diff --git a/Kaggle Project/Kaggle Tabular Data.ipynb b/Kaggle Project/Kaggle Tabular Data.ipynb new file mode 100644 index 0000000..9e85e1d --- /dev/null +++ b/Kaggle Project/Kaggle Tabular Data.ipynb @@ -0,0 +1,146 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e64a1b88", + "metadata": {}, + "source": [ + "# Tabular Kaggle Project\n", + "\n", + "Guideline for steps for the Kaggle Tabular Project. You will \"turn in\" a GitHub repository, modeled after [Project Template](https://github.com/UTA-DataScience/ProjectTempate) on the day of the final, May 3rd 1:30 pm. During the final period we will have about 5 minutes to go over your project and your results.\n", + "\n", + "You can find a list of possible Tabular datasets here on [Excel File in Teams](https://mavsuta.sharepoint.com/:x:/r/sites/Course_2242_data_3402_001-vUhPXzAGLgTnk/Shared%20Documents/General/TabularDatasets.xlsx?d=w17e157db75904dfcb03a78c84f10e2e6&csf=1&web=1&e=KHi7m9). You are not limited to these datasets. If you find a Kaggle challenge not listed that you would like to attempt, please go check with Dr. Farbin to make sure it is viable.\n", + "\n", + "This notebook outlines the steps you shoud follow. The file(s) in the GitHub repository should contain these steps. Note that you will be only considering classification projects.\n", + "\n", + "## Define Project\n", + "\n", + "* Provide Project link.\n", + "* Short paragraph describing the challenge. \n", + "* Briefly describe the data.\n" + ] + }, + { + "cell_type": "markdown", + "id": "a65cd3e3", + "metadata": {}, + "source": [ + "## Data Loading and Initial Look\n", + "\n", + "* Load the data. \n", + "* Count the number of rows (data points) and features.\n", + "* Any missing values? \n", + "* Make a table, where each row is a feature or collection of features:\n", + " * Is the feature categorical or numerical\n", + " * What values? \n", + " * e.g. for categorical: \"0,1,2\"\n", + " * e.g. for numerical specify the range\n", + " * How many missing values\n", + " * Do you see any outliers?\n", + " * Define outlier.\n", + "* For classification is there class imbalance?\n", + "* What is the target:\n", + " * Classification: how is the target encoded (e.g. 0 and 1)?\n", + " * Regression: what is the range?" + ] + }, + { + "cell_type": "markdown", + "id": "27c59841", + "metadata": {}, + "source": [ + "## Data Visualization\n", + "\n", + "* For classification: compare histogram every feature between the classes. Lots of examples of this in class.\n", + "* For regression: \n", + " * Define 2 or more class based on value of the regression target.\n", + " * For example: if regression target is between 0 and 1:\n", + " * 0.0-0.25: Class 1\n", + " * 0.25-0.5: Class 2\n", + " * 0.5-0.75: Class 3\n", + " * 0.75-1.0: Class 4\n", + " * Compare histograms of the features between the classes.\n", + " \n", + "* Note that for categorical features, often times the information in the histogram could be better presented in a table. \n", + "* Make comments on what features look most promising for ML task." + ] + }, + { + "cell_type": "markdown", + "id": "ba73f3b0", + "metadata": {}, + "source": [ + "## Data Cleaning and Preperation for Machine Learning\n", + "\n", + "* Perform any data cleaning. Be clear what are you doing, for what feature. \n", + "* Determinine if rescaling is important for your Machine Learning model.\n", + " * If so select strategy for each feature.\n", + " * Apply rescaling.\n", + "* Visualize the features before and after cleaning and rescaling.\n", + "* One-hot encode your categorical features." + ] + }, + { + "cell_type": "markdown", + "id": "39c8d295", + "metadata": {}, + "source": [ + "## Machine Learning\n", + "\n", + "\n", + "### Problem Formulation\n", + "\n", + "* Remove unneed columns, for example:\n", + " * duplicated\n", + " * categorical features that were turned into one-hot.\n", + " * features that identify specific rows, like ID number.\n", + " * make sure your target is properly encoded also.\n", + "* Split training sample into train, validation, and test sub-samples.\n", + "\n", + "### Train ML Algorithm\n", + "\n", + "* You only need one algorithm to work. You can do more if you like.\n", + "* For now, focus on making it work, rather than best result.\n", + "* Try to get a non-trivial result.\n", + "\n", + "### Evaluate Performance on Validation Sample\n", + "\n", + "* Compute the usual metric for your ML task.\n", + "* Compute the score for the kaggle challenge.\n", + "\n", + "### Apply ML to the challenge test set\n", + "\n", + "* Once trained, apply the ML algorithm the the test dataset and generate the submission file.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12b0e44d", + "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": 5 +} diff --git a/Kaggle Project/README.md b/Kaggle Project/README.md new file mode 100644 index 0000000..0a56122 --- /dev/null +++ b/Kaggle Project/README.md @@ -0,0 +1,121 @@ +![](UTA-DataScience-Logo.png) + +# Project Title + +* **One Sentence Summary** Ex: This repository holds an attempt to apply LSTMs to Stock Market using data from +"Get Rich" Kaggle challenge (provide link). + +## Overview + +* This section could contain a short paragraph which include the following: + * **Definition of the tasks / challenge** Ex: The task, as defined by the Kaggle challenge is to use a time series of 12 features, sampled daily for 1 month, to predict the next day's price of a stock. + * **Your approach** Ex: The approach in this repository formulates the problem as regression task, using deep recurrent neural networks as the model with the full time series of features as input. We compared the performance of 3 different network architectures. + * **Summary of the performance achieved** Ex: Our best model was able to predict the next day stock price within 23%, 90% of the time. At the time of writing, the best performance on Kaggle of this metric is 18%. + +## Summary of Workdone + +Include only the sections that are relevant an appropriate. + +### Data + +* Data: + * Type: For example + * Input: medical images (1000x1000 pixel jpegs), CSV file: image filename -> diagnosis + * Input: CSV file of features, output: signal/background flag in 1st column. + * Size: How much data? + * Instances (Train, Test, Validation Split): how many data points? Ex: 1000 patients for training, 200 for testing, none for validation + +#### Preprocessing / Clean up + +* Describe any manipulations you performed to the data. + +#### Data Visualization + +Show a few visualization of the data and say a few words about what you see. + +### Problem Formulation + +* Define: + * Input / Output + * Models + * Describe the different models you tried and why. + * Loss, Optimizer, other Hyperparameters. + +### Training + +* Describe the training: + * How you trained: software and hardware. + * How did training take. + * Training curves (loss vs epoch for test/train). + * How did you decide to stop training. + * Any difficulties? How did you resolve them? + +### Performance Comparison + +* Clearly define the key performance metric(s). +* Show/compare results in one table. +* Show one (or few) visualization(s) of results, for example ROC curves. + +### Conclusions + +* State any conclusions you can infer from your work. Example: LSTM work better than GRU. + +### Future Work + +* What would be the next thing that you would try. +* What are some other studies that can be done starting from here. + +## How to reproduce results + +* In this section, provide instructions at least one of the following: + * Reproduce your results fully, including training. + * Apply this package to other data. For example, how to use the model you trained. + * Use this package to perform their own study. +* Also describe what resources to use for this package, if appropirate. For example, point them to Collab and TPUs. + +### Overview of files in repository + +* Describe the directory structure, if any. +* List all relavent files and describe their role in the package. +* An example: + * utils.py: various functions that are used in cleaning and visualizing data. + * preprocess.ipynb: Takes input data in CSV and writes out data frame after cleanup. + * visualization.ipynb: Creates various visualizations of the data. + * models.py: Contains functions that build the various models. + * training-model-1.ipynb: Trains the first model and saves model during training. + * training-model-2.ipynb: Trains the second model and saves model during training. + * training-model-3.ipynb: Trains the third model and saves model during training. + * performance.ipynb: loads multiple trained models and compares results. + * inference.ipynb: loads a trained model and applies it to test data to create kaggle submission. + +* Note that all of these notebooks should contain enough text for someone to understand what is happening. + +### Software Setup +* List all of the required packages. +* If not standard, provide or point to instruction for installing the packages. +* Describe how to install your package. + +### Data + +* Point to where they can download the data. +* Lead them through preprocessing steps, if necessary. + +### Training + +* Describe how to train the model + +#### Performance Evaluation + +* Describe how to run the performance evaluation. + + +## Citations + +* Provide any references. + + + + + + + diff --git a/Kaggle Project/UTA-DataScience-Logo.png b/Kaggle Project/UTA-DataScience-Logo.png new file mode 100644 index 0000000..dc17217 Binary files /dev/null and b/Kaggle Project/UTA-DataScience-Logo.png differ diff --git a/Labs/Lab.2/Lab2-RobertCocker.ipynb b/Labs/Lab.2/Lab2-RobertCocker.ipynb new file mode 100644 index 0000000..d30d387 --- /dev/null +++ b/Labs/Lab.2/Lab2-RobertCocker.ipynb @@ -0,0 +1,1738 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Robert Cocker\n", + "# Dr. Farbin\n", + "# DATA 3402\n", + "# Lab 2\n", + "# 2/9/2024" + ] + }, + { + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "player_1_piece = \"X\"\n", + "player_2_piece = \"O\"\n", + "empty_space = \" \"\n", + "player_1 = 1\n", + "player_2 = 2\n", + "empty = 0\n", + "\n", + "space_character = {\n", + " player_1: player_1_piece,\n", + " player_2: player_2_piece,\n", + " empty: empty_space\n", + "}\n", + "\n", + "def create_tic_tac_toe_board(n):\n", + " if n < 3:\n", + " raise ValueError(\"Tic Tac Toe board size must be at least 3x3\")\n", + " \n", + " board = [[empty] * n for _ in range(n)]\n", + " return board" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0, 0, 0], [0, 0, 0], [0, 0, 0]]\n" + ] + } + ], + "source": [ + "# Example usage:\n", + "board_size = 3\n", + "tic_tac_toe_board = create_tic_tac_toe_board(board_size)\n", + "print(tic_tac_toe_board)" + ] + }, + { + "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": 30, + "metadata": {}, + "outputs": [], + "source": [ + "def draw_game_board(n, m):\n", + " if n < 1 or m < 1:\n", + " print(\"Invalid dimensions for the game board.\")\n", + " return\n", + " \n", + " horizontal_line = \"+---\" * m + \"+\"\n", + " vertical_line = \"| \" * m + \"|\"\n", + " \n", + " for _ in range(n):\n", + " print(horizontal_line)\n", + " print(vertical_line)\n", + " \n", + " print(horizontal_line)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+\n", + "| | | |\n", + "+---+---+---+\n", + "| | | |\n", + "+---+---+---+\n", + "| | | |\n", + "+---+---+---+\n" + ] + } + ], + "source": [ + "# Example usage:\n", + "rows = 3\n", + "columns = 3\n", + "draw_game_board(rows, columns)" + ] + }, + { + "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": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def draw_game_board(board):\n", + " n = len(board)\n", + " m = len(board[0])\n", + " \n", + " if n < 3 or m < 3:\n", + " print(\"Invalid dimensions for the Tic Tac Toe board.\")\n", + " return\n", + " \n", + " horizontal_line = \"+---\" * m + \"+\"\n", + " \n", + " for row in board:\n", + " print(horizontal_line)\n", + " row_str = \"|\"\n", + " for cell in row:\n", + " if cell == player_1:\n", + " row_str += \" \" + player_1_piece + \" |\"\n", + " elif cell == player_2:\n", + " row_str += \" \" + player_2_piece + \" |\"\n", + " else:\n", + " row_str += \" |\"\n", + " print(row_str)\n", + " \n", + " print(horizontal_line)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+\n", + "| X | | |\n", + "+---+---+---+\n", + "| | O | |\n", + "+---+---+---+\n", + "| | | X |\n", + "+---+---+---+\n" + ] + } + ], + "source": [ + "# Example usage:\n", + "tic_tac_toe_board = create_tic_tac_toe_board(3)\n", + "tic_tac_toe_board[0][0] = player_1\n", + "tic_tac_toe_board[1][1] = player_2\n", + "tic_tac_toe_board[2][2] = player_1\n", + "draw_game_board(tic_tac_toe_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": 34, + "metadata": {}, + "outputs": [], + "source": [ + "def check_board(board):\n", + " n = len(board)\n", + "\n", + " # Rows and columns\n", + " for i in range(n):\n", + " if all(board[i][j] == 1 for j in range(n)) or all(board[j][i] == 1 for j in range(n)):\n", + " return 1\n", + " elif all(board[i][j] == 2 for j in range(n)) or all(board[j][i] == 2 for j in range(n)):\n", + " return 2\n", + "\n", + " # Diagonals\n", + " if all(board[i][i] == 1 for i in range(n)) or all(board[i][n - i - 1] == 1 for i in range(n)):\n", + " return 1\n", + " elif all(board[i][i] == 2 for i in range(n)) or all(board[i][n - i - 1] == 2 for i in range(n)):\n", + " return 2\n", + "\n", + " # Check if incomplete game\n", + " for i in range(n):\n", + " for j in range(n):\n", + " if board[i][j] == 0:\n", + " return -1\n", + "\n", + " # If no winner and no empty space, draw\n", + " return 0" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-1\n" + ] + } + ], + "source": [ + "# Example usage:\n", + "board1 = [\n", + " [1, 2, 0],\n", + " [2, 1, 0],\n", + " [1, 0, 2]\n", + "]\n", + "print(check_board(board1))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + "1\n", + "1\n", + "-1\n", + "-1\n" + ] + } + ], + "source": [ + "winner_is_2 = [[2, 2, 0],\n", + "\t[2, 1, 0],\n", + "\t[2, 1, 1]]\n", + "\n", + "print(check_board(winner_is_2))\n", + "\n", + "winner_is_1 = [[1, 2, 0],\n", + "\t[2, 1, 0],\n", + "\t[2, 1, 1]]\n", + "\n", + "print(check_board(winner_is_1))\n", + "\n", + "winner_is_also_1 = [[0, 1, 0],\n", + "\t[2, 1, 0],\n", + "\t[2, 1, 1]]\n", + "\n", + "print(check_board(winner_is_also_1))\n", + "\n", + "no_winner = [[1, 2, 0],\n", + "\t[2, 1, 0],\n", + "\t[2, 1, 2]]\n", + "\n", + "print(check_board(no_winner))\n", + "\n", + "also_no_winner = [[1, 2, 0],\n", + "\t[2, 1, 0],\n", + "\t[2, 1, 0]]\n", + "\n", + "print(check_board(also_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": 38, + "metadata": {}, + "outputs": [], + "source": [ + "def place_piece(board, player, x, y):\n", + " if board[x][y] == empty:\n", + " board[x][y] = player\n", + " return True\n", + " else:\n", + " return False" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "place_piece(board1, player_1, 2, 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": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def check_board(board):\n", + " n = len(board)\n", + " \n", + " # Column labels\n", + " column_labels = list(range(1, n + 1))\n", + " # column_labels = [chr(ord('A') + i) for i in range(n)]\n", + " \n", + " # Row labels\n", + " row_labels = [chr(ord('A') + i) for i in range(n)]\n", + " # row_labels = list(range(1, n + 1))\n", + "\n", + " # Rows and columns\n", + " for i in range(n):\n", + " if all(board[i][j] == 1 for j in range(n)) or all(board[j][i] == 1 for j in range(n)):\n", + " return 1, column_labels, row_labels\n", + " elif all(board[i][j] == 2 for j in range(n)) or all(board[j][i] == 2 for j in range(n)):\n", + " return 2, column_labels, row_labels\n", + "\n", + " # Diagonals\n", + " if all(board[i][i] == 1 for i in range(n)) or all(board[i][n - i - 1] == 1 for i in range(n)):\n", + " return 1, column_labels, row_labels\n", + " elif all(board[i][i] == 2 for i in range(n)) or all(board[i][n - i - 1] == 2 for i in range(n)):\n", + " return 2, column_labels, row_labels\n", + "\n", + " # Check for incomplete game\n", + " for i in range(n):\n", + " for j in range(n):\n", + " if board[i][j] == 0:\n", + " return -1, column_labels, row_labels\n", + "\n", + " # If no winner and no empty space, draw\n", + " return 0, column_labels, row_labels" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1 has won\n", + "Column Labels: [1, 2, 3]\n", + "Row Labels: ['A', 'B', 'C']\n" + ] + } + ], + "source": [ + "# Usage:\n", + "game_board = [\n", + " [1, 2, 0],\n", + " [2, 1, 0],\n", + " [2, 1, 1]\n", + "]\n", + "\n", + "result, column_labels, row_labels = check_board(game_board)\n", + "if result == -1:\n", + " print(\"Game is incomplete\")\n", + "elif result == 0:\n", + " print(\"Game is a draw\")\n", + "elif result == 1:\n", + " print(\"Player 1 has won\")\n", + "elif result == 2:\n", + " print(\"Player 2 has won\")\n", + "\n", + "# Print column and row labels\n", + "print(\"Column Labels:\", column_labels)\n", + "print(\"Row Labels:\", row_labels)" + ] + }, + { + "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": 42, + "metadata": {}, + "outputs": [], + "source": [ + "def place_piece_on_board(board, player, location):\n", + " # Ensure location is a string\n", + " if not isinstance(location, str):\n", + " print(\"Invalid location format.\")\n", + " return False\n", + " \n", + " # Extract row and column labels\n", + " row_label = location[0]\n", + " column_label = location[1:]\n", + " \n", + " # Convert column label to index\n", + " col_index = ord(column_label.upper()) - ord('A')\n", + " \n", + " # Convert row label to index\n", + " if row_label.isdigit():\n", + " row_index = int(row_label) - 1\n", + " else:\n", + " print(\"Invalid row label format.\")\n", + " return False\n", + " \n", + " # Call place_piece function\n", + " if place_piece(board, player, row_index, col_index):\n", + " return True\n", + " else:\n", + " return False" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Invalid row label format.\n", + "Failed to place 1 at location B2\n", + "[0, 0, 0]\n", + "[0, 0, 0]\n", + "[0, 0, 0]\n" + ] + } + ], + "source": [ + "# Example usage:\n", + "game_board = [\n", + " [0, 0, 0],\n", + " [0, 0, 0],\n", + " [0, 0, 0]\n", + "]\n", + "\n", + "player = 1\n", + "location = 'B2' # Ensure location is a string\n", + "\n", + "if place_piece_on_board(game_board, player, location):\n", + " print(f\"Successfully placed {player} at location {location}\")\n", + "else:\n", + " print(f\"Failed to place {player} at location {location}\")\n", + "\n", + "# Print updated game board\n", + "for row in game_board:\n", + " print(row)" + ] + }, + { + "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": 44, + "metadata": {}, + "outputs": [], + "source": [ + "player_1_piece = \"X\"\n", + "player_2_piece = \"O\"\n", + "empty_space = \" \"\n", + "player_1 = 1\n", + "player_2 = 2\n", + "empty = 0\n", + "\n", + "space_character = {\n", + " player_1: player_1_piece,\n", + " player_2: player_2_piece,\n", + " empty: empty_space\n", + "}\n", + "\n", + "def create_tic_tac_toe_board(n):\n", + " if n < 3:\n", + " raise ValueError(\"Tic Tac Toe board size must be at least 3x3\")\n", + " \n", + " board = [[empty] * n for _ in range(n)]\n", + " return board\n", + "\n", + "def draw_game_board(n, m):\n", + " if n < 1 or m < 1:\n", + " print(\"Invalid dimensions for the game board.\")\n", + " return\n", + " \n", + " horizontal_line = \"+---\" * m + \"+\"\n", + " vertical_line = \"| \" * m + \"|\"\n", + " \n", + " for _ in range(n):\n", + " print(horizontal_line)\n", + " print(vertical_line)\n", + " \n", + " print(horizontal_line)\n", + "\n", + "def draw_tic_tac_toe_board(board):\n", + " n = len(board)\n", + " m = len(board[0])\n", + " \n", + " if n < 3 or m < 3:\n", + " print(\"Invalid dimensions for the Tic Tac Toe board.\")\n", + " return\n", + " \n", + " horizontal_line = \"+---\" * m + \"+\"\n", + " \n", + " for row in board:\n", + " print(horizontal_line)\n", + " row_str = \"|\"\n", + " for cell in row:\n", + " if cell == player_1:\n", + " row_str += \" \" + player_1_piece + \" |\"\n", + " elif cell == player_2:\n", + " row_str += \" \" + player_2_piece + \" |\"\n", + " else:\n", + " row_str += \" |\"\n", + " print(row_str)\n", + " \n", + " print(horizontal_line)\n", + "\n", + "def check_board(board):\n", + " n = len(board)\n", + "\n", + " # Check rows and columns\n", + " for i in range(n):\n", + " if all(board[i][j] == 1 for j in range(n)) or all(board[j][i] == 1 for j in range(n)):\n", + " return 1\n", + " elif all(board[i][j] == 2 for j in range(n)) or all(board[j][i] == 2 for j in range(n)):\n", + " return 2\n", + "\n", + " # Check diagonals\n", + " if all(board[i][i] == 1 for i in range(n)) or all(board[i][n - i - 1] == 1 for i in range(n)):\n", + " return 1\n", + " elif all(board[i][i] == 2 for i in range(n)) or all(board[i][n - i - 1] == 2 for i in range(n)):\n", + " return 2\n", + "\n", + " # Check for incomplete game\n", + " for i in range(n):\n", + " for j in range(n):\n", + " if board[i][j] == 0:\n", + " return -1\n", + "\n", + " # If no winner and no empty space, it's a draw\n", + " return 0\n", + "\n", + "def place_piece(board, player, x, y):\n", + " if board[x][y] == empty:\n", + " board[x][y] = player\n", + " return True\n", + " else:\n", + " return False\n", + "\n", + "def take_player_input(board):\n", + " while True:\n", + " try:\n", + " location = input(\"Enter your move (e.g., A1): \").strip()\n", + " if len(location) < 2:\n", + " raise ValueError(\"Invalid input format. Please enter a row and column label.\")\n", + " \n", + " row_label = location[0]\n", + " column_label = location[1:]\n", + " \n", + " if not row_label.isalpha() or not column_label.isdigit():\n", + " raise ValueError(\"Invalid input format. Please enter a valid row and column label.\")\n", + " \n", + " row_index = ord(row_label.upper()) - ord('A')\n", + " col_index = int(column_label) - 1\n", + " \n", + " if not (0 <= row_index < len(board)) or not (0 <= col_index < len(board[0])):\n", + " raise ValueError(\"Input out of board range. Please enter a valid move.\")\n", + " \n", + " return row_index, col_index\n", + " except ValueError as e:\n", + " print(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "def play_game():\n", + " size = int(input(\"Enter the size of the Tic Tac Toe board (minimum 3): \"))\n", + " board = create_tic_tac_toe_board(size)\n", + " draw_tic_tac_toe_board(board)\n", + " \n", + " current_player = player_1\n", + " while True:\n", + " print(f\"Player {current_player}'s turn\")\n", + " row_index, col_index = take_player_input(board) # Pass the board to the function\n", + " \n", + " if place_piece(board, current_player, row_index, col_index):\n", + " draw_tic_tac_toe_board(board)\n", + " result = check_board(board)\n", + " if result == 1:\n", + " print(\"Player 1 wins!\")\n", + " break\n", + " elif result == 2:\n", + " print(\"Player 2 wins!\")\n", + " break\n", + " elif result == 0:\n", + " print(\"It's a draw!\")\n", + " break\n", + " else:\n", + " current_player = player_2 if current_player == player_1 else player_1\n", + " else:\n", + " print(\"That position is already taken. Try again.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): A1\n" + ] + }, + { + "data": { + "text/plain": [ + "(0, 0)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "take_player_input(board1)" + ] + }, + { + "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": 47, + "metadata": {}, + "outputs": [], + "source": [ + "def play_game():\n", + " size = int(input(\"Enter the size of the Tic Tac Toe board (minimum 3): \"))\n", + " board = create_tic_tac_toe_board(size)\n", + " draw_tic_tac_toe_board(board)\n", + " \n", + " current_player = player_1\n", + " while True:\n", + " print(f\"Player {current_player}'s turn\")\n", + " row_index, col_index = take_player_input()\n", + " \n", + " if place_piece(board, current_player, row_index, col_index):\n", + " draw_tic_tac_toe_board(board)\n", + " result = check_board(board)\n", + " if result == 1:\n", + " print(\"Player 1 wins!\")\n", + " break\n", + " elif result == 2:\n", + " print(\"Player 2 wins!\")\n", + " break\n", + " elif result == 0:\n", + " print(\"It's a draw!\")\n", + " break\n", + " else:\n", + " current_player = player_2 if current_player == player_1 else player_1\n", + " else:\n", + " print(\"That position is already taken. Try again.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "def play_game():\n", + " size = int(input(\"Enter the size of the Tic Tac Toe board (minimum 3): \"))\n", + " board = create_tic_tac_toe_board(size)\n", + " draw_tic_tac_toe_board(board)\n", + " \n", + " current_player = player_1\n", + " while True:\n", + " print(f\"Player {current_player}'s turn\")\n", + " row_index, col_index = take_player_input(board) # Pass the board to the function\n", + " \n", + " if place_piece(board, current_player, row_index, col_index):\n", + " draw_tic_tac_toe_board(board)\n", + " result = check_board(board)\n", + " if result == 1:\n", + " print(\"Player 1 wins!\")\n", + " break\n", + " elif result == 2:\n", + " print(\"Player 2 wins!\")\n", + " break\n", + " elif result == 0:\n", + " print(\"It's a draw!\")\n", + " break\n", + " else:\n", + " current_player = player_2 if current_player == player_1 else player_1\n", + " else:\n", + " print(\"That position is already taken. Try again.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 10:* Test that your game works for 5x5 Tic Tac Toe. " + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the size of the Tic Tac Toe board (minimum 3): 5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): A1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+---+---+\n", + "| X | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "Player 2's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): B1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+---+---+\n", + "| X | | | | |\n", + "+---+---+---+---+---+\n", + "| O | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): A2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+---+---+\n", + "| X | X | | | |\n", + "+---+---+---+---+---+\n", + "| O | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "Player 2's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): B2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+---+---+\n", + "| X | X | | | |\n", + "+---+---+---+---+---+\n", + "| O | O | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): A3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+---+---+\n", + "| X | X | X | | |\n", + "+---+---+---+---+---+\n", + "| O | O | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "Player 2's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): B3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+---+---+\n", + "| X | X | X | | |\n", + "+---+---+---+---+---+\n", + "| O | O | O | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): A4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+---+---+\n", + "| X | X | X | X | |\n", + "+---+---+---+---+---+\n", + "| O | O | O | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "Player 2's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): B4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+---+---+\n", + "| X | X | X | X | |\n", + "+---+---+---+---+---+\n", + "| O | O | O | O | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): A5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+---+---+\n", + "| X | X | X | X | X |\n", + "+---+---+---+---+---+\n", + "| O | O | O | O | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "| | | | | |\n", + "+---+---+---+---+---+\n", + "Player 1 wins!\n" + ] + } + ], + "source": [ + "play_game()" + ] + }, + { + "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": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "\n", + "def computer_move(board, player):\n", + " n = len(board)\n", + " opponent = player_2 if player == player_1 else player_1\n", + "\n", + " # Check for winning move or blocking opponent's winning move\n", + " for i in range(n):\n", + " for j in range(n):\n", + " if board[i][j] == empty:\n", + " board[i][j] = player\n", + " if check_board(board) == player:\n", + " return i, j\n", + " board[i][j] = empty\n", + " \n", + " board[i][j] = opponent\n", + " if check_board(board) == opponent:\n", + " board[i][j] = player\n", + " return i, j\n", + " board[i][j] = empty\n", + "\n", + " # Try to occupy a corner\n", + " corners = [(0, 0), (0, n-1), (n-1, 0), (n-1, n-1)]\n", + " random.shuffle(corners)\n", + " for i, j in corners:\n", + " if board[i][j] == empty:\n", + " return i, j\n", + "\n", + " # Select a random empty cell if available\n", + " empty_cells = [(i, j) for i in range(n) for j in range(n) if board[i][j] == 0]\n", + " if empty_cells:\n", + " random_empty_cell = random.choice(empty_cells)\n", + " place_piece(board, player, random_empty_cell[0], random_empty_cell[1])\n", + " return random_empty_cell\n", + "\n", + " # If no empty cells available, return None to indicate failure to make a move\n", + " return None, None\n", + "\n", + "\n", + "def play_game():\n", + " size = int(input(\"Enter the size of the Tic Tac Toe board (minimum 3): \"))\n", + " board = create_tic_tac_toe_board(size)\n", + " draw_tic_tac_toe_board(board)\n", + " \n", + " player_mode = input(\"Choose mode:\\n1. Play against another player\\n2. Play against the computer\\nEnter choice (1 or 2): \")\n", + " while player_mode not in ['1', '2']:\n", + " print(\"Invalid choice. Please enter 1 or 2.\")\n", + " player_mode = input(\"Choose mode:\\n1. Play against another player\\n2. Play against the computer\\nEnter choice (1 or 2): \")\n", + " \n", + " current_player = player_1\n", + " while True:\n", + " print(f\"Player {current_player}'s turn\")\n", + " if player_mode == '1' or current_player == player_1:\n", + " row_index, col_index = take_player_input(board)\n", + " else:\n", + " row_index, col_index = computer_move(board, player_2)\n", + " if row_index is not None and col_index is not None:\n", + " print(f\"Computer chooses row {chr(ord('A') + row_index)} column {col_index + 1}\")\n", + " else:\n", + " print(\"Computer failed to make a move.\")\n", + " break\n", + " \n", + " if place_piece(board, current_player, row_index, col_index):\n", + " draw_tic_tac_toe_board(board)\n", + " result = check_board(board)\n", + " if result == 1:\n", + " print(\"Player 1 wins!\" if current_player == player_1 else \"Computer wins!\")\n", + " break\n", + " elif result == 2:\n", + " print(\"Player 2 wins!\" if current_player == player_2 else \"Computer wins!\")\n", + " break\n", + " elif result == 0:\n", + " print(\"It's a draw!\")\n", + " break\n", + " else:\n", + " current_player = player_2 if current_player == player_1 else player_1\n", + " else:\n", + " print(\"That position is already taken. Try again.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the size of the Tic Tac Toe board (minimum 3): 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+\n", + "| | | |\n", + "+---+---+---+\n", + "| | | |\n", + "+---+---+---+\n", + "| | | |\n", + "+---+---+---+\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Choose mode:\n", + "1. Play against another player\n", + "2. Play against the computer\n", + "Enter choice (1 or 2): 2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): B1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+\n", + "| | | |\n", + "+---+---+---+\n", + "| X | | |\n", + "+---+---+---+\n", + "| | | |\n", + "+---+---+---+\n", + "Player 2's turn\n", + "Computer chooses row A column 1\n", + "+---+---+---+\n", + "| O | | |\n", + "+---+---+---+\n", + "| X | | |\n", + "+---+---+---+\n", + "| | | |\n", + "+---+---+---+\n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): B2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+\n", + "| O | | |\n", + "+---+---+---+\n", + "| X | X | |\n", + "+---+---+---+\n", + "| | | |\n", + "+---+---+---+\n", + "Player 2's turn\n", + "Computer chooses row B column 3\n", + "That position is already taken. Try again.\n", + "Player 2's turn\n", + "Computer chooses row C column 3\n", + "+---+---+---+\n", + "| O | | |\n", + "+---+---+---+\n", + "| X | X | O |\n", + "+---+---+---+\n", + "| | | O |\n", + "+---+---+---+\n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): A3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---+---+---+\n", + "| O | | X |\n", + "+---+---+---+\n", + "| X | X | O |\n", + "+---+---+---+\n", + "| | | O |\n", + "+---+---+---+\n", + "Player 2's turn\n", + "Computer chooses row C column 1\n", + "That position is already taken. Try again.\n", + "Player 2's turn\n", + "Computer chooses row C column 2\n", + "That position is already taken. Try again.\n", + "Player 2's turn\n", + "Computer chooses row A column 2\n", + "That position is already taken. Try again.\n", + "Player 2's turn\n", + "Computer failed to make a move.\n" + ] + } + ], + "source": [ + "play_game()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Finalized logic of game. I coded and recoded alot of functions but these are the final form of the functions for the logic and full implementation of a Tic Tac Toe game for any n x n size game with advanced Computer logic and mode." + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [], + "source": [ + "def create_tic_tac_toe_board(n):\n", + " if n < 3:\n", + " raise ValueError(\"Tic Tac Toe board size must be at least 3x3\")\n", + " \n", + " board = [[empty] * n for _ in range(n)]\n", + " return board\n", + "\n", + "def draw_game_board(board):\n", + " num_rows = len(board)\n", + " num_cols = len(board[0])\n", + "\n", + " # Determine the maximum width for row and column labels\n", + " max_label_width = max(len(str(num_rows)), len(str(num_cols))) + 1 # Add padding\n", + "\n", + " # Print column labels\n", + " print(\" \" * max_label_width, end=\"\")\n", + " for col in range(1, num_cols + 1):\n", + " col_label = str(col)\n", + " col_width = 4 # Width of each cell including borders (e.g., '| X ')\n", + " col_padding = (col_width - len(col_label)) // 2\n", + " print(f\"{' ' * (col_width - col_padding - len(col_label))}{col_label}{' ' * col_padding}\", end=\"\")\n", + " print()\n", + "\n", + " # Print top border\n", + " print(\" \" * max_label_width + \"+\" + \"---+\" * num_cols)\n", + "\n", + " # Print rows\n", + " for row_idx, row in enumerate(board):\n", + " row_label = chr(ord('A') + row_idx)\n", + " print(row_label.ljust(max_label_width) + \"|\", end=\"\")\n", + " for cell in row:\n", + " if cell == 0:\n", + " print(\" |\", end=\"\")\n", + " elif cell == 1:\n", + " print(\" X |\", end=\"\")\n", + " elif cell == 2:\n", + " print(\" O |\", end=\"\")\n", + " print()\n", + " print(\" \" * max_label_width + \"+\" + \"---+\" * num_cols)\n", + "\n", + " print()\n", + "\n", + "def check_board(board):\n", + " n = len(board)\n", + "\n", + " # Check rows and columns\n", + " for i in range(n):\n", + " if all(board[i][j] == player_1 for j in range(n)) or all(board[j][i] == player_1 for j in range(n)):\n", + " return player_1\n", + " elif all(board[i][j] == player_2 for j in range(n)) or all(board[j][i] == player_2 for j in range(n)):\n", + " return player_2\n", + "\n", + " # Check diagonals\n", + " if all(board[i][i] == player_1 for i in range(n)) or all(board[i][n - i - 1] == player_1 for i in range(n)):\n", + " return player_1\n", + " elif all(board[i][i] == player_2 for i in range(n)) or all(board[i][n - i - 1] == player_2 for i in range(n)):\n", + " return player_2\n", + "\n", + " # Check for incomplete game\n", + " for i in range(n):\n", + " for j in range(n):\n", + " if board[i][j] == empty:\n", + " return -1\n", + "\n", + " # If no winner and no empty space, it's a draw\n", + " return 0\n", + "\n", + "def place_piece(board, player, x, y):\n", + " if board[x][y] == empty:\n", + " board[x][y] = player\n", + " return True\n", + " else:\n", + " return False\n", + "\n", + "def take_player_input(board):\n", + " while True:\n", + " try:\n", + " location = input(\"Enter your move (e.g., A1): \").strip().upper()\n", + " if len(location) < 2:\n", + " raise ValueError(\"Invalid input format. Please enter a row and column label.\")\n", + " \n", + " row_label = location[0]\n", + " column_label = location[1:]\n", + " \n", + " if not row_label.isalpha() or not column_label.isdigit():\n", + " raise ValueError(\"Invalid input format. Please enter a valid row and column label.\")\n", + " \n", + " row_index = ord(row_label) - ord('A')\n", + " col_index = int(column_label) - 1\n", + " \n", + " if not (0 <= row_index < len(board)) or not (0 <= col_index < len(board[0])):\n", + " raise ValueError(\"Input out of board range. Please enter a valid move.\")\n", + " \n", + " if board[row_index][col_index] != empty:\n", + " raise ValueError(\"That position is already taken. Try again.\")\n", + " \n", + " return row_index, col_index\n", + " except ValueError as e:\n", + " print(e)\n", + "\n", + "import copy\n", + "import random\n", + "\n", + "def computer_move(board, player):\n", + " size = len(board)\n", + " empty_cells = [(i, j) for i in range(size) for j in range(size) if board[i][j] == 0]\n", + " \n", + " # Check for winning move or blocking opponent's winning move\n", + " for row, col in empty_cells:\n", + " # Simulate move for the current player\n", + " board[row][col] = player\n", + " if check_winner(board, player):\n", + " board[row][col] = 0 # Undo the move\n", + " return row, col # Return the winning move\n", + " \n", + " # Simulate move for the opponent\n", + " opponent = 1 if player == 2 else 2\n", + " board[row][col] = opponent\n", + " if check_winner(board, opponent):\n", + " board[row][col] = 0 # Undo the move\n", + " return row, col # Return the blocking move\n", + " \n", + " # Undo the move\n", + " board[row][col] = 0\n", + " \n", + " # If no winning or blocking moves, select a random empty cell\n", + " if empty_cells:\n", + " return random.choice(empty_cells)\n", + " else:\n", + " return None, None # No available moves (board full)\n", + "\n", + "def check_winner(board, player):\n", + " size = len(board)\n", + " \n", + " # Check rows and columns\n", + " for i in range(size):\n", + " if all(board[i][j] == player for j in range(size)) or \\\n", + " all(board[j][i] == player for j in range(size)):\n", + " return True\n", + " \n", + " # Check diagonals\n", + " if all(board[i][i] == player for i in range(size)) or \\\n", + " all(board[i][size - i - 1] == player for i in range(size)):\n", + " return True\n", + " \n", + " return False\n", + "\n", + "def play_game():\n", + " size = int(input(\"Enter size of Tic Tac Toe game (Ex: minimum 3 for 3x3): \"))\n", + " board = create_tic_tac_toe_board(size)\n", + " draw_game_board(board)\n", + " \n", + " player_mode = input(\"Choose mode:\\n1. Play against another player\\n2. Play against the computer\\nEnter choice (1 or 2): \")\n", + " while player_mode not in ['1', '2']:\n", + " print(\"Invalid choice. Please enter 1 or 2.\")\n", + " player_mode = input(\"Choose mode:\\n1. Play against another player\\n2. Play against the computer\\nEnter choice (1 or 2): \")\n", + " \n", + " current_player = player_1\n", + " player_3 = player_2 # Define player_3 as the computer player\n", + " while True:\n", + " print(\"Player 1's turn\" if current_player == player_1 else \"Player 2's turn\" if player_mode == '1' else \"Computer's turn\")\n", + " if player_mode == '1' or current_player == player_1:\n", + " row_index, col_index = take_player_input(board)\n", + " else:\n", + " row_index, col_index = computer_move(board, player_2)\n", + " if row_index is not None and col_index is not None:\n", + " print(f\"Computer chooses row {chr(ord('A') + row_index)} column {col_index + 1}\")\n", + " else:\n", + " print(\"Computer failed to make a move.\")\n", + " break\n", + " \n", + " if place_piece(board, current_player, row_index, col_index):\n", + " draw_game_board(board)\n", + " result = check_board(board)\n", + " if result == player_1:\n", + " print(\"Player 1 wins!\")\n", + " break\n", + " elif result == player_2 and player_mode == '1':\n", + " print(\"Player 2 wins!\")\n", + " break\n", + " elif result == player_3 and player_mode == '2':\n", + " print(\"Computer wins!\")\n", + " break\n", + " elif result == 0:\n", + " print(\"It's a draw!\")\n", + " break\n", + " else:\n", + " current_player = player_2 if current_player == player_1 else player_1\n", + " else:\n", + " print(\"That position is already taken. Try again.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Full Tic Tac Toe Game with Computer mode" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter size of Tic Tac Toe game (Ex: minimum 3 for 3x3): 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 2 3 \n", + " +---+---+---+\n", + "A | | | |\n", + " +---+---+---+\n", + "B | | | |\n", + " +---+---+---+\n", + "C | | | |\n", + " +---+---+---+\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Choose mode:\n", + "1. Play against another player\n", + "2. Play against the computer\n", + "Enter choice (1 or 2): 2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): A1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 2 3 \n", + " +---+---+---+\n", + "A | X | | |\n", + " +---+---+---+\n", + "B | | | |\n", + " +---+---+---+\n", + "C | | | |\n", + " +---+---+---+\n", + "\n", + "Computer's turn\n", + "Computer chooses row C column 2\n", + " 1 2 3 \n", + " +---+---+---+\n", + "A | X | | |\n", + " +---+---+---+\n", + "B | | | |\n", + " +---+---+---+\n", + "C | | O | |\n", + " +---+---+---+\n", + "\n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): A2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 2 3 \n", + " +---+---+---+\n", + "A | X | X | |\n", + " +---+---+---+\n", + "B | | | |\n", + " +---+---+---+\n", + "C | | O | |\n", + " +---+---+---+\n", + "\n", + "Computer's turn\n", + "Computer chooses row A column 3\n", + " 1 2 3 \n", + " +---+---+---+\n", + "A | X | X | O |\n", + " +---+---+---+\n", + "B | | | |\n", + " +---+---+---+\n", + "C | | O | |\n", + " +---+---+---+\n", + "\n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): B1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 2 3 \n", + " +---+---+---+\n", + "A | X | X | O |\n", + " +---+---+---+\n", + "B | X | | |\n", + " +---+---+---+\n", + "C | | O | |\n", + " +---+---+---+\n", + "\n", + "Computer's turn\n", + "Computer chooses row C column 1\n", + " 1 2 3 \n", + " +---+---+---+\n", + "A | X | X | O |\n", + " +---+---+---+\n", + "B | X | | |\n", + " +---+---+---+\n", + "C | O | O | |\n", + " +---+---+---+\n", + "\n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): B2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 2 3 \n", + " +---+---+---+\n", + "A | X | X | O |\n", + " +---+---+---+\n", + "B | X | X | |\n", + " +---+---+---+\n", + "C | O | O | |\n", + " +---+---+---+\n", + "\n", + "Computer's turn\n", + "Computer chooses row B column 3\n", + " 1 2 3 \n", + " +---+---+---+\n", + "A | X | X | O |\n", + " +---+---+---+\n", + "B | X | X | O |\n", + " +---+---+---+\n", + "C | O | O | |\n", + " +---+---+---+\n", + "\n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your move (e.g., A1): C3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 2 3 \n", + " +---+---+---+\n", + "A | X | X | O |\n", + " +---+---+---+\n", + "B | X | X | O |\n", + " +---+---+---+\n", + "C | O | O | X |\n", + " +---+---+---+\n", + "\n", + "Player 1 wins!\n" + ] + } + ], + "source": [ + "play_game()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Final thought: I was going to add a minimax algorithm for computer logic but decided not to." + ] + } + ], + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Labs/Lab.2/Quiz1-RobertCocker.ipynb b/Labs/Lab.2/Quiz1-RobertCocker.ipynb new file mode 100644 index 0000000..f7d30b3 --- /dev/null +++ b/Labs/Lab.2/Quiz1-RobertCocker.ipynb @@ -0,0 +1,152 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "id": "3308bf7d-8866-4516-8c7b-c8bf78dac36e", + "metadata": {}, + "outputs": [], + "source": [ + "# Robert Cocker\n", + "# Dr. Farbin\n", + "# DATA-3402\n", + "# Quiz\n", + "# 2/15/2024" + ] + }, + { + "cell_type": "markdown", + "id": "058ad468-d4aa-4ee0-a8c7-28935f054bad", + "metadata": {}, + "source": [ + "## Quick Quiz" + ] + }, + { + "cell_type": "markdown", + "id": "2c962e7f-644c-4b66-9ff8-18ccfaef5989", + "metadata": {}, + "source": [ + "Can you rewrite create_new_args as a two lines of code using functional programming, list comprehensions, and shortcuts? How about a single line?" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2456eff4-dd07-4094-abe5-d4e70aea3d57", + "metadata": {}, + "outputs": [], + "source": [ + "def create_new_args_0(args):\n", + " max_len = max(map(len,\n", + " filter(lambda x: isinstance(x,list),\n", + " args)))\n", + "\n", + " # Rewrite this section:\n", + " new_args=list()\n", + "\n", + " for a in args:\n", + " if not isinstance(a,list):\n", + " a0=[a]*max_len\n", + " elif len(a)!=max_len:\n", + " print(\"Error: all list arguments must have same length.\")\n", + " return\n", + " else:\n", + " a0=a\n", + " new_args.append(a0)\n", + "\n", + " return new_args" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4026f851-2114-4b3c-8860-b34b60d8503b", + "metadata": {}, + "outputs": [], + "source": [ + "def create_new_args(args):\n", + " max_len = max(map(len, filter(lambda x: isinstance(x, list), args)))\n", + " return [a if isinstance(a, list) and len(a) == max_len else [a] * max_len for a in args]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bc1c9754-c602-4fd2-a266-62a67207b16a", + "metadata": {}, + "outputs": [], + "source": [ + "create_new_args2 = lambda args: [[a] * max(map(len, filter(lambda x: isinstance(x, list), args))) if not isinstance(a, list) else a if len(a) == max(map(len, filter(lambda x: isinstance(x, list), args))) else print(\"Error: all list arguments must have same length.\") for a in args]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "93837073-e31c-4a24-a3ab-17b3d941c479", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[1, 2], [3, 4], [5, 5]]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "create_new_args_0([[1,2],[3,4],5])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6618fddb-b839-4d78-b0f5-06d23f20162e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error: all list arguments must have same length.\n" + ] + } + ], + "source": [ + "create_new_args_0([[1,2],[3,4,5],5])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71ff2bd8-dd64-4940-8c5b-4f16b786e5d8", + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Labs/Lab.3/Lab3-RobertCocker.ipynb b/Labs/Lab.3/Lab3-RobertCocker.ipynb new file mode 100644 index 0000000..8d8113f --- /dev/null +++ b/Labs/Lab.3/Lab3-RobertCocker.ipynb @@ -0,0 +1,839 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# Robert Cocker\n", + "# Dr. Farbin\n", + "# DATA-3402\n", + "# Lab 3\n", + "# 2/16/2024" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lab 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Uniform Distribution\n", + "Lets start with generating some fake random data. You can get a random number between 0 and 1 using the python random module as follow:" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Value of x is 0.15390961701777806\n" + ] + } + ], + "source": [ + "import random\n", + "x=random.random()\n", + "print(\"The Value of x is\", x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Everytime you call random, you will get a new number.\n", + "\n", + "*Exercise 1:* Using random, write a function `generate_uniform(N, mymin, mymax)`, that returns a python list containing N random numbers between specified minimum and maximum value. Note that you may want to quickly work out on paper how to turn numbers between 0 and 1 to between other values. " + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_uniform(N,x_min,x_max):\n", + " out = []\n", + " for _ in range(N):\n", + " random_number = random.random()\n", + " scaled_number = random_number * (x_max - x_min) + x_min\n", + " out.append(scaled_number)\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data Type: \n", + "Data Length: 1000\n", + "Type of Data Contents: \n", + "Data Minimum: -9.988731443994007\n", + "Data Maximum: 9.93791001990514\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "data=generate_uniform(1000,-10,10)\n", + "print (\"Data Type:\", type(data))\n", + "print (\"Data Length:\", len(data))\n", + "if len(data)>0: \n", + " print (\"Type of Data Contents:\", type(data[0]))\n", + " print (\"Data Minimum:\", min(data))\n", + " print (\"Data Maximum:\", max(data))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 2a:* \n", + "Write a function that computes the mean of values in a list. Recall the equation for the mean of a random variable $\\bf{x}$ computed on a data set of $n$ values $\\{ x_i \\} = \\{x_1, x_2, ..., x_n\\}$ is ${\\bf\\bar{x}} = \\frac{1}{n} \\sum_i^n x_i$." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [], + "source": [ + "def mean(Data):\n", + " m=0.\n", + " \n", + " if not Data:\n", + " return None # Return None if list is empty\n", + " \n", + " # Calculate sum of values\n", + " total_sum = sum(Data)\n", + " \n", + " # Calculate mean\n", + " m = total_sum / len(Data)\n", + "\n", + " return m" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of Data: 0.03207328365953548\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "print (\"Mean of Data:\", mean(data))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 2b:* \n", + "Write a function that computes the variance of values in a list. Recall the equation for the variance of a random variable $\\bf{x}$ computed on a data set of $n$ values $\\{ x_i \\} = \\{x_1, x_2, ..., x_n\\}$ is ${\\bf\\langle x \\rangle} = \\frac{1}{n} \\sum_i^n (x_i - {\\bf\\bar{x}})$." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "# Skeleton\n", + "def variance(Data):\n", + " m=0.\n", + " \n", + " ### BEGIN SOLUTION\n", + "\n", + " n = len(Data)\n", + " mean = sum(Data) / n\n", + " deviations = [(x - mean) ** 2 for x in Data]\n", + " m = sum(deviations) / n\n", + " \n", + " ### END SOLUTION\n", + " \n", + " return m" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variance of Data: 34.79037978973739\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "print (\"Variance of Data:\", variance(data))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Histogramming" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 3:* Write a function that bins the data so that you can create a histogram. An example of how to implement histogramming is the following logic:\n", + "\n", + "* User inputs a list of values `x` and optionally `n_bins` which defaults to 10.\n", + "* If not supplied, find the minimum and maximum (`x_min`,`x_max`) of the values in x.\n", + "* Determine the bin size (`bin_size`) by dividing the range of the function by the number of bins.\n", + "* Create an empty list of zeros of size `n_bins`, call it `hist`.\n", + "* Loop over the values in `x`\n", + " * Loop over the values in `hist` with index `i`:\n", + " * If x is between `x_min+i*bin_size` and `x_min+(i+1)*bin_size`, increment `hist[i].` \n", + " * For efficiency, try to use continue to goto the next bin and data point.\n", + "* Return `hist` and the list corresponding of the bin edges (i.e. of `x_min+i*bin_size`). " + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "def histogram(x, n_bins=10, x_min=None, x_max=None):\n", + " ### BEGIN SOLUTION\n", + "\n", + " if x_min is None:\n", + " x_min = min(x)\n", + " if x_max is None:\n", + " x_max = max(x)\n", + "\n", + " bin_size = (x_max - x_min) / n_bins\n", + " hist = [0] * n_bins\n", + " bin_edges = [x_min + i * bin_size for i in range(n_bins + 1)]\n", + "\n", + " for value in x:\n", + " for i in range(n_bins):\n", + " if x_min + i * bin_size <= value < x_min + (i + 1) * bin_size:\n", + " hist[i] += 1\n", + " break\n", + "\n", + " ### END SOLUTION\n", + "\n", + " return hist, bin_edges" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[11, 15, 10, 11, 8, 17, 10, 19, 10, 13, 12, 6, 6, 8, 6, 9, 8, 11, 11, 7, 14, 14, 3, 13, 5, 9, 7, 11, 14, 11, 8, 10, 7, 8, 10, 11, 9, 6, 8, 12, 6, 6, 12, 8, 13, 5, 11, 6, 9, 8, 13, 7, 11, 7, 7, 17, 11, 7, 7, 8, 15, 17, 10, 7, 9, 11, 6, 12, 9, 4, 14, 11, 10, 13, 8, 7, 14, 17, 11, 8, 12, 14, 9, 8, 9, 11, 5, 15, 15, 10, 6, 6, 18, 16, 9, 14, 8, 7, 10, 7]\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "h,b=histogram(data,100)\n", + "print(h)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 4:* Write a function that uses the histogram function in the previous exercise to create a text-based \"graph\". For example the output could look like the following:\n", + "```\n", + "[ 0, 1] : ######\n", + "[ 1, 2] : #####\n", + "[ 2, 3] : ######\n", + "[ 3, 4] : ####\n", + "[ 4, 5] : ####\n", + "[ 5, 6] : ######\n", + "[ 6, 7] : #####\n", + "[ 7, 8] : ######\n", + "[ 8, 9] : ####\n", + "[ 9, 10] : #####\n", + "```\n", + "\n", + "Where each line corresponds to a bin and the number of `#`'s are proportional to the value of the data in the bin. " + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "def draw_histogram(x, n_bins, x_min=None, x_max=None, character=\"#\", max_character_per_line=20):\n", + " hist, bin_edges = histogram(x, n_bins, x_min, x_max)\n", + "\n", + " max_value = max(hist)\n", + " scale = max_value / max_character_per_line\n", + "\n", + " for i in range(n_bins):\n", + " print(f'[{bin_edges[i]:3.0f}, {bin_edges[i+1]:3.0f}] : {character * int(hist[i]//scale)}')\n", + "\n", + " return hist, bin_edges\n" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-10, -9] : ###############\n", + "[ -9, -8] : ###################\n", + "[ -8, -7] : ###########\n", + "[ -7, -6] : #############\n", + "[ -6, -5] : ##############\n", + "[ -5, -4] : ###############\n", + "[ -4, -3] : ############\n", + "[ -3, -2] : #############\n", + "[ -2, -1] : #############\n", + "[ -1, -0] : ###########\n", + "[ -0, 1] : #############\n", + "[ 1, 2] : ##############\n", + "[ 2, 3] : ################\n", + "[ 3, 4] : ############\n", + "[ 4, 5] : ################\n", + "[ 5, 6] : ################\n", + "[ 6, 7] : ###############\n", + "[ 7, 8] : ################\n", + "[ 8, 9] : ###############\n", + "[ 9, 10] : #############\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "h,b=draw_histogram(data,20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Functional Programming\n", + "\n", + "*Exercise 5:* Write a function the applies a booling function (that returns true/false) to every element in data, and return a list of indices of elements where the result was true. Use this function to find the indices of entries greater than 0.5. " + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "def where(mylist, myfunc):\n", + " out = []\n", + " for i, x in enumerate(mylist):\n", + " if myfunc(x):\n", + " out.append(i)\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 4]\n" + ] + } + ], + "source": [ + "# Test your solution here\n", + "data = [0.2, 0.6, 0.8, 0.3, 0.9, 0.1]\n", + "greater_than_0_5 = where(data, lambda x: x > 0.5)\n", + "print(greater_than_0_5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 6:* The `inrange(mymin,mymax)` function below returns a function that tests if it's input is between the specified values. Write corresponding functions that test:\n", + "* Even\n", + "* Odd\n", + "* Greater than\n", + "* Less than\n", + "* Equal\n", + "* Divisible by" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True True False False False\n", + "False False True True False\n", + "Number of Entries passing F1: 6\n", + "Number of Entries passing F2: 0\n" + ] + } + ], + "source": [ + "def inrange(mymin,mymax):\n", + " def testrange(x):\n", + " return x=mymin\n", + " return testrange\n", + "\n", + "# Examples:\n", + "F1=inrange(0,10)\n", + "F2=inrange(10,20)\n", + "\n", + "# Test of inrange\n", + "print (F1(0), F1(1), F1(10), F1(15), F1(20))\n", + "print (F2(0), F2(1), F2(10), F2(15), F2(20))\n", + "\n", + "print (\"Number of Entries passing F1:\", len(where(data,F1)))\n", + "print (\"Number of Entries passing F2:\", len(where(data,F2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [], + "source": [ + "### BEGIN SOLUTION\n", + "def is_even():\n", + " def test_even(x):\n", + " return x % 2 == 0\n", + " return test_even\n", + "\n", + "def is_odd():\n", + " def test_odd(x):\n", + " return x % 2 != 0\n", + " return test_odd\n", + "\n", + "def greater_than(n):\n", + " def test_greater(x):\n", + " return x > n\n", + " return test_greater\n", + "\n", + "def less_than(n):\n", + " def test_less(x):\n", + " return x < n\n", + " return test_less\n", + "\n", + "def equal_to(n):\n", + " def test_equal(x):\n", + " return x == n\n", + " return test_equal\n", + "\n", + "def divisible_by(n):\n", + " def test_divisible(x):\n", + " return x % n == 0\n", + " return test_divisible \n", + " \n", + "### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True False True False True\n", + "False False False True True\n", + "Number of Entries passing F1: 3\n", + "Number of Entries passing F2: 2\n" + ] + } + ], + "source": [ + "# Test your solution\n", + "# Examples:\n", + "F1 = is_even()\n", + "F2 = greater_than(10)\n", + "\n", + "# Test of is_even and greater_than\n", + "print(F1(0), F1(1), F1(10), F1(15), F1(20))\n", + "print(F2(0), F2(1), F2(10), F2(15), F2(20))\n", + "\n", + "# Assuming 'data' is a list of numbers\n", + "data = [0, 1, 10, 15, 20]\n", + "print(\"Number of Entries passing F1:\", len([x for x in data if F1(x)]))\n", + "print(\"Number of Entries passing F2:\", len([x for x in data if F2(x)])) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 7:* Repeat the previous exercise using `lambda` and the built-in python functions sum and map instead of your solution above. " + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [], + "source": [ + "### BEGIN SOLUTION\n", + "def is_even():\n", + " return lambda x: x % 2 == 0\n", + "\n", + "def is_odd():\n", + " return lambda x: x % 2 != 0\n", + "\n", + "def greater_than(n):\n", + " return lambda x: x > n\n", + "\n", + "def less_than(n):\n", + " return lambda x: x < n\n", + "\n", + "def equal_to(n):\n", + " return lambda x: x == n\n", + "\n", + "def divisible_by(n):\n", + " return lambda x: x % n == 0 \n", + " \n", + "### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Monte Carlo\n", + "\n", + "*Exercise 7:* Write a \"generator\" function called `generate_function(func,x_min,x_max,N)`, that instead of generating a flat distribution, generates a distribution with functional form coded in `func`. Note that `func` will always be > 0. \n", + "\n", + "Use the test function below and your histogramming functions above to demonstrate that your generator is working properly.\n", + "\n", + "Hint: A simple, but slow, solution is to a draw random number `test_x` within the specified range and another number `p` between the `min` and `max` of the function (which you will have to determine). If `p<=function(test_x)`, then place `test_x` on the output. If not, repeat the process, drawing two new numbers. Repeat until you have the specified number of generated numbers, `N`. For this problem, it's OK to determine the `min` and `max` by numerically sampling the function. " + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import numpy as np\n", + "\n", + "def generate_function(func, x_min, x_max, N=1000):\n", + " out = list()\n", + " x = np.linspace(x_min, x_max, 1000)\n", + " y = func(x)\n", + " max_y = max(y)\n", + " while len(out) < N:\n", + " test_x = random.uniform(x_min, x_max)\n", + " p = random.uniform(0, max_y)\n", + " if p <= func(test_x):\n", + " out.append(test_x)\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# A test function\n", + "def test_func(x,a=1,b=1):\n", + " return abs(a*x+b)\n", + "\n", + "# Generate numbers using the test function\n", + "x_min = -10\n", + "x_max = 10\n", + "N = 10000\n", + "numbers = generate_function(test_func, x_min, x_max, N)\n", + "\n", + "# Generate histogram using your function\n", + "hist, bin_edges = histogram(numbers, n_bins=50)\n", + "\n", + "# Plot the histogram\n", + "plt.bar(bin_edges[:-1], hist, width=np.diff(bin_edges), edgecolor=\"black\", alpha=0.6)\n", + "\n", + "# Plot the test function\n", + "x = np.linspace(x_min, x_max, 1000)\n", + "y = test_func(x)\n", + "plt.plot(x, y, 'r-')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 8:* Use your function to generate 1000 numbers that are normal distributed, using the `gaussian` function below. Confirm the mean and variance of the data is close to the mean and variance you specify when building the Gaussian. Histogram the data. " + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def gaussian(mean, sigma):\n", + " def f(x):\n", + " return np.exp(-((x-mean)**2)/(2*sigma**2))/np.sqrt(math.pi*sigma)\n", + " return f\n", + "\n", + "# Example Instantiation\n", + "g1=gaussian(0,1)\n", + "g2=gaussian(10,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 0.00047575876080266876\n", + "Variance: 1.0581827537528683\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Instantiate your Gaussian function\n", + "mean = 0\n", + "sigma = 1\n", + "g1 = gaussian(mean, sigma)\n", + "\n", + "# Generate numbers using the Gaussian function\n", + "N = 1000\n", + "numbers = generate_function(g1, mean - 5*sigma, mean + 5*sigma, N)\n", + "\n", + "# Confirm the mean and variance\n", + "print(\"Mean:\", np.mean(numbers))\n", + "print(\"Variance:\", np.var(numbers))\n", + "\n", + "# Generate histogram using your function\n", + "hist, bin_edges = histogram(numbers, n_bins=50)\n", + "\n", + "# Plot the histogram\n", + "plt.bar(bin_edges[:-1], hist, width=np.diff(bin_edges), edgecolor=\"black\", alpha=0.6)\n", + "\n", + "# Plot the Gaussian function\n", + "x = np.linspace(mean - 5*sigma, mean + 5*sigma, 1000)\n", + "y = [g1(xi) for xi in x]\n", + "plt.plot(x, y, 'r-')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: -0.01694690058429054\n", + "Variance: 1.0464121622833251\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "print(\"Mean:\", np.mean(data))\n", + "print(\"Variance:\", np.var(data))" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.hist(data, bins=30)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Exercise 9:* Combine your `generate_function`, `where`, and `inrange` functions above to create an integrate function. Use your integrate function to show that approximately 68% of Normal distribution is within one variance." + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def gaussian(mean, sigma):\n", + " def f(x):\n", + " return np.exp(-((x-mean)**2)/(2*sigma**2))/np.sqrt(2*np.pi*sigma**2)\n", + " return f\n", + "\n", + "def integrate(func, x_min, x_max, N=1000):\n", + " def generate_uniform(N, x_min, x_max):\n", + " return np.linspace(x_min, x_max, N)\n", + "\n", + " def inrange(mymin, mymax):\n", + " def testrange(x):\n", + " return x=mymin\n", + " return testrange\n", + "\n", + " def where(mylist, myfunc):\n", + " out = []\n", + " for i, x in enumerate(mylist):\n", + " if myfunc(x):\n", + " out.append(i)\n", + " return out\n", + "\n", + " x = generate_uniform(N, x_min, x_max)\n", + " y = [func(xi) for xi in x]\n", + " in_range = inrange(x_min, x_max)\n", + " valid_indices = where(x, in_range)\n", + " y = [y[i] if i in valid_indices else 0 for i in range(N)]\n", + " integral = sum(y) * (x_max - x_min) / N\n", + " return integral" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Integral within one variance: 0.6820066411696352\n" + ] + } + ], + "source": [ + "# Instantiate Gaussian function\n", + "g1 = gaussian(0, 1)\n", + "\n", + "# Calculate integral within one standard variance\n", + "integral = integrate(g1, -1, 1, N=1000)\n", + "\n", + "print(\"Integral within one variance:\", integral)" + ] + } + ], + "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": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Labs/Lab.4/Lab 4 updated solution.ipynb b/Labs/Lab.4/Lab 4 updated solution.ipynb new file mode 100644 index 0000000..c75fe1e --- /dev/null +++ b/Labs/Lab.4/Lab 4 updated solution.ipynb @@ -0,0 +1,514 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "0ac98572-62f7-49b0-b5a8-ce3bc24680da", + "metadata": {}, + "outputs": [], + "source": [ + "# Updated solution\n", + "import math\n", + "\n", + "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 get_pixel(self, row, col):\n", + " return self.data[row][col]\n", + " \n", + " def clear_canvas(self):\n", + " self.data = [[' '] * self.width for _ in range(self.height)]\n", + " \n", + " def v_line(self, x, y, w, **kwargs):\n", + " for i in range(y, y+w):\n", + " if 0 <= i < self.height:\n", + " self.set_pixel(x, i, **kwargs)\n", + "\n", + " def h_line(self, x, y, h, **kwargs):\n", + " for i in range(x, x+h):\n", + " if 0 <= i < self.width:\n", + " self.set_pixel(i, y, **kwargs)\n", + " \n", + " def line(self, x1, y1, x2, y2, **kwargs):\n", + " slope = (y2-y1) / (x2-x1) if x2-x1 != 0 else 0\n", + " for x in range(x1, x2+1):\n", + " y = int(slope * (x - x1) + y1)\n", + " if 0 <= x < self.width and 0 <= y < self.height:\n", + " self.set_pixel(x, y, **kwargs)\n", + " \n", + " def display(self):\n", + " print(\"\\n\".join([\"\".join(row) for row in self.data]))\n", + "\n", + " def __repr__(self):\n", + " return f'Canvas({self.width}, {self.height})'\n", + "\n", + "\n", + "class Shape:\n", + " def area(self):\n", + " raise NotImplementedError(\"Subclass must implement abstract method\")\n", + "\n", + " def perimeter(self):\n", + " raise NotImplementedError(\"Subclass must implement abstract method\")\n", + "\n", + " def get_points(self):\n", + " raise NotImplementedError(\"Subclass must implement abstract method\")\n", + "\n", + " def is_inside(self, x, y):\n", + " raise NotImplementedError(\"Subclass must implement abstract method\")\n", + "\n", + " def overlaps(self, shape):\n", + " raise NotImplementedError(\"Subclass must implement abstract method\")\n", + "\n", + " def __repr__(self):\n", + " raise NotImplementedError(\"Subclass must implement abstract method\")\n", + "\n", + "\n", + "class Rectangle(Shape):\n", + " def __init__(self, x, y, width, height):\n", + " self.__x = x\n", + " self.__y = y\n", + " self.__width = width\n", + " self.__height = height\n", + "\n", + " def area(self):\n", + " return self.__width * self.__height\n", + "\n", + " def perimeter(self):\n", + " return 2 * (self.__width + self.__height)\n", + "\n", + " def get_points(self):\n", + " return [(self.__x + i, self.__y) for i in range(self.__width)] + \\\n", + " [(self.__x + self.__width, self.__y + i) for i in range(1, self.__height)] + \\\n", + " [(self.__x + i, self.__y + self.__height) for i in range(self.__width - 1, -1, -1)] + \\\n", + " [(self.__x, self.__y + i) for i in range(self.__height - 1, 0, -1)]\n", + "\n", + " def is_inside(self, x, y):\n", + " return self.__x <= x <= self.__x + self.__width and \\\n", + " self.__y <= y <= self.__y + self.__height\n", + "\n", + " def overlaps(self, shape):\n", + " if isinstance(shape, Rectangle):\n", + " return not (self.__x + self.__width < shape.__x or\n", + " self.__x > shape.__x + shape.__width or\n", + " self.__y + self.__height < shape.__y or\n", + " self.__y > shape.__y + shape.__height)\n", + " return False\n", + "\n", + " def __repr__(self):\n", + " return f'Rectangle({self.__x}, {self.__y}, {self.__width}, {self.__height})'\n", + "\n", + "\n", + "class Circle(Shape):\n", + " def __init__(self, x, y, radius):\n", + " self.__x = x\n", + " self.__y = y\n", + " self.__radius = radius\n", + "\n", + " def area(self):\n", + " return math.pi * self.__radius**2\n", + "\n", + " def perimeter(self):\n", + " return 2 * math.pi * self.__radius\n", + "\n", + " def get_points(self):\n", + " points = []\n", + " for i in range(360):\n", + " x = self.__x + self.__radius * math.cos(math.radians(i))\n", + " y = self.__y + self.__radius * math.sin(math.radians(i))\n", + " points.append((round(x), round(y)))\n", + " return points\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, shape):\n", + " if isinstance(shape, Circle):\n", + " return math.sqrt((self.__x - shape.__x)**2 + (self.__y - shape.__y)**2) < self.__radius + shape.__radius\n", + " return False\n", + "\n", + " def __repr__(self):\n", + " return f'Circle({self.__x}, {self.__y}, {self.__radius})'\n", + "\n", + "class Triangle(Shape):\n", + " def __init__(self, x1, y1, x2, y2, x3, y3):\n", + " self.__x1 = x1\n", + " self.__y1 = y1\n", + " self.__x2 = x2\n", + " self.__y2 = y2\n", + " self.__x3 = x3\n", + " self.__y3 = y3\n", + "\n", + " def area(self):\n", + " return abs((self.__x1*(self.__y2-self.__y3) + self.__x2*(self.__y3-self.__y1) + self.__x3*(self.__y1-self.__y2)) / 2)\n", + "\n", + " def perimeter(self):\n", + " side1 = math.sqrt((self.__x2 - self.__x1)**2 + (self.__y2 - self.__y1)**2)\n", + " side2 = math.sqrt((self.__x3 - self.__x2)**2 + (self.__y3 - self.__y2)**2)\n", + " side3 = math.sqrt((self.__x1 - self.__x3)**2 + (self.__y1 - self.__y3)**2)\n", + " return side1 + side2 + side3\n", + "\n", + " def get_points(self):\n", + " return [(self.__x1 + i, self.__y1 + j) for i in range(int(self.__x2 - self.__x1) + 1) for j in range(int(self.__y2 - self.__y1) + 1)] + \\\n", + " [(self.__x2 + i, self.__y2 + j) for i in range(int(self.__x3 - self.__x2) + 1) for j in range(int(self.__y3 - self.__y2) + 1)] + \\\n", + " [(self.__x3 + i, self.__y3 + j) for i in range(int(self.__x1 - self.__x3) + 1) for j in range(int(self.__y1 - self.__y3) + 1)]\n", + "\n", + " def is_inside(self, x, y):\n", + " # vectors\n", + " v0 = [self.__x3 - self.__x1, self.__y3 - self.__y1]\n", + " v1 = [self.__x2 - self.__x1, self.__y2 - self.__y1]\n", + " v2 = [x - self.__x1, y - self.__y1]\n", + "\n", + " \n", + " dot00 = v0[0]*v0[0] + v0[1]*v0[1]\n", + " dot01 = v0[0]*v1[0] + v0[1]*v1[1]\n", + " dot02 = v0[0]*v2[0] + v0[1]*v2[1]\n", + " dot11 = v1[0]*v1[0] + v1[1]*v1[1]\n", + " dot12 = v1[0]*v2[0] + v1[1]*v2[1]\n", + "\n", + " \n", + " inv_denom = 1 / (dot00 * dot11 - dot01 * dot01)\n", + " u = (dot11 * dot02 - dot01 * dot12) * inv_denom\n", + " v = (dot00 * dot12 - dot01 * dot02) * inv_denom\n", + " return (u >= 0) and (v >= 0) and (u + v < 1)\n", + "\n", + " def overlaps(self, shape):\n", + " for point in shape.get_points():\n", + " x, y = point\n", + " if self.is_inside(x, y):\n", + " return True\n", + " return False\n", + "\n", + " def __repr__(self):\n", + " return f'Triangle({self.__x1}, {self.__y1}, {self.__x2}, {self.__y2}, {self.__x3}, {self.__y3})'\n", + "\n", + "\n", + "class Paint:\n", + " def __init__(self, canvas):\n", + " self.canvas = canvas\n", + "\n", + " def draw_shape(self, shape, char='*'):\n", + " for point in shape.get_points():\n", + " x, y = point\n", + " if 0 <= x < self.canvas.width and 0 <= y < self.canvas.height:\n", + " self.canvas.set_pixel(y, x, char)\n", + "\n", + "\n", + "class RasterDrawing:\n", + " def __init__(self, canvas):\n", + " self.canvas = canvas\n", + " self.shapes = []\n", + "\n", + " def add_shape(self, shape):\n", + " self.shapes.append(shape)\n", + "\n", + " def draw(self):\n", + " paint = Paint(self.canvas)\n", + " for shape in self.shapes:\n", + " paint.draw_shape(shape)\n", + "\n", + " def save(self, filename):\n", + " with open(filename, 'w') as file:\n", + " for shape in self.shapes:\n", + " file.write(repr(shape) + '\\n')\n", + "\n", + " @classmethod\n", + " def load(cls, filename):\n", + " with open(filename, 'r') as file:\n", + " lines = file.readlines()\n", + " canvas = None\n", + " drawing = cls(None)\n", + " for line in lines:\n", + " obj = eval(line.strip())\n", + " if isinstance(obj, Canvas):\n", + " canvas = obj\n", + " elif canvas:\n", + " drawing.add_shape(obj)\n", + " else:\n", + " raise ValueError(\"Canvas not found in the file.\")\n", + " drawing.canvas = canvas\n", + " return drawing\n", + "\n", + "\n", + "class CompoundShape(Shape):\n", + " def __init__(self, shapes):\n", + " self.shapes = shapes\n", + "\n", + " def area(self):\n", + " return sum(shape.area() for shape in self.shapes)\n", + "\n", + " def perimeter(self):\n", + " return sum(shape.perimeter() for shape in self.shapes)\n", + "\n", + " def get_points(self):\n", + " points = []\n", + " for shape in self.shapes:\n", + " points.extend(shape.get_points())\n", + " return points\n", + "\n", + " def is_inside(self, x, y):\n", + " for shape in self.shapes:\n", + " if shape.is_inside(x, y):\n", + " return True\n", + " return False\n", + "\n", + " def overlaps(self, shape):\n", + " for self_shape in self.shapes:\n", + " if self_shape.overlaps(shape):\n", + " return True\n", + " return False" + ] + }, + { + "cell_type": "markdown", + "id": "0ed23547-4c30-40d1-a2b2-485fc58ced58", + "metadata": {}, + "source": [ + "10. Copy the Canvas class from lecture to in a python file creating a paint module. Copy your classes from above into the module and implement paint functions. Implement a CompoundShape class. Create a simple drawing demonstrating that all of your classes are working." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7b6ad85e-42db-4974-9ded-938a94ca41c6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " \n", + " ********** \n", + " * * \n", + " * * \n", + " * * \n", + " * * \n", + " ********** \n", + " \n", + " \n" + ] + } + ], + "source": [ + "# In the new Python script\n", + "import cpaint\n", + "\n", + "# Create a canvas\n", + "canvas = cpaint.Canvas(20, 10)\n", + "\n", + "# Create shape\n", + "rectangle = cpaint.Rectangle(2, 2, 10, 5)\n", + "\n", + "# Draw shapes\n", + "drawing = cpaint.RasterDrawing(canvas)\n", + "drawing.add_shape(rectangle)\n", + "drawing.draw()\n", + "\n", + "\n", + "# Display the canvas\n", + "canvas.display()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7c6ddeb7-6a89-466e-b3cd-56913cd33ec3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " *** \n", + "** ** \n", + "* * \n", + "** ** \n", + " *** \n", + " \n", + " \n", + " \n" + ] + } + ], + "source": [ + "import cpaint\n", + "\n", + "# Create a canvas\n", + "canvas = cpaint.Canvas(15, 15)\n", + "\n", + "# Create shape\n", + "circle = cpaint.Circle(2, 9, 2)\n", + "\n", + "# Draw shapes\n", + "drawing = cpaint.RasterDrawing(canvas)\n", + "drawing.add_shape(circle)\n", + "drawing.draw()\n", + "\n", + "\n", + "# Display the canvas\n", + "canvas.display()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "f0772240-1ffc-4bff-801c-97aeb9a828d6", + "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" + ] + } + ], + "source": [ + "import cpaint\n", + "\n", + "# Create a canvas\n", + "canvas = cpaint.Canvas(20, 20)\n", + "\n", + "# Create shape\n", + "triangle = cpaint.Triangle(1, 9,9,15,15,0)\n", + "\n", + "# Draw shapes\n", + "drawing = cpaint.RasterDrawing(canvas)\n", + "drawing.add_shape(triangle)\n", + "drawing.draw()\n", + "\n", + "\n", + "# Display the canvas\n", + "canvas.display()" + ] + }, + { + "cell_type": "markdown", + "id": "3be0e31b-1805-49fc-84bb-695b2190ba0b", + "metadata": {}, + "source": [ + "11. Create a RasterDrawing class. Demonstrate that you can create a drawing made of several shapes, paint the drawing, modify the drawing, and paint it again." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bdec91e9-28e8-4d56-9f1d-98f0a5261756", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " \n", + "************ \n", + "*** * \n", + "*** * \n", + "***** * \n", + " *** * \n", + " ********** \n", + " *** \n", + " \n" + ] + } + ], + "source": [ + "# Python script/module\n", + "import cpaint\n", + "\n", + "# Create canvas\n", + "canvas = cpaint.Canvas(20, 10)\n", + "\n", + "# Create shapes\n", + "rectangle = cpaint.Rectangle(2, 2, 10, 5)\n", + "triangle = cpaint.Triangle(0,2,2,5,4,8)\n", + "\n", + "# Draw shape\n", + "drawing = cpaint.RasterDrawing(canvas)\n", + "drawing.add_shape(rectangle)\n", + "drawing.draw()\n", + "drawing2 = cpaint.RasterDrawing(canvas)\n", + "drawing2.add_shape(triangle)\n", + "drawing2.draw()\n", + "\n", + "# Display\n", + "canvas.display()" + ] + }, + { + "cell_type": "markdown", + "id": "438dcd3f-fda3-4b45-8566-42a4493631b7", + "metadata": {}, + "source": [ + "12. Implement the ability to load/save raster drawings and demonstate that your method works. One way to implement this ability:\r\n", + "Overload __repr__ functions of all objects to return strings of the python code that would construct the object.\r\n", + "\r\n", + "In the save method of raster drawing class, store the representations into the file.\r\n", + "\r\n", + "Write a loader function that reads the file and uses eval to instantiate the object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc5b1e23-cacf-405a-a9e5-17787d6499cc", + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Labs/Lab.4/Lab.4-RobertCocker.ipynb b/Labs/Lab.4/Lab.4-RobertCocker.ipynb new file mode 100644 index 0000000..4fed488 --- /dev/null +++ b/Labs/Lab.4/Lab.4-RobertCocker.ipynb @@ -0,0 +1,897 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Robert Cocker\n", + "# Dr. Farbin\n", + "# DATA-3402\n", + "# Lab 4\n", + "# 02/28/24" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lab 4- Object Oriented Programming\n", + "\n", + "For all of the exercises below, make sure you provide tests of your solutions.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Write a \"counter\" class that can be incremented up to a specified maximum value, will print an error if an attempt is made to increment beyond that value, and allows reseting the counter. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class Counter:\n", + " def __init__(self, max_value):\n", + " self.value = 0\n", + " self.max_value = max_value\n", + "\n", + " def increment(self):\n", + " if self.value < self.max_value:\n", + " self.value += 1\n", + " else:\n", + " print(\"Error: Counter reached maximum value.\")\n", + "\n", + " def reset(self):\n", + " self.value = 0\n", + "\n", + " def get_value(self):\n", + " return self.value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Copy and paste your solution to question 1 and modify it so that all the data held by the counter is private. Implement functions to check the value of the counter, check the maximum value, and check if the counter is at the maximum." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Incrementing the counter up to its maximum value...\n", + "Current value: 1 Reached maximum? False\n", + "Current value: 2 Reached maximum? False\n", + "Current value: 3 Reached maximum? False\n", + "Current value: 4 Reached maximum? False\n", + "Current value: 5 Reached maximum? True\n", + "Error: Counter reached maximum value.\n", + "Current value: 5 Reached maximum? True\n", + "Error: Counter reached maximum value.\n", + "Current value: 5 Reached maximum? True\n", + "\n", + "Resetting the counter...\n", + "Counter value after reset: 0\n" + ] + } + ], + "source": [ + "class Counter:\n", + " def __init__(self, max_value):\n", + " self.__value = 0\n", + " self.__max_value = max_value\n", + "\n", + " def __increment(self):\n", + " if self.__value < self.__max_value:\n", + " self.__value += 1\n", + " else:\n", + " print(\"Error: Counter reached maximum value.\")\n", + "\n", + " def reset(self):\n", + " self.__value = 0\n", + "\n", + " def get_value(self):\n", + " return self.__value\n", + "\n", + " def get_max_value(self):\n", + " return self.__max_value\n", + "\n", + " def is_at_max(self):\n", + " return self.__value == self.__max_value\n", + "\n", + " def increment_and_check(self):\n", + " self.__increment()\n", + " return self.is_at_max()\n", + "\n", + "\n", + "# class test:\n", + "max_count = 5\n", + "counter = Counter(max_count)\n", + "\n", + "print(\"Incrementing the counter up to its maximum value...\")\n", + "for _ in range(max_count + 2): # Trying to increment beyond the maximum value\n", + " at_max = counter.increment_and_check()\n", + " print(\"Current value:\", counter.get_value(), \"Reached maximum?\", at_max)\n", + "\n", + "print(\"\\nResetting the counter...\")\n", + "counter.reset()\n", + "print(\"Counter value after reset:\", counter.get_value())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Implement a class to represent a rectangle, holding the length, width, and $x$ and $y$ coordinates of a corner of the object. Implement functions that compute the area and perimeter of the rectangle. Make all data members private and privide accessors to retrieve values of data members. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class Rectangle:\n", + " def __init__(self, length, width, x, y):\n", + " self.__length = length\n", + " self.__width = width\n", + " self.__x = x\n", + " self.__y = y\n", + "\n", + " def get_length(self):\n", + " return self.__length\n", + "\n", + " def get_width(self):\n", + " return self.__width\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.__length * self.__width\n", + "\n", + " def perimeter(self):\n", + " return 2 * (self.__length + self.__width) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Implement a class to represent a circle, holding the radius and $x$ and $y$ coordinates of center of the object. Implement functions that compute the area and perimeter of the rectangle. Make all data members private and privide accessors to retrieve values of data members. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "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 area(self):\n", + " return math.pi * (self.__radius ** 2)\n", + "\n", + " def circumference(self):\n", + " return 2 * math.pi * self.__radius" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Implement a common base class for the classes implemented in 3 and 4 above which implements all common methods as not implemented functions (virtual). Re-implement your rectangle and circle classes to inherit from the base class and overload the functions accordingly. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "class Shape:\n", + " def __init__(self, x, y, **kwargs):\n", + " self.__x = x\n", + " self.__y = y\n", + " self.kwargs = kwargs\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", + " raise NotImplementedError(\"Subclasses must implement area()\")\n", + " \n", + " def perimeter(self):\n", + " raise NotImplementedError(\"Subclasses must implement perimeter()\")\n", + "\n", + "class Rectangle(Shape):\n", + " def __init__(self, length, width, x, y, **kwargs):\n", + " super().__init__(x, y, **kwargs)\n", + " self.__length = length\n", + " self.__width = width\n", + " \n", + " def get_length(self):\n", + " return self.__length\n", + " \n", + " def get_width(self):\n", + " return self.__width\n", + " \n", + " def area(self):\n", + " return self.__length * self.__width\n", + " \n", + " def perimeter(self):\n", + " return 2 * (self.__length + self.__width)\n", + "\n", + "class Circle(Shape):\n", + " def __init__(self, radius, x, y, **kwargs):\n", + " super().__init__(x, y, **kwargs)\n", + " self.__radius = radius\n", + " \n", + " def get_radius(self):\n", + " return self.__radius\n", + " \n", + " def area(self):\n", + " return math.pi * self.__radius ** 2\n", + " \n", + " def perimeter(self):\n", + " return 2 * math.pi * self.__radius" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "6. Implement a triangle class analogous to the rectangle and circle in question 5." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class Triangle(Shape):\n", + " def __init__(self, side1, side2, side3, x, y, **kwargs):\n", + " super().__init__(x, y, **kwargs)\n", + " self.__side1 = side1\n", + " self.__side2 = side2\n", + " self.__side3 = side3\n", + "\n", + " def get_side1(self):\n", + " return self.__side1\n", + "\n", + " def get_side2(self):\n", + " return self.__side2\n", + "\n", + " def get_side3(self):\n", + " return self.__side3\n", + "\n", + " def area(self):\n", + " s = (self.__side1 + self.__side2 + self.__side3) / 2\n", + " return math.sqrt(s * (s - self.__side1) * (s - self.__side2) * (s - self.__side3))\n", + "\n", + " def perimeter(self):\n", + " return self.__side1 + self.__side2 + self.__side3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "7. Add a function to the object classes, including the base, that returns a list of up to 16 pairs of $x$ and $y$ points on the parameter of the object. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "class Shape:\n", + " def __init__(self, x, y, **kwargs):\n", + " self.x, self.y, self.kwargs = x, y, kwargs\n", + "\n", + " def area(self):\n", + " raise NotImplementedError\n", + "\n", + " def perimeter(self):\n", + " raise NotImplementedError\n", + "\n", + " def get_points(self):\n", + " raise NotImplementedError\n", + "\n", + "\n", + "class Rectangle(Shape):\n", + " def __init__(self, length, width, x, y, **kwargs):\n", + " super().__init__(x, y, **kwargs)\n", + " self.length, self.width = length, width\n", + "\n", + " def area(self):\n", + " return self.length * self.width\n", + "\n", + " def perimeter(self):\n", + " return 2 * (self.length + self.width)\n", + "\n", + " def get_points(self):\n", + " return [(self.x + i % 4 * self.length / 4, self.y + i // 4 * self.width / 4) for i in range(16)]\n", + "\n", + "\n", + "class Circle(Shape):\n", + " def __init__(self, radius, x, y, **kwargs):\n", + " super().__init__(x, y, **kwargs)\n", + " self.radius = radius\n", + "\n", + " def area(self):\n", + " return math.pi * self.radius ** 2\n", + "\n", + " def perimeter(self):\n", + " return 2 * math.pi * self.radius\n", + "\n", + " def get_points(self):\n", + " return [(self.x + self.radius * math.cos(2 * math.pi * i / 16), self.y + self.radius * math.sin(2 * math.pi * i / 16)) for i in range(16)]\n", + "\n", + "\n", + "class Triangle(Shape):\n", + " def __init__(self, side1, side2, side3, x, y, **kwargs):\n", + " super().__init__(x, y, **kwargs)\n", + " self.side1, self.side2, self.side3 = side1, side2, side3\n", + "\n", + " def area(self):\n", + " s = (self.side1 + self.side2 + self.side3) / 2\n", + " return math.sqrt(s * (s - self.side1) * (s - self.side2) * (s - self.side3))\n", + "\n", + " def perimeter(self):\n", + " return self.side1 + self.side2 + self.side3\n", + "\n", + " def get_points(self):\n", + " return [(self.x + i % 8 * self.side1 / 8, self.y + i // 8 * self.side1 / 8) for i in range(16)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "8. Add a function to the object classes, including the base, that tests if a given set of $x$ and $y$ coordinates are inside of the object. You'll have to think through how to determine if a set of coordinates are inside an object for each object type." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "class Shape:\n", + " def __init__(self, x, y, **kwargs):\n", + " self.x, self.y, self.kwargs = x, y, kwargs\n", + "\n", + " def area(self):\n", + " raise NotImplementedError\n", + "\n", + " def perimeter(self):\n", + " raise NotImplementedError\n", + "\n", + " def get_points(self):\n", + " raise NotImplementedError\n", + " \n", + " def contains(self, x, y):\n", + " raise NotImplementedError(\"Subclasses must implement contains()\")\n", + " \n", + "\n", + "class Rectangle(Shape):\n", + " def __init__(self, length, width, x, y, **kwargs):\n", + " super().__init__(x, y, **kwargs)\n", + " self.length, self.width = length, width\n", + "\n", + " def area(self):\n", + " return self.length * self.width\n", + "\n", + " def perimeter(self):\n", + " return 2 * (self.length + self.width)\n", + "\n", + " def get_points(self):\n", + " return [(self.x + i % 4 * self.length / 4, self.y + i // 4 * self.width / 4) for i in range(16)]\n", + " \n", + " def contains(self, x, y):\n", + " return self.x <= x <= self.x + self.length and self.y <= y <= self.y + self.width\n", + "\n", + "\n", + "\n", + "class Circle(Shape):\n", + " def __init__(self, radius, x, y, **kwargs):\n", + " super().__init__(x, y, **kwargs)\n", + " self.radius = radius\n", + "\n", + " def area(self):\n", + " return math.pi * self.radius ** 2\n", + "\n", + " def perimeter(self):\n", + " return 2 * math.pi * self.radius\n", + "\n", + " def get_points(self):\n", + " return [(self.x + self.radius * math.cos(2 * math.pi * i / 16), self.y + self.radius * math.sin(2 * math.pi * i / 16)) for i in range(16)]\n", + " \n", + " def contains(self, x, y):\n", + " return (x - self.x) ** 2 + (y - self.y) ** 2 <= self.radius ** 2\n", + "\n", + "\n", + "class Triangle(Shape):\n", + " def __init__(self, side1, side2, side3, x, y, **kwargs):\n", + " super().__init__(x, y, **kwargs)\n", + " self.side1, self.side2, self.side3 = side1, side2, side3\n", + "\n", + " def area(self):\n", + " s = (self.side1 + self.side2 + self.side3) / 2\n", + " return math.sqrt(s * (s - self.side1) * (s - self.side2) * (s - self.side3))\n", + "\n", + " def perimeter(self):\n", + " return self.side1 + self.side2 + self.side3\n", + "\n", + " def get_points(self):\n", + " return [(self.x + i % 8 * self.side1 / 8, self.y + i // 8 * self.side1 / 8) for i in range(16)]\n", + " \n", + " def contains(self, x, y):\n", + " height = self.side1 * math.sqrt(3) / 2\n", + " base_y = self.y + height \n", + " return self.y <= y <= base_y and self.x - self.side1/2 <= x <= self.x + self.side1/2\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "9. Add a function in the base class of the object classes that returns true/false testing that the object overlaps with another object." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "class Shape:\n", + " def __init__(self, x, y, **kwargs):\n", + " self.x, self.y, self.kwargs = x, y, kwargs\n", + "\n", + " def area(self):\n", + " raise NotImplementedError\n", + "\n", + " def perimeter(self):\n", + " raise NotImplementedError\n", + "\n", + " def get_points(self):\n", + " raise NotImplementedError\n", + " \n", + " def contains(self, x, y):\n", + " raise NotImplementedError(\"Subclasses must implement contains()\")\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, **kwargs):\n", + " super().__init__(x, y, **kwargs)\n", + " self.length, self.width = length, width\n", + "\n", + " def area(self):\n", + " return self.length * self.width\n", + "\n", + " def perimeter(self):\n", + " return 2 * (self.length + self.width)\n", + "\n", + " def get_points(self):\n", + " return [(self.x + i % 4 * self.length / 4, self.y + i // 4 * self.width / 4) for i in range(16)]\n", + " \n", + " def contains(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, Circle):\n", + " dx = max(self.x - other.x, 0, other.x - (self.x + self.length))\n", + " dy = max(self.y - other.y, 0, other.y - (self.y + self.width))\n", + " return dx * dx + dy * dy < other.radius * other.radius\n", + " elif isinstance(other, Triangle):\n", + " rect_center_x = self.x + self.length / 2\n", + " rect_center_y = self.y + self.width / 2\n", + " return other.contains(rect_center_x, rect_center_y)\n", + " else:\n", + " raise NotImplementedError(\"Overlaps with Rectangle with shape of type \" + type(other).__name__)\n", + " \n", + " \n", + "\n", + "class Circle(Shape):\n", + " def __init__(self, radius, x, y, **kwargs):\n", + " super().__init__(x, y, **kwargs)\n", + " self.radius = radius\n", + "\n", + " def area(self):\n", + " return math.pi * self.radius ** 2\n", + "\n", + " def perimeter(self):\n", + " return 2 * math.pi * self.radius\n", + "\n", + " def get_points(self):\n", + " return [(self.x + self.radius * math.cos(2 * math.pi * i / 16), self.y + self.radius * math.sin(2 * math.pi * i / 16)) for i in range(16)]\n", + " \n", + " def contains(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, Circle):\n", + " return ((self.x - other.x) ** 2 + (self.y - other.y) ** 2) ** 0.5 < self.radius + other.radius\n", + " elif isinstance(other, Rectangle):\n", + " dx = max(other.x - self.x, 0, self.x - (other.x + other.length))\n", + " dy = max(other.y - self.y, 0, self.y - (other.y + other.width))\n", + " return dx * dx + dy * dy < self.radius * self.radius\n", + " elif isinstance(other, Triangle):\n", + " return other.contains(self.x, self.y)\n", + " else:\n", + " raise NotImplementedError(\"Overlaps with shape of type \" + type(other).__name__)\n", + " \n", + " \n", + "\n", + "class Triangle(Shape):\n", + " def __init__(self, side1, side2, side3, x, y, **kwargs):\n", + " super().__init__(x, y, **kwargs)\n", + " self.side1, self.side2, self.side3 = side1, side2, side3\n", + "\n", + " def area(self):\n", + " s = (self.side1 + self.side2 + self.side3) / 2\n", + " return math.sqrt(s * (s - self.side1) * (s - self.side2) * (s - self.side3))\n", + "\n", + " def perimeter(self):\n", + " return self.side1 + self.side2 + self.side3\n", + "\n", + " def get_points(self):\n", + " return [(self.x + i % 8 * self.side1 / 8, self.y + i // 8 * self.side1 / 8) for i in range(16)]\n", + " \n", + " def contains(self, x, y):\n", + " height = self.side1 * math.sqrt(3) / 2\n", + " base_y = self.y + height \n", + " return self.y <= y <= base_y and self.x - self.side1/2 <= x <= self.x + self.side1/2\n", + " \n", + " def overlaps(self, other):\n", + " if isinstance(other, Circle):\n", + " return self.contains(other.x, other.y)\n", + " elif isinstance(other, Rectangle):\n", + " rect_center_x = other.x + other.length / 2\n", + " rect_center_y = other.y + other.width / 2\n", + " return self.contains(rect_center_x, rect_center_y)\n", + " else:\n", + " raise NotImplementedError(\"Overlaps with Triangle with shape of type \" + type(other).__name__)\n", + "\n", + "# I had to refactor and rewrite my code into a cpaint py file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "10. Copy the `Canvas` class from lecture to in a python file creating a `paint` module. Copy your classes from above into the module and implement paint functions. Implement a `CompoundShape` class. Create a simple drawing demonstrating that all of your classes are working." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " \n", + " ********** \n", + " * * \n", + " * * \n", + " * * \n", + " * * \n", + " ********** \n", + " \n", + " \n" + ] + } + ], + "source": [ + "# In the new Python script\n", + "import cpaint\n", + "\n", + "# Create a canvas\n", + "canvas = cpaint.Canvas(20, 10)\n", + "\n", + "# Create shape\n", + "rectangle = cpaint.Rectangle(2, 2, 10, 5)\n", + "\n", + "# Draw shapes\n", + "drawing = cpaint.RasterDrawing(canvas)\n", + "drawing.add_shape(rectangle)\n", + "drawing.draw()\n", + "\n", + "# Display the canvas\n", + "canvas.display()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "11. Create a `RasterDrawing` class. Demonstrate that you can create a drawing made of several shapes, paint the drawing, modify the drawing, and paint it again. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " \n", + " ********** \n", + " * * \n", + " * * \n", + " * * \n", + " * * \n", + " ********** \n", + " \n", + " \n" + ] + } + ], + "source": [ + "# Python script/module\n", + "import cpaint\n", + "\n", + "# Create canvas\n", + "canvas = cpaint.Canvas(20, 10)\n", + "\n", + "# Create shapes\n", + "rectangle = cpaint.Rectangle(2, 2, 10, 5)\n", + "\n", + "# Draw shape\n", + "drawing = cpaint.RasterDrawing(canvas)\n", + "drawing.add_shape(rectangle)\n", + "drawing.draw()\n", + "\n", + "# Display\n", + "canvas.display()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12. Implement the ability to load/save raster drawings and demonstate that your method works. One way to implement this ability:\n", + "\n", + " * Overload `__repr__` functions of all objects to return strings of the python code that would construct the object.\n", + " \n", + " * In the save method of raster drawing class, store the representations into the file.\n", + " * Write a loader function that reads the file and uses `eval` to instantiate the object.\n", + "\n", + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class foo:\n", + " def __init__(self,a,b=None):\n", + " self.a=a\n", + " self.b=b\n", + " \n", + " def __repr__(self):\n", + " return \"foo(\"+repr(self.a)+\",\"+repr(self.b)+\")\"\n", + " \n", + " def save(self,filename):\n", + " f=open(filename,\"w\")\n", + " f.write(self.__repr__())\n", + " f.close()\n", + " \n", + " \n", + "def foo_loader(filename):\n", + " f=open(filename,\"r\")\n", + " tmp=eval(f.read())\n", + " f.close()\n", + " return tmp\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "foo(1,'hello')\n" + ] + } + ], + "source": [ + "# Test\n", + "print(repr(foo(1,\"hello\")))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an object and save it\n", + "ff=foo(1,\"hello\")\n", + "ff.save(\"Test.foo\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "'cat' is not recognized as an internal or external command,\n", + "operable program or batch file.\n" + ] + } + ], + "source": [ + "# Check contents of the saved file\n", + "!cat Test.foo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "foo(1,'hello')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load the object\n", + "ff_reloaded=foo_loader(\"Test.foo\")\n", + "ff_reloaded" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'Canvas' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[10], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcpaint\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Create raster drawing\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m c1 \u001b[38;5;241m=\u001b[39m \u001b[43mCanvas\u001b[49m(\u001b[38;5;241m20\u001b[39m, \u001b[38;5;241m10\u001b[39m)\n\u001b[1;32m 5\u001b[0m drawing \u001b[38;5;241m=\u001b[39m RasterDrawing(c1)\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# Add shapes\u001b[39;00m\n", + "\u001b[0;31mNameError\u001b[0m: name 'Canvas' is not defined" + ] + } + ], + "source": [ + "import cpaint\n", + "\n", + "# Create raster drawing\n", + "c1 = Canvas(20, 10)\n", + "drawing = RasterDrawing(c1)\n", + "\n", + "# Add shapes\n", + "drawing.add_shape(Rectangle(2, 2, 10, 5))\n", + "drawing.add_shape(Circle(15, 5, 3))\n", + "drawing.add_shape(Triangle(5, 7, 12, 7, 8, 2))\n", + "\n", + "# Draw raster drawing\n", + "drawing.draw()\n", + "c1.display()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'RasterDrawing' object has no attribute 'save'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[11], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Save drawing\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mdrawing\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdrawing.txt\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Load drawing\u001b[39;00m\n\u001b[1;32m 5\u001b[0m loaded_drawing \u001b[38;5;241m=\u001b[39m RasterDrawing\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdrawing.txt\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'RasterDrawing' object has no attribute 'save'" + ] + } + ], + "source": [ + "# Save drawing\n", + "drawing.save('drawing.txt')\n", + "\n", + "# Load drawing\n", + "loaded_drawing = RasterDrawing.load('drawing.txt')\n", + "\n", + "# Draw loaded drawing\n", + "loaded_drawing.draw()\n", + "loaded_drawing.c1.display()" + ] + }, + { + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Labs/Lab.5/Lab.5 - Matrix Representations.ipynb b/Labs/Lab.5/Lab.5 - Matrix Representations.ipynb new file mode 100644 index 0000000..a2248f4 --- /dev/null +++ b/Labs/Lab.5/Lab.5 - Matrix Representations.ipynb @@ -0,0 +1,1082 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lab 5\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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": {}, + "outputs": [], + "source": [ + "class Matrix:\n", + " def __init__(self, *args):\n", + " if len(args) == 2:\n", + " n, m = args\n", + " self.data = [[0] * m for _ in range(n)]\n", + " elif len(args) == 1:\n", + " if not isinstance(args[0], list):\n", + " raise TypeError(\"Argument must be a list of lists\")\n", + " rows = len(args[0])\n", + " cols = len(args[0][0])\n", + " for row in args[0]:\n", + " if len(row) != cols:\n", + " raise ValueError(\"All rows must have the same number of columns\")\n", + " self.data = args[0]\n", + " else:\n", + " raise TypeError(\"Invalid arguments\")\n", + " \n", + " def shape(self):\n", + " return len(self.data), len(self.data[0])\n", + "\n", + " def transpose(self):\n", + " transposed = [[self.data[j][i] for j in range(len(self.data))] for i in range(len(self.data[0]))]\n", + " return Matrix(transposed)\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[m_0:m_1]])\n", + "\n", + " def scalarmul(self, c):\n", + " return Matrix([[c * self.data[i][j] for j in range(len(self.data[0]))] for i in range(len(self.data))])\n", + "\n", + " def add(self, other):\n", + " if self.shape() != other.shape():\n", + " raise ValueError(\"Matrix dimensions must match\")\n", + " return Matrix([[self.data[i][j] + other.data[i][j] for j in range(len(self.data[0]))] for i in range(len(self.data))])\n", + "\n", + " def sub(self, other):\n", + " if self.shape() != other.shape():\n", + " raise ValueError(\"Matrix dimensions must match\")\n", + " return Matrix([[self.data[i][j] - other.data[i][j] for j in range(len(self.data[0]))] for i in range(len(self.data))])\n", + "\n", + " def mat_mult(self, other):\n", + " if self.shape()[1] != other.shape()[0]:\n", + " print(\"Matrix A dimensions:\", self.shape())\n", + " print(\"Matrix B dimensions:\", other.shape())\n", + " raise ValueError(\"Number of columns in first matrix must equal the number of rows in second matrix\")\n", + " \n", + " result = []\n", + " for i in range(self.shape()[0]):\n", + " row = []\n", + " for j in range(other.shape()[1]):\n", + " dot_product = sum(self.data[i][k] * other.data[k][j] for k in range(self.shape()[1]))\n", + " row.append(dot_product)\n", + " result.append(row)\n", + " \n", + " print(\"Result matrix dimensions:\", len(result), \"x\", len(result[0]))\n", + " return Matrix(result)\n", + "\n", + " def element_mult(self, other):\n", + " if self.shape() != other.shape():\n", + " raise ValueError(\"Matrix dimensions must match\")\n", + " return Matrix([[self.data[i][j] * other.data[i][j] for j in range(len(self.data[0]))] for i in range(len(self.data))])\n", + "\n", + " def equals(self, other):\n", + " return self.data == other.data\n", + "\n", + " def __getitem__(self, key):\n", + " if isinstance(key, tuple):\n", + " if len(key) == 2:\n", + " i, j = key\n", + " return self.data[i][j]\n", + " elif len(key) == 1:\n", + " i = key[0]\n", + " return Matrix([self.data[i]])\n", + " elif len(key) == 3:\n", + " i_start, i_end, j_end = key\n", + " return Matrix([row[:j_end] for row in self.data[i_start:i_end]])\n", + " else:\n", + " raise ValueError(\"Invalid slice\")\n", + " elif isinstance(key, int):\n", + " return self.data[key]\n", + " else:\n", + " raise TypeError(\"Invalid index type\")\n", + "\n", + " def __setitem__(self, key, value):\n", + " if isinstance(key, tuple):\n", + " i, j = key\n", + " self.data[i][j] = value\n", + " elif isinstance(key, int):\n", + " self.data[key] = value\n", + " else:\n", + " raise TypeError(\"Invalid index type\")\n", + "\n", + " def __eq__(self, other):\n", + " if not isinstance(other, Matrix):\n", + " return False\n", + " if len(self.data) != len(other.data):\n", + " return False\n", + " for i in range(len(self.data)):\n", + " if len(self.data[i]) != len(other.data[i]):\n", + " return False\n", + " for j in range(len(self.data[i])):\n", + " if self.data[i][j] != other.data[i][j]:\n", + " return False\n", + " return True\n", + " \n", + " # Overload operators \n", + " # * operator\n", + " def __mul__(self, other):\n", + " if isinstance(other, (int, float)):\n", + " result_matrix = self.scalarmul(other)\n", + " return result_matrix, result_matrix.shape()\n", + " elif isinstance(other, Matrix):\n", + " result_matrix = self.mat_mult(other)\n", + " return result_matrix, result_matrix.shape()\n", + " else:\n", + " raise TypeError(\"Unsupported operand type for *: Matrix and {}\".format(type(other)))\n", + "\n", + " # Enable reverse multiplication\n", + " def __rmul__(self, other):\n", + " return self.__mul__(other)\n", + "\n", + " # + operator\n", + " def __add__(self, other):\n", + " if isinstance(other, Matrix):\n", + " result_matrix = self.add(other)\n", + " return result_matrix, result_matrix.shape()\n", + " else:\n", + " raise TypeError(\"Unsupported operand type for +: Matrix and {}\".format(type(other)))\n", + "\n", + " # - operator\n", + " def __sub__(self, other):\n", + " if isinstance(other, Matrix):\n", + " result_matrix = self.sub(other)\n", + " return result_matrix, result_matrix.shape()\n", + " else:\n", + " raise TypeError(\"Unsupported operand type for -: Matrix and {}\".format(type(other)))\n", + "\n", + " # == operator\n", + " def __eq__(self, other):\n", + " if isinstance(other, Matrix):\n", + " return self.equals(other)\n", + " else:\n", + " return False\n", + "\n", + " # = operator for matrix assignment\n", + " def __setattr__(self, name, value):\n", + " if name == 'data':\n", + " object.__setattr__(self, name, value)\n", + " else:\n", + " super().__setattr__(name, value)\n", + " \n", + " @classmethod\n", + " def from_matrix(cls, other):\n", + " return cls(other.to_list())\n", + "\n", + " def __str__(self):\n", + " return '\\n'.join([' '.join(map(str, row)) for row in self.data])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0, 0, 0], [0, 0, 0]]\n" + ] + } + ], + "source": [ + "# Test Matrix class\n", + "m1 = Matrix(2, 3)\n", + "print(m1.data) # Output: [[0, 0, 0], [0, 0, 0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1, 2, 3], [4, 5, 6]]\n" + ] + } + ], + "source": [ + "m2 = Matrix([[1, 2, 3], [4, 5, 6]])\n", + "print(m2.data) # Output: [[1, 2, 3], [4, 5, 6]]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1, 2], [3, 4], [5, 6]]\n" + ] + } + ], + "source": [ + "m3 = Matrix([[1, 2], [3, 4], [5, 6]])\n", + "print(m3.data) # Output: [[1, 2], [3, 4], [5, 6]]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + "[[1, 2, 3], [4, 5, 10]]\n" + ] + } + ], + "source": [ + "# Test indexing\n", + "print(m2[0, 1]) # Output: 2\n", + "m2[1, 2] = 10\n", + "print(m2.data) # Output: [[1, 2, 3], [4, 5, 10]]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n" + ] + } + ], + "source": [ + "# Testing equality protocol\n", + "m4 = Matrix(2, 3)\n", + "print(m1 == m4) # Output: True\n", + "print(m1 == m2) # Output: False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# See cell 1 (Matrix class)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Test updated class\n", + "M = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape: (3, 3)\n" + ] + } + ], + "source": [ + "print(\"Shape:\", M.shape())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transpose:\n", + "1 4 7\n", + "2 5 8\n", + "3 6 9\n" + ] + } + ], + "source": [ + "print(\"Transpose:\")\n", + "print(M.transpose())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Row 1:\n", + "4 5 6\n" + ] + } + ], + "source": [ + "print(\"Row 1:\")\n", + "print(M.row(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Column 2:\n", + "3\n", + "6\n", + "9\n" + ] + } + ], + "source": [ + "print(\"Column 2:\")\n", + "print(M.column(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matrix as a list of lists:\n", + "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n" + ] + } + ], + "source": [ + "print(\"Matrix as a list of lists:\")\n", + "print(M.to_list())" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Block:\n", + "1 2\n", + "4 5\n" + ] + } + ], + "source": [ + "print(\"Block:\")\n", + "print(M.block(0, 2, 0, 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Slice:\n", + "[1, 2]\n" + ] + } + ], + "source": [ + "print(\"Slice:\")\n", + "print(M[0, :2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def constant(n, m, c):\n", + " return Matrix([[c] * m for _ in range(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": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Constant matrix:\n", + "5 5 5\n", + "5 5 5\n", + "5 5 5\n" + ] + } + ], + "source": [ + "# Test functions\n", + "print(\"Constant matrix:\")\n", + "print(constant(3, 3, 5))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Zeros matrix:\n", + "0 0\n", + "0 0\n" + ] + } + ], + "source": [ + "print(\"Zeros matrix:\")\n", + "print(zeros(2, 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ones matrix:\n", + "1 1 1\n", + "1 1 1\n" + ] + } + ], + "source": [ + "print(\"Ones matrix:\")\n", + "print(ones(2, 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Identity matrix:\n", + "1 0 0 0\n", + "0 1 0 0\n", + "0 0 1 0\n", + "0 0 0 1\n" + ] + } + ], + "source": [ + "print(\"Identity matrix:\")\n", + "print(eye(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Refer to cell 1 Matrix class" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scalar multiplication:\n", + "2 4 6\n", + "8 10 12\n" + ] + } + ], + "source": [ + "# Test updated class\n", + "M = Matrix([[1, 2, 3], [4, 5, 6]])\n", + "N = Matrix([[7, 8, 9], [10, 11, 12]])\n", + "\n", + "print(\"Scalar multiplication:\")\n", + "print(M.scalarmul(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matrix addition:\n", + "8 10 12\n", + "14 16 18\n" + ] + } + ], + "source": [ + "print(\"Matrix addition:\")\n", + "print(M.add(N))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matrix subtraction:\n", + "-6 -6 -6\n", + "-6 -6 -6\n" + ] + } + ], + "source": [ + "print(\"Matrix subtraction:\")\n", + "print(M.sub(N))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matrix multiplication:\n", + "Result matrix dimensions: 3 x 3\n", + "27 30 33\n", + "61 68 75\n", + "95 106 117\n" + ] + } + ], + "source": [ + "M2 = Matrix([[1, 2], [3, 4], [5, 6]])\n", + "N2 = Matrix([[7, 8, 9], [10, 11, 12]])\n", + "print(\"Matrix multiplication:\")\n", + "print(M2.mat_mult(N2))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Element-wise multiplication:\n", + "7 16 27\n", + "40 55 72\n" + ] + } + ], + "source": [ + "print(\"Element-wise multiplication:\")\n", + "print(M.element_mult(N))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Equality check:\n", + "True\n" + ] + } + ], + "source": [ + "print(\"Equality check:\")\n", + "print(M.equals(Matrix([[1, 2, 3], [4, 5, 6]])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of scalar multiplication: 2 4\n", + "6 8\n", + "Dimensions of the result: (2, 2)\n" + ] + } + ], + "source": [ + "M = Matrix([[1, 2], [3, 4]])\n", + "N = Matrix([[5, 6], [7, 8]])\n", + "\n", + "# Scalar multiplication\n", + "result1, shape1 = 2 * M\n", + "\n", + "print(\"Result of scalar multiplication:\", result1)\n", + "print(\"Dimensions of the result:\", shape1)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result matrix dimensions: 2 x 2\n", + "Result of matrix multiplication: 19 22\n", + "43 50\n", + "Dimensions of the result: (2, 2)\n" + ] + } + ], + "source": [ + "# Matrix multiplication\n", + "result2, shape2 = M * N\n", + "\n", + "print(\"Result of matrix multiplication:\", result2)\n", + "print(\"Dimensions of the result:\", shape2)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of matrix addition: 6 8\n", + "10 12\n", + "Dimensions of the result: (2, 2)\n" + ] + } + ], + "source": [ + "# Matrix addition\n", + "result3, shape3 = M + N\n", + "\n", + "print(\"Result of matrix addition:\", result3)\n", + "print(\"Dimensions of the result:\", shape3)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result of matrix subtraction: -4 -4\n", + "-4 -4\n", + "Dimensions of the result: (2, 2)\n" + ] + } + ], + "source": [ + "# Matrix subtraction\n", + "result4, shape4 = M - N\n", + "\n", + "print(\"Result of matrix subtraction:\", result4)\n", + "print(\"Dimensions of the result:\", shape4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result matrix dimensions: 2 x 2\n", + "Result matrix dimensions: 2 x 2\n", + "Result matrix dimensions: 2 x 2\n", + "Result matrix dimensions: 2 x 2\n", + "Property 1 (Associativity): (AB)C = A(BC) - True\n" + ] + } + ], + "source": [ + "A = Matrix([[1, 2], [3, 4]])\n", + "B = Matrix([[5, 6], [7, 8]])\n", + "C = Matrix([[9, 10], [11, 12]])\n", + "\n", + "# Associative\n", + "result1 = (A * B)[0] * C\n", + "result2 = A * (B * C)[0]\n", + "print(\"Property 1 (Associativity): (AB)C = A(BC) -\", result1 == result2)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result matrix dimensions: 2 x 2\n", + "Result matrix dimensions: 2 x 2\n", + "Result matrix dimensions: 2 x 2\n", + "Property 2 (Distributive property): A(B+C) = AB + AC - False\n" + ] + } + ], + "source": [ + "# Distributive\n", + "result3 = A * (B + C)[0]\n", + "result4 = A * B + A * C\n", + "print(\"Property 2 (Distributive property): A(B+C) = AB + AC -\", result3 == result4)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result matrix dimensions: 2 x 2\n", + "Result matrix dimensions: 2 x 2\n", + "Property 3 (Non-commutativity): AB ≠ BA - True\n" + ] + } + ], + "source": [ + "# Non-commutative: AB ≠ BA\n", + "result5 = A * B\n", + "result6 = B * A\n", + "print(\"Property 3 (Non-commutativity): AB ≠ BA -\", result5 != result6)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result matrix dimensions: 2 x 2\n", + "Property 4 (Identity matrix property): AI = A - False\n" + ] + } + ], + "source": [ + "# Identity matrix\n", + "I = Matrix([[1, 0], [0, 1]])\n", + "result7 = A * I\n", + "print(\"Property 4 (Identity matrix property): AI = A -\", result7 == A)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(AB)C = A(BC) : True\n" + ] + } + ], + "source": [ + "# Overloading operators not coded properly. An easier implementation would be utilizing numpy library\n", + "import numpy as np\n", + "\n", + "A2 = np.array([[2, 1],\n", + " [3, 4]])\n", + "B2 = np.array([[5, 6],\n", + " [7, 8]])\n", + "C2 = np.array([[9, 10],\n", + " [11, 12]])\n", + "\n", + "# (AB)C\n", + "result1 = np.dot(np.dot(A2, B2), C2)\n", + "# A(BC)\n", + "result2 = np.dot(A2, np.dot(B2, C2))\n", + "# (AB)C = A(BC)\n", + "associative_property = np.array_equal(result1, result2)\n", + "print(\"(AB)C = A(BC) :\", associative_property)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A(B+C) = AB + AC : True\n" + ] + } + ], + "source": [ + "# A(B+C)\n", + "result3 = np.dot(A2, B2 + C2)\n", + "# AB + AC\n", + "result4 = np.dot(A2, B2) + np.dot(A2, C2)\n", + "# A(B+C) = AB + AC\n", + "distributive_property = np.array_equal(result3, result4)\n", + "print(\"A(B+C) = AB + AC :\", distributive_property)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AB != BA : True\n" + ] + } + ], + "source": [ + "# AB, BA\n", + "AB = np.dot(A2, B2)\n", + "BA = np.dot(B2, A2)\n", + "# AB != BA\n", + "commutativity = np.array_equal(AB, BA)\n", + "print(\"AB != BA :\", not commutativity)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AI = A : True\n" + ] + } + ], + "source": [ + "# identity matrix\n", + "I = np.identity(2)\n", + "# AI\n", + "result5 = np.dot(A2, I)\n", + "# AI = A\n", + "identity_property = np.array_equal(result5, A2)\n", + "print(\"AI = A :\", identity_property)" + ] + }, + { + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Labs/Lab.5/Quiz-2.ipynb b/Labs/Lab.5/Quiz-2.ipynb new file mode 100644 index 0000000..22a4ef7 --- /dev/null +++ b/Labs/Lab.5/Quiz-2.ipynb @@ -0,0 +1,82 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cbd392fb-c0a5-464e-98a8-ea378338e6af", + "metadata": {}, + "source": [ + "## Quiz 2" + ] + }, + { + "cell_type": "markdown", + "id": "41bccbdb-1d91-41a8-997e-9f1657883981", + "metadata": {}, + "source": [ + "Write a function make_deck that returns a list of all of the cards in a standard card deck. The return should be a list of tuples of pairs of suit and value. For example the 10 of Clubs would be ('Clubs', 10) and Queen of Hearts would be ('Hearts', 'Queen'). Recall that a deck has 52 cards, divided into 4 suits (Clubs, Diamonds, Hearts, and Spades), and that each suit has 13 cards: 2 to 10, Jack, Queen, King, and Ace" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "559e3b46-d6d0-43db-89ea-86ffaed99d4e", + "metadata": {}, + "outputs": [], + "source": [ + "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", + " deck = [(suit, value) for suit in suits for value in values]\n", + " return deck" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "53c27bee-eea9-406a-8ad2-3d3b3c3fd2f1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('Clubs', '2'), ('Clubs', '3'), ('Clubs', '4'), ('Clubs', '5'), ('Clubs', '6'), ('Clubs', '7'), ('Clubs', '8'), ('Clubs', '9'), ('Clubs', '10'), ('Clubs', 'Jack'), ('Clubs', 'Queen'), ('Clubs', 'King'), ('Clubs', 'Ace'), ('Diamonds', '2'), ('Diamonds', '3'), ('Diamonds', '4'), ('Diamonds', '5'), ('Diamonds', '6'), ('Diamonds', '7'), ('Diamonds', '8'), ('Diamonds', '9'), ('Diamonds', '10'), ('Diamonds', 'Jack'), ('Diamonds', 'Queen'), ('Diamonds', 'King'), ('Diamonds', 'Ace'), ('Hearts', '2'), ('Hearts', '3'), ('Hearts', '4'), ('Hearts', '5'), ('Hearts', '6'), ('Hearts', '7'), ('Hearts', '8'), ('Hearts', '9'), ('Hearts', '10'), ('Hearts', 'Jack'), ('Hearts', 'Queen'), ('Hearts', 'King'), ('Hearts', 'Ace'), ('Spades', '2'), ('Spades', '3'), ('Spades', '4'), ('Spades', '5'), ('Spades', '6'), ('Spades', '7'), ('Spades', '8'), ('Spades', '9'), ('Spades', '10'), ('Spades', 'Jack'), ('Spades', 'Queen'), ('Spades', 'King'), ('Spades', 'Ace')]\n" + ] + } + ], + "source": [ + "deck = make_deck()\n", + "print(deck)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "630af2f7-afda-4550-9b72-46df7783fcd5", + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Labs/Lab.6/Lab.6 - RobertCocker.ipynb b/Labs/Lab.6/Lab.6 - RobertCocker.ipynb new file mode 100644 index 0000000..d2bf868 --- /dev/null +++ b/Labs/Lab.6/Lab.6 - RobertCocker.ipynb @@ -0,0 +1,25645 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 290, + "metadata": {}, + "outputs": [], + "source": [ + "# Robert Cocker\n", + "# Dr. Farin\n", + "# DATA 3402\n", + "# Lab 6\n", + "# 3/16/2024" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lab 6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You are tasked with evaluating card counting strategies for black jack. In order to do so, you will use object oriented programming to create a playable casino style black jack game where a computer dealer plays against $n$ computer players and possibily one human player. If you don't know the rules of blackjack or card counting, please google it. \n", + "\n", + "A few requirements:\n", + "* The game should utilize multiple 52-card decks. Typically the game is played with 6 decks.\n", + "* Players should have chips.\n", + "* Dealer's actions are predefined by rules of the game (typically hit on 16). \n", + "* The players should be aware of all shown cards so that they can count cards.\n", + "* Each player could have a different strategy.\n", + "* The system should allow you to play large numbers of games, study the outcomes, and compare average winnings per hand rate for different strategies." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Begin by creating a classes to represent cards and decks. The deck should support more than one 52-card set. The deck should allow you to shuffle and draw cards. Include a \"plastic\" card, placed randomly in the deck. Later, when the plastic card is dealt, shuffle the cards before the next deal." + ] + }, + { + "cell_type": "code", + "execution_count": 303, + "metadata": {}, + "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 __str__(self):\n", + " return f\"{self.rank} of {self.suit}\"\n", + "\n", + "class Deck:\n", + " def __init__(self, num_decks=1):\n", + " self.num_decks = num_decks\n", + " self.cards = [Card(suit, rank) for _ in range(num_decks) for suit in ['Clubs', 'Hearts', 'Spades', 'Diamonds'] \n", + " for rank in ['2', '3', '4', '5', '6', '7', '8', '9', '10', 'Jack', 'Queen', 'King', 'Ace']]\n", + " self.plastic_position = random.randint(0, 52 * num_decks) # position of plastic card\n", + "\n", + " def shuffle(self):\n", + " random.shuffle(self.cards)\n", + "\n", + " def draw(self):\n", + " return self.cards.pop(0)\n", + "\n", + " def needs_reshuffle(self):\n", + " return self.cards and len(self.cards) == self.plastic_position\n", + "\n", + "class Player:\n", + " def __init__(self, name, chips=0):\n", + " self.name = name\n", + " self.chips = chips\n", + " self.hand = Hand()\n", + "\n", + " def buy_chips(self):\n", + " self.chips += int(input(f\"{self.name}, how many chips would you like to buy? (Maximum bet 10,000 chips) \"))\n", + "\n", + " def receive_card(self, card):\n", + " self.hand.add_card(card)\n", + "\n", + " def clear_hand(self):\n", + " self.hand.clear()\n", + "\n", + " def get_hand_value(self):\n", + " return self.hand.get_value()\n", + "\n", + " def buy_chips(self):\n", + " self.chips += int(input(f\"{self.name}, how many chips would you like to buy? \"))\n", + "\n", + " def make_bet(self, amount):\n", + " if amount > self.chips:\n", + " print(\"Insufficient chips. Please bet a lower amount.\")\n", + " return False\n", + " else:\n", + " self.chips -= amount\n", + " return True\n", + "\n", + "class HumanPlayer(Player):\n", + " pass\n", + " \n", + "class Hand:\n", + " def __init__(self):\n", + " self.cards = []\n", + "\n", + " def add_card(self, card):\n", + " self.cards.append(card)\n", + "\n", + " def get_value(self):\n", + " total_value = 0\n", + " num_aces = 0\n", + " for card in self.cards:\n", + " if card.rank in ['Jack', 'Queen', 'King']:\n", + " total_value += 10\n", + " elif card.rank == 'Ace':\n", + " num_aces += 1\n", + " total_value += 11 # Initial value of Ace is 11\n", + " else:\n", + " total_value += int(card.rank)\n", + "\n", + " # Adjust Ace value if needed\n", + " while total_value > 21 and num_aces:\n", + " total_value -= 10 # Change Ace value to 1\n", + " num_aces -= 1\n", + "\n", + " return total_value\n", + "\n", + " def is_busted(self):\n", + " return self.get_value() > 21\n", + " \n", + " def clear(self):\n", + " self.cards.clear()\n", + "\n", + "class Game:\n", + " def __init__(self, verbose=True):\n", + " self.players = []\n", + " self.dealer = Dealer(\"Dealer\", deck=Deck())\n", + " self.running = True\n", + " self.verbose = verbose\n", + "\n", + " def select_mode(self):\n", + " mode = input(\"Choose mode (auto/regular): \").lower()\n", + " return mode\n", + " \n", + " def setup_game(self):\n", + " num_players = int(input(\"Enter the number of players (up to 7): \"))\n", + " if num_players < 1 or num_players > 7:\n", + " raise ValueError(\"Number of players must be between 1 and 7\")\n", + " mode = self.select_mode()\n", + " for i in range(num_players): \n", + " name = input(f\"Enter name for Player {i+1}: \")\n", + " if mode == 'auto' and i != 0:\n", + " self.players.append(CardCountPlayer2(name))\n", + " else:\n", + " player_type = input(f\"Enter type for Player {i+1} (human/card_counter): \")\n", + " if player_type == \"human\":\n", + " self.players.append(HumanPlayer(name))\n", + " elif player_type == \"card_counter\":\n", + " self.players.append(CardCounterPlayer2(name))\n", + " else:\n", + " raise ValueError(\"Invalid player type. Choose 'human' or 'card_counter'.\")\n", + " \n", + " # dealer\n", + " self.dealer = Dealer(\"Dealer\", deck=Deck())\n", + " self.players.append(self.dealer)\n", + "\n", + " def start_round(self):\n", + " self.dealer.deck = Deck()\n", + " self.dealer.deck.shuffle()\n", + " for player in self.players:\n", + " player.clear_hand()\n", + " self.dealer.deal_initial_cards(self.players + [self.dealer])\n", + "\n", + " def start_game(self):\n", + " print(\"\\nWelcome to the Blackjack Game!\\n\")\n", + " for player in self.players:\n", + " chips = int(input(f\"{player.name}, how many chips would you like to buy? (Maximum bet 10,000 chips) \"))\n", + " player.buy_chips(chips)\n", + "\n", + " def play_round(self):\n", + " print(\"\\nStarting a new round...\")\n", + " for player in self.players:\n", + " # Skipping human players in auto mode\n", + " if isinstance(player, HumanPlayer):\n", + " continue\n", + " print(f\"\\n{player.name}'s turn:\")\n", + " if player.chips <= 0: # Skip if player has no chips\n", + " print(f\"{player.name} has no chips and skips this round.\")\n", + " continue\n", + " max_bet = min(player.chips, 10000) # max bet 10,000 chips\n", + " print(f\"You have {player.chips} chips.\")\n", + " bet = int(input(f\"Enter your bet amount (maximum bet: {max_bet:,} chips): \"))\n", + " while bet > player.chips or bet <= 0: # Ensure the bet is valid\n", + " print(\"Invalid bet amount. Please enter a valid bet.\")\n", + " bet = int(input(f\"Enter your bet amount (maximum bet: {max_bet:,} chips): \"))\n", + " if player.make_bet(bet):\n", + " self.dealer.deal_card(player)\n", + " print(f\"{player.name}'s hand: {', '.join(str(card) for card in player.hand.cards)}\")\n", + " if isinstance(player, CardCounterPlayer): # Check if player is a CardCounterPlayer\n", + " for card in player.hand.cards:\n", + " player.count_card(card)\n", + " while True:\n", + " decision = player.make_decision()\n", + " print(f\"{player.name} decides to {decision}.\")\n", + " if decision == 'hit':\n", + " self.dealer.deal_card(player)\n", + " print(f\"{player.name}'s hand: {', '.join(str(card) for card in player.hand.cards)}\")\n", + " if player.get_hand_value() > 21:\n", + " print(f\"{player.name} busts.\")\n", + " break\n", + " else:\n", + " print(f\"{player.name} stays.\")\n", + " break\n", + " else:\n", + " print(f\"{player.name} cannot make a bet and skips this round.\")\n", + " player.clear_hand()\n", + " \n", + " print(\"\\nDealer's turn:\")\n", + " self.dealer.deal_card(self.dealer)\n", + " self.dealer.deal_card(self.dealer)\n", + " bet = 1000\n", + " self.end_round(bet) \n", + " print(f\"Dealer's hand: {self.dealer.hand.cards[0]}, [Hidden]\")\n", + " return bet\n", + " \n", + " def end_round(self, bet, verbose=False):\n", + " aggregated_output = \"\"\n", + " for player in self.players:\n", + " if not isinstance(player, Dealer):\n", + " if player.hand.is_busted():\n", + " print(f\"{player.name} busted.\")\n", + " elif self.dealer.hand.is_busted() or player.hand.get_value() > self.dealer.hand.get_value():\n", + " print(f\"{player.name} wins {bet} chips.\")\n", + " player.chips += bet\n", + " elif player.hand.get_value() < self.dealer.hand.get_value():\n", + " print(f\"{player.name} loses {bet} chips.\")\n", + " player.chips -= bet\n", + " elif player.hand.get_value() == self.dealer.hand.get_value():\n", + " print(f\"{player.name} ties with the dealer.\")\n", + " else:\n", + " if mode == 'auto':\n", + " aggregated_output += player.chips\n", + " if verbose:\n", + " print(f\"Simulation running...\")\n", + " \n", + " return aggregated_output\n", + " \n", + "\n", + " def play(self):\n", + " print(\"\\nWelcome to Blackjack!\\n\")\n", + " mode = self.select_mode()\n", + " card_counter_winnings = 0\n", + "\n", + " if mode == 'auto':\n", + " num_games, num_rounds, initial_chips = self.get_auto_settings()\n", + " self.setup_auto_mode(initial_chips)\n", + " all_winnings = self.play_auto_mode(num_games, num_rounds, initial_chips)\n", + " if self.verbose:\n", + " print(\"Simulation running...\\n\")\n", + " self.display_auto_results(all_winnings, num_games, num_rounds)\n", + " elif mode == 'regular':\n", + " self.setup_regular_mode()\n", + " self.play_regular_mode()\n", + " else:\n", + " print(\"Invalid mode. Please choose either 'auto' or 'regular'.\")\n", + " if self.verbose:\n", + " print(f\"CardCounterPlayer's total winnings: {card_counter_winnings}\")\n", + "\n", + " def get_auto_settings(self):\n", + " return (\n", + " int(input(\"Enter number of games: \")),\n", + " int(input(\"Enter number of rounds: \")),\n", + " int(input(\"Enter initial amount of chips per player: \"))\n", + " )\n", + " \n", + " def setup_auto_mode(self, initial_chips):\n", + " self.players.append(Dealer(\"Dealer\", deck=Deck()))\n", + " for _ in range(4): # maximum of 7 players including dealer\n", + " self.players.append(CardCounterPlayer(f\"Player {len(self.players) + 1}\"))\n", + " \n", + " def play_auto_mode(self, num_games, num_rounds, initial_chips):\n", + " all_winnings = []\n", + " dealer_index = None\n", + " \n", + " for i, player in enumerate(self.players):\n", + " if isinstance(player, Dealer):\n", + " dealer_index = i\n", + " break\n", + " \n", + " # Initial chips for players 10,000\n", + " for player in self.players:\n", + " if isinstance(player, Player):\n", + " player.chips = 100000\n", + " \n", + " for _ in range(num_games):\n", + " winnings_per_game = []\n", + " for _ in range(num_rounds):\n", + " self.start_round()\n", + " bet = 100 \n", + " self.end_round(bet)\n", + " card_counter_winnings = sum(player.chips - initial_chips for player in self.players if not isinstance(player, Dealer))\n", + " winnings_per_game.append(card_counter_winnings)\n", + " all_winnings += winnings_per_game\n", + " return all_winnings\n", + " \n", + " def display_auto_results(self, all_winnings, num_games, num_rounds):\n", + " if all_winnings:\n", + " average_winnings_per_round = np.mean(all_winnings) / (num_games * num_rounds)\n", + " std_dev = np.std(all_winnings)\n", + " net_winnings = [w for w in all_winnings if w > 0]\n", + " net_loss = [w for w in all_winnings if w < 0]\n", + " prob_win = len(net_winnings) / len(all_winnings)\n", + " prob_loss = len(net_loss) / len(all_winnings)\n", + " plt.hist(all_winnings, bins=20)\n", + " plt.title('Aggregate Histogram of Winnings')\n", + " plt.xlabel('Winnings')\n", + " plt.ylabel('Frequency')\n", + " plt.show()\n", + " print(\"Aggregate Statistics:\")\n", + " print(f\"Average winnings per round: {average_winnings_per_round}\")\n", + " print(f\"Standard deviation of winnings: {std_dev}\")\n", + " print(f\"Probability of net winning after {num_rounds} rounds: {prob_win}\")\n", + " print(f\"Probability of net losing after {num_rounds} rounds: {prob_loss}\")\n", + " \n", + " def setup_regular_mode(self):\n", + " num_players = int(input(\"Enter the number of players (up to 7): \"))\n", + " if num_players < 1 or num_players > 7:\n", + " raise ValueError(\"Number of players must be between 1 and 7\")\n", + " for i in range(num_players):\n", + " name = input(f\"Enter name for Player {i+1}: \")\n", + " player_type = input(f\"Enter type for Player {i+1} (human/card_counter): \")\n", + " if player_type == \"human\":\n", + " self.players.append(HumanPlayer(name))\n", + " elif player_type == \"card_counter\":\n", + " self.players.append(CardCounterPlayer(name))\n", + " else:\n", + " raise ValueError(\"Invalid player type. Choose 'human' or 'card_counter'.\")\n", + " for player in self.players:\n", + " if not isinstance(player, Dealer):\n", + " chips = int(input(f\"{player.name}, how many chips would you like to buy? (Maximum bet 10,000 chips) \"))\n", + " player.buy_chips()\n", + " \n", + " def play_regular_mode(self):\n", + " self.setup_game()\n", + " while self.running:\n", + " self.start_round()\n", + " bet = self.play_round()\n", + " self.end_round(bet)\n", + " self.running = input(\"Do you want to play another round? (yes/no): \").lower() == 'yes'\n", + "\n", + "\n", + "class Dealer(Player):\n", + " def __init__(self, name, deck):\n", + " super().__init__(name)\n", + " self.deck = deck\n", + " self.hand = Hand()\n", + "\n", + " def buy_chips(self):\n", + " pass\n", + "\n", + " def receive_card(self, card):\n", + " self.hand.add_card(card)\n", + "\n", + " def clear_hand(self):\n", + " self.hand.clear()\n", + "\n", + " def get_hand_value(self):\n", + " return self.hand.get_value()\n", + "\n", + " def deal_card(self, player=None):\n", + " card = self.deck.draw()\n", + " if player:\n", + " player.receive_card(card)\n", + " else:\n", + " self.hand.add_card(card)\n", + "\n", + " def deal_initial_cards(self, players):\n", + " for _ in range(2):\n", + " for player in players:\n", + " self.deal_card(player)\n", + " self.deal_card()\n", + "\n", + "class CardCounterPlayer(Player):\n", + " def __init__(self, name):\n", + " super().__init__(name)\n", + " self.card_count = 0\n", + "\n", + " def count_card(self, card):\n", + " if card.rank in ['2', '3', '4', '5', '6']:\n", + " self.card_count += 1\n", + " elif card.rank in ['10', 'Jack', 'Queen', 'King', 'Ace']:\n", + " self.card_count -= 1\n", + "\n", + " def make_decision(self):\n", + " if self.card_count <= -2:\n", + " return 'hit'\n", + " elif self.card_count >= -0:\n", + " return 'stay'\n", + " else:\n", + " return 'hit' \n", + "\n", + "import random\n", + "\n", + "class CardCounterPlayer2(Player):\n", + " def __init__(self, name):\n", + " super().__init__(name)\n", + " self.q_table = {} \n", + " self.epsilon = 0.1 \n", + " self.alpha = 0.8 # Learning rate\n", + " self.gamma = 0.9 \n", + " self.state = None\n", + " self.action_space = ['hit', 'stay']\n", + "\n", + " def get_state(self, dealer_card):\n", + " return (self.hand.get_value(), dealer_card.rank)\n", + "\n", + " def get_action(self):\n", + " if random.random() < self.epsilon:\n", + " return random.choice(self.action_space)\n", + " else:\n", + " if self.state in self.q_table:\n", + " return max(self.q_table[self.state], key=self.q_table[self.state].get)\n", + " else:\n", + " return random.choice(self.action_space)\n", + "\n", + " def update_q_table(self, next_state, reward):\n", + " if self.state not in self.q_table:\n", + " self.q_table[self.state] = {'hit': 0, 'stay': 0}\n", + "\n", + " if next_state not in self.q_table:\n", + " self.q_table[next_state] = {'hit': 0, 'stay': 0}\n", + "\n", + " max_next_q = max(self.q_table[next_state].values())\n", + " self.q_table[self.state][self.action] += self.alpha * (reward + self.gamma * max_next_q - self.q_table[self.state][self.action])\n", + "\n", + " def make_decision(self, dealer_card):\n", + " self.state = self.get_state(dealer_card)\n", + " self.action = self.get_action()\n", + " return self.action\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawn card: 10 of Diamonds\n" + ] + } + ], + "source": [ + "# Test the class\n", + "deck = Deck(num_decks=6) # Create a deck with 6 sets of 52 cards\n", + "deck.shuffle() # Shuffle deck\n", + "\n", + "# Draw a card\n", + "card = deck.draw()\n", + "print(\"Drawn card:\", card)\n", + "\n", + "# Check deck if needs reshuffling (plastic card)\n", + "if deck.needs_reshuffle():\n", + " deck.shuffle()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Now design your game on a UML diagram. You may want to create classes to represent, players, a hand, and/or the game. As you work through the lab, update your UML diagram. At the end of the lab, submit your diagram (as pdf file) along with your notebook. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# See UML diagram in Lab.6 folder" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Begin with implementing the skeleton (ie define data members and methods/functions, but do not code the logic) of the classes in your UML diagram." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# See cell 1 for class implementations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Complete the implementation by coding the logic of all functions. For now, just implement the dealer player and human player." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# See cell 1 for class implementation. Updated classes as I went along through the steps." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Test. Demonstrate game play. For example, create a game of several dealer players and show that the game is functional through several rounds." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Starting a new round...\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, how many chips would you like to buy? 20000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You have 20000 chips.\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your bet amount (maximum bet: 10,000 chips): 5000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1's hand: 4 of Diamonds, Queen of Spades, 7 of Diamonds, 4 of Spades\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, how many chips would you like to buy? 20000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You have 20000 chips.\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your bet amount (maximum bet: 10,000 chips): 5000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2's hand: Queen of Diamonds, 4 of Clubs, 2 of Hearts, King of Clubs\n", + "\n", + "Dealer's turn:\n", + "Dealer's hand: King of Hearts, [Hidden]\n", + "\n", + "Round ended. Evaluating hands...\n", + "Dealer's hand value: 46\n", + "Player 1's hand value: 25\n", + "Player 1 loses 5000 chips.\n", + "Player 2's hand value: 26\n", + "Player 2 loses 5000 chips.\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Do you want to play another round? (yes/no): yes\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Starting a new round...\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, how many chips would you like to buy? 50000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You have 60000 chips.\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your bet amount (maximum bet: 10,000 chips): 5000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1's hand: Jack of Clubs, 5 of Clubs, Jack of Hearts, 3 of Clubs\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, how many chips would you like to buy? 50000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You have 60000 chips.\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your bet amount (maximum bet: 10,000 chips): 5000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2's hand: 3 of Diamonds, Queen of Hearts, King of Spades, 2 of Diamonds\n", + "\n", + "Dealer's turn:\n", + "Dealer's hand: 2 of Spades, [Hidden]\n", + "\n", + "Round ended. Evaluating hands...\n", + "Dealer's hand value: 44\n", + "Player 1's hand value: 28\n", + "Player 1 loses 5000 chips.\n", + "Player 2's hand value: 25\n", + "Player 2 loses 5000 chips.\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Do you want to play another round? (yes/no): no\n" + ] + } + ], + "source": [ + "# See cell 1 for logic and class implementations.\n", + "# Testing blackjack game\n", + "num_players = 2\n", + "player_info = [(\"Player 1\", \"human\"), (\"Player 2\", \"card_counter\")]\n", + "game = Game(player_info)\n", + "game.play()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "6. Implement a new player with the following strategy:\n", + "\n", + " * Assign each card a value: \n", + " * Cards 2 to 6 are +1 \n", + " * Cards 7 to 9 are 0 \n", + " * Cards 10 through Ace are -1\n", + " * Compute the sum of the values for all cards seen so far.\n", + " * Hit if sum is very negative, stay if sum is very positive. Select a threshold for hit/stay, e.g. 0 or -2. " + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Starting a new round...\n", + "\n", + "Player 1's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 1, how many chips would you like to buy? 50000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You have 50000 chips.\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your bet amount (maximum bet: 10,000 chips): 500\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 1's hand: Queen of Diamonds, King of Spades, 5 of Spades, 2 of Clubs\n", + "\n", + "Player 2's turn:\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Player 2, how many chips would you like to buy? 50000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You have 50000 chips.\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your bet amount (maximum bet: 10,000 chips): 500\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Player 2's hand: 9 of Spades, 3 of Diamonds, Queen of Hearts, 9 of Hearts\n", + "Player 2 decides to hit.\n", + "Player 2's hand: 9 of Spades, 3 of Diamonds, Queen of Hearts, 9 of Hearts, Jack of Spades\n", + "Player 2 busts.\n", + "\n", + "Dealer's turn:\n", + "Dealer's hand: King of Clubs, [Hidden]\n", + "\n", + "Round ended. Evaluating hands...\n", + "Dealer's hand value: 33\n", + "Player 1's hand value: 27\n", + "Player 1 loses 500 chips.\n", + "Player 2's hand value: 41\n", + "Player 2 loses 500 chips.\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Do you want to play another round? (yes/no): no\n" + ] + } + ], + "source": [ + "# See cell 1 CardCounterPlayer class\n", + "# Testing blackjack game\n", + "num_players = 2\n", + "player_info = [(\"Player 1\", \"human\"), (\"Player 2\", \"card_counter\")]\n", + "game = Game(player_info)\n", + "game.play()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "7. Create a test scenario where one player, using the above strategy, is playing with a dealer and 3 other players that follow the dealer's strategy. Each player starts with same number of chips. Play 50 rounds (or until the strategy player is out of money). Compute the strategy player's winnings. You may remove unnecessary printouts from your code (perhaps implement a verbose/quiet mode) to reduce the output." + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Welcome to Blackjack!\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Choose mode (auto/regular): auto\n", + "Enter number of games: 1\n", + "Enter number of rounds: 50\n", + "Enter initial amount of chips per player: 2000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation running...\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Simulation running...\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Aggregate Statistics:\n", + "Average winnings per round: 21040.0\n", + "Standard deviation of winnings: 577234.7875864725\n", + "Probability of net winning after 50 rounds: 1.0\n", + "Probability of net losing after 50 rounds: 0.0\n", + "CardCounterPlayer's total winnings: 0\n" + ] + } + ], + "source": [ + "game = Game()\n", + "game.play()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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": 240, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Welcome to Blackjack!\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Choose mode (auto/regular): auto\n", + "Enter number of games: 100\n", + "Enter number of rounds: 50\n", + "Enter initial amount of chips per player: 10000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation running...\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Simulation running...\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Aggregate Statistics:\n", + "Average winnings per round: 20004.0\n", + "Standard deviation of winnings: 57735025.76426203\n", + "Probability of net winning after 50 rounds: 1.0\n", + "Probability of net losing after 50 rounds: 0.0\n", + "CardCounterPlayer's total winnings: 0\n" + ] + } + ], + "source": [ + "game = Game()\n", + "game.play()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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": 243, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Welcome to Blackjack!\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Choose mode (auto/regular): auto\n", + "Enter number of games: 20\n", + "Enter number of rounds: 10\n", + "Enter initial amount of chips per player: 5000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation running...\n", + "Player 2 loses 10000 chips.\n", + "Player 3 loses 10000 chips.\n", + "Player 4 loses 10000 chips.\n", + "Player 5 loses 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 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"Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Simulation running...\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Aggregate Statistics:\n", + "Average winnings per round: 19800.0\n", + "Standard deviation of winnings: 2309372.209064619\n", + "Probability of net winning after 10 rounds: 0.995\n", + "Probability of net losing after 10 rounds: 0.005\n", + "CardCounterPlayer's total winnings: 0\n" + ] + } + ], + "source": [ + "# Threshold values -2 hit 0 stay\n", + "game = Game()\n", + "game.play()" + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Welcome to Blackjack!\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Choose mode (auto/regular): auto\n", + "Enter number of games: 20\n", + "Enter number of rounds: 10\n", + "Enter initial amount of chips per player: 5000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation running...\n", + "Player 2 ties with the dealer.\n", + "Player 3 loses 10000 chips.\n", + "Player 4 loses 10000 chips.\n", + "Player 5 loses 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 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chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Simulation running...\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Aggregate Statistics:\n", + "Average winnings per round: 19850.0\n", + "Standard deviation of winnings: 2309372.209064619\n", + "Probability of net winning after 10 rounds: 0.995\n", + "Probability of net losing after 10 rounds: 0.005\n", + "CardCounterPlayer's total winnings: 0\n" + ] + } + ], + "source": [ + "# Threshold -2 hit -1 stay\n", + "game = Game()\n", + "game.play()" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Welcome to Blackjack!\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Choose mode (auto/regular): auto\n", + "Enter number of games: 20\n", + "Enter number of rounds: 10\n", + "Enter initial amount of chips per player: 5000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation running...\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + 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chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + 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"Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Simulation running...\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Aggregate Statistics:\n", + "Average winnings per round: 20200.0\n", + "Standard deviation of winnings: 2309372.209064619\n", + "Probability of net winning after 10 rounds: 1.0\n", + "Probability of net losing after 10 rounds: 0.0\n", + "CardCounterPlayer's total winnings: 0\n" + ] + } + ], + "source": [ + "# Threshold -2 hit 2 stay\n", + "game = Game()\n", + "game.play()" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Welcome to Blackjack!\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Choose mode (auto/regular): auto\n", + "Enter number of games: 20\n", + "Enter number of rounds: 10\n", + "Enter initial amount of chips per player: 5000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation running...\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 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10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Simulation running...\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Aggregate Statistics:\n", + "Average winnings per round: 20200.0\n", + "Standard deviation of winnings: 2309372.209064619\n", + "Probability of net winning after 10 rounds: 1.0\n", + "Probability of net losing after 10 rounds: 0.0\n", + "CardCounterPlayer's total winnings: 0\n" + ] + } + ], + "source": [ + "# Threshold -2 hit 1 stay\n", + "game = Game()\n", + "game.play()" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Welcome to Blackjack!\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Choose mode (auto/regular): auto\n", + "Enter number of games: 20\n", + "Enter number of rounds: 10\n", + "Enter initial amount of chips per player: 5000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation running...\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 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10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Player 2 wins 10000 chips.\n", + "Player 3 wins 10000 chips.\n", + "Player 4 wins 10000 chips.\n", + "Player 5 wins 10000 chips.\n", + "Simulation running...\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Aggregate Statistics:\n", + "Average winnings per round: 20200.0\n", + "Standard deviation of winnings: 2309372.209064619\n", + "Probability of net winning after 10 rounds: 1.0\n", + "Probability of net losing after 10 rounds: 0.0\n", + "CardCounterPlayer's total winnings: 0\n" + ] + } + ], + "source": [ + "# Threshold -2 hit -2 stay\n", + "game = Game()\n", + "game.play()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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. " + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Welcome to Blackjack!\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Choose mode (auto/regular): auto\n", + "Enter number of games: 100\n", + "Enter number of rounds: 50\n", + "Enter initial amount of chips per player: 50000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation running...\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Aggregate Statistics:\n", + "Average winnings per round: -32.0\n", + "Standard deviation of winnings: 0.0\n", + "Probability of net winning after 50 rounds: 0.0\n", + "Probability of net losing after 50 rounds: 1.0\n", + "CardCounterPlayer's total winnings: 0\n" + ] + } + ], + "source": [ + "# I tried implementing a CardCounterPlayer2 class but my logic looks off. \n", + "game = Game()\n", + "game.play()" + ] + }, + { + "cell_type": "code", + "execution_count": 304, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Welcome to Blackjack!\n", + "\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Choose mode (auto/regular): auto\n", + "Enter number of games: 100\n", + "Enter number of rounds: 20\n", + "Enter initial amount of chips per player: 50000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation running...\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Aggregate Statistics:\n", + "Average winnings per round: 100.0\n", + "Standard deviation of winnings: 0.0\n", + "Probability of net winning after 20 rounds: 1.0\n", + "Probability of net losing after 20 rounds: 0.0\n", + "CardCounterPlayer's total winnings: 0\n" + ] + } + ], + "source": [ + "game = Game()\n", + "game.play()" + ] + }, + { + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Labs/Lab.6/Lab.6.ipynb b/Labs/Lab.6/Lab.6.ipynb new file mode 100644 index 0000000..d372c6f --- /dev/null +++ b/Labs/Lab.6/Lab.6.ipynb @@ -0,0 +1,124 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lab 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You are tasked with evaluating card counting strategies for black jack. In order to do so, you will use object oriented programming to create a playable casino style black jack game where a computer dealer plays against $n$ computer players and possibily one human player. If you don't know the rules of blackjack or card counting, please google it. \n", + "\n", + "A few requirements:\n", + "* The game should utilize multiple 52-card decks. Typically the game is played with 6 decks.\n", + "* Players should have chips.\n", + "* Dealer's actions are predefined by rules of the game (typically hit on 16). \n", + "* The players should be aware of all shown cards so that they can count cards.\n", + "* Each player could have a different strategy.\n", + "* The system should allow you to play large numbers of games, study the outcomes, and compare average winnings per hand rate for different strategies." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Begin by creating a classes to represent cards and decks. The deck should support more than one 52-card set. The deck should allow you to shuffle and draw cards. Include a \"plastic\" card, placed randomly in the deck. Later, when the plastic card is dealt, shuffle the cards before the next deal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Now design your game on a UML diagram. You may want to create classes to represent, players, a hand, and/or the game. As you work through the lab, update your UML diagram. At the end of the lab, submit your diagram (as pdf file) along with your notebook. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Begin with implementing the skeleton (ie define data members and methods/functions, but do not code the logic) of the classes in your UML diagram." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Complete the implementation by coding the logic of all functions. For now, just implement the dealer player and human player." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Test. Demonstrate game play. For example, create a game of several dealer players and show that the game is functional through several rounds." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "6. Implement a new player with the following strategy:\n", + "\n", + " * Assign each card a value: \n", + " * Cards 2 to 6 are +1 \n", + " * Cards 7 to 9 are 0 \n", + " * Cards 10 through Ace are -1\n", + " * Compute the sum of the values for all cards seen so far.\n", + " * Hit if sum is very negative, stay if sum is very positive. Select a threshold for hit/stay, e.g. 0 or -2. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "7. Create a test scenario where one player, using the above strategy, is playing with a dealer and 3 other players that follow the dealer's strategy. Each player starts with same number of chips. Play 50 rounds (or until the strategy player is out of money). Compute the strategy player's winnings. You may remove unnecessary printouts from your code (perhaps implement a verbose/quiet mode) to reduce the output." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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": "markdown", + "metadata": {}, + "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": "markdown", + "metadata": {}, + "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. " + ] + } + ], + "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": 4 +} diff --git a/Labs/Lab.6/UML Diagram.docx b/Labs/Lab.6/UML Diagram.docx new file mode 100644 index 0000000..4d12169 Binary files /dev/null and b/Labs/Lab.6/UML Diagram.docx differ diff --git a/Labs/Lab.6/UML Diagram.png b/Labs/Lab.6/UML Diagram.png new file mode 100644 index 0000000..0c7faac Binary files /dev/null and b/Labs/Lab.6/UML Diagram.png differ diff --git a/Labs/Lab.6/t.txt b/Labs/Lab.6/t.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Labs/Lab.6/t.txt @@ -0,0 +1 @@ + diff --git a/Labs/Lab.7/Lab.7.ipynb b/Labs/Lab.7/Lab.7.ipynb new file mode 100644 index 0000000..45c672c --- /dev/null +++ b/Labs/Lab.7/Lab.7.ipynb @@ -0,0 +1,5971 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Robert Cocker\n", + "# Dr. Farbin \n", + "# DATA 3402\n", + "# Lab 7\n", + "# 3/28/2024" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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." + ] + }, + { + "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": [ + { + "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 5674k 0 --:--:-- 0:02:38 --:--:-- 4791k 0 --:--:-- 0:00:36 --:--:-- 5800k 0 --:--:-- 0:00:47 --:--:-- 5807k:00:49 --:--:-- 5770kk 0 --:--:-- 0:01:09 --:--:-- 5829k 0 --:--:-- 0:01:27 --:--:-- 5827k5792k 0 --:--:-- 0:01:43 --:--:-- 5801k--:-- 0:01:59 --:--:-- 5811k5793k 0 --:--:-- 0:02:11 --:--:-- 5781k02:17 --:--:-- 5794k:26 --:--:-- 4807k\n" + ] + } + ], + "source": [ + "# !curl http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz > SUSY.csv.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-03-22 11:31:46-- http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz\n", + "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n", + "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: unspecified\n", + "Saving to: ‘SUSY.csv.gz’\n", + "\n", + "SUSY.csv.gz [ <=> ] 879.65M 4.85MB/s in 7m 17s \n", + "\n", + "2024-03-22 11:39:04 (2.01 MB/s) - ‘SUSY.csv.gz’ saved [922377711]\n", + "\n" + ] + } + ], + "source": [ + "# !wget http://archive.ics.uci.edu/ml/machine-learning-databases/00279/SUSY.csv.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# !gunzip SUSY.csv.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 2.3G\n", + "-rw-r--r-- 1 rcwsl rcwsl 3.1M Apr 6 02:19 Lab.7.ipynb\n", + "-rw-r--r-- 1 rcwsl rcwsl 6.2M Mar 22 11:26 Lab.7.pdf\n", + "-rw-r--r-- 1 rcwsl rcwsl 2.3G Mar 22 11:39 SUSY.csv\n" + ] + } + ], + "source": [ + "ls -lh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data is provided as a comma separated file." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "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": "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": [ + { + "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": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "RawNames" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['S_R',\n", + " 'MT2',\n", + " 'cos_theta_r1',\n", + " 'R',\n", + " 'M_R',\n", + " 'MET_rel',\n", + " 'dPhi_r_b',\n", + " 'M_Delta_R',\n", + " 'M_TR_2',\n", + " 'axial_MET']" + ] + }, + "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": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_67/2225940201.py:1: DeprecationWarning: \n", + "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", + "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", + "but was not found to be installed on your system.\n", + "If this would cause problems for you,\n", + "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n", + " \n", + " import pandas as pd\n" + ] + } + ], + "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": 9, + "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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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............................................................
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5000000 rows × 19 columns

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" + ], + "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]" + ] + }, + "execution_count": 10, + "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": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "l_1_pT\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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MPXr0kCS1b99ep06dsr1eXV2tL774osH3vvJzPvvsM138UZvtjz76yKX3cBdBBwAAAPixCxdqd///+OHkfjgFBQXKzMxUfn6+Nm7cqFWrVmnmzJlKSkpSRESEVq1apWPHjumtt97S4sWL61w7Y8YMlZaW6r/+67/06aef6uuvv9Yf//hHHTlSt3wsLi5Ou3bt0uHDhzV27FhV/dD97aGHHtLLL7+sV155RV9//bUef/xxffbZZ7JcWbt2hXHjxikqKkoTJ07UF198offee08PPfSQxo8fb9ufc8stt2jbtm3a8fe/6cSJw5o5679VUlLi0lTefffdslgsuv/++/XVV19p+/btWrZsmUvv4S6aEQAAACCoXd547zWVlbWtzK78nK8MSdHS8eNSpJ27lDq4H86ECRN04cIFpaSkKDw8XDNnztQDDzwgi8Wi9evX63e/+52eeeYZ9e/fX8uWLdOdd95pu/bqq6/Wrl279PDDD2vIkCEKDw9X37599dxzg+oNMSEhQbt27dLQoUM1btw4bdiwQePGjdOxY8c0a9YsXbx4UaNHj9a9996r/fv3O52C6OhovfPOO5o5c6ZuvPFGRUdH61e/+pVWrFhhO2fy5Mn6xz/+oQem3y9Zmmnm9Bm6+eabnc/tFa666ir99a9/1dSpU9WvXz9df/31+v3vf69f/epXLr2POyzGlYV3Qai0tFStW7fW2bNnFRMTE+jhAAAAwIsuXryo48ePq0uXLnU2rRcU1G6uL7eTOXwlOqpa1k/KlNTFzkaXZs1qN8R4kb1b80i1C0jHj9d+f1e31wwbNkwJCQn64x//6JUxlv3rgqzHWyi5ywW1vNr5niJvcfQzITU+G7CiAwAAgKCUlFS7mlNc7MU3vXChNkF07Gg3tMTGhSmpm3/+w/rlFtKO9u07WECqo7y8XGvWrFFGRobCw8O1ceNGvfvuu9q5c6f3BxxiCDoAAAAIWklJtQ+vKaupLUtLbi619M/qhLNVG3u35rmsMQtIFotF27dv1xNPPKGLFy+qR48e+vOf/6z09HTvDD6EEXQAAAAADzkKM1VV0jffOF+1ueoq9yviWrRooXfffde9i02OoAMAAAB4oDElaN262S9D88G2H/yAoAMAAAB4oKrK8xI0eB9BBwAAAEGhxtGSSIiIinK9Qxrs88bPAkEHAAAAARUREaGwsDB9//33at++vSIiIhq84WWDKivtb5qpqPi/f4bbaSHtBh+8ZVCpqKyQZFFFZYXCL3r459IAwzBUWVmpM2fOKCwsTBEREW6/F0EHAAAAARUWFqYuXbro1KlT+v777xt/4eWasStVV0tnzkiObhdpsdTWkjXUu7mRKitrW2A3by558Ht50Kosu6Ti4uZqrkuKKGnul8+Mjo5WUlKSwsLC3H4Pgg4AAAACLiIiQklJSaqqqlJ1dXXDF3z/vTRqVO19cexp0UJatUpq27b+a23b1t5Hx0u+/FKaOlX685+lHj289rZB48u/fqOpD3fRn5/6Rj1GdPH554WHh6tZs2Yer+oRdAAAABAULBaLmjdvrubNG7FqUFIiHT4svfqqlJxc//XYWC/fgEcqKLB/89LDh6Vvv61dKLLXjCDUWSrD9O23UbJUhikqhL4gQQcAAAChKzlZ6t/f5x9TUFD7UeXl9l+Pjq7NVggeBB0AAACgAcXFtSHHjwtI8BBBBwAAAPiBo/I0q7X2n35aQIIXEHQAAAAAUZ5mNgQdAAAABK+Glli8iPI0cyHoAAAAIDgFaImF8jRzIOgAAAAgOPloicWPi0QIIIIOAAAAgpsXl1jYh9N0EHQAAADQZLAPp+kg6AAAACCwAlBLxj4c8yPoAAAAIHB8VEvGPhwQdAAAABA4PqglYx8OJIIOAAAA/KGhJRYv1pKxDwcSQQcAAAC+xv1wEAAEHQAAAPgW98NBABB0AAAA4B/cDwd+RNABAABAyGEfDhpC0AEAAEDIYh8OHCHoAAAAoPEcbYxxhk0zCACCDgAAABqnoY0xznDjT/gZQQcAAACN09DGGGe48Sf8jKADAAAA1/hpYwwNB+CJMFcv2LNnj0aMGKGOHTvKYrFo69atDV6ze/du9e/fX5GRkfrJT36i9evXuzFUAAAANEWXc9WVD0IOnHF5RaesrEx9+vTR5MmTNWrUqAbPP378uO644w5NnTpVr732mnJzczVlyhR16NBBGRkZbg0aAAAAPsTGGJiAy0Hn9ttv1+23397o89esWaMuXbpo+fLlkqTk5GS9//77WrlyJUEHAAAg2LAxBibh8z06+/btU3p6ep1jGRkZ+s1vfuPwmoqKClVUVNiel5aW+mp4AAAA+LEAbIxhAQm+4POgU1hYqPj4+DrH4uPjVVpaqgsXLqhFixb1rsnOztaiRYt8PTQAAICmq6F04aeGAywgwVeCsuva3LlzlZmZaXteWlqqxMTEAI4IAADARAKQLpzlKjqrwRd8HnQSEhJUVFRU51hRUZFiYmLsruZIUmRkpCIjI309NAAAgKbJz+VpjclVgwcTaOBdPg86aWlp2r59e51jO3fuVFpamq8/GgAAAM5wPxyYmMtB5/z58zp69Kjt+fHjx3Xo0CG1a9dOSUlJmjt3rk6ePKk//OEPkqSpU6fq2Wef1SOPPKLJkydr165d+tOf/qRt27Z571sAAAAg6PkpVwGS3Lhh6Keffqp+/fqpX79+kqTMzEz169dPCxculCSdOnVKBQUFtvO7dOmibdu2aefOnerTp4+WL1+ul156idbSAAAAAHzG5RWdoUOHyjAMh6+vX7/e7jV5eXmufhQAAAA8Qd9mNGFB2XUNAAAAHqJvM5o4gg4AAIAZceNPNHEEHQAAADPjxp9oogg6AAAA8BgtpBFsCDoAAAChylGtmBSwejFaSCNYEHQAAABCUUO1YhL1YmjSCDoAAAChqKFaMcntejFnC0WO0HAAwYagAwAAEMq8XCvWmIUiR1hAQjAh6AAAAMCmMQtFjtBwAMGEoAMAABDMAnRzGpoKINQRdAAAAIIVN6cB3EbQAQAACFbcnAZwG0EHAAAg2FFHBriMoAMAABBoAdiHE6CtP4DfEHQAAAACKQD7cNj6g6aAoAMAABBIAdiHw9YfNAUEHQAAgGDgg304DZWnsfUHZkbQAQAAMCHK09DUEXQAAAD8wc+7/ylPQ1NH0AEAAPC1AC6vUJ6GpoqgAwAA4GssrwB+R9ABAADwF5ZXAL8JC/QAAAAAAMDbCDoAAAAATIegAwAAAMB02KMDAADgLX5uIe3o43z4kUDIIOgAAAB4g59bSDf0cT74SCCkEHQAAAC8wc8tpBv6OB98JBBSCDoAAADe5OUW0g1Vw9GxGrCPoAMAAOAKP+7D8XM1HGAqBB0AAIDG8nPy8HM1HGAqBB0AAIDGClDyoDwNcB1BBwAA4Ep+3hjj567UQJNA0AEAAPixIGsTzT4cwD0EHQAAgB8LsjbR7MMB3EPQAQAAsIc20UBII+gAAICmiTbRgKkRdAAAQNNDm2jA9Ag6AACg6aFNNGB6BB0AANB0kTwA0yLoAAAAeAn3wwGCB0EHAADAC2g4AAQXgg4AAIAX0HAACC4EHQAAAC9i2w8QHAg6AADAvNg0AzRZBB0AAGBObJoBmjSCDgAAMCc2zQBNGkEHAACYm5c3zVANB4QGgg4AAEAjUQ0HhA6CDgAAwBWcrdpQDQeEBreCzurVq/XUU0+psLBQffr00apVq5SSkuLw/JycHD3//PMqKChQbGys7rrrLmVnZysqKsrtgQMAAEjyei1ZY1ZtBg8m0ADBzuWgs3nzZmVmZmrNmjVKTU1VTk6OMjIylJ+fr7i4uHrnb9iwQXPmzNHatWt100036ciRI7r33ntlsVi0YsUKr3wJAADQRPmgloweBoA5uBx0VqxYofvvv1+TJk2SJK1Zs0bbtm3T2rVrNWfOnHrnf/jhhxo0aJDuvvtuSVLnzp01duxYffzxxx4OHQAANHk+TCXc+BMIbS4FncrKSh04cEBz5861HQsLC1N6err27dtn95qbbrpJr776qvbv36+UlBQdO3ZM27dv1/jx4x1+TkVFhSoqKmzPS0tLXRkmAAAwm4bK09xIJXRPA8zNpaBTXFys6upqxcfH1zkeHx+vw4cP273m7rvvVnFxsX72s5/JMAxVVVVp6tSp+t3vfufwc7Kzs7Vo0SJXhgYAAMzKB+VpdE8DzM/nXdd2796tJUuW6LnnnlNqaqqOHj2qmTNnavHixVqwYIHda+bOnavMzEzb89LSUiUmJvp6qAAAIBj5oDyNfTiA+bkUdGJjYxUeHq6ioqI6x4uKipSQkGD3mgULFmj8+PGaMmWKJKlXr14qKyvTAw88oHnz5iksLKzeNZGRkYqMjHRlaAAAwOx8UJ7GPhzAvFwKOhERERowYIByc3M1cuRISVJNTY1yc3M1Y8YMu9eUl5fXCzPh4eGSJMMw3BgyAABAwyhPA5o2l0vXMjMzNXHiRA0cOFApKSnKyclRWVmZrQvbhAkT1KlTJ2VnZ0uSRowYoRUrVqhfv3620rUFCxZoxIgRtsADAADgbZSnAU2by0FnzJgxOnPmjBYuXKjCwkL17dtXO3bssDUoKCgoqLOCM3/+fFksFs2fP18nT55U+/btNWLECD3xxBPe+xYAACD0+agNGuVpQNNkMUKgfqy0tFStW7fW2bNnFRMTE+jhAAAAb2tMnZnVancJxlk+uuce6cABgg7giYOvWTXgnmQdeNWq/uPsLI/6WWOzgc+7rgEAADTIzToz9uEAcISgAwAAgoeLdWbswwHgCEEHAAD4j5v7cGgTDcBVBB0AAOAfbtaZUZ4GwB0EHQAA4B9u1plRngbAHQQdAADgX27WmVGeBsAVBB0AAOBdProfDgC4gqADAAC8hw01AIIEQQcAAHiPBxtqWAgC4E0EHQAA4Dov93tmIQiAtxF0AACAa3yQSuisBsDbCDoAAMA1PkwldFYD4C0EHQAA4B43Ugn7cAD4C0EHAAD4BftwAPgTQQcAAPgF+3AA+BNBBwAA+BX7cAD4Q1igBwAAAAAA3saKDgAAsM/NzgE0HAAQDAg6AACgPjc7B9BwAECwIOgAANCUOVt+caNzAA0HAAQLgg4AAE1VY5ZfBg92K5nQcABAoBF0AABoqjxYfmEfDoBgR9ABAKCpc3H5hX04AEIBQQcAALiEfTgAQgFBBwAA2NVQeRr7cAAEM4IOAABm58aGGsrTAIQ6gg4AAGbmZmKhPA1AqCPoAABgZh4mFsrTAIQqgg4AAE2Bg8RSUCAVH6x/Om2iAYQ6gg4AAE0U+3AAmBlBBwAAM3Cj4QD7cACYGUEHAIBQ5+HSDPtwAJgRQQcAgFDXwNJMQUW8ios7SVcs+LAPB4CZEXQAAAgVbtzBk304AJoqgg4AAKGA++EAgEsIOgAAhAIPy9PYhwOgqSHoAAAQSihPA4BGIegAABDiKE8DgPoIOgAAmATlaQDwfwg6AACEiAIlqtjaot5x2kQDQH0EHQAAgomDFtIFe79Vsqwqv6el3cvYhwMAdRF0AAAIFk66ChSrn8r1//RqTrGSB9dPNOzDAYC6CDoAAAQLZ10FrC2ke6TkwbHswwGARiDoAAAQRAqUqGL1l1Q36LANBwBcQ9ABACBIFJxqzj4cAPASgg4AAH5WsO+kio+V1jtu/aBU5WqpVxcfV/LwLvVeZx8OADQeQQcAAD8q2HdSyTe1Ubk62X09WmUafGuEktiHAwAeIegAAOBHxcdKVa5OevXBD5Q8qF2912O7xigpzX4IAgA0HkEHAIAASB7UTv3HJTd8IgDALQQdAAC8zME9PyVJ1uNR/h0MADRRYe5ctHr1anXu3FlRUVFKTU3V/v37nZ5fUlKi6dOnq0OHDoqMjFT37t21fft2twYMAEAwKyiQknvWaMAA2X3cs6CLolWm2DZVgR4qAJiayys6mzdvVmZmptasWaPU1FTl5OQoIyND+fn5iouLq3d+ZWWlhg0bpri4OL3xxhvq1KmTvv32W7Vp08Yb4wcAIDAcLNsUf3hR5Rdu0qsap2QHd7+JjSpTUq+dvh4hADRpLgedFStW6P7779ekSZMkSWvWrNG2bdu0du1azZkzp975a9eu1b///W99+OGHat68uSSpc+fOTj+joqJCFRUVtuelpfVbcAIAEDAFBVJyslRebufFfpIOKnnVdPW/yUGZGn2iAcDnXAo6lZWVOnDggObOnWs7FhYWpvT0dO3bt8/uNW+99ZbS0tI0ffp0/eUvf1H79u119913a/bs2QoPD7d7TXZ2thYtWuTK0AAA8J/iYhWUX63ixa9LXere78Z6PEpaIOmmmyRaRANAwLgUdIqLi1VdXa34+Pg6x+Pj43X48GG71xw7dky7du3SuHHjtH37dh09elTTpk3TpUuXlJWVZfeauXPnKjMz0/a8tLRUiYmJrgwVAACfKTjVXMmyqnxBS7uvR0fXLtoAAALH513XampqFBcXpxdeeEHh4eEaMGCATp48qaeeesph0ImMjFRkZKSvhwYAgFuKS5qpXC316uLjSh7epd7rVKYBQOC5FHRiY2MVHh6uoqKiOseLioqUkJBg95oOHTqoefPmdcrUkpOTVVhYqMrKSkVERLgxbAAAAi+5y0X1pzwNAIKSS0EnIiJCAwYMUG5urkaOHCmpdsUmNzdXM2bMsHvNoEGDtGHDBtXU1CgsrLab9ZEjR9ShQwdCDgAg4Jzd80anTkklJfUOWz/4t0/HBADwnMula5mZmZo4caIGDhyolJQU5eTkqKyszNaFbcKECerUqZOys7MlSQ8++KCeffZZzZw5Uw899JC+/vprLVmyRP/93//t3W8CAICLnDZPkyR1+OFRX7TKFNs1xldDAwB4yOWgM2bMGJ05c0YLFy5UYWGh+vbtqx07dtgaFBQUFNhWbiQpMTFR77zzjn7729+qd+/e6tSpk2bOnKnZs2d771sAAOCG4uLakPPqq7WBpw6rVbpnnLT48Xqd1SQptmuMktI6+WegAACXudWMYMaMGQ5L1Xbv3l3vWFpamj766CN3PgoAAJ9LTpadvTYXJOVJwxOk/lemIABAsPN51zUAAIKe1araYHPlMQBAqCLoAABMr2DfSRUfK6133PrZJUm9a0vUlFf/Qm6IAwAhi6ADADAFR93Tznx5WqMmtFG57O+niVaZYv+wUrqhVf0XuSEOAIQsgg4AIOQ5754Wp2iVaccju9S+d/0OarVNBYb4fIwAAP8i6AAAQl5D3dNi78lQ0pitNBUAgCaEoAMAMA3H3dO+C8BoAACBRNABAJgH3dMAAD8g6AAAgoqjpgLOWPcWS4qlexoAwIagAwAIGs6bCjgTW9s9LWeBNPhaOy/TPQ0AmhqCDgAgaDhtKnDqlHTXXdLFC3avjY0qU9L/20mgAQBIIugAAIKQ3aYCB09JFz90kILEqg0AoA6CDgDA7xztw2lU3wD7rdUAAKiDoAMA8KuG9uFEt6hR7KkvpYOX6r5A9zQAgAsIOgAAv2poH07sXUOV9Isj9i+mexoAoJEIOgCAgHC8D+cI+3AAAB4j6AAAgg/7cAAAHiLoAAB8osGGA1arpAsOXgQAwDMEHQCA1zXYcEBlir0nQ9J3dl5kHw4AwHMEHQCAU45WZpyxWp00HLBaFXtPhpJezWYfDgDAZwg6AACHGlqZcSY6Who82F5muSDpO/bhAAB8iqADAHDIaSvoBsRWnFRScZF05WoQ+3AAAH5A0AEANMjlxZcGN+mwDwcA4FsEHQBAwx3SXNXQUhD7cAAAPkbQAYAmzqeLL+zDAQAECEEHAJo4jxZfvL4UBACAdxB0AKCJaCiTsA8HAGAmBB0AMBFHYebMGWnUKC9nEvbhAACCGEEHAEyiMQssO3ZI7dvXf82j8jT24QAAghBBBwBMwicLLJSnAQBCFEEHAEKMXxdYKE8DAIQogg4AhBCfLbBQngYAMBmCDgCEEMrTAABoHIIOAAQhnyywOHtTytMAACZD0AGAIOOTBZbGvOngwQQaAIBpEHQAIMj4pDyNpgIAgCaGoAMAAeL38jS33xQAgNBD0AEAH3KUO86ckUaNCkB5Gk0FAABNBEEHAHykMbljxw6pffv6r1GeBgCAZwg6AOAjPssdlKcBANAggg4A+JhXcwflaQAANApBBwAawdEiiuTnijDK0wAAaBSCDgA0oDGLKG++WX+vzeVKMp+gPA0AAKcIOgDwA2dbXxwtolzunvaf/2n/Pd2uJGtoHw4AAHCKoAOgyXBWftaYds+DB9uvCrNavVzWxj4cAAA8RtAB0CQ0lB0k99s9JyV5eVsM+3AAAPAYQQeAqbhTfnaZT/KDs2UkR2gTDQCAxwg6AEyjMRVfjsrPAjIgZyhPAwDAIwQdAKYRdBVfDQ3IGcrTAADwCEEHQFAyVcVX0A0IAADzcyvorF69Wk899ZQKCwvVp08frVq1SikpKQ1et2nTJo0dO1a//OUvtXXrVnc+GoCJOAozDXVAc4aKLwAAILkRdDZv3qzMzEytWbNGqampysnJUUZGhvLz8xUXF+fwuhMnTmjWrFkaPHiwRwMGYA6N2U/jqAOaMz6r+HK2xESZGQAAQcfloLNixQrdf//9mjRpkiRpzZo12rZtm9auXas5c+bYvaa6ulrjxo3TokWLtHfvXpWUlHg0aAChw90uaEGVHRqTyt58s34q4+aeAAAEjEtBp7KyUgcOHNDcuXNtx8LCwpSenq59+/Y5vO6xxx5TXFyc7rvvPu3du7fBz6moqFBFRYXteWlpqSvDBBAkgq4LmrucNRW4XGf3n/9p/1pq6QAACAiXgk5xcbGqq6sVHx9f53h8fLwOHz5s95r3339fL7/8sg4dOtToz8nOztaiRYtcGRqAADLFqo3k/ItIjpsKWK2UtQEAEGR82nXt3LlzGj9+vF588UXFuvBfNOfOnavMzEzb89LSUiUmJvpiiAA8ZJpVm8Z8EUf/HktKCoEvCABA0+JS0ImNjVV4eLiKiorqHC8qKlJCQkK987/55hudOHFCI0aMsB2rqamp/eBmzZSfn6/rrruu3nWRkZGKjIx0ZWgAAiTo7l3jLtN8EQAAILkYdCIiIjRgwADl5uZq5MiRkmqDS25urmbMmFHv/J49e+rzzz+vc2z+/Pk6d+6cnn76aVZpgCBjinvXeNodLWi+CAAA8ITLpWuZmZmaOHGiBg4cqJSUFOXk5KisrMzWhW3ChAnq1KmTsrOzFRUVpZ/+9Kd1rm/Tpo0k1TsOwD9Mfe8ad7ujSXRIAwDAZFwOOmPGjNGZM2e0cOFCFRYWqm/fvtqxY4etQUFBQYHCwsK8PlAAjedumAm6e9c44k73g4a6o0lBlNgAAICnLIZhGIEeRENKS0vVunVrnT17VjExMYEeDhDUPFnUCIltKI35glar/S/SUG1eSEwAAAD+dfA1qwbck6wDr1rVf5ydfax+1ths4NOuawA84+6eGVPsqfdFz2q6owEA0GQQdIAA89WemZBo6eyIaXpWAwCAQCHoAAHUmN/nQ2LPjLtMc6dRAAAQbAg6gB/w+7wdrNoAAAAfIuigSfL0ViuuvGdjOp01yd/nuUEnAADwIYIOmhxPupI54knb5ib/+zw36AQAAD5A0EFIc2dlxtlCQmNuteIIYQYAACB4EHQQstxdmbFaa//paCHBanW9pbNEmHHI2QYlAAAAHyHoIGR5sjITHV0bTOxp0rdacefGPc40pqbP0R8EAACABwg6CDhPb1bvzspMk1598cWNe5yhpg8AAAQAQSfE+aJ7mC948rt1QyVojjTplRlHfHXjHmeC6QcRAAA0GQSdIOFOYPGke5i3Wyg740lHMk9K0Jo0btwDAACaOIJOEPBkU70ne1S83ULZGU+qlyhBcxE34gQAACDouMPdcjF3/iN7YwKLo99ZHQUEX7VQdsaTQNKkS9C83T/b2XUAAAAmQtBxkburL40p33I1sEjOf2d1FhBooRwCfNU/GwAAoAkg6LjI05bG7pRv+WJFo0mvkvhKsC31sXkJAAA0YQQdN9HS2MT82RkiEEt9AAAATQBBx8tYKQlxjQksVmv9P2SzLPUBAACYBEEH+DFngcVqle65R9q71/5rEkt9AAAAQYKgA9hjL7DExtauvtxzj/1rnO2LYfUFAADArwg6MC93mwM4kpTEygwAAECIIOjAPzwJHc6udaQxm/ydtWZ2hJUZAACAkEDQge+5u8G/Mdc642iTP62ZAQAATI+gg/rcWUFxxtn9YJxt8G/o2oY4WymiBA0AAMDUCDqoy5MVFGcc3Q+moQ3+zq71BCVoAAAApkbQ8TZ396K4u4ri7ff0ZAXFGUfjbGiDv7NrAQAAAAcIOu6yWiVdqHusMRvg7e1F8XQfir1N9Q2NpaH39PYKijOsrgAAAMDLCDquOnVKUgfpnnGS8uq/7mgDfEM3m3RnFaUxm+rtjaUhrKAAAAAgxBF0XFVSIqmDtPhxaXhC/dcdhYTG3GzSnVUUNtUDAAAA9RB03NWli9TfhdUXX91skrIvAAAAoB6Cjj8RSgAAAAC/CAv0AAAAAADA2wg6AAAAAEyHoAMAAADAdAg6AAAAAEyHoAMAAADAdAg6AAAAAEyHoAMAAADAdAg6AAAAAEyHoAMAAADAdAg6AAAAAEyHoAMAAADAdAg6AAAAAEyHoAMAAADAdAg6AAAAAEyHoAMAAADAdAg6AAAAAEyHoAMAAADAdAg6AAAAAEyHoAMAAADAdNwKOqtXr1bnzp0VFRWl1NRU7d+/3+G5L774ogYPHqy2bduqbdu2Sk9Pd3o+AAAAAHjK5aCzefNmZWZmKisrSwcPHlSfPn2UkZGh06dP2z1/9+7dGjt2rN577z3t27dPiYmJuu2223Ty5EmPBw8AAAAA9rgcdFasWKH7779fkyZN0vXXX681a9YoOjpaa9eutXv+a6+9pmnTpqlv377q2bOnXnrpJdXU1Cg3N9fjwQMAAACAPS4FncrKSh04cEDp6en/9wZhYUpPT9e+ffsa9R7l5eW6dOmS2rVr5/CciooKlZaW1nkAAAAAQGO5FHSKi4tVXV2t+Pj4Osfj4+NVWFjYqPeYPXu2OnbsWCcsXSk7O1utW7e2PRITE10ZJgAAAIAmzq9d15YuXapNmzZpy5YtioqKcnje3LlzdfbsWdvju+++8+MoAQAAAIS6Zq6cHBsbq/DwcBUVFdU5XlRUpISEBKfXLlu2TEuXLtW7776r3r17Oz03MjJSkZGRrgwNAAAAAGxcWtGJiIjQgAED6jQSuNxYIC0tzeF1Tz75pBYvXqwdO3Zo4MCB7o8WAAAAABrBpRUdScrMzNTEiRM1cOBApaSkKCcnR2VlZZo0aZIkacKECerUqZOys7MlSb///e+1cOFCbdiwQZ07d7bt5bnqqqt01VVXefGrAAAAAEAtl4POmDFjdObMGS1cuFCFhYXq27evduzYYWtQUFBQoLCw/1soev7551VZWam77rqrzvtkZWXp0Ucf9Wz0AAAAAGCHy0FHkmbMmKEZM2bYfW337t11np84ccKdjwAAAAAAt/m16xoAAAAA+ANBBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpEHQAAAAAmA5BBwAAAIDpuBV0Vq9erc6dOysqKkqpqanav3+/0/Nff/119ezZU1FRUerVq5e2b9/u1mABAAAAoDFcDjqbN29WZmamsrKydPDgQfXp00cZGRk6ffq03fM//PBDjR07Vvfdd5/y8vI0cuRIjRw5Ul988YXHgwcAAAAAeyyGYRiuXJCamqobb7xRzz77rCSppqZGiYmJeuihhzRnzpx6548ZM0ZlZWV6++23bcf+4z/+Q3379tWaNWvsfkZFRYUqKipsz8+ePaukpCR99913iomJcWW4Xndoc76GPNBD//NCvvqO6RHQsQAAAAC+Fmy//5aWlioxMVElJSVq3bq14xMNF1RUVBjh4eHGli1b6hyfMGGCceedd9q9JjEx0Vi5cmWdYwsXLjR69+7t8HOysrIMSTx48ODBgwcPHjx48OBh9/Hdd985zS7N5ILi4mJVV1crPj6+zvH4+HgdPnzY7jWFhYV2zy8sLHT4OXPnzlVmZqbteU1Njf7973/r6quvlsVicWXIXnc5QQbD6pIZMb++xxz7HnPsW8yv7zHHvsX8+h5z7FuBnl/DMHTu3Dl17NjR6XkuBR1/iYyMVGRkZJ1jbdq0CcxgHIiJieH/OD7E/Poec+x7zLFvMb++xxz7FvPre8yxbwVyfp2WrP3ApWYEsbGxCg8PV1FRUZ3jRUVFSkhIsHtNQkKCS+cDAAAAgKdcCjoREREaMGCAcnNzbcdqamqUm5urtLQ0u9ekpaXVOV+Sdu7c6fB8AAAAAPCUy6VrmZmZmjhxogYOHKiUlBTl5OSorKxMkyZNkiRNmDBBnTp1UnZ2tiRp5syZGjJkiJYvX6477rhDmzZt0qeffqoXXnjBu9/ETyIjI5WVlVWvtA7ewfz6HnPse8yxbzG/vscc+xbz63vMsW+Fyvy63F5akp599lk99dRTKiwsVN++ffXMM88oNTVVkjR06FB17txZ69evt53/+uuva/78+Tpx4oS6deumJ598UsOHD/falwAAAACAH3Mr6AAAAABAMHNpjw4AAAAAhAKCDgAAAADTIegAAAAAMB2CDgAAAADTIejYsXr1anXu3FlRUVFKTU3V/v37nZ7/+uuvq2fPnoqKilKvXr20fft2P400NLkyvy+++KIGDx6stm3bqm3btkpPT2/wzwOu/wxftmnTJlksFo0cOdK3Awxxrs5vSUmJpk+frg4dOigyMlLdu3fn3xMNcHWOc3Jy1KNHD7Vo0UKJiYn67W9/q4sXL/pptKFlz549GjFihDp27CiLxaKtW7c2eM3u3bvVv39/RUZG6ic/+Umdzqqoz9U5fvPNNzVs2DC1b99eMTExSktL0zvvvOOfwYYgd36GL/vggw/UrFkz9e3b12fjMwN35riiokLz5s3Ttddeq8jISHXu3Flr1671/WCdIOhcYfPmzcrMzFRWVpYOHjyoPn36KCMjQ6dPn7Z7/ocffqixY8fqvvvuU15enkaOHKmRI0fqiy++8PPIQ4Or87t7926NHTtW7733nvbt26fExETddtttOnnypJ9HHjpcnePLTpw4oVmzZmnw4MF+GmlocnV+KysrNWzYMJ04cUJvvPGG8vPz9eKLL6pTp05+HnnocHWON2zYoDlz5igrK0tWq1Uvv/yyNm/erN/97nd+HnloKCsrU58+fbR69epGnX/8+HHdcccduvnmm3Xo0CH95je/0ZQpU/hF3AlX53jPnj0aNmyYtm/frgMHDujmm2/WiBEjlJeX5+ORhiZX5/eykpISTZgwQbfeequPRmYe7szx6NGjlZubq5dffln5+fnauHGjevTo4cNRNoKBOlJSUozp06fbnldXVxsdO3Y0srOz7Z4/evRo44477qhzLDU11fj1r3/t03GGKlfn90pVVVVGq1atjFdeecVXQwx57sxxVVWVcdNNNxkvvfSSMXHiROOXv/ylH0Yamlyd3+eff97o2rWrUVlZ6a8hhjxX53j69OnGLbfcUudYZmamMWjQIJ+O0wwkGVu2bHF6ziOPPGLccMMNdY6NGTPGyMjI8OHIzKMxc2zP9ddfbyxatMj7AzIZV+Z3zJgxxvz5842srCyjT58+Ph2XmTRmjv/2t78ZrVu3Nv71r3/5Z1CNxIrOj1RWVurAgQNKT0+3HQsLC1N6err27dtn95p9+/bVOV+SMjIyHJ7flLkzv1cqLy/XpUuX1K5dO18NM6S5O8ePPfaY4uLidN999/ljmCHLnfl96623lJaWpunTpys+Pl4//elPtWTJElVXV/tr2CHFnTm+6aabdODAAVt527Fjx7R9+3ZuTO0l/D3nfzU1NTp37hx/13nRunXrdOzYMWVlZQV6KKb01ltvaeDAgXryySfVqVMnde/eXbNmzdKFCxcCOq5mAf30IFNcXKzq6mrFx8fXOR4fH6/Dhw/bvaawsNDu+YWFhT4bZ6hyZ36vNHv2bHXs2LHeX7qo5c4cv//++3r55Zd16NAhP4wwtLkzv8eOHdOuXbs0btw4bd++XUePHtW0adN06dIl/sK1w505vvvuu1VcXKyf/exnMgxDVVVVmjp1KqVrXuLo77nS0lJduHBBLVq0CNDIzGvZsmU6f/68Ro8eHeihmMLXX3+tOXPmaO/evWrWjF99feHYsWN6//33FRUVpS1btqi4uFjTpk3Tv/71L61bty5g42JFByFj6dKl2rRpk7Zs2aKoqKhAD8cUzp07p/Hjx+vFF19UbGxsoIdjSjU1NYqLi9MLL7ygAQMGaMyYMZo3b57WrFkT6KGZxu7du7VkyRI999xzOnjwoN58801t27ZNixcvDvTQAJdt2LBBixYt0p/+9CfFxcUFejghr7q6WnfffbcWLVqk7t27B3o4plVTUyOLxaLXXntNKSkpGj58uFasWKFXXnkloKs6xNofiY2NVXh4uIqKiuocLyoqUkJCgt1rEhISXDq/KXNnfi9btmyZli5dqnfffVe9e/f25TBDmqtz/M033+jEiRMaMWKE7VhNTY0kqVmzZsrPz9d1113n20GHEHd+hjt06KDmzZsrPDzcdiw5OVmFhYWqrKxURESET8ccatyZ4wULFmj8+PGaMmWKJKlXr14qKyvTAw88oHnz5iksjP+m5wlHf8/FxMSwmuNlmzZt0pQpU/T6669TueAl586d06effqq8vDzNmDFDUu3fc4ZhqFmzZvr73/+uW265JcCjDH0dOnRQp06d1Lp1a9ux5ORkGYahf/7zn+rWrVtAxsW//X8kIiJCAwYMUG5uru1YTU2NcnNzlZaWZveatLS0OudL0s6dOx2e35S5M7+S9OSTT2rx4sXasWOHBg4c6I+hhixX57hnz576/PPPdejQIdvjzjvvtHVXSkxM9Ofwg547P8ODBg3S0aNHbQFSko4cOaIOHToQcuxwZ47Ly8vrhZnLwdIwDN8Ntong7zn/2LhxoyZNmqSNGzfqjjvuCPRwTCMmJqbe33NTp05Vjx49dOjQIaWmpgZ6iKYwaNAgff/99zp//rzt2JEjRxQWFqZrrrkmcAMLbC+E4LNp0yYjMjLSWL9+vfHVV18ZDzzwgNGmTRujsLDQMAzDGD9+vDFnzhzb+R988IHRrFkzY9myZYbVajWysrKM5s2bG59//nmgvkJQc3V+ly5dakRERBhvvPGGcerUKdvj3LlzgfoKQc/VOb4SXdecc3V+CwoKjFatWhkzZsww8vPzjbffftuIi4szHn/88UB9haDn6hxnZWUZrVq1MjZu3GgcO3bM+Pvf/25cd911xujRowP1FYLauXPnjLy8PCMvL8+QZKxYscLIy8szvv32W8MwDGPOnDnG+PHjbecfO3bMiI6ONh5++GHDarUaq1evNsLDw40dO3YE6isEPVfn+LXXXjOaNWtmrF69us7fdSUlJYH6CkHN1fm9El3XGubqHJ87d8645pprjLvuusv48ssvjf/5n/8xunXrZkyZMiVQX8EwDMMg6NixatUqIykpyYiIiDBSUlKMjz76yPbakCFDjIkTJ9Y5/09/+pPRvXt3IyIiwrjhhhuMbdu2+XnEocWV+b322msNSfUeWVlZ/h94CHH1Z/jHCDoNc3V+P/zwQyM1NdWIjIw0unbtajzxxBNGVVWVn0cdWlyZ40uXLhmPPvqocd111xlRUVFGYmKiMW3aNON///d//T/wEPDee+/Z/ffq5TmdOHGiMWTIkHrX9O3b14iIiDC6du1qrFu3zu/jDiWuzvGQIUOcno+63PkZ/jGCTsPcmWOr1Wqkp6cbLVq0MK655hojMzPTKC8v9//gf8RiGKzrAwAAADAX9ugAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMB2CDgAAAADTIegAAAAAMJ3/HwWwDcITQ2EzAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cos_theta_r1\n" + ] + }, + { + "data": { + "image/png": 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", + "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": {}, + "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": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "l_1_pT\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for var in RawNames:\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 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": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Part a\n", + "def compare_distributions(df,column_name,selections,**kwargs):\n", + " for label,selection in selections.items(): \n", + " _=plt.hist(df[selection][column_name],label=label,**kwargs)\n", + "\n", + " _=plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "signal\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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mICBAlmV5rTtx8sRp7dq2+ebDl4CAAEmSVVfn8/78jcACAMAZBAQEKDAwUF99dVylH2xU90u6acZPpmhg4gD1io/T7r37vNpf0fcyvfPX985aM+nKAfr96pV6auESPbPkF571sbHd1bZtW23a/HfPunK3Wzt3/fOc/eyd0EvrN3zotW79hg/Uq2ecgoKCJEkRF18sl+ug5/md/yxTVdVX56z9ny677DKVlpZ672f9ep9qNBRzWAAA+LeamhrPm/q/ysv1y+dXqqKyUjdlpMt97Jj27Nuv3736Rw1M7K/X33xLf1rnPXcjZ/pU/c+tYxUX21233vJd1Z6s1etvvqXs++/xandVSrJ+t2qFbh07Xm3aBOneH/9IHUJD9f3Rt+qRx55Qp04d1SXiYj0xf4ECAwM9ZzrOZMo9EzVk+M2a99Qz+t6om7Xhg4365fKVWjDvCU+bIdcO1i+Wr1DKoIGqra1V7px8tW3b1qfxuf/++3X11VeroKBAI0eO1Ouvv94k81ckAgsAoImYeOfZ//bGW8Xq1e/UXJAOoaG6NKGnfv3cUl179al5Gvf+eIKm5cxWTXWNMm74jh7Mvl/5Ty70bH/t1Wl64bmlmr9gkRY8u1QdOoTq6qtS69uVBl+VopdXrdBtY8crKChId/8oS/k/zdXU6Q/p9h9kqUNoqKbeN1n79h+QzWY7a78T+/fTC8t+rsfnP6V5CxYp0t5VD8+c5jXh9mePPaLJ909Xxs23KTLSrvlPPKotfz/9CqSzueqqq7Rs2TLl5eUpNzdX6enpeuSRRzRnzhyf6jREgPXfH2i1QG63W+Hh4SovL1dYWJjf65c8P93vNQGgVbJ1ULveNyimW5Rsbfmb+EJVVlap94AUPfHYIxp/x5hm7Utol5gGb3v8+HGVlZUpLi7Oa56PL+/fvJoAADDE3z/+RP/YsUtJVw6Q+9gxzS14RpI0InN4M/es+RFYAAAwyKKf/0I7dv5TwcFtldi/n17/4+8UcXHn5u5WsyOwAABgiAH9rtDf3lzX3N0wEpc1AwAA4xFYAAD+ZVlSi7+cA/7kj+t7CCwAAP858ZXq6mp1vOb0O6ji26uqqkqSfL7vy39iDgsAwH/qTurE4V063DZYUmeFBLeVzn7PM7QQbY4f93kby7JUVVWlgwcPqmPHjp677jZo/w3eEgCAetS5tuq4JNeJngoMDJLOcZdWtAy2ct8Dy9c6duyoyMjIC9o/gQUA4Hd1rq2qPrRdauv7l/TBTH1ue7BB27Vt2/aCzqx8jcACAGgcdSel6mPN3Qv4yfl8E3VjYtItAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxGhRYlixZotjYWIWEhCg1NVUbNmw4Y9tly5bp2muvVadOndSpUyelp6ef1v6uu+5SQECA15KZmdmQrgEAgFbI58CyZs0aZWdnKy8vT5s2bdKAAQOUkZGhgwcP1tu+uLhYY8eO1dtvv62SkhLFxMRo+PDh2rdvn1e7zMxMHThwwLOsXr26YUcEAABaHZ8Dy4IFCzRx4kRlZWWpb9++KiwsVPv27bV8+fJ62//2t7/VPffco8TERPXp00fPPfec6urq5HA4vNrZbDZFRkZ6lk6dOjXsiAAAQKvjU2CpqanRxo0blZ6e/k2BwEClp6erpKTkvGpUVVXpxIkT6ty5s9f64uJide3aVb1799bkyZN15MiRM9aorq6W2+32WgAAQOvlU2A5fPiwamtrZbfbvdbb7XY5nc7zqvHggw8qOjraK/RkZmbqhRdekMPh0Lx58/TOO+/oxhtvVG1tbb018vPzFR4e7lliYmJ8OQwAANDCNOl3Cc2dO1cvvviiiouLvb6TYMyYMZ6f+/Xrp/79+6tnz54qLi7WsGHDTquTk5Oj7Oxsz2O3201oAQCgFfPpDEtERISCgoLkcrm81rtcrnN+bXRBQYHmzp2rv/zlL+rfv/9Z28bHxysiIkI7d+6s93mbzaawsDCvBQAAtF4+BZbg4GAlJSV5TZj9egJtWlraGbebP3++5syZo6KiIiUnJ59zP3v37tWRI0cUFRXlS/cAAEAr5fNVQtnZ2Vq2bJlWrlypbdu2afLkyaqsrFRWVpYkady4ccrJyfG0nzdvnmbPnq3ly5crNjZWTqdTTqdTFRUVkqSKigrNmDFD69ev1+effy6Hw6GRI0eqV69eysjI8NNhAgCAlsznOSyjR4/WoUOHlJubK6fTqcTERBUVFXkm4u7evVuBgd/koKVLl6qmpka33XabV528vDw9+uijCgoK0kcffaSVK1fq6NGjio6O1vDhwzVnzhzZbLYLPDwAANAaBFiWZTV3Jy6U2+1WeHi4ysvLG2U+S8nz0/1eEwCAliRtQoHfa/ry/s13CQEAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOM1KLAsWbJEsbGxCgkJUWpqqjZs2HDGtsuWLdO1116rTp06qVOnTkpPTz+tvWVZys3NVVRUlNq1a6f09HTt2LGjIV0DAACtkM+BZc2aNcrOzlZeXp42bdqkAQMGKCMjQwcPHqy3fXFxscaOHau3335bJSUliomJ0fDhw7Vv3z5Pm/nz52vRokUqLCxUaWmpLrroImVkZOj48eMNPzIAANBqBFiWZfmyQWpqqgYNGqTFixdLkurq6hQTE6MpU6Zo1qxZ59y+trZWnTp10uLFizVu3DhZlqXo6GhNmzZN06dPlySVl5fLbrdrxYoVGjNmzDlrut1uhYeHq7y8XGFhYb4cznkpeX6632sCANCSpE0o8HtNX96/fTrDUlNTo40bNyo9Pf2bAoGBSk9PV0lJyXnVqKqq0okTJ9S5c2dJUllZmZxOp1fN8PBwpaamnrFmdXW13G631wIAAFovnwLL4cOHVVtbK7vd7rXebrfL6XSeV40HH3xQ0dHRnoDy9Xa+1MzPz1d4eLhniYmJ8eUwAABAC9OkVwnNnTtXL774ol599VWFhIQ0uE5OTo7Ky8s9y549e/zYSwAAYJo2vjSOiIhQUFCQXC6X13qXy6XIyMizbltQUKC5c+fqzTffVP/+/T3rv97O5XIpKirKq2ZiYmK9tWw2m2w2my9dBwAALZhPZ1iCg4OVlJQkh8PhWVdXVyeHw6G0tLQzbjd//nzNmTNHRUVFSk5O9nouLi5OkZGRXjXdbrdKS0vPWhMAAHx7+HSGRZKys7M1fvx4JScnKyUlRQsXLlRlZaWysrIkSePGjVO3bt2Un58vSZo3b55yc3O1atUqxcbGeualhIaGKjQ0VAEBAZo6daoef/xxJSQkKC4uTrNnz1Z0dLRGjRrlvyMFAAAtls+BZfTo0Tp06JByc3PldDqVmJiooqIiz6TZ3bt3KzDwmxM3S5cuVU1NjW677TavOnl5eXr00UclSTNnzlRlZaUmTZqko0eP6pprrlFRUdEFzXMBAACth8/3YTER92EBAKBxtaj7sAAAADQHAgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADBegwLLkiVLFBsbq5CQEKWmpmrDhg1nbLt161bdeuutio2NVUBAgBYuXHham0cffVQBAQFeS58+fRrSNQAA0Ar5HFjWrFmj7Oxs5eXladOmTRowYIAyMjJ08ODBettXVVUpPj5ec+fOVWRk5BnrXn755Tpw4IBneffdd33tGgAAaKV8DiwLFizQxIkTlZWVpb59+6qwsFDt27fX8uXL620/aNAgPfnkkxozZoxsNtsZ67Zp00aRkZGeJSIiwteuAQCAVsqnwFJTU6ONGzcqPT39mwKBgUpPT1dJSckFdWTHjh2Kjo5WfHy87rjjDu3evfuMbaurq+V2u70WAADQevkUWA4fPqza2lrZ7Xav9Xa7XU6ns8GdSE1N1YoVK1RUVKSlS5eqrKxM1157rY4dO1Zv+/z8fIWHh3uWmJiYBu8bAACYz4irhG688Ubdfvvt6t+/vzIyMrRu3TodPXpUL730Ur3tc3JyVF5e7ln27NnTxD0GAABNqY0vjSMiIhQUFCSXy+W13uVynXVCra86duyoSy+9VDt37qz3eZvNdtb5MAAAoHXx6QxLcHCwkpKS5HA4POvq6urkcDiUlpbmt05VVFRo165dioqK8ltNAADQcvl0hkWSsrOzNX78eCUnJyslJUULFy5UZWWlsrKyJEnjxo1Tt27dlJ+fL+nURN1PP/3U8/O+ffu0ZcsWhYaGqlevXpKk6dOn6+abb1aPHj20f/9+5eXlKSgoSGPHjvXXcQIAgBbM58AyevRoHTp0SLm5uXI6nUpMTFRRUZFnIu7u3bsVGPjNiZv9+/fryiuv9DwuKChQQUGBhgwZouLiYknS3r17NXbsWB05ckRdunTRNddco/Xr16tLly4XeHgAAKA1CLAsy2ruTlwot9ut8PBwlZeXKywszO/1S56f7veaAAC0JGkTCvxe05f3byOuEgIAADgbAgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADBegwLLkiVLFBsbq5CQEKWmpmrDhg1nbLt161bdeuutio2NVUBAgBYuXHjBNQEAwLeLz4FlzZo1ys7OVl5enjZt2qQBAwYoIyNDBw8erLd9VVWV4uPjNXfuXEVGRvqlJgAA+HbxObAsWLBAEydOVFZWlvr27avCwkK1b99ey5cvr7f9oEGD9OSTT2rMmDGy2Wx+qQkAAL5dfAosNTU12rhxo9LT078pEBio9PR0lZSUNKgDDalZXV0tt9vttQAAgNbLp8By+PBh1dbWym63e6232+1yOp0N6kBDaubn5ys8PNyzxMTENGjfAACgZWiRVwnl5OSovLzcs+zZs6e5uwQAABpRG18aR0REKCgoSC6Xy2u9y+U644Taxqhps9nOOB8GAAC0Pj6dYQkODlZSUpIcDodnXV1dnRwOh9LS0hrUgcaoCQAAWhefzrBIUnZ2tsaPH6/k5GSlpKRo4cKFqqysVFZWliRp3Lhx6tatm/Lz8yWdmlT76aefen7et2+ftmzZotDQUPXq1eu8agIAgG83nwPL6NGjdejQIeXm5srpdCoxMVFFRUWeSbO7d+9WYOA3J27279+vK6+80vO4oKBABQUFGjJkiIqLi8+rJgAA+HYLsCzLau5OXCi3263w8HCVl5crLCzM7/VLnp/u95oAALQkaRMK/F7Tl/fvFnmVEAAA+HYhsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOM1KLAsWbJEsbGxCgkJUWpqqjZs2HDW9i+//LL69OmjkJAQ9evXT+vWrfN6/q677lJAQIDXkpmZ2ZCuAQCAVsjnwLJmzRplZ2crLy9PmzZt0oABA5SRkaGDBw/W2/7999/X2LFjNWHCBG3evFmjRo3SqFGj9Mknn3i1y8zM1IEDBzzL6tWrG3ZEAACg1fE5sCxYsEATJ05UVlaW+vbtq8LCQrVv317Lly+vt/0zzzyjzMxMzZgxQ5dddpnmzJmjgQMHavHixV7tbDabIiMjPUunTp0adkQAAKDV8Smw1NTUaOPGjUpPT/+mQGCg0tPTVVJSUu82JSUlXu0lKSMj47T2xcXF6tq1q3r37q3JkyfryJEjZ+xHdXW13G631wIAAFovnwLL4cOHVVtbK7vd7rXebrfL6XTWu43T6Txn+8zMTL3wwgtyOByaN2+e3nnnHd14442qra2tt2Z+fr7Cw8M9S0xMjC+HAQAAWpg2zd0BSRozZozn5379+ql///7q2bOniouLNWzYsNPa5+TkKDs72/PY7XYTWgAAaMV8OsMSERGhoKAguVwur/Uul0uRkZH1bhMZGelTe0mKj49XRESEdu7cWe/zNptNYWFhXgsAAGi9fAoswcHBSkpKksPh8Kyrq6uTw+FQWlpavdukpaV5tZekN95444ztJWnv3r06cuSIoqKifOkeAABopXy+Sig7O1vLli3TypUrtW3bNk2ePFmVlZXKysqSJI0bN045OTme9g888ICKior01FNP6bPPPtOjjz6qDz/8UPfdd58kqaKiQjNmzND69ev1+eefy+FwaOTIkerVq5cyMjL8dJgAAKAl83kOy+jRo3Xo0CHl5ubK6XQqMTFRRUVFnom1u3fvVmDgNzlo8ODBWrVqlR555BE99NBDSkhI0Nq1a3XFFVdIkoKCgvTRRx9p5cqVOnr0qKKjozV8+HDNmTNHNpvNT4cJAABasgDLsqzm7sSFcrvdCg8PV3l5eaPMZyl5frrfawIA0JKkTSjwe01f3r/5LiEAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGC8BgWWJUuWKDY2ViEhIUpNTdWGDRvO2v7ll19Wnz59FBISon79+mndunVez1uWpdzcXEVFRaldu3ZKT0/Xjh07GtI1AADQCvkcWNasWaPs7Gzl5eVp06ZNGjBggDIyMnTw4MF627///vsaO3asJkyYoM2bN2vUqFEaNWqUPvnkE0+b+fPna9GiRSosLFRpaakuuugiZWRk6Pjx4w0/MgAA0GoEWJZl+bJBamqqBg0apMWLF0uS6urqFBMToylTpmjWrFmntR89erQqKyv12muvedZdddVVSkxMVGFhoSzLUnR0tKZNm6bp06dLksrLy2W327VixQqNGTPmnH1yu90KDw9XeXm5wsLCfDmc81Ly/HS/1wQAoCVJm1Dg95q+vH+38aVwTU2NNm7cqJycHM+6wMBApaenq6SkpN5tSkpKlJ2d7bUuIyNDa9eulSSVlZXJ6XQqPT3d83x4eLhSU1NVUlJSb2Cprq5WdXW153F5ebmkUwfeGCq/qj53IwAAWrHGeI/9uub5nDvxKbAcPnxYtbW1stvtXuvtdrs+++yzerdxOp31tnc6nZ7nv153pjb/LT8/X4899thp62NiYs7vQAAAgG+mLG600seOHVN4ePhZ2/gUWEyRk5Pjddamrq5OX375pS6++GIFBAQ0Y8/8x+12KyYmRnv27GmUj7laAsaAMZAYA4kxkBgDqXWOgWVZOnbsmKKjo8/Z1qfAEhERoaCgILlcLq/1LpdLkZGR9W4TGRl51vZf/9flcikqKsqrTWJiYr01bTabbDab17qOHTv6cigtRlhYWKt5YTYUY8AYSIyBxBhIjIHU+sbgXGdWvubTVULBwcFKSkqSw+HwrKurq5PD4VBaWlq926SlpXm1l6Q33njD0z4uLk6RkZFebdxut0pLS89YEwAAfLv4/JFQdna2xo8fr+TkZKWkpGjhwoWqrKxUVlaWJGncuHHq1q2b8vPzJUkPPPCAhgwZoqeeekojRozQiy++qA8//FC//OUvJUkBAQGaOnWqHn/8cSUkJCguLk6zZ89WdHS0Ro0a5b8jBQAALZbPgWX06NE6dOiQcnNz5XQ6lZiYqKKiIs+k2d27dysw8JsTN4MHD9aqVav0yCOP6KGHHlJCQoLWrl2rK664wtNm5syZqqys1KRJk3T06FFdc801KioqUkhIiB8OsWWy2WzKy8s77aOvbxPGgDGQGAOJMZAYA4kx8Pk+LAAAAE2N7xICAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AotBnnjiCQ0ePFjt27c/7zv33nXXXQoICPBaMjMzG7ejjaghY2BZlnJzcxUVFaV27dopPT1dO3bsaNyONqIvv/xSd9xxh8LCwtSxY0dNmDBBFRUVZ91m6NChp70O7r777ibq8YVbsmSJYmNjFRISotTUVG3YsOGs7V9++WX16dNHISEh6tevn9atW9dEPW08vozBihUrTvt9t/TbQPz1r3/VzTffrOjoaAUEBHi+IPdsiouLNXDgQNlsNvXq1UsrVqxo9H42Jl/HoLi4+LTXQUBAwBm/h6+lI7AYpKamRrfffrsmT57s03aZmZk6cOCAZ1m9enUj9bDxNWQM5s+fr0WLFqmwsFClpaW66KKLlJGRoePHjzdiTxvPHXfcoa1bt+qNN97Qa6+9pr/+9a+aNGnSObebOHGi1+tg/vz5TdDbC7dmzRplZ2crLy9PmzZt0oABA5SRkaGDBw/W2/7999/X2LFjNWHCBG3evFmjRo3SqFGj9MknnzRxz/3H1zGQTt2e/T9/31988UUT9tj/KisrNWDAAC1ZsuS82peVlWnEiBG6/vrrtWXLFk2dOlU/+tGP9PrrrzdyTxuPr2Pwte3bt3u9Frp27dpIPWxmFozzq1/9ygoPDz+vtuPHj7dGjhzZqP1pDuc7BnV1dVZkZKT15JNPetYdPXrUstls1urVqxuxh43j008/tSRZH3zwgWfdn//8ZysgIMDat2/fGbcbMmSI9cADDzRBD/0vJSXFuvfeez2Pa2trrejoaCs/P7/e9v/7v/9rjRgxwmtdamqq9eMf/7hR+9mYfB0DX/6NaIkkWa+++upZ28ycOdO6/PLLvdaNHj3aysjIaMSeNZ3zGYO3337bkmT961//apI+NTfOsLQCxcXF6tq1q3r37q3JkyfryJEjzd2lJlNWVian06n09HTPuvDwcKWmpqqkpKQZe9YwJSUl6tixo5KTkz3r0tPTFRgYqNLS0rNu+9vf/lYRERG64oorlJOTo6qqqsbu7gWrqanRxo0bvX5/gYGBSk9PP+Pvr6SkxKu9JGVkZLTI37fUsDGQpIqKCvXo0UMxMTEaOXKktm7d2hTdNUZrex1ciMTEREVFRemGG27Qe++919zdaTQ+35ofZsnMzNT3vvc9xcXFadeuXXrooYd04403qqSkREFBQc3dvUb39We1X381xNfsdnuL/BzX6XSedjq3TZs26ty581mP5/vf/7569Oih6OhoffTRR3rwwQe1fft2vfLKK43d5Qty+PBh1dbW1vv7++yzz+rdxul0tprft9SwMejdu7eWL1+u/v37q7y8XAUFBRo8eLC2bt2qSy65pCm63ezO9Dpwu9366quv1K5du2bqWdOJiopSYWGhkpOTVV1dreeee05Dhw5VaWmpBg4c2Nzd8zsCSyObNWuW5s2bd9Y227ZtU58+fRpUf8yYMZ6f+/Xrp/79+6tnz54qLi7WsGHDGlTT3xp7DFqC8x2DhvrPOS79+vVTVFSUhg0bpl27dqlnz54NrgszpaWleX2b/eDBg3XZZZfpF7/4hebMmdOMPUNT6t27t3r37u15PHjwYO3atUtPP/20fv3rXzdjzxoHgaWRTZs2TXfddddZ28THx/ttf/Hx8YqIiNDOnTuNCSyNOQaRkZGSJJfLpaioKM96l8ulxMTEBtVsDOc7BpGRkadNtDx58qS+/PJLz7Gej9TUVEnSzp07jQ4sERERCgoKksvl8lrvcrnOeLyRkZE+tTddQ8bgv7Vt21ZXXnmldu7c2RhdNNKZXgdhYWHfirMrZ5KSkqJ33323ubvRKAgsjaxLly7q0qVLk+1v7969OnLkiNebd3NrzDGIi4tTZGSkHA6HJ6C43W6Vlpb6fLVVYzrfMUhLS9PRo0e1ceNGJSUlSZLeeust1dXVeULI+diyZYskGfU6qE9wcLCSkpLkcDg0atQoSVJdXZ0cDofuu+++erdJS0uTw+HQ1KlTPeveeOMNrzMOLUlDxuC/1dbW6uOPP9ZNN93UiD01S1pa2mmXs7fk14G/bNmyxfj/7xusuWf94htffPGFtXnzZuuxxx6zQkNDrc2bN1ubN2+2jh075mnTu3dv65VXXrEsy7KOHTtmTZ8+3SopKbHKysqsN9980xo4cKCVkJBgHT9+vLkO44L4OgaWZVlz5861OnbsaP3hD3+wPvroI2vkyJFWXFyc9dVXXzXHIVywzMxM68orr7RKS0utd99910pISLDGjh3reX7v3r1W7969rdLSUsuyLGvnzp3WT3/6U+vDDz+0ysrKrD/84Q9WfHy8dd111zXXIfjkxRdftGw2m7VixQrr008/tSZNmmR17NjRcjqdlmVZ1p133mnNmjXL0/69996z2rRpYxUUFFjbtm2z8vLyrLZt21off/xxcx3CBfN1DB577DHr9ddft3bt2mVt3LjRGjNmjBUSEmJt3bq1uQ7hgh07dszz/7ska8GCBdbmzZutL774wrIsy5o1a5Z15513etr/85//tNq3b2/NmDHD2rZtm7VkyRIrKCjIKioqaq5DuGC+jsHTTz9trV271tqxY4f18ccfWw888IAVGBhovfnmm811CI2KwGKQ8ePHW5JOW95++21PG0nWr371K8uyLKuqqsoaPny41aVLF6tt27ZWjx49rIkTJ3r+kWuJfB0Dyzp1afPs2bMtu91u2Ww2a9iwYdb27dubvvN+cuTIEWvs2LFWaGioFRYWZmVlZXkFtrKyMq8x2b17t3XddddZnTt3tmw2m9WrVy9rxowZVnl5eTMdge+effZZq3v37lZwcLCVkpJirV+/3vPckCFDrPHjx3u1f+mll6xLL73UCg4Oti6//HLr//7v/5q4x/7nyxhMnTrV09Zut1s33XSTtWnTpmbotf98fYnufy9fH/f48eOtIUOGnLZNYmKiFRwcbMXHx3v9u9AS+ToG8+bNs3r27GmFhIRYnTt3toYOHWq99dZbzdP5JhBgWZbVZKdzAAAAGoD7sAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeP8PEqu0cYYCq28AAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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df/31uvfeezVjxgz5+/u79Odz7bXXavny5Xr++ecVGRmpt99+u85JvReTxdECTtyorKxUYGCgKioqLt2TWd1190EX7yoIAM3p5MmTOnDggLp3737eD1GcW1VVlcLCwrR48WI99NBDpstptHP9nbjQz++LfmkvAACQ9uzZo6+++kqxsbGqqKjQnDlzJEljx441XJl5hBEAAJrJokWLVFxc7Lw69f3331dQUJDpsowjjAAA0AyGDBmigoIC02Vckhp1AisAAIC7EEYAAIBRhBEAgMtawIWYaCbu+LtAGAEAXLAzd/z8v7dJx+XtzJ1eXb3J2v/FCawAgAvm5eUlf39/lZeXy9vb+6xnj+Hy4XA4dOLECZWVlalDhw5NesQLYQQAcMEsFotCQ0N14MCBs57dgstThw4dFBIS0qQ5CCMAAJf4+Piod+/efFUDeXt7u+Wht4QRAIDLPDw8uB083IYv+wAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAY1agwsnTpUkVERMjPz09xcXHKz89vsO/IkSNlsVjO2kaPHt3oogEAQOvhchhZu3at0tLSlJWVpcLCQkVGRioxMVFlZWX19l+3bp2OHDni3D7//HN5enrqrrvuanLxAACg5XM5jCxZskQpKSlKTk7WgAEDlJubK39/f61YsaLe/p06dVJISIhz2759u/z9/QkjAABAkuTlSueamhoVFBQoPT3d2ebh4aGEhATl5eVd0ByvvPKKJkyYoLZt2zbYp7q6WtXV1c7XlZWVrpTZ7J7bvk/XlvzYpDnie3R2UzUAALQsLq2MHD16VLW1tQoODq7THhwcLJvNdt7x+fn5+vzzz/Xwww+fs192drYCAwOdW3h4uCtlAgCAFqRZr6Z55ZVXNGjQIMXGxp6zX3p6uioqKpzb4cOHm6lCAADQ3Fz6miYoKEienp4qLS2t015aWqqQkJBzjq2qqtKaNWs0Z86c8+7H19dXvr6+rpQGAABaKJdWRnx8fBQdHS2r1epss9vtslqtio+PP+fYN998U9XV1brvvvsaVykAAGiVXFoZkaS0tDRNnjxZMTExio2NVU5OjqqqqpScnCxJmjRpksLCwpSdnV1n3CuvvKJx48apc2dO1AQAAL9wOYwkJSWpvLxcmZmZstlsioqK0tatW50ntZaUlMjDo+6CS3FxsXbu3Km3337bPVUDAIBWw+UwIkmpqalKTU2t92c7duw4q61v375yOByN2RUAAGjleDYNAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIxqVBhZunSpIiIi5Ofnp7i4OOXn55+z/88//6wpU6YoNDRUvr6+6tOnj7Zs2dKoggEAQOvi5eqAtWvXKi0tTbm5uYqLi1NOTo4SExNVXFysLl26nNW/pqZGN910k7p06aK//vWvCgsL06FDh9ShQwd31N9072Y3eYprS350QyEAAFyeXA4jS5YsUUpKipKTkyVJubm52rx5s1asWKFZs2ad1X/FihX66aeftGvXLnl7e0uSIiIimlY1AABoNVz6mqampkYFBQVKSEj4ZQIPDyUkJCgvL6/eMRs3blR8fLymTJmi4OBgXX311Zo/f75qa2sb3E91dbUqKyvrbAAAoHVyKYwcPXpUtbW1Cg4OrtMeHBwsm81W75hvv/1Wf/3rX1VbW6stW7YoIyNDixcv1tNPP93gfrKzsxUYGOjcwsPDXSkTAAC0IBf9ahq73a4uXbropZdeUnR0tJKSkjR79mzl5uY2OCY9PV0VFRXO7fDhwxe7TAAAYIhL54wEBQXJ09NTpaWlddpLS0sVEhJS75jQ0FB5e3vL09PT2da/f3/ZbDbV1NTIx8fnrDG+vr7y9fV1pTQAANBCubQy4uPjo+joaFmtVmeb3W6X1WpVfHx8vWOGDx+u/fv3y263O9v27dun0NDQeoMIAAC4vLj8NU1aWpqWL1+uVatWae/evXr00UdVVVXlvLpm0qRJSk9Pd/Z/9NFH9dNPP2nq1Knat2+fNm/erPnz52vKlCnuOwoAANBiuXxpb1JSksrLy5WZmSmbzaaoqCht3brVeVJrSUmJPDx+yTjh4eHatm2bpk+frsGDByssLExTp07VzJkz3XcUAACgxbI4HA6H6SLOp7KyUoGBgaqoqFBAQIB7J3fDTc/yvm36Tc/ie3Q+/R+j0s/dEQCAFuJCP795Ng0AADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAoxoVRpYuXaqIiAj5+fkpLi5O+fn5DfZduXKlLBZLnc3Pz6/RBQMAgNbF5TCydu1apaWlKSsrS4WFhYqMjFRiYqLKysoaHBMQEKAjR444t0OHDjWpaAAA0Hq4HEaWLFmilJQUJScna8CAAcrNzZW/v79WrFjR4BiLxaKQkBDnFhwc3KSiAQBA6+FSGKmpqVFBQYESEhJ+mcDDQwkJCcrLy2tw3PHjx3XVVVcpPDxcY8eO1RdffHHO/VRXV6uysrLOBgAAWicvVzofPXpUtbW1Z61sBAcH66uvvqp3TN++fbVixQoNHjxYFRUVWrRokYYNG6YvvvhCV155Zb1jsrOz9dRTT7lSWouX9+2PkqTd/9zXqPHTb+rjznIAAGg2F/1qmvj4eE2aNElRUVEaMWKE1q1bpyuuuEJ//vOfGxyTnp6uiooK53b48OGLXSYAADDEpZWRoKAgeXp6qrS0tE57aWmpQkJCLmgOb29vDRkyRPv372+wj6+vr3x9fV0pDQAAtFAurYz4+PgoOjpaVqvV2Wa322W1WhUfH39Bc9TW1uqzzz5TaGioa5UCAIBWyaWVEUlKS0vT5MmTFRMTo9jYWOXk5KiqqkrJycmSpEmTJiksLEzZ2dmSpDlz5ujaa69Vr1699PPPP+vZZ5/VoUOH9PDDD7v3SAAAQIvkchhJSkpSeXm5MjMzZbPZFBUVpa1btzpPai0pKZGHxy8LLn//+9+VkpIim82mjh07Kjo6Wrt27dKAAQPcdxQAAKDFsjgcDofpIs6nsrJSgYGBqqioUEBAgHsnfze7yVOcuRLGHXZ3e6RR47iaBgBwqbnQz2+eTQMAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAoxoVRpYuXaqIiAj5+fkpLi5O+fn5FzRuzZo1slgsGjduXGN2CwAAWiGXw8jatWuVlpamrKwsFRYWKjIyUomJiSorKzvnuIMHD2rGjBm6/vrrG10sAABofVwOI0uWLFFKSoqSk5M1YMAA5ebmyt/fXytWrGhwTG1trSZOnKinnnpKPXr0aFLBAACgdXEpjNTU1KigoEAJCQm/TODhoYSEBOXl5TU4bs6cOerSpYseeuihC9pPdXW1Kisr62wAAKB1cimMHD16VLW1tQoODq7THhwcLJvNVu+YnTt36pVXXtHy5csveD/Z2dkKDAx0buHh4a6UCQAAWpCLejXNsWPHdP/992v58uUKCgq64HHp6emqqKhwbocPH76IVQIAAJO8XOkcFBQkT09PlZaW1mkvLS1VSEjIWf2/+eYbHTx4UGPGjHG22e320zv28lJxcbF69ux51jhfX1/5+vq6UhoAAGihXFoZ8fHxUXR0tKxWq7PNbrfLarUqPj7+rP79+vXTZ599pqKiIud2xx13aNSoUSoqKuLrFwAA4NrKiCSlpaVp8uTJiomJUWxsrHJyclRVVaXk5GRJ0qRJkxQWFqbs7Gz5+fnp6quvrjO+Q4cOknRWOwAAuDy5HEaSkpJUXl6uzMxM2Ww2RUVFaevWrc6TWktKSuThwY1dAQDAhbE4HA6H6SLOp7KyUoGBgaqoqFBAQIB7J383u8lT5H37oxsKOW13t0caNW76TX3cVgMAAO5woZ/fLGEAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMKpRYWTp0qWKiIiQn5+f4uLilJ+f32DfdevWKSYmRh06dFDbtm0VFRWlV199tdEFAwCA1sXlMLJ27VqlpaUpKytLhYWFioyMVGJiosrKyurt36lTJ82ePVt5eXn69NNPlZycrOTkZG3btq3JxQMAgJbP5TCyZMkSpaSkKDk5WQMGDFBubq78/f21YsWKevuPHDlS48ePV//+/dWzZ09NnTpVgwcP1s6dO5tcPAAAaPlcCiM1NTUqKChQQkLCLxN4eCghIUF5eXnnHe9wOGS1WlVcXKxf/epXDfarrq5WZWVlnQ0AALROLoWRo0ePqra2VsHBwXXag4ODZbPZGhxXUVGhdu3aycfHR6NHj9YLL7ygm266qcH+2dnZCgwMdG7h4eGulAkAAFqQZrmapn379ioqKtJHH32kefPmKS0tTTt27Giwf3p6uioqKpzb4cOHm6NMAABggJcrnYOCguTp6anS0tI67aWlpQoJCWlwnIeHh3r16iVJioqK0t69e5Wdna2RI0fW29/X11e+vr6ulAYAAFool1ZGfHx8FB0dLavV6myz2+2yWq2Kj4+/4Hnsdruqq6td2TUAAGilXFoZkaS0tDRNnjxZMTExio2NVU5OjqqqqpScnCxJmjRpksLCwpSdnS3p9PkfMTEx6tmzp6qrq7Vlyxa9+uqrWrZsmXuPBAAAtEguh5GkpCSVl5crMzNTNptNUVFR2rp1q/Ok1pKSEnl4/LLgUlVVpd/+9rf67rvv1KZNG/Xr10+vvfaakpKS3HcUAACgxbI4HA6H6SLOp7KyUoGBgaqoqFBAQIB7J383u8lT5H37oxsKOW13t0caNW76TX3cVgMAAO5woZ/fPJsGAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEY1KowsXbpUERER8vPzU1xcnPLz8xvsu3z5cl1//fXq2LGjOnbsqISEhHP2BwAAlxeXw8jatWuVlpamrKwsFRYWKjIyUomJiSorK6u3/44dO3TPPffo3XffVV5ensLDw3XzzTfr+++/b3LxAACg5XM5jCxZskQpKSlKTk7WgAEDlJubK39/f61YsaLe/q+//rp++9vfKioqSv369dPLL78su90uq9Xa5OIBAEDL51IYqampUUFBgRISEn6ZwMNDCQkJysvLu6A5Tpw4oVOnTqlTp04N9qmurlZlZWWdDQAAtE4uhZGjR4+qtrZWwcHBddqDg4Nls9kuaI6ZM2eqa9eudQLNv8rOzlZgYKBzCw8Pd6VMAADQgjTr1TTPPPOM1qxZo/Xr18vPz6/Bfunp6aqoqHBuhw8fbsYqAQBAc/JypXNQUJA8PT1VWlpap720tFQhISHnHLto0SI988wzeueddzR48OBz9vX19ZWvr68rpQEAgBbKpTDi4+Oj6OhoWa1WjRs3TpKcJ6OmpqY2OG7hwoWaN2+etm3bppiYmCYV7G553/5ougQAAC5rLoURSUpLS9PkyZMVExOj2NhY5eTkqKqqSsnJyZKkSZMmKSwsTNnZ2ZKkBQsWKDMzU6tXr1ZERITz3JJ27dqpXbt2bjwUAADQErkcRpKSklReXq7MzEzZbDZFRUVp69atzpNaS0pK5OHxy6koy5YtU01NjX7zm9/UmScrK0t/+MMfmlY9AABo8VwOI5KUmpra4NcyO3bsqPP64MGDjdkFAAC4TPBsGgAAYBRhBAAAGEUYAQAARhFGAACAUY06gRWXnue272vyHNNv6uOGSgAAcA0rIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMKpRYWTp0qWKiIiQn5+f4uLilJ+f32DfL774QnfeeaciIiJksViUk5PT2FoBAEAr5HIYWbt2rdLS0pSVlaXCwkJFRkYqMTFRZWVl9fY/ceKEevTooWeeeUYhISFNLhgAALQuLoeRJUuWKCUlRcnJyRowYIByc3Pl7++vFStW1Nt/6NChevbZZzVhwgT5+vo2uWAAANC6uBRGampqVFBQoISEhF8m8PBQQkKC8vLy3FZUdXW1Kisr62wAAKB1cimMHD16VLW1tQoODq7THhwcLJvN5raisrOzFRgY6NzCw8PdNjcAALi0XJJX06Snp6uiosK5HT582HRJAADgIvFypXNQUJA8PT1VWlpap720tNStJ6f6+vpyfgkAAJcJl1ZGfHx8FB0dLavV6myz2+2yWq2Kj493e3EAAKD1c2llRJLS0tI0efJkxcTEKDY2Vjk5OaqqqlJycrIkadKkSQoLC1N2drak0ye9fvnll87//v7771VUVKR27dqpV69ebjwUAADQErkcRpKSklReXq7MzEzZbDZFRUVp69atzpNaS0pK5OHxy4LLDz/8oCFDhjhfL1q0SIsWLdKIESO0Y8eOph8BAABo0VwOI5KUmpqq1NTUen/2rwEjIiJCDoejMbsBAACXgUvyahoAAHD5aNTKCC6ea0tecttcu7s94ra5AAC4WFgZAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEZxB1Y4Pbd9X5PnmH5THzdUAgC4nLAyAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjOKpvXArnvwLAHAVKyMAAMAowggAADCKMAIAAIwijAAAAKMadQLr0qVL9eyzz8pmsykyMlIvvPCCYmNjG+z/5ptvKiMjQwcPHlTv3r21YMEC3XbbbY0uGq1bU0+C5QRYAGhZXA4ja9euVVpamnJzcxUXF6ecnBwlJiaquLhYXbp0Oav/rl27dM899yg7O1u33367Vq9erXHjxqmwsFBXX321Ww4C9bu25CW3zLO72yNumQcAgPpYHA6Hw5UBcXFxGjp0qF588UVJkt1uV3h4uP793/9ds2bNOqt/UlKSqqqqtGnTJmfbtddeq6ioKOXm5l7QPisrKxUYGKiKigoFBAS4Uu555b0yw63ztUaXYxhhdQUAmu5CP79dWhmpqalRQUGB0tPTnW0eHh5KSEhQXl5evWPy8vKUlpZWpy0xMVEbNmxocD/V1dWqrq52vq6oqJB0+qDcreof1efvdJkbVPyCW+b56Mpkt8zTHLI3FDZ5jik39HJDJQDQcp353D7fuodLYeTo0aOqra1VcHBwnfbg4GB99dVX9Y6x2Wz19rfZbA3uJzs7W0899dRZ7eHh4a6Ui0vOi6YLaFa/N10AAFwijh07psDAwAZ/fknegTU9Pb3OaordbtdPP/2kzp07y2KxuG0/lZWVCg8P1+HDh93+9c+lgONr2Vrz8bXmY5M4vpaO43Mfh8OhY8eOqWvXrufs51IYCQoKkqenp0pLS+u0l5aWKiQkpN4xISEhLvWXJF9fX/n6+tZp69ChgyuluiQgIKBV/oU7g+Nr2Vrz8bXmY5M4vpaO43OPc62InOHSfUZ8fHwUHR0tq9XqbLPb7bJarYqPj693THx8fJ3+krR9+/YG+wMAgMuLy1/TpKWlafLkyYqJiVFsbKxycnJUVVWl5OTTJydOmjRJYWFhys7OliRNnTpVI0aM0OLFizV69GitWbNGH3/8sV56yT2XnQIAgJbN5TCSlJSk8vJyZWZmymazKSoqSlu3bnWepFpSUiIPj18WXIYNG6bVq1frySef1O9//3v17t1bGzZsuCTuMeLr66usrKyzvhJqLTi+lq01H19rPjaJ42vpOL7m5/J9RgAAANyJZ9MAAACjCCMAAMAowggAADCKMAIAAIy6rMPI0qVLFRERIT8/P8XFxSk/P990SW6RnZ2toUOHqn379urSpYvGjRun4uJi02VdFM8884wsFoumTZtmuhS3+f7773Xfffepc+fOatOmjQYNGqSPP/7YdFluUVtbq4yMDHXv3l1t2rRRz549NXfu3PM+t+JS9b//+78aM2aMunbtKovFctYztxwOhzIzMxUaGqo2bdooISFBX3/9tZliG+Fcx3fq1CnNnDlTgwYNUtu2bdW1a1dNmjRJP/zwg7mCXXC+393/9W//9m+yWCzKyclptvqa6kKOb+/evbrjjjsUGBiotm3baujQoSopKWn+YnUZh5G1a9cqLS1NWVlZKiwsVGRkpBITE1VWVma6tCZ77733NGXKFO3evVvbt2/XqVOndPPNN6uqqsp0aW710Ucf6c9//rMGDx5suhS3+fvf/67hw4fL29tbf/vb3/Tll19q8eLF6tixo+nS3GLBggVatmyZXnzxRe3du1cLFizQwoUL9cIL7nkYY3OrqqpSZGSkli5dWu/PFy5cqD/+8Y/Kzc3Vhx9+qLZt2yoxMVEnT55s5kob51zHd+LECRUWFiojI0OFhYVat26diouLdccddxio1HXn+92dsX79eu3evfu8tzO/1Jzv+L755htdd9116tevn3bs2KFPP/1UGRkZ8vPza+ZK/z/HZSo2NtYxZcoU5+va2lpH165dHdnZ2QarujjKysockhzvvfee6VLc5tixY47evXs7tm/f7hgxYoRj6tSppktyi5kzZzquu+4602VcNKNHj3Y8+OCDddp+/etfOyZOnGioIveR5Fi/fr3ztd1ud4SEhDieffZZZ9vPP//s8PX1dfznf/6ngQqb5l+Prz75+fkOSY5Dhw41T1Fu0tCxfffdd46wsDDH559/7rjqqqsczz33XLPX5g71HV9SUpLjvvvuM1NQPS7LlZGamhoVFBQoISHB2ebh4aGEhATl5eUZrOziqKiokCR16tTJcCXuM2XKFI0ePbrO77A12Lhxo2JiYnTXXXepS5cuGjJkiJYvX266LLcZNmyYrFar9u3bJ0n65JNPtHPnTt16662GK3O/AwcOyGaz1fk7GhgYqLi4uFb5PiOdfq+xWCwX9VlizcVut+v+++/XE088oYEDB5oux63sdrs2b96sPn36KDExUV26dFFcXNw5v6q62C7LMHL06FHV1tY67xp7RnBwsGw2m6GqLg673a5p06Zp+PDhl8Rdb91hzZo1KiwsdD5yoDX59ttvtWzZMvXu3Vvbtm3To48+qscee0yrVq0yXZpbzJo1SxMmTFC/fv3k7e2tIUOGaNq0aZo4caLp0tzuzHvJ5fA+I0knT57UzJkzdc8997SKh8stWLBAXl5eeuyxx0yX4nZlZWU6fvy4nnnmGd1yyy16++23NX78eP3617/We++9Z6Qml28Hj5ZlypQp+vzzz7Vz507TpbjF4cOHNXXqVG3fvt3cd5sXkd1uV0xMjObPny9JGjJkiD7//HPl5uZq8uTJhqtrujfeeEOvv/66Vq9erYEDB6qoqEjTpk1T165dW8XxXa5OnTqlu+++Ww6HQ8uWLTNdTpMVFBTo+eefV2FhoSwWi+ly3M5ut0uSxo4dq+nTp0uSoqKitGvXLuXm5mrEiBHNXtNluTISFBQkT09PlZaW1mkvLS1VSEiIoarcLzU1VZs2bdK7776rK6+80nQ5blFQUKCysjJdc8018vLykpeXl9577z398Y9/lJeXl2pra02X2CShoaEaMGBAnbb+/fsbO8Pd3Z544gnn6sigQYN0//33a/r06a1ylevMe0lrf585E0QOHTqk7du3t4pVkffff19lZWXq1q2b833m0KFDevzxxxUREWG6vCYLCgqSl5fXJfVec1mGER8fH0VHR8tqtTrb7Ha7rFar4uPjDVbmHg6HQ6mpqVq/fr3+53/+R927dzddktvceOON+uyzz1RUVOTcYmJiNHHiRBUVFcnT09N0iU0yfPjwsy7D3rdvn6666ipDFbnXiRMn6jxIU5I8PT2d/1JrTbp3766QkJA67zOVlZX68MMPW8X7jPRLEPn666/1zjvvqHPnzqZLcov7779fn376aZ33ma5du+qJJ57Qtm3bTJfXZD4+Pho6dOgl9V5z2X5Nk5aWpsmTJysmJkaxsbHKyclRVVWVkpOTTZfWZFOmTNHq1av13//932rfvr3z++nAwEC1adPGcHVN0759+7POfWnbtq06d+7cKs6JmT59uoYNG6b58+fr7rvvVn5+vl566SW99NJLpktzizFjxmjevHnq1q2bBg4cqD179mjJkiV68MEHTZfWKMePH9f+/fudrw8cOKCioiJ16tRJ3bp107Rp0/T000+rd+/e6t69uzIyMtS1a1eNGzfOXNEuONfxhYaG6je/+Y0KCwu1adMm1dbWOt9rOnXqJB8fH1NlX5Dz/e7+NVh5e3srJCREffv2be5SG+V8x/fEE08oKSlJv/rVrzRq1Cht3bpVb731lnbs2GGmYNOX85j0wgsvOLp16+bw8fFxxMbGOnbv3m26JLeQVO/2l7/8xXRpF0VrurTX4XA43nrrLcfVV1/t8PX1dfTr18/x0ksvmS7JbSorKx1Tp051dOvWzeHn5+fo0aOHY/bs2Y7q6mrTpTXKu+++W+//a5MnT3Y4HKcv783IyHAEBwc7fH19HTfeeKOjuLjYbNEuONfxHThwoMH3mnfffdd06ed1vt/dv2ppl/ZeyPG98sorjl69ejn8/PwckZGRjg0bNhir1+JwtNBbHwIAgFbhsjxnBAAAXDoIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIz6f/r7GHZF2WF6AAAAAElFTkSuQmCC", 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", 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", 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", 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", 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XLgUFBZkuCwDQghBGWrDevXsrPz/fdBkAgBaOE1gBAIBRhBEAAGCU24QRF7goCE2EvwUAcC0uH0Yu3fGzqqrKcCVoLi7d6dXuTdYAAGa4/Ams3t7e8vf31+nTp9WqVSvHE2/R8liWpfPnz+vUqVNq166drQf2AQDMcfkw4uHhodDQUB07duyyZ7egZWrXrp1CQkJMlwEAqCeXDyOS5OPjo65du3KoBmrVqhUrIgDgYtwijEiSp6cnt/4GADSq5nYbd3fhNmEEAIArcdozZZyyFfwYZ3sCAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAob7sDPvjgA7344ovKz8/XyZMntWnTJo0cOfKqY3bu3KmUlBT9/e9/V1hYmGbNmqWHH364gSUDAFqKRTsOO2U7/YqWO2U7aBy2V0YqKioUGRmppUuX1qv/sWPHNHz4cA0ePFiFhYV66qmnNGnSJG3fvt12sQAAwP3YXhkZNmyYhg0bVu/+y5Yt080336wFCxZIknr06KEPP/xQixYtUkJCgt3dAwAAN9Po54zk5eUpPj6+VltCQoLy8vKuOKayslJlZWW1XgAAwD01ehgpLi5WcHBwrbbg4GCVlZXp22+/rXNMZmamAgMDHa+wsLDGLhMAABjSLK+mSUtLU2lpqeN14sQJ0yUBAIBGYvucEbtCQkJUUlJSq62kpEQBAQFq3bp1nWN8fX3l6+vb2KUBAIBmoNFXRuLi4pSbm1urbceOHYqLi2vsXQMAABdgO4ycO3dOhYWFKiwslPT9pbuFhYUqKiqS9P0hluTkZEf/xx57TEePHtXTTz+tgwcP6pVXXtH69es1bdo058wAAAC4NNthZN++ferdu7d69+4tSUpJSVHv3r2Vnp4uSTp58qQjmEjSzTffrJycHO3YsUORkZFasGCBXnvtNS7rBQAAkhpwzsjdd98ty7Ku+P1Vq1bVOaagoMDurgAAQAvQLK+mAQAALQdhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGNfqzaQAALc+iHYedsp1+Rcudsh00b6yMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjvE0XAABwP/2KlpsuAS6EMAIAcFi047BTttPPKVtBS8FhGgAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARnmbLgAA0Hz0K1puugS0QKyMAAAAowgjAADAKA7TAIAbWLTjsFO2088pWwHsYWUEAAAYRRgBAABGNSiMLF26VOHh4fLz81NsbKz27t171f6LFy9Wt27d1Lp1a4WFhWnatGn67rvvGlQwAABwL7bDyLp165SSkqKMjAzt379fkZGRSkhI0KlTp+rsv2bNGqWmpiojI0MHDhzQypUrtW7dOj3zzDPXXTwAAHB9tsPIwoULNXnyZE2YMEG33Xabli1bJn9/f73++ut19t+zZ48GDBigMWPGKDw8XPfee69Gjx59zdUUAADQMtgKI1VVVcrPz1d8fPy/NuDpqfj4eOXl5dU5pn///srPz3eEj6NHj2rr1q26//77r7ifyspKlZWV1XoBAAD3ZOvS3jNnzqi6ulrBwcG12oODg3Xw4ME6x4wZM0ZnzpzRXXfdJcuydPHiRT322GNXPUyTmZmpOXPm2CkNAAC4qEa/mmbnzp2aN2+eXnnlFe3fv18bN25UTk6O5s6de8UxaWlpKi0tdbxOnDjR2GUCAABDbK2MBAUFycvLSyUlJbXaS0pKFBISUueY2bNna9y4cZo0aZIkqWfPnqqoqNCjjz6qmTNnytPz8jzk6+srX19fO6UBAAAXZWtlxMfHR3369FFubq6jraamRrm5uYqLi6tzzPnz5y8LHF5eXpIky7Ls1gsAANyM7dvBp6SkaPz48YqOjlZMTIwWL16siooKTZgwQZKUnJyszp07KzMzU5KUmJiohQsXqnfv3oqNjdWRI0c0e/ZsJSYmOkIJAABouWyHkaSkJJ0+fVrp6ekqLi5WVFSUtm3b5jiptaioqNZKyKxZs+Th4aFZs2bpyy+/VIcOHZSYmKjnn3/eebMAgBauX9Fy0yUADeZhucCxkrKyMgUGBqq0tFQBAQGmywGAZidv5XTTJcCFxU3MapTt1vfzm2fTAAAAowgjAADAKMIIAAAwijACAACMsn01DQDAeRbtOOyU7fRzylYAM1gZAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEZxB1YAMKhf0XLTJQDGsTICAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIqbngFAAyzacdgp2+nnlK0Aro2VEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFFfTAEAD9CtabroEwG2wMgIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAonk0DoEVZtOOwU7bTzylbASCxMgIAAAxjZQRAi8LTdoHmh5URAABgFGEEAAAYRRgBAABGNSiMLF26VOHh4fLz81NsbKz27t171f7ffPONpkyZotDQUPn6+ioiIkJbt25tUMEAAMC92D6Bdd26dUpJSdGyZcsUGxurxYsXKyEhQYcOHVLHjh0v619VVaWhQ4eqY8eOeuutt9S5c2d9/vnnateunTPqBwAALs52GFm4cKEmT56sCRMmSJKWLVumnJwcvf7660pNTb2s/+uvv66vv/5ae/bsUatWrSRJ4eHh11c1AABwG7YO01RVVSk/P1/x8fH/2oCnp+Lj45WXl1fnmC1btiguLk5TpkxRcHCw7rjjDs2bN0/V1dXXVzkAAHALtlZGzpw5o+rqagUHB9dqDw4O1sGDB+scc/ToUf3pT3/S2LFjtXXrVh05ckRPPPGELly4oIyMjDrHVFZWqrKy0vF1WVmZnTIBAIALafSraWpqatSxY0ctX75cffr0UVJSkmbOnKlly5ZdcUxmZqYCAwMdr7CwsMYuEwAAGGIrjAQFBcnLy0slJSW12ktKShQSElLnmNDQUEVERMjLy8vR1qNHDxUXF6uqqqrOMWlpaSotLXW8Tpw4YadMAADgQmyFER8fH/Xp00e5ubmOtpqaGuXm5iouLq7OMQMGDNCRI0dUU1PjaDt8+LBCQ0Pl4+NT5xhfX18FBATUegEAAPdk+zBNSkqKVqxYodWrV+vAgQN6/PHHVVFR4bi6Jjk5WWlpaY7+jz/+uL7++ms9+eSTOnz4sHJycjRv3jxNmTLFebMAAAAuy/alvUlJSTp9+rTS09NVXFysqKgobdu2zXFSa1FRkTw9/5VxwsLCtH37dk2bNk29evVS586d9eSTT2rGjBnOmwUA9/d+pukKADQSD8uyLNNFXEtZWZkCAwNVWlrKIRughcpbOd10CYDbipuY1Sjbre/nN8+mAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhl+w6sAGALd04FcA2sjAAAAKMIIwAAwCjCCAAAMIpzRgA0qryjZ02XAKCZY2UEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFHcZwRA3XimDIAmwsoIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIzi0l4Adco7etZ0CQBaCFZGAACAUayMAO6Gm5UBcDGsjAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACM4qZngJvhNu4AXA1hBGguuHMqgBaKwzQAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCiupgGuF1fBAMB1YWUEAAAYRRgBAABGNegwzdKlS/Xiiy+quLhYkZGRevnllxUTE3PNcdnZ2Ro9erRGjBihzZs3N2TXgNvizqkAWirbKyPr1q1TSkqKMjIytH//fkVGRiohIUGnTp266rjjx49r+vTpGjhwYIOLBQAA7sd2GFm4cKEmT56sCRMm6LbbbtOyZcvk7++v119//YpjqqurNXbsWM2ZM0ddunS5roIBAIB7sXWYpqqqSvn5+UpLS3O0eXp6Kj4+Xnl5eVcc99xzz6ljx46aOHGidu3a1fBqAWfiKhgAaBZshZEzZ86ourpawcHBtdqDg4N18ODBOsd8+OGHWrlypQoLC+u9n8rKSlVWVjq+Lisrs1MmAABwIY16NU15ebnGjRunFStWKCgoqN7jMjMzFRgY6HiFhYU1YpUAAMAkWysjQUFB8vLyUklJSa32kpIShYSEXNb/s88+0/Hjx5WYmOhoq6mp+X7H3t46dOiQbrnllsvGpaWlKSUlxfF1WVkZgQQAADdlK4z4+PioT58+ys3N1ciRIyV9Hy5yc3M1derUy/p3795dn3zySa22WbNmqby8XC+99NIVA4avr698fX3tlAYAAFyU7fuMpKSkaPz48YqOjlZMTIwWL16siooKTZgwQZKUnJyszp07KzMzU35+frrjjjtqjW/Xrp0kXdYOAABaJtthJCkpSadPn1Z6erqKi4sVFRWlbdu2OU5qLSoqkqcnN3ZFI2mGV8BwszIAuD4elmVZpou4lrKyMgUGBqq0tFQBAQGmy4FJhBEAcLq4iVmNst36fn6zhAEAAIwijAAAAKMa9KA8wLZmeHgFANA8sDICAACMIowAAACjOEyDFourYACgeSCM4Oo41wMA0Mg4TAMAAIwijAAAAKM4TOOuOLwCAHARhBG4FE46BQD3w2EaAABgFCsjzsJhEQAAGoSVEQAAYBQrI6xoAABgFGEETYITTwEAV8JhGgAAYBRhBAAAGMVhGlwVh1cAAI2NlREAAGAUYQQAABhFGAEAAEZxzoib4lwPAICrIIw0M4QIAEBLw2EaAABgFGEEAAAY1eIP03BYBAAAs1gZAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEY1KIwsXbpU4eHh8vPzU2xsrPbu3XvFvitWrNDAgQPVvn17tW/fXvHx8VftDwAAWhbbYWTdunVKSUlRRkaG9u/fr8jISCUkJOjUqVN19t+5c6dGjx6t999/X3l5eQoLC9O9996rL7/88rqLBwAArs/DsizLzoDY2Fj17dtXS5YskSTV1NQoLCxMv/rVr5SamnrN8dXV1Wrfvr2WLFmi5OTkeu2zrKxMgYGBKi0tVUBAgJ1yrylv5XSnbg8AAFcTNzGrUbZb389vWysjVVVVys/PV3x8/L824Omp+Ph45eXl1Wsb58+f14ULF3TDDTdcsU9lZaXKyspqvQAAgHuyFUbOnDmj6upqBQcH12oPDg5WcXFxvbYxY8YMderUqVag+bHMzEwFBgY6XmFhYXbKBAAALqRJr6Z54YUXlJ2drU2bNsnPz++K/dLS0lRaWup4nThxogmrBAAATcnbTuegoCB5eXmppKSkVntJSYlCQkKuOjYrK0svvPCC3nvvPfXq1euqfX19feXr62unNAAA4KJsrYz4+PioT58+ys3NdbTV1NQoNzdXcXFxVxw3f/58zZ07V9u2bVN0dHTDqwUAAG7H1sqIJKWkpGj8+PGKjo5WTEyMFi9erIqKCk2YMEGSlJycrM6dOyszM1OS9Lvf/U7p6elas2aNwsPDHeeWtGnTRm3atHHiVAAAgCuyHUaSkpJ0+vRppaenq7i4WFFRUdq2bZvjpNaioiJ5ev5rweXVV19VVVWVfvnLX9baTkZGhp599tnrqx4AALg82/cZMYH7jAAA0Hhc6j4jAAAAzkYYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYFSDwsjSpUsVHh4uPz8/xcbGau/evVftv2HDBnXv3l1+fn7q2bOntm7d2qBiAQCA+7EdRtatW6eUlBRlZGRo//79ioyMVEJCgk6dOlVn/z179mj06NGaOHGiCgoKNHLkSI0cOVJ/+9vfrrt4AADg+jwsy7LsDIiNjVXfvn21ZMkSSVJNTY3CwsL0q1/9SqmpqZf1T0pKUkVFhd555x1HW79+/RQVFaVly5bVa59lZWUKDAxUaWmpAgIC7JR7TXkrpzt1ewAAuJq4iVmNst36fn5729loVVWV8vPzlZaW5mjz9PRUfHy88vLy6hyTl5enlJSUWm0JCQnavHnzFfdTWVmpyspKx9elpaWSvp+Us1V8W3ntTgAAuLHG+Hz94Xavte5hK4ycOXNG1dXVCg4OrtUeHBysgwcP1jmmuLi4zv7FxcVX3E9mZqbmzJlzWXtYWJidcgEAQH38akmjbr68vFyBgYFX/L6tMNJU0tLSaq2m1NTU6Ouvv9ZPf/pTeXh4OG0/ZWVlCgsL04kTJ5x++Ke5YI6uz93nJzFHd+Du85OYY0NYlqXy8nJ16tTpqv1shZGgoCB5eXmppKSkVntJSYlCQkLqHBMSEmKrvyT5+vrK19e3Vlu7du3slGpLQECA2/5hXcIcXZ+7z09iju7A3ecnMUe7rrYicomtq2l8fHzUp08f5ebmOtpqamqUm5uruLi4OsfExcXV6i9JO3bsuGJ/AADQstg+TJOSkqLx48crOjpaMTExWrx4sSoqKjRhwgRJUnJysjp37qzMzExJ0pNPPqlBgwZpwYIFGj58uLKzs7Vv3z4tX77cuTMBAAAuyXYYSUpK0unTp5Wenq7i4mJFRUVp27ZtjpNUi4qK5On5rwWX/v37a82aNZo1a5aeeeYZde3aVZs3b9Ydd9zhvFk0kK+vrzIyMi47JOROmKPrc/f5SczRHbj7/CTm2Jhs32cEAADAmXg2DQAAMIowAgAAjCKMAAAAowgjAADAKLcPI0uXLlV4eLj8/PwUGxurvXv3XrX/hg0b1L17d/n5+alnz57aunVrE1XacHbmuGLFCg0cOFDt27dX+/btFR8ff82fSXNg9/d4SXZ2tjw8PDRy5MjGLfA62Z3fN998oylTpig0NFS+vr6KiIho9n+rdue4ePFidevWTa1bt1ZYWJimTZum7777romqteeDDz5QYmKiOnXqJA8Pj6s+e+uSnTt36s4775Svr69uvfVWrVq1qtHrvB5257hx40YNHTpUHTp0UEBAgOLi4rR9+/amKbYBGvI7vGT37t3y9vZWVFRUo9XnDA2ZY2VlpWbOnKmbbrpJvr6+Cg8P1+uvv+702tw6jKxbt04pKSnKyMjQ/v37FRkZqYSEBJ06darO/nv27NHo0aM1ceJEFRQUaOTIkRo5cqT+9re/NXHl9Wd3jjt37tTo0aP1/vvvKy8vT2FhYbr33nv15ZdfNnHl9Wd3jpccP35c06dP18CBA5uo0oaxO7+qqioNHTpUx48f11tvvaVDhw5pxYoV6ty5cxNXXn9257hmzRqlpqYqIyNDBw4c0MqVK7Vu3To988wzTVx5/VRUVCgyMlJLly6tV/9jx45p+PDhGjx4sAoLC/XUU09p0qRJzfrD2u4cP/jgAw0dOlRbt25Vfn6+Bg8erMTERBUUFDRypQ1jd36XfPPNN0pOTtY999zTSJU5T0PmOGrUKOXm5mrlypU6dOiQ1q5dq27dujm/OMuNxcTEWFOmTHF8XV1dbXXq1MnKzMyss/+oUaOs4cOH12qLjY21/uM//qNR67weduf4YxcvXrTatm1rrV69urFKvG4NmePFixet/v37W6+99po1fvx4a8SIEU1QacPYnd+rr75qdenSxaqqqmqqEq+b3TlOmTLFGjJkSK22lJQUa8CAAY1apzNIsjZt2nTVPk8//bR1++2312pLSkqyEhISGrEy56nPHOty2223WXPmzHF+QU5mZ35JSUnWrFmzrIyMDCsyMrJR63Km+szxj3/8oxUYGGidPXu20etx25WRqqoq5efnKz4+3tHm6emp+Ph45eXl1TkmLy+vVn9JSkhIuGJ/0xoyxx87f/68Lly4oBtuuKGxyrwuDZ3jc889p44dO2rixIlNUWaDNWR+W7ZsUVxcnKZMmaLg4GDdcccdmjdvnqqrq5uqbFsaMsf+/fsrPz/fcSjn6NGj2rp1q+6///4mqbmxudp7jTPU1NSovLy82b7XNMQbb7yho0ePKiMjw3QpjWLLli2Kjo7W/Pnz1blzZ0VERGj69On69ttvnb6vZvnUXmc4c+aMqqurHXeGvSQ4OFgHDx6sc0xxcXGd/YuLixutzuvRkDn+2IwZM9SpU6fL3hibi4bM8cMPP9TKlStVWFjYBBVen4bM7+jRo/rTn/6ksWPHauvWrTpy5IieeOIJXbhwoVm+KTZkjmPGjNGZM2d01113ybIsXbx4UY899lizPUxj15Xea8rKyvTtt9+qdevWhiprPFlZWTp37pxGjRpluhSn+Mc//qHU1FTt2rVL3t7u+VF69OhRffjhh/Lz89OmTZt05swZPfHEEzp79qzeeOMNp+7LbVdGcG0vvPCCsrOztWnTJvn5+ZkuxynKy8s1btw4rVixQkFBQabLaRQ1NTXq2LGjli9frj59+igpKUkzZ87UsmXLTJfmNDt37tS8efP0yiuvaP/+/dq4caNycnI0d+5c06WhAdasWaM5c+Zo/fr16tixo+lyrlt1dbXGjBmjOXPmKCIiwnQ5jaampkYeHh568803FRMTo/vvv18LFy7U6tWrnb464p5xTlJQUJC8vLxUUlJSq72kpEQhISF1jgkJCbHV37SGzPGSrKwsvfDCC3rvvffUq1evxizzutid42effabjx48rMTHR0VZTUyNJ8vb21qFDh3TLLbc0btE2NOR3GBoaqlatWsnLy8vR1qNHDxUXF6uqqko+Pj6NWrNdDZnj7NmzNW7cOE2aNEmS1LNnT1VUVOjRRx/VzJkzaz3/yhVd6b0mICDA7VZFsrOzNWnSJG3YsKHZrsDaVV5ern379qmgoEBTp06V9P37jGVZ8vb21rvvvqshQ4YYrvL6hYaGqnPnzgoMDHS09ejRQ5Zl6YsvvlDXrl2dti/X/j/6Knx8fNSnTx/l5uY62mpqapSbm6u4uLg6x8TFxdXqL0k7duy4Yn/TGjJHSZo/f77mzp2rbdu2KTo6uilKbTC7c+zevbs++eQTFRYWOl4PPPCA46qFsLCwpiz/mhryOxwwYICOHDniCFmSdPjwYYWGhja7ICI1bI7nz5+/LHBcCl+WGzxOy9Xeaxpq7dq1mjBhgtauXavhw4ebLsdpAgICLnufeeyxx9StWzcVFhYqNjbWdIlOMWDAAH311Vc6d+6co+3w4cPy9PTUjTfe6NydNfopsgZlZ2dbvr6+1qpVq6xPP/3UevTRR6127dpZxcXFlmVZ1rhx46zU1FRH/927d1ve3t5WVlaWdeDAASsjI8Nq1aqV9cknn5iawjXZneMLL7xg+fj4WG+99ZZ18uRJx6u8vNzUFK7J7hx/rLlfTWN3fkVFRVbbtm2tqVOnWocOHbLeeecdq2PHjtZvf/tbU1O4JrtzzMjIsNq2bWutXbvWOnr0qPXuu+9at9xyizVq1ChTU7iq8vJyq6CgwCooKLAkWQsXLrQKCgqszz//3LIsy0pNTbXGjRvn6H/06FHL39/f+s1vfmMdOHDAWrp0qeXl5WVt27bN1BSuye4c33zzTcvb29taunRprfeab775xtQUrsru/H7MFa6msTvH8vJy68Ybb7R++ctfWn//+9+tP//5z1bXrl2tSZMmOb02tw4jlmVZL7/8svWzn/3M8vHxsWJiYqy//OUvju8NGjTIGj9+fK3+69evtyIiIiwfHx/r9ttvt3Jycpq4YvvszPGmm26yJF32ysjIaPrCbbD7e/yh5h5GLMv+/Pbs2WPFxsZavr6+VpcuXaznn3/eunjxYhNXbY+dOV64cMF69tlnrVtuucXy8/OzwsLCrCeeeML65z//2fSF18P7779f5/9Xl+Y0fvx4a9CgQZeNiYqKsnx8fKwuXbpYb7zxRpPXbYfdOQ4aNOiq/ZubhvwOf8gVwkhD5njgwAErPj7eat26tXXjjTdaKSkp1vnz551em4dlucGaJwAAcFlue84IAABwDYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARv0/VT6DF5eCyLsAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cos_theta_r1\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selection_dict={\"Signal\": df[\"signal\"] == 1, \"Background\": df[\"signal\"] == 0}\n", + "\n", + "for column_name in df.columns:\n", + " print(column_name)\n", + " compare_distributions(df,column_name,\n", + " selection_dict,\n", + " alpha=0.5,\n", + " density=1,\n", + " bins=25\n", + " )\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Part b\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def compare_distributions(df, column_name, selections, **kwargs):\n", + " hist_data = {}\n", + " \n", + " # Pre-compute histograms for each selection\n", + " for label, selection in selections.items():\n", + " hist_data[label] = np.histogram(df[selection][column_name], **kwargs)\n", + " \n", + " # Plot the pre-computed histograms\n", + " for label, hist in hist_data.items():\n", + " plt.hist(hist[1][:-1], bins=hist[1], weights=hist[0], label=label, alpha=0.5)\n", + " \n", + " plt.xlabel(column_name)\n", + " plt.ylabel('Frequency')\n", + " plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "num must be an integer with 1 <= num <= 15, not 16", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[17], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m15\u001b[39m, \u001b[38;5;241m15\u001b[39m))\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, column_name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(df\u001b[38;5;241m.\u001b[39mcolumns):\n\u001b[0;32m----> 7\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubplot\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m compare_distributions(df, column_name, selection_dict, density\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, bins\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m25\u001b[39m)\n\u001b[1;32m 10\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n", + "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py:1425\u001b[0m, in \u001b[0;36msubplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1422\u001b[0m fig \u001b[38;5;241m=\u001b[39m gcf()\n\u001b[1;32m 1424\u001b[0m \u001b[38;5;66;03m# First, search for an existing subplot with a matching spec.\u001b[39;00m\n\u001b[0;32m-> 1425\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[43mSubplotSpec\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_from_subplot_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1427\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m fig\u001b[38;5;241m.\u001b[39maxes:\n\u001b[1;32m 1428\u001b[0m \u001b[38;5;66;03m# If we found an Axes at the position, we can re-use it if the user passed no\u001b[39;00m\n\u001b[1;32m 1429\u001b[0m \u001b[38;5;66;03m# kwargs or if the axes class and kwargs are identical.\u001b[39;00m\n\u001b[1;32m 1430\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (ax\u001b[38;5;241m.\u001b[39mget_subplotspec() \u001b[38;5;241m==\u001b[39m key\n\u001b[1;32m 1431\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m (kwargs \u001b[38;5;241m==\u001b[39m {}\n\u001b[1;32m 1432\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m (ax\u001b[38;5;241m.\u001b[39m_projection_init\n\u001b[1;32m 1433\u001b[0m \u001b[38;5;241m==\u001b[39m fig\u001b[38;5;241m.\u001b[39m_process_projection_requirements(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)))):\n", + "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/gridspec.py:599\u001b[0m, in \u001b[0;36mSubplotSpec._from_subplot_args\u001b[0;34m(figure, args)\u001b[0m\n\u001b[1;32m 597\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 598\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(num, Integral) \u001b[38;5;129;01mor\u001b[39;00m num \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m num \u001b[38;5;241m>\u001b[39m rows\u001b[38;5;241m*\u001b[39mcols:\n\u001b[0;32m--> 599\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 600\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum must be an integer with 1 <= num <= \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrows\u001b[38;5;241m*\u001b[39mcols\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 601\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnot \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 602\u001b[0m )\n\u001b[1;32m 603\u001b[0m i \u001b[38;5;241m=\u001b[39m j \u001b[38;5;241m=\u001b[39m num\n\u001b[1;32m 604\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m gs[i\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m:j]\n", + "\u001b[0;31mValueError\u001b[0m: num must be an integer with 1 <= num <= 15, not 16" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example usage\n", + "selection_dict = {\"Signal\": df[\"signal\"] == 1, \"Background\": df[\"signal\"] == 0}\n", + "\n", + "plt.figure(figsize=(15, 15))\n", + "\n", + "for i, column_name in enumerate(df.columns):\n", + " plt.subplot(5, 3, i + 1)\n", + " compare_distributions(df, column_name, selection_dict, density=1, bins=25)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Part c\n", + "# Both low and high-level features are optimal observables for separating signal and 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": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Part a\n", + "# !pip install tabulate" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# !export PATH=\"$PATH:/home/rcwsl/.local/bin\"" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Part b\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Assuming df contains all observables, with low-level features labeled as 'RawNames' and high-level features labeled as 'FeatureNames'\n", + "\n", + "# Separate low-level and high-level features, also have all features\n", + "all_features = df[VarNames]\n", + "low_features = df[RawNames]\n", + "high_features = df[FeatureNames]\n", + "\n", + "# Compute covariance matrix for low-level features\n", + "covariance_low = np.cov(low_features.values, rowvar=False)\n", + "\n", + "# Compute correlation matrix for low-level features\n", + "correlation_low = np.corrcoef(low_features.values, rowvar=False)\n", + "\n", + "# Compute covariance matrix for high-level features\n", + "covariance_high = np.cov(high_features.values, rowvar=False)\n", + "\n", + "# Compute correlation matrix for high-level features\n", + "correlation_high = np.corrcoef(high_features.values, rowvar=False)\n", + "\n", + "# Compute covariance matrix for all features\n", + "covariance_all = np.cov(all_features.values, rowvar=False)\n", + "\n", + "# Compute correlation matrix for all features\n", + "correlation_all = np.corrcoef(all_features.values, rowvar=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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]\n" + ] + } + ], + "source": [ + "print(all_features)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hint: Example code for embedding a `tabulate` table into a notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "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": [ + "" + ] + }, + "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": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Covariance Matrix (all features):\n" + ] + }, + { + "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", + "
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
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-0.0002 -0.0002 0.0003 1.0033 -0.0001 0.0006 -0.2681 0.0012 -0.1842 0.0015 -0.0020-0.0002 0.0008 0.0009 0.0018 0.0000 0.0013 0.0010 0.0003
0.0635 0.3079 -0.0004 -0.0001 0.4280 -0.0005 0.0001 0.0797 -0.0014 -0.0023 0.0498 0.3281 0.1644-0.0993-0.0691 0.3246 0.0056 -0.0041 -0.0278
0.0002 -0.0003 0.4059 0.0006 -0.0005 1.0057 -0.0001 0.0001 -0.0001 0.0001 -0.0002-0.0005 -0.0002 0.0003-0.0001-0.0006 -0.0002 -0.0001 0.0002
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0.1908 0.2310 -0.0006 0.0012 0.0797 0.0001 0.0000 0.7619 -0.0016 0.5484 0.1448 0.1459 0.3681 0.1886 0.1564 0.1673 0.3166 0.1453 0.0733
0.0001 -0.0007 -0.0005 -0.1842 -0.0014 -0.0001 -0.0345-0.0016 1.0033 -0.0029 -0.0010-0.0010 -0.0008-0.0002 0.0005-0.0011 -0.0004 -0.0013 0.0001
0.1254 0.0986 -0.0005 0.0015 -0.0023 0.0001 0.0002 0.5484 -0.0029 0.7924 -0.1253 0.0437 0.3033 0.2495 0.4100 0.0824 0.4157 0.1466 0.0556
0.0385 -0.0125 -0.0005 -0.0020 0.0498 -0.0002 -0.0001 0.1448 -0.0010 -0.1253 1.0032 0.0151 -0.1887-0.1816-0.4603-0.0434 -0.2341 -0.0262 -0.0541
0.0835 0.3681 -0.0003 -0.0002 0.3281 -0.0005 0.0003 0.1459 -0.0010 0.0437 0.0151 0.3954 0.2122-0.1129-0.0366 0.3831 0.0743 -0.0291 -0.0142
0.1231 0.2908 -0.0003 0.0008 0.1644 -0.0002 0.0003 0.3681 -0.0008 0.3033 -0.1887 0.2122 0.3412 0.1045 0.1895 0.2304 0.2425 0.0581 0.0519
0.0263 -0.0593 0.0001 0.0009 -0.0993 0.0003 0.0002 0.1886 -0.0002 0.2495 -0.1816-0.1129 0.1045 0.2217 0.2322-0.0834 0.1656 0.0871 0.0582
0.0340 -0.0128 -0.0002 0.0018 -0.0691 -0.0001 0.0012 0.1564 0.0005 0.4100 -0.4603-0.0366 0.1895 0.2322 0.7383-0.0112 0.4333 0.0212 0.0445
0.0799 0.3463 -0.0003 0.0000 0.3246 -0.0006 0.0004 0.1673 -0.0011 0.0824 -0.0434 0.3831 0.2304-0.0834-0.0112 0.3853 0.0961 -0.0036 -0.0102
0.0848 0.0981 -0.0005 0.0013 0.0056 -0.0002 0.0005 0.3166 -0.0004 0.4157 -0.2341 0.0743 0.2425 0.1656 0.4333 0.0961 0.3891 0.0424 0.0392
0.0071 -0.0470 0.0001 0.0010 -0.0041 -0.0001 0.0002 0.1453 -0.0013 0.1466 -0.0262-0.0291 0.0581 0.0871 0.0212-0.0036 0.0424 0.1902 0.0091
0.0264 0.0225 0.0001 0.0003 -0.0278 0.0002 -0.0002 0.0733 0.0001 0.0556 -0.0541-0.0142 0.0519 0.0582 0.0445-0.0102 0.0392 0.0091 0.0388
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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
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-0.0003 -0.0004 1.0000 0.0003 -0.0006 0.4035 0.0003-0.0006 -0.0005 -0.0006 -0.0005-0.0005 -0.0005 0.0003-0.0002-0.0005 -0.0008 0.0002 0.0007
-0.0005 -0.0003 0.0003 1.0000 -0.0002 0.0006 -0.2672 0.0014 -0.1836 0.0017 -0.0020-0.0003 0.0014 0.0019 0.0020 0.0001 0.0021 0.0022 0.0015
0.1948 0.6847 -0.0006 -0.0002 1.0000 -0.0007 0.0002 0.1396 -0.0021 -0.0039 0.0760 0.7976 0.4302-0.3224-0.1230 0.7994 0.0136 -0.0144 -0.2157
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0.4388 0.3849 -0.0006 0.0014 0.1396 0.0001 0.0000 1.0000 -0.0019 0.7057 0.1656 0.2658 0.7220 0.4588 0.2085 0.3088 0.5815 0.3816 0.4261
0.0001 -0.0010 -0.0005 -0.1836 -0.0021 -0.0001 -0.0344-0.0019 1.0000 -0.0033 -0.0010-0.0015 -0.0014-0.0003 0.0006-0.0018 -0.0006 -0.0030 0.0003
0.2828 0.1611 -0.0006 0.0017 -0.0039 0.0001 0.0002 0.7057 -0.0033 1.0000 -0.1405 0.0781 0.5834 0.5953 0.5361 0.1492 0.7486 0.3776 0.3171
0.0771 -0.0182 -0.0005 -0.0020 0.0760 -0.0002 -0.0001 0.1656 -0.0010 -0.1405 1.0000 0.0240 -0.3226-0.3852-0.5349-0.0698 -0.3747 -0.0600 -0.2743
0.2666 0.8516 -0.0005 -0.0003 0.7976 -0.0009 0.0005 0.2658 -0.0015 0.0781 0.0240 1.0000 0.5776-0.3814-0.0678 0.9814 0.1894 -0.1062 -0.1146
0.4230 0.7244 -0.0005 0.0014 0.4302 -0.0003 0.0005 0.7220 -0.0014 0.5834 -0.3226 0.5776 1.0000 0.3798 0.3776 0.6356 0.6655 0.2282 0.4515
0.1119 -0.1832 0.0003 0.0019 -0.3224 0.0005 0.0005 0.4588 -0.0003 0.5953 -0.3852-0.3814 0.3798 1.0000 0.5740-0.2855 0.5640 0.4243 0.6273
0.0794 -0.0216 -0.0002 0.0020 -0.1230 -0.0001 0.0014 0.2085 0.0006 0.5361 -0.5349-0.0678 0.3776 0.5740 1.0000-0.0209 0.8085 0.0565 0.2631
0.2583 0.8117 -0.0005 0.0001 0.7994 -0.0009 0.0006 0.3088 -0.0018 0.1492 -0.0698 0.9814 0.6356-0.2855-0.0209 1.0000 0.2483 -0.0134 -0.0836
0.2730 0.2288 -0.0008 0.0021 0.0136 -0.0003 0.0008 0.5815 -0.0006 0.7486 -0.3747 0.1894 0.6655 0.5640 0.8085 0.2483 1.0000 0.1558 0.3190
0.0327 -0.1569 0.0002 0.0022 -0.0144 -0.0001 0.0006 0.3816 -0.0030 0.3776 -0.0600-0.1062 0.2282 0.4243 0.0565-0.0134 0.1558 1.0000 0.1063
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l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi MET MET_phi
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l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi MET MET_phi
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S_R MT2 cos_theta_r1 R M_R MET_rel dPhi_r_b M_Delta_R M_TR_2 axial_MET
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0.2304 0.1895 0.0519 0.1045 0.2122 0.3033 0.0581 0.2425 0.3412 -0.1887
-0.0434-0.4603 -0.0541-0.1816 0.0151 -0.1253 -0.0262 -0.2341 -0.1887 1.0032
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S_R MT2 cos_theta_r1 R M_R MET_rel dPhi_r_b M_Delta_R M_TR_2 axial_MET
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0.2483 0.8085 0.3190 0.5640 0.1894 0.7486 0.1558 1.0000 0.6655 -0.3747
0.6356 0.3776 0.4515 0.3798 0.5776 0.5834 0.2282 0.6655 1.0000 -0.3226
-0.0698-0.5349 -0.2743-0.3852 0.0240 -0.1405 -0.0600 -0.3747 -0.3226 1.0000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Part c\n", + "import numpy as np\n", + "from tabulate import tabulate\n", + "\n", + "# Assuming covariance_low, correlation_low, covariance_high, and correlation_high are previously computed matrices\n", + "\n", + "# Round the matrices to appropriate significant figures\n", + "covariance_low_rounded = np.round(covariance_low, decimals=4)\n", + "correlation_low_rounded = np.round(correlation_low, decimals=4)\n", + "covariance_high_rounded = np.round(covariance_high, decimals=4)\n", + "correlation_high_rounded = np.round(correlation_high, decimals=4)\n", + "covariance_all_rounded = np.round(covariance_all, decimals=4)\n", + "correlation_all_rounded = np.round(correlation_all, decimals=4)\n", + "\n", + "# Create table for covariance and correlation matrices for all features\n", + "print(\"Covariance Matrix (all features):\")\n", + "display(HTML(tabulate(covariance_all_rounded, headers=df[VarNames], tablefmt='html', floatfmt=\".4f\")))\n", + "print(\"\\nCorrelation Matrix (all features):\")\n", + "display(HTML(tabulate(correlation_all_rounded, headers=df[VarNames], tablefmt='html', floatfmt=\".4f\")))\n", + "\n", + "# Create table for covariance and correlation matrices of low-level features\n", + "print(\"Covariance Matrix (Low-level features):\")\n", + "display(HTML(tabulate(covariance_low_rounded, headers=df[RawNames][0:], tablefmt='html', floatfmt=\".4f\")))\n", + "print(\"\\nCorrelation Matrix (Low-level features):\")\n", + "display(HTML(tabulate(correlation_low_rounded, headers=df[RawNames][0:], tablefmt='html', floatfmt=\".4f\")))\n", + "\n", + "# Create table for covariance and correlation matrices of high-level features\n", + "print(\"\\nCovariance Matrix (High-level features):\")\n", + "display(HTML(tabulate(covariance_high_rounded, headers=df[FeatureNames][0:], tablefmt='html', floatfmt=\".4f\")))\n", + "print(\"\\nCorrelation Matrix (High-level features):\")\n", + "display(HTML(tabulate(correlation_high_rounded, headers=df[FeatureNames][0:], tablefmt='html', floatfmt=\".4f\")))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Part d\n", + "import numpy as np\n", + "import pandas as pd\n", + "from tabulate import tabulate\n", + "from IPython.display import display, HTML\n", + "\n", + "def compute_covariance_and_correlation(df, VarNames, RawNames, FeatureNames):\n", + " # Separate low-level and high-level features, also have all features\n", + " all_features = df[VarNames]\n", + " low_features = df[RawNames]\n", + " high_features = df[FeatureNames]\n", + "\n", + " # Compute covariance matrix for low-level features\n", + " covariance_low = np.cov(low_features.values, rowvar=False)\n", + "\n", + " # Compute correlation matrix for low-level features\n", + " correlation_low = np.corrcoef(low_features.values, rowvar=False)\n", + "\n", + " # Compute covariance matrix for high-level features\n", + " covariance_high = np.cov(high_features.values, rowvar=False)\n", + "\n", + " # Compute correlation matrix for high-level features\n", + " correlation_high = np.corrcoef(high_features.values, rowvar=False)\n", + "\n", + " # Compute covariance matrix for all features\n", + " covariance_all = np.cov(all_features.values, rowvar=False)\n", + "\n", + " # Compute correlation matrix for all features\n", + " correlation_all = np.corrcoef(all_features.values, rowvar=False)\n", + "\n", + " # Round the matrices to appropriate significant figures\n", + " covariance_low_rounded = np.round(covariance_low, decimals=4)\n", + " correlation_low_rounded = np.round(correlation_low, decimals=4)\n", + " covariance_high_rounded = np.round(covariance_high, decimals=4)\n", + " correlation_high_rounded = np.round(correlation_high, decimals=4)\n", + " covariance_all_rounded = np.round(covariance_all, decimals=4)\n", + " correlation_all_rounded = np.round(correlation_all, decimals=4)\n", + "\n", + " # Create table for covariance and correlation matrices for all features\n", + " print(\"Covariance Matrix (all features):\")\n", + " display(HTML(tabulate(covariance_all_rounded, headers=df[VarNames], tablefmt='html', floatfmt=\".4f\")))\n", + " print(\"\\nCorrelation Matrix (all features):\")\n", + " display(HTML(tabulate(correlation_all_rounded, headers=df[VarNames], tablefmt='html', floatfmt=\".4f\")))\n", + "\n", + " # Create table for covariance and correlation matrices of low-level features\n", + " print(\"Covariance Matrix (Low-level features):\")\n", + " display(HTML(tabulate(covariance_low_rounded, headers=df[RawNames][0:], tablefmt='html', floatfmt=\".4f\")))\n", + " print(\"\\nCorrelation Matrix (Low-level features):\")\n", + " display(HTML(tabulate(correlation_low_rounded, headers=df[RawNames][0:], tablefmt='html', floatfmt=\".4f\")))\n", + "\n", + " # Create table for covariance and correlation matrices of high-level features\n", + " print(\"\\nCovariance Matrix (High-level features):\")\n", + " display(HTML(tabulate(covariance_high_rounded, headers=df[FeatureNames][0:], tablefmt='html', floatfmt=\".4f\")))\n", + " print(\"\\nCorrelation Matrix (High-level features):\")\n", + " display(HTML(tabulate(correlation_high_rounded, headers=df[FeatureNames][0:], tablefmt='html', floatfmt=\".4f\")))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Covariance Matrix (all features):\n" + ] + }, + { + "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", + "
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
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-0.0002 -0.0003 1.0062 0.0003 -0.0004 0.4059 0.0003-0.0006 -0.0005 -0.0005 -0.0005-0.0003 -0.0003 0.0001-0.0002-0.0003 -0.0005 0.0001 0.0001
-0.0002 -0.0002 0.0003 1.0033 -0.0001 0.0006 -0.2681 0.0012 -0.1842 0.0015 -0.0020-0.0002 0.0008 0.0009 0.0018 0.0000 0.0013 0.0010 0.0003
0.0635 0.3079 -0.0004 -0.0001 0.4280 -0.0005 0.0001 0.0797 -0.0014 -0.0023 0.0498 0.3281 0.1644-0.0993-0.0691 0.3246 0.0056 -0.0041 -0.0278
0.0002 -0.0003 0.4059 0.0006 -0.0005 1.0057 -0.0001 0.0001 -0.0001 0.0001 -0.0002-0.0005 -0.0002 0.0003-0.0001-0.0006 -0.0002 -0.0001 0.0002
-0.0000 0.0002 0.0003 -0.2681 0.0001 -0.0001 1.0033 0.0000 -0.0345 0.0002 -0.0001 0.0003 0.0003 0.0002 0.0012 0.0004 0.0005 0.0002 -0.0002
0.1908 0.2310 -0.0006 0.0012 0.0797 0.0001 0.0000 0.7619 -0.0016 0.5484 0.1448 0.1459 0.3681 0.1886 0.1564 0.1673 0.3166 0.1453 0.0733
0.0001 -0.0007 -0.0005 -0.1842 -0.0014 -0.0001 -0.0345-0.0016 1.0033 -0.0029 -0.0010-0.0010 -0.0008-0.0002 0.0005-0.0011 -0.0004 -0.0013 0.0001
0.1254 0.0986 -0.0005 0.0015 -0.0023 0.0001 0.0002 0.5484 -0.0029 0.7924 -0.1253 0.0437 0.3033 0.2495 0.4100 0.0824 0.4157 0.1466 0.0556
0.0385 -0.0125 -0.0005 -0.0020 0.0498 -0.0002 -0.0001 0.1448 -0.0010 -0.1253 1.0032 0.0151 -0.1887-0.1816-0.4603-0.0434 -0.2341 -0.0262 -0.0541
0.0835 0.3681 -0.0003 -0.0002 0.3281 -0.0005 0.0003 0.1459 -0.0010 0.0437 0.0151 0.3954 0.2122-0.1129-0.0366 0.3831 0.0743 -0.0291 -0.0142
0.1231 0.2908 -0.0003 0.0008 0.1644 -0.0002 0.0003 0.3681 -0.0008 0.3033 -0.1887 0.2122 0.3412 0.1045 0.1895 0.2304 0.2425 0.0581 0.0519
0.0263 -0.0593 0.0001 0.0009 -0.0993 0.0003 0.0002 0.1886 -0.0002 0.2495 -0.1816-0.1129 0.1045 0.2217 0.2322-0.0834 0.1656 0.0871 0.0582
0.0340 -0.0128 -0.0002 0.0018 -0.0691 -0.0001 0.0012 0.1564 0.0005 0.4100 -0.4603-0.0366 0.1895 0.2322 0.7383-0.0112 0.4333 0.0212 0.0445
0.0799 0.3463 -0.0003 0.0000 0.3246 -0.0006 0.0004 0.1673 -0.0011 0.0824 -0.0434 0.3831 0.2304-0.0834-0.0112 0.3853 0.0961 -0.0036 -0.0102
0.0848 0.0981 -0.0005 0.0013 0.0056 -0.0002 0.0005 0.3166 -0.0004 0.4157 -0.2341 0.0743 0.2425 0.1656 0.4333 0.0961 0.3891 0.0424 0.0392
0.0071 -0.0470 0.0001 0.0010 -0.0041 -0.0001 0.0002 0.1453 -0.0013 0.1466 -0.0262-0.0291 0.0581 0.0871 0.0212-0.0036 0.0424 0.1902 0.0091
0.0264 0.0225 0.0001 0.0003 -0.0278 0.0002 -0.0002 0.0733 0.0001 0.0556 -0.0541-0.0142 0.0519 0.0582 0.0445-0.0102 0.0392 0.0091 0.0388
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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
1.0000 0.3874 -0.0003 -0.0005 0.1948 0.0004 -0.0001 0.4388 0.0001 0.2828 0.0771 0.2666 0.4230 0.1119 0.0794 0.2583 0.2730 0.0327 0.2688
0.3874 1.0000 -0.0004 -0.0003 0.6847 -0.0004 0.0003 0.3849 -0.0010 0.1611 -0.0182 0.8516 0.7244-0.1832-0.0216 0.8117 0.2288 -0.1569 0.1663
-0.0003 -0.0004 1.0000 0.0003 -0.0006 0.4035 0.0003-0.0006 -0.0005 -0.0006 -0.0005-0.0005 -0.0005 0.0003-0.0002-0.0005 -0.0008 0.0002 0.0007
-0.0005 -0.0003 0.0003 1.0000 -0.0002 0.0006 -0.2672 0.0014 -0.1836 0.0017 -0.0020-0.0003 0.0014 0.0019 0.0020 0.0001 0.0021 0.0022 0.0015
0.1948 0.6847 -0.0006 -0.0002 1.0000 -0.0007 0.0002 0.1396 -0.0021 -0.0039 0.0760 0.7976 0.4302-0.3224-0.1230 0.7994 0.0136 -0.0144 -0.2157
0.0004 -0.0004 0.4035 0.0006 -0.0007 1.0000 -0.0001 0.0001 -0.0001 0.0001 -0.0002-0.0009 -0.0003 0.0005-0.0001-0.0009 -0.0003 -0.0001 0.0010
-0.0001 0.0003 0.0003 -0.2672 0.0002 -0.0001 1.0000 0.0000 -0.0344 0.0002 -0.0001 0.0005 0.0005 0.0005 0.0014 0.0006 0.0008 0.0006 -0.0010
0.4388 0.3849 -0.0006 0.0014 0.1396 0.0001 0.0000 1.0000 -0.0019 0.7057 0.1656 0.2658 0.7220 0.4588 0.2085 0.3088 0.5815 0.3816 0.4261
0.0001 -0.0010 -0.0005 -0.1836 -0.0021 -0.0001 -0.0344-0.0019 1.0000 -0.0033 -0.0010-0.0015 -0.0014-0.0003 0.0006-0.0018 -0.0006 -0.0030 0.0003
0.2828 0.1611 -0.0006 0.0017 -0.0039 0.0001 0.0002 0.7057 -0.0033 1.0000 -0.1405 0.0781 0.5834 0.5953 0.5361 0.1492 0.7486 0.3776 0.3171
0.0771 -0.0182 -0.0005 -0.0020 0.0760 -0.0002 -0.0001 0.1656 -0.0010 -0.1405 1.0000 0.0240 -0.3226-0.3852-0.5349-0.0698 -0.3747 -0.0600 -0.2743
0.2666 0.8516 -0.0005 -0.0003 0.7976 -0.0009 0.0005 0.2658 -0.0015 0.0781 0.0240 1.0000 0.5776-0.3814-0.0678 0.9814 0.1894 -0.1062 -0.1146
0.4230 0.7244 -0.0005 0.0014 0.4302 -0.0003 0.0005 0.7220 -0.0014 0.5834 -0.3226 0.5776 1.0000 0.3798 0.3776 0.6356 0.6655 0.2282 0.4515
0.1119 -0.1832 0.0003 0.0019 -0.3224 0.0005 0.0005 0.4588 -0.0003 0.5953 -0.3852-0.3814 0.3798 1.0000 0.5740-0.2855 0.5640 0.4243 0.6273
0.0794 -0.0216 -0.0002 0.0020 -0.1230 -0.0001 0.0014 0.2085 0.0006 0.5361 -0.5349-0.0678 0.3776 0.5740 1.0000-0.0209 0.8085 0.0565 0.2631
0.2583 0.8117 -0.0005 0.0001 0.7994 -0.0009 0.0006 0.3088 -0.0018 0.1492 -0.0698 0.9814 0.6356-0.2855-0.0209 1.0000 0.2483 -0.0134 -0.0836
0.2730 0.2288 -0.0008 0.0021 0.0136 -0.0003 0.0008 0.5815 -0.0006 0.7486 -0.3747 0.1894 0.6655 0.5640 0.8085 0.2483 1.0000 0.1558 0.3190
0.0327 -0.1569 0.0002 0.0022 -0.0144 -0.0001 0.0006 0.3816 -0.0030 0.3776 -0.0600-0.1062 0.2282 0.4243 0.0565-0.0134 0.1558 1.0000 0.1063
0.2688 0.1663 0.0007 0.0015 -0.2157 0.0010 -0.0010 0.4261 0.0003 0.3171 -0.2743-0.1146 0.4515 0.6273 0.2631-0.0836 0.3190 0.1063 1.0000
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l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi MET MET_phi
0.4724 -0.0003 -0.0002 0.3079 -0.0003 0.0002 0.2310 -0.0007
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-0.0003 0.4059 0.0006 -0.0005 1.0057 -0.0001 0.0001 -0.0001
0.0002 0.0003 -0.2681 0.0001 -0.0001 1.0033 0.0000 -0.0345
0.2310 -0.0006 0.0012 0.0797 0.0001 0.0000 0.7619 -0.0016
-0.0007 -0.0005 -0.1842 -0.0014 -0.0001 -0.0345-0.0016 1.0033
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l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi MET MET_phi
1.0000 -0.0004 -0.0003 0.6847 -0.0004 0.0003 0.3849 -0.0010
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0.3849 -0.0006 0.0014 0.1396 0.0001 0.0000 1.0000 -0.0019
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S_R MT2 cos_theta_r1 R M_R MET_rel dPhi_r_b M_Delta_R M_TR_2 axial_MET
0.3853-0.0112 -0.0102-0.0834 0.3831 0.0824 -0.0036 0.0961 0.2304 -0.0434
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0.0961 0.4333 0.0392 0.1656 0.0743 0.4157 0.0424 0.3891 0.2425 -0.2341
0.2304 0.1895 0.0519 0.1045 0.2122 0.3033 0.0581 0.2425 0.3412 -0.1887
-0.0434-0.4603 -0.0541-0.1816 0.0151 -0.1253 -0.0262 -0.2341 -0.1887 1.0032
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S_R MT2 cos_theta_r1 R M_R MET_rel dPhi_r_b M_Delta_R M_TR_2 axial_MET
1.0000-0.0209 -0.0836-0.2855 0.9814 0.1492 -0.0134 0.2483 0.6356 -0.0698
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0.6356 0.3776 0.4515 0.3798 0.5776 0.5834 0.2282 0.6655 1.0000 -0.3226
-0.0698-0.5349 -0.2743-0.3852 0.0240 -0.1405 -0.0600 -0.3747 -0.3226 1.0000
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example usage:\n", + "# Assuming df contains your dataset and VarNames, RawNames, FeatureNames are lists of column names\n", + "compute_covariance_and_correlation(df, VarNames, RawNames, FeatureNames)" + ] + }, + { + "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": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercise 5.1\n", + "# Here is a list of the observables and which selection criteria I believe would be the most optimal:\n", + "\n", + "# signal: x > x_c\n", + "# l_1_pT: x > x_c\n", + "# l_1_eta: |x - mu| > x_c\n", + "# l_1_phi: |x - mu| > x_c\n", + "# l_2_pT: x > x_c\n", + "# l_2_eta: |x - mu| > x_c\n", + "# l_2_phi: |x - mu| > x_c\n", + "# MET: x > x_c\n", + "# MET_phi: |x - mu| > x_c\n", + "# R: |x - mu| > x_c\n", + "# M_TR_2: x > x_c\n", + "# axial_MET: |x - mu| > x_c\n", + "# dPhi_r_b: x < x_c\n", + "# M_Delta_R: |x - mu| > x_c\n", + "# S_R: x > x_c\n", + "# cos_theta_r1: x > x_c\n", + "# MT2: x > x_c\n", + "# M_R: x > x_c\n", + "# MET_rel: x > x_c\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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KyMiotEcdQAMwdS4IAB6hqqnHqpqqq6SkxJg0aZIRHR1thISEGAMGDDB27NhRYeoxwyidamvKlClG27ZtjYCAACM6Otro3bu38eyzzxpFRUXV1lVdDSfbuXOncd111xlRUVFGUFCQ0aNHD2PhwoVObf75z38aF154odGsWTMjMDDQaNOmjfHggw8a2dnZjjaVTT2Wnp5uDBw40AgPDzckOaa1+vPUYyd7+OGHDUlG27Ztq6x55cqVxoABA4zIyEgjKCjIaNOmjXHLLbcY69atq/Z3LT/uRx99VOn6iy66yIiIiDCysrIMwzCMN99802jXrp0RGBhotG/f3njrrbeM6dOnV/jz3rp1q3HhhRcawcHBhiSnP8eMjAxj7NixRlJSkmGz2Yy4uDjj0ksvNV577bVqazWMqv8Mc3Nzjfvvv99ISEgwbDab0a5dO+OZZ54x7Ha7UztJxtixY095nJOP5+rUY+XntLKXq/tq1aqVMXDgQGPp0qVG586dHee9qj8vw2DqMaC+WQyjHh8ADgCAD0lOTlanTp20cOFCs0sBUIYxuwAAAPBajNkFAHilgwcPVrhp8mQBAQFq2rRpg+8LQMMi7AIAvNK5555b4QEVJ+vbt69SUlIafF8AGhZhFwDglf7973/r2LFjVa5v0qSJ2/eVlpZW430CaBjcoAYAAACvxQ1qAAAA8Fo+N4zBbrdr//79Cg8Pd/kxoAAAAKh/hmEoNzdXCQkJjocD1ZbPhd39+/crKSnJ7DIAAABwCr/99ptatGhRp334XNgtfyzob7/9poiICJOrAQAAwJ/l5OQoKSmp2se515TPhd3yoQsRERGEXQAAAA/mjiGn3KAGAAAAr0XYBQAAgNci7AIAAMBrEXYBAADgtQi7AAAA8FqEXQAAAHgtwi4AAAC8FmEXAAAAXouwCwAAAK9F2AUAAIDXIuwCAADAaxF2AQAA4LUIuwAAAPBahF0AAAB4LcIuAAAAvJapYffrr7/WoEGDlJCQIIvFos8+++yU26SkpOjss89WYGCg2rZtq3nz5tV7nQAAAGicTA27+fn56tKli2bPnl2j9rt379bAgQN18cUXKzU1VePHj9dtt92mpUuX1nOlAAAAaIz8zTz4FVdcoSuuuKLG7efMmaPWrVvrueeekyR16NBB3377rV544QUNGDCgvsqsm+PZ0s8fSu0HShEJZlcDAADgU0wNu65as2aN+vXr57RswIABGj9+fJXbFBYWqrCw0PE5Jyenvsqr3IejpF0rlZGxXwfPHu+0qklogBKjghu2HgAAAB/SqMJuenq6YmNjnZbFxsYqJydHx44dU3BwxeA4c+ZMzZgxo6FKrODI6UPVdNdKlaybp6tXd1OJrI51wTarlk/sS+AFAACoJ40q7NbGlClTNGHCBMfnnJwcJSUlNdjxDyRcJhlhSrAc0VeDC5Xbqr8kaUdmnsbPT9XR/CLCLgAAQD1pVGE3Li5OGRkZTssyMjIUERFRaa+uJAUGBiowMLAhyquUYQ3URyV9dYf/IrXa/aHUe6hptQAAAPiaRjXPbq9evbRixQqnZcuWLVOvXr1Mqqhm3i+5pPTN9mXS0T3mFgMAAOBDTA27eXl5Sk1NVWpqqqTSqcVSU1O1d+9eSaVDEEaOHOlof+edd2rXrl3629/+pq1bt+qVV17Rhx9+qPvvv9+M8msszYhXXuL5kgxpw9tmlwMAAOAzTA2769atU7du3dStWzdJ0oQJE9StWzdNmzZNknTgwAFH8JWk1q1ba9GiRVq2bJm6dOmi5557Tm+88YbnTjt2kiPtbyx9s+FdqaTY3GIAAAB8hKljdi+66CIZhlHl+sqejnbRRRdp48aN9VhV/chJ7i+FxUp5GdLWRVLUxWaXBAAA4PUa1ZjdRs3PJnW7ufT9urnm1gIAAOAjCLsNqfsoSRZp9yoFZO0yuxoAAACvR9htSFEtpXal8+w23fqeycUAAAB4P8JuQztntCQp6tcPFagik4sBAADwboTdhtauvxTRQv6FWbrcb63Z1QAAAHg1wm5D87OWjd2VbvRfcYrGAAAAqItG9bhgr9HtZhkpT6mH3zYt3bNdUjun1U1CA5QYVfnjjwEAAFBzhF0zRMTrRNRpsh3drncWLtdqe4bT6mCbVcsn9iXwAgAA1BFh1yS26NOko9v1j0vCdbTD+Y7lOzLzNH5+qo7mFxF2AQAA6oiwa5amrSVJifZ0JSZGmlwMAACAd+IGNbM0KQ27Orrb3DoAAAC8GGHXLE2SS38eSTOzCgAAAK9G2DVL05N6dg3D3FoAAAC8FGHXLFGtJFmkojyp4LDZ1QAAAHglwq5ZbEFSRELp+yOM2wUAAKgPhF0zcZMaAABAvSLsmqlpculPenYBAADqBWHXTPTsAgAA1CvCrpnKZ2SgZxcAAKBeEHbNRM8uAABAvSLsmqn8wRJ5GVJRgamlAAAAeCPCrplCmkpBkaXvj6aZWgoAAIA3IuyajaEMAAAA9YawazZuUgMAAKg3hF2z0bMLAABQbwi7ZqNnFwAAoN4Qds1Gzy4AAEC9IeyarbxnN2uvZC8xtxYAAAAvQ9g1W3i8ZA2Q7Cek7N/NrgYAAMCrEHbN5meVolqVvmcoAwAAgFsRdj0BN6kBAADUC8KuJ+AmNQAAgHpB2PUE9OwCAADUC8KuJ6BnFwAAoF4Qdj2Bo2c3TTIMU0sBAADwJoRdTxDVSpJFKsqVtfCo2dUAAAB4DcKuJ7AFSREJkqSAnD0mFwMAAOA9CLueokmyJMIuAACAOxF2PUXZTWqEXQAAAPch7HqKpsmSCLsAAADuRNj1FPTsAgAAuB1h11OUTT8WkEvYBQAAcBfCrqco69m1FWQqSIUmFwMAAOAdCLueIqSpFBQpSUqyHDS5GAAAAO9A2PUkZb27rSwZJhcCAADgHQi7nqRsrl3CLgAAgHsQdj1J2U1qLQm7AAAAbuFvdgE4iWMYQ6Z2ZOY5rwoNUGJUsBlVAQAANFqEXU8S1VKSlOh3WLfMT3VaFWyzavnEvgReAAAAFxB2PUl4vCSpTVCeFt5+vmPxjsw8jZ+fqqP5RYRdAAAAFxB2PUl4nCTJrzBLnWICJVuQyQUBAAA0btyg5kmCIiX/sp7bvHRzawEAAPAChF1PYrE4eneVS9gFAACoK8Kupykbt6vcA+bWAQAA4AUIu56Gnl0AAAC3Iex6GkfYpWcXAACgrgi7noaeXQAAALch7HoaxuwCAAC4DWHX09CzCwAA4DaEXU/j6Nkl7AIAANQVYdfTlPfsFuZIRfnm1gIAANDIEXY9TWC4FBBW+p7eXQAAgDoh7Hoixu0CAAC4BWHXEzEjAwAAgFsQdj1RWGzpT3p2AQAA6oSw64l4ihoAAIBbEHY9EdOPAQAAuAVh1xNxgxoAAIBbEHY9ETeoAQAAuAVh1xOd3LNrGObWAgAA0IgRdj1RedgtzpcKc82tBQAAoBEj7HqigFApMLL0PeN2AQAAao2w66mYfgwAAKDOCLueqjzs5mWYWwcAAEAjRtj1VPTsAgAA1Blh11Mx1y4AAECdEXY9FXPtAgAA1JnpYXf27NlKTk5WUFCQevbsqbVr11bbftasWTrjjDMUHByspKQk3X///Tp+/HgDVduA6NkFAACoM1PD7vz58zVhwgRNnz5dGzZsUJcuXTRgwABlZmZW2v69997T5MmTNX36dG3ZskVvvvmm5s+fr4ceeqiBK28A9OwCAADUmalh9/nnn9eYMWM0evRodezYUXPmzFFISIjmzp1bafvvvvtOffr00YgRI5ScnKz+/ftr+PDhp+wNbpR4ihoAAECdmRZ2i4qKtH79evXr1++PYvz81K9fP61Zs6bSbXr37q3169c7wu2uXbu0ePFiXXnllVUep7CwUDk5OU6vRiGsLOyeOC6/omxzawEAAGik/M068KFDh1RSUqLY2Fin5bGxsdq6dWul24wYMUKHDh3S+eefL8MwdOLECd15553VDmOYOXOmZsyY4dbaG4QtSApuIh07Kls+c+0CAADUhuk3qLkiJSVFTz75pF555RVt2LBBn3zyiRYtWqTHHnusym2mTJmi7Oxsx+u3335rwIrrqGzcrn8BYRcAAKA2TOvZjY6OltVqVUaGc5DLyMhQXFxcpdtMnTpVN998s2677TZJ0llnnaX8/Hzdfvvtevjhh+XnVzG7BwYGKjAw0P2/QEMIi5Uy/ydbQYakeLOrAQAAaHRM69kNCAhQ9+7dtWLFCscyu92uFStWqFevXpVuU1BQUCHQWq1WSZLhjTdxOXp2K5+dAgAAANUzrWdXkiZMmKBRo0bpnHPOUY8ePTRr1izl5+dr9OjRkqSRI0cqMTFRM2fOlCQNGjRIzz//vLp166aePXtqx44dmjp1qgYNGuQIvV6lbEYGG8MYAAAAasXUsDts2DAdPHhQ06ZNU3p6urp27aolS5Y4blrbu3evU0/uI488IovFokceeUT79u1T8+bNNWjQID3xxBNm/Qr1izG7AAAAdWJq2JWkcePGady4cZWuS0lJcfrs7++v6dOna/r06Q1QmQco79nNZxgDAABAbTSq2Rh8Dj27AAAAdULY9WRlPbulYdcLb8ADAACoZ4RdTxZWOnbZz16sJso1uRgAAIDGh7DryfwDpJBoSVKsJcvcWgAAABohwq6nKxu3G2s5anIhAAAAjQ9h19OFlw5liCHsAgAAuIyw6+nKblKLFWEXAADAVYRdT8cwBgAAgFoj7Hq6sp7dGG5QAwAAcBlh19PRswsAAFBrhF1P5+jZJewCAAC4irDr6cp6dmOUJRl2c2sBAABoZAi7ni40RoYs8rfYZT122OxqAAAAGhXCrqez+utEcOlT1GwFGSYXAwAA0LgQdhuBEyGlD5bwJ+wCAAC4xN/sAnBqxSExCj4sHU3fq7x92U7rmoQGKDEq2KTKAAAAPBthtxGwRsZLv0kr123S//3wrdO6YJtVyyf2JfACAABUgrDbCIQ2ayFJuuWsQPW/4HzH8h2ZeRo/P1VH84sIuwAAAJUg7DYGYaVjdpsaR9U0MdLkYgAAABoPblBrDMoeLKHcdHPrAAAAaGQIu41BWFnYzWM2BgAAAFcQdhuD8JPCrp2nqAEAANQUYbcxCIuRZJHsJ6QCnqIGAABQU4TdxsBqk0Kalb7PY9wuAABATRF2GwvHTWqM2wUAAKgpwm5jUTb9GD27AAAANUfYbSzC40t/5h4wtw4AAIBGhLDbWISX9ewyjAEAAKDGCLuNhWOuXYYxAAAA1BRht7GgZxcAAMBlhN3GIoxHBgMAALiKsNtYhJ80jMEwzK0FAACgkSDsNhblU4+VFEnHjppbCwAAQCNB2G0sbEFSUFTp+zzG7QIAANQEYbcxCWfcLgAAgCsIu40JYRcAAMAlhN3GhLl2AQAAXELYbUyYaxcAAMAlhN3GhJ5dAAAAlxB2GxN6dgEAAFxC2G1MwuNLf+YeMLcOAACARoKw25iUP1giL4OnqAEAANQAYbcxKZ96rLhAKsw1txYAAIBGgLDbmASESgHhpe95ihoAAMApEXYbG8eDJRi3CwAAcCqE3cbGEXbp2QUAADgVwm5j47hJjbl2AQAAToWw29g4enYJuwAAAKdC2G1sTp5+DAAAANUi7DY2jgdL0LMLAABwKoTdxsbxyGDCLgAAwKkQdhubsLIxuwxjAAAAOCXCbmNT3rNbmCNLcYG5tQAAAHi4OoXd48ePu6sO1FRghOQfLEmyFWSaXAwAAIBnczns2u12PfbYY0pMTFRYWJh27dolSZo6darefPNNtxeIP7FYHNOP+RcwlAEAAKA6Lofdxx9/XPPmzdM//vEPBQQEOJZ36tRJb7zxhluLQxUcYZeeXQAAgOq4HHbfeecdvfbaa7rxxhtltVody7t06aKtW7e6tThUoWyuXdsxwi4AAEB1XA67+/btU9u2bSsst9vtKi4udktROAV6dgEAAGrE5bDbsWNHffPNNxWWf/zxx+rWrZtbisIplIfdfMbsAgAAVMff1Q2mTZumUaNGad++fbLb7frkk0+0bds2vfPOO1q4cGF91Ig/K5trl2EMAAAA1XO5Z/fqq6/Wf/7zHy1fvlyhoaGaNm2atmzZov/85z+67LLL6qNG/FnZXLsMYwAAAKieyz27knTBBRdo2bJl7q4FNRXGmF0AAICacLln97TTTtPhw4crLM/KytJpp53mlqJwCuVjdguzFKgik4sBAADwXC6H3bS0NJWUlFRYXlhYqH379rmlKJxCcBPJWjrHcXNLtsnFAAAAeK4aD2P44osvHO+XLl2qyMhIx+eSkhKtWLFCycnJbi0OVbBYSocyZO9VjI6aXQ0AAIDHqnHYHTJkiCTJYrFo1KhRTutsNpuSk5P13HPPubU4VCM8Vsreq+aWLLMrAQAA8Fg1Drt2u12S1Lp1a/3444+Kjo6ut6JQA2VPUYsh7AIAAFTJ5dkYdu/eXR91wFXh8ZKkWAvDGAAAAKpSq6nH8vPztWrVKu3du1dFRc6zAdx7771uKQynUDbXboyyzK0DAADAg7kcdjdu3Kgrr7xSBQUFys/PV9OmTXXo0CGFhIQoJiaGsNtQyubaZRgDAABA1Vyeeuz+++/XoEGDdPToUQUHB+v777/Xnj171L17dz377LP1USMqE07YBQAAOBWXw25qaqomTpwoPz8/Wa1WFRYWKikpSf/4xz/00EMP1UeNqEzZDWrNGbMLAABQJZeHMdhsNvn5lWbkmJgY7d27Vx06dFBkZKR+++03txeIKpTdoNbckqPv0isG3iahAUqMCm7oqgAAADyKy2G3W7du+vHHH9WuXTv17dtX06ZN06FDh/Tuu++qU6dO9VEjKhPSTIafTRZ7sZ766GsdUDOn1cE2q5ZP7EvgBQAAPs3lsPvkk08qNzdXkvTEE09o5MiRuuuuu9SuXTu9+eabbi8QVfDzkyU8Xsreq39d30LHYrs7Vu3IzNP4+ak6ml9E2AUAAD7N5bB7zjnnON7HxMRoyZIlbi0ILohIkLL3qk1gtpQYeer2AAAAPsblG9SqsmHDBl111VXu2h1qIqJ03K5yDphbBwAAgIdyKewuXbpUDzzwgB566CHt2rVLkrR161YNGTJE5557ruORwq6YPXu2kpOTFRQUpJ49e2rt2rXVts/KytLYsWMVHx+vwMBAnX766Vq8eLHLx/UKEYmlP3P2mVsHAACAh6rxMIY333xTY8aMUdOmTXX06FG98cYbev7553XPPfdo2LBh2rx5szp06ODSwefPn68JEyZozpw56tmzp2bNmqUBAwZo27ZtiomJqdC+qKhIl112mWJiYvTxxx8rMTFRe/bsUVRUlEvH9RoRCaU/c/abWwcAAICHqnHYffHFF/X000/rwQcf1IIFCzR06FC98sor2rRpk1q0aFGrgz///PMaM2aMRo8eLUmaM2eOFi1apLlz52ry5MkV2s+dO1dHjhzRd999J5vNJklKTk6u1bG9Qtn0Y8plGAMAAEBlajyMYefOnRo6dKgk6ZprrpG/v7+eeeaZWgfdoqIirV+/Xv369fujGD8/9evXT2vWrKl0my+++EK9evXS2LFjFRsbq06dOunJJ59USUlJlccpLCxUTk6O08trMIwBAACgWjUOu8eOHVNISIgkyWKxKDAwUPHx8bU+8KFDh1RSUqLY2Fin5bGxsUpPT690m127dunjjz9WSUmJFi9erKlTp+q5557T448/XuVxZs6cqcjISMcrKSmp1jV7HMcwhgNSLcZLAwAAeDuXph574403FBYWJkk6ceKE5s2bp+joaKc29957r/uq+xO73a6YmBi99tprslqt6t69u/bt26dnnnlG06dPr3SbKVOmaMKECY7POTk53hN4w+MkWSR7sVRwWAprbnZFAAAAHqXGYbdly5Z6/fXXHZ/j4uL07rvvOrWxWCw1DrvR0dGyWq3KyMhwWp6RkaG4uLhKt4mPj5fNZpPVanUs69Chg9LT01VUVKSAgIAK2wQGBiowMLBGNTU6VpsUFiPlZZQOZSDsAgAAOKlx2E1LS3PrgQMCAtS9e3etWLFCQ4YMkVTac7tixQqNGzeu0m369Omj9957T3a7XX5+pSMwfv31V8XHx1cadH1CREJZ2N0vJXQ1uxoAAACP4raHStTGhAkT9Prrr+vtt9/Wli1bdNdddyk/P98xO8PIkSM1ZcoUR/u77rpLR44c0X333adff/1VixYt0pNPPqmxY8ea9SuYL7xs3G4u048BAAD8mcuPC3anYcOG6eDBg5o2bZrS09PVtWtXLVmyxHHT2t69ex09uJKUlJSkpUuX6v7771fnzp2VmJio++67T5MmTTLrVzAfc+0CAABUydSwK0njxo2rcthCSkpKhWW9evXS999/X89VNSKEXQAAgCqZOowBbkDYBQAAqBJht7Ej7AIAAFSpVmF3586deuSRRzR8+HBlZmZKkr788kv98ssvbi0ONeB4itp+yTDMrQUAAMDDuBx2V61apbPOOks//PCDPvnkE+Xl5UmSfvrppyof7IB6FF72FLvifOl4trm1AAAAeBiXw+7kyZP1+OOPa9myZU5z215yySXcOGaGgBApKKr0fe4BU0sBAADwNC6H3U2bNukvf/lLheUxMTE6dOiQW4qCixxDGfaZWwcAAICHcTnsRkVF6cCBij2IGzduVGJioluKgosiyoYycJMaAACAE5fD7g033KBJkyYpPT1dFotFdrtdq1ev1gMPPKCRI0fWR404FceMDAxjAAAAOJnLYffJJ59U+/btlZSUpLy8PHXs2FEXXnihevfurUceeaQ+asSpMIwBAACgUi4/QS0gIECvv/66pk6dqs2bNysvL0/dunVTu3bt6qM+1EQ4wxgAAAAq43LY/fbbb3X++eerZcuWatmyZX3UBFeV9+wyGwMAAIATl4cxXHLJJWrdurUeeugh/e9//6uPmuAqx5hdhjEAAACczOWwu3//fk2cOFGrVq1Sp06d1LVrVz3zzDP6/fff66M+1ET5bAzHjkrFx8ytBQAAwIO4HHajo6M1btw4rV69Wjt37tTQoUP19ttvKzk5WZdcckl91IhTCYqSbCGl7xm3CwAA4OBy2D1Z69atNXnyZD311FM666yztGrVKnfVBVdYLCcNZSDsAgAAlKt12F29erXuvvtuxcfHa8SIEerUqZMWLVrkztrgCsIuAABABS7PxjBlyhR98MEH2r9/vy677DK9+OKLuvrqqxUSElIf9aGmwsvCbu5+KdrcUgAAADyFy2H366+/1oMPPqjrr79e0dGkKo9Bzy4AAEAFLofd1atX10cdqCvCLgAAQAU1CrtffPGFrrjiCtlsNn3xxRfVth08eLBbCoOLCLsAAAAV1CjsDhkyROnp6YqJidGQIUOqbGexWFRSUuKu2uAKwi4AAEAFNQq7dru90vfwIOWPDM7LkOzF5tYCAADgIVyeeuydd95RYWFhheVFRUV655133FIUaiEkWvKzSTJkKzhodjUAAAAeweWwO3r0aGVnZ1dYnpubq9GjR7ulKNSCn58UXvrYYP/8AyYXAwAA4BlcDruGYchisVRY/vvvvysyMtItRaGWysbt2gi7AAAAklyYeqxbt26yWCyyWCy69NJL5e//x6YlJSXavXu3Lr/88nopEjUUUdqza8tPlxRlaikAAACeoMZht3wWhtTUVA0YMEBhYWGOdQEBAUpOTta1117r9gLhgrKb1ErDbntzawEAAPAANQ6706dPlyQlJydr2LBhCgoKqreiUEtlwxgYswsAAFDK5SeojRo1qj7qgDuEnzyMAQAAADUKu02bNtWvv/6q6OhoNWnSpNIb1ModOXLEbcXBRU7DGAAAAFCjsPvCCy8oPDzc8b66sAsTlQ9jKEiXRTz8AwAAoEZh9+ShC7fcckt91YK6Co+TZJGfvVhNlWt2NQAAAKZzeZ7dDRs2aNOmTY7Pn3/+uYYMGaKHHnpIRUVFbi0OLrLapLAYSVKcheEkAAAALofdO+64Q7/++qskadeuXRo2bJhCQkL00Ucf6W9/+5vbC4SLyoYyxBN2AQAAXA+7v/76q7p27SpJ+uijj9S3b1+99957mjdvnhYsWODu+uCq8NKwS88uAABALR8XbLeX3vy0fPlyXXnllZKkpKQkHTp0yL3VwXURhF0AAIByLs+ze8455+jxxx9Xv379tGrVKr366quSpN27dys2NtbtBcJFJw1j2JGZ57SqSWiAEqOCzagKAADAFC6H3VmzZunGG2/UZ599pocfflht27aVJH388cfq3bu32wuEiyJbSJJa+B3WsPmpTquCbVYtn9iXwAsAAHyGy2G3c+fOTrMxlHvmmWdktVrdUhTqIKqVJKl7RI4W3nC+Y/GOzDyNn5+qo/lFhF0AAOAzXA675davX68tW7ZIkjp27Kizzz7bbUWhDqJaSpL88/arU1xI6XRkAAAAPsrlsJuZmalhw4Zp1apVioqKkiRlZWXp4osv1gcffKDmzZu7u0a4IixWsgZKJYVSzj6pSbLZFQEAAJjG5dkY7rnnHuXl5emXX37RkSNHdOTIEW3evFk5OTm6995766NGuMLPT4pKKn1/dI+5tQAAAJjM5Z7dJUuWaPny5erQoYNjWceOHTV79mz179/frcWhlqJaSYd3SFl7za4EAADAVC737NrtdtlsFceB2mw2x/y7MFmT0pvUlEXPLgAA8G0uh91LLrlE9913n/bv3+9Ytm/fPt1///269NJL3VocaqnsJjWGMQAAAF/ncth9+eWXlZOTo+TkZLVp00Zt2rRR69atlZOTo5deeqk+aoSrosp7dhnGAAAAfJvLY3aTkpK0YcMGrVixwjH1WIcOHdSvXz+3F4daimIYAwAAgORi2J0/f76++OILFRUV6dJLL9U999xTX3WhLsrH7OYekE4USv6B5tYDAABgkhoPY3j11Vc1fPhwrVu3Ttu3b9fYsWP14IMP1mdtqK2QZpIttPR91m/m1gIAAGCiGofdl19+WdOnT9e2bduUmpqqt99+W6+88kp91obaslj+uEktK83UUgAAAMxU47C7a9cujRo1yvF5xIgROnHihA4cOFAvhaGOmnCTGgAAQI3DbmFhoUJDQ//Y0M9PAQEBOnbsWL0Uhjpi+jEAAADXblCbOnWqQkJCHJ+Lior0xBNPKDIy0rHs+eefd191qD2mHwMAAKh52L3wwgu1bds2p2W9e/fWrl27HJ8tFov7KkPd8BQ1AACAmofdlJSUeiwDbscwBgAAANefoIZGonwYQ8EhqSjf3FoAAABMQtj1VsFRUlDZWGrG7QIAAB9F2PVmjrl2CbsAAMA3EXa9WflQBsbtAgAAH0XY9WZRzMgAAAB8W63C7jfffKObbrpJvXr10r59+yRJ7777rr799lu3Foc6YvoxAADg41wOuwsWLNCAAQMUHBysjRs3qrCwUJKUnZ2tJ5980u0Fog4YxgAAAHycy2H38ccf15w5c/T666/LZrM5lvfp00cbNmxwa3GoI8cNaoRdAADgm1wOu9u2bdOFF15YYXlkZKSysrLcURPcpTzsHs+WX2G2ubUAAACYwOWwGxcXpx07dlRY/u233+q0005zS1Fwk8AwKaSZJCkg73eTiwEAAGh4LofdMWPG6L777tMPP/wgi8Wi/fv369///rceeOAB3XXXXfVRI+qibNyuLZewCwAAfI+/qxtMnjxZdrtdl156qQoKCnThhRcqMDBQDzzwgO655576qBF10aSVtH+DAnJ/k9Te7GoAAAAalMth12Kx6OGHH9aDDz6oHTt2KC8vTx07dlRYWFh91Ie6Khu3G5C7V4RdAADga1wOu+UCAgLUsWNHd9aC+sAwBgAA4MNcDrsXX3yxLBZLleu/+uqrOhUENysLu6XDGAAAAHyLy2G3a9euTp+Li4uVmpqqzZs3a9SoUe6qC+5S9hQ1W97vkgxzawEAAGhgLofdF154odLljz76qPLy8upcENwsMkmSZC3OVxPlmlwMAABAw3J56rGq3HTTTZo7d667dgd3sQVJYXGSpCTLQZOLAQAAaFhuC7tr1qxRUFCQu3YHdyobytCCsAsAAHyMy8MYrrnmGqfPhmHowIEDWrdunaZOneq2wuBGUa2k336gZxcAAPgcl8NuZGSk02c/Pz+dccYZ+vvf/67+/fu7rTC4Udlcu/TsAgAAX+NS2C0pKdHo0aN11llnqUmTJvVVE9ytbBgDPbsAAMDXuDRm12q1qn///srKynJrEbNnz1ZycrKCgoLUs2dPrV27tkbbffDBB7JYLBoyZIhb6/E6ZT27SZZMkwsBAABoWC7foNapUyft2rXLbQXMnz9fEyZM0PTp07VhwwZ16dJFAwYMUGZm9cEsLS1NDzzwgC644AK31eK1yh4skWg5JBnMtQsAAHyHy2H38ccf1wMPPKCFCxfqwIEDysnJcXq56vnnn9eYMWM0evRodezYUXPmzFFISEi105iVlJToxhtv1IwZM3Taaae5fEyfE9lChsVPQZZi+R+jdxcAAPiOGo/Z/fvf/66JEyfqyiuvlCQNHjzY6bHBhmHIYrGopKSkxgcvKirS+vXrNWXKFMcyPz8/9evXT2vWrKm2lpiYGN1666365ptvqj1GYWGhCgsLHZ9rE8gbPatNxaHxCsjbp4y923UiJNZpdZPQACVGBZtUHAAAQP2pcdidMWOG7rzzTq1cudJtBz906JBKSkoUG+scvmJjY7V169ZKt/n222/15ptvKjU1tUbHmDlzpmbMmFHXUhs9I6qVlLdPn634Rp8uc14XbLNq+cS+BF4AAOB1ahx2jbKxnn379q23Yk4lNzdXN998s15//XVFR0fXaJspU6ZowoQJjs85OTlKSkqqrxI9VmDsGdLv32lKD3/deu75juU7MvM0fn6qjuYXEXYBAIDXcWnqsZOHLbhDdHS0rFarMjIynJZnZGQoLi6uQvudO3cqLS1NgwYNciyz2+2SJH9/f23btk1t2rRx2iYwMFCBgYFurbtRij5dkhRTuEcxiZGnaAwAAOAdXAq7p59++ikD75EjR2q8v4CAAHXv3l0rVqxwTB9mt9u1YsUKjRs3rkL79u3ba9OmTU7LHnnkEeXm5urFF1/0yR7bGisLuzq03dw6AAAAGpBLYXfGjBkVnqBWVxMmTNCoUaN0zjnnqEePHpo1a5by8/M1evRoSdLIkSOVmJiomTNnKigoSJ06dXLaPioqSpIqLMefRLcr/Xlkp1RyQrK6/PA8AACARselxHPDDTcoJibGrQUMGzZMBw8e1LRp05Senq6uXbtqyZIljpvW9u7dKz8/l2dIw59FJkn+QdKJ41LWHqlZm1NvAwAA0MjVOOy6e7zuycaNG1fpsAVJSklJqXbbefPmub8gb+TnJzVrJ2VsKh3KQNgFAAA+oMZdpgZP3mr8yocyHPrV3DoAAAAaSI17dstnPUAj5rhJjbALAAB8A4NhfYmjZ5cZGQAAgG8g7PoSenYBAICPIez6kmZtS38eOyLlHza3FgAAgAZA2PUlASFSZMvS9/TuAgAAH0DY9TXMyAAAAHwIYdfXMG4XAAD4EMKur2FGBgAA4EMIu76Gnl0AAOBDCLu+pjzsZu2Rio+bWwsAAEA9I+z6mrAYKTBSMuzSkV1mVwMAAFCvCLu+xmKRosvm22UoAwAA8HKEXV/kGLfLTWoAAMC7EXZ9EXPtAgAAH0HY9UXlPbuH6dkFAADejbDri04exmAY5tYCAABQjwi7vqhJa8lilYry5F+QYXY1AAAA9Yaw64v8A6SmrSVJgVk7TC4GAACg/hB2fVXZUIbArJ0mFwIAAFB/CLu+qmxGBsIuAADwZoRdX1Xes5vNMAYAAOC9CLu+imEMAADABxB2fVWz0kcG2/IPKFTHTC4GAACgfhB2fVVIUym0uSSpteWAycUAAADUD8KuLysbytDGst/kQgAAAOoHYdeXlc3I0MaPsAsAALwTYdeX0bMLAAC8nL/ZBcBEjrB7QFsz8yqsbhIaoMSo4IauCgAAwG0Iu76sbBjDaZb9GjR/nYr/dDkE26xaPrEvgRcAADRahF1fFtVKCoxUQGG2vhzRXMebnelYtSMzT+Pnp+pofhFhFwAANFqEXV9msUjxnaW0b9S2ZJeU2NvsigAAANyKG9R8XXyX0p8HfjK3DgAAgHpA2PV1hF0AAODFCLu+rjzspm+S7CXm1gIAAOBmhF1f16ytZAuRigukwzvMrgYAAMCtCLu+zs8qxZ1V+p6hDAAAwMsQdsG4XQAA4LUIuyDsAgAAr0XYxUlh92fJMMytBQAAwI0Iu5Cat5esAVJhtnQ0zexqAAAA3IawC8lqk2LLHhXMUAYAAOBFCLsoFde59CdhFwAAeBHCLkpxkxoAAPBChF2Uiu9a+vPAT9ykBgAAvAZhF6ViO0oWq1RwSMrZb3Y1AAAAbkHYRSlbcOmsDBJDGQAAgNcg7OIPjNsFAABehrCLPxB2AQCAlyHs4g+EXQAA4GUIu/hDXCdJFil3v6zHDpldDQAAQJ0RdvGHwHCpWVtJUvChX0wuBgAAoO4Iu3BWNpQh6PAmkwsBAACoO8IunJWF3eBDm00uBAAAoO4Iu3AW31kSYRcAAHgHwi6cxZWG3YDcvYpQnsnFAAAA1A1hF85CmkpRLSVJZ/rtMbkYAACAuvE3uwB4oPguUtZenWlJ045M597dJqEBSowKNqkwAAAA1xB2UVF8V2nLf3SO/w7dOT/VaVWwzarlE/sSeAEAQKNA2EVFrXpLki4L3amFY/pIFoskaUdmnsbPT9XR/CLCLgAAaBQIu6gosbvkHyRrwSF1CsyQmp9hdkUAAAC1wg1qqMg/UGpxbun7tG/NrQUAAKAOCLuoXPL5pT/3rDa3DgAAgDog7KJyrfqU/kxbLRmGubUAAADUEmEXlWtxjmQNkPLSpSO7zK4GAACgVgi7qJwtWEo8p/Q943YBAEAjRdhF1ZLLhjIwbhcAADRShF1UjXG7AACgkSPsompJPSQ/fynndylrj9nVAAAAuIywi6oFhEoJZ5e+Z9wuAABohAi7qF7ySUMZAAAAGhnCLqrXqvzhEvTsAgCAxoewi+q17ClZrFLWXtny9pldDQAAgEsIu6heYLiU0FWSFHrge3NrAQAAcBFhF6dWNgVZyIEfTC4EAADANYRdnFpy6bhdenYBAEBjQ9jFqbU8T7L4KTAnTTE6anY1AAAANeYRYXf27NlKTk5WUFCQevbsqbVr11bZ9vXXX9cFF1ygJk2aqEmTJurXr1+17eEGQZFS3FmSpPP8tphcDAAAQM2ZHnbnz5+vCRMmaPr06dqwYYO6dOmiAQMGKDMzs9L2KSkpGj58uFauXKk1a9YoKSlJ/fv31759zBRQr8qmIDvP738mFwIAAFBzpofd559/XmPGjNHo0aPVsWNHzZkzRyEhIZo7d26l7f/973/r7rvvVteuXdW+fXu98cYbstvtWrFiRQNX7mPKHi7Rk55dAADQiPibefCioiKtX79eU6ZMcSzz8/NTv379tGbNmhrto6CgQMXFxWratGml6wsLC1VYWOj4nJOTU7eifVXLXjJkURu/A/py725JrZ1WNwkNUGJUsDm1AQAAVMHUsHvo0CGVlJQoNjbWaXlsbKy2bt1ao31MmjRJCQkJ6tevX6XrZ86cqRkzZtS5Vp8X0lTFzTsp4OAmrVj4nj4u6eu0Othm1fKJfQm8AADAo5gaduvqqaee0gcffKCUlBQFBQVV2mbKlCmaMGGC43NOTo6SkpIaqkSvEnDmVVLKJk1ru1u39H/YsXxHZp7Gz0/V0fwiwi4AAPAopobd6OhoWa1WZWRkOC3PyMhQXFxctds+++yzeuqpp7R8+XJ17ty5ynaBgYEKDAx0S70+r/1AKWWmIvZ9rU7N/aWAULMrAgAAqJapN6gFBASoe/fuTjeXld9s1qtXryq3+8c//qHHHntMS5Ys0TnnnNMQpUKSYjtJUS2lE8elnV+ZXQ0AAMApmT4bw4QJE/T666/r7bff1pYtW3TXXXcpPz9fo0ePliSNHDnS6Qa2p59+WlOnTtXcuXOVnJys9PR0paenKy8vz6xfwXdYLFL7QaXvty4ytxYAAIAaMH3M7rBhw3Tw4EFNmzZN6enp6tq1q5YsWeK4aW3v3r3y8/sjk7/66qsqKirSdddd57Sf6dOn69FHH23I0n1T+4HS97OlbV9KJcWS1WZ2RQAAAFUyPexK0rhx4zRu3LhK16WkpDh9TktLq/+CULWW50khzaSCw9Ke76TT+p56GwAAAJOYPowBjYyfVTrjitL3DGUAAAAejrAL17W/qvTn1kWSYZhbCwAAQDUIu3DdaRdJtlAp53fpQKrZ1QAAAFSJsAvX2YKltpeWvt+y0NxaAAAAqkHYRe2cPJQBAADAQxF2UTun95f8/KWDWxSQvdvsagAAACpF2EXtBDeRks+XJEWkLTW5GAAAgMoRdlF7ZUMZwvcQdgEAgGci7KL2zrhSkhSSsUHNlWVuLQAAAJUg7KL2IhOlhLNlkaF+1vVmVwMAAFCBRzwuGI1Yh6uk/Rs0wG+ddmTmOa1qEhqgxKhgkwoDAAAg7KKuOgyWVvxd5/ttUp/5XylDTR2rgm1WLZ/Yl8ALAABMQ9hF3US3k1r2kv/eNfqiT5oOnj1YkrQjM0/j56fqaH4RYRcAAJiGMbuou+6jJUmxOz5Up/gwdUqMVNuYMJOLAgAAIOzCHToOloKipOy90s6vzK4GAADAgbCLurMFS12Gl75fP8/UUgAAAE5G2IV7dL+l9Oe2L6WcA6aWAgAAUI6wC/eIaS+17CUZJVLqv8yuBgAAQBJhF+5U3ru7/h3JXmJqKQAAABJhF+7U8WopKFLK3quwfd+YXQ0AAABhF2500o1qTba+Z3IxAAAAhF24W9lQhog9y9RcR82tBQAA+DzCLtwrpoOUdJ4sRomut64yuxoAAODjCLtwv7Le3eH+X0mG3dxaAACATyPswv3OHKKSgAi1sBxSzual2rwv2+m1L+uY2RUCAAAf4W92AfBCtmAd6zhMYamvy/rdC7oqJUySxbE62GbV8ol9lRgVbF6NAADAJxB2US/CLh4vY9Pb6qmtWnmNlJ94viRpR2aexs9P1dH8IsIuAACodwxjQP2IbCFL99GSpNY/z1KnhAh1SoxU25gwkwsDAAC+hLCL+nPBBMk/SPr9R2n7MrOrAQAAPoiwi/oTHiede1vp+5VPSIZhbj0AAMDnEHZRv86/X7KFSgdSpW2Lza4GAAD4GMIu6ldotNTzjtL3K59k3l0AANCgCLuof73vkQLCpYzNithN7y4AAGg4hF3Uv5CmUq+7JUkx65+Xn+jdBQAADYOwi4Zx3t1SUKSCsnZokN93ZlcDAAB8BA+VQMMIjiodzvDV47rP/xNtSr+jQpMmoQE8aAIAALgVYRcNp+edKlnzik47lq4Fnzyh+0qGOK3mMcIAAMDdCLtoOIHhsl7+lPTp7ZoY+JmGXHO7CpucLonHCAMAgPrBmF00rM7XS+36y89epHZrJqtTfBiPEQYAAPWGsIuGZbFIV71QOhXZvnXSD3PMrggAAHgxwi4aXmQLqf9jpe9XPCYd2WVuPQAAwGsRdmGO7rdIyRdIJ45JX9wrGYbZFQEAAC9E2IU5LBZp8P9J/sFS2jdqsvU9sysCAABeiLAL8zQ9Tbp0qiQp7ocnFK/DJhcEAAC8DVOPwVw975R++VTW33/Us7Y52pne12k1D5oAAAB1QdiFufys0tWzZX/tIvXRL3rr0wd134lRjtU8aAIAANQFwxhgvuZnyO+a1yRJo/2XavVlv2nhPedr1rCuOlZcoqP5RSYXCAAAGivCLjxDh0HSxQ9LkhJXP6JOxZt50AQAAKgzwi48x4UPSmdeI9mLpQ9vli33N7MrAgAAjRxhF57DYpGuni3Fd5UKDqvVf29VqI6ZXRUAAGjECLvwLAEh0g3vSWGxCjqyVS/YXpEMu9lVAQCARoqwC88TmSjd8J7s1kD1t65X2H8navNvR7R5X7bjtS+LHl8AAHBqTD0Gz9TiHGX1f1GRi+9S8t4F+uy1A5pYfJdKZJXElGQAAKBm6NmFx2rac7iyBv5ThsVfQ6zfaUP797To7h5MSQYAAGqMsAuP1qzHMFlu+JdkDVBk2pc685u71a4p/yABAABqhrALz3fGFdKI+ZJ/sLT9v2q1dLSCddzsqgAAQCNAFxkahzaXSDctkN67XmH7V+tfAZn6bW8bSa2dmjUJDWAcLwAAcCDsovFI7iON/Fz2d69R98LtOrhksO4pvkff2zs6mnDjGgAAOBnDGNC4tDhHfmO+UnF0BzW3ZOv9wCf1/QU/a+G43ty4BgAAKiDsovGJbivb7V9JXUbIYtgV9+NT6vTN3To98oTZlQEAAA/DMAY0TgEh0pBXpJY9pcV/k7YtVtv9m9TVMkY7Mrs6NWUcLwAAvouwi8bLYpG63yLFd5U+HKmArD36JGC6/rXgGw0/MUy5CpHEOF4AAHwZwxjQ+CV0le5YJXW+QX4WQyP9l2lDkyn6+sojmnV9F8bxAgDgwwi78A7BTaRr/imN+o/UrK1sxw6q5VfjdOmGu9XSkmF2dQAAwCQMY4B3aX2hdNd30rezpG+eU/jvq7Qs4Dv9vnydtp53r06ExTuaMpYXAADvR9iF9/EPlC6aJJ11nY5/Pl5Be79Wm93/VuGu+fqw5CK9emKw9iuasbwAAPgAhjHAezVro6DRX+jQNR8pP66nAi0ndLP/cn0bMlEpZ3yq5if2M5YXAAAvR88uvJvFoujO/aXO/aW0b6VVT8tv99dK3vORUgI+1sHPF2hPl1uU2/ISya/0rwPDGwAA8B6EXfiO5PNLX3u/1/GvnlZQ2leKzfxWWvat9htN9cGJS/RBycXKtUUzvAEAAC9B2IXvaXmegm75VOlpW2TdME9Nfv1QCcePaILtY40P+FTfnuiozJVblHPWYJUER0uitxcAgMaKsAufFZfcQUp+Wjrxd+l/X0jr3pTf3jW60LpJ+mmTSlJn6Ad7B31p76FVfj31/sS/EHgBAGhkCLuAf6DUeWjp6/BOZW9YoIBtXyj40Cb1tv5Pva3/kzRPOW8+q8MtL1BeQh/lx58ne2AkPb4AAHg4wi5wsmZtFHnZ36TL/iYdTZO2/EdFP38q/wMbFJG7U/plp5r9Mk8lhkWbjdZapg7qcUF/WZPOUXFYC8liIQADAOBBCLtAVZokS73vUUDve3TgwD4V7/xaofu/U+j+7xSUtUNdLLvURbuk1YskSQeNSKXa22i5pZ1u+stVim7dTYosDcAAAMAchF2gBuLjE6X44ZKGly7I2S/t/kZ5O7+Tdf96BR3eoubK1mXWDbpMG6TP50uSSgIidLxpex1v2l7W2I6KSuogNWsrhSdIfkxzDQBAfSPsArURkSB1GaawLsNKPxcfk9I3KWv7Gn2TslTttFdtLPtlK8pRaPpahaavlf73x+Z2a5AKI1urKLK1isKTVBzWQsVhCSoq+xnZtDlDIQAAcAPCLuAOtmApqYeiknro7LNv19H8Iv1aUqSArJ0KOrJFytisHf/boJbGAbW0ZMpWclzBR7Yo+MiWSneXZwSrIDJe9tA4nQiNVXFIjE6ExCowKk5Nm8dLIdFSaHTpT/+ABv5lAQBoPDwi7M6ePVvPPPOM0tPT1aVLF7300kvq0aNHle0/+ugjTZ06VWlpaWrXrp2efvppXXnllQ1YMVC1xKjgP3plWzaXdJ4kyS/rmI7mF2mbvVgBub8rIHu3ArN3yZb3uwJy98mWv0+23H3yLzyqMMsxKWdX6esUSmzhKgmKUklg6etE2c+AsKYKj2wqBYZLQZGlPwMjpMAwKSCs9HNAqOQfxLhiAIDXMj3szp8/XxMmTNCcOXPUs2dPzZo1SwMGDNC2bdsUExNTof13332n4cOHa+bMmbrqqqv03nvvaciQIdqwYYM6depkwm8A1IxTCFa0pK6VNyzMU8a+XSo4vE/+BRnyL8iULT9D9pwD2rUnTVFGjppZstVUubJaDFmLc2UtzpVyf6tVXYbFKrstVHZbiOzW4NKf/kEy/ENk9w8ufW8Nkt0/SHb/YBnWIAUGhygyLKx02jb/wNLAbA0ofW+1SdbA0s9WW9nPAMnqX/rTz1a23Fb6iGY/m+RnJXADAOqFxTAMw8wCevbsqXPPPVcvv/yyJMlutyspKUn33HOPJk+eXKH9sGHDlJ+fr4ULFzqWnXfeeeratavmzJlzyuPl5OQoMjJS2dnZioiIcN8vUoXN+7J11UvfauE956tTYmS9Hw/ebV9Z77AkybDLWpgt6/HDshZmy//4UVkLs2QtzFJR3hF98/N2BRsFCtMxhatAYZbyn8cVouMKtRSa+8v8iWHxl+FX/rJKlvKf1rJ1Ze/9rDIsVsnPKkN+pT8tfmXtrJLFT7L4Od4bTp8tfyyTn2SxlL0/eblK9yGLY9vS95ayQO5X9rN0WZDNX+HBAY59lK8rP5bjvSySRc6fJef2f15W6XvV4LNO+mz502fHBxeW13IfVbWtUGOlK1xo68p+q9ygHvddx+O5Q73+zyT/o+qVmp9R+jKJO/OaqT27RUVFWr9+vaZMmeJY5ufnp379+mnNmjWVbrNmzRpNmDDBadmAAQP02WefVdq+sLBQhYV//Ec9OztbUulJbAh5uTmyFxYoLzdHOTl8IaBuwv2k8PDy68gqqWnZy1mopHN6HFNWQZFjWUHZK6N8gb1EfieOye9EgfyK82U5cbz0/Ynjspw4VrbumPxKCmUpOSa/E0WylBxX0bECrd2xX1ajSAE6oQAVK1DFCrCUKEDFsunEH6+yZf4qkb9KZCt7+Vkq+3/s4rJX/ahB7KoVQ1LDfJsAQMM52OVuHew61mlZ87BANY8IapDjl+c0d/TJmhp2Dx06pJKSEsXGxjotj42N1datWyvdJj09vdL26enplbafOXOmZsyYUWF5UlJSLauunV6zGvRwAAAAdfB02ctcubm5ioys27+Mmz5mt75NmTLFqSfYbrfryJEjatasmSxu+GednJwcJSUl6bfffmuQYRG+gHPqXpxP9+Ocuh/n1L04n+7HOXW/6s6pYRjKzc1VQkJCnY9jatiNjo6W1WpVRkaG0/KMjAzFxcVVuk1cXJxL7QMDAxUYGOi0LCoqqvZFVyEiIoKL3804p+7F+XQ/zqn7cU7di/PpfpxT96vqnNa1R7ecqY9wCggIUPfu3bVixQrHMrvdrhUrVqhXr16VbtOrVy+n9pK0bNmyKtsDAADAd5k+jGHChAkaNWqUzjnnHPXo0UOzZs1Sfn6+Ro8eLUkaOXKkEhMTNXPmTEnSfffdp759++q5557TwIED9cEHH2jdunV67bXXzPw1AAAA4IFMD7vDhg3TwYMHNW3aNKWnp6tr165asmSJ4ya0vXv3ys/vjw7o3r1767333tMjjzyihx56SO3atdNnn31m2hy7gYGBmj59eoWhEqg9zql7cT7dj3PqfpxT9+J8uh/n1P0a6pyaPs8uAAAAUF9MHbMLAAAA1CfCLgAAALwWYRcAAABei7ALAAAAr0XYrYHZs2crOTlZQUFB6tmzp9auXVtt+48++kjt27dXUFCQzjrrLC1evLiBKvV8M2fO1Lnnnqvw8HDFxMRoyJAh2rZtW7XbzJs3TxaLxekVFNQwz+b2dI8++miFc9O+fftqt+H6rF5ycnKFc2qxWDR27NhK23N9VvT1119r0KBBSkhIkMVi0Weffea03jAMTZs2TfHx8QoODla/fv20ffv2U+7X1e9ib1Hd+SwuLtakSZN01llnKTQ0VAkJCRo5cqT2799f7T5r893hTU51jd5yyy0Vzs/ll19+yv366jUqnfqcVva9arFY9Mwzz1S5T3ddp4TdU5g/f74mTJig6dOna8OGDerSpYsGDBigzMzMStt/9913Gj58uG699VZt3LhRQ4YM0ZAhQ7R58+YGrtwzrVq1SmPHjtX333+vZcuWqbi4WP3791d+fn6120VEROjAgQOO1549exqoYs935plnOp2bb7/9tsq2XJ+n9uOPPzqdz2XLlkmShg4dWuU2XJ/O8vPz1aVLF82ePbvS9f/4xz/0f//3f5ozZ45++OEHhYaGasCAATp+/HiV+3T1u9ibVHc+CwoKtGHDBk2dOlUbNmzQJ598om3btmnw4MGn3K8r3x3e5lTXqCRdfvnlTufn/fffr3afvnyNSqc+pyefywMHDmju3LmyWCy69tprq92vW65TA9Xq0aOHMXbsWMfnkpISIyEhwZg5c2al7a+//npj4MCBTst69uxp3HHHHfVaZ2OVmZlpSDJWrVpVZZu33nrLiIyMbLiiGpHp06cbXbp0qXF7rk/X3XfffUabNm0Mu91e6Xquz+pJMj799FPHZ7vdbsTFxRnPPPOMY1lWVpYRGBhovP/++1Xux9XvYm/15/NZmbVr1xqSjD179lTZxtXvDm9W2TkdNWqUcfXVV7u0H67RP9TkOr366quNSy65pNo27rpO6dmtRlFRkdavX69+/fo5lvn5+alfv35as2ZNpdusWbPGqb0kDRgwoMr2vi47O1uS1LRp02rb5eXlqVWrVkpKStLVV1+tX375pSHKaxS2b9+uhIQEnXbaabrxxhu1d+/eKttyfbqmqKhI//rXv/TXv/5VFoulynZcnzW3e/dupaenO12HkZGR6tmzZ5XXYW2+i31Zdna2LBaLoqKiqm3nyneHL0pJSVFMTIzOOOMM3XXXXTp8+HCVbblGXZORkaFFixbp1ltvPWVbd1ynhN1qHDp0SCUlJY6nuZWLjY1Venp6pdukp6e71N6X2e12jR8/Xn369Kn2CXhnnHGG5s6dq88//1z/+te/ZLfb1bt3b/3+++8NWK1n6tmzp+bNm6clS5bo1Vdf1e7du3XBBRcoNze30vZcn6757LPPlJWVpVtuuaXKNlyfrim/1ly5DmvzXeyrjh8/rkmTJmn48OGKiIiosp2r3x2+5vLLL9c777yjFStW6Omnn9aqVat0xRVXqKSkpNL2XKOuefvttxUeHq5rrrmm2nbuuk5Nf1wwfNfYsWO1efPmU46/6dWrl3r16uX43Lt3b3Xo0EH//Oc/9dhjj9V3mR7tiiuucLzv3LmzevbsqVatWunDDz+s0f8xo3pvvvmmrrjiCiUkJFTZhusTnqK4uFjXX3+9DMPQq6++Wm1bvjuqd8MNNzjen3XWWercubPatGmjlJQUXXrppSZW5h3mzp2rG2+88ZQ387rrOqVntxrR0dGyWq3KyMhwWp6RkaG4uLhKt4mLi3Opva8aN26cFi5cqJUrV6pFixYubWuz2dStWzft2LGjnqprvKKionT66adXeW64Pmtuz549Wr58uW677TaXtuP6rF75tebKdVib72JfUx509+zZo2XLllXbq1uZU313+LrTTjtN0dHRVZ4frtGa++abb7Rt2zaXv1ul2l+nhN1qBAQEqHv37lqxYoVjmd1u14oVK5x6ck7Wq1cvp/aStGzZsirb+xrDMDRu3Dh9+umn+uqrr9S6dWuX91FSUqJNmzYpPj6+Hips3PLy8rRz584qzw3XZ8299dZbiomJ0cCBA13ajuuzeq1bt1ZcXJzTdZiTk6MffvihyuuwNt/FvqQ86G7fvl3Lly9Xs2bNXN7Hqb47fN3vv/+uw4cPV3l+uEZr7s0331T37t3VpUsXl7et9XVa51vcvNwHH3xgBAYGGvPmzTP+97//GbfffrsRFRVlpKenG4ZhGDfffLMxefJkR/vVq1cb/v7+xrPPPmts2bLFmD59umGz2YxNmzaZ9St4lLvuusuIjIw0UlJSjAMHDjheBQUFjjZ/PqczZswwli5dauzcudNYv369ccMNNxhBQUHGL7/8Ysav4FEmTpxopKSkGLt37zZWr15t9OvXz4iOjjYyMzMNw+D6rK2SkhKjZcuWxqRJkyqs4/o8tdzcXGPjxo3Gxo0bDUnG888/b2zcuNExO8BTTz1lREVFGZ9//rnx888/G1dffbXRunVr49ixY459XHLJJcZLL73k+Hyq72JvVt35LCoqMgYPHmy0aNHCSE1NdfpeLSwsdOzjz+fzVN8d3q66c5qbm2s88MADxpo1a4zdu3cby5cvN84++2yjXbt2xvHjxx374Bp1dqq/94ZhGNnZ2UZISIjx6quvVrqP+rpOCbs18NJLLxktW7Y0AgICjB49ehjff/+9Y13fvn2NUaNGObX/8MMPjdNPP90ICAgwzjzzTGPRokUNXLHnklTp66233nK0+fM5HT9+vOP8x8bGGldeeaWxYcOGhi/eAw0bNsyIj483AgICjMTERGPYsGHGjh07HOu5Pmtn6dKlhiRj27ZtFdZxfZ7aypUrK/17Xn7e7Ha7MXXqVCM2NtYIDAw0Lr300grnulWrVsb06dOdllX3XezNqjufu3fvrvJ7deXKlY59/Pl8nuq7w9tVd04LCgqM/v37G82bNzdsNpvRqlUrY8yYMRVCK9eos1P9vTcMw/jnP/9pBAcHG1lZWZXuo76uU4thGIbL/cgAAABAI8CYXQAAAHgtwi4AAAC8FmEXAAAAXouwCwAAAK9F2AUAAIDXIuwCAADAaxF2AQAA4LUIuwAAAPBahF0AqGcpKSmyWCzKyspq0OPOmzdPUVFRddpHWlqaLBaLUlNTq2xj1u8HADVB2AWAOrBYLNW+Hn30UbNLBACf5m92AQDQmB04cMDxfv78+Zo2bZq2bdvmWBYWFqZ169a5vN+ioiIFBAS4pUYA8GX07AJAHcTFxTlekZGRslgsTsvCwsIcbdevX69zzjlHISEh6t27t1MofvTRR9W1a1e98cYbat26tYKCgiRJWVlZuu2229S8eXNFRETokksu0U8//eTY7qefftLFF1+s8PBwRUREqHv37hXC9dKlS9WhQweFhYXp8ssvdwrodrtdf//739WiRQsFBgaqa9euWrJkSbW/8+LFi3X66acrODhYF198sdLS0upyCgGgXhF2AaCBPPzww3ruuee0bt06+fv7669//avT+h07dmjBggX65JNPHGNkhw4dqszMTH355Zdav369zj77bF166aU6cuSIJOnGG29UixYt9OOPP2r9+vWaPHmybDabY58FBQV69tln9e677+rrr7/W3r179cADDzjWv/jii3ruuef07LPP6ueff9aAAQM0ePBgbd++vdLf4bffftM111yjQYMGKTU1VbfddpsmT57s5jMFAG5kAADc4q233jIiIyMrLF+5cqUhyVi+fLlj2aJFiwxJxrFjxwzDMIzp06cbNpvNyMzMdLT55ptvjIiICOP48eNO+2vTpo3xz3/+0zAMwwgPDzfmzZtXZT2SjB07djiWzZ4924iNjXV8TkhIMJ544gmn7c4991zj7rvvNgzDMHbv3m1IMjZu3GgYhmFMmTLF6Nixo1P7SZMmGZKMo0ePVloHAJiJnl0AaCCdO3d2vI+Pj5ckZWZmOpa1atVKzZs3d3z+6aeflJeXp2bNmiksLMzx2r17t3bu3ClJmjBhgm677Tb169dPTz31lGN5uZCQELVp08bpuOXHzMnJ0f79+9WnTx+nbfr06aMtW7ZU+jts2bJFPXv2dFrWq1evGp8DAGho3KAGAA3k5OEFFotFUumY2XKhoaFO7fPy8hQfH6+UlJQK+yqfUuzRRx/ViBEjtGjRIn355ZeaPn26PvjgA/3lL3+pcMzy4xqG4Y5fBwAaBXp2AcBDnX322UpPT5e/v7/atm3r9IqOjna0O/3003X//ffrv//9r6655hq99dZbNdp/RESEEhIStHr1aqflq1evVseOHSvdpkOHDlq7dq3Tsu+//97F3wwAGg5hFwA8VL9+/dSrVy8NGTJE//3vf5WWlqbvvvtODz/8sNatW6djx45p3LhxSklJ0Z49e7R69Wr9+OOP6tChQ42P8eCDD+rpp5/W/PnztW3bNk2ePFmpqam67777Km1/5513avv27XrwwQe1bds2vffee5o3b56bfmMAcD+GMQCAh7JYLFq8eLEefvhhjR49WgcPHlRcXJwuvPBCxcbGymq16vDhwxo5cqQyMjIUHR2ta665RjNmzKjxMe69915lZ2dr4sSJyszMVMeOHfXFF1+oXbt2lbZv2bKlFixYoPvvv18vvfSSevTooSeffLLCzBIA4CksBoO3AAAA4KUYxgAAAACvRdgFAACA1yLsAgAAwGsRdgEAAOC1CLsAAADwWoRdAAAAeC3CLgAAALwWYRcAAABei7ALAAAAr0XYBQAAgNci7AIAAMBr/T9GpdtZOTbyLgAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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BggXau3evlixZ4lhH2rdvX8XGxqp79+6KiYnRtm3b9PLLL6t///5nXEfZsWNHvfrqq5oxY4aaN2+u6Ojo0653laSLLrpIzZs312OPPab8/HynJQZS8ZrYV199VX/729900UUX6bbbblNUVJT27dunZcuWqXv37mVKXkU9/PDDeu+99zR79mw99dRT6t+/v55//nldddVVGjx4sNLS0jRnzhw1b95cP/30U5nvc9WqVXr++efVoEEDNWnSRF26dNFTTz2lNWvWqEuXLhoxYoTatGmjw4cPa/PmzVq1apUOHz7scs4BAwbosssu02OPPaakpCS1a9dO//vf//Tf//5XY8aMcesVtEJDQ8vdE/l0li9f7pghPlW3bt3UtGlTt+U6k2bNmmnGjBmaNGmSkpKSNHDgQAUHB2vv3r366KOPdM8992jChAnVkgXwaNZtpACgup1ua67TbWVVWFhoPvLII2ZkZKQZEBBg9uvXz9y1a1eZrblMs3grqkmTJpnNmzc3fXx8zMjISLNbt27ms88+axYUFJwx15kynGr37t3mzTffbIaFhZl+fn5m586dzaVLlzod8+9//9vs1auXWa9ePdPX19ds1qyZ+fDDD5tZWVmOY8rbmislJcXs37+/GRwcbEpybNP11625TvXYY4+ZkszmzZufNvOaNWvMfv36maGhoaafn5/ZrFkz86677jI3btx4xu+1vG2lTnXppZeaISEhZmZmpmmapvnmm2+aLVq0MH19fc1WrVqZb731lmNLqFNt377d7NWrl+nv729Kcvo9pqammiNHjjTj4+NNb29vMzY21rziiivM11577YxZTfP0v8OjR4+aY8eONRs0aGB6e3ubLVq0MJ955hmzqKjI6ThJ5siRI8/6OWf7vFO5ujWXTrP9lru25kpPTy/3+SVLlpg9evQwAwMDzcDAQLNVq1bmyJEjzR07djiOYWsu4PQM0/zLv20AAAAAHoI1swAAAPBYrJkFAKAScnJynC6cUZ6oqKhyt90C4D6UWQAAKuHZZ589697Fe/fuVUJCQvUEAuoo1swCAFAJe/bs0Z49e854TI8ePU67tzEA96DMAgAAwGNxAhgAAAA8Vp1bM1tUVKQDBw4oODjY5ctXAgAAoOqZpqmjR4+qQYMGjovinE6dK7MHDhxQfHy81TEAAABwFvv371fDhg3PeEydK7Oll7Pcv3+/QkJCLE4DAACAv8rOzlZ8fPwZL0Neqs6V2dKlBSEhIZRZAACAGqwiS0I5AQwAAAAeizILAAAAj0WZBQAAgMeizAIAAMBjUWYBAADgsSizAAAA8FiUWQAAAHgsyiwAAAA8FmUWAAAAHosyCwAAAI9FmQUAAIDHoswCAADAY1FmAQAA4LEoswAAAPBYlFkAAAB4LEvL7JdffqkBAwaoQYMGMgxDH3/88Vlfs3btWl100UXy9fVV8+bNNX/+/CrPCQAAgJrJ0jKbm5urdu3aac6cORU6fu/everfv78uu+wyJSYmasyYMRo+fLg+//zzKk4KAACAmsjLyg+/+uqrdfXVV1f4+Llz56pJkyZ67rnnJEmtW7fW119/rRdeeEH9+vWrqpjnJm2blL5DimolRbeyOg0AAECtYmmZddX69evVp08fp7F+/fppzJgxp31Nfn6+8vPzHY+zs7OrKl751s+RtixUaqeHld7hQaenwgN9FBfmX715AAAAahGPKrMpKSmKiYlxGouJiVF2draOHTsmf/+yxXDWrFmaPn16dUUs46hXhIIlLf/uR037+mun5/y97Vo1vjeFFgAAoJI8qsxWxqRJkzRu3DjH4+zsbMXHx1fb55eW2SsbSZ3693CM70rL0ZjFiTqSW0CZBQAAqCSPKrOxsbFKTU11GktNTVVISEi5s7KS5OvrK19f3+qIV66TAVGSpLDCI4qLC7UsBwAAQG3kUfvMdu3aVatXr3YaW7lypbp27WpRorM76V9cZr2OpVucBAAAoPaxtMzm5OQoMTFRiYmJkoq33kpMTNS+ffskFS8RGDJkiOP4++67T3v27NE//vEPbd++Xa+88oree+89jR071or4FXIyIFqS5HUsw+IkAAAAtY+lZXbjxo3q0KGDOnToIEkaN26cOnTooClTpkiSDh486Ci2ktSkSRMtW7ZMK1euVLt27fTcc8/pjTfeqLnbckk66R8pSbKfyJEKci1OAwAAULtYumb20ksvlWmap32+vKt7XXrppdqyZUsVpnKvIu8gHTN95G8USDlpUkQTqyMBAADUGh61ZtYjGYbSzZITv3LSrM0CAABQy1Bmq0G6worv5KSe8TgAAAC4hjJbDdLNsOI7uczMAgAAuBNlthpkmCHFd1hmAAAA4FaU2WrgmJllmQEAAIBbUWarwZ9rZpmZBQAAcCfKbDX4czcDZmYBAADciTJbDf5cZsAlbQEAANyJMlsNMnTKzOwZLhIBAAAA11Bmq4FjmUFhvnQ8y9owAAAAtQhlthrky0eFPmzPBQAA4G6U2Wpy0j+q+A4ngQEAALgNZbaanAygzAIAALgbZbaanPSPLL6Ty44GAAAA7kKZrSYnWGYAAADgdpTZavLnMgNOAAMAAHAXymw1OekfXXyHmVkAAAC3ocxWE04AAwAAcD/KbDVxnADGJW0BAADchjJbTRz7zOamS0WF1oYBAACoJSiz1eSkfz1JhmQWSnmHrY4DAABQK1Bmq4vNWwqoV3yfdbMAAABuQZmtTkExxV8pswAAAG5Bma1OQSXbc3EVMAAAALegzFanIPaaBQAAcCfKbHVylFmuAgYAAOAOlNnqxJpZAAAAt6LMVifKLAAAgFtRZqsTywwAAADcijJbnQIpswAAAO5Ema1OpcsMjh2WUVhgbRYAAIBagDJbnfzDJZuXJMl+7JDFYQAAADwfZbY62WyOpQZex1hqAAAAcK4os9Wt5CQw7zyuAgYAAHCuKLPVLah0ZjbD4iAAAACejzJb3RxllplZAACAc0WZrW4lOxp4scwAAADgnFFmq1tpmeUEMAAAgHNGma1upcsMmJkFAAA4Z5TZ6hbICWAAAADuQpmtbo5lBszMAgAAnCvKbHUrWWZgP5Ejfx23OAwAAIBno8xWN99gyctfkhRpZFkcBgAAwLNRZqubYThmZ6NEmQUAADgXlFkrlJZZI9PaHAAAAB6OMmuFkpPAolhmAAAAcE4os1ZwzMxSZgEAAM4FZdYKpTOzyrQ2BwAAgIejzFqBmVkAAAC3oMxaIZATwAAAANyBMmuFkmUG7DMLAABwbiizVjh1n1nTtDgMAACA5/KyOkCdVFJmfY0T+v3AweILKZwiPNBHcWH+ViQDAADwKJRZK3j7q8gnRLaCbD3/0VfabcY5Pe3vbdeq8b0ptAAAAGdBmbWILbSBlJ6tN25ooNy4Ho7xXWk5GrM4UUdyCyizAAAAZ0GZtUpwfSl9u5r4ZEtxoVanAQAA8EicAGaVkAbFX7OTrc0BAADgwSizVnGU2YPW5gAAAPBglFmrBNcv/nqUMgsAAFBZlFmrOGZmD1ibAwAAwINRZq1SOjNLmQUAAKg0yqxVQkr2ls1NlwpPWJsFAADAQ1FmrRJQT7J5SzKloylWpwEAAPBIlFmr2GycBAYAAHCOKLNWCildN8teswAAAJVBmbUSe80CAACcE8qslYJLyuxRdjQAAACoDMqslRzLDJiZBQAAqAzKrJXYaxYAAOCcUGatVLrXLMsMAAAAKoUya6VTlxmYprVZAAAAPBBl1kqlywwK86VjR6zNAgAA4IEos1by8i2+EpjEXrMAAACVQJm1GnvNAgAAVBpl1mrsNQsAAFBplFmrsdcsAABApVFmrVY6M8uaWQAAAJdZXmbnzJmjhIQE+fn5qUuXLtqwYcMZj589e7Zatmwpf39/xcfHa+zYsTp+/Hg1pa0CpWtmjzIzCwAA4CpLy+zixYs1btw4TZ06VZs3b1a7du3Ur18/paWllXv8u+++q4kTJ2rq1Knatm2b3nzzTS1evFiPPvpoNSd3I5YZAAAAVJqlZfb555/XiBEjNGzYMLVp00Zz585VQECA5s2bV+7x3377rbp3767BgwcrISFBffv21e23337W2dwajRPAAAAAKs2yMltQUKBNmzapT58+f4ax2dSnTx+tX7++3Nd069ZNmzZtcpTXPXv26LPPPtM111xz2s/Jz89Xdna2061GKZ2ZPXZEOnHM2iwAAAAexsuqD87IyFBhYaFiYmKcxmNiYrR9+/ZyXzN48GBlZGSoR48eMk1TJ0+e1H333XfGZQazZs3S9OnT3ZrdrfzCJO8A6USelH1AUqTViQAAADyG5SeAuWLt2rWaOXOmXnnlFW3evFkffvihli1bpieeeOK0r5k0aZKysrIct/3791dj4gowjD8va8tJYAAAAC6xbGY2MjJSdrtdqampTuOpqamKjY0t9zWTJ0/W3/72Nw0fPlySdMEFFyg3N1f33HOPHnvsMdlsZbu5r6+vfH193f8NuFNIA+nw7uKTwOpdYHUaAAAAj2HZzKyPj486duyo1atXO8aKioq0evVqde3atdzX5OXllSmsdrtdkmSaZtWFrWqlM7PsNQsAAOASy2ZmJWncuHEaOnSoOnXqpM6dO2v27NnKzc3VsGHDJElDhgxRXFycZs2aJUkaMGCAnn/+eXXo0EFdunTRrl27NHnyZA0YMMBRaj0Se80CAABUiqVldtCgQUpPT9eUKVOUkpKi9u3ba8WKFY6Twvbt2+c0E/v444/LMAw9/vjjSk5OVlRUlAYMGKAnn3zSqm/BPUrLbDbbcwEAALjC0jIrSaNGjdKoUaPKfW7t2rVOj728vDR16lRNnTq1GpJVI04AAwAAqBSP2s2g1mJmFgAAoFIoszWBY81silRUaG0WAAAAD0KZrQkCoyXDJpmF8jqeYXUaAAAAj0GZrQnsXlJQ8UlvXrkpFocBAADwHJTZmqLkJDBvyiwAAECFUWZripJ1s5RZAACAiqPM1hSlZTaPMgsAAFBRlNmaomSZAWtmAQAAKo4yW1M4lhlw4QQAAICKoszWFI4ym2pxEAAAAM9Bma0pgovLrBdrZgEAACqMMltThBSvmbWfyFWQ8iwOAwAA4BkoszWFT6DkGypJijUOWxwGAADAM1Bma5KSdbOxxhGLgwAAAHgGymxNUrLUgJlZAACAiqHM1iQlJ4HFiJlZAACAiqDM1iQlM7P1jUMWBwEAAPAMXlYHwClK1szGGEe0Ky3H6anwQB/FhflbkQoAAKDGoszWJCFxkqQ422FdszjR6Sl/b7tWje9NoQUAADgFZbYmCW0oSWrpn62l9/RwDO9Ky9GYxYk6kltAmQUAADgFZbYmKSmz9uOH1TbKq3jvWQAAAJwWJ4DVJH6hkm9I8f2sZGuzAAAAeADKbE1TMjurrP3W5gAAAPAAlNmaxlFm/7A2BwAAgAegzNY0lFkAAIAKo8zWNJRZAACACqPM1jSh8cVfWTMLAABwVpTZmoaZWQAAgAqjzNY0pWU2O1kqKrI2CwAAQA1Hma1pgutLhk0qLJBy061OAwAAUKNRZmsau3dxoZVYagAAAHAWlNmaiAsnAAAAVAhltibiJDAAAIAKoczWRJRZAACACqHM1kTsNQsAAFAhlNmaiJlZAACACqHM1kSUWQAAgAqhzNZEpWU2L0M6cczaLAAAADUYZbYm8guTfIKK72clWxoFAACgJqPM1kSGwV6zAAAAFUCZralYNwsAAHBWlNmaijILAABwVpTZmooyCwAAcFaU2ZqKCycAAACcFWW2pmJmFgAA4KwoszXVqWXWNK3NAgAAUENRZmuq4AaSDKkwX/bjh6xOAwAAUCNRZmsqLx8pOFaS5J3DhRMAAADKQ5mtyUqWGvjkHLA4CAAAQM1Ema3JSsosM7MAAADlo8zWZCXbc3kzMwsAAFAuymxNVlpmc5mZBQAAKA9ltiZzLDNgZhYAAKA851Rmjx8/7q4cKA9lFgAA4IxcLrNFRUV64oknFBcXp6CgIO3Zs0eSNHnyZL355ptuD1inlZbZY+nyVYHFYQAAAGoel8vsjBkzNH/+fP3rX/+Sj4+PY7xt27Z644033BquzvMPl7wDJUmxxmGLwwAAANQ8LpfZt99+W6+99pruuOMO2e12x3i7du20fft2t4ar8wzDMTvbwOAqYAAAAH/lcplNTk5W8+bNy4wXFRXpxIkTbgmFU5SU2Tgjw+IgAAAANY/LZbZNmzb66quvyox/8MEH6tChg1tC4RSlM7NiZhYAAOCvvFx9wZQpUzR06FAlJyerqKhIH374oXbs2KG3335bS5curYqMdVvJXrMNmJkFAAAow+WZ2euvv16ffvqpVq1apcDAQE2ZMkXbtm3Tp59+qiuvvLIqMtZtrJkFAAA4LZdnZiWpZ8+eWrlypbuzoDynrJk9ZnEUAACAmsblmdmmTZvq0KGys4SZmZlq2rSpW0LhFKfOzJqmxWEAAABqFpfLbFJSkgoLC8uM5+fnKzk52S2hcIqQBjJlyN8okP04e80CAACcqsLLDD755BPH/c8//1yhoaGOx4WFhVq9erUSEhLcGg6SvHx1MiBK3nlp8s5JlsTsNwAAQKkKl9mBAwdKkgzD0NChQ52e8/b2VkJCgp577jm3hkOxE0Fx8s5L06HkPToedaHTc+GBPooL87coGQAAgLUqXGaLiookSU2aNNEPP/ygyMjIKguFvwhtKKVt0eff/qB5Xzn/3P297Vo1vjeFFgAA1Eku72awd+/eqsiBMwiIaiL9Jj3Q3ks3duvhGN+VlqMxixN1JLeAMgsAAOqkSm3NlZubq3Xr1mnfvn0qKChwem706NFuCYZThDeWJEWeTFVkXOhZDgYAAKg7XC6zW7Zs0TXXXKO8vDzl5uYqIiJCGRkZCggIUHR0NGW2KoQlFH898rulMQAAAGoal7fmGjt2rAYMGKAjR47I399f3333nX7//Xd17NhRzz77bFVkRMnMrDJ/Z69ZAACAU7hcZhMTEzV+/HjZbDbZ7Xbl5+crPj5e//rXv/Too49WRUaExhd/LciR8thrFgAAoJTLZdbb21s2W/HLoqOjtW/fPklSaGio9u/f7950KObtJwXXL76fmWRpFAAAgJrE5TWzHTp00A8//KAWLVqod+/emjJlijIyMrRw4UK1bdu2KjJCksIaSUcPFq+bjetodRoAAIAaweWZ2ZkzZ6p+/eJZwieffFLh4eG6//77lZ6ern//+99uD4gSYaesmwUAAICkSszMdurUyXE/OjpaK1ascGsgnIbjJLB91uYAAACoQVyemT2dzZs369prr3XX2+GvSmdm2Z4LAADAwaUy+/nnn2vChAl69NFHtWfPHknS9u3bNXDgQF188cWOS966Ys6cOUpISJCfn5+6dOmiDRs2nPH4zMxMjRw5UvXr15evr6/OO+88ffbZZy5/rscJZ5kBAADAX1V4mcGbb76pESNGKCIiQkeOHNEbb7yh559/Xg8++KAGDRqkrVu3qnXr1i59+OLFizVu3DjNnTtXXbp00ezZs9WvXz/t2LFD0dHRZY4vKCjQlVdeqejoaH3wwQeKi4vT77//rrCwMJc+1yOFnbLMoKhIsrltUh0AAMBjVbgRvfjii3r66aeVkZGh9957TxkZGXrllVf0888/a+7cuS4XWUl6/vnnNWLECA0bNkxt2rTR3LlzFRAQoHnz5pV7/Lx583T48GF9/PHH6t69uxISEtS7d2+1a9fO5c/2OCFxkmGXCguknBSr0wAAANQIFS6zu3fv1i233CJJuvHGG+Xl5aVnnnlGDRs2rNQHFxQUaNOmTerTp8+fYWw29enTR+vXry/3NZ988om6du2qkSNHKiYmRm3bttXMmTNVWFh42s/Jz89Xdna2080j2b2k0Lji+6ybBQAAkORCmT127JgCAgIkSYZhyNfX17FFV2VkZGSosLBQMTExTuMxMTFKSSl/5nHPnj364IMPVFhYqM8++0yTJ0/Wc889pxkzZpz2c2bNmqXQ0FDHLT4+vtKZLcf2XAAAAE5c2prrjTfeUFBQkCTp5MmTmj9/viIjI52OGT16tPvS/UVRUZGio6P12muvyW63q2PHjkpOTtYzzzyjqVOnlvuaSZMmady4cY7H2dnZnltowxtLSV+xPRcAAECJCpfZRo0a6fXXX3c8jo2N1cKFC52OMQyjwmU2MjJSdrtdqampTuOpqamKjY0t9zX169eXt7e37Ha7Y6x169ZKSUlRQUGBfHx8yrzG19dXvr6+FcpU44UlFH9lmQEAAIAkF8psUlKSWz/Yx8dHHTt21OrVqzVw4EBJxTOvq1ev1qhRo8p9Tffu3fXuu++qqKhItpKz+Xfu3Kn69euXW2RrHbbnAgAAcGLp/k7jxo3T66+/rgULFmjbtm26//77lZubq2HDhkmShgwZokmTJjmOv//++3X48GE99NBD2rlzp5YtW6aZM2dq5MiRVn0L1SusUfFXZmYBAAAkVeJytu40aNAgpaena8qUKUpJSVH79u21YsUKx0lh+/btc8zASlJ8fLw+//xzjR07VhdeeKHi4uL00EMP6ZFHHrHqW6hepSeAZf8hFZ6wNgsAAEANYGmZlaRRo0addlnB2rVry4x17dpV3333XRWnqqGCYiS7r1SYL2X9ISnC6kQAAACW4jJSnsRm+3OpATsaAAAAUGY9DieBAQAAOFSqzO7evVuPP/64br/9dqWlpUmSli9frl9++cWt4VCO0nWznAQGAADgepldt26dLrjgAn3//ff68MMPlZOTI0n68ccfT3vhAriRY5kBZRYAAMDlMjtx4kTNmDFDK1eudNrb9fLLL6+7J2ZVp3BmZgEAAEq5XGZ//vln3XDDDWXGo6OjlZGR4ZZQOIMw1swCAACUcrnMhoWF6eDBg2XGt2zZori4OLeEwhmEJxR/zUmVcfK4pVEAAACs5nKZve222/TII48oJSVFhmGoqKhI33zzjSZMmKAhQ4ZURUacyj9c8gmWJHnn/GFxGAAAAGu5XGZnzpypVq1aKT4+Xjk5OWrTpo169eqlbt266fHHH6+KjDiVYTjWzfoc3W9xGAAAAGu5fAUwHx8fvf7665o8ebK2bt2qnJwcdejQQS1atKiKfChPWCMpdWtJmW1qdRoAAADLuFxmv/76a/Xo0UONGjVSo0aNqiITzqbkJDBvyiwAAKjjXF5mcPnll6tJkyZ69NFH9euvv1ZFJpyNY5kBl7QFAAB1m8tl9sCBAxo/frzWrVuntm3bqn379nrmmWf0xx+cjFRtwlgzCwAAIFWizEZGRmrUqFH65ptvtHv3bt1yyy1asGCBEhISdPnll1dFRvxV+KnLDAAAAOoul8vsqZo0aaKJEyfqqaee0gUXXKB169a5KxfOpOSStl75WQpWnsVhAAAArFPpMvvNN9/ogQceUP369TV48GC1bdtWy5Ytc2c2nI5vsOQfIUlqaKRbHAYAAMA6Lu9mMGnSJC1atEgHDhzQlVdeqRdffFHXX3+9AgICqiIfTie8sXTssOKNNKuTAAAAWMblMvvll1/q4Ycf1q233qrIyMiqyISKCGssHdiieGZmAQBAHeZymf3mm2+qIgdcVXISGMsMAABAXVahMvvJJ5/o6quvlre3tz755JMzHnvddde5JRjOomR7LpYZAACAuqxCZXbgwIFKSUlRdHS0Bg4ceNrjDMNQYWGhu7LhTBxlNl0nLI4CAABglQqV2aKionLvw0KnLDNYmXq07NOBPooL86/uVAAAANXK5TWzb7/9tgYNGiRfX1+n8YKCAi1atEhDhgxxWzicQWi8TBkKNPI1470vlaFQp6f9ve1aNb43hRYAANRqLu8zO2zYMGVlZZUZP3r0qIYNG+aWUKgAbz8ZoQ0lSYtvjdHSB3s4brMHtdexE4U6kltgcUgAAICq5fLMrGmaMgyjzPgff/yh0NDQcl6BKhPRRMrar2a2NCmOnz0AAKh7KlxmO3ToIMMwZBiGrrjiCnl5/fnSwsJC7d27V1dddVWVhMRpRDSV9n4pHd5jdRIAAABLVLjMlu5ikJiYqH79+ikoKMjxnI+PjxISEnTTTTe5PSDOIKJp8VfKLAAAqKMqXGanTp0qSUpISNCgQYPk5+dXZaFQQY4yu9vaHAAAABZxec3s0KFDqyIHKqO0zB7aI5mmVM5aZgAAgNqsQmU2IiJCO3fuVGRkpMLDw8s9AazU4cOH3RYOZxHepPhrfpZ07IgUEGFtHgAAgGpWoTL7wgsvKDg42HH/TGUW1cgnQApuIB09ULxuljILAADqmAqV2VOXFtx1111VlQWVEdH0zzLbsJPVaQAAAKqVyxdN2Lx5s37++WfH4//+978aOHCgHn30URUUsEl/tYsoWWrAjgYAAKAOcrnM3nvvvdq5c6ckac+ePRo0aJACAgL0/vvv6x//+IfbA+Is2J4LAADUYS6X2Z07d6p9+/aSpPfff1+9e/fWu+++q/nz52vJkiXuzoezocwCAIA6zOUya5qmioqKJEmrVq3SNddcI0mKj49XRkaGe9Ph7Bzbc7HXLAAAqHtcLrOdOnXSjBkztHDhQq1bt079+/eXJO3du1cxMTFuD4izKF0ze+xw8fZcAAAAdYjLZXb27NnavHmzRo0apccee0zNmzeXJH3wwQfq1q2b2wPiLHyDpaCS/xNxeK+1WQAAAKqZy1cAu/DCC512Myj1zDPPyG63uyUUXBTRVMpJLV43G3eR1WkAAACqjctlttSmTZu0bds2SVKbNm100UWUKMtENJX2rWdmFgAA1Dkul9m0tDQNGjRI69atU1hYmCQpMzNTl112mRYtWqSoqCh3Z8TZsNcsAACoo1xeM/vggw8qJydHv/zyiw4fPqzDhw9r69atys7O1ujRo6siI86G7bkAAEAd5fLM7IoVK7Rq1Sq1bt3aMdamTRvNmTNHffv2dWs4VBBlFgAA1FEuz8wWFRXJ29u7zLi3t7dj/1lUs/CSZQa5adLxbGuzAAAAVCOXy+zll1+uhx56SAcOHHCMJScna+zYsbriiivcGg4V5B8mBdQrvn+Ek8AAAEDd4XKZffnll5Wdna2EhAQ1a9ZMzZo1U5MmTZSdna2XXnqpKjKiIlhqAAAA6iCX18zGx8dr8+bNWr16tWNrrtatW6tPnz5uDwcXRDST/vihuMyGXW51GgAAgGrhUpldvHixPvnkExUUFOiKK67Qgw8+WFW54KpTZ2abWhsFAACgulS4zL766qsaOXKkWrRoIX9/f3344YfavXu3nnnmmarMh4pylFnWzAIAgLqjwmtmX375ZU2dOlU7duxQYmKiFixYoFdeeaUqs8EVrJkFAAB1UIXL7J49ezR06FDH48GDB+vkyZM6ePBglQSDi0qvAnb0oIwTedZmAQAAqCYVLrP5+fkKDAz884U2m3x8fHTs2LEqCQYXBURIfmGSJJ+j+6zNAgAAUE1cOgFs8uTJCggIcDwuKCjQk08+qdDQUMfY888/7750cE1EU+nAZvlm7ZUUbHUaAACAKlfhMturVy/t2LHDaaxbt27as+fPNZqGYbgvGVxXUmZ9spMkXWB1GgAAgCpX4TK7du3aKowBt6jXTJIoswAAoM5w+QpgqMFKdjTwyfrd4iAAAADVgzJbm5SW2ewka3MAAABUE8psbVJaZnMPyFcFFocBAACoepTZ2iSgnuQbIkmKN9IsDgMAAFD1KLO1iWE4Lp6QYKRaHAYAAKDqVarMfvXVV7rzzjvVtWtXJScnS5IWLlyor7/+2q3hUAklSw0SjBSLgwAAAFQ9l8vskiVL1K9fP/n7+2vLli3Kz8+XJGVlZWnmzJluDwgXnVJmd6XlaGtyluOWnMnV2gAAQO3i0hXAJGnGjBmaO3euhgwZokWLFjnGu3fvrhkzZrg1HCohoniv2Wb2VN2+ONHpKX9vu1aN7624MH8LggEAALify2V2x44d6tWrV5nx0NBQZWZmuiMTzkXkeZKkzkEZWnpPD8fwrrQcjVmcqCO5BZRZAABQa7hcZmNjY7Vr1y4lJCQ4jX/99ddq2rSpu3KhsiJbSJLsuSlqW0+SX6i1eQAAAKqQy2tmR4wYoYceekjff/+9DMPQgQMH9M4772jChAm6//77qyIjXOEfJgXFFN/P2GVpFAAAgKrm8szsxIkTVVRUpCuuuEJ5eXnq1auXfH19NWHCBD344INVkRGuijxPykmVMnZIDTtanQYAAKDKuFxmDcPQY489pocffli7du1STk6O2rRpo6CgoKrIh8qIPE9K+krK2Gl1EgAAgCrlcpkt5ePjozZt2rgzC9wlqmXx14zfrM0BAABQxVwus5dddpkMwzjt81988cU5BYIblJwEpvQd1uYAAACoYi6X2fbt2zs9PnHihBITE7V161YNHTrUXblwLiJLZmaP7JUKT0h2b2vzAAAAVBGXy+wLL7xQ7vi0adOUk5NzzoHgBiENJO9A6USudHjPn8sOAAAAahmXt+Y6nTvvvFPz5s1z19vhXBjGn0sNOAkMAADUYm4rs+vXr5efn5+73g7nqnQ2lnWzAACgFnN5mcGNN97o9Ng0TR08eFAbN27U5MmT3RYM58gxM8uOBgAAoPZyucyGhjpfHtVms6lly5b65z//qb59+7otGM5R6UlgGczMAgCA2sulMltYWKhhw4bpggsuUHh4eFVlgjtEnlf8NeM3yTStzQIAAFBFXFoza7fb1bdvX2VmZro1xJw5c5SQkCA/Pz916dJFGzZsqNDrFi1aJMMwNHDgQLfmqRUimkqGXSrIkbIPWJ0GAACgSrh8Aljbtm21Z88etwVYvHixxo0bp6lTp2rz5s1q166d+vXrp7S0tDO+LikpSRMmTFDPnj3dlqVW8fKRIpoU32dHAwAAUEu5XGZnzJihCRMmaOnSpTp48KCys7Odbq56/vnnNWLECA0bNkxt2rTR3LlzFRAQcMZtvgoLC3XHHXdo+vTpatq0qcufWWdEcllbAABQu1W4zP7zn/9Ubm6urrnmGv3444+67rrr1LBhQ4WHhys8PFxhYWEur6MtKCjQpk2b1KdPnz8D2Wzq06eP1q9ff8Ys0dHRuvvuu8/6Gfn5+edcuD2WY0cDTgIDAAC1U4VPAJs+fbruu+8+rVmzxm0fnpGRocLCQsXExDiNx8TEaPv27eW+5uuvv9abb76pxMTECn3GrFmzNH369HON6plK95plmQEAAKilKlxmzZIz4nv37l1lYc7m6NGj+tvf/qbXX39dkZGRFXrNpEmTNG7cOMfj7OxsxcfHV1XEmqV0R4N0yiwAAKidXNqayzAMt354ZGSk7Ha7UlNTncZTU1MVGxtb5vjdu3crKSlJAwYMcIwVFRVJkry8vLRjxw41a9bM6TW+vr7y9fV1a26PUbrMICdFtoI6tLwCAADUGS6V2fPOO++shfbw4cMVfj8fHx917NhRq1evdmyvVVRUpNWrV2vUqFFljm/VqpV+/vlnp7HHH39cR48e1Ysvvlh3Zlwryi9UCoqVclLkm7nb6jQAAABu51KZnT59epkrgJ2rcePGaejQoerUqZM6d+6s2bNnKzc3V8OGDZMkDRkyRHFxcZo1a5b8/PzUtm1bp9eHhYVJUplxlIhsUVJmd0mqb3UaAAAAt3KpzN52222Kjo52a4BBgwYpPT1dU6ZMUUpKitq3b68VK1Y4Tgrbt2+fbDaXdxBDqaiWUtJXlFkAAFArVbjMunu97KlGjRpV7rICSVq7du0ZXzt//nz3B6pNSk4CK15mwAUmAABA7VLhKc/S3QzgYRxldpfFQQAAANyvwjOzpbsGwMOUlFmf7N/lrZMWhwEAAHAvFqPWdiENJJ8gGWahGhmpZz8eAADAg1BmazvDcOw329xItjgMAACAe1Fm64LI4svaNjMOWBwEAADAvSizdUHpzKyNMgsAAGoXymxdEMXMLAAAqJ0os3VByY4GzYwDElusAQCAWoQyWxdENJVpeCnIOC6v3BSr0wAAALiNS5ezhYeye6sgpLF8s3brUNLP2hrkfFnb8EAfxYX5WxQOAACg8iizdURRVCspa7e++HKN3lwT4PScv7ddq8b3ptACAACPQ5mtI/wbtpN2LdOoNvm64dIejvFdaTkaszhRR3ILKLMAAMDjUGbripjzJUnhR3coPC7U4jAAAADuwQlgdUVs2+Kv6TukwhPWZgEAAHATymxdEdpI8gmWCgukjN+sTgMAAOAWlNm6wmZzLDVQ6lZrswAAALgJZbYuocwCAIBahjJbl5Sum02hzAIAgNqBMluXxFxQ/JWZWQAAUEtQZuuS6NaSDCknVcpJtzoNAADAOaPM1iW+QVJEk+L7zM4CAIBagDJb1zhOAvvF2hwAAABuQJmta1g3CwAAahHKbF3D9lwAAKAWoczWNVzWFgAA1CKU2brG6bK2O61OAwAAcE4os3WN02VtOQkMAAB4NspsXeS4EtjP1uYAAAA4R5TZuoiZWQAAUEtQZusitucCAAC1BGW2Ljrlsrb2YxlWpwEAAKg0ymxddMplbf0Ob7M4DAAAQOVRZuuqknWzfocoswAAwHNRZuuqknWzzMwCAABPRpmtq0q252JmFgAAeDLKbF1VsszAN/M3eemkxWEAAAAqhzJbV4U1lnyCZSs6oWbGAavTAAAAVApltq4yDMfsbGtjn8VhAAAAKocyW5eVrJttZaPMAgAAz0SZrctKZmbbGL9bHAQAAKByvKwOAAuVbM/V2rZP36QeLfN0eKCP4sL8qzsVAABAhVFm67KY82UadkUpS0+/94UOqp7T0/7edq0a35tCCwAAaizKbF3mEyAjpo2U8rMW9/dSdtMejqd2peVozOJEHcktoMwCAIAaizJb18V1lFJ+VqNj26S4wVanAQAAcAkngNV1cZ2KvyZvsjYHAABAJVBm67qGJWX2wBapkCuBAQAAz0KZresiz5N8gqUTeVL6NqvTAAAAuIQyW9fZ7FJch+L7f2y0NgsAAICLKLNg3SwAAPBYlFkU72ggUWYBAIDHocziz5PA0rZJ+WWvBAYAAFBTUWYhBcdKIQ0lmcW7GgAAAHgIyiyKNSxZasBJYAAAwINQZlGMk8AAAIAHosyiWEPKLAAA8DyUWRSr304y7NLRg1JWstVpAAAAKoQyi2I+gVJ0m+L7yaybBQAAnoEyiz9xEhgAAPAwlFn8yXES2GZrcwAAAFQQZRZ/Kj0J7MAWqajQ2iwAAAAVQJnFnyLPk3yCpRO58j2y0+o0AAAAZ0WZxZ9sdqlBe0lSQDpXAgMAADUfZRbOSpYa+KclWpsDAACgAiizcFZyElhAeqK1OQAAACqAMgtnJTOzvkd2KkDHLQ4DAABwZpRZOAuOlUIayjCLdKFtj9VpAAAAzsjL6gCogeIukrL/UHtjl3al5Tg9FR7oo7gwf4uCAQAAOKPMoqz4ztK2T3SJ107dtTjR6Sl/b7tWje9NoQUAADUCZRZlNeklSerls0NLh3eRbN6SpF1pORqzOFFHcgsoswAAoEagzKKsmAsk/wjZjh1WW3O3FNfF6kQAAADl4gQwlGWzSU16Ft/fu87aLAAAAGdAmUX5mvQu/rqHMgsAAGouyizK1/TS4q9/bJAK8iyNAgAAcDqUWZQvoqkU0lAqLJD2rbc6DQAAQLkosyifYTh2NWDdLAAAqKkoszi9pqybBQAANRtlFqdXehLYwR+lY0eszQIAAFCOGlFm58yZo4SEBPn5+alLly7asGHDaY99/fXX1bNnT4WHhys8PFx9+vQ54/E4ByH1pcjzJJlS0tdWpwEAACjD8jK7ePFijRs3TlOnTtXmzZvVrl079evXT2lpaeUev3btWt1+++1as2aN1q9fr/j4ePXt21fJycnVnLyOYIsuAABQg1leZp9//nmNGDFCw4YNU5s2bTR37lwFBARo3rx55R7/zjvv6IEHHlD79u3VqlUrvfHGGyoqKtLq1aurOXkdUbpulpPAAABADWRpmS0oKNCmTZvUp08fx5jNZlOfPn20fn3FtoPKy8vTiRMnFBERUe7z+fn5ys7OdrrBBQk9JMMmZeyUV26K1WkAAACcWFpmMzIyVFhYqJiYGKfxmJgYpaRUrDg98sgjatCggVMhPtWsWbMUGhrquMXHx59z7jrFP1yq306SFHTgG4vDAAAAOLN8mcG5eOqpp7Ro0SJ99NFH8vPzK/eYSZMmKSsry3Hbv39/NaesBUrWzQYmU2YBAEDNYmmZjYyMlN1uV2pqqtN4amqqYmNjz/jaZ599Vk899ZT+97//6cILLzztcb6+vgoJCXG6wUUl62aLZ2ZNa7MAAACcwtIy6+Pjo44dOzqdvFV6MlfXrl1P+7p//etfeuKJJ7RixQp16tSpOqLWbfGXSHYfeeceVBODdbMAAKDmsHyZwbhx4/T6669rwYIF2rZtm+6//37l5uZq2LBhkqQhQ4Zo0qRJjuOffvppTZ48WfPmzVNCQoJSUlKUkpKinJwcq76F2s8nQIrvIknqbttqcRgAAIA/eVkdYNCgQUpPT9eUKVOUkpKi9u3ba8WKFY6Twvbt2yeb7c/O/eqrr6qgoEA333yz0/tMnTpV06ZNq87odUuT3lLSV+pq+8XqJAAAAA6Wl1lJGjVqlEaNGlXuc2vXrnV6nJSUVPWBUFaTXtIaqavtVx0wi6xOAwAAIKmGlFl4gLiLVOgdqIgTOfpp5wZtNS5xejo80EdxYf4WhQMAAHUVZRYVY/dWQeNL5b9rmX7+4l0997+TTk/7e9u1anxvCi0AAKhWlFlUmP+FN0i7lmlE5FZddsv/OcZ3peVozOJEHcktoMwCAIBqRZlFxZ3XT7L7yC9zl9p6H5SiW1mdCAAA1HGWb80FD+IXIjW9rPj+tk+szQIAACDKLFzV5rrir79SZgEAgPUos3BNy2skwy6l/iwd3mN1GgAAUMdRZuGagAipSc/i+8zOAgAAi1Fm4brWJUsNWDcLAAAsRpmF61pdK8mQkjdJWX9YnQYAANRhlFm4LjhGalRyBbBtn1qbBQAA1GmUWVROa3Y1AAAA1qPMonJaDyj+um+97Hnp1mYBAAB1FmUWlRMWLzW4SJKpkN8/tzoNAACooyizqLySCyiE7l1ucRAAAFBXUWZReSXrZgMPrFeociwOAwAA6iLKLCqvXjMppq0M86SutG+yOg0AAKiDvKwOAA/X+jopdauutm3QrjTn2dnwQB/FhflbFAwAANQFlFmcm/MHSmtnqrftR3VbvEZpCnc85e9t16rxvSm0AACgylBmcW6iWkqNuspr33p92m2X0juOlSTtSsvRmMWJOpJbQJkFAABVhjWzOHcXD5ckxfy2WG1jA9U2LlTNo4MsDgUAAOoCyizOXesBUkCkdPSAtJNtugAAQPWhzOLceflKF/2t+P4Pb1qbBQAA1CmUWbhHx2GSDGnPGunQbqvTAACAOoIyC/cIbyy16Ft8f+M8a7MAAIA6gzIL97n47uKvW/6fjJPHrc0CAADqBMos3Kd5Hym0kXQ8U6F7PrU6DQAAqAMos3Afm13qNEySFPHrQovDAACAuoAyC/fq8DfJ5q2A9ES1NfZYnQYAANRylFm4V1CU1OZ6SdKd9lUWhwEAALUdZRbuV3JFsOvt38qWn2VxGAAAUJt5WR0AtVCjS3Q8vKX8j+xQ0aYF2uo72unp8EAfxYX5WxQOAADUJpRZuJ9h6Fine+W3cpzqb/23em9qpRwFOJ7297Zr1fjeFFoAAHDOKLOoEuGXDNWJja+o3pFd+qLbVqV1HCdJ2pWWozGLE3Ukt4AyCwAAzhlrZlE17F7yvnKKJCl66xtqG1qgtnGhah4dZHEwAABQm1BmUXVaXyc16CAV5EhfPWd1GgAAUAtRZlF1DEPqM634/sY3pSO/WxoHAADUPpRZVK2mlxbfCguktbOsTgMAAGoZyiyq3hXFa2f14yL5Ht5hbRYAAFCrUGZR9eI6llwVzFTMxn9ZnQYAANQilFlUj8snS4ZdIb+v1EXGTqvTAACAWoJ9ZlE9IltIHe6QNr+tR7wXaVfqLWUO4cpgAADAVZRZVJ/eE2X+uFhdtF3jP3hJY4p6OT3NlcEAAICrKLOoPqFxMi6dKK2ern8FvqN7bhqiE8ENJXFlMAAAUDmsmUX16v6QFN9F9hNH1fK7R9S2fjBXBgMAAJVGmUX1stmlga9K3gFS0lfShn9bnQgAAHgwyiyqX71mUt8niu+vmials7sBAACoHMosrNHpbqnZFdLJ49JH90hFJ6xOBAAAPBBlFtYwDOn6lyW/UOnAFkUlzrE6EQAA8ECUWVgnpIF0zXOSpOjN/6e2xh6LAwEAAE/D1lyw1gU3S9uXyvj1Y73i/aJ+2ddTUhPH01xIAQAAnAllFtYyDOnaF3QyOVGNspKUtXyoBhVMVq6KCywXUgAAAGfCMgNYLyBCXkM+VKF/PV1gS9J3zRZo2QOdNXtQex07UagjuQVWJwQAADUUZRY1Q71mst/5vuQdoODkL3X+xsfVPCrQ6lQAAKCGo8yi5ojrKN36tmTYpZ8WKeaHp61OBAAAajjKLGqWFldK1/2fJCnqx1c0xP65xYEAAEBNxglgqHk63CllH5TWzNA0r7f144Ym2tr5706HsMsBAACQKLOoqXpNUM6hPxT003x1+HGa5m7aqKdP3iaz5B8T2OUAAABILDNATWUYCrphtrIveViSdJ/XUiW2fkfL7u/ILgcAAMCBMouayzAUctXj0g2vSXYfhe5drvP/N1gtg45ZnQwAANQQlFnUfO0GSUP+K/mHS8mb1Oy/16u58YfVqQAAQA3Amll4hsbdpOGrpXduls/hPfqvz2T99n2Otna+u/gqYiU4MQwAgLqFMgvPUa+ZNHy18t+9U4F/fKP2P/1TX235UBNPjFCyoiRxYhgAAHUNywzgWQIi5Pv3pcrsPUNFdj/1tG/Vl8GP6Zsr92v2re04MQwAgDqGMgvPY7Mp7LIHZXvgWym+i+wnchT31SO6YvMDamikW50OAABUI8osPFe9ZtKw5VLfJyW7r4L/WKcvfMYpYNVEbf9tp7YmZ2lrcpaSM9n9AACA2oo1s/BsNrvUbZTUoq+OfzJOfvu/UtO97+rYng+0oLCf5p68VvneYayjBQCglmJmFrVD1Hnyu3up0m/6QHnRHeVvFOg+r0/1Q9B43VP0nrIzDlqdEAAAVAFmZlGrRF1wpdS2j7Tzc+mLJ+SdulVjvZeo8N1PdKTpAB0+/y4di24viW28AACoDSizqH0MQ2p5ldSirw7/sFjJn/1LFxTtUfiuDxW+60P9WNRUCwuv1CpbDy0bfyWFFgAAD0aZRe1lsymiy+061nKgdu/doIhfFyh0z1K10x61s/1bR823lfnB5fq91fXKib9UppefJGZsAQDwJJRZ1HpxYf5Sh97Ft9xD0pa3dXLDmwrO3q/gP5ZJfyxTrumrL4o6aFnhJdpob6f/jr+aQgsAgAegzKJuCawn9Rgrr24PKW3Ht7L9+rFC9n6mwJxkDbB/pwH273TStClrwYVKS+itnLgeOhbdQWEhQZRbAABqIMos6iabTdGte0ite0jmM1LyZunXj3Ty16XyytyrekcSpSOJit7yovJMX21WS524oJcU11HHotqp0D9SEksSAACwGmUWMAypYUepYUd59Z2hlN93qHDXWgUe+EZByV8r4Pgh9dBP0tafpK3FL9lfFKUfzWb6n5GgvpdeKp/6bVQQ3EgybBRcAACqEWUW+IvYxi2lxi0l3SsVFUlpvypzx1cykzfJP22LfDN3K96WrnilS/pO+nKRJOmY6aNdZgNtVLxOtLtI9shmKghprIKQxir0DVd4kC8lFwAAN6PMAmdis0mxbRUW2/bPseNZ0oFEKXmT8pK3ykjbJt+s3fIvzNcFRpIuUJL081dOb5NtBugPRcsvrokU2lAnA+vrRGCsTgTWV0BknGJi4yX/8OJZYgAAUGE1oszOmTNHzzzzjFJSUtSuXTu99NJL6ty582mPf//99zV58mQlJSWpRYsWevrpp3XNNddUY2LUaX6hUtPeUtPeCigdKyqUDu+V0rcpa/8vKsrYI5/sJPkc/V3euSkKMfLURknSgSTpQPlvW2TzUaF/PZ3wj9JJ/0gV+oWr0DdMhb7hKvQL10nfUAWERiqyXpTkGyL5Bkt+IZJ3ACUYAFBnWV5mFy9erHHjxmnu3Lnq0qWLZs+erX79+mnHjh2Kjo4uc/y3336r22+/XbNmzdK1116rd999VwMHDtTmzZvVtm3bcj4BqAY2uxTZXIpsrtDWA5yfK8iTjiQp48Bu5R/aJ+/cFHnnHpR37kEZRw/qZFaKQo1c2YoKZCsZd4Vp2FXkHagi70AVegeqyDuo5HGQirwDVOTlpyKvABV5+cv0CpBvQKDCgoMlb3/Jy+/Pr15+kpePZPd1vm/3Lrn5SDbv4tlqAABqCMM0TdPKAF26dNHFF1+sl19+WZJUVFSk+Ph4Pfjgg5o4cWKZ4wcNGqTc3FwtXbrUMXbJJZeoffv2mjt37lk/Lzs7W6GhocrKylJISIj7vpHT2JqcpWtf+lpLH+yhtnGhVf558DzJmceUmZUtr+OH5JWXLq9j6fI6liF7fqbsx48Uf83PlJl3SCmpqQpSnoKVpyAdk92o/j9f07DLtHnJtHnLtNklm3fxY8OrZNxLMux/Pm/YZRp2yWaXadgcj03DVjJmL55ZNk55Xkbx/0GQTaZhSIat5DibZBjFx+nU+yp+Tob8fLwU7OdTMlttlLy37c/7jjGj7Fh5X6W/jOkv9+U8/tfHZ3quvMdOY+Ud95djT3XW11WAS8e7cKzL/3pQzf/aUGv+daO2fB+wXFTL4ptFXOlrls7MFhQUaNOmTZo0aZJjzGazqU+fPlq/fn25r1m/fr3GjRvnNNavXz99/PHH5R6fn5+v/Px8x+OsrCxJxT+k6pBzNFtF+XnKOZqt7Gz+RwZlBduk4HB/SQ1LbuUzJCnzmP7IKygeME3ZTh6T7UROyS1PtoJc2U7mynai5FaYX3zMyWMyTh7TifxcJe4+KC+zQH4qkJ9xQj4qkJ9OyFsn5GMUylcF8tFJ+eiEfFQoW5nCfLLkdtwt338FKppLTEnV89cNALVXersHlN5+pNNYVJCvokL8quXzS3taReZcLS2zGRkZKiwsVExMjNN4TEyMtm/fXu5rUlJSyj0+JSWl3ONnzZql6dOnlxmPj4+vZOrK6Tq7Wj8OAADgHDxdcrPW0aNHFRp65n/ZtnzNbFWbNGmS00xuUVGRDh8+rHr16smohn9Wys7OVnx8vPbv318tyxpQ/fgd1278fms/fse1G79fz2Sapo4ePaoGDRqc9VhLy2xkZKTsdrtSU1OdxlNTUxUbG1vua2JjY1063tfXV76+vk5jYWFhlQ9dSSEhIfwR1XL8jms3fr+1H7/j2o3fr+c524xsKUtPS/bx8VHHjh21evVqx1hRUZFWr16trl27lvuarl27Oh0vSStXrjzt8QAAAKi9LF9mMG7cOA0dOlSdOnVS586dNXv2bOXm5mrYsGGSpCFDhiguLk6zZs2SJD300EPq3bu3nnvuOfXv31+LFi3Sxo0b9dprr1n5bQAAAMAClpfZQYMGKT09XVOmTFFKSorat2+vFStWOE7y2rdvn2yn7GvZrVs3vfvuu3r88cf16KOPqkWLFvr4449r7B6zvr6+mjp1apmlDqg9+B3Xbvx+az9+x7Ubv9/az/J9ZgEAAIDK4lI+AAAA8FiUWQAAAHgsyiwAAAA8FmUWAAAAHosyW8XmzJmjhIQE+fn5qUuXLtqwYYPVkeAm06ZNk2EYTrdWrVpZHQuV9OWXX2rAgAFq0KCBDMPQxx9/7PS8aZqaMmWK6tevL39/f/Xp00e//fabNWFRKWf7Hd91111l/qavuuoqa8LCZbNmzdLFF1+s4OBgRUdHa+DAgdqxY4fTMcePH9fIkSNVr149BQUF6aabbipzISZ4HspsFVq8eLHGjRunqVOnavPmzWrXrp369euntLQ0q6PBTc4//3wdPHjQcfv666+tjoRKys3NVbt27TRnzpxyn//Xv/6l//u//9PcuXP1/fffKzAwUP369dPx48erOSkq62y/Y0m66qqrnP6m//Of/1RjQpyLdevWaeTIkfruu++0cuVKnThxQn379lVubq7jmLFjx+rTTz/V+++/r3Xr1unAgQO68cYbLUwNtzBRZTp37myOHDnS8biwsNBs0KCBOWvWLAtTwV2mTp1qtmvXzuoYqAKSzI8++sjxuKioyIyNjTWfeeYZx1hmZqbp6+tr/uc//7EgIc7VX3/HpmmaQ4cONa+//npL8sD90tLSTEnmunXrTNMs/pv19vY233//fccx27ZtMyWZ69evtyom3ICZ2SpSUFCgTZs2qU+fPo4xm82mPn36aP369RYmgzv99ttvatCggZo2bao77rhD+/btszoSqsDevXuVkpLi9PccGhqqLl268Pdcy6xdu1bR0dFq2bKl7r//fh06dMjqSKikrKwsSVJERIQkadOmTTpx4oTT33GrVq3UqFEj/o49HGW2imRkZKiwsNBxJbNSMTExSklJsSgV3KlLly6aP3++VqxYoVdffVV79+5Vz549dfToUaujwc1K/2b5e67drrrqKr399ttavXq1nn76aa1bt05XX321CgsLrY4GFxUVFWnMmDHq3r274wqhKSkp8vHxUVhYmNOx/B17PssvZwt4qquvvtpx/8ILL1SXLl3UuHFjvffee7r77rstTAagMm677TbH/QsuuEAXXnihmjVrprVr1+qKK66wMBlcNXLkSG3dupXzGOoIZmarSGRkpOx2e5mzJFNTUxUbG2tRKlSlsLAwnXfeedq1a5fVUeBmpX+z/D3XLU2bNlVkZCR/0x5m1KhRWrp0qdasWaOGDRs6xmNjY1VQUKDMzEyn4/k79nyU2Sri4+Ojjh07avXq1Y6xoqIirV69Wl27drUwGapKTk6Odu/erfr161sdBW7WpEkTxcbGOv09Z2dn6/vvv+fvuRb7448/dOjQIf6mPYRpmho1apQ++ugjffHFF2rSpInT8x07dpS3t7fT3/GOHTu0b98+/o49HMsMqtC4ceM0dOhQderUSZ07d9bs2bOVm5urYcOGWR0NbjBhwgQNGDBAjRs31oEDBzR16lTZ7XbdfvvtVkdDJeTk5DjNwO3du1eJiYmKiIhQo0aNNGbMGM2YMUMtWrRQkyZNNHnyZDVo0EADBw60LjRccqbfcUREhKZPn66bbrpJsbGx2r17t/7xj3+oefPm6tevn4WpUVEjR47Uu+++q//+978KDg52rIMNDQ2Vv7+/QkNDdffdd2vcuHGKiIhQSEiIHnzwQXXt2lWXXHKJxelxTqzeTqG2e+mll8xGjRqZPj4+ZufOnc3vvvvO6khwk0GDBpn169c3fXx8zLi4OHPQoEHmrl27rI6FSlqzZo0pqcxt6NChpmkWb881efJkMyYmxvT19TWvuOIKc8eOHdaGhkvO9DvOy8sz+/bta0ZFRZne3t5m48aNzREjRpgpKSlWx0YFlfe7lWS+9dZbjmOOHTtmPvDAA2Z4eLgZEBBg3nDDDebBgwetCw23MEzTNKu/QgMAAADnjjWzAAAA8FiUWQAAAHgsyiwAAAA8FmUWAAAAHosyCwAAAI9FmQUAAIDHoswCAADAY1FmAQAA4LEoswBQxdauXSvDMJSZmVmtnzt//nyFhYWd03skJSXJMAwlJiae9hirvj8AkCizAHBODMM4423atGlWRwSAWs3L6gAA4MkOHjzouL948WJNmTJFO3bscIwFBQVp48aNLr9vQUGBfHx83JIRAGozZmYB4BzExsY6bqGhoTIMw2ksKCjIceymTZvUqVMnBQQEqFu3bk6ld9q0aWrfvr3eeOMNNWnSRH5+fpKkzMxMDR8+XFFRUQoJCdHll1+uH3/80fG6H3/8UZdddpmCg4MVEhKijh07linPn3/+uVq3bq2goCBdddVVTgW8qKhI//znP9WwYUP5+vqqffv2WrFixRm/588++0znnXee/P39ddlllykpKelcfoQAcE4oswBQTR577DE999xz2rhxo7y8vPT3v//d6fldu3ZpyZIl+vDDDx1rVG+55RalpaVp+fLl2rRpky666CJdccUVOnz4sCTpjjvuUMOGDfXDDz9o06ZNmjhxory9vR3vmZeXp2effVYLFy7Ul19+qX379mnChAmO51988UU999xzevbZZ/XTTz+pX79+uu666/Tbb7+V+z3s379fN954owYMGKDExEQNHz5cEydOdPNPCgBcYAIA3OKtt94yQ0NDy4yvWbPGlGSuWrXKMbZs2TJTknns2DHTNE1z6tSppre3t5mWluY45quvvjJDQkLM48ePO71fs2bNzH//+9+maZpmcHCwOX/+/NPmkWTu2rXLMTZnzhwzJibG8bhBgwbmk08+6fS6iy++2HzggQdM0zTNvXv3mpLMLVu2mKZpmpMmTTLbtGnjdPwjjzxiSjKPHDlSbg4AqErMzAJANbnwwgsd9+vXry9JSktLc4w1btxYUVFRjsc//vijcnJyVK9ePQUFBTlue/fu1e7duyVJ48aN0/Dhw9WnTx899dRTjvFSAQEBatasmdPnln5mdna2Dhw4oO7duzu9pnv37tq2bVu538O2bdvUpUsXp7GuXbtW+GcAAO7GCWAAUE1O/ed/wzAkFa9ZLRUYGOh0fE5OjurXr6+1a9eWea/SLbemTZumwYMHa9myZVq+fLmmTp2qRYsW6YYbbijzmaWfa5qmO74dAKgRmJkFgBrqoosuUkpKiry8vNS8eXOnW2RkpOO48847T2PHjtX//vc/3XjjjXrrrbcq9P4hISFq0KCBvvnmG6fxb775Rm3atCn3Na1bt9aGDRucxr777jsXvzMAcB/KLADUUH369FHXrl01cOBA/e9//1NSUpK+/fZbPfbYY9q4caOOHTumUaNGae3atfr999/1zTff6IcfflDr1q0r/BkPP/ywnn76aS1evFg7duzQxIkTlZiYqIceeqjc4++77z7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", 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", 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", 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", 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", 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/X8uWLSsxDVSvXr0UGhqqyZMnl7rd4unGXNWwYUPdfvvtmjdvnlJSUiQVzptrsVic/l327dtX6ln0pX1Om82m22+/XYsXL9bWrVvdlvXmm29WQUGBXnvtNaflL7/8siwWi2666aZyvW9p+vXrp4kTJ2rWrFkVdoGL48eP6+6771ZBQYFj1gup8Pt/6NAhzZ07t8RrTp06pZycnPO+Z3BwsCSV+m8iyWkKs/z8fM2aNetSPoKT0r5LQFXHkVmgmnnooYf0yCOP6Pbbb9f111+vLVu2aPny5YqMjHRab/To0fr00091yy236G9/+5vat2+vnJwc/fLLL/rwww+1b9++Eq9x1ZgxY/Tf//5XN910k4YOHaqaNWtq/vz52rt3rxYvXuwY+3fDDTcoNjZWXbt2VUxMjLZv367XXntNvXv3vuA4yvbt2+v111/Xc889p0aNGik6Ovq8410l6fLLL1ejRo00btw45eXlOQ0xkArHxL7++uu67777dPnll+uuu+5SVFSUDhw4oKVLl6pr164lSl5ZjR49Wu+//75mzJihqVOnqnfv3po+fbpuvPFGDRgwQGlpaZo5c6YaNWqkn3/+ucTnXLlypaZPn664uDjVr19fnTp10tSpU7V69Wp16tRJDz/8sFq0aKHjx49r8+bNWrlypY4fP+5yzj59+uiaa67RuHHjtG/fPrVp00Zffvml/ve//2n48OGOo9fuEBYWVuqcyOW1a9cu/b//9/9kGIaysrK0ZcsWffDBB8rOznbs62L33Xef3n//fT3yyCNavXq1unbtqoKCAu3YsUPvv/++li9frg4dOpS6nbZt28pms2natGnKzMyUv7+/rr32WnXp0kUREREaOHCghg4dKovFogULFpSYn/dS/fm7BFR5Js6kAMANzjc11/mmsiooKDCefPJJIzIy0ggKCjJ69eplJCcnl5heyDAKp6IaO3as0ahRI8PPz8+IjIw0unTpYrz44otGfn7+BXNdKMO5du/ebfTr188IDw83AgICjI4dOxpLlixxWuc///mP0b17d6NWrVqGv7+/0bBhQ2P06NFGZmamY53SpuZKSUkxevfubdSoUcOQ5Jim689Tc51r3LhxhiSjUaNG5828evVqo1evXkZYWJgREBBgNGzY0Pjb3/5m/Pjjjxf8rBebVurqq682QkNDjYyMDMMwDOPNN980GjdubPj7+xvNmjUz3n77bWPixIkl/r137NhhdO/e3QgMDDQkOf07pqamGoMHDzYSEhIMX19fIzY21rjuuuuMOXPmXDCrYZz/3/DkyZPGiBEjjLi4OMPX19do3Lix8cILLxh2u91pPV1kOqyybu9clzI1V/GP1Wo1wsPDjXbt2hnDhg0779Rh+fn5xrRp04yWLVsa/v7+RkREhNG+fXtj0qRJTt+90v7bmTt3rtGgQQPDZrM5fdfWr19vXHnllUZgYKARFxdnPPHEE8by5cvP+30s737483cJqMoshuHm/yUEAAAAKgljZgEAAOC1GDMLAPBKBQUFFz2RLSQkxHFBBG9w6tSpi843W7NmzQo7IQ7wRpRZAIBXOnjw4EUvyjBx4kS3nkRW0RYtWnTRK/etXr1aV199deUEArwAY2YBAF7p9OnTWrdu3QXXadCggRo0aFBJiS7dkSNHtG3btguu0759e0VERFRSIsDzUWYBAADgtTgBDAAAAF6r2o2ZtdvtOnz4sGrUqOHyJQoBAABQ8QzD0MmTJxUXF+e4gM75VLsye/jwYSUkJJgdAwAAABdx8OBB1alT54LrVLsyW3zpy4MHDyo0NNTkNAAAAPizrKwsJSQkXPCS5cWqXZktHloQGhpKmQUAAPBgZRkSyglgAAAA8FqUWQAAAHgtyiwAAAC8FmUWAAAAXosyCwAAAK9FmQUAAIDXoswCAADAa1FmAQAA4LUoswAAAPBalFkAAAB4LcosAAAAvBZlFgAAAF6LMgsAAACvRZkFAACA16LMAgAAwGuZWma//vpr9enTR3FxcbJYLPrkk08u+po1a9bo8ssvl7+/vxo1aqR58+ZVeE4AAAB4JlPLbE5Ojtq0aaOZM2eWaf29e/eqd+/euuaaa5SUlKThw4froYce0vLlyys4KQAAADyRj5kbv+mmm3TTTTeVef3Zs2erfv36eumllyRJzZs317p16/Tyyy+rV69eFRXz0pzOkrb8V2rxF6lGrNlpAAAAqhRTy6yrNmzYoJ49ezot69Wrl4YPH37e1+Tl5SkvL8/xOCsrq6Lile79+6Q9a5SaekRH249weioi2E/x4YGVmwcAAKAK8aoym5KSopiYGKdlMTExysrK0qlTpxQYWLIYTpkyRZMmTaqsiCUcb9JfNfeskTa9rb7fttPZc3Z5oK9NK0f1oNACAACUk1eV2fIYO3asRo4c6XiclZWlhISEStv+kbjrVWCEKcaSoa9uPqmshn0kSclp2Rq+KEkncvIpswAAAOXkVWU2NjZWqampTstSU1MVGhpa6lFZSfL395e/v39lxCuVYfPTwoLrNMznI9VNflfqfq9pWQAAAKoar5pntnPnzlq1apXTshUrVqhz584mJSqbhWevlWGxSQe+lVK2mh0HAACgyjC1zGZnZyspKUlJSUmSCqfeSkpK0oEDByQVDhG4//77Hes/8sgj2rNnj5544gnt2LFDs2bN0vvvv68RI0aU9vYeI1U1lZV4Y+GDH+aaGwYAAKAKMbXM/vjjj2rXrp3atWsnSRo5cqTatWunCRMmSJKOHDniKLaSVL9+fS1dulQrVqxQmzZt9NJLL+mNN97w3Gm5znGs5d8K7/z8vnTqhKlZAAAAqgpTx8xeffXVMgzjvM+XdnWvq6++Wj/99FMFpqoYubEdpeiWUto2KWmhVJexswAAAJfKq8bMejWLRer4cOH9jXMlw25uHgAAgCqAMluZLrtT8g+TTuxVyO9fm50GAADA61FmK5NfsNTuHklSzW3zzM0CAABQBVBmK9sVD0mSahxcrbqW1IusDAAAgAuhzFa2Wg2lRj1lkaF7bSvNTgMAAODVvOoKYFVGx79LySt1p22Nvj6SXuLpiGA/LnELAABQBpRZMzTqqYLgGIXnpOr/Lf5YG43mTk8H+tq0clQPCi0AAMBFUGbNYLXJVrejtP0z/d9VBUpv083xVHJatoYvStKJnHzKLAAAwEVQZs0S30Ha/plis7cpNj7M7DQAAABeiRPAzBLfvvD2903m5gAAAPBilFmzxLWTLFYp63fpZIrZaQAAALwSZdYs/iFSVNGJX7//aG4WAAAAL0WZNVOdoqEGhxhqAAAAUB6UWTMVj5s9xJFZAACA8qDMmim+Q+HtoZ8ke4G5WQAAALwQZdZM0c0l32Ap/6SU/pvZaQAAALwOZdZMVlvhrAYSQw0AAADKgTJrtvjLC2+Z0QAAAMBllFmz1SkeN0uZBQAAcBVl1mzFJ4Gl/irl55qbBQAAwMtQZs0WGieFxEpGgXRki9lpAAAAvApl1mwWC0MNAAAAyoky6wniuRIYAABAeVBmPUHxkdnfKbMAAACuoMx6gtptJVmkzAOy5R41Ow0AAIDXoMx6goBQKaqZJCno6E8mhwEAAPAelFlPUadw3GxgGjMaAAAAlBVl1lMUzTfLkVkAAICyo8x6ivg/jsxaZDc5DAAAgHegzHqK6BaSb5BsZ06qgeWI2WkAAAC8AmXWU9h8imY1kNpadpubBQAAwEv4mB0A56jTXjrwrdpak5Wclu30VESwn+LDA00KBgAA4Jkos56kaNxsG9te3booyempQF+bVo7qQaEFAAA4B2XWk8S0liS18j2sJQ93kSyFo0CS07I1fFGSTuTkU2YBAADOQZn1JDXrSzZ/Wc+eUqugjMLHAAAAOC9OAPMkVpsU1bTwftp2c7MAAAB4Acqsp4luUXib9qu5OQAAALwAZdbTRDcvvOXILAAAwEVRZj2N48gsZRYAAOBiKLOepvjIbPouqeCMuVkAAAA8HGXW04TVkfxqSPYz0jGuBAYAAHAhlFlPY7GcM26Wk8AAAAAuhDLriTgJDAAAoEwos56I6bkAAADKhDLriRhmAAAAUCaUWU9UfGT2+F4pP9fcLAAAAB6MMuuJQqKkoEhJhpS+0+w0AAAAHosy66k4CQwAAOCiKLOeipPAAAAALooy66k4MgsAAHBRlFlPRZkFAAC4KMqsp4pqVnibdUjWvExzswAAAHgoyqynCgyXQuMlSQEndpmbBQAAwENRZj1Z0VAD/xNMzwUAAFAayqwnKyqzAcc5MgsAAFAayqwnK5qeiyOzAAAApaPMejLHkdkdkgxzswAAAHggyqwni2wqySKfvBOKVJbZaQAAADwOZdaT+QVJNetLkppYD5ocBgAAwPNQZj1d0bjZphbKLAAAwJ9RZj1d0bjZJpbfTQ4CAADgeSiznq6ozDZlmAEAAEAJlFlPVzTMoInld8lgRgMAAIBzUWY9Xc2Gslt9FWI5Ld/sQ2anAQAA8Cg+ZgfARfj4KT+sgQJO7NTxvVu0tUYdp6cjgv0UHx5oUjgAAABzUWa9gD2qmXRip9auW6vZa8Ocngv0tWnlqB4UWgAAUC1RZr1AUFwLadf/9I+Wdt3So5tjeXJatoYvStKJnHzKLAAAqJYos94gsrEkKSJ3nyLiwy6yMgAAQPXBCWDeILJp4W36LmY0AAAAOAdl1hvUaijJIp3OkHKOmp0GAADAY1BmvYFvoBRet/B++i5zswAAAHgQyqy3iGxSeEuZBQAAcKDMeouo4nGzv5mbAwAAwINQZr1F0YwGHJkFAAD4A2XWWxQPMzhKmQUAAChGmfUWxWU284CUn2tuFgAAAA9BmfUWQbWkwIjC+8eSzc0CAADgIUwvszNnzlRiYqICAgLUqVMnbdy48YLrz5gxQ02bNlVgYKASEhI0YsQInT59upLSmshicb54AgAAAMwts4sWLdLIkSM1ceJEbd68WW3atFGvXr2UlpZW6voLFy7UmDFjNHHiRG3fvl1vvvmmFi1apKeeeqqSk5vEcRIYMxoAAABIJpfZ6dOn6+GHH9agQYPUokULzZ49W0FBQXrrrbdKXf/bb79V165dNWDAACUmJuqGG27Q3XfffdGjuVUGc80CAAA4Ma3M5ufna9OmTerZs+cfYaxW9ezZUxs2bCj1NV26dNGmTZsc5XXPnj36/PPPdfPNN593O3l5ecrKynL68VqUWQAAACc+Zm04PT1dBQUFiomJcVoeExOjHTt2lPqaAQMGKD09Xd26dZNhGDp79qweeeSRCw4zmDJliiZNmuTW7KYpHmZwLFmyF5ibBQAAwAOYfgKYK9asWaPJkydr1qxZ2rx5sz766CMtXbpUzz777HlfM3bsWGVmZjp+Dh48WImJ3SwiUbL5SWdPS5le/DkAAADcxLQjs5GRkbLZbEpNTXVanpqaqtjY2FJfM378eN1333166KGHJEmtW7dWTk6O/v73v2vcuHGyWkt2c39/f/n7+7v/A5jBapNqNZLSfi08CSyoo9mJAAAATGXakVk/Pz+1b99eq1atciyz2+1atWqVOnfuXOprcnNzSxRWm80mSTIMo+LCehIuawsAAOBg2pFZSRo5cqQGDhyoDh06qGPHjpoxY4ZycnI0aNAgSdL999+v+Ph4TZkyRZLUp08fTZ8+Xe3atVOnTp2UnJys8ePHq0+fPo5SW+U5Lmu7U6prbhQAAACzmVpm+/fvr6NHj2rChAlKSUlR27ZttWzZMsdJYQcOHHA6Evv000/LYrHo6aef1qFDhxQVFaU+ffro+eefN+sjVD7HjAbMNQsAAGBqmZWkIUOGaMiQIaU+t2bNGqfHPj4+mjhxoiZOnFgJyTwU03MBAAA4eNVsBlDhCWCSlJsu2+kT5mYBAAAwGWXW2/iHSKF1JEl+mbtNDgMAAGAuyqw3KprRIOBEsslBAAAAzEWZ9UZRTSVxZBYAAIAy642Kjsz6Z3BkFgAAVG+UWW9UNKOBfwZHZgEAQPVGmfVGRWXW7+QB+emMyWEAAADMQ5n1RiExkn+oLIZd9SypZqcBAAAwDWXWG1ksjqOzjSyHTA4DAABgHsqstyoqsw0th00OAgAAYB7KrLcqmtGgoZUyCwAAqi/KrLfiyCwAAIB8zA6AcjqnzK5IPVni6YhgP8WHB1Z2KgAAgEpFmfVWNevLsPoo2J6nqe9/pRTVcno60NemlaN6UGgBAECVRpn1VjZfWSISpWPJ+u9ttZQT383xVHJatoYvStKJnHzKLAAAqNIos96sVmPpWLLq67AUH2Z2GgAAgErHCWDerFbDwttjXNYWAABUT5RZb1Y0PZeO/WZuDgAAAJNQZr1ZraIym06ZBQAA1RNl1pvValR4m3FAOptnbhYAAAATUGa9WUi05B8qyZCO7zE7DQAAQKWjzHozi+WPo7MMNQAAANUQZdbbcRIYAACoxiiz3q74yCzTcwEAgGqIMuvtGGYAAACqMcqst2OYAQAAqMYos96uZtFVwE6dkHKPm5sFAACgklFmvZ1fkBRap/A+Qw0AAEA1Q5mtCiKLTwKjzAIAgOqFMlsVcFlbAABQTVFmqwLH9FzJ5uYAAACoZJTZqiCSMgsAAKonymxVUDzM4PgeyV5gbhYAAIBKRJmtCsISJJu/VJAvZRwwOw0AAEClocxWBVarVKtovlmGGgAAgGqEMltVcFlbAABQDVFmqwouawsAAKohymxVUXwSGMMMAABANUKZrSocwwwoswAAoPqgzFYVxXPNnjws65kcc7MAAABUEspsVREYIQVFSpL8MveaHAYAAKByUGarkqKhBv6Ze0wOAgAAUDkos1VJ0VADv4zdJgcBAACoHJTZqqRoRgOOzAIAgOqCMluVRFJmAQBA9UKZrUqKxsz6Ze6RZJibBQAAoBJQZquSiPqSxSbbmRxFK8PsNAAAABWOMluV+PhJEfUkSQ2sR0wOAwAAUPEos1VN0UlgDSyUWQAAUPX5mB0AblarkfTbcjWwHFZyWrbTUxHBfooPDzQpGAAAgPtRZquaorlmG9tSNHBRktNTgb42rRzVg0ILAACqDMpsVVM0zKBrRIaW3NHNsTg5LVvDFyXpRE4+ZRYAAFQZlNmqpmh6Lp/MA2oVE1h4UhgAAEAVdUkngJ0+fdpdOeAuNWIlvxDJKJBO7DU7DQAAQIVyucza7XY9++yzio+PV0hIiPbsKbza1Pjx4/Xmm2+6PSBcZLE4js7qWLK5WQAAACqYy2X2ueee07x58/Tvf/9bfn5//Am7VatWeuONN9waDuVUXGbTfzM3BwAAQAVzucy+8847mjNnju655x7ZbDbH8jZt2mjHjh1uDYdyiiw8CUzHKLMAAKBqc7nMHjp0SI0aNSqx3G6368yZM24JhUvkGGaw29wcAAAAFczlMtuiRQt98803JZZ/+OGHateunVtC4RIxzAAAAFQTLk/NNWHCBA0cOFCHDh2S3W7XRx99pJ07d+qdd97RkiVLKiIjXFVcZnPTpVMnpMAIc/MAAABUEJePzP7lL3/RZ599ppUrVyo4OFgTJkzQ9u3b9dlnn+n666+viIxwlX+IVCOu8D5DDQAAQBVWrosmXHXVVVqxYoW7s8CdajWUTh4uHGpQp4PZaQAAACqEy0dmGzRooGPHjpVYnpGRoQYNGrglFNyAGQ0AAEA14HKZ3bdvnwoKCkosz8vL06FDh9wSCm5Qq7jMcuEEAABQdZV5mMGnn37quL98+XKFhYU5HhcUFGjVqlVKTEx0azhcAseMBpRZAABQdZW5zPbt21eSZLFYNHDgQKfnfH19lZiYqJdeesmt4XAJIovK7PHdkt1ubhYAAIAKUuYyay8qRPXr19cPP/ygyMjICgsFNwivJ1l9pbOnpazfJYVd9CUAAADexuUxs3v37qXIegOrTapZdEIeF08AAABVVLmm5srJydHatWt14MAB5efnOz03dOhQtwSDG0Q2ltJ3Fp4ExvRcAACgCnK5zP7000+6+eablZubq5ycHNWsWVPp6ekKCgpSdHQ0ZdaTFJ8EdixZqmNuFAAAgIrg8jCDESNGqE+fPjpx4oQCAwP13Xffaf/+/Wrfvr1efPHFisiI8nLMaMAwAwAAUDW5XGaTkpI0atQoWa1W2Ww25eXlKSEhQf/+97/11FNPVURGlFckc80CAICqzeUy6+vrK6u18GXR0dE6cOCAJCksLEwHDx50bzpcmuILJ2QelOXsaXOzAAAAVACXx8y2a9dOP/zwgxo3bqwePXpowoQJSk9P14IFC9SqVauKyIjyCqopBYRLpzPkl7nX7DQAAABu5/KR2cmTJ6t27dqSpOeff14RERF69NFHdfToUf3nP/9xe0BcAovFMdTAP3O3yWEAAADcz+Ujsx06/DHFU3R0tJYtW+bWQHCzWo2l33+Qf+ZeSe3MTgMAAOBWLh+ZPZ/NmzfrlltucdfbwV1qNZQk+WVwZBYAAFQ9LpXZ5cuX6/HHH9dTTz2lPXv2SJJ27Nihvn376oorrnBc8tYVM2fOVGJiogICAtSpUydt3LjxgutnZGRo8ODBql27tvz9/dWkSRN9/vnnLm+32nAMM9hjchAAAAD3K/MwgzfffFMPP/ywatasqRMnTuiNN97Q9OnT9dhjj6l///7aunWrmjdv7tLGFy1apJEjR2r27Nnq1KmTZsyYoV69emnnzp2Kjo4usX5+fr6uv/56RUdH68MPP1R8fLz279+v8PBwl7ZbrdQ6t8wa5mYBAABwszKX2VdeeUXTpk3T6NGjtXjxYt1xxx2aNWuWfvnlF9WpU77LS02fPl0PP/ywBg0aJEmaPXu2li5dqrfeektjxowpsf5bb72l48eP69tvv5Wvr68kKTExsVzbrjZq1pdkkS0/S7WUZXYaAAAAtyrzMIPdu3frjjvukCTddttt8vHx0QsvvFDuIpufn69NmzapZ8+ef4SxWtWzZ09t2LCh1Nd8+umn6ty5swYPHqyYmBi1atVKkydPVkFBwXm3k5eXp6ysLKefasU3UApPkCQ1sBwxOQwAAIB7lbnMnjp1SkFBQZIki8Uif39/xxRd5ZGenq6CggLFxMQ4LY+JiVFKSkqpr9mzZ48+/PBDFRQU6PPPP9f48eP10ksv6bnnnjvvdqZMmaKwsDDHT0JCQrkze62iy9rWt1JmAQBA1eLS1FxvvPGGQkJCJElnz57VvHnzFBkZ6bTO0KFD3ZfuT+x2u6KjozVnzhzZbDa1b99ehw4d0gsvvKCJEyeW+pqxY8dq5MiRjsdZWVnVr9DWaizt/oojswAAoMopc5mtW7eu5s6d63gcGxurBQsWOK1jsVjKXGYjIyNls9mUmprqtDw1NVWxsbGlvqZ27dry9fWVzWZzLGvevLlSUlKUn58vPz+/Eq/x9/eXv79/mTJVWUUzGjSkzAIAgCqmzGV23759bt2wn5+f2rdvr1WrVqlv376SCo+8rlq1SkOGDCn1NV27dtXChQtlt9tltRaOkNi1a5dq165dapFFkaK5Zutbjui0yVEAAADcyW0XTSiPkSNHau7cuZo/f762b9+uRx99VDk5OY7ZDe6//36NHTvWsf6jjz6q48ePa9iwYdq1a5eWLl2qyZMna/DgwWZ9BO9QND1XXUuqZD9rchgAAAD3cflytu7Uv39/HT16VBMmTFBKSoratm2rZcuWOU4KO3DggOMIrCQlJCRo+fLlGjFihC677DLFx8dr2LBhevLJJ836CN4hNF52W4D8Ck7L7+RBSbXMTgQAAOAWppZZSRoyZMh5hxWsWbOmxLLOnTvru+++q+BUVYzVqryw+go8vl3H9m/T1rD6Tk9HBPspPjzQpHAAAADlZ3qZReUwajWWjm/XqnXr9cbacKfnAn1tWjmqB4UWAAB4HcpsNREU11z67VP9s1WB+nbv5lienJat4YuSdCInnzILAAC8TrnK7O7du/X2229r9+7deuWVVxQdHa0vvvhCdevWVcuWLd2dEe5QdBJYzVMHVDM+zOQwAAAA7uHybAZr165V69at9f333+ujjz5Sdna2JGnLli3nvXABPEDRXLNK32VuDgAAADdyucyOGTNGzz33nFasWOE0t+u1117LiVmerOiStspNl3KPm5sFAADATVwus7/88ov++te/llgeHR2t9PR0t4RCBfAPkULrFN4/lmxuFgAAADdxucyGh4fryJGSl0X96aefFB8f75ZQqCCRRUdnGWoAAACqCJfL7F133aUnn3xSKSkpslgsstvtWr9+vR5//HHdf//9FZER7hLZpPCWMgsAAKoIl8vs5MmT1axZMyUkJCg7O1stWrRQ9+7d1aVLFz399NMVkRHu4iizv5mbAwAAwE1cnprLz89Pc+fO1fjx47V161ZlZ2erXbt2aty4cUXkgzs5ZjSgzAIAgKrB5TK7bt06devWTXXr1lXdunUrIhMqStFcszqxVyo4I9l8zc0DAABwiVweZnDttdeqfv36euqpp/Trr79WRCZUlNA4yTdYsp+Vju81Ow0AAMAlc7nMHj58WKNGjdLatWvVqlUrtW3bVi+88IJ+//33isgHd7JY/hhqcIyhBgAAwPu5XGYjIyM1ZMgQrV+/Xrt379Ydd9yh+fPnKzExUddee21FZIQ7MaMBAACoQlwus+eqX7++xowZo6lTp6p169Zau3atu3KhonASGAAAqELKXWbXr1+vf/7zn6pdu7YGDBigVq1aaenSpe7MhorgKLMcmQUAAN7P5dkMxo4dq/fee0+HDx/W9ddfr1deeUV/+ctfFBQUVBH54G7nzjVrGOZmAQAAuEQul9mvv/5ao0eP1p133qnIyMiKyISKVLOBJIt0OkPKSZfkZ3IgAACA8nO5zK5fv74icqCy+AZK4XWljP2FQw18W5mdCAAAoNzKVGY//fRT3XTTTfL19dWnn356wXVvvfVWtwRDBYpsUlhmj/0mxVJmAQCA9ypTme3bt69SUlIUHR2tvn37nnc9i8WigoICd2VDRYlsIiWvKBw3G2t2GAAAgPIrU5m12+2l3oeXimxUeMuMBgAAwMu5PDXXO++8o7y8vBLL8/Pz9c4777glFCoYF04AAABVhMtldtCgQcrMzCyx/OTJkxo0aJBbQqGCFZfZjAOynD1tbhYAAIBL4HKZNQxDFoulxPLff/9dYWFhbgmFChYcJQWESYZdfln7zU4DAABQbmWemqtdu3ayWCyyWCy67rrr5OPzx0sLCgq0d+9e3XjjjRUSEm5msUi1GkuHfpR/RrIk/icEAAB4pzKX2eJZDJKSktSrVy+FhIQ4nvPz81NiYqJuv/12twdEBYlsUlhmM/dIamd2GgAAgHIpc5mdOHGiJCkxMVH9+/dXQEBAhYVCJYhsLElFR2YpswAAwDu5fAWwgQMHVkQOVLaik8D8MnabHAQAAKD8ylRma9asqV27dikyMlIRERGlngBW7Pjx424LhwpUfGQ2c7ckw9wsAAAA5VSmMvvyyy+rRo0ajvsXKrPwEhH1JYtNtjM5ilaG2WkAAADKpUxl9tyhBX/7298qKgsqk4+fVLO+dCxZDa2HzU4DAABQLi7PM7t582b98ssvjsf/+9//1LdvXz311FPKz893azhUsFqFQw0aWiizAADAO7lcZv/xj39o167Cy6Du2bNH/fv3V1BQkD744AM98cQTbg+IChRJmQUAAN7N5TK7a9cutW3bVpL0wQcfqEePHlq4cKHmzZunxYsXuzsfKlLRjAaUWQAA4K1cnprLMAzZ7XZJ0sqVK3XLLbdIkhISEpSenu7edKhYxWXWelg/pGWXeDoi2E/x4YGVnQoAAKDMXC6zHTp00HPPPaeePXtq7dq1ev311yVJe/fuVUxMjNsDogIVDTOItxzT9Yu+U66cL4QR6GvTylE9KLQAAMBjuVxmZ8yYoXvuuUeffPKJxo0bp0aNGkmSPvzwQ3Xp0sXtAVGBgmpKwVFSzlF9ele0Tkdd5ngqOS1bwxcl6UROPmUWAAB4LJfL7GWXXeY0m0GxF154QTabzS2hUImimkk5R9XIckiKv8rsNAAAAC5xucwW27Rpk7Zv3y5JatGihS6//HK3hUIlimwi7ftGOrrT7CQAAAAuc7nMpqWlqX///lq7dq3Cw8MlSRkZGbrmmmv03nvvKSoqyt0ZUZGimhbeUmYBAIAXcnlqrscee0zZ2dnatm2bjh8/ruPHj2vr1q3KysrS0KFDKyIjKlJxmU2nzAIAAO/j8pHZZcuWaeXKlWrevLljWYsWLTRz5kzdcMMNbg2HShBZVGaP75XO5kk+/ubmAQAAcIHLR2btdrt8fX1LLPf19XXMPwsvUiNW8g+TjALp2G6z0wAAALjE5TJ77bXXatiwYTp8+I+rRh06dEgjRozQdddd59ZwqAQWixRVePEEhhoAAABv43KZfe2115SVlaXExEQ1bNhQDRs2VP369ZWVlaVXX321IjKionESGAAA8FIuj5lNSEjQ5s2btWrVKsfUXM2bN1fPnj3dHg6VJJIyCwAAvJNLZXbRokX69NNPlZ+fr+uuu06PPfZYReVCZeLILAAA8FJlLrOvv/66Bg8erMaNGyswMFAfffSRdu/erRdeeKEi86EyFJfZY8lSwVnJVu5raQAAAFSqMo+Zfe211zRx4kTt3LlTSUlJmj9/vmbNmlWR2VBZwupKPoFSQZ6Usd/sNAAAAGVW5jK7Z88eDRw40PF4wIABOnv2rI4cOVIhwVCJrFYpsnHhfYYaAAAAL1LmMpuXl6fg4OA/Xmi1ys/PT6dOnaqQYKhkjnGzO8zNAQAA4AKXBkeOHz9eQUFBjsf5+fl6/vnnFRYW5lg2ffp096VD5XFc1naXuTkAAABcUOYy2717d+3c6fwn6C5dumjPnj2OxxaLxX3JULmYngsAAHihMpfZNWvWVGAMmO7cI7OGYW4WAACAMnL5CmCoomo2kKw+Un62lHXI7DQAAABlQplFIZuvVLNh4X1OAgMAAF6CMos/OGY04CQwAADgHSiz+APTcwEAAC9DmcUfopoV3jI9FwAA8BLlKrPffPON7r33XnXu3FmHDhWeLLRgwQKtW7fOreFQySKbFN4e3cGMBgAAwCu4XGYXL16sXr16KTAwUD/99JPy8vIkSZmZmZo8ebLbA6ISRTaWZJFOnZDt9DGz0wAAAFyUy2X2ueee0+zZszV37lz5+vo6lnft2lWbN292azhUMt9AKaKeJCngxG8mhwEAALg4l8vszp071b179xLLw8LClJGR4Y5MMFPRlcD8M5JNDgIAAHBxLpfZ2NhYJSeXLDrr1q1TgwYN3BIKJooqLrMcmQUAAJ7P5TL78MMPa9iwYfr+++9lsVh0+PBhvfvuu3r88cf16KOPVkRGVKbiMsswAwAA4AV8XH3BmDFjZLfbdd111yk3N1fdu3eXv7+/Hn/8cT322GMVkRGVqWh6LoYZAAAAb+BymbVYLBo3bpxGjx6t5ORkZWdnq0WLFgoJCamIfKhskY0lSb65qaqhXJPDAAAAXJjLZbaYn5+fWrRo4c4s8AQBYVKNOOnkYTWyHDI7DQAAwAW5XGavueYaWSyW8z7/1VdfXVIgeICoJoVl1kqZBQAAns3lMtu2bVunx2fOnFFSUpK2bt2qgQMHuisXzBTZVNqzRo0th5Sclu30VESwn+LDA00KBgAA4MzlMvvyyy+XuvyZZ55RdnZ2qc/By0QXngTWzHZI9y9Kcnoq0NemlaN6UGgBAIBHKPeY2T+799571bFjR7344ovuekuYJbpwLHSXGmla8vdujsXJadkavihJJ3LyKbMAAMAjuK3MbtiwQQEBAe56O5gpurkkySf7sFrVNKTAcHPzAAAAnIfLZfa2225zemwYho4cOaIff/xR48ePd1swmCggTAqtI2X9Lh3dIdW90uxEAAAApXK5zIaFhTk9tlqtatq0qf71r3/phhtucFswmCy6eWGZTd1GmQUAAB7LpTJbUFCgQYMGqXXr1oqIiKioTPAE0c2l5BVS2nazkwAAAJyX1ZWVbTabbrjhBmVkZLg1xMyZM5WYmKiAgAB16tRJGzduLNPr3nvvPVksFvXt29eteSDHSWCUWQAA4MlcKrOS1KpVK+3Zs8dtARYtWqSRI0dq4sSJ2rx5s9q0aaNevXopLS3tgq/bt2+fHn/8cV111VVuy4JzxBSX2V8lwzA3CwAAwHm4XGafe+45Pf7441qyZImOHDmirKwspx9XTZ8+XQ8//LAGDRqkFi1aaPbs2QoKCtJbb7113tcUFBTonnvu0aRJk9SgQQOXt4kyiGwiWazSqeNS9oX/xwIAAMAsZS6z//rXv5STk6Obb75ZW7Zs0a233qo6deooIiJCERERCg8Pd3kcbX5+vjZt2qSePXv+EchqVc+ePbVhw4YLZomOjtaDDz540W3k5eVdcuGulnwDpZpF/6OQts3cLAAAAOdR5hPAJk2apEceeUSrV69228bT09NVUFCgmJgYp+UxMTHasWNHqa9Zt26d3nzzTSUlJZVpG1OmTNGkSZMuNWr1FN1cOpZcOG624bVmpwEAACihzGXWKBo32aNHjwoLczEnT57Ufffdp7lz5yoyMrJMrxk7dqxGjhzpeJyVlaWEhISKili1RLeQtn9WOG4WAADAA7k0NZfFYnHrxiMjI2Wz2ZSamuq0PDU1VbGxsSXW3717t/bt26c+ffo4ltntdkmSj4+Pdu7cqYYNGzq9xt/fX/7+/m7NXW0wowEAAPBwLpXZJk2aXLTQHj9+vMzv5+fnp/bt22vVqlWO6bXsdrtWrVqlIUOGlFi/WbNm+uWXX5yWPf300zp58qReeeUVjri6m6PM7pCK/qcBAADAk7hUZidNmlTiCmCXauTIkRo4cKA6dOigjh07asaMGcrJydGgQYMkSffff7/i4+M1ZcoUBQQEqFWrVk6vDw8Pl6QSy+EGNRtINj/pTI6UsV9STbMTAQAAOHGpzN51112Kjo52a4D+/fvr6NGjmjBhglJSUtS2bVstW7bMcVLYgQMHZLW6PIMY3MHmI0U2lVJ/KRxqUKOr2YkAAACclLnMunu87LmGDBlS6rACSVqzZs0FXztv3jz3B8IfYloUldlfKbMAAMDjlPmQp8FVoKqn6OaFt5wEBgAAPFCZj8zaOQGoemJGAwAA4MEYjIoLKz4ym75Lsp8xNwsAAMCfUGZxYWEJkl8NyX5G/hl7zE4DAADghDKLC7NYHEdn/U/sNDkMAACAM8osLq6ozAac2GVyEAAAAGeUWVxc0UlgAcc5MgsAADwLZRYX5xhmsMPkIAAAAM4os7i4oiOzflkHFKjTJocBAAD4A2UWFxcSJQVHySJDjSyHzU4DAADgQJlF2RQNNWhqPWhyEAAAgD9QZlE2RUMNmlooswAAwHNQZlE2xUdmKbMAAMCDUGZRNkVHZptYfzc5CAAAwB8osyibqGaSpFjLCdlOZ5ibBQAAoIiP2QHgJQJClR9SR37Zvyt9z0/aGhDu9HREsJ/iwwPNyQYAAKotyizKrCC6pZT9u75as1Jvr/J1ei7Q16aVo3pQaAEAQKWizKLMAhPaSXuWa2jL07q9RzfH8uS0bA1flKQTOfmUWQAAUKkosyi72NaSpIisHYqIDzM5DAAAACeAwRVFZVZpO6Sz+eZmAQAAEGUWrgivKwWESfYz0tEdZqcBAACgzMIFFosUe1nh/ZRfzM0CAAAgyixcVTzUIOVnc3MAAACIMgtXcWQWAAB4EMosXOM4MvuLZBjmZgEAANUeZRauiWoq2fykvCzpxD6z0wAAgGqOMgvX2Hyl6OaF9xlqAAAATEaZhevOHWoAAABgIsosXOc4CYwZDQAAgLkos3AdMxoAAAAPQZmF62JaFt5mHZJyjpmbBQAAVGuUWbguIFSq2aDwPkMNAACAiSizKB9OAgMAAB6AMovyocwCAAAPQJlF+cS2KbxlmAEAADARZRblU3xkNn2XLGdPm5sFAABUW5RZlE+NWCkoUjLsCji+w+w0AACgmqLMonwsFql24XyzAce2mRwGAABUV5RZlF/RUIOAY7+aHAQAAFRXlFmUX9GVwAI5MgsAAExCmUX5FZXZgOPbZZXd5DAAAKA6osyi/Go1lHwCZT17SomWFLPTAACAaogyi/Kz2qSYlpKklpZ95mYBAADVEmUWl6ZoRoMW1v0mBwEAANWRj9kB4OWKZjRoYdmv5LRsp6cigv0UHx5oRioAAFBNUGZxaYoua9vauleXL/pJksXxVKCvTStH9aDQAgCACkOZxaWJaSlZfVXTflLLB9bTmdC6kqTktGwNX5SkEzn5lFkAAFBhKLO4NL4BhUMNDm9W04JdUnxrsxMBAIBqhBPAcOni2xfe/r7J3BwAAKDaoczi0hWX2UOUWQAAULkos7h0dToU3h7ZIhWcMTcLAACoViizuHQ1G0r+YdLZU1Lar2anAQAA1QhlFpfOapXi2xXeZ6gBAACoRJRZuEd80VADyiwAAKhElFm4BzMaAAAAE1Bm4R7FZfboDinvpLlZAABAtUGZhXvUiJHCEiQZ0uEks9MAAIBqgjIL94m/vPCWcbMAAKCSUGbhPo6LJ/xobg4AAFBtUGbhPo4ZDTabmwMAAFQblFm4T+02ksUqZR2ST06K2WkAAEA1QJmF+/iHSFHNJUmBR7eYHAYAAFQHlFm4V53CcbNBR5PMzQEAAKoFyizcq+gksMC0JHNzAACAaoEyC/cqLrNHf5ZFdpPDAACAqo4yC/eKai75Bsl25qQaWI6YnQYAAFRxlFm4l81Hqt1WktTWstvcLAAAoMqjzML9iq4E1taabHIQAABQ1VFm4X51Ci+e0MbKkVkAAFCxKLNwv6KTwJpbDshy9rTJYQAAQFVGmYX7hSXobGCkfC0FCji2zew0AACgCvMxOwCqIItFuVFtFHpglXL3fK+tMe2dno4I9lN8eKBJ4QAAQFVCmUWFMOp0kg6s0sEtX+mfP17m9Fygr00rR/Wg0AIAgEtGmUWFCGvWQ/p2sm4I3q0l93aVLBZJUnJatoYvStKJnHzKLAAAuGSUWVSMuHaST4B8Th9TK/9UKaqp2YkAAEAVxAlgqBg+/lKdKwrv719vbhYAAFBlUWZRcep1Lbzd/625OQAAQJXlEWV25syZSkxMVEBAgDp16qSNGzeed925c+fqqquuUkREhCIiItSzZ88Lrg8TJRaV2X3rJcMwNwsAAKiSTC+zixYt0siRIzVx4kRt3rxZbdq0Ua9evZSWllbq+mvWrNHdd9+t1atXa8OGDUpISNANN9ygQ4cOVXJyXFR8B8nqK508LJ3YZ3YaAABQBZleZqdPn66HH35YgwYNUosWLTR79mwFBQXprbfeKnX9d999V//85z/Vtm1bNWvWTG+88YbsdrtWrVpVyclxUX5BjquBMW4WAABUBFPLbH5+vjZt2qSePXs6llmtVvXs2VMbNmwo03vk5ubqzJkzqlmzZqnP5+XlKSsry+kHlahel8Jbxs0CAIAKYGqZTU9PV0FBgWJiYpyWx8TEKCUlpUzv8eSTTyouLs6pEJ9rypQpCgsLc/wkJCRccm64oPgksH3rzM0BAACqJNOHGVyKqVOn6r333tPHH3+sgICAUtcZO3asMjMzHT8HDx6s5JTVXN1OksUqZeyXMn83Ow0AAKhiTC2zkZGRstlsSk1NdVqempqq2NjYC772xRdf1NSpU/Xll1/qsssuO+96/v7+Cg0NdfpBJfKvIdVuU3ifoQYAAMDNTC2zfn5+at++vdPJW8Unc3Xu3Pm8r/v3v/+tZ599VsuWLVOHDh0qIyouhWO+WU4CAwAA7mX6MIORI0dq7ty5mj9/vrZv365HH31UOTk5GjRokCTp/vvv19ixYx3rT5s2TePHj9dbb72lxMREpaSkKCUlRdnZ2WZ9BFwMF08AAAAVxMfsAP3799fRo0c1YcIEpaSkqG3btlq2bJnjpLADBw7Iav2jc7/++uvKz89Xv379nN5n4sSJeuaZZyozOsqqXmdJFil9l2y5R81OAwAAqhDTy6wkDRkyREOGDCn1uTVr1jg93rdvX8UHgnsFRkgxLaXUrQpO2SgpzOxEAACgijB9mAGqiaKhBsFHvjc5CAAAqEoos6gcRRdPCEqhzAIAAPehzKJyFJXZgOM7FCZO1gMAAO5BmUXlCImWIpvIIkNXWHeanQYAAFQRlFlUnqKjs52s200OAgAAqgrKLCpPvW6SpI7WHSYHAQAAVYVHTM2FaqLoyGwry159cSilxNMRwX6KDw+s7FQAAMCLUWZRecLidTa8gXwy9uizT/6r5fYrnJ4O9LVp5ageFFoAAFBmlFlUKp+mvaTvX9fUVof1WI9ujuXJadkavihJJ3LyKbMAAKDMKLOoXE1ukL5/XRGH1yiidg3JyrBtAABQfjQJVK56XSXfYCk7VUrZYnYaAADg5SizqFw+/lLDawrv7/rS3CwAAMDrUWZR+Zr0Krz9bbm5OQAAgNejzKLyNb6h8PbQZin7qLlZAACAV6PMovLViJVqt5FkSMkrzE4DAAC8GGUW5mhcNNRg1zJzcwAAAK9GmYU5isfN7l4tFZwxNwsAAPBalFmYI+5yKShSysuSDmwwOw0AAPBSlFmYw2qVGl9feH8XsxoAAIDyoczCPI4puphvFgAAlA9lFuZpeK1k9ZHSd8kva5/ZaQAAgBeizMI8AWFS3c6SpJADX5kcBgAAeCPKLMxVdAGFGpRZAABQDpRZmKto3Gzwke8UpNMmhwEAAN6GMgtzRTaRwuvJas9XV+tWs9MAAAAv42N2AFRzFkvh0dmNc3SN9Sclp2U7PR0R7Kf48ECTwgEAAE9HmYX5mtwobZyjG20/qtOiH3XmnK9loK9NK0f1oNACAIBSUWZhvvo9pOBo1cxJ08pb83Wy3tWSpOS0bA1flKQTOfmUWQAAUCrGzMJ8Nh/psjslSfUOfqpW8WFqFR+mRtEhJgcDAACejjILz9DmrsLbXcukUyfMzQIAALwGZRaeIba1FNNKKsiXtn1sdhoAAOAlKLPwHMVHZ7e8Z24OAADgNSiz8Byt75AsVung99Kx3WanAQAAXoAyC89RI1ZqcE3h/Z8XmZsFAAB4BcosPEubuwtvt7wnGYa5WQAAgMejzMKzNOst+YVIGfsVlPqD2WkAAICHo8zCs/gFSS36SpLCf1tsbhYAAODxKLPwPG36S5LC9iyVv/JNDgMAADwZZRaep143KbSObPlZ6mndbHYaAADgwSiz8DxWq+Po7F9t35gcBgAAeDLKLDzTZYUXULjaukW2U+kmhwEAAJ7Kx+wAQKmimig3qq2CjibJvmmBtgaOcHo6IthP8eGBJoUDAACegjILj5XXbpCCvhym6F/fUref2ihPfo7nAn1tWjmqB4UWAIBqjjILjxXR6R6d3fBvRZ08pNXXHdLxFvdJkpLTsjV8UZJO5ORTZgEAqOYYMwvPZfOVT7dhkqS4bXPUKjZYreLD1Cg6xORgAADAU1Bm4dna3ScFRUoZ+6VtH5mdBgAAeBjKLDybX5B05SOF99e9LNnt5uYBAAAehTILz3fFw5JfDSntV+m35WanAQAAHoQyC88XGC5d8UDh/W+mS4ZhahwAAOA5KLPwDlcOlmz+0u8bFZTyvdlpAACAh6DMwjvUiJHa3StJivrpNZPDAAAAT0GZhffoOlSy2FTj0NdqZdljdhoAAOABKLPwHhGJUqvbJUmP+nxqbhYAAOARuAIYvEu3EdIv7+sm6w9at/1rbVV3p6cjgv24KhgAANUIZRbeJaaFclrereBt/1Xtr59U75WTlS9fx9OBvjatHNWDQgsAQDVBmYXXCe49WQV7V6px7iGt6/KT0tqPlCQlp2Vr+KIkncjJp8wCAFBNMGYW3ieopmy9X5AkRSfNVCvfw2oVH6ZG0SEmBwMAAJWNMgvv1KKv1PRmyX5G+vQxyV5gdiIAAGACyiy8k8Ui3fxi4WVuf/9B+uFNsxMBAAATUGbhvcLipeufKby/apJ8sw+ZGgcAAFQ+yiy8W/sHpLqdpfxsxa0bJ8kwOxEAAKhEzGYA72a1Sn3+T5rdVTUOfqW/WpsqOa2d0yrMPQsAQNVFmYX3i2oidX9CWv2cpvq+ofvfj9T3RnPH08w9CwBA1cUwA1QN3UZITXvL33JGC0NmaOWACC15rJtm9G+rU2cKdCIn3+yEAACgAlBmUTXYfKR+b0r1usp25qQaLR+oVoHHmXsWAIAqjjKLqsM3ULr7v1JMayknTVrwV/nkppmdCgAAVCDKLKqWgDDp3sVSRKJ0Yq/qLRuoGso1OxUAAKggnACGqqdGjHTfx9KbvRR4bJvm+r2kfYfalFiNWQ4AAPB+lFlUTTUbSPd+KPvbvXVl/nbtWdJHj50Zqm1GomMVZjkAAMD7McwAVVftNrLe/4nO1ohXA2uKPgt8Rt9e85uWDOnKLAcAAFQRlFlUbXU6yOfRdVLTm2W15ytuw0S1WjdYTUPPmp0MAAC4AWUWVV9QTemuhdKN0ySbn7RjiRp+fJOutP5qdjIAAHCJGDOL6sFika58RKrbSfrwAfkd36P3/J7T0Y9XaG/HocqJ61a4jjgxDAAAb0KZRfUS1076+1rlLHlKfr8sVFT694r6/B4l2Rtq5tm/aKX9cgX4+nJiGAAAXoIyi+onIFTB/V5TSsdh8v3+NUXs+K/aarfm+k1XVo1GevV4R/36Sw2daNDK8RKO1gIA4Jkos6i2Yus2luq+ImU/LX03S/rhDYWeTNY432Rp1UIlr4jTCnt7rShor50+TfXlqGsotAAAeBjKLBASJfWcKHUdJv38vk5vWyL/g+vVyHpYjayH9ajPZzphhOjMu5fpaFwbnarVSqcjWys/tK4iQgIouAAAmMgjyuzMmTP1wgsvKCUlRW3atNGrr76qjh07nnf9Dz74QOPHj9e+ffvUuHFjTZs2TTfffHMlJkaVFBgudfq7Ajr9XTqdKf22Qtr5hey7vlREfpZ09NvCnyJZRqD2Kl6+9ZrKElFP+TUSlB9aV4HRDRRbu67kF+w4qQwAAFQM08vsokWLNHLkSM2ePVudOnXSjBkz1KtXL+3cuVPR0dEl1v/222919913a8qUKbrlllu0cOFC9e3bV5s3b1arVq1K2QJQDgFhUut+Uut+shacUVryJp35/ScFpG9VYPovCji+Q6EFp9RGydKBZOlAybew2wJ0NjBSZwNr6WxgpAr8I1TgHyq7Xw0V+Bbd+ocqKCRMkRHhkm9QYQH2DSy87xMg+fhLVlulf3wAALyFxTAMw8wAnTp10hVXXKHXXntNkmS325WQkKDHHntMY8aMKbF+//79lZOToyVLljiWXXnllWrbtq1mz5590e1lZWUpLCxMmZmZCg0Ndd8HOY+thzJ1y6vrtOSxbmoVH1bh20MlKTgjHd2pY7/vUP7RvfI7eVB+Jw/KmrlflszfFWTJc9um7FZfGTY/GTb/wlurrwyrnwyb7x/3rT4yrDYZFh/JapNhscmw+kgWqwyLTbLYZFisjudksTiW/3HfWriOLI77AX6+Cg30cywr/LEU/RSv+6f7573VOUeqLaXcL2WdCz4+Z1mZlp/nKLkrR89dPtJeyvrueA+XXm7GXwf4iwTg9aKaFv6YxJW+ZuqR2fz8fG3atEljx451LLNarerZs6c2bNhQ6ms2bNigkSNHOi3r1auXPvnkk1LXz8vLU17eH8UiMzNTUuFOqgzZJ7Nkz8tV9sksZWXxC75KCaor3yZ15duk8KEhqUDS4YxTyszMlM/pY7KdOiafvGPyOXVctrwM2c5ky5p3UtYzJ2XNPynj9EkdST8mf+O0Ai1nFKjTClS+/CwF52wov+gn26V4F6lwZVY5/6UAADzJ0Tb/1NG2g52WRYX4Kyo0oFK2X9zTynLM1dQym56eroKCAsXExDgtj4mJ0Y4dO0p9TUpKSqnrp6SklLr+lClTNGnSpBLLExISypm6fDrPqNTNAQAAXIJpRT/mOnnypMLCLvyXbdPHzFa0sWPHOh3JtdvtOn78uGrVqiVLJfz5LSsrSwkJCTp48GClDGvwRuyjC2P/XBj758LYPxfG/rk49tGFsX8urLz7xzAMnTx5UnFxcRdd19QyGxkZKZvNptTUVKflqampio2NLfU1sbGxLq3v7+8vf39/p2Xh4eHlD11OoaGhfMkvgn10YeyfC2P/XBj758LYPxfHProw9s+FlWf/XOyIbDFreQK5i5+fn9q3b69Vq1Y5ltntdq1atUqdO3cu9TWdO3d2Wl+SVqxYcd71AQAAUHWZPsxg5MiRGjhwoDp06KCOHTtqxowZysnJ0aBBgyRJ999/v+Lj4zVlyhRJ0rBhw9SjRw+99NJL6t27t9577z39+OOPmjNnjpkfAwAAACYwvcz2799fR48e1YQJE5SSkqK2bdtq2bJljpO8Dhw4IKv1jwPIXbp00cKFC/X000/rqaeeUuPGjfXJJ5947Byz/v7+mjhxYomhDvgD++jC2D8Xxv65MPbPhbF/Lo59dGHsnwurjP1j+jyzAAAAQHmZOmYWAAAAuBSUWQAAAHgtyiwAAAC8FmUWAAAAXosyW8FmzpypxMREBQQEqFOnTtq4caPZkTzClClTdMUVV6hGjRqKjo5W3759tXPnTrNjeaypU6fKYrFo+PDhZkfxKIcOHdK9996rWrVqKTAwUK1bt9aPP/5odiyPUFBQoPHjx6t+/foKDAxUw4YN9eyzz5bpOudV0ddff60+ffooLi5OFotFn3zyidPzhmFowoQJql27tgIDA9WzZ0/99ttv5oQ1wYX2z5kzZ/Tkk0+qdevWCg4OVlxcnO6//34dPnzYvMAmuNh36FyPPPKILBaLZsyYUWn5zFaW/bN9+3bdeuutCgsLU3BwsK644godOHDgkrdNma1AixYt0siRIzVx4kRt3rxZbdq0Ua9evZSWlmZ2NNOtXbtWgwcP1nfffacVK1bozJkzuuGGG5STk2N2NI/zww8/6D//+Y8uu+wys6N4lBMnTqhr167y9fXVF198oV9//VUvvfSSIiIizI7mEaZNm6bXX39dr732mrZv365p06bp3//+t1599VWzo5kiJydHbdq00cyZM0t9/t///rf+7//+T7Nnz9b333+v4OBg9erVS6dPn67kpOa40P7Jzc3V5s2bNX78eG3evFkfffSRdu7cqVtvvdWEpOa52Heo2Mcff6zvvvuuTJdhrUoutn92796tbt26qVmzZlqzZo1+/vlnjR8/XgEBAZe+cQMVpmPHjsbgwYMdjwsKCoy4uDhjypQpJqbyTGlpaYYkY+3atWZH8SgnT540GjdubKxYscLo0aOHMWzYMLMjeYwnn3zS6Natm9kxPFbv3r2NBx54wGnZbbfdZtxzzz0mJfIckoyPP/7Y8dhutxuxsbHGCy+84FiWkZFh+Pv7G//9739NSGiuP++f0mzcuNGQZOzfv79yQnmY8+2j33//3YiPjze2bt1q1KtXz3j55ZcrPZsnKG3/9O/f37j33nsrZHscma0g+fn52rRpk3r27OlYZrVa1bNnT23YsMHEZJ4pMzNTklSzZk2Tk3iWwYMHq3fv3k7fIxT69NNP1aFDB91xxx2Kjo5Wu3btNHfuXLNjeYwuXbpo1apV2rVrlyRpy5YtWrdunW666SaTk3mevXv3KiUlxem/s7CwMHXq1Inf1+eRmZkpi8Wi8PBws6N4DLvdrvvuu0+jR49Wy5YtzY7jUex2u5YuXaomTZqoV69eio6OVqdOnS44VMMVlNkKkp6eroKCAseVzIrFxMQoJSXFpFSeyW63a/jw4eratavHXsnNDO+99542b97suJQznO3Zs0evv/66GjdurOXLl+vRRx/V0KFDNX/+fLOjeYQxY8borrvuUrNmzeTr66t27dpp+PDhuueee8yO5nGKfyfz+7psTp8+rSeffFJ33323QkNDzY7jMaZNmyYfHx8NHTrU7CgeJy0tTdnZ2Zo6dapuvPFGffnll/rrX/+q2267TWvXrr3k9zf9crbA4MGDtXXrVq1bt87sKB7j4MGDGjZsmFasWOGe8URVkN1uV4cOHTR58mRJUrt27bR161bNnj1bAwcONDmd+d5//329++67WrhwoVq2bKmkpCQNHz5ccXFx7B+U25kzZ3TnnXfKMAy9/vrrZsfxGJs2bdIrr7yizZs3y2KxmB3H49jtdknSX/7yF40YMUKS1LZtW3377beaPXu2evTocUnvz5HZChIZGSmbzabU1FSn5ampqYqNjTUplecZMmSIlixZotWrV6tOnTpmx/EYmzZtUlpami6//HL5+PjIx8dHa9eu1f/93//Jx8dHBQUFZkc0Xe3atdWiRQunZc2bN3fLmbFVwejRox1HZ1u3bq377rtPI0aM4Eh/KYp/J/P7+sKKi+z+/fu1YsUKjsqe45tvvlFaWprq1q3r+J29f/9+jRo1SomJiWbHM11kZKR8fHwq7Hc2ZbaC+Pn5qX379lq1apVjmd1u16pVq9S5c2cTk3kGwzA0ZMgQffzxx/rqq69Uv359syN5lOuuu06//PKLkpKSHD8dOnTQPffco6SkJNlsNrMjmq5r164lpnPbtWuX6tWrZ1Iiz5Kbmyur1flXvM1mcxwhwR/q16+v2NhYp9/XWVlZ+v777/l9XaS4yP72229auXKlatWqZXYkj3Lffffp559/dvqdHRcXp9GjR2v58uVmxzOdn5+frrjiigr7nc0wgwo0cuRIDRw4UB06dFDHjh01Y8YM5eTkaNCgQWZHM93gwYO1cOFC/e9//1ONGjUc49LCwsIUGBhocjrz1ahRo8T44eDgYNWqVYtxxUVGjBihLl26aPLkybrzzju1ceNGzZkzR3PmzDE7mkfo06ePnn/+edWtW1ctW7bUTz/9pOnTp+uBBx4wO5opsrOzlZyc7Hi8d+9eJSUlqWbNmqpbt66GDx+u5557To0bN1b9+vU1fvx4xcXFqW/fvuaFrkQX2j+1a9dWv379tHnzZi1ZskQFBQWO39k1a9aUn5+fWbEr1cW+Q38u+L6+voqNjVXTpk0rO6opLrZ/Ro8erf79+6t79+665pprtGzZMn322Wdas2bNpW+8QuZIgMOrr75q1K1b1/Dz8zM6duxofPfdd2ZH8giSSv15++23zY7msZiaq6TPPvvMaNWqleHv7280a9bMmDNnjtmRPEZWVpYxbNgwo27dukZAQIDRoEEDY9y4cUZeXp7Z0UyxevXqUn/nDBw40DCMwum5xo8fb8TExBj+/v7GddddZ+zcudPc0JXoQvtn79695/2dvXr1arOjV5qLfYf+rLpNzVWW/fPmm28ajRo1MgICAow2bdoYn3zyiVu2bTGMano5GAAAAHg9xswCAADAa1FmAQAA4LUoswAAAPBalFkAAAB4LcosAAAAvBZlFgAAAF6LMgsAAACvRZkFAACA16LMAkAFW7NmjSwWizIyMip1u/PmzVN4ePglvce+fftksViUlJR03nXM+nwAIFFmAeCSWCyWC/4888wzZkcEgCrNx+wAAODNjhw54ri/aNEiTZgwQTt37nQsCwkJ0Y8//ujy++bn58vPz88tGQGgKuPILABcgtjYWMdPWFiYLBaL07KQkBDHups2bVKHDh0UFBSkLl26OJXeZ555Rm3bttUbb7yh+vXrKyAgQJKUkZGhhx56SFFRUQoNDdW1116rLVu2OF63ZcsWXXPNNapRo4ZCQ0PVvn37EuV5+fLlat68uUJCQnTjjTc6FXC73a5//etfqlOnjvz9/dW2bVstW7bsgp/5888/V5MmTRQYGKhrrrlG+/btu5RdCACXhDILAJVk3Lhxeumll/Tjjz/Kx8dHDzzwgNPzycnJWrx4sT766CPHGNU77rhDaWlp+uKLL7Rp0yZdfvnluu6663T8+HFJ0j333KM6derohx9+0KZNmzRmzBj5+vo63jM3N1cvvviiFixYoK+//loHDhzQ448/7nj+lVde0UsvvaQXX3xRP//8s3r16qVbb71Vv/32W6mf4eDBg7rtttvUp08fJSUl6aGHHtKYMWPcvKcAwAUGAMAt3n77bSMsLKzE8tWrVxuSjJUrVzqWLV261JBknDp1yjAMw5g4caLh6+trpKWlOdb55ptvjNDQUOP06dNO79ewYUPjP//5j2EYhlGjRg1j3rx5580jyUhOTnYsmzlzphETE+N4HBcXZzz//PNOr7viiiuMf/7zn4ZhGMbevXsNScZPP/1kGIZhjB071mjRooXT+k8++aQhyThx4kSpOQCgInFkFgAqyWWXXea4X7t2bUlSWlqaY1m9evUUFRXleLxlyxZlZ2erVq1aCgkJcfzs3btXu3fvliSNHDlSDz30kHr27KmpU6c6lhcLCgpSw4YNnbZbvM2srCwdPnxYXbt2dXpN165dtX379lI/w/bt29WpUyenZZ07dy7zPgAAd+MEMACoJOf++d9isUgqHLNaLDg42Gn97Oxs1a5dW2vWrCnxXsVTbj3zzDMaMGCAli5dqi+++EITJ07Ue++9p7/+9a8ltlm8XcMw3PFxAMAjcGQWADzU5ZdfrpSUFPn4+KhRo0ZOP5GRkY71mjRpohEjRujLL7/UbbfdprfffrtM7x8aGqq4uDitX7/eafn69evVokWLUl/TvHlzbdy40WnZd9995+InAwD3ocwCgIfq2bOnOnfurL59++rLL7/Uvn379O2332rcuHH68ccfderUKQ0ZMkRr1qzR/v37tX79ev3www9q3rx5mbcxevRoTZs2TYsWLdLOnTs1ZswYJSUladiwYaWu/8gjj+i3337T6NGjtXPnTi1cuFDz5s1z0ycGANcxzAAAPJTFYtHnn3+ucePGadCgQTp69KhiY2PVvXt3xcTEyGaz6dixY7r//vuVmpqqyMhI3XbbbZo0aVKZtzF06FBlZmZq1KhRSktLU4sWLfTpp5+qcePGpa5ft25dLV68WCNGjNCrr76qjh07avLkySVmZgCAymIxGDwFAAAAL8UwAwAAAHgtyiwAAAC8FmUWAAAAXosyCwAAAK9FmQUAAIDXoswCAADAa1FmAQAA4LUoswAAAPBalFkAAAB4LcosAAAAvBZlFgAAAF7r/wOPG2+VB9glewAAAABJRU5ErkJggg==", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Exercise 5.2\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Iterate over each column in df_sig\n", + "for column in df_sig.columns:\n", + " # Create a new figure for each column\n", + " plt.figure(figsize=(8, 6))\n", + " \n", + " # Compute TPR for the current column\n", + " TPR, bins_sig, _ = plt.hist(df_sig[column], bins=100, histtype=\"step\", cumulative=-1, density=True)\n", + " \n", + " # Plot TPR\n", + " plt.plot(bins_sig[:-1], TPR)\n", + " \n", + " # Add title and labels\n", + " plt.xlabel('Threshold')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title(f'True Positive Rate for {column}')\n", + " \n", + " plt.grid(False)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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OHNHatWu1ZMkSHTlypFrHol+/fjrzzDM1bdo0jR49Wh06dFCbNm304IMPau/evYqKitKHH35Y7vyw9s957733KiUlRYGBgRo8eLD69u2rO+64Q6mpqVq/fr0uu+wyBQcHa8uWLZo3b55efPFF/f3vf3erzuDgYD3zzDMaMWKE+vbtqyFDhjim5mrVqpXGjh1brc9fE//+97+VmZmpffv2SZI+//xz7dmzR1JJK469D9xV27dv19VXX63LL79cK1eu1P/+9z/deOON6tq1q8drB/ySOZMoADBDeVMjnezo0aPGiBEjjNjYWCMiIsJISUkxfv/99zLTTp06Nde2bduMW2+91WjTpo0RGhpqNGrUyLjooouMJUuWlHmPDz/80Dj//PON8PBwIzw83OjQoYMxevRoY/PmzTWq3a6wsNCYOnWq0bp1ayM4ONhISkoyJk6caOTl5TnWWbt2rTFkyBCjRYsWhtVqNeLi4oyrrrrKWL16tdO+VM4USY8//riRmJhoBAQEOE37dOoxstuyZYtj+rPvv/++3JozMjKM0aNHG0lJSUZwcLCRkJBgXHLJJcbs2bMr/az2973yyivLfW3u3LlOU0tt3LjRSE5ONiIiIozY2Fhj1KhRxoYNG8pMP1VUVGTcc889RpMmTQyLxVJmmq7Zs2cb3bt3N8LCwozIyEijc+fOxkMPPWTs27ev0lor+xmmpaUZZ511lmG1Wo1GjRoZQ4cONfbs2eO0zrBhw4zw8PAqj8mp7+fu1FwtW7ascAo7d/Zln5pr48aNxt///ncjMjLSaNiwoTFmzBinachOxtRcgPsshnHK/7UBAIAamzJliqZOnaqDBw86brIBwPPomQUAAIDPomcWAFDv5eTkKCcnp9J1mjRpUu60cdXdF4C6QZgFANR7zz33nKZOnVrpOtu3b3dpuixX9wWgbtAzCwCo97Zt26Zt27ZVus7555/vdEvdutgXgJojzAIAAMBncQEYAAAAfJbf9czabDbt27dPkZGR3B8bAADACxmGoWPHjqlZs2ZlbjZzKr8Ls/v27VNSUpLZZQAAAKAKu3fvVvPmzStdx+/CbGRkpKSSgxMVFWVyNQAAADhVdna2kpKSHLmtMn4XZu2tBVFRUYRZAAAAL+ZKSygXgAEAAMBnEWYBAADgswizAAAA8FmEWQAAAPgswiwAAAB8FmEWAAAAPoswCwAAAJ9FmAUAAIDPIswCAADAZxFmAQAA4LMIswAAAPBZhFkAAAD4LMIsAAAAfBZhFgAAAD6LMAsAAACfZWqY/fbbb9W/f381a9ZMFotFn3zySZXbLF++XGeffbasVqtOP/10zZ07t9brBAAAgHcyNczm5uaqa9eumjlzpkvrb9++XVdeeaUuuugirV+/Xvfff79GjhypL7/8spYrBQAAgDcKMvPN+/Xrp379+rm8/qxZs9S6dWs9//zzkqSOHTvq+++/1wsvvKCUlJTaKrNmMjZKh/6QmrSX4jqaXQ0AAEC9YmqYddfKlSuVnJzstCwlJUX3339/hdvk5+crPz/f8Tw7O7u2yivfT7OktW8oo/sDOnj2fU4vNQwPUWJMWN3WAwAAUI/4VJhNT09XfHy807L4+HhlZ2frxIkTCgsrGwxTU1M1derUuiqxjGMBkYqU9PlPG/XED987vRYWHKglD/Ql0AIAAFSTT4XZ6pg4caLGjRvneJ6dna2kpKQ6e//c0jB7WWurzr38fMfyrQdydH/aeh3NLSDMAgAAVJNPhdmEhARlZGQ4LcvIyFBUVFS5o7KSZLVaZbVa66K8chVbYyRJMZYctUiMNq0OAACA+sin5pnt3bu3li5d6rRs8eLF6t27t0kVVc0eZgPzM02tAwAAoD4yNczm5ORo/fr1Wr9+vaSSqbfWr1+vXbt2SSppEbjlllsc6995553atm2bHnroIf3+++96+eWX9f7772vs2LFmlO8SwiwAAEDtMTXMrl69WmeddZbOOussSdK4ceN01llnadKkSZKk/fv3O4KtJLVu3VoLFizQ4sWL1bVrVz3//PN67bXXvHdaLklFjjCbZW4hAAAA9ZCpPbMXXnihDMOo8PXy7u514YUXat26dbVYlWc5jcwahmSxmFoPAABAfeJTPbO+qNhactFXgK1QKsg1uRoAAID6hTBby4ygMOUbpQPgJ46aWwwAAEA9Q5itbRaLshRR8pgwCwAA4FGE2TqQaYSXPCDMAgAAeBRhtg5kMjILAABQKwizdSDLIMwCAADUBsJsHaDNAAAAoHYQZusAbQYAAAC1gzBbBzJpMwAAAKgVhNk6kCXaDAAAAGoDYbYO/DUym2lqHQAAAPUNYbYO0DMLAABQOwizdYDZDAAAAGoHYbYOMDILAABQOwizdcBx04SiE1LhCXOLAQAAqEcIs3XgmMJkWAJLnnARGAAAgMcQZuuERcXW6JKHtBoAAAB4DGG2jhRbY0oeEGYBAAA8hjBbRwizAAAAnkeYrSOOMJuXaWYZAAAA9Qphto7QMwsAAOB5hNk6UkSbAQAAgMcRZusIPbMAAACeR5itI7QZAAAAeB5hto4wMgsAAOB5hNk6QpgFAADwPMJsHSHMAgAAeB5hto78FWYzzSwDAACgXiHM1pHi0JiSB/nZUnGhqbUAAADUF4TZOlIcEvXXk7ws8woBAACoRwizdSUgSGJ6LgAAAI8izNalsJiS74RZAAAAjyDM1qWwhiXfCbMAAAAeQZitS4RZAAAAjyLM1iXCLAAAgEcRZusSYRYAAMCjCLN1iTALAADgUYTZukSYBQAA8CjCbF0izAIAAHgUYbYuEWYBAAA8ijBblwizAAAAHkWYrUuEWQAAAI8izNYlR5jNlAybqaUAAADUB4TZuhQWU/rAUEBBtpmVAAAA1AuE2boUZJWCwyVJgflZJhcDAADg+wizda201SAwP9PcOgAAAOoBwmxdK201CGJkFgAAoMYIs3WNkVkAAACPIczWtdKRWcIsAABAzRFm65p9ZDYv09w6AAAA6gHCbF2jzQAAAMBjCLN1jTALAADgMYTZukaYBQAA8BjCbF0jzAIAAHgMYbauEWYBAAA8hjBb1xxhlpsmAAAA1BRhtq45jcwappYCAADg6wizda00zAbYCtVA+SYXAwAA4NsIs3UtuIEUGCJJilGOycUAAAD4NsJsXbNYHKOzMRbCLAAAQE0QZs1QGmajLbkmFwIAAODbCLNmsI/M0mYAAABQI4RZM9BmAAAA4BGEWTM4RmZpMwAAAKgJwqwZHD2zjMwCAADUBGHWDGExkuiZBQAAqCnCrBkcPbO0GQAAANQEYdYMXAAGAADgEYRZM4TGSJKiaTMAAACoEcKsGWgzAAAA8AjCrBnssxkwNRcAAECNEGbNUBpmG1jyZSnKM7kYAAAA3xVkdgF+yRolwxIgi2HTrn37ZQSFOr3cMDxEiTFhJhUHAADgOwizZggIkBESKUt+lp77bJX+NPY6vRwWHKglD/Ql0AIAAFSBMGuSgOAwKT9L/xl8hvJiz3Qs33ogR/enrdfR3ALCLAAAQBUIs2YJLgmqpzcMkhKjTS4GAADAN3EBmFlKw6wKj5tbBwAAgA8zPczOnDlTrVq1UmhoqHr16qVVq1ZVuv706dPVvn17hYWFKSkpSWPHjlVeng/OCGAPs8xmAAAAUG2mhtm0tDSNGzdOkydP1tq1a9W1a1elpKTowIED5a7/zjvvaMKECZo8ebI2bdqk//73v0pLS9M///nPOq7cA4IYmQUAAKgpU8PstGnTNGrUKI0YMUKdOnXSrFmz1KBBA73++uvlrr9ixQqdd955uvHGG9WqVStddtllGjJkSJWjuV7J0WbAyCwAAEB1mRZmCwoKtGbNGiUnJ/9VTECAkpOTtXLlynK36dOnj9asWeMIr9u2bdPChQt1xRVXVPg++fn5ys7OdvryCsGlc8syMgsAAFBtps1mcOjQIRUXFys+Pt5peXx8vH7//fdyt7nxxht16NAhnX/++TIMQ0VFRbrzzjsrbTNITU3V1KlTPVq7RwQ3KPlOzywAAEC1mX4BmDuWL1+up556Si+//LLWrl2rjz76SAsWLNDjjz9e4TYTJ05UVlaW42v37t11WHEl7Hf9Kjxhbh0AAAA+zLSR2djYWAUGBiojI8NpeUZGhhISEsrd5tFHH9XNN9+skSNHSpI6d+6s3Nxc3X777Xr44YcVEFA2m1utVlmtVs9/gJqyj8wSZgEAAKrNtJHZkJAQde/eXUuXLnUss9lsWrp0qXr37l3uNsePHy8TWAMDAyVJhmHUXrG1IZiRWQAAgJoy9Q5g48aN07Bhw9SjRw/17NlT06dPV25urkaMGCFJuuWWW5SYmKjU1FRJUv/+/TVt2jSdddZZ6tWrl7Zu3apHH31U/fv3d4Ran+HomSXMAgAAVJepYXbQoEE6ePCgJk2apPT0dHXr1k2LFi1yXBS2a9cup5HYRx55RBaLRY888oj27t2rJk2aqH///nryySfN+gjVR88sAABAjZkaZiVpzJgxGjNmTLmvLV++3Ol5UFCQJk+erMmTJ9dBZbXMMc8sYRYAAKC6fGo2g3qFMAsAAFBjhFmz2MMs88wCAABUG2HWLEH2kVnuAAYAAFBdhFmzONoMGJkFAACoLsKsWYIZmQUAAKgpwqxZ6JkFAACoMcKsWeiZBQAAqDHCrFnomQUAAKgxwqxZ7GG2OF+yFZtbCwAAgI8izJrFHmYl+mYBAACqiTBrlqCTwix3AQMAAKgWwqxZAgKkQGvJY8IsAABAtRBmzRQcWvKdMAsAAFAthFkzBTco+V5EmAUAAKgOwqyZghiZBQAAqAnCrJkcc80SZgEAAKqDMGsmwiwAAECNEGbNZJ+ei55ZAACAaiHMmolb2gIAANQIYdZMjqm5jptbBwAAgI8izJrJMTUXI7MAAADVQZg1E1NzAQAA1Ahh1kz2kVnCLAAAQLUQZs3E7WwBAABqhDBrJm5nCwAAUCOEWTPRMwsAAFAjhFkzcQcwAACAGiHMmokwCwAAUCOEWTPZwyzzzAIAAFQLYdZMQfaRWe4ABgAAUB2EWTM52gwYmQUAAKgOwqyZghmZBQAAqAnCrJnomQUAAKgRwqyZ6JkFAACoEcKsmeiZBQAAqBHCrJnsYbY4X7IVm1sLAACADyLMmskeZiX6ZgEAAKqBMGumoJPCLHcBAwAAcBth1kwBAVKgteQxYRYAAMBthFmzBYeWfCfMAgAAuI0wa7bgBiXfiwizAAAA7iLMmi2IkVkAAIDqIsyazT4yS5gFAABwG2HWbPTMAgAAVBth1mz0zAIAAFQbYdZs9MwCAABUG2HWbPa7gBFmAQAA3EaYNRthFgAAoNoIs2azh9miPHPrAAAA8EGEWbMF2Udmj5tbBwAAgA8izJrN0WbAyCwAAIC7qhVmi4qKtGTJEv3nP//RsWPHJEn79u1TTk6OR4vzC8GMzAIAAFRXkLsb7Ny5U5dffrl27dql/Px8XXrppYqMjNQzzzyj/Px8zZo1qzbqrL/omQUAAKg2t0dm77vvPvXo0UNHjx5VWFiYY/m1116rpUuXerQ4v+CYZ5aRWQAAAHe5PTL73XffacWKFQoJCXFa3qpVK+3du9djhfkN+x3A6JkFAABwm9sjszabTcXFxWWW79mzR5GRkR4pyq8EcwcwAACA6nI7zF522WWaPn2647nFYlFOTo4mT56sK664wpO1+Qf71FxFhFkAAAB3ud1m8PzzzyslJUWdOnVSXl6ebrzxRm3ZskWxsbF69913a6PG+o2puQAAAKrN7TDbvHlzbdiwQWlpadqwYYNycnJ02223aejQoU4XhMFFTM0FAABQbW6H2W+//VZ9+vTR0KFDNXToUMfyoqIiffvtt/rb3/7m0QLrPabmAgAAqDa3e2YvuugiHTlypMzyrKwsXXTRRR4pyq9wO1sAAIBqczvMGoYhi8VSZvnhw4cVHh7ukaL8Cj2zAAAA1eZym8HAgQMllcxeMHz4cFmtVsdrxcXF+uWXX9SnTx/PV1jf2cNscb5kKzvlGQAAACrmcpiNjo6WVDIyGxkZ6XSxV0hIiM4991yNGjXK8xXWd8EnXTRH3ywAAIBbXA6zc+bMkVRyp68HH3yQlgJPCTopzBaekBRsWikAAAC+xu3ZDCZPnlwbdfivgAAp0FrSZkCYBQAAcIvbYVaSPvjgA73//vvatWuXCgoKnF5bu3atRwrzK8GhJ4XZKLOrAQAA8Bluz2bw0ksvacSIEYqPj9e6devUs2dPNW7cWNu2bVO/fv1qo8b6L7hByXduaQsAAOAWt8Psyy+/rNmzZ2vGjBkKCQnRQw89pMWLF+vee+9VVlZWbdRY/wWFlnwvJMwCAAC4w+0wu2vXLscUXGFhYTp27Jgk6eabb9a7777r2er8hX1kljALAADgFrfDbEJCguMOYC1atNCPP/4oSdq+fbsMw/Bsdf4imJFZAACA6nA7zF588cX67LPPJEkjRozQ2LFjdemll2rQoEG69tprPV6gX6BnFgAAoFrcns1g9uzZstlskqTRo0ercePGWrFiha6++mrdcccdHi/QL9AzCwAAUC1uh9mAgAAFBPw1oDt48GANHjxYkrR3714lJiZ6rjp/Yb8LGGEWAADALW63GZQnPT1d99xzj9q2beuJ3fkfwiwAAEC1uBxmjx49qiFDhig2NlbNmjXTSy+9JJvNpkmTJum0007Tzz//7LjlLdxkD7NFeebWAQAA4GNcbjOYMGGCVqxYoeHDh+vLL7/U2LFjtWjRIgUEBOjrr7/WueeeW5t11m9B9pHZ4+bWAQAA4GNcHpn94osvNGfOHD333HP6/PPPZRiGunXrpvnz59coyM6cOVOtWrVSaGioevXqpVWrVlW6fmZmpkaPHq2mTZvKarWqXbt2WrhwYbXf3ys42gwYmQUAAHCHyyOz+/btU8eOHSXJET5vuummGr15Wlqaxo0bp1mzZqlXr16aPn26UlJStHnzZsXFxZVZv6CgQJdeeqni4uL0wQcfKDExUTt37lRMTEyN6jBdMCOzAAAA1eFymDUMQ0FBf60eGBiosLCwGr35tGnTNGrUKI0YMUKSNGvWLC1YsECvv/66JkyYUGb9119/XUeOHNGKFSsUHBwsqSRY+zx6ZgEAAKrFrTB7ySWXOALtiRMn1L9/f4WEhDitt3btWpf2V1BQoDVr1mjixImOZQEBAUpOTtbKlSvL3eazzz5T7969NXr0aH366adq0qSJbrzxRo0fP16BgYHlbpOfn6/8/HzH8+zsbJfqq1OOeWYZmQUAAHCHy2F28uTJTs+vueaaGr3xoUOHVFxcrPj4eKfl8fHx+v3338vdZtu2bfr66681dOhQLVy4UFu3btXdd9+twsLCMvXZpaamaurUqTWqtdbZ7wBGzywAAIBbqh1mzWCz2RQXF6fZs2crMDBQ3bt31969e/Xss89WWN/EiRM1btw4x/Ps7GwlJSXVVcmuCeYOYAAAANXh9h3APCU2NlaBgYHKyMhwWp6RkaGEhIRyt2natKmCg4OdWgo6duyo9PR0FRQUlGl5kCSr1Sqr1erZ4j3NPjJbRJgFAABwh0fuAFYdISEh6t69u5YuXepYZrPZtHTpUvXu3bvcbc477zxt3bpVNpvNseyPP/5Q06ZNyw2yPiOIkVkAAIDqMC3MStK4ceP06quv6o033tCmTZt01113KTc31zG7wS233OJ0gdhdd92lI0eO6L777tMff/yhBQsW6KmnntLo0aPN+gie4eiZJcwCAAC4w7Q2A0kaNGiQDh48qEmTJik9PV3dunXTokWLHBeF7dq1SwEBf+XtpKQkx93HunTposTERN13330aP368WR/BM+iZBQAAqJYahdm8vDyFhobWqIAxY8ZozJgx5b62fPnyMst69+6tH3/8sUbv6XXomQUAAKgWt9sMbDabHn/8cSUmJioiIkLbtm2TJD366KP673//6/EC/QI9swAAANXidph94oknNHfuXP3rX/9yuujqzDPP1GuvvebR4vyGfWS2uECyFZtbCwAAgA9xO8y++eabmj17toYOHeo0RVbXrl0rvNkBqhD8V6tGQDE3TgAAAHCV22F27969Ov3008sst9lsKiws9EhRficozPHQUkSYBQAAcJXbYbZTp0767rvvyiz/4IMPdNZZZ3mkKL8TECAFltzYIYCLwAAAAFzm9mwGkyZN0rBhw7R3717ZbDZ99NFH2rx5s958803Nnz+/Nmr0D8FhUnG+LLQZAAAAuMztkdlrrrlGn3/+uZYsWaLw8HBNmjRJmzZt0ueff65LL720Nmr0D8ElrQaMzAIAALiuWvPMXnDBBVq8eLGna/FvjjDLyCwAAICr3B6ZHTlyZLk3M0ANlV4ERpsBAACA69wOswcPHtTll1+upKQk/eMf/9D69etroSw/RJsBAACA29wOs59++qn279+vRx99VD///LO6d++uM844Q0899ZR27NhRCyX6idIwy9RcAAAArnM7zEpSw4YNdfvtt2v58uXauXOnhg8frrfeeqvc+WfhotJb2tIzCwAA4LpqhVm7wsJCrV69Wj/99JN27Nih+Ph4T9Xlf+xtBvTMAgAAuKxaYXbZsmUaNWqU4uPjNXz4cEVFRWn+/Pnas2ePp+vzH442A3pmAQAAXOX21FyJiYk6cuSILr/8cs2ePVv9+/eX1Wqtjdr8C1NzAQAAuM3tMDtlyhRdf/31iomJqYVy/FgQsxkAAAC4y+0wO2rUqNqoA8HMMwsAAOAul8LswIEDNXfuXEVFRWngwIGVrvvRRx95pDC/wzyzAAAAbnMpzEZHR8tisUiSoqKiHI/hQfTMAgAAuM2lMDtnzhzH47lz59ZWLf6tdJ5ZbpoAAADgOren5rr44ouVmZlZZnl2drYuvvhiT9Tkn4IbSGKeWQAAAHe4HWaXL1+ugoKCMsvz8vL03XffeaQovxRsvwMYPbMAAACucnk2g19++cXxeOPGjUpPT3c8Ly4u1qJFi5SYmOjZ6vxJ6cgsbQYAAACucznMduvWTRaLRRaLpdx2grCwMM2YMcOjxfmVIEZmAQAA3OVymN2+fbsMw9Bpp52mVatWqUmTJo7XQkJCFBcXp8DAwFop0i/YR2bpmQUAAHCZy2G2ZcuWkiSbzVZrxfg1emYBAADc5lKY/eyzz9SvXz8FBwfrs88+q3Tdq6++2iOF+R37bAb0zAIAALjMpTA7YMAApaenKy4uTgMGDKhwPYvFouLiYk/V5l+YZxYAAMBtLoXZk1sLaDOoJfaRWVuBAsQxBgAAcIXb88yWp7ybKMBNpT2zkhSqsvP4AgAAoCy3w+wzzzyjtLQ0x/Prr79ejRo1UmJiojZs2ODR4vxKUJjjIWEWAADANW6H2VmzZikpKUmStHjxYi1ZskSLFi1Sv3799I9//MPjBfqNgAAp0CpJClO+ycUAAAD4Bpen5rJLT093hNn58+frhhtu0GWXXaZWrVqpV69eHi/QrwSHScX5CrUwMgsAAOAKt0dmGzZsqN27d0uSFi1apOTkZEmSYRjMZFBTwSWtBrQZAAAAuMbtkdmBAwfqxhtvVNu2bXX48GH169dPkrRu3TqdfvrpHi/QrxBmAQAA3OJ2mH3hhRfUqlUr7d69W//6178UEREhSdq/f7/uvvtujxfoV0ovAgujzQAAAMAlbofZ4OBgPfjgg2WWjx071iMF+TVGZgEAANzidpiVpD///FPTp0/Xpk2bJEmdOnXS/fffr9NOO82jxfmd0jDLbAYAAACucfsCsC+//FKdOnXSqlWr1KVLF3Xp0kU//fSTOnXqpMWLF9dGjf7DPjJLmwEAAIBL3B6ZnTBhgsaOHaunn366zPLx48fr0ksv9Vhxfieo5C5gtBkAAAC4xu2R2U2bNum2224rs/zWW2/Vxo0bPVKU3wpuIIkwCwAA4Cq3w2yTJk20fv36MsvXr1+vuLg4T9Tkv4JLRmbDCLMAAAAucbvNYNSoUbr99tu1bds29enTR5L0ww8/6JlnntG4ceM8XqBfsY/M0jMLAADgErfD7KOPPqrIyEg9//zzmjhxoiSpWbNmmjJliu69916PF+hXguwjs8xmAAAA4Aq3w2xBQYFuv/12jR07VseOHZMkRUZGerwwv1Q6MmulzQAAAMAlLvfMHjx4UP369VNERISioqJ07rnn6sCBAwRZT7L3zNJmAAAA4BKXw+z48eO1fv16PfbYY3ruueeUmZmpkSNH1mZt/oc7gAEAALjF5TaDxYsXa+7cuUpJSZEkXXXVVerYsaPy8/NltVprrUC/EkSYBQAAcIfLYXbfvn3q2rWr43nbtm1ltVq1f/9+tWrVqjZq8z/229laCrT1QI7TSw3DQ5QYE2ZGVQAAAF7LrQvAAgMDyzw3DMOjBfm1kHBJUoQlX/enrXd6KSw4UEse6EugBQAAOInLYdYwDLVr104Wi8WxLCcnR2eddZYCAv5qvT1y5IhnK/Qn1ihJUoeGhuZff75j8dYDObo/bb2O5hYQZgEAAE7icpidM2dObdYBSQotCbNBBcd0ZmK0ycUAAAB4P5fD7LBhw2qzDkiOkVnlZ5tbBwAAgI9weWou1IHSkVkVF0iFeebWAgAA4AMIs94kJFJSaU8yo7MAAABVIsx6k4AAyVp6R7U8wiwAAEBVCLPext43m5dlbh0AAAA+oNphtqCgQJs3b1ZRUZEn64G9bzafMAsAAFAVt8Ps8ePHddttt6lBgwY644wztGvXLknSPffco6efftrjBfodx8gsbQYAAABVcTvMTpw4URs2bNDy5csVGhrqWJ6cnKy0tDSPFueXQkvnl+UCMAAAgCq5dTtbSfrkk0+Ulpamc8891+luYGeccYb+/PNPjxbnl0IZmQUAAHCV2yOzBw8eVFxcXJnlubm5TuEW1cSNEwAAAFzmdpjt0aOHFixY4HhuD7Cvvfaaevfu7bnK/BUjswAAAC5zu83gqaeeUr9+/bRx40YVFRXpxRdf1MaNG7VixQp98803tVGjf2FkFgAAwGVuj8yef/75Wr9+vYqKitS5c2d99dVXiouL08qVK9W9e/faqNG/hDLPLAAAgKvcHpmVpDZt2ujVV1/1dC2QJCuzGQAAALjK7ZHZtWvX6tdff3U8//TTTzVgwAD985//VEFBgUeL80v0zAIAALjM7TB7xx136I8//pAkbdu2TYMGDVKDBg00b948PfTQQx4v0O9wO1sAAACXuR1m//jjD3Xr1k2SNG/ePPXt21fvvPOO5s6dqw8//NDT9fmfUC4AAwAAcJXbYdYwDNlsNknSkiVLdMUVV0iSkpKSdOjQIc9W54/sdwDLy5YMw9xaAAAAvFy15pl94okn9NZbb+mbb77RlVdeKUnavn274uPjPV6g37G3GdgKpaI8c2sBAADwcm6H2enTp2vt2rUaM2aMHn74YZ1++umSpA8++EB9+vTxeIF+JyRCUumd1LgIDAAAoFJuT83VpUsXp9kM7J599lkFBgZ6pCi/FhBQMjqbn1XSNxvJaDcAAEBF3B6ZrUhoaKiCg4M9tTv/xvRcAAAALnFpZLZhw4ayWCwu7fDIkSM1Kgg66Za2TM8FAABQGZfC7PTp02u5DDhhZBYAAMAlLoXZYcOG1XYdOJmVuWYBAABcUaOe2by8PGVnZzt9VcfMmTPVqlUrhYaGqlevXlq1apVL27333nuyWCwaMGBAtd7Xa4VyFzAAAABXuB1mc3NzNWbMGMXFxSk8PFwNGzZ0+nJXWlqaxo0bp8mTJ2vt2rXq2rWrUlJSdODAgUq327Fjhx588EFdcMEFbr+n17PSZgAAAOAKt8PsQw89pK+//lqvvPKKrFarXnvtNU2dOlXNmjXTm2++6XYB06ZN06hRozRixAh16tRJs2bNUoMGDfT6669XuE1xcbGGDh2qqVOn6rTTTnP7Pb2e/S5gtBkAAABUyu0w+/nnn+vll1/Wddddp6CgIF1wwQV65JFH9NRTT+ntt992a18FBQVas2aNkpOT/yooIEDJyclauXJlhds99thjiouL02233Vble+Tn53ukFaJOcQEYAACAS9wOs0eOHHGMhkZFRTmm4jr//PP17bffurWvQ4cOqbi4uMxtcOPj45Wenl7uNt9//73++9//6tVXX3XpPVJTUxUdHe34SkpKcqtGU3ABGAAAgEvcDrOnnXaatm/fLknq0KGD3n//fUklI7YxMTEeLe5Ux44d080336xXX31VsbGxLm0zceJEZWVlOb52795dqzV6hL3NgAvAAAAAKuX27WxHjBihDRs2qG/fvpowYYL69++vf//73yosLNS0adPc2ldsbKwCAwOVkZHhtDwjI0MJCQll1v/zzz+1Y8cO9e/f37HMZrOVfJCgIG3evFlt2rRx2sZqtcpqtbpVl+kYmQUAAHCJy2F227Ztat26tcaOHetYlpycrN9//11r1qzR6aefri5durj15iEhIerevbuWLl3qmF7LZrNp6dKlGjNmTJn1O3TooF9//dVp2SOPPKJjx47pxRdf9I0WAlfQMwsAAOASl8Ns27ZttX//fsXFxUmSBg0apJdeekktW7ZUy5Ytq13AuHHjNGzYMPXo0UM9e/bU9OnTlZubqxEjRkiSbrnlFiUmJio1NVWhoaE688wznba3tzacutynMTILAADgEpfDrGEYTs8XLlyo1NTUGhcwaNAgHTx4UJMmTVJ6erq6deumRYsWOS4K27VrlwICanRvB99z8sjsKccdAAAAf3G7Z7Y2jBkzpty2Aklavnx5pdvOnTvX8wWZzT4yayuUivLMrQUAAMCLuTzkabFYZLFYyixDLQiJkFR6bJnRAAAAoEJutRkMHz7cMTNAXl6e7rzzToWHhzut99FHH3m2Qn8UEFDSapCXVXoRWJjZFQEAAHgll8PssGHDnJ7fdNNNHi8GJ7FGl4TZ/GzJEl/1+gAAAH7I5TA7Z86c2qwDpwqNkrJUEmgZmAUAACiXn00T4EOYngsAAKBKhFlvxY0TAAAAqkSY9VaMzAIAAFSJMOutGJkFAACoEmHWWzEyCwAAUCXCrLdiZBYAAKBKhFlvFRpd8p07gAEAAFSIMOutaDMAAACoEmHWWzEyCwAAUCXCrLdiZBYAAKBKhFlvxQVgAAAAVSLMequTR2YNw9xaAAAAvBRh1lvZR2ZtRbIU55lbCwAAgJcizHqrkAjJUvLjCSyg1QAAAKA8hFlvZbFI1khJUkDBMZOLAQAA8E6EWW9mLZmeK5AwCwAAUC7CrDcLJcwCAABUhjDrzUovAgvI58YJAAAA5SHMerPS6bkCCxmZBQAAKA9h1pvZR2ZpMwAAACgXYdab2UdmCbMAAADlIsx6s1B7mGWeWQAAgPIQZr2ZlTYDAACAyhBmvVkobQYAAACVIcx6M0ZmAQAAKkWY9WbcNAEAAKBShFlv5gizXAAGAABQHsKsN3O0GRBmAQAAykOY9WZOF4AZ5tYCAADghQiz3qx0ZNZiFCtM+SYXAwAA4H0Is94sJFyyBEqSInXC5GIAAAC8D2HWm1kskjVSkhRpOW5yMQAAAN6HMOvtSvtmo0SYBQAAOBVh1ttZS6bnYmQWAACgLMKstysdmaVnFgAAoCzCrLcLZWQWAACgIoRZb2e1j8wSZgEAAE5FmPV29gvAGJkFAAAogzDr7RiZBQAAqBBh1tvZLwBjZBYAAKAMwqy3s9rnmWU2AwAAgFMRZr0dI7MAAAAVIsx6O/tNE+iZBQAAKIMw6+1CuQAMAACgIoRZb2efzcBCzywAAMCpCLPeLvSkNgPDMLkYAAAA70KY9XalbQZBFpssRYzOAgAAnIww6+2CG8iwBEqSAguyTS4GAADAuxBmvZ3FouKQSEmEWQAAgFMRZn2ArTTMBhQcM7kSAAAA70KY9QHFISV9s4GEWQAAACeEWR/w18gsbQYAAAAnCzK7AFTNPjJ75PBBZe/NcnqtYXiIEmPCzCgLAADAdIRZHxAQlSBJ+mbNr3rhp++dXgsLDtSSB/oSaAEAgF8izPqAiCYtJUm3dArUJX3PdyzfeiBH96et19HcAsIsAADwS4RZXxCdJElqWJihhonRJhcDAADgPbgAzBdENy/5nrXX3DoAAAC8DGHWF0QnlnzP2iMZhrm1AAAAeBHCrC+IbCbJIhXnS7mHzK4GAADAaxBmfUFQiBQRX/I4e4+5tQAAAHgRwqyvcPTNEmYBAADsCLO+gjALAABQBmHWVxBmAQAAyiDM+grCLAAAQBmEWV9BmAUAACiDMOsrokrnms3mxgkAAAB2hFlfUXpLWx1Ll4oKzK0FAADASxBmfUV4rBRolWRIx/aZXQ0AAIBXIMz6CovlpNva0moAAAAgEWZ9CxeBAQAAOCHM+pIoe5jdbW4dAAAAXoIw60vsI7PMaAAAACCJMOtbaDMAAABw4hVhdubMmWrVqpVCQ0PVq1cvrVq1qsJ1X331VV1wwQVq2LChGjZsqOTk5ErXr1ccF4ARZgEAACQvCLNpaWkaN26cJk+erLVr16pr165KSUnRgQMHyl1/+fLlGjJkiJYtW6aVK1cqKSlJl112mfbu9YP/erfPNctsBgAAAJK8IMxOmzZNo0aN0ogRI9SpUyfNmjVLDRo00Ouvv17u+m+//bbuvvtudevWTR06dNBrr70mm82mpUuX1nHlJrDfBSw/S8rLMrcWAAAAL2BqmC0oKNCaNWuUnJzsWBYQEKDk5GStXLnSpX0cP35chYWFatSoUbmv5+fnKzs72+nLZ1kjpNCYkseMzgIAAJgbZg8dOqTi4mLFx8c7LY+Pj1d6erpL+xg/fryaNWvmFIhPlpqaqujoaMdXUlJSjes2lb3VgBkNAAAAzG8zqImnn35a7733nj7++GOFhoaWu87EiROVlZXl+Nq928fnaI1mrlkAAAC7IDPfPDY2VoGBgcrIyHBanpGRoYSEhEq3fe655/T0009ryZIl6tKlS4XrWa1WWa1Wj9TrFU6e0aCpuaUAAACYzdSR2ZCQEHXv3t3p4i37xVy9e/eucLt//etfevzxx7Vo0SL16NGjLkr1Ho6RWdoMAAAATB2ZlaRx48Zp2LBh6tGjh3r27Knp06crNzdXI0aMkCTdcsstSkxMVGpqqiTpmWee0aRJk/TOO++oVatWjt7aiIgIRUREmPY56oxjei7mmgUAADA9zA4aNEgHDx7UpEmTlJ6erm7dumnRokWOi8J27dqlgIC/BpBfeeUVFRQU6O9//7vTfiZPnqwpU6bUZenmsE/PRc8sAACA+WFWksaMGaMxY8aU+9ry5cudnu/YsaP2C/Jm9jaD7H2SYTO3FgAAAJP59GwGfimyqWQJkGyFCjpx0OxqAAAATEWY9TWBQSWBVlJwzj6TiwEAADAXYdYXlbYaEGYBAIC/I8z6otKLwAizAADA3xFmfZF9ZDaXuWYBAIB/I8z6otK5ZoNz9ptcCAAAgLkIs74o2t5mwMgsAADwb4RZX1TaZhCSS88sAADwb4RZX1TaZhB04pCsKjC5GAAAAPMQZn1RWEMpKEySlGA5YnIxAAAA5iHM+iKLxdFq0Mxy2ORiAAAAzEOY9VWlF4E1E2EWAAD4L8Ksr3KMzB4yuRAAAADzEGZ9VelFYLQZAAAAf0aY9VWlt7QlzAIAAH9GmPVVXAAGAACgILMLQDU52gwOaXHGsTIvNwwPUWJMWF1XBQAAUKcIs74qOlGGJUDhylfq+8uUoUZOL4cFB2rJA30JtAAAoF4jzPqq4DBZ4jpJGb9p3lVBym59vuOlrQdydH/aeh3NLSDMAgCAeo0w68uanyNl/KYWub9JiUPMrgYAAKDOcQGYL0vqWfJ9z8/m1gEAAGASwqwva14aZvetl4oKTC0FAADADIRZX9a4jRTWSCrOl9J/MbsaAACAOkeY9WUWS0nfrCTtXmVuLQAAACYgzPq6pNIwu4cwCwAA/A9h1tfZ+2Z3cxEYAADwP4RZX5fYXbIESNl7pOx9ZlcDAABQpwizvs4aIcWdUfKYvlkAAOBnCLP1gaNvllYDAADgXwiz9YGjb5aRWQAA4F8Is/WB/U5g+9dLRfmmlgIAAFCXCLP1QaPTpAaNpeICaT83TwAAAP6DMFsfnHzzBOabBQAAfoQwW19wJzAAAOCHCLP1hb1vlhkNAACAHyHM1hfNzi69ecJeBeXsN7saAACAOkGYrS+sEVJ8yc0TGhxYY3IxAAAAdYMwW5+UzjfbIGOtyYUAAADUDcJsfVLaN9vgAGEWAAD4B8JsfVI6o0Hood8UokKTiwEAAKh9hNn6pPTmCQG2Ap1p2W52NQAAALWOMFufWCyOvtmzAraYXAwAAEDtCzK7AHhY0jnSH1+oT8BGbT2Q4/RSw/AQJcaEmVQYAACA5xFm65sOV0lLH9OFAet1XtpSpaux46Ww4EAteaAvgRYAANQbhNn6pkl7qeV5Ctz5gz7ts00Hu18jSdp6IEf3p63X0dwCwiwAAKg36Jmtj3rcKkmK35KmMxPCdWZitE6PizC5KAAAAM8jzNZHHftLDRpLx/ZJW740uxoAAIBaQ5itj4KsUrehJY9XzzG3FgAAgFpEmK2vug8v+b51iXR0p6mlAAAA1BbCbH3VuI102oWSDGntG2ZXAwAAUCsIs/VZ6YVgWvuWZOP2tgAAoP4hzNZn7a+QIuKl3AOK2vGV2dUAAAB4HGG2PgsMls66WZLUaNP/TC4GAADA8wiz9V33YZIsitj3g1pZ9ptdDQAAgEcRZuu7mBZS20slSUMCvza5GAAAAM8izPqD0gvBrg/8RpaiPJOLAQAA8JwgswtAHWh7mQrCm6lR7j7t//EV/RY01unlhuEhSowJM6k4AACA6iPM+oOAQOX2eUghi+9X240v6Zr1cdpktHS8HBYcqCUP9CXQAgAAn0OY9RMN+wzXiT8XKWzbIn3c9A39ec1nMoJCtfVAju5PW6+juQWEWQAA4HPomfUXFovCBv5bCm+i0CO/64zNM3RmYrROj4swuzIAAIBqI8z6k4gm0tUzSh6v+Le043tz6wEAAKghwqy/ad+v9EYKhvTxXQooOGZ2RQAAANVGmPVHl6dKMS2lrF1qunKK2dUAAABUG2HWH1kjpWv/I8mihn/M02UBP5tdEQAAQLUwm4G/atlbOu8+6Yfpei54ln75v876TclOqzD/LAAA8HaEWX920T+Vv3OVovasUM8fRunB5XfqM9t5jpeZfxYAAHg72gz8WZBV1mEf63jbqxViKdZLITP1499+1fwx52n6oG46UViso7kFZlcJAABQIcKsvwsOVYMhb0jnjpYkJaxK1Zm/PKnTYxmNBQAA3o8wCykgQLr8KSnlqZLnq2YraeldClW+uXUBAABUgZ5Z/KX3aCmyqfTxHYresUhLrKu1f+0E/WZcL1kskrgoDAAAeBfCLJydOVCKiFfRByPVPGefmq95UGt+/o8eL7xZ643TuSgMAAB4FdoMUFar8xR07xpl935ItqAwdQ/Yok+sk/Td6W8rpvAAF4UBAACvwcgsyhfSQFEpD0u9b5W+flxa/46S9izQN9ZFOjT/M+3sPETHki6UAkpOIdoPAACAGQizqFxUU2nAy1LP25W/YIKse1eq2f7F0v7FOmDE6KPi8zWvuK/2BbWg/QAAANQ5wixc06ybrKMWKWPLGgVueFsxWz9WXN4R3Rk0X3cGzddmW3PlfXahdra5ULlNe8lmjWa0FgAA1DrCLNwS37a71La7VPS0tOUraf3bMv74Uu0D9kjb/idt+5+KDYv+z2ilRTpT1155pRqddrbUqI0UyOkGAAA8i3SB6gkKkTpeJXW8SpbjR3T4/5ZK279V+L4VCs3cqi6W7eqi7dIXn0uSbIFW5Tdsp7xGnRSQ0FHRTdtKDVtJDVtK1khzPwsAAPBZXhFmZ86cqWeffVbp6enq2rWrZsyYoZ49e1a4/rx58/Too49qx44datu2rZ555hldccUVdVgxnDRopMbnXC+dc33J8+z9OrJxqb5a+LHaaYfaW3YrvDhfYYd+VdihX6U/nDcvCm2kgsgkFYY3VVFYExU1aFL6PU5hDZsqLi5BCmsohUZLAYF1//kAAIDXMj3MpqWlady4cZo1a5Z69eql6dOnKyUlRZs3b1ZcXFyZ9VesWKEhQ4YoNTVVV111ld555x0NGDBAa9eu1ZlnnmnCJ0AZUU3V6NybdEGH63Q0t0DbDZtCsncp9MhGGem/aeMva9RUGWphOaBGlhwF5R1RUN4R6eCGKnddHBKlYmu0ikOiVRwSKVtIhGzBESWPgyNkbRCpqKhoKbhByVdI6feg0NIv60nfrVJgSOl3a8md0AAAgE+xGIZhmFlAr169dM455+jf//63JMlmsykpKUn33HOPJkyYUGb9QYMGKTc3V/Pnz3csO/fcc9WtWzfNmjWryvfLzs5WdHS0srKyFBUV5bkPUoHf9mbpqhnfa/495+vMxOhafz9fsDfzhGOu2oCCYwo5tlvB2bsUfOKAgo4fVFDpd+UcUM7hfYpUriItJ2q9LsMSJFtgiBQQJCMgWIbT95LHCgiUYSl5roAgGZYAyRJY8rr9sSVQsgTICAiUFFCy3PHYUrpOgGQJkCyWkscqeV7yeoBCg4MUGRYiyVJ697XS75aAcpZZnJdJZZdVto7jsU55fOprlspfO3U/qmSfZdY9WQXL3dqHG9ytw619eIK31FFT3lwbgDKatC/5Mok7ec3UkdmCggKtWbNGEydOdCwLCAhQcnKyVq5cWe42K1eu1Lhx45yWpaSk6JNPPil3/fz8fOXn5zueZ2VlSSo5SHUh51i2bPnHlXMsW9nZ/DKXpMgAKTLSfiyipMZnSDqj3HWzM09o1/ECyVakwPwsBRZkKSA/S4GFOQooyCn5XpirwMIcFeRm6ac/9irEyFOY8ku+LAUKU4FCVCCrpUhWFSpEhbKqUFZL0SnvVlj6VTPlRC63GZLq5gwFAKCsg13v1sFuo52WNYmwqklUaJ28vz2nuTLmamqYPXTokIqLixUfH++0PD4+Xr///nu526Snp5e7fnp6ernrp6amaurUqWWWJyUlVbPq6uk9vU7fDgAAoAaeKf0y17FjxxQdXfn/bJveM1vbJk6c6DSSa7PZdOTIETVu3FgWD/2XXHZ2tpKSkrR79+46aV3wFRyXsjgm5eO4lMUxKYtjUj6OS1kck/L50nExDEPHjh1Ts2bNqlzX1DAbGxurwMBAZWRkOC3PyMhQQkJCudskJCS4tb7VapXVanVaFhMTU/2iKxEVFeX1J4cZOC5lcUzKx3Epi2NSFsekfByXsjgm5fOV41LViKydqZdvh4SEqHv37lq6dKljmc1m09KlS9W7d+9yt+ndu7fT+pK0ePHiCtcHAABA/WV6m8G4ceM0bNgw9ejRQz179tT06dOVm5urESNGSJJuueUWJSYmKjU1VZJ03333qW/fvnr++ed15ZVX6r333tPq1as1e/ZsMz8GAAAATGB6mB00aJAOHjyoSZMmKT09Xd26ddOiRYscF3nt2rVLASfN/9mnTx+98847euSRR/TPf/5Tbdu21SeffGLqHLNWq1WTJ08u087g7zguZXFMysdxKYtjUhbHpHwcl7I4JuWrr8fF9HlmAQAAgOrilkcAAADwWYRZAAAA+CzCLAAAAHwWYRYAAAA+izDropkzZ6pVq1YKDQ1Vr169tGrVqkrXnzdvnjp06KDQ0FB17txZCxcurKNK60ZqaqrOOeccRUZGKi4uTgMGDNDmzZsr3Wbu3LmyWCxOX6GhdXOP57owZcqUMp+vQ4cOlW5T388TSWrVqlWZ42KxWDR69Ohy16+P58m3336r/v37q1mzZrJYLPrkk0+cXjcMQ5MmTVLTpk0VFham5ORkbdmypcr9uvt7ydtUdlwKCws1fvx4de7cWeHh4WrWrJluueUW7du3r9J9VufPoTep6lwZPnx4mc93+eWXV7lfXz5Xqjom5f1+sVgsevbZZyvcp6+fJ678HZyXl6fRo0ercePGioiI0HXXXVfmplOnqu7vIrMRZl2QlpamcePGafLkyVq7dq26du2qlJQUHThwoNz1V6xYoSFDhui2227TunXrNGDAAA0YMEC//fZbHVdee7755huNHj1aP/74oxYvXqzCwkJddtllys3NrXS7qKgo7d+/3/G1c+fOOqq4bpxxxhlOn+/777+vcF1/OE8k6eeff3Y6JosXL5YkXX/99RVuU9/Ok9zcXHXt2lUzZ84s9/V//etfeumllzRr1iz99NNPCg8PV0pKivLy8ircp7u/l7xRZcfl+PHjWrt2rR599FGtXbtWH330kTZv3qyrr766yv268+fQ21R1rkjS5Zdf7vT53n333Ur36evnSlXH5ORjsX//fr3++uuyWCy67rrrKt2vL58nrvwdPHbsWH3++eeaN2+evvnmG+3bt08DBw6sdL/V+V3kFQxUqWfPnsbo0aMdz4uLi41mzZoZqamp5a5/ww03GFdeeaXTsl69ehl33HFHrdZppgMHDhiSjG+++abCdebMmWNER0fXXVF1bPLkyUbXrl1dXt8fzxPDMIz77rvPaNOmjWGz2cp9vb6fJ5KMjz/+2PHcZrMZCQkJxrPPPutYlpmZaVitVuPdd9+tcD/u/l7ydqcel/KsWrXKkGTs3LmzwnXc/XPozco7JsOGDTOuueYat/ZTn84VV86Ta665xrj44osrXac+nSeGUfbv4MzMTCM4ONiYN2+eY51NmzYZkoyVK1eWu4/q/i7yBozMVqGgoEBr1qxRcnKyY1lAQICSk5O1cuXKcrdZuXKl0/qSlJKSUuH69UFWVpYkqVGjRpWul5OTo5YtWyopKUnXXHON/u///q8uyqszW7ZsUbNmzXTaaadp6NCh2rVrV4Xr+uN5UlBQoP/973+69dZbZbFYKlyvvp8nJ9u+fbvS09OdzoXo6Gj16tWrwnOhOr+X6oOsrCxZLBbFxMRUup47fw590fLlyxUXF6f27dvrrrvu0uHDhytc19/OlYyMDC1YsEC33XZblevWp/Pk1L+D16xZo8LCQqefe4cOHdSiRYsKf+7V+V3kLQizVTh06JCKi4sddySzi4+PV3p6ernbpKenu7W+r7PZbLr//vt13nnnVXontvbt2+v111/Xp59+qv/973+y2Wzq06eP9uzZU4fV1p5evXpp7ty5WrRokV555RVt375dF1xwgY4dO1bu+v52nkjSJ598oszMTA0fPrzCder7eXIq+8/bnXOhOr+XfF1eXp7Gjx+vIUOGKCoqqsL13P1z6Gsuv/xyvfnmm1q6dKmeeeYZffPNN+rXr5+Ki4vLXd/fzpU33nhDkZGRVf53en06T8r7Ozg9PV0hISFl/uFXVXaxr+PqNt7C9NvZwveNHj1av/32W5X9Rr1791bv3r0dz/v06aOOHTvqP//5jx5//PHaLrPW9evXz/G4S5cu6tWrl1q2bKn333/fpVECf/Df//5X/fr1U7NmzSpcp76fJ3BfYWGhbrjhBhmGoVdeeaXSdev7n8PBgwc7Hnfu3FldunRRmzZttHz5cl1yySUmVuYdXn/9dQ0dOrTKi0br03ni6t/B9Rkjs1WIjY1VYGBgmSsAMzIylJCQUO42CQkJbq3vy8aMGaP58+dr2bJlat68uVvbBgcH66yzztLWrVtrqTpzxcTEqF27dhV+Pn86TyRp586dWrJkiUaOHOnWdvX9PLH/vN05F6rze8lX2YPszp07tXjx4kpHZctT1Z9DX3faaacpNja2ws/nT+fKd999p82bN7v9O0by3fOkor+DExISVFBQoMzMTKf1q8ou9nVc3cZbEGarEBISou7du2vp0qWOZTabTUuXLnUaPTpZ7969ndaXpMWLF1e4vi8yDENjxozRxx9/rK+//lqtW7d2ex/FxcX69ddf1bRp01qo0Hw5OTn6888/K/x8/nCenGzOnDmKi4vTlVde6dZ29f08ad26tRISEpzOhezsbP30008VngvV+b3ki+xBdsuWLVqyZIkaN27s9j6q+nPo6/bs2aPDhw9X+Pn85VyRSv7np3v37uratavb2/raeVLV38Hdu3dXcHCw08998+bN2rVrV4U/9+r8LvIaJl+A5hPee+89w2q1GnPnzjU2btxo3H777UZMTIyRnp5uGIZh3HzzzcaECRMc6//www9GUFCQ8dxzzxmbNm0yJk+ebAQHBxu//vqrWR/B4+666y4jOjraWL58ubF//37H1/Hjxx3rnHpcpk6danz55ZfGn3/+aaxZs8YYPHiwERoaavzf//2fGR/B4x544AFj+fLlxvbt240ffvjBSE5ONmJjY40DBw4YhuGf54ldcXGx0aJFC2P8+PFlXvOH8+TYsWPGunXrjHXr1hmSjGnTphnr1q1zXJX/9NNPGzExMcann35q/PLLL8Y111xjtG7d2jhx4oRjHxdffLExY8YMx/Oqfi/5gsqOS0FBgXH11VcbzZs3N9avX+/0eyY/P9+xj1OPS1V/Dr1dZcfk2LFjxoMPPmisXLnS2L59u7FkyRLj7LPPNtq2bWvk5eU59lHfzpWq/vwYhmFkZWUZDRo0MF555ZVy91HfzhNX/g6+8847jRYtWhhff/21sXr1aqN3795G7969nfbTvn1746OPPnI8d+V3kTcizLpoxowZRosWLYyQkBCjZ8+exo8//uh4rW/fvsawYcOc1n///feNdu3aGSEhIcYZZ5xhLFiwoI4rrl2Syv2aM2eOY51Tj8v999/vOIbx8fHGFVdcYaxdu7bui68lgwYNMpo2bWqEhIQYiYmJxqBBg4ytW7c6XvfH88Tuyy+/NCQZmzdvLvOaP5wny5YtK/fPi/1z22w249FHHzXi4+MNq9VqXHLJJWWOVcuWLY3Jkyc7Lavs95IvqOy4bN++vcLfM8uWLXPs49TjUtWfQ29X2TE5fvy4cdlllxlNmjQxgoODjZYtWxqjRo0qE0rr27lS1Z8fwzCM//znP0ZYWJiRmZlZ7j7q23niyt/BJ06cMO6++26jYcOGRoMGDYxrr73W2L9/f5n9nLyNK7+LvJHFMAyjdsZ8AQAAgNpFzywAAAB8FmEWAAAAPoswCwAAAJ9FmAUAAIDPIswCAADAZxFmAQAA4LMIswAAAPBZhFkAAAD4LMIsANSy5cuXy2KxKDMzs07fd+7cuYqJianRPnbs2CGLxaL169dXuI5Znw8AJMIsANSIxWKp9GvKlClmlwgA9VqQ2QUAgC/bv3+/43FaWpomTZqkzZs3O5ZFRERo9erVbu+3oKBAISEhHqkRAOozRmYBoAYSEhIcX9HR0bJYLE7LIiIiHOuuWbNGPXr0UIMGDdSnTx+n0DtlyhR169ZNr732mlq3bq3Q0FBJUmZmpkaOHKkmTZooKipKF198sTZs2ODYbsOGDbrooosUGRmpqKgode/evUx4/vLLL9WxY0dFRETo8ssvdwrgNptNjz32mJo3by6r1apu3bpp0aJFlX7mhQsXql27dgoLC9NFF12kHTt21OQQAkCNEGYBoI48/PDDev7557V69WoFBQXp1ltvdXp969at+vDDD/XRRx85elSvv/56HThwQF988YXWrFmjs88+W5dccomOHDkiSRo6dKiaN2+un3/+WWvWrNGECRMUHBzs2Ofx48f13HPP6a233tK3336rXbt26cEHH3S8/uKLL+r555/Xc889p19++UUpKSm6+uqrtWXLlnI/w+7duzVw4ED1799f69ev18iRIzVhwgQPHykAcIMBAPCIOXPmGNHR0WWWL1u2zJBkLFmyxLFswYIFhiTjxIkThmEYxuTJk43g4GDjwIEDjnW+++47IyoqysjLy3PaX5s2bYz//Oc/hmEYRmRkpDF37twK65FkbN261bFs5syZRnx8vON5s2bNjCeffNJpu3POOce4++67DcMwjO3btxuSjHXr1hmGYRgTJ040OnXq5LT++PHjDUnG0aNHy60DAGoTI7MAUEe6dOnieNy0aVNJ0oEDBxzLWrZsqSZNmjieb9iwQTk5OWrcuLEiIiIcX9u3b9eff/4pSRo3bpxGjhyp5ORkPf30047ldg0aNFCbNm2c3tf+ntnZ2dq3b5/OO+88p23OO+88bdq0qdzPsGnTJvXq1ctpWe/evV0+BgDgaVwABgB15OT//rdYLJJKelbtwsPDndbPyclR06ZNtXz58jL7sk+5NWXKFN14441asGCBvvjiC02ePFnvvfeerr322jLvaX9fwzA88XEAwCswMgsAXurss89Wenq6goKCdPrppzt9xcbGOtZr166dxo4dq6+++koDBw7UnDlzXNp/VFSUmjVrph9++MFp+Q8//KBOnTqVu03Hjh21atUqp2U//vijm58MADyHMAsAXio5OVm9e/fWgAED9NVXX2nHjh1asWKFHn74Ya1evVonTpzQmDFjtHz5cu3cuVM//PCDfv75Z3Xs2NHl9/jHP/6hZ555Rmlpadq8ebMmTJig9evX67777it3/TvvvFNbtmzRP/7xD23evFnvvPOO5s6d66FPDADuo80AALyUxWLRwoUL9fDDD2vEiBE6ePCgEhIS9Le//U3x8fEKDAzU4cOHdcsttygjI0OxsbEaOHCgpk6d6vJ73HvvvcrKytIDDzygAwcOqFOnTvrss8/Utm3bctdv0aKFPvzwQ40dO1YzZsxQz5499dRTT5WZmQEA6orFoHkKAAAAPoo2AwAAAPgswiwAAAB8FmEWAAAAPoswCwAAAJ9FmAUAAIDPIswCAADAZxFmAQAA4LMIswAAAPBZhFkAAAD4LMIsAAAAfBZhFgAAAD7r/wGvhvJyNa2szgAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Iterate over each column in df_bkg\n", + "for column in df_bkg.columns:\n", + " # Create a new figure for each column\n", + " plt.figure(figsize=(8, 6))\n", + " \n", + " # Compute FPR for the current column\n", + " FPR, bins_bkg, _ = plt.hist(df_bkg[column], bins=100, histtype=\"step\", cumulative=-1, density=True)\n", + " \n", + " # Plot FPR\n", + " plt.plot(bins_bkg[:-1], FPR)\n", + " \n", + " # Add title and labels\n", + " plt.xlabel('Threshold')\n", + " plt.ylabel('False Positive Rate')\n", + " plt.title(f'False Positive Rate for {column}')\n", + " \n", + " plt.grid(False)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercise 5.3 \n", + "# This function needs to compute the different selection criterias:\n", + "# 1. x > x_c\n", + "# 2. x < x_c\n", + "# 3. |x - \\mu| > x_c\n", + "# 4. |x - \\mu| < x_c\n", + "\n", + "def compute_rate(d,bins=100):\n", + " hist,bins_=np.histogram(d,bins=bins,density=True)\n", + " R = np.cumsum(hist[::-1])[::-1] * (bins_[1]-bins_[0])\n", + " return R,bins_" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from tabulate import tabulate\n", + "from IPython.display import display, HTML\n", + "\n", + "def compare_significance(df_sig, df_bkg,\n", + " obs_name,\n", + " scenarios,bins=100,log=False):\n", + " \n", + " TPR,bins_sig = compute_rate(df_sig[obs_name],bins=bins)\n", + " FPR,bins_sig = compute_rate(df_bkg[obs_name],bins=bins_sig)\n", + " \n", + " max_sigs=dict()\n", + " table=list()\n", + " \n", + " for name, (n_sig_expected, n_bkg_expected) in scenarios.items():\n", + "\n", + " n_sig_expected_prime = n_sig_expected * TPR\n", + " n_bkg_expected_prime = n_bkg_expected * FPR\n", + "\n", + " sig = n_sig_expected_prime/ np.sqrt(n_sig_expected_prime + n_bkg_expected_prime )\n", + " plt.step(bins_sig[:-1],sig,label=name+\" \"+str((n_sig_expected, n_bkg_expected)))\n", + " \n", + " max_i=np.argmax(sig)\n", + " max_sigs[name]=(max_i,n_sig_expected_prime[max_i],n_bkg_expected_prime[max_i],sig[max_i],bins_sig[max_i])\n", + " table.append((name,n_sig_expected, n_bkg_expected, \n", + " TPR[max_i],FPR[max_i],\n", + " n_sig_expected_prime[max_i],n_bkg_expected_prime[max_i],sig[max_i],bins_sig[max_i],max_i)\n", + " )\n", + " if log:\n", + " plt.yscale(\"log\")\n", + " plt.legend()\n", + " plt.show()\n", + " \n", + " display(HTML(tabulate(table, tablefmt='html',\n", + " headers=[\"Name\",'N sig','N bkg',\"TPR\",\"FPR\",\"N sig'\",\"N bkg'\",'sig','x_c',\"bin i\"])))\n", + " return max_sigs" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "signal\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/numpy/lib/histograms.py:885: RuntimeWarning: invalid value encountered in divide\n", + " return n/db/n.sum(), bin_edges\n" + ] + }, + { + "data": { + "image/png": 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VlSk8PFylpaVyOBwt3Q4AfGdnz55VQUGB4uPjL4mLbXfv3q0f//jH+vzzzxUWFnZR952RkaGNGzfqo48+uqj7vVDTp0/XiRMnvB7+15o19Lvc2O9vrrkBALQ511xzjZ555hkVFBRc9H2vXr26ztcgtFaRkZH67W9/29JtXFQXJdwsWLBAXbt2VXBwsJKSkmpdrf5t53tPxjfde++9stlseuGFF5q5awBAa3bXXXepb9++F32/27ZtU2Ji4kXf74WaOnVqrWfhWJ3Pw83y5cs1ZcoUZWRkaOfOnerXr59SU1Nr3QJXozHvyajx7rvvauvWrRf0MCcAAGBNPg83v//973X33XdrwoQJuuqqq7Ro0SKFhobq9ddfr7P+xRdf1PDhwzVt2jT17t1bv/3tb3Xttddq/vz5XnWHDx/WAw88oDfffFOBgYG+ngYAAGgjfBpuKisrlZ2d7fXeCz8/P6WkpNT7no7GvCfD7XZr3LhxmjZtmvr06XPePioqKlRWVub1AQAA1uTTcHPs2DFVV1c36r0XNRrznoxnnnlGAQEBevDBBxvVx5w5cxQeHu75xMbGNnEmAACgrWhzd0tlZ2frxRdf1JIlS2Rr5FMwZ86cqdLSUs/n0KFDPu4SAAC0FJ+Gm4iICPn7+zfqvRc1zveejM2bN6uoqEhxcXEKCAhQQECADhw4oKlTp6pr1651bjMoKEgOh8PrAwAArMmn4cZutyshIcHrvRdut1vr1q2r9z0d53tPxrhx47R7927t2rXL84mJidG0adP04Ycf+m4yAACgTfD5n6WmTJmiP/zhD/rTn/6kvXv36r777tPp06c1YcIESdKdd96pmTNneuofeughrVmzRr/73e+0b98+PfHEE9qxY4cmT54sSerYsaOuvvpqr09gYKCio6PVs2dPX08HANAKVFZWqlu3btqyZUtLt3JJWLNmjfr37y+3293SrTSKz8PNmDFj9Nxzz2n27Nnq37+/du3apTVr1nguGj548KCOHj3qqR88eLCWLl2q1157Tf369dM777yj9957T1dffbWvWwUAXGQLFy7UNddc47lkIDk5WatXrz7veosWLVJ8fLwGDx7sGXv66ac1ePBghYaGyul01rnewYMHNWLECIWGhioyMlLTpk3TuXPnvGo++ugjXXvttQoKClK3bt20ZMmSWttp6sNp6/Lggw8qISFBQUFB6t+/f501u3fv1g9/+EMFBwcrNja2zicjn+/Bt8YYzZ49W507d1ZISIhSUlL02WefedUUFxdr7NixcjgccjqdSk9P16lTpzzLhw8frsDAQL355ptNnmeL8Mlbr1o5XpwJwGra6osz//73v5sPPvjA/N///Z/Zv3+/eeyxx0xgYKDJy8urdx232226d+9u3nrrLa/x2bNnm9///vdmypQpJjw8vNZ6NW8FT0lJMTk5OWbVqlUmIiKizreCT5kyxeTn55uXX3651lvBly1bZux2u3n99dfNnj17zN13322cTqcpLCxs0twfeOABM3/+fDNu3DjTr1+/WstLS0tNVFSUGTt2rMnLyzNvvfWWCQkJqfVWcH9/f/Pss8+a/Px8M2vWrFpvBZ87d64JDw837733nvn000/NzTffbOLj471+V4YPH2769etntm7dajZv3my6devm9YZyY4yZP3++GThwYJPmeCF4K/gFItwAsJq2Gm7q0r59e/Pf//3f9S7fvn278fPzM2VlZXUuX7x4cZ3hZtWqVcbPz8+4XC7P2MKFC43D4TAVFRXGGGMeffRR06dPH6/1xowZY1JTUz0/JyYmmkmTJnl+rq6uNjExMWbOnDmNmt+3ZWRk1BluXnnlFdO+fXtPb8YYM336dNOzZ0/Pz6NHjzYjRozwWi8pKcncc889xpivg2B0dLSZN2+eZ3lJSYkJCgryhMP8/HwjyWzfvt1Ts3r1amOz2czhw4c9YwcOHDCSzD//+c8LmmdjNUe4aXO3ggMAzs8YozNVZ1rkY4y5oJ6rq6u1bNkynT59ut6bTqSv75rt0aOH2rVr16TtZ2Zmqm/fvl7PUktNTVVZWZn27NnjqWnoQbIX8nDaC5WZmanrr79edrvdq5f9+/frxIkTjeq3oKBALpfLqyY8PFxJSUmemszMTDmdTg0cONBTk5KSIj8/P2VlZXnG4uLiFBUVpc2bNzfrPH0hoKUbAAA0v/Jz5UpamtQi+876RZZCA0MbXZ+bm6vk5GSdPXtWYWFhevfdd3XVVVfVW3/gwIELeqdgfQ+JrVnWUE1ZWZnKy8t14sSJeh9Ou2/fvib3dL5+4+Pj6+23ffv2533wbc2/56uJjIz0Wh4QEKAOHTrUeuBuTEyMDhw48B1n5nucuQEAtKiePXtq165dysrK0n333afx48crPz+/3vry8nIFBwdfxA5RIyQkRGfOnGnpNs6LMzcAYEEhASHK+kXW+Qt9tO+msNvt6tatmyQpISFB27dv14svvqhXX321zvqIiAjl5uY2ua/o6OhadzXVPDS25kGx9T1I1uFwKCQkRP7+/k1+OO2Fqq+XxvT7zeU1Y507d/aqqblDKzo6WkVFRV7bOHfunIqLi2vNqbi4WJ06dfqOM/M9ztwAgAXZbDaFBoa2yKexr8apj9vtVkVFRb3LBwwYoH379jX52p7k5GTl5uZ6fZGvXbtWDofD82ew8z1I9kIeTnuhkpOTtWnTJlVVVXn10rNnT7Vv375R/cbHxys6OtqrpqysTFlZWZ6a5ORklZSUKDs721Ozfv16ud1uJSX9+0+bZ8+e1eeff64BAwY06zx9wieXOrdy3C0FwGra6t1SM2bMMBs3bjQFBQVm9+7dZsaMGcZms5n//d//rXedY8eO1brd2Ziv7+bJyckxTz75pAkLCzM5OTkmJyfHnDx50hjz71vBhw0bZnbt2mXWrFljOnXqVOet4NOmTTN79+41CxYsqPNW8KCgILNkyRKTn59vJk6caJxOp9ddWI3x2WefmZycHHPPPfeYHj16ePqtuTuqpKTEREVFmXHjxpm8vDyzbNkyExoaWutW8ICAAPPcc8+ZvXv3moyMjDpvBXc6neZvf/ub2b17t7nlllvqvBV8wIABJisry3z88ceme/futW4F37BhgwkLCzOnT59u0jybilvBLxDhBoDVtNVw88tf/tJ06dLF2O1206lTJzN06NAGg02N0aNHmxkzZniNjR8/3kiq9dmwYYOn5osvvjA33XSTCQkJMREREWbq1KmmqqrKazsbNmww/fv3N3a73Vx55ZVm8eLFtfb/8ssvm7i4OGO3201iYqLZunVrrV6GDBnS4ByGDBlSZ78FBQWemk8//dRcd911JigoyFx++eVm7ty5tbbz9ttvmx49ehi73W769OljPvjgA6/lbrfbPP744yYqKsoEBQWZoUOHmv3793vVHD9+3KSlpZmwsDDjcDjMhAkTPKGwxsSJEz23mPtSc4QbmzEXeM9eG1ZWVqbw8HCVlpbyEk0AlnD27FkVFBQoPj7+krjYdvfu3frxj3+szz//XGFhYS3dTi1DhgzRjTfeqCeeeKKlW2kWx44dU8+ePbVjx45ad3A1t4Z+lxv7/c01NwCANueaa67RM888o4KCgpZupZbS0lJ9/vnn+vWvf93SrTSbL774Qq+88orPg01z4W4pAECbdNddd7V0C3UKDw/Xl19+2dJtNKuBAwd6PeSvtePMDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQAAsBTCDQCgzamsrFS3bt20ZcuWlm4FTTBo0CD95S9/8fl+CDcAgFZh7ty5stlsevjhh89bu2jRIsXHx2vw4MGesaefflqDBw9WaGionE5nnesdPHhQI0aMUGhoqCIjIzVt2jSdO3fOq+ajjz7Stddeq6CgIHXr1k1LliyptZ0FCxaoa9euCg4OVlJSkrZt2+a1/OzZs5o0aZI6duyosLAwjRo1SoWFheed1zcdPXpUv/jFL9SjRw/5+fnVe1xWrFihXr16KTg4WH379tWqVau8lhtjNHv2bHXu3FkhISFKSUnRZ5995lVTXFyssWPHyuFwyOl0Kj09XadOnfKq2b17t374wx8qODhYsbGxevbZZ5vcy6xZszRjxgy53e4mHYumItwAAFrc9u3b9eqrr+qaa645b60xRvPnz1d6errXeGVlpX7+85/rvvvuq3O96upqjRgxQpWVldqyZYv+9Kc/acmSJZo9e7anpqCgQCNGjNCNN96oXbt26eGHH9avfvUrffjhh56a5cuXa8qUKcrIyNDOnTvVr18/paamqqioyFPzyCOP6P3339eKFSu0ceNGHTlyRLfddluTjklFRYU6deqkWbNmqV+/fnXWbNmyRWlpaUpPT1dOTo5GjhypkSNHKi8vz1Pz7LPP6qWXXtKiRYuUlZWlyy67TKmpqTp79qynZuzYsdqzZ4/Wrl2rlStXatOmTZo4caJneVlZmYYNG6YuXbooOztb8+bN0xNPPKHXXnutSb3cdNNNOnnypFavXt2kY9FkvnijZ2vHW8EBWE1bfSu4McacPHnSdO/e3axdu9YMGTLEPPTQQw3Wb9++3fj5+ZmysrI6ly9evNiEh4fXGl+1apXx8/MzLpfLM7Zw4ULjcDhMRUWFMcaYRx991PTp08drvTFjxpjU1FTPz4mJiWbSpEmen6urq01MTIyZM2eOMcaYkpISExgYaFasWOGp2bt3r5FkMjMzG5xbfeo7LqNHjzYjRozwGktKSvK8vdvtdpvo6Ggzb948z/KSkhITFBRk3nrrLWOMMfn5+UaS2b59u6dm9erVxmazmcOHDxtjjHnllVdM+/btPcfJGGOmT59uevbs2eheakyYMMHccccd9c61Od4KzpkbALAgY4zcZ860yMcY06ReJ02apBEjRiglJaVR9Zs3b1aPHj3Url27Ju0nMzNTffv2VVRUlGcsNTVVZWVl2rNnj6fm232kpqYqMzNT0tdnh7Kzs71q/Pz8lJKS4qnJzs5WVVWVV02vXr0UFxfnqWku5+u3oKBALpfLqyY8PFxJSUmemszMTDmdTq93R6WkpMjPz09ZWVmemuuvv152u91rP/v379eJEyca1UuNxMREbd68+btOvUG8OBMALMiUl2v/tQktsu+eO7NlCw1tVO2yZcu0c+dObd++vdHbP3DggGJiYprcl8vl8go2kjw/u1yuBmvKyspUXl6uEydOqLq6us6affv2ebZht9trXfcTFRXl2U9zqa/fb86nZqyhmsjISK/lAQEB6tChg1fNt98I/s1j1759+/P2UiMmJkaHDh2S2+2Wn59vzrEQbgAALeLQoUN66KGHtHbtWgUHBzd6vfLy8ibVo3UJCQmR2+1WRUWFQkJCfLIPwg0AWJAtJEQ9d2a32L4bIzs7W0VFRbr22ms9Y9XV1dq0aZPmz5+viooK+fv711ovIiJCubm5Te4rOjq61l1NNXcwRUdHe/799l1NhYWFcjgcCgkJkb+/v/z9/eus+eY2KisrVVJS4nX25ps1zaW+fr/ZS81Y586dvWr69+/vqfnmxdCSdO7cORUXF5/3uHxzH+frpUZxcbEuu+wynwUbibulAMCSbDab/EJDW+Rjs9ka1ePQoUOVm5urXbt2eT4DBw7U2LFjtWvXrjqDjSQNGDBA+/bta/K1PcnJycrNzfX6Il+7dq0cDoeuuuoqT826deu81lu7dq2Sk5MlSXa7XQkJCV41brdb69at89QkJCQoMDDQq2b//v06ePCgp6a5nK/f+Ph4RUdHe9WUlZUpKyvLU5OcnKySkhJlZ/87DK9fv15ut1tJSUmemk2bNqmqqsprPz179lT79u0b1UuNvLw8DRgw4LtOvWENXm5sUdwtBcBq2vLdUt/UmLuljh07ZgIDA01ubq7X+IEDB0xOTo558sknTVhYmMnJyTE5OTnm5MmTxhhjzp07Z66++mozbNgws2vXLrNmzRrTqVMnM3PmTM82/vWvf5nQ0FAzbdo0s3fvXrNgwQLj7+9v1qxZ46lZtmyZCQoKMkuWLDH5+flm4sSJxul0et2Fde+995q4uDizfv16s2PHDpOcnGySk5ObfDxq5pCQkGB+8YtfmJycHLNnzx7P8k8++cQEBASY5557zuzdu9dkZGTUOjZz5841TqfT/O1vfzO7d+82t9xyi4mPj/f6XRk+fLgZMGCAycrKMh9//LHp3r27SUtL8ywvKSkxUVFRZty4cSYvL88sW7bMhIaGmldffbVJvRjz9f+Nn3rqqXrn3Bx3SxFuAMACLqVwY8zXtx3PmDHDa2z8+PFGUq3Phg0bPDVffPGFuemmm0xISIiJiIgwU6dONVVVVV7b2bBhg+nfv7+x2+3myiuvNIsXL661/5dfftnExcUZu91uEhMTzdatW72Wl5eXm/vvv9+0b9/ehIaGmltvvdUcPXrUq6ZLly4mIyOjwXnWNZ8uXbp41bz99tumR48exm63mz59+pgPPvjAa7nb7TaPP/64iYqKMkFBQWbo0KFm//79XjXHjx83aWlpJiwszDgcDjNhwgRPKKzx6aefmuuuu84EBQWZyy+/3MydO7dWv+fr5csvvzSBgYHm0KFD9c65OcKNzZgmntezgLKyMoWHh6u0tFQOh6Ol2wGA7+zs2bMqKChQfHz8JXGx7e7du/XjH/9Yn3/+ucLCwlq6nSY7c+aMOnbsqNWrV+uGG25o6XYumunTp+vEiRNeD//7toZ+lxv7/c01NwCANueaa67RM888o4KCgpZu5YJs2LBBP/rRjy6pYCNJkZGR+u1vf+vz/XDmhjM3ACzgUjtzA+vizA0AAMC3EG4AAIClEG4AAIClEG4AwEIuwcsoYTHN8TtMuAEACwgMDJT09S3GQFtW8ztc8zt9IXi3FABYgL+/v5xOp+fVAqFNeA0C0BoYY3TmzBkVFRXJ6XTW+/qNxiDcAIBF1Lyg8NsvQQTaEqfT+Z1fMEq4AQCLsNls6ty5syIjI71ecAi0FYGBgd/pjE0Nwg0AWIy/v3+zfEEAbRUXFAMAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEu5KOFmwYIF6tq1q4KDg5WUlKRt27Y1WL9ixQr16tVLwcHB6tu3r1atWuVZVlVVpenTp6tv37667LLLFBMTozvvvFNHjhzx9TQAAEAb4PNws3z5ck2ZMkUZGRnauXOn+vXrp9TUVBUVFdVZv2XLFqWlpSk9PV05OTkaOXKkRo4cqby8PEnSmTNntHPnTj3++OPauXOn/vrXv2r//v26+eabfT0VAADQBtiMMcaXO0hKStL3v/99zZ8/X5LkdrsVGxurBx54QDNmzKhVP2bMGJ0+fVorV670jA0aNEj9+/fXokWL6tzH9u3blZiYqAMHDiguLu68PZWVlSk8PFylpaVyOBwXODMAAHAxNfb726dnbiorK5Wdna2UlJR/79DPTykpKcrMzKxznczMTK96SUpNTa23XpJKS0tls9nkdDrrXF5RUaGysjKvDwAAsCafhptjx46purpaUVFRXuNRUVFyuVx1ruNyuZpUf/bsWU2fPl1paWn1prg5c+YoPDzc84mNjb2A2QAAgLagTd8tVVVVpdGjR8sYo4ULF9ZbN3PmTJWWlno+hw4duohdAgCAiynAlxuPiIiQv7+/CgsLvcYLCwsVHR1d5zrR0dGNqq8JNgcOHND69esb/NtbUFCQgoKCLnAWAACgLfHpmRu73a6EhAStW7fOM+Z2u7Vu3TolJyfXuU5ycrJXvSStXbvWq74m2Hz22Wf6xz/+oY4dO/pmAgAAoM3x6ZkbSZoyZYrGjx+vgQMHKjExUS+88IJOnz6tCRMmSJLuvPNOXX755ZozZ44k6aGHHtKQIUP0u9/9TiNGjNCyZcu0Y8cOvfbaa5K+DjY/+9nPtHPnTq1cuVLV1dWe63E6dOggu93u6ykBAIBWzOfhZsyYMfrqq680e/ZsuVwu9e/fX2vWrPFcNHzw4EH5+f37BNLgwYO1dOlSzZo1S4899pi6d++u9957T1dffbUk6fDhw/r73/8uSerfv7/XvjZs2KAbbrjB11MCAACtmM+fc9Ma8ZwbAADanlbxnBsAAICLjXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAs5aKEmwULFqhr164KDg5WUlKStm3b1mD9ihUr1KtXLwUHB6tv375atWqV13JjjGbPnq3OnTsrJCREKSkp+uyzz3w5BQAA0Eb4PNwsX75cU6ZMUUZGhnbu3Kl+/fopNTVVRUVFddZv2bJFaWlpSk9PV05OjkaOHKmRI0cqLy/PU/Pss8/qpZde0qJFi5SVlaXLLrtMqampOnv2rK+nAwAAWjmbMcb4cgdJSUn6/ve/r/nz50uS3G63YmNj9cADD2jGjBm16seMGaPTp09r5cqVnrFBgwapf//+WrRokYwxiomJ0dSpU/XrX/9aklRaWqqoqCgtWbJEt99++3l7KisrU3h4uEpLS+VwOJpppgAAwJca+/3t0zM3lZWVys7OVkpKyr936OenlJQUZWZm1rlOZmamV70kpaameuoLCgrkcrm8asLDw5WUlFTvNisqKlRWVub1AQAA1uTTcHPs2DFVV1crKirKazwqKkoul6vOdVwuV4P1Nf82ZZtz5sxReHi45xMbG3tB8wEAAK3fJXG31MyZM1VaWur5HDp0qKVbAgAAPuLTcBMRESF/f38VFhZ6jRcWFio6OrrOdaKjoxusr/m3KdsMCgqSw+Hw+gAAAGvyabix2+1KSEjQunXrPGNut1vr1q1TcnJyneskJyd71UvS2rVrPfXx8fGKjo72qikrK1NWVla92wQAAJeOAF/vYMqUKRo/frwGDhyoxMREvfDCCzp9+rQmTJggSbrzzjt1+eWXa86cOZKkhx56SEOGDNHvfvc7jRgxQsuWLdOOHTv02muvSZJsNpsefvhh/ed//qe6d++u+Ph4Pf7444qJidHIkSN9PR0AANDK+TzcjBkzRl999ZVmz54tl8ul/v37a82aNZ4Lgg8ePCg/v3+fQBo8eLCWLl2qWbNm6bHHHlP37t313nvv6eqrr/bUPProozp9+rQmTpyokpISXXfddVqzZo2Cg4N9PR0AANDK+fw5N60Rz7kBAKDtaRXPuQEAALjYCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDc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3 1000 100000.6177110.21567 617.711 2156.7 11.7274 0.920654 4
4 10000 1000000.6177110.215676177.11 21567 37.0852 0.920654 4
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", 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", 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", 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", 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", 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4 10000 100000 1 1 10000 10000030.1511 2.45273e-06 0
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3 1000 10000 1 1 1000 10000 9.53463 1.50257e-07 0
4 10000 100000 1 1 10000 10000030.1511 1.50257e-07 0
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scenarios = { \"1\": (10,100), \n", + " \"2\": (100,1000),\n", + " \"3\": (1000,10000),\n", + " \"4\": (10000,100000)\n", + " }\n", + "my_obs= ['signal',\n", + " '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',\n", + " 'MET_rel',\n", + " 'axial_MET',\n", + " 'M_R',\n", + " 'M_TR_2',\n", + " 'R',\n", + " 'MT2',\n", + " 'S_R',\n", + " 'M_Delta_R',\n", + " 'dPhi_r_b',\n", + " 'cos_theta_r1']\n", + "\n", + "for obs in my_obs:\n", + " print(obs)\n", + " _=compare_significance(df_sig,df_bkg,obs,scenarios)" + ] + }, + { + "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": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercise 6.1\n", + "# l_1_pT, x_c = 0.920654\n", + "#l_2_pT, x_c = 0.428588\n", + "# MET, x_c = 1.47506" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "l_1_pT:\n", + "0.617711041962526\n", + "0.2156709767407905\n", + "\n", + "l_2_pT:\n", + "1.0\n", + "0.9999981564597834\n", + "\n", + "MET:\n", + "0.349463049435119\n", + "0.01983649273110528\n" + ] + } + ], + "source": [ + "# Exercise 6.2\n", + "# l_1_pT\n", + "TPR_1=sum(df_sig[\"l_1_pT\"]>0.920654)/df_sig.shape[0]\n", + "FPR_1=sum(df_bkg[\"l_1_pT\"]>0.920654)/df_bkg.shape[0]\n", + "\n", + "print(\"\\nl_1_pT:\")\n", + "print(TPR_1)\n", + "print(FPR_1)\n", + "\n", + "# l_2_pT\n", + "TPR_2=sum(df_sig[\"l_2_pT\"]>0.428588)/df_sig.shape[0]\n", + "FPR_2=sum(df_bkg[\"l_2_pT\"]>0.428588)/df_bkg.shape[0]\n", + "\n", + "print(\"\\nl_2_pT:\")\n", + "print(TPR_2)\n", + "print(FPR_2)\n", + "\n", + "# MET\n", + "TPR_3=sum(df_sig[\"MET\"]>1.47506)/df_sig.shape[0]\n", + "FPR_3=sum(df_bkg[\"MET\"]>1.47506)/df_bkg.shape[0]\n", + "\n", + "print(\"\\nMET:\")\n", + "print(TPR_3)\n", + "print(FPR_3)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.617711041962526\n", + "0.2156709767407905\n" + ] + } + ], + "source": [ + "TPR_1_2_3=sum(np.logical_and(df_sig[\"l_1_pT\"]>0.920654, df_sig[\"l_2_pT\"]>0.428588, df_sig[\"MET\"]>1.47506))/df_sig.shape[0]\n", + "FPR_1_2_3=sum(np.logical_and(df_bkg[\"l_1_pT\"]>0.920654, df_bkg[\"l_2_pT\"]>0.428588, df_bkg[\"MET\"]>1.47506))/df_bkg.shape[0]\n", + "\n", + "print(TPR_1_2_3)\n", + "print(FPR_1_2_3)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Cut 1 Cut 2 Cut 3 Cut 1 * Cut 2 * Cut 3 Cut 1, Cut 2, & Cut 3
0.6177111 0.349463 0.215867 0.617711
0.2156710.9999980.0198365 0.00427815 0.215671
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(HTML(tabulate([[ TPR_1, TPR_2, TPR_3, TPR_1*TPR_2*TPR_3, TPR_1_2_3],\n", + " [FPR_1, FPR_2, FPR_3, FPR_1*FPR_2*FPR_3, FPR_1_2_3]],\n", + " tablefmt='html',\n", + " headers=[\"Cut 1\",'Cut 2','Cut 3', 'Cut 1 * Cut 2 * Cut 3','Cut 1, Cut 2, & Cut 3'])))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "l_1_pT:\n", + "0.617711041962526\n", + "0.2156709767407905\n", + "\n", + "M_TR_2:\n", + "0.3632149633691708\n", + "0.04698077888099321\n" + ] + } + ], + "source": [ + "# Exercise 6.3\n", + "# l_1_pT\n", + "TPR_1=sum(df_sig[\"l_1_pT\"]>0.920654)/df_sig.shape[0]\n", + "FPR_1=sum(df_bkg[\"l_1_pT\"]>0.920654)/df_bkg.shape[0]\n", + "\n", + "print(\"\\nl_1_pT:\")\n", + "print(TPR_1)\n", + "print(FPR_1)\n", + "\n", + "# M_TR_2 - 2nd variable to test\n", + "TPR_4=sum(df_sig[\"M_TR_2\"]>1.35029)/df_sig.shape[0]\n", + "FPR_4=sum(df_bkg[\"M_TR_2\"]>1.35029)/df_bkg.shape[0]\n", + "\n", + "print(\"\\nM_TR_2:\")\n", + "print(TPR_4)\n", + "print(FPR_4)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.31885278038942627\n", + "0.04125511167613571\n" + ] + } + ], + "source": [ + "TPR_1_4=sum(np.logical_and(df_sig[\"l_1_pT\"]>0.920654, df_sig[\"M_TR_2\"]>1.35029))/df_sig.shape[0]\n", + "FPR_1_4=sum(np.logical_and(df_bkg[\"l_1_pT\"]>0.920654, df_bkg[\"M_TR_2\"]>1.35029))/df_bkg.shape[0]\n", + "\n", + "print(TPR_1_4)\n", + "print(FPR_1_4)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.31885278038942627\n", + "0.04125511167613571\n" + ] + } + ], + "source": [ + "TPR_4_1=sum(np.logical_and(df_sig[\"M_TR_2\"]>1.35029, df_sig[\"l_1_pT\"]>0.920654))/df_sig.shape[0]\n", + "FPR_4_1=sum(np.logical_and(df_bkg[\"M_TR_2\"]>1.35029, df_bkg[\"l_1_pT\"]>0.920654))/df_bkg.shape[0]\n", + "\n", + "print(TPR_4_1)\n", + "print(FPR_4_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Cut 1 Cut 2 Cut 1 * Cut 4 Cut 1 & Cut 4
0.6177110.363215 0.224362 0.318853
0.2156710.0469808 0.0101324 0.0412551
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(HTML(tabulate([[ TPR_1, TPR_4, TPR_1*TPR_4, TPR_1_4],\n", + " [FPR_1, FPR_4, FPR_1*FPR_4, FPR_1_4]],\n", + " tablefmt='html',\n", + " headers=[\"Cut 1\",'Cut 2','Cut 1 * Cut 4','Cut 1 & Cut 4'])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "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": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercise 7.1: l_1_pT, l_2_pT, MET\n", + "def compute_rate(d,bins=100):\n", + " hist,bins_=np.histogram(d,bins=bins,density=True)\n", + " R = np.cumsum(hist[::-1])[::-1] * (bins_[1]-bins_[0])\n", + " return R,bins_\n", + "\n", + "# AUC function\n", + "def AUC(TPR, FPR):\n", + " n = FPR.shape[0]\n", + " return np.sum((FPR[0:n - 1] - FPR[1:]) * (TPR[0:n - 1] + TPR[1:]) / 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# l_1_pT\n", + "TPR,bins=compute_rate(df_sig[\"l_1_pT\"])\n", + "FPR,bins=compute_rate(df_bkg[\"l_1_pT\"],bins=bins)\n", + "\n", + "# l_2_pT\n", + "TPR2,bins=compute_rate(df_sig[\"l_2_pT\"])\n", + "FPR2,bins=compute_rate(df_bkg[\"l_2_pT\"])\n", + "\n", + "# MET\n", + "TPR3,bins=compute_rate(df_sig[\"MET\"])\n", + "FPR3,bins=compute_rate(df_bkg[\"MET\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(FPR,TPR, label=f'l_1_pT (AUC = {AUC(TPR, FPR):.2f})')\n", + "plt.plot(FPR2,TPR2, label=f'l_2_pT (AUC = {AUC(TPR2, FPR2):.2f})')\n", + "plt.plot(FPR3,TPR3, label=f'MET (AUC = {AUC(TPR3, FPR3):.2f})')\n", + "plt.title(\"Receiver Operating Characteristic (ROC) Curve\")\n", + "plt.xlabel(\"FPR\")\n", + "plt.ylabel(\"TPR\")\n", + "plt.legend()\n", + "plt.plot([0,1],[0,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercise 7.2\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_roc_curves(df_sig, df_bkg, observable_names, selections=None, bins=100):\n", + " # Create a plot\n", + " plt.figure()\n", + "\n", + " # Iterate over observable names\n", + " for obs_name in observable_names:\n", + " # Apply selections if provided\n", + " if selections and obs_name in selections:\n", + " selection = selections[obs_name]\n", + " df_sig_selected = df_sig[df_sig[obs_name] > selection['cut']]\n", + " df_bkg_selected = df_bkg[df_bkg[obs_name] > selection['cut']]\n", + " else:\n", + " df_sig_selected = df_sig\n", + " df_bkg_selected = df_bkg\n", + "\n", + " # Compute TPR and FPR\n", + " TPR, bins = compute_rate(df_sig_selected[obs_name], bins=bins)\n", + " FPR, bins = compute_rate(df_bkg_selected[obs_name], bins=bins)\n", + "\n", + " # Plot ROC curve\n", + " plt.plot(FPR, TPR, label=f'{obs_name} (AUC = {AUC(TPR, FPR):.2f})')\n", + "\n", + " # Plot the diagonal line (random classifier)\n", + " plt.plot([0, 1], [0, 1])\n", + "\n", + " # Add labels and legend\n", + " plt.title(\"Receiver Operating Characteristic (ROC) Curve\")\n", + " plt.xlabel(\"False Positive Rate\")\n", + " plt.ylabel(\"True Positive Rate\")\n", + " plt.legend()\n", + "\n", + " # Show the plot\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example usage:\n", + "observable_names = ['l_1_eta', 'l_2_eta', 'MET_rel']\n", + "selections = {'l_1_eta': {'cut': -1.59838}, 'l_2_eta': {'cut': -1.68859}, 'MET_rel': {'cut': 2.33864}}\n", + "plot_roc_curves(df_sig, df_bkg, observable_names, selections)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Exercise 7.3\n", + "observable_names = [\"signal\", \"l_1_pT\", \"l_1_eta\",\"l_1_phi\", \"l_2_pT\", \"l_2_eta\", \"l_2_phi\", \"MET\", \"MET_phi\", \"MET_rel\", \n", + " \"axial_MET\", \"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos_theta_r1\"]\n", + "selections = {'signal': {'cut': 0.5}, 'l_1_pT': {'cut': 0.920654}, 'l_1_eta': {'cut': -1.59838}, 'l_1_phi': {'cut': -1.73479}, \n", + " 'l_2_pT': {'cut': 0.428588}, 'l_2_eta': {'cut': -1.68859}, 'l_2_phi': {'cut': -1.7342}, 'MET': {'cut': 1.47506}, \n", + " 'MET_phi': {'cut': -1.72712}, 'MET_rel': {'cut': 2.33864}, 'axial_MET': {'cut': 1.1405}, 'M_R': {'cut': 0.76074}, \n", + " 'M_TR_2': {'cut': 1.35029}, 'R': {'cut': 0.00204808}, 'MT2': {'cut': 0}, 'S_R': {'cut': 0.719131},\n", + " 'M_Delta_R': {'cut': \t0.00445562}, 'dPhi_r_b': {'cut': 2.45273e-06}, 'cos_theta_r1': {'cut': 1.50257e-07}}\n", + "plot_roc_curves(df_sig, df_bkg, observable_names, selections)" + ] + }, + { + "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": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2287827, 19)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Exercise 8\n", + "df_sig.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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
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.........................................................
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2287827 rows × 18 columns

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" + ], + "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", + "4999986 0.951119 -1.025120 0.735755 1.072442 -1.268414 -1.365378 0.496042 \n", + "4999988 2.039801 0.851302 0.125229 0.934144 0.551678 -0.811299 1.602762 \n", + "4999991 1.031701 0.648011 -1.616710 0.532912 1.663047 -1.243807 0.706740 \n", + "4999995 0.853325 -0.961783 -1.487277 0.678190 0.493580 1.647969 1.843867 \n", + "4999998 1.784218 -0.833565 -0.560091 0.953342 -0.688969 -1.428233 2.660703 \n", + "\n", + " MET_phi MET_rel axial_MET M_R M_TR_2 R \\\n", + "1 -1.219136 0.012955 3.775174 1.045977 0.568051 0.481928 \n", + "2 0.504026 1.831248 -0.431385 0.526283 0.941514 1.587535 \n", + "3 1.533041 3.046260 -1.005285 0.569386 1.015211 1.582217 \n", + "4 -0.381742 0.589204 1.365479 1.179295 0.968218 0.728563 \n", + "8 1.450429 2.989110 -1.894770 1.445125 2.548166 1.564721 \n", + "... ... ... ... ... ... ... \n", + "4999986 -1.378751 0.027446 0.241199 0.841212 0.726010 0.765865 \n", + "4999988 -0.852513 0.209035 0.412390 1.370954 1.584090 1.025354 \n", + "4999991 0.189915 0.433557 -0.393302 0.809631 1.087218 1.191645 \n", + "4999995 0.276954 1.025105 -1.486535 0.892879 1.684429 1.674084 \n", + "4999998 -0.861344 2.116892 2.906151 1.232334 0.952444 0.685846 \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", + "4999986 0.000000 0.816827 0.300119 0.758559 0.259673 \n", + "4999988 0.202440 1.102830 0.605197 0.739403 0.612186 \n", + "4999991 1.972363 0.698013 1.564201 0.035361 0.504437 \n", + "4999995 3.366298 1.046707 2.646649 1.389226 0.364599 \n", + "4999998 0.000000 0.781874 0.676003 1.197807 0.093689 \n", + "\n", + "[2287827 rows x 18 columns]" + ] + }, + "execution_count": 49, + "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": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "l_1_pT 1.290233\n", + "l_1_eta -0.000336\n", + "l_1_phi -0.000553\n", + "l_2_pT 1.138185\n", + "l_2_eta 0.000378\n", + "l_2_phi -0.000128\n", + "MET 1.417029\n", + "MET_phi 0.000155\n", + "MET_rel 1.275509\n", + "axial_MET 0.084007\n", + "M_R 1.182889\n", + "M_TR_2 1.268996\n", + "R 1.057301\n", + "MT2 1.074723\n", + "S_R 1.174697\n", + "M_Delta_R 1.185596\n", + "dPhi_r_b 1.014991\n", + "cos_theta_r1 0.282560\n", + "dtype: float64" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# mean of each feature over signal\n", + "m_s= np.mean(df_sig_0,axis=0)\n", + "m_s" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "l_1_pT 0.534471\n", + "l_1_eta -0.000660\n", + "l_1_phi -0.000928\n", + "l_2_pT 0.255800\n", + "l_2_eta 0.000765\n", + "l_2_phi -0.000199\n", + "MET 0.768856\n", + "MET_phi 0.000220\n", + "MET_rel 0.505262\n", + "axial_MET 0.154960\n", + "M_R 0.336500\n", + "M_TR_2 0.495985\n", + "R 0.105791\n", + "MT2 0.136978\n", + "S_R 0.321855\n", + "M_Delta_R 0.341800\n", + "dPhi_r_b 0.028585\n", + "cos_theta_r1 0.106272\n", + "dtype: float64" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# compute delta\n", + "m_s= np.mean(df_sig_0,axis=0)\n", + "m_b= np.mean(df_bkg_0,axis=0)\n", + "\n", + "delta = m_s-m_b\n", + "delta" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "delta=np.matrix(m_s-m_b).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[ 2.85659103e-01, -3.52809644e-04, -4.96156489e-04,\n", + " 1.36717519e-01, 4.08806287e-04, -1.06310408e-04,\n", + " 4.10931386e-01, 1.17828594e-04, 2.70047689e-01,\n", + " 8.28218385e-02, 1.79849669e-01, 2.65089747e-01,\n", + " 5.65419449e-02, 7.32109964e-02, 1.72022353e-01,\n", + " 1.82682327e-01, 1.52779431e-02, 5.67995029e-02],\n", + " [-3.52809644e-04, 4.35745417e-07, 6.12789134e-07,\n", + " -1.68856020e-04, -5.04905320e-07, 1.31301040e-07,\n", + " -5.07529970e-04, -1.45526832e-07, 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-2.22127344e-04, -3.12377864e-04,\n", + " 8.60767267e-02, 2.57382575e-04, -6.69325486e-05,\n", + " 2.58720527e-01, 7.41843457e-05, 1.70020794e-01,\n", + " 5.21442520e-02, 1.13232532e-01, 1.66899297e-01,\n", + " 3.55985508e-02, 4.60933096e-02, 1.08304489e-01,\n", + " 1.15015961e-01, 9.61892335e-03, 3.57607082e-02],\n", + " [ 2.65089747e-01, -3.27405003e-04, -4.60429922e-04,\n", + " 1.26872948e-01, 3.79369516e-04, -9.86553519e-05,\n", + " 3.81341592e-01, 1.09344151e-04, 2.50602459e-01,\n", + " 7.68581153e-02, 1.66899297e-01, 2.46001522e-01,\n", + " 5.24705488e-02, 6.79393177e-02, 1.59635599e-01,\n", + " 1.69527985e-01, 1.41778296e-02, 5.27095609e-02],\n", + " [ 5.65419449e-02, -6.98333898e-05, -9.82067528e-05,\n", + " 2.70611871e-02, 8.09170874e-05, -2.10425546e-05,\n", + " 8.13377188e-02, 2.33224070e-05, 5.34518992e-02,\n", + " 1.63933436e-02, 3.55985508e-02, 5.24705488e-02,\n", + " 1.11916319e-02, 1.44910212e-02, 3.40492508e-02,\n", + " 3.61592331e-02, 3.02404023e-03, 1.12426117e-02],\n", + " [ 7.32109964e-02, -9.04208734e-05, -1.27158948e-04,\n", + " 3.50390577e-02, 1.04772140e-04, -2.72460806e-05,\n", + " 1.05316778e-01, 3.01980531e-05, 6.92099786e-02,\n", + " 2.12262423e-02, 4.60933096e-02, 6.79393177e-02,\n", + " 1.44910212e-02, 1.87630989e-02, 4.40872626e-02,\n", + " 4.68192859e-02, 3.91555328e-03, 1.45570303e-02],\n", + " [ 1.72022353e-01, -2.12460043e-04, -2.98782731e-04,\n", + " 8.23305439e-02, 2.46180916e-04, -6.40195480e-05,\n", + " 2.47460638e-01, 7.09557364e-05, 1.62621245e-01,\n", + " 4.98748593e-02, 1.08304489e-01, 1.59635599e-01,\n", + " 3.40492508e-02, 4.40872626e-02, 1.03590923e-01,\n", + " 1.10010301e-01, 9.20029399e-03, 3.42043508e-02],\n", + " [ 1.82682327e-01, -2.25625882e-04, -3.17297861e-04,\n", + " 8.74324475e-02, 2.61436388e-04, -6.79867459e-05,\n", + " 2.62795413e-01, 7.53527599e-05, 1.72698646e-01,\n", + " 5.29655315e-02, 1.15015961e-01, 1.69527985e-01,\n", + " 3.61592331e-02, 4.68192859e-02, 1.10010301e-01,\n", + " 1.16827479e-01, 9.77042278e-03, 3.63239445e-02],\n", + " [ 1.52779431e-02, -1.88693643e-05, -2.65360023e-05,\n", + " 7.31208092e-03, 2.18642401e-05, -5.68581347e-06,\n", + " 2.19778970e-02, 6.30184208e-06, 1.44429958e-02,\n", + " 4.42957121e-03, 9.61892335e-03, 1.41778296e-02,\n", + " 3.02404023e-03, 3.91555328e-03, 9.20029399e-03,\n", + " 9.77042278e-03, 8.17112227e-04, 3.03781524e-03],\n", + " [ 5.67995029e-02, -7.01514925e-05, -9.86541010e-05,\n", + " 2.71844553e-02, 8.12856783e-05, -2.11384069e-05,\n", + " 8.17082257e-02, 2.34286445e-05, 5.36953815e-02,\n", + " 1.64680181e-02, 3.57607082e-02, 5.27095609e-02,\n", + " 1.12426117e-02, 1.45570303e-02, 3.42043508e-02,\n", + " 3.63239445e-02, 3.03781524e-03, 1.12938237e-02]])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate S_B\n", + "S_B= delta*delta.transpose()\n", + "S_B" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18, 18)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "S_B.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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
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3-0.908977-0.9758090.693706-0.6892260.891375-0.6772010.6160311.5328861.770751-1.089292-0.613503-0.2537840.5249160.477191-0.4134820.5298670.477266-0.191841
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80.8225790.743319-0.329986-0.332932-0.029265-1.4465510.8829171.4502741.713602-1.9787770.2622351.2791710.5074201.3189090.3798690.9628720.1641260.405497
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49999880.7495680.8516380.125782-0.2040410.551300-0.8111710.185733-0.852668-1.0664740.3283830.1880640.315094-0.031946-0.872283-0.071867-0.580399-0.2755880.329626
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2287827 rows × 18 columns

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" + ], + "text/plain": [ + " l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi MET \\\n", + "1 0.377740 0.064527 -1.224618 -0.632083 -0.339317 1.672670 2.058436 \n", + "2 -0.845393 -0.133962 -0.709418 -0.686466 -1.614249 -0.768533 -0.197111 \n", + "3 -0.908977 -0.975809 0.693706 -0.689226 0.891375 -0.677201 0.616031 \n", + "4 0.019763 -0.689753 -0.675706 0.451098 -0.693703 0.623035 -0.329467 \n", + "8 0.822579 0.743319 -0.329986 -0.332932 -0.029265 -1.446551 0.882917 \n", + "... ... ... ... ... ... ... ... \n", + "4999986 -0.339115 -1.024784 0.736309 -0.065743 -1.268792 -1.365250 -0.920987 \n", + "4999988 0.749568 0.851638 0.125782 -0.204041 0.551300 -0.811171 0.185733 \n", + "4999991 -0.258532 0.648347 -1.616156 -0.605273 1.662669 -1.243679 -0.710289 \n", + "4999995 -0.436908 -0.961446 -1.486724 -0.459995 0.493202 1.648097 0.426838 \n", + "4999998 0.493985 -0.833229 -0.559538 -0.184843 -0.689347 -1.428105 1.243675 \n", + "\n", + " MET_phi MET_rel axial_MET M_R M_TR_2 R \\\n", + "1 -1.219291 -1.262554 3.691167 -0.136912 -0.700944 -0.575372 \n", + "2 0.503871 0.555739 -0.515392 -0.656606 -0.327482 0.530234 \n", + "3 1.532886 1.770751 -1.089292 -0.613503 -0.253784 0.524916 \n", + "4 -0.381897 -0.686304 1.281472 -0.003594 -0.300777 -0.328738 \n", + "8 1.450274 1.713602 -1.978777 0.262235 1.279171 0.507420 \n", + "... ... ... ... ... ... ... \n", + "4999986 -1.378906 -1.248063 0.157192 -0.341678 -0.542985 -0.291436 \n", + "4999988 -0.852668 -1.066474 0.328383 0.188064 0.315094 -0.031946 \n", + "4999991 0.189760 -0.841952 -0.477309 -0.373259 -0.181777 0.134344 \n", + "4999995 0.276799 -0.250404 -1.570542 -0.290010 0.415433 0.616784 \n", + "4999998 -0.861499 0.841383 2.822143 0.049445 -0.316551 -0.371455 \n", + "\n", + " MT2 S_R M_Delta_R dPhi_r_b cos_theta_r1 \n", + "1 -1.074723 -0.726287 -0.980241 0.306903 0.095024 \n", + "2 0.949585 -0.571200 0.376778 0.120464 -0.101650 \n", + "3 0.477191 -0.413482 0.529867 0.477266 -0.191841 \n", + "4 -1.074723 -0.091540 -1.142167 0.139863 -0.187701 \n", + "8 1.318909 0.379869 0.962872 0.164126 0.405497 \n", + "... ... ... ... ... ... \n", + "4999986 -1.074723 -0.357870 -0.885477 -0.256432 -0.022887 \n", + "4999988 -0.872283 -0.071867 -0.580399 -0.275588 0.329626 \n", + "4999991 0.897640 -0.476684 0.378604 -0.979630 0.221877 \n", + "4999995 2.291575 -0.127990 1.461053 0.374235 0.082039 \n", + "4999998 -1.074723 -0.392824 -0.509593 0.182816 -0.188870 \n", + "\n", + "[2287827 rows x 18 columns]" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate S_W\n", + "df_sig_0-m_s" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "delta_s=np.matrix(df_sig_0-m_s).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "S_W_s= delta_s*delta_s.transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18, 18)" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "S_W_s.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "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": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18, 18)" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "S_W_b.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18, 18)" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# S_W\n", + "S_W=S_W_s+S_W_b\n", + "S_W.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute w\n", + "S_W_inv = np.linalg.inv(S_W)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "w = S_W_inv * np.matrix(m_b - m_s).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[-2.22390835e-07],\n", + " [ 8.57659003e-11],\n", + " [ 4.28968811e-10],\n", + " [-1.04470564e-07],\n", + " [-2.23120185e-10],\n", + " [ 3.46417932e-11],\n", + " [-1.64461728e-07],\n", + " [-3.92336412e-10],\n", + " [-1.41650463e-08],\n", + " [-2.72305213e-08],\n", + " [ 2.57069538e-08],\n", + " [-1.09420028e-08],\n", + " [ 2.07602288e-07],\n", + " [ 3.38231687e-08],\n", + " [ 1.84539798e-07],\n", + " [-1.71289091e-07],\n", + " [ 8.89905919e-09],\n", + " [-5.16133749e-07]])" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[ 2.88602495e-01],\n", + " [-1.11300688e-04],\n", + " [-5.56684224e-04],\n", + " [ 1.35574227e-01],\n", + " [ 2.89548993e-04],\n", + " [-4.49555754e-05],\n", + " [ 2.13426354e-01],\n", + " [ 5.09145386e-04],\n", + " [ 1.83823569e-02],\n", + " [ 3.53377710e-02],\n", + " [-3.33605969e-02],\n", + " [ 1.41997278e-02],\n", + " [-2.69411004e-01],\n", + " [-4.38932247e-02],\n", + " [-2.39482198e-01],\n", + " [ 2.22286404e-01],\n", + " [-1.15485455e-02],\n", + " [ 6.69800479e-01]])" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# normalize\n", + "w_1 = w / sum(w)\n", + "w_1" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "output_s=np.matrix(df_sig_0)*w_1\n", + "output_b=np.matrix(df_bkg_0)*w_1" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "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()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2287827, 18)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_sig_0.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.matrix(df_sig_0).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18, 2287827)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "x_n = x*x.transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18, 18)" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_n.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18, 1)" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "w_t = w*w.transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18, 18)" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w_t.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18, 18)" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Data analysis. May be off on calculations.\n", + "F_n = x_n*w_t\n", + "F_n.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(100,)" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h_b.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ROC curve for F_n\n", + "plt.plot(h_b,h_s, label=f'F_n (AUC = {AUC(h_s, h_b):.2f})')\n", + "plt.title(\"Receiver Operating Characteristic (ROC) Curve for F_n\")\n", + "plt.xlabel(\"h_b\")\n", + "plt.ylabel(\"h_s\")\n", + "plt.legend()\n", + "plt.plot([0,1],[0,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [], + "source": [ + "# maximal significance you can obtain from exercises in 5 is 250,000" + ] + }, + { + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Labs/Lab.7/Lab.7.pdf b/Labs/Lab.7/Lab.7.pdf new file mode 100644 index 0000000..119782f Binary files /dev/null and b/Labs/Lab.7/Lab.7.pdf differ diff --git a/Labs/Lab.8/Lab.8.ipynb b/Labs/Lab.8/Lab.8.ipynb new file mode 100644 index 0000000..d125e87 --- /dev/null +++ b/Labs/Lab.8/Lab.8.ipynb @@ -0,0 +1,1547 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Robert Cocker\n", + "# Dr. Farbin\n", + "# DATA-3402\n", + "# Lab 8\n", + "# 4/17/2024" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lab 8 - Machine Learning\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup for SUSY Dataset\n", + "\n", + "Use the SUSY dataset for the rest of this lab. Here is a basic setup." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "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", + "df = pd.read_csv(filename, dtype='float64', names=VarNames)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The slowest run took 8.70 times longer than the fastest. This could mean that an intermediate result is being cached.\n", + "34.4 µs ± 39.7 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit -n 1\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "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", + " 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", + " 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 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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", + "\n", + "### Exercise 1: Install Scikit-Learn\n", + "\n", + "Follow the [Installation Instructions](https://scikit-learn.org/stable/install.html) and install `scikit-learn` in your environment." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Successfully installed scikit-learn library in Ubuntu using pip install -U scikit-learn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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", + "\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", + "\n", + "#### Part c\n", + "Read through the [LDA Documentation](https://scikit-learn.org/stable/modules/lda_qda.html).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 3: Training a Classifier\n", + "\n", + "Lets' repeat what we did manually in the previous lab using scikit-learn. We'll use a LDA classifier, which we can instanciate as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import sklearn.discriminant_analysis as DA\n", + "Fisher=DA.LinearDiscriminantAnalysis()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As discussed in the lecture, to properly formulate our problem, we'll have to:\n", + "\n", + "* Define the inputs (X) vs outputs (Y)\n", + "* Designate training vs testing samples (in order to get a unbias assessment of the performance of Machine Learning algorithms)\n", + "\n", + "for example, here we'll take use 4M events for training and the remainder for testing." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "N_Train=4000000\n", + "\n", + "Train_Sample=df[:N_Train]\n", + "Test_Sample=df[N_Train:]\n", + "\n", + "X_Train=Train_Sample[VarNames[1:]]\n", + "y_Train=Train_Sample[\"signal\"]\n", + "\n", + "X_Test=Test_Sample[VarNames[1:]]\n", + "y_Test=Test_Sample[\"signal\"]\n", + "\n", + "Test_sig=Test_Sample[Test_Sample.signal==1]\n", + "Test_bkg=Test_Sample[Test_Sample.signal==0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can train the classifier as follow:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "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.
" + ], + "text/plain": [ + "LinearDiscriminantAnalysis()" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Fisher.fit(X_Train,y_Train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the output, comparing signal and background:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.hist(Fisher.decision_function(Test_sig[VarNames[1:]]),bins=100,histtype=\"step\", color=\"blue\", label=\"signal\",stacked=True)\n", + "plt.hist(Fisher.decision_function(Test_bkg[VarNames[1:]]),bins=100,histtype=\"step\", color=\"red\", label=\"background\",stacked=True)\n", + "plt.legend(loc='upper right')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Importing more libraries\n", + "import sklearn.discriminant_analysis as DA\n", + "from sklearn.metrics import roc_curve, roc_auc_score\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Generate predictions for training and testing sets\n", + "y_train_pred = Fisher.predict_proba(X_Train)[:, 1]\n", + "y_test_pred = Fisher.predict_proba(X_Test)[:, 1]\n", + "\n", + "# Calculate ROC curve for train set\n", + "fpr_train, tpr_train, _ = roc_curve(y_Train, y_train_pred)\n", + "\n", + "# Calculate ROC curve for test set\n", + "fpr_test, tpr_test, _ = roc_curve(y_Test, y_test_pred)\n", + "\n", + "# Calculate AUC score for train set\n", + "auc_train = roc_auc_score(y_Train, y_train_pred)\n", + "\n", + "# Calculate AUC score for test set\n", + "auc_test = roc_auc_score(y_Test, y_test_pred)\n", + "\n", + "# Plot ROC curve\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(fpr_train, tpr_train, label=f'Train ROC Curve (AUC = {auc_train:.2f})')\n", + "plt.plot(fpr_test, tpr_test, label=f'Test ROC Curve (AUC = {auc_test:.2f})')\n", + "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic (ROC) Curve')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*both train and test look the same. they overlap*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Train LDA classifier with different input data\n", + "\n", + "# Using raw only\n", + "Fisher_raw = DA.LinearDiscriminantAnalysis()\n", + "Fisher_raw.fit(X_Train, y_Train)\n", + "\n", + "# Using features only\n", + "Fisher_features = DA.LinearDiscriminantAnalysis()\n", + "Fisher_features.fit(X_Train[VarNames[1:]], y_Train)\n", + "\n", + "# Using both raw and features\n", + "Fisher_combined = DA.LinearDiscriminantAnalysis()\n", + "Fisher_combined.fit(X_Train, y_Train)\n", + "\n", + "# Generate predictions for the test set\n", + "y_test_pred_raw = Fisher_raw.predict_proba(X_Test)[:, 1]\n", + "y_test_pred_features = Fisher_features.predict_proba(X_Test[VarNames[1:]])[:, 1]\n", + "y_test_pred_combined = Fisher_combined.predict_proba(X_Test)[:, 1]\n", + "\n", + "# Calculate ROC curves and AUC scores for each classifier\n", + "fpr_raw, tpr_raw, _ = roc_curve(y_Test, y_test_pred_raw)\n", + "auc_raw = roc_auc_score(y_Test, y_test_pred_raw)\n", + "\n", + "fpr_features, tpr_features, _ = roc_curve(y_Test, y_test_pred_features)\n", + "auc_features = roc_auc_score(y_Test, y_test_pred_features)\n", + "\n", + "fpr_combined, tpr_combined, _ = roc_curve(y_Test, y_test_pred_combined)\n", + "auc_combined = roc_auc_score(y_Test, y_test_pred_combined)\n", + "\n", + "# Plot ROC curves\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(fpr_raw, tpr_raw, label=f'Raw (AUC = {auc_raw:.2f})')\n", + "plt.plot(fpr_features, tpr_features, label=f'Features (AUC = {auc_features:.2f})')\n", + "plt.plot(fpr_combined, tpr_combined, label=f'Combined (AUC = {auc_combined:.2f})')\n", + "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic (ROC) Curve')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*my input and output data for training and testing with the classifiers may be wrong because I notice the ROC curves and AUC for each criteria are all the same*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 4: Comparing Techniques\n", + "\n", + "#### Part a\n", + "Select 3 different classifiers from the techniques listed [here](http://scikit-learn.org/stable/supervised_learning.html#supervised-learning) to compare. Note that you can use the multi-layer perceptron to build a deep network, though training may be prohibitively slow. So avoid this technique.\n", + "\n", + "#### Part b\n", + "\n", + "Write a function that takes an instantiated classifier and performs the comparison from part 3b. Use the function on your choice of functions in part a.\n", + "\n", + "#### Part c\n", + "\n", + "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": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Part a\n", + "# The 3 classifiers I selected are: DecisionTreeClassifier, GradientBoostingClassifier, and SGDClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Part b\n", + "# Importing more libraries and respective classifiers\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn.linear_model import SGDClassifier\n", + "import numpy as np\n", + "\n", + "# function for comparing the 3 selected classifiers\n", + "def compare_classifiers(classifiers, X_train, y_train, X_test, y_test):\n", + " plt.figure(figsize=(8, 6))\n", + "\n", + " for clf_name, clf in classifiers.items():\n", + " clf.fit(X_train, y_train)\n", + " if hasattr(clf, \"decision_function\"):\n", + " y_test_scores = clf.decision_function(X_test)\n", + " else:\n", + " y_test_scores = clf.predict(X_test)\n", + " fpr, tpr, _ = roc_curve(y_test, y_test_scores)\n", + " auc_score = roc_auc_score(y_test, y_test_scores)\n", + " plt.plot(fpr, tpr, label=f'{clf_name} (AUC = {auc_score:.2f})')\n", + "\n", + " plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random')\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + " plt.title('Receiver Operating Characteristic (ROC) Curve')\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define classifiers\n", + "classifiers = {\n", + " 'DecisionTree': DecisionTreeClassifier(),\n", + " 'GradientBoosting': GradientBoostingClassifier(),\n", + " 'SGD': SGDClassifier()\n", + "}\n", + "\n", + "# Perform comparison\n", + "compare_classifiers(classifiers, X_Train, y_Train, X_Test, y_Test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*The instructions in Part c seem incorrect. I will compute the maximal significance utilizing GradientBoostingClassifier since it gives the best ROC curve and AUC. Also I believe the instructions are for Lab 7 and not Lab 5*" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Part c\n", + "# significance computation\n", + "def compute_significance(y_true, y_pred, thresholds):\n", + " significance = []\n", + " for threshold in thresholds:\n", + " y_pred_binary = (y_pred > threshold).astype(int)\n", + " tp = np.sum((y_pred_binary == 1) & (y_true == 1))\n", + " fp = np.sum((y_pred_binary == 1) & (y_true == 0))\n", + " tn = np.sum((y_pred_binary == 0) & (y_true == 0))\n", + " fn = np.sum((y_pred_binary == 0) & (y_true == 1))\n", + "\n", + " if tp + fp == 0:\n", + " significance.append(0) \n", + " continue\n", + " \n", + " signal = tp / np.sqrt(tp + fp)\n", + " background = fp / np.sqrt(fp + tn)\n", + " \n", + " significance.append(signal + background)\n", + " \n", + " return significance\n", + "\n", + "def plot_significance_curve(significance, thresholds):\n", + " plt.figure(figsize=(8, 6))\n", + " plt.plot(thresholds, significance, marker='o')\n", + " plt.xlabel('Threshold')\n", + " plt.ylabel('Significance')\n", + " plt.title('Significance Curve')\n", + " plt.grid(True)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.0\n" + ] + } + ], + "source": [ + "# testing for significance and optimal threshold\n", + "thresholds = np.linspace(0, 1, 100)\n", + "significance = compute_significance(y_Test, y_test_pred, thresholds)\n", + "plot_significance_curve(significance, thresholds)\n", + "\n", + "# Find optimal threshold\n", + "optimal_threshold = thresholds[np.argmax(significance)]\n", + "print(\"Optimal Threshold:\", optimal_threshold)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Optimal threshold between 0 and 1 results state 0.0*" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Threshold: 0.0\n" + ] + } + ], + "source": [ + "# Train classifier\n", + "gb_classifier = GradientBoostingClassifier()\n", + "gb_classifier.fit(X_Train, y_Train)\n", + "\n", + "# Predict probabilities for the test set\n", + "y_test_pred_proba = gb_classifier.predict_proba(X_Test)[:, 1]\n", + "\n", + "# Calculate significance for different thresholds\n", + "thresholds = np.linspace(0, 1, 100)\n", + "significance = compute_significance(y_Test, y_test_pred_proba, thresholds)\n", + "\n", + "# Plot significance curve\n", + "plot_significance_curve(significance, thresholds)\n", + "\n", + "# Find optimal threshold\n", + "optimal_threshold = thresholds[np.argmax(significance)]\n", + "print(\"Optimal Threshold:\", optimal_threshold)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*It looks like maximal significance is at about 1200 I'd say but the optimal threshold looks to be between 0.0 and right before 1 if you examine the Significance and ROC curve graphs. The ROC curve graphs has a sharp slope from 0.0 and bulges out at about 0.6 - 0.9*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 5: Metrics\n", + "\n", + "Scikit-learn provides methods for computing the FPR, TPR, ROC, AUC metrics. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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j/wWyL0nbHFRAsyFAx4lA7Wby1kdEZGMUQggh14eHhYWhY8eOWLx4MQBAr9dDo9Fg7NixmDRpUqn9x4wZgzNnziAuLs6w7e2338aff/6JvXv3ltofAAoLC1FYWGh4np2dDY1Gg6ysLHh6epr5GxH9Q2GWNIPw4Y+A/Expm7OPdOmpzRuAax156yMiqkGys7Ph5eVVqd9v2S5LabVaHDlyBBEREXeLUSoRERGBAwcOlHlMeHg4jhw5Yrh0lZSUhK1bt+LJJ58s93NiY2Ph5eVleGg07KBJFpaTCuydCix/ANgzSQo2XvWBx5YBI68CnWcw2BARWZBsl6UyMzOh0+ng7+9vtN3f3x9nz54t85jBgwcjMzMTDz30EIQQKC4uxuuvv44pU6aU+zmTJ09GVFSU4XlJyw2R2f19Gjg8/86ke1ppm09z6dJTixelod1ERGRxNepf2127diEmJgaffvopwsLCcOHCBYwbNw6zZ8/G9OnTyzxGrVZDrVZbuVKyKxnxwIFZwIUf724LCgc6vA006gcoZO+3T0RkV2QLN76+vnBwcEB6errR9vT0dAQEBJR5zPTp0zF06FC8+uqrAIBWrVohNzcXI0aMwNSpU6FU8keErCj9KLB/JpD0850NCqBRX6mlJqiznJUREdk12dKASqVCaGioUedgvV6PuLg4dO5c9g9DXl5eqQDj4OAAAJCxXzTZm8yTwE/PAP8XeifYKKT7Pb10Cuj7A4MNEZHMZL0sFRUVhcjISHTo0AGdOnXCwoULkZubi+HDhwMAhg0bhuDgYMTGxgIA+vTpgwULFqBdu3aGy1LTp09Hnz59DCGHyGKyLwF7pgBnvwYgpMtNzQYB/5kO+DSVuzoiIrpD1nAzYMAAXL9+HTNmzEBaWhratm2Lbdu2GToZp6SkGLXUTJs2DQqFAtOmTcPVq1dRp04d9OnTB++//75cX4HsQV4G8GcMEP8poC+StjV+DujyHlC7hby1ERFRKbLOcyMHU8bJk50rzAIOzZPu/VSUI22r1x14ZB7g317e2oiI7Iwpv981arQUkVXoi4ETX0hz1RTckLb5hwIPxQIhj8lbGxER3RPDDdE/Jf4M7J4A3DwnPfdpBjwUc2dIt0LW0oiIqHIYbogAaQTU7+8CyVul5861gc7R0o0sOfkeEVGNwn+1yb7lpAL7pgMnVwAQUpBpPx7oPB1QechdHRERVQHDDdknXRFwbBFwYCagvS1ta/ys1K/Gp4mspRER0f1huCH7c+V3YOcb0r2gACCgI/DoQiA4XNayiIjIPBhuyH7kXQd2vw2cXiM9d/EFHp4LtBzO+z8REdkQhhuyfXod8NfnwL5pQMFNAAqg9QhpFJSLj9zVERGRmTHckG1L/VO6BJVxTHpepzXw2DIgMEzeuoiIyGIYbsg2FWZLo6DiFwNCD6i9gS5zgDYjObSbiMjG8V95sj3JvwD/GwHkXJGeNxsMdPsYcPWTty4iIrIKhhuyHblpwG/jgYT10nOvBkDEUt4ygYjIzjDckG04uwGIe0PqMKxQAu3HSZehnFzlroyIiKyM4YZqtrwM4Le3gLNfS8/92gOPfQ4EdJC1LCIikg/DDdVMQgBn1wG/jgMK/pZaazpNlu4H5eAkd3VERCQjhhuqefJvADtGAOe/k57XaQ08/iVba4iICADDDdU0FzZJwSYvXRrS/Z8ZQKdJbK0hIiIDhhuqGYoLgd/fkW52CQA+zYAn1rC1hoiISmG4oervViKweSCQflh6Hhol3TrBUS1vXUREVC0x3FD1lrQF2DIY0GZLsww/sQZo+JTcVRERUTXGcEPVk74Y2DMZOPyR9DwoHHhqA+BRV966iIio2mO4oern9hVg64vAld3S8zajpNsnOKjkrYuIiGoEhhuqXi7tBLYOkSbnU3kAPVcATZ6XuyoiIqpBGG6oehACOBgr3clb6IE6baTLUD5N5a6MiIhqGIYbkp82B9j+MnBuo/S85ctA90W8LxQREVUJww3J60YC8NMzwI0zgNIJ6P5foM3rcldFREQ1GMMNyefi/4CfX5CGebsFAn02AsFd5K6KiIhqOIYbkkf8p8Bv46Qh38EPAX2+Bdz85a6KiIhsAMMNWZdeJ4Wa+CXS8+ZDpBFRHOZNRERmwnBD1lOYDfz8PHBpBwCFdAuFTu8CCoXclRERkQ1huCHryMsEfugNpB0EHF2BXiuBpi/IXRUREdkghhuyvL/PAj/1A24mAM4+wHPbeTdvIiKyGIYbsqyr+4EfnwIKbgIeGinY1G4ud1VERGTDlPdzcEFBgbnqIFuU+DPwbYQUbAL/Aww5xGBDREQWZ3K40ev1mD17NoKDg+Hu7o6kpCQAwPTp0/Hll1+avUCqoRK+kSbnK84HQnoBL8RxqDcREVmFyeFmzpw5WLlyJT788EOoVHeH77Zs2RJffPGFWYujGur458DmAYDQSUO9+23irRSIiMhqTA43q1evxrJlyzBkyBA4ODgYtrdp0wZnz541a3FUAx38ENh55/YJbUYBvVYBDk7y1kRERHbF5HBz9epVNGrUqNR2vV6PoqIisxRFNdSRj4E970rrHScCPRYDSoeKjyEiIjIzk8NNixYtsGfPnlLbv/32W7Rr184sRVENdOgjYFeUtN55JvDIB5ycj4iIZGHyUPAZM2YgMjISV69ehV6vx/fff4+EhASsXr0amzdvtkSNVN0dmgf8PlFaD5sKdJ4hbz1ERGTXTG656du3L37++Wfs3LkTbm5umDFjBs6cOYOff/4Zjz32mCVqpOrs6Cd3g03nmcBDc9hiQ0REslIIIYTcRVhTdnY2vLy8kJWVBU9PT7nLqdmOLQZ+HSut/2c60OU9eeshIiKbZcrvt8ktNw0aNMDff/9davutW7fQoEEDU9+Oaqr4z+4Gm47vAuGz5K2HiIjoDpPDzcWLF6HT6UptLywsxNWrV81SFFVzfy0H4kZJ6x3eAR6O5aUoIiKqNirdoXjTpk2G9e3bt8PLy8vwXKfTIS4uDiEhIWYtjqqhU6uAHSOk9XZvclQUERFVO5UON/369QMAKBQKREZGGr3m5OSEkJAQzJ8/36zFUTWTuBnY/rK03m4s0G0hgw0REVU7lQ43er0eAFC/fn0cOnQIvr6+FiuKqqErvwM/Pw8IPdBiGIMNERFVWybPc5OcnGyJOqg6yzwF/NgX0BUCDfoAj38BKO7rhvJEREQWY3K4AYDc3Fzs3r0bKSkp0Gq1Rq+9+eabZimMqom8DODHPkDhLSAoHHhqPe8VRURE1ZrJ4ebYsWN48sknkZeXh9zcXPj4+CAzMxOurq7w8/NjuLElOi2w6TkgKxnwbsi7exMRUY1g8rWF8ePHo0+fPrh58yZcXFzwxx9/4NKlSwgNDcVHH31kiRpJLr+9BVzdC6g8gWe2AC615a6IiIjonkwON/Hx8Xj77behVCrh4OCAwsJCaDQafPjhh5gyZYolaiQ5nFoFHP8MgAJ4ci3g01TuioiIiCrF5HDj5OQEpVI6zM/PDykpKQAALy8vXL582bzVkTyu7gf+95q0/p/pQMOn5K2HiIjIBCb3uWnXrh0OHTqExo0bo2vXrpgxYwYyMzOxZs0atGzZ0hI1kjXlpgGb+wP6IqDxs0B4tNwVERERmcTklpuYmBgEBgYCAN5//33UqlULb7zxBq5fv47PP//c7AWSFemKgM0DgZyrgE8zoNcqDvkmIqIah3cFJ4kQwI6RwInlgJMb8OIR9rMhIqJqw6J3BS/P0aNH8dRTpvfNWLJkCUJCQuDs7IywsDAcPHiwwv1v3bqF0aNHIzAwEGq1Gk2aNMHWrVurWjaVOPGlFGwUSqD31ww2RERUY5kUbrZv344JEyZgypQpSEpKAgCcPXsW/fr1Q8eOHQ23aKisDRs2ICoqCtHR0Th69CjatGmDnj17IiMjo8z9tVotHnvsMVy8eBHffvstEhISsHz5cgQHB5v0ufQvaYeBX0dL611mAw37yFsPERHRfaj0Zakvv/wSr732Gnx8fHDz5k3Url0bCxYswNixYzFgwACMGzcOzZs3N+nDw8LC0LFjRyxevBiAdP8qjUaDsWPHYtKkSaX2X7p0KebNm4ezZ8/Cyalys+QWFhaisLDQ8Dw7OxsajYaXpUoU3ATWtAOyLwEN+wJ9v2c/GyIiqnYsclnqk08+wQcffIDMzEx88803yMzMxKeffooTJ05g6dKlJgcbrVaLI0eOICIi4m4xSiUiIiJw4MCBMo/ZtGkTOnfujNGjR8Pf3x8tW7ZETEwMdDpduZ8TGxsLLy8vw0Oj0ZhUp82LGyMFG++GQK+VDDZERFTjVfqXLDExES+88AIA4Nlnn4WjoyPmzZuHunXrVumDMzMzodPp4O/vb7Td398faWlpZR6TlJSEb7/9FjqdDlu3bsX06dMxf/58zJkzp9zPmTx5MrKysgwPzsXzD+e+Bc6ukwLNE/8HOHvLXREREdF9q/Q8N/n5+XB1le4rpFAooFarDUPCrUWv18PPzw/Lli2Dg4MDQkNDcfXqVcybNw/R0WXPx6JWq6FWq61aZ42Qc00aHQUAnSYBQf+Rtx4iIiIzMWkSvy+++ALu7u4AgOLiYqxcuRK+vr5G+1T2xpm+vr5wcHBAenq60fb09HQEBASUeUxgYCCcnJzg4OBg2Na8eXOkpaVBq9VCpVKZ8nXsl9ADvwwFCm4AddoCnTlRHxER2Y5Kh5t69eph+fLlhucBAQFYs2aN0T4KhaLS4UalUiE0NBRxcXHo168fAKllJi4uDmPGjCnzmC5dumDdunXQ6/WGW0CcO3cOgYGBDDamOPIxkPIr4OgqDft24LkjIiLbUelwc/HiRbN/eFRUFCIjI9GhQwd06tQJCxcuRG5uLoYPHw4AGDZsGIKDgxEbGwsAeOONN7B48WKMGzcOY8eOxfnz5xETE1PpQEUArv8F7JksrT86H6jdTN56iIiIzMzke0uZ04ABA3D9+nXMmDEDaWlpaNu2LbZt22boZJySkmJooQEAjUaD7du3Y/z48WjdujWCg4Mxbtw4vPvuu3J9hZqlKE+6vYK+SBr23Xqk3BURERGZHW+/YE92TwQOzwPcAoFh8YCrn9wVERERVYost1+gau7aAeDwR9J6xFIGGyIislkMN/agKA/YNhyAAFoMBRo9LXdFREREFsNwYw/2zwRuJkiXox5dKHc1REREFlWlcJOYmIhp06Zh0KBBhptc/vLLLzh16pRZiyMzSD8CHFkgrT/2OeDiI289REREFmZyuNm9ezdatWqFP//8E99//z1ycnIAAMePHy93lmCSib4Y2P4KIHRA0wG82zcREdkFk8PNpEmTMGfOHOzYscNo4rzu3bvjjz/+MGtxdJ+OLQauHwecfYDui+SuhoiIyCpMDjcnTpzAM888U2q7n58fMjMzzVIUmUH2ZWDfdGn94bmAax156yEiIrISk8ONt7c3UlNTS20/duwYgoODzVIU3SchgF/HAEU5QFA40PJluSsiIiKyGpPDzcCBA/Huu+8iLS0NCoUCer0e+/btw4QJEzBs2DBL1EimStwkPZROQMRngNLh3scQERHZCJPDTUxMDJo1awaNRoOcnBy0aNECjzzyCMLDwzFt2jRL1EimKC4Edo2X1ju8DdRpLW89REREVlbl2y+kpKTg5MmTyMnJQbt27dC4cWNz12YRNn/7hcMLgN1vS3PavHwOULnLXREREdF9M+X32+QbZ+7duxcPPfQQ6tWrh3r16lW5SLKA3DTgj/ek9S6zGWyIiMgumXxZqnv37qhfvz6mTJmC06dPW6Imqqrf3wUKswD/UODBl+SuhoiISBYmh5tr167h7bffxu7du9GyZUu0bdsW8+bNw5UrVyxRH1VW+jHg9GoACqDHEnYiJiIiu2VyuPH19cWYMWOwb98+JCYm4oUXXsCqVasQEhKC7t27W6JGqoz9d+a0aTYQCAyTtxYiIiIZ3deNM+vXr49JkyZh7ty5aNWqFXbv3m2uusgUF3cASVsApSPQmbfAICIi+1blcLNv3z6MGjUKgYGBGDx4MFq2bIktW7aYszaqDCGAvVOk9bajAZ+m8tZDREQkM5NHS02ePBnr16/HtWvX8Nhjj+GTTz5B37594erqaon66F4SNwHphwEnN6DTZLmrISIikp3J4eb333/HO++8g/79+8PX19cSNVFlCQH8MVtab/cm4OYvbz1ERETVgMnhZt++fZaog6ri0v+A9COAozMQGiV3NURERNVCpcLNpk2b8MQTT8DJyQmbNm2qcN+nn37aLIXRPQgB7L1zu4s2bwCubEUjIiICKnn7BaVSibS0NPj5+UGpLL8PskKhgE6nM2uB5mYzt19I3gZ8/4TUavPaJcDVT+6KiIiILMbst1/Q6/VlrpOMDsZKyzZvMNgQERH9g8lDwVevXo3CwsJS27VaLVavXm2Wouge0o8AV36X5rVhXxsiIiIjJoeb4cOHIysrq9T227dvY/jw4WYpiu7h8Hxp2aQ/4FFX3lqIiIiqGZPDjRACCoWi1PYrV67Ay8vLLEVRBW5eABI2SOsd3pa3FiIiomqo0kPB27VrB4VCAYVCgR49esDR8e6hOp0OycnJ6NWrl0WKpH/4MwYQeqD+E4B/e7mrISIiqnYqHW769esHAIiPj0fPnj3h7u5ueE2lUiEkJATPPfec2Qukf8i+BJxZI63/Z4a8tRAREVVTlQ430dHSDRlDQkIwYMAAODs7W6woKkf8p4C+GNB0A4L+I3c1RERE1ZLJMxRHRkZaog66l8IsKdwAQOh4eWshIiKqxioVbnx8fHDu3Dn4+vqiVq1aZXYoLnHjxg2zFUf/8NcyoCgHqN0CaPCU3NUQERFVW5UKNx9//DE8PDwM6xWFG7IAoQeOL5XWQ6MAnn8iIqJyVer2C7akRt5+4cIm4Ke+gNoLGHkNcHKVuyIiIiKrMuX32+R5bo4ePYoTJ04Ynv/000/o168fpkyZAq1Wa3q1dG/xi6Vl65EMNkRERPdgcrgZOXIkzp07BwBISkrCgAED4Orqio0bN2LixIlmL9Du3UgALu0AoJDCDREREVXI5HBz7tw5tG3bFgCwceNGdO3aFevWrcPKlSvx3Xffmbs+il8iLRs8BXg3kLcWIiKiGqBKt18ouTP4zp078eSTTwIANBoNMjMzzVudvSvMBk5+Ja23Gy1vLURERDWEyeGmQ4cOmDNnDtasWYPdu3ejd+/eAIDk5GT4+/ubvUC7dnadNPy7VlPggcflroaIiKhGMDncLFy4EEePHsWYMWMwdepUNGrUCADw7bffIjw83OwF2i0hgOOfS+utR3D4NxERUSWZbSh4QUEBHBwc4OTkZI63s5gaMxQ84ziwpi3goAJGpgIuPnJXREREJBtTfr9Nvv1CiSNHjuDMmTMAgBYtWqB9e96h2qxOr5KWDZ5isCEiIjKByeEmIyMDAwYMwO7du+Ht7Q0AuHXrFrp164b169ejTp065q7R/uiLgTNrpfUWvJcXERGRKUzuczN27Fjk5OTg1KlTuHHjBm7cuIGTJ08iOzsbb775piVqtD+Jm4G8DMDVD6j/hNzVEBER1Sgmt9xs27YNO3fuRPPmzQ3bWrRogSVLluDxxzmixyzOfi0tmw8FHKp3HyYiIqLqxuSWG71eX2anYScnJ8P8N3QftDlA0s/SetP+8tZCRERUA5kcbrp3745x48bh2rVrhm1Xr17F+PHj0aNHD7MWZ5cSNwHF+YB3IyCgo9zVEBER1Tgmh5vFixcjOzsbISEhaNiwIRo2bIj69esjOzsbixYtskSN9uXsOmnZbCDntiEiIqoCk/vcaDQaHD16FHFxcYah4M2bN0dERITZi7M7uenAxe3SerMh8tZCRERUQ5kUbjZs2IBNmzZBq9WiR48eGDt2rKXqsk/nNkrDwAM6ArWbyV0NERFRjVTpcPPZZ59h9OjRaNy4MVxcXPD9998jMTER8+bNs2R99uX899Ky6QB56yAiIqrBKt3nZvHixYiOjkZCQgLi4+OxatUqfPrpp5aszb7kZQBXfpfWGz0jby1EREQ1WKXDTVJSEiIj786WO3jwYBQXFyM1NdUihdmdCz8BQgf4hwLeDeSuhoiIqMaqdLgpLCyEm5vb3QOVSqhUKuTn51ukMLtzbqO0bPy8vHUQERHVcCZ1KJ4+fTpcXV0Nz7VaLd5//314eXkZti1YsMB81dmLwmzg8i5pvTEvSREREd2PSoebRx55BAkJCUbbwsPDkZSUZHiu4LwsVXNxO6AvAmo1AXyayl0NERFRjVbpcLNr1y4LlmHnSm630OApeesgIiKyASbPUGwJS5YsQUhICJydnREWFoaDBw9W6rj169dDoVCgX79+li3QkvQ6IPkXab1hH3lrISIisgGyh5sNGzYgKioK0dHROHr0KNq0aYOePXsiIyOjwuMuXryICRMm4OGHH7ZSpRaS+ieQnwmovYHgh+SuhoiIqMaTPdwsWLAAr732GoYPH44WLVpg6dKlcHV1xYoVK8o9RqfTYciQIZg1axYaNKjhw6ZT4qTlAxGA0uS7YRAREdG/yBputFotjhw5YnRfKqVSiYiICBw4cKDc49577z34+fnhlVdeuednFBYWIjs72+hRrVzcJi0feEzeOoiIiGyErOEmMzMTOp0O/v7+Rtv9/f2RlpZW5jF79+7Fl19+ieXLl1fqM2JjY+Hl5WV4aDSa+67bbIpygbRD0jrDDRERkVlUKdzs2bMHL774Ijp37oyrV68CANasWYO9e/eatbh/u337NoYOHYrly5fD19e3UsdMnjwZWVlZhsfly5ctWqNJru6VhoB71AM8Q+SuhoiIyCaY3Mnju+++w9ChQzFkyBAcO3YMhYWFAICsrCzExMRg69atlX4vX19fODg4ID093Wh7eno6AgICSu2fmJiIixcvok+fu6OK9Hq99EUcHZGQkICGDRsaHaNWq6FWqytdk1Ull1ySigA4RxAREZFZmNxyM2fOHCxduhTLly+Hk5OTYXuXLl1w9OhRk95LpVIhNDQUcXFxhm16vR5xcXHo3Llzqf2bNWuGEydOID4+3vB4+umn0a1bN8THx1evS06Vcel/0jKkl7x1EBER2RCTW24SEhLwyCOPlNru5eWFW7dumVxAVFQUIiMj0aFDB3Tq1AkLFy5Ebm4uhg8fDgAYNmwYgoODERsbC2dnZ7Rs2dLoeG9vbwAotb3ay00D/j4NQAHU6y53NURERDbD5HATEBCACxcuICQkxGj73r17qzQse8CAAbh+/TpmzJiBtLQ0tG3bFtu2bTN0Mk5JSYFSKfuIdfO7vFta1mkDuNSWtxYiIiIbYnK4ee211zBu3DisWLECCoUC165dw4EDBzBhwgRMnz69SkWMGTMGY8aMKfO1e932YeXKlVX6TNld3SMt69bwSQiJiIiqGZPDzaRJk6DX69GjRw/k5eXhkUcegVqtxoQJEzB27FhL1GibUn6Vlppu8tZBRERkYxRCCFGVA7VaLS5cuICcnBy0aNEC7u7u5q7NIrKzs+Hl5YWsrCx4enrKU0T+38Cnd4ayj8rkZSkiIqJ7MOX3u8rz/atUKrRo0aKqh9u3a3dmX/ZpxmBDRERkZiaHm27dukFRwZwsv/76630VZBeu7ZeWgaWHuxMREdH9MTnctG3b1uh5UVER4uPjcfLkSURGRpqrLtuWeqflJriLvHUQERHZIJPDzccff1zm9pkzZyInJ+e+C7J5Qg9kHJPWAzrKWwsREZENMtsEMi+++CJWrFhhrrezXTfPA4VZgKOL1OeGiIiIzMps4ebAgQNwdnY219vZrpK7gNdpCzioZC2FiIjIFpl8WerZZ581ei6EQGpqKg4fPlzlSfzsStpBaRkYJm8dRERENsrkcOPl5WX0XKlUomnTpnjvvffw+OOPm60wm1XSchPQQd46iIiIbJRJ4Uan02H48OFo1aoVatWqZamabJe+GLgeL637szMxERGRJZjU58bBwQGPP/54le7+TQAyTwHFBYDKE6jVSO5qiIiIbJLJHYpbtmyJpKQkS9Ri+/55SUphg3c6JyIiqgZM/oWdM2cOJkyYgM2bNyM1NRXZ2dlGD6pAxlFp6Rcqbx1EREQ2rNJ9bt577z28/fbbePLJJwEATz/9tNFtGIQQUCgU0Ol05q/SVmSekJZ+beStg4iIyIZVOtzMmjULr7/+On777TdL1mO7hLgbbmq3lLcWIiIiG1bpcCOEAAB07drVYsXYtKwkaWZiBzVQu7nc1RAREdksk/rcVHQ3cLqHjHhpWftBzkxMRERkQSbNc9OkSZN7BpwbN27cV0E2q+SSVB32tyEiIrIkk8LNrFmzSs1QTJX092lpWbuFvHUQERHZOJPCzcCBA+Hn52epWmxbSbjxfVDeOoiIiGxcpfvcsL/NfdAVATcTpHW23BAREVlUpcNNyWgpqoKsZOm+Uo6ugEc9uashIiKyaZW+LKXX6y1Zh227cUZa+jQF2AJGRERkUbzBkTWU9Lfx4fw2RERElsZwYw2ZJ6WlL2cmJiIisjSGG2swDAPnSCkiIiJLY7ixNL0OuHlWWudtF4iIiCyO4cbSsi8BxQXSPaW8GshdDRERkc1juLG0kvltajUGlA7y1kJERGQHGG4s7eZ5aendSN46iIiI7ATDjaXduNNy49NM3jqIiIjsBMONpd26IC29G8tbBxERkZ1guLG0rCRp6c3OxERERNbAcGNJep00WgrgSCkiIiIrYbixpJxrgL4IUDoB7sFyV0NERGQXGG4sKTtZWnpoOAyciIjIShhuLCk7RVp6hchaBhERkT1huLGknKvSkpekiIiIrIbhxpJKOhN71JO3DiIiIjvCcGNJt69ISw+NvHUQERHZEYYbS8q5E254WYqIiMhqGG4sydByU1feOoiIiOwIw42lFBcA+deldfa5ISIishqGG0vJTZOWjs6Acy15ayEiIrIjDDeWcvuytHQLAhQKeWshIiKyIww3llIyxw372xAREVkVw42l5KZKS7cgeesgIiKyMww3lpKXIS1d/eStg4iIyM4w3FgKww0REZEsGG4sJS9dWrr6y1sHERGRnWG4sZSca9LSPVDeOoiIiOwMw42l5N2ZwI+XpYiIiKyK4cYShLg7O7FLHXlrISIisjMMN5ZQlAPoCqV1F195ayEiIrIzDDeWUHLrBSc3QOUuby1ERER2plqEmyVLliAkJATOzs4ICwvDwYMHy913+fLlePjhh1GrVi3UqlULERERFe4vi9w7I6XcAuStg4iIyA7JHm42bNiAqKgoREdH4+jRo2jTpg169uyJjIyMMvfftWsXBg0ahN9++w0HDhyARqPB448/jqtXr1q58grkZ0pL9rchIiKyOoUQQshZQFhYGDp27IjFixcDAPR6PTQaDcaOHYtJkybd83idTodatWph8eLFGDZs2D33z87OhpeXF7KysuDp6Xnf9Zfp5FfA9peB+k8Az261zGcQERHZEVN+v2VtudFqtThy5AgiIiIM25RKJSIiInDgwIFKvUdeXh6Kiorg4+NT5uuFhYXIzs42elhcwU1pqa5l+c8iIiIiI7KGm8zMTOh0Ovj7G8/i6+/vj7S0tEq9x7vvvougoCCjgPRPsbGx8PLyMjw0Gs19131PhSXhxtvyn0VERERGZO9zcz/mzp2L9evX44cffoCzs3OZ+0yePBlZWVmGx+XLly1fWP4NaelS2/KfRUREREYc5fxwX19fODg4ID093Wh7eno6AgIqHmn00UcfYe7cudi5cydat25d7n5qtRpqtdos9VZawZ1w48zLUkRERNYma8uNSqVCaGgo4uLiDNv0ej3i4uLQuXPnco/78MMPMXv2bGzbtg0dOnSwRqmmMYQbttwQERFZm6wtNwAQFRWFyMhIdOjQAZ06dcLChQuRm5uL4cOHAwCGDRuG4OBgxMbGAgA++OADzJgxA+vWrUNISIihb467uzvc3avJhHkFf0tL57I7ORMREZHlyB5uBgwYgOvXr2PGjBlIS0tD27ZtsW3bNkMn45SUFCiVdxuYPvvsM2i1Wjz//PNG7xMdHY2ZM2das/TylYyWYrghIiKyOtnnubE2q8xzs7gWUHgLeOkMULuZZT6DiIjIjtSYeW5skl4HFGZJ6+xQTEREZHUMN+ZWlAPgTmMY57khIiKyOoYbcytptVE6AY5WHoJOREREDDdmx0tSREREsmK4MbfCW9JS7SVrGURERPaK4cbcSlpuVAw3REREcmC4MTftnXDDlhsiIiJZMNyYW8EtacmRUkRERLJguDE3ttwQERHJiuHG3AoZboiIiOTEcGNu7FBMREQkK4YbczPMc+MtaxlERET2iuHG3LTZ0tLJQ946iIiI7BTDjbmVhBu1he44TkRERBViuDE3bY60VLHlhoiISA4MN+ZWdFtaOrnLWwcREZGdYrgxt6JcaclwQ0REJAuGG3MruSzl5CZvHURERHaK4cachB4oYp8bIiIiOTHcmFPJJSmA4YaIiEgmDDfmVHJJSqEEHF3krYWIiMhOMdyYU3GetHR0BRQKeWshIiKyUww35lRUEm7YakNERCQXhhtzMnQm5jBwIiIiuTDcmBPnuCEiIpIdw405GcIN57ghIiKSC8ONOZX0uWG4ISIikg3DjTkVs0MxERGR3BhuzKk4X1oy3BAREcmG4cacGG6IiIhkx3BjTsUF0tLRWd46iIiI7BjDjTnp7oQbB4YbIiIiuTDcmBNbboiIiGTHcGNOOq20dFDJWwcREZEdY7gxJ12htHRQy1sHERGRHWO4MSd9ScsNww0REZFcGG7Mqbik5YaXpYiIiOTCcGNOnOeGiIhIdgw35sSh4ERERLJjuDGnkqHg7HNDREQkG4YbcyppueFlKSIiItkw3JgT57khIiKSHcONOTHcEBERyY7hxpw4iR8REZHsHOUuwKaw5YbIJgkhUFxcDJ1OJ3cpRDbNyckJDg4O9/0+DDfmxJYbIpuj1WqRmpqKvLw8uUshsnkKhQJ169aFu7v7fb0Pw405ldx+QcmWGyJboNfrkZycDAcHBwQFBUGlUkGhUMhdFpFNEkLg+vXruHLlCho3bnxfLTgMN+ZUclnKkS03RLZAq9VCr9dDo9HA1dVV7nKIbF6dOnVw8eJFFBUV3Ve4YYdicyq5LMWWGyKbolTyn0oiazBXyyj/xpqL0AP6YmmdHYqJiIhkw3BjLrqiu+sMN0RERLJhuDEX/T/CjdJJvjqIiIjsHMONuTDcEBHZhISEBAQEBOD27dtyl2JTtFotQkJCcPjwYYt/FsONuZSMlAIAJQehEZG8XnrpJSgUCigUCjg5OaF+/fqYOHEiCgoKSu27efNmdO3aFR4eHnB1dUXHjh2xcuXKMt/3u+++w6OPPgovLy+4u7ujdevWeO+993Djxg0LfyPrmTx5MsaOHQsPDw+5S7GYJUuWICQkBM7OzggLC8PBgwfveczChQvRtGlTuLi4QKPRYPz48WX+eQKAuXPnQqFQ4K233jJsU6lUmDBhAt59911zfY1yMdyYS0nLjdIR4DwYRLZLCKAoV56HECaV2qtXL6SmpiIpKQkff/wxPv/8c0RHRxvts2jRIvTt2xddunTBn3/+ib/++gsDBw7E66+/jgkTJhjtO3XqVAwYMAAdO3bEL7/8gpMnT2L+/Pk4fvw41qxZc9+ntrK0Wu29d6qilJQUbN68GS+99NJ9vY8la7xfGzZsQFRUFKKjo3H06FG0adMGPXv2REZGRrnHrFu3DpMmTUJ0dDTOnDmDL7/8Ehs2bMCUKVNK7Xvo0CF8/vnnaN26danXhgwZgr179+LUqVNm/U6lCDuTlZUlAIisrCzzvvGtJCE+ghALXc37vkQkm/z8fHH69GmRn59/d6M2R/q7LsdDm1Pp2iMjI0Xfvn2Ntj377LOiXbt2hucpKSnCyclJREVFlTr+v//9rwAg/vjjDyGEEH/++acAIBYuXFjm5928ebPcWi5fviwGDhwoatWqJVxdXUVoaKjhfcuqc9y4caJr166G5127dhWjR48W48aNE7Vr1xaPPvqoGDRokOjfv7/RcVqtVtSuXVusWrVKCCGETqcTMTExIiQkRDg7O4vWrVuLjRs3llunEELMmzdPdOjQwWhbZmamGDhwoAgKChIuLi6iZcuWYt26dUb7lFWjEEKcOHFC9OrVS7i5uQk/Pz/x4osviuvXrxuO++WXX0SXLl2El5eX8PHxEb179xYXLlyosMb71alTJzF69GjDc51OJ4KCgkRsbGy5x4wePVp0797daFtUVJTo0qWL0bbbt2+Lxo0bix07doiuXbuKcePGlXqvbt26iWnTppX5OWX+nbvDlN/vatFyY2rz2MaNG9GsWTM4OzujVatW2Lp1q5UqrYDhvlLsb0NE1c/Jkyexf/9+qFR3R3N+++23KCoqKtVCAwAjR46Eu7s7vv76awDA2rVr4e7ujlGjRpX5/t7e3mVuz8nJQdeuXXH16lVs2rQJx48fx8SJE6HX602qf9WqVVCpVNi3bx+WLl2KIUOG4Oeff0ZOTo5hn+3btyMvLw/PPPMMACA2NharV6/G0qVLcerUKYwfPx4vvvgidu/eXe7n7NmzBx06dDDaVlBQgNDQUGzZsgUnT57EiBEjMHTo0FK/Vf+u8datW+jevTvatWuHw4cPY9u2bUhPT0f//v0Nx+Tm5iIqKgqHDx9GXFwclEolnnnmmQrPT0xMDNzd3St8pKSklHmsVqvFkSNHEBERYdimVCoRERGBAwcOlPuZ4eHhOHLkiOE7JyUlYevWrXjyySeN9hs9ejR69+5t9P7/1qlTJ+zZs6fc181B9s4hJc1jS5cuRVhYGBYuXIiePXsiISEBfn5+pfbfv38/Bg0ahNjYWDz11FNYt24d+vXrh6NHj6Jly5YyfIM7DJelGG6IbJqjK/Bmzr33s9Rnm2Dz5s1wd3dHcXExCgsLoVQqsXjxYsPr586dg5eXFwIDA0sdq1Kp0KBBA5w7dw4AcP78eTRo0ABOTqb9G7du3Tpcv34dhw4dgo+PDwCgUaNGJr0HADRu3Bgffvih4XnDhg3h5uaGH374AUOHDjV81tNPPw0PDw8UFhYiJiYGO3fuROfOnQEADRo0wN69e/H555+ja9euZX7OpUuXSoWb4OBgowA4duxYbN++Hd988w06depUbo1z5sxBu3btEBMTY9i2YsUKaDQanDt3Dk2aNMFzzz1n9FkrVqxAnTp1cPr06XJ/015//XWjgFSWoKCgMrdnZmZCp9PB39/faLu/vz/Onj1b7vsNHjwYmZmZeOihhww3kn399deNLkutX78eR48exaFDh+5Z26VLlyrc537JHm4WLFiA1157DcOHDwcALF26FFu2bMGKFSswadKkUvt/8skn6NWrF9555x0AwOzZs7Fjxw4sXrwYS5cutWrtRkrmueEcN0S2TaEAnNzkrqJSunXrhs8++wy5ubn4+OOP4ejoWOrHtLKEif19SsTHx6Ndu3aGYFNVoaGhRs8dHR3Rv39/rF27FkOHDkVubi5++uknrF+/HgBw4cIF5OXl4bHHHjM6TqvVol27duV+Tn5+PpydnY226XQ6xMTE4JtvvsHVq1eh1WpRWFhY6pYc/67x+PHj+O2338q8CWRiYiKaNGmC8+fPY8aMGfjzzz+RmZlpaLFJSUkpN9z4+Pjc9/k01a5duxATE4NPP/0UYWFhuHDhAsaNG4fZs2dj+vTpuHz5MsaNG4cdO3aUOn//5uLiYvEb0coabkqaxyZPnmzYdq/msQMHDiAqKspoW8+ePfHjjz+WuX9hYSEKCwsNz7Ozs++/8LKw5YaIqhk3NzdDK8mKFSvQpk0bfPnll3jllVcAAE2aNEFWVhauXbtW6v/0tVotEhMT0a1bN8O+e/fuRVFRkUmtNy4uLhW+rlQqSwWnoqKiUvu5uZUOlEOGDEHXrl2RkZGBHTt2wMXFBb169QIAw+WqLVu2IDg42Og4tbr8+//5+vri5s2bRtvmzZuHTz75BAsXLkSrVq3g5uaGt956q1Sn4X/XmJOTgz59+uCDDz4o9TklrWV9+vTBAw88gOXLlyMoKAh6vR4tW7assENyTEyMUWtQWU6fPo169eqV+f0cHByQnp5utD09PR0BAQHlvt/06dMxdOhQvPrqqwCAVq1aITc3FyNGjMDUqVNx5MgRZGRkoH379oZjdDodfv/9dyxevBiFhYWGe0XduHEDderUqbD++yVrn5uKmsfS0tLKPCYtLc2k/WNjY+Hl5WV4aDQa8xT/b0IvNRnXkP+jIyL7olQqMWXKFEybNg35+fkAgOeeew5OTk6YP39+qf2XLl2K3NxcDBo0CIB0WSInJweffvppme9/69atMre3bt0a8fHx5Q4Vr1OnDlJTU422xcfHV+o7hYeHQ6PRYMOGDVi7di1eeOEFQ/Bq0aIF1Go1UlJS0KhRI6NHRb8D7dq1w+nTp4227du3D3379sWLL76INm3aGF2uq0j79u1x6tQphISElKrBzc0Nf//9NxISEjBt2jT06NEDzZs3LxWsyvL6668jPj6+wkd5l6VUKhVCQ0MRFxdn2KbX6xEXF2e4fFeWvLy8UvdYKwkrQgj06NEDJ06cMKqhQ4cOGDJkCOLj441ugnny5MkKW8/MoVp0KLakyZMnIysry/C4fPmyZT4o6D/AuFzgJQsPbyMiqqIXXngBDg4OWLJkCQCgXr16+PDDD7Fw4UJMnToVZ8+eRWJiIhYsWICJEyfi7bffRlhYGAAgLCzMsG3ixIk4cOAALl26hLi4OLzwwgtYtWpVmZ85aNAgBAQEoF+/fti3bx+SkpLw3XffGVrnu3fvjsOHD2P16tU4f/48oqOjcfLkyUp/p8GDB2Pp0qXYsWMHhgwZYtju4eGBCRMmYPz48Vi1ahUSExNx9OhRLFq0qNxaAelKwIEDB6DT6QzbGjdujB07dmD//v04c+YMRo4cWarloyyjR4/GjRs3MGjQIBw6dAiJiYnYvn07hg8fDp1Oh1q1aqF27dpYtmwZLly4gF9//bXUlYmy+Pj4lApL/344OpZ/YSYqKgrLly/HqlWrcObMGbzxxhvIzc01dA8BgGHDhhldVenTpw8+++wzrF+/HsnJydixYwemT5+OPn36wMHBAR4eHmjZsqXRw83NDbVr1y51eW3Pnj14/PHH7/k978s9x1NZUGFhoXBwcBA//PCD0fZhw4aJp59+usxjNBqN+Pjjj422zZgxQ7Ru3bpSn2mxoeBEZHMqGpZa3ZU1xFoIIWJjY0WdOnVETs7dYeU//fSTePjhh4Wbm5twdnYWoaGhYsWKFWW+74YNG8QjjzwiPDw8hJubm2jdurV47733KhwKfvHiRfHcc88JT09P4erqKjp06CD+/PNPw+szZswQ/v7+wsvLS4wfP16MGTOm1FDwsoYUCyHE6dOnBQDxwAMPCL1eb/SaXq8XCxcuFE2bNhVOTk6iTp06omfPnmL37t3l1lpUVCSCgoLEtm3bDNv+/vtv0bdvX+Hu7i78/PzEtGnTxLBhw4zOb3k1njt3TjzzzDPC29tbuLi4iGbNmom33nrLUOuOHTtE8+bNhVqtFq1btxa7du0SAEr9LprbokWLRL169YRKpRKdOnUyDM3/5/eJjIw0PC8qKhIzZ84UDRs2FM7OzkKj0YhRo0ZV+N+9rHOyf/9+4e3tLfLy8so8xlxDwRVCVLGXmJmEhYWhU6dOWLRoEQCpeaxevXoYM2ZMmR2KBwwYgLy8PPz888+GbeHh4WjdunWlOhRnZ2fDy8sLWVlZ8PT0NN8XISKbU1BQgOTkZNSvX/+enSTJdixZsgSbNm3C9u3b5S7F5gwYMABt2rQpc/I/oOK/c6b8fss+WioqKgqRkZHo0KEDOnXqhIULFxo1jw0bNgzBwcGIjY0FAIwbNw5du3bF/Pnz0bt3b6xfvx6HDx/GsmXL5PwaRERkI0aOHIlbt27h9u3bNn0LBmvTarVo1aoVxo8fb/HPkj3cDBgwANevX8eMGTOQlpaGtm3bYtu2bYZOwykpKUadmMLDw7Fu3TpMmzYNU6ZMQePGjfHjjz/KO8cNERHZDEdHR0ydOlXuMmyOSqXCtGnTrPJZsl+WsjZeliKiyuJlKSLrMtdlKZsfLUVEdL/s7P8BiWRjrr9rDDdEROUomTPF0rOpEpGkZPLCf86LUxWy97khIqquHBwc4O3tjYyMDACAq6srFAqFzFUR2Sa9Xo/r16/D1dW1wnl6KoPhhoioAiVT0pcEHCKyHKVSiXr16t33/0Qw3BARVUChUCAwMBB+fn5l3vOIiMxHpVKVus1DVTDcEBFVgoODw333AyAi62CHYiIiIrIpDDdERERkUxhuiIiIyKbYXZ+bkgmCsrOzZa6EiIiIKqvkd7syE/3ZXbi5ffs2AECj0chcCREREZnq9u3b8PLyqnAfu7u3lF6vx7Vr1+Dh4WH2ybiys7Oh0Whw+fJl3rfKgnierYPn2Tp4nq2H59o6LHWehRC4ffs2goKC7jlc3O5abpRKJerWrWvRz/D09ORfHCvgebYOnmfr4Hm2Hp5r67DEeb5Xi00JdigmIiIim8JwQ0RERDaF4caM1Go1oqOjoVar5S7FpvE8WwfPs3XwPFsPz7V1VIfzbHcdiomIiMi2seWGiIiIbArDDREREdkUhhsiIiKyKQw3REREZFMYbky0ZMkShISEwNnZGWFhYTh48GCF+2/cuBHNmjWDs7MzWrVqha1bt1qp0prNlPO8fPlyPPzww6hVqxZq1aqFiIiIe/53IYmpf55LrF+/HgqFAv369bNsgTbC1PN869YtjB49GoGBgVCr1WjSpAn/7agEU8/zwoUL0bRpU7i4uECj0WD8+PEoKCiwUrU10++//44+ffogKCgICoUCP/744z2P2bVrF9q3bw+1Wo1GjRph5cqVFq8Tgipt/fr1QqVSiRUrVohTp06J1157TXh7e4v09PQy99+3b59wcHAQH374oTh9+rSYNm2acHJyEidOnLBy5TWLqed58ODBYsmSJeLYsWPizJkz4qWXXhJeXl7iypUrVq68ZjH1PJdITk4WwcHB4uGHHxZ9+/a1TrE1mKnnubCwUHTo0EE8+eSTYu/evSI5OVns2rVLxMfHW7nymsXU87x27VqhVqvF2rVrRXJysti+fbsIDAwU48ePt3LlNcvWrVvF1KlTxffffy8AiB9++KHC/ZOSkoSrq6uIiooSp0+fFosWLRIODg5i27ZtFq2T4cYEnTp1EqNHjzY81+l0IigoSMTGxpa5f//+/UXv3r2NtoWFhYmRI0datM6aztTz/G/FxcXCw8NDrFq1ylIl2oSqnOfi4mIRHh4uvvjiCxEZGclwUwmmnufPPvtMNGjQQGi1WmuVaBNMPc+jR48W3bt3N9oWFRUlunTpYtE6bUllws3EiRPFgw8+aLRtwIABomfPnhasTAhelqokrVaLI0eOICIiwrBNqVQiIiICBw4cKPOYAwcOGO0PAD179ix3f6raef63vLw8FBUVwcfHx1Jl1nhVPc/vvfce/Pz88Morr1ijzBqvKud506ZN6Ny5M0aPHg1/f3+0bNkSMTEx0Ol01iq7xqnKeQ4PD8eRI0cMl66SkpKwdetWPPnkk1ap2V7I9TtodzfOrKrMzEzodDr4+/sbbff398fZs2fLPCYtLa3M/dPS0ixWZ01XlfP8b++++y6CgoJK/YWiu6pynvfu3Ysvv/wS8fHxVqjQNlTlPCclJeHXX3/FkCFDsHXrVly4cAGjRo1CUVERoqOjrVF2jVOV8zx48GBkZmbioYceghACxcXFeP311zFlyhRrlGw3yvsdzM7ORn5+PlxcXCzyuWy5IZsyd+5crF+/Hj/88AOcnZ3lLsdm3L59G0OHDsXy5cvh6+srdzk2Ta/Xw8/PD8uWLUNoaCgGDBiAqVOnYunSpXKXZlN27dqFmJgYfPrppzh69Ci+//57bNmyBbNnz5a7NDIDttxUkq+vLxwcHJCenm60PT09HQEBAWUeExAQYNL+VLXzXOKjjz7C3LlzsXPnTrRu3dqSZdZ4pp7nxMREXLx4EX369DFs0+v1AABHR0ckJCSgYcOGli26BqrKn+fAwEA4OTnBwcHBsK158+ZIS0uDVquFSqWyaM01UVXO8/Tp0zF06FC8+uqrAIBWrVohNzcXI0aMwNSpU6FU8v/9zaG830FPT0+LtdoAbLmpNJVKhdDQUMTFxRm26fV6xMXFoXPnzmUe07lzZ6P9AWDHjh3l7k9VO88A8OGHH2L27NnYtm0bOnToYI1SazRTz3OzZs1w4sQJxMfHGx5PP/00unXrhvj4eGg0GmuWX2NU5c9zly5dcOHCBUN4BIBz584hMDCQwaYcVTnPeXl5pQJMSaAUvOWi2cj2O2jR7so2Zv369UKtVouVK1eK06dPixEjRghvb2+RlpYmhBBi6NChYtKkSYb99+3bJxwdHcVHH30kzpw5I6KjozkUvBJMPc9z584VKpVKfPvttyI1NdXwuH37tlxfoUYw9Tz/G0dLVY6p5zklJUV4eHiIMWPGiISEBLF582bh5+cn5syZI9dXqBFMPc/R0dHCw8NDfP311yIpKUn873//Ew0bNhT9+/eX6yvUCLdv3xbHjh0Tx44dEwDEggULxLFjx8SlS5eEEEJMmjRJDB061LB/yVDwd955R5w5c0YsWbKEQ8Gro0WLFol69eoJlUolOnXqJP744w/Da127dhWRkZFG+3/zzTeiSZMmQqVSiQcffFBs2bLFyhXXTKac5wceeEAAKPWIjo62fuE1jKl/nv+J4abyTD3P+/fvF2FhYUKtVosGDRqI999/XxQXF1u56prHlPNcVFQkZs6cKRo2bCicnZ2FRqMRo0aNEjdv3rR+4TXIb7/9Vua/tyXnNjIyUnTt2rXUMW3bthUqlUo0aNBAfPXVVxavUyEE29+IiIjIdrDPDREREdkUhhsiIiKyKQw3REREZFMYboiIiMimMNwQERGRTWG4ISIiIpvCcENEREQ2heGGiIiIbArDDREZWblyJby9veUuo8oUCgV+/PHHCvd56aWX0K9fP6vUQ0TWx3BDZINeeuklKBSKUo8LFy7IXRpWrlxpqEepVKJu3boYPnw4MjIyzPL+qampeOKJJwAAFy9ehEKhQHx8vNE+n3zyCVauXGmWzyvPzJkzDd/TwcEBGo0GI0aMwI0bN0x6HwYxItM5yl0AEVlGr1698NVXXxltq1OnjkzVGPP09ERCQgL0ej2OHz+O4cOH49q1a9i+fft9v3dAQMA99/Hy8rrvz6mMBx98EDt37oROp8OZM2fw8ssvIysrCxs2bLDK5xPZK7bcENkotVqNgIAAo4eDgwMWLFiAVq1awc3NDRqNBqNGjUJOTk6573P8+HF069YNHh4e8PT0RGhoKA4fPmx4fe/evXj44Yfh4uICjUaDN998E7m5uRXWplAoEBAQgKCgIDzxxBN48803sXPnTuTn50Ov1+O9995D3bp1oVar0bZtW2zbts1wrFarxZgxYxAYGAhnZ2c88MADiI2NNXrvkstS9evXBwC0a9cOCoUCjz76KADj1pBly5YhKCgIer3eqMa+ffvi5ZdfNjz/6aef0L59ezg7O6NBgwaYNWsWiouLK/yejo6OCAgIQHBwMCIiIvDCCy9gx44dhtd1Oh1eeeUV1K9fHy4uLmjatCk++eQTw+szZ87EqlWr8NNPPxlagXbt2gUAuHz5Mvr37w9vb2/4+Pigb9++uHjxYoX1ENkLhhsiO6NUKvHf//4Xp06dwqpVq/Drr79i4sSJ5e4/ZMgQ1K1bF4cOHcKRI0cwadIkODk5AQASExPRq1cvPPfcc/jrr7+wYcMG7N27F2PGjDGpJhcXF+j1ehQXF+OTTz7B/Pnz8dFHH+Gvv/5Cz5498fTTT+P8+fMAgP/+97/YtGkTvvnmGyQkJGDt2rUICQkp830PHjwIANi5cydSU1Px/fffl9rnhRdewN9//43ffvvNsO3GjRvYtm0bhgwZAgDYs2cPhg0bhnHjxuH06dP4/PPPsXLlSrz//vuV/o4XL17E9u3boVKpDNv0ej3q1q2LjRs34vTp05gxYwamTJmCb775BgAwYcIE9O/fH7169UJqaipSU1MRHh6OoqIi9OzZEx4eHtizZw/27dsHd3d39OrVC1qtttI1Edksi993nIisLjIyUjg4OAg3NzfD4/nnny9z340bN4ratWsbnn/11VfCy8vL8NzDw0OsXLmyzGNfeeUVMWLECKNte/bsEUqlUuTn55d5zL/f/9y5c6JJkyaiQ4cOQgghgoKCxPvvv290TMeOHcWoUaOEEEKMHTtWdO/eXej1+jLfH4D44YcfhBBCJCcnCwDi2LFjRvtERkaKvn37Gp737dtXvPzyy4bnn3/+uQgKChI6nU4IIUSPHj1ETEyM0XusWbNGBAYGllmDEEJER0cLpVIp3NzchLOzswAgAIgFCxaUe4wQQowePVo899xz5dZa8tlNmzY1OgeFhYXCxcVFbN++vcL3J7IH7HNDZKO6deuGzz77zPDczc0NgNSKERsbi7NnzyI7OxvFxcUoKChAXl4eXF1dS71PVFQUXn31VaxZs8ZwaaVhw4YApEtWf/31F9auXWvYXwgBvV6P5ORkNG/evMzasrKy4O7uDr1ej4KCAjz00EP44osvkJ2djWvXrqFLly5G+3fp0gXHjx8HIF1Seuyxx9C0aVP06tULTz31FB5//PH7OldDhgzBa6+9hk8//RRqtRpr167FwIEDoVQqDd9z3759Ri01Op2uwvMGAE2bNsWmTZtQUFCA//u//0N8fDzGjh1rtM+SJUuwYsUKpKSkID8/H1qtFm3btq2w3uPHj+PChQvw8PAw2l5QUIDExMQqnAEi28JwQ2Sj3Nzc0KhRI6NtFy9exFNPPYU33ngD77//Pnx8fLB371688sor0Gq1Zf5Iz5w5E4MHD8aWLVvwyy+/IDo6GuvXr8czzzyDnJwcjBw5Em+++Wap4+rVq1dubR4eHjh69CiUSiUCAwPh4uICAMjOzr7n92rfvj2Sk5Pxyy+/YOfOnejfvz8iIiLw7bff3vPY8vTp0wdCCGzZsgUdO3bEnj178PHHHxtez8nJwaxZs/Dss8+WOtbZ2bnc91WpVIb/BnPnzkXv3r0xa9YszJ49GwCwfv16TJgwAfPnz0fnzp3h4eGBefPm4c8//6yw3pycHISGhhqFyhLVpdM4kZwYbojsyJEjR6DX6zF//nxDq0RJ/46KNGnSBE2aNMH48eMxaNAgfPXVV3jmmWfQvn17nD59ulSIuhelUlnmMZ6enggKCsK+ffvQtWtXw/Z9+/ahU6dORvsNGDAAAwYMwPPPP49evXrhxo0b8PHxMXq/kv4tOp2uwnqcnZ3x7LPPYu3atbhw4QKaNm2K9u3bG15v3749EhISTP6e/zZt2jR0794db7zxhuF7hoeHY9SoUYZ9/t3yolKpStXfvn17bNiwAX5+fvD09LyvmohsETsUE9mRRo0aoaioCIsWLUJSUhLWrFmDpUuXlrt/fn4+xowZg127duHSpUvYt28fDh06ZLjc9O6772L//v0YM2YM4uPjcf78efz0008mdyj+p3feeQcffPABNmzYgISEBEyaNAnx8fEYN24cAGDBggX4+uuvcfbsWZw7dw4bN25EQEBAmRMP+vn5wcXFBdu2bUN6ejqysrLK/dwhQ4Zgy5YtWLFihaEjcYkZM2Zg9erVmDVrFk6dOoUzZ85g/fr1mDZtmknfrXPnzmjdujViYmIAAI0bN8bhw4exfft2nDt3DtOnT8ehQ4eMjgkJCcFff/2FhIQEZGZmoqioCEOGDIGvry/69u2LPXv2IDk5Gbt27cKbb76JK1eumFQTkU2Su9MPEZlfWZ1QSyxYsEAEBgYKFxcX0bNnT7F69WoBQNy8eVMIYdzht7CwUAwcOFBoNBqhUqlEUFCQGDNmjFFn4YMHD4rHHntMuLu7Czc3N9G6detSHYL/6d8div9Np9OJmTNniuDgYOHk5CTatGkjfvnlF8Pry5YtE23bthVubm7C09NT9OjRQxw9etTwOv7RoVgIIZYvXy40Go1QKpWia9eu5Z4fnU4nAgMDBQCRmJhYqq5t27aJ8PBw4eLiIjw9PUWnTp3EsmXLyv0e0dHRok2bNqW2f/3110KtVouUlBRRUFAgXnrpJeHl5SW8vb3FG2+8ISZNmmR0XEZGhuH8AhC//fabEEKI1NRUMWzYMOHr6yvUarVo0KCBeO2110RWVla5NRHZC4UQQsgbr4iIiIjMh5eliIiIyKYw3BAREZFNYbghIiIim8JwQ0RERDaF4YaIiIhsCsMNERER2RSGGyIiIrIpDDdERERkUxhuiIiIyKYw3BAREZFNYbghIiIim/L/64IS2IqZyNEAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import roc_curve, auc\n", + "fpr, tpr, _ = roc_curve(y_Test, Fisher.decision_function(X_Test))\n", + "\n", + "roc_auc = auc(fpr, tpr)\n", + "\n", + "plt.plot(fpr,tpr,color='darkorange',label='ROC curve (area = %0.2f)' % roc_auc)\n", + "plt.legend(loc=\"lower right\")\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Final Model Results\n", + "*Training the model took about 8 >= hours. The compute on the inputs and outputs do not seem accurate. I may not have implemented the training and test sets properly. Some of the model training involved seemed a bit redundant in certain functions when training can be done outside of the class functions. This would significantly cut down on training time. The dataset was also a large dataset at 2.2 Gb. This is my first experience with training ML models so I have a clearer understanding now of best practices in machine learning. I was able to obtain a model accuracy score of 80% with GradientBoostingClassifier. Below are the rest of the results.*" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Importing more sci-kit learn functions\n", + "from sklearn.metrics import precision_recall_curve, precision_score, recall_score, f1_score, accuracy_score\n", + "\n", + "# Compute function to compute performance metrics and maximal significance for given scenarios\n", + "def compute_metrics(clf, X_train, y_train, X_test, y_test):\n", + " # Train the classifier\n", + " clf.fit(X_train, y_train)\n", + " \n", + " # Predict probabilities for the test set\n", + " y_pred_proba = clf.predict_proba(X_test)[:, 1]\n", + " \n", + " # Compute ROC curve and AUC\n", + " fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n", + " auc = roc_auc_score(y_test, y_pred_proba)\n", + " \n", + " # Compute precision-recall curve\n", + " precision, recall, _ = precision_recall_curve(y_test, y_pred_proba)\n", + " \n", + " # Compute precision, recall, F1-score, and accuracy\n", + " y_pred = clf.predict(X_test)\n", + " precision_score_val = precision_score(y_test, y_pred)\n", + " recall_score_val = recall_score(y_test, y_pred)\n", + " f1_score_val = f1_score(y_test, y_pred)\n", + " accuracy = accuracy_score(y_test, y_pred)\n", + " \n", + " # Compute maximal significance\n", + " thresholds = np.linspace(0, 1, 1000)\n", + " significance = compute_significance(y_test, y_pred_proba, thresholds)\n", + " max_significance = np.max(significance)\n", + " \n", + " # Return metrics\n", + " return {\n", + " 'TPR': tpr,\n", + " 'FPR': fpr,\n", + " 'ROC': (fpr, tpr),\n", + " 'AUC': auc,\n", + " 'Precision': precision_score_val,\n", + " 'Recall': recall_score_val,\n", + " 'F1 Score': f1_score_val,\n", + " 'Accuracy': accuracy,\n", + " 'Max Significance': max_significance\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results:\n", + "\n", + "Scenario: Raw\n", + "TPR: [0.00000000e+00 2.18229591e-06 8.72918362e-06 ... 9.99997818e-01\n", + " 1.00000000e+00 1.00000000e+00]\n", + "FPR: [0. 0. 0. ... 0.9999557 0.9999557 1. ]\n", + "ROC: (array([0. , 0. , 0. , ..., 0.9999557, 0.9999557,\n", + " 1. ]), array([0.00000000e+00, 2.18229591e-06, 8.72918362e-06, ...,\n", + " 9.99997818e-01, 1.00000000e+00, 1.00000000e+00]))\n", + "AUC: 0.8708145295492099\n", + "Precision: 0.822944362135363\n", + "Recall: 0.7139686578661947\n", + "F1 Score: 0.7645930174132318\n", + "Accuracy: 0.798542\n", + "Max Significance: 1194.2812321152603\n", + "\n", + "Scenario: Features\n", + "TPR: [0.00000000e+00 2.18229591e-06 8.72918362e-06 ... 9.99997818e-01\n", + " 1.00000000e+00 1.00000000e+00]\n", + "FPR: [0. 0. 0. ... 0.9999557 0.9999557 1. ]\n", + "ROC: (array([0. , 0. , 0. , ..., 0.9999557, 0.9999557,\n", + " 1. ]), array([0.00000000e+00, 2.18229591e-06, 8.72918362e-06, ...,\n", + " 9.99997818e-01, 1.00000000e+00, 1.00000000e+00]))\n", + "AUC: 0.8708145295492099\n", + "Precision: 0.822944362135363\n", + "Recall: 0.7139686578661947\n", + "F1 Score: 0.7645930174132318\n", + "Accuracy: 0.798542\n", + "Max Significance: 1194.2812321152603\n", + "\n", + "Scenario: Combined\n", + "TPR: [0.00000000e+00 2.18229591e-06 8.72918362e-06 ... 9.99997818e-01\n", + " 1.00000000e+00 1.00000000e+00]\n", + "FPR: [0. 0. 0. ... 0.9999557 0.9999557 1. ]\n", + "ROC: (array([0. , 0. , 0. , ..., 0.9999557, 0.9999557,\n", + " 1. ]), array([0.00000000e+00, 2.18229591e-06, 8.72918362e-06, ...,\n", + " 9.99997818e-01, 1.00000000e+00, 1.00000000e+00]))\n", + "AUC: 0.8708145295492099\n", + "Precision: 0.822944362135363\n", + "Recall: 0.7139686578661947\n", + "F1 Score: 0.7645930174132318\n", + "Accuracy: 0.798542\n", + "Max Significance: 1194.2812321152603\n" + ] + } + ], + "source": [ + "# Define scenarios\n", + "scenarios = {\n", + " 'Raw': X_Train, \n", + " 'Features': X_Train[VarNames[1:]], \n", + " 'Combined': X_Train\n", + "}\n", + "\n", + "# Initialize dictionary to store results\n", + "results = {}\n", + "\n", + "# Compute metrics for each scenario\n", + "for scenario, X_train_scenario in scenarios.items():\n", + " metrics = compute_metrics(gb_classifier, X_train_scenario, y_Train, X_Test, y_Test)\n", + " results[scenario] = metrics\n", + "\n", + "# Print results\n", + "print(\"Results:\")\n", + "for scenario, metrics in results.items():\n", + " print(\"\\nScenario:\", scenario)\n", + " for metric, value in metrics.items():\n", + " print(f\"{metric}: {value}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DataFrame/Table" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TPRFPRROCAUCPrecisionRecallF1 ScoreAccuracyMax Significance
scenario
Raw[0.0, 2.1822959062311096e-06, 8.72918362492443...[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...0.8708150.8229440.7139690.7645930.7985421194.281232
Features[0.0, 2.1822959062311096e-06, 8.72918362492443...[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...0.8708150.8229440.7139690.7645930.7985421194.281232
Combined[0.0, 2.1822959062311096e-06, 8.72918362492443...[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...0.8708150.8229440.7139690.7645930.7985421194.281232
\n", + "
" + ], + "text/plain": [ + " TPR \\\n", + "scenario \n", + "Raw [0.0, 2.1822959062311096e-06, 8.72918362492443... \n", + "Features [0.0, 2.1822959062311096e-06, 8.72918362492443... \n", + "Combined [0.0, 2.1822959062311096e-06, 8.72918362492443... \n", + "\n", + " FPR \\\n", + "scenario \n", + "Raw [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", + "Features [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", + "Combined [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", + "\n", + " ROC AUC \\\n", + "scenario \n", + "Raw ([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 0.870815 \n", + "Features ([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 0.870815 \n", + "Combined ([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 0.870815 \n", + "\n", + " Precision Recall F1 Score Accuracy Max Significance \n", + "scenario \n", + "Raw 0.822944 0.713969 0.764593 0.798542 1194.281232 \n", + "Features 0.822944 0.713969 0.764593 0.798542 1194.281232 \n", + "Combined 0.822944 0.713969 0.764593 0.798542 1194.281232 " + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_table = pd.DataFrame.from_dict(results, orient='index')\n", + "df_table.index.name = 'scenario'\n", + "df_table" + ] + }, + { + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Quizzes/Quiz1-RobertCocker.ipynb b/Quizzes/Quiz1-RobertCocker.ipynb new file mode 100644 index 0000000..f7d30b3 --- /dev/null +++ b/Quizzes/Quiz1-RobertCocker.ipynb @@ -0,0 +1,152 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "id": "3308bf7d-8866-4516-8c7b-c8bf78dac36e", + "metadata": {}, + "outputs": [], + "source": [ + "# Robert Cocker\n", + "# Dr. Farbin\n", + "# DATA-3402\n", + "# Quiz\n", + "# 2/15/2024" + ] + }, + { + "cell_type": "markdown", + "id": "058ad468-d4aa-4ee0-a8c7-28935f054bad", + "metadata": {}, + "source": [ + "## Quick Quiz" + ] + }, + { + "cell_type": "markdown", + "id": "2c962e7f-644c-4b66-9ff8-18ccfaef5989", + "metadata": {}, + "source": [ + "Can you rewrite create_new_args as a two lines of code using functional programming, list comprehensions, and shortcuts? How about a single line?" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2456eff4-dd07-4094-abe5-d4e70aea3d57", + "metadata": {}, + "outputs": [], + "source": [ + "def create_new_args_0(args):\n", + " max_len = max(map(len,\n", + " filter(lambda x: isinstance(x,list),\n", + " args)))\n", + "\n", + " # Rewrite this section:\n", + " new_args=list()\n", + "\n", + " for a in args:\n", + " if not isinstance(a,list):\n", + " a0=[a]*max_len\n", + " elif len(a)!=max_len:\n", + " print(\"Error: all list arguments must have same length.\")\n", + " return\n", + " else:\n", + " a0=a\n", + " new_args.append(a0)\n", + "\n", + " return new_args" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4026f851-2114-4b3c-8860-b34b60d8503b", + "metadata": {}, + "outputs": [], + "source": [ + "def create_new_args(args):\n", + " max_len = max(map(len, filter(lambda x: isinstance(x, list), args)))\n", + " return [a if isinstance(a, list) and len(a) == max_len else [a] * max_len for a in args]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bc1c9754-c602-4fd2-a266-62a67207b16a", + "metadata": {}, + "outputs": [], + "source": [ + "create_new_args2 = lambda args: [[a] * max(map(len, filter(lambda x: isinstance(x, list), args))) if not isinstance(a, list) else a if len(a) == max(map(len, filter(lambda x: isinstance(x, list), args))) else print(\"Error: all list arguments must have same length.\") for a in args]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "93837073-e31c-4a24-a3ab-17b3d941c479", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[1, 2], [3, 4], [5, 5]]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "create_new_args_0([[1,2],[3,4],5])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6618fddb-b839-4d78-b0f5-06d23f20162e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error: all list arguments must have same length.\n" + ] + } + ], + "source": [ + "create_new_args_0([[1,2],[3,4,5],5])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71ff2bd8-dd64-4940-8c5b-4f16b786e5d8", + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/README.md b/README.md index 85b5ba9..1ace78e 100644 --- a/README.md +++ b/README.md @@ -1 +1,3 @@ -# DATA3402.Spring.2024 +# DATA-3402 Python for Data Science II +Python programming for data science @ UTA. +Spring 2024 semester