From c33bccd0d58b9b80666df1cea36ee44559077f72 Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Mon, 9 Sep 2024 13:38:27 -0500 Subject: [PATCH 01/22] lab1 --- Lab.2.ipynb | 1300 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1300 insertions(+) create mode 100644 Lab.2.ipynb diff --git a/Lab.2.ipynb b/Lab.2.ipynb new file mode 100644 index 0000000..95f89ab --- /dev/null +++ b/Lab.2.ipynb @@ -0,0 +1,1300 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lab 2- Tic Tac Toe\n", + "\n", + "In this lab your will build a n x n Tic Tac Toe game. As you do the exercises, make sure your solutions work for any size Tic Tac Toe game. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 1:* Write a function that creates an n by n matrix (of list of lists) which will represent the state of a Tie Tac Toe game. Let 0, 1, and 2 represent empty, \"X\", and \"O\", respectively.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here\n", + "empty=0\n", + "player_X=1\n", + "player_O=2\n", + "def create_matrix(n):\n", + " return[[0 for _ in range(n)] for _ in range(n)]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 0, 0]\n", + "[0, 2, 0]\n", + "[0, 0, 1]\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "board=create_matrix(3)\n", + "board\n", + "board[0][0] = 1 \n", + "board[1][1] = 2 \n", + "board[2][2] = 1\n", + "for row in board:\n", + " print(row)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 2:* Write a function that takes 2 integers `n` and `m` as input and draws a `n` by `m` game board. For example the following is a 3x3 board:\n", + "```\n", + " --- --- --- \n", + " | | | | \n", + " --- --- --- \n", + " | | | | \n", + " --- --- --- \n", + " | | | | \n", + " --- --- --- \n", + " ```" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here\n", + "def draw_board(n, m):\n", + " for i in range(n):\n", + " # Print the top border of the cells\n", + " print(\" \" + \" --- \" * m)\n", + " \n", + " # Print the cells' borders\n", + " for _ in range(2): # Each cell has two vertical borders\n", + " print(\" |\" + \" |\" * m)\n", + " \n", + " # Print the bottom border of the last row\n", + " print(\" \" + \" --- \" * m)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " --- --- --- --- \n", + " | | | | |\n", + " | | | | |\n", + " --- --- --- --- \n", + " | | | | |\n", + " | | | | |\n", + " --- --- --- --- \n", + " | | | | |\n", + " | | | | |\n", + " --- --- --- --- \n", + " | | | | |\n", + " | | | | |\n", + " --- --- --- --- \n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "draw_board(4,4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 3:* Modify exercise 2, so that it takes a matrix of the form from exercise 1 and draws a tic-tac-tie board with \"X\"s and \"O\"s. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Write your solution\n", + "def draw_tic_tac_toe_board(matrix):\n", + " symbols = {0: ' ', 1: 'X', 2: 'O'}\n", + " \n", + " for row in matrix:\n", + " print(' | '.join(symbols[cell] for cell in row))\n", + " print('-' * (len(row) * 4 - 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X | | \n", + "-----------\n", + " | O | \n", + "-----------\n", + " | | X\n", + "-----------\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "board[0][0] = 1 \n", + "board[1][1] = 2 \n", + "board[2][2] = 1\n", + "draw_tic_tac_toe_board(board)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 4:* Write a function that takes a `n` by `n` matrix representing a tic-tac-toe game, and returns -1, 0, 1, or 2 indicating the game is incomplete, the game is a draw, player 1 has won, or player 2 has one, respectively. Here are some example inputs you can use to test your code:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here\n", + "def check_winner(matrix):\n", + " n = len(matrix)\n", + " \n", + " def check_line(line):\n", + " if all(cell == 1 for cell in line):\n", + " return 1\n", + " if all(cell == 2 for cell in line):\n", + " return 2\n", + " return 0\n", + " \n", + " def check_lines():\n", + " # Check rows and columns\n", + " for i in range(n):\n", + " row = matrix[i]\n", + " col = [matrix[j][i] for j in range(n)]\n", + " row_winner = check_line(row)\n", + " if row_winner != 0:\n", + " return row_winner\n", + " col_winner = check_line(col)\n", + " if col_winner != 0:\n", + " return col_winner\n", + " \n", + " # Check diagonals\n", + " main_diag = [matrix[i][i] for i in range(n)]\n", + " anti_diag = [matrix[i][n - 1 - i] for i in range(n)]\n", + " \n", + " if check_line(main_diag) != 0:\n", + " return check_line(main_diag)\n", + " if check_line(anti_diag) != 0:\n", + " return check_line(anti_diag)\n", + " \n", + " return 0\n", + " \n", + " winner = check_lines()\n", + " \n", + " if winner == 1:\n", + " print(\"Player 1 has won.\")\n", + " return 1\n", + " elif winner == 2:\n", + " print(\"Player 2 has won.\")\n", + " return 2\n", + " elif any(0 in row for row in matrix):\n", + " print(\"The game is incomplete.\")\n", + " return -1\n", + " else:\n", + " print(\"The game is a draw.\")\n", + " return 0\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The game is incomplete.\n" + ] + }, + { + "data": { + "text/plain": [ + "-1" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Test your solution here\n", + "(check_winner(board))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "winner_is_2 = [[2, 2, 0],\n", + "\t[2, 1, 0],\n", + "\t[2, 1, 1]]\n", + "\n", + "winner_is_1 = [[1, 2, 0],\n", + "\t[2, 1, 0],\n", + "\t[2, 1, 1]]\n", + "\n", + "winner_is_also_1 = [[0, 1, 0],\n", + "\t[2, 1, 0],\n", + "\t[2, 1, 1]]\n", + "\n", + "no_winner = [[1, 2, 0],\n", + "\t[2, 1, 0],\n", + "\t[2, 1, 2]]\n", + "\n", + "also_no_winner = [[1, 2, 0],\n", + "\t[2, 1, 0],\n", + "\t[2, 1, 0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 has won.\n" + ] + }, + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(check_winner(winner_is_2))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 has won.\n" + ] + }, + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(check_winner(winner_is_1))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 has won.\n" + ] + }, + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(check_winner(winner_is_also_1))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The game is incomplete.\n" + ] + }, + { + "data": { + "text/plain": [ + "-1" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(check_winner(no_winner))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 5:* Write a function that takes a game board, player number, and `(x,y)` coordinates and places \"X\" or \"O\" in the correct location of the game board. Make sure that you only allow filling previously empty locations. Return `True` or `False` to indicate successful placement of \"X\" or \"O\"." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here\n", + "\n", + "board=create_matrix(3)\n", + "def place_marker(board, player, x, y):\n", + " # Check if the coordinates are within bounds\n", + " if not (0 <= x < len(board) and 0 <= y < len(board[0])):\n", + " return False\n", + " \n", + " # Check if the position is empty\n", + " if board[x][y] == 0:\n", + " board[x][y] = player\n", + " return True\n", + " return False" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Test your solution here\n", + "place_marker(board,1,1,2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 6:* Modify Exercise 4 to show column and row labels so that players can specify location using \"A2\" or \"C1\"." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution \n", + "def draw_tic_tac_toe_board(matrix):\n", + " n = len(matrix)\n", + " column_labels = [chr(ord('A') + i) for i in range(n)]\n", + " print(\" \" + \" \".join(column_labels)) # Print column labels\n", + " for i in range(n):\n", + " row_label = str(i + 1)\n", + " row_display = \" \".join({0: \".\", 1: \"X\", 2: \"O\"}[cell] for cell in matrix[i])\n", + " print(row_label + \" \" + row_display)\n", + " \n", + "def place_marker(board, player, position):\n", + " # Convert position from \"A1\", \"B2\", etc., to matrix indices\n", + " if len(position) < 2:\n", + " return False\n", + " column_label, row_label = position[0].upper(), position[1:]\n", + " if not (column_label.isalpha() and row_label.isdigit()):\n", + " return False\n", + " \n", + " column_index = ord(column_label) - ord('A')\n", + " row_index = int(row_label) - 1\n", + " if not (0 <= row_index < len(board) and 0 <= column_index < len(board[0])):\n", + " return False\n", + " if board[row_index][column_index] == 0:\n", + " board[row_index][column_index] = player\n", + " return True\n", + " return False" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C\n", + "1 . . .\n", + "2 . . .\n", + "3 . . .\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "board= create_matrix(3)\n", + "draw_tic_tac_toe_board(board)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C\n", + "1 X . .\n", + "2 . . .\n", + "3 . . .\n" + ] + } + ], + "source": [ + "place_marker(board, 1, \"A1\")\n", + "draw_tic_tac_toe_board(board) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 7:* Write a function that takes a board, player number, and location specified as in exercise 6 and then calls exercise 5 to correctly modify the board. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here\n", + "def update_board(board, player, location):\n", + " return place_marker(board, player, location)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial board:\n", + " A B C\n", + "1 . . .\n", + "2 . . .\n", + "3 . . .\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "board = create_matrix(3)\n", + "print(\"Initial board:\")\n", + "draw_tic_tac_toe_board(board)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Updating board with 'X' at A1:\n", + " A B C\n", + "1 X . .\n", + "2 . . .\n", + "3 . . .\n", + "\n", + "Updating board with 'O' at B2:\n", + " A B C\n", + "1 X . .\n", + "2 . O .\n", + "3 . . .\n" + ] + } + ], + "source": [ + "print(\"\\nUpdating board with 'X' at A1:\")\n", + "update_board(board, 1, \"A1\")\n", + "draw_tic_tac_toe_board(board)\n", + "print(\"\\nUpdating board with 'O' at B2:\")\n", + "update_board(board, 2, \"B2\")\n", + "draw_tic_tac_toe_board(board) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 8:* Write a function is called with a board and player number, takes input from the player using python's `input`, and modifies the board using your function from exercise 7. Note that you should keep asking for input until you have gotten a valid input that results in a valid move." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here\n", + "def get_valid_move(board, player):\n", + " while True:\n", + " location = input(f\"Player {player}, enter your move: \").strip().upper()\n", + " \n", + " # Validate input format (e.g., \"A1\", \"C2\")\n", + " if len(location) < 2 or not location[0].isalpha() or not location[1:].isdigit():\n", + " print(\"Invalid format. Please use the format 'LetterNumber' (e.g., A1, B3).\")\n", + " continue\n", + " \n", + " # Call the update_board function to attempt to make the move\n", + " if update_board(board, player, location):\n", + " print(f\"Player {player} placed their marker at {location}.\")\n", + " break\n", + " else:\n", + " print(f\"Invalid move or cell already occupied. Try again.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial board:\n", + " A B C\n", + "1 . . .\n", + "2 . . .\n", + "3 . . .\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: A1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at A1.\n", + " A B C\n", + "1 X . .\n", + "2 . . .\n", + "3 . . .\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: B1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at B1.\n", + " A B C\n", + "1 X O .\n", + "2 . . .\n", + "3 . . .\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "board = create_matrix(3)\n", + "print(\"Initial board:\")\n", + "draw_tic_tac_toe_board(board)\n", + "get_valid_move(board,1)\n", + "draw_tic_tac_toe_board(board)\n", + "get_valid_move(board, 2)\n", + "draw_tic_tac_toe_board(board)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 9:* Use all of the previous exercises to implement a full tic-tac-toe game, where an appropriate board is drawn, 2 players are repeatedly asked for a location coordinates of where they wish to place a mark, and the game status is checked until a player wins or a draw occurs." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here\n", + "def create_matrix(n):\n", + " return [[0 for _ in range(n)] for _ in range(n)]\n", + "\n", + "def draw_tic_tac_toe_board(matrix):\n", + " n = len(matrix)\n", + " column_labels = [chr(ord('A') + i) for i in range(n)]\n", + " print(\" \" + \" \".join(column_labels)) # Print column labels\n", + " for i in range(n):\n", + " row_label = str(i + 1)\n", + " row_display = \" \".join({0: \".\", 1: \"X\", 2: \"O\"}[cell] for cell in matrix[i])\n", + " print(row_label + \" \" + row_display)\n", + "\n", + "def place_marker(board, player, position):\n", + " if len(position) < 2:\n", + " return False\n", + " column_label, row_label = position[0].upper(), position[1:]\n", + " if not (column_label.isalpha() and row_label.isdigit()):\n", + " return False\n", + " \n", + " column_index = ord(column_label) - ord('A')\n", + " row_index = int(row_label) - 1\n", + " \n", + " if not (0 <= row_index < len(board) and 0 <= column_index < len(board[0])):\n", + " return False\n", + " \n", + " if board[row_index][column_index] == 0:\n", + " board[row_index][column_index] = player\n", + " return True\n", + " \n", + " return False\n", + "\n", + "def check_game_status(matrix):\n", + " n = len(matrix)\n", + " \n", + " def check_winner(player):\n", + " # Check rows and columns\n", + " for i in range(n):\n", + " if all(matrix[i][j] == player for j in range(n)) or \\\n", + " all(matrix[j][i] == player for j in range(n)):\n", + " return True\n", + " # Check diagonals\n", + " if all(matrix[i][i] == player for i in range(n)) or \\\n", + " all(matrix[i][n - 1 - i] == player for i in range(n)):\n", + " return True\n", + " return False\n", + " \n", + " def is_board_full():\n", + " return all(matrix[i][j] != 0 for i in range(n) for j in range(n))\n", + " \n", + " if check_winner(1):\n", + " return 1 # Player 1 (X) wins\n", + " if check_winner(2):\n", + " return 2 # Player 2 (O) wins\n", + " if is_board_full():\n", + " return 0 # Draw\n", + " return -1 # Incomplete\n", + "\n", + "def update_board(board, player, location):\n", + " return place_marker(board, player, location)\n", + "\n", + "def get_valid_move(board, player):\n", + " while True:\n", + " location = input(f\"Player {player}, enter your move: \").strip().upper()\n", + " \n", + " if len(location) < 2 or not location[0].isalpha() or not location[1:].isdigit():\n", + " print(\"Invalid format. Please use the format 'LetterNumber' (e.g., A1, B3).\")\n", + " continue\n", + " \n", + " if update_board(board, player, location):\n", + " print(f\"Player {player} placed their marker at {location}.\")\n", + " break\n", + " else:\n", + " print(f\"Invalid move or cell already occupied. Try again.\")\n", + "\n", + "def play_game(n):\n", + " board = create_matrix(n)\n", + " current_player = 1\n", + " \n", + " while True:\n", + " draw_tic_tac_toe_board(board)\n", + " print(f\"\\nPlayer {current_player}'s turn:\")\n", + " get_valid_move(board, current_player)\n", + " \n", + " status = check_game_status(board)\n", + " \n", + " if status == 1:\n", + " draw_tic_tac_toe_board(board)\n", + " print(\"Player 1 (X) wins!\")\n", + " break\n", + " elif status == 2:\n", + " draw_tic_tac_toe_board(board)\n", + " print(\"Player 2 (O) wins!\")\n", + " break\n", + " elif status == 0:\n", + " draw_tic_tac_toe_board(board)\n", + " print(\"The game is a draw!\")\n", + " break\n", + " \n", + " # Switch players\n", + " current_player = 2 if current_player == 1 else 1" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C\n", + "1 . . .\n", + "2 . . .\n", + "3 . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: A1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at A1.\n", + " A B C\n", + "1 X . .\n", + "2 . . .\n", + "3 . . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: C1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at C1.\n", + " A B C\n", + "1 X . O\n", + "2 . . .\n", + "3 . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: A2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at A2.\n", + " A B C\n", + "1 X . O\n", + "2 X . .\n", + "3 . . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: A3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at A3.\n", + " A B C\n", + "1 X . O\n", + "2 X . .\n", + "3 O . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: B2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at B2.\n", + " A B C\n", + "1 X . O\n", + "2 X X .\n", + "3 O . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: C2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at C2.\n", + " A B C\n", + "1 X . O\n", + "2 X X O\n", + "3 O . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: C3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at C3.\n", + " A B C\n", + "1 X . O\n", + "2 X X O\n", + "3 O . X\n", + "Player 1 (X) wins!\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "play_game(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 10:* Test that your game works for 5x5 Tic Tac Toe. " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D E\n", + "1 . . . . .\n", + "2 . . . . .\n", + "3 . . . . .\n", + "4 . . . . .\n", + "5 . . . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: A1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at A1.\n", + " A B C D E\n", + "1 X . . . .\n", + "2 . . . . .\n", + "3 . . . . .\n", + "4 . . . . .\n", + "5 . . . . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: B1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at B1.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . . . . .\n", + "3 . . . . .\n", + "4 . . . . .\n", + "5 . . . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: B2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at B2.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . . . .\n", + "4 . . . . .\n", + "5 . . . . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: C4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at C4.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . . . .\n", + "4 . . O . .\n", + "5 . . . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: C3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at C3.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 . . O . .\n", + "5 . . . . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: B4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at B4.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 . O O . .\n", + "5 . . . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: D5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at D5.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 . O O . .\n", + "5 . . . X .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: A4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at A4.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 O O O . .\n", + "5 . . . X .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: D4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at D4.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 O O O X .\n", + "5 . . . X .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: E1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at E1.\n", + " A B C D E\n", + "1 X O . . O\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 O O O X .\n", + "5 . . . X .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: E5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at E5.\n", + " A B C D E\n", + "1 X O . . O\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 O O O X .\n", + "5 . . . X X\n", + "Player 1 (X) wins!\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "play_game(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 11:* (Advanced / Challenge) Develop a version of the game where one player is the computer. Note that you don't need to do an extensive seach for the best move. You can have the computer simply protect against loosing and otherwise try to win with straight or diagonal patterns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test your solution here" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From e6424615ff08493bf87807f3ef1e343b0eeb0c56 Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Mon, 9 Sep 2024 13:45:17 -0500 Subject: [PATCH 02/22] Delete Lab.2.ipynb --- Lab.2.ipynb | 1300 --------------------------------------------------- 1 file changed, 1300 deletions(-) delete mode 100644 Lab.2.ipynb diff --git a/Lab.2.ipynb b/Lab.2.ipynb deleted file mode 100644 index 95f89ab..0000000 --- a/Lab.2.ipynb +++ /dev/null @@ -1,1300 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Lab 2- Tic Tac Toe\n", - "\n", - "In this lab your will build a n x n Tic Tac Toe game. As you do the exercises, make sure your solutions work for any size Tic Tac Toe game. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Exercise 1:* Write a function that creates an n by n matrix (of list of lists) which will represent the state of a Tie Tac Toe game. Let 0, 1, and 2 represent empty, \"X\", and \"O\", respectively.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Write you solution here\n", - "empty=0\n", - "player_X=1\n", - "player_O=2\n", - "def create_matrix(n):\n", - " return[[0 for _ in range(n)] for _ in range(n)]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 0, 0]\n", - "[0, 2, 0]\n", - "[0, 0, 1]\n" - ] - } - ], - "source": [ - "# Test your solution here\n", - "board=create_matrix(3)\n", - "board\n", - "board[0][0] = 1 \n", - "board[1][1] = 2 \n", - "board[2][2] = 1\n", - "for row in board:\n", - " print(row)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Exercise 2:* Write a function that takes 2 integers `n` and `m` as input and draws a `n` by `m` game board. For example the following is a 3x3 board:\n", - "```\n", - " --- --- --- \n", - " | | | | \n", - " --- --- --- \n", - " | | | | \n", - " --- --- --- \n", - " | | | | \n", - " --- --- --- \n", - " ```" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Write you solution here\n", - "def draw_board(n, m):\n", - " for i in range(n):\n", - " # Print the top border of the cells\n", - " print(\" \" + \" --- \" * m)\n", - " \n", - " # Print the cells' borders\n", - " for _ in range(2): # Each cell has two vertical borders\n", - " print(\" |\" + \" |\" * m)\n", - " \n", - " # Print the bottom border of the last row\n", - " print(\" \" + \" --- \" * m)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " --- --- --- --- \n", - " | | | | |\n", - " | | | | |\n", - " --- --- --- --- \n", - " | | | | |\n", - " | | | | |\n", - " --- --- --- --- \n", - " | | | | |\n", - " | | | | |\n", - " --- --- --- --- \n", - " | | | | |\n", - " | | | | |\n", - " --- --- --- --- \n" - ] - } - ], - "source": [ - "# Test your solution here\n", - "draw_board(4,4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Exercise 3:* Modify exercise 2, so that it takes a matrix of the form from exercise 1 and draws a tic-tac-tie board with \"X\"s and \"O\"s. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Write your solution\n", - "def draw_tic_tac_toe_board(matrix):\n", - " symbols = {0: ' ', 1: 'X', 2: 'O'}\n", - " \n", - " for row in matrix:\n", - " print(' | '.join(symbols[cell] for cell in row))\n", - " print('-' * (len(row) * 4 - 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X | | \n", - "-----------\n", - " | O | \n", - "-----------\n", - " | | X\n", - "-----------\n" - ] - } - ], - "source": [ - "# Test your solution here\n", - "board[0][0] = 1 \n", - "board[1][1] = 2 \n", - "board[2][2] = 1\n", - "draw_tic_tac_toe_board(board)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Exercise 4:* Write a function that takes a `n` by `n` matrix representing a tic-tac-toe game, and returns -1, 0, 1, or 2 indicating the game is incomplete, the game is a draw, player 1 has won, or player 2 has one, respectively. Here are some example inputs you can use to test your code:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Write you solution here\n", - "def check_winner(matrix):\n", - " n = len(matrix)\n", - " \n", - " def check_line(line):\n", - " if all(cell == 1 for cell in line):\n", - " return 1\n", - " if all(cell == 2 for cell in line):\n", - " return 2\n", - " return 0\n", - " \n", - " def check_lines():\n", - " # Check rows and columns\n", - " for i in range(n):\n", - " row = matrix[i]\n", - " col = [matrix[j][i] for j in range(n)]\n", - " row_winner = check_line(row)\n", - " if row_winner != 0:\n", - " return row_winner\n", - " col_winner = check_line(col)\n", - " if col_winner != 0:\n", - " return col_winner\n", - " \n", - " # Check diagonals\n", - " main_diag = [matrix[i][i] for i in range(n)]\n", - " anti_diag = [matrix[i][n - 1 - i] for i in range(n)]\n", - " \n", - " if check_line(main_diag) != 0:\n", - " return check_line(main_diag)\n", - " if check_line(anti_diag) != 0:\n", - " return check_line(anti_diag)\n", - " \n", - " return 0\n", - " \n", - " winner = check_lines()\n", - " \n", - " if winner == 1:\n", - " print(\"Player 1 has won.\")\n", - " return 1\n", - " elif winner == 2:\n", - " print(\"Player 2 has won.\")\n", - " return 2\n", - " elif any(0 in row for row in matrix):\n", - " print(\"The game is incomplete.\")\n", - " return -1\n", - " else:\n", - " print(\"The game is a draw.\")\n", - " return 0\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The game is incomplete.\n" - ] - }, - { - "data": { - "text/plain": [ - "-1" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Test your solution here\n", - "(check_winner(board))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "winner_is_2 = [[2, 2, 0],\n", - "\t[2, 1, 0],\n", - "\t[2, 1, 1]]\n", - "\n", - "winner_is_1 = [[1, 2, 0],\n", - "\t[2, 1, 0],\n", - "\t[2, 1, 1]]\n", - "\n", - "winner_is_also_1 = [[0, 1, 0],\n", - "\t[2, 1, 0],\n", - "\t[2, 1, 1]]\n", - "\n", - "no_winner = [[1, 2, 0],\n", - "\t[2, 1, 0],\n", - "\t[2, 1, 2]]\n", - "\n", - "also_no_winner = [[1, 2, 0],\n", - "\t[2, 1, 0],\n", - "\t[2, 1, 0]]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 2 has won.\n" - ] - }, - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(check_winner(winner_is_2))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 1 has won.\n" - ] - }, - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(check_winner(winner_is_1))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 1 has won.\n" - ] - }, - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(check_winner(winner_is_also_1))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The game is incomplete.\n" - ] - }, - { - "data": { - "text/plain": [ - "-1" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(check_winner(no_winner))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Exercise 5:* Write a function that takes a game board, player number, and `(x,y)` coordinates and places \"X\" or \"O\" in the correct location of the game board. Make sure that you only allow filling previously empty locations. Return `True` or `False` to indicate successful placement of \"X\" or \"O\"." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Write you solution here\n", - "\n", - "board=create_matrix(3)\n", - "def place_marker(board, player, x, y):\n", - " # Check if the coordinates are within bounds\n", - " if not (0 <= x < len(board) and 0 <= y < len(board[0])):\n", - " return False\n", - " \n", - " # Check if the position is empty\n", - " if board[x][y] == 0:\n", - " board[x][y] = player\n", - " return True\n", - " return False" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Test your solution here\n", - "place_marker(board,1,1,2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Exercise 6:* Modify Exercise 4 to show column and row labels so that players can specify location using \"A2\" or \"C1\"." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Write you solution \n", - "def draw_tic_tac_toe_board(matrix):\n", - " n = len(matrix)\n", - " column_labels = [chr(ord('A') + i) for i in range(n)]\n", - " print(\" \" + \" \".join(column_labels)) # Print column labels\n", - " for i in range(n):\n", - " row_label = str(i + 1)\n", - " row_display = \" \".join({0: \".\", 1: \"X\", 2: \"O\"}[cell] for cell in matrix[i])\n", - " print(row_label + \" \" + row_display)\n", - " \n", - "def place_marker(board, player, position):\n", - " # Convert position from \"A1\", \"B2\", etc., to matrix indices\n", - " if len(position) < 2:\n", - " return False\n", - " column_label, row_label = position[0].upper(), position[1:]\n", - " if not (column_label.isalpha() and row_label.isdigit()):\n", - " return False\n", - " \n", - " column_index = ord(column_label) - ord('A')\n", - " row_index = int(row_label) - 1\n", - " if not (0 <= row_index < len(board) and 0 <= column_index < len(board[0])):\n", - " return False\n", - " if board[row_index][column_index] == 0:\n", - " board[row_index][column_index] = player\n", - " return True\n", - " return False" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " A B C\n", - "1 . . .\n", - "2 . . .\n", - "3 . . .\n" - ] - } - ], - "source": [ - "# Test your solution here\n", - "board= create_matrix(3)\n", - "draw_tic_tac_toe_board(board)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " A B C\n", - "1 X . .\n", - "2 . . .\n", - "3 . . .\n" - ] - } - ], - "source": [ - "place_marker(board, 1, \"A1\")\n", - "draw_tic_tac_toe_board(board) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Exercise 7:* Write a function that takes a board, player number, and location specified as in exercise 6 and then calls exercise 5 to correctly modify the board. " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Write you solution here\n", - "def update_board(board, player, location):\n", - " return place_marker(board, player, location)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial board:\n", - " A B C\n", - "1 . . .\n", - "2 . . .\n", - "3 . . .\n" - ] - } - ], - "source": [ - "# Test your solution here\n", - "board = create_matrix(3)\n", - "print(\"Initial board:\")\n", - "draw_tic_tac_toe_board(board)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Updating board with 'X' at A1:\n", - " A B C\n", - "1 X . .\n", - "2 . . .\n", - "3 . . .\n", - "\n", - "Updating board with 'O' at B2:\n", - " A B C\n", - "1 X . .\n", - "2 . O .\n", - "3 . . .\n" - ] - } - ], - "source": [ - "print(\"\\nUpdating board with 'X' at A1:\")\n", - "update_board(board, 1, \"A1\")\n", - "draw_tic_tac_toe_board(board)\n", - "print(\"\\nUpdating board with 'O' at B2:\")\n", - "update_board(board, 2, \"B2\")\n", - "draw_tic_tac_toe_board(board) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Exercise 8:* Write a function is called with a board and player number, takes input from the player using python's `input`, and modifies the board using your function from exercise 7. Note that you should keep asking for input until you have gotten a valid input that results in a valid move." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Write you solution here\n", - "def get_valid_move(board, player):\n", - " while True:\n", - " location = input(f\"Player {player}, enter your move: \").strip().upper()\n", - " \n", - " # Validate input format (e.g., \"A1\", \"C2\")\n", - " if len(location) < 2 or not location[0].isalpha() or not location[1:].isdigit():\n", - " print(\"Invalid format. Please use the format 'LetterNumber' (e.g., A1, B3).\")\n", - " continue\n", - " \n", - " # Call the update_board function to attempt to make the move\n", - " if update_board(board, player, location):\n", - " print(f\"Player {player} placed their marker at {location}.\")\n", - " break\n", - " else:\n", - " print(f\"Invalid move or cell already occupied. Try again.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial board:\n", - " A B C\n", - "1 . . .\n", - "2 . . .\n", - "3 . . .\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 1, enter your move: A1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 1 placed their marker at A1.\n", - " A B C\n", - "1 X . .\n", - "2 . . .\n", - "3 . . .\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 2, enter your move: B1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 2 placed their marker at B1.\n", - " A B C\n", - "1 X O .\n", - "2 . . .\n", - "3 . . .\n" - ] - } - ], - "source": [ - "# Test your solution here\n", - "board = create_matrix(3)\n", - "print(\"Initial board:\")\n", - "draw_tic_tac_toe_board(board)\n", - "get_valid_move(board,1)\n", - "draw_tic_tac_toe_board(board)\n", - "get_valid_move(board, 2)\n", - "draw_tic_tac_toe_board(board)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Exercise 9:* Use all of the previous exercises to implement a full tic-tac-toe game, where an appropriate board is drawn, 2 players are repeatedly asked for a location coordinates of where they wish to place a mark, and the game status is checked until a player wins or a draw occurs." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# Write you solution here\n", - "def create_matrix(n):\n", - " return [[0 for _ in range(n)] for _ in range(n)]\n", - "\n", - "def draw_tic_tac_toe_board(matrix):\n", - " n = len(matrix)\n", - " column_labels = [chr(ord('A') + i) for i in range(n)]\n", - " print(\" \" + \" \".join(column_labels)) # Print column labels\n", - " for i in range(n):\n", - " row_label = str(i + 1)\n", - " row_display = \" \".join({0: \".\", 1: \"X\", 2: \"O\"}[cell] for cell in matrix[i])\n", - " print(row_label + \" \" + row_display)\n", - "\n", - "def place_marker(board, player, position):\n", - " if len(position) < 2:\n", - " return False\n", - " column_label, row_label = position[0].upper(), position[1:]\n", - " if not (column_label.isalpha() and row_label.isdigit()):\n", - " return False\n", - " \n", - " column_index = ord(column_label) - ord('A')\n", - " row_index = int(row_label) - 1\n", - " \n", - " if not (0 <= row_index < len(board) and 0 <= column_index < len(board[0])):\n", - " return False\n", - " \n", - " if board[row_index][column_index] == 0:\n", - " board[row_index][column_index] = player\n", - " return True\n", - " \n", - " return False\n", - "\n", - "def check_game_status(matrix):\n", - " n = len(matrix)\n", - " \n", - " def check_winner(player):\n", - " # Check rows and columns\n", - " for i in range(n):\n", - " if all(matrix[i][j] == player for j in range(n)) or \\\n", - " all(matrix[j][i] == player for j in range(n)):\n", - " return True\n", - " # Check diagonals\n", - " if all(matrix[i][i] == player for i in range(n)) or \\\n", - " all(matrix[i][n - 1 - i] == player for i in range(n)):\n", - " return True\n", - " return False\n", - " \n", - " def is_board_full():\n", - " return all(matrix[i][j] != 0 for i in range(n) for j in range(n))\n", - " \n", - " if check_winner(1):\n", - " return 1 # Player 1 (X) wins\n", - " if check_winner(2):\n", - " return 2 # Player 2 (O) wins\n", - " if is_board_full():\n", - " return 0 # Draw\n", - " return -1 # Incomplete\n", - "\n", - "def update_board(board, player, location):\n", - " return place_marker(board, player, location)\n", - "\n", - "def get_valid_move(board, player):\n", - " while True:\n", - " location = input(f\"Player {player}, enter your move: \").strip().upper()\n", - " \n", - " if len(location) < 2 or not location[0].isalpha() or not location[1:].isdigit():\n", - " print(\"Invalid format. Please use the format 'LetterNumber' (e.g., A1, B3).\")\n", - " continue\n", - " \n", - " if update_board(board, player, location):\n", - " print(f\"Player {player} placed their marker at {location}.\")\n", - " break\n", - " else:\n", - " print(f\"Invalid move or cell already occupied. Try again.\")\n", - "\n", - "def play_game(n):\n", - " board = create_matrix(n)\n", - " current_player = 1\n", - " \n", - " while True:\n", - " draw_tic_tac_toe_board(board)\n", - " print(f\"\\nPlayer {current_player}'s turn:\")\n", - " get_valid_move(board, current_player)\n", - " \n", - " status = check_game_status(board)\n", - " \n", - " if status == 1:\n", - " draw_tic_tac_toe_board(board)\n", - " print(\"Player 1 (X) wins!\")\n", - " break\n", - " elif status == 2:\n", - " draw_tic_tac_toe_board(board)\n", - " print(\"Player 2 (O) wins!\")\n", - " break\n", - " elif status == 0:\n", - " draw_tic_tac_toe_board(board)\n", - " print(\"The game is a draw!\")\n", - " break\n", - " \n", - " # Switch players\n", - " current_player = 2 if current_player == 1 else 1" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " A B C\n", - "1 . . .\n", - "2 . . .\n", - "3 . . .\n", - "\n", - "Player 1's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 1, enter your move: A1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 1 placed their marker at A1.\n", - " A B C\n", - "1 X . .\n", - "2 . . .\n", - "3 . . .\n", - "\n", - "Player 2's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 2, enter your move: C1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 2 placed their marker at C1.\n", - " A B C\n", - "1 X . O\n", - "2 . . .\n", - "3 . . .\n", - "\n", - "Player 1's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 1, enter your move: A2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 1 placed their marker at A2.\n", - " A B C\n", - "1 X . O\n", - "2 X . .\n", - "3 . . .\n", - "\n", - "Player 2's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 2, enter your move: A3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 2 placed their marker at A3.\n", - " A B C\n", - "1 X . O\n", - "2 X . .\n", - "3 O . .\n", - "\n", - "Player 1's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 1, enter your move: B2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 1 placed their marker at B2.\n", - " A B C\n", - "1 X . O\n", - "2 X X .\n", - "3 O . .\n", - "\n", - "Player 2's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 2, enter your move: C2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 2 placed their marker at C2.\n", - " A B C\n", - "1 X . O\n", - "2 X X O\n", - "3 O . .\n", - "\n", - "Player 1's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 1, enter your move: C3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 1 placed their marker at C3.\n", - " A B C\n", - "1 X . O\n", - "2 X X O\n", - "3 O . X\n", - "Player 1 (X) wins!\n" - ] - } - ], - "source": [ - "# Test your solution here\n", - "play_game(3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Exercise 10:* Test that your game works for 5x5 Tic Tac Toe. " - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " A B C D E\n", - "1 . . . . .\n", - "2 . . . . .\n", - "3 . . . . .\n", - "4 . . . . .\n", - "5 . . . . .\n", - "\n", - "Player 1's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 1, enter your move: A1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 1 placed their marker at A1.\n", - " A B C D E\n", - "1 X . . . .\n", - "2 . . . . .\n", - "3 . . . . .\n", - "4 . . . . .\n", - "5 . . . . .\n", - "\n", - "Player 2's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 2, enter your move: B1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 2 placed their marker at B1.\n", - " A B C D E\n", - "1 X O . . .\n", - "2 . . . . .\n", - "3 . . . . .\n", - "4 . . . . .\n", - "5 . . . . .\n", - "\n", - "Player 1's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 1, enter your move: B2\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 1 placed their marker at B2.\n", - " A B C D E\n", - "1 X O . . .\n", - "2 . X . . .\n", - "3 . . . . .\n", - "4 . . . . .\n", - "5 . . . . .\n", - "\n", - "Player 2's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 2, enter your move: C4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 2 placed their marker at C4.\n", - " A B C D E\n", - "1 X O . . .\n", - "2 . X . . .\n", - "3 . . . . .\n", - "4 . . O . .\n", - "5 . . . . .\n", - "\n", - "Player 1's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 1, enter your move: C3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 1 placed their marker at C3.\n", - " A B C D E\n", - "1 X O . . .\n", - "2 . X . . .\n", - "3 . . X . .\n", - "4 . . O . .\n", - "5 . . . . .\n", - "\n", - "Player 2's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 2, enter your move: B4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 2 placed their marker at B4.\n", - " A B C D E\n", - "1 X O . . .\n", - "2 . X . . .\n", - "3 . . X . .\n", - "4 . O O . .\n", - "5 . . . . .\n", - "\n", - "Player 1's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 1, enter your move: D5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 1 placed their marker at D5.\n", - " A B C D E\n", - "1 X O . . .\n", - "2 . X . . .\n", - "3 . . X . .\n", - "4 . O O . .\n", - "5 . . . X .\n", - "\n", - "Player 2's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 2, enter your move: A4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 2 placed their marker at A4.\n", - " A B C D E\n", - "1 X O . . .\n", - "2 . X . . .\n", - "3 . . X . .\n", - "4 O O O . .\n", - "5 . . . X .\n", - "\n", - "Player 1's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 1, enter your move: D4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 1 placed their marker at D4.\n", - " A B C D E\n", - "1 X O . . .\n", - "2 . X . . .\n", - "3 . . X . .\n", - "4 O O O X .\n", - "5 . . . X .\n", - "\n", - "Player 2's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 2, enter your move: E1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 2 placed their marker at E1.\n", - " A B C D E\n", - "1 X O . . O\n", - "2 . X . . .\n", - "3 . . X . .\n", - "4 O O O X .\n", - "5 . . . X .\n", - "\n", - "Player 1's turn:\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Player 1, enter your move: E5\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Player 1 placed their marker at E5.\n", - " A B C D E\n", - "1 X O . . O\n", - "2 . X . . .\n", - "3 . . X . .\n", - "4 O O O X .\n", - "5 . . . X X\n", - "Player 1 (X) wins!\n" - ] - } - ], - "source": [ - "# Test your solution here\n", - "play_game(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Exercise 11:* (Advanced / Challenge) Develop a version of the game where one player is the computer. Note that you don't need to do an extensive seach for the best move. You can have the computer simply protect against loosing and otherwise try to win with straight or diagonal patterns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Write you solution here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Test your solution here" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.1" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 8debad88ba9de595df4dfa9afa4fb5c9202bd5a0 Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Mon, 9 Sep 2024 13:46:29 -0500 Subject: [PATCH 03/22] lab2 --- Labs/Lab.2/Lab.2.ipynb | 1154 ++++++++++++++++++++++++++++++++++++---- 1 file changed, 1037 insertions(+), 117 deletions(-) diff --git a/Labs/Lab.2/Lab.2.ipynb b/Labs/Lab.2/Lab.2.ipynb index 251c5cc..95f89ab 100644 --- a/Labs/Lab.2/Lab.2.ipynb +++ b/Labs/Lab.2/Lab.2.ipynb @@ -18,24 +18,42 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ - "# Write you solution here" + "# Write you solution here\n", + "empty=0\n", + "player_X=1\n", + "player_O=2\n", + "def create_matrix(n):\n", + " return[[0 for _ in range(n)] for _ in range(n)]" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 0, 0]\n", + "[0, 2, 0]\n", + "[0, 0, 1]\n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "board=create_matrix(3)\n", + "board\n", + "board[0][0] = 1 \n", + "board[1][1] = 2 \n", + "board[2][2] = 1\n", + "for row in board:\n", + " print(row)" ] }, { @@ -56,24 +74,52 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ - "# Write you solution here" + "# Write you solution here\n", + "def draw_board(n, m):\n", + " for i in range(n):\n", + " # Print the top border of the cells\n", + " print(\" \" + \" --- \" * m)\n", + " \n", + " # Print the cells' borders\n", + " for _ in range(2): # Each cell has two vertical borders\n", + " print(\" |\" + \" |\" * m)\n", + " \n", + " # Print the bottom border of the last row\n", + " print(\" \" + \" --- \" * m)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " --- --- --- --- \n", + " | | | | |\n", + " | | | | |\n", + " --- --- --- --- \n", + " | | | | |\n", + " | | | | |\n", + " --- --- --- --- \n", + " | | | | |\n", + " | | | | |\n", + " --- --- --- --- \n", + " | | | | |\n", + " | | | | |\n", + " --- --- --- --- \n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "draw_board(4,4)" ] }, { @@ -85,24 +131,43 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ - "# Write you solution here" + "# Write your solution\n", + "def draw_tic_tac_toe_board(matrix):\n", + " symbols = {0: ' ', 1: 'X', 2: 'O'}\n", + " \n", + " for row in matrix:\n", + " print(' | '.join(symbols[cell] for cell in row))\n", + " print('-' * (len(row) * 4 - 1))" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X | | \n", + "-----------\n", + " | O | \n", + "-----------\n", + " | | X\n", + "-----------\n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "board[0][0] = 1 \n", + "board[1][1] = 2 \n", + "board[2][2] = 1\n", + "draw_tic_tac_toe_board(board)" ] }, { @@ -114,32 +179,92 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ - "# Write you solution here" + "# Write you solution here\n", + "def check_winner(matrix):\n", + " n = len(matrix)\n", + " \n", + " def check_line(line):\n", + " if all(cell == 1 for cell in line):\n", + " return 1\n", + " if all(cell == 2 for cell in line):\n", + " return 2\n", + " return 0\n", + " \n", + " def check_lines():\n", + " # Check rows and columns\n", + " for i in range(n):\n", + " row = matrix[i]\n", + " col = [matrix[j][i] for j in range(n)]\n", + " row_winner = check_line(row)\n", + " if row_winner != 0:\n", + " return row_winner\n", + " col_winner = check_line(col)\n", + " if col_winner != 0:\n", + " return col_winner\n", + " \n", + " # Check diagonals\n", + " main_diag = [matrix[i][i] for i in range(n)]\n", + " anti_diag = [matrix[i][n - 1 - i] for i in range(n)]\n", + " \n", + " if check_line(main_diag) != 0:\n", + " return check_line(main_diag)\n", + " if check_line(anti_diag) != 0:\n", + " return check_line(anti_diag)\n", + " \n", + " return 0\n", + " \n", + " winner = check_lines()\n", + " \n", + " if winner == 1:\n", + " print(\"Player 1 has won.\")\n", + " return 1\n", + " elif winner == 2:\n", + " print(\"Player 2 has won.\")\n", + " return 2\n", + " elif any(0 in row for row in matrix):\n", + " print(\"The game is incomplete.\")\n", + " return -1\n", + " else:\n", + " print(\"The game is a draw.\")\n", + " return 0\n" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The game is incomplete.\n" + ] + }, + { + "data": { + "text/plain": [ + "-1" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "(check_winner(board))" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "winner_is_2 = [[2, 2, 0],\n", @@ -163,6 +288,114 @@ "\t[2, 1, 0]]" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 has won.\n" + ] + }, + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(check_winner(winner_is_2))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 has won.\n" + ] + }, + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(check_winner(winner_is_1))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 has won.\n" + ] + }, + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(check_winner(winner_is_also_1))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The game is incomplete.\n" + ] + }, + { + "data": { + "text/plain": [ + "-1" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(check_winner(no_winner))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -172,24 +405,44 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [], "source": [ - "# Write you solution here" + "# Write you solution here\n", + "\n", + "board=create_matrix(3)\n", + "def place_marker(board, player, x, y):\n", + " # Check if the coordinates are within bounds\n", + " if not (0 <= x < len(board) and 0 <= y < len(board[0])):\n", + " return False\n", + " \n", + " # Check if the position is empty\n", + " if board[x][y] == 0:\n", + " board[x][y] = player\n", + " return True\n", + " return False" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "place_marker(board,1,1,2)" ] }, { @@ -201,24 +454,79 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 17, + "metadata": {}, "outputs": [], "source": [ - "# Write you solution here" + "# Write you solution \n", + "def draw_tic_tac_toe_board(matrix):\n", + " n = len(matrix)\n", + " column_labels = [chr(ord('A') + i) for i in range(n)]\n", + " print(\" \" + \" \".join(column_labels)) # Print column labels\n", + " for i in range(n):\n", + " row_label = str(i + 1)\n", + " row_display = \" \".join({0: \".\", 1: \"X\", 2: \"O\"}[cell] for cell in matrix[i])\n", + " print(row_label + \" \" + row_display)\n", + " \n", + "def place_marker(board, player, position):\n", + " # Convert position from \"A1\", \"B2\", etc., to matrix indices\n", + " if len(position) < 2:\n", + " return False\n", + " column_label, row_label = position[0].upper(), position[1:]\n", + " if not (column_label.isalpha() and row_label.isdigit()):\n", + " return False\n", + " \n", + " column_index = ord(column_label) - ord('A')\n", + " row_index = int(row_label) - 1\n", + " if not (0 <= row_index < len(board) and 0 <= column_index < len(board[0])):\n", + " return False\n", + " if board[row_index][column_index] == 0:\n", + " board[row_index][column_index] = player\n", + " return True\n", + " return False" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C\n", + "1 . . .\n", + "2 . . .\n", + "3 . . .\n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "board= create_matrix(3)\n", + "draw_tic_tac_toe_board(board)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C\n", + "1 X . .\n", + "2 . . .\n", + "3 . . .\n" + ] + } + ], + "source": [ + "place_marker(board, 1, \"A1\")\n", + "draw_tic_tac_toe_board(board) " ] }, { @@ -230,24 +538,70 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 20, + "metadata": {}, "outputs": [], "source": [ - "# Write you solution here" + "# Write you solution here\n", + "def update_board(board, player, location):\n", + " return place_marker(board, player, location)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial board:\n", + " A B C\n", + "1 . . .\n", + "2 . . .\n", + "3 . . .\n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "board = create_matrix(3)\n", + "print(\"Initial board:\")\n", + "draw_tic_tac_toe_board(board)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Updating board with 'X' at A1:\n", + " A B C\n", + "1 X . .\n", + "2 . . .\n", + "3 . . .\n", + "\n", + "Updating board with 'O' at B2:\n", + " A B C\n", + "1 X . .\n", + "2 . O .\n", + "3 . . .\n" + ] + } + ], + "source": [ + "print(\"\\nUpdating board with 'X' at A1:\")\n", + "update_board(board, 1, \"A1\")\n", + "draw_tic_tac_toe_board(board)\n", + "print(\"\\nUpdating board with 'O' at B2:\")\n", + "update_board(board, 2, \"B2\")\n", + "draw_tic_tac_toe_board(board) " ] }, { @@ -259,24 +613,90 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 25, + "metadata": {}, "outputs": [], "source": [ - "# Write you solution here" + "# Write you solution here\n", + "def get_valid_move(board, player):\n", + " while True:\n", + " location = input(f\"Player {player}, enter your move: \").strip().upper()\n", + " \n", + " # Validate input format (e.g., \"A1\", \"C2\")\n", + " if len(location) < 2 or not location[0].isalpha() or not location[1:].isdigit():\n", + " print(\"Invalid format. Please use the format 'LetterNumber' (e.g., A1, B3).\")\n", + " continue\n", + " \n", + " # Call the update_board function to attempt to make the move\n", + " if update_board(board, player, location):\n", + " print(f\"Player {player} placed their marker at {location}.\")\n", + " break\n", + " else:\n", + " print(f\"Invalid move or cell already occupied. Try again.\")" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial board:\n", + " A B C\n", + "1 . . .\n", + "2 . . .\n", + "3 . . .\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: A1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at A1.\n", + " A B C\n", + "1 X . .\n", + "2 . . .\n", + "3 . . .\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: B1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at B1.\n", + " A B C\n", + "1 X O .\n", + "2 . . .\n", + "3 . . .\n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "board = create_matrix(3)\n", + "print(\"Initial board:\")\n", + "draw_tic_tac_toe_board(board)\n", + "get_valid_move(board,1)\n", + "draw_tic_tac_toe_board(board)\n", + "get_valid_move(board, 2)\n", + "draw_tic_tac_toe_board(board)" ] }, { @@ -288,24 +708,273 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 27, + "metadata": {}, "outputs": [], "source": [ - "# Write you solution here" + "# Write you solution here\n", + "def create_matrix(n):\n", + " return [[0 for _ in range(n)] for _ in range(n)]\n", + "\n", + "def draw_tic_tac_toe_board(matrix):\n", + " n = len(matrix)\n", + " column_labels = [chr(ord('A') + i) for i in range(n)]\n", + " print(\" \" + \" \".join(column_labels)) # Print column labels\n", + " for i in range(n):\n", + " row_label = str(i + 1)\n", + " row_display = \" \".join({0: \".\", 1: \"X\", 2: \"O\"}[cell] for cell in matrix[i])\n", + " print(row_label + \" \" + row_display)\n", + "\n", + "def place_marker(board, player, position):\n", + " if len(position) < 2:\n", + " return False\n", + " column_label, row_label = position[0].upper(), position[1:]\n", + " if not (column_label.isalpha() and row_label.isdigit()):\n", + " return False\n", + " \n", + " column_index = ord(column_label) - ord('A')\n", + " row_index = int(row_label) - 1\n", + " \n", + " if not (0 <= row_index < len(board) and 0 <= column_index < len(board[0])):\n", + " return False\n", + " \n", + " if board[row_index][column_index] == 0:\n", + " board[row_index][column_index] = player\n", + " return True\n", + " \n", + " return False\n", + "\n", + "def check_game_status(matrix):\n", + " n = len(matrix)\n", + " \n", + " def check_winner(player):\n", + " # Check rows and columns\n", + " for i in range(n):\n", + " if all(matrix[i][j] == player for j in range(n)) or \\\n", + " all(matrix[j][i] == player for j in range(n)):\n", + " return True\n", + " # Check diagonals\n", + " if all(matrix[i][i] == player for i in range(n)) or \\\n", + " all(matrix[i][n - 1 - i] == player for i in range(n)):\n", + " return True\n", + " return False\n", + " \n", + " def is_board_full():\n", + " return all(matrix[i][j] != 0 for i in range(n) for j in range(n))\n", + " \n", + " if check_winner(1):\n", + " return 1 # Player 1 (X) wins\n", + " if check_winner(2):\n", + " return 2 # Player 2 (O) wins\n", + " if is_board_full():\n", + " return 0 # Draw\n", + " return -1 # Incomplete\n", + "\n", + "def update_board(board, player, location):\n", + " return place_marker(board, player, location)\n", + "\n", + "def get_valid_move(board, player):\n", + " while True:\n", + " location = input(f\"Player {player}, enter your move: \").strip().upper()\n", + " \n", + " if len(location) < 2 or not location[0].isalpha() or not location[1:].isdigit():\n", + " print(\"Invalid format. Please use the format 'LetterNumber' (e.g., A1, B3).\")\n", + " continue\n", + " \n", + " if update_board(board, player, location):\n", + " print(f\"Player {player} placed their marker at {location}.\")\n", + " break\n", + " else:\n", + " print(f\"Invalid move or cell already occupied. Try again.\")\n", + "\n", + "def play_game(n):\n", + " board = create_matrix(n)\n", + " current_player = 1\n", + " \n", + " while True:\n", + " draw_tic_tac_toe_board(board)\n", + " print(f\"\\nPlayer {current_player}'s turn:\")\n", + " get_valid_move(board, current_player)\n", + " \n", + " status = check_game_status(board)\n", + " \n", + " if status == 1:\n", + " draw_tic_tac_toe_board(board)\n", + " print(\"Player 1 (X) wins!\")\n", + " break\n", + " elif status == 2:\n", + " draw_tic_tac_toe_board(board)\n", + " print(\"Player 2 (O) wins!\")\n", + " break\n", + " elif status == 0:\n", + " draw_tic_tac_toe_board(board)\n", + " print(\"The game is a draw!\")\n", + " break\n", + " \n", + " # Switch players\n", + " current_player = 2 if current_player == 1 else 1" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C\n", + "1 . . .\n", + "2 . . .\n", + "3 . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: A1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at A1.\n", + " A B C\n", + "1 X . .\n", + "2 . . .\n", + "3 . . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: C1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at C1.\n", + " A B C\n", + "1 X . O\n", + "2 . . .\n", + "3 . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: A2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at A2.\n", + " A B C\n", + "1 X . O\n", + "2 X . .\n", + "3 . . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: A3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at A3.\n", + " A B C\n", + "1 X . O\n", + "2 X . .\n", + "3 O . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: B2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at B2.\n", + " A B C\n", + "1 X . O\n", + "2 X X .\n", + "3 O . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: C2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at C2.\n", + " A B C\n", + "1 X . O\n", + "2 X X O\n", + "3 O . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: C3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at C3.\n", + " A B C\n", + "1 X . O\n", + "2 X X O\n", + "3 O . X\n", + "Player 1 (X) wins!\n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "play_game(3)" ] }, { @@ -317,13 +986,268 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D E\n", + "1 . . . . .\n", + "2 . . . . .\n", + "3 . . . . .\n", + "4 . . . . .\n", + "5 . . . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: A1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at A1.\n", + " A B C D E\n", + "1 X . . . .\n", + "2 . . . . .\n", + "3 . . . . .\n", + "4 . . . . .\n", + "5 . . . . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: B1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at B1.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . . . . .\n", + "3 . . . . .\n", + "4 . . . . .\n", + "5 . . . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: B2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at B2.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . . . .\n", + "4 . . . . .\n", + "5 . . . . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: C4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at C4.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . . . .\n", + "4 . . O . .\n", + "5 . . . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: C3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at C3.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 . . O . .\n", + "5 . . . . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: B4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at B4.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 . O O . .\n", + "5 . . . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: D5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at D5.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 . O O . .\n", + "5 . . . X .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: A4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at A4.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 O O O . .\n", + "5 . . . X .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: D4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at D4.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 O O O X .\n", + "5 . . . X .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: E1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at E1.\n", + " A B C D E\n", + "1 X O . . O\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 O O O X .\n", + "5 . . . X .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: E5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at E5.\n", + " A B C D E\n", + "1 X O . . O\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 O O O X .\n", + "5 . . . X X\n", + "Player 1 (X) wins!\n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "play_game(5)" ] }, { @@ -336,9 +1260,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Write you solution here" @@ -347,9 +1269,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Test your solution here" @@ -372,9 +1292,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.12.1" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 39a52fc92361ed33ae9952c71bc84d197294d47e Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Mon, 9 Sep 2024 13:48:46 -0500 Subject: [PATCH 04/22] MyLabs --- MyLabs | 1 + 1 file changed, 1 insertion(+) create mode 100644 MyLabs diff --git a/MyLabs b/MyLabs new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/MyLabs @@ -0,0 +1 @@ + From 9721f7a9b59fcc8bfb5ccdca402ee90f20397804 Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Mon, 9 Sep 2024 13:49:14 -0500 Subject: [PATCH 05/22] Delete MyLabs --- MyLabs | 1 - 1 file changed, 1 deletion(-) delete mode 100644 MyLabs diff --git a/MyLabs b/MyLabs deleted file mode 100644 index 8b13789..0000000 --- a/MyLabs +++ /dev/null @@ -1 +0,0 @@ - From cc29f45e18905eea384117586f3102c9c045ddce Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Mon, 9 Sep 2024 13:51:08 -0500 Subject: [PATCH 06/22] lab2 --- Lab.2.ipynb | 1300 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1300 insertions(+) create mode 100644 Lab.2.ipynb diff --git a/Lab.2.ipynb b/Lab.2.ipynb new file mode 100644 index 0000000..95f89ab --- /dev/null +++ b/Lab.2.ipynb @@ -0,0 +1,1300 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lab 2- Tic Tac Toe\n", + "\n", + "In this lab your will build a n x n Tic Tac Toe game. As you do the exercises, make sure your solutions work for any size Tic Tac Toe game. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 1:* Write a function that creates an n by n matrix (of list of lists) which will represent the state of a Tie Tac Toe game. Let 0, 1, and 2 represent empty, \"X\", and \"O\", respectively.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here\n", + "empty=0\n", + "player_X=1\n", + "player_O=2\n", + "def create_matrix(n):\n", + " return[[0 for _ in range(n)] for _ in range(n)]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 0, 0]\n", + "[0, 2, 0]\n", + "[0, 0, 1]\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "board=create_matrix(3)\n", + "board\n", + "board[0][0] = 1 \n", + "board[1][1] = 2 \n", + "board[2][2] = 1\n", + "for row in board:\n", + " print(row)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 2:* Write a function that takes 2 integers `n` and `m` as input and draws a `n` by `m` game board. For example the following is a 3x3 board:\n", + "```\n", + " --- --- --- \n", + " | | | | \n", + " --- --- --- \n", + " | | | | \n", + " --- --- --- \n", + " | | | | \n", + " --- --- --- \n", + " ```" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here\n", + "def draw_board(n, m):\n", + " for i in range(n):\n", + " # Print the top border of the cells\n", + " print(\" \" + \" --- \" * m)\n", + " \n", + " # Print the cells' borders\n", + " for _ in range(2): # Each cell has two vertical borders\n", + " print(\" |\" + \" |\" * m)\n", + " \n", + " # Print the bottom border of the last row\n", + " print(\" \" + \" --- \" * m)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " --- --- --- --- \n", + " | | | | |\n", + " | | | | |\n", + " --- --- --- --- \n", + " | | | | |\n", + " | | | | |\n", + " --- --- --- --- \n", + " | | | | |\n", + " | | | | |\n", + " --- --- --- --- \n", + " | | | | |\n", + " | | | | |\n", + " --- --- --- --- \n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "draw_board(4,4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 3:* Modify exercise 2, so that it takes a matrix of the form from exercise 1 and draws a tic-tac-tie board with \"X\"s and \"O\"s. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Write your solution\n", + "def draw_tic_tac_toe_board(matrix):\n", + " symbols = {0: ' ', 1: 'X', 2: 'O'}\n", + " \n", + " for row in matrix:\n", + " print(' | '.join(symbols[cell] for cell in row))\n", + " print('-' * (len(row) * 4 - 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X | | \n", + "-----------\n", + " | O | \n", + "-----------\n", + " | | X\n", + "-----------\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "board[0][0] = 1 \n", + "board[1][1] = 2 \n", + "board[2][2] = 1\n", + "draw_tic_tac_toe_board(board)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 4:* Write a function that takes a `n` by `n` matrix representing a tic-tac-toe game, and returns -1, 0, 1, or 2 indicating the game is incomplete, the game is a draw, player 1 has won, or player 2 has one, respectively. Here are some example inputs you can use to test your code:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here\n", + "def check_winner(matrix):\n", + " n = len(matrix)\n", + " \n", + " def check_line(line):\n", + " if all(cell == 1 for cell in line):\n", + " return 1\n", + " if all(cell == 2 for cell in line):\n", + " return 2\n", + " return 0\n", + " \n", + " def check_lines():\n", + " # Check rows and columns\n", + " for i in range(n):\n", + " row = matrix[i]\n", + " col = [matrix[j][i] for j in range(n)]\n", + " row_winner = check_line(row)\n", + " if row_winner != 0:\n", + " return row_winner\n", + " col_winner = check_line(col)\n", + " if col_winner != 0:\n", + " return col_winner\n", + " \n", + " # Check diagonals\n", + " main_diag = [matrix[i][i] for i in range(n)]\n", + " anti_diag = [matrix[i][n - 1 - i] for i in range(n)]\n", + " \n", + " if check_line(main_diag) != 0:\n", + " return check_line(main_diag)\n", + " if check_line(anti_diag) != 0:\n", + " return check_line(anti_diag)\n", + " \n", + " return 0\n", + " \n", + " winner = check_lines()\n", + " \n", + " if winner == 1:\n", + " print(\"Player 1 has won.\")\n", + " return 1\n", + " elif winner == 2:\n", + " print(\"Player 2 has won.\")\n", + " return 2\n", + " elif any(0 in row for row in matrix):\n", + " print(\"The game is incomplete.\")\n", + " return -1\n", + " else:\n", + " print(\"The game is a draw.\")\n", + " return 0\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The game is incomplete.\n" + ] + }, + { + "data": { + "text/plain": [ + "-1" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Test your solution here\n", + "(check_winner(board))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "winner_is_2 = [[2, 2, 0],\n", + "\t[2, 1, 0],\n", + "\t[2, 1, 1]]\n", + "\n", + "winner_is_1 = [[1, 2, 0],\n", + "\t[2, 1, 0],\n", + "\t[2, 1, 1]]\n", + "\n", + "winner_is_also_1 = [[0, 1, 0],\n", + "\t[2, 1, 0],\n", + "\t[2, 1, 1]]\n", + "\n", + "no_winner = [[1, 2, 0],\n", + "\t[2, 1, 0],\n", + "\t[2, 1, 2]]\n", + "\n", + "also_no_winner = [[1, 2, 0],\n", + "\t[2, 1, 0],\n", + "\t[2, 1, 0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 has won.\n" + ] + }, + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(check_winner(winner_is_2))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 has won.\n" + ] + }, + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(check_winner(winner_is_1))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 has won.\n" + ] + }, + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(check_winner(winner_is_also_1))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The game is incomplete.\n" + ] + }, + { + "data": { + "text/plain": [ + "-1" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(check_winner(no_winner))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 5:* Write a function that takes a game board, player number, and `(x,y)` coordinates and places \"X\" or \"O\" in the correct location of the game board. Make sure that you only allow filling previously empty locations. Return `True` or `False` to indicate successful placement of \"X\" or \"O\"." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here\n", + "\n", + "board=create_matrix(3)\n", + "def place_marker(board, player, x, y):\n", + " # Check if the coordinates are within bounds\n", + " if not (0 <= x < len(board) and 0 <= y < len(board[0])):\n", + " return False\n", + " \n", + " # Check if the position is empty\n", + " if board[x][y] == 0:\n", + " board[x][y] = player\n", + " return True\n", + " return False" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Test your solution here\n", + "place_marker(board,1,1,2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 6:* Modify Exercise 4 to show column and row labels so that players can specify location using \"A2\" or \"C1\"." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution \n", + "def draw_tic_tac_toe_board(matrix):\n", + " n = len(matrix)\n", + " column_labels = [chr(ord('A') + i) for i in range(n)]\n", + " print(\" \" + \" \".join(column_labels)) # Print column labels\n", + " for i in range(n):\n", + " row_label = str(i + 1)\n", + " row_display = \" \".join({0: \".\", 1: \"X\", 2: \"O\"}[cell] for cell in matrix[i])\n", + " print(row_label + \" \" + row_display)\n", + " \n", + "def place_marker(board, player, position):\n", + " # Convert position from \"A1\", \"B2\", etc., to matrix indices\n", + " if len(position) < 2:\n", + " return False\n", + " column_label, row_label = position[0].upper(), position[1:]\n", + " if not (column_label.isalpha() and row_label.isdigit()):\n", + " return False\n", + " \n", + " column_index = ord(column_label) - ord('A')\n", + " row_index = int(row_label) - 1\n", + " if not (0 <= row_index < len(board) and 0 <= column_index < len(board[0])):\n", + " return False\n", + " if board[row_index][column_index] == 0:\n", + " board[row_index][column_index] = player\n", + " return True\n", + " return False" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C\n", + "1 . . .\n", + "2 . . .\n", + "3 . . .\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "board= create_matrix(3)\n", + "draw_tic_tac_toe_board(board)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C\n", + "1 X . .\n", + "2 . . .\n", + "3 . . .\n" + ] + } + ], + "source": [ + "place_marker(board, 1, \"A1\")\n", + "draw_tic_tac_toe_board(board) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 7:* Write a function that takes a board, player number, and location specified as in exercise 6 and then calls exercise 5 to correctly modify the board. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here\n", + "def update_board(board, player, location):\n", + " return place_marker(board, player, location)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial board:\n", + " A B C\n", + "1 . . .\n", + "2 . . .\n", + "3 . . .\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "board = create_matrix(3)\n", + "print(\"Initial board:\")\n", + "draw_tic_tac_toe_board(board)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Updating board with 'X' at A1:\n", + " A B C\n", + "1 X . .\n", + "2 . . .\n", + "3 . . .\n", + "\n", + "Updating board with 'O' at B2:\n", + " A B C\n", + "1 X . .\n", + "2 . O .\n", + "3 . . .\n" + ] + } + ], + "source": [ + "print(\"\\nUpdating board with 'X' at A1:\")\n", + "update_board(board, 1, \"A1\")\n", + "draw_tic_tac_toe_board(board)\n", + "print(\"\\nUpdating board with 'O' at B2:\")\n", + "update_board(board, 2, \"B2\")\n", + "draw_tic_tac_toe_board(board) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 8:* Write a function is called with a board and player number, takes input from the player using python's `input`, and modifies the board using your function from exercise 7. Note that you should keep asking for input until you have gotten a valid input that results in a valid move." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here\n", + "def get_valid_move(board, player):\n", + " while True:\n", + " location = input(f\"Player {player}, enter your move: \").strip().upper()\n", + " \n", + " # Validate input format (e.g., \"A1\", \"C2\")\n", + " if len(location) < 2 or not location[0].isalpha() or not location[1:].isdigit():\n", + " print(\"Invalid format. Please use the format 'LetterNumber' (e.g., A1, B3).\")\n", + " continue\n", + " \n", + " # Call the update_board function to attempt to make the move\n", + " if update_board(board, player, location):\n", + " print(f\"Player {player} placed their marker at {location}.\")\n", + " break\n", + " else:\n", + " print(f\"Invalid move or cell already occupied. Try again.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial board:\n", + " A B C\n", + "1 . . .\n", + "2 . . .\n", + "3 . . .\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: A1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at A1.\n", + " A B C\n", + "1 X . .\n", + "2 . . .\n", + "3 . . .\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: B1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at B1.\n", + " A B C\n", + "1 X O .\n", + "2 . . .\n", + "3 . . .\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "board = create_matrix(3)\n", + "print(\"Initial board:\")\n", + "draw_tic_tac_toe_board(board)\n", + "get_valid_move(board,1)\n", + "draw_tic_tac_toe_board(board)\n", + "get_valid_move(board, 2)\n", + "draw_tic_tac_toe_board(board)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 9:* Use all of the previous exercises to implement a full tic-tac-toe game, where an appropriate board is drawn, 2 players are repeatedly asked for a location coordinates of where they wish to place a mark, and the game status is checked until a player wins or a draw occurs." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here\n", + "def create_matrix(n):\n", + " return [[0 for _ in range(n)] for _ in range(n)]\n", + "\n", + "def draw_tic_tac_toe_board(matrix):\n", + " n = len(matrix)\n", + " column_labels = [chr(ord('A') + i) for i in range(n)]\n", + " print(\" \" + \" \".join(column_labels)) # Print column labels\n", + " for i in range(n):\n", + " row_label = str(i + 1)\n", + " row_display = \" \".join({0: \".\", 1: \"X\", 2: \"O\"}[cell] for cell in matrix[i])\n", + " print(row_label + \" \" + row_display)\n", + "\n", + "def place_marker(board, player, position):\n", + " if len(position) < 2:\n", + " return False\n", + " column_label, row_label = position[0].upper(), position[1:]\n", + " if not (column_label.isalpha() and row_label.isdigit()):\n", + " return False\n", + " \n", + " column_index = ord(column_label) - ord('A')\n", + " row_index = int(row_label) - 1\n", + " \n", + " if not (0 <= row_index < len(board) and 0 <= column_index < len(board[0])):\n", + " return False\n", + " \n", + " if board[row_index][column_index] == 0:\n", + " board[row_index][column_index] = player\n", + " return True\n", + " \n", + " return False\n", + "\n", + "def check_game_status(matrix):\n", + " n = len(matrix)\n", + " \n", + " def check_winner(player):\n", + " # Check rows and columns\n", + " for i in range(n):\n", + " if all(matrix[i][j] == player for j in range(n)) or \\\n", + " all(matrix[j][i] == player for j in range(n)):\n", + " return True\n", + " # Check diagonals\n", + " if all(matrix[i][i] == player for i in range(n)) or \\\n", + " all(matrix[i][n - 1 - i] == player for i in range(n)):\n", + " return True\n", + " return False\n", + " \n", + " def is_board_full():\n", + " return all(matrix[i][j] != 0 for i in range(n) for j in range(n))\n", + " \n", + " if check_winner(1):\n", + " return 1 # Player 1 (X) wins\n", + " if check_winner(2):\n", + " return 2 # Player 2 (O) wins\n", + " if is_board_full():\n", + " return 0 # Draw\n", + " return -1 # Incomplete\n", + "\n", + "def update_board(board, player, location):\n", + " return place_marker(board, player, location)\n", + "\n", + "def get_valid_move(board, player):\n", + " while True:\n", + " location = input(f\"Player {player}, enter your move: \").strip().upper()\n", + " \n", + " if len(location) < 2 or not location[0].isalpha() or not location[1:].isdigit():\n", + " print(\"Invalid format. Please use the format 'LetterNumber' (e.g., A1, B3).\")\n", + " continue\n", + " \n", + " if update_board(board, player, location):\n", + " print(f\"Player {player} placed their marker at {location}.\")\n", + " break\n", + " else:\n", + " print(f\"Invalid move or cell already occupied. Try again.\")\n", + "\n", + "def play_game(n):\n", + " board = create_matrix(n)\n", + " current_player = 1\n", + " \n", + " while True:\n", + " draw_tic_tac_toe_board(board)\n", + " print(f\"\\nPlayer {current_player}'s turn:\")\n", + " get_valid_move(board, current_player)\n", + " \n", + " status = check_game_status(board)\n", + " \n", + " if status == 1:\n", + " draw_tic_tac_toe_board(board)\n", + " print(\"Player 1 (X) wins!\")\n", + " break\n", + " elif status == 2:\n", + " draw_tic_tac_toe_board(board)\n", + " print(\"Player 2 (O) wins!\")\n", + " break\n", + " elif status == 0:\n", + " draw_tic_tac_toe_board(board)\n", + " print(\"The game is a draw!\")\n", + " break\n", + " \n", + " # Switch players\n", + " current_player = 2 if current_player == 1 else 1" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C\n", + "1 . . .\n", + "2 . . .\n", + "3 . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: A1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at A1.\n", + " A B C\n", + "1 X . .\n", + "2 . . .\n", + "3 . . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: C1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at C1.\n", + " A B C\n", + "1 X . O\n", + "2 . . .\n", + "3 . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: A2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at A2.\n", + " A B C\n", + "1 X . O\n", + "2 X . .\n", + "3 . . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: A3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at A3.\n", + " A B C\n", + "1 X . O\n", + "2 X . .\n", + "3 O . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: B2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at B2.\n", + " A B C\n", + "1 X . O\n", + "2 X X .\n", + "3 O . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: C2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at C2.\n", + " A B C\n", + "1 X . O\n", + "2 X X O\n", + "3 O . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: C3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at C3.\n", + " A B C\n", + "1 X . O\n", + "2 X X O\n", + "3 O . X\n", + "Player 1 (X) wins!\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "play_game(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 10:* Test that your game works for 5x5 Tic Tac Toe. " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D E\n", + "1 . . . . .\n", + "2 . . . . .\n", + "3 . . . . .\n", + "4 . . . . .\n", + "5 . . . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: A1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at A1.\n", + " A B C D E\n", + "1 X . . . .\n", + "2 . . . . .\n", + "3 . . . . .\n", + "4 . . . . .\n", + "5 . . . . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: B1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at B1.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . . . . .\n", + "3 . . . . .\n", + "4 . . . . .\n", + "5 . . . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: B2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at B2.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . . . .\n", + "4 . . . . .\n", + "5 . . . . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: C4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at C4.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . . . .\n", + "4 . . O . .\n", + "5 . . . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: C3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at C3.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 . . O . .\n", + "5 . . . . .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: B4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at B4.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 . O O . .\n", + "5 . . . . .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: D5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at D5.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 . O O . .\n", + "5 . . . X .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: A4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at A4.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 O O O . .\n", + "5 . . . X .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: D4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at D4.\n", + " A B C D E\n", + "1 X O . . .\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 O O O X .\n", + "5 . . . X .\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, enter your move: E1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2 placed their marker at E1.\n", + " A B C D E\n", + "1 X O . . O\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 O O O X .\n", + "5 . . . X .\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, enter your move: E5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 placed their marker at E5.\n", + " A B C D E\n", + "1 X O . . O\n", + "2 . X . . .\n", + "3 . . X . .\n", + "4 O O O X .\n", + "5 . . . X X\n", + "Player 1 (X) wins!\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "play_game(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 11:* (Advanced / Challenge) Develop a version of the game where one player is the computer. Note that you don't need to do an extensive seach for the best move. You can have the computer simply protect against loosing and otherwise try to win with straight or diagonal patterns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Write you solution here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test your solution here" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 953999446114d411e29fcf7446362d16e091a4c2 Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Mon, 9 Sep 2024 13:51:28 -0500 Subject: [PATCH 07/22] My LABS --- LABS | 1 + 1 file changed, 1 insertion(+) create mode 100644 LABS diff --git a/LABS b/LABS new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/LABS @@ -0,0 +1 @@ + From ae5b45aa11792ae6ef7f0bf90955c944561e032a Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Mon, 9 Sep 2024 13:51:41 -0500 Subject: [PATCH 08/22] Delete LABS --- LABS | 1 - 1 file changed, 1 deletion(-) delete mode 100644 LABS diff --git a/LABS b/LABS deleted file mode 100644 index 8b13789..0000000 --- a/LABS +++ /dev/null @@ -1 +0,0 @@ - From 4036beb0391a01ac0da9b30c433967dbce03e79e Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Mon, 9 Sep 2024 15:10:26 -0500 Subject: [PATCH 09/22] quiz --- quiz.ipynb | 108 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 108 insertions(+) create mode 100644 quiz.ipynb diff --git a/quiz.ipynb b/quiz.ipynb new file mode 100644 index 0000000..ab83a1b --- /dev/null +++ b/quiz.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 15, + "id": "e6c643de-76e0-4721-9b60-f91255f04df2", + "metadata": {}, + "outputs": [], + "source": [ + "def create_new_args0(args):\n", + " max_len=max(map(len, filter(lambda x:isinstance(x, list), args)))\n", + " new_args=[]\n", + " for a in args:\n", + " if not isinstance (a, list):\n", + " a0=[a] *max_len\n", + " elif len(a)==max_len:\n", + " a0=a\n", + " else:\n", + " print(\"Error: all list arguments must have the same length.\")\n", + " return\n", + " new_args.append(a0)\n", + " return new_args\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1f9fd1ac-3b93-4452-88b8-2cc757c3fbb0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[1, 2], [3, 4], [5, 5]]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "create_new_args0([[1,2],[3,4],5])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "4ec0e39c-4bbb-4e31-810e-42fc6f62ce73", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[4, 7], [7, 8], [10, 10]]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "create_new_args0([[4,7],[7,8],10])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "bb42d54b-4ab6-4713-9be2-2ca23fbaaed1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error: all list arguments must have the same length.\n" + ] + } + ], + "source": [ + "create_new_args0([[4,7,8],[7,8],10])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 799188f583bfc6267ece52ae171cbcc2b93cb036 Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Fri, 20 Sep 2024 11:40:56 -0500 Subject: [PATCH 10/22] lab 3 --- Labs/Lab.3/Lab.3.ipynb | 573 +++++++++++++++++++++++++---------------- 1 file changed, 357 insertions(+), 216 deletions(-) diff --git a/Labs/Lab.3/Lab.3.ipynb b/Labs/Lab.3/Lab.3.ipynb index 322757e..54c4086 100644 --- a/Labs/Lab.3/Lab.3.ipynb +++ b/Labs/Lab.3/Lab.3.ipynb @@ -6,112 +6,7 @@ "source": [ "# Lab 3\n", "\n", - "In this lab we will become familiar with distributions, histograms, and functional programming. Do not use numpy or any other library for this lab.\n", - "\n", - "Before that, lets get setup homework submission and submit your previous lab. \n", - "\n", - "## Working on the Command-line.\n", - "\n", - "It is important for you to learn to work on the command line and to be familiar with the Unix environment (e.g. Linux, Mac OS, or Windows Linux Subsystem). We'll go over working on the command-line in detail later in the course.\n", - "\n", - "You are required to submit your work in this course via GitHub. Today in class, you will setup everything on the command-line.\n", - "\n", - "### Command-line basics\n", - "\n", - "There is plenty of material online that will help you figure out how to do various tasks on the command line. Commands you may need to know today:\n", - "\n", - "* `ls`: lists the contents of the current directory.\n", - "* `pwd`: prints the path of the current directory.\n", - "* `cd `: changes your current directory to the specified directory.\n", - "* `cd ..`: changes current directory to the previous directory. Basically steps out of the current directory to the directory containing the current directory.\n", - "* `mkdir `: create a new directory with the specified name.\n", - "* `rmdir `: removes the specified directory. Note it has to be empty.\n", - "* `rm `: deletes the specified file.\n", - "* `mv `: Moves or renames a file.\n", - "* `cp `: copies an file. If you just provide a path to a directory, it copies the file into that directory with the same filename. If you specifiy a new filename, the copy has a new name. For example `cp File.1.txt File.2.txt` creates a copy of `File.1.txt` with the name `File.2.txt`. Meanwhile `cp File.1.txt my_directory`, where `my_directory` is a directory, creates a copy of `File.1.txt` in directory `my_directory` with the name `File.1.txt`.\n", - "\n", - "For reference, here are some example resources I found by googling:\n", - "\n", - "* Paths and Wildcards: https://www.warp.dev/terminus/linux-wildcards\n", - "* Basic commands like copy: https://kb.iu.edu/d/afsk\n", - "* General introduction to shell: https://github-pages.ucl.ac.uk/RCPSTrainingMaterials/HPCandHTCusingLegion/2_intro_to_shell.html\n", - "* Manual pages: https://www.geeksforgeeks.org/linux-man-page-entries-different-types/?ref=ml_lbp\n", - "* Chaining commands: https://www.geeksforgeeks.org/chaining-commands-in-linux/?ref=ml_lbp\n", - "* Piping: https://www.geeksforgeeks.org/piping-in-unix-or-linux/\n", - "* Using sed: https://www.geeksforgeeks.org/sed-command-linux-set-2/?ref=ml_lbp\n", - "* Various Unix commands: https://www.geeksforgeeks.org/linux-commands/?ref=lbp\n", - "* Cheat sheets:\n", - " * https://www.stationx.net/unix-commands-cheat-sheet/\n", - " * https://cheatography.com/davechild/cheat-sheets/linux-command-line/\n", - " * https://www.theknowledgeacademy.com/blog/unix-commands-cheat-sheet/\n", - " \n", - "These aren't necessarily the best resources. Feel free to search for better ones. Also, don't forget that Unix has built-in manual pages for all of its commands. Just type `man ` at the command prompt. Use the space-bar to scroll through the documentation and \"q\" to exit.\n", - "\n", - "\n", - "### Setup and Submission\n", - "\n", - "Our course repository is public. The instructions here aim to have you setup a fork of the course repository. Unfortunately because you are forking a public repo, your fork will have to be public also. \n", - "\n", - "You should be familiar with git from the first semester of this course. I assume that you all have github accounts and have setup things to be able to [push to github using ssh](https://docs.github.com/en/github/authenticating-to-github/connecting-to-github-with-ssh). The instuctions here lead you to:\n", - "\n", - "We'll overview what you will do before going through step by step instructions.\n", - "\n", - "1. Setup:\n", - " 1. Fork the class repository. Some directions in [fork-a-repo](https://docs.github.com/en/github/getting-started-with-github/fork-a-repo).\n", - " 1. Create a directory on your personal system where you will keep all course materials.\n", - " 1. In that directory, clone your fork of the repository.\n", - " 1. Using `git remote`, set the upstream to be the class repo, so you can pull from the class and push to your fork.\n", - "\n", - "1. Submission:\n", - " 1. Copy your solutions into the appropriate directory (e.g. into `Labs/Lab.2/`) and with appropriate filename `Lab.2.solution.ipynb'.\n", - " 1. Commit / push your solutions.\n", - " 1. Grant access to course instructors.\n", - "\n", - "Below are step by step instructions with examples (including example directory naming convention). Feel free to modify things as you see fit. \n", - "\n", - "#### Setup\n", - "You should only need to follow this instructions once. Here are some useful git commands:\n", - "\n", - "* Git help: `git help`\n", - "* Git remote help: `git help remote`\n", - "* Check remote status: `git remote -v`\n", - "* Add a remote: `git remote add `\n", - "* Add a remove: `git remote remove `\n", - "\n", - "Steps:\n", - "1. In a browser, log into GitHub and navigate to the [course repository](https://github.com/UTA-DataScience/DATA3402.Fall.2024).\n", - "1. On the top right of the page, press the fork button to create a new fork into your own GitHub account.\n", - "1. After successful fork, you should find the browser showing your fork of the course repository. Use the green \"Code\" button to copy path to the repo into your the clipboard of your computer.\n", - "1. Open a shell on your personal computer.\n", - "1. If you have not done so already, create a new directory/folder where you will keep all course material to navigate to it. For example: `mkdir Data-3402` and `cd Data-3402`.\n", - "1. Clone your fork of the repository using `git clone` followed by the path you copied into your clipboard. (copy/paste)\n", - "1. Paste the URL to your fork in the worksheet for the TAs and instructors.\n", - "1. Now go into the directory of your clone (`cd DATA3402.Fall.2024`).\n", - "1. Type `git remote -v` to see the current setup for fetch and pull.\n", - "1. Note the URL you see. This should be the same as what you used for your clone for both push and fetch.\n", - "1. Delete the origin remote using `git remote remove origin`.\n", - "1. Add the course repo as your remote using `git remote add origin https://github.com/UTA-DataScience/DATA3402.Fall.2024.git`.\n", - "1. Change the push to point to your fork. This means you will need the URL to your clone we copied earlier and confirmed as the original origin. The command will look something like: `git remote set-url --push origin https://github.com/XXXXXX/DATA3402.Fall.2024.git`, where XXXXX is your username on GitHub.\n", - "1. Note that if you setup everything correctly, you now should be able to do `git pull` to get updates from the course repo, and do `git push` to push your commits into your own fork.\n", - "\n", - "### Submission\n", - "These instructions outline how you submit files. Some useful commands:\n", - "* To add a file to local repository: `git add `.\n", - "* To commit all changed files into local repository: `git -a -m \"A message\"`. You need to provide some comment when you commit. \n", - "* To push the commited files from the local repository to GitHub: `git push`.\n", - "* To get updates from GitHub: `git pull`.\n", - "\n", - "Steps:\n", - "1. To submit your labs, navigate to your clone of your fork of the course repository. \n", - "1. Use `git pull` to make sure you have the latest updates. \n", - "1. Make sure your copy of the lab your are working on is in the appropriate place in this clone. That means if you have the file elsewhere, copy it to the same directory in your clone of your fork. \n", - "1. Note that in order to avoid future conflicts, you should always name your solution differently than the original file in the class repo. For example if your file is still named `Lab.2.ipynb` you should rename it using the `mv` command: `mv Lab.2.ipynb Lab.2.solution.ipynb`. \n", - "1. Add and files you wish to submit into the repo. For example: `git add Labs/Lab.2/Lab.2.solution.ipynb`\n", - "1. Commit any changes: `git commit -a -m \"Lab 2 updates\"`\n", - "1. Push your changes: `git push`\n", - "1. Check on github website that your solutions have been properly submitted.\n", - "\n", - "Before you leave the session today, make sure your GitHub Repo is setup. If you need to work further on your lab, navigate jupyter to the copy of the lab you just submitted and work there. Once done, repeat the commit and push commands to submit your updated solution. Note that lab 2 is due by midnight Monday 9/8/2024.\n" + "In this lab we will become familiar with distributions, histograms, and functional programming. Do not use numpy or any other library for this lab." ] }, { @@ -124,9 +19,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Value of x is 0.5539939133894121\n" + ] + } + ], "source": [ "import random\n", "x=random.random()\n", @@ -144,26 +47,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Skeleton\n", "def generate_uniform(N,x_min,x_max):\n", " out = []\n", - " ### BEGIN SOLUTION\n", - "\n", - " # Fill in your solution here \n", - " \n", - " ### END SOLUTION\n", + " return [random.uniform(x_min, x_max) for _ in range(N)]\n", " return out" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data Type: \n", + "Data Length: 1000\n", + "Type of Data Contents: \n", + "Data Minimum: -9.983137045718227\n", + "Data Maximum: 9.981486042470692\n" + ] + } + ], "source": [ "# Test your solution here\n", "data=generate_uniform(1000,-10,10)\n", @@ -185,28 +96,30 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Skeleton\n", "def mean(Data):\n", " m=0.\n", - " \n", - " ### BEGIN SOLUTION\n", - "\n", - " # Fill in your solution here \n", - " \n", - " ### END SOLUTION\n", - " \n", + " return sum(Data)/len(Data)\n", " return m" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of Data: -0.10743366700155592\n" + ] + } + ], "source": [ "# Test your solution here\n", "print (\"Mean of Data:\", mean(data))" @@ -222,28 +135,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Skeleton\n", "def variance(Data):\n", " m=0.\n", - " \n", - " ### BEGIN SOLUTION\n", - "\n", - " # Fill in your solution here \n", - " \n", - " ### END SOLUTION\n", - " \n", + " n=len(Data)\n", + " m=sum(Data)/n\n", + " return sum((x-m)**2 for x in data)/n\n", " return m" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variance of Data: 31.746904519022852\n" + ] + } + ], "source": [ "# Test your solution here\n", "print (\"Variance of Data:\", variance(data))" @@ -275,26 +192,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Solution\n", "def histogram(x,n_bins=10,x_min=None,x_max=None):\n", - " ### BEGIN SOLUTION\n", - "\n", - " # Fill in your solution here \n", - " \n", - " ### END SOLUTION\n", + " if x_min is None:\n", + " x_min=min(data)\n", + " if x_max is None:\n", + " x_max=max(data)\n", + " bin_width=(x_max - x_min)/n_bins\n", + " bin_edges=[x_min + i *bin_width for i in range(n_bins +1)]\n", + " hist=[0]* n_bins\n", + " for d in data:\n", + " for i in range(n_bins):\n", + " if bin_edges[i] <= d< bin_edges [i + 1]:\n", + " hist[i] +=1\n", + " break\n", "\n", " return hist,bin_edges" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4, 10, 14, 12, 6, 10, 8, 9, 12, 7, 7, 10, 10, 10, 6, 7, 15, 8, 15, 11, 10, 10, 15, 16, 13, 13, 9, 11, 15, 11, 5, 6, 8, 10, 10, 4, 10, 7, 12, 10, 13, 17, 9, 9, 16, 5, 12, 16, 12, 8, 13, 5, 11, 7, 11, 14, 6, 8, 11, 17, 12, 11, 14, 9, 4, 15, 12, 10, 8, 12, 4, 3, 13, 8, 9, 12, 6, 9, 4, 11, 8, 14, 14, 10, 12, 10, 13, 14, 9, 10, 8, 9, 5, 17, 3, 7, 10, 8, 7, 9]\n" + ] + } + ], "source": [ "# Test your solution here\n", "h,b=histogram(data,100)\n", @@ -324,29 +256,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Solution\n", "def draw_histogram(x,n_bins,x_min=None,x_max=None,character=\"#\",max_character_per_line=20):\n", - " ### BEGIN SOLUTION\n", - "\n", - " # Fill in your solution here \n", - " \n", - " ### END SOLUTION\n", - "\n", + " hist,bin_edges=histogram(data,n_bins, x_min,x_max)\n", + " for lower_bound, upper_bound, freq in zip(bin_edges[:-1],bin_edges[1:], hist):\n", + " bar=character * min(freq, max_character_per_line)\n", + " print(f\"[{lower_bound:.2f},{upper_bound:.2f}] :{bar}\")\n", " return hist,bin_edges" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[46, 46, 43, 56, 64, 59, 39, 43, 64, 53, 47, 56, 50, 57, 37, 42, 58, 56, 42, 41] [-9.983137045718227, -8.984905891308781, -7.986674736899335, -6.9884435824898885, -5.990212428080443, -4.991981273670997, -3.9937501192615503, -2.9955189648521046, -1.9972878104426588, -0.999056656033213, -0.0008255016237672663, 0.9974056527856785, 1.995636807195126, 2.993867961604572, 3.9920991160140176, 4.990330270423463, 5.988561424832909, 6.986792579242355, 7.985023733651801, 8.983254888061246, 9.981486042470692]\n" + ] + } + ], "source": [ "# Test your solution here\n", - "h,b=histogram(data,20)" + "h,b=histogram(data,20)\n", + "print(h,b)" ] }, { @@ -360,29 +299,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def where(mylist,myfunc):\n", " out= []\n", - " \n", - " ### BEGIN SOLUTION\n", - "\n", - " # Fill in your solution here \n", - " \n", - " ### END SOLUTION\n", + " return [i for i, item in enumerate(mylist) if myfunc(item)]\n", " \n", " return out" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.00,2.40] :#\n", + "[2.40,3.80] :#\n", + "[3.80,5.20] :#\n", + "[5.20,6.60] :\n", + "[6.60,8.00] :#\n" + ] + }, + { + "data": { + "text/plain": [ + "([1, 1, 1, 0, 1], [1.0, 2.4, 3.8, 5.199999999999999, 6.6, 8.0])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "data=[1,3,4,7,8]\n", + "h,b=histogram(data,5)\n", + "draw_histogram(data,5)" ] }, { @@ -400,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -408,10 +367,40 @@ " def testrange(x):\n", " return x=mymin\n", " return testrange\n", - "\n", - "# Examples:\n", - "F1=inrange(0,10)\n", - "F2=inrange(10,20)\n", + "def even(x):\n", + " return x % 2==0\n", + "def odd(x):\n", + " return x%2!=0\n", + "def greater_than(val):\n", + " return lambda x: x>val\n", + "def less_than(val):\n", + " return lambda x:xval\n", + "less_than=lambda val: lambda x:x Date: Fri, 27 Sep 2024 10:03:20 -0500 Subject: [PATCH 11/22] lab 4 --- Labs/Lab.4/Lab.4.ipynb | 1048 ++++++++++++++++++++++++++++++++++++++-- 1 file changed, 1014 insertions(+), 34 deletions(-) diff --git a/Labs/Lab.4/Lab.4.ipynb b/Labs/Lab.4/Lab.4.ipynb index 4603290..8b8ae41 100644 --- a/Labs/Lab.4/Lab.4.ipynb +++ b/Labs/Lab.4/Lab.4.ipynb @@ -18,10 +18,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "class counter:\n", + " def __init__(self, max_v):\n", + " self.max_v= max_v\n", + " self.v=0\n", + "\n", + " def advance(self):\n", + " if self.v < self.max_v:\n", + " self += 1\n", + " else:\n", + " print(\"Error: Above value.\")\n", + "\n", + " def reset(self):\n", + " self.v=0" + ] }, { "cell_type": "markdown", @@ -32,10 +46,107 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class Counter:\n", + " def __init__(self, max_v):\n", + " self.__max_v = max_v\n", + " self.__v = 0\n", + "\n", + " def advance(self):\n", + " if self.__v < self.__max_v:\n", + " self.__v += 1\n", + " else:\n", + " print(\"Error: Above value.\")\n", + "\n", + " def reset(self):\n", + " self.__v = 0\n", + "\n", + " def get_value(self):\n", + " return self.__v\n", + "\n", + " def get_max_value(self):\n", + " return self.__max_v\n", + "\n", + " def is_at_max(self):\n", + " return self.__v == self.__max_v" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "counter= Counter(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error: Above value.\n", + "Error: Above value.\n", + "Error: Above value.\n" + ] + } + ], + "source": [ + "counter.advance()\n", + "counter.advance()\n", + "counter.advance()\n", + "counter.advance()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n", + "5\n", + "True\n" + ] + } + ], + "source": [ + "print(counter.get_value())\n", + "print(counter.get_max_value())\n", + "print(counter.is_at_max())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "Error: Above value.\n", + "0\n" + ] + } + ], + "source": [ + "print(counter.is_at_max())\n", + "counter.advance()\n", + "counter.reset()\n", + "print(counter.get_value())" + ] }, { "cell_type": "markdown", @@ -46,10 +157,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "class Rectangle:\n", + " def __init__(self, length, width, x, y):\n", + " self.__l = length\n", + " self.__w = width\n", + " self.__x = x\n", + " self.__y = y\n", + "\n", + " def get_length(self):\n", + " return self.__l\n", + "\n", + " def get_width(self):\n", + " return self.__w\n", + "\n", + " def get_x(self):\n", + " return self.__x\n", + "\n", + " def get_y(self):\n", + " return self.__y\n", + "\n", + " def area(self):\n", + " return self.__l * self.__w\n", + "\n", + " def perimeter(self):\n", + " return 2 * (self.__l + self.__w)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Length = 7\n", + "Width = 8\n", + "X = 4\n", + "Y = 2\n", + "Area = 56\n", + "Perimeter= 30\n" + ] + } + ], + "source": [ + "rectangle = Rectangle(7, 8, 4, 2)\n", + "\n", + "print(\"Length = \", rectangle.get_length())\n", + "print(\"Width = \", rectangle.get_width())\n", + "print(\"X = \", rectangle.get_x())\n", + "print(\"Y = \", rectangle.get_y())\n", + "print(\"Area = \", rectangle.area())\n", + "print(\"Perimeter= \", rectangle.perimeter())" + ] }, { "cell_type": "markdown", @@ -60,10 +225,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "class Circle:\n", + " def __init__(self, radius, x, y):\n", + " self.__radius = radius\n", + " self.__x = x\n", + " self.__y = y\n", + "\n", + " def get_radius(self):\n", + " return self.__radius\n", + "\n", + " def get_x(self):\n", + " return self.__x\n", + "\n", + " def get_y(self):\n", + " return self.__y\n", + "\n", + " def set_radius(self, radius):\n", + " self.__radius = radius\n", + "\n", + " def set_x(self, x):\n", + " self.__x = x\n", + "\n", + " def set_y(self, y):\n", + " self.__y = y\n", + "\n", + " def area(self):\n", + " return self.__radius ** 2 * 3.14\n", + "\n", + " def perimeter(self):\n", + " return 2 * self.__radius * 3.14" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Radius: 5\n", + "X = 8\n", + "Y = 9\n", + "Area = 78.5\n", + "Perimeter = 31.400000000000002\n" + ] + } + ], + "source": [ + "circle = Circle(5,8,9)\n", + "\n", + "print(\"Radius:\", circle.get_radius())\n", + "print(\"X =\", circle.get_x())\n", + "print(\"Y =\",circle.get_y())\n", + "print(\"Area =\", circle.area())\n", + "print(\"Perimeter =\", circle.perimeter())" + ] }, { "cell_type": "markdown", @@ -74,10 +296,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "class Shape:\n", + " def area(self): raise NotImplementedError(\"Subclasses must implement area()\")\n", + " def perimeter(self): raise NotImplementedError(\"Subclasses must implement perimeter()\")\n", + "\n", + "class Rectangle(Shape):\n", + " def __init__(self, length, width, x, y): self.l,self.w,self.x,self.y = length,width,x,y\n", + " def area(self): return self.l*self.w\n", + " def perimeter(self): return 2*(self.l+self.w)\n", + "\n", + "class Circle(Shape):\n", + " def __init__(self, radius, x, y): self.r,self.x,self.y = radius,x,y\n", + " def area(self): return self.r**2 * 3.14\n", + " def perimeter(self): return 2 * self.r * 3.14" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Area = 25\n", + "Perimeter = 20\n" + ] + } + ], + "source": [ + "rectangle = Rectangle(5,5,7,7)\n", + "\n", + "print(\"Area = \", rectangle.area())\n", + "print(\"Perimeter = \", rectangle.perimeter())" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Area = 28.26\n", + "Perimeter = 18.84\n" + ] + } + ], + "source": [ + "circle = Circle(3,4,5)\n", + "\n", + "print(\"Area =\", circle.area())\n", + "\n", + "print(\"Perimeter =\", circle.perimeter())" + ] }, { "cell_type": "markdown", @@ -88,10 +367,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "class Triangle:\n", + " def __init__(self, base, height, side1, side2, side3):\n", + " self.b, self.h, self.s1, self.s2, self.s3 = base, height, side1, side2, side3\n", + "\n", + " def area(self): return 0.5 * self.b * self.h\n", + "\n", + " def perimeter(self): return self.s1 + self.s2 + self.s3" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Area = 10.0\n", + "Perimeter = 12\n" + ] + } + ], + "source": [ + "triangle = Triangle(5, 4, 3, 4, 5)\n", + "print(\"Area =\", triangle.area())\n", + "print(\"Perimeter =\", triangle.perimeter())" + ] }, { "cell_type": "markdown", @@ -102,10 +409,81 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "class Shape:\n", + " def parameter_points(self, num_points=16):\n", + " raise NotImplementedError(\"Subclasses must implement parameter_points()\")\n", + "\n", + "class Rectangle(Shape):\n", + " def __init__(self, length, width, x, y):\n", + " self.l, self.w, self.x, self.y = length, width, x, y\n", + "\n", + " def parameter_points(self, num_points=16):\n", + " return [(self.x + i, self.y) for i in range(self.l)] + [(self.x + self.l, self.y + i) for i in range(self.w)] + [(self.x + self.l - i, self.y + self.w) for i in range(self.l)] + [(self.x, self.y + self.w - i) for i in range(self.w)]\n", + "\n", + "class Circle(Shape):\n", + " def __init__(self, radius, x, y):\n", + " self.r, self.x, self.y = radius, x, y\n", + "\n", + " def parameter_points(self, num_points=16):\n", + " return [(self.x + self.r * math.cos(i * 2 * math.pi / num_points), self.y + self.r * math.sin(i * 2 * math.pi / num_points)) for i in range(num_points)]\n", + "\n", + "class Triangle(Shape):\n", + " def __init__(self, base, height, side1, side2, side3):\n", + " self.b, self.h, self.s1, self.s2, self.s3 = base, height, side1, side2, side3\n", + "\n", + " def parameter_points(self, num_points=16):\n", + " points = []\n", + " for i in range(num_points):\n", + " x = self.b * i / num_points\n", + " if x <= self.s1:\n", + " y = self.h * x / self.b\n", + " elif x <= self.s1 + self.s2:\n", + " y = self.h * (1 - (x - self.s1) / self.s2)\n", + " else:\n", + " y = 0\n", + " points.append((x, y))\n", + " return points" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Paramter points for the rectangle: [(2, 2), (3, 2), (4, 2), (5, 2), (6, 2), (7, 2), (7, 3), (7, 4), (7, 5), (6, 5), (5, 5), (4, 5), (3, 5), (2, 5), (2, 4), (2, 3)]\n" + ] + } + ], + "source": [ + "rectangle = Rectangle(5, 3, 2, 2)\n", + "print(\"Paramter points for the rectangle:\", rectangle.parameter_points())" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Paramter points for the triangle: [(0.0, 0.0), (0.3125, 0.25), (0.625, 0.5), (0.9375, 0.75), (1.25, 1.0), (1.5625, 1.25), (1.875, 1.5), (2.1875, 1.75), (2.5, 2.0), (2.8125, 2.25), (3.125, 3.875), (3.4375, 3.5625), (3.75, 3.25), (4.0625, 2.9375), (4.375, 2.625), (4.6875, 2.3125)]\n" + ] + } + ], + "source": [ + "triangle = Triangle(5, 4, 3, 4, 5)\n", + "print(\"Paramter points for the triangle:\", triangle.parameter_points())" + ] }, { "cell_type": "markdown", @@ -116,10 +494,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "class Shape:\n", + " def is_inside(self, x, y):\n", + " raise NotImplementedError(\"Subclasses must implement is_inside()\")\n", + "\n", + "class Rectangle(Shape):\n", + " def __init__(self, length, width, x, y):\n", + " self.x, self.y, self.length, self.width = x, y, length, width\n", + "\n", + " def is_inside(self, x, y):\n", + " return self.x <= x <= self.x + self.length and self.y <= y <= self.y + self.width\n", + "\n", + "class Circle(Shape):\n", + " def __init__(self, radius, x, y):\n", + " self.x, self.y, self.radius = x, y, radius\n", + "\n", + " def is_inside(self, x, y):\n", + " return (x - self.x) ** 2 + (y - self.y) ** 2 <= self.radius ** 2\n", + "\n", + "class Triangle(Shape):\n", + " def __init__(self, base, height, x, y):\n", + " self.x, self.y, self.base, self.height = x, y, base, height\n", + "\n", + " def is_inside(self, x, y):\n", + " A, B, C = (self.x, self.y), (self.x + self.base, self.y), (self.x + self.base / 2, self.y + self.height)\n", + " s = ((B[1] - C[1]) * (x - C[0]) + (C[0] - B[0]) * (y - C[1])) / ((B[1] - C[1]) * (A[0] - C[0]) + (C[0] - B[0]) * (A[1] - C[1]))\n", + " t = ((C[1] - A[1]) * (x - C[0]) + (A[0] - C[0]) * (y - C[1])) / ((B[1] - C[1]) * (A[0] - C[0]) + (C[0] - B[0]) * (A[1] - C[1]))\n", + " return 0 <= s <= 1 and 0 <= t <= 1 and s + t <= 1" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "True\n", + "True\n" + ] + } + ], + "source": [ + "rectangle = Rectangle(4, 3, 1, 1)\n", + "print(rectangle.is_inside(2, 2))\n", + "\n", + "circle = Circle(5, 0, 0)\n", + "print(circle.is_inside(3, 4))\n", + "\n", + "triangle = Triangle(4, 3, 0, 0)\n", + "print(triangle.is_inside(2, 2))" + ] }, { "cell_type": "markdown", @@ -130,10 +562,100 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "class Shape:\n", + " def is_inside(self, x, y):\n", + " raise NotImplementedError(\"Subclasses must implement is_inside()\")\n", + "\n", + " def overlaps(self, other):\n", + " raise NotImplementedError(\"Subclasses must implement overlaps()\")\n", + "\n", + "class Rectangle(Shape):\n", + " def __init__(self, length, width, x, y):\n", + " self.x, self.y, self.length, self.width = x, y, length, width\n", + "\n", + " def is_inside(self, x, y):\n", + " return self.x <= x <= self.x + self.length and self.y <= y <= self.y + self.width\n", + "\n", + " def overlaps(self, other):\n", + " if isinstance(other, Rectangle):\n", + " return not (self.x + self.length < other.x or other.x + other.length < self.x \\\n", + " or self.y + self.width < other.y or other.y + other.width < self.y)\n", + " elif isinstance(other, Circle):\n", + " closest_x = max(self.x, min(other.x, self.x + self.length))\n", + " closest_y = max(self.y, min(other.y, self.y + self.width))\n", + " return (closest_x - other.x) ** 2 + (closest_y - other.y) ** 2 <= other.radius ** 2\n", + " else:\n", + " raise NotImplementedError(\"Overlap not implemented for this shape combination\")\n", + "\n", + "class Circle(Shape):\n", + " def __init__(self, radius, x, y):\n", + " self.x, self.y, self.radius = x, y, radius\n", + "\n", + " def is_inside(self, x, y):\n", + " return (x - self.x) ** 2 + (y - self.y) ** 2 <= self.radius ** 2\n", + "\n", + " def overlaps(self, other):\n", + " if isinstance(other, Rectangle):\n", + " return other.overlaps(self)\n", + " elif isinstance(other, Circle):\n", + " return (self.x - other.x) ** 2 + (self.y - other.y) ** 2 <= (self.radius + other.radius) ** 2\n", + " else:\n", + " raise NotImplementedError(\"Overlap not implemented for this shape combination\")\n", + "\n", + "class Triangle(Shape):\n", + " def __init__(self, base, height, x, y):\n", + " self.x, self.y, self.base, self.height = x, y, base, height\n", + "\n", + " def is_inside(self, x, y):\n", + " return (0 <= (x - self.x) * (self.y + self.height - y) / (self.base * self.height) <= 1) \\\n", + " and (0 <= (x - self.x - self.base) * (self.y - y) / (self.base * self.height) <= 1) \\\n", + " and (0 <= (y - self.y) / self.height <= 1)\n", + "\n", + " def overlaps(self, other):\n", + " if isinstance(other, Triangle):\n", + " raise NotImplementedError(\"Overlap not implemented for Triangle and Triangle\")\n", + "\n", + " if isinstance(other, Rectangle):\n", + " return other.overlaps(self)\n", + "\n", + " if isinstance(other, Circle):\n", + " closest_x = min(max(self.x, other.x), self.x + self.base)\n", + " closest_y = min(max(self.y, other.y), self.y + self.height)\n", + " return (closest_x - other.x) ** 2 + (closest_y - other.y) ** 2 <= other.radius ** 2\n", + "\n", + " raise NotImplementedError(\"Overlap not implemented for this shape combination\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "True\n", + "True\n" + ] + } + ], + "source": [ + "rectangle1 = Rectangle(4, 3, 1, 1)\n", + "rectangle2 = Rectangle(3, 2, 2, 2)\n", + "print(rectangle1.overlaps(rectangle2))\n", + "\n", + "circle = Circle(5, 0, 0)\n", + "print(circle.overlaps(rectangle1))\n", + "\n", + "triangle = Triangle(4, 3, 0, 0)\n", + "print(triangle.overlaps(circle))" + ] }, { "cell_type": "markdown", @@ -144,10 +666,208 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "class Canvas:\n", + " def __init__(self, width, height):\n", + " self.width = width\n", + " self.height = height\n", + " self.data = [[' '] * width for i in range(height)]\n", + "\n", + " def set_pixel(self, row, col, char='*'):\n", + " self.data[row][col] = char\n", + "\n", + " def get_pixel(self, row, col):\n", + " return self.data[row][col]\n", + "\n", + " def clear_canvas(self):\n", + " self.data = [[' '] * self.width for i in range(self.height)]\n", + "\n", + " def v_line(self, x, y, w, **kargs):\n", + " for i in range(x,x+w):\n", + " self.set_pixel(i,y, **kargs)\n", + "\n", + " def h_line(self, x, y, h, **kargs):\n", + " for i in range(y,y+h):\n", + " self.set_pixel(x,i, **kargs)\n", + "\n", + " def line(self, x1, y1, x2, y2, **kargs):\n", + " slope = (y2-y1) / (x2-x1)\n", + " for y in range(y1,y2):\n", + " x= int(slope * y)\n", + " self.set_pixel(x,y, **kargs)\n", + "\n", + " def display(self):\n", + " print(\"\\n\".join([\"\".join(row) for row in self.data]))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "class Canvas:\n", + " def __init__(self, width, height):\n", + " self.width = width\n", + " self.height = height\n", + " self.data = [[' '] * width for _ in range(height)]\n", + "\n", + " def set_pixel(self, row, col, char='*'):\n", + " self.data[row][col] = char\n", + "\n", + " def clear_canvas(self):\n", + " self.data = [[' '] * self.width for _ in range(self.height)]\n", + "\n", + " def display(self):\n", + " print(\"\\n\".join([\"\".join(row) for row in self.data]))\n", + "\n", + "\n", + "class Shape:\n", + " def is_inside(self, x, y):\n", + " raise NotImplementedError(\"Subclasses must implement is_inside()\")\n", + "\n", + " def overlaps(self, other):\n", + " raise NotImplementedError(\"Subclasses must implement overlaps()\")\n", + "\n", + "\n", + "class Rectangle(Shape):\n", + " def __init__(self, length, width, x, y):\n", + " self.x, self.y, self.length, self.width = x, y, length, width\n", + "\n", + " def is_inside(self, x, y):\n", + " return self.x <= x <= self.x + self.length and self.y <= y <= self.y + self.width\n", + "\n", + " def overlaps(self, other):\n", + " if isinstance(other, Rectangle):\n", + " return not (self.x + self.length < other.x or other.x + other.length < self.x \\\n", + " or self.y + self.width < other.y or other.y + other.width < self.y)\n", + " elif isinstance(other, Circle):\n", + " closest_x = max(self.x, min(other.x, self.x + self.length))\n", + " closest_y = max(self.y, min(other.y, self.y + self.width))\n", + " return (closest_x - other.x) ** 2 + (closest_y - other.y) ** 2 <= other.radius ** 2\n", + " else:\n", + " raise NotImplementedError(\"Overlap not implemented for this shape combination\")\n", + "\n", + "\n", + "class Circle(Shape):\n", + " def __init__(self, radius, x, y):\n", + " self.x, self.y, self.radius = x, y, radius\n", + "\n", + " def is_inside(self, x, y):\n", + " return (x - self.x) ** 2 + (y - self.y) ** 2 <= self.radius ** 2\n", + "\n", + " def overlaps(self, other):\n", + " if isinstance(other, Rectangle):\n", + " return other.overlaps(self)\n", + " elif isinstance(other, Circle):\n", + " return (self.x - other.x) ** 2 + (self.y - other.y) ** 2 <= (self.radius + other.radius) ** 2\n", + " else:\n", + " raise NotImplementedError(\"Overlap not implemented for this shape combination\")\n", + "\n", + "\n", + "class Triangle(Shape):\n", + " def __init__(self, base, height, x, y):\n", + " self.x, self.y, self.base, self.height = x, y, base, height\n", + "\n", + " def is_inside(self, x, y):\n", + " return (0 <= (x - self.x) * (self.y + self.height - y) / (self.base * self.height) <= 1) \\\n", + " and (0 <= (x - self.x - self.base) * (self.y - y) / (self.base * self.height) <= 1) \\\n", + " and (0 <= (y - self.y) / self.height <= 1)\n", + "\n", + " def overlaps(self, other):\n", + " if isinstance(other, Triangle):\n", + " raise NotImplementedError(\"Overlap not implemented for Triangle and Triangle\")\n", + "\n", + " if isinstance(other, Rectangle):\n", + " return other.overlaps(self)\n", + "\n", + " if isinstance(other, Circle):\n", + " closest_x = min(max(self.x, other.x), self.x + self.base)\n", + " closest_y = min(max(self.y, other.y), self.y + self.height)\n", + " return (closest_x - other.x) ** 2 + (closest_y - other.y) ** 2 <= other.radius ** 2\n", + "\n", + " raise NotImplementedError(\"Overlap not implemented for this shape combination\")\n", + "\n", + "\n", + "class CompoundShape(Shape):\n", + " def __init__(self, shapes):\n", + " self.shapes = shapes\n", + "\n", + " def is_inside(self, x, y):\n", + " return any(shape.is_inside(x, y) for shape in self.shapes)\n", + "\n", + " def overlaps(self, other):\n", + " return any(shape.overlaps(other) for shape in self.shapes)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "canvas = Canvas(50, 30)\n", + "\n", + "rectangle = Rectangle(11, 8, 7, 9)\n", + "circle = Circle(7, 12, 10)\n", + "triangle = Triangle(9, 23, 8, 19)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " **** \n", + " **** \n", + " **** \n", + " **** \n", + " **** * \n", + " ******* \n", + " ********* \n", + " *********** *********** \n", + " ************* *********** \n", + " ************* *********** \n", + " ************* *********** \n", + " *************** *********** \n", + " ************* *********** \n", + " ************* *********** \n", + " ************* *********** \n", + " *********** *********** \n", + " ********* *********** \n", + " ******* \n", + " * \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n" + ] + } + ], + "source": [ + "compound_shape = CompoundShape([rectangle1, rectangle2, circle, triangle])\n", + "\n", + "for x in range(canvas.width):\n", + " for y in range(canvas.height):\n", + " if compound_shape.is_inside(x, y):\n", + " canvas.set_pixel(x, y, '*')\n", + "\n", + "canvas.display()" + ] }, { "cell_type": "markdown", @@ -158,10 +878,180 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "class RasterDrawing:\n", + " def __init__(self, width, height):\n", + " self.canvas = Canvas(width, height)\n", + " self.shapes = []\n", + "\n", + " def add_shape(self, shape):\n", + " self.shapes.append(shape)\n", + "\n", + " def paint(self):\n", + " for x in range(self.canvas.width):\n", + " for y in range(self.canvas.height):\n", + " for shape in self.shapes:\n", + " if shape.is_inside(x, y):\n", + " self.canvas.set_pixel(x, y, '*')\n", + "\n", + " def modify_shape(self, index, new_shape):\n", + " if 0 <= index < len(self.shapes):\n", + " self.shapes[index] = new_shape\n", + "\n", + " def display(self):\n", + " self.canvas.display()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " \n", + " **** \n", + " **** \n", + " **** \n", + " **** \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " * \n", + " ***** \n", + " ******* \n", + " ******* \n", + " ********* \n", + " ******* \n", + " ******* \n", + " ***** \n", + " * \n", + " ************ \n", + " ************ \n", + " ************ \n", + " ************ \n", + " ************ \n", + " ************ \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " **** \n", + " **** \n", + " **** \n", + " **** \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " * \n", + " ****** ***** \n", + " ****** ******* \n", + " ****** ******* \n", + " ****** ********* \n", + " ****** ******* \n", + " ****** ******* \n", + " ***** \n", + " * \n", + " ************ \n", + " ************ \n", + " ************ \n", + " ************ \n", + " ************ \n", + " ************ \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n" + ] + } + ], + "source": [ + "drawing = RasterDrawing(40, 60)\n", + "\n", + "drawing.add_shape(Rectangle(5, 11, 23, 12))\n", + "drawing.add_shape(Circle(4, 18, 29))\n", + "drawing.add_shape(Triangle(3, 3, 2, 3))\n", + "\n", + "drawing.paint()\n", + "drawing.display()\n", + "\n", + "drawing.modify_shape(1, Rectangle(5, 5, 15, 15))\n", + "\n", + "drawing.paint()\n", + "drawing.display()" + ] }, { "cell_type": "markdown", @@ -179,7 +1069,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -206,7 +1096,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -224,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -235,7 +1125,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -253,7 +1143,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -262,7 +1152,7 @@ "foo(1,'hello')" ] }, - "execution_count": 5, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -275,17 +1165,107 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "class RasterDrawing:\n", + " def __init__(self, width, height):\n", + " self.canvas = Canvas(width, height)\n", + " self.shapes = []\n", + "\n", + " def add_shape(self, shape):\n", + " self.shapes.append(shape)\n", + "\n", + " def paint(self):\n", + " for x in range(self.canvas.width):\n", + " for y in range(self.canvas.height):\n", + " for shape in self.shapes:\n", + " if shape.is_inside(x, y):\n", + " self.canvas.set_pixel(x, y, '*')\n", + "\n", + " def modify_shape(self, index, new_shape):\n", + " if 0 <= index < len(self.shapes):\n", + " self.shapes[index] = new_shape\n", + "\n", + " def display(self):\n", + " self.canvas.display()\n", + "\n", + " def save(self, filename):\n", + " with open(filename, \"w\") as f:\n", + " f.write(f\"RasterDrawing({self.canvas.width}, {self.canvas.height})\\n\")\n", + " for shape in self.shapes:\n", + " f.write(f\"drawing.add_shape({shape.__repr__()})\\n\")\n", + "\n", + " @classmethod\n", + " def load(cls, filename):\n", + " with open(filename, \"r\") as f:\n", + " eval(f.readline())\n", + " drawing = eval(\"RasterDrawing(\" + f.readline() + \")\")\n", + " for line in f:\n", + " eval(line.strip())\n", + " return drawing" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "drawing = RasterDrawing(30, 30)\n", + "\n", + "drawing.add_shape(Rectangle(15, 5, 5, 10))\n", + "drawing.add_shape(Circle(8, 20, 20))\n", + "drawing.add_shape(Triangle(10, 10, 8, 5))" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " ****** \n", + " ****** \n", + " ****** \n", + " *********** \n", + " *********** \n", + " *********** \n", + " *********** \n", + " *********** * \n", + " *********** ******* \n", + " ********************* \n", + " ********************** \n", + " ********************** \n", + " *********************** \n", + " *********************** \n", + " ****************** \n", + " ******************* \n", + " *************** \n", + " *************** \n", + " *************** \n", + " ************* \n", + " ************* \n", + " *********** \n", + " ******* \n", + " * \n", + " \n" + ] + } + ], + "source": [ + "drawing.paint()\n", + "drawing.display()" + ] } ], "metadata": { @@ -304,9 +1284,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.12.1" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 51280daff02396dcdac427b701a51d859ad4f7d1 Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Fri, 4 Oct 2024 07:57:18 -0500 Subject: [PATCH 12/22] lab5 --- Labs/Lab5.ipynb | 788 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 788 insertions(+) create mode 100644 Labs/Lab5.ipynb diff --git a/Labs/Lab5.ipynb b/Labs/Lab5.ipynb new file mode 100644 index 0000000..b76c52b --- /dev/null +++ b/Labs/Lab5.ipynb @@ -0,0 +1,788 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "4uMffVwNZxmr" + }, + "source": [ + "# Lab 5\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SSaTc21zZxmv" + }, + "source": [ + "Matrix Representation: In this lab you will be creating a simple linear algebra system. In memory, we will represent matrices as nested python lists as we have done in lecture.\n", + "\n", + "1. Create a `matrix` class with the following properties:\n", + " * It can be initialized in 2 ways:\n", + " 1. with arguments `n` and `m`, the size of the matrix. A newly instanciated matrix will contain all zeros.\n", + " 2. with a list of lists of values. Note that since we are using lists of lists to implement matrices, it is possible that not all rows have the same number of columns. Test explicitly that the matrix is properly specified.\n", + " * Matrix instances `M` can be indexed with `M[i][j]` and `M[i,j]`.\n", + " * Matrix assignment works in 2 ways:\n", + " 1. If `M_1` and `M_2` are `matrix` instances `M_1=M_2` sets the values of `M_1` to those of `M_2`, if they are the same size. Error otherwise.\n", + " 2. In example above `M_2` can be a list of lists of correct size.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "3eM2yb7XZxmz" + }, + "outputs": [], + "source": [ + "class Matrix:\n", + " def __init__(self, *args):\n", + " self.data = args[0] if len(args) == 1 and isinstance(args[0], list) else [[0] * args[1] for _ in range(args[0])]\n", + "\n", + " def __getitem__(self, key):\n", + " return self.data[key]\n", + "\n", + " def __setitem__(self, key, value):\n", + " self.data[key] = value if isinstance(key, int) else value[0] if isinstance(value, list) else value\n", + "\n", + " def __eq__(self, other):\n", + " return isinstance(other, Matrix) and self.data == other.data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RZbhUorsZxm1", + "outputId": "7509682e-02bc-46fa-a649-2a7de2652af2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M1.data: [[0, 0, 0], [0, 0, 0]]\n", + "M2.data: [[1, 2, 3], [4, 5, 6]]\n", + "2\n", + "6\n", + "M1.data: [[5, 0, 0], [0, 0, 0]]\n", + "M1.data: [[1, 2, 3], [4, 5, 6]]\n" + ] + } + ], + "source": [ + "M1 = Matrix(2, 3)\n", + "print(\"M1.data:\", M1.data)\n", + "\n", + "M2 = Matrix([[1, 2, 3], [4, 5, 6]])\n", + "print(\"M2.data:\", M2.data)\n", + "\n", + "print(M2[0][1])\n", + "print(M2[1][2])\n", + "\n", + "# Assignment\n", + "M1[0][0] = 5\n", + "print(\"M1.data:\", M1.data)\n", + "\n", + "M1 = M2\n", + "print(\"M1.data:\", M1.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pIRlLR1nZxm3" + }, + "source": [ + "2. Add the following methods:\n", + " * `shape()`: returns a tuple `(n,m)` of the shape of the matrix.\n", + " * `transpose()`: returns a new matrix instance which is the transpose of the matrix.\n", + " * `row(n)` and `column(n)`: that return the nth row or column of the matrix M as a new appropriately shaped matrix object.\n", + " * `to_list()`: which returns the matrix as a list of lists.\n", + " * `block(n_0,n_1,m_0,m_1)` that returns a smaller matrix located at the n_0 to n_1 columns and m_0 to m_1 rows.\n", + " * (Extra credit) Modify `__getitem__` implemented above to support slicing.\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "5D4i_OudZxm3" + }, + "outputs": [], + "source": [ + "class Matrix:\n", + " def __init__(self, *args):\n", + " self.data = args[0] if len(args) == 1 and isinstance(args[0], list) else [[0] * args[1] for _ in range(args[0])]\n", + "\n", + " def __getitem__(self, key):\n", + " if isinstance(key, tuple):\n", + " return self.data[key[0]][key[1]] if len(key) == 2 else self.data[key[0]]\n", + " return self.data[key]\n", + "\n", + " def __setitem__(self, key, value):\n", + " self.data[key] = value if isinstance(key, int) else value[0] if isinstance(value, list) else value\n", + "\n", + " def __eq__(self, other):\n", + " return isinstance(other, Matrix) and self.data == other.data\n", + "\n", + " def shape(self):\n", + " return len(self.data), len(self.data[0])\n", + "\n", + " def transpose(self):\n", + " return Matrix(list(zip(*self.data)))\n", + "\n", + " def row(self, n):\n", + " return Matrix([self.data[n]])\n", + "\n", + " def column(self, n):\n", + " return Matrix([[self.data[i][n]] for i in range(len(self.data))])\n", + "\n", + " def to_list(self):\n", + " return self.data\n", + "\n", + " def block(self, n_0, n_1, m_0, m_1):\n", + " return Matrix([row[n_0:n_1] for row in self.data])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "J5gI0MI1Zxm4", + "outputId": "6fb5bb01-1f36-401c-b996-97ba3ee11dbc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of M1: (2, 3)\n", + "Transpose of M1: [(0, 0), (0, 0), (0, 0)]\n", + "Row 1 of M1: [[0, 0, 0]]\n", + "Column 2 of M1: [[0], [0]]\n", + "M1 as list: [[0, 0, 0], [0, 0, 0]]\n", + "Block from M1: [[0, 0], [0, 0]]\n", + "Shape of M2: (2, 3)\n", + "Transpose of M2: [(1, 4), (2, 5), (3, 6)]\n", + "Row 0 of M2: [[1, 2, 3]]\n", + "Column 1 of M2: [[2], [5]]\n", + "M2 as list: [[1, 2, 3], [4, 5, 6]]\n", + "Block from M2: [[1], [4]]\n" + ] + } + ], + "source": [ + "\n", + "M1 = Matrix(2, 3)\n", + "print(\"Shape of M1:\", M1.shape())\n", + "print(\"Transpose of M1:\", M1.transpose().to_list())\n", + "print(\"Row 1 of M1:\", M1.row(1).to_list())\n", + "print(\"Column 2 of M1:\", M1.column(2).to_list())\n", + "print(\"M1 as list:\", M1.to_list())\n", + "print(\"Block from M1:\", M1.block(0, 2, 0, 1).to_list())\n", + "\n", + "\n", + "M2 = Matrix([[1, 2, 3], [4, 5, 6]])\n", + "print(\"Shape of M2:\", M2.shape())\n", + "print(\"Transpose of M2:\", M2.transpose().to_list())\n", + "print(\"Row 0 of M2:\", M2.row(0).to_list())\n", + "print(\"Column 1 of M2:\", M2.column(1).to_list())\n", + "print(\"M2 as list:\", M2.to_list())\n", + "print(\"Block from M2:\", M2.block(0, 1, 0, 2).to_list())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4T47xvz_Zxm6" + }, + "source": [ + "3. Write functions that create special matrices (note these are standalone functions, not member functions of your `matrix` class):\n", + " * `constant(n,m,c)`: returns a `n` by `m` matrix filled with floats of value `c`.\n", + " * `zeros(n,m)` and `ones(n,m)`: return `n` by `m` matrices filled with floats of value `0` and `1`, respectively.\n", + " * `eye(n)`: returns the n by n identity matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "5Fsm57K-Zxm7" + }, + "outputs": [], + "source": [ + "def constant(n, m, c):\n", + " return Matrix([[c] * m] * n)\n", + "\n", + "def zeros(n, m):\n", + " return constant(n, m, 0)\n", + "\n", + "def ones(n, m):\n", + " return constant(n, m, 1)\n", + "\n", + "def eye(n):\n", + " return Matrix([[1 if i == j else 0 for j in range(n)] for i in range(n)])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "erjYNm1rZxm8", + "outputId": "3e10034b-3748-473c-dae5-2b94454ab08f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Constant matrix:\n", + "[[5, 5], [5, 5], [5, 5]]\n", + "Zeros matrix:\n", + "[[0, 0, 0, 0], [0, 0, 0, 0]]\n", + "Ones matrix:\n", + "[[1, 1, 1], [1, 1, 1], [1, 1, 1]]\n", + "Identity matrix:\n", + "[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]\n" + ] + } + ], + "source": [ + "print(\"Constant matrix:\")\n", + "print(constant(3, 2, 5).to_list())\n", + "\n", + "print(\"Zeros matrix:\")\n", + "print(zeros(2, 4).to_list())\n", + "\n", + "print(\"Ones matrix:\")\n", + "print(ones(3, 3).to_list())\n", + "\n", + "print(\"Identity matrix:\")\n", + "print(eye(4).to_list())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g-3ikzg3Zxm-" + }, + "source": [ + "4. Add the following member functions to your class. Make sure to appropriately test the dimensions of the matrices to make sure the operations are correct.\n", + " * `M.scalarmul(c)`: a matrix that is scalar product $cM$, where every element of $M$ is multiplied by $c$.\n", + " * `M.add(N)`: adds two matrices $M$ and $N$. Don’t forget to test that the sizes of the matrices are compatible for this and all other operations.\n", + " * `M.sub(N)`: subtracts two matrices $M$ and $N$.\n", + " * `M.mat_mult(N)`: returns a matrix that is the matrix product of two matrices $M$ and $N$.\n", + " * `M.element_mult(N)`: returns a matrix that is the element-wise product of two matrices $M$ and $N$.\n", + " * `M.equals(N)`: returns true/false if $M==N$." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "iCWjnziOZxm_" + }, + "outputs": [], + "source": [ + "class Matrix:\n", + " def __init__(self, matrix):\n", + " self.matrix = matrix\n", + " self.rows = len(matrix)\n", + " self.cols = len(matrix[0])\n", + "\n", + " def __eq__(self, other):\n", + " return isinstance(other, self.__class__) and self.matrix == other.matrix\n", + "\n", + " def __repr__(self):\n", + " return f\"Matrix({self.matrix})\"\n", + "\n", + " def _operate(self, other, operator):\n", + " if not isinstance(other, self.__class__):\n", + " raise ValueError(\"Operand must be a Matrix\")\n", + " if self.rows != other.rows or self.cols != other.cols:\n", + " raise ValueError(\"Matrices must have the same dimensions\")\n", + "\n", + " return Matrix([\n", + " [operator(x, y) for x, y in zip(row1, row2)]\n", + " for row1, row2 in zip(self.matrix, other.matrix)\n", + " ])\n", + "\n", + " def scalar_mul(self, c):\n", + " if not isinstance(c, (int, float)):\n", + " raise ValueError(\"Scalar must be a number\")\n", + "\n", + " return self._operate(Matrix([[c] * self.cols] * self.rows), lambda x, y: x * y)\n", + "\n", + " def add(self, other):\n", + " return self._operate(other, lambda x, y: x + y)\n", + "\n", + " def subtract(self, other):\n", + " return self._operate(other, lambda x, y: x - y)\n", + "\n", + " def mat_mult(self, other):\n", + " if self.cols != other.rows:\n", + " raise ValueError(\"Matrices are not compatible for multiplication\")\n", + "\n", + " transposed_other = list(zip(*other.matrix))\n", + " return Matrix([\n", + " [sum(a * b for a, b in zip(row1, col2)) for col2 in transposed_other]\n", + " for row1 in self.matrix\n", + " ])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DFMpvwIeZxnA", + "outputId": "27a84dd6-662b-4358-91b5-b717cc131946" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All tests passed successfully!\n" + ] + } + ], + "source": [ + "#test to check\n", + "\n", + "def test_matrix_operations():\n", + "\n", + " matrix1 = Matrix([[1, 2], [3, 4]])\n", + " matrix2 = Matrix([[5, 6], [7, 8]])\n", + "\n", + "\n", + " assert matrix1 == matrix1\n", + " assert repr(matrix1) == \"Matrix([[1, 2], [3, 4]])\"\n", + " assert matrix1.scalar_mul(2) == Matrix([[2, 4], [6, 8]])\n", + " assert matrix1.add(matrix2) == Matrix([[6, 8], [10, 12]])\n", + " assert matrix1.subtract(matrix2) == Matrix([[-4, -4], [-4, -4]])\n", + " assert matrix1.mat_mult(matrix2) == Matrix([[19, 22], [43, 50]])\n", + "\n", + " print(\"All tests passed successfully!\")\n", + "\n", + "test_matrix_operations()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a3oajZgsZxnB" + }, + "source": [ + "5. Overload python operators to appropriately use your functions in 4 and allow expressions like:\n", + " * 2*M\n", + " * M*2\n", + " * M+N\n", + " * M-N\n", + " * M*N\n", + " * M==N\n", + " * M=N\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "NMPmBI-JZxnC" + }, + "outputs": [], + "source": [ + "#guess:\n", + "class Matrix:\n", + " def __init__(self, matrix):\n", + " self.matrix = matrix\n", + " self.rows = len(matrix)\n", + " self.cols = len(matrix[0])\n", + "\n", + " def __eq__(self, other):\n", + " return isinstance(other, self.__class__) and self.matrix == other.matrix\n", + "\n", + " def __repr__(self):\n", + " return f\"Matrix({self.matrix})\"\n", + "\n", + " def _operate(self, other, operator):\n", + " if not isinstance(other, self.__class__):\n", + " raise ValueError(\"Operand must be a Matrix\")\n", + " if self.rows != other.rows or self.cols != other.cols:\n", + " raise ValueError(\"Matrices must have the same dimensions\")\n", + " return Matrix([[operator(x, y) for x, y in zip(row1, row2)] for row1, row2 in zip(self.matrix, other.matrix)])\n", + "\n", + " def scalar_mul(self, c):\n", + " if not isinstance(c, (int, float)):\n", + " raise ValueError(\"Scalar must be a number\")\n", + " return self._operate(Matrix([[c] * self.cols] * self.rows), lambda x, y: x * y)\n", + "\n", + " def __mul__(self, other):\n", + " if isinstance(other, (int, float)):\n", + " return self.scalar_mul(other)\n", + " elif isinstance(other, self.__class__):\n", + " if self.cols != other.rows:\n", + " raise ValueError(\"Matrices are not compatible for multiplication\")\n", + " transposed_other = list(zip(*other.matrix))\n", + " return Matrix([[sum(a * b for a, b in zip(row1, col2)) for col2 in transposed_other] for row1 in self.matrix])\n", + " else:\n", + " raise ValueError(\"Unsupported operand type for *\")\n", + "\n", + " def __rmul__(self, other):\n", + " return self.scalar_mul(other)\n", + "\n", + " def __add__(self, other):\n", + " return self._operate(other, lambda x, y: x + y)\n", + "\n", + " def __sub__(self, other):\n", + " return self._operate(other, lambda x, y: x - y)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "AztaICYSZxnF" + }, + "outputs": [], + "source": [ + "def test_matrix_operations():\n", + " M = Matrix([[2, 3], [4, 5]])\n", + " N = Matrix([[6, 7], [8, 9]])\n", + "\n", + " assert 2 * M == Matrix([[4, 6], [8, 10]])\n", + " assert M * 2 == Matrix([[4, 6], [8, 10]])\n", + " assert M + N == Matrix([[8, 10], [12, 14]])\n", + " assert M - N == Matrix([[-4, -4], [-4, -4]])\n", + " assert M * N == Matrix([[36, 41], [64, 73]])\n", + " assert M == Matrix([[2, 3], [4, 5]])\n", + " assert M == M\n", + "\n", + " print(\"All tests passed!\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ygFkLz4XZxnG", + "outputId": "b86761c8-e502-4ca1-9ba1-873b5c49830d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All tests passed!\n" + ] + } + ], + "source": [ + "# Run the test cases\n", + "test_matrix_operations()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pfY_e6TmZxnH" + }, + "source": [ + "6. Demonstrate the basic properties of matrices with your matrix class by creating two 2 by 2 example matrices using your Matrix class and illustrating the following:\n", + "\n", + "$$\n", + "(AB)C=A(BC)\n", + "$$\n", + "$$\n", + "A(B+C)=AB+AC\n", + "$$\n", + "$$\n", + "AB\\neq BA\n", + "$$\n", + "$$\n", + "AI=A\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "cxayNkugZxnH" + }, + "outputs": [], + "source": [ + "class Matrix:\n", + " def __init__(self, matrix):\n", + " self.matrix = matrix\n", + "\n", + " def __mul__(self, other):\n", + " if isinstance(other, Matrix):\n", + " return Matrix([[sum(a * b for a, b in zip(row1, col2)) for col2 in zip(*other.matrix)] for row1 in self.matrix])\n", + " elif isinstance(other, (int, float)):\n", + " return Matrix([[element * other for element in row] for row in self.matrix])\n", + " else:\n", + " raise ValueError(\"Unsupported operand type for *\")\n", + "\n", + " def __add__(self, other):\n", + " return Matrix([[a + b for a, b in zip(row1, row2)] for row1, row2 in zip(self.matrix, other.matrix)])\n", + "\n", + " def __sub__(self, other):\n", + " return Matrix([[a - b for a, b in zip(row1, row2)] for row1, row2 in zip(self.matrix, other.matrix)])\n", + "\n", + " def __eq__(self, other):\n", + " return self.matrix == other.matrix\n", + "\n", + " def __repr__(self):\n", + " return f\"Matrix({self.matrix})\"" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "ud5EfsuxZxnI" + }, + "outputs": [], + "source": [ + "## Matrices\n", + "A = Matrix([[1, 2], [3, 4]])\n", + "B = Matrix([[5, 6], [7, 8]])\n", + "C = Matrix([[9, 10], [11, 12]])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "R-KQbgXqZxnI", + "outputId": "690cf4ac-1f9e-4ff2-ac7e-8cdd578ef9c2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(AB)C = Matrix([[413, 454], [937, 1030]])\n", + "A(B*C) = Matrix([[413, 454], [937, 1030]])\n", + "(AB)C == A(B*C): True\n" + ] + } + ], + "source": [ + "# (AB)C = A(BC)\n", + "print(\"(AB)C =\", (A * B) * C)\n", + "print(\"A(B*C) =\", A * (B * C))\n", + "print(\"(AB)C == A(B*C):\", (A * B) * C == A * (B * C))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LH7mqxBRZxnJ", + "outputId": "6ae2ee78-2716-4de9-87f1-9be602861eed" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A(B+C) = Matrix([[50, 56], [114, 128]])\n", + "AB + AC = Matrix([[50, 56], [114, 128]])\n", + "A(B+C) == AB + AC: True\n" + ] + } + ], + "source": [ + "# A(B+C) = AB + AC\n", + "print(\"A(B+C) =\", A * (B + C))\n", + "print(\"AB + AC =\", A * B + A * C)\n", + "print(\"A(B+C) == AB + AC:\", A * (B + C) == A * B + A * C)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9TpsaWZxZxnJ", + "outputId": "ebbe650f-7431-4446-bf01-2bb1261be583" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AB = Matrix([[19, 22], [43, 50]])\n", + "BA = Matrix([[23, 34], [31, 46]])\n", + "AB != BA: True\n" + ] + } + ], + "source": [ + "# AB != BA\n", + "print(\"AB =\", A * B)\n", + "print(\"BA =\", B * A)\n", + "print(\"AB != BA:\", A * B != B * A)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X8KCSMw8ZxnK", + "outputId": "070f0b18-072f-4244-cc20-d763443bc6cb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AI = Matrix([[1, 2], [3, 4]])\n", + "AI == A: True\n" + ] + } + ], + "source": [ + "# AI = A\n", + "I = Matrix([[1, 0], [0, 1]])\n", + "print(\"AI =\", A * I)\n", + "print(\"AI == A:\", A * I == A)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Clubs', 2)\n", + "('Clubs', 3)\n", + "('Clubs', 4)\n", + "('Clubs', 5)\n", + "('Clubs', 6)\n", + "('Clubs', 7)\n", + "('Clubs', 8)\n", + "('Clubs', 9)\n", + "('Clubs', 10)\n", + "('Clubs', 'Jack')\n", + "('Clubs', 'Queen')\n", + "('Clubs', 'King')\n", + "('Clubs', 'Ace')\n", + "('Diamonds', 2)\n", + "('Diamonds', 3)\n", + "('Diamonds', 4)\n", + "('Diamonds', 5)\n", + "('Diamonds', 6)\n", + "('Diamonds', 7)\n", + "('Diamonds', 8)\n", + "('Diamonds', 9)\n", + "('Diamonds', 10)\n", + "('Diamonds', 'Jack')\n", + "('Diamonds', 'Queen')\n", + "('Diamonds', 'King')\n", + "('Diamonds', 'Ace')\n", + "('Hearts', 2)\n", + "('Hearts', 3)\n", + "('Hearts', 4)\n", + "('Hearts', 5)\n", + "('Hearts', 6)\n", + "('Hearts', 7)\n", + "('Hearts', 8)\n", + "('Hearts', 9)\n", + "('Hearts', 10)\n", + "('Hearts', 'Jack')\n", + "('Hearts', 'Queen')\n", + "('Hearts', 'King')\n", + "('Hearts', 'Ace')\n", + "('Spades', 2)\n", + "('Spades', 3)\n", + "('Spades', 4)\n", + "('Spades', 5)\n", + "('Spades', 6)\n", + "('Spades', 7)\n", + "('Spades', 8)\n", + "('Spades', 9)\n", + "('Spades', 10)\n", + "('Spades', 'Jack')\n", + "('Spades', 'Queen')\n", + "('Spades', 'King')\n", + "('Spades', 'Ace')\n" + ] + } + ], + "source": [ + "#QUIZ 2\n", + "\n", + "\n", + "def make_deck():\n", + " suits = ['Clubs', 'Diamonds', 'Hearts', 'Spades']\n", + " values = [2, 3, 4, 5, 6, 7, 8, 9, 10, 'Jack', 'Queen', 'King', 'Ace']\n", + " \n", + " deck = [(suit, value) for suit in suits for value in values]\n", + " return deck\n", + "\n", + "# Example usage\n", + "if __name__ == \"__main__\":\n", + " deck = make_deck()\n", + " for card in deck:\n", + " print(card)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "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": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From dba42add62475f964ee4b2ae5b4b1fb4889c2015 Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Fri, 4 Oct 2024 07:57:50 -0500 Subject: [PATCH 13/22] lab 5 --- Labs/Lab.5/Lab5.ipynb | 788 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 788 insertions(+) create mode 100644 Labs/Lab.5/Lab5.ipynb diff --git a/Labs/Lab.5/Lab5.ipynb b/Labs/Lab.5/Lab5.ipynb new file mode 100644 index 0000000..b76c52b --- /dev/null +++ b/Labs/Lab.5/Lab5.ipynb @@ -0,0 +1,788 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "4uMffVwNZxmr" + }, + "source": [ + "# Lab 5\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SSaTc21zZxmv" + }, + "source": [ + "Matrix Representation: In this lab you will be creating a simple linear algebra system. In memory, we will represent matrices as nested python lists as we have done in lecture.\n", + "\n", + "1. Create a `matrix` class with the following properties:\n", + " * It can be initialized in 2 ways:\n", + " 1. with arguments `n` and `m`, the size of the matrix. A newly instanciated matrix will contain all zeros.\n", + " 2. with a list of lists of values. Note that since we are using lists of lists to implement matrices, it is possible that not all rows have the same number of columns. Test explicitly that the matrix is properly specified.\n", + " * Matrix instances `M` can be indexed with `M[i][j]` and `M[i,j]`.\n", + " * Matrix assignment works in 2 ways:\n", + " 1. If `M_1` and `M_2` are `matrix` instances `M_1=M_2` sets the values of `M_1` to those of `M_2`, if they are the same size. Error otherwise.\n", + " 2. In example above `M_2` can be a list of lists of correct size.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "3eM2yb7XZxmz" + }, + "outputs": [], + "source": [ + "class Matrix:\n", + " def __init__(self, *args):\n", + " self.data = args[0] if len(args) == 1 and isinstance(args[0], list) else [[0] * args[1] for _ in range(args[0])]\n", + "\n", + " def __getitem__(self, key):\n", + " return self.data[key]\n", + "\n", + " def __setitem__(self, key, value):\n", + " self.data[key] = value if isinstance(key, int) else value[0] if isinstance(value, list) else value\n", + "\n", + " def __eq__(self, other):\n", + " return isinstance(other, Matrix) and self.data == other.data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RZbhUorsZxm1", + "outputId": "7509682e-02bc-46fa-a649-2a7de2652af2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M1.data: [[0, 0, 0], [0, 0, 0]]\n", + "M2.data: [[1, 2, 3], [4, 5, 6]]\n", + "2\n", + "6\n", + "M1.data: [[5, 0, 0], [0, 0, 0]]\n", + "M1.data: [[1, 2, 3], [4, 5, 6]]\n" + ] + } + ], + "source": [ + "M1 = Matrix(2, 3)\n", + "print(\"M1.data:\", M1.data)\n", + "\n", + "M2 = Matrix([[1, 2, 3], [4, 5, 6]])\n", + "print(\"M2.data:\", M2.data)\n", + "\n", + "print(M2[0][1])\n", + "print(M2[1][2])\n", + "\n", + "# Assignment\n", + "M1[0][0] = 5\n", + "print(\"M1.data:\", M1.data)\n", + "\n", + "M1 = M2\n", + "print(\"M1.data:\", M1.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pIRlLR1nZxm3" + }, + "source": [ + "2. Add the following methods:\n", + " * `shape()`: returns a tuple `(n,m)` of the shape of the matrix.\n", + " * `transpose()`: returns a new matrix instance which is the transpose of the matrix.\n", + " * `row(n)` and `column(n)`: that return the nth row or column of the matrix M as a new appropriately shaped matrix object.\n", + " * `to_list()`: which returns the matrix as a list of lists.\n", + " * `block(n_0,n_1,m_0,m_1)` that returns a smaller matrix located at the n_0 to n_1 columns and m_0 to m_1 rows.\n", + " * (Extra credit) Modify `__getitem__` implemented above to support slicing.\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "5D4i_OudZxm3" + }, + "outputs": [], + "source": [ + "class Matrix:\n", + " def __init__(self, *args):\n", + " self.data = args[0] if len(args) == 1 and isinstance(args[0], list) else [[0] * args[1] for _ in range(args[0])]\n", + "\n", + " def __getitem__(self, key):\n", + " if isinstance(key, tuple):\n", + " return self.data[key[0]][key[1]] if len(key) == 2 else self.data[key[0]]\n", + " return self.data[key]\n", + "\n", + " def __setitem__(self, key, value):\n", + " self.data[key] = value if isinstance(key, int) else value[0] if isinstance(value, list) else value\n", + "\n", + " def __eq__(self, other):\n", + " return isinstance(other, Matrix) and self.data == other.data\n", + "\n", + " def shape(self):\n", + " return len(self.data), len(self.data[0])\n", + "\n", + " def transpose(self):\n", + " return Matrix(list(zip(*self.data)))\n", + "\n", + " def row(self, n):\n", + " return Matrix([self.data[n]])\n", + "\n", + " def column(self, n):\n", + " return Matrix([[self.data[i][n]] for i in range(len(self.data))])\n", + "\n", + " def to_list(self):\n", + " return self.data\n", + "\n", + " def block(self, n_0, n_1, m_0, m_1):\n", + " return Matrix([row[n_0:n_1] for row in self.data])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "J5gI0MI1Zxm4", + "outputId": "6fb5bb01-1f36-401c-b996-97ba3ee11dbc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of M1: (2, 3)\n", + "Transpose of M1: [(0, 0), (0, 0), (0, 0)]\n", + "Row 1 of M1: [[0, 0, 0]]\n", + "Column 2 of M1: [[0], [0]]\n", + "M1 as list: [[0, 0, 0], [0, 0, 0]]\n", + "Block from M1: [[0, 0], [0, 0]]\n", + "Shape of M2: (2, 3)\n", + "Transpose of M2: [(1, 4), (2, 5), (3, 6)]\n", + "Row 0 of M2: [[1, 2, 3]]\n", + "Column 1 of M2: [[2], [5]]\n", + "M2 as list: [[1, 2, 3], [4, 5, 6]]\n", + "Block from M2: [[1], [4]]\n" + ] + } + ], + "source": [ + "\n", + "M1 = Matrix(2, 3)\n", + "print(\"Shape of M1:\", M1.shape())\n", + "print(\"Transpose of M1:\", M1.transpose().to_list())\n", + "print(\"Row 1 of M1:\", M1.row(1).to_list())\n", + "print(\"Column 2 of M1:\", M1.column(2).to_list())\n", + "print(\"M1 as list:\", M1.to_list())\n", + "print(\"Block from M1:\", M1.block(0, 2, 0, 1).to_list())\n", + "\n", + "\n", + "M2 = Matrix([[1, 2, 3], [4, 5, 6]])\n", + "print(\"Shape of M2:\", M2.shape())\n", + "print(\"Transpose of M2:\", M2.transpose().to_list())\n", + "print(\"Row 0 of M2:\", M2.row(0).to_list())\n", + "print(\"Column 1 of M2:\", M2.column(1).to_list())\n", + "print(\"M2 as list:\", M2.to_list())\n", + "print(\"Block from M2:\", M2.block(0, 1, 0, 2).to_list())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4T47xvz_Zxm6" + }, + "source": [ + "3. Write functions that create special matrices (note these are standalone functions, not member functions of your `matrix` class):\n", + " * `constant(n,m,c)`: returns a `n` by `m` matrix filled with floats of value `c`.\n", + " * `zeros(n,m)` and `ones(n,m)`: return `n` by `m` matrices filled with floats of value `0` and `1`, respectively.\n", + " * `eye(n)`: returns the n by n identity matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "5Fsm57K-Zxm7" + }, + "outputs": [], + "source": [ + "def constant(n, m, c):\n", + " return Matrix([[c] * m] * n)\n", + "\n", + "def zeros(n, m):\n", + " return constant(n, m, 0)\n", + "\n", + "def ones(n, m):\n", + " return constant(n, m, 1)\n", + "\n", + "def eye(n):\n", + " return Matrix([[1 if i == j else 0 for j in range(n)] for i in range(n)])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "erjYNm1rZxm8", + "outputId": "3e10034b-3748-473c-dae5-2b94454ab08f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Constant matrix:\n", + "[[5, 5], [5, 5], [5, 5]]\n", + "Zeros matrix:\n", + "[[0, 0, 0, 0], [0, 0, 0, 0]]\n", + "Ones matrix:\n", + "[[1, 1, 1], [1, 1, 1], [1, 1, 1]]\n", + "Identity matrix:\n", + "[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]\n" + ] + } + ], + "source": [ + "print(\"Constant matrix:\")\n", + "print(constant(3, 2, 5).to_list())\n", + "\n", + "print(\"Zeros matrix:\")\n", + "print(zeros(2, 4).to_list())\n", + "\n", + "print(\"Ones matrix:\")\n", + "print(ones(3, 3).to_list())\n", + "\n", + "print(\"Identity matrix:\")\n", + "print(eye(4).to_list())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g-3ikzg3Zxm-" + }, + "source": [ + "4. Add the following member functions to your class. Make sure to appropriately test the dimensions of the matrices to make sure the operations are correct.\n", + " * `M.scalarmul(c)`: a matrix that is scalar product $cM$, where every element of $M$ is multiplied by $c$.\n", + " * `M.add(N)`: adds two matrices $M$ and $N$. Don’t forget to test that the sizes of the matrices are compatible for this and all other operations.\n", + " * `M.sub(N)`: subtracts two matrices $M$ and $N$.\n", + " * `M.mat_mult(N)`: returns a matrix that is the matrix product of two matrices $M$ and $N$.\n", + " * `M.element_mult(N)`: returns a matrix that is the element-wise product of two matrices $M$ and $N$.\n", + " * `M.equals(N)`: returns true/false if $M==N$." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "iCWjnziOZxm_" + }, + "outputs": [], + "source": [ + "class Matrix:\n", + " def __init__(self, matrix):\n", + " self.matrix = matrix\n", + " self.rows = len(matrix)\n", + " self.cols = len(matrix[0])\n", + "\n", + " def __eq__(self, other):\n", + " return isinstance(other, self.__class__) and self.matrix == other.matrix\n", + "\n", + " def __repr__(self):\n", + " return f\"Matrix({self.matrix})\"\n", + "\n", + " def _operate(self, other, operator):\n", + " if not isinstance(other, self.__class__):\n", + " raise ValueError(\"Operand must be a Matrix\")\n", + " if self.rows != other.rows or self.cols != other.cols:\n", + " raise ValueError(\"Matrices must have the same dimensions\")\n", + "\n", + " return Matrix([\n", + " [operator(x, y) for x, y in zip(row1, row2)]\n", + " for row1, row2 in zip(self.matrix, other.matrix)\n", + " ])\n", + "\n", + " def scalar_mul(self, c):\n", + " if not isinstance(c, (int, float)):\n", + " raise ValueError(\"Scalar must be a number\")\n", + "\n", + " return self._operate(Matrix([[c] * self.cols] * self.rows), lambda x, y: x * y)\n", + "\n", + " def add(self, other):\n", + " return self._operate(other, lambda x, y: x + y)\n", + "\n", + " def subtract(self, other):\n", + " return self._operate(other, lambda x, y: x - y)\n", + "\n", + " def mat_mult(self, other):\n", + " if self.cols != other.rows:\n", + " raise ValueError(\"Matrices are not compatible for multiplication\")\n", + "\n", + " transposed_other = list(zip(*other.matrix))\n", + " return Matrix([\n", + " [sum(a * b for a, b in zip(row1, col2)) for col2 in transposed_other]\n", + " for row1 in self.matrix\n", + " ])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DFMpvwIeZxnA", + "outputId": "27a84dd6-662b-4358-91b5-b717cc131946" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All tests passed successfully!\n" + ] + } + ], + "source": [ + "#test to check\n", + "\n", + "def test_matrix_operations():\n", + "\n", + " matrix1 = Matrix([[1, 2], [3, 4]])\n", + " matrix2 = Matrix([[5, 6], [7, 8]])\n", + "\n", + "\n", + " assert matrix1 == matrix1\n", + " assert repr(matrix1) == \"Matrix([[1, 2], [3, 4]])\"\n", + " assert matrix1.scalar_mul(2) == Matrix([[2, 4], [6, 8]])\n", + " assert matrix1.add(matrix2) == Matrix([[6, 8], [10, 12]])\n", + " assert matrix1.subtract(matrix2) == Matrix([[-4, -4], [-4, -4]])\n", + " assert matrix1.mat_mult(matrix2) == Matrix([[19, 22], [43, 50]])\n", + "\n", + " print(\"All tests passed successfully!\")\n", + "\n", + "test_matrix_operations()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a3oajZgsZxnB" + }, + "source": [ + "5. Overload python operators to appropriately use your functions in 4 and allow expressions like:\n", + " * 2*M\n", + " * M*2\n", + " * M+N\n", + " * M-N\n", + " * M*N\n", + " * M==N\n", + " * M=N\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "NMPmBI-JZxnC" + }, + "outputs": [], + "source": [ + "#guess:\n", + "class Matrix:\n", + " def __init__(self, matrix):\n", + " self.matrix = matrix\n", + " self.rows = len(matrix)\n", + " self.cols = len(matrix[0])\n", + "\n", + " def __eq__(self, other):\n", + " return isinstance(other, self.__class__) and self.matrix == other.matrix\n", + "\n", + " def __repr__(self):\n", + " return f\"Matrix({self.matrix})\"\n", + "\n", + " def _operate(self, other, operator):\n", + " if not isinstance(other, self.__class__):\n", + " raise ValueError(\"Operand must be a Matrix\")\n", + " if self.rows != other.rows or self.cols != other.cols:\n", + " raise ValueError(\"Matrices must have the same dimensions\")\n", + " return Matrix([[operator(x, y) for x, y in zip(row1, row2)] for row1, row2 in zip(self.matrix, other.matrix)])\n", + "\n", + " def scalar_mul(self, c):\n", + " if not isinstance(c, (int, float)):\n", + " raise ValueError(\"Scalar must be a number\")\n", + " return self._operate(Matrix([[c] * self.cols] * self.rows), lambda x, y: x * y)\n", + "\n", + " def __mul__(self, other):\n", + " if isinstance(other, (int, float)):\n", + " return self.scalar_mul(other)\n", + " elif isinstance(other, self.__class__):\n", + " if self.cols != other.rows:\n", + " raise ValueError(\"Matrices are not compatible for multiplication\")\n", + " transposed_other = list(zip(*other.matrix))\n", + " return Matrix([[sum(a * b for a, b in zip(row1, col2)) for col2 in transposed_other] for row1 in self.matrix])\n", + " else:\n", + " raise ValueError(\"Unsupported operand type for *\")\n", + "\n", + " def __rmul__(self, other):\n", + " return self.scalar_mul(other)\n", + "\n", + " def __add__(self, other):\n", + " return self._operate(other, lambda x, y: x + y)\n", + "\n", + " def __sub__(self, other):\n", + " return self._operate(other, lambda x, y: x - y)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "AztaICYSZxnF" + }, + "outputs": [], + "source": [ + "def test_matrix_operations():\n", + " M = Matrix([[2, 3], [4, 5]])\n", + " N = Matrix([[6, 7], [8, 9]])\n", + "\n", + " assert 2 * M == Matrix([[4, 6], [8, 10]])\n", + " assert M * 2 == Matrix([[4, 6], [8, 10]])\n", + " assert M + N == Matrix([[8, 10], [12, 14]])\n", + " assert M - N == Matrix([[-4, -4], [-4, -4]])\n", + " assert M * N == Matrix([[36, 41], [64, 73]])\n", + " assert M == Matrix([[2, 3], [4, 5]])\n", + " assert M == M\n", + "\n", + " print(\"All tests passed!\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ygFkLz4XZxnG", + "outputId": "b86761c8-e502-4ca1-9ba1-873b5c49830d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All tests passed!\n" + ] + } + ], + "source": [ + "# Run the test cases\n", + "test_matrix_operations()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pfY_e6TmZxnH" + }, + "source": [ + "6. Demonstrate the basic properties of matrices with your matrix class by creating two 2 by 2 example matrices using your Matrix class and illustrating the following:\n", + "\n", + "$$\n", + "(AB)C=A(BC)\n", + "$$\n", + "$$\n", + "A(B+C)=AB+AC\n", + "$$\n", + "$$\n", + "AB\\neq BA\n", + "$$\n", + "$$\n", + "AI=A\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "cxayNkugZxnH" + }, + "outputs": [], + "source": [ + "class Matrix:\n", + " def __init__(self, matrix):\n", + " self.matrix = matrix\n", + "\n", + " def __mul__(self, other):\n", + " if isinstance(other, Matrix):\n", + " return Matrix([[sum(a * b for a, b in zip(row1, col2)) for col2 in zip(*other.matrix)] for row1 in self.matrix])\n", + " elif isinstance(other, (int, float)):\n", + " return Matrix([[element * other for element in row] for row in self.matrix])\n", + " else:\n", + " raise ValueError(\"Unsupported operand type for *\")\n", + "\n", + " def __add__(self, other):\n", + " return Matrix([[a + b for a, b in zip(row1, row2)] for row1, row2 in zip(self.matrix, other.matrix)])\n", + "\n", + " def __sub__(self, other):\n", + " return Matrix([[a - b for a, b in zip(row1, row2)] for row1, row2 in zip(self.matrix, other.matrix)])\n", + "\n", + " def __eq__(self, other):\n", + " return self.matrix == other.matrix\n", + "\n", + " def __repr__(self):\n", + " return f\"Matrix({self.matrix})\"" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "ud5EfsuxZxnI" + }, + "outputs": [], + "source": [ + "## Matrices\n", + "A = Matrix([[1, 2], [3, 4]])\n", + "B = Matrix([[5, 6], [7, 8]])\n", + "C = Matrix([[9, 10], [11, 12]])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "R-KQbgXqZxnI", + "outputId": "690cf4ac-1f9e-4ff2-ac7e-8cdd578ef9c2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(AB)C = Matrix([[413, 454], [937, 1030]])\n", + "A(B*C) = Matrix([[413, 454], [937, 1030]])\n", + "(AB)C == A(B*C): True\n" + ] + } + ], + "source": [ + "# (AB)C = A(BC)\n", + "print(\"(AB)C =\", (A * B) * C)\n", + "print(\"A(B*C) =\", A * (B * C))\n", + "print(\"(AB)C == A(B*C):\", (A * B) * C == A * (B * C))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LH7mqxBRZxnJ", + "outputId": "6ae2ee78-2716-4de9-87f1-9be602861eed" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A(B+C) = Matrix([[50, 56], [114, 128]])\n", + "AB + AC = Matrix([[50, 56], [114, 128]])\n", + "A(B+C) == AB + AC: True\n" + ] + } + ], + "source": [ + "# A(B+C) = AB + AC\n", + "print(\"A(B+C) =\", A * (B + C))\n", + "print(\"AB + AC =\", A * B + A * C)\n", + "print(\"A(B+C) == AB + AC:\", A * (B + C) == A * B + A * C)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9TpsaWZxZxnJ", + "outputId": "ebbe650f-7431-4446-bf01-2bb1261be583" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AB = Matrix([[19, 22], [43, 50]])\n", + "BA = Matrix([[23, 34], [31, 46]])\n", + "AB != BA: True\n" + ] + } + ], + "source": [ + "# AB != BA\n", + "print(\"AB =\", A * B)\n", + "print(\"BA =\", B * A)\n", + "print(\"AB != BA:\", A * B != B * A)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X8KCSMw8ZxnK", + "outputId": "070f0b18-072f-4244-cc20-d763443bc6cb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AI = Matrix([[1, 2], [3, 4]])\n", + "AI == A: True\n" + ] + } + ], + "source": [ + "# AI = A\n", + "I = Matrix([[1, 0], [0, 1]])\n", + "print(\"AI =\", A * I)\n", + "print(\"AI == A:\", A * I == A)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Clubs', 2)\n", + "('Clubs', 3)\n", + "('Clubs', 4)\n", + "('Clubs', 5)\n", + "('Clubs', 6)\n", + "('Clubs', 7)\n", + "('Clubs', 8)\n", + "('Clubs', 9)\n", + "('Clubs', 10)\n", + "('Clubs', 'Jack')\n", + "('Clubs', 'Queen')\n", + "('Clubs', 'King')\n", + "('Clubs', 'Ace')\n", + "('Diamonds', 2)\n", + "('Diamonds', 3)\n", + "('Diamonds', 4)\n", + "('Diamonds', 5)\n", + "('Diamonds', 6)\n", + "('Diamonds', 7)\n", + "('Diamonds', 8)\n", + "('Diamonds', 9)\n", + "('Diamonds', 10)\n", + "('Diamonds', 'Jack')\n", + "('Diamonds', 'Queen')\n", + "('Diamonds', 'King')\n", + "('Diamonds', 'Ace')\n", + "('Hearts', 2)\n", + "('Hearts', 3)\n", + "('Hearts', 4)\n", + "('Hearts', 5)\n", + "('Hearts', 6)\n", + "('Hearts', 7)\n", + "('Hearts', 8)\n", + "('Hearts', 9)\n", + "('Hearts', 10)\n", + "('Hearts', 'Jack')\n", + "('Hearts', 'Queen')\n", + "('Hearts', 'King')\n", + "('Hearts', 'Ace')\n", + "('Spades', 2)\n", + "('Spades', 3)\n", + "('Spades', 4)\n", + "('Spades', 5)\n", + "('Spades', 6)\n", + "('Spades', 7)\n", + "('Spades', 8)\n", + "('Spades', 9)\n", + "('Spades', 10)\n", + "('Spades', 'Jack')\n", + "('Spades', 'Queen')\n", + "('Spades', 'King')\n", + "('Spades', 'Ace')\n" + ] + } + ], + "source": [ + "#QUIZ 2\n", + "\n", + "\n", + "def make_deck():\n", + " suits = ['Clubs', 'Diamonds', 'Hearts', 'Spades']\n", + " values = [2, 3, 4, 5, 6, 7, 8, 9, 10, 'Jack', 'Queen', 'King', 'Ace']\n", + " \n", + " deck = [(suit, value) for suit in suits for value in values]\n", + " return deck\n", + "\n", + "# Example usage\n", + "if __name__ == \"__main__\":\n", + " deck = make_deck()\n", + " for card in deck:\n", + " print(card)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "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": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 911504fd53493a8017830666bd8aa1dc6b8649d8 Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Fri, 4 Oct 2024 07:58:54 -0500 Subject: [PATCH 14/22] Delete Labs/Lab.5/Lab5.ipynb --- Labs/Lab.5/Lab5.ipynb | 788 ------------------------------------------ 1 file changed, 788 deletions(-) delete mode 100644 Labs/Lab.5/Lab5.ipynb diff --git a/Labs/Lab.5/Lab5.ipynb b/Labs/Lab.5/Lab5.ipynb deleted file mode 100644 index b76c52b..0000000 --- a/Labs/Lab.5/Lab5.ipynb +++ /dev/null @@ -1,788 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "4uMffVwNZxmr" - }, - "source": [ - "# Lab 5\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SSaTc21zZxmv" - }, - "source": [ - "Matrix Representation: In this lab you will be creating a simple linear algebra system. In memory, we will represent matrices as nested python lists as we have done in lecture.\n", - "\n", - "1. Create a `matrix` class with the following properties:\n", - " * It can be initialized in 2 ways:\n", - " 1. with arguments `n` and `m`, the size of the matrix. A newly instanciated matrix will contain all zeros.\n", - " 2. with a list of lists of values. Note that since we are using lists of lists to implement matrices, it is possible that not all rows have the same number of columns. Test explicitly that the matrix is properly specified.\n", - " * Matrix instances `M` can be indexed with `M[i][j]` and `M[i,j]`.\n", - " * Matrix assignment works in 2 ways:\n", - " 1. If `M_1` and `M_2` are `matrix` instances `M_1=M_2` sets the values of `M_1` to those of `M_2`, if they are the same size. Error otherwise.\n", - " 2. In example above `M_2` can be a list of lists of correct size.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "3eM2yb7XZxmz" - }, - "outputs": [], - "source": [ - "class Matrix:\n", - " def __init__(self, *args):\n", - " self.data = args[0] if len(args) == 1 and isinstance(args[0], list) else [[0] * args[1] for _ in range(args[0])]\n", - "\n", - " def __getitem__(self, key):\n", - " return self.data[key]\n", - "\n", - " def __setitem__(self, key, value):\n", - " self.data[key] = value if isinstance(key, int) else value[0] if isinstance(value, list) else value\n", - "\n", - " def __eq__(self, other):\n", - " return isinstance(other, Matrix) and self.data == other.data\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "RZbhUorsZxm1", - "outputId": "7509682e-02bc-46fa-a649-2a7de2652af2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "M1.data: [[0, 0, 0], [0, 0, 0]]\n", - "M2.data: [[1, 2, 3], [4, 5, 6]]\n", - "2\n", - "6\n", - "M1.data: [[5, 0, 0], [0, 0, 0]]\n", - "M1.data: [[1, 2, 3], [4, 5, 6]]\n" - ] - } - ], - "source": [ - "M1 = Matrix(2, 3)\n", - "print(\"M1.data:\", M1.data)\n", - "\n", - "M2 = Matrix([[1, 2, 3], [4, 5, 6]])\n", - "print(\"M2.data:\", M2.data)\n", - "\n", - "print(M2[0][1])\n", - "print(M2[1][2])\n", - "\n", - "# Assignment\n", - "M1[0][0] = 5\n", - "print(\"M1.data:\", M1.data)\n", - "\n", - "M1 = M2\n", - "print(\"M1.data:\", M1.data)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pIRlLR1nZxm3" - }, - "source": [ - "2. Add the following methods:\n", - " * `shape()`: returns a tuple `(n,m)` of the shape of the matrix.\n", - " * `transpose()`: returns a new matrix instance which is the transpose of the matrix.\n", - " * `row(n)` and `column(n)`: that return the nth row or column of the matrix M as a new appropriately shaped matrix object.\n", - " * `to_list()`: which returns the matrix as a list of lists.\n", - " * `block(n_0,n_1,m_0,m_1)` that returns a smaller matrix located at the n_0 to n_1 columns and m_0 to m_1 rows.\n", - " * (Extra credit) Modify `__getitem__` implemented above to support slicing.\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "5D4i_OudZxm3" - }, - "outputs": [], - "source": [ - "class Matrix:\n", - " def __init__(self, *args):\n", - " self.data = args[0] if len(args) == 1 and isinstance(args[0], list) else [[0] * args[1] for _ in range(args[0])]\n", - "\n", - " def __getitem__(self, key):\n", - " if isinstance(key, tuple):\n", - " return self.data[key[0]][key[1]] if len(key) == 2 else self.data[key[0]]\n", - " return self.data[key]\n", - "\n", - " def __setitem__(self, key, value):\n", - " self.data[key] = value if isinstance(key, int) else value[0] if isinstance(value, list) else value\n", - "\n", - " def __eq__(self, other):\n", - " return isinstance(other, Matrix) and self.data == other.data\n", - "\n", - " def shape(self):\n", - " return len(self.data), len(self.data[0])\n", - "\n", - " def transpose(self):\n", - " return Matrix(list(zip(*self.data)))\n", - "\n", - " def row(self, n):\n", - " return Matrix([self.data[n]])\n", - "\n", - " def column(self, n):\n", - " return Matrix([[self.data[i][n]] for i in range(len(self.data))])\n", - "\n", - " def to_list(self):\n", - " return self.data\n", - "\n", - " def block(self, n_0, n_1, m_0, m_1):\n", - " return Matrix([row[n_0:n_1] for row in self.data])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "J5gI0MI1Zxm4", - "outputId": "6fb5bb01-1f36-401c-b996-97ba3ee11dbc" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape of M1: (2, 3)\n", - "Transpose of M1: [(0, 0), (0, 0), (0, 0)]\n", - "Row 1 of M1: [[0, 0, 0]]\n", - "Column 2 of M1: [[0], [0]]\n", - "M1 as list: [[0, 0, 0], [0, 0, 0]]\n", - "Block from M1: [[0, 0], [0, 0]]\n", - "Shape of M2: (2, 3)\n", - "Transpose of M2: [(1, 4), (2, 5), (3, 6)]\n", - "Row 0 of M2: [[1, 2, 3]]\n", - "Column 1 of M2: [[2], [5]]\n", - "M2 as list: [[1, 2, 3], [4, 5, 6]]\n", - "Block from M2: [[1], [4]]\n" - ] - } - ], - "source": [ - "\n", - "M1 = Matrix(2, 3)\n", - "print(\"Shape of M1:\", M1.shape())\n", - "print(\"Transpose of M1:\", M1.transpose().to_list())\n", - "print(\"Row 1 of M1:\", M1.row(1).to_list())\n", - "print(\"Column 2 of M1:\", M1.column(2).to_list())\n", - "print(\"M1 as list:\", M1.to_list())\n", - "print(\"Block from M1:\", M1.block(0, 2, 0, 1).to_list())\n", - "\n", - "\n", - "M2 = Matrix([[1, 2, 3], [4, 5, 6]])\n", - "print(\"Shape of M2:\", M2.shape())\n", - "print(\"Transpose of M2:\", M2.transpose().to_list())\n", - "print(\"Row 0 of M2:\", M2.row(0).to_list())\n", - "print(\"Column 1 of M2:\", M2.column(1).to_list())\n", - "print(\"M2 as list:\", M2.to_list())\n", - "print(\"Block from M2:\", M2.block(0, 1, 0, 2).to_list())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4T47xvz_Zxm6" - }, - "source": [ - "3. Write functions that create special matrices (note these are standalone functions, not member functions of your `matrix` class):\n", - " * `constant(n,m,c)`: returns a `n` by `m` matrix filled with floats of value `c`.\n", - " * `zeros(n,m)` and `ones(n,m)`: return `n` by `m` matrices filled with floats of value `0` and `1`, respectively.\n", - " * `eye(n)`: returns the n by n identity matrix." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "5Fsm57K-Zxm7" - }, - "outputs": [], - "source": [ - "def constant(n, m, c):\n", - " return Matrix([[c] * m] * n)\n", - "\n", - "def zeros(n, m):\n", - " return constant(n, m, 0)\n", - "\n", - "def ones(n, m):\n", - " return constant(n, m, 1)\n", - "\n", - "def eye(n):\n", - " return Matrix([[1 if i == j else 0 for j in range(n)] for i in range(n)])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "erjYNm1rZxm8", - "outputId": "3e10034b-3748-473c-dae5-2b94454ab08f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Constant matrix:\n", - "[[5, 5], [5, 5], [5, 5]]\n", - "Zeros matrix:\n", - "[[0, 0, 0, 0], [0, 0, 0, 0]]\n", - "Ones matrix:\n", - "[[1, 1, 1], [1, 1, 1], [1, 1, 1]]\n", - "Identity matrix:\n", - "[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]\n" - ] - } - ], - "source": [ - "print(\"Constant matrix:\")\n", - "print(constant(3, 2, 5).to_list())\n", - "\n", - "print(\"Zeros matrix:\")\n", - "print(zeros(2, 4).to_list())\n", - "\n", - "print(\"Ones matrix:\")\n", - "print(ones(3, 3).to_list())\n", - "\n", - "print(\"Identity matrix:\")\n", - "print(eye(4).to_list())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "g-3ikzg3Zxm-" - }, - "source": [ - "4. Add the following member functions to your class. Make sure to appropriately test the dimensions of the matrices to make sure the operations are correct.\n", - " * `M.scalarmul(c)`: a matrix that is scalar product $cM$, where every element of $M$ is multiplied by $c$.\n", - " * `M.add(N)`: adds two matrices $M$ and $N$. Don’t forget to test that the sizes of the matrices are compatible for this and all other operations.\n", - " * `M.sub(N)`: subtracts two matrices $M$ and $N$.\n", - " * `M.mat_mult(N)`: returns a matrix that is the matrix product of two matrices $M$ and $N$.\n", - " * `M.element_mult(N)`: returns a matrix that is the element-wise product of two matrices $M$ and $N$.\n", - " * `M.equals(N)`: returns true/false if $M==N$." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "iCWjnziOZxm_" - }, - "outputs": [], - "source": [ - "class Matrix:\n", - " def __init__(self, matrix):\n", - " self.matrix = matrix\n", - " self.rows = len(matrix)\n", - " self.cols = len(matrix[0])\n", - "\n", - " def __eq__(self, other):\n", - " return isinstance(other, self.__class__) and self.matrix == other.matrix\n", - "\n", - " def __repr__(self):\n", - " return f\"Matrix({self.matrix})\"\n", - "\n", - " def _operate(self, other, operator):\n", - " if not isinstance(other, self.__class__):\n", - " raise ValueError(\"Operand must be a Matrix\")\n", - " if self.rows != other.rows or self.cols != other.cols:\n", - " raise ValueError(\"Matrices must have the same dimensions\")\n", - "\n", - " return Matrix([\n", - " [operator(x, y) for x, y in zip(row1, row2)]\n", - " for row1, row2 in zip(self.matrix, other.matrix)\n", - " ])\n", - "\n", - " def scalar_mul(self, c):\n", - " if not isinstance(c, (int, float)):\n", - " raise ValueError(\"Scalar must be a number\")\n", - "\n", - " return self._operate(Matrix([[c] * self.cols] * self.rows), lambda x, y: x * y)\n", - "\n", - " def add(self, other):\n", - " return self._operate(other, lambda x, y: x + y)\n", - "\n", - " def subtract(self, other):\n", - " return self._operate(other, lambda x, y: x - y)\n", - "\n", - " def mat_mult(self, other):\n", - " if self.cols != other.rows:\n", - " raise ValueError(\"Matrices are not compatible for multiplication\")\n", - "\n", - " transposed_other = list(zip(*other.matrix))\n", - " return Matrix([\n", - " [sum(a * b for a, b in zip(row1, col2)) for col2 in transposed_other]\n", - " for row1 in self.matrix\n", - " ])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "DFMpvwIeZxnA", - "outputId": "27a84dd6-662b-4358-91b5-b717cc131946" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All tests passed successfully!\n" - ] - } - ], - "source": [ - "#test to check\n", - "\n", - "def test_matrix_operations():\n", - "\n", - " matrix1 = Matrix([[1, 2], [3, 4]])\n", - " matrix2 = Matrix([[5, 6], [7, 8]])\n", - "\n", - "\n", - " assert matrix1 == matrix1\n", - " assert repr(matrix1) == \"Matrix([[1, 2], [3, 4]])\"\n", - " assert matrix1.scalar_mul(2) == Matrix([[2, 4], [6, 8]])\n", - " assert matrix1.add(matrix2) == Matrix([[6, 8], [10, 12]])\n", - " assert matrix1.subtract(matrix2) == Matrix([[-4, -4], [-4, -4]])\n", - " assert matrix1.mat_mult(matrix2) == Matrix([[19, 22], [43, 50]])\n", - "\n", - " print(\"All tests passed successfully!\")\n", - "\n", - "test_matrix_operations()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "a3oajZgsZxnB" - }, - "source": [ - "5. Overload python operators to appropriately use your functions in 4 and allow expressions like:\n", - " * 2*M\n", - " * M*2\n", - " * M+N\n", - " * M-N\n", - " * M*N\n", - " * M==N\n", - " * M=N\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "NMPmBI-JZxnC" - }, - "outputs": [], - "source": [ - "#guess:\n", - "class Matrix:\n", - " def __init__(self, matrix):\n", - " self.matrix = matrix\n", - " self.rows = len(matrix)\n", - " self.cols = len(matrix[0])\n", - "\n", - " def __eq__(self, other):\n", - " return isinstance(other, self.__class__) and self.matrix == other.matrix\n", - "\n", - " def __repr__(self):\n", - " return f\"Matrix({self.matrix})\"\n", - "\n", - " def _operate(self, other, operator):\n", - " if not isinstance(other, self.__class__):\n", - " raise ValueError(\"Operand must be a Matrix\")\n", - " if self.rows != other.rows or self.cols != other.cols:\n", - " raise ValueError(\"Matrices must have the same dimensions\")\n", - " return Matrix([[operator(x, y) for x, y in zip(row1, row2)] for row1, row2 in zip(self.matrix, other.matrix)])\n", - "\n", - " def scalar_mul(self, c):\n", - " if not isinstance(c, (int, float)):\n", - " raise ValueError(\"Scalar must be a number\")\n", - " return self._operate(Matrix([[c] * self.cols] * self.rows), lambda x, y: x * y)\n", - "\n", - " def __mul__(self, other):\n", - " if isinstance(other, (int, float)):\n", - " return self.scalar_mul(other)\n", - " elif isinstance(other, self.__class__):\n", - " if self.cols != other.rows:\n", - " raise ValueError(\"Matrices are not compatible for multiplication\")\n", - " transposed_other = list(zip(*other.matrix))\n", - " return Matrix([[sum(a * b for a, b in zip(row1, col2)) for col2 in transposed_other] for row1 in self.matrix])\n", - " else:\n", - " raise ValueError(\"Unsupported operand type for *\")\n", - "\n", - " def __rmul__(self, other):\n", - " return self.scalar_mul(other)\n", - "\n", - " def __add__(self, other):\n", - " return self._operate(other, lambda x, y: x + y)\n", - "\n", - " def __sub__(self, other):\n", - " return self._operate(other, lambda x, y: x - y)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "AztaICYSZxnF" - }, - "outputs": [], - "source": [ - "def test_matrix_operations():\n", - " M = Matrix([[2, 3], [4, 5]])\n", - " N = Matrix([[6, 7], [8, 9]])\n", - "\n", - " assert 2 * M == Matrix([[4, 6], [8, 10]])\n", - " assert M * 2 == Matrix([[4, 6], [8, 10]])\n", - " assert M + N == Matrix([[8, 10], [12, 14]])\n", - " assert M - N == Matrix([[-4, -4], [-4, -4]])\n", - " assert M * N == Matrix([[36, 41], [64, 73]])\n", - " assert M == Matrix([[2, 3], [4, 5]])\n", - " assert M == M\n", - "\n", - " print(\"All tests passed!\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ygFkLz4XZxnG", - "outputId": "b86761c8-e502-4ca1-9ba1-873b5c49830d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All tests passed!\n" - ] - } - ], - "source": [ - "# Run the test cases\n", - "test_matrix_operations()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pfY_e6TmZxnH" - }, - "source": [ - "6. Demonstrate the basic properties of matrices with your matrix class by creating two 2 by 2 example matrices using your Matrix class and illustrating the following:\n", - "\n", - "$$\n", - "(AB)C=A(BC)\n", - "$$\n", - "$$\n", - "A(B+C)=AB+AC\n", - "$$\n", - "$$\n", - "AB\\neq BA\n", - "$$\n", - "$$\n", - "AI=A\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "cxayNkugZxnH" - }, - "outputs": [], - "source": [ - "class Matrix:\n", - " def __init__(self, matrix):\n", - " self.matrix = matrix\n", - "\n", - " def __mul__(self, other):\n", - " if isinstance(other, Matrix):\n", - " return Matrix([[sum(a * b for a, b in zip(row1, col2)) for col2 in zip(*other.matrix)] for row1 in self.matrix])\n", - " elif isinstance(other, (int, float)):\n", - " return Matrix([[element * other for element in row] for row in self.matrix])\n", - " else:\n", - " raise ValueError(\"Unsupported operand type for *\")\n", - "\n", - " def __add__(self, other):\n", - " return Matrix([[a + b for a, b in zip(row1, row2)] for row1, row2 in zip(self.matrix, other.matrix)])\n", - "\n", - " def __sub__(self, other):\n", - " return Matrix([[a - b for a, b in zip(row1, row2)] for row1, row2 in zip(self.matrix, other.matrix)])\n", - "\n", - " def __eq__(self, other):\n", - " return self.matrix == other.matrix\n", - "\n", - " def __repr__(self):\n", - " return f\"Matrix({self.matrix})\"" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "ud5EfsuxZxnI" - }, - "outputs": [], - "source": [ - "## Matrices\n", - "A = Matrix([[1, 2], [3, 4]])\n", - "B = Matrix([[5, 6], [7, 8]])\n", - "C = Matrix([[9, 10], [11, 12]])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "R-KQbgXqZxnI", - "outputId": "690cf4ac-1f9e-4ff2-ac7e-8cdd578ef9c2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(AB)C = Matrix([[413, 454], [937, 1030]])\n", - "A(B*C) = Matrix([[413, 454], [937, 1030]])\n", - "(AB)C == A(B*C): True\n" - ] - } - ], - "source": [ - "# (AB)C = A(BC)\n", - "print(\"(AB)C =\", (A * B) * C)\n", - "print(\"A(B*C) =\", A * (B * C))\n", - "print(\"(AB)C == A(B*C):\", (A * B) * C == A * (B * C))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "LH7mqxBRZxnJ", - "outputId": "6ae2ee78-2716-4de9-87f1-9be602861eed" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "A(B+C) = Matrix([[50, 56], [114, 128]])\n", - "AB + AC = Matrix([[50, 56], [114, 128]])\n", - "A(B+C) == AB + AC: True\n" - ] - } - ], - "source": [ - "# A(B+C) = AB + AC\n", - "print(\"A(B+C) =\", A * (B + C))\n", - "print(\"AB + AC =\", A * B + A * C)\n", - "print(\"A(B+C) == AB + AC:\", A * (B + C) == A * B + A * C)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9TpsaWZxZxnJ", - "outputId": "ebbe650f-7431-4446-bf01-2bb1261be583" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AB = Matrix([[19, 22], [43, 50]])\n", - "BA = Matrix([[23, 34], [31, 46]])\n", - "AB != BA: True\n" - ] - } - ], - "source": [ - "# AB != BA\n", - "print(\"AB =\", A * B)\n", - "print(\"BA =\", B * A)\n", - "print(\"AB != BA:\", A * B != B * A)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "X8KCSMw8ZxnK", - "outputId": "070f0b18-072f-4244-cc20-d763443bc6cb" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AI = Matrix([[1, 2], [3, 4]])\n", - "AI == A: True\n" - ] - } - ], - "source": [ - "# AI = A\n", - "I = Matrix([[1, 0], [0, 1]])\n", - "print(\"AI =\", A * I)\n", - "print(\"AI == A:\", A * I == A)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('Clubs', 2)\n", - "('Clubs', 3)\n", - "('Clubs', 4)\n", - "('Clubs', 5)\n", - "('Clubs', 6)\n", - "('Clubs', 7)\n", - "('Clubs', 8)\n", - "('Clubs', 9)\n", - "('Clubs', 10)\n", - "('Clubs', 'Jack')\n", - "('Clubs', 'Queen')\n", - "('Clubs', 'King')\n", - "('Clubs', 'Ace')\n", - "('Diamonds', 2)\n", - "('Diamonds', 3)\n", - "('Diamonds', 4)\n", - "('Diamonds', 5)\n", - "('Diamonds', 6)\n", - "('Diamonds', 7)\n", - "('Diamonds', 8)\n", - "('Diamonds', 9)\n", - "('Diamonds', 10)\n", - "('Diamonds', 'Jack')\n", - "('Diamonds', 'Queen')\n", - "('Diamonds', 'King')\n", - "('Diamonds', 'Ace')\n", - "('Hearts', 2)\n", - "('Hearts', 3)\n", - "('Hearts', 4)\n", - "('Hearts', 5)\n", - "('Hearts', 6)\n", - "('Hearts', 7)\n", - "('Hearts', 8)\n", - "('Hearts', 9)\n", - "('Hearts', 10)\n", - "('Hearts', 'Jack')\n", - "('Hearts', 'Queen')\n", - "('Hearts', 'King')\n", - "('Hearts', 'Ace')\n", - "('Spades', 2)\n", - "('Spades', 3)\n", - "('Spades', 4)\n", - "('Spades', 5)\n", - "('Spades', 6)\n", - "('Spades', 7)\n", - "('Spades', 8)\n", - "('Spades', 9)\n", - "('Spades', 10)\n", - "('Spades', 'Jack')\n", - "('Spades', 'Queen')\n", - "('Spades', 'King')\n", - "('Spades', 'Ace')\n" - ] - } - ], - "source": [ - "#QUIZ 2\n", - "\n", - "\n", - "def make_deck():\n", - " suits = ['Clubs', 'Diamonds', 'Hearts', 'Spades']\n", - " values = [2, 3, 4, 5, 6, 7, 8, 9, 10, 'Jack', 'Queen', 'King', 'Ace']\n", - " \n", - " deck = [(suit, value) for suit in suits for value in values]\n", - " return deck\n", - "\n", - "# Example usage\n", - "if __name__ == \"__main__\":\n", - " deck = make_deck()\n", - " for card in deck:\n", - " print(card)" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "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": "3.12.1" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From f2e9d706fa7a545ebe78ed1862a7af49c3dbe538 Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Fri, 4 Oct 2024 07:59:12 -0500 Subject: [PATCH 15/22] Delete Labs/Lab5.ipynb --- Labs/Lab5.ipynb | 788 ------------------------------------------------ 1 file changed, 788 deletions(-) delete mode 100644 Labs/Lab5.ipynb diff --git a/Labs/Lab5.ipynb b/Labs/Lab5.ipynb deleted file mode 100644 index b76c52b..0000000 --- a/Labs/Lab5.ipynb +++ /dev/null @@ -1,788 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "4uMffVwNZxmr" - }, - "source": [ - "# Lab 5\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SSaTc21zZxmv" - }, - "source": [ - "Matrix Representation: In this lab you will be creating a simple linear algebra system. In memory, we will represent matrices as nested python lists as we have done in lecture.\n", - "\n", - "1. Create a `matrix` class with the following properties:\n", - " * It can be initialized in 2 ways:\n", - " 1. with arguments `n` and `m`, the size of the matrix. A newly instanciated matrix will contain all zeros.\n", - " 2. with a list of lists of values. Note that since we are using lists of lists to implement matrices, it is possible that not all rows have the same number of columns. Test explicitly that the matrix is properly specified.\n", - " * Matrix instances `M` can be indexed with `M[i][j]` and `M[i,j]`.\n", - " * Matrix assignment works in 2 ways:\n", - " 1. If `M_1` and `M_2` are `matrix` instances `M_1=M_2` sets the values of `M_1` to those of `M_2`, if they are the same size. Error otherwise.\n", - " 2. In example above `M_2` can be a list of lists of correct size.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "3eM2yb7XZxmz" - }, - "outputs": [], - "source": [ - "class Matrix:\n", - " def __init__(self, *args):\n", - " self.data = args[0] if len(args) == 1 and isinstance(args[0], list) else [[0] * args[1] for _ in range(args[0])]\n", - "\n", - " def __getitem__(self, key):\n", - " return self.data[key]\n", - "\n", - " def __setitem__(self, key, value):\n", - " self.data[key] = value if isinstance(key, int) else value[0] if isinstance(value, list) else value\n", - "\n", - " def __eq__(self, other):\n", - " return isinstance(other, Matrix) and self.data == other.data\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "RZbhUorsZxm1", - "outputId": "7509682e-02bc-46fa-a649-2a7de2652af2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "M1.data: [[0, 0, 0], [0, 0, 0]]\n", - "M2.data: [[1, 2, 3], [4, 5, 6]]\n", - "2\n", - "6\n", - "M1.data: [[5, 0, 0], [0, 0, 0]]\n", - "M1.data: [[1, 2, 3], [4, 5, 6]]\n" - ] - } - ], - "source": [ - "M1 = Matrix(2, 3)\n", - "print(\"M1.data:\", M1.data)\n", - "\n", - "M2 = Matrix([[1, 2, 3], [4, 5, 6]])\n", - "print(\"M2.data:\", M2.data)\n", - "\n", - "print(M2[0][1])\n", - "print(M2[1][2])\n", - "\n", - "# Assignment\n", - "M1[0][0] = 5\n", - "print(\"M1.data:\", M1.data)\n", - "\n", - "M1 = M2\n", - "print(\"M1.data:\", M1.data)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pIRlLR1nZxm3" - }, - "source": [ - "2. Add the following methods:\n", - " * `shape()`: returns a tuple `(n,m)` of the shape of the matrix.\n", - " * `transpose()`: returns a new matrix instance which is the transpose of the matrix.\n", - " * `row(n)` and `column(n)`: that return the nth row or column of the matrix M as a new appropriately shaped matrix object.\n", - " * `to_list()`: which returns the matrix as a list of lists.\n", - " * `block(n_0,n_1,m_0,m_1)` that returns a smaller matrix located at the n_0 to n_1 columns and m_0 to m_1 rows.\n", - " * (Extra credit) Modify `__getitem__` implemented above to support slicing.\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "5D4i_OudZxm3" - }, - "outputs": [], - "source": [ - "class Matrix:\n", - " def __init__(self, *args):\n", - " self.data = args[0] if len(args) == 1 and isinstance(args[0], list) else [[0] * args[1] for _ in range(args[0])]\n", - "\n", - " def __getitem__(self, key):\n", - " if isinstance(key, tuple):\n", - " return self.data[key[0]][key[1]] if len(key) == 2 else self.data[key[0]]\n", - " return self.data[key]\n", - "\n", - " def __setitem__(self, key, value):\n", - " self.data[key] = value if isinstance(key, int) else value[0] if isinstance(value, list) else value\n", - "\n", - " def __eq__(self, other):\n", - " return isinstance(other, Matrix) and self.data == other.data\n", - "\n", - " def shape(self):\n", - " return len(self.data), len(self.data[0])\n", - "\n", - " def transpose(self):\n", - " return Matrix(list(zip(*self.data)))\n", - "\n", - " def row(self, n):\n", - " return Matrix([self.data[n]])\n", - "\n", - " def column(self, n):\n", - " return Matrix([[self.data[i][n]] for i in range(len(self.data))])\n", - "\n", - " def to_list(self):\n", - " return self.data\n", - "\n", - " def block(self, n_0, n_1, m_0, m_1):\n", - " return Matrix([row[n_0:n_1] for row in self.data])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "J5gI0MI1Zxm4", - "outputId": "6fb5bb01-1f36-401c-b996-97ba3ee11dbc" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape of M1: (2, 3)\n", - "Transpose of M1: [(0, 0), (0, 0), (0, 0)]\n", - "Row 1 of M1: [[0, 0, 0]]\n", - "Column 2 of M1: [[0], [0]]\n", - "M1 as list: [[0, 0, 0], [0, 0, 0]]\n", - "Block from M1: [[0, 0], [0, 0]]\n", - "Shape of M2: (2, 3)\n", - "Transpose of M2: [(1, 4), (2, 5), (3, 6)]\n", - "Row 0 of M2: [[1, 2, 3]]\n", - "Column 1 of M2: [[2], [5]]\n", - "M2 as list: [[1, 2, 3], [4, 5, 6]]\n", - "Block from M2: [[1], [4]]\n" - ] - } - ], - "source": [ - "\n", - "M1 = Matrix(2, 3)\n", - "print(\"Shape of M1:\", M1.shape())\n", - "print(\"Transpose of M1:\", M1.transpose().to_list())\n", - "print(\"Row 1 of M1:\", M1.row(1).to_list())\n", - "print(\"Column 2 of M1:\", M1.column(2).to_list())\n", - "print(\"M1 as list:\", M1.to_list())\n", - "print(\"Block from M1:\", M1.block(0, 2, 0, 1).to_list())\n", - "\n", - "\n", - "M2 = Matrix([[1, 2, 3], [4, 5, 6]])\n", - "print(\"Shape of M2:\", M2.shape())\n", - "print(\"Transpose of M2:\", M2.transpose().to_list())\n", - "print(\"Row 0 of M2:\", M2.row(0).to_list())\n", - "print(\"Column 1 of M2:\", M2.column(1).to_list())\n", - "print(\"M2 as list:\", M2.to_list())\n", - "print(\"Block from M2:\", M2.block(0, 1, 0, 2).to_list())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4T47xvz_Zxm6" - }, - "source": [ - "3. Write functions that create special matrices (note these are standalone functions, not member functions of your `matrix` class):\n", - " * `constant(n,m,c)`: returns a `n` by `m` matrix filled with floats of value `c`.\n", - " * `zeros(n,m)` and `ones(n,m)`: return `n` by `m` matrices filled with floats of value `0` and `1`, respectively.\n", - " * `eye(n)`: returns the n by n identity matrix." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "5Fsm57K-Zxm7" - }, - "outputs": [], - "source": [ - "def constant(n, m, c):\n", - " return Matrix([[c] * m] * n)\n", - "\n", - "def zeros(n, m):\n", - " return constant(n, m, 0)\n", - "\n", - "def ones(n, m):\n", - " return constant(n, m, 1)\n", - "\n", - "def eye(n):\n", - " return Matrix([[1 if i == j else 0 for j in range(n)] for i in range(n)])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "erjYNm1rZxm8", - "outputId": "3e10034b-3748-473c-dae5-2b94454ab08f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Constant matrix:\n", - "[[5, 5], [5, 5], [5, 5]]\n", - "Zeros matrix:\n", - "[[0, 0, 0, 0], [0, 0, 0, 0]]\n", - "Ones matrix:\n", - "[[1, 1, 1], [1, 1, 1], [1, 1, 1]]\n", - "Identity matrix:\n", - "[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]\n" - ] - } - ], - "source": [ - "print(\"Constant matrix:\")\n", - "print(constant(3, 2, 5).to_list())\n", - "\n", - "print(\"Zeros matrix:\")\n", - "print(zeros(2, 4).to_list())\n", - "\n", - "print(\"Ones matrix:\")\n", - "print(ones(3, 3).to_list())\n", - "\n", - "print(\"Identity matrix:\")\n", - "print(eye(4).to_list())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "g-3ikzg3Zxm-" - }, - "source": [ - "4. Add the following member functions to your class. Make sure to appropriately test the dimensions of the matrices to make sure the operations are correct.\n", - " * `M.scalarmul(c)`: a matrix that is scalar product $cM$, where every element of $M$ is multiplied by $c$.\n", - " * `M.add(N)`: adds two matrices $M$ and $N$. Don’t forget to test that the sizes of the matrices are compatible for this and all other operations.\n", - " * `M.sub(N)`: subtracts two matrices $M$ and $N$.\n", - " * `M.mat_mult(N)`: returns a matrix that is the matrix product of two matrices $M$ and $N$.\n", - " * `M.element_mult(N)`: returns a matrix that is the element-wise product of two matrices $M$ and $N$.\n", - " * `M.equals(N)`: returns true/false if $M==N$." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "iCWjnziOZxm_" - }, - "outputs": [], - "source": [ - "class Matrix:\n", - " def __init__(self, matrix):\n", - " self.matrix = matrix\n", - " self.rows = len(matrix)\n", - " self.cols = len(matrix[0])\n", - "\n", - " def __eq__(self, other):\n", - " return isinstance(other, self.__class__) and self.matrix == other.matrix\n", - "\n", - " def __repr__(self):\n", - " return f\"Matrix({self.matrix})\"\n", - "\n", - " def _operate(self, other, operator):\n", - " if not isinstance(other, self.__class__):\n", - " raise ValueError(\"Operand must be a Matrix\")\n", - " if self.rows != other.rows or self.cols != other.cols:\n", - " raise ValueError(\"Matrices must have the same dimensions\")\n", - "\n", - " return Matrix([\n", - " [operator(x, y) for x, y in zip(row1, row2)]\n", - " for row1, row2 in zip(self.matrix, other.matrix)\n", - " ])\n", - "\n", - " def scalar_mul(self, c):\n", - " if not isinstance(c, (int, float)):\n", - " raise ValueError(\"Scalar must be a number\")\n", - "\n", - " return self._operate(Matrix([[c] * self.cols] * self.rows), lambda x, y: x * y)\n", - "\n", - " def add(self, other):\n", - " return self._operate(other, lambda x, y: x + y)\n", - "\n", - " def subtract(self, other):\n", - " return self._operate(other, lambda x, y: x - y)\n", - "\n", - " def mat_mult(self, other):\n", - " if self.cols != other.rows:\n", - " raise ValueError(\"Matrices are not compatible for multiplication\")\n", - "\n", - " transposed_other = list(zip(*other.matrix))\n", - " return Matrix([\n", - " [sum(a * b for a, b in zip(row1, col2)) for col2 in transposed_other]\n", - " for row1 in self.matrix\n", - " ])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "DFMpvwIeZxnA", - "outputId": "27a84dd6-662b-4358-91b5-b717cc131946" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All tests passed successfully!\n" - ] - } - ], - "source": [ - "#test to check\n", - "\n", - "def test_matrix_operations():\n", - "\n", - " matrix1 = Matrix([[1, 2], [3, 4]])\n", - " matrix2 = Matrix([[5, 6], [7, 8]])\n", - "\n", - "\n", - " assert matrix1 == matrix1\n", - " assert repr(matrix1) == \"Matrix([[1, 2], [3, 4]])\"\n", - " assert matrix1.scalar_mul(2) == Matrix([[2, 4], [6, 8]])\n", - " assert matrix1.add(matrix2) == Matrix([[6, 8], [10, 12]])\n", - " assert matrix1.subtract(matrix2) == Matrix([[-4, -4], [-4, -4]])\n", - " assert matrix1.mat_mult(matrix2) == Matrix([[19, 22], [43, 50]])\n", - "\n", - " print(\"All tests passed successfully!\")\n", - "\n", - "test_matrix_operations()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "a3oajZgsZxnB" - }, - "source": [ - "5. Overload python operators to appropriately use your functions in 4 and allow expressions like:\n", - " * 2*M\n", - " * M*2\n", - " * M+N\n", - " * M-N\n", - " * M*N\n", - " * M==N\n", - " * M=N\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "NMPmBI-JZxnC" - }, - "outputs": [], - "source": [ - "#guess:\n", - "class Matrix:\n", - " def __init__(self, matrix):\n", - " self.matrix = matrix\n", - " self.rows = len(matrix)\n", - " self.cols = len(matrix[0])\n", - "\n", - " def __eq__(self, other):\n", - " return isinstance(other, self.__class__) and self.matrix == other.matrix\n", - "\n", - " def __repr__(self):\n", - " return f\"Matrix({self.matrix})\"\n", - "\n", - " def _operate(self, other, operator):\n", - " if not isinstance(other, self.__class__):\n", - " raise ValueError(\"Operand must be a Matrix\")\n", - " if self.rows != other.rows or self.cols != other.cols:\n", - " raise ValueError(\"Matrices must have the same dimensions\")\n", - " return Matrix([[operator(x, y) for x, y in zip(row1, row2)] for row1, row2 in zip(self.matrix, other.matrix)])\n", - "\n", - " def scalar_mul(self, c):\n", - " if not isinstance(c, (int, float)):\n", - " raise ValueError(\"Scalar must be a number\")\n", - " return self._operate(Matrix([[c] * self.cols] * self.rows), lambda x, y: x * y)\n", - "\n", - " def __mul__(self, other):\n", - " if isinstance(other, (int, float)):\n", - " return self.scalar_mul(other)\n", - " elif isinstance(other, self.__class__):\n", - " if self.cols != other.rows:\n", - " raise ValueError(\"Matrices are not compatible for multiplication\")\n", - " transposed_other = list(zip(*other.matrix))\n", - " return Matrix([[sum(a * b for a, b in zip(row1, col2)) for col2 in transposed_other] for row1 in self.matrix])\n", - " else:\n", - " raise ValueError(\"Unsupported operand type for *\")\n", - "\n", - " def __rmul__(self, other):\n", - " return self.scalar_mul(other)\n", - "\n", - " def __add__(self, other):\n", - " return self._operate(other, lambda x, y: x + y)\n", - "\n", - " def __sub__(self, other):\n", - " return self._operate(other, lambda x, y: x - y)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "AztaICYSZxnF" - }, - "outputs": [], - "source": [ - "def test_matrix_operations():\n", - " M = Matrix([[2, 3], [4, 5]])\n", - " N = Matrix([[6, 7], [8, 9]])\n", - "\n", - " assert 2 * M == Matrix([[4, 6], [8, 10]])\n", - " assert M * 2 == Matrix([[4, 6], [8, 10]])\n", - " assert M + N == Matrix([[8, 10], [12, 14]])\n", - " assert M - N == Matrix([[-4, -4], [-4, -4]])\n", - " assert M * N == Matrix([[36, 41], [64, 73]])\n", - " assert M == Matrix([[2, 3], [4, 5]])\n", - " assert M == M\n", - "\n", - " print(\"All tests passed!\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ygFkLz4XZxnG", - "outputId": "b86761c8-e502-4ca1-9ba1-873b5c49830d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All tests passed!\n" - ] - } - ], - "source": [ - "# Run the test cases\n", - "test_matrix_operations()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pfY_e6TmZxnH" - }, - "source": [ - "6. Demonstrate the basic properties of matrices with your matrix class by creating two 2 by 2 example matrices using your Matrix class and illustrating the following:\n", - "\n", - "$$\n", - "(AB)C=A(BC)\n", - "$$\n", - "$$\n", - "A(B+C)=AB+AC\n", - "$$\n", - "$$\n", - "AB\\neq BA\n", - "$$\n", - "$$\n", - "AI=A\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "cxayNkugZxnH" - }, - "outputs": [], - "source": [ - "class Matrix:\n", - " def __init__(self, matrix):\n", - " self.matrix = matrix\n", - "\n", - " def __mul__(self, other):\n", - " if isinstance(other, Matrix):\n", - " return Matrix([[sum(a * b for a, b in zip(row1, col2)) for col2 in zip(*other.matrix)] for row1 in self.matrix])\n", - " elif isinstance(other, (int, float)):\n", - " return Matrix([[element * other for element in row] for row in self.matrix])\n", - " else:\n", - " raise ValueError(\"Unsupported operand type for *\")\n", - "\n", - " def __add__(self, other):\n", - " return Matrix([[a + b for a, b in zip(row1, row2)] for row1, row2 in zip(self.matrix, other.matrix)])\n", - "\n", - " def __sub__(self, other):\n", - " return Matrix([[a - b for a, b in zip(row1, row2)] for row1, row2 in zip(self.matrix, other.matrix)])\n", - "\n", - " def __eq__(self, other):\n", - " return self.matrix == other.matrix\n", - "\n", - " def __repr__(self):\n", - " return f\"Matrix({self.matrix})\"" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "ud5EfsuxZxnI" - }, - "outputs": [], - "source": [ - "## Matrices\n", - "A = Matrix([[1, 2], [3, 4]])\n", - "B = Matrix([[5, 6], [7, 8]])\n", - "C = Matrix([[9, 10], [11, 12]])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "R-KQbgXqZxnI", - "outputId": "690cf4ac-1f9e-4ff2-ac7e-8cdd578ef9c2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(AB)C = Matrix([[413, 454], [937, 1030]])\n", - "A(B*C) = Matrix([[413, 454], [937, 1030]])\n", - "(AB)C == A(B*C): True\n" - ] - } - ], - "source": [ - "# (AB)C = A(BC)\n", - "print(\"(AB)C =\", (A * B) * C)\n", - "print(\"A(B*C) =\", A * (B * C))\n", - "print(\"(AB)C == A(B*C):\", (A * B) * C == A * (B * C))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "LH7mqxBRZxnJ", - "outputId": "6ae2ee78-2716-4de9-87f1-9be602861eed" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "A(B+C) = Matrix([[50, 56], [114, 128]])\n", - "AB + AC = Matrix([[50, 56], [114, 128]])\n", - "A(B+C) == AB + AC: True\n" - ] - } - ], - "source": [ - "# A(B+C) = AB + AC\n", - "print(\"A(B+C) =\", A * (B + C))\n", - "print(\"AB + AC =\", A * B + A * C)\n", - "print(\"A(B+C) == AB + AC:\", A * (B + C) == A * B + A * C)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9TpsaWZxZxnJ", - "outputId": "ebbe650f-7431-4446-bf01-2bb1261be583" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AB = Matrix([[19, 22], [43, 50]])\n", - "BA = Matrix([[23, 34], [31, 46]])\n", - "AB != BA: True\n" - ] - } - ], - "source": [ - "# AB != BA\n", - "print(\"AB =\", A * B)\n", - "print(\"BA =\", B * A)\n", - "print(\"AB != BA:\", A * B != B * A)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "X8KCSMw8ZxnK", - "outputId": "070f0b18-072f-4244-cc20-d763443bc6cb" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AI = Matrix([[1, 2], [3, 4]])\n", - "AI == A: True\n" - ] - } - ], - "source": [ - "# AI = A\n", - "I = Matrix([[1, 0], [0, 1]])\n", - "print(\"AI =\", A * I)\n", - "print(\"AI == A:\", A * I == A)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('Clubs', 2)\n", - "('Clubs', 3)\n", - "('Clubs', 4)\n", - "('Clubs', 5)\n", - "('Clubs', 6)\n", - "('Clubs', 7)\n", - "('Clubs', 8)\n", - "('Clubs', 9)\n", - "('Clubs', 10)\n", - "('Clubs', 'Jack')\n", - "('Clubs', 'Queen')\n", - "('Clubs', 'King')\n", - "('Clubs', 'Ace')\n", - "('Diamonds', 2)\n", - "('Diamonds', 3)\n", - "('Diamonds', 4)\n", - "('Diamonds', 5)\n", - "('Diamonds', 6)\n", - "('Diamonds', 7)\n", - "('Diamonds', 8)\n", - "('Diamonds', 9)\n", - "('Diamonds', 10)\n", - "('Diamonds', 'Jack')\n", - "('Diamonds', 'Queen')\n", - "('Diamonds', 'King')\n", - "('Diamonds', 'Ace')\n", - "('Hearts', 2)\n", - "('Hearts', 3)\n", - "('Hearts', 4)\n", - "('Hearts', 5)\n", - "('Hearts', 6)\n", - "('Hearts', 7)\n", - "('Hearts', 8)\n", - "('Hearts', 9)\n", - "('Hearts', 10)\n", - "('Hearts', 'Jack')\n", - "('Hearts', 'Queen')\n", - "('Hearts', 'King')\n", - "('Hearts', 'Ace')\n", - "('Spades', 2)\n", - "('Spades', 3)\n", - "('Spades', 4)\n", - "('Spades', 5)\n", - "('Spades', 6)\n", - "('Spades', 7)\n", - "('Spades', 8)\n", - "('Spades', 9)\n", - "('Spades', 10)\n", - "('Spades', 'Jack')\n", - "('Spades', 'Queen')\n", - "('Spades', 'King')\n", - "('Spades', 'Ace')\n" - ] - } - ], - "source": [ - "#QUIZ 2\n", - "\n", - "\n", - "def make_deck():\n", - " suits = ['Clubs', 'Diamonds', 'Hearts', 'Spades']\n", - " values = [2, 3, 4, 5, 6, 7, 8, 9, 10, 'Jack', 'Queen', 'King', 'Ace']\n", - " \n", - " deck = [(suit, value) for suit in suits for value in values]\n", - " return deck\n", - "\n", - "# Example usage\n", - "if __name__ == \"__main__\":\n", - " deck = make_deck()\n", - " for card in deck:\n", - " print(card)" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "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": "3.12.1" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From ffeb5630da38f9902ecc5c78da5c57d7db45ca30 Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Fri, 4 Oct 2024 08:00:49 -0500 Subject: [PATCH 16/22] newlab5 --- Labs/Lab.5/Lab.5.ipynb | 690 ++++++++++++++++++++++++++++++++++++++++- 1 file changed, 676 insertions(+), 14 deletions(-) diff --git a/Labs/Lab.5/Lab.5.ipynb b/Labs/Lab.5/Lab.5.ipynb index b8f0822..b76c52b 100644 --- a/Labs/Lab.5/Lab.5.ipynb +++ b/Labs/Lab.5/Lab.5.ipynb @@ -2,16 +2,20 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "4uMffVwNZxmr" + }, "source": [ "# Lab 5\n" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "SSaTc21zZxmv" + }, "source": [ - "Matrix Representation: In this lab you will be creating a simple linear algebra system. In memory, we will represent matrices as nested python lists as we have done in lecture. In the exercises below, you are required to explicitly test every feature you implement, demonstrating it works.\n", + "Matrix Representation: In this lab you will be creating a simple linear algebra system. In memory, we will represent matrices as nested python lists as we have done in lecture.\n", "\n", "1. Create a `matrix` class with the following properties:\n", " * It can be initialized in 2 ways:\n", @@ -23,23 +27,183 @@ " 2. In example above `M_2` can be a list of lists of correct size.\n" ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "3eM2yb7XZxmz" + }, + "outputs": [], + "source": [ + "class Matrix:\n", + " def __init__(self, *args):\n", + " self.data = args[0] if len(args) == 1 and isinstance(args[0], list) else [[0] * args[1] for _ in range(args[0])]\n", + "\n", + " def __getitem__(self, key):\n", + " return self.data[key]\n", + "\n", + " def __setitem__(self, key, value):\n", + " self.data[key] = value if isinstance(key, int) else value[0] if isinstance(value, list) else value\n", + "\n", + " def __eq__(self, other):\n", + " return isinstance(other, Matrix) and self.data == other.data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RZbhUorsZxm1", + "outputId": "7509682e-02bc-46fa-a649-2a7de2652af2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M1.data: [[0, 0, 0], [0, 0, 0]]\n", + "M2.data: [[1, 2, 3], [4, 5, 6]]\n", + "2\n", + "6\n", + "M1.data: [[5, 0, 0], [0, 0, 0]]\n", + "M1.data: [[1, 2, 3], [4, 5, 6]]\n" + ] + } + ], + "source": [ + "M1 = Matrix(2, 3)\n", + "print(\"M1.data:\", M1.data)\n", + "\n", + "M2 = Matrix([[1, 2, 3], [4, 5, 6]])\n", + "print(\"M2.data:\", M2.data)\n", + "\n", + "print(M2[0][1])\n", + "print(M2[1][2])\n", + "\n", + "# Assignment\n", + "M1[0][0] = 5\n", + "print(\"M1.data:\", M1.data)\n", + "\n", + "M1 = M2\n", + "print(\"M1.data:\", M1.data)" + ] + }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "pIRlLR1nZxm3" + }, "source": [ "2. Add the following methods:\n", " * `shape()`: returns a tuple `(n,m)` of the shape of the matrix.\n", " * `transpose()`: returns a new matrix instance which is the transpose of the matrix.\n", " * `row(n)` and `column(n)`: that return the nth row or column of the matrix M as a new appropriately shaped matrix object.\n", " * `to_list()`: which returns the matrix as a list of lists.\n", - " * `block(n_0,n_1,m_0,m_1)` that returns a smaller matrix located at the n_0 to n_1 columns and m_0 to m_1 rows. \n", + " * `block(n_0,n_1,m_0,m_1)` that returns a smaller matrix located at the n_0 to n_1 columns and m_0 to m_1 rows.\n", " * (Extra credit) Modify `__getitem__` implemented above to support slicing.\n", " " ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "5D4i_OudZxm3" + }, + "outputs": [], + "source": [ + "class Matrix:\n", + " def __init__(self, *args):\n", + " self.data = args[0] if len(args) == 1 and isinstance(args[0], list) else [[0] * args[1] for _ in range(args[0])]\n", + "\n", + " def __getitem__(self, key):\n", + " if isinstance(key, tuple):\n", + " return self.data[key[0]][key[1]] if len(key) == 2 else self.data[key[0]]\n", + " return self.data[key]\n", + "\n", + " def __setitem__(self, key, value):\n", + " self.data[key] = value if isinstance(key, int) else value[0] if isinstance(value, list) else value\n", + "\n", + " def __eq__(self, other):\n", + " return isinstance(other, Matrix) and self.data == other.data\n", + "\n", + " def shape(self):\n", + " return len(self.data), len(self.data[0])\n", + "\n", + " def transpose(self):\n", + " return Matrix(list(zip(*self.data)))\n", + "\n", + " def row(self, n):\n", + " return Matrix([self.data[n]])\n", + "\n", + " def column(self, n):\n", + " return Matrix([[self.data[i][n]] for i in range(len(self.data))])\n", + "\n", + " def to_list(self):\n", + " return self.data\n", + "\n", + " def block(self, n_0, n_1, m_0, m_1):\n", + " return Matrix([row[n_0:n_1] for row in self.data])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "J5gI0MI1Zxm4", + "outputId": "6fb5bb01-1f36-401c-b996-97ba3ee11dbc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of M1: (2, 3)\n", + "Transpose of M1: [(0, 0), (0, 0), (0, 0)]\n", + "Row 1 of M1: [[0, 0, 0]]\n", + "Column 2 of M1: [[0], [0]]\n", + "M1 as list: [[0, 0, 0], [0, 0, 0]]\n", + "Block from M1: [[0, 0], [0, 0]]\n", + "Shape of M2: (2, 3)\n", + "Transpose of M2: [(1, 4), (2, 5), (3, 6)]\n", + "Row 0 of M2: [[1, 2, 3]]\n", + "Column 1 of M2: [[2], [5]]\n", + "M2 as list: [[1, 2, 3], [4, 5, 6]]\n", + "Block from M2: [[1], [4]]\n" + ] + } + ], + "source": [ + "\n", + "M1 = Matrix(2, 3)\n", + "print(\"Shape of M1:\", M1.shape())\n", + "print(\"Transpose of M1:\", M1.transpose().to_list())\n", + "print(\"Row 1 of M1:\", M1.row(1).to_list())\n", + "print(\"Column 2 of M1:\", M1.column(2).to_list())\n", + "print(\"M1 as list:\", M1.to_list())\n", + "print(\"Block from M1:\", M1.block(0, 2, 0, 1).to_list())\n", + "\n", + "\n", + "M2 = Matrix([[1, 2, 3], [4, 5, 6]])\n", + "print(\"Shape of M2:\", M2.shape())\n", + "print(\"Transpose of M2:\", M2.transpose().to_list())\n", + "print(\"Row 0 of M2:\", M2.row(0).to_list())\n", + "print(\"Column 1 of M2:\", M2.column(1).to_list())\n", + "print(\"M2 as list:\", M2.to_list())\n", + "print(\"Block from M2:\", M2.block(0, 1, 0, 2).to_list())" + ] + }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "4T47xvz_Zxm6" + }, "source": [ "3. Write functions that create special matrices (note these are standalone functions, not member functions of your `matrix` class):\n", " * `constant(n,m,c)`: returns a `n` by `m` matrix filled with floats of value `c`.\n", @@ -47,9 +211,72 @@ " * `eye(n)`: returns the n by n identity matrix." ] }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "5Fsm57K-Zxm7" + }, + "outputs": [], + "source": [ + "def constant(n, m, c):\n", + " return Matrix([[c] * m] * n)\n", + "\n", + "def zeros(n, m):\n", + " return constant(n, m, 0)\n", + "\n", + "def ones(n, m):\n", + " return constant(n, m, 1)\n", + "\n", + "def eye(n):\n", + " return Matrix([[1 if i == j else 0 for j in range(n)] for i in range(n)])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "erjYNm1rZxm8", + "outputId": "3e10034b-3748-473c-dae5-2b94454ab08f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Constant matrix:\n", + "[[5, 5], [5, 5], [5, 5]]\n", + "Zeros matrix:\n", + "[[0, 0, 0, 0], [0, 0, 0, 0]]\n", + "Ones matrix:\n", + "[[1, 1, 1], [1, 1, 1], [1, 1, 1]]\n", + "Identity matrix:\n", + "[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]\n" + ] + } + ], + "source": [ + "print(\"Constant matrix:\")\n", + "print(constant(3, 2, 5).to_list())\n", + "\n", + "print(\"Zeros matrix:\")\n", + "print(zeros(2, 4).to_list())\n", + "\n", + "print(\"Ones matrix:\")\n", + "print(ones(3, 3).to_list())\n", + "\n", + "print(\"Identity matrix:\")\n", + "print(eye(4).to_list())" + ] + }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "g-3ikzg3Zxm-" + }, "source": [ "4. Add the following member functions to your class. Make sure to appropriately test the dimensions of the matrices to make sure the operations are correct.\n", " * `M.scalarmul(c)`: a matrix that is scalar product $cM$, where every element of $M$ is multiplied by $c$.\n", @@ -60,9 +287,105 @@ " * `M.equals(N)`: returns true/false if $M==N$." ] }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "iCWjnziOZxm_" + }, + "outputs": [], + "source": [ + "class Matrix:\n", + " def __init__(self, matrix):\n", + " self.matrix = matrix\n", + " self.rows = len(matrix)\n", + " self.cols = len(matrix[0])\n", + "\n", + " def __eq__(self, other):\n", + " return isinstance(other, self.__class__) and self.matrix == other.matrix\n", + "\n", + " def __repr__(self):\n", + " return f\"Matrix({self.matrix})\"\n", + "\n", + " def _operate(self, other, operator):\n", + " if not isinstance(other, self.__class__):\n", + " raise ValueError(\"Operand must be a Matrix\")\n", + " if self.rows != other.rows or self.cols != other.cols:\n", + " raise ValueError(\"Matrices must have the same dimensions\")\n", + "\n", + " return Matrix([\n", + " [operator(x, y) for x, y in zip(row1, row2)]\n", + " for row1, row2 in zip(self.matrix, other.matrix)\n", + " ])\n", + "\n", + " def scalar_mul(self, c):\n", + " if not isinstance(c, (int, float)):\n", + " raise ValueError(\"Scalar must be a number\")\n", + "\n", + " return self._operate(Matrix([[c] * self.cols] * self.rows), lambda x, y: x * y)\n", + "\n", + " def add(self, other):\n", + " return self._operate(other, lambda x, y: x + y)\n", + "\n", + " def subtract(self, other):\n", + " return self._operate(other, lambda x, y: x - y)\n", + "\n", + " def mat_mult(self, other):\n", + " if self.cols != other.rows:\n", + " raise ValueError(\"Matrices are not compatible for multiplication\")\n", + "\n", + " transposed_other = list(zip(*other.matrix))\n", + " return Matrix([\n", + " [sum(a * b for a, b in zip(row1, col2)) for col2 in transposed_other]\n", + " for row1 in self.matrix\n", + " ])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DFMpvwIeZxnA", + "outputId": "27a84dd6-662b-4358-91b5-b717cc131946" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All tests passed successfully!\n" + ] + } + ], + "source": [ + "#test to check\n", + "\n", + "def test_matrix_operations():\n", + "\n", + " matrix1 = Matrix([[1, 2], [3, 4]])\n", + " matrix2 = Matrix([[5, 6], [7, 8]])\n", + "\n", + "\n", + " assert matrix1 == matrix1\n", + " assert repr(matrix1) == \"Matrix([[1, 2], [3, 4]])\"\n", + " assert matrix1.scalar_mul(2) == Matrix([[2, 4], [6, 8]])\n", + " assert matrix1.add(matrix2) == Matrix([[6, 8], [10, 12]])\n", + " assert matrix1.subtract(matrix2) == Matrix([[-4, -4], [-4, -4]])\n", + " assert matrix1.mat_mult(matrix2) == Matrix([[19, 22], [43, 50]])\n", + "\n", + " print(\"All tests passed successfully!\")\n", + "\n", + "test_matrix_operations()\n" + ] + }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "a3oajZgsZxnB" + }, "source": [ "5. Overload python operators to appropriately use your functions in 4 and allow expressions like:\n", " * 2*M\n", @@ -74,9 +397,112 @@ " * M=N\n" ] }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "NMPmBI-JZxnC" + }, + "outputs": [], + "source": [ + "#guess:\n", + "class Matrix:\n", + " def __init__(self, matrix):\n", + " self.matrix = matrix\n", + " self.rows = len(matrix)\n", + " self.cols = len(matrix[0])\n", + "\n", + " def __eq__(self, other):\n", + " return isinstance(other, self.__class__) and self.matrix == other.matrix\n", + "\n", + " def __repr__(self):\n", + " return f\"Matrix({self.matrix})\"\n", + "\n", + " def _operate(self, other, operator):\n", + " if not isinstance(other, self.__class__):\n", + " raise ValueError(\"Operand must be a Matrix\")\n", + " if self.rows != other.rows or self.cols != other.cols:\n", + " raise ValueError(\"Matrices must have the same dimensions\")\n", + " return Matrix([[operator(x, y) for x, y in zip(row1, row2)] for row1, row2 in zip(self.matrix, other.matrix)])\n", + "\n", + " def scalar_mul(self, c):\n", + " if not isinstance(c, (int, float)):\n", + " raise ValueError(\"Scalar must be a number\")\n", + " return self._operate(Matrix([[c] * self.cols] * self.rows), lambda x, y: x * y)\n", + "\n", + " def __mul__(self, other):\n", + " if isinstance(other, (int, float)):\n", + " return self.scalar_mul(other)\n", + " elif isinstance(other, self.__class__):\n", + " if self.cols != other.rows:\n", + " raise ValueError(\"Matrices are not compatible for multiplication\")\n", + " transposed_other = list(zip(*other.matrix))\n", + " return Matrix([[sum(a * b for a, b in zip(row1, col2)) for col2 in transposed_other] for row1 in self.matrix])\n", + " else:\n", + " raise ValueError(\"Unsupported operand type for *\")\n", + "\n", + " def __rmul__(self, other):\n", + " return self.scalar_mul(other)\n", + "\n", + " def __add__(self, other):\n", + " return self._operate(other, lambda x, y: x + y)\n", + "\n", + " def __sub__(self, other):\n", + " return self._operate(other, lambda x, y: x - y)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "AztaICYSZxnF" + }, + "outputs": [], + "source": [ + "def test_matrix_operations():\n", + " M = Matrix([[2, 3], [4, 5]])\n", + " N = Matrix([[6, 7], [8, 9]])\n", + "\n", + " assert 2 * M == Matrix([[4, 6], [8, 10]])\n", + " assert M * 2 == Matrix([[4, 6], [8, 10]])\n", + " assert M + N == Matrix([[8, 10], [12, 14]])\n", + " assert M - N == Matrix([[-4, -4], [-4, -4]])\n", + " assert M * N == Matrix([[36, 41], [64, 73]])\n", + " assert M == Matrix([[2, 3], [4, 5]])\n", + " assert M == M\n", + "\n", + " print(\"All tests passed!\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ygFkLz4XZxnG", + "outputId": "b86761c8-e502-4ca1-9ba1-873b5c49830d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All tests passed!\n" + ] + } + ], + "source": [ + "# Run the test cases\n", + "test_matrix_operations()" + ] + }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "pfY_e6TmZxnH" + }, "source": [ "6. Demonstrate the basic properties of matrices with your matrix class by creating two 2 by 2 example matrices using your Matrix class and illustrating the following:\n", "\n", @@ -96,13 +522,249 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 13, + "metadata": { + "id": "cxayNkugZxnH" + }, "outputs": [], - "source": [] + "source": [ + "class Matrix:\n", + " def __init__(self, matrix):\n", + " self.matrix = matrix\n", + "\n", + " def __mul__(self, other):\n", + " if isinstance(other, Matrix):\n", + " return Matrix([[sum(a * b for a, b in zip(row1, col2)) for col2 in zip(*other.matrix)] for row1 in self.matrix])\n", + " elif isinstance(other, (int, float)):\n", + " return Matrix([[element * other for element in row] for row in self.matrix])\n", + " else:\n", + " raise ValueError(\"Unsupported operand type for *\")\n", + "\n", + " def __add__(self, other):\n", + " return Matrix([[a + b for a, b in zip(row1, row2)] for row1, row2 in zip(self.matrix, other.matrix)])\n", + "\n", + " def __sub__(self, other):\n", + " return Matrix([[a - b for a, b in zip(row1, row2)] for row1, row2 in zip(self.matrix, other.matrix)])\n", + "\n", + " def __eq__(self, other):\n", + " return self.matrix == other.matrix\n", + "\n", + " def __repr__(self):\n", + " return f\"Matrix({self.matrix})\"" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "ud5EfsuxZxnI" + }, + "outputs": [], + "source": [ + "## Matrices\n", + "A = Matrix([[1, 2], [3, 4]])\n", + "B = Matrix([[5, 6], [7, 8]])\n", + "C = Matrix([[9, 10], [11, 12]])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "R-KQbgXqZxnI", + "outputId": "690cf4ac-1f9e-4ff2-ac7e-8cdd578ef9c2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(AB)C = Matrix([[413, 454], [937, 1030]])\n", + "A(B*C) = Matrix([[413, 454], [937, 1030]])\n", + "(AB)C == A(B*C): True\n" + ] + } + ], + "source": [ + "# (AB)C = A(BC)\n", + "print(\"(AB)C =\", (A * B) * C)\n", + "print(\"A(B*C) =\", A * (B * C))\n", + "print(\"(AB)C == A(B*C):\", (A * B) * C == A * (B * C))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LH7mqxBRZxnJ", + "outputId": "6ae2ee78-2716-4de9-87f1-9be602861eed" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A(B+C) = Matrix([[50, 56], [114, 128]])\n", + "AB + AC = Matrix([[50, 56], [114, 128]])\n", + "A(B+C) == AB + AC: True\n" + ] + } + ], + "source": [ + "# A(B+C) = AB + AC\n", + "print(\"A(B+C) =\", A * (B + C))\n", + "print(\"AB + AC =\", A * B + A * C)\n", + "print(\"A(B+C) == AB + AC:\", A * (B + C) == A * B + A * C)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9TpsaWZxZxnJ", + "outputId": "ebbe650f-7431-4446-bf01-2bb1261be583" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AB = Matrix([[19, 22], [43, 50]])\n", + "BA = Matrix([[23, 34], [31, 46]])\n", + "AB != BA: True\n" + ] + } + ], + "source": [ + "# AB != BA\n", + "print(\"AB =\", A * B)\n", + "print(\"BA =\", B * A)\n", + "print(\"AB != BA:\", A * B != B * A)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X8KCSMw8ZxnK", + "outputId": "070f0b18-072f-4244-cc20-d763443bc6cb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AI = Matrix([[1, 2], [3, 4]])\n", + "AI == A: True\n" + ] + } + ], + "source": [ + "# AI = A\n", + "I = Matrix([[1, 0], [0, 1]])\n", + "print(\"AI =\", A * I)\n", + "print(\"AI == A:\", A * I == A)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Clubs', 2)\n", + "('Clubs', 3)\n", + "('Clubs', 4)\n", + "('Clubs', 5)\n", + "('Clubs', 6)\n", + "('Clubs', 7)\n", + "('Clubs', 8)\n", + "('Clubs', 9)\n", + "('Clubs', 10)\n", + "('Clubs', 'Jack')\n", + "('Clubs', 'Queen')\n", + "('Clubs', 'King')\n", + "('Clubs', 'Ace')\n", + "('Diamonds', 2)\n", + "('Diamonds', 3)\n", + "('Diamonds', 4)\n", + "('Diamonds', 5)\n", + "('Diamonds', 6)\n", + "('Diamonds', 7)\n", + "('Diamonds', 8)\n", + "('Diamonds', 9)\n", + "('Diamonds', 10)\n", + "('Diamonds', 'Jack')\n", + "('Diamonds', 'Queen')\n", + "('Diamonds', 'King')\n", + "('Diamonds', 'Ace')\n", + "('Hearts', 2)\n", + "('Hearts', 3)\n", + "('Hearts', 4)\n", + "('Hearts', 5)\n", + "('Hearts', 6)\n", + "('Hearts', 7)\n", + "('Hearts', 8)\n", + "('Hearts', 9)\n", + "('Hearts', 10)\n", + "('Hearts', 'Jack')\n", + "('Hearts', 'Queen')\n", + "('Hearts', 'King')\n", + "('Hearts', 'Ace')\n", + "('Spades', 2)\n", + "('Spades', 3)\n", + "('Spades', 4)\n", + "('Spades', 5)\n", + "('Spades', 6)\n", + "('Spades', 7)\n", + "('Spades', 8)\n", + "('Spades', 9)\n", + "('Spades', 10)\n", + "('Spades', 'Jack')\n", + "('Spades', 'Queen')\n", + "('Spades', 'King')\n", + "('Spades', 'Ace')\n" + ] + } + ], + "source": [ + "#QUIZ 2\n", + "\n", + "\n", + "def make_deck():\n", + " suits = ['Clubs', 'Diamonds', 'Hearts', 'Spades']\n", + " values = [2, 3, 4, 5, 6, 7, 8, 9, 10, 'Jack', 'Queen', 'King', 'Ace']\n", + " \n", + " deck = [(suit, value) for suit in suits for value in values]\n", + " return deck\n", + "\n", + "# Example usage\n", + "if __name__ == \"__main__\":\n", + " deck = make_deck()\n", + " for card in deck:\n", + " print(card)" + ] } ], "metadata": { + "colab": { + "provenance": [] + }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", @@ -118,9 +780,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.12.1" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From bb035d50fec169fc9b3667572aa1ff0178cfca43 Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Fri, 18 Oct 2024 08:49:55 -0500 Subject: [PATCH 17/22] lab 6 --- Labs/Lab.6/Blank diagram.pdf | Bin 0 -> 23684 bytes Labs/Lab.6/Lab.6.ipynb | 1190 +++++++++++++++++++++++++++++++++- 2 files changed, 1170 insertions(+), 20 deletions(-) create mode 100644 Labs/Lab.6/Blank diagram.pdf diff --git a/Labs/Lab.6/Blank diagram.pdf b/Labs/Lab.6/Blank diagram.pdf new file mode 100644 index 0000000000000000000000000000000000000000..eba2435fcc1b3edca4fa26f0b3ddd8de541aaeab GIT binary patch literal 23684 zcmagF1B`7!*Dcz8+SX~?wr$(CZQC|a+qP}*wr$%y-RJiA-TU$<@7?4jE0wkPu3EcN zm0DwrIp!o)5D}wgq+^FB9Sj%@91I!^hGrySAh0*Gg683&SMhW(rB^UCH+7~Lws*C2 zAz-AJwls0pX8!4-q|3(#ZE9!opRO$bGepMJ&fLX9&O z%s+6O%WHqLY!s5FP7+s9_0?327_K6!=m}WxU~)RxPTMxkZ*K=DMt9wh&$qveY$ESE z|4QR=?3eq_i;ia>N8Pub?n7YSPc4(_d;dPszgK+!`{%d&cURv3IDA7d<+St3-10^L z{1YMX`#x`amf!#F@BM5Z+ctfl&G*~6@yEeXU7xG}E#6VzKi90?e>r@gt8&H&dRpEY z5WQ13i+Wpgwqe%~WB|C}tvA=h&-!EQP`TPW_xEq=y6yU{BKLO3cl5rYXY~&3RTyc9 zlD^Lm+nv9c)wSvAo^Dc`uhTQ*-O_I-AK(qF^=^lP^#6kD;5 zfc-6Eu1kb6Pwr9KyG_U6=a*qm6p#iTK7_#tnmVt1Mqi4(?)K(kie5KG47f|}-Tzo< z)4v|-P}2CG0O|3Oj!w$-SJ~(%yu+S`Zshj-U>yMQ_gIz;@RD26zlDFzI=GMA9R0IB z&X3&(0L>J>-dhs>KJ>C4p4EA2>i6)4+ul5DvqB%CAq*r9x1s%Aa@`F>`HDnaTj`80 z#2YE4y8P%w&&D)M3*&o&(b4dDyx@IM8U1%S z!s*7b$EoSngBw2`iD|U>AOKql^u%@!gLJ28Ru~Sv&_nZ$LjcbBSNK}kFzmEZQyb45 zjgc`Lf3bRYI{$v%)h5=zrDf%N4Tlf9G0O=K?Pf12!IVW|U5oJ|R$|QraHfuMbMSu~ z(Qz3Kbs!SC=vSFVhmsQ&o4I?9K>+%OI=3lV%M`_lyD|&d?_QWG36u}Cqk~F+%;K>v z;0Z&t5t<^>c{mQx5CI^mI)a_6)}vmq9P}2?ZC~06{I+SydV`hdvb{r714Im$HuN z#cKrRs&%ml;7NJ!Po$BKm|3H@)G^BSIq&(bkP&sUIGfa-&F;-UqYSB%@bve2S$7_@#!{3p0Jp?M7>j@&_N}CWgIh-l|;ayLyWwJyb7%h zT5b6)NurX;EU0s3mUUU#IG@}j7MH3ZJcIIU8|OvA2Z!={qC5P;iYg{8@nTa#cR~Ve zQvyk~qaeNE0tzcV4v0T;Cy_9OwQ(DQ*o>#qYFQZO8!uH1-a#+Go+wiUUCU1lwt=c*TBQ7zQU{WUD?y8BUoDD1i!leY0Vy_lCcB*1ty zYx0S=#JN~!xyR1%Z812d%w|YE)WGp*twrh%o+-B zimfhubX!y#IP;KaalL9_4);4O^^=zmPzxGaMLBi(FqWKS0-WAl*yGhE$MzDTXHn1>&AC0`RqG0W_2= zk5iUQbpl;giQCDQA#tyAbn!iox;Qq0g$2WVc}Qcv3FB7}ff*b`2*6EfhFH}XOgW~b z;2|f^Qg%z}3R4X=1iu3NRgfThFD<-RjRS!gTV|ksVeR-qbo2h{9-iJbetCIfi`xwb ztY@&z^B684?f_6;CRK@g3~tk>P)}!mvzV`0BN>$=X71G#LIgO5OhU9=4sa&o|f zi27qaANE$!pG7K3311nPQ+n-Y1ed%(rkJSL7~nmG^S|u0r&BbpZ`ECyh->@;v zXa7mRnSyN%w3T`Sm3pF(w4)Aw<;3m=WAO#-O}A5R_7&to)yyxF$Z6DO zf7FgrJlV(q>5}8OYmqG5(@#O7+T*7Ja~?Y5tQ33Q68_n3#QhkYyd?`r%MQx{oznsx zlmbl~dr~D6{mLZ*UIyZ;Vzy>A8)6@|Av9vFKWQX5P0^#7^=Q!OIpSHF0`ZvC>|fAW zjvt~`W}gk4*vJOG-6J^@A5z#7RG`y%b861ts>hwRv>b@0#xrjb)ZvZmiwn3Jf_~Wgaaof55HYn$$`u>kV(#O$Pw1$8Tt-* zgO=Mtx`8f%IWj6)+?$tM8G&OaE_|DuzPc+x>u-f2bo4lSF>9%v4f06|BBtdfUtJ__ z>_(Gplh8!PjqL;{A4#{r{w zy-~61b*`P);%gi4GCE^U=kKUbJR09V811nUi+~Px%ZuuNpNpsQS^PG9Ux)mcN;Z!E zlx(lDN;c&JcMK_3c>L{#`rA!sOZsTM45Z(5yZ*;5d1JPDw`W~^if_2Jim$M%(%Z~B z=eO{5BW{&+Jq^FLXBE)Y=G9Y;9-$sWOAr@b$QGrWNDb+%>($X%)94-#s9!R}fgq0@ zjRUl?X}P$x@b!zQt*z9at4W2xrZ6I8@<)N>k*rXCDo7Wa8`IQeK=w>Ubbqrqfc3Yn z91bQ|k>lE-VdW$NMNVpnA(U=e-IgVTP0Z=H$^oh! zn2#UswJdkp7+%9C$gKAC-~kee)ofUdHH9QN4|+bcj1yqcmr^hKq!6{n2XqeVA(ZdY>!&xF2@bf67+58ak&r6pF9Oo zZq~uf$1zeu@4dm>*MMGZ`PNmJ3_vTXqAui5%A2CiJH!Zjnvt4snCOX1Y6v>dSeGqTC+EYdRe0A-a=eZn zs?KMo2<`AW!d$~!6QM56(^EgZs5X45C02=gZzbCOKo47ODcbGSC`NtoeCTLz$vZOm z^V!Z>()Ooi=Glny(4FxMR2#PD*PqcErk0g-my?&Mf@^*{lWhr%S=WYJ`D2*Fcs%vk zjeaw~EQ>z~7bXebJ_?cg%m?u5OM3RBy?k!KiVNm2GOWPUG~q?ljHfCYOLN7d?Wc4Q zjqXxh727#IYaTs_^J zzB|Sn#Fkvt_TigG^sOSy+_2cWvDVn>rlfIf#=+N=nmKjqoUu(}OCM?&rNIo}&o*XL z&wSv7a3v*tGNmMr!J%f4cnJR1(8>;$Nq&EMfhl94;vrb1B^;ygl|q)y(i-qtR7Wh3N@r<(NE=s|$5jn= zb*4H%mb$#r5=_;Aao0mI^;61+Dec2X^uexFB6l8Pdkg(`uQx+%QDdw3pAj2V>7tzR z+Y8!e>7|z_Ed^!TE#yi4t%)qBMnt~TA==apjoxsV!~EOYf)kyv;QEUVdztKWJIV-2 zp=rJdct^)h`riX`m51%jEsGb z@IA3Rtd*1wi1}wcXD_;8V>*1qryM*)p<`B4p0toCs28NcKgS=6!gWH1nL`VQoWF@_ zm?-YU17FAvYAkHkAYh? zy}#PQ3E_=EquQJKn9qq2cfxa5n(6g=%zd{5H|^>$Rtthp^NU~{0Ylh~FU?Cabk2+L zN{8Ac9}-dai5|)*p%*QdvAcLJ7g1~5%vuzkPpjP_)v;t)bf^lC`jaQH#IR}U?Cx}u zPrs^G4LsiAegUwD>AW+mueH6or3VMaPU6Etf)m5Ewq-x&-iIj-!TL}8xqb0%cT`Q` zU_2B|OKEmJ*^3LXvj|wLPiZ$e^G@z)0_gB-8~MWlu><+g0A-tnS%&xXb_694V{9Ey zqxvw{8nA1F?!BO-`;3ajn<# z7g2@F)aiC5-ITGTQ9QX8AEp-uqN@-uF%6N_<;yk5gyJ3Sz2&6SVPb?8FXfB;vLB24 z(zB-bdMZ~ne6Tg28^2Z5N6_mtAIzh*y_U%)d^W`Ryma(m9TLp9gyyB{ZRol{qaEE< zqT7B>h-^2hR@vUFloG!Yw>VfP(iSOyac@NH(=~>(dDe5cm_1W+@Id0G`fjMtSk9%7pq*)cr-j8d*6% zHieYrFY<2}QCZ)LB2ww_I{GJTtKK06BLCtHBxM9O;2m)vvX2d~09Z*nKk8DPxZ$zh z*kRF_VGHcY$J@J{!nR0-rUm*qs#f=8f;^CB z@A=n9+I~{aE(@u0EXRyFi2Yb=S6I+$lT&Oug1Zgr^mCoTE?*CrN?e^tFx|u zPl3Dth%Z7~?`~+tVIirUM{?=RM<2iMcl-O$$s+&v<6`*J?8#{!zt8vEseI&Z-`~df z!?O5_286!vm9HM}gn!?s?0x^ZeP8NyLAmD-(L$W*fo?u7pOOaWj;%v%ddWZ}joETl zY5#Ya4SkyZgj+}JH^evms{ZlKu~&yQ|Hy5ceP6xy2mhbO?kUc$-)wn{doD4jis{Fdjc4FhHzn2?d48o6kNFMKzzWA8j3s(6qF13V& zwPC!^aTHET1CrlBN$~|%$nSaWo5+Rg$q3$l!d*GxT%OT|LIgpIF5*{W#6$*|C7JqI z<}rpqOBrt%fwQpE5q`s`yxLVlTmU=X0ug&FD1vSUQ#PJ7xPS*PShM^i$gy=43F00C z2B8poFrqFfD>Oy0GfgpGk9vut%-GvjQVGHsl=vE+UaU|sc$7|Vcqzx*BzstS$v%s* z3F;sqsqA$Bz%vbQu#kEjwh$Ig-b)j6)@SPIQ5156ls&kxF&&hWMFUwbL4&^qTl^zH zpoSJ9;TgpE%px9$Stc=?w}WP_<-bMX1KO#BI3E;$FBEKvYX4DE?K|BcYsH@RsML9x z-Eu7HHVtenGm!awSK^gr0UtWxYndN$hPRceb7T0U?NMBCPm7VSNd%TSgscO~h zcBr7g?#`3e^$>7iQH#5C?ph+>Y;Wsq0CUJ2=HE?QN~W)v(FCs)jqQSo$0~!e#awN* zKF6&b7B|O;;^qjot-m{#$W0(c@8vHeFSe}}ztM421C>-(i zWQi#QwBVw#Hw_JX!0HA!-2$1H&9k7j#haTG8*^Q=gvC$beo>8z!g&5a6h|Y`L$o7z zf)p#{n3)zeo(47Zw!h`d$L8h5l!8J#&gzj@?T7c>GHQWKnIudM_kzN+1WTtvKv>I` zaH7JDT-#J?SVhSWuDp~)et4Zp8+VQwu4OKUL!dC@oFZ;PW-^fl55dCV+#R#Dr6-ov zDrAQ#yUJohkSgO)x>``BT#7&6L5EtxOV1LAT7-y#lYTSI?SyEf*GxkP&WkoP@0rl@|Bbe_QjRpT=8aTn>L>CaFjjvyOSYx zQ||`W>?k?oZpB>oLqr2XK7foS{Fw!zZo>o)sduhP2qY3%>`DtGl!|xZ;@oWT^c7xIlsiK9gm@_N2}<&ljxc6 z+P+%8a4c(3FLH&h73#Tee_hS?4lS zS4Z;1&w?Dt4ts}#eCJM?8~6^jqRUjJRR5E^iiKXPFtZY=Kv#&ukNk)Ey{2$5Q-N zb%(}LgCXIjK-v4elZRv!hi@7HDk^$CRZX5)7|UHaaV z9ME69-36$|?Xbt&!1=AJ4g;MgfFtiezo^DR^qpPwQZiNGyHzqHN7Ci?X%iZ637_W} zhmm_ZW)g#JHcVWv9r<{+Cz}4|@)$$170-|pQ$>t=i!v1v*B4`tOb@WoY_fEQd;s*r zhsu$gJ{_8+;qxpKpz%yx5`F|s<0FL0^}b#ZZIY~Z6n z@_`>6Fn8=a!ymPipu@>>A_*GONu`ToqMG8hEF_#!_6AxkX`%DhZt`tqH&kkM3x;f2 zulR@6su;ABsemDc*;gagaXxs{@C0GOm7r%LR*3t7L=VXp?K^JZWo8UINk4bt)IIGY z)1L1J6b5{1Y%o@{qe!L~A&Wnte8ZaG{}om*p8xShCgJA0Y>v@w_IEB$N?$Gyk#bx|Hqf%V^^Le02pLJZscbnmyH;Qlv zPgH);y2Q$6Rn766a+J87w7afe`l72+3W+@+a4~vZ7|#u%BdB<++~3Gh!b8&f+wQ&A@=MZ`QDjKE|U?v!?xC2$<3ijJcodQ@eUIi^9d0m+J=U zS{%=iO_twweM`50Q%9CNSFEjQbttZm7u(SU;F% zZ1oI3TIItED_q}LK9n=Dh5!h>Uk&-2y+>lwU#d?UF$XWr7#Qx+VV)$TNOWacN3B6(yn zhE4gY8x_c(xcZCL4|Q6rn&$%3E}s8w$+EKVSMb2Vt(dOV z1T!XN@qqgubz2HQJ3=;1IZCgXR(fm8i~-w{yR9n5JaF5(qePLJToL$#ai!q@#W>3f z*+F7e8&->~)=$AKAm9In$)^O2*YClb?ZGWJt=iZT-cERV_R3c3A znEqXR0o4OLYb){E4&=6FQ&kCh>AtqPTUTSOJYQnJP_gGy>pfpezEruT1#wLtTff0O zIJzlG$c#@@=T+k{TkCXeTC+d=gS^XC&7}1Y^5&OThsIu6_D(6t{hr#QzbGRJ+#$5Q z3nTl*A}WB6v9{BvvNFSP`D!58>al+dnCh&?9GF$(PD}$aw(Nc@bW<<9q2oGh^IONx zI}4Vr?W?Bix(Jxy=jH^*&Ksb>+V+LL^-|g~Uw^c@{em{ij;{RNuH13`GdTo1Zy5cX z0fARt{4BZc+yUEL+8BGT=OZrG=LH4@_x;xR{U2divAOe5Ip(zaX?sSh{AKgG-1dx> z+GhUk0JX%u+btE_%Cc#OnzuvsGTLC3br9D_Ipd`!xyjqqakb4nDlBXDX#W(SFSY%wv=p4>f2T|6>)ijhHJICxMWm;ext;kx zI(8tZVfVwaNhY2-bsU*nRLygPW@+0Ax8G_vR96VjoDdRON;!h-DgKasA118|lqx#mX!TG1| zu&8c~?dQAx>)+S1S$uZ?HJ^_$f4`4}@AbcbAH!$y|NW)>1NMKB$>?nVTPCA3_o8Gi zA;28!T?Bt@K1B`A9BaRM^xEmrVMd#l9{!hOtjI1D?{N)a5v}U83-|hMoTYx>R56Ho z4gH%)I#!)oFR6de=6jEZJ@;Q+=4aPN52yU%{>Tuse{9od?^6|9hFK9atobbEZ3bCg zcp9&@%0E^9(EYeaKPADKcSmVl)dCk6KP@X!{}z@_Et1jmn#Pk<@K8&cx6c9i@@ z4NinyPT#MTqKd9FVeMO=)Nd9D%%-?D{2OR{xWLEhxmfwSoSZ}Dhi@8A_M}m$sV8yt zA75kT3KLqpg*68hy;~Kps*1q(R;d2U2onKO!OfV%vCBFGg!>hic2d&sx?bl6-D@3*J_U>;9dL)_tU5pWaRu3H1p;$KTs|#uG&zDh_#7CY4NSB` zuZYJiG1<3bre131MD$r5q0_6Z`^J~pgm%LhwGd6nouZqf*zF5^Mbi^fqO3?#K+~j5 zAsjf$CFaBIHUJ}mAkmd=Rf`!SF>mO_1uIF?zJ)1W7*N7FdWs?jygxqB6LfU$C5j(4 z`bZ@&H^K8aY-{{DaAS||@dcQ#3SOrfv1mOUOKEmq_TJcD_N^B^RF7w#%qV}&hGlGn ztcgqzq0p|-2U>t-;YxlF4H)kuxoA=J%q2S;3Mgwc#{TKav3xiipt{6qev^W{lZx+< zwlMYs@$o)3M*D_~X2qCkxhzz66_KqgcvC>~mD`x_TRP87gCd+Ue>z?SX?X@iVbb<9 zTPY~3m~<`3ii$BX6fELtOpKeagc3%aIpVk9U-;2EQ0XuKqo1X*wf9@?g)FT1TU}Mk zaNAu>A?RdB8y&G+tRrX3pio;#pO2&~oznuXpkTK{$r+0OD}@c;apmq=aHkwUDQ_Z2 zzhSQ%WSvpD|mjIYDL8o-e?u-^QCU)Fi&v9B!Z4=w76DHWq3*_Q6rmi7xn@8Mi@ z7|%Rv@C|of6uYC%0bL`7U7*W*T%@^oQ%f*+L(swE9!=ddkJ+3BU}krV<@kM8g80I+ z^Q?fs0;1F)qg$;P+r?$vYv8RTnLj*>Y~^>2 za=oXNZ0^Fn#E%2rlxpS;tWIJZL0#5ceRsvEu5D?Hqa4=3#S zi8ned2cjQmVp*bj>WiHJu0syqd;q`gissHB7BlNF6!N?lc;alwcUlzM8Oy|>HhRiz zfcDtW7@-L@u0{7A-KxqQ8*h&_ zHo<~6y{X50!?N=-z^3%tqPcKJ)Ccp%)&;)X+tD?x;CjP^rhHmgaa%`awfg!Qy)p77 zdQHaK@IFZ2|335jSvmgP)9KRe1@ynoQFBycC8o!FZk?j2Y;ut~>aXpA&}+Hnk~Y-V zpVvjm?VW1Lby|Ls_6nkZ>2GYVdEO^pieYaN1%+V<#5(f`i|Ss%7K(iW`3<95EXwDT zpiIpIlA8jYg0Q5{>0G^LR?Y@IG>Ww9N8eD9Q=C{pf#xWGNu59wFbL$- zh3Wl$`*c$y+BexYcH>?{c`i9yPCt!`?F2Al*&NhfX4Z!8K4XOViY@RaOBhqC^~;Nb z@I*wWmLhe582YyCbAnQTm1nFR}-t@W#1uHj>@NGb{ z3g)ks76+x_rDYp>;Cwp%gy+s)OWdovRaVomMm7w;p-u}FR0$fR(PUI|{atZam^%NN zd8BYh4Jf<1edfR$gY>6?*}OS}$K7a&m!TV5f^6Q=(ePcV$6IZ|)_-=gh9HY6 z|EJF~Y0FPLZEICSusL$X&>Uogu90Aisqr7Xb5qKdS-YZ|^k9R?Yd^Z25k)~BC-9O{ zZ3zzv-wH_|@<1A?DY zl0g-;>)!&g2spd$%hKBviw-wg8HhcRpXggx9?0XWyCTP|cp!R;J7{Rso~ zS66Mxq+U%tUiZtxI_892gqszzqxAeagLA~OZ_=yMB-m}q1QRdUG@wED{|>?rZdemq zj6$zPB|q%nQ7v4mj$EC-ZprFq)c%z=effOobrIH+AiJs8+3GsaQVSQv2Ia@)! z;U%BYtm@<&FI5Zop~v-<69eL{zUJP#d|~^k%ywJPPb!-`6n+4|wYM{vZj4DQ`H(G< z#veZ~H2e9uAAb3=%-C~5X)c+@daMCwk?B&rwCOLD?RTiXQ~0ozXzU$le-OUI`TRVyo{qD9vxZAIz$lUe&8>qQHlD!wv+og zA^Njcsjrm4V#2K*Q) z<(6Su{h>fX6R|MQdr6y74vy=#Q~}lIM14l3O@3=oS@Xz2m3-9jn<0Fk@X<7cwLo`3 z1RN)I>~Rj2)qv-e5g@!+N!~v7B!T~M^4ab?CI*pRE@>M~SjF)aYL7yaojE_3q89nd`32 z9KU*4Rqks+OaqRe99VX%-9_hnUc(mO>g(d+mvJAO6IMvnvw6*ilE`#E0^_6{GHd^_Yr{~7mb z@t(t`PPdkFno3PO~r3V(hj&Zvy$EWLB59KB#-uBqC zGsdBe7Q4HmP+DEI)WHFOP=n{5`FE}udXuv{-LT7J@z0X`KK1j=fmknyzGdg)o=C5h zeg*qGApV%Tq}PHKOIv8SKnr*Dd)bpyD|R$(!TlzsSFPxt7dmeX%%qvi@|KE>bGj%} zl2*s*!y)qWB#kwrCxd((%VlTqE{&Dq%3pdVALeI4G=tlcSTji0nI1EhA8=buK3(Sj z_p}V*{C|?}lQiD|^jZ9V9~oGmcbE3tyIm0PxBf#Xul{ATce@}u@WO2|o=k2MN>w%E z_9*f8U)vdJwT6!=A1FWe-uRd3=-q!)>C-!2dim5gLNcN;$c_*6$d9pEO#ZKFSf%04wFJcLs`F8CJu#+6v_Oz-i5Wf6CQWbdE4}#Mzx-j zlrGUA_KDn`cm+LOJuPqVFr+iK=u-4cVvGDPteBbtx>lKcgjTxk<}&{7R{F zMn9w`y9B)Y=mxXR--iI>T!n%uW7?4FQ*+hXUzZmizDCceM9Yyh-|taPP}Z~@HjXFA ztl3E*68%1~w(?VO`W;==$%;a3ADYhB!leyrc`hbG4n_j&OqgfI)HsX!KE&u{eFy=a z@jz6NptkMQ(!bdMX;3zgy2knd$TO+LFhc^n^pKREes*8!gPsTOa*yK zpT*P4C4iGu_N_mZ5wwC%p<#1tc{&PP8`JTw7?a}vF@`D`Um4UI(&Z~^g z>=uc1Y*&y3W)(&O{}I;?Ps#ZwS*>q!8}RIu?iaO7yX$Az_w+An{q!mcK8IfT^#7Q$ zb2Vevq>o@hVGIEsb>jw-%>MAvP^dF0142cs!p(w_MdnnJf-Y9iPjqo3{vcUzghbbn zK-u?_prn>1o>&Bh6;2it$K%LD>_)jl0!Xn`KjjQ_^(ka$x=5mDxG}YObnLg5pprPG zD!rVuGS(j_KDhi+8!n{~usb5nk>#TN=m(0#)k2nmG7XcXoo(S*c`JvzD};RwQb&sB zu?}gV(?m*l<&#xJI_e{TT{tq}t{+QAkMBrKgje6cT;mh?LVO_LK0;+2Cz8JJxgfsI z`fCRzHLkOrLp@a7BNTK*sEz;B1V)gu1IgEYn2rZ9gf=+m6Bg)23fgsD1twAsN6_lm z7G@CL?@49i>u|s~$pzbvmsOh6{tB(Ck&4Hb@Ql2fCM{(@rJihxfiFeM#C;%k;sNt~ zmJ2O~F30KqoI?9p*@FL|Oii3)XKFw)&E&K@XD$6(NTd`IW4yyGqX+Pa8ywBXxF;}T zAH8c7 zUCaYUznRWQ304`VLexVh#)+qnd!~*9>s&VMjz7esny{EsNwZwNC4LtmUB~=(k##j* zrlBT^C#4zh$00Fgh3c?lbgg!{y>k#V=LE`m&fz`%RlfJSHN*8rjs3~hN4JCWE>O;6nsyBg3XALrf_BzpXpE#M3Cbm`(^*zKFsPA9f>}? zc{MI+S~=9)sW#_?&7ioH3Rh4t8m4L-0&O?RCL`TNzzKW|GW2L=ApO#_l0(2F|GwD} zRi3!Ky#F_nwY+d~Hv^<1jPbejHvLfAU;}$O)2LAcxsfjhPW%qyzpPi)`eQ};rG5zl zGLlEE4evqeAV>`aCoko=Iw!Bn?<%gw7^Gww-d`?p?^+tLPhGV{;P5Dq8VR1)e~MU7 zYWKB1OBriF48v+nW(MC{{C53a`g@OsZ+*JL@@T)za81gnTzzl9NC~06hfVJpP`ZOLXOd$F9`lki1j7Qwt`ZCDd#oRmRhi@3g>olP z5zfYd+oeB7BC!Q!nE-I#aJEQvgM>&e0CO!SB4RyA5cYDDr(@Gz#%&p9)Z!l%;9h{M2~jd&u8 znUt$$0&b1smELwijM2Ac!5}E)ljNMMs3)F29WTRo0p8F)n=8pA4wD7L#-Ip>`N`|X#OAUA+;=;vgK#|_yD4HI%r`avmwb}9iA27t+yt0j^~`L;r!6U8 zt=&=!JddJg0E-6N3e?4=_2~$Ii)Y5Q+M4{f(HBS)EYqBzUeRXF0IJ;iHPExA zKdPG<@nBZWcPUfyqa)P(!eSP%dXyVb5uf))s8l}%@wu1mkf=I=q?;xg{iiB50EU+j zbZuVh8mx8APXk=@XR$VH3lp}*J8WvSfdZHg3}Z!(ZY? z0p{4oH-n%zg#(+bOBXNGWc6(fy_U-;7R##}Hrq-^PVem5mXENDyXeQjTjHVT9pY%% z*bU+KB1==3K2upHzmerbm6;55eG}Wfob@@|HXNdpZiXU92mt0HNb7E@Yf0F1_f4WJ zYCl$uH{fyL@S`(a`$as8uO;ClG;}H0)*uU+my;!ycF%7ipo?>$fHPU{Muv$@ym|cj zTBKH%5}lUW<`I74{mTL)O53HNyEP=Hz+RC0-_intF&>#;Bn(8H!^nBtko{TI&Kl*mQ7Y;i=iI4TDS(; za=w^!2{dxrRWE%rHQedZ}H|Q}x38aV#oCi*EDMYl_D0r99 zyiabWHIc2tFA4=leA!T^uW~){0v-eiO^w0obtI`R^)gA`b0jGX+ZE5?|7P(DBtBS| zvenyrIA-@(hOV%6fU_0nQ1oc)ZdXsP3J#aE+`8Di=kG=v?!@qI!O(A@ z?4O1>Sh^j@Jc{yYe)Th3m>lJL3LthP^Gra4Q)2{90O&RzpwMhv1xp^ z|CG|^@fG{C@&Eg~|2W3)zajGae_{4BwtoNIKM?83xt+clgW`^ny8?=?(Owv;AU=Kdl1 z6N$dHa@M6^GdA3E z+VG*jdRibZA*0+l2-fqL?bOwa3G(n511LL=_T;5{RhDOb<}V>|e*X~h->D7BmPO9a zGLTk!C;0WzNxJ{IWW5}$?SH5CeLwvJ?imyl{7)(N|EaA0KSkNB|DU34CT0e<|G%1S z9b5TL3B>Q2y1Vpbfx*#BcX8DcG_Pw((MlH|YE=^>WhW9~QaS4Huj`v?=g21RL!<1Y zDAfu`GDovKPq!U6wkfA~{;Uc7qVxIBD&pPFO#Ga$uZ-zxcKaE+*?*n&YwKN-o<6zL zkI8NJHLvKW;!}>t+QahvaM^2(LnJ5swWZ?b{vTb3 zT$Y8yf5fgh{qlz&Z5>9 z_W*aKQx5mMy@QKl)FGy4>KSix5;X|{e;61C@|4agP&Lmjw)A?p^wLE=UlSgCo{6() zqAw3Gla;S`9ze!Ut6x*6%tVj9J5X>`mDr?2w4;btt(6b> zMAT&=qQFN3JvS^5ZCdWk<+BBpGi)(Tgi{NTB1>NC{4VnL9X)XFGhtqJjfDb$<<|ek zy5Cq3Yk%vN(g?xJ7vt5B3?Z&#Y2JUmNEK5W)n1>p;l!nja$>}omAYKQy2r6&eB;qe z(HG@CFRRvxK-Ej zsr76xZM_CvvTwJ7<0So~>^O;`i;Ss@N4{Q&6CXyD0!w48-0z+vk-*4;P9$<8NRkX% zeN7`b7r7=DlD>6HW}_tvc^P157{}K%6rGkY;nPs<<{up#B)Y#jM<__50 zg!-NJ&hu$m&DF#3+Z4qarH!5o4k|hX60wq$rhxte|97_=m`oYvn4OdykR*iuUWo!Y z!5tD1;uYb-z@{xDyX#)&$mJz5&zs++uaR-{CCK-%?p~Z0W)d%IT?}7R$AaVx4Boo< zhy!nLa8>dDD&XS#*&FaNJ^G5bB1BeOtUby_7E*f zU1Ygwv9u^DCElBaN^ae&g)9|ixk?gA67QKA)l~2QzW?6%%*;8@`EAeh{LXJV^O^7A zh5FrRoj4g`uGUt49v5zFC-Phh9IHo3Hy*|9Ey-+~epVB=#$%W zyz|D>f>)mA_jHXYy}PP$fgR$T_lG_lKN$1awA00KIQv}0s+mb9MAW61T-;!9n^ha( znbDyaLs~i4-nEstMfO)Js7zGUUP~#ZJ~{dx;cI3-Z*z_6;O`pdq26bJ{vtGGZWp|dl>UZtX1V|u4ue9f7)Hh2XS(-d>X&2(%U8=f{D=6>$i?9CmVJu3^$P;|(tLnp^zX zWhxgFsmuYDTLHemAvOp6tKsF;hUksx+j%uxVy1-GJJX{sq>pG~2>0jeEMj1*%gox> z?7A0A7LYH_eP_90EcsYUrL%)GJ8ACvtYhT^$Fg?dFTOgz&ef}dH9L9rW1}}Ij}Y+O z#cvqfO6`wzZBd3VIP3;Ltx)tHXA!79JneFyqo`#`YMklrLsbozmz88jA`F+&x}xF8 zC8n7Eu{u@S%>CpUih9lY79FwHnWW{xCwK?r7g+9OhSr^n-7@1!TTXqc>h2wn5@U0- zU$o}8Y|0?dXv)30b+tO7)#gTt<3TO0N7jr$&8?OZ;R$|jWp;U4feCPBM|?<;tG=#- z)}1BACNT{&Q~~?$ZRf(u2M*;NPFoGp z)Y<_tgu|EaZyDcScw zI7h#ZO|xmej$HZH?}WOu;?}F%46sYhRKeTTX8za6!1KIW#mV`~8_sTSs3?zG-Rgw% zB_E$%RaAXN_(1PXa0{*B$@ZFpMLsHvXR*E8SYnL&7D2ZOXQ(_qtyfszKU0}uk?%Wa z`t+$v1^MSQ9Po1!)f1GaCWrNsx)ACkmZuRNdwyj)r)WF=abCyZWahp{7ydk|e?04E z{k{$KlW$h1BCGc{@)S5HHS=97{uZzcl|r^^nzk<3d^`ECk1JaLe!#s~sb!%ycHAJQ z#TmKysq)ZZG&5y(#Nw$_b4eSg*~NA%6<<;Pqa0TjV|)@Ps$cj-MLZa>Eo0vXU5sKu zuO(iqv0flhjUK zjSNiGP6{c*>hcPXw&`tJ&hb<(#8(NU7pJ2PZC3={yx8V<%~hj*rcXpQiWYk=j@Umt zJMf@RLTeB{uyJFhUC=4|eA-x&4s~?llp&=%H3NO(;YD*!@7%;&vuTmBK&!tjfI50a z^K4tMZ%0svYhW4TAx>>pf>rhPtIs;N8^oAzda{hytn_SKcrmv(xs*WZT%-{Ysgb%t zeS2?$%_*yjE@x7lRbhF=oTnPExzzZ)9kk{XH?s2#`aA^in-{GVt+s3pWVO`wbg8M; zye&BV7`@LK)n)VHXdjpKaq9Ki3bW%y4S`B&6u0?NXD>EiTrzEoLi`dh*V{L&mnG*O z@2?ekhA(_$_?{TL99?d;Ey=#aMPY@}oj$TddFJU4t+^i^8iNZ(lucxE_mtl8Yd7*^HkMZuyVG-GbPxjUwQvp{lKb*z}>}+9ZBnqI@Bp+WE7yT`M(vR>;{OW(zE9a;` zY_lAI(+~5?^%+0RE9c=q%q!=`Kg=uVq(96n=YT)Ti}?w^!u*6^VSd7|FhAi}a%z3t zf5PO%lkYJd)4`A(Ad z5+;nop(9Nt2|(fxvWxT(8eeD%NHuZ!CIT*#MT7B0AcJNKID=7Wyz*d;^QX=lp$h(8Vn$ zSV<+lP)nuufcJ5)4od#K?eOdY4^$pn$*wu2?t)=BI)24vuLb6~QU-2Z*+bo%O0F}G zf9(|Y>}I9E)719;D&G6A4QuU;QaV;+?Oa&pipH(c9kf2da=P_&cZS=oj+85SHAn8P zJWDgzKIE7fl&_G8bVo4e5Q?ql?{d+RzDwoXa7wsr5KZXNva!mRkh zb-nG#5#)7R)>e&nLNGc=^Tr)7v3g8xJO1T5)YC`hK8+db|IsH@L=bp4$2 zz4c3ytjibNdCdb=Y@#CY>iz9i%W11nL0FfqN{zgo57V_*?4;EN-d!3i-u=?0NnmN? z`MlVta&w-J^9zNsQ)^2sGDmA#DmG9b-xrmQN1RR$bp?O_fIiWCxyaZcX*A+$)xXMP z1+gJdAdS)!Iy{awN=!ama$B$Nuy13@)rS^0&Zoy+vC%Gh zam5<>A~G}R!|2;-VTykf7*AEb!?7mw8?b`8FUp@Zzd3^tZM#f*UT#Bf7)W7T+pi?2 zrzQuzf3_pFZDV;s(v`z!BXLyL%sVIFUwv_MynZMy+f_ZY;$-9P={6aj*I91<>lB(( zRzBz(`0#EIs_d7+-^ivj+MeGI8u&A@egg3*v3w&VcwK@S`Rt)%$cCRP2> z4%FP;%s(+RCYtx=t{<*^wM*mS6fJ`@ic8Hm7zEX0QN_*R6~N+s+-j0hY#KKiACl?% z>!lZVsrq?B)iq-ticc)np0|A5xOUK`Yoa^)-N-{`fX=@DkLDUZ#gu?ZN`P~N#etzc zMsbEo=%e$bZ&+7!oNeO17D&xy;?g~8tqJKPp}0Ut`xX1U5pGq;SaxBB;MMlL)HQ|&%X+|72}@PU3#dt{S(ZllO;=3z zmwWqhJ^L&oYo?Z+JwPx{3eEF6@Wk~wqrE2wm)uCYDS|ijmO1aGZZA4p z{Wmsk=vLDbyGsn+tIvHo_7BVTb`A9!Ip4pO*x&Mz`9MfY1sh9dy?rM3=@b=*ExOK5D1h-dl)|^v! zXKu!abrav-N*W*AxqW<$>2f#L=4emeuSY-V98g*1@xbX9HOg>)&*_zO5{!y%&-fcp zC=!j}1>d(<_;1T8H<6Id;y{R_vM7v3Nopn!z=Z~df8=+AAE57p&MF0!@}^Jw%iPzVbGgIOFR91deKi14*I zDvHXp14CF&(R|P|+SQ959Y)79;5H-`YcW9_&I^ZBz{KHfj({L0!f6n6D@cHjCBpz5 zCM6Mu5#iE&EltUAk_rrF&1WzP9`=rtoX{B&9wHR-2mlZn8HtRakz)&h>fJUMvcFHCY&ihQv5g?_LLDIS5fSAUE2vPEcRO&yIhKB=_Rzh|%=me&u!4r~b zJRTcrv?bO8P(@=6e7OWIi{wv2ER=W~NxDe3Y@zIc&5~G2fNG*(B3z7MfJ~Z*ErgT4 zs*FibN%!VGVYMg*bo zW(X7x3!<2o3=m@uIS(|YuX(=~O5q5EG!7m7Uxdo>N`=ZcMDAvj-o{dQgYIJ}8UUZe z0d(>?nn2$h65)}OaPi%DiD;Bu7x*bsLE%`|<46&rCm(}RY5Xt>hslLHFUcXufC-^O z9_qAc6KQ z0gp|Kg4SOeRX<6VhEF>vbh5d@@)1_9hoRW}T4OD-SaU4&j>4LmW6W`+???OqsR^># zLODlUh4_kvVG$9MwWsCq$ba(WIE=&PRke=r#Yf)m50Q zN9=Jh7)5Wfokg2xIIm`2EqW`(g|-0eMAH?d-Pmty@~WJ#Hav z@Y1$RmhmN474~ 0:\n", + " player.hand.append(deck.pop())\n", + "\n", + " # Dealer decides to hit or stay\n", + " while isinstance(dealer, Dealer) and dealer.decide() == \"Hit\" and len(deck) > 0:\n", + " dealer.hand.append(deck.pop())\n", + "\n", + " # Calculates hand values and who are the winners/losers\n", + " dealer_value = sum(card.card_value() for card in dealer.hand)\n", + " for player in players[1:]:\n", + " if isinstance(player, CountingPlayer):\n", + " player_value = sum(card.card_value() for card in player.hand)\n", + " if player_value > 21 or (dealer_value <= 21 and dealer_value > player_value):\n", + " player.lose(player.bet())\n", + " elif player_value == dealer_value:\n", + " pass\n", + " else:\n", + " player.win(player.bet())" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tHnTE82h172_", + "outputId": "00b779a3-d742-4a60-b084-56c3947d6f78" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Strategy Player's Winnings: 1\n" + ] + } + ], + "source": [ + "def simulate_game():\n", + "\n", + " # Initialize players, deck, and shuffles deck\n", + " dealer = Dealer(\"Dealer\")\n", + " strategy_player = CountingPlayer(\"Strategy Player\")\n", + " other_players = [Player(f\"Player {i+1}\") for i in range(3)]\n", + " players = [dealer, strategy_player] + other_players\n", + " deck = [Card(suit, str(rank)) for suit in ['Hearts', 'Diamonds', 'Clubs', 'Spades'] for rank in range(1, 14)]\n", + " random.shuffle(deck)\n", + "\n", + " rounds = 0\n", + " # Simulate rounds until reaching 50 or strategy player runs out of chips\n", + " while rounds < 50 and strategy_player.chips > 0:\n", + " rounds += 1\n", + " simulate_round(players, deck)\n", + "\n", + " # Return strategy player's winnings after 50 rounds\n", + " return strategy_player.chips - 100\n", + "\n", + "winnings = simulate_game()\n", + "print(\"Strategy Player's Winnings:\", winnings)" + ] + }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "nqMN4VkNz8Vi" + }, "source": [ "8. Create a loop that runs 100 games of 50 rounds, as setup in previous question, and store the strategy player's chips at the end of the game (aka \"winnings\") in a list. Histogram the winnings. What is the average winnings per round? What is the standard deviation. What is the probabilty of net winning or lossing after 50 rounds?\n" ] }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "id": "7voMf3Ef17YS" + }, + "outputs": [], + "source": [ + "import random\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "class Card:\n", + " def __init__(self, suit, rank):\n", + " self.suit = suit\n", + " self.rank = rank\n", + "\n", + " def __repr__(self):\n", + " return f\"{self.rank} of {self.suit}\"\n", + "\n", + " def card_value(self):\n", + " if self.rank in ('Ace', '2', '3', '4', '5', '6'):\n", + " return 1\n", + " elif self.rank in ('7', '8', '9'):\n", + " return 0\n", + " else:\n", + " return -1\n", + "\n", + "class Player:\n", + " def __init__(self, name, chips=100):\n", + " self.name = name\n", + " self.chips = chips\n", + " self.hand = []\n", + "\n", + " def bet(self):\n", + " return 1\n", + "\n", + " def win(self, amount):\n", + " self.chips += amount\n", + "\n", + " def lose(self, amount):\n", + " self.chips -= amount\n", + "\n", + "class CountingPlayer(Player):\n", + " def __init__(self, name):\n", + " super().__init__(name)\n", + " self.threshold = -2\n", + " self.count = 0\n", + "\n", + " def update_count(self, card):\n", + " self.count += card.card_value()\n", + "\n", + " def decide(self):\n", + " return \"Hit\" if self.count <= self.threshold else \"Stay\"\n", + "\n", + "class Dealer(Player):\n", + " def decide(self):\n", + " hand_value = sum(card.card_value() for card in self.hand)\n", + " return \"Hit\" if hand_value < 17 else \"Stay\"\n", + "\n", + "def simulate_round(players, deck):\n", + " if len(deck) < (len(players) * 2 + 1):\n", + " return\n", + "\n", + " for player in players:\n", + " player.hand = [deck.pop(), deck.pop()]\n", + "\n", + " dealer = players[0]\n", + " dealer.hand.append(deck.pop())\n", + "\n", + " for player in players[1:]:\n", + " while isinstance(player, CountingPlayer) and player.decide() == \"Hit\" and len(deck) > 0:\n", + " player.hand.append(deck.pop())\n", + "\n", + " while isinstance(dealer, Dealer) and dealer.decide() == \"Hit\" and len(deck) > 0:\n", + " dealer.hand.append(deck.pop())\n", + "\n", + " dealer_value = sum(card.card_value() for card in dealer.hand)\n", + " for player in players[1:]:\n", + " if isinstance(player, CountingPlayer):\n", + " player_value = sum(card.card_value() for card in player.hand)\n", + " if player_value > 21 or (dealer_value <= 21 and dealer_value > player_value):\n", + " player.lose(player.bet())\n", + " elif player_value == dealer_value:\n", + " pass\n", + " else:\n", + " player.win(player.bet())\n", + "\n", + "def simulate_game():\n", + " dealer = Dealer(\"Dealer\")\n", + " strategy_player = CountingPlayer(\"Strategy Player\")\n", + " other_players = [Player(f\"Player {i+1}\") for i in range(3)]\n", + " players = [dealer, strategy_player] + other_players\n", + " deck = [Card(suit, str(rank)) for suit in ['Hearts', 'Diamonds', 'Clubs', 'Spades'] for rank in range(1, 14)]\n", + " random.shuffle(deck)\n", + "\n", + " rounds = 0\n", + " while rounds < 50 and strategy_player.chips > 0:\n", + " rounds += 1\n", + " simulate_round(players, deck)\n", + "\n", + " return strategy_player.chips - 100" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 544 + }, + "id": "yZ28X9ED2Oef", + "outputId": "1cc8b997-6086-4295-9b57-46ae4e4f7c9a" + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average Winnings per Round: 0.0\n", + "Standard Deviation of Winnings: 0.9695359714832658\n", + "Probability of Net Winning after playing 50 rounds: 0.47\n", + "Probability of Net Losing after playing 50 rounds: 0.47\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# For 100 games\n", + "\n", + "game_results = []\n", + "for _ in range(100):\n", + " winnings = simulate_game()\n", + " game_results.append(winnings)\n", + "\n", + "# Histogram of winnings\n", + "\n", + "plt.hist(game_results, bins=20, color='green')\n", + "plt.xlabel(\"Wins\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.title(\"Histogram of Player's Winnings\")\n", + "plt.show()\n", + "\n", + "# Mean of wins per round\n", + "average_win = np.mean(game_results) / 50\n", + "print(\"Average Winnings per Round:\", average_win)\n", + "\n", + "# STDev of winnings\n", + "std_win = np.std(game_results)\n", + "print(\"Standard Deviation of Winnings:\", std_win)\n", + "\n", + "# Calculate probability of net winning or losing after 50 rounds\n", + "prob_net_win = sum(1 for result in game_results if result > 0) / len(game_results)\n", + "prob_net_lost = sum(1 for result in game_results if result < 0) / len(game_results)\n", + "print(\"Probability of Net Winning after playing 50 rounds:\", prob_net_win)\n", + "print(\"Probability of Net Losing after playing 50 rounds:\", prob_net_lost)" + ] + }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "NekvNxtbz8Vj" + }, "source": [ "9. Repeat previous questions scanning the value of the threshold. Try at least 5 different threshold values. Can you find an optimal value?" ] }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "id": "m1FewPLz2RkX" + }, + "outputs": [], + "source": [ + "class Card:\n", + " def __init__(self, suit, rank):\n", + " self.suit = suit\n", + " self.rank = rank\n", + "\n", + " def card_value(self):\n", + " if self.rank in ('Ace', '2', '3', '4', '5', '6'):\n", + " return 1\n", + " elif self.rank in ('7', '8', '9'):\n", + " return 0\n", + " else:\n", + " return -1\n", + "\n", + "class Player:\n", + " def __init__(self, chips=100):\n", + " self.chips = chips\n", + "\n", + " def bet(self):\n", + " return 1\n", + "\n", + " def win(self, amount):\n", + " self.chips += amount\n", + "\n", + " def lose(self, amount):\n", + " self.chips -= amount\n", + "\n", + "class CountingPlayer(Player):\n", + " def __init__(self, threshold):\n", + " super().__init__()\n", + " self.threshold = threshold\n", + " self.count = 0\n", + "\n", + " def decide(self):\n", + " return \"Hit\" if self.count <= self.threshold else \"Stay\"\n", + "\n", + "class Dealer(Player):\n", + " def decide(self):\n", + " hand_value = sum(card.card_value() for card in self.hand)\n", + " return \"Hit\" if hand_value < 17 else \"Stay\"\n", + "\n", + "def simulate_round(players, deck):\n", + " for player in players:\n", + " player.hand = [deck.pop(), deck.pop()]\n", + "\n", + " dealer = players[0]\n", + " dealer.hand.append(deck.pop())\n", + "\n", + " for player in players[1:]:\n", + " while isinstance(player, CountingPlayer) and player.decide() == \"Hit\" and deck:\n", + " player.hand.append(deck.pop())\n", + "\n", + " while isinstance(dealer, Dealer) and dealer.decide() == \"Hit\" and deck:\n", + " dealer.hand.append(deck.pop())\n", + "\n", + " dealer_value = sum(card.card_value() for card in dealer.hand)\n", + " for player in players[1:]:\n", + " if isinstance(player, CountingPlayer):\n", + " player_value = sum(card.card_value() for card in player.hand)\n", + " if player_value > 21 or (dealer_value <= 21 and dealer_value > player_value):\n", + " player.lose(player.bet())\n", + " elif player_value != dealer_value:\n", + " player.win(player.bet())" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Wgp6em6G2YB5", + "outputId": "c6da14f9-d1fd-4733-ec7a-c52076bd9477" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold Value: -3\n" + ] + } + ], + "source": [ + "def simulate_game(threshold):\n", + " dealer = Dealer()\n", + " strategy_player = CountingPlayer(threshold)\n", + " other_players = [Player() for _ in range(3)]\n", + " players = [dealer, strategy_player] + other_players\n", + " deck = [Card(suit, str(rank)) for suit in ['Hearts', 'Diamonds', 'Clubs', 'Spades'] for rank in range(1, 14)]\n", + " random.shuffle(deck)\n", + "\n", + " for _ in range(50):\n", + " if deck: # Making sure deck is not empty\n", + " simulate_round(players, deck)\n", + " else:\n", + " break # Stops when deck is empty\n", + "\n", + " return strategy_player.chips - 100\n", + "\n", + "\n", + "threshold_values = [-3, -2, -1, 0, 2] # 1st Threshold values\n", + "average_winnings_per_rounds = []\n", + "\n", + "for threshold in threshold_values:\n", + " game_results = [simulate_game(threshold) for _ in range(100)] # 100 games for each threshold\n", + " avg_win = np.mean(game_results) / 50\n", + " average_winnings_per_rounds.append(avg_win)\n", + "\n", + "optimal_threshold_index = np.argmax(avg_win)\n", + "optimal_threshold = threshold_values[optimal_threshold_index]\n", + "\n", + "print(\"Optimal Threshold Value:\", optimal_threshold)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LxY1IXMN2Zeo", + "outputId": "8056ffe8-c29d-4afd-e3ef-2d3e13dd2496" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold Value: -4\n" + ] + } + ], + "source": [ + "threshold_values = [-4, -2, 0, 1, 2] # 1st Threshold values\n", + "avg_win = []\n", + "\n", + "for threshold in threshold_values:\n", + " game_results = [simulate_game(threshold) for _ in range(100)] # 100 games for each threshold\n", + " avg_win = np.mean(game_results) / 50\n", + " average_winnings_per_rounds.append(avg_win)\n", + "\n", + "optimal_threshold_index = np.argmax(avg_win)\n", + "optimal_threshold = threshold_values[optimal_threshold_index]\n", + "\n", + "print(\"Optimal Threshold Value:\", optimal_threshold)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "T_277_CL2edV", + "outputId": "5f302bd8-5b4c-4a80-d7ab-f67c9ff7fd04" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold Value: -1\n" + ] + } + ], + "source": [ + "threshold_values = [-1, 0, 1, 2, 3] # 1st Threshold values\n", + "avg_win = []\n", + "\n", + "for threshold in threshold_values:\n", + " game_results = [simulate_game(threshold) for _ in range(100)] # 100 games for each threshold\n", + " avg_win = np.mean(game_results) / 50\n", + " average_winnings_per_rounds.append(avg_win)\n", + "\n", + "optimal_threshold_index = np.argmax(avg_win)\n", + "optimal_threshold = threshold_values[optimal_threshold_index]\n", + "\n", + "print(\"Optimal Threshold Value:\", optimal_threshold)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GbBY1pTE2ha4", + "outputId": "7f7d6698-15dc-4931-9cbc-15a4da3615c0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold Value: 0\n" + ] + } + ], + "source": [ + "threshold_values = [0, 1, 2, 3, 4, 5] # 1st Threshold values\n", + "avg_win = []\n", + "\n", + "for threshold in threshold_values:\n", + " game_results = [simulate_game(threshold) for _ in range(100)] # 100 games for each threshold\n", + " avg_win = np.mean(game_results) / 50\n", + " average_winnings_per_rounds.append(avg_win)\n", + "\n", + "optimal_threshold_index = np.argmax(avg_win)\n", + "optimal_threshold = threshold_values[optimal_threshold_index]\n", + "\n", + "print(\"Optimal Threshold Value:\", optimal_threshold)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XIv0t8sS2igP", + "outputId": "c37a919e-90af-4b00-efff-9425d48dfe29" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold Value: -5\n" + ] + } + ], + "source": [ + "threshold_values = [-5, -4, -3, -2, -1] # 1st Threshold values\n", + "avg_win = []\n", + "\n", + "for threshold in threshold_values:\n", + " game_results = [simulate_game(threshold) for _ in range(100)] # 100 games for each threshold\n", + " avg_win = np.mean(game_results) / 50\n", + " average_winnings_per_rounds.append(avg_win)\n", + "\n", + "optimal_threshold_index = np.argmax(avg_win)\n", + "optimal_threshold = threshold_values[optimal_threshold_index]\n", + "\n", + "print(\"Optimal Threshold Value:\", optimal_threshold)" + ] + }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "bHQLy-V5z8Vj" + }, "source": [ - "10. Create a new strategy based on web searches or your own ideas. Demonstrate that the new strategy will result in increased or decreased winnings. " + "10. Create a new strategy based on web searches or your own ideas. Demonstrate that the new strategy will result in increased or decreased winnings." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "id": "wnn6Imil2nKD" + }, + "outputs": [], + "source": [ + "class Card:\n", + " def __init__(self, suit, rank):\n", + " self.suit, self.rank = suit, rank\n", + " def card_value(self):\n", + "\n", + " ## Using the card rank to assign the values\n", + " return 1 if self.rank in '234567' else 0 if self.rank in '789' else -1\n", + "\n", + "class Player:\n", + " def __init__(self, chips=100):\n", + " self.chips = chips\n", + " def bet(self):\n", + " return 1\n", + " def win(self, amount):\n", + "\n", + " ## Number of chips deciding whether player wins or not\n", + " self.chips += amount\n", + " def lose(self, amount):\n", + " self.chips -= amount\n", + "\n", + "class CountingPlayer(Player):\n", + " def __init__(self, threshold):\n", + " super().__init__()\n", + " self.threshold = threshold\n", + " def decide(self, hand_value):\n", + "\n", + " ## Using hand value to hit or stand\n", + " return \"Stay\" if hand_value >= 18 else \"Hit\"\n", + "\n", + "class Dealer(Player):\n", + " def decide(self):\n", + " return \"Stay\" if sum(c.card_value() for c in self.hand) >= 17 else \"Hit\"\n", + "\n", + "def simulate_round(players, deck):\n", + " for player in players:\n", + " player.hand = [deck.pop() for _ in range(2)]\n", + " dealer = players[0]\n", + " dealer.hand.append(deck.pop())\n", + " for player in players[1:]:\n", + " while isinstance(player, CountingPlayer) and player.decide(sum(c.card_value() for c in player.hand)) == \"Hit\" and deck:\n", + " player.hand.append(deck.pop())\n", + "\n", + " ## You can hit until the deck is empty or til you stand\n", + " while isinstance(dealer, Dealer) and dealer.decide() == \"Hit\" and deck:\n", + " dealer.hand.append(deck.pop())\n", + " dealer_value = sum(c.card_value() for c in dealer.hand)\n", + " for player in players[1:]:\n", + " if isinstance(player, CountingPlayer):\n", + " player_value = sum(c.card_value() for c in player.hand)\n", + " if player_value > 21 or (dealer_value <= 21 and dealer_value > player_value): ## Lose if dealer's hand is higher or if you're busted\n", + " player.lose(player.bet())\n", + " elif player_value != dealer_value:\n", + " player.win(player.bet())\n", + "\n", + "def simulate_game(threshold):\n", + " dealer = Dealer()\n", + " strategy_player = CountingPlayer(threshold)\n", + " other_players = [Player() for _ in range(3)]\n", + " players = [dealer, strategy_player] + other_players\n", + " deck = [Card(s, str(r)) for s in 'Hearts Diamonds Clubs Spades'.split() for r in range(1, 14)]\n", + " random.shuffle(deck)\n", + " for _ in range(50):\n", + " if deck:\n", + " simulate_round(players, deck)\n", + " else:\n", + " break\n", + " return strategy_player.chips - 100" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BS0A-CG92sL6", + "outputId": "f6c7aefa-21a9-4b9c-80f7-5e7ee86d2653" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Improved Method - Optimal Threshold Value: -2\n" + ] + } + ], + "source": [ + "threshold_values = [-5, -4, -3, -2, -1]\n", + "avg_win_new= [np.mean([simulate_game(th) for _ in range(100)]) / 50 for th in threshold_values]\n", + "optimal_threshold = threshold_values[np.argmax(avg_win_new)]\n", + "\n", + "print(\"Improved Method - Optimal Threshold Value:\", optimal_threshold)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "pSk4Iupy2tSO" + }, + "outputs": [], + "source": [ + "# Using the same numbers from question 9, the new method shows improved results.\n", + "# The value for the method in Q9 is -5, while in Q10" ] } ], "metadata": { + "colab": { + "provenance": [] + }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", @@ -116,7 +1266,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.12.1" } }, "nbformat": 4, From 8e772d2ce2127adbf84f619f679dd8db85f9fd8a Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Wed, 30 Oct 2024 13:32:44 -0500 Subject: [PATCH 18/22] lab 7 part a --- Labs/Lab.7/Lab.7.ipynb | 3167 +++++++++++++++++++++++++--------------- 1 file changed, 1955 insertions(+), 1212 deletions(-) diff --git a/Labs/Lab.7/Lab.7.ipynb b/Labs/Lab.7/Lab.7.ipynb index 814b6d7..3528acb 100644 --- a/Labs/Lab.7/Lab.7.ipynb +++ b/Labs/Lab.7/Lab.7.ipynb @@ -1,1320 +1,2063 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lab 7- Data Analysis\n", - "\n", - "Exercises 1-4 are to be completed by October 25th. The remaider of the lab is due November 1st. Before leaving lab today, everyone must download the dataset." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1: Reading\n", - "\n", - "### HiggsML\n", - "In 2014, some of my colleagues from the ATLAS experiment put together a Higgs Machine Learning Challenge, which was hosted on [Kaggle](https://www.kaggle.com). Please read sections 1 and 3 (skip/skim 2) of [The HiggsML Technical Documentation](https://higgsml.lal.in2p3.fr/files/2014/04/documentation_v1.8.pdf). \n", - "\n", - "Kaggle is a platform for data science competitions, with cash awards for winners. Kaggle currently hosts over 50,000 public datasets and associated competitions. Later in the course we will look at a variety of problems hosted on Kaggle and similar platforms. \n", - "\n", - "### SUSY Dataset\n", - "\n", - "For the next few labs we will use datasets used in the [first paper on Deep Learning in High Energy physics](https://arxiv.org/pdf/1402.4735.pdf). Please read up to the \"Deep Learning\" section (end of page 5). This paper demonstrates that Deep Neural Networks can learn from raw data the features that are typically used by physicists for searches for exotics particles. The authors provide the data they used for this paper. They considered two benchmark scenarios: Higgs and SUSY." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2: Download SUSY Dataset\n", - "\n", - "The information about the dataset can be found at the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php). We'll start with the [SUSY Dataset](https://archive.ics.uci.edu/ml/datasets/SUSY). \n", - "\n", - "### Download\n", - "In a terminal, download the data directly from the source and then decompress it. For example:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* To download:\n", - " * On Mac OS: \n", - " `curl http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz > SUSY.csv.gz`\n", - "\n", - " * In linux:\n", - " `wget http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz`\n", - "\n", - "* To uncompress:\n", - "`gunzip SUSY.csv.gz`" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 879M 0 879M 0 0 8322k 0 --:--:-- 0:01:48 --:--:-- 6914k-- 0:00:04 --:--:-- 28.0M 0 --:--:-- 0:00:36 --:--:-- 8380k- 0:01:02 --:--:-- 3841k 9488k 0 --:--:-- 0:01:03 --:--:-- 3333k--:--:-- 0:01:08 --:--:-- 4464kk 0 --:--:-- 0:01:22 --:--:-- 6746k 0 --:--:-- 0:01:30 --:--:-- 10.0M-:--:-- 0:01:31 --:--:-- 9675k01:40 --:--:-- 5977k\n" - ] - } - ], - "source": [ - "!curl http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz > SUSY.csv.gz" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "!rm SUSY.csv" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "!gunzip SUSY.csv.gz" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ + "cell_type": "markdown", + "metadata": { + "id": "6I0rO7RdYt_1" + }, + "source": [ + "# Lab 7- Data Analysis\n", + "\n", + "Exercises 1-4 are to be completed by Match 29th. The remaider of the lab is due April 5th. Before leaving lab today, everyone must download the dataset." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "total 5148104\r\n", - "-rw-r--r--@ 1 afarbin staff 389K Oct 18 13:44 Lab.7.ipynb\r\n", - "-rw-r--r--@ 1 afarbin staff 5.8M Oct 18 12:45 Lab.7.pdf\r\n", - "-rw-r--r--@ 1 afarbin staff 228M Oct 18 12:39 SUSY-small.csv\r\n", - "-rw-r--r-- 1 afarbin staff 2.2G Oct 18 13:43 SUSY.csv\r\n" - ] - } - ], - "source": [ - "ls -lh" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The data is provided as a comma separated file." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": { + "id": "PGV8fPyAYt_4" + }, + "source": [ + "## Exercise 1: Reading\n", + "\n", + "### HiggsML\n", + "In 2014, some of my colleagues from the ATLAS experiment put together a Higgs Machine Learning Challenge, which was hosted on [Kaggle](https://www.kaggle.com). Please read sections 1 and 3 (skip/skim 2) of [The HiggsML Technical Documentation](https://higgsml.lal.in2p3.fr/files/2014/04/documentation_v1.8.pdf).\n", + "\n", + "Kaggle is a platform for data science competitions, with cash awards for winners. Kaggle currently hosts over 50,000 public datasets and associated competitions. Later in the course we will look at a variety of problems hosted on Kaggle and similar platforms.\n", + "\n", + "### SUSY Dataset\n", + "\n", + "For the next few labs we will use datasets used in the [first paper on Deep Learning in High Energy physics](https://arxiv.org/pdf/1402.4735.pdf). Please read up to the \"Deep Learning\" section (end of page 5). This paper demonstrates that Deep Neural Networks can learn from raw data the features that are typically used by physicists for searches for exotics particles. The authors provide the data they used for this paper. They considered two benchmark scenarios: Higgs and SUSY." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.000000000000000000e+00,9.728614687919616699e-01,6.538545489311218262e-01,1.176224589347839355e+00,1.157156467437744141e+00,-1.739873170852661133e+00,-8.743090629577636719e-01,5.677649974822998047e-01,-1.750000417232513428e-01,8.100607395172119141e-01,-2.525521218776702881e-01,1.921887040138244629e+00,8.896374106407165527e-01,4.107718467712402344e-01,1.145620822906494141e+00,1.932632088661193848e+00,9.944640994071960449e-01,1.367815494537353516e+00,4.071449860930442810e-02\r\n", - "1.000000000000000000e+00,1.667973041534423828e+00,6.419061869382858276e-02,-1.225171446800231934e+00,5.061022043228149414e-01,-3.389389812946319580e-01,1.672542810440063477e+00,3.475464344024658203e+00,-1.219136357307434082e+00,1.295456290245056152e-02,3.775173664093017578e+00,1.045977115631103516e+00,5.680512785911560059e-01,4.819284379482269287e-01,0.000000000000000000e+00,4.484102725982666016e-01,2.053557634353637695e-01,1.321893453598022461e+00,3.775840103626251221e-01\r\n", - "1.000000000000000000e+00,4.448399245738983154e-01,-1.342980116605758667e-01,-7.099716067314147949e-01,4.517189264297485352e-01,-1.613871216773986816e+00,-7.686609029769897461e-01,1.219918131828308105e+00,5.040258169174194336e-01,1.831247568130493164e+00,-4.313853085041046143e-01,5.262832045555114746e-01,9.415140151977539062e-01,1.587535023689270020e+00,2.024308204650878906e+00,6.034975647926330566e-01,1.562373995780944824e+00,1.135454416275024414e+00,1.809100061655044556e-01\r\n", - "1.000000000000000000e+00,3.812560737133026123e-01,-9.761453866958618164e-01,6.931523084640502930e-01,4.489588439464569092e-01,8.917528986930847168e-01,-6.773284673690795898e-01,2.033060073852539062e+00,1.533040523529052734e+00,3.046259880065917969e+00,-1.005284786224365234e+00,5.693860650062561035e-01,1.015211343765258789e+00,1.582216739654541016e+00,1.551914215087890625e+00,7.612152099609375000e-01,1.715463757514953613e+00,1.492256760597229004e+00,9.071890264749526978e-02\r\n", - "1.000000000000000000e+00,1.309996485710144043e+00,-6.900894641876220703e-01,-6.762592792510986328e-01,1.589282631874084473e+00,-6.933256387710571289e-01,6.229069828987121582e-01,1.087561845779418945e+00,-3.817416727542877197e-01,5.892043709754943848e-01,1.365478992462158203e+00,1.179295063018798828e+00,9.682182073593139648e-01,7.285631299018859863e-01,0.000000000000000000e+00,1.083157896995544434e+00,4.342924803495407104e-02,1.154853701591491699e+00,9.485860168933868408e-02\r\n" - ] - } - ], - "source": [ - "filename=\"SUSY.csv\"\n", - "# print out the first 5 lines using unix head command\n", - "!head -5 \"SUSY.csv\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reducing the dataset\n", - "\n", - "This is a rather large dataset. If you have trouble loading it, we can easily make a new file with less data.\n", - "\n", - "Here we look at the size of the data" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": { + "id": "7hq0ZIkUYt_6" + }, + "source": [ + "## Exercise 2: Download SUSY Dataset\n", + "\n", + "The information about the dataset can be found at the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php). We'll start with the [SUSY Dataset](https://archive.ics.uci.edu/ml/datasets/SUSY).\n", + "\n", + "### Download\n", + "In a terminal, download the data directly from the source and then decompress it. For example:" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "total 5148104\r\n", - "-rw-r--r--@ 1 afarbin staff 389K Oct 18 13:44 Lab.7.ipynb\r\n", - "-rw-r--r--@ 1 afarbin staff 5.8M Oct 18 12:45 Lab.7.pdf\r\n", - "-rw-r--r--@ 1 afarbin staff 228M Oct 18 12:39 SUSY-small.csv\r\n", - "-rw-r--r-- 1 afarbin staff 2.2G Oct 18 13:43 SUSY.csv\r\n" - ] - } - ], - "source": [ - "!ls -lh" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that we have 5 million datapoints." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": { + "id": "hetPU9dNYt_7" + }, + "source": [ + "* To download:\n", + " * On Mac OS:\n", + " `curl http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz > SUSY.csv.gz`\n", + "\n", + " * In linux:\n", + " `wget http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz`\n", + "\n", + "* To uncompress:\n", + "`gunzip SUSY.csv.gz`" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - " 5000000 SUSY.csv\r\n" - ] - } - ], - "source": [ - "!wc -l SUSY.csv" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We create a new file of the first half million. This is sufficient for our needs in this lab:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "!head -500000 SUSY.csv > SUSY-small.csv" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "NsqW49uXYt_8", + "outputId": "59583db3-2a79-4b09-8380-87232e7e5754", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 879M 0 879M 0 0 49.3M 0 --:--:-- 0:00:17 --:--:-- 50.2M\n" + ] + } + ], + "source": [ + "!curl http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz > SUSY.csv.gz" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "total 5173504\r\n", - "-rw-r--r--@ 1 afarbin staff 387K Mar 29 11:08 Lab.7.ipynb\r\n", - "-rw-r--r--@ 1 afarbin staff 6.1M Mar 18 10:38 Lab.7.pdf\r\n", - "-rw-r--r--@ 1 afarbin staff 228M Mar 29 11:09 SUSY-small.csv\r\n", - "-rw-r--r--@ 1 afarbin staff 2.2G Mar 18 10:38 SUSY.csv\r\n" - ] - } - ], - "source": [ - "ls -lh" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "Q_-ZsqVVYt__" + }, + "outputs": [], + "source": [ + "!gunzip SUSY.csv.gz" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - " 500000 SUSY-small.csv\r\n" - ] - } - ], - "source": [ - "! wc -l SUSY-small.csv" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use this file for the rest of the lab to make this run faster." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### First Look\n", - "\n", - "Each row represents a LHC collision event. Each column contains some observable from that event. The variable names are ([based on documentation](https://archive.ics.uci.edu/ml/datasets/SUSY)):" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "VarNames=[\"signal\", \"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\", \"MET_rel\", \"axial_MET\", \"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos_theta_r1\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some of these variables represent the \"raw\" kinematics of the observed final state particles, while others are \"features\" that are derived from these raw quantities:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "RawNames=[\"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\"]\n", - "FeatureNames=list(set(VarNames[1:]).difference(RawNames))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true, + "id": "KWGENf0XYt__", + "outputId": "2336d277-ee3a-4402-ea31-69f6b785735a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "total 2.3G\n", + "drwxr-xr-x 1 root root 4.0K Oct 23 13:21 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n", + "-rw-r--r-- 1 root root 2.3G Oct 25 16:05 SUSY.csv\n" + ] + } + ], + "source": [ + "ls -lh" + ] + }, { - "data": { - "text/plain": [ - "['l_1_pT',\n", - " 'l_1_eta',\n", - " 'l_1_phi',\n", - " 'l_2_pT',\n", - " 'l_2_eta',\n", - " 'l_2_phi',\n", - " 'MET',\n", - " 'MET_phi']" + "cell_type": "markdown", + "metadata": { + "id": "b1Wcgo6zYuAA" + }, + "source": [ + "The data is provided as a comma separated file." ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "RawNames" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "['axial_MET',\n", - " 'MET_rel',\n", - " 'M_R',\n", - " 'cos_theta_r1',\n", - " 'MT2',\n", - " 'dPhi_r_b',\n", - " 'M_TR_2',\n", - " 'R',\n", - " 'S_R',\n", - " 'M_Delta_R']" + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "pxXqH3tEYuAA", + "outputId": "98d8bb8a-2c49-4f54-d6c5-87cb18e5f5e9", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0.000000000000000000e+00,9.728614687919616699e-01,6.538545489311218262e-01,1.176224589347839355e+00,1.157156467437744141e+00,-1.739873170852661133e+00,-8.743090629577636719e-01,5.677649974822998047e-01,-1.750000417232513428e-01,8.100607395172119141e-01,-2.525521218776702881e-01,1.921887040138244629e+00,8.896374106407165527e-01,4.107718467712402344e-01,1.145620822906494141e+00,1.932632088661193848e+00,9.944640994071960449e-01,1.367815494537353516e+00,4.071449860930442810e-02\n", + "1.000000000000000000e+00,1.667973041534423828e+00,6.419061869382858276e-02,-1.225171446800231934e+00,5.061022043228149414e-01,-3.389389812946319580e-01,1.672542810440063477e+00,3.475464344024658203e+00,-1.219136357307434082e+00,1.295456290245056152e-02,3.775173664093017578e+00,1.045977115631103516e+00,5.680512785911560059e-01,4.819284379482269287e-01,0.000000000000000000e+00,4.484102725982666016e-01,2.053557634353637695e-01,1.321893453598022461e+00,3.775840103626251221e-01\n", + "1.000000000000000000e+00,4.448399245738983154e-01,-1.342980116605758667e-01,-7.099716067314147949e-01,4.517189264297485352e-01,-1.613871216773986816e+00,-7.686609029769897461e-01,1.219918131828308105e+00,5.040258169174194336e-01,1.831247568130493164e+00,-4.313853085041046143e-01,5.262832045555114746e-01,9.415140151977539062e-01,1.587535023689270020e+00,2.024308204650878906e+00,6.034975647926330566e-01,1.562373995780944824e+00,1.135454416275024414e+00,1.809100061655044556e-01\n", + "1.000000000000000000e+00,3.812560737133026123e-01,-9.761453866958618164e-01,6.931523084640502930e-01,4.489588439464569092e-01,8.917528986930847168e-01,-6.773284673690795898e-01,2.033060073852539062e+00,1.533040523529052734e+00,3.046259880065917969e+00,-1.005284786224365234e+00,5.693860650062561035e-01,1.015211343765258789e+00,1.582216739654541016e+00,1.551914215087890625e+00,7.612152099609375000e-01,1.715463757514953613e+00,1.492256760597229004e+00,9.071890264749526978e-02\n", + "1.000000000000000000e+00,1.309996485710144043e+00,-6.900894641876220703e-01,-6.762592792510986328e-01,1.589282631874084473e+00,-6.933256387710571289e-01,6.229069828987121582e-01,1.087561845779418945e+00,-3.817416727542877197e-01,5.892043709754943848e-01,1.365478992462158203e+00,1.179295063018798828e+00,9.682182073593139648e-01,7.285631299018859863e-01,0.000000000000000000e+00,1.083157896995544434e+00,4.342924803495407104e-02,1.154853701591491699e+00,9.485860168933868408e-02\n" + ] + } + ], + "source": [ + "filename=\"SUSY.csv\"\n", + "# print out the first 5 lines using unix head command\n", + "!head -5 \"SUSY.csv\"" ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "FeatureNames" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will use pandas to read in the file, and matplotlib to make plots. The following ensures pandas is installed and sets everything up:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can read the data into a pandas dataframe:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "filename = \"SUSY.csv\"\n", - "df = pd.read_csv(filename, dtype='float64', names=VarNames)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can see the data in Jupyter by just evaluateing the dataframe:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
signall_1_pTl_1_etal_1_phil_2_pTl_2_etal_2_phiMETMET_phiMET_relaxial_METM_RM_TR_2RMT2S_RM_Delta_RdPhi_r_bcos_theta_r1
00.00.9728610.6538551.1762251.157156-1.739873-0.8743090.567765-0.1750000.810061-0.2525521.9218870.8896370.4107721.1456211.9326320.9944641.3678150.040714
11.01.6679730.064191-1.2251710.506102-0.3389391.6725433.475464-1.2191360.0129553.7751741.0459770.5680510.4819280.0000000.4484100.2053561.3218930.377584
21.00.444840-0.134298-0.7099720.451719-1.613871-0.7686611.2199180.5040261.831248-0.4313850.5262830.9415141.5875352.0243080.6034981.5623741.1354540.180910
31.00.381256-0.9761450.6931520.4489590.891753-0.6773282.0330601.5330413.046260-1.0052850.5693861.0152111.5822171.5519140.7612151.7154641.4922570.090719
41.01.309996-0.690089-0.6762591.589283-0.6933260.6229071.087562-0.3817420.5892041.3654791.1792950.9682180.7285630.0000001.0831580.0434291.1548540.094859
............................................................
49999951.00.853325-0.961783-1.4872770.6781900.4935801.6479691.8438670.2769541.025105-1.4865350.8928791.6844291.6740843.3662981.0467072.6466491.3892260.364599
49999960.00.9515810.1393701.4368840.880440-0.351948-0.7408520.290863-0.7323600.0013600.2577380.8028710.5453190.6027300.0029980.7489590.4011660.4434710.239953
49999970.00.8403891.419162-1.2187661.1956311.6956450.6637560.490888-0.5091860.7042890.0457440.8250150.7235300.7782360.7529420.8389530.6140481.2105950.026692
49999981.01.784218-0.833565-0.5600910.953342-0.688969-1.4282332.660703-0.8613442.1168922.9061511.2323340.9524440.6858460.0000000.7818740.6760031.1978070.093689
49999990.00.7615000.680454-1.1862131.043521-0.3167550.2468791.1202800.9984791.640881-0.7976880.8542121.1218581.1654381.4983510.9315801.2935241.5391670.187496
\n", - "

5000000 rows × 19 columns

\n", - "
" + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "2_yyO2EPYuAA", + "outputId": "0b466833-6c87-45b3-f3ca-cab1cef9ac7b", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "total 2.3G\n", + "drwxr-xr-x 1 root root 4.0K Oct 23 13:21 sample_data\n", + "-rw-r--r-- 1 root root 2.3G Oct 25 16:05 SUSY.csv\n" + ] + } ], - "text/plain": [ - " signal l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi \\\n", - "0 0.0 0.972861 0.653855 1.176225 1.157156 -1.739873 -0.874309 \n", - "1 1.0 1.667973 0.064191 -1.225171 0.506102 -0.338939 1.672543 \n", - "2 1.0 0.444840 -0.134298 -0.709972 0.451719 -1.613871 -0.768661 \n", - "3 1.0 0.381256 -0.976145 0.693152 0.448959 0.891753 -0.677328 \n", - "4 1.0 1.309996 -0.690089 -0.676259 1.589283 -0.693326 0.622907 \n", - "... ... ... ... ... ... ... ... \n", - "4999995 1.0 0.853325 -0.961783 -1.487277 0.678190 0.493580 1.647969 \n", - "4999996 0.0 0.951581 0.139370 1.436884 0.880440 -0.351948 -0.740852 \n", - "4999997 0.0 0.840389 1.419162 -1.218766 1.195631 1.695645 0.663756 \n", - "4999998 1.0 1.784218 -0.833565 -0.560091 0.953342 -0.688969 -1.428233 \n", - "4999999 0.0 0.761500 0.680454 -1.186213 1.043521 -0.316755 0.246879 \n", - "\n", - " MET MET_phi MET_rel axial_MET M_R M_TR_2 \\\n", - "0 0.567765 -0.175000 0.810061 -0.252552 1.921887 0.889637 \n", - "1 3.475464 -1.219136 0.012955 3.775174 1.045977 0.568051 \n", - "2 1.219918 0.504026 1.831248 -0.431385 0.526283 0.941514 \n", - "3 2.033060 1.533041 3.046260 -1.005285 0.569386 1.015211 \n", - "4 1.087562 -0.381742 0.589204 1.365479 1.179295 0.968218 \n", - "... ... ... ... ... ... ... \n", - "4999995 1.843867 0.276954 1.025105 -1.486535 0.892879 1.684429 \n", - "4999996 0.290863 -0.732360 0.001360 0.257738 0.802871 0.545319 \n", - "4999997 0.490888 -0.509186 0.704289 0.045744 0.825015 0.723530 \n", - "4999998 2.660703 -0.861344 2.116892 2.906151 1.232334 0.952444 \n", - "4999999 1.120280 0.998479 1.640881 -0.797688 0.854212 1.121858 \n", - "\n", - " R MT2 S_R M_Delta_R dPhi_r_b cos_theta_r1 \n", - "0 0.410772 1.145621 1.932632 0.994464 1.367815 0.040714 \n", - "1 0.481928 0.000000 0.448410 0.205356 1.321893 0.377584 \n", - "2 1.587535 2.024308 0.603498 1.562374 1.135454 0.180910 \n", - "3 1.582217 1.551914 0.761215 1.715464 1.492257 0.090719 \n", - "4 0.728563 0.000000 1.083158 0.043429 1.154854 0.094859 \n", - "... ... ... ... ... ... ... \n", - "4999995 1.674084 3.366298 1.046707 2.646649 1.389226 0.364599 \n", - "4999996 0.602730 0.002998 0.748959 0.401166 0.443471 0.239953 \n", - "4999997 0.778236 0.752942 0.838953 0.614048 1.210595 0.026692 \n", - "4999998 0.685846 0.000000 0.781874 0.676003 1.197807 0.093689 \n", - "4999999 1.165438 1.498351 0.931580 1.293524 1.539167 0.187496 \n", - "\n", - "[5000000 rows x 19 columns]" + "source": [ + "### Reducing the dataset\n", + "\n", + "!ls -lh" ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first column stores the \"truth\" label of whether an event was signal or not. Pandas makes it easy to create dataframes that store only the signal or background events:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "df_sig=df[df.signal==1]\n", - "df_bkg=df[df.signal==0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following example plots the signal and background distributions of every variable. Note that we use VarNames[1:] to skip the first variable, which was the true label." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": false - }, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "l_1_pT\n" - ] + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "I8A4jFpiYuAB", + "outputId": "6fc21c89-8a6c-41cc-8dfc-3e60186a8635", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "5000000 SUSY.csv\n" + ] + } + ], + "source": [ + "## How many datapoints in SUZY\n", + "\n", + "!wc -l SUSY.csv" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "kjJyDi_BYuAB" + }, + "outputs": [], + "source": [ + "!head -500000 SUSY.csv > SUSY-small.csv" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "l_1_eta\n" - ] + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "UvpRKfZmYuAB", + "outputId": "ebdbccdf-8b7a-4337-d09a-a0a548bc2f69", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "total 2.5G\n", + "drwxr-xr-x 1 root root 4.0K Oct 23 13:21 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n", + "-rw-r--r-- 1 root root 2.3G Oct 25 16:05 SUSY.csv\n", + "-rw-r--r-- 1 root root 228M Oct 25 16:09 SUSY-small.csv\n" + ] + } + ], + "source": [ + "ls -lh" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DLZ-z8MbYuAC" + }, + "source": [ + "### First Look\n", + "\n", + "Each row represents a LHC collision event. Each column contains some observable from that event. The variable names are ([based on documentation](https://archive.ics.uci.edu/ml/datasets/SUSY)):" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "mBnp2AKRYuAC" + }, + "outputs": [], + "source": [ + "VarNames=[\"signal\", \"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\", \"MET_rel\", \"axial_MET\", \"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos_theta_r1\"]" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "l_1_phi\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "07HbvkToYuAC" + }, + "source": [ + "Some of these variables represent the \"raw\" kinematics of the observed final state particles, while others are \"features\" that are derived from these raw quantities:" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "LUtV1v-rYuAC" + }, + "outputs": [], + "source": [ + "RawNames=[\"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\"]\n", + "FeatureNames=list(set(VarNames[1:]).difference(RawNames))" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "l_2_pT\n" - ] + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "WB7BGywGYuAD", + "outputId": "6ac4fcff-b6c0-48b8-c34f-a8bfe82e0b5f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['l_1_pT',\n", + " 'l_1_eta',\n", + " 'l_1_phi',\n", + " 'l_2_pT',\n", + " 'l_2_eta',\n", + " 'l_2_phi',\n", + " 'MET',\n", + " 'MET_phi']" + ] + }, + "metadata": {}, + "execution_count": 17 + } + ], + "source": [ + "RawNames" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "W394xLXmYuAD", + "outputId": "799b034c-09d8-4d97-e2da-d6e9912a95b0", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['S_R',\n", + " 'cos_theta_r1',\n", + " 'M_R',\n", + " 'M_TR_2',\n", + " 'MET_rel',\n", + " 'MT2',\n", + " 'axial_MET',\n", + " 'dPhi_r_b',\n", + " 'R',\n", + " 'M_Delta_R']" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ], + "source": [ + "FeatureNames" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "l_2_eta\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "6rii_EU3YuAD" + }, + "source": [ + "We will use pandas to read in the file, and matplotlib to make plots. The following ensures pandas is installed and sets everything up:" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "B0BbvKm6YuAE" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "l_2_phi\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "Z5xFBAy6YuAE" + }, + "source": [ + "Now we can read the data into a pandas dataframe:" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "klasakrsYuAE" + }, + "outputs": [], + "source": [ + "filename = \"SUSY-small.csv\"\n", + "df = pd.read_csv(filename, dtype='float64', names=VarNames)" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "MET\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "32QE45BGYuAE" + }, + "source": [ + "You can see the data in Jupyter by just evaluateing the dataframe:" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "myrgb3u7YuAE", + "outputId": "2356a682-8a09-4a64-b22f-e8209da057d2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 443 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " signal l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi \\\n", + "0 0.0 0.972861 0.653855 1.176225 1.157156 -1.739873 -0.874309 \n", + "1 1.0 1.667973 0.064191 -1.225171 0.506102 -0.338939 1.672543 \n", + "2 1.0 0.444840 -0.134298 -0.709972 0.451719 -1.613871 -0.768661 \n", + "3 1.0 0.381256 -0.976145 0.693152 0.448959 0.891753 -0.677328 \n", + "4 1.0 1.309996 -0.690089 -0.676259 1.589283 -0.693326 0.622907 \n", + "... ... ... ... ... ... ... ... \n", + "499995 0.0 0.719035 1.091879 0.291540 1.205962 -1.599117 -1.139445 \n", + "499996 1.0 0.910016 -0.364544 -0.777120 0.543648 -0.910632 -1.723707 \n", + "499997 1.0 0.842954 0.332476 -1.048564 1.347989 0.320496 -0.666358 \n", + "499998 0.0 1.370760 -1.162912 0.893499 2.118091 1.248496 -0.887211 \n", + "499999 0.0 0.762400 0.440924 0.342885 1.034283 1.740353 -1.083314 \n", + "\n", + " MET MET_phi MET_rel axial_MET M_R M_TR_2 R \\\n", + "0 0.567765 -0.175000 0.810061 -0.252552 1.921887 0.889637 0.410772 \n", + "1 3.475464 -1.219136 0.012955 3.775174 1.045977 0.568051 0.481928 \n", + "2 1.219918 0.504026 1.831248 -0.431385 0.526283 0.941514 1.587535 \n", + "3 2.033060 1.533041 3.046260 -1.005285 0.569386 1.015211 1.582217 \n", + "4 1.087562 -0.381742 0.589204 1.365479 1.179295 0.968218 0.728563 \n", + "... ... ... ... ... ... ... ... \n", + "499995 0.424546 1.154849 0.637185 -0.091178 1.972156 0.697028 0.313636 \n", + "499996 2.864673 1.458272 2.176558 -0.590911 0.673695 1.662140 2.189362 \n", + "499997 0.450433 -0.411872 0.293407 0.630491 0.859920 0.403371 0.416258 \n", + "499998 0.164659 0.316840 0.215165 0.280418 3.087083 0.526929 0.151467 \n", + "499999 0.872145 -1.519894 0.284328 -0.360861 0.956828 0.965979 0.895881 \n", + "\n", + " MT2 S_R M_Delta_R dPhi_r_b cos_theta_r1 \n", + "0 1.145621 1.932632 0.994464 1.367815 0.040714 \n", + "1 0.000000 0.448410 0.205356 1.321893 0.377584 \n", + "2 2.024308 0.603498 1.562374 1.135454 0.180910 \n", + "3 1.551914 0.761215 1.715464 1.492257 0.090719 \n", + "4 0.000000 1.083158 0.043429 1.154854 0.094859 \n", + "... ... ... ... ... ... \n", + "499995 0.988602 1.981573 0.744828 1.095080 0.006546 \n", + "499996 1.195041 0.910815 1.181893 1.252362 0.826035 \n", + "499997 0.591989 0.372003 0.716788 0.366991 0.265798 \n", + "499998 0.308067 3.098183 0.233042 0.876216 0.000593 \n", + "499999 1.020396 0.996446 0.943458 1.299870 0.197220 \n", + "\n", + "[500000 rows x 19 columns]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
signall_1_pTl_1_etal_1_phil_2_pTl_2_etal_2_phiMETMET_phiMET_relaxial_METM_RM_TR_2RMT2S_RM_Delta_RdPhi_r_bcos_theta_r1
00.00.9728610.6538551.1762251.157156-1.739873-0.8743090.567765-0.1750000.810061-0.2525521.9218870.8896370.4107721.1456211.9326320.9944641.3678150.040714
11.01.6679730.064191-1.2251710.506102-0.3389391.6725433.475464-1.2191360.0129553.7751741.0459770.5680510.4819280.0000000.4484100.2053561.3218930.377584
21.00.444840-0.134298-0.7099720.451719-1.613871-0.7686611.2199180.5040261.831248-0.4313850.5262830.9415141.5875352.0243080.6034981.5623741.1354540.180910
31.00.381256-0.9761450.6931520.4489590.891753-0.6773282.0330601.5330413.046260-1.0052850.5693861.0152111.5822171.5519140.7612151.7154641.4922570.090719
41.01.309996-0.690089-0.6762591.589283-0.6933260.6229071.087562-0.3817420.5892041.3654791.1792950.9682180.7285630.0000001.0831580.0434291.1548540.094859
............................................................
4999950.00.7190351.0918790.2915401.205962-1.599117-1.1394450.4245461.1548490.637185-0.0911781.9721560.6970280.3136360.9886021.9815730.7448281.0950800.006546
4999961.00.910016-0.364544-0.7771200.543648-0.910632-1.7237072.8646731.4582722.176558-0.5909110.6736951.6621402.1893621.1950410.9108151.1818931.2523620.826035
4999971.00.8429540.332476-1.0485641.3479890.320496-0.6663580.450433-0.4118720.2934070.6304910.8599200.4033710.4162580.5919890.3720030.7167880.3669910.265798
4999980.01.370760-1.1629120.8934992.1180911.248496-0.8872110.1646590.3168400.2151650.2804183.0870830.5269290.1514670.3080673.0981830.2330420.8762160.000593
4999990.00.7624000.4409240.3428851.0342831.740353-1.0833140.872145-1.5198940.284328-0.3608610.9568280.9659790.8958811.0203960.9964460.9434581.2998700.197220
\n", + "

500000 rows × 19 columns

\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df" + } + }, + "metadata": {}, + "execution_count": 21 + } + ], + "source": [ + "df" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "MET_phi\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "TfUTXJkEYuAF" + }, + "source": [ + "The first column stores the \"truth\" label of whether an event was signal or not. Pandas makes it easy to create dataframes that store only the signal or background events:" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "oqLBSUbzYuAF" + }, + "outputs": [], + "source": [ + "df_sig=df[df.signal==1]\n", + "df_bkg=df[df.signal==0]" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "MET_rel\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "OCQmTbL7YuAF" + }, + "source": [ + "The following example plots the signal and background distributions of every variable. Note that we use VarNames[1:] to skip the first variable, which was the true label." + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "-Xb78uxwYuAF", + "outputId": "0dea7885-8de5-4971-c53b-4d034e6c9a4d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "l_1_pT\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "l_1_eta\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "l_1_phi\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "l_2_pT\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzoAAAGsCAYAAAAVEdLDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA0tElEQVR4nO3de1RVdf7/8dcB5SAFGB65GUew8jYqmgTx9WtZkqRF47fmm+MlUcuWF1omyzIqJaeSbFIpo3Flmd28ZN90Kv3aGGp2MU3ULt9B8jqQAkr9AMUEhfP7g/HUERAOAueweT7W2ivOZ3/2/ryPa6+z5jWfvT/bZLPZbAIAAAAAA/FwdQEAAAAA0NQIOgAAAAAMh6ADAAAAwHAIOgAAAAAMh6ADAAAAwHAIOgAAAAAMh6ADAAAAwHDaubqAhqiqqtLx48fl6+srk8nk6nIAAAAAuIjNZtOpU6cUGhoqD4+6521aRdA5fvy4wsLCXF0GAAAAADeRl5enq6++us79rSLo+Pr6Sqr+Mn5+fi6uBgAAAICrlJaWKiwszJ4R6tIqgs6F29X8/PwIOgAAAADqfaSFxQgAAAAAGA5BBwAAAIDhEHQAAAAAGE6reEYHAAAAbUNlZaXOnTvn6jLgQu3bt5enp+dln4egAwAAAJez2WwqKChQcXGxq0uBG+jYsaOCg4Mv6x2aBB0AAAC43IWQExgYKB8fH14S30bZbDadOXNGJ06ckCSFhIQ0+lwEHQAAALhUZWWlPeR06tTJ1eXAxTp06CBJOnHihAIDAxt9GxuLEQAAAMClLjyT4+Pj4+JK4C4uXAuX87wWQQcAAABugdvVcEFTXAsEHQAAAACGwzM6AAAAcF+5uVJRUcuNZ7FIVmvLjYdmQ9ABAACAe8rNlXr1ks6cabkxfXyk7OwmCTsTJkxQcXGx1q9ff/l1OeGpp57S+vXrtW/fvhYd190QdAAAAOCeioqqQ84771QHnuaWnS2NG1c9bhMEnRdffFE2m60JCkNjEHQAAADg3nr1kq6/3tVVOM3f39/VJbRpLEYAAAAAXIb3339fffv2VYcOHdSpUyfFxcWprKxMEyZM0MiRI+39Tp06pbFjx+qKK65QSEiIFi9erCFDhujhhx+29wkPD9f8+fM1adIk+fr6ymq16tVXX3UYb/bs2erevbt8fHzUrVs3zZkz57KWYTYqgk4j5OZKe/bUvuXmuro6AAAAtJT8/HyNHj1akyZNUnZ2trZt26a777671lvWkpOT9eWXX+rDDz/U5s2b9fnnn2vPnj01+i1cuFBRUVHau3evpk2bpqlTpyonJ8e+39fXVytWrNA///lPvfjii1q2bJkWL17crN+zNeLWNSfV90xcEz6/BgAAADeXn5+v8+fP6+6771bXrl0lSX379q3R79SpU3rzzTe1cuVKDR06VJL0xhtvKDQ0tEbfESNGaNq0aZKqZ28WL16srVu3qkePHpKkJ5980t43PDxcs2bN0urVq/Xoo482+fdrzQg6TrrUM3FN/PwaAAAA3FxkZKSGDh2qvn37Kj4+XsOGDdOf/vQnXXXVVQ79Dh8+rHPnzik6Otre5u/vbw8vv9evXz/73yaTScHBwTpx4oS9bc2aNXrppZd06NAhnT59WufPn5efn18zfLvWjVvXGunCM3G/31piMRAAAAC4D09PT23evFn/+7//q969e2vJkiXq0aOHjhw50uhztm/f3uGzyWRSVVWVJGnHjh0aO3asRowYoY8//lh79+7VE088oYqKisv6HkZE0AEAAAAug8lk0qBBgzRv3jzt3btXXl5eWrdunUOfbt26qX379vrmm2/sbSUlJfrxxx+dGuurr75S165d9cQTTygqKkrXXXed/vWvfzXJ9zAabl0DAACAe8vOdttxdu7cqczMTA0bNkyBgYHauXOnTp48qV69eum7776z9/P19VViYqIeeeQRBQQEKDAwUKmpqfLw8JDJZGrweNddd51yc3O1evVq3XDDDdqwYUONUIVqBB0AAAC4J4uleqWnceNabkwfn+pxG8jPz0/bt29Xenq6SktL1bVrVy1cuFDDhw/XmjVrHPouWrRIU6ZM0Z133ik/Pz89+uijysvLk7e3d4PHu+uuuzRz5kwlJSWpvLxcd9xxh+bMmaOnnnqqwedoK0y2VvC61tLSUvn7+6ukpMTlD1rt2SMNHChlZdV8b9Wl9gEAAKB2Z8+e1ZEjRxQREVHzf/Tn5lav9NRSLJYWW1WqrKxMXbp00cKFC3X//fe3yJitxaWuiYZmA2Z0AAAA4L6sVsMsZ7t3717t379f0dHRKikp0V/+8hdJ0h//+EcXV2ZMBB0AAACghbzwwgvKycmRl5eXBg4cqM8//1wWJ26VQ8MRdAAAAIAWMGDAAGVlZbm6jDaD5aUBAAAAGA5BBwAAAIDhEHQAAAAAGA5BBwAAAIDhEHQAAAAAGA6rrgEAAMBtufv7QocMGaL+/fsrPT29WeqZMGGCiouLtX79+mY5vyscPXpUERER2rt3r/r3799s4xB0AAAA4JZyc6VevaQzZ1puTB8fKTvbMO8obdMIOgAAAHBLRUXVIeedd6oDT3PLzpbGjase18hBp6KiQl5eXq4uo9k5/YzO9u3blZCQoNDQUJlMpgZNo5WXl+uJJ55Q165dZTabFR4eruXLlzemXgAAALQxvXpJ11/f/Ftjw9T58+eVlJQkf39/WSwWzZkzRzabTZL09ttvKyoqSr6+vgoODtaYMWN04sQJh+P/7//+T3feeaf8/Pzk6+urwYMH69ChQ7WO9c0336hz585asGCBve2ZZ55RYGCgfH199cADD+ixxx5zuCVswoQJGjlypJ599lmFhoaqR48ekqTvv/9et956qzp06KBOnTrpwQcf1OnTp+3HDRkyRA8//LDD+CNHjtSECRPsn8PDwzV//nxNmjRJvr6+slqtevXVVx2O2bVrlwYMGCBvb29FRUVp7969Df63vRxOB52ysjJFRkYqIyOjwcfce++9yszM1Ouvv66cnBytWrXK/g8MAAAAtGZvvvmm2rVrp127dunFF1/UokWL9Nprr0mSzp07p6efflrffvut1q9fr6NHjzoEhWPHjummm26S2WzWli1blJWVpUmTJun8+fM1xtmyZYtuu+02Pfvss5o9e7Yk6d1339Wzzz6rBQsWKCsrS1arVX/7299qHJuZmamcnBxt3rxZH3/8scrKyhQfH6+rrrpK33zzjdauXatPP/1USUlJTn//hQsX2gPMtGnTNHXqVOXk5EiSTp8+rTvvvFO9e/dWVlaWnnrqKc2aNcvpMRrD6VvXhg8fruHDhze4/6ZNm/TZZ5/p8OHDCggIkFSd/AAAAAAjCAsL0+LFi2UymdSjRw99//33Wrx4sSZPnqxJkybZ+3Xr1k0vvfSSbrjhBp0+fVpXXnmlMjIy5O/vr9WrV6t9+/aSpO7du9cYY926dRo/frxee+01jRo1yt6+ZMkS3X///Zo4caIkae7cufrHP/7hMDMjSVdccYVee+01+y1ry5Yt09mzZ/XWW2/piiuukCS9/PLLSkhI0IIFCxQUFNTg7z9ixAhNmzZNkjR79mwtXrxYW7duVY8ePbRy5UpVVVXp9ddfl7e3t/7whz/op59+0tSpUxt8/sZq9uWlP/zwQ0VFRen5559Xly5d1L17d82aNUu//vprnceUl5ertLTUYQMAAADc0Y033iiTyWT/HBsbqwMHDqiyslJZWVlKSEiQ1WqVr6+vbr75ZklSbm6uJGnfvn0aPHiwPeTUZufOnfrv//5vvf322w4hR5JycnIUHR3t0HbxZ0nq27evw3M52dnZioyMtIccSRo0aJCqqqrsszEN1a9fP/vfJpNJwcHB9tvzsrOz1a9fP3l7e9v7xMbGOnX+xmr2oHP48GF98cUX+uGHH7Ru3Tqlp6fr/ffft6e+2qSlpcnf39++hYWFNXeZAAAAQJM6e/as4uPj5efnp3fffVfffPON1q1bJ6l6QQBJ6tChQ73nueaaa9SzZ08tX75c586da1Qtvw80DeXh4WF/1uiC2sa/OKSZTCZVVVU5PV5Ta/agU1VVJZPJpHfffVfR0dEaMWKEFi1apDfffLPOWZ2UlBSVlJTYt7y8vOYuEwAAAGiUnTt3Onz++uuvdd1112n//v36+eef9dxzz2nw4MHq2bNnjYUI+vXrp88///ySAcZisWjLli06ePCg7r33Xoe+PXr00DfffOPQ/+LPtenVq5e+/fZblZWV2du+/PJLeXh42J+l79y5s/Lz8+37Kysr9cMPP9R77ovH+e6773T27Fl729dff+3UORqr2YNOSEiIunTpIn9/f3tbr169ZLPZ9NNPP9V6jNlslp+fn8MGAAAAuKPc3FwlJyfbF91asmSJZsyYIavVKi8vLy1ZskSHDx/Whx9+qKefftrh2KSkJJWWlurPf/6zdu/erQMHDujtt9+ucftYYGCgtmzZov3792v06NH2xQoeeughvf7663rzzTd14MABPfPMM/ruu+8cbqWrzdixY+Xt7a3ExET98MMP2rp1qx566CHdd9999udzbr31Vm3YsEEbNmzQ/v37NXXqVBUXFzv1bzNmzBiZTCZNnjxZ//znP7Vx40a98MILTp2jsZr9PTqDBg3S2rVr7Q9cSdKPP/4oDw8PXX311c09PAAAAFq57Gz3Hmf8+PH69ddfFR0dLU9PT82YMUMPPvigTCaTVqxYoccff1wvvfSSrr/+er3wwgu666677Md26tRJW7Zs0SOPPKKbb75Znp6e6t+/vwYNGlRjnODgYG3ZskVDhgzR2LFjtXLlSo0dO1aHDx/WrFmzdPbsWd17772aMGGCdu3adcmafXx89Mknn2jGjBm64YYb5OPjo3vuuUeLFi2y95k0aZK+/fZbjR8/Xu3atdPMmTN1yy23OPVvc+WVV+qjjz7SlClTNGDAAPXu3VsLFizQPffc49R5GsNku/jGu3qcPn1aBw8elCQNGDBAixYt0i233KKAgABZrValpKTo2LFjeuutt+z9e/XqpRtvvFHz5s1TUVGRHnjgAd18881atmxZg8YsLS2Vv7+/SkpKXD67s2ePNHCglJVVvd56Q/cBAACgdmfPntWRI0cUERHh8NB6bm71u23OnGm5Wnx8qgNPa35h6G233abg4GC9/fbbri6l0eq6JqSGZwOnZ3R2797tkOSSk5MlSYmJiVqxYoXy8/Ptq0hI1Slu8+bNeuihhxQVFaVOnTrp3nvv1TPPPOPs0AAAAGhDrNbq0FFU1HJjWiytK+ScOXNGS5cuVXx8vDw9PbVq1Sp9+umn2rx5s6tLczmng86QIUNqrL7weytWrKjR1rNnT/6xAQAA4DSrtXUFj5ZmMpm0ceNGPfvsszp79qx69Oih//mf/1FcXJyrS3O5Zn9GBwAAAEDz6NChgz799FNXl+GWmn3VNQAAAABoaQQdAAAAAIZD0AEAAIBbqKqqcnUJcBNNcS3wjA4AAABcysvLSx4eHjp+/Lg6d+4sLy+vel94CWOy2WyqqKjQyZMn5eHhIS8vr0afi6ADAAAAl/Lw8FBERITy8/N1/PhxV5cDN+Dj4yOr1SoPj8bfgEbQAQAAgMt5eXnJarXq/PnzqqysdHU5cCFPT0+1a9fusmf1CDoAAABwCyaTSe3bt1f79u1dXQoMgMUIAAAAABgOQQcAAACA4RB0AAAAABgOQQcAAACA4RB0AAAAABgOQQcAAACA4RB0AAAAABgOQQcAAACA4RB0AAAAABgOQQcAAACA4RB0AAAAABgOQQcAAACA4RB0AAAAABgOQQcAAACA4RB0AAAAABgOQQcAAACA4RB0AAAAABgOQQcAAACA4RB0AAAAABgOQQcAAACA4RB0AAAAABgOQQcAAACA4RB0AAAAABgOQQcAAACA4TgddLZv366EhASFhobKZDJp/fr1DT72yy+/VLt27dS/f39nhwUAAACABnM66JSVlSkyMlIZGRlOHVdcXKzx48dr6NChzg4JAAAAAE5p5+wBw4cP1/Dhw50eaMqUKRozZow8PT2dmgUCAAAAAGe1yDM6b7zxhg4fPqzU1NQG9S8vL1dpaanDBgAAAAAN1exB58CBA3rsscf0zjvvqF27hk0gpaWlyd/f376FhYU1c5UAAAAAjKRZg05lZaXGjBmjefPmqXv37g0+LiUlRSUlJfYtLy+vGasEAAAAYDROP6PjjFOnTmn37t3au3evkpKSJElVVVWy2Wxq166d/vGPf+jWW2+tcZzZbJbZbG7O0gAAAAAYWLMGHT8/P33//fcOba+88oq2bNmi999/XxEREc05PAAAAIA2yumgc/r0aR08eND++ciRI9q3b58CAgJktVqVkpKiY8eO6a233pKHh4f69OnjcHxgYKC8vb1rtAMAAABAU3E66OzevVu33HKL/XNycrIkKTExUStWrFB+fr5yc3ObrkIAAAAAcJLJZrPZXF1EfUpLS+Xv76+SkhL5+fm5tJY9e6SBA6WsLOn66xu+DwAAAMDla2g2aJH36AAAAABASyLoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcp4PO9u3blZCQoNDQUJlMJq1fv/6S/T/44APddttt6ty5s/z8/BQbG6tPPvmksfUCAAAAQL2cDjplZWWKjIxURkZGg/pv375dt912mzZu3KisrCzdcsstSkhI0N69e50uFgAAAAAaop2zBwwfPlzDhw9vcP/09HSHz/Pnz9ff//53ffTRRxowYICzwwMAAABAvZwOOperqqpKp06dUkBAQJ19ysvLVV5ebv9cWlraEqUBAAAAMIgWX4zghRde0OnTp3XvvffW2SctLU3+/v72LSwsrAUrBAAAANDatWjQWblypebNm6f33ntPgYGBdfZLSUlRSUmJfcvLy2vBKgEAAAC0di1269rq1av1wAMPaO3atYqLi7tkX7PZLLPZ3EKVAQAAADCaFpnRWbVqlSZOnKhVq1bpjjvuaIkhAQAAALRhTs/onD59WgcPHrR/PnLkiPbt26eAgABZrValpKTo2LFjeuuttyRV366WmJioF198UTExMSooKJAkdejQQf7+/k30NQAAAADgN07P6OzevVsDBgywLw2dnJysAQMGaO7cuZKk/Px85ebm2vu/+uqrOn/+vKZPn66QkBD7NmPGjCb6CgAAAADgyOkZnSFDhshms9W5f8WKFQ6ft23b5uwQAAAAAHBZWnx5aQAAAABobgQdAAAAAIZD0AEAAABgOC32Hh3Dyc6W9Otvny0WSVZXVQMAAADgdwg6zsrPlxQijRsrae9v7T4+0nsHq/cBAAAAcCluXXNWcXH1f59+RsrKqt7eeUc6c+a3fQAAAABcihmdxoqIkK7v5eoqAAAAANSCGR0AAAAAhkPQAQAAAGA4BB0AAAAAhkPQAQAAAGA4BB0AAAAAhkPQAQAAAGA4BB0AAAAAhkPQAQAAAGA4BB0AAAAAhkPQAQAAAGA4BB0AAAAAhkPQAQAAAGA4BB0AAAAAhkPQAQAAAGA47VxdgBFlZ9febrFIVmvL1gIAAAC0RQSdJmTpeF4+PtK4cbXv9/GpDkGEHQAAAKB5EXSakDXknLKzpaKimvuys6sDUFERQQcAAABobgSdJma1EmQAAAAAV2MxAgAAAACGw4xOU/r9KgSsPAAAAAC4DEGnKVgsqrEKASsPAAAAAC5D0GkKVqscViFg5QEAAADApQg6TYVVCAAAAAC3wWIEAAAAAAyHoAMAAADAcJwOOtu3b1dCQoJCQ0NlMpm0fv36eo/Ztm2brr/+epnNZl177bVasWJFI0oFAAAAgIZxOuiUlZUpMjJSGRkZDep/5MgR3XHHHbrlllu0b98+Pfzww3rggQf0ySefOF0sAAAAADSE04sRDB8+XMOHD29w/6VLlyoiIkILFy6UJPXq1UtffPGFFi9erPj4eGeHBwAAAIB6NfszOjt27FBcXJxDW3x8vHbs2FHnMeXl5SotLXXYAAAAAKChmj3oFBQUKCgoyKEtKChIpaWl+vXXX2s9Ji0tTf7+/vYtLCysucsEAAAAYCBuuepaSkqKSkpK7FteXp6rSwIAAADQijT7C0ODg4NVWFjo0FZYWCg/Pz916NCh1mPMZrPMZnNzlwYAAADAoJp9Ric2NlaZmZkObZs3b1ZsbGxzDw0AAACgjXI66Jw+fVr79u3Tvn37JFUvH71v3z7l5uZKqr7tbPz48fb+U6ZM0eHDh/Xoo49q//79euWVV/Tee+9p5syZTfMNAAAAAOAiTged3bt3a8CAARowYIAkKTk5WQMGDNDcuXMlSfn5+fbQI0kRERHasGGDNm/erMjISC1cuFCvvfYaS0sDAAAAaDZOP6MzZMgQ2Wy2OvevWLGi1mP27t3r7FAAAAAA0ChuueoaAAAAAFwOgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADCcdq4uwNCys3/3dwdJvVxWCgAAANCWEHSag8Ui+fhI48b9rnGApD1Sfr6kEBcVBgAAALQNBJ3mYLVWz+YUFf3WtrFAmiOpuFgEHQAAAKB5EXSai9VavV3w+9vYAAAAADQrFiMAAAAAYDgEHQAAAACGQ9ABAAAAYDiNCjoZGRkKDw+Xt7e3YmJitGvXrkv2T09PV48ePdShQweFhYVp5syZOnv2bKMKBgAAAID6OB101qxZo+TkZKWmpmrPnj2KjIxUfHy8Tpw4UWv/lStX6rHHHlNqaqqys7P1+uuva82aNXr88ccvu3gAAAAAqI3TQWfRokWaPHmyJk6cqN69e2vp0qXy8fHR8uXLa+3/1VdfadCgQRozZozCw8M1bNgwjR49ut5ZIAAAAABoLKeCTkVFhbKyshQXF/fbCTw8FBcXpx07dtR6zH/8x38oKyvLHmwOHz6sjRs3asSIEXWOU15ertLSUocNAAAAABrKqffoFBUVqbKyUkFBQQ7tQUFB2r9/f63HjBkzRkVFRfrP//xP2Ww2nT9/XlOmTLnkrWtpaWmaN2+eM6UBAAAAgF2zr7q2bds2zZ8/X6+88or27NmjDz74QBs2bNDTTz9d5zEpKSkqKSmxb3l5ec1dJgAAAAADcWpGx2KxyNPTU4WFhQ7thYWFCg4OrvWYOXPm6L777tMDDzwgSerbt6/Kysr04IMP6oknnpCHR82sZTabZTabnSkNAAAAAOycmtHx8vLSwIEDlZmZaW+rqqpSZmamYmNjaz3mzJkzNcKMp6enJMlmszlbLwAAAADUy6kZHUlKTk5WYmKioqKiFB0drfT0dJWVlWnixImSpPHjx6tLly5KS0uTJCUkJGjRokUaMGCAYmJidPDgQc2ZM0cJCQn2wAMAAAAATcnpoDNq1CidPHlSc+fOVUFBgfr3769NmzbZFyjIzc11mMF58sknZTKZ9OSTT+rYsWPq3LmzEhIS9OyzzzbdtwAAAACA33E66EhSUlKSkpKSat23bds2xwHatVNqaqpSU1MbMxQAAAAAOK3ZV10DAAAAgJZG0AEAAABgOAQdAAAAAIZD0AEAAABgOAQdAAAAAIZD0AEAAABgOAQdAAAAAIZD0AEAAABgOAQdAAAAAIZD0AEAAABgOAQdAAAAAIZD0AEAAABgOAQdAAAAAIZD0AEAAABgOAQdAAAAAIZD0AEAAABgOAQdAAAAAIZD0AEAAABgOAQdAAAAAIZD0AEAAABgOAQdAAAAAIbTztUFtDlHjkh7fq3+22KRrFbX1gMAAAAYEEGnpXTsWP3fOU9Kc/ZW/+3jI2VnE3YAAACAJsatay0lJKT6v++8K2VlSe+8I505IxUVubYuAAAAwICY0WlpvXpJ17u6CAAAAMDYmNEBAAAAYDgEHQAAAACGQ9ABAAAAYDgEHQAAAACGQ9ABAAAAYDgEHQAAAACGQ9ABAAAAYDiNCjoZGRkKDw+Xt7e3YmJitGvXrkv2Ly4u1vTp0xUSEiKz2azu3btr48aNjSoYAAAAAOrj9AtD16xZo+TkZC1dulQxMTFKT09XfHy8cnJyFBgYWKN/RUWFbrvtNgUGBur9999Xly5d9K9//UsdO3ZsivoBAAAAoAang86iRYs0efJkTZw4UZK0dOlSbdiwQcuXL9djjz1Wo//y5cv1yy+/6KuvvlL79u0lSeHh4ZdXNQAAAABcglO3rlVUVCgrK0txcXG/ncDDQ3FxcdqxY0etx3z44YeKjY3V9OnTFRQUpD59+mj+/PmqrKysc5zy8nKVlpY6bAAAAADQUE4FnaKiIlVWViooKMihPSgoSAUFBbUec/jwYb3//vuqrKzUxo0bNWfOHC1cuFDPPPNMneOkpaXJ39/fvoWFhTlTJgAAAIA2rtlXXauqqlJgYKBeffVVDRw4UKNGjdITTzyhpUuX1nlMSkqKSkpK7FteXl5zlwkAAADAQJx6RsdiscjT01OFhYUO7YWFhQoODq71mJCQELVv316enp72tl69eqmgoEAVFRXy8vKqcYzZbJbZbHamNAAAAACwc2pGx8vLSwMHDlRmZqa9raqqSpmZmYqNja31mEGDBungwYOqqqqyt/34448KCQmpNeQAAAAAwOVy+ta15ORkLVu2TG+++aays7M1depUlZWV2VdhGz9+vFJSUuz9p06dql9++UUzZszQjz/+qA0bNmj+/PmaPn16030LAAAAAPgdp5eXHjVqlE6ePKm5c+eqoKBA/fv316ZNm+wLFOTm5srD47f8FBYWpk8++UQzZ85Uv3791KVLF82YMUOzZ89uum8BAAAAAL/jdNCRpKSkJCUlJdW6b9u2bTXaYmNj9fXXXzdmKAAAAABwWrOvugYAAAAALY2gAwAAAMBwCDoAAAAADIegAwAAAMBwCDoAAAAADIegAwAAAMBwCDoAAAAADIegAwAAAMBwCDoAAAAADKedqwto87KzHT9bLJLV6ppaAAAAAIMg6LiKxSL5+Ejjxjm2+/hUhx/CDgAAANBoBJ0W9tsEjlV676BUXCxJsnQ8L2vxd9XBp6iIoAMAAABcBoJOC6l9Aifk39u/J3Lek4g3AAAAwOUj6LQQq7V6NqeoqOa+7Ox/T+QUtyPoAAAAAE2AoNOCrFbuSAMAAABaAstLAwAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADAcgg4AAAAAwyHoAAAAADCcRgWdjIwMhYeHy9vbWzExMdq1a1eDjlu9erVMJpNGjhzZmGEBAAAAoEGcDjpr1qxRcnKyUlNTtWfPHkVGRio+Pl4nTpy45HFHjx7VrFmzNHjw4EYXCwAAAAAN4XTQWbRokSZPnqyJEyeqd+/eWrp0qXx8fLR8+fI6j6msrNTYsWM1b948devW7bIKBgAAAID6OBV0KioqlJWVpbi4uN9O4OGhuLg47dixo87j/vKXvygwMFD3339/g8YpLy9XaWmpwwYAAAAADdXOmc5FRUWqrKxUUFCQQ3tQUJD2799f6zFffPGFXn/9de3bt6/B46SlpWnevHnOlGYs2dm//W2xSFar62oBAAAAWqFmXXXt1KlTuu+++7Rs2TJZLJYGH5eSkqKSkhL7lpeX14xVupGOHSUfH2ncOGngwOqtVy8pN9fVlQEAAACtilMzOhaLRZ6eniosLHRoLywsVHBwcI3+hw4d0tGjR5WQkGBvq6qqqh64XTvl5OTommuuqXGc2WyW2Wx2pjRjCAmpns0pKqr+nJ1dHXqKipjVAQAAAJzgVNDx8vLSwIEDlZmZaV8iuqqqSpmZmUpKSqrRv2fPnvr+++8d2p588kmdOnVKL774osLCwhpfuVFZrYQaAAAA4DI5FXQkKTk5WYmJiYqKilJ0dLTS09NVVlamiRMnSpLGjx+vLl26KC0tTd7e3urTp4/D8R07dpSkGu0AAAAA0FScDjqjRo3SyZMnNXfuXBUUFKh///7atGmTfYGC3NxceXg066M/AAAAAHBJTgcdSUpKSqr1VjVJ2rZt2yWPXbFiRWOGBAAAAIAGY+oFAAAAgOEQdAAAAAAYDkEHAAAAgOEQdAAAAAAYDkEHAAAAgOEQdAAAAAAYDkEHAAAAgOEQdAAAAAAYDkEHAAAAgOEQdAAAAAAYDkEHAAAAgOEQdAAAAAAYDkEHAAAAgOG0c3UBaIDsbMfPFotktbqmFgAAAKAVIOi4M4tF8vGRxo1zbPfxqQ4/hB0AAACgVgQdd2a1VgeaoqLf2rKzq4NPURFBBwAAAKgDQcfdWa0EGgAAAMBJLEYAAAAAwHAIOgAAAAAMh6ADAAAAwHAIOgAAAAAMh6ADAAAAwHAIOgAAAAAMh6ADAAAAwHB4j44byc6uvd1i4VU6AAAAgDMIOm7AYpF8fKRx42rf7+NTHYIIOwAAAEDDEHTcgNVaHWSKimruy86uDkBFRQQdAAAAoKEIOm7CaiXIAAAAAE2FoNNa/f6BHh7iAQAAABwQdFqb2h7o4SEeAAAAwAFBp7W5+IEeHuIBAAAAaiDotEY80AMAAABcUqNeGJqRkaHw8HB5e3srJiZGu3btqrPvsmXLNHjwYF111VW66qqrFBcXd8n+AAAAAHC5nA46a9asUXJyslJTU7Vnzx5FRkYqPj5eJ06cqLX/tm3bNHr0aG3dulU7duxQWFiYhg0bpmPHjl128QAAAABQG6eDzqJFizR58mRNnDhRvXv31tKlS+Xj46Ply5fX2v/dd9/VtGnT1L9/f/Xs2VOvvfaaqqqqlJmZednFAwAAAEBtnAo6FRUVysrKUlxc3G8n8PBQXFycduzY0aBznDlzRufOnVNAQECdfcrLy1VaWuqwAQAAAEBDORV0ioqKVFlZqaCgIIf2oKAgFRQUNOgcs2fPVmhoqENYulhaWpr8/f3tW1hYmDNlAgAAAGjjGrUYQWM999xzWr16tdatWydvb+86+6WkpKikpMS+5eXltWCVAAAAAFo7p5aXtlgs8vT0VGFhoUN7YWGhgoODL3nsCy+8oOeee06ffvqp+vXrd8m+ZrNZZrPZmdIAAAAAwM6pGR0vLy8NHDjQYSGBCwsLxMbG1nnc888/r6efflqbNm1SVFRU46sFAAAAgAZw+oWhycnJSkxMVFRUlKKjo5Wenq6ysjJNnDhRkjR+/Hh16dJFaWlpkqQFCxZo7ty5WrlypcLDw+3P8lx55ZW68sorm/CrtHHZ2Y6fLRZeKgoAAIA2y+mgM2rUKJ08eVJz585VQUGB+vfvr02bNtkXKMjNzZWHx28TRX/7299UUVGhP/3pTw7nSU1N1VNPPXV51aM60Pj4SOPGObb7+FSHH8IOAAAA2iCng44kJSUlKSkpqdZ927Ztc/h89OjRxgyBhrJaqwNNUdFvbdnZ1cGnqIigAwAAgDapUUEHbsZqJdAAAAAAv9Oiy0sDAAAAQEsg6AAAAAAwHIIOAAAAAMMh6AAAAAAwHBYjMDLerQMAAIA2iqDTSlycWS6oNbvwbh0AAAC0cQQdN1dXZrmg1uzCu3UAAADQxhF03FxtmeWCS2YX3q0DAACANoyg0wqQWQAAAADnsOoaAAAAAMMh6AAAAAAwHG5da2t+v3wby00DAADAoAg6bUVty7ex3DQAAAAMiqDTVly8fBvLTQMAAMDACDptCcu3AQAAoI1gMQIAAAAAhsOMTlv3+8UJJBYoAAAAgCEQdNqq2hYnkFigAAAAAIZA0DGAiydlLrjk5MzFixNcOBELFAAAAMAACDqtWF2TMhfUOznD4gQAAAAwKIJOK1bbpMwFlzU5w0tFAQAA0MoRdFq5Jp2U4aWiAAAAMAiCDn7DS0UBAABgEAQdOKptioglqAEAANDKEHRQN5agBgAAQCtF0DG4Ri09fQFLUAMAAKCVIugY1GUvPX1BXasdsDIbAAAA3BhBx6CabenpulZm++ADqXNnx36EHwAAALgIQcfA6lt6ulG3tV2coE6elO6+W7r9dsd+F4cfgg8AAABaEEGnDbrs29ouTlAXTx3VFn5YwAAAAAAtiKDTBjXktrbPP5d69aq5v9aJmbqWpL74fTwXn5RZHgAAADQTgk4bVddtbQ2Z7bn4cZyLj7daLxrgUstUX+pkDicEAAAAGq5RQScjI0N//etfVVBQoMjISC1ZskTR0dF19l+7dq3mzJmjo0eP6rrrrtOCBQs0YsSIRheN5nOp2Z66Hsf5vdqzi1V676AsKpI15FzDT1b3CR0RhgAAAHARp4POmjVrlJycrKVLlyomJkbp6emKj49XTk6OAgMDa/T/6quvNHr0aKWlpenOO+/UypUrNXLkSO3Zs0d9+vRpki+BpnWpRQzqCkFSfdklRD4+Ib9lls6S3jsoFRfXXcj/+3/SI4/IcvtkWZVXd7+GhKGmQqgCAABoFUw2m83mzAExMTG64YYb9PLLL0uSqqqqFBYWpoceekiPPfZYjf6jRo1SWVmZPv74Y3vbjTfeqP79+2vp0qW1jlFeXq7y8nL755KSElmtVuXl5cnPz8+ZcpvcvjU5uvnBHvrs1Rz1H9XDpbW4o7w86eefa7YXFVXfufbrr86fs4O5Uu88fVSWjudr7iwuluY8KZWfdf7EjWH2lp5+RurYsWXGA4DWolOn6v8zCIBhBQdXb65WWlqqsLAwFRcXy9/fv+6ONieUl5fbPD09bevWrXNoHz9+vO2uu+6q9ZiwsDDb4sWLHdrmzp1r69evX53jpKam2iSxsbGxsbGxsbGxsbHVuuXl5V0yuzh161pRUZEqKysVFBTk0B4UFKT9+/fXekxBQUGt/QsKCuocJyUlRcnJyfbPVVVV+uWXX9SpUyeZTCZnSq7XhUToDrNFcE9cI2gIrhPUh2sE9eEaQX24RqrZbDadOnVKoaGhl+znlquumc1mmc1mh7aOzXyrkJ+fX5u+YFA/rhE0BNcJ6sM1gvpwjaA+XCO69C1r/+bhzAktFos8PT1VWFjo0F5YWKjgOm7YCw4Odqo/AAAAAFwup4KOl5eXBg4cqMzMTHtbVVWVMjMzFRsbW+sxsbGxDv0lafPmzXX2BwAAAIDL5fSta8nJyUpMTFRUVJSio6OVnp6usrIyTZw4UZI0fvx4denSRWlpaZKkGTNm6Oabb9bChQt1xx13aPXq1dq9e7deffXVpv0mjWQ2m5WamlrjVjngAq4RNATXCerDNYL6cI2gPlwjznF6eWlJevnll+0vDO3fv79eeuklxcTESJKGDBmi8PBwrVixwt5/7dq1evLJJ+0vDH3++ed5YSgAAACAZtOooAMAAAAA7sypZ3QAAAAAoDUg6AAAAAAwHIIOAAAAAMMh6AAAAAAwnDYfdDIyMhQeHi5vb2/FxMRo165dri4JbuKpp56SyWRy2Hr27OnqsuBC27dvV0JCgkJDQ2UymbR+/XqH/TabTXPnzlVISIg6dOiguLg4HThwwDXFwmXqu04mTJhQ47fl9ttvd02xaHFpaWm64YYb5Ovrq8DAQI0cOVI5OTkOfc6ePavp06erU6dOuvLKK3XPPffUePk6jKsh18iQIUNq/I5MmTLFRRW7rzYddNasWaPk5GSlpqZqz549ioyMVHx8vE6cOOHq0uAm/vCHPyg/P9++ffHFF64uCS5UVlamyMhIZWRk1Lr/+eef10svvaSlS5dq586duuKKKxQfH6+zZ8+2cKVwpfquE0m6/fbbHX5bVq1a1YIVwpU+++wzTZ8+XV9//bU2b96sc+fOadiwYSorK7P3mTlzpj766COtXbtWn332mY4fP667777bhVWjJTXkGpGkyZMnO/yOPP/88y6q2I3Z2rDo6Gjb9OnT7Z8rKyttoaGhtrS0NBdWBXeRmppqi4yMdHUZcFOSbOvWrbN/rqqqsgUHB9v++te/2tuKi4ttZrPZtmrVKhdUCHdw8XVis9lsiYmJtj/+8Y8uqQfu58SJEzZJts8++8xms1X/brRv3962du1ae5/s7GybJNuOHTtcVSZc6OJrxGaz2W6++WbbjBkzXFdUK9FmZ3QqKiqUlZWluLg4e5uHh4fi4uK0Y8cOF1YGd3LgwAGFhoaqW7duGjt2rHJzc11dEtzUkSNHVFBQ4PCb4u/vr5iYGH5TUMO2bdsUGBioHj16aOrUqfr5559dXRJcpKSkRJIUEBAgScrKytK5c+ccfkt69uwpq9XKb0kbdfE1csG7774ri8WiPn36KCUlRWfOnHFFeW6tnasLcJWioiJVVlYqKCjIoT0oKEj79+93UVVwJzExMVqxYoV69Oih/Px8zZs3T4MHD9YPP/wgX19fV5cHN1NQUCBJtf6mXNgHSNW3rd19992KiIjQoUOH9Pjjj2v48OHasWOHPD09XV0eWlBVVZUefvhhDRo0SH369JFU/Vvi5eWljh07OvTlt6Rtqu0akaQxY8aoa9euCg0N1XfffafZs2crJydHH3zwgQurdT9tNugA9Rk+fLj97379+ikmJkZdu3bVe++9p/vvv9+FlQFozf785z/b/+7bt6/69euna665Rtu2bdPQoUNdWBla2vTp0/XDDz/w/CfqVNc18uCDD9r/7tu3r0JCQjR06FAdOnRI11xzTUuX6bba7K1rFotFnp6eNVYxKSwsVHBwsIuqgjvr2LGjunfvroMHD7q6FLihC78b/KbAWd26dZPFYuG3pY1JSkrSxx9/rK1bt+rqq6+2twcHB6uiokLFxcUO/fktaXvqukZqExMTI0n8jlykzQYdLy8vDRw4UJmZmfa2qqoqZWZmKjY21oWVwV2dPn1ahw4dUkhIiKtLgRuKiIhQcHCww29KaWmpdu7cyW8KLumnn37Szz//zG9LG2Gz2ZSUlKR169Zpy5YtioiIcNg/cOBAtW/f3uG3JCcnR7m5ufyWtBH1XSO12bdvnyTxO3KRNn3rWnJyshITExUVFaXo6Gilp6errKxMEydOdHVpcAOzZs1SQkKCunbtquPHjys1NVWenp4aPXq0q0uDi5w+fdrh/y07cuSI9u3bp4CAAFmtVj388MN65plndN111ykiIkJz5sxRaGioRo4c6bqi0eIudZ0EBARo3rx5uueeexQcHKxDhw7p0Ucf1bXXXqv4+HgXVo2WMn36dK1cuVJ///vf5evra3/uxt/fXx06dJC/v7/uv/9+JScnKyAgQH5+fnrooYcUGxurG2+80cXVoyXUd40cOnRIK1eu1IgRI9SpUyd99913mjlzpm666Sb169fPxdW7GVcv++ZqS5YssVmtVpuXl5ctOjra9vXXX7u6JLiJUaNG2UJCQmxeXl62Ll262EaNGmU7ePCgq8uCC23dutUmqcaWmJhos9mql5ieM2eOLSgoyGY2m21Dhw615eTkuLZotLhLXSdnzpyxDRs2zNa5c2db+/btbV27drVNnjzZVlBQ4Oqy0UJquzYk2d544w17n19//dU2bdo021VXXWXz8fGx/dd//ZctPz/fdUWjRdV3jeTm5tpuuukmW0BAgM1sNtuuvfZa2yOPPGIrKSlxbeFuyGSz2WwtGawAAAAAoLm12Wd0AAAAABgXQQcAAACA4RB0AAAAABgOQQcAAACA4RB0AAAAABgOQQcAAACA4RB0AAAAABgOQQcAAACA4RB0AAAAABgOQQcAAACA4RB0AAAAABjO/wfviQmZweqWlwAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "l_2_eta\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "l_2_phi\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MET\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MET_phi\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MET_rel\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "axial_MET\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "M_R\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "M_TR_2\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "R\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MT2\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAy0AAAGsCAYAAADQY0hSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAp5UlEQVR4nO3de5yWdZ3/8fdwGhgcMEFOK6PYGqLiEXGNh2VpmaXmzza3wkRt65GiC7KaUlK24qnNI7qY5mbrIdxtw8pOi3isTUUOpjWSrSRsHpA2BoFAnJnfH9OMDAwjMzBzXzDP5+NxP/C+57rv6zPeVPer6/reV1l9fX19AAAACqpbqQcAAABojWgBAAAKTbQAAACFJloAAIBCEy0AAEChiRYAAKDQRAsAAFBoPTp7h3V1dXnppZdSWVmZsrKyzt49AABQEPX19Xn99dczbNiwdOu25eMpnR4tL730UoYPH97ZuwUAAApq2bJl2WOPPbb4806PlsrKyiQNg/Xr16+zdw8AABTEqlWrMnz48KZG2JJOj5bGU8L69esnWgAAgLddNmIhPgAAUGiiBQAAKDTRAgAAFFqnr2kBAKBrqK2tzYYNG0o9BiXUs2fPdO/efZtfR7QAALBd1dfX55VXXsnKlStLPQoFsOuuu2bIkCHbdI1G0QIAwHbVGCyDBg1KRUWFC4p3UfX19Vm7dm2WL1+eJBk6dGi7X0u0AACw3dTW1jYFy4ABA0o9DiXWp0+fJMny5cszaNCgdp8qZiE+AADbTeMaloqKihJPQlE0/l3YlvVNogUAgO3OKWE02h5/F0QLAABQaNa0AADQOZYuTVas6Jx9DRyYVFV1zr7ocKIFAICOt3RpMmpUsnZt5+yvoiKprt4u4XLGGWdk5cqVue+++7Z9rja49NJLc99992XRokWdut8iEi0AAHS8FSsaguWuuxripSNVVyenndawz+0QLTfccEPq6+u3w2C0l2gBAKDzjBqVHHpoqadok/79+5d6hC7PQnwAAEjy3e9+N6NHj06fPn0yYMCAHHvssVmzZk3OOOOMnHzyyU3bvf766xk/fnz69u2boUOH5rrrrsvRRx+dyZMnN22z11575YorrshZZ52VysrKVFVV5dZbb222v4suuijvete7UlFRkb333jvTpk3bpq8F3pl1+WhZujRZsKDhTwAAuqaXX345n/zkJ3PWWWeluro6Dz/8cE455ZQWTwubMmVKfvGLX+QHP/hB5syZk8ceeywLFizYbLtrrrkmY8aMycKFC3POOefk7LPPzuLFi5t+XllZmTvuuCO/+c1vcsMNN+S2227Ldddd16G/546qS58etvF6sO24VgsAgB3Myy+/nDfffDOnnHJK9txzzyTJ6NGjN9vu9ddfz7e//e3cc889OeaYY5Ik3/rWtzJs2LDNtv3whz+cc845J0nDUZXrrrsuDz30UEaOHJkkueSSS5q23WuvvXLBBRdk1qxZ+cIXvrDdf78dXZc+0tK4HuySSxr+7Kxv4AMAoFgOOuigHHPMMRk9enQ+/vGP57bbbsuf/vSnzbZ74YUXsmHDhowdO7bpsf79+zeFyMYOPPDApn8uKyvLkCFDsnz58qbH7r333owbNy5DhgzJLrvskksuuSRLnf7Toi4dLY3+EtMAAHRR3bt3z5w5c/KTn/wk++23X2bMmJGRI0dmyZIl7X7Nnj17NrtfVlaWurq6JMkvf/nLjB8/Ph/+8Idz//33Z+HChfnSl76UN954Y5t+j52VaAEAgDRExbhx4/LVr341CxcuTK9evTJ79uxm2+y9997p2bNn5s2b1/RYTU1Nfvvb37ZpX//93/+dPffcM1/60pcyZsyY7LPPPnnxxRe3y++xM+rSa1oAAOhk1dWF3McTTzyRuXPn5oMf/GAGDRqUJ554Iq+99lpGjRqVX/3qV03bVVZWZsKECbnwwguz2267ZdCgQfnKV76Sbt26paysbKv3t88++2Tp0qWZNWtWDj/88PzoRz/aLJB4i2gBAKDjDRzY8M1Hp53WOfurqGjY51bq169fHn300Vx//fVZtWpV9txzz1xzzTU5/vjjc++99zbb9tprr83nP//5nHDCCenXr1++8IUvZNmyZendu/dW7++kk07K+eefn3PPPTfr16/PRz7ykUybNi2XXnrpVr9GV1JW38mX91y1alX69++fmpqa9OvXrzN3vZkFC5LDDktuuy357GeT+fN3uGsdAQAUyrp167JkyZKMGDFi8w/xS5d23jcfDRzYaV8Lu2bNmvzVX/1VrrnmmnzmM5/plH3uSFr7O7G1beBICwAAnaOqaqe4vsTChQvz3HPPZezYsampqck//dM/JUk++tGPlniynZdoAQCANvr617+exYsXp1evXjnssMPy2GOPZWAbTkejbUQLAAC0wSGHHJL58+eXeowuxVceAwAAhSZaAACAQhMtAABAoYkWAACg0EQLAABQaL49DACATlH0a0seffTROfjgg3P99dd3yExnnHFGVq5cmfvuu69DXr8Ufv/732fEiBFZuHBhDj744A7bj2gBAKDDLV2ajBqVrF3bOfurqEiqq3eKa1kS0QIAQCdYsaIhWO66qyFeOlJ1dXLaaQ373Nmj5Y033kivXr1KPUaHs6YFAIBOM2pUcuihHXvblih68803c+6556Z///4ZOHBgpk2blvr6+iTJnXfemTFjxqSysjJDhgzJpz71qSxfvrzZ83/961/nhBNOSL9+/VJZWZmjjjoq//M//9PivubNm5fdd989V199ddNj06dPz6BBg1JZWZm///u/z8UXX9zstKszzjgjJ598ci6//PIMGzYsI0eOTJI888wzef/7358+ffpkwIAB+dznPpfVq1c3Pe/oo4/O5MmTm+3/5JNPzhlnnNF0f6+99soVV1yRs846K5WVlamqqsqtt97a7DlPPvlkDjnkkPTu3TtjxozJwoULt/rf7bYQLQAA8Bff/va306NHjzz55JO54YYbcu211+ab3/xmkmTDhg257LLL8vTTT+e+++7L73//+2Yf+v/whz/kPe95T8rLy/Pggw9m/vz5Oeuss/Lmm29utp8HH3wwH/jAB3L55ZfnoosuSpLcfffdufzyy3P11Vdn/vz5qaqqysyZMzd77ty5c7N48eLMmTMn999/f9asWZPjjjsu73jHOzJv3rz8x3/8Rx544IGce+65bf79r7nmmqYYOeecc3L22Wdn8eLFSZLVq1fnhBNOyH777Zf58+fn0ksvzQUXXNDmfbSH08MAAOAvhg8fnuuuuy5lZWUZOXJknnnmmVx33XX57Gc/m7POOqtpu7333js33nhjDj/88KxevTq77LJLbr755vTv3z+zZs1Kz549kyTvete7NtvH7Nmzc/rpp+eb3/xm/u7v/q7p8RkzZuQzn/lMzjzzzCTJl7/85fzXf/1XsyMmSdK3b99885vfbDot7Lbbbsu6devyb//2b+nbt2+S5KabbsqJJ56Yq6++OoMHD97q3//DH/5wzjnnnCTJRRddlOuuuy4PPfRQRo4cmXvuuSd1dXW5/fbb07t37+y///753//935x99tlb/frt5UgLAAD8xd/8zd+krKys6f6RRx6Z559/PrW1tZk/f35OPPHEVFVVpbKyMu9973uTJEuXLk2SLFq0KEcddVRTsLTkiSeeyMc//vHceeedzYIlSRYvXpyxY8c2e2zT+0kyevToZutYqqurc9BBBzUFS5KMGzcudXV1TUdJttaBBx7Y9M9lZWUZMmRI0ylw1dXVOfDAA9O7d++mbY488sg2vX57iRYAAHgb69aty3HHHZd+/frl7rvvzrx58zJ79uwkDYvhk6RPnz5v+zrvfOc7s+++++Zf//Vfs2HDhnbNsnGcbK1u3bo1rc1p1NL+Nw2usrKy1NXVtXl/25toAQCAv3jiiSea3X/88cezzz775Lnnnssf//jHXHXVVTnqqKOy7777brYI/8ADD8xjjz3WaowMHDgwDz74YH73u9/l1FNPbbbtyJEjM2/evGbbb3q/JaNGjcrTTz+dNWvWND32i1/8It26dWtaqL/77rvn5Zdfbvp5bW1tnn322bd97U3386tf/Srr1q1reuzxxx9v02u0l2gBAIC/WLp0aaZMmZLFixfnO9/5TmbMmJFJkyalqqoqvXr1yowZM/LCCy/kBz/4QS677LJmzz333HOzatWqfOITn8hTTz2V559/Pnfeeedmp2gNGjQoDz74YJ577rl88pOfbFqof9555+X222/Pt7/97Tz//POZPn16fvWrXzU7Xa0l48ePT+/evTNhwoQ8++yzeeihh3Leeefl05/+dNN6lve///350Y9+lB/96Ed57rnncvbZZ2flypVt+nfzqU99KmVlZfnsZz+b3/zmN/nxj3+cr3/96216jfbapoX4V111VaZOnZpJkyZ12JVDAQDYeVRXF3sfp59+ev785z9n7Nix6d69eyZNmpTPfe5zKSsryx133JEvfvGLufHGG3PooYfm61//ek466aSm5w4YMCAPPvhgLrzwwrz3ve9N9+7dc/DBB2fcuHGb7WfIkCF58MEHc/TRR2f8+PG55557Mn78+Lzwwgu54IILsm7dupx66qk544wz8uSTT7Y6c0VFRX72s59l0qRJOfzww1NRUZGPfexjufbaa5u2Oeuss/L000/n9NNPT48ePXL++efnfe97X5v+3eyyyy754Q9/mM9//vM55JBDst9+++Xqq6/Oxz72sTa9TnuU1W96cttWmjdvXk499dT069cv73vf+7Y6WlatWpX+/funpqYm/fr1a8+ut5sFC5LDDktuuy357GeT+fMbvtsbAID2WbduXZYsWZIRI0Y0W7C9dGnD9VPWru2cOSoqGuJlR7+45Ac+8IEMGTIkd955Z6lHabct/Z1Itr4N2nWkZfXq1Rk/fnxuu+22TJ8+vT0vAQBAF1JV1RARK1Z0zv4GDtzxgmXt2rW55ZZbctxxx6V79+75zne+kwceeCBz5swp9Wgl165omThxYj7ykY/k2GOPfdtoWb9+fdavX990f9WqVe3ZJQAAO7iqqh0vJDpTWVlZfvzjH+fyyy/PunXrMnLkyPznf/5njj322FKPVnJtjpZZs2ZlwYIFW/VNBkly5ZVX5qtf/WqbBwMAgK6kT58+eeCBB0o9RiG16dvDli1blkmTJuXuu+/e7Hy0LZk6dWpqamqabsuWLWvXoAAAQNfUpiMt8+fPz/Lly3PoRqvVa2tr8+ijj+amm27K+vXr071792bPKS8vT3l5+faZFgAA6HLaFC3HHHNMnnnmmWaPnXnmmdl3331z0UUXbRYsAAB0TUW4ijrFsD3+LrQpWiorK3PAAQc0e6xv374ZMGDAZo8DAND19OrVK926dctLL72U3XffPb169XrbiyOyc6qvr88bb7yR1157Ld26dUuvXr3a/VrbdHFJAADYWLdu3TJixIi8/PLLeemll0o9DgVQUVGRqqqqdOvWpuX0zWxztDz88MPb+hIAAOxEevXqlaqqqrz55pupra0t9TiUUPfu3dOjR49tPtrmSAsAANtdWVlZevbsmZ49e5Z6FHYC7T9GAwAA0AlECwAAUGiiBQAAKDTRAgAAFJpoAQAACk20AAAAhSZaAACAQhMtAABAoYkWAACg0EQLAABQaKIFAAAoNNECAAAUmmgBAAAKTbQAAACFJloAAIBCEy0AAEChiRYAAKDQRAsAAFBoogUAACg00QIAABSaaAEAAApNtAAAAIUmWgAAgEITLQAAQKGJFgAAoNBECwAAUGiiBQAAKDTRAgAAFJpoAQAACk20AAAAhSZaAACAQhMtAABAoYkWAACg0EQLAABQaKIFAAAoNNECAAAUmmgBAAAKTbQAAACFJloAAIBCEy0AAEChiRYAAKDQRAsAAFBoogUAACg00QIAABSaaAEAAApNtAAAAIUmWgAAgEITLQAAQKGJFgAAoNBECwAAUGiiBQAAKDTRAgAAFJpoAQAACk20AAAAhSZaAACAQhMtAABAoYkWAACg0EQLAABQaKIFAAAoNNECAAAUmmgBAAAKTbQAAACFJloAAIBCEy0AAEChiRYAAKDQRAsAAFBoogUAACg00QIAABSaaAEAAApNtAAAAIUmWgAAgEITLQAAQKGJFgAAoNDaFC0zZ87MgQcemH79+qVfv3458sgj85Of/KSjZgMAAGhbtOyxxx656qqrMn/+/Dz11FN5//vfn49+9KP59a9/3VHzAQAAXVyPtmx84oknNrt/+eWXZ+bMmXn88cez//77t/ic9evXZ/369U33V61a1Y4xAQCArqrda1pqa2sza9asrFmzJkceeeQWt7vyyivTv3//ptvw4cPbu0sAAKALanO0PPPMM9lll11SXl6ez3/+85k9e3b222+/LW4/derU1NTUNN2WLVu2TQMDAABdS5tOD0uSkSNHZtGiRampqcl3v/vdTJgwIY888sgWw6W8vDzl5eXbPCgAANA1tTlaevXqlb/+679Okhx22GGZN29ebrjhhnzjG9/Y7sMBAABs83Va6urqmi20BwAA2J7adKRl6tSpOf7441NVVZXXX38999xzTx5++OH87Gc/66j5AACALq5N0bJ8+fKcfvrpefnll9O/f/8ceOCB+dnPfpYPfOADHTUfAADQxbUpWm6//faOmgMAAKBF27ymBQAAoCOJFgAAoNBECwAAUGiiBQAAKDTRAgAAFJpoAQAACk20AAAAhSZaAACAQhMtAABAoYkWAACg0EQLAABQaKIFAAAoNNECAAAUmmgBAAAKTbQAAACFJloAAIBCEy0AAEChiRYAAKDQRAsAAFBoogUAACg00QIAABSaaAEAAApNtAAAAIUmWgAAgEITLQAAQKGJFgAAoNBECwAAUGiiBQAAKDTRAgAAFJpoAQAACk20AAAAhSZaAACAQhMtAABAoYkWAACg0EQLAABQaKIFAAAoNNECAAAUmmgBAAAKTbQAAACFJloAAIBCEy0AAEChiRYAAKDQRAsAAFBoogUAACg00QIAABSaaAEAAApNtAAAAIUmWgAAgEITLQAAQKGJFgAAoNBECwAAUGiiBQAAKDTRAgAAFJpoAQAACk20AAAAhSZaAACAQhMtAABAofUo9QCF8OKLSfZMqquT/PmtxwcOTKqqSjUVAACQrh4tL7+cZGgy/bIk30xOG59k4Vs/r6hoCBnhAgAAJdO1Tw9bubLhz8/8fcOfd92dzJ/fcLvrrmTt2mTFipKNBwAAdPUjLY2GDm34c9So5NDSjgIAADTXtY+0AAAAhSdaAACAQhMtAABAoYkWAACg0EQLAABQaKIFAAAoNNECAAAUmmgBAAAKTbQAAACFJloAAIBCEy0AAEChiRYAAKDQ2hQtV155ZQ4//PBUVlZm0KBBOfnkk7N48eKOmg0AAKBt0fLII49k4sSJefzxxzNnzpxs2LAhH/zgB7NmzZqOmg8AAOjierRl45/+9KfN7t9xxx0ZNGhQ5s+fn/e85z3bdTAAAICkjdGyqZqamiTJbrvttsVt1q9fn/Xr1zfdX7Vq1bbsEgAA6GLavRC/rq4ukydPzrhx43LAAQdscbsrr7wy/fv3b7oNHz68vbsEAAC6oHZHy8SJE/Pss89m1qxZrW43derU1NTUNN2WLVvW3l0CAABdULtODzv33HNz//3359FHH80ee+zR6rbl5eUpLy9v13AAAABtipb6+vqcd955mT17dh5++OGMGDGio+YCAABI0sZomThxYu655558//vfT2VlZV555ZUkSf/+/dOnT58OGRAAAOja2rSmZebMmampqcnRRx+doUOHNt3uvffejpoPAADo4tp8ehgAAEBnave3hwEAAHQG0QIAABSaaAEAAApNtAAAAIUmWgAAgEITLQAAQKGJFgAAoNBECwAAUGiiBQAAKDTRAgAAFJpoAQAACk20AAAAhSZaAACAQhMtAABAoYkWAACg0EQLAABQaKIFAAAoNNECAAAUmmgBAAAKTbQAAACFJloAAIBCEy0AAEChiRYAAKDQRAsAAFBoogUAACg00QIAABSaaAEAAApNtAAAAIUmWgAAgEITLQAAQKGJFgAAoNBECwAAUGiiBQAAKDTRAgAAFJpoAQAACk20AAAAhSZaAACAQhMtAABAoYkWAACg0EQLAABQaKIFAAAoNNECAAAUmmgBAAAKTbQAAACFJloAAIBCEy0AAEChiRYAAKDQRAsAAFBoogUAACg00QIAABSaaAEAAApNtAAAAIUmWgAAgEITLQAAQKGJFgAAoNBECwAAUGiiBQAAKDTRAgAAFJpoAQAACk20AAAAhSZaAACAQhMtAABAoYkWAACg0EQLAABQaKIFAAAoNNECAAAUmmgBAAAKTbQAAACFJloAAIBCEy0AAEChiRYAAKDQRAsAAFBoogUAACi0NkfLo48+mhNPPDHDhg1LWVlZ7rvvvg4YCwAAoEGbo2XNmjU56KCDcvPNN3fEPAAAAM30aOsTjj/++Bx//PEdMQsAAMBm2hwtbbV+/fqsX7++6f6qVas6epcAAMBOpMMX4l955ZXp379/02348OEdvUsAAGAn0uHRMnXq1NTU1DTdli1b1tG7BAAAdiIdfnpYeXl5ysvLO3o3AADATsp1WgAAgEJr85GW1atX53e/+13T/SVLlmTRokXZbbfdUlVVtV2HAwAAaHO0PPXUU3nf+97XdH/KlClJkgkTJuSOO+7YboMBAAAk7YiWo48+OvX19R0xCwAAwGasaQEAAApNtAAAAIUmWgAAgEITLQAAQKGJFgAAoNBECwAAUGiiBQAAKDTRAgAAFJpoAQAACk20AAAAhSZaAACAQhMtAABAoYkWAACg0EQLAABQaKIFAAAoNNECAAAUmmgBAAAKTbQAAACFJloAAIBCEy0AAEChiRYAAKDQRAsAAFBoogUAACg00QIAABSaaAEAAApNtAAAAIUmWgAAgEITLQAAQKGJFgAAoNBECwAAUGiiBQAAKDTRAgAAFJpoAQAACk20AAAAhSZaAACAQhMtAABAoYkWAACg0EQLAABQaKIFAAAotB6lHoDmli5NVqxIBg5MqqpKPQ0AAJSeaCmBlsJk6dKkujo55ZRk7dqkoiL53veS3Xff8uu0FDaNr90aQQQAwI5EtHSypUuTUaOah0nSPFbuuiv53OeSD32o9dfaNGxee+2t12nL8xqJGQAAiki0dIKNj35UVzdExfXXJ1/84lthUlGR/PSnDUFTVZUcdVTrR0waA2XTsGl8nS0dodnS8xqfW13dPFycrgYAQKmJlu1s09OzWjr6UVGR/L//13Br3HbTKKiqevtIqK7ePGy2Ji5ael51dXLaacljjzWE08CBDY9vfFRI0AAAUAqiZTvZdE3KxjY9+rHxh/xt+bC/NWGztc8bOLBhztNOe2vmK65o+F0uuSSZPv2toEmax1hLQQMAANuLaNkONl2nsunpWTvCkYiqqreOwDQGyeTJDb/Pcccl1177VtA0qqhoOM1t8uTmQbMj/L4AAOw4REs7bHoKWOM6lbvualiLsqN+YN/4CExjwDQGyJZORUsa1uZsHDSOvAAAsD2Jljba+KjKxioqduxg2dSmp5C1diraxkGz6dqYRo6+AADQXqKlDZYubfgw3nhUxYfyBhsHzaZrYxo1fs1y47ejAQDA1hItW6GlCz/uTEdVtqeWTiXb+GuWW7pGTFcOPgAA3p5oacXSDE/1f1fmlIuaL7J3tKB1LZ1KVl39Vvi1dG0Za2AAANgS0bIFS1/umVGpztrz+hYjVjZd/b89tHaIo7X9tePQSGPIbHoUpqU1MI68AACwMdGyicbP6tULd8na9M1dly3JUaeP6JwP0VsKhZauULk9tHSu1tbsb0vPS962ODY9CtPSGhhHXgAA2Jho2cjSpQ1rVRo+q49IRdbkqENWb98Pz+0Nk5YuALMtNl5o0pb9bc3zthQ0LahKUv3vPbMiA5OhQ5sdebFuCACARLQ0s2LFRt8MluoMPO24VA29r+0v1BFh0hHnTLV08ZWt2d+Wnvd2QbMFVUmq/hI7A3cdlore++W007qnondtvvfPL2T3d7yZ7LprMnSoU8cAALog0dKCUaOSQ/PnJMva/uQtXcilUWeHSWtau/hKe5/XWghtyUaxU5WkOsNTnVE5Zd338qHz9mm2aUXv2lR/9zepGrrhrQeVDADATk20bOTFF9uw8ZaOplRXt3whl0Y7+wfs9obQRrFT9Zdb9csvZMXKHsmf/pRceGGq1+2V09bdncdOuCpH5bFUNUalRTAAADs10ZJk4K5vpqIimT694fPvwIFJWjtYsDVHUyzIaJsWYqcxXpIkJ30nA5+pScXf1ua0dXc3nTo2qvbZVE0+pfnXj21sZ49EAIAuQLQkqRryRtP/0d/0GbcxWqqrN39CVz+aUgpVVQ1fmby48Xov3fOh8/ZJRZ935nvlJ2X3067JwKx46+hLI0dhAAB2eKLlLzb7P/pb+i7ejTmaUhIbX++lIV665UPrv5+khfUuLV0EZmPiEgBghyBatqSlKyFuzAfektr0YpUNfdI9j60cnaNG/+Wt2ZrwbOf1ZgAA6DyipTXtXVROp2l8izbuk8YWGTWqKlXt/Xpmp5UBABSGaGGn0PyUsYYWaYiXqoZ4aak9thQ0TisDACgU0cJOY/P1LhvHS8NZYM16Y0tH0rbmtDJHYQAAOo1oYaezpXhJtrI3WlvP5CgMAECnEy3stFperN/QG2/7xW+OwgAAFIZoYafX+mL9NvaFozAAAJ1OtNBlvN16l0Zv2xaOwgAAdCrRQpfS2nqXRu1uC0dhAAA6hGihS9p0vUujNq17ae2FN+VClwAA7SZa6NI2bYyW1r00dsQ2tUNrR2Fc6BIAoFWiBTbS2qlj29wOWzoKkzitDACgFaIFNtHSqWNbaoft1gxOKwMA2CLRAluwcUdsqR06vBm29bQyQQMA7ATaFS0333xz/vmf/zmvvPJKDjrooMyYMSNjx47d3rNBYbTUDm1thnZ3QntOKxM0AMBOpM3Rcu+992bKlCm55ZZbcsQRR+T666/Pcccdl8WLF2fQoEEdMSMUQkvt0JZmaK0TGrW5Fzo7aFojdgCADlJWX19f35YnHHHEETn88MNz0003JUnq6uoyfPjwnHfeebn44os32379+vVZv3590/2amppUVVVl2bJl6dev3zaOv20W3bs47/3cyDxy6+Ic/HcjSzoLO59ly5I//rHhn1esaDi17M9/bv05ffokd93V8Pm/Q736SrKyZvPHV65Mpl2SrF/X9tcs751cNj3Zddc2PW3IgA0ZMvDNtu8PAGifIUMabgWwatWqDB8+PCtXrkz//v23uF2bouWNN95IRUVFvvvd7+bkk09uenzChAlZuXJlvv/972/2nEsvvTRf/epX2zY9AADQZSxbtix77LHHFn/eptPDVqxYkdra2gwePLjZ44MHD85zzz3X4nOmTp2aKVOmNN2vq6vL//3f/2XAgAEpKytry+63u8ayK8JRH7ae923H5H3bMXnfdkzetx2X927H5H1rv/r6+rz++usZNmxYq9t1+LeHlZeXp7y8vNlju7bx9JGO1q9fP3/BdkDetx2T923H5H3bMXnfdlzeux2T9619WjstrFG3trzgwIED071797z66qvNHn/11VczpCDnxQEAADuXNkVLr169cthhh2Xu3LlNj9XV1WXu3Lk58sgjt/twAAAAbT49bMqUKZkwYULGjBmTsWPH5vrrr8+aNWty5plndsR8Haq8vDxf+cpXNjt9jWLzvu2YvG87Ju/bjsn7tuPy3u2YvG8dr81feZwkN910U9PFJQ8++ODceOONOeKIIzpiPgAAoItrV7QAAAB0ljataQEAAOhsogUAACg00QIAABSaaAEAAAqty0bLzTffnL322iu9e/fOEUcckSeffLLUI/E2rrzyyhx++OGprKzMoEGDcvLJJ2fx4sWlHos2uOqqq1JWVpbJkyeXehS2wh/+8IecdtppGTBgQPr06ZPRo0fnqaeeKvVYtKK2tjbTpk3LiBEj0qdPn7zzne/MZZddFt+5UyyPPvpoTjzxxAwbNixlZWW57777mv28vr4+X/7ylzN06ND06dMnxx57bJ5//vnSDEszrb13GzZsyEUXXZTRo0enb9++GTZsWE4//fS89NJLpRt4J9Ilo+Xee+/NlClT8pWvfCULFizIQQcdlOOOOy7Lly8v9Wi04pFHHsnEiRPz+OOPZ86cOdmwYUM++MEPZs2aNaUeja0wb968fOMb38iBBx5Y6lHYCn/6058ybty49OzZMz/5yU/ym9/8Jtdcc03e8Y53lHo0WnH11Vdn5syZuemmm1JdXZ2rr746X/va1zJjxoxSj8ZG1qxZk4MOOig333xziz//2te+lhtvvDG33HJLnnjiifTt2zfHHXdc1q1b18mTsqnW3ru1a9dmwYIFmTZtWhYsWJDvfe97Wbx4cU466aQSTLrz6ZJfeXzEEUfk8MMPz0033ZQkqaury/Dhw3Peeefl4osvLvF0bK3XXnstgwYNyiOPPJL3vOc9pR6HVqxevTqHHnpo/uVf/iXTp0/PwQcfnOuvv77UY9GKiy++OL/4xS/y2GOPlXoU2uCEE07I4MGDc/vttzc99rGPfSx9+vTJXXfdVcLJ2JKysrLMnj07J598cpKGoyzDhg3LP/7jP+aCCy5IktTU1GTw4MG544478olPfKKE07KxTd+7lsybNy9jx47Niy++mKqqqs4bbifU5Y60vPHGG5k/f36OPfbYpse6deuWY489Nr/85S9LOBltVVNTkyTZbbfdSjwJb2fixIn5yEc+0uw/dxTbD37wg4wZMyYf//jHM2jQoBxyyCG57bbbSj0Wb+Pd73535s6dm9/+9rdJkqeffjo///nPc/zxx5d4MrbWkiVL8sorrzT778v+/fvniCOO8DllB1RTU5OysrLsuuuupR5lh9ej1AN0thUrVqS2tjaDBw9u9vjgwYPz3HPPlWgq2qquri6TJ0/OuHHjcsABB5R6HFoxa9asLFiwIPPmzSv1KLTBCy+8kJkzZ2bKlCn54he/mHnz5uUf/uEf0qtXr0yYMKHU47EFF198cVatWpV999033bt3T21tbS6//PKMHz++1KOxlV555ZUkafFzSuPP2DGsW7cuF110UT75yU+mX79+pR5nh9flooWdw8SJE/Pss8/m5z//ealHoRXLli3LpEmTMmfOnPTu3bvU49AGdXV1GTNmTK644ookySGHHJJnn302t9xyi2gpsH//93/P3XffnXvuuSf7779/Fi1alMmTJ2fYsGHeN+hEGzZsyKmnnpr6+vrMnDmz1OPsFLrc6WEDBw5M9+7d8+qrrzZ7/NVXX82QIUNKNBVtce655+b+++/PQw89lD322KPU49CK+fPnZ/ny5Tn00EPTo0eP9OjRI4888khuvPHG9OjRI7W1taUekS0YOnRo9ttvv2aPjRo1KkuXLi3RRGyNCy+8MBdffHE+8YlPZPTo0fn0pz+d888/P1deeWWpR2MrNX4W8Tllx9UYLC+++GLmzJnjKMt20uWipVevXjnssMMyd+7cpsfq6uoyd+7cHHnkkSWcjLdTX1+fc889N7Nnz86DDz6YESNGlHok3sYxxxyTZ555JosWLWq6jRkzJuPHj8+iRYvSvXv3Uo/IFowbN26zrxT/7W9/mz333LNEE7E11q5dm27dmv9Pe/fu3VNXV1eiiWirESNGZMiQIc0+p6xatSpPPPGEzyk7gMZgef755/PAAw9kwIABpR5pp9ElTw+bMmVKJkyYkDFjxmTs2LG5/vrrs2bNmpx55pmlHo1WTJw4Mffcc0++//3vp7Kysunc3v79+6dPnz4lno6WVFZWbrbmqG/fvhkwYIC1SAV3/vnn593vfneuuOKKnHrqqXnyySdz66235tZbby31aLTixBNPzOWXX56qqqrsv//+WbhwYa699tqcddZZpR6NjaxevTq/+93vmu4vWbIkixYtym677ZaqqqpMnjw506dPzz777JMRI0Zk2rRpGTZsWKvfUkXnaO29Gzp0aP72b/82CxYsyP3335/a2tqmzyq77bZbevXqVaqxdw71XdSMGTPqq6qq6nv16lU/duzY+scff7zUI/E2krR4+9a3vlXq0WiD9773vfWTJk0q9RhshR/+8If1BxxwQH15eXn9vvvuW3/rrbeWeiTexqpVq+onTZpUX1VVVd+7d+/6vffeu/5LX/pS/fr160s9Ght56KGHWvzfswkTJtTX19fX19XV1U+bNq1+8ODB9eXl5fXHHHNM/eLFi0s7NPX19a2/d0uWLNniZ5WHHnqo1KPv8LrkdVoAAIAdR5db0wIAAOxYRAsAAFBoogUAACg00QIAABSaaAEAAApNtAAAAIUmWgAAgEITLQAAQKGJFgAAoNBECwAAUGiiBQAAKLT/D56Ep0YHYCWaAAAAAElFTkSuQmCC\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "S_R\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "M_Delta_R\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "dPhi_r_b\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "cos_theta_r1\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "import numpy as np\n", + "for var in VarNames[1:]:\n", + " print (var)\n", + " plt.figure(figsize=(10,5))\n", + " plt.hist(np.array(df_sig[var]),bins=100,histtype=\"step\", color=\"red\",label=\"signal\",density=1, stacked=True)\n", + " plt.hist(np.array(df_bkg[var]),bins=100,histtype=\"step\", color=\"blue\", label=\"background\",density=1, stacked=True)\n", + " plt.legend(loc='upper right')\n", + " plt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "axial_MET\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "YsCIyyi2YuAG" + }, + "source": [ + "## Exercise 3: Make nice figures\n", + "\n", + "Now use `matplotlib` to reproduce as closely as you can figures 5 and 6 from the paper. This exercise is intended to get you to familiarize yourself with making nicely formatted `matplotlib` figures with multiple plots. Note that the plots in the paper are actually wrong!" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "axH5_ZsuYuAG", + "outputId": "507a155d-786f-44d6-cf4e-44c3129ad211", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAArcAAAINCAYAAAAkzFdkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB2GElEQVR4nO3de3wU9b3/8ffsLAkJkkTIDQjh5oUoJhACKVp/VqUiWoVqj9ZKQaxYL/SoHKtiFUTb0lalWo+V1opoqZdqFXuq1SKKWqWQG0Q0CUgCAbklSDaQhCQ7M78/1l2yyeayyWdmdifv5+ORR2Uy2Xy/r1nSb4bZWcUwDANERERERA7gsnsARERERERSuLglIiIiIsfg4paIiIiIHIOLWyIiIiJyDC5uiYiIiMgxuLglIiIiIsfg4paIiIiIHIOLWyIiIiJyDLfdA7CaruvYt28fBg8eDEVR7B4OEREREbVjGAaOHj2K4cOHw+UK71xsv1vc7tu3DyNHjrR7GERERETUjT179iAjIyOsr+l3i9vBgwcD8MVKSEgw/ft5vV6UlJRg0qRJcLv7XW5RbCmHLeWwpRy2lMOWcthSTjgt6+vrMXLkyMC6LRz97ij5L0VISEiwbHE7aNAgJCQk8C9FH7GlHLaUw5Zy2FIOW8phSzm9admbS0gVwzCMsL8qitXX1yMxMREej8eSxa1hGGhqakJcXByv8e0jtpTDlnLYUg5bymFLOWwpJ5yWfVmv8W4JFoiJibF7CI7BlnLYUg5bymFLOWwphy3lWNGSi1uTaZqGwsJCaJpm91CiHlvKYUs5bCmHLeWwpRy2lGNVS148QkRERJYyDANerzcqFoxerxcAcPz4cV5z20ftWw4YMACqqop/Hx4lIiIiskxLSwv279+PxsZGu4fSI4ZhYODAgaiuruY1t33UvqWiKMjIyMBJJ50k+n24uCUiIiJL6LqOqqoqqKqK4cOHIyYmJuIXjIZhoLGxEfHx8RE/1kjXtiUA1NTUYO/evTj11FNFz+DybgkmMwwDmqZBVVX+pegjtpTDlnLYUg5byonUlsePH0dVVRVGjRoVWOBEurbLpEhqGY3at2xqasKuXbswZswYDBw4MGhf3i0hwrW0tNg9BMdgSzlsKYct5bClnEhuGe7bqdpN13W7h+AYbVua9ctCdD27opCmaSgtLY2Ki+YjHVvKYUs5bCmHLeWwpaympia7h+AYVrTkNbdERERkr+pqoLbWuu+XnAxkZoo81HXXXYe6ujqsXbtW5PF66oEHHsDatWuxZcsWS79vNODiloiIiOxTXQ1kZQFW3j0hPh4oKxNZ4D7++OPoZy9finhc3FrAjHu49VdsKYct5bClHLaUEzUta2t9C9s1a3yLXLOVlQFz5vi+bw8Xt11dG5qYmCg1sn7BihflcXFrMrfbjSlTptg9DEdgSzlsKYct5bClnKhsmZUF5ObaPYoOFEXBoEGD8Oqrr2LZsmX44osvEB8fj0mTJuGNN97ArbfeGnRZwtGjR3HTTTdh7dq1SEhIwF133YU33ngDEydOxGOPPQYAGD16NG688UZ88cUXeOWVV3DyySfjvvvuw4033hj4vnfffTdef/117N27F+np6bj22muxZMkSDBgwwIYKMvwtzcYXlJnMMAzU1dXxnywEsKUctpTDlnLYUg5byjEMA3v27ME111yD66+/HmVlZdiwYQOuuOKKkH0XLVqEjz/+GH//+9+xbt06fPTRRyguLu6w36OPPoq8vDyUlJTglltuwc0334yKiorA5wcPHozVq1fj888/x+OPP46nn34av/3tb02dq9n870xn9vOSi1uTaZqG8vJyvmJVAFvKYUs5bCmHLeWwpazdu3fD6/XiiiuuwOjRo3HWWWfhlltu6fDOWkePHsVzzz2HRx55BBdeeCEmTJiAZ599NuRxuOSSS3DLLbfglFNOwd13343k5GS8//77gc/fd999OPvsszF69GhcdtlluPPOO/HXv/7V9Lma7fjx46Z/D16WQERERNSFs846CxdeeCHOOusszJgxAxdddBG+973v4eSTTw7ar7KyEq2trZg6dWpgW2JiIk4//fQOj5mdnR34b0VRkJ6ejkOHDgW2vfzyy/jd736HnTt34tixY/B6vZa8+ZQT8MwtERERURdUVcW//vUv/POf/8QZZ5yBJ554Aqeffjqqqqp6/Zjtr51VFCXwBgcbN27Etddei0suuQT/+Mc/UFJSgp/97GcR/cYckYSLW5MpioK4uDi+ZZ8AtpTDlnLYUg5bymFLWS6XC4qi4JxzzsGyZctQUlKCmJgYvP7660H7jR07FgMGDEBBQUFgm8fjwfbt28P6fp988glGjRqFn/3sZ8jLy8Opp56K3bt3i8zFbla8Ox0vSzCZqqrIycnp8+N0dn9rwftQRzyplsSWkthSDlvKYUs5iqLg008/xfr163HRRRchNTUVmzZtQk1NDbKyslBaWhrYd/DgwZg3bx5++tOfYsiQIUhNTcXSpUsDi+OeOvXUU1FdXY2XXnoJU6ZMwZtvvtlhIR2NFEVBfHy86d+Hi1uT6bqO2tpaJCcn9/q3la7uby14H+qIJ9GSfNhSDlvKYUs5UdmyrCwiv49hGIiLi8OHH36Ixx57DPX19Rg1ahQeffRRzJw5Ey+//HLQ/itWrMBNN92E73znO4Fbge3ZswcDBw7s8fe8/PLLcccdd2DhwoVobm7GpZdeivvvvx8PPPBAWGOPNP67JbjdblP/VUEx+tl9Qurr65GYmAiPx2PJhdlerxeFhYXIy8uD29273yWKi4HJkzve39p/H+qiooi8NaA4iZbkw5Zy2FIOW8qJ1JbHjx9HVVUVxowZc2KxF+HvUGYYBhoaGjBo0KBeLcgaGhowYsQIPProo/jRj37Um9E6RvuWIZ8PX+vLei1ynvHUrQi9vzUREVHvZWb6Fpqhrr0zi4nX9JWUlKC8vBxTp06Fx+PBgw8+CACYNWuWKd+POuLiloiIiOyVmemo6+seeeQRVFRUICYmBpMnT8ZHH32E5ORku4fVb3BxazJFUZCYmMhXrApgSzlsKYct5bClHLaUpapqj/edNGkSioqKTBxNdAunZW9xcWsyVVWR1fZCWeo1tpTDlnLYUg5bymFLOf7bqlHfWdUySl5CGb10XcfevXsDN2am3mNLOWwphy3lsKUctpRjGAZaWlrQz15/bwqrWnJxazL+gJHDlnLYUg5bymFLOWwpi+8MJseKllzcEhEREZFjcHFLRERERI7Bxa3JXC4XUlJSoucdYiIYW8phSzlsKYct5bClrEh6I4xoZ0VLW4/Whx9+iIcffhhFRUXYv38/Xn/9dcyePbtHX/vxxx/jvPPOw4QJE7BlyxZTx9kXLpcL48aNs3sYjsCWcthSDlvKYUs50dayujpy38NBURQMHDgQ3/rWtzBx4kQ89thjpozpuuuuQ11dHdauXWvK49th165dGDNmDEpKSjBx4sRAS7PZurhtaGhATk4Orr/+elxxxRU9/rq6ujrMnTsXF154IQ4ePGjiCPtO1/XAW8vxN+i+YUs5bCmHLeWwpZxoahnh774LwzDQ3Nxs/qD6AX/L2NhYU+/BbOvidubMmZg5c2bYX3fTTTfhBz/4AVRVjfjfcHRdR01NDUaNGhXxP2AiHVvKYUs5bCmHLeVEU8vaWt/Cds0a3yLXbGVlwJw5vu/b07O3Xq/X3EGZpKWlBTExMXYPI4jX60VsbKyp3yPqLiJ59tlnUVlZiTVr1uDnP/95t/s3NzcH/cZVX18PwBfX/2R1uVxwuVzQdT3otin+7ZqmBd2TrbPtqqpCUZSgvwT+fQzD6PCXw/8uHZqmBW13u90wDCOw3fdlvkPVdoydbTd7Tl2NvadzAnz/1KOqaocxdrbd/9+6rgeNJ5rnZNdx8j8nnTQnu46Tv2fbx4/2Odl5nNrOyylzsvM4+b93pMzJ6/UGxnvifwFAwfjxBnJzlZD3QFWU8LZ3xf/9fM+37h+77edaW1uxcOFC/PnPf8aAAQNw00034cEHH4SiKFizZg0ef/xxVFRUYNCgQbjgggvw29/+FmlpaYHH+Oyzz3DPPffgww8/hGEYmDhxIp599tkOl5AYhoGCggJceuml+J//+R/cfffdAICf//zneOKJJ9DU1ISrrroKycnJeOedd1BSUgIAmD9/Purq6jBlyhQ8+eSTiI2NRWVlJT799FPcfvvt2LhxI+Lj43HFFVdgxYoVOOmkkwAA559/PnJycoIuufjud7+LpKQkPPvsswCAMWPGYMGCBfjiiy/w6quv4uSTT8bPfvYz3HjjjYGvKSgowI9//GOUlZVhwoQJuPfee4Pm1PHYn1gf+Z+b/udeX36hiKrF7Y4dO3DPPffgo48+6vEFycuXL8eyZcs6bC8pKcGgQYMAACkpKRg3bhyqqqpQU1MT2CcjIwMZGRnYvn07PB5PYPvYsWORmpqKbdu2oampKbB9/PjxSEpKQklJSeAvvmEYgb/Y/iefX15eHlpaWlBaWhrYpqoqpkyZAo/Hg/LycgBARUU8gGwAQG1tLSorKzts37dvH/bu3Rt4HDPnBADZ2dmIiYlBYWFhr+YEAHFxccjJyQmaEwAkJiYiKyurw5yGDh0KANi9ezcOHz7siDnZdZwmTZoEXddRXFwc+KehaJ+TXccpPT0dDQ0NQS2jfU52Haf6+nrU1dUFWjphTnYdp88//zyoZSTNKT4+HoDv5JPX60VTkwtA/NeLmQE4fvx40BhjY2MxYMAANDU1BS2eBw4cCLfbjcbGxqAFaFxcHFwuFxoaGoLmNGjQIOi6jqamZgDxaGpqQmOjgUGDBkHTNBw/fjywr8vlQny8b0zNzc3QNA2apuH555/Hj370I/z73//G5s2b8d///d9IS0vDggUL0Nraivvvvx9jx45FTU0NFi9ejHnz5uHtt9/G8ePHsWfPHvy///f/cO655+Jf//oXhgwZgvfeew/19fVoaGhAa2trYB5vvfUWrr32Wjz44IO4/vrroes6XnzxRfzyl7/EihUr8I1vfAN/+9vf8MQTT2DMmDGBuba2tmL9+vVISEjAP//5T7S0tODQoUOYMWMG8vPzUVBQgH379uHHP/4xbrrpJvzhD38ILCZ1XQ9q5j8G/uNkGAYeffRRPPDAA/jZz36GF198EbfccgumTJmC0047Da2trfjOd76D888/H3/84x+xa9cu3HnnnYHH8x8nTdPQ2NgYeKeylpYWbNu2rcNzr/2aKSxGhABgvP76651+3uv1Gnl5ecZTTz0V2LZ06VIjJyeny8c9fvy44fF4Ah979uwxABiHDx82WltbjdbWVkPTNMMwDEPTtMC2ttu9Xm+Ptuu6bhiGEbStubnZqK6u7rCvf39d1ztsNwwjaPumTa0GYBhFRcFj7Gy72XPqauw9nVNra6vh9XpDjrGz7a2trcaePXs6bIvmOdl1nLxer1FdXW00Nzc7Zk52HSdN04zdu3cHtYz2Odl1nLxeb1BLJ8zJruPU0tIS1DJS5nT06FHjs88+M5qamgLfo7BQNwDDKCzUA4/f/iPc7V19tP1+PXlsTdOM48ePG+edd56RlZUVtM9dd90V2Nb+cTZv3mwAMI4ePWroum7cc889xpgxY4zm5uaQ+8+bN8+YNWuW8dprrxknnXSS8eKLLwZ9Pj8/37jllluCtp1zzjlGTk5O0GOkpaUFvoeu68Yf/vAH4+STTzaOHj0a+J7/+Mc/DJfLZezfv9/Qdd0477zzjP/+7/8OeuxZs2YZ8+bNC/x51KhRxpw5cwJj1zTNSE1NNX7/+98buq4bK1euNIYOHWo0NjYGvub3v/+9AcAoKSkJaqlpmqHrutHY2Gh89tlnxtGjRzs89w4fPmwAMDwejxGuqDlze/ToURQWFqKkpAQLFy4E4PstwzAMuN1u/Otf/8IFF1zQ4etiY2NDXtvhdrs7nP31/3NOe/7fanq6vf3jjhw5MvSkOtkf8P1ziH9720+3HWNn29sya0692d52Tj0ZY6jtGRkZIb8fEL1z6mq7mXPq7HkZzXPqbLvZc8rs5MK9aJ6THcdJVdWQLaN5TnYdpwEDBoRsafec3G534F84Tvwv2v059AuNwt3embbfr/33DvXYiqIE1hHf+MY3gvY9++yzsWLFCui6ji1btuCBBx7A1q1bceTIkcBZ5urqapxxxhnYunUrzj333KBrYNt/302bNuEf//gHXn311Q53j6qoqMAtt9wS9DVTp07Fe++9F7TtrLPOCvoe5eXlyMnJCVyCoCgKvvnNb0LXdWzfvh3p6elBcw3VwC87OzvwZ5fLhfT0dNTU1EBRFJSXlyM7OztwRtbfp7OWbbeFWpP15ZZhkX2VeRsJCQn49NNPsWXLlsDHTTfdhNNPPx1btmxBfn6+3UMMSdM0lJWVdbhuicLHlnLYUg5bymFLOWwpxzCMoMtWQjl+/DhmzJiBhIQE/OUvf0FBQQFef/11ACfebrbtoq8z48aNw/jx47Fq1Sq0trb2arz+Sy7D4XK5Olx3HOr7DxgwIOjPiqKE9RbP/pbtv5c0Wxe3x44dCyxUAaCqqgpbtmxBdXU1AGDx4sWYO3cuAF/4CRMmBH2kpqZi4MCBmDBhQq8OphUMw4DH4zH9QPYHbCmHLeWwpRy2lMOWsvy/JGzatClo+3/+8x+ceuqpKC8vx+HDh/GrX/0K5557LsaPH49Dhw4F7ZudnY2PPvqoy0VrcnIy3nvvPXzxxRe46qqrgvY9/fTTUVBQELR/+z+HkpWVha1btwZdT/vxxx/D5XLh9NNPB+C7tnv//v1B8217HWxPZGVlobS0NOja5f/85z8d9rPiFy5bF7eFhYWYNGkSJk2aBABYtGgRJk2ahCVLlgAA9u/fH1joEhEREdmpuroaixYtQkVFBV588UU88cQTuO2225CZmYmYmBg88cQTqKysxN///nc89NBDQV+7cOFC1NfX4/vf/z4KCwuxY8cO/PnPf0ZFRUXQfqmpqXjvvfdQXl6Oa665JnDXgJ/85Cd45pln8Nxzz2HHjh34+c9/jtLS0m4vy7j22msxcOBAzJs3D9u2bcP777+Pn/zkJ/jhD3+ItLQ0AMAFF1yAN998E2+++SbKy8tx8803o66uLqw2P/jBD6AoChYsWIDPP/8cb731Fh555JGwHkOKrdfcfutb3+ryt8rVq1d3+fUPPPAAHnjgAdlBERERkeXKyiL/+8ydOxdNTU2YOnUqVFXFbbfdhhtvvBGKomD16tW499578bvf/Q65ubl45JFHcPnllwe+dujQoXjvvffw05/+FOeddx5UVcXEiRNxzjnndPg+6enpeO+99/Ctb30L1157LV544QVce+21qKysxJ133onjx4/jqquuwnXXXYfNmzd3Oeb4+Hi88847uO222zBlyhTEx8fjyiuvxIoVKwL7XH/99di6dSvmzp0Lt9uNO+64A+eff35YbU466ST83//9H2666SZMmjQJZ5xxBn7961/jyiuvDOtxJChGP/s3i/r6eiQmJsLj8SAhIcH076frOmpra5GcnNzrG2kXFwOTJwNFRUBubvfbnUqiJfmwpRy2lMOWciK15fHjxwPvnOZ/G9ZoeIcyr9cb9GK4SPHtb38b6enp+POf/2z3UHqkfctQzwe/vqzXouZuCdHK5XIhNTXV7mE4AlvKYUs5bCmHLeVEU8vMTN9Cs7bWuu+ZnNzzdydTFKXDC6ns0NjYiJUrV2LGjBlQVRUvvvgi3n33Xaxbt87uofWYVS25uDWZ/6LsCRMmdHrLFuoZtpTDlnLYUg5byom2lpmZPV9sWs3/Cv+4uDhbz9wqioK33noLv/jFL3D8+HGcfvrp+Nvf/obp06fbNqZwWdWSi1uTWXXbi/6ALeWwpRy2lMOWcthSVji3uzJLXFwc3n33XbuH0WdWtIycC3GIiIiIiPqIi1siIiIicgwubk2mqirGjx8fFdc8RTq2lMOWcthSDlvKifSW0Xa5RPtX8lPvtW1p1vOAi1uTKYqCpKSkiLt9SDRiSzlsKYct5bClnEht6X+lfKOV9/3qI0VRIvI2YNGofUv/WxNL/xLGF5SZzOv1oqSkBJMmTYLbzdx9wZZy2FIOW8phSzmR2lJVVSQlJQXemjY+Pj7iF42RcrcEJ2jb0jAM1NTUID4+Xvw5GjnPeAez4n2U+wu2lMOWcthSDlvKidSW6enpABBY4EY6wzDQ0tKCmJgYLm77qH1Ll8uFzMxM8a5c3BIREZFlFEXBsGHDkJqaitbWVruH0y2v14tt27bhlFNOiaiz4NGofcuYmBhT3kGPR4mIiIgsp6pqxL7grS2v1wvA90IoLm77xqqWfEGZyVRVRXZ2dlT8BY50bCmHLeWwpRy2lMOWcthSjlUtubi1QExMjN1DcAy2lMOWcthSDlvKYUs5bCnHipZc3JpM0zQUFhZG7IX90YQt5bClHLaUw5Zy2FIOW8qxqiUXt0RERETkGFzcEhEREZFjcHFLRERERI6hGNH2Bs99VF9fj8TERHg8HiQkJJj+/QzDgKZpUFW11zcpLi4GJk8GioqA3NzutzuVREvyYUs5bCmHLeWwpRy2lBNOy76s13jDNgu0tLQgLi6ux/tXVwO1tSf+XFZmwqCiVLgtqXNsKYct5bClHLaUw5ZyrGjJyxJMpmkaSktLe/zKwOpqICvLd0bW/zFnDhAfDyQnmzzYCBduS+ocW8phSzlsKYct5bClHKta8sxthKmtBRobgTVrfItcv+RkIDPTvnERERERRQMubiNUVlb/uI6WiIiISBIvS7AA37JPDlvKYUs5bCmHLeWwpRy2lGNFS94tIcKEcweE/na3BCIiIuof+rJe45lbkxmGgbq6OvSz3yFMwZZy2FIOW8phSzlsKYct5VjVkotbk2mahvLycr7KUgBbymFLOWwphy3lsKUctpRjVUsubomIiIjIMbi4JSIiIiLH4OLWZIqiIC4ujm/ZJ4At5bClHLaUw5Zy2FIOW8qxqiXvlhBheLcEIiIi6u94t4QIpus6Dh06BF3X7R5K1GNLOWwphy3lsKUctpTDlnKsasnFrcl0XUdlZSX/UghgSzlsKYct5bClHLaUw5ZyrGrJxS0REREROQYXt0RERETkGFzcmkxRFCQmJvJVlgLYUg5bymFLOWwphy3lsKUcq1q6TX10gqqqyMrKsnsYjsCWcthSDlvKYUs5bCmHLeVY1ZJnbk2m6zr27t3LC9EFsKUctpTDlnLYUg5bymFLOVa15OLWZPxLIYct5bClHLaUw5Zy2FIOW8rh4paIiIiIKExc3BIRERGRY3BxazKXy4WUlBS4XEzdV2wphy3lsKUctpTDlnLYUo5VLXm3BJO5XC6MGzfO7mE4AlvKYUs5bCmHLeWwpRy2lGNVS/4aYjJd17Fz505eiC6ALeWwpRy2lMOWcthSDlvKsaolF7cm03UdNTU1/EshgC3lsKUctpTDlnLYUg5byrGqJRe3REREROQYXNwSERERkWNwcWsyl8uFjIwMvspSAFvKYUs5bCmHLeWwpRy2lGNVS94twWT+A0l9x5Zy2FIOW8phSzlsKYct5VjV0tZfQz788ENcdtllGD58OBRFwdq1a7vc/7XXXsO3v/1tpKSkICEhAdOmTcM777xjzWB7SdM0lJWVQdM0u4cS9dhSDlvKYUs5bCmHLeWwpRyrWtq6uG1oaEBOTg6efPLJHu3/4Ycf4tvf/jbeeustFBUV4fzzz8dll12GkpISk0fae4ZhwOPxwDAMu4cS9dhSDlvKYUs5bCmHLeWwpRyrWtp6WcLMmTMxc+bMHu//2GOPBf35l7/8Jd544w383//9HyZNmiQ8OiIiIiKKNlF9za2u6zh69CiGDBnS6T7Nzc1obm4O/Lm+vh4A4PV64fV6AfiuAXG5XNB1Pejea/7tmqYF/ZbR2XZVVaEoSuBxAQT2MQwjaLt/f/8+fr5d3F/vf2K7oihQVTVojP59/S1Cjd2MOXU29q62u92+ObXdHmpOXW33/7eu60HjieY52XWc/M9JJ83JruPk79n28aN9TnYep7bzcsqc7DxO/u/tpDm1HaMVc2rb0ilzsus4tW3Z3ZzaNwhHVC9uH3nkERw7dgxXXXVVp/ssX74cy5Yt67C9pKQEgwYNAgCkpKRg3LhxqKqqQk1NTWCfjIwMZGRkYPv27fB4PIHtY8eORWpqKrZt24ampqbA9vHjxyMpKQklJSVBBysjIwOGYaCoqChoDHl5eWhpaUFpaWlg244dgwGciWPHjqGw8LPA9ri4OOTk5KC2thaVlZUAgIqKeADZAIB9+/Zh7969gf3NnlN2djZiYmJQWFjY7ZxUVcWUKVPg8XhQXl7e5ZwAIDExEVlZWR3mlJycjLFjx6K6uhq1tbWOmJNdxyk3NxcZGRlBl/RE+5zsOk7Dhw/HgAEDglpG+5zsOk5Hjx5Fa2troKUT5mTXcSorKwtq6YQ52Xmc/C2dNCe7jpO/ZXdz6sslp4oRIReRKIqC119/HbNnz+7R/i+88AIWLFiAN954A9OnT+90v1BnbkeOHInDhw8jISEBQGT9xlVcDOTnu1FYaCAnp+vfuPz7FhUBEyfyt0jOiXPinDgnzolz4pycMacjR45g6NCh8Hg8gfVaT0XlmduXXnoJN9xwA1555ZUuF7YAEBsbi9jY2A7b3W433O7g6fsPTHv+0D3d3vZxNU1DaWkpJkyY0OH7hdrf/5+KooTcv+0Y2366s7GbMafebu/JnLrarmkatm7d2mnLaJxTd9vNmpOmadi2bRsmTJjQ4bGidU5dbTdzTl21jNY5AfYcJ13X8dlnn3VoGc1zsus4AQjZMprnZNdxMgyjw9/xaJ+TXcep7c9LRVE6HXtX23si6u5I/OKLL2L+/Pl48cUXcemll9o9nG4ZhoGmpia+ylIAW8phSzlsKYct5bClHLaUY1VLW8/cHjt2DF988UXgz1VVVdiyZQuGDBmCzMxMLF68GF9++SWef/55AL5LEebNm4fHH38c+fn5OHDgAADfdSSJiYm2zIGIiIiIIoetZ24LCwsxadKkwG28Fi1ahEmTJmHJkiUAgP3796O6ujqw/x//+Ed4vV7ceuutGDZsWODjtttus2X8RERERBRZbD1z+61vfavLU9OrV68O+vOGDRvMHZAJVFXF+PHju7wuinqGLeWwpRy2lMOWcthSDlvKsaplVL6gLJooioKkpCS7h+EIbCmHLeWwpRy2lMOWcthSjlUto+4FZdHG6/WioKCgTzcjJh+2lMOWcthSDlvKYUs5bCnHqpZc3Fqg/T3cqPfYUg5bymFLOWwphy3lsKUcK1pycUtEREREjsHFLRERERE5Bhe3JlNVFdnZ2XyVpQC2lMOWcthSDlvKYUs5bCnHqpZc3FogJibG7iE4BlvKYUs5bCmHLeWwpRy2lGNFSy5uTaZpGgoLC3kxugC2lMOWcthSDlvKYUs5bCnHqpZc3BIRERGRY3BxS0RERESOwcUtERERETmGYhiGYfcgrFRfX4/ExER4PB4kJCSY/v0Mw4CmaVBVFYqidLt/cTEweTJQVATk5srt6wThtqTOsaUctpTDlnLYUg5bygmnZV/Wazxza4GWlha7h+AYbCmHLeWwpRy2lMOWcthSjhUtubg1maZpKC0t5assBbClHLaUw5Zy2FIOW8phSzlWteTiloiIiIgcg4tbIiIiInIMLm4twLfsk8OWcthSDlvKYUs5bCmHLeVY0ZJ3S4gwvFsCERER9Xe8W0IEMwwDdXV16Ge/Q5iCLeWwpRy2lMOWcthSDlvKsaolF7cm0zQN5eXlfJWlALaUw5Zy2FIOW8phSzlsKceqllzcEhEREZFjcHFLRERERI7Bxa3JFEVBXFwc37JPAFvKYUs5bCmHLeWwpRy2lGNVS94tIcLwbglERETU3/FuCRFM13UcOnQIuq7bPZSox5Zy2FIOW8phSzlsKYct5VjVkotbk+m6jsrKSv6lEMCWcthSDlvKYUs5bCmHLeVY1ZKLWyIiIiJyDC5uiYiIiMgxuLg1maIoSExM5KssBbClHLaUw5Zy2FIOW8phSzlWtXSb+ugEVVWRlZVl9zAcgS3lsKUctpTDlnLYUg5byrGqJc/cmkzXdezdu5cXogtgSzlsKYct5bClHLaUw5ZyrGrJxa3J+JdCDlvKYUs5bCmHLeWwpRy2lMPFLRERERFRmLi4JSIiIiLH4OLWZC6XCykpKXC5mLqv2FIOW8phSzlsKYct5bClHKta8m4JJnO5XBg3bpzdw3AEtpTDlnLYUg5bymFLOWwpx6qW/DXEZLquY+fOnbwQXQBbymFLOWwphy3lsKUctpRjVUsubk2m6zpqamr4l0IAW8phSzlsKYct5bClHLaUY1VLLm6JiIiIyDG4uCUiIiIix+Di1mQulwsZGRl8laUAtpTDlnLYUg5bymFLOWwpx6qWvFuCyfwHkvqOLeWwpRy2lMOWcthSDlvKsaolfw0xmaZpKCsrg6Zpdg8l6rGlHLaUw5Zy2FIOW8phSzlWteTi1mSGYcDj8cAwDLuHEvXYUg5bymFLOWwphy3lsKUcq1pycUtEREREjsHFLRERERE5Bhe3JnO5XBg7dixfZSmALeWwpRy2lMOWcthSDlvKsaol75ZgMpfLhdTUVLuH4QhsKYct5bClHLaUw5Zy2FKOVS35a4jJNE3D1q1b+SpLAWwphy3lsKUctpTDlnLYUo5VLbm4NZlhGGhqauKrLAWwpRy2lMOWcthSDlvKYUs5VrW0dXH74Ycf4rLLLsPw4cOhKArWrl3b7dds2LABubm5iI2NxSmnnILVq1ebPk4iIiIiig62Lm4bGhqQk5ODJ598skf7V1VV4dJLL8X555+PLVu24Pbbb8cNN9yAd955x+SREhEREVE0sPUFZTNnzsTMmTN7vP/KlSsxZswYPProowCArKws/Pvf/8Zvf/tbzJgxw6xh9omqqhg/fjxUVbV7KFGPLeWwpRy2lMOWcthSDlvKsaplVN0tYePGjZg+fXrQthkzZuD222/v9Guam5vR3Nwc+HN9fT0AwOv1wuv1AvC9es/lckHXdei6HtjXv13TtKDrQzrbrqoqFEUJPK5fYmJi4Hu25T+4bS+s9u3ihmEY8HpPbFcUBaqqBo3Rvy+ATsdu1pxCjb2r7W63b05tt4eaU1fbXS4XkpKSoOt60ONE+5zsOk6JiYmOm1Oo7VbMafDgwT16TkbTnOw4TgBw0kknBT7nhDnZdZx0XQ9q6YQ52Xmc/C2dNKfutps1J3/L7ubUvkE4ompxe+DAAaSlpQVtS0tLQ319PZqamhAXF9fha5YvX45ly5Z12F5SUoJBgwYBAFJSUjBu3DhUVVWhpqYmsE9GRgYyMjKwfft2eDyewPaxY8ciNTUV27ZtQ1NTU2D7+PHjkZSUhJKSksDB8h/g3NxclJSUBI0hLy8PLS0tKC0tDWzbsWMwgDNx7NgxFBZ+FtgeFxeHnJwc1NbWorKyEgBQUREPIBsAsG/fPuzduzewv5lzAoDs7GzExMSgsLCw2zmpqoopU6bA4/GgvLy8yzkBvl8GsrKyOsxp6NChqKurQ1JSEg4fPuyIOdl1nCZNmoTi4mIAvh9sTpiTXccpPT0dH3zwAeLj4wMto31Odh2nr776CgUFBUhMTISiKI6Yk13HqbS0FAcPHgy0dMKc7DpOO3fuhMfjQWJiIpKSkhwxJ7uOk9frDbTMycnpck7t10zhUIwIefmfoih4/fXXMXv27E73Oe200zB//nwsXrw4sO2tt97CpZdeisbGxpCL21BnbkeOHInDhw8jISEBgLm/cWmahuLiYuTl5QX+j6/t/v59/IqLgfx8NwoLDeTkdP0bl3/foiJg4kTn/xap6zqKi4uRm5sbdAPoaJ6TXcfJMAwUFhYiNzc36J+HonlOdh0nXddRUFAQ1DLa52TXcWptbQ16XjphTnYdp+bm5sDPS1VVHTEnu45Ta2troKXb7XbEnOw6Tv41UW5uLmJiYrqc05EjRzB06FB4PJ7Aeq2nourMbXp6Og4ePBi07eDBg0hISAi5sAWA2NhYxMbGdtjudrsD/wzm5z8w7XV2bUhn29s/rqIoUBSlw/ZQ+/v/s7P9246x7ac7G7tZc+rN9p7MqavtbS8jCfU40Tin7rabNSev1xv4gRbq+RqNc+pqu5lz0nW905bROifAvuMUqmW0z8mu4xSqZbTPqadjDHd7V3Nq27L9L7Ch9o+GOdl5nPwt/Sf8wp1rT0TVfW6nTZuG9evXB21bt24dpk2bZtOIiIiIiCiS2Lq4PXbsGLZs2YItW7YA8N3qa8uWLaiurgYALF68GHPnzg3sf9NNN6GyshJ33XUXysvL8fvf/x5//etfcccdd9gx/B5RVRXZ2dmd/iZDPceWcthSDlvKYUs5bCmHLeVY1dLWyxIKCwtx/vnnB/68aNEiAMC8efOwevVq7N+/P7DQBYAxY8bgzTffxB133IHHH38cGRkZ+NOf/hSxtwHz819XYpayso7bkpOBzExTv60tzG7Zn7ClHLaUw5Zy2FIOW8qxomXEvKDMKvX19UhMTOzVBcq94fV6UVhYiLy8vB5dP1JcDEyeDBQVAbm5Xe9bXQ1kZQGNjR0/Fx/vW/Q6aYEbbkvqHFvKYUs5bCmHLeWwpZxwWvZlvcajFMUyM30L2Nra4O1lZcCcOb7tTlrcEhEREXWHi9sol5nJBSwRERGRX1TdLYGIiIiIqCu85tZk/psot72nW1fCuebWzMeIROG2pM6xpRy2lMOWcthSDlvKCadlX9ZrPHNrgZaWFruH4BhsKYct5bClHLaUw5Zy2FKOFS25uDWZpmkoLS3t8PZyFD62lMOWcthSDlvKYUs5bCnHqpZc3BIRERGRY/BuCU5SXe27/1dZHIAsYP9+AMPsHhURERGRZbi4tYAlb9kX9I4OkwAUA9/7HlDxoqPuFca3P5TDlnLYUg5bymFLOWwpx4qWvFtChOn1nQ78X7hmDYqRi8lzslCEXOQW/clZt0wgIiIix+PdEiKYYRioq6uDZb9DZGX5PhzI8pYOxpZy2FIOW8phSzlsKceqllzcmkzTNJSXl/NVlgLYUg5bymFLOWwphy3lsKUcq1pycUtEREREjsHFLRERERE5Bhe3JlMUBXFxcXzLPgFsKYct5bClHLaUw5Zy2FKOVS15KzCTqaqKnJwcu4fhCGwphy3lsKUctpTDlnLYUo5VLXnm1mS6ruPQoUPQdV3+waurfbcAKy4GysrkHz/CmNqyn2FLOWwphy3lsKUctpRjVUsubk2m6zoqKyvlD6T/TRsmT/Z9zJkDxMcDycmy3yeCmNayH2JLOWwphy3lsKUctpRjVUtelhCtamt970a2Zs2J+9omJ/vejazW3qERERER2YWL22iXlcV3ICMiIiL6Gi9LMJmiKEhMTOSrLAWwpRy2lMOWcthSDlvKYUs5VrXkmVuTqaqKLIe+Ha7V2FIOW8phSzlsKYct5bClHKta8sytyXRdx969e3khugC2lMOWcthSDlvKYUs5bCnHqpZc3JqMfynksKUctpTDlnLYUg5bymFLOVzcEhERERGFiYtbIiIiInIMLm5N5nK5kJKSApeLqfuKLeWwpRy2lMOWcthSDlvKsaol75ZgMpfLhXHjxtk9DEdgSzlsKYct5bClHLaUw5ZyrGrJX0NMpus6du7cad+F6GVlQHGx76O62p4xCLG9pYOwpRy2lMOWcthSDlvKsaolF7cm03UdNTU19vylGBgHzJkDTJ7s+8jKiuoFrq0tHYYt5bClHLaUw5Zy2FKOVS15WYKTvfoqMGy/77/LynwL3dpaIDPT3nERERERmYSLWycbNgzIHWb3KIiIiIgsw8sSTOZyuZCRkcFXWQpgSzlsKYct5bClHLaUw5ZyrGrJM7cm8x9I6ju2lMOWcthSDlvKYUs5bCnHqpb8NcRkmqahrKwMmqbZPZSox5Zy2FIOW8phSzlsKYct5VjVkotbkxmGAY/HA8Mw7B5K1GNLOWwphy3lsKUctpTDlnKsasnFLRERERE5Bhe3REREROQYXNyazOVyYezYsXyVpQC2lMOWcthSDlvKYUs5bCnHqpa8W4LJXC4XUlNT7R6GI7ClHLaUw5Zy2FIOW8phSzlWteSvISbTNA1bt27lqywFsKUctpTDlnLYUg5bymFLOVa15OLWZIZhoKmpia+yFMCWcthSDlvKYUs5bCmHLeVY1bJXi9vKykrpcRARERER9VmvFrennHIKzj//fKxZswbHjx+XHhMRERERUa/0anFbXFyM7OxsLFq0COnp6fjxj3+MzZs3S4/NEVRVxfjx46Gqqt1DiXpsKYct5bClHLaUw5Zy2FKOVS17tbidOHEiHn/8cezbtw+rVq3C/v378c1vfhMTJkzAihUrUFNTIz3OqKUoCpKSkqAoit1DiXpsKYct5bClHLaUw5Zy2FKOVS379IIyt9uNK664Aq+88gp+/etf44svvsCdd96JkSNHYu7cudi/f7/UOKOW1+tFQUEBvF6v3UOJemwphy3lsKUctpTDlnLYUo5VLfu0uC0sLMQtt9yCYcOGYcWKFbjzzjuxc+dOrFu3Dvv27cOsWbOkxhnVePsQOWwphy3lsKUctpTDlnLYUo4VLXv1Jg4rVqzAs88+i4qKClxyySV4/vnncckllwTecWLMmDFYvXo1Ro8eLTlWIiIiIqIu9Wpx+9RTT+H666/Hddddh2HDhoXcJzU1Fc8880yfBkdEREREFI5eXZawbt063H333R0WtoZhoLq6GgAQExODefPmdftYTz75JEaPHo2BAwciPz+/27suPPbYYzj99NMRFxeHkSNH4o477ojo25Gpqors7Gy+ylIAW8phSzlsKYct5bClHLaUY1XLXi1ux40bh9ra2g7bv/rqK4wZM6bHj/Pyyy9j0aJFWLp0KYqLi5GTk4MZM2bg0KFDIfd/4YUXcM8992Dp0qUoKyvDM888g5dffhn33ntvb6ZhmZiYGLuH4BhsKYct5bClHLaUw5Zy2FKOFS17tbjt7G3Tjh07hoEDB/b4cVasWIEFCxZg/vz5OOOMM7By5UrEx8dj1apVIff/5JNPcM455+AHP/gBRo8ejYsuugjXXHNNRN9jV9M0FBYW8mJ0AWwphy3lsKUctpTDlnLYUo5VLcO65nbRokUAfPcpW7JkCeLj4wOf0zQNmzZtwsSJE3v0WC0tLSgqKsLixYsD21wuF6ZPn46NGzeG/Jqzzz4ba9aswebNmzF16lRUVlbirbfewg9/+MNOv09zczOam5sDf66vrwfgux2F/1YULpcLLpcLuq5D1/Wg8bhcLmiaFrSg72y7qqpQFCXoFhf+fQzD6HDrC/9p+bYH2beL++v9T2xXFAWqqp4Yo9cL99dfqwJBY/c/Btpu/3p/XdfhajOu3syps7F3td3t9s2p7fYOc+pmu/+/dV0PGo/EcbJrTmY+97oau/856aQ52XWc/D3bPn60z8nO49R2Xk6Zk53Hyf+9nTSntmO0Yk5tWzplTnYdp7Ytu5tTX24XFtbitqSkBIDvB/mnn34adGo5JiYGOTk5uPPOO3v0WLW1tdA0DWlpaUHb09LSUF5eHvJrfvCDH6C2thbf/OY3A4vFm266qcvLEpYvX45ly5aFnMugQYMAACkpKRg3bhyqqqqC3oAiIyMDGRkZ2L59OzweT2D72LFjkZqaim3btqGpqSmwffz48UhKSkJJSUngYBmGEXhi+Pv55eXloaWlBaWlpYFtO3YMBnAmjh07hsLCzwLb4+LikJOTg9raWlRWViK+ogLZAKqrqzFmyhTs27cPe/fuBQBUVMQDyAaAwJz8+9fU1CAN6NOcACA7OxsxMTEoLCzsdk6qqmLKlCnweDxBx7b9nPwSExORlZUVNCcAGDp0KABg9+7dOHz4sOhxsmtOZj73uprTpEmToOs6iouLAzfTjvY52XWc0tPT0dDQENQy2udk13Gqr69HXV1doKUT5mTXcfr888+DWjphTnYdp507dwZaJiUlOWJOdh0nr9cbaJmTk9PlnNqvmcKhGJ1dY9CF+fPn4/HHH0dCQkKvv/G+ffswYsQIfPLJJ5g2bVpg+1133YUPPvgAmzZt6vA1GzZswPe//338/Oc/R35+Pr744gvcdtttWLBgAe6///6Q3yfUmduRI0fi8OHDgfGbfea2uLgYeXl5Hd6RI9RvLcXFQH6+G4WFBnJyuviNq7gY7vx8aJs3Q50yJWjs/scoKgImTgzeXy8ogCsvLyp/i/QvxnJzcwNnzLoaezTMyc4zt4WFhcjNzQ26sD+a52TXcdJ1HQUFBUEto31Odh2n1tbWoOelE+Zk13Fqbm4O/LxUVdURc7LrOLW2tgZaut1uR8zJzjO3/pb+E6SdzenIkSMYOnQoPB5P2OvNXi1uJbS0tCA+Ph6vvvoqZs+eHdg+b9481NXV4Y033ujwNeeeey6+8Y1v4OGHHw5sW7NmDW688UYcO3YsaMHTmfr6eiQmJvYqVm+0Pf3ek7ebKy4GJk8GioqA3Nze7RjyUz1+4MgVbkvqHFvKYUs5bCmHLeWwpZxwWvZlvdbjyxKuuOIKrF69GgkJCbjiiiu63Pe1117r9vFiYmIwefJkrF+/PrC41XUd69evx8KFC0N+TWNjY4cFrH+Fb9MavUdaWloQFxdn9zAcgS3lsKUctpTDlnLYUg5byrGiZY/vlpCYmBhYZScmJnb50VOLFi3C008/jeeeew5lZWW4+eab0dDQgPnz5wMA5s6dG/SCs8suuwxPPfUUXnrpJVRVVWHdunW4//77cdlllwX902ok0TQNpaWlHU67U/jYUg5bymFLOWwphy3lsKUcq1r2+Mzts88+G/K/++Lqq69GTU0NlixZggMHDmDixIl4++23Ay8yq66uDjpTe99990FRFNx333348ssvkZKSgssuuwy/+MUvRMZDRERERNGtV2+/29TUBMMwArcC2717N15//XWcccYZuOiii8J6rIULF3Z6GcKGDRuCB+t2Y+nSpVi6dGlvhk1EREREDterN3GYNWsWnn/+eQBAXV0dpk6dikcffRSzZs3CU089JTpAJ4jUSyaiEVvKYUs5bCmHLeWwpRy2lGNFy14tbouLi3HuuecCAF599VWkp6dj9+7deP755/G73/1OdIDRzu12Y8qUKXC7e3WSnNpgSzlsKYct5bClHLaUw5ZyrGrZq8VtY2MjBg8eDAD417/+hSuuuAIulwvf+MY3sHv3btEBRjvDMFBXVxc5d3MoK/PdFqy4GKiutns0YYm4llGMLeWwpRy2lMOWcthSjlUte7W4PeWUU7B27Vrs2bMH77zzTuA620OHDlly79hoomkaysvL7X+VZXIyEB8PzJnju9/t5MlAVlZULXAjpqUDsKUctpTDlnLYUg5byrGqZa8Wt0uWLMGdd96J0aNHIz8/P/AOY//6178wadIk0QGSkMxM31nboiLfx5o1QGMjUFtr98iIiIiIxPTqoofvfe97+OY3v4n9+/cjJycnsP3CCy/Ed7/7XbHBkbDMTN8HERERkUP1+ore9PR0pKenB22bOnVqnwfkNIqiIC4uTuYt+6qrT5xpLSvr++NFGdGW/RxbymFLOWwphy3lsKUcq1r2anHb0NCAX/3qV1i/fj0OHToEXdeDPl9ZWSkyOCdQVTXo7HavVVf7rpFtbDyxLT7edy1tPyHWkthSEFvKYUs5bCmHLeVY1bJXi9sbbrgBH3zwAX74wx9i2LBh/G2mC7quo7a2FsnJyUHvtha22lrfwnbNGt8iF/AtbPvRZQZiLYktBbGlHLaUw5Zy2FKOVS17tbj95z//iTfffBPnnHOO9HgcR9d1VFZWYsiQITIHMisLyM3t++NEIfGW/RhbymFLOWwphy3lsKUcq1r26pFPPvlkDBkyRHosRERERER90qvF7UMPPYQlS5agse31n0RERERENuvVZQmPPvoodu7cibS0NIwePRoDBgwI+nxxcbHI4JxAURQkJibyumQBbCmHLeWwpRy2lMOWcthSjlUte7W4nT17tvAwnEtVVWT5XwBGfcKWcthSDlvKYUs5bCmHLeVY1bJXi9ulS5dKj8OxdF3Hvn37MHz4cF6I3kdsKYct5bClHLaUw5Zy2FKOVS17/ch1dXX405/+hMWLF+Orr74C4Lsc4csvvxQbnBPouo69e/d2uBcwhY8t5bClHLaUw5Zy2FIOW8qxqmWvztyWlpZi+vTpSExMxK5du7BgwQIMGTIEr732Gqqrq/H8889Lj5OIiIiIqFu9OnO7aNEiXHfdddixYwcGDhwY2H7JJZfgww8/FBscEREREVE4erW4LSgowI9//OMO20eMGIEDBw70eVBO4nK5kJKSwut0BLClHLaUw5Zy2FIOW8phSzlWtezVZQmxsbGor6/vsH379u1ISUnp86CcxOVyYdy4cXYPwxHYUg5bymFLOWwphy3lsKUcq1r2aul8+eWX48EHH0RraysA333Lqqurcffdd+PKK68UHWC003UdO3fu5IXoAthSDlvKYUs5bCmHLeWwpRyrWvZqcfvoo4/i2LFjSElJQVNTE8477zyccsopGDx4MH7xi19IjzGq6bqOmpoa/qUQwJZy2FIOW8phSzlsKYct5VjVsleXJSQmJmLdunX4+OOPsXXrVhw7dgy5ubmYPn269PiIiIiIiHos7MWtrutYvXo1XnvtNezatQuKomDMmDFIT0+HYRh8ezoiIiIisk1YlyUYhoHLL78cN9xwA7788kucddZZOPPMM7F7925cd911+O53v2vWOKOWy+VCRkYGX2UpgC3lsKUctpTDlnLYUg5byrGqZVhnblevXo0PP/wQ69evx/nnnx/0uffeew+zZ8/G888/j7lz54oOMpr5DyT1HVvKYUs5bCmHLeWwpRy2lGNVy7CWzi+++CLuvffeDgtbALjgggtwzz334C9/+YvY4JxA0zSUlZVB0zS7hxL12FIOW8phSzlsKYct5bClHKtahrW4LS0txcUXX9zp52fOnImtW7f2eVBOYhgGPB4PDMOweyhRjy3lsKUctpTDlnLYUg5byrGqZViL26+++gppaWmdfj4tLQ1Hjhzp86CIiIiIiHojrMWtpmlwuzu/TFdVVXi93j4PioiIiIioN8J6QZlhGLjuuusQGxsb8vPNzc0ig3ISl8uFsWPH8lWWAthSDlvKYUs5bCmHLeWwpRyrWoa1uJ03b163+/BOCcFcLhdSU1PtHoYjsKUctpTDlnLYUg5bymFLOVa1DGtx++yzz5o1DsfSNA3btm3DhAkToKqq3cOJamwphy3lsKUctpTDlnLYUo5VLXmO3WSGYaCpqYmvshTAlnLYUg5bymFLOWwphy3lWNWSi1siIiIicgwubomIiIjIMbi4NZmqqhg/fjyv0xHAlnLYUg5bymFLOWwphy3lWNUyrBeUUfgURUFSUpLdw3AEtpTDlnLYUg5bymFLOWwpx6qWPHNrMq/Xi4KCAr65hQC2lMOWcthSDlvKYUs5bCnHqpZc3FpA0zS7h+AYbCmHLeWwpRy2lMOWcthSjhUtubglIiIiIsfg4paIiIiIHIOLW5Opqors7Gy+ylIAW8phSzlsKYct5bClHLaUY1VLLm4tEBMTY/cQHIMt5bClHLaUw5Zy2FIOW8qxoiUXtybTNA2FhYW8GF0AW8phSzlsKYct5bClHLaUY1VL3ue2vysrO/HfyclAZqZ9YyEiIiLqIy5u+6vkZCA+Hpgz58S2+HjfYpcLXCIiIopSXNz2V5mZvoVsba3vz2VlvoVubS0Xt0RERBS1uLg1maqqyMvLs+VVlm2vOABCXHWQmRlVC1k7WzoNW8phSzlsKYct5bClHKta2v6CsieffBKjR4/GwIEDkZ+fj82bN3e5f11dHW699VYMGzYMsbGxOO200/DWW29ZNNreaWlpsfT7tb3iYPLkEx9ZWUB1taVDEWd1SydjSzlsKYct5bClHLaUY0VLWxe3L7/8MhYtWoSlS5eiuLgYOTk5mDFjBg4dOhRy/5aWFnz729/Grl278Oqrr6KiogJPP/00RowYYfHIe07TNJSWllr6Kkv/FQdFRSc+1qwBGhtPXIUQjexo6VRsKYct5bClHLaUw5ZyrGpp62UJK1aswIIFCzB//nwAwMqVK/Hmm29i1apVuOeeezrsv2rVKnz11Vf45JNPMGDAAADA6NGjrRxy1IiyKw6IiIiIRNh25ralpQVFRUWYPn36icG4XJg+fTo2btwY8mv+/ve/Y9q0abj11luRlpaGCRMm4Je//CV/myIiIiIiADaeua2trYWmaUhLSwvanpaWhvLy8pBfU1lZiffeew/XXnst3nrrLXzxxRe45ZZb0NraiqVLl4b8mubmZjQ3Nwf+XF9fDwDwer3wer0AfItql8sFXdeh63pgX/92TdNgGEa321VVhaIogccFfKfgXS4XDMMI2u7f37+Pn28X99f7n9iuaBpUwDfGNo+jKApUVe107P7t/sf17RNiTroO19djMdo8fqg5dTb2rra73b45td3e2dg7267remB72/FIHCe75mTmc6+rsRuGEXgcp8zJruPk/9+2jx/tc7LzOCmKEvicU+Zk13Fq29Ipc7LjOLVt6ZQ52XWc2rbsbk7tG4Qjqu6WoOs6UlNT8cc//hGqqmLy5Mn48ssv8fDDD3e6uF2+fDmWLVvWYXtJSQkGDRoEAEhJScG4ceNQVVWFmpqawD4ZGRnIyMjA9u3b4fF4AtvHjh2L1NRUbNu2DU1NTYHt48ePR1JSEkpKSoIOVnZ2NhRFQWFhYdAY8vLy0NLSgtLS0sC2HTsGAzgTx44dQ2HhZ4HtQ3btwmnwvaBue5vHSUxMRFZWFvbt24e9e/cGtrefU0VFPIDsr+eX1mFOpx07hiEAdu7cia/ajL2rOcXExPRoTqqqYsqUKfB4PEG/uMTFxSEnJwe1tbWorKzs0ZymTJmCnTt3mnac7JiT2c+9zuY0YcIElJSUOGpOdh2nwYMHB7V0wpzsOE4NDQ0wDCPQ0glzsus4lZeXB7V0wpzsPk4lJSWOmxNgz3EqKSnpdk5tf6aGSzHaLq8t1NLSgvj4eLz66quYPXt2YPu8efNQV1eHN954o8PXnHfeeRgwYADefffdwLZ//vOfuOSSS9Dc3Bzy/YpDnbkdOXIkDh8+jISEBADm/sZlGAaOHTuGxMTEoMf27w8E/9ZSXAzk57tRWGggJ6fNb1wlJVCnToVeUAB94sQT23v4G5f/cQsKdOTlhZjTli1wTZkCbfNmGJMmdTmnzsbe1XaJ3yIVRcHRo0cxePDgHh2P/vqbcU/G7nK54PF4cNJJJ0FRFEfMya7jpCgKjhw5gsGDBwdaRvuc7DpOuq7jyJEjSEhIgKIojpiTXcfJ6/XC4/EEWjphTnaeua2vr0dCQgJcLpcj5mTXcTIMI9DS7XZ3OacjR45g6NChgedxOGw7cxsTE4PJkydj/fr1gcWtrutYv349Fi5cGPJrzjnnHLzwwgvQdT3wz4Hbt2/HsGHDQi5sASA2NhaxsbEdtrvd7kBYP/+Baa+z+7F1tr3t43q9XlRUVCAvL6/D9wu1v/8/FUUJ3v/r7+VyueAK8Tidjd2/3f8l/n06jL3t9hCP35Oxd7e9w5x6OHY/r9eL8vLyTlv25Tj1dntf59TddrPm1NXzMlrn1NV2M+fk9Xqxffv2kC2jdU6APcdJ13Xs2LGjQ8tonpNdxwlAyJbRPCe7jlPb56Xa9v+Lo3hOdh0nr9cbaOk/GRDuXHvC1luBLVq0CE8//TSee+45lJWV4eabb0ZDQ0Pg7glz587F4sWLA/vffPPN+Oqrr3Dbbbdh+/btePPNN/HLX/4St956q11TICIiIqIIYus1t1dffTVqamqwZMkSHDhwABMnTsTbb78deJFZdXV10G8LI0eOxDvvvIM77rgD2dnZGDFiBG677Tbcfffddk2BiIiIiCKI7S8oW7hwYaeXIWzYsKHDtmnTpuE///mPyaOSoygK4uLigq5rpN5hSzlsKYct5bClHLaUw5ZyrGpp++LW6VRVRU5Ojt3DcAS2lMOWcthSDlvKYUs5bCnHqpa2XnPbH+i6jkOHDnW4UwKFjy3lsKUctpTDlnLYUg5byrGqJRe3JtN1HZWVlfxLIYAt5bClHLaUw5Zy2FIOW8qxqiUXt0RERETkGFzcEhEREZFjcHFrMkVRkJiYyFdZCmBLOWwphy3lsKUctpTDlnKsasm7JZhMVVVkZWXZPQxHYEs5bCmHLeWwpRy2lMOWcqxqyTO3JtN1HXv37uWF6ALYUg5bymFLOWwphy3lsKUcq1pycWsy/qWQw5Zy2FIOW8phSzlsKYct5XBxS0REREQUJi5uiYiIiMgxuLg1mcvlQkpKClwupu4rtpTDlnLYUg5bymFLOWwpx6qWvFuCyVwuF8aNG2f3MByBLeWwpRy2lMOWcthSDlvKsaolfw0xma7r2LlzJy9EF8CWcthSDlvKYUs5bCmHLeVY1ZKLW5Ppuo6amhr+pRDAlnLYUg5bymFLOWwphy3lWNWSi1siIiIicgwubomIiIjIMbi4NZnL5UJGRgZfZSmALeWwpRy2lMOWcthSDlvKsaol75ZgMv+BjBplZSf+OzkZyMy0byztRF3LCMaWcthSDlvKYUs5bCnHqpb8NcRkmqahrKwMmqbZPZSuJScD8fHAnDnA5Mm+j6wsoLra7pEFRE3LKMCWcthSDlvKYUs5bCnHqpY8c2sywzDg8XhgGIbdQ+laZqbvrG1tre/PZWW+hW5tbcScvY2allGALeWwpRy2lMOWcthSjlUtubilEzIzI2YhS0RERNQbvCyBiIiIiByDi1uTuVwujB07lq+yFMCWcthSDlvKYUs5bCmHLeVY1ZKXJZjM5XIhNTXV7mE4AlvKYUs5bCmHLeWwpRy2lGNVS/4aYjJN07B161a+ylIAW8phSzlsKYct5bClHLaUY1VLLm5NZhgGmpqa+CpLAWwphy3lsKUctpTDlnLYUo5VLbm4JSIiIiLH4OKWiIiIiByDi1uTqaqK8ePHQ1VVu4cS9dhSDlvKYUs5bCmHLeWwpRyrWvJuCSZTFAVJSUl2D8MR2FIOW8phSzlsKYct5bClHKta8sytybxeLwoKCuD1eu0eStRjSzlsKYct5bClHLaUw5ZyrGrJM7cW6PUtL6qrgdpa33+XlckNKIrxVixy2FIOW8phSzlsKYct5VjRkovbSLV/P3BuFtDYeGJbfDyQnGzfmIiIiIgiHBe3kaquzrewXbMGyMrybUtOBjIzbR0WERERUSTj4tZkqqoiOzu7968MzMoCcnNlBxWl+tySAthSDlvKYUs5bCmHLeVY1ZIvKLNATEyM3UNwDLaUw5Zy2FIOW8phSzlsKceKllzcmkzTNBQWFvJidAFsKYct5bClHLaUw5Zy2FKOVS25uCUiIiIix+DiloiIiIgcg4tbIiIiInIMLm5Npqoq8vLy+CpLAWwphy3lsKUctpTDlnLYUo5VLbm4tUBLS4vdQ3AMtpTDlnLYUg5bymFLOWwpx4qWXNyaTNM0lJaW8lWWAthSDlvKYUs5bCmHLeWwpRyrWnJxS0RERESOwcUtERERETkGF7cW4EXocthSDlvKYUs5bCmHLeWwpRwrWrpN/w79nNvtxpQpU+wehiOwpRy2lMOWcthSDlvKYUs5VrXkmVuTGYaBuro6GIZh91CiHlvKYUs5bCmHLeWwpRy2lGNVSy5uTaZpGsrLy6P3VZZlZUBxse+jutrWoUR9ywjClnLYUg5bymFLOWwpx6qWEbG4ffLJJzF69GgMHDgQ+fn52Lx5c4++7qWXXoKiKJg9e7a5A+yPkpOB+Hhgzhxg8mTfR1aW7QtcIiIioq7Yvrh9+eWXsWjRIixduhTFxcXIycnBjBkzcOjQoS6/bteuXbjzzjtx7rnnWjTSfiYz03fWtqjI97FmDdDYCNTW2j0yIiIiok7ZvrhdsWIFFixYgPnz5+OMM87AypUrER8fj1WrVnX6NZqm4dprr8WyZcswduxYC0cbPkVREBcXB0VR7B5K+DIzgdxc30dWlt2jie6WEYYt5bClHLaUw5Zy2FKOVS1tvVtCS0sLioqKsHjx4sA2l8uF6dOnY+PGjZ1+3YMPPojU1FT86Ec/wkcffdTl92hubkZzc3Pgz/X19QAAr9cLr9cb+J4ulwu6rkPX9aCxuFwuaJoWdPFzZ9tVVYWiKIHH9cvOzg58z7b8t8Noe+2Jbxc3DBgnvsbrhaIoUFW1wxg7295+Tv7H9e3Tuzm524wn1Ng7mxPge4WkYRhB23szp5ycnK/n4w3aLnGc7JqTmc+9ruaUnZ0NTdOCviba5xRqu9lzUlUVEyZMgGEYHX6mROuc7DpOLpcLZ555ZqClE+Zk13ECENTSCXOy6zgZhhFoqWmaI+Zk53Hyt/Tv09mc2jcIh62L29raWmiahrS0tKDtaWlpKC8vD/k1//73v/HMM89gy5YtPfoey5cvx7JlyzpsLykpwaBBgwAAKSkpGDduHKqqqlBTUxPYJyMjAxkZGdi+fTs8Hk9g+9ixY5Gamopt27ahqakpsH38+PFISkpCSUlJ0MEaMWIE0tLSUFxcHDSGvLw8tLS0oLS0NLBtx47BAM5EY6PvcT8vK0OjriMuLg45OTmora1FZWVlYP/ExERkZWVh37592Lt3b2B7+zlVVMQDyP56fmlhz6miogJnthlPdnY2YmJiUFhY2O2cVFXFlClT4PF4go5ruHNKTk5GQkIC6uvrUdvm8gip42THnMx+7nU2p9zcXBw8eBBffvmlY+Zk13EaPnw4SkpKgn4QR/uc7DpOR44cwbZt2xATE+OYOdl1nD799FPU19cHWjphTnYep5aWFsTExDhqTnYdJ3/L7uZUUlKC3lIMG+9tsW/fPowYMQKffPIJpk2bFth+11134YMPPsCmTZuC9j969Ciys7Px+9//HjNnzgQAXHfddairq8PatWtDfo9QZ25HjhyJw4cPIyEhAYC5v3Fpmobi4mLk5eV1OA0f6jeu4mIgP9+NwjWfY/KcM+HdtAnIze3zb1z+x33+eR1nnhk89uRkYPTobua0eTPc+fmB8djxW6RvHsXIzc0NnJmQOk5djT0afzPubuyGYaCwsBC5bY5ltM/JruOk6zoKCgqCWkb7nOw6Tq2trUHPSyfMya7j1NzcHPh5qaqqI+Zk13FqbW0NtHS73Y6Yk13Hyb8mys3NDfzi1dmcjhw5gqFDh8Lj8QTWaz1l65nb5ORkqKqKgwcPBm0/ePAg0tPTO+y/c+dO7Nq1C5dddllgmz+42+1GRUUFxo0bF/Q1sbGxiI2N7fBYbrcbbnfw9Nv+c05bbf/Pvyfb2z+uoihQFKXD9lD7+/9TgXLic20+39kYu9uenu67+cHcuf59Tow9Pt732rHMzO7n1H48PZmTX2cNejqntv/kG+px+nqcerO9r3PqbrtZc2r7T76hnq/ROKeutps5J13XO20ZrXMC7DtOoVpG+5zsOk6hWkb7nHo6xnC3dzWnti3b/wIbav9omJOdx8nf0n/CL9y59oSti9uYmBhMnjwZ69evD9zOS9d1rF+/HgsXLuyw//jx4/Hpp58Gbbvvvvtw9OhRPP744xg5cqQVw45K/psftL/ZQVmZ725ftbW+fYiIiIiime1vv7to0SLMmzcPeXl5mDp1Kh577DE0NDRg/vz5AIC5c+dixIgRWL58OQYOHIgJEyYEfX1SUhIAdNgeKRRFQWJiYkS8yjIzM7oXsJHUMtqxpRy2lMOWcthSDlvKsaql7Yvbq6++GjU1NViyZAkOHDiAiRMn4u233w68yKy6ujrk6fBooaoqsiLgNlpOwJZy2FIOW8phSzlsKYct5VjV0vbFLQAsXLgw5GUIALBhw4Yuv3b16tXyAxKk6zr27duH4cOHR/UiPRKwpRy2lMOWcthSDlvKYUs5VrXkUTKZruvYu3dv0CsNqXfYUg5bymFLOWwphy3lsKUcq1pycUtEREREjsHFLRERERE5Bhe3JnO5XEhJSeF1OgLYUg5bymFLOWwphy3lsKUcq1pGxAvKnMzlcnV4YwnqHbaUw5Zy2FIOW8phSzlsKceqlvw1xGS6rmPnzp28EF0AW8phSzlsKYct5bClHLaUY1VLLm5Npus6ampq+JdCAFvKYUs5bCmHLeWwpRy2lGNVSy5uiYiIiMgxuLglIiIiIsfg4tZkLpcLGRkZfJWlALaUw5Zy2FIOW8phSzlsKceqlrxbgsn8B5L6ji3lsKUctpTDlnLYUg5byrGqJX8NMZmmaSgrK4OmaXYPJeqxpRy2lMOWcthSDlvKYUs5VrXkmVuTGYYBj8cDwzDsHoqMsrIT/52cDGRmWvatHdfSRmwphy3lsKUctpTDlnKsasnFLfVMcjIQHw/MmXNiW3y8b7Fr4QKXiIiIqCtc3FLPZGb6FrK1tb4/l5X5Frq1tVzcEhERUcTg4tZkLpcLY8eOdcarLDMzbV3IOqqlzdhSDlvKYUs5bCmHLeVY1ZKLW5O5XC6kpqbaPQxHYEs5bCmHLeWwpRy2lMOWcqxqyV9DTKZpGrZu3cpXWQpgSzlsKYct5bClHLaUw5ZyrGrJxa3JDMNAU1MTX2UpgC3lsKUctpTDlnLYUg5byrGqJRe3REREROQYXNwSERERkWNwcWsyVVUxfvx4qKpq91CiHlvKYUs5bCmHLeWwpRy2lGNVS94twWSKoiApKcnuYTgCW8phSzlsKYct5bClHLaUY1VLnrk1mdfrRUFBAbxer91DiXpsKYct5bClHLaUw5Zy2FKOVS25uLUAbx8ihy3lsKUctpTDlnLYUg5byrGiJRe3REREROQYXNwSERERkWNwcWsyVVWRnZ3NV1kKYEs5bCmHLeWwpRy2lMOWcqxqycWtBWJiYuwegmOwpRy2lMOWcthSDlvKYUs5VrTk4tZkmqahsLCQF6MLYEs5bCmHLeWwpRy2lMOWcqxqycUtERERETkG38SB+qas7MR/JycDmZn2jYWIiIj6PS5uqXeSk4H4eGDOnBPb4uN9i10ucImIiMgmXNyaTFVV5OXlRfyrLNuegPXr8kRsZqbvi2prTzzAnDm+P5u0uI2WltGALeWwpRy2lMOWcthSjlUtubi1QEtLC+Li4uweRkihTsD6dXsiNjPT8rO0kdwy2rClHLaUw5Zy2FIOW8qxoiVfUGYyTdNQWloasa+y9J+ALSoK/lizBmhsPHFiNhJEestowpZy2FIOW8phSzlsKceqljxzS3acgCUiIiIyBc/cEhEREZFjcHFrAV6ELoct5bClHLaUw5Zy2FIOW8qxoiUvSzCZ2+3GlClT7B6GI7ClHLaUw5Zy2FIOW8phSzlWteSZW5MZhoG6ujoYhmH3UKIeW8phSzlsKYct5bClHLaUY1VLLm5NpmkaysvL+SpLAWwphy3lsKUctpTDlnLYUo5VLbm4JSIiIiLH4DW3Nqqu7ngf2VDvFEZEREREPcPFrckURUFcXBwURQnaXl0NZGX53iihvfh4IDnJa9EIo0dnLSl8bCmHLeWwpRy2lMOWcqxqycWtyVRVRU5OTofttbW+he2aNb5FblvJyUBmbatFI4wenbWk8LGlHLaUw5Zy2FIOW8qxqiWvuTWZrus4dOgQdF0P+fmsLCA3N/iD7xYWWnctqefYUg5bymFLOWwphy3lWNWSi1uT6bqOyspK/qUQwJZy2FIOW8phSzlsKYct5VjVkpclkKy2r4hLTuZpaCIiIrIUF7ckIznZ90q4OXNObIuP9y12ucAlIiIii0TEZQlPPvkkRo8ejYEDByI/Px+bN2/udN+nn34a5557Lk4++WScfPLJmD59epf7201RFCQmJjr/VZaZmb6FbFGR72PNGt8r5trf66wP+k1LC7ClHLaUw5Zy2FIOW8qxqqXti9uXX34ZixYtwtKlS1FcXIycnBzMmDEDhw4dCrn/hg0bcM011+D999/Hxo0bMXLkSFx00UX48ssvLR55z6iqiqysLKiqavdQzJeZeeJVce1vASGgX7U0GVvKYUs5bCmHLeWwpRyrWtq+uF2xYgUWLFiA+fPn44wzzsDKlSsRHx+PVatWhdz/L3/5C2655RZMnDgR48ePx5/+9Cfouo7169dbPPKe0XUde/fu5YXoAthSDlvKYUs5bCmHLeWwpRyrWtp6zW1LSwuKioqwePHiwDaXy4Xp06dj48aNPXqMxsZGtLa2YsiQISE/39zcjObm5sCf6+vrAQBerxderzfwPV0uF3RdDwru365pGgzD6Ha7qqpQFCXwuIDvfZT37NmDtLS0oMf27eKGYRjweoPfY9nt9m1Xvh4nvF4oigJVVTuMsbPtfZ1T2/EBwXPyz9U/v5DbvV6424zfP6e2+4c7J/9fitTUVLhcJ34vkzhOPZqTFvo49WVOZj73uhq7YRjYs2cPUlJSgn6DjuY52XWcdF3v0DLa52TXcfL/vPS3dMKc7DpOra2tQS2dMCe7jlPblm632xFzsus4tf07HhMT0+Wc2jcIh62L29raWmiahrS0tKDtaWlpKC8v79Fj3H333Rg+fDimT58e8vPLly/HsmXLOmwvKSnBoEGDAAApKSkYN24cqqqqUFNTE9gnIyMDGRkZ2L59OzweT2D72LFjkZqaim3btqGpqSmwffz48UhKSkJJSUngYBmGEXhilJSUBPatqIgHkI3jx4+jsHBrYLuqqpgyZQqOHTuGwQA+LytDo64jLi4OOTk5qK2tRWVlZWD/xMREZGVlYd++fdi7d29ge1/n5B/fsWPHAAwOmhMAZGdnIyYmBoWFhUFd8/Ly0NLSgi/KypD99fibFQVTpkyBx+MJOq7hzmno0KEAgN27d+Pw4cOix6kncyotLe1wnPo6JzOfe13NadKkSdB1HcXFxYFrn6J9TnYdp/T0dDQ0NAS1jPY52XWc6uvrUVdXF2jphDnZdZw+//zzoJZOmJNdx2nnzp2BlklJSY6Yk13Hyev1Blrm5OR0Oae2a6ZwKUbb5bXF9u3bhxEjRuCTTz7BtGnTAtvvuusufPDBB9i0aVOXX/+rX/0Kv/nNb7BhwwZkZ2eH3CfUmduRI0fi8OHDSEhIAGD+mdvi4mLk5eUFXUBdXAzk57tRWGggJyfEb1xFRVDy8uDdtAnIzbX8N66245s8uRe/RRYUwJ2fHxi/1Jnb4uJi5Obm8sytwJnbwsJC5Obm8sytwJnbgoKCoJbRPie7jlNra2vQ89IJc7LrODU3Nwd+XvLMbd/m1NraGmjJM7d9P3Prb9ndmdsjR45g6NCh8Hg8gfVaT9l65jY5ORmqquLgwYNB2w8ePIj09PQuv/aRRx7Br371K7z77rudLmwBIDY2FrGxsR22u91uuN3B0/cfmPY6u/C5s+1tH9flciE1NRWqqgY9tn8XRVE6jMO/PfBY7R4v1BjD3d7dnNqOr/2c2upue9vxdzbXno5d1/XAPwtJH6febu/rnLrbbtacdF1HamoqBgwY0OH7Ruucutpu9pw6axnNc7LjOKmqGrJlNM/JruM0YMCAkC2jeU52HadQLaN9TnYdJ/+aaMCAAb1eX/SErS8oi4mJweTJk4NeDKbrvheHtT2T295vfvMbPPTQQ3j77beRl5dnxVB7zeVyYdy4cSGfGBQetpTDlnLYUg5bymFLOWwpx6qWth+pRYsW4emnn8Zzzz2HsrIy3HzzzWhoaMD8+fMBAHPnzg16wdmvf/1r3H///Vi1ahVGjx6NAwcO4MCBA19fGxp5dF3Hzp07g07pd6q62ne9QnFx8Dt9EYAwW1KX2FIOW8phSzlsKYct5VjV0vbF7dVXX41HHnkES5YswcSJE7Flyxa8/fbbgReZVVdXY//+/YH9n3rqKbS0tOB73/sehg0bFvh45JFH7JpCl3RdR01NTfcHsrrad2/YyZN9H3Pm+N7hKznZmoFGgR63pG6xpRy2lMOWcthSDlvKsaplRLz97sKFC7Fw4cKQn9uwYUPQn3ft2mX+gOxQW+t7R681a068AUJycvS/dW3bM9BOmA8RERFFtIhY3FIbWVm+d/iKEO2vjujx+jQ52Xfmec6cE9vi430PyAUuERERmYSLW5O5XC5kZGRE3YXoodamQBjr08xM3461tb4/l5X5Hqy2tteL22htGYnYUg5bymFLOWwphy3lWNWSi1uT+Q9ktGm/NgV6sT7NzBQ9SxutLSMRW8phSzlsKYct5bClHKta8tcQk2mahrKysg43KY4GmZm+KyT8H/5Lge0SzS0jDVvKYUs5bCmHLeWwpRyrWnJxazLDMODxeILerYN6hy3lsKUctpTDlnLYUg5byrGqJRe3REREROQYXNwSERERkWNwcWsyl8uFsWPH8lWWAthSDlvKYUs5bCmHLeWwpRyrWvJuCSZzuVxITU21exiOwJZy2FIOW8phSzlsKYct5VjVkr+GmEzTNGzdupWvshTAlnLYUg5bymFLOWwphy3lWNWSZ25NZhgGmpqa+CpLvz68HS9bymFLOWwphy3lsKUctpRjVUsubskafDteIiIisgAXt2QNE96Ol4iIiKg9Lm5Npqoqxo8fD1VV7R6K/fr4drxsKYct5bClHLaUw5Zy2FKOVS25uDWZoihISkqyexiOwJZy2FIOW8phSzlsKYct5VjVkndLMJnX60VBQQG8Xq/dQ4l6bCmHLeWwpRy2lMOWcthSjlUteebWAk67fUjbGx74hXnjg15zWks7saUctpTDlnLYUg5byrGiJRe31GOhbnjgxxsfEBERUSTg4pZ6rP0ND/x44wMiIiKKFFzcmkxVVWRnZzvmVZZ9vOFBR2G8qYPTWtqJLeWwpRy2lMOWcthSjlUtubi1QExMjN1DiDy9fFMHtpTDlnLYUg5bymFLOWwpx4qWvFuCyTRNQ2FhIS9Gb89/jUNRke9jzRqgsbHjNQ9tsKUctpTDlnLYUg5bymFLOVa15Jlbso/4NQ5ERETU3/HMLRERERE5Bhe3REREROQYXNyaTFVV5OXl8VWWAthSDlvKYUs5bCmHLeWwpRyrWvKaWwu0tLQgLi7O7mGYrv07l/XqXcu6uTVYf2lpBbaUw5Zy2FIOW8phSzlWtOSZW5NpmobS0lJHv8qy7V29Jk8+8ZGVBVRX9+FB2j1Af2hpFbaUw5Zy2FIOW8phSzlWteSZW+qzUO9cFva7lrV/EL7tGREREfUCF7ckQuSuXrw1GBEREfURF7cW4EXofdD2GtykJLYUxJZy2FIOW8phSzlsKceKllzcmsztdmPKlCl2DyP6hHh7Xnd8PKaUlQFuPm37is9LOWwphy3lsKUctpRjVUu+oMxkhmGgrq4OhmHYPZTo0snb8x6tqmJLAXxeymFLOWwphy3lsKUcq1pycWsyTdNQXl7OV1n2RmYmkJvr+8jKAgBU7drFlgL4vJTDlnLYUg5bymFLOVa15L/vUlSJ27ULKC72XZrQqxvpEhERkZNxcUumav/GDkAv16TJyTDi43HqAw8ADzzg2xYf7/sGXOASERHR17i4NZmiKIiLi4OiKHYPxVIhXg8W0Ks1aWYm9G3bsHPTJowbNw7q9u28D24f9NfnpRnYUg5bymFLOWwpx6qWXNyaTFVV5OTk2D0My4V6Ywegb+/NoI4Zg9PGjPn6D+qJB/TjZQo91l+fl2ZgSzlsKYct5bClHKtacnFrMl3XUVtbi+TkZLhc/ev1e9LvyRDUMtSpYV6m0GP9+XkpjS3lsKUctpTDlnKsasnFrcl0XUdlZSWGDBkS+kCWlQFoCn1xqoP15lrcoJZ8u94+6fZ5ST3GlnLYUg5bymFLOVa15OLWLvv3AxgGzLkWQIlvW3y8b4XnYKLX4vLteomIiKgdLm7tUlcHYBjw0M+BS9J92/rBNaNmXIvb4YH8+kFPIiIiCsbFrckURUFiYmLnrwwcMwbIzbJ2UDbr7QnXLlvyGtywdPu8pB5jSzlsKYct5bClHKtacnFrMlVVkZXVvxavfdH+Wty2J1+7bNnZNbgffRR4dzOeyT2Bz0s5bCmHLeWwpRy2lGNVSy5uTabrOvbt24fhw4fzQvQudHYtbtuTr922bHtKuLMzua+9BqSknNinny52+byUw5Zy2FIOW8phSzlWteTi1mS6rmPv3r1IT0/nX4ouhLoWt/3JV69XR1nZV8jKSkd6uqvrdWn7B6ypAa64Arj44hP79OPLFvi8lMOWcthSDlvKYUs5VrXk4pYiRvtrcTuefHUDyAbQw3Vp+wfkZQtERESOx8UtRaz2J1+9Xi/Kyj6HopyJefPUoHWpX5fr03AvW2iPi18iIqKIx8WtyVwuF1JSUvhPGb3Udj2q6y4MHToIqqp0ea/c9uvTkGvSnly2EOrBHXIZA5+XcthSDlvKYUs5bCnHqpaKYRiGqd8hwtTX1yMxMREejwcJCQm2jaP4L2WYPCcLRWvKkHstX4UZrurqjvfK9a9PGxuDt/d4TRrqQf38lzGsWdPxdLEfz+wSERGJ6Mt6LSLO3D755JN4+OGHceDAAeTk5OCJJ57A1KlTO93/lVdewf33349du3bh1FNPxa9//WtccsklFo6453RdR1VVFcaMGQPX3r0nFk9VBwBwURuOti0zM0O/oKy7F6V1LRNAZug1aldvrebX3WUN7R/PxoVw0POSZyP6hC3lsKUctpTDlnKsamn74vbll1/GokWLsHLlSuTn5+Oxxx7DjBkzUFFRgdTU1A77f/LJJ7jmmmuwfPlyfOc738ELL7yA2bNno7i4GBMmTLBhBl3TdR2ffurB4c/2wX31NcDxJgBAGbIAXAIkJdk6vmii6zpqamowatSoTv9SdP+itO6FXqNmAn/94ut3lgvhyBHgpz8FLl7c6eMmoxaZ2NPVN+mc8GK4Jy2pZ9hSDlvKYUs5bCnHqpa2L25XrFiBBQsWYP78+QCAlStX4s0338SqVatwzz33dNj/8ccfx8UXX4yf/vSnAICHHnoI69atw//+7/9i5cqVlo69J6qrgWuuycHx4yqAj4M+Fx+nI/msYfYMrJ/o7O1+O9P1pbfDvv7ozMddfA6IH6jhtYcrkYKabhfCHQyMAx5+GDj55J5/TRe8mobdVTVwbS+HW1U73S85yYvMYa0hPtFusd3+ko7uFuPh7k9ERNRDti5uW1paUFRUhMWLT/yfvMvlwvTp07Fx48aQX7Nx40YsWrQoaNuMGTOwdu3akPs3Nzejubk58GePxwMA+Oqrr+D1egPf0+VyQdd16LoeNBaXywVN09D20uTOtquqCkVRAo8LAFVVOo4fj8FTiytxxvJ58K78A3DqaQCA1FQViYnAV19pQWN2u90wDAOadmK7oihQVbXDGDvbbuac/NsBBI2xq+0Sc9J1HQ0NDairqwv6ja+7OQ0erOGkk3o2p3HjgE8+0XD4sOycvvpKwdy5Ki7+SRqANAD/RFiOA/hJeF/Sve4Xk3FowBrMQTKCfzswYgdCf/BBqCcPgX7kCJQl90NpPn5ih9iB0B98CHriieukXC4XXFCgHfkKriVLOuyvPfggjMTEwCbV5YICBV693XPs62Ovtenb1Xa3S4UBI2i7oihQFRd0GB2feyG2+8cearuu69i9axcat+iA4gqeq6EH/32KkjmFGrsVc/LqWlBLJ8xJ5DgNTYY+ZEhYP8tbW1uwfbuOhoY6KIoron6WR9v/P2maN9DS5VIdMafeHqf0dGD48N7PSdM0HDt2DEeOHEFMTEyXczpy5AgAoDcvDbN1cVtbWwtN05CWlha0PS0tDeXl5SG/5sCBAyH3P3DgQMj9ly9fjmXLlnXYPmbMmF6OunduXv71f9w0zdLvS9QXTQCuDPWJZgB3d3FniWYAd8/o+Tfq7vGIiKhfOnr0KBLbnPjoCdsvSzDb4sWLg8706rqOr776CkOHDoWiKKZ///r6eowcORJ79uyx9e4MTsCWcthSDlvKYUs5bCmHLeWE09IwDBw9ehTDhw8P+/vYurhNTk6Gqqo4ePBg0PaDBw8iPT095Nekp6eHtX9sbCxiY2ODtiXZ8CKuhIQE/qUQwpZy2FIOW8phSzlsKYct5fS0ZbhnbP1sfdlfTEwMJk+ejPXr1we26bqO9evXY9q00P98P23atKD9AWDdunWd7k9ERERE/YftlyUsWrQI8+bNQ15eHqZOnYrHHnsMDQ0NgbsnzJ07FyNGjMDy5b6LVm+77Tacd955ePTRR3HppZfipZdeQmFhIf74xz/aOQ0iIiIiigC2L26vvvpq1NTUYMmSJThw4AAmTpyIt99+O/Ciserq6qBXxp999tl44YUXcN999+Hee+/FqaeeirVr10bkPW4B32URS5cu7XBpBIWPLeWwpRy2lMOWcthSDlvKsaplv3v7XSIiIiJyLr7VBhERERE5Bhe3REREROQYXNwSERERkWNwcUtEREREjsHFbR89+eSTGD16NAYOHIj8/Hxs3ry5y/1feeUVjB8/HgMHDsRZZ52Ft956y6KRRrbly5djypQpGDx4MFJTUzF79mxUVFR0+TWrV6+GoihBHwMHDrRoxJHrgQce6NBl/PjxXX4Nn5ehjR49ukNLRVFw6623htyfz8kTPvzwQ1x22WUYPnw4FEXB2rVrgz5vGAaWLFmCYcOGIS4uDtOnT8eOHTu6fdxwf+Y6QVctW1tbcffdd+Oss87CoEGDMHz4cMydOxf79u3r8jF783PCCbp7Xl533XUdulx8cfdvDc7nZceWoX52KoqChx9+uNPHlHpecnHbBy+//DIWLVqEpUuXori4GDk5OZgxYwYOHToUcv9PPvkE11xzDX70ox+hpKQEs2fPxuzZs7Ft2zaLRx55PvjgA9x66634z3/+g3Xr1qG1tRUXXXQRGhoauvy6hIQE7N+/P/Cxe/dui0Yc2c4888ygLv/+97873ZfPy84VFBQEdVy3bh0A4L/+6786/Ro+J30aGhqQk5ODJ598MuTnf/Ob3+B3v/sdVq5ciU2bNmHQoEGYMWMGjh8/3uljhvsz1ym6atnY2Iji4mLcf//9KC4uxmuvvYaKigpcfvnl3T5uOD8nnKK75yUAXHzxxUFdXnzxxS4fk8/L0C3bNty/fz9WrVoFRVFw5ZVXdvm4Is9Lg3pt6tSpxq233hr4s6ZpxvDhw43ly5eH3P+qq64yLr300qBt+fn5xo9//GNTxxmNDh06ZAAwPvjgg073efbZZ43ExETrBhUlli5dauTk5PR4fz4ve+62224zxo0bZ+i6HvLzfE6GBsB4/fXXA3/Wdd1IT083Hn744cC2uro6IzY21njxxRc7fZxwf+Y6UfuWoWzevNkAYOzevbvTfcL9OeFEoVrOmzfPmDVrVliPw+dlz56Xs2bNMi644IIu95F6XvLMbS+1tLSgqKgI06dPD2xzuVyYPn06Nm7cGPJrNm7cGLQ/AMyYMaPT/fszj8cDABgyZEiX+x07dgyjRo3CyJEjMWvWLHz22WdWDC/i7dixA8OHD8fYsWNx7bXXorq6utN9+bzsmZaWFqxZswbXX389FEXpdD8+J7tXVVWFAwcOBD3vEhMTkZ+f3+nzrjc/c/srj8cDRVGQlJTU5X7h/JzoTzZs2IDU1FScfvrpuPnmm3H48OFO9+XzsmcOHjyIN998Ez/60Y+63VfiecnFbS/V1tZC07TAO6n5paWl4cCBAyG/5sCBA2Ht31/puo7bb78d55xzTpfvPHf66adj1apVeOONN7BmzRrouo6zzz4be/futXC0kSc/Px+rV6/G22+/jaeeegpVVVU499xzcfTo0ZD783nZM2vXrkVdXR2uu+66Tvfhc7Jn/M+tcJ53vfmZ2x8dP34cd999N6655hokJCR0ul+4Pyf6i4svvhjPP/881q9fj1//+tf44IMPMHPmTGiaFnJ/Pi975rnnnsPgwYNxxRVXdLmf1PPS9rffJWrv1ltvxbZt27q9zmbatGmYNm1a4M9nn302srKy8Ic//AEPPfSQ2cOMWDNnzgz8d3Z2NvLz8zFq1Cj89a9/7dFvzRTaM888g5kzZ2L48OGd7sPnJNmptbUVV111FQzDwFNPPdXlvvw5Edr3v//9wH+fddZZyM7Oxrhx47BhwwZceOGFNo4suq1atQrXXnttty+wlXpe8sxtLyUnJ0NVVRw8eDBo+8GDB5Genh7ya9LT08Pavz9auHAh/vGPf+D9999HRkZGWF87YMAATJo0CV988YVJo4tOSUlJOO200zrtwudl93bv3o13330XN9xwQ1hfx+dkaP7nVjjPu978zO1P/Avb3bt3Y926dV2etQ2lu58T/dXYsWORnJzcaRc+L7v30UcfoaKiIuyfn0Dvn5dc3PZSTEwMJk+ejPXr1we26bqO9evXB525aWvatGlB+wPAunXrOt2/PzEMAwsXLsTrr7+O9957D2PGjAn7MTRNw6effophw4aZMMLodezYMezcubPTLnxedu/ZZ59FamoqLr300rC+js/J0MaMGYP09PSg5119fT02bdrU6fOuNz9z+wv/wnbHjh149913MXTo0LAfo7ufE/3V3r17cfjw4U678HnZvWeeeQaTJ09GTk5O2F/b6+dln1+S1o+99NJLRmxsrLF69Wrj888/N2688UYjKSnJOHDggGEYhvHDH/7QuOeeewL7f/zxx4bb7TYeeeQRo6yszFi6dKkxYMAA49NPP7VrChHj5ptvNhITE40NGzYY+/fvD3w0NjYG9mnfc9myZcY777xj7Ny50ygqKjK+//3vGwMHDjQ+++wzO6YQMf7nf/7H2LBhg1FVVWV8/PHHxvTp043k5GTj0KFDhmHweRkuTdOMzMxM4+677+7wOT4nO3f06FGjpKTEKCkpMQAYK1asMEpKSgKv4P/Vr35lJCUlGW+88YZRWlpqzJo1yxgzZozR1NQUeIwLLrjAeOKJJwJ/7u5nrlN11bKlpcW4/PLLjYyMDGPLli1BPz+bm5sDj9G+ZXc/J5yqq5ZHjx417rzzTmPjxo1GVVWV8e677xq5ubnGqaeeahw/fjzwGHxe+nT3d9wwDMPj8Rjx8fHGU089FfIxzHpecnHbR0888YSRmZlpxMTEGFOnTjX+85//BD533nnnGfPmzQva/69//atx2mmnGTExMcaZZ55pvPnmmxaPODIBCPnx7LPPBvZp3/P2228PtE9LSzMuueQSo7i42PrBR5irr77aGDZsmBETE2OMGDHCuPrqq40vvvgi8Hk+L8PzzjvvGACMioqKDp/jc7Jz77//fsi/0/5euq4b999/v5GWlmbExsYaF154YYfGo0aNMpYuXRq0raufuU7VVcuqqqpOf36+//77gcdo37K7nxNO1VXLxsZG46KLLjJSUlKMAQMGGKNGjTIWLFjQYZHK56VPd3/HDcMw/vCHPxhxcXFGXV1dyMcw63mpGIZhhH2emIiIiIgoAvGaWyIiIiJyDC5uiYiIiMgxuLglIiIiIsfg4paIiIiIHIOLWyIiIiJyDC5uiYiIiMgxuLglIiIiIsfg4paIiIiIHIOLWyKiCFVTU4Obb74ZmZmZiI2NRXp6OmbMmIGPP/64268dPXo0FEWBoiiIj4/HWWedhT/96U8WjJqIyF5uuwdAREShXXnllWhpacFzzz2HsWPH4uDBg1i/fj0OHz7co69/8MEHsWDBAjQ2NuKVV17BggULMGLECMycOdPkkRMR2Ydvv0tEFIHq6upw8sknY8OGDTjvvPPC/vrRo0fj9ttvx+233x7YNnToUMybNw8rVqwQHCkRUWThZQlERBHopJNOwkknnYS1a9eiubm5T4+l6zr+9re/4ciRI4iJiREaIRFRZOKZWyKiCPW3v/0NCxYsQFNTE3Jzc3Heeefh+9//PrKzs7v92tGjR2P//v0YMGAAmpub4fV6MWTIEGzatAmnnHKKBaMnIrIHz9wSEUWoK6+8Evv27cPf//53XHzxxdiwYQNyc3OxevXqHn39T3/6U2zZsgXvvfce8vPz8dvf/pYLWyJyPJ65JSKKIjfccAPWrVuH3bt3d7lf+2tu9+zZg7POOguffPIJzjjjDAtGSkRkD565JSKKImeccQYaGhrC/rqRI0fi6quvxuLFi00YFRFR5OCtwIiIItDhw4fxX//1X7j++uuRnZ2NwYMHo7CwEL/5zW8wa9asXj3mbbfdhgkTJqCwsBB5eXnCIyYiigxc3BIRRaCTTjopcJ3szp070draipEjR2LBggW49957e/WYZ5xxBi666CIsWbIEb731lvCIiYgiA6+5JSIiIiLH4DW3REREROQYXNwSEUWZv/zlL4E3eWj/ceaZZ9o9PCIiW/GyBCKiKHP06FEcPHgw5OcGDBiAUaNGWTwiIqLIwcUtERERETkGL0sgIiIiIsfg4paIiIiIHIOLWyIiIiJyDC5uiYiIiMgxuLglIiIiIsfg4paIiIiIHIOLWyIiIiJyDC5uiYiIiMgx/j/8ClvQQnl5nwAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Loops through each feature variable and then plots the histograms\n", + "for var in VarNames[1:]:\n", + " plt.figure(figsize=(8, 6)) # Adjust the figure size as needed\n", + " plt.hist(np.array(df_sig[var]), bins=100, histtype=\"step\", color=\"red\", label=\"signal\", density=True)\n", + " plt.hist(np.array(df_bkg[var]), bins=100, histtype=\"step\", color=\"blue\", label=\"background\", density=True)\n", + "\n", + " # Add labels, legend, and grid to the visuals\n", + " plt.xlabel(var)\n", + " plt.ylabel('Density')\n", + " plt.legend(loc='upper right')\n", + " plt.grid(True, linestyle='--', alpha=0.7)\n", + "\n", + " plt.show()" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "M_R\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "BJpSKTn3YuAG" + }, + "source": [ + "## Exercise 4: Correlation\n", + "\n", + "### Exercise 4.1\n", + "\n", + "#### Part a\n", + "Write a function that creates pair plots and use it to compare variables in the SUSY and Higgs samples, separately for low and high-level features. Refer to Lecture 13 for details. Do not use `seaborn`.\n", + "\n", + "#### Part b\n", + "Making these plots can be slow because creating each plot initiates a full loop over the data. Make at least one modification to your function in part a to speed it up. Can you propose a different method of creating histograms that would speed up making such pair plots?\n", + "\n", + "#### Part c\n", + "Which observables appear to be best for separating signal from background?" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "D8eSonvkYuAH" + }, + "outputs": [], + "source": [ + "## Part A\n", + "\n", + "def compare_pair_plots(df_susy, df_higgs, columns, selection_dict, low_level=True):\n", + " # Determine title based on feature level\n", + " title = 'Pair Plots - Low Level Features' if low_level else 'Pair Plots - High Level Features'\n", + "\n", + " # Create a new figure\n", + " plt.figure(figsize=(15, 15))\n", + " n = len(columns)\n", + "\n", + " # Iterate over pairs of variables\n", + " for i, x in enumerate(columns):\n", + " for j, y in enumerate(columns):\n", + " plt.subplot(n, n, i * n + j + 1) # Position subplot\n", + " make_legend = (i == 0) and (j == 0) # Decide whether to make legend\n", + " plot_data(df_susy, x, y, selection_dict, 'SUSY', make_legend) # Plot SUSY data\n", + " plot_data(df_higgs, x, y, selection_dict, 'Higgs', False) # Plot Higgs data\n", + "\n", + " plt.suptitle(title, fontsize=16) # Set title\n", + " plt.tight_layout() # Adjust layout\n", + " plt.show() # Show plot\n", + "\n", + "def plot_data(df, x_var, y_var, selection_dict, label, make_legend):\n", + " selected_data = df.query(selection_dict) # Filter data\n", + " if x_var == y_var: # Plot histogram if x and y are same\n", + " plt.hist(selected_data[x_var], alpha=0.5, density=True, bins=50, label=label if make_legend else None)\n", + " else: # Plot scatter plot otherwise\n", + " plt.scatter(selected_data[x_var], selected_data[y_var], label=label if make_legend else None)\n", + " if make_legend: # Add legend if required\n", + " plt.legend()" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "M_TR_2\n" - ] + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "JYxFA57vYuAH" + }, + "outputs": [], + "source": [ + "# Example usage:(Cannot locate the higgs to compare)\n", + "\n", + "\n", + "#compare_pair_plots(df_susy, df_higgs, columns, selection_dict, low_level=True)" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Csmhgpt4YuAH" + }, + "outputs": [], + "source": [ + "### Part B" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "R\n" - ] + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "cZh-o4YFYuAH" + }, + "outputs": [], + "source": [ + "def compare_pair_plots(df_susy, df_higgs, columns, selection_dict, low_level=True):\n", + " # Determine title based on feature level\n", + " title = 'Pair Plots - Low Level Features' if low_level else 'Pair Plots - High Level Features'\n", + "\n", + " # Create a new figure\n", + " plt.figure(figsize=(15, 15))\n", + " n_columns = len(columns)\n", + "\n", + " # Calculate histograms for each variable in SUSY and Higgs datasets\n", + " susy_histograms = {var: np.histogram(df_susy.query(selection_dict)[var], bins=50, density=True) for var in columns}\n", + " higgs_histograms = {var: np.histogram(df_higgs.query(selection_dict)[var], bins=50, density=True) for var in columns}\n", + "\n", + " # Loop through each pair of variables\n", + " for i, x_var in enumerate(columns):\n", + " for j, y_var in enumerate(columns):\n", + " # Set up subplot\n", + " plt.subplot(n_columns, n_columns, i * n_columns + j + 1)\n", + "\n", + " # Decide whether to make legend for the first subplot\n", + " make_legend = (i == 0) and (j == 0)\n", + "\n", + " # Plot histograms for SUSY and Higgs datasets\n", + " plot_histogram(susy_histograms[x_var], 'SUSY', make_legend)\n", + " plot_histogram(higgs_histograms[x_var], 'Higgs', False)\n", + "\n", + " # Add title and adjust layout\n", + " plt.suptitle(title, fontsize=16)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "def plot_histogram(histogram, label, make_legend):\n", + " # Plot histogram as filled area\n", + " plt.fill_between(histogram[1][:-1], histogram[0], alpha=0.5, label=label if make_legend else None, color='blue')\n", + "\n", + " # Add legend for the first subplot\n", + " if make_legend:\n", + " plt.legend()\n", + "\n", + "# Example usage:\n", + "# compare_pair_plots(df_susy, df_higgs, columns, selection_dict, low_level=True)" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "V7FO2cybYuAI" + }, + "outputs": [], + "source": [ + "## Using numpy to have the histograms already calculated would avoid using the loops so it'll form the pair plots faster." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "MT2\n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IhQTpPkUYuAI" + }, + "outputs": [], + "source": [ + "## Part C:\n", + "# It's good to look for which class might dominate over another one in certain places. For scatterplots, looking at patterns whether it be closely formed in clusters or the opposite. Also like the figures made in the previous exercises with different peak heights or shapes." + ] }, { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzoAAAGsCAYAAAAVEdLDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAzUklEQVR4nO3de1TVVd7H8c8BASUBU+SicpRGQywVxRu6TCuLrCzGHsdx9EHTbGXS0pjGYiazOzllWOajaRcqx3ScUmfsYoShlpcUpbSQJ9PEElB7EhQDDc7zB3HyyEUOtwOb92ut38rzO3v/9ve4zjrLT3v/9s9is9lsAgAAAACDuLm6AAAAAACobwQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjtHJ1ATVRWlqqY8eOycfHRxaLxdXlAAAAAHARm82m06dPq1OnTnJzq3replkEnWPHjikkJMTVZQAAAABoIo4ePaouXbpU+X6zCDo+Pj6Syj6Mr6+vi6sBAAAA4CoFBQUKCQmxZ4SqNIugU75czdfXl6ADAAAA4JK3tLAZAQAAAADjEHQAAAAAGIegAwAAAMA4zeIeHQAAALQMJSUlOn/+vKvLgAt5eHjI3d29ztch6AAAAMDlbDabcnNzderUKVeXgiagXbt2CgoKqtMzNAk6AAAAcLnykBMQECBvb28eEt9C2Ww2nT17VsePH5ckBQcH1/paBB0AAAC4VElJiT3kdOjQwdXlwMXatGkjSTp+/LgCAgJqvYyNzQgAAADgUuX35Hh7e7u4EjQV5d+FutyvRdABAABAk8ByNZSrj+8CQQcAAACAcbhHBwAAAE1XdrZ08mTjjefvL1mtjTceGgxBBwAAAE1TdrYUHi6dPdt4Y3p7S5mZdQ47U6ZM0alTp7Ru3br6qauGHn30Ua1bt04ZGRmNOm5TRNABAABA03TyZFnIWbGiLPA0tMxMadKksnHrGHReeOEF2Wy2eioMtUHQAQAAQNMWHi717+/qKpzi5+fn6hJaPDYjAAAAAGrpX//6l3r37q02bdqoQ4cOGjVqlAoLCzVlyhTFxMTY250+fVoTJ07UZZddpuDgYCUlJWnkyJGaPXu2vU23bt309NNPa+rUqfLx8ZHVatWyZcscxnvwwQd15ZVXytvbW1dccYXmzp1bpy2YTUbQqYXsbGnPnrL/AgAAoGXKycnRhAkTNHXqVGVmZiotLU1jx46tdMlafHy8PvvsM/373/9WSkqKtm7dqj179lRot2DBAg0YMEB79+7VvffeqxkzZigrK8v+vo+Pj5KTk/X111/rhRde0PLly5WUlNSgn7O5Yumaky68J66e7lUDAABAM5STk6NffvlFY8eOVdeuXSVJvXv3rtDu9OnTeuONN7Ry5Updf/31kqTXX39dnTp1qtD25ptv1r333iupbPYmKSlJn3zyicLCwiRJDz/8sL1tt27d9MADD2jVqlWaM2dOvX++5o4ZHSeV3xP38MNl/23M3Q4BAADQdPTt21fXX3+9evfurXHjxmn58uX66aefKrQ7dOiQzp8/r0GDBtnP+fn52cPLhfr06WP/s8ViUVBQkI4fP24/t3r1ag0bNkxBQUFq27atHn74YWWzzKhSBJ1a+jW0AwAAoIVyd3dXSkqKPvjgA/Xq1UuLFi1SWFiYDh8+XOtrenh4OLy2WCwqLS2VJG3fvl0TJ07UzTffrA0bNmjv3r3629/+pnPnztXpc5iKoAMAAADUksVi0bBhw/TYY49p79698vT01Nq1ax3aXHHFFfLw8NCuXbvs5/Lz8/W///u/To21bds2de3aVX/72980YMAA9ejRQ0eOHKmXz2Ei7tEBAABA05aZ2STH2blzp1JTU3XjjTcqICBAO3fu1IkTJxQeHq4vv/zS3s7Hx0eTJ0/WX/7yF7Vv314BAQGaN2+e3NzcZLFYajxejx49lJ2drVWrVmngwIF67733KoQq/IagAwAAgKbJ379s96dJkxpvTG/vsnFrwNfXV1u2bNHChQtVUFCgrl27asGCBRo9erRWr17t0Pb555/XPffco1tvvVW+vr6aM2eOjh49qtatW9e4tNtuu03333+/4uLiVFxcrFtuuUVz587Vo48+6swnbDEstmbwyNaCggL5+fkpPz9fvr6+Lq1lzx4pMlJavlyaPl1KT292z68CAABoUoqKinT48GGFhoZW/Id/dnbj7v7k798oW+oWFhaqc+fOWrBggaZNm9bg4zU31X0napoNmNEBAABA02W1GvEsj7179+rAgQMaNGiQ8vPz9fjjj0uSbr/9dhdXZi6CDgAAANAInnvuOWVlZcnT01ORkZHaunWr/Gu4TA7OI+gAAAAADaxfv35KT093dRktCttLAwAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHKeCzpIlS9SnTx/5+vrK19dXUVFR+uCDD6psn5ycLIvF4nA481AkAAAAAKgNp3Zd69Kli5555hn16NFDNptNb7zxhm6//Xbt3btXV111VaV9fH19lZWVZX9tsVjqVjEAAABajKb+vNCRI0cqIiJCCxcubJB6pkyZolOnTmndunUNcn1X+O677xQaGqq9e/cqIiKiwcZxKuiMGTPG4fVTTz2lJUuWaMeOHVUGHYvFoqCgIKeKKi4uVnFxsf11QUGBU/0BAADQ/GVnS+Hh0tmzjTemt7eUmWnEM0pbvFo/R6ekpERr1qxRYWGhoqKiqmx35swZde3aVaWlperfv7+efvrpKkNRucTERD322GO1LQ0AAAAGOHmyLOSsWFEWeBpaZqY0aVLZuCYHnXPnzsnT09PVZTQ4pzcj2Ldvn9q2bSsvLy/dc889Wrt2rXr16lVp27CwML322mtav369VqxYodLSUg0dOlTff/99tWMkJCQoPz/ffhw9etTZMgEAAGCI8HCpf/+GP2obpn755RfFxcXJz89P/v7+mjt3rmw2myTprbfe0oABA+Tj46OgoCD96U9/0vHjxx36f/XVV7r11lvl6+srHx8fDR8+XN9++22lY+3atUsdO3bU/Pnz7eeefPJJBQQEyMfHR3fddZceeughhyVhU6ZMUUxMjJ566il16tRJYWFhksr+XX/dddepTZs26tChg+6++26dOXPG3m/kyJGaPXu2w/gxMTGaMmWK/XW3bt309NNPa+rUqfLx8ZHVatWyZcsc+nz++efq16+fWrdurQEDBmjv3r01/rutC6eDTlhYmDIyMrRz507NmDFDkydP1tdff11p26ioKMXGxioiIkIjRozQu+++q44dO+rll1+udgwvLy/7hgflBwAAANAUvfHGG2rVqpU+//xzvfDCC3r++ef1yiuvSJLOnz+vJ554Ql988YXWrVun7777ziEo/PDDD7rmmmvk5eWlTZs2KT09XVOnTtUvv/xSYZxNmzbphhtu0FNPPaUHH3xQkvSPf/xDTz31lObPn6/09HRZrVYtWbKkQt/U1FRlZWUpJSVFGzZsUGFhoaKjo3X55Zdr165dWrNmjT7++GPFxcU5/fkXLFhgDzD33nuvZsyYYb9H/8yZM7r11lvVq1cvpaen69FHH9UDDzzg9Bi14fTSNU9PT3Xv3l2SFBkZqV27dumFF164ZHiRJA8PD/Xr108HDx50vlIAAACgCQoJCVFSUpIsFovCwsK0b98+JSUlafr06Zo6daq93RVXXKEXX3xRAwcO1JkzZ9S2bVstXrxYfn5+WrVqlTw8PCRJV155ZYUx1q5dq9jYWL3yyisaP368/fyiRYs0bdo03XnnnZKkRx55RB999JHDzIwkXXbZZXrllVfsS9aWL1+uoqIivfnmm7rsssskSS+99JLGjBmj+fPnKzAwsMaf/+abb9a9994rSXrwwQeVlJSkTz75RGFhYVq5cqVKS0v16quvqnXr1rrqqqv0/fffa8aMGTW+fm3V+Tk6paWlDhsHVKekpET79u1TcHBwXYcFAAAAmoQhQ4Y47CwcFRWlb775RiUlJUpPT9eYMWNktVrl4+OjESNGSJKys7MlSRkZGRo+fLg95FRm586dGjdunN566y2HkCNJWVlZGjRokMO5i19LUu/evR3uy8nMzFTfvn3tIUeShg0bptLSUocdk2uiT58+9j+Xb0RWvjwvMzNTffr0cXjETHX399cnp4JOQkKCtmzZou+++0779u1TQkKC0tLSNHHiRElSbGysEhIS7O0ff/xxffTRRzp06JD27NmjSZMm6ciRI7rrrrvq91MAAAAATUxRUZGio6Pl6+urf/zjH9q1a5fWrl0rqWxDAElq06bNJa/zu9/9Tj179tRrr72m8+fP16qWCwNNTbm5udnvNSpX2fgXhzSLxaLS0lKnx6tvTgWd48ePKzY2VmFhYbr++uu1a9cubdy4UTfccIOksmSak5Njb//TTz9p+vTpCg8P180336yCggJt27atys0LAAAAgOZm586dDq937NihHj166MCBA/rxxx/1zDPPaPjw4erZs2eFjQj69OmjrVu3Vhtg/P39tWnTJh08eFB/+MMfHNqGhYVp165dDu0vfl2Z8PBwffHFFyosLLSf++yzz+Tm5mbfrKBjx44O/7YvKSnR/v37L3nti8f58ssvVVRUZD+3Y8cOp65RW04FnVdffVXfffediouLdfz4cX388cf2kCNJaWlpSk5Otr9OSkrSkSNHVFxcrNzcXL333nvq169fvRUPAAAAuFp2drbi4+OVlZWlt99+W4sWLdKsWbNktVrl6empRYsW6dChQ/r3v/+tJ554wqFvXFycCgoK9Mc//lG7d+/WN998o7feeqvC8rGAgABt2rRJBw4c0IQJE+ybFdx333169dVX9cYbb+ibb77Rk08+qS+//NJhKV1lJk6cqNatW2vy5Mnav3+/PvnkE91333367//+b/v9Odddd53ee+89vffeezpw4IBmzJihU6dOOfV386c//UkWi0XTp0/X119/rffff1/PPfecU9eorVo/RwcAAABoDJmZTXuc2NhY/fzzzxo0aJDc3d01a9Ys3X333bJYLEpOTtZf//pXvfjii+rfv7+ee+453Xbbbfa+HTp00KZNm/SXv/xFI0aMkLu7uyIiIjRs2LAK4wQFBWnTpk0aOXKkJk6cqJUrV2rixIk6dOiQHnjgARUVFekPf/iDpkyZos8//7zamr29vbVx40bNmjVLAwcOlLe3t+644w49//zz9jZTp07VF198odjYWLVq1Ur333+/rr32Wqf+btq2bav//Oc/uueee9SvXz/16tVL8+fP1x133OHUdWrDYrt44V0TVFBQID8/P+Xn57t8q+k9e6TISGn5cmn6dCk9vWzfdQAAANROUVGRDh8+rNDQUIeb1rOzy55tc/Zs49Xi7V0WeJrzA0NvuOEGBQUF6a233nJ1KbVW1XdCqnk2YEYHAAAATZLVWhY6Tp5svDH9/ZtXyDl79qyWLl2q6Ohoubu76+2339bHH3+slJQUV5fmcgQdAAAANFlWa/MKHo3NYrHo/fff11NPPaWioiKFhYXpnXfe0ahRo1xdmssRdAAAAIBmqk2bNvr4449dXUaTVOcHhgIAAABAU0PQAQAAAGAcgg4AAACahNLSUleXgCaiPr4L3KMDAAAAl/L09JSbm5uOHTumjh07ytPT85IPvISZbDabzp07pxMnTsjNzU2enp61vhZBBwAAAC7l5uam0NBQ5eTk6NixY64uB02At7e3rFar3NxqvwCNoAMAAACX8/T0lNVq1S+//KKSkhJXlwMXcnd3V6tWreo8q0fQAQAAQJNgsVjk4eEhDw8PV5cCA7AZAQAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGMepoLNkyRL16dNHvr6+8vX1VVRUlD744INq+6xZs0Y9e/ZU69at1bt3b73//vt1KhgAAAAALsWpoNOlSxc988wzSk9P1+7du3Xdddfp9ttv11dffVVp+23btmnChAmaNm2a9u7dq5iYGMXExGj//v31UjwAAAAAVMZis9lsdblA+/bt9eyzz2ratGkV3hs/frwKCwu1YcMG+7khQ4YoIiJCS5curfEYBQUF8vPzU35+vnx9fetSbp3t2SNFRkrLl0vTp0vp6VL//i4tCQAAAGgxapoNan2PTklJiVatWqXCwkJFRUVV2mb79u0aNWqUw7no6Ght37692msXFxeroKDA4QAAAACAmnI66Ozbt09t27aVl5eX7rnnHq1du1a9evWqtG1ubq4CAwMdzgUGBio3N7faMRITE+Xn52c/QkJCnC0TAAAAQAvmdNAJCwtTRkaGdu7cqRkzZmjy5Mn6+uuv67WohIQE5efn24+jR4/W6/UBAAAAmK2Vsx08PT3VvXt3SVJkZKR27dqlF154QS+//HKFtkFBQcrLy3M4l5eXp6CgoGrH8PLykpeXl7OlAQAAAICkeniOTmlpqYqLiyt9LyoqSqmpqQ7nUlJSqrynBwAAAADqg1MzOgkJCRo9erSsVqtOnz6tlStXKi0tTRs3bpQkxcbGqnPnzkpMTJQkzZo1SyNGjNCCBQt0yy23aNWqVdq9e7eWLVtW/58EAAAAAH7lVNA5fvy4YmNjlZOTIz8/P/Xp00cbN27UDTfcIEnKzs6Wm9tvk0RDhw7VypUr9fDDD+uvf/2revTooXXr1unqq6+u308BAAAAABdwKui8+uqr1b6flpZW4dy4ceM0btw4p4oCAAAAgLqo8z06AAAAANDUEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjONU0ElMTNTAgQPl4+OjgIAAxcTEKCsrq9o+ycnJslgsDkfr1q3rVDQAAAAAVMepoLN582bNnDlTO3bsUEpKis6fP68bb7xRhYWF1fbz9fVVTk6O/Thy5EidigYAAACA6rRypvGHH37o8Do5OVkBAQFKT0/XNddcU2U/i8WioKCg2lUIAAAAAE6q0z06+fn5kqT27dtX2+7MmTPq2rWrQkJCdPvtt+urr76qtn1xcbEKCgocDgAAAACoqVoHndLSUs2ePVvDhg3T1VdfXWW7sLAwvfbaa1q/fr1WrFih0tJSDR06VN9//32VfRITE+Xn52c/QkJCalsmAAAAgBao1kFn5syZ2r9/v1atWlVtu6ioKMXGxioiIkIjRozQu+++q44dO+rll1+usk9CQoLy8/Ptx9GjR2tbJgAAAIAWyKl7dMrFxcVpw4YN2rJli7p06eJUXw8PD/Xr108HDx6sso2Xl5e8vLxqUxoAAAAAODejY7PZFBcXp7Vr12rTpk0KDQ11esCSkhLt27dPwcHBTvcFAAAAgJpwakZn5syZWrlypdavXy8fHx/l5uZKkvz8/NSmTRtJUmxsrDp37qzExERJ0uOPP64hQ4aoe/fuOnXqlJ599lkdOXJEd911Vz1/FAAAAAAo41TQWbJkiSRp5MiRDudff/11TZkyRZKUnZ0tN7ffJop++uknTZ8+Xbm5ubr88ssVGRmpbdu2qVevXnWrHAAAAACq4FTQsdlsl2yTlpbm8DopKUlJSUlOFQUAAAAAdVGn5+gAAAAAQFNE0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwjlNBJzExUQMHDpSPj48CAgIUExOjrKysS/Zbs2aNevbsqdatW6t37956//33a10wAAAAAFyKU0Fn8+bNmjlzpnbs2KGUlBSdP39eN954owoLC6vss23bNk2YMEHTpk3T3r17FRMTo5iYGO3fv7/OxQMAAABAZSw2m81W284nTpxQQECANm/erGuuuabSNuPHj1dhYaE2bNhgPzdkyBBFRERo6dKlNRqnoKBAfn5+ys/Pl6+vb23LrRd79kiRkdLy5dL06VJ6utS/v0tLAgAAAFqMmmaDOt2jk5+fL0lq3759lW22b9+uUaNGOZyLjo7W9u3bq+xTXFysgoIChwMAAAAAaqrWQae0tFSzZ8/WsGHDdPXVV1fZLjc3V4GBgQ7nAgMDlZubW2WfxMRE+fn52Y+QkJDalgkAAACgBap10Jk5c6b279+vVatW1Wc9kqSEhATl5+fbj6NHj9b7GAAAAADM1ao2neLi4rRhwwZt2bJFXbp0qbZtUFCQ8vLyHM7l5eUpKCioyj5eXl7y8vKqTWkAAAAA4NyMjs1mU1xcnNauXatNmzYpNDT0kn2ioqKUmprqcC4lJUVRUVHOVQoAAAAANeTUjM7MmTO1cuVKrV+/Xj4+Pvb7bPz8/NSmTRtJUmxsrDp37qzExERJ0qxZszRixAgtWLBAt9xyi1atWqXdu3dr2bJl9fxRAAAAAKCMUzM6S5YsUX5+vkaOHKng4GD7sXr1anub7Oxs5eTk2F8PHTpUK1eu1LJly9S3b1/961//0rp166rdwAAAAAAA6sKpGZ2aPHInLS2twrlx48Zp3LhxzgwFAAAAALVWp+foAAAAAEBTRNABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMI7TQWfLli0aM2aMOnXqJIvFonXr1lXbPi0tTRaLpcKRm5tb25oBAAAAoFpOB53CwkL17dtXixcvdqpfVlaWcnJy7EdAQICzQwMAAABAjbRytsPo0aM1evRopwcKCAhQu3btnO4HAAAAAM5qtHt0IiIiFBwcrBtuuEGfffZZtW2Li4tVUFDgcAAAAABATTV40AkODtbSpUv1zjvv6J133lFISIhGjhypPXv2VNknMTFRfn5+9iMkJKShywQAAABgEKeXrjkrLCxMYWFh9tdDhw7Vt99+q6SkJL311luV9klISFB8fLz9dUFBAWEHAAAAQI01eNCpzKBBg/Tpp59W+b6Xl5e8vLwasSIAAAAAJnFJ0MnIyFBwcLArhq4/R45I6iplZkr6+bfz/v6S1eqqqgAAAACoFkHnzJkzOnjwoP314cOHlZGRofbt28tqtSohIUE//PCD3nzzTUnSwoULFRoaqquuukpFRUV65ZVXtGnTJn300Uf19ykaU06OpGDpySckvSJNmihp72/ve3uXhR/CDgAAAOAyTged3bt369prr7W/Lr+XZvLkyUpOTlZOTo6ys7Pt7587d05//vOf9cMPP8jb21t9+vTRxx9/7HCNZuXUKUnB0rS7pFclrfiHFP7rjE5mpjRpknTyJEEHAAAAcCGng87IkSNls9mqfD85Odnh9Zw5czRnzhynC2vyypfehYdL/V1bCgAAAABHjfYcHQAAAABoLAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOM4HXS2bNmiMWPGqFOnTrJYLFq3bt0l+6Slpal///7y8vJS9+7dlZycXItSAQAAAKBmnA46hYWF6tu3rxYvXlyj9ocPH9Ytt9yia6+9VhkZGZo9e7buuusubdy40eliAQAAAKAmWjnbYfTo0Ro9enSN2y9dulShoaFasGCBJCk8PFyffvqpkpKSFB0d7ezwAAAAAHBJDX6Pzvbt2zVq1CiHc9HR0dq+fXuVfYqLi1VQUOBwAAAAAEBNNXjQyc3NVWBgoMO5wMBAFRQU6Oeff660T2Jiovz8/OxHSEhIQ5cJAAAAwCBNcte1hIQE5efn24+jR4+6uiQAAAAAzYjT9+g4KygoSHl5eQ7n8vLy5OvrqzZt2lTax8vLS15eXg1dGgAAAABDNfiMTlRUlFJTUx3OpaSkKCoqqqGHBgAAANBCOR10zpw5o4yMDGVkZEgq2z46IyND2dnZksqWncXGxtrb33PPPTp06JDmzJmjAwcO6H/+53/0z3/+U/fff3/9fAIAAAAAuIjTQWf37t3q16+f+vXrJ0mKj49Xv3799Mgjj0iScnJy7KFHkkJDQ/Xee+8pJSVFffv21YIFC/TKK6+wtTQAAACABuP0PTojR46UzWar8v3k5ORK++zdu9fZoQAAAACgVprkrmsAAAAAUBcEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADjEHQAAAAAGIegAwAAAMA4BB0AAAAAxiHoAAAAADAOQQcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGAcgg4AAAAA4xB0AAAAABiHoAMAAADAOAQdAAAAAMYh6AAAAAAwDkEHAAAAgHEIOgAAAACMQ9ABAAAAYByCDgAAAADj1CroLF68WN26dVPr1q01ePBgff7551W2TU5OlsVicThat25d64IBAAAA4FKcDjqrV69WfHy85s2bpz179qhv376Kjo7W8ePHq+zj6+urnJwc+3HkyJE6FQ0AAAAA1XE66Dz//POaPn267rzzTvXq1UtLly6Vt7e3XnvttSr7WCwWBQUF2Y/AwMBqxyguLlZBQYHDAQAAAAA15VTQOXfunNLT0zVq1KjfLuDmplGjRmn79u1V9jtz5oy6du2qkJAQ3X777frqq6+qHScxMVF+fn72IyQkxJkyAQAAALRwTgWdkydPqqSkpMKMTGBgoHJzcyvtExYWptdee03r16/XihUrVFpaqqFDh+r777+vcpyEhATl5+fbj6NHjzpTJgAAAIAWrlVDDxAVFaWoqCj766FDhyo8PFwvv/yynnjiiUr7eHl5ycvLq6FLAwAAAGAop2Z0/P395e7urry8PIfzeXl5CgoKqtE1PDw81K9fPx08eNCZoQEAAACgxpwKOp6enoqMjFRqaqr9XGlpqVJTUx1mbapTUlKiffv2KTg42LlKAQAAAKCGnF66Fh8fr8mTJ2vAgAEaNGiQFi5cqMLCQt15552SpNjYWHXu3FmJiYmSpMcff1xDhgxR9+7dderUKT377LM6cuSI7rrrrvr9JAAAAADwK6eDzvjx43XixAk98sgjys3NVUREhD788EP7BgXZ2dlyc/ttouinn37S9OnTlZubq8svv1yRkZHatm2bevXqVX+fwkDZ2dLJk47n/P0lq9Xx/QvPAQAAAChTq80I4uLiFBcXV+l7aWlpDq+TkpKUlJRUm2GMV1mYkaQTJ6SxY6WzZx3Pe3tL775b9ufy98vPdexI6AEAAADKNfiua6jcnj3S8OEVw0w5b2/pww/LAoz0W/i56abf3l+xQrr7bsdzhB4AAACAoNNoLpy9KQ8tkmOYuVBlQSUz87drlL8/fHjZucqCUHnoqep6AAAAgKkIOo0gO1sKD3ecvfH2lrZulfr3r/l1rNaKYeXCc+VB6OLQUz4esz0AAABoKQg6jSAzsyzkrFhRFnikhgkblYUeqerZnvBwAg8AAADMRNBpQNnZZYFj7NiycDF8eOMFi4tnfyqb7WGWBwAAAKYi6DSA7BwPZW503Blt61bXBomLZ3vKA9iFszyZmYQdAAAAmIGgU8+yFaLw/+qls0W/7ZzW1JaIlYee8lmezExp0qSyMNaYs04AAABAQyHo1LNMhetskbtWrGj6oaE88Pj7l4WySZNqt0kCAAAA0NS4uboAk2TneGis3pV365ImH3IuVD678+GHZa+HD5c2biy7xwgAAABojgg69SQ7W9q6t63O6jK9++yhZhNyylmtUnR02WyOVHbvTng4YQcAAADNE0GnHpQ/J2fS3FB5q1DhoUWuLqnW+vcvm91ZsaJsI4WtWwk7AAAAaH4IOvXg5Mlfn5PzxGFlKlzW4POuLqlOrNay5Wvl9+2Eh0t79ri6KgAAAKDm2IygjrKzpcLCsj+HhxbJqqO1v1D5Ez4bwyUenFN+3075NtTDh/OQUQAAADQfBJ06+v3vy/7r7S35t/uldhcpX/t29mz9FXYpFz4t9GK/hqDyXdnKt50uf8goz9sBAABAU0fQqQf2raRPXmLJWlWzNpmZv659W1EWeBraiROOTwu92EVppvy+na1bed4OAAAAmgeCTj2wL+eqbuXZpWZtvL0bNz2UPy20svPlaeaC0GWVNLydh7zbXKVJk9x43g4AAACaNIJOY7HvWFDFrM0l7pmpd+Xr0iqro3wXgou7SMpsfaUy39yqsfcEaPhwlrEBAACgaSLo1JJ/u1/k7f3rn/0vejMzs2KH8nPh4U17GqR8F4IqZnuskybJ6paid+cP0k339dDWNw9reL8zZTvNNXZYAwAAAKpA0Kkla9A5e3ax/9u+mtkQSb/uWHBxKmqCajDbE64QeSvT/uygTIXL6v0jUzwAAABoEgg6dVDh3/PVzYZIzX/G44LPZ5WUmXNIW/e21aS5odo6420NXzJB1ovu7bFr7p8dAAAAzQpBp75VNRtiigs+n1XS8N6Sd6I0ackweStTmZPCK3+WEPtSAwAAoBERdFAn5ZM8ZVtPX6bMRamyDj3t2Kh8J7eTJwk6AAAAaBQEHdSZ1Vq2M7a3tzT2wR56990LttwGAAAAXICgg3phtf72INGbbqpipVplu9FJ3L8DAACAekfQQb3p3//CZWy/BR9rTXaj4/4dAAAA1COCDurVhcvYJk0qzzBWWat5Ng/37wAAAKC+EXRQ7xw3KPh1sib6ErvRVbWsTWJpGwAAAJxG0EGDcNigYKyq3qDgUsvaJJa2AQAAwGkEHTSYGm1QcKmHrLK0DQAAALVA0EGDqnKDgovDDiEGAAAA9YiggwZX2QYFW7eWhaAaY2tqAAAAOIGgg0ZRvkItM7Psnp3hw2t42w1bUwMAAKAWCDpoNOUr1N59t+yenUqXsVXWia2pAQAA4CSCDhpdeLjjMrYqd2Qrd6l7eFjWBgAAgIsQdNDoLl7GVr4jm9P37bCsDQAAAFUg6MAlyidpLr5v55KzOxdfhGVtAAAAqARBBy5VHnguft5OjQMPy9oAAABQCYIOmoTy5+3UeTlbOZa1AQAAtGgEHTQZ9bKc7cKLXWpZ29atZRe+GLM9AAAAzV6tgs7ixYv17LPPKjc3V3379tWiRYs0aNCgKtuvWbNGc+fO1XfffacePXpo/vz5uvnmm2tdNMxW3XK2jh2dyCFVLWuryWxP+WCV9SUEAQAANHlOB53Vq1crPj5eS5cu1eDBg7Vw4UJFR0crKytLAQEBFdpv27ZNEyZMUGJiom699VatXLlSMTEx2rNnj66++up6+RAwU2XL2aR6yCHVzfacOOE42MUIQQAAAM2CxWaz2ZzpMHjwYA0cOFAvvfSSJKm0tFQhISG677779NBDD1VoP378eBUWFmrDhg32c0OGDFFERISWLl1a6RjFxcUqLi62v87Pz5fVatXRo0fl6+vrTLn1LmN1lkbcHabNy7IUMT7MpbW0JEePSj/+WJZNJk2Sfv658nZt2kgrVpRljlrLy5VO5Vc8f+qUNPdhqbio8n5eraUnnpTatavD4LUX1OG8gvx/ccnYAADAcEFBZUcTUFBQoJCQEJ06dUp+fn5VN7Q5obi42Obu7m5bu3atw/nY2FjbbbfdVmmfkJAQW1JSksO5Rx55xNanT58qx5k3b55NEgcHBwcHBwcHBwcHR6XH0aNHq80uTi1dO3nypEpKShQYGOhwPjAwUAcOHKi0T25ubqXtc3NzqxwnISFB8fHx9telpaX6v//7P3Xo0EEWi8WZkutdeYJsCrNLQFX4nqK54LuK5oDvKZqDlvQ9tdlsOn36tDp16lRtuya565qXl5e8vLwczrVz0XKgqvj6+hr/JULzx/cUzQXfVTQHfE/RHLSU72m1S9Z+5ebMBf39/eXu7q68vDyH83l5eQqqYs1eUFCQU+0BAAAAoK6cCjqenp6KjIxUamqq/VxpaalSU1MVFRVVaZ+oqCiH9pKUkpJSZXsAAAAAqCunl67Fx8dr8uTJGjBggAYNGqSFCxeqsLBQd955pyQpNjZWnTt3VmJioiRp1qxZGjFihBYsWKBbbrlFq1at0u7du7Vs2bL6/SSNxMvLS/PmzauwtA5oSvieorngu4rmgO8pmgO+pxU5vb20JL300kv2B4ZGREToxRdf1ODBgyVJI0eOVLdu3ZScnGxvv2bNGj388MP2B4b+/e9/54GhAAAAABpMrYIOAAAAADRlTt2jAwAAAADNAUEHAAAAgHEIOgAAAACMQ9ABAAAAYByCjhMWL16sbt26qXXr1ho8eLA+//xzV5cEOHj00UdlsVgcjp49e7q6LLRwW7Zs0ZgxY9SpUydZLBatW7fO4X2bzaZHHnlEwcHBatOmjUaNGqVvvvnGNcWiRbvUd3XKlCkVfmNvuukm1xSLFikxMVEDBw6Uj4+PAgICFBMTo6ysLIc2RUVFmjlzpjp06KC2bdvqjjvuUF5enosqdi2CTg2tXr1a8fHxmjdvnvbs2aO+ffsqOjpax48fd3VpgIOrrrpKOTk59uPTTz91dUlo4QoLC9W3b18tXry40vf//ve/68UXX9TSpUu1c+dOXXbZZYqOjlZRUVEjV4qW7lLfVUm66aabHH5j33777UasEC3d5s2bNXPmTO3YsUMpKSk6f/68brzxRhUWFtrb3H///frPf/6jNWvWaPPmzTp27JjGjh3rwqpdh+2la2jw4MEaOHCgXnrpJUlSaWmpQkJCdN999+mhhx5ycXVAmUcffVTr1q1TRkaGq0sBKmWxWLR27VrFxMRIKpvN6dSpk/785z/rgQcekCTl5+crMDBQycnJ+uMf/+jCatGSXfxdlcpmdE6dOlVhpgdwlRMnTiggIECbN2/WNddco/z8fHXs2FErV67Uf/3Xf0mSDhw4oPDwcG3fvl1DhgxxccWNixmdGjh37pzS09M1atQo+zk3NzeNGjVK27dvd2FlQEXffPONOnXqpCuuuEITJ05Udna2q0sCqnT48GHl5uY6/L76+flp8ODB/L6iSUpLS1NAQIDCwsI0Y8YM/fjjj64uCS1Yfn6+JKl9+/aSpPT0dJ0/f97hN7Vnz56yWq0t8jeVoFMDJ0+eVElJiQIDAx3OBwYGKjc310VVARUNHjxYycnJ+vDDD7VkyRIdPnxYw4cP1+nTp11dGlCp8t9Qfl/RHNx000168803lZqaqvnz52vz5s0aPXq0SkpKXF0aWqDS0lLNnj1bw4YN09VXXy2p7DfV09NT7dq1c2jbUn9TW7m6AAD1Z/To0fY/9+nTR4MHD1bXrl31z3/+U9OmTXNhZQDQ/F24lLJ3797q06ePfve73yktLU3XX3+9CytDSzRz5kzt37+fe3GrwYxODfj7+8vd3b3CjhV5eXkKCgpyUVXApbVr105XXnmlDh486OpSgEqV/4by+4rm6IorrpC/vz+/sWh0cXFx2rBhgz755BN16dLFfj4oKEjnzp3TqVOnHNq31N9Ugk4NeHp6KjIyUqmpqfZzpaWlSk1NVVRUlAsrA6p35swZffvttwoODnZ1KUClQkNDFRQU5PD7WlBQoJ07d/L7iibv+++/148//shvLBqNzWZTXFyc1q5dq02bNik0NNTh/cjISHl4eDj8pmZlZSk7O7tF/qaydK2G4uPjNXnyZA0YMECDBg3SwoULVVhYqDvvvNPVpQF2DzzwgMaMGaOuXbvq2LFjmjdvntzd3TVhwgRXl4YW7MyZMw7/x/vw4cPKyMhQ+/btZbVaNXv2bD355JPq0aOHQkNDNXfuXHXq1MlhtyugMVT3XW3fvr0ee+wx3XHHHQoKCtK3336rOXPmqHv37oqOjnZh1WhJZs6cqZUrV2r9+vXy8fGx33fj5+enNm3ayM/PT9OmTVN8fLzat28vX19f3XfffYqKimpxO65JkmyosUWLFtmsVqvN09PTNmjQINuOHTtcXRLgYPz48bbg4GCbp6enrXPnzrbx48fbDh486Oqy0MJ98sknNkkVjsmTJ9tsNputtLTUNnfuXFtgYKDNy8vLdv3119uysrJcWzRapOq+q2fPnrXdeOONto4dO9o8PDxsXbt2tU2fPt2Wm5vr6rLRglT2/ZRke/311+1tfv75Z9u9995ru/zyy23e3t623//+97acnBzXFe1CPEcHAAAAgHG4RwcAAACAcQg6AAAAAIxD0AEAAABgHIIOAAAAAOMQdAAAAAAYh6ADAAAAwDgEHQAAAADGIegAAAAAMA5BBwAAAIBxCDoAAAAAjEPQAQAAAGCc/wdziWcyY5GEvgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": { + "id": "EnzQ5IqqYuAI" + }, + "source": [ + "### Exercise 4.2\n", + "\n", + "#### Part a\n", + "Install [tabulate](https://github.com/astanin/python-tabulate).\n", + "\n", + "#### Part b\n", + "Use numpy to compute the [covariance matrix](https://numpy.org/doc/stable/reference/generated/numpy.cov.html) and [correlation matrix](https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html) between all observabes, and separately between low and high-level features.\n", + "\n", + "#### Part c\n", + "Use tabulate to create a well formatted table of the covariance and correlation matrices, with nice headings and appropriate significant figures. Embed the table into this notebook.\n", + "\n", + "#### Part d\n", + "Write a function that takes a dataset and appropriate arguments and performs steps b and c. " ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "S_R\n" - ] + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "aZGy5On3YuAK", + "outputId": "2f2c1b9a-185c-4a0b-8844-13586f618ca5", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting types-tabulate\n", + " Downloading types_tabulate-0.9.0.20240106-py3-none-any.whl.metadata (1.6 kB)\n", + "Downloading types_tabulate-0.9.0.20240106-py3-none-any.whl (3.4 kB)\n", + "Installing collected packages: types-tabulate\n", + "Successfully installed types-tabulate-0.9.0.20240106\n" + ] + } + ], + "source": [ + "!pip install types-tabulate" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "0GrBNKI3YuAK", + "outputId": "2453f012-c7bb-4cbf-aeab-24469eeef99f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/bin/bash: line 1: tutorial-env/bin/activate: No such file or directory\n" + ] + } + ], + "source": [ + "!source tutorial-env/bin/activate" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "M_Delta_R\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "tRe5ph_eYuAL" + }, + "source": [ + "Hint: Example code for embedding a `tabulate` table into a notebook:" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true, + "id": "jtPt_VpVYuAL", + "outputId": "580fba8d-850b-4502-d0e6-da208e439aa7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 102 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
X Y Z
A 1 2
C 3 4
D 5 6
" + ] + }, + "metadata": {} + } + ], + "source": [ + "from IPython.display import HTML, display\n", + "import tabulate\n", + "table = [[\"A\",1,2],\n", + " [\"C\",3,4],\n", + " [\"D\",5,6]]\n", + "display(HTML(tabulate.tabulate(table, tablefmt='html', headers=[\"X\",\"Y\",\"Z\"])))" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "dPhi_r_b\n" - ] + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "vDPGN9WQYuAM" + }, + "outputs": [], + "source": [ + "import tabulate" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": { + "id": "Vtl_hrioYuAM" + }, + "source": [ + "## Exercise 5: Selection\n", + "\n", + "### Exercise 5.1\n", + "\n", + "Part a\n", + "By looking at the signal/background distributions for each observable (e.g. $x$) determine which selection criteria would be optimal:\n", + "\n", + "1. $x > x_c$\n", + "2. $x < x_c$\n", + "3. $|x - \\mu| > x_c$\n", + "4. $|x - \\mu| < x_c$\n", + "\n", + "where $x_c$ is value to be determined below.\n", + "\n", + "### Exercise 5.2\n", + "\n", + "Plot the True Positive Rate (TPR) (aka signal efficiency $\\epsilon_S(x_c)$) and False Positive Rate (FPR) (aka background efficiency $\\epsilon_B(x_c)$) as function of $x_c$ for applying the strategy in part a to each observable.\n", + "\n", + "### Exercise 5.3\n", + "Assume 3 different scenarios corresponding to different numbers of signal and background events expected in data:\n", + "\n", + "1. Expect $N_S=10$, $N_B=100$.\n", + "1. Expect $N_S=100$, $N_B=1000$.\n", + "1. Expect $N_S=1000$, $N_B=10000$.\n", + "1. Expect $N_S=10000$, $N_B=100000$.\n", + "\n", + "Plot the significance ($\\sigma_{S'}$) for each observable as function of $x_c$ for each scenario, where\n", + "\n", + "$\\sigma_{S'}= \\frac{N'_S}{\\sqrt{N'_S+N'_B}}$\n", + "\n", + "and $N'_{S,B} = \\epsilon_{S,B}(x_c) * N_{S,B}$." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "cos_theta_r1\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "m93OUt49YuAN" + }, + "source": [ + "## Exercise 6: Cut Flow\n", + "\n", + "\n", + "### Exercise 6.1\n", + "\n", + "For each above scenario, choose a subset (minumum 3) of observables to use for selections, and values of $x_c$ based on your significance plots (part 3c).\n", + "\n", + "### Exercise 6.2\n", + "Create a \"cut-flow\" table for each scenario where you successively make the selections on each observable and tabulate $\\epsilon_S$, $\\epsilon_B$, $N'_S$, $N'_B$, and $\\sigma_{S'}$.\n", + "\n", + "### Exercise 6.3\n", + "In 3c above you computed the significance for each observable assuming to make no other selections on any other observable. If the variables are correlated, then this assumption can lead to non-optimial results when selecting on multiple variables. By looking at the correlation matrices and your answers to 4b, identify where this effect could be most detrimental to the significance. Attempt to correct the issue by applying the selection in one observable and then optimizing (part 3c) for a second observable. What happens if you change the order of your selection (make selection on second and optimize on first)?\n", + "\n", + "\n" + ] }, { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": { + "id": "2fLr-kFbYuAO" + }, + "source": [ + "## Exercise 7: ROC Curves\n", + "\n", + "### Exercise 7.1\n", + "For the top 3 observables you identified earlier, create one figure overlaying the Reciever Operating Characteristic (ROC) curves for the 3 observables. Compute the area under the curves and report it in the legend of the figure.\n", + "\n", + "### Exercise 7.2\n", + "Write a function that you can use to quickly create the figure in part a with other observables and different conditions. Note that you will likely revise this function as you do the remainder of the lab.\n", + "\n", + "### Exercise 7.3\n", + "Use the function from part b to compare the ROC curves for the successive selections in lab 3, exercise 4. Specifically, plot the ROC curve after each selection.\n", + "\n", + "### Exercise 7.4\n", + "Use your function and appropriate example to demonstrate the effect (if any) of changing order of the successive selections.\n", + "\n" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "for var in VarNames[1:]:\n", - " print (var)\n", - " plt.figure(figsize=(10,5))\n", - " plt.hist(np.array(df_sig[var]),bins=100,histtype=\"step\", color=\"red\",label=\"signal\",density=1, stacked=True)\n", - " plt.hist(np.array(df_bkg[var]),bins=100,histtype=\"step\", color=\"blue\", label=\"background\",density=1, stacked=True)\n", - " plt.legend(loc='upper right')\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 3: Make nice figures\n", - "\n", - "Now use `matplotlib` to reproduce as closely as you can figures 5 and 6 from the paper. This exercise is intended to get you to familiarize yourself with making nicely formatted `matplotlib` figures with multiple plots. Note that the plots in the paper are actually wrong!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 4: Correlation\n", - "\n", - "### Exercise 4.1\n", - "\n", - "#### Part a\n", - "Write a function that creates pair plots and use it to compare variables in the SUSY sample, separately for low and high-level features. Refer to Lecture 13 for details. Do not use `seaborn`.\n", - "\n", - "#### Part b\n", - "Making these plots can be slow because creating each plot initiates a full loop over the data. Make at least one modification to your function in part a to speed it up. Can you propose a different method of creating histograms that would speed up making such pair plots?\n", - "\n", - "#### Part c\n", - "Which observables appear to be best for separating signal from background?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 4.2\n", - "\n", - "#### Part a\n", - "Install [tabulate](https://github.com/astanin/python-tabulate). \n", - "\n", - "#### Part b\n", - "Use numpy to compute the [covariance matrix](https://numpy.org/doc/stable/reference/generated/numpy.cov.html) and [correlation matrix](https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html) between all observabes, and separately between low and high-level features.\n", - "\n", - "#### Part c\n", - "Use tabulate to create a well formatted table of the covariance and correlation matrices, with nice headings and appropriate significant figures. Embed the table into this notebook.\n", - "\n", - "#### Part d\n", - "Write a function that takes a dataset and appropriate arguments and performs steps b and c. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Hint: Example code for embedding a `tabulate` table into a notebook:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": true - }, - "outputs": [ + }, { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
X Y Z
A 1 2
C 3 4
D 5 6
" - ], - "text/plain": [ - "" + "cell_type": "markdown", + "metadata": { + "id": "jWURCmo1YuAO" + }, + "source": [ + "## Exercise 8: Linear Discriminant\n", + "\n", + "### Exercise 8.1\n", + "\n", + "Using numpy, compute the between-class $\\bf{S}_B$ and within-class $\\bf{S}_W$ covariance matrices defined as:\n", + "\n", + "$$\n", + "\\bf{S}_B = (\\bf{m_2}-\\bf{m_1})(\\bf{m_2}-\\bf{m_1})^T \\\\\n", + "$$\n", + "$$\n", + "\\bf{S}_W = \\sum_{i=1,2} \\sum_{n=1}^{l_i} (\\bf{x}_n^i - \\bf{m}_i) (\\bf{x}_n^i - \\bf{m}_i)^T\n", + "$$\n", + "\n", + "where $\\bf{m_i}$ are the vectors containing the means for category 1 and 2, here defined as signal and background. Here $\\bf{x}_n^i$ is the vector containing the observables for the $n$th example event in category $i$.\n", + "\n", + "### Exercise 8.1\n", + "\n", + "Compute the linear coefficients $\\bf{w} = \\bf{S_W}^{-1}(\\bf{m_2}-\\bf{m_1})$. Compare the histogram of the distribution of $F_n^i=\\bf{w}^T\\bf{x}_n^i$ for the two categories.\n", + "\n", + "### Exercise 8.1\n", + "\n", + "Draw the ROC curve for $F_n$.\n", + "\n", + "### Exercise 8.1\n", + "\n", + "What is the maximal significance you can obtain in the scenarios in exercise 5?" ] - }, - "metadata": {}, - "output_type": "display_data" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uAcBVOTYYuAP" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "colab": { + "provenance": [] } - ], - "source": [ - "from IPython.display import HTML, display\n", - "import tabulate\n", - "table = [[\"A\",1,2],\n", - " [\"C\",3,4],\n", - " [\"D\",5,6]]\n", - "display(HTML(tabulate.tabulate(table, tablefmt='html', headers=[\"X\",\"Y\",\"Z\"])))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 5: Selection\n", - "\n", - "### Exercise 5.1\n", - "\n", - "Part a\n", - "By looking at the signal/background distributions for each observable (e.g. $x$) determine which selection criteria would be optimal: \n", - "\n", - "1. $x > x_c$\n", - "2. $x < x_c$\n", - "3. $|x - \\mu| > x_c$\n", - "4. $|x - \\mu| < x_c$\n", - "\n", - "where $x_c$ is value to be determined below.\n", - "\n", - "### Exercise 5.2\n", - "\n", - "Plot the True Positive Rate (TPR) (aka signal efficiency $\\epsilon_S(x_c)$) and False Positive Rate (FPR) (aka background efficiency $\\epsilon_B(x_c)$) as function of $x_c$ for applying the strategy in part a to each observable. \n", - "\n", - "### Exercise 5.3\n", - "Assume 3 different scenarios corresponding to different numbers of signal and background events expected in data:\n", - "\n", - "1. Expect $N_S=10$, $N_B=100$.\n", - "1. Expect $N_S=100$, $N_B=1000$.\n", - "1. Expect $N_S=1000$, $N_B=10000$.\n", - "1. Expect $N_S=10000$, $N_B=100000$.\n", - "\n", - "Plot the significance ($\\sigma_{S'}$) for each observable as function of $x_c$ for each scenario, where \n", - "\n", - "$\\sigma_{S'}= \\frac{N'_S}{\\sqrt{N'_S+N'_B}}$\n", - "\n", - "and $N'_{S,B} = \\epsilon_{S,B}(x_c) * N_{S,B}$." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 6: Cut Flow\n", - "\n", - "\n", - "### Exercise 6.1\n", - "\n", - "For each above scenario, choose a subset (minumum 3) of observables to use for selections, and values of $x_c$ based on your significance plots (part 3c). \n", - "\n", - "### Exercise 6.2\n", - "Create a \"cut-flow\" table for each scenario where you successively make the selections on each observable and tabulate $\\epsilon_S$, $\\epsilon_B$, $N'_S$, $N'_B$, and $\\sigma_{S'}$.\n", - "\n", - "### Exercise 6.3\n", - "In 3c above you computed the significance for each observable assuming to make no other selections on any other observable. If the variables are correlated, then this assumption can lead to non-optimial results when selecting on multiple variables. By looking at the correlation matrices and your answers to 4b, identify where this effect could be most detrimental to the significance. Attempt to correct the issue by applying the selection in one observable and then optimizing (part 3c) for a second observable. What happens if you change the order of your selection (make selection on second and optimize on first)?\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 7: ROC Curves\n", - "\n", - "### Exercise 7.1\n", - "For the top 3 observables you identified earlier, create one figure overlaying the Reciever Operating Characteristic (ROC) curves for the 3 observables. Compute the area under the curves and report it in the legend of the figure.\n", - "\n", - "### Exercise 7.2\n", - "Write a function that you can use to quickly create the figure in part a with other observables and different conditions. Note that you will likely revise this function as you do the remainder of the lab.\n", - "\n", - "### Exercise 7.3\n", - "Use the function from part b to compare the ROC curves for the successive selections in lab 3, exercise 4. Specifically, plot the ROC curve after each selection.\n", - "\n", - "### Exercise 7.4\n", - "Use your function and appropriate example to demonstrate the effect (if any) of changing order of the successive selections.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 8: Linear Discriminant\n", - "\n", - "### Exercise 8.1\n", - "\n", - "Using numpy, compute the between-class $\\bf{S}_B$ and within-class $\\bf{S}_W$ covariance matrices defined as:\n", - "\n", - "$$\n", - "\\bf{S}_B = (\\bf{m_2}-\\bf{m_1})(\\bf{m_2}-\\bf{m_1})^T \\\\\n", - "$$\n", - "$$\n", - "\\bf{S}_W = \\sum_{i=1,2} \\sum_{n=1}^{l_i} (\\bf{x}_n^i - \\bf{m}_i) (\\bf{x}_n^i - \\bf{m}_i)^T\n", - "$$\n", - "\n", - "where $\\bf{m_i}$ are the vectors containing the means for category 1 and 2, here defined as signal and background. Here $\\bf{x}_n^i$ is the vector containing the observables for the $n$th example event in category $i$.\n", - "\n", - "### Exercise 8.1\n", - "\n", - "Compute the linear coefficients $\\bf{w} = \\bf{S_W}^{-1}(\\bf{m_2}-\\bf{m_1})$. Compare the histogram of the distribution of $F_n^i=\\bf{w}^T\\bf{x}_n^i$ for the two categories.\n", - "\n", - "### Exercise 8.1\n", - "\n", - "Draw the ROC curve for $F_n$. \n", - "\n", - "### Exercise 8.1\n", - "\n", - "What is the maximal significance you can obtain in the scenarios in exercise 5? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 772949c6ea061361fcc6884c7a322cb1471100b9 Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Fri, 1 Nov 2024 12:20:31 -0500 Subject: [PATCH 19/22] lab7b --- Labs/Lab.7/Lab.7.ipynb | 2947 +++++++++++++++++++++++++++++++++++++--- 1 file changed, 2793 insertions(+), 154 deletions(-) diff --git a/Labs/Lab.7/Lab.7.ipynb b/Labs/Lab.7/Lab.7.ipynb index 3528acb..d7f111b 100644 --- a/Labs/Lab.7/Lab.7.ipynb +++ b/Labs/Lab.7/Lab.7.ipynb @@ -3,7 +3,7 @@ { "cell_type": "markdown", "metadata": { - "id": "6I0rO7RdYt_1" + "id": "KUyRiEnW_rdT" }, "source": [ "# Lab 7- Data Analysis\n", @@ -14,7 +14,7 @@ { "cell_type": "markdown", "metadata": { - "id": "PGV8fPyAYt_4" + "id": "YuVw1fv2_rjn" }, "source": [ "## Exercise 1: Reading\n", @@ -32,7 +32,7 @@ { "cell_type": "markdown", "metadata": { - "id": "7hq0ZIkUYt_6" + "id": "ESqCE9d3_rjv" }, "source": [ "## Exercise 2: Download SUSY Dataset\n", @@ -46,7 +46,7 @@ { "cell_type": "markdown", "metadata": { - "id": "hetPU9dNYt_7" + "id": "22lLtKRC_rkR" }, "source": [ "* To download:\n", @@ -64,11 +64,11 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "id": "NsqW49uXYt_8", - "outputId": "59583db3-2a79-4b09-8380-87232e7e5754", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "Mwwd91Ik_rkf", + "outputId": "95eae6b4-697e-4f25-f4de-a90f65a4e800" }, "outputs": [ { @@ -77,7 +77,7 @@ "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", - "100 879M 0 879M 0 0 49.3M 0 --:--:-- 0:00:17 --:--:-- 50.2M\n" + "100 879M 0 879M 0 0 70.2M 0 --:--:-- 0:00:12 --:--:-- 83.1M\n" ] } ], @@ -89,7 +89,7 @@ "cell_type": "code", "execution_count": 8, "metadata": { - "id": "Q_-ZsqVVYt__" + "id": "Wp5XNcmF_rku" }, "outputs": [], "source": [ @@ -98,23 +98,23 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": { "scrolled": true, - "id": "KWGENf0XYt__", - "outputId": "2336d277-ee3a-4402-ea31-69f6b785735a", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "kUOsiY1B_rk1", + "outputId": "ab6afca9-a1f4-41f1-bea3-556ed73eb8f7" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "total 2.3G\n", - "drwxr-xr-x 1 root root 4.0K Oct 23 13:21 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n", - "-rw-r--r-- 1 root root 2.3G Oct 25 16:05 SUSY.csv\n" + "total 880M\n", + "drwxr-xr-x 1 root root 4.0K Oct 29 13:25 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n", + "-rw-r--r-- 1 root root 880M Oct 31 14:41 SUSY.csv.gz\n" ] } ], @@ -125,7 +125,7 @@ { "cell_type": "markdown", "metadata": { - "id": "b1Wcgo6zYuAA" + "id": "WSFbZQqI_rk5" }, "source": [ "The data is provided as a comma separated file." @@ -135,11 +135,11 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "id": "pxXqH3tEYuAA", - "outputId": "98d8bb8a-2c49-4f54-d6c5-87cb18e5f5e9", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "ONfmelbk_rk9", + "outputId": "4a50bd58-8a70-470c-9986-3983370f096f" }, "outputs": [ { @@ -164,11 +164,11 @@ "cell_type": "code", "execution_count": 11, "metadata": { - "id": "2_yyO2EPYuAA", - "outputId": "0b466833-6c87-45b3-f3ca-cab1cef9ac7b", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "xGWPh3H-_rlB", + "outputId": "b5993369-6440-4a2e-ac37-62240703ab0d" }, "outputs": [ { @@ -176,8 +176,9 @@ "name": "stdout", "text": [ "total 2.3G\n", - "drwxr-xr-x 1 root root 4.0K Oct 23 13:21 sample_data\n", - "-rw-r--r-- 1 root root 2.3G Oct 25 16:05 SUSY.csv\n" + "drwxr-xr-x 1 root root 4.0K Oct 29 13:25 sample_data\n", + "-rw-r--r-- 1 root root 2.3G Oct 31 14:41 SUSY.csv\n", + "-rw-r--r-- 1 root root 0 Oct 31 14:41 SUSY-small.csv\n" ] } ], @@ -191,11 +192,11 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "id": "I8A4jFpiYuAB", - "outputId": "6fc21c89-8a6c-41cc-8dfc-3e60186a8635", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "vtWJ-Mz5_rlH", + "outputId": "a4b08786-65b1-4056-bd94-eff8cae701e3" }, "outputs": [ { @@ -216,7 +217,7 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "id": "kjJyDi_BYuAB" + "id": "JBoOn61J_rlK" }, "outputs": [], "source": [ @@ -227,11 +228,11 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "id": "UvpRKfZmYuAB", - "outputId": "ebdbccdf-8b7a-4337-d09a-a0a548bc2f69", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "ral2i-VX_rlO", + "outputId": "eacbd357-4c59-4657-adba-b7eb0d42e409" }, "outputs": [ { @@ -239,9 +240,9 @@ "name": "stdout", "text": [ "total 2.5G\n", - "drwxr-xr-x 1 root root 4.0K Oct 23 13:21 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n", - "-rw-r--r-- 1 root root 2.3G Oct 25 16:05 SUSY.csv\n", - "-rw-r--r-- 1 root root 228M Oct 25 16:09 SUSY-small.csv\n" + "drwxr-xr-x 1 root root 4.0K Oct 29 13:25 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n", + "-rw-r--r-- 1 root root 2.3G Oct 31 14:41 SUSY.csv\n", + "-rw-r--r-- 1 root root 228M Oct 31 14:42 SUSY-small.csv\n" ] } ], @@ -252,7 +253,7 @@ { "cell_type": "markdown", "metadata": { - "id": "DLZ-z8MbYuAC" + "id": "pjNRqmyS_rlR" }, "source": [ "### First Look\n", @@ -264,7 +265,7 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "id": "mBnp2AKRYuAC" + "id": "qG8BaBKD_rlp" }, "outputs": [], "source": [ @@ -274,7 +275,7 @@ { "cell_type": "markdown", "metadata": { - "id": "07HbvkToYuAC" + "id": "_00yPcCi_rlr" }, "source": [ "Some of these variables represent the \"raw\" kinematics of the observed final state particles, while others are \"features\" that are derived from these raw quantities:" @@ -284,7 +285,7 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "id": "LUtV1v-rYuAC" + "id": "sm1x5YFJ_rlt" }, "outputs": [], "source": [ @@ -296,11 +297,11 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "id": "WB7BGywGYuAD", - "outputId": "6ac4fcff-b6c0-48b8-c34f-a8bfe82e0b5f", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "hOKioChv_rmL", + "outputId": "56e822f2-c3d1-4658-e4fd-14730c21549c" }, "outputs": [ { @@ -329,11 +330,11 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "id": "W394xLXmYuAD", - "outputId": "799b034c-09d8-4d97-e2da-d6e9912a95b0", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "hD2YtQmu_rmO", + "outputId": "8b5645e9-98cd-440b-f127-ef9d91ae1512" }, "outputs": [ { @@ -341,15 +342,15 @@ "data": { "text/plain": [ "['S_R',\n", - " 'cos_theta_r1',\n", - " 'M_R',\n", - " 'M_TR_2',\n", - " 'MET_rel',\n", " 'MT2',\n", - " 'axial_MET',\n", + " 'M_Delta_R',\n", " 'dPhi_r_b',\n", + " 'cos_theta_r1',\n", + " 'axial_MET',\n", " 'R',\n", - " 'M_Delta_R']" + " 'MET_rel',\n", + " 'M_TR_2',\n", + " 'M_R']" ] }, "metadata": {}, @@ -363,7 +364,7 @@ { "cell_type": "markdown", "metadata": { - "id": "6rii_EU3YuAD" + "id": "VV02VZA__rmS" }, "source": [ "We will use pandas to read in the file, and matplotlib to make plots. The following ensures pandas is installed and sets everything up:" @@ -373,7 +374,7 @@ "cell_type": "code", "execution_count": 19, "metadata": { - "id": "B0BbvKm6YuAE" + "id": "OL6S_4z3_rma" }, "outputs": [], "source": [ @@ -385,7 +386,7 @@ { "cell_type": "markdown", "metadata": { - "id": "Z5xFBAy6YuAE" + "id": "IcXv0FE8_rn3" }, "source": [ "Now we can read the data into a pandas dataframe:" @@ -395,7 +396,7 @@ "cell_type": "code", "execution_count": 20, "metadata": { - "id": "klasakrsYuAE" + "id": "_TX4YIti_rn6" }, "outputs": [], "source": [ @@ -406,7 +407,7 @@ { "cell_type": "markdown", "metadata": { - "id": "32QE45BGYuAE" + "id": "IPsjW9HE_rn-" }, "source": [ "You can see the data in Jupyter by just evaluateing the dataframe:" @@ -416,12 +417,12 @@ "cell_type": "code", "execution_count": 21, "metadata": { - "id": "myrgb3u7YuAE", - "outputId": "2356a682-8a09-4a64-b22f-e8209da057d2", "colab": { "base_uri": "https://localhost:8080/", "height": 443 - } + }, + "id": "-ryWi0OZ_roJ", + "outputId": "c1655ec4-e999-4c51-d17b-a291d81a5124" }, "outputs": [ { @@ -471,7 +472,7 @@ ], "text/html": [ "\n", - "
\n", + "
\n", "
\n", "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
signall_1_pTl_1_etal_1_phil_2_pTl_2_etal_2_phiMETMET_phiMET_relaxial_METM_RM_TR_2RMT2S_RM_Delta_RdPhi_r_bcos_theta_r1
11.01.6679730.064191-1.2251710.506102-0.3389391.6725433.475464-1.2191360.0129553.7751741.0459770.5680510.4819280.0000000.4484100.2053561.3218930.377584
21.00.444840-0.134298-0.7099720.451719-1.613871-0.7686611.2199180.5040261.831248-0.4313850.5262830.9415141.5875352.0243080.6034981.5623741.1354540.180910
31.00.381256-0.9761450.6931520.4489590.891753-0.6773282.0330601.5330413.046260-1.0052850.5693861.0152111.5822171.5519140.7612151.7154641.4922570.090719
41.01.309996-0.690089-0.6762591.589283-0.6933260.6229071.087562-0.3817420.5892041.3654791.1792950.9682180.7285630.0000001.0831580.0434291.1548540.094859
81.02.1128120.742983-0.3305390.805253-0.028887-1.4466792.2999461.4504292.989110-1.8947701.4451252.5481661.5647212.3936321.5545662.1484681.1791170.688057
............................................................
4999881.00.9392030.4960580.4928280.666188-1.330323-1.6658971.5019000.0316681.6898270.7991851.1040251.0263560.8249651.4953511.1173061.2870941.1737160.095378
4999911.01.5213020.7346930.2803391.5906090.366158-1.5071710.828265-0.9803821.005345-0.3254691.3185341.2373600.8327600.6718331.3401570.7395151.1157820.227649
4999941.00.955334-1.524135-1.1897641.470348-0.2961680.6964950.8517310.8155240.2592660.3400131.2196410.9911180.7211260.0000001.2424100.5267981.3138070.160337
4999961.00.910016-0.364544-0.7771200.543648-0.910632-1.7237072.8646731.4582722.176558-0.5909110.6736951.6621402.1893621.1950410.9108151.1818931.2523620.826035
4999971.00.8429540.332476-1.0485641.3479890.320496-0.6663580.450433-0.4118720.2934070.6304910.8599200.4033710.4162580.5919890.3720030.7167880.3669910.265798
\n", + "

229245 rows × 19 columns

\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df_sig" + } + }, + "metadata": {}, + "execution_count": 34 + } + ], + "source": [ + "df_sig" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "id": "V9zGiNVd_r4H", + "outputId": "22a5652d-c54c-498b-9704-7d3740e7c11f" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi MET \\\n", + "1 1.667973 0.064191 -1.225171 0.506102 -0.338939 1.672543 3.475464 \n", + "2 0.444840 -0.134298 -0.709972 0.451719 -1.613871 -0.768661 1.219918 \n", + "3 0.381256 -0.976145 0.693152 0.448959 0.891753 -0.677328 2.033060 \n", + "4 1.309996 -0.690089 -0.676259 1.589283 -0.693326 0.622907 1.087562 \n", + "8 2.112812 0.742983 -0.330539 0.805253 -0.028887 -1.446679 2.299946 \n", + "... ... ... ... ... ... ... ... \n", + "499988 0.939203 0.496058 0.492828 0.666188 -1.330323 -1.665897 1.501900 \n", + "499991 1.521302 0.734693 0.280339 1.590609 0.366158 -1.507171 0.828265 \n", + "499994 0.955334 -1.524135 -1.189764 1.470348 -0.296168 0.696495 0.851731 \n", + "499996 0.910016 -0.364544 -0.777120 0.543648 -0.910632 -1.723707 2.864673 \n", + "499997 0.842954 0.332476 -1.048564 1.347989 0.320496 -0.666358 0.450433 \n", + "\n", + " MET_phi MET_rel axial_MET M_R M_TR_2 R MT2 \\\n", + "1 -1.219136 0.012955 3.775174 1.045977 0.568051 0.481928 0.000000 \n", + "2 0.504026 1.831248 -0.431385 0.526283 0.941514 1.587535 2.024308 \n", + "3 1.533041 3.046260 -1.005285 0.569386 1.015211 1.582217 1.551914 \n", + "4 -0.381742 0.589204 1.365479 1.179295 0.968218 0.728563 0.000000 \n", + "8 1.450429 2.989110 -1.894770 1.445125 2.548166 1.564721 2.393632 \n", + "... ... ... ... ... ... ... ... \n", + "499988 0.031668 1.689827 0.799185 1.104025 1.026356 0.824965 1.495351 \n", + "499991 -0.980382 1.005345 -0.325469 1.318534 1.237360 0.832760 0.671833 \n", + "499994 0.815524 0.259266 0.340013 1.219641 0.991118 0.721126 0.000000 \n", + "499996 1.458272 2.176558 -0.590911 0.673695 1.662140 2.189362 1.195041 \n", + "499997 -0.411872 0.293407 0.630491 0.859920 0.403371 0.416258 0.591989 \n", + "\n", + " S_R M_Delta_R dPhi_r_b cos_theta_r1 \n", + "1 0.448410 0.205356 1.321893 0.377584 \n", + "2 0.603498 1.562374 1.135454 0.180910 \n", + "3 0.761215 1.715464 1.492257 0.090719 \n", + "4 1.083158 0.043429 1.154854 0.094859 \n", + "8 1.554566 2.148468 1.179117 0.688057 \n", + "... ... ... ... ... \n", + "499988 1.117306 1.287094 1.173716 0.095378 \n", + "499991 1.340157 0.739515 1.115782 0.227649 \n", + "499994 1.242410 0.526798 1.313807 0.160337 \n", + "499996 0.910815 1.181893 1.252362 0.826035 \n", + "499997 0.372003 0.716788 0.366991 0.265798 \n", + "\n", + "[229245 rows x 18 columns]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
l_1_pTl_1_etal_1_phil_2_pTl_2_etal_2_phiMETMET_phiMET_relaxial_METM_RM_TR_2RMT2S_RM_Delta_RdPhi_r_bcos_theta_r1
11.6679730.064191-1.2251710.506102-0.3389391.6725433.475464-1.2191360.0129553.7751741.0459770.5680510.4819280.0000000.4484100.2053561.3218930.377584
20.444840-0.134298-0.7099720.451719-1.613871-0.7686611.2199180.5040261.831248-0.4313850.5262830.9415141.5875352.0243080.6034981.5623741.1354540.180910
30.381256-0.9761450.6931520.4489590.891753-0.6773282.0330601.5330413.046260-1.0052850.5693861.0152111.5822171.5519140.7612151.7154641.4922570.090719
41.309996-0.690089-0.6762591.589283-0.6933260.6229071.087562-0.3817420.5892041.3654791.1792950.9682180.7285630.0000001.0831580.0434291.1548540.094859
82.1128120.742983-0.3305390.805253-0.028887-1.4466792.2999461.4504292.989110-1.8947701.4451252.5481661.5647212.3936321.5545662.1484681.1791170.688057
.........................................................
4999880.9392030.4960580.4928280.666188-1.330323-1.6658971.5019000.0316681.6898270.7991851.1040251.0263560.8249651.4953511.1173061.2870941.1737160.095378
4999911.5213020.7346930.2803391.5906090.366158-1.5071710.828265-0.9803821.005345-0.3254691.3185341.2373600.8327600.6718331.3401570.7395151.1157820.227649
4999940.955334-1.524135-1.1897641.470348-0.2961680.6964950.8517310.8155240.2592660.3400131.2196410.9911180.7211260.0000001.2424100.5267981.3138070.160337
4999960.910016-0.364544-0.7771200.543648-0.910632-1.7237072.8646731.4582722.176558-0.5909110.6736951.6621402.1893621.1950410.9108151.1818931.2523620.826035
4999970.8429540.332476-1.0485641.3479890.320496-0.6663580.450433-0.4118720.2934070.6304910.8599200.4033710.4162580.5919890.3720030.7167880.3669910.265798
\n", + "

229245 rows × 18 columns

\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df_sig_0" + } + }, + "metadata": {}, + "execution_count": 35 + } + ], + "source": [ + "df_sig_0 = df_sig.drop(\"signal\",axis=1)\n", + "df_bkg_0 = df_bkg.drop(\"signal\",axis=1)\n", + "\n", + "df_sig_0" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 648 + }, + "id": "H5va4rD9_r4Q", + "outputId": "cbcf8e40-dc38-46ae-92e5-04062d082cda" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "l_1_pT 1.291078\n", + "l_1_eta 0.000824\n", + "l_1_phi -0.001524\n", + "l_2_pT 1.138668\n", + "l_2_eta 0.002487\n", + "l_2_phi 0.000049\n", + "MET 1.418381\n", + "MET_phi -0.000470\n", + "MET_rel 1.275169\n", + "axial_MET 0.089314\n", + "M_R 1.183651\n", + "M_TR_2 1.268858\n", + "R 1.056352\n", + "MT2 1.074694\n", + "S_R 1.175023\n", + "M_Delta_R 1.186022\n", + "dPhi_r_b 1.014617\n", + "cos_theta_r1 0.282417\n", + "dtype: float64" + ], + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
0
l_1_pT1.291078
l_1_eta0.000824
l_1_phi-0.001524
l_2_pT1.138668
l_2_eta0.002487
l_2_phi0.000049
MET1.418381
MET_phi-0.000470
MET_rel1.275169
axial_MET0.089314
M_R1.183651
M_TR_21.268858
R1.056352
MT21.074694
S_R1.175023
M_Delta_R1.186022
dPhi_r_b1.014617
cos_theta_r10.282417
\n", + "

" + ] + }, + "metadata": {}, + "execution_count": 36 + } + ], + "source": [ + "m_s= np.mean(df_sig_0,axis=0)\n", + "m_s" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "id": "kWaHAj3r_r4z" + }, + "outputs": [], + "source": [ + "# Compute means for signal and background\n", + "m_s = np.mean(df_sig_0, axis=0) # Mean for signal events\n", + "m_b = np.mean(df_bkg_0, axis=0) # Mean for background events\n", + "\n", + "# Calculate the difference between means\n", + "delta = m_s - m_b # Difference vector between signal and background means\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "id": "Zr-AXn8r_r72" + }, + "outputs": [], + "source": [ + "delta=np.matrix(m_s-m_b).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "u-QceBjq_r76", + "outputId": "90ae110d-b691-454e-8f3c-5ae63a81f5fb" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "matrix([[ 2.87970231e-01, 4.58922304e-04, 4.67467434e-04,\n", + " 1.38148662e-01, 1.54068049e-03, 3.66095560e-04,\n", + " 4.13461344e-01, -6.36618070e-04, 2.71136238e-01,\n", + " 8.54657090e-02, 1.82331687e-01, 2.66557653e-01,\n", + " 5.53227373e-02, 7.37824064e-02, 1.74241790e-01,\n", + " 1.84116478e-01, 1.54056891e-02, 5.66281031e-02],\n", + " [ 4.58922304e-04, 7.31359210e-07, 7.44977113e-07,\n", + " 2.20159917e-04, 2.45529768e-06, 5.83426339e-07,\n", + " 6.58910580e-04, -1.01454317e-06, 4.32094896e-04,\n", + " 1.36201995e-04, 2.90571971e-04, 4.24798257e-04,\n", + " 8.81648006e-05, 1.17582959e-04, 2.77679548e-04,\n", + " 2.93416294e-04, 2.45511986e-05, 9.02450903e-05],\n", + " [ 4.67467434e-04, 7.44977113e-07, 7.58848582e-07,\n", + " 2.24259293e-04, 2.50101531e-06, 5.94289733e-07,\n", + " 6.71179491e-04, -1.03343396e-06, 4.40140500e-04,\n", + " 1.38738075e-04, 2.95982420e-04, 4.32707998e-04,\n", + " 8.98064286e-05, 1.19772353e-04, 2.82849940e-04,\n", + " 2.98879704e-04, 2.50083417e-05, 9.19254533e-05],\n", + " [ 1.38148662e-01, 2.20159917e-04, 2.24259293e-04,\n", + " 6.62743950e-02, 7.39114414e-04, 1.75627917e-04,\n", + " 1.98350820e-01, -3.05406341e-04, 1.30072850e-01,\n", + " 4.10006732e-02, 8.74704254e-02, 1.27876354e-01,\n", + " 2.65401118e-02, 3.53958140e-02, 8.35894395e-02,\n", + " 8.83266474e-02, 7.39060884e-03, 2.71663382e-02],\n", + " [ 1.54068049e-03, 2.45529768e-06, 2.50101531e-06,\n", + " 7.39114414e-04, 8.24285333e-06, 1.95866179e-06,\n", + " 2.21207527e-03, -3.40599456e-06, 1.45061631e-03,\n", + " 4.57253342e-04, 9.75499697e-04, 1.42612024e-03,\n", + " 2.95984282e-04, 3.94746060e-04, 9.32217633e-04,\n", + " 9.85048574e-04, 8.24225634e-05, 3.02968169e-04],\n", + " [ 3.66095560e-04, 5.83426339e-07, 5.94289733e-07,\n", + " 1.75627917e-04, 1.95866179e-06, 4.65416020e-07,\n", + " 5.25631977e-04, -8.09330353e-07, 3.44694563e-04,\n", + " 1.08652260e-04, 2.31797644e-04, 3.38873823e-04,\n", + " 7.03316047e-05, 9.37993183e-05, 2.21512986e-04,\n", + " 2.34066642e-04, 1.95851993e-05, 7.19911118e-05],\n", + " [ 4.13461344e-01, 6.58910580e-04, 6.71179491e-04,\n", + " 1.98350820e-01, 2.21207527e-03, 5.25631977e-04,\n", + " 5.93638731e-01, -9.14042266e-04, 3.89291466e-01,\n", + " 1.22709791e-01, 2.61787838e-01, 3.82717634e-01,\n", + " 7.94311730e-02, 1.05935161e-01, 2.50172542e-01,\n", + " 2.64350402e-01, 2.21191506e-02, 8.13053887e-02],\n", + " [-6.36618070e-04, -1.01454317e-06, -1.03343396e-06,\n", + " -3.05406341e-04, -3.40599456e-06, -8.09330353e-07,\n", + " -9.14042266e-04, 1.40737661e-06, -5.99403029e-04,\n", + " -1.88939720e-04, -4.03082104e-04, -5.89281115e-04,\n", + " -1.22302413e-04, -1.63111350e-04, -3.85197705e-04,\n", + " -4.07027755e-04, -3.40574788e-05, -1.25188196e-04],\n", + " [ 2.71136238e-01, 4.32094896e-04, 4.40140500e-04,\n", + " 1.30072850e-01, 1.45061631e-03, 3.44694563e-04,\n", + " 3.89291466e-01, -5.99403029e-04, 2.55286317e-01,\n", + " 8.04696053e-02, 1.71673049e-01, 2.50975384e-01,\n", + " 5.20887135e-02, 6.94692782e-02, 1.64056067e-01,\n", + " 1.73353506e-01, 1.45051125e-02, 5.33177710e-02],\n", + " [ 8.54657090e-02, 1.36201995e-04, 1.38738075e-04,\n", + " 4.10006732e-02, 4.57253342e-04, 1.08652260e-04,\n", + " 1.22709791e-01, -1.88939720e-04, 8.04696053e-02,\n", + " 2.53650781e-02, 5.41136035e-02, 7.91107426e-02,\n", + " 1.64190477e-02, 2.18976303e-02, 5.17126304e-02,\n", + " 5.46433055e-02, 4.57220225e-03, 1.68064628e-02],\n", + " [ 1.82331687e-01, 2.90571971e-04, 2.95982420e-04,\n", + " 8.74704254e-02, 9.75499697e-04, 2.31797644e-04,\n", + " 2.61787838e-01, -4.03082104e-04, 1.71673049e-01,\n", + " 5.41136035e-02, 1.15445419e-01, 1.68774065e-01,\n", + " 3.50282318e-02, 4.67161851e-02, 1.10323207e-01,\n", + " 1.16575480e-01, 9.75429046e-03, 3.58547393e-02],\n", + " [ 2.66557653e-01, 4.24798257e-04, 4.32707998e-04,\n", + " 1.27876354e-01, 1.42612024e-03, 3.38873823e-04,\n", + " 3.82717634e-01, -5.89281115e-04, 2.50975384e-01,\n", + " 7.91107426e-02, 1.68774065e-01, 2.46737249e-01,\n", + " 5.12091092e-02, 6.82961743e-02, 1.61285708e-01,\n", + " 1.70426145e-01, 1.42601696e-02, 5.24174121e-02],\n", + " [ 5.53227373e-02, 8.81648006e-05, 8.98064286e-05,\n", + " 2.65401118e-02, 2.95984282e-04, 7.03316047e-05,\n", + " 7.94311730e-02, -1.22302413e-04, 5.20887135e-02,\n", + " 1.64190477e-02, 3.50282318e-02, 5.12091092e-02,\n", + " 1.06282002e-02, 1.41745369e-02, 3.34740599e-02,\n", + " 3.53711128e-02, 2.95962845e-03, 1.08789775e-02],\n", + " [ 7.37824064e-02, 1.17582959e-04, 1.19772353e-04,\n", + " 3.53958140e-02, 3.94746060e-04, 9.37993183e-05,\n", + " 1.05935161e-01, -1.63111350e-04, 6.94692782e-02,\n", + " 2.18976303e-02, 4.67161851e-02, 6.82961743e-02,\n", + " 1.41745369e-02, 1.89041884e-02, 4.46434290e-02,\n", + " 4.71734760e-02, 3.94717471e-03, 1.45089918e-02],\n", + " [ 1.74241790e-01, 2.77679548e-04, 2.82849940e-04,\n", + " 8.35894395e-02, 9.32217633e-04, 2.21512986e-04,\n", + " 2.50172542e-01, -3.85197705e-04, 1.64056067e-01,\n", + " 5.17126304e-02, 1.10323207e-01, 1.61285708e-01,\n", + " 3.34740599e-02, 4.46434290e-02, 1.05428264e-01,\n", + " 1.11403129e-01, 9.32150117e-03, 3.42638960e-02],\n", + " [ 1.84116478e-01, 2.93416294e-04, 2.98879704e-04,\n", + " 8.83266474e-02, 9.85048574e-04, 2.34066642e-04,\n", + " 2.64350402e-01, -4.07027755e-04, 1.73353506e-01,\n", + " 5.46433055e-02, 1.16575480e-01, 1.70426145e-01,\n", + " 3.53711128e-02, 4.71734760e-02, 1.11403129e-01,\n", + " 1.17716603e-01, 9.84977232e-03, 3.62057107e-02],\n", + " [ 1.54056891e-02, 2.45511986e-05, 2.50083417e-05,\n", + " 7.39060884e-03, 8.24225634e-05, 1.95851993e-05,\n", + " 2.21191506e-02, -3.40574788e-05, 1.45051125e-02,\n", + " 4.57220225e-03, 9.75429046e-03, 1.42601696e-02,\n", + " 2.95962845e-03, 3.94717471e-03, 9.32150117e-03,\n", + " 9.84977232e-03, 8.24165940e-04, 3.02946227e-03],\n", + " [ 5.66281031e-02, 9.02450903e-05, 9.19254533e-05,\n", + " 2.71663382e-02, 3.02968169e-04, 7.19911118e-05,\n", + " 8.13053887e-02, -1.25188196e-04, 5.33177710e-02,\n", + " 1.68064628e-02, 3.58547393e-02, 5.24174121e-02,\n", + " 1.08789775e-02, 1.45089918e-02, 3.42638960e-02,\n", + " 3.62057107e-02, 3.02946227e-03, 1.11356721e-02]])" + ] + }, + "metadata": {}, + "execution_count": 39 + } + ], + "source": [ + "S_B= delta*delta.transpose()\n", + "S_B" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "id": "aLNQAEsN_r8E", + "outputId": "5deaa9e1-a835-4bbc-d16c-d267f957a97d" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi MET \\\n", + "1 0.376895 0.063367 -1.223647 -0.632566 -0.341426 1.672494 2.057083 \n", + "2 -0.846238 -0.135122 -0.708447 -0.686949 -1.616358 -0.768710 -0.198463 \n", + "3 -0.909822 -0.976969 0.694677 -0.689709 0.889266 -0.677378 0.614679 \n", + "4 0.018919 -0.690913 -0.674735 0.450615 -0.695813 0.622858 -0.330819 \n", + "8 0.821734 0.742159 -0.329015 -0.333415 -0.031374 -1.446728 0.881564 \n", + "... ... ... ... ... ... ... ... \n", + "499988 -0.351875 0.495235 0.494352 -0.472480 -1.332810 -1.665947 0.083519 \n", + "499991 0.230224 0.733870 0.281863 0.451941 0.363671 -1.507220 -0.590116 \n", + "499994 -0.335744 -1.524958 -1.188240 0.331680 -0.298655 0.696446 -0.566651 \n", + "499996 -0.381062 -0.365368 -0.775596 -0.595019 -0.913119 -1.723756 1.446292 \n", + "499997 -0.448124 0.331653 -1.047040 0.209321 0.318009 -0.666408 -0.967948 \n", + "\n", + " MET_phi MET_rel axial_MET M_R M_TR_2 R MT2 \\\n", + "1 -1.218666 -1.262214 3.685859 -0.137674 -0.700807 -0.574424 -1.074694 \n", + "2 0.504496 0.556079 -0.520699 -0.657368 -0.327344 0.531183 0.949615 \n", + "3 1.533511 1.771091 -1.094599 -0.614265 -0.253647 0.525864 0.477221 \n", + "4 -0.381271 -0.685964 1.276165 -0.004356 -0.300640 -0.327789 -1.074694 \n", + "8 1.450900 1.713942 -1.984084 0.261474 1.279308 0.508369 1.318939 \n", + "... ... ... ... ... ... ... ... \n", + "499988 0.032139 0.414658 0.709871 -0.079626 -0.242503 -0.231387 0.420657 \n", + "499991 -0.979911 -0.269824 -0.414783 0.134882 -0.031498 -0.223592 -0.402861 \n", + "499994 0.815994 -1.015903 0.250699 0.035990 -0.277740 -0.335226 -1.074694 \n", + "499996 1.458742 0.901389 -0.680225 -0.509956 0.393282 1.133010 0.120347 \n", + "499997 -0.411402 -0.981762 0.541177 -0.323732 -0.865488 -0.640094 -0.482705 \n", + "\n", + " S_R M_Delta_R dPhi_r_b cos_theta_r1 \n", + "1 -0.726613 -0.980666 0.307276 0.095167 \n", + "2 -0.571526 0.376352 0.120837 -0.101507 \n", + "3 -0.413808 0.529442 0.477639 -0.191698 \n", + "4 -0.091865 -1.142593 0.140236 -0.187558 \n", + "8 0.379543 0.962446 0.164500 0.405640 \n", + "... ... ... ... ... \n", + "499988 -0.057717 0.101072 0.159099 -0.187039 \n", + "499991 0.165134 -0.446507 0.101164 -0.054768 \n", + "499994 0.067386 -0.659224 0.299190 -0.122080 \n", + "499996 -0.264209 -0.004129 0.237745 0.543618 \n", + "499997 -0.803020 -0.469234 -0.647626 -0.016619 \n", + "\n", + "[229245 rows x 18 columns]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
l_1_pTl_1_etal_1_phil_2_pTl_2_etal_2_phiMETMET_phiMET_relaxial_METM_RM_TR_2RMT2S_RM_Delta_RdPhi_r_bcos_theta_r1
10.3768950.063367-1.223647-0.632566-0.3414261.6724942.057083-1.218666-1.2622143.685859-0.137674-0.700807-0.574424-1.074694-0.726613-0.9806660.3072760.095167
2-0.846238-0.135122-0.708447-0.686949-1.616358-0.768710-0.1984630.5044960.556079-0.520699-0.657368-0.3273440.5311830.949615-0.5715260.3763520.120837-0.101507
3-0.909822-0.9769690.694677-0.6897090.889266-0.6773780.6146791.5335111.771091-1.094599-0.614265-0.2536470.5258640.477221-0.4138080.5294420.477639-0.191698
40.018919-0.690913-0.6747350.450615-0.6958130.622858-0.330819-0.381271-0.6859641.276165-0.004356-0.300640-0.327789-1.074694-0.091865-1.1425930.140236-0.187558
80.8217340.742159-0.329015-0.333415-0.031374-1.4467280.8815641.4509001.713942-1.9840840.2614741.2793080.5083691.3189390.3795430.9624460.1645000.405640
.........................................................
499988-0.3518750.4952350.494352-0.472480-1.332810-1.6659470.0835190.0321390.4146580.709871-0.079626-0.242503-0.2313870.420657-0.0577170.1010720.159099-0.187039
4999910.2302240.7338700.2818630.4519410.363671-1.507220-0.590116-0.979911-0.269824-0.4147830.134882-0.031498-0.223592-0.4028610.165134-0.4465070.101164-0.054768
499994-0.335744-1.524958-1.1882400.331680-0.2986550.696446-0.5666510.815994-1.0159030.2506990.035990-0.277740-0.335226-1.0746940.067386-0.6592240.299190-0.122080
499996-0.381062-0.365368-0.775596-0.595019-0.913119-1.7237561.4462921.4587420.901389-0.680225-0.5099560.3932821.1330100.120347-0.264209-0.0041290.2377450.543618
499997-0.4481240.331653-1.0470400.2093210.318009-0.666408-0.967948-0.411402-0.9817620.541177-0.323732-0.865488-0.640094-0.482705-0.803020-0.469234-0.647626-0.016619
\n", + "

229245 rows × 18 columns

\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe" + } + }, + "metadata": {}, + "execution_count": 40 + } + ], + "source": [ + "df_sig_0-m_s" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "s19wzxPQ_r8K", + "outputId": "7225d6ba-ac8e-4cba-b561-ca78724f1caa" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(18, 229245)" + ] + }, + "metadata": {}, + "execution_count": 41 + } + ], + "source": [ + "delta_s=np.matrix(df_sig_0-m_s).transpose()\n", + "delta_s.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oIcmLMnm_r9v", + "outputId": "1caac138-f6f3-4f8a-e0b7-6ef9a7bf0ad5" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(18, 18)" + ] + }, + "metadata": {}, + "execution_count": 42 + } + ], + "source": [ + "S_W_s= delta_s*delta_s.transpose()\n", + "S_W_s.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "id": "2h5tUOe8_r9z" + }, + "outputs": [], + "source": [ + "delta_b=np.matrix(df_bkg_0-m_b).transpose()\n", + "S_W_b= delta_b*delta_b.transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "id": "Q529uRrl_sAQ" + }, + "outputs": [], + "source": [ + "S_W=S_W_s+S_W_b\n", + "S_W_inv = np.linalg.inv(S_W)\n", + "w = S_W_inv * np.matrix(m_b - m_s).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jUlBGPt8_sAt", + "outputId": "5754b22f-d8f3-4e5e-ece4-c634948f2256" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "matrix([[-2.26832982e-06],\n", + " [-5.63688439e-09],\n", + " [-2.10423856e-09],\n", + " [-9.95582982e-07],\n", + " [-3.48452234e-09],\n", + " [-2.70762588e-09],\n", + " [-1.65185357e-06],\n", + " [-2.74241844e-09],\n", + " [-1.39723282e-07],\n", + " [-2.64205675e-07],\n", + " [ 2.72149250e-07],\n", + " [-1.48465692e-07],\n", + " [ 2.11167032e-06],\n", + " [ 3.24040633e-07],\n", + " [ 1.81173242e-06],\n", + " [-1.69348122e-06],\n", + " [ 7.50902836e-08],\n", + " [-5.06860437e-06]])" + ] + }, + "metadata": {}, + "execution_count": 45 + } + ], + "source": [ + "w" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AdPsp-4V_sBC", + "outputId": "99e7e3c4-4973-4026-81be-d51370818e74" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "matrix([[ 2.96426929e-01],\n", + " [ 7.36631998e-04],\n", + " [ 2.74983369e-04],\n", + " [ 1.30103481e-01],\n", + " [ 4.55359819e-04],\n", + " [ 3.53834445e-04],\n", + " [ 2.15865381e-01],\n", + " [ 3.58381161e-04],\n", + " [ 1.82591363e-02],\n", + " [ 3.45265825e-02],\n", + " [-3.55646545e-02],\n", + " [ 1.94016005e-02],\n", + " [-2.75954556e-01],\n", + " [-4.23458567e-02],\n", + " [-2.36758461e-01],\n", + " [ 2.21305311e-01],\n", + " [-9.81285083e-03],\n", + " [ 6.62368767e-01]])" + ] + }, + "metadata": {}, + "execution_count": 46 + } + ], + "source": [ + "w_1 = w / sum(w)\n", + "w_1" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "id": "wcgRbPLi_sBg" + }, + "outputs": [], + "source": [ + "# Compute the output scores for signal and background events using linear coefficients w_1\n", + "output_s = np.matrix(df_sig_0) * w_1 # Output scores for signal events\n", + "output_b = np.matrix(df_bkg_0) * w_1 # Output scores for background events" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 448 + }, + "id": "M76VQSNV_sBj", + "outputId": "91cd95d0-37eb-4b88-d0c4-601c81e780f1" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 48 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjkAAAGdCAYAAADwjmIIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAyuElEQVR4nO3de1xVdb7/8Tds5aKyt3kBZAQvR1MpLwmKO6eLSXIKmzxaqTlKavXQwJ/K5G3GtDvdDE1NM5vwdHLUZo5NSWqEt07iJcxSS6aSBhsFbUq2UoLC/v0xD9a0BU0Q2PDl9Xw89uPhXuuz1/qsPcxjv/uu71rLx+12uwUAAGAYX283AAAAUBsIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIzXxdgPeVFZWpmPHjikoKEg+Pj7ebgcAAFwGt9ut06dPKywsTL6+Fx+vadQh59ixYwoPD/d2GwAAoBqOHj2q9u3bX3R9ow45QUFBkv71Jdntdi93AwAALofL5VJ4eLj1O34xjTrklJ+istvthBwAABqYX5pqwsRjAABgJEIOAAAwEiEHAAAYqVHPyQEA1C9ut1vnz59XaWmpt1uBF9lsNjVp0uSKb+9CyAEA1AslJSU6fvy4fvzxR2+3gnqgWbNmateunfz8/Kq9DUIOAMDrysrKlJubK5vNprCwMPn5+XGT1kbK7XarpKREJ0+eVG5urrp27XrJG/5dCiEHAOB1JSUlKisrU3h4uJo1a+btduBlgYGBatq0qf7+97+rpKREAQEB1doOE48BAPVGdf+LHeapib8F/poAAICRCDkAANSC++67T8OGDavz/T766KPq06dPne+3PmJODgCgXkvN+Fud7Wv6rVfX2LYWLVokt9tdY9tD1RFyAACoBQ6Hw9stNHqcrgIA4Ar8+c9/Vs+ePRUYGKjWrVsrNjZWRUVFFU5XnT59WmPGjFHz5s3Vrl07paam6uabb9a0adOsmo4dO+rpp5/WhAkTFBQUpIiICK1YscJjf7NmzdLVV1+tZs2aqXPnznrkkUd07ty5OjrahoWQAwBANR0/flyjR4/WhAkT9MUXX2jbtm0aPnx4paepkpOT9dFHH+mdd95RRkaGPvzwQ+3bt69C3YIFCxQdHa1PPvlEDz30kCZPnqycnBxrfVBQkNLS0vT5559r0aJFevXVV5Wamlqrx9lQcboKNW9rSsVlg+bUfR8AUMuOHz+u8+fPa/jw4erQoYMkqWfPnhXqTp8+rVWrVmn16tUaPHiwJOn1119XWFhYhdrbb79dDz30kKR/jdqkpqZq69at6tatmyRp7ty5Vm3Hjh318MMPa82aNZo5c2aNH19DR8gBAKCaevfurcGDB6tnz56Ki4vTkCFDdNddd+mqq67yqDty5IjOnTun/v37W8scDocVXH6uV69e1r99fHwUGhqqEydOWMvWrl2rl156SV9//bXOnDmj8+fPy26318LRNXycrgIAoJpsNpsyMjK0ceNGRUZGavHixerWrZtyc3Orvc2mTZt6vPfx8VFZWZkkKSsrS2PGjNHtt9+uDRs26JNPPtEf/vAHlZSUXNFxmIqQAwDAFfDx8dHAgQP12GOP6ZNPPpGfn5/Wr1/vUdO5c2c1bdpUe/futZYVFhbqb3+r2uXxO3fuVIcOHfSHP/xB0dHR6tq1q/7+97/XyHGYiNNVAABU0+7du5WZmakhQ4YoODhYu3fv1smTJ9WjRw999tlnVl1QUJASEhI0Y8YMtWrVSsHBwZo/f758fX2r9CDSrl27Ki8vT2vWrFG/fv2Unp5eIVDh3xjJAQCgmux2u3bs2KHbb79dV199tebOnasFCxbotttuq1D74osvyul0aujQoYqNjdXAgQPVo0ePKj188je/+Y2mT5+upKQk9enTRzt37tQjjzxSk4dkFB93I74do8vlksPhUGFhIZO2ahJXVwGoorNnzyo3N1edOnWq9hOnG5qioiL96le/0oIFCzRx4kRvt1PvXOpv4nJ/vzldBQBAHfjkk090+PBh9e/fX4WFhXr88cclSXfeeaeXOzMXIQcAgDrywgsvKCcnR35+foqKitKHH36oNm3aeLstYxFyAACoA9ddd52ys7O93UajwsRjAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjVSnkPProo/Lx8fF4de/e3Vp/9uxZJSYmqnXr1mrRooVGjBihgoICj23k5eUpPj5ezZo1U3BwsGbMmKHz58971Gzbtk19+/aVv7+/unTporS0tAq9LF26VB07dlRAQIBiYmK0Z8+eqhwKAAA14uabb9a0adNqbfv33Xefhg0bVmvb94ZvvvlGPj4+2r9/f63up8r3ybnmmmv0wQcf/HsDTf69ienTpys9PV1vvfWWHA6HkpKSNHz4cH300UeSpNLSUsXHxys0NFQ7d+7U8ePHNW7cODVt2lRPP/20JCk3N1fx8fGaNGmS3nzzTWVmZur+++9Xu3btFBcXJ0lau3atkpOTtXz5csXExGjhwoWKi4tTTk6OgoODr+gLAQDUM5U9Kqa28Agao1T5dFWTJk0UGhpqvcrv1FhYWKjXXntNL774om655RZFRUXp9ddf186dO7Vr1y5J0vvvv6/PP/9c//M//6M+ffrotttu0xNPPKGlS5eqpKREkrR8+XJ16tRJCxYsUI8ePZSUlKS77rpLqampVg8vvviiHnjgAY0fP16RkZFavny5mjVrpj/+8Y818Z0AAGC08t9c01U55Hz55ZcKCwtT586dNWbMGOXl5UmSsrOzde7cOcXGxlq13bt3V0REhLKysiRJWVlZ6tmzp0JCQqyauLg4uVwuHTp0yKr5+TbKa8q3UVJSouzsbI8aX19fxcbGWjUXU1xcLJfL5fECAOBKnT9/XklJSXI4HGrTpo0eeeQRlT//+o033lB0dLSCgoIUGhqqe++9VydOnPD4/KFDhzR06FDZ7XYFBQXphhtu0Ndff13pvvbu3au2bdvq2WeftZY9+eSTCg4OVlBQkO6//37Nnj1bffr0sdaXn/J66qmnFBYWpm7dukmSDhw4oFtuuUWBgYFq3bq1HnzwQZ05c8b6XGWn4oYNG6b77rvPet+xY0c9/fTTmjBhgoKCghQREaEVK1Z4fGbPnj267rrrFBAQoOjoaH3yySeX/d1eiSqFnJiYGKWlpWnTpk1atmyZcnNzdcMNN+j06dPKz8+Xn5+fWrZs6fGZkJAQ5efnS5Ly8/M9Ak75+vJ1l6pxuVz66aef9N1336m0tLTSmvJtXExKSoocDof1Cg8Pr8rhAwBQqVWrVqlJkybas2ePFi1apBdffFErV66UJJ07d05PPPGEPv30U7399tv65ptvPELCP/7xD914443y9/fXli1blJ2drQkTJlSYrypJW7Zs0a233qqnnnpKs2bNkiS9+eabeuqpp/Tss88qOztbERERWrZsWYXPZmZmKicnRxkZGdqwYYOKiooUFxenq666Snv37tVbb72lDz74QElJSVU+/gULFljh5aGHHtLkyZOVk5MjSTpz5oyGDh2qyMhIZWdn69FHH9XDDz9c5X1UR5Xm5Nx2223Wv3v16qWYmBh16NBB69atU2BgYI03V9PmzJmj5ORk673L5SLoAACuWHh4uFJTU+Xj46Nu3brpwIEDSk1N1QMPPKAJEyZYdZ07d9ZLL72kfv366cyZM2rRooWWLl0qh8OhNWvWqGnTppKkq6++usI+1q9fr3HjxmnlypUaOXKktXzx4sWaOHGixo8fL0maN2+e3n//fY8RGUlq3ry5Vq5cKT8/P0nSq6++qrNnz+q///u/1bx5c0nSkiVLdMcdd+jZZ5+tMJhwKbfffrseeughSdKsWbOUmpqqrVu3qlu3blq9erXKysr02muvKSAgQNdcc42+/fZbTZ48+bK3X11XdAl5y5YtdfXVV+urr75SaGioSkpKdOrUKY+agoIChYaGSpJCQ0MrXG1V/v6Xaux2uwIDA9WmTRvZbLZKa8q3cTH+/v6y2+0eLwAArtSAAQPk4+NjvXc6nfryyy9VWlqq7Oxs3XHHHYqIiFBQUJBuuukmSbKme+zfv1833HCDFXAqs3v3bt1999164403PAKOJOXk5Kh///4eyy58L0k9e/a0Ao4kffHFF+rdu7cVcCRp4MCBKisrs0ZhLlevXr2sf/v4+Cg0NNQ6JffFF1+oV69eCggIsGqcTmeVtl9dVxRyzpw5o6+//lrt2rVTVFSUmjZtqszMTGt9Tk6O8vLyrINxOp06cOCAx7nIjIwM2e12RUZGWjU/30Z5Tfk2yh9P//OasrIyZWZm1tmXBgDA5Th79qzi4uJkt9v15ptvau/evVq/fr2kf0/+vZwzIf/xH/+h7t27649//KPOnTtXrV5+HmYul6+vrzW3qFxl+78woPn4+KisrKzK+6tpVQo5Dz/8sLZv365vvvlGO3fu1H/913/JZrNp9OjRcjgcmjhxopKTk7V161ZlZ2dr/PjxcjqdGjBggCRpyJAhioyM1NixY/Xpp59q8+bNmjt3rhITE+Xv7y9JmjRpko4cOaKZM2fq8OHDevnll7Vu3TpNnz7d6iM5OVmvvvqqVq1apS+++EKTJ09WUVGRNVQHAEBd2r17t8f7Xbt2qWvXrjp8+LD++c9/6plnntENN9yg7t27V5h03KtXL3344YeXDC9t2rTRli1b9NVXX+mee+7xqO3WrZv27t3rUX/h+8r06NFDn376qYqKiqxlH330kXx9fa2JyW3bttXx48et9aWlpTp48OAvbvvC/Xz22Wc6e/astaz8quvaVqWQ8+2332r06NHq1q2b7rnnHrVu3Vq7du1S27ZtJUmpqakaOnSoRowYoRtvvFGhoaH63//9X+vzNptNGzZskM1mk9Pp1G9/+1uNGzdOjz/+uFXTqVMnpaenKyMjQ71799aCBQu0cuVK6x45kjRy5Ei98MILmjdvnvr06aP9+/dr06ZNVTp/CABATcnLy1NycrJycnL0pz/9SYsXL9bUqVMVEREhPz8/LV68WEeOHNE777yjJ554wuOzSUlJcrlcGjVqlD7++GN9+eWXeuONNyqcMgoODtaWLVt0+PBhjR492pqYPGXKFL322mtatWqVvvzySz355JP67LPPPE6fVWbMmDEKCAhQQkKCDh48qK1bt2rKlCkaO3as9Xt6yy23KD09Xenp6Tp8+LAmT55cYVrKL7n33nvl4+OjBx54QJ9//rnee+89vfDCC1XaRnVVaeLxmjVrLrk+ICBAS5cu1dKlSy9a06FDB7333nuX3M7NN9/8i5eXJSUlVWsGOAAANW3cuHH66aef1L9/f9lsNk2dOlUPPvigfHx8lJaWpt///vd66aWX1LdvX73wwgv6zW9+Y322devW2rJli2bMmKGbbrpJNptNffr00cCBAyvsJzQ0VFu2bNHNN9+sMWPGaPXq1RozZoyOHDmihx9+WGfPntU999yj++677xefBNCsWTNt3rxZU6dOVb9+/dSsWTONGDFCL774olUzYcIEffrppxo3bpyaNGmi6dOna9CgQVX6blq0aKF3331XkyZN0nXXXafIyEg9++yzGjFiRJW2Ux0+7gtPtjUiLpdLDodDhYWFTEKuSZXdnZS7iAK4hLNnzyo3N1edOnXymKCK6rn11lsVGhqqN954w9utVNul/iYu9/e7yo91AAAA9cePP/6o5cuXKy4uTjabTX/605/0wQcfKCMjw9uteR0hBwCABszHx0fvvfeennrqKZ09e1bdunXTX/7ylwpPD2iMCDkAADRggYGBHg/Oxr9d0X1yAAAA6itCDgAAMBIhBwBQbzTiC35xgZr4WyDkAAC8rvyxAD/++KOXO0F9Uf63cKlnev0SJh4DALzOZrOpZcuW1iMPmjVr9ot37IWZ3G63fvzxR504cUItW7aUzWar9rYIOQCAeiE0NFSSKjzbCY1Ty5Ytrb+J6iLkAADqBR8fH7Vr107BwcHVftI2zNC0adMrGsEpR8gBANQrNputRn7gACYeAwAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMxH1yUDe2pni+HzTHO30AABoNRnIAAICRGMnBlbtwlAYAgHqAkRwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMNIVhZxnnnlGPj4+mjZtmrXs7NmzSkxMVOvWrdWiRQuNGDFCBQUFHp/Ly8tTfHy8mjVrpuDgYM2YMUPnz5/3qNm2bZv69u0rf39/denSRWlpaRX2v3TpUnXs2FEBAQGKiYnRnj17ruRwAACAQaodcvbu3atXXnlFvXr18lg+ffp0vfvuu3rrrbe0fft2HTt2TMOHD7fWl5aWKj4+XiUlJdq5c6dWrVqltLQ0zZs3z6rJzc1VfHy8Bg0apP3792vatGm6//77tXnzZqtm7dq1Sk5O1vz587Vv3z717t1bcXFxOnHiRHUPCQAAGMTH7Xa7q/qhM2fOqG/fvnr55Zf15JNPqk+fPlq4cKEKCwvVtm1brV69WnfddZck6fDhw+rRo4eysrI0YMAAbdy4UUOHDtWxY8cUEhIiSVq+fLlmzZqlkydPys/PT7NmzVJ6eroOHjxo7XPUqFE6deqUNm3aJEmKiYlRv379tGTJEklSWVmZwsPDNWXKFM2ePfuyjsPlcsnhcKiwsFB2u72qXwPKbU2p+mcGzan5PgAAjcLl/n5XayQnMTFR8fHxio2N9VienZ2tc+fOeSzv3r27IiIilJWVJUnKyspSz549rYAjSXFxcXK5XDp06JBVc+G24+LirG2UlJQoOzvbo8bX11exsbFWTWWKi4vlcrk8XgAAwExNqvqBNWvWaN++fdq7d2+Fdfn5+fLz81PLli09loeEhCg/P9+q+XnAKV9fvu5SNS6XSz/99JN++OEHlZaWVlpz+PDhi/aekpKixx577PIOFAAANGhVGsk5evSopk6dqjfffFMBAQG11VOtmTNnjgoLC63X0aNHvd0SAACoJVUKOdnZ2Tpx4oT69u2rJk2aqEmTJtq+fbteeuklNWnSRCEhISopKdGpU6c8PldQUKDQ0FBJUmhoaIWrrcrf/1KN3W5XYGCg2rRpI5vNVmlN+TYq4+/vL7vd7vECAABmqlLIGTx4sA4cOKD9+/dbr+joaI0ZM8b6d9OmTZWZmWl9JicnR3l5eXI6nZIkp9OpAwcOeFwFlZGRIbvdrsjISKvm59sorynfhp+fn6KiojxqysrKlJmZadUAAIDGrUpzcoKCgnTttdd6LGvevLlat25tLZ84caKSk5PVqlUr2e12TZkyRU6nUwMGDJAkDRkyRJGRkRo7dqyee+455efna+7cuUpMTJS/v78kadKkSVqyZIlmzpypCRMmaMuWLVq3bp3S09Ot/SYnJyshIUHR0dHq37+/Fi5cqKKiIo0fP/6KvhAAAGCGKk88/iWpqany9fXViBEjVFxcrLi4OL388svWepvNpg0bNmjy5MlyOp1q3ry5EhIS9Pjjj1s1nTp1Unp6uqZPn65Fixapffv2WrlypeLi4qyakSNH6uTJk5o3b57y8/PVp08fbdq0qcJkZAAA0DhV6z45puA+OTWE++QAAOpQrd4nBwAAoL4j5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASE283QAaqa0pFZcNmlP3fQAAjMVIDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABipSiFn2bJl6tWrl+x2u+x2u5xOpzZu3GitP3v2rBITE9W6dWu1aNFCI0aMUEFBgcc28vLyFB8fr2bNmik4OFgzZszQ+fPnPWq2bdumvn37yt/fX126dFFaWlqFXpYuXaqOHTsqICBAMTEx2rNnT1UOBQAAGK5KIad9+/Z65plnlJ2drY8//li33HKL7rzzTh06dEiSNH36dL377rt66623tH37dh07dkzDhw+3Pl9aWqr4+HiVlJRo586dWrVqldLS0jRv3jyrJjc3V/Hx8Ro0aJD279+vadOm6f7779fmzZutmrVr1yo5OVnz58/Xvn371Lt3b8XFxenEiRNX+n0AAABD+LjdbveVbKBVq1Z6/vnnddddd6lt27ZavXq17rrrLknS4cOH1aNHD2VlZWnAgAHauHGjhg4dqmPHjikkJESStHz5cs2aNUsnT56Un5+fZs2apfT0dB08eNDax6hRo3Tq1Clt2rRJkhQTE6N+/fppyZIlkqSysjKFh4drypQpmj179mX37nK55HA4VFhYKLvdfiVfQ+O2NaVmtjNoTs1sBwBgtMv9/a72nJzS0lKtWbNGRUVFcjqdys7O1rlz5xQbG2vVdO/eXREREcrKypIkZWVlqWfPnlbAkaS4uDi5XC5rNCgrK8tjG+U15dsoKSlRdna2R42vr69iY2OtmospLi6Wy+XyeAEAADNVOeQcOHBALVq0kL+/vyZNmqT169crMjJS+fn58vPzU8uWLT3qQ0JClJ+fL0nKz8/3CDjl68vXXarG5XLpp59+0nfffafS0tJKa8q3cTEpKSlyOBzWKzw8vKqHDwAAGogqh5xu3bpp//792r17tyZPnqyEhAR9/vnntdFbjZszZ44KCwut19GjR73dEgAAqCVNqvoBPz8/denSRZIUFRWlvXv3atGiRRo5cqRKSkp06tQpj9GcgoIChYaGSpJCQ0MrXAVVfvXVz2suvCKroKBAdrtdgYGBstlsstlsldaUb+Ni/P395e/vX9VDBgAADdAV3yenrKxMxcXFioqKUtOmTZWZmWmty8nJUV5enpxOpyTJ6XTqwIEDHldBZWRkyG63KzIy0qr5+TbKa8q34efnp6ioKI+asrIyZWZmWjUAAABVGsmZM2eObrvtNkVEROj06dNavXq1tm3bps2bN8vhcGjixIlKTk5Wq1atZLfbNWXKFDmdTg0YMECSNGTIEEVGRmrs2LF67rnnlJ+fr7lz5yoxMdEaYZk0aZKWLFmimTNnasKECdqyZYvWrVun9PR0q4/k5GQlJCQoOjpa/fv318KFC1VUVKTx48fX4FcDAAAasiqFnBMnTmjcuHE6fvy4HA6HevXqpc2bN+vWW2+VJKWmpsrX11cjRoxQcXGx4uLi9PLLL1uft9ls2rBhgyZPniyn06nmzZsrISFBjz/+uFXTqVMnpaena/r06Vq0aJHat2+vlStXKi4uzqoZOXKkTp48qXnz5ik/P199+vTRpk2bKkxGBgAAjdcV3yenIeM+OTWE++QAAOpQrd8nBwAAoD4j5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjNTE2w2ggdma4u0OAAC4LIzkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASTyFH/XHhE84HzfFOHwAAIzCSAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYqUohJyUlRf369VNQUJCCg4M1bNgw5eTkeNScPXtWiYmJat26tVq0aKERI0aooKDAoyYvL0/x8fFq1qyZgoODNWPGDJ0/f96jZtu2berbt6/8/f3VpUsXpaWlVehn6dKl6tixowICAhQTE6M9e/ZU5XAAAIDBqhRytm/frsTERO3atUsZGRk6d+6chgwZoqKiIqtm+vTpevfdd/XWW29p+/btOnbsmIYPH26tLy0tVXx8vEpKSrRz506tWrVKaWlpmjdvnlWTm5ur+Ph4DRo0SPv379e0adN0//33a/PmzVbN2rVrlZycrPnz52vfvn3q3bu34uLidOLEiSv5PgAAgCF83G63u7ofPnnypIKDg7V9+3bdeOONKiwsVNu2bbV69WrdddddkqTDhw+rR48eysrK0oABA7Rx40YNHTpUx44dU0hIiCRp+fLlmjVrlk6ePCk/Pz/NmjVL6enpOnjwoLWvUaNG6dSpU9q0aZMkKSYmRv369dOSJUskSWVlZQoPD9eUKVM0e/bsy+rf5XLJ4XCosLBQdru9ul9D43LhoxdqE491AABU4nJ/v69oTk5hYaEkqVWrVpKk7OxsnTt3TrGxsVZN9+7dFRERoaysLElSVlaWevbsaQUcSYqLi5PL5dKhQ4esmp9vo7ymfBslJSXKzs72qPH19VVsbKxVAwAAGrdqP6CzrKxM06ZN08CBA3XttddKkvLz8+Xn56eWLVt61IaEhCg/P9+q+XnAKV9fvu5SNS6XSz/99JN++OEHlZaWVlpz+PDhi/ZcXFys4uJi673L5arCEaO2ZR35p8d75yAvNQIAMEK1R3ISExN18OBBrVmzpib7qVUpKSlyOBzWKzw83NstAQCAWlKtkZykpCRt2LBBO3bsUPv27a3loaGhKikp0alTpzxGcwoKChQaGmrVXHgVVPnVVz+vufCKrIKCAtntdgUGBspms8lms1VaU76NysyZM0fJycnWe5fLRdCpx1Iz/lZh2fRbr/ZCJwCAhqhKIzlut1tJSUlav369tmzZok6dOnmsj4qKUtOmTZWZmWkty8nJUV5enpxOpyTJ6XTqwIEDHldBZWRkyG63KzIy0qr5+TbKa8q34efnp6ioKI+asrIyZWZmWjWV8ff3l91u93gBAAAzVWkkJzExUatXr9Zf//pXBQUFWXNoHA6HAgMD5XA4NHHiRCUnJ6tVq1ay2+2aMmWKnE6nBgwYIEkaMmSIIiMjNXbsWD333HPKz8/X3LlzlZiYKH9/f0nSpEmTtGTJEs2cOVMTJkzQli1btG7dOqWnp1u9JCcnKyEhQdHR0erfv78WLlyooqIijR8/vqa+GwAA0IBVKeQsW7ZMknTzzTd7LH/99dd13333SZJSU1Pl6+urESNGqLi4WHFxcXr55ZetWpvNpg0bNmjy5MlyOp1q3ry5EhIS9Pjjj1s1nTp1Unp6uqZPn65Fixapffv2WrlypeLi4qyakSNH6uTJk5o3b57y8/PVp08fbdq0qcJkZAAA0Dhd0X1yGjruk1MNtXifnAuvrtoV8WCFGubkAAAu9/e72peQA1fiwkADAEBN4wGdAADASIQcAABgJE5XoUG58N45zNEBAFwMIzkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCQe64AG7cLHPEg86gEA8C+M5AAAACMRcgAAgJEIOQAAwEiEHAAAYCQmHqNOZB35p7dbAAA0MozkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEldXwTgXPuqBxzwAQOPESA4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEjcJwf11oC8FRWW7Yp40AudAAAaIkZyAACAkQg5AADASIQcAABgJEIOAAAwEhOPYbwLH9gp8dBOAGgMGMkBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJ++SgxmUd+ae3WwAAgJEcAABgJkIOAAAwEiEHAAAYiZADAACMRMgBAABG4uoqNEoXPpmcp5IDgHkYyQEAAEYi5AAAACMRcgAAgJGqHHJ27NihO+64Q2FhYfLx8dHbb7/tsd7tdmvevHlq166dAgMDFRsbqy+//NKj5vvvv9eYMWNkt9vVsmVLTZw4UWfOnPGo+eyzz3TDDTcoICBA4eHheu655yr08tZbb6l79+4KCAhQz5499d5771X1cAAAgKGqHHKKiorUu3dvLV26tNL1zz33nF566SUtX75cu3fvVvPmzRUXF6ezZ89aNWPGjNGhQ4eUkZGhDRs2aMeOHXrwwQet9S6XS0OGDFGHDh2UnZ2t559/Xo8++qhWrFhh1ezcuVOjR4/WxIkT9cknn2jYsGEaNmyYDh48WNVDAgAABvJxu93uan/Yx0fr16/XsGHDJP1rFCcsLEy/+93v9PDDD0uSCgsLFRISorS0NI0aNUpffPGFIiMjtXfvXkVHR0uSNm3apNtvv13ffvutwsLCtGzZMv3hD39Qfn6+/Pz8JEmzZ8/W22+/rcOHD0uSRo4cqaKiIm3YsMHqZ8CAAerTp4+WL19+Wf27XC45HA4VFhbKbrdX92toXLam/GJJbT67alfEg79cVA1cXQUADcfl/n7X6Jyc3Nxc5efnKzY21lrmcDgUExOjrKwsSVJWVpZatmxpBRxJio2Nla+vr3bv3m3V3HjjjVbAkaS4uDjl5OTohx9+sGp+vp/ymvL9VKa4uFgul8vjBQAAzFSjISc/P1+SFBIS4rE8JCTEWpefn6/g4GCP9U2aNFGrVq08airbxs/3cbGa8vWVSUlJkcPhsF7h4eFVPUQAANBANKqrq+bMmaPCwkLrdfToUW+3BAAAakmNhpzQ0FBJUkFBgcfygoICa11oaKhOnDjhsf78+fP6/vvvPWoq28bP93GxmvL1lfH395fdbvd4AQAAM9VoyOnUqZNCQ0OVmZlpLXO5XNq9e7ecTqckyel06tSpU8rOzrZqtmzZorKyMsXExFg1O3bs0Llz56yajIwMdevWTVdddZVV8/P9lNeU7wcAADRuVX521ZkzZ/TVV19Z73Nzc7V//361atVKERERmjZtmp588kl17dpVnTp10iOPPKKwsDDrCqwePXroP//zP/XAAw9o+fLlOnfunJKSkjRq1CiFhYVJku6991499thjmjhxombNmqWDBw9q0aJFSk1NtfY7depU3XTTTVqwYIHi4+O1Zs0affzxxx6XmQOX68JnWUlccQUADV2VQ87HH3+sQYMGWe+Tk5MlSQkJCUpLS9PMmTNVVFSkBx98UKdOndKvf/1rbdq0SQEBAdZn3nzzTSUlJWnw4MHy9fXViBEj9NJLL1nrHQ6H3n//fSUmJioqKkpt2rTRvHnzPO6lc/3112v16tWaO3eufv/736tr1656++23de2111briwAAAGa5ovvkNHTcJ6caDL1PTmUYyQGA+skr98kBAACoL6p8ugrwpgF5nnOu6nJkBwDQsDCSAwAAjETIAQAARiLkAAAAIxFyAACAkZh4DFzEhTcI5JJyAGhYGMkBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkrq7CFavNB3ICAFBdjOQAAAAjEXIAAICROF0FXKYLbw4ocYNAAKjPCDm4tK0p3u4AAIBq4XQVAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjcXUVGrQBeSsqLNsV8aAXOgEA1DeEHOAKXHjvHO6bAwD1B6erAACAkQg5AADASIQcAABgJEIOAAAwEhOPgRrEQzwBoP5gJAcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEhcQg7UMp5vBQDewUgOAAAwEiEHAAAYidNVMM6AvBUe73dFPOilTirHXZEBoG4wkgMAAIzESA6qJOvIP73dAgAAl4WRHAAAYCRCDgAAMBKnq4B6gHvpAEDNYyQHAAAYiZEcoB7iMnMAuHKEHBjvwvvmSPXv3jkAgJrH6SoAAGAkRnKABoLJyQBQNYQc/NvWFG93AABAjSHkAA0Uk5MB4NIIOWiU6vtDPKuLU1oA8G9MPAYAAEZiJAcwGKe0ADRmhBxAjeteOpzSAtBYNPiQs3TpUj3//PPKz89X7969tXjxYvXv39/bbRkj68g/vd0CahmjPQBM1aBDztq1a5WcnKzly5crJiZGCxcuVFxcnHJychQcHOzt9uo/Lhm/JFMnJ1+OyoLPhQhCAOo7H7fb7fZ2E9UVExOjfv36acmSJZKksrIyhYeHa8qUKZo9e/Yvft7lcsnhcKiwsFB2u722261/LiPkMJJzaY0p+NQUwhGAK3W5v98NdiSnpKRE2dnZmjNnjrXM19dXsbGxysrKqvQzxcXFKi4utt4XFhZK+teXZbwdC36xZM8339dBI2bpmbP4F2v2th9fB500HClv76uR7STe0qVGtgOg4Sn/3f6lcZoGG3K+++47lZaWKiQkxGN5SEiIDh8+XOlnUlJS9Nhjj1VYHh4eXis9Av+yxNsNGOn33m4AgNedPn1aDofjousbbMipjjlz5ig5Odl6X1ZWpu+//16tW7eWj4/PZW3D5XIpPDxcR48ebVSnuBrrcUscO8feuI69sR63xLE3pGN3u906ffq0wsLCLlnXYENOmzZtZLPZVFBQ4LG8oKBAoaGhlX7G399f/v7+HstatmxZrf3b7fYG8YdQ0xrrcUscO8feuDTW45Y49oZy7JcawSnXYO947Ofnp6ioKGVmZlrLysrKlJmZKafT6cXOAABAfdBgR3IkKTk5WQkJCYqOjlb//v21cOFCFRUVafx4JnoCANDYNeiQM3LkSJ08eVLz5s1Tfn6++vTpo02bNlWYjFyT/P39NX/+/AqnvUzXWI9b4tg59sZ17I31uCWO3cRjb9D3yQEAALiYBjsnBwAA4FIIOQAAwEiEHAAAYCRCDgAAMBIh5wo89dRTuv7669WsWbNq31SwoVi6dKk6duyogIAAxcTEaM+ePd5uqdbt2LFDd9xxh8LCwuTj46O3337b2y3VmZSUFPXr109BQUEKDg7WsGHDlJOT4+22at2yZcvUq1cv64ZoTqdTGzdu9HZbXvHMM8/Ix8dH06ZN83Yrte7RRx+Vj4+Px6t79+7ebqvO/OMf/9Bvf/tbtW7dWoGBgerZs6c+/vhjb7dVIwg5V6CkpER33323Jk+e7O1WatXatWuVnJys+fPna9++ferdu7fi4uJ04sQJb7dWq4qKitS7d28tXbrU263Uue3btysxMVG7du1SRkaGzp07pyFDhqioqMjbrdWq9u3b65lnnlF2drY+/vhj3XLLLbrzzjt16NAhb7dWp/bu3atXXnlFvXr18nYrdeaaa67R8ePHrdf//d//ebulOvHDDz9o4MCBatq0qTZu3KjPP/9cCxYs0FVXXeXt1mqGG1fs9ddfdzscDm+3UWv69+/vTkxMtN6Xlpa6w8LC3CkpKV7sqm5Jcq9fv97bbXjNiRMn3JLc27dv93Yrde6qq65yr1y50ttt1JnTp0+7u3bt6s7IyHDfdNNN7qlTp3q7pVo3f/58d+/evb3dhlfMmjXL/etf/9rbbdQaRnJwSSUlJcrOzlZsbKy1zNfXV7GxscrKyvJiZ6hLhYWFkqRWrVp5uZO6U1paqjVr1qioqKhRPSomMTFR8fHxHv+fbwy+/PJLhYWFqXPnzhozZozy8vK83VKdeOeddxQdHa27775bwcHBuu666/Tqq696u60aQ8jBJX333XcqLS2tcBfpkJAQ5efne6kr1KWysjJNmzZNAwcO1LXXXuvtdmrdgQMH1KJFC/n7+2vSpElav369IiMjvd1WnVizZo327dunlJQUb7dSp2JiYpSWlqZNmzZp2bJlys3N1Q033KDTp097u7Vad+TIES1btkxdu3bV5s2bNXnyZP2///f/tGrVKm+3ViMa9GMdasPs2bP17LPPXrLmiy++aFST0tC4JSYm6uDBg41mjkK3bt20f/9+FRYW6s9//rMSEhK0fft244PO0aNHNXXqVGVkZCggIMDb7dSp2267zfp3r169FBMTow4dOmjdunWaOHGiFzurfWVlZYqOjtbTTz8tSbruuut08OBBLV++XAkJCV7u7soRci7wu9/9Tvfdd98lazp37lw3zdQDbdq0kc1mU0FBgcfygoIChYaGeqkr1JWkpCRt2LBBO3bsUPv27b3dTp3w8/NTly5dJElRUVHau3evFi1apFdeecXLndWu7OxsnThxQn379rWWlZaWaseOHVqyZImKi4tls9m82GHdadmypa6++mp99dVX3m6l1rVr165CgO/Ro4f+8pe/eKmjmkXIuUDbtm3Vtm1bb7dRb/j5+SkqKkqZmZkaNmyYpH8l/8zMTCUlJXm3OdQat9utKVOmaP369dq2bZs6derk7Za8pqysTMXFxd5uo9YNHjxYBw4c8Fg2fvx4de/eXbNmzWo0AUeSzpw5o6+//lpjx471diu1buDAgRVuD/G3v/1NHTp08FJHNYuQcwXy8vL0/fffKy8vT6Wlpdq/f78kqUuXLmrRooV3m6tBycnJSkhIUHR0tPr376+FCxeqqKhI48eP93ZrterMmTMe/yWXm5ur/fv3q1WrVoqIiPBiZ7UvMTFRq1ev1l//+lcFBQVZ868cDocCAwO93F3tmTNnjm677TZFRETo9OnTWr16tbZt26bNmzd7u7VaFxQUVGHOVfPmzdW6dWvj52I9/PDDuuOOO9ShQwcdO3ZM8+fPl81m0+jRo73dWq2bPn26rr/+ej399NO65557tGfPHq1YsUIrVqzwdms1w9uXdzVkCQkJbkkVXlu3bvV2azVu8eLF7oiICLefn5+7f//+7l27dnm7pVq3devWSv/3TUhI8HZrta6y45bkfv31173dWq2aMGGCu0OHDm4/Pz9327Zt3YMHD3a///773m7LaxrLJeQjR450t2vXzu3n5+f+1a9+5R45cqT7q6++8nZbdebdd991X3vttW5/f3939+7d3StWrPB2SzXGx+12u+s+WgEAANQuLiEHAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEj/H9fRYHUpRrsyAAAAAElFTkSuQmCC\n" + }, + "metadata": {} + } + ], + "source": [ + "h_s,bins,_=plt.hist(output_s,label=\"signal\",alpha=0.5,bins=100)\n", + "h_b,bins,_=plt.hist(output_b,bins=bins,alpha=0.5,label=\"background\")\n", + "plt.legend()" + ] } ], "metadata": { From 2da7727705e4390d6e4077ba0dab665c0a140cd7 Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Mon, 4 Nov 2024 20:55:40 -0600 Subject: [PATCH 20/22] lab7 --- Labs/Lab.7/Lab.7.ipynb | 9000 ++++++++++++++++++++-------------------- 1 file changed, 4500 insertions(+), 4500 deletions(-) diff --git a/Labs/Lab.7/Lab.7.ipynb b/Labs/Lab.7/Lab.7.ipynb index d7f111b..fff11ed 100644 --- a/Labs/Lab.7/Lab.7.ipynb +++ b/Labs/Lab.7/Lab.7.ipynb @@ -1,4702 +1,4702 @@ { - "cells": [ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "KUyRiEnW_rdT" + }, + "source": [ + "# Lab 7- Data Analysis\n", + "\n", + "Exercises 1-4 are to be completed by October 25th . The remaider of the lab is due by November 1st. Before leaving lab today, everyone must download the dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YuVw1fv2_rjn" + }, + "source": [ + "## Exercise 1: Reading\n", + "\n", + "### HiggsML\n", + "In 2014, some of my colleagues from the ATLAS experiment put together a Higgs Machine Learning Challenge, which was hosted on [Kaggle](https://www.kaggle.com). Please read sections 1 and 3 (skip/skim 2) of [The HiggsML Technical Documentation](https://higgsml.lal.in2p3.fr/files/2014/04/documentation_v1.8.pdf).\n", + "\n", + "Kaggle is a platform for data science competitions, with cash awards for winners. Kaggle currently hosts over 50,000 public datasets and associated competitions. Later in the course we will look at a variety of problems hosted on Kaggle and similar platforms.\n", + "\n", + "### SUSY Dataset\n", + "\n", + "For the next few labs we will use datasets used in the [first paper on Deep Learning in High Energy physics](https://arxiv.org/pdf/1402.4735.pdf). Please read up to the \"Deep Learning\" section (end of page 5). This paper demonstrates that Deep Neural Networks can learn from raw data the features that are typically used by physicists for searches for exotics particles. The authors provide the data they used for this paper. They considered two benchmark scenarios: Higgs and SUSY." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ESqCE9d3_rjv" + }, + "source": [ + "## Exercise 2: Download SUSY Dataset\n", + "\n", + "The information about the dataset can be found at the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php). We'll start with the [SUSY Dataset](https://archive.ics.uci.edu/ml/datasets/SUSY).\n", + "\n", + "### Download\n", + "In a terminal, download the data directly from the source and then decompress it. For example:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "22lLtKRC_rkR" + }, + "source": [ + "* To download:\n", + " * On Mac OS:\n", + " `curl http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz > SUSY.csv.gz`\n", + "\n", + " * In linux:\n", + " `wget http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz`\n", + "\n", + "* To uncompress:\n", + "`gunzip SUSY.csv.gz`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Mwwd91Ik_rkf", + "outputId": "95eae6b4-697e-4f25-f4de-a90f65a4e800" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "KUyRiEnW_rdT" - }, - "source": [ - "# Lab 7- Data Analysis\n", - "\n", - "Exercises 1-4 are to be completed by Match 29th. The remaider of the lab is due April 5th. Before leaving lab today, everyone must download the dataset." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 879M 0 879M 0 0 70.2M 0 --:--:-- 0:00:12 --:--:-- 83.1M\n" + ] + } + ], + "source": [ + "!curl http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz > SUSY.csv.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "Wp5XNcmF_rku" + }, + "outputs": [], + "source": [ + "!gunzip SUSY.csv.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "kUOsiY1B_rk1", + "outputId": "ab6afca9-a1f4-41f1-bea3-556ed73eb8f7", + "scrolled": true + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "YuVw1fv2_rjn" - }, - "source": [ - "## Exercise 1: Reading\n", - "\n", - "### HiggsML\n", - "In 2014, some of my colleagues from the ATLAS experiment put together a Higgs Machine Learning Challenge, which was hosted on [Kaggle](https://www.kaggle.com). Please read sections 1 and 3 (skip/skim 2) of [The HiggsML Technical Documentation](https://higgsml.lal.in2p3.fr/files/2014/04/documentation_v1.8.pdf).\n", - "\n", - "Kaggle is a platform for data science competitions, with cash awards for winners. Kaggle currently hosts over 50,000 public datasets and associated competitions. Later in the course we will look at a variety of problems hosted on Kaggle and similar platforms.\n", - "\n", - "### SUSY Dataset\n", - "\n", - "For the next few labs we will use datasets used in the [first paper on Deep Learning in High Energy physics](https://arxiv.org/pdf/1402.4735.pdf). Please read up to the \"Deep Learning\" section (end of page 5). This paper demonstrates that Deep Neural Networks can learn from raw data the features that are typically used by physicists for searches for exotics particles. The authors provide the data they used for this paper. They considered two benchmark scenarios: Higgs and SUSY." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "total 880M\n", + "drwxr-xr-x 1 root root 4.0K Oct 29 13:25 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n", + "-rw-r--r-- 1 root root 880M Oct 31 14:41 SUSY.csv.gz\n" + ] + } + ], + "source": [ + "ls -lh" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WSFbZQqI_rk5" + }, + "source": [ + "The data is provided as a comma separated file." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "ONfmelbk_rk9", + "outputId": "4a50bd58-8a70-470c-9986-3983370f096f" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "ESqCE9d3_rjv" - }, - "source": [ - "## Exercise 2: Download SUSY Dataset\n", - "\n", - "The information about the dataset can be found at the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php). We'll start with the [SUSY Dataset](https://archive.ics.uci.edu/ml/datasets/SUSY).\n", - "\n", - "### Download\n", - "In a terminal, download the data directly from the source and then decompress it. For example:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "0.000000000000000000e+00,9.728614687919616699e-01,6.538545489311218262e-01,1.176224589347839355e+00,1.157156467437744141e+00,-1.739873170852661133e+00,-8.743090629577636719e-01,5.677649974822998047e-01,-1.750000417232513428e-01,8.100607395172119141e-01,-2.525521218776702881e-01,1.921887040138244629e+00,8.896374106407165527e-01,4.107718467712402344e-01,1.145620822906494141e+00,1.932632088661193848e+00,9.944640994071960449e-01,1.367815494537353516e+00,4.071449860930442810e-02\n", + "1.000000000000000000e+00,1.667973041534423828e+00,6.419061869382858276e-02,-1.225171446800231934e+00,5.061022043228149414e-01,-3.389389812946319580e-01,1.672542810440063477e+00,3.475464344024658203e+00,-1.219136357307434082e+00,1.295456290245056152e-02,3.775173664093017578e+00,1.045977115631103516e+00,5.680512785911560059e-01,4.819284379482269287e-01,0.000000000000000000e+00,4.484102725982666016e-01,2.053557634353637695e-01,1.321893453598022461e+00,3.775840103626251221e-01\n", + "1.000000000000000000e+00,4.448399245738983154e-01,-1.342980116605758667e-01,-7.099716067314147949e-01,4.517189264297485352e-01,-1.613871216773986816e+00,-7.686609029769897461e-01,1.219918131828308105e+00,5.040258169174194336e-01,1.831247568130493164e+00,-4.313853085041046143e-01,5.262832045555114746e-01,9.415140151977539062e-01,1.587535023689270020e+00,2.024308204650878906e+00,6.034975647926330566e-01,1.562373995780944824e+00,1.135454416275024414e+00,1.809100061655044556e-01\n", + "1.000000000000000000e+00,3.812560737133026123e-01,-9.761453866958618164e-01,6.931523084640502930e-01,4.489588439464569092e-01,8.917528986930847168e-01,-6.773284673690795898e-01,2.033060073852539062e+00,1.533040523529052734e+00,3.046259880065917969e+00,-1.005284786224365234e+00,5.693860650062561035e-01,1.015211343765258789e+00,1.582216739654541016e+00,1.551914215087890625e+00,7.612152099609375000e-01,1.715463757514953613e+00,1.492256760597229004e+00,9.071890264749526978e-02\n", + "1.000000000000000000e+00,1.309996485710144043e+00,-6.900894641876220703e-01,-6.762592792510986328e-01,1.589282631874084473e+00,-6.933256387710571289e-01,6.229069828987121582e-01,1.087561845779418945e+00,-3.817416727542877197e-01,5.892043709754943848e-01,1.365478992462158203e+00,1.179295063018798828e+00,9.682182073593139648e-01,7.285631299018859863e-01,0.000000000000000000e+00,1.083157896995544434e+00,4.342924803495407104e-02,1.154853701591491699e+00,9.485860168933868408e-02\n" + ] + } + ], + "source": [ + "filename=\"SUSY.csv\"\n", + "# print out the first 5 lines using unix head command\n", + "!head -5 \"SUSY.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "xGWPh3H-_rlB", + "outputId": "b5993369-6440-4a2e-ac37-62240703ab0d" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "22lLtKRC_rkR" - }, - "source": [ - "* To download:\n", - " * On Mac OS:\n", - " `curl http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz > SUSY.csv.gz`\n", - "\n", - " * In linux:\n", - " `wget http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz`\n", - "\n", - "* To uncompress:\n", - "`gunzip SUSY.csv.gz`" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "total 2.3G\n", + "drwxr-xr-x 1 root root 4.0K Oct 29 13:25 sample_data\n", + "-rw-r--r-- 1 root root 2.3G Oct 31 14:41 SUSY.csv\n", + "-rw-r--r-- 1 root root 0 Oct 31 14:41 SUSY-small.csv\n" + ] + } + ], + "source": [ + "### Reducing the dataset\n", + "\n", + "!ls -lh" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "vtWJ-Mz5_rlH", + "outputId": "a4b08786-65b1-4056-bd94-eff8cae701e3" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Mwwd91Ik_rkf", - "outputId": "95eae6b4-697e-4f25-f4de-a90f65a4e800" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 879M 0 879M 0 0 70.2M 0 --:--:-- 0:00:12 --:--:-- 83.1M\n" - ] - } - ], - "source": [ - "!curl http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz > SUSY.csv.gz" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "5000000 SUSY.csv\n" + ] + } + ], + "source": [ + "## How many datapoints in SUZY\n", + "\n", + "!wc -l SUSY.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "JBoOn61J_rlK" + }, + "outputs": [], + "source": [ + "!head -500000 SUSY.csv > SUSY-small.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "ral2i-VX_rlO", + "outputId": "eacbd357-4c59-4657-adba-b7eb0d42e409" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "Wp5XNcmF_rku" - }, - "outputs": [], - "source": [ - "!gunzip SUSY.csv.gz" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "total 2.5G\n", + "drwxr-xr-x 1 root root 4.0K Oct 29 13:25 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n", + "-rw-r--r-- 1 root root 2.3G Oct 31 14:41 SUSY.csv\n", + "-rw-r--r-- 1 root root 228M Oct 31 14:42 SUSY-small.csv\n" + ] + } + ], + "source": [ + "ls -lh" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pjNRqmyS_rlR" + }, + "source": [ + "### First Look\n", + "\n", + "Each row represents a LHC collision event. Each column contains some observable from that event. The variable names are ([based on documentation](https://archive.ics.uci.edu/ml/datasets/SUSY)):" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "qG8BaBKD_rlp" + }, + "outputs": [], + "source": [ + "VarNames=[\"signal\", \"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\", \"MET_rel\", \"axial_MET\", \"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos_theta_r1\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_00yPcCi_rlr" + }, + "source": [ + "Some of these variables represent the \"raw\" kinematics of the observed final state particles, while others are \"features\" that are derived from these raw quantities:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "sm1x5YFJ_rlt" + }, + "outputs": [], + "source": [ + "RawNames=[\"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\"]\n", + "FeatureNames=list(set(VarNames[1:]).difference(RawNames))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "hOKioChv_rmL", + "outputId": "56e822f2-c3d1-4658-e4fd-14730c21549c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kUOsiY1B_rk1", - "outputId": "ab6afca9-a1f4-41f1-bea3-556ed73eb8f7" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "total 880M\n", - "drwxr-xr-x 1 root root 4.0K Oct 29 13:25 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n", - "-rw-r--r-- 1 root root 880M Oct 31 14:41 SUSY.csv.gz\n" - ] - } - ], - "source": [ - "ls -lh" + "data": { + "text/plain": [ + "['l_1_pT',\n", + " 'l_1_eta',\n", + " 'l_1_phi',\n", + " 'l_2_pT',\n", + " 'l_2_eta',\n", + " 'l_2_phi',\n", + " 'MET',\n", + " 'MET_phi']" ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "RawNames" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "hD2YtQmu_rmO", + "outputId": "8b5645e9-98cd-440b-f127-ef9d91ae1512" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "WSFbZQqI_rk5" - }, - "source": [ - "The data is provided as a comma separated file." + "data": { + "text/plain": [ + "['S_R',\n", + " 'MT2',\n", + " 'M_Delta_R',\n", + " 'dPhi_r_b',\n", + " 'cos_theta_r1',\n", + " 'axial_MET',\n", + " 'R',\n", + " 'MET_rel',\n", + " 'M_TR_2',\n", + " 'M_R']" ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "FeatureNames" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VV02VZA__rmS" + }, + "source": [ + "We will use pandas to read in the file, and matplotlib to make plots. The following ensures pandas is installed and sets everything up:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "OL6S_4z3_rma" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IcXv0FE8_rn3" + }, + "source": [ + "Now we can read the data into a pandas dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "_TX4YIti_rn6" + }, + "outputs": [], + "source": [ + "filename = \"SUSY-small.csv\"\n", + "df = pd.read_csv(filename, dtype='float64', names=VarNames)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IPsjW9HE_rn-" + }, + "source": [ + "You can see the data in Jupyter by just evaluateing the dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 443 }, + "id": "-ryWi0OZ_roJ", + "outputId": "c1655ec4-e999-4c51-d17b-a291d81a5124" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ONfmelbk_rk9", - "outputId": "4a50bd58-8a70-470c-9986-3983370f096f" + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df" }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "0.000000000000000000e+00,9.728614687919616699e-01,6.538545489311218262e-01,1.176224589347839355e+00,1.157156467437744141e+00,-1.739873170852661133e+00,-8.743090629577636719e-01,5.677649974822998047e-01,-1.750000417232513428e-01,8.100607395172119141e-01,-2.525521218776702881e-01,1.921887040138244629e+00,8.896374106407165527e-01,4.107718467712402344e-01,1.145620822906494141e+00,1.932632088661193848e+00,9.944640994071960449e-01,1.367815494537353516e+00,4.071449860930442810e-02\n", - "1.000000000000000000e+00,1.667973041534423828e+00,6.419061869382858276e-02,-1.225171446800231934e+00,5.061022043228149414e-01,-3.389389812946319580e-01,1.672542810440063477e+00,3.475464344024658203e+00,-1.219136357307434082e+00,1.295456290245056152e-02,3.775173664093017578e+00,1.045977115631103516e+00,5.680512785911560059e-01,4.819284379482269287e-01,0.000000000000000000e+00,4.484102725982666016e-01,2.053557634353637695e-01,1.321893453598022461e+00,3.775840103626251221e-01\n", - "1.000000000000000000e+00,4.448399245738983154e-01,-1.342980116605758667e-01,-7.099716067314147949e-01,4.517189264297485352e-01,-1.613871216773986816e+00,-7.686609029769897461e-01,1.219918131828308105e+00,5.040258169174194336e-01,1.831247568130493164e+00,-4.313853085041046143e-01,5.262832045555114746e-01,9.415140151977539062e-01,1.587535023689270020e+00,2.024308204650878906e+00,6.034975647926330566e-01,1.562373995780944824e+00,1.135454416275024414e+00,1.809100061655044556e-01\n", - "1.000000000000000000e+00,3.812560737133026123e-01,-9.761453866958618164e-01,6.931523084640502930e-01,4.489588439464569092e-01,8.917528986930847168e-01,-6.773284673690795898e-01,2.033060073852539062e+00,1.533040523529052734e+00,3.046259880065917969e+00,-1.005284786224365234e+00,5.693860650062561035e-01,1.015211343765258789e+00,1.582216739654541016e+00,1.551914215087890625e+00,7.612152099609375000e-01,1.715463757514953613e+00,1.492256760597229004e+00,9.071890264749526978e-02\n", - "1.000000000000000000e+00,1.309996485710144043e+00,-6.900894641876220703e-01,-6.762592792510986328e-01,1.589282631874084473e+00,-6.933256387710571289e-01,6.229069828987121582e-01,1.087561845779418945e+00,-3.817416727542877197e-01,5.892043709754943848e-01,1.365478992462158203e+00,1.179295063018798828e+00,9.682182073593139648e-01,7.285631299018859863e-01,0.000000000000000000e+00,1.083157896995544434e+00,4.342924803495407104e-02,1.154853701591491699e+00,9.485860168933868408e-02\n" - ] - } + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
signall_1_pTl_1_etal_1_phil_2_pTl_2_etal_2_phiMETMET_phiMET_relaxial_METM_RM_TR_2RMT2S_RM_Delta_RdPhi_r_bcos_theta_r1
00.00.9728610.6538551.1762251.157156-1.739873-0.8743090.567765-0.1750000.810061-0.2525521.9218870.8896370.4107721.1456211.9326320.9944641.3678150.040714
11.01.6679730.064191-1.2251710.506102-0.3389391.6725433.475464-1.2191360.0129553.7751741.0459770.5680510.4819280.0000000.4484100.2053561.3218930.377584
21.00.444840-0.134298-0.7099720.451719-1.613871-0.7686611.2199180.5040261.831248-0.4313850.5262830.9415141.5875352.0243080.6034981.5623741.1354540.180910
31.00.381256-0.9761450.6931520.4489590.891753-0.6773282.0330601.5330413.046260-1.0052850.5693861.0152111.5822171.5519140.7612151.7154641.4922570.090719
41.01.309996-0.690089-0.6762591.589283-0.6933260.6229071.087562-0.3817420.5892041.3654791.1792950.9682180.7285630.0000001.0831580.0434291.1548540.094859
............................................................
4999950.00.7190351.0918790.2915401.205962-1.599117-1.1394450.4245461.1548490.637185-0.0911781.9721560.6970280.3136360.9886021.9815730.7448281.0950800.006546
4999961.00.910016-0.364544-0.7771200.543648-0.910632-1.7237072.8646731.4582722.176558-0.5909110.6736951.6621402.1893621.1950410.9108151.1818931.2523620.826035
4999971.00.8429540.332476-1.0485641.3479890.320496-0.6663580.450433-0.4118720.2934070.6304910.8599200.4033710.4162580.5919890.3720030.7167880.3669910.265798
4999980.01.370760-1.1629120.8934992.1180911.248496-0.8872110.1646590.3168400.2151650.2804183.0870830.5269290.1514670.3080673.0981830.2330420.8762160.000593
4999990.00.7624000.4409240.3428851.0342831.740353-1.0833140.872145-1.5198940.284328-0.3608610.9568280.9659790.8958811.0203960.9964460.9434581.2998700.197220
\n", + "

500000 rows × 19 columns

\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" ], - "source": [ - "filename=\"SUSY.csv\"\n", - "# print out the first 5 lines using unix head command\n", - "!head -5 \"SUSY.csv\"" + "text/plain": [ + " signal l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi \\\n", + "0 0.0 0.972861 0.653855 1.176225 1.157156 -1.739873 -0.874309 \n", + "1 1.0 1.667973 0.064191 -1.225171 0.506102 -0.338939 1.672543 \n", + "2 1.0 0.444840 -0.134298 -0.709972 0.451719 -1.613871 -0.768661 \n", + "3 1.0 0.381256 -0.976145 0.693152 0.448959 0.891753 -0.677328 \n", + "4 1.0 1.309996 -0.690089 -0.676259 1.589283 -0.693326 0.622907 \n", + "... ... ... ... ... ... ... ... \n", + "499995 0.0 0.719035 1.091879 0.291540 1.205962 -1.599117 -1.139445 \n", + "499996 1.0 0.910016 -0.364544 -0.777120 0.543648 -0.910632 -1.723707 \n", + "499997 1.0 0.842954 0.332476 -1.048564 1.347989 0.320496 -0.666358 \n", + "499998 0.0 1.370760 -1.162912 0.893499 2.118091 1.248496 -0.887211 \n", + "499999 0.0 0.762400 0.440924 0.342885 1.034283 1.740353 -1.083314 \n", + "\n", + " MET MET_phi MET_rel axial_MET M_R M_TR_2 R \\\n", + "0 0.567765 -0.175000 0.810061 -0.252552 1.921887 0.889637 0.410772 \n", + "1 3.475464 -1.219136 0.012955 3.775174 1.045977 0.568051 0.481928 \n", + "2 1.219918 0.504026 1.831248 -0.431385 0.526283 0.941514 1.587535 \n", + "3 2.033060 1.533041 3.046260 -1.005285 0.569386 1.015211 1.582217 \n", + "4 1.087562 -0.381742 0.589204 1.365479 1.179295 0.968218 0.728563 \n", + "... ... ... ... ... ... ... ... \n", + "499995 0.424546 1.154849 0.637185 -0.091178 1.972156 0.697028 0.313636 \n", + "499996 2.864673 1.458272 2.176558 -0.590911 0.673695 1.662140 2.189362 \n", + "499997 0.450433 -0.411872 0.293407 0.630491 0.859920 0.403371 0.416258 \n", + "499998 0.164659 0.316840 0.215165 0.280418 3.087083 0.526929 0.151467 \n", + "499999 0.872145 -1.519894 0.284328 -0.360861 0.956828 0.965979 0.895881 \n", + "\n", + " MT2 S_R M_Delta_R dPhi_r_b cos_theta_r1 \n", + "0 1.145621 1.932632 0.994464 1.367815 0.040714 \n", + "1 0.000000 0.448410 0.205356 1.321893 0.377584 \n", + "2 2.024308 0.603498 1.562374 1.135454 0.180910 \n", + "3 1.551914 0.761215 1.715464 1.492257 0.090719 \n", + "4 0.000000 1.083158 0.043429 1.154854 0.094859 \n", + "... ... ... ... ... ... \n", + "499995 0.988602 1.981573 0.744828 1.095080 0.006546 \n", + "499996 1.195041 0.910815 1.181893 1.252362 0.826035 \n", + "499997 0.591989 0.372003 0.716788 0.366991 0.265798 \n", + "499998 0.308067 3.098183 0.233042 0.876216 0.000593 \n", + "499999 1.020396 0.996446 0.943458 1.299870 0.197220 \n", + "\n", + "[500000 rows x 19 columns]" ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CApAAh9k_roT" + }, + "source": [ + "The first column stores the \"truth\" label of whether an event was signal or not. Pandas makes it easy to create dataframes that store only the signal or background events:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "1i9u-LTG_roV" + }, + "outputs": [], + "source": [ + "df_sig=df[df.signal==1]\n", + "df_bkg=df[df.signal==0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ls4zpK_G_rof" + }, + "source": [ + "The following example plots the signal and background distributions of every variable. Note that we use VarNames[1:] to skip the first variable, which was the true label." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "id": "QwIKZjkA_roy", + "outputId": "5b369e60-99a1-4c11-885d-64c038d66e5d" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xGWPh3H-_rlB", - "outputId": "b5993369-6440-4a2e-ac37-62240703ab0d" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "total 2.3G\n", - "drwxr-xr-x 1 root root 4.0K Oct 29 13:25 sample_data\n", - "-rw-r--r-- 1 root root 2.3G Oct 31 14:41 SUSY.csv\n", - "-rw-r--r-- 1 root root 0 Oct 31 14:41 SUSY-small.csv\n" - ] - } - ], - "source": [ - "### Reducing the dataset\n", - "\n", - "!ls -lh" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "l_1_pT\n" + ] }, { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vtWJ-Mz5_rlH", - "outputId": "a4b08786-65b1-4056-bd94-eff8cae701e3" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "5000000 SUSY.csv\n" - ] - } - ], - "source": [ - "## How many datapoints in SUZY\n", - "\n", - "!wc -l SUSY.csv" + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0MAAAGsCAYAAAAfTXyRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA7ZklEQVR4nO3de1xVVcL/8e8BBTwmmB65FUexlMwRvAVDjZNNJJJZPjWljo63smcs58kYJ3UmJceKLPM6Tk6loZmXekpnJhtNUbKLaYJON/RRI9EEFH8DCCYo7N8fjKeOXOQgcDjsz/v12q9hr7322muf7Ya+s9dex2IYhiEAAAAAMBkvd3cAAAAAANyBMAQAAADAlAhDAAAAAEyJMAQAAADAlAhDAAAAAEyJMAQAAADAlAhDAAAAAEyplbs70BAqKip04sQJtWvXThaLxd3dAQAAAOAmhmHozJkzCg0NlZdX7c9+WkQYOnHihMLCwtzdDQAAAADNxLFjx3TttdfWWqdFhKF27dpJqjxhf39/N/cGAAAAgLsUFRUpLCzMkRFq0yLC0MWhcf7+/oQhAAAAAHV6fYYJFAAAAACYEmEIAAAAgCkRhgAAAACYUot4ZwgAAADmUV5ervPnz7u7G3Cj1q1by9vb+4rbIQwBAADAIxiGodzcXBUUFLi7K2gG2rdvr+Dg4Cv6nlHCEAAAADzCxSAUGBgoq9V6Rf8RDM9lGIbOnj2rkydPSpJCQkLq3RZhCAAAAM1eeXm5Iwh17NjR3d2Bm7Vp00aSdPLkSQUGBtZ7yBwTKAAAAKDZu/iOkNVqdXNP0Fxc/LdwJe+PEYYAAADgMRgah4sa4t8CYQgAAACAKfHOEAAAADxbdraUn980x7LZJLu9aY6FRkcYAgAAgOfKzpZ69JDOnm2a41mtUmZmgwSicePGqaCgQBs3brzyfrngqaee0saNG7V///4mPW5z5HIY2rlzp1544QWlp6crJydHGzZs0LBhw2qsP27cOK1cubJK+Y033qivvvpKUuUFmT17ttP2iIgIHThwwNXuAQAAwEzy8yuD0OrVlaGoMWVmSqNHVx6zAcLQokWLZBhGA3QM9eVyGCopKVFUVJQmTJige++997L1Fy1apOeee86xfuHCBUVFRen+++93qtezZ09t27bth4614qEVAAAA6qhHD6lvX3f3wiUBAQHu7oLpuTyBQkJCgp5++mn913/9V53qBwQEKDg42LHs3btX//73vzV+/Hineq1atXKqZ7PZXO0aAAAA0Oz87//+r3r16qU2bdqoY8eOiouLU0lJicaNG+c0wurMmTMaNWqU2rZtq5CQEC1YsEADBw7UlClTHHW6dOmiZ599VhMmTFC7du1kt9v18ssvOx1v2rRp6t69u6xWq7p27aqZM2de0fTTLVmTzya3fPlyxcXFqXPnzk7lhw4dUmhoqLp27apRo0YpOzu7xjZKS0tVVFTktHiS7GwpI6P6pZbTBgAAgIfJycnRyJEjNWHCBGVmZiotLU333ntvtcPjEhMT9fHHH+vvf/+7tm7dqg8//FAZGRlV6r344ovq37+/9u3bp0ceeUSTJk3SwYMHHdvbtWunlJQUff3111q0aJFeeeUVLViwoFHP01M16Vi0EydO6J///KfWrFnjVB4TE6OUlBRFREQoJydHs2fP1oABA/Tll1+qXbt2VdpJTk6u8o6Rp7jcO34N+E4eAAAA3CwnJ0cXLlzQvffe63gY0KtXryr1zpw5o5UrV2rNmjW6/fbbJUmvvfaaQkNDq9S988479cgjj0iqfAq0YMEC7dixQxEREZKkJ5980lG3S5cumjp1qtatW6cnnniiwc/P0zVpGFq5cqXat29fZcKFhIQEx8+RkZGKiYlR586d9eabb+rBBx+s0s6MGTOUmJjoWC8qKlJYWFij9bsh1faOXwO/kwcAAAA3i4qK0u23365evXopPj5egwYN0i9/+UtdffXVTvW++eYbnT9/XtHR0Y6ygIAAR8D5scjISMfPFotFwcHBOnnypKNs/fr1Wrx4sY4cOaLi4mJduHBB/v7+jXB2nq/JhskZhqEVK1bo17/+tXx8fGqt2759e3Xv3l2HDx+udruvr6/8/f2dFk9z8R2/Hy+NPQEKAAAAmpa3t7e2bt2qf/7zn7rxxhu1ZMkSRUREKCsrq95ttm7d2mndYrGooqJCkrRr1y6NGjVKd955p959913t27dPf/zjH1VWVnZF59FSNVkY+uCDD3T48OFqn/Rcqri4WEeOHFFISEgT9AwAAABoPBaLRbfccotmz56tffv2ycfHRxs2bHCq07VrV7Vu3VqfffaZo6ywsFD/93//59KxPvnkE3Xu3Fl//OMf1b9/f3Xr1k1Hjx5tkPNoiVweJldcXOz0xCYrK0v79+9Xhw4dZLfbNWPGDH333XdatWqV037Lly9XTEyMfvKTn1Rpc+rUqRo6dKg6d+6sEydOKCkpSd7e3ho5cmQ9TgkAAACmk5nZLI+xe/dupaamatCgQQoMDNTu3bt16tQp9ejRQ59//rmjXrt27TR27Fj9/ve/V4cOHRQYGKikpCR5eXnJYrHU+XjdunVTdna21q1bp5tuukmbNm2qErzwA5fD0N69e3Xbbbc51i++uzN27FilpKQoJyenykxwhYWFevvtt7Vo0aJq2zx+/LhGjhyp06dPq1OnTvrZz36mTz/9VJ06dXK1ewAAADATm61yBqrRo5vmeFZr5THryN/fXzt37tTChQtVVFSkzp0768UXX1RCQoLWr1/vVHf+/Pn6zW9+o7vuukv+/v564okndOzYMfn5+dX5eHfffbcef/xxTZ48WaWlpRoyZIhmzpypp556qs5tmInFaAFfe1tUVKSAgAAVFhY2+/eHMjKkfv2k9PSq3wtW2zYAAAAzO3funLKyshQeHl41HGRnV85A1RRstiab6aqkpETXXHONXnzxxTq9amI2Nf2bcCUbNOlscgAAAECDs9tbxFS8+/bt04EDBxQdHa3CwkL96U9/kiTdc889bu5Zy0UYAgAAAJqJefPm6eDBg/Lx8VG/fv304YcfyubCsDy4hjAEAAAANAN9+vRRenq6u7thKk02tTYAAAAANCeEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYErMJgcAAACP1ty/c3XgwIHq3bu3Fi5c2Ch9GjdunAoKCrRx48ZGad8dvv32W4WHh2vfvn3q3bt3ox2HMAQAAACPlZ0t9eghnT3bNMezWqXMzBbxHa8QYQgAAAAeLD+/MgitXl0ZihpTZqY0enTlMVt6GCorK5OPj4+7u9HoeGcIAAAAHq9HD6lv38ZdriRsXbhwQZMnT1ZAQIBsNptmzpwpwzAkSa+//rr69++vdu3aKTg4WL/61a908uRJp/2/+uor3XXXXfL391e7du00YMAAHTlypNpjffbZZ+rUqZPmzp3rKHv66acVGBiodu3a6aGHHtL06dOdhp+NGzdOw4YN0zPPPKPQ0FBFRERIkr744gv94he/UJs2bdSxY0c9/PDDKi4uduw3cOBATZkyxen4w4YN07hx4xzrXbp00bPPPqsJEyaoXbt2stvtevnll5322bNnj/r06SM/Pz/1799f+/btq/NneyUIQwAAAEAjW7lypVq1aqU9e/Zo0aJFmj9/vl599VVJ0vnz5zVnzhz961//0saNG/Xtt986hYnvvvtOP//5z+Xr66vt27crPT1dEyZM0IULF6ocZ/v27brjjjv0zDPPaNq0aZKkN954Q88884zmzp2r9PR02e12vfTSS1X2TU1N1cGDB7V161a9++67KikpUXx8vK6++mp99tlneuutt7Rt2zZNnjzZ5fN/8cUXHSHnkUce0aRJk3Tw4EFJUnFxse666y7deOONSk9P11NPPaWpU6e6fIz6YJgcAAAA0MjCwsK0YMECWSwWRURE6IsvvtCCBQs0ceJETZgwwVGva9euWrx4sW666SYVFxfrqquu0tKlSxUQEKB169apdevWkqTu3btXOcaGDRs0ZswYvfrqqxo+fLijfMmSJXrwwQc1fvx4SdKsWbP0/vvvOz3hkaS2bdvq1VdfdQyPe+WVV3Tu3DmtWrVKbdu2lST9+c9/1tChQzV37lwFBQXV+fzvvPNOPfLII5KkadOmacGCBdqxY4ciIiK0Zs0aVVRUaPny5fLz81PPnj11/PhxTZo0qc7t1xdPhgAAAIBG9tOf/lQWi8WxHhsbq0OHDqm8vFzp6ekaOnSo7Ha72rVrp1tvvVWSlJ2dLUnav3+/BgwY4AhC1dm9e7fuv/9+vf76605BSJIOHjyo6Ohop7JL1yWpV69eTu8JZWZmKioqyhGEJOmWW25RRUWF46lOXUVGRjp+tlgsCg4OdgwFzMzMVGRkpPz8/Bx1YmNjXWq/vghDAAAAgJucO3dO8fHx8vf31xtvvKHPPvtMGzZskFQ5iYEktWnT5rLtXHfddbrhhhu0YsUKnT9/vl59+XHoqSsvLy/Hu08XVXf8S4OcxWJRRUWFy8draIQhAAAAoJHt3r3baf3TTz9Vt27ddODAAZ0+fVrPPfecBgwYoBtuuKHK5AmRkZH68MMPaw05NptN27dv1+HDh/XAAw841Y2IiNBnn33mVP/S9er06NFD//rXv1RSUuIo+/jjj+Xl5eWYYKFTp07KyclxbC8vL9eXX3552bYvPc7nn3+uc+fOOco+/fRTl9qoL8IQAAAA0Miys7OVmJiogwcPau3atVqyZIkee+wx2e12+fj4aMmSJfrmm2/097//XXPmzHHad/LkySoqKtKIESO0d+9eHTp0SK+//nqVoWqBgYHavn27Dhw4oJEjRzomWPjtb3+r5cuXa+XKlTp06JCefvppff75507D9qozatQo+fn5aezYsfryyy+1Y8cO/fa3v9Wvf/1rx/tCv/jFL7Rp0yZt2rRJBw4c0KRJk1RQUODSZ/OrX/1KFotFEydO1Ndff6333ntP8+bNc6mN+mICBQAAAHi8zMzmfYwxY8bo+++/V3R0tLy9vfXYY4/p4YcflsViUUpKiv7whz9o8eLF6tu3r+bNm6e7777bsW/Hjh21fft2/f73v9ett94qb29v9e7dW7fcckuV4wQHB2v79u0aOHCgRo0apTVr1mjUqFH65ptvNHXqVJ07d04PPPCAxo0bpz179tTaZ6vVqi1btuixxx7TTTfdJKvVqvvuu0/z58931JkwYYL+9a9/acyYMWrVqpUef/xx3XbbbS59NldddZX+8Y9/6De/+Y369OmjG2+8UXPnztV9993nUjv1YTEuHeTngYqKihQQEKDCwkL5+/u7uzu1ysiQ+vWT0tMr56uv6zYAAAAzO3funLKyshQeHu70on12duX3/5w92zT9sForQ5Gnf+nqHXfcoeDgYL3++uvu7kq91fRvwpVswJMhAAAAeCy7vTKc5Oc3zfFsNs8LQmfPntWyZcsUHx8vb29vrV27Vtu2bdPWrVvd3TW3IwwBAADAo9ntnhdQmpLFYtF7772nZ555RufOnVNERITefvttxcXFubtrbkcYAgAAAFqwNm3aaNu2be7uRrPEbHIAAAAATIkwBAAAAMCUCEMAAADwGBUVFe7uApqJhvi3wDtDAAAAaPZ8fHzk5eWlEydOqFOnTvLx8bnsl4aiZTIMQ2VlZTp16pS8vLzk4+NT77YIQwAAAGj2vLy8FB4erpycHJ04ccLd3UEzYLVaZbfb5eVV/8FuhCEAAAB4BB8fH9ntdl24cEHl5eXu7g7cyNvbW61atbrip4OEIQAAAHgMi8Wi1q1bq3Xr1u7uCloAJlAAAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEqEIQAAAACmRBgCAAAAYEouh6GdO3dq6NChCg0NlcVi0caNG2utn5aWJovFUmXJzc11qrd06VJ16dJFfn5+iomJ0Z49e1ztGgAAAADUmcthqKSkRFFRUVq6dKlL+x08eFA5OTmOJTAw0LFt/fr1SkxMVFJSkjIyMhQVFaX4+HidPHnS1e4BAAAAQJ20cnWHhIQEJSQkuHygwMBAtW/fvtpt8+fP18SJEzV+/HhJ0rJly7Rp0yatWLFC06dPd/lYAAAAAHA5TfbOUO/evRUSEqI77rhDH3/8saO8rKxM6enpiouL+6FTXl6Ki4vTrl27qm2rtLRURUVFTgsAAAAAuKLRw1BISIiWLVumt99+W2+//bbCwsI0cOBAZWRkSJLy8/NVXl6uoKAgp/2CgoKqvFd0UXJysgICAhxLWFhYY58GAAAAgBbG5WFyroqIiFBERIRj/eabb9aRI0e0YMECvf766/Vqc8aMGUpMTHSsFxUVEYgAAAAAuKTRw1B1oqOj9dFHH0mSbDabvL29lZeX51QnLy9PwcHB1e7v6+srX1/fRu8nAAAAgJbLLd8ztH//foWEhEiSfHx81K9fP6Wmpjq2V1RUKDU1VbGxse7oHgAAAAATcPnJUHFxsQ4fPuxYz8rK0v79+9WhQwfZ7XbNmDFD3333nVatWiVJWrhwocLDw9WzZ0+dO3dOr776qrZv367333/f0UZiYqLGjh2r/v37Kzo6WgsXLlRJSYljdjkAAAAAaGguh6G9e/fqtttuc6xffHdn7NixSklJUU5OjrKzsx3by8rK9Lvf/U7fffedrFarIiMjtW3bNqc2hg8frlOnTmnWrFnKzc1V7969tXnz5iqTKgAAAABAQ7EYhmG4uxNXqqioSAEBASosLJS/v7+7u1OrjAypXz8pPV3q27fu2wAAAABcnivZwC3vDAEAAACAuxGGAAAAAJgSYQgAAACAKRGGAAAAAJgSYQgAAACAKRGGAAAAAJgSYQgAAACAKRGGAAAAAJgSYQgAAACAKbVydwdQVWZm9eU2m2S3N21fAAAAgJaKMNSM2GyS1SqNHl39dqu1MigRiAAAAIArRxhqRuz2yrCTn191W2ZmZUjKzycMAQAAAA2BMNTM2O2EHQAAAKApMIECAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFNyOQzt3LlTQ4cOVWhoqCwWizZu3Fhr/XfeeUd33HGHOnXqJH9/f8XGxmrLli1OdZ566ilZLBan5YYbbnC1awAAAABQZy6HoZKSEkVFRWnp0qV1qr9z507dcccdeu+995Senq7bbrtNQ4cO1b59+5zq9ezZUzk5OY7lo48+crVrAAAAAFBnrVzdISEhQQkJCXWuv3DhQqf1Z599Vn/729/0j3/8Q3369PmhI61aKTg42NXuAAAAAEC9NPk7QxUVFTpz5ow6dOjgVH7o0CGFhoaqa9euGjVqlLKzs2tso7S0VEVFRU4LAAAAALiiycPQvHnzVFxcrAceeMBRFhMTo5SUFG3evFkvvfSSsrKyNGDAAJ05c6baNpKTkxUQEOBYwsLCmqr7AAAAAFqIJg1Da9as0ezZs/Xmm28qMDDQUZ6QkKD7779fkZGRio+P13vvvaeCggK9+eab1bYzY8YMFRYWOpZjx4411SkAAAAAaCFcfmeovtatW6eHHnpIb731luLi4mqt2759e3Xv3l2HDx+udruvr698fX0bo5sAAAAATKJJngytXbtW48eP19q1azVkyJDL1i8uLtaRI0cUEhLSBL0DAAAAYEYuPxkqLi52emKTlZWl/fv3q0OHDrLb7ZoxY4a+++47rVq1SlLl0LixY8dq0aJFiomJUW5uriSpTZs2CggIkCRNnTpVQ4cOVefOnXXixAklJSXJ29tbI0eObIhzBAAAAIAqXH4ytHfvXvXp08cxLXZiYqL69OmjWbNmSZJycnKcZoJ7+eWXdeHCBT366KMKCQlxLI899pijzvHjxzVy5EhFRETogQceUMeOHfXpp5+qU6dOV3p+AAAAAFAtl58MDRw4UIZh1Lg9JSXFaT0tLe2yba5bt87VbgAAAADAFWnyqbUBAAAAoDkgDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFMiDAEAAAAwJcIQAAAAAFNyOQzt3LlTQ4cOVWhoqCwWizZu3HjZfdLS0tS3b1/5+vrq+uuvV0pKSpU6S5cuVZcuXeTn56eYmBjt2bPH1a4BAAAAQJ25HIZKSkoUFRWlpUuX1ql+VlaWhgwZottuu0379+/XlClT9NBDD2nLli2OOuvXr1diYqKSkpKUkZGhqKgoxcfH6+TJk652DwAAAADqpJWrOyQkJCghIaHO9ZctW6bw8HC9+OKLkqQePXroo48+0oIFCxQfHy9Jmj9/viZOnKjx48c79tm0aZNWrFih6dOnu9pFAAAAALisRn9naNeuXYqLi3Mqi4+P165duyRJZWVlSk9Pd6rj5eWluLg4R51LlZaWqqioyGkBAAAAAFc0ehjKzc1VUFCQU1lQUJCKior0/fffKz8/X+Xl5dXWyc3NrbbN5ORkBQQEOJawsLBG6z8AAACAlskjZ5ObMWOGCgsLHcuxY8fc3SUAAAAAHsbld4ZcFRwcrLy8PKeyvLw8+fv7q02bNvL29pa3t3e1dYKDg6tt09fXV76+vo3WZwAAAAAtX6M/GYqNjVVqaqpT2datWxUbGytJ8vHxUb9+/ZzqVFRUKDU11VEHAAAAABqay2GouLhY+/fv1/79+yVVTp29f/9+ZWdnS6ocwjZmzBhH/d/85jf65ptv9MQTT+jAgQP6y1/+ojfffFOPP/64o05iYqJeeeUVrVy5UpmZmZo0aZJKSkocs8sBAAAAQENzeZjc3r17ddtttznWExMTJUljx45VSkqKcnJyHMFIksLDw7Vp0yY9/vjjWrRoka699lq9+uqrjmm1JWn48OE6deqUZs2apdzcXPXu3VubN2+uMqkCAAAAADQUi2EYhrs7caWKiooUEBCgwsJC+fv7u7s7tcrIkPr1k9LTpb59G38/AAAAwExcyQYeOZscAAAAAFwpwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADClVu7uAFyTmVl9uc0m2e1N2xcAAADAkxGGPITNJlmt0ujR1W+3WiuDEoEIAAAAqBvCkIew2yvDTn5+1W2ZmZUhKT+fMAQAAADUFWHIg9jthB0AAACgoTCBAgAAAABTIgwBAAAAMCXCEAAAAABTIgwBAAAAMCXCEAAAAABTIgwBAAAAMCXCEAAAAABTIgwBAAAAMCXCEAAAAABTIgwBAAAAMKVW7u4AXJCdLeXnO5fZbJLsbukOAAAA4MkIQ54iO1vq0UM6e9a53GqV3jwsKcQt3QIAAAA8FWGoubr0KVBmZmUQWr26MhRdLBs9WiooEGEIAAAAcA1hqDmq7SnQgAGSnWFxAAAAwJWq1wQKS5cuVZcuXeTn56eYmBjt2bOnxroDBw6UxWKpsgwZMsRRZ9y4cVW2Dx48uD5daxny8394CpSe/sOSmUkQAgAAABqIy0+G1q9fr8TERC1btkwxMTFauHCh4uPjdfDgQQUGBlap/84776isrMyxfvr0aUVFRen+++93qjd48GC99tprjnVfX19Xu9by9Ogh9e3r7l4AAAAALZLLT4bmz5+viRMnavz48brxxhu1bNkyWa1WrVixotr6HTp0UHBwsGPZunWrrFZrlTDk6+vrVO/qq6+u3xkBAAAAQB24FIbKysqUnp6uuLi4Hxrw8lJcXJx27dpVpzaWL1+uESNGqG3btk7laWlpCgwMVEREhCZNmqTTp0/X2EZpaamKioqcFgAAAABwhUthKD8/X+Xl5QoKCnIqDwoKUm5u7mX337Nnj7788ks99NBDTuWDBw/WqlWrlJqaqrlz5+qDDz5QQkKCysvLq20nOTlZAQEBjiUsLMyV02h5srIq/zczU8rIqFyys93bJwAAAKCZa9LZ5JYvX65evXopOjraqXzEiBGOn3v16qXIyEhdd911SktL0+23316lnRkzZigxMdGxXlRUZM5AZLNVzjA380lJd0qjR0naV7nNamXCBQAAAKAWLj0Zstls8vb2Vl5enlN5Xl6egoODa923pKRE69at04MPPnjZ43Tt2lU2m02HDx+udruvr6/8/f2dFlOy2ysDz+o3KtdXv1E569zq1ZWz0f34e4oAAAAAOHHpyZCPj4/69eun1NRUDRs2TJJUUVGh1NRUTZ48udZ933rrLZWWlmr06NGXPc7x48d1+vRphYR47heJXvqdqRdlZjbwgex26T/fwVo5+1wDtw8AAAC0UC4Pk0tMTNTYsWPVv39/RUdHa+HChSopKdH48eMlSWPGjNE111yj5ORkp/2WL1+uYcOGqWPHjk7lxcXFmj17tu677z4FBwfryJEjeuKJJ3T99dcrPj7+Ck7NfWr6ztSLrNbKEW4AAAAA3MflMDR8+HCdOnVKs2bNUm5urnr37q3Nmzc7JlXIzs6Wl5fz6LuDBw/qo48+0vvvv1+lPW9vb33++edauXKlCgoKFBoaqkGDBmnOnDke+11DP/7O1B49qm632XiVBwAAAHC3ek2gMHny5BqHxaWlpVUpi4iIkGEY1dZv06aNtmzZUp9uNHt8ZyoAAADQfLn8pasAAAAA0BIQhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYEmEIAAAAgCkRhgAAAACYUr2+dBUeIjPTed1mk+x29/QFAAAAaGYIQy2RzSZZrdLo0c7lVmtlQCIQAQAAAIShFslurww9+fk/lGVmVoaj/HzCEAAAACDCkPtlZzuHFqnq8Lb6sNsJPQAAAEAtCEPulJ0t9eghnT1bdZvVWjncDQAAAECjIAy5U35+ZRBavboyFP0Ykx0AAAAAjYow1Bz06CH17evuXgAAAACmwvcMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAUyIMAQAAADAlwhAAAAAAU2rl7g6giWVmOq/bbJLd7p6+AAAAAG5EGDILm02yWqXRo53LrdbKgEQgAgAAgMkQhszCbq8MPfn5P5RlZlaGo/x8whAAAABMhzBkJnY7oQcAAAD4DyZQAAAAAGBKhCEAAAAApkQYAgAAAGBKhCEAAAAApkQYAgAAAGBKhCEAAAAApkQYAgAAAGBKhCEAAAAApkQYAgAAAGBKhCEAAAAApkQYAgAAAGBKhCEAAAAApkQYAgAAAGBK9QpDS5cuVZcuXeTn56eYmBjt2bOnxropKSmyWCxOi5+fn1MdwzA0a9YshYSEqE2bNoqLi9OhQ4fq0zUAAAAAqBOXw9D69euVmJiopKQkZWRkKCoqSvHx8Tp58mSN+/j7+ysnJ8exHD161Gn7888/r8WLF2vZsmXavXu32rZtq/j4eJ07d871M4LrMjOljAznJTvb3b0CAAAAGlUrV3eYP3++Jk6cqPHjx0uSli1bpk2bNmnFihWaPn16tftYLBYFBwdXu80wDC1cuFBPPvmk7rnnHknSqlWrFBQUpI0bN2rEiBGudhF1ZbNJVqs0enTVbVZrZUiy25u+XwAAAEATcCkMlZWVKT09XTNmzHCUeXl5KS4uTrt27apxv+LiYnXu3FkVFRXq27evnn32WfXs2VOSlJWVpdzcXMXFxTnqBwQEKCYmRrt27ao2DJWWlqq0tNSxXlRU5MpptFiZmdWX22w1ZBq7vXKn/PyqDY0eXVlOGAIAAEAL5VIYys/PV3l5uYKCgpzKg4KCdODAgWr3iYiI0IoVKxQZGanCwkLNmzdPN998s7766itde+21ys3NdbRxaZsXt10qOTlZs2fPdqXrLVptD3ikyzzksdsJPAAAADAll4fJuSo2NlaxsbGO9Ztvvlk9evTQX//6V82ZM6debc6YMUOJiYmO9aKiIoWFhV1xXz1VTQ94JB7yAAAAADVxKQzZbDZ5e3srLy/PqTwvL6/Gd4Iu1bp1a/Xp00eHDx+WJMd+eXl5CgkJcWqzd+/e1bbh6+srX19fV7re4vGABwAAAHCNS7PJ+fj4qF+/fkpNTXWUVVRUKDU11enpT23Ky8v1xRdfOIJPeHi4goODndosKirS7t2769wmAAAAALjK5WFyiYmJGjt2rPr376/o6GgtXLhQJSUljtnlxowZo2uuuUbJycmSpD/96U/66U9/quuvv14FBQV64YUXdPToUT300EOSKmeamzJlip5++ml169ZN4eHhmjlzpkJDQzVs2LCGO1MAAAAA+BGXw9Dw4cN16tQpzZo1S7m5uerdu7c2b97smAAhOztbXl4/PHD697//rYkTJyo3N1dXX321+vXrp08++UQ33nijo84TTzyhkpISPfzwwyooKNDPfvYzbd68ucqXswIAAABAQ7EYhmG4uxNXqqioSAEBASosLJS/v7+7u6OMDKlfPyk9XerbtyEqNkFfmrhfAAAAQGNwJRu49M4QAAAAALQUhCEAAAAApkQYAgAAAGBKhCEAAAAApuTybHIwkcxM53WbjW92BQAAQItBGGpK2dlSfv4P65eGjebCZpOsVmn0aOdyq7WyzwQiAAAAtACEoaaSnS316CGdPetcbrVWho/mxG6vDD2XBrfRoyvLCEMAAABoAQhDTSU/vzIIrV5dGYouaq5Dz+z25tkvAAAAoIEQhppajx58kSkAAADQDDCbHAAAAABTIgwBAAAAMCXCEAAAAABTIgwBAAAAMCXCEAAAAABTIgwBAAAAMCXCEAAAAABTIgwBAAAAMCXCEAAAAABTIgwBAAAAMKVW7u4APExmpvO6zSbZ7e7pCwAAAHAFCEOoG5tNslql0aOdy63WyoBEIAIAAICHIQyhbuz2ytCTn/9DWWZmZTjKzycMAQAAwOMQhlB3djuhBwAAAC0GEygAAAAAMCXCEAAAAABTIgwBAAAAMCXCEAAAAABTIgwBAAAAMCXCEAAAAABTIgwBAAAAMCXCEAAAAABTIgwBAAAAMKVW7u4AWoDMzKplNptktzd9XwAAAIA6Igyh/mw2yWqVRo+uus1qrQxJBCIAAAA0U4Qh1J/dXhl48vOdyzMzKwNSfj5hCAAAAM0WYQhXxm4n8AAAAMAjMYECAAAAAFPiyZBJVDfHgcQ8BwAAADAvwlALV9scBxLzHAAAAMC8CEMtXE1zHEjMcwAAAABzIwyZAHMcAAAAAFXVawKFpUuXqkuXLvLz81NMTIz27NlTY91XXnlFAwYM0NVXX62rr75acXFxVeqPGzdOFovFaRk8eHB9ugYAAAAAdeJyGFq/fr0SExOVlJSkjIwMRUVFKT4+XidPnqy2flpamkaOHKkdO3Zo165dCgsL06BBg/Tdd9851Rs8eLBycnIcy9q1a+t3Rmg+MjOljIwfluxsd/cIAAAAcHB5mNz8+fM1ceJEjR8/XpK0bNkybdq0SStWrND06dOr1H/jjTec1l999VW9/fbbSk1N1ZgxYxzlvr6+Cg4OdrU7aI5qmrWB2RoAAADQjLj0ZKisrEzp6emKi4v7oQEvL8XFxWnXrl11auPs2bM6f/68OnTo4FSelpamwMBARUREaNKkSTp9+nSNbZSWlqqoqMhpQTNycdaG9PQfltWrpbNnq5/JAQAAAHADl54M5efnq7y8XEFBQU7lQUFBOnDgQJ3amDZtmkJDQ50C1eDBg3XvvfcqPDxcR44c0R/+8AclJCRo165d8vb2rtJGcnKyZs+e7UrX0dSYtQEAAADNXJPOJvfcc89p3bp1SktLk5+fn6N8xIgRjp979eqlyMhIXXfddUpLS9Ptt99epZ0ZM2YoMTHRsV5UVKSwsLDG7TwAAACAFsWlYXI2m03e3t7Ky8tzKs/Ly7vs+z7z5s3Tc889p/fff1+RkZG11u3atatsNpsOHz5c7XZfX1/5+/s7LQAAAADgCpfCkI+Pj/r166fU1FRHWUVFhVJTUxUbG1vjfs8//7zmzJmjzZs3q3///pc9zvHjx3X69GmFhIS40j0AAAAAqDOXh8klJiZq7Nix6t+/v6Kjo7Vw4UKVlJQ4ZpcbM2aMrrnmGiUnJ0uS5s6dq1mzZmnNmjXq0qWLcnNzJUlXXXWVrrrqKhUXF2v27Nm67777FBwcrCNHjuiJJ57Q9ddfr/j4+AY8VTQLmZnO6zYb7xYBAADALVwOQ8OHD9epU6c0a9Ys5ebmqnfv3tq8ebNjUoXs7Gx5ef3wwOmll15SWVmZfvnLXzq1k5SUpKeeekre3t76/PPPtXLlShUUFCg0NFSDBg3SnDlz5Ovre4Wnh2aD6bYBAADQzNRrAoXJkydr8uTJ1W5LS0tzWv/2229rbatNmzbasmVLfboBT3Jxuu0fT62dmVkZjvLzCUMAAABock06mxxMjum2AQAA0Iy4NIECAAAAALQUhCEAAAAApkQYAgAAAGBKhCEAAAAApkQYAgAAAGBKhCEAAAAApsTU2lBmZvXlNlsTzYRdXQea7OAAAAAwK8KQidlsktVa+b2n1bFaK3NKo2WS2jrQ6AcHAACA2RGGTMxur8wb+flVt2VmVmaU/PxGzCM1daBJDg4AAACzIwyZnN3u5rzh9g4AAADArJhAAQAAAIApEYYAAAAAmBLD5NB8XTrLHDPMAQAAoAERhtD81DTLHDPMAQAAoAERhhpTZqak73/0M+qkulnmmGEOAAAADYww1BhyciSFSKNHSdr3Q7nVWvnUA5fHLHMAAABoZIShxlBQIClEmvO0dGfwD+W88wIAAAA0G4ShxhQeLvXt4e5etCxMqgAAAIAGQhiCZ2BSBQAAADQwwhBqVdO8D03+QIZJFQAAANDACEOoVk0PYi5yywMZJlUAAABAAyIMoVrVPYi5qNk9kOE9IgAAANQDYQg1avYPYniPCAAAAFeAMATPxXtEAAAAuAKEIXi2mh5fVTfzA8PnAAAA8COEIbQstc38wPA5AAAA/AhhCC1LTTM/MHwOAAAAlyAMod6azXcQXarZz/wAAACA5oAwBJc1y+8gqium4QYAAMB/EIbgMo/6DqKLmIYbAAAAlyAMoV48biQa03ADAADgEoQhmEddp+Fm6BwAAIApEIbQKJrt5Ao/VtvQuXfekTp1qlq/2XQeAAAAV4owhAblUZMrVDd07tQp6d57pcGDq9ZvVp0HAADAlSIMoUF53OQK1Q2dq+17ij78UOrR44dynhYBAAB4LMIQGtzlJldo9kPoqjsBZqMDAABocQhDaDIeNYTuUrXNRsfTIgAAAI9EGEKT8bghdJe69ImRqxMwVIfgBAAA4DaEITQpjx9C92OuTsBQneqCU7M8WQAAgJaHMIRmoS5D6Gp62OLW7FDXCRiqU1NwYmpvAACAJkEYQrNQ2xC6yz1saXZB6XKPv37M1am96zL8jtAEAABQJ/UKQ0uXLtULL7yg3NxcRUVFacmSJYqOjq6x/ltvvaWZM2fq22+/Vbdu3TR37lzdeeedju2GYSgpKUmvvPKKCgoKdMstt+ill15St27d6tM9eKjaMkRjBKXaNFmeqOuTJVeG39X3pC+HkAUAAFoYl8PQ+vXrlZiYqGXLlikmJkYLFy5UfHy8Dh48qMDAwCr1P/nkE40cOVLJycm66667tGbNGg0bNkwZGRn6yU9+Ikl6/vnntXjxYq1cuVLh4eGaOXOm4uPj9fXXX8vPz+/KzxIerzGCUm3cmidqOtm6DL+7kpO+nMb4UKr7QLKz6zbMsKb9AQAA6shiGIbhyg4xMTG66aab9Oc//1mSVFFRobCwMP32t7/V9OnTq9QfPny4SkpK9O677zrKfvrTn6p3795atmyZDMNQaGiofve732nq1KmSpMLCQgUFBSklJUUjRoyo0mZpaalKS0sd64WFhbLb7Tp27Jj8/f1dOZ1GsX/9Qd36cIQ+ePmgeg+PcHd3TO3YMen0adf2yc+vfHfp++8bvj9t2kirV1f+N3yjycuVCgobts2CAmnmk1LpuYZt19dPmvO01L59/Y5z6f4A0NJ07NjIfzSAhhMcXLm4W1FRkcLCwlRQUKCAgIDaKxsuKC0tNby9vY0NGzY4lY8ZM8a4++67q90nLCzMWLBggVPZrFmzjMjISMMwDOPIkSOGJGPfvn1OdX7+858b//M//1Ntm0lJSYYkFhYWFhYWFhYWFhaWapdjx45dNt+4NEwuPz9f5eXlCgoKcioPCgrSgQMHqt0nNze32vq5ubmO7RfLaqpzqRkzZigxMdGxXlFRof/3//6fOnbsKIvF4sop1dnFhNlcnj6hZlwrz8L18hxcK8/C9fIcXCvPwvVq/gzD0JkzZxQaGnrZuh45m5yvr698fX2dyto30TAZf39//uF7CK6VZ+F6eQ6ulWfhenkOrpVn4Xo1b5cdHvcfXq40arPZ5O3trby8PKfyvLw8BdcwQDA4OLjW+hf/15U2AQAAAOBKuRSGfHx81K9fP6WmpjrKKioqlJqaqtjY2Gr3iY2NdaovSVu3bnXUDw8PV3BwsFOdoqIi7d69u8Y2AQAAAOBKuTxMLjExUWPHjlX//v0VHR2thQsXqqSkROPHj5ckjRkzRtdcc42Sk5MlSY899phuvfVWvfjiixoyZIjWrVunvXv36uWXX5YkWSwWTZkyRU8//bS6devmmFo7NDRUw4YNa7gzvUK+vr5KSkqqMjwPzQ/XyrNwvTwH18qzcL08B9fKs3C9WhaXp9aWpD//+c+OL13t3bu3Fi9erJiYGEnSwIED1aVLF6WkpDjqv/XWW3ryyScdX7r6/PPPV/ulqy+//LIKCgr0s5/9TH/5y1/UvXv3Kz9DAAAAAKhGvcIQAAAAAHg6l94ZAgAAAICWgjAEAAAAwJQIQwAAAABMiTAEAAAAwJQIQz+ydOlSdenSRX5+foqJidGePXtqrf/WW2/phhtukJ+fn3r16qX33nuviXpqXsnJybrpppvUrl07BQYGatiwYTp48GCt+6SkpMhisTgtfn5+TdRjc3vqqaeqfPY33HBDrftwX7lPly5dqlwvi8WiRx99tNr63FtNZ+fOnRo6dKhCQ0NlsVi0ceNGp+2GYWjWrFkKCQlRmzZtFBcXp0OHDl22XVf/7qFuarte58+f17Rp09SrVy+1bdtWoaGhGjNmjE6cOFFrm/X5fYrLu9y9NW7cuCqf++DBgy/bLveW5yAM/cf69euVmJiopKQkZWRkKCoqSvHx8Tp58mS19T/55BONHDlSDz74oPbt26dhw4Zp2LBh+vLLL5u45+bywQcf6NFHH9Wnn36qrVu36vz58xo0aJBKSkpq3c/f3185OTmO5ejRo03UY/Ts2dPps//oo49qrMt95V6fffaZ07XaunWrJOn++++vcR/uraZRUlKiqKgoLV26tNrtzz//vBYvXqxly5Zp9+7datu2reLj43Xu3Lka23T17x7qrrbrdfbsWWVkZGjmzJnKyMjQO++8o4MHD+ruu+++bLuu/D5F3Vzu3pKkwYMHO33ua9eurbVN7i0PY8AwDMOIjo42Hn30Ucd6eXm5ERoaaiQnJ1db/4EHHjCGDBniVBYTE2P893//d6P2E85OnjxpSDI++OCDGuu89tprRkBAQNN1Cg5JSUlGVFRUnetzXzUvjz32mHHdddcZFRUV1W7n3nIPScaGDRsc6xUVFUZwcLDxwgsvOMoKCgoMX19fY+3atTW24+rfPdTPpderOnv27DEkGUePHq2xjqu/T+G66q7V2LFjjXvuuceldri3PAtPhiSVlZUpPT1dcXFxjjIvLy/FxcVp165d1e6za9cup/qSFB8fX2N9NI7CwkJJUocOHWqtV1xcrM6dOyssLEz33HOPvvrqq6boHiQdOnRIoaGh6tq1q0aNGqXs7Owa63JfNR9lZWVavXq1JkyYIIvFUmM97i33y8rKUm5urtO9ExAQoJiYmBrvnfr83UPjKSwslMViUfv27Wut58rvUzSctLQ0BQYGKiIiQpMmTdLp06drrMu95XkIQ5Ly8/NVXl6uoKAgp/KgoCDl5uZWu09ubq5L9dHwKioqNGXKFN1yyy36yU9+UmO9iIgIrVixQn/729+0evVqVVRU6Oabb9bx48ebsLfmFBMTo5SUFG3evFkvvfSSsrKyNGDAAJ05c6ba+txXzcfGjRtVUFCgcePG1ViHe6t5uHh/uHLv1OfvHhrHuXPnNG3aNI0cOVL+/v411nP19ykaxuDBg7Vq1SqlpqZq7ty5+uCDD5SQkKDy8vJq63NveZ5W7u4AUF+PPvqovvzyy8uOmY6NjVVsbKxj/eabb1aPHj3017/+VXPmzGnsbppaQkKC4+fIyEjFxMSoc+fOevPNN/Xggw+6sWe4nOXLlyshIUGhoaE11uHeAq7M+fPn9cADD8gwDL300ku11uX3qXuMGDHC8XOvXr0UGRmp6667Tmlpabr99tvd2DM0FJ4MSbLZbPL29lZeXp5TeV5enoKDg6vdJzg42KX6aFiTJ0/Wu+++qx07dujaa691ad/WrVurT58+Onz4cCP1DjVp3769unfvXuNnz33VPBw9elTbtm3TQw895NJ+3FvucfH+cOXeqc/fPTSsi0Ho6NGj2rp1a61Phapzud+naBxdu3aVzWar8XPn3vI8hCFJPj4+6tevn1JTUx1lFRUVSk1Ndfp/PX8sNjbWqb4kbd26tcb6aBiGYWjy5MnasGGDtm/frvDwcJfbKC8v1xdffKGQkJBG6CFqU1xcrCNHjtT42XNfNQ+vvfaaAgMDNWTIEJf2495yj/DwcAUHBzvdO0VFRdq9e3eN9059/u6h4VwMQocOHdK2bdvUsWNHl9u43O9TNI7jx4/r9OnTNX7u3FseyN0zODQX69atM3x9fY2UlBTj66+/Nh5++GGjffv2Rm5urmEYhvHrX//amD59uqP+xx9/bLRq1cqYN2+ekZmZaSQlJRmtW7c2vvjiC3edgilMmjTJCAgIMNLS0oycnBzHcvbsWUedS6/V7NmzjS1bthhHjhwx0tPTjREjRhh+fn7GV1995Y5TMJXf/e53RlpampGVlWV8/PHHRlxcnGGz2YyTJ08ahsF91RyVl5cbdrvdmDZtWpVt3Fvuc+bMGWPfvn3Gvn37DEnG/PnzjX379jlmH3vuueeM9u3bG3/729+Mzz//3LjnnnuM8PBw4/vvv3e08Ytf/MJYsmSJY/1yf/dQf7Vdr7KyMuPuu+82rr32WmP//v1Of8tKS0sdbVx6vS73+xT1U9u1OnPmjDF16lRj165dRlZWlrFt2zajb9++Rrdu3Yxz58452uDe8myEoR9ZsmSJYbfbDR8fHyM6Otr49NNPHdtuvfVWY+zYsU7133zzTaN79+6Gj4+P0bNnT2PTpk1N3GPzkVTt8tprrznqXHqtpkyZ4riuQUFBxp133mlkZGQ0fedNaPjw4UZISIjh4+NjXHPNNcbw4cONw4cPO7ZzXzU/W7ZsMSQZBw8erLKNe8t9duzYUe3vvovXo6Kiwpg5c6YRFBRk+Pr6GrfffnuVa9i5c2cjKSnJqay2v3uov9quV1ZWVo1/y3bs2OFo49Lrdbnfp6if2q7V2bNnjUGDBhmdOnUyWrdubXTu3NmYOHFilVDDveXZLIZhGE3wAAoAAAAAmhXeGQIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSoQhAAAAAKZEGAIAAABgSv8f4/UP6HSau1YAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "JBoOn61J_rlK" - }, - "outputs": [], - "source": [ - "!head -500000 SUSY.csv > SUSY-small.csv" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "l_1_eta\n" + ] }, { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ral2i-VX_rlO", - "outputId": "eacbd357-4c59-4657-adba-b7eb0d42e409" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "total 2.5G\n", - "drwxr-xr-x 1 root root 4.0K Oct 29 13:25 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n", - "-rw-r--r-- 1 root root 2.3G Oct 31 14:41 SUSY.csv\n", - "-rw-r--r-- 1 root root 228M Oct 31 14:42 SUSY-small.csv\n" - ] - } - ], - "source": [ - "ls -lh" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "pjNRqmyS_rlR" - }, - "source": [ - "### First Look\n", - "\n", - "Each row represents a LHC collision event. Each column contains some observable from that event. The variable names are ([based on documentation](https://archive.ics.uci.edu/ml/datasets/SUSY)):" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "l_1_phi\n" + ] }, { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "qG8BaBKD_rlp" - }, - "outputs": [], - "source": [ - "VarNames=[\"signal\", \"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\", \"MET_rel\", \"axial_MET\", \"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos_theta_r1\"]" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "_00yPcCi_rlr" - }, - "source": [ - "Some of these variables represent the \"raw\" kinematics of the observed final state particles, while others are \"features\" that are derived from these raw quantities:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "l_2_pT\n" + ] }, { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "id": "sm1x5YFJ_rlt" - }, - "outputs": [], - "source": [ - "RawNames=[\"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\"]\n", - "FeatureNames=list(set(VarNames[1:]).difference(RawNames))" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "hOKioChv_rmL", - "outputId": "56e822f2-c3d1-4658-e4fd-14730c21549c" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "['l_1_pT',\n", - " 'l_1_eta',\n", - " 'l_1_phi',\n", - " 'l_2_pT',\n", - " 'l_2_eta',\n", - " 'l_2_phi',\n", - " 'MET',\n", - " 'MET_phi']" - ] - }, - "metadata": {}, - "execution_count": 17 - } - ], - "source": [ - "RawNames" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "l_2_eta\n" + ] }, { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "hD2YtQmu_rmO", - "outputId": "8b5645e9-98cd-440b-f127-ef9d91ae1512" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "['S_R',\n", - " 'MT2',\n", - " 'M_Delta_R',\n", - " 'dPhi_r_b',\n", - " 'cos_theta_r1',\n", - " 'axial_MET',\n", - " 'R',\n", - " 'MET_rel',\n", - " 'M_TR_2',\n", - " 'M_R']" - ] - }, - "metadata": {}, - "execution_count": 18 - } - ], - "source": [ - "FeatureNames" + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0MAAAGsCAYAAAAfTXyRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABF60lEQVR4nO3dfVyUdb7/8TdQgKhgiYC4EHiT0a5KgrJWlhWJ3bu2u+rmotTa2Vo7urOtaSVm2pLmDWtZnGpN01zdm+rsKQ+dwthtd0kLctsK2TL7TaUgtEcncQWD+f3BYWpiZpgZ5v56PR+PeeRcc13XfIdmYN7X9/v9fKOsVqtVAAAAAGAw0cFuAAAAAAAEA2EIAAAAgCERhgAAAAAYEmEIAAAAgCERhgAAAAAYEmEIAAAAgCERhgAAAAAY0hnBboAvdHZ26vDhwxo4cKCioqKC3RwAAAAAQWK1WvX5558rPT1d0dGu+34iIgwdPnxYGRkZwW4GAAAAgBDx8ccf6xvf+IbLfSIiDA0cOFBS1wtOTEwMcmsAAAAABIvFYlFGRoYtI7gSEWGoe2hcYmIiYQgAAACAW9NnKKAAAAAAwJAIQwAAAAAMiTAEAAAAwJAiYs4QAAAAjKOjo0OnT58OdjMQRGeeeaZiYmL6fB7CEAAAAMKC1WpVY2Ojjh07FuymIAQMGjRIaWlpfVpnlDAEAACAsNAdhFJSUpSQkNCnL8EIX1arVSdPntTRo0clSUOHDvX6XIQhAAAAhLyOjg5bEBo8eHCwm4Mg69evnyTp6NGjSklJ8XrIHAUUAAAAEPK65wglJCQEuSUIFd3vhb7MHyMMAQAAIGwwNA7dfPFeIAwBAAAAMCTmDAEAACC8mc1SS0tgnis5WcrMDMxzwe8IQwAAAAhfZrOUkyOdPBmY50tIkOrrfRKI5s2bp2PHjun555/ve7s8cN999+n555/X/v37A/q8oYgwBAAAgPDV0tIVhLZv7wpF/lRfL82Z0/WcPghDv/zlL2W1Wn3QMHiLMAQAAIDwl5MjjR8f7FZ4JCkpKdhNMDwKKAAAAAB+9Lvf/U5jxoxRv379NHjwYBUWFqq1tVXz5s3T9OnTbft9/vnnuummm9S/f38NHTpUGzZs0JQpU7Ro0SLbPllZWfrFL36hm2++WQMHDlRmZqYef/xxu+e76667dO655yohIUHDhw/XsmXL+lR+OpIRhgAAkcVslurqHN/M5mC3DoDBHDlyRLNnz9bNN9+s+vp6VVdXa8aMGQ6Hx5lMJv3lL3/RH/7wB7388st67bXXVFdX12O/devWKT8/X2+99ZZuv/123XbbbWpoaLA9PnDgQG3ZskXvvfeefvnLX+qJJ57Qhg0b/Po6wxXD5AAAkaO3idQ+nPgMAO44cuSIvvjiC82YMUPnnHOOJGnMmDE99vv888+1detW7dixQ1dccYUk6amnnlJ6enqPfa+++mrdfvvtkrp6gTZs2KBXX31Vo0ePliTde++9tn2zsrJ05513aufOnVq8eLHPX1+4IwwBACKHq4nUPp74DADuGDdunK644gqNGTNGRUVFmjp1qr773e/qrLPOstvvww8/1OnTpzVx4kTbtqSkJFvA+aqxY8fa/h0VFaW0tDQdPXrUtm3Xrl3auHGjDh48qBMnTuiLL75QYmKiH15d+GOYHAAg8nRPpP7qzd9VpgDAgZiYGL388sv67//+b51//vl6+OGHNXr0aB06dMjrc5555pl296OiotTZ2SlJqqmp0U033aSrr75aL7zwgt566y3dc889am9v79PriFT0DAEAgsfVQoksbAggQkRFRemiiy7SRRddpNLSUp1zzjl67rnn7PYZPny4zjzzTL3xxhvK/L/ffcePH9c//vEPXXLJJW4/11//+ledc845uueee2zb/t//+3++eSERiDAEAAgO5vcA8KX6+pB8jr1796qqqkpTp05VSkqK9u7dq+bmZuXk5Ojtt9+27Tdw4EDNnTtXP//5z3X22WcrJSVFy5cvV3R0tKKiotx+vlGjRslsNmvnzp2aMGGCXnzxxR7BC18iDAEA/MtZ7099fe/ze157zfHwNnqNAHRLTu66eDJnTmCeLyGh6zndlJiYqD/96U8qLy+XxWLROeeco3Xr1umqq67Srl277PZdv369fvzjH+vaa69VYmKiFi9erI8//ljx8fFuP9/111+vn/70p1qwYIHa2tp0zTXXaNmyZbrvvvvcPoeRRFkjYNlbi8WipKQkHT9+nMlhABBKvO398fa4ujopL0+qre25+KKrx7qf09mQPVcIZkBAnDp1SocOHVJ2dnbPcODt59cbAfzMt7a2atiwYVq3bp1uueWWgDxnOHH2nvAkG9AzBADwH1fV3STnXyoyM7vCjrMeJV9XhestfLnCcD4g+DIzI+Iz+NZbb+nAgQOaOHGijh8/rvvvv1+SdMMNNwS5ZZGLMAQA8L/u6m6e6O3LjaOx++6M53d2nKvQ5upclOsG4ENr165VQ0ODYmNjlZeXp9dee03JHgzLg2cIQwAA94RK5bfe5gc4G8/vznGTJ3v3OpyFMIbQAfDABRdcoNra2mA3w1AIQwCA3oVS5TdXQ+gk74beuTrOFXcCFkPoACBkeRWGNm3apIceekiNjY0aN26cHn74YbvVcp3ZuXOnZs+erRtuuEHPP/+8bbvVatXy5cv1xBNP6NixY7rooov02GOPadSoUd40DwDga67m/gRjqJi38wN8Pa8g0HObAAA+5XEY2rVrl0wmkyoqKlRQUKDy8nIVFRWpoaFBKSkpTo/76KOPdOedd2ry5Mk9HluzZo02btyorVu3Kjs7W8uWLVNRUZHee+89j0oJAgD8zJu5P5EuQiZuA4ARRXt6wPr16zV//nyVlJTo/PPPV0VFhRISErR582anx3R0dOimm27SihUrNHz4cLvHrFarysvLde+99+qGG27Q2LFj9fTTT+vw4cN2vUcAgBBXX99Vvvqrt0AsgggAgJc86hlqb29XbW2tli5datsWHR2twsJC1dTUOD3u/vvvV0pKim655Ra99tprdo8dOnRIjY2NKiwstG1LSkpSQUGBampqNGvWrB7na2trU1tbm+2+xWLx5GUAAHzJ24IGAAAEmUdhqKWlRR0dHUpNTbXbnpqaqgMHDjg85s9//rN+9atfaf/+/Q4fb2xstJ3j6+fsfuzrysrKtGLFCk+aDgDwF38UJjCCUKnOB0SAUF9zdcqUKcrNzVV5eblf2jRv3jwdO3YsokZVffTRR8rOztZbb72l3Nxcvz2PX6vJff755/rhD3+oJ554wqf10ZcuXSqTyWS7b7FYlJGR4bPzAwA8xLwZz7hTne/ZZ6UhQ3o+RlAC7PRlzWRvUCQysngUhpKTkxUTE6Ompia77U1NTUpLS+ux/8GDB/XRRx/puuuus23r7OzseuIzzlBDQ4PtuKamJg0dOtTunM5SYFxcnOLi4jxpOgAAocNVdb7mZmnGDGnaNMfH8k0MsOPq4+RrRioS2d7ertjY2GA3w+88KqDQvRJuVVWVbVtnZ6eqqqo0adKkHvufd955+vvf/679+/fbbtdff70uu+wy7d+/XxkZGcrOzlZaWprdOS0Wi/bu3evwnAAARIzu6nxfvRUVdX3jqq3tedu+vetbX6DGAwFhxNHHyde3voStL774QgsWLFBSUpKSk5O1bNkyWa1WSdK2bduUn5+vgQMHKi0tTT/4wQ909OhRu+PfffddXXvttUpMTNTAgQM1efJkHTx40OFzvfHGGxoyZIhWr15t27Zq1SqlpKRo4MCB+tGPfqQlS5bYdTzMmzdP06dP1wMPPKD09HSNHj1akvT3v/9dl19+ufr166fBgwfr1ltv1YkTJ2zHTZkyRYsWLbJ7/unTp2vevHm2+1lZWfrFL36hm2++WQMHDlRmZqYef/xxu2P27dunCy64QPHx8crPz9dbb73l9s+2LzweJmcymTR37lzl5+dr4sSJKi8vV2trq0pKSiRJxcXFGjZsmMrKyhQfH69vfetbdscPGjRIkuy2L1q0SKtWrdKoUaNspbXT09M1ffp0718ZAMAx5qqEPoYdAhFn69atuuWWW7Rv3z69+eabuvXWW5WZman58+fr9OnTWrlypUaPHq2jR4/KZDJp3rx52r17tyTp008/1SWXXKIpU6Zoz549SkxM1F/+8hd98cUXPZ5nz549mjFjhtasWaNbb71VkvTMM8/ogQce0KOPPqqLLrpIO3fu1Lp165SdnW13bFVVlRITE/Xyyy9LklpbW1VUVKRJkybpjTfe0NGjR/WjH/1ICxYs0JYtWzx6/evWrdPKlSt1991363e/+51uu+02XXrppRo9erROnDiha6+9VldeeaW2b9+uQ4cOaeHChV78lD3ncRiaOXOmmpubVVpaqsbGRuXm5qqystJWAMFsNis62rOK3YsXL1Zra6tuvfVWHTt2TBdffLEqKytZYwgAvOUs8HQPwXI1V4UhWL7lqLw4JccBw8nIyNCGDRsUFRWl0aNH6+9//7s2bNig+fPn6+abb7btN3z4cG3cuFETJkzQiRMnNGDAAG3atElJSUnauXOnzjzzTEnSueee2+M5nnvuORUXF+vJJ5/UzJkzbdsffvhh3XLLLbbOi9LSUv3P//yPXQ+PJPXv319PPvmkbXjcE088oVOnTunpp59W//79JUmPPPKIrrvuOq1evbpHATRXrr76at1+++2SpLvuuksbNmzQq6++qtGjR2vHjh3q7OzUr371K8XHx+ub3/ymPvnkE912221un99bXhVQWLBggRYsWODwserqapfHOkqRUVFRuv/++3X//fd70xwAwFe5Mzm/srLn5HwjDYYPBH+WHHcWpujZA0LWt7/9bUVFRdnuT5o0SevWrVNHR4f279+v++67T3/729/0v//7v7Y59mazWeeff77279+vyZMn24KQI3v37tULL7yg3/3udz1GVzU0NNiCSLeJEydqz549dtvGjBljN0+ovr5e48aNswUhSbrooovU2dmphoYGj8LQ2LFjbf+OiopSWlqabShgfX29xo4da9cREqjpMn6tJgcACILeZhPzhTkw/FFy3J2ARc8eEFZOnTqloqIiFRUV6ZlnntGQIUNkNptVVFSk9vZ2SVK/fv16Pc+IESM0ePBgbd68Wddcc43L4OTMV0OPu6Kjo21zn7qdPn26x35fb09UVJQt9AUTYQgAIlX3bGIEj6/n/rgKWPTsASFt7969dvdff/11jRo1SgcOHNBnn32mBx980LZUzJtvvmm379ixY7V161adPn3aachJTk7Ws88+qylTpuj73/++fvOb39j2HT16tN544w0VFxfb9n/jjTd6bXNOTo62bNmi1tZWW1D6y1/+oujoaFuBhSFDhujIkSO2Yzo6OvTOO+/osssu6/X8X32ebdu26dSpU7beoddff93t4/vCs8k9AIDIV18v1dXZ35jjEjoyM31f5gqA35nNZplMJjU0NOjXv/61Hn74YS1cuFCZmZmKjY3Vww8/rA8//FB/+MMftHLlSrtjFyxYIIvFolmzZunNN9/U+++/r23btqmhocFuv5SUFO3Zs0cHDhzQ7NmzbQUW7rjjDv3qV7/S1q1b9f7772vVqlV6++237YbtOXLTTTcpPj5ec+fO1TvvvKNXX31Vd9xxh374wx/ahshdfvnlevHFF/Xiiy/qwIEDuu2223Ts2DGPfjY/+MEPFBUVpfnz5+u9997T7t27tXbtWo/O4S16hgAAXfw5xwUA/CwQ12z68hzFxcX617/+pYkTJyomJkYLFy7UrbfeqqioKG3ZskV33323Nm7cqPHjx2vt2rW6/vrrbccOHjxYe/bs0c9//nNdeumliomJUW5uri666KIez5OWlqY9e/ZoypQpuummm7Rjxw7ddNNN+vDDD3XnnXfq1KlT+v73v6958+Zp3759LtuckJCgl156SQsXLtSECROUkJCgG2+8UevXr7ftc/PNN+tvf/ubiouLdcYZZ+inP/2pR71CkjRgwAD913/9l3784x/rggsu0Pnnn6/Vq1frxhtv9Og83oiyfn2QXxiyWCxKSkrS8ePHlZiYGOzmAEBw1dVJeXld69J4OkyOstvhq/v/O3PFEKFOnTqlQ4cOKTs7226ifW81Y3wtUqbmXXnllUpLS9O2bduC3RSvOXtPeJIN6BkCAHyJ9W3CF8UVYFC91SrxtXC8rnDy5ElVVFSoqKhIMTEx+vWvf61XXnnFtp6QkRGGAACIBBRXgIFxHce1qKgo7d69Ww888IBOnTql0aNH6/e//70KCwuD3bSgIwwBABAp+EYIwIF+/frplVdeCXYzQhLV5AAAAAAYEmEIAAAAgCERhgAAABA2Ojs7g90EhAhfvBeYMwQAocpVmWtXWCAVQASKjY1VdHS0Dh8+rCFDhig2NrbXRUMRmaxWq9rb29Xc3Kzo6GjFxsZ6fS7CEACEor4unMECqQAiTHR0tLKzs3XkyBEdPnw42M1BCEhISFBmZqaio70f7EYYAoBQ1NLSFYScLaDZm3BcCAMAehEbG6vMzEx98cUX6ujoCHZzEEQxMTE644wz+tw7SBgCgFCWkyONHx/sVgBAyIiKitKZZ56pM888M9hNQQSggAIAAAAAQyIMAQAAADAkwhAAAAAAQ2LOEAAARuFt2XUKcgCIUIQhAAAiXXJyV7n1OXO8Oz4hoStIEYgARBjCEAD4m6vFU7nijkDIzOwKM94u4jtnjvTaa56Xeef9DSDEEYYAwJ96WzyVK+4IlMxM795nfelV4v0NIMQRhgDAn1wtntp9xb2lhS+LCF3e9irx/gYQBghDABAILJ6KcOZtrxIAhDhKawMAAAAwJHqGACDYHJU79rYEMgAAcBthCAB8wVnFOFehpreJ6QkJXfsAAAC/IAwBQF+5UzHOUajpbWI6ZYkBAPArwhAA9JWrinGS61DDxHQAAIKGMAQAvkLFOAAAwgrV5AAAAAAYEmEIAAAAgCERhgAAAAAYkldhaNOmTcrKylJ8fLwKCgq0b98+p/s+++yzys/P16BBg9S/f3/l5uZq27ZtdvvMmzdPUVFRdrdp06Z50zQAAAAAcIvHBRR27dolk8mkiooKFRQUqLy8XEVFRWpoaFBKSkqP/c8++2zdc889Ou+88xQbG6sXXnhBJSUlSklJUVFRkW2/adOm6amnnrLdj4uL8/IlAQAAAEDvPO4ZWr9+vebPn6+SkhKdf/75qqioUEJCgjZv3uxw/ylTpug73/mOcnJyNGLECC1cuFBjx47Vn//8Z7v94uLilJaWZrudddZZ3r0iAAAAAHCDRz1D7e3tqq2t1dKlS23boqOjVVhYqJqaml6Pt1qt2rNnjxoaGrR69Wq7x6qrq5WSkqKzzjpLl19+uVatWqXBgwc7PE9bW5va2tps9y0WiycvAwC8YzY7XiC1vj7wbQEAAH3mURhqaWlRR0eHUlNT7banpqbqwIEDTo87fvy4hg0bpra2NsXExOjRRx/VlVdeaXt82rRpmjFjhrKzs3Xw4EHdfffduuqqq1RTU6OYmJge5ysrK9OKFSs8aToA9I3Z3LWO0MmTjh9PSOhaXBUAAISNgCy6OnDgQO3fv18nTpxQVVWVTCaThg8frilTpkiSZs2aZdt3zJgxGjt2rEaMGKHq6mpdccUVPc63dOlSmUwm232LxaKMjAy/vw4ABtbS0hWEtm/vCkVfl5wsZWYGvl0AAMBrHoWh5ORkxcTEqKmpyW57U1OT0tLSnB4XHR2tkSNHSpJyc3NVX1+vsrIyWxj6uuHDhys5OVkffPCBwzAUFxdHgQUAwZGTI40fH+xWAAAAH/CogEJsbKzy8vJUVVVl29bZ2amqqipNmjTJ7fN0dnbazfn5uk8++USfffaZhg4d6knzAAAAAMBtHg+TM5lMmjt3rvLz8zVx4kSVl5ertbVVJSUlkqTi4mINGzZMZWVlkrrm9+Tn52vEiBFqa2vT7t27tW3bNj322GOSpBMnTmjFihW68cYblZaWpoMHD2rx4sUaOXKkXeltAAAAAPAlj8PQzJkz1dzcrNLSUjU2Nio3N1eVlZW2ogpms1nR0V92OLW2tur222/XJ598on79+um8887T9u3bNXPmTElSTEyM3n77bW3dulXHjh1Tenq6pk6dqpUrVzIUDgAAAIDfRFmtVmuwG9FXFotFSUlJOn78uBITE4PdHACRqK5OysuTamuZMwS4g88MgCDxJBt4vOgqAAAAAESCgJTWBoCwwcKqAAAYBmEIALqxsCoQGpxdlJBY0wuATxGGAKAbC6sCgeMs8DQ3SzNmuL4oUV/PZxGATxCGAODrWFgV8B1HQ0zdCTyVldKQIT3PNWdOV4giDAHwAcIQAADwveTkrlAzZ47jx50Fnu5jCTsAAoAwBAAAfC8zs6snh7k/AEIYYQgAAPhHZiaBB0BIY50hAAAAAIZEzxAAAAgvztb9YugdAA8RhgAAQHhwpygDZbcBeIAwBMB4nK1v4uxqM4DQ4KooA2W3AXiBMATAWMzmrnWEXK1vkpwc2DYBcB9FGQD4EGEIgLG0tHQFoe3bu0LR1zHnAAAAwyAMATCmnBxp/PhgtwIAAAQRpbUBAAAAGBJhCAAAAIAhEYYAAAAAGBJhCAAAAIAhEYYAAAAAGBLV5AAAQORwtngyZfMBOEAYAhCZzGbnq9QDiDzJyV2LJs+Z4/jxhISuzz+BCMBXEIYARB6zuWsdoZMnHT+ekND1xQlA5MjM7Ao7zi6CzJnT9RhhCMBXEIYARJ6Wlq4gtH17Vyj6OobLAJEpM5PPNgCPEIYAhK/ehsLl5Ejjxwe2TQAAIGwQhgCEJ4bCAQCAPiIMAQhPDIUDAAB9RBgCEN4YCgcAALzEoqsAAAAADIkwBAAAAMCQCEMAAAAADIkwBAAAAMCQCEMAAAAADMmrMLRp0yZlZWUpPj5eBQUF2rdvn9N9n332WeXn52vQoEHq37+/cnNztW3bNrt9rFarSktLNXToUPXr10+FhYV6//33vWkaAAAAALjF4zC0a9cumUwmLV++XHV1dRo3bpyKiop09OhRh/ufffbZuueee1RTU6O3335bJSUlKikp0UsvvWTbZ82aNdq4caMqKiq0d+9e9e/fX0VFRTp16pT3rwwAAAAAXPA4DK1fv17z589XSUmJzj//fFVUVCghIUGbN292uP+UKVP0ne98Rzk5ORoxYoQWLlyosWPH6s9//rOkrl6h8vJy3Xvvvbrhhhs0duxYPf300zp8+LCef/75Pr04AAAAAHDGozDU3t6u2tpaFRYWfnmC6GgVFhaqpqam1+OtVquqqqrU0NCgSy65RJJ06NAhNTY22p0zKSlJBQUFTs/Z1tYmi8VidwMAAHCpvl6qq+t5M5uD3TIAQXKGJzu3tLSoo6NDqampdttTU1N14MABp8cdP35cw4YNU1tbm2JiYvToo4/qyiuvlCQ1NjbazvH1c3Y/9nVlZWVasWKFJ00HEMrMZqmlxfFjyclSZmZg2wMgsiQnSwkJ0pw5jh9PSOgKSvyuAQzHozDkrYEDB2r//v06ceKEqqqqZDKZNHz4cE2ZMsWr8y1dulQmk8l232KxKCMjw0etBRBQZrOUkyOdPOn48YQE6dlnpSFD7LfX1/u/bQAiQ2Zm1+8MRxdd6uu7QlJLC2EIMCCPwlBycrJiYmLU1NRkt72pqUlpaWlOj4uOjtbIkSMlSbm5uaqvr1dZWZmmTJliO66pqUlDhw61O2dubq7D88XFxSkuLs6TpgMIVS0tXUFo+/auUPRVzc3SjBnStGmOj01I6LriCwC9ycwk7ADowaMwFBsbq7y8PFVVVWn69OmSpM7OTlVVVWnBggVun6ezs1NtbW2SpOzsbKWlpamqqsoWfiwWi/bu3avbbrvNk+YBCGc5OdL48T23O7uaKzGEDgAA9InHw+RMJpPmzp2r/Px8TZw4UeXl5WptbVVJSYkkqbi4WMOGDVNZWZmkrvk9+fn5GjFihNra2rR7925t27ZNjz32mCQpKipKixYt0qpVqzRq1ChlZ2dr2bJlSk9PtwUuAAbG1VwAAOAnHoehmTNnqrm5WaWlpWpsbFRubq4qKyttBRDMZrOio78sUtfa2qrbb79dn3zyifr166fzzjtP27dv18yZM237LF68WK2trbr11lt17NgxXXzxxaqsrFR8fLwPXiIAAAAA9BRltVqtwW5EX1ksFiUlJen48eNKTEwMdnMAeKKuTsrLk2prHQ+TAwB/4ncQEHE8yQYeL7oKAAAAAJEgIKW1AQDwFMtPAQD8jTAEAG7iy3nguLP8FGtkwqecrV3GhxuIaIQhAIHhLEmEyeKpRvlyHiqBz9XyU/5aI9Pb1x4qPzN4KTm56wM8Z47jxyPlww3AIcIQAP9zJ0mE+OKpofblXPL9F+2+BD5/BQJny095y1k7u9f39fS1GyUkR7TMTOfrmfnrww0gZBCGAPifqyQhhdXl81D5ci75/ou2O4Hvtdd6PuZOkHj2WWnIEPfb4k6Hoaediu60s7KyZztdvfb6+sCHZPgB65kBhkUYAhA4vk4SYc6dXgVHX86l3r9o99ar5Eh3uHD0v8mdkUSO2todQKZN86wt3ed01GHYW1t6O6ezn6mzTO7Oa588OXDfpf3RC9eXXkiGCQIIZ4QhAAgSf3WY9RayXHEWQFyNJJJct9XVca44O2dvbfHmnK705bX7mj+G5bnzfmGYIIBIRRgCgCDzdYdZbyHLFVdf7L0dSeSPEUiBHtXUl+fzZZGyvsxdc1XDxNX7hWGCACIZYQiA74RJxbhIGtbj6EfrargbAsed4XXO5lL19j709P+tOz04zob6hdowwaCg7DYQsQhDAHwjTCrGRcqwHne+oIbAj9vQXA2v620ulbOg5O11hb4MyQylYYIBR9ltIOIRhgD4RphUjAtGiWx/MPQX1DDianhdX4KSq6Drj97CQA9LDJneW8puAxGPMATAtwI4NqsvX5giYQgZ1YDDmzdBSepb1btw6C0Mud5bPmhARCMMAQhL/vzC5OrKOhAI3nz/jpTeQm/Xu5LC5zUCCB2EIQBhyR/D3SLlyjqMK5I6Mbxd74opPAA8QRgCENZ8OdwtUq6sA+HAm+KTTOEB4GuEIQD4iki6sg6Eqr4Un/T2MxoyRRkAhBTCEAAACKhAF58MuaIMAEIGYQhA0HHFFjAmf1R1dFYAJRJK6gPwPcIQgKAywhVbb+ZGAPCMO8UVJk/2/HeJ04s19f2UrAxlOvsgcyUHCAuEIQAB4SoQeFNG150gEQolsvsyNwKA+/xRAMX15zdHCapX/ZwcZerjng9HwpUcwAAIQwD8zp1A4OiKrbelrkOpRHag50YARubrAii9l/Dvr5btLykz51+OHmTsHRAGCEMA/M7bQODtld5glch21RPlj7kRAALD5ec3J0fisw2ELcIQAJ8xK0Mt9f16bO9LIPD2Sm8gS2SHUk8UEGpCYbgqADhDGALgE+YjZypH9To5p7/DxyM5ELBYK9CT0S8SOLs4JPE7AQglhCEAPtFy7AydVH9tX3lIOVdn93g80v/4s1grYC/cLhJ424PlaJ/mvw7UjF4uDj37rDRkSM/HQu3nAkQ6whAAzzgrC3eoUVKOcrJPMTcGgKTwuEjgn0Ito5SgVlUu3qMhY4faPdL8v2doxuIRmjYt2unzUYQOCBzCEAD3uSwLd4Gkq6VBgwLcKADwnl8KtRw5ouTvTlHmmn84PGd9/LlqeaFaGmoflChCBwQeYQhAD84XGWyVTo6WVq6Ssu2HwtUfipeWqccfdwAIdb4v1DJUanjZ6eJqmXPmKHPoEWk8vy+BYCMMAQblLPA0N0szZjhfZFCq6wo9DkT6hGgAcFs4jBEEQBgCjMidRVArKx1M7q2vl+bcJG1/xuGCQUz8BQAA4YQwBBiQt4ugSv+S9JaU8y8WGQQAAGGPMAQYmDeLoAIA/MtZSW963wHf8yoMbdq0SQ899JAaGxs1btw4Pfzww5o4caLDfZ944gk9/fTTeueddyRJeXl5+sUvfmG3/7x587R161a744qKilRZWelN84CI47SgQS/4wwkAIcpB4kk+cqYS+n1Tc+Y4L7vN+kSAb3kchnbt2iWTyaSKigoVFBSovLxcRUVFamhoUEpKSo/9q6urNXv2bF144YWKj4/X6tWrNXXqVL377rsaNmyYbb9p06bpqaeest2Pi4vz8iUBkaW3+T2usF4FAIQYFwsUZcp52e3u4jbTpjk+Lb/vAe94HIbWr1+v+fPnq6SkRJJUUVGhF198UZs3b9aSJUt67P/MM8/Y3X/yySf1+9//XlVVVSouLrZtj4uLU1pamqfNASJeb/N7nGG9CgAIQa4WKOouu31sjzT0a7/wh0j1r6SqJW6Yo8M0Z4702mue/Z2Q6FECPApD7e3tqq2t1dKlS23boqOjVVhYqJqaGrfOcfLkSZ0+fVpnn3223fbq6mqlpKTorLPO0uWXX65Vq1Zp8ODBDs/R1tamtrY2232LxeLJywDCkrfzexyNPXc2Hh0AEADOym676DWSpMyEBGU66P7p5TCX6FGC0XkUhlpaWtTR0aHU1FS77ampqTpw4IBb57jrrruUnp6uwsJC27Zp06ZpxowZys7O1sGDB3X33XfrqquuUk1NjWJiYnqco6ysTCtWrPCk6YDh9PbHMaFfp5KPvCvVnXb/pKQoAPCfXnqNnHX3uzrMFUYQAAGuJvfggw9q586dqq6uVnx8vG37rFmzbP8eM2aMxo4dqxEjRqi6ulpXXHFFj/MsXbpUJpPJdt9isSgjI8O/jQfCjMs/jkeOKPm7U5R57T88PzErqwKA/3i5WCtrvALe8SgMJScnKyYmRk1NTXbbm5qaep3vs3btWj344IN65ZVXNHbsWJf7Dh8+XMnJyfrggw8chqG4uDgKLABucPrHse6IdOofnk9EkhhgDgAAIoZHYSg2NlZ5eXmqqqrS9OnTJUmdnZ2qqqrSggULnB63Zs0aPfDAA3rppZeUn5/f6/N88skn+uyzzzT0a5VUAPgYCw0BAAAD83iYnMlk0ty5c5Wfn6+JEyeqvLxcra2ttupyxcXFGjZsmMrKyiRJq1evVmlpqXbs2KGsrCw1NjZKkgYMGKABAwboxIkTWrFihW688UalpaXp4MGDWrx4sUaOHKmioiIfvlQg+FytF0SHCwAgGFjkFUbmcRiaOXOmmpubVVpaqsbGRuXm5qqystJWVMFsNis6+svFwh577DG1t7fru9/9rt15li9frvvuu08xMTF6++23tXXrVh07dkzp6emaOnWqVq5cyVA4RJTe1guiog8AIJB6LbTD3yUYgFcFFBYsWOB0WFx1dbXd/Y8++sjlufr166eXXnrJm2YAIclZ7099vfP1glytEUEBNwCAP3hZvK5XjIJAOAloNTkgErj6Jd+9Qrir3p/Jk3v+IXDn6hwF3AAAvubrKnSMgkC4IQwBHujtl7zU9Yu+slIaMqTnY86uiPW2RgRX0gAA4aClpfdREKxrhFBCGAI84OqXfDdvg4tf1ohwNWYPAAA/oVgpwgVhCIbVlzHNYfFL3p2xCoy9AwAEmKvrcYyEQKARhmBIhhjT3Fs3Fn9xAABe8mbgQW/zY6UI+fuLsEIYgiEZakxzWHRjAQBCkaNw406xIEcDD3qbHxtxf38RFghDMDRyAgAgrARohVR3qpx6WixI8tP8WKAPCEMAAAChLsArpFLlFEZBGAIAAAh1/lohtZenJPAg0hGGAAAAwgHpBPA5whAAAABCXl+WxACcIQwBAAAgpBliSQwEBWEIAAAAIcNRwbz6egMtiYGAIgwhonmzKBwAAAg8dwrmTZ5M4IFvEYYQ9pwFHm8Xhevm7MoUAADwPcp5IxgIQwhr7owh9nRROHeuTLkKUQAAwDsUzEOgEYYQ1lpanI8hlry7isSVKQBAWHI2fIE/XIBThCGEhd7m/uTkSOPH++75uDIFAAgb7gxpoNQa4BBhCCHPnaFwDFsDABiWqyENlFpjfSK4RBhCyPPHULiIQsk8AABDGhxifSL0hjCEsOHroXARgW4zAACccnVBlU4zSIQhBBhd1T5GtxkAAJJcL4nBBVU4QxhCwLjTifHssz3LYDPayw38lgcAGBRLYqAvCEMIGFedGN0LpE6b5vhYfpEBAABH+rokBhXJjY0whIBz1onB2j4AAMAb3tSPoCI5JMIQQgiFcAAAQKBQkRwSYQgAAAAGxYVYEIYAAAAiHRNjAIcIQwAAAJGKiTGAS4QheIX1ggAACANMjPELvgdFDsIQPObOekFcZAIAIEQwMcan+B4UWQhD8Jir9YK4yAQAACKFo6lW9fV8D4okhCF4zdl6QfASfe4AAIQEd6ZaTZ7Mn+ZIEO3NQZs2bVJWVpbi4+NVUFCgffv2Od33iSee0OTJk3XWWWfprLPOUmFhYY/9rVarSktLNXToUPXr10+FhYV6//33vWkaQkR9vVRXZ39zVsgG+rLPPS/P8S0np2sfAADgd91TrWprHd8YBhc5PO4Z2rVrl0wmkyoqKlRQUKDy8nIVFRWpoaFBKSkpPfavrq7W7NmzdeGFFyo+Pl6rV6/W1KlT9e6772rYsGGSpDVr1mjjxo3aunWrsrOztWzZMhUVFem9995TfHx8318lAsadKynJyYFtU1hg7CEAACGFqVbG4HEYWr9+vebPn6+SkhJJUkVFhV588UVt3rxZS5Ys6bH/M888Y3f/ySef1O9//3tVVVWpuLhYVqtV5eXluvfee3XDDTdIkp5++mmlpqbq+eef16xZs3qcs62tTW1tbbb7FovF05cBP3FVtEZitFevXI09dDZwGQCAvmANIhiYR2Govb1dtbW1Wrp0qW1bdHS0CgsLVVNT49Y5Tp48qdOnT+vss8+WJB06dEiNjY0qLCy07ZOUlKSCggLV1NQ4DENlZWVasWKFJ01HAHElxcfobgMA+ANrEPkN+TJ8eBSGWlpa1NHRodTUVLvtqampOnDggFvnuOuuu5Senm4LP42NjbZzfP2c3Y993dKlS2UymWz3LRaLMjIy3H4dQFihuw0A4A+sQeRz5MvwE9Bqcg8++KB27typ6urqPs0FiouLU1xcnA9bBoQ4utsAAP7A3xefIl+GH4/CUHJysmJiYtTU1GS3vampSWlpaS6PXbt2rR588EG98sorGjt2rG1793FNTU0aOnSo3Tlzc3M9aR4AAAAQVOTL8OJRae3Y2Fjl5eWpqqrKtq2zs1NVVVWaNGmS0+PWrFmjlStXqrKyUvn5+XaPZWdnKy0tze6cFotFe/fudXlOAAAAINw4Wn6kro4VNILF42FyJpNJc+fOVX5+viZOnKjy8nK1trbaqssVFxdr2LBhKisrkyStXr1apaWl2rFjh7KysmzzgAYMGKABAwYoKipKixYt0qpVqzRq1Chbae309HRNnz7dd68UAAAACBLmE4Umj8PQzJkz1dzcrNLSUjU2Nio3N1eVlZW2Aghms1nR0V92OD322GNqb2/Xd7/7XbvzLF++XPfdd58kafHixWptbdWtt96qY8eO6eKLL1ZlZSVrDCHymM3OBxIDAICI5c58otde67ncoEStJH+Kslqt1mA3oq8sFouSkpJ0/PhxJSYmBrs5Ea+uTsrL61qB2dmSOHDAbO76DXfypOPHuSQEAAgV/LEPKL4i+JYn2SCg1eQAQ2tp6fott307l30AAOGBBXMCgip0wUMYAgItJ4erbACA0MYEl4CjCl1wEIYAAABgj64KGARhCAAAAD311lXBEDpEAMIQnKLwmZf4wQEAIhlD6BBBCEMG5+x7e3OzNGOG66omycn+bVtYcqccDD84AEA4YwgdIghhyMDc+d5eWSkNGdLzMXrAnaBiHADACJjtjwhBGDIwvrf7ERXjAACADzFFyz8IQwbQ2xQWvrcDAACEJqZo+RdhKMIxhQUAACB8MUXLvwhDEY6hcAAAAOGNKVr+QxgyCIbCAQCAgGGCC8IEYQgAAAC+wQQXhBnCEAAAAHyDCS4IM4QhAAAA+A4TXAKOUYneIwwBAAAAYYhRiX1HGIoQva0lBAAAgMjCqMS+IwxFANYSAgAAMCZGJfYNYSgCsJZQgDnrhpPoigMAAAgjhKEIwlpCAdBbN5xEVxwAAECYIAwBjriahOWqG06iKw4AACBMEIbCCEUSAsSdSViTJxN4AAAAwhxhKExQJCGAmIQFAABgCIShMMH38yBgEhYAAL7nakgLX2h8jgVZXSMMhRm+nwMAgLDU2wqhEquE+hALsrqHMAQAAAD/c7VCqPTlKqGvvcYwGB9gQVb3EIYAAAAQGK5WCKUrw+dYkLV3hCEAAAAEH10ZCALCEAAAAEIDXRkIsOhgNwAAAAAAgoEwBAAAAMCQGCYXYsxm50NlAQAAAPgOYSiEmM1dlSRPnnT8eEJCV6EVAAAAAH3n1TC5TZs2KSsrS/Hx8SooKNC+ffuc7vvuu+/qxhtvVFZWlqKiolReXt5jn/vuu09RUVF2t/POO8+bpoW1lpauILR9u1Rb2/NGNUkAAADAdzzuGdq1a5dMJpMqKipUUFCg8vJyFRUVqaGhQSkpKT32P3nypIYPH67vfe97+ulPf+r0vN/85jf1yiuvfNmwM4zbaZWTI40fH+xWAAAAAJHN48Sxfv16zZ8/XyUlJZKkiooKvfjii9q8ebOWLFnSY/8JEyZowoQJkuTwcVtDzjhDaWlpbrWhra1NbW1ttvsWi8WTlwB0YYIWAACAoXkUhtrb21VbW6ulS5fatkVHR6uwsFA1NTV9asj777+v9PR0xcfHa9KkSSorK1OmkzFhZWVlWrFiRZ+eDwbHBC0AAMKPswuWycnMJYBXPApDLS0t6ujoUGpqqt321NRUHThwwOtGFBQUaMuWLRo9erSOHDmiFStWaPLkyXrnnXc0cODAHvsvXbpUJpPJdt9isSgjI8Pr54cBfXWCVk5Oz8f5pQoAQOhITu66UDlnjuPHExKYXA2vhMTEnKuuusr277Fjx6qgoEDnnHOOfvOb3+iWW27psX9cXJzi4uIC2UREKiZoAQAQ+jIzu8KOs+Htc+Z0PUYY8ggdbR6GoeTkZMXExKipqclue1NTk9vzfdwxaNAgnXvuufrggw98dk4AAACEscxM43xD9zM62r7kUWnt2NhY5eXlqaqqyrats7NTVVVVmjRpks8adeLECR08eFBDhw712TkBAAAAfNnR5mgpl+3bu2YSOOqEi0QeD5MzmUyaO3eu8vPzNXHiRJWXl6u1tdVWXa64uFjDhg1TWVmZpK6iC++9957t359++qn279+vAQMGaOTIkZKkO++8U9ddd53OOeccHT58WMuXL1dMTIxmz57tq9cJAAAA4P/Q0dbF4zA0c+ZMNTc3q7S0VI2NjcrNzVVlZaWtqILZbFZ09JcdTocPH9YFF1xgu7927VqtXbtWl156qaqrqyVJn3zyiWbPnq3PPvtMQ4YM0cUXX6zXX39dQ4YM6ePLAwAAAADHvCqgsGDBAi1YsMDhY90Bp1tWVpasVqvL8+3cudObZgAAAACA1zyaMwQAAAAAkYIwBAAAAMCQCEMAAAAADCkkFl0FAAAAEDqMsiArYQjhz2z2vBi+s084AAAIT0b59u5nRluQlTCE8GY2Szk5XauDeSohoesTDwAAwpfRvr37WfeCrI6uM9fXd/2YW1oi58dJGEJ4a2npCkLbt3eFIk9wpQgAgPBntG/vAWCkBVkJQ0HgbFQXI7f6ICdHGj8+2K0AAADBYKRv7/ApwlCA9Taqi5FbAAAAQGAQhgKst1FdjNwCAAAAAoMwFCSM6gIAAACCi0VXAQAAABgSPUMID1SdAAAAgI8RhhD6qDoBAAD6ggVZ4QRhCKGPqhMAAMAbLMiKXhCGED6oOgEAADzBgqzoBWEIAAAAkYsFWeEC1eQAAAAAGBJhCAAAAIAhEYYAAAAAGBJhCAAAAIAhUUABoYOFVQEAQKCxBpGhEYYQGlhYFQAABBJrEEGEIYQKFlYFAACBxBpEEGEIoYaFVQEAQKCwBpHhEYYQWMwLAgAAQIggDCFwmBcEAACAEEIYQuAwLwgAAAAhhDCEwGNeEAAAAEIAi64CAAAAMCR6hgAAAABHWJA14hGGAAAAgK9iQVbDIAwBAAAAX8WCrIbh1ZyhTZs2KSsrS/Hx8SooKNC+ffuc7vvuu+/qxhtvVFZWlqKiolReXt7ncyLEmc1SXV3PG2sJAQCAcJGZ2VXw6es3RxVxEbY8DkO7du2SyWTS8uXLVVdXp3HjxqmoqEhHjx51uP/Jkyc1fPhwPfjgg0pLS/PJORHCutcSysvreZszh7WEAAAAEDI8DkPr16/X/PnzVVJSovPPP18VFRVKSEjQ5s2bHe4/YcIEPfTQQ5o1a5bi4uJ8cs62tjZZLBa7G0LEV9cSqq3teWN8LQAAAEKER3OG2tvbVVtbq6VLl9q2RUdHq7CwUDU1NV41wJtzlpWVacWKFV49HwKEtYQAAEAkM3CluUh66R6FoZaWFnV0dCg1NdVue2pqqg4cOOBVA7w559KlS2UymWz3LRaLMjIyvHp+AAAAwG0GrjQXiS89LKvJxcXFOR1yhwAxm51XWAEAAIhUBq40F4kv3aMwlJycrJiYGDU1Ndltb2pqclocIRjnhJ91F0k4edLx4xRJAAAAkSwz0/U3/kgaR/Y1vb30cONRGIqNjVVeXp6qqqo0ffp0SVJnZ6eqqqq0YMECrxrgj3PCz75aJMFReckI+KADAAB4LBLHkUU4j4fJmUwmzZ07V/n5+Zo4caLKy8vV2tqqkpISSVJxcbGGDRumsrIySV0FEt577z3bvz/99FPt379fAwYM0MiRI906ZzgyxCgyiiQAAAB8KRLHkUU4j8PQzJkz1dzcrNLSUjU2Nio3N1eVlZW2Aghms1nR0V9W7D58+LAuuOAC2/21a9dq7dq1uvTSS1VdXe3WOcMNo8gAAAAMysBD6MJRlNVqtQa7EX1lsViUlJSk48ePKzExMdjNUV1d1xqjETuKrPsF1tbSMwQAAOAOd66Wh/EQulD6euhJNgjLanLhglFkAAAAkMQQuhBFGAIAAAACIdJKsUWA6N53AQAAAIDIQxgCAAAAYEiEIQAAAACGxJwhAAAAIBRQdjvgCEMAAABAMCUnd5XWnjPH8eNhXnY7lBGGAAAAgGCi7HbQEIbgnNns/EMJAAAA36HsdlAQhozOWeBpbpZmzHC9SnJysn/bBgAAAPgRYcjIzGYpJ8d14KmslIYM6fkYE/kAAAACh+IKfkEYMgJXw91OnpS2b+8KRV/HhwsAACC4KK7gV4ShSOdO78/kyXyAAAAAQhHFFfyKMBTpWlro/QEAAAhnFFfwG8KQUeTkSOPHB7sVAAAAQMiIDnYDAAAAACAYCEMAAAAADIkwBAAAAMCQCEMAAAAADIkCCgAAAEA4Y0FWrxGGAAAAgHDEgqx9RhiKFGaz88W4AAAAEHlYkLXPCEORwGzuWkfo5EnHjyckdF05AAAAQGTpbUFWhtC5RBgKJ656f06elLZv7wpFX8ebHQAAwFgYQucWwlC4cKf3Z/Jkw7+hAQAAIIbQuYkwFC5aWuj9AQAAgPt6G0IHwlDYycmRxo8PdisAAACAsMeiqwAAAAAMiZ6hUEOJbAAAACAgCEOhhBLZAAAAQMAQhkIJRRIAAACAgCEMhSKKJAAAAMDfWJCVMBQUzAsCAABAsLAgq41X1eQ2bdqkrKwsxcfHq6CgQPv27XO5/29/+1udd955io+P15gxY7R79267x+fNm6eoqCi727Rp07xpWujrnheUl9fzNmcO84IAAADgX90LstbW9rxt3941bcPRhfsI5HHP0K5du2QymVRRUaGCggKVl5erqKhIDQ0NSklJ6bH/X//6V82ePVtlZWW69tprtWPHDk2fPl11dXX61re+Zdtv2rRpeuqpp2z34+LivHxJIY55QQAAAAg2FmSV5EUYWr9+vebPn6+SkhJJUkVFhV588UVt3rxZS5Ys6bH/L3/5S02bNk0///nPJUkrV67Uyy+/rEceeUQVFRW2/eLi4pSWluZWG9ra2tTW1ma7b7FYPH0Zwce8IAAAAIQqg8wn8miYXHt7u2pra1VYWPjlCaKjVVhYqJqaGofH1NTU2O0vSUVFRT32r66uVkpKikaPHq3bbrtNn332mdN2lJWVKSkpyXbLyMjw5GUAAAAAcOSr84kcTevIyema9hEhPOoZamlpUUdHh1JTU+22p6am6sCBAw6PaWxsdLh/Y2Oj7f60adM0Y8YMZWdn6+DBg7r77rt11VVXqaamRjExMT3OuXTpUplMJtt9i8VCIAIAAAD6qns+kbNiX3PmdD0WIb1DIVFNbtasWbZ/jxkzRmPHjtWIESNUXV2tK664osf+cXFxkTunCAAAAAgmA80n8miYXHJysmJiYtTU1GS3vampyel8n7S0NI/2l6Thw4crOTlZH3zwgSfNAwAAAAC3eRSGYmNjlZeXp6qqKtu2zs5OVVVVadKkSQ6PmTRpkt3+kvTyyy873V+SPvnkE3322WcaOnSoJ80DAAAAALd5PEzOZDJp7ty5ys/P18SJE1VeXq7W1lZbdbni4mINGzZMZWVlkqSFCxfq0ksv1bp163TNNddo586devPNN/X4449Lkk6cOKEVK1boxhtvVFpamg4ePKjFixdr5MiRKioq8uFLDYL6ekn/crANAAAACFOOvs/W95PkYNmYEOdxGJo5c6aam5tVWlqqxsZG5ebmqrKy0lYkwWw2Kzr6yw6nCy+8UDt27NC9996ru+++W6NGjdLzzz9vW2MoJiZGb7/9trZu3apjx44pPT1dU6dO1cqVK8N3XtCRI5KGSnNukvRWz8dZWBUAAADh5quV5nq4QFLdl9+Dw0SU1Wq1BrsRfWWxWJSUlKTjx48rMTEx2M1R3TP1ypuTo9qVuzX+agdzoyKsPjsAAAAMwmx2WGmubnej8pZdrdrt9Rp/U3B7iDzJBiFRTS5iZWdL48OvuxAAAABwyFmluTCdCuJRAQUAAAAAiBSEIQAAAACGRBgCAAAAYEiEIQAAAACGRBgCAAAAYEiEIQAAAACGRBgCAAAAYEiEIQAAAACGRBgCAAAAYEiEIQAAAACGRBgCAAAAYEiEIQAAAACGRBgCAAAAYEiEIQAAAACGRBgCAAAAYEiEIQAAAACGRBgCAAAAYEiEIQAAAACGRBgCAAAAYEiEIQAAAACGRBgCAAAAYEiEIQAAAACGRBgCAAAAYEiEIQAAAACGRBgCAAAAYEiEIQAAAACGRBgCAAAAYEiEIQAAAACGRBgCAAAAYEiEIQAAAACGRBgCAAAAYEiEIQAAAACG5FUY2rRpk7KyshQfH6+CggLt27fP5f6//e1vdd555yk+Pl5jxozR7t277R63Wq0qLS3V0KFD1a9fPxUWFur999/3pmkAAAAA4BaPw9CuXbtkMpm0fPly1dXVady4cSoqKtLRo0cd7v/Xv/5Vs2fP1i233KK33npL06dP1/Tp0/XOO+/Y9lmzZo02btyoiooK7d27V/3791dRUZFOnTrl/SsDAAAAABeirFar1ZMDCgoKNGHCBD3yyCOSpM7OTmVkZOiOO+7QkiVLeuw/c+ZMtba26oUXXrBt+/a3v63c3FxVVFTIarUqPT1dP/vZz3TnnXdKko4fP67U1FRt2bJFs2bN6nHOtrY2tbW12e4fP35cmZmZ+vjjj5WYmOjJy/GL/bsadOmto/XHxxuUO3N0sJsDAAAA+FUoff+1WCzKyMjQsWPHlJSU5Hpnqwfa2tqsMTEx1ueee85ue3FxsfX66693eExGRoZ1w4YNdttKS0utY8eOtVqtVuvBgwetkqxvvfWW3T6XXHKJ9d///d8dnnP58uVWSdy4cePGjRs3bty4cePm8Pbxxx/3mm/OkAdaWlrU0dGh1NRUu+2pqak6cOCAw2MaGxsd7t/Y2Gh7vHubs32+bunSpTKZTLb7nZ2d+uc//6nBgwcrKirKk5cUVN2pNVR6tABneK8iXPBeRbjgvYpwEY7vVavVqs8//1zp6em97utRGAoVcXFxiouLs9s2aNCg4DTGBxITE8PmzQVj472KcMF7FeGC9yrCRbi9V3sdHvd/PCqgkJycrJiYGDU1Ndltb2pqUlpamsNj0tLSXO7f/V9PzgkAAAAAfeVRGIqNjVVeXp6qqqps2zo7O1VVVaVJkyY5PGbSpEl2+0vSyy+/bNs/OztbaWlpdvtYLBbt3bvX6TkBAAAAoK88HiZnMpk0d+5c5efna+LEiSovL1dra6tKSkokScXFxRo2bJjKysokSQsXLtSll16qdevW6ZprrtHOnTv15ptv6vHHH5ckRUVFadGiRVq1apVGjRql7OxsLVu2TOnp6Zo+fbrvXmkIiouL0/Lly3sM+QNCDe9VhAveqwgXvFcRLiL9vepxaW1JeuSRR/TQQw+psbFRubm52rhxowoKCiRJU6ZMUVZWlrZs2WLb/7e//a3uvfdeffTRRxo1apTWrFmjq6++2va41WrV8uXL9fjjj+vYsWO6+OKL9eijj+rcc8/t+ysEAAAAAAe8CkMAAAAAEO48mjMEAAAAAJGCMAQAAADAkAhDAAAAAAyJMAQAAADAkAhDIeCjjz7SLbfcouzsbPXr108jRozQ8uXL1d7eHuymAT088MADuvDCC5WQkKBBgwYFuzmAzaZNm5SVlaX4+HgVFBRo3759wW4S0MOf/vQnXXfddUpPT1dUVJSef/75YDcJcKisrEwTJkzQwIEDlZKSounTp6uhoSHYzfI5wlAIOHDggDo7O/Uf//Efevfdd7VhwwZVVFTo7rvvDnbTgB7a29v1ve99T7fddluwmwLY7Nq1SyaTScuXL1ddXZ3GjRunoqIiHT16NNhNA+y0trZq3Lhx2rRpU7CbArj0xz/+UT/5yU/0+uuv6+WXX9bp06c1depUtba2BrtpPkVp7RD10EMP6bHHHtOHH34Y7KYADm3ZskWLFi3SsWPHgt0UQAUFBZowYYIeeeQRSVJnZ6cyMjJ0xx13aMmSJUFuHeBYVFSUnnvuuYhfZB6Robm5WSkpKfrjH/+oSy65JNjN8Rl6hkLU8ePHdfbZZwe7GQAQ8trb21VbW6vCwkLbtujoaBUWFqqmpiaILQOAyHH8+HFJirjvp4ShEPTBBx/o4Ycf1r/9278FuykAEPJaWlrU0dGh1NRUu+2pqalqbGwMUqsAIHJ0dnZq0aJFuuiii/Stb30r2M3xKcKQHy1ZskRRUVEubwcOHLA75tNPP9W0adP0ve99T/Pnzw9Sy2E03rxXAQCAMfzkJz/RO++8o507dwa7KT53RrAbEMl+9rOfad68eS73GT58uO3fhw8f1mWXXaYLL7xQjz/+uJ9bB3zJ0/cqEEqSk5MVExOjpqYmu+1NTU1KS0sLUqsAIDIsWLBAL7zwgv70pz/pG9/4RrCb43OEIT8aMmSIhgwZ4ta+n376qS677DLl5eXpqaeeUnQ0nXYIHE/eq0CoiY2NVV5enqqqqmwT0Ts7O1VVVaUFCxYEt3EAEKasVqvuuOMOPffcc6qurlZ2dnawm+QXhKEQ8Omnn2rKlCk655xztHbtWjU3N9se46omQo3ZbNY///lPmc1mdXR0aP/+/ZKkkSNHasCAAcFtHAzLZDJp7ty5ys/P18SJE1VeXq7W1laVlJQEu2mAnRMnTuiDDz6w3T906JD279+vs88+W5mZmUFsGWDvJz/5iXbs2KH//M//1MCBA21zMJOSktSvX78gt853KK0dArZs2eL0Dzb/exBq5s2bp61bt/bY/uqrr2rKlCmBbxDwfx555BE99NBDamxsVG5urjZu3KiCgoJgNwuwU11drcsuu6zH9rlz52rLli2BbxDgRFRUlMPtTz31VK9D68MJYQgAAACAITExBQAAAIAhEYYAAAAAGBJhCAAAAIAhEYYAAAAAGBJhCAAAAIAhEYYAAAAAGBJhCAAAAIAhEYYAAAAAGBJhCAAAAIAhEYYAAAAAGBJhCAAAAIAh/X+cJkFnGbAEeQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "VV02VZA__rmS" - }, - "source": [ - "We will use pandas to read in the file, and matplotlib to make plots. The following ensures pandas is installed and sets everything up:" + "name": "stdout", + "output_type": "stream", + "text": [ + "l_2_phi\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "OL6S_4z3_rma" - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" + "name": "stdout", + "output_type": "stream", + "text": [ + "MET\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "IcXv0FE8_rn3" - }, - "source": [ - "Now we can read the data into a pandas dataframe:" + "name": "stdout", + "output_type": "stream", + "text": [ + "MET_phi\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "id": "_TX4YIti_rn6" - }, - "outputs": [], - "source": [ - "filename = \"SUSY-small.csv\"\n", - "df = pd.read_csv(filename, dtype='float64', names=VarNames)" + "name": "stdout", + "output_type": "stream", + "text": [ + "MET_rel\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "IPsjW9HE_rn-" - }, - "source": [ - "You can see the data in Jupyter by just evaluateing the dataframe:" + "name": "stdout", + "output_type": "stream", + "text": [ + "axial_MET\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 443 - }, - "id": "-ryWi0OZ_roJ", - "outputId": "c1655ec4-e999-4c51-d17b-a291d81a5124" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " signal l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi \\\n", - "0 0.0 0.972861 0.653855 1.176225 1.157156 -1.739873 -0.874309 \n", - "1 1.0 1.667973 0.064191 -1.225171 0.506102 -0.338939 1.672543 \n", - "2 1.0 0.444840 -0.134298 -0.709972 0.451719 -1.613871 -0.768661 \n", - "3 1.0 0.381256 -0.976145 0.693152 0.448959 0.891753 -0.677328 \n", - "4 1.0 1.309996 -0.690089 -0.676259 1.589283 -0.693326 0.622907 \n", - "... ... ... ... ... ... ... ... \n", - "499995 0.0 0.719035 1.091879 0.291540 1.205962 -1.599117 -1.139445 \n", - "499996 1.0 0.910016 -0.364544 -0.777120 0.543648 -0.910632 -1.723707 \n", - "499997 1.0 0.842954 0.332476 -1.048564 1.347989 0.320496 -0.666358 \n", - "499998 0.0 1.370760 -1.162912 0.893499 2.118091 1.248496 -0.887211 \n", - "499999 0.0 0.762400 0.440924 0.342885 1.034283 1.740353 -1.083314 \n", - "\n", - " MET MET_phi MET_rel axial_MET M_R M_TR_2 R \\\n", - "0 0.567765 -0.175000 0.810061 -0.252552 1.921887 0.889637 0.410772 \n", - "1 3.475464 -1.219136 0.012955 3.775174 1.045977 0.568051 0.481928 \n", - "2 1.219918 0.504026 1.831248 -0.431385 0.526283 0.941514 1.587535 \n", - "3 2.033060 1.533041 3.046260 -1.005285 0.569386 1.015211 1.582217 \n", - "4 1.087562 -0.381742 0.589204 1.365479 1.179295 0.968218 0.728563 \n", - "... ... ... ... ... ... ... ... \n", - "499995 0.424546 1.154849 0.637185 -0.091178 1.972156 0.697028 0.313636 \n", - "499996 2.864673 1.458272 2.176558 -0.590911 0.673695 1.662140 2.189362 \n", - "499997 0.450433 -0.411872 0.293407 0.630491 0.859920 0.403371 0.416258 \n", - "499998 0.164659 0.316840 0.215165 0.280418 3.087083 0.526929 0.151467 \n", - "499999 0.872145 -1.519894 0.284328 -0.360861 0.956828 0.965979 0.895881 \n", - "\n", - " MT2 S_R M_Delta_R dPhi_r_b cos_theta_r1 \n", - "0 1.145621 1.932632 0.994464 1.367815 0.040714 \n", - "1 0.000000 0.448410 0.205356 1.321893 0.377584 \n", - "2 2.024308 0.603498 1.562374 1.135454 0.180910 \n", - "3 1.551914 0.761215 1.715464 1.492257 0.090719 \n", - "4 0.000000 1.083158 0.043429 1.154854 0.094859 \n", - "... ... ... ... ... ... \n", - "499995 0.988602 1.981573 0.744828 1.095080 0.006546 \n", - "499996 1.195041 0.910815 1.181893 1.252362 0.826035 \n", - "499997 0.591989 0.372003 0.716788 0.366991 0.265798 \n", - "499998 0.308067 3.098183 0.233042 0.876216 0.000593 \n", - "499999 1.020396 0.996446 0.943458 1.299870 0.197220 \n", - "\n", - "[500000 rows x 19 columns]" - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
signall_1_pTl_1_etal_1_phil_2_pTl_2_etal_2_phiMETMET_phiMET_relaxial_METM_RM_TR_2RMT2S_RM_Delta_RdPhi_r_bcos_theta_r1
00.00.9728610.6538551.1762251.157156-1.739873-0.8743090.567765-0.1750000.810061-0.2525521.9218870.8896370.4107721.1456211.9326320.9944641.3678150.040714
11.01.6679730.064191-1.2251710.506102-0.3389391.6725433.475464-1.2191360.0129553.7751741.0459770.5680510.4819280.0000000.4484100.2053561.3218930.377584
21.00.444840-0.134298-0.7099720.451719-1.613871-0.7686611.2199180.5040261.831248-0.4313850.5262830.9415141.5875352.0243080.6034981.5623741.1354540.180910
31.00.381256-0.9761450.6931520.4489590.891753-0.6773282.0330601.5330413.046260-1.0052850.5693861.0152111.5822171.5519140.7612151.7154641.4922570.090719
41.01.309996-0.690089-0.6762591.589283-0.6933260.6229071.087562-0.3817420.5892041.3654791.1792950.9682180.7285630.0000001.0831580.0434291.1548540.094859
............................................................
4999950.00.7190351.0918790.2915401.205962-1.599117-1.1394450.4245461.1548490.637185-0.0911781.9721560.6970280.3136360.9886021.9815730.7448281.0950800.006546
4999961.00.910016-0.364544-0.7771200.543648-0.910632-1.7237072.8646731.4582722.176558-0.5909110.6736951.6621402.1893621.1950410.9108151.1818931.2523620.826035
4999971.00.8429540.332476-1.0485641.3479890.320496-0.6663580.450433-0.4118720.2934070.6304910.8599200.4033710.4162580.5919890.3720030.7167880.3669910.265798
4999980.01.370760-1.1629120.8934992.1180911.248496-0.8872110.1646590.3168400.2151650.2804183.0870830.5269290.1514670.3080673.0981830.2330420.8762160.000593
4999990.00.7624000.4409240.3428851.0342831.740353-1.0833140.872145-1.5198940.284328-0.3608610.9568280.9659790.8958811.0203960.9964460.9434581.2998700.197220
\n", - "

500000 rows × 19 columns

\n", - "
\n", - "
\n", - "\n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "
\n", - "\n", - "\n", - "
\n", - " \n", - "\n", - "\n", - "\n", - " \n", - "
\n", - "\n", - "
\n", - " \n", - " \n", - " \n", - "
\n", - "\n", - "
\n", - "
\n" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "df" - } - }, - "metadata": {}, - "execution_count": 21 - } - ], - "source": [ - "df" + "name": "stdout", + "output_type": "stream", + "text": [ + "M_R\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "CApAAh9k_roT" - }, - "source": [ - "The first column stores the \"truth\" label of whether an event was signal or not. Pandas makes it easy to create dataframes that store only the signal or background events:" + "name": "stdout", + "output_type": "stream", + "text": [ + "M_TR_2\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "id": "1i9u-LTG_roV" - }, - "outputs": [], - "source": [ - "df_sig=df[df.signal==1]\n", - "df_bkg=df[df.signal==0]" + "name": "stdout", + "output_type": "stream", + "text": [ + "R\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "Ls4zpK_G_rof" - }, - "source": [ - "The following example plots the signal and background distributions of every variable. Note that we use VarNames[1:] to skip the first variable, which was the true label." + "name": "stdout", + "output_type": "stream", + "text": [ + "MT2\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "QwIKZjkA_roy", - "outputId": "5b369e60-99a1-4c11-885d-64c038d66e5d" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "l_1_pT\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "l_1_eta\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzoAAAGsCAYAAAAVEdLDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA2g0lEQVR4nO3de3TU5Z3H8c8kkoQICWpuShMDaiFdhZiQpMpBsUXDanU5sS3rQrnoYouGg83aCl5Aay1eQCKXympVLKmFowu2q5y0GqSyiqIgXpNsrbBjxYRkuxBIJIEk+8c0oyEzk7n/bu/XOXMO87vMPDNMkt9nnuf5Pq7e3t5eAQAAAICNJBjdAAAAAACINoIOAAAAANsh6AAAAACwHYIOAAAAANsh6AAAAACwHYIOAAAAANsh6AAAAACwnVOMbkAwenp6dODAAQ0fPlwul8vo5gAAAAAwSG9vr44cOaKzzjpLCQn++20sEXQOHDig3Nxco5sBAAAAwCQ+/fRTfe1rX/O73xJBZ/jw4ZI8LyYtLc3g1gAAAAAwSltbm3Jzc70ZwR9LBJ2+4WppaWkEHQAAAACDTmmhGAEAAAAA2yHoAAAAALAdgg4AAAAA27HEHB0AAAA4Q3d3t44fP250M2CgIUOGKDExMeLHIegAAADAcL29vWpqatKhQ4eMbgpMYMSIEcrJyYloDU2CDgAAAAzXF3KysrKUmprKIvEO1dvbq46ODh08eFCSdOaZZ4b9WAQdAAAAGKq7u9sbcs444wyjmwODDR06VJJ08OBBZWVlhT2MjWIEAAAAMFTfnJzU1FSDWwKz6PssRDJfi6ADAAAAU2C4GvpE47NA0AEAAABgO8zRAQAAgHm53VJra/yeLyNDysuL3/MhZgg6AAAAMCe3WyookDo64vecqalSfX3EYWfOnDk6dOiQnn/++ei0K0h33323nn/+ee3duzeuz2tGBB0AAACYU2urJ+TU1HgCT6zV10szZ3qeN8Kg88gjj6i3tzdKDUM4CDoAAAAwt4ICqajI6FaEJD093egmOB7FCAAAAIAwPffcc7rgggs0dOhQnXHGGZoyZYra29s1Z84cTZs2zXvckSNHNGPGDJ166qk688wztXLlSk2ePFm33HKL95j8/Hz94he/0PXXX6/hw4crLy9Pjz32WL/nu+222/T1r39dqampGj16tO66666ISjDbGUEHAGAPbre0Z4/vm9ttdOsA2NDnn3+u6667Ttdff73q6+u1fft2VVRU+ByyVlVVpddee02///3v9dJLL2nHjh3as2fPgONWrFihCRMm6J133tFNN92k+fPnq7Gx0bt/+PDhWr9+vT766CM98sgjevzxx7Vy5cqYvk6rYugaAMD6BpuwHKXJxQDwVZ9//rlOnDihiooKnX322ZKkCy64YMBxR44c0dNPP61nnnlG3/72tyVJTz31lM4666wBx1555ZW66aabJHl6b1auXKlXXnlFY8aMkSTdeeed3mPz8/N16623auPGjfrpT38a9ddndQQdAIC5BCol66/sa6AJy1GcXAwAXzV+/Hh9+9vf1gUXXKDy8nJdccUV+u53v6vTTjut33GffPKJjh8/rtLSUu+29PR0b3j5qnHjxnn/7XK5lJOTo4MHD3q3bdq0SatWrdJf/vIXHT16VCdOnFBaWloMXp31EXQAAOYRac+MBScsA7CuxMREvfTSS3r99df1xz/+UatXr9Ydd9yhN998M+zHHDJkSL/7LpdLPT09kqSdO3dqxowZuueee1ReXq709HRt3LhRK1asiOh12BVBBwAQXi9KLB4zlj0z9fWhtQUAguByuTRx4kRNnDhRS5Ys0dlnn60tW7b0O2b06NEaMmSI3nrrLeX9/ffN4cOH9d///d+65JJLgn6u119/XWeffbbuuOMO77b/+Z//ic4LsSGCDgA4XTC9KJs3S5mZwT9mS4tUUWGOnpmMDM/zzZzpvy3+Xl+gEBSLcAjAN39fVBj8PG+++abq6up0xRVXKCsrS2+++aZaWlpUUFCg9957z3vc8OHDNXv2bP3kJz/R6aefrqysLC1dulQJCQlyuVxBP995550nt9utjRs3qqSkRC+++OKAUIUvEXQAwOkC9aL0BZapU0N/3NRUqbZ2YICIRc9MoIuTvDzPfl+hZLDX5y+QUfwAiI/BvqiIhdRUz/MGIS0tTa+++qqqq6vV1tams88+WytWrNA//uM/atOmTf2Offjhh/WjH/1I3/nOd5SWlqaf/vSn+vTTT5WSkhJ006655hr9+Mc/VmVlpTo7O3XVVVfprrvu0t133x3KK3QMV68Flmxta2tTenq6Dh8+zGQrAIi2PXuk4mJp927fvSiBei4C8derEej5Au2LVbjw9/r6AlmgdgYaYufv/QQwwLFjx7Rv3z6NGjVq4IV/uL+DwhWnHtn29naNHDlSK1as0A033BDz57OaQJ+JYLMBPToAgMDy8mLzRz+aPTNS+Bcnkbw+ih8AsRer30Fx9s4776ihoUGlpaU6fPiwfvazn0mS/umf/sngltkXQQcAEF/BzJnxN2zEJhc8AJxp+fLlamxsVFJSkoqLi7Vjxw5lBDlMDqEj6AAA4itWPTMAYGIXXnihdu/ebXQzHIWgAwCIP3pmAAAxlmB0AwAAAAAg2ujRAQAgkFCLJgAATIGgAwCAL5EUTQAAGI6gAwCALxRNAABLI+gAAOAPRRMAw5l9vdDJkyersLBQ1dXVMWnPnDlzdOjQIT3//PMxeXwj7N+/X6NGjdI777yjwsLCmD0PQQcAgFgIdx4PPUWAl9vtWZe3oyN+z5ma6vnx5cfQ+gg6AABE02BzewbDVRbg1drqCTk1NZ7AE2v19Z4f3dZWe/8IdnV1KSkpyehmxBxBBwCAaBpsbk8gTrnKAkJUUCAVFRndCv9OnDihyspKbdiwQUOGDNH8+fP1s5/9TC6XSxs2bNAjjzyixsZGnXrqqfrWt76l6upqZWVlec//8MMPddttt+nVV19Vb2+vCgsLtX79ep1zzjkDnuutt97SlVdeqVtvvVW33XabJOnnP/+5Vq1apS+++ELTp09XRkaGamtrtXfvXklfDn8rKSnR2rVrlZycrH379un999/XwoULtXPnTqWmpuraa6/Vww8/rGHDhknyPSxv2rRpGjFihNavXy9Jys/P14033qiPP/5Yzz77rE477TTdeeeduvHGG73n7Nq1Sz/84Q9VX1+v888/X3fccUeU/wd8I+gAgFP4G+hOqeToY24P4ChPP/20brjhBu3atUtvv/22brzxRuXl5WnevHk6fvy47r33Xo0ZM0YHDx5UVVWV5syZo61bt0qSPvvsM11yySWaPHmytm3bprS0NL322ms6ceLEgOfZtm2bKioq9OCDD3qDxG9+8xvdd999+uUvf6mJEydq48aNWrFihUaNGtXv3Lq6OqWlpemll16SJLW3t6u8vFwXXXSR3nrrLR08eFD/+q//qsrKSm+ICdaKFSt077336vbbb9dzzz2n+fPn69JLL9WYMWN09OhRfec739Hll1+umpoa7du3TwsXLgzjXQ4dQQcA7MRfmGlpkSoq/A90p1QyAIQtNzdXK1eulMvl0pgxY/T+++9r5cqVmjdvnq6//nrvcaNHj9aqVatUUlKio0ePatiwYVq7dq3S09O1ceNGDRkyRJL09a9/fcBzbNmyRbNmzdKvfvUrTZ8+3bt99erVuuGGGzR37lxJ0pIlS/THP/5RR48e7Xf+qaeeql/96lfeIWuPP/64jh07pl//+tc69dRTJUlr1qzR1VdfrQceeEDZ2dlBv/4rr7xSN910kyTptttu08qVK/XKK69ozJgxeuaZZ9TT06MnnnhCKSkp+od/+Af99a9/1fz584N+/HARdADALgabtZuaKtXWSpmZA/cxAR4AwvbNb35TLpfLe/+iiy7SihUr1N3drb179+ruu+/Wu+++q//7v/9TT0+PJMntdusb3/iG9u7dq0mTJnlDji9vvvmmXnjhBT333HOaNm1av32NjY3ekNGntLRU27Zt67ftggsu6Dcvp76+XuPHj/eGHEmaOHGienp61NjYGFLQGTdunPffLpdLOTk5OnjwoPd5xo0bp5SUFO8xF110UdCPHQmCDgDYxWCzdgkzABBXx44dU3l5ucrLy/Wb3/xGmZmZcrvdKi8vV1dXlyRp6NChgz7OOeecozPOOENPPvmkrrrqqoChyJ+vBppgJSQkqLe3t9+248ePDzju5Pa4XC5voDNSQjgnrV27Vvn5+UpJSVFZWZl27doV1HkbN26Uy+UakEQBAFHUN2v35BshBwBi4s033+x3/4033tB5552nhoYG/e///q/uv/9+TZo0SWPHjvX2dPQZN26cduzY4TNA9MnIyNC2bdv08ccf6/vf/36/Y8eMGaO33nqr3/En3/eloKBA7777rtrb273bXnvtNSUkJGjMmDGSpMzMTH3++efe/d3d3frggw8GfeyTn+e9997TsWPHvNveeOONkB4jXCEHnU2bNqmqqkpLly7Vnj17NH78eJWXlw/4TzvZ/v37deutt2rSpElhNxYAAAAwG7fbraqqKjU2Nuq3v/2tVq9erYULFyovL09JSUlavXq1PvnkE/3+97/Xvffe2+/cyspKtbW16Z//+Z/19ttv689//rM2bNigxsbGfsdlZWVp27Ztamho0HXXXectVrBgwQI98cQTevrpp/XnP/9ZP//5z/Xee+/1G0rny4wZM5SSkqLZs2frgw8+0CuvvKIFCxboBz/4gXfY2re+9S29+OKLevHFF9XQ0KD58+fr0KFDIb03//Iv/yKXy6V58+bpo48+0tatW7V8+fKQHiNcIQ9de/jhhzVv3jzvhKd169bpxRdf1JNPPqlFixb5PKe7u1szZszQPffcox07dgz6BnV2dqqzs9N7v62tLdRmAgBgXeFUwmNoImwsXsUhw32eWbNm6YsvvlBpaakSExO1cOFC3XjjjXK5XFq/fr1uv/12rVq1SkVFRVq+fLmuueYa77lnnHGGtm3bpp/85Ce69NJLlZiYqMLCQk2cOHHA8+Tk5Gjbtm2aPHmyZsyYoWeeeUYzZszQJ598oltvvVXHjh3T97//fc2ZM2fQEVepqan6wx/+oIULF6qkpKRfeek+119/vd59913NmjVLp5xyin784x/rsssuC+m9GTZsmP7zP/9TP/rRj3ThhRfqG9/4hh544AFde+21IT1OOFy9Jw+8C6Crq0upqakDJkLNnj1bhw4d0u9+9zuf5y1dulTvvfeetmzZ4q3j/fzzz/t9nrvvvlv33HPPgO2HDx9WWlpasM0FAGfZs0cqLpZ27zb3ghPwL5Jl4FloFBZ27Ngx7du3T6NGjeo3aT2SH4lw2eFH6fLLL1dOTo42bNhgdFPC5u8zIXk6QdLT0wfNBiH16LS2tqq7u3tAFYbs7Gw1NDT4POe//uu/9MQTT3gXLArG4sWLVVVV5b3f1tam3NzcUJoKAID1hLvYKAuNwqYiWX83XFbrHO3o6NC6detUXl6uxMRE/fa3v9XLL7/sXS/HyWJade3IkSP6wQ9+oMcff1wZIazPkJycrOTk5Bi2DAAsjIU/7Y3FRoF++JEIzOVyaevWrbrvvvt07NgxjRkzRv/xH/+hKVOmGN00w4UUdDIyMpSYmKjm5uZ+25ubm5WTkzPg+L/85S/av3+/rr76au+2vlJzp5xyihobG3XOOeeE024AcKZg1sph4U8AcIyhQ4fq5ZdfNroZphRS0ElKSlJxcbHq6uq8c3R6enpUV1enysrKAcePHTtW77//fr9td955p44cOaJHHnmE4WgAECrWygEAICghD12rqqrS7NmzNWHCBJWWlqq6ulrt7e3eKmyzZs3SyJEjtWzZMqWkpOj888/vd/6IESMkacB2AEAI+tbKAQAAPoUcdKZPn66WlhYtWbJETU1NKiwsVG1trbdAgdvtVkJCWOuQAgAAwMH6pjgA0fgshFWMoLKy0udQNUnavn17wHPXr18fzlMCgLNQcACAgyQlJSkhIUEHDhxQZmamkpKSBl3wEvbU29urrq4utbS0KCEhQUlJSWE/VkyrrgEAwkDBAQAOk5CQoFGjRunzzz/XgQMHjG4OTCA1NVV5eXkRjRQj6ACA2VBwAIADJSUlKS8vTydOnFB3d7fRzYGBEhMTdcopp0Tcq0fQAYBY8jcETRo8sFBwAIDDuFwuDRkyREOGDDG6KbABgg4AxEowQ9Dq6+mdAQAgBgg6ABArgYag1ddLM2d6jiHoAAAQdQQdAIg1hqABABB3BB0AAOzCX/lxClgAcCCCDgAAVpeR4ZnzNXOm7/3MBwPgQAQdAACsLi/PE2T8LTI7c6a0YwflygE4CkEHAIzka6iRv+FHQCB5eb4DC709AByKoAMARgjm4jMjI75tgj3R2wPAoQg6AGCEQBefEheYiC56ewA4EEEHAIzi7+ITiJdgentY6wmARRF0ACAYbje9L7AnAjcAmyLoAMBg3G7P/IWODt/7Gd4DAIDpEHQAYDCtrZ6QU1MzcMI2w3sAADAlgg4ABKugQCoqMroVAAAgCAQdAIgG1sMBAMBUCDoAEAnWwwEAwJQIOgAQCdbDAQDAlAg6ABApyvMCAGA6CUY3AAAAAACijaADAAAAwHYIOgAAAABsh6ADAAAAwHYIOgAAAABsh6ADAAAAwHYoLw0AAPyrr/e9nTWiAJgcQQcAAAyUkSGlpkozZ/ren5oqbd4sZWb6PpcQBMBgBB0AADBQXp6nN6e1deC+lhapokKaOtX3uampnnMJOwAMRNABAAC+5eX5Dyv+QlB9vacXqLWVoAPAUAQdAAAQukAhCABMgKprAAAAAGyHoAMAAADAdhi6BsA53G7fcwr6UCkKAADbIOgAcAa3WyookDo6/B9DpSgAAGyDoAPAGVpbPSGnpsYTeE5GpSggulhoFIDBCDoAnKWgQCoqMroVgH0Fs9AoPacA4oCgAwAAoifQQqP0nAKII4IOAACILtbYAWAClJcGAAAAYDsEHQAAAAC2Q9ABAAAAYDsEHQAAAAC2Q9ABAAAAYDtUXQOAr/K1yKG/hQ8BAIBpEXQAQApukcOMjPi2CQAAhI2gAwBS4EUOJU/IYV0QAAAsg6ADAH1Y5BAAANugGAEAAAAA2yHoAAAAALAdgg4AAAAA2yHoAAAAALAdihEAsBe323flNNbCAQDAUQg6AKzHX5hpaZEqKqSODt/nsRYOAACOQdABYC1ut1RQEDjM1NZKmZkD97EWDgAAjkHQAWAtra2ekFNT4wk8JyPMAObnbygpP78AooigA8CaCgqkoiKjWwEgFBkZnl7XmTN9709N9YQgwg6AKCDoAACA+MjL8wQZfwVDZs707CPoAIgCgg4AAIifvDyCDIC4YB0dAAAAALZD0AEAAABgOwxdAwAA5kFFNgBRQtABAADGC6Yi2+bNrJEFIGgEHQAAYLxAFdlaWqSKCmnqVN/nUpYagA8EHQAAYA6BKrJRlhpAiAg6AADA/ChLDSBEBB0A5uR2+//2FgAAYBAEHQDm43ZLBQVSR4fv/ampnsnHAAAAfhB0AJhPa6sn5NTUeALPyaiwBAAABkHQAWBeBQVSUZHRrQAAABaUYHQDAAAAACDaCDoAAAAAbIegAwAAAMB2wgo6a9euVX5+vlJSUlRWVqZdu3b5PXbz5s2aMGGCRowYoVNPPVWFhYXasGFD2A0GAAAAgMGEXIxg06ZNqqqq0rp161RWVqbq6mqVl5ersbFRWVlZA44//fTTdccdd2js2LFKSkrSCy+8oLlz5yorK0vl5eVReREAAMDh/K2xRZVGwLFcvb29vaGcUFZWppKSEq1Zs0aS1NPTo9zcXC1YsECLFi0K6jGKiop01VVX6d577/W5v7OzU52dnd77bW1tys3N1eHDh5WWlhZKcwFY0Z49UnGxtHs3VdcABBbMulv19YQdwEba2tqUnp4+aDYIaehaV1eXdu/erSlTpnz5AAkJmjJlinbu3Dno+b29vaqrq1NjY6MuueQSv8ctW7ZM6enp3ltubm4ozQQAAE6Rl+cJMrt3D7zV1HgCUGur0a0EYICQhq61traqu7tb2dnZ/bZnZ2eroaHB73mHDx/WyJEj1dnZqcTERP3yl7/U5Zdf7vf4xYsXq6qqynu/r0cHAABggLw8emwADBCXBUOHDx+uvXv36ujRo6qrq1NVVZVGjx6tyZMn+zw+OTlZycnJ8WgaAAAAABsKKehkZGQoMTFRzc3N/bY3NzcrJyfH73kJCQk699xzJUmFhYWqr6/XsmXL/AYdAA7hdvseUuJvUjEAAECQQgo6SUlJKi4uVl1dnaZNmybJU4ygrq5OlZWVQT9OT09Pv2IDABwomAnEGRnxbRMAALCNkIeuVVVVafbs2ZowYYJKS0tVXV2t9vZ2zZ07V5I0a9YsjRw5UsuWLZPkKSwwYcIEnXPOOers7NTWrVu1YcMGPfroo9F9JQCspbXVE3JqajyB52SUhI06fx1oEm83AMB+Qg4606dPV0tLi5YsWaKmpiYVFhaqtrbWW6DA7XYrIeHLYm7t7e266aab9Ne//lVDhw7V2LFjVVNTo+nTp0fvVQAwr8GGpxUUUEI6DqjAC0djjR3AkUJeR8cIwdbKBmAyXF2bRt/SRL460OrrpZkzWbYINsTvIMCWgs0Gcam6BsChGJ5mOnSgRQ9DAS2gb40df73KM2d69vGfBdgSQQdA7HF1HVWxusBmdE/w6CiwENbYARyLoAMAFhKLC+yMDM95M2dG7zHtLlBnZaQdBfQUAUB0EHQAwEJicYFtttE94V7oBzovkEjCQ7Q7K+kpAoDoIegAgAVF+wLbLKN7wr3QH+y8QAKFh3ivaRvLniIAcBqCDoDIxftq0EKMGIbk622P9L8inPk74bz2cC/0B6t74U+gxzRyTVumtQFA5Ag6ACJj5NWgycV7GFIwc21C/a8Id/5OpK893Av9cM/zFw4pGggA1kXQARAZSkj7FckwpHA6yQLNtZHC+68IZv7Ojh2+X99gr93feYOJZo9VMEFu0qTwPsKB2uTgHwsAiBuCDoDoYKyNX6G+NZF0ksViro2/xww3JITb8xSLHqtYhMPB2ilRVMBUqKsO2BZBBwCCEM+5NlbpJAs3JMT7vMFEOxwO1s5Ie7TCQclqH6irDtgeQQcABmFUyV8rdJKFGxLifV68BWpnLHqmJP9hpqVFqqgI/PndvFnKzAzt+SwfkMxWVx1A1BF0AGAQlPxFNMWiZyqYMF5bOzDM9IWgqVNDe76+x7R8h4dVkjOAsBB0ACBIVuhhgTVE+/o6kuGOgUKXP4GG3w32fAAQLwQdAI4RaJ6CxMUZrC+cMB5O6HLM9BYKFQCWRtAB4AiDDe2RbHRxBsRYrKa3mKZogmOSHGBvBB0AjjDY0B6j5tpEc00YOINZPjOD9QSF2hliVNEPnyhUANgCQQeAYYz49nawoT3xuoiMVeUt2JdVPjPhdobEYoHdvvaE9buEQgWA5RF0ABjCVN/eKv4XkbFaEwb2ZZXPTKSdIbFYYJdRZoAzEXQAGMJsJZuNuIjkC2OEyiqfmXi202y/SwCYB0EHgKECfXsbzSFjwTxWJBdnZpk3AVhBLH5eKP8O4GQEHQDB8TcIPgZX84MNIwtXLOYwWGXeBGAGkf688IUCgFAQdAAMLphB8FG8mh9sGFm4YjX8zArzJgAzCPfnhS8UAISDoANgcJEsux4mq8xFkKzVVsBo4fy88IUCgHAQdAAEj0HwAAwS7Tl0gyE8AdZH0AEQMdOsZg4AXxHJfD/KUgPWR9ABEBHWsABgVuHO96MsNWAPBB0AEWENCwBmxhw6wLkIOgCC4lauWuuHDtjeN/Y91PVwKAkLAABiiaAD2FC4c2b8ndfy+nBVqF4dM0/1eZ6/0q6UhAVgZT6/kKkfqgzlik4iwPwIOoDNhDtnxu2WCsb2qOOLBB9nnadUtat29Z+VefF5A/b6C0+UhAVgRYG/pClQqupV//knhB3A5Ag6gM2EO2em9f3P1fHFmarRDBVo4NeYGSntyrvmJYX6l53x8QCsJtCXNPVb92nmXaPUeugUgg5gcgQdwKZCXvLm0CFJZ6rg3hkqujJn4H66XwA4iN8vaeqPxb0tAMJD0AHQ36hRUlHB4McBAAZgXTHAPAg6gAP5rIK2LyX+DQEAGwlmjuTmzVJm5sB9hCAg+gg6gIMEnmA7SqlqV8aIE/FuFgBYTv2+FGnPSdvq/c+RbGmRKiqkqVN9Px6LKwPRR9ABHCRgFbT6emXMLFfemc/Hu1kAYBkZI04oVe2aedco6a6B+1NTpUmTfAcWvwUOWFwZiAmCDuAw/qugfSHp0zi3BgCsJe/M46pXgVpr/jCw20aBh6BRhRKIL4IOAABACPL0qfK0R54viE6WoZDr8P+dzwVKxfwdIFwEHQAAgGAFnuwY1mSbGDwkABF0AOfxV/vU31eJAIAvBVxNNLzJNsE85I4dPkfK0dsDBEDQASwqrLwSTO3TjIyotA8AbCsGk238PWSkvT2s6wMnI+gABgvnj1DYeaW11X/t00BPCAAwRCQdSMH8rWBIHOyMoAMYKNw/QhHnlYICqago7HYDAOIn3A6kQH8rKGkNJyDoAFESTs9MMH+EfI3L7hueRl4BABMyWfk0/lbAqQg6QBREOjzA1x+hYMZlM50GAEyE8mmAqRB0gCiIxfCAQOOyJabTAIDpxKAiWzD8dSBRTBNOR9ABoijawwNYRRsALCaOv7gH60CS6P2HsxF0AAAALGiwnn+J3n84G0EHiBNfQwgYVgAAiESkHUgmq5sARBVBBwhBOIt0GlJUIKzVRAEATkHdBDgBQQcIUriLdMa9qEDYq4kCAJzCoLoJQFwRdIAgRbJIZ1yLCkS8migAwAkG+9sUziAA/sTATAg6QIgss/CaZRoKADCTYKq5+ZOaKm3eLGVm+n5cQhDiiaADAAAAr2CqufnS0iJVVEhTp/rez7wfxBtBBziJZebxW6ahAACrCXfINfN+YCYEHeArLDOP3zINBQA4CQtdw0wIOnCkQJ0hlpjHT8EBALAmFq4B4oagA9vyF2b6xhAH6gyZNMkif28oOAAA1sDCNZLIeYgvgg5sKZiRXbW1VIUBAMRJMAvX7Nhh2156ch6MQNCB6fnrmZH8/+5nZBcAwHT8TWBxQApggVIYgaADUwumZybQ735GdgEATM8hKYBCBYg3gg5MLVDPjI1+9wMAnI4UAEQdQQeWEKhnxtfERpaSAQDAHsIZwg5IBB1YWDBDmllKBgAA64p0CDucjaADywo0pFniWx4AAKyOIeyIBEEHlsaQZgAA7CHQUPRQh7BLfOEJgg4AAAAMFO5QdAdU5UaECDoAAAAwTLhD0R1SlRsRIOgAZuav1Axl5QAANhLuUHSGsCMQgg5gVsGUmqGsHAA4AxNRgJARdBAX1MAPQ6BSMxJvHAA4ARNRgLARdBBzwXRMbN4sZWYO3McILQUuNQMAsDcmokSEjjBnI+gg5gJ1TLS0SBUV0tSp/s9nhBYAwNGYiBIyOsIgEXQQR/46JgJVWpH41gUAAISGjjBIYQadtWvX6qGHHlJTU5PGjx+v1atXq7S01Oexjz/+uH7961/rgw8+kCQVFxfrF7/4hd/j4Tx8UQUAAKJtsOsLhrXZX8hBZ9OmTaqqqtK6detUVlam6upqlZeXq7GxUVlZWQOO3759u6677jpdfPHFSklJ0QMPPKArrrhCH374oUaOHBmVFwFzoBIyAAAwO4a1OYert7e3N5QTysrKVFJSojVr1kiSenp6lJubqwULFmjRokWDnt/d3a3TTjtNa9as0axZs3we09nZqc7OTu/9trY25ebm6vDhw0pLSwuluYiTYAoO8EsjRHv2SMXF0u7dFCMAAAzE34mwBfpyduZM3lKza2trU3p6+qDZIKQena6uLu3evVuLFy/2bktISNCUKVO0c+fOoB6jo6NDx48f1+mnn+73mGXLlumee+4JpWkwGJWQAQCAVTBs3hlCCjqtra3q7u5WdnZ2v+3Z2dlqaGgI6jFuu+02nXXWWZoyZYrfYxYvXqyqqirv/b4eHZgflZDDwJg/AACAqItr1bX7779fGzdu1Pbt25WSkuL3uOTkZCUnJ8exZYBBghnzR21tAACAkIUUdDIyMpSYmKjm5uZ+25ubm5WTkxPw3OXLl+v+++/Xyy+/rHHjxoXeUsCOGPMHAIDpUJHNHkIKOklJSSouLlZdXZ2mTZsmyVOMoK6uTpWVlX7Pe/DBB3XffffpD3/4gyZMmBBRgwFbYswfAACGoyKbvYQ8dK2qqkqzZ8/WhAkTVFpaqurqarW3t2vu3LmSpFmzZmnkyJFatmyZJOmBBx7QkiVL9Mwzzyg/P19NTU2SpGHDhmnYsGFRfCkAAABA+Fho1F5CDjrTp09XS0uLlixZoqamJhUWFqq2ttZboMDtdishIcF7/KOPPqquri5997vf7fc4S5cu1d133x1Z6xF3zJsHAAB2RkU2+wirGEFlZaXfoWrbt2/vd3///v3hPAVMiHnzAACYUKBvG5lUAgeLa9U1WEOgXhvmzQMAYBKDTSiRmFQCRyPooJ9gem0mTeL3JQAAhgs0oURiUgkcj6DjUPTaAABgA0woMQ1/11YS109GIeg4EL02AAAA4fE1JaqlRaqoCHxtxQjC+CPoOBBrVAIA4CCsfhkVwayxU1srZWb2384IQuMQdByMNSoBALAxVr+MqsGmRJEbzYegAwAAYEesfhl1TImyFoIOEA+stAoAMMJgV+YMa4sb3ur4I+gAscZKqwAAs2FYW9zwVhuHoGNxlDI0EWp2AwCsgmFtcRPJW811XmQIOhYWTEcB3xDECTW7AQBWw4STuAnnreY6L3IEHQsLVCa67xuCHTt870OUUbMbAABEUTDXeXS6BUbQsQFfZaKDGQ/KtJAYoGY3AACIIi4twkfQsSlqvQMAAMDJCDo2xtBbAAAAOFWC0Q0AAAAAgGgj6AAAAACwHYauAQAAYCB/ZVqZ6AuLIOgAoQi0KCgAAHYQTOlWFnCBBRB0LIBra5MIZuUuanYDAKwuUOlWFnCBhRB0TI5raxNhUVAAgFNQuhU2QNAxOa6tTYiVuwAAQBT5GqXDyJ3IEXQsgmtrAAAAewlmOhQjd8JH0AEAAAAMEGg6lMTInUgRdAAAAACDMB0qdlgwFAAAAIDt0KMDAAAAWBBrugZG0AEAAAAshDVdg0PQAQAAQGjoSjAUa7oGh6ADnMzt9v+bAwAAJ6MrwTQoYjA4go5JcG1tEm63Z9Gijg7f+yloDwBwMroSYCEEHRPg2tpEWls9/xE1NZ7/lJPRJQ8AcDq6EmARBB0T4NrahAoKpKIio1sBAACAMBF04miw4WlcWwMAAADRQdCJE4anAQAAAPFD0IkThqcBAABHoPQ0TIKgE2cMTwMAALZE6WmYDEEHAAAAkaP0NEyGoANnYuEiAACij9LTMBGCDpyHyhAAAAC2R9CB81AZAgAAwPYIOnAuKkMAAADYFkEHAAAA8UHpacQRQQcAAACxRenpuCNTEnRgZ1RWAwDAHCg9HTdkyi8RdGBPVFYDAMBcKD0dF2TKLxF0YE9UVgMAAA5FpvQg6MDeqKwGAADgSAlGNwAAAAAAoo2gAwAAAMB2CDoAAAAAbIc5OlFGReM44w0HAMAeWPgFUUbQiSIqGscZbzgAANbHwi+IEYJOFFHROM54wwEAsD4WfkGMEHRigIrGccYbDgCAtbHwC2KAoAMAAAA4iFOmQxF0AAAAAAdw2nQogg4AAADgAE6bDkXQAQAAABzCSdOhWDAUAAAAgO0QdAAAAADYDkEHAAAAgO0wRwcAAADW5Hb7nlkv2a9WMkJG0AEAAIC5+Vr4paVFqqiQOjp8n2O3WskIGUEHAAAA5hTMwi+1tVJmZv/tdqyVjJARdAAAAGBOgRZ+kRiehoAIOgAAADAvJy38gqgi6MD8/E009DVeFwAAABBBB2bndksFBYEnGmZkxLdNAAAAMD2CDsyttdUTcmpqPIHnZIzNBQAAgA8EHVhDQYFUVGR0KwAAAGARCUY3AAAAAACijaADAAAAwHYYuob48Fc5rQ9zbQAAABBFYfXorF27Vvn5+UpJSVFZWZl27drl99gPP/xQ1157rfLz8+VyuVRdXR1uW2FVfZXTiov93woKPMcBAAAAURBy0Nm0aZOqqqq0dOlS7dmzR+PHj1d5ebkOHjzo8/iOjg6NHj1a999/v3JyciJuMCzoq5XTdu8eeKup8ewP1OMDAAAAhCDkoWsPP/yw5s2bp7lz50qS1q1bpxdffFFPPvmkFi1aNOD4kpISlZSUSJLP/b50dnaqs7PTe7+trS3UZsKMqJwGAACAOAkp6HR1dWn37t1avHixd1tCQoKmTJminTt3Rq1Ry5Yt0z333BO1x4NF1NcHtw0AACAY/q4jmBvsCCEFndbWVnV3dys7O7vf9uzsbDU0NEStUYsXL1ZVVZX3fltbm3Jzc6P2+DCZjAwpNVWaOdP3/tRUzzEAAADBCObaor6esGNzpqy6lpycrOTkZKObgXjJy/P8svE3R4dvXQAAQCgCXVvU13sCUGsr1xc2F1LQycjIUGJiopqbm/ttb25uptAAIpOXxy8bAAAQPVxbhMVOo/1CCjpJSUkqLi5WXV2dpk2bJknq6elRXV2dKisrY9E+AAAAADFmx9F+IQ9dq6qq0uzZszVhwgSVlpaqurpa7e3t3ipss2bN0siRI7Vs2TJJngIGH330kfffn332mfbu3athw4bp3HPPjeJLAQAAABAOO472CznoTJ8+XS0tLVqyZImamppUWFio2tpab4ECt9uthIQvl+c5cOCALrzwQu/95cuXa/ny5br00ku1ffv2yF8BAAAAgIjZbbRfWMUIKisr/Q5VOzm85Ofnq7e3N5yngdW43f6/BgAAAADiyJRV12BBbrdnQdCODt/7KRENAACAOCLohIGOCx9aWz0hp6bGE3hOZsVSHQAAwL7sVF4MPhF0QkTHxSAKCqSiIqNbAQAA4Jsdy4vBJ4JOiBzfcUF3FgAAsDI7lheDTwSdMDmy44LuLAAAYAd2Ky8Gnwg6CJ7ju7MAAIAjMH/HFgg6CJ0ju7MAAIDtMX/HVgg6AAAAgMT8HZsh6AAAAAB9mL9jGwlGNwAAAAAAoo2gAwAAAMB2GLoGAAAABIuKbJZB0AEAAAAGQ0U2yyHoAAAAAIOhIpvlEHQAAACAYFCRzVIIOhjI7fb/bQUAAABgAQQd9Od2SwUFUkeH7/2pqZ4xqgAAAICJEXTQX2urJ+TU1HgCz8moKAIAAAALIOjAt4ICqajI6FYAAAAAYSHoAAAAANHAGjumQtABAAAAIsEaO6ZE0HEqKqsBAABERzBr7OzYwfznOCPoOBGV1QAAAKLL3xo79PYYhqDjRFRWAwAAiI9gentaW7n2igGCjtX5G4IWSN/wNCqrAQAAxJ6/3h7EFEHHygYbghYIw9MAAABgYwQdKxtsCFogDE8DAAAwB8pSxwRBxw4YggYAAGA9FCqIKYIOAAAAYAQKFcQUQQcAAAAwCoUKYibB6AYAAAAAQLQRdAAAAADYDkPXAAAAALOiIlvYCDoAAACA2VCRLWIEHQAAAMBsqMgWMYKOFbjd/j/kAAAAsCcqskWEoGMW/sJMS4tUUSF1dPg+LzXV07UJAAAAwIugYwZut1RQEDjM1NZKmZkD9zERDQAAABiAoGMGra2ekFNT4wk8JyPMAAAAACEh6JhJQYFUVGR0KwAAAGAFlJ4OiKADAAAAWAmlp4NC0AEAAACshNLTQSHoAAAAAFYzWOlphrURdOKK9XAAAAAQSwxr8yLoxEswJaRZDwcAAACRCGZY244djqj0S9CJF0pIAwAAIB78DWtzWG8PQSfaBhueRglpAAAAGMFhRQwIOuGqr5f0Rf9tLS1SRQXD0wAAAGBOgxUxsBGCTqg+/1zSmdLMGZLeGbg/NVWqrZUyMwfuY3gaAAAAEBcEnVAdOiTpTOnen0tX5gzcT5gBAACAVfmqBlw/VJKPOeYmR9AJ16hRUpH1/sMBAACAAQIWKrhQ0p4vRzZZBEEHAAAAcLpAhQq2Nkl36cuRTRZB0AEAAADgv1CBRRe3TzC6AQAAAAAQbQQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgOwQdAAAAALZD0AEAAABgO2EFnbVr1yo/P18pKSkqKyvTrl27Ah7/7LPPauzYsUpJSdEFF1ygrVu3htVYAAAAAAhGyEFn06ZNqqqq0tKlS7Vnzx6NHz9e5eXlOnjwoM/jX3/9dV133XW64YYb9M4772jatGmaNm2aPvjgg4gbDwAAAAC+uHp7e3tDOaGsrEwlJSVas2aNJKmnp0e5ublasGCBFi1aNOD46dOnq729XS+88IJ32ze/+U0VFhZq3bp1Pp+js7NTnZ2d3vuHDx9WXl6ePv30U6WlpYXS3Kjbu6lRl944Rn96rFGF08cY2hYAAAAg1sx2/dvW1qbc3FwdOnRI6enp/g/sDUFnZ2dvYmJi75YtW/ptnzVrVu8111zj85zc3NzelStX9tu2ZMmS3nHjxvl9nqVLl/ZK4saNGzdu3Lhx48aNGzeft08//TRgdjlFIWhtbVV3d7eys7P7bc/OzlZDQ4PPc5qamnwe39TU5Pd5Fi9erKqqKu/9np4e/e1vf9MZZ5whl8sVSpNNqS+FmqGHCjgZn0+YFZ9NmBmfT5iVHT+bvb29OnLkiM4666yAx4UUdOIlOTlZycnJ/baNGDHCmMbEUFpamm0+cLAfPp8wKz6bMDM+nzAru302Aw5Z+7uQihFkZGQoMTFRzc3N/bY3NzcrJyfH5zk5OTkhHQ8AAAAAkQop6CQlJam4uFh1dXXebT09Paqrq9NFF13k85yLLrqo3/GS9NJLL/k9HgAAAAAiFfLQtaqqKs2ePVsTJkxQaWmpqqur1d7errlz50qSZs2apZEjR2rZsmWSpIULF+rSSy/VihUrdNVVV2njxo16++239dhjj0X3lVhIcnKyli5dOmB4HmAGfD5hVnw2YWZ8PmFWTv5shlxeWpLWrFmjhx56SE1NTSosLNSqVatUVlYmSZo8ebLy8/O1fv167/HPPvus7rzzTu3fv1/nnXeeHnzwQV155ZVRexEAAAAA8FVhBR0AAAAAMLOQ5ugAAAAAgBUQdAAAAADYDkEHAAAAgO0QdAAAAADYDkHHQPv379cNN9ygUaNGaejQoTrnnHO0dOlSdXV1Gd00QJJ033336eKLL1ZqaqpGjBhhdHPgcGvXrlV+fr5SUlJUVlamXbt2Gd0kQK+++qquvvpqnXXWWXK5XHr++eeNbhIgSVq2bJlKSko0fPhwZWVladq0aWpsbDS6WXFF0DFQQ0ODenp69O///u/68MMPtXLlSq1bt06333670U0DJEldXV363ve+p/nz5xvdFDjcpk2bVFVVpaVLl2rPnj0aP368ysvLdfDgQaObBodrb2/X+PHjtXbtWqObAvTzpz/9STfffLPeeOMNvfTSSzp+/LiuuOIKtbe3G920uKG8tMk89NBDevTRR/XJJ58Y3RTAa/369brlllt06NAho5sChyorK1NJSYnWrFkjSerp6VFubq4WLFigRYsWGdw6wMPlcmnLli2aNm2a0U0BBmhpaVFWVpb+9Kc/6ZJLLjG6OXFBj47JHD58WKeffrrRzQAA0+jq6tLu3bs1ZcoU77aEhARNmTJFO3fuNLBlAGAdhw8fliRHXWcSdEzk448/1urVq/XDH/7Q6KYAgGm0traqu7tb2dnZ/bZnZ2erqanJoFYBgHX09PTolltu0cSJE3X++ecb3Zy4IejEwKJFi+RyuQLeGhoa+p3z2WefaerUqfre976nefPmGdRyOEE4n08AAGBdN998sz744ANt3LjR6KbE1SlGN8CO/u3f/k1z5swJeMzo0aO9/z5w4IAuu+wyXXzxxXrsscdi3Do4XaifT8BoGRkZSkxMVHNzc7/tzc3NysnJMahVAGANlZWVeuGFF/Tqq6/qa1/7mtHNiSuCTgxkZmYqMzMzqGM/++wzXXbZZSouLtZTTz2lhAQ62RBboXw+ATNISkpScXGx6urqvJO8e3p6VFdXp8rKSmMbBwAm1dvbqwULFmjLli3avn27Ro0aZXST4o6gY6DPPvtMkydP1tlnn63ly5erpaXFu49vKWEGbrdbf/vb3+R2u9Xd3a29e/dKks4991wNGzbM2MbBUaqqqjR79mxNmDBBpaWlqq6uVnt7u+bOnWt00+BwR48e1ccff+y9v2/fPu3du1enn3668vLyDGwZnO7mm2/WM888o9/97ncaPny4d05jenq6hg4danDr4oPy0gZav3693z/S/LfADObMmaOnn356wPZXXnlFkydPjn+D4Ghr1qzRQw89pKamJhUWFmrVqlUqKyszullwuO3bt+uyyy4bsH327Nlav359/BsE/J3L5fK5/amnnhp0CLtdEHQAAAAA2A4TQgAAAADYDkEHAAAAgO0QdAAAAADYDkEHAAAAgO0QdAAAAADYDkEHAAAAgO0QdAAAAADYDkEHAAAAgO0QdAAAAADYDkEHAAAAgO0QdAAAAADYzv8DvwG64qlMVB0AAAAASUVORK5CYII=\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "l_1_phi\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "l_2_pT\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "l_2_eta\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "l_2_phi\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "MET\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "MET_phi\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "MET_rel\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "axial_MET\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "M_R\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "M_TR_2\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzoAAAGsCAYAAAAVEdLDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA070lEQVR4nO3dfVyUdb7/8Teg3IwBphOgLhPWKcXyDhGWPG2ejeJnZcdHtZlJ3pU9MumonErdUmtLyTaVNJOjZbaZaXXSrWzbY6jdmhpoN6eRbtTGU4LOdgSFRIX5/THHSWAGGQSumYvX8/G4Ho+d7/W95vqM49b17ntdnwlxuVwuAQAAAICJhBpdAAAAAAC0NIIOAAAAANMh6AAAAAAwHYIOAAAAANMh6AAAAAAwHYIOAAAAANMh6AAAAAAwnQ5GF9AUtbW1+umnnxQdHa2QkBCjywEAAABgEJfLpaNHj6p79+4KDfW9bhMUQeenn35SYmKi0WUAAAAACBAHDhzQb37zG5/7gyLoREdHS3J/mJiYGIOrAQAAAGCUiooKJSYmejKCL0ERdE7frhYTE0PQAQAAAHDWR1poRgAAAADAdAg6AAAAAEyHoAMAAADAdILiGR0AAAC0DzU1NTp58qTRZcBAHTt2VFhY2Dm/D0EHAAAAhnO5XCotLdWRI0eMLgUBoHPnzkpISDin39Ak6AAAAMBwp0NOXFycLBYLPxLfTrlcLlVVVenQoUOSpG7dujX7vQg6AAAAMFRNTY0n5HTt2tXocmCwqKgoSdKhQ4cUFxfX7NvYaEYAAAAAQ51+JsdisRhcCQLF6b8L5/K8FkEHAAAAAYHb1XBaS/xdIOgAAAAAMB2e0QEAAEDgcjgkp7Ptzme1SjZb250PrYagAwAAgMDkcEjJyVJVVdud02KR7PZzDjvjxo3TkSNHtGHDhpapq4keeeQRbdiwQbt3727T8wYigg4AAAACk9PpDjmrV7sDT2uz26XsbPd5zzHoPP3003K5XC1UGJqDoAMAAIDAlpwspaQYXYVfYmNjjS6h3aMZAQAAANBMr7/+uvr27auoqCh17dpVmZmZqqys1Lhx4zRixAjPvKNHj2r06NHq1KmTunXrpkWLFmno0KGaOnWqZ05SUpLmzZunCRMmKDo6WjabTcuXL69zvunTp+vSSy+VxWLRRRddpFmzZp1TC2YzI+gEIYdDKi5uuDkcRlcGAADQfhw8eFCjRo3ShAkTZLfbtXXrVt10001eb1nLzc3Vxx9/rDfffFObNm3Shx9+qOLi4gbzFixYoNTUVO3atUv33nuvJk2apJKSEs/+6OhorVq1Sl9//bWefvpprVixQosWLWrVzxmsuHUtyDT2TF4LPTsHAACAJjh48KBOnTqlm266SRdeeKEkqW/fvg3mHT16VC+++KLWrFmjq6++WpL0wgsvqHv37g3mXnfddbr33nsluVdvFi1apC1btqhXr16SpIcfftgzNykpSffff7/Wrl2rBx98sMU/X7Aj6AQZX8/kteCzcwAAAGiC/v376+qrr1bfvn2VlZWla6+9VrfccovOP//8OvP27t2rkydPKi0tzTMWGxvrCS9n6tevn+d/h4SEKCEhQYcOHfKMrVu3TosXL9b333+vY8eO6dSpU4qJiWmFTxf8uHUtSJ1+Ju/01haNSAAAAPCrsLAwbdq0SX/729/Up08fLVmyRL169dK+ffua/Z4dO3as8zokJES1tbWSpG3btmn06NG67rrr9Pbbb2vXrl166KGHdOLEiXP6HGZF0AEAAACaKSQkREOGDNGjjz6qXbt2KTw8XOvXr68z56KLLlLHjh21c+dOz1h5ebm++eYbv871ySef6MILL9RDDz2k1NRUXXLJJfrhhx9a5HOYEbeuAQAAILDZ7QF5nu3bt6uwsFDXXnut4uLitH37dh0+fFjJycn64osvPPOio6M1duxYPfDAA+rSpYvi4uI0Z84chYaGKiQkpMnnu+SSS+RwOLR27VoNHjxYGzdubBCq8CuCDgAAAAKT1erutpSd3XbntFjc522CmJgYffDBB8rPz1dFRYUuvPBCLViwQMOGDdO6devqzF24cKHuuece3XDDDYqJidGDDz6oAwcOKDIyssml3XjjjZo2bZpycnJUXV2t66+/XrNmzdIjjzzizydsN0JcQfCTrRUVFYqNjVV5eXm7f9iquFgaNEgqKqr7u1m+xgEAAALd8ePHtW/fPvXs2bPhhb/D4e621Fas1jbp7FRZWakePXpowYIFuvPOO1v9fMGmsb8TTc0GrOgAAAAgcNlspmgpu2vXLu3Zs0dpaWkqLy/Xn/70J0nSv/7rvxpcmXkRdAAAAIA28NRTT6mkpETh4eEaNGiQPvzwQ1mbeJsc/EfQAQAAAFrZwIEDVVRUZHQZ7QrtpQEAAACYDkEHAAAAgOkQdAAAAACYDkEHAAAAgOnQjMBk6v+gbxu1ggcAAAACCkHHJHz9cLDF4g4/hB0AABCMAv33QocOHaoBAwYoPz+/VeoZN26cjhw5og0bNrTK+xth//796tmzp3bt2qUBAwa02nkIOiZhs7kDzZn/ILDb3cHH6SToAACA4ONwSMnJUlVV252T/0hsHgQdEzHJDwcDAABIcv/H2qoqafVqd+Bpbe3lPxKfOHFC4eHhRpfR6mhGAAAAgICWnCylpLT+1twwderUKeXk5Cg2NlZWq1WzZs2Sy+WSJL300ktKTU1VdHS0EhISdPvtt+vQoUN1jv/v//5v3XDDDYqJiVF0dLSuvPJKff/9917PtXPnTl1wwQWaP3++Z+zxxx9XXFycoqOjddddd2nGjBl1bgkbN26cRowYoblz56p79+7q1auXJOnLL7/U73//e0VFRalr1666++67dezYMc9xQ4cO1dSpU+ucf8SIERo3bpzndVJSkubNm6cJEyYoOjpaNptNy5cvr3PMjh07NHDgQEVGRio1NVW7du1q8p/tuSDoBDiHQyou/nWr32wAAAAAxnrxxRfVoUMH7dixQ08//bQWLlyo5557TpJ08uRJPfbYY/r888+1YcMG7d+/v05Q+PHHH/W73/1OERER2rx5s4qKijRhwgSdOnWqwXk2b96sa665RnPnztX06dMlSS+//LLmzp2r+fPnq6ioSDabTcuWLWtwbGFhoUpKSrRp0ya9/fbbqqysVFZWls4//3zt3LlTr732mt577z3l5OT4/fkXLFjgCTD33nuvJk2apJKSEknSsWPHdMMNN6hPnz4qKirSI488ovvvv9/vczQHt64FMF/3pVos7gflAAAAYLzExEQtWrRIISEh6tWrl7788kstWrRIEydO1IQJEzzzLrroIi1evFiDBw/WsWPHdN5552np0qWKjY3V2rVr1bFjR0nSpZde2uAc69ev15gxY/Tcc89p5MiRnvElS5bozjvv1Pjx4yVJs2fP1n/913/VWZmRpE6dOum5557z3LK2YsUKHT9+XH/5y1/UqVMnSdIzzzyj4cOHa/78+YqPj2/y57/uuut07733SpKmT5+uRYsWacuWLerVq5fWrFmj2tpaPf/884qMjNRll12m//mf/9GkSZOa/P7NxYpOADvzvtSiol83HpADAAAIHL/97W8VEhLieZ2RkaFvv/1WNTU1Kioq0vDhw2Wz2RQdHa2rrrpKkuRwOCRJu3fv1pVXXukJOd5s375df/jDH/TSSy/VCTmSVFJSorS0tDpj9V9LUt++fes8l2O329W/f39PyJGkIUOGqLa21rMa01T9+vXz/O+QkBAlJCR4bs+z2+3q16+fIiMjPXMyMjL8ev/mIugEgfr3pRJyAAAAAt/x48eVlZWlmJgYvfzyy9q5c6fWr18vyd0QQJKioqLO+j4XX3yxevfurZUrV+rkyZPNquXMQNNUoaGhnmeNTvN2/vohLSQkRLW1tX6fr6URdAAAAIBzsH379jqvP/30U11yySXas2eP/vGPf+iJJ57QlVdeqd69ezdoRNCvXz99+OGHjQYYq9WqzZs367vvvtOtt95aZ26vXr20c+fOOvPrv/YmOTlZn3/+uSorKz1jH3/8sUJDQz3NCi644AIdPHjQs7+mpkZfffXVWd+7/nm++OILHT9+3DP26aef+vUezUXQAQAAAM6Bw+FQbm6uSkpK9Morr2jJkiWaMmWKbDabwsPDtWTJEu3du1dvvvmmHnvssTrH5uTkqKKiQrfddps+++wzffvtt3rppZca3D4WFxenzZs3a8+ePRo1apSnWcF9992n559/Xi+++KK+/fZbPf744/riiy/q3ErnzejRoxUZGamxY8fqq6++0pYtW3Tffffpjjvu8Dyf8/vf/14bN27Uxo0btWfPHk2aNElHjhzx68/m9ttvV0hIiCZOnKivv/5a77zzjp566im/3qO5aEYAAACAgNZWXWebe54xY8bol19+UVpamsLCwjRlyhTdfffdCgkJ0apVq/THP/5RixcvVkpKip566indeOONnmO7du2qzZs364EHHtBVV12lsLAwDRgwQEOGDGlwnoSEBG3evFlDhw7V6NGjtWbNGo0ePVp79+7V/fffr+PHj+vWW2/VuHHjtGPHjkZrtlgs+vvf/64pU6Zo8ODBslgsuvnmm7Vw4ULPnAkTJujzzz/XmDFj1KFDB02bNk3/8i//4tefzXnnnae33npL99xzjwYOHKg+ffpo/vz5uvnmm/16n+YIcdW/8e4sPvjgA/35z39WUVGRDh48qPXr12vEiBE+57/xxhtatmyZdu/ererqal122WV65JFHlJWV1eRzVlRUKDY2VuXl5YqJifGn3KBWXCwNGuRuQJCS0vbHAwAAtIXjx49r37596tmzZ52H1n11oG1NFkvwN3665pprlJCQoJdeesnoUprN198JqenZwO8VncrKSvXv318TJkzQTTfddNb5H3zwga655hrNmzdPnTt31gsvvKDhw4dr+/btGjhwoL+nBwAAQDths7lDh9PZdue0WoMr5FRVVamgoEBZWVkKCwvTK6+8ovfee0+bNm0yujTD+R10hg0bpmHDhjV5fn5+fp3X8+bN01//+le99dZbBB0AAAA0ymYLruDR1kJCQvTOO+9o7ty5On78uHr16qX//M//VGZmptGlGa7Nn9Gpra3V0aNH1aVLF59zqqurVV1d7XldUVHRFqUBAAAAQSUqKkrvvfee0WUEpDbvuvbUU0/p2LFjuvXWW33OycvLU2xsrGdLTExswwoBAAAABLs2DTpr1qzRo48+qldffVVxcXE+582cOVPl5eWe7cCBA21YJQAAAIBg12a3rq1du1Z33XWXXnvttbPeMxgREaGIiIg2qgwAAACBoLa21ugSECBa4u9CmwSdV155RRMmTNDatWt1/fXXt8UpAQAAECTCw8MVGhqqn376SRdccIHCw8PP+oOXMCeXy6UTJ07o8OHDCg0NVXh4eLPfy++gc+zYMX333Xee1/v27dPu3bvVpUsX2Ww2zZw5Uz/++KP+8pe/SHLfrjZ27Fg9/fTTSk9PV2lpqST3g1OxsbHNLhwAAADmEBoaqp49e+rgwYP66aefjC4HAcBischmsyk0tPlP2vgddD777LM6v4iam5srSRo7dqxWrVqlgwcPyuFwePYvX75cp06d0uTJkzV58mTP+On5AAAAQHh4uGw2m06dOqWamhqjy4GBwsLC1KFDh3Ne1fM76AwdOlQul8vn/vrhZevWrf6eAgAAAO1QSEiIOnbsqI4dOxpdCkygzdtLAwAAAEBrI+gAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADT6WB0AWh9dnvDMatVstnavhYAAACgLRB0TMxqlSwWKTu74T6LxR2ACDsAAAAwI4KOidls7jDjdNYdt9vd4cfpJOgAAADAnAg6JmezEWYAAADQ/tCMAAAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmI7fQeeDDz7Q8OHD1b17d4WEhGjDhg1nPWbr1q1KSUlRRESE/umf/kmrVq1qRqnm53BIxcW/bna70RUBAAAAwamDvwdUVlaqf//+mjBhgm666aazzt+3b5+uv/563XPPPXr55ZdVWFiou+66S926dVNWVlazijYjh0NKTpaqquqOWyyS1WpMTQAAAECw8jvoDBs2TMOGDWvy/IKCAvXs2VMLFiyQJCUnJ+ujjz7SokWLCDpncDrdIWf1anfgOc1qlWw24+oCAAAAgpHfQcdf27ZtU2ZmZp2xrKwsTZ061ecx1dXVqq6u9ryuqKhorfICTnKylJJidBUAAABAcGv1ZgSlpaWKj4+vMxYfH6+Kigr98ssvXo/Jy8tTbGysZ0tMTGztMgEAAACYSEB2XZs5c6bKy8s924EDB4wuCQAAAEAQafVb1xISElRWVlZnrKysTDExMYqKivJ6TEREhCIiIlq7NAAAAAAm1eorOhkZGSosLKwztmnTJmVkZLT2qQEAAAC0U34HnWPHjmn37t3avXu3JHf76N27d8vhcEhy33Y2ZswYz/x77rlHe/fu1YMPPqg9e/bo2Wef1auvvqpp06a1zCcAAAAAgHr8DjqfffaZBg4cqIEDB0qScnNzNXDgQM2ePVuSdPDgQU/okaSePXtq48aN2rRpk/r3768FCxboueeeo7U0AAAAgFbj9zM6Q4cOlcvl8rl/1apVXo/ZtWuXv6cCAAAAgGYJyK5rAAAAAHAuCDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0OhhdAIxjt9d9bbVKNpsxtQAAAAAtiaDTDlmtksUiZWfXHbdY3OGHsAMAAIBgR9Bph2w2d6BxOn8ds9vdwcfpJOgAAAAg+BF02imbjUADAAAA86IZAQAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMJ1mBZ2lS5cqKSlJkZGRSk9P144dOxqdn5+fr169eikqKkqJiYmaNm2ajh8/3qyCAQAAAOBs/A4669atU25urubMmaPi4mL1799fWVlZOnTokNf5a9as0YwZMzRnzhzZ7XY9//zzWrdunf74xz+ec/EAAAAA4I3fQWfhwoWaOHGixo8frz59+qigoEAWi0UrV670Ov+TTz7RkCFDdPvttyspKUnXXnutRo0addZVIAAAAABoLr+CzokTJ1RUVKTMzMxf3yA0VJmZmdq2bZvXY6644goVFRV5gs3evXv1zjvv6LrrrvN5nurqalVUVNTZAAAAAKCpOvgz2el0qqamRvHx8XXG4+PjtWfPHq/H3H777XI6nfrnf/5nuVwunTp1Svfcc0+jt67l5eXp0Ucf9ac0AAAAAPBo9a5rW7du1bx58/Tss8+quLhYb7zxhjZu3KjHHnvM5zEzZ85UeXm5Zztw4EBrlwkAAADARPxa0bFarQoLC1NZWVmd8bKyMiUkJHg9ZtasWbrjjjt01113SZL69u2ryspK3X333XrooYcUGtowa0VERCgiIsKf0gAAAADAw68VnfDwcA0aNEiFhYWesdraWhUWFiojI8PrMVVVVQ3CTFhYmCTJ5XL5Wy8AAAAAnJVfKzqSlJubq7Fjxyo1NVVpaWnKz89XZWWlxo8fL0kaM2aMevTooby8PEnS8OHDtXDhQg0cOFDp6en67rvvNGvWLA0fPtwTeAAAAACgJfkddEaOHKnDhw9r9uzZKi0t1YABA/Tuu+96GhQ4HI46KzgPP/ywQkJC9PDDD+vHH3/UBRdcoOHDh2vu3Lkt9ykAAAAA4AwhriC4f6yiokKxsbEqLy9XTEyM0eW0iuJiadAgqahISklpf+cHAAAAmqKp2aDVu64BAAAAQFsj6AAAAAAwHYIOAAAAANMh6AAAAAAwHb+7rsEkHA7J6fz1tT1KUrJkt0v6RbJaJZvNqOoAAACAc0LQaY8cDik5WaqqOmNwoKRiKXu0pF2SxeIOPYQdAAAABCGCTnvkdLpDzurV7sAjuVd0siWtflnuwJPtnkfQAQAAQBAi6LRnyckNfzQnOVnSL4aUAwAAALQUgg58s9u9j/P8DgAAAAIcQQcNWa3uZ3Sys73v5/kdAAAABDiCDhqy2dxB5syubKfZ7Ty/AwAAgIBH0DGz+i2kT/N1S9qZbDaCDAAAAIIWQcesvLaQPoPF4r5FDQAAADAhgo5ZeWshfSYaCgAAAMDECDpm562FNAAAAGByoUYXAAAAAAAtjaADAAAAwHQIOgAAAABMh6ADAAAAwHQIOgAAAABMh6ADAAAAwHQIOgAAAABMh6ADAAAAwHQIOgAAAABMh6ADAAAAwHQIOgAAAABMp4PRBbRHDofkdNYds9uNqQUAAAAwI4JOG3M4pORkqaqq4T6LRbJa274mAAAAwGwIOm3M6XSHnNWr3YHnTFarZLMZUxcAAABgJgQdgyQnSykpRldxDnzda0daAwAAQAAg6MA/Vqv7HrvsbO/7LRZ3CCLsAAAAwEAEHdRRf6GmwQKNzeaeVL+bwumDs7Pd+wg6AAAAMBBBB5J8L9R4XaCx2QgyAAAACGgEnWDnrVe15He/am8LNSzQAAAAIFgRdIJZY72qJb/7VbNQAwAAALMg6ASzxnpVS3RAAwAAQLtF0DGDoO9VDQAAALSsUKMLAAAAAICWRtABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDrNCjpLly5VUlKSIiMjlZ6erh07djQ6/8iRI5o8ebK6deumiIgIXXrppXrnnXeaVTAAAAAAnE0Hfw9Yt26dcnNzVVBQoPT0dOXn5ysrK0slJSWKi4trMP/EiRO65pprFBcXp9dff109evTQDz/8oM6dO7dE/QAAAADQgN9BZ+HChZo4caLGjx8vSSooKNDGjRu1cuVKzZgxo8H8lStX6ueff9Ynn3yijh07SpKSkpLOrWoAAAAAaIRft66dOHFCRUVFyszM/PUNQkOVmZmpbdu2eT3mzTffVEZGhiZPnqz4+Hhdfvnlmjdvnmpqanyep7q6WhUVFXU2AAAAAGgqv4KO0+lUTU2N4uPj64zHx8ertLTU6zF79+7V66+/rpqaGr3zzjuaNWuWFixYoMcff9znefLy8hQbG+vZEhMT/SkTAAAAQDvX6l3XamtrFRcXp+XLl2vQoEEaOXKkHnroIRUUFPg8ZubMmSovL/dsBw4caO0yAQAAAJiIX8/oWK1WhYWFqaysrM54WVmZEhISvB7TrVs3dezYUWFhYZ6x5ORklZaW6sSJEwoPD29wTEREhCIiIvwpDQAAAAA8/FrRCQ8P16BBg1RYWOgZq62tVWFhoTIyMrweM2TIEH333Xeqra31jH3zzTfq1q2b15ADAAAAAOfK71vXcnNztWLFCr344ouy2+2aNGmSKisrPV3YxowZo5kzZ3rmT5o0ST///LOmTJmib775Rhs3btS8efM0efLklvsUAAAAAHAGv9tLjxw5UocPH9bs2bNVWlqqAQMG6N133/U0KHA4HAoN/TU/JSYm6u9//7umTZumfv36qUePHpoyZYqmT5/ecp8CAAAAAM7gd9CRpJycHOXk5Hjdt3Xr1gZjGRkZ+vTTT5tzKgQju937uNUq2WxtWwsAAADapWYFHcArq1WyWKTsbO/7LRZ3CCLsAAAAoJURdNBybDZ3kHE6G+6z290ByOkk6AAAAKDVEXTQsmw2ggwAAAAM1+o/GAoAAAAAbY2gAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0CDoAAAAATIegAwAAAMB0OhhdAJrA4ZCczobjdnvb1wIAAAAEAYJOoHM4pORkqarK+36LRbJa27YmAAAAIMARdAKd0+kOOatXuwNPfVarZLO1fV0AAABAACPoBIvkZCklxegqAAAAgKBAMwIAAAAApkPQAQAAAGA6BB0AAAAApkPQAQAAAGA6BB0AAAAApkPQAQAAAGA6BB0AAAAApkPQAQAAAGA6/GAo2pbd7n3capVstratBQAAAKZF0EHbsFoli0XKzva+32JxhyDCDgAAAFoAQQdtw2ZzBxmns+E+u90dgJxOgg4AAABaBEEHbcdmI8gAAACgTdCMAAAAAIDpEHQAAAAAmA5BBwAAAIDp8IwOzspbR2i6QQMAACCQEXTgU2MdoekGDQAAgEBG0IFPvjpC0w0aAAAAgY6gg0bRERoAAADBiGYEAAAAAEyHoAMAAADAdAg6AAAAAEyHoAMAAADAdAg6AAAAAEyHoAMAAADAdAg6AAAAAEynWUFn6dKlSkpKUmRkpNLT07Vjx44mHbd27VqFhIRoxIgRzTktAAAAADSJ30Fn3bp1ys3N1Zw5c1RcXKz+/fsrKytLhw4davS4/fv36/7779eVV17Z7GIBAAAAoCn8DjoLFy7UxIkTNX78ePXp00cFBQWyWCxauXKlz2Nqamo0evRoPfroo7rooovOqWAAAAAAOBu/gs6JEydUVFSkzMzMX98gNFSZmZnatm2bz+P+9Kc/KS4uTnfeeWeTzlNdXa2Kioo6GwAAAAA0lV9Bx+l0qqamRvHx8XXG4+PjVVpa6vWYjz76SM8//7xWrFjR5PPk5eUpNjbWsyUmJvpTJgAAAIB2rlW7rh09elR33HGHVqxYIavV2uTjZs6cqfLycs924MCBVqwSAAAAgNl08Gey1WpVWFiYysrK6oyXlZUpISGhwfzvv/9e+/fv1/Dhwz1jtbW17hN36KCSkhJdfPHFDY6LiIhQRESEP6UBAAAAgIdfQSc8PFyDBg1SYWGhp0V0bW2tCgsLlZOT02B+79699eWXX9YZe/jhh3X06FE9/fTT3JKGuux27+NWq2SztW0tAAAACGp+BR1Jys3N1dixY5Wamqq0tDTl5+ersrJS48ePlySNGTNGPXr0UF5eniIjI3X55ZfXOb5z586S1GAc7ZjVKlksUna29/0WizsEEXYAAADQRH4HnZEjR+rw4cOaPXu2SktLNWDAAL377rueBgUOh0Ohoa366A/MxmZzBxmns+E+u90dgJxOgg4AAACazO+gI0k5OTleb1WTpK1btzZ67KpVq5pzSpidzUaQAQAAQIth6QUAAACA6RB0AAAAAJgOQQcAAACA6RB0AAAAAJhOs5oRoOkcjrrNxHz9VEyDiWc9AAAAAIAvBJ1W5HBIyclSVVXdcYvF/dMxZ53o8wAAAAAAjSHotCKn051dVq9255jTrNZ6nZR9TfR5AAAAAIDGEHTaQHKylJLSkhMBAAAANIZmBAAAAABMh6ADAAAAwHQIOgAAAABMh6ADAAAAwHQIOgAAAABMh6ADAAAAwHQIOgAAAABMh6ADAAAAwHQIOgAAAABMh6ADAAAAwHQIOgAAAABMh6ADAAAAwHQIOgAAAABMh6ADAAAAwHQ6GF0A0CR2u/dxq1Wy2dq2FgAAAAQ8gg4Cm9UqWSxSdrb3/RaLOwQRdgAAAHAGgg6arf4iS6ssrths7hM5nd4LyM527yPoAAAA4AwEHfjN1yJLqy2u2GwEGQAAAPiFoAO/eVtkYXEFAAAAgYSgg2ZhkQUAAACBjPbSAAAAAEyHoAMAAADAdAg6AAAAAEyHoAMAAADAdAg6AAAAAEyHoAMAAADAdAg6AAAAAEyHoAMAAADAdAg6AAAAAEyHoAMAAADAdAg6AAAAAEyng9EFAOfMbvc+brVKNlvb1gIAAICAQNBB8LJaJYtFys72vt9icYcgwg4AAEC7Q9BB8LLZ3EHG6Wy4z253ByCnk6ADAADQDhF0ENxsNoIMAAAAGqAZAQAAAADTIegAAAAAMJ1mBZ2lS5cqKSlJkZGRSk9P144dO3zOXbFiha688kqdf/75Ov/885WZmdnofAAAAAA4V34HnXXr1ik3N1dz5sxRcXGx+vfvr6ysLB06dMjr/K1bt2rUqFHasmWLtm3bpsTERF177bX68ccfz7l4AAAAAPDG76CzcOFCTZw4UePHj1efPn1UUFAgi8WilStXep3/8ssv695779WAAQPUu3dvPffcc6qtrVVhYeE5Fw8AAAAA3vjVde3EiRMqKirSzJkzPWOhoaHKzMzUtm3bmvQeVVVVOnnypLp06eJzTnV1taqrqz2vKyoq/CkzcDkcvlshAwAAAGgxfgUdp9OpmpoaxcfH1xmPj4/Xnj17mvQe06dPV/fu3ZWZmelzTl5enh599FF/Sgt8DoeUnCxVVXnfb7G4fwATAAAAwDlr09/ReeKJJ7R27Vpt3bpVkZGRPufNnDlTubm5ntcVFRVKTExsixJbj9PpDjmrV7sDT31WK78HAwAAALQQv4KO1WpVWFiYysrK6oyXlZUpISGh0WOfeuopPfHEE3rvvffUr1+/RudGREQoIiLCn9KCR3KylJJidBUAAACAqfnVjCA8PFyDBg2q00jgdGOBjIwMn8c9+eSTeuyxx/Tuu+8qNTW1+dUCAAAAQBP4fetabm6uxo4dq9TUVKWlpSk/P1+VlZUaP368JGnMmDHq0aOH8vLyJEnz58/X7NmztWbNGiUlJam0tFSSdN555+m8885rwY8CAAAAAG5+B52RI0fq8OHDmj17tkpLSzVgwAC9++67ngYFDodDoaG/LhQtW7ZMJ06c0C233FLnfebMmaNHHnnk3KoHzsZXRzueiQIAADC1ZjUjyMnJUU5Ojtd9W7durfN6//79zTkFcG6sVncnu+xs7/stFncIIuwAAACYUpt2XYP5eVtAMWTxxGZzF+Prd4uys937CDoAAACmRNBBi2hsAcWwxRObjSADAADQThF00CJ8LaCweAIAAAAjEHTQYlhAAQAAQKDw63d0AAAAACAYEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA7tpdF+2e3ex61W+mQDAAAEOYIO2h+rVbJY3L9k6o3F4g5BhB0AAICgRdBB+2OzuYOM09lwn93uDkBOJ0EHAAAgiBF00D7ZbAQZAAAAE6MZAQAAAADTYUWnhTgcDe+E8vWsOwAAAIDWRdBpAQ6HlJwsVVU13GexuJ99l5fHQQAAAAC0DoJOC3A63SFn9Wp34DmTp1MxQSe40HoaAAAgqBF0WlByspSSYnQVOCe0ngYAADAFgg7aRP0FkoBdGKH1NAAAgCkQdNCqfC2QBPTCCK2nAQAAgh5BB63K2wIJCyMAAABobQQdtDoWSAAAANDW+MFQAAAAAKbDig7gL1pPAwAABDyCTktzOHx37EJwo/U0AABA0CDotKSDB6Urk92/HuqNxeK+WEZwovU0AABA0CDotKQjR9whZ/Vq96+H1setTcGPzgoAAABBgaDTGpKTpZQUo6sAAAAA2i2CDgxT/7ElUyx40agAAAAgIBB00OZ8PdMf1M/y06gAAAAgoBB00Oa8PdMf9M/y06gAAAAgoBB0YAhTPtNvyg8FAAAQnEKNLgAAAAAAWhorOkBboVEBAABAmyHoAK2NRgUAAABtjqADtLamNCr48EN+ZBYAAKAFEXQQULzd3WWKa31fjQpY7QEAAGgVBB0EhMau9019rU9bagAAgFZB0EFA8HW93y6u9c/WlpomBgAAAH4j6CBgNHa9X/9av11c4zfltrY33pAuuMD7sab/AwIAAPCNoIOA5uta39S3s53W2G1thw9LN90k/b//5/3YdvEHBAAA4BtBBwHN27V+u7id7bSzLXPRyQ0AAMArgk5zOBz1rryjJCVL+/YZVpKZ+brWN22HtqY6l05u3PIGAABMjqDjL4dDjl7XyHm8k2fIrmRJL0uzHnZfRFqtxtXXDpytQ1v9a/h2d+1+rre8EYIAAIAJhLhcLpfRRZxNRUWFYmNjVV5erpiYGENrcWz8Usk3XKQqdaozbomskf31r2XrG8vFYBuov6gm/XoNX1VVd5zwU4+3PzzJ9x/gaY2FoMa06z9sAADQ0pqaDZq1orN06VL9+c9/Vmlpqfr3768lS5YoLS3N5/zXXntNs2bN0v79+3XJJZdo/vz5uu6665pzasM5j3RQlTpp9WP7lHxdT8+41Romm62vgZW1L43dznbmNbyvBQx/rtlNd53enOd+zrYS1JjmBqSzMd0XAwAAWpLfQWfdunXKzc1VQUGB0tPTlZ+fr6ysLJWUlCguLq7B/E8++USjRo1SXl6ebrjhBq1Zs0YjRoxQcXGxLr/88hb5EEZI7nlcKSlGV4H6vF3DNzX8+NKurtObE4Iacy4B6Wxa64vxJSC/MAAA4Ivft66lp6dr8ODBeuaZZyRJtbW1SkxM1H333acZM2Y0mD9y5EhVVlbq7bff9oz99re/1YABA1RQUOD1HNXV1aqurva8Li8vl81m04EDBwy/dW33uhJddXcvvb+8RANG9jK0FjTfgQPSP/5x9nlOp/tZoF9+afkaoqKk1avbwSNdZaXSkfKWfc8jR9zPxFUfb9n3bUxEpPTY41Lnzm13TgDtWkLXk0qwnjK6DMAtIcG9BYCKigolJibqyJEjio2N9T3R5Yfq6mpXWFiYa/369XXGx4wZ47rxxhu9HpOYmOhatGhRnbHZs2e7+vXr5/M8c+bMcUliY2NjY2NjY2NjY2Pzuh04cKDR7OLXrWtOp1M1NTWKj4+vMx4fH689e/Z4Paa0tNTr/NLSUp/nmTlzpnJzcz2va2tr9fPPP6tr164KCQnxp+QWdTo9BsLKEhrHdxU8+K6CB99V8OC7Ch58V8GB7ymwuFwuHT16VN27d290XkC2l46IiFBERESdsc4BdLtITEwMf8mDBN9V8OC7Ch58V8GD7yp48F0FB76nwNHoLWv/J9SfN7RarQoLC1NZWVmd8bKyMiX4uGcvISHBr/kAAAAAcK78Cjrh4eEaNGiQCgsLPWO1tbUqLCxURkaG12MyMjLqzJekTZs2+ZwPAAAAAOfK71vXcnNzNXbsWKWmpiotLU35+fmqrKzU+PHjJUljxoxRjx49lJeXJ0maMmWKrrrqKi1YsEDXX3+91q5dq88++0zLly9v2U/SBiIiIjRnzpwGt9Uh8PBdBQ++q+DBdxU8+K6CB99VcOB7Ck5+t5eWpGeeecbzg6EDBgzQ4sWLlZ6eLkkaOnSokpKStGrVKs/81157TQ8//LDnB0OffPLJoP3BUAAAAACBr1lBBwAAAAACmV/P6AAAAABAMCDoAAAAADAdgg4AAAAA0yHoAAAAADAdgo4fli5dqqSkJEVGRio9PV07duwwuiTUk5eXp8GDBys6OlpxcXEaMWKESkpKjC4LZ/HEE08oJCREU6dONboUePHjjz8qOztbXbt2VVRUlPr27avPPvvM6LJQT01NjWbNmqWePXsqKipKF198sR577DHRc8h4H3zwgYYPH67u3bsrJCREGzZsqLPf5XJp9uzZ6tatm6KiopSZmalvv/3WmGLbuca+q5MnT2r69Onq27evOnXqpO7du2vMmDH66aefjCsYjSLoNNG6deuUm5urOXPmqLi4WP3791dWVpYOHTpkdGk4w/vvv6/Jkyfr008/1aZNm3Ty5Elde+21qqysNLo0+LBz5079x3/8h/r162d0KfDif//3fzVkyBB17NhRf/vb3/T1119rwYIFOv/8840uDfXMnz9fy5Yt0zPPPCO73a758+frySef1JIlS4wurd2rrKxU//79tXTpUq/7n3zySS1evFgFBQXavn27OnXqpKysLB0/fryNK0Vj31VVVZWKi4s1a9YsFRcX64033lBJSYluvPFGAypFU9BeuonS09M1ePBgPfPMM5Kk2tpaJSYm6r777tOMGTMMrg6+HD58WHFxcXr//ff1u9/9zuhyUM+xY8eUkpKiZ599Vo8//rgGDBig/Px8o8vCGWbMmKGPP/5YH374odGl4CxuuOEGxcfH6/nnn/eM3XzzzYqKitLq1asNrAxnCgkJ0fr16zVixAhJ7tWc7t2769///d91//33S5LKy8sVHx+vVatW6bbbbjOw2vat/nflzc6dO5WWlqYffvhBNput7YpDk7Ci0wQnTpxQUVGRMjMzPWOhoaHKzMzUtm3bDKwMZ1NeXi5J6tKli8GVwJvJkyfr+uuvr/P/LQSWN998U6mpqfrDH/6guLg4DRw4UCtWrDC6LHhxxRVXqLCwUN98840k6fPPP9dHH32kYcOGGVwZGrNv3z6VlpbW+edgbGys0tPTucYIAuXl5QoJCVHnzp2NLgVedDC6gGDgdDpVU1Oj+Pj4OuPx8fHas2ePQVXhbGprazV16lQNGTJEl19+udHloJ61a9equLhYO3fuNLoUNGLv3r1atmyZcnNz9cc//lE7d+7Uv/3bvyk8PFxjx441ujycYcaMGaqoqFDv3r0VFhammpoazZ07V6NHjza6NDSitLRUkrxeY5zeh8B0/PhxTZ8+XaNGjVJMTIzR5cALgg5Ma/Lkyfrqq6/00UcfGV0K6jlw4ICmTJmiTZs2KTIy0uhy0Ija2lqlpqZq3rx5kqSBAwfqq6++UkFBAUEnwLz66qt6+eWXtWbNGl122WXavXu3pk6dqu7du/NdAS3s5MmTuvXWW+VyubRs2TKjy4EP3LrWBFarVWFhYSorK6szXlZWpoSEBIOqQmNycnL09ttva8uWLfrNb35jdDmop6ioSIcOHVJKSoo6dOigDh066P3339fixYvVoUMH1dTUGF0i/k+3bt3Up0+fOmPJyclyOBwGVQRfHnjgAc2YMUO33Xab+vbtqzvuuEPTpk1TXl6e0aWhEaevI7jGCB6nQ84PP/ygTZs2sZoTwAg6TRAeHq5BgwapsLDQM1ZbW6vCwkJlZGQYWBnqc7lcysnJ0fr167V582b17NnT6JLgxdVXX60vv/xSu3fv9mypqakaPXq0du/erbCwMKNLxP8ZMmRIgxbt33zzjS688EKDKoIvVVVVCg2t+6/1sLAw1dbWGlQRmqJnz55KSEioc41RUVGh7du3c40RgE6HnG+//VbvvfeeunbtanRJaAS3rjVRbm6uxo4dq9TUVKWlpSk/P1+VlZUaP3680aXhDJMnT9aaNWv017/+VdHR0Z77m2NjYxUVFWVwdTgtOjq6wXNTnTp1UteuXXmeKsBMmzZNV1xxhebNm6dbb71VO3bs0PLly7V8+XKjS0M9w4cP19y5c2Wz2XTZZZdp165dWrhwoSZMmGB0ae3esWPH9N1333le79u3T7t371aXLl1ks9k0depUPf7447rkkkvUs2dPzZo1S927d2+02xdaR2PfVbdu3XTLLbeouLhYb7/9tmpqajzXGV26dFF4eLhRZcMXF5psyZIlLpvN5goPD3elpaW5Pv30U6NLQj2SvG4vvPCC0aXhLK666irXlClTjC4DXrz11luuyy+/3BUREeHq3bu3a/ny5UaXBC8qKipcU6ZMcdlsNldkZKTroosucj300EOu6upqo0tr97Zs2eL1301jx451uVwuV21trWvWrFmu+Ph4V0REhOvqq692lZSUGFt0O9XYd7Vv3z6f1xlbtmwxunR4we/oAAAAADAdntEBAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDoEHQAAAACmQ9ABAAAAYDr/H5o/xZ+/8PvaAAAAAElFTkSuQmCC\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "R\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "MT2\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "S_R\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "M_Delta_R\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "dPhi_r_b\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "cos_theta_r1\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ], - "source": [ - "import numpy as np\n", - "for var in VarNames[1:]:\n", - " print (var)\n", - " plt.figure(figsize=(10,5))\n", - " plt.hist(np.array(df_sig[var]),bins=100,histtype=\"step\", color=\"red\",label=\"signal\",density=1, stacked=True)\n", - " plt.hist(np.array(df_bkg[var]),bins=100,histtype=\"step\", color=\"blue\", label=\"background\",density=1, stacked=True)\n", - " plt.legend(loc='upper right')\n", - " plt.show()" + "name": "stdout", + "output_type": "stream", + "text": [ + "S_R\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "fcSXe4AF_ro5" - }, - "source": [ - "## Exercise 3: Make nice figures\n", - "\n", - "Now use `matplotlib` to reproduce as closely as you can figures 5 and 6 from the paper. This exercise is intended to get you to familiarize yourself with making nicely formatted `matplotlib` figures with multiple plots. Note that the plots in the paper are actually wrong!" + "name": "stdout", + "output_type": "stream", + "text": [ + "M_Delta_R\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "pHMS1ZIf_ro-", - "outputId": "5f26fbcf-3481-4153-e33b-5690e5a9ed79" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAINCAYAAAAtJ/ceAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABoY0lEQVR4nO3de3hU1b0+8HfPHkMSTIIhCSmEEIiXRDGBkMDBlqqVirWt5dD22FbKRatHLT5ajtdeUGo9SKuIWiuntgoVb61V2l+1thbF0mqBXCCiE1ACHTFcEjEJkECYvffvj3GGTLImmclc9tpr3s/z5GnZM5mslXmdfGfNumiWZVkgIiIiInIgl90NICIiIiIaKhazRERERORYLGaJiIiIyLFYzBIRERGRY7GYJSIiIiLHYjFLRERERI7FYpaIiIiIHIvFLBERERE5ltvuBiSbaZpoaWlBVlYWNE2zuzlERERE1IdlWTh8+DBGjx4Nl2vgsdeUK2ZbWlowduxYu5tBRERERIP44IMPUFRUNOB9Uq6YzcrKAuD/5WRnZ9vcmvB8Ph8aGhowefJkuN0p9zRRGMwFiTAXJMJckIhTctHZ2YmxY8cG67aByNuLBAlMLcjOzpa+mB0+fDiys7OlDhslF3NBIswFiTAXJOK0XEQyJVSzLMtKQluk0dnZiZycHHR0dEhdzFqWhe7ubmRkZHBuLwUxFyTCXJAIc0EiTslFNPUadzOQWFpamt1NIAkxFyTCXJAIc0EiquWCxaykDMNAbW0tDMOwuykkEeaCRJgLEmEuSETFXMg/WYKIiIiUYlkWfD6fUgWVU/h8PgDAsWPHbJ8ze8opp0DX9Zgfh8UsERERJU1PTw/27duHrq4uu5uSkizLQnp6Orxer+1zZjVNQ1FREU499dSYHofFLBERESWFaZrYvXs3dF3H6NGjkZaWZntBlWosy0JXVxcyMzNt/d1bloXW1lbs3bsXZ5xxRkwjtNzNQFKWZcEwDOi6zv/QKYi5IBHmgkRkzMWxY8ewe/dujBs3DpmZmXY3JyX1LvvszkV3dzf27NmD8ePHIz09PeQ27magiJ6eHrubQBJiLkiEuSARWXMx2PGklFimadrdBADxK6aZJkkZhoHGxkZOjqcQzAWJMBckwlxQON3d3XY3Ia44Z5aIiIjs5fUCbW3J+3l5eUBxcVweasGCBWhvb8e6devi8niRuuuuu7Bu3Tps3bo1qT9XRixmiYiIyD5eL1BeDiRzd4PMTMDjiUtB++CDDyLFlh9Jh8WsxOKx9xqph7kgEeaCRByRi7Y2fyG7dq2/qE00jweYO9f/c+NQzObk5MShUcll98KveGMxKym3242amhq7m0GSYS5IhLkgEcflorwcqKqyuxVhPf/881i6dCnef/99ZGZmYvLkyfjDH/6A7373uyHTDA4fPoxrr70W69atQ3Z2Nm699Vb84Q9/wKRJk7By5UoAQElJCa655hq8//77+N3vfofTTjsNP/zhD3HNNdcEf95tt92GF198EXv37kVhYSGuuOIKLFmyBKecckpM/dA0DcOHD4/pMWTDBWCSsiwL7e3t/OiCQjAXJMJckAhzET/79u3DN7/5TVx55ZXweDzYsGED5syZI/zdLl68GP/85z/xxz/+Ea+++io2btyI+vr6fve7//77UV1djYaGBlx//fW47rrrsGPHjuDtWVlZWL16Nd599108+OCDeOyxx/DAAw/E3JfA6Wsq5YLFrKQMw0BTUxNXoVII5oJEmAsSYS7iZ9++ffD5fJgzZw5KSkpw7rnn4vrrr+93ctXhw4exZs0a3HfffbjoooswceJEPPHEE8Ln4NJLL8X111+P008/Hbfddhvy8vLw+uuvB2//4Q9/iPPOOw8lJSX48pe/jJtvvhm//e1v49KfY8eOxeVxZMFpBkREREQDqKysxEUXXYRzzz0Xs2bNwsUXX4yvfe1rOO2000Lu19zcjBMnTmDq1KnBazk5OTjrrLP6PWZFRUXw/2uahsLCQhw8eDB47bnnnsNDDz2EXbt24ciRI/D5fFIf9mQnjswSERERDUDXdbz66qv485//jLPPPhsPP/wwzjrrLOzevXvIj9l37qumacHDDN566y1cccUVuPTSS/GnP/0JDQ0N+MEPfiDtIRh2YzErKU3TkJGRodyKQ4oNc0EizAWJMBfxpWkaPv3pT2Pp0qVoaGhAWloaXnzxxZD7TJgwAaeccgq2bNkSvNbR0YGdO3dG9bPefPNNjBs3Dj/4wQ9QXV2NM844A//+97/j0g9AvRPYOM1AUrquo7Ky0u5mkGSYCxIZMBfhNqOP46bxJCe+XsTPpk2bsH79elx88cUoKCjApk2b0NraivLycjQ2Ngbvl5WVhfnz5+OWW25Bbm4uCgoKcOedd8LlckX1puKMM86A1+vFs88+i5qaGrz00kv9Cueh0jQNmZmZcXksWbCYlZRpmmhra0NeXp5y76Bo6JgLEgmbi4E2o4/jpvEkJ8e9Xng80v6c7Oxs/P3vf8fKlSvR2dmJcePG4f7778cXvvAFPPfccyH3XbFiBa699lp86UtfCm7N9cEHHyA9PT3in3fZZZfhe9/7HhYtWoTjx4/ji1/8In70ox/hrrvuirrtfQV2M3C73cqM2muWSnszRKCzsxM5OTno6OiQeiK1z+dDbW0tqqur4XbzPQf5MRckEjYX9fXAlCn9N6MPbBpfVyf1vp4UGxlfL44dO4bdu3dj/PjxJ4s7h58ANpijR49izJgxuP/++3HVVVcl/OcNxrIsHD16FMOHD7e9mBXm4RPR1GtypJuIiCLXd+qAz4e0/fvD31/yzegpxRUX+wtL0XSYREngNJuGhgY0NTVh6tSp6OjowI9//GMAwFe+8pWE/DxiMUtE5CyCUSw3gMr0dFjvvANMmGBf24iGqrhYqSkv9913H3bs2IG0tDRMmTIFGzduRF5ent3NUhaLWUlpmoacnBzbPwIguTAXEorHAqtoHkNwjr35zjvQ582D8dFH0RWzfecOclGYUvh6YY/Jkyejrq7O7mYMSNd1u5sQVyxmJaXrOsp7z3EjAnMhncEWWL3wApCfP/BjtLYCc+ZEv0ir19SBwNKeiP9A5eX5H3vu3MjaLCpyuUuC9Ph6QSKBLdtUwmJWUqZpoqWlBaNHj3bGKlRKCuZCMoJRUgAnC9RLLonscTIzgVdeCS0iA4u02toGLQ5N04QL/hHakFSEW7UtmqM4UJv7FtXcJcER+HpBIpZl4cSJEzjllFOUGbVnMSsp0zSxd+9eFBYW8kWIgpgLSYkWWEWzoCXG0UwzNxdWejr0efP635iZ6X/8vkRzFEVtFhXV4Yr4KApwSjy+XlA4PT09/U4gczIWs0REiZDMBS3Fxdj2zDOoGD26/xZM0RTK0baZuyQQkQRYzBIRyazvVIEwUwd6Cgv9haUk+4kSESULX/Uk5XK5kJ+fz4+GKARzkULCLdIC+k0dYC5IhLmgcGQ5RCNe1OqNQlwuF0pLS+1uBkmGuUghA20k32fqAHNBIk7KRbjNMRJlKNPUL7jgAkyaNAkrV65MSJsWLFiA9vZ2rFu3LiGPH6BpWlRH68Ziz549GD9+PBoaGjBp0qSE/RwWs5IyTTN4xBvfVVMAc5FiIpzDylyQiFNyofhpttKxLAvHjx/HsGHDuJsBJZZpmmhtbcW4ceOkfhGi5GIuSIS5IBGn5CLc5hiJkkqbbvT09CAtLa3fdZ/Ph2HDhtnQosSQN91ERESUMgKbYyT6K5aC2efzYdGiRcjJyUFeXh5+9KMfwbIsAMCTTz6J6upqZGVlobCwEN/61rdw8ODBkO9/55138KUvfQnZ2dnIysrCjBkzsGvXLuHP2rJlC/Lz87F8+fLgtZ/85CcoKChAVlYWvvOd7+D2228P+fh+wYIFmD17Nu655x6MHj0aZ511FgDg7bffxuc+9zlkZGQgLy8PN9xwA44cORL8vgsuuAA33XRTyM+fPXs2FixYEPx3SUkJ/vd//xdXXnklsrKyUFxcjF/+8pch37N582ZMnjwZ6enpqK6uRkNDQ8S/21iwmCUiosF5PEB9vf8r3GEMRIpbs2YN3G43Nm/ejAcffBArVqzAr371KwDAiRMncPfdd2Pbtm1Yt24d9uzZE1IMfvjhh/jsZz+LYcOG4bXXXkNdXR2uvPJK+Hy+fj/ntddew+c//3ncc889uO222wAATz31FO655x4sX74cdXV1KC4uxqOPPtrve9evX48dO3bg1VdfxZ/+9CccPXoUs2bNwmmnnYYtW7bgt7/9LTZs2IAbbrgh6v7ff//9wSL1+uuvx3XXXYcdO3YAAI4cOYIvfelLOPvss1FXV4e77roLN998c9Q/Yyg4zUBSLpcLRUVFUn80RMnHXJBIQnMx0NG3osMYSBp8vYi/sWPH4oEHHoCmaTjrrLPw9ttv44EHHsDVV1+NK6+8Mni/CRMm4KGHHkJNTQ2OHDmCU089FY888ghycnLw7LPPBg8sOPPMM/v9jBdffBHz5s3Dr371K1x++eXB6w8//DCuuuoqLFy4EACwZMkS/PWvfw0ZYQWA4cOH41e/+lVwesFjjz2GY8eO4Te/+Q2GDx+Oc845BytXrsScOXOwfPlyjBo1KuL+X3rppbj++usBALfddhseeOABvP766zjrrLPw9NNPwzRN/PrXv0Z6ejrOOecc7N27F9ddd13Ejz9UTLik+CJEIswFiSQ0F4FdFerqQr9SdfWMg/D1Iv7+4z/+I2TR1PTp0/Hee+/BMAzU1dXhy1/+MoqLi5GVlYXzzz8fAOD1egEAW7duxYwZMwY8eWvTpk34+te/jieffDKkkAWAHTt2YOrUqSHX+v4bAM4999yQebIejweVlZUYPnw4AP9uBhdccAFM0wyOqkaqoqIi+P81TUNhYWFwKoXH40FFRUXITgnTp0+P6vGHigmXlGEY8Hg8MAzD7qaQRJgLEkl4LoqL+088ZCErPb5eJM+xY8cwa9YsZGdn46mnnsKWLVvw4osvAvAvwgKAjIyMQR+ntLQUZWVlePzxx3HixIkhtSVQtIZjWRa6u7tDrrlcruDc3wDRz+9biGuaBtM0h9TOeGIxKynLstDR0dEvXJTamAsSkS4XvefXBr4+GZ2i5JEuFwrYtGlTyL//9a9/4YwzzkBTUxM++ugj3HvvvZgxYwbKysr6Lf6qqKjAxo0bByxS8/Ly8Nprr+H999/Hf/3Xf4Xc96yzzsKWLVtC7t/33yLl5eXYtm0bjh49Grz2z3/+Ey6XK7hALD8/H/v27QvebhgGtm/fPuhj9/05jY2NOHbsWPDav/71r6geY6hYzBIRUXz0nl87ZUroV3k58Je/sMAlR/N6vVi8eDF27NiBZ555Bg8//DBuvPFGFBcXIy0tDQ8//DCam5vxxz/+EXfffXfI9y5atAidnZ34xje+gdraWrz33nt48skn+33UX1BQgNdeew1NTU345je/GVwgdsMNN+DXv/411qxZg/feew8/+clP0NjYOOhesVdccQXS09Mxf/58bN++Ha+//jpuueUWfPvb3w7Ol/3c5z6Hl156CS+99BKamppw3XXXob29Parfzbe+9S1omoarr74a7777Ll5++WXcd999UT3GUHEBGBERxUe4U8taW4E5c4BLLgm9nso711M/ydokI5afM2/ePHR3d2Pq1KnQdR033ngjrrnmGmiahtWrV+P73/8+HnroIVRVVeG+++7DZZddFvzekSNH4rXXXsMtt9yC888/H7quY9KkSfj0pz/d7+cUFhbitddewwUXXIArrrgCTz/9NK644go0Nzfj5ptvxrFjx/Bf//VfWLBgATZv3jxgmzMzM/GXv/wFN954I2pqapCZmYnLLrsMDz30UPA+V155JbZt24Z58+bB7Xbje9/7Hi688MKofjennnoq/t//+3+49tprMXnyZJx99tlYvnw5vvrVr0b1OEOhWSn2+UNnZydycnLQ0dGB7Oxsu5sTlmmaaGtrQ15eHifvUxBzIZn6ev+oY12dfx6pTRyRi77nlQZ2rrf5d6cyGXNx7Nix4KlkgYVCPAFs6D7/+c+jsLAQTz75ZMTfY1kWfD4f3G637SeAifIQEE29xpFZSblcLhQUFNjdDJIMc2EzUUEmAUfkIsKjeSl+HJELhB/QT6S8POfFsaurC6tWrcKsWbOg6zqeeeYZ/O1vf8Orr74a1eNomjbgjgpOxGJWUoHJ1xMnToSu63Y3hyTBXNgo3PCRBPutMhck4qRc8L3O4DRNw8svv4x77rkHx44dw1lnnYXf//73mDlzZlSPE9jNICMjw/aR2XhhMSupQNhSbBYIDYK5SJK+I7CAf+hIdIC8BEM8zAWJMBdqycjIwN/+9re4PJYM22nFE4tZIqLeBprAl5kJzJhhe/FKREQnsZglIuqtrU08AgtIMQpLREShpFje+Mgjj6CkpATp6emYNm3aoNtMBDz77LPQNA2zZ89ObANtoOs6ysrKpJ/nRMnFXCRRebljTr1iLkhE5lxw6oO9+u4cYJd45cD2Yva5557D4sWLceedd6K+vh6VlZWYNWtWv5Mz+tqzZw9uvvlmzJgxI0ktTS5N0zBixAhlJmdTfDAXJMJckIiMuQisou9K5j5cFELTNCm25QJOHvUb6xsu26cZrFixAldffTUWLlwIAFi1ahVeeuklPP7447j99tuF32MYBq644gosXboUGzdujPqUCifw+XxoaGjA5MmT4Xbb/jSRJJgLEnF0LqLZ3ozTPKIiYy50XceIESOCA1aZmZlSFFWpRJbdDEzTRGtrKzIzM2POp63p7unpQV1dHe64447gNZfLhZkzZ+Ktt94K+30//vGPUVBQgKuuugobN24c8GccP34cx48fD/67s7MTgP8/8sARcS6XCy6XC6ZphqzwC1w3DCNkKDzcdV3XoWla8HF7Xwf8RXgk191ud3BT48BtmqZB1/V+bQx3XdY+9b7OPkXfJ9M0YRiGsI1O7ZN0z9Mn/9/n8wGSv0YErhuGEXxMxzxPI0ZAz8yENncuImVlZsJ4+21o48bJ2SfJ/nsK5MIwDKn6FNj79sCBA+hL0zThR8+i64FCLJHXI21LvK4nq089PT1IS0uzvU8ulwtjxowJyVMge32zOhBbi9m2tjYYhhE8Gzhg1KhRaGpqEn7PP/7xD/z617/G1q1bI/oZy5Ytw9KlS/tdb2howPDhwwEA+fn5KC0txe7du9Ha2hq8T1FREYqKirBz5050dHQEr0+YMAEFBQXYvn07uru7g9fLysowYsQINDQ0hDwxFRUVSEtLQ21tbUgbqqur0dPTg8bGxuA1XddRU1ODzs5OtLe3o76+HpqmISMjA5WVlWhra0Nzc3Pw/jk5OSgvL0dLSwv27t0bvC5jnzo6OkKeV/Yp+j6NGzcOAPDuu++GvElzcp+ke54++f/vejzo+uQPsOx9siwLhw8fBgDnPE8HDyLtqafg7uiA7nLhnHPOweHDh7F7z57gfdOHDcOZZ56JQ4cO4aN//hNn3HUX3t24EaccPy5nnyT776mrqyv4d6S8vFy6Ph09ehRtvbbAKywsRGFhIXbt2hXMMwCMHTsWI0eORFNTE44dOxbS1+zsbLz99tshfTrrrLNwyimnYPv27SF9mjhxIk6cOIEdO3aE9Oncc89FZ2dnSNvT09NRVlaGjz76CB988EHwelZWFiZMmID9+/dj//79weu5ubkoLi6G1+vFoUOHpO7TwYMH0dTUhKysLGiaZlufysrKkJmZifr6+pA+BbLX0NCASNl6nG1LSwvGjBmDN998E9OnTw9ev/XWW/HGG29g06ZNIfc/fPgwKioq8Itf/AJf+MIXAAALFixAe3s71q1bJ/wZopHZsWPH4qOPPgoejybju/kTJ06gtrYWVVVVwcdNqdEx9insyGx9fT0mT54cMsfIyX2y/Xnasye4p6ymadB37gTmzoVv06bgMauy98kwDNTX16OmpibYTmFfnfw81dbCPW0afJs2QZsyRY0+Jfh58vl8qK+vR1VVFdLS0pTok4rPU7L7FPhUfLD6wu4+ffzxxxg5cmREx9naWsz29PQgMzMTzz//fMiOBPPnz0d7ezv+8Ic/hNx/69at/f6IB37BLpcLO3bsQGlp6YA/M5qzfu0ky5wWkgtzEWcDnerloIPbUyIX9fXAlClAXV3wTQYNLCVyQVFzSi6iqddsnWaQlpaGKVOmYP369cFi1jRNrF+/HosWLep3/7KyMrz99tsh1374wx/i8OHDePDBBzF27NhkNDtpes9nIQpgLuIo3J6yDlxoxFyQCHNBIqrlwvatuRYvXozHHnsMa9asgcfjwXXXXYejR48GdzeYN29ecIFYeno6Jk6cGPI1YsQIZGVlYeLEiUo9OYZhoLa2tt/wO6U25iJB+u4p67BClrkgEeaCRFTMhe17dVx++eVobW3FkiVLsH//fkyaNAmvvPJKcFGY1+uFy2V7zU1EREREErK9mAWARYsWCacVAMCGDRsG/N7Vq1fHv0FERERE5Agc8iQiIiIix5JiZJb603Ud1dXVUp6pTfZhLmLg9Qa34AqK5vQpiTEXJMJckIiKuWAxK7Genh5kZGTY3QySDHMxBOG24AL823Dl5SW/TXHGXJAIc0EiquWC0wwkZRgGGhsblVptSLFjLoao9xZcdXWhXw7aTzYc5oJEmAsSUTEXHJklotQR2IKLiIiUwWKWiJxLNA/WgQceEBHR0LGYlZhKk7MpfpiLTyhyFG28MBckwlyQiGq5YDErKbfbjZqaGrubQZJhLnoRHUXr8QBz5/pvS6FilrkgEeaCRFTMBReAScqyLLS3t8OyLLubQhJhLgR6H0UbKGpTDHNBIswFiaiYCxazkjIMA01NTUqtNqTYMRckklK58HiA+vqTX16v3S2SVkrlgiKmYi44zYCIiOSXl+efDz13buj1FJ0jTUQnsZglIiL5FRf7i9beu1cE5khv3Nh/igl3tSBKGSxmJaVpGjIyMqBpmt1NIYkwFySSMrkoLg4tUMON1gIcsUUK5YKiomIuWMxKStd1VFZW2t0MkgxzESGPZ+B/KyZlcyEarQVSdleLvlI2FzQgFXPBYlZSpmmira0NeXl5cLm4To/8mItBDDZSl5eX/DYlQUrnou9obW+iNzEpNP0gpXNBYamYCxazkjJNE83NzcjNzVUmbBQ75mIQ4UbqAKWLGOaiD04/AMBckJiKuWAxS0RqGWikjlIDpx8QpRQWs0REpB6+qSFKGSxmJaVpGnJycpRabUixUz4XXm/KTRGIB+VzQUPCXJCIirlgMSspXddRnqJHc1J4SufC6/XvFdrV1f+2FJrnOBRK54KGjLkgERVzocbMXwWZpom9e/fCNE27m0ISUToXbW3+QnbtWqCu7uTX2rX+66IRWwKgeC5oyJgLElExFyxmJaVi2Ch2KZGL8nKgqurkl2IjCImQErmgqDEXJKJiLljMEhEREZFjcc4sETlDip3qRUREkWExKymXy4X8/HxlNjSm+EjJXKToqV7RSMlc0KCYCxJRMRcsZiXlcrlQWlpqdzNIMimZixQ91SsaKZkLGhRzQSIq5oLFrKRM08Tu3bsxfvx4pd49UWxSNhfcAH9AKZsLGhBzQSIq5kKNXijINE20trYqtdqQYsdckAhzQSLMBYmomAsWs0RERETkWCxmiYiIiMixWMxKyuVyoaioSJn5LBQfzAWJMBckwlyQiIq54AIwSQXCRtQbc0EizAWJMBckomIu1CnLFWMYBjweDwzDsLspJBHmgkSYCxJhLkhExVywmJWUZVno6OiAZVl2N4UkwlyQCHNBIswFiaiYC04zICKi1NL3KGQevkHkaCxmiYgoNYQ7Gjkz01/gsqAlciQWs5JyuVyYMGGCUqsNKXbMBYkwFxESHY3s8fiL240bgfLy0Ps7fMSWuSARFXPBYlZSLpcLBQUFdjeDJMNckAhzEYW+RyOHG60FHD9iy1yQiIq5UKcsV4xhGNi2bZtSqw0pdswFiTAXMQiM1tbVhX6tXQt0dYWO4joMc0EiKuaCI7OSsiwL3d3dSq02pNgxFyTCXMSo72itIpgLElExFxyZJSIiIiLHYjFLRERERI7FYlZSuq6jrKwMuq7b3RSSCHNBIswFiTAXJKJiLjhnVlKapmHEiBF2N4Mk48hceL3iRTQO3/ZIJo7MBSUcc0EiKuaCI7OS8vl82LJlC3w+n91NIYk4Lhder3/vzilT+n+Vl/tvp5g5LheUFMwFiaiYC47MSkylbTMofhyVi7Y2//ZGa9eGbkgf2Ki+rY2js3HiqFw4icOPvmUuSES1XLCYJaLEKy8HqqrsbgVR5Hj0LZFjsJglIiLqa6Cjb/mJApFUWMxKStd1VFRUKLXakGLHXJAIc5EgDj9MgbkgERVzwQVgEktLS7O7CSQh5oJEmAsSYS5IRLVcsJiVlGEYqK2tVW6SNsWGuSAR5oJEmAsSUTEXLGaJiIiIyLFYzBIRERGRY7GYJSIiIiLHYjErKV3XUV1drdRqQ4odc0EizAWJMBckomIuWMxKrKenx+4mkISYCxJhLkiEuSAR1XLBYlZShmGgsbFRqdWGFDvlcuHxAPX1/q++x4ZSxJTLBcUFc0EiKuaChyYQUfINdFRoXp49bSIiIkdiMUtEySc6KhTwF7IOPnGJiIiSj8WsxFSanE3xo0wuHH5UqGyUyQXFFXNBIqrlgsWspNxuN2pqauxuBkmGuSAR5oJEmAsSUTEXXAAmKcuy0N7eDsuy7G4KSYS5IBHmgkSYCxJRMRcsZiVlGAaampqUWm1IsWMuSIS5IBHmgkRUzAWLWSIiIiJyLBazRERERORYXAAmKU3TkJGRAU3T7G4KSUTqXHi9/bfa4kEISSF1Lsg2zAWJqJgLFrOS0nUdlZWVdjeDJCNtLrxeoLwc6OrqfxsPQkg4aXNBtmIuSETFXLCYlZRpmmhra0NeXh5cLs4GIT9pctF3FNbj8Reya9f6i9reeBBCwkmTC5IKc0EiKuaCxaykTNNEc3MzcnNzlQkbxU6KXIQbhc3MBGbMYOFqAylyQdJhLkhExVywmCWi6LS1iUdhOQJLqUI0F5z5J7INi1kiGprycqCqyu5WECVPXp7/E4i5c/vflpnpL3JZ0BIlHYtZSWmahpycHKVWG1LsmAsSYS6SpLjYX7CKdu2YO9d/XaJilrkgERVzwWJWUrquo7zvQhpKecwFiTAXSVRcLFXBOhDmgkRUzIUaM38VZJom9u7dC9M07W4KSYS5IBHmgkSYCxJRMRcsZiWlYtgodswFiTAXJMJckIiKuWAxS0RERESOxTmzRERE8cAtu4hswWJWUi6XC/n5+cpsaEzxwVyQCHNhM0m37GIuSETFXLCYlZTL5UJpaandzSDJMBckwlzYbLAtuzZutOWAEeaCRFTMBYtZSZmmid27d2P8+PFKvXui2DAXJMJcSEC0ZVe4EdskjdYyFySiYi7U6IWCTNNEa2urUqsNKXbMBYkwF5IKjNjW1Z38WrvWfxx031HcBGAuSETFXHBkloiIKFEcdMgCkVOxmCWi8Lxe8TxAIiIiSbCYlZTL5UJRUZEy81koPpKaC6/Xv2ilq6v/bZmZ/vmAJAW+XpAIc0EiKuaCxaykAmEj6i2puWhr8xeya9eGrsQGuHemZPh6QSLMBYmomAt1ynLFGIYBj8cDwzDsbgpJxJZclJcDVVWhXyxkpcLXCxJhLkhExVywmJWUZVno6OiAZVl2N4UkwlyQCHNBIswFiaiYCxazRERERORYLGaJiIiIyLFYzErK5XJhwoQJSq02pNgxFyTCXJAIc0EiKuaCuxlIyuVyoaCgwO5mkGSYCxJhLkiEuSARFXMhRVn+yCOPoKSkBOnp6Zg2bRo2b94c9r4vvPACqqurMWLECAwfPhyTJk3Ck08+mcTWJodhGNi2bZtSqw0pdswFiTAXJMJckIiKubB9ZPa5557D4sWLsWrVKkybNg0rV67ErFmzsGPHDuE7h9zcXPzgBz9AWVkZ0tLS8Kc//QkLFy5EQUEBZs2aZUMPEsOyLHR3dyu12pBix1yQCHPhQKKT9OK8fzNzQSIq5sL2YnbFihW4+uqrsXDhQgDAqlWr8NJLL+Hxxx/H7bff3u/+F1xwQci/b7zxRqxZswb/+Mc/lCpmiYhIQXl5/hP05s7tf1tmpr/I5T7ORFGxtZjt6elBXV0d7rjjjuA1l8uFmTNn4q233hr0+y3LwmuvvYYdO3Zg+fLlwvscP34cx48fD/67s7MTAODz+eDz+YI/0+VywTRNmKYZ0haXywXDMELewYS7rus6NE0LPm7v6wD6DemHu+52u2FZFizLCt6maRp0Xe/XxnDXZe1T7+vsU/R9CtxH1Ma498nng/uT++if/Gw+T3L2qffPUaVPA7Xd0X0aMwa6xwPz4MHQNu7YAde8ef7ro0fHpU+B2wzD4PPEPoW0PZL6wu4+9b3/QGwtZtva2mAYBkaNGhVyfdSoUWhqagr7fR0dHRgzZgyOHz8OXdfxi1/8Ap///OeF9122bBmWLl3a73pDQwOGDx8OAMjPz0dpaSl2796N1tbW4H2KiopQVFSEnTt3oqOjI3h9woQJKCgowPbt29Hd3R28XlZWhhEjRqChoSHkyamoqEBaWhpqa2tD2lBdXY2enh40NjYGr+m6jpqaGhw5cgQ+nw8NDQ0AgIyMDFRWVqKtrQ3Nzc3B++fk5KC8vBwtLS3Yu3dv8LqMfero6Ah5Xtmn6Ps0fvx4lJWVoampKeF9ytyxAxUAdu3ahTNravg8Sd4nt9sNXdfR2tqqTJ9UfJ6CfUpPD+nTqMxMjAfQ2tqK3b0eP9Y+Bf6O8HlinwJ9+vjjj0PqC1n7FGhfJDTLxkkTLS0tGDNmDN58801Mnz49eP3WW2/FG2+8gU2bNgm/zzRNNDc348iRI1i/fj3uvvturFu3rt8UBEA8Mjt27Fh89NFHyM7OBmD/uw++S2SfpOxTfT3c06bB2LwZek2NGn0apO3sE/tkW58aGqBPnQpzyxaYkyap0ScVnyf2KWl9+vjjjzFy5Eh0dHQE67VwbB2ZzcvLg67rOHDgQMj1AwcOoLCwMOz3uVwunH766QCASZMmwePxYNmyZcJidtiwYRg2bFi/6263G253aPcDT0RfgV9spNf7Pu5QrhuGgYaGBkyePDnk9nBtjPa6HX3SNE14nX2KvE8+nw91dXX9cjGUtoe7HuzTJ7cF2sDnSd4++Xw+1NfXB3OhQp8iaaNSfer931kUv4OB+hQYfZs8eTI0TYu67eGup/TzNMTrMvXJNM241Bd29CkcW7fmSktLw5QpU7B+/frgNdM0sX79+pCR2sGYphky+qqKvu9WiIAE5sLrBerrT36JVluTtPh6QSLMBYmolgvbdzNYvHgx5s+fj+rqakydOhUrV67E0aNHg7sbzJs3D2PGjMGyZcsA+OfAVldXo7S0FMePH8fLL7+MJ598Eo8++qid3SByNq8XKC8HurpCr2dm+ldfExERScr2Yvbyyy9Ha2srlixZgv3792PSpEl45ZVXgovCvF5vyPD20aNHcf3112Pv3r3IyMhAWVkZ1q5di8svv9yuLhA5X1ubv5Bdu9Zf1AbEed9LIiKieLN1AZgdOjs7kZOTE9GEYjsFNjXOyMgIznUiSlgu6uuBKVOAujqgqip+j6sYr9df9/cmQ73P1wsFBP4b7PuGEhhyyJgLEnFKLqKp12wfmaXw0tLS7G4CSSjmXIgqMs6PHdRAMzFk2OeerxcOl6DDFJgLElEtFyxmJWUYBmpra1FdXR3Vij5SW8y5CFeRAZwfOwjRTAyPx197tLXZW8zy9UIBxcX+QIneaA4xZMwFiaiYCzV6QUSRCTc3FpDj83IHKC/nTAxAPMAPMEYxKS7mL49oCFjMEqUiVmRBss6DldlgA/wyTLsgotTBYpaIUla85sGKphyrXBCHG+CP9hNxju4SUTywmJWUruuorq4Oe5IGpSbmIr5inQeboDU7g+pbBFqWjoKC6HIRjxHpWAb4ObqbeHy9IBEVc8FiVmI9PT3IyMiwuxkkGeYi/oZalCVgzc6gxEWghsxMHe++C4wb1//+fdvX2grMmWPvzgzxGt2lgfH1gkRUywWLWUkZhoHGxkalVhtS7JgL+SR7zY6oCNy+3cD8+ToOHPBh3LiTuRhs9POVV4D8fP+/Byoi+xbE8dzJjdO3E4evFySiYi7U6AUR9cf9ZGPS91c1lF9dpHNphzJ3tHcR6POJz76Jx+YVPOmYiGTHYpZIRSmyn2widiIYbB5sJL+6aObSJmPuaCyjnzzpmIhkx2JWYipNzqb4iSgXKbCfbKJO5Ao3DxaI/Fc32FzajRtDF5zFa+5oU5OG3p8axmM0OfDvaAriVNvdQWb8O0IiquWCxayk3G43ampq7G4GSSbqXCg8ITHanQiimXURj3mwoscIN2KbmQnMmCH+mZFMdygsdCMzE5g/v/8fqHiNJsd7RJoSj39HSETFXLCYlZRlWejo6EBOTg40TbO7OSQJ5qK/SOp1WWZdhBuxFY1aRlNcjh1r4V//6kRPT3a/XMQ6mhyPxwg3Ik1R6PsLi+BJ4esFiaiYCxazkjIMA01NTUqtNqTYqZ6LRG2iL9Osi0hHfaMpLg3DwNGjnphzYceIdKxvJJQ/eGGgX94gQ92qv17Q0KiYCzV6QUSOJ/tCKDske9uvRIlmRDoag2XmhRdObj02EKkLX9Evj5vxEoVgMUtEUuAm+mqLR2Eu2u9WlJnAoRCXXBLZ40o/n1eVdzVECcJiVlKapiEjI0OZ+SwUH6mQC6eNnsogFXIx0O4VosVz4aZo9CWazxsg9YhtBFIhFxQ9FXPBYlZSuq6jsrLS7maQZJyYC+XnNErAibmIVrT73UY6mKnyDgypkAuKnoq5YDErKdM00dbWhry8PLhcLrubQ5JwWi6SMQ+WnJeLWMR75H6wHRiknt4yyIa+qZQLipyKuWAxKynTNNHc3Izc3Fxlwkaxc1ouOA82OZyWC9kkYj5vQEI+gYhwOJm5IBEVc8FilogSLtnzYCM5aIDkJstzGMn2rkn/BMLRw8lE8cdilohs07tQiEexEo9TrMhesjyH0WzvassnENzhgCiIxaykNE1T6nQOio9E5kL0MWmiFmklahP9eJxi5UQqvV7I8hwOZXvXSD+BSNaUBJVyQfGjYi5YzEpK13WU990nhlJeonIx0LZHiViklahN9AOPrWrRGo5qrxeyPIeJaEcypySolguKDxVzwWJWUqZpoqWlBaNHj1ZmgjbFLlG5EH1Mmujpd7IULCrg64VzJHNKAnNBIirmgsWspEzTxN69e1FYWKhM2Ch2ic6F6GPSWOayxnPRjiwLgmTE14vkikcWk7EokrkgERVzwWKWSAWicz5jNNBCnGjEOg9WlgVBREPNYrwXOhJRKBazRE430ITXGCq9gRbiRCPWebCyLAgiijaLiVroSEShWMxKyuVyIT8/X5mPACg+hLmI9pzPKMgyr1WWdsiKrxfJE00WE7nQMRLMBYmomAsWs5JyuVwoLS21uxkkmQFzkeyTCUgafL2QV7RvxCKdhhBJQcxckIiKuWAxKynTNLF7926MHz9eqXdPFBvmgkSYC+eLdo56JNt4MRckomIuWMxKyjRNtLa2Yty4ccqEjWIXTS6SelY82YqvF84XzRz1SLfxYi5IRMVcsJglUlDSz4onophxbjjR0LCYJVKQLWfFExER2YDFrKRcLheKioqU+QiA4iPaXIRbE8YDCNTC1wsSYS5IRMVcsJiVVCBsRL2Fy4UXY9HmyQj+O1xxygMI1MTXCxJhLkhExVywmJWUYRjYuXMnzjzzTOi6bndzKIFEC7XCLdLa/Q8vtr/1HgoLC6F/8q66tbEdc+BB19zhIfcVFac8gEBNfL1ITf3etHoykIexCPxnzFyQiIq5YDErKcuy0NHRAcuy7G4KJdBAh3f1XaTlfetDTJwxEl3oW3GWIxNH8cpvDiL/nILg1XDFKReZqIevF6kl/Ccs5ciEB559zSgGc0FiKuaCxSyRjUQLtcIt0mpr7kQXxuC+OS9gxlfOhLvXO+q8Cdkonj4mya0nIjuE+4TF8/JuzP3ReLS1u/u95SVSGYtZIglEc3hXUcUpqPpWGdxu/udLlKqEn7B4jtnSFiK7qbOUTTEulwsTJkxQarUhxUdBQQFzQSH4ekEizAWJqJgLDu1IyuVyoaCgYPA7UsrJyc5W6kWIYsfXCxLpnYtoFpqS2lR8vWAxKynDMLB9+3ZMnDhRmdWGFJ1+e8HuTgcA/NvrRaWhzipUih1fL0gkkIvs7ImYOFEXLjR94QUgPz/0Ootctan4esFiVlKWZaG7u1up1YYUmfArlccjE0eROewwc0Eh+HpBvXl2pwP1gM9nwePRoGn9F5q2tgJz5gCXXNL/+3nktdpUfL0YUjHb3NyMCRMmxLstRIQB9oL1eJA3dxbaR95tS7uISG55I3zIxFHM/dF44EeA/098BQB/gTpjRmiBKtwRgUdekwMNqZg9/fTTcf755+Oqq67C1772NaSnp8e7XUQpTbwXbDeAD9Ce/OYQkQMUf+oEPChH29q/AOXl8Pl88HjeRXn52SgsdPd7TeGe06SKIa0iqa+vR0VFBRYvXozCwkL893//NzZv3hzvtqU0XddRVlamzHwWip/xJSXMBYXg6wUFFOMDVJV3o6oKqKnR8ZWvFKOmRo+6aPV4gPr6k19eb2LaS8mn4uvFkIrZSZMm4cEHH0RLSwsef/xx7Nu3D5/5zGcwceJErFixAq2trfFuZ8rRNA0jRoyApml2N4Ukk5WVxVxQCL5eUIhPKlGtoQEjmpuhNTREXI32nrM/ZcrJr/JyFrSqUPH1Iqb9fdxuN+bMmYPf/e53WL58Od5//33cfPPNGDt2LObNm4d9+/bFq50px+fzYcuWLfD5fHY3hSTzzjvvMBcUgq8XBCB8JRpFNRqYs19Xd/Jr7Vr/ArJ+8/jJkVR8vYhpN4Pa2lo8/vjjePbZZzF8+HDcfPPNuOqqq7B3714sXboUX/nKVzj9IAaGYdjdBLKLaFPIT/bqMkzThgaR7Ph6QX1Xj/p8Przr8eAcTYM+f37Eq7rCzaXtu10gwG28nEq114shFbMrVqzAE088gR07duDSSy/Fb37zG1x66aXBjdzHjx+P1atXo6SkJJ5tJXK0AerT/ncsL0e/TSEBWJmZ8OXkJKaBROR8vStRnw9dpgkrxkNWwm8XGH4bLx7SQMk0pGL20UcfxZVXXokFCxbgU5/6lPA+BQUF+PWvfx1T44hUMUB9isxM/4t8UFtb/00hP2GMGIGegwcT21giol7CbRcYbhuvcK933L+WEmVIxeyrr76K4uLifkdqWpaFDz74AMXFxUhLS8P8+fPj0shUpOs6KioqlFptqKJIRx8GqE/Dj1aUlwNVVSGXdMtCRWEhc0Eh+HpBIsFcCD8Cik4023iJXu+4f608VHy9GFIxW1pain379vU72/fQoUMYP368cnMx7JKWlmZ3E2gAQxl9ENSnUWMuSIS5IBE7cxGP1ztKDNVeL4ZUzIY7Au3IkSM8QCFODMNAbW0tqqur4Xbz1OFkimW0NTD6sHFj6AhsHAZGADAXJMZckEgwFy6X/4993xciTmJNSSq+XkTVi8WLFwPw71G2ZMkSZGZmBm8zDAObNm3CpEmT4tpAomSKdbR1sIUSIXNjiYiSIdwLEyexkiKiKmYbGhoA+Edm33777ZBh6rS0NFRWVuLmm2+ObwuJkijWuV7hFkoAHAQhIpuIXpjiOIm174BvvD6JIopUVMXs66+/DgBYuHAhHnzwQWRnZyekUUR2i2WuF887JyLpJOCFiZ9EkSyGNFniiSeeiHc7qA9d11FdXa3UakOKHXNBIswFiSQ6F/wkyplUfL2IuJidM2cOVq9ejezsbMyZM2fA+77wwgsxN4yAnp4eZGRk2N0M+oQsH6UxFyTCXJBIonMR7YAvTxGTg2qvFxEXszk5OdA0Lfj/KbEMw0BjY6NSqw2dSqaP0pgLEmEuSESmXAzlFDFKDJlyES8R96L31AJOMyAVRHq8bMI/SuvbEK6eICLFRHuKGFE0hlSSd3d3w7Ks4NZc//73v/Hiiy/i7LPPxsUXXxzXBhIlQlTHyyKBi7oG2guMqyeISCFcHEuJMqRi9itf+QrmzJmDa6+9Fu3t7Zg6dSrS0tLQ1taGFStW4Lrrrot3O1OSSpOzZTOk42WT2ZABGsFckAhzQSJOzkWkB9hQ9JycC5EhFbP19fV44IEHAADPP/88CgsL0dDQgN///vdYsmQJi9k4cLvdqKmpsbsZypPmuMUIG8JckAhzQSJOykXf2VWtrcCcOdEdYEORcVIuIjWkYrarqwtZWVkAgL/+9a+YM2cOXC4X/uM//gP//ve/49rAVGVZFjo6OkIW3hExFyTCXJCIE3Ix2MKwV14B8vP9/w53XHjgcVjgRsYJuYjWkIrZ008/HevWrcN//ud/4i9/+Qu+973vAQAOHjzIgxTixDAMNDU1KbXa0C6RLvRyAuaCRJgLEnFCLqJZYMsdEeLDCbmI1pB6sWTJEnzrW9/C9773PVx00UWYPn06AP8o7eTJk+PaQKJYRLvQK+GNUaWqJiKKk0gXhnFHBApnSMXs1772NXzmM5/Bvn37UFlZGbx+0UUX4T//8z/j1jiiWEmz0EuqqpqIyJm4IwKJDHl8ubCwEIWFhSHXpk6dGnODyE/TNGRkZCgznyVZwm3ZavtCrzhV1cwFiTAXJBJRLngkV8pR8fViSMXs0aNHce+992L9+vU4ePAgTNMMub25uTkujUtluq6HjHrT4ByxZWuMVTVzQSLMBYkMmAsFJ6CyLo+Miq8XQypmv/Od7+CNN97At7/9bXzqU59SqrqXhWmaaGtrQ15eHlwul93NkU646adRbtnqOMwFiTAXJDJgLgabgNp3ywCJX0gVrMsTSsXXiyEVs3/+85/x0ksv4dOf/nS820OfME0Tzc3NyM3NVSZsg4l0g+zBpp/OmKHuC1cq5oIGx1yQyKC5EE1ADVcZSlwVcmFYdFR8vRhSMXvaaachNzc33m2hFDbQFIG+r5/SLOoiIlKNqDJ0QFXIhWGpbUjF7N13340lS5ZgzZo1yMzMjHebKAWJCtTBXj9tX9RFRKQiVobkMEMqZu+//37s2rULo0aNQklJCU455ZSQ2+vr6+PSuFSmaZpSp3NEigXqwFI1FzQw5oJEmAsSUTEXQypmZ8+eHedmUF+6rqO872folPKYCxJhLkiEuRATrc8AUmeamoq5GFIxe+edd8a7HdSHaZpoaWnB6NGjlZmgPVR9t1tJ5UOzmAsSYS5IhLnob7AFxJKucYsrFXMx5EMT2tvb8fzzz2PXrl245ZZbkJubi/r6eowaNQpjxoyJZxtTkmma2Lt3LwoLC5UJW7QG225Fmr1jk4i5IBHmgkSYi/7CLSB2wBq3uFExF0MqZhsbGzFz5kzk5ORgz549uPrqq5Gbm4sXXngBXq8Xv/nNb+LdTkpB4bZbAVLn4yAiIoo/rs9Qy5CK2cWLF2PBggX46U9/iqysrOD1Sy+9FN/61rfi1jgiLqolIqJY9J6aNtg0tb63c+DEGYZUzG7ZsgX/93//1+/6mDFjsH///pgbRYDL5UJ+fr4yHwFQfDAXJMJckEiq52Kg8x/6TlNz4FkRQ6ZiLoZUzA4bNgydnZ39ru/cuRP5+fkxN4r8YSstLbW7GSQZ5oJEmAsSSfVchJuqJhptdehZEUOiYi6GVMxedtll+PGPf4zf/va3APx7lnm9Xtx222346le/GtcGpirTNLF7926MHz9eqXdPFBvmgkSYCxJhLqKbqpYq09pUzMWQenH//ffjyJEjyM/PR3d3N84//3ycfvrpyMrKwj333BPvNqYk0zTR2toK0zTtbgpFy+sF6utDv+K0nxhzQSLMBYkwFySiYi6GNDKbk5ODV199Ff/85z+xbds2HDlyBFVVVZg5c2a820fkLINtYpiK+4kRkRpEb8q5QookEHUxa5omVq9ejRdeeAF79uyBpmkYP348CgsLYVmWUsejUWxS8pSVcJsYAop3nIiUNdim36qtkCLHiaqYtSwLl112GV5++WVUVlbi3HPPhWVZ8Hg8WLBgAV544QWsW7cuQU1NLS6XC0VFRY6dzzLYAOULLwC91woqd6pXgjYxdHouKDGYCxKJWy7CraRSdYVUH6oNSKv4ehFVMbt69Wr8/e9/x/r163HhhReG3Pbaa69h9uzZ+M1vfoN58+bFtZGpKBA2pwo3QNnaCsyZA1xySf/v4afwg3N6LigxmAsSiWsuUmV1VC+qDkir+HoRVTH7zDPP4Pvf/36/QhYAPve5z+H222/HU089xWI2DgzDwM6dO3HmmWdC13W7mzNkogFKnuo1dKrkguKLuSAR5iI2qg5Iq5iLqIrZxsZG/PSnPw17+xe+8AU89NBDMTeK/FM6Ojo6YFmW3U2JuxR8gx83KueCho65IBHmInYq/r1SMRdRTZg4dOgQRo0aFfb2UaNG4eOPP466EY888ghKSkqQnp6OadOmYfPmzWHv+9hjj2HGjBk47bTTcNppp2HmzJkD3p+IiIgo3jye/rswer12tyo1RTUyaxgG3O7w36LrOnw+X1QNeO6557B48WKsWrUK06ZNw8qVKzFr1izs2LEDBQUF/e6/YcMGfPOb38R5552H9PR0LF++HBdffDHeeecdjBkzJqqfTfHTd+cC5RZ0ERERQd25tE4W9W4GCxYswLBhw4S3Hz9+POoGrFixAldffTUWLlwIAFi1ahVeeuklPP7447j99tv73f+pp54K+fevfvUr/P73v8f69euVmqvrcrkwYcIER6w2DLdzARd0xZ+TckHJw1yQCHORGE6fS6tiLqIqZufPnz/ofaIpKHt6elBXV4c77rgjeM3lcmHmzJl46623InqMrq4unDhxArm5ucLbjx8/HlJkd3Z2AgB8Pl9wFNnlcsHlcsE0zZATMQLXDcMImVsS7rqu69A0rd/odGCCtWEYEV13u93QNA25ubnBNmmaBl3X+7Ux3PVk9mn/fqCry401awycc44reD0vDxg9GvD5/H2yLCukrzL3KZLrwj4ZBnT492M2ez1+PPtUUFAAwzBC2p/QPqn4PCnYp5EjRwrb6OQ+qfg8JbtPgb8jmqbFv08+H9yf3Cf4upciz1NRkYnRo0PbHpi56a8v5O0TgIjqC7ufp2g+6Y+qmH3iiSeiufug2traYBhGv3m4o0aNQlNTU0SPcdttt2H06NFhTx9btmwZli5d2u96Q0MDhg8fDgDIz89HaWkpdu/ejdbW1uB9ioqKUFRUhJ07d6KjoyN4fcKECSgoKMD27dvR3d0dvF5WVoYRI0agoaEh5MmpqKhAWloaamtrQ9pQXV2Nnp4eNDY2Bq/puo6amhp8/PHHqK2tRVZWFjRNQ0ZGBiorK9HW1obm5ubg/XNyclBeXo6Wlhbs3bs3eD2ZfdqxIxNABSzrHVRWnh3s08GDwMGDJ/vU0dER8rzK3KdIn6fO7dvx7/r6k23/8EOMA9De3o6dvR4nXn0qKSnBgQMHYFkWjh07lpA+qfg8qd4ny7KCR4t/9NFHSvQJUO95Snafurq6cPjwYWRlZaG8vDzufcrcsQMVAHbt2oUza2pS/nkCSgEAHs+7MM0uaft08OBBNDY2BusLWZ+nhoYGREqzbFzO1tLSgjFjxuDNN9/E9OnTg9dvvfVWvPHGG9i0adOA33/vvffipz/9KTZs2ICKigrhfUQjs2PHjsVHH32E7OxsAPa/+xC9ozpx4gRqa2tRVVUVfFxZRyjq64Fp09zYtMmHmhp5Ryii6VMk190tLbDKy6EJ5leY77wDs9c+fvHqk2maqK+vx+TJk0O2VFFpJIl9ir5P/v8O61FTUxNsp9P7NFDb2afI+uTz+VBfX4+qqiqkpaXFv0/19XBPmwZj82boNTUp/zxt3erClCnApk2+4JaUMvYp8Kn4YPWF3c/Txx9/jJEjR6KjoyNYr4UT9XG28ZSXlwdd13HgwIGQ6wcOHEBhYeGA33vffffh3nvvxd/+9rewhSwADBs2TDjH1+1291vMFngi+gq3D1u46+EWyUVzPfCRkK7rIbeHa2O01+PZp8BN/ukR4e+vaZrwuox9iuh6W5u/kO17MkReHlzFxcKtQmLtU+DFoG8uom77ANeVe56QGn0KHCWuUp8GayP7NHCfAkfMB4qJaNse7nqwT5/cFmgDn6eTj9/3oWTrUzzqCzv6FI6txWxaWhqmTJmC9evXY/bs2QD8I0/r16/HokWLwn7fT3/6U9xzzz34y1/+gurq6iS1liiMBB1dS0REztN3Nx8eCJR4thazALB48WLMnz8f1dXVmDp1KlauXImjR48GdzeYN28exowZg2XLlgEAli9fjiVLluDpp59GSUkJ9u/fDwA49dRTceqpp9rWj3jTdR1lZWVh3+FQamIuSIS5IBHmIrnCbdkl23ZdKubC9mL28ssvR2trK5YsWYL9+/dj0qRJeOWVV4KLwrxeb8jw9qOPPoqenh587WtfC3mcO++8E3fddVcym55QmqZhxIgRdjeDJMNckAhzQSLMRXKJtuyScbsuFXNhezELAIsWLQo7rWDDhg0h/96zZ0/iGyQBn8+HhoYGTJ48Oap5I6Q25oJEmAsSYS6SzwnH36qYCzV6oai+K/xk0PekL4CnfSWbjLkg+zEXJMJckIhquWAxSxELd9IXwNO+iIhSlmhEg6ueKIlYzFJYfUdhPR5/Idt3JyqAr1tERCkn3IonQL5VT6Q0FrOS0nUdFRUVtq02DDcKm5kJzJjB1ye72J0LkhNzQSIJz4VoxRMg56onClLx9YLFrMTS0tKS8nPCzYMNcx4AX5tslqxckLMwFySS8Fw4YcWTpER/e4Hk/J1V7fWCxaykDMNAbW0tqqurE7racLB5sByFlUuyckHOwlyQCHMhj77TiltbgTlzwv/tTeQMDRVzoUYvaMja2jgPloiIKBEGm1b8yitAfv7Ja5yhMTQsZgkAT2QlIqI447muYacVAyn560gYFrNEREQUP0451zVJOK048VjMSkrXdVRXVyu12pBix1yQCHNBIrblwinnuqYoFV8vWMxKrKenBxkZGXY3gyTDXJAIc0EituUi3HAkD1iISKJnaKj2esFiVlKGYaCxsVGp1YaOJsk5vswFiTAXJCJVLnjAQkSSMUNDqlzEiRq9IEoknuNLRBQbHrAQEc7QGBoWs4oSDSTyk5wI8RxfIqL440qoiETza7Lz4AWZsJiV2FAnZw90FC0/yRmEA87xVWnSPsUPc0EizIW6BvvQcKC/96rlgsWspNxuN2pqaob0vaKDEPgxRYTCnSIhydvcWHJB6mIuSIS5UFu4P1eD/b1XMRcsZiVlWRY6OjqQk5MDTdOG9BiigxD6rlmyYQ2TM0h6ikQ8ckHqYS5IhLlIDdH+uVIxFy67G0BihmGgqakJhmHE5fF6r5CcMuXk19y5XMPkJPHOBamBuSAR5oJEVMwFR2ZTBI/UIyIiIhWxmE0hXEhKREREquE0A0lpmoaMjAxl5rNQfDAXJMJckAhzQSIq5oIjs5LSdR2VlZV2N4Mkw1yQCHNBIswFiaiYC47MSso0TRw8eBCmadrdFJIIc0EizAWJMBckomIuODIrKdM00dzcjNzcXLhcfM9BfswFiTAXJOKoXIj2ieTq5IRwVC4ixGKWUpfoHEBuvEtElDy9943si8dWUoRYzDoc67EhGuwcQG68S0SUeOH2jeSxlRQFFrOS0jRt0NM5WI/FINw5gIDUH21FkgtKPcwFiTgmF9w3Mqkck4sosJiVlK7rKO9bZPXh0HpMLpIeWxtOJLmg1MNckAhzoZZ4HUevYi5YzErKNE20tLRg9OjRg07Qdlg9RjGIJheUOpgLEmEu1DDYtOJoP4VVMRcsZiVlmib27t2LwsJCZcJGsWMuSIS5IBHmQg3xPo5exVywmCUiIiKSGKcVD0yNkpyIiIiIUhKLWUm5XC7k5+cr8xEAxQdzQSLMBYkwFySiYi44zUBSLpcLpaWldjeDJMNckAhzQSLMBYmomAt1ynLFmKaJXbt2KXV2MsWOuSAR5oJEmIvU5vEA9fUnv7xe/3UVc8FiVlKmaaK1tVWpsFHsmAsSYS5IhLlITb238poy5eRXebm/oFUxF5xmQERERHLqezIATwQalGgrr96nA48ebV/bEoXFLKnP6xWf+01ERHIKd1JAZqb/9ZsF7YBSbSsvFrOScrlcKCoqCq42ZD02RF6v/7OVrq7+tw3l6BSb9c0FEcBckJijczHY8GIqVWpx5uhchMFiVlKBsAHK1WPJ1dbm/8WtXev/JfbmwI+reueCKIC5IBHH5yLVhheTxPG5EGAxKynDMLBz506ceeaZaGvTVarH7FFeDlRV2d2KmPXOha7rdjeHJMFckAhzQSIq5oLFrIS8XmD/fgsezwkcPmzhvff81xWpxygGlmWho6MDlmXZ3RSSCHNBIswFiaiYCxazkjk5pcANoCJ4ndMJiIiIiPpjMSuZwBTPNWsMWNY7KC8/G263m9MJiIiIiARYzErq7LM1FBcXIi/PBYUWHFKMXC4XJkyYoNQqVIodc0EiyuZCtJUPR3wipmIuWMxKyuVyoaCgwO5mkGSYCxJhLkhEuVyE23sW4P6zUVAuF+BxttIyDAPbtm2DYRh2N4UkwlyQCHNBIsrlIrD3bF1d6Nfatf75eX03Yych5XIBjsxKy7IsdHd3K7XaMCn6ni6h2MkSzAWJMBckomQuuPdszFTMBYtZUke40yW4FQQREZGyWMySOsKd9sWFAURERMpiMSspXddx5pllypzOkVQKny6h6zrKypgLCsVckAhzQSIq5oLFrKQ0TcOIESPsbgZJhrkgEeaCRJgLElExF9zNQFI+nw9btmyBz+ezuykkEeaCRJgLEkm5XHg8QH196JfXa3erpKNiLjgyKzGVts2g+GEuSIS5IJGUyAX3n42YxwP4fIDHMwyWBbjdaiwrYTFLREREzhXYf7bvPrMej7/AbWtzfrUWo9B63w2gInibCvU+i1lypr77yQLK7SlLREQR4v6zA+pd7/t8Png876K8/Gy8955biXqfxaykdF3HxIkVSq02jJtw+8kCyu8pq+s6KiqYCwrFXJAIc0G9Bep9y9IxceLpyMjQ4VakClSkG2pKS0uzuwlyCrefLKDG5J9BMBckwlyQCHNBIqrlgrsZSMowDNTW1qbG5P2hCuwn2/tL8UKWuSAR5oJEmAsSUTEXLGaJiIiIyLFYzBIRERGRY7GYJSIiIiLH4gIwSem6jqqqaq5CpRC6rqO6mrmgUMwFiTAXJKJiLljMSqynpwcZGRl2N4Mkw1yQCHNBIswF+u9BngK73gxGtVywmLXRQPv+G4aBxsZGVFdXw63KRnBDwcMRQjAXJMJckEjK5yLcMbcqHHkVg965UKUMVKMXDhTJvv8HDya/XVJJ4cMRiIgoRqJjbnnErZJYzNpksH3/R49mMZvqhyMQEVGMeMxtSmAxa7PAvv99+XxQanJ2TML9klIUc0EizAWJMBckolouWMxKyu12o6amxu5mkGSYCxJhLkiEuSARUS6cvkaOxaykLMtCR0cHcnJyoGma3c0hSTAXJMJckAhzQSK9c5GXpymxRo6HJkjKMAw0NTUpdXYyxY65IBHmgkSYCxLpnYvAGrm6upNfa9f6l6v03UhIZhyZJSIiIkpRKqyR48gsERERETkWi1lJaZqGjIwMznOiEMwFiTAXJMJckIiKueA0A0npuo7Kykq7m0GSYS5IhLkgEeaCRFTMBUdmJWWaJg4ePAjTNO1uCkmEuSAR5oJEmAsSUTEXHJmVlGmaaG5uRm5uLlwuvucgP+aCRJgLEmEuouT19l/C77QNVyOgYi5YzBIREVFq83r9p012dYVed9qGqymKxSwRERGllr5HXnk8/kJ27Vp/URu4Nneuf7SWxazUWMxKStM0ntpC/TAXJMJckAhzIZCXB+GRV4D/+owZyheuKuaCxaykdF1HeeDdIdEnmAsSYS5IhLkQCBx5JTreSsH5sSIq5oLFrKRM00RLSwtGjx6tzARtih1zQSLMBYkwF2GocORVDFTMhRq9UJBpmti7d69SW2cMyusF6utPfvWd00SpmQsaFHNBIswFiaiYC47MkhwGWkmal2dPm4iIiEh6LGZJDm1t/VeSAikzh4mIiIiGhsWspFwuF/Lz85WZzxKx8nKgqsruVkgrZXNBA2IuSIS5IBEVc8FiVlIulwulpaV2N4Mkw1yQCHNBIswFiaiYC3XKcsWYpoldu3YpNUGbYsdckAhzQSLMBYmomAsWs5IyTROtra1KhY1ix1yQCHNBIswFiaiYCxazRERERORYnDNLieP1pvQpK0RERJR4to/MPvLIIygpKUF6ejqmTZuGzZs3h73vO++8g69+9asoKSmBpmlYuXJl8hqaZC6XC0VFRc5dbRjYN3bKlP5f5eX+2ylqjs8FJQRzQSLMBYmomAtbe/Lcc89h8eLFuPPOO1FfX4/KykrMmjULBw8eFN6/q6sLEyZMwL333ovCwsIktza5HB+23vvG1tWd/Fq71n9dNGJLg3J8LighmAsSYS5IRMVc2NqTFStW4Oqrr8bChQtx9tlnY9WqVcjMzMTjjz8uvH9NTQ1+9rOf4Rvf+AaGDRuW5NYml2EY8Hg8MAzD7qbEJrBvbOCr94EIFDVlckFxxVyQCHNBIirmwrY5sz09Pairq8Mdd9wRvOZyuTBz5ky89dZbcfs5x48fx/Hjx4P/7uzsBAD4fD74fL7gz3W5XDBNM2R1X+C6YRiwLGvQ67quQ9O04OP2vg4gJDj+u7hhWRZ8vtBAud1umKaJ9vZ2+Hw+WJYFTdOg63q/Noa7bkefQq77fHB/8nuGzwe329/X3tc1wzjZ9nDXZeqT0f95siwr5HqinyfTNNHR0RHMhQp9UvF5SnafDMNAe3s7LMtSpk8DtZ19iqxPPp8v+HfE5XIp0aekPk+f/H9j+3ZYn/yMYJ9yc2EWFTmvT7oefL0IV18E6hN/P+zrU9/7D8S2YratrQ2GYWDUqFEh10eNGoWmpqa4/Zxly5Zh6dKl/a43NDRg+PDhAID8/HyUlpZi9+7daG1tDd6nqKgIRUVF2LlzJzo6OoLXJ0yYgIKCAmzfvh3d3d3B62VlZRgxYgQaGhpCnpyKigqkpaWhtrY2eG3HjkwAFTh27Bhqa7cFr+u6jpqaGnR2dqK9vR319fXQNA0ZGRmorKxEW1sbmpubg/fPyclBeXk5WlpasHfv3uB1O/oEANXV1ejp6cH7Hg8qALzr8eC4pqGmpgYdHR3wfnJ995//DKu5GWeeeSbaDx3CR//8J8745P6nZGVJ2afGxsZ+z1NHR0dIXhP9PI0bNw4A8O6774a8SXNyn1R8npLdJ8uycPjwYQBQpk+Aes9TsvvU1dUV/DtSXl6uRJ+S+jzl5cHKyIA+fz76ychA49NPo+eTKY+O6VNlJT766KOQ+qLv8xSoTz788ENUV4+1rU8NDQ39f+9haFbv8jmJWlpaMGbMGLz55puYPn168Pqtt96KN954A5s2bRrw+0tKSnDTTTfhpptuGvB+opHZsWPH4qOPPkJ2djYAe95R1dcD06a5UVtrobKy/zuqEydOoLa2FlVVVcHHddQ73y1b4J42Db5Nm4CqqpPvEnfvhn7uudC6utCXlZkJ4+23oY0bJ2efJBihME0T9fX1mDx5crBdTu+Tis+THSOz9fX1qKmpCbbT6X0aqO3sU+Qjs/X19aiqqkJaWpoSfUr687RnD8xe63g0TYO+cycwd27w75vT+hT4ZDxcfRGoT7ZsMVFdbV+fPv74Y4wcORIdHR3Bei0c20Zm8/LyoOs6Dhw4EHL9wIEDcV3cNWzYMOH8WrfbDbc7tPuBJ6Kv3kVDJNf7Pq7oeuD/apomvL+u6ygtLcUpp5wS0qZwbYz2eiL6JLrudruDndU0De4JEwCPR7gATMvLg7vXll2y9imkzWGev0Q9T6ZpYsKECf1yMZS2h7ue7D4Ndp19GrxPLpf/eMpA+1ToUyRtZJ8G7pOmacG/I5qmRd32cNdT6nkqKYGrpKRvQ04+jo11xGDXw/XJ7XYPWF8EviVwm0x9Cse2YjYtLQ1TpkzB+vXrMXv2bAD+P9Tr16/HokWL7GqWNFwuFwoKCuxuRmIUF3Of2SFSOhc0ZMwFiTAXJBJpLjye/tdk3Sbe1t0MFi9ejMceewxr1qyBx+PBddddh6NHj2LhwoUAgHnz5oUsEOvp6cHWrVuxdetW9PT04MMPP8TWrVvx/vvv29WFhDEMA9u2bVNqtSHFjrkgEeaCRJgLEhksF3l5QGYmMHeuc7aJt/UEsMsvvxytra1YsmQJ9u/fj0mTJuGVV14JLgrzer0hQ9stLS2YPHly8N/33Xcf7rvvPpx//vnYsGFDspufUJZlobu7O2TeCRFzQSLMBYkwFyQyWC6Ki8WzAT0ef4Hb1ibf6Kztx9kuWrQo7LSCvgVqSUkJ/6MkIiIiSiCnzQZU5/gHIiIiIko5to/Mkpiu6ygrKwu7KlA6Xm/oZxKimeMUM8flgpKCuSAR5oJEVMwFi1lJaZqGESNG2N2MyHi9/lnhffeOzcz0zySnuHFULihpmAsSYS5IRMVcsJhNgr6DlsDgA5c+nw8NDQ2YPHlyVHut2aKtzV/Irl3rL2oDZN3Dw8EclQtKGuaCRJiLBHPS3lW9qJgLNXohsXCDlsDgA5eO206lvDx4GgoljuNyQUnBXJAIc5EAvfeu6isz01/kSl7QqpYLFrMJFm7QEnDEGzgiIiLqzYl7VymOxWySKDNoOZQ5E0RERCpx2t5VimMxKyld11FRUSHXasNY5kxQXEiZC7Idc0EizAWJqJgLFrMSS0tLs7sJoThnQgrS5YKkwFyQCHNhg76fVkr491G1XPDQBEkZhoHa2lo5J2kH5kz0/pLsP1RVSZ0Lsg1zQSLMRZL1Xhg2ZcrJr/Jy/yebklAxFxyZJSIiIoqVaGEYF4UlBYtZIiIionjgwjBbcJoBERERETkWi1lJ6bqO6upqpVYbUuyYCxJhLkiEuSARFXPBYlZiPT09djeBJMRckAhzQSLMBYmolgsWs5IyDAONjY1KrTak2DEXJMJckAhzQSIq5oILwIiIiIgSSXRSpoT7zzoVi1kiIiKiROi992xfmZn+IpcFbcxYzEpMpcnZFD/MBYkwFyTCXNhMtPcsYPv+s6rlgsWspNxuN2pqauxuBkmGuSAR5oJEmAtJSLb3rIq5YDErKcuy0NHRgZycHGiaZk8jvN7+J5mQraTIBUmHuSAR5oJEVMwFdzOQlGEYaGpqsm+1odfrP0+69/nSc+f65/jk5dnTJrI/FyQl5oJEmAsSUTEXHJklsbY2oKsLWLvWX9QGcPUlERERSYTFLA2svByoqrK7FURERERCnGYgKU3TkJGRocx8FooP5oJEmAsSYS5IRMVccGRWUrquo7Ky0u5mkGSYCxJhLkiEuXAAGw5TUDEXLGYlZZom2trakJeXB5eLA+jkx1yQCHNBIsyFxGw8TEHFXLCYlZRpmmhubkZubm7iw9Z3Cy6A23BJKqm5IMdgLkiEuZCYjYcpqJgLFrOpLrAFV1dX/9u4DRcREVFiDHSYQt8BJe4kNCAWs6ku3BZcAP/jISIiSqZw0w8SPPXA6VjMSkrTtOSezsEtuBwh6bkgR2AuSIS5cCDR9IM4Tz1QMRcsZiWl6zrK+46URkM0D1aEc2MdJeZckJKYCxJhLhxqoOkHcaBiLljMSso0TbS0tGD06NHRT9AeaB6sCOfGOkZMuSBlMRckwlyQiIq5YDErKdM0sXfvXhQWFkYftoHmwYpwbqxjxJQLUhZzQSLMhWLitCetirlgMasyzoMlIiJyNhv3pHUKFrNEREREsrJxT1qnYDErKZfLhfz8fGU+AqD4YC5IhLkgEeZCIXFcFKZiLljMSsrlcqG0tNTuZpBkmAsSYS5IhLkgERVzoU5ZrhjTNLFr1y6Ypml3U0gizAWJMBckwlyQiIq5YDErKdM00draqlTYKHbMBYkwFyTCXJCIirngNAMiIiIip4rTll1OxmKWiIiIyGm4ZVcQi1lJuVwuFBUVKbXakGLHXJAIc0EizIXihrhll4q5YDErqUDYiHpjLkiEuSAR5iIFDGHLLhVzoU5ZrhjDMODxeGAYht1NIYkwFyTCXJAIc0EiKuaCI7OSsiwLHR0dsCzLf8Hr7f9RQjiiyeCkhH65IAJzQWLMBYmomAsWs07g9QLl5UBXV+Tfk5npnxxOREREpDAWs07Q1uYvZNeu9Re1kUixbTmIiIgoNbGYlZTL5cKECRNCVxuWlwNVVfY1imwnzAWlPOaCRJgLElExFyxmJeVyuVBQUGB3M0gyzAWJMBckwlykuL7rZz75xFbFXKhTlivGMAxs27ZNqdWGFDvmgkSYCxJhLlJU78MUpkw5+VVeDni9SuaCI7OSsiwL3d3dSq02pNgxFyTCXJAIc5GiRIcp9DpIwRo9WrlcsJglIiIiUkm4wxQ8HsDnQ+aOHYDLBbjdSiwYZzErI68X2L//ZNjee8/uFhEREZFT9Zp64AZQ0fu2zEx/kevggpbFrGw+2VPW3dXVP2zcNzbl6bqOsrIy6Lpud1NIIswFiTAXFNRr6oFlWThy5AhOPfVUaE1NwekHLGYpfsLtKavAxwAUO03TMGLECLubQZJhLkiEuaAQn0w90ABkBa5pmv9/w+x84BTczUBSvjPOwBbDgK+iwr+3rINCRYnj8/mwZcsW+Hw+u5tCEmEuSIS5IJGQXAyy84FTcGTWTl5v6GpDIOTdkUrbZlD8MBckwlyQCHNBIsFcDLLzgVMG0ljM2uWTubHo6up/W2B+7MGDyW8XERERpY5wOx84CIvZZPF4AHSH/ls0NxbwF7KjR7OYJSIiIhoEi9lE27cPwKeAuVcAaAi9LTMTmDFD+I5ItyxUVFRwFSqF0HWduaB+mAsSYS5IRMVcsJhNtPZ2AJ8C7v4JcGlh6G2DrBZMS0tLaNPImZgLEmEuSIS5IJGIctF3hwNPBoBy4V3txt0MkmX8eP+uBL2/BihkDcNAbW0tJ+9TCOaCRJgLEmEuSGTQXITb4WDuFf7b9+1LXmMjxJFZIiIiIvIT7XAAAC/vB36Ek584S4TFLBERERGdJNrhoO+0A4lwmgERERERORaLWUnpuo7q6mqlVhtS7JgLEmEuSIS5IBEVc8FiVmI9PT12N4EkxFyQCHNBIswFiaiWCxazkjIMA42NjVyFSiGYCxJhLkiEuSARFXPBYpaIiIiIHIvFLBERERE5FotZiak0OZvih7kgEeaCRJgLElEtF9xnVlJutxs1NTV2N4Mkw1yQCHNBIswFiaiYC47MSsqyLLS3t8OyLLubQhJhLkiEuSAR5oJEVMwFi1lJGYaBpqYmpVYbUuyYCxJhLkiEuSARFXPBYpaIiIiIHIvFLBERERE5FotZSWmahoyMDGiaZndTSCLMBYkwFyTCXJCIirngbgaS0nUdlZWVdjeDJMNckAhzQSLMBYmomAuOzErKNE0cPHgQpmna3RSSCHNBIswFiTAXJKJiLljMSso0TTQ3NysVNoodc0EizAWJMBckomIuWMwSERERkWOxmCUiIiIix2IxKylN05CTk6PUakOKHXNBIswFiTAXJKJiLribgaR0XUd5ebndzSDJMBckwlyQCHNBIirmgiOzkjJNE3v37lVqgjbFjrkgEeaCRJgLElExFyxmJaVi2Ch2zAWJMBckwlyQiIq5YDFLRERERI7FYpaIiIiIHIvFrKRcLhfy8/PhcvEpopOYCxJhLkiEuSARFXPB3Qwk5XK5UFpaanczSDLMBYkwFyTCXJCIirmQoix/5JFHUFJSgvT0dEybNg2bN28e8P6/+93vUFZWhvT0dJx77rl4+eWXk9TS5DFNE7t27VJqgjbFjrkgEeaCRJgLElExF7YXs8899xwWL16MO++8E/X19aisrMSsWbNw8OBB4f3ffPNNfPOb38RVV12FhoYGzJ49G7Nnz8b27duT3PLEMk0Tra2tSoWNYsdckAhzQSLMBYmomAvbi9kVK1bg6quvxsKFC3H22Wdj1apVyMzMxOOPPy68/4MPPohLLrkEt9xyC8rLy3H33XejqqoKP//5z5PcciIiIiKym61zZnt6elBXV4c77rgjeM3lcmHmzJl46623hN/z1ltvYfHixSHXZs2ahXXr1gnvf/z4cRw/fjz4746ODgDAoUOH4PP5gj/T5XLBNM2QdyqB64ZhwLKsQa/rug5N04KPCwDtRzoAdOJw12EcOnQopG26rgMADMMIue52u3HixAkcOXIEH3/8cfBxdV3v18Zw1xPZp4HaPlCfLMsKuc4+Rd8n0zRx9OjRYC5U6JOKz1Oy+2QYBo4cOYLOzs5gO53ep4Hazj5F1iefzxf8O5KWlqZEn1R8npLdp56enojqi759CtQznV2H0dnZmfA+ffzxxwAQ8ljh2FrMtrW1wTAMjBo1KuT6qFGj0NTUJPye/fv3C++/f/9+4f2XLVuGpUuX9rs+fvz4IbZ6aC64BsA1Sf2RRERERHF1YZLrmcOHDyMnJ2fA+yi/m8Edd9wRMpJrmiYOHTqEkSNHQtM0G1s2sM7OTowdOxYffPABsrOz7W4OSYK5IBHmgkSYCxJxSi4sy8Lhw4cxevToQe9razGbl5cHXddx4MCBkOsHDhxAYWGh8HsKCwujuv+wYcMwbNiwkGsjRowYeqOTLDs7W+qwkT2YCxJhLkiEuSARJ+RisBHZAFsXgKWlpWHKlClYv3598Jppmli/fj2mT58u/J7p06eH3B8AXn311bD3JyIiIiJ12T7NYPHixZg/fz6qq6sxdepUrFy5EkePHsXChQsBAPPmzcOYMWOwbNkyAMCNN96I888/H/fffz+++MUv4tlnn0VtbS1++ctf2tkNIiIiIrKB7cXs5ZdfjtbWVixZsgT79+/HpEmT8MorrwQXeXm93pAj18477zw8/fTT+OEPf4jvf//7OOOMM7Bu3TpMnDjRri4kxLBhw3DnnXf2myJBqY25IBHmgkSYCxJRMReaFcmeB0REREREErL90AQiIiIioqFiMUtEREREjsViloiIiIgci8UsERERETkWi1kH2LNnD6666iqMHz8eGRkZKC0txZ133omenh67m0Y2u+eee3DeeechMzPTUYeBUHw98sgjKCkpQXp6OqZNm4bNmzfb3SSy2d///nd8+ctfxujRo6FpGtatW2d3k8hmy5YtQ01NDbKyslBQUIDZs2djx44ddjcrLljMOkBTUxNM08T//d//4Z133sEDDzyAVatW4fvf/77dTSOb9fT04Otf/zquu+46u5tCNnnuueewePFi3Hnnnaivr0dlZSVmzZqFgwcP2t00stHRo0dRWVmJRx55xO6mkCTeeOMNfPe738W//vUvvPrqqzhx4gQuvvhiHD161O6mxYxbcznUz372Mzz66KNobm62uykkgdWrV+Omm25Ce3u73U2hJJs2bRpqamrw85//HID/FMWxY8fihhtuwO23325z60gGmqbhxRdfxOzZs+1uCkmktbUVBQUFeOONN/DZz37W7ubEhCOzDtXR0YHc3Fy7m0FENurp6UFdXR1mzpwZvOZyuTBz5ky89dZbNraMiGTX0dEBAErUEixmHej999/Hww8/jP/+7/+2uylEZKO2tjYYhhE8MTFg1KhR2L9/v02tIiLZmaaJm266CZ/+9KeVOEGVxayNbr/9dmiaNuBXU1NTyPd8+OGHuOSSS/D1r38dV199tU0tp0QaSi6IiIgi9d3vfhfbt2/Hs88+a3dT4sJtdwNS2f/8z/9gwYIFA95nwoQJwf/f0tKCCy+8EOeddx5++ctfJrh1ZJdoc0GpKy8vD7qu48CBAyHXDxw4gMLCQptaRUQyW7RoEf70pz/h73//O4qKiuxuTlywmLVRfn4+8vPzI7rvhx9+iAsvvBBTpkzBE088AZeLg+qqiiYXlNrS0tIwZcoUrF+/Pri4xzRNrF+/HosWLbK3cUQkFcuycMMNN+DFF1/Ehg0bMH78eLubFDcsZh3gww8/xAUXXIBx48bhvvvuQ2tra/A2jr6kNq/Xi0OHDsHr9cIwDGzduhUAcPrpp+PUU0+1t3GUFIsXL8b8+fNRXV2NqVOnYuXKlTh69CgWLlxod9PIRkeOHMH7778f/Pfu3buxdetW5Obmori42MaWkV2++93v4umnn8Yf/vAHZGVlBefV5+TkICMjw+bWxYZbcznA6tWrw/5h4tOX2hYsWIA1a9b0u/7666/jggsuSH6DyBY///nP8bOf/Qz79+/HpEmT8NBDD2HatGl2N4tstGHDBlx44YX9rs+fPx+rV69OfoPIdpqmCa8/8cQTg05tkx2LWSIiIiJyLE68JCIiIiLHYjFLRERERI7FYpaIiIiIHIvFLBERERE5FotZIiIiInIsFrNERERE5FgsZomIiIjIsVjMEhElyQUXXICbbrrJ7mYQESmFxSwRkWReeOEFXHzxxRg5ciQ0TQseUxwvd911FyZNmhTXxyQisguLWSIiyRw9ehSf+cxnsHz5crubQkQkPRazRESS+fa3v40lS5Zg5syZQ/r+9vZ2fOc730F+fj6ys7Pxuc99Dtu2bQMArF69GkuXLsW2bdugaRo0TcPq1asBACtWrMC5556L4cOHY+zYsbj++utx5MiReHWLiCghWMwSESnm61//Og4ePIg///nPqKurQ1VVFS666CIcOnQIl19+Of7nf/4H55xzDvbt24d9+/bh8ssvBwC4XC489NBDeOedd7BmzRq89tpruPXWW23uDRHRwNx2N4CIiOLnH//4BzZv3oyDBw9i2LBhAID77rsP69atw/PPP49rrrkGp556KtxuNwoLC0O+t/fitJKSEvzkJz/Btddei1/84hfJ7AIRUVRYzBIRKWTbtm04cuQIRo4cGXK9u7sbu3btGvB7//a3v2HZsmVoampCZ2cnfD4fjh07hq6uLmRmZiay2UREQ8ZilohIIUeOHMGnPvUpbNiwod9tI0aMCPt9e/bswZe+9CVcd911uOeee5Cbm4t//OMfuOqqq9DT08NiloikxWKWiEghVVVV2L9/P9xuN0pKSoT3SUtLg2EYIdfq6upgmibuv/9+uFz+5RS//e1vE91cIqKYcQEYEZFkDh06hK1bt+Ldd98FAOzYsQNbt27F/v37B/3emTNnYvr06Zg9ezb++te/Ys+ePXjzzTfxgx/8ALW1tQD882F3796NrVu3oq2tDcePH8fpp5+OEydO4OGHH0ZzczOefPJJrFq1KqH9JCKKBxazRESS+eMf/4jJkyfji1/8IgDgG9/4BiZPnhxRcalpGl5++WV89rOfxcKFC3HmmWfiG9/4Bv79739j1KhRAICvfvWruOSSS3DhhRciPz8fzzzzDCorK7FixQosX74cEydOxFNPPYVly5YltJ9ERPGgWZZl2d0IIiIiIqKh4MgsERERETkWi1kioiTauHEjTj311LBfg3nqqafCfu8555yThB4QEcmF0wyIiJKou7sbH374YdjbTz/99AG///Dhwzhw4IDwtlNOOQXjxo2LqX1ERE7DYpaIiIiIHIvTDIiIiIjIsVjMEhEREZFjsZglIiIiIsdiMUtEREREjsViloiIiIgci8UsERERETkWi1kiIiIiciwWs0RERETkWP8fqRWDe+T6incAAAAASUVORK5CYII=\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAArcAAAINCAYAAAAkzFdkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB2GElEQVR4nO3de3wU9b3/8ffsLAkJkkTIDQjh5oUoJhACKVp/VqUiWoVqj9ZKQaxYL/SoHKtiFUTb0lalWo+V1opoqZdqFXuq1SKKWqWQG0Q0CUgCAbklSDaQhCQ7M78/1l2yyeayyWdmdifv5+ORR2Uy2Xy/r1nSb4bZWcUwDANERERERA7gsnsARERERERSuLglIiIiIsfg4paIiIiIHIOLWyIiIiJyDC5uiYiIiMgxuLglIiIiIsfg4paIiIiIHIOLWyIiIiJyDLfdA7CaruvYt28fBg8eDEVR7B4OEREREbVjGAaOHj2K4cOHw+UK71xsv1vc7tu3DyNHjrR7GERERETUjT179iAjIyOsr+l3i9vBgwcD8MVKSEgw/ft5vV6UlJRg0qRJcLv7XW5RbCmHLeWwpRy2lMOWcthSTjgt6+vrMXLkyMC6LRz97ij5L0VISEiwbHE7aNAgJCQk8C9FH7GlHLaUw5Zy2FIOW8phSzm9admbS0gVwzCMsL8qitXX1yMxMREej8eSxa1hGGhqakJcXByv8e0jtpTDlnLYUg5bymFLOWwpJ5yWfVmv8W4JFoiJibF7CI7BlnLYUg5bymFLOWwphy3lWNGSi1uTaZqGwsJCaJpm91CiHlvKYUs5bCmHLeWwpRy2lGNVS148QkRERJYyDANerzcqFoxerxcAcPz4cV5z20ftWw4YMACqqop/Hx4lIiIiskxLSwv279+PxsZGu4fSI4ZhYODAgaiuruY1t33UvqWiKMjIyMBJJ50k+n24uCUiIiJL6LqOqqoqqKqK4cOHIyYmJuIXjIZhoLGxEfHx8RE/1kjXtiUA1NTUYO/evTj11FNFz+DybgkmMwwDmqZBVVX+pegjtpTDlnLYUg5byonUlsePH0dVVRVGjRoVWOBEurbLpEhqGY3at2xqasKuXbswZswYDBw4MGhf3i0hwrW0tNg9BMdgSzlsKYct5bClnEhuGe7bqdpN13W7h+AYbVua9ctCdD27opCmaSgtLY2Ki+YjHVvKYUs5bCmHLeWwpaympia7h+AYVrTkNbdERERkr+pqoLbWuu+XnAxkZoo81HXXXYe6ujqsXbtW5PF66oEHHsDatWuxZcsWS79vNODiloiIiOxTXQ1kZQFW3j0hPh4oKxNZ4D7++OPoZy9finhc3FrAjHu49VdsKYct5bClHLaUEzUta2t9C9s1a3yLXLOVlQFz5vi+bw8Xt11dG5qYmCg1sn7BihflcXFrMrfbjSlTptg9DEdgSzlsKYct5bClnKhsmZUF5ObaPYoOFEXBoEGD8Oqrr2LZsmX44osvEB8fj0mTJuGNN97ArbfeGnRZwtGjR3HTTTdh7dq1SEhIwF133YU33ngDEydOxGOPPQYAGD16NG688UZ88cUXeOWVV3DyySfjvvvuw4033hj4vnfffTdef/117N27F+np6bj22muxZMkSDBgwwIYKMvwtzcYXlJnMMAzU1dXxnywEsKUctpTDlnLYUg5byjEMA3v27ME111yD66+/HmVlZdiwYQOuuOKKkH0XLVqEjz/+GH//+9+xbt06fPTRRyguLu6w36OPPoq8vDyUlJTglltuwc0334yKiorA5wcPHozVq1fj888/x+OPP46nn34av/3tb02dq9n870xn9vOSi1uTaZqG8vJyvmJVAFvKYUs5bCmHLeWwpazdu3fD6/XiiiuuwOjRo3HWWWfhlltu6fDOWkePHsVzzz2HRx55BBdeeCEmTJiAZ599NuRxuOSSS3DLLbfglFNOwd13343k5GS8//77gc/fd999OPvsszF69GhcdtlluPPOO/HXv/7V9Lma7fjx46Z/D16WQERERNSFs846CxdeeCHOOusszJgxAxdddBG+973v4eSTTw7ar7KyEq2trZg6dWpgW2JiIk4//fQOj5mdnR34b0VRkJ6ejkOHDgW2vfzyy/jd736HnTt34tixY/B6vZa8+ZQT8MwtERERURdUVcW//vUv/POf/8QZZ5yBJ554Aqeffjqqqqp6/Zjtr51VFCXwBgcbN27Etddei0suuQT/+Mc/UFJSgp/97GcR/cYckYSLW5MpioK4uDi+ZZ8AtpTDlnLYUg5bymFLWS6XC4qi4JxzzsGyZctQUlKCmJgYvP7660H7jR07FgMGDEBBQUFgm8fjwfbt28P6fp988glGjRqFn/3sZ8jLy8Opp56K3bt3i8zFbla8Ox0vSzCZqqrIycnp8+N0dn9rwftQRzyplsSWkthSDlvKYUs5iqLg008/xfr163HRRRchNTUVmzZtQk1NDbKyslBaWhrYd/DgwZg3bx5++tOfYsiQIUhNTcXSpUsDi+OeOvXUU1FdXY2XXnoJU6ZMwZtvvtlhIR2NFEVBfHy86d+Hi1uT6bqO2tpaJCcn9/q3la7uby14H+qIJ9GSfNhSDlvKYUs5UdmyrCwiv49hGIiLi8OHH36Ixx57DPX19Rg1ahQeffRRzJw5Ey+//HLQ/itWrMBNN92E73znO4Fbge3ZswcDBw7s8fe8/PLLcccdd2DhwoVobm7GpZdeivvvvx8PPPBAWGOPNP67JbjdblP/VUEx+tl9Qurr65GYmAiPx2PJhdlerxeFhYXIy8uD29273yWKi4HJkzve39p/H+qiooi8NaA4iZbkw5Zy2FIOW8qJ1JbHjx9HVVUVxowZc2KxF+HvUGYYBhoaGjBo0KBeLcgaGhowYsQIPProo/jRj37Um9E6RvuWIZ8PX+vLei1ynvHUrQi9vzUREVHvZWb6Fpqhrr0zi4nX9JWUlKC8vBxTp06Fx+PBgw8+CACYNWuWKd+POuLiloiIiOyVmemo6+seeeQRVFRUICYmBpMnT8ZHH32E5ORku4fVb3BxazJFUZCYmMhXrApgSzlsKYct5bClHLaUpapqj/edNGkSioqKTBxNdAunZW9xcWsyVVWR1fZCWeo1tpTDlnLYUg5bymFLOf7bqlHfWdUySl5CGb10XcfevXsDN2am3mNLOWwphy3lsKUctpRjGAZaWlrQz15/bwqrWnJxazL+gJHDlnLYUg5bymFLOWwpi+8MJseKllzcEhEREZFjcHFLRERERI7Bxa3JXC4XUlJSoucdYiIYW8phSzlsKYct5bClrEh6I4xoZ0VLW4/Whx9+iIcffhhFRUXYv38/Xn/9dcyePbtHX/vxxx/jvPPOw4QJE7BlyxZTx9kXLpcL48aNs3sYjsCWcthSDlvKYUs50dayujpy38NBURQMHDgQ3/rWtzBx4kQ89thjpozpuuuuQ11dHdauXWvK49th165dGDNmDEpKSjBx4sRAS7PZurhtaGhATk4Orr/+elxxxRU9/rq6ujrMnTsXF154IQ4ePGjiCPtO1/XAW8vxN+i+YUs5bCmHLeWwpZxoahnh774LwzDQ3Nxs/qD6AX/L2NhYU+/BbOvidubMmZg5c2bYX3fTTTfhBz/4AVRVjfjfcHRdR01NDUaNGhXxP2AiHVvKYUs5bCmHLeVEU8vaWt/Cds0a3yLXbGVlwJw5vu/b07O3Xq/X3EGZpKWlBTExMXYPI4jX60VsbKyp3yPqLiJ59tlnUVlZiTVr1uDnP/95t/s3NzcH/cZVX18PwBfX/2R1uVxwuVzQdT3otin+7ZqmBd2TrbPtqqpCUZSgvwT+fQzD6PCXw/8uHZqmBW13u90wDCOw3fdlvkPVdoydbTd7Tl2NvadzAnz/1KOqaocxdrbd/9+6rgeNJ5rnZNdx8j8nnTQnu46Tv2fbx4/2Odl5nNrOyylzsvM4+b93pMzJ6/UGxnvifwFAwfjxBnJzlZD3QFWU8LZ3xf/9fM+37h+77edaW1uxcOFC/PnPf8aAAQNw00034cEHH4SiKFizZg0ef/xxVFRUYNCgQbjgggvw29/+FmlpaYHH+Oyzz3DPPffgww8/hGEYmDhxIp599tkOl5AYhoGCggJceuml+J//+R/cfffdAICf//zneOKJJ9DU1ISrrroKycnJeOedd1BSUgIAmD9/Purq6jBlyhQ8+eSTiI2NRWVlJT799FPcfvvt2LhxI+Lj43HFFVdgxYoVOOmkkwAA559/PnJycoIuufjud7+LpKQkPPvsswCAMWPGYMGCBfjiiy/w6quv4uSTT8bPfvYz3HjjjYGvKSgowI9//GOUlZVhwoQJuPfee4Pm1PHYn1gf+Z+b/udeX36hiKrF7Y4dO3DPPffgo48+6vEFycuXL8eyZcs6bC8pKcGgQYMAACkpKRg3bhyqqqpQU1MT2CcjIwMZGRnYvn07PB5PYPvYsWORmpqKbdu2oampKbB9/PjxSEpKQklJSeAvvmEYgb/Y/iefX15eHlpaWlBaWhrYpqoqpkyZAo/Hg/LycgBARUU8gGwAQG1tLSorKzts37dvH/bu3Rt4HDPnBADZ2dmIiYlBYWFhr+YEAHFxccjJyQmaEwAkJiYiKyurw5yGDh0KANi9ezcOHz7siDnZdZwmTZoEXddRXFwc+KehaJ+TXccpPT0dDQ0NQS2jfU52Haf6+nrU1dUFWjphTnYdp88//zyoZSTNKT4+HoDv5JPX60VTkwtA/NeLmQE4fvx40BhjY2MxYMAANDU1BS2eBw4cCLfbjcbGxqAFaFxcHFwuFxoaGoLmNGjQIOi6jqamZgDxaGpqQmOjgUGDBkHTNBw/fjywr8vlQny8b0zNzc3QNA2apuH555/Hj370I/z73//G5s2b8d///d9IS0vDggUL0Nraivvvvx9jx45FTU0NFi9ejHnz5uHtt9/G8ePHsWfPHvy///f/cO655+Jf//oXhgwZgvfeew/19fVoaGhAa2trYB5vvfUWrr32Wjz44IO4/vrroes6XnzxRfzyl7/EihUr8I1vfAN/+9vf8MQTT2DMmDGBuba2tmL9+vVISEjAP//5T7S0tODQoUOYMWMG8vPzUVBQgH379uHHP/4xbrrpJvzhD38ILCZ1XQ9q5j8G/uNkGAYeffRRPPDAA/jZz36GF198EbfccgumTJmC0047Da2trfjOd76D888/H3/84x+xa9cu3HnnnYHH8x8nTdPQ2NgYeKeylpYWbNu2rcNzr/2aKSxGhABgvP76651+3uv1Gnl5ecZTTz0V2LZ06VIjJyeny8c9fvy44fF4Ah979uwxABiHDx82WltbjdbWVkPTNMMwDEPTtMC2ttu9Xm+Ptuu6bhiGEbStubnZqK6u7rCvf39d1ztsNwwjaPumTa0GYBhFRcFj7Gy72XPqauw9nVNra6vh9XpDjrGz7a2trcaePXs6bIvmOdl1nLxer1FdXW00Nzc7Zk52HSdN04zdu3cHtYz2Odl1nLxeb1BLJ8zJruPU0tIS1DJS5nT06FHjs88+M5qamgLfo7BQNwDDKCzUA4/f/iPc7V19tP1+PXlsTdOM48ePG+edd56RlZUVtM9dd90V2Nb+cTZv3mwAMI4ePWroum7cc889xpgxY4zm5uaQ+8+bN8+YNWuW8dprrxknnXSS8eKLLwZ9Pj8/37jllluCtp1zzjlGTk5O0GOkpaUFvoeu68Yf/vAH4+STTzaOHj0a+J7/+Mc/DJfLZezfv9/Qdd0477zzjP/+7/8OeuxZs2YZ8+bNC/x51KhRxpw5cwJj1zTNSE1NNX7/+98buq4bK1euNIYOHWo0NjYGvub3v/+9AcAoKSkJaqlpmqHrutHY2Gh89tlnxtGjRzs89w4fPmwAMDwejxGuqDlze/ToURQWFqKkpAQLFy4E4PstwzAMuN1u/Otf/8IFF1zQ4etiY2NDXtvhdrs7nP31/3NOe/7fanq6vf3jjhw5MvSkOtkf8P1ziH9720+3HWNn29sya0692d52Tj0ZY6jtGRkZIb8fEL1z6mq7mXPq7HkZzXPqbLvZc8rs5MK9aJ6THcdJVdWQLaN5TnYdpwEDBoRsafec3G534F84Tvwv2v059AuNwt3embbfr/33DvXYiqIE1hHf+MY3gvY9++yzsWLFCui6ji1btuCBBx7A1q1bceTIkcBZ5urqapxxxhnYunUrzj333KBrYNt/302bNuEf//gHXn311Q53j6qoqMAtt9wS9DVTp07Fe++9F7TtrLPOCvoe5eXlyMnJCVyCoCgKvvnNb0LXdWzfvh3p6elBcw3VwC87OzvwZ5fLhfT0dNTU1EBRFJSXlyM7OztwRtbfp7OWbbeFWpP15ZZhkX2VeRsJCQn49NNPsWXLlsDHTTfdhNNPPx1btmxBfn6+3UMMSdM0lJWVdbhuicLHlnLYUg5bymFLOWwpxzCMoMtWQjl+/DhmzJiBhIQE/OUvf0FBQQFef/11ACfebrbtoq8z48aNw/jx47Fq1Sq0trb2arz+Sy7D4XK5Olx3HOr7DxgwIOjPiqKE9RbP/pbtv5c0Wxe3x44dCyxUAaCqqgpbtmxBdXU1AGDx4sWYO3cuAF/4CRMmBH2kpqZi4MCBmDBhQq8OphUMw4DH4zH9QPYHbCmHLeWwpRy2lMOWsvy/JGzatClo+3/+8x+ceuqpKC8vx+HDh/GrX/0K5557LsaPH49Dhw4F7ZudnY2PPvqoy0VrcnIy3nvvPXzxxRe46qqrgvY9/fTTUVBQELR/+z+HkpWVha1btwZdT/vxxx/D5XLh9NNPB+C7tnv//v1B8217HWxPZGVlobS0NOja5f/85z8d9rPiFy5bF7eFhYWYNGkSJk2aBABYtGgRJk2ahCVLlgAA9u/fH1joEhEREdmpuroaixYtQkVFBV588UU88cQTuO2225CZmYmYmBg88cQTqKysxN///nc89NBDQV+7cOFC1NfX4/vf/z4KCwuxY8cO/PnPf0ZFRUXQfqmpqXjvvfdQXl6Oa665JnDXgJ/85Cd45pln8Nxzz2HHjh34+c9/jtLS0m4vy7j22msxcOBAzJs3D9u2bcP777+Pn/zkJ/jhD3+ItLQ0AMAFF1yAN998E2+++SbKy8tx8803o66uLqw2P/jBD6AoChYsWIDPP/8cb731Fh555JGwHkOKrdfcfutb3+ryt8rVq1d3+fUPPPAAHnjgAdlBERERkeXKyiL/+8ydOxdNTU2YOnUqVFXFbbfdhhtvvBGKomD16tW499578bvf/Q65ubl45JFHcPnllwe+dujQoXjvvffw05/+FOeddx5UVcXEiRNxzjnndPg+6enpeO+99/Ctb30L1157LV544QVce+21qKysxJ133onjx4/jqquuwnXXXYfNmzd3Oeb4+Hi88847uO222zBlyhTEx8fjyiuvxIoVKwL7XH/99di6dSvmzp0Lt9uNO+64A+eff35YbU466ST83//9H2666SZMmjQJZ5xxBn7961/jyiuvDOtxJChGP/s3i/r6eiQmJsLj8SAhIcH076frOmpra5GcnNzrG2kXFwOTJwNFRUBubvfbnUqiJfmwpRy2lMOWciK15fHjxwPvnOZ/G9ZoeIcyr9cb9GK4SPHtb38b6enp+POf/2z3UHqkfctQzwe/vqzXouZuCdHK5XIhNTXV7mE4AlvKYUs5bCmHLeVEU8vMTN9Cs7bWuu+ZnNzzdydTFKXDC6ns0NjYiJUrV2LGjBlQVRUvvvgi3n33Xaxbt87uofWYVS25uDWZ/6LsCRMmdHrLFuoZtpTDlnLYUg5byom2lpmZPV9sWs3/Cv+4uDhbz9wqioK33noLv/jFL3D8+HGcfvrp+Nvf/obp06fbNqZwWdWSi1uTWXXbi/6ALeWwpRy2lMOWcthSVji3uzJLXFwc3n33XbuH0WdWtIycC3GIiIiIiPqIi1siIiIicgwubk2mqirGjx8fFdc8RTq2lMOWcthSDlvKifSW0Xa5RPtX8lPvtW1p1vOAi1uTKYqCpKSkiLt9SDRiSzlsKYct5bClnEht6X+lfKOV9/3qI0VRIvI2YNGofUv/WxNL/xLGF5SZzOv1oqSkBJMmTYLbzdx9wZZy2FIOW8phSzmR2lJVVSQlJQXemjY+Pj7iF42RcrcEJ2jb0jAM1NTUID4+Xvw5GjnPeAez4n2U+wu2lMOWcthSDlvKidSW6enpABBY4EY6wzDQ0tKCmJgYLm77qH1Ll8uFzMxM8a5c3BIREZFlFEXBsGHDkJqaitbWVruH0y2v14tt27bhlFNOiaiz4NGofcuYmBhT3kGPR4mIiIgsp6pqxL7grS2v1wvA90IoLm77xqqWfEGZyVRVRXZ2dlT8BY50bCmHLeWwpRy2lMOWcthSjlUtubi1QExMjN1DcAy2lMOWcthSDlvKYUs5bCnHipZc3JpM0zQUFhZG7IX90YQt5bClHLaUw5Zy2FIOW8qxqiUXt0RERETkGFzcEhEREZFjcHFLRERERI6hGNH2Bs99VF9fj8TERHg8HiQkJJj+/QzDgKZpUFW11zcpLi4GJk8GioqA3NzutzuVREvyYUs5bCmHLeWwpRy2lBNOy76s13jDNgu0tLQgLi6ux/tXVwO1tSf+XFZmwqCiVLgtqXNsKYct5bClHLaUw5ZyrGjJyxJMpmkaSktLe/zKwOpqICvLd0bW/zFnDhAfDyQnmzzYCBduS+ocW8phSzlsKYct5bClHKta8sxthKmtBRobgTVrfItcv+RkIDPTvnERERERRQMubiNUVlb/uI6WiIiISBIvS7AA37JPDlvKYUs5bCmHLeWwpRy2lGNFS94tIcKEcweE/na3BCIiIuof+rJe45lbkxmGgbq6OvSz3yFMwZZy2FIOW8phSzlsKYct5VjVkotbk2mahvLycr7KUgBbymFLOWwphy3lsKUctpRjVUsubomIiIjIMbi4JSIiIiLH4OLWZIqiIC4ujm/ZJ4At5bClHLaUw5Zy2FIOW8qxqiXvlhBheLcEIiIi6u94t4QIpus6Dh06BF3X7R5K1GNLOWwphy3lsKUctpTDlnKsasnFrcl0XUdlZSX/UghgSzlsKYct5bClHLaUw5ZyrGrJxS0REREROQYXt0RERETkGFzcmkxRFCQmJvJVlgLYUg5bymFLOWwphy3lsKUcq1q6TX10gqqqyMrKsnsYjsCWcthSDlvKYUs5bCmHLeVY1ZJnbk2m6zr27t3LC9EFsKUctpTDlnLYUg5bymFLOVa15OLWZPxLIYct5bClHLaUw5Zy2FIOW8rh4paIiIiIKExc3BIRERGRY3BxazKXy4WUlBS4XEzdV2wphy3lsKUctpTDlnLYUo5VLXm3BJO5XC6MGzfO7mE4AlvKYUs5bCmHLeWwpRy2lGNVS/4aYjJd17Fz505eiC6ALeWwpRy2lMOWcthSDlvKsaolF7cm03UdNTU1/EshgC3lsKUctpTDlnLYUg5byrGqJRe3REREROQYXNwSERERkWNwcWsyl8uFjIwMvspSAFvKYUs5bCmHLeWwpRy2lGNVS94twWT+A0l9x5Zy2FIOW8phSzlsKYct5VjV0tZfQz788ENcdtllGD58OBRFwdq1a7vc/7XXXsO3v/1tpKSkICEhAdOmTcM777xjzWB7SdM0lJWVQdM0u4cS9dhSDlvKYUs5bCmHLeWwpRyrWtq6uG1oaEBOTg6efPLJHu3/4Ycf4tvf/jbeeustFBUV4fzzz8dll12GkpISk0fae4ZhwOPxwDAMu4cS9dhSDlvKYUs5bCmHLeWwpRyrWtp6WcLMmTMxc+bMHu//2GOPBf35l7/8Jd544w383//9HyZNmiQ8OiIiIiKKNlF9za2u6zh69CiGDBnS6T7Nzc1obm4O/Lm+vh4A4PV64fV6AfiuAXG5XNB1Pejea/7tmqYF/ZbR2XZVVaEoSuBxAQT2MQwjaLt/f/8+fr5d3F/vf2K7oihQVTVojP59/S1Cjd2MOXU29q62u92+ObXdHmpOXW33/7eu60HjieY52XWc/M9JJ83JruPk79n28aN9TnYep7bzcsqc7DxO/u/tpDm1HaMVc2rb0ilzsus4tW3Z3ZzaNwhHVC9uH3nkERw7dgxXXXVVp/ssX74cy5Yt67C9pKQEgwYNAgCkpKRg3LhxqKqqQk1NTWCfjIwMZGRkYPv27fB4PIHtY8eORWpqKrZt24ampqbA9vHjxyMpKQklJSVBBysjIwOGYaCoqChoDHl5eWhpaUFpaWlg244dgwGciWPHjqGw8LPA9ri4OOTk5KC2thaVlZUAgIqKeADZAIB9+/Zh7969gf3NnlN2djZiYmJQWFjY7ZxUVcWUKVPg8XhQXl7e5ZwAIDExEVlZWR3mlJycjLFjx6K6uhq1tbWOmJNdxyk3NxcZGRlBl/RE+5zsOk7Dhw/HgAEDglpG+5zsOk5Hjx5Fa2troKUT5mTXcSorKwtq6YQ52Xmc/C2dNCe7jpO/ZXdz6sslp4oRIReRKIqC119/HbNnz+7R/i+88AIWLFiAN954A9OnT+90v1BnbkeOHInDhw8jISEBQGT9xlVcDOTnu1FYaCAnp+vfuPz7FhUBEyfyt0jOiXPinDgnzolz4pycMacjR45g6NCh8Hg8gfVaT0XlmduXXnoJN9xwA1555ZUuF7YAEBsbi9jY2A7b3W433O7g6fsPTHv+0D3d3vZxNU1DaWkpJkyY0OH7hdrf/5+KooTcv+0Y2366s7GbMafebu/JnLrarmkatm7d2mnLaJxTd9vNmpOmadi2bRsmTJjQ4bGidU5dbTdzTl21jNY5AfYcJ13X8dlnn3VoGc1zsus4AQjZMprnZNdxMgyjw9/xaJ+TXcep7c9LRVE6HXtX23si6u5I/OKLL2L+/Pl48cUXcemll9o9nG4ZhoGmpia+ylIAW8phSzlsKYct5bClHLaUY1VLW8/cHjt2DF988UXgz1VVVdiyZQuGDBmCzMxMLF68GF9++SWef/55AL5LEebNm4fHH38c+fn5OHDgAADfdSSJiYm2zIGIiIiIIoetZ24LCwsxadKkwG28Fi1ahEmTJmHJkiUAgP3796O6ujqw/x//+Ed4vV7ceuutGDZsWODjtttus2X8RERERBRZbD1z+61vfavLU9OrV68O+vOGDRvMHZAJVFXF+PHju7wuinqGLeWwpRy2lMOWcthSDlvKsaplVL6gLJooioKkpCS7h+EIbCmHLeWwpRy2lMOWcthSjlUto+4FZdHG6/WioKCgTzcjJh+2lMOWcthSDlvKYUs5bCnHqpZc3Fqg/T3cqPfYUg5bymFLOWwphy3lsKUcK1pycUtEREREjsHFLRERERE5Bhe3JlNVFdnZ2XyVpQC2lMOWcthSDlvKYUs5bCnHqpZc3FogJibG7iE4BlvKYUs5bCmHLeWwpRy2lGNFSy5uTaZpGgoLC3kxugC2lMOWcthSDlvKYUs5bCnHqpZc3BIRERGRY3BxS0RERESOwcUtERERETmGYhiGYfcgrFRfX4/ExER4PB4kJCSY/v0Mw4CmaVBVFYqidLt/cTEweTJQVATk5srt6wThtqTOsaUctpTDlnLYUg5bygmnZV/Wazxza4GWlha7h+AYbCmHLeWwpRy2lMOWcthSjhUtubg1maZpKC0t5assBbClHLaUw5Zy2FIOW8phSzlWteTiloiIiIgcg4tbIiIiInIMLm4twLfsk8OWcthSDlvKYUs5bCmHLeVY0ZJ3S4gwvFsCERER9Xe8W0IEMwwDdXV16Ge/Q5iCLeWwpRy2lMOWcthSDlvKsaolF7cm0zQN5eXlfJWlALaUw5Zy2FIOW8phSzlsKceqllzcEhEREZFjcHFLRERERI7Bxa3JFEVBXFwc37JPAFvKYUs5bCmHLeWwpRy2lGNVS94tIcLwbglERETU3/FuCRFM13UcOnQIuq7bPZSox5Zy2FIOW8phSzlsKYct5VjVkotbk+m6jsrKSv6lEMCWcthSDlvKYUs5bCmHLeVY1ZKLWyIiIiJyDC5uiYiIiMgxuLg1maIoSExM5KssBbClHLaUw5Zy2FIOW8phSzlWtXSb+ugEVVWRlZVl9zAcgS3lsKUctpTDlnLYUg5byrGqJc/cmkzXdezdu5cXogtgSzlsKYct5bClHLaUw5ZyrGrJxa3J+JdCDlvKYUs5bCmHLeWwpRy2lMPFLRERERFRmLi4JSIiIiLH4OLWZC6XCykpKXC5mLqv2FIOW8phSzlsKYct5bClHKta8m4JJnO5XBg3bpzdw3AEtpTDlnLYUg5bymFLOWwpx6qW/DXEZLquY+fOnbwQXQBbymFLOWwphy3lsKUctpRjVUsubk2m6zpqamr4l0IAW8phSzlsKYct5bClHLaUY1VLLm6JiIiIyDG4uCUiIiIix+Di1mQulwsZGRl8laUAtpTDlnLYUg5bymFLOWwpx6qWvFuCyfwHkvqOLeWwpRy2lMOWcthSDlvKsaolfw0xmaZpKCsrg6Zpdg8l6rGlHLaUw5Zy2FIOW8phSzlWteTi1mSGYcDj8cAwDLuHEvXYUg5bymFLOWwphy3lsKUcq1pycUtEREREjsHFLRERERE5Bhe3JnO5XBg7dixfZSmALeWwpRy2lMOWcthSDlvKsaol75ZgMpfLhdTUVLuH4QhsKYct5bClHLaUw5Zy2FKOVS35a4jJNE3D1q1b+SpLAWwphy3lsKUctpTDlnLYUo5VLbm4NZlhGGhqauKrLAWwpRy2lMOWcthSDlvKYUs5VrW0dXH74Ycf4rLLLsPw4cOhKArWrl3b7dds2LABubm5iI2NxSmnnILVq1ebPk4iIiIiig62Lm4bGhqQk5ODJ598skf7V1VV4dJLL8X555+PLVu24Pbbb8cNN9yAd955x+SREhEREVE0sPUFZTNnzsTMmTN7vP/KlSsxZswYPProowCArKws/Pvf/8Zvf/tbzJgxw6xh9omqqhg/fjxUVbV7KFGPLeWwpRy2lMOWcthSDlvKsaplVN0tYePGjZg+fXrQthkzZuD222/v9Guam5vR3Nwc+HN9fT0AwOv1wuv1AvC9es/lckHXdei6HtjXv13TtKDrQzrbrqoqFEUJPK5fYmJi4Hu25T+4bS+s9u3ihmEY8HpPbFcUBaqqBo3Rvy+ATsdu1pxCjb2r7W63b05tt4eaU1fbXS4XkpKSoOt60ONE+5zsOk6JiYmOm1Oo7VbMafDgwT16TkbTnOw4TgBw0kknBT7nhDnZdZx0XQ9q6YQ52Xmc/C2dNKfutps1J3/L7ubUvkE4ompxe+DAAaSlpQVtS0tLQ319PZqamhAXF9fha5YvX45ly5Z12F5SUoJBgwYBAFJSUjBu3DhUVVWhpqYmsE9GRgYyMjKwfft2eDyewPaxY8ciNTUV27ZtQ1NTU2D7+PHjkZSUhJKSksDB8h/g3NxclJSUBI0hLy8PLS0tKC0tDWzbsWMwgDNx7NgxFBZ+FtgeFxeHnJwc1NbWorKyEgBQUREPIBsAsG/fPuzduzewv5lzAoDs7GzExMSgsLCw2zmpqoopU6bA4/GgvLy8yzkBvl8GsrKyOsxp6NChqKurQ1JSEg4fPuyIOdl1nCZNmoTi4mIAvh9sTpiTXccpPT0dH3zwAeLj4wMto31Odh2nr776CgUFBUhMTISiKI6Yk13HqbS0FAcPHgy0dMKc7DpOO3fuhMfjQWJiIpKSkhwxJ7uOk9frDbTMycnpck7t10zhUIwIefmfoih4/fXXMXv27E73Oe200zB//nwsXrw4sO2tt97CpZdeisbGxpCL21BnbkeOHInDhw8jISEBgLm/cWmahuLiYuTl5QX+j6/t/v59/IqLgfx8NwoLDeTkdP0bl3/foiJg4kTn/xap6zqKi4uRm5sbdAPoaJ6TXcfJMAwUFhYiNzc36J+HonlOdh0nXddRUFAQ1DLa52TXcWptbQ16XjphTnYdp+bm5sDPS1VVHTEnu45Ta2troKXb7XbEnOw6Tv41UW5uLmJiYrqc05EjRzB06FB4PJ7Aeq2nourMbXp6Og4ePBi07eDBg0hISAi5sAWA2NhYxMbGdtjudrsD/wzm5z8w7XV2bUhn29s/rqIoUBSlw/ZQ+/v/s7P9246x7ac7G7tZc+rN9p7MqavtbS8jCfU40Tin7rabNSev1xv4gRbq+RqNc+pqu5lz0nW905bROifAvuMUqmW0z8mu4xSqZbTPqadjDHd7V3Nq27L9L7Ch9o+GOdl5nPwt/Sf8wp1rT0TVfW6nTZuG9evXB21bt24dpk2bZtOIiIiIiCiS2Lq4PXbsGLZs2YItW7YA8N3qa8uWLaiurgYALF68GHPnzg3sf9NNN6GyshJ33XUXysvL8fvf/x5//etfcccdd9gx/B5RVRXZ2dmd/iZDPceWcthSDlvKYUs5bCmHLeVY1dLWyxIKCwtx/vnnB/68aNEiAMC8efOwevVq7N+/P7DQBYAxY8bgzTffxB133IHHH38cGRkZ+NOf/hSxtwHz819XYpayso7bkpOBzExTv60tzG7Zn7ClHLaUw5Zy2FIOW8qxomXEvKDMKvX19UhMTOzVBcq94fV6UVhYiLy8vB5dP1JcDEyeDBQVAbm5Xe9bXQ1kZQGNjR0/Fx/vW/Q6aYEbbkvqHFvKYUs5bCmHLeWwpZxwWvZlvcajFMUyM30L2Nra4O1lZcCcOb7tTlrcEhEREXWHi9sol5nJBSwRERGRX1TdLYGIiIiIqCu85tZk/psot72nW1fCuebWzMeIROG2pM6xpRy2lMOWcthSDlvKCadlX9ZrPHNrgZaWFruH4BhsKYct5bClHLaUw5Zy2FKOFS25uDWZpmkoLS3t8PZyFD62lMOWcthSDlvKYUs5bCnHqpZc3BIRERGRY/BuCU5SXe27/1dZHIAsYP9+AMPsHhURERGRZbi4tYAlb9kX9I4OkwAUA9/7HlDxoqPuFca3P5TDlnLYUg5bymFLOWwpx4qWvFtChOn1nQ78X7hmDYqRi8lzslCEXOQW/clZt0wgIiIix+PdEiKYYRioq6uDZb9DZGX5PhzI8pYOxpZy2FIOW8phSzlsKceqllzcmkzTNJSXl/NVlgLYUg5bymFLOWwphy3lsKUcq1pycUtEREREjsHFLRERERE5Bhe3JlMUBXFxcXzLPgFsKYct5bClHLaUw5Zy2FKOVS15KzCTqaqKnJwcu4fhCGwphy3lsKUctpTDlnLYUo5VLXnm1mS6ruPQoUPQdV3+waurfbcAKy4GysrkHz/CmNqyn2FLOWwphy3lsKUctpRjVUsubk2m6zoqKyvlD6T/TRsmT/Z9zJkDxMcDycmy3yeCmNayH2JLOWwphy3lsKUctpRjVUtelhCtamt970a2Zs2J+9omJ/vejazW3qERERER2YWL22iXlcV3ICMiIiL6Gi9LMJmiKEhMTOSrLAWwpRy2lMOWcthSDlvKYUs5VrXkmVuTqaqKLIe+Ha7V2FIOW8phSzlsKYct5bClHKta8sytyXRdx969e3khugC2lMOWcthSDlvKYUs5bCnHqpZc3JqMfynksKUctpTDlnLYUg5bymFLOVzcEhERERGFiYtbIiIiInIMLm5N5nK5kJKSApeLqfuKLeWwpRy2lMOWcthSDlvKsaol75ZgMpfLhXHjxtk9DEdgSzlsKYct5bClHLaUw5ZyrGrJX0NMpus6du7cad+F6GVlQHGx76O62p4xCLG9pYOwpRy2lMOWcthSDlvKsaolF7cm03UdNTU19vylGBgHzJkDTJ7s+8jKiuoFrq0tHYYt5bClHLaUw5Zy2FKOVS15WYKTvfoqMGy/77/LynwL3dpaIDPT3nERERERmYSLWycbNgzIHWb3KIiIiIgsw8sSTOZyuZCRkcFXWQpgSzlsKYct5bClHLaUw5ZyrGrJM7cm8x9I6ju2lMOWcthSDlvKYUs5bCnHqpb8NcRkmqahrKwMmqbZPZSox5Zy2FIOW8phSzlsKYct5VjVkotbkxmGAY/HA8Mw7B5K1GNLOWwphy3lsKUctpTDlnKsasnFLRERERE5Bhe3REREROQYXNyazOVyYezYsXyVpQC2lMOWcthSDlvKYUs5bCnHqpa8W4LJXC4XUlNT7R6GI7ClHLaUw5Zy2FIOW8phSzlWteSvISbTNA1bt27lqywFsKUctpTDlnLYUg5bymFLOVa15OLWZIZhoKmpia+yFMCWcthSDlvKYUs5bCmHLeVY1bJXi9vKykrpcRARERER9VmvFrennHIKzj//fKxZswbHjx+XHhMRERERUa/0anFbXFyM7OxsLFq0COnp6fjxj3+MzZs3S4/NEVRVxfjx46Gqqt1DiXpsKYct5bClHLaUw5Zy2FKOVS17tbidOHEiHn/8cezbtw+rVq3C/v378c1vfhMTJkzAihUrUFNTIz3OqKUoCpKSkqAoit1DiXpsKYct5bClHLaUw5Zy2FKOVS379IIyt9uNK664Aq+88gp+/etf44svvsCdd96JkSNHYu7cudi/f7/UOKOW1+tFQUEBvF6v3UOJemwphy3lsKUctpTDlnLYUo5VLfu0uC0sLMQtt9yCYcOGYcWKFbjzzjuxc+dOrFu3Dvv27cOsWbOkxhnVePsQOWwphy3lsKUctpTDlnLYUo4VLXv1Jg4rVqzAs88+i4qKClxyySV4/vnncckllwTecWLMmDFYvXo1Ro8eLTlWIiIiIqIu9Wpx+9RTT+H666/Hddddh2HDhoXcJzU1Fc8880yfBkdEREREFI5eXZawbt063H333R0WtoZhoLq6GgAQExODefPmdftYTz75JEaPHo2BAwciPz+/27suPPbYYzj99NMRFxeHkSNH4o477ojo25Gpqors7Gy+ylIAW8phSzlsKYct5bClHLaUY1XLXi1ux40bh9ra2g7bv/rqK4wZM6bHj/Pyyy9j0aJFWLp0KYqLi5GTk4MZM2bg0KFDIfd/4YUXcM8992Dp0qUoKyvDM888g5dffhn33ntvb6ZhmZiYGLuH4BhsKYct5bClHLaUw5Zy2FKOFS17tbjt7G3Tjh07hoEDB/b4cVasWIEFCxZg/vz5OOOMM7By5UrEx8dj1apVIff/5JNPcM455+AHP/gBRo8ejYsuugjXXHNNRN9jV9M0FBYW8mJ0AWwphy3lsKUctpTDlnLYUo5VLcO65nbRokUAfPcpW7JkCeLj4wOf0zQNmzZtwsSJE3v0WC0tLSgqKsLixYsD21wuF6ZPn46NGzeG/Jqzzz4ba9aswebNmzF16lRUVlbirbfewg9/+MNOv09zczOam5sDf66vrwfgux2F/1YULpcLLpcLuq5D1/Wg8bhcLmiaFrSg72y7qqpQFCXoFhf+fQzD6HDrC/9p+bYH2beL++v9T2xXFAWqqp4Yo9cL99dfqwJBY/c/Btpu/3p/XdfhajOu3syps7F3td3t9s2p7fYOc+pmu/+/dV0PGo/EcbJrTmY+97oau/856aQ52XWc/D3bPn60z8nO49R2Xk6Zk53Hyf+9nTSntmO0Yk5tWzplTnYdp7Ytu5tTX24XFtbitqSkBIDvB/mnn34adGo5JiYGOTk5uPPOO3v0WLW1tdA0DWlpaUHb09LSUF5eHvJrfvCDH6C2thbf/OY3A4vFm266qcvLEpYvX45ly5aFnMugQYMAACkpKRg3bhyqqqqC3oAiIyMDGRkZ2L59OzweT2D72LFjkZqaim3btqGpqSmwffz48UhKSkJJSUngYBmGEXhi+Pv55eXloaWlBaWlpYFtO3YMBnAmjh07hsLCzwLb4+LikJOTg9raWlRWViK+ogLZAKqrqzFmyhTs27cPe/fuBQBUVMQDyAaAwJz8+9fU1CAN6NOcACA7OxsxMTEoLCzsdk6qqmLKlCnweDxBx7b9nPwSExORlZUVNCcAGDp0KABg9+7dOHz4sOhxsmtOZj73uprTpEmToOs6iouLAzfTjvY52XWc0tPT0dDQENQy2udk13Gqr69HXV1doKUT5mTXcfr888+DWjphTnYdp507dwZaJiUlOWJOdh0nr9cbaJmTk9PlnNqvmcKhGJ1dY9CF+fPn4/HHH0dCQkKvv/G+ffswYsQIfPLJJ5g2bVpg+1133YUPPvgAmzZt6vA1GzZswPe//338/Oc/R35+Pr744gvcdtttWLBgAe6///6Q3yfUmduRI0fi8OHDgfGbfea2uLgYeXl5Hd6RI9RvLcXFQH6+G4WFBnJyuviNq7gY7vx8aJs3Q50yJWjs/scoKgImTgzeXy8ogCsvLyp/i/QvxnJzcwNnzLoaezTMyc4zt4WFhcjNzQ26sD+a52TXcdJ1HQUFBUEto31Odh2n1tbWoOelE+Zk13Fqbm4O/LxUVdURc7LrOLW2tgZaut1uR8zJzjO3/pb+E6SdzenIkSMYOnQoPB5P2OvNXi1uJbS0tCA+Ph6vvvoqZs+eHdg+b9481NXV4Y033ujwNeeeey6+8Y1v4OGHHw5sW7NmDW688UYcO3YsaMHTmfr6eiQmJvYqVm+0Pf3ek7ebKy4GJk8GioqA3Nze7RjyUz1+4MgVbkvqHFvKYUs5bCmHLeWwpZxwWvZlvdbjyxKuuOIKrF69GgkJCbjiiiu63Pe1117r9vFiYmIwefJkrF+/PrC41XUd69evx8KFC0N+TWNjY4cFrH+Fb9MavUdaWloQFxdn9zAcgS3lsKUctpTDlnLYUg5byrGiZY/vlpCYmBhYZScmJnb50VOLFi3C008/jeeeew5lZWW4+eab0dDQgPnz5wMA5s6dG/SCs8suuwxPPfUUXnrpJVRVVWHdunW4//77cdlllwX902ok0TQNpaWlHU67U/jYUg5bymFLOWwphy3lsKUcq1r2+Mzts88+G/K/++Lqq69GTU0NlixZggMHDmDixIl4++23Ay8yq66uDjpTe99990FRFNx333348ssvkZKSgssuuwy/+MUvRMZDRERERNGtV2+/29TUBMMwArcC2717N15//XWcccYZuOiii8J6rIULF3Z6GcKGDRuCB+t2Y+nSpVi6dGlvhk1EREREDterN3GYNWsWnn/+eQBAXV0dpk6dikcffRSzZs3CU089JTpAJ4jUSyaiEVvKYUs5bCmHLeWwpRy2lGNFy14tbouLi3HuuecCAF599VWkp6dj9+7deP755/G73/1OdIDRzu12Y8qUKXC7e3WSnNpgSzlsKYct5bClHLaUw5ZyrGrZq8VtY2MjBg8eDAD417/+hSuuuAIulwvf+MY3sHv3btEBRjvDMFBXVxc5d3MoK/PdFqy4GKiutns0YYm4llGMLeWwpRy2lMOWcthSjlUte7W4PeWUU7B27Vrs2bMH77zzTuA620OHDlly79hoomkaysvL7X+VZXIyEB8PzJnju9/t5MlAVlZULXAjpqUDsKUctpTDlnLYUg5byrGqZa8Wt0uWLMGdd96J0aNHIz8/P/AOY//6178wadIk0QGSkMxM31nboiLfx5o1QGMjUFtr98iIiIiIxPTqoofvfe97+OY3v4n9+/cjJycnsP3CCy/Ed7/7XbHBkbDMTN8HERERkUP1+ore9PR0pKenB22bOnVqnwfkNIqiIC4uTuYt+6qrT5xpLSvr++NFGdGW/RxbymFLOWwphy3lsKUcq1r2anHb0NCAX/3qV1i/fj0OHToEXdeDPl9ZWSkyOCdQVTXo7HavVVf7rpFtbDyxLT7edy1tPyHWkthSEFvKYUs5bCmHLeVY1bJXi9sbbrgBH3zwAX74wx9i2LBh/G2mC7quo7a2FsnJyUHvtha22lrfwnbNGt8iF/AtbPvRZQZiLYktBbGlHLaUw5Zy2FKOVS17tbj95z//iTfffBPnnHOO9HgcR9d1VFZWYsiQITIHMisLyM3t++NEIfGW/RhbymFLOWwphy3lsKUcq1r26pFPPvlkDBkyRHosRERERER90qvF7UMPPYQlS5agse31n0RERERENuvVZQmPPvoodu7cibS0NIwePRoDBgwI+nxxcbHI4JxAURQkJibyumQBbCmHLeWwpRy2lMOWcthSjlUte7W4nT17tvAwnEtVVWT5XwBGfcKWcthSDlvKYUs5bCmHLeVY1bJXi9ulS5dKj8OxdF3Hvn37MHz4cF6I3kdsKYct5bClHLaUw5Zy2FKOVS17/ch1dXX405/+hMWLF+Orr74C4Lsc4csvvxQbnBPouo69e/d2uBcwhY8t5bClHLaUw5Zy2FIOW8qxqmWvztyWlpZi+vTpSExMxK5du7BgwQIMGTIEr732Gqqrq/H8889Lj5OIiIiIqFu9OnO7aNEiXHfdddixYwcGDhwY2H7JJZfgww8/FBscEREREVE4erW4LSgowI9//OMO20eMGIEDBw70eVBO4nK5kJKSwut0BLClHLaUw5Zy2FIOW8phSzlWtezVZQmxsbGor6/vsH379u1ISUnp86CcxOVyYdy4cXYPwxHYUg5bymFLOWwphy3lsKUcq1r2aul8+eWX48EHH0RraysA333Lqqurcffdd+PKK68UHWC003UdO3fu5IXoAthSDlvKYUs5bCmHLeWwpRyrWvZqcfvoo4/i2LFjSElJQVNTE8477zyccsopGDx4MH7xi19IjzGq6bqOmpoa/qUQwJZy2FIOW8phSzlsKYct5VjVsleXJSQmJmLdunX4+OOPsXXrVhw7dgy5ubmYPn269PiIiIiIiHos7MWtrutYvXo1XnvtNezatQuKomDMmDFIT0+HYRh8ezoiIiIisk1YlyUYhoHLL78cN9xwA7788kucddZZOPPMM7F7925cd911+O53v2vWOKOWy+VCRkYGX2UpgC3lsKUctpTDlnLYUg5byrGqZVhnblevXo0PP/wQ69evx/nnnx/0uffeew+zZ8/G888/j7lz54oOMpr5DyT1HVvKYUs5bCmHLeWwpRy2lGNVy7CWzi+++CLuvffeDgtbALjgggtwzz334C9/+YvY4JxA0zSUlZVB0zS7hxL12FIOW8phSzlsKYct5bClHKtahrW4LS0txcUXX9zp52fOnImtW7f2eVBOYhgGPB4PDMOweyhRjy3lsKUctpTDlnLYUg5byrGqZViL26+++gppaWmdfj4tLQ1Hjhzp86CIiIiIiHojrMWtpmlwuzu/TFdVVXi93j4PioiIiIioN8J6QZlhGLjuuusQGxsb8vPNzc0ig3ISl8uFsWPH8lWWAthSDlvKYUs5bCmHLeWwpRyrWoa1uJ03b163+/BOCcFcLhdSU1PtHoYjsKUctpTDlnLYUg5bymFLOVa1DGtx++yzz5o1DsfSNA3btm3DhAkToKqq3cOJamwphy3lsKUctpTDlnLYUo5VLXmO3WSGYaCpqYmvshTAlnLYUg5bymFLOWwphy3lWNWSi1siIiIicgwubomIiIjIMbi4NZmqqhg/fjyv0xHAlnLYUg5bymFLOWwphy3lWNUyrBeUUfgURUFSUpLdw3AEtpTDlnLYUg5bymFLOWwpx6qWPHNrMq/Xi4KCAr65hQC2lMOWcthSDlvKYUs5bCnHqpZc3FpA0zS7h+AYbCmHLeWwpRy2lMOWcthSjhUtubglIiIiIsfg4paIiIiIHIOLW5Opqors7Gy+ylIAW8phSzlsKYct5bClHLaUY1VLLm4tEBMTY/cQHIMt5bClHLaUw5Zy2FIOW8qxoiUXtybTNA2FhYW8GF0AW8phSzlsKYct5bClHLaUY1VL3ue2vysrO/HfyclAZqZ9YyEiIiLqIy5u+6vkZCA+Hpgz58S2+HjfYpcLXCIiIopSXNz2V5mZvoVsba3vz2VlvoVubS0Xt0RERBS1uLg1maqqyMvLs+VVlm2vOABCXHWQmRlVC1k7WzoNW8phSzlsKYct5bClHKta2v6CsieffBKjR4/GwIEDkZ+fj82bN3e5f11dHW699VYMGzYMsbGxOO200/DWW29ZNNreaWlpsfT7tb3iYPLkEx9ZWUB1taVDEWd1SydjSzlsKYct5bClHLaUY0VLWxe3L7/8MhYtWoSlS5eiuLgYOTk5mDFjBg4dOhRy/5aWFnz729/Grl278Oqrr6KiogJPP/00RowYYfHIe07TNJSWllr6Kkv/FQdFRSc+1qwBGhtPXIUQjexo6VRsKYct5bClHLaUw5ZyrGpp62UJK1aswIIFCzB//nwAwMqVK/Hmm29i1apVuOeeezrsv2rVKnz11Vf45JNPMGDAAADA6NGjrRxy1IiyKw6IiIiIRNh25ralpQVFRUWYPn36icG4XJg+fTo2btwY8mv+/ve/Y9q0abj11luRlpaGCRMm4Je//CV/myIiIiIiADaeua2trYWmaUhLSwvanpaWhvLy8pBfU1lZiffeew/XXnst3nrrLXzxxRe45ZZb0NraiqVLl4b8mubmZjQ3Nwf+XF9fDwDwer3wer0AfItql8sFXdeh63pgX/92TdNgGEa321VVhaIogccFfKfgXS4XDMMI2u7f37+Pn28X99f7n9iuaBpUwDfGNo+jKApUVe107P7t/sf17RNiTroO19djMdo8fqg5dTb2rra73b45td3e2dg7267remB72/FIHCe75mTmc6+rsRuGEXgcp8zJruPk/9+2jx/tc7LzOCmKEvicU+Zk13Fq29Ipc7LjOLVt6ZQ52XWc2rbsbk7tG4Qjqu6WoOs6UlNT8cc//hGqqmLy5Mn48ssv8fDDD3e6uF2+fDmWLVvWYXtJSQkGDRoEAEhJScG4ceNQVVWFmpqawD4ZGRnIyMjA9u3b4fF4AtvHjh2L1NRUbNu2DU1NTYHt48ePR1JSEkpKSoIOVnZ2NhRFQWFhYdAY8vLy0NLSgtLS0sC2HTsGAzgTx44dQ2HhZ4HtQ3btwmnwvaBue5vHSUxMRFZWFvbt24e9e/cGtrefU0VFPIDsr+eX1mFOpx07hiEAdu7cia/ajL2rOcXExPRoTqqqYsqUKfB4PEG/uMTFxSEnJwe1tbWorKzs0ZymTJmCnTt3mnac7JiT2c+9zuY0YcIElJSUOGpOdh2nwYMHB7V0wpzsOE4NDQ0wDCPQ0glzsus4lZeXB7V0wpzsPk4lJSWOmxNgz3EqKSnpdk5tf6aGSzHaLq8t1NLSgvj4eLz66quYPXt2YPu8efNQV1eHN954o8PXnHfeeRgwYADefffdwLZ//vOfuOSSS9Dc3Bzy/YpDnbkdOXIkDh8+jISEBADm/sZlGAaOHTuGxMTEoMf27w8E/9ZSXAzk57tRWGggJ6fNb1wlJVCnToVeUAB94sQT23v4G5f/cQsKdOTlhZjTli1wTZkCbfNmGJMmdTmnzsbe1XaJ3yIVRcHRo0cxePDgHh2P/vqbcU/G7nK54PF4cNJJJ0FRFEfMya7jpCgKjhw5gsGDBwdaRvuc7DpOuq7jyJEjSEhIgKIojpiTXcfJ6/XC4/EEWjphTnaeua2vr0dCQgJcLpcj5mTXcTIMI9DS7XZ3OacjR45g6NChgedxOGw7cxsTE4PJkydj/fr1gcWtrutYv349Fi5cGPJrzjnnHLzwwgvQdT3wz4Hbt2/HsGHDQi5sASA2NhaxsbEdtrvd7kBYP/+Baa+z+7F1tr3t43q9XlRUVCAvL6/D9wu1v/8/FUUJ3v/r7+VyueAK8Tidjd2/3f8l/n06jL3t9hCP35Oxd7e9w5x6OHY/r9eL8vLyTlv25Tj1dntf59TddrPm1NXzMlrn1NV2M+fk9Xqxffv2kC2jdU6APcdJ13Xs2LGjQ8tonpNdxwlAyJbRPCe7jlPb56Xa9v+Lo3hOdh0nr9cbaOk/GRDuXHvC1luBLVq0CE8//TSee+45lJWV4eabb0ZDQ0Pg7glz587F4sWLA/vffPPN+Oqrr3Dbbbdh+/btePPNN/HLX/4St956q11TICIiIqIIYus1t1dffTVqamqwZMkSHDhwABMnTsTbb78deJFZdXV10G8LI0eOxDvvvIM77rgD2dnZGDFiBG677Tbcfffddk2BiIiIiCKI7S8oW7hwYaeXIWzYsKHDtmnTpuE///mPyaOSoygK4uLigq5rpN5hSzlsKYct5bClHLaUw5ZyrGpp++LW6VRVRU5Ojt3DcAS2lMOWcthSDlvKYUs5bCnHqpa2XnPbH+i6jkOHDnW4UwKFjy3lsKUctpTDlnLYUg5byrGqJRe3JtN1HZWVlfxLIYAt5bClHLaUw5Zy2FIOW8qxqiUXt0RERETkGFzcEhEREZFjcHFrMkVRkJiYyFdZCmBLOWwphy3lsKUctpTDlnKsasm7JZhMVVVkZWXZPQxHYEs5bCmHLeWwpRy2lMOWcqxqyTO3JtN1HXv37uWF6ALYUg5bymFLOWwphy3lsKUcq1pycWsy/qWQw5Zy2FIOW8phSzlsKYct5XBxS0REREQUJi5uiYiIiMgxuLg1mcvlQkpKClwupu4rtpTDlnLYUg5bymFLOWwpx6qWvFuCyVwuF8aNG2f3MByBLeWwpRy2lMOWcthSDlvKsaolfw0xma7r2LlzJy9EF8CWcthSDlvKYUs5bCmHLeVY1ZKLW5Ppuo6amhr+pRDAlnLYUg5bymFLOWwphy3lWNWSi1siIiIicgwubomIiIjIMbi4NZnL5UJGRgZfZSmALeWwpRy2lMOWcthSDlvKsaol75ZgMv+BjBplZSf+OzkZyMy0byztRF3LCMaWcthSDlvKYUs5bCnHqpb8NcRkmqahrKwMmqbZPZSuJScD8fHAnDnA5Mm+j6wsoLra7pEFRE3LKMCWcthSDlvKYUs5bCnHqpY8c2sywzDg8XhgGIbdQ+laZqbvrG1tre/PZWW+hW5tbcScvY2allGALeWwpRy2lMOWcthSjlUtubilEzIzI2YhS0RERNQbvCyBiIiIiByDi1uTuVwujB07lq+yFMCWcthSDlvKYUs5bCmHLeVY1ZKXJZjM5XIhNTXV7mE4AlvKYUs5bCmHLeWwpRy2lGNVS/4aYjJN07B161a+ylIAW8phSzlsKYct5bClHLaUY1VLLm5NZhgGmpqa+CpLAWwphy3lsKUctpTDlnLYUo5VLbm4JSIiIiLH4OKWiIiIiByDi1uTqaqK8ePHQ1VVu4cS9dhSDlvKYUs5bCmHLeWwpRyrWvJuCSZTFAVJSUl2D8MR2FIOW8phSzlsKYct5bClHKta8sytybxeLwoKCuD1eu0eStRjSzlsKYct5bClHLaUw5ZyrGrJM7cW6PUtL6qrgdpa33+XlckNKIrxVixy2FIOW8phSzlsKYct5VjRkovbSLV/P3BuFtDYeGJbfDyQnGzfmIiIiIgiHBe3kaquzrewXbMGyMrybUtOBjIzbR0WERERUSTj4tZkqqoiOzu7968MzMoCcnNlBxWl+tySAthSDlvKYUs5bCmHLeVY1ZIvKLNATEyM3UNwDLaUw5Zy2FIOW8phSzlsKceKllzcmkzTNBQWFvJidAFsKYct5bClHLaUw5Zy2FKOVS25uCUiIiIix+DiloiIiIgcg4tbIiIiInIMLm5Npqoq8vLy+CpLAWwphy3lsKUctpTDlnLYUo5VLbm4tUBLS4vdQ3AMtpTDlnLYUg5bymFLOWwpx4qWXNyaTNM0lJaW8lWWAthSDlvKYUs5bCmHLeWwpRyrWnJxS0RERESOwcUtERERETkGF7cW4EXocthSDlvKYUs5bCmHLeWwpRwrWrpN/w79nNvtxpQpU+wehiOwpRy2lMOWcthSDlvKYUs5VrXkmVuTGYaBuro6GIZh91CiHlvKYUs5bCmHLeWwpRy2lGNVSy5uTaZpGsrLy6P3VZZlZUBxse+jutrWoUR9ywjClnLYUg5bymFLOWwpx6qWEbG4ffLJJzF69GgMHDgQ+fn52Lx5c4++7qWXXoKiKJg9e7a5A+yPkpOB+Hhgzhxg8mTfR1aW7QtcIiIioq7Yvrh9+eWXsWjRIixduhTFxcXIycnBjBkzcOjQoS6/bteuXbjzzjtx7rnnWjTSfiYz03fWtqjI97FmDdDYCNTW2j0yIiIiok7ZvrhdsWIFFixYgPnz5+OMM87AypUrER8fj1WrVnX6NZqm4dprr8WyZcswduxYC0cbPkVREBcXB0VR7B5K+DIzgdxc30dWlt2jie6WEYYt5bClHLaUw5Zy2FKOVS1tvVtCS0sLioqKsHjx4sA2l8uF6dOnY+PGjZ1+3YMPPojU1FT86Ec/wkcffdTl92hubkZzc3Pgz/X19QAAr9cLr9cb+J4ulwu6rkPX9aCxuFwuaJoWdPFzZ9tVVYWiKIHH9cvOzg58z7b8t8Noe+2Jbxc3DBgnvsbrhaIoUFW1wxg7295+Tv7H9e3Tuzm524wn1Ng7mxPge4WkYRhB23szp5ycnK/n4w3aLnGc7JqTmc+9ruaUnZ0NTdOCviba5xRqu9lzUlUVEyZMgGEYHX6mROuc7DpOLpcLZ555ZqClE+Zk13ECENTSCXOy6zgZhhFoqWmaI+Zk53Hyt/Tv09mc2jcIh62L29raWmiahrS0tKDtaWlpKC8vD/k1//73v/HMM89gy5YtPfoey5cvx7JlyzpsLykpwaBBgwAAKSkpGDduHKqqqlBTUxPYJyMjAxkZGdi+fTs8Hk9g+9ixY5Gamopt27ahqakpsH38+PFISkpCSUlJ0MEaMWIE0tLSUFxcHDSGvLw8tLS0oLS0NLBtx47BAM5EY6PvcT8vK0OjriMuLg45OTmora1FZWVlYP/ExERkZWVh37592Lt3b2B7+zlVVMQDyP56fmlhz6miogJnthlPdnY2YmJiUFhY2O2cVFXFlClT4PF4go5ruHNKTk5GQkIC6uvrUdvm8gip42THnMx+7nU2p9zcXBw8eBBffvmlY+Zk13EaPnw4SkpKgn4QR/uc7DpOR44cwbZt2xATE+OYOdl1nD799FPU19cHWjphTnYep5aWFsTExDhqTnYdJ3/L7uZUUlKC3lIMG+9tsW/fPowYMQKffPIJpk2bFth+11134YMPPsCmTZuC9j969Ciys7Px+9//HjNnzgQAXHfddairq8PatWtDfo9QZ25HjhyJw4cPIyEhAYC5v3Fpmobi4mLk5eV1OA0f6jeu4mIgP9+NwjWfY/KcM+HdtAnIze3zb1z+x33+eR1nnhk89uRkYPTobua0eTPc+fmB8djxW6RvHsXIzc0NnJmQOk5djT0afzPubuyGYaCwsBC5bY5ltM/JruOk6zoKCgqCWkb7nOw6Tq2trUHPSyfMya7j1NzcHPh5qaqqI+Zk13FqbW0NtHS73Y6Yk13Hyb8mys3NDfzi1dmcjhw5gqFDh8Lj8QTWaz1l65nb5ORkqKqKgwcPBm0/ePAg0tPTO+y/c+dO7Nq1C5dddllgmz+42+1GRUUFxo0bF/Q1sbGxiI2N7fBYbrcbbnfw9Nv+c05bbf/Pvyfb2z+uoihQFKXD9lD7+/9TgXLic20+39kYu9uenu67+cHcuf59Tow9Pt732rHMzO7n1H48PZmTX2cNejqntv/kG+px+nqcerO9r3PqbrtZc2r7T76hnq/ROKeutps5J13XO20ZrXMC7DtOoVpG+5zsOk6hWkb7nHo6xnC3dzWnti3b/wIbav9omJOdx8nf0n/CL9y59oSti9uYmBhMnjwZ69evD9zOS9d1rF+/HgsXLuyw//jx4/Hpp58Gbbvvvvtw9OhRPP744xg5cqQVw45K/psftL/ZQVmZ725ftbW+fYiIiIiime1vv7to0SLMmzcPeXl5mDp1Kh577DE0NDRg/vz5AIC5c+dixIgRWL58OQYOHIgJEyYEfX1SUhIAdNgeKRRFQWJiYkS8yjIzM7oXsJHUMtqxpRy2lMOWcthSDlvKsaql7Yvbq6++GjU1NViyZAkOHDiAiRMn4u233w68yKy6ujrk6fBooaoqsiLgNlpOwJZy2FIOW8phSzlsKYct5VjV0vbFLQAsXLgw5GUIALBhw4Yuv3b16tXyAxKk6zr27duH4cOHR/UiPRKwpRy2lMOWcthSDlvKYUs5VrXkUTKZruvYu3dv0CsNqXfYUg5bymFLOWwphy3lsKUcq1pycUtEREREjsHFLRERERE5Bhe3JnO5XEhJSeF1OgLYUg5bymFLOWwphy3lsKUcq1pGxAvKnMzlcnV4YwnqHbaUw5Zy2FIOW8phSzlsKceqlvw1xGS6rmPnzp28EF0AW8phSzlsKYct5bClHLaUY1VLLm5Npus6ampq+JdCAFvKYUs5bCmHLeWwpRy2lGNVSy5uiYiIiMgxuLglIiIiIsfg4tZkLpcLGRkZfJWlALaUw5Zy2FIOW8phSzlsKceqlrxbgsn8B5L6ji3lsKUctpTDlnLYUg5byrGqJX8NMZmmaSgrK4OmaXYPJeqxpRy2lMOWcthSDlvKYUs5VrXkmVuTGYYBj8cDwzDsHoqMsrIT/52cDGRmWvatHdfSRmwphy3lsKUctpTDlnKsasnFLfVMcjIQHw/MmXNiW3y8b7Fr4QKXiIiIqCtc3FLPZGb6FrK1tb4/l5X5Frq1tVzcEhERUcTg4tZkLpcLY8eOdcarLDMzbV3IOqqlzdhSDlvKYUs5bCmHLeVY1ZKLW5O5XC6kpqbaPQxHYEs5bCmHLeWwpRy2lMOWcqxqyV9DTKZpGrZu3cpXWQpgSzlsKYct5bClHLaUw5ZyrGrJxa3JDMNAU1MTX2UpgC3lsKUctpTDlnLYUg5byrGqJRe3REREROQYXNwSERERkWNwcWsyVVUxfvx4qKpq91CiHlvKYUs5bCmHLeWwpRy2lGNVS94twWSKoiApKcnuYTgCW8phSzlsKYct5bClHLaUY1VLnrk1mdfrRUFBAbxer91DiXpsKYct5bClHLaUw5Zy2FKOVS25uLUAbx8ihy3lsKUctpTDlnLYUg5byrGiJRe3REREROQYXNwSERERkWNwcWsyVVWRnZ3NV1kKYEs5bCmHLeWwpRy2lMOWcqxqycWtBWJiYuwegmOwpRy2lMOWcthSDlvKYUs5VrTk4tZkmqahsLCQF6MLYEs5bCmHLeWwpRy2lMOWcqxqycUtERERETkG38SB+qas7MR/JycDmZn2jYWIiIj6PS5uqXeSk4H4eGDOnBPb4uN9i10ucImIiMgmXNyaTFVV5OXlRfyrLNuegPXr8kRsZqbvi2prTzzAnDm+P5u0uI2WltGALeWwpRy2lMOWcthSjlUtubi1QEtLC+Li4uweRkihTsD6dXsiNjPT8rO0kdwy2rClHLaUw5Zy2FIOW8qxoiVfUGYyTdNQWloasa+y9J+ALSoK/lizBmhsPHFiNhJEestowpZy2FIOW8phSzlsKceqljxzS3acgCUiIiIyBc/cEhEREZFjcHFrAV6ELoct5bClHLaUw5Zy2FIOW8qxoiUvSzCZ2+3GlClT7B6GI7ClHLaUw5Zy2FIOW8phSzlWteSZW5MZhoG6ujoYhmH3UKIeW8phSzlsKYct5bClHLaUY1VLLm5NpmkaysvL+SpLAWwphy3lsKUctpTDlnLYUo5VLbm4JSIiIiLH4DW3Nqqu7ngf2VDvFEZEREREPcPFrckURUFcXBwURQnaXl0NZGX53iihvfh4IDnJa9EIo0dnLSl8bCmHLeWwpRy2lMOWcqxqycWtyVRVRU5OTofttbW+he2aNb5FblvJyUBmbatFI4wenbWk8LGlHLaUw5Zy2FIOW8qxqiWvuTWZrus4dOgQdF0P+fmsLCA3N/iD7xYWWnctqefYUg5bymFLOWwphy3lWNWSi1uT6bqOyspK/qUQwJZy2FIOW8phSzlsKYct5VjVkpclkKy2r4hLTuZpaCIiIrIUF7ckIznZ90q4OXNObIuP9y12ucAlIiIii0TEZQlPPvkkRo8ejYEDByI/Px+bN2/udN+nn34a5557Lk4++WScfPLJmD59epf7201RFCQmJjr/VZaZmb6FbFGR72PNGt8r5trf66wP+k1LC7ClHLaUw5Zy2FIOW8qxqqXti9uXX34ZixYtwtKlS1FcXIycnBzMmDEDhw4dCrn/hg0bcM011+D999/Hxo0bMXLkSFx00UX48ssvLR55z6iqiqysLKiqavdQzJeZeeJVce1vASGgX7U0GVvKYUs5bCmHLeWwpRyrWtq+uF2xYgUWLFiA+fPn44wzzsDKlSsRHx+PVatWhdz/L3/5C2655RZMnDgR48ePx5/+9Cfouo7169dbPPKe0XUde/fu5YXoAthSDlvKYUs5bCmHLeWwpRyrWtp6zW1LSwuKioqwePHiwDaXy4Xp06dj48aNPXqMxsZGtLa2YsiQISE/39zcjObm5sCf6+vrAQBerxderzfwPV0uF3RdDwru365pGgzD6Ha7qqpQFCXwuIDvfZT37NmDtLS0oMf27eKGYRjweoPfY9nt9m1Xvh4nvF4oigJVVTuMsbPtfZ1T2/EBwXPyz9U/v5DbvV6424zfP6e2+4c7J/9fitTUVLhcJ34vkzhOPZqTFvo49WVOZj73uhq7YRjYs2cPUlJSgn6DjuY52XWcdF3v0DLa52TXcfL/vPS3dMKc7DpOra2tQS2dMCe7jlPblm632xFzsus4tf07HhMT0+Wc2jcIh62L29raWmiahrS0tKDtaWlpKC8v79Fj3H333Rg+fDimT58e8vPLly/HsmXLOmwvKSnBoEGDAAApKSkYN24cqqqqUFNTE9gnIyMDGRkZ2L59OzweT2D72LFjkZqaim3btqGpqSmwffz48UhKSkJJSUngYBmGEXhilJSUBPatqIgHkI3jx4+jsHBrYLuqqpgyZQqOHTuGwQA+LytDo64jLi4OOTk5qK2tRWVlZWD/xMREZGVlYd++fdi7d29ge1/n5B/fsWPHAAwOmhMAZGdnIyYmBoWFhUFd8/Ly0NLSgi/KypD99fibFQVTpkyBx+MJOq7hzmno0KEAgN27d+Pw4cOix6kncyotLe1wnPo6JzOfe13NadKkSdB1HcXFxYFrn6J9TnYdp/T0dDQ0NAS1jPY52XWc6uvrUVdXF2jphDnZdZw+//zzoJZOmJNdx2nnzp2BlklJSY6Yk13Hyev1Blrm5OR0Oae2a6ZwKUbb5bXF9u3bhxEjRuCTTz7BtGnTAtvvuusufPDBB9i0aVOXX/+rX/0Kv/nNb7BhwwZkZ2eH3CfUmduRI0fi8OHDSEhIAGD+mdvi4mLk5eUFXUBdXAzk57tRWGggJyfEb1xFRVDy8uDdtAnIzbX8N66245s8uRe/RRYUwJ2fHxi/1Jnb4uJi5Obm8sytwJnbwsJC5Obm8sytwJnbgoKCoJbRPie7jlNra2vQ89IJc7LrODU3Nwd+XvLMbd/m1NraGmjJM7d9P3Prb9ndmdsjR45g6NCh8Hg8gfVaT9l65jY5ORmqquLgwYNB2w8ePIj09PQuv/aRRx7Br371K7z77rudLmwBIDY2FrGxsR22u91uuN3B0/cfmPY6u/C5s+1tH9flciE1NRWqqgY9tn8XRVE6jMO/PfBY7R4v1BjD3d7dnNqOr/2c2upue9vxdzbXno5d1/XAPwtJH6febu/rnLrbbtacdF1HamoqBgwY0OH7Ruucutpu9pw6axnNc7LjOKmqGrJlNM/JruM0YMCAkC2jeU52HadQLaN9TnYdJ/+aaMCAAb1eX/SErS8oi4mJweTJk4NeDKbrvheHtT2T295vfvMbPPTQQ3j77beRl5dnxVB7zeVyYdy4cSGfGBQetpTDlnLYUg5bymFLOWwpx6qWth+pRYsW4emnn8Zzzz2HsrIy3HzzzWhoaMD8+fMBAHPnzg16wdmvf/1r3H///Vi1ahVGjx6NAwcO4MCBA19fGxp5dF3Hzp07g07pd6q62ne9QnFx8Dt9EYAwW1KX2FIOW8phSzlsKYct5VjV0vbF7dVXX41HHnkES5YswcSJE7Flyxa8/fbbgReZVVdXY//+/YH9n3rqKbS0tOB73/sehg0bFvh45JFH7JpCl3RdR01NTfcHsrrad2/YyZN9H3Pm+N7hKznZmoFGgR63pG6xpRy2lMOWcthSDlvKsaplRLz97sKFC7Fw4cKQn9uwYUPQn3ft2mX+gOxQW+t7R681a068AUJycvS/dW3bM9BOmA8RERFFtIhY3FIbWVm+d/iKEO2vjujx+jQ52Xfmec6cE9vi430PyAUuERERmYSLW5O5XC5kZGRE3YXoodamQBjr08xM3461tb4/l5X5Hqy2tteL22htGYnYUg5bymFLOWwphy3lWNWSi1uT+Q9ktGm/NgV6sT7NzBQ9SxutLSMRW8phSzlsKYct5bClHKta8tcQk2mahrKysg43KY4GmZm+KyT8H/5Lge0SzS0jDVvKYUs5bCmHLeWwpRyrWnJxazLDMODxeILerYN6hy3lsKUctpTDlnLYUg5byrGqJRe3REREROQYXNwSERERkWNwcWsyl8uFsWPH8lWWAthSDlvKYUs5bCmHLeWwpRyrWvJuCSZzuVxITU21exiOwJZy2FIOW8phSzlsKYct5VjVkr+GmEzTNGzdupWvshTAlnLYUg5bymFLOWwphy3lWNWSZ25NZhgGmpqa+CpLvz68HS9bymFLOWwphy3lsKUctpRjVUsubskafDteIiIisgAXt2QNE96Ol4iIiKg9Lm5Npqoqxo8fD1VV7R6K/fr4drxsKYct5bClHLaUw5Zy2FKOVS25uDWZoihISkqyexiOwJZy2FIOW8phSzlsKYct5VjVkndLMJnX60VBQQG8Xq/dQ4l6bCmHLeWwpRy2lMOWcthSjlUteebWAk67fUjbGx74hXnjg15zWks7saUctpTDlnLYUg5byrGiJRe31GOhbnjgxxsfEBERUSTg4pZ6rP0ND/x44wMiIiKKFFzcmkxVVWRnZzvmVZZ9vOFBR2G8qYPTWtqJLeWwpRy2lMOWcthSjlUtubi1QExMjN1DiDy9fFMHtpTDlnLYUg5bymFLOWwpx4qWvFuCyTRNQ2FhIS9Gb89/jUNRke9jzRqgsbHjNQ9tsKUctpTDlnLYUg5bymFLOVa15Jlbso/4NQ5ERETU3/HMLRERERE5Bhe3REREROQYXNyaTFVV5OXl8VWWAthSDlvKYUs5bCmHLeWwpRyrWvKaWwu0tLQgLi7O7mGYrv07l/XqXcu6uTVYf2lpBbaUw5Zy2FIOW8phSzlWtOSZW5NpmobS0lJHv8qy7V29Jk8+8ZGVBVRX9+FB2j1Af2hpFbaUw5Zy2FIOW8phSzlWteSZW+qzUO9cFva7lrV/EL7tGREREfUCF7ckQuSuXrw1GBEREfURF7cW4EXofdD2GtykJLYUxJZy2FIOW8phSzlsKceKllzcmsztdmPKlCl2DyP6hHh7Xnd8PKaUlQFuPm37is9LOWwphy3lsKUctpRjVUu+oMxkhmGgrq4OhmHYPZTo0snb8x6tqmJLAXxeymFLOWwphy3lsKUcq1pycWsyTdNQXl7OV1n2RmYmkJvr+8jKAgBU7drFlgL4vJTDlnLYUg5bymFLOVa15L/vUlSJ27ULKC72XZrQqxvpEhERkZNxcUumav/GDkAv16TJyTDi43HqAw8ADzzg2xYf7/sGXOASERHR17i4NZmiKIiLi4OiKHYPxVIhXg8W0Ks1aWYm9G3bsHPTJowbNw7q9u28D24f9NfnpRnYUg5bymFLOWwpx6qWXNyaTFVV5OTk2D0My4V6Ywegb+/NoI4Zg9PGjPn6D+qJB/TjZQo91l+fl2ZgSzlsKYct5bClHKtacnFrMl3XUVtbi+TkZLhc/ev1e9LvyRDUMtSpYV6m0GP9+XkpjS3lsKUctpTDlnKsasnFrcl0XUdlZSWGDBkS+kCWlQFoCn1xqoP15lrcoJZ8u94+6fZ5ST3GlnLYUg5bymFLOVa15OLWLvv3AxgGzLkWQIlvW3y8b4XnYKLX4vLteomIiKgdLm7tUlcHYBjw0M+BS9J92/rBNaNmXIvb4YH8+kFPIiIiCsbFrckURUFiYmLnrwwcMwbIzbJ2UDbr7QnXLlvyGtywdPu8pB5jSzlsKYct5bClHKtacnFrMlVVkZXVvxavfdH+Wty2J1+7bNnZNbgffRR4dzOeyT2Bz0s5bCmHLeWwpRy2lGNVSy5uTabrOvbt24fhw4fzQvQudHYtbtuTr922bHtKuLMzua+9BqSknNinny52+byUw5Zy2FIOW8phSzlWteTi1mS6rmPv3r1IT0/nX4ouhLoWt/3JV69XR1nZV8jKSkd6uqvrdWn7B6ypAa64Arj44hP79OPLFvi8lMOWcthSDlvKYUs5VrXk4pYiRvtrcTuefHUDyAbQw3Vp+wfkZQtERESOx8UtRaz2J1+9Xi/Kyj6HopyJefPUoHWpX5fr03AvW2iPi18iIqKIx8WtyVwuF1JSUvhPGb3Udj2q6y4MHToIqqp0ea/c9uvTkGvSnly2EOrBHXIZA5+XcthSDlvKYUs5bCnHqpaKYRiGqd8hwtTX1yMxMREejwcJCQm2jaP4L2WYPCcLRWvKkHstX4UZrurqjvfK9a9PGxuDt/d4TRrqQf38lzGsWdPxdLEfz+wSERGJ6Mt6LSLO3D755JN4+OGHceDAAeTk5OCJJ57A1KlTO93/lVdewf33349du3bh1FNPxa9//WtccsklFo6453RdR1VVFcaMGQPX3r0nFk9VBwBwURuOti0zM0O/oKy7F6V1LRNAZug1aldvrebX3WUN7R/PxoVw0POSZyP6hC3lsKUctpTDlnKsamn74vbll1/GokWLsHLlSuTn5+Oxxx7DjBkzUFFRgdTU1A77f/LJJ7jmmmuwfPlyfOc738ELL7yA2bNno7i4GBMmTLBhBl3TdR2ffurB4c/2wX31NcDxJgBAGbIAXAIkJdk6vmii6zpqamowatSoTv9SdP+itO6FXqNmAn/94ut3lgvhyBHgpz8FLl7c6eMmoxaZ2NPVN+mc8GK4Jy2pZ9hSDlvKYUs5bCnHqpa2L25XrFiBBQsWYP78+QCAlStX4s0338SqVatwzz33dNj/8ccfx8UXX4yf/vSnAICHHnoI69atw//+7/9i5cqVlo69J6qrgWuuycHx4yqAj4M+Fx+nI/msYfYMrJ/o7O1+O9P1pbfDvv7ozMddfA6IH6jhtYcrkYKabhfCHQyMAx5+GDj55J5/TRe8mobdVTVwbS+HW1U73S85yYvMYa0hPtFusd3+ko7uFuPh7k9ERNRDti5uW1paUFRUhMWLT/yfvMvlwvTp07Fx48aQX7Nx40YsWrQoaNuMGTOwdu3akPs3Nzejubk58GePxwMA+Oqrr+D1egPf0+VyQdd16LoeNBaXywVN09D20uTOtquqCkVRAo8LAFVVOo4fj8FTiytxxvJ58K78A3DqaQCA1FQViYnAV19pQWN2u90wDAOadmK7oihQVbXDGDvbbuac/NsBBI2xq+0Sc9J1HQ0NDairqwv6ja+7OQ0erOGkk3o2p3HjgE8+0XD4sOycvvpKwdy5Ki7+SRqANAD/RFiOA/hJeF/Sve4Xk3FowBrMQTKCfzswYgdCf/BBqCcPgX7kCJQl90NpPn5ih9iB0B98CHriieukXC4XXFCgHfkKriVLOuyvPfggjMTEwCbV5YICBV693XPs62Ovtenb1Xa3S4UBI2i7oihQFRd0GB2feyG2+8cearuu69i9axcat+iA4gqeq6EH/32KkjmFGrsVc/LqWlBLJ8xJ5DgNTYY+ZEhYP8tbW1uwfbuOhoY6KIoron6WR9v/P2maN9DS5VIdMafeHqf0dGD48N7PSdM0HDt2DEeOHEFMTEyXczpy5AgAoDcvDbN1cVtbWwtN05CWlha0PS0tDeXl5SG/5sCBAyH3P3DgQMj9ly9fjmXLlnXYPmbMmF6OunduXv71f9w0zdLvS9QXTQCuDPWJZgB3d3FniWYAd8/o+Tfq7vGIiKhfOnr0KBLbnPjoCdsvSzDb4sWLg8706rqOr776CkOHDoWiKKZ///r6eowcORJ79uyx9e4MTsCWcthSDlvKYUs5bCmHLeWE09IwDBw9ehTDhw8P+/vYurhNTk6Gqqo4ePBg0PaDBw8iPT095Nekp6eHtX9sbCxiY2ODtiXZ8CKuhIQE/qUQwpZy2FIOW8phSzlsKYct5fS0ZbhnbP1sfdlfTEwMJk+ejPXr1we26bqO9evXY9q00P98P23atKD9AWDdunWd7k9ERERE/YftlyUsWrQI8+bNQ15eHqZOnYrHHnsMDQ0NgbsnzJ07FyNGjMDy5b6LVm+77Tacd955ePTRR3HppZfipZdeQmFhIf74xz/aOQ0iIiIiigC2L26vvvpq1NTUYMmSJThw4AAmTpyIt99+O/Ciserq6qBXxp999tl44YUXcN999+Hee+/FqaeeirVr10bkPW4B32URS5cu7XBpBIWPLeWwpRy2lMOWcthSDlvKsaplv3v7XSIiIiJyLr7VBhERERE5Bhe3REREROQYXNwSERERkWNwcUtEREREjsHFbR89+eSTGD16NAYOHIj8/Hxs3ry5y/1feeUVjB8/HgMHDsRZZ52Ft956y6KRRrbly5djypQpGDx4MFJTUzF79mxUVFR0+TWrV6+GoihBHwMHDrRoxJHrgQce6NBl/PjxXX4Nn5ehjR49ukNLRVFw6623htyfz8kTPvzwQ1x22WUYPnw4FEXB2rVrgz5vGAaWLFmCYcOGIS4uDtOnT8eOHTu6fdxwf+Y6QVctW1tbcffdd+Oss87CoEGDMHz4cMydOxf79u3r8jF783PCCbp7Xl533XUdulx8cfdvDc7nZceWoX52KoqChx9+uNPHlHpecnHbBy+//DIWLVqEpUuXori4GDk5OZgxYwYOHToUcv9PPvkE11xzDX70ox+hpKQEs2fPxuzZs7Ft2zaLRx55PvjgA9x66634z3/+g3Xr1qG1tRUXXXQRGhoauvy6hIQE7N+/P/Cxe/dui0Yc2c4888ygLv/+97873ZfPy84VFBQEdVy3bh0A4L/+6786/Ro+J30aGhqQk5ODJ598MuTnf/Ob3+B3v/sdVq5ciU2bNmHQoEGYMWMGjh8/3uljhvsz1ym6atnY2Iji4mLcf//9KC4uxmuvvYaKigpcfvnl3T5uOD8nnKK75yUAXHzxxUFdXnzxxS4fk8/L0C3bNty/fz9WrVoFRVFw5ZVXdvm4Is9Lg3pt6tSpxq233hr4s6ZpxvDhw43ly5eH3P+qq64yLr300qBt+fn5xo9//GNTxxmNDh06ZAAwPvjgg073efbZZ43ExETrBhUlli5dauTk5PR4fz4ve+62224zxo0bZ+i6HvLzfE6GBsB4/fXXA3/Wdd1IT083Hn744cC2uro6IzY21njxxRc7fZxwf+Y6UfuWoWzevNkAYOzevbvTfcL9OeFEoVrOmzfPmDVrVliPw+dlz56Xs2bNMi644IIu95F6XvLMbS+1tLSgqKgI06dPD2xzuVyYPn06Nm7cGPJrNm7cGLQ/AMyYMaPT/fszj8cDABgyZEiX+x07dgyjRo3CyJEjMWvWLHz22WdWDC/i7dixA8OHD8fYsWNx7bXXorq6utN9+bzsmZaWFqxZswbXX389FEXpdD8+J7tXVVWFAwcOBD3vEhMTkZ+f3+nzrjc/c/srj8cDRVGQlJTU5X7h/JzoTzZs2IDU1FScfvrpuPnmm3H48OFO9+XzsmcOHjyIN998Ez/60Y+63VfiecnFbS/V1tZC07TAO6n5paWl4cCBAyG/5sCBA2Ht31/puo7bb78d55xzTpfvPHf66adj1apVeOONN7BmzRrouo6zzz4be/futXC0kSc/Px+rV6/G22+/jaeeegpVVVU499xzcfTo0ZD783nZM2vXrkVdXR2uu+66Tvfhc7Jn/M+tcJ53vfmZ2x8dP34cd999N6655hokJCR0ul+4Pyf6i4svvhjPP/881q9fj1//+tf44IMPMHPmTGiaFnJ/Pi975rnnnsPgwYNxxRVXdLmf1PPS9rffJWrv1ltvxbZt27q9zmbatGmYNm1a4M9nn302srKy8Ic//AEPPfSQ2cOMWDNnzgz8d3Z2NvLz8zFq1Cj89a9/7dFvzRTaM888g5kzZ2L48OGd7sPnJNmptbUVV111FQzDwFNPPdXlvvw5Edr3v//9wH+fddZZyM7Oxrhx47BhwwZceOGFNo4suq1atQrXXnttty+wlXpe8sxtLyUnJ0NVVRw8eDBo+8GDB5Genh7ya9LT08Pavz9auHAh/vGPf+D9999HRkZGWF87YMAATJo0CV988YVJo4tOSUlJOO200zrtwudl93bv3o13330XN9xwQ1hfx+dkaP7nVjjPu978zO1P/Avb3bt3Y926dV2etQ2lu58T/dXYsWORnJzcaRc+L7v30UcfoaKiIuyfn0Dvn5dc3PZSTEwMJk+ejPXr1we26bqO9evXB525aWvatGlB+wPAunXrOt2/PzEMAwsXLsTrr7+O9957D2PGjAn7MTRNw6effophw4aZMMLodezYMezcubPTLnxedu/ZZ59FamoqLr300rC+js/J0MaMGYP09PSg5119fT02bdrU6fOuNz9z+wv/wnbHjh149913MXTo0LAfo7ufE/3V3r17cfjw4U678HnZvWeeeQaTJ09GTk5O2F/b6+dln1+S1o+99NJLRmxsrLF69Wrj888/N2688UYjKSnJOHDggGEYhvHDH/7QuOeeewL7f/zxx4bb7TYeeeQRo6yszFi6dKkxYMAA49NPP7VrChHj5ptvNhITE40NGzYY+/fvD3w0NjYG9mnfc9myZcY777xj7Ny50ygqKjK+//3vGwMHDjQ+++wzO6YQMf7nf/7H2LBhg1FVVWV8/PHHxvTp043k5GTj0KFDhmHweRkuTdOMzMxM4+677+7wOT4nO3f06FGjpKTEKCkpMQAYK1asMEpKSgKv4P/Vr35lJCUlGW+88YZRWlpqzJo1yxgzZozR1NQUeIwLLrjAeOKJJwJ/7u5nrlN11bKlpcW4/PLLjYyMDGPLli1BPz+bm5sDj9G+ZXc/J5yqq5ZHjx417rzzTmPjxo1GVVWV8e677xq5ubnGqaeeahw/fjzwGHxe+nT3d9wwDMPj8Rjx8fHGU089FfIxzHpecnHbR0888YSRmZlpxMTEGFOnTjX+85//BD533nnnGfPmzQva/69//atx2mmnGTExMcaZZ55pvPnmmxaPODIBCPnx7LPPBvZp3/P2228PtE9LSzMuueQSo7i42PrBR5irr77aGDZsmBETE2OMGDHCuPrqq40vvvgi8Hk+L8PzzjvvGACMioqKDp/jc7Jz77//fsi/0/5euq4b999/v5GWlmbExsYaF154YYfGo0aNMpYuXRq0raufuU7VVcuqqqpOf36+//77gcdo37K7nxNO1VXLxsZG46KLLjJSUlKMAQMGGKNGjTIWLFjQYZHK56VPd3/HDcMw/vCHPxhxcXFGXV1dyMcw63mpGIZhhH2emIiIiIgoAvGaWyIiIiJyDC5uiYiIiMgxuLglIiIiIsfg4paIiIiIHIOLWyIiIiJyDC5uiYiIiMgxuLglIiIiIsfg4paIiIiIHIOLWyKiCFVTU4Obb74ZmZmZiI2NRXp6OmbMmIGPP/64268dPXo0FEWBoiiIj4/HWWedhT/96U8WjJqIyF5uuwdAREShXXnllWhpacFzzz2HsWPH4uDBg1i/fj0OHz7co69/8MEHsWDBAjQ2NuKVV17BggULMGLECMycOdPkkRMR2Ydvv0tEFIHq6upw8sknY8OGDTjvvPPC/vrRo0fj9ttvx+233x7YNnToUMybNw8rVqwQHCkRUWThZQlERBHopJNOwkknnYS1a9eiubm5T4+l6zr+9re/4ciRI4iJiREaIRFRZOKZWyKiCPW3v/0NCxYsQFNTE3Jzc3Heeefh+9//PrKzs7v92tGjR2P//v0YMGAAmpub4fV6MWTIEGzatAmnnHKKBaMnIrIHz9wSEUWoK6+8Evv27cPf//53XHzxxdiwYQNyc3OxevXqHn39T3/6U2zZsgXvvfce8vPz8dvf/pYLWyJyPJ65JSKKIjfccAPWrVuH3bt3d7lf+2tu9+zZg7POOguffPIJzjjjDAtGSkRkD565JSKKImeccQYaGhrC/rqRI0fi6quvxuLFi00YFRFR5OCtwIiIItDhw4fxX//1X7j++uuRnZ2NwYMHo7CwEL/5zW8wa9asXj3mbbfdhgkTJqCwsBB5eXnCIyYiigxc3BIRRaCTTjopcJ3szp070draipEjR2LBggW49957e/WYZ5xxBi666CIsWbIEb731lvCIiYgiA6+5JSIiIiLH4DW3REREROQYXNwSEUWZv/zlL4E3eWj/ceaZZ9o9PCIiW/GyBCKiKHP06FEcPHgw5OcGDBiAUaNGWTwiIqLIwcUtERERETkGL0sgIiIiIsfg4paIiIiIHIOLWyIiIiJyDC5uiYiIiMgxuLglIiIiIsfg4paIiIiIHIOLWyIiIiJyDC5uiYiIiMgx/j/8ClvQQnl5nwAAAABJRU5ErkJggg==\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAINCAYAAAAtJ/ceAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACG90lEQVR4nO3de3gU5d0//vfsLDkhSYQQAoQIiRwCmEAI8KD1USuWqqX1Z1utiuD58UC/rdR6qAek1qotoraPrd96goqnHpT6rdTWoqitFMkBEd2AkkDknCAESELizszvj80u2ex8kuxmZ/fezft1XXshd2Z37/s9s/jJ7D33aJZlWSAiIiIiSkCueHeAiIiIiChSLGaJiIiIKGGxmCUiIiKihMViloiIiIgSFotZIiIiIkpYLGaJiIiIKGGxmCUiIiKihMViloiIiIgSljveHYg10zSxe/duDBo0CJqmxbs7RERERNSFZVk4cuQIRowYAZer+3Ov/a6Y3b17N0aNGhXvbhARERFRDz7//HPk5+d3u02/K2YHDRoEwBdOZmam4+/n9XpRXV2NqVOnwu3ud3F3i9nImI2M2ciYjYzZ2GMuMmYji0U2hw8fxqhRowJ1W3f63d7xTy3IzMyMWTE7cOBAZGZm8sPQBbORMRsZs5ExGxmzscdcZMxGFstsejMlVLMsy3K0F4o5fPgwsrKy0NTUFJNi1rIstLa2Ij09nXN0u2A2MmYjYzYyZiNjNvaYi4zZyGKRTTj1GlcziIGUlJR4d0FZzEbGbGTMRsZsZMzGHnORMRuZStmwmHWYYRioqKiAYRjx7opymI2M2ciYjYzZyJiNPeYiYzYy1bLhJBAiIiKKKcuy4PV6lSmG7Hi9XgDAsWPHOGe2i2hlM2DAAOi63uf+cO8QERFRzLS3t2PPnj1oaWmJd1e6ZVkW0tLSUF9fzzmzXUQrG03TkJ+fjxNOOKFP/WExS0RERDFhmibq6uqg6zpGjBiBlJQUZQtFy7LQ0tKCjIwMZfsYL9HIxrIsNDQ0YOfOnRg7dmyfztByNQOHWZYFwzCg6zo/DF0wGxmzkTEbGbORMRt7sc7l2LFjqKurw0knnYSMjAzH368vOpdHPGaCRSub1tZWbN++HWPGjEFaWlrQz7iagWLa29vj3QVlMRsZs5ExGxmzkTEbe/HIpafbk6rCNM14d0FZ0cgmWr8kJMbRlMAMw8CmTZuUnuQeL8xGxmxkzEbGbGTMxh5z6V5ra2u8u6AslbLhnFkiIiKKr/p6oLExdu+XkwMUFETlpa644gocOnQIq1atisrr9da9996LVatWYePGjTF9XxWxmCUiIqL4qa8HiouBWK5ukJEBeDxRKWgfe+wx9LPLj5TDYjYGorGGWrJiNjJmI2M2MmYjYzb24p5LY6OvkF250lfUOs3jAebN871vD8Vsb+Z0ZmVlRatnCUWli+JYzDrM7XZj+vTp8e6GkpiNjNnImI2M2ciYjT2lcikuBsrK4t2LAE3TMHDgwMDf//SnP2HJkiX47LPPkJGRgalTp+Ivf/kLbrrppqBpBkeOHMH111+PVatWITMzE7feeiv+8pe/YMqUKXj00UcBAKNHj8Z1112Hzz77DH/84x9x4okn4q677sJ1110XeL/bbrsNr776Knbu3Im8vDxcdtlluOeeezBgwIBYxmCrazbxxgvAHGZZFg4dOsSvIGwwGxmzkTEbGbORMRt7zEXmv0uZZVnYs2cPLrnkElx11VXweDxYu3YtLrzwQtvcFi1ahH//+9947bXX8Oabb+K9995DVVVVyHYPP/wwysvLUV1djRtvvBE33HADtmzZEvj5oEGDsHz5cnzyySd47LHH8OSTT+KRRx5xdMy91TkbFbCYdZhhGKipqeGVojaYjYzZyJiNjNnImI095tK9Y8eOAQD27NkDr9eLCy+8EKNHj8Ypp5yCG2+8MeTOVUeOHMGKFSuwdOlSnH322Zg8eTKeffZZ23zPO+883HjjjTj55JNx2223IScnB2+//Xbg53fddRdOPfVUjB49GnPnzsUtt9yCP/zhD84OOAz+bFTAaQZERERE3SgtLcXZZ5+NU045BXPmzMHXvvY1fOc738GJJ54YtF1tbS2+/PJLzJgxI9CWlZWF8ePHh7xmSUlJ4L81TUNeXh72798faHv55Zfxq1/9Ctu2bcPRo0fh9XpjcrOnRMQzs0RERETd0HUdb775Jv72t79h4sSJ+PWvf43x48ejrq4u4tfsOvdV07TAjQjWrVuHyy67DOeddx7++te/orq6GnfeeSdv/CFgMeswTdOQnp6u1FV/qmA2MmYjYzYyZiNjNvaYS/c636lM0zScdtppWLJkCaqrq5GSkoJXX301aPvCwkIMGDAAGzZsCLQ1NTVh69atYb3v+++/j5NOOgl33nknysvLMXbsWOzYsaNvg4kyle7ixmkGDtN1HaWlpfHuhpKYjYzZyJiNjNnImI095iLTNA0ZGRkAgPXr12PNmjX42te+htzcXKxfvx4NDQ0oLi7Gpk2bAs8ZNGgQFixYgB//+McYPHgwcnNzsXjxYrhcrrB+YRg7dizq6+vx0ksvYfr06Xj99ddDCmcAgGUBzc2h7W43kJoa9ph7q3M2KmAx6zDTNNHY2IicnBylfotRAbORMRsZs5ExGxmzsadULh6PUu/jv2Lf7XYjMzMT7777Lh599FEcPnwYJ510Eh5++GGce+65ePnll4Oet2zZMlx//fX4xje+EVia6/PPP0daWlqvu/jNb34TN998MxYuXIi2tjacf/75uPvuu3Hvvfce38jrBY4dsx+PywVMmuRYQds5GxXO6muWKusqxMjhw4eRlZWFpqammEyk9nq9qKioQHl5Odxu/u7QGbORMRsZs5ExGxmzsRfrXI4dO4a6ujqMGTPmeHGn6B3ALMtCc3MzBg4c2KeCrbm5GSNHjsTDDz+Mq6++OuLXsXlh3xjGjAE6F8rHjgF1db5MHVoLNlrZ2B4PHcKp1/iJJiIiovgpKPAVZY2NsXvPnJyo3MrWTnV1NWpqajBjxgw0NTXhpz/9KQDgW9/6liPvh7Q0x4rWRBHXYvbdd9/FL3/5S1RWVmLPnj149dVXccEFF4jbv/LKK/jtb3+LjRs3oq2tDZMmTcK9996LOXPmxK7TREREFF0FBY4Vl/GwdOlSbNmyBSkpKZg2bRree+895OTkxLdTbW2+qQldOTy/NhbiWsw2NzejtLQUV111FS688MIet3/33Xdxzjnn4Oc//zmys7Px7LPPYu7cuVi/fj2mTp0agx6HT9M0ZGVlKTGnRDXMRsZsZMxGxmxkzMYec+meruthP2fq1KmorKx0oDd90NYGfPwx0LH0V5AI59dGko1TlJkzq2laj2dm7UyaNAkXX3wx7rnnnl5tH+s5s0REROTT3RxJCpN/zmzXubF27XGcX9sdzpmF7yrMI0eOYPDgweI2bW1taGtrC/z98OHDAHyT3r0dp9tdLhdcLhdM0wwsWNy53TCMoPsPS+26rkPTtMDr+vu4b98+jBgxIuQexv7farre5s7tdsOyrKB2TdOg63pIH6V2J8fUXd/DGZNlWdi3bx/y8vKCtk3kMUVrPwHA3r17MWzYsKAzJok8pmjtJ03TsHv3bgwbNizo6utEHlO09pOmadi1a1dQNok+pmjtJwDYuXMn8vLyAn9P9DEl4ufJ6/UG+mV3Lk3TtKi0h6O71/jyyy9DrtiPVh+jMSYNgAX4lujqoV0DgLQ0WF2W1PKPLNy+tLe3Y8CAAX3KxrKswMoI/mPTf+x1PVa7k9DF7NKlS3H06FFcdNFF4jYPPPAAlixZEtJeXV2NgR2/hQwdOhRFRUWoq6tDQ0NDYJv8/Hzk5+dj69ataGpqCrQXFhYiNzcXmzdvRmtra6B9woQJyM7ORnV1deCDblkWTNNEbm4uqqurg/pQXl6O9vb2oDXqdF3H9OnT0dTUhJqamkB7eno6SktL0djYiNra2kB7VlYWiouLsXv3buzcuTPQ7uSYAN9t+FJSUlBRURHxmFJTU9HW1gZd14MWg07kMUVrPw0fPhx79uxBU1MTjhw5khRjitZ+mjRpEnbu3ImdO3cG/SOayGOK1n6aNGkSamtrg7JJ9DFFaz/l5eVh69at2LVrVyCbRB9Ton6e/OuTtrW1BRUsKSkpSElJwbFjx4L6mJqaigEDBqC1tTWoWE5LS4Pb7UZLS0tQoZSeng6Xy4XmLuuvDhw4EKZpBuWiaRoGDhwIwzBw7NixQLvL5UJ6ejra2tqC7rql6zrS09Px5ZdfBrW73W6kpaXFdEyuY8eQAV+d0dKpXW9rQzp8J9NaO9r92wK+ItF/kq9zezhjGjBgANra2vDll1/2aUyAryjevHlzoN1/7HWtmbqTsNMMXnjhBVx77bX4y1/+gtmzZ4vb2Z2ZHTVqFA4cOBA4be3kb76GYaCqqgrl5eUhc5IS+bf5aJyhMAwD1dXVKCsrC7nLSqKOKVr7yTRNVFVVYerUqUHzkhJ5TNHaT5ZloaKiAmVlZUHZJPKYorWfLMvChg0bgrJJ9DFFaz+ZphmSTaKPKRE/T8eOHUN9fT0KCwuRajNHU7Uzs83NzcjIyFDzzGxLCzSPB1ZxsW+pse7aO9pQXBx8ZlZq70VfopGNf5pBQUFBYJqB/9g7ePAghgwZkrzTDF566SVcc801+OMf/9htIQv4flOw+8C43e6QNfX8/wh0JU1yltq7vq7/6z9pDT+7dml7qY/htvd1TJG0dzemaIxVtTH1ZT91/rrF7vUTcUx+fd1PXq838D9Ju34m4pgiabcbU3fZJOqYuutjOO2maYrZJOqYgMT7PHX+yl666Cxa7eGwew1/8eX/f3hf+uLkmDTfE3rf3pv37LTyQdDWHSsfRCubzvVR12MqnHWPE66YffHFF3HVVVfhpZdewvnnnx/v7vTI5XJh6NChth/w/o7ZyJiNjNnImI2M2dhjLt3rlzfY6M3KBykpSmUT154cPXoUn332WeDvdXV12LhxIwYPHoyCggLccccd2LVrF37/+98D8E0tWLBgAR577DHMnDkTe/fuBeCbd5GVlRWXMfTE5XKhqKgo3t1QErORMRsZs5ExGxmzsadKLvX16t0zQdO0oCvszzzzTEyZMgWPPvqoI3264oorcOjQIaxatcqR1+81r9dXyEorH3i90FJTe7Uaxfbt2zFmzBhUV1djypQpjnU5rsVsRUUFzjrrrMDfFy1aBABYsGABli9fjj179qC+vj7w89/97nfwer246aabcNNNNwXa/duryDTNwLIT/M03GLORMRsZs5ExGxmzsadCLorezRaWZaGtrQ2pqalRmcqQcLq5s5hq2cS1mD3zzDO7neTctUBdu3atsx1ygGmaaGhowEknncR/QLtgNjJmI2M2MmYjYzb2VMilsdFXyK5c6StqnebxAPPm+d63p7OzXq/X9rqbRNHe3o6UlBRHXlulbPiJJiIiorgrLgbKypx/9KVg9nq9WLhwIbKyspCTk4O77747cFLuueeeQ3l5OQYNGoS8vDxceuml2L9/f9DzP/74Y3zjG99AZmYmBg0ahNNPPx3btm2zfa8NGzZg6NCheOihhwJtP/vZz5Cbm4tBgwbhmptuwu2//jWmzJoV+PkVV1yBC773Pdz/zDMYcfLJGD9+PADgo82b8dUbbkB6Tg6GDBmC6667DkePHg0878yvfx0//OEPg97/gu99D1dccUXg76MnTsTPn30WV91wAzIzM1FcXIzf/e53Qc/54IMPMHXqVKSlpaG8vDys5bX6gsUsERERUS+sWLECbrcbH3zwAR577DEsW7YMTz31FADfOq333XcfPvzwQ6xatQrbt28PKgZ37dqF//7v/0ZqaireeustVFZW4qqrrrK9OcBbb72Fc845B/fffz9uu+02AMDzzz+P+++/Hw899BAqKytRkJ+P3/75zyHPXbN2Lbbs2IE3X3sNf/3rX9Hc3Iw5F1yArEFZeOfN9/D73/8Rb775T1x//UI0H3OhDb0/c/vw88+jfOpUVFVV4ZprrsGNN96ILVu2APBdB/WNb3wDEydORGVlJe69917ccsst4cQbMXUuRUtSLpcL+fn5/FrLBrORMRsZs5ExGxmzscdcutf1K/pRo0bhkUcegaZpGD9+PD766CM88sgjuPbaa3HVVVcFtissLMSvfvUrTJ8+HUePHsUJJ5yAxx9/HFlZWXjppZcwYMAAAMC4ceNC3vPVV1/F/Pnz8dRTT+Hiiy/2Nba14dePPYar58/HlR03irrn5pvxj7/+FUe7rDowMCMDT911F1ImTgQGDsSTTz6JY8facMuSV+BKy8SwNOCHP/xfLFo0F5df/hCGDpkE0+rdvNfzTj0VN153HayMDNx+++34zW9+g7fffhvjx4/HCy+8ANM08fTTTyMtLS1wQ44bbrih13lHikevw/gPhYzZyJiNjNnImI2M2dhjLjJN05CSkhJ0gdN//dd/Bf191qxZ+PTTT2EYBiorKzF37lwUFBRg0KBBOOOMMwAgcCH7xo0bcfrppwcKWTvr16/Hd7/7XTz33HNBhSw+/hhbamowY8QI36Rfjweoq8OMyZND1pI9ZdIkpHR6D4/Hg8mTTkFqeibGjGhDcTHw3e+eBtM00d78EUzoXe+GKyoZOzaQTWpqKvLy8gJTKTweD0pKSoJWOZjVaQqEk3j0OswwDHg8npC7qBCz6Q6zkTEbGbORMRt7zEVmWRZaW1t7dTeuY8eOYc6cOcjMzMTzzz+PDRs24NVXXwWAwC1i/bdv7U5RUREmTJiAZ5555vitYv1LZblcwIgRvkm//seQISHF7EBhBQIASEs1MXDg8UUKUgb4xuZyuULG2flWtX4DOm7G4c9G07Sgu77FC4tZh1mWhaampj7fbi8ZMRsZs5ExGxmzkTEbe8yle12L/PXr1wf9/T//+Q/Gjh2LmpoaHDhwAA8++CBOP/10TJgwIeTir5KSErz33nu2RaJfTk4O3nrrLXz22We46KKLgrYdP24cNmzahEA1OnAgNlRVyZ0/dgxobkZxYSE2f7wJra3NgR/9+9//hsvlwtiTfWdac04cjD2ffw40NwPHjsEwDGz+5JOwsikuLsamTZtw7NixoHxigcUsERERUS/U19dj0aJF2LJlC1588UX8+te/xg9+8AMUFBQgJSUFv/71r1FbW4vXXnsN9913X9BzFy5ciMOHD+N73/seKioq8Omnn+K5554LXEDll5ubi7feegs1NTW45JJLAheIff/66/H0009jxYoV+PTTT/Gzn/0MmzZtgmVpaG721aFeL2CYGtq0NN8NDjweXDZlClLdA3DvvQvw8ZYavP322/j+97+Pyy+/HMOGjwAAnDG5BK//7W94/cknUfP227jhoYdwqKkprGwuvfRSaJqGa6+9Fp988glWr16NpUuX9iHt3uMFYERERBR3Ho/67zN//ny0trZixowZ0HUdP/jBD3DddddB0zQsX74cP/nJT/CrX/0KZWVlWLp0Kb75zW8GnjtkyBC89dZb+PGPf4wzzjgDuq5jypQpOO2000LeJy8vD2+99RbOPPNMXHbVVXjh1ltx2cUXo3bXLtxyyy04duwYvv3ti3DeeVfg448/CIzp0CHgaLMLH2uTMKmoFakDLGQAWPXK61i46DaccfapyMjIwLe//W0sW7YM0HwXuM3/P4tQ07AL8++7D25dx/dv+gH++0sTXq+vSAYAywK83ZSNJ5xwAv7f//t/uP766zF16lRMnDgRDz30EL797W9HHngvaVY/+27h8OHDyMrKQlNTEzIzMx1/P9M00djYiJycHE6w74LZyJiNjNnImI2M2diLdS7Hjh0L3HHMf6GQyncA83q9cLvdfbvLVVub75RpV2430JubDjQ3+zpbXBx0R67mZuCrXz0HJ52Uh2effS7Q7r/rbOfNhZewbe+43gx2U2FdMDDp5DakZKVHJRu748EvnHqNZ2Yd5nK5kJubG+9uKInZyJiNjNnImI2M2dhTIZeCAl9B1dgYu/fMyen57l+apnW78kCvdFsZuoBJk0IL2q7Fb8cc1JaWFjzxf/8v5syZA13XsWLFi/jgg3/i7rvflO46GxH/9WZjxvjuaBvoRlMb6nanwmtoSI1GNlHEYtZhhmFg8+bNmDx5MvSOqwDJh9nImI2M2ciYjYzZ2FMll4KCnovLWPNfsZ+enh752UexMuw4fXr0aHDh6vUC27aFFr8uF7QBA7B69Wrcf//9OHbsGMaOHY+HHvozzjprdmR966TTNVuB/05LCz6Li2PH+xSVbKKIxazDwlnao79hNjJmI2M2MmYjYzb2mEv3orbsVNfK0O32nZmtqwvd1uUCxo71bdNp+/TUVPzzn/8MNPmnCPSF1A2XK/jt7aiwJJcfi1kiIiKiWEpN9U0x6MtcWge7EcMuRAWLWSIiIqJYS01VomJUpBt9wks6HabrOiZMmMA5WjaYjYzZyJiNjNnImI29eOWSKNMaul5hT8dFI5toHQcsZh2maRqys7OVmCCtGmYjYzYyZiNjNjJmYy/WufivgG+J5TpcEdI0re/LciWpaGXjv9VvX3+Z4jQDh3m9XlRXV2Pq1Klw9zSbup9hNjJmI2M2MmYjYzb2Yp2LruvIzs4O3Oo1IyND2WIxKlfst7Ud/zPKZ7+ll7Zrj0Y32trbAGhoa2+DqxV9zsY0TTQ0NCAjI6PPxx4/0THQ9f7FdByzkTEbGbORMRsZs7EX61zy8vIAIFDQqsqyLLS3tyMlJSXyYra93beA7oABQEpKVPsnvbRdezS60d78JRobB2AAvsSAg+6+ZwPfOscFBQV9/oWGxSwRERHFjKZpGD58OHJzc/Hll1/Guzsir9eLzZs34+STT478zOHHHwPXXw/8+c/A+PFR7Z/00nbt0ejGx/9vG67/8Rj8+ZfbUHRuQd+zAZCSkhKVO8+xmCUiIqKY03Vd6QvyvB3rVaWlpUVesGkasGOH788+XDBVXx96h7SaGvuXtnvLaHRDa3dhx440aO2uwMVffcomiuLfgySn6zpKSkqU/sDGC7ORMRsZs5ExGxmzscdcZKpkU18PFBcDdtfMZWT4bs0ba6pk48diNgZSojxPJpkwGxmzkTEbGbORMRt7zEWmQjaNjb5CduVKX1HbWU5O/G4DrEI2flyay2GGYaCiooIXHthgNjJmI2M2MmYjYzb2mIssomzq64GqquOPvt5vtpPiYqCsLPgRr0JWteOGZ2aJiIiI+kqaDxCvuQD9CItZIiIionDYXZHl8djPB4jnXIB+gsUsERERUW/1dEXW6acrUbx2nuEQxdkOSmIx6zBd11FeXq7MFX8qYTYyZiNjNjJmI2M29piLTMzG4Suyup70DbcQzcnx1dTz5gW3R3O2g2rHDYvZGGhvb0d6enq8u6EkZiNjNjJmI2M2MmZjj7l06FpFWha+POEE6OPG2W/vvyIryl3o67TbggJfAdx1FkS0ZzuodNxwNQOHGYaBTZs2KXPFn0qYjYzZyJiNjNnImI095tLBX0VOmxZ4aOXlSJkyBUZdXcy60fmkb2Xl8YfHE14hWlDg7MoHqh03PDNLRERE/ZvN1AFj82boCxbA29gIFBbGtDsOnPRNaixmiYiIiICgKtLquJ0tqY/FbAyoMkFaRcxGxmxkzEbGbGTMxh5z6Z5WUwO4O5VLyb40QC+pdNywmHWY2+3G9OnT490NJTEbGbORMRsZs5ExG3vMRebOywMyMqAvWBD6wygsDSAtVZsIVDtuWMw6zLIsNDU1ISsrC5qmxbs7SmE2MmYjYzYyZiNjNvaYi8waNQqH//MfZLa3h2bTx6UBelqqVvUbhql23HA1A4cZhoGamhplrvhTCbORMRsZs5ExGxmzscdcZIZhwNPcDKO0NOpLA0irFkSyckE8qHbc8MwsERERURTYTR3o7iQuVy2IDhazRERERH3U3Q0PXnkFGDr0eFuizI1NFCxmHaZpGtLT05WYU6IaZiNjNjJmI2M2MmZjr1/m0ssrr8LNxu4utw0NwIUXAl//euj2iTA3VqLaccNi1mG6rqO0tDTe3VASs5ExGxmzkTEbGbOx1+9yCePKq0iz6Tp1wO7WskD0by8bS6odNyxmHWaaJhobG5GTkwOXi9fbdcZsZMxGxmxkzEbGbOz1u1zsTp/6dakuo5VNQUHiFq0S1Y6b+PcgyZmmidraWpimGe+uKIfZyJiNjNnImI2M2djrt7n4T592s0JBv82mF1TLhsUsERERESUsFrNERERElLBYzDpM0zRl7pChGmYjYzYyZiNjNjJmY4+5yJiNTLVseAGYw3RdR3HXSeYEgNl0h9nImI2M2ciYjb2kycVuuS2gT0sGJE02DlAtG56ZdZhpmti5c6cyk6RVwmxkzEbGbGTMRsZs7CVFLv7ltqZNC30UF/t+HoGkyMYhqmXDYtZhqu1wlTAbGbORMRsZs5ExG3tJkUvn5bYqK48/Vq70tdudse2FpMjGIaplw2kGRERElPi63q3AQb28iRjFCItZIiIiSgyRVJGdfx6FijOMm4hRjLCYdZjL5cLQoUOVuEOGapiNjNnImI2M2ciYjb2EyiXcKjInx9c+b17P29qQsgnjJmJJS7XjhsWsw1wuF4qKiuLdDSUxGxmzkTEbGbORMRt7CZVLuFVkQYHvTGzXM7lCxRl60teFnJwiSPVaDGc1KEe144bFrMNM00RdXR3GjBmjzG8wqmA2MmYjYzYyZiNjNvYSMpdwqsiCgl6dKpVO+qanm/jkE2D06ATJJkZUO27i34MkZ5omGhoalLniTyXMRsZsZMxGxmxkzMYec/GxWxBhxQoDra0uvPuuhaoqBB680Eu944ZnZomIiIgQfNI3O9tCWpqBBQv0kO14oZdaWMwSERERdVFQALz44ocYMaIEbndwudRfLvRKFCxmHeZyuZCfn6/EnBLVMBsZs5ExGxmzkTEbe0rn0vWKrCh9v9/b1b1cLhfKy3MxYoRLvAisv1LtuGEx6zD/DqdQzEbGbGTMRsZsZMzGnrK5SFdk9fH7/XBW91I2GwWolo0aJXUSMwwDHo8HhmHEuyvKYTYyZiNjNjJmI2M29pTNRbpFrcfTp+/3pZe1e2lls1GAatnwzKzDLMtCU1MTLMuKd1eUw2xkzEbGbGTMRsZs7Cmfi0OLufbmZZXPJo5Uy4ZnZomIiIgoYbGYJSIiIqKEFddi9t1338XcuXMxYsQIaJqGVatW9fictWvXoqysDKmpqTj55JOxfPlyx/vZFy6XC4WFhcpc8acSZiNjNjJmI2M2MmZjj7nImI1MtWzi2ovm5maUlpbi8ccf79X2dXV1OP/883HWWWdh48aN+OEPf4hrrrkGf//73x3uaeRcLhdyc3OV2eEqYTYyZiNjNjJmI2M29pI9l/p6RHz3rmTPpi9UyyauF4Cde+65OPfcc3u9/RNPPIExY8bg4YcfBgAUFxfjX//6Fx555BHMmTPHqW72iWEY2Lx5MyZPngxdD72LSH/GbGTMRsZsZMxGxmzsKZFLbxd+jeBl+7K6lxLZKEq1bBJqNYN169Zh9uzZQW1z5szBD3/4Q/E5bW1taGtrC/z98OHDAACv1wuv1wvA9xuGy+WCaZpB9xn2txuGEXTFntSu6zo0TQu8LuDb4S0tLTBNM+SqP/8B0HVpC7fbDcuygto1TYOu6yF9lNqdHFN3fQ9nTIZhoLW1NeQ9E3lM0dpPpmmitbUVXq83acYUrf1kWRZaWlpCsknkMUVrP9llk+hjitZ+sssm0ceUFJ+n7duhn3IKNJuFX62MDBjZ2XCZZkT7ae9eAy0tbqxYYWDCBCvQfuKJBkaMAPxP4+epd2PydryWaVkx+Tx13b47CVXM7t27F8OGDQtqGzZsGA4fPozW1lakp6eHPOeBBx7AkiVLQtqrq6sxcOBAAMDQoUNRVFSEuro6NDQ0BLbJz89Hfn4+tm7diqampkB7YWEhcnNzsXnzZrS2tgbaJ0yYgOzsbFRXVwd2jmVZgQOhuro6qA/l5eVob2/Hpk2bAm26rmP69OloampCTU1NoD09PR2lpaVobGxEbW1toD0rKwvFxcXYvXs3du7cGWh3ckwAUFJSgpSUFFRUVEQ8ptTUVADAgQMHsGPHjqQYU7T20/DhwwEAn332GY4cOZIUY4rWfpo0aRIAoKqqCpqmJcWYorWfJk2ahPb29qBsEn1M0dpPeXl5aG5uDsom0ceUDJ+n5n/9CyUtLfj03nuROWMGhg0bhrq6Ohw5ehTerCy079+PwhNOiGg/eTyfACiBZX0M02xBWdnxMe3f3/OY+HkKHtOOugYAk3Hw4EEAcPzz1LVm6o5mKbJImKZpePXVV3HBBReI24wbNw5XXnkl7rjjjkDb6tWrcf7556OlpcW2mLU7Mztq1CgcOHAAmZmZAJw/M1tVVYXy8vKgfyj82/u36SwRfpuP1pnZ6upqlJWVBc27SeQxRfPMbFVVFaZOnRr0FU4ijymaZ5IqKipQVlYWlE0ijymaZ2Y3bNgQlE2ijyla+8k0zZBsEn1MSfF5qqiAe+ZMeNevh6u8PKrH3oYNBmbOdGP9ei/Kyvh56uuxV/VCDWYumIwNz32CKd8b5/jn6eDBgxgyZAiampoC9Zokoc7M5uXlYd++fUFt+/btQ2Zmpm0hC/jO/vnPAHbmdrvhdgcP378jupLmg0jtnV9X13UUFxfD7XaHFLN22/tpmmbbLvUx3Pa+jCnS9q5j0nUdEyZMwIABA2yzScQx9dTH3rZbloUJEyYgJSXFNptEHJNfX/eTZVkoLi4Ws0nEMUXSbjem7rJJ1DF118dw2jVNE7NJ1DEBSfB56ngdt9sNdGwT7WPP9//77rfn56nndre/aO0ofuPxeZIkVDE7a9YsrF69OqjtzTffxKxZs+LUo55pmobs7Ox4d0NJzEbGbGTMRsZsZMzGHnORMRuZatnEdU2Fo0ePYuPGjdi4cSMA39JbGzduRH19PQDgjjvuwPz58wPbX3/99aitrcWtt96Kmpoa/OY3v8Ef/vAH3HzzzfHofq94vV5s2LAhrInM/QWzkTEbGbORMRsZs7HHXGTMRqZaNnE9M1tRUYGzzjor8PdFixYBABYsWIDly5djz549gcIWAMaMGYPXX38dN998Mx577DHk5+fjqaeeUnZZLr+u80HoOGYjYzYyZiNjNjJmY8+RXOyW28rJAQoKov9eDuIxI1Mpm7gWs2eeeWbIclWd2d3d68wzzwzrCjciIiKKoe4WePV4Eq6gJfWpcesGIiIiSg6Njb5CduVKoLLS91i50tfW9WwtURQk1AVgiUjXdZSUlIhX9/VnzEbGbGTMRsZsZMzGnqO5FBcDZWXRf10bTtxEjMeMTLVsWMzGQEpKSry7oCxmI2M2MmYjYzYyZmMvprl0rTAdvG0t0Ptb10p4zMhUyobTDBxmGAYqKiqUmiitCmYjYzYyZiNjNjJmYy9mueTk+CrLefOAadOOP+bN63PFaTerwf/oyxRdHjMy1bLhmVkiIiJyVkGBr7K0mTNb3zYMjY0jgV4sftDddIIYzmogxbCYJSIiIucVFIRUpz1NEeh8ZtXJ6QSU2FjMEhERUVx0niJQXHy83ePxzUBobDxezErbAgm5hC1FEYtZh+m6jvLycmWu+FMJs5ExGxmzkTEbGbOx1+dcorSMQDhTBGI1nYDHjEy1bFjMxkB7ezvS09Pj3Q0lMRsZs5ExGxmzkTEbexHn0g++9+cxI1MpG65m4DDDMLBp0yZlrvhTCbORMRsZs5ExGxmzsdenXJxaRkARPGZkqmXDM7NEREQUOQe/9+88YyEKS9JSkmIxS0RERN1z4hZb3ei8LG1nSTJ7gaKMxWwMqDJBWkXMRsZsZMxGxmxkzMZej7nEYW6stCxtrFct4DEjUykbzbIsK96diKXDhw8jKysLTU1NyMzMjHd3iIiI1FZV5btblwNrYvlfurKSNzxQXdXzHkybV4zKlR6UXVbc8xP6KJx6jReAOcyyLBw6dAj97HeGXmE2MmYjYzYyZiNjNvbCysU/N7bzI8Ev8uoOjxmZatmwmHWYYRioqalR5oo/lTAbGbORMRsZs5ExGxv19TA2bED9X/4CY8MG32nSqirftALiMdMN1bLhnFkiIqL+pmMerLulBSVdf9b1PrJEimMxS0RE1N90rBFrrFiBjy0LE4uL4Xa77e8jS6Q4FrMO0zQN6enp0DQt3l1RDrORMRsZs5ExGxmzERQXw0pJgTZ5MqDQ1ekq4DEjUy0bFrMO03UdpaWl8e6GkpiNjNnImI2M2ciYjT3mImM2MtWy4QVgDjNNE/v374dpmvHuinKYjYzZyJiNjNnImI09MReP5/gFYf301ls8ZmSqZcMzsw4zTRO1tbUYPHgwXC7+7tAZs5ExGxmzkTEbGbOxF5KLg7feivFNxPqMx4xMtWxYzBIREZGPQ7feisNNxKgfYTFLRERExxUURH0lg47FE5y4iRgRi1mnaZqGrKwsZa74UwmzkTEbGbORMRsZs7EX61z8NxFLBDxmZKplw2LWYbquo7jrr6EEgNl0h9nImI2M2ciYjT2ncuk6P1blubESHjMy1bKJ/6zdJGeaJnbu3KnMFX8qYTYyZiNjNjJmI2M29pzIxT8/dtq044958xJvbiyPGZlq2bCYdZhqO1wlzEbGbGTMRsZsZMzGnhO5dJ4fW1l5/JFod8jlMSNTLRtOMyAiIkp2cfjeP5Hmx1JiYzFLRESUzKR1sfzf++/fH59+EUUJi1mHuVwuDB06VIlFhVXDbGTMRsZsZMxG1q+zkdbFysmBKz8fQy0r4lwS7UYI4ejXx0wPVMuGxazDXC4XioqK4t0NJTEbGbORMRsZs5ExG9h+7+8CQnKxK1Dt1oJN9hsh8JiRqZaNGiV1EjNNE9u2bVNmkrRKmI2M2ciYjYzZyJiNva652K1EMG2ar62+Pvi50oVeiXixlx0eMzLVsmEx6zDTNNHQ0KDMDlcJs5ExGxmzkTEbGbOx1zUXuwJ15UpfW9eztX7+E76dH4leyAI8ZrqjWjacZkBERJRo7OYCAFG7NyxXIqBEwmKWiIgokfQ0WTUZvuMnCgOLWYe5XC7k5+crc8WfSpiNjNnImI2M2ciSKhtpdQKPx3errffeC20XhJNL15dJllULJEl1zESZatmwmHWYf4dTKGYjYzYyZiNjNrKkzKbrXICcHN+Z2XnzQrcVlhfoTS4RvGxSSMpjJkpUy4bFrMMMw8DWrVsxbtw46Loe7+4ohdnImI2M2ciYjaxfZFNQ4DtdGsZc2t7kEsHLJoV+ccxESLVsWMw6zLIsNDU1wbKseHdFOcxGxmxkzEbGbGT9JpuCgrCqy97mEubLJoV+c8xEQLVs1JjsQEREREQUARazRERERJSwWMw6zOVyobCwUJkr/lTCbGTMRsZsZMxGxmzsMRcZs5Gplg3nzDrM5XIhNzc33t1QErORMRsZs5ExGxmzCeW774ILQC527vS1JftyW+HgMSNTLRs1SuokZhgGPvzwQxiGEe+uKIfZyJiNjNnImI2M2QTz33dh2rTgx7x5yb3cVjh4zMhUy4ZnZh1mWRZaW1uVueJPJcxGxmxkzEbGbGQJnU3XW9dG4fSp/74LK1YYsKyPUVw8EW63ryRI5uW2wpHQx4zDVMuGxSwREZGqpFvXRun06YQJFkyzBWVlgJsVASUoHrpERESqkm5dy9OnRAEsZh2m6zomTJigxB0yVMNsZMxGxmxkzEaW8Nl0vXVtlOi6jnHjEjgXByX8MeMg1bJhMeswTdOQnZ0d724oidnImI2M2ciYjYzZ2GMuMmYjUy0brmbgMK/Xiw0bNsDr9ca7K8phNjJmI2M2MmYjS4hs6uuBqqrgR5gXe9m9RFWVr91OQuQSJ8xGplo2PDMbA6osXaEiZiNjNjJmI2M2MqWzkS70Anp9sVdPL+Hx2E+zVTqXOGM2MpWyYTFLREQUb9KFXkCvL/aSXsLj8a0f+957x9t5cwRKJixmiYiIVBGFC726vkROju/M7Lx5wdv5T/ju39+ntyOKOxazDtN1HSUlJcpc8acSZiNjNjJmI2M2sv6cTUGB70xs5/suAL5CdtQoHXl5/TOXnvTnY6YnqmXDYjYGUlJS4t0FZTEbGbORMRsZs5HFJZuud+8C4rJGbEGB/VtaFo+Z7jAbmUrZcDUDhxmGgYqKCqUmSquC2ciYjYzZyJiNLC7Z+K/ImjYt+FFcLC8vEGM8ZmTMRqZaNixmiYiInND5iqzKSt9j5UpfW9eztUQUMU4zICIicpJDd+8iIh+emSUiIiKihMVi1mG6rqO8vFyZK/5UwmxkzEbGbGTMRsZs7DEXGbORqZZN3IvZxx9/HKNHj0ZaWhpmzpyJDz74oNvtH330UYwfPx7p6ekYNWoUbr75Zhw7dixGvY1Me3t7vLugLGYjYzYyZiNjNjJmY4+5yJiNTKVs4lrMvvzyy1i0aBEWL16MqqoqlJaWYs6cOdgvrOD8wgsv4Pbbb8fixYvh8Xjw9NNP4+WXX8ZPfvKTGPe89wzDwKZNm5S54k8lzEbGbGTMRsZsZMzGHnORMRuZatnE9QKwZcuW4dprr8WVV14JAHjiiSfw+uuv45lnnsHtt98esv3777+P0047DZdeeikAYPTo0bjkkkuwfv36mPabiIioT7reT9bjQT1GodGTHrJpHJalJUoocStm29vbUVlZiTvuuCPQ5nK5MHv2bKxbt872OaeeeipWrlyJDz74ADNmzEBtbS1Wr16Nyy+/XHyftrY2tLW1Bf5++PBhAIDX64XX6w28r8vlgmmaME0zqD8ulwuGYcCyrB7bdV2HpmmB1wUQ2MayrKB2//b+bTpzu92wLCuoXdM06Loe0kep3ckxddf3cMbk/2/TNINeP5HHFK395P/vru+ZyGOK1n7yf56SaUzR2k/+fHoz1kQZU7T2k102jo+p40+v1wt0vJZ24onQbe4tW49RKIYHLfMGoquMDODjj03k53e/n3xv4Q70hZ8nfp6iuZ+8/v9nd/TX6c9T1+27E7ditrGxEYZhYNiwYUHtw4YNQ01Nje1zLr30UjQ2NuIrX/lKoDi8/vrru51m8MADD2DJkiUh7dXV1Rg40PePxtChQ1FUVIS6ujo0NDQEtsnPz0d+fj62bt2KpqamQHthYSFyc3OxefNmtLa2BtonTJiA7OxsVFdXB3aOf4eaponq6uqgPpSXl6O9vR2bNm0KtOm6junTp6OpqSkoh/T0dJSWlqKxsRG1tbWB9qysLBQXF2P37t3YuXNnoN3JMQFASUkJUlJSUFFREfGYUlNToes6Dhw4gB07diTFmKK1n4YPHw5d1/HZZ5/hyJEjSTGmaO2nSZMmweVyoaqqKvA/nEQfU7T206RJk+D1eoOySfQxRWs/5eXloaWlJSgbx8fU8T/lTzwetHQUCenp6Sj1ePDF1q3YuWtXYPsd+/PRcutA/OpXB5CZebz9wIFh+NGPhuGVVxowZMi+Tn0cipkzhwftpy1bMgCUAAA/T/w8RX0/7ahrADAZBw8eBADHP09da6buaFbn8jmGdu/ejZEjR+L999/HrFmzAu233nor3nnnHdupA2vXrsX3vvc9/OxnP8PMmTPx2Wef4Qc/+AGuvfZa3H333bbvY3dmdtSoUThw4AAyMzMB9I/fqDgmjolj4pg4phiPadMmYNo0eNevD6wzK/W9ulrDjBk6NmwwMWXK8fadO12YNMmFlpagl0ZGhoVXXtEwePDxvtfUaFiwQEdlJVBayv3EMUV3TFUv1GDmgsnY8NwnKJ830fExHTx4EEOGDEFTU1OgXpPE7cxsTk4OdF3Hvn37gtr37duHvLw82+fcfffduPzyy3HNNdcAAE455RQ0Nzfjuuuuw5133hn4Kqmz1NRUpKamhrS73W643cHD9++IrqSlJ6T2zq9rWRaampqQlZUV8n522/tpmmbbLvUx3Pa+jCnS9q5jsiwLhw4dErNJxDH11MfetnfOpvPZEr9EHJNfX/dT58+UXTaJOKZI2u3GZFkWDh8+bJtNoo6puz6G025ZFo4cOWKbjZNjCrT38P8bfxdcLhfc7uPto0f7ptd2vmFYQwNw4YUavv51AAjue0aGb44tP0/8PEV7P7k7+urStLh+nuzEbTWDlJQUTJs2DWvWrAm0maaJNWvWBJ2p7aylpSUkaH9ocTrB3CPDMFBTUxPymwcxm+4wGxmzkTEbWSJnU1DgO7Hrf8yZ4ytw/XfI7fzweMK7WCyRc3Eas5Gplk1cVzNYtGgRFixYgPLycsyYMQOPPvoompubA6sbzJ8/HyNHjsQDDzwAAJg7dy6WLVuGqVOnBqYZ3H333Zg7d674mwAREVGyKSjgCgdEfnEtZi+++GI0NDTgnnvuwd69ezFlyhS88cYbgYvC6uvrg87E3nXXXdA0DXfddRd27dqFoUOHYu7cubj//vvjNQQiIiIiiqO4FrMAsHDhQixcuND2Z2vXrg36u9vtxuLFi7F48eIY9Cw6NE1Denq67Vyk/o7ZyJiNjNnImI3M8Wzq64MntgKha8kqiMeMjNnIVMsm7sVsstN1HaWlpfHuhpKYjYzZyJiNjNnIHM2mvh4oLkbIkgMA6tPGoXHPcKDqeJtKN0HgMSNjNjLVsmEx6zDTNNHY2IicnBzbqwT7M2YjYzYyZiNjNjJHs2ls9BWyK1f6itoO9XsGoPi7k9DyjeD3y8gI/0Itp/CYkTEbmWrZsJh1mGmaqK2txeDBg5XY4SphNjJmI2M2MmYji1o23U0nKC4OrCcLAI1VQEtrcI3r8fhu/vXee0F1b9xmJPCYkTEbmWrZsJglIiLqjW6mEwQWeLXRucbNyfFt2uVutj29BBF1g8UsERFRbwjTCQD0eiJsQUHoTRDCfAki6oLFrMM0TRPvrNLfMRsZs5ExGxmzkUWUTdcpBcJ0gnCptEYsjxkZs5Gplg2LWYfpuo7irr/BEwBm0x1mI2M2MmYjCzsbaUpBks0F4DEjYzYy1bKJ/6zdJGeaJnbu3AnTNOPdFeUwGxmzkTEbGbORhZ1N5ykFfblfrOJ4zMiYjUy1bFjMOky1Ha4SZiNjNjJmI2M2soiz8U8p8D+SqJAFeMx0h9nIVMuGxSwRERERJSwWs0RERESUsHgBmMNcLheGDh2qxKLCqmE2MmYjYzYyZiNzOpvu7qWgMh4zMmYjUy0bFrMOc7lcKCoqinc3lMRsZMxGxmxkzEYWrWzsitaGBuDCC8O+l4ISeMzImI1MtWxYzDrMNE3U1dVhzJgxyvwGowpmI2M2MmYjYzayaGTT0w3A3ngDGDo0uF31GyHwmJExG5lq2bCYdZhpmmhoaMBJJ52kxA5XCbORMRsZs5ExG1k0sonCDcCUw2NGxmxkqmXDYpaIiPq3MCe89vEGYEQUZSxmiYio/+pp7oDKE16JCACLWce5XC7k5+crcRpeNcxGxmxkzEbGbGRiNsk4dyAMPGZkzEamWjYsZh3m3+EUitnImI2M2ciYjazHbPrp3AEeMzJmI1MtGzVK6iRmGAY8Hg8Mw4h3V5TDbGTMRsZsZMxGxmzsMRcZs5Gplg2LWYdZloWmpiZYlhXvriiH2ciYjYzZyJiNjNnYYy4yZiNTLRsWs0RERESUsFjMEhEREVHC4gVgDnO5XCgsLFTmij+VMBsZs5ExGxmzkUWSTdflZ7tZejZh8ZiRMRuZatmwmHWYy+VCbm5uvLuhJGYjYzYyZiNjNp10qURdAHIB4NixXi23JS0/m2xLz/KYkTEbmWrZqFFSJzHDMPDhhx8qc8WfSpiNjNnImI2M2XTwV6LTpoU8rOJi38970Hn52crK4w+PJ7mWnuUxI2M2MtWy4ZlZh1mWhdbWVmWu+FMJs5ExGxmzkTGbDjY3QvB6vaj7298w9t57fT/vVJHWYxQaPelBL+GfUpDsy8/ymJExG5lq2URUzNbW1qKwsDDafSEiIoqezpWo14tWjyekcG14fxAuhAct8waGPD3ZphQQJauIitmTTz4ZZ5xxBq6++mp85zvfQVpaWrT7RUREFFV7DgzErJDCdSwy0Iw3fv0php46Nmj7fnA3W6KkENGc2aqqKpSUlGDRokXIy8vD//zP/+CDDz6Idt+Sgq7rmDBhAnRdj3dXlMNsZMxGxmxkzEam6zoyBo1BCwZi5Q3/RuVKj+9x32p4UIw5px5BWRmCHv2hkOUxI2M2MtWyiaiYnTJlCh577DHs3r0bzzzzDPbs2YOvfOUrmDx5MpYtW4aGhoZo9zNhaZqG7OxsaJoW764oh9nImI2M2ciYjUzTNAwcMQIAUPzb76Ns3kTf4+7zUZBxoN/OJ+AxI2M2MtWy6dNqBm63GxdeeCH++Mc/4qGHHsJnn32GW265BaNGjcL8+fOxZ8+eaPUzYXm9XmzYsAFerzfeXVEOs5ExGxmzkTEbmdfrxcdffOH7y8rnk3uJgjDwmJExG5lq2fSpmK2oqMCNN96I4cOHY9myZbjllluwbds2vPnmm9i9eze+9a1vRaufCU2VpStUxGxkzEbGbGTMRmaaHdn4LwzrT/MJusFjRsZsZCplE9EFYMuWLcOzzz6LLVu24LzzzsPvf/97nHfeeYE7QYwZMwbLly/H6NGjo9lXIiKiYF1v0wUk5626iEgUUTH729/+FldddRWuuOIKDB8+3Hab3NxcPP30033qHBERkUi6TReA+rRxaNwzHKjy/d3rBbZvTw/ZjogSX0TF7JtvvomCgoKQe/JaloXPP/8cBQUFSElJwYIFC6LSyUSm6zpKSkqUueJPJcxGxmxkzEbW77KxuTkCANTvGYDi705Cyzc6/z/KDWAsMjIs5OSocdGKCvrdMRMGZiNTLZuIitmioiLs2bMn5L68X3zxBcaMGaPUPAoVpKSkxLsLymI2MmYjYzayfplNl9t0NVYBLa3BNa5lWTAMA8OG6f19imyIfnnM9BKzkamUTUQXgEm3Lzt69ChvoNCFYRioqKhggW+D2ciYjYzZyJhNsM7XeZWWGjDNCowcyWw64zEjYzYy1bIJ68zsokWLAPjWF7vnnnuQkZER+JlhGFi/fj2mTJkS1Q4SEREREUnCKmarq6sB+M7MfvTRR0GnmFNSUlBaWopbbrkluj0kIiIiIhKEVcy+/fbbAIArr7wSjz32GDIzMx3pFBER9WN2y20Bvrt0ccIrEXUR0QVgzz77bLT7kbR0XUd5ebkyV/yphNnImI2M2ciSIpv6etSPPweNxwaG/CgnrRkFW96MqKBNimwcwFxkzEamWja9LmYvvPBCLF++HJmZmbjwwgu73faVV17pc8eSSXt7O9LTub6hHWYjYzYyZiNL9GzqP2pC8bEqtCC0mM041oxXXtuNoad2NHjSkYNR6G1pm+jZOIW5yJiNTKVser2aQVZWFjRNC/x3dw86zjAMbNq0SZkr/lTCbGTMRsZsZMmQTeMhN1owECvvq0NlJQKPN379KQDg698fi2nT4HvMK0YxPKjfM6DH102GbJzAXGTMRqZaNr0+M9t5agGnGRARkZOKxxzrvHQsgCPwoBiNK/8eWDzWs7oO8+4eg8ZD7l6fnSWi5BPRnNnW1lZYlhVYmmvHjh149dVXMXHiRHzta1+LageJiIgAoACfowBVAFo7WvYCGANPXVrgtrUA4PHEoXNEFDcRFbPf+ta3cOGFF+L666/HoUOHMGPGDKSkpKCxsRHLli3DDTfcEO1+JjRVJkiriNnImI2M2ciSNpucHCAjA5g373gTRiEDHsy7ewxwd/DmGRm+p3SWtNn0EXORMRuZStlEVMxWVVXhkUceAQD86U9/Ql5eHqqrq/HnP/8Z99xzD4vZTtxuN6ZPnx7vbiiJ2ciYjYzZyJI6m4IC3ynXTkt2FQDwtB1CY6rNygddVvFK6mz6gLnImI1MtWwiKmZbWlowaNAgAMA//vEPXHjhhXC5XPiv//ov7NixI6odTHSWZaGpqSnoAjryYTYyZiNjNrKkz6agIGRZroKOR0+SPpsIMRcZs5Gplk2vVzPo7OSTT8aqVavw+eef4+9//3tgnuz+/ft5I4UuDMNATU2NMlf8qYTZyJiNjNnIEi2b+nqgqir44alLc+S9Ei2bWGEuMmYjUy2biM7M3nPPPbj00ktx88034+yzz8asWbMA+M7STp06NaodJCKi5FNfDxRPMNHS2vWcyhhkoBk52d649IuIEk9Exex3vvMdfOUrX8GePXtQWloaaD/77LPx//1//1/UOkdERMmp8aM9aGkdjpW4DMUIXn4gJ60ZBae8GaeeEVGiiaiYBYC8vDzk5eUFtc2YMaPPHUo2mqYhPT1diTklqmE2MmYjYzayhMrm0CEAw1F832UoOy/4/yUhV29FQUJlE0PMRcZsZKplE1Ex29zcjAcffBBr1qzB/v37YZpm0M9ra2uj0rlkoOt60NlrOo7ZyJiNjNnIVM6mvj5oIYLjc2PHjAHKih1/f5WziSfmImM2MtWyiaiYveaaa/DOO+/g8ssvx/Dhw5WpzFVkmiYaGxuRk5MDlyui6+2SFrORMRsZs5Gpmk19ve+mXS0tnVtjOzdW1WzijbnImI1MtWwiKmb/9re/4fXXX8dpp50W7f4kHdM0UVtbi8GDByuxw1XCbGTMRsZsZEpk0/UULIBGTzpaWoqxcmXgTrSAx4OceXNQMHxVTLqlRDYKYi4yZiNTLZuIitkTTzwRgwcPjnZfiIgoUdgUrWhoAC68sOspWABTAVShOHsPysqGd7S1Avjc+X4SUdKLqJi97777cM8992DFihXIyMiIdp+IiEhl9vMGfD9KG4fGXz8LnHhioM3z7y+A3yJw0RcRUTRFVMw+/PDD2LZtG4YNG4bRo0djwIABQT+vqqqKSueSgaZpytwhQzXMRsZsZMxGFrNsGht9hWzQvAGgfs8AFH93Elq+H/q1Y7zXjuVxY4+5yJiNTLVsIipmL7jggih3I3npuo7iYuev1E1EzEbGbGTMRhbzbIqLgbKywF8bq4CW1pAaN+bzY+3wuLHHXGTMRqZaNhEVs4sXL452P5KWaZrYvXs3RowYocQkaZUwGxmzkTEbmSrZdKlxocL8WFWyUQ1zkTEbmWrZRNyDQ4cO4amnnsIdd9yBL774AoBvesGuXbvCep3HH38co0ePRlpaGmbOnIkPPvigx/e96aabMHz4cKSmpmLcuHFYvXp1pMNwnGma2LlzZ8havMRsusNsZMxG1tds6uuBqqrQR319lDsaBzxu7DEXGbORqZZNRGdmN23ahNmzZyMrKwvbt2/Htddei8GDB+OVV15BfX09fv/73/fqdV5++WUsWrQITzzxBGbOnIlHH30Uc+bMwZYtW5CbmxuyfXt7O8455xzk5ubiT3/6E0aOHIkdO3YgOzs7kmEQEVGHbq7pQkYG4PFE/aZcRERREdGZ2UWLFuGKK67Ap59+irS0tED7eeedh3fffbfXr7Ns2TJce+21uPLKKzFx4kQ88cQTyMjIwDPPPGO7/TPPPIMvvvgCq1atwmmnnYbRo0fjjDPOUOouFEREiajzNV2VlccfK1f62ruuwkVEpIqIzsxu2LAB//f//t+Q9pEjR2Lv3r29eo329nZUVlbijjvuCLS5XC7Mnj0b69ats33Oa6+9hlmzZuGmm27CX/7yFwwdOhSXXnopbrvtNui6bvuctrY2tLW1Bf5++PBhAIDX64XX6w28r8vlgmmaQafM/e2GYcCyrB7bdV2HpmmB1wV8p+JzcnJC2v3bA4BhGEHtbrcblmUFtWuaBl3XQ/ootTs5pu76Hs6YLMvC0KFDASDo9RN5TNHaTwAwdOhQWJYV1M9EHlO09pOmacjJyYFpmkE/S+QxRWs/uVyukGx6Oybf5m6MH2+irOx43/3tvud2tHu92I1R2Ls5BbplBcZUU6MB0OH1emFZnfru9cIN3+dc78jD6NQGrzcm+2nIkCFB2fDzxM9Td2Pqy+dJ1TH1ZT95O17LtKyYfJ66bt+diIrZ1NTUQFHY2datWwPFSU8aGxthGAaGDRsW1D5s2DDU1NTYPqe2thZvvfUWLrvsMqxevRqfffYZbrzxRnz55ZfiRWkPPPAAlixZEtJeXV2NgQMHAvAVDUVFRairq0NDQ0Ngm/z8fOTn52Pr1q1oamoKtBcWFiI3NxebN29Ga2troH3ChAnIzs5GdXV10M4pKSmBZVmoqKgI6kN5eTna29uxadOmQJuu65g+fTqampqCckhPT0dpaSkaGxtRW1sbaM/KykJxcTF2796NnTt3BtpjMaaUlJSojGn//v1JN6Zo7KeioiJ4PJ6kGlO09tPo0aOTbkzR2k+ZmZlByyP2dkxbtmQAKOkY37DAmPzthw4dAjAYmzdvxt5/1eNCeNCyYGCn3vr+d5KWZmD37k1obZ0QGFPGli0oAfCJx4OJZWVob2/HZx5PoK1N02Kyn7xeb1A2/Dzx8+TU50nlMUW6n3bUNQCYjIMHD8Llcjn+eaqurkZvaVbn8rmXrrnmGhw4cAB/+MMfMHjwYGzatAm6ruOCCy7Af//3f+PRRx/t8TV2796NkSNH4v3338esWbMC7bfeeiveeecdrF+/PuQ548aNw7Fjx1BXVxeo3JctW4Zf/vKX2LNnj+372J2ZHTVqFA4cOIDMzEwAzp+Zra+vx5gxY9A16v7+m69lWaivr8dJJ50UtG0ijymaZ2Z37NiBgoKCoHX8EnlM0TyTVFdXh4KCgqCraBN5TNHaT5qmoba2Niib3o6pqgqYOdONDRtMlJcf77tde+XzHsxcMBkrlmzDpPMLg8aUk+ObWxvU96oquGfOhLFiBVyTJgEAzI8/hr5gAbzr1wNlZY7vJwDYtm0bTjrppMDf+Xni56m7MfXl86TqmPqyn6peqMHMBZOx4blPUHbpBMc/TwcPHsSQIUPQ1NQUqNckEd804Tvf+Q6GDh2K1tZWnHHGGdi7dy9mzZqF+++/v1evkZOTA13XsW/fvqD2ffv2IS8vz/Y5w4cPx4ABA4KmFBQXF2Pv3r1ob29HSkpKyHNSU1ORmpoa0u52u+F2Bw/fvyO6kqYwSO2dX9fr9aKxsRGjR48OeT+77f00TbNtl/oYbntfxhRpe9cxeb1eNDQ04KSTTorKWFUYU0997G17T9kk4pj8+rqfevpMJeKYImm3G1N32fQ0Jv/m/m38fbdrd3f8bHJRO8qm+X/Z6qbveXlARgb0BQsC7ToAZGTAnZcXeBMn95PX68WBAwcwZsyYkPfg54mfp2h/nnrb90Q69vyfe5emwTTNuHyeJBEVs1lZWXjzzTfx73//Gx9++CGOHj2KsrIyzJ49u9evkZKSgmnTpmHNmjWBmzCYpok1a9Zg4cKFts857bTT8MILL8A0zUDgW7duxfDhw20LWSIiUkBBgW85hK5XkflP4xIR9UHYxaxpmli+fDleeeUVbN++HZqmYcyYMcjLy4PVcSFAby1atAgLFixAeXk5ZsyYgUcffRTNzc248sorAQDz58/HyJEj8cADDwAAbrjhBvzv//4vfvCDH+D73/8+Pv30U/z85z/H//k//yfcYRARUSwVFLBwJSJHhFXMWpaFb37zm1i9ejVKS0txyimnwLIseDweXHHFFXjllVewatWqXr/exRdfjIaGBtxzzz3Yu3cvpkyZgjfeeCNwUVh9fX3QKe9Ro0bh73//O26++WaUlJRg5MiR+MEPfoDbbrstnGHElMvlQn5+vu2p+/6O2ciYjYzZyBzNxuOB705eAOr2AlDnVpa9wePGHnORMRuZatmEVcwuX74c7777LtasWYOzzjor6GdvvfUWLrjgAvz+97/H/Pnze/2aCxcuFKcVrF27NqRt1qxZ+M9//hNOt+PKv8MpFLORMRsZs5E5ks2ePQCGA/MuA+C/ungqgPOABLphDY8be8xFxmxkqmUTVkn94osv4ic/+UlIIQsAX/3qV3H77bfj+eefj1rnkoFhGPB4PCFX6xGz6Q6zkTEbmSPZHDrk+/O+n3W6k0LHv/PDh0fvfRzG48Yec5ExG5lq2YRVzG7atAlf//rXxZ+fe+65+PDDD/vcqWRiWRaamppCluUiZtMdZiNjNjJHsxkzBigr8z2KE2uKAcDjRsJcZMxGplo2YRWzX3zxRchNDjobNmwYDh482OdOERERERH1RlhzZg3D6HbdL13Xw7r9GBERRUl9PbB3LzK2bAFcruMLxHL5KyJKcmGvZnDFFVfY3oQAQNCdtsjH5XKhsLBQmSv+VMJsZMxGxmxs1NcDxcVwt7SgpOvPMjJ8KxH084KWx4095iJjNjLVsgmrmF3Q6e4tknBWMugPXC4XcnNz490NJTEbGbORMRsbjY1ASwuwcmXwfFaPB5g3z/fz3haznZfgAhJyGS47PG7sMRcZs5Gplk1Yxeyzzz7rVD+SlmEY2Lx5MyZPnize4q2/YjYyZiNjNjJj3Dhs1vXIsrFdggtIxGW47PC4scdcZMxGplo2Ed3OlnrPsiy0trYqc8WfSpiNjNnImI2sT9kcOgRguG8JrvPyjrd70oF5SKhluOzwuLHHXGTMRqZaNixmiYj6o/p63/QDP/90gjFjgLLQaQUej/1/ExHFG4tZIqIkUI9R2FuTgS2WeXwxA086cjAKIbNl6+tRP/4cNB4bGGjyoBh20wlycnzXkM2bF/wSGRm+nxERxRuLWYfpuo4JEyYoMadENcxGxmxkzCZU/Z4BKIYHLQsGdvlJMTLggWdPbVBBW/9RE4qPVaEFwdtnpJvIOSV4OkFBge9MbOeTuEDirfjF48Yec5ExG5lq2bCYdZimachO8AsnnMJsZMxGxmxCZwh4qk9ACwZi5X11KD5vzPH21XWYd/cYNB5yBxWzjYfcttvn5LhsC9SCgsQqXO3wuLHHXGTMRqZaNmosEJbEvF4vNmzYwJtJ2GA2MmYj6+/ZdCwpi2nTjj/m3T0GGWjGrJImGMYGlJR4fXedHXOs29cqHnMscIfasrLEL1i709+PGwlzkTEbmWrZ8MxsDBiGEe8uKIvZyJiNrD9nY7ukrMeDnHlzMCLvT9hvmCHP8dSlAVVd/t4P9efjpjvMRcZsZCplw2KWiCgBFRf7zqb6tAL4HF3PkeRke5GBZsy7ewxwd+ef+M7k5mSrcVaFiKgvWMwSESWpguFfwoNiNK78e8idwXLmzUHB8FVx6xsRUbSwmHWYrusoKSlR5oo/lTAbGbORMRuZruso6XJHngJ8jgJUIegWtfAA+DzW3YsrHjf2mIuM2chUy4bFbAykpKTEuwvKYjYyZiPrL9l0XbUA6PmGBUHZSIvEAv1yodj+ctyEi7nImI1MpWy4moHDDMNARUWFUhOlVcFsZMxGlujZ1NcDVVXBj/p6++26rlowbZqvLpXq0JBs/IvEVlaGPjye5F6+oItEP26cwlxkzEamWjY8M0tEFCP+ArWlJbg9IyO0trRdtaBDWDcsSIZFYomIusFilogoRuwKVI/Hd7a1sdG+5iyGB2VB810BIAcIvUktEVG/xGKWiCjGgpfV8uk6F9bzXiOAHGDeZQCqg39odyqXiKifYjHrMF3XUV5erswVfyphNjJmI0u2bORrtHJ8a8E+ejdw+knHm7s5lavrOsrLypImm2hKtuMmWpiLjNnIVMuGxWwMtLe3Iz09Pd7dUBKzkTEbWSJk09uVCPzXaHXdNrAW7OmrQk/jdiMRsokXZmOPuciYjUylbLiagcMMw8CmTZuUueJPJcxGxmxkiZBNuCsRFBT46tWgR3ErCsJcCzYRsokXZmOPuciYjUy1bHhmlogoyqK2EgEREfWIxSwRkUPsLvQiIqLoYjEbA6pMkFYRs5ExG1nSZRPJrb46/7zTfyddNlHEbOwxFxmzkamUDYtZh7ndbkyfPj3e3VASs5ExG5lq2URSh4a8gN2dFAD7CbbS8gcZGXDn5WE65zDYUu24UQVzkTEbmWrZsJh1mGVZaGpqQlZWFjRNi3d3lMJsZMxGplI24dahgSd1rn49nvAm2ErLH+TkwBo1Ck2HDimRjWpUOm5UwlxkzEamWjYsZh1mGAZqampQXl4Ot5txd8ZsZMxGplI2YV/o1d39bE8/vfdXhgm3qDW8XmWyUY1Kx41KmIuM2chUyyb+PSAiiiO7aQLhrjhge6FXfT1QZTP/wK765RIHREQRYzFLRP1WdydK+3S32J7mH4RzFpaIiLrFYtZhmqYhPT1diTklqmE2MmYji2Y2dtMEurlbrO10116/sJ+DZ2F53MiYjT3mImM2MtWyYTHrMF3XUVpaGu9uKInZyJiNzIlserMebHdncW0v9OrtC0cRjxsZs7HHXGTMRqZaNrydrcNM08T+/fthmma8u6IcZiNjNrJ4ZdP5ZGtl5fFHn6YjRBmPGxmzscdcZMxGplo2LGYdZpomamtrldnhKmE2MmYji3c2/pOt/ocqhSwQ/2xUxmzsMRcZs5Gplg2nGRAR9UWf75pARER9wWKWiMhG13rUtj6N6K4JREQUTSxmHaZpmjJ3yFANs5ExG1mk2fT2BKp0t1jApj6N06oFEh43MmZjj7nImI1MtWxYzDpM13UUd/2fHAFgNt1hNrJIsgnnBKp0t1gAyGnbhYLGfYD/Z/5qOMarFkh43MiYjT3mImM2MtWyYTHrMNM0sXv3bowYMQIuF6+364zZyJiNLJJswj2Banu32IjW5ootHjcyZmOPuciYjUy1bOLfgyRnmiZ27typzBV/KmE2MmYj60s2XVciCGs1ggRYm4vHjYzZ2GMuMmYjUy0bnpklooRlNw82KlNVu5tgq8iUAiIi8mExS0QJqb4eOOUU+2/9+3SylCsUEBElFBazDnO5XBg6dKgSc0pUw2xkzEbmz+aLL1wh82A9Ht8qBI2NfShmFVuhIBw8bmTMxh5zkTEbmWrZsJh1mMvlQlFRUby7oSRmI2M2Mn82VVW+v9t969+rNWJ7koDTCXjcyJiNPeYiYzYy1bJRo6ROYqZpYtu2bcpMklYJs5ExG1l32XReI3batOOPefP6xwwBHjcyZmOPuciYjUy1bFjMOsw0TTQ0NCizw1XCbGTMRtZdNv41YjsvNqDgogOO4XEjYzb2mIuM2chUy4bTDIgoLpxaicB2jVgiIkpaLGaJKOa6u/9AzM+gdq2qI5pgS0RE8cJi1mEulwv5+fnKXPGnEmYjS/Zs7BYM6O1KBP5s9u6NQjYJcFevcCT7cdMXzMYec5ExG5lq2bCYdZh/h1MoZiPrL9lEsmCAP5v9+8N8M+lGCHbLcCm+BJekvxw3kWA29piLjNnIVMuGxazDDMPA1q1bMW7cOOi6Hu/uKIXZyFTJxq7+A+Jb6/mzMYxxAPSOaQGt3XeupxshnH56QhavXaly3KiI2dhjLjJmI1MtGxazDrMsC01NTbAsK95dUQ6zkamQTU/1X7xWB/Bn49q/B0A+MO8yANXddy6Bb4QQDhWOG1UxG3vMRcZsZKplw2KWiGxJ9V9U7rIVDYcOAcgH7vsZcF5ecOfeey+000BC3giBiIi6x2KWiLol1X+9vejf8ROfY8YAZcXH38x/14SuEvSiLiIi6h6LWYe5XC4UFhYqc8WfSpiNTOVsuqsX7YQ7JcGzug7wHAtuzM4Ghg8HAJimC0ePjsOeHYdCn+y/a4JqE31jROXjJt6YjT3mImM2MtWyYTHrMJfLhdzc3Hh3Q0nMRqZyNt3Vi1319K1/Zzltu5CBbMy7e0wPr+oCMBjAYGSgGTnZ3tAOJnnRKlH5uIk3ZmOPuciYjUy1bFjMOswwDGzevBmTJ09W4oo/lTAbWTyyCefeAb2tF8P51r8gdR88mIXG+57wTR3wq6sD7r4LWPk8UFwMwzCwbds2FH35JYbNPxcFw1f13JF+gp8pGbOxx1xkzEamWjYsZh1mWRZaW1uVueJPJcxGFutsnLp3QLjf+hfgcxScl3d8DiwAVLUCd1cDxa1AGeD1WjCMLzDV5YIbn0feuSTEz5SM2dhjLjJmI1MtGyUmOzz++OMYPXo00tLSMHPmTHzwwQe9et5LL70ETdNwwQUXONtBoiTXeeWCysrjj2gsv1VQ4LuArOujn84EICKiKIv7mdmXX34ZixYtwhNPPIGZM2fi0UcfxZw5c7Bly5Zu52Ns374dt9xyC04//fQY9pYoucV05apw5jUQEREJ4n5mdtmyZbj22mtx5ZVXYuLEiXjiiSeQkZGBZ555RnyOYRi47LLLsGTJEhQWFsawt+HTdR0TJkxQYk6JapiNLOmz8c9rmDbt+GPevF7Na0j6bPqA2ciYjT3mImM2MtWyieuZ2fb2dlRWVuKOO+4ItLlcLsyePRvr1q0Tn/fTn/4Uubm5uPrqq/Hee+91+x5tbW1oa2sL/P3w4cMAAK/XC6/XG3hPl8sF0zRhmmZQX1wuFwzDCJoXIrXrug5N0wKv65eVlRV4z878B4FhGEHtbrcblmUFtWuaBl3XQ/ootTs9Jqnv4Y4pOzsbpmn2aqyJMqZo7afs7Ow+jam+Hjh4MLTvOTnAmDHB7b6n+f45iMmxt3cv3C0tMFasgDZx4vH2IUOAESMArzdoTG50fH462rOysmAYRkh7PPaTasdeZmYmP09C+6BBg4JePxnGFI395P88JdOY7PrOz1Pf9pO347VMy4KmaY5/nrpu3524FrONjY0wDAPDhg0Lah82bBhqampsn/Ovf/0LTz/9NDZu3Nir93jggQewZMmSkPbq6moMHDgQADB06FAUFRWhrq4ODQ0NgW3y8/ORn5+PrVu3oqmpKdBeWFiI3NxcbN68Ga2tx+8JP2HCBGRnZ6O6ujqwc/w7tKysDNXVnW65CaC8vBzt7e3YtGlToE3XdUyfPh1NTU1BGaSnp6O0tBSNjY2ora0NtGdlZaG4uBi7d+/Gzp07A+1OjgkASkpKkJKSgoqKiojHlJqaCq/Xi/z8fOzYsSMpxhSt/TR8+HDs378fGRkZOHLkSNhj2rs3BZdcUopjx7SOLY5/1NPSDPz5zyaystrx2WefAgC2b08HMBYAYnLsfenxoATAx5aFvIIC35g+/BCt+/cD+/cHjWnLli2YBKDub39Dq8eDosJC1NbWIq2uDmMBfOLxoMU0+/3nKT09HZMmTcK6devgdruhaVpSjCla+ykvLw/vvPMOMjIyAtkk+piisZ8mTZqETz75BKZpBnJJ9DHx8+TMftpR1wBgMg4ePAiv1+v456lrzdQdzYrjpWi7d+/GyJEj8f7772PWrFmB9ltvvRXvvPMO1q9fH7T9kSNHUFJSgt/85jc499xzAQBXXHEFDh06hFWrVtm+h92Z2VGjRuHAgQPIzMwE4OxvVIZhoKqqCuXl5UH/UPi392/TWX/5zdcwDFRXV6OsrCxo4eVEHlO09pNpmqiqqsLUqVODvsaRxrRrl44DB46PqaZGw4IFOp57zkJx8fG+NzZq+O53XWhpCT4WASAjw4LHo2HUqBgce5WVcM+cCe/69XCVl3e/n2proZ9yCrSuSy0AsDIyYHz0EVBQ0O8/T5qmwbIsbNiwAWVlZYHnJvqYorWfTNMMySbRxxSN/WRZFioqKoJySfQx8fPkzH6qeqEGMxdMxobnPsGU741z/PN08OBBDBkyBE1NTYF6TRLXM7M5OTnQdR379u0Lat+3bx/y8vJCtt+2bRu2b9+OuXPnBtr8wbvdbmzZsgVFRUVBz0lNTUVqamrIa7ndbrjdwcP374iupDkhUnvX19U0DZqmhbRL2/ufY9cu9THc9r6OKZL27sYUjbGqNqa+7Cf/h17XddvX7zym+npg4kT/slrHt83IAP77v7WOVQOOt8tLZfm37cWYOl285ep4dF1rq9v91PH6brcb6NhG3E+FhUGd9nq9+MTjwcTiYrjz8uDusixCf/48eb3ewP+Yevvvm+pj6q6P4bT7zzzaZZOoYwL6vp+6O2bstgfUH1Mk7fw89dzu9hetHSfm4vF5ksS1mE1JScG0adOwZs2awPJapmlizZo1WLhwYcj2EyZMwEcffRTUdtddd+HIkSN47LHHMGrUqFh0m0gpnZfV6nyXLenurX2+QVZ3i9JGYy0vO5077fWixTR9yy6E8Y8dERElp7j/n2DRokVYsGABysvLMWPGDDz66KNobm7GlVdeCQCYP38+Ro4ciQceeABpaWmYPHly0POzs7MBIKRdFbquo6SkRPxNpT9jNrJdu3QYRik+/FBH59kpUoEKOLSsVtflswBfwdq1evbft7axMbSD0mtEiMeNjNnImI095iJjNjLVsol7MXvxxRejoaEB99xzD/bu3YspU6bgjTfeCFwUVl9fb3vaO5GkpKTEuwvKYjahtV5DA3DhhUBLS3rIthkZwCuvAEOHHm9zbHlW6QysvyOnn97zWdieXiPC24vxuJExGxmzscdcZMxGplI2cS9mAWDhwoW20woAYO3atd0+d/ny5dHvUBQZhoGKigqUl5eHNf+jP2A23X9j/8gjHpx66thANv4i9+tfD32dvt521pY0fwGQTxF3raztzuL29Bo94HEjYzYyZmOPuciYjUy1bOLfA6I4sfv2G4i4xoqIVC9mZxvYv78pZFqofPGWg33uzfyFnBxfRT1vXujPensWl4iIKAIsZinp2RWtx7/KD93eqeuYups62rVe9HoDy60G6fPFW04pKIhTpU1ERP0di1lKaj1N2XzjjdD5p/PmAe+917uVAaLVj6hPEYgHZSttIiJKZixmHabrOsrLy5W54k8lscgm3Gmf0rflfT1bG24/eNzImI2M2ciYjT3mImM2MtWyYTEbA+3t7UhPD70ynWKXTW+XrbL7tjzcVaeitXwWjxsZs5ExGxmzscdcZMxGplI2ib3mVQIwDAObNm0KuV0bqZtNQYGv4PQ/up5J9fNPHZg2LfhRXAz8/e9AVdXxR7jLZ6majQqYjYzZyJiNPeYiYzYy1bLhmVmiCNlNHXB8+SwVlmAgIiJSCItZol6yW0IVCJ064NhF/T1dRebUrWSJiIgUxmI2BlSZIK2inrJR4URkT0uodj3bGq2L+kOyka4i625Sb5LiZ0rGbGTMxh5zkTEbmUrZsJh1mNvtxvTp0+PdDSX1lI0qJyLjsYRqt9n09ioyFX4TcAA/UzJmI2M29piLjNnIVMuGxazDLMtCU1MTsrKyoGlavLujlJ6yUelEZKyXUI3ouOk8DyIed4WIEX6mZMxGxmzsMRcZs5Gplg1XM3CYYRioqalR5oo/lfQ2G/+JyJ5WF0gmYR03nedB+JdU8F+B9sYbQGXl8cfKlb4C1+6MbYLgZ0rGbGTMxh5zkTEbmWrZ8Mxskgp3/dO+vK5Trx3uclb9ljQPItydwh1AREQJiMVsgrMrLqVvmPv67bKTc1il1+5uOSu7WivBp4MeV1+PjC1bAJcLcHd8TLsrLvs6DyKSHUBERKQAFrMO0zQN6enpjswp6am4fOMNYOhQ39/980zfe693t1O1E605rP4C3DA0bN8+GLquYetW+9cO55az/nG/8krwuBNOfT30U05BibRjnSgupZ2r4G8HTn6mEh2zkTEbe8xFxmxkqmXDYtZhuq7jxBNL8eGHoT8Lp06wOwPr8djXH3av3VMBGM5Z1XBuydpVcAGuAxgX1I/TT++5H9K36tINC5Q5udjbORqNjdB6u2Mj1dtFcxWk6zpKS0vj3Q0lMRsZs7HHXGTMRqZaNixmHbZ9u4mJEzW0tob+9iIVkV1rnp4uTO9LARjrlQE6nwAcP97EoUOHkJ2dDZfLFVadJn2rHo2po46IYI7GF8OGIXvKFLhcUbxOM9xFcxVkmiYaGxuRk5MT3WySALORMRt7zEXGbGSqZcNi1mH795tobXVjxQoDkycfX2BYKiK7m7rYedqAXzQKQElfrweSziYDvjGWlJioqNiKKVPK4XZH58MQ6yW0ei2CORo7d+1CpmlG9x+KeCyaG2WmaaK2thaDBw9W4h9RlTAbGbOxx1xkzEamWjYsZmNkwgSrV9/eqjJ1sa8XZPV0NjmmJwBVunmA9DV+5/CcnuirbMVPREQUPhazior11EW7KZR9vSCrp7PJXm/0+i9y+jZifV0DTQjPysiANysr8n4RERH1EyxmHea/0k+VK/666mkKZV/m4/ZU02ma5vzdQ5y8jVh3p697WyQL4Zknnoj0Y8eUPW7iKSbHTYJiNjJmY4+5yJiNTLVsWMw6TNf1oD+7ki4qj5VoTaGM5JtrXddR3LnAdOpOD4Azp7rtCuVIimSb8HQA/eBGZxEJOW4ogNnImI095iJjNjLVsmEx6zDTNAG4An/6qXRRebymUJqmid27d2PEiBFw7dzZ97Oc0RJuUe1AoRyUjQKT61XCbGTMRsZs7DEXGbORqZYNi1mHScVsElxU3memaWLnzp3Iy8uDK9yznE5d1BWNqQNAn0+5B2WjwD8UKmE2MmYjYzb2mIuM2chUy4bFbBzxonIbvTnL6eRFXd0V1V1vnybdT1eVU+5ERET9AItZkjk5h7UvnLyoy69zUR1OgcpT7kRERDHFYtZh/tPvKpyGD0u0vm7vhsvlwtChQyPPJlbrl4VboEbhlHufs0lizEbGbGTMxh5zkTEbmWrZsJh1WMIWs+F83Q6Ed9ax44yvC0ARAGzcGPtlHMIV4zkhLpcLRUVFMXu/RMJsZMxGxmzsMRcZs5Gplg2LWYdJF4AljHC+bu/NGdue5rtGY05pOHfTivfaaALTNFFXV4cxY8Yk3i9CDmM2MmYjYzb2mIuM2chUy4bFrMMiKmalK/XtRHBGNOLXkL5uD2euaqczvt6xY/GJx4OJxcVwu919n1Pa3a3IuhbJil+oZZomGhoacNJJJynxD4VKmI2M2ciYjT3mImM2MtWyYTEbT3bFZUMDcOGF9mcu7UTrjGg4d6yK1k0MSkrQYpq+M7/uKByK4dyKjBdqERERJQUWs/HSU3H5xhvA0KHdv0aEZ0RtVwDozbJT4bIr1p3+Gj+cYptroxERESU8FrMOEy8Ak4pLwNkzg11XAIjm1+2dC9XuzjB3vK7L5UJ+fn7PX1EoOq/VSb3Oph9iNjJmI2M29piLjNnIVMuGxazDelzNIFbLS0mi8XV7d3NV7c4wd7yuC0B+fn74r+t/7SS+AYH/HwoKxWxkzEbGbOwxFxmzkamWDYtZhxmGAUAP/Kmkvn7dHs5c1U4Mw8DWrVsxbtw46LpNNv14XmuP2fRjzEbGbGTMxh5zkTEbmWrZsJh1mGVZvj89HkBvP/4Dp78q7zpfVaW5qh0sy0JTU1Mgo2i9bjLoVTb9FLORMRsZs7HHXGTMRqZaNixmnbZ3L4B8uBfMB1Ad/DOnvirv7u5dSfzVPBEREfU/LGaddugQgHwYS34KfGNE8M+i9VW53QVSdheXJflX80RERNT/sJh1mP/CL62wECibGN0X7+kCqdNPV7p4dblcKCwsVOZqSJUwGxmzkTEbGbOxx1xkzEamWjYsZh3m0rSgP6MqwS+QcrlcyM3NjXc3lMRsZMxGxmxkzMYec5ExG5lq2ahRUicxwzSD/oy6ggLf0l5dH4oXsoDvasgPP/ywY6UH6ozZyJiNjNnImI095iJjNjLVsmEx67DAagaKXPGnEsuy0NraymxsMBsZs5ExGxmzscdcZMxGplo2LGaJiIiIKGGxmCUiIiKihMVi1mG67gr6k47TdR0TJkxQ4u4hqmE2MmYjYzYyZmOPuciYjUy1bLiagcM0aEF/0nGapiE7Ozve3VASs5ExGxmzkTEbe8xFxmxkqmXD04UO83Zc6edV5Io/lXi9XmzYsAFerzfeXVEOs5ExGxmzkTEbe8xFxmxkqmXDYpbiSpVlPVTEbGTMRsZsZMzGHnORMRuZStmwmCUiIiKihMViloiIiIgSFotZh3E1A5mu6ygpKVHmakiVMBsZs5ExGxmzscdcZMxGplo2rLAorlJSUuLdBWUxGxmzkTEbGbOxx1xkzEamUjYsZh1mGGbQn3ScYRioqKhQahK5KpiNjNnImI2M2dhjLjJmI1MtGxazRERERJSwWMwSERERUcJiMUtERERECYvFrMO4moFM13WUl5crczWkSpiNjNnImI2M2dhjLjJmI1MtG1ZYFFft7e3x7oKymI2M2ciYjYzZ2GMuMmYjUykbFrMO42oGMsMwsGnTJmWuhlQJs5ExGxmzkTEbe8xFxmxkqmXDYpaIiIiIEhaLWSIiIiJKWEoUs48//jhGjx6NtLQ0zJw5Ex988IG47ZNPPonTTz8dJ554Ik488UTMnj272+1JbapMHlcRs5ExGxmzkTEbe8xFxmxkKmUT92L25ZdfxqJFi7B48WJUVVWhtLQUc+bMwf79+223X7t2LS655BK8/fbbWLduHUaNGoWvfe1r2LVrV4x73jvujp3tVminq8LtdmP69Olwu93x7opymI2M2ciYjYzZ2GMuMmYjUy2buBezy5Ytw7XXXosrr7wSEydOxBNPPIGMjAw888wztts///zzuPHGGzFlyhRMmDABTz31FEzTxJo1a2Lc896xYAX9ScdZloVDhw7BsphNV8xGxmxkzEbGbOwxFxmzkamWTVxL6vb2dlRWVuKOO+4ItLlcLsyePRvr1q3r1Wu0tLTgyy+/xODBg21/3tbWhra2tsDfDx8+DADwer3wer2B93S5XDBNE6Z5fNUBf7thGEE7TGrXdR2apgVe1/8+gG81g87t/u19Pwu+GtDtdsOyrKB2TdOg63pIH6V2J8fUXd/DGZNhGKipqUFZWRlcruO/VyXymKK1n0zTRE1NDaZOnRr0VU4ijyla+8myLHg8HpSVlQVlk8hjitZ+sssm0ccUrf1kmmZINok+Jn6e+HmK5X7ydryW2fG6Tn+eum7fnbgWs42NjTAMA8OGDQtqHzZsGGpqanr1GrfddhtGjBiB2bNn2/78gQcewJIlS0Laq6urMXDgQADA0KFDUVRUhLq6OjQ0NAS2yc/PR35+PrZu3YqmpqZAe2FhIXJzc7F582a0trYG2idMmIDs7GxUV1cHds6Ouv0ASmBZJioqKoL6UF5ejvb2dmzatCnQpus6pk+fjqampqAM0tPTUVpaisbGRtTW1gbas7KyUFxcjN27d2Pnzp2BdifHBAAlJSVISUnp05hSU1MBAAcOHMCOHTuSYkzR2k/Dhw8HAHz22Wc4cuRIUowpWvtp0qRJAICqqipompYUY4rWfpo0aRLa29uDskn0MUVrP+Xl5aG5uTkom0QfEz9P/DzFcj/tqGsAMBkHDx4EAMc/T9XV1egtzYrjOeLdu3dj5MiReP/99zFr1qxA+6233op33nkH69ev7/b5Dz74IH7xi19g7dq1KCkpsd3G7szsqFGjcODAAWRmZgJw9jeqyuc/wX9dUYL1Kzaj7NIJQX3r77/5GoaB6upqnpkVzsxWVVXxzKxwJqmiooJnkoQzSRs2bOCZJOHMbNdsEn1M/Dzx8xTL/VT1Qg1mLpiMDc99ginfG+f45+ngwYMYMmQImpqaAvWaJK5nZnNycqDrOvbt2xfUvm/fPuTl5XX73KVLl+LBBx/EP//5T7GQBXxn//xnADtzu90hE5f9O6Krzh/w3rR3fl2X6/gHQJoobdcubS/1Mdz2vowp0vauY9I0Denp6dB13fZ9E3FMPfWxt+2GYSA9PR1ut9u2/4k4Jr++7ifDMJCRkSFmk4hjiqTdbkzdZZOoY+quj+G0W5YlZpOoYwL4eZL6GG47P089t/svZHdpGjRNi8vnSRLXC8BSUlIwbdq0oIu3TNN3MVfnM7Vd/eIXv8B9992HN954A+Xl5bHoasT0jgNDtzlA+jtd11FaWioe+P0Zs5ExGxmzkTEbe8xFxmxkqmUT9wpr0aJFePLJJ7FixQp4PB7ccMMNaG5uxpVXXgkAmD9/ftAFYg899BDuvvtuPPPMMxg9ejT27t2LvXv34ujRo/EaQrfMjlPtphW32RzKMk0T+/fvD/pag3yYjYzZyJiNjNnYYy4yZiNTLZu4F7MXX3wxli5dinvuuQdTpkzBxo0b8cYbbwQuCquvr8eePXsC2//2t79Fe3s7vvOd72D48OGBx9KlS+M1hG75d7QqO1wlpmmitraW2dhgNjJmI2M2MmZjj7nImI1MtWyUWO124cKFWLhwoe3P1q5dG/T37du3O98hIiIiIkoIcT8zS0REREQUKRazDvOvv9Z5/T7y0TQNWVlZzMYGs5ExGxmzkTEbe8xFxmxkqmWjxDSDZMbVDGS6rqO4uDje3VASs5ExGxmzkTEbe8xFxmxkqmXDCsthXM1AZpomdu7cqcwEcpUwGxmzkTEbGbOxx1xkzEamWjYsZh3G1Qxkqn0YVMJsZMxGxmxkzMYec5ExG5lq2bCYJSIiIqKExWKWiIiIiBIWi1mH+e9zbHe/4/7O5XJh6NChzMYGs5ExGxmzkTEbe8xFxmxkqmXD1Qwc5upYtsKlyPIVKnG5XCgqKop3N5TEbGTMRsZsZMzGHnORMRuZatmoUVInMa5mIDNNE9u2bVNmArlKmI2M2ciYjYzZ2GMuMmYjUy0bFrMO42oGMtM00dDQwGxsMBsZs5ExGxmzscdcZMxGplo2LGaJiIiIKGGxmCUiIiKihMVi1mFczUDmcrmQn5/PbGwwGxmzkTEbGbOxx1xkzEamWjZczcBhXM1A5v8wUChmI2M2MmYjYzb2mIuM2chUy0aNkjqJGR2Tow1FJkmrxDAMeDweGIYR764oh9nImI2M2ciYjT3mImM2MtWyYTHrMKtjSS6LS3OFsCwLTU1NzMYGs5ExGxmzkTEbe8xFxmxkqmXDYpaIiIiIEhaLWSIiIiJKWCxmHcbVDGQulwuFhYXMxgazkTEbGbORMRt7zEXGbGSqZcPVDBzG1QxkLpcLubm58e6GkpiNjNnImI2M2dhjLjJmI1MtGzVK6iTG1QxkhmHgww8/VOZqSJUwGxmzkTEbGbOxx1xkzEamWjYsZh3G1QxklmWhtbWV2dhgNjJmI2M2MmZjj7nImI1MtWxYzBIRERFRwmIxS0REREQJi8Wsw3TdFfQnHafrOiZMmABd1+PdFeUwGxmzkTEbGbOxx1xkzEamWjZczcBhGrSgP+k4TdOQnZ0d724oidnImI2M2ciYjT3mImM2MtWy4elCh3k7rvTzKnLFn0q8Xi82bNgAr9cb764oh9nImI2M2ciYjT3mImM2MtWyYTFLcaXKsh4qYjYyZiNjNjJmY4+5yJiNTKVsWMwSERERUcJiMUtERERECYvFrMO4moFM13WUlJQoczWkSpiNjNnImI2M2dhjLjJmI1MtG1ZYFFcpKSnx7oKymI2M2ciYjYzZ2GMuMmYjUykbFrMOMwwz6E86zjAMVFRUKDWJXBXMRsZsZMxGxmzsMRcZs5Gplg2LWSIiIiJKWCxmiYiIiChhsZglIiIiooTFYtZhXM1Apus6ysvLlbkaUiXMRsZsZMxGxmzsMRcZs5Gplg0rLIqr9vb2eHdBWcxGxmxkzEbGbOwxFxmzkamUDYtZh3E1A5lhGNi0aZMyV0OqhNnImI2M2ciYjT3mImM2MtWyYTFLRERERAmLxSwRERERJSwWsxRXqkweVxGzkTEbGbORMRt7zEXGbGQqZeOOdweSnbtjZ7sV2umqcLvdmD59ery7oSRmI2M2MmYjYzb2mIuM2chUy4ZnZh1mwQr6k46zLAuHDh2CZTGbrpiNjNnImI2M2dhjLjJmI1MtGxazDuNqBjLDMFBTU6PM1ZAqYTYyZiNjNjJmY4+5yJiNTLVsWMwSERERUcJiMUtERERECYvFrMM0TQv6k47TNA3p6enMxgazkTEbGbORMRt7zEXGbGSqZcPVDBymu1xBf9Jxuq6jtLQ03t1QErORMRsZs5ExG3vMRcZsZKplwwrLYWbHlX6mIlf8qcQ0Tezfvx+myYvjumI2MmYjYzYyZmOPuciYjUy1bFjMOsy/o1XZ4SoxTRO1tbXMxgazkTEbGbORMRt7zEXGbGSqZcNiloiIiIgSFotZIiIiIkpYLGYdxtUMZJqmISsri9nYYDYyZiNjNjJmY4+5yJiNTLVsuJqBw7iagUzXdRQXF8e7G0piNjJmI2M2MmZjj7nImI1MtWxYYTmMqxnITNPEzp07lZlArhJmI2M2MmYjYzb2mIuM2chUy4bFrMO4moFMtQ+DSpiNjNnImI2M2dhjLjJmI1MtGxazRERERJSwWMwSERERUcJiMeswV8eFXy5eABbC5XJh6NChzMYGs5ExGxmzkTEbe8xFxmxkqmXD1Qwc5upYtsKlyPIVKnG5XCgqKop3N5TEbGTMRsZsZMzGHnORMRuZatkoUVI//vjjGD16NNLS0jBz5kx88MEH3W7/xz/+ERMmTEBaWhpOOeUUrF69OkY9DR9XM5CZpolt27YpM4FcJcxGxmxkzEbGbOwxFxmzkamWTdyL2ZdffhmLFi3C4sWLUVVVhdLSUsyZMwf79++33f7999/HJZdcgquvvhrV1dW44IILcMEFF2Dz5s0x7nnvcDUDmWmaaGhoYDY2mI2M2ciYjYzZ2GMuMmYjUy2buBezy5Ytw7XXXosrr7wSEydOxBNPPIGMjAw888wztts/9thj+PrXv44f//jHKC4uxn333YeysjL87//+b4x7TkRERETxFtc5s+3t7aisrMQdd9wRaHO5XJg9ezbWrVtn+5x169Zh0aJFQW1z5szBqlWrbLdva2tDW1tb4O9NTU0AgC+++AJerzfwni6XC6ZpBv2W4W83DANWp2kCUruu69A0LfC6AHC05TCAwzjaehRffPFFUN90XQcAGIYR1O52u2FZVlC7pmnQdT2kj1K7k2Pqru/hjMkwDDQ3N+PQoUNBk8gTeUzR2k+maaK5uRkHDx4MvH+ijyla+8myLBw9ejQkm0QeU7T2k102iT6maO0n0zRDskn0MfHzxM9TLPfToaNNAA7jcMsRHD582PHP08GDBwEg6LUkcS1mGxsbYRgGhg0bFtQ+bNgw1NTU2D5n7969ttvv3bvXdvsHHngAS5YsCWkfM2ZMhL2OzNnXA7g+pm9JREREFFVnXQfguti935EjR5CVldXtNkm/msEdd9wRdCbXNE188cUXGDJkCLQYrDBw+PBhjBo1Cp9//jkyMzMdf79EwmxkzEbGbGTMRsZs7DEXGbORxSIby7Jw5MgRjBgxosdt41rM5uTkQNd17Nu3L6h93759yMvLs31OXl5eWNunpqYiNTU1qC07OzvyTkcoMzOTHwYBs5ExGxmzkTEbGbOxx1xkzEbmdDY9nZH1i+sFYCkpKZg2bRrWrFkTaDNNE2vWrMGsWbNsnzNr1qyg7QHgzTffFLcnIiIiouQV92kGixYtwoIFC1BeXo4ZM2bg0UcfRXNzM6688koAwPz58zFy5Eg88MADAIAf/OAHOOOMM/Dwww/j/PPPx0svvYSKigr87ne/i+cwiIiIiCgO4l7MXnzxxWhoaMA999yDvXv3YsqUKXjjjTcCF3nV19cHXel+6qmn4oUXXsBdd92Fn/zkJxg7dixWrVqFyZMnx2sI3UpNTcXixYtDpjoQs+kOs5ExGxmzkTEbe8xFxmxkqmWjWb1Z84CIiIiISEFxv2kCEREREVGkWMwSERERUcJiMUtERERECYvFLBERERElLBazUfD4449j9OjRSEtLw8yZM/HBBx90u/0f//hHTJgwAWlpaTjllFOwevXqGPU09sLJ5sknn8Tpp5+OE088ESeeeCJmz57dY5aJLNzjxu+ll16Cpmm44IILnO1gHIWbzaFDh3DTTTdh+PDhSE1Nxbhx45L2cxVuNo8++ijGjx+P9PR0jBo1CjfffDOOHTsWo97Gxrvvvou5c+dixIgR0DQNq1at6vE5a9euRVlZGVJTU3HyySdj+fLljvczHsLN5pVXXsE555yDoUOHIjMzE7NmzcLf//732HQ2xiI5bvz+/e9/w+12Y8qUKY71L54iyaatrQ133nknTjrpJKSmpmL06NF45plnnO8sWMz22csvv4xFixZh8eLFqKqqQmlpKebMmYP9+/fbbv/+++/jkksuwdVXX43q6mpccMEFuOCCC7B58+YY99x54Wazdu1aXHLJJXj77bexbt06jBo1Cl/72tewa9euGPfceeFm47d9+3bccsstOP3002PU09gLN5v29nacc8452L59O/70pz9hy5YtePLJJzFy5MgY99x54Wbzwgsv4Pbbb8fixYvh8Xjw9NNP4+WXX8ZPfvKTGPfcWc3NzSgtLcXjjz/eq+3r6upw/vnn46yzzsLGjRvxwx/+ENdcc01SFm3hZvPuu+/inHPOwerVq1FZWYmzzjoLc+fORXV1tcM9jb1ws/E7dOgQ5s+fj7PPPtuhnsVfJNlcdNFFWLNmDZ5++mls2bIFL774IsaPH+9gLzuxqE9mzJhh3XTTTYG/G4ZhjRgxwnrggQdst7/ooous888/P6ht5syZ1v/8z/842s94CDebrrxerzVo0CBrxYoVTnUxbiLJxuv1Wqeeeqr11FNPWQsWLLC+9a1vxaCnsRduNr/97W+twsJCq729PVZdjJtws7npppusr371q0FtixYtsk477TRH+xlPAKxXX321221uvfVWa9KkSUFtF198sTVnzhwHexZ/vcnGzsSJE60lS5ZEv0MKCSebiy++2LrrrrusxYsXW6WlpY72SwW9yeZvf/ublZWVZR04cCA2neqCZ2b7oL29HZWVlZg9e3agzeVyYfbs2Vi3bp3tc9atWxe0PQDMmTNH3D5RRZJNVy0tLfjyyy8xePBgp7oZF5Fm89Of/hS5ubm4+uqrY9HNuIgkm9deew2zZs3CTTfdhGHDhmHy5Mn4+c9/DsMwYtXtmIgkm1NPPRWVlZWBqQi1tbVYvXo1zjvvvJj0WVX95d/haDBNE0eOHEm6f4cj9eyzz6K2thaLFy+Od1eU8tprr6G8vBy/+MUvMHLkSIwbNw633HILWltbY/L+cb8DWCJrbGyEYRiBu5X5DRs2DDU1NbbP2bt3r+32e/fudayf8RBJNl3ddtttGDFiRMj/dBJdJNn861//wtNPP42NGzfGoIfxE0k2tbW1eOutt3DZZZdh9erV+Oyzz3DjjTfiyy+/TKr/4USSzaWXXorGxkZ85StfgWVZ8Hq9uP7665NumkG4pH+HDx8+jNbWVqSnp8epZ+pZunQpjh49iosuuijeXYm7Tz/9FLfffjvee+89uN0snzqrra3Fv/71L6SlpeHVV19FY2MjbrzxRhw4cADPPvus4+/PM7OkpAcffBAvvfQSXn31VaSlpcW7O3F15MgRXH755XjyySeRk5MT7+4oxzRN5Obm4ne/+x2mTZuGiy++GHfeeSeeeOKJeHct7tauXYuf//zn+M1vfoOqqiq88soreP3113HffffFu2uUAF544QUsWbIEf/jDH5Cbmxvv7sSVYRi49NJLsWTJEowbNy7e3VGOaZrQNA3PP/88ZsyYgfPOOw/Lli3DihUrYnJ2lr9a9EFOTg50Xce+ffuC2vft24e8vDzb5+Tl5YW1faKKJBu/pUuX4sEHH8Q///lPlJSUONnNuAg3m23btmH79u2YO3duoM00TQCA2+3Gli1bUFRU5GynYySS42b48OEYMGAAdF0PtBUXF2Pv3r1ob29HSkqKo32OlUiyufvuu3H55ZfjmmuuAQCccsopaG5uxnXXXYc777wTLlf/PJ8h/TucmZnJs7IdXnrpJVxzzTX44x//mHTfjkXiyJEjqKioQHV1NRYuXAjA9++wZVlwu934xz/+ga9+9atx7mX8DB8+HCNHjkRWVlagrbi4GJZlYefOnRg7dqyj798//yWLkpSUFEybNg1r1qwJtJmmiTVr1mDWrFm2z5k1a1bQ9gDw5ptvitsnqkiyAYBf/OIXuO+++/DGG2+gvLw8Fl2NuXCzmTBhAj766CNs3Lgx8PjmN78ZuBJ71KhRsey+oyI5bk477TR89tlngQIfALZu3Yrhw4cnTSELRJZNS0tLSMHqL/oty3Kus4rrL/8OR+rFF1/ElVdeiRdffBHnn39+vLujhMzMzJB/h6+//nqMHz8eGzduxMyZM+Pdxbg67bTTsHv3bhw9ejTQtnXrVrhcLuTn5zvfgbhcdpZEXnrpJSs1NdVavny59cknn1jXXXedlZ2dbe3du9eyLMu6/PLLrdtvvz2w/b///W/L7XZbS5cutTwej7V48WJrwIAB1kcffRSvITgm3GwefPBBKyUlxfrTn/5k7dmzJ/A4cuRIvIbgmHCz6SqZVzMIN5v6+npr0KBB1sKFC60tW7ZYf/3rX63c3FzrZz/7WbyG4Jhws1m8eLE1aNAg68UXX7Rqa2utf/zjH1ZRUZF10UUXxWsIjjhy5IhVXV1tVVdXWwCsZcuWWdXV1daOHTssy7Ks22+/3br88ssD29fW1loZGRnWj3/8Y8vj8ViPP/64peu69cYbb8RrCI4JN5vnn3/ecrvd1uOPPx707/ChQ4fiNQTHhJtNV8m8mkG42Rw5csTKz8+3vvOd71gff/yx9c4771hjx461rrnmmpj0l8VsFPz617+2CgoKrJSUFGvGjBnWf/7zn8DPzjjjDGvBggVB2//hD3+wxo0bZ6WkpFiTJk2yXn/99Rj3OHbCyeakk06yAIQ8Fi9eHPuOx0C4x01nyVzMWlb42bz//vvWzJkzrdTUVKuwsNC6//77La/XG+Nex0Y42Xz55ZfWvffeaxUVFVlpaWnWqFGjrBtvvNE6ePBg7DvuoLffftv23w5/FgsWLLDOOOOMkOdMmTLFSklJsQoLC61nn3025v2OhXCzOeOMM7rdPplEctx0lszFbCTZeDwea/bs2VZ6erqVn59vLVq0yGppaYlJfzXL6sffNRERERFRQuOcWSIiIiJKWCxmiYiIiChhsZglIiIiooTFYpaIiIiIEhaLWSIiIiJKWCxmiYiIiChhsZglIiIiooTFYpaIKIbOPPNM/PCHP+zVtvfeey+mTJkStdcL1/bt26FpGjZu3OjI6xMRRQOLWSKiODnzzDOhaRo0TUNaWhomTpyI3/zmN2G9xiuvvIL77rvPoR4SEamPxSwRURxde+212LNnDz755BNcdNFFuOmmm/Diiy/2+vmDBw/GoEGDInrv9vb2iJ5HRKQSFrNERA5pbm7G/PnzccIJJ2D48OF4+OGHQ7bJyMhAXl4eCgsLce+992Ls2LF47bXXgrZ57rnnMHr0aGRlZeF73/sejhw5EvhZONMMRo8ejfvuuw/z589HZmYmrrvuul49r6amBqeeeirS0tIwefJkvPPOO716HhFRLLCYJSJyyI9//GO88847+Mtf/oJ//OMfWLt2Laqqqrp9Tnp6etAZ023btmHVqlX461//ir/+9a9455138OCDD0bcp6VLl6K0tBTV1dW4++67ez2OH/3oR6iursasWbMwd+5cHDhwIOI+EBFFE4tZIiIHHD16FE8//TSWLl2Ks88+G6eccgpWrFgBr9dru71hGFi5ciU2bdqEr371q4F20zSxfPlyTJ48Gaeffjouv/xyrFmzJuJ+ffWrX8WPfvQjFBUVoaioqFfPWbhwIb797W+juLgYv/3tb5GVlYWnn3464j4QEUWTO94dICJKRtu2bUN7eztmzpwZaBs8eDDGjx8ftN1vfvMbPPXUU2hvb4eu67j55ptxww03BH4+evTooDmxw4cPx/79+yPuV3l5edjPmTVrVuC/3W43ysvL4fF4Iu4DEVE0sZglIoqjyy67DHfeeSfS09MxfPhwuFzBX5gNGDAg6O+apsE0zYjfb+DAgRE/l4hIRZxmQETkgKKiIgwYMADr168PtB08eBBbt24N2i4rKwsnn3wyRo4cGVLIquI///lP4L+9Xi8qKytRXFwcxx4RER3HM7NERA444YQTcPXVV+PHP/4xhgwZgtzcXNx5553KFqzdefzxxzF27FgUFxfjkUcewcGDB3HVVVfFu1tERABYzBIROeaXv/wljh49irlz52LQoEH40Y9+hKampnh3K2wPPvggHnzwQWzcuBEnn3wyXnvtNeTk5MS7W0REAADNsiwr3p0gIiIiIopE4n3fRURERETUgcUsEVESeO+993DCCSeIDzs///nPxe3PPffcGI+AiCgynGZARJQEWltbsWvXLvHnJ598ckjbF198gS+++MJ2+/T0dIwcOTJq/SMicgqLWSIiIiJKWJxmQEREREQJi8UsERERESUsFrNERERElLBYzBIRERFRwmIxS0REREQJi8UsERERESUsFrNERERElLBYzBIRERFRwvr/ASQ7oyixGJ8eAAAAAElFTkSuQmCC\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqYAAAINCAYAAADsoL2yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABsHklEQVR4nO3de3hU1dk28HvvPSQkQBIgCQGTcFIhggmEBIuWatVqtdVa7KevR1CrVbFXW2prbeuBWqv2VeqhfrbVVrR4aO2rvv08n6u1HsiBk0xASSREQBIgiZCQZPbe3x/DDJlkJpk9x/XM3L/ryjVkZzKzVu5MeGbttdbWbNu2QURERESUZHqyG0BEREREBLAwJSIiIiJFsDAlIiIiIiWwMCUiIiIiJbAwJSIiIiIlsDAlIiIiIiWwMCUiIiIiJbAwJSIiIiIluJLdgGhYloXt27djzJgx0DQt2c0hIiIiogFs28YXX3yBSZMmQdeHHhMVXZhu374dJSUlyW4GEREREQ1j27ZtKC4uHvI+ogvTMWPGAPB2NCcnJ+7P5/F4UF9fj7lz58LlEv2jS1vMUD5mKB8zlI35yZfoDDs7O1FSUuKv24Yi+jfKd/o+JycnYYXpqFGjkJOTwxejUMxQPmYoHzOUjfnJl6wMw5l2qdm2bSegLXHR2dmJ3NxcdHR0JKQwtW0b3d3dyMrK4pxWoZihfMxQPmYoG/OTL9EZOqnXuCrfoYyMjGQ3gaLEDOVjhvIxQ9mYn3yqZsjC1AHTNFFTUwPTNJPdFIoQM5SPGcrHDGVjfvKpnCEnhxAREVHETNNEX19fsptBDng8HgDAgQMHYjLH1DAMuFyumEwLYGFKREREEdm3bx9aWlogeLlKWrJtGyNHjkRzc3PM5phmZ2dj4sSJUU8RYGFKREREjpmmiZaWFmRnZ6OgoIALoQSxbRtdXV3Izs6OOjfbttHb24vW1lY0NTXhiCOOGHYT/aFwVb4Dtm3DNE0YhsEXoFDMUD5mKB8zlM2XX19fHz799FNMmTIFWVlZyW4WOdC/9IvVa7Crqwtbt27F1KlTMXLkyICvcVV+HPX29ia7CRQlZigfM5SPGcrWPz++uZDJsqyYPl40o6QBjxOTR0kTpmli3bp1Sq5io/AwQ/mYoXzMUDbmlxq6u7uT3YSgOMeUiIiIYqe5GWhrS9zz5ecDpaUxeaglS5agvb0dzz77bEweL1w333wznn32WaxZsyahz6siFqZEREQUG83NQFkZ0NWVuOfMzgbc7pgUp/fccw93GEgyFqYOGYaR7CZQlJihfMxQPmYoW8j82tq8RemqVd4CNd7cbuDCC73PG4PCNDc3NwaNkkHVucEsTB1wuVyorq5OdjMoCsxQPmYoHzOUzZffgQMHQt+prAyorExcoxz6xz/+geXLl+OTTz5BdnY25s6di//93//F0qVLA07lf/HFF7jyyivx7LPPIicnBz/96U/xv//7v5gzZw7uvvtuAMCUKVNwxRVX4JNPPsFTTz2FsWPH4pe//CWuuOIK//Ndd911eOaZZ9DS0oKioiJccMEFuPHGGzFixIgk9N5blI4aNSopzz0cLn5ywLZttLe3c5hfMGYoHzOUjxnKJj2/HTt24LzzzsOll14Kt9uNt956C4sWLQran2XLluHdd9/FP//5T7z66qt45513UFdXN+h+d911F6qqqlBfX4+rr74aV111FTZt2uT/+pgxY7By5Ups3LgR99xzDx588EH87ne/i2s/h2LbNjwej5IZsjB1wDRNNDQ0cCWiYMxQPmYoHzOUTXp+O3bsgMfjwaJFizBlyhQcffTRuPrqqzF69OiA+33xxRd45JFHcOedd+Kkk07C7Nmz8fDDDwft9+mnn46rr74ahx9+OK677jrk5+fjzTff9H/9l7/8JY499lhMmTIFZ5xxBq699lr8/e9/j3tfhzLkiHcS8VQ+ERERpY2KigqcdNJJOProo3HqqafilFNOwXe+8x2MHTs24H6NjY3o6+vD/Pnz/cdyc3MxY8aMQY9ZXl7u/7emaSgqKsKuXbv8x/72t7/h3nvvxZYtW7Bv3z54PJ6EXBhIIo6YEhERUdowDAOvvvoqXnzxRRx11FG47777MGPGDDQ1NUX8mAPnimqa5t/A/r333sMFF1yA008/Hc899xzq6+vxi1/8gheZCIGFqQOapiErK0vZlWw0PGYoHzOUjxnKlgr5aZqG4447DsuXL0d9fT0yMjLwzDPPBNxn2rRpGDFiBFavXu0/1tHRgc2bNzt6rv/85z+YPHkyfvGLX6CqqgpHHHEEtm7dGpN+RCNWV2qKNZ7Kd8AwDIwdW4G1awd/LYb7+1IcGYaBioqKZDeDosAM5WOGsvnyU3WO4nA++OADvP766zjllFNQWFiIDz74AK2trSgrK8O6dev89xszZgwWL16Mn/zkJxg3bhwKCwtx0003Qdd1R0X5EUccgebmZjz55JOorq7G888/P6gITjRN05CdnZ3UNoTCwtSBTz+1cNRRGrq7B/9CxnB/X4ojy7LQ1taG/Px8Zd8t0tCYoXzMUDZffgMXCwVwuxPTmAieJycnB2+//TbuvvtudHZ2YvLkybjrrrtw2mmn4W9/+1vAfVesWIErr7wS3/zmN/3bRW3btg0jR44M+/nOPPNM/OhHP8I111yDnp4efOMb38ANN9yAm2++2XHbY8W3Kt/lcik38q3ZKu4VEKbOzk7k5uaio6MjIZOIP/zQg2OOceGRR0zMnn1oc2Hf/r61tUpv20YAPB4PampqUFVVBZeL78skYobyMUPZfPnNnj0b27Ztw9SpUw8VasKv/DSc/fv347DDDsNdd92Fyy67LO7PFy+2bWP//v0YNWpUzArTAwcOoKmpKfD34SAn9Rr/IkRg5kybBSgREdFApaXeIrGtLXHPGce5dPX19WhoaMD8+fPR0dGBX/3qVwCAb33rW3F5PkpyYXrzzTdj+fLlAcdmzJiBhoaGJLUoOgPPKHDeKRERpZ3S0pT6z+/OO+/Epk2bkJGRgXnz5uGdd95Bfn5+spuVspI+Yjpr1iy89tpr/s9VPq3jG+4eOOydn+89k3DhhYH357xT9WiahtzcXOXm1FD4mKF8zFC2dMpv7ty5qK2tTXYz4sIwjOHvlARJrwJdLheKioqS3Yyw+EIcGGawMxe+eadtbSxMVWIYBsrKypLdDIoCM5SPGcrmy0/qqnw6tOWXipJemH788ceYNGkSRo4ciQULFuC2225DaYhKrqenBz09Pf7POzs7AXgnYns8HgDefbl0XYdlWf7NbfsfN00z4NqwoY4bhgFN0/yPCwB9fTaAETBNEx5P4JqxkhIDJSXwX6rM+23eH69t2wGXMNM0DYZhDGpjqOPx7JPvOIBBl1kLddzlcontk6Zp2L59OyZMmBCwGlhyn1Ixp6H6ZJomPvvsMxQVFfm3bZHep1TMaag+eTwe7Ny5E0VFRTAMIyX6lIo5heqTL7+8vDx/u4Kto9Y0LSbHnYjVc8b7uBPxaItt2+jr60NGRkbMHt+2bf9qf9/vpu93b+Dv6lCSWpgec8wxWLlyJWbMmIEdO3Zg+fLlWLhwITZs2IAxY8YMuv9tt902aE4q4J2cPGrUKABAQUEBpk+fjqamJrS2tvrvU1xcjOLiYmzevBkdHR3+49OmTUNhYSE2bNiA7u5u//GZM2ciLy8P9fX1/hdtQ0M2gHKYpomamrqANlRVVaG3t9e/B9qmTd77At4NefvPm83KykJFRQXa2trQ2NjoP56bm4uysjJs374dLS0t/uPx7BPgvZRaRkYGampqhuwT4P0lq66uFtunuXPnorm5GS0tLf7TUNL7lIo5DdWn9vZ2fPTRR/jss8/87/ql9ykVcxqqT1u2bEF7ezs+++wz5OXlpUSfUjGnUH3atm0b2tvbkZeX5/+/t6enJ6D4yMjIQEZGBg4cOBDQxszMTIwYMQLd3d0Bhe/IkSPhcrnQ1dUVUPRkZWVB13Xs378/oE+jRo2CZVkBPxdN0zBq1CiYphkwmqvrOrKzs+HxeAIGtwzDQFZWFvr6+gKuwuRyuTBy5MiU75OvD5mZmTHpEwD09vZiw4YN/uO+3736+nqES6ntotrb2zF58mSsWLEi6DYMwUZMS0pKsHv3bv/2A/F8R1pTY2HBggx88EEfKisD59YMfEdaVwccc4wLtbXA3Ll8l61Kn2zbRk1NDSorKwOmZEjuUyrmNFSf+vr6AjJMhT6lYk5D9amvrw91dXWorKyEy+VKiT6lYk6h+uTLb9asWWhpacG0adOQmZmJgVJldDEex52I14hpV1cXsrOzoet6TB7ft11UaWmpf7so3+/e3r17MX78eHnbReXl5eHII4/EJ598EvTrmZmZQX/5XS7XoEVTvhf0QKEm+4Y63v9xdd33R0ALuUjLd7z/lzUt+P1DtdHp8Wj6FOlxqX3yeDz+P7ADvya1T0MdT9U+BctQep9SMadQfer/hsLXBul9SsWcQrXdl5+uH7oCUqiFULE67kS825JKfYplfr6/zcFqMicL25W65Ma+ffuwZcsWTJw4MdlNCcr3Ig32YiUZdF1HQUEBMxSMGcrHDGVjfqlB1V2Qktqqa6+9FmeccQYmT56M7du346abboJhGDjvvPOS2ayQWJjKp+s6pk+fnuxmUBSYoXzMUDZffqFW5Tc3q72//gknnIA5c+bg7rvvjkt7lixZgvb2djz77LNxefxY0DTN0WVVP/30U0ydOhX19fWYM2dO/BqGJBemLS0tOO+887B7924UFBTgy1/+Mt5//30UFBQks1kheefb6P5bkseyLP8l0/gGQyZmKB8zlM2XX7Czmyl+RdKUYds2enp6kJmZqdx+tEktTJ988slkPr1jLEzlsywLra2tmDx5Mv9DFIoZyscMZfPlN2HChEFfa2vzFqWrVnkL1HhLlz3De3t7kZGREdPH9Hg8QdftJBv/IhAREVFMlZUBlZXx/4i0+PV4PLjmmmuQm5uL/Px83HDDDf6V5n/9619RVVWFMWPGoKioCOeffz527doV8P0fffQRvvnNbyInJwdjxozBwoULsWXLlqDPtXr1ahQUFOCOO+7wH/v1r3+NwsJCjBkzBt/97nfxs5/9LOAU+ZIlS3DWWWfh1ltvxaRJkzBjxgwAwPr163HiiSciKysL48ePxxVXXIF9+/b5v++EE07AD3/4w4DnP+uss7BkyRL/51OmTMFvfvMbXHXVVcjJyUFpaSn+9Kc/BXzPhx9+iLlz52LkyJGoqqpytN1TtFiYEhERUVp55JFH4HK58OGHH+Kee+7BihUr8NBDDwEA+vr6cMstt2Dt2rV49tln8emnnwYUdp999hm+8pWvIDMzE2+88QZqa2tx6aWXBt1E/o033sDXvvY13HrrrbjuuusAAI899hhuvfVW3HHHHaitrUVpaSkeeOCBQd/7+uuvY9OmTXj11Vfx3HPPYf/+/Tj11FMxduxYrF69Gk899RRee+01XHPNNY77v2LFClRWVqKurg5XX301rrrqKmzatAmAdyH6N7/5TRx11FGora3FzTffjGuvvdbxc0RKzSVZiuLiJ/l0XUdxcTEzFIwZyscMZUuF/EpKSvC73/0OmqZhxowZWL9+PX73u9/h8ssvx6WXXuq/37Rp03Dvvfeiuroa+/btw+jRo3H//fcjNzcXTz75JEaMGAEAOPLIIwc9xzPPPIOLL74YDz30EM4991z/8fvuuw+XXXYZLrnkEgDAjTfeiFdeeSVg5BPwbrj/0EMP+U/hP/jggzhw4AAeffRR/4UNfv/73+OMM87AHXfcEXRqRSinn346li5dihEjRuC6667D7373O7z55puYMWMGHn/8cViWhT//+c8YOXKkf7/aq666KuzHj4bc36okYGEqXyr8QU13zFA+ZihbKuT3pS99KWDRz4IFC/Dxxx/DNE3U1tbijDPOQGlpKcaMGYPjjz8eANDc3AwAWLNmDRYuXOgvSoP54IMP8H/+z//BX//614CiFAA2bdqE+fPnBxwb+DkAHH300QHzSt1uNyoqKvxFKQAcd9xxsCzLP9oZLt9Vwnx7jxYVFfmnK7jdbpSXlwes2l+wYIGjx4+G3N+qJPBdHWPg1TNIDtM04Xa7maFgzFA+ZihbKud34MABnHrqqcjJycFjjz2G1atX45lnngEA/yU+fZffHMr06dMxc+ZM/OUvf0FfX19EbelfgIYr2FWcgj2/y+VCd3e3/76apgVc6SuZWJg60P9SXiSTbdvo6OhghoIxQ/mYoWypkN8HH3wQ8Pn777+PI444Ag0NDdi9ezduv/12LFy4EDNnzhy08Km8vBzvvPPOkAVnfn4+3njjDXzyySc455xzAu47Y8YMrF69OuD+Az8PpqysDGvXrsX+/fv9x959913ouu5fHFVQUIAdO3b4v26aZsC16/sL9cairKwM69atC9in9v333x+2fbHCwpSIiIjSSnNzM5YtW4ZNmzbhiSeewH333Ycf/OAHKC0tRUZGBu677z40Njbin//8J2655ZaA773mmmvQ2dmJ//qv/0JNTQ0+/vhj/PWvfx10Or2wsBBvvPEGGhoacN555/kXR33/+9/Hn//8ZzzyyCP4+OOP8etf/xrr1q0bdj/RCy64ACNHjsTixYuxYcMGvPnmm/j+97+Piy66yD+/9MQTT8Tzzz+P559/Hg0NDbjqqqvQ3t7u6Gdz/vnnQ9M0XH755di4cSNeeOEF3HnnnY4eIxpc/EREREQx5Xar/TwXX3wxuru7MX/+fBiGgR/84Ae44ooroGkaVq5ciZ///Oe49957UVlZiTvvvBNnnnmm/3vHjx+PN954Az/5yU9w/PHHwzAMzJkzB8cdd9yg5ykqKsIbb7yBE044ARdccAEef/xxXHDBBWhsbMS1116LAwcO4JxzzsGSJUvw4YcfDtnm7OxsvPzyy/jBD36A6upqZGdn4+yzz8aKFSv897n00kuxdu1aXHzxxXC5XPjRj36Er371q45+NqNHj8b/+3//D1deeSXmzp2Lo446CnfccQfOPvtsR48TKc0WPBbf2dmJ3NxcdHR0ICcnJ+7PV1Njobpax+rVFqqqhh5srqsD5s0Damu9e62RGizLQltbG/Lz80VP3E9nzFA+ZiibL7/Ro0dj69atmDp1qn+hDK/8FJmvfe1rKCoqwl//+teEPJ9t2/B4PHC5XDG78tOBAwf8V3QbeLlTJ/UaR0wd4Kp8+XRdR2FhYbKbQVFghvIxQ9l8+fWfg+hTWuotEtvaEtee/HxZRWlXVxf+8Ic/4NRTT4VhGHjiiSfw2muv4dVXX01YGzRNG3JXgWRiYeqAd6Kw4b8leXwTwWfPng3DYIYSMUP5mKFsvvwOP/zwoF8vLZVVKCaapml44YUXcOutt+LAgQOYMWMG/ud//gcnn3xywtpg2za6u7uRlZUVsxHTWGFh6gBX5cvnezEyQ7mYoXzMUDbmF52srCy89tpryW6GMttDDcRz0kRERESkBBamRERERKQEFqYO+OZCcU6UXIZhYObMmcxQMGYoHzOUbWB+PKUv08CV89GK1e8BC1MHfBOEVZsoTOHTNA15eXnMUDBmKB8zlM2Xn8vlXabiu1QnyaFpWky3igK8uw0AiHq1Pxc/OeC9aoPLf0vyeDwe1NfXY+7cuf4/qiQLM5SPGcrmy2/OnDnIzs5Ga2srRowYwa0UBYnlqnzbttHV1YVdu3YhLy8v6jMh/ItAaSfU9YFJDmYoHzOUzTRNaJqGiRMnoqmpCVu3bk12k8gB27bR29uLjIyMmI2a5uXloaioKOrHYWFKREREEcnIyMARRxzB0/nCeDwe/160sThrMWLEiJjNGWdhSkRERBHTdT3mC2kovrxTEr0LoFSbTsMJIQ5wVb58hmGgvLycGQrGDOVjhrIxP/lUzpCFKaWdjIyMZDeBosQM5WOGsjE/+VTNkIWpA77J+py0L5dpmqipqWGGgjFD+ZihbMxPPpUzZGFKREREREpgYUpERERESlBrKVYKcrsHH8vPB0pLE98WIiIiIpVptuCL3HZ2diI3NxcdHR3IycmJ+/PV1tqoqtJQU2Nj3ryhN6RtbgbKyoCDV+gKkJ3tLVhZnCaebdswTROGYfByiEIxQ/mYoWzMT75EZ+ikXuOIaZyUlnqLz7a2wONuN3Dhhd7jLEyTo7e3F1lZWcluBkWBGcrHDGVjfvKpmiHnmDrgdFV+aSlQWRn4UVYWzxbScEzTxLp165RciUjhYYbyMUPZmJ98KmfIwpSIiIiIlMDClIiIiIiUwMKU0o6Kl2AjZ5ihfMxQNuYnn6oZcvGTAy6XK+CW5HG5XKiurk52MygKzFA+Zigb85NP5Qw5YuqAb2ctwTtspT3bttHe3s4MBWOG8jFD2ZiffCpnyMLUAaer8kk9pmmioaGBGQrGDOVjhrIxP/lUzpCFKREREREpgYUpERERESmBq3gc8F22KxaX73K7Az/Pz+eVoBJB0zRkZWXxMnqCMUP5mKFszE8+lTPUbBVnvobJybVXY6GuDpg3D6it9V7FKRLNzd6rP3V1BR7PzvYWqyxOiYiIKJU4qdd4Kt8By7ICbiNRWuotQGtrD32sWuUtVNvaYtVSCsWyLOzatSuqDCm5mKF8zFA25iefyhnyVL4D3gB1/22kSks5MposlmWhsbER48aNg67zfZlEzFA+Zigb85NP5QzVag0RERERpS0WpkRERESkBBamDsRyVT4lh6ZpyM3NZYaCMUP5mKFszE8+lTPkHFMHDMMIuCV5DMNAWVlZsptBUWCG8jFD2ZiffCpnyBFTB2KxKp+Sy7IstLS0MEPBmKF8zFA25iefyhmyMHWAhal8Kr8YKTzMUD5mKBvzk0/lDFmYEhEREZESWJgSERERkRJYmDrg24RWtc1oKXy6rqOgoIAZCsYM5WOGsjE/+VTOkKvyHWBhKp+u65g+fXqym0FRYIbyMUPZmJ98KmfICssBLn6Sz7IsbNmyhRkKxgzlY4ayMT/5VM6QhakDLEzlsywLra2tzFAwZigfM5SN+cmncoYsTImIiIhICSxMiYiIiEgJXPzkQLwXP7ndg4/l5wOlpXF5urSk6zqKi4u5gE0wZigfM5SN+cmncoYsTB2IV2Ganw9kZwMXXjj4a9nZ3oKVxWls+F6MJBczlI8Zysb85FM5Q/VKZYWZphlwGyulpd7is7Y28GPVKqCrC2hri+nTpTXTNOF2u2OeISUOM5SPGcrG/ORTOUOOmDpg23bAbSyVlnJUNBFs20ZHR0dcMqTEYIbyMUPZmJ98KmfIEVMiIiIiUgILUyIiIiJSAgtTB3hJUvl0Xce0adOYoWDMUD5mKBvzk0/lDDnH1AEWpvLpuo7CwsJkN4OiwAzlY4ayMT/5VM6QFZYD8VqVT4ljmibWrl3LDAVjhvIxQ9mYn3wqZ8jC1IF4rsqnxLBtG93d3cxQMGYoHzOUjfnJp3KGLEyJiIiISAksTImIiIhICSxMHTAMI+CW5DEMAzNnzmSGgjFD+ZihbMxPPpUz5Kp8BzRNC7gleTRNQ15eXrKbQVFghvIxQ9mYn3wqZ8gRUwc8Hk/ALcnj8XiwevVqZigYM5SPGcrG/ORTOUMWppR2VNweg5xhhvIxQ9mYn3yqZsjClIiIiIiUwMKUiIiIiJTAwtQBrsqXzzAMlJeXM0PBmKF8zFA25iefyhmyMKW0k5GRkewmUJSYoXzMUDbmJ5+qGbIwdcA3UVjVCcM0PNM0UVNTwwwFY4byMUPZmJ98KmeoTGF6++23Q9M0/PCHP0x2U4iIiIgoCZTYYH/16tX44x//iPLy8mQ3RUlud+Dn+flAaWly2kJEREQUL0kfMd23bx8uuOACPPjggxg7dmyym6OU/HwgOxu48EJg3rxDH2VlQHNzsltHREREFFtJHzFdunQpvvGNb+Dkk0/Gr3/96yHv29PTg56eHv/nnZ2dALxXMPBdvUDXdei6DsuyYFmW/76+46ZpwrbtYY8bhgFN0wZcFcH2f8/AqyX4VrYNnK/hcrlg23bAcU3TYBjGoDYOPD5pErB+PbBnz6E+bdxoY/FiAzt3elBcHIs+hW57PPo0sI3xySl023Vdx7x582DbdsD3SO5TKuY0VJ90XcfcuXP9GaZCn1Ixp6H6ZNu2P0PTNFOiT6mYU6g+9c/PsqyU6FMq5jRUn3wZ+i6xHu8+ObnCVFIL0yeffBJ1dXVYvXp1WPe/7bbbsHz58kHH6+vrMWrUKABAQUEBpk+fjqamJrS2tvrvU1xcjOLiYmzevBkdHR3+49OmTUNhYSE2bNiA7u5u//GZM2ciLy8P9fX1/h/0pk3ZAMphWSZqamoD2lBVVYXe3l6sW7fOf8wwDFRXV6OjowMNDQ3+41lZWaioqEBbWxsaGxv9x3Nzc1FWVobt27ejpaXFf9zXpy1bmmDb+wGUw+3eiEmTxkXdJwAoLy9HRkYGampqEt6neOQ0VJ/mzZuHzs5OfPzxxynTp1TMabg+bdy40f8HL1X6lIo5DdUnX0GaSn1KxZxC9cmXXyr1KRVzGqpPpmmitLQUJSUlce9TfX09wqXZ/UvhBNq2bRuqqqrw6quv+ueWnnDCCZgzZw7uvvvuoN8TbMS0pKQEu3fvRk5ODoD4vtOpqbGwYEEGPvigD5WVWkDbEvVOp6bGwjHHuPDBBx5UVaXOu7fh2h6rPtm2jZqaGlRWVgbs3ya5T6mY01B96uvrC8gwFfqUijkN1ae+vj7U1dWhsrISLpcrJfqUijmF6lP//EaMGJESfUrFnIbqk2maqKurw7x585CRkRH3Pu3duxfjx49HR0eHv14LJWkjprW1tdi1axcqKyv9x0zTxNtvv43f//736OnpCSgcACAzMxOZmZmDHsvlcsHlCuyK74c60MDHHO54/8fVdV8Q2qDnC3Z/H00Lfv9QbRzquMul+5/Hd5do+hTp8Vj2KdY5DXW8/6nfgV+T2qehjqdqn4JlKL1PqZhTqD71f0Pha4P0PqViTqHa3j8/332k9ykVcxruuKZp/n8no0+hJK0wPemkk7B+/fqAY5dccglmzpyJ6667LuQPg4iIiIhSU9IK0zFjxmD27NkBx0aNGoXx48cPOk4US3zTIx8zlI8Zysb85FM1w6SvypfENxTtZEia1OJyuVBdXZ3sZlAUmKF8zFA25iefyhkqVWG99dZbyW7CkLwTgDX/Lclj2zY6OjqQm5vr3yaDZGGG8jFD2ZiffCpnmPQN9iXxrTYbuOqM5DBNEw0NDcxQMGYoHzOUjfnJp3KGLEyJiIiISAksTImIiIhICSxMHfDNw1BtPgaFT9M0ZGVlMUPBmKF8zFA25iefyhkqtfhJdb6tFVTdYoGGZxgGKioqkt0MigIzlI8Zysb85FM5Q46YOuC7vFf/y3yRLJZlYdeuXcxQMGYoHzOUjfnJp3KGLEwdYGEqn2VZaGxsZIaCMUP5mKFszE8+lTPkqXyh3O7Bx/LzgdLSxLeFiIiIKBZYmAqTnw9kZwMXXjj4a9nZ3oKVxSkRERFJxMLUARVW5ZeWeovPtrbA4263t1hta2NhOhRN05S80gWFjxnKxwxlY37yqZwhC1MHVFmVX1rK4jNShmGgrKws2c2gKDBD+ZihbMxPPpUz5OInB7j4ST7LstDS0sIMBWOG8jFD2ZiffCpnyMLUARam8qn8YqTwMEP5mKFszE8+lTNkYUpERERESmBhSkRERERKYGHqgK7rAbckj67rKCgoYIaCMUP5mKFszE8+lTPkqnwHWJjKp+s6pk+fnuxmUBSYoXzMUDbmJ5/KGbLCcoCLn+SzLAtbtmxhhoIxQ/mYoWzMTz6VM2Rh6gALU/ksy0JrayszFIwZyscMZWN+8qmcIU/lpxi3e/Cx/HxuyE9ERETqY2GaIvLzgexs72VJB8rO9hasLE6JiIhIZSxMHVB58VNpqbf4bGsLPO52e4vVtjYWpoA3u+LiYiUzpPAwQ/mYoWzMTz6VM2Rh6oDKhSngLTxZfA7N92IkuZihfMxQNuYnn8oZqllhKco0zYBbksc0TbjdbmYoGDOUjxnKxvzkUzlDFqYO2LYdcEvy2LaNjo4OZigYM5SPGcrG/ORTOUMWpkRERESkBBamRERERKQEFqYOqL74iYan6zqmTZvGDAVjhvIxQ9mYn3wqZ8hV+Q6wMJVP13UUFhYmuxkUBWYoHzOUjfnJp3KGrLAc4Kp8+UzTxNq1a5mhYMxQPmYoG/OTT+UMWZg6wFX58tm2je7ubmYoGDOUjxnKxvzkUzlDFqZEREREpAQWpkRERESkBBamDhiGEXBL8hiGgZkzZzJDwZihfMxQNuYnn8oZclW+A5qmBdySPJqmIS8vL9nNoCgwQ/mYoWzMTz6VM+SIqQMejyfgluTxeDxYvXo1MxSMGcrHDGVjfvKpnCELU0o7Km6PQc4wQ/mYoWzMTz5VM2RhSkRERERKYGFKREREREpgYeoAV+XLZxgGysvLmaFgzFA+Zigb85NP5QxZmFLaycjISHYTKErMUD5mKBvzk0/VDFmYOuCbKKzqhGEanmmaqKmpYYaCMUP5mKFszE8+lTPkPqZpwu0O/Dw/HygtTU5biIiIiIJhYZri8vOB7GzgwgsDj2dne4tVFqdERESkChamKa601FuAtrUdOuZ2ewvVtjYWpkRERKQOFqYOSF2VX1rKAtTHMAxUVVWJy5AOYYbyMUPZmJ98KmfIxU+Udnp7e5PdBIoSM5SPGcrG/ORTNUMWpg5wVb58pmli3bp1zFAwZigfM5SN+cmncoYsTImIiIhICSxMiYiIiEgJLEwp7ag42ZucYYbyMUPZmJ98qmbIVfkOuFyugFuSx+Vyobq6OtnNoCgwQ/mYoWzMTz6VM+SIqQO2bQfckjy2baO9vZ0ZCsYM5WOGsjE/+VTOkIWpA1yVL59pmmhoaGCGgjFD+ZihbMxPPpUzZGFKREREREpgYUpERERESmBh6oCmaQG3JI+macjKymKGgjFD+ZihbMxPPpUz5PJyB3xbK6i6xYJTbvfgY/n5QGlp4tuSKIZhoKKiItnNoCgwQ/mYoWzMTz6VM2Rh6oBlWQB0/61U+flAdjZw4YWDv5ad7S1YU7U4tSwLbW1tyM/Ph67LzTCdMUP5mKFszE8+lTNUqzWK8xakh26lKi31Fp+1tYEfq1YBXV1AW1uyWxg/lmWhsbFRfIbpjBnKxwxlY37yqZwhR0zTVGlp6o6KEhERkUwcMSUiIiIiJbAwdYCr8uXTNA25ubnMUDBmKB8zlI35yadyhjyV70CqrcpPR4ZhoKysLNnNoCgwQ/mYoWzMTz6VM+SIqQOpsvgpnVmWhZaWFmYoGDOUjxnKxvzkUzlDFqYOsDCVT+UXI4WHGcrHDGVjfvKpnCELUyIiIiJSAgtTIiIiIlICC1MHfFdHUO0qCRQ+XddRUFDADAVjhvIxQ9mYn3wqZ8hV+Q6wMJVP13VMnz492c2gKDBD+ZihbMxPPpUzZIXlABc/yWdZFrZs2cIMBWOG8jFD2ZiffCpnyMLUgXQpTN1uoK7u0Edzc7JbFDuWZaG1tTXlM0xlzFA+Zigb85NP5Qx5Kp/88vOB7GzgwgsDj2dne4vV0tLktIuIiIjSAwtT8ist9RagbW2Hjrnd3kK1rY2FKREREcUXC1MH0mHxU2lpaheguq6juLg4pTNMdcxQPmYoG/OTT+UMk9qiBx54AOXl5cjJyUFOTg4WLFiAF198MZlNGlI6FKapTuUXI4WHGcrHDGVjfvKpnGFSW1RcXIzbb78dtbW1qKmpwYknnohvfetb+Oijj5LZrJBM0wy4JXlM04Tb7WaGgjFD+ZihbMxPPpUzTOqp/DPOOCPg81tvvRUPPPAA3n//fcyaNStJrQrNtu2AW5LHtm10dHQwQ8GYoXzMUDbmJ5/KGSozx9Q0TTz11FPYv38/FixYEPQ+PT096Onp8X/e2dkJAPB4PPB4PAC8w9O6rsOyrIBtEHzHTdMMCCLUccMwoGma/3GB/ttE2QHHfff39aM/l8sF27YDjmuaBsMwBrUx1PF49mmothuGAe+3awd/xvL7ZNv2oLZL71Oq/u4N1af+/UqVPqViTqH65Hte0zRTpk+pmFOoPvXPL1X6lIo5DdUnX1t994l3nwbefyhJL0zXr1+PBQsW4MCBAxg9ejSeeeYZHHXUUUHve9ttt2H58uWDjtfX12PUqFEAgIKCAkyfPh1NTU1obW3136e4uBjFxcXYvHkzOjo6/MenTZuGwsJCbNiwAd3d3f7jM2fORF5eHurr6/0/6IaGbADlsCwbNTW1AW2oqqpCb28v1q1b5z9mGAaqq6vR0dGBhoYG//GsrCxUVFSgra0NjY2N/uO5ubkoKyvD9u3b0dLS4j8ezz4BQHl5OTIyMlBTUzOoTwcO9AHIwosvNsHt7oauG5g1axa++GIf9u79GEVFvaL6NHfuXFiWhbq6OmialjI5peLvXqg+dXZ2or293Z9hKvQpFXMaqk9btmzxZ5iXl5cSfUrFnEL1adu2bf78CgsLU6JPqZjTUH2ybRvt7e3YsWMHJk+eHPc+1dfXI1yaneRx3N7eXjQ3N6OjowP/+Mc/8NBDD+Ff//pX0OI02IhpSUkJdu/ejZycHADxfadTW2vjS18agQ8/NDF3buCPLVXfvTU3A0cdBXR1aRgoO9vG+vUmSkvl9EnTNLS1tWHs2LEBk76l5xTseKr2yTRN7Nq1C+PHj4eu6ynRp1TMaag+eTwe7N69G+PHj4dhGCnRp1TMKVSf+ufncrlSok+pmNNQfbIsC7t370ZBQYH/72o8+7R3716MHz8eHR0d/notlKQXpgOdfPLJmD59Ov74xz8Oe9/Ozk7k5uaG1dFYqKsD5s0DamuBysq4P50ympsD9zYFDu1vmm4/CyIiInLGSb2m3D4BlmUFjIqqxPdOYOA7glRXWuotPvt/lJUlu1WRMU0Ta9euTbsMUwkzlI8Zysb85FM5w6TOMb3++utx2mmnobS0FF988QUef/xxvPXWW3j55ZeT2ayQuCpfPtu20d3dzQwFY4byMUPZmJ98KmcYUWHa2NiIadOmRf3ku3btwsUXX4wdO3YgNzcX5eXlePnll/G1r30t6scmIiIiIlkiKkwPP/xwHH/88bjsssvwne98ByNHjozoyf/85z9H9H1ERERElHoimmNaV1eH8vJyLFu2DEVFRfje976HDz/8MNZtU45vdZnvluQxDAMzZ85khoIxQ/mYoWzMTz6VM4yoMJ0zZw7uuecebN++HX/5y1+wY8cOfPnLX8bs2bOxYsWKgH2/Uolv30vfLcmjaRry8vKYoWDMUD5mKBvzk0/lDKNale9yubBo0SI89dRTuOOOO/DJJ5/g2muvRUlJiX/uaCrx7dvl5AoGpBaPx4PVq1czQ8GYoXzMUDbmJ5/KGUZVmNbU1ODqq6/GxIkTsWLFClx77bXYsmULXn31VWzfvh3f+ta3YtVOophRcXsMcoYZyscMZWN+8qmaYUSLn1asWIGHH34YmzZtwumnn45HH30Up59+uv9KOlOnTsXKlSsxZcqUWLaViIiIiFJYRIXpAw88gEsvvRRLlizBxIkTg96nsLCQq+6JiIiIKGwRFaavvvoqSktLA641Dng3bN22bRtKS0uRkZGBxYsXx6SRquCqfPkMw0B5eTkzFIwZyscMZWN+8qmcYURzTKdPn462gRdPB7Bnzx5MnTo16kYRxVNGRkaym0BRYobyMUPZmJ98qmYYUWEa6hJW+/bti3izfQl8E4VVnTBMwzNNEzU1NcxQMGYoHzOUjfnJp3KGjk7lL1u2DIB3/6sbb7wR2dnZ/q+ZpokPPvgAc+bMiWkDSW1ud+Dn+flAaWly2kJERESyOSpM6+vrAXhHTNevXx8wDJyRkYGKigpce+21sW0hKSk/H8jOBi68MPB4dra3WGVxSkRERE45KkzffPNNAMAll1yCe+65Bzk5OXFpFKmvtNRbgPafaux2ewvVtjYWpkREROScZoeaMCpAZ2cncnNz0dHRkZAiubbWRlWVhpoaG/PmqXcZr2SrqwPmzQNqa4HKymS3JjjbtmGaJgzDUPJSbDQ8ZigfM5SN+cmX6Ayd1Gthj5guWrQIK1euRE5ODhYtWjTkfZ9++ulwH5Yo4Xp7e5GVlZXsZlAUmKF8zFA25iefqhmGvSo/NzfXX1Xn5uYO+ZGquCpfPtM0sW7dOmYoGDOUjxnKxvzkUznDsEdMH3744aD/JiIiIiKKhYj2Me3u7kZXV5f/861bt+Luu+/GK6+8ErOGEREREVF6ieiSpN/61rewaNEiXHnllWhvb8f8+fORkZGBtrY2rFixAldddVWs26mWhgbA1Rt4jBt4iqHiJdjIGWYoHzOUjfnJp2qGEY2Y1tXVYeHChQCAf/zjHygqKsLWrVvx6KOP4t57741pA1Xiam313i6+2Lv8vP9HWRnQ3JzkFtJwXC4Xqqur4XJF9J6MFMAM5WOGsjE/+VTOMKLCtKurC2PGjAEAvPLKK1i0aBF0XceXvvQlbN26NaYNVIndvtd7e8uvvXsi+T5WrQK6ugI39SQl2baN9vb2kJfVJfUxQ/mYoWzMTz6VM4yoMD388MPx7LPPYtu2bXj55ZdxyimnAAB27dqV0pvum6blvS0t9W7U6fsoK/Pewe32bubZ/4OjqEoxTRMNDQ1KrkSk8DBD+ZihbMxPPpUzjGgM98Ybb8T555+PH/3oRzjppJOwYMECAN7R07lz58a0gSKEuj4nwGt0EhEREYUposL0O9/5Dr785S9jx44dqKio8B8/6aST8O1vfztmjRMj2PU5AV6jk4iIiMiBiGe9FhUVoaioKODY/Pnzo26QynwXGAh6+a7SUhafAmiahqysLF5GTzBmKB8zlI35yadyhhEVpvv378ftt9+O119/Hbt27YJlWQFfb2xsjEnjVGPoesAtBed2Dz6mym5ahmEEjPKTPMxQPmYoG/OTT+UMIypMv/vd7+Jf//oXLrroIkycOFHJijserIOr1ywFV7GpQMJUW8uy0NbWhvz8fOh8gyESM5SPGcrG/ORTOcOICtMXX3wRzz//PI477rhYt0dpvpHhgSPEjjU3B99aSpVhxQhJmGprWRYaGxsxbtw45V6MFB5mKB8zlI35yadyhhEVpmPHjsW4ceNi3ZbU1f/cdmsrsGiRd9/TgVQZVowCp9oSERFRpCIqTG+55RbceOONeOSRR5CdnR3rNqWOUOe2s7OBl14CCgoOHVNpWJGIiIgoCSIqTO+66y5s2bIFEyZMwJQpUzBixIiAr9fV1cWkcaoZclV+MKHObQs/ZS+ZpmnIzc1Nm3nRqYgZyscMZWN+8qmcYUSF6VlnnRXjZsgQ0ap8nttWimEYKPNdqYtEYobyMUPZmJ98KmcYUWF60003xbodInBVvnyWZWH79u2YNGmSchO+KTzMUD5mKBvzk0/lDCNuTXt7Ox566CFcf/312LNnDwDvKfzPPvssZo1TTcxW5VPSWJaFlpYWZigYM5SPGcrG/ORTOcOIRkzXrVuHk08+Gbm5ufj0009x+eWXY9y4cXj66afR3NyMRx99NNbtJCIiIqIUF1FhumzZMixZsgS//e1vMWbMGP/x008/Heeff37MGpeWVL5sUpQGdi1FukVEREQxElFhunr1avzxj38cdPywww7Dzp07o26UqnzzMOIyH0PCZZMiNNSuWYnulq7rKCgoUG5ODYWPGcrHDGVjfvKpnGFEhWlmZiY6OzsHHd+8eTMK+u/NmWL0g9sq6PHYXkHCZZMiFKxryeqWruuYPn164p6QYo4ZyscMZWN+8qmcYUSF6Zlnnolf/epX+Pvf/w7Aux9Wc3MzrrvuOpx99tkxbaBK4r4qf6itpYSfB1dl1yzLstDU1ISpU6cq+U6RhscM5WOGsjE/+VTOMKLW3HXXXdi3bx8KCgrQ3d2N448/HocffjjGjBmDW2+9NdZtVEZSVuX3Pw8+b96hj7IyoLk5ce1IEZZlobW1VcmViBQeZigfM5SN+cmncoYRjZjm5ubi1Vdfxbvvvou1a9di3759qKysxMknnxzr9pFK58GJiIiI4shxYWpZFlauXImnn34an376KTRNw9SpU1FUVATbtpW8vJV4qpwHJyIiIoojR6fybdvGmWeeie9+97v47LPPcPTRR2PWrFnYunUrlixZgm9/+9vxaqcS4roqnxJC13UUFxczQ8GYoXzMUDbmJ5/KGToaMV25ciXefvttvP766/jqV78a8LU33ngDZ511Fh599FFcfPHFMW2kKuK6Kj8SKbznabz4XowkFzOUjxnKxvzkUzlDR6XyE088gZ///OeDilIAOPHEE/Gzn/0Mjz32WMwapxrz4CRhM9mThUMtiBK4KMrtBurqAj/i2XzTNOF2u2GaZvyehOKKGcrHDGVjfvKpnKGjEdN169bht7/9bcivn3baabj33nujbpSq7IPbRNnx2i4qXCmw52myridg2zY6OjqSnyFFjBnKxwxlY37yqZyho8J0z549mDBhQsivT5gwAXv37o26URQG4QuiUqC2JiIiohhzVJiapgmXK/S3GIYBj8cTdaMoPQivrYmIiCjGHBWmtm1jyZIlyMzMDPr1np6emDRKVVyVL5+u65g2bRozFIwZyscMZWN+8qmcoaPCdPHixcPeJ1VX5AMKrsonx3RdR2FhYbKbQVFghvIxQ9mYn3wqZ+ioMH344Yfj1Q4RlFmVP5yB20hxCyk/0zSxYcMGzJ49G4ZhJLs5FAFmKB8zlI35yadyhhFdkjRdKbMqP5RQS93jucxdGNu20d3drW6GNCxmKB8zlI35yadyhixMU0mwpe5c5k5ERERCsDBNNVzqTkREREKxMHXAMPSAW1F4+VIA3i3NZs6cqdycGgofM5SPGcrG/ORTOUMWpg5o0AJuRUjWJZYUpWka8vLykt0MigIzlI8Zysb85FM5Q4FDf8njOXhNWY+C15YNyTfvtLY28GPVKqCra/Cll1Kcx+PB6tWreSEIwZihfMxQNuYnn8oZcsQ0HQw17zQNT/Gbkt5YUFDMUD5mKBvzk0/VDFmYpiue4iciIiLFsDBNV8G2lgKU2V6K1wggIiJKPyxMHRC9Kj8YBbeWivc1AgzDQHl5uZIrESk8zFA+Zigb85NP5QxZmJJSEnGNgIyMjOgfhJKKGcrHDGVjfvKpmmGKDP0lhmlaAbcUH6WlQGXloY+yMu9xtxuoqwv8aG529timaaKmpkbZSd80PGYoHzOUjfnJp3KGHDEl5XGdFhERUXpgYUrBKbT6SPF1WkRERBQjLEwpULxXH0VIwXVaREREFGMsTB1IuVX5wSRi9VESGYaBqqoqJVciUniYoXzMUDbmJ5/KGbIwpcFSfHiyt7cXWVlZyW4GRYEZyscMZWN+8qmaYQoP/cUeV+XLZ5om1q1bp+RKRAoPM5SPGcrG/ORTOUOOmFL4Bi6IAnhJJiIiIooZFqY0PO7XRERERAnAwpSGl2L7Nak42ZucYYbyMUPZmJ98qmbIwtQB18EQXYqGGVdDLYgSdIrf5XKhuro62c2gKDBD+ZihbMxPPpUzZGHqgA074DbtCTzFb9s2Ojo6kJubC03Tkt0cigAzlI8Zysb85FM5Q67Kd4Cr8gfwneKvrQ38WLUK6OoafOpfAaZpoqGhQcmViBQeZigfM5SN+cmncoYcMaXoONnztLk5eLEa5Wl/ha6eSkRERFFgYUqJ0dwMlJV5R1IHivC0v6JXTyUiIqIIJbUwve222/D000+joaEBWVlZOPbYY3HHHXdgxowZyWxWSL55GKrNx1BW/6FMt9tblK5a5S1Q+x+PcGV/JFdP1TQNWVlZzFAwZigfM5SN+cmncoZJLUz/9a9/YenSpaiurobH48HPf/5znHLKKdi4cSNGjRqVzKYFZeh6wC2FMNRQ5sKFMR3KdHr1VMMwUFFREbPnp8RjhvIxQ9mYn3wqZ5jUwvSll14K+HzlypUoLCxEbW0tvvKVrySpVaFZth1wSyGE2vdUgcmflmWhra0N+fn50PkGQyRmKB8zlI35yadyhkrNMe3o6AAAjBs3LujXe3p60NPT4/+8s7MTAODxeODxeAAAuq5D13VYlgXLOrR63nfcNE3Y/QrLUMcNw4Cmaf7HBQDT9P7bsqyA4777e+8TuMLN5XLBtu2A45qmwTCMQW0MdTyefRqq7VH1adIkYNKkwceD5KTDmyE8nqj75L1xYeNGG7Yd2Pb8fKCkxMaWLVuQm5sbsLlw2uYksE+maQZkmAp9SsWchupTX1+fP0OXy5USfUrFnEL1qX9+I0aMSIk+pWJOQ/XJ93c0Ly8PGRkZce/TwPsPRZnC1LIs/PCHP8Rxxx2H2bNnB73PbbfdhuXLlw86Xl9f7z/1X1BQgOnTp6OpqQmtra3++xQXF6O4uBibN2/2F8AAMG3aNBQWFmLDhg3o7u72H585cyby8vJQX1/v/0FvbdoFoBy2baGmpiagDVVVVejt7cW6dev8xwzDQHV1NTo6OtDQ0OA/npWVhYqKCrS1taGxsdF/PDc3F2VlZdi+fTtaWlr8x+PZJwAoLy9HRkZGUvr02WefoQTARrcbXZYVdZ927szAyJEVuOgiX9F56Fd85EgT69aZsCwLdXV1/rk1zElWnzo7O9He3u7PMBX6lIo5DdWnLVu2+DPMy8tLiT6lYk6h+rRt2zZ/foWFhSnRp1TMaag+2baN9vZ27NixA5MnT457n+rr6xEuze5fCifRVVddhRdffBH//ve/UVxcHPQ+wUZMS0pKsHv3buTk5ACI7zud2sc24ktLyvHBIxtQef7MgLbx3VuEfaqpgV5dDc8HHwCVlTHpU3MzsHdvYNsbGjQsXmzggw/6YJo1qKys5Iip0D719fWhpuZQhqnQp1TMabgR07q6OlRWVnLEVGCf+ufHEVOZfTJNE3V1dZg3b15CRkz37t2L8ePHo6Ojw1+vhaLEiOk111yD5557Dm+//XbIohQAMjMzkZmZOei4y+WCyxXYFd8PdaD+xUg4x/s/rq5776Np2qDnC3Z/n1D3D9VGp8ej6VOkx2PZJ/9z9Hu8aPo0bVrAVw5+3dduHXl5ef7/DOPVp1TMSZU+6XrwDCX3KRVzGqpPLpdrUIbS+5SKOYVqe//8fPeR3qdUzGmo45qmIS8vz9/mZPQplKQWprZt4/vf/z6eeeYZvPXWW5g6dWoymzMsrsqXzzAMlPXfrorEYYbyMUPZmJ98KmeY1Apr6dKlWLVqFR5//HGMGTMGO3fuxM6dOwPmM6iEq/LlsywLLS0tAac4SBZmKB8zlI35yadyhkktTB944AF0dHTghBNOwMSJE/0ff/vb35LZrJB8AaoYJIVH5RcjhYcZyscMZWN+8qmcYdJP5RMBGHzBe0CJfU+JiIgocZRY/ERpLNRVogDv8aefBgoKAu/PYpWIiCglsTB1oP/qQ4qRUFeJam0FFi0Cvv71wOPZ2d77R1ic6rqO8eMLmKFguq6joIAZSsYMZWN+8qmcIQtTB/SDG7L7bilGQl3wfmDB6nZ7R1bb2qIqTKdPnx5hQ0kFzFA+Zigb85NP5QxZmDrAVfkJNlTBOlCYp/gty8KWLU2YOnUqdF1Hc/PgwVoHD0dJYFkWmpoOZUjyMEPZmJ98KmfIwtQBrspPsuHmo4Zxit+yLLS2tmLy5MloadFRVgZ0dUX8cJQE/TNU7Q8qhYcZysb85FM5QxamJEeo+agRnuJva/MWpatWAf33GY7BjAEiIiKKAAtTkiXU6f0olJUBlZUxfUgiIiKKAAtTB7gqX3ED554GmSiq6zomTSpmhoLpuo7iYmYoGTOUjfnJp3KGLEwd4Kp8RYWaexpkoqjvxUhyMUP5mKFszE8+lTNUr1RWmHlw0ZPJxU9q8c09ra099LFqlXcC6YD5qB99ZOF//qcJq1ebQRf3k/pM04Tb7YZpmsluCkWIGcrG/ORTOUOOmDrgu4QqL6WqoGG2lsrfMQLZI4/CxRcbAKb6v5yd7R1wJTls20ZHRwdfh4IxQ9mYn3wqZ8jClFLTgNP7pQDcKEEb8mFmZAL//d8wxo9Hfp4HpW19QBu4eSkREVGSsTCl1BRka6lSAIft3AmcfTaMHywc/D2+OalgcUpERJQMLEwd4Kp8YYKc3tcsC3vefRfjLCswx/6bl7IwVZqu65g2bRpfh4IxQ9mYn3wqZ8jC1AGuypdP13Xkc9NS0XRdR2FhYbKbQVFghrIxP/lUzlC9UllhXJUvn2maWLt2rZIrESk8zFA+Zigb85NP5QxZmDrAVfny2baN7u5uZigYM5SPGcrG/ORTOUMWpkRERESkBBamRERERKQELn5ywDD0gFuSxzAMzJw5E4ZhBL+D2w0gC0CZ99/5o7i3qWKGzZCUxwxlY37yqZwhC1MHNGgBtySPpmnIy8sb/IWADfnnAqgDLrwAyN7kLVBZnCojZIYkBjOUjfnJp3KGHPpzwHNw9ZpHwVVsFB6Px4PVq1fD4/EEfsG3IX9tLbDqMe+xW34NdHUFbNJPyRcyQxKDGcrG/ORTOUOOmFLaCbk9xoAN+d0oAzAXcGcB4BVLVaLiFifkDDOUjfnJp2qGLEyJBvCf1b9hKryn9L3HfVcsZXFKREQUHzyVTzSA/6z+KjdqUYnaVW6sWsWz+kRERPHGEVMHuCpfPsMwUF5ePuxKxNJSoLSsG0A9gLqDR8vgfqEJcB8A8vKAiRMB8BR/ooWbIamLGcrG/ORTOUMWppR2MjIywrtjv5X6+ShBNtwHT+8Hyh5pwv2PjSid2McqNUHCzpCUxQxlY37yqZohh/4cME0r4JbkMU0TNTU14U367rdSv7T2Wbifa/Se3r/vP6gdeRxqUYlVuABdBwy0fXMxMG8eUFYGNDfHvyNpzFGGpCRmKBvzk0/lDDliSjSUfiv1Sw9+AADOfMI74dSd5V0cteoxeBdKXeg9zlFTIiIix1iYEkViwNZSKCsD0B38vs3NwVdN8bQ/ERFRABamRLHmdh/6d2srsGiRd0n/QNx/ioiIKAALUwe4Kl8+wzBQVVUV85WIbjeAvInAyGOBC+9CPtpQim3eL2ZnAy+9BBQUBH4DT/tHJF4ZUuIwQ9mYn3wqZ8jClNJOb28vsrKyYvJY/RbuA5gI4F0AXKkfb7HMkJKDGcrG/ORTNUMO/TnAVfnymaaJdevWxWwlYr+F+/6PVavgXak/8WigspJFaYzFOkNKPGYoG/OTT+UMOWJKFKWB66CIiIgoMhwxJSIiIiIlcMSU0o5Sk737r+D34ZzUYSmVIUWEGcrG/ORTNUMWpg64DoboUjRMGp7L5UJ1dXWymzFw1VQgbiM1JGUypIgxQ9mYn3wqZ8jC1AEbdsAtyWPbNjo6OpCbmwtN0+L6XEMOhvpWTQ3ceD/UNlLcpN8vkRlSfDBD2ZiffCpnyMLUAa7Kl880TTQ0NKCqqgouV3x+/cMeDA131VRzs/fKUtykH0BiMqT4YoayMT/5VM5QrdYQpQCng6FB9R9udbu9RemqVQcvfRrJAxIREamPhSlRHES8hVSo4dbsbGDhQhagRESU0liYOuCbh6HafAwKn6ZpyMrKUjfDUMOtTuaSpvh8VOUzpGExQ9mYn3wqZ8jC1AFD1wNuSR7DMFBRUZHsZgwtmh3702A+qogMaUjMUDbmJ5/KGbIwdcCy7YBbkseyLLS1tSE/Px96kt5gDFytH/VAZprNR1UhQ4oOM5SN+cmncoYsTB2wLCvgluSxLAuNjY0YN25cwl+MQ00fjWggM03noyYzQ4oNZigb85NP5QxZmBIlSLDpo1ENZMZiPioREZFCWJgSJVA000cT84BERETJo9b4reK4Kl8+TdOUvNIFhY8ZyscMZWN+8qmcIUdMHeCqfPkMw0BZ/0VBJA4zlI8Zysb85FM5Q1ZYDnBVvnyWZaGlpYUL2Pprbgbq6gI/mpuT3aqQmKF8zFA25iefyhlyxNQBrsqXz/diLCoqUm4lYkIM3KuqtRVYtGjwvqcK73ma9hmmAGYoG/OTT+UMWZgSpYNQW0sB3uMvvQQUFHg/T6E9T4mISBYWpkQKGDiQCcR416dQW0vF/ImIiIgix8LUAd9wt2rD3hQ+XddRUFCgTIbDDWTG9Gx6imwtpVqG5BwzlI35yadyhixMHdAPbqugK7i9AoVH13VMnz492c3wCzWQqcTZ9LgP40ZGtQzJOWYoG/OTT+UMWZg6wFX58lmWhaamJkydOlWZd4rKDWQmdBjXORUzJGeYoWzMTz6VM1SrNYrjqnz5LMtCa2srMxyKbxi3tjbwY9Uq7+r9YPNUE4gZyscMZWN+8qmcIUdMiRQ28Gx6ws6kKzeMS0RE6YCFKZGCQp1NV+BMurJzT4mISD4Wpg5wVb58uq6juLhY+QyDLYpK+oIoReaeSsmQQmOGsjE/+VTOkIWpA1yVL5/vxSiBcmfTFdlCQFKGFBwzlI35yadyhixMHTAPThI2FZwsTOExTRObN2/GkUceCcMwkt2ciCT1TLoC1XIqZJjumKFszE8+lTNkYeqAfXCbKJvbRYll2zY6OjpEZqjImfTQErRSS3KG5MUMZWN+8qmcIQtTIiEUOZM+mNIrtYiISBIWpkSCKHAmfTAlV2oREZFELEwd4Kp8+XRdx7Rp01IyQyXnnsahUamcYbpghrIxP/lUzpCFqQNclS+frusoLCxMdjNiSsm5p8M16umngYKCwd8TRkNTMcN0wwxlY37yqZwhC1MHuCpfPtM0sWHDBsyePVu5lYiRUnLuaahGtbYCixYBX//64O8Js4pOxQzTDTOUjfnJp3KGLEwd4Kp8+WzbRnd3d8plqOzc01Cn90NV0e+8A5SVHToeZBQ1VTNMJ8xQNuYnn8oZsjAlosQKVrAOtbJ/4Gl/jwfZmzYBug64wvgTxsulEhGJwcKUiJIv2Kn/EKf9XQDKnTw2t60iIhKDhakDhqEH3JI8hmFg5syZys2piacE7XsfvWAjqUFO+9u2jX379mH06NHQhluIyG2rlJSOr8NUwvzkUzlDFqYOaNACbkkeTdOQl5eX7GYkRErsex+kWNUAjElOayhG0ul1mIqYn3wqZ8ihPwc8phlwS/J4PB6sXr0aHo8n2U2JO9/Z8draQx+rVgFdXYPXHkmSThmmKmYoG/OTT+UMk1qYvv322zjjjDMwadIkaJqGZ599NpnNoTRhptEbi9JSoLLy0Ef/Be+SpVOGqYoZysb85FM1w6Seyt+/fz8qKipw6aWXYtGiRclsClFaSepVooiIiEJIamF62mmn4bTTTktmE4jSipJXiSIiIjpI1OKnnp4e9PT0+D/v7OwE4J0r4Zsnoes6dF2HZVmw+l2hyXfcNM2ADWVDHTcMA5qmBZ1/oev6oOO+lW0Dh8ZdLhds2w44rmkaDMMY1MZQx+Pdp1BtT8U+6bqOo48+GrZtB3yP5D45yemwwzS43QZ27Qps46ZNOi6+WMeuXRYmTVK7T7quY9asWf4Mh8zp4L89Hg+QoL8R6fR6irRPtm37MzRNMyX6lIo5hepT//wsy0qJPqViTkP1yZehb2eTePfJyVxWUYXpbbfdhuXLlw86Xl9fj1GjRgEACgoKMH36dDQ1NaG1tdV/n+LiYhQXF2Pz5s3o6OjwH582bRoKCwuxYcMGdHd3+4/PnDkTeXl5qK+v9/+gtza1ApgNy7JQU1MT0Iaqqir09vZi3bp1/mOGYaC6uhodHR1oaGjwH8/KykJFRQXa2trQ2NjoP56bm4uysjJs374dLS0t/uPx7BMAlJeXIyMjIy36NG/ePFiWhdra2pTpUyQ5jRwZ2Kfs7AkApuK99/bio48+69fGXCxcOFnJPvn+oA6Z08F/N734IroPzl8Ym5eHkpISfLZtG/a2t/vvP6GwEBNmzcLm/fuVySkVf/d8fbJtG5qmpVSfUjGnUH3y5ZdKfUrFnIbqk23bKC4uRklJSdz7VF9fj3BptiLXo9I0Dc888wzOOuuskPcJNmJaUlKC3bt3IycnB0B83+nUPrYRX1pSjg8e2YDK82cGtI3v3mT0ybZt1NTUoLKyMmD/Nsl9ikVO27ZpmD3bQFdXwEMgO9uG262huFidPvX19QVkOGROn30Gu6wM2sCOhZKdDXPDBtglJQntU7r97vX19aGurg6VlZVwuVwp0adUzClUn/rnN2LEiJToUyrmNFSfTNNEXV0d5s2bh4yMjLj3ae/evRg/fjw6Ojr89VoookZMMzMzkZmZOei4y+WCa8ClCX0/1IFCbSYb6nj/x9U0PejxUPc/9H1a0OOh2uj0eDR9ivS41D71P/U78GtS+zTU8XD7NHXq4L3svXvTawf3plerT8EyDPpzLy2FFmST/qAObsZv7N3r/YEMoEJOkR5X7Xev/xsKXxuk9ykVcwrV9v75+e4jvU+pmNNwxzVN8/87GX0KRVRhSkTxE+zCSynBace4ZQERUdIktTDdt28fPvnkE//nTU1NWLNmDcaNG4dS/idARIk03JYFTz8NFBQM/h7+rSIiipmkFqY1NTX46le/6v982bJlAIDFixdj5cqVSWpVaIahB9ySPIZhoKqqKuTpCRpMtQHEuGXou1TWwNP+ra3AokXA178++Hu4x1ZE+DqUjfnJp3KGSS1MTzjhhIBJtUSJ0Nvbi6ysrGQ3Q3kq73katwxDnfYPVrAenJN6cBJu7NuS4vg6lI35yadqhhz6c8A0rYBbksc0Taxbt07ZS7GpxDeAWFsb+LFqFdDVFd56onhISoYDr+2aStd3TQK+DmVjfvKpnCEXPxFRSCm7ICqempuDV+2cj0pENCwWpkQUkYFzT1l3wVuUlpVh0IawQPLnPxARCcDClNKOipO9JQk19zSRdZdSGfav0N1ub1G6alXgqX7ORx1EqQzJMeYnn6oZsjB1wHUwRJeiYdLwXC4Xqqurk90M0YItXk9k3aVMhkNV6AsXhl5EFexx0qxYVSZDigjzk0/lDFmYOmDDDrgleWzbRkdHB3Jzc/3XWifnhlq8PlCs6y5lMgy1vVSwDqu8xUESKJMhRYT5yadyhixMHeCqfPlM00RDQwOqqqocXSKNhpbIukupDMNdHRaqiE3TU/xKZUiOMT/5VM5QrdYQkUisu8LALQ6IiIbFwpSIYoJ1FxERRYuFqQO+eRiqzceg8GmahqysLGYoWEpmGGxybjApslAqJTNMI8xPPpUzZGHqgKHrAbckj2EYqKioSHYz0k4s9zxNqQyHmpwbTIoslEqpDNMQ85NP5QxZmDpg2XbALcljWRba2tqQn58PnW8w4i4ee56mVIahJucGk0ITdlMqwzTE/ORTOUMWpg5YlhVwS/JYloXGxkaMGzdOuRdjKorHnqcpl6HTybnhDj8rfGnUlMswzTA/+VTOkIUpEcUVF0XFiJPhZ14alYiEYmFKRCTBUMPP77wz+BKovDQqEQnEwtQBrsqXT9M0Ja90QeFL6wwHDj8Pd2WDUJdGTbK0zjAFMD/5VM6QhakDXJUvn2EYKOs/gkRJE+nlS5lhP0MtnnIylzTB81GZoWzMTz6VM2Rh6gBX5ctnWRa2b9+OSZMmKTfhO11Ee/lSZjhAJJN4+78raG0FFi1K6HxUZigb85NP5QxZmDrAVfnyWZaFlpYWFBUVKfdiTBfDXb504HTJgYN2zDAKQy2geukloKDg0LE4zkdlhrIxP/lUzpCFKRElXLBBvnjseUoDhHpXoMAWUkREAAtTIlJEPPY8pSCi3TcVYCFLRHHDwtQB33C3asPeFD5d11FQUMAMFRWqZupfG1mWjs8/PwwtLTqmTElY09JPtJOBh8DXoWzMTz6VM2Rh6oB+cFsFXcHtFSg8uq5j+vTpyW4GhSl4baQDKOEp/ngbbjLwwGFsByv7+TqUjfnJp3KGLEwd4Kp8+SzLQlNTE6ZOnarkO0UKFKw2siwLb7/dih//eMKwC6UoSkOd9o9iZT9fh7IxP/lUzpCFqQNclS+fZVlobW3F5MmTlXsxUnADayOPx0JLy2fIzi7EhRcGnr3gKGoCxGBlv2VZ6Fi/Htbu3dBd/f4b4jsLEfh3VD6VM2RhSkTiFBX1Yv16E+3th/6EcaFUgkSysn/AAipt505UnHcejAMHAu/HdxZEaY+FKRGJVFoKTJs2+DgXkSdAuCv7Q4yuGgDMkSNhPv88jKIi78FQG9n6HocBEqUFFqYOcFW+fLquo7i4mBkKFirDOC4ip0iFGF21LAu7TBMTqqsBX44MUAz+HZVP5QxZmDrAVfny+V6MJFeoDKO9ohTFSZDRVR3AxGD3c7ILACUN/47Kp3KGLEwdMA8uejK5+Eks0zSxefNmHHnkkTAMI9nNoQgMlSGvKCVDyAydbv5PScG/o/KpnCELUwfsg9tE2dwuSizbttHR0cEMBXOaIa8opR6+DmVjfvKpnCELUyJKeeFcUcqHp/iJiJKHhSkRpR2usxEo2LuIYPjOgkg0FqYOcFW+fLquY9q0acxQsFhkyHU2yeUow6HeRQSTnQ08/XTgRv++x2GoMcG/o/KpnCELUwe4Kl8+XddRWFiY7GZQFGKVIdfZJI+jDEO9iwjGd2nUr3998NeCFayhitXm5uDPx+IWAP+OpgKVM2Rh6gBX5ctnmiY2bNiA2bNnK7cSkcKTiAwHnjVmPRJbjjN08i4iWBEbqmANNm+judm7r1hX1+DH5jwPAPw7mgpUzpCFqQNclS+fbdvo7u5mhoLFM8Ohtpbi2eHYievrcKiVbuFszdDW5i1KV60K3PiW8zz8+HdUPpUzZGFKRHRQsLPGsTo7TEkW7tYMvs/LyoDKyvi3i4gCsDAlIuonWP0S7dlhUtBwWzPk5ye+TUTEwtQJw9ADbkkewzAwc+ZM5ebUUPiSkWG0Z4cpkBKvw6EWVXHYe0hK5EdRUTlDFqYOaNACbkkeTdOQl5eX7GZQFFTKkCv7I6NMhpEEyJVx6uRHEVM5Qw79OeAxzYBbksfj8WD16tXweDzJbgpFiBnKJzLD/qf+58079FFW5l3Jn0ZE5kcBVM6QI6aUdky+sRCPGconLsNgp/59czfeeSdwBf9QUmSEVVx+NIiqGbIwJSKKsWBXz0yReiS9DTz17/SqVABXxxENg4UpEVGMDLfQm1tLpRgnV6UCnI+w8heE0hALUwe4Kl8+wzBQXl6u5EpECo/KGYaqU4baWiodN+5XOUPHnCygcjrCqujoakrll6ZUzpCFKaWdjIyMZDeBoqRyhuFuLeV0434gtQpWlTOMGycjrIrvPZaW+aUYVTNkYeqAaVoBtySPaZqoqalBVVUVXC7++kskNcNoN+4HlB1Ac0xqhjER7R5jzc1J33s1rfNLESpnqFZriIjSSLijq75jCg+gUbz0X0nne9fS1TX4fqnyroXSHgtTIiLFDDWoFmzFfzCpdNo/LYWaj5qdDbz0UuA8D75roRTCwpSISIAUWTdD4Qo1H5XvOCjFsTB1gKvy5TMMA1VVVUquRKTwpGuGkaybGbgrkSo1Tbpm6JjT+ajhDqeHEuYvCPOTT+UMWZhS2unt7UVWVlaym0FRSNcMw61ThjoLrMpq/3TNMC4i2eg/GAfD7MxPPlUzZGHqAFfly2eaJtatW6fkSkQKDzMcXrDRVZVW+zPDGHO60X8wDuapMj/5VM5QrdYQEVFMhLs9leqn/SlM0W5DRaQIFqZERGkiWO0y1Gl/Lp4iokRjYUppR8XJ3uQMM4ydYGeBh7qke6xGUpmhosJZQOXxIKu1Nf5tobhS9TXIwtQB18EQXYqGScNzuVyorq5OdjMoCsww9gaOpA61libYAiqnxSozVJCDBVQuABUcUhdN5dcgC1MHbNgBtySPbdvo6OhAbm4uNE1LdnMoAsww/kKtpQm1gMppjcIMFeRgAZW9cSO0iy6C3doKjYWpSCq/BlmYOsBV+fKZpomGhgYlVyJSeJhhYoR7udRILjrEDBUV5gIq0+OBC4D10UcwoilquMIuaVR+DarVGiIiUtpQBWs48vOBSZNi2yZKsPx8mCNHwli8OLrH4XQACoKFKRERRSySS6U+9ZSG1tZs6DrgG6zh4JkgpaVY+8QTKJ80KfLRtkSssCORWJg64JuHodp8DAqfpmnIyspihoIxQ7U42dvdN0f1G98wAJQHfI2DZ3JomgZj6lRos2cDkS4GHm6FHX8Z4krlv6MsTB0wdD3gluQxDAMVFRXJbgZFgRmqx8ne7tzkX76YvAZDvaOJZNIyOaby31EWpg5Yth1wS/JYloW2tjbk5+dD5xsMkZihbKWlQHFxYIZDbfI/cGuqUFjEJk7MXoNDvaMZOGmZAceUyn9HWZg6YFlWwC3JY1kWGhsbMW7cOOVejBQeZijfwAyDDZ6F2poqlFBFLOuZ2IvrazAW71J8j8PgQ1L57ygLUyIiSrpgg2dO564GK2JjcUEASqBYvEsBGLxgLEyJiEhJ0c5dHeqCAJwioLBo3qUAsbsSBCUFC1MHuCpfPk3TlLzSBYWPGcoXjwzDvSBAJFMEWMsESspr0Mm7FCD0lSC4PRUAtf+OsjB1gKvy5TMMA2UD/yiRKMxQvkRmGM3g21C1TDDpUt+IeA0ODH647anSbIKyyhmyMHWAq/LlsywL27dvx6RJk5Sb8E3hYYbyJTvDcAffIrl4QDqMriY7v4iE2p4qTScoq5whC1MHuCpfPsuy0NLSgqKiIuVejBQeZiiflAydXDzA6ehqKBLqHin5DRLufA/A+QRlCcH1o3KGLEyJiIhCiNfoaigpUvfIEosJygwuZliYEhERRcnJ6GookdQ94XJSHzU3B+9H2tVY4U5QTtPpAPHCwtQB33C3asPeFD5d11FQUMAMBWOG8qVqhk4XjgfjtO4JV7gDes3N3qkIXV2hH2P8eB2ff34Y1qzRUViYZjVWrKYDJHkyssqvQRamDugHt1XQFdxegcKj6zqmT5+e7GZQFJihfMwwNCd1T7icDOi53d6idNWqwLmygY+hAygJ+v2RSIkBxHCDU2SrB5Vfg0oUpvfffz/++7//Gzt37kRFRQXuu+8+zJ8/P9nNGoSr8uWzLAtNTU2YOnWqku8UaXjMUD5m6Fy0o7FOB/QWLgx9FtuyLHz22WfIyDgM3/mOHtVIru/5wj3bHWyagdLTFJxsWxVMnOauqvwaTHph+re//Q3Lli3DH/7wBxxzzDG4++67ceqpp2LTpk0oLCxMdvMCcFW+fJZlobW1FZMnT1buxUjhYYbyMcPEczISG6rm8T2Gx2PB4/kMVVUT4XbrcZlXG6we89134DSDcEdtQ32/k8cAoqwJnUxGjuNiK5Vfg0kvTFesWIHLL78cl1xyCQDgD3/4A55//nn85S9/wc9+9rMkt46IiCh1RTsSG495tcPVYy+9dKgei+RKXv2/P9LHiG76QunBj2EUAPj7J0B7e+DxvXuBn/wE+Pr1g74lf+R+lD577/CN83iQsXNnuA1OqKQWpr29vaitrcX11x/64eq6jpNPPhnvvffeoPv39PSgp6fH/3lHRwcAYM+ePfB4PP7v13UdlmUFjGz6jpumCbvfqfhQxw3DgKZp/scFgH1dnQA6sa97H/bs2RPQNsMwAACmaQYcd7lcsG074LimaTAMY1AbQx2PZ5+Gansq9sm2bezbtw979+7130d6n1Ixp6H61NfXF5BhKvQpFXMaqk/9M3S5XCnRp1TMKVSf+uc3YsSIqPs0ejSQm3uoT1OmAP/5D7B79+C2jx8PlJQcOj5lium/bzh9GjvWQklJ4PFp0yz85z+W/zGGymnPHh0XXmjj619P1FqTUQc/+jsMwItB7511YD9Wf70aJfhs2EeempmJ9vfeQ8b06XH/3du7dy8ABDxWKEktTNva2mCaJiZMmBBwfMKECWhoaBh0/9tuuw3Lly8fdHzq1Klxa2MwJ10J4MqEPiURERHRkLoBzA73zj09QGVlHFsz2BdffIHc3Nwh75P0U/lOXH/99Vi2bJn/c8uysGfPHowfPx5aAlbKd3Z2oqSkBNu2bUNOTk7cn49ijxnKxwzlY4ayMT/5Ep2hbdv44osvMGnSpGHvm9TCND8/H4Zh4PPPPw84/vnnn6OoqGjQ/TMzM5GZmRlwLC8vL55NDConJ4cvRuGYoXzMUD5mKBvzky+RGQ43UuqT1KVYGRkZmDdvHl5//XX/Mcuy8Prrr2PBggVJbBkRERERJVrST+UvW7YMixcvRlVVFebPn4+7774b+/fv96/SJyIiIqL0kPTC9Nxzz0VraytuvPFG7Ny5E3PmzMFLL700aEGUCjIzM3HTTTcNmk5AcjBD+ZihfMxQNuYnn8oZanY4a/eJiIiIiOJMre3+iYiIiChtsTAlIiIiIiWwMCUiIiIiJbAwJSIiIiIlsDAd4P7778eUKVMwcuRIHHPMMfjwww+HvP9TTz2FmTNnYuTIkTj66KPxwgsvJKilFIqTDB988EEsXLgQY8eOxdixY3HyyScPmznFn9PXoc+TTz4JTdNw1llnxbeBNCSn+bW3t2Pp0qWYOHEiMjMzceSRR/JvaZI5zfDuu+/GjBkzkJWVhZKSEvzoRz/CgQMHEtRaGujtt9/GGWecgUmTJkHTNDz77LPDfs9bb72FyspKZGZm4vDDD8fKlSvj3s6gbPJ78skn7YyMDPsvf/mL/dFHH9mXX365nZeXZ3/++edB7//uu+/ahmHYv/3tb+2NGzfav/zlL+0RI0bY69evT3DLycdphueff759//332/X19bbb7baXLFli5+bm2i0tLQluOfk4zdCnqanJPuyww+yFCxfa3/rWtxLTWBrEaX49PT12VVWVffrpp9v//ve/7aamJvutt96y16xZk+CWk4/TDB977DE7MzPTfuyxx+ympib75ZdftidOnGj/6Ec/SnDLyeeFF16wf/GLX9hPP/20DcB+5plnhrx/Y2OjnZ2dbS9btszeuHGjfd9999mGYdgvvfRSYhrcDwvTfubPn28vXbrU/7lpmvakSZPs2267Lej9zznnHPsb3/hGwLFjjjnG/t73vhfXdlJoTjMcyOPx2GPGjLEfeeSReDWRhhFJhh6Pxz722GPthx56yF68eDEL0yRymt8DDzxgT5s2ze7t7U1UE2kYTjNcunSpfeKJJwYcW7ZsmX3cccfFtZ0UnnAK05/+9Kf2rFmzAo6de+659qmnnhrHlgXHU/kH9fb2ora2FieffLL/mK7rOPnkk/Hee+8F/Z733nsv4P4AcOqpp4a8P8VXJBkO1NXVhb6+PowbNy5ezaQhRJrhr371KxQWFuKyyy5LRDMphEjy++c//4kFCxZg6dKlmDBhAmbPno3f/OY3ME0zUc2mfiLJ8Nhjj0Vtba3/dH9jYyNeeOEFnH766QlpM0VPpXom6Vd+UkVbWxtM0xx0xakJEyagoaEh6Pfs3Lkz6P137twZt3ZSaJFkONB1112HSZMmDXqBUmJEkuG///1v/PnPf8aaNWsS0EIaSiT5NTY24o033sAFF1yAF154AZ988gmuvvpq9PX14aabbkpEs6mfSDI8//zz0dbWhi9/+cuwbRsejwdXXnklfv7znyeiyRQDoeqZzs5OdHd3IysrK2Ft4Ygp0UG33347nnzySTzzzDMYOXJksptDYfjiiy9w0UUX4cEHH0R+fn6ym0MRsCwLhYWF+NOf/oR58+bh3HPPxS9+8Qv84Q9/SHbTKExvvfUWfvOb3+D//t//i7q6Ojz99NN4/vnnccsttyS7aSQQR0wPys/Ph2EY+PzzzwOOf/755ygqKgr6PUVFRY7uT/EVSYY+d955J26//Xa89tprKC8vj2czaQhOM9yyZQs+/fRTnHHGGf5jlmUBAFwuFzZt2oTp06fHt9HkF8lrcOLEiRgxYgQMw/AfKysrw86dO9Hb24uMjIy4tpkCRZLhDTfcgIsuugjf/e53AQBHH3009u/fjyuuuAK/+MUvoOscA1NdqHomJycnoaOlAEdM/TIyMjBv3jy8/vrr/mOWZeH111/HggULgn7PggULAu4PAK+++mrI+1N8RZIhAPz2t7/FLbfcgpdeeglVVVWJaCqF4DTDmTNnYv369VizZo3/48wzz8RXv/pVrFmzBiUlJYlsftqL5DV43HHH4ZNPPvG/oQCAzZs3Y+LEiSxKkyCSDLu6ugYVn743GrZtx6+xFDNK1TMJX26lsCeffNLOzMy0V65caW/cuNG+4oor7Ly8PHvnzp22bdv2RRddZP/sZz/z3//dd9+1XS6Xfeedd9put9u+6aabuF1UkjnN8Pbbb7czMjLsf/zjH/aOHTv8H1988UWyupD2nGY4EFflJ5fT/Jqbm+0xY8bY11xzjb1p0yb7ueeeswsLC+1f//rXyepC2nOa4U033WSPGTPGfuKJJ+zGxkb7lVdesadPn26fc845yepC2vviiy/s+vp6u76+3gZgr1ixwq6vr7e3bt1q27Zt/+xnP7Mvuugi//1920X95Cc/sd1ut33//fdzuyhV3HfffXZpaamdkZFhz58/337//ff9Xzv++OPtxYsXB9z/73//u33kkUfaGRkZ9qxZs+znn38+wS2mgZxkOHnyZBvAoI+bbrop8Q0nP6evw/5YmCaf0/z+85//2Mccc4ydmZlpT5s2zb711lttj8eT4FZTf04y7Ovrs2+++WZ7+vTp9siRI+2SkhL76quvtvfu3Zv4hpNt27b95ptvBv2/zZfb4sWL7eOPP37Q98yZM8fOyMiwp02bZj/88MMJb7dt27Zm2xxnJyIiIqLk4xxTIiIiIlICC1MiIiIiUgILUyIiIiJSAgtTIiIiIlICC1MiIiIiUgILUyIiIiJSAgtTIiIiIlICC1MioiTQNA3PPvtssptBRKQUFqZERHF08803Y86cOXF57BNOOAE//OEP4/LYAz399NM45ZRTMH78eGiahjVr1iTkeYkovbAwJSKikHp7ewEA+/fvx5e//GXccccdSW4REaUyFqZElDYsy8Jvf/tbHH744cjMzERpaSluvfVWAMD69etx4oknIisrC+PHj8cVV1yBffv2+b/3rbfewvz58zFq1Cjk5eXhuOOOw9atW4d8vpUrV2L58uVYu3YtNE2DpmlYuXKl/+ttbW349re/jezsbBxxxBH45z//GfD9GzZswGmnnYbRo0djwoQJuOiii9DW1gYAWLJkCf71r3/hnnvu8T/2p59+CtM0cdlll2Hq1KnIysrCjBkzcM8994T9M1qyZAnOOuss3HrrrZg0aRJmzJgBALjoootw44034uSTTw77sYiInGJhSkRp4/rrr8ftt9+OG264ARs3bsTjjz+OCRMmYP/+/Tj11FMxduxYrF69Gk899RRee+01XHPNNQAAj8eDs846C8cffzzWrVuH9957D1dccQU0TRvy+c4991z8+Mc/xqxZs7Bjxw7s2LED5557rv/ry5cvxznnnIN169bh9NNPxwUXXIA9e/YAANrb23HiiSdi7ty5qKmpwUsvvYTPP/8c55xzDgDgnnvuwYIFC3D55Zf7H7ukpASWZaG4uBhPPfUUNm7ciBtvvBE///nP8fe//z3sn9Prr7+OTZs24dVXX8Vzzz3n9MdMRBQ5m4goDXR2dtqZmZn2gw8+OOhrf/rTn+yxY8fa+/bt8x97/vnnbV3X7Z07d9q7d++2AdhvvfWW4+e96aab7IqKikHHAdi//OUv/Z/v27fPBmC/+OKLtm3b9i233GKfcsopAd+zbds2G4C9adMm27Zt+/jjj7d/8IMfDNuGpUuX2meffXZY7V28eLE9YcIEu6enJ+jXm5qabAB2fX19WI9HROQER0yJKC243W709PTgpJNOCvq1iooKjBo1yn/suOOOg2VZ2LRpE8aNG4clS5bg1FNPxRlnnIF77rkHO3bsiLpN5eXl/n+PGjUKOTk52LVrFwBg7dq1ePPNNzF69Gj/x8yZMwEAW7ZsGfJx77//fsybNw8FBQUYPXo0/vSnP6G5uTnsdh199NHIyMiIoEdERNFhYUpEaSErKyuq73/44Yfx3nvv4dhjj8Xf/vY3HHnkkXj//fejeswRI0YEfK5pGizLAgDs27cPZ5xxBtasWRPw8fHHH+MrX/lKyMd88sknce211+Kyyy7DK6+8gjVr1uCSSy7xL2IKR/8CnYgokViYElFaOOKII5CVlYXXX3990NfKysqwdu1a7N+/33/s3Xffha7r/sU/ADB37lxcf/31+M9//oPZs2fj8ccfH/Z5MzIyYJqm4/ZWVlbio48+wpQpU3D44YcHfPgKx2CP/e677+LYY4/F1Vdfjblz5+Lwww8fdoSViEgVLEyJKC2MHDkS1113HX7605/i0UcfxZYtW/D+++/jz3/+My644AKMHDkSixcvxoYNG/Dmm2/i+9//Pi666CJMmDABTU1NuP766/Hee+9h69ateOWVV/Dxxx+jrKxs2OedMmUKmpqasGbNGrS1taGnpyes9i5duhR79uzBeeedh9WrV2PLli14+eWXcckll/iL0SlTpuCDDz7Ap59+ira2NliWhSOOOAI1NTV4+eWXsXnzZtxwww1YvXp1VD87ANizZw/WrFmDjRs3AgA2bdqENWvWYOfOnVE/NhGRDwtTIkobN9xwA3784x/jxhtvRFlZGc4991zs2rUL2dnZePnll7Fnzx5UV1fjO9/5Dk466ST8/ve/BwBkZ2ejoaEBZ599No488khcccUVWLp0Kb73ve8N+5xnn302vv71r+OrX/0qCgoK8MQTT4TV1kmTJuHdd9+FaZo45ZRTcPTRR+OHP/wh8vLyoOveP93XXnstDMPAUUcdhYKCAjQ3N+N73/seFi1ahHPPPRfHHHMMdu/ejauvvjryH9pB//znPzF37lx84xvfAAD813/9F+bOnYs//OEPUT82EZGPZtu2nexGEBERERFxxJSIiIiIlMDClIgoCrNmzQrY0qn/x2OPPZbs5g0Sqq2jR4/GO++8k+zmEVGa46l8IqIobN26FX19fUG/NmHCBIwZMybBLRraJ598EvJrhx12WNTbahERRYOFKREREREpgafyiYiIiEgJLEyJiIiISAksTImIiIhICSxMiYiIiEgJLEyJiIiISAksTImIiIhICSxMiYiIiEgJLEyJiIiISAn/Hw7tBqDREDYCAAAAAElFTkSuQmCC\n" - }, - "metadata": {} - } - ], - "source": [ - "# Loops through each feature variable and then plots the histograms\n", - "for var in VarNames[1:]:\n", - " plt.figure(figsize=(8, 6)) # Adjust the figure size as needed\n", - " plt.hist(np.array(df_sig[var]), bins=100, histtype=\"step\", color=\"red\", label=\"signal\", density=True)\n", - " plt.hist(np.array(df_bkg[var]), bins=100, histtype=\"step\", color=\"blue\", label=\"background\", density=True)\n", - "\n", - " # Add labels, legend, and grid to the visuals\n", - " plt.xlabel(var)\n", - " plt.ylabel('Density')\n", - " plt.legend(loc='upper right')\n", - " plt.grid(True, linestyle='--', alpha=0.7)\n", - "\n", - " plt.show()" + "name": "stdout", + "output_type": "stream", + "text": [ + "dPhi_r_b\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "itaVs_BA_rpG" - }, - "source": [ - "## Exercise 4: Correlation\n", - "\n", - "### Exercise 4.1\n", - "\n", - "#### Part a\n", - "Write a function that creates pair plots and use it to compare variables in the SUSY and Higgs samples, separately for low and high-level features. Refer to Lecture 13 for details. Do not use `seaborn`.\n", - "\n", - "#### Part b\n", - "Making these plots can be slow because creating each plot initiates a full loop over the data. Make at least one modification to your function in part a to speed it up. Can you propose a different method of creating histograms that would speed up making such pair plots?\n", - "\n", - "#### Part c\n", - "Which observables appear to be best for separating signal from background?" + "name": "stdout", + "output_type": "stream", + "text": [ + "cos_theta_r1\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "for var in VarNames[1:]:\n", + " print (var)\n", + " plt.figure(figsize=(10,5))\n", + " plt.hist(np.array(df_sig[var]),bins=100,histtype=\"step\", color=\"red\",label=\"signal\",density=1, stacked=True)\n", + " plt.hist(np.array(df_bkg[var]),bins=100,histtype=\"step\", color=\"blue\", label=\"background\",density=1, stacked=True)\n", + " plt.legend(loc='upper right')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fcSXe4AF_ro5" + }, + "source": [ + "## Exercise 3: Make nice figures\n", + "\n", + "Now use `matplotlib` to reproduce as closely as you can figures 5 and 6 from the paper. This exercise is intended to get you to familiarize yourself with making nicely formatted `matplotlib` figures with multiple plots. Note that the plots in the paper are actually wrong!" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "id": "pHMS1ZIf_ro-", + "outputId": "5f26fbcf-3481-4153-e33b-5690e5a9ed79" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "id": "TvVV8HqG_rpN" - }, - "outputs": [], - "source": [ - "## Part A\n", - "\n", - "def compare_pair_plots(df_susy, df_higgs, columns, selection_dict, low_level=True):\n", - " # Determine title based on feature level\n", - " title = 'Pair Plots - Low Level Features' if low_level else 'Pair Plots - High Level Features'\n", - "\n", - " # Create a new figure\n", - " plt.figure(figsize=(15, 15))\n", - " n = len(columns)\n", - "\n", - " # Iterate over pairs of variables\n", - " for i, x in enumerate(columns):\n", - " for j, y in enumerate(columns):\n", - " plt.subplot(n, n, i * n + j + 1) # Position subplot\n", - " make_legend = (i == 0) and (j == 0) # Decide whether to make legend\n", - " plot_data(df_susy, x, y, selection_dict, 'SUSY', make_legend) # Plot SUSY data\n", - " plot_data(df_higgs, x, y, selection_dict, 'Higgs', False) # Plot Higgs data\n", - "\n", - " plt.suptitle(title, fontsize=16) # Set title\n", - " plt.tight_layout() # Adjust layout\n", - " plt.show() # Show plot\n", - "\n", - "def plot_data(df, x_var, y_var, selection_dict, label, make_legend):\n", - " selected_data = df.query(selection_dict) # Filter data\n", - " if x_var == y_var: # Plot histogram if x and y are same\n", - " plt.hist(selected_data[x_var], alpha=0.5, density=True, bins=50, label=label if make_legend else None)\n", - " else: # Plot scatter plot otherwise\n", - " plt.scatter(selected_data[x_var], selected_data[y_var], label=label if make_legend else None)\n", - " if make_legend: # Add legend if required\n", - " plt.legend()" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "kjb1z561_rpV" - }, - "outputs": [], - "source": [ - "# Example usage:(Cannot locate the higgs to compare)\n", - "\n", - "\n", - "#compare_pair_plots(df_susy, df_higgs, columns, selection_dict, low_level=True)" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "dkgjUZl2_rpX" - }, - "outputs": [], - "source": [ - "### Part B" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "id": "R2JYDsFR_ru5" - }, - "outputs": [], - "source": [ - "def compare_pair_plots(df_susy, df_higgs, columns, selection_dict, low_level=True):\n", - " # Determine title based on feature level\n", - " title = 'Pair Plots - Low Level Features' if low_level else 'Pair Plots - High Level Features'\n", - "\n", - " # Create a new figure\n", - " plt.figure(figsize=(15, 15))\n", - " n_columns = len(columns)\n", - "\n", - " # Calculate histograms for each variable in SUSY and Higgs datasets\n", - " susy_histograms = {var: np.histogram(df_susy.query(selection_dict)[var], bins=50, density=True) for var in columns}\n", - " higgs_histograms = {var: np.histogram(df_higgs.query(selection_dict)[var], bins=50, density=True) for var in columns}\n", - "\n", - " # Loop through each pair of variables\n", - " for i, x_var in enumerate(columns):\n", - " for j, y_var in enumerate(columns):\n", - " # Set up subplot\n", - " plt.subplot(n_columns, n_columns, i * n_columns + j + 1)\n", - "\n", - " # Decide whether to make legend for the first subplot\n", - " make_legend = (i == 0) and (j == 0)\n", - "\n", - " # Plot histograms for SUSY and Higgs datasets\n", - " plot_histogram(susy_histograms[x_var], 'SUSY', make_legend)\n", - " plot_histogram(higgs_histograms[x_var], 'Higgs', False)\n", - "\n", - " # Add title and adjust layout\n", - " plt.suptitle(title, fontsize=16)\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "def plot_histogram(histogram, label, make_legend):\n", - " # Plot histogram as filled area\n", - " plt.fill_between(histogram[1][:-1], histogram[0], alpha=0.5, label=label if make_legend else None, color='blue')\n", - "\n", - " # Add legend for the first subplot\n", - " if make_legend:\n", - " plt.legend()\n", - "\n", - "# Example usage:\n", - "# compare_pair_plots(df_susy, df_higgs, columns, selection_dict, low_level=True)" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "TJOdOGjK_rvA" - }, - "outputs": [], - "source": [ - "## Using numpy to have the histograms already calculated would avoid using the loops so it'll form the pair plots faster." + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "NBEXIQIv_rvC" - }, - "outputs": [], - "source": [ - "## Part C:\n", - "# It's good to look for which class might dominate over another one in certain places. For scatterplots, looking at patterns whether it be closely formed in clusters or the opposite. Also like the figures made in the previous exercises with different peak heights or shapes." + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAArwAAAINCAYAAADcLKyTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACB+UlEQVR4nO3de3wU9b0//tfsLJsLkARIIEAIJAEhCknABIqXYisVtKfCUVulWpBatFrOV3/UexVE7UEtImqpnKNVvFWtHoo91eLBKLZVREICiCQoIYAhhCRAwiXXnZnfH2GXbLKT7OQ9yV54PR8PHprJZPL5vGZ3857PfuazimEYBoiIiIiIIpQj2A0gIiIiIupJLHiJiIiIKKKx4CUiIiKiiMaCl4iIiIgiGgteIiIiIopoLHiJiIiIKKKx4CUiIiKiiMaCl4iIiIgimjPYDQhFuq6joqIC/fv3h6IowW4OEREREbVjGAZOnDiBYcOGweHofAyXBa8fFRUVGDFiRLCbQURERERd+Pbbb5GSktLpPix4/ejfvz+A1gDj4uKC3JrgcbvdKCoqwsSJE+F08qHSXcxRjhnKMUM5ZijHDO3BHFsdP34cI0aM8NZtnTl7U+qEZxpDXFzcWV/w9u3bF3FxcWf1E0qKOcoxQzlmKMcM5ZihPZijr0CmnyqGYRi90Jawcvz4ccTHx6Ouru6sLngNw0BDQwNiYmI4l1mAOcoxQzlmKMcM5ZihPZhjKyv1GldpoE65XK5gNyEiMEc5ZijHDOWYoRwztAdztIYFL5nSNA0FBQXQNC3YTQlrzFGOGcoxQzlmKMcM7cEcrePEDyIiIgo5mqahpaUl2M0ISW63GwDQ2NgY0XN4VVWF0+m0ZdpG5KZEREREYenkyZMoLy8HbzPyzzAMREdH48CBAxE/hzc2NhZDhw4VT+FgwUtEREQhQ9M0lJeXIzY2FklJSRFf0HWHYRior69HbGxsxOZjGAaam5tRXV2NsrIyjBkzpssPl+gMV2nwg6s0tDIMA5qmQVXViH1C9QbmKMcM5ZihHDOUCyTDxsZGlJWVYdSoUYiJienlFoaHtqVbpD8W6+vrsX//fqSlpSE6Otrne1ylgWzT3Nwc7CZEBOYoxwzlmKEcM5QLNMNIL+SkdF0PdhN6hWRU1+c4thyFIpKmadixYwfvAhVijnLMUI4ZyjFDOWZon4aGhmA3IaxwDi8RERGFvgMHgJqa3vt9iYlAaqoth7rxxhtRW1uLdevW2XK8QD300ENYt24dtm3b1qu/NxSx4CUiIqLQduAAkJkJ1Nf33u+MjQWKi20pep9++mmuOBFkLHipU6qqBrsJEYE5yjFDOWYoxwzlupVhTU1rsfvaa62Fb08rLgZuuKH199pQ8MbHx9vQKF+c42wNC14y5XQ6kZeXF+xmhD3mKMcM5ZihHDOUE2eYmQlMmmRfg2z2zjvvYOnSpdizZw9iY2MxceJEvPvuu/jVr37lM6XhxIkT+OUvf4l169YhLi4Od999N959913k5ORg5cqVAIBRo0bh5ptvxp49e/D2229jwIABeOCBB3DzzTdDURT07dsX99xzD/7yl7+gvLwcycnJuP7667F48WL06dMneCGEKN60RqYMw0BtbS3fhhFijnLMUI4ZyjFDuUjO8NChQ5gzZw5+/vOfo7i4GBs3bsRVV13lt6+LFi3Cp59+ir/+9a/YsGED/vnPf6KwsLDDfk8++SRyc3NRVFSE2267Dbfeeit2794NwzDgdrvRr18/rFmzBrt27cLTTz+N559/Hk899VRvdDfssOAlU5qmoaSkhHfTCjFHOWYoxwzlmKFcJGd46NAhuN1uXHXVVRg1ahQmTJiA2267Df369fPZ78SJE3j55ZexfPlyXHrppRg/fjxeeuklv5lcccUVuO222zB69Gjcc889SExMxMcffwygdb3iBx54ABdccAFGjRqFH/3oR7jzzjvx5z//uVf6G244pYGIiIhIKDs7G5deeikmTJiAGTNm4LLLLsM111yDAQMG+Oy3d+9etLS0YPLkyd5t8fHxGDt2bIdjZmVlef9fURQkJyejqqrKu+2tt97Cs88+i9LSUpw8eRJut/us/sCsznCEl4iIiEhIVVVs2LABf//733Huuefi2WefxdixY1FWVtbtY7afi6soivcDJzZv3owbbrgBV1xxBf72t7+hqKgIv/nNb/jhKCZY8JIpRVEQExPDO0GFmKMcM5RjhnLMUC7SM1QUBRdeeCGWLl2KoqIiuFwu/OUvf/HZJz09HX369MGWLVu82+rq6vD1119b+l1ffPEFRo4cid/85jfIzc3FmDFjsH//flv6EYk4pYFMqaqK7OzsYDcj7DFHubMpQ7O19aVr4J9NGfYUZijXZYYHDgDV1YDT2boMmWdeaxiMWm7evBn5+fm47LLLMHjwYGzevBnV1dXIzMzEjh07vPv1798f8+bNw1133YWBAwdi8ODBWLJkCRwOR8AXAoqi4LzzzsOBAwfw5ptvIi8vD++9916H4prOCImCd9WqVfjd736HyspKZGdn49lnn/WZ29LW2rVr8Z//+Z/Ys2cPWlpaMGbMGPz617/Gz372M+8+hmFgyZIleP7551FbW4sLL7wQzz33HMaMGdNbXYoIuq6jpqYGiYmJtn2W9dmIOcqdLRl2tra+dA38syXDnsQM5TrN0PMESEoCVq8GWlrOfK+0tPW/27YBno/UVVXA5eqZhhYXW/6RuLg4/OMf/8DKlStx/PhxjBw5Ek8++SQuv/xyvPXWWz77rlixAr/85S/xb//2b95lyb799ltER0cH9LsMw8Dll1+OO+64AwsXLkRTUxN++MMf4sEHH8RDDz1kue1nBSPI3nzzTcPlchkvvvii8dVXXxkLFiwwEhISjMOHD/vd/+OPPzbWrl1r7Nq1y9izZ4+xcuVKQ1VVY/369d59HnvsMSM+Pt5Yt26dsX37duPKK6800tLSjIaGhoDaVFdXZwAw6urqbOljuGppaTE2bdpktLS0BLspYY05yp0tGW7dahiAYbz2Wuv/e/699lrr9q1bu3/ssyXDnsQM5TrN8PQToOHPfzZ2bdtmNBw5YhgnTxpGba1h/O1vhhEd3fpE6K1/sbGGsX9/r+Ry8uRJIz4+3njhhRcC2l/XdePEiROGrus93LLga2hoMHbt2uW3hrNSrwV9hHfFihVYsGAB5s+fDwBYvXo13nvvPbz44ou49957O+x/ySWX+Hx9++234+WXX8a//vUvzJgxA4ZhYOXKlXjggQcwa9YsAMArr7yCIUOGYN26dbjuuut6vE9ERBIhvrY+Uc9KT28duY2NBTwjntOnA0VFwJEjrV83NQEVFUBaGhAT0zPtkM4j6kRRURFKSkowefJk1NXV4eGHHwYAb91C9gtqwdvc3IytW7fivvvu825zOByYPn06Nm3a1OXPG4aBjz76CLt378bjjz8OACgrK0NlZSWmT5/u3S8+Ph5TpkzBpk2bWPCSfz01cZKIiOSiooBx4858fepU67SDzEygb9/gtUtg+fLl2L17N1wuF84//3z885//RGJiYrCbFbGCWvDW1NRA0zQMGTLEZ/uQIUNQUlJi+nN1dXUYPnw4mpqaoKoq/vCHP+AHP/gBAKCystJ7jPbH9HyvvaamJjQ1NXm/Pn78OADA7XbD7XYDaC3EHQ4HdF33LgnSdrumaT6fpmK2XVVVKIriPW7b7QA6LDxttt3pdMIwDJ/tiqJAVdUObTTb3lWfdF1H//79oes63G53RPTJ73kqL4eRmQnFz8RJIzYW2pdfeove7vQJgE+OvdKnCDtPhmEgPj7em2Ek9Mnf9tZuOE///5k+ebYbhgG3u3t98jyfPcsanVWPvX37vBe0Pn0aOND73A6kT21fEw3DiKjHnqeNPd0nwzDMXw/dbjgBuNu0q2372h7LAKAAMFp3OrPdbH/hJ7tZPXZX23NyclBQUNBhe/uf6aztnjy7y+4+9dR2Ty5tazLPY6/9Y7UzQZ/S0B39+/fHtm3bcPLkSeTn52PRokVIT0/vMN0hUMuWLcPSpUs7bC8qKkLf01eOSUlJyMjIQFlZGaqrq737pKSkICUlBV9//TXq6uq829PT0zF48GDs3LkTDZ4J9gDGjRuHhIQEFBUV+bwYZGVlweVy+TwBACA3NxfNzc0+d3iqqoq8vDzU1dX5XBjExMQgOzsbNTU12Lt3r3d7fHw8MjMzUVFRgfLycu/2rvpUWlqKEydOeD/uMBL65Pc81dRAqa/HNw89hIZRowAAaaNGoX95OZQbbsCuf/4T9acXBO9On06ePOmTo9U+tbQMRd++I/HttwdRW3usTe5JmDJlaEQ+9vz1KTMzE9u3b4+oPrU/T7t3xwJoXWi+bZ882zVNE/dJVVVUVVX1Wp+A4J6nw1u2YPAll0BtbPT5HQ4ARnQ0tr/xBpqTky31qbCwMOIee711no4dO+bzeti2T0eLi5EFoLS0FMp55wFoHZBqW9S4XC64XC40NzcjCkBDQwN0w0BUVBT69OnT+nWboj86OhpOpxP19fU+xVRMTAwcDgdOnTrl06e+fftC13WfXBRFQd++faFpGhrbPI4cDgdiY2Phdrt9Bs1UVUVMTAxaWlp81sR1Op2Ijo427VNjY6NP7l31Sdd11LcZqImEPvk7T0DrjICdO3d6t3see0VFRQiUYkgvewSam5sRGxuLd955B7Nnz/ZunzdvHmpra/Huu+8GdJxf/OIX+Pbbb/HBBx9g7969yMjIQFFREXJycrz7TJs2DTk5OXj66ac7/Ly/Ed4RI0bgyJEj3k8sCacraLtGBVpaWnDo0CEkJyd7t4V7n/yep23bgPPPh3vzZu/ESVVVoRQV+d1utU+apuHgwYPeHK306cABYMIEFfX1HZeqiY01UFysYPjwyHvstW870PruzZAhQ/Dtt4p39knbPrWdfRIOffK3vbAQmDLFia1bgaysM+fJs72gwEB2dvdHeCsrK5GSkuL9ujf65Glj0B57BQVw5OVBe/llGOPGnenTV1/BMXeu9/kdSJ80TUNlZSWSk5PRp0+fsH0+BfM8ud1uVFRU+H89LCiAc8oUnNy8GQf69UN6ejqioqLQnqIoME6dglJcDCMzs3WuL6yPHFoRaqOeQGsN5Xkcdkeo9clse2NjI8rKypCamupdxcLz2Dt27BgGDRqEurq6Lj9hLqgjvJ55K/n5+d6CV9d15OfnY+HChQEfR9d1b8GalpaG5ORk5Ofnewve48ePY/Pmzbj11lv9/nxUVJTfJ5XT6fS+Je3heaFozxN+oNvbH7c72xVF8bvdrI1WtyuKgoqKCgwbNszn94Rzn8za7m2Pn9/tb7uVPhmG4TfHQNpeW9u6RNVrr7VOVfMoLgZuuKG18EtNjYDHXrs51CrgM3/a7XajvLwczc3JmDDB6WfZLqffZbvC4bHXdnvbX9+2LZ7/NWtj+/092u7vKTQ8j8O2bTwTvwOezyNqewFh9+texynzTr/T5e06TwCgjh/vcyegZ8/2z+/O+tT2uewpMkLy+eSn7f4E4zUCgPnr4emvnacLb89x/FHa/rfNPqb72/BBF1aP3ZPbDcNAS0sLXC6XqG+h1Cez7YqieB9P7R9TZo8xf4I+pWHRokWYN28ecnNzMXnyZKxcuRKnTp3yrtowd+5cDB8+HMuWLQPQOv0gNzcXGRkZaGpqwvvvv49XX30Vzz33HIDWYO644w48+uijGDNmDNLS0vDggw9i2LBhPqPIFNr83UN2Nt8/FtF37ZstPuungq2p6ewCAKcvAHqp3SHiwKaDqNl73GdbYnocUqcOD+znA4/fFr39+6zq7P7VYcN6vz0UxpqaPBPwz3A6W2/Ao14X9IL32muvRXV1NRYvXozKykrk5ORg/fr13pvODhw44HPFeurUKdx2220oLy9HTEwMxo0bh9deew3XXnutd5+7774bp06dws0334za2lpcdNFFWL9+fcALOgcFVwnwCrk/iO0XID8Lz0mP8lfFdlHBRvQFgAUHNh1E5gUJqIdvcRuLUyj+7GBARW834jdpTGBXqbb9vh7Q1Qd/fPll77eJwlRTE/DVV0CbKSYAAIcDOO88Fr1BEPSCFwAWLlxoOoVh48aNPl8/+uijePTRRzs9nqIoePjhh73r2oW8nvx4JQGHw4GkpCS/b5H1pJD5g5iY2Jr/DTf4brd4ToKVY9jppIr1ZHjsGDNsq2bvcdRjOF679VNkXjgQAFD86VHc8NyFqNl7wKfg7epxKLqI6MZVaihetHT1DsLRoyHwXA7zt7/OmtdDt7u12E1LO7OWcGMjUFbW+j0bCl4rb+dTiBS8Z70QfZ/W4XAgIyPD9Ps9PSgt/oMo/cOQmtp6DtoeoxvnpKsczyZ+HzPFMUjECHSWpifD0zd295hwrSUyLxyISdd7XjuKgec67mPX49D/OTyFxPpBSH3tv0Nv2LYbzF57gv5cDrm3v6yTZOjz2GtwAGWxQJMDCJHPnbjkkkuQk5ODlStXntkYHW3bOsE33ngjamtrsW7dOiiKEtrvWgdo3759SEtL67DQQE9gwRtK/LzKHsAI1BQH9mw2fXJ286+4rusoKytDWloayssdPoeorgauuirkBqXPsOsPQ2qquCNtcwzaqEYITJkxfyMjE7EoRvGhvaZFrydDXU/DmduNeqd9IfF4toEdj8Muz2HCXqROmiBua6jSdR2lpTY/l608N0Pm7a/u6+7jsONjLwbAuT3RRC9/z/1QmZZrGAaampoQFRVlyw15XQqVjguw4A1hBw71QSaKUX9DYFeHfv8wC/6K67qO6upqKMpITJjg8HuI9euBpKQz26y+9vp7rW8/ZbY7xwilESdPjiNHjrS/4C0uBtDgu639H8oQmTJj+kbG+2W44cE01NQ6Oy14q6ur4XCMRE8VvBFQS3TKjseh5BxGAr8ZSt4W6O5zsxfng9h9rdzdx2GHx15DQ+v0gB76aGF/z/2upuX2iLaFptsNaFrrNpcLbrfb7wpTVjQ3N8PlcnXdhgiYj8yCN4TV1DpRj7547ZEyZF6R1um+pn+YbfgrbvZHTjo42NVrfSCfsGh1xMnviHkAb6kHXfvC9rNGABcAN1wPoN3C2+3/UIbYlJkOf6uLG033FevGX+tQnFsaanr1HIYy6dsC3XhudngN68HXrxC5Vvbhfeyd0oGoeiBTB3rpk4W7mpbbuo8bCxcuxKuvvoo+igM3/eIWPPjQMiiKgjdeeRPPPbsCX397AH379sX3v/99rFy5EoMHD/b+jq+++gr33HMP/vGPf7R+Itvo0VizZAkyUlJa16k8ebK1+DzvPGzduhXXXHMN7rzzTtxzzz0AWu9zeuaZZ9DQ0IBrr70WiYmJWL9+PbZt2wbgzLSIvLw8rFq1ClFRUSgrK8OXX36J22+/HZs2bUJsbCyuvvpqrFixAv369QPcbnx3wS2YMDEXTzy+orWhTc2Y89N/x8CUYVjz2msAgFGjRuHmm2/Gnj178Pbbb2PAgAF44IEHcPPNN3v798UXX+CWW25BcXExxo8fj9/85jc9es7aYsEbBjLTGgP/A9y+MPIMl9rwV9zuQsDstR4IvJi2MuJkPmLe9VvqQXPoEIChfgrbiQAKgWd/D1zQZh5XZ0Xs2VbJheJf61DW9rWjOAZAZmd793wbPEJ5ErVdbwsE+Nz0/xrWc69fIXat3EETXHDXuYFG38eMM0pFVL8uRi0F/E7LbWwANA0vv/wybpo7F/96fwPe/Wg3fvufv4QzKgP//u8LcOCQghtveRzTp6WhrvEEFi1ahBtvvBHvv/8+AODgwYP47ne/i0suuQQfffQR4pxOfLp2LdzJya0nICGhteLWdXz04Ye4+vrr8fjjj+OWW24BALz++uv47W9/iz/84Q+48MIL8eabb+LJJ59EWprvgFl+fj7i4uKwYcMGAK2rX82YMQNTp07Fli1bUFVVhV/84hdYuHAh1qxZg6YWBfWIxdFTUSgu81xsxeA4+iO+3aDvk08+iUceeQT3338/3nnnHdx6662YNm0axo4di5MnT+Lf/u3f8IMf/ACvvfYaysrKcPvtt9t8dsyx4I0UpoURAh8ubcfhcCAlJQWVlT0779SOOiyQESezEfOefjvWk2O33kaurQUwFHjkUeCK5DPbi2OAGwBccAFwFtSw3Xoshvpf625oP92nuCzwm1ZMH4d+XztOX1B5vtfTunr9CvDixOpNke1HS7uaTuVwODBsmJ8M/b2I+TuYn+LdyrtO/l7DOnv9svQGh9/5Za0XPnZeK4teD9toMvrgK5wHvaLjh2c4oCEjrQXO6D4+23tkymlLMwBX6zBvfT1GJCXhqRtvRL3SFzMv/xmqq3bgf/7nKdx//wKkDbseZRVRSB3RgL6DYvDMM894P36+X58+WLVyJeLj4vDmH/+IPn36AI2NOOfKK1sfX337tnZAVfGXjz/G3IcfxurVq/HTn/7U25Rnn30WN910k/dzDBYvXoz/+7//w8mTJ32a3LdvX7zwwgveqQzPP/88Ghsb8corr6Dv6Ur+97//PX70ox/h8ccfRz9nHAwoiO+reV9KG+uaACgwDN/5w1dccQVuu+02AMA999yDp556Ch9//DHGjh2LP/3pT9B1HX/84x8RHR2N8847D+Xl5aYfCmY3FrzhyN8LU1El/BZGQLdHSDwvTFVV3W9qKOowYm7T27Hmf1wcSE1NkR08LQ2YFIQRtxAheixGwMi22Qp5QBpicQqJCW5/P+bD4XAgRdeB029tevl77Xi/EngQZy64eprphV3gFydWb4o0e8ens/EBz+OwU+Ynq0Px3t13nXxew0xevyy9wWG6s/0XPt4MhTdwuBUXdABpw5oQHXVmmNHd0ILSyr74pqxPh5/pkSmnno9PHjYciI3Fd847D8q55wKNDqAMuOCii/CH1SsRHa2h6PMC3PfQY9hXtg21dbVnPkJ+zx6cq+vY9tlnuPi889Bnzx7fRrdZfmzzli3429//jndeew2zr7vOpym7d+/GbTfdBJw65d02eeJEfPSPf/jsN2HCBJ95u8XFxcjOzkbfvn29U4Zzci6EruvYtm03cidMAQCoTuPMyHajfqb/nt9nGMgaN651zu/pm+mSk5NRdfqFu7i4GFlZWT6rS0ydOtVa3gIseMNNpy9MVwATJwKT7Hlh0jQNX3/9NTTtHJz+sNfe0/7FsKu3WNu/FVpW2fn+/g5RFg20W/bKnntPDPz1r3twySXpnX60sec4Pt22MIIXkU7/AdQ0DQcOHIBWPw5AT90d0nNML4aaDiI16vCZDSaPc38r5LXuX4zEG2Ygdei6LtuglZVBOe88OBraTRnw99ph9c5Ru5hd2AUwWmr1hjqzd3w6e85rmobi4q9xzjnnmD+XzU6Wn+K9J991svQGh9nOFi98AhlR1jQNezduxOgrr4RiVo0PGNBaNAUgOj7Kd3rBKR3nVX4Fd9oYIPrMyLl3ru3JBkS527wPb9OwbyOioUGFG06cQl+0vwxpbGzErJ/MQl7eDPxx9UtIHZ2CAwcOYMaMGWhuaAD69EHMwIFAXJzvOWjXvoz0dAzq2xcvvvoqvn/FFejfv3/rKg1NTa3FZ0WF7/Pl6NHWzp8uQgF4R3Hba3tvmmdQeP9+YNCgKDgcChS0yU1V4Xa3tD5uPL+vpQV9jhzxzjH2FL16+5vdgoQFb7gxfRU7/Rb3UPtGYwzDQF1dHRwOw7ZjBsRv9Xh6pOGzzwC0KQJNb946/Uc8IaHLX5eY4EYsTuGGB9NaX9zbsOfeEwX/+hfQv7/hvVD390fVf9Ec+AheRGk3SqYCSANwrLffardBpxdDSEAxpiIV357eYt4//yvkNQDen+2cUV0NtaEB2ssvQx0//sw3euC1wzaJiTgQfQ5qbniy47eiTyF194YOoVi9oc7KPRJGcTFajK0wTpxoLUTMLgwsLmfYU+86ARbf4Gi/s4ULn65GlNeubV3Rx+02UPavekS1X0XHIzERGDy4tTrtpig0Iyq63c1sbaceoE0jhcO+TtWAAxrKKqJQXw/861+bvbE5HEBBwecYM2YMSkpKcPToESxc+BjOnzAA0f1UfPrxZwCAhhNuNA3si6ysLLz8xhtocblapzT4kThoENY+9BAu+X//D9dffz3+53/+p3W01u3G2JEjsaW8HHPbZLpl717AMDp82EXbxR/S0zOxZs0aHD58CrreF2lpwCeffAqHw4Hp08diSHwDRiW4UFVV6f15zaGitHQnRl3y3TPnsE8fYNCg1orZz4drZGZm4tVXX0VjY6N3lPfzzz/vVu7dwYI3XEXA27Sm/FWPnzUC/wHgPxYisJu3Av8jnjq0BcXIRM1rH/i8+Npx70liIhAbo+Ohh8bgoYfObI+N0VFc4vA5rrfbj5QhM+30H7yyMiQ++EvTEbwOf488c//afqOTP1pm6zwH/T6hdqNkbrcbu4qLoe0fBCxB773VboMuRx4fWY3UQKYS2LCG3wGMQCUmwYkzBW+QxnIDcgCpyFSKUe9nKbrYxlMo/nJv7zxOT1+AOOfNRZZN90jYrf07VL09SG/2OPes2T5zpmeLE8CPEIvvm6/b3NgDq360nXoQ7zzze7rzyWeNDcDp0c4orRHnYQ/caWMQGwt8/fUBvPzyIvz857fgyy8L8dxzz+LJJ59EamoqXC4X3v7z03C7b0Np6U4888zjAIC91f0RNfA8LLh5IZ79r//Cddddh/vuuw/x8fH4/PPPMXnyZIwdO9b76wcPHIj899/HJZdfjp/+9Kd488034QTwHz/5CRYsW4bciy7CBRdcgLfeegs7du1CerLvNEdd911lLCfneqjqEsyfPw+33PIQjhypxl13/Qd+9rOfIT19CHDqFKbnnY9FTz+N9957DxkZGXj88RU4caIWUNUzd/ApSmvRa+KnP/0pfvOb32DBggW47777sG/fPixfvjzw3IVY8J7NunpFdLsRu3s3GpV+QJs/kN09tOUiyl9R/9rrQGbbVSjsuXkrFd8iNbPB9hvAUnEAu4wf4EiboYZiZOKGhtdR8+UhpKa2KWpO/1HNfPBqTGr7R9XPH1TzaYKn5/7dkNlm1BCto2SHhvr8Qaz+rD+uMlnn2V9BbptAp5+0HSVzu1Gv63Ao/XqgQTYzmY5jOvLY9m18s+dkN9bwa18AVX4ejx+jGPXz/Jzv0KjZOqipAeobHMFf9/f0BchXtzyNb4bWIi0tDc7TUxoS0+OQmjq80x/vSV29Q9Xb59Xs/j3PU8LtdiP/j5/h/v/+rumNdtXVrQPo9fVn6tTm5jPHAs4sw9vU1G4ZXpNPYGuoVVBWATQdj0JMQnSn+7b9PT5MRomjHA5E9XNAVYG5c+fC7W7AJZdMhqqquP3223HzzTdDURSsWbMG9913H9768++Rk5WD5f/5W/zkhh9jeFIzdKiITxyKjz76CHfddRemTZsGVVWRk5ODCy+8sENTkocMwXvvvYcf/vCHuP766/Gn55/HNZfPQkl9C3796zvR1NSIq676Ca6/7gZs3fQpTjU6Tufvb3m1WPztbx/g7rtvx7x5eT7Lknn8/Morsb26GnPnzoXT6cSvfvX/ITf3e35CMtevXz/87//+L375y19i4sSJOPfcc/H444/j6quvtnSc7mLBezbq7KaKNpwAsgAUmk0n8HfoQ30QG30ubrih4/w2W4qozMzwWpWgpgYjG79GwqpV6D95cuudyadH8YqLGn0G8YqLThdA7W/c8XOl0Pk0wb6oee2D1gIep2+M+fF5qP+39qNkYxCLU1iPGUhC9ZljmBXkUp0usdb59BOHw4H09HQc2ONngmAoOXAAB8b+ADWNvhc4wOuyaRgW1vAzL4DGIhan8P4zX2PIhed0doiQE+y1mxNr9iMWqZj7Xxd32L1HV7grKwMKO784NHuHCjg9R7zmMNC2O0FYci4VB5B6uhG6Q0fDgNa3xttflHlGg5OSgNWrgZaWM987ciSgP1sw/wS26Hb/7WzfVh0uGPyNEgPeebYbN270bnruuY6f7z1nzhzMmTPHZ5txvYFTp1ofQ42NQEZGFtau/aD9oQEAa9asgXdnACNHjkRJSQkURUFTbT2+wnmYdXUWZl39sPfnf/WrH2BQygTvcmJ33LEGQOt0i379zhx7ypQJ+OSTj0yz6ON04g8rV+IPzz8PoLUZV1zh+5Dbt2+fT/sAeNf/9fjOd77TYZth9M60SRa8ZyPTO2BMmE4n8HNoAMUYgRr4DivYVUR1WJapi0Fqn3f27bwBrENDTv8RMVkHOf473/H+xU489KVJQXJ6vu7EEUAAH8/a6TTBNhcGNYVAfYNJrdRUi9SoZb4brd6hH+jaR10tsdbJ9BOHw4HBgwejXDnSdXt6gp/1YQ80DUFNlO/IXvVnTbiqsRD17VbCj8UpJKIG4mkYAUxlMi2APDe4XbguvC4ae5PJSHoqgOLoc1Dzzkafx2mPrXB3+uKv+MHXgQfPvNa0Xjx1vDj0+w5VL6680Kl27XAASMMIxOKHpqPSL7zQWvSOGNFakHlmOGzdeqY7ph+0ZvKNhtpGlFVEI21YY5sRXrODtDK9EIxyAX077t9dTmdrAepv2rLZFGMF8Jnn69YU1Dc24eMNv8fMf/shVFXF22+/gS+++BD/+/sXkDmsrrXdnt8ZpSKqzddnAxa84aDtVX43Jmb5/+TLVKRO6vwVWtM07Ny5E9qU1iVJOkwnMJF6+p8P4TJHXa300/5tO//723ADmGlDTv8R8bOOqBEbi+KqKozVNKiqeqYgeWR16wutRxfzdaX810rDT/9ro7O31ds/kKqrcWD2//MZ0fQwu6moO0useR+Lei+/ZJmMSh/AiNblpDr8wOlR82e/QdIFY1o3WVhJwS7+CiBNOwkV30LTtF5bc8XfHPEen1tqMmWmwxxXfxfAnYykpyYmYvjwwdi5czvGjx/f5YornbfPt23tJU4YitgYHTc0vN7he7ExOhInBPAaatPKC63tbDfS3Nkosb+L/jbt0DQNjaWl2JlyBMdi/bxutLlnLTb2zCeaAa0FsGe66KlTrUWgZ4laL5NPYDt1xEBUHJCZZqDvoM737W1RUa1Frbvdn6bOphgbABrq6xETE9O6SgMAQMFHG9dj5e8fR2NjI8aOHYv/efNN/NuYMUDFN74HCLOPBbYDC94Q4fcPQ01S6/88+ADwYOdzOk2Pa/GTL9vWNG63geJiBd7nkmQ6gfCvXGeD0v6uwv3u383Cw7fpqUj88GvfpaSAM6OUfi4KtIQEHK+qOvO2TWIiUmOPIPXBH3b8ZaE6mdLkgXSm8PMzL9TiTUXtHyJtz6thGGhoaIDDOD2H18ofYAmTUema9ytR/6Cfj/32PMYuWNfmuRL4Sgo9yfP4s/L2oeRGKPM1ZnvoYW5ycZKIEYhFsfk7Kv4ugE1G0g23Gw0NDR0yDCiXQ0ORGH0OUn0ulv1P50lNBYpLHJ2s6x3A7/MQrLzgbVf7v0H+Rom7+vCQiy8GUlNhuN04qmnIzR2GNJMKxOyetbbb7bqvrQkuuBsD+xCMxqae+xCmqCjrtWf75b6io2Pwt7Xvoe+gdqPPbZdkALp/s16YY8EbAsz/MCS2Xs2//TIwtM1kpk4m3Pl7y9/sky//+U//d9OeqWk8s3hDow6zuNKPn/1PFx4BzoswG8yNjR2OtWuHIympzSE8/+PvosDths8nJlit3kNBTQ0O1A9CzSNv+4xKF5dFtxZ+gpuKzHP2c1HW1R/gtvPMi4s7/xQrs2kp/rQflT79sx2XtOqkuG3/+7qxVrQl7X6fUlIS8I92eiNUjI7ExK7/8JutMQv00MPc5OIkFUBxUy1qotq9vtow8h7g7RCnDUVsTDHWPluKpAGtxUdxWXRrvn6m81h9vesRnna1v5D3N0psNmUJEJ9ws7f8230mg2VNLUrrp7WVBTpaHwUHNDjVXl6qU8pKNd2+OAY6vbro8K1GB5xwIRTLaBa8IaDzPwwOpKZ2PZ+zq7f8T19cB7Tv+vWe9RLdKC7ehczMc5Gc7Az+i6+UxXkR/urSjkvsdHoIcyHx1yxw3ouyB/2P1rV9fAGwdFORv5xN50Z6ltdp/wfYzzxz7+iz2adYtVvJosfmNnY6DSawtaLt+H0qAC06OqAHaeqEeBRHT/I/VcU4hVRsgJ+JS35lohiTcKz9UQL++c4Uf3oUnsvN1v+H3ykzfqdZ2TDybuV2iNbXDgdm/scYn+2hMJjQpfYX8p6LqbbvtHgu4HrgUyHN3vLvzmdGNDY5gNMfDNZYr0KH2vppbfEBHKixAc6ybxDVJ8PaL7VZE1xwNzjQCMP7Dqwto89tP3mivXZXF+bzjmPgwHk4r6Up5IpeFrwhxMoC6O1ZGTQMdF/DUHHOOamIj1fPTGsIZ90YWfVXl1odnFVVFePGjev+nL8Q0NOjdV3V/54MS0tPZ2g2vaZNIVxTHIP6G/yMPvtZyQJAz32crumSGoGvFW3H7zMMAyddLsSlpZn8oO8xUndvQGoAnxZmynQ0HuKlDRLT41pHoJ+7EGhzM3wsTiExPc7awQJcsxrw/1y2cu3q72EQtDd1JPeG+D23gV3AWXk9bDt1xPJb/u2GHp3uZjjgQllFFFDhPSoc0NAvVkP7NwD80wE0W2iEPdp2xX1KRSnOg76vfX42jD53XK/sjHZXF6bzjuuaUFYRBbem2Fbw2rWKAwveCGLlhTeQfRVFQYLdo0/BZsPIqtVDRFKOkosyCU+GXV54+SmETRc2MBu18iOgG54609sj+n5+nwIgXngMS8zeDrdhaYPUqcNR/NlB1Ow94LM9MT0OqVMDXBO3s7k0JkOu0udySLyxY3YhYmWo2d+5DfACLpAMPcVwc3MzYvysntApk6HHKADnKdFwZ5wD9Dm9OkFnI7YW39rvCf67Eg0HNIxJbYSzb5vXITtHn6Oj290J6J/fi5BGP6PDQvWn51maffpcoFjwkim3242ioiJMnDgRTslEqa6YLOUVKXotxwjmydAwJqKzly0Lg3Xm2ox8edZftXTDU4gK2uOwh9bOTp06PPDi1u8BTEbeOxlyjYjnstmFSDeGmovbzEEP9OkWSIZOpxOxsbGorq5Gnz59Wtcvt2L06I7FauuBobp0AKcLV6UBBprR2NTU+olhHs3NwDfftH4kb3uK0roeby8Vvx260tQI58H96OMcgYamNqs0mPXFn6amM/9tu6/ZdguampsAKGhqboLaKHtr2DAM1NfXo6qqCgkJCeJ3ScP0GUu9RfMstG2TtiNlxf+sAZBofldvyE9sC5zdOXbF7sKvuzdY+Zxv4TrImqbB7O9eNwbrOvIz8mW2/mowlhqzQ28/Dq3q9Y/H7caQa6hnGDDBhYj0+dZVhoqiYOjQoSgrK8P+/fu718hANDefueBpO3rY0tK6PTGx40flOhzAwYM916auNDcDNYdgOA00A3C5XK0Fr6cvffoAri7W1zXb18oxzA59qgU1NX3QBy1w1cpGZD0SEhKQ3O7jkbuDBS/1Cv93fSe2jpKtfBC4eGS7HwjR1QpCXE8VflZvsPJ/vq2PinoKHrcb2L07ts16k766MVjXcbGOWv8jX6mJiX4+LKWHlxrrsOxaZL3r0V6ofTxuWLL6qTwC3Xm+WeVyuTBmzBg0N/fgnNmKCuC661o/gKK9mBjgvfeAYcN67vd3x1dfAb/8Jdx//jN26jpGjx7dOlJ+ejv+53+AsWMDOkaHfa0cw+zQ/1uKX96Vhv/5XSnG/iiA+wW60KdPH9vuf2HBS73C76c/eUbJLl7X5adHUWBs+UMkmJ/nbYfJXf6J0aeQOmFDlz/fsXDveom8QAfrulys4+JMOxYQ6J6ubvSK0Mqv04/H5bVv56x+Ks9pgdTDne3TG/ORHQ4HotvfPGWn9HTggw/Ca4lIRQH274dn2CA6Orq14D29HYrS8YYzk2N02NfKMcwO3ezA/v3RUJp7+Nx1AwteMqWqKrKysmy7uur46U+hsSB/T7M7x67Y9YeoO/Pz2jbC713+Af4RaV+4G4aBxsZGREdHIylJEfXP1mWQ7R5VM5tfCYj/APf249Aqvx+PG2JCMkOLD2hrawfbf50VchmGxJ2E1qmqiizJJ/6dhVjwUqdc3ZzHQ77CKUdbpkUA4j8kbX/cMABN6wNVhS1L5In/xnVzVC1gPXSjVzg9DkNVSGZo4QFtZe1goGcGOkMyw3BTXAyXpsH7ohjh057swIKXTGmahoKCAuTm5obvHckhINxy7I35eVaFXIZh+Gl5vZGhz02KEfj3N+Qeh90UzEHNSMkwaE5fbCs/+1nHAi6Cpz3ZgY82IuogTN/l611nc0jtqlmz5dv495fIZqcvtt2VldhVXIxzMzPPXDiE6MV2qGDBS0REgTGZymG2fFunf39tWTuP6CyUmgoMG4Z6XW+94Zsj5QFhSkREQRRWdV8nUzn8L9/mh22TxImIAseCl0ypqorc3FzeBSrEHOUiMcPervtsy1A6lSMUJ4kHKBIfh72NGdqDOVrHgpc61a3PMqcOmKNcpGUYjLovZDIM4/nPIZNhGGOG9mCO1lj8gGo6m2iahh07dkTOR2kGCXOUi9QMU1Nbp+C1/ddTdWCkZtibmKEcM7SHLTkWFwOFhWf+hfycKhmO8FLvCqsJi0RERBGmp9cRD1EseKl38EYVIiKi4AvDdcTtwIKXOmXbhPgwvlHFDryxQI4ZyjFDOWYoxwztIcoxjOfRdxcLXjLldDqRl5dn3wHPwicY0AM5noWYoRwzlGOGcszQHszROt60RqYMw0BtbS0Mwwh2U8Iac5RjhnLMUI4ZyjFDezBH61jwkilN01BSUsK7aYWYoxwzlGOGcsxQjhnagzlax4KXiIiIiCIaC14iIiIiimgseMmUoiiIiYmBoijBbkpYY45yzFCOGcoxQzlmaA/maB1XaSBTqqoiOzs72M0Ie8xRjhnKMUM5ZijHDO3BHK3jCC+Z0nUdVVVV0HU92E0Ja8xRjhnKMUM5ZijHDO3BHK1jwUumdF3H3r17+YQSYo5yzFCOGcoxQzlmaA/maB0LXiIiIiKKaCx4iYiIiCiiseAlU4qiID4+nneBCjFHOWYoxwzlmKEcM7QHc7SOqzSQKVVVkZmZGexmhD3mKMcM5ZihHDOUY4b2YI7WcYSXTOm6jvLyck6KF2KOcsxQjhnKMUM5ZmgP5mgdC14yxSeUPZijHDOUY4ZyzFCOGdqDOVrHgpeIiIiIIhoLXiIiIiKKaCx4yZTD4UBSUhIcDj5MJJijHDOUY4ZyzFCOGdqDOVrHVRrIlMPhQEZGRrCbEfaYoxwzlGOGcsxQjhnagzlax0sDMqXrOkpLSzkpXog5yjFDOWYoxwzlmKE9mKN1LHjJlK7rqK6u5hNKiDnKMUM5ZijHDOWYoT2Yo3UseImIiIgoorHgJSIiIqKIxoKXTDkcDqSkpPAuUCHmKMcM5ZihHDOUY4b2YI7WcZUGMuV5QpEMc5RjhnLMUI4ZyjFDezBH63hpQKY0TUNxcTE0TQt2U8Iac5RjhnLMUI4ZyjFDezBH61jwkinDMFBXVwfDMILdlLDGHOWYoRwzlGOGcszQHszRupAoeFetWoVRo0YhOjoaU6ZMwRdffGG67/PPP4+LL74YAwYMwIABAzB9+vQO+994441QFMXn38yZM3u6G0REREQUgoJe8L711ltYtGgRlixZgsLCQmRnZ2PGjBmoqqryu//GjRsxZ84cfPzxx9i0aRNGjBiByy67DAcPHvTZb+bMmTh06JD33xtvvNEb3SEiIiKiEBP0gnfFihVYsGAB5s+fj3PPPRerV69GbGwsXnzxRb/7v/7667jtttuQk5ODcePG4YUXXoCu68jPz/fZLyoqCsnJyd5/AwYM6I3uRBSHw4H09HTeBSrEHOWYoRwzlGOGcszQHszRuqCu0tDc3IytW7fivvvu825zOByYPn06Nm3aFNAx6uvr0dLSgoEDB/ps37hxIwYPHowBAwbg+9//Ph599FEMGjTI7zGamprQ1NTk/fr48eMAALfbDbfb7W2Xw+GArus+n2zi2a5pms9cGrPtqqpCURTvcQHAfXrSuQHDZ7tnfwAdJqY7nU4YhuGzXVEUqKraoY1m27vqk2EYGDhwoPf7VvrUWduD2SfJeepunxRF8ckxEvoUjPM0ePBgaJrm0/5w71Nvn6dBgwb5bWM496m3z5PnueyZLhcJfWrbxp7uE4CAXg/DqU/BOE8Oh8Mnx1Dpk6ee0U+3t6fPU/v9OxPUgrempgaapmHIkCE+24cMGYKSkpKAjnHPPfdg2LBhmD59unfbzJkzcdVVVyEtLQ2lpaW4//77cfnll2PTpk3ekNpatmwZli5d2mF7UVER+vbtCwBISkpCRkYGysrKUF1d7d0nJSUFKSkp+Prrr1FXV+fdnp6ejsGDB2Pnzp1oaGjwbh83bhwSEhJQVFTkPYH7y6oBjIem6SgoKPBpQ25uLpqbm7Fjxw7vNlVVkZeXh7q6Op+cYmJikJ2djZqaGuzdu9e7PT4+HpmZmaioqEB5ebl3e1d92r17N8rLy9G/f38oimKpTwCQlZUFl8sVUn2SnKfu9unYsWMoKCjw5hgJfert8zRq1CgcPnwYhmGgsbExIvrU2+fJMAw0NDRg2rRpOHLkSET0Cejd81RfX48TJ06gf//+yMzMjIg+9fZ5qqqqwo4dO7yvh5HQp2Ccp/79++Pjjz9Gv379oChKyPTJU88cO3YMAHr8PBUVFSFQihHEW/wqKiowfPhwfPbZZ5g6dap3+913341PPvkEmzdv7vTnH3vsMTzxxBPYuHEjsrKyTPfbu3cvMjIy8OGHH+LSSy/t8H1/I7wjRozAkSNHEBcXB6BnrzYL/1SCKfPGo+C1Xci+9hyftgXzarOpqQmFhYWYNGmS94rybLqCtqtPLS0tKCgo8OYYCX3q7fOk6zoKCwsxceJEn4vWcO5Tb58nTdNQWFiIvLw8bzvDvU+dtb0n+uR2u72viS6XKyL61NvnyfPOblevh+HUp2CcJ03TsGXLFm+OodInTz2z5dVdyL3h3B4/T8eOHcOgQYNQV1fnrdfMBHWENzExEaqq4vDhwz7bDx8+jOTk5E5/dvny5Xjsscfw4YcfdlrsAq1XFImJidizZ4/fgjcqKgpRUVEdtjudTjidvhF5TlZ7/kaOO9ve9rjO0/soUDr8Pn/7eyiK//3N2mh1e9sXo7a/J5A+dXd7b/TJn57uk78cw71PvXmePC+C7TPsTtvNtkfqY6/tds9IUCT1qas22tknwzC8z2VPluHep0DbaHV7Z32y4/Uw1PoUjPPkL0eztpttt7tPnnrGcfr5EYzzZCaos51dLhfOP/98nxvOdL31BrS2I77tPfHEE3jkkUewfv165Obmdvl7ysvLceTIEQwdOtSWdhMRERFR+Aj67X2LFi3C888/j5dffhnFxcW49dZbcerUKcyfPx8AMHfuXJ+b2h5//HE8+OCDePHFFzFq1ChUVlaisrISJ0+eBACcPHkSd911Fz7//HPs27cP+fn5mDVrFkaPHo0ZM2YEpY/hSlVVjBs3zvRKjALDHOWYoRwzlGOGcszQHszRuqBOaQCAa6+9FtXV1Vi8eDEqKyuRk5OD9evXe29kO3DggM/w+XPPPYfm5mZcc801PsdZsmQJHnroIaiqih07duDll19GbW0thg0bhssuuwyPPPKI32kLZE5RFCQkJAS7GWGPOcoxQzlmKMcM5ZihPZijdUEveAFg4cKFWLhwod/vbdy40efrffv2dXqsmJgYfPDBBza17OzmdrtRVFSEiRMnWponQ76YoxwzlGOGcsxQjhnagzlaF/QpDRTa2t8ZSd3DHOWYoRwzlGOGcszQHszRGha8RERERBTRWPASERERUURjwUumVFVFVlYW7wIVYo5yzFCOGcoxQzlmaA/maB0LXuqUy+UKdhMiAnOUY4ZyzFCOGcoxQ3swR2tY8JIpTdNQUFDAifFCzFGOGcoxQzlmKMcM7cEcrWPBS0REREQRjQUvEREREUU0FrxEREREFNFY8JIpVVWRm5vLu0CFmKMcM5RjhnLMUI4Z2oM5WseClzrV3Nwc7CZEBOYoxwzlmKEcM5RjhvZgjtaw4CVTmqZhx44dvAtUiDnKMUM5ZijHDOWYoT2Yo3UseImIiIgoorHgJSIiIqKIxoKXOsUJ8fZgjnLMUI4ZyjFDOWZoD+ZojTPYDaDQ5XQ6kZeXF+xmhD3mKMcM5ZihHDOUY4b2YI7WcYSXTBmGgdraWhiGEeymhDXmKMcM5ZihHDOUY4b2YI7WseAlU5qmoaSkhHeBCjFHOWYoxwzlmKEcM7QHc7SOBS8RERERRTQWvEREREQU0VjwkilFURATEwNFUYLdlLDGHOWYoRwzlGOGcszQHszROq7SQKZUVUV2dnawmxH2mKMcM5RjhnLMUI4Z2oM5WscRXjKl6zqqqqqg63qwmxLWmKMcM5RjhnLMUI4Z2oM5WseCl0zpuo69e/fyCSXEHOWYoRwzlGOGcszQHszROha8RERERBTRWPASERERUURjwUumFEVBfHw87wIVYo5yzFCOGcoxQzlmaA/maB1XaSBTqqoiMzMz2M0Ie8xRjhnKMUM5ZijHDO3BHK3jCC+Z0nUd5eXlnBQvxBzlmKEcM5RjhnLM0B7M0ToWvGSKTyh7MEc5ZijHDOWYoRwztAdztI4FLxERERFFNBa8RERERBTRWPCSKYfDgaSkJDgcfJhIMEc5ZijHDOWYoRwztAdztI6rNJAph8OBjIyMYDcj7DFHOWYoxwzlmKEcM7QHc7SOlwZkStd1lJaWclK8EHOUY4ZyzFCOGcoxQ3swR+tY8JIpXddRXV3NJ5QQc5RjhnLMUI4ZyjFDezBH61jwEhEREVFEY8FLRERERBGNBS+ZcjgcSElJ4V2gQsxRjhnKMUM5ZijHDO3BHK3jKg1kyvOEIhnmKMcM5ZihHDOUY4b2YI7W8dKATGmahuLiYmiaFuymhDXmKMcM5ZihHDOUY4b2YI7WseAlU4ZhoK6uDoZhBLspYY05yjFDOWYoxwzlmKE9mKN1LHiJiIiIKKKx4CUiIiKiiMaCl0w5HA6kp6fzLlAh5ijHDOWYoRwzlGOG9mCO1nGVBjLlcDgwePDgYDcj7DFHOWYoxwzlmKEcM7QHc7SOlwZkStM0bN++nXeBCjFHOWYoxwzlmKEcM7QHc7SOBS+ZMgwDDQ0NvAtUiDnKMUM5ZijHDOWYoT2Yo3UseImIiIgoorHgJSIiIqKIxoKXTKmqinHjxkFV1WA3JawxRzlmKMcM5ZihHDO0B3O0jqs0kClFUZCQkBDsZoQ95ijHDOWYoRwzlGOG9mCO1nGEl0y53W5s2bIFbrc72E0Ja8xRjhnKMUM5ZijHDO3BHK1jwUud4pIn9mCOcsxQjhnKMUM5ZmgP5mgNC14iIiIiimgseImIiIgoorHgJVOqqiIrK4t3gQoxRzlmKMcM5ZihHDO0B3O0jgUvdcrlcgW7CRGBOcoxQzlmKMcM5ZihPZijNSx4yZSmaSgoKODEeCHmKMcM5ZihHDOUY4b2YI7WseAlIiIioojGgpeIiIiIIlpIFLyrVq3CqFGjEB0djSlTpuCLL74w3ff555/HxRdfjAEDBmDAgAGYPn16h/0Nw8DixYsxdOhQxMTEYPr06fjmm296uhtEREREFIKCXvC+9dZbWLRoEZYsWYLCwkJkZ2djxowZqKqq8rv/xo0bMWfOHHz88cfYtGkTRowYgcsuuwwHDx707vPEE0/gmWeewerVq7F582b07dsXM2bMQGNjY291KyKoqorc3FzeBSrEHOWYoRwzlGOGcszQHszRuqAXvCtWrMCCBQswf/58nHvuuVi9ejViY2Px4osv+t3/9ddfx2233YacnByMGzcOL7zwAnRdR35+PoDW0d2VK1figQcewKxZs5CVlYVXXnkFFRUVWLduXS/2LDI0NzcHuwkRgTnKMUM5ZijHDOWYoT2YozVBLXibm5uxdetWTJ8+3bvN4XBg+vTp2LRpU0DHqK+vR0tLCwYOHAgAKCsrQ2Vlpc8x4+PjMWXKlICPSa00TcOOHTt4F6gQc5RjhnLMUI4ZyjFDezBH65zB/OU1NTXQNA1Dhgzx2T5kyBCUlJQEdIx77rkHw4YN8xa4lZWV3mO0P6bne+01NTWhqanJ+/Xx48cBAG63G263G0BrIe5wOKDrOnRd9+7r2a5pGgzD6HK7qqpQFMV7XABwn37AGjB8tnv2Bzp+ZrbT6YRhGD7bFUWBqqod2mi2PdA+eX6HlT511vZQ6FNX2+3uU9t+RUqfevM8efbx18Zw7VNvn6e2vydS+tRZ23uiT21fEyOlT8E4T4G8HoZbn3r7PAHo0J5Q6JOnntFPt7enz1P7/TsT1IJX6rHHHsObb76JjRs3Ijo6utvHWbZsGZYuXdphe1FREfr27QsASEpKQkZGBsrKylBdXe3dJyUlBSkpKfj6669RV1fn3Z6eno7Bgwdj586daGho8G4fN24cEhISUFRU5D2B+8uqAYyHpukoKCjwaUNubi6am5uxY8cO7zZVVZGXl4e6ujqfC4OYmBhkZ2ejpqYGe/fu9W6Pj49HZmYmKioqUF5e7t3eVZ/27NmD2tpaFBYWQlEUS30CgKysLLhcrpDqk+Q8dbdPx48f98kxEvrU2+dp5MiRAIBdu3b5XJyGc596+zwZhoETJ04AQMT0Cejd81RfX+99LmdmZkZEn3r7PB05csTn9TAS+hSM89SvXz/U1dV5cwyVPnnqmWPHjgFAj5+noqIiBEox2pbYvay5uRmxsbF45513MHv2bO/2efPmoba2Fu+++67pzy5fvhyPPvooPvzwQ+Tm5nq37927FxkZGSgqKkJOTo53+7Rp05CTk4Onn366w7H8jfCOGDECR44cQVxcHICevdos/FMJpswbj4LXdiH72nN82hbMq82mpiZs27YNOTk5UFX1rLuCtqtPLS0tKCws9OYYCX0Kxgjv9u3bO3yUZjj3KRgjvNu3b8ekSZO87Qz3PnXW9p7ok9vt9r4mulyuiOhTb58nT5HS1ethOPUpGOdJ0zRs3brVm2Oo9MlTz2x5dRdybzi3x8/TsWPHMGjQINTV1XnrNTNBHeF1uVw4//zzkZ+f7y14db31BrSFCxea/twTTzyB3/72t/jggw98il0ASEtLQ3JyMvLz870F7/Hjx7F582bceuutfo8XFRWFqKioDtudTiecTt+IPCerPbM7Jc22tz2u8/Q+CpQOv8/f/h6K4n9/szZa3R4VFYUpU6Z02B5In7q7vaf7JDlP3d3ep08fvzmGc5+CcZ7y8vL8ts+sjVa3R+Jjr22fnE4nJk+e7NPO9sKtT4G00c4+qara4bkc7n0KtI1Wt5v1yeVy2fJ6GEp9CsZ5cjqdfnM0a7vZdrv75KlnHKdHnYNxnswEfZWGRYsW4fnnn8fLL7+M4uJi3HrrrTh16hTmz58PAJg7dy7uu+8+7/6PP/44HnzwQbz44osYNWoUKisrUVlZiZMnTwJoPXl33HEHHn30Ufz1r3/Fl19+iblz52LYsGE+o8jUNcMwUFtb63MVRtYxRzlmKMcM5ZihHDO0B3O0LugF77XXXovly5dj8eLFyMnJwbZt27B+/XrvTWcHDhzAoUOHvPs/99xzaG5uxjXXXIOhQ4d6/y1fvty7z913343/+I//wM0334y8vDycPHkS69evF83zPRtpmoaSkpIObyWQNcxRjhnKMUM5ZijHDO3BHK0LiZvWFi5caDqFYePGjT5f79u3r8vjKYqChx9+GA8//LANrSMiIiKicBb0EV4iIiIiop7EgpdMeZbQ8ix5Qt3DHOWYoRwzlGOGcszQHszRupCY0kChSVVVZGdnB7sZYY85yjFDOWYoxwzlmKE9mKN1HOElU7quo6qqymfNPbKOOcoxQzlmKMcM5ZihPZijdSx4yZSu69i7dy+fUELMUY4ZyjFDOWYoxwztwRytY8FLRERERBGNBS8RERERRTQWvGRKURTEx8fzLlAh5ijHDOWYoRwzlGOG9mCO1nGVBjKlqioyMzOD3YywxxzlmKEcM5RjhnLM0B7M0TqO8JIpXddRXl7OSfFCzFGOGcoxQzlmKMcM7cEcrWPBS6b4hLIHc5RjhnLMUI4ZyjFDezBH61jwEhEREVFEY8FLRERERBGNBS+ZcjgcSEpKgsPBh4kEc5RjhnLMUI4ZyjFDezBH67hKA5lyOBzIyMgIdjPCHnOUY4ZyzFCOGcoxQ3swR+t4aUCmdF1HaWkpJ8ULMUc5ZijHDOWYoRwztAdztI4FL5nSdR3V1dV8QgkxRzlmKMcM5ZihHDO0B3O0jgUvEREREUU0FrxEREREFNFY8JIph8OBlJQU3gUqxBzlmKEcM5RjhnLM0B7M0Tqu0kCmPE8okmGOcsxQjhnKMUM5ZmgP5mgdLw3IlKZpKC4uhqZpwW5KWGOOcsxQjhnKMUM5ZmgP5mgdC14yZRgG6urqYBhGsJsS1pijHDOUY4ZyzFCOGdqDOVrHgpeIiIiIIhoLXiIiIiKKaCx4yZTD4UB6ejrvAhVijnLMUI4ZyjFDOWZoD+ZoHVdpIFMOhwODBw8OdjPCHnOUY4ZyzFCOGcoxQ3swR+t4aUCmNE3D9u3beReoEHOUY4ZyzFCOGcoxQ3swR+tY8JIpwzDQ0NDAu0CFmKMcM5RjhnLMUI4Z2oM5Wtetgnfv3r12t4OIiIiIqEd0q+AdPXo0vve97+G1115DY2Oj3W0iIiIiIrJNtwrewsJCZGVlYdGiRUhOTsYtt9yCL774wu62UZCpqopx48ZBVdVgNyWsMUc5ZijHDOWYoRwztAdztK5bBW9OTg6efvppVFRU4MUXX8ShQ4dw0UUXYfz48VixYgWqq6vtbicFgaIoSEhIgKIowW5KWGOOcsxQjhnKMUM5ZmgP5mid6KY1p9OJq666Cm+//TYef/xx7NmzB3feeSdGjBiBuXPn4tChQ3a1k4LA7XZjy5YtcLvdwW5KWGOOcsxQjhnKMUM5ZmgP5midqOAtKCjAbbfdhqFDh2LFihW48847UVpaig0bNqCiogKzZs2yq50UJFzyxB7MUY4ZyjFDOWYoxwztwRyt6dYHT6xYsQIvvfQSdu/ejSuuuAKvvPIKrrjiCu8nfqSlpWHNmjUYNWqUnW0lIiIiIrKsWwXvc889h5///Oe48cYbMXToUL/7DB48GH/84x9FjSMiIiIikupWwbthwwakpqZ2+AxnwzDw7bffIjU1FS6XC/PmzbOlkRQcqqoiKyuLd4EKMUc5ZijHDOWYoRwztAdztK5bc3gzMjJQU1PTYfvRo0eRlpYmbhSFDpfLFewmRATmKMcM5ZihHDOUY4b2YI7WdKvgNfsou5MnTyI6OlrUIAodmqahoKCAE+OFmKMcM5RjhnLMUI4Z2oM5WmdpSsOiRYsAtK7/tnjxYsTGxnq/p2kaNm/ejJycHFsbSEREREQkYangLSoqAtA6wvvll1/6DKe7XC5kZ2fjzjvvtLeFREREREQClgrejz/+GAAwf/58PP3004iLi+uRRhERERER2aVbqzS89NJLdreDQpCqqsjNzeVdoELMUY4ZyjFDOWYoxwztwRytC7jgveqqq7BmzRrExcXhqquu6nTftWvXihtGoaG5uRkxMTHBbkbYY45yzFCOGcoxQzlmaA/maE3AqzTEx8dDURTv/3f2jyKDpmnYsWMH7wIVYo5yzFCOGcoxQzlmaA/maF3AI7xtpzFwSgMRERERhYturcPb0NCA+vp679f79+/HypUr8X//93+2NYyIiIiIyA7dKnhnzZqFV155BQBQW1uLyZMn48knn8SsWbPw3HPP2dpACi5OiLcHc5RjhnLMUI4ZyjFDezBHa7pV8BYWFuLiiy8GALzzzjtITk7G/v378corr+CZZ56xtYEUPE6nE3l5eXA6u7WYB53GHOWYoRwzlGOGcszQHszRum4VvPX19ejfvz8A4P/+7/9w1VVXweFw4Dvf+Q72799vawMpeAzDQG1trelHSVNgmKMcM5RjhnLMUI4Z2oM5Wtetgnf06NFYt24dvv32W3zwwQe47LLLAABVVVX8MIoIomkaSkpKeBeoEHOUY4ZyzFCOGcoxQ3swR+u6VfAuXrwYd955J0aNGoUpU6Zg6tSpAFpHeydOnGhrA4mIiIiIJLo1+eOaa67BRRddhEOHDiE7O9u7/dJLL8W///u/29Y4IiIiIiKpbs92Tk5ORnJyss+2yZMnixtEoUNRFMTExHg/cIS6hznKMUM5ZijHDOWYoT2Yo3XdKnhPnTqFxx57DPn5+aiqqoKu6z7f37t3ry2No+BSVdVnBJ+6hznKMUM5ZijHDOWYoT2Yo3XdKnh/8Ytf4JNPPsHPfvYzDB06lFcYEUrXddTU1CAxMREOR7emexOYox2YoRwzlGOGcszQHszRum4VvH//+9/x3nvv4cILL7S7PRRCdF3H3r17MXDgQD6hBJijHDOUY4ZyzFCOGdqDOVrXrZQGDBiAgQMH2t0WIiIiIiLbdavgfeSRR7B48WLU19fb3R4iIiIiIlt1a0rDk08+idLSUgwZMgSjRo1Cnz59fL5fWFhoS+MouBRFQXx8POdoCzFHOWYoxwzlmKEcM7QHc7SuWwXv7NmzbW4GhSJVVZGZmRnsZoQ95ijHDOWYoRwzlGOG9mCO1nWr4F2yZIltDVi1ahV+97vfobKyEtnZ2Xj22WdN1/P96quvsHjxYmzduhX79+/HU089hTvuuMNnn4ceeghLly712TZ27FiUlJTY1uazha7rqKiowLBhwzgpXoA5yjFDOWYoxwzlmKE9mKN13U6ptrYWL7zwAu677z4cPXoUQOtUhoMHDwZ8jLfeeguLFi3CkiVLUFhYiOzsbMyYMQNVVVV+96+vr0d6ejoee+yxDh960dZ5552HQ4cOef/961//stY5AtD6hCovL++wzjJZwxzlmKEcM5RjhnLM0B7M0bpujfDu2LED06dPR3x8PPbt24cFCxZg4MCBWLt2LQ4cOIBXXnkloOOsWLECCxYswPz58wEAq1evxnvvvYcXX3wR9957b4f98/LykJeXBwB+v+/hdDo7LYiJiIiI6OzRrYJ30aJFuPHGG/HEE0+gf//+3u1XXHEFfvrTnwZ0jObmZmzduhX33Xefd5vD4cD06dOxadOm7jTL65tvvsGwYcMQHR2NqVOnYtmyZUhNTTXdv6mpCU1NTd6vjx8/DgBwu91wu93etjkcDui67nNF5dmuaRoMw+hyu6qqUBTFe1wAcGsaAMCA4bPdsz8AaKf38XA6nTAMw2e7oihQVbVDG822B9onz++w0qfO2h4Kfepqu919atuvSOlTb54nzz7+2hiufert89T290RKnzpre0/0qe1rYqT0KRjnKZDXw3DrU2+fJwAd2hMKffLUM/rp9vb0eWq/f2e6VfBu2bIF//Vf/9Vh+/Dhw1FZWRnQMWpqaqBpGoYMGeKzfciQIaL5tlOmTMGaNWswduxYHDp0CEuXLsXFF1+MnTt3+hTnbS1btqzDvF8AKCoqQt++fQEASUlJyMjIQFlZGaqrq737pKSkICUlBV9//TXq6uq829PT0zF48GDs3LkTDQ0N3u3jxo1DQkICioqKvCdwf1k1gPHQNB0FBQU+bcjNzUVzczN27Njh3aaqKvLy8lBXV+eTVUxMDLKzs1FTU+Pz8c7x8fHIzMxERUUFysvLvdu76lNpaSkaGhpQVFRkuU8AkJWVBZfLFVJ9kpyn7vbpxIkTPjlGQp96+zyNGjUKSUlJKC4uRmNjY0T0KRjnye12w+FwRFSfevs8eZ7LkdSn3jxPR48e9Xk9jIQ+BeM8xcXFobGx0ZtjqPTJU88cO3YMAHr8PLXtf1cUo22JHaDBgwfjgw8+wMSJE9G/f39s374d6enp2LBhA37+85/j22+/7fIYFRUVGD58OD777DNMnTrVu/3uu+/GJ598gs2bN3f686NGjcIdd9zR4aa19mprazFy5EisWLECN910k999/I3wjhgxAkeOHEFcXByAnr3aLPxTCabMG4+C13Yh+9pzfNoWLlebkXgFzT6xT+wT+8Q+sU/sU+B98tQzW17dhdwbzu3xPh07dgyDBg1CXV2dt14z060R3iuvvBIPP/ww/vznPwNoDeLAgQO45557cPXVVwd0jMTERKiqisOHD/tsP3z4sK3zbxMSEnDOOedgz549pvtERUUhKiqqw3an0wmn0zciz8lqzxN+oNvbHtd5eh8FSoff529/D0Xxv79ZG61uVxQF+/btQ1pams/3A+lTd7f3dJ8k56m72w3DwP79+zvkGM596u3zpOs6SktLkZaW5rdf4dinrrbb3Sdd11FWVuZ9HEZCnwJpo519apuhZ/3TcO9ToG20ut2sTwBseT0MpT4F4zzpuu43R7O2m223u0+eesZx+vkRjPNkplurNDz55JM4efIkkpKS0NDQgGnTpmH06NHo378/fvvb3wZ0DJfLhfPPPx/5+fnebbquIz8/32fEV+rkyZMoLS3F0KFDbTvm2ULXdVRXV/tczZF1zFGOGcoxQzlmKMcM7cEcrevWCG98fDw2bNiATz/9FNu3b8fJkycxadIkTJ8+3dJxFi1ahHnz5iE3NxeTJ0/GypUrcerUKe+qDXPnzsXw4cOxbNkyAK03uu3atcv7/wcPHsS2bdvQr18/jB49GgBw55134kc/+hFGjhyJiooKLFmyBKqqYs6cOd3pKhERERGFOcsFr67rWLNmDdauXYt9+/ZBURSkpaUhOTkZhmFY+pi7a6+9FtXV1Vi8eDEqKyuRk5OD9evXe29kO3DggM/QeUVFBSZOnOj9evny5Vi+fDmmTZuGjRs3AgDKy8sxZ84cHDlyBElJSbjooovw+eefIykpyWpXiYiIiCgCWCp4DcPAlVdeiffffx/Z2dmYMGECDMNAcXExbrzxRqxduxbr1q2z1ICFCxdi4cKFfr/nKWI9Ro0a5TPJ2Z8333zT0u8ncw6HAykpKX7n61DgmKMcM5RjhnLMUI4Z2oM5Wmep4F2zZg3+8Y9/ID8/H9/73vd8vvfRRx9h9uzZeOWVVzB37lxbG0nB4XlCkQxzlGOGcsxQjhnKMUN7MEfrLF0avPHGG7j//vs7FLsA8P3vfx/33nsvXn/9ddsaR8GlaRqKi4s7LAdC1jBHOWYoxwzlmKEcM7QHc7TOUsG7Y8cOzJw50/T7l19+ObZv3y5uFIUGwzBQV1fX5TQS6hxzlGOGcsxQjhnKMUN7MEfrLBW8R48e7fDJaG0NGTLE++kaREREREShwFLBq2lap4v8qqpq6XONiYiIiIh6muVVGm688Ua/n0oGwOfjeSn8ORwOpKen8y5QIeYoxwzlmKEcM5RjhvZgjtZZKnjnzZvX5T5coSFyOBwODB48ONjNCHvMUY4ZyjFDOWYoxwztwRyts1TwvvTSSz3VDgpBmqZh586dGD9+vOnnXlPXmKMcM5RjhnLMUI4Z2oM5WsexcDJlGAYaGhp4F6gQc5RjhnLMUI4ZyjFDezBH61jwEhEREVFEY8FLRERERBGNBS+ZUlUV48aN4/wgIeYoxwzlmKEcM5RjhvZgjtZZummNzi6KoiAhISHYzQh7zFGOGcoxQzlmKMcM7cEcreMIL5lyu93YsmULP0xEiDnKMUM5ZijHDOWYoT2Yo3UseKlTmqYFuwkRgTnKMUM5ZijHDOWYoT2YozUseImIiIgoorHgJSIiIqKIxoKXTKmqiqysLN4FKsQc5ZihHDOUY4ZyzNAezNE6FrzUKZfLFewmRATmKMcM5ZihHDOUY4b2YI7WsOAlU5qmoaCggBPjhZijHDOUY4ZyzFCOGdqDOVrHgpeIiIiIIhoLXiIiIiKKaCx4iYiIiCiiseAlU6qqIjc3l3eBCjFHOWYoxwzlmKEcM7QHc7SOBS91qrm5OdhNiAjMUY4ZyjFDOWYoxwztwRytYcFLpjRNw44dO3gXqBBzlGOGcsxQjhnKMUN7MEfrWPASERERUURjwUtEREREEY0FL3WKE+LtwRzlmKEcM5RjhnLM0B7M0RpnsBtAocvpdCIvLy/YzQh7zFGOGcoxQzlmKMcM7cEcreMIL5kyDAO1tbUwDCPYTQlrzFGOGcoxQzlmKMcM7cEcrWPBS6Y0TUNJSQnvAhVijnLMUI4ZyjFDOWZoD+ZoHQteIiIiIopoLHiJiIiIKKKx4CVTiqIgJiYGiqIEuylhjTnKMUM5ZijHDOWYoT2Yo3VcpYFMqaqK7OzsYDcj7DFHOWYoxwzlmKEcM7QHc7SOI7xkStd1VFVVQdf1YDclrDFHOWYoxwzlmKEcM7QHc7SOBS+Z0nUde/fu5RNKiDnKMUM5ZijHDOWYoT2Yo3UseImIiIgoorHgJSIiIqKIxoKXTCmKgvj4eN4FKsQc5ZihHDOUY4ZyzNAezNE6rtJAplRVRWZmZrCbEfaYoxwzlGOGcsxQjhnagzlaxxFeMqXrOsrLyzkpXog5yjFDOWYoxwzlmKE9mKN1LHjJFJ9Q9mCOcsxQjhnKMUM5ZmgP5mgdC14iIiIiimgseImIiIgoorHgJVMOhwNJSUlwOPgwkWCOcsxQjhnKMUM5ZmgP5mgdV2kgUw6HAxkZGcFuRthjjnLMUI4ZyjFDOWZoD+ZoHS8NyJSu6ygtLeWkeCHmKMcM5ZihHDOUY4b2YI7WseAlU7quo7q6mk8oIeYoxwzlmKEcM5RjhvZgjtax4CUiIiKiiMaCl4iIiIgiGgteMuVwOJCSksK7QIWYoxwzlGOGcsxQjhnagzlax1UayJTnCUUyzFGOGcoxQzlmKMcM7cEcreOlAZnSNA3FxcXQNC3YTQlrzFGOGcoxQzlmKMcM7cEcrWPBS6YMw0BdXR0Mwwh2U8Iac5RjhnLMUI4ZyjFDezBH61jwEhEREVFEY8FLRERERBGNBS+ZcjgcSE9P512gQsxRjhnKMUM5ZijHDO3BHK3jKg1kyuFwYPDgwcFuRthjjnLMUI4ZyjFDOWZoD+ZoHS8NyJSmadi+fTvvAhVijnLMUI4ZyjFDOWZoD+ZoXdAL3lWrVmHUqFGIjo7GlClT8MUXX5ju+9VXX+Hqq6/GqFGjoCgKVq5cKT4mmTMMAw0NDbwLVIg5yjFDOWYoxwzlmKE9mKN1QS1433rrLSxatAhLlixBYWEhsrOzMWPGDFRVVfndv76+Hunp6XjssceQnJxsyzGJiIiIKLIFteBdsWIFFixYgPnz5+Pcc8/F6tWrERsbixdffNHv/nl5efjd736H6667DlFRUbYck4iIiIgiW9AK3ubmZmzduhXTp08/0xiHA9OnT8emTZtC5phnM1VVMW7cOKiqGuymhDXmKMcM5ZihHDOUY4b2YI7WBW2VhpqaGmiahiFDhvhsHzJkCEpKSnr1mE1NTWhqavJ+ffz4cQCA2+2G2+0G0Fo4OxwO6LoOXde9+3q2a5rmM5fGbLuqqlAUxXtcAHCfnnRuwPDZ7tkfQIeJ6U6nE4Zh+GxXFAWqqnZoo9n2rvqk6zr69evn/R1W+tRZ24PZJ8l56m6fAPjkGAl9CsZ5SkhIiLg+9fZ56t+/PxRFiag+9fZ58jyXI6lPbdvY031qm2Gk9ClY56ltjqHSJ089o59ub0+fp/b7d4bLkgFYtmwZli5d2mF7UVER+vbtCwBISkpCRkYGysrKUF1d7d0nJSUFKSkp+Prrr1FXV+fdnp6ejsGDB2Pnzp1oaGjwbh83bhwSEhJQVFTkPYH7y6oBjIem6SgoKPBpQ25uLpqbm7Fjxw7vNlVVkZeXh7q6Op9CPiYmBtnZ2aipqcHevXu92+Pj45GZmYmKigqUl5d7t3fVp5KSEnz77beIj4+HoiiW+gQAWVlZcLlcIdUnyXnqbp+OHj2KLVu2eHOMhD719nkaOXIkysvL4XQ6fS5Ow7lPvX2eDMPAyZMn8b3vfQ9Hjx6NiD4BvXue6uvrUVdX5z12JPSpt8/T4cOHsX37du/rYST0KRjnqV+/fsjPz0dcXBwURQmZPnnqmWPHjgFAj5+noqIiBEoxgnSLX3NzM2JjY/HOO+9g9uzZ3u3z5s1DbW0t3n333U5/ftSoUbjjjjtwxx13iI/pb4R3xIgROHLkCOLi4gD07NVm4Z9KMGXeeBS8tgvZ157j07ZgXm02NTWhsLAQkyZNgqqqZ+UVtB19amlpQUFBgTfHSOhTb58nXddRWFiIiRMn+ryFF8596u3zpGkaCgsLkZeX521nuPeps7b3RJ/cbrf3NdHlckVEn3r7PHmmHnb1ehhOfQrGedI0DVu2bPHmGCp98tQzW17dhdwbzu3x83Ts2DEMGjQIdXV13nrNTNBGeF0uF84//3zk5+d7i1Nd15Gfn4+FCxf26jGjoqL83gTndDq9b0l7eE5We2bzaMy2tz2u8/Q+CpQOv8/f/h6K4n9/szZa3d72xajt7wmkT93d3ht98qen++Qvx3DvU2+eJ8+LYPsMu9N2s+2R+thru90zEhRJfeqqjXb2yTAM73PZk2W49ynQNlrd3lmf7Hg9DLU+BeM8+cvRrO1m2+3uk6eecZx+fgTjPJkJ6pSGRYsWYd68ecjNzcXkyZOxcuVKnDp1CvPnzwcAzJ07F8OHD8eyZcsAtI7g7tq1y/v/Bw8exLZt29CvXz+MHj06oGMSERER0dklqAXvtddei+rqaixevBiVlZXIycnB+vXrvTedHThwwOdKoqKiAhMnTvR+vXz5cixfvhzTpk3Dxo0bAzomBU5VVWRlZZleiVFgmKMcM5RjhnLMUI4Z2oM5Whf0m9YWLlxoOt3AU8R6jBo1KqBPFensmGSNy+UKdhMiAnOUY4ZyzFCOGcoxQ3swR2uC/tHCFLo0TUNBQUGHyeJkDXOUY4ZyzFCOGcoxQ3swR+tY8BIRERFRRGPBS0REREQRjQUvEREREUU0FrxkSlVV5Obm8i5QIeYoxwzlmKEcM5RjhvZgjtax4KVONTc3B7sJEYE5yjFDOWYoxwzlmKE9mKM1LHjJlKZp2LFjB+8CFWKOcsxQjhnKMUM5ZmgP5mgdC14iIiIiimgseImIiIgoorHgpU5xQrw9mKMcM5RjhnLMUI4Z2oM5WhP0jxam0OV0OpGXlxfsZoQ95ijHDOWYoRwzlGOG9mCO1nGEl0wZhoHa2loYhhHspoQ15ijHDOWYoRwzlGOG9mCO1rHgJVOapqGkpIR3gQoxRzlmKMcM5ZihHDO0B3O0jgUvEREREUU0FrxEREREFNFY8JIpRVEQExMDRVGC3ZSwxhzlmKEcM5RjhnLM0B7M0Tqu0kCmVFVFdnZ2sJsR9pijHDOUY4ZyzFCOGdqDOVrHEV4ypes6qqqqoOt6sJsS1pijHDOUY4ZyzFCOGdqDOVrHgpdM6bqOvXv38gklxBzlmKEcM5RjhnLM0B7M0ToWvEREREQU0VjwEhEREVFEY8FLphRFQXx8PO8CFWKOcsxQjhnKMUM5ZmgP5mgdV2kgU6qqIjMzM9jNCHvMUY4ZyjFDOWYoxwztwRyt4wgvmdJ1HeXl5ZwUL8Qc5ZihHDOUY4ZyzNAezNE6Frxkik8oezBHOWYoxwzlmKEcM7QHc7SOBS8RERERRTQWvEREREQU0VjwkimHw4GkpCQ4HHyYSDBHOWYoxwzlmKEcM7QHc7SOqzSQKYfDgYyMjGA3I+wxRzlmKMcM5ZihHDO0B3O0jpcGZErXdZSWlnJSvBBzlGOGcsxQjhnKMUN7MEfrWPCSKV3XUV1dzSeUEHOUY4ZyzFCOGcoxQ3swR+tY8BIRERFRRGPBS0REREQRjQUvmXI4HEhJSeFdoELMUY4ZyjFDOWYoxwztwRyt4yoNZMrzhCIZ5ijHDOWYoRwzlGOG9mCO1vHSgExpmobi4mJomhbspoQ15ijHDOWYoRwzlGOG9mCO1rHgJVOGYaCurg6GYQS7KWGNOcoxQzlmKMcM5ZihPZijdSx4iYiIiCiiseAlIiIioojGgpdMORwOpKen8y5QIeYoxwzlmKEcM5RjhvZgjtZxlQYy5XA4MHjw4GA3I+wxRzlmKMcM5ZihHDO0B3O0jpcGZErTNGzfvp13gQoxRzlmKMcM5ZihHDO0B3O0jgUvmTIMAw0NDbwLVIg5yjFDOWYoxwzlmKE9mKN1LHiJiIiIKKKx4CUiIiKiiMaCl0ypqopx48ZBVdVgNyWsMUc5ZijHDOWYoRwztAdztI6rNJApRVGQkJAQ7GaEPeYoxwzlmKEcM5RjhvZgjtZxhJdMud1ubNmyBW63O9hNCWvMUY4ZyjFDOWYoxwztwRytY8FLneKSJ/ZgjnLMUI4ZyjFDOWZoD+ZoDQteIiIiIopoLHiJiIiIKKKx4CVTqqoiKyuLd4EKMUc5ZijHDOWYoRwztAdztI4FL3XK5XIFuwkRgTnKMUM5ZijHDOWYoT2YozUseMmUpmkoKCjgxHgh5ijHDOWYoRwzlGOG9mCO1rHgJSIiIqKIxoKXiIiIiCIaC14iIiIiimgseMmUqqrIzc3lXaBCzFGOGcoxQzlmKMcM7cEcrWPBS51qbm4OdhMiAnOUY4ZyzFCOGcoxQ3swR2tY8JIpTdOwY8cO3gUqxBzlmKEcM5RjhnLM0B7M0ToWvEREREQU0VjwEhEREVFEC4mCd9WqVRg1ahSio6MxZcoUfPHFF53u//bbb2PcuHGIjo7GhAkT8P777/t8/8Ybb4SiKD7/Zs6c2ZNdiFicEG8P5ijHDOWYoRwzlGOG9mCO1gS94H3rrbewaNEiLFmyBIWFhcjOzsaMGTNQVVXld//PPvsMc+bMwU033YSioiLMnj0bs2fPxs6dO332mzlzJg4dOuT998Ybb/RGdyKK0+lEXl4enE5nsJsS1pijHDOUY4ZyzFCOGdqDOVoX9IJ3xYoVWLBgAebPn49zzz0Xq1evRmxsLF588UW/+z/99NOYOXMm7rrrLmRmZuKRRx7BpEmT8Pvf/95nv6ioKCQnJ3v/DRgwoDe6E1EMw0BtbS0Mwwh2U8Iac5RjhnLMUI4ZyjFDezBH64J6adDc3IytW7fivvvu825zOByYPn06Nm3a5PdnNm3ahEWLFvlsmzFjBtatW+ezbePGjRg8eDAGDBiA73//+3j00UcxaNAgv8dsampCU1OT9+vjx48DANxuN9xut7ddDocDuq5D13Wf9jocDmia5vPAM9uuqioURfEeFwDcp++yNGD4bPfsD6DDnZhOpxOGYfhsVxQFqqp2aKPZ9q761NzcjOLiYkyaNAmqqlrqU2dtD2afJOepu31yu90+OUZCn3r7POm6jpKSEkycONHnbbxw7lNvnydN01BcXIy8vDxvO8O9T521vSf61Pa57HK5IqJPvX2eWlpaAno9DKc+BeM8eZ7PnhxDpU+eekY/3d6ePk/t9+9MUAvempoaaJqGIUOG+GwfMmQISkpK/P5MZWWl3/0rKyu9X8+cORNXXXUV0tLSUFpaivvvvx+XX345Nm3a5HfOy7Jly7B06dIO24uKitC3b18AQFJSEjIyMlBWVobq6mrvPikpKUhJScHXX3+Nuro67/b09HQMHjwYO3fuRENDg3f7uHHjkJCQgKKiIu8J3F9WDWA8NE1HQUGBTxtyc3PR3NyMHTt2eLepqoq8vDzU1dX55BQTE4Ps7GzU1NRg79693u3x8fHIzMxERUUFysvLvdu76tOePXtQW1uLwsJCKIpiqU8AkJWVBZfLFVJ9kpyn7vbp+PHjPjlGQp96+zyNHDkSALBr1y6fi9Nw7lNvnyfDMHDixAkAiJg+Ab17nurr673P5czMzIjoU2+fpyNHjvi8HkZCn4Jxnvr164e6ujpvjqHSJ089c+zYMQDo8fNUVFSEQClGEMfDKyoqMHz4cHz22WeYOnWqd/vdd9+NTz75BJs3b+7wMy6XCy+//DLmzJnj3faHP/wBS5cuxeHDh/3+nr179yIjIwMffvghLr300g7f9zfCO2LECBw5cgRxcXEAevZqs/BPJZgybzwKXtuF7GvP8WlbMK82m5qaUFhYyBFeYZ9aWlpQUFDAEV7hCG9hYSFHeAV90jQNhYWFHOEVjvB6XhM5wtu9Pnne2eUIr3yEd8uWLSE3wuupZ7a8ugu5N5zb4+fp2LFjGDRoEOrq6rz1mpmgjvAmJiZCVdUOherhw4eRnJzs92eSk5Mt7Q+0XlEkJiZiz549fgveqKgoREVFddjudDo7TAj3nKz2/I0cd7a97XGdp/dRoJhOQPe3XVH872/WRqvbnU4nYmNj4XQ6OxQZgbbR6vae7pPkPHV3u8Ph8JtjOPept8+TpmmIiYnpkGF32m62PRIfe237pCgKYmNjoShKxPQpkDba3SfPc9kzqhYJfQqkjVa3m/VJVVVbXg9DqU/BOE+e57O/18Rg9slTzzhOPz+CcZ7MBPWmNZfLhfPPPx/5+fnebbquIz8/32fEt62pU6f67A8AGzZsMN0fAMrLy3HkyBEMHTrUnoafJVRVRXZ2tukDkwLDHOWYoRwzlGOGcszQHszRuqCv0rBo0SI8//zzePnll1FcXIxbb70Vp06dwvz58wEAc+fO9bmp7fbbb8f69evx5JNPoqSkBA899BAKCgqwcOFCAMDJkydx11134fPPP8e+ffuQn5+PWbNmYfTo0ZgxY0ZQ+hiudF1HVVWVz9sXZB1zlGOGcsxQjhnKMUN7MEfrgl7wXnvttVi+fDkWL16MnJwcbNu2DevXr/femHbgwAEcOnTIu/8FF1yAP/3pT/jv//5vZGdn45133sG6deswfvx4AK1XPTt27MCVV16Jc845BzfddBPOP/98/POf//Q7bYHM6bqOvXv38gklxBzlmKEcM5RjhnLM0B7M0bqQWLF44cKF3hHa9jZu3Nhh249//GP8+Mc/9rt/TEwMPvjgAzubR0RERERhLOgjvEREREREPYkFL5nyrJHouRuZuoc5yjFDOWYoxwzlmKE9mKN1ITGlgUKTqqrIzMwMdjPCHnOUY4ZyzFCOGcoxQ3swR+s4wkumdF1HeXk5J8ULMUc5ZijHDOWYoRwztAdztI4FL5niE8oezFGOGcoxQzlmKMcM7cEcrWPBS0REREQRjQUvEREREUU0FrxkyuFwICkpye/nZlPgmKMcM5RjhnLMUI4Z2oM5WsdVGsiUw+FARkZGsJsR9pijHDOUY4ZyzFCOGdqDOVrHSwMypes6SktLOSleiDnKMUM5ZijHDOWYoT2Yo3UseMmUruuorq7mE0qIOcoxQzlmKMcM5ZihPZijdSx4iYiIiCiiseAlIiIioojGgpdMORwOpKSk8C5QIeYoxwzlmKEcM5RjhvZgjtZxlQYy5XlCkQxzlGOGcsxQjhnKMUN7MEfreGlApjRNQ3FxMTRNC3ZTwhpzlGOGcsxQjhnKMUN7MEfrWPCSKcMwUFdXB8Mwgt2UsMYc5ZihHDOUY4ZyzNAezNE6FrxEREREFNFY8BIRERFRRGPBS6YcDgfS09N5F6gQc5RjhnLMUI4ZyjFDezBH67hKA5lyOBwYPHhwsJsR9pijHDOUY4ZyzFCOGdqDOVrHSwMypWkatm/fzrtAhZijHDOUY4ZyzFCOGdqDOVrHgpdMGYaBhoYG3gUqxBzlmKEcM5RjhnLM0B7M0ToWvEREREQU0VjwEhEREVFEY8FLplRVxbhx46CqarCbEtaYoxwzlGOGcsxQjhnagzlax1UayJSiKEhISAh2M8Iec5RjhnLMUI4ZyjFDezBH6zjCS6bcbje2bNkCt9sd7KaENeYoxwzlmKEcM5RjhvZgjtax4KVOcckTezBHOWYoxwzlmKEcM7QHc7SGBS8RERERRTQWvEREREQU0VjwkilVVZGVlcW7QIWYoxwzlGOGcsxQjhnagzlax4KXOuVyuYLdhIjAHOWYoRwzlGOGcszQHszRGha8ZErTNBQUFHBivBBzlGOGcsxQjhnKMUN7MEfrWPASERERUURjwUtEREREEY0FLxERERFFNBa8ZEpVVeTm5vIuUCHmKMcM5ZihHDOUY4b2YI7WseClTjU3Nwe7CRGBOcoxQzlmKMcM5ZihPZijNSx4yZSmadixYwfvAhVijnLMUI4ZyjFDOWZoD+ZoHQteIiIiIopoLHiJiIiIKKKx4KVOcUK8PZijHDOUY4ZyzFCOGdqDOVrjDHYDKHQ5nU7k5eUFuxlhjznKMUM5ZijHDOWYoT2Yo3Uc4SVThmGgtrYWhmEEuylhjTnKMUM5ZijHDOWYoT2Yo3UseMmUpmkoKSnhXaBCzFGOGcoxQzlmKMcM7cEcrWPBS0REREQRjQUvEREREUU0FrxkSlEUxMTEQFGUYDclrDFHOWYoxwzlmKEcM7QHc7SOqzSQKVVVkZ2dHexmhD3mKMcM5ZihHDOUY4b2YI7WcYSXTOm6jqqqKui6HuymhDXmKMcM5ZihHDOUY4b2YI7WseAlU7quY+/evXxCCTFHOWYoxwzlmKEcM7QHc7SOBS8RERERRTQWvEREREQU0VjwkilFURAfH8+7QIWYoxwzlGOGcsxQjhnagzlax1UayJSqqsjMzAx2M8Iec5RjhnLMUI4ZyjFDezBH6zjCS6Z0XUd5eTknxQsxRzlmKMcM5ZihHDO0B3O0jgUvmeITyh7MUY4ZyjFDOWYoxwztwRytY8FLRERERBGNBS8RERERRTQWvGTK4XAgKSkJDgcfJhLMUY4ZyjFDOWYoxwztwRyt4yoNZMrhcCAjIyPYzQh7zFGOGcoxQzlmKMcM7cEcrQuJS4NVq1Zh1KhRiI6OxpQpU/DFF190uv/bb7+NcePGITo6GhMmTMD777/v833DMLB48WIMHToUMTExmD59Or755pue7EJE0nUdpaWlnBQvxBzlmKEcM5RjhnLM0B7M0bqgF7xvvfUWFi1ahCVLlqCwsBDZ2dmYMWMGqqqq/O7/2WefYc6cObjppptQVFSE2bNnY/bs2di5c6d3nyeeeALPPPMMVq9ejc2bN6Nv376YMWMGGhsbe6tbEUHXdVRXV/MJJcQc5ZihHDOUY4ZyzNAezNG6oBe8K1aswIIFCzB//nyce+65WL16NWJjY/Hiiy/63f/pp5/GzJkzcddddyEzMxOPPPIIJk2ahN///vcAWkd3V65ciQceeACzZs1CVlYWXnnlFVRUVGDdunW92DMiIiIiCgVBncPb3NyMrVu34r777vNuczgcmD59OjZt2uT3ZzZt2oRFixb5bJsxY4a3mC0rK0NlZSWmT5/u/X58fDymTJmCTZs24brrrutwzKamJjQ1NXm/rqurAwAcPXoUbrfb2y6HwwFd132uqDzbNU2DYRhdbldVFYqieI8LALUn6wAcx4n6Ezh69KhP21RVBQBomuaz3el0wjAMn+2KokBV1Q5tNNveVZ+amppw8uRJHDt2DKqqWupTZ20PZp8k56m7fWppafHJMRL61NvnSdd1nDp1ypthJPSpt8+Tpmk4efIkjh8/7m1nuPeps7b3RJ/cbrf3uexyuSKiT719npqbmwN6PQynPgXjPHmez21fE0OhT5565nj9CRw/frzHz9OxY8cAwOdYZoJa8NbU1EDTNAwZMsRn+5AhQ1BSUuL3ZyorK/3uX1lZ6f2+Z5vZPu0tW7YMS5cu7bA9LS0tsI7Y5JKbAdzcq7+SiIiIyFbf6+V65sSJE4iPj+90H67SAOC+++7zGTXWdR1Hjx7FoEGDoChKEFsWXMePH8eIESPw7bffIi4uLtjNCVvMUY4ZyjFDOWYoxwztwRxbGYaBEydOYNiwYV3uG9SCNzExEaqq4vDhwz7bDx8+jOTkZL8/k5yc3On+nv8ePnwYQ4cO9dknJyfH7zGjoqIQFRXlsy0hIcFKVyJaXFzcWf2EsgtzlGOGcsxQjhnKMUN7MEd0ObLrEdSb1lwuF84//3zk5+d7t+m6jvz8fEydOtXvz0ydOtVnfwDYsGGDd/+0tDQkJyf77HP8+HFs3rzZ9JhEREREFLmCPqVh0aJFmDdvHnJzczF58mSsXLkSp06dwvz58wEAc+fOxfDhw7Fs2TIAwO23345p06bhySefxA9/+EO8+eabKCgowH//938DaJ1Yfccdd+DRRx/FmDFjkJaWhgcffBDDhg3D7Nmzg9VNIiIiIgqSoBe81157Laqrq7F48WJUVlYiJycH69ev9950duDAAZ+Pzrvgggvwpz/9CQ888ADuv/9+jBkzBuvWrcP48eO9+9x99904deoUbr75ZtTW1uKiiy7C+vXrER0d3ev9C2dRUVFYsmRJh+keZA1zlGOGcsxQjhnKMUN7MEfrFCOQtRyIiIiIiMJU0D94goiIiIioJ7HgJSIiIqKIxoKXiIiIiCIaC14iIiIiimgseMnHb3/7W1xwwQWIjY0N+MM3brzxRiiK4vNv5syZPdvQENadDA3DwOLFizF06FDExMRg+vTp+Oabb3q2oSHs6NGjuP766xEXF4eEhATcdNNNOHnyZKc/c8kll3R4HP7yl7/spRaHhlWrVmHUqFGIjo7GlClT8MUXX3S6/9tvv41x48YhOjoaEyZMwPvvv99LLQ1dVjJcs2ZNh8fc2b4a0D/+8Q/86Ec/wrBhw6AoCtatW9flz2zcuBGTJk1CVFQURo8ejTVr1vR4O0OZ1Qw3btzY4XGoKAoqKyt7p8FhggUv+WhubsaPf/xj3HrrrZZ+bubMmTh06JD33xtvvNFDLQx93cnwiSeewDPPPIPVq1dj8+bN6Nu3L2bMmIHGxsYebGnouv766/HVV19hw4YN+Nvf/oZ//OMfuPnmrj+YfcGCBT6PwyeeeKIXWhsa3nrrLSxatAhLlixBYWEhsrOzMWPGDFRVVfnd/7PPPsOcOXNw0003oaioCLNnz8bs2bOxc+fOXm556LCaIdD6SVdtH3P79+/vxRaHnlOnTiE7OxurVq0KaP+ysjL88Ic/xPe+9z1s27YNd9xxB37xi1/ggw8+6OGWhi6rGXrs3r3b57E4ePDgHmphmDKI/HjppZeM+Pj4gPadN2+eMWvWrB5tTzgKNENd143k5GTjd7/7nXdbbW2tERUVZbzxxhs92MLQtGvXLgOAsWXLFu+2v//974aiKMbBgwdNf27atGnG7bff3gstDE2TJ082fvWrX3m/1jTNGDZsmLFs2TK/+//kJz8xfvjDH/psmzJlinHLLbf0aDtDmdUMrbxOno0AGH/5y1863efuu+82zjvvPJ9t1157rTFjxowebFn4CCTDjz/+2ABgHDt2rFfaFK44wku22LhxIwYPHoyxY8fi1ltvxZEjR4LdpLBRVlaGyspKTJ8+3bstPj4eU6ZMwaZNm4LYsuDYtGkTEhISkJub6902ffp0OBwObN68udOfff3115GYmIjx48fjvvvuQ319fU83NyQ0Nzdj69atPo8hh8OB6dOnmz6GNm3a5LM/AMyYMeOsfMwB3csQAE6ePImRI0dixIgRmDVrFr766qveaG7E4OPQPjk5ORg6dCh+8IMf4NNPPw12c0JO0D9pjcLfzJkzcdVVVyEtLQ2lpaW4//77cfnll2PTpk1QVTXYzQt5nnlWnk8X9BgyZMhZOQersrKyw1txTqcTAwcO7DSPn/70pxg5ciSGDRuGHTt24J577sHu3buxdu3anm5y0NXU1EDTNL+PoZKSEr8/U1lZycdcG93JcOzYsXjxxReRlZWFuro6LF++HBdccAG++uorpKSk9Eazw57Z4/D48eNoaGhATExMkFoWPoYOHYrVq1cjNzcXTU1NeOGFF3DJJZdg8+bNmDRpUrCbFzJY8J4F7r33Xjz++OOd7lNcXIxx48Z16/jXXXed9/8nTJiArKwsZGRkYOPGjbj00ku7dcxQ09MZng0CzbC72s7xnTBhAoYOHYpLL70UpaWlyMjI6PZxicxMnToVU6dO9X59wQUXIDMzE//1X/+FRx55JIgto7PJ2LFjMXbsWO/XF1xwAUpLS/HUU0/h1VdfDWLLQgsL3rPAr3/9a9x4442d7pOenm7b70tPT0diYiL27NkTMQVvT2aYnJwMADh8+DCGDh3q3X748GHk5OR065ihKNAMk5OTO9wk5Ha7cfToUW9WgZgyZQoAYM+ePRFf8CYmJkJVVRw+fNhn++HDh00zS05OtrR/pOtOhu316dMHEydOxJ49e3qiiRHJ7HEYFxfH0V2ByZMn41//+lewmxFSWPCeBZKSkpCUlNRrv6+8vBxHjhzxKd7CXU9mmJaWhuTkZOTn53sL3OPHj2Pz5s2WV8sIZYFmOHXqVNTW1mLr1q04//zzAQAfffQRdF33FrGB2LZtGwBE1OPQjMvlwvnnn4/8/HzMnj0bAKDrOvLz87Fw4UK/PzN16lTk5+fjjjvu8G7bsGGDz4jl2aQ7GbanaRq+/PJLXHHFFT3Y0sgyderUDsvhnc2PQ7ts27btrHjtsyTYd81RaNm/f79RVFRkLF261OjXr59RVFRkFBUVGSdOnPDuM3bsWGPt2rWGYRjGiRMnjDvvvNPYtGmTUVZWZnz44YfGpEmTjDFjxhiNjY3B6kZQWc3QMAzjscceMxISEox3333X2LFjhzFr1iwjLS3NaGhoCEYXgm7mzJnGxIkTjc2bNxv/+te/jDFjxhhz5szxfr+8vNwYO3assXnzZsMwDGPPnj3Gww8/bBQUFBhlZWXGu+++a6Snpxvf/e53g9WFXvfmm28aUVFRxpo1a4xdu3YZN998s5GQkGBUVlYahmEYP/vZz4x7773Xu/+nn35qOJ1OY/ny5UZxcbGxZMkSo0+fPsaXX34ZrC4EndUMly5danzwwQdGaWmpsXXrVuO6664zoqOjja+++ipYXQi6EydOeF/zABgrVqwwioqKjP379xuGYRj33nuv8bOf/cy7/969e43Y2FjjrrvuMoqLi41Vq1YZqqoa69evD1YXgs5qhk899ZSxbt0645tvvjG+/PJL4/bbbzccDofx4YcfBqsLIYkFL/mYN2+eAaDDv48//ti7DwDjpZdeMgzDMOrr643LLrvMSEpKMvr06WOMHDnSWLBggfcPxNnIaoaG0bo02YMPPmgMGTLEiIqKMi699FJj9+7dvd/4EHHkyBFjzpw5Rr9+/Yy4uDhj/vz5PhcMZWVlPpkeOHDA+O53v2sMHDjQiIqKMkaPHm3cddddRl1dXZB6EBzPPvuskZqaarhcLmPy5MnG559/7v3etGnTjHnz5vns/+c//9k455xzDJfLZZx33nnGe++918stDj1WMrzjjju8+w4ZMsS44oorjMLCwiC0OnR4lshq/8+T27x584xp06Z1+JmcnBzD5XIZ6enpPq+NZyOrGT7++ONGRkaGER0dbQwcONC45JJLjI8++ig4jQ9himEYRq8NJxMRERER9TKuw0tEREREEY0FLxERERFFNBa8RERERBTRWPASERERUURjwUtEREREEY0FLxERERFFNBa8RERERBTRWPASEYWQSy65xOfjfoNl48aNUBQFtbW1pvs89NBD3o/DJiIKZSx4iYjCTEtLC+655x5MmDABffv2xbBhwzB37lxUVFT0ajvuvPNO5Ofn9+rvJCLqDha8RERhpr6+HoWFhXjwwQdRWFiItWvXYvfu3bjyyit7tR39+vXDoEGDevV3EhF1BwteIqIwEx8fjw0bNuAnP/kJxo4di+985zv4/e9/j61bt+LAgQNd/vy+ffugKArefPNNXHDBBYiOjsb48ePxySefdNh369atyM3NRWxsLC644ALs3r3b+z1OaSCicMGCl4goAtTV1UFRFCQkJAT8M3fddRd+/etfo6ioCFOnTsWPfvQjHDlyxGef3/zmN3jyySdRUFAAp9OJn//85za3nIio57HgJSIKc42NjbjnnnswZ84cxMXFBfxzCxcuxNVXX43MzEw899xziI+Pxx//+EeffX77299i2rRpOPfcc3Hvvffis88+Q2Njo91dICLqUSx4iYjCWEtLC37yk5/AMAw899xzln526tSp3v93Op3Izc1FcXGxzz5ZWVne/x86dCgAoKqqStBiIqLe5wx2A4iIqHs8xe7+/fvx0UcfWRrdDVSfPn28/68oCgBA13Xbfw8RUU/iCC8RURjyFLvffPMNPvzww26tlvD55597/9/tdmPr1q3IzMy0s5lERCGBI7xERGGmpaUF11xzDQoLC/G3v/0NmqahsrISADBw4EC4XK6AjrNq1SqMGTMGmZmZeOqpp3Ds2DHelEZEEYkFLxFRmDl48CD++te/AkCHZcE+/vhjXHLJJQEd57HHHsNjjz2Gbdu2YfTo0fjrX/+KxMREm1tLRBR8imEYRrAbQUREvWffvn1IS0tDUVER19ElorMC5/ASERERUURjwUtEFGL++c9/ol+/fqb/uvKf//mfpj97+eWX90IPiIhCC6c0EBGFmIaGBhw8eND0+6NHj+70548ePYqjR4/6/V5MTAyGDx8uah8RUbhhwUtEREREEY1TGoiIiIgoorHgJSIiIqKIxoKXiIiIiCIaC14iIiIiimgseImIiIgoorHgJSIiIqKIxoKXiIiIiCIaC14iIiIiimj/P5+HDfzZ2rr6AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "BqSmKXRa_rvE" - }, - "source": [ - "### Exercise 4.2\n", - "\n", - "#### Part a\n", - "Install [tabulate](https://github.com/astanin/python-tabulate).\n", - "\n", - "#### Part b\n", - "Use numpy to compute the [covariance matrix](https://numpy.org/doc/stable/reference/generated/numpy.cov.html) and [correlation matrix](https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html) between all observabes, and separately between low and high-level features.\n", - "\n", - "#### Part c\n", - "Use tabulate to create a well formatted table of the covariance and correlation matrices, with nice headings and appropriate significant figures. Embed the table into this notebook.\n", - "\n", - "#### Part d\n", - "Write a function that takes a dataset and appropriate arguments and performs steps b and c. " + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "ue0lwpfy_rwt" - }, - "source": [ - "Hint: Example code for embedding a `tabulate` table into a notebook:" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "scrolled": true, - "colab": { - "base_uri": "https://localhost:8080/", - "height": 102 - }, - "id": "I_4en7R5_rwx", - "outputId": "cad32c77-900e-4dd2-f1d4-0116fb57bf94" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
X Y Z
A 1 2
C 3 4
D 5 6
" - ] - }, - "metadata": {} - } - ], - "source": [ - "from IPython.display import HTML, display\n", - "import tabulate\n", - "table = [[\"A\",1,2],\n", - " [\"C\",3,4],\n", - " [\"D\",5,6]]\n", - "display(HTML(tabulate.tabulate(table, tablefmt='html', headers=[\"X\",\"Y\",\"Z\"])))" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "id": "g-0mMHEW_rw0" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from tabulate import tabulate\n", - "\n", - "# Function to compute covariance and correlation matrices\n", - "def compute_matrices(df):\n", - " covariance_matrix = np.cov(df, rowvar=False)\n", - " correlation_matrix = np.corrcoef(df, rowvar=False)\n", - " return covariance_matrix, correlation_matrix\n", - "\n", - "# Function to format matrices for display\n", - "def format_matrix(matrix):\n", - " return [[\"{:.3f}\".format(value) for value in row] for row in matrix]\n", - "\n", - "# Function to display matrices using tabulate\n", - "def display_matrix(matrix, headers):\n", - " formatted_matrix = format_matrix(matrix)\n", - " table = tabulate(formatted_matrix, headers=headers)\n", - " print(table)\n", - "\n", - "# Function to analyze dataset\n", - "def analyze_dataset(dataset, feature_names=None):\n", - " if feature_names is None:\n", - " feature_names = [\"Feature \" + str(i) for i in range(dataset.shape[1])]\n", - "\n", - " low_level_features = dataset[:, :5]\n", - " high_level_features = dataset[:, 5:]\n", - "\n", - " covariance_low, correlation_low = compute_matrices(low_level_features)\n", - " covariance_high, correlation_high = compute_matrices(high_level_features)\n", - "\n", - " print(\"Low-Level Features Analysis:\")\n", - " display_matrix(covariance_low, feature_names[:5])\n", - " display_matrix(correlation_low, feature_names[:5])\n", - "\n", - " print(\"\\nHigh-Level Features Analysis:\")\n", - " display_matrix(covariance_high, feature_names[5:])\n", - " display_matrix(correlation_high, feature_names[5:])" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nHIScqea_rxF", - "outputId": "93dae5b6-6754-4ff6-d3f2-deffc6866f68" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Low-Level Features Analysis:\n", - " Feature 0 Feature 1 Feature 2 Feature 3 Feature 4\n", - "----------- ----------- ----------- ----------- -----------\n", - " 0.077 -0.004 -0.003 -0.01 -0\n", - " -0.004 0.09 -0.018 0.007 -0.018\n", - " -0.003 -0.018 0.076 0.013 -0.01\n", - " -0.01 0.007 0.013 0.095 0.014\n", - " -0 -0.018 -0.01 0.014 0.075\n", - " Feature 0 Feature 1 Feature 2 Feature 3 Feature 4\n", - "----------- ----------- ----------- ----------- -----------\n", - " 1 -0.052 -0.041 -0.112 -0.006\n", - " -0.052 1 -0.213 0.072 -0.22\n", - " -0.041 -0.213 1 0.155 -0.126\n", - " -0.112 0.072 0.155 1 0.16\n", - " -0.006 -0.22 -0.126 0.16 1\n", - "\n", - "High-Level Features Analysis:\n", - " Feature 5 Feature 6 Feature 7 Feature 8 Feature 9\n", - "----------- ----------- ----------- ----------- -----------\n", - " 0.08 -0.006 -0.001 0.005 0.011\n", - " -0.006 0.084 0.004 0.013 0.005\n", - " -0.001 0.004 0.092 -0.018 -0.001\n", - " 0.005 0.013 -0.018 0.084 0.012\n", - " 0.011 0.005 -0.001 0.012 0.102\n", - " Feature 5 Feature 6 Feature 7 Feature 8 Feature 9\n", - "----------- ----------- ----------- ----------- -----------\n", - " 1 -0.067 -0.008 0.06 0.118\n", - " -0.067 1 0.042 0.153 0.058\n", - " -0.008 0.042 1 -0.201 -0.006\n", - " 0.06 0.153 -0.201 1 0.127\n", - " 0.118 0.058 -0.006 0.127 1\n" - ] - } - ], - "source": [ - "# checking if it works:\n", - "dataset = np.random.rand(100, 10) # Example dataset with 100 samples and 10 features\n", - "analyze_dataset(dataset)" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "FjCYpGx__rxH" - }, - "source": [ - "## Exercise 5: Selection\n", - "\n", - "### Exercise 5.1\n", - "\n", - "Part a\n", - "By looking at the signal/background distributions for each observable (e.g. $x$) determine which selection criteria would be optimal:\n", - "\n", - "1. $x > x_c$\n", - "2. $x < x_c$\n", - "3. $|x - \\mu| > x_c$\n", - "4. $|x - \\mu| < x_c$\n", - "\n", - "where $x_c$ is value to be determined below.\n", - "\n", - "### Exercise 5.2\n", - "\n", - "Plot the True Positive Rate (TPR) (aka signal efficiency $\\epsilon_S(x_c)$) and False Positive Rate (FPR) (aka background efficiency $\\epsilon_B(x_c)$) as function of $x_c$ for applying the strategy in part a to each observable.\n", - "\n", - "### Exercise 5.3\n", - "Assume 3 different scenarios corresponding to different numbers of signal and background events expected in data:\n", - "\n", - "1. Expect $N_S=10$, $N_B=100$.\n", - "1. Expect $N_S=100$, $N_B=1000$.\n", - "1. Expect $N_S=1000$, $N_B=10000$.\n", - "1. Expect $N_S=10000$, $N_B=100000$.\n", - "\n", - "Plot the significance ($\\sigma_{S'}$) for each observable as function of $x_c$ for each scenario, where\n", - "\n", - "$\\sigma_{S'}= \\frac{N'_S}{\\sqrt{N'_S+N'_B}}$\n", - "\n", - "and $N'_{S,B} = \\epsilon_{S,B}(x_c) * N_{S,B}$." + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "uBhzDKKW_r0J", - "outputId": "3fbf3f3f-755d-4a45-c22b-ff07b0c6de53" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ], - "source": [ - "# Define expected number of signal and background events\n", - "n_sig_expected = 1000\n", - "n_bkg_expected = 10000\n", - "\n", - "# Define threshold values\n", - "threshold_values = np.linspace(0, 1, 100) # Adjust range and number of points as needed\n", - "\n", - "# Calculate TPR and FPR for each threshold value\n", - "tpr_values = [(1 - threshold) for threshold in threshold_values]\n", - "fpr_values = [threshold for threshold in threshold_values]\n", - "\n", - "# Plot TPR and FPR as functions of the threshold value\n", - "plt.plot(threshold_values, tpr_values, label='True Positive Rate (TPR)')\n", - "plt.plot(threshold_values, fpr_values, label='False Positive Rate (FPR)')\n", - "plt.xlabel('Threshold Value')\n", - "plt.ylabel('Rate')\n", - "plt.title('TPR and FPR vs. Threshold Value')\n", - "plt.legend()\n", - "plt.show()\n" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "mIj1zAGk_r0T" - }, - "source": [ - "## Exercise 6: Cut Flow\n", - "\n", - "\n", - "### Exercise 6.1\n", - "\n", - "For each above scenario, choose a subset (minumum 3) of observables to use for selections, and values of $x_c$ based on your significance plots (part 3c).\n", - "\n", - "### Exercise 6.2\n", - "Create a \"cut-flow\" table for each scenario where you successively make the selections on each observable and tabulate $\\epsilon_S$, $\\epsilon_B$, $N'_S$, $N'_B$, and $\\sigma_{S'}$.\n", - "\n", - "### Exercise 6.3\n", - "In 3c above you computed the significance for each observable assuming to make no other selections on any other observable. If the variables are correlated, then this assumption can lead to non-optimial results when selecting on multiple variables. By looking at the correlation matrices and your answers to 4b, identify where this effect could be most detrimental to the significance. Attempt to correct the issue by applying the selection in one observable and then optimizing (part 3c) for a second observable. What happens if you change the order of your selection (make selection on second and optimize on first)?\n", - "\n", - "\n" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 619 - }, - "id": "NQayCwMA_r0n", - "outputId": "9a72ceaf-e662-4c13-b298-cdbdbc08f2f6" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA5gklEQVR4nO3de3RU9b3//9ckZCYBkiBKCIGgXBSsiB4RafCGLYWiiyNf6bdW++VibT22gVOkHhW0UvASsH6r1iL2WAu0yqHVA9rlBY7SBooFa5F8hSK0KIocCMX+jkkIZHL7/P6gGTLJJJk9mX2beT7WmrWcmT2ZT7bIfvn+vD+fHTDGGAEAADgkw+0BAACA9EL4AAAAjiJ8AAAARxE+AACAowgfAADAUYQPAADgKMIHAABwFOEDAAA4qofbA2irublZhw8fVm5urgKBgNvDAQAAcTDGqKamRkVFRcrI6Ly24bnwcfjwYRUXF7s9DAAAkIBPPvlEgwYN6vQYz4WP3NxcSacGn5eX5/JoAABAPKqrq1VcXBy5jnfGc+GjZaolLy+P8AEAgM/E0zJBwykAAHAU4QMAADiK8AEAABzluZ6PeBhj1NjYqKamJreH4ilZWVnKzMx0exgAAHTKd+Gjvr5eR44c0YkTJ9weiucEAgENGjRIvXv3dnsoAAB0yFfho7m5WQcOHFBmZqaKiooUDAbZiOwfjDE6duyYDh06pHPPPZcKCADAs3wVPurr69Xc3Kzi4mL17NnT7eF4Tr9+/fTRRx+poaGB8AEA8CxfNpx2tW1ruqIKBADwA67iAADAUYQPAADgKMIHAABwlKXwsWLFCo0ePTpy35WSkhK9/vrrkffr6upUWlqqM888U71799b06dN19OjRpA/aj7Zs2aKpU6eqqKhIgUBAL730kttDAgCkOGOM6uvrYz6MMa6Ny9Jql0GDBmnp0qU699xzZYzR6tWrdf3112vnzp264IILdMcdd+jVV1/VCy+8oPz8fM2ZM0c33HCD3nrrLbvG7xu1tbW66KKL9I1vfEM33HCD28MBAKQ4Y4x+/vOf65NPPon5/sKFCxUMBh0e1SmWwsfUqVOjnj/00ENasWKFtm/frkGDBunZZ5/VmjVr9IUvfEGStHLlSp1//vnavn27Pv/5zydv1K0YY3SywZ2dTnOyMuNeYTJlyhRNmTLF5hEBANKZMUYNDQ2STm1P0VHwcFvC+3w0NTXphRdeUG1trUpKSrRjxw41NDRo4sSJkWNGjhypwYMHa9u2bR2Gj3A4rHA4HHleXV1taRwnG5r0ufs3JvZLdNOeJZPVM+irrVIAACmqs0rHnXfe2a7KkZWV5dTQ2rF85dy1a5dKSkpUV1en3r17a/369frc5z6niooKBYNB9enTJ+r4/v37q7KyssOfV1ZWpsWLF1seOAAA6ax1lUPquNJRXFysXr16eWovKMvhY8SIEaqoqFBVVZVefPFFzZo1S5s3b054AAsWLND8+fMjz6urq1VcXBz353OyMrVnyeSEv787crLYRRQA4Lyu+jlaVzqysrI8FTykBMJHMBjU8OHDJUljxozRO++8oyeeeEI33nij6uvr9dlnn0VVP44eParCwsIOf14oFFIoFLI+8n8IBAJMfQAAUl68/RxerHS01e2rdnNzs8LhsMaMGaOsrCxt2rRJ06dPlyTt27dPBw8eVElJSbcHCgBAumg7pWKM0cqVK2O2MbTt5/BipaMtS+FjwYIFmjJligYPHqyamhqtWbNG5eXl2rhxo/Lz83Xrrbdq/vz56tu3r/Ly8jR37lyVlJTYttLFT44fP679+/dHnh84cEAVFRXq27evBg8e7OLIAABe0tWUSmt+qHLEYil8/O1vf9PMmTN15MgR5efna/To0dq4caO+9KUvSZIee+wxZWRkaPr06QqHw5o8ebKeeuopWwbuN3/60590zTXXRJ639LnMmjVLq1atcmlUAAC3xds4KkmFhYW65ZZbImHDD1WOWALGzS3OYqiurlZ+fr6qqqqUl5cX9V5dXZ0OHDigIUOGKDs726URehfnBwD8xUrjqOTtsNHZ9bstOjUBAHBQKjWOJorwAQCAQ6xsBOblKkd3ET4AALCJnzcCsxPhAwCAJGkdNjpbHit5fyMwOxE+AABIgnRYIpsshA8AABIQ75RK2+WxUvpVOtoifAAAYJHf763iNsIHAABxYIls8hA+AADoAktkkyvD7QGki7KyMo0dO1a5ubkqKCjQtGnTtG/fPreHBQCIwRij+vr6yKO2trbTJbLBYDDyIHh0jcqHQzZv3qzS0lKNHTtWjY2NWrhwoSZNmqQ9e/aoV69ebg8PAPAP9HPYj/DhkA0bNkQ9X7VqlQoKCrRjxw5dddVVLo0KACDRz+E0/4cPY6SGE+58d1ZPKcE/gFVVVZKkvn37JnNEAIAutF0i29lmYPRz2MP/4aPhhPRwkTvfvfCwFLQ+ZdLc3Kx58+bp8ssv16hRo2wYGAAgFjYC8wb/hw8fKi0t1e7du7V161a3hwIAKS3ejcCk9puBUeWwj//DR1bPUxUIt77bojlz5uiVV17Rli1bNGjQIBsGBQCQrDWOSoQNJ/k/fAQCCU19OM0Yo7lz52r9+vUqLy/XkCFD3B4SAKQcGkf9wf/hwydKS0u1Zs0avfzyy8rNzY00NuXn5ysnJ8fl0QGA/7ERmH8QPhyyYsUKSdKECROiXl+5cqVmz57t/IAAwOfi7eegyuE9hA+HGGPcHgIApAw2AvM3wgcAwBfo50gdhA8AgOfRz5FaCB8AAM+hnyO1ET4AAJ5CP0fqI3wAAFxHP0d6IXwAABzFjd1A+AAAOIYbu0EifAAAbBbvlAo3dksfhA8AgG1YIotYCB8AgKRhiSziQfhwSFlZmdatW6e9e/cqJydH48eP17JlyzRixIgOP/PnP/9Z999/v3bs2KGPP/5Yjz32mObNm+fcoAHAApbIIl4Zbg8gXWzevFmlpaXavn273njjDTU0NGjSpEmqra3t8DMnTpzQ0KFDtXTpUhUWFjo4WgDomjFG9fX1kUdtbW2XS2SDwaCCwSDBI81R+XDIhg0bop6vWrVKBQUF2rFjh6666qqYnxk7dqzGjh0rSbrnnntsHyMAxMtKlUOi0oFovg8fxhidbDzpynfn9MhJ+D+mqqoqSVLfvn2TOSQAsA0bgSFZfB8+Tjae1Lg141z57rdvfls9s3pa/lxzc7PmzZunyy+/XKNGjbJhZACQXKxaQTL5Pnz4UWlpqXbv3q2tW7e6PRQAiIlVK7CT78NHTo8cvX3z2659t1Vz5szRK6+8oi1btmjQoEE2jAoAuodVK7Cb78NHIBBIaOrDacYYzZ07V+vXr1d5ebmGDBni9pAAIIJ+DjjJ9+HDL0pLS7VmzRq9/PLLys3NjdxAKT8/Xzk5pyooM2fO1MCBA1VWVibp1F8Ae/bsifzzf//3f6uiokK9e/fW8OHD3flFAKQc+jngNMKHQ1asWCFJmjBhQtTrK1eu1OzZsyVJBw8eVEbG6a1XDh8+rH/6p3+KPH/00Uf16KOP6uqrr1Z5ebndQwaQoujngNsIHw4xxnR5TNtAcc4558T1OQCIF/0c8ALCBwCkOPo54DWEDwBIYfRzwIsIHwCQQujngB8QPgAgRdDPAb+wdFfbsrIyjR07Vrm5uSooKNC0adO0b9++qGMmTJigQCAQ9bj99tuTOmgAwCmt7yzLXWXhF5YqHy23hR87dqwaGxu1cOFCTZo0SXv27FGvXr0ix33rW9/SkiVLIs979vT+JmAA4Df0c8CvLIWPeG8L37NnTxUWFiZnhAAASfRzIHV0q+ejo9vCP//883ruuedUWFioqVOn6vvf/36H1Y9wOKxwOBx5Xl1d3Z0hAUBKop8DqSTh8NHRbeFvvvlmnX322SoqKtJ7772nu+++W/v27dO6deti/pyysjItXrw40WEAQEqKt8ohUemA/wRMgltofvvb39brr7+urVu3dnp31t/+9rf64he/qP3792vYsGHt3o9V+SguLlZVVZXy8vKijq2rq9OBAwc0ZMgQZWdnJzLslMb5AVKDlSqHRKUD3lBdXa38/PyY1++2LK12adFyW/jf/e53Xd4Wfty4cZKk/fv3x3w/FAopLy8v6pGK4lkp1NYzzzyjK6+8UmeccYbOOOMMTZw4UX/84x8dGjEAJyW6aoWVK/AjS9MuidwWvqKiQpI0YMCAhAaYKuJdKdRaeXm5brrpJo0fP17Z2dlatmyZJk2apD//+c8aOHCgw78BALuwagXpxlL46Oq28B988IHWrFmja6+9Vmeeeabee+893XHHHbrqqqs0evRoW34Bv4h3pVBrzz//fNTzn/3sZ/rP//xPbdq0STNnzrRtrADsxaoVpDtL4aOr28IHg0G9+eabevzxx1VbW6vi4mJNnz5d9913X9IG3JYxRubkSdt+fmcCOTkJ/6XQ0Uqhzpw4cUINDQ2WPgPAW1i1AiQw7dKZ4uJibd68uVsDssqcPKl9l4xx9DtbjHh3hwIJbKDW0Uqhrtx9990qKirSxIkTLX8nAG9oaGhg1QrSHvd2cUFpaal2796trVu3xv2ZpUuXau3atSovL2clC+AzbW9p34J+DqQr34ePQE6ORry7w7XvtqplpdCWLVu6XCnU4tFHH9XSpUv15ptvpn3vDOA3nU2ztKxWAdKN/8NHIJDQ1IfTElkpJEmPPPKIHnroIW3cuFGXXnqpzaME0F1WmkmzsrKcHBrgGb4PH37R1UohSZo5c6YGDhyosrIySdKyZct0//33a82aNTrnnHMin+ndu7d69+7tzi8CoEM0kwLxSWiTMVi3YsUKVVVVacKECRowYEDk8atf/SpyzMGDB3XkyJGoz9TX1+srX/lK1GceffRRN34FADFwS3vAOiofDolnF/vy8vKo5x999JE9gwGQFGwOBiSG8AEAcWJzMCA5CB8AEAf6OYDkIXwAQAfa7s/B5mBAchA+ACAG+jkA+xA+AED0cwBOInwASHv0cwDOInwASHvc7A1wFuEDQFriZm+AewgfANION3sD3EX4AJDyuNkb4C2ED4eUlZVp3bp12rt3r3JycjR+/HgtW7ZMI0aM6PAz69at08MPP6z9+/eroaFB5557rr73ve9pxowZDo4c8DeaSQHv4cZyDtm8ebNKS0u1fft2vfHGG2poaNCkSZNUW1vb4Wf69u2re++9V9u2bdN7772nW265Rbfccos2btzo4MgBf2l9ozdu9gZ4U8DEc8czB1VXVys/P19VVVXKy8uLeq+urk4HDhzQkCFDlJ2d7dIIk+PYsWMqKCjQ5s2bddVVV8X9uUsuuUTXXXedHnjggXbvpdL5ARJhpcohUekAkqmz63dbvp92Mcaosb7Zle/uEcxI+C+uqqoqSaeqG/Ewxui3v/2t9u3bp2XLliX0nUAqYgt0wH98Hz4a65v179/d7Mp33/bE1coKZVr+XHNzs+bNm6fLL79co0aN6vTYqqoqDRw4UOFwWJmZmXrqqaf0pS99KdEhAymFLdABf/J9+PCj0tJS7d69W1u3bu3y2NzcXFVUVOj48ePatGmT5s+fr6FDh2rChAn2DxTwuI42B6PKAXib78NHj2CGbnviate+26o5c+bolVde0ZYtWzRo0KAuj8/IyNDw4cMlSRdffLHef/99lZWVET6QlmItmW3BqhXAP3wfPgKBQEJTH04zxmju3Llav369ysvLNWTIkIR+TnNzs8LhcJJHB3hfV82kbA4G+Ifvw4dflJaWas2aNXr55ZeVm5uryspKSVJ+fr5ycnIkSTNnztTAgQNVVlYm6dTeIJdeeqmGDRumcDis1157Tb/85S+1YsUK134PwElWmknZHAzwD8KHQ1oCQ9vpkpUrV2r27NmSpIMHDyoj4/RUTm1trb7zne/o0KFDysnJ0ciRI/Xcc8/pxhtvdGrYgGtoJgVSF+HDIfFsp1JeXh71/MEHH9SDDz5o04gAb7GyBTrNpIC/ET4AuI4t0IH0QvgA4LqOlsxKVDqAVET4AOCKts2kLejnAFIf4QOA4zqbZmHJLJD6CB8AbGelmZQls0DqI3wAsBXNpADaInwASDruNAugM4QPAEnF5mAAukL4AJBU3GkWQFcIHwC6hTvNArCK8OGQsrIyrVu3Tnv37lVOTo7Gjx+vZcuWacSIEXF9fu3atbrpppt0/fXX66WXXrJ3sECcuNMsgERkdH0IkmHz5s0qLS3V9u3b9cYbb6ihoUGTJk1SbW1tl5/96KOPdOedd+rKK690YKRAx4wxqq+vjzxqa2u50ywAy6h8OGTDhg1Rz1etWqWCggLt2LFDV111VYefa2pq0te//nUtXrxYv//97/XZZ5/ZPFIgNitLZiWmWQB0zPfhwxijxnDYle/uEQol/JdrVVWVJKlv376dHrdkyRIVFBTo1ltv1e9///uEvgtIBu6/AiBZfB8+GsNh/XjWV1z57n9d/aKysrMtf665uVnz5s3T5ZdfrlGjRnV43NatW/Xss8+qoqKiG6MEEsf9VwDYwffhw49KS0u1e/dubd26tcNjampqNGPGDD3zzDM666yzHBwdcAr3XwFgF9+Hjx6hkP519YuufbdVc+bM0SuvvKItW7Zo0KBBHR73wQcf6KOPPtLUqVMjrzU3N5/63h49tG/fPg0bNsz6oIEOcP8VAE7xffgIBAIJTX04zRijuXPnav369SovL9eQIUM6PX7kyJHatWtX1Gv33Xefampq9MQTT6i4uNjO4SLNcP8VAE6ytNS2rKxMY8eOVW5urgoKCjRt2jTt27cv6pi6ujqVlpbqzDPPVO/evTV9+nQdPXo0qYP2o9LSUj333HNas2aNcnNzVVlZqcrKSp08eTJyzMyZM7VgwQJJUnZ2tkaNGhX16NOnj3JzczVq1ChK3kiqeJpJW6ZaCB4AustS5aNlr4qxY8eqsbFRCxcu1KRJk7Rnzx716tVLknTHHXfo1Vdf1QsvvKD8/HzNmTNHN9xwg9566y1bfgG/WLFihSRpwoQJUa+vXLlSs2fPliQdPHhQGRlsvQJn0EwKwC0BY4xJ9MPHjh1TQUGBNm/erKuuukpVVVXq16+f1qxZo6985dQKlL179+r888/Xtm3b9PnPf77Ln1ldXa38/HxVVVUpLy8v6r26ujodOHBAQ4YMUbYPplqcxvlBvDqbZlm4cCGVNQCWdXb9bqtb/5vddq+KHTt2qKGhQRMnTowcM3LkSA0ePFjbtm2L+TPC4bCqq6ujHgCSK96dSWkmBeCEhBtOY+1VUVlZqWAwqD59+kQd279/f1VWVsb8OWVlZVq8eHGiwwDQBZpJAXhNwpWPlr0q1q5d260BLFiwQFVVVZFHR39BAkgMzaQAvCahykdHe1UUFhaqvr5en332WVT14+jRoyosLIz5s0KhkEIJ7JcBoGM0kwLwMkvho6u9KsaMGaOsrCxt2rRJ06dPlyTt27dPBw8eVElJSfJGDaBD7EwKwOsshY/S0lKtWbNGL7/8cmSvCknKz89XTk6O8vPzdeutt2r+/Pnq27ev8vLyNHfuXJWUlMS10gVA93U0zUIzKQCvsBQ+4tmr4rHHHlNGRoamT5+ucDisyZMn66mnnkrKYAG0F2tb9BY0kwLwIsvTLl3Jzs7W8uXLtXz58oQHBSA+Xa1kYZoFgBf5/t4uQLpp20za2UoWplkAeBHhA/CRziodrGQB4BfcSMQhK1as0OjRo5WXl6e8vDyVlJTo9ddfd3tY8JnOmklb79fBnh0AvIzKh0MGDRqkpUuX6txzz5UxRqtXr9b111+vnTt36oILLnB7ePAomkkBpCLCh0OmTp0a9fyhhx7SihUrtH37dsIHYqKZFECq8n34MMbINDS78t2BrIyE/m+zqalJL7zwgmpra9l8DR3qalt0mkkB+JX/w0dDsw7f/wdXvrtoyXgFgplxH79r1y6VlJSorq5OvXv31vr16/W5z33OxhHCT+KdYpGYZgHgb74PH34yYsQIVVRUqKqqSi+++KJmzZqlzZs3E0DAFAuAtOL78BHIylDRkvGufbcVwWBQw4cPl3TqPjjvvPOOnnjiCf30pz+1Y3jwOPbrAJCu/B8+AgFLUx9e0tzcrHA47PYw4AL26wCQznwfPvxiwYIFmjJligYPHqyamhqtWbNG5eXl2rhxo9tDgwu62q+DsAEglRE+HPK3v/1NM2fO1JEjR5Sfn6/Ro0dr48aN+tKXvuT20OAA9usAgNMIHw559tln3R4CXEIzKQBEY3t1wGbs1wEA0ah8ADZou5KlBc2kAED4AJKus2kWplgAgGkXIOk6W8nCFAsAUPkAuo2VLABgDeED6AZWsgCAdUy7AN3AShYAsI7KB2ARK1kAoHsIH4AFrGQBgO5j2gWwgJUsANB9VD5csHTpUi1YsEDf/e539fjjj7s9HHSClSwAkHyED4e98847+ulPf6rRo0e7PRR0gZUsAGAPpl0cdPz4cX3961/XM888ozPOOMPt4aALrGQBAHv4vvLRtizuJKul9tLSUl133XWaOHGiHnzwQRtHhkSxkgUA7Of78NHQ0KCHH37Yle9euHBh3GX3tWvX6t1339U777xj86iQKFayAIAzfB8+/OCTTz7Rd7/7Xb3xxhvKzs52ezjoACtZAMAZvg8fWVlZWrhwoWvfHY8dO3bob3/7my655JLIa01NTdqyZYt+8pOfKBwOKzMz065hogOsZAEAd/g+fAQCAc+Xw7/4xS9q165dUa/dcsstGjlypO6++26ChwtYyQIA7vF9+PCD3NxcjRo1Kuq1Xr166cwzz2z3OpzBShYAcA/hA2kh3ikWiWkWALAb4cMl5eXlbg8hbTDFAgDewiZjSHlMsQCAt1D5QEpiszAA8C7CB1IOm4UBgLcx7YKUw2ZhAOBtVD6Q0tgsDAC8x5fho7m52e0heJIxxu0huKKzZbRMswCA9/gqfASDQWVkZOjw4cPq16+fgsEg/yf7D8YYHTt2TIFAIK2mFrpaRgsA8B5fhY+MjAwNGTJER44c0eHDh90ejucEAgENGjQorbZrZxktAPiPr8KHdKr6MXjwYDU2Nqqpqcnt4XhKVlZWWgQPltECgL/5LnxIikwt8H+16YdltADgfyy1ha+wjBYA/M9y5WPLli364Q9/qB07dujIkSNav369pk2bFnl/9uzZWr16ddRnJk+erA0bNnR7sEg/8d4QjikWAPAPy+GjtrZWF110kb7xjW/ohhtuiHnMl7/8Za1cuTLyPBQKJT5CpC1uCAcAqcly+JgyZYqmTJnS6TGhUEiFhYUJDwqQWMkCAKnKlobT8vJyFRQU6IwzztAXvvAFPfjggzrzzDNjHhsOhxUOhyPPq6ur7RgSfI6VLACQOpLecPrlL39Zv/jFL7Rp0yYtW7ZMmzdv1pQpUzpcFltWVqb8/PzIo7i4ONlDgo8YY1RfXx95tGiZYml5EDwAwL+SXvn42te+FvnnCy+8UKNHj9awYcNUXl6uL37xi+2OX7BggebPnx95Xl1dTQBJU+xWCgDpwfaltkOHDtVZZ52l/fv3x3w/FAopLy8v6oH0xDJaAEgPtm8ydujQIf3973/XgAED7P4q+AzLaAEgPVkOH8ePH4+qYhw4cEAVFRXq27ev+vbtq8WLF2v69OkqLCzUBx98oLvuukvDhw/X5MmTkzpw+BvLaAEgfVkOH3/60590zTXXRJ639GvMmjVLK1as0HvvvafVq1frs88+U1FRkSZNmqQHHniAvT4QhWW0AJC+LIePCRMmyBjT4fsbN27s1oCQflhGCwDpxZc3loM/dXQ3WqZYACC9ED7gCJbRAgBacFdbOIJltACAFlQ+YAuW0QIAOkL4QNKxjBYA0BmmXZB0LKMFAHSGygdsxTJaAEBbhA90W2f9HUyxAADaInygW1hCCwCwip4PdAv9HQAAq6h8IGno7wAAxIPwAcvYJh0A0B2ED1hCjwcAoLvo+YAlbJMOAOguKh9IGNukAwASQfhAp9jDAwCQbIQPdIj+DgCAHej5QIfYwwMAYAcqH4gLe3gAAJKF8IEo7OEBALAb4QMR9HgAAJxAzwci2MMDAOAEKh+IiT08AAB2IXykMfbwAAC4gfCRpujvAAC4hZ6PNMUeHgAAt1D5AHt4AAAcRfhII+zhAQDwAsJHmqDHAwDgFfR8pAn28AAAeAWVjzTEHh4AADcRPtIQPR4AADcRPlJUZxuIAQDgJsJHCqK5FADgZTScpiA2EAMAeBmVjxTHBmIAAK8hfKQINhADAPgF4SMF0OMBAPATej5SABuIAQD8hMpHimEDMQCA1xE+Ugw9HgAAryN8+BAbiAEA/Izw4TM0lwIA/I6GU59hAzEAgN9R+fAxNhADAPiR5crHli1bNHXqVBUVFSkQCOill16Ket8Yo/vvv18DBgxQTk6OJk6cqL/+9a/JGi9aaWkubXkQPAAAfmA5fNTW1uqiiy7S8uXLY77/yCOP6Mc//rGefvppvf322+rVq5cmT56surq6bg82HRljVF9fH/UAAMDPLE+7TJkyRVOmTIn5njFGjz/+uO677z5df/31kqRf/OIX6t+/v1566SV97Wtf695o0wzNpQCAVJTUhtMDBw6osrJSEydOjLyWn5+vcePGadu2bTE/Ew6HVV1dHfXAKTSXAgBSUVIbTisrKyVJ/fv3j3q9f//+kffaKisr0+LFi5M5jJREcykAIFW4vtR2wYIFqqqqijyYYoiN5lIAQKpIauWjsLBQknT06FENGDAg8vrRo0d18cUXx/xMKBRSKBRK5jB8rfXupTSXAgBSUVLDx5AhQ1RYWKhNmzZFwkZ1dbXefvttffvb307mV6UkGkwBAN1hjJFpaI7r2EBWhmtVdMvh4/jx49q/f3/k+YEDB1RRUaG+fftq8ODBmjdvnh588EGde+65GjJkiL7//e+rqKhI06ZNS+a4U1JHDaY0lwIA2moXNIx07On/p4YjtXF9vmjJeAWCmTaNrnOWw8ef/vQnXXPNNZHn8+fPlyTNmjVLq1at0l133aXa2lrddttt+uyzz3TFFVdow4YNys7OTt6o00DrBlOaSwEgvXRZwbAYNLzGcviYMGGCjDEdvh8IBLRkyRItWbKkWwNLdy2NpQCA1GJ3sMga0Ev9br9I6uL/WQNZ7q054d4uAAAkidMVi1hBw81ejngRPlzUemWLxOoWAPAyLwSLtvwQNGIhfLiElS0A4F3dbebsSioHi3gQPlzC1ukA4A4v9FykcrCIB+HDA9g6HQCSg2DhD4QPD2BlCwBY58bUCMEiOQgfAADPoYKR2ggfDuK+LQBAsADhwzGsbgGQrqLCBlMjEOHDMdy3BUCqiesmZt0IG1QwUhfhwwXctwWA1zE1AjsRPlzA6hYAXmPn1IjUPmwQLNIb4QMAUpydVQwrNzEjbKAF4cMm3LcFgBuSufcFUyOwC+HDBqxsAWAHp/swCBawC+HDBty3BUB3JXv3TqoY8BLCh824bwuAttxYSUKwgJcQPmzGyhYAyVxJQgUDqYDwAQDd4PRKEoIFUgHhAwDixEoSIDkIH0nCTeMAf2MlCeAcwkcSsLQW8B/6MAD3ED6SgJvGAd7hxs3OCBaANYSPJOOmcYCzqGAA/kP4SDKW1gL2sXvjLYIF4AzCBwBPSGbDJzc7A7yN8AHAFXZOlxAqAG8jfCSAO9YC1ti9PwZhA4jNGKPGcDjmez1CIdf+uyF8WMSyWiCa09MlBA2kq86CRMzjZbR20d069tGHMd//19UvKis7O1nDs4TwYRF3rEU6o+ET6D6rIULqOkj4DeGjG7hjLVKd3ctYCRtINV0FCzdCRL9zhupri5cp0Kak2CMUcmwMbRE+uoFltfAzpkuAjnm1OtFRkOiMm70dHSF8AGmAhk/gNC9UJxIJEZI3g0QiCB9ACmIZK9KVX4JFqoSIRBE+AJ/rTlWD6RJ4lRemPdK9OmEnwkccWu/rwZ4ecFuiVQ2mS+AVVCfsY4xRY30XN1b8hx5B9/77J3x0gX094KZkVjUIGnACwcI+XQULY4zW/9939eknx+P6ebc9cbWyQpnJGp4lhI8udLSvB3t6oLvsXm1C2EB3MO1hHyvVidafsRIsvI7wYUHrfT3Y0wNWsDkXvITqRHJ4NUScVdxb/+t7l3R57noEM2wbQ1cIHxawrwfixeZccAvBIjFWg4RblYh4goWbvRzxInwA3cRqEzjF6WDhx2kPr1Yj2oq3OtGWH4JFPAgfgEWsNkF3+aWfwm/VCUKEfxA+gE6w2gRWMe3RNa9WJxIJEukeIhJF+ABaoaqBzhAsoqVSiJAIEk5Kevj4wQ9+oMWLF0e9NmLECO3duzfZX2WL1huKSWwqlsqoaqA1+imieXGKQ0qdhku7GGNkTp6M69hATk5qbTJ2wQUX6M033zz9JT38UWBhQ7HUleymUMKGv3gxWDg57eHFlRxUJzpnJUS0+pA++j8zFH7//bgOH/HuDgV69kxgdN1nSyro0aOHCgsL7fjRtupoQzGJTcX8pjtLXalq+AvBwt0KBSGic06ECD+yJXz89a9/VVFRkbKzs1VSUqKysjINHjzYjq+yTesNxSQ2FfMyqhqpK52DRTzVCi8Ei3QJEVICQcKlEBE6/3yd89wvpS7+vQRychwaUXtJDx/jxo3TqlWrNGLECB05ckSLFy/WlVdeqd27dys3N7fd8eFwWOFWf7lUV1cne0gJYUMx76IpNDWkU7DwwrRHuq7kSKjyEPsHOR4k4g0RbbnZyxGvpIePKVOmRP559OjRGjdunM4++2z9+te/1q233tru+LKysnYNqkALmkJTQ9ugkU7Bwiv9E2kZJDwyfZHKISJRtneC9unTR+edd572798f8/0FCxZo/vz5kefV1dUqLi62e1jwKKoa/mN3BSOdg4WVe3T46c+6n/sgEgkSqRwiEmV7+Dh+/Lg++OADzZgxI+b7oVBIoVDI7mHAg6hqeF+qBAsv7EeRitMefgkRiVYeYiFIJEfSw8edd96pqVOn6uyzz9bhw4e1aNEiZWZm6qabbkr2V8FnqGp4ixd6LpwIFkx7xMcvUxpUHlJD0sPHoUOHdNNNN+nvf/+7+vXrpyuuuELbt29Xv379kv1V8DCqGu7yQrBoK5GgQbCwLtWrEQSJ1JD08LF27dpk/0j4jDFGx55+T/Ufd71yiapG9/m1mdPpYJEK+1F0GSwIEfAJf2w9ahO2Uk+OtlUOU9/UYfCgqmGNX3suvBgsfBUiYn/I9mDBlIa/GGN0sjHxZcQ5PVJse3U/YCv1xFnp3Rhw3zgFgpmR54SN0/wYLNzY+MrrwcKLvRJUI7ytu6GhxawNs7T3/0v8vmlv3/y2emal0PbqfsBW6vHpTu9G8Ow8ZfRiZ1jJnamRZAcLtza+cipYeLUa0VY8wYIQYQ+vhIZUkLbhozW2Uj+NFSnW+aGCQbCgVyJdJSswSN4MDSP7jtTqL69O6LM5PVJoe3U/Yiv1U7rTKJouQUOKDhvpGCzc3PjKq9UJeiWSL9WrDN0JDa252bfRHYSPNBZvo2g6VTXsrGIkMjXixWBhV7XCC9UJqhH2SCRIEBpSG+EjjSTaKJouQSPZwaKttkEjlYOFX5swCRGnpdJ0RbICg0RoSBbCR4pK90ZRp/sw4gkWrZ/7NVjEFSpsDhJUJzqXStMVVBlSF+EjBaXbJl/JXklitYrRNmikbLCgOuGoREOEF0JDLIkECUJD6iJ8pIjWlY5U3+TL7obPzoJFi5bXuhs0UjlY0IR5ml97HpiugF0IHz5kZUrFb5t8JXO6pKsKhjFGCrSfXvJ6sHC7nyLeUJEKQcKvUxhMV8DrCB8+Y2VKxeu9G3Y3fHY5NfIje6dGrAYLv/RT+DFU+HUKI9EQQWiA1xE+PC6V7pti13SJHyoY9FMkh19DhETPA9Aa4cPDuqpyeHlKpTtVDSvTJd2tYCQzWJiOD6CfIga/9EEwhQEkX9qED7/cwTbexlEvTakkM2h0VMVo/V2Jho1EpkacDhZ+7KfwSzWCKQz4njFSw4nk/bysnpaXrCdL2oSPhoYGPfzww24PI4ofGkeTvV9GZ9MlyQ4WbbUNGl4MFm6GCkIEYFGyw0BX37Xyy1LlruT9zIWHpWCv5P08C9ImfHTErTvYerVx1M5lrHZOl1iuYBAs2vHLlAYhApbYFRDsCANpJG3CR1ZWlhYuXBjzdaf+IvPaXhzJXG1y1uAh+sp9ZW2mNEIJVTWsVjHsrmB4LVh4sVeCagQscapikIoBofBC6ZYNyZkuyerZ/Z+RoLQJH4FAwNU713ZW6XBqSiXRqkasCkZjw+mVJL/5cYX+fiisVXf/0fKYuurD6GiViDmpU2HDx8HCi9MchAhEUDGITzLDQDxc7NNIprQJH06Ld4msXVMqyWoCjbcvI97xtw4bxhhlNoUVaI5u/k1WsGgrVtBwM1h4sVeCEOFDdoQEAkL8UiQMOI3wYQMrS2STVeVItKrR2XRJsqdKosKGT3ouvFidkOiV8B2qCO05WTEgIHgO4SNJnFwi252qRkvYkJS06ZKOKhhtBp1w2HAzWHgxREgECcckIzQQEGIjEKQ1wkcS2NnPkYyg0RIQYoWNeMbStqqRzApGugcLQkQSUFWwLyQQEGATwkcSmIZmW/o5jDFae/9dOvyX+C7s8VQ12o4lVrBo2+DZI5hxuqqR5LDhh2BBdcIm6VZVoIoARBA+EhCrmbRFd/s5Wlc6GsJ1HQaPRKsanU6X2Dw1InUdNrwYLAgRrSSrypCOoYGAAEQQPizqqpk0EMxURqtplq5+VrxTKt/8yWplhbI7XdoaT1XDrumSeCoYBAsXpXqVgaoC4CuEjzhYaSYNZGV0+nMSWZESyCzSL++riLpIxqxqDOypaXMuiDxPdLrEah9GvCtCCBYJIDTEh4AA+ArhowvJaia10r8RyOynYO6Nimxxqh7tqxqtg4Yx+vjWb6qxfJc+fD6uXyuiq70vOgwWrV5LduNmSgaLREKEV0NDsndY9NO/RwBJQfjoQqLNpG2nVDrq32gfNKS2YSPeoBHPX+Gtw4YxRnVZUl3b34FgcVoqVR6oMgDwCMJHG4k2k1qZUgnl3y4FWm5m10lVw2LQ6Gq6pG3YmLVhtuMrQlwLFn6uPBAaAKQYwkcriTaTWptSKZICpy/A8VQ14gkaHVYxWkk0bHiuWmE1SPg9RBAaAKQYwkcrHU2xSNHNpN2dUpm95FJlZQXirmrEEzR8GSz8Uo2g8gAASZXW4SPeKRZjjJqaG9QYDnd7SiW/6gMdvHpOJGDEuhRljRyhwtU/i7wbyMlOStCQ2oeNpASLVA8RhAYASKq0DR/xTrEYY/SrBKdUetd8oksqHlPAmMgxGc31UYGjbdC47c3btKtmn/TSNZZ/J1uqGF0FC7emNBIJEoQIAPCE9A0fnUyxZBX3VmNTvQJ1AUtTKr1rjmjM1u9FwkbboCFFh40Og0YcF8hYQaPLYOHFXgmqEQCQdtI2fLTWMsVijNGLD9ynQ1t2S1vaH9d2SuXKP9yjzKbTd3JNqKoR4wKaUAXD7QoFIQIAECfCh05PsTTU1enQX3fHPqbNlEp+1QfKajjeYdiIt6rRZQWjo1DR+jUvBAtCBAAgTmkTPjprLm2sq1OgOVP1dac314quckhtKx0ZzfUqLn9DgZwcSQlWNYxRjjFRPSGSTgcLO0IFvRIA0CVjjE42NHV9oI/lZGW6tqFj+oSPhmYdvv8PMd976rb/oybTEP1iIEuBVuGjbaVj7yDpxg1fjr4od1XVaB023AoWBAkg7aXDhbU7jJH+99PbtOdI7L7AVLFnyWT1DLoTA9ImfHTkWN2hdsEjkFmkK/5wn3q06uf4uF+9Zn7v9AZj4Sy1Dxt9ztPqiU9HXs/JzD5d1ehO2Ii3WkGwAGLiYntaulxY4W3pEz56BFSw8JLI09qaWq3+3r+oyTS0m2LJr/pYpbf/QeHg6Y+HszKjLuxRQcMY6ZfTlHPgTQV2Drc2LqoVcEm6XJC52CJRnxuQpxduL0nZv35zstrv2O2UtAkfjeGwnvzWjTHfG799sYKNp6scfymqV3WvDsKGlHjQkNqHDYKFp6XqBZoLMlL9wpoMbvZEpLq0CR8N9bEvIIHMIs371mGdyD79fjgrU+U3vK6cHjnWqhpUMSzx+oWdC3Rq4WIbjQsr3JQ24SOjsUmhPnPbvZ5f9bFOhP6gcPD0f4T/VFenvj8a1fkt6mMFDY8HCy9d7Lmwe0M6XZC52ALekTbhozGjXhN+f1e71/9SVK8NRyrVU6eXu+YY0z542DBd4mQY4GKfuFS+QHNBBuAG28LH8uXL9cMf/lCVlZW66KKL9OSTT+qyyy6z6+u6lBGUZt/R/kL/uaZG/a/K5uiw0SZoGGN0UqHosNHN0EAYOMUPF3Yu0ACQXLaEj1/96leaP3++nn76aY0bN06PP/64Jk+erH379qmgoMCOr+xSTma2thw+1O71UMEFOnnnO4q6v2yrqkaqhQSvXey5sANA+gkY03Z7ze4bN26cxo4dq5/85CeSpObmZhUXF2vu3Lm65557Ov1sdXW18vPzVVVVpby8vKSN6US4QWMW/abd6ycVUuwb2zvD6TDAxR4AYAcr1++kVz7q6+u1Y8cOLViwIPJaRkaGJk6cqG3btiX76+IXCOikshP+uF0hgTAAAEg3SQ8fn376qZqamtS/f/+o1/v376+9e/e2Oz4cDiscDkeeV1fbM72Rk5WpPUsmd+vzhAQAALrP9dUuZWVlWrx4se3fEwgEXNvDHgAAnJaR7B941llnKTMzU0ePHo16/ejRoyosLGx3/IIFC1RVVRV5fPLJJ8keEgAA8JCkh49gMKgxY8Zo06ZNkdeam5u1adMmlZSUtDs+FAopLy8v6gEAAFKXLfMQ8+fP16xZs3TppZfqsssu0+OPP67a2lrdcsstdnwdAADwEVvCx4033qhjx47p/vvvV2VlpS6++GJt2LChXRMqAABIP7bs89Eddu3zAQAA7GPl+p30ng8AAIDOED4AAICjCB8AAMBRhA8AAOAowgcAAHAU4QMAADiK8AEAABxF+AAAAI7y3G1eW/Y8q66udnkkAAAgXi3X7Xj2LvVc+KipqZEkFRcXuzwSAABgVU1NjfLz8zs9xnPbqzc3N+vw4cPKzc1VIBBI6s+urq5WcXGxPvnkE7ZutxHn2RmcZ2dwnp3DuXaGXefZGKOamhoVFRUpI6Pzrg7PVT4yMjI0aNAgW78jLy+PP9gO4Dw7g/PsDM6zczjXzrDjPHdV8WhBwykAAHAU4QMAADgqrcJHKBTSokWLFAqF3B5KSuM8O4Pz7AzOs3M4187wwnn2XMMpAABIbWlV+QAAAO4jfAAAAEcRPgAAgKMIHwAAwFEpFz6WL1+uc845R9nZ2Ro3bpz++Mc/dnr8Cy+8oJEjRyo7O1sXXnihXnvtNYdG6m9WzvMzzzyjK6+8UmeccYbOOOMMTZw4sct/LzjF6p/nFmvXrlUgENC0adPsHWCKsHqeP/vsM5WWlmrAgAEKhUI677zz+LsjDlbP8+OPP64RI0YoJydHxcXFuuOOO1RXV+fQaP1py5Ytmjp1qoqKihQIBPTSSy91+Zny8nJdcsklCoVCGj58uFatWmX7OGVSyNq1a00wGDQ///nPzZ///GfzrW99y/Tp08ccPXo05vFvvfWWyczMNI888ojZs2ePue+++0xWVpbZtWuXwyP3F6vn+eabbzbLly83O3fuNO+//76ZPXu2yc/PN4cOHXJ45P5i9Ty3OHDggBk4cKC58sorzfXXX+/MYH3M6nkOh8Pm0ksvNddee63ZunWrOXDggCkvLzcVFRUOj9xfrJ7n559/3oRCIfP888+bAwcOmI0bN5oBAwaYO+64w+GR+8trr71m7r33XrNu3Tojyaxfv77T4z/88EPTs2dPM3/+fLNnzx7z5JNPmszMTLNhwwZbx5lS4eOyyy4zpaWlkedNTU2mqKjIlJWVxTz+q1/9qrnuuuuiXhs3bpz5l3/5F1vH6XdWz3NbjY2NJjc316xevdquIaaERM5zY2OjGT9+vPnZz35mZs2aRfiIg9XzvGLFCjN06FBTX1/v1BBTgtXzXFpaar7whS9EvTZ//nxz+eWX2zrOVBJP+LjrrrvMBRdcEPXajTfeaCZPnmzjyIxJmWmX+vp67dixQxMnToy8lpGRoYkTJ2rbtm0xP7Nt27ao4yVp8uTJHR6PxM5zWydOnFBDQ4P69u1r1zB9L9HzvGTJEhUUFOjWW291Ypi+l8h5/s1vfqOSkhKVlpaqf//+GjVqlB5++GE1NTU5NWzfSeQ8jx8/Xjt27IhMzXz44Yd67bXXdO211zoy5nTh1nXQczeWS9Snn36qpqYm9e/fP+r1/v37a+/evTE/U1lZGfP4yspK28bpd4mc57buvvtuFRUVtfsDj9MSOc9bt27Vs88+q4qKCgdGmBoSOc8ffvihfvvb3+rrX/+6XnvtNe3fv1/f+c531NDQoEWLFjkxbN9J5DzffPPN+vTTT3XFFVfIGKPGxkbdfvvtWrhwoRNDThsdXQerq6t18uRJ5eTk2PK9KVP5gD8sXbpUa9eu1fr165Wdne32cFJGTU2NZsyYoWeeeUZnnXWW28NJac3NzSooKNC///u/a8yYMbrxxht177336umnn3Z7aCmlvLxcDz/8sJ566im9++67WrdunV599VU98MADbg8NSZAylY+zzjpLmZmZOnr0aNTrR48eVWFhYczPFBYWWjoeiZ3nFo8++qiWLl2qN998U6NHj7ZzmL5n9Tx/8MEH+uijjzR16tTIa83NzZKkHj16aN++fRo2bJi9g/ahRP48DxgwQFlZWcrMzIy8dv7556uyslL19fUKBoO2jtmPEjnP3//+9zVjxgx985vflCRdeOGFqq2t1W233aZ7771XGRn8v3MydHQdzMvLs63qIaVQ5SMYDGrMmDHatGlT5LXm5mZt2rRJJSUlMT9TUlISdbwkvfHGGx0ej8TOsyQ98sgjeuCBB7RhwwZdeumlTgzV16ye55EjR2rXrl2qqKiIPP75n/9Z11xzjSoqKlRcXOzk8H0jkT/Pl19+ufbv3x8Jd5L0l7/8RQMGDCB4dCCR83zixIl2AaMl8BluSZY0rl0HbW1nddjatWtNKBQyq1atMnv27DG33Xab6dOnj6msrDTGGDNjxgxzzz33RI5/6623TI8ePcyjjz5q3n//fbNo0SKW2sbB6nleunSpCQaD5sUXXzRHjhyJPGpqatz6FXzB6nlui9Uu8bF6ng8ePGhyc3PNnDlzzL59+8wrr7xiCgoKzIMPPujWr+ALVs/zokWLTG5urvmP//gP8+GHH5r/+q//MsOGDTNf/epX3foVfKGmpsbs3LnT7Ny500gyP/rRj8zOnTvNxx9/bIwx5p577jEzZsyIHN+y1Pbf/u3fzPvvv2+WL1/OUttEPPnkk2bw4MEmGAyayy67zGzfvj3y3tVXX21mzZoVdfyvf/1rc95555lgMGguuOAC8+qrrzo8Yn+ycp7PPvtsI6ndY9GiRc4P3Ges/nlujfARP6vn+Q9/+IMZN26cCYVCZujQoeahhx4yjY2NDo/af6yc54aGBvODH/zADBs2zGRnZ5vi4mLzne98x/zP//yP8wP3kd/97ncx/75tObezZs0yV199dbvPXHzxxSYYDJqhQ4ealStX2j7OgDHUrwAAgHNSpucDAAD4A+EDAAA4ivABAAAcRfgAAACOInwAAABHET4AAICjCB8AAMBRhA8AAOAowgcAAHAU4QMAADiK8AEAABxF+AAAAI76/wHxa8H03rBPTgAAAABJRU5ErkJggg==\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
Name N sig N bkg TPR FPR N sig' N bkg' sig x_c bin i
1 10 100 1 1 10 100 0.953463 1 99
2 100 1000 1 1 100 1000 3.01511 1 99
2.1 200 2000 1 1 200 2000 4.26401 1 99
2.2 300 3000 1 1 300 3000 5.22233 1 99
2.3 400 4000 1 1 400 4000 6.03023 1 99
2.4 500 5000 1 1 500 5000 6.742 1 99
3 1000 10000 1 1 1000 10000 9.53463 1 99
4 10000 100000 1 1 10000 10000030.1511 1 99
" - ] - }, - "metadata": {} - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from IPython.display import HTML, display\n", - "import tabulate\n", - "\n", - "def compare_significance(scenarios, log=False):\n", - " max_sigs = dict()\n", - " table = []\n", - "\n", - " for name, (n_sig_expected, n_bkg_expected) in scenarios.items():\n", - " TPR = np.linspace(0, 1, 100)\n", - " FPR = np.linspace(0, 1, 100)\n", - "\n", - " # Calculate expected number of signal and background events passing the threshold cut\n", - " n_sig_expected_prime = n_sig_expected * TPR\n", - " n_bkg_expected_prime = n_bkg_expected * FPR\n", - "\n", - " # Calculate significance, handle division by zero\n", - " with np.errstate(divide='ignore', invalid='ignore'):\n", - " sig = np.divide(n_sig_expected_prime, np.sqrt(n_sig_expected_prime + n_bkg_expected_prime))\n", - "\n", - " # Plot significance as a function of TPR\n", - " plt.step(TPR, sig, label=name)\n", - "\n", - " # Find maximum significance and store relevant data\n", - " max_i = np.nanargmax(sig) # Use np.nanargmax to ignore NaN values\n", - " max_sigs[name] = (max_i, n_sig_expected_prime[max_i], n_bkg_expected_prime[max_i], sig[max_i], TPR[max_i])\n", - "\n", - " # Append data to table\n", - " table.append((name, n_sig_expected, n_bkg_expected, TPR[max_i], FPR[max_i], n_sig_expected_prime[max_i],\n", - " n_bkg_expected_prime[max_i], sig[max_i], TPR[max_i], max_i))\n", - "\n", - " # Display plot\n", - " if log:\n", - " plt.yscale(\"log\")\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - " # Display table\n", - " display(HTML(tabulate.tabulate(table, tablefmt='html',\n", - " headers=[\"Name\", 'N sig', 'N bkg', \"TPR\", \"FPR\", \"N sig'\", \"N bkg'\", 'sig', 'x_c', \"bin i\"])))\n", - "\n", - " return max_sigs\n", - "\n", - "# Define scenarios\n", - "scenarios = {\"1\": (10, 100),\n", - " \"2\": (100, 1000),\n", - " \"2.1\": (200, 2000),\n", - " \"2.2\": (300, 3000),\n", - " \"2.3\": (400, 4000),\n", - " \"2.4\": (500, 5000),\n", - " \"3\": (1000, 10000),\n", - " \"4\": (10000, 100000)}\n", - "\n", - "# Compare significance\n", - "max_sigs = compare_significance(scenarios)\n" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "-6OTqp5d_r2C" - }, - "source": [ - "## Exercise 7: ROC Curves\n", - "\n", - "### Exercise 7.1\n", - "For the top 3 observables you identified earlier, create one figure overlaying the Reciever Operating Characteristic (ROC) curves for the 3 observables. Compute the area under the curves and report it in the legend of the figure.\n", - "\n", - "### Exercise 7.2\n", - "Write a function that you can use to quickly create the figure in part a with other observables and different conditions. Note that you will likely revise this function as you do the remainder of the lab.\n", - "\n", - "### Exercise 7.3\n", - "Use the function from part b to compare the ROC curves for the successive selections in lab 3, exercise 4. Specifically, plot the ROC curve after each selection.\n", - "\n", - "### Exercise 7.4\n", - "Use your function and appropriate example to demonstrate the effect (if any) of changing order of the successive selections.\n", - "\n" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 619 - }, - "id": "i6a6O4RI_r2b", - "outputId": "0af5a310-266c-4a97-a409-cefd36dc7ec2" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
Name N sig N bkg TPR FPR N sig' N bkg' sig x_c bin i
1 10 100 1 1 10 100 0.953463 1 99
2 100 1000 1 1 100 1000 3.01511 1 99
2.1 200 2000 1 1 200 2000 4.26401 1 99
2.2 300 3000 1 1 300 3000 5.22233 1 99
2.3 400 4000 1 1 400 4000 6.03023 1 99
2.4 500 5000 1 1 500 5000 6.742 1 99
3 1000 10000 1 1 1000 10000 9.53463 1 99
4 10000 100000 1 1 10000 10000030.1511 1 99
" - ] - }, - "metadata": {} - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from IPython.display import HTML, display\n", - "import tabulate\n", - "\n", - "def compare_significance(scenarios, log=False):\n", - " max_sigs = dict()\n", - " table = []\n", - "\n", - " for name, (n_sig_expected, n_bkg_expected) in scenarios.items():\n", - " TPR = np.linspace(0, 1, 100)\n", - " FPR = np.linspace(0, 1, 100)\n", - "\n", - " # Calculate expected number of signal and background events passing the threshold cut\n", - " n_sig_expected_prime = n_sig_expected * TPR\n", - " n_bkg_expected_prime = n_bkg_expected * FPR\n", - "\n", - " # Calculate significance, handle division by zero or taking square root of negative numbers\n", - " with np.errstate(divide='ignore', invalid='ignore'):\n", - " sig = np.divide(n_sig_expected_prime, np.sqrt(n_sig_expected_prime + n_bkg_expected_prime))\n", - "\n", - " # Plot significance as a function of TPR\n", - " plt.step(TPR, sig, label=name)\n", - "\n", - " # Find maximum significance and store relevant data\n", - " max_i = np.nanargmax(sig)\n", - " max_sigs[name] = (max_i, n_sig_expected_prime[max_i], n_bkg_expected_prime[max_i], sig[max_i], TPR[max_i])\n", - "\n", - " # Append data to table\n", - " table.append((name, n_sig_expected, n_bkg_expected, TPR[max_i], FPR[max_i], n_sig_expected_prime[max_i],\n", - " n_bkg_expected_prime[max_i], sig[max_i], TPR[max_i], max_i))\n", - "\n", - " # Display plot\n", - " if log:\n", - " plt.yscale(\"log\")\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - " # Display table\n", - " display(HTML(tabulate.tabulate(table, tablefmt='html',\n", - " headers=[\"Name\", 'N sig', 'N bkg', \"TPR\", \"FPR\", \"N sig'\", \"N bkg'\", 'sig', 'x_c', \"bin i\"])))\n", - "\n", - " return max_sigs\n", - "\n", - "# Define scenarios\n", - "scenarios = {\"1\": (10, 100),\n", - " \"2\": (100, 1000),\n", - " \"2.1\": (200, 2000),\n", - " \"2.2\": (300, 3000),\n", - " \"2.3\": (400, 4000),\n", - " \"2.4\": (500, 5000),\n", - " \"3\": (1000, 10000),\n", - " \"4\": (10000, 100000)}\n", - "\n", - "# Compare significance\n", - "max_sigs = compare_significance(scenarios)\n" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "4KVNhaw0_r2l" - }, - "source": [ - "## Exercise 8: Linear Discriminant\n", - "\n", - "### Exercise 8.1\n", - "\n", - "Using numpy, compute the between-class $\\bf{S}_B$ and within-class $\\bf{S}_W$ covariance matrices defined as:\n", - "\n", - "$$\n", - "\\bf{S}_B = (\\bf{m_2}-\\bf{m_1})(\\bf{m_2}-\\bf{m_1})^T \\\\\n", - "$$\n", - "$$\n", - "\\bf{S}_W = \\sum_{i=1,2} \\sum_{n=1}^{l_i} (\\bf{x}_n^i - \\bf{m}_i) (\\bf{x}_n^i - \\bf{m}_i)^T\n", - "$$\n", - "\n", - "where $\\bf{m_i}$ are the vectors containing the means for category 1 and 2, here defined as signal and background. Here $\\bf{x}_n^i$ is the vector containing the observables for the $n$th example event in category $i$.\n", - "\n", - "### Exercise 8.1\n", - "\n", - "Compute the linear coefficients $\\bf{w} = \\bf{S_W}^{-1}(\\bf{m_2}-\\bf{m_1})$. Compare the histogram of the distribution of $F_n^i=\\bf{w}^T\\bf{x}_n^i$ for the two categories.\n", - "\n", - "### Exercise 8.1\n", - "\n", - "Draw the ROC curve for $F_n$.\n", - "\n", - "### Exercise 8.1\n", - "\n", - "What is the maximal significance you can obtain in the scenarios in exercise 5?" + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqYAAAINCAYAAADsoL2yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABsHklEQVR4nO3de3hU1dk28HvvPSQkQBIgCQGTcFIhggmEBIuWatVqtdVa7KevR1CrVbFXW2prbeuBWqv2VeqhfrbVVrR4aO2rvv08n6u1HsiBk0xASSREQBIgiZCQZPbe3x/DDJlkJpk9x/XM3L/ryjVkZzKzVu5MeGbttdbWbNu2QURERESUZHqyG0BEREREBLAwJSIiIiJFsDAlIiIiIiWwMCUiIiIiJbAwJSIiIiIlsDAlIiIiIiWwMCUiIiIiJbAwJSIiIiIluJLdgGhYloXt27djzJgx0DQt2c0hIiIiogFs28YXX3yBSZMmQdeHHhMVXZhu374dJSUlyW4GEREREQ1j27ZtKC4uHvI+ogvTMWPGAPB2NCcnJ+7P5/F4UF9fj7lz58LlEv2jS1vMUD5mKB8zlI35yZfoDDs7O1FSUuKv24Yi+jfKd/o+JycnYYXpqFGjkJOTwxejUMxQPmYoHzOUjfnJl6wMw5l2qdm2bSegLXHR2dmJ3NxcdHR0JKQwtW0b3d3dyMrK4pxWoZihfMxQPmYoG/OTL9EZOqnXuCrfoYyMjGQ3gaLEDOVjhvIxQ9mYn3yqZsjC1AHTNFFTUwPTNJPdFIoQM5SPGcrHDGVjfvKpnCEnhxAREVHETNNEX19fsptBDng8HgDAgQMHYjLH1DAMuFyumEwLYGFKREREEdm3bx9aWlogeLlKWrJtGyNHjkRzc3PM5phmZ2dj4sSJUU8RYGFKREREjpmmiZaWFmRnZ6OgoIALoQSxbRtdXV3Izs6OOjfbttHb24vW1lY0NTXhiCOOGHYT/aFwVb4Dtm3DNE0YhsEXoFDMUD5mKB8zlM2XX19fHz799FNMmTIFWVlZyW4WOdC/9IvVa7Crqwtbt27F1KlTMXLkyICvcVV+HPX29ia7CRQlZigfM5SPGcrWPz++uZDJsqyYPl40o6QBjxOTR0kTpmli3bp1Sq5io/AwQ/mYoXzMUDbmlxq6u7uT3YSgOMeUiIiIYqe5GWhrS9zz5ecDpaUxeaglS5agvb0dzz77bEweL1w333wznn32WaxZsyahz6siFqZEREQUG83NQFkZ0NWVuOfMzgbc7pgUp/fccw93GEgyFqYOGYaR7CZQlJihfMxQPmYoW8j82tq8RemqVd4CNd7cbuDCC73PG4PCNDc3NwaNkkHVucEsTB1wuVyorq5OdjMoCsxQPmYoHzOUzZffgQMHQt+prAyorExcoxz6xz/+geXLl+OTTz5BdnY25s6di//93//F0qVLA07lf/HFF7jyyivx7LPPIicnBz/96U/xv//7v5gzZw7uvvtuAMCUKVNwxRVX4JNPPsFTTz2FsWPH4pe//CWuuOIK//Ndd911eOaZZ9DS0oKioiJccMEFuPHGGzFixIgk9N5blI4aNSopzz0cLn5ywLZttLe3c5hfMGYoHzOUjxnKJj2/HTt24LzzzsOll14Kt9uNt956C4sWLQran2XLluHdd9/FP//5T7z66qt45513UFdXN+h+d911F6qqqlBfX4+rr74aV111FTZt2uT/+pgxY7By5Ups3LgR99xzDx588EH87ne/i2s/h2LbNjwej5IZsjB1wDRNNDQ0cCWiYMxQPmYoHzOUTXp+O3bsgMfjwaJFizBlyhQcffTRuPrqqzF69OiA+33xxRd45JFHcOedd+Kkk07C7Nmz8fDDDwft9+mnn46rr74ahx9+OK677jrk5+fjzTff9H/9l7/8JY499lhMmTIFZ5xxBq699lr8/e9/j3tfhzLkiHcS8VQ+ERERpY2KigqcdNJJOProo3HqqafilFNOwXe+8x2MHTs24H6NjY3o6+vD/Pnz/cdyc3MxY8aMQY9ZXl7u/7emaSgqKsKuXbv8x/72t7/h3nvvxZYtW7Bv3z54PJ6EXBhIIo6YEhERUdowDAOvvvoqXnzxRRx11FG47777MGPGDDQ1NUX8mAPnimqa5t/A/r333sMFF1yA008/Hc899xzq6+vxi1/8gheZCIGFqQOapiErK0vZlWw0PGYoHzOUjxnKlgr5aZqG4447DsuXL0d9fT0yMjLwzDPPBNxn2rRpGDFiBFavXu0/1tHRgc2bNzt6rv/85z+YPHkyfvGLX6CqqgpHHHEEtm7dGpN+RCNWV2qKNZ7Kd8AwDIwdW4G1awd/LYb7+1IcGYaBioqKZDeDosAM5WOGsvnyU3WO4nA++OADvP766zjllFNQWFiIDz74AK2trSgrK8O6dev89xszZgwWL16Mn/zkJxg3bhwKCwtx0003Qdd1R0X5EUccgebmZjz55JOorq7G888/P6gITjRN05CdnZ3UNoTCwtSBTz+1cNRRGrq7B/9CxnB/X4ojy7LQ1taG/Px8Zd8t0tCYoXzMUDZffgMXCwVwuxPTmAieJycnB2+//TbuvvtudHZ2YvLkybjrrrtw2mmn4W9/+1vAfVesWIErr7wS3/zmN/3bRW3btg0jR44M+/nOPPNM/OhHP8I111yDnp4efOMb38ANN9yAm2++2XHbY8W3Kt/lcik38q3ZKu4VEKbOzk7k5uaio6MjIZOIP/zQg2OOceGRR0zMnn1oc2Hf/r61tUpv20YAPB4PampqUFVVBZeL78skYobyMUPZfPnNnj0b27Ztw9SpUw8VasKv/DSc/fv347DDDsNdd92Fyy67LO7PFy+2bWP//v0YNWpUzArTAwcOoKmpKfD34SAn9Rr/IkRg5kybBSgREdFApaXeIrGtLXHPGce5dPX19WhoaMD8+fPR0dGBX/3qVwCAb33rW3F5PkpyYXrzzTdj+fLlAcdmzJiBhoaGJLUoOgPPKHDeKRERpZ3S0pT6z+/OO+/Epk2bkJGRgXnz5uGdd95Bfn5+spuVspI+Yjpr1iy89tpr/s9VPq3jG+4eOOydn+89k3DhhYH357xT9WiahtzcXOXm1FD4mKF8zFC2dMpv7ty5qK2tTXYz4sIwjOHvlARJrwJdLheKioqS3Yyw+EIcGGawMxe+eadtbSxMVWIYBsrKypLdDIoCM5SPGcrmy0/qqnw6tOWXipJemH788ceYNGkSRo4ciQULFuC2225DaYhKrqenBz09Pf7POzs7AXgnYns8HgDefbl0XYdlWf7NbfsfN00z4NqwoY4bhgFN0/yPCwB9fTaAETBNEx5P4JqxkhIDJSXwX6rM+23eH69t2wGXMNM0DYZhDGpjqOPx7JPvOIBBl1kLddzlcontk6Zp2L59OyZMmBCwGlhyn1Ixp6H6ZJomPvvsMxQVFfm3bZHep1TMaag+eTwe7Ny5E0VFRTAMIyX6lIo5heqTL7+8vDx/u4Kto9Y0LSbHnYjVc8b7uBPxaItt2+jr60NGRkbMHt+2bf9qf9/vpu93b+Dv6lCSWpgec8wxWLlyJWbMmIEdO3Zg+fLlWLhwITZs2IAxY8YMuv9tt902aE4q4J2cPGrUKABAQUEBpk+fjqamJrS2tvrvU1xcjOLiYmzevBkdHR3+49OmTUNhYSE2bNiA7u5u//GZM2ciLy8P9fX1/hdtQ0M2gHKYpomamrqANlRVVaG3t9e/B9qmTd77At4NefvPm83KykJFRQXa2trQ2NjoP56bm4uysjJs374dLS0t/uPx7BPgvZRaRkYGampqhuwT4P0lq66uFtunuXPnorm5GS0tLf7TUNL7lIo5DdWn9vZ2fPTRR/jss8/87/ql9ykVcxqqT1u2bEF7ezs+++wz5OXlpUSfUjGnUH3atm0b2tvbkZeX5/+/t6enJ6D4yMjIQEZGBg4cOBDQxszMTIwYMQLd3d0Bhe/IkSPhcrnQ1dUVUPRkZWVB13Xs378/oE+jRo2CZVkBPxdN0zBq1CiYphkwmqvrOrKzs+HxeAIGtwzDQFZWFvr6+gKuwuRyuTBy5MiU75OvD5mZmTHpEwD09vZiw4YN/uO+3736+nqES6ntotrb2zF58mSsWLEi6DYMwUZMS0pKsHv3bv/2A/F8R1pTY2HBggx88EEfKisD59YMfEdaVwccc4wLtbXA3Ll8l61Kn2zbRk1NDSorKwOmZEjuUyrmNFSf+vr6AjJMhT6lYk5D9amvrw91dXWorKyEy+VKiT6lYk6h+uTLb9asWWhpacG0adOQmZmJgVJldDEex52I14hpV1cXsrOzoet6TB7ft11UaWmpf7so3+/e3r17MX78eHnbReXl5eHII4/EJ598EvTrmZmZQX/5XS7XoEVTvhf0QKEm+4Y63v9xdd33R0ALuUjLd7z/lzUt+P1DtdHp8Wj6FOlxqX3yeDz+P7ADvya1T0MdT9U+BctQep9SMadQfer/hsLXBul9SsWcQrXdl5+uH7oCUqiFULE67kS825JKfYplfr6/zcFqMicL25W65Ma+ffuwZcsWTJw4MdlNCcr3Ig32YiUZdF1HQUEBMxSMGcrHDGVjfqlB1V2Qktqqa6+9FmeccQYmT56M7du346abboJhGDjvvPOS2ayQWJjKp+s6pk+fnuxmUBSYoXzMUDZffqFW5Tc3q72//gknnIA5c+bg7rvvjkt7lixZgvb2djz77LNxefxY0DTN0WVVP/30U0ydOhX19fWYM2dO/BqGJBemLS0tOO+887B7924UFBTgy1/+Mt5//30UFBQks1kheefb6P5bkseyLP8l0/gGQyZmKB8zlM2XX7Czmyl+RdKUYds2enp6kJmZqdx+tEktTJ988slkPr1jLEzlsywLra2tmDx5Mv9DFIoZyscMZfPlN2HChEFfa2vzFqWrVnkL1HhLlz3De3t7kZGREdPH9Hg8QdftJBv/IhAREVFMlZUBlZXx/4i0+PV4PLjmmmuQm5uL/Px83HDDDf6V5n/9619RVVWFMWPGoKioCOeffz527doV8P0fffQRvvnNbyInJwdjxozBwoULsWXLlqDPtXr1ahQUFOCOO+7wH/v1r3+NwsJCjBkzBt/97nfxs5/9LOAU+ZIlS3DWWWfh1ltvxaRJkzBjxgwAwPr163HiiSciKysL48ePxxVXXIF9+/b5v++EE07AD3/4w4DnP+uss7BkyRL/51OmTMFvfvMbXHXVVcjJyUFpaSn+9Kc/BXzPhx9+iLlz52LkyJGoqqpytN1TtFiYEhERUVp55JFH4HK58OGHH+Kee+7BihUr8NBDDwEA+vr6cMstt2Dt2rV49tln8emnnwYUdp999hm+8pWvIDMzE2+88QZqa2tx6aWXBt1E/o033sDXvvY13HrrrbjuuusAAI899hhuvfVW3HHHHaitrUVpaSkeeOCBQd/7+uuvY9OmTXj11Vfx3HPPYf/+/Tj11FMxduxYrF69Gk899RRee+01XHPNNY77v2LFClRWVqKurg5XX301rrrqKmzatAmAdyH6N7/5TRx11FGora3FzTffjGuvvdbxc0RKzSVZiuLiJ/l0XUdxcTEzFIwZyscMZUuF/EpKSvC73/0OmqZhxowZWL9+PX73u9/h8ssvx6WXXuq/37Rp03Dvvfeiuroa+/btw+jRo3H//fcjNzcXTz75JEaMGAEAOPLIIwc9xzPPPIOLL74YDz30EM4991z/8fvuuw+XXXYZLrnkEgDAjTfeiFdeeSVg5BPwbrj/0EMP+U/hP/jggzhw4AAeffRR/4UNfv/73+OMM87AHXfcEXRqRSinn346li5dihEjRuC6667D7373O7z55puYMWMGHn/8cViWhT//+c8YOXKkf7/aq666KuzHj4bc36okYGEqXyr8QU13zFA+ZihbKuT3pS99KWDRz4IFC/Dxxx/DNE3U1tbijDPOQGlpKcaMGYPjjz8eANDc3AwAWLNmDRYuXOgvSoP54IMP8H/+z//BX//614CiFAA2bdqE+fPnBxwb+DkAHH300QHzSt1uNyoqKvxFKQAcd9xxsCzLP9oZLt9Vwnx7jxYVFfmnK7jdbpSXlwes2l+wYIGjx4+G3N+qJPBdHWPg1TNIDtM04Xa7maFgzFA+ZihbKud34MABnHrqqcjJycFjjz2G1atX45lnngEA/yU+fZffHMr06dMxc+ZM/OUvf0FfX19EbelfgIYr2FWcgj2/y+VCd3e3/76apgVc6SuZWJg60P9SXiSTbdvo6OhghoIxQ/mYoWypkN8HH3wQ8Pn777+PI444Ag0NDdi9ezduv/12LFy4EDNnzhy08Km8vBzvvPPOkAVnfn4+3njjDXzyySc455xzAu47Y8YMrF69OuD+Az8PpqysDGvXrsX+/fv9x959913ouu5fHFVQUIAdO3b4v26aZsC16/sL9cairKwM69atC9in9v333x+2fbHCwpSIiIjSSnNzM5YtW4ZNmzbhiSeewH333Ycf/OAHKC0tRUZGBu677z40Njbin//8J2655ZaA773mmmvQ2dmJ//qv/0JNTQ0+/vhj/PWvfx10Or2wsBBvvPEGGhoacN555/kXR33/+9/Hn//8ZzzyyCP4+OOP8etf/xrr1q0bdj/RCy64ACNHjsTixYuxYcMGvPnmm/j+97+Piy66yD+/9MQTT8Tzzz+P559/Hg0NDbjqqqvQ3t7u6Gdz/vnnQ9M0XH755di4cSNeeOEF3HnnnY4eIxpc/EREREQx5Xar/TwXX3wxuru7MX/+fBiGgR/84Ae44ooroGkaVq5ciZ///Oe49957UVlZiTvvvBNnnnmm/3vHjx+PN954Az/5yU9w/PHHwzAMzJkzB8cdd9yg5ykqKsIbb7yBE044ARdccAEef/xxXHDBBWhsbMS1116LAwcO4JxzzsGSJUvw4YcfDtnm7OxsvPzyy/jBD36A6upqZGdn4+yzz8aKFSv897n00kuxdu1aXHzxxXC5XPjRj36Er371q45+NqNHj8b/+3//D1deeSXmzp2Lo446CnfccQfOPvtsR48TKc0WPBbf2dmJ3NxcdHR0ICcnJ+7PV1Njobpax+rVFqqqhh5srqsD5s0Damu9e62RGizLQltbG/Lz80VP3E9nzFA+ZiibL7/Ro0dj69atmDp1qn+hDK/8FJmvfe1rKCoqwl//+teEPJ9t2/B4PHC5XDG78tOBAwf8V3QbeLlTJ/UaR0wd4Kp8+XRdR2FhYbKbQVFghvIxQ9l8+fWfg+hTWuotEtvaEtee/HxZRWlXVxf+8Ic/4NRTT4VhGHjiiSfw2muv4dVXX01YGzRNG3JXgWRiYeqAd6Kw4b8leXwTwWfPng3DYIYSMUP5mKFsvvwOP/zwoF8vLZVVKCaapml44YUXcOutt+LAgQOYMWMG/ud//gcnn3xywtpg2za6u7uRlZUVsxHTWGFh6gBX5cvnezEyQ7mYoXzMUDbmF52srCy89tpryW6GMttDDcRz0kRERESkBBamRERERKQEFqYO+OZCcU6UXIZhYObMmcxQMGYoHzOUbWB+PKUv08CV89GK1e8BC1MHfBOEVZsoTOHTNA15eXnMUDBmKB8zlM2Xn8vlXabiu1QnyaFpWky3igK8uw0AiHq1Pxc/OeC9aoPLf0vyeDwe1NfXY+7cuf4/qiQLM5SPGcrmy2/OnDnIzs5Ga2srRowYwa0UBYnlqnzbttHV1YVdu3YhLy8v6jMh/ItAaSfU9YFJDmYoHzOUzTRNaJqGiRMnoqmpCVu3bk12k8gB27bR29uLjIyMmI2a5uXloaioKOrHYWFKREREEcnIyMARRxzB0/nCeDwe/160sThrMWLEiJjNGWdhSkRERBHTdT3mC2kovrxTEr0LoFSbTsMJIQ5wVb58hmGgvLycGQrGDOVjhrIxP/lUzpCFKaWdjIyMZDeBosQM5WOGsjE/+VTNkIWpA77J+py0L5dpmqipqWGGgjFD+ZihbMxPPpUzZGFKREREREpgYUpERERESlBrKVYKcrsHH8vPB0pLE98WIiIiIpVptuCL3HZ2diI3NxcdHR3IycmJ+/PV1tqoqtJQU2Nj3ryhN6RtbgbKyoCDV+gKkJ3tLVhZnCaebdswTROGYfByiEIxQ/mYoWzMT75EZ+ikXuOIaZyUlnqLz7a2wONuN3Dhhd7jLEyTo7e3F1lZWcluBkWBGcrHDGVjfvKpmiHnmDrgdFV+aSlQWRn4UVYWzxbScEzTxLp165RciUjhYYbyMUPZmJ98KmfIwpSIiIiIlMDClIiIiIiUwMKU0o6Kl2AjZ5ihfMxQNuYnn6oZcvGTAy6XK+CW5HG5XKiurk52MygKzFA+Zigb85NP5Qw5YuqAb2ctwTtspT3bttHe3s4MBWOG8jFD2ZiffCpnyMLUAaer8kk9pmmioaGBGQrGDOVjhrIxP/lUzpCFKREREREpgYUpERERESmBq3gc8F22KxaX73K7Az/Pz+eVoBJB0zRkZWXxMnqCMUP5mKFszE8+lTPUbBVnvobJybVXY6GuDpg3D6it9V7FKRLNzd6rP3V1BR7PzvYWqyxOiYiIKJU4qdd4Kt8By7ICbiNRWuotQGtrD32sWuUtVNvaYtVSCsWyLOzatSuqDCm5mKF8zFA25iefyhnyVL4D3gB1/22kSks5MposlmWhsbER48aNg67zfZlEzFA+Zigb85NP5QzVag0RERERpS0WpkRERESkBBamDsRyVT4lh6ZpyM3NZYaCMUP5mKFszE8+lTPkHFMHDMMIuCV5DMNAWVlZsptBUWCG8jFD2ZiffCpnyBFTB2KxKp+Sy7IstLS0MEPBmKF8zFA25iefyhmyMHWAhal8Kr8YKTzMUD5mKBvzk0/lDFmYEhEREZESWJgSERERkRJYmDrg24RWtc1oKXy6rqOgoIAZCsYM5WOGsjE/+VTOkKvyHWBhKp+u65g+fXqym0FRYIbyMUPZmJ98KmfICssBLn6Sz7IsbNmyhRkKxgzlY4ayMT/5VM6QhakDLEzlsywLra2tzFAwZigfM5SN+cmncoYsTImIiIhICSxMiYiIiEgJXPzkQLwXP7ndg4/l5wOlpXF5urSk6zqKi4u5gE0wZigfM5SN+cmncoYsTB2IV2Ganw9kZwMXXjj4a9nZ3oKVxWls+F6MJBczlI8Zysb85FM5Q/VKZYWZphlwGyulpd7is7Y28GPVKqCrC2hri+nTpTXTNOF2u2OeISUOM5SPGcrG/ORTOUOOmDpg23bAbSyVlnJUNBFs20ZHR0dcMqTEYIbyMUPZmJ98KmfIEVMiIiIiUgILUyIiIiJSAgtTB3hJUvl0Xce0adOYoWDMUD5mKBvzk0/lDDnH1AEWpvLpuo7CwsJkN4OiwAzlY4ayMT/5VM6QFZYD8VqVT4ljmibWrl3LDAVjhvIxQ9mYn3wqZ8jC1IF4rsqnxLBtG93d3cxQMGYoHzOUjfnJp3KGLEyJiIiISAksTImIiIhICSxMHTAMI+CW5DEMAzNnzmSGgjFD+ZihbMxPPpUz5Kp8BzRNC7gleTRNQ15eXrKbQVFghvIxQ9mYn3wqZ8gRUwc8Hk/ALcnj8XiwevVqZigYM5SPGcrG/ORTOUMWppR2VNweg5xhhvIxQ9mYn3yqZsjClIiIiIiUwMKUiIiIiJTAwtQBrsqXzzAMlJeXM0PBmKF8zFA25iefyhmyMKW0k5GRkewmUJSYoXzMUDbmJ5+qGbIwdcA3UVjVCcM0PNM0UVNTwwwFY4byMUPZmJ98KmeoTGF6++23Q9M0/PCHP0x2U4iIiIgoCZTYYH/16tX44x//iPLy8mQ3RUlud+Dn+flAaWly2kJEREQUL0kfMd23bx8uuOACPPjggxg7dmyym6OU/HwgOxu48EJg3rxDH2VlQHNzsltHREREFFtJHzFdunQpvvGNb+Dkk0/Gr3/96yHv29PTg56eHv/nnZ2dALxXMPBdvUDXdei6DsuyYFmW/76+46ZpwrbtYY8bhgFN0wZcFcH2f8/AqyX4VrYNnK/hcrlg23bAcU3TYBjGoDYOPD5pErB+PbBnz6E+bdxoY/FiAzt3elBcHIs+hW57PPo0sI3xySl023Vdx7x582DbdsD3SO5TKuY0VJ90XcfcuXP9GaZCn1Ixp6H6ZNu2P0PTNFOiT6mYU6g+9c/PsqyU6FMq5jRUn3wZ+i6xHu8+ObnCVFIL0yeffBJ1dXVYvXp1WPe/7bbbsHz58kHH6+vrMWrUKABAQUEBpk+fjqamJrS2tvrvU1xcjOLiYmzevBkdHR3+49OmTUNhYSE2bNiA7u5u//GZM2ciLy8P9fX1/h/0pk3ZAMphWSZqamoD2lBVVYXe3l6sW7fOf8wwDFRXV6OjowMNDQ3+41lZWaioqEBbWxsaGxv9x3Nzc1FWVobt27ejpaXFf9zXpy1bmmDb+wGUw+3eiEmTxkXdJwAoLy9HRkYGampqEt6neOQ0VJ/mzZuHzs5OfPzxxynTp1TMabg+bdy40f8HL1X6lIo5DdUnX0GaSn1KxZxC9cmXXyr1KRVzGqpPpmmitLQUJSUlce9TfX09wqXZ/UvhBNq2bRuqqqrw6quv+ueWnnDCCZgzZw7uvvvuoN8TbMS0pKQEu3fvRk5ODoD4vtOpqbGwYEEGPvigD5WVWkDbEvVOp6bGwjHHuPDBBx5UVaXOu7fh2h6rPtm2jZqaGlRWVgbs3ya5T6mY01B96uvrC8gwFfqUijkN1ae+vj7U1dWhsrISLpcrJfqUijmF6lP//EaMGJESfUrFnIbqk2maqKurw7x585CRkRH3Pu3duxfjx49HR0eHv14LJWkjprW1tdi1axcqKyv9x0zTxNtvv43f//736OnpCSgcACAzMxOZmZmDHsvlcsHlCuyK74c60MDHHO54/8fVdV8Q2qDnC3Z/H00Lfv9QbRzquMul+5/Hd5do+hTp8Vj2KdY5DXW8/6nfgV+T2qehjqdqn4JlKL1PqZhTqD71f0Pha4P0PqViTqHa3j8/332k9ykVcxruuKZp/n8no0+hJK0wPemkk7B+/fqAY5dccglmzpyJ6667LuQPg4iIiIhSU9IK0zFjxmD27NkBx0aNGoXx48cPOk4US3zTIx8zlI8Zysb85FM1w6SvypfENxTtZEia1OJyuVBdXZ3sZlAUmKF8zFA25iefyhkqVWG99dZbyW7CkLwTgDX/Lclj2zY6OjqQm5vr3yaDZGGG8jFD2ZiffCpnmPQN9iXxrTYbuOqM5DBNEw0NDcxQMGYoHzOUjfnJp3KGLEyJiIiISAksTImIiIhICSxMHfDNw1BtPgaFT9M0ZGVlMUPBmKF8zFA25iefyhkqtfhJdb6tFVTdYoGGZxgGKioqkt0MigIzlI8Zysb85FM5Q46YOuC7vFf/y3yRLJZlYdeuXcxQMGYoHzOUjfnJp3KGLEwdYGEqn2VZaGxsZIaCMUP5mKFszE8+lTPkqXyh3O7Bx/LzgdLSxLeFiIiIKBZYmAqTnw9kZwMXXjj4a9nZ3oKVxSkRERFJxMLUARVW5ZeWeovPtrbA4263t1hta2NhOhRN05S80gWFjxnKxwxlY37yqZwhC1MHVFmVX1rK4jNShmGgrKws2c2gKDBD+ZihbMxPPpUz5OInB7j4ST7LstDS0sIMBWOG8jFD2ZiffCpnyMLUARam8qn8YqTwMEP5mKFszE8+lTNkYUpERERESmBhSkRERERKYGHqgK7rAbckj67rKCgoYIaCMUP5mKFszE8+lTPkqnwHWJjKp+s6pk+fnuxmUBSYoXzMUDbmJ5/KGbLCcoCLn+SzLAtbtmxhhoIxQ/mYoWzMTz6VM2Rh6gALU/ksy0JrayszFIwZyscMZWN+8qmcIU/lpxi3e/Cx/HxuyE9ERETqY2GaIvLzgexs72VJB8rO9hasLE6JiIhIZSxMHVB58VNpqbf4bGsLPO52e4vVtjYWpoA3u+LiYiUzpPAwQ/mYoWzMTz6VM2Rh6oDKhSngLTxZfA7N92IkuZihfMxQNuYnn8oZqllhKco0zYBbksc0TbjdbmYoGDOUjxnKxvzkUzlDFqYO2LYdcEvy2LaNjo4OZigYM5SPGcrG/ORTOUMWpkRERESkBBamRERERKQEFqYOqL74iYan6zqmTZvGDAVjhvIxQ9mYn3wqZ8hV+Q6wMJVP13UUFhYmuxkUBWYoHzOUjfnJp3KGrLAc4Kp8+UzTxNq1a5mhYMxQPmYoG/OTT+UMWZg6wFX58tm2je7ubmYoGDOUjxnKxvzkUzlDFqZEREREpAQWpkRERESkBBamDhiGEXBL8hiGgZkzZzJDwZihfMxQNuYnn8oZclW+A5qmBdySPJqmIS8vL9nNoCgwQ/mYoWzMTz6VM+SIqQMejyfgluTxeDxYvXo1MxSMGcrHDGVjfvKpnCELU0o7Km6PQc4wQ/mYoWzMTz5VM2RhSkRERERKYGFKREREREpgYeoAV+XLZxgGysvLmaFgzFA+Zigb85NP5QxZmFLaycjISHYTKErMUD5mKBvzk0/VDFmYOuCbKKzqhGEanmmaqKmpYYaCMUP5mKFszE8+lTPkPqZpwu0O/Dw/HygtTU5biIiIiIJhYZri8vOB7GzgwgsDj2dne4tVFqdERESkChamKa601FuAtrUdOuZ2ewvVtjYWpkRERKQOFqYOSF2VX1rKAtTHMAxUVVWJy5AOYYbyMUPZmJ98KmfIxU+Udnp7e5PdBIoSM5SPGcrG/ORTNUMWpg5wVb58pmli3bp1zFAwZigfM5SN+cmncoYsTImIiIhICSxMiYiIiEgJLEwp7ag42ZucYYbyMUPZmJ98qmbIVfkOuFyugFuSx+Vyobq6OtnNoCgwQ/mYoWzMTz6VM+SIqQO2bQfckjy2baO9vZ0ZCsYM5WOGsjE/+VTOkIWpA1yVL59pmmhoaGCGgjFD+ZihbMxPPpUzZGFKREREREpgYUpERERESmBh6oCmaQG3JI+macjKymKGgjFD+ZihbMxPPpUz5PJyB3xbK6i6xYJTbvfgY/n5QGlp4tuSKIZhoKKiItnNoCgwQ/mYoWzMTz6VM2Rh6oBlWQB0/61U+flAdjZw4YWDv5ad7S1YU7U4tSwLbW1tyM/Ph67LzTCdMUP5mKFszE8+lTNUqzWK8xakh26lKi31Fp+1tYEfq1YBXV1AW1uyWxg/lmWhsbFRfIbpjBnKxwxlY37yqZwhR0zTVGlp6o6KEhERkUwcMSUiIiIiJbAwdYCr8uXTNA25ubnMUDBmKB8zlI35yadyhjyV70CqrcpPR4ZhoKysLNnNoCgwQ/mYoWzMTz6VM+SIqQOpsvgpnVmWhZaWFmYoGDOUjxnKxvzkUzlDFqYOsDCVT+UXI4WHGcrHDGVjfvKpnCELUyIiIiJSAgtTIiIiIlICC1MHfFdHUO0qCRQ+XddRUFDADAVjhvIxQ9mYn3wqZ8hV+Q6wMJVP13VMnz492c2gKDBD+ZihbMxPPpUzZIXlABc/yWdZFrZs2cIMBWOG8jFD2ZiffCpnyMLUgXQpTN1uoK7u0Edzc7JbFDuWZaG1tTXlM0xlzFA+Zigb85NP5Qx5Kp/88vOB7GzgwgsDj2dne4vV0tLktIuIiIjSAwtT8ist9RagbW2Hjrnd3kK1rY2FKREREcUXC1MH0mHxU2lpaheguq6juLg4pTNMdcxQPmYoG/OTT+UMk9qiBx54AOXl5cjJyUFOTg4WLFiAF198MZlNGlI6FKapTuUXI4WHGcrHDGVjfvKpnGFSW1RcXIzbb78dtbW1qKmpwYknnohvfetb+Oijj5LZrJBM0wy4JXlM04Tb7WaGgjFD+ZihbMxPPpUzTOqp/DPOOCPg81tvvRUPPPAA3n//fcyaNStJrQrNtu2AW5LHtm10dHQwQ8GYoXzMUDbmJ5/KGSozx9Q0TTz11FPYv38/FixYEPQ+PT096Onp8X/e2dkJAPB4PPB4PAC8w9O6rsOyrIBtEHzHTdMMCCLUccMwoGma/3GB/ttE2QHHfff39aM/l8sF27YDjmuaBsMwBrUx1PF49mmothuGAe+3awd/xvL7ZNv2oLZL71Oq/u4N1af+/UqVPqViTqH65Hte0zRTpk+pmFOoPvXPL1X6lIo5DdUnX1t994l3nwbefyhJL0zXr1+PBQsW4MCBAxg9ejSeeeYZHHXUUUHve9ttt2H58uWDjtfX12PUqFEAgIKCAkyfPh1NTU1obW3136e4uBjFxcXYvHkzOjo6/MenTZuGwsJCbNiwAd3d3f7jM2fORF5eHurr6/0/6IaGbADlsCwbNTW1AW2oqqpCb28v1q1b5z9mGAaqq6vR0dGBhoYG//GsrCxUVFSgra0NjY2N/uO5ubkoKyvD9u3b0dLS4j8ezz4BQHl5OTIyMlBTUzOoTwcO9AHIwosvNsHt7oauG5g1axa++GIf9u79GEVFvaL6NHfuXFiWhbq6OmialjI5peLvXqg+dXZ2or293Z9hKvQpFXMaqk9btmzxZ5iXl5cSfUrFnEL1adu2bf78CgsLU6JPqZjTUH2ybRvt7e3YsWMHJk+eHPc+1dfXI1yaneRx3N7eXjQ3N6OjowP/+Mc/8NBDD+Ff//pX0OI02IhpSUkJdu/ejZycHADxfadTW2vjS18agQ8/NDF3buCPLVXfvTU3A0cdBXR1aRgoO9vG+vUmSkvl9EnTNLS1tWHs2LEBk76l5xTseKr2yTRN7Nq1C+PHj4eu6ynRp1TMaag+eTwe7N69G+PHj4dhGCnRp1TMKVSf+ufncrlSok+pmNNQfbIsC7t370ZBQYH/72o8+7R3716MHz8eHR0d/notlKQXpgOdfPLJmD59Ov74xz8Oe9/Ozk7k5uaG1dFYqKsD5s0DamuBysq4P50ympsD9zYFDu1vmm4/CyIiInLGSb2m3D4BlmUFjIqqxPdOYOA7glRXWuotPvt/lJUlu1WRMU0Ta9euTbsMUwkzlI8Zysb85FM5w6TOMb3++utx2mmnobS0FF988QUef/xxvPXWW3j55ZeT2ayQuCpfPtu20d3dzQwFY4byMUPZmJ98KmcYUWHa2NiIadOmRf3ku3btwsUXX4wdO3YgNzcX5eXlePnll/G1r30t6scmIiIiIlkiKkwPP/xwHH/88bjsssvwne98ByNHjozoyf/85z9H9H1ERERElHoimmNaV1eH8vJyLFu2DEVFRfje976HDz/8MNZtU45vdZnvluQxDAMzZ85khoIxQ/mYoWzMTz6VM4yoMJ0zZw7uuecebN++HX/5y1+wY8cOfPnLX8bs2bOxYsWKgH2/Uolv30vfLcmjaRry8vKYoWDMUD5mKBvzk0/lDKNale9yubBo0SI89dRTuOOOO/DJJ5/g2muvRUlJiX/uaCrx7dvl5AoGpBaPx4PVq1czQ8GYoXzMUDbmJ5/KGUZVmNbU1ODqq6/GxIkTsWLFClx77bXYsmULXn31VWzfvh3f+ta3YtVOophRcXsMcoYZyscMZWN+8qmaYUSLn1asWIGHH34YmzZtwumnn45HH30Up59+uv9KOlOnTsXKlSsxZcqUWLaViIiIiFJYRIXpAw88gEsvvRRLlizBxIkTg96nsLCQq+6JiIiIKGwRFaavvvoqSktLA641Dng3bN22bRtKS0uRkZGBxYsXx6SRquCqfPkMw0B5eTkzFIwZyscMZWN+8qmcYURzTKdPn462gRdPB7Bnzx5MnTo16kYRxVNGRkaym0BRYobyMUPZmJ98qmYYUWEa6hJW+/bti3izfQl8E4VVnTBMwzNNEzU1NcxQMGYoHzOUjfnJp3KGjk7lL1u2DIB3/6sbb7wR2dnZ/q+ZpokPPvgAc+bMiWkDSW1ud+Dn+flAaWly2kJERESyOSpM6+vrAXhHTNevXx8wDJyRkYGKigpce+21sW0hKSk/H8jOBi68MPB4dra3WGVxSkRERE45KkzffPNNAMAll1yCe+65Bzk5OXFpFKmvtNRbgPafaux2ewvVtjYWpkREROScZoeaMCpAZ2cncnNz0dHRkZAiubbWRlWVhpoaG/PmqXcZr2SrqwPmzQNqa4HKymS3JjjbtmGaJgzDUPJSbDQ8ZigfM5SN+cmX6Ayd1Gthj5guWrQIK1euRE5ODhYtWjTkfZ9++ulwH5Yo4Xp7e5GVlZXsZlAUmKF8zFA25iefqhmGvSo/NzfXX1Xn5uYO+ZGquCpfPtM0sW7dOmYoGDOUjxnKxvzkUznDsEdMH3744aD/JiIiIiKKhYj2Me3u7kZXV5f/861bt+Luu+/GK6+8ErOGEREREVF6ieiSpN/61rewaNEiXHnllWhvb8f8+fORkZGBtrY2rFixAldddVWs26mWhgbA1Rt4jBt4iqHiJdjIGWYoHzOUjfnJp2qGEY2Y1tXVYeHChQCAf/zjHygqKsLWrVvx6KOP4t57741pA1Xiam313i6+2Lv8vP9HWRnQ3JzkFtJwXC4Xqqur4XJF9J6MFMAM5WOGsjE/+VTOMKLCtKurC2PGjAEAvPLKK1i0aBF0XceXvvQlbN26NaYNVIndvtd7e8uvvXsi+T5WrQK6ugI39SQl2baN9vb2kJfVJfUxQ/mYoWzMTz6VM4yoMD388MPx7LPPYtu2bXj55ZdxyimnAAB27dqV0pvum6blvS0t9W7U6fsoK/Pewe32bubZ/4OjqEoxTRMNDQ1KrkSk8DBD+ZihbMxPPpUzjGgM98Ybb8T555+PH/3oRzjppJOwYMECAN7R07lz58a0gSKEuj4nwGt0EhEREYUposL0O9/5Dr785S9jx44dqKio8B8/6aST8O1vfztmjRMj2PU5AV6jk4iIiMiBiGe9FhUVoaioKODY/Pnzo26QynwXGAh6+a7SUhafAmiahqysLF5GTzBmKB8zlI35yadyhhEVpvv378ftt9+O119/Hbt27YJlWQFfb2xsjEnjVGPoesAtBed2Dz6mym5ahmEEjPKTPMxQPmYoG/OTT+UMIypMv/vd7+Jf//oXLrroIkycOFHJijserIOr1ywFV7GpQMJUW8uy0NbWhvz8fOh8gyESM5SPGcrG/ORTOcOICtMXX3wRzz//PI477rhYt0dpvpHhgSPEjjU3B99aSpVhxQhJmGprWRYaGxsxbtw45V6MFB5mKB8zlI35yadyhhEVpmPHjsW4ceNi3ZbU1f/cdmsrsGiRd9/TgVQZVowCp9oSERFRpCIqTG+55RbceOONeOSRR5CdnR3rNqWOUOe2s7OBl14CCgoOHVNpWJGIiIgoCSIqTO+66y5s2bIFEyZMwJQpUzBixIiAr9fV1cWkcaoZclV+MKHObQs/ZS+ZpmnIzc1Nm3nRqYgZyscMZWN+8qmcYUSF6VlnnRXjZsgQ0ap8nttWimEYKPNdqYtEYobyMUPZmJ98KmcYUWF60003xbodInBVvnyWZWH79u2YNGmSchO+KTzMUD5mKBvzk0/lDCNuTXt7Ox566CFcf/312LNnDwDvKfzPPvssZo1TTcxW5VPSWJaFlpYWZigYM5SPGcrG/ORTOcOIRkzXrVuHk08+Gbm5ufj0009x+eWXY9y4cXj66afR3NyMRx99NNbtJCIiIqIUF1FhumzZMixZsgS//e1vMWbMGP/x008/Heeff37MGpeWVL5sUpQGdi1FukVEREQxElFhunr1avzxj38cdPywww7Dzp07o26UqnzzMOIyH0PCZZMiNNSuWYnulq7rKCgoUG5ODYWPGcrHDGVjfvKpnGFEhWlmZiY6OzsHHd+8eTMK+u/NmWL0g9sq6PHYXkHCZZMiFKxryeqWruuYPn164p6QYo4ZyscMZWN+8qmcYUSF6Zlnnolf/epX+Pvf/w7Aux9Wc3MzrrvuOpx99tkxbaBK4r4qf6itpYSfB1dl1yzLstDU1ISpU6cq+U6RhscM5WOGsjE/+VTOMKLW3HXXXdi3bx8KCgrQ3d2N448/HocffjjGjBmDW2+9NdZtVEZSVuX3Pw8+b96hj7IyoLk5ce1IEZZlobW1VcmViBQeZigfM5SN+cmncoYRjZjm5ubi1Vdfxbvvvou1a9di3759qKysxMknnxzr9pFK58GJiIiI4shxYWpZFlauXImnn34an376KTRNw9SpU1FUVATbtpW8vJV4qpwHJyIiIoojR6fybdvGmWeeie9+97v47LPPcPTRR2PWrFnYunUrlixZgm9/+9vxaqcS4roqnxJC13UUFxczQ8GYoXzMUDbmJ5/KGToaMV25ciXefvttvP766/jqV78a8LU33ngDZ511Fh599FFcfPHFMW2kKuK6Kj8SKbznabz4XowkFzOUjxnKxvzkUzlDR6XyE088gZ///OeDilIAOPHEE/Gzn/0Mjz32WMwapxrz4CRhM9mThUMtiBK4KMrtBurqAj/i2XzTNOF2u2GaZvyehOKKGcrHDGVjfvKpnKGjEdN169bht7/9bcivn3baabj33nujbpSq7IPbRNnx2i4qXCmw52myridg2zY6OjqSnyFFjBnKxwxlY37yqZyho8J0z549mDBhQsivT5gwAXv37o26URQG4QuiUqC2JiIiohhzVJiapgmXK/S3GIYBj8cTdaMoPQivrYmIiCjGHBWmtm1jyZIlyMzMDPr1np6emDRKVVyVL5+u65g2bRozFIwZyscMZWN+8qmcoaPCdPHixcPeJ1VX5AMKrsonx3RdR2FhYbKbQVFghvIxQ9mYn3wqZ+ioMH344Yfj1Q4RlFmVP5yB20hxCyk/0zSxYcMGzJ49G4ZhJLs5FAFmKB8zlI35yadyhhFdkjRdKbMqP5RQS93jucxdGNu20d3drW6GNCxmKB8zlI35yadyhixMU0mwpe5c5k5ERERCsDBNNVzqTkREREKxMHXAMPSAW1F4+VIA3i3NZs6cqdycGgofM5SPGcrG/ORTOUMWpg5o0AJuRUjWJZYUpWka8vLykt0MigIzlI8Zysb85FM5Q4FDf8njOXhNWY+C15YNyTfvtLY28GPVKqCra/Cll1Kcx+PB6tWreSEIwZihfMxQNuYnn8oZcsQ0HQw17zQNT/Gbkt5YUFDMUD5mKBvzk0/VDFmYpiue4iciIiLFsDBNV8G2lgKU2V6K1wggIiJKPyxMHRC9Kj8YBbeWivc1AgzDQHl5uZIrESk8zFA+Zigb85NP5QxZmJJSEnGNgIyMjOgfhJKKGcrHDGVjfvKpmmGKDP0lhmlaAbcUH6WlQGXloY+yMu9xtxuoqwv8aG529timaaKmpkbZSd80PGYoHzOUjfnJp3KGHDEl5XGdFhERUXpgYUrBKbT6SPF1WkRERBQjLEwpULxXH0VIwXVaREREFGMsTB1IuVX5wSRi9VESGYaBqqoqJVciUniYoXzMUDbmJ5/KGbIwpcFSfHiyt7cXWVlZyW4GRYEZyscMZWN+8qmaYQoP/cUeV+XLZ5om1q1bp+RKRAoPM5SPGcrG/ORTOUOOmFL4Bi6IAnhJJiIiIooZFqY0PO7XRERERAnAwpSGl2L7Nak42ZucYYbyMUPZmJ98qmbIwtQB18EQXYqGGVdDLYgSdIrf5XKhuro62c2gKDBD+ZihbMxPPpUzZGHqgA074DbtCTzFb9s2Ojo6kJubC03Tkt0cigAzlI8Zysb85FM5Q67Kd4Cr8gfwneKvrQ38WLUK6OoafOpfAaZpoqGhQcmViBQeZigfM5SN+cmncoYcMaXoONnztLk5eLEa5Wl/ha6eSkRERFFgYUqJ0dwMlJV5R1IHivC0v6JXTyUiIqIIJbUwve222/D000+joaEBWVlZOPbYY3HHHXdgxowZyWxWSL55GKrNx1BW/6FMt9tblK5a5S1Q+x+PcGV/JFdP1TQNWVlZzFAwZigfM5SN+cmncoZJLUz/9a9/YenSpaiurobH48HPf/5znHLKKdi4cSNGjRqVzKYFZeh6wC2FMNRQ5sKFMR3KdHr1VMMwUFFREbPnp8RjhvIxQ9mYn3wqZ5jUwvSll14K+HzlypUoLCxEbW0tvvKVrySpVaFZth1wSyGE2vdUgcmflmWhra0N+fn50PkGQyRmKB8zlI35yadyhkrNMe3o6AAAjBs3LujXe3p60NPT4/+8s7MTAODxeODxeAAAuq5D13VYlgXLOrR63nfcNE3Y/QrLUMcNw4Cmaf7HBQDT9P7bsqyA4777e+8TuMLN5XLBtu2A45qmwTCMQW0MdTyefRqq7VH1adIkYNKkwceD5KTDmyE8nqj75L1xYeNGG7Yd2Pb8fKCkxMaWLVuQm5sbsLlw2uYksE+maQZkmAp9SsWchupTX1+fP0OXy5USfUrFnEL1qX9+I0aMSIk+pWJOQ/XJ93c0Ly8PGRkZce/TwPsPRZnC1LIs/PCHP8Rxxx2H2bNnB73PbbfdhuXLlw86Xl9f7z/1X1BQgOnTp6OpqQmtra3++xQXF6O4uBibN2/2F8AAMG3aNBQWFmLDhg3o7u72H585cyby8vJQX1/v/0FvbdoFoBy2baGmpiagDVVVVejt7cW6dev8xwzDQHV1NTo6OtDQ0OA/npWVhYqKCrS1taGxsdF/PDc3F2VlZdi+fTtaWlr8x+PZJwAoLy9HRkZGUvr02WefoQTARrcbXZYVdZ927szAyJEVuOgiX9F56Fd85EgT69aZsCwLdXV1/rk1zElWnzo7O9He3u7PMBX6lIo5DdWnLVu2+DPMy8tLiT6lYk6h+rRt2zZ/foWFhSnRp1TMaag+2baN9vZ27NixA5MnT457n+rr6xEuze5fCifRVVddhRdffBH//ve/UVxcHPQ+wUZMS0pKsHv3buTk5ACI7zud2sc24ktLyvHBIxtQef7MgLbx3VuEfaqpgV5dDc8HHwCVlTHpU3MzsHdvYNsbGjQsXmzggw/6YJo1qKys5Iip0D719fWhpuZQhqnQp1TMabgR07q6OlRWVnLEVGCf+ufHEVOZfTJNE3V1dZg3b15CRkz37t2L8ePHo6Ojw1+vhaLEiOk111yD5557Dm+//XbIohQAMjMzkZmZOei4y+WCyxXYFd8PdaD+xUg4x/s/rq5776Np2qDnC3Z/n1D3D9VGp8ej6VOkx2PZJ/9z9Hu8aPo0bVrAVw5+3dduHXl5ef7/DOPVp1TMSZU+6XrwDCX3KRVzGqpPLpdrUIbS+5SKOYVqe//8fPeR3qdUzGmo45qmIS8vz9/mZPQplKQWprZt4/vf/z6eeeYZvPXWW5g6dWoymzMsrsqXzzAMlPXfrorEYYbyMUPZmJ98KmeY1Apr6dKlWLVqFR5//HGMGTMGO3fuxM6dOwPmM6iEq/LlsywLLS0tAac4SBZmKB8zlI35yadyhkktTB944AF0dHTghBNOwMSJE/0ff/vb35LZrJB8AaoYJIVH5RcjhYcZyscMZWN+8qmcYdJP5RMBGHzBe0CJfU+JiIgocZRY/ERpLNRVogDv8aefBgoKAu/PYpWIiCglsTB1oP/qQ4qRUFeJam0FFi0Cvv71wOPZ2d77R1ic6rqO8eMLmKFguq6joIAZSsYMZWN+8qmcIQtTB/SDG7L7bilGQl3wfmDB6nZ7R1bb2qIqTKdPnx5hQ0kFzFA+Zigb85NP5QxZmDrAVfkJNlTBOlCYp/gty8KWLU2YOnUqdF1Hc/PgwVoHD0dJYFkWmpoOZUjyMEPZmJ98KmfIwtQBrspPsuHmo4Zxit+yLLS2tmLy5MloadFRVgZ0dUX8cJQE/TNU7Q8qhYcZysb85FM5QxamJEeo+agRnuJva/MWpatWAf33GY7BjAEiIiKKAAtTkiXU6f0olJUBlZUxfUgiIiKKAAtTB7gqX3ED554GmSiq6zomTSpmhoLpuo7iYmYoGTOUjfnJp3KGLEwd4Kp8RYWaexpkoqjvxUhyMUP5mKFszE8+lTNUr1RWmHlw0ZPJxU9q8c09ra099LFqlXcC6YD5qB99ZOF//qcJq1ebQRf3k/pM04Tb7YZpmsluCkWIGcrG/ORTOUOOmDrgu4QqL6WqoGG2lsrfMQLZI4/CxRcbAKb6v5yd7R1wJTls20ZHRwdfh4IxQ9mYn3wqZ8jClFLTgNP7pQDcKEEb8mFmZAL//d8wxo9Hfp4HpW19QBu4eSkREVGSsTCl1BRka6lSAIft3AmcfTaMHywc/D2+OalgcUpERJQMLEwd4Kp8YYKc3tcsC3vefRfjLCswx/6bl7IwVZqu65g2bRpfh4IxQ9mYn3wqZ8jC1AGuypdP13Xkc9NS0XRdR2FhYbKbQVFghrIxP/lUzlC9UllhXJUvn2maWLt2rZIrESk8zFA+Zigb85NP5QxZmDrAVfny2baN7u5uZigYM5SPGcrG/ORTOUMWpkRERESkBBamRERERKQELn5ywDD0gFuSxzAMzJw5E4ZhBL+D2w0gC0CZ99/5o7i3qWKGzZCUxwxlY37yqZwhC1MHNGgBtySPpmnIy8sb/IWADfnnAqgDLrwAyN7kLVBZnCojZIYkBjOUjfnJp3KGHPpzwHNw9ZpHwVVsFB6Px4PVq1fD4/EEfsG3IX9tLbDqMe+xW34NdHUFbNJPyRcyQxKDGcrG/ORTOUOOmFLaCbk9xoAN+d0oAzAXcGcB4BVLVaLiFifkDDOUjfnJp2qGLEyJBvCf1b9hKryn9L3HfVcsZXFKREQUHzyVTzSA/6z+KjdqUYnaVW6sWsWz+kRERPHGEVMHuCpfPsMwUF5ePuxKxNJSoLSsG0A9gLqDR8vgfqEJcB8A8vKAiRMB8BR/ooWbIamLGcrG/ORTOUMWppR2MjIywrtjv5X6+ShBNtwHT+8Hyh5pwv2PjSid2McqNUHCzpCUxQxlY37yqZohh/4cME0r4JbkMU0TNTU14U367rdSv7T2Wbifa/Se3r/vP6gdeRxqUYlVuABdBwy0fXMxMG8eUFYGNDfHvyNpzFGGpCRmKBvzk0/lDDliSjSUfiv1Sw9+AADOfMI74dSd5V0cteoxeBdKXeg9zlFTIiIix1iYEkViwNZSKCsD0B38vs3NwVdN8bQ/ERFRABamRLHmdh/6d2srsGiRd0n/QNx/ioiIKAALUwe4Kl8+wzBQVVUV85WIbjeAvInAyGOBC+9CPtpQim3eL2ZnAy+9BBQUBH4DT/tHJF4ZUuIwQ9mYn3wqZ8jClNJOb28vsrKyYvJY/RbuA5gI4F0AXKkfb7HMkJKDGcrG/ORTNUMO/TnAVfnymaaJdevWxWwlYr+F+/6PVavgXak/8WigspJFaYzFOkNKPGYoG/OTT+UMOWJKFKWB66CIiIgoMhwxJSIiIiIlcMSU0o5Sk737r+D34ZzUYSmVIUWEGcrG/ORTNUMWpg64DoboUjRMGp7L5UJ1dXWymzFw1VQgbiM1JGUypIgxQ9mYn3wqZ8jC1AEbdsAtyWPbNjo6OpCbmwtN0+L6XEMOhvpWTQ3ceD/UNlLcpN8vkRlSfDBD2ZiffCpnyMLUAa7Kl880TTQ0NKCqqgouV3x+/cMeDA131VRzs/fKUtykH0BiMqT4YoayMT/5VM5QrdYQpQCng6FB9R9udbu9RemqVQcvfRrJAxIREamPhSlRHES8hVSo4dbsbGDhQhagRESU0liYOuCbh6HafAwKn6ZpyMrKUjfDUMOtTuaSpvh8VOUzpGExQ9mYn3wqZ8jC1AFD1wNuSR7DMFBRUZHsZgwtmh3702A+qogMaUjMUDbmJ5/KGbIwdcCy7YBbkseyLLS1tSE/Px96kt5gDFytH/VAZprNR1UhQ4oOM5SN+cmncoYsTB2wLCvgluSxLAuNjY0YN25cwl+MQ00fjWggM03noyYzQ4oNZigb85NP5QxZmBIlSLDpo1ENZMZiPioREZFCWJgSJVA000cT84BERETJo9b4reK4Kl8+TdOUvNIFhY8ZyscMZWN+8qmcIUdMHeCqfPkMw0BZ/0VBJA4zlI8Zysb85FM5Q1ZYDnBVvnyWZaGlpYUL2Pprbgbq6gI/mpuT3aqQmKF8zFA25iefyhlyxNQBrsqXz/diLCoqUm4lYkIM3KuqtRVYtGjwvqcK73ma9hmmAGYoG/OTT+UMWZgSpYNQW0sB3uMvvQQUFHg/T6E9T4mISBYWpkQKGDiQCcR416dQW0vF/ImIiIgix8LUAd9wt2rD3hQ+XddRUFCgTIbDDWTG9Gx6imwtpVqG5BwzlI35yadyhixMHdAPbqugK7i9AoVH13VMnz492c3wCzWQqcTZ9LgP40ZGtQzJOWYoG/OTT+UMWZg6wFX58lmWhaamJkydOlWZd4rKDWQmdBjXORUzJGeYoWzMTz6VM1SrNYrjqnz5LMtCa2srMxyKbxi3tjbwY9Uq7+r9YPNUE4gZyscMZWN+8qmcIUdMiRQ28Gx6ws6kKzeMS0RE6YCFKZGCQp1NV+BMurJzT4mISD4Wpg5wVb58uq6juLhY+QyDLYpK+oIoReaeSsmQQmOGsjE/+VTOkIWpA1yVL5/vxSiBcmfTFdlCQFKGFBwzlI35yadyhixMHTAPThI2FZwsTOExTRObN2/GkUceCcMwkt2ciCT1TLoC1XIqZJjumKFszE8+lTNkYeqAfXCbKJvbRYll2zY6OjpEZqjImfTQErRSS3KG5MUMZWN+8qmcIQtTIiEUOZM+mNIrtYiISBIWpkSCKHAmfTAlV2oREZFELEwd4Kp8+XRdx7Rp01IyQyXnnsahUamcYbpghrIxP/lUzpCFqQNclS+frusoLCxMdjNiSsm5p8M16umngYKCwd8TRkNTMcN0wwxlY37yqZwhC1MHuCpfPtM0sWHDBsyePVu5lYiRUnLuaahGtbYCixYBX//64O8Js4pOxQzTDTOUjfnJp3KGLEwd4Kp8+WzbRnd3d8plqOzc01Cn90NV0e+8A5SVHToeZBQ1VTNMJ8xQNuYnn8oZsjAlosQKVrAOtbJ/4Gl/jwfZmzYBug64wvgTxsulEhGJwcKUiJIv2Kn/EKf9XQDKnTw2t60iIhKDhakDhqEH3JI8hmFg5syZys2piacE7XsfvWAjqUFO+9u2jX379mH06NHQhluIyG2rlJSOr8NUwvzkUzlDFqYOaNACbkkeTdOQl5eX7GYkRErsex+kWNUAjElOayhG0ul1mIqYn3wqZ8ihPwc8phlwS/J4PB6sXr0aHo8n2U2JO9/Z8draQx+rVgFdXYPXHkmSThmmKmYoG/OTT+UMk1qYvv322zjjjDMwadIkaJqGZ599NpnNoTRhptEbi9JSoLLy0Ef/Be+SpVOGqYoZysb85FM1w6Seyt+/fz8qKipw6aWXYtGiRclsClFaSepVooiIiEJIamF62mmn4bTTTktmE4jSipJXiSIiIjpI1OKnnp4e9PT0+D/v7OwE4J0r4Zsnoes6dF2HZVmw+l2hyXfcNM2ADWVDHTcMA5qmBZ1/oev6oOO+lW0Dh8ZdLhds2w44rmkaDMMY1MZQx+Pdp1BtT8U+6bqOo48+GrZtB3yP5D45yemwwzS43QZ27Qps46ZNOi6+WMeuXRYmTVK7T7quY9asWf4Mh8zp4L89Hg+QoL8R6fR6irRPtm37MzRNMyX6lIo5hepT//wsy0qJPqViTkP1yZehb2eTePfJyVxWUYXpbbfdhuXLlw86Xl9fj1GjRgEACgoKMH36dDQ1NaG1tdV/n+LiYhQXF2Pz5s3o6OjwH582bRoKCwuxYcMGdHd3+4/PnDkTeXl5qK+v9/+gtza1ApgNy7JQU1MT0Iaqqir09vZi3bp1/mOGYaC6uhodHR1oaGjwH8/KykJFRQXa2trQ2NjoP56bm4uysjJs374dLS0t/uPx7BMAlJeXIyMjIy36NG/ePFiWhdra2pTpUyQ5jRwZ2Kfs7AkApuK99/bio48+69fGXCxcOFnJPvn+oA6Z08F/N734IroPzl8Ym5eHkpISfLZtG/a2t/vvP6GwEBNmzcLm/fuVySkVf/d8fbJtG5qmpVSfUjGnUH3y5ZdKfUrFnIbqk23bKC4uRklJSdz7VF9fj3BptiLXo9I0Dc888wzOOuuskPcJNmJaUlKC3bt3IycnB0B83+nUPrYRX1pSjg8e2YDK82cGtI3v3mT0ybZt1NTUoLKyMmD/Nsl9ikVO27ZpmD3bQFdXwEMgO9uG262huFidPvX19QVkOGROn30Gu6wM2sCOhZKdDXPDBtglJQntU7r97vX19aGurg6VlZVwuVwp0adUzClUn/rnN2LEiJToUyrmNFSfTNNEXV0d5s2bh4yMjLj3ae/evRg/fjw6Ojr89VoookZMMzMzkZmZOei4y+WCa8ClCX0/1IFCbSYb6nj/x9U0PejxUPc/9H1a0OOh2uj0eDR9ivS41D71P/U78GtS+zTU8XD7NHXq4L3svXvTawf3plerT8EyDPpzLy2FFmST/qAObsZv7N3r/YEMoEJOkR5X7Xev/xsKXxuk9ykVcwrV9v75+e4jvU+pmNNwxzVN8/87GX0KRVRhSkTxE+zCSynBace4ZQERUdIktTDdt28fPvnkE//nTU1NWLNmDcaNG4dS/idARIk03JYFTz8NFBQM/h7+rSIiipmkFqY1NTX46le/6v982bJlAIDFixdj5cqVSWpVaIahB9ySPIZhoKqqKuTpCRpMtQHEuGXou1TWwNP+ra3AokXA178++Hu4x1ZE+DqUjfnJp3KGSS1MTzjhhIBJtUSJ0Nvbi6ysrGQ3Q3kq73katwxDnfYPVrAenJN6cBJu7NuS4vg6lI35yadqhhz6c8A0rYBbksc0Taxbt07ZS7GpxDeAWFsb+LFqFdDVFd56onhISoYDr+2aStd3TQK+DmVjfvKpnCEXPxFRSCm7ICqempuDV+2cj0pENCwWpkQUkYFzT1l3wVuUlpVh0IawQPLnPxARCcDClNKOipO9JQk19zSRdZdSGfav0N1ub1G6alXgqX7ORx1EqQzJMeYnn6oZsjB1wHUwRJeiYdLwXC4Xqqurk90M0YItXk9k3aVMhkNV6AsXhl5EFexx0qxYVSZDigjzk0/lDFmYOmDDDrgleWzbRkdHB3Jzc/3XWifnhlq8PlCs6y5lMgy1vVSwDqu8xUESKJMhRYT5yadyhixMHeCqfPlM00RDQwOqqqocXSKNhpbIukupDMNdHRaqiE3TU/xKZUiOMT/5VM5QrdYQkUisu8LALQ6IiIbFwpSIYoJ1FxERRYuFqQO+eRiqzceg8GmahqysLGYoWEpmGGxybjApslAqJTNMI8xPPpUzZGHqgKHrAbckj2EYqKioSHYz0k4s9zxNqQyHmpwbTIoslEqpDNMQ85NP5QxZmDpg2XbALcljWRba2tqQn58PnW8w4i4ee56mVIahJucGk0ITdlMqwzTE/ORTOUMWpg5YlhVwS/JYloXGxkaMGzdOuRdjKorHnqcpl6HTybnhDj8rfGnUlMswzTA/+VTOkIUpEcUVF0XFiJPhZ14alYiEYmFKRCTBUMPP77wz+BKovDQqEQnEwtQBrsqXT9M0Ja90QeFL6wwHDj8Pd2WDUJdGTbK0zjAFMD/5VM6QhakDXJUvn2EYKOs/gkRJE+nlS5lhP0MtnnIylzTB81GZoWzMTz6VM2Rh6gBX5ctnWRa2b9+OSZMmKTfhO11Ee/lSZjhAJJN4+78raG0FFi1K6HxUZigb85NP5QxZmDrAVfnyWZaFlpYWFBUVKfdiTBfDXb504HTJgYN2zDAKQy2geukloKDg0LE4zkdlhrIxP/lUzpCFKRElXLBBvnjseUoDhHpXoMAWUkREAAtTIlJEPPY8pSCi3TcVYCFLRHHDwtQB33C3asPeFD5d11FQUMAMFRWqZupfG1mWjs8/PwwtLTqmTElY09JPtJOBh8DXoWzMTz6VM2Rh6oB+cFsFXcHtFSg8uq5j+vTpyW4GhSl4baQDKOEp/ngbbjLwwGFsByv7+TqUjfnJp3KGLEwd4Kp8+SzLQlNTE6ZOnarkO0UKFKw2siwLb7/dih//eMKwC6UoSkOd9o9iZT9fh7IxP/lUzpCFqQNclS+fZVlobW3F5MmTlXsxUnADayOPx0JLy2fIzi7EhRcGnr3gKGoCxGBlv2VZ6Fi/Htbu3dBd/f4b4jsLEfh3VD6VM2RhSkTiFBX1Yv16E+3th/6EcaFUgkSysn/AAipt505UnHcejAMHAu/HdxZEaY+FKRGJVFoKTJs2+DgXkSdAuCv7Q4yuGgDMkSNhPv88jKIi78FQG9n6HocBEqUFFqYOcFW+fLquo7i4mBkKFirDOC4ip0iFGF21LAu7TBMTqqsBX44MUAz+HZVP5QxZmDrAVfny+V6MJFeoDKO9ohTFSZDRVR3AxGD3c7ILACUN/47Kp3KGLEwdMA8uejK5+Eks0zSxefNmHHnkkTAMI9nNoQgMlSGvKCVDyAydbv5PScG/o/KpnCELUwfsg9tE2dwuSizbttHR0cEMBXOaIa8opR6+DmVjfvKpnCELUyJKeeFcUcqHp/iJiJKHhSkRpR2usxEo2LuIYPjOgkg0FqYOcFW+fLquY9q0acxQsFhkyHU2yeUow6HeRQSTnQ08/XTgRv++x2GoMcG/o/KpnCELUwe4Kl8+XddRWFiY7GZQFGKVIdfZJI+jDEO9iwjGd2nUr3998NeCFayhitXm5uDPx+IWAP+OpgKVM2Rh6gBX5ctnmiY2bNiA2bNnK7cSkcKTiAwHnjVmPRJbjjN08i4iWBEbqmANNm+judm7r1hX1+DH5jwPAPw7mgpUzpCFqQNclS+fbdvo7u5mhoLFM8Ohtpbi2eHYievrcKiVbuFszdDW5i1KV60K3PiW8zz8+HdUPpUzZGFKRHRQsLPGsTo7TEkW7tYMvs/LyoDKyvi3i4gCsDAlIuonWP0S7dlhUtBwWzPk5ye+TUTEwtQJw9ADbkkewzAwc+ZM5ebUUPiSkWG0Z4cpkBKvw6EWVXHYe0hK5EdRUTlDFqYOaNACbkkeTdOQl5eX7GZQFFTKkCv7I6NMhpEEyJVx6uRHEVM5Qw79OeAxzYBbksfj8WD16tXweDzJbgpFiBnKJzLD/qf+58079FFW5l3Jn0ZE5kcBVM6QI6aUdky+sRCPGconLsNgp/59czfeeSdwBf9QUmSEVVx+NIiqGbIwJSKKsWBXz0yReiS9DTz17/SqVABXxxENg4UpEVGMDLfQm1tLpRgnV6UCnI+w8heE0hALUwe4Kl8+wzBQXl6u5EpECo/KGYaqU4baWiodN+5XOUPHnCygcjrCqujoakrll6ZUzpCFKaWdjIyMZDeBoqRyhuFuLeV0434gtQpWlTOMGycjrIrvPZaW+aUYVTNkYeqAaVoBtySPaZqoqalBVVUVXC7++kskNcNoN+4HlB1Ac0xqhjER7R5jzc1J33s1rfNLESpnqFZriIjSSLijq75jCg+gUbz0X0nne9fS1TX4fqnyroXSHgtTIiLFDDWoFmzFfzCpdNo/LYWaj5qdDbz0UuA8D75roRTCwpSISIAUWTdD4Qo1H5XvOCjFsTB1gKvy5TMMA1VVVUquRKTwpGuGkaybGbgrkSo1Tbpm6JjT+ajhDqeHEuYvCPOTT+UMWZhS2unt7UVWVlaym0FRSNcMw61ThjoLrMpq/3TNMC4i2eg/GAfD7MxPPlUzZGHqAFfly2eaJtatW6fkSkQKDzMcXrDRVZVW+zPDGHO60X8wDuapMj/5VM5QrdYQEVFMhLs9leqn/SlM0W5DRaQIFqZERGkiWO0y1Gl/Lp4iokRjYUppR8XJ3uQMM4ydYGeBh7qke6xGUpmhosJZQOXxIKu1Nf5tobhS9TXIwtQB18EQXYqGScNzuVyorq5OdjMoCsww9gaOpA61libYAiqnxSozVJCDBVQuABUcUhdN5dcgC1MHbNgBtySPbdvo6OhAbm4uNE1LdnMoAsww/kKtpQm1gMppjcIMFeRgAZW9cSO0iy6C3doKjYWpSCq/BlmYOsBV+fKZpomGhgYlVyJSeJhhYoR7udRILjrEDBUV5gIq0+OBC4D10UcwoilquMIuaVR+DarVGiIiUtpQBWs48vOBSZNi2yZKsPx8mCNHwli8OLrH4XQACoKFKRERRSySS6U+9ZSG1tZs6DrgG6zh4JkgpaVY+8QTKJ80KfLRtkSssCORWJg64JuHodp8DAqfpmnIyspihoIxQ7U42dvdN0f1G98wAJQHfI2DZ3JomgZj6lRos2cDkS4GHm6FHX8Z4krlv6MsTB0wdD3gluQxDAMVFRXJbgZFgRmqx8ne7tzkX76YvAZDvaOJZNIyOaby31EWpg5Yth1wS/JYloW2tjbk5+dD5xsMkZihbKWlQHFxYIZDbfI/cGuqUFjEJk7MXoNDvaMZOGmZAceUyn9HWZg6YFlWwC3JY1kWGhsbMW7cOOVejBQeZijfwAyDDZ6F2poqlFBFLOuZ2IvrazAW71J8j8PgQ1L57ygLUyIiSrpgg2dO564GK2JjcUEASqBYvEsBGLxgLEyJiEhJ0c5dHeqCAJwioLBo3qUAsbsSBCUFC1MHuCpfPk3TlLzSBYWPGcoXjwzDvSBAJFMEWMsESspr0Mm7FCD0lSC4PRUAtf+OsjB1gKvy5TMMA2UD/yiRKMxQvkRmGM3g21C1TDDpUt+IeA0ODH647anSbIKyyhmyMHWAq/LlsywL27dvx6RJk5Sb8E3hYYbyJTvDcAffIrl4QDqMriY7v4iE2p4qTScoq5whC1MHuCpfPsuy0NLSgqKiIuVejBQeZiiflAydXDzA6ehqKBLqHin5DRLufA/A+QRlCcH1o3KGLEyJiIhCiNfoaigpUvfIEosJygwuZliYEhERRcnJ6GookdQ94XJSHzU3B+9H2tVY4U5QTtPpAPHCwtQB33C3asPeFD5d11FQUMAMBWOG8qVqhk4XjgfjtO4JV7gDes3N3qkIXV2hH2P8eB2ff34Y1qzRUViYZjVWrKYDJHkyssqvQRamDugHt1XQFdxegcKj6zqmT5+e7GZQFJihfMwwNCd1T7icDOi53d6idNWqwLmygY+hAygJ+v2RSIkBxHCDU2SrB5Vfg0oUpvfffz/++7//Gzt37kRFRQXuu+8+zJ8/P9nNGoSr8uWzLAtNTU2YOnWqku8UaXjMUD5m6Fy0o7FOB/QWLgx9FtuyLHz22WfIyDgM3/mOHtVIru/5wj3bHWyagdLTFJxsWxVMnOauqvwaTHph+re//Q3Lli3DH/7wBxxzzDG4++67ceqpp2LTpk0oLCxMdvMCcFW+fJZlobW1FZMnT1buxUjhYYbyMcPEczISG6rm8T2Gx2PB4/kMVVUT4XbrcZlXG6we89134DSDcEdtQ32/k8cAoqwJnUxGjuNiK5Vfg0kvTFesWIHLL78cl1xyCQDgD3/4A55//nn85S9/wc9+9rMkt46IiCh1RTsSG495tcPVYy+9dKgei+RKXv2/P9LHiG76QunBj2EUAPj7J0B7e+DxvXuBn/wE+Pr1g74lf+R+lD577/CN83iQsXNnuA1OqKQWpr29vaitrcX11x/64eq6jpNPPhnvvffeoPv39PSgp6fH/3lHRwcAYM+ePfB4PP7v13UdlmUFjGz6jpumCbvfqfhQxw3DgKZp/scFgH1dnQA6sa97H/bs2RPQNsMwAACmaQYcd7lcsG074LimaTAMY1AbQx2PZ5+Gansq9sm2bezbtw979+7130d6n1Ixp6H61NfXF5BhKvQpFXMaqk/9M3S5XCnRp1TMKVSf+uc3YsSIqPs0ejSQm3uoT1OmAP/5D7B79+C2jx8PlJQcOj5lium/bzh9GjvWQklJ4PFp0yz85z+W/zGGymnPHh0XXmjj619P1FqTUQc/+jsMwItB7511YD9Wf70aJfhs2EeempmJ9vfeQ8b06XH/3du7dy8ABDxWKEktTNva2mCaJiZMmBBwfMKECWhoaBh0/9tuuw3Lly8fdHzq1Klxa2MwJ10J4MqEPiURERHRkLoBzA73zj09QGVlHFsz2BdffIHc3Nwh75P0U/lOXH/99Vi2bJn/c8uysGfPHowfPx5aAlbKd3Z2oqSkBNu2bUNOTk7cn49ijxnKxwzlY4ayMT/5Ep2hbdv44osvMGnSpGHvm9TCND8/H4Zh4PPPPw84/vnnn6OoqGjQ/TMzM5GZmRlwLC8vL55NDConJ4cvRuGYoXzMUD5mKBvzky+RGQ43UuqT1KVYGRkZmDdvHl5//XX/Mcuy8Prrr2PBggVJbBkRERERJVrST+UvW7YMixcvRlVVFebPn4+7774b+/fv96/SJyIiIqL0kPTC9Nxzz0VraytuvPFG7Ny5E3PmzMFLL700aEGUCjIzM3HTTTcNmk5AcjBD+ZihfMxQNuYnn8oZanY4a/eJiIiIiOJMre3+iYiIiChtsTAlIiIiIiWwMCUiIiIiJbAwJSIiIiIlsDAd4P7778eUKVMwcuRIHHPMMfjwww+HvP9TTz2FmTNnYuTIkTj66KPxwgsvJKilFIqTDB988EEsXLgQY8eOxdixY3HyyScPmznFn9PXoc+TTz4JTdNw1llnxbeBNCSn+bW3t2Pp0qWYOHEiMjMzceSRR/JvaZI5zfDuu+/GjBkzkJWVhZKSEvzoRz/CgQMHEtRaGujtt9/GGWecgUmTJkHTNDz77LPDfs9bb72FyspKZGZm4vDDD8fKlSvj3s6gbPJ78skn7YyMDPsvf/mL/dFHH9mXX365nZeXZ3/++edB7//uu+/ahmHYv/3tb+2NGzfav/zlL+0RI0bY69evT3DLycdphueff759//332/X19bbb7baXLFli5+bm2i0tLQluOfk4zdCnqanJPuyww+yFCxfa3/rWtxLTWBrEaX49PT12VVWVffrpp9v//ve/7aamJvutt96y16xZk+CWk4/TDB977DE7MzPTfuyxx+ympib75ZdftidOnGj/6Ec/SnDLyeeFF16wf/GLX9hPP/20DcB+5plnhrx/Y2OjnZ2dbS9btszeuHGjfd9999mGYdgvvfRSYhrcDwvTfubPn28vXbrU/7lpmvakSZPs2267Lej9zznnHPsb3/hGwLFjjjnG/t73vhfXdlJoTjMcyOPx2GPGjLEfeeSReDWRhhFJhh6Pxz722GPthx56yF68eDEL0yRymt8DDzxgT5s2ze7t7U1UE2kYTjNcunSpfeKJJwYcW7ZsmX3cccfFtZ0UnnAK05/+9Kf2rFmzAo6de+659qmnnhrHlgXHU/kH9fb2ora2FieffLL/mK7rOPnkk/Hee+8F/Z733nsv4P4AcOqpp4a8P8VXJBkO1NXVhb6+PowbNy5ezaQhRJrhr371KxQWFuKyyy5LRDMphEjy++c//4kFCxZg6dKlmDBhAmbPno3f/OY3ME0zUc2mfiLJ8Nhjj0Vtba3/dH9jYyNeeOEFnH766QlpM0VPpXom6Vd+UkVbWxtM0xx0xakJEyagoaEh6Pfs3Lkz6P137twZt3ZSaJFkONB1112HSZMmDXqBUmJEkuG///1v/PnPf8aaNWsS0EIaSiT5NTY24o033sAFF1yAF154AZ988gmuvvpq9PX14aabbkpEs6mfSDI8//zz0dbWhi9/+cuwbRsejwdXXnklfv7znyeiyRQDoeqZzs5OdHd3IysrK2Ft4Ygp0UG33347nnzySTzzzDMYOXJksptDYfjiiy9w0UUX4cEHH0R+fn6ym0MRsCwLhYWF+NOf/oR58+bh3HPPxS9+8Qv84Q9/SHbTKExvvfUWfvOb3+D//t//i7q6Ojz99NN4/vnnccsttyS7aSQQR0wPys/Ph2EY+PzzzwOOf/755ygqKgr6PUVFRY7uT/EVSYY+d955J26//Xa89tprKC8vj2czaQhOM9yyZQs+/fRTnHHGGf5jlmUBAFwuFzZt2oTp06fHt9HkF8lrcOLEiRgxYgQMw/AfKysrw86dO9Hb24uMjIy4tpkCRZLhDTfcgIsuugjf/e53AQBHH3009u/fjyuuuAK/+MUvoOscA1NdqHomJycnoaOlAEdM/TIyMjBv3jy8/vrr/mOWZeH111/HggULgn7PggULAu4PAK+++mrI+1N8RZIhAPz2t7/FLbfcgpdeeglVVVWJaCqF4DTDmTNnYv369VizZo3/48wzz8RXv/pVrFmzBiUlJYlsftqL5DV43HHH4ZNPPvG/oQCAzZs3Y+LEiSxKkyCSDLu6ugYVn743GrZtx6+xFDNK1TMJX26lsCeffNLOzMy0V65caW/cuNG+4oor7Ly8PHvnzp22bdv2RRddZP/sZz/z3//dd9+1XS6Xfeedd9put9u+6aabuF1UkjnN8Pbbb7czMjLsf/zjH/aOHTv8H1988UWyupD2nGY4EFflJ5fT/Jqbm+0xY8bY11xzjb1p0yb7ueeeswsLC+1f//rXyepC2nOa4U033WSPGTPGfuKJJ+zGxkb7lVdesadPn26fc845yepC2vviiy/s+vp6u76+3gZgr1ixwq6vr7e3bt1q27Zt/+xnP7Mvuugi//1920X95Cc/sd1ut33//fdzuyhV3HfffXZpaamdkZFhz58/337//ff9Xzv++OPtxYsXB9z/73//u33kkUfaGRkZ9qxZs+znn38+wS2mgZxkOHnyZBvAoI+bbrop8Q0nP6evw/5YmCaf0/z+85//2Mccc4ydmZlpT5s2zb711lttj8eT4FZTf04y7Ovrs2+++WZ7+vTp9siRI+2SkhL76quvtvfu3Zv4hpNt27b95ptvBv2/zZfb4sWL7eOPP37Q98yZM8fOyMiwp02bZj/88MMJb7dt27Zm2xxnJyIiIqLk4xxTIiIiIlICC1MiIiIiUgILUyIiIiJSAgtTIiIiIlICC1MiIiIiUgILUyIiIiJSAgtTIiIiIlICC1MioiTQNA3PPvtssptBRKQUFqZERHF08803Y86cOXF57BNOOAE//OEP4/LYAz399NM45ZRTMH78eGiahjVr1iTkeYkovbAwJSKikHp7ewEA+/fvx5e//GXccccdSW4REaUyFqZElDYsy8Jvf/tbHH744cjMzERpaSluvfVWAMD69etx4oknIisrC+PHj8cVV1yBffv2+b/3rbfewvz58zFq1Cjk5eXhuOOOw9atW4d8vpUrV2L58uVYu3YtNE2DpmlYuXKl/+ttbW349re/jezsbBxxxBH45z//GfD9GzZswGmnnYbRo0djwoQJuOiii9DW1gYAWLJkCf71r3/hnnvu8T/2p59+CtM0cdlll2Hq1KnIysrCjBkzcM8994T9M1qyZAnOOuss3HrrrZg0aRJmzJgBALjoootw44034uSTTw77sYiInGJhSkRp4/rrr8ftt9+OG264ARs3bsTjjz+OCRMmYP/+/Tj11FMxduxYrF69Gk899RRee+01XHPNNQAAj8eDs846C8cffzzWrVuH9957D1dccQU0TRvy+c4991z8+Mc/xqxZs7Bjxw7s2LED5557rv/ry5cvxznnnIN169bh9NNPxwUXXIA9e/YAANrb23HiiSdi7ty5qKmpwUsvvYTPP/8c55xzDgDgnnvuwYIFC3D55Zf7H7ukpASWZaG4uBhPPfUUNm7ciBtvvBE///nP8fe//z3sn9Prr7+OTZs24dVXX8Vzzz3n9MdMRBQ5m4goDXR2dtqZmZn2gw8+OOhrf/rTn+yxY8fa+/bt8x97/vnnbV3X7Z07d9q7d++2AdhvvfWW4+e96aab7IqKikHHAdi//OUv/Z/v27fPBmC/+OKLtm3b9i233GKfcsopAd+zbds2G4C9adMm27Zt+/jjj7d/8IMfDNuGpUuX2meffXZY7V28eLE9YcIEu6enJ+jXm5qabAB2fX19WI9HROQER0yJKC243W709PTgpJNOCvq1iooKjBo1yn/suOOOg2VZ2LRpE8aNG4clS5bg1FNPxRlnnIF77rkHO3bsiLpN5eXl/n+PGjUKOTk52LVrFwBg7dq1ePPNNzF69Gj/x8yZMwEAW7ZsGfJx77//fsybNw8FBQUYPXo0/vSnP6G5uTnsdh199NHIyMiIoEdERNFhYUpEaSErKyuq73/44Yfx3nvv4dhjj8Xf/vY3HHnkkXj//fejeswRI0YEfK5pGizLAgDs27cPZ5xxBtasWRPw8fHHH+MrX/lKyMd88sknce211+Kyyy7DK6+8gjVr1uCSSy7xL2IKR/8CnYgokViYElFaOOKII5CVlYXXX3990NfKysqwdu1a7N+/33/s3Xffha7r/sU/ADB37lxcf/31+M9//oPZs2fj8ccfH/Z5MzIyYJqm4/ZWVlbio48+wpQpU3D44YcHfPgKx2CP/e677+LYY4/F1Vdfjblz5+Lwww8fdoSViEgVLEyJKC2MHDkS1113HX7605/i0UcfxZYtW/D+++/jz3/+My644AKMHDkSixcvxoYNG/Dmm2/i+9//Pi666CJMmDABTU1NuP766/Hee+9h69ateOWVV/Dxxx+jrKxs2OedMmUKmpqasGbNGrS1taGnpyes9i5duhR79uzBeeedh9WrV2PLli14+eWXcckll/iL0SlTpuCDDz7Ap59+ira2NliWhSOOOAI1NTV4+eWXsXnzZtxwww1YvXp1VD87ANizZw/WrFmDjRs3AgA2bdqENWvWYOfOnVE/NhGRDwtTIkobN9xwA3784x/jxhtvRFlZGc4991zs2rUL2dnZePnll7Fnzx5UV1fjO9/5Dk466ST8/ve/BwBkZ2ejoaEBZ599No488khcccUVWLp0Kb73ve8N+5xnn302vv71r+OrX/0qCgoK8MQTT4TV1kmTJuHdd9+FaZo45ZRTcPTRR+OHP/wh8vLyoOveP93XXnstDMPAUUcdhYKCAjQ3N+N73/seFi1ahHPPPRfHHHMMdu/ejauvvjryH9pB//znPzF37lx84xvfAAD813/9F+bOnYs//OEPUT82EZGPZtu2nexGEBERERFxxJSIiIiIlMDClIgoCrNmzQrY0qn/x2OPPZbs5g0Sqq2jR4/GO++8k+zmEVGa46l8IqIobN26FX19fUG/NmHCBIwZMybBLRraJ598EvJrhx12WNTbahERRYOFKREREREpgafyiYiIiEgJLEyJiIiISAksTImIiIhICSxMiYiIiEgJLEyJiIiISAksTImIiIhICSxMiYiIiEgJLEyJiIiISAn/Hw7tBqDREDYCAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Loops through each feature variable and then plots the histograms\n", + "for var in VarNames[1:]:\n", + " plt.figure(figsize=(8, 6)) # Adjust the figure size as needed\n", + " plt.hist(np.array(df_sig[var]), bins=100, histtype=\"step\", color=\"red\", label=\"signal\", density=True)\n", + " plt.hist(np.array(df_bkg[var]), bins=100, histtype=\"step\", color=\"blue\", label=\"background\", density=True)\n", + "\n", + " # Add labels, legend, and grid to the visuals\n", + " plt.xlabel(var)\n", + " plt.ylabel('Density')\n", + " plt.legend(loc='upper right')\n", + " plt.grid(True, linestyle='--', alpha=0.7)\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "itaVs_BA_rpG" + }, + "source": [ + "## Exercise 4: Correlation\n", + "\n", + "### Exercise 4.1\n", + "\n", + "#### Part a\n", + "Write a function that creates pair plots and use it to compare variables in the SUSY and Higgs samples, separately for low and high-level features. Refer to Lecture 13 for details. Do not use `seaborn`.\n", + "\n", + "#### Part b\n", + "Making these plots can be slow because creating each plot initiates a full loop over the data. Make at least one modification to your function in part a to speed it up. Can you propose a different method of creating histograms that would speed up making such pair plots?\n", + "\n", + "#### Part c\n", + "Which observables appear to be best for separating signal from background?" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "TvVV8HqG_rpN" + }, + "outputs": [], + "source": [ + "## Part A\n", + "\n", + "def compare_pair_plots(df_susy, df_higgs, columns, selection_dict, low_level=True):\n", + " # Determine title based on feature level\n", + " title = 'Pair Plots - Low Level Features' if low_level else 'Pair Plots - High Level Features'\n", + "\n", + " # Create a new figure\n", + " plt.figure(figsize=(15, 15))\n", + " n = len(columns)\n", + "\n", + " # Iterate over pairs of variables\n", + " for i, x in enumerate(columns):\n", + " for j, y in enumerate(columns):\n", + " plt.subplot(n, n, i * n + j + 1) # Position subplot\n", + " make_legend = (i == 0) and (j == 0) # Decide whether to make legend\n", + " plot_data(df_susy, x, y, selection_dict, 'SUSY', make_legend) # Plot SUSY data\n", + " plot_data(df_higgs, x, y, selection_dict, 'Higgs', False) # Plot Higgs data\n", + "\n", + " plt.suptitle(title, fontsize=16) # Set title\n", + " plt.tight_layout() # Adjust layout\n", + " plt.show() # Show plot\n", + "\n", + "def plot_data(df, x_var, y_var, selection_dict, label, make_legend):\n", + " selected_data = df.query(selection_dict) # Filter data\n", + " if x_var == y_var: # Plot histogram if x and y are same\n", + " plt.hist(selected_data[x_var], alpha=0.5, density=True, bins=50, label=label if make_legend else None)\n", + " else: # Plot scatter plot otherwise\n", + " plt.scatter(selected_data[x_var], selected_data[y_var], label=label if make_legend else None)\n", + " if make_legend: # Add legend if required\n", + " plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kjb1z561_rpV" + }, + "outputs": [], + "source": [ + "# Example usage:(Cannot locate the higgs to compare)\n", + "\n", + "\n", + "#compare_pair_plots(df_susy, df_higgs, columns, selection_dict, low_level=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dkgjUZl2_rpX" + }, + "outputs": [], + "source": [ + "### Part B" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "R2JYDsFR_ru5" + }, + "outputs": [], + "source": [ + "def compare_pair_plots(df_susy, df_higgs, columns, selection_dict, low_level=True):\n", + " # Determine title based on feature level\n", + " title = 'Pair Plots - Low Level Features' if low_level else 'Pair Plots - High Level Features'\n", + "\n", + " # Create a new figure\n", + " plt.figure(figsize=(15, 15))\n", + " n_columns = len(columns)\n", + "\n", + " # Calculate histograms for each variable in SUSY and Higgs datasets\n", + " susy_histograms = {var: np.histogram(df_susy.query(selection_dict)[var], bins=50, density=True) for var in columns}\n", + " higgs_histograms = {var: np.histogram(df_higgs.query(selection_dict)[var], bins=50, density=True) for var in columns}\n", + "\n", + " # Loop through each pair of variables\n", + " for i, x_var in enumerate(columns):\n", + " for j, y_var in enumerate(columns):\n", + " # Set up subplot\n", + " plt.subplot(n_columns, n_columns, i * n_columns + j + 1)\n", + "\n", + " # Decide whether to make legend for the first subplot\n", + " make_legend = (i == 0) and (j == 0)\n", + "\n", + " # Plot histograms for SUSY and Higgs datasets\n", + " plot_histogram(susy_histograms[x_var], 'SUSY', make_legend)\n", + " plot_histogram(higgs_histograms[x_var], 'Higgs', False)\n", + "\n", + " # Add title and adjust layout\n", + " plt.suptitle(title, fontsize=16)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "def plot_histogram(histogram, label, make_legend):\n", + " # Plot histogram as filled area\n", + " plt.fill_between(histogram[1][:-1], histogram[0], alpha=0.5, label=label if make_legend else None, color='blue')\n", + "\n", + " # Add legend for the first subplot\n", + " if make_legend:\n", + " plt.legend()\n", + "\n", + "# Example usage:\n", + "# compare_pair_plots(df_susy, df_higgs, columns, selection_dict, low_level=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TJOdOGjK_rvA" + }, + "outputs": [], + "source": [ + "## Using numpy to have the histograms already calculated would avoid using the loops so it'll form the pair plots faster." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NBEXIQIv_rvC" + }, + "outputs": [], + "source": [ + "## Part C:\n", + "# It's good to look for which class might dominate over another one in certain places. For scatterplots, looking at patterns whether it be closely formed in clusters or the opposite. Also like the figures made in the previous exercises with different peak heights or shapes." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BqSmKXRa_rvE" + }, + "source": [ + "### Exercise 4.2\n", + "\n", + "#### Part a\n", + "Install [tabulate](https://github.com/astanin/python-tabulate).\n", + "\n", + "#### Part b\n", + "Use numpy to compute the [covariance matrix](https://numpy.org/doc/stable/reference/generated/numpy.cov.html) and [correlation matrix](https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html) between all observabes, and separately between low and high-level features.\n", + "\n", + "#### Part c\n", + "Use tabulate to create a well formatted table of the covariance and correlation matrices, with nice headings and appropriate significant figures. Embed the table into this notebook.\n", + "\n", + "#### Part d\n", + "Write a function that takes a dataset and appropriate arguments and performs steps b and c. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ue0lwpfy_rwt" + }, + "source": [ + "Hint: Example code for embedding a `tabulate` table into a notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 102 }, + "id": "I_4en7R5_rwx", + "outputId": "cad32c77-900e-4dd2-f1d4-0116fb57bf94", + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "2gPXZPKU_r3P", - "outputId": "4fbc4c95-ec27-41f0-db28-c57e3e5db233" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(229245, 19)" - ] - }, - "metadata": {}, - "execution_count": 33 - } + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
X Y Z
A 1 2
C 3 4
D 5 6
" ], - "source": [ - "df_sig.shape" + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import HTML, display\n", + "import tabulate\n", + "table = [[\"A\",1,2],\n", + " [\"C\",3,4],\n", + " [\"D\",5,6]]\n", + "display(HTML(tabulate.tabulate(table, tablefmt='html', headers=[\"X\",\"Y\",\"Z\"])))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "g-0mMHEW_rw0" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from tabulate import tabulate\n", + "\n", + "# Function to compute covariance and correlation matrices\n", + "def compute_matrices(df):\n", + " covariance_matrix = np.cov(df, rowvar=False)\n", + " correlation_matrix = np.corrcoef(df, rowvar=False)\n", + " return covariance_matrix, correlation_matrix\n", + "\n", + "# Function to format matrices for display\n", + "def format_matrix(matrix):\n", + " return [[\"{:.3f}\".format(value) for value in row] for row in matrix]\n", + "\n", + "# Function to display matrices using tabulate\n", + "def display_matrix(matrix, headers):\n", + " formatted_matrix = format_matrix(matrix)\n", + " table = tabulate(formatted_matrix, headers=headers)\n", + " print(table)\n", + "\n", + "# Function to analyze dataset\n", + "def analyze_dataset(dataset, feature_names=None):\n", + " if feature_names is None:\n", + " feature_names = [\"Feature \" + str(i) for i in range(dataset.shape[1])]\n", + "\n", + " low_level_features = dataset[:, :5]\n", + " high_level_features = dataset[:, 5:]\n", + "\n", + " covariance_low, correlation_low = compute_matrices(low_level_features)\n", + " covariance_high, correlation_high = compute_matrices(high_level_features)\n", + "\n", + " print(\"Low-Level Features Analysis:\")\n", + " display_matrix(covariance_low, feature_names[:5])\n", + " display_matrix(correlation_low, feature_names[:5])\n", + "\n", + " print(\"\\nHigh-Level Features Analysis:\")\n", + " display_matrix(covariance_high, feature_names[5:])\n", + " display_matrix(correlation_high, feature_names[5:])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "nHIScqea_rxF", + "outputId": "93dae5b6-6754-4ff6-d3f2-deffc6866f68" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 443 - }, - "id": "RL_uXjmq_r3Y", - "outputId": "390f95ab-3f5c-4a48-8974-9229f5caa19f" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " signal l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi \\\n", - "1 1.0 1.667973 0.064191 -1.225171 0.506102 -0.338939 1.672543 \n", - "2 1.0 0.444840 -0.134298 -0.709972 0.451719 -1.613871 -0.768661 \n", - "3 1.0 0.381256 -0.976145 0.693152 0.448959 0.891753 -0.677328 \n", - "4 1.0 1.309996 -0.690089 -0.676259 1.589283 -0.693326 0.622907 \n", - "8 1.0 2.112812 0.742983 -0.330539 0.805253 -0.028887 -1.446679 \n", - "... ... ... ... ... ... ... ... \n", - "499988 1.0 0.939203 0.496058 0.492828 0.666188 -1.330323 -1.665897 \n", - "499991 1.0 1.521302 0.734693 0.280339 1.590609 0.366158 -1.507171 \n", - "499994 1.0 0.955334 -1.524135 -1.189764 1.470348 -0.296168 0.696495 \n", - "499996 1.0 0.910016 -0.364544 -0.777120 0.543648 -0.910632 -1.723707 \n", - "499997 1.0 0.842954 0.332476 -1.048564 1.347989 0.320496 -0.666358 \n", - "\n", - " MET MET_phi MET_rel axial_MET M_R M_TR_2 R \\\n", - "1 3.475464 -1.219136 0.012955 3.775174 1.045977 0.568051 0.481928 \n", - "2 1.219918 0.504026 1.831248 -0.431385 0.526283 0.941514 1.587535 \n", - "3 2.033060 1.533041 3.046260 -1.005285 0.569386 1.015211 1.582217 \n", - "4 1.087562 -0.381742 0.589204 1.365479 1.179295 0.968218 0.728563 \n", - "8 2.299946 1.450429 2.989110 -1.894770 1.445125 2.548166 1.564721 \n", - "... ... ... ... ... ... ... ... \n", - "499988 1.501900 0.031668 1.689827 0.799185 1.104025 1.026356 0.824965 \n", - "499991 0.828265 -0.980382 1.005345 -0.325469 1.318534 1.237360 0.832760 \n", - "499994 0.851731 0.815524 0.259266 0.340013 1.219641 0.991118 0.721126 \n", - "499996 2.864673 1.458272 2.176558 -0.590911 0.673695 1.662140 2.189362 \n", - "499997 0.450433 -0.411872 0.293407 0.630491 0.859920 0.403371 0.416258 \n", - "\n", - " MT2 S_R M_Delta_R dPhi_r_b cos_theta_r1 \n", - "1 0.000000 0.448410 0.205356 1.321893 0.377584 \n", - "2 2.024308 0.603498 1.562374 1.135454 0.180910 \n", - "3 1.551914 0.761215 1.715464 1.492257 0.090719 \n", - "4 0.000000 1.083158 0.043429 1.154854 0.094859 \n", - "8 2.393632 1.554566 2.148468 1.179117 0.688057 \n", - "... ... ... ... ... ... \n", - "499988 1.495351 1.117306 1.287094 1.173716 0.095378 \n", - "499991 0.671833 1.340157 0.739515 1.115782 0.227649 \n", - "499994 0.000000 1.242410 0.526798 1.313807 0.160337 \n", - "499996 1.195041 0.910815 1.181893 1.252362 0.826035 \n", - "499997 0.591989 0.372003 0.716788 0.366991 0.265798 \n", - "\n", - "[229245 rows x 19 columns]" - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
signall_1_pTl_1_etal_1_phil_2_pTl_2_etal_2_phiMETMET_phiMET_relaxial_METM_RM_TR_2RMT2S_RM_Delta_RdPhi_r_bcos_theta_r1
11.01.6679730.064191-1.2251710.506102-0.3389391.6725433.475464-1.2191360.0129553.7751741.0459770.5680510.4819280.0000000.4484100.2053561.3218930.377584
21.00.444840-0.134298-0.7099720.451719-1.613871-0.7686611.2199180.5040261.831248-0.4313850.5262830.9415141.5875352.0243080.6034981.5623741.1354540.180910
31.00.381256-0.9761450.6931520.4489590.891753-0.6773282.0330601.5330413.046260-1.0052850.5693861.0152111.5822171.5519140.7612151.7154641.4922570.090719
41.01.309996-0.690089-0.6762591.589283-0.6933260.6229071.087562-0.3817420.5892041.3654791.1792950.9682180.7285630.0000001.0831580.0434291.1548540.094859
81.02.1128120.742983-0.3305390.805253-0.028887-1.4466792.2999461.4504292.989110-1.8947701.4451252.5481661.5647212.3936321.5545662.1484681.1791170.688057
............................................................
4999881.00.9392030.4960580.4928280.666188-1.330323-1.6658971.5019000.0316681.6898270.7991851.1040251.0263560.8249651.4953511.1173061.2870941.1737160.095378
4999911.01.5213020.7346930.2803391.5906090.366158-1.5071710.828265-0.9803821.005345-0.3254691.3185341.2373600.8327600.6718331.3401570.7395151.1157820.227649
4999941.00.955334-1.524135-1.1897641.470348-0.2961680.6964950.8517310.8155240.2592660.3400131.2196410.9911180.7211260.0000001.2424100.5267981.3138070.160337
4999961.00.910016-0.364544-0.7771200.543648-0.910632-1.7237072.8646731.4582722.176558-0.5909110.6736951.6621402.1893621.1950410.9108151.1818931.2523620.826035
4999971.00.8429540.332476-1.0485641.3479890.320496-0.6663580.450433-0.4118720.2934070.6304910.8599200.4033710.4162580.5919890.3720030.7167880.3669910.265798
\n", - "

229245 rows × 19 columns

\n", - "
\n", - "
\n", - "\n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "
\n", - "\n", - "\n", - "
\n", - " \n", - "\n", - "\n", - "\n", - " \n", - "
\n", - "\n", - "
\n", - " \n", - " \n", - " \n", - "
\n", - "\n", - "
\n", - "
\n" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "df_sig" - } - }, - "metadata": {}, - "execution_count": 34 - } - ], - "source": [ - "df_sig" + "name": "stdout", + "output_type": "stream", + "text": [ + "Low-Level Features Analysis:\n", + " Feature 0 Feature 1 Feature 2 Feature 3 Feature 4\n", + "----------- ----------- ----------- ----------- -----------\n", + " 0.077 -0.004 -0.003 -0.01 -0\n", + " -0.004 0.09 -0.018 0.007 -0.018\n", + " -0.003 -0.018 0.076 0.013 -0.01\n", + " -0.01 0.007 0.013 0.095 0.014\n", + " -0 -0.018 -0.01 0.014 0.075\n", + " Feature 0 Feature 1 Feature 2 Feature 3 Feature 4\n", + "----------- ----------- ----------- ----------- -----------\n", + " 1 -0.052 -0.041 -0.112 -0.006\n", + " -0.052 1 -0.213 0.072 -0.22\n", + " -0.041 -0.213 1 0.155 -0.126\n", + " -0.112 0.072 0.155 1 0.16\n", + " -0.006 -0.22 -0.126 0.16 1\n", + "\n", + "High-Level Features Analysis:\n", + " Feature 5 Feature 6 Feature 7 Feature 8 Feature 9\n", + "----------- ----------- ----------- ----------- -----------\n", + " 0.08 -0.006 -0.001 0.005 0.011\n", + " -0.006 0.084 0.004 0.013 0.005\n", + " -0.001 0.004 0.092 -0.018 -0.001\n", + " 0.005 0.013 -0.018 0.084 0.012\n", + " 0.011 0.005 -0.001 0.012 0.102\n", + " Feature 5 Feature 6 Feature 7 Feature 8 Feature 9\n", + "----------- ----------- ----------- ----------- -----------\n", + " 1 -0.067 -0.008 0.06 0.118\n", + " -0.067 1 0.042 0.153 0.058\n", + " -0.008 0.042 1 -0.201 -0.006\n", + " 0.06 0.153 -0.201 1 0.127\n", + " 0.118 0.058 -0.006 0.127 1\n" + ] + } + ], + "source": [ + "# checking if it works:\n", + "dataset = np.random.rand(100, 10) # Example dataset with 100 samples and 10 features\n", + "analyze_dataset(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FjCYpGx__rxH" + }, + "source": [ + "## Exercise 5: Selection\n", + "\n", + "### Exercise 5.1\n", + "\n", + "Part a\n", + "By looking at the signal/background distributions for each observable (e.g. $x$) determine which selection criteria would be optimal:\n", + "\n", + "1. $x > x_c$\n", + "2. $x < x_c$\n", + "3. $|x - \\mu| > x_c$\n", + "4. $|x - \\mu| < x_c$\n", + "\n", + "where $x_c$ is value to be determined below.\n", + "\n", + "### Exercise 5.2\n", + "\n", + "Plot the True Positive Rate (TPR) (aka signal efficiency $\\epsilon_S(x_c)$) and False Positive Rate (FPR) (aka background efficiency $\\epsilon_B(x_c)$) as function of $x_c$ for applying the strategy in part a to each observable.\n", + "\n", + "### Exercise 5.3\n", + "Assume 3 different scenarios corresponding to different numbers of signal and background events expected in data:\n", + "\n", + "1. Expect $N_S=10$, $N_B=100$.\n", + "1. Expect $N_S=100$, $N_B=1000$.\n", + "1. Expect $N_S=1000$, $N_B=10000$.\n", + "1. Expect $N_S=10000$, $N_B=100000$.\n", + "\n", + "Plot the significance ($\\sigma_{S'}$) for each observable as function of $x_c$ for each scenario, where\n", + "\n", + "$\\sigma_{S'}= \\frac{N'_S}{\\sqrt{N'_S+N'_B}}$\n", + "\n", + "and $N'_{S,B} = \\epsilon_{S,B}(x_c) * N_{S,B}$." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "uBhzDKKW_r0J", + "outputId": "3fbf3f3f-755d-4a45-c22b-ff07b0c6de53" + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define expected number of signal and background events\n", + "n_sig_expected = 1000\n", + "n_bkg_expected = 10000\n", + "\n", + "# Define threshold values\n", + "threshold_values = np.linspace(0, 1, 100) # Adjust range and number of points as needed\n", + "\n", + "# Calculate TPR and FPR for each threshold value\n", + "tpr_values = [(1 - threshold) for threshold in threshold_values]\n", + "fpr_values = [threshold for threshold in threshold_values]\n", + "\n", + "# Plot TPR and FPR as functions of the threshold value\n", + "plt.plot(threshold_values, tpr_values, label='True Positive Rate (TPR)')\n", + "plt.plot(threshold_values, fpr_values, label='False Positive Rate (FPR)')\n", + "plt.xlabel('Threshold Value')\n", + "plt.ylabel('Rate')\n", + "plt.title('TPR and FPR vs. Threshold Value')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mIj1zAGk_r0T" + }, + "source": [ + "## Exercise 6: Cut Flow\n", + "\n", + "\n", + "### Exercise 6.1\n", + "\n", + "For each above scenario, choose a subset (minumum 3) of observables to use for selections, and values of $x_c$ based on your significance plots (part 3c).\n", + "\n", + "### Exercise 6.2\n", + "Create a \"cut-flow\" table for each scenario where you successively make the selections on each observable and tabulate $\\epsilon_S$, $\\epsilon_B$, $N'_S$, $N'_B$, and $\\sigma_{S'}$.\n", + "\n", + "### Exercise 6.3\n", + "In 3c above you computed the significance for each observable assuming to make no other selections on any other observable. If the variables are correlated, then this assumption can lead to non-optimial results when selecting on multiple variables. By looking at the correlation matrices and your answers to 4b, identify where this effect could be most detrimental to the significance. Attempt to correct the issue by applying the selection in one observable and then optimizing (part 3c) for a second observable. What happens if you change the order of your selection (make selection on second and optimize on first)?\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 619 }, + "id": "NQayCwMA_r0n", + "outputId": "9a72ceaf-e662-4c13-b298-cdbdbc08f2f6" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 423 - }, - "id": "V9zGiNVd_r4H", - "outputId": "22a5652d-c54c-498b-9704-7d3740e7c11f" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi MET \\\n", - "1 1.667973 0.064191 -1.225171 0.506102 -0.338939 1.672543 3.475464 \n", - "2 0.444840 -0.134298 -0.709972 0.451719 -1.613871 -0.768661 1.219918 \n", - "3 0.381256 -0.976145 0.693152 0.448959 0.891753 -0.677328 2.033060 \n", - "4 1.309996 -0.690089 -0.676259 1.589283 -0.693326 0.622907 1.087562 \n", - "8 2.112812 0.742983 -0.330539 0.805253 -0.028887 -1.446679 2.299946 \n", - "... ... ... ... ... ... ... ... \n", - "499988 0.939203 0.496058 0.492828 0.666188 -1.330323 -1.665897 1.501900 \n", - "499991 1.521302 0.734693 0.280339 1.590609 0.366158 -1.507171 0.828265 \n", - "499994 0.955334 -1.524135 -1.189764 1.470348 -0.296168 0.696495 0.851731 \n", - "499996 0.910016 -0.364544 -0.777120 0.543648 -0.910632 -1.723707 2.864673 \n", - "499997 0.842954 0.332476 -1.048564 1.347989 0.320496 -0.666358 0.450433 \n", - "\n", - " MET_phi MET_rel axial_MET M_R M_TR_2 R MT2 \\\n", - "1 -1.219136 0.012955 3.775174 1.045977 0.568051 0.481928 0.000000 \n", - "2 0.504026 1.831248 -0.431385 0.526283 0.941514 1.587535 2.024308 \n", - "3 1.533041 3.046260 -1.005285 0.569386 1.015211 1.582217 1.551914 \n", - "4 -0.381742 0.589204 1.365479 1.179295 0.968218 0.728563 0.000000 \n", - "8 1.450429 2.989110 -1.894770 1.445125 2.548166 1.564721 2.393632 \n", - "... ... ... ... ... ... ... ... \n", - "499988 0.031668 1.689827 0.799185 1.104025 1.026356 0.824965 1.495351 \n", - "499991 -0.980382 1.005345 -0.325469 1.318534 1.237360 0.832760 0.671833 \n", - "499994 0.815524 0.259266 0.340013 1.219641 0.991118 0.721126 0.000000 \n", - "499996 1.458272 2.176558 -0.590911 0.673695 1.662140 2.189362 1.195041 \n", - "499997 -0.411872 0.293407 0.630491 0.859920 0.403371 0.416258 0.591989 \n", - "\n", - " S_R M_Delta_R dPhi_r_b cos_theta_r1 \n", - "1 0.448410 0.205356 1.321893 0.377584 \n", - "2 0.603498 1.562374 1.135454 0.180910 \n", - "3 0.761215 1.715464 1.492257 0.090719 \n", - "4 1.083158 0.043429 1.154854 0.094859 \n", - "8 1.554566 2.148468 1.179117 0.688057 \n", - "... ... ... ... ... \n", - "499988 1.117306 1.287094 1.173716 0.095378 \n", - "499991 1.340157 0.739515 1.115782 0.227649 \n", - "499994 1.242410 0.526798 1.313807 0.160337 \n", - "499996 0.910815 1.181893 1.252362 0.826035 \n", - "499997 0.372003 0.716788 0.366991 0.265798 \n", - "\n", - "[229245 rows x 18 columns]" - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
l_1_pTl_1_etal_1_phil_2_pTl_2_etal_2_phiMETMET_phiMET_relaxial_METM_RM_TR_2RMT2S_RM_Delta_RdPhi_r_bcos_theta_r1
11.6679730.064191-1.2251710.506102-0.3389391.6725433.475464-1.2191360.0129553.7751741.0459770.5680510.4819280.0000000.4484100.2053561.3218930.377584
20.444840-0.134298-0.7099720.451719-1.613871-0.7686611.2199180.5040261.831248-0.4313850.5262830.9415141.5875352.0243080.6034981.5623741.1354540.180910
30.381256-0.9761450.6931520.4489590.891753-0.6773282.0330601.5330413.046260-1.0052850.5693861.0152111.5822171.5519140.7612151.7154641.4922570.090719
41.309996-0.690089-0.6762591.589283-0.6933260.6229071.087562-0.3817420.5892041.3654791.1792950.9682180.7285630.0000001.0831580.0434291.1548540.094859
82.1128120.742983-0.3305390.805253-0.028887-1.4466792.2999461.4504292.989110-1.8947701.4451252.5481661.5647212.3936321.5545662.1484681.1791170.688057
.........................................................
4999880.9392030.4960580.4928280.666188-1.330323-1.6658971.5019000.0316681.6898270.7991851.1040251.0263560.8249651.4953511.1173061.2870941.1737160.095378
4999911.5213020.7346930.2803391.5906090.366158-1.5071710.828265-0.9803821.005345-0.3254691.3185341.2373600.8327600.6718331.3401570.7395151.1157820.227649
4999940.955334-1.524135-1.1897641.470348-0.2961680.6964950.8517310.8155240.2592660.3400131.2196410.9911180.7211260.0000001.2424100.5267981.3138070.160337
4999960.910016-0.364544-0.7771200.543648-0.910632-1.7237072.8646731.4582722.176558-0.5909110.6736951.6621402.1893621.1950410.9108151.1818931.2523620.826035
4999970.8429540.332476-1.0485641.3479890.320496-0.6663580.450433-0.4118720.2934070.6304910.8599200.4033710.4162580.5919890.3720030.7167880.3669910.265798
\n", - "

229245 rows × 18 columns

\n", - "
\n", - "
\n", - "\n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "
\n", - "\n", - "\n", - "
\n", - " \n", - "\n", - "\n", - "\n", - " \n", - "
\n", - "\n", - "
\n", - " \n", - " \n", - " \n", - "
\n", - "\n", - "
\n", - "
\n" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "df_sig_0" - } - }, - "metadata": {}, - "execution_count": 35 - } - ], - "source": [ - "df_sig_0 = df_sig.drop(\"signal\",axis=1)\n", - "df_bkg_0 = df_bkg.drop(\"signal\",axis=1)\n", - "\n", - "df_sig_0" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 648 - }, - "id": "H5va4rD9_r4Q", - "outputId": "cbcf8e40-dc38-46ae-92e5-04062d082cda" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "l_1_pT 1.291078\n", - "l_1_eta 0.000824\n", - "l_1_phi -0.001524\n", - "l_2_pT 1.138668\n", - "l_2_eta 0.002487\n", - "l_2_phi 0.000049\n", - "MET 1.418381\n", - "MET_phi -0.000470\n", - "MET_rel 1.275169\n", - "axial_MET 0.089314\n", - "M_R 1.183651\n", - "M_TR_2 1.268858\n", - "R 1.056352\n", - "MT2 1.074694\n", - "S_R 1.175023\n", - "M_Delta_R 1.186022\n", - "dPhi_r_b 1.014617\n", - "cos_theta_r1 0.282417\n", - "dtype: float64" - ], - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
0
l_1_pT1.291078
l_1_eta0.000824
l_1_phi-0.001524
l_2_pT1.138668
l_2_eta0.002487
l_2_phi0.000049
MET1.418381
MET_phi-0.000470
MET_rel1.275169
axial_MET0.089314
M_R1.183651
M_TR_21.268858
R1.056352
MT21.074694
S_R1.175023
M_Delta_R1.186022
dPhi_r_b1.014617
cos_theta_r10.282417
\n", - "

" - ] - }, - "metadata": {}, - "execution_count": 36 - } + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Name N sig N bkg TPR FPR N sig' N bkg' sig x_c bin i
1 10 100 1 1 10 100 0.953463 1 99
2 100 1000 1 1 100 1000 3.01511 1 99
2.1 200 2000 1 1 200 2000 4.26401 1 99
2.2 300 3000 1 1 300 3000 5.22233 1 99
2.3 400 4000 1 1 400 4000 6.03023 1 99
2.4 500 5000 1 1 500 5000 6.742 1 99
3 1000 10000 1 1 1000 10000 9.53463 1 99
4 10000 100000 1 1 10000 10000030.1511 1 99
" ], - "source": [ - "m_s= np.mean(df_sig_0,axis=0)\n", - "m_s" + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from IPython.display import HTML, display\n", + "import tabulate\n", + "\n", + "def compare_significance(scenarios, log=False):\n", + " max_sigs = dict()\n", + " table = []\n", + "\n", + " for name, (n_sig_expected, n_bkg_expected) in scenarios.items():\n", + " TPR = np.linspace(0, 1, 100)\n", + " FPR = np.linspace(0, 1, 100)\n", + "\n", + " # Calculate expected number of signal and background events passing the threshold cut\n", + " n_sig_expected_prime = n_sig_expected * TPR\n", + " n_bkg_expected_prime = n_bkg_expected * FPR\n", + "\n", + " # Calculate significance, handle division by zero\n", + " with np.errstate(divide='ignore', invalid='ignore'):\n", + " sig = np.divide(n_sig_expected_prime, np.sqrt(n_sig_expected_prime + n_bkg_expected_prime))\n", + "\n", + " # Plot significance as a function of TPR\n", + " plt.step(TPR, sig, label=name)\n", + "\n", + " # Find maximum significance and store relevant data\n", + " max_i = np.nanargmax(sig) # Use np.nanargmax to ignore NaN values\n", + " max_sigs[name] = (max_i, n_sig_expected_prime[max_i], n_bkg_expected_prime[max_i], sig[max_i], TPR[max_i])\n", + "\n", + " # Append data to table\n", + " table.append((name, n_sig_expected, n_bkg_expected, TPR[max_i], FPR[max_i], n_sig_expected_prime[max_i],\n", + " n_bkg_expected_prime[max_i], sig[max_i], TPR[max_i], max_i))\n", + "\n", + " # Display plot\n", + " if log:\n", + " plt.yscale(\"log\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " # Display table\n", + " display(HTML(tabulate.tabulate(table, tablefmt='html',\n", + " headers=[\"Name\", 'N sig', 'N bkg', \"TPR\", \"FPR\", \"N sig'\", \"N bkg'\", 'sig', 'x_c', \"bin i\"])))\n", + "\n", + " return max_sigs\n", + "\n", + "# Define scenarios\n", + "scenarios = {\"1\": (10, 100),\n", + " \"2\": (100, 1000),\n", + " \"2.1\": (200, 2000),\n", + " \"2.2\": (300, 3000),\n", + " \"2.3\": (400, 4000),\n", + " \"2.4\": (500, 5000),\n", + " \"3\": (1000, 10000),\n", + " \"4\": (10000, 100000)}\n", + "\n", + "# Compare significance\n", + "max_sigs = compare_significance(scenarios)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-6OTqp5d_r2C" + }, + "source": [ + "## Exercise 7: ROC Curves\n", + "\n", + "### Exercise 7.1\n", + "For the top 3 observables you identified earlier, create one figure overlaying the Reciever Operating Characteristic (ROC) curves for the 3 observables. Compute the area under the curves and report it in the legend of the figure.\n", + "\n", + "### Exercise 7.2\n", + "Write a function that you can use to quickly create the figure in part a with other observables and different conditions. Note that you will likely revise this function as you do the remainder of the lab.\n", + "\n", + "### Exercise 7.3\n", + "Use the function from part b to compare the ROC curves for the successive selections in lab 3, exercise 4. Specifically, plot the ROC curve after each selection.\n", + "\n", + "### Exercise 7.4\n", + "Use your function and appropriate example to demonstrate the effect (if any) of changing order of the successive selections.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 619 }, + "id": "i6a6O4RI_r2b", + "outputId": "0af5a310-266c-4a97-a409-cefd36dc7ec2" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "id": "kWaHAj3r_r4z" - }, - "outputs": [], - "source": [ - "# Compute means for signal and background\n", - "m_s = np.mean(df_sig_0, axis=0) # Mean for signal events\n", - "m_b = np.mean(df_bkg_0, axis=0) # Mean for background events\n", - "\n", - "# Calculate the difference between means\n", - "delta = m_s - m_b # Difference vector between signal and background means\n" + "data": { + "image/png": "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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "id": "Zr-AXn8r_r72" - }, - "outputs": [], - "source": [ - "delta=np.matrix(m_s-m_b).transpose()" + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Name N sig N bkg TPR FPR N sig' N bkg' sig x_c bin i
1 10 100 1 1 10 100 0.953463 1 99
2 100 1000 1 1 100 1000 3.01511 1 99
2.1 200 2000 1 1 200 2000 4.26401 1 99
2.2 300 3000 1 1 300 3000 5.22233 1 99
2.3 400 4000 1 1 400 4000 6.03023 1 99
2.4 500 5000 1 1 500 5000 6.742 1 99
3 1000 10000 1 1 1000 10000 9.53463 1 99
4 10000 100000 1 1 10000 10000030.1511 1 99
" + ], + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from IPython.display import HTML, display\n", + "import tabulate\n", + "\n", + "def compare_significance(scenarios, log=False):\n", + " max_sigs = dict()\n", + " table = []\n", + "\n", + " for name, (n_sig_expected, n_bkg_expected) in scenarios.items():\n", + " TPR = np.linspace(0, 1, 100)\n", + " FPR = np.linspace(0, 1, 100)\n", + "\n", + " # Calculate expected number of signal and background events passing the threshold cut\n", + " n_sig_expected_prime = n_sig_expected * TPR\n", + " n_bkg_expected_prime = n_bkg_expected * FPR\n", + "\n", + " # Calculate significance, handle division by zero or taking square root of negative numbers\n", + " with np.errstate(divide='ignore', invalid='ignore'):\n", + " sig = np.divide(n_sig_expected_prime, np.sqrt(n_sig_expected_prime + n_bkg_expected_prime))\n", + "\n", + " # Plot significance as a function of TPR\n", + " plt.step(TPR, sig, label=name)\n", + "\n", + " # Find maximum significance and store relevant data\n", + " max_i = np.nanargmax(sig)\n", + " max_sigs[name] = (max_i, n_sig_expected_prime[max_i], n_bkg_expected_prime[max_i], sig[max_i], TPR[max_i])\n", + "\n", + " # Append data to table\n", + " table.append((name, n_sig_expected, n_bkg_expected, TPR[max_i], FPR[max_i], n_sig_expected_prime[max_i],\n", + " n_bkg_expected_prime[max_i], sig[max_i], TPR[max_i], max_i))\n", + "\n", + " # Display plot\n", + " if log:\n", + " plt.yscale(\"log\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " # Display table\n", + " display(HTML(tabulate.tabulate(table, tablefmt='html',\n", + " headers=[\"Name\", 'N sig', 'N bkg', \"TPR\", \"FPR\", \"N sig'\", \"N bkg'\", 'sig', 'x_c', \"bin i\"])))\n", + "\n", + " return max_sigs\n", + "\n", + "# Define scenarios\n", + "scenarios = {\"1\": (10, 100),\n", + " \"2\": (100, 1000),\n", + " \"2.1\": (200, 2000),\n", + " \"2.2\": (300, 3000),\n", + " \"2.3\": (400, 4000),\n", + " \"2.4\": (500, 5000),\n", + " \"3\": (1000, 10000),\n", + " \"4\": (10000, 100000)}\n", + "\n", + "# Compare significance\n", + "max_sigs = compare_significance(scenarios)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4KVNhaw0_r2l" + }, + "source": [ + "## Exercise 8: Linear Discriminant\n", + "\n", + "### Exercise 8.1\n", + "\n", + "Using numpy, compute the between-class $\\bf{S}_B$ and within-class $\\bf{S}_W$ covariance matrices defined as:\n", + "\n", + "$$\n", + "\\bf{S}_B = (\\bf{m_2}-\\bf{m_1})(\\bf{m_2}-\\bf{m_1})^T \\\\\n", + "$$\n", + "$$\n", + "\\bf{S}_W = \\sum_{i=1,2} \\sum_{n=1}^{l_i} (\\bf{x}_n^i - \\bf{m}_i) (\\bf{x}_n^i - \\bf{m}_i)^T\n", + "$$\n", + "\n", + "where $\\bf{m_i}$ are the vectors containing the means for category 1 and 2, here defined as signal and background. Here $\\bf{x}_n^i$ is the vector containing the observables for the $n$th example event in category $i$.\n", + "\n", + "### Exercise 8.1\n", + "\n", + "Compute the linear coefficients $\\bf{w} = \\bf{S_W}^{-1}(\\bf{m_2}-\\bf{m_1})$. Compare the histogram of the distribution of $F_n^i=\\bf{w}^T\\bf{x}_n^i$ for the two categories.\n", + "\n", + "### Exercise 8.1\n", + "\n", + "Draw the ROC curve for $F_n$.\n", + "\n", + "### Exercise 8.1\n", + "\n", + "What is the maximal significance you can obtain in the scenarios in exercise 5?" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "2gPXZPKU_r3P", + "outputId": "4fbc4c95-ec27-41f0-db28-c57e3e5db233" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "u-QceBjq_r76", - "outputId": "90ae110d-b691-454e-8f3c-5ae63a81f5fb" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "matrix([[ 2.87970231e-01, 4.58922304e-04, 4.67467434e-04,\n", - " 1.38148662e-01, 1.54068049e-03, 3.66095560e-04,\n", - " 4.13461344e-01, -6.36618070e-04, 2.71136238e-01,\n", - " 8.54657090e-02, 1.82331687e-01, 2.66557653e-01,\n", - " 5.53227373e-02, 7.37824064e-02, 1.74241790e-01,\n", - " 1.84116478e-01, 1.54056891e-02, 5.66281031e-02],\n", - " [ 4.58922304e-04, 7.31359210e-07, 7.44977113e-07,\n", - " 2.20159917e-04, 2.45529768e-06, 5.83426339e-07,\n", - " 6.58910580e-04, -1.01454317e-06, 4.32094896e-04,\n", - " 1.36201995e-04, 2.90571971e-04, 4.24798257e-04,\n", - " 8.81648006e-05, 1.17582959e-04, 2.77679548e-04,\n", - " 2.93416294e-04, 2.45511986e-05, 9.02450903e-05],\n", - " [ 4.67467434e-04, 7.44977113e-07, 7.58848582e-07,\n", - " 2.24259293e-04, 2.50101531e-06, 5.94289733e-07,\n", - " 6.71179491e-04, -1.03343396e-06, 4.40140500e-04,\n", - " 1.38738075e-04, 2.95982420e-04, 4.32707998e-04,\n", - " 8.98064286e-05, 1.19772353e-04, 2.82849940e-04,\n", - " 2.98879704e-04, 2.50083417e-05, 9.19254533e-05],\n", - " [ 1.38148662e-01, 2.20159917e-04, 2.24259293e-04,\n", - " 6.62743950e-02, 7.39114414e-04, 1.75627917e-04,\n", - " 1.98350820e-01, -3.05406341e-04, 1.30072850e-01,\n", - " 4.10006732e-02, 8.74704254e-02, 1.27876354e-01,\n", - " 2.65401118e-02, 3.53958140e-02, 8.35894395e-02,\n", - " 8.83266474e-02, 7.39060884e-03, 2.71663382e-02],\n", - " [ 1.54068049e-03, 2.45529768e-06, 2.50101531e-06,\n", - " 7.39114414e-04, 8.24285333e-06, 1.95866179e-06,\n", - " 2.21207527e-03, -3.40599456e-06, 1.45061631e-03,\n", - " 4.57253342e-04, 9.75499697e-04, 1.42612024e-03,\n", - " 2.95984282e-04, 3.94746060e-04, 9.32217633e-04,\n", - " 9.85048574e-04, 8.24225634e-05, 3.02968169e-04],\n", - " [ 3.66095560e-04, 5.83426339e-07, 5.94289733e-07,\n", - " 1.75627917e-04, 1.95866179e-06, 4.65416020e-07,\n", - " 5.25631977e-04, -8.09330353e-07, 3.44694563e-04,\n", - " 1.08652260e-04, 2.31797644e-04, 3.38873823e-04,\n", - " 7.03316047e-05, 9.37993183e-05, 2.21512986e-04,\n", - " 2.34066642e-04, 1.95851993e-05, 7.19911118e-05],\n", - " [ 4.13461344e-01, 6.58910580e-04, 6.71179491e-04,\n", - " 1.98350820e-01, 2.21207527e-03, 5.25631977e-04,\n", - " 5.93638731e-01, -9.14042266e-04, 3.89291466e-01,\n", - " 1.22709791e-01, 2.61787838e-01, 3.82717634e-01,\n", - " 7.94311730e-02, 1.05935161e-01, 2.50172542e-01,\n", - " 2.64350402e-01, 2.21191506e-02, 8.13053887e-02],\n", - " [-6.36618070e-04, -1.01454317e-06, -1.03343396e-06,\n", - " -3.05406341e-04, -3.40599456e-06, -8.09330353e-07,\n", - " -9.14042266e-04, 1.40737661e-06, -5.99403029e-04,\n", - " -1.88939720e-04, -4.03082104e-04, -5.89281115e-04,\n", - " -1.22302413e-04, -1.63111350e-04, -3.85197705e-04,\n", - " -4.07027755e-04, -3.40574788e-05, -1.25188196e-04],\n", - " [ 2.71136238e-01, 4.32094896e-04, 4.40140500e-04,\n", - " 1.30072850e-01, 1.45061631e-03, 3.44694563e-04,\n", - " 3.89291466e-01, -5.99403029e-04, 2.55286317e-01,\n", - " 8.04696053e-02, 1.71673049e-01, 2.50975384e-01,\n", - " 5.20887135e-02, 6.94692782e-02, 1.64056067e-01,\n", - " 1.73353506e-01, 1.45051125e-02, 5.33177710e-02],\n", - " [ 8.54657090e-02, 1.36201995e-04, 1.38738075e-04,\n", - " 4.10006732e-02, 4.57253342e-04, 1.08652260e-04,\n", - " 1.22709791e-01, -1.88939720e-04, 8.04696053e-02,\n", - " 2.53650781e-02, 5.41136035e-02, 7.91107426e-02,\n", - " 1.64190477e-02, 2.18976303e-02, 5.17126304e-02,\n", - " 5.46433055e-02, 4.57220225e-03, 1.68064628e-02],\n", - " [ 1.82331687e-01, 2.90571971e-04, 2.95982420e-04,\n", - " 8.74704254e-02, 9.75499697e-04, 2.31797644e-04,\n", - " 2.61787838e-01, -4.03082104e-04, 1.71673049e-01,\n", - " 5.41136035e-02, 1.15445419e-01, 1.68774065e-01,\n", - " 3.50282318e-02, 4.67161851e-02, 1.10323207e-01,\n", - " 1.16575480e-01, 9.75429046e-03, 3.58547393e-02],\n", - " [ 2.66557653e-01, 4.24798257e-04, 4.32707998e-04,\n", - " 1.27876354e-01, 1.42612024e-03, 3.38873823e-04,\n", - " 3.82717634e-01, -5.89281115e-04, 2.50975384e-01,\n", - " 7.91107426e-02, 1.68774065e-01, 2.46737249e-01,\n", - " 5.12091092e-02, 6.82961743e-02, 1.61285708e-01,\n", - " 1.70426145e-01, 1.42601696e-02, 5.24174121e-02],\n", - " [ 5.53227373e-02, 8.81648006e-05, 8.98064286e-05,\n", - " 2.65401118e-02, 2.95984282e-04, 7.03316047e-05,\n", - " 7.94311730e-02, -1.22302413e-04, 5.20887135e-02,\n", - " 1.64190477e-02, 3.50282318e-02, 5.12091092e-02,\n", - " 1.06282002e-02, 1.41745369e-02, 3.34740599e-02,\n", - " 3.53711128e-02, 2.95962845e-03, 1.08789775e-02],\n", - " [ 7.37824064e-02, 1.17582959e-04, 1.19772353e-04,\n", - " 3.53958140e-02, 3.94746060e-04, 9.37993183e-05,\n", - " 1.05935161e-01, -1.63111350e-04, 6.94692782e-02,\n", - " 2.18976303e-02, 4.67161851e-02, 6.82961743e-02,\n", - " 1.41745369e-02, 1.89041884e-02, 4.46434290e-02,\n", - " 4.71734760e-02, 3.94717471e-03, 1.45089918e-02],\n", - " [ 1.74241790e-01, 2.77679548e-04, 2.82849940e-04,\n", - " 8.35894395e-02, 9.32217633e-04, 2.21512986e-04,\n", - " 2.50172542e-01, -3.85197705e-04, 1.64056067e-01,\n", - " 5.17126304e-02, 1.10323207e-01, 1.61285708e-01,\n", - " 3.34740599e-02, 4.46434290e-02, 1.05428264e-01,\n", - " 1.11403129e-01, 9.32150117e-03, 3.42638960e-02],\n", - " [ 1.84116478e-01, 2.93416294e-04, 2.98879704e-04,\n", - " 8.83266474e-02, 9.85048574e-04, 2.34066642e-04,\n", - " 2.64350402e-01, -4.07027755e-04, 1.73353506e-01,\n", - " 5.46433055e-02, 1.16575480e-01, 1.70426145e-01,\n", - " 3.53711128e-02, 4.71734760e-02, 1.11403129e-01,\n", - " 1.17716603e-01, 9.84977232e-03, 3.62057107e-02],\n", - " [ 1.54056891e-02, 2.45511986e-05, 2.50083417e-05,\n", - " 7.39060884e-03, 8.24225634e-05, 1.95851993e-05,\n", - " 2.21191506e-02, -3.40574788e-05, 1.45051125e-02,\n", - " 4.57220225e-03, 9.75429046e-03, 1.42601696e-02,\n", - " 2.95962845e-03, 3.94717471e-03, 9.32150117e-03,\n", - " 9.84977232e-03, 8.24165940e-04, 3.02946227e-03],\n", - " [ 5.66281031e-02, 9.02450903e-05, 9.19254533e-05,\n", - " 2.71663382e-02, 3.02968169e-04, 7.19911118e-05,\n", - " 8.13053887e-02, -1.25188196e-04, 5.33177710e-02,\n", - " 1.68064628e-02, 3.58547393e-02, 5.24174121e-02,\n", - " 1.08789775e-02, 1.45089918e-02, 3.42638960e-02,\n", - " 3.62057107e-02, 3.02946227e-03, 1.11356721e-02]])" - ] - }, - "metadata": {}, - "execution_count": 39 - } - ], - "source": [ - "S_B= delta*delta.transpose()\n", - "S_B" + "data": { + "text/plain": [ + "(229245, 19)" ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sig.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 443 }, + "id": "RL_uXjmq_r3Y", + "outputId": "390f95ab-3f5c-4a48-8974-9229f5caa19f" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 423 - }, - "id": "aLNQAEsN_r8E", - "outputId": "5deaa9e1-a835-4bbc-d16c-d267f957a97d" + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df_sig" }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi MET \\\n", - "1 0.376895 0.063367 -1.223647 -0.632566 -0.341426 1.672494 2.057083 \n", - "2 -0.846238 -0.135122 -0.708447 -0.686949 -1.616358 -0.768710 -0.198463 \n", - "3 -0.909822 -0.976969 0.694677 -0.689709 0.889266 -0.677378 0.614679 \n", - "4 0.018919 -0.690913 -0.674735 0.450615 -0.695813 0.622858 -0.330819 \n", - "8 0.821734 0.742159 -0.329015 -0.333415 -0.031374 -1.446728 0.881564 \n", - "... ... ... ... ... ... ... ... \n", - "499988 -0.351875 0.495235 0.494352 -0.472480 -1.332810 -1.665947 0.083519 \n", - "499991 0.230224 0.733870 0.281863 0.451941 0.363671 -1.507220 -0.590116 \n", - "499994 -0.335744 -1.524958 -1.188240 0.331680 -0.298655 0.696446 -0.566651 \n", - "499996 -0.381062 -0.365368 -0.775596 -0.595019 -0.913119 -1.723756 1.446292 \n", - "499997 -0.448124 0.331653 -1.047040 0.209321 0.318009 -0.666408 -0.967948 \n", - "\n", - " MET_phi MET_rel axial_MET M_R M_TR_2 R MT2 \\\n", - "1 -1.218666 -1.262214 3.685859 -0.137674 -0.700807 -0.574424 -1.074694 \n", - "2 0.504496 0.556079 -0.520699 -0.657368 -0.327344 0.531183 0.949615 \n", - "3 1.533511 1.771091 -1.094599 -0.614265 -0.253647 0.525864 0.477221 \n", - "4 -0.381271 -0.685964 1.276165 -0.004356 -0.300640 -0.327789 -1.074694 \n", - "8 1.450900 1.713942 -1.984084 0.261474 1.279308 0.508369 1.318939 \n", - "... ... ... ... ... ... ... ... \n", - "499988 0.032139 0.414658 0.709871 -0.079626 -0.242503 -0.231387 0.420657 \n", - "499991 -0.979911 -0.269824 -0.414783 0.134882 -0.031498 -0.223592 -0.402861 \n", - "499994 0.815994 -1.015903 0.250699 0.035990 -0.277740 -0.335226 -1.074694 \n", - "499996 1.458742 0.901389 -0.680225 -0.509956 0.393282 1.133010 0.120347 \n", - "499997 -0.411402 -0.981762 0.541177 -0.323732 -0.865488 -0.640094 -0.482705 \n", - "\n", - " S_R M_Delta_R dPhi_r_b cos_theta_r1 \n", - "1 -0.726613 -0.980666 0.307276 0.095167 \n", - "2 -0.571526 0.376352 0.120837 -0.101507 \n", - "3 -0.413808 0.529442 0.477639 -0.191698 \n", - "4 -0.091865 -1.142593 0.140236 -0.187558 \n", - "8 0.379543 0.962446 0.164500 0.405640 \n", - "... ... ... ... ... \n", - "499988 -0.057717 0.101072 0.159099 -0.187039 \n", - "499991 0.165134 -0.446507 0.101164 -0.054768 \n", - "499994 0.067386 -0.659224 0.299190 -0.122080 \n", - "499996 -0.264209 -0.004129 0.237745 0.543618 \n", - "499997 -0.803020 -0.469234 -0.647626 -0.016619 \n", - "\n", - "[229245 rows x 18 columns]" - ], - "text/html": [ - "\n", - "
\n", - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
l_1_pTl_1_etal_1_phil_2_pTl_2_etal_2_phiMETMET_phiMET_relaxial_METM_RM_TR_2RMT2S_RM_Delta_RdPhi_r_bcos_theta_r1
10.3768950.063367-1.223647-0.632566-0.3414261.6724942.057083-1.218666-1.2622143.685859-0.137674-0.700807-0.574424-1.074694-0.726613-0.9806660.3072760.095167
2-0.846238-0.135122-0.708447-0.686949-1.616358-0.768710-0.1984630.5044960.556079-0.520699-0.657368-0.3273440.5311830.949615-0.5715260.3763520.120837-0.101507
3-0.909822-0.9769690.694677-0.6897090.889266-0.6773780.6146791.5335111.771091-1.094599-0.614265-0.2536470.5258640.477221-0.4138080.5294420.477639-0.191698
40.018919-0.690913-0.6747350.450615-0.6958130.622858-0.330819-0.381271-0.6859641.276165-0.004356-0.300640-0.327789-1.074694-0.091865-1.1425930.140236-0.187558
80.8217340.742159-0.329015-0.333415-0.031374-1.4467280.8815641.4509001.713942-1.9840840.2614741.2793080.5083691.3189390.3795430.9624460.1645000.405640
.........................................................
499988-0.3518750.4952350.494352-0.472480-1.332810-1.6659470.0835190.0321390.4146580.709871-0.079626-0.242503-0.2313870.420657-0.0577170.1010720.159099-0.187039
4999910.2302240.7338700.2818630.4519410.363671-1.507220-0.590116-0.979911-0.269824-0.4147830.134882-0.031498-0.223592-0.4028610.165134-0.4465070.101164-0.054768
499994-0.335744-1.524958-1.1882400.331680-0.2986550.696446-0.5666510.815994-1.0159030.2506990.035990-0.277740-0.335226-1.0746940.067386-0.6592240.299190-0.122080
499996-0.381062-0.365368-0.775596-0.595019-0.913119-1.7237561.4462921.4587420.901389-0.680225-0.5099560.3932821.1330100.120347-0.264209-0.0041290.2377450.543618
499997-0.4481240.331653-1.0470400.2093210.318009-0.666408-0.967948-0.411402-0.9817620.541177-0.323732-0.865488-0.640094-0.482705-0.803020-0.469234-0.647626-0.016619
\n", - "

229245 rows × 18 columns

\n", - "
\n", - "
\n", - "\n", - "
\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "
\n", - "\n", - "\n", - "
\n", - " \n", - "\n", - "\n", - "\n", - " \n", - "
\n", - "\n", - "
\n", - "
\n" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe" - } - }, - "metadata": {}, - "execution_count": 40 - } + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
signall_1_pTl_1_etal_1_phil_2_pTl_2_etal_2_phiMETMET_phiMET_relaxial_METM_RM_TR_2RMT2S_RM_Delta_RdPhi_r_bcos_theta_r1
11.01.6679730.064191-1.2251710.506102-0.3389391.6725433.475464-1.2191360.0129553.7751741.0459770.5680510.4819280.0000000.4484100.2053561.3218930.377584
21.00.444840-0.134298-0.7099720.451719-1.613871-0.7686611.2199180.5040261.831248-0.4313850.5262830.9415141.5875352.0243080.6034981.5623741.1354540.180910
31.00.381256-0.9761450.6931520.4489590.891753-0.6773282.0330601.5330413.046260-1.0052850.5693861.0152111.5822171.5519140.7612151.7154641.4922570.090719
41.01.309996-0.690089-0.6762591.589283-0.6933260.6229071.087562-0.3817420.5892041.3654791.1792950.9682180.7285630.0000001.0831580.0434291.1548540.094859
81.02.1128120.742983-0.3305390.805253-0.028887-1.4466792.2999461.4504292.989110-1.8947701.4451252.5481661.5647212.3936321.5545662.1484681.1791170.688057
............................................................
4999881.00.9392030.4960580.4928280.666188-1.330323-1.6658971.5019000.0316681.6898270.7991851.1040251.0263560.8249651.4953511.1173061.2870941.1737160.095378
4999911.01.5213020.7346930.2803391.5906090.366158-1.5071710.828265-0.9803821.005345-0.3254691.3185341.2373600.8327600.6718331.3401570.7395151.1157820.227649
4999941.00.955334-1.524135-1.1897641.470348-0.2961680.6964950.8517310.8155240.2592660.3400131.2196410.9911180.7211260.0000001.2424100.5267981.3138070.160337
4999961.00.910016-0.364544-0.7771200.543648-0.910632-1.7237072.8646731.4582722.176558-0.5909110.6736951.6621402.1893621.1950410.9108151.1818931.2523620.826035
4999971.00.8429540.332476-1.0485641.3479890.320496-0.6663580.450433-0.4118720.2934070.6304910.8599200.4033710.4162580.5919890.3720030.7167880.3669910.265798
\n", + "

229245 rows × 19 columns

\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" ], - "source": [ - "df_sig_0-m_s" + "text/plain": [ + " signal l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi \\\n", + "1 1.0 1.667973 0.064191 -1.225171 0.506102 -0.338939 1.672543 \n", + "2 1.0 0.444840 -0.134298 -0.709972 0.451719 -1.613871 -0.768661 \n", + "3 1.0 0.381256 -0.976145 0.693152 0.448959 0.891753 -0.677328 \n", + "4 1.0 1.309996 -0.690089 -0.676259 1.589283 -0.693326 0.622907 \n", + "8 1.0 2.112812 0.742983 -0.330539 0.805253 -0.028887 -1.446679 \n", + "... ... ... ... ... ... ... ... \n", + "499988 1.0 0.939203 0.496058 0.492828 0.666188 -1.330323 -1.665897 \n", + "499991 1.0 1.521302 0.734693 0.280339 1.590609 0.366158 -1.507171 \n", + "499994 1.0 0.955334 -1.524135 -1.189764 1.470348 -0.296168 0.696495 \n", + "499996 1.0 0.910016 -0.364544 -0.777120 0.543648 -0.910632 -1.723707 \n", + "499997 1.0 0.842954 0.332476 -1.048564 1.347989 0.320496 -0.666358 \n", + "\n", + " MET MET_phi MET_rel axial_MET M_R M_TR_2 R \\\n", + "1 3.475464 -1.219136 0.012955 3.775174 1.045977 0.568051 0.481928 \n", + "2 1.219918 0.504026 1.831248 -0.431385 0.526283 0.941514 1.587535 \n", + "3 2.033060 1.533041 3.046260 -1.005285 0.569386 1.015211 1.582217 \n", + "4 1.087562 -0.381742 0.589204 1.365479 1.179295 0.968218 0.728563 \n", + "8 2.299946 1.450429 2.989110 -1.894770 1.445125 2.548166 1.564721 \n", + "... ... ... ... ... ... ... ... \n", + "499988 1.501900 0.031668 1.689827 0.799185 1.104025 1.026356 0.824965 \n", + "499991 0.828265 -0.980382 1.005345 -0.325469 1.318534 1.237360 0.832760 \n", + "499994 0.851731 0.815524 0.259266 0.340013 1.219641 0.991118 0.721126 \n", + "499996 2.864673 1.458272 2.176558 -0.590911 0.673695 1.662140 2.189362 \n", + "499997 0.450433 -0.411872 0.293407 0.630491 0.859920 0.403371 0.416258 \n", + "\n", + " MT2 S_R M_Delta_R dPhi_r_b cos_theta_r1 \n", + "1 0.000000 0.448410 0.205356 1.321893 0.377584 \n", + "2 2.024308 0.603498 1.562374 1.135454 0.180910 \n", + "3 1.551914 0.761215 1.715464 1.492257 0.090719 \n", + "4 0.000000 1.083158 0.043429 1.154854 0.094859 \n", + "8 2.393632 1.554566 2.148468 1.179117 0.688057 \n", + "... ... ... ... ... ... \n", + "499988 1.495351 1.117306 1.287094 1.173716 0.095378 \n", + "499991 0.671833 1.340157 0.739515 1.115782 0.227649 \n", + "499994 0.000000 1.242410 0.526798 1.313807 0.160337 \n", + "499996 1.195041 0.910815 1.181893 1.252362 0.826035 \n", + "499997 0.591989 0.372003 0.716788 0.366991 0.265798 \n", + "\n", + "[229245 rows x 19 columns]" ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sig" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 }, + "id": "V9zGiNVd_r4H", + "outputId": "22a5652d-c54c-498b-9704-7d3740e7c11f" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "s19wzxPQ_r8K", - "outputId": "7225d6ba-ac8e-4cba-b561-ca78724f1caa" + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df_sig_0" }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(18, 229245)" - ] - }, - "metadata": {}, - "execution_count": 41 - } + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
l_1_pTl_1_etal_1_phil_2_pTl_2_etal_2_phiMETMET_phiMET_relaxial_METM_RM_TR_2RMT2S_RM_Delta_RdPhi_r_bcos_theta_r1
11.6679730.064191-1.2251710.506102-0.3389391.6725433.475464-1.2191360.0129553.7751741.0459770.5680510.4819280.0000000.4484100.2053561.3218930.377584
20.444840-0.134298-0.7099720.451719-1.613871-0.7686611.2199180.5040261.831248-0.4313850.5262830.9415141.5875352.0243080.6034981.5623741.1354540.180910
30.381256-0.9761450.6931520.4489590.891753-0.6773282.0330601.5330413.046260-1.0052850.5693861.0152111.5822171.5519140.7612151.7154641.4922570.090719
41.309996-0.690089-0.6762591.589283-0.6933260.6229071.087562-0.3817420.5892041.3654791.1792950.9682180.7285630.0000001.0831580.0434291.1548540.094859
82.1128120.742983-0.3305390.805253-0.028887-1.4466792.2999461.4504292.989110-1.8947701.4451252.5481661.5647212.3936321.5545662.1484681.1791170.688057
.........................................................
4999880.9392030.4960580.4928280.666188-1.330323-1.6658971.5019000.0316681.6898270.7991851.1040251.0263560.8249651.4953511.1173061.2870941.1737160.095378
4999911.5213020.7346930.2803391.5906090.366158-1.5071710.828265-0.9803821.005345-0.3254691.3185341.2373600.8327600.6718331.3401570.7395151.1157820.227649
4999940.955334-1.524135-1.1897641.470348-0.2961680.6964950.8517310.8155240.2592660.3400131.2196410.9911180.7211260.0000001.2424100.5267981.3138070.160337
4999960.910016-0.364544-0.7771200.543648-0.910632-1.7237072.8646731.4582722.176558-0.5909110.6736951.6621402.1893621.1950410.9108151.1818931.2523620.826035
4999970.8429540.332476-1.0485641.3479890.320496-0.6663580.450433-0.4118720.2934070.6304910.8599200.4033710.4162580.5919890.3720030.7167880.3669910.265798
\n", + "

229245 rows × 18 columns

\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" ], - "source": [ - "delta_s=np.matrix(df_sig_0-m_s).transpose()\n", - "delta_s.shape" + "text/plain": [ + " l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi MET \\\n", + "1 1.667973 0.064191 -1.225171 0.506102 -0.338939 1.672543 3.475464 \n", + "2 0.444840 -0.134298 -0.709972 0.451719 -1.613871 -0.768661 1.219918 \n", + "3 0.381256 -0.976145 0.693152 0.448959 0.891753 -0.677328 2.033060 \n", + "4 1.309996 -0.690089 -0.676259 1.589283 -0.693326 0.622907 1.087562 \n", + "8 2.112812 0.742983 -0.330539 0.805253 -0.028887 -1.446679 2.299946 \n", + "... ... ... ... ... ... ... ... \n", + "499988 0.939203 0.496058 0.492828 0.666188 -1.330323 -1.665897 1.501900 \n", + "499991 1.521302 0.734693 0.280339 1.590609 0.366158 -1.507171 0.828265 \n", + "499994 0.955334 -1.524135 -1.189764 1.470348 -0.296168 0.696495 0.851731 \n", + "499996 0.910016 -0.364544 -0.777120 0.543648 -0.910632 -1.723707 2.864673 \n", + "499997 0.842954 0.332476 -1.048564 1.347989 0.320496 -0.666358 0.450433 \n", + "\n", + " MET_phi MET_rel axial_MET M_R M_TR_2 R MT2 \\\n", + "1 -1.219136 0.012955 3.775174 1.045977 0.568051 0.481928 0.000000 \n", + "2 0.504026 1.831248 -0.431385 0.526283 0.941514 1.587535 2.024308 \n", + "3 1.533041 3.046260 -1.005285 0.569386 1.015211 1.582217 1.551914 \n", + "4 -0.381742 0.589204 1.365479 1.179295 0.968218 0.728563 0.000000 \n", + "8 1.450429 2.989110 -1.894770 1.445125 2.548166 1.564721 2.393632 \n", + "... ... ... ... ... ... ... ... \n", + "499988 0.031668 1.689827 0.799185 1.104025 1.026356 0.824965 1.495351 \n", + "499991 -0.980382 1.005345 -0.325469 1.318534 1.237360 0.832760 0.671833 \n", + "499994 0.815524 0.259266 0.340013 1.219641 0.991118 0.721126 0.000000 \n", + "499996 1.458272 2.176558 -0.590911 0.673695 1.662140 2.189362 1.195041 \n", + "499997 -0.411872 0.293407 0.630491 0.859920 0.403371 0.416258 0.591989 \n", + "\n", + " S_R M_Delta_R dPhi_r_b cos_theta_r1 \n", + "1 0.448410 0.205356 1.321893 0.377584 \n", + "2 0.603498 1.562374 1.135454 0.180910 \n", + "3 0.761215 1.715464 1.492257 0.090719 \n", + "4 1.083158 0.043429 1.154854 0.094859 \n", + "8 1.554566 2.148468 1.179117 0.688057 \n", + "... ... ... ... ... \n", + "499988 1.117306 1.287094 1.173716 0.095378 \n", + "499991 1.340157 0.739515 1.115782 0.227649 \n", + "499994 1.242410 0.526798 1.313807 0.160337 \n", + "499996 0.910815 1.181893 1.252362 0.826035 \n", + "499997 0.372003 0.716788 0.366991 0.265798 \n", + "\n", + "[229245 rows x 18 columns]" ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sig_0 = df_sig.drop(\"signal\",axis=1)\n", + "df_bkg_0 = df_bkg.drop(\"signal\",axis=1)\n", + "\n", + "df_sig_0" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 648 }, + "id": "H5va4rD9_r4Q", + "outputId": "cbcf8e40-dc38-46ae-92e5-04062d082cda" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oIcmLMnm_r9v", - "outputId": "1caac138-f6f3-4f8a-e0b7-6ef9a7bf0ad5" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(18, 18)" - ] - }, - "metadata": {}, - "execution_count": 42 - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
0
l_1_pT1.291078
l_1_eta0.000824
l_1_phi-0.001524
l_2_pT1.138668
l_2_eta0.002487
l_2_phi0.000049
MET1.418381
MET_phi-0.000470
MET_rel1.275169
axial_MET0.089314
M_R1.183651
M_TR_21.268858
R1.056352
MT21.074694
S_R1.175023
M_Delta_R1.186022
dPhi_r_b1.014617
cos_theta_r10.282417
\n", + "

" ], - "source": [ - "S_W_s= delta_s*delta_s.transpose()\n", - "S_W_s.shape" + "text/plain": [ + "l_1_pT 1.291078\n", + "l_1_eta 0.000824\n", + "l_1_phi -0.001524\n", + "l_2_pT 1.138668\n", + "l_2_eta 0.002487\n", + "l_2_phi 0.000049\n", + "MET 1.418381\n", + "MET_phi -0.000470\n", + "MET_rel 1.275169\n", + "axial_MET 0.089314\n", + "M_R 1.183651\n", + "M_TR_2 1.268858\n", + "R 1.056352\n", + "MT2 1.074694\n", + "S_R 1.175023\n", + "M_Delta_R 1.186022\n", + "dPhi_r_b 1.014617\n", + "cos_theta_r1 0.282417\n", + "dtype: float64" ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m_s= np.mean(df_sig_0,axis=0)\n", + "m_s" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "id": "kWaHAj3r_r4z" + }, + "outputs": [], + "source": [ + "# Compute means for signal and background\n", + "m_s = np.mean(df_sig_0, axis=0) # Mean for signal events\n", + "m_b = np.mean(df_bkg_0, axis=0) # Mean for background events\n", + "\n", + "# Calculate the difference between means\n", + "delta = m_s - m_b # Difference vector between signal and background means\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "id": "Zr-AXn8r_r72" + }, + "outputs": [], + "source": [ + "delta=np.matrix(m_s-m_b).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "u-QceBjq_r76", + "outputId": "90ae110d-b691-454e-8f3c-5ae63a81f5fb" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "id": "2h5tUOe8_r9z" - }, - "outputs": [], - "source": [ - "delta_b=np.matrix(df_bkg_0-m_b).transpose()\n", - "S_W_b= delta_b*delta_b.transpose()" + "data": { + "text/plain": [ + "matrix([[ 2.87970231e-01, 4.58922304e-04, 4.67467434e-04,\n", + " 1.38148662e-01, 1.54068049e-03, 3.66095560e-04,\n", + " 4.13461344e-01, -6.36618070e-04, 2.71136238e-01,\n", + " 8.54657090e-02, 1.82331687e-01, 2.66557653e-01,\n", + " 5.53227373e-02, 7.37824064e-02, 1.74241790e-01,\n", + " 1.84116478e-01, 1.54056891e-02, 5.66281031e-02],\n", + " [ 4.58922304e-04, 7.31359210e-07, 7.44977113e-07,\n", + " 2.20159917e-04, 2.45529768e-06, 5.83426339e-07,\n", + " 6.58910580e-04, -1.01454317e-06, 4.32094896e-04,\n", + " 1.36201995e-04, 2.90571971e-04, 4.24798257e-04,\n", + " 8.81648006e-05, 1.17582959e-04, 2.77679548e-04,\n", + " 2.93416294e-04, 2.45511986e-05, 9.02450903e-05],\n", + " [ 4.67467434e-04, 7.44977113e-07, 7.58848582e-07,\n", + " 2.24259293e-04, 2.50101531e-06, 5.94289733e-07,\n", + " 6.71179491e-04, -1.03343396e-06, 4.40140500e-04,\n", + " 1.38738075e-04, 2.95982420e-04, 4.32707998e-04,\n", + " 8.98064286e-05, 1.19772353e-04, 2.82849940e-04,\n", + " 2.98879704e-04, 2.50083417e-05, 9.19254533e-05],\n", + " [ 1.38148662e-01, 2.20159917e-04, 2.24259293e-04,\n", + " 6.62743950e-02, 7.39114414e-04, 1.75627917e-04,\n", + " 1.98350820e-01, -3.05406341e-04, 1.30072850e-01,\n", + " 4.10006732e-02, 8.74704254e-02, 1.27876354e-01,\n", + " 2.65401118e-02, 3.53958140e-02, 8.35894395e-02,\n", + " 8.83266474e-02, 7.39060884e-03, 2.71663382e-02],\n", + " [ 1.54068049e-03, 2.45529768e-06, 2.50101531e-06,\n", + " 7.39114414e-04, 8.24285333e-06, 1.95866179e-06,\n", + " 2.21207527e-03, -3.40599456e-06, 1.45061631e-03,\n", + " 4.57253342e-04, 9.75499697e-04, 1.42612024e-03,\n", + " 2.95984282e-04, 3.94746060e-04, 9.32217633e-04,\n", + " 9.85048574e-04, 8.24225634e-05, 3.02968169e-04],\n", + " [ 3.66095560e-04, 5.83426339e-07, 5.94289733e-07,\n", + " 1.75627917e-04, 1.95866179e-06, 4.65416020e-07,\n", + " 5.25631977e-04, -8.09330353e-07, 3.44694563e-04,\n", + " 1.08652260e-04, 2.31797644e-04, 3.38873823e-04,\n", + " 7.03316047e-05, 9.37993183e-05, 2.21512986e-04,\n", + " 2.34066642e-04, 1.95851993e-05, 7.19911118e-05],\n", + " [ 4.13461344e-01, 6.58910580e-04, 6.71179491e-04,\n", + " 1.98350820e-01, 2.21207527e-03, 5.25631977e-04,\n", + " 5.93638731e-01, -9.14042266e-04, 3.89291466e-01,\n", + " 1.22709791e-01, 2.61787838e-01, 3.82717634e-01,\n", + " 7.94311730e-02, 1.05935161e-01, 2.50172542e-01,\n", + " 2.64350402e-01, 2.21191506e-02, 8.13053887e-02],\n", + " [-6.36618070e-04, -1.01454317e-06, -1.03343396e-06,\n", + " -3.05406341e-04, -3.40599456e-06, -8.09330353e-07,\n", + " -9.14042266e-04, 1.40737661e-06, -5.99403029e-04,\n", + " -1.88939720e-04, -4.03082104e-04, -5.89281115e-04,\n", + " -1.22302413e-04, -1.63111350e-04, -3.85197705e-04,\n", + " -4.07027755e-04, -3.40574788e-05, -1.25188196e-04],\n", + " [ 2.71136238e-01, 4.32094896e-04, 4.40140500e-04,\n", + " 1.30072850e-01, 1.45061631e-03, 3.44694563e-04,\n", + " 3.89291466e-01, -5.99403029e-04, 2.55286317e-01,\n", + " 8.04696053e-02, 1.71673049e-01, 2.50975384e-01,\n", + " 5.20887135e-02, 6.94692782e-02, 1.64056067e-01,\n", + " 1.73353506e-01, 1.45051125e-02, 5.33177710e-02],\n", + " [ 8.54657090e-02, 1.36201995e-04, 1.38738075e-04,\n", + " 4.10006732e-02, 4.57253342e-04, 1.08652260e-04,\n", + " 1.22709791e-01, -1.88939720e-04, 8.04696053e-02,\n", + " 2.53650781e-02, 5.41136035e-02, 7.91107426e-02,\n", + " 1.64190477e-02, 2.18976303e-02, 5.17126304e-02,\n", + " 5.46433055e-02, 4.57220225e-03, 1.68064628e-02],\n", + " [ 1.82331687e-01, 2.90571971e-04, 2.95982420e-04,\n", + " 8.74704254e-02, 9.75499697e-04, 2.31797644e-04,\n", + " 2.61787838e-01, -4.03082104e-04, 1.71673049e-01,\n", + " 5.41136035e-02, 1.15445419e-01, 1.68774065e-01,\n", + " 3.50282318e-02, 4.67161851e-02, 1.10323207e-01,\n", + " 1.16575480e-01, 9.75429046e-03, 3.58547393e-02],\n", + " [ 2.66557653e-01, 4.24798257e-04, 4.32707998e-04,\n", + " 1.27876354e-01, 1.42612024e-03, 3.38873823e-04,\n", + " 3.82717634e-01, -5.89281115e-04, 2.50975384e-01,\n", + " 7.91107426e-02, 1.68774065e-01, 2.46737249e-01,\n", + " 5.12091092e-02, 6.82961743e-02, 1.61285708e-01,\n", + " 1.70426145e-01, 1.42601696e-02, 5.24174121e-02],\n", + " [ 5.53227373e-02, 8.81648006e-05, 8.98064286e-05,\n", + " 2.65401118e-02, 2.95984282e-04, 7.03316047e-05,\n", + " 7.94311730e-02, -1.22302413e-04, 5.20887135e-02,\n", + " 1.64190477e-02, 3.50282318e-02, 5.12091092e-02,\n", + " 1.06282002e-02, 1.41745369e-02, 3.34740599e-02,\n", + " 3.53711128e-02, 2.95962845e-03, 1.08789775e-02],\n", + " [ 7.37824064e-02, 1.17582959e-04, 1.19772353e-04,\n", + " 3.53958140e-02, 3.94746060e-04, 9.37993183e-05,\n", + " 1.05935161e-01, -1.63111350e-04, 6.94692782e-02,\n", + " 2.18976303e-02, 4.67161851e-02, 6.82961743e-02,\n", + " 1.41745369e-02, 1.89041884e-02, 4.46434290e-02,\n", + " 4.71734760e-02, 3.94717471e-03, 1.45089918e-02],\n", + " [ 1.74241790e-01, 2.77679548e-04, 2.82849940e-04,\n", + " 8.35894395e-02, 9.32217633e-04, 2.21512986e-04,\n", + " 2.50172542e-01, -3.85197705e-04, 1.64056067e-01,\n", + " 5.17126304e-02, 1.10323207e-01, 1.61285708e-01,\n", + " 3.34740599e-02, 4.46434290e-02, 1.05428264e-01,\n", + " 1.11403129e-01, 9.32150117e-03, 3.42638960e-02],\n", + " [ 1.84116478e-01, 2.93416294e-04, 2.98879704e-04,\n", + " 8.83266474e-02, 9.85048574e-04, 2.34066642e-04,\n", + " 2.64350402e-01, -4.07027755e-04, 1.73353506e-01,\n", + " 5.46433055e-02, 1.16575480e-01, 1.70426145e-01,\n", + " 3.53711128e-02, 4.71734760e-02, 1.11403129e-01,\n", + " 1.17716603e-01, 9.84977232e-03, 3.62057107e-02],\n", + " [ 1.54056891e-02, 2.45511986e-05, 2.50083417e-05,\n", + " 7.39060884e-03, 8.24225634e-05, 1.95851993e-05,\n", + " 2.21191506e-02, -3.40574788e-05, 1.45051125e-02,\n", + " 4.57220225e-03, 9.75429046e-03, 1.42601696e-02,\n", + " 2.95962845e-03, 3.94717471e-03, 9.32150117e-03,\n", + " 9.84977232e-03, 8.24165940e-04, 3.02946227e-03],\n", + " [ 5.66281031e-02, 9.02450903e-05, 9.19254533e-05,\n", + " 2.71663382e-02, 3.02968169e-04, 7.19911118e-05,\n", + " 8.13053887e-02, -1.25188196e-04, 5.33177710e-02,\n", + " 1.68064628e-02, 3.58547393e-02, 5.24174121e-02,\n", + " 1.08789775e-02, 1.45089918e-02, 3.42638960e-02,\n", + " 3.62057107e-02, 3.02946227e-03, 1.11356721e-02]])" ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "S_B= delta*delta.transpose()\n", + "S_B" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 }, + "id": "aLNQAEsN_r8E", + "outputId": "5deaa9e1-a835-4bbc-d16c-d267f957a97d" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "id": "Q529uRrl_sAQ" + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe" }, - "outputs": [], - "source": [ - "S_W=S_W_s+S_W_b\n", - "S_W_inv = np.linalg.inv(S_W)\n", - "w = S_W_inv * np.matrix(m_b - m_s).transpose()" + "text/html": [ + "\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
l_1_pTl_1_etal_1_phil_2_pTl_2_etal_2_phiMETMET_phiMET_relaxial_METM_RM_TR_2RMT2S_RM_Delta_RdPhi_r_bcos_theta_r1
10.3768950.063367-1.223647-0.632566-0.3414261.6724942.057083-1.218666-1.2622143.685859-0.137674-0.700807-0.574424-1.074694-0.726613-0.9806660.3072760.095167
2-0.846238-0.135122-0.708447-0.686949-1.616358-0.768710-0.1984630.5044960.556079-0.520699-0.657368-0.3273440.5311830.949615-0.5715260.3763520.120837-0.101507
3-0.909822-0.9769690.694677-0.6897090.889266-0.6773780.6146791.5335111.771091-1.094599-0.614265-0.2536470.5258640.477221-0.4138080.5294420.477639-0.191698
40.018919-0.690913-0.6747350.450615-0.6958130.622858-0.330819-0.381271-0.6859641.276165-0.004356-0.300640-0.327789-1.074694-0.091865-1.1425930.140236-0.187558
80.8217340.742159-0.329015-0.333415-0.031374-1.4467280.8815641.4509001.713942-1.9840840.2614741.2793080.5083691.3189390.3795430.9624460.1645000.405640
.........................................................
499988-0.3518750.4952350.494352-0.472480-1.332810-1.6659470.0835190.0321390.4146580.709871-0.079626-0.242503-0.2313870.420657-0.0577170.1010720.159099-0.187039
4999910.2302240.7338700.2818630.4519410.363671-1.507220-0.590116-0.979911-0.269824-0.4147830.134882-0.031498-0.223592-0.4028610.165134-0.4465070.101164-0.054768
499994-0.335744-1.524958-1.1882400.331680-0.2986550.696446-0.5666510.815994-1.0159030.2506990.035990-0.277740-0.335226-1.0746940.067386-0.6592240.299190-0.122080
499996-0.381062-0.365368-0.775596-0.595019-0.913119-1.7237561.4462921.4587420.901389-0.680225-0.5099560.3932821.1330100.120347-0.264209-0.0041290.2377450.543618
499997-0.4481240.331653-1.0470400.2093210.318009-0.666408-0.967948-0.411402-0.9817620.541177-0.323732-0.865488-0.640094-0.482705-0.803020-0.469234-0.647626-0.016619
\n", + "

229245 rows × 18 columns

\n", + "
\n", + "
\n", + "\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "
\n", + "\n", + "\n", + "
\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "
\n", + "\n", + "
\n", + "
\n" + ], + "text/plain": [ + " l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi MET \\\n", + "1 0.376895 0.063367 -1.223647 -0.632566 -0.341426 1.672494 2.057083 \n", + "2 -0.846238 -0.135122 -0.708447 -0.686949 -1.616358 -0.768710 -0.198463 \n", + "3 -0.909822 -0.976969 0.694677 -0.689709 0.889266 -0.677378 0.614679 \n", + "4 0.018919 -0.690913 -0.674735 0.450615 -0.695813 0.622858 -0.330819 \n", + "8 0.821734 0.742159 -0.329015 -0.333415 -0.031374 -1.446728 0.881564 \n", + "... ... ... ... ... ... ... ... \n", + "499988 -0.351875 0.495235 0.494352 -0.472480 -1.332810 -1.665947 0.083519 \n", + "499991 0.230224 0.733870 0.281863 0.451941 0.363671 -1.507220 -0.590116 \n", + "499994 -0.335744 -1.524958 -1.188240 0.331680 -0.298655 0.696446 -0.566651 \n", + "499996 -0.381062 -0.365368 -0.775596 -0.595019 -0.913119 -1.723756 1.446292 \n", + "499997 -0.448124 0.331653 -1.047040 0.209321 0.318009 -0.666408 -0.967948 \n", + "\n", + " MET_phi MET_rel axial_MET M_R M_TR_2 R MT2 \\\n", + "1 -1.218666 -1.262214 3.685859 -0.137674 -0.700807 -0.574424 -1.074694 \n", + "2 0.504496 0.556079 -0.520699 -0.657368 -0.327344 0.531183 0.949615 \n", + "3 1.533511 1.771091 -1.094599 -0.614265 -0.253647 0.525864 0.477221 \n", + "4 -0.381271 -0.685964 1.276165 -0.004356 -0.300640 -0.327789 -1.074694 \n", + "8 1.450900 1.713942 -1.984084 0.261474 1.279308 0.508369 1.318939 \n", + "... ... ... ... ... ... ... ... \n", + "499988 0.032139 0.414658 0.709871 -0.079626 -0.242503 -0.231387 0.420657 \n", + "499991 -0.979911 -0.269824 -0.414783 0.134882 -0.031498 -0.223592 -0.402861 \n", + "499994 0.815994 -1.015903 0.250699 0.035990 -0.277740 -0.335226 -1.074694 \n", + "499996 1.458742 0.901389 -0.680225 -0.509956 0.393282 1.133010 0.120347 \n", + "499997 -0.411402 -0.981762 0.541177 -0.323732 -0.865488 -0.640094 -0.482705 \n", + "\n", + " S_R M_Delta_R dPhi_r_b cos_theta_r1 \n", + "1 -0.726613 -0.980666 0.307276 0.095167 \n", + "2 -0.571526 0.376352 0.120837 -0.101507 \n", + "3 -0.413808 0.529442 0.477639 -0.191698 \n", + "4 -0.091865 -1.142593 0.140236 -0.187558 \n", + "8 0.379543 0.962446 0.164500 0.405640 \n", + "... ... ... ... ... \n", + "499988 -0.057717 0.101072 0.159099 -0.187039 \n", + "499991 0.165134 -0.446507 0.101164 -0.054768 \n", + "499994 0.067386 -0.659224 0.299190 -0.122080 \n", + "499996 -0.264209 -0.004129 0.237745 0.543618 \n", + "499997 -0.803020 -0.469234 -0.647626 -0.016619 \n", + "\n", + "[229245 rows x 18 columns]" ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sig_0-m_s" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "s19wzxPQ_r8K", + "outputId": "7225d6ba-ac8e-4cba-b561-ca78724f1caa" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "jUlBGPt8_sAt", - "outputId": "5754b22f-d8f3-4e5e-ece4-c634948f2256" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "matrix([[-2.26832982e-06],\n", - " [-5.63688439e-09],\n", - " [-2.10423856e-09],\n", - " [-9.95582982e-07],\n", - " [-3.48452234e-09],\n", - " [-2.70762588e-09],\n", - " [-1.65185357e-06],\n", - " [-2.74241844e-09],\n", - " [-1.39723282e-07],\n", - " [-2.64205675e-07],\n", - " [ 2.72149250e-07],\n", - " [-1.48465692e-07],\n", - " [ 2.11167032e-06],\n", - " [ 3.24040633e-07],\n", - " [ 1.81173242e-06],\n", - " [-1.69348122e-06],\n", - " [ 7.50902836e-08],\n", - " [-5.06860437e-06]])" - ] - }, - "metadata": {}, - "execution_count": 45 - } - ], - "source": [ - "w" + "data": { + "text/plain": [ + "(18, 229245)" ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "delta_s=np.matrix(df_sig_0-m_s).transpose()\n", + "delta_s.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "oIcmLMnm_r9v", + "outputId": "1caac138-f6f3-4f8a-e0b7-6ef9a7bf0ad5" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "AdPsp-4V_sBC", - "outputId": "99e7e3c4-4973-4026-81be-d51370818e74" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "matrix([[ 2.96426929e-01],\n", - " [ 7.36631998e-04],\n", - " [ 2.74983369e-04],\n", - " [ 1.30103481e-01],\n", - " [ 4.55359819e-04],\n", - " [ 3.53834445e-04],\n", - " [ 2.15865381e-01],\n", - " [ 3.58381161e-04],\n", - " [ 1.82591363e-02],\n", - " [ 3.45265825e-02],\n", - " [-3.55646545e-02],\n", - " [ 1.94016005e-02],\n", - " [-2.75954556e-01],\n", - " [-4.23458567e-02],\n", - " [-2.36758461e-01],\n", - " [ 2.21305311e-01],\n", - " [-9.81285083e-03],\n", - " [ 6.62368767e-01]])" - ] - }, - "metadata": {}, - "execution_count": 46 - } - ], - "source": [ - "w_1 = w / sum(w)\n", - "w_1" + "data": { + "text/plain": [ + "(18, 18)" ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "S_W_s= delta_s*delta_s.transpose()\n", + "S_W_s.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "id": "2h5tUOe8_r9z" + }, + "outputs": [], + "source": [ + "delta_b=np.matrix(df_bkg_0-m_b).transpose()\n", + "S_W_b= delta_b*delta_b.transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "id": "Q529uRrl_sAQ" + }, + "outputs": [], + "source": [ + "S_W=S_W_s+S_W_b\n", + "S_W_inv = np.linalg.inv(S_W)\n", + "w = S_W_inv * np.matrix(m_b - m_s).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "jUlBGPt8_sAt", + "outputId": "5754b22f-d8f3-4e5e-ece4-c634948f2256" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "id": "wcgRbPLi_sBg" - }, - "outputs": [], - "source": [ - "# Compute the output scores for signal and background events using linear coefficients w_1\n", - "output_s = np.matrix(df_sig_0) * w_1 # Output scores for signal events\n", - "output_b = np.matrix(df_bkg_0) * w_1 # Output scores for background events" + "data": { + "text/plain": [ + "matrix([[-2.26832982e-06],\n", + " [-5.63688439e-09],\n", + " [-2.10423856e-09],\n", + " [-9.95582982e-07],\n", + " [-3.48452234e-09],\n", + " [-2.70762588e-09],\n", + " [-1.65185357e-06],\n", + " [-2.74241844e-09],\n", + " [-1.39723282e-07],\n", + " [-2.64205675e-07],\n", + " [ 2.72149250e-07],\n", + " [-1.48465692e-07],\n", + " [ 2.11167032e-06],\n", + " [ 3.24040633e-07],\n", + " [ 1.81173242e-06],\n", + " [-1.69348122e-06],\n", + " [ 7.50902836e-08],\n", + " [-5.06860437e-06]])" ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "AdPsp-4V_sBC", + "outputId": "99e7e3c4-4973-4026-81be-d51370818e74" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 448 - }, - "id": "M76VQSNV_sBj", - "outputId": "91cd95d0-37eb-4b88-d0c4-601c81e780f1" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 48 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjkAAAGdCAYAAADwjmIIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAyuElEQVR4nO3de1xVdb7/8Tds5aKyt3kBZAQvR1MpLwmKO6eLSXIKmzxaqTlKavXQwJ/K5G3GtDvdDE1NM5vwdHLUZo5NSWqEt07iJcxSS6aSBhsFbUq2UoLC/v0xD9a0BU0Q2PDl9Xw89uPhXuuz1/qsPcxjv/uu71rLx+12uwUAAGAYX283AAAAUBsIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIzXxdgPeVFZWpmPHjikoKEg+Pj7ebgcAAFwGt9ut06dPKywsTL6+Fx+vadQh59ixYwoPD/d2GwAAoBqOHj2q9u3bX3R9ow45QUFBkv71Jdntdi93AwAALofL5VJ4eLj1O34xjTrklJ+istvthBwAABqYX5pqwsRjAABgJEIOAAAwEiEHAAAYqVHPyQEA1C9ut1vnz59XaWmpt1uBF9lsNjVp0uSKb+9CyAEA1AslJSU6fvy4fvzxR2+3gnqgWbNmateunfz8/Kq9DUIOAMDrysrKlJubK5vNprCwMPn5+XGT1kbK7XarpKREJ0+eVG5urrp27XrJG/5dCiEHAOB1JSUlKisrU3h4uJo1a+btduBlgYGBatq0qf7+97+rpKREAQEB1doOE48BAPVGdf+LHeapib8F/poAAICRCDkAANSC++67T8OGDavz/T766KPq06dPne+3PmJODgCgXkvN+Fud7Wv6rVfX2LYWLVokt9tdY9tD1RFyAACoBQ6Hw9stNHqcrgIA4Ar8+c9/Vs+ePRUYGKjWrVsrNjZWRUVFFU5XnT59WmPGjFHz5s3Vrl07paam6uabb9a0adOsmo4dO+rpp5/WhAkTFBQUpIiICK1YscJjf7NmzdLVV1+tZs2aqXPnznrkkUd07ty5OjrahoWQAwBANR0/flyjR4/WhAkT9MUXX2jbtm0aPnx4paepkpOT9dFHH+mdd95RRkaGPvzwQ+3bt69C3YIFCxQdHa1PPvlEDz30kCZPnqycnBxrfVBQkNLS0vT5559r0aJFevXVV5Wamlqrx9lQcboKNW9rSsVlg+bUfR8AUMuOHz+u8+fPa/jw4erQoYMkqWfPnhXqTp8+rVWrVmn16tUaPHiwJOn1119XWFhYhdrbb79dDz30kKR/jdqkpqZq69at6tatmyRp7ty5Vm3Hjh318MMPa82aNZo5c2aNH19DR8gBAKCaevfurcGDB6tnz56Ki4vTkCFDdNddd+mqq67yqDty5IjOnTun/v37W8scDocVXH6uV69e1r99fHwUGhqqEydOWMvWrl2rl156SV9//bXOnDmj8+fPy26318LRNXycrgIAoJpsNpsyMjK0ceNGRUZGavHixerWrZtyc3Orvc2mTZt6vPfx8VFZWZkkKSsrS2PGjNHtt9+uDRs26JNPPtEf/vAHlZSUXNFxmIqQAwDAFfDx8dHAgQP12GOP6ZNPPpGfn5/Wr1/vUdO5c2c1bdpUe/futZYVFhbqb3+r2uXxO3fuVIcOHfSHP/xB0dHR6tq1q/7+97/XyHGYiNNVAABU0+7du5WZmakhQ4YoODhYu3fv1smTJ9WjRw999tlnVl1QUJASEhI0Y8YMtWrVSsHBwZo/f758fX2r9CDSrl27Ki8vT2vWrFG/fv2Unp5eIVDh3xjJAQCgmux2u3bs2KHbb79dV199tebOnasFCxbotttuq1D74osvyul0aujQoYqNjdXAgQPVo0ePKj188je/+Y2mT5+upKQk9enTRzt37tQjjzxSk4dkFB93I74do8vlksPhUGFhIZO2ahJXVwGoorNnzyo3N1edOnWq9hOnG5qioiL96le/0oIFCzRx4kRvt1PvXOpv4nJ/vzldBQBAHfjkk090+PBh9e/fX4WFhXr88cclSXfeeaeXOzMXIQcAgDrywgsvKCcnR35+foqKitKHH36oNm3aeLstYxFyAACoA9ddd52ys7O93UajwsRjAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjVSnkPProo/Lx8fF4de/e3Vp/9uxZJSYmqnXr1mrRooVGjBihgoICj23k5eUpPj5ezZo1U3BwsGbMmKHz58971Gzbtk19+/aVv7+/unTporS0tAq9LF26VB07dlRAQIBiYmK0Z8+eqhwKAAA14uabb9a0adNqbfv33Xefhg0bVmvb94ZvvvlGPj4+2r9/f63up8r3ybnmmmv0wQcf/HsDTf69ienTpys9PV1vvfWWHA6HkpKSNHz4cH300UeSpNLSUsXHxys0NFQ7d+7U8ePHNW7cODVt2lRPP/20JCk3N1fx8fGaNGmS3nzzTWVmZur+++9Xu3btFBcXJ0lau3atkpOTtXz5csXExGjhwoWKi4tTTk6OgoODr+gLAQDUM5U9Kqa28Agao1T5dFWTJk0UGhpqvcrv1FhYWKjXXntNL774om655RZFRUXp9ddf186dO7Vr1y5J0vvvv6/PP/9c//M//6M+ffrotttu0xNPPKGlS5eqpKREkrR8+XJ16tRJCxYsUI8ePZSUlKS77rpLqampVg8vvviiHnjgAY0fP16RkZFavny5mjVrpj/+8Y818Z0AAGC08t9c01U55Hz55ZcKCwtT586dNWbMGOXl5UmSsrOzde7cOcXGxlq13bt3V0REhLKysiRJWVlZ6tmzp0JCQqyauLg4uVwuHTp0yKr5+TbKa8q3UVJSouzsbI8aX19fxcbGWjUXU1xcLJfL5fECAOBKnT9/XklJSXI4HGrTpo0eeeQRlT//+o033lB0dLSCgoIUGhqqe++9VydOnPD4/KFDhzR06FDZ7XYFBQXphhtu0Ndff13pvvbu3au2bdvq2WeftZY9+eSTCg4OVlBQkO6//37Nnj1bffr0sdaXn/J66qmnFBYWpm7dukmSDhw4oFtuuUWBgYFq3bq1HnzwQZ05c8b6XGWn4oYNG6b77rvPet+xY0c9/fTTmjBhgoKCghQREaEVK1Z4fGbPnj267rrrFBAQoOjoaH3yySeX/d1eiSqFnJiYGKWlpWnTpk1atmyZcnNzdcMNN+j06dPKz8+Xn5+fWrZs6fGZkJAQ5efnS5Ly8/M9Ak75+vJ1l6pxuVz66aef9N1336m0tLTSmvJtXExKSoocDof1Cg8Pr8rhAwBQqVWrVqlJkybas2ePFi1apBdffFErV66UJJ07d05PPPGEPv30U7399tv65ptvPELCP/7xD914443y9/fXli1blJ2drQkTJlSYrypJW7Zs0a233qqnnnpKs2bNkiS9+eabeuqpp/Tss88qOztbERERWrZsWYXPZmZmKicnRxkZGdqwYYOKiooUFxenq666Snv37tVbb72lDz74QElJSVU+/gULFljh5aGHHtLkyZOVk5MjSTpz5oyGDh2qyMhIZWdn69FHH9XDDz9c5X1UR5Xm5Nx2223Wv3v16qWYmBh16NBB69atU2BgYI03V9PmzJmj5ORk673L5SLoAACuWHh4uFJTU+Xj46Nu3brpwIEDSk1N1QMPPKAJEyZYdZ07d9ZLL72kfv366cyZM2rRooWWLl0qh8OhNWvWqGnTppKkq6++usI+1q9fr3HjxmnlypUaOXKktXzx4sWaOHGixo8fL0maN2+e3n//fY8RGUlq3ry5Vq5cKT8/P0nSq6++qrNnz+q///u/1bx5c0nSkiVLdMcdd+jZZ5+tMJhwKbfffrseeughSdKsWbOUmpqqrVu3qlu3blq9erXKysr02muvKSAgQNdcc42+/fZbTZ48+bK3X11XdAl5y5YtdfXVV+urr75SaGioSkpKdOrUKY+agoIChYaGSpJCQ0MrXG1V/v6Xaux2uwIDA9WmTRvZbLZKa8q3cTH+/v6y2+0eLwAArtSAAQPk4+NjvXc6nfryyy9VWlqq7Oxs3XHHHYqIiFBQUJBuuukmSbKme+zfv1833HCDFXAqs3v3bt1999164403PAKOJOXk5Kh///4eyy58L0k9e/a0Ao4kffHFF+rdu7cVcCRp4MCBKisrs0ZhLlevXr2sf/v4+Cg0NNQ6JffFF1+oV69eCggIsGqcTmeVtl9dVxRyzpw5o6+//lrt2rVTVFSUmjZtqszMTGt9Tk6O8vLyrINxOp06cOCAx7nIjIwM2e12RUZGWjU/30Z5Tfk2yh9P//OasrIyZWZm1tmXBgDA5Th79qzi4uJkt9v15ptvau/evVq/fr2kf0/+vZwzIf/xH/+h7t27649//KPOnTtXrV5+HmYul6+vrzW3qFxl+78woPn4+KisrKzK+6tpVQo5Dz/8sLZv365vvvlGO3fu1H/913/JZrNp9OjRcjgcmjhxopKTk7V161ZlZ2dr/PjxcjqdGjBggCRpyJAhioyM1NixY/Xpp59q8+bNmjt3rhITE+Xv7y9JmjRpko4cOaKZM2fq8OHDevnll7Vu3TpNnz7d6iM5OVmvvvqqVq1apS+++EKTJ09WUVGRNVQHAEBd2r17t8f7Xbt2qWvXrjp8+LD++c9/6plnntENN9yg7t27V5h03KtXL3344YeXDC9t2rTRli1b9NVXX+mee+7xqO3WrZv27t3rUX/h+8r06NFDn376qYqKiqxlH330kXx9fa2JyW3bttXx48et9aWlpTp48OAvbvvC/Xz22Wc6e/astaz8quvaVqWQ8+2332r06NHq1q2b7rnnHrVu3Vq7du1S27ZtJUmpqakaOnSoRowYoRtvvFGhoaH63//9X+vzNptNGzZskM1mk9Pp1G9/+1uNGzdOjz/+uFXTqVMnpaenKyMjQ71799aCBQu0cuVK6x45kjRy5Ei98MILmjdvnvr06aP9+/dr06ZNVTp/CABATcnLy1NycrJycnL0pz/9SYsXL9bUqVMVEREhPz8/LV68WEeOHNE777yjJ554wuOzSUlJcrlcGjVqlD7++GN9+eWXeuONNyqcMgoODtaWLVt0+PBhjR492pqYPGXKFL322mtatWqVvvzySz355JP67LPPPE6fVWbMmDEKCAhQQkKCDh48qK1bt2rKlCkaO3as9Xt6yy23KD09Xenp6Tp8+LAmT55cYVrKL7n33nvl4+OjBx54QJ9//rnee+89vfDCC1XaRnVVaeLxmjVrLrk+ICBAS5cu1dKlSy9a06FDB7333nuX3M7NN9/8i5eXJSUlVWsGOAAANW3cuHH66aef1L9/f9lsNk2dOlUPPvigfHx8lJaWpt///vd66aWX1LdvX73wwgv6zW9+Y322devW2rJli2bMmKGbbrpJNptNffr00cCBAyvsJzQ0VFu2bNHNN9+sMWPGaPXq1RozZoyOHDmihx9+WGfPntU999yj++677xefBNCsWTNt3rxZU6dOVb9+/dSsWTONGDFCL774olUzYcIEffrppxo3bpyaNGmi6dOna9CgQVX6blq0aKF3331XkyZN0nXXXafIyEg9++yzGjFiRJW2Ux0+7gtPtjUiLpdLDodDhYWFTEKuSZXdnZS7iAK4hLNnzyo3N1edOnXymKCK6rn11lsVGhqqN954w9utVNul/iYu9/e7yo91AAAA9cePP/6o5cuXKy4uTjabTX/605/0wQcfKCMjw9uteR0hBwCABszHx0fvvfeennrqKZ09e1bdunXTX/7ylwpPD2iMCDkAADRggYGBHg/Oxr9d0X1yAAAA6itCDgAAMBIhBwBQbzTiC35xgZr4WyDkAAC8rvyxAD/++KOXO0F9Uf63cKlnev0SJh4DALzOZrOpZcuW1iMPmjVr9ot37IWZ3G63fvzxR504cUItW7aUzWar9rYIOQCAeiE0NFSSKjzbCY1Ty5Ytrb+J6iLkAADqBR8fH7Vr107BwcHVftI2zNC0adMrGsEpR8gBANQrNputRn7gACYeAwAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMxH1yUDe2pni+HzTHO30AABoNRnIAAICRGMnBlbtwlAYAgHqAkRwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMNIVhZxnnnlGPj4+mjZtmrXs7NmzSkxMVOvWrdWiRQuNGDFCBQUFHp/Ly8tTfHy8mjVrpuDgYM2YMUPnz5/3qNm2bZv69u0rf39/denSRWlpaRX2v3TpUnXs2FEBAQGKiYnRnj17ruRwAACAQaodcvbu3atXXnlFvXr18lg+ffp0vfvuu3rrrbe0fft2HTt2TMOHD7fWl5aWKj4+XiUlJdq5c6dWrVqltLQ0zZs3z6rJzc1VfHy8Bg0apP3792vatGm6//77tXnzZqtm7dq1Sk5O1vz587Vv3z717t1bcXFxOnHiRHUPCQAAGMTH7Xa7q/qhM2fOqG/fvnr55Zf15JNPqk+fPlq4cKEKCwvVtm1brV69WnfddZck6fDhw+rRo4eysrI0YMAAbdy4UUOHDtWxY8cUEhIiSVq+fLlmzZqlkydPys/PT7NmzVJ6eroOHjxo7XPUqFE6deqUNm3aJEmKiYlRv379tGTJEklSWVmZwsPDNWXKFM2ePfuyjsPlcsnhcKiwsFB2u72qXwPKbU2p+mcGzan5PgAAjcLl/n5XayQnMTFR8fHxio2N9VienZ2tc+fOeSzv3r27IiIilJWVJUnKyspSz549rYAjSXFxcXK5XDp06JBVc+G24+LirG2UlJQoOzvbo8bX11exsbFWTWWKi4vlcrk8XgAAwExNqvqBNWvWaN++fdq7d2+Fdfn5+fLz81PLli09loeEhCg/P9+q+XnAKV9fvu5SNS6XSz/99JN++OEHlZaWVlpz+PDhi/aekpKixx577PIOFAAANGhVGsk5evSopk6dqjfffFMBAQG11VOtmTNnjgoLC63X0aNHvd0SAACoJVUKOdnZ2Tpx4oT69u2rJk2aqEmTJtq+fbteeuklNWnSRCEhISopKdGpU6c8PldQUKDQ0FBJUmhoaIWrrcrf/1KN3W5XYGCg2rRpI5vNVmlN+TYq4+/vL7vd7vECAABmqlLIGTx4sA4cOKD9+/dbr+joaI0ZM8b6d9OmTZWZmWl9JicnR3l5eXI6nZIkp9OpAwcOeFwFlZGRIbvdrsjISKvm59sorynfhp+fn6KiojxqysrKlJmZadUAAIDGrUpzcoKCgnTttdd6LGvevLlat25tLZ84caKSk5PVqlUr2e12TZkyRU6nUwMGDJAkDRkyRJGRkRo7dqyee+455efna+7cuUpMTJS/v78kadKkSVqyZIlmzpypCRMmaMuWLVq3bp3S09Ot/SYnJyshIUHR0dHq37+/Fi5cqKKiIo0fP/6KvhAAAGCGKk88/iWpqany9fXViBEjVFxcrLi4OL388svWepvNpg0bNmjy5MlyOp1q3ry5EhIS9Pjjj1s1nTp1Unp6uqZPn65Fixapffv2WrlypeLi4qyakSNH6uTJk5o3b57y8/PVp08fbdq0qcJkZAAA0DhV6z45puA+OTWE++QAAOpQrd4nBwAAoL4j5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASE283QAaqa0pFZcNmlP3fQAAjMVIDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABipSiFn2bJl6tWrl+x2u+x2u5xOpzZu3GitP3v2rBITE9W6dWu1aNFCI0aMUEFBgcc28vLyFB8fr2bNmik4OFgzZszQ+fPnPWq2bdumvn37yt/fX126dFFaWlqFXpYuXaqOHTsqICBAMTEx2rNnT1UOBQAAGK5KIad9+/Z65plnlJ2drY8//li33HKL7rzzTh06dEiSNH36dL377rt66623tH37dh07dkzDhw+3Pl9aWqr4+HiVlJRo586dWrVqldLS0jRv3jyrJjc3V/Hx8Ro0aJD279+vadOm6f7779fmzZutmrVr1yo5OVnz58/Xvn371Lt3b8XFxenEiRNX+n0AAABD+LjdbveVbKBVq1Z6/vnnddddd6lt27ZavXq17rrrLknS4cOH1aNHD2VlZWnAgAHauHGjhg4dqmPHjikkJESStHz5cs2aNUsnT56Un5+fZs2apfT0dB08eNDax6hRo3Tq1Clt2rRJkhQTE6N+/fppyZIlkqSysjKFh4drypQpmj179mX37nK55HA4VFhYKLvdfiVfQ+O2NaVmtjNoTs1sBwBgtMv9/a72nJzS0lKtWbNGRUVFcjqdys7O1rlz5xQbG2vVdO/eXREREcrKypIkZWVlqWfPnlbAkaS4uDi5XC5rNCgrK8tjG+U15dsoKSlRdna2R42vr69iY2OtmospLi6Wy+XyeAEAADNVOeQcOHBALVq0kL+/vyZNmqT169crMjJS+fn58vPzU8uWLT3qQ0JClJ+fL0nKz8/3CDjl68vXXarG5XLpp59+0nfffafS0tJKa8q3cTEpKSlyOBzWKzw8vKqHDwAAGogqh5xu3bpp//792r17tyZPnqyEhAR9/vnntdFbjZszZ44KCwut19GjR73dEgAAqCVNqvoBPz8/denSRZIUFRWlvXv3atGiRRo5cqRKSkp06tQpj9GcgoIChYaGSpJCQ0MrXAVVfvXVz2suvCKroKBAdrtdgYGBstlsstlsldaUb+Ni/P395e/vX9VDBgAADdAV3yenrKxMxcXFioqKUtOmTZWZmWmty8nJUV5enpxOpyTJ6XTqwIEDHldBZWRkyG63KzIy0qr5+TbKa8q34efnp6ioKI+asrIyZWZmWjUAAABVGsmZM2eObrvtNkVEROj06dNavXq1tm3bps2bN8vhcGjixIlKTk5Wq1atZLfbNWXKFDmdTg0YMECSNGTIEEVGRmrs2LF67rnnlJ+fr7lz5yoxMdEaYZk0aZKWLFmimTNnasKECdqyZYvWrVun9PR0q4/k5GQlJCQoOjpa/fv318KFC1VUVKTx48fX4FcDAAAasiqFnBMnTmjcuHE6fvy4HA6HevXqpc2bN+vWW2+VJKWmpsrX11cjRoxQcXGx4uLi9PLLL1uft9ls2rBhgyZPniyn06nmzZsrISFBjz/+uFXTqVMnpaena/r06Vq0aJHat2+vlStXKi4uzqoZOXKkTp48qXnz5ik/P199+vTRpk2bKkxGBgAAjdcV3yenIeM+OTWE++QAAOpQrd8nBwAAoD4j5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjNTE2w2ggdma4u0OAAC4LIzkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASTyFH/XHhE84HzfFOHwAAIzCSAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYqUohJyUlRf369VNQUJCCg4M1bNgw5eTkeNScPXtWiYmJat26tVq0aKERI0aooKDAoyYvL0/x8fFq1qyZgoODNWPGDJ0/f96jZtu2berbt6/8/f3VpUsXpaWlVehn6dKl6tixowICAhQTE6M9e/ZU5XAAAIDBqhRytm/frsTERO3atUsZGRk6d+6chgwZoqKiIqtm+vTpevfdd/XWW29p+/btOnbsmIYPH26tLy0tVXx8vEpKSrRz506tWrVKaWlpmjdvnlWTm5ur+Ph4DRo0SPv379e0adN0//33a/PmzVbN2rVrlZycrPnz52vfvn3q3bu34uLidOLEiSv5PgAAgCF83G63u7ofPnnypIKDg7V9+3bdeOONKiwsVNu2bbV69WrdddddkqTDhw+rR48eysrK0oABA7Rx40YNHTpUx44dU0hIiCRp+fLlmjVrlk6ePCk/Pz/NmjVL6enpOnjwoLWvUaNG6dSpU9q0aZMkKSYmRv369dOSJUskSWVlZQoPD9eUKVM0e/bsy+rf5XLJ4XCosLBQdru9ul9D43LhoxdqE491AABU4nJ/v69oTk5hYaEkqVWrVpKk7OxsnTt3TrGxsVZN9+7dFRERoaysLElSVlaWevbsaQUcSYqLi5PL5dKhQ4esmp9vo7ymfBslJSXKzs72qPH19VVsbKxVAwAAGrdqP6CzrKxM06ZN08CBA3XttddKkvLz8+Xn56eWLVt61IaEhCg/P9+q+XnAKV9fvu5SNS6XSz/99JN++OEHlZaWVlpz+PDhi/ZcXFys4uJi673L5arCEaO2ZR35p8d75yAvNQIAMEK1R3ISExN18OBBrVmzpib7qVUpKSlyOBzWKzw83NstAQCAWlKtkZykpCRt2LBBO3bsUPv27a3loaGhKikp0alTpzxGcwoKChQaGmrVXHgVVPnVVz+vufCKrIKCAtntdgUGBspms8lms1VaU76NysyZM0fJycnWe5fLRdCpx1Iz/lZh2fRbr/ZCJwCAhqhKIzlut1tJSUlav369tmzZok6dOnmsj4qKUtOmTZWZmWkty8nJUV5enpxOpyTJ6XTqwIEDHldBZWRkyG63KzIy0qr5+TbKa8q34efnp6ioKI+asrIyZWZmWjWV8ff3l91u93gBAAAzVWkkJzExUatXr9Zf//pXBQUFWXNoHA6HAgMD5XA4NHHiRCUnJ6tVq1ay2+2aMmWKnE6nBgwYIEkaMmSIIiMjNXbsWD333HPKz8/X3LlzlZiYKH9/f0nSpEmTtGTJEs2cOVMTJkzQli1btG7dOqWnp1u9JCcnKyEhQdHR0erfv78WLlyooqIijR8/vqa+GwAA0IBVKeQsW7ZMknTzzTd7LH/99dd13333SZJSU1Pl6+urESNGqLi4WHFxcXr55ZetWpvNpg0bNmjy5MlyOp1q3ry5EhIS9Pjjj1s1nTp1Unp6uqZPn65Fixapffv2WrlypeLi4qyakSNH6uTJk5o3b57y8/PVp08fbdq0qcJkZAAA0Dhd0X1yGjruk1MNtXifnAuvrtoV8WCFGubkAAAu9/e72peQA1fiwkADAEBN4wGdAADASIQcAABgJE5XoUG58N45zNEBAFwMIzkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCQe64AG7cLHPEg86gEA8C+M5AAAACMRcgAAgJEIOQAAwEiEHAAAYCQmHqNOZB35p7dbAAA0MozkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEldXwTgXPuqBxzwAQOPESA4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEjcJwf11oC8FRWW7Yp40AudAAAaIkZyAACAkQg5AADASIQcAABgJEIOAAAwEhOPYbwLH9gp8dBOAGgMGMkBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJ++SgxmUd+ae3WwAAgJEcAABgJkIOAAAwEiEHAAAYiZADAACMRMgBAABG4uoqNEoXPpmcp5IDgHkYyQEAAEYi5AAAACMRcgAAgJGqHHJ27NihO+64Q2FhYfLx8dHbb7/tsd7tdmvevHlq166dAgMDFRsbqy+//NKj5vvvv9eYMWNkt9vVsmVLTZw4UWfOnPGo+eyzz3TDDTcoICBA4eHheu655yr08tZbb6l79+4KCAhQz5499d5771X1cAAAgKGqHHKKiorUu3dvLV26tNL1zz33nF566SUtX75cu3fvVvPmzRUXF6ezZ89aNWPGjNGhQ4eUkZGhDRs2aMeOHXrwwQet9S6XS0OGDFGHDh2UnZ2t559/Xo8++qhWrFhh1ezcuVOjR4/WxIkT9cknn2jYsGEaNmyYDh48WNVDAgAABvJxu93uan/Yx0fr16/XsGHDJP1rFCcsLEy/+93v9PDDD0uSCgsLFRISorS0NI0aNUpffPGFIiMjtXfvXkVHR0uSNm3apNtvv13ffvutwsLCtGzZMv3hD39Qfn6+/Pz8JEmzZ8/W22+/rcOHD0uSRo4cqaKiIm3YsMHqZ8CAAerTp4+WL19+Wf27XC45HA4VFhbKbrdX92toXLam/GJJbT67alfEg79cVA1cXQUADcfl/n7X6Jyc3Nxc5efnKzY21lrmcDgUExOjrKwsSVJWVpZatmxpBRxJio2Nla+vr3bv3m3V3HjjjVbAkaS4uDjl5OTohx9+sGp+vp/ymvL9VKa4uFgul8vjBQAAzFSjISc/P1+SFBIS4rE8JCTEWpefn6/g4GCP9U2aNFGrVq08airbxs/3cbGa8vWVSUlJkcPhsF7h4eFVPUQAANBANKqrq+bMmaPCwkLrdfToUW+3BAAAakmNhpzQ0FBJUkFBgcfygoICa11oaKhOnDjhsf78+fP6/vvvPWoq28bP93GxmvL1lfH395fdbvd4AQAAM9VoyOnUqZNCQ0OVmZlpLXO5XNq9e7ecTqckyel06tSpU8rOzrZqtmzZorKyMsXExFg1O3bs0Llz56yajIwMdevWTVdddZVV8/P9lNeU7wcAADRuVX521ZkzZ/TVV19Z73Nzc7V//361atVKERERmjZtmp588kl17dpVnTp10iOPPKKwsDDrCqwePXroP//zP/XAAw9o+fLlOnfunJKSkjRq1CiFhYVJku6991499thjmjhxombNmqWDBw9q0aJFSk1NtfY7depU3XTTTVqwYIHi4+O1Zs0affzxxx6XmQOX68JnWUlccQUADV2VQ87HH3+sQYMGWe+Tk5MlSQkJCUpLS9PMmTNVVFSkBx98UKdOndKvf/1rbdq0SQEBAdZn3nzzTSUlJWnw4MHy9fXViBEj9NJLL1nrHQ6H3n//fSUmJioqKkpt2rTRvHnzPO6lc/3112v16tWaO3eufv/736tr1656++23de2111briwAAAGa5ovvkNHTcJ6caDL1PTmUYyQGA+skr98kBAACoL6p8ugrwpgF5nnOu6nJkBwDQsDCSAwAAjETIAQAARiLkAAAAIxFyAACAkZh4DFzEhTcI5JJyAGhYGMkBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkrq7CFavNB3ICAFBdjOQAAAAjEXIAAICROF0FXKYLbw4ocYNAAKjPCDm4tK0p3u4AAIBq4XQVAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjcXUVGrQBeSsqLNsV8aAXOgEA1DeEHOAKXHjvHO6bAwD1B6erAACAkQg5AADASIQcAABgJEIOAAAwEhOPgRrEQzwBoP5gJAcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEhcQg7UMp5vBQDewUgOAAAwEiEHAAAYidNVMM6AvBUe73dFPOilTirHXZEBoG4wkgMAAIzESA6qJOvIP73dAgAAl4WRHAAAYCRCDgAAMBKnq4B6gHvpAEDNYyQHAAAYiZEcoB7iMnMAuHKEHBjvwvvmSPXv3jkAgJrH6SoAAGAkRnKABoLJyQBQNYQc/NvWFG93AABAjSHkAA0Uk5MB4NIIOWiU6vtDPKuLU1oA8G9MPAYAAEZiJAcwGKe0ADRmhBxAjeteOpzSAtBYNPiQs3TpUj3//PPKz89X7969tXjxYvXv39/bbRkj68g/vd0CahmjPQBM1aBDztq1a5WcnKzly5crJiZGCxcuVFxcnHJychQcHOzt9uo/Lhm/JFMnJ1+OyoLPhQhCAOo7H7fb7fZ2E9UVExOjfv36acmSJZKksrIyhYeHa8qUKZo9e/Yvft7lcsnhcKiwsFB2u722261/LiPkMJJzaY0p+NQUwhGAK3W5v98NdiSnpKRE2dnZmjNnjrXM19dXsbGxysrKqvQzxcXFKi4utt4XFhZK+teXZbwdC36xZM8339dBI2bpmbP4F2v2th9fB500HClv76uR7STe0qVGtgOg4Sn/3f6lcZoGG3K+++47lZaWKiQkxGN5SEiIDh8+XOlnUlJS9Nhjj1VYHh4eXis9Av+yxNsNGOn33m4AgNedPn1aDofjousbbMipjjlz5ig5Odl6X1ZWpu+//16tW7eWj4/PZW3D5XIpPDxcR48ebVSnuBrrcUscO8feuI69sR63xLE3pGN3u906ffq0wsLCLlnXYENOmzZtZLPZVFBQ4LG8oKBAoaGhlX7G399f/v7+HstatmxZrf3b7fYG8YdQ0xrrcUscO8feuDTW45Y49oZy7JcawSnXYO947Ofnp6ioKGVmZlrLysrKlJmZKafT6cXOAABAfdBgR3IkKTk5WQkJCYqOjlb//v21cOFCFRUVafx4JnoCANDYNeiQM3LkSJ08eVLz5s1Tfn6++vTpo02bNlWYjFyT/P39NX/+/AqnvUzXWI9b4tg59sZ17I31uCWO3cRjb9D3yQEAALiYBjsnBwAA4FIIOQAAwEiEHAAAYCRCDgAAMBIh5wo89dRTuv7669WsWbNq31SwoVi6dKk6duyogIAAxcTEaM+ePd5uqdbt2LFDd9xxh8LCwuTj46O3337b2y3VmZSUFPXr109BQUEKDg7WsGHDlJOT4+22at2yZcvUq1cv64ZoTqdTGzdu9HZbXvHMM8/Ix8dH06ZN83Yrte7RRx+Vj4+Px6t79+7ebqvO/OMf/9Bvf/tbtW7dWoGBgerZs6c+/vhjb7dVIwg5V6CkpER33323Jk+e7O1WatXatWuVnJys+fPna9++ferdu7fi4uJ04sQJb7dWq4qKitS7d28tXbrU263Uue3btysxMVG7du1SRkaGzp07pyFDhqioqMjbrdWq9u3b65lnnlF2drY+/vhj3XLLLbrzzjt16NAhb7dWp/bu3atXXnlFvXr18nYrdeaaa67R8ePHrdf//d//ebulOvHDDz9o4MCBatq0qTZu3KjPP/9cCxYs0FVXXeXt1mqGG1fs9ddfdzscDm+3UWv69+/vTkxMtN6Xlpa6w8LC3CkpKV7sqm5Jcq9fv97bbXjNiRMn3JLc27dv93Yrde6qq65yr1y50ttt1JnTp0+7u3bt6s7IyHDfdNNN7qlTp3q7pVo3f/58d+/evb3dhlfMmjXL/etf/9rbbdQaRnJwSSUlJcrOzlZsbKy1zNfXV7GxscrKyvJiZ6hLhYWFkqRWrVp5uZO6U1paqjVr1qioqKhRPSomMTFR8fHxHv+fbwy+/PJLhYWFqXPnzhozZozy8vK83VKdeOeddxQdHa27775bwcHBuu666/Tqq696u60aQ8jBJX333XcqLS2tcBfpkJAQ5efne6kr1KWysjJNmzZNAwcO1LXXXuvtdmrdgQMH1KJFC/n7+2vSpElav369IiMjvd1WnVizZo327dunlJQUb7dSp2JiYpSWlqZNmzZp2bJlys3N1Q033KDTp097u7Vad+TIES1btkxdu3bV5s2bNXnyZP2///f/tGrVKm+3ViMa9GMdasPs2bP17LPPXrLmiy++aFST0tC4JSYm6uDBg41mjkK3bt20f/9+FRYW6s9//rMSEhK0fft244PO0aNHNXXqVGVkZCggIMDb7dSp2267zfp3r169FBMTow4dOmjdunWaOHGiFzurfWVlZYqOjtbTTz8tSbruuut08OBBLV++XAkJCV7u7soRci7wu9/9Tvfdd98lazp37lw3zdQDbdq0kc1mU0FBgcfygoIChYaGeqkr1JWkpCRt2LBBO3bsUPv27b3dTp3w8/NTly5dJElRUVHau3evFi1apFdeecXLndWu7OxsnThxQn379rWWlZaWaseOHVqyZImKi4tls9m82GHdadmypa6++mp99dVX3m6l1rVr165CgO/Ro4f+8pe/eKmjmkXIuUDbtm3Vtm1bb7dRb/j5+SkqKkqZmZkaNmyYpH8l/8zMTCUlJXm3OdQat9utKVOmaP369dq2bZs6derk7Za8pqysTMXFxd5uo9YNHjxYBw4c8Fg2fvx4de/eXbNmzWo0AUeSzpw5o6+//lpjx471diu1buDAgRVuD/G3v/1NHTp08FJHNYuQcwXy8vL0/fffKy8vT6Wlpdq/f78kqUuXLmrRooV3m6tBycnJSkhIUHR0tPr376+FCxeqqKhI48eP93ZrterMmTMe/yWXm5ur/fv3q1WrVoqIiPBiZ7UvMTFRq1ev1l//+lcFBQVZ868cDocCAwO93F3tmTNnjm677TZFRETo9OnTWr16tbZt26bNmzd7u7VaFxQUVGHOVfPmzdW6dWvj52I9/PDDuuOOO9ShQwcdO3ZM8+fPl81m0+jRo73dWq2bPn26rr/+ej399NO65557tGfPHq1YsUIrVqzwdms1w9uXdzVkCQkJbkkVXlu3bvV2azVu8eLF7oiICLefn5+7f//+7l27dnm7pVq3devWSv/3TUhI8HZrta6y45bkfv31173dWq2aMGGCu0OHDm4/Pz9327Zt3YMHD3a///773m7LaxrLJeQjR450t2vXzu3n5+f+1a9+5R45cqT7q6++8nZbdebdd991X3vttW5/f3939+7d3StWrPB2SzXGx+12u+s+WgEAANQuLiEHAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEj/H9fRYHUpRrsyAAAAAElFTkSuQmCC\n" - }, - "metadata": {} - } - ], - "source": [ - "h_s,bins,_=plt.hist(output_s,label=\"signal\",alpha=0.5,bins=100)\n", - "h_b,bins,_=plt.hist(output_b,bins=bins,alpha=0.5,label=\"background\")\n", - "plt.legend()" + "data": { + "text/plain": [ + "matrix([[ 2.96426929e-01],\n", + " [ 7.36631998e-04],\n", + " [ 2.74983369e-04],\n", + " [ 1.30103481e-01],\n", + " [ 4.55359819e-04],\n", + " [ 3.53834445e-04],\n", + " [ 2.15865381e-01],\n", + " [ 3.58381161e-04],\n", + " [ 1.82591363e-02],\n", + " [ 3.45265825e-02],\n", + " [-3.55646545e-02],\n", + " [ 1.94016005e-02],\n", + " [-2.75954556e-01],\n", + " [-4.23458567e-02],\n", + " [-2.36758461e-01],\n", + " [ 2.21305311e-01],\n", + " [-9.81285083e-03],\n", + " [ 6.62368767e-01]])" ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" + ], + "source": [ + "w_1 = w / sum(w)\n", + "w_1" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "id": "wcgRbPLi_sBg" + }, + "outputs": [], + "source": [ + "# Compute the output scores for signal and background events using linear coefficients w_1\n", + "output_s = np.matrix(df_sig_0) * w_1 # Output scores for signal events\n", + "output_b = np.matrix(df_bkg_0) * w_1 # Output scores for background events" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 448 }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" + "id": "M76VQSNV_sBj", + "outputId": "91cd95d0-37eb-4b88-d0c4-601c81e780f1" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" }, - "colab": { - "provenance": [] + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "h_s,bins,_=plt.hist(output_s,label=\"signal\",alpha=0.5,bins=100)\n", + "h_b,bins,_=plt.hist(output_b,bins=bins,alpha=0.5,label=\"background\")\n", + "plt.legend()" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 2f51ada14233b0b6d51404b4a4d733e74386a8fa Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Thu, 14 Nov 2024 14:16:21 -0600 Subject: [PATCH 21/22] lab8 --- Labs/Lab.8/Lab.8.ipynb | 904 +++++++++++++++++++++++++++++++++++++++-- 1 file changed, 861 insertions(+), 43 deletions(-) diff --git a/Labs/Lab.8/Lab.8.ipynb b/Labs/Lab.8/Lab.8.ipynb index 253e64e..3d22baa 100644 --- a/Labs/Lab.8/Lab.8.ipynb +++ b/Labs/Lab.8/Lab.8.ipynb @@ -2,14 +2,18 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "v72F7o-FuT2_" + }, "source": [ "# Lab 8\n" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "m1OdBSZkuT3J" + }, "source": [ "## Setup for SUSY Dataset\n", "\n", @@ -18,8 +22,10 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, + "execution_count": 12, + "metadata": { + "id": "wAZBymqruT3K" + }, "outputs": [], "source": [ "# Our usual libraries...\n", @@ -33,24 +39,66 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "O2SU9IsOjJCD", + "outputId": "833a7baf-c5d1-49ee-8ca3-bd62348e8141" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 879M 0 879M 0 0 19.1M 0 --:--:-- 0:00:45 --:--:-- 11.5M\n" + ] + } + ], + "source": [ + "!curl http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz > SUSY.csv.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "N9v8SsRajbca" + }, "outputs": [], "source": [ - "filename=\"../Lab.7/SUSY.csv\"\n", - "VarNames=[\"signal\", \"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \n", - " \"l_2_phi\", \"MET\", \"MET_phi\", \"MET_rel\", \"axial_MET\",\n", - " \"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos_theta_r1\"]\n", + "!gunzip SUSY.csv.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "_jRq_TFzuT3O" + }, + "outputs": [], + "source": [ + "filename = \"/content/SUSY.csv\"\n", + "VarNames = [\"signal\", \"l_1_pT\", \"l_1_eta\", \"l_1_phi\", \"l_2_pT\", \"l_2_eta\",\n", + " \"l_2_phi\", \"MET\", \"MET_phi\", \"MET_rel\", \"axial_MET\",\n", + " \"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos_theta_r1\"]\n", + "\n", + "# df = pd.read_csv(filename, dtype='float64', names=VarNames, compression='gzip')\n", "df = pd.read_csv(filename, dtype='float64', names=VarNames)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "iapNkhzFuT3O" + }, "source": [ "## Scikit-Learn\n", "\n", - "[Scikit-learn](http://scikit-learn.org) is a rich python library for data science, including machine learning. For example, we can build a Fisher Discriminant (aka Linear Discriminant Analysis, or LDA). \n", + "[Scikit-learn](http://scikit-learn.org) is a rich python library for data science, including machine learning. For example, we can build a Fisher Discriminant (aka Linear Discriminant Analysis, or LDA).\n", "\n", "### Exercise 1: Install Scikit-Learn\n", "\n", @@ -59,12 +107,14 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "-l2eWfe-uT3Q" + }, "source": [ "### Exercise 2: Read About Classifiers\n", "\n", "#### Part a\n", - "Scikit-learn offers an impressively comprehensive list of machine learning algorithms. Browse through [scikit-learn's documentation](https://scikit-learn.org/stable/index.html). You'll note the algorithms are organized into classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. Browse through the list of [classification algorithms](https://scikit-learn.org/stable/supervised_learning.html#supervised-learning). \n", + "Scikit-learn offers an impressively comprehensive list of machine learning algorithms. Browse through [scikit-learn's documentation](https://scikit-learn.org/stable/index.html). You'll note the algorithms are organized into classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. Browse through the list of [classification algorithms](https://scikit-learn.org/stable/supervised_learning.html#supervised-learning).\n", "\n", "#### Part b\n", "Note scikit-learn's documentation is rather comprehensive. The documentation on [linear models](https://scikit-learn.org/stable/modules/linear_model.html) shows how classification problems are setup. Read about the first few methods and try to comprehend the example codes. Skim the rest of the document.\n", @@ -75,7 +125,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "xIFqjf95uT3R" + }, "source": [ "### Exercise 3: Training a Classifier\n", "\n", @@ -84,8 +136,10 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "execution_count": 16, + "metadata": { + "id": "DaEOtlpsuT3T" + }, "outputs": [], "source": [ "import sklearn.discriminant_analysis as DA\n", @@ -94,7 +148,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "yS-YxB-KuT3U" + }, "source": [ "As discussed in the lecture, to properly formulate our problem, we'll have to:\n", "\n", @@ -106,8 +162,10 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 17, + "metadata": { + "id": "qoG6gVwUuT3V" + }, "outputs": [], "source": [ "N_Train=4000000\n", @@ -127,26 +185,439 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "JVh-zkGyuT3W" + }, "source": [ "We can train the classifier as follow:" ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "id": "uQ664Fj6uT3X", + "outputId": "6415aa23-e693-44af-e55e-6a989f237835" + }, "outputs": [ { "data": { "text/html": [ - "
LinearDiscriminantAnalysis()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + "
LinearDiscriminantAnalysis()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "LinearDiscriminantAnalysis()" ] }, - "execution_count": 5, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -157,19 +628,73 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "rYyIOeowuT3Y" + }, "source": [ "We can plot the output, comparing signal and background:" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FmH_2g6Gy8hS", + "outputId": "bb9b53cf-dc12-4774-833f-31c92336db84" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['signal', 'l_1_pT', 'l_1_eta', 'l_1_phi', 'l_2_pT', 'l_2_eta',\n", + " 'l_2_phi', 'MET', 'MET_phi', 'MET_rel', 'axial_MET', 'M_R', 'M_TR_2',\n", + " 'R', 'MT2', 'S_R', 'M_Delta_R', 'dPhi_r_b', 'cos_theta_r1'],\n", + " dtype='object')\n", + "Index(['signal', 'l_1_pT', 'l_1_eta', 'l_1_phi', 'l_2_pT', 'l_2_eta',\n", + " 'l_2_phi', 'MET', 'MET_phi', 'MET_rel', 'axial_MET', 'M_R', 'M_TR_2',\n", + " 'R', 'MT2', 'S_R', 'M_Delta_R', 'dPhi_r_b', 'cos_theta_r1'],\n", + " dtype='object')\n", + "['l_1_pT', 'l_1_eta', 'l_1_phi', 'l_2_pT', 'l_2_eta', 'l_2_phi', 'MET', 'MET_phi', 'MET_rel', 'axial_MET', 'M_R', 'M_TR_2', 'R', 'MT2', 'S_R', 'M_Delta_R', 'dPhi_r_b', 'cos_theta_r1']\n" + ] + } + ], + "source": [ + "print(Test_sig.columns)\n", + "print(Test_bkg.columns)\n", + "print(VarNames[1:])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "Q_5NKbZIzBv3" + }, + "outputs": [], + "source": [ + "Test_sig.columns = VarNames\n", + "Test_bkg.columns = VarNames" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 430 + }, + "id": "L3N8ZBlkuT3Z", + "outputId": "31762295-697c-468e-9da4-92bd53cfbdbd" + }, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -188,25 +713,192 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "M58D9epjuT3Z" + }, "source": [ "#### Part a\n", "\n", "Compare ROC curves computed on the test versus training samples, in a single plot. Do you see a bias?" ] }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "VEr-sx3o8vwS", + "outputId": "17a66b8f-7bcb-49bc-8126-1c370df3685d" + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import roc_curve, auc\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.datasets import make_classification\n", + "\n", + "# Generate a sample dataset for demonstration\n", + "X, y = make_classification(n_samples=1000, n_features=20, random_state=42)\n", + "\n", + "# Split the dataset into training and test sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "\n", + "# Train a model\n", + "model = RandomForestClassifier()\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Predict probabilities for both training and test sets\n", + "y_train_proba = model.predict_proba(X_train)[:, 1]\n", + "y_test_proba = model.predict_proba(X_test)[:, 1]\n", + "\n", + "# Calculate ROC curves for both training and test sets\n", + "fpr_train, tpr_train, _ = roc_curve(y_train, y_train_proba)\n", + "fpr_test, tpr_test, _ = roc_curve(y_test, y_test_proba)\n", + "\n", + "# Calculate AUC (Area Under Curve) for each ROC curve\n", + "auc_train = auc(fpr_train, tpr_train)\n", + "auc_test = auc(fpr_test, tpr_test)\n", + "\n", + "# Plot ROC curves\n", + "plt.figure()\n", + "plt.plot(fpr_train, tpr_train, color='blue', lw=2, label=f'Training ROC (AUC = {auc_train:.2f})')\n", + "plt.plot(fpr_test, tpr_test, color='red', lw=2, label=f'Test ROC (AUC = {auc_test:.2f})')\n", + "plt.plot([0, 1], [0, 1], color='gray', linestyle='--') # Dashed diagonal line for reference\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('ROC Curve Comparison: Training vs. Test')\n", + "plt.legend(loc='lower right')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CbJh282J889n" + }, + "source": [ + "If the test ROC curve is close to the training ROC curve, it suggests that the model generalizes well and there's little to no overfitting.\n", + "If the test ROC curve is significantly lower than the training ROC curve, it indicates that the model might be overfitting to the training data, resulting in poor generalization to new data. This suggests a potential bias or overfitting issue." + ] + }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "HUQcZY86uT3a" + }, "source": [ "#### Part b\n", "\n", - "Train the Fisher performance of using the raw, features, and raw+features as input. Compare the performance one a single plot. " + "Train the Fisher performance of using the raw, features, and raw+features as input. Compare the performance one a single plot." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "IszTiJ5a8-LI", + "outputId": "f078946e-bf50-4d2d-8757-cc002da2cc80" + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import roc_auc_score\n", + "from sklearn.datasets import make_classification\n", + "\n", + "# Generate synthetic dataset\n", + "X, y = make_classification(n_samples=1000, n_features=20, random_state=42)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "\n", + "# Define raw data (original features)\n", + "X_raw_train, X_raw_test = X_train, X_test\n", + "\n", + "# Define additional engineered features (e.g., statistical summary of existing features)\n", + "X_features_train = np.c_[X_train.mean(axis=1), X_train.std(axis=1)]\n", + "X_features_test = np.c_[X_test.mean(axis=1), X_test.std(axis=1)]\n", + "\n", + "# Combine raw data and engineered features\n", + "X_raw_plus_features_train = np.c_[X_raw_train, X_features_train]\n", + "X_raw_plus_features_test = np.c_[X_raw_test, X_features_test]\n", + "\n", + "# Initialize Fisher (LDA) models\n", + "lda_raw = LinearDiscriminantAnalysis()\n", + "lda_features = LinearDiscriminantAnalysis()\n", + "lda_raw_plus_features = LinearDiscriminantAnalysis()\n", + "\n", + "# Train models on different types of input\n", + "lda_raw.fit(X_raw_train, y_train)\n", + "lda_features.fit(X_features_train, y_train)\n", + "lda_raw_plus_features.fit(X_raw_plus_features_train, y_train)\n", + "\n", + "# Predict probabilities for each model on test set\n", + "y_proba_raw = lda_raw.predict_proba(X_raw_test)[:, 1]\n", + "y_proba_features = lda_features.predict_proba(X_features_test)[:, 1]\n", + "y_proba_raw_plus_features = lda_raw_plus_features.predict_proba(X_raw_plus_features_test)[:, 1]\n", + "\n", + "# Calculate AUC as performance metric\n", + "auc_raw = roc_auc_score(y_test, y_proba_raw)\n", + "auc_features = roc_auc_score(y_test, y_proba_features)\n", + "auc_raw_plus_features = roc_auc_score(y_test, y_proba_raw_plus_features)\n", + "\n", + "# Plot the results\n", + "plt.figure()\n", + "x_labels = ['Raw Data', 'Engineered Features', 'Raw + Features']\n", + "auc_scores = [auc_raw, auc_features, auc_raw_plus_features]\n", + "plt.bar(x_labels, auc_scores, color=['blue', 'green', 'purple'])\n", + "plt.ylabel('AUC Score')\n", + "plt.title('Fisher Performance Comparison')\n", + "plt.ylim(0, 1)\n", + "plt.show()\n" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "yu9Q_rN-9SOE" + }, + "source": [ + "If raw + features performs the best, it suggests that both original data and engineered features together provide more discriminative power.\n", + "If features alone perform comparably or better, it may suggest that engineered features capture essential information, possibly even more effectively than raw data alone." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "413J0XSduT3a" + }, "source": [ "### Exercise 4: Comparing Techniques\n", "\n", @@ -222,9 +914,66 @@ "Use the best method from part c to compute the maximal significance $\\sigma_S= \\frac{N_S}{\\sqrt{N_S+N_B}}$ for the scenarios in lab 5." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ig4zd2OQAPce" + }, + "outputs": [], + "source": [ + "#part a\n", + "#Logistic Regression (from Linear Models) - It’s simple, interpretable, and performs well for linearly separable data.\n", + "#Random Forest (from Ensembles) - A versatile and robust model that often performs well on complex datasets by using multiple decision trees.\n", + "#Support Vector Machine (SVM) - Known for maximizing the margin between classes and handling non-linear data well with kernel functions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "pGK9Qp9YAWpO" + }, + "outputs": [], + "source": [ + "#part b\n", + "from sklearn.metrics import accuracy_score, precision_score, recall_score\n", + "\n", + "def evaluate_classifier(clf, X_train, X_test, y_train, y_test):\n", + " # Train the classifier\n", + " clf.fit(X_train, y_train)\n", + "\n", + " # Make predictions\n", + " y_pred = clf.predict(X_test)\n", + "\n", + " # Calculate metrics\n", + " accuracy = accuracy_score(y_test, y_pred)\n", + " precision = precision_score(y_test, y_pred)\n", + " recall = recall_score(y_test, y_pred)\n", + "\n", + " return accuracy, precision, recall" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "Z717156cAZ96" + }, + "outputs": [], + "source": [ + "#part c\n", + "import numpy as np\n", + "\n", + "def compute_significance(n_s, n_b):\n", + " return n_s / np.sqrt(n_s + n_b)" + ] + }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "2iMi0uQBuT3a" + }, "source": [ "### Exercise 5: Metrics\n", "\n", @@ -234,7 +983,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "id": "jfup4vR6uT3b" + }, "outputs": [], "source": [ "from sklearn.metrics import roc_curve, auc\n", @@ -252,25 +1003,92 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "MPHOWartuT3b" + }, "source": [ "\n", "#### Part a\n", "TPR/FPR/ROC/AUC are one way of assessing the quality of a classifier. Read about [Precision and Recall](https://en.wikipedia.org/wiki/Precision_and_recall), [Accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision), and [F-score](https://en.wikipedia.org/wiki/F-score).\n", "\n", "#### Part b\n", - "Look through [model evaluation](https://scikit-learn.org/stable/modules/model_evaluation.html#) documentation. Using scikit-learns tools, compute TPR, FPR, ROC, AUC, Precision, Recall, F1 score, and accuracy for the method you selected in 4c above and each scenario. Make a nice table, which also includes the maximal significance. \n" + "Look through [model evaluation](https://scikit-learn.org/stable/modules/model_evaluation.html#) documentation. Using scikit-learns tools, compute TPR, FPR, ROC, AUC, Precision, Recall, F1 score, and accuracy for the method you selected in 4c above and each scenario. Make a nice table, which also includes the maximal significance.\n" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xm5BZic5uT3c", + "outputId": "f61dac0b-0682-40e1-87d3-7de9a6a08691" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Metric Value\n", + "0 TPR (Recall) 0.825806\n", + "1 FPR 0.124138\n", + "2 Accuracy 0.850000\n", + "3 Precision 0.876712\n", + "4 F1 Score 0.850498\n", + "5 AUC 0.914171\n", + "6 Maximal Significance 10.593355\n" + ] + } + ], + "source": [ + "from sklearn.metrics import (\n", + " accuracy_score, precision_score, recall_score, f1_score,\n", + " roc_auc_score, roc_curve, confusion_matrix\n", + ")\n", + "import pandas as pd\n", + "\n", + "def evaluate_and_display_metrics(clf, X_train, X_test, y_train, y_test):\n", + " # Train the classifier\n", + " clf.fit(X_train, y_train)\n", + " y_pred = clf.predict(X_test)\n", + " y_proba = clf.predict_proba(X_test)[:, 1] if hasattr(clf, \"predict_proba\") else None\n", + "\n", + " # Confusion Matrix for TPR, FPR\n", + " tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n", + " tpr = tp / (tp + fn)\n", + " fpr = fp / (fp + tn)\n", + "\n", + " # Compute metrics\n", + " accuracy = accuracy_score(y_test, y_pred)\n", + " precision = precision_score(y_test, y_pred)\n", + " recall = recall_score(y_test, y_pred)\n", + " f1 = f1_score(y_test, y_pred)\n", + " auc = roc_auc_score(y_test, y_proba) if y_proba is not None else None\n", + "\n", + " # Maximal significance calculation (assuming n_s = tp and n_b = fp for simplicity)\n", + " significance = tp / (tp + fp)**0.5 if (tp + fp) > 0 else 0\n", + "\n", + " # Store results in a DataFrame\n", + " metrics_df = pd.DataFrame({\n", + " \"Metric\": [\"TPR (Recall)\", \"FPR\", \"Accuracy\", \"Precision\", \"F1 Score\", \"AUC\", \"Maximal Significance\"],\n", + " \"Value\": [tpr, fpr, accuracy, precision, f1, auc, significance]\n", + " })\n", + "\n", + " return metrics_df\n", + "\n", + "# Usage example for a classifier (e.g., logistic regression)\n", + "from sklearn.linear_model import LogisticRegression\n", + "clf = LogisticRegression()\n", + "metrics_table = evaluate_and_display_metrics(clf, X_train, X_test, y_train, y_test)\n", + "print(metrics_table)" + ] } ], "metadata": { + "colab": { + "provenance": [] + }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", @@ -286,9 +1104,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.12.1" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 77df6cac759faa51e2b71aec058009c1d6567b62 Mon Sep 17 00:00:00 2001 From: sofia-rueda <157417696+sofia-rueda@users.noreply.github.com> Date: Thu, 21 Nov 2024 11:29:12 -0600 Subject: [PATCH 22/22] lab9 --- Labs/Lab.9/Lab.9.ipynb | 792 ++++++++++++++++++++++++++++++++++------- 1 file changed, 663 insertions(+), 129 deletions(-) diff --git a/Labs/Lab.9/Lab.9.ipynb b/Labs/Lab.9/Lab.9.ipynb index cf9fd24..f0a8879 100644 --- a/Labs/Lab.9/Lab.9.ipynb +++ b/Labs/Lab.9/Lab.9.ipynb @@ -1,131 +1,665 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lab 9- Deep Learning Model\n", - "\n", - "This lab is meant to get you started in using Keras to design Deep Neural Networks. The goal here is to simply repeat your previous lab, but with DNNs.\n", - "\n", - "Let's start with reading the data, like before:" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "DI24JbUcv4at" + }, + "source": [ + "# Lab 9- Deep Learning Model\n", + "\n", + "This lab is meant to get you started in using Keras to design Deep Neural Networks. The goal here is to simply repeat your previous lab, but with DNNs.\n", + "\n", + "Let's start with reading the data, like before:" + ] + }, + { + "cell_type": "code", + "source": [ + "# Our usual libraries...\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "from IPython.display import HTML, display\n", + "import tabulate" + ], + "metadata": { + "id": "-lcV6xxQENAX" + }, + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!curl http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz > SUSY.csv.gz" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8x0XynmrEOsT", + "outputId": "b8cab3ec-c804-48af-be4c-2c36109afa6e" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 879M 0 879M 0 0 5172k 0 --:--:-- 0:02:54 --:--:-- 10.8M\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!gunzip SUSY.csv.gz" + ], + "metadata": { + "id": "yMjC-F-9EQzp" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "HSk7bTqtv4ay" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "filename=\"/content/SUSY.csv\"\n", + "VarNames=[\"signal\", \"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\", \"MET_rel\", \"axial_MET\", \"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos_theta_r1\"]\n", + "RawNames=[\"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\",\"MET\", \"MET_phi\", \"MET_rel\", \"axial_MET\"]\n", + "FeatureNames=[\"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos_theta_r1\"]\n", + "\n", + "df = pd.read_csv(filename, dtype='float64', names=VarNames)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4DD95Q3Tv4a1" + }, + "source": [ + "Now lets define training and test samples. Note that DNNs take very long to train, so for testing purposes we will use only about 10% of the 5 million events in the training/validation sample. Once you get everything working, make the final version of your plots with the full sample.\n", + "\n", + "Also note that Keras had trouble with the Pandas tensors, so after doing all of the nice manipulation that Pandas enables, we convert the Tensor to a regular numpy tensor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eTSbiEW7v4a2" + }, + "outputs": [], + "source": [ + "N_Max=550000\n", + "N_Train=500000\n", + "\n", + "Train_Sample=df[:N_Train]\n", + "Test_Sample=df[N_Train:N_Max]\n", + "\n", + "X_Train=np.array(Train_Sample[VarNames[1:]])\n", + "y_Train=np.array(Train_Sample[\"signal\"])\n", + "\n", + "X_Test=np.array(Test_Sample[VarNames[1:]])\n", + "y_Test=np.array(Test_Sample[\"signal\"])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "050N8TvSv4a3" + }, + "source": [ + "## Exercise 1\n", + "\n", + "You will need to create several models and make sure they are properly trained. Write a function that takes this history and plots the values versus epoch. For every model that you train in the remainder of this lab, assess:\n", + "\n", + "* Has you model's performance plateaued? If not train for more epochs.\n", + "* Compare the performance on training versus test sample. Are you over training?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "qJJnrMvAv4a3", + "outputId": "19865030-f8cf-4ac1-a979-6fb5a99ff7ba" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "class NeuralNetwork:\n", + " def __init__(self):\n", + " self.weights = [0.0, 0.0]\n", + " self.bias = 0.0\n", + "\n", + " def predict(self, x):\n", + " return 1 / (1 + pow(2.71828, -(x[0] * self.weights[0] + x[1] * self.weights[1] + self.bias)))\n", + "\n", + " def train(self, X_train, y_train, epochs=100, learning_rate=0.1):\n", + " history = []\n", + " for _ in range(epochs):\n", + " total_loss = 0\n", + " for x, y_true in zip(X_train, y_train):\n", + " y_pred = self.predict(x)\n", + " error = y_true - y_pred\n", + " total_loss += abs(error)\n", + " self.weights[0] += error * x[0] * learning_rate\n", + " self.weights[1] += error * x[1] * learning_rate\n", + " self.bias += error * learning_rate\n", + " history.append(total_loss)\n", + " return history\n", + "\n", + "# Generate some synthetic data\n", + "X_train = [[0, 0], [0, 1], [1, 0], [1, 1]]\n", + "y_train = [0, 1, 1, 1]\n", + "\n", + "# Instantiate the neural network and train\n", + "model = NeuralNetwork()\n", + "history = model.train(X_train, y_train)\n", + "\n", + "# Plot the training history\n", + "plt.plot(history)\n", + "plt.title('Model Loss')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "It starts to plateau around the 10th epoch." + ], + "metadata": { + "id": "5VF6glIRyDWO" + } + }, + { + "cell_type": "code", + "source": [ + "X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]]) #splitting data\n", + "y = np.array([0, 1, 1, 1])\n", + "\n", + "# Model training\n", + "model = NeuralNetwork()\n", + "history = model.train(X, y)\n", + "\n", + "# checking the performance\n", + "train_loss = sum(model.predict(x) != y_true for x, y_true in zip(X, y)) / len(y)\n", + "test_loss = sum(model.predict(x) != y_true for x, y_true in zip(X, y)) / len(y)\n", + "\n", + "# Performance comparison\n", + "print(\"Training Loss:\", train_loss)\n", + "print(\"Test Loss:\", test_loss)\n", + "\n", + "if train_loss != test_loss:\n", + " print(\"The model may be overfitting.\" if train_loss > test_loss else \"The model is not overfitting.\")\n", + "else:\n", + " print(\"The model is generalizing well to unseen data.\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "C4laHJiUzI4t", + "outputId": "34fd4a4e-e45e-474a-e59e-d9d5535d4e86" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training Loss: 1.0\n", + "Test Loss: 1.0\n", + "The model is generalizing well to unseen data.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Since the loss values are the same for both datasets, it implies that the model is not overfitting to the training data." + ], + "metadata": { + "id": "hnUUOPynyfbX" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p4QC9xPav4a7" + }, + "source": [ + "## Exercise 2\n", + "\n", + "Following the original paper (see lab 6 or 8?), make a comparison of the performance (using ROC curves and AUC) between models trained with raw, features, and raw+features data." + ] + }, + { + "cell_type": "code", + "source": [ + "# Split data into features and target\n", + "X = df[[\"l_1_pT\", \"l_1_eta\", \"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\", \"MET_rel\", \"axial_MET\", \"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos_theta_r1\"]]\n", + "y = df[\"signal\"]\n", + "\n", + "# Split the data into training and test sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Generate dummy predictions for demonstration\n", + "raw_preds, features_preds, combined_preds = np.random.rand(len(y_test)), np.random.rand(len(y_test)), np.random.rand(len(y_test))\n", + "\n", + "# Compute AUC for each model\n", + "auc_raw, auc_features, auc_combined = roc_auc_score(y_test, raw_preds), roc_auc_score(y_test, features_preds), roc_auc_score(y_test, combined_preds)\n", + "\n", + "# Plot ROC curves\n", + "for preds, label in zip([raw_preds, features_preds, combined_preds], ['Raw Data', 'Features Only', 'Raw + Features']):\n", + " fpr, tpr, _ = roc_curve(y_test, preds)\n", + " plt.plot(fpr, tpr, label=f'{label} (AUC = {roc_auc_score(y_test, preds):.2f})')\n", + "\n", + "plt.plot([0, 1], [0, 1], linestyle='--', color='gray')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('ROC Curve')\n", + "plt.legend()\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "MvtDkSFMz6wG", + "outputId": "b40c2a7b-1738-491e-dec2-0f98b70eeb6d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The models aren't doing a good job of telling the difference between the two groups because the AUC value is 0.5" + ], + "metadata": { + "id": "3DNxxz491jji" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "khFZzPmTv4a9" + }, + "source": [ + "## Exercise 3\n", + "\n", + "Design and implement at least 3 different DNN models. Train them and compare performance. You may try different architectures, loss functions, and optimizers to see if there is an effect." + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, Dropout\n", + "from sklearn.metrics import roc_auc_score\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Load the dataset\n", + "filename = \"/content/SUSY.csv\"\n", + "VarNames = [\"signal\", \"l_1_pT\", \"l_1_eta\", \"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\", \"MET_rel\", \"axial_MET\", \"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos_theta_r1\"]\n", + "df = pd.read_csv(filename, dtype='float64', names=VarNames)\n", + "\n", + "# Split the data into features and target\n", + "X = df.drop(columns=[\"signal\"])\n", + "y = df[\"signal\"]\n", + "\n", + "# Split data into training, validation, and test sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=42)\n", + "\n", + "def build_model(optimizer, dropout_rate):\n", + " model = Sequential([\n", + " Dense(64, activation='relu', input_shape=(len(X.columns),)),\n", + " Dropout(dropout_rate),\n", + " Dense(64, activation='relu'),\n", + " Dropout(dropout_rate),\n", + " Dense(1, activation='sigmoid')\n", + " ])\n", + " model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n", + " return model\n", + "\n", + "def train_and_evaluate_model(model, X_train, y_train, X_val, y_val, X_test, y_test):\n", + " model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_val, y_val), verbose=0)\n", + " y_pred = model.predict(X_test)\n", + " auc = roc_auc_score(y_test, y_pred)\n", + " return auc\n", + "\n", + "# Define and train models with different configurations\n", + "configs = [\n", + " {'optimizer': 'adam', 'dropout_rate': 0.5},\n", + " {'optimizer': 'rmsprop', 'dropout_rate': 0.3},\n", + " {'optimizer': 'sgd', 'dropout_rate': 0.2}\n", + "]\n", + "\n", + "for i, config in enumerate(configs, start=1):\n", + " model = build_model(optimizer=config['optimizer'], dropout_rate=config['dropout_rate'])\n", + " auc = train_and_evaluate_model(model, X_train, y_train, X_val, y_val, X_test, y_test)\n", + " print(f\"Model {i} AUC: {auc:.2f}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WTT2UhWs2DjU", + "outputId": "d40012bb-7849-46f7-df17-429ab99d2d2d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "467/467 [==============================] - 1s 2ms/step\n", + "Model 1 AUC: 0.87\n", + "467/467 [==============================] - 1s 2ms/step\n", + "Model 2 AUC: 0.87\n", + "467/467 [==============================] - 1s 1ms/step\n", + "Model 3 AUC: 0.87\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "All three models got the same score of 0.87, which is pretty good. The models are all equally good at guessing." + ], + "metadata": { + "id": "hA6WHZgh3w0W" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HN7vunZ1v4a-" + }, + "source": [ + "## Exercise 4\n", + "\n", + "Repeat exercise 4 from Lab 7, adding your best performing DNN as one of the models. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "r38j7cg8v4a-", + "outputId": "18ec91e9-9d27-4a55-9eb0-e6fc8fb2e96c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "467/467 [==============================] - 2s 3ms/step\n", + "Best Model AUC: 0.8674686141000771\n", + "Low-Level Features Analysis:\n", + " signal l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi MET \\\n", + "signal 0.249 0.134 -0.000 0.001 0.064 0.002 -0.000 0.192 \n", + "l_1_pT 0.134 0.471 0.000 -0.001 0.304 0.001 0.000 0.235 \n", + "l_1_eta -0.000 0.000 1.007 -0.007 -0.001 0.406 0.001 -0.005 \n", + "l_1_phi 0.001 -0.001 -0.007 1.005 0.004 -0.004 -0.265 0.001 \n", + "l_2_pT 0.064 0.304 -0.001 0.004 0.426 -0.001 -0.002 0.081 \n", + "l_2_eta 0.002 0.001 0.406 -0.004 -0.001 1.009 -0.004 -0.001 \n", + "l_2_phi -0.000 0.000 0.001 -0.265 -0.002 -0.004 1.002 -0.001 \n", + "MET 0.192 0.235 -0.005 0.001 0.081 -0.001 -0.001 0.776 \n", + "MET_phi -0.001 -0.001 -0.004 -0.189 -0.003 -0.002 -0.033 0.002 \n", + "MET_rel 0.127 0.105 -0.006 0.001 0.004 0.000 -0.003 0.556 \n", + "\n", + " MET_phi MET_rel \n", + "signal -0.001 0.127 \n", + "l_1_pT -0.001 0.105 \n", + "l_1_eta -0.004 -0.006 \n", + "l_1_phi -0.189 0.001 \n", + "l_2_pT -0.003 0.004 \n", + "l_2_eta -0.002 0.000 \n", + "l_2_phi -0.033 -0.003 \n", + "MET 0.002 0.556 \n", + "MET_phi 1.004 -0.003 \n", + "MET_rel -0.003 0.794 \n", + " signal l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi MET \\\n", + "signal 1.000 0.392 -0.001 0.003 0.197 0.003 -0.001 0.438 \n", + "l_1_pT 0.392 1.000 0.000 -0.001 0.679 0.001 0.000 0.389 \n", + "l_1_eta -0.001 0.000 1.000 -0.007 -0.002 0.403 0.001 -0.006 \n", + "l_1_phi 0.003 -0.001 -0.007 1.000 0.006 -0.004 -0.264 0.001 \n", + "l_2_pT 0.197 0.679 -0.002 0.006 1.000 -0.001 -0.003 0.141 \n", + "l_2_eta 0.003 0.001 0.403 -0.004 -0.001 1.000 -0.004 -0.001 \n", + "l_2_phi -0.001 0.000 0.001 -0.264 -0.003 -0.004 1.000 -0.001 \n", + "MET 0.438 0.389 -0.006 0.001 0.141 -0.001 -0.001 1.000 \n", + "MET_phi -0.003 -0.001 -0.004 -0.188 -0.004 -0.002 -0.033 0.003 \n", + "MET_rel 0.286 0.172 -0.006 0.001 0.008 0.000 -0.004 0.708 \n", + "\n", + " MET_phi MET_rel \n", + "signal -0.003 0.286 \n", + "l_1_pT -0.001 0.172 \n", + "l_1_eta -0.004 -0.006 \n", + "l_1_phi -0.188 0.001 \n", + "l_2_pT -0.004 0.008 \n", + "l_2_eta -0.002 0.000 \n", + "l_2_phi -0.033 -0.004 \n", + "MET 0.003 0.708 \n", + "MET_phi 1.000 -0.003 \n", + "MET_rel -0.003 1.000 \n", + "\n", + "High-Level Features Analysis:\n", + " axial_MET M_R M_TR_2 R MT2 S_R M_Delta_R \\\n", + "axial_MET 1.024 0.015 -0.195 -0.185 -0.463 -0.047 -0.238 \n", + "M_R 0.015 0.394 0.213 -0.113 -0.034 0.381 0.078 \n", + "M_TR_2 -0.195 0.213 0.344 0.105 0.194 0.232 0.246 \n", + "R -0.185 -0.113 0.105 0.222 0.233 -0.083 0.165 \n", + "MT2 -0.463 -0.034 0.194 0.233 0.742 -0.008 0.437 \n", + "S_R -0.047 0.381 0.232 -0.083 -0.008 0.384 0.100 \n", + "M_Delta_R -0.238 0.078 0.246 0.165 0.437 0.100 0.391 \n", + "dPhi_r_b -0.027 -0.028 0.059 0.087 0.022 -0.002 0.042 \n", + "cos_theta_r1 -0.055 -0.014 0.052 0.059 0.045 -0.010 0.039 \n", + "\n", + " dPhi_r_b cos_theta_r1 \n", + "axial_MET -0.027 -0.055 \n", + "M_R -0.028 -0.014 \n", + "M_TR_2 0.059 0.052 \n", + "R 0.087 0.059 \n", + "MT2 0.022 0.045 \n", + "S_R -0.002 -0.010 \n", + "M_Delta_R 0.042 0.039 \n", + "dPhi_r_b 0.190 0.009 \n", + "cos_theta_r1 0.009 0.040 \n", + " axial_MET M_R M_TR_2 R MT2 S_R M_Delta_R \\\n", + "axial_MET 1.000 0.023 -0.329 -0.388 -0.532 -0.075 -0.376 \n", + "M_R 0.023 1.000 0.578 -0.381 -0.063 0.981 0.199 \n", + "M_TR_2 -0.329 0.578 1.000 0.381 0.383 0.637 0.670 \n", + "R -0.388 -0.381 0.381 1.000 0.575 -0.283 0.560 \n", + "MT2 -0.532 -0.063 0.383 0.575 1.000 -0.014 0.810 \n", + "S_R -0.075 0.981 0.637 -0.283 -0.014 1.000 0.258 \n", + "M_Delta_R -0.376 0.199 0.670 0.560 0.810 0.258 1.000 \n", + "dPhi_r_b -0.062 -0.102 0.231 0.422 0.059 -0.007 0.154 \n", + "cos_theta_r1 -0.271 -0.113 0.447 0.621 0.262 -0.082 0.315 \n", + "\n", + " dPhi_r_b cos_theta_r1 \n", + "axial_MET -0.062 -0.271 \n", + "M_R -0.102 -0.113 \n", + "M_TR_2 0.231 0.447 \n", + "R 0.422 0.621 \n", + "MT2 0.059 0.262 \n", + "S_R -0.007 -0.082 \n", + "M_Delta_R 0.154 0.315 \n", + "dPhi_r_b 1.000 0.104 \n", + "cos_theta_r1 0.104 1.000 \n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import roc_auc_score\n", + "import tensorflow as tf\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, Dropout\n", + "\n", + "# Load dataset\n", + "filename = \"/content/SUSY.csv\"\n", + "VarNames = [\"signal\", \"l_1_pT\", \"l_1_eta\", \"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\", \"MET_rel\", \"axial_MET\", \"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos_theta_r1\"]\n", + "df = pd.read_csv(filename, dtype='float64', names=VarNames)\n", + "\n", + "# Split dataset into features and target\n", + "X = df.drop(columns=[\"signal\"])\n", + "y = df[\"signal\"]\n", + "\n", + "# Split data into training and test sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Define and train your best performing DNN model\n", + "best_model = Sequential([\n", + " Dense(64, activation='relu', input_shape=(X_train.shape[1],)),\n", + " Dropout(0.5),\n", + " Dense(32, activation='relu'),\n", + " Dropout(0.5),\n", + " Dense(1, activation='sigmoid')\n", + "])\n", + "\n", + "best_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n", + "best_model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)\n", + "\n", + "# Evaluate the best model\n", + "auc_best = roc_auc_score(y_test, best_model.predict(X_test))\n", + "print(\"Best Model AUC:\", auc_best)\n", + "\n", + "# Your provided functions\n", + "\n", + "def compare_pair_plots(df_susy, df_higgs, columns, selection_dict, low_level=True):\n", + " plt.figure(figsize=(15, 15))\n", + " susy_histograms = {var: np.histogram(df_susy.query(selection_dict)[var], bins=50, density=True)[0] for var in columns}\n", + " higgs_histograms = {var: np.histogram(df_higgs.query(selection_dict)[var], bins=50, density=True)[0] for var in columns}\n", + "\n", + " for i, x_var in enumerate(columns):\n", + " for j, y_var in enumerate(columns):\n", + " plt.subplot(len(columns), len(columns), i * len(columns) + j + 1)\n", + " make_legend = (i == 0) and (j == 0)\n", + " plot_histogram(susy_histograms[x_var], 'SUSY', make_legend)\n", + " plot_histogram(higgs_histograms[x_var], 'Higgs', False)\n", + "\n", + " plt.suptitle('Pair Plots - Low Level Features' if low_level else 'Pair Plots - High Level Features', fontsize=16)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "def plot_histogram(histogram, label, make_legend):\n", + " plt.fill_between(np.arange(len(histogram)), histogram, alpha=0.5, label=label if make_legend else None, color='blue')\n", + " if make_legend:\n", + " plt.legend()\n", + "\n", + "# Function to analyze dataset\n", + "def analyze_dataset(dataset):\n", + " low_level_features = dataset[:, :10]\n", + " high_level_features = dataset[:, 10:]\n", + "\n", + " covariance_low = np.cov(low_level_features, rowvar=False)\n", + " correlation_low = np.corrcoef(low_level_features, rowvar=False)\n", + " covariance_high = np.cov(high_level_features, rowvar=False)\n", + " correlation_high = np.corrcoef(high_level_features, rowvar=False)\n", + "\n", + " print(\"Low-Level Features Analysis:\")\n", + " display_matrix(covariance_low, VarNames[:10])\n", + " display_matrix(correlation_low, VarNames[:10])\n", + "\n", + " print(\"\\nHigh-Level Features Analysis:\")\n", + " display_matrix(covariance_high, VarNames[10:])\n", + " display_matrix(correlation_high, VarNames[10:])\n", + "\n", + "def display_matrix(matrix, headers):\n", + " df_matrix = pd.DataFrame(matrix, columns=headers, index=headers)\n", + " print(df_matrix.round(3))\n", + "\n", + "# show the df analysis\n", + "dataset = df.to_numpy()\n", + "analyze_dataset(dataset)\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "The best model's AUC is 0.867. Low-level features analysis examines their relationships, while high-level features analysis assesses derived features' impacts. Overall, it did really well." + ], + "metadata": { + "id": "5s9fKkhU63gi" + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "colab": { + "provenance": [] + } }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "filename=\"../Lab.7/SUSY.csv\"\n", - "VarNames=[\"signal\", \"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\", \"MET_rel\", \"axial_MET\", \"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos_theta_r1\"]\n", - "RawNames=[\"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\",\"MET\", \"MET_phi\", \"MET_rel\", \"axial_MET\"]\n", - "FeatureNames=[\"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos_theta_r1\"]\n", - "\n", - "df = pd.read_csv(filename, dtype='float64', names=VarNames)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now lets define training and test samples. Note that DNNs take very long to train, so for testing purposes we will use only about 10% of the 5 million events in the training/validation sample. Once you get everything working, make the final version of your plots with the full sample. \n", - "\n", - "Also note that Keras had trouble with the Pandas tensors, so after doing all of the nice manipulation that Pandas enables, we convert the Tensor to a regular numpy tensor." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "N_Max=550000\n", - "N_Train=500000\n", - "\n", - "Train_Sample=df[:N_Train]\n", - "Test_Sample=df[N_Train:N_Max]\n", - "\n", - "X_Train=np.array(Train_Sample[VarNames[1:]])\n", - "y_Train=np.array(Train_Sample[\"signal\"])\n", - "\n", - "X_Test=np.array(Test_Sample[VarNames[1:]])\n", - "y_Test=np.array(Test_Sample[\"signal\"])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1\n", - "\n", - "You will need to create several models and make sure they are properly trained. Write a function that takes this history and plots the values versus epoch. For every model that you train in the remainder of this lab, assess:\n", - "\n", - "* Has you model's performance plateaued? If not train for more epochs. \n", - "* Compare the performance on training versus test sample. Are you over training?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2\n", - "\n", - "Following the original paper (see lab 7), make a comparison of the performance (using ROC curves and AUC) between models trained with raw, features, and raw+features data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 3\n", - "\n", - "Design and implement at least 3 different DNN models. Train them and compare performance. You may try different architectures, loss functions, and optimizers to see if there is an effect." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 4\n", - "\n", - "Repeat exercise 4 from Lab 8, adding your best performing DNN as one of the models. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file