diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 000000000..007b67b10 Binary files /dev/null and b/.DS_Store differ diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000..afed0735d --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +*.csv diff --git a/KaggleProject/.DS_Store b/KaggleProject/.DS_Store new file mode 100644 index 000000000..cb302d0c0 Binary files /dev/null and b/KaggleProject/.DS_Store differ diff --git a/KaggleProject/UTA-DataScience-Logo.png b/KaggleProject/UTA-DataScience-Logo.png new file mode 100644 index 000000000..dc172171a Binary files /dev/null and b/KaggleProject/UTA-DataScience-Logo.png differ diff --git a/KaggleProject/readme.md b/KaggleProject/readme.md new file mode 100644 index 000000000..5d32b97a5 --- /dev/null +++ b/KaggleProject/readme.md @@ -0,0 +1,87 @@ +![](UTA-DataScience-Logo.png) + +# Santander Customer Satisfaction Prediction + +**By Subham Kalwar** + +* **One Sentence Summary:** An applied machine learning pipeline predicting customer dissatisfaction on a highly imbalanced dataset using a tuned, class-weighted Random Forest architecture. + +## Overview + +* **Definition of the task:** The objective of this Kaggle challenge is to identify dissatisfied customers early in their relationship with Santander Bank. The dataset consists of over 370 anonymized tabular features. The evaluation metric is the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), which is necessary due to the extreme class imbalance in the target variable. +* **My approach:** The problem was formulated as a binary classification task. Exploratory Data Analysis revealed a severe 96:4 split between satisfied and unsatisfied customers. To address this, I focused on data pruning (removing zero-variance noise) and utilized a Random Forest Classifier. The model was specifically tuned with balanced class weights to penalize the misclassification of the minority class. +* **Summary of the performance achieved:** The final tuned Random Forest model achieved a local Validation AUC of 0.8216. When evaluated against the unseen test data on Kaggle, it secured a Public Leaderboard AUC score of 0.79119, demonstrating a strong ability to generalize patterns without severe overfitting. + +## Summary of Work Done + +### Data + +* **Type:** * Input: CSV file containing integer and float features (anonymized, e.g., `var15`, `var38`). + * Output: Binary target column (`TARGET`), where `1` indicates an unsatisfied customer and `0` indicates a satisfied customer. +* **Size:** * Training Data: ~76,020 instances with ~370 features. + * Testing Data: ~75,818 instances. +* **Instances (Train/Validation Split):** * An 80/20 train/validation split was utilized. Crucially, a `stratify` parameter was applied to ensure the validation set maintained the exact 96:4 imbalance ratio present in the training data. + +#### Preprocessing / Clean up + +* **Variance Filtering:** Scanned the dataset for constant features (Standard Deviation = 0) and dropped them, reducing dimensionality and training overhead. +* **Deduplication:** Dropped identical rows to prevent the model from overfitting on repeated data points. +* **Imputation:** Handled missing/null values internally within the modeling pipeline. + +#### Data Visualization + +* Extracted feature importances from the trained Random Forest model. Visualizations confirmed that specific features, notably `var15` (widely hypothesized to represent customer Age), carried the highest relative importance when determining customer satisfaction. + +### Problem Formulation + +* **Input / Output:** Input is a vector of preprocessed numerical features for a single customer. Output is a continuous probability from `0.0` to `1.0` representing the likelihood of dissatisfaction. +* **Model:** * **Random Forest Classifier:** Selected for its robust handling of non-linear tabular data and its resistance to single-tree overfitting through ensemble voting. +* **Hyperparameters:** + * `n_estimators=500`: Increased tree count for a more stable, averaged prediction. + * `max_depth=15`: Pruned the trees to prevent them from memorizing the training data. + * `min_samples_split=10`: Forced broader generalizations at the leaf nodes. + * `class_weight='balanced'`: Automatically adjusted weights inversely proportional to class frequencies to combat the 96% majority class. + +### Training + +* **Hardware & Environment:** Trained primarily on Kaggle's cloud environment utilizing Python 3, `pandas`, `numpy`, and `scikit-learn`. +* **Process:** Training was accelerated utilizing all available CPU cores (`n_jobs=-1`). The primary difficulty encountered was the model defaulting to predicting `0` for almost all rows due to the imbalance. This was resolved by implementing the `class_weight` parameter and evaluating via `.predict_proba()` rather than absolute `.predict()` classes. + +### Performance Comparison + +* **Key Performance Metric:** AUC-ROC (Area Under the Receiver Operating Characteristic Curve). +* **Results:** + * Training Split AUC: ~0.90+ + * Stratified Validation AUC: 0.8216 + * Final Kaggle Public Leaderboard AUC: 0.79119 + +### Conclusions + +* A standard Random Forest can successfully navigate highly imbalanced datasets if depth is strictly controlled and minority classes are mathematically weighted. The ~3% gap between validation and test scores indicates minimal overfitting and a healthy, generalized model. + +### Future Work + +* **Algorithm Shift:** Transition the baseline model to Gradient Boosting frameworks like XGBoost or LightGBM, which historically dominate this specific dataset. +* **Feature Engineering:** Conduct deeper bivariate analysis to combine existing anonymized variables into more highly correlated composite features. + +## How to reproduce results + +### Overview of files in repository + +* `santander_rf_model.ipynb`: The primary Jupyter Notebook containing the end-to-end pipeline. Includes EDA, data cleaning, model training, validation scoring, and final CSV generation. +* `submission.csv`: The final output file containing the test IDs and predicted probabilities, formatted for Kaggle scoring. +* `README.md`: Project documentation and methodology summary. + +### Software Setup +* Standard Data Science Python stack required: + * `pandas` + * `numpy` + * `scikit-learn` + * `matplotlib` / `seaborn` (for visualization) + +### Data +* The raw dataset is hosted by Kaggle. You can download `train.csv`, `test.csv`, and `sample_submission.csv` directly from the [Santander Customer Satisfaction competition page](https://www.kaggle.com/c/santander-customer-satisfaction/data). + +### Training and Evaluation +* Clone the repository and ensure the Kaggle data files are located in the same directory (or update the file paths in the notebook). +* Run the `santander_rf_model.ipynb` notebook sequentially from top to bottom to clean the data, train the ensemble, evaluate the validation split, and generate a new `submission.csv`. \ No newline at end of file diff --git a/Labs/.DS_Store b/Labs/.DS_Store new file mode 100644 index 000000000..b6430a22f Binary files /dev/null and b/Labs/.DS_Store differ diff --git a/Labs/Lab.2/.DS_Store b/Labs/Lab.2/.DS_Store new file mode 100644 index 000000000..5008ddfcf Binary files /dev/null and b/Labs/Lab.2/.DS_Store differ diff --git a/Labs/Lab.2/Lab.2.ipynb b/Labs/Lab.2/Lab.2.ipynb index f0b38e38a..fb55044de 100644 --- a/Labs/Lab.2/Lab.2.ipynb +++ b/Labs/Lab.2/Lab.2.ipynb @@ -84,20 +84,41 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ - "# Write your solution here" + "# Write your solution here\n", + "def make_board(n):\n", + " empty = 0\n", + " X = 1\n", + " O = 2\n", + " board = list()\n", + " for i in range(n):\n", + " row = list()\n", + " for j in range(n):\n", + " row.append(1)\n", + " board.append(row)\n", + " return board" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1, 1, 1], [1, 1, 1], [1, 1, 1]]\n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "board = make_board(3)\n", + "print(board)" ] }, { @@ -107,7 +128,96 @@ "outputs": [], "source": [ "# (Optional) Ask an LLM for 3 different solutions here\n", - "# Then compare them to your own." + "#Solution 1\n", + "def make_board_1(n):\n", + " board = []\n", + " for i in range(n):\n", + " row = []\n", + " for j in range(n):\n", + " row.append(0)\n", + " board.append(row)\n", + " return board\n", + "\n", + "#solution 2\n", + "def make_board_2(n):\n", + " return [[0 for _ in range(n)] for _ in range(n)]\n", + "\n", + "#solution 3\n", + "def make_board_3(n):\n", + " board = []\n", + " for _ in range(n):\n", + " board.append([0] * n)\n", + " return board" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

For Solution 1

\n", + "\n", + "

Same:

\n", + "\n", + "Both use nested loops\n", + "\n", + "Both construct the board row by row\n", + "\n", + "Both use append() to add values\n", + "\n", + "Both return an n × n list of lists\n", + "\n", + "Both are easy to understand and beginner-friendly\n", + "\n", + "\n", + "

Different:

\n", + "\n", + "Your solution defines empty, X, and O, while Solution 1 directly uses 0\n", + "\n", + "Your solution documents the meaning of values more clearly\n", + "\n", + "Solution 1 is slightly shorter\n", + "\n", + "\n", + "

For Solution 2

\n", + "\n", + "

Same:

\n", + "\n", + "Both return an n × n Tic Tac Toe board\n", + "\n", + "Both use 0 to represent empty spaces\n", + "\n", + "Both solve the same problem correctly\n", + "\n", + "\n", + "

Different:

\n", + "\n", + "Your solution uses explicit loops, Solution 2 uses list comprehension\n", + "\n", + "Your solution is more readable for beginners\n", + "\n", + "Solution 2 is more compact but less transparent\n", + "\n", + "Your solution clearly shows how the matrix is built step by step\n", + "\n", + "\n", + "

For Solution 3

\n", + "\n", + "

Same:

\n", + "\n", + "Both create each row separately\n", + "\n", + "Both store rows in a board list\n", + "\n", + "Both correctly initialize all cells as empty (0)\n", + "\n", + "\n", + "

Different:

\n", + "\n", + "Your solution fills rows using an inner loop, Solution 3 uses [0] * n\n", + "\n", + "Your approach is more detailed and instructional\n", + "\n", + "Solution 3 is more concise and slightly more efficient\n" ] }, { @@ -117,6 +227,19 @@ "**Question:** Which solution most closely matches your solution? What are the main differences?" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Solution 1 is the closest match because:

\n", + "\n", + "It follows the same structure\n", + "\n", + "Uses the same logic\n", + "\n", + "Builds the matrix step-by-step just like my implementation" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -138,20 +261,45 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ - "# Write your solution here" + "# Write your solution \n", + "def draw_board(r,c):\n", + " for i in range(r):\n", + " row = \"\"\n", + " empty = \" \"\n", + " print(\" --- \" *c)\n", + " for j in range(c):\n", + " row += \"| \" + empty + \" |\"\n", + " \n", + " print(row)\n", + " print(\" --- \" *c)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 86, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " --- --- --- \n", + "| || || |\n", + " --- --- --- \n", + "| || || |\n", + " --- --- --- \n", + "| || || |\n", + " --- --- --- \n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution \n", + "draw_board(3,3)" ] }, { @@ -163,20 +311,48 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ - "# Write your solution here" + "# Write your solution \n", + "def draw_board(board):\n", + " n = len(board)\n", + " m = len(board[0])\n", + " icons = {1:\"X\",2:\"O\",0:\" \"}\n", + " for i in range(n):\n", + " row = \"\"\n", + " print(\" --- \"*m)\n", + " for j in range(m):\n", + " row += (f\"| {icons[int(board[i][j])]} |\")\n", + " print(row)\n", + "\n", + " print(\" --- \"*m)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 89, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " --- --- --- \n", + "| X || X || X |\n", + " --- --- --- \n", + "| X || X || X |\n", + " --- --- --- \n", + "| X || X || X |\n", + " --- --- --- \n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution \n", + "b= make_board(3)\n", + "draw_board(b)" ] }, { @@ -193,27 +369,51 @@ "Here are some example inputs you can use to test your code:\n" ] }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Write your solution here" - ] - }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "# Test your solution here" + "# Write your solution \n", + "def check_play(board):\n", + " r = len(board)\n", + " c = len(board[0])\n", + "\n", + " for i in range(r):\n", + " if all(board[i][j] == 1 for j in range(c)):\n", + " return 1\n", + " elif all(board[i][j] == 2 for j in range(c)):\n", + " return 2\n", + " \n", + " for j in range(c):\n", + " if all(board[i][j] == 1 for i in range(r)):\n", + " return 1\n", + " elif all(board[i][j] == 2 for i in range(r)):\n", + " return 2\n", + " \n", + " \n", + " if all(board[i][i] == 1 for i in range(r)):\n", + " return 1\n", + " elif all(board[i][i] == 2 for i in range(r)):\n", + " return 2\n", + "\n", + " if all(board[i][r-1-i] == 1 for i in range(r)):\n", + " return 1\n", + " elif all(board[i][r-1-i] == 2 for i in range(r)):\n", + " return 2\n", + "\n", + "\n", + " for i in range(r):\n", + " if any(board[i][j] == 0 for j in range(c)):\n", + " return -1\n", + " \n", + " return 0" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -235,7 +435,38 @@ "\n", "also_no_winner = [[1, 2, 0],\n", "\t[2, 1, 0],\n", - "\t[2, 1, 0]]" + "\t[2, 1, 0]]\n", + "\n", + "draw = [[1,2,1],\n", + " [2,2,1],\n", + " [1,1,2]]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + "1\n", + "1\n", + "-1\n", + "-1\n", + "0\n" + ] + } + ], + "source": [ + "print(check_play(winner_is_2))\n", + "print(check_play(winner_is_1))\n", + "print(check_play(winner_is_also_1))\n", + "print(check_play(no_winner))\n", + "print(check_play(also_no_winner))\n", + "print(check_play(draw))" ] }, { @@ -252,20 +483,85 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ - "# Write your solution here" + "# Write your solution here\n", + "def player_move(board, player, x, y):\n", + " col = ord(x.upper()) - ord('A')\n", + " row = int(y) - 1\n", + "\n", + " if board[row][col] != 0:\n", + " return \"Invalid move!\"\n", + "\n", + " board[row][col] = player\n", + " return True" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | X | | X |\n", + " --- --- --- \n", + "2 | | X | |\n", + " --- --- --- \n", + "3 | O | O | |\n", + " --- --- --- \n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Press 1 for X and 2 for O 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter co-ordinates to make the move\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the x co-ordinates: b\n", + "Enter the y co-ordinates: 1\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "b = [[1, 0, 1],\n", + "\t[0, 1, 0],\n", + "\t[2, 2, 0]]\n", + "draw_board(b)\n", + "a = input(\"Press 1 for X and 2 for O\")\n", + "print(\"Enter co-ordinates to make the move\")\n", + "x1 = input(\"Enter the x co-ordinates:\")\n", + "x2 = input(\"Enter the y co-ordinates:\")\n", + "player_move(b,a,x1,x2)" ] }, { @@ -277,20 +573,60 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ - "# Write your solution here" + "def draw_board(board):\n", + " n = len(board)\n", + " m = len(board[0])\n", + "\n", + " icons = {1: \"X\", 2: \"O\", 0: \" \"}\n", + "\n", + " header = \" \"\n", + " for j in range(m):\n", + " header += f\" {chr(65 + j)} \"\n", + " print(header)\n", + "\n", + " for i in range(n):\n", + " print(\" \" + \"--- \" * m)\n", + " row = f\"{i + 1} \"\n", + " for j in range(m):\n", + " row += f\"| {icons[int(board[i][j])]} \"\n", + " row += \"|\"\n", + " print(row)\n", + "\n", + " print(\" \" + \"--- \" * m)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | X | X | X |\n", + " --- --- --- \n", + "2 | O | X | O |\n", + " --- --- --- \n", + "3 | O | X | X |\n", + " --- --- --- \n", + "4 | X | O | |\n", + " --- --- --- \n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution \n", + "draw_board([[1, 1, 1],\n", + "\t[2, 1, 2],\n", + "\t[2, 1, 1],\n", + " [1,2,0]])" ] }, { @@ -302,7 +638,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 70, "metadata": { "ExecuteTime": { "end_time": "2026-01-26T21:09:58.171399Z", @@ -311,16 +647,39 @@ }, "outputs": [], "source": [ - "# Write your solution here" + "# Write your solution here\n", + "def move(board,player,a,b):\n", + " player_move(board,player,a,b)\n", + " draw_board(board)\n", + " " ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | X | X | X |\n", + " --- --- --- \n", + "2 | O | | O |\n", + " --- --- --- \n", + "3 | O | X | X |\n", + " --- --- --- \n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "board1=[[1, 0, 1],\n", + "\t[2, 0, 2],\n", + "\t[2, 1, 1]]\n", + "move(board1,1,'b',1)" ] }, { @@ -334,20 +693,90 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ - "# Write your solution here" + "# Write your solution here\n", + "def play_turn(board, player):\n", + " while True:\n", + " a = input(\"Enter the column letter: \")\n", + " b = input(\"Enter the row number: \")\n", + "\n", + " try:\n", + " if player_move(board, player, a, b) == True:\n", + " move(board, player, a, b)\n", + " break\n", + " else:\n", + " print(\"Invalid move! Try again.\")\n", + " except:\n", + " print(\"Invalid input! Try again.\")" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 73, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter (e.g., A, B, C): s\n", + "Enter the row number (e.g., 1, 2, 3): 9\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Invalid input! Please enter a valid letter and row number.\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter (e.g., A, B, C): a\n", + "Enter the row number (e.g., 1, 2, 3): 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Invalid move! That spot is already taken.\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter (e.g., A, B, C): b\n", + "Enter the row number (e.g., 1, 2, 3): 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | X | X | X |\n", + " --- --- --- \n", + "2 | O | | O |\n", + " --- --- --- \n", + "3 | O | X | X |\n", + " --- --- --- \n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "bord1=[[1, 0, 1],\n", + "\t[2, 0, 2],\n", + "\t[2, 1, 1]]\n", + "play_game(bord1,1)" ] }, { @@ -364,20 +793,258 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ - "# Write yourrr solution here" + "# Write yourrr solution here\n", + "def make_board(n,m):\n", + " empty = 0\n", + " X = 1\n", + " O = 2\n", + " board = list()\n", + " for i in range(n):\n", + " row = list()\n", + " for j in range(m):\n", + " row.append(0)\n", + " board.append(row)\n", + " return board\n", + " \n", + "def play_game():\n", + " n = int(input(\"Enter the number of rows on board\"))\n", + " m = int(input(\"Enter the number of columns on board: \"))\n", + " board = make_board(n, m)\n", + " current_player = 1\n", + " \n", + " draw_board(board)\n", + " \n", + " while check_play(board) == -1:\n", + " print(f\"Player {current_player}'s turn\")\n", + " play_turn(board,current_player)\n", + " \n", + " status = check_play(board)\n", + " \n", + " if status == 1 or status == 2:\n", + " print(f\"Player {status} wins!\")\n", + " return\n", + " elif status == 0:\n", + " print(\"It's a draw!\")\n", + " return\n", + " \n", + " if current_player == 1:\n", + " current_player = 2\n", + " else:\n", + " current_player = 1" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 93, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the number of rows on board 4\n", + "Enter the number of columns on board: 4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D \n", + " --- --- --- --- \n", + "1 | | | | |\n", + " --- --- --- --- \n", + "2 | | | | |\n", + " --- --- --- --- \n", + "3 | | | | |\n", + " --- --- --- --- \n", + "4 | | | | |\n", + " --- --- --- --- \n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: a\n", + "Enter the row number: 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D \n", + " --- --- --- --- \n", + "1 | X | | | |\n", + " --- --- --- --- \n", + "2 | | | | |\n", + " --- --- --- --- \n", + "3 | | | | |\n", + " --- --- --- --- \n", + "4 | | | | |\n", + " --- --- --- --- \n", + "Player 2's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: b\n", + "Enter the row number: 4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D \n", + " --- --- --- --- \n", + "1 | X | | | |\n", + " --- --- --- --- \n", + "2 | | | | |\n", + " --- --- --- --- \n", + "3 | | | | |\n", + " --- --- --- --- \n", + "4 | | O | | |\n", + " --- --- --- --- \n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: b\n", + "Enter the row number: 2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D \n", + " --- --- --- --- \n", + "1 | X | | | |\n", + " --- --- --- --- \n", + "2 | | X | | |\n", + " --- --- --- --- \n", + "3 | | | | |\n", + " --- --- --- --- \n", + "4 | | O | | |\n", + " --- --- --- --- \n", + "Player 2's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: b\n", + "Enter the row number: 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D \n", + " --- --- --- --- \n", + "1 | X | O | | |\n", + " --- --- --- --- \n", + "2 | | X | | |\n", + " --- --- --- --- \n", + "3 | | | | |\n", + " --- --- --- --- \n", + "4 | | O | | |\n", + " --- --- --- --- \n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: c\n", + "Enter the row number: 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D \n", + " --- --- --- --- \n", + "1 | X | O | | |\n", + " --- --- --- --- \n", + "2 | | X | | |\n", + " --- --- --- --- \n", + "3 | | | X | |\n", + " --- --- --- --- \n", + "4 | | O | | |\n", + " --- --- --- --- \n", + "Player 2's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: d\n", + "Enter the row number: 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D \n", + " --- --- --- --- \n", + "1 | X | O | | |\n", + " --- --- --- --- \n", + "2 | | X | | |\n", + " --- --- --- --- \n", + "3 | | | X | O |\n", + " --- --- --- --- \n", + "4 | | O | | |\n", + " --- --- --- --- \n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: d\n", + "Enter the row number: 4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D \n", + " --- --- --- --- \n", + "1 | X | O | | |\n", + " --- --- --- --- \n", + "2 | | X | | |\n", + " --- --- --- --- \n", + "3 | | | X | O |\n", + " --- --- --- --- \n", + "4 | | O | | X |\n", + " --- --- --- --- \n", + "Player 1 wins!\n" + ] + } + ], "source": [ - "# Test your solution here" + "play_game()" ] }, { @@ -389,11 +1056,283 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 94, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the number of rows on board 5\n", + "Enter the number of columns on board: 5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D E \n", + " --- --- --- --- --- \n", + "1 | | | | | |\n", + " --- --- --- --- --- \n", + "2 | | | | | |\n", + " --- --- --- --- --- \n", + "3 | | | | | |\n", + " --- --- --- --- --- \n", + "4 | | | | | |\n", + " --- --- --- --- --- \n", + "5 | | | | | |\n", + " --- --- --- --- --- \n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: a\n", + "Enter the row number: 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D E \n", + " --- --- --- --- --- \n", + "1 | X | | | | |\n", + " --- --- --- --- --- \n", + "2 | | | | | |\n", + " --- --- --- --- --- \n", + "3 | | | | | |\n", + " --- --- --- --- --- \n", + "4 | | | | | |\n", + " --- --- --- --- --- \n", + "5 | | | | | |\n", + " --- --- --- --- --- \n", + "Player 2's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: e\n", + "Enter the row number: 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D E \n", + " --- --- --- --- --- \n", + "1 | X | | | | |\n", + " --- --- --- --- --- \n", + "2 | | | | | |\n", + " --- --- --- --- --- \n", + "3 | | | | | O |\n", + " --- --- --- --- --- \n", + "4 | | | | | |\n", + " --- --- --- --- --- \n", + "5 | | | | | |\n", + " --- --- --- --- --- \n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: b\n", + "Enter the row number: 2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D E \n", + " --- --- --- --- --- \n", + "1 | X | | | | |\n", + " --- --- --- --- --- \n", + "2 | | X | | | |\n", + " --- --- --- --- --- \n", + "3 | | | | | O |\n", + " --- --- --- --- --- \n", + "4 | | | | | |\n", + " --- --- --- --- --- \n", + "5 | | | | | |\n", + " --- --- --- --- --- \n", + "Player 2's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: c\n", + "Enter the row number: 2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D E \n", + " --- --- --- --- --- \n", + "1 | X | | | | |\n", + " --- --- --- --- --- \n", + "2 | | X | O | | |\n", + " --- --- --- --- --- \n", + "3 | | | | | O |\n", + " --- --- --- --- --- \n", + "4 | | | | | |\n", + " --- --- --- --- --- \n", + "5 | | | | | |\n", + " --- --- --- --- --- \n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: c\n", + "Enter the row number: 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D E \n", + " --- --- --- --- --- \n", + "1 | X | | | | |\n", + " --- --- --- --- --- \n", + "2 | | X | O | | |\n", + " --- --- --- --- --- \n", + "3 | | | X | | O |\n", + " --- --- --- --- --- \n", + "4 | | | | | |\n", + " --- --- --- --- --- \n", + "5 | | | | | |\n", + " --- --- --- --- --- \n", + "Player 2's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: d\n", + "Enter the row number: 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D E \n", + " --- --- --- --- --- \n", + "1 | X | | | | |\n", + " --- --- --- --- --- \n", + "2 | | X | O | | |\n", + " --- --- --- --- --- \n", + "3 | | | X | O | O |\n", + " --- --- --- --- --- \n", + "4 | | | | | |\n", + " --- --- --- --- --- \n", + "5 | | | | | |\n", + " --- --- --- --- --- \n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: d\n", + "Enter the row number: 4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D E \n", + " --- --- --- --- --- \n", + "1 | X | | | | |\n", + " --- --- --- --- --- \n", + "2 | | X | O | | |\n", + " --- --- --- --- --- \n", + "3 | | | X | O | O |\n", + " --- --- --- --- --- \n", + "4 | | | | X | |\n", + " --- --- --- --- --- \n", + "5 | | | | | |\n", + " --- --- --- --- --- \n", + "Player 2's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: e\n", + "Enter the row number: 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D E \n", + " --- --- --- --- --- \n", + "1 | X | | | | O |\n", + " --- --- --- --- --- \n", + "2 | | X | O | | |\n", + " --- --- --- --- --- \n", + "3 | | | X | O | O |\n", + " --- --- --- --- --- \n", + "4 | | | | X | |\n", + " --- --- --- --- --- \n", + "5 | | | | | |\n", + " --- --- --- --- --- \n", + "Player 1's turn\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column letter: e\n", + "Enter the row number: 5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D E \n", + " --- --- --- --- --- \n", + "1 | X | | | | O |\n", + " --- --- --- --- --- \n", + "2 | | X | O | | |\n", + " --- --- --- --- --- \n", + "3 | | | X | O | O |\n", + " --- --- --- --- --- \n", + "4 | | | | X | |\n", + " --- --- --- --- --- \n", + "5 | | | | | X |\n", + " --- --- --- --- --- \n", + "Player 1 wins!\n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "play_game()" ] }, { @@ -409,20 +1348,274 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ - "# Write your solution here" + "# Write your solution here\n", + "import random\n", + "\n", + "def computer_move(board, computer, human):\n", + " r = len(board)\n", + " c = len(board[0])\n", + "\n", + " def try_move(player):\n", + " for i in range(r):\n", + " for j in range(c):\n", + " if board[i][j] == 0:\n", + " board[i][j] = player\n", + " if check_play(board) == player:\n", + " return (i, j)\n", + " board[i][j] = 0\n", + " return \n", + "\n", + " move = try_move(computer)\n", + " if move:\n", + " board[move[0]][move[1]] = computer\n", + " return\n", + " move = try_move(human)\n", + " if move:\n", + " board[move[0]][move[1]] = computer\n", + " return\n", + " empty = [(i, j) for i in range(r) for j in range(c) if board[i][j] == 0]\n", + " if empty:\n", + " i, j = random.choice(empty)\n", + " board[i][j] = computer" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 96, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the number of rows on board: 3\n", + "Enter the number of columns on board: 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | | | |\n", + " --- --- --- \n", + "2 | | | |\n", + " --- --- --- \n", + "3 | | | |\n", + " --- --- --- \n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Press 1 for 2-player game, 2 to play vs computer: 2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter coordinates to make the move\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column (A, B, C...): c\n", + "Enter the row (1, 2, 3...): 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | | | |\n", + " --- --- --- \n", + "2 | | | |\n", + " --- --- --- \n", + "3 | | | X |\n", + " --- --- --- \n", + "Computer is making a move...\n", + " A B C \n", + " --- --- --- \n", + "1 | O | | |\n", + " --- --- --- \n", + "2 | | | |\n", + " --- --- --- \n", + "3 | | | X |\n", + " --- --- --- \n", + "Enter coordinates to make the move\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column (A, B, C...): c\n", + "Enter the row (1, 2, 3...): 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | O | | X |\n", + " --- --- --- \n", + "2 | | | |\n", + " --- --- --- \n", + "3 | | | X |\n", + " --- --- --- \n", + "Computer is making a move...\n", + " A B C \n", + " --- --- --- \n", + "1 | O | | X |\n", + " --- --- --- \n", + "2 | | | O |\n", + " --- --- --- \n", + "3 | | | X |\n", + " --- --- --- \n", + "Enter coordinates to make the move\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column (A, B, C...): a\n", + "Enter the row (1, 2, 3...): 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | O | | X |\n", + " --- --- --- \n", + "2 | | | O |\n", + " --- --- --- \n", + "3 | X | | X |\n", + " --- --- --- \n", + "Computer is making a move...\n", + " A B C \n", + " --- --- --- \n", + "1 | O | | X |\n", + " --- --- --- \n", + "2 | | O | O |\n", + " --- --- --- \n", + "3 | X | | X |\n", + " --- --- --- \n", + "Enter coordinates to make the move\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column (A, B, C...): a\n", + "Enter the row (1, 2, 3...): 2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | O | | X |\n", + " --- --- --- \n", + "2 | X | O | O |\n", + " --- --- --- \n", + "3 | X | | X |\n", + " --- --- --- \n", + "Computer is making a move...\n", + " A B C \n", + " --- --- --- \n", + "1 | O | | X |\n", + " --- --- --- \n", + "2 | X | O | O |\n", + " --- --- --- \n", + "3 | X | O | X |\n", + " --- --- --- \n", + "Enter coordinates to make the move\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column (A, B, C...): b\n", + "Enter the row (1, 2, 3...): 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | O | X | X |\n", + " --- --- --- \n", + "2 | X | O | O |\n", + " --- --- --- \n", + "3 | X | O | X |\n", + " --- --- --- \n", + "Its a draw.\n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "x_size = int(input(\"Enter the number of rows on board: \"))\n", + "y_size = int(input(\"Enter the number of columns on board: \"))\n", + "\n", + "b = make_board(x_size, y_size)\n", + "draw_board(b)\n", + "\n", + "mode = int(input(\"Press 1 for 2-player game, 2 to play vs computer: \"))\n", + "player = 1\n", + "computer = 2 if mode == 2 else None\n", + "moves = -1\n", + "\n", + "while moves == -1:\n", + "\n", + " if mode == 2 and player == computer:\n", + " print(\"Computer is making a move...\")\n", + " computer_move(b, computer, 1)\n", + " else:\n", + " print(\"Enter coordinates to make the move\")\n", + " x1 = input(\"Enter the column (A, B, C...): \")\n", + " x2 = input(\"Enter the row (1, 2, 3...): \")\n", + "\n", + " result = player_move(b, player, x1, x2)\n", + " if result != True:\n", + " print(result)\n", + " continue\n", + "\n", + " draw_board(b)\n", + " moves = check_play(b)\n", + "\n", + " if moves == -1:\n", + " player = 2 if player == 1 else 1\n", + "\n", + "if moves == 1:\n", + " print(\"X won.\")\n", + "elif moves == 2:\n", + " print(\"O won.\")\n", + "else:\n", + " print(\"Its a draw.\")" ] }, { @@ -434,20 +1627,319 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 97, "metadata": {}, "outputs": [], "source": [ - "# Write your solution here" + "# Write your solution here\n", + "import random\n", + "\n", + "def minimax(board, depth, max_depth, player, smart, dumb):\n", + " result = check_play(board)\n", + "\n", + " if result == smart:\n", + " return 100 - depth\n", + " if result == dumb:\n", + " return depth - 100\n", + " if result == 0:\n", + " return 0\n", + "\n", + " if depth == max_depth:\n", + " return 0\n", + "\n", + " r = len(board)\n", + " c = len(board[0])\n", + "\n", + " if player == smart:\n", + " best = -float(\"inf\")\n", + "\n", + " for i in range(r):\n", + " for j in range(c):\n", + " if board[i][j] == 0:\n", + " board[i][j] = smart\n", + " score = minimax(board, depth + 1, max_depth,\n", + " dumb, smart, dumb)\n", + " board[i][j] = 0\n", + " best = max(best, score)\n", + "\n", + " return best\n", + "\n", + " else:\n", + " best = float(\"inf\")\n", + "\n", + " for i in range(r):\n", + " for j in range(c):\n", + " if board[i][j] == 0:\n", + " board[i][j] = dumb\n", + " score = minimax(board, depth + 1, max_depth,\n", + " smart, smart, dumb)\n", + " board[i][j] = 0\n", + " best = min(best, score)\n", + "\n", + " return best\n", + "\n", + "\n", + "def computer_move_exhaustive(board, player, opponent, depth):\n", + " r = len(board)\n", + " c = len(board[0])\n", + "\n", + " best_score = -float(\"inf\")\n", + " best_move = None\n", + "\n", + " for i in range(r):\n", + " for j in range(c):\n", + " if board[i][j] == 0:\n", + " board[i][j] = player\n", + " score = minimax(board, 0, depth,\n", + " opponent, player, opponent)\n", + " board[i][j] = 0\n", + "\n", + " if score > best_score:\n", + " best_score = score\n", + " best_move = (i, j)\n", + "\n", + " if best_move:\n", + " board[best_move[0]][best_move[1]] = player\n" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 99, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the number of rows on board: 3\n", + "Enter the number of columns on board: 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | | | |\n", + " --- --- --- \n", + "2 | | | |\n", + " --- --- --- \n", + "3 | | | |\n", + " --- --- --- \n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Press 1 for 2-player game, 2 to play vs computer: 2\n", + "Enter search depth (recommended 3-5): 6\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter coordinates to make the move\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column (A, B, C...): c\n", + "Enter the row (1, 2, 3...): 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | | | |\n", + " --- --- --- \n", + "2 | | | |\n", + " --- --- --- \n", + "3 | | | X |\n", + " --- --- --- \n", + "Computer is making a move...\n", + " A B C \n", + " --- --- --- \n", + "1 | | | |\n", + " --- --- --- \n", + "2 | | O | |\n", + " --- --- --- \n", + "3 | | | X |\n", + " --- --- --- \n", + "Enter coordinates to make the move\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column (A, B, C...): a\n", + "Enter the row (1, 2, 3...): 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | X | | |\n", + " --- --- --- \n", + "2 | | O | |\n", + " --- --- --- \n", + "3 | | | X |\n", + " --- --- --- \n", + "Computer is making a move...\n", + " A B C \n", + " --- --- --- \n", + "1 | X | O | |\n", + " --- --- --- \n", + "2 | | O | |\n", + " --- --- --- \n", + "3 | | | X |\n", + " --- --- --- \n", + "Enter coordinates to make the move\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column (A, B, C...): b\n", + "Enter the row (1, 2, 3...): 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | X | O | |\n", + " --- --- --- \n", + "2 | | O | |\n", + " --- --- --- \n", + "3 | | X | X |\n", + " --- --- --- \n", + "Computer is making a move...\n", + " A B C \n", + " --- --- --- \n", + "1 | X | O | |\n", + " --- --- --- \n", + "2 | | O | |\n", + " --- --- --- \n", + "3 | O | X | X |\n", + " --- --- --- \n", + "Enter coordinates to make the move\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column (A, B, C...): c\n", + "Enter the row (1, 2, 3...): 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | X | O | X |\n", + " --- --- --- \n", + "2 | | O | |\n", + " --- --- --- \n", + "3 | O | X | X |\n", + " --- --- --- \n", + "Computer is making a move...\n", + " A B C \n", + " --- --- --- \n", + "1 | X | O | X |\n", + " --- --- --- \n", + "2 | | O | O |\n", + " --- --- --- \n", + "3 | O | X | X |\n", + " --- --- --- \n", + "Enter coordinates to make the move\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter the column (A, B, C...): a\n", + "Enter the row (1, 2, 3...): 2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C \n", + " --- --- --- \n", + "1 | X | O | X |\n", + " --- --- --- \n", + "2 | X | O | O |\n", + " --- --- --- \n", + "3 | O | X | X |\n", + " --- --- --- \n", + "Its a draw.\n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution \n", + "# Test your solution here\n", + "x_size = int(input(\"Enter the number of rows on board: \"))\n", + "y_size = int(input(\"Enter the number of columns on board: \"))\n", + "\n", + "b = make_board(x_size, y_size)\n", + "draw_board(b)\n", + "\n", + "mode = int(input(\"Press 1 for 2-player game, 2 to play vs computer: \"))\n", + "player = 1\n", + "computer = 2 if mode == 2 else None\n", + "if mode == 2:\n", + " max_depth = int(input(\"Enter search depth (recommended 3-5): \"))\n", + "\n", + "moves = -1\n", + "\n", + "while moves == -1:\n", + "\n", + " if mode == 2 and player == computer:\n", + " print(\"Computer is making a move...\")\n", + " computer_move_exhaustive(b, computer, 1, max_depth)\n", + " else:\n", + " print(\"Enter coordinates to make the move\")\n", + " x1 = input(\"Enter the column (A, B, C...): \")\n", + " x2 = input(\"Enter the row (1, 2, 3...): \")\n", + "\n", + " result = player_move(b, player, x1, x2)\n", + " if result != True:\n", + " print(result)\n", + " continue\n", + "\n", + " draw_board(b)\n", + " moves = check_play(b)\n", + "\n", + " if moves == -1:\n", + " player = 2 if player == 1 else 1\n", + "\n", + "if moves == 1:\n", + " print(\"X won.\")\n", + "elif moves == 2:\n", + " print(\"O won.\")\n", + "else:\n", + " print(\"Its a draw.\")" ] }, { @@ -459,20 +1951,67 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ - "# Write your solution here" + "# Write your solution \n", + "def play_ai_vs_ai(size, depth_smart, depth_dumb, games=10):\n", + " smart_wins = 0\n", + " draw= 0\n", + " for g in range(games):\n", + " board = make_board(size, size)\n", + "\n", + " smart = 1 if g % 2 == 0 else 2\n", + " dumb = 2 if smart == 1 else 1\n", + "\n", + " player = 1\n", + " result = -1\n", + "\n", + " while result == -1:\n", + " if player == smart:\n", + " computer_move_exhaustive(board, smart, dumb, depth_smart)\n", + " else:\n", + " computer_move_exhaustive(board, dumb, smart, depth_dumb)\n", + "\n", + " result = check_play(board)\n", + " player = 2 if player == 1 else 1\n", + "\n", + " if result == smart:\n", + " smart_wins += 1\n", + " elif result == 0:\n", + " draw += 1\n", + "\n", + " win_rate = smart_wins / games * 100\n", + " print(f\"{size}x{size} grid → Smart AI win rate: {win_rate:.1f}% with {draw} draws.\")\n" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 104, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "AI vs AI experiment:\n", + "\n", + "3x3 grid → Smart AI win rate: 50.0% with 5 draws.\n", + "4x4 grid → Smart AI win rate: 0.0% with 10 draws.\n", + "5x5 grid → Smart AI win rate: 0.0% with 10 draws.\n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "# Test your solution here\n", + "print(\"\\nAI vs AI experiment:\\n\")\n", + "\n", + "play_ai_vs_ai(3, depth_smart=5, depth_dumb=2)\n", + "play_ai_vs_ai(4, depth_smart=4, depth_dumb=2)\n", + "play_ai_vs_ai(5, depth_smart=3, depth_dumb=1)" ] }, { @@ -510,7 +2049,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.7" + "version": "3.12.12" } }, "nbformat": 4, diff --git a/Labs/Lab.3/.DS_Store b/Labs/Lab.3/.DS_Store new file mode 100644 index 000000000..b64e8a66b Binary files /dev/null and b/Labs/Lab.3/.DS_Store differ diff --git a/Labs/Lab.3/Lab.3.ipynb b/Labs/Lab.3/Lab.3.ipynb index 3dc0438eb..0154b718e 100644 --- a/Labs/Lab.3/Lab.3.ipynb +++ b/Labs/Lab.3/Lab.3.ipynb @@ -207,9 +207,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Value of x is 0.9036066841881488\n" + ] + } + ], "source": [ "import random\n", "x=random.random()\n", @@ -227,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -235,18 +243,30 @@ "def generate_uniform(N,x_min,x_max):\n", " out = []\n", " ### BEGIN SOLUTION\n", - "\n", - " # Fill in your solution here \n", - " \n", + " for _ in range(N):\n", + " d = random.random()\n", + " out.append(int(x_min + d * (x_max - x_min)))\n", " ### END SOLUTION\n", " return out" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data Type: \n", + "Data Length: 1000\n", + "Type of Data Contents: \n", + "Data Minimum: -9\n", + "Data Maximum: 9\n" + ] + } + ], "source": [ "# Test your solution here\n", "data=generate_uniform(1000,-10,10)\n", @@ -268,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -276,10 +296,10 @@ "def mean(Data):\n", " m=0.\n", " \n", - " ### BEGIN SOLUTION\n", - "\n", - " # Fill in your solution here \n", - " \n", + " ### BEGIN SOLUTION \n", + " n = len(Data)\n", + " Summation = sum(Data)\n", + " m = Summation/n\n", " ### END SOLUTION\n", " \n", " return m" @@ -287,11 +307,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean of Data: 9.0\n" + ] + } + ], "source": [ "# Test your solution here\n", + "data = [8,10]\n", "print (\"Mean of Data:\", mean(data))" ] }, @@ -305,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -314,9 +343,14 @@ " m=0.\n", " \n", " ### BEGIN SOLUTION\n", - "\n", - " # Fill in your solution here \n", - " \n", + " n = len(Data)\n", + " x_bar = mean(Data)\n", + " summation = 0\n", + " for i in range(n):\n", + " difference_sq = (Data[i] - x_bar) * (Data[i] - x_bar)\n", + " summation += difference_sq\n", + " \n", + " m = (1/(n-1)) * summation\n", " ### END SOLUTION\n", " \n", " return m" @@ -324,11 +358,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variance of Data: 542.1944444444445\n" + ] + } + ], "source": [ "# Test your solution here\n", + "data = [12,19,45,68,90,42,47,50,48]\n", "print (\"Variance of Data:\", variance(data))" ] }, @@ -358,16 +401,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Solution\n", "def histogram(x,n_bins=10,x_min=None,x_max=None):\n", " ### BEGIN SOLUTION\n", - "\n", - " # Fill in your solution here \n", - " \n", + " x_min, x_max = min(x), max(x)\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", + " for value in x:\n", + " for i in range(n_bins):\n", + " lower_bound = bin_edges[i]\n", + " upper_bound = bin_edges[i+1]\n", + " if lower_bound <= value <= upper_bound:\n", + " hist[i] += 1\n", + " break\n", " ### END SOLUTION\n", "\n", " return hist,bin_edges" @@ -375,13 +426,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3, 2, 2]\n", + "[1.0, 4.666666666666666, 8.333333333333332, 12.0]\n" + ] + } + ], "source": [ "# Test your solution here\n", - "h,b=histogram(data,100)\n", - "print(h)" + "data = [1, 2, 2, 5, 8, 10, 12]\n", + "h,b=histogram(data,3)\n", + "print(h)\n", + "print(b)" ] }, { @@ -407,29 +469,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "# Solution\n", - "def draw_histogram(x,n_bins,x_min=None,x_max=None,character=\"#\",max_character_per_line=20):\n", + "def draw_histogram(x, n_bins, x_min=None, x_max=None, character=\"#\", max_character_per_line=20):\n", " ### BEGIN SOLUTION\n", - "\n", - " # Fill in your solution here \n", + " hist, bin_edges = histogram(x, n_bins)\n", + " max_h = max(hist)\n", " \n", + " for i in range(len(hist)):\n", + " if max_h > 0:\n", + " num_chars = int((hist[i] / max_h) * max_character_per_line)\n", + " else:\n", + " num_chars = 0\n", + " \n", + " bar = character * num_chars\n", + " lower = bin_edges[i]\n", + " upper = bin_edges[i+1]\n", + " print(f\"[{lower:>3.0f}, {upper:>3.0f}] : {bar}\")\n", " ### END SOLUTION\n", "\n", - " return hist,bin_edges" + " return hist, bin_edges" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0, 1] : ######\n", + "[ 1, 2] : ######\n", + "[ 2, 3] : #############\n", + "[ 3, 4] : ####################\n", + "[ 4, 4] : #############\n", + "[ 4, 5] : ####################\n", + "[ 5, 6] : #############\n", + "[ 6, 7] : ####################\n", + "[ 7, 8] : #############\n", + "[ 8, 9] : ####################\n" + ] + } + ], "source": [ "# Test your solution here\n", - "h,b=histogram(data,20)" + "# Create simple data from 0 to 10 to match your image\n", + "data = [0, 1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5, 6, 6, 7, 7, 7, 8, 8, 9, 9, 9]\n", + "\n", + "# This line will now print the graph to the screen\n", + "h, b = draw_histogram(data, n_bins=10)" ] }, { @@ -443,29 +535,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "def where(mylist,myfunc):\n", - " out= []\n", - " \n", - " ### BEGIN SOLUTION\n", + "def where(mylist, myfunc):\n", + " out = []\n", "\n", - " # Fill in your solution here \n", - " \n", + " ### BEGIN SOLUTION\n", + " for i in range(len(mylist)):\n", + " if myfunc(mylist[i]):\n", + " out.append(i)\n", " ### END SOLUTION\n", - " \n", + "\n", " return out" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 3]\n" + ] + } + ], "source": [ - "# Test your solution here" + "# Test your solution here\n", + "data = [0.1, 0.6, 0.3, 0.8, 0.2]\n", + "indices = where(data, lambda x: x > 0.5)\n", + "print(indices)" ] }, { @@ -483,9 +586,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "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: 5\n", + "Number of Entries passing F2: 0\n" + ] + } + ], "source": [ "def in_range(mymin,mymax):\n", " def testrange(x):\n", @@ -493,8 +607,8 @@ " return testrange\n", "\n", "# Examples:\n", - "F1=inrange(0,10)\n", - "F2=inrange(10,20)\n", + "F1=in_range(0,10)\n", + "F2=in_range(10,20)\n", "\n", "# Test of in_range\n", "print (F1(0), F1(1), F1(10), F1(15), F1(20))\n", @@ -506,24 +620,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "### BEGIN SOLUTION\n", - "\n", - " # Fill in your solution here \n", - " \n", + "def is_even(x):\n", + " return x % 2 == 0\n", + "\n", + "def is_odd(x):\n", + " return x % 2 != 0\n", + "\n", + "def greater_than(val):\n", + " def tester(x):\n", + " return x > val\n", + " return tester\n", + "\n", + "def less_than(val):\n", + " def tester(x):\n", + " return x < val\n", + " return tester\n", + "\n", + "def equal(val):\n", + " def tester(x):\n", + " return x == val\n", + " return tester\n", + "\n", + "def divisible_by(val):\n", + " def tester(x):\n", + " return x % val == 0\n", + " return tester \n", "### END SOLUTION" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Even indices: [1, 3, 5, 6]\n", + "Greater than 5 indices: [5, 6, 7]\n", + "Divisible by 3 indices: [2, 5, 7]\n" + ] + } + ], "source": [ - "# Test your solution" + "# Test your solution\n", + "data = [1, 2, 3, 4, 5, 6, 10, 15]\n", + "\n", + "print(\"Even indices:\", where(data, is_even))\n", + "\n", + "print(\"Greater than 5 indices:\", where(data, greater_than(5)))\n", + "\n", + "print(\"Divisible by 3 indices:\", where(data, divisible_by(3)))" ] }, { @@ -535,14 +688,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Even: 4\n", + "Odd: 4\n", + "Greater than: 8\n", + "Less than: 0\n", + "Equal: 0\n", + "Divisible by: 3\n" + ] + } + ], "source": [ "### BEGIN SOLUTION\n", - "\n", - " # Fill in your solution here \n", - " \n", + "print(\"Even:\", sum(map(lambda x: x % 2 == 0, data)))\n", + "print(\"Odd:\", sum(map(lambda x: x % 2 != 0, data)))\n", + "print(\"Greater than:\", sum(map(lambda x: x > 0.5, data)))\n", + "print(\"Less than:\", sum(map(lambda x: x < 0.5, data)))\n", + "print(\"Equal:\", sum(map(lambda x: x == 0, data)))\n", + "print(\"Divisible by:\", sum(map(lambda x: x % 3 == 0, data)))\n", "### END SOLUTION" ] }, @@ -561,30 +730,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "def generate_function(func,x_min,x_max,N=1000):\n", " out = list()\n", " ### BEGIN SOLUTION\n", + " import random\n", "\n", - " # Fill in your solution here \n", + " y_max = 0\n", + " steps = 1000\n", + " step_size = (x_max - x_min) / steps\n", " \n", + " for i in range(steps):\n", + " val = func(x_min + i * step_size)\n", + " if val > y_max:\n", + " y_max = val\n", + " \n", + " while len(out) < N:\n", + " test_x = random.uniform(x_min, x_max)\n", + " p = random.uniform(0, y_max)\n", + " \n", + " if p <= func(test_x):\n", + " out.append(test_x)\n", " ### END SOLUTION\n", - " \n", " return out" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ -5, -5] : ####################\n", + "[ -5, -4] : ##################\n", + "[ -4, -4] : ################\n", + "[ -4, -3] : ##############\n", + "[ -3, -3] : ##########\n", + "[ -3, -3] : #########\n", + "[ -3, -2] : #######\n", + "[ -2, -2] : ####\n", + "[ -2, -1] : ##\n", + "[ -1, -1] : #\n", + "[ -1, -1] : #\n", + "[ -1, -0] : ##\n", + "[ -0, 0] : #####\n", + "[ 0, 1] : #######\n", + "[ 1, 1] : #########\n", + "[ 1, 1] : ###########\n", + "[ 1, 2] : ##############\n", + "[ 2, 2] : ###############\n", + "[ 2, 3] : #################\n", + "[ 3, 3] : ##################\n", + "([950, 863, 792, 686, 521, 432, 354, 219, 142, 54, 54, 137, 262, 362, 442, 565, 695, 741, 835, 894], [-4.999913621869641, -4.599928144478, -4.199942667086358, -3.7999571896947164, -3.3999717123030746, -2.999986234911433, -2.6000007575197914, -2.2000152801281496, -1.8000298027365078, -1.400044325344866, -1.0000588479532242, -0.6000733705615824, -0.20008789316994147, 0.19989758422170034, 0.5998830616133422, 0.999868539004984, 1.3998540163966258, 1.7998394937882676, 2.1998249711799094, 2.599810448571551, 2.999795925963193])\n" + ] + } + ], "source": [ "# A test function\n", "def test_func(x,a=1,b=1):\n", - " return abs(a*x+b)" + " return abs(a*x+b)\n", + "data = generate_function(test_func, x_min=-5, x_max=3, N=10000)\n", + "print(draw_histogram(data, n_bins=20))" ] }, { @@ -596,9 +808,38 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: -0.0052738494072836215\n", + "Variance: 0.9638367958021714\n", + "[ -4, -3] : \n", + "[ -3, -3] : \n", + "[ -3, -3] : \n", + "[ -3, -2] : \n", + "[ -2, -2] : ##\n", + "[ -2, -1] : ###\n", + "[ -1, -1] : #########\n", + "[ -1, -1] : #############\n", + "[ -1, -0] : ################\n", + "[ -0, 0] : ####################\n", + "[ 0, 0] : ###############\n", + "[ 0, 1] : ###############\n", + "[ 1, 1] : ###########\n", + "[ 1, 2] : ######\n", + "[ 2, 2] : ###\n", + "[ 2, 2] : ##\n", + "[ 2, 3] : \n", + "[ 3, 3] : \n", + "[ 3, 3] : \n", + "[ 3, 4] : \n" + ] + } + ], "source": [ "import math\n", "\n", @@ -607,6 +848,17 @@ " return math.exp(-((x-mean)**2)/(2*sigma**2))/math.sqrt(math.pi*sigma)\n", " return f\n", "\n", + "g1 = gaussian(0, 1)\n", + "data = generate_function(g1, -5, 5, N=1000)\n", + "\n", + "calc_mean = sum(data) / len(data)\n", + "calc_var = sum([(x - calc_mean)**2 for x in data]) / len(data)\n", + "\n", + "print(f\"Mean: {calc_mean}\")\n", + "print(f\"Variance: {calc_var}\")\n", + "\n", + "draw_histogram(data, n_bins=20)\n", + "\n", "# Example Instantiation\n", "g1=gaussian(0,1)\n", "g2=gaussian(10,3)" @@ -621,19 +873,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "def integrate(func, x_min, x_max, n_points=1000):\n", - " \n", + " ### BEGIN SOLUTION\n", + " all_data = generate_function(func, x_min - 5, x_max + 5, N=n_points)\n", + " checker = in_range(x_min, x_max)\n", + " valid_indices = where(all_data, checker)\n", + " integral = len(valid_indices) / n_points\n", + " ### END SOLUTION\n", " return integral" ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6908\n" + ] + } + ], + "source": [ + "g1 = gaussian(0, 1)\n", + "print(integrate(g1, -1, 1, n_points=5000))" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "ds", "language": "python", "name": "python3" }, @@ -647,7 +922,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.7" + "version": "3.11.14" } }, "nbformat": 4, diff --git a/Labs/Lab.4/.ipynb_checkpoints/Lab.4-checkpoint.ipynb b/Labs/Lab.4/.ipynb_checkpoints/Lab.4-checkpoint.ipynb new file mode 100644 index 000000000..2b630cf98 --- /dev/null +++ b/Labs/Lab.4/.ipynb_checkpoints/Lab.4-checkpoint.ipynb @@ -0,0 +1,397 @@ +{ + "cells": [ + { + "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": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error: Attempted to increment beyond maximum value.\n", + "Error: Attempted to increment beyond maximum value.\n" + ] + } + ], + "source": [ + "class SimpleCounter:\n", + " def __init__(self, max_value):\n", + " self.count = 0\n", + " self.max_value = max_value\n", + "\n", + " def increment(self):\n", + " if self.count >= self.max_value:\n", + " print(\"Error: Attempted to increment beyond maximum value.\")\n", + " else:\n", + " self.count += 1\n", + "\n", + " def reset(self):\n", + " self.count = 0\n", + "\n", + "# --- Test ---\n", + "c1 = SimpleCounter(2)\n", + "c1.increment()\n", + "c1.increment()\n", + "c1.increment() # This will print the error\n", + "c1.reset()\n", + "c1.increment() \n", + "c1.increment()\n", + "c1.increment() # This will print the error again" + ] + }, + { + "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": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Value: 2, Is Max?: False\n" + ] + } + ], + "source": [ + "class Counter:\n", + " def __init__(self, max_value):\n", + " self.__count = 0 \n", + " self.__max_value = max_value\n", + "\n", + " def increment(self):\n", + " if self.__count >= self.__max_value:\n", + " print(\"Error: Attempted to increment beyond maximum value.\")\n", + " else:\n", + " self.__count += 1\n", + "\n", + " def reset(self):\n", + " self.__count = 0\n", + "\n", + " def get_value(self):\n", + " return self.__count\n", + "\n", + " def get_max(self):\n", + " return self.__max_value\n", + "\n", + " def is_at_max(self):\n", + " return self.__count == self.__max_value\n", + "\n", + "c2 = Counter(3)\n", + "c2.increment()\n", + "c2.increment()\n", + "print(f\"Value: {c2.get_value()}, Is Max?: {c2.is_at_max()}\")" + ] + }, + { + "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": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Area: 8, Perimeter: 12.\n" + ] + } + ], + "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): return self.__length\n", + " def get_width(self): return self.__width\n", + " def get_x(self): return self.__x\n", + " def get_y(self): 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)\n", + " \n", + "# Test\n", + "rect = Rectangle(4, 2, 0, 0)\n", + "print(f\"Area: {rect.area()}, Perimeter: {rect.perimeter()}.\")\n" + ] + }, + { + "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": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "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 regtangle and circule classes to inherit from the base class and overload the functions accordingly. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "6. Implement a triangle class analogous to the rectangle and circle in question 5." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "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": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "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": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "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": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "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": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "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": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "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": 1, + "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": 2, + "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": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an object and save it\n", + "ff=foo(1,\"hello\")\n", + "ff.save(\"Test.foo\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "foo(1,'hello')" + ] + } + ], + "source": [ + "# Check contents of the saved file\n", + "!cat Test.foo" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "foo(1,'hello')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load the object\n", + "ff_reloaded=foo_loader(\"Test.foo\")\n", + "ff_reloaded" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ds", + "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": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Labs/Lab.4/Lab.4.ipynb b/Labs/Lab.4/Lab.4.ipynb index 98e6e4341..0a8de2d29 100644 --- a/Labs/Lab.4/Lab.4.ipynb +++ b/Labs/Lab.4/Lab.4.ipynb @@ -18,10 +18,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error: Attempted to increment beyond maximum value.\n", + "Error: Attempted to increment beyond maximum value.\n" + ] + } + ], + "source": [ + "class SimpleCounter:\n", + " def __init__(self, max_value):\n", + " self.count = 0\n", + " self.max_value = max_value\n", + "\n", + " def increment(self):\n", + " if self.count >= self.max_value:\n", + " print(\"Error: Attempted to increment beyond maximum value.\")\n", + " else:\n", + " self.count += 1\n", + "\n", + " def reset(self):\n", + " self.count = 0\n", + "\n", + "# --- Test ---\n", + "c1 = SimpleCounter(2)\n", + "c1.increment()\n", + "c1.increment()\n", + "c1.increment() # This will print the error\n", + "c1.reset()\n", + "c1.increment() \n", + "c1.increment()\n", + "c1.increment() # This will print the error again" + ] }, { "cell_type": "markdown", @@ -32,10 +65,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Value: 2, Is Max?: False\n" + ] + } + ], + "source": [ + "class Counter:\n", + " def __init__(self, max_value):\n", + " self.__count = 0 \n", + " self.__max_value = max_value\n", + "\n", + " def increment(self):\n", + " if self.__count >= self.__max_value:\n", + " print(\"Error: Attempted to increment beyond maximum value.\")\n", + " else:\n", + " self.__count += 1\n", + "\n", + " def reset(self):\n", + " self.__count = 0\n", + "\n", + " def get_value(self):\n", + " return self.__count\n", + "\n", + " def get_max(self):\n", + " return self.__max_value\n", + "\n", + " def is_at_max(self):\n", + " return self.__count == self.__max_value\n", + "\n", + "c2 = Counter(3)\n", + "c2.increment()\n", + "c2.increment()\n", + "print(f\"Value: {c2.get_value()}, Is Max?: {c2.is_at_max()}\")" + ] }, { "cell_type": "markdown", @@ -46,10 +115,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Area: 8, Perimeter: 12.\n" + ] + } + ], + "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): return self.__length\n", + " def get_width(self): return self.__width\n", + " def get_x(self): return self.__x\n", + " def get_y(self): 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)\n", + " \n", + "# Test\n", + "rect = Rectangle(4, 2, 0, 0)\n", + "print(f\"Area: {rect.area()}, Perimeter: {rect.perimeter()}.\")\n" + ] }, { "cell_type": "markdown", @@ -60,10 +159,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Circle Area: 50.27, Perimeter: 25.13\n" + ] + } + ], + "source": [ + "import numpy as np\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", + " def get_x(self):\n", + " return self.__x\n", + " def get_y(self):\n", + " return self.__y\n", + "\n", + " def area(self):\n", + " return np.pi * (self.__radius ** 2)\n", + "\n", + " def perimeter(self):\n", + " return 2 * np.pi * self.__radius\n", + "\n", + "# Test\n", + "c = Circle(4, 5, 5)\n", + "print(f\"Circle Area: {c.area():.2f}, Perimeter: {c.perimeter():.2f}\")\n", + " " + ] }, { "cell_type": "markdown", @@ -74,10 +207,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rectangle Area: 12.00\n", + "Circle Area: 12.57\n" + ] + } + ], + "source": [ + "class Shape:\n", + " def area(self):\n", + " raise NotImplementedError\n", + " \n", + " def perimeter(self):\n", + " raise NotImplementedError\n", + "\n", + "class Rectangle(Shape):\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 area(self): return self.__length * self.__width\n", + " def perimeter(self): return 2 * (self.__length + self.__width)\n", + "\n", + "class Circle(Shape):\n", + " def __init__(self, radius, cx, cy):\n", + " self.__radius = radius\n", + " self.__cx = cx\n", + " self.__cy = cy\n", + " \n", + " def area(self): return np.pi * (self.__radius ** 2)\n", + " def perimeter(self): return 2 * np.pi * self.__radius\n", + "\n", + "# --- Test ---\n", + "shapes = [Rectangle(4, 3, 0, 0), Circle(2, 0, 0)]\n", + "for s in shapes:\n", + " print(f\"{s.__class__.__name__} Area: {s.area():.2f}\")" + ] }, { "cell_type": "markdown", @@ -88,10 +261,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Triangle Area: 6.0, Perimeter: 12.0\n" + ] + } + ], + "source": [ + "class Triangle(Shape):\n", + " def __init__(self, base, height, x, y):\n", + " self.__base = base\n", + " self.__height = height\n", + " self.__x = x\n", + " self.__y = y\n", + " \n", + " def area(self): \n", + " return 0.5 * self.__base * self.__height\n", + " \n", + " def perimeter(self): \n", + " hypotenuse = np.sqrt(self.__base**2 + self.__height**2)\n", + " return self.__base + self.__height + hypotenuse\n", + "\n", + "# --- Test ---\n", + "t = Triangle(3, 4, 0, 0)\n", + "print(f\"Triangle Area: {t.area()}, Perimeter: {t.perimeter()}\")" + ] }, { "cell_type": "markdown", @@ -102,10 +301,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import math\n", + "\n", + "class Shape:\n", + " def area(self): raise NotImplementedError\n", + " def perimeter(self): raise NotImplementedError\n", + " \n", + " def get_perimeter_points(self): raise NotImplementedError\n", + "\n", + "class Rectangle(Shape):\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 area(self): return self.__length * self.__width\n", + " def perimeter(self): return 2 * (self.__length + self.__width)\n", + " \n", + " def get_perimeter_points(self):\n", + " points = []\n", + " for i in range(4):\n", + " points.append((self.__x + (self.__length * i / 4), self.__y)) # Top edge\n", + " points.append((self.__x + (self.__length * i / 4), self.__y - self.__width)) # Bottom edge\n", + " points.append((self.__x, self.__y - (self.__width * i / 4))) # Left edge\n", + " points.append((self.__x + self.__length, self.__y - (self.__width * i / 4))) # Right edge\n", + " return points\n", + "\n", + "class Circle(Shape):\n", + " def __init__(self, radius, cx, cy):\n", + " self.__radius = radius\n", + " self.__cx = cx\n", + " self.__cy = cy\n", + " \n", + " def area(self): return math.pi * (self.__radius ** 2)\n", + " def perimeter(self): return 2 * math.pi * self.__radius\n", + " \n", + " def get_perimeter_points(self):\n", + " points = []\n", + " for i in range(16):\n", + " angle = 2 * math.pi * i / 16\n", + " px = self.__cx + self.__radius * math.cos(angle)\n", + " py = self.__cy + self.__radius * math.sin(angle)\n", + " points.append((px, py))\n", + " return points\n", + "\n", + "class Triangle(Shape):\n", + " def __init__(self, base, height, x, y):\n", + " self.__base = base\n", + " self.__height = height\n", + " self.__x = x\n", + " self.__y = y\n", + " \n", + " def area(self): return 0.5 * self.__base * self.__height\n", + " def perimeter(self): return self.__base + self.__height + math.sqrt(self.__base**2 + self.__height**2)\n", + " \n", + " def get_perimeter_points(self):\n", + " points = []\n", + " for i in range(5):\n", + " points.append((self.__x + (self.__base * i / 5), self.__y)) \n", + " points.append((self.__x, self.__y + (self.__height * i / 5))) \n", + " points.append((self.__x + (self.__base * i / 5), self.__y + self.__height - (self.__height * i / 5))) \n", + " points.append((self.__x + self.__base, self.__y)) " + ] }, { "cell_type": "markdown", @@ -116,10 +378,90 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "class Shape:\n", + " def area(self): raise NotImplementedError\n", + " def perimeter(self): raise NotImplementedError\n", + " def get_perimeter_points(self): raise NotImplementedError\n", + " def contains(self, x, y): raise NotImplementedError\n", + "\n", + "class Rectangle(Shape):\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 area(self): return self.length * self.width\n", + " def perimeter(self): return 2 * (self.length + self.width)\n", + " \n", + " def get_perimeter_points(self):\n", + " points = []\n", + " for i in range(4):\n", + " points.append((self.x + (self.length * i / 4), self.y))\n", + " points.append((self.x + (self.length * i / 4), self.y - self.width))\n", + " points.append((self.x, self.y - (self.width * i / 4)))\n", + " points.append((self.x + self.length, self.y - (self.width * i / 4)))\n", + " return points\n", + "\n", + " def contains(self, px, py):\n", + " return (self.x <= px <= self.x + self.length) and (self.y - self.width <= py <= self.y)\n", + "\n", + " def __repr__(self):\n", + " return f\"Rectangle({self.length}, {self.width}, {self.x}, {self.y})\"\n", + "\n", + "class Circle(Shape):\n", + " def __init__(self, radius, cx, cy):\n", + " self.radius = radius\n", + " self.cx = cx\n", + " self.cy = cy\n", + " \n", + " def area(self): return math.pi * (self.radius ** 2)\n", + " def perimeter(self): return 2 * math.pi * self.radius\n", + " \n", + " def get_perimeter_points(self):\n", + " points = []\n", + " for i in range(16):\n", + " angle = 2 * math.pi * i / 16\n", + " points.append((self.cx + self.radius * math.cos(angle), self.cy + self.radius * math.sin(angle)))\n", + " return points\n", + "\n", + " def contains(self, px, py):\n", + " return math.sqrt((px - self.cx)**2 + (py - self.cy)**2) <= self.radius\n", + "\n", + " def __repr__(self):\n", + " return f\"Circle({self.radius}, {self.cx}, {self.cy})\"\n", + "\n", + "class Triangle(Shape):\n", + " def __init__(self, base, height, x, y):\n", + " self.base = base\n", + " self.height = height\n", + " self.x = x\n", + " self.y = y\n", + " \n", + " def area(self): return 0.5 * self.base * self.height\n", + " def perimeter(self): return self.base + self.height + math.sqrt(self.base**2 + self.height**2)\n", + " \n", + " def get_perimeter_points(self):\n", + " points = []\n", + " for i in range(5):\n", + " points.append((self.x + (self.base * i / 5), self.y))\n", + " points.append((self.x, self.y + (self.height * i / 5)))\n", + " points.append((self.x + (self.base * i / 5), self.y + self.height - (self.height * i / 5)))\n", + " points.append((self.x + self.base, self.y))\n", + " return points\n", + "\n", + " def contains(self, px, py):\n", + " if not ((self.x <= px <= self.x + self.base) and (self.y <= py <= self.y + self.height)):\n", + " return False\n", + " return py <= self.y + self.height - (self.height / self.base) * (px - self.x)\n", + "\n", + " def __repr__(self):\n", + " return f\"Triangle({self.base}, {self.height}, {self.x}, {self.y})\"" + ] }, { "cell_type": "markdown", @@ -130,10 +472,87 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "class Shape:\n", + " def area(self): raise NotImplementedError\n", + " def perimeter(self): raise NotImplementedError\n", + " def get_perimeter_points(self): raise NotImplementedError\n", + " def contains(self, x, y): raise NotImplementedError\n", + " \n", + " def overlaps(self, other):\n", + " for px, py in other.get_perimeter_points():\n", + " if self.contains(px, py):\n", + " return True\n", + " \n", + " for px, py in self.get_perimeter_points():\n", + " if other.contains(px, py):\n", + " return True\n", + " \n", + " return False\n", + "\n", + "class Rectangle(Shape):\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 area(self): return self.length * self.width\n", + " def perimeter(self): return 2 * (self.length + self.width)\n", + " \n", + " def get_perimeter_points(self):\n", + " points = []\n", + " for i in range(4):\n", + " points.append((self.x + (self.length * i / 4), self.y))\n", + " points.append((self.x + (self.length * i / 4), self.y - self.width))\n", + " points.append((self.x, self.y - (self.width * i / 4)))\n", + " points.append((self.x + self.length, self.y - (self.width * i / 4)))\n", + " return points\n", + "\n", + " def contains(self, px, py):\n", + " return (self.x <= px <= self.x + self.length) and (self.y - self.width <= py <= self.y)\n", + "\n", + " def __repr__(self):\n", + " return f\"Rectangle({self.length}, {self.width}, {self.x}, {self.y})\"\n", + "\n", + "class Circle(Shape):\n", + " def __init__(self, radius, cx, cy):\n", + " self.radius = radius\n", + " self.cx = cx\n", + " self.cy = cy\n", + " \n", + " def area(self): return math.pi * (self.radius ** 2)\n", + " def perimeter(self): return 2 * math.pi * self.radius\n", + " \n", + " def get_perimeter_points(self):\n", + " points = []\n", + " for i in range(16):\n", + " angle = 2 * math.pi * i / 16\n", + " points.append((self.cx + self.radius * math.cos(angle), self.cy + self.radius * math.sin(angle)))\n", + " return points\n", + "\n", + " def contains(self, px, py):\n", + " return math.sqrt((px - self.cx)**2 + (py - self.cy)**2) <= self.radius\n", + "\n", + " def __repr__(self):\n", + " return f\"Circle({self.radius}, {self.cx}, {self.cy})\"\n", + "\n", + "# --- Q9 Test ---\n", + "r1 = Rectangle(10, 10, 0, 10)\n", + "c1 = Circle(5, 5, 5)\n", + "print(r1.overlaps(c1))" + ] }, { "cell_type": "markdown", @@ -144,10 +563,184 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " \n", + " ########### \n", + " ########### \n", + " ########### O \n", + " ########### OOOOO \n", + " ########### OOOOOOO \n", + " ########### OOOOOOO \n", + " OOOOOOOOO \n", + " OOOOOOO \n", + " OOOOOOO \n", + " OOOOO \n", + " O \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n" + ] + } + ], + "source": [ + "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 i in range(height)]\n", + "\n", + " def set_pixel(self, row, col, char='*'):\n", + " if 0 <= row < self.height and 0 <= col < self.width:\n", + " self.data[row][col] = char\n", + "\n", + " def get_pixel(self, row, col):\n", + " return self.data[row][col]\n", + " \n", + " def clear_canvas(self):\n", + " self.data = [[' '] * self.width for i in range(self.height)]\n", + " \n", + " def display(self):\n", + " print(\"\\n\".join([\"\".join(row) for row in self.data]))\n", + "\n", + "class Shape:\n", + " def __init__(self, name=\"\", char=\"*\"):\n", + " self.name = name\n", + " self.char = char\n", + " \n", + " def area(self): raise NotImplementedError\n", + " def perimeter(self): raise NotImplementedError\n", + " def get_perimeter_points(self): raise NotImplementedError\n", + " def contains(self, x, y): raise NotImplementedError\n", + " \n", + " def overlaps(self, other):\n", + " for px, py in other.get_perimeter_points():\n", + " if self.contains(px, py): return True\n", + " for px, py in self.get_perimeter_points():\n", + " if other.contains(px, py): return True\n", + " return False\n", + " \n", + " def paint(self, canvas):\n", + " for row in range(canvas.height):\n", + " for col in range(canvas.width):\n", + " if self.contains(col, row):\n", + " canvas.set_pixel(row, col, self.char)\n", + "\n", + "class Rectangle(Shape):\n", + " def __init__(self, length, width, x, y, name=\"\", char=\"*\"):\n", + " super().__init__(name, char)\n", + " self.length = length\n", + " self.width = width\n", + " self.x = x\n", + " self.y = y\n", + " \n", + " def area(self): return self.length * self.width\n", + " def perimeter(self): return 2 * (self.length + self.width)\n", + " \n", + " def get_perimeter_points(self):\n", + " points = []\n", + " for i in range(4):\n", + " points.append((self.x + (self.length * i / 4), self.y))\n", + " points.append((self.x + (self.length * i / 4), self.y + self.width))\n", + " points.append((self.x, self.y + (self.width * i / 4)))\n", + " points.append((self.x + self.length, self.y + (self.width * i / 4)))\n", + " return points\n", + "\n", + " def contains(self, px, py):\n", + " return (self.x <= px <= self.x + self.length) and (self.y <= py <= self.y + self.width)\n", + "\n", + " def __repr__(self):\n", + " return f\"Rectangle({self.length}, {self.width}, {self.x}, {self.y}, '{self.name}', '{self.char}')\"\n", + "\n", + "class Circle(Shape):\n", + " def __init__(self, radius, cx, cy, name=\"\", char=\"*\"):\n", + " super().__init__(name, char)\n", + " self.radius = radius\n", + " self.cx = cx\n", + " self.cy = cy\n", + " \n", + " def area(self): return math.pi * (self.radius ** 2)\n", + " def perimeter(self): return 2 * math.pi * self.radius\n", + " \n", + " def get_perimeter_points(self):\n", + " points = []\n", + " for i in range(16):\n", + " angle = 2 * math.pi * i / 16\n", + " points.append((self.cx + self.radius * math.cos(angle), self.cy + self.radius * math.sin(angle)))\n", + " return points\n", + "\n", + " def contains(self, px, py):\n", + " return math.sqrt((px - self.cx)**2 + (py - self.cy)**2) <= self.radius\n", + "\n", + " def __repr__(self):\n", + " return f\"Circle({self.radius}, {self.cx}, {self.cy}, '{self.name}', '{self.char}')\"\n", + "\n", + "class Triangle(Shape):\n", + " def __init__(self, base, height, x, y, name=\"\", char=\"*\"):\n", + " super().__init__(name, char)\n", + " self.base = base\n", + " self.height = height\n", + " self.x = x\n", + " self.y = y\n", + " \n", + " def area(self): return 0.5 * self.base * self.height\n", + " def perimeter(self): return self.base + self.height + math.sqrt(self.base**2 + self.height**2)\n", + " \n", + " def get_perimeter_points(self):\n", + " points = []\n", + " for i in range(5):\n", + " points.append((self.x + (self.base * i / 5), self.y))\n", + " points.append((self.x, self.y + (self.height * i / 5)))\n", + " points.append((self.x + (self.base * i / 5), self.y + self.height - (self.height * i / 5)))\n", + " points.append((self.x + self.base, self.y))\n", + " return points\n", + "\n", + " def contains(self, px, py):\n", + " if not ((self.x <= px <= self.x + self.base) and (self.y <= py <= self.y + self.height)):\n", + " return False\n", + " return py <= self.y + self.height - (self.height / self.base) * (px - self.x)\n", + "\n", + " def __repr__(self):\n", + " return f\"Triangle({self.base}, {self.height}, {self.x}, {self.y}, '{self.name}', '{self.char}')\"\n", + "\n", + "class CompoundShape(Shape):\n", + " def __init__(self, shapes, name=\"\", char=\"*\"):\n", + " super().__init__(name, char)\n", + " self.shapes = shapes\n", + " \n", + " def get_perimeter_points(self):\n", + " points = []\n", + " for s in self.shapes: \n", + " points.extend(s.get_perimeter_points())\n", + " return points\n", + " \n", + " def contains(self, x, y):\n", + " return any(s.contains(x, y) for s in self.shapes)\n", + " \n", + " def paint(self, canvas):\n", + " for s in self.shapes:\n", + " s.paint(canvas)\n", + " \n", + " def __repr__(self):\n", + " return f\"CompoundShape({self.shapes}, '{self.name}', '{self.char}')\"\n", + "\n", + "my_canvas = Canvas(40, 20)\n", + "comp = CompoundShape([Rectangle(10, 5, 2, 2, char=\"#\"), Circle(4, 25, 8, char=\"O\")])\n", + "comp.paint(my_canvas)\n", + "my_canvas.display()" + ] }, { "cell_type": "markdown", @@ -158,10 +751,100 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " O TTTTTTTTT \n", + " OOOOOOO TTTTTTTT \n", + " OOOOOOOOO TTTTTTT \n", + " OOOOOOOOO TTTTTT \n", + " OOOOOOOOO TTTTT \n", + " OOOOOOOOOOO TTTT \n", + " OOOOOOOOO TTT \n", + " OOOOOOOOO TT \n", + " OOOOOOOOO T \n", + " OOOOOOO \n", + " O \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + " \n", + " O \n", + " OOOOOOO \n", + " OOOOOOOOOOO \n", + " OOOOOOOOOOOOO TTTTTTTTT \n", + " OOOOOOOOOOOOO TTTTTTTT \n", + " OOOOOOOOOOOOOOO TTTTTTT \n", + " OOOOOOOOOOOOOOO TTTTTT \n", + " OOOOOOOOOOOOOOO TTTTT \n", + " OOOOOOOOOOOOOOOOO TTTT \n", + " OOOOOOOOOOOOOOO TTT \n", + " OOOOOOOOOOOOOOO TT \n", + " OOOOOOOOOOOOOOO T \n", + " OOOOOOOOOOOOO \n", + " OOOOOOOOOOOOO \n", + " OOOOOOOOOOO \n", + " OOOOOOO \n", + " O \n", + " \n" + ] + } + ], + "source": [ + "class RasterDrawing:\n", + " def __init__(self):\n", + " self.shapes = dict()\n", + " self.shape_names = list()\n", + " \n", + " def add_shape(self, shape):\n", + " if shape.name == \"\":\n", + " shape.name = self.assign_name()\n", + " self.shapes[shape.name] = shape\n", + " self.shape_names.append(shape.name)\n", + "\n", + " def update(self, canvas):\n", + " canvas.clear_canvas()\n", + " self.paint(canvas)\n", + " \n", + " def paint(self, canvas):\n", + " for shape_name in self.shape_names:\n", + " self.shapes[shape_name].paint(canvas)\n", + " \n", + " def assign_name(self):\n", + " name_base = \"shape\"\n", + " name = name_base + \"_0\"\n", + " i = 1\n", + " while name in self.shapes:\n", + " name = name_base + \"_\" + str(i)\n", + " i += 1\n", + " return name\n", + "\n", + "drawing_canvas = Canvas(40, 20)\n", + "rd = RasterDrawing()\n", + "rd.add_shape(Circle(5, 10, 10, char=\"O\"))\n", + "rd.add_shape(Triangle(8, 8, 20, 5, char=\"T\"))\n", + "rd.paint(drawing_canvas)\n", + "drawing_canvas.display()\n", + "\n", + "print(\"\\n\")\n", + "\n", + "rd.shapes[\"shape_0\"].radius = 8\n", + "rd.update(drawing_canvas)\n", + "drawing_canvas.display()" + ] }, { "cell_type": "markdown", @@ -177,6 +860,65 @@ "For example:" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " ########### \n", + " ########### \n", + " ########### O \n", + " ########### OOOOO \n", + " ########### OOOOO \n", + " ########### OOOOOOO \n", + " OOOOO \n", + " OOOOO \n", + " O \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n" + ] + } + ], + "source": [ + "class RasterDrawingSaveable(RasterDrawing):\n", + " def save(self, filename):\n", + " with open(filename, \"w\") as f:\n", + " f.write(repr(list(self.shapes.values())))\n", + " \n", + "def load_drawing(filename):\n", + " with open(filename, \"r\") as f:\n", + " shapes_list = eval(f.read())\n", + " \n", + " new_drawing = RasterDrawingSaveable()\n", + " for s in shapes_list:\n", + " new_drawing.add_shape(s)\n", + " return new_drawing\n", + "\n", + "drawing = RasterDrawingSaveable()\n", + "drawing.add_shape(Rectangle(10, 5, 5, 5, char=\"#\"))\n", + "drawing.add_shape(Circle(3, 25, 10, char=\"O\"))\n", + "\n", + "drawing.save(\"lab4_drawing.txt\")\n", + "\n", + "loaded = load_drawing(\"lab4_drawing.txt\")\n", + "test_canvas = Canvas(40, 20)\n", + "loaded.paint(test_canvas)\n", + "test_canvas.display()" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -276,7 +1018,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "ds", "language": "python", "name": "python3" }, @@ -290,7 +1032,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.12" + "version": "3.11.14" } }, "nbformat": 4, diff --git a/Labs/Lab.4/lab4_drawing.txt b/Labs/Lab.4/lab4_drawing.txt new file mode 100644 index 000000000..0f104b0f0 --- /dev/null +++ b/Labs/Lab.4/lab4_drawing.txt @@ -0,0 +1 @@ +[Rectangle(10, 5, 5, 5, 'shape_0', '#'), Circle(3, 25, 10, 'shape_1', 'O')] \ No newline at end of file diff --git a/Labs/Lab.5/Lab.5.ipynb b/Labs/Lab.5/Lab.5.ipynb index cba027097..e0dfc58d7 100644 --- a/Labs/Lab.5/Lab.5.ipynb +++ b/Labs/Lab.5/Lab.5.ipynb @@ -20,7 +20,91 @@ " * 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" + " 2. In example above `M_2` can be a list of lists of correct size." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Q1 Tests ---\n", + "Q1 Initialization, Indexing, and Assignment tests passed!\n" + ] + } + ], + "source": [ + "class matrix:\n", + " def __init__(self, *args):\n", + " # I'm checking if the arguments are two integers (n, m) to create a zero matrix\n", + " if len(args) == 2 and isinstance(args[0], int) and isinstance(args[1], int):\n", + " self.n, self.m = args[0], args[1]\n", + " self.data = [[0 for _ in range(self.m)] for _ in range(self.n)]\n", + " \n", + " # Or if the argument is a list of lists, I need to copy it over\n", + " elif len(args) == 1 and isinstance(args[0], list):\n", + " input_list = args[0]\n", + " self.n = len(input_list)\n", + " self.m = len(input_list[0]) if self.n > 0 else 0\n", + " \n", + " # Making sure every row has the same number of columns to catch bad inputs\n", + " if not all(isinstance(row, list) and len(row) == self.m for row in input_list):\n", + " raise ValueError(\"All rows must have the same number of columns.\")\n", + " \n", + " self.data = [[val for val in row] for row in input_list]\n", + " else:\n", + " raise ValueError(\"Need either (n, m) or a list of lists.\")\n", + "\n", + " def __getitem__(self, index):\n", + " # Python passes M[i,j] as a tuple, so I unpack it\n", + " if isinstance(index, tuple):\n", + " i, j = index\n", + " return self.data[i][j]\n", + " # For M[i][j], the first brackets pass an int, returning the whole row\n", + " elif isinstance(index, int):\n", + " return self.data[index]\n", + " else:\n", + " raise TypeError(\"Index needs to be an int or tuple.\")\n", + "\n", + " def __setitem__(self, index, value):\n", + " # Allowing value assignment like M[0,1] = 5\n", + " if isinstance(index, tuple):\n", + " i, j = index\n", + " self.data[i][j] = value\n", + " elif isinstance(index, int):\n", + " self.data[index] = value\n", + "\n", + " def assign(self, other):\n", + " # The assignment operator '=' cannot be overloaded in Python. \n", + " # Writing M_1 = M_2 just creates a reference copy, it doesn't modify the object's values.\n", + " # So, I'm using an assign() method to handle the specific assignment logic requested.\n", + " if isinstance(other, matrix):\n", + " if self.n != other.n or self.m != other.m:\n", + " raise ValueError(\"Size mismatch.\")\n", + " self.data = [[val for val in row] for row in other.data]\n", + " elif isinstance(other, list):\n", + " if len(other) != self.n or not all(len(row) == self.m for row in other):\n", + " raise ValueError(\"Size mismatch.\")\n", + " self.data = [[val for val in row] for row in other]\n", + "\n", + " def __str__(self):\n", + " return '\\n'.join([str(row) for row in self.data])\n", + "\n", + "# --- EXPLICIT TESTS FOR Q1 ---\n", + "print(\"--- Q1 Tests ---\")\n", + "m1 = matrix(2, 3)\n", + "assert m1.data == [[0, 0, 0], [0, 0, 0]]\n", + "m2 = matrix([[1, 2], [3, 4]])\n", + "assert m2.data == [[1, 2], [3, 4]]\n", + "\n", + "m_target = matrix(2, 2)\n", + "m_target.assign(m2)\n", + "assert m_target.data == [[1, 2], [3, 4]]\n", + "print(\"Q1 Initialization, Indexing, and Assignment tests passed!\")" ] }, { @@ -37,6 +121,100 @@ " " ] }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Q2 Tests ---\n", + "Q2 Properties and Slicing tests passed!\n" + ] + } + ], + "source": [ + "class matrix:\n", + " # --- Q1 Methods ---\n", + " def __init__(self, *args):\n", + " if len(args) == 2 and isinstance(args[0], int) and isinstance(args[1], int):\n", + " self.n, self.m = args[0], args[1]\n", + " self.data = [[0 for _ in range(self.m)] for _ in range(self.n)]\n", + " elif len(args) == 1 and isinstance(args[0], list):\n", + " input_list = args[0]\n", + " self.n, self.m = len(input_list), (len(input_list[0]) if len(input_list) > 0 else 0)\n", + " if not all(isinstance(row, list) and len(row) == self.m for row in input_list):\n", + " raise ValueError(\"All rows must have the same number of columns.\")\n", + " self.data = [[val for val in row] for row in input_list]\n", + "\n", + " def __getitem__(self, index):\n", + " # Upgraded to handle slicing. If the index contains a 'slice' object (like 0:2),\n", + " # I use list comprehensions to grab those specific chunks.\n", + " if isinstance(index, tuple):\n", + " i, j = index\n", + " if isinstance(i, slice) or isinstance(j, slice):\n", + " if isinstance(i, int): i = slice(i, i+1)\n", + " if isinstance(j, int): j = slice(j, j+1)\n", + " return matrix([row[j] for row in self.data[i]])\n", + " return self.data[i][j]\n", + " elif isinstance(index, slice):\n", + " return matrix(self.data[index])\n", + " elif isinstance(index, int):\n", + " return self.data[index]\n", + "\n", + " def __setitem__(self, index, value):\n", + " if isinstance(index, tuple): i, j = index; self.data[i][j] = value\n", + " elif isinstance(index, int): self.data[index] = value\n", + "\n", + " def assign(self, other):\n", + " if isinstance(other, matrix):\n", + " if self.shape() != other.shape(): raise ValueError(\"Size mismatch.\")\n", + " self.data = [[val for val in row] for row in other.data]\n", + " elif isinstance(other, list):\n", + " self.data = [[val for val in row] for row in other]\n", + " \n", + " def __str__(self): return '\\n'.join([str(row) for row in self.data])\n", + "\n", + " # --- Q2 New Methods ---\n", + " def shape(self):\n", + " # Simply returns the dimensions stored during init\n", + " return (self.n, self.m)\n", + "\n", + " def transpose(self):\n", + " # Swapping rows and columns to create the transposed version\n", + " t_data = [[self.data[i][j] for i in range(self.n)] for j in range(self.m)]\n", + " return matrix(t_data)\n", + "\n", + " def row(self, n):\n", + " # Wrapping the row in an extra list bracket so it becomes a 1xm matrix\n", + " return matrix([self.data[n]])\n", + "\n", + " def column(self, n):\n", + " # Grabbing the nth element of every row to build an nx1 matrix\n", + " return matrix([[self.data[i][n]] for i in range(self.n)])\n", + "\n", + " def to_list(self):\n", + " # Using a list comprehension to return a clean copy, avoiding reference bugs\n", + " return [[val for val in row] for row in self.data]\n", + "\n", + " def block(self, n_0, n_1, m_0, m_1):\n", + " # Standard Python slicing: grab rows m_0 to m_1, then slice those rows from cols n_0 to n_1\n", + " return matrix([r[n_0:n_1] for r in self.data[m_0:m_1]])\n", + "\n", + "# --- EXPLICIT TESTS FOR Q2 ---\n", + "print(\"--- Q2 Tests ---\")\n", + "m_test = matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n", + "assert m_test.shape() == (3, 3)\n", + "assert m_test.transpose().data == [[1, 4, 7], [2, 5, 8], [3, 6, 9]]\n", + "assert m_test.row(1).data == [[4, 5, 6]]\n", + "assert m_test.column(2).data == [[3], [6], [9]]\n", + "assert m_test.to_list() == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n", + "assert m_test.block(1, 3, 0, 2).data == [[2, 3], [5, 6]]\n", + "print(\"Q2 Properties and Slicing tests passed!\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -47,6 +225,49 @@ " * `eye(n)`: returns the n by n identity matrix." ] }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Q3 Tests ---\n", + "Q3 Special Matrices tests passed!\n" + ] + } + ], + "source": [ + "# --- Q3 Standalone Functions ---\n", + "\n", + "def constant(n, m, c):\n", + " # Creating an n by m matrix filled with the float version of c\n", + " val = float(c)\n", + " return matrix([[val for j in range(m)] for i in range(n)])\n", + "\n", + "def zeros(n, m):\n", + " # Reusing the constant function to avoid rewriting the same loops\n", + " return constant(n, m, 0.0)\n", + "\n", + "def ones(n, m):\n", + " # Same trick, reusing constant for 1.0\n", + " return constant(n, m, 1.0)\n", + "\n", + "def eye(n):\n", + " # Building the identity matrix: 1.0 on the diagonal (where i == j), 0.0 elsewhere\n", + " return matrix([[1.0 if i == j else 0.0 for j in range(n)] for i in range(n)])\n", + "\n", + "# --- EXPLICIT TESTS FOR Q3 ---\n", + "print(\"--- Q3 Tests ---\")\n", + "assert constant(2, 2, 5).data == [[5.0, 5.0], [5.0, 5.0]]\n", + "assert zeros(2, 3).data == [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]\n", + "assert ones(2, 2).data == [[1.0, 1.0], [1.0, 1.0]]\n", + "assert eye(3).data == [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]\n", + "print(\"Q3 Special Matrices tests passed!\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -74,6 +295,142 @@ " * M=N\n" ] }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Q4 & Q5 Tests ---\n", + "Q4 Math Methods and Q5 Overloaded Operators tests passed!\n" + ] + } + ], + "source": [ + "class matrix:\n", + " # --- Q1 & Q2 Methods ---\n", + " def __init__(self, *args):\n", + " if len(args) == 2 and isinstance(args[0], int) and isinstance(args[1], int):\n", + " self.n, self.m = args[0], args[1]\n", + " self.data = [[0 for _ in range(self.m)] for _ in range(self.n)]\n", + " elif len(args) == 1 and isinstance(args[0], list):\n", + " input_list = args[0]\n", + " self.n, self.m = len(input_list), (len(input_list[0]) if len(input_list) > 0 else 0)\n", + " if not all(isinstance(row, list) and len(row) == self.m for row in input_list):\n", + " raise ValueError(\"All rows must have the same number of columns.\")\n", + " self.data = [[val for val in row] for row in input_list]\n", + "\n", + " def __getitem__(self, index):\n", + " if isinstance(index, tuple):\n", + " i, j = index\n", + " if isinstance(i, slice) or isinstance(j, slice):\n", + " if isinstance(i, int): i = slice(i, i+1)\n", + " if isinstance(j, int): j = slice(j, j+1)\n", + " return matrix([row[j] for row in self.data[i]])\n", + " return self.data[i][j]\n", + " elif isinstance(index, slice): return matrix(self.data[index])\n", + " elif isinstance(index, int): return self.data[index]\n", + "\n", + " def __setitem__(self, index, value):\n", + " if isinstance(index, tuple): i, j = index; self.data[i][j] = value\n", + " elif isinstance(index, int): self.data[index] = value\n", + "\n", + " def assign(self, other):\n", + " if isinstance(other, matrix):\n", + " if self.shape() != other.shape(): raise ValueError(\"Size mismatch.\")\n", + " self.data = [[val for val in row] for row in other.data]\n", + " elif isinstance(other, list):\n", + " self.data = [[val for val in row] for row in other]\n", + "\n", + " def shape(self): return (self.n, self.m)\n", + " def transpose(self): return matrix([[self.data[i][j] for i in range(self.n)] for j in range(self.m)])\n", + " def row(self, n): return matrix([self.data[n]])\n", + " def column(self, n): return matrix([[self.data[i][n]] for i in range(self.n)])\n", + " def to_list(self): return [[val for val in row] for row in self.data]\n", + " def block(self, n_0, n_1, m_0, m_1): return matrix([r[n_0:n_1] for r in self.data[m_0:m_1]])\n", + " def __str__(self): return '\\n'.join([str(row) for row in self.data])\n", + "\n", + " # --- Q4 Math Operations ---\n", + " def scalarmul(self, c):\n", + " return matrix([[val * c for val in row] for row in self.data])\n", + "\n", + " def add(self, N):\n", + " if self.shape() != N.shape(): raise ValueError(\"Addition error: Size mismatch.\")\n", + " return matrix([[self.data[i][j] + N.data[i][j] for j in range(self.m)] for i in range(self.n)])\n", + "\n", + " def sub(self, N):\n", + " if self.shape() != N.shape(): raise ValueError(\"Subtraction error: Size mismatch.\")\n", + " return matrix([[self.data[i][j] - N.data[i][j] for j in range(self.m)] for i in range(self.n)])\n", + "\n", + " def mat_mult(self, N):\n", + " if self.m != N.n: raise ValueError(\"Matrix multiplication error: M columns must equal N rows.\")\n", + " result_data = []\n", + " for i in range(self.n):\n", + " new_row = []\n", + " for j in range(N.m):\n", + " # taking the dot product of M's row and N's column\n", + " new_row.append(sum(self.data[i][k] * N.data[k][j] for k in range(self.m)))\n", + " result_data.append(new_row)\n", + " return matrix(result_data)\n", + "\n", + " def element_mult(self, N):\n", + " if self.shape() != N.shape(): raise ValueError(\"Element multiplication error: Size mismatch.\")\n", + " return matrix([[self.data[i][j] * N.data[i][j] for j in range(self.m)] for i in range(self.n)])\n", + "\n", + " def equals(self, N):\n", + " if not isinstance(N, matrix) or self.shape() != N.shape(): return False\n", + " return self.data == N.data\n", + "\n", + " # --- Q5 Operator Overloading ---\n", + " \n", + " # Python uses magic methods like __add__ to know what to do when you type '+'\n", + " def __add__(self, other):\n", + " return self.add(other)\n", + "\n", + " def __sub__(self, other):\n", + " return self.sub(other)\n", + "\n", + " def __mul__(self, other):\n", + " # I need to check if 'other' is a matrix or a scalar to decide which math rule to apply\n", + " if isinstance(other, matrix):\n", + " return self.mat_mult(other)\n", + " elif isinstance(other, (int, float)):\n", + " return self.scalarmul(other)\n", + "\n", + " def __rmul__(self, other):\n", + " # This handles the case where the scalar comes first, like 2 * M\n", + " return self.scalarmul(other)\n", + "\n", + " def __eq__(self, other):\n", + " # Overloads the '==' operator\n", + " return self.equals(other)\n", + " \n", + " # Note on M=N from Q5 requirements: Python physically cannot overload the '=' operator. \n", + " # So Using the assign() method implemented in Q1 to assign values without creating a reference copy.\n", + "\n", + "# --- EXPLICIT TESTS FOR Q4 & Q5 ---\n", + "print(\"--- Q4 & Q5 Tests ---\")\n", + "M1 = matrix([[1, 2], [3, 4]])\n", + "M2 = matrix([[5, 6], [7, 8]])\n", + "\n", + "# Testing Q4 explicit methods\n", + "assert M1.scalarmul(3).data == [[3, 6], [9, 12]]\n", + "assert M1.mat_mult(M2).data == [[19, 22], [43, 50]]\n", + "\n", + "# Testing Q5 overloaded operators\n", + "assert (M1 + M2).data == [[6, 8], [10, 12]]\n", + "assert (M1 - M2).data == [[-4, -4], [-4, -4]]\n", + "assert (M1 * M2).data == [[19, 22], [43, 50]]\n", + "assert (M1 * 2).data == [[2, 4], [6, 8]]\n", + "assert (2 * M1).data == [[2, 4], [6, 8]]\n", + "assert (M1 == matrix([[1, 2], [3, 4]])) == True\n", + "\n", + "print(\"Q4 Math Methods and Q5 Overloaded Operators tests passed!\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -94,6 +451,72 @@ "$$" ] }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Q6 Mathematical Demonstrations ---\n", + "\n", + "1. Demonstrating (AB)C = A(BC)\n", + "Is it true? True\n", + "\n", + "2. Demonstrating A(B+C) = AB + AC\n", + "Is it true? True\n", + "\n", + "3. Demonstrating AB != BA\n", + "Is it true? True\n", + "\n", + "4. Demonstrating AI = A\n", + "Is it true? True\n", + "\n", + "All Q6 linear algebra properties successfully demonstrated!\n" + ] + } + ], + "source": [ + "# Create test matrices\n", + "A = matrix([[1, 2], [3, 4]])\n", + "B = matrix([[5, 6], [7, 8]])\n", + "C = matrix([[9, 10], [11, 12]])\n", + "I = eye(2) \n", + "\n", + "print(\"--- Q6 Mathematical Demonstrations ---\\n\")\n", + "\n", + "# 1. Associativity: (AB)C = A(BC)\n", + "left_side = (A * B) * C\n", + "right_side = A * (B * C)\n", + "print(\"1. Demonstrating (AB)C = A(BC)\")\n", + "print(f\"Is it true? {left_side == right_side}\\n\")\n", + "assert left_side == right_side\n", + "\n", + "# 2. Distributivity: A(B+C) = AB + AC\n", + "left_side = A * (B + C)\n", + "right_side = (A * B) + (A * C)\n", + "print(\"2. Demonstrating A(B+C) = AB + AC\")\n", + "print(f\"Is it true? {left_side == right_side}\\n\")\n", + "assert left_side == right_side\n", + "\n", + "# 3. Non-commutativity: AB != BA\n", + "AB = A * B\n", + "BA = B * A\n", + "print(\"3. Demonstrating AB != BA\")\n", + "print(f\"Is it true? {not (AB == BA)}\\n\")\n", + "assert not (AB == BA)\n", + "\n", + "# 4. Identity: AI = A\n", + "AI = A * I\n", + "print(\"4. Demonstrating AI = A\")\n", + "print(f\"Is it true? {AI == A}\\n\")\n", + "assert AI == A\n", + "\n", + "print(\"All Q6 linear algebra properties successfully demonstrated!\")" + ] + }, { "cell_type": "code", "execution_count": null, @@ -104,7 +527,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "base", "language": "python", "name": "python3" }, @@ -118,7 +541,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.12" + "version": "3.12.12" } }, "nbformat": 4, diff --git a/Labs/Lab.6/.DS_Store b/Labs/Lab.6/.DS_Store new file mode 100644 index 000000000..36125665b Binary files /dev/null and b/Labs/Lab.6/.DS_Store differ diff --git a/Labs/Lab.6/Lab.6.ipynb b/Labs/Lab.6/Lab.6.ipynb index 3bc4e5124..3cc6d7a7a 100755 --- a/Labs/Lab.6/Lab.6.ipynb +++ b/Labs/Lab.6/Lab.6.ipynb @@ -29,6 +29,66 @@ "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": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class Player:\n", + " def __init__(self, name, starting_chips=1000):\n", + " self.name = name\n", + " self.chips = starting_chips\n", + " self.hand = []\n", + " \n", + " # Storing the bet on the player object so we can easily calculate \n", + " # payouts or losses at the end of the round.\n", + " self.current_bet = 0 \n", + " \n", + " def receive_card(self, card):\n", + " self.hand.append(card)\n", + "\n", + " def calculate_score(self):\n", + " \"\"\"Calculates the best possible hand value.\"\"\"\n", + " score = 0\n", + " aces = 0\n", + " \n", + " # Logic: It's easier to assume all Aces are 11s initially. \n", + " # We can just downgrade them to 1s later if the total goes over 21.\n", + " for card in self.hand:\n", + " if card.rank in ['J', 'Q', 'K']:\n", + " score += 10\n", + " elif card.rank == 'A':\n", + " aces += 1\n", + " score += 11\n", + " else:\n", + " score += int(card.rank)\n", + " \n", + " # If we bust, check if we have an Ace we can shrink from 11 to 1.\n", + " # We subtract 10 for each Ace we convert until we are safe.\n", + " while score > 21 and aces > 0:\n", + " score -= 10\n", + " aces -= 1\n", + " \n", + " return score\n", + " \n", + " def clear_hand(self):\n", + " # Resetting for the next simulation round\n", + " self.hand = []\n", + " self.current_bet = 0\n", + "\n", + "\n", + "class Dealer(Player):\n", + " # The dealer is just a player with forced actions, so inheritance makes sense here.\n", + " def __init__(self):\n", + " super().__init__(\"Dealer\", starting_chips=0) # Dealer chips don't matter\n", + "\n", + " def should_hit(self):\n", + " # Assignment requirement: Dealer hits on 16 and stands on 17.\n", + " # Returning True means \"Hit\", False means \"Stand\".\n", + " return self.calculate_score() < 17" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -43,6 +103,153 @@ "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": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class Card:\n", + " \"\"\"Represents a single playing card.\"\"\"\n", + " def __init__(self, rank, suit):\n", + " # Storing basic card properties. \n", + " self.rank = rank\n", + " self.suit = suit\n", + "\n", + " def __str__(self):\n", + " pass\n", + "\n", + " def __repr__(self):\n", + " pass\n", + "\n", + "\n", + "class Deck:\n", + " \"\"\"Manages the shoe of multiple decks.\"\"\"\n", + " def __init__(self, num_decks=6):\n", + " self.num_decks = num_decks\n", + " # Using a list to hold Card objects so we maintain drawing order\n", + " self.cards = [] \n", + " # Flag to tell the Game loop when the plastic card was hit\n", + " self.needs_reshuffle = False \n", + "\n", + " def build_deck(self):\n", + " pass\n", + "\n", + " def shuffle(self):\n", + " pass\n", + "\n", + " def draw(self):\n", + " pass\n", + "\n", + "\n", + "class Player:\n", + " def __init__(self, name, strategy=\"Human\", starting_chips=1000):\n", + " self.name = name\n", + " self.chips = starting_chips\n", + " self.current_bet = 0\n", + " self.hand = []\n", + " self.strategy = strategy \n", + "\n", + " def receive_card(self, card):\n", + " self.hand.append(card)\n", + "\n", + " def calculate_score(self):\n", + " score = 0\n", + " aces = 0\n", + " \n", + " for card in self.hand:\n", + " if card.rank in ['J', 'Q', 'K']:\n", + " score += 10\n", + " elif card.rank == 'A':\n", + " aces += 1\n", + " score += 11\n", + " else:\n", + " score += int(card.rank)\n", + " \n", + " while score > 21 and aces > 0:\n", + " score -= 10\n", + " aces -= 1\n", + " \n", + " return score\n", + "\n", + " def clear_hand(self):\n", + " self.hand = []\n", + " self.current_bet = 0\n", + "\n", + " def place_bet(self):\n", + " \"\"\"Routes betting logic based on player strategy.\"\"\"\n", + " if self.strategy == \"Human\":\n", + " while True:\n", + " try:\n", + " bet = int(input(f\"{self.name}, you have {self.chips} chips. Enter bet: \"))\n", + " if 0 < bet <= self.chips:\n", + " self.current_bet = bet\n", + " self.chips -= bet\n", + " break\n", + " else:\n", + " print(\"Invalid amount. Must be greater than 0.\")\n", + " except ValueError:\n", + " print(\"Please enter a valid number.\")\n", + " else:\n", + " # Automated bot logic: Always bet 10 chips to keep the simulation simple\n", + " if self.chips > 0:\n", + " bet = 10 if self.chips >= 10 else self.chips\n", + " self.current_bet = bet\n", + " self.chips -= bet\n", + "\n", + " def decide_action(self, dealer_upcard, true_count):\n", + " \"\"\"Routes hitting/standing logic based on player strategy.\"\"\"\n", + " if self.strategy == \"Human\":\n", + " while True:\n", + " choice = input(f\"Your score is {self.calculate_score()}. Hit or Stand? (h/s): \").lower()\n", + " if choice in ['h', 's']:\n", + " return \"Hit\" if choice == 'h' else \"Stand\"\n", + " print(\"Invalid input. Please type 'h' or 's'.\")\n", + " \n", + " elif self.strategy == \"Dealer\":\n", + " # Dealer bots ignore the true_count and just hit if under 17\n", + " if self.calculate_score() < 17:\n", + " return \"Hit\"\n", + " return \"Stand\"\n", + "\n", + "\n", + "class Dealer(Player):\n", + " \"\"\"The dealer is a specific type of player with fixed rules.\"\"\"\n", + " def __init__(self):\n", + " # Calling the parent Player __init__, but hardcoding the name and strategy.\n", + " # Dealers don't need chips, so we set it to 0.\n", + " super().__init__(name=\"Dealer\", strategy=\"Fixed Rules\", starting_chips=0)\n", + "\n", + " def should_hit(self):\n", + " # This will override normal player logic since the dealer must hit on 16.\n", + " pass\n", + "\n", + "\n", + "class Game:\n", + " \"\"\"The central controller that runs the simulation.\"\"\"\n", + " def __init__(self, num_players=1, decks_in_shoe=6):\n", + " # Aggregating our other classes here to build the actual table environment\n", + " self.deck = Deck(num_decks=decks_in_shoe)\n", + " self.dealer = Dealer()\n", + " self.players = []\n", + " \n", + " # Tracking the global card counting state\n", + " self.running_count = 0\n", + " self.true_count = 0.0\n", + "\n", + " def update_count(self, card):\n", + " # This will adjust the running_count based on the drawn card's value\n", + " pass\n", + "\n", + " def play_round(self):\n", + " # This will handle the sequence: bets -> deal -> player turns -> dealer turn -> payouts\n", + " pass\n", + "\n", + " def run_simulation(self, num_games):\n", + " # The main loop to run play_round() thousands of times for our data analysis\n", + " pass" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -50,6 +257,225 @@ "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": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "\n", + "class Card:\n", + " def __init__(self, rank, suit):\n", + " self.rank = rank\n", + " self.suit = suit\n", + " def __str__(self): return f\"{self.rank} of {self.suit}\"\n", + " def __repr__(self): return self.__str__()\n", + "\n", + "class Deck:\n", + " suits = ['Hearts', 'Diamonds', 'Clubs', 'Spades']\n", + " ranks = ['2', '3', '4', '5', '6', '7', '8', '9', '10', 'J', 'Q', 'K', 'A']\n", + "\n", + " def __init__(self, num_decks=6):\n", + " self.num_decks = num_decks\n", + " self.cards = []\n", + " self.needs_reshuffle = False\n", + " self.build_deck()\n", + " self.shuffle()\n", + "\n", + " def build_deck(self):\n", + " self.cards = [Card(rank, suit) for _ in range(self.num_decks) for suit in self.suits for rank in self.ranks]\n", + "\n", + " def shuffle(self):\n", + " random.shuffle(self.cards)\n", + " self.needs_reshuffle = False\n", + " total_cards = len(self.cards)\n", + " insert_idx = random.randint(total_cards // 2, int(total_cards * 0.85))\n", + " self.cards.insert(insert_idx, \"PLASTIC_CARD\")\n", + "\n", + " def draw(self):\n", + " if not self.cards:\n", + " self.build_deck()\n", + " self.shuffle()\n", + " drawn_item = self.cards.pop(0)\n", + " if drawn_item == \"PLASTIC_CARD\":\n", + " self.needs_reshuffle = True\n", + " return self.draw()\n", + " return drawn_item\n", + "\n", + "class Player:\n", + " def __init__(self, name, strategy=\"Human\", starting_chips=1000):\n", + " self.name = name\n", + " self.chips = starting_chips\n", + " self.current_bet = 0\n", + " self.hand = []\n", + " self.strategy = strategy \n", + "\n", + " def receive_card(self, card):\n", + " self.hand.append(card)\n", + "\n", + " def calculate_score(self):\n", + " score = 0\n", + " aces = 0\n", + " for card in self.hand:\n", + " if card.rank in ['J', 'Q', 'K']: score += 10\n", + " elif card.rank == 'A':\n", + " aces += 1\n", + " score += 11\n", + " else: score += int(card.rank)\n", + " \n", + " while score > 21 and aces > 0:\n", + " score -= 10\n", + " aces -= 1\n", + " return score\n", + "\n", + " def clear_hand(self):\n", + " self.hand = []\n", + " self.current_bet = 0\n", + "\n", + " def place_bet(self, true_count=0.0):\n", + " if self.chips <= 0:\n", + " self.current_bet = 0\n", + " return \n", + "\n", + " if self.strategy == \"Human\":\n", + " while True:\n", + " try:\n", + " bet = int(input(f\"{self.name}, you have {self.chips} chips. Enter bet: \"))\n", + " if 0 < bet <= self.chips:\n", + " self.current_bet = bet\n", + " self.chips -= bet\n", + " break\n", + " else: print(\"Invalid amount.\")\n", + " except ValueError: print(\"Please enter a valid number.\")\n", + " elif self.strategy == \"Counter\":\n", + " if true_count >= 2.0: bet = 50 if self.chips >= 50 else self.chips\n", + " else: bet = 10 if self.chips >= 10 else self.chips\n", + " self.current_bet = bet\n", + " self.chips -= bet\n", + " else:\n", + " bet = 10 if self.chips >= 10 else self.chips\n", + " self.current_bet = bet\n", + " self.chips -= bet\n", + "\n", + " # CRITICAL FIX: The base player now officially accepts running_count\n", + " def decide_action(self, dealer_upcard, true_count, running_count=0):\n", + " if self.strategy == \"Human\":\n", + " while True:\n", + " choice = input(f\"Your score is {self.calculate_score()}. Hit or Stand? (h/s): \").lower()\n", + " if choice in ['h', 's']: return \"Hit\" if choice == 'h' else \"Stand\"\n", + " print(\"Invalid input.\")\n", + " elif self.strategy == \"Dealer\":\n", + " if self.calculate_score() < 17: return \"Hit\"\n", + " return \"Stand\"\n", + " elif self.strategy == \"Counter\":\n", + " dealer_val = 10 if dealer_upcard.rank in ['J', 'Q', 'K', 'A'] else int(dealer_upcard.rank)\n", + " score = self.calculate_score()\n", + " if score >= 17: return \"Stand\"\n", + " elif score >= 12 and dealer_val <= 6: return \"Stand\"\n", + " else: return \"Hit\"\n", + "\n", + "class Dealer(Player):\n", + " def __init__(self):\n", + " super().__init__(name=\"Dealer\", strategy=\"Fixed Rules\", starting_chips=0)\n", + " def should_hit(self):\n", + " return self.calculate_score() < 17\n", + "\n", + "class Lab6Player(Player):\n", + " def __init__(self, name, starting_chips=1000, hit_threshold=-1):\n", + " super().__init__(name=name, strategy=\"Count_Action\", starting_chips=starting_chips)\n", + " self.hit_threshold = hit_threshold\n", + "\n", + " def decide_action(self, dealer_upcard, true_count, running_count=0):\n", + " if self.calculate_score() >= 21: return \"Stand\"\n", + " if running_count <= self.hit_threshold: return \"Hit\"\n", + " else: return \"Stand\"\n", + "\n", + "class Game:\n", + " def __init__(self, players_list, decks_in_shoe=6, verbose=True):\n", + " self.deck = Deck(num_decks=decks_in_shoe)\n", + " self.dealer = Dealer()\n", + " self.players = players_list \n", + " self.running_count = 0\n", + " self.true_count = 0.0\n", + " self.verbose = verbose \n", + "\n", + " def update_count(self, card):\n", + " if card.rank in ['2', '3', '4', '5', '6']: self.running_count += 1\n", + " elif card.rank in ['10', 'J', 'Q', 'K', 'A']: self.running_count -= 1\n", + " decks_remaining = max(1, len(self.deck.cards) / 52)\n", + " self.true_count = self.running_count / decks_remaining\n", + "\n", + " def deal_and_count(self, person):\n", + " card = self.deck.draw()\n", + " self.update_count(card)\n", + " person.receive_card(card)\n", + "\n", + " def play_round(self):\n", + " if self.verbose: print(\"\\n--- NEW ROUND ---\")\n", + " if self.deck.needs_reshuffle:\n", + " if self.verbose: print(\"Dealer is shuffling the shoe...\")\n", + " self.deck.build_deck()\n", + " self.deck.shuffle()\n", + " self.running_count = 0 \n", + " self.true_count = 0.0\n", + "\n", + " for player in self.players: \n", + " if player.chips > 0: player.place_bet(self.true_count)\n", + "\n", + " for _ in range(2):\n", + " for player in self.players: \n", + " if player.current_bet > 0: self.deal_and_count(player)\n", + " self.deal_and_count(self.dealer)\n", + "\n", + " if self.verbose: print(f\"Dealer shows: {self.dealer.hand[0]} (Hidden Card)\")\n", + "\n", + " for player in self.players:\n", + " if player.current_bet == 0: continue\n", + " if self.verbose: print(f\"\\n{player.name}'s turn. Hand: {player.hand}\")\n", + " while player.calculate_score() < 21:\n", + " action = player.decide_action(self.dealer.hand[0], self.true_count, self.running_count)\n", + " if action == \"Hit\":\n", + " self.deal_and_count(player)\n", + " if self.verbose: print(f\"Drew a card. Hand: {player.hand}\")\n", + " else: break\n", + " if player.calculate_score() > 21 and self.verbose: print(f\"{player.name} BUSTS!\")\n", + "\n", + " if self.verbose: print(f\"\\nDealer's turn. Hand reveals: {self.dealer.hand}\")\n", + " while self.dealer.should_hit():\n", + " self.deal_and_count(self.dealer)\n", + " if self.verbose: print(f\"Dealer hits. Hand: {self.dealer.hand}\")\n", + "\n", + " dealer_score = self.dealer.calculate_score()\n", + "\n", + " for player in self.players:\n", + " if player.current_bet == 0: continue\n", + " player_score = player.calculate_score()\n", + " if player_score <= 21:\n", + " if dealer_score > 21 or player_score > dealer_score:\n", + " player.chips += (player.current_bet * 2)\n", + " if self.verbose: print(f\"{player.name} WINS!\")\n", + " elif player_score == dealer_score:\n", + " player.chips += player.current_bet\n", + " if self.verbose: print(f\"{player.name} PUSHES.\")\n", + " else: \n", + " if self.verbose: print(f\"{player.name} LOSES.\")\n", + " else:\n", + " if self.verbose: print(f\"{player.name} LOSES.\")\n", + " player.clear_hand()\n", + " \n", + " self.dealer.clear_hand()\n", + "\n", + " def run_simulation(self, num_games=1, target_player_name=None):\n", + " for i in range(1, num_games + 1):\n", + " if target_player_name:\n", + " target_player = next((p for p in self.players if p.name == target_player_name), None)\n", + " if target_player and target_player.chips <= 0:\n", + " print(f\"Simulation stopped early at round {i}: {target_player.name} is out of money!\")\n", + " break\n", + " self.play_round()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -57,6 +483,128 @@ "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": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def place_bet(self):\n", + " \"\"\"Handles betting for both humans and computer players.\"\"\"\n", + " if self.strategy == \"Human\":\n", + " # ... (Keep your existing human input loop here) ...\n", + " pass\n", + " else:\n", + " # Logic: Automated testing. Computer players bet a flat 10 chips \n", + " # so we can easily track their win/loss rate over time.\n", + " if self.chips > 0:\n", + " bet = 10 if self.chips >= 10 else self.chips\n", + " self.current_bet = bet\n", + " self.chips -= bet\n", + "\n", + "def decide_action(self, dealer_upcard, true_count, running_count=0):\n", + " \"\"\"Routes the decision based on the player's assigned strategy.\"\"\"\n", + " if self.strategy == \"Human\":\n", + " # ... (Keep your existing human input loop here) ...\n", + " pass\n", + " elif self.strategy == \"Dealer\":\n", + " # Logic: These players mimic the dealer. They ignore the true_count \n", + " # and dealer_upcard, and simply hit if they are under 17.\n", + " if self.calculate_score() < 17:\n", + " return \"Hit\"\n", + " return \"Stand\"\n", + " \n", + "def __init__(self, players_list, decks_in_shoe=6):\n", + " self.deck = Deck(num_decks=decks_in_shoe)\n", + " self.dealer = Dealer()\n", + " \n", + " # Logic: Instead of hardcoding one human, we pass a list of Player objects\n", + " # so we can test different table compositions (e.g., 3 bots, 1 human).\n", + " self.players = players_list \n", + " \n", + " self.running_count = 0\n", + " self.true_count = 0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting automated test for 3 rounds...\n", + "\n", + "\n", + "--- NEW ROUND ---\n", + "Dealer shows: 4 of Diamonds (Hidden Card)\n", + "\n", + "Bot Alice's turn. Hand: [4 of Clubs, J of Spades]\n", + "\n", + "Bot Bob's turn. Hand: [4 of Diamonds, 2 of Spades]\n", + "\n", + "Bot Charlie's turn. Hand: [9 of Diamonds, 6 of Spades]\n", + "\n", + "Dealer's turn. Hand reveals: [4 of Diamonds, 4 of Clubs]\n", + "Dealer hits. Hand: [4 of Diamonds, 4 of Clubs, 2 of Diamonds]\n", + "Dealer hits. Hand: [4 of Diamonds, 4 of Clubs, 2 of Diamonds, 5 of Clubs]\n", + "Dealer hits. Hand: [4 of Diamonds, 4 of Clubs, 2 of Diamonds, 5 of Clubs, J of Hearts]\n", + "\n", + "--- NEW ROUND ---\n", + "Dealer shows: A of Hearts (Hidden Card)\n", + "\n", + "Bot Alice's turn. Hand: [6 of Clubs, 9 of Spades]\n", + "\n", + "Bot Bob's turn. Hand: [K of Spades, 6 of Hearts]\n", + "\n", + "Bot Charlie's turn. Hand: [2 of Diamonds, 6 of Diamonds]\n", + "\n", + "Dealer's turn. Hand reveals: [A of Hearts, A of Clubs]\n", + "Dealer hits. Hand: [A of Hearts, A of Clubs, Q of Hearts]\n", + "Dealer hits. Hand: [A of Hearts, A of Clubs, Q of Hearts, 7 of Spades]\n", + "\n", + "--- NEW ROUND ---\n", + "Dealer shows: 6 of Clubs (Hidden Card)\n", + "\n", + "Bot Alice's turn. Hand: [A of Spades, Q of Hearts]\n", + "\n", + "Bot Bob's turn. Hand: [9 of Clubs, 6 of Diamonds]\n", + "\n", + "Bot Charlie's turn. Hand: [9 of Spades, K of Clubs]\n", + "\n", + "Dealer's turn. Hand reveals: [6 of Clubs, 2 of Spades]\n", + "Dealer hits. Hand: [6 of Clubs, 2 of Spades, Q of Clubs]\n", + "\n", + "--- FINAL CHIP COUNTS ---\n", + "Bot Alice: 1800 chips\n", + "Bot Bob: 0 chips\n", + "Bot Charlie: 1800 chips\n" + ] + } + ], + "source": [ + "# --- SIMULATION TEST ---\n", + "\n", + "# 1. Create our automated players\n", + "bot_1 = Player(name=\"Bot Alice\", strategy=\"Dealer\", starting_chips=1000)\n", + "bot_2 = Player(name=\"Bot Bob\", strategy=\"Dealer\", starting_chips=1000)\n", + "bot_3 = Player(name=\"Bot Charlie\", strategy=\"Dealer\", starting_chips=1000)\n", + "\n", + "# 2. Initialize the game with these players\n", + "test_table = Game(players_list=[bot_1, bot_2, bot_3], decks_in_shoe=6)\n", + "\n", + "# 3. Run a short simulation to prove it works\n", + "print(\"Starting automated test for 3 rounds...\\n\")\n", + "test_table.run_simulation(num_games=3)\n", + "\n", + "# 4. Print final results to verify chips updated correctly\n", + "print(\"\\n--- FINAL CHIP COUNTS ---\")\n", + "for p in test_table.players:\n", + " print(f\"{p.name}: {p.chips} chips\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -71,6 +619,66 @@ " * 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": 6, + "metadata": {}, + "outputs": [], + "source": [ + "class Lab6Player(Player):\n", + " def __init__(self, name, starting_chips=1000, hit_threshold=-1):\n", + " # Call the parent __init__ to set up the hand, chips, etc.\n", + " super().__init__(name=name, strategy=\"Count_Action\", starting_chips=starting_chips)\n", + " \n", + " # Storing the threshold so we can easily test different values later (like 0 or -2)\n", + " self.hit_threshold = hit_threshold\n", + "\n", + " def decide_action(self, dealer_upcard, true_count, running_count=0):\n", + " \"\"\"\n", + " LAB 6 STRATEGY: \n", + " Ignore the dealer's card and our own hand score (mostly).\n", + " Hit if the sum is very negative, stay if very positive.\n", + " \"\"\"\n", + " # Absolute safeguard: It is mathematically useless to hit on a 21 or higher.\n", + " if self.calculate_score() >= 21:\n", + " return \"Stand\"\n", + " \n", + " # The core Lab 6 logic: Compare the running count sum to our threshold\n", + " if running_count <= self.hit_threshold:\n", + " return \"Hit\"\n", + " else:\n", + " return \"Stand\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- NEW ROUND ---\n", + "Dealer shows: 10 of Diamonds (Hidden Card)\n", + "\n", + "Q6 Bot's turn. Hand: [J of Spades, 6 of Diamonds]\n", + "Drew a card. Hand: [J of Spades, 6 of Diamonds, 3 of Hearts]\n", + "\n", + "Dealer's turn. Hand reveals: [10 of Diamonds, Q of Clubs]\n", + "Q6 Bot LOSES.\n" + ] + } + ], + "source": [ + "count_bot = Lab6Player(name=\"Q6 Bot\", hit_threshold=-2)\n", + "\n", + "# Load it into a game and play just ONE round to see the prints\n", + "q6_table = Game(players_list=[count_bot], decks_in_shoe=6)\n", + "q6_table.run_simulation(num_games=1)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -78,6 +686,159 @@ "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": 8, + "metadata": {}, + "outputs": [], + "source": [ + "class Game:\n", + " # 1. Added the verbose parameter here\n", + " def __init__(self, players_list, decks_in_shoe=6, verbose=True):\n", + " self.deck = Deck(num_decks=decks_in_shoe)\n", + " self.dealer = Dealer()\n", + " self.players = players_list \n", + " self.running_count = 0\n", + " self.true_count = 0.0\n", + " self.verbose = verbose \n", + "\n", + " def update_count(self, card):\n", + " if card.rank in ['2', '3', '4', '5', '6']: self.running_count += 1\n", + " elif card.rank in ['10', 'J', 'Q', 'K', 'A']: self.running_count -= 1\n", + " decks_remaining = max(1, len(self.deck.cards) / 52)\n", + " self.true_count = self.running_count / decks_remaining\n", + "\n", + " def deal_and_count(self, person):\n", + " card = self.deck.draw()\n", + " self.update_count(card)\n", + " person.receive_card(card)\n", + "\n", + " def play_round(self):\n", + " # 2. Wrapped print statements in \"if self.verbose:\" checks\n", + " if self.verbose: print(\"\\n--- NEW ROUND ---\")\n", + " \n", + " if self.deck.needs_reshuffle:\n", + " if self.verbose: print(\"Dealer is shuffling the shoe...\")\n", + " self.deck.build_deck()\n", + " self.deck.shuffle()\n", + " self.running_count = 0 \n", + " self.true_count = 0.0\n", + "\n", + " for player in self.players: \n", + " player.place_bet(self.true_count)\n", + "\n", + " for _ in range(2):\n", + " for player in self.players: self.deal_and_count(player)\n", + " self.deal_and_count(self.dealer)\n", + "\n", + " if self.verbose: print(f\"Dealer shows: {self.dealer.hand[0]} (Hidden Card)\")\n", + "\n", + " for player in self.players:\n", + " if self.verbose: print(f\"\\n{player.name}'s turn. Hand: {player.hand}\")\n", + " while player.calculate_score() < 21:\n", + " action = player.decide_action(self.dealer.hand[0], self.true_count, self.running_count)\n", + " if action == \"Hit\":\n", + " self.deal_and_count(player)\n", + " if self.verbose: print(f\"Drew a card. Hand: {player.hand}\")\n", + " else: break\n", + " if player.calculate_score() > 21:\n", + " if self.verbose: print(f\"{player.name} BUSTS!\")\n", + "\n", + " if self.verbose: print(f\"\\nDealer's turn. Hand reveals: {self.dealer.hand}\")\n", + " while self.dealer.should_hit():\n", + " self.deal_and_count(self.dealer)\n", + " if self.verbose: print(f\"Dealer hits. Hand: {self.dealer.hand}\")\n", + "\n", + " dealer_score = self.dealer.calculate_score()\n", + "\n", + " for player in self.players:\n", + " player_score = player.calculate_score()\n", + " if player_score <= 21:\n", + " if dealer_score > 21 or player_score > dealer_score:\n", + " player.chips += (player.current_bet * 2)\n", + " if self.verbose: print(f\"{player.name} WINS!\")\n", + " elif player_score == dealer_score:\n", + " player.chips += player.current_bet\n", + " if self.verbose: print(f\"{player.name} PUSHES.\")\n", + " else: \n", + " if self.verbose: print(f\"{player.name} LOSES.\")\n", + " else:\n", + " if self.verbose: print(f\"{player.name} LOSES.\")\n", + " player.clear_hand()\n", + " \n", + " self.dealer.clear_hand()\n", + "\n", + " # 3. Updated simulation loop to check for bankruptcy\n", + " def run_simulation(self, num_games=1, target_player_name=None):\n", + " for i in range(1, num_games + 1):\n", + " # If a target player is provided, check if they are broke before dealing\n", + " if target_player_name:\n", + " target_player = next((p for p in self.players if p.name == target_player_name), None)\n", + " if target_player and target_player.chips <= 0:\n", + " print(f\"Simulation stopped early at round {i}: {target_player.name} is out of money!\")\n", + " break\n", + " \n", + " self.play_round()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting 50-round simulation... (Quiet Mode Active)\n", + "\n", + "--- FINAL RESULTS AFTER 50 ROUNDS ---\n", + "Dealer Bot 1:\n", + " Ending Chips: 930\n", + " Net Winnings: -70 chips\n", + "\n", + "Dealer Bot 2:\n", + " Ending Chips: 830\n", + " Net Winnings: -170 chips\n", + "\n", + "Dealer Bot 3:\n", + " Ending Chips: 950\n", + " Net Winnings: -50 chips\n", + "\n", + "Strategy Player:\n", + " Ending Chips: 790\n", + " Net Winnings: -210 chips\n", + "\n" + ] + } + ], + "source": [ + "# --- STEP 7: TEST SCENARIO ---\n", + "\n", + "# 1. Create the players (Each starts with exactly 1000 chips)\n", + "bot_1 = Player(name=\"Dealer Bot 1\", strategy=\"Dealer\", starting_chips=1000)\n", + "bot_2 = Player(name=\"Dealer Bot 2\", strategy=\"Dealer\", starting_chips=1000)\n", + "bot_3 = Player(name=\"Dealer Bot 3\", strategy=\"Dealer\", starting_chips=1000)\n", + "\n", + "# The Lab 6 Strategy Player. We will use a threshold of 0 for this test.\n", + "strat_player = Lab6Player(name=\"Strategy Player\", starting_chips=1000, hit_threshold=0)\n", + "\n", + "# 2. Initialize the game table. Note that we set verbose=False!\n", + "q7_table = Game(players_list=[bot_1, bot_2, bot_3, strat_player], decks_in_shoe=6, verbose=False)\n", + "\n", + "# 3. Play 50 rounds, targeting our Strategy Player for the bankruptcy check\n", + "print(\"Starting 50-round simulation... (Quiet Mode Active)\\n\")\n", + "q7_table.run_simulation(num_games=50, target_player_name=\"Strategy Player\")\n", + "\n", + "# 4. Compute and print the final winnings\n", + "print(\"--- FINAL RESULTS AFTER 50 ROUNDS ---\")\n", + "for p in q7_table.players:\n", + " winnings = p.chips - 1000\n", + " print(f\"{p.name}:\")\n", + " print(f\" Ending Chips: {p.chips}\")\n", + " print(f\" Net Winnings: {winnings} chips\\n\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -85,6 +846,122 @@ "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": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running 100 games of 50 rounds each... Please wait.\n", + "\n", + "--- FINAL STATISTICAL REPORT ---\n", + "Average Winnings (Per 50-Round Game): -208.60 chips\n", + "Average Winnings (PER ROUND): -4.17 chips\n", + "Standard Deviation of Winnings: 67.25 chips\n", + "Probability of a Net Win: 0.0%\n", + "Probability of a Net Loss: 100.0%\n", + "Probability of Breaking Even: 0.0%\n", + "\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# --- 1. THE MONTE CARLO LOOP ---\n", + "num_simulations = 100\n", + "rounds_per_game = 50\n", + "winnings_list = []\n", + "\n", + "print(f\"Running {num_simulations} games of {rounds_per_game} rounds each... Please wait.\\n\")\n", + "\n", + "for _ in range(num_simulations):\n", + " # CRITICAL LOGIC: We MUST re-instantiate the players inside the loop. \n", + " # If we don't, they will carry their chip counts over from the previous game!\n", + " # We need everyone starting fresh with exactly 1000 chips.\n", + " bot_1 = Player(name=\"Dealer Bot 1\", strategy=\"Dealer\", starting_chips=1000)\n", + " bot_2 = Player(name=\"Dealer Bot 2\", strategy=\"Dealer\", starting_chips=1000)\n", + " bot_3 = Player(name=\"Dealer Bot 3\", strategy=\"Dealer\", starting_chips=1000)\n", + " strat_player = Lab6Player(name=\"Strategy Player\", starting_chips=1000, hit_threshold=0)\n", + " \n", + " # Initialize the table in quiet mode\n", + " q8_table = Game(players_list=[bot_1, bot_2, bot_3, strat_player], decks_in_shoe=6, verbose=False)\n", + " \n", + " # Run the 50 rounds\n", + " q8_table.run_simulation(num_games=rounds_per_game, target_player_name=\"Strategy Player\")\n", + " \n", + " # Record the net winnings (ending chips minus starting chips)\n", + " net_winnings = strat_player.chips - 1000\n", + " winnings_list.append(net_winnings)\n", + "\n", + "# --- 2. STATISTICAL ANALYSIS ---\n", + "# Convert our list to a numpy array for easy mathematical calculations\n", + "winnings_array = np.array(winnings_list)\n", + "\n", + "# 1. Average winnings per game, and per round\n", + "avg_winnings_per_game = np.mean(winnings_array)\n", + "avg_winnings_per_round = avg_winnings_per_game / rounds_per_game \n", + "\n", + "# 2. Standard Deviation\n", + "std_dev = np.std(winnings_array)\n", + "\n", + "# 3. Probabilities (Counting outcomes and dividing by total games)\n", + "prob_winning = np.sum(winnings_array > 0) / num_simulations\n", + "prob_losing = np.sum(winnings_array < 0) / num_simulations\n", + "prob_breaking_even = np.sum(winnings_array == 0) / num_simulations\n", + "\n", + "# Print the final report\n", + "print(\"--- FINAL STATISTICAL REPORT ---\")\n", + "print(f\"Average Winnings (Per 50-Round Game): {avg_winnings_per_game:.2f} chips\")\n", + "print(f\"Average Winnings (PER ROUND): {avg_winnings_per_round:.2f} chips\")\n", + "print(f\"Standard Deviation of Winnings: {std_dev:.2f} chips\")\n", + "print(f\"Probability of a Net Win: {prob_winning * 100:.1f}%\")\n", + "print(f\"Probability of a Net Loss: {prob_losing * 100:.1f}%\")\n", + "print(f\"Probability of Breaking Even: {prob_breaking_even * 100:.1f}%\\n\")\n", + "\n", + "# --- 3. HISTOGRAM GENERATION ---\n", + "plt.figure(figsize=(10, 6))\n", + "# Using 15 bins to get a nice distribution curve\n", + "plt.hist(winnings_list, bins=15, color='cornflowerblue', edgecolor='black')\n", + "\n", + "# Adding lines to easily visualize the mean and the break-even point\n", + "plt.axvline(avg_winnings_per_game, color='red', linestyle='dashed', linewidth=2, label=f'Mean Winnings: {avg_winnings_per_game:.2f}')\n", + "plt.axvline(0, color='green', linestyle='dashed', linewidth=2, label='Break Even (0)')\n", + "\n", + "plt.title('Histogram of Lab 6 Strategy Player Winnings\\n(100 Games, 50 Rounds Each)')\n", + "plt.xlabel('Net Winnings (Chips)')\n", + "plt.ylabel('Frequency (Number of Games)')\n", + "plt.legend()\n", + "plt.grid(axis='y', alpha=0.75)\n", + "\n", + "# Display the chart\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Conclusion & Analysis

\n", + "\n", + "\n", + "The Monte Carlo simulation of 100 games demonstrates that the custom card-counting strategy outlined in Step 6 yields a negative expected value, with the average net winnings consistently falling below the break-even point. While tracking the running count provides valuable information about deck composition, strictly dictating hit/stay actions based solely on a count threshold—while completely ignoring the player's current hand total and the dealer's upcard—leads to mathematically unfavorable decisions, such as standing on a low total or hitting on a 19. Ultimately, this data proves that while card counting is an effective tool for adjusting bet sizes, it cannot profitably replace standard Basic Strategy for actual gameplay decisions." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -92,19 +969,281 @@ "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": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting threshold scan. Testing 5 values...\n", + "Threshold -4: Average Net Winnings = -138.70 chips\n", + "Threshold -2: Average Net Winnings = -183.90 chips\n", + "Threshold 0: Average Net Winnings = -205.60 chips\n", + "Threshold 2: Average Net Winnings = -246.80 chips\n", + "Threshold 4: Average Net Winnings = -272.80 chips\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "CONCLUSION: The 'optimal' threshold found is -4, yielding -138.70 chips on average.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "thresholds_to_test = [-4, -2, 0, 2, 4] \n", + "num_simulations = 100\n", + "rounds_per_game = 50\n", + "\n", + "# Dictionary to store the average winnings for each threshold\n", + "results = {}\n", + "\n", + "print(f\"Starting threshold scan. Testing {len(thresholds_to_test)} values...\")\n", + "for threshold in thresholds_to_test:\n", + " # print(f\"Testing threshold: {threshold}...\")\n", + " winnings_for_this_threshold = []\n", + " \n", + " for _ in range(num_simulations):\n", + " # We must re-initialize the table with fresh players for EVERY single game\n", + " bot_1 = Player(name=\"Dealer Bot 1\", strategy=\"Dealer\", starting_chips=1000)\n", + " bot_2 = Player(name=\"Dealer Bot 2\", strategy=\"Dealer\", starting_chips=1000)\n", + " bot_3 = Player(name=\"Dealer Bot 3\", strategy=\"Dealer\", starting_chips=1000)\n", + " \n", + " # NOTE: We pass the current 'threshold' variable from our outer loop into the bot!\n", + " strat_player = Lab6Player(name=\"Strategy Player\", starting_chips=1000, hit_threshold=threshold)\n", + " \n", + " # Set up and run the game quietly\n", + " q9_table = Game(players_list=[bot_1, bot_2, bot_3, strat_player], decks_in_shoe=6, verbose=False)\n", + " q9_table.run_simulation(num_games=rounds_per_game, target_player_name=\"Strategy Player\")\n", + " \n", + " # Record net winnings\n", + " winnings_for_this_threshold.append(strat_player.chips - 1000)\n", + " \n", + " # Calculate the average winnings for this specific threshold and save it to the dictionary\n", + " avg_win = np.mean(winnings_for_this_threshold)\n", + " results[threshold] = avg_win\n", + " print(f\"Threshold {threshold:>2}: Average Net Winnings = {avg_win:.2f} chips\")\n", + "\n", + "# --- PLOTTING THE RESULTS ---\n", + "# Extract data for our line graph\n", + "x_values = list(results.keys())\n", + "y_values = list(results.values())\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(x_values, y_values, marker='o', linestyle='-', color='purple', linewidth=2, markersize=8)\n", + "\n", + "# Programmatically find the \"optimal\" (maximum) value in our dictionary\n", + "optimal_threshold = max(results, key=results.get)\n", + "best_avg = results[optimal_threshold]\n", + "\n", + "# Highlight the optimal value on the graph with a gold star/dot\n", + "plt.scatter([optimal_threshold], [best_avg], color='gold', s=200, zorder=5, label=f'Optimal: {optimal_threshold} ({best_avg:.1f} chips)')\n", + "plt.axhline(0, color='red', linestyle='dashed', alpha=0.5, label='Break Even (0)')\n", + "\n", + "plt.title('Average Winnings vs. Hit Threshold\\n(100 Games of 50 Rounds per Threshold)')\n", + "plt.xlabel('Hit Threshold Value')\n", + "plt.ylabel('Average Net Winnings (Chips)')\n", + "plt.xticks(thresholds_to_test)\n", + "plt.grid(True, alpha=0.3)\n", + "plt.legend()\n", + "\n", + "# Display the final chart\n", + "plt.show()\n", + "\n", + "print(f\"\\nCONCLUSION: The 'optimal' threshold found is {optimal_threshold}, yielding {best_avg:.2f} chips on average.\")" + ] + }, { "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": "markdown", + "metadata": {}, + "source": [ + "

The Strategy: Martingale

\n", + "\n", + "\n", + "Instead of changing how we play our cards, we are going to change how we bet.\n", + "The rule of Martingale is simple: Double your bet after every loss, and reset to your base bet after a win. In theory, the logic sounds foolproof: if you keep doubling your bet, your eventual win will recover all previous losses plus a small profit. However, in reality (and in our simulation), a long losing streak will cause your bets to grow exponentially until you completely run out of chips." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "class MartingalePlayer(Player):\n", + " def __init__(self, name, starting_chips=1000, base_bet=10):\n", + " # Initialize using the parent class\n", + " super().__init__(name=name, strategy=\"Martingale\", starting_chips=starting_chips)\n", + " \n", + " # New state variables specifically for the Martingale strategy\n", + " self.base_bet = base_bet\n", + " self.last_bet = base_bet\n", + " self.last_chips = starting_chips \n", + " self.first_bet_made = False\n", + "\n", + " def place_bet(self, true_count=0.0):\n", + " if self.chips <= 0:\n", + " self.current_bet = 0\n", + " return\n", + "\n", + " # Logic: Check if our chips went down since the start of the last round\n", + " if not self.first_bet_made:\n", + " bet = self.base_bet\n", + " self.first_bet_made = True\n", + " else:\n", + " if self.chips < self.last_chips:\n", + " # We lost the last hand! Double the bet.\n", + " bet = self.last_bet * 2\n", + " else:\n", + " # We won or pushed. Reset to base bet.\n", + " bet = self.base_bet\n", + "\n", + " # Safeguard: You can't bet more chips than you currently have (going \"all in\")\n", + " bet = min(bet, self.chips)\n", + "\n", + " # Update our tracking variables BEFORE deducting the new bet\n", + " self.last_chips = self.chips\n", + " self.last_bet = bet\n", + " \n", + " self.current_bet = bet\n", + " self.chips -= bet\n", + "\n", + " def decide_action(self, dealer_upcard, true_count, running_count=0):\n", + " # We will use standard Simplified Basic Strategy for playing the cards, \n", + " # so we can isolate and test just the betting strategy.\n", + " dealer_val = 10 if dealer_upcard.rank in ['J', 'Q', 'K', 'A'] else int(dealer_upcard.rank)\n", + " score = self.calculate_score()\n", + " \n", + " if score >= 17: return \"Stand\"\n", + " elif score >= 12 and dealer_val <= 6: return \"Stand\"\n", + " else: return \"Hit\"" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulating 100 games to compare Martingale vs. Flat Betting... \n", + "\n", + "--- RESULTS AFTER 100 GAMES ---\n", + "MARTINGALE STRATEGY:\n", + " Average Net Winnings: -136.10 chips\n", + " Bankruptcy Rate: 20.0%\n", + " Win Rate (Finished with >1000 chips): 62.0%\n", + "\n", + "CONTROL STRATEGY (Flat Betting):\n", + " Average Net Winnings: -35.40 chips\n", + " Bankruptcy Rate: 0.0%\n", + " Win Rate (Finished with >1000 chips): 37.0%\n" + ] + }, + { + "data": { + "image/png": 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eeKBAFb1DaXokjcmdNm2aC1I0FrywPcweXUzQmOQLLrjApe9qvLgCdFVbDp1mS6nqmqpJ6cPqWdMJsMYFa7opBcF7o+rnCvo1tlgZBgqS1Euqseiqcq2ezL1RD5k3nlW9pQrI1WZ9vjc+WGnH6iXX99L7K41dAbumvNJFAd17vaTanqJUYa2vXmJtT21XPacAUQGAqlMrkPPWLyyNaddFA1XCV3Xpww8/3I3tVWp16LbOi7IxFCRqHLXGbCvYWbJkiatCrmNK+8bjtVUV9bXdFWjvLciK5jjNa/to6jNNDahj5cYbb3SfqfH86n3++OOP3XdXcFWYbaEx2t6YbAXYBaXvrnHZ+m46jhXQeVOBeVkgCkT1vQ844AC3/9Wbrh7y0OBPlcp1U7CuiwpF7cQTT3T7RxcMhg0b5o5xHasbN27cY11ta/Wo63ltO22vvaXKK0D2fj+6kKZjRxdGRPvNo99D586dXSq9Lpjot75161Y3LEF/e1R3wBujrmPwhRdecFOm6benC0O6Feb42xtlVGjIgrJC1KOv4QW6qKBp/PT3cG+/HwDwja9l5gCgDFdfz09ulamfeeYZV/Vc1YObN2/uqkw//fTTe1T/VmViVVbOzdy5cwPHHHNMoEKFCmHVhvOqvq6q6JG8SuGhli9fHujbt6+rQF25cuXAmWee6apka73//ve/wfVWrFjhnqtevbpb7+STT3bV5/OqphzaHvn5558DZ599dqBOnTqBlJSUQL169QLdu3cPTJo0KbA3kVWj9Xptx0suuSTw119/7bH+zJkz3XZUJXat27BhQ/f4tddeC1tv+PDhgQYNGgQrmnttXrp0aaBHjx7ue2q5V+k6v+rra9euzfV4Cd2/qiCtNlerVs3txxNPPDHw3XffufVCK9jnRuupqrWqyOt7qSJ37dq13X7wqpp7MjIyApdffrl7XlXaQ9uhf+t9clPQ4zSv7SOqhn/bbbe590lNTXWV3tu1a+eqxK9evXqftkWzZs1cxfloLViwINcK6LJr167A1VdfHTj44IMDVapUCZQvX961XftV1fwj93PkcR3JO0ZU3T8/uVVfnzZtmtu/qnKuY/bmm292MyNEfq623VlnneW2nbd/o6m+ruNdx71mL/j8889z/Q6XXnqpa4N+PzqOOnXqFLj77rvD1nvppZfczApaJ7Rqfn7HX15/LyJ/m7n91jQDgNqg6vA6trTP3n33XbfN+vTpk+82AAC/JOg//l0SAACUVkpB1vzD6uFUwSoUH1XBVpVw9YKHzhcdj/LbFvPmzXNzj6sK+8CBA31rI2KLevyV4aB6F5rrHABiDUE5AGCvlP4sOrFVZWulp6qqvNKkvXRiFA1VuF65cqVL71W6rdKJlXbevn37qKY0i6dtsWjRIjdmWAGXLhIpjVpjnxF/NARFx40u2GgogqZSVAq+hkCo0CFV2AHEIsaUAwD2SgGOxpUvXbrUVVNXoSvNR66echQtjVfWWGyNhVelbY2F1Rh7PY43Bd0WmspMxeY0blm1BwjI45emMpw9e7arZbFp0yZXG0DTuqneAgE5gFhFTzkAAAAAAD6hDCUAAAAAAD4hKAcAAAAAwCcE5QCAqGku5oMOOsjNL+1RwbdzzjnHzeOtolyauzgv27Ztc/Mea75izV+tObU1djg3mqP8hBNOcHMca75xzQu9ePHiArdVY+BVjVvzRtesWdPNNa57jTPV3OiaXzme6HsnJCTscdO84JFU1E9zPGtfai5yFfrTHO8F8eyzz4a9f3JyshsTrmNk4cKFFutGjhzp2l0UtP1y2+a6aX8UB9V/0PtrHvm9uf322+2www4L+z0DAEoOhd4AAFFZtWqVq2asoEvBt0eFtlavXm1HHnmkO7lXQJcXBdazZs2ye+65x1q3bu2muTr33HPd684777zgen/88YcLWhS0v/rqq7Zr1y6744477Nhjj7W5c+da7dq1823r2rVrXbCpqssDBgyw66+/3urUqWPr1693FeSHDRtmX331lWt7PGnevLm98MILYct0wSOSphXTtlEhtSOOOMI++ugju+GGG9yFjIJOLTV58mQXzGvfaRozFdyaMWOG27fVq1e3eHHMMcfkGiCrQrjfbrrpJjfDwpQpU+ySSy7xuzkAEH98myEdAFAqDRs2LNCwYcNAdnZ22PLQx6ecckqgadOmub7+vffeC+h/Py+++GLY8hNPPDHQoEGDQFZWVnBZv379ArVq1Qps3rw5uGzp0qWBlJQU14696dGjh1t35syZuT6/bt26wHPPPReIJ126dAm0adNmr+v9+uuvgYSEhMCYMWPCll9xxRWB8uXLB9avX5/v6ydPnuz286xZs8KWjxo1yi1/5plnArFsxIgRrp1FQb8F/SZK0pIlS1z777///gKtP2jQoEDr1q0DOTk5xd42AEA40tcBAAWWmZnpphpSb3ZoL7lEPs7LW2+95VLR+/XrF7ZcPXTqhf/+++/d46ysLHv33XftzDPPDOtNbNq0qXXr1s29T37UE//xxx/blVdeaccdd1yu6yiN/YILLghbpnTtjh07Wo0aNdznKq1X3zkQUIwTnpJ86qmnujZq3uzy5cu7Kbn0WJRJoMeaoknZA5qmKZKWnXbaae6zlMav91FGQKgdO3a4nsz99tvPraN1O3To4OZiLk5vv/22+86RPad6vHPnTvvwww8L9b5qu/zzzz9hy9955x07+uij3XRmmgrtxBNPtG+//TZsHU2HltuwiNxSzfV40KBBwanS9L6HHHJIcP+Eeu+991w2hlL0tZ3zSvnWdGs6NjTNlt5PGQeXXnqpFRXNr67t26pVK/f+DRs2tN69e9svv/yyx7qa7mvo0KGuDWq3MkB69erlMhAijRs3zn0v/e60jTXfe6QLL7zQFixY4LIYAAAli6AcAFBgCpiV+q2guLCUSq4gSWOMQx188MHB52XRokUu+POWR66rAEYp0XmZPn26u1fQG+1Y3KuuusoFx2+++aZLtb/uuutcCnekn3/+2YYPH+7mbNe6Cta0/ogRI+ypp56yMWPGuDTxzZs3uwBe38ej4EcpzQquJk2aZP/9739dYNi/f38X0HuGDBliEydOdKn3CoQVZOqChvZDYWnbKrjXPmjRooXdeuutYW3z9oOGB9SrVy/f/RStJUuWuHsNW/Bo+MLpp5/uLoLoYoMugmzcuNENXdDwgsJSsK20bNVAeOONN9x37tOnT1hNgk8//dR9tjcn+v333+/2vdLuQ+kCgfaNgmCtp/fWUApdPCoIXeDQupG30Is9uiilC0Ua1qF9rVoI2ke6EPDnn38G19Pwgc6dO7uaCArip02b5o4hbdP09PSwz9V76Lcwfvx4dyxqvncF7zomQx1++OEuaNf3AgCUsIiecwAA8nTvvfe6lNjVq1fnu5XyS19v1apV4KSTTtpj+apVq9x7e+nSX3/9tXv80ksv7bGu1tFzek1err76arfOH3/8EbZc6bm7d+8O3kLT5SMpJV/r3HnnnYGaNWuGpfbq+ymNe8WKFcFlc+fOdZ9Zv379wPbt24PL3377bbf8nXfeCS474IADAu3bt3fvH+rUU091r/eGA7Rt2zZwxhlnBIrKrbfeGpgwYULgs88+c0MJlLacnJwcOO6448KGIGg4wf7775/re6SmpgauvPLKAqWvf/fdd+47bt26NfDhhx8G6tWr5z7L+976TA1baNeuXdjna/06deoEOnXqFFw2YMCAXI+r3FLN9bhu3bqBLVu2BJfpuE1MTAyMHTs2uKxjx47u83fu3BlcptfUqFEj7D0feOAB93jTpk2BaKnNem1ut7vuuivP1+nYzMzMdL+ZG2+8Mbhcx6NeO3369L2mr2u7hh7jP/zwQ56/q2OOOcZtDwBAyaKnHABQYOrJU1pwrVq19mmr5VfVOrc05MK8T17UI60K7N5NvduhVABO1d61PCkpya2jHlH1TK9ZsyZsXfVsK8XYowwAUQ+v0o8jly9btszdq5dfacbnn3++exzac6peTPV2ej2jSn3/4IMP7JZbbrHPP/98jx7taN199912zTXXuGwHfZaqqatn9osvvnDbpqi3/VFHHeW2oXqiVXRPxd30OV6mhL6njiulT4cOgVCvrYYuKNVaKfyFoe+oz/XUrVvXpXl7+0G9xhrmoOwGDQ3w6DVKGw+lQndy9tlnu570lStXRtUW9WzrsyJvl112WXAd7X9lV2hmg9TUVLeNdK9q9b///ntwPR0P6hXXcbo3p5xyijuOIzMdvG0QStsm2u8FANh3BOUAgAJTQKgAK/QkP1pKz80t9XrDhg3uXinG3nqS17oKCnOrGO5p0qRJrsGHAmYvIFJKeagffvjBevTo4f795JNPumrhWk/p3RIZEHtt9SiAym+5l27vjafWWPHQCwS6qeK5rFu3zt0/8sgjLj1eY7wVZOq9zzjjjCKdVswbVx861jiv/aRAVrUFIr9jXjRVnrahLnZoWICCS1Xa93ifoenSImnKPFXkVyp7YXjHUCiNv/b2o95X7x+Zoi+Ry1SXQPtAgfNFF11kjRo1srZt2xZ4bL8u8mg8feQt9HtrqIKmJ9P+VUq6hoto22ksfOixp1kF9PmF2Qb6/pLbxR1dmNjXiz4AgOgxJRoAoMDUQ66ATIGZCpgVRrt27Vwgo+AmdFy5V8xKgY5orLOKp+VW5ErLWrZsGda7GUmFwjRtlwqIeYG2KJD3io1FBiwaK6zAWMXAQt9bwVhR8jINNB5dvbS50Xzvou2s4nO6KZj3es3Vk5tbUa99EdpTrf2k7aFp7kID1Mj9tDfKEvC2ty4qZGdnu/H2r7/+up111lnBfRA5FlrUg642eVOnaZ9o3vlI3gWMaOl9dXFH3zFSbss09lw3tUEXMMaOHeuKHqr4nAqo7avnn3/eBfzqLY/8fqEXoDTWf8WKFVbUdLFrX7NgAADRo6ccAFBgmm/aKxRWWCq0tW3bNld4K5TmSFbPqIpaiQJ2BZ4qoKbCVp7ly5e7Iml5BbMeBYIKxtXj/eWXXxaobQrQ9LmhmQDqOSzqecwVcKvCtgrF5dZ7qlto2nVo+rUqkKunWWnfhU3rjqRt76WaexR8ant4z3lUhE4XS5SKXhia417BsIYEqJda20JDAFTsLbTomS786BjxKrKLgl8NIQit3K6LRJo/vTC8yvg6xkKLBup4U091XtTb3KVLF7v33nvd459++smKgra315PtUeG1yJTynj17ukrpyj4oSiqAp9R5AEDJoqccAFBgSv0W9RJGVkX/7bff3M3rZVTAqN5Q0Ym+d7KvgEK92BrXvGXLFtfjrZ5zVZtWT2FoQKzeYY3lVZq5eocVOCmYU2+epoPaG73fSSed5MbeKpjVvzVuVp87b948++STT8KmW9P4W00fpd5PTaWm1GpNjxUZKBUFVc7WtlCb1DYFpuqpVHr3nDlz3PRboosU+v7a3gpm9bwuEoQGq6oYrymvBgwYEFa5PZIuTowePdpdGFEVcW1P9bw/8cQT1r1797Bx1G3atHHjnVVJXvtE+0FTzGldjUsvaPp6JH0HZQgMGzbMBeJKnVegrvH1+p5KcVdPtKqgqzK9xrt7VP1c+/+cc86xm2++2bVf6f3qfS8sVdXXBQYdkzqm9F4KthWwe0MqRJ+r3unjjz/epY6rbQ8//LDLrFCAvjdaP7epyHRsaSo80ffX/tPFL+3vH3/80W2HyFT1wYMH2yuvvOIunOh3oQsLung0c+ZM9x6FmR1Bx7qGRGimAQBACSvhwnIAgFLu2GOPDfTq1SvPCti53fRcKFXWvv76610lblXyPvjgg3OtBi2zZ88OHH/88YEKFSoEqlSp4iqR//XXXwVu765duwKPPvpooHPnzoFq1aq5SuOqrK3voWry69evD1v/mWeecVXH09LSAs2bN3eVup9++mn3PVTROrSitqrMR9J61157ba6VsO+///6w5T///HPg7LPPdlXGU1JS3Pbo3r17YNKkScF1brnllkCHDh0C1atXD7ZJlbjXrVsXXOeXX35x769187Nw4UK37xo2bOjeq1y5cq469+jRo912iqTK39p3TZo0cfupdevWgUceeSRQEF719VmzZu3xnCqd6z1VVdyrDK4K9ar8rTZVrFjR7XNV4I/0/vvvBw499FBX+V7b4rHHHsuz+nrkfvD2m6q4h1JVfB2D+o5q1z333LPHe7777ruBnj17um2n9bTPtC2//PLLfaq+rvfzbNy4MXDZZZe599bxrmNW79+lSxd3C6V1b7jhBtdeHTt6jY5Hb7aBvI45b9tE/iZ1jOt99jazAgCg6CXoPyV9IQAAUHoppVg9liqgFlp5HP6ZMGGC63nWsAKluAPROvbYY11xRM1lDgAoWQTlAICo6Fpup06d7PDDD7fHHnuMrRcD+vXr58aoRxYIAwpC0+Gp/oKGn2hYAwCgZDGmHAAQdTEqFU9TVXMV6gqt2A1/eOPPgcLQeHJNXUdADgD+oKccAAAAAACf0L0BAAAAAIBPCMoBAAAAAPAJQTkAAAAAAD4p84XeVIRo1apVVrlyZVecCAAAAACA4p6tZuvWrdagQYO9FsUt80G5AvLGjRv73QwAAAAAQJz5+++/rVGjRvEdlKuH3NsYVapU8bs5AAAAQNABjx1g6VvTrX7l+vbHoD/YMkAZsWXLFtc57MWjcR2UeynrCsgJygEAABBLEsslmu3+3z3nqkDZU5Ah1BR6AwAAAADAJwTlAAAAAAD4hKAcAAAAAACflPkx5QUtV5+VlWXZ2dl+NwUIk5SUZMnJyUznBwBAGTXrilmWHci2pIQkv5sCwCdxH5RnZmZaenq67dixw699AOSrQoUKVr9+fUtNTWVLAQBQxqjqOoD4FtdBeU5Oji1ZssT1RmpSdwU9BamOB5RUBocuGq1du9Ydp61atbLEREacAAAAAGVJXAflCngUmGv+OPVGArGmfPnylpKSYsuWLXPHa7ly5fxuEgAAAIAiFNdBuYfeR8Qyjk8AAMquJ358wrZlbrNKqZXsysOv9Ls5AHxAUA4AAAD45M6Zd9rKrSutYeWGBOVAnGKAKgAAAAAAPqGnPA/Tpk0rsZ3Qu3dviweff/65devWzTZu3GjVqlXLc73PPvvMBg4caL/99luBUrdHjhxpb7/9ts2dO9dKk8cee8w+/vhje+edd/xuCgAAAACf0FNeSq1evdquu+46a968uaWlpblidQruP/300yL9nK5du9rgwYOtJA0bNsxuvfXWYED+7LPPuqr4kbennnqq0J+h1yuQL8h63k3zhTdp0sSGDBliGRkZ+/x5V1xxhc2aNcu++uqrqNsPAAAAoGygp7wUWrp0qR1zzDGut/m+++6zgw8+2Hbv3m0fffSRXXvttfbHH3+U+NRd2dnZLmjdV998840tXLjQ+vXrF7a8SpUq9ueff4Ytq1q1qpWEyZMn28knn+y28c8//2yXXHKJVaxY0e666659el9dTDnvvPPs0Ucftc6dOxdZewEAAACUHvSUl0JK7VbP6w8//GBnnXWWtW7d2tq0aeN6cL/77rvgesuXL7fTTz/dKlWq5ILas88+2/7555+wtO9DDz3UnnvuOWvWrJkLcs855xzbunWre/7iiy+2mTNn2sMPPxzsLdYFAaWh69+6CNChQwcXXH755Zeu9/j666+3OnXquKm7FGiqJzgaL7/8svXo0WOPqb/0efXq1Qu7abqw3OgzTzzxRKtVq5b7Tl26dLE5c+YEn9d3lT59+rj39R7nRRc/9HnKRjj11FPttNNOC3s/b7jD4Ycf7tqt7IVRo0ZZVlbWXj9P76Ue9J07d0a1nQAAAACUDQTlpcyGDRvsww8/dD3i6q2N5I3VVu/1GWec4dZXYD19+nRbtGiR9e/fP2x9LVNQ+O6777qb1r3nnnvccwrGjz76aJdmnZ6e7m4KTEPTzMeOHWu///67663X4zfeeMOmTJnigtaWLVvaSSed5NpQUF988YUL9PeFLioMGDDAXSjQRYpWrVpZr169ghcbvAsF6gHXd4rmwsGCBQtsxowZ1rFjx+AyXZy44IIL3AUJjYN//PHHXcr96NGj9/p5+q7qgdcFFgAAAADxh/T1Uuavv/5yAfcBBxyQ73qffPKJzZs3z5YsWRIMpNUjrh51BYVHHHGEW5aTk+MCyMqVK7vHF154oRuXroBSvcypqalWoUIF11Mc6c4773Q90rJ9+3abOHGie6+ePXu6ZU8++aS7GPD000/bzTffXKDvp574Bg0a7LF88+bNrsffo39rXH1uunfvHvZYQXL16tXdBQf1dNeuXTusB3xvzj33XEtKSnI938oG0HsMHz48+Ly21S233OIuBIh6ypXarosUI0aMyPfzdGFFy/W91aMPAAAAIL4QlJcyCshFadD5Ue+1gvHQnu2DDjrIBYB6zgvKlUrtBeRSv359W7NmTYHaEtqjrR539fhqrLsnJSXFjjzySPd5BaU07sjUdVEbQ1PG86vKrvbfcccdroq70vU13n3Hjh0unb8wHnroITvhhBPc++iiiIYJ6OKFUu3lxx9/dBc6vJ5x0bq7du1yn6uLGvlRGr7WAwAAABB/CMpLGaViKyBXoKv09PyC99wC98jlCpxD6Tn1nhdEaPp8XhcL8mpHXjQOXFOmRVIQrnT4gtBY+LVr19r48eOtadOmbsy70vAzMzOtMNS77X32/vvv79Lg1Xt+9913u+XaXhpD3rdv3z1em9sFhkhK7/d60wEAQHxpXbO1VS1X1epWrOt3UwD4hKC8lKlRo4Ybp/2f//zHjWGOHFe+adMm1xuuXnH1DP/999/B3nKNd1Ya+IEHHljgz1P6unp990bBqdbV9F6qKC7qOZ89e3ZUU6q1b9/etXNfaCz5hAkT3Dhy0TZYt25d2Dq6GFGQ75UbpbKLV5ztsMMOc5Xh87tokNfnKcNAPer63gAAIP58NuAzv5sAM9ehs2XLlpjeFircTEdO2URQXgop4OzUqZNLDde4bhVZ03hnjd/WuG71oivdWsvPP/9812Os51W1XeOWoymkpvT277//3o151jhuXRTIjS4OXHPNNW7suNbRfN6ark1p2ZdddlmBP08XHFQobl8oONb4eX1P/XFVmyIrtet7aey80u3Vk64x53nRhQ6NX1ePuKZr0zZXxXvv4oZS5TXOXBc/NJWbevU1nv+XX35xven5fZ4uIGgMeosWLfbpOwMAAKDwAfk1l59nGdvWx/QmTKtU0yY+9SKBeRlEUJ6H3r17W6zab7/93PhqjWEeOnSoq+itq2aakktBuShlXFXVr7vuOjvuuONcoKi5tjUndjRuuukmV8BMPe/qGVbhuLyoarsCV423Voq3gmJVJs8v4I2kKub/+te/XM+zUsUL45lnnrErr7zS9T7r4sCYMWPc9wj14IMPurHhKkbXsGFDd9EhL5qXPHRaNm1Pvac3L7suJKhyvYJ1XYhQr7gK8V1++eV7/byXXnrJVbcHAACAP9SJo4B8aO80a1w79yl3/fb32p324LT1rq30lpc9CQFvMHAZpQNXVcSVtq2Uj1BKG1aQqSC3IGN/UTJUtVz7S1XTy7Jff/3Vjj/+eDfNmo7RvHCcAgAAFB8NJxx8ZT8bf2k1a9FwzymHY8Gildtt8DObbPwTr5FhWQbi0EjMU46Yc+utt7oCbYUd811arFq1yqZOnZpvQA4AAMq289883056/iR3DyA+kb6OmKMg9d///reVdT169PC7CQAAwGczl860lVtXWsPKDf1uCgCf0FMOAAAAAIBPCMoBAAAAAPAJQTkAAAAAAD4hKAcAAAAAwCcE5QAAAAAA+ISgHAAAAAAAnzAlWh7Wrl3rJnwvCZpMvnbt2lZaNGvWzAYPHuxuJalr16526KGH2vjx40v0cwEAAACgTAblY8eOtTfffNP++OMPK1++vHXq1Mnuvfde23///YPrXHzxxTZlypSw13Xs2NG+++67Yg3Ir7n8PMvYtr7YPiNUWqWaNvGpF6MKzL3tctVVV9mkSZPCnhs4cKBNnDjRBgwYYM8++2yh26XXKvDetGlT2PJZs2ZZxYoVC/2+AAAAAIAYCMpnzpxp1157rR1xxBGWlZVlt956q/Xo0cN+++23sKDv5JNPtsmTJwcfp6amFmu71EOugHxo7zRrXLt8sX7W32t32oPT1rvPjLa3vHHjxvbyyy/bQw895C5qyK5du+yll16yJk2a7FO7du/enedzpalXHwAAIJZdcdgVtjljs1VNq+p3UwDE45jyDz/80PX4tmnTxg455BAXeC9fvtx+/PHHsPXS0tKsXr16wVuNGjVKpH0KyFs0rFist30J+g877DAXfCvbwKN/K1hv37592Hbu3LmzVatWzWrWrGmnnnqqLVq0KPj80qVLLSEhwV599VWXIl6uXDl7/vnn7ZJLLrHNmze753QbOXJkMH09NIVczz311FPWp08fq1ChgrVq1creeeedsLbqsZbr4kG3bt1cL79e5/XCr1+/3s4991xr1KiRe4927dq5iwv5yczMtGHDhlnDhg3dRRxlUHz++eeF3p4AAAAlbUTXETbupHHuHkB8iqlCbwoAJTLoVqBVp04da926tV1xxRW2Zs0an1oYexQ4h2YRPPPMM3bppZeGrbN9+3YbMmSISzv/9NNPLTEx0QXQOTk5Yev961//suuvv95+//13O/74413grfHu6enp7nbTTTfl2Y5Ro0bZ2WefbfPmzbNevXrZ+eefbxs2bAgG/WeddZadccYZNnfuXJdyr6yIUOrhP/zww+3dd9+1X3/91a688kq78MIL7fvvv8/3u3/99dcuW0Cf269fP5dVsXDhwqi3IwAAAADEdaG3QCDgAkf16LZt2za4vGfPni7Yatq0qS1ZssRuv/126969u+tNVw96pIyMDHfzeMXasrOz3S2UHutzvVtoW9z9/92Kk/f+kW0oqAsuuMCGDx/uto16nhWkqofZ6zHWe/bt2zfsNerVrlu3rs2fP99ta+9zb7jhBhesexSQ6z217h7bJqK9Gr9+zjnnuH+PHj3aHn30URdQK0jW+HbVCbjvvvvc87q48ssvv9iYMWOC79OgQQMbOnRo8P0GDRrkevjVe3/kkUeGfb5u6unX9/z777/da0Wv12t0YULvXVZ43zm3YxgAAAD7Rh1ViUlJlmNJlh2IqT7LILXNtTEnh/PBUiKa8/aYCcoVhKm386uvvgpb3r9//+C/FUB26NDBBejvvffeHsGmVzxOvbaRFMRVqlQpbJkOao1lDw3iRY9zAjmWE8i2nJziDYLcZwRy3Geqt7igvABN30mB79NPP+0CN/1by7zn9Z6LFy+2O++803744QeXJu71kP/111/WsmXL4Pc/+OCDw9rgjSuPbJc+R9stdPmBBx4YfJyUlGSVK1e2lStXumXqeVc6fej6qqLuvbduausDDzxgb7zxhq1atSp4cUWp9N7rvD9CeqyAX+0ILQooeo3S9KPZlrFO30nbe9myZS7LAQAAAEVH2Z2Hduhsa1PLWXZG8dauKqwNqZl2aIddriA1nTSlw7Zt20pXUH7ddde5McdffPGFG1Ocn/r167ugPK8UZfUaq8c9tKdcY6xbtGjhen5DKXBToKMedwV/Hj1OTEi0xIQkS0xMsuLkPiMhcY827I0CX930mssvv9xtQ3nsscfcstDnlWmgbfDkk0+6XmUFtxqzLXreyzioXr16WBtSUlKC64RS73lycnLYco0DD32sdbzPVyAZuX7oe+umXnS1XUXr1DaNEb/xxhvdHx3vdXof7z297zd79mx3H0oXJaLZlqWBtp+O+7L2vQAAiHdNH25qK7eutIaVG9qyG5b53Zy4pA6subO/sgvaVLPmaRUsFi1ev8Pmzt5kF1wy0Jo3b+53c1AA0Uyv7WtQrp5OBZNvvfWWS7feb7/99voa9fQqZVnBeW4UYOaW1u4FcZHLvCJmunm8f+u//39p8fDeP7INBX59QoJL8VfRM1FPeej76Mqfeqoff/xxO/bYY90yLxsh8rtHtkHbUUFxbu3KbZtFructO+CAA+z9998Pe94r5uetozadfvrpbhy56MKBLryoBz63z1GRO7VNVwu971VWed85t2MYAACUHfx/3h/q+MnJzrZEy7akhPCaS7FCbXNt/L9OKsS+aPaTr7mwmg5NVb5ffPFFl+68evVqd9u5c2ewy1/Fxb799ltXLEyBe+/eva1WrVphY5/jnXa4Am/dIne+er9Vcf2JJ55w6eqfffZZWCZBflRlXftAxeHWrVtnO3bsKFT7VNhNc9GrkNyCBQvcOHFv/nQv4FYa/fTp0+2bb75x30Ov0bGQF41LVzG5iy66yFWc15h6FbLTPPe6AAAAAAAApYGvPeUqACaahiuUqolrqjQFmCoINnXqVDd1lnrHNZ3WK6+84oL44qY5xEvLZ0Sm5nt0NU3VyVVVXWPyNQb7kUce2WOb56ZTp0529dVXu3H9ylAYMWJEcFq0aCgD4vXXX3eF2B5++GE7+uijXfX1a665JpjVoAJ+CqxPOukklwqv6uuq1u5V5M+NjpO7777bva/Gr+vig95b1d8BAAAAoDRICBSm5Hcpy+WvWrWqC+5yG1OuQFBBY+hYXaVEX3P5eZaxbX2JtDGtUk2b+NSLVrt2bYsXqtA+adIkNxQB+cvrOAUAAKVfo3GNgmPKVwxZ4Xdz4pIKQg++sp+Nv7SatWhY0WLRopXbbfAzm2z8E6+5Wlko3XFoTBZ6izUKjhUkRzM4f19oJ5X1gHzChAl2xBFHuN5sTdt2//33u4r7AAAAABDPCMrzoCC5rAfKJUlF25RqrsJzTZo0cSnnqpQPAAAAAPGMoBwlQlOd6QYAAAAAiJHq6wAAAAAAxDOCcgAAAAAAfEL6OgAAAOCT5/s+bxlZGZaW/L9pYgHEH4JyAAAAwCddm3Vl2wNxjvR1AAAAAAB8QlAOAAAAAIBPSF8HAAAAfPL50s+DY8pJZQfiEz3liNrIkSPt0EMPLVNb7sILL7QxY8YUeP2MjAxr0qSJ/fjjj8XaLgAAULZd8OYFdvILJ7t7APGJoLwUuvjiiy0hISF4q1mzpp188sk2b948i1XNmjULa7N3u+eee/xumttu7733nl133XXBZYFAwF18aNCggZUvX966du1q8+fPDz6flpZmN910k/3rX//yqdUAAAAAygKC8lJKQXh6erq7ffrpp5acnGynnnpqvq/ZvXu3+enOO+8Mttm7hQbCfnnsscesX79+Vrly5eCy++67z8aNG+eemzVrltWrV89OPPFE27p1a3Cd888/37788kv7/ffffWo5AAAAgNKOoLyUUk+tAkXdlEquHtu///7b1q5d655funSp64l+9dVXXS9vuXLl7Pnnn3fPTZ482Q488EC37IADDrAJEyaEvbfeq3Xr1lahQgVr3ry53X777fkG9EuWLLGWLVvaNddcYzk5OXmup6DXa7N3q1ixontNo0aNbNKkSWHrz5kzx32HxYsXu8ebN2+2K6+80urUqWNVqlSx7t27288//7xHWv1zzz3neuarVq1q55xzTlggHUmf/dprr9lpp50W1ks+fvx4u/XWW61v377Wtm1bmzJliu3YscNefPHF4HrKUOjUqZO99NJLeb4/AAAAAOSHQm95GPftOHfbm8PqH2bvnPtO2LLTXjrN5qTP2etrhxw9xN321bZt2+yFF15wgbECxcgA+8EHH3SBuAL5J5980kaMGOF6gNu3b28//fSTXXHFFS44HjBgQDB4fvbZZ13q9i+//OKe17Jhw4bt8dm//vqr9ejRw7127NixhWp/YmKiC571Ha6++urgcgXARx99tLswoED5lFNOsRo1atj777/vAu7HH3/cjj/+eFuwYIFbLosWLbK3337b3n33Xdu4caOdffbZLkV+9OjReaaub9q0yTp06BB2kWH16tXue3m07bp06WLffPONXXXVVcHlRx55pOstBwAAAIDCICjPw5aMLbZy68q9bsDGVRvvsWztjrUFeq0+o7AUdFaqVMn9e/v27Va/fn23TAFuqMGDB7veXs9dd93lgnRv2X777We//fabC3C9oPy2224Lrq8e56FDh9orr7yyR1D+7bffupT54cOHu/HVe6MLBKHv7X0P9eQrFVzp4suWLbOmTZu6HuyXX37Z/v3vf7v1ZsyY4S4QrFmzxgXI8sADD7gA/PXXX3c96KLX6YKCl4quAm5K788rKFdGQVJSkut99yggl7p164atq8dqX6iGDRu69wAAAACAwiAoz0OVtCrWsHLDvW7A2hVq57qsIK/VZxRWt27dbOLEie7fGzZscCnoPXv2tB9++MEFtZ7QHmCltivF/bLLLnO9356srCzX8+xRkKv07b/++sv1wut5pYuHWr58uZ1wwgl2991324033ligNt98882uSF1kUCvqtVcqvVLBb7nlFps5c6YLwNXTLapyrrZEZgLs3LnT9Y6HXkQIHRuuixV6n7zo9QrylSYfKXKZeusjl6kInNLaAQAAAKAwCMqLIbU8Mp29OCjdXOnqnsMPP9wF1kpPV6Acup7HG++tdTp27Bj2fuotlu+++86lko8aNcpOOukk957qsVbveqjatWu79HY9pyA/MmjPTa1atcLaHEm95UpZV1Cue32+XuO1XQH2559/vsfrqlWrFvx3SkpK2HMKovMb5673V1CdmZlpqampbpnGuns95vpMj4L7yN5zXRDRtgAAAACAwqDQWxmh4FOp6+r5zYsCSvVMq3CaguPQm9LY5euvv3Y97Spypl72Vq1a7ZGy7fUQK/VcxeIUPOdXTK2gzjvvPJeirl5x9dYrSPccdthhLkhWlfnItnuBe2F4860rhd+jbaHAfPr06cFlCtrVe6/CbpFj6tXLDwAAAACFQVBeSmVkZLggVTdNyaWpxZTe3bt373xfpwrlKsj28MMPuwJpCoJVBE7juUVBrlLT1QOutPBHHnnE3nrrrVzfS73wmt9bgbJS5/X5+VHg7rXZu23ZsiUsGFbQq553pcyffvrpweeUKq+ib2eccYZ99NFHbhy3iq5pjPrs2bOtsNTLrYD/q6++CrvAobH4Y8aMcd9dgbfS7lWNXhcOQqnIW2hBOAAAAACIBkF5KfXhhx+61GrdlIquubQ1tZeKpuXn8ssvt6eeesoVQ2vXrp2rKK5/ez3lCoQ1RnzQoEGuF1mBr6ZEy4uKzX3wwQduvHWvXr1c0bm83HHHHcE2e7fI4nHqHdc0ZypEp9740EBZVdePO+44u/TSS92UbUqzV3AemVIeLRWJU+X3UGqXAvOBAwe6jIGVK1faxx9/HDZeXYXuNE3bWWedtU+fDwAA4teKISssMCLg7gHEp4SAoqkyTD2xGhet4Cly3POuXbvc9FcKSJWGjfik42D//fd32QHqjS+ofv36udR1r0J8cbaP4xQAAKB4KDt08JX9bPyl1axFw/9fjymWLFq53QY/s8nGP/GatWjRwu/mYB/j0Ej0lCPu6YLM1KlTbd26dVENHzjkkEMKXHkeAAAAAHJD9XXAzKXxR0PTqEXOuQ4AAAAA0SIoBwAAAHwy6vNRtjljs1VNq2ojuo5gPwBxiKAcAAAA8MmTc560lVtXWsPKDQnKgTjFmHIzVzkciFUcnwAAAEDZFddBeUpKirvfsWOH300B8uQdn97xCgAAAKDsiOv09aSkJKtWrZqtWbPGPa5QoYKbDxuIlR5yBeQ6PnWc6ngFAAAAULbEdVAu9erVc/deYA7EGgXk3nEKAAAAoGyJ+6BcPeP169e3OnXq2O7du/3eH0AYpazTQw4AAACUXXEflHsU+BD8AAAAAABKUlwXegMAAAAAwE8E5QAAAAAA+IT0dQAAAMAnXZp1sXU71lmtCrXYB0CcIigHAAAAfPJC3xfY9kCcI30dAAAAAACfEJQDAAAAAOATgnIAAAAAAHzCmHIAAADAJ92ndLd/tv9jdSvWtc8GfMZ+AOIQQTkAAADgkwXrF9jKrStt867N7AMgTpG+DgAAAACATwjKAQAAAADwCUE5AAAAAAA+ISgHAAAAAMAnBOUAAAAAAPiEoBwAAAAAAJ8QlAMAAAAA4BOCcgAAAAAAfJLs1wcDAAAA8e6OLnfYtsxtVim1kt9NAeATgnIAAADAJ1cefiXbHohzpK8DAAAAAOATgnIAAAAAAHxC+joAAADgk/St6ZYdyLakhCSrX7k++wGIQwTlAAAAgE+OePIIW7l1pTWs3NBWDFnBfgDiEOnrAAAAAAD4hKAcAAAAAACfEJQDAAAAAOATgnIAAAAAAHxCUA4AAAAAgE8IygEAAAAA8AlBOQAAAAAAPiEoBwAAAADAJwTlAAAAAAD4JNmvDwYAAADi3acXfWpZOVmWnMhpORCv+PUDAAAAPtm/1v5seyDOkb4OAAAAAIBPCMoBAAAAAPAJ6esAAACAT1785UXbsXuHVUipYOe1O4/9AMQhgnIAAADAJ8OmD7OVW1daw8oNCcqBOEX6OgAAAAAAPiEoBwAAAADAJwTlAAAAAAD4hKAcAAAAAIB4DMrHjh1rRxxxhFWuXNnq1KljZ5xxhv35559h6wQCARs5cqQ1aNDAypcvb127drX58+f71mYAAAAAAMpEUD5z5ky79tpr7bvvvrPp06dbVlaW9ejRw7Zv3x5c57777rNx48bZY489ZrNmzbJ69erZiSeeaFu3bvWz6QAAAAAAlO4p0T788MOwx5MnT3Y95j/++KMdd9xxrpd8/Pjxduutt1rfvn3dOlOmTLG6devaiy++aFdddZVPLQcAAAAAoIzNU75582Z3X6NGDXe/ZMkSW716tes996SlpVmXLl3sm2++yTUoz8jIcDfPli1b3H12dra7xbJ169YF2xurqlSpYrVq1fK7GQAAAGVOrJ+rllU5OTmWmJRkOZZk2YHYLLmltrk25uRwnJTB33PMBOXqFR8yZIh17tzZ2rZt65YpIBf1jIfS42XLluU5Tn3UqFF7LF+0aJFVqlTJYpVS9t+b9qZlZ+6wWJaUWsFO6d3XKlas6HdTAAAASr1qydUsq3yWu1+4cKHfzYlLGzZssEM7dLa1qeUsOyPVYtGG1Ew7tMMuW7t2LUF5KbFt27bSF5QPGjTI5s2bZ1999dUezyUkJOwRwEcu8wwfPtwF9x71PDdu3NhatGjhenlj1eLFi+3Hbz62wb3SrFHt8haLVqzdaePfz7Bzz7/Ymjdv7ndzAAAASr2fW/3sdxPins7D587+yi5oU82ap1WIye2xeP0Omzt7k11wyUDOw0uJaDKgYyIov+666+ydd96xL774who1ahRcrqJuXo95/fr1g8vXrFmzR+95aHq7bpGSkpLcLVYlJiZaTna2Namdai0alrNYlGjZlpO9w7U1lrclAAAAEO15uM51kxJyYvg8PJvz8FIkmnjJ10ET6vFWD/mbb75pn332me23335hz+uxAnNVZvdkZma6qu2dOnXyocUAAAAAABQdX3vKNR2aqqj/97//dXOVe2PIq1at6uYkV4r64MGDbcyYMdaqVSt3078rVKhg5513np9NBwAAAACgdAflEydOdPddu3bdY2q0iy++2P172LBhtnPnThs4cKBt3LjROnbsaB9//LEL4gEAAIDS7KppV9mGXRusRrka9njvx/1uDoB4C8qVvr436i0fOXKkuwEAAABlyXsL37OVW1daw8oN/W4KAJ/E5kR8AAAAAADEAYJyAAAAAAB8QlAOAAAAAIBPCMoBAAAAAPAJQTkAAAAAAD4hKAcAAAAAwCcE5QAAAAAA+ISgHAAAAAAAnyT79cEAAABAvDu37bm2cddGq16uut9NAeATgnIAAADAJ/f3uJ9tD8Q50tcBAAAAAPAJQTkAAAAAAD4hKAcAAAAAwCeMKQcAAAB8csBjB9iqrausQeUG9segP9gPQByipxwAAADwybbMbbY1c6u7BxCfCMoBAAAAAPAJQTkAAAAAAD4hKAcAAAAAwCcE5QAAAAAA+ISgHAAAAAAAnxCUAwAAAADgE4JyAAAAAAB8QlAOAAAAAIBPkv36YAAAACDeTTp1ku3cvdPKp5T3uykAfEJQDgAAAPjk1Nansu2BOEf6OgAAAAAAPiEoBwAAAADAJ6SvAwAAAP9n2rRpJbItevfu7e5/XPWjZWZnWmpSqh3e4HD2AxCHCMoBAAAAn5z+8um2cutKa1i5oa0YsoL9AMQh0tcBAAAAAPAJQTkAAAAAAD4hKAcAAAAAwCcE5QAAAAAA+ISgHAAAAACA0lR9/e+//7alS5fajh07rHbt2tamTRtLS0sr+tYBAAAAAFCGFTgoX7ZsmU2aNMleeuklF5QHAoHgc6mpqXbsscfalVdeaWeeeaYlJtIBDwAAAADA3hQoer7hhhusXbt2tnDhQrvzzjtt/vz5tnnzZsvMzLTVq1fb+++/b507d7bbb7/dDj74YJs1a1ZB3hYAAAAAgLhWoJ5y9YQvWrTIpapHqlOnjnXv3t3dRowY4QJ09aofccQRxdFeAAAAAADiKyi///77C/yGvXr12pf2AAAAAHHj92t/t4AFLMES/G4KgNJS6G3nzp1uPHmFChXcY/WKv/XWW3bggQfaSSedVBxtBAAAAMqkymmV/W4CAJ9FXZHt9NNPt6lTp7p/b9q0yTp27GgPPvignXHGGTZx4sTiaCMAAAAAAGVS1EH5nDlzXKV1ef31161u3bqut1yB+iOPPFIcbQQAAAAAoEyKOn1dc5NXrvy/NJuPP/7Y+vbt66ZAO+qoo1xwDgAAAKBgxn07zrZkbLEqaVVsyNFD2GxAHIq6p7xly5b29ttvu7nKP/roI+vRo4dbvmbNGqtSpUpxtBEAAAAokxSUj5o5yt0DiE9RB+V33HGH3XTTTdasWTM78sgj7eijjw72mrdv37442ggAAAAAQJkUdfr6WWedZZ07d7b09HQ75JBDgsuPP/5469OnT1G3DwAAAACAMivqnnKpV6+eG1c+ffp0N0WaHHHEEXbAAQcUdfsAAAAAACizog7K169f73rFW7dubb169XI95nL55Zfb0KFDi6ONAAAAAACUSVEH5TfeeKOlpKTY8uXLrUKFCsHl/fv3tw8//LCo2wcAAAAAQJkV9ZhyFXRT1fVGjRqFLW/VqhVTogEAAAAAUJw95du3bw/rIfesW7fO0tLSon07AAAAAADiVtRB+XHHHWdTp04NPk5ISLCcnBy7//77rVu3bkXdPgAAAAAAyqyo09cVfHft2tVmz55tmZmZNmzYMJs/f75t2LDBvv766+JpJQAAAFAGHVb/MGtctbHVrlDb76YAKC1B+UEHHWTz5s2ziRMnWlJSkktn79u3r1177bVWv3794mklAAAAUAa9c+47fjcBQGkLyr15ykeNGlX0rQEAAAAAII4UKijftWuX6y1fs2aNG08e6rTTTiuqtgEAAAAAUKZFHZRrLvKLLrrIVVuPpKJv2dnZRdU2AAAAAADKtKirrw8aNMj69etn6enprpc89EZADgAAABTcaS+dZkc/fbS7BxCfou4pV8r6kCFDrG7dusXTIgAAACBOzEmfYyu3rrSGlRv63RQApaWn/KyzzrLPP/+8eFoDAAAAAEAcibqn/LHHHnPp619++aW1a9fOUlJSwp6//vrri7J9AAAAAACUWVEH5S+++KJ99NFHVr58eddjruJuHv2boBwAAAAAgGIKym+77Ta788477ZZbbrHExKiz3wEAAAAAwP+JOqrOzMy0/v37E5ADAAAAAFDSQfmAAQPslVde2dfPBQAAAAAg7kWdvq65yO+77z43rvzggw/eo9DbuHHj4n6jAgAAAABQLEH5L7/8Yu3bt3f//vXXX8OeCy36BgAAAAAAijgonzFjRrQvAQAAAJCLIUcPsS0ZW6xKWhW2DxCnog7KAQAAABRdUA4gvhUqKJ81a5a99tprtnz5cleNPdSbb75ZVG0DAAAAAKBMi7r6+ssvv2zHHHOM/fbbb/bWW2/Z7t273b8/++wzq1q1avG0EgAAAACAMijqoHzMmDH20EMP2bvvvmupqan28MMP2++//25nn322NWnSpHhaCQAAAJRBWzO2ujHlugcQn6IOyhctWmSnnHKK+3daWppt377dVV2/8cYb7YknniiONgIAAABl0oH/OdCq3lPV3QOIT1EH5TVq1LCtW/93Ja9hw4bBadE2bdpkO3bsiOq9vvjiC+vdu7c1aNDABfZvv/122PMXX3yxWx56O+qoo6JtMgAAAAAAZSMoP/bYY2369Onu30pZv+GGG+yKK66wc889144//vio3ku97Icccog99thjea5z8sknW3p6evD2/vvvR9tkAAAAAADKRvV1BdC7du1y/x4+fLilpKTYV199ZX379rXbb789qvfq2bOnu+VHKfL16tWLtpkAAAAAAJS9oFzp657ExEQbNmyYuxWXzz//3OrUqWPVqlWzLl262OjRo91jAAAAAADicp5ymT9/vmVnZwcfJyUlWZs2bawoqRe9X79+1rRpU1uyZInrie/evbv9+OOPrgc9NxkZGe7m2bJli7tXW0PbG2tycnIsMSnJcizJsgNRjyooEWqba2NOTkxvSwAAgMIKBAIlsvFyO5fi/MofnIejOETzey5wUP7ll1/akCFDbNasWe6xCq6psJv3h0tF2D766CM74YQTrKj0798/+O+2bdtahw4dXID+3nvvuXT53IwdO9ZGjRqVa9X4SpUqWazasGGDHdqhs61NLWfZGakWizakZtqhHXbZ2rVr+Z8GAAAok0oqKF+4cKG7z8rKCt57y1CyOA9Hcdi2bVvRB+UTJkywCy+8MGzZjBkzXJCsP16PPPKITZw4sUiD8kj169d3n5ffHyyNc9fFg9Ce8saNG1uLFi2sSpUqFqsWL15sc2d/ZRe0qWbN0ypYLFq8fofNnb3JLrhkoDVv3tzv5gAAABS5BQsWlMhWbdWqlbtPTk4O3nvLULI4D0dx8DK2izQoVw+5Kq2HatSokQuSRQG7N395cVm/fr39/fffLjjPi9Lac0ttV3q9brFK4/NzsrMt0bItKSHHYpHa5tqYmBjT2xIAAKCwlP1ZEnI7l+L8yh+ch6M4RPN7LnBQvnLlyrBgeMqUKWFV0VUATkFztF36f/31V/Cxxo3PnTvXvZduI0eOtDPPPNN97tKlS+3f//631apVy/r06RPV5wAAAAAAEIsKHJRXrlzZBc1ez3jkmG49F216+OzZs61bt27Bx17a+YABA1wq/C+//GJTp061TZs2ucBc677yyiuuLQAAAEBp999z/muZ2ZmWmhSbNYUAxFBQ3rFjRxcgd+3aNdfnn332WbdONPRe+RXTUOE4AAAAoKw6vMHhfjcBQGkJytWLrSJuNWvWtJtvvjk4V/iaNWvs3nvvteeff94+/vjj4mwrAAAAAADxGZQrdfzRRx+1G2+80caNG+dS1VUIY/Pmza5a5Pjx490c4gAAAAAAoIiDchk4cKD17t3bXn/99eC0ZJq64ayzznLTjgEAAAAouHcXvGs7d++08inl7dTWp7LpgDgUVVAuCr7VWw4AAABg31z97tW2cutKa1i5oa0YsoLNCcShxIKs9O233xb4Dbdv327z58/flzYBAAAAABAXChSUX3TRRXbiiSfaq6++6uYWz81vv/3m5hFv2bKlzZkzp6jbCQAAAABAfKavK+B+/PHH7Y477rDzzz/fWrdubQ0aNLBy5crZxo0b7Y8//nA95Jq7fPr06da2bdvibzkAAAAAAPEQlKekpNigQYPcTb3gX375pS1dutR27txphxxyiBtjrursNWrUKP4WAwAAAAAQr4XeDjvsMHcDAAAAAAAlMKYcAAAAAADEQE85AAAAABTU2rVrbcuWLTG7wZYtW2ZZWVl+NwNxjKAcAAAAKGHTpk1z97t27Qree8uKUu/evc3vgPyay8+zjG3rLVZt35Fh/6z+2zJ2V/W7KYhTBOUAAACAT8ollrPyieXdfVmkHnIF5EN7p1nj2uUtFn33+0YbPTXLsrPpLUcpCcqXLFli++23X/G0BgAAAIgjE9tMtHiggLxFw4oWi5b9s9PvJiDORV3orWXLlm76s+effz6YbgMAAAAAAEogKP/555+tffv2NnToUKtXr55dddVV9sMPPxTiowEAAAAAiG9RB+Vt27a1cePG2cqVK23y5Mm2evVq69y5s7Vp08YtVzEHAAAAAABQjPOUJycnW58+fezVV1+1e++91xYtWmQ33XSTNWrUyC666CJLT08v7FsDAAAAcWHyisn26LJH3T2A+FTooHz27Nk2cOBAq1+/vushV0CuwPyzzz5zveinn3560bYUAAAAKGO+2PiFTV8/3d0DiE9RV19XAK609T///NN69eplU6dOdfeJif+L71WZ/fHHH7cDDjigONoLAAAAAED8BuUTJ060Sy+91C655BJX6C03TZo0saeffroo2gcAAAAAQJkVdVC+cOHCva6TmppqAwYMKGybAAAAAACIC1GPKVfq+muvvbbHci2bMmVKUbULAAAAAIAyL+qg/J577rFatWrtsbxOnTo2ZsyYomoXAAAAAABlXtRB+bJly1wxt0hNmza15cuXF1W7AAAAAAAo86IOytUjPm/evD2W//zzz1azZs2iahcAAAAAAGVe1EH5OeecY9dff73NmDHDsrOz3U1zk99www3uOQAAAAAAUEzV1++++26Xwn788cdbcvL/Xp6Tk2MXXXQRY8oBAACAKHSo2sG2Zm21ysmV2W5AnIo6KNd0Z6+88ordddddLmW9fPny1q5dOzemHAAAAPFl2rRpJfI5vXv3trLo2ibX+t0EAKUtKPe0bt3a3QAAAAAAQAkF5RpD/uyzz9qnn35qa9ascanroTS+HAAAAAAAFENQroJuCspPOeUUa9u2rSUkJET7FgAAAAAAoDBB+csvv2yvvvqq9erViw0IAAAA7IMhfwyxjbs3WvWU6jbugHFsSyAOFarQW8uWLYunNQAAAEAcUUC+fvd6v5sBoDTNUz506FB7+OGHLRAIFE+LAAAAAACIE1H3lH/11Vc2Y8YM++CDD6xNmzaWkpIS9vybb75ZlO0DAAAAAKDMijoor1atmvXp06d4WgMAAAAAQByJOiifPHly8bQEAAAAAIA4E/WYcsnKyrJPPvnEHn/8cdu6datbtmrVKtu2bVtRtw8AAAAAgDIr6p7yZcuW2cknn2zLly+3jIwMO/HEE61y5cp233332a5du2zSpEnF01IAAAAAAOK9p/yGG26wDh062MaNG618+fLB5Rpn/umnnxZ1+wAAAAAAKLMKVX3966+/dvOVh2ratKmtXLmyKNsGAAAAAECZFnVQnpOTY9nZ2XssX7FihUtjBwAAAFAwFze82DJyMiwtMY1NBsSpqNPXNYZ8/PjxwccJCQmuwNuIESOsV69eRd0+AAAAoMzqUqOL9ajVw90DiE9R95Q/9NBD1q1bNzvooINcYbfzzjvPFi5caLVq1bKXXnqpeFoJAAAAAEAZFHVQ3qBBA5s7d64LwOfMmePS2S+77DI7//zzwwq/AQAAAACAIg7KRcH3pZde6m4AAAAACmfFrhWWE8ixxIREa1SuEZsRiENRB+VTp07N9/mLLrpoX9oDAAAAxI3bF95u63evt5opNW1yu8l+NwdAaQjKNU95qN27d9uOHTvcFGkVKlQgKAcAAAAAoLiqr2/cuDHspsrrf/75p3Xu3JlCbwAAAAAAFGdQnptWrVrZPffcs0cvOgAAAAAAKOagXJKSkmzVqlVF9XYAAAAAAJR5UY8pf+edd8IeBwIBS09Pt8cee8yOOeaYomwbAAAAAABlWtRB+RlnnBH2OCEhwWrXrm3du3e3Bx98sCjbBgAAAABAmRZ1UJ6Tk1M8LQEAAAAAIM4U2ZhyAAAAAABQzD3lQ4YMKfC648aNi/btAQAAAACIG1EH5T/99JPNmTPHsrKybP/993fLFixY4KqvH3bYYWFjzQEAAADk7cEDHrScQI4lJpDACsSrqIPy3r17W+XKlW3KlClWvXp1t2zjxo12ySWX2LHHHmtDhw4tjnYCAAAAZU6NlBp+NwGAz6K+JKcK62PHjg0G5KJ/33333VRfBwAAAACgOIPyLVu22D///LPH8jVr1tjWrVujfTsAAAAAAOJW1Onrffr0canq6jE/6qij3LLvvvvObr75Zuvbt29xtBEAAAAokz5c96Htyt5l5ZLK2cm1Tva7OQBKQ1A+adIku+mmm+yCCy6w3bt3/+9NkpPtsssus/vvv7842ggAAIA4N23aNCuLXkl/xdbvXm81U2oSlANxKuqgvEKFCjZhwgQXgC9atMgCgYC1bNnSKlasWDwtBAAAAACgjCr03Avp6enu1rp1axeQKzgHAAAAAADFGJSvX7/ejj/+eBeM9+rVywXmcvnllzMdGgAAAAAAxRmU33jjjZaSkmLLly93qeye/v3724cffhjt2wEAAAAAELeiHlP+8ccf20cffWSNGjUKW96qVStbtmxZUbYNAAAAAIAyLeqe8u3bt4f1kHvWrVtnaWlpRdUuAAAAAADKvKiD8uOOO86mTp0afJyQkGA5OTmuGnu3bt2Kun0AAAAAAJRZUaevK/ju2rWrzZ492zIzM23YsGE2f/5827Bhg3399dfF00oAAAAAAMqgqHvKDzroIJs3b54deeSRduKJJ7p09r59+9pPP/1kLVq0KJ5WAgAAAGVQg7QG1rhcY3cPID5F1VO+e/du69Gjhz3++OM2atSo4msVAAAAEAdGtx7tdxMAlKaeck2F9uuvv7px5AAAAAAAoITT1y+66CJ7+umnrSh88cUX1rt3b2vQoIEL9N9+++2w5wOBgI0cOdI9X758eTeWXePXAQAAAACIy0JvKu721FNP2fTp061Dhw5WsWLFsOfHjRtX4PfSePRDDjnELrnkEjvzzDP3eP6+++5z7/fss89a69at7e6773bj2P/880+rXLlytE0HAAAAAKB0B+VKXz/ssMPcvxcsWBD2XLRp7T179nS33KiXfPz48Xbrrbe6QnIyZcoUq1u3rr344ot21VVXRdt0AAAAIKY8uORB25K1xaokV7Gh+w31uzkAYjkoX7x4se233342Y8YMKwlLliyx1atXu8JynrS0NOvSpYt98803eQblGRkZ7ubZsmWLu8/Ozna3WKW53hOTkizHkiw7EPWoghKhtrk25uTE9LYEAAAlRx0pKLxft/1q63evt5opNQu1LXWuu2PHjjyfX7hwoa+7Z/ny5ZYTCMT0OW7AkiwpOdndx2obOQ8vfaKJlwoclLdq1crS09OtTp067nH//v3tkUcecT3XxUEBuUS+vx4vW7Ysz9eNHTs218rwixYtskqVKlms0jzvh3bobGtTy1l2RqrFog2pmXZoh122du1agnIAAOAQlPu3LdUR9evPsy2QnZnnOiuX/m5+ytydbS32P9hWJ9Wz7IxyFosyq++wrifuZxsr1LOFGRUsFnEeXvps27at6IPyyD8S77//vguAi1tkSrzakV+a/PDhw23IkCFhVw8bN27s5lCvUqWKxSplIsyd/ZVd0KaaNU+LzT8Gi9fvsLmzN9kFlwy05s2b+90cAAAQAyKHM6Lwoh0KumnTJvv+i/ftupNTrH6N3Dt1atWq5esu+eGPjXbPGwvs8g4HW6u6NS0Wrdy43j6fPs+uOPJga1UvNtvIeXjp42VsF8uY8pJSr169YI95/fr1g8vXrFmTb++8Utx1i5SUlORusSoxMdFysrMt0bItKSHHYpHa5tqYmBjT2xIAAJQcpsr1d1sqRbZBjXLWrF5Krs/Xq+tv7/Tf/6RYdlaWJcTwOa7aFutt5Dy89IkmXkqM5o9E5B+K4vwjrPHrCsxV5T208vvMmTOtU6dOxfa5AAAAAACUlKjS1y+++OJgL/SuXbvs6quv3mNKtDfffDOqPPu//vorrLjb3LlzrUaNGtakSRMbPHiwjRkzxo1n103/rlChgp133nkF/gwAAAAAAEp9UD5gwICwxxdccME+f/js2bOtW7duwcfeWHB9luYmHzZsmO3cudMGDhxoGzdutI4dO9rHH3/MHOUAAAAAgPgKyidPnlzkH961a9d8q0wqPX7kyJHuBgAAAABAWRObE/EBAAAAABAHYrb6OgAAAFDW9ajVw3Zk77AKSbE5JS6A4kdQDgAAAPjk3Prnsu2BOEf6OgAAAAAAPiEoBwAAAADAJwTlAAAAAAD4hDHlAAAAgE8u+eUSW797vdVMqWmT2xX9FMQAYh895QAAAAAA+ISgHAAAAAAAn5C+DgAAAORi8+bNtmPHjmLdNtnZ2cH79PT0qF67du3a4OsBlF4E5QAAAEAuAfmTEx6wQObWYt02O07YalbebMf2rfb8E/dG9dqdGbtty6a1lpVdIc91Vv/zj5WEenXrlsjnAGURQTkAAAAQQT3kCsgH90qxBjVTi237nLY5wdYGzKpWSLD7LqwY1WvnLNxmD7+dYzn0lgOlGkE5AAAAkAcF5M3qlSu+k/FtCWZZZslJCVF/zop1GcXWLgAlh0JvAAAAAAD4hKAcAAAAAACfEJQDAAAAAOATxpQDAAAAPnmsfnPLCAQsLSGBfQDEKYJyAAAAwCedKlZh2wNxjvR1AAAAAAB8QlAOAAAAAIBPSF8HAAAAfPLN9i3BMeWksgPxiaAcAAAA8Mmg9MWWnrXb6ien2JyWh7IfgDhE+joAAAAAAD4hKAcAAAAAwCcE5QAAAAAA+ISgHAAAAAAAn1DoDQAAAABiXEbmblu2bJnFsipVqljt2rX9bkapQ1AOAABQBk2bNs3vJgAoIuu3ZNriJcvsnjuus7S0tJjdrmmVatrEp14kMI8SQTkAAAAAxLBtO7MtNTHLbjw11Vo3rmax6O+1O+3Baetty5YtBOVRIigHAAAAgFKgUe1y1qJhRYtdGX43oFSi0BsAAAAAAD6hpxwAAADwyZyWh7LtgThHTzkAAAAAAD4hKAcAAAAAwCcE5QAAAAAA+IQx5QAAAIBPHly30rZkZ1uVpCQbWqsh+wGIQwTlAAAAgE9e2LTW0rN2W/3kFIJyIE6Rvg4AAAAAgE8IygEAAAAA8AlBOQAAAAAAPiEoBwAAAADAJwTlAAAAAAD4hKAcAAAAAACfEJQDAAAAAOATgnIAAAAAAHyS7NcHAwAAAPHuqAqVbUN2ltVI4rQciFf8+gEAAACfTGjQgm0PxDnS1wEAAAAA8AlBOQAAAAAAPiEoBwAAAADAJ4wpBwAAAHxy1vI/bG3WbqudnGKvNzmA/QDEIYJyAAAAwCeLM3dZetZu25qTzT4A4hTp6wAAAAAA+ISgHAAAAAAAnxCUAwAAAADgE4JyAAAAAAB8QlAOAAAAAIBPCMoBAAAAAPAJQTkAAAAAAD4hKAcAAAAAwCfJfn0wAAAAEO9urNXAtufkWMVE+sqAeEVQDgAAAPjkwmp1ysS2X/3PP7ku37Bxs2XnZNvGDRtsdaVsi0W5tbFe3bp+NwtxhEtyAAAAAAD4hKAcAAAAAACfkL4OAAAA+OSfrEzLDpglJZjVTU5lPwBxiKAcAAAA8EnPpb9ZetZuq5+cYnNaHsp+AOIQ6esAAAAAAPiEoBwAAAAAAJ8QlAMAAAAA4BOCcgAAAAAAfEJQDgAAAACATwjKAQAAAADwCUE5AAAAAAA+ISgHAAAAAMAnMR2Ujxw50hISEsJu9erV87tZAAAAAAAUiWSLcW3atLFPPvkk+DgpKcnX9gAAAABF5dXG+1u2znHZpEDcivmgPDk5md5xAAAAlEkt08r73QQAPov5oHzhwoXWoEEDS0tLs44dO9qYMWOsefPmea6fkZHhbp4tW7a4++zsbHeLVTk5OZaYlGQ5lmTZgdgcVaC2uTbm5MT0tgQAAGaBQCCmN4PO0Xbs2GGxat26de4+YP87P4tJCUmWlJxsgQTaWNTbMdbOx3UcujYSK5Qa0cRLMR2UKwifOnWqtW7d2v755x+7++67rVOnTjZ//nyrWbNmrq8ZO3asjRo1ao/lixYtskqVKlms2rBhgx3aobOtTS1n2RmpFos2pGbaoR122dq1awnKAQDYB3/++Wdcbz91oPz682wLZGdarMrKzraDD+9kW6tUs1XJsXlullg/w7r32GS7aqiNaRaLSmsbt2TEVtyQWX2HdT1xP9tYoZ4tzKhgsYhYIdy2bdusTATlPXv2DP67Xbt2dvTRR1uLFi1sypQpNmTIkFxfM3z48LDndBW2cePG7nVVqlSxWLV48WKbO/sru6BNNWueFps/tMXrd9jc2ZvsgksG5putAAAA8rdgwYK43kSbNm2y77943647OcXq14jNgPenv7bZYx+tsn6tmlqDClWL7XPe2rzOdgZyrHxCovWpWiuq1y5N32yffbzM+rVuag0qFl8b90VpbWPdtDoWS1ZuXG+fT59nVxx5sLWql3vnpN+IFcJ5GdulPiiPVLFiRRecK6U9L0pz1y2SCsTFcpG4xMREy8nOtkTLtqSEHItFaptrY2JiTG9LAABinWaUiXdK7WxQo5w1q5disWjlukTLzsqyhMD/zs+Ky+i1yyw9a7fVT06xM6tWj+7FgewSaeM+KaVtjLXz8QT7vzYSK5Qa0cRLsTVYogCpTr///rvVr1/f76YAAAAAALDPYjoov+mmm2zmzJm2ZMkS+/777+2ss85yaQADBgzwu2kAAAAAAOyzmE5fX7FihZ177rmu+mXt2rXtqKOOsu+++86aNm3qd9MAAAAAACjbQfnLL7/sdxMAAAAAAIjP9HUAAAAAAMoygnIAAAAAAHxCUA4AAAAAgE8IygEAAAAA8ElMF3oDAAAAyrLaySlh9wDiD0E5AAAA4JOPmrVh2wNxjvR1AAAAAAB8QlAOAAAAAIBPCMoBAAAAAPAJY8oBAAAAn9y8eqltys6yaknJdn+9ZuwHIA4RlAMAAAA++XTbJkvP2m31qb4OxC3S1wEAAAAA8AlBOQAAAAAAPiEoBwAAAADAJwTlAAAAAAD4hKAcAAAAAACfEJQDAAAAAOATgnIAAAAAAHxCUA4AAAAAgE+S/fpgAAAAIN6dUaWmbc7OsqpJnJYD8YpfPwAAAOCTO+o0ZtsDcY70dQAAAAAAfEJQDgAAAACATwjKAQAAAADwCWPKAQAAAJ90XvyL/ZOVaXWTU+2r5u3YD0AcoqccAAAA8MmOnGzblpPj7gHEJ4JyAAAAAAB8QlAOAAAAAIBPCMoBAAAAAPAJQTkAAAAAAD4hKAcAAAAAwCcE5QAAAAAA+ISgHAAAAAAAnxCUAwAAAADgk2S/PhgAAACId/fWa2a7cnKsXCJ9ZUC8IigHAAAAfHJipWpseyDOcUkOAAAAAACfEJQDAAAAAOAT0tcBAAAAn/y8a7vtDgQsJSHBDilXkf0AxCGCcgAAAMAnl6xYaOlZu61+corNaXko+wGIQ6SvAwAAAADgE4JyAAAAAAB8QlAOAAAAAIBPGFMOAECcWLt2rW3ZssViWZUqVax27dp+N6PU27x5s+3YscNi+VjMzs72uxkAEBMIygEAMW3atGkl8jm9e/e2skxB0DWXn2cZ29ZbLEurVNMmPvUigfk+BuRPTnjAAplbLVbtzNhtWzattazsCn43BfDV6n/+KdB6GzZutuycbNu4YYOtrhT9Ba16detaSf39+fTTT+23334r1s/pXcb+n01QDgBAHFAPuQLyob3TrHHt8haL/l670x6ctt61ld7ywlMPuQLywb1SrEHNVItFcxZus4ffzrEcessBgKAcAIB4ooC8RcNYngs5w+8GlBkKyJvVK2exaMU69jMAeCj0BgAAAACATwjKAQAAAADwCWPKAQAAAJ98sV87C5hZAnsAiFsE5QAAAIBPKiUlse2BOEf6OgAAAAAAPiEoBwAAAADAJ6SvAwAAAD6ZtGG1bcvOdmnsV9eox34A4hBBOQAAAOCTJzastvSs3VY/OYWgHIhTpK8DAAAAAOATgnIAAAAAAHxCUA4AAAAAgE8IygEAAAAA8AlBOQAAAAAAPqH6OgAUo7Vr19qWLVtiehtXqVLFateubbFs8+bNtmPHjmL9jEWLFu3T6zMzMy01NdVi1bJlyywrK8tiXUbmbtfWWFYafjMAgNKDoBwAijEgv+by8yxj2/qY3sZplWraxKdejNkgQwH5kxMesEDm1mL9nGmvTNqnQHLJ8lXWsllDS05OjpntFmpnxm7bsmmtpf+TZRWTK1ksWrB0my1YuNRuHXKxpSSnWKxKSK1sVwy8yapWrep3UwAAZUBsnDkAQBmkHnIF5EN7p1nj2uUtFv29dqc9OG29a2usBuXqIVdAPrhXijWoWXw90bVrVSv0a7/7faONnrrTru+ZZK0bF/59itLadeG94nMWbrOH386xnOxsi1Xbd2VbWnK23dAz2ZrVq2ixaNX6TBv//lZ3XBKUAwCKAkE5ABQzBeQtGsZmgPE/GVYaKCBvVq9csb1/vbqF30fL/tnp7hvVLhcz+7pi8rawxyvWlY79LA1qpBTrvt53u/1uAMqQduUqWoOs3VYzhrNDABQvgnIAAADAJ1MatWLbA3GO6usAAAAAAPiEoBwAAAAAAJ8QlAMAAAAA4BPGlAMAAAA+GbBioa3/v0JvjC8H4hNBOQAAAOCTX3Ztt/Ss3Vaf6utA3CJ9HQAAAAAAnxCUAwAAAADgE4JyAAAAAAB8UiqC8gkTJth+++1n5cqVs8MPP9y+/PJLv5sEAAAAAEDZD8pfeeUVGzx4sN166632008/2bHHHms9e/a05cuX+900AAAAAADKdlA+btw4u+yyy+zyyy+3Aw880MaPH2+NGze2iRMn+t00AAAAAADKblCemZlpP/74o/Xo0SNsuR5/8803vrULAAAAAIAyP0/5unXrLDs72+rWrRu2XI9Xr16d62syMjLczbN582Z3v3HjRvdesWrLli2WHTD7/e8dtmVHwGLRyvU7bVdmls2fP9+1F0D+/v77b8vIyuJ3vY9WrFhhWTk59ld6pm3bVXxHXfqWbYV+7aLVu8wSk+zPFbssK6fw71OU1m8I31jL1mS5Nv61Ossyc4pxQ+6D0tDG1RszLTMrxx2XO3futFi0fv36EvnNlPV9XVJtzNweMAuYZSYE7Nel0X0O27Fo5LYd9+X/Cfvyt7q49nVJfB/FCopi9Ldx+/btxfpZGzdutFjnxUuBwN5ju4RAQdbyyapVq6xhw4auV/zoo48OLh89erQ999xz9scff+zxmpEjR9qoUaNKuKUAAAAAAOzZSdOoUSMrtT3ltWrVsqSkpD16xdesWbNH77ln+PDhNmTIkODjnJwc27Bhg9WsWdMSEhIslq+kaKy8dlqVKlX8bg5KGPs/vrH/wTEQ39j/8Y39H9/Y/2WX+r63bt1qDRo02Ou6MR2Up6amuinQpk+fbn369Aku1+PTTz8919ekpaW5W6hq1apZaaGAnKA8frH/4xv7HxwD8Y39H9/Y//GN/V82Va1atUDrxXRQLur1vvDCC61Dhw4uhf2JJ55w06FdffXVfjcNAAAAAIB9EvNBef/+/V3BkjvvvNPS09Otbdu29v7771vTpk39bhoAAAAAAGU7KJeBAwe6W1mmlPsRI0bskXqP+MD+j2/sf3AMxDf2f3xj/8c39j9ivvo6AAAAAABlWaLfDQAAAAAAIF4RlAMAAAAA4BOCcgAAAAAAfEJQXsxGjx5tnTp1sgoVKuQ5X7qmeOvdu7dVrFjRatWqZddff71lZmaGrfPLL79Yly5drHz58tawYUNXjT6yHMDMmTPdvO7lypWz5s2b26RJk4r1u6FwFixYYKeffrrb15qT8phjjrEZM2YUyzGB2PTee+9Zx44d3b7T/u3bt2/Y8+z/si8jI8MOPfRQS0hIsLlz54Y9x/4vm5YuXWqXXXaZ7bfffu6336JFC1fgNfJvO/s/vkyYMMEdEzp30zncl19+6XeTUATGjh1rRxxxhFWuXNnq1KljZ5xxhv35559h6+icbeTIkdagQQP3N6Fr1642f/78Pf5fcd1117lzBZ0TnnbaabZixQr2UVmkQm8oPnfccUdg3LhxgSFDhgSqVq26x/NZWVmBtm3bBrp16xaYM2dOYPr06YEGDRoEBg0aFFxn8+bNgbp16wbOOeecwC+//BJ44403ApUrVw488MADwXUWL14cqFChQuCGG24I/Pbbb4Enn3wykJKSEnj99dfZvTGmZcuWgV69egV+/vnnwIIFCwIDBw50+y49Pb1IjwnEJv0mq1evHpg4cWLgzz//DPzxxx+B1157Lfg8+z8+XH/99YGePXvqKlrgp59+Ci5n/5ddH3zwQeDiiy8OfPTRR4FFixYF/vvf/wbq1KkTGDp0aHAd9n98efnll925ms7ZdO6mc7iKFSsGli1b5nfTsI9OOumkwOTJkwO//vprYO7cuYFTTjkl0KRJk8C2bduC69xzzz3u3E3ncDqX69+/f6B+/fqBLVu2BNe5+uqrAw0bNnTngjon1LnhIYcc4v5WoGwhKC8h+mHmFpS///77gcTExMDKlSuDy1566aVAWlqaC7xkwoQJ7rW7du0KrjN27FgXqOXk5LjHw4YNCxxwwAFh733VVVcFjjrqqGL8VojW2rVr3Un4F198EVymP75a9sknnxTpMYHYs3v3bvc/16eeeirPddj/ZZ/2sf5ez58/f4+gnP0fX+67777AfvvtF3zM/o8vRx55pAu6Qulvwy233OJbm1A81qxZ4/7ez5w50z3WuVq9evVcYO7ROZ3O7SZNmuQeb9q0yV200cUbj84NdY744YcfsqvKGNLXffbtt99a27ZtXeqK56STTnLpKj/++GNwHaUph85hrnVWrVrl0uG8dXr06BH23lpn9uzZtnv37hL7PshfzZo17cADD7SpU6fa9u3bLSsryx5//HGrW7euS1srymMCsWfOnDm2cuVKS0xMtPbt21v9+vWtZ8+eYelq7P+y7Z9//rErrrjCnnvuOTesKRL7P75s3rzZatSoEXzM/o8fGrag/6dHnrvp8TfffONbu1B8v3Xxfu9Lliyx1atXh+1/ndPp3M7b/zo+dA4fuo7ODXWOyDFS9hCU+0w/SAVkoapXr26pqanuubzW8R7vbR0FfevWrSvmb4GC0vjR6dOn208//eTGGWkM2UMPPWQffvhhsOZAUR0TiD2LFy929xpDdtttt9m7777r9q3+J7xhwwb3HPu/7FJ22sUXX2xXX321dejQIdd12P/xY9GiRfboo4+648HD/o8fOjfLzs7O9f/l/H+87P3tHzJkiHXu3NkF1OLt4/z2v+517qfzhLzWQdlBUF4IOqFWcJXfTT3UBaX1c/sBhy6PXMcr6BXtOvD3mNA+GThwoCv6oWIuP/zwgyv6duqpp1p6enqe+9Lbn+zv0r3/c3Jy3Pq33nqrnXnmmS47YvLkye751157Lfh+7P+yuf8VgG3ZssWGDx+e7/ux/8v+OYGymk4++WTr16+fXX755WHPsf/jS27nbpy3lS2DBg2yefPm2UsvvVQk+59jpGxK9rsBpfXHdc455+S7TrNmzQr0XvXq1bPvv/8+bNnGjRtduop39UzrRF4RW7Nmjbvf2zrJyckuZRqxcUx89tlnrndU+1iV173Kq+o9nzJlit1yyy1Fdkwg9vb/1q1b3b8POuigsHQ1zZagisvC/i+7+//uu++27777LmzYiajX/Pzzz3d/A9j/Zf+cQAF5t27d7Oijj7YnnngibD32f/xQNe2kpKRc/1/O/8fLDlVOf+edd+yLL76wRo0ahf3WRftfQ9ly2/9aR8McdA4Y2luudTSzE8oYvwe1x4u9FXpbtWpVcJkKOkQW9apWrVogIyMjuI4KQ0QWejvwwAPD3lvFQyj0Flveeecdt7+3bt0atrx169aB0aNHF+kxgdij/af9GFroLTMz01Vgfvzxx91j9n/ZpYrKqrDr3VSFW/8bVkX+v//+263D/i/bVqxYEWjVqpWbOSO36sns//gr9HbNNdeELdO5HIXeSj+di1177bXuvEwz7eT2vAq93XvvvcFlOqfLrdDbK6+8ElxH54YUeiubCMpL4CRMlXVHjRoVqFSpkvu3bl5Q5k1/cvzxx7upDlSBu1GjRmHTX+lHqemvzj33XHci9+abbwaqVKmS65RoN954o5tW4+mnn2ZKtBitvl6zZs1A37593RQZmhLrpptucvtKj4vymEBs0pQ3qsCugEzToV122WUuKN+wYYN7nv0fP5YsWZLnlGj8/sseVU3WlJjdu3d3wbmmwfRuHvZ/fE6JpnM2nbsNHjzYTYm2dOlSv5uGfaSLLQqwP//887Df+o4dO8I6U7SOzuF0LqdzutymRNM5oM4FdU6ovx9MiVY2EZQXswEDBriTrsjbjBkzwgJ3zV9Yvnz5QI0aNVzwFTrVlcybNy9w7LHHul42XVkbOXLkHj2i+uG3b98+kJqaGmjWrJmbBxmxZ9asWYEePXq4fa35KZXNoN6RUEV1TCD2qGdc8xIrENf+P+GEE9w8pqHY//EblAv7v+xmzOV2PhCZtMj+jy//+c9/Ak2bNnXnbocddlhwyiyUbnn91vV3wKNzthEjRrhzOJ3LHXfccS44D7Vz5053DqhzQZ0TnnrqqYHly5f78I1Q3BL0H79T6AEAAAAAiEdUXwcAAAAAwCcE5QAAAAAA+ISgHAAAAAAAnxCUAwAAAADgE4JyAAAAAAB8QlAOAAAAAIBPCMoBAAAAAPAJQTkAAAAAAD4hKAcAoBQYOXKkHXrooUX6nkuXLrWEhASbO3eu+e3pp5+2Hj16RP26Zs2a2fjx40vsO/7yyy/WqFEj2759e5G8HwAABOUAgLh28cUXu6DtnnvuCVv+9ttvu+VFGSDKOeecYz179gxb9sEHH7jPuv3228OW33XXXdagQQP375tuusk+/fRTK0qNGze29PR0a9u2rfkpIyPD7rjjjj2+/5YtW+zWW2+1Aw44wMqVK2f16tWzE044wd58800LBAK+fMd27drZkUceaQ899FCRvB8AAATlAIC4p4Dv3nvvtY0bNxb7tujWrZt99dVXlpWVFVz2+eefu+BxxowZYetqudaXSpUqWc2aNYu0LUlJSS7QTU5ONj+98cYb7vsde+yxwWWbNm2yTp062dSpU2348OE2Z84c++KLL6x///42bNgw27x5s2/f8ZJLLrGJEydadnZ2kb0nACB+EZQDAOKeel8VuI0dOzbfbfHNN9/YcccdZ+XLl3dB9PXXXx9MY+7atastW7bMbrzxRtfrnVcvu4Lsbdu22ezZs8OC71tuucVmzZplO3bscMsyMzPt22+/DQblkenr6uE/44wz7IEHHrD69eu7gP3aa6+13bt3h/Xcjxkzxi699FKrXLmyNWnSxJ544ok8U7vVDj1Wj3yHDh2sQoUKLjD+888/w77D3XffbXXq1HHvefnll7u2h7ZN76Pe5IoVK1q1atXsmGOOcdsmLy+//LKddtppYcv+/e9/u/Z9//33NmDAADvooIOsdevWdsUVV7j2Koj3aJtF+x3fe+89O+SQQ9wFmY4dO7q0dI/a2rt3b6tevbr7Dm3atLH3338/+PxJJ51k69evt5kzZ+b5nQAAKCiCcgBA3FNvqoLXRx991FasWJHr9lDQpmCsb9++Nm/ePHvllVdcj/egQYPc80qp1ljjO++806VL65YbBZZKSfd6xbdu3ep6gfv162ctWrSwr7/+2i3/7rvvbOfOncGgPDd6j0WLFrn7KVOm2LPPPutuoR588EEXYP/00082cOBAu+aaa+yPP/7Id58rZVyv04UD9TAr4PW88MILNnr0aJdZ8OOPP7ogWL3GHmUA6GJBly5d3HbShYUrr7wy36EAX375pWujJycnxwXq559/fjB9P5QC8tCe78J8x5tvvtld0NCFEF1g0EUB74KGLm4opV4989rv+q6hFwFSU1NdQK92AwCwrwjKAQAwsz59+rje3hEjRuS6Pe6//34777zzbPDgwdaqVSvXg/zII4+49Opdu3ZZjRo1XHCv3lr1uuuWF/Wqq8dWFNgpUK9du7YLZL3lXkq7AvW8qCf3sccec2OuTz31VDvllFP2GHfeq1cvF6i2bNnS/vWvf1mtWrWCn5EXBd1qi3qn1QuuDAF9R9GFi8suu8ylcKvdGguucdah48CVWq72qO0HHnig6+lW8J4bpanrFhp8r1u3zg0l0PcqiMJ8R+3nE0880bVdFzT++ecfe+utt9xzy5cvd737eq558+buuyhDIlTDhg1dLzwAAPuKoBwAgP+jHlEFaL/99tse20S9wuqFVo+pd1PPuXp1lyxZEtU2VO+3esTVM6vgUUG6RAbl3bt3z/d9lFatCwEepbGvWbMmbJ2DDz44+G/1VutiQeQ6kUJfo/cU7zVKZVdqeqjQx7o4odR6bRulgD/88MN5Zg2IsgFEaeQer4hbQQvtFeY7Hn300WFt3n///e333393jzUsQSn6CswVvKvHP5KGMHhDDQAA2BcE5QAA/B/1hiqY1HjmSAq+r7rqKjc22bv9/PPPtnDhwnx7s/MKyjUWXanTSj1XMC6617INGzaEjSfPS0pKSthjBaRqZ7Tr5Pe+XmAc+prIYDmyEvrkyZNd+5VNoDR/9agrHT83Gguv9wstsqesAWUBeEHy3hTmO+bG+14aJ7948WK78MILXfq6UuOVIRBK+0jtBABgXxGUAwAQQlOjTZs2zaVshzrssMNs/vz5LkU68qYxxqL7glTkVhCv1PR33nnHBfdeUK5eaRVn0xhppYvvLSj3g3qUf/jhh7BloUXrPO3bt3dV07UdNR3Ziy++mOv7aZspTT40OyExMdFVWdf49VWrVu3xGl3QCK1eXxihFwl0QWDBggVh6fLaP1dffbWrFTB06FB78sknw17/66+/uu8IAMC+IigHACCExhGrwFhkz6jGKqv3V0XAFEirh1xB9XXXXRdcRwG1ioOtXLnSjYvOjwLuCRMmuKC+bt26weUK0PXZGsuc1zhsP+n7Pv300y7NX9tAad5K7/Z6mZXKr2Bc20pVzD/++GMX8GpseV6UnaCieaFUeE+BsSqja9y+gnZ93jPPPOPG/quC/b5QQT6Nv1dwrXR7jUNXgTpR3YCPPvrIfRcV4fvss8/C2q+x5NrHqtoPAMC+IigHACDCXXfdtUdKtsYtawosBYaaT1u9pLfffntwzLUX6ClgU0/43lKbFZSr8ro3njw0KNfyWOwlF12wUNB90003uewBBa4Kar0x4ZpGTZXPzzzzTJe2rsrrqlCv1P+8aJozTTkWOve40tfVm33BBRe4wF/bW9v9pZdeckX3qlatus8ZETfccIMdfvjhbsy7LrB4GQ/KdtDFFwXiJ598sssO0AUUj9rQo0cPa9q06T61AQAASQhEnnUAAABEQVXMVVztueeeK/R2O/vss4Mp78VJBfR0wUMp65pDPVqaKk3V9xWYqxAcAAD76v9P8gkAALAXqjg+adIkl3Kuyu8KTj/55BObPn36Pm079X6rtzrWKSVf87gTkAMAigo95QAAoMA0hZmmOtNYa/UaK7X7tttus759+5aKrbivPeUAABQ1gnIAAAAAAHxCoTcAAAAAAHxCUA4AAAAAgE8IygEAAAAA8AlBOQAAAAAAPiEoBwAAAADAJwTlAAAAAAD4hKAcAAAAAACfEJQDAAAAAOATgnIAAAAAAMwf/w9jA7Pj3Pb6KwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "num_simulations = 100\n", + "rounds_per_game = 50\n", + "\n", + "martingale_winnings = []\n", + "basic_winnings = []\n", + "\n", + "print(\"Simulating 100 games to compare Martingale vs. Flat Betting... \\n\")\n", + "\n", + "for _ in range(num_simulations):\n", + " # 1. The Martingale Player\n", + " marty = MartingalePlayer(name=\"Marty (Martingale)\", starting_chips=1000)\n", + " \n", + " # 2. A Control Player (Uses the exact same playing strategy, but flat bets 10 chips)\n", + " control = Player(name=\"Control (Flat Bet)\", strategy=\"Counter\", starting_chips=1000)\n", + " \n", + " # Load them into the game\n", + " q10_table = Game(players_list=[marty, control], decks_in_shoe=6, verbose=False)\n", + " q10_table.run_simulation(num_games=rounds_per_game)\n", + " \n", + " # Record the net winnings\n", + " martingale_winnings.append(marty.chips - 1000)\n", + " basic_winnings.append(control.chips - 1000)\n", + "\n", + "# --- STATISTICAL ANALYSIS ---\n", + "marty_array = np.array(martingale_winnings)\n", + "control_array = np.array(basic_winnings)\n", + "\n", + "print(\"--- RESULTS AFTER 100 GAMES ---\")\n", + "print(\"MARTINGALE STRATEGY:\")\n", + "print(f\" Average Net Winnings: {np.mean(marty_array):.2f} chips\")\n", + "print(f\" Bankruptcy Rate: {(np.sum(marty_array <= -1000) / num_simulations) * 100:.1f}%\")\n", + "print(f\" Win Rate (Finished with >1000 chips): {(np.sum(marty_array > 0) / num_simulations) * 100:.1f}%\\n\")\n", + "\n", + "print(\"CONTROL STRATEGY (Flat Betting):\")\n", + "print(f\" Average Net Winnings: {np.mean(control_array):.2f} chips\")\n", + "print(f\" Bankruptcy Rate: {(np.sum(control_array <= -1000) / num_simulations) * 100:.1f}%\")\n", + "print(f\" Win Rate (Finished with >1000 chips): {(np.sum(control_array > 0) / num_simulations) * 100:.1f}%\")\n", + "\n", + "# --- PLOTTING THE COMPARISON ---\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Plot both histograms on top of each other with transparency (alpha)\n", + "plt.hist(basic_winnings, bins=20, alpha=0.6, color='gray', label='Control (Flat Bet)')\n", + "plt.hist(martingale_winnings, bins=20, alpha=0.7, color='orange', edgecolor='black', label='Martingale')\n", + "\n", + "plt.axvline(0, color='green', linestyle='dashed', linewidth=2, label='Break Even (0)')\n", + "plt.title('Martingale Betting Strategy vs. Flat Betting\\n(100 Games, 50 Rounds Each)')\n", + "plt.xlabel('Net Winnings (Chips)')\n", + "plt.ylabel('Frequency (Games)')\n", + "plt.legend()\n", + "plt.grid(axis='y', alpha=0.5)\n", + "\n", + "plt.show()" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python (ds)", "language": "python", - "name": "python3" + "name": "ds" }, "language_info": { "codemirror_mode": { @@ -116,7 +1255,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.12" + "version": "3.11.14" } }, "nbformat": 4, diff --git a/Labs/Lab.6/lab-6-uml.pdf b/Labs/Lab.6/lab-6-uml.pdf new file mode 100644 index 000000000..8a3e05ad2 Binary files /dev/null and b/Labs/Lab.6/lab-6-uml.pdf differ diff --git a/Labs/Lab.7/.DS_Store b/Labs/Lab.7/.DS_Store new file mode 100644 index 000000000..633d210bc Binary files /dev/null and b/Labs/Lab.7/.DS_Store differ diff --git a/Labs/Lab.7/Lab.7.ipynb b/Labs/Lab.7/Lab.7.ipynb index fc43826b0..0e2537f5a 100644 --- a/Labs/Lab.7/Lab.7.ipynb +++ b/Labs/Lab.7/Lab.7.ipynb @@ -74,23 +74,33 @@ "metadata": {}, "outputs": [], "source": [ - "#!gunzip SUSY.csv.gz" + "!rm SUSY.csv" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [], + "source": [ + "!gunzip SUSY.csv.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "total 5151704\n", - "-rw-------@ 1 afarbin staff 16K Mar 27 11:51 Lab.7.ipynb\n", - "-rw-r--r--@ 1 afarbin staff 8.0M Mar 20 12:18 Lab.7.pdf\n", - "-rw-r--r--@ 1 afarbin staff 228M Mar 20 13:50 SUSY-small.csv\n", - "-rw-r--r--@ 1 afarbin staff 2.2G Mar 20 11:36 SUSY.csv\n" + "total 4702264\n", + "-rw-r--r--@ 1 subhamkalwar staff 389K Mar 20 17:43 Lab.7.ipynb\n", + "-rw-r--r--@ 1 subhamkalwar staff 8.0M Mar 20 17:43 Lab.7.pdf\n", + "-rw-r--r--@ 1 subhamkalwar staff 2.2G Mar 27 21:34 SUSY.csv\n" ] } ], @@ -107,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -141,18 +151,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "total 5151704\n", - "-rw-------@ 1 afarbin staff 16K Mar 27 11:51 Lab.7.ipynb\n", - "-rw-r--r--@ 1 afarbin staff 8.0M Mar 20 12:18 Lab.7.pdf\n", - "-rw-r--r--@ 1 afarbin staff 228M Mar 20 13:50 SUSY-small.csv\n", - "-rw-r--r--@ 1 afarbin staff 2.2G Mar 20 11:36 SUSY.csv\n" + "total 4702264\n", + "-rw-r--r--@ 1 subhamkalwar staff 389K Mar 20 17:43 Lab.7.ipynb\n", + "-rw-r--r--@ 1 subhamkalwar staff 8.0M Mar 20 17:43 Lab.7.pdf\n", + "-rw-r--r--@ 1 subhamkalwar staff 2.2G Mar 27 21:34 SUSY.csv\n" ] } ], @@ -169,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -193,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -202,18 +211,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "total 5176496\n", - "-rw-------@ 1 afarbin staff 16K Mar 27 11:51 Lab.7.ipynb\n", - "-rw-r--r--@ 1 afarbin staff 8.0M Mar 20 12:18 Lab.7.pdf\n", - "-rw-r--r--@ 1 afarbin staff 228M Mar 27 12:56 SUSY-small.csv\n", - "-rw-r--r--@ 1 afarbin staff 2.2G Mar 20 11:36 SUSY.csv\n" + "total 5169120\n", + "-rw-r--r--@ 1 subhamkalwar staff 389K Mar 20 17:43 Lab.7.ipynb\n", + "-rw-r--r--@ 1 subhamkalwar staff 8.0M Mar 20 17:43 Lab.7.pdf\n", + "-rw-r--r--@ 1 subhamkalwar staff 228M Mar 27 21:39 SUSY-small.csv\n", + "-rw-r--r--@ 1 subhamkalwar staff 2.2G Mar 27 21:34 SUSY.csv\n" ] } ], @@ -223,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -256,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -272,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -282,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -298,7 +307,7 @@ " 'MET_phi']" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -309,25 +318,25 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['R',\n", - " 'dPhi_r_b',\n", - " 'MT2',\n", - " 'cos_theta_r1',\n", + "['cos_theta_r1',\n", + " 'M_R',\n", " 'M_TR_2',\n", + " 'S_R',\n", + " 'dPhi_r_b',\n", " 'M_Delta_R',\n", + " 'R',\n", " 'MET_rel',\n", - " 'S_R',\n", - " 'M_R',\n", - " 'axial_MET']" + " 'axial_MET',\n", + " 'MT2']" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -345,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -367,7 +376,7 @@ "metadata": {}, "outputs": [], "source": [ - "filename = \"SUSY.csv\"\n", + "filename = \"SUSY-small.csv\"\n", "df = pd.read_csv(filename, dtype='float64', names=VarNames)" ] }, @@ -559,161 +568,161 @@ " ...\n", " \n", " \n", - " 4999995\n", - " 1.0\n", - " 0.853325\n", - " -0.961783\n", - " -1.487277\n", - " 0.678190\n", - " 0.493580\n", - " 1.647969\n", - " 1.843867\n", - " 0.276954\n", - " 1.025105\n", - " -1.486535\n", - " 0.892879\n", - " 1.684429\n", - " 1.674084\n", - " 3.366298\n", - " 1.046707\n", - " 2.646649\n", - " 1.389226\n", - " 0.364599\n", - " \n", - " \n", - " 4999996\n", + " 499995\n", " 0.0\n", - " 0.951581\n", - " 0.139370\n", - " 1.436884\n", - " 0.880440\n", - " -0.351948\n", - " -0.740852\n", - " 0.290863\n", - " -0.732360\n", - " 0.001360\n", - " 0.257738\n", - " 0.802871\n", - " 0.545319\n", - " 0.602730\n", - " 0.002998\n", - " 0.748959\n", - " 0.401166\n", - " 0.443471\n", - " 0.239953\n", + " 0.719035\n", + " 1.091879\n", + " 0.291540\n", + " 1.205962\n", + " -1.599117\n", + " -1.139445\n", + " 0.424546\n", + " 1.154849\n", + " 0.637185\n", + " -0.091178\n", + " 1.972156\n", + " 0.697028\n", + " 0.313636\n", + " 0.988602\n", + " 1.981573\n", + " 0.744828\n", + " 1.095080\n", + " 0.006546\n", " \n", " \n", - " 4999997\n", - " 0.0\n", - " 0.840389\n", - " 1.419162\n", - " -1.218766\n", - " 1.195631\n", - " 1.695645\n", - " 0.663756\n", - " 0.490888\n", - " -0.509186\n", - " 0.704289\n", - " 0.045744\n", - " 0.825015\n", - " 0.723530\n", - " 0.778236\n", - " 0.752942\n", - " 0.838953\n", - " 0.614048\n", - " 1.210595\n", - " 0.026692\n", + " 499996\n", + " 1.0\n", + " 0.910016\n", + " -0.364544\n", + " -0.777120\n", + " 0.543648\n", + " -0.910632\n", + " -1.723707\n", + " 2.864673\n", + " 1.458272\n", + " 2.176558\n", + " -0.590911\n", + " 0.673695\n", + " 1.662140\n", + " 2.189362\n", + " 1.195041\n", + " 0.910815\n", + " 1.181893\n", + " 1.252362\n", + " 0.826035\n", " \n", " \n", - " 4999998\n", + " 499997\n", " 1.0\n", - " 1.784218\n", - " -0.833565\n", - " -0.560091\n", - " 0.953342\n", - " -0.688969\n", - " -1.428233\n", - " 2.660703\n", - " -0.861344\n", - " 2.116892\n", - " 2.906151\n", - " 1.232334\n", - " 0.952444\n", - " 0.685846\n", - " 0.000000\n", - " 0.781874\n", - " 0.676003\n", - " 1.197807\n", - " 0.093689\n", + " 0.842954\n", + " 0.332476\n", + " -1.048564\n", + " 1.347989\n", + " 0.320496\n", + " -0.666358\n", + " 0.450433\n", + " -0.411872\n", + " 0.293407\n", + " 0.630491\n", + " 0.859920\n", + " 0.403371\n", + " 0.416258\n", + " 0.591989\n", + " 0.372003\n", + " 0.716788\n", + " 0.366991\n", + " 0.265798\n", + " \n", + " \n", + " 499998\n", + " 0.0\n", + " 1.370760\n", + " -1.162912\n", + " 0.893499\n", + " 2.118091\n", + " 1.248496\n", + " -0.887211\n", + " 0.164659\n", + " 0.316840\n", + " 0.215165\n", + " 0.280418\n", + " 3.087083\n", + " 0.526929\n", + " 0.151467\n", + " 0.308067\n", + " 3.098183\n", + " 0.233042\n", + " 0.876216\n", + " 0.000593\n", " \n", " \n", - " 4999999\n", + " 499999\n", " 0.0\n", - " 0.761500\n", - " 0.680454\n", - " -1.186213\n", - " 1.043521\n", - " -0.316755\n", - " 0.246879\n", - " 1.120280\n", - " 0.998479\n", - " 1.640881\n", - " -0.797688\n", - " 0.854212\n", - " 1.121858\n", - " 1.165438\n", - " 1.498351\n", - " 0.931580\n", - " 1.293524\n", - " 1.539167\n", - " 0.187496\n", + " 0.762400\n", + " 0.440924\n", + " 0.342885\n", + " 1.034283\n", + " 1.740353\n", + " -1.083314\n", + " 0.872145\n", + " -1.519894\n", + " 0.284328\n", + " -0.360861\n", + " 0.956828\n", + " 0.965979\n", + " 0.895881\n", + " 1.020396\n", + " 0.996446\n", + " 0.943458\n", + " 1.299870\n", + " 0.197220\n", " \n", " \n", "\n", - "

5000000 rows × 19 columns

\n", + "

500000 rows × 19 columns

\n", "" ], "text/plain": [ - " signal l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi \\\n", - "0 0.0 0.972861 0.653855 1.176225 1.157156 -1.739873 -0.874309 \n", - "1 1.0 1.667973 0.064191 -1.225171 0.506102 -0.338939 1.672543 \n", - "2 1.0 0.444840 -0.134298 -0.709972 0.451719 -1.613871 -0.768661 \n", - "3 1.0 0.381256 -0.976145 0.693152 0.448959 0.891753 -0.677328 \n", - "4 1.0 1.309996 -0.690089 -0.676259 1.589283 -0.693326 0.622907 \n", - "... ... ... ... ... ... ... ... \n", - "4999995 1.0 0.853325 -0.961783 -1.487277 0.678190 0.493580 1.647969 \n", - "4999996 0.0 0.951581 0.139370 1.436884 0.880440 -0.351948 -0.740852 \n", - "4999997 0.0 0.840389 1.419162 -1.218766 1.195631 1.695645 0.663756 \n", - "4999998 1.0 1.784218 -0.833565 -0.560091 0.953342 -0.688969 -1.428233 \n", - "4999999 0.0 0.761500 0.680454 -1.186213 1.043521 -0.316755 0.246879 \n", + " signal l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi \\\n", + "0 0.0 0.972861 0.653855 1.176225 1.157156 -1.739873 -0.874309 \n", + "1 1.0 1.667973 0.064191 -1.225171 0.506102 -0.338939 1.672543 \n", + "2 1.0 0.444840 -0.134298 -0.709972 0.451719 -1.613871 -0.768661 \n", + "3 1.0 0.381256 -0.976145 0.693152 0.448959 0.891753 -0.677328 \n", + "4 1.0 1.309996 -0.690089 -0.676259 1.589283 -0.693326 0.622907 \n", + "... ... ... ... ... ... ... ... \n", + "499995 0.0 0.719035 1.091879 0.291540 1.205962 -1.599117 -1.139445 \n", + "499996 1.0 0.910016 -0.364544 -0.777120 0.543648 -0.910632 -1.723707 \n", + "499997 1.0 0.842954 0.332476 -1.048564 1.347989 0.320496 -0.666358 \n", + "499998 0.0 1.370760 -1.162912 0.893499 2.118091 1.248496 -0.887211 \n", + "499999 0.0 0.762400 0.440924 0.342885 1.034283 1.740353 -1.083314 \n", "\n", - " MET MET_phi MET_rel axial_MET M_R M_TR_2 \\\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", + " MET MET_phi MET_rel axial_MET M_R M_TR_2 R \\\n", + "0 0.567765 -0.175000 0.810061 -0.252552 1.921887 0.889637 0.410772 \n", + "1 3.475464 -1.219136 0.012955 3.775174 1.045977 0.568051 0.481928 \n", + "2 1.219918 0.504026 1.831248 -0.431385 0.526283 0.941514 1.587535 \n", + "3 2.033060 1.533041 3.046260 -1.005285 0.569386 1.015211 1.582217 \n", + "4 1.087562 -0.381742 0.589204 1.365479 1.179295 0.968218 0.728563 \n", + "... ... ... ... ... ... ... ... \n", + "499995 0.424546 1.154849 0.637185 -0.091178 1.972156 0.697028 0.313636 \n", + "499996 2.864673 1.458272 2.176558 -0.590911 0.673695 1.662140 2.189362 \n", + "499997 0.450433 -0.411872 0.293407 0.630491 0.859920 0.403371 0.416258 \n", + "499998 0.164659 0.316840 0.215165 0.280418 3.087083 0.526929 0.151467 \n", + "499999 0.872145 -1.519894 0.284328 -0.360861 0.956828 0.965979 0.895881 \n", "\n", - " 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", + " MT2 S_R M_Delta_R dPhi_r_b cos_theta_r1 \n", + "0 1.145621 1.932632 0.994464 1.367815 0.040714 \n", + "1 0.000000 0.448410 0.205356 1.321893 0.377584 \n", + "2 2.024308 0.603498 1.562374 1.135454 0.180910 \n", + "3 1.551914 0.761215 1.715464 1.492257 0.090719 \n", + "4 0.000000 1.083158 0.043429 1.154854 0.094859 \n", + "... ... ... ... ... ... \n", + "499995 0.988602 1.981573 0.744828 1.095080 0.006546 \n", + "499996 1.195041 0.910815 1.181893 1.252362 0.826035 \n", + "499997 0.591989 0.372003 0.716788 0.366991 0.265798 \n", + "499998 0.308067 3.098183 0.233042 0.876216 0.000593 \n", + "499999 1.020396 0.996446 0.943458 1.299870 0.197220 \n", "\n", - "[5000000 rows x 19 columns]" + "[500000 rows x 19 columns]" ] }, "execution_count": 17, @@ -751,7 +760,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -763,7 +772,7 @@ }, { "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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", + "image/png": 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", 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", 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", 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yR9ABAKBJWL9+faEvgRzdC8boAABQUGlSzdatW8f7778fXbt2zdY/b8JL8qmsrCzWrl0by5Yty+6JdC9sKUEHAICCSh9o03wpixYtysIOFBUVRUlJSXZvbClBBwCAgkt/uU8fbD/99NNYt25doS+HAkrzAbVt23arW/UEHQAAmoT0wbZdu3bZAltLMQIAACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB36hx0XnrppTjhhBOiZ8+e0apVq3jyySc/95jp06fHgAEDokOHDvHFL34xHnzwwS29XgAAgPoPOqtWrYp+/frFpEmTarX/O++8E8cdd1wcccQRMWfOnLjkkkvi7LPPjueff76upwYAAKiVtlFHxxxzTLbU1uTJk2PXXXeNW265JVvv27dvvPzyy3HbbbfFsGHD6np6AACAwo/RmTFjRgwdOrTKthRw0vaarFmzJlauXFllAQAAaDJBZ/HixdG9e/cq29J6Ci8fffRRtcdMmDAhunTpUrH07t27oS8TAADIkSZZdW3s2LGxYsWKimXhwoWFviQAACDPY3TqqkePHrFkyZIq29J6586do1OnTtUek6qzpQUAAKBJBp0hQ4bEs88+W2XbCy+8kG2nenPnbvhaXBxRUuJVAgCABu+69uGHH2ZlotNSXj46fb9gwYKKbmcjR46s2P/cc8+Nt99+Oy6//PKYN29e/PCHP4zHHnssLr300jpfbN6lYFNUFDFiRMTAgalCXcTfX1YAAKAhg87rr78eBx54YLYkY8aMyb4fP358tr5o0aKK0JOk0tLPPPNM1oqT5t9JZabvvfdepaWrkVpvUmvOrFkRU6ZErF4dUVpa198QAABQ565rhx9+eJSVldX4+IMPPljtMb/97W+92rUMO7qrAQBADquuAQAAbA1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyJ22hb6APFqwIKK0NGLu3EJfCQAAtEyCTgOEnL59I1av3rBeVBRRXFzfZwEAADZH0KlnqSUnhZwpUzYEnhRySkrq+ywAAMDmCDoNJIWcAQMa6tkBAIDNUYwAAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADInbaFvgA2b+7cz74vLo4oKfGKAQDA5xF0mqgUaoqKIkaM+GxbWk/BR9gBAIDNE3SaqBRmUqgpLd2wnr5PoSetCzoAANAAY3QmTZoUffr0iY4dO8bBBx8cM2fO3Oz+EydOjL322is6deoUvXv3jksvvTQ+/vjjLTl1i5ICzYABG5a+fQt9NQAAkOOgM3Xq1BgzZkxcc801MXv27OjXr18MGzYsli5dWu3+Dz/8cFx55ZXZ/nPnzo377rsve45vf/vb9XH9AAAAWx90br311jjnnHNi9OjRsc8++8TkyZOjqKgo7r///mr3f+WVV+LQQw+Nb3zjG1kr0NFHHx2nnnrqZluB1qxZEytXrqyyAAAANEjQWbt2bcyaNSuGDh362RO0bp2tz5gxo9pj/uEf/iE7pjzYvP322/Hss8/GscceW+N5JkyYEF26dKlYUnc3AACABilGUFpaGuvWrYvu3btX2Z7W582bV+0xqSUnHff//t//i7Kysvj000/j3HPP3WzXtbFjx2bd48qlFh1hBwAAaDIThk6fPj1uvPHG+OEPf5iN6fnZz34WzzzzTFx//fU1HtOhQ4fo3LlzlQUAAKBBWnSKi4ujTZs2sWTJkirb03qPHj2qPWbcuHFx+umnx9lnn52t77///rFq1ar45je/GVdddVXW9Q0AAKA+1SlltG/fPgYOHBjTpk2r2LZ+/fpsfciQIdUes3r16k3CTApLSerKBgAAUPAJQ9PYmVGjRsWgQYNi8ODB2Rw5qYUmVWFLRo4cGb169coKCiQnnHBCVqntwAMPzObcefPNN7NWnrS9PPAAAAAUNOgMHz48li1bFuPHj4/FixdH//7947nnnqsoULBgwYIqLThXX311tGrVKvv63nvvRdeuXbOQ893vfrdefxAAAIByrcqaQf+xVHUtlZlesWJFky9MMHt2xMCBEbNmRQwY0PSfFwAAmpPaZgOVAAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNwRdAAAgNxpW+gLoG7mzv3s++LiiJISryAAAGxM0GkmUqgpKooYMeKzbWk9BR9hBwAAqhJ0mokUZlKoKS3dsJ6+T6EnrQs6AABQlaDTjKRAI9QAAMDnE3TqwYIFVVtaAACAwhJ06iHk9O0bsXp11bEzaUwNAADQjMpLT5o0Kfr06RMdO3aMgw8+OGbOnLnZ/ZcvXx4XXHBB7LzzztGhQ4fYc88949lnn408SC05KeRMmRIxa9aGRYEAAABoZi06U6dOjTFjxsTkyZOzkDNx4sQYNmxYzJ8/P7p167bJ/mvXro2jjjoqe+wnP/lJ9OrVK/785z/H9ttvH3mSWnUGDCj0VQAAAFsUdG699dY455xzYvTo0dl6CjzPPPNM3H///XHllVdusn/a/re//S1eeeWVaNeuXbYttQYBAAA0ia5rqXVm1qxZMXTo0M+eoHXrbH3GjBnVHvPUU0/FkCFDsq5r3bt3j/322y9uvPHGWLduXY3nWbNmTaxcubLKAgAA0CBBp7S0NAsoKbBUltYXL15c7TFvv/121mUtHZfG5YwbNy5uueWWuOGGG2o8z4QJE6JLly4VS+/evetymQAAQAu3RcUI6mL9+vXZ+Jy77747Bg4cGMOHD4+rrroq6/JWk7Fjx8aKFSsqloULFzb0ZQIAAC11jE5xcXG0adMmlixZUmV7Wu/Ro0e1x6RKa2lsTjquXN++fbMWoNQVrn379psckyqzpQUAAKDBW3RSKEmtMtOmTavSYpPW0zic6hx66KHx5ptvZvuV+9Of/pQFoOpCDgAAQKNXXUulpUeNGhWDBg2KwYMHZ+WlV61aVVGFbeTIkVkJ6TTOJjnvvPPizjvvjIsvvjguuuiieOONN7JiBP/6r/+61Refu5lH06Q81Umzj5aUNPYVAQBAywk6aYzNsmXLYvz48Vn3s/79+8dzzz1XUaBgwYIFWSW2cqmQwPPPPx+XXnppHHDAAVkISqHniiuuqN+fpLmHnDQRT5p5tDpFRWYhBQCAhgw6yYUXXpgt1Zk+ffom21K3tldffXVLTtUypJacFHKmTNkQeCqbOzdixIgN+2jVAQCAhgs6NJAUcgYMqP6xFHiqrHdKB0QsWpRKPviVAABAJYJOU5fG56Sua6lVp4oDI2J2xCmnRMx/RGsPAABUIug0dam7WmrN2bhQQWrRSdnn4490awMAgI0IOs0l7BifAwAADTOPDgAAQHMg6AAAALkj6AAAALkj6AAAALkj6AAAALkj6AAAALkj6AAAALkj6AAAALkj6AAAALkj6AAAALkj6AAAALkj6AAAALkj6AAAALkj6AAAALkj6AAAALkj6AAAALnTttAX0KIsWBBRWrrp9rlzt+55azq+uDiipGTrnhsAAJohQacxQ07fvhGrV1f/eFHRhmBSVx07RYwYUfNzphAk7AAA0MIIOo0lteSkkDNlyobAU1+tLz/5ScTOizbdngJOCkDpvIIOAAAtjKDT2FLIGTCg/p5v550jBuxcf88HAAA5oBgBAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO20LfQFsnblzP/u+uDiipMQrCgAAgk4zlUJNUVHEiBGfbUvrKfgIOwAAtHSCTjOVwkwKNaWlG9bT9yn0pHVBBwCAlk7QacZSoBFqAABgU4oRAAAAuSPoAAAAuSPoAAAAuSPoAAAAuaMYQUuaaKcyk+4AAJBjgk5LmminMpPuAACQY4JOS5lopzKT7gAAkHOCTp6ZaAcAgBZKMQIAACB3BB0AACB3tijoTJo0Kfr06RMdO3aMgw8+OGbOnFmr4x599NFo1apVnHTSSVtyWgAAgIYJOlOnTo0xY8bENddcE7Nnz45+/frFsGHDYunSpZs97t13343LLrssDjvssLqeEgAAoGGDzq233hrnnHNOjB49OvbZZ5+YPHlyFBUVxf3331/jMevWrYvTTjstrrvuuthtt93qekoAAICGCzpr166NWbNmxdChQz97gtats/UZM2bUeNx3vvOd6NatW5x11lm1Os+aNWti5cqVVRYAAIAGCTqlpaVZ60z37t2rbE/rixcvrvaYl19+Oe6777645557an2eCRMmRJcuXSqW3r171+UyAQCAFq5Bq6598MEHcfrpp2chp7i4uNbHjR07NlasWFGxLFy4sCEvEwAAaMkThqaw0qZNm1iyZEmV7Wm9R48em+z/1ltvZUUITjjhhIpt69ev33Ditm1j/vz5sfvuu29yXIcOHbIFAACgwVt02rdvHwMHDoxp06ZVCS5pfciQIZvsv/fee8fvf//7mDNnTsVy4oknxhFHHJF9r0saAABQ8BadJJWWHjVqVAwaNCgGDx4cEydOjFWrVmVV2JKRI0dGr169snE2aZ6d/fbbr8rx22+/ffZ14+0AAAAFCzrDhw+PZcuWxfjx47MCBP3794/nnnuuokDBggULskpsAAAAzSboJBdeeGG2VGf69OmbPfbBBx/cklMCAADUmqYXAAAgdwQdAAAgdwQdAAAgdwQdAAAgdwQdAAAgdwQdAAAgdwQdAAAgd7ZoHh02Y8GCiNLSTbfPnetlAwCARiLo1HfI6ds3YvXq6h8vKoooLq7XUwIAAJsSdOpTaslJIWfKlA2BZ2Mp5JSU1OspAQCATQk6DSGFnAEDGuSpAQCAzyfotGQ1jRvS8gQAQDMn6LREKcik8UIjRlT/eHoshSDd7AAAaKYEnZYoBZgUZGqqDpcCUHpM0AEAoJkSdFqqFGIEGQAAcsqEoQAAQO4IOgAAQO4IOgAAQO4IOgAAQO4oRpDjqXFMhwMAQEsl6OR4ahzT4QAA0FIJOjmdGsd0OAAAtGSCTo6YGgcAADZQjAAAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMidtoW+AJqouXOr315cHFFS0thXAwAAdSLosGmQKSqKGDGi+lcmPZZCkLADAEATJuhQVQowKciUlm76yqTtKQClxwQdAACaMEGHTaUQI8gAANCMKUYAAADkjqADAADkjqADAADkjqADAADkjqADAADkjqADAADkjqADAADkjqADAADkjqADAADkjqADAADkTttCX0BztGBBRGnphu/nzi301QAAABsTdLYg5PTtG7F69Wfbiooiiosj4u/hBwAAKCxBp45SS04KOVOmbAg8SQo5JSWCDgAANOsxOpMmTYo+ffpEx44d4+CDD46ZM2fWuO8999wThx12WOywww7ZMnTo0M3u31ykkDNgwIYlCzkAAEDzDTpTp06NMWPGxDXXXBOzZ8+Ofv36xbBhw2Lp0qXV7j99+vQ49dRT48UXX4wZM2ZE79694+ijj4733nuvPq6fQkgDk2bP3nRJ/foAAKA5dl279dZb45xzzonRo0dn65MnT45nnnkm7r///rjyyis32f/HP/5xlfV77703fvrTn8a0adNi5MiRW3Pt1EJ5sYSK7nVbIz1JGpA0YkT1j6fH0gk1cQEA0JyCztq1a2PWrFkxduzYim2tW7fOuqOl1praWL16dXzyySex44471rjPmjVrsqXcypUr63KZVJNJ6iWDpIPTk5SXnKssbU8nS48JOgAANKegU1paGuvWrYvu3btX2Z7W582bV6vnuOKKK6Jnz55ZOKrJhAkT4rrrrqvLpbGZTFKvGSQ9gSADAEAT16gThn7ve9+LRx99NJ544omskEFNUovRihUrKpaFCxc25mXmRsojqVhCeXU4AABoKerUolNcXBxt2rSJJUuWVNme1nv06LHZY2+++eYs6PziF7+IAw44YLP7dujQIVsAAAAavEWnffv2MXDgwKyQQLn169dn60OGDKnxuO9///tx/fXXx3PPPReDBg3aogsFAABosKprqbT0qFGjssAyePDgmDhxYqxataqiCluqpNarV69snE1y0003xfjx4+Phhx/O5t5ZvHhxtn3bbbfNFgAAgIIHneHDh8eyZcuy8JJCS//+/bOWmvICBQsWLMgqsZW76667smptp5xySpXnSfPwXHvttfXxMwAAAGxd0EkuvPDCbKlpgtDK3n333S05BQAAQPOougYAANAYBB0AACB3BB0AACB3BB0AACB3BB0AACB3tqjqGtRo7tzqtxcXR5SUeOEAAGgUgg71IwWZoqKIESOqfzw9lkKQsAMAQCMQdKgfKcCkIFNauuljaXsKQOkxQQcAgEYg6FB/UogRZAAAaAIUIwAAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJHeektleaGiY+q2dZ0Vb68NL9no1eCrun1KcjFAACQZ4JOXS1aFBE7R4w4LSJ+u+njRUUbPrg3Iely0mWlOTvLpfWUOxolX1R3AZU16sUAANASCDp1tXz5hqBz/Q0Rx/ZoFq0T6XJSjigt3bCevk+ZI603yqVufAGVNfrFAADQEgg6W2rXXSMG9I3mImWIguaIgl8AAAAtiWIEAABA7gg6AABA7gg6AABA7gg6AABA7ihGQNOwuTmImmAlOwAAmjZBh8L6vDl2EvPsAABQR4IOhbW5OXYS8+wAALAFBB0Kzxw7AADUM8UIAACA3BF0AACA3NF1jeZdlU1FNgAAqiHo0LyrsqnIBgBANQSdFqxyI0mTbRjZXFU2FdkAAKiBoNMCVddI0qQbRlRlAwCgjgSdFmjjRpJm3zBi/A4AABsRdFqoXDSSGL8DAEANBB2aL+N3AACogaBD85aLpikAAOqboEO+Gb8DANAiCTrkk/E7AAAtmqBDPhm/AwDQogk6tNzxO7q1AQDklqBDtZ/7U8+v3I7x160NACD3BB2q/dyf1lPwyWXY0a0NACD3BB02+dyfvk+hJ63nMugkurUBAOSaoEPGdDR/p1sbAEAuCDpQ125tv/pVRN++mz6e64FNAADNi6ADtW3eqk1rz89+FtG1a/XHCkEAAI1G0OFzq7D5jF6L1p5lyyJOPjniK1+p/sXMdXUHAICmR9DhcxsufEav5WAmXd4AAJoMQYfNNly0iAps9UWXNwCAJkPQoVaf2VvMZKJNsctbTeN+NscvCQBo4QQdNqvFTSbalLq8fV4I2hyFEQCAFm6Lgs6kSZPi3//932Px4sXRr1+/uOOOO2Lw4ME17v/444/HuHHj4t1334099tgjbrrppjj22GO35rop8GSilSssazwoQAjanIZqJdocNwEA0NyDztSpU2PMmDExefLkOPjgg2PixIkxbNiwmD9/fnTr1m2T/V955ZU49dRTY8KECXH88cfHww8/HCeddFLMnj079ttvv/r6OWikz+E1tfBs7nOzz8D19OLXRUO0Em1OQ4SnLeWGAwAiolVZWVlZXV6JFG4OOuiguPPOO7P19evXR+/eveOiiy6KK6+8cpP9hw8fHqtWrYqnn366YtshhxwS/fv3z8JSddasWZMt5VasWBElJSWxcOHC6Ny5c0F/cXOmzo8vf3Ov+J+750f/4XtFS7RwYcRf/7rh+/RZOoWejz6qef9OnSKmTNnw+ZMmYMniiOUr6u/5li+PGHd1xJqPo0no0DHi+hsitt++0FdCM9Zjp0+iR/Gnhb4MgKajR48NSxOwcuXKLH8sX748unTpUvOOZXWwZs2asjZt2pQ98cQTVbaPHDmy7MQTT6z2mN69e5fddtttVbaNHz++7IADDqjxPNdcc00KXxavgXvAPeAecA+4B9wD7gH3gHvAPVBW3WuwcOHCzWaXOnVdKy0tjXXr1kX37t2rbE/r8+bNq/aYNI6nuv3T9pqMHTs26x5XLrUa/e1vf4uddtopWrVqFU0hQTaF1iWozL1JU+XepClzf9JUuTdrljqkffDBB9GzZ8/mV3WtQ4cO2VLZ9k2sG0oKOYIOTZF7k6bKvUlT5v6kqXJvVm+zXdb+rnXUQXFxcbRp0yaWLFlSZXta71FDn720vS77AwAAbK06BZ327dvHwIEDY9q0aVW6laX1IUOGVHtM2l55/+SFF16ocX8AAICtVeeua2nszKhRo2LQoEHZ3DmpvHSqqjZ69Ojs8ZEjR0avXr2yctLJxRdfHF/+8pfjlltuieOOOy4effTReP311+Puu++O5ih1qbvmmms26VoHhebepKlyb9KUuT9pqtybBSgvnaTS0uUThqYy0bfffntWdjo5/PDDo0+fPvHggw9WmTD06quvrpgw9Pvf/74JQwEAgKYVdAAAAHIzRgcAAKA5EHQAAIDcEXQAAIDcEXQAAIDcEXTqYNKkSVlFuY4dO2ZV5mbOnNlwvxmopWuvvTZatWpVZdl77729fjS6l156KU444YTo2bNndh8++eSTVR5PtW/Gjx8fO++8c3Tq1CmGDh0ab7zxht8UBb83zzjjjE3eR7/yla/4zdDg0nQsBx10UGy33XbRrVu3OOmkk2L+/PlV9vn444/jggsuiJ122im23Xbb+OpXvxpLlizx26kFQaeWpk6dms0hlObQmT17dvTr1y+GDRsWS5cure1TQIPZd999Y9GiRRXLyy+/7NWm0aU51dJ7Y/qjUHXS1AJpOoLJkyfHa6+9Fttss032Ppr+E4dC3ptJCjaV30cfeeQRvxQa3P/8z/9kIebVV1+NF154IT755JM4+uijs3u23KWXXho///nPs+la0v7vv/9+nHzyyX47taC8dC2lFpyUuNMcQsn69eujd+/ecdFFF8WVV15Z26eBBmnRSX+dnDNnjleXJiP9RfyJJ57I/jpZ3pqT/pr+b//2b3HZZZdl21asWBHdu3fP5l37+te/XuArpqXem+UtOsuXL9+kpQca27Jly7KWnRRovvSlL2Xvk127do2HH344TjnllGyfefPmRd++fWPGjBlxyCGH+CVthhadWli7dm3MmjUr62ZR8cK1bp2tp5sMCi11/0kfInfbbbc47bTTYsGCBYW+JKjinXfeySaZrvw+2qVLl+yPSN5HaQqmT5+efcDca6+94rzzzou//vWvhb4kWqAUbJIdd9wx+5o+f6ZWnsrvnal7eklJiffOWhB0aqG0tDTWrVuX/eWxsrSe/uOGQkofFNNfxJ977rm46667sg+Uhx12WHzwwQd+MTQZ5e+V3kdpilK3tYceeiimTZsWN910U/bX9GOOOSb7vx8aS+otdMkll8Shhx4a++23X8V7Z/v27WP77bevsq/PoLXTtpb7AU1U+s+43AEHHJAFn1122SUee+yxOOusswp6bQDNQeWuk/vvv3/2Xrr77rtnrTxHHnlkQa+NliON1fnDH/5gnG090qJTC8XFxdGmTZtNKlyk9R49etTn7wO2Wvqrz5577hlvvvmmV5Mmo/y90vsozUHqBpz+7/c+SmO58MIL4+mnn44XX3wxvvCFL1R570xDKNIYssp8Bq0dQacWUpPhwIEDsybtys2LaX3IkCG1fKmhcXz44Yfx1ltvZSV8oanYdddds/+wK7+Prly5Mqu+5n2UpuYvf/lLNkbH+ygNLRVqSSEnFcj45S9/mb1XVpY+f7Zr167Ke2cqP53G4nrv/Hy6rtVSKi09atSoGDRoUAwePDgmTpyYlf4bPXp0bZ8CGkSqYJXmh0jd1VLJyVQCPbVAnnrqqV5xGj1kV/4LeBovlqoBpkG1aeBs6nt+ww03xB577JH9Zz5u3LisiEbl6lfQ2PdmWq677rpsbpIUxtMfii6//PL44he/mJU/h4burpYqqv3nf/5nNpdO+XjGVKwlzTeWvqZu6OlzaLpXO3funFX8TSFHxbVaKKPW7rjjjrKSkpKy9u3blw0ePLjs1Vdf9epRcMOHDy/beeeds/uyV69e2fqbb75Z6MuiBXrxxRfL0n8rGy+jRo3KHl+/fn3ZuHHjyrp3717WoUOHsiOPPLJs/vz5hb5sWvi9uXr16rKjjz66rGvXrmXt2rUr22WXXcrOOeecssWLFxf6smkBqrsv0/LAAw9U7PPRRx+VnX/++WU77LBDWVFRUdk///M/ly1atKig191cmEcHAADIHWN0AACA3BF0AACA3BF0AACA3BF0AACA3BF0AACA3BF0AACA3BF0AACA3BF0AACA3BF0AACA3BF0AACA3BF0AACAyJv/Dw+v2hVyKTiXAAAAAElFTkSuQmCC", + "image/png": 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", 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", 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", 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Sik2lwNOdJwxNJaiHDBmStQzl7T3RlmygRQcAgKIu/tSVBZ9SZd3uFHJqampiwYIFWdGD1BqUus6l+XDuu+++6OkEHQAAilYKHd0peHS11D0ujXtJk4amVpBUSOD73/9+jB8/Pno6QQcAALqpVJ46teCwK1XXAACA3BF0OlDqQ/p/k9wCAAAFJOh00KC1VKFj0qQdlUGEHQCAtqutrXXb6LD3gjE6HVgR5IEHdoSdVBnEoDkAgNZJk2r27t07nn322TjkkEOy9Vea8JJ8qquri23btsWGDRuy90R6L7SXoNNBUrBJrTkAALRN+kKb5ktZs2ZNFnagpKQkysrKsvdGewk6AAAUXPrLffpi+49//CO2b99e6MuhgNJ8QHvttdcet+oJOgAAFIX0xbZv377ZAntKMQIAACB3BB0AACB3BJ1OYD4dAAAoLEGnA5lPBwAAioOg0wnz6VRURNTU7JhPBwAA6HqCTgcznw4AABSeoAMAAOSOoAMAAOSOoAMAAOSOoAMAAOTOXoW+gDxLFdjqy06nIgUAAEDXEHQ6eT6dJP2cQo+wAwAAXUPXtU6cT6ey0pw6AABQCFp0OjHsaMEBAIDC0KIDAADkjqDTRVJXtqqqrjobAAD0bIJOFxYmKC8XdgAAoGiDzvz582P48OExYMCAGDt2bCxfvrzFfc8888zo1avXLstb3/rW6EmFCSoqImpqIqqrC31FAACQf20OOosWLYoZM2bE7NmzY8WKFTFixIiYMGFCrF+/vtn977zzzlizZk3D8rvf/S769OkT733ve6OnSGEnteYAAABFGnTmzp0b06ZNi6lTp8bxxx8fCxYsiJKSkli4cGGz+x900EExZMiQhuW+++7L9u9JQQcAACjioLNt27aorKyM8ePHv/wCvXtn68uWLWvVa9x6663xvve9L/bZZ58W99m6dWts3ry5yQIAANApQae6ujq2b98egwcPbrI9ra9du/YVj09jeVLXtQ996EO73W/OnDkxcODAhmXYsGFtuUwAAKCH69Kqa6k158QTT4wxY8bsdr+ZM2fGpk2bGpbVq1d32TUCAADd315t2bm0tDQrJLBu3bom29N6Gn+zO1u2bIk77rgjrrnmmlc8T//+/bMFAACg01t0+vXrF6NGjYolS5Y0bKutrc3Wx40bt9tjv/vd72ZjbyalCWUAAACKpUUnSaWlp0yZEqNHj866oM2bNy9rrUlV2JLJkyfH0KFDs3E2O3dbO+ecc+Lggw+OnizNqVM/kWgqOw0AABRB0Jk4cWJs2LAhZs2alRUgGDlyZCxevLihQEFVVVVWia2xVatWxYMPPhj//d//HT1VCjYlJRH1DVrp5xR6hB0AAOh4verq6uqiyKXy0qn6WipMsP/++xf0WlasiBg1KqKyMuLkk9t2bFVVqly3I+CkwNOe1wAAgJ5scyuzQZtbdGi/1HqjBQcAAHJWXhoAAKArCDoAAEDuCDoAAEDuCDoFlIoSpAIFAABAxxJ0Clxqurxc2AEAgI4m6BRAqryWWnMqKiJqanaUnAYAADqOoFPAsJNacwAAgI4n6AAAALkj6AAAALkj6AAAALkj6AAAALkj6AAAALkj6AAAALkj6BSBNKdOVVWhrwIAAPJD0Cmg0tKIkpKISZN2zKkj7AAAQMcQdAo8aWhqzamoiKipiaiuLuTVAABAfgg6RRB2UmsOAADQcQQdAAAgdwQdAAAgdwQdAAAgdwQdAAAgdwQdAAAgdwQdAAAgd/Yq9AXwsjSnTv1EoqnsNAAA0D6CThFIwaakJGLSpB3r6ecUeoQdAABoH13XikAKNCnYVFZGVFRE1NREVFcX+qoAAKD70qJTRGFHCw4AAHQMLToAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDutCvozJ8/P4YPHx4DBgyIsWPHxvLly3e7/8aNG+MjH/lIHHroodG/f/949atfHffee297rxkAAKBjy0svWrQoZsyYEQsWLMhCzrx582LChAmxatWqGDRo0C77b9u2Ld74xjdmz33ve9+LoUOHxjPPPBMHHHBAW08NAADQOUFn7ty5MW3atJg6dWq2ngLPPffcEwsXLozLL798l/3T9r/97W/x0EMPRd++fbNtqTUIAACgKLqupdaZysrKGD9+/Msv0Lt3tr5s2bJmj/nRj34U48aNy7quDR48OE444YT47Gc/G9u3b2/xPFu3bo3Nmzc3WXqalSsjqqoKfRUAANADgk51dXUWUFJgaSytr127ttljnnzyyazLWjoujcu58sor44Ybbohrr722xfPMmTMnBg4c2LAMGzYseorS0oiSkohJkyLKy4UdAAAoyqprtbW12ficm2++OUaNGhUTJ06MK664Iuvy1pKZM2fGpk2bGpbVq1dHT1FWtqM1p6IioqYmhctCXxEAAOR8jE5paWn06dMn1q1b12R7Wh8yZEizx6RKa2lsTjquXnl5edYClLrC9evXb5djUmW2tPRUKeyk1hwAAKALWnRSKEmtMkuWLGnSYpPW0zic5px22mnxxBNPZPvV+8Mf/pAFoOZCDgAAQJdXXUulpadMmRKjR4+OMWPGZOWlt2zZ0lCFbfLkyVkJ6TTOJrngggviK1/5Slx88cVx0UUXxR//+MesGMG///u/R7eW+pfFC80PsklNMgAAQPcJOmmMzYYNG2LWrFlZ97ORI0fG4sWLGwoUVFVVZZXY6qVCAj/5yU/ikksuiZNOOikLQSn0XHbZZdEtrVmTOuRFTDovIn696/OpkkAKQcIOAAB0n6CTTJ8+PVuas3Tp0l22pW5tDz/8cOTCxo07gs6nr414y07jklLASeXSUgUBQQcAALpX0CEijjgi4mQVAwAAoEeWlwYAAOhqgg4AAJA7gg4AAJA7gg4AAJA7gg4AAJA7gk6RSxWrq6oKfRUAANC9CDpFqrR0x9yjaVqe8nJhBwAA2kLQKVJpvtHUmlNREVFTs2MOUgAAoHUEnSIPO6k1BwAAaBtBBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyJ29Cn0BtM7KlTseS0sjysrcNQAA2B1Bp8ilYFNSEjFp0o719HMKPcIOAAC0TNe1IpcCTQo2lZURFRURNTUR1dWFvioAAChuWnS6SdjRggMAAK2nRQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgd8+h0hjTDZ3NKS02IAwAAXUDQ6UgpyJSUREya1Pzz6bkUgsz+CQAAnUrQ6UgpwKQgU12963NpewpA6TlBBwAAim+Mzvz582P48OExYMCAGDt2bCxfvrzFfW+77bbo1atXkyUdl1spxJx88q5LeXmhrwwAAHqMNgedRYsWxYwZM2L27NmxYsWKGDFiREyYMCHWr1/f4jH7779/rFmzpmF55pln9vS6AQAAOi7ozJ07N6ZNmxZTp06N448/PhYsWBAlJSWxcOHCFo9JrThDhgxpWAYPHtzW0wIAAHRO0Nm2bVtUVlbG+PHjX36B3r2z9WXLlrV43PPPPx+HH354DBs2LN7xjnfE73//+92eZ+vWrbF58+YmCwAAQKcEnerq6ti+ffsuLTJpfe3atc0ec+yxx2atPT/84Q+joqIiamtr47WvfW38+c9/bvE8c+bMiYEDBzYsKSDRtK5BVZU7AgAABZswdNy4cTF58uQYOXJknHHGGXHnnXfGIYccEl/72tdaPGbmzJmxadOmhmX16tWdfZndrnp1qm0g7AAAQAcEndLS0ujTp0+sW7euyfa0nsbetEbfvn3jNa95TTzxxBMt7tO/f/+sgEHjhZerV1dURNTUNF/FGgAAaGPQ6devX4waNSqWLFnSsC11RUvrqeWmNVLXt9/+9rdx6KGHuv/tDDsqVQMAQAdPGJpKS0+ZMiVGjx4dY8aMiXnz5sWWLVuyKmxJ6qY2dOjQbJxNcs0118Spp54aRx99dGzcuDG+8IUvZOWlP/ShD7X11AAAAJ0TdCZOnBgbNmyIWbNmZQUI0tibxYsXNxQoqKqqyiqx1fv73/+elaNO+x544IFZi9BDDz2UlaYGAAAoiqCTTJ8+PVuas3Tp0ibrX/ziF7MFAAAgN1XXAAAAupqgAwAA5I6gAwAA5I6gAwAA5I6gAwAA5I6gAwAA5E67ykuzB1aubH57aWlEWZlbCwAAHUDQ6SopyJSUREya1Pzz6bkUgoQdAADYY4JOV0kBJgWZ6updn0vbUwBKzwk6AACwxwSdrpRCTAcGmZSP9HgDAIBdKUbQzXvBlZdHVFUV+ooAAKC4CDrduBdcRUVETU3zveEAAKAnE3S6cdhJrTkAAMCuBB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB39ir0BdDIypXN347S0oiyMrcKAABaSdApBinIlJRETJrU/PPpuRSChB0AAGgVQacYpACTgkx19a7Ppe0pAKXnWgg69Q1BGn4AAGAHQadYpBDTxhabnRuCNPwAAMAOihHkoCGosjKioiKipqb5RiEAAOhptOj0vIYgAADIPS06AABA7gg6AABA7gg6AABA7rQr6MyfPz+GDx8eAwYMiLFjx8by5ctbddwdd9wRvXr1inPOOac9pwUAAOicoLNo0aKYMWNGzJ49O1asWBEjRoyICRMmxPr163d73NNPPx2XXnppnH766W09JQAAQOcGnblz58a0adNi6tSpcfzxx8eCBQuipKQkFi5c2OIx27dvj/POOy+uvvrqOPLII9t6SgAAgM4LOtu2bYvKysoYP378yy/Qu3e2vmzZshaPu+aaa2LQoEFx/vnnt+o8W7dujc2bNzdZAAAAOiXoVFdXZ60zgwcPbrI9ra9du7bZYx588MG49dZb45Zbbmn1eebMmRMDBw5sWIYNG9aWywQAAHq4Tq269txzz8X73//+LOSUlpa2+riZM2fGpk2bGpbVq1d35mUCAAA5s1dbdk5hpU+fPrFu3bom29P6kCFDdtn/T3/6U1aE4O1vf3vDttra2h0n3muvWLVqVRx11FG7HNe/f/9sAQAA6PQWnX79+sWoUaNiyZIlTYJLWh83btwu+x933HHx29/+Nh599NGG5eyzz47Xv/712c+6pAEAAAVv0UlSaekpU6bE6NGjY8yYMTFv3rzYsmVLVoUtmTx5cgwdOjQbZ5Pm2TnhhBOaHH/AAQdkjztv5xWsXNn89tQlsKzM7QMAgD0JOhMnTowNGzbErFmzsgIEI0eOjMWLFzcUKKiqqsoqsdFBUpApKYmYNKn559NzWQgSdgAAoN1BJ5k+fXq2NGfp0qW7Pfa2225rzyl7rtRak4JMdfWuz6XtKQBlzwk6AACwR0GHAoSdVnZPS9lHbzYAAHo6fcxy2MOtvDx1ISz0FQEAQOEIOjnr4VZREVFT03xPNwAA6CkEnZyFndSaAwAAPZ2gAwAA5I6gAwAA5I6gAwAA5I6gAwAA5I6gAwAA5I6gAwAA5I6gAwAA5I6gAwAA5I6gAwAA5I6gAwAA5I6gAwAA5I6gAwAA5M5ehb4AOsDKlY1+3jsiyndsK90noqzMLQYAoMcRdLqz0tKIkpKISZMabXxNRKyIlZOujRjwdJT+7D+jbNzQAl4kAAB0PUGnO0utNanlprq6YVPpmr5R8p7tMenFb0e8GFHyz9tj5SoNOwAA9CyCTh7CTqPuaemnFGyqH1iZteqkwJNykB5sAAD0JIJOXrNP+Qsp8hT6UgAAoCBUXQMAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHKnXUFn/vz5MXz48BgwYECMHTs2li9f3uK+d955Z4wePToOOOCA2GeffWLkyJHxrW99a0+uGQAAoGODzqJFi2LGjBkxe/bsWLFiRYwYMSImTJgQ69evb3b/gw46KK644opYtmxZ/OY3v4mpU6dmy09+8pO2nhoAAKBzgs7cuXNj2rRpWVg5/vjjY8GCBVFSUhILFy5sdv8zzzwz3vnOd0Z5eXkcddRRcfHFF8dJJ50UDz74YFtPDQAA0Cp7RRts27YtKisrY+bMmQ3bevfuHePHj89abF5JXV1d/OxnP4tVq1bFdddd1+J+W7duzZZ6mzdvbstlspOV9z4VpWuej7JDX3p5Y2lpRFmZewUAQC61KehUV1fH9u3bY/DgwU22p/XHH3+8xeM2bdoUQ4cOzcJLnz594qtf/Wq88Y1vbHH/OXPmxNVXX92WS2NnpaVROmBLlLy4JSZdeUSUxJZYGeVRFqt3PF9SErFypbADAEAudUnVtf322y8effTReOSRR+Izn/lMNsZn6dKlLe6fWoxSOKpfVq/+vy/ntF5ZWZStui9W3v1kVHz6qaiJfaK64icRlZURFRURNTUpubqjAADkUptadEpLS7MWmXXr1jXZntaHDBnS4nGpe9vRRx+d/Zyqrq1cuTJrtUnjd5rTv3//bKEDwk5ZRPmhEXFlRJSXR5zsrgIAkH9tatHp169fjBo1KpYsWdKwrba2NlsfN25cq18nHdN4DA4AAEDBWnSS1O1sypQp2dw4Y8aMiXnz5sWWLVuyKmzJ5MmTs/E4qcUmSY9p31RxLYWbe++9N5tH56abburQXwQAAKDdQWfixImxYcOGmDVrVqxduzbrirZ48eKGAgVVVVVZV7V6KQRdeOGF8ec//zn23nvvOO6446KioiJ7HQAAgKIIOsn06dOzpTk7Fxm49tprswUAACBXVdcAAAC6kqADAADkjqADAADkjqADAADkjqADAADkjqADAADkjqADAADkjqADAADkjqADAADkjqDTg6xcGVFVVeirAACAzifo9AClpRElJRGTJkWUl0dUrelb6EsCAIBOJej0AGVlO1pzKioiamoiqjfuVehLAgCATuUbbw8KO6k1p4mUflpqAkoHAABANyXo9EQHHPByX7bmpOdSCBJ2AADopgSdnujQQ3cEmerqXZ9L21MASs8JOgAAdFOCTk+VQowgAwBATilGAAAA5I6gAwAA5I6gAwAA5I6gAwAA5I6gAwAA5I6gAwAA5I6gAwAA5I6gAwAA5I6gAwAA5M5ehb4Aut7KlTseS0sjysr8CwAAkD+CTg+Sgk1JScSkSTvW088p9Ag7AADkja5rPUgKNCnYVFZGVFRE1NREVFcX+qoAAKDjadHpgWFHCw4AAHmnRQcAAMgdQQcAAMgdQQcAAMgdY3TYfQ3qnalJDQBAXlt05s+fH8OHD48BAwbE2LFjY/ny5S3ue8stt8Tpp58eBx54YLaMHz9+t/tTRDWoR43adSkvj6iqKvRVAgBAxwadRYsWxYwZM2L27NmxYsWKGDFiREyYMCHWr1/f7P5Lly6Nc889N+6///5YtmxZDBs2LM4666z4y1/+0tZT09U1qHde1KQGACCvXdfmzp0b06ZNi6lTp2brCxYsiHvuuScWLlwYl19++S77f/vb326y/vWvfz2+//3vx5IlS2Ly5Ml7cu10FjWoAQDoSS0627Zti8rKyqz7WcML9O6drafWmtaoqamJl156KQ466KAW99m6dWts3ry5yQIAANApQae6ujq2b98egwcPbrI9ra9du7ZVr3HZZZfFYYcd1iQs7WzOnDkxcODAhiV1dwMAACjK8tKf+9zn4o477oi77rorK2TQkpkzZ8amTZsaltWrV3flZQIAAD1pjE5paWn06dMn1q1b12R7Wh8yZMhuj73++uuzoPPTn/40TjrppN3u279//2wBAADo9Badfv36xahRo7JCAvVqa2uz9XHjxrV43Oc///n49Kc/HYsXL47Ro0e360IBAAA6repaKi09ZcqULLCMGTMm5s2bF1u2bGmowpYqqQ0dOjQbZ5Ncd911MWvWrLj99tuzuXfqx/Lsu+++2UJhpUrS5gAFACB6etCZOHFibNiwIQsvKbSMHDkya6mpL1BQVVWVVWKrd9NNN2XV2t7znvc0eZ00D89VV13VEb8DezgvaHpMgSdVlQYAgB4ZdJLp06dnS0sThDb29NNPt+/K6JJ5QR94YEfYqa4WdAAAyI8urbpG8YWd8vJCXwUAAHQ8QQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMiddpWXpodLdambY+ZRAACKhKBD+2YZbY6ZRwEAKBKCDm2fZTTNLrqztN3MowAAFAlBh7aHnbQAAEARU4yAhgaZqio3AwCAfBB0erjGw27Ky4UdAADyQdDp4eqH3VRURNTUND/8BgAAuhtBhyzspNYcAADIC0EHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADInb0KfQEUl5UrdzyWlu6YSLTdL7Czdr8gAAC0naBDQw4pKYmYNGnHevo5ZZZWZ5OdX2BnbX5BAABoP0GHTMofKYdUV+94THkl/dzqXNL4BXbWrhcEAID2E3RokDLIHuWQPX4BAADoGIoRAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAudOuoDN//vwYPnx4DBgwIMaOHRvLly9vcd/f//738e53vzvbv1evXjFv3rw9uV4AAICODzqLFi2KGTNmxOzZs2PFihUxYsSImDBhQqxfv77Z/WtqauLII4+Mz33uczFkyBD/JN3IypURVVWFvgoAAGi7vdp6wNy5c2PatGkxderUbH3BggVxzz33xMKFC+Pyyy/fZf9TTjklW5Lmnqf4lJZGlJRETJq04zEFnrKyDnjh9EItnbBDTgAAAO0IOtu2bYvKysqYOXNmw7bevXvH+PHjY9myZdFRtm7dmi31Nm/e3GGvzStLmSNlkgce2BF2qqv3MIc0Tk7N6dA0BQAAbQw61dXVsX379hg8eHCT7Wn98ccf77D7OWfOnLj66qv9+xRQyhzl5R34YinIpMS0s7S9Q9IUAADsQde1rpBajNI4oMYtOsOGDSvoNbGHUogRZAAAKMagU1paGn369Il169Y12Z7WO7LQQP/+/bMFAACg06uu9evXL0aNGhVLlixp2FZbW5utjxs3rl0XAAAAUPCua6lL2ZQpU2L06NExZsyYbF6cLVu2NFRhmzx5cgwdOjQbZ1NfwOCxxx5r+Pkvf/lLPProo7HvvvvG0Ucf3dG/DwAAQNuDzsSJE2PDhg0xa9asWLt2bYwcOTIWL17cUKCgqqoqq8RW79lnn43XvOY1DevXX399tpxxxhmxdOlS/wQAAEBxFCOYPn16tjRn5/AyfPjwqKura9/VURRSYTRT3QAAkNsxOvQsjae/SaWmq6oKfUUAANCNy0uT04lDdyedqCWakwAAaCNBh66bOPSVmo1akp5PQcg8PAAAtJKgQ3E0G6Xmouak5zq9OQkAgLwRdCi8FGCEGAAAOpBiBAAAQO4IOgAAQO7oukabC6MpggYAQLETdGhzYTRF0AAAKHaCDm0qjFawImgtzbOjeQkAgGYIOhR3YbRXmmdH8xIAAM0QdOi+8+yYYwcAgBYIOhQ/8+wAANBGykvTLqkxparKzQMAoDgJOrR7yEx5ubADAEBxEnRo15CZioqImprmh84AAEChGaNDu8JOas0pGkpPAwCwE0GH7kvpaQAAWiDo0CGNKQWZt1PpaQAAWiDo0CGNKQWbt1PpaQAAmiHosMeNKUU9b6fxOwAAPZKgQz4bU4zfAQDo0QQdOrTxpCBjdZpj/A4AQI8m6NChjScFG6vT7ZqcAADoTIIOHdZ48sADRTxWpznG7wAA5JagQz4nEd0d43cAAHJP0CHfY3XaO34nNU81l9yK+hcDAKCeoEP+x+q0ZfyO1h4AgFwQdOgw3XasTmNaewAAckHQodPG6tSP9e92vb209gAAdHuCDh1u595fRd+NrbW09gAAdBuCDp2aBxqP7T/99JyEnfaO7bnzzohDDmn+2G5/YwAAiougQ6fmgZ0LFKTv+qlrW+6+1++utWfDhoh3vSviTW9q/lghCACgwwk6dMn3/7TUf9dv/L0+V40ZLbX2JEIQAEDxB5358+fHF77whVi7dm2MGDEibrzxxhgzZkyL+3/3u9+NK6+8Mp5++uk45phj4rrrrou3vOUte3LddMPv/zsHniTXrTyFDEG7k6t0CQDQQUFn0aJFMWPGjFiwYEGMHTs25s2bFxMmTIhVq1bFoEGDdtn/oYceinPPPTfmzJkTb3vb2+L222+Pc845J1asWBEnnHBCW09PTgJP+l7f+Ht8c9/Ze8z38c4IQbvT3oDUGXrMPzIA0NV61dXV1bXlgBRuTjnllPjKV76SrdfW1sawYcPioosuissvv3yX/SdOnBhbtmyJu+++u2HbqaeeGiNHjszCUnO2bt2aLfU2bdoUZWVlsXr16th///2jkB5dtCrO+Ldj439uXhUjJx5b0GvJg9WrI1at2jGG54UXmj63994RFRU7vgvTjHVrIzZuatut2bgx4spPRWx9sThuaf8BEZ++NuKAAzrtFEMOfimGlP6j014fAHqEIUN2LEVg8+bNWf7YuHFjDBw4sOUd69pg69atdX369Km76667mmyfPHly3dlnn93sMcOGDav74he/2GTbrFmz6k466aQWzzN79uwUvizugfeA94D3gPeA94D3gPeA94D3gPdAXXP3YPXq1bvNLm3qulZdXR3bt2+PwYMHN9me1h9//PFmj0njeJrbP21vycyZM7PucfVSq9Hf/va3OPjgg6NXr15RDAmyGFqXyBfvLbyv6E58ZuF9RaGkDmnPPfdcHHbYYd2v6lr//v2zpbEDOrFrS3ukkCPo4L1Fd+EzC+8tuhOfWbyS3XZZ+z+9ow1KS0ujT58+sW7duibb0/qQFvrspe1t2R8AAGBPtSno9OvXL0aNGhVLlixp0q0srY8bN67ZY9L2xvsn9913X4v7AwAA7Kk2d11LY2emTJkSo0ePzubOSeWlU1W1qVOnZs9Pnjw5hg4dmpWTTi6++OI444wz4oYbboi3vvWtcccdd8SvfvWruPnmm6M7Sl3qZs+evUvXOvDeohj5zMJ7i+7EZxYFLS+dpNLS9ROGpjLRX/7yl7Oy08mZZ54Zw4cPj9tuu63JhKGf+tSnGiYM/fznP2/CUAAAoLiCDgAAQG7G6AAAAHQHgg4AAJA7gg4AAJA7gg4AAJA7gk4bzJ8/P6soN2DAgKzK3PLlyzvvX4Ye4aqrropevXo1WY477rhCXxbd0M9//vN4+9vfHocddlj2PvrBD37Q5PlUd2bWrFlx6KGHxt577x3jx4+PP/7xjwW7XvLz3vrABz6wy+fYm970poJdL91DmobklFNOif322y8GDRoU55xzTqxatarJPi+++GJ85CMfiYMPPjj23XffePe7373LJPSwO4JOKy1atCibQyjNobNixYoYMWJETJgwIdavX9/al4Bm/dM//VOsWbOmYXnwwQfdKdoszWeWPpfSH2Sak8r6p6kAFixYEL/85S9jn332yT7D0hcJ2JP3VpKCTePPse985ztuKrv1P//zP1mIefjhh7OJ5F966aU466yzsvdbvUsuuSR+/OMfZ9OUpP2fffbZeNe73uXO0mrKS7dSasFJf3lIcwgltbW1MWzYsLjooovi8ssvb/0dh51adNJfRx999FH3hQ6T/qJ+1113ZX8hrW/NSX+N/9jHPhaXXnpptm3Tpk0xePDgbM6z973vfe4+7Xpv1bfobNy4cZeWHmiLDRs2ZC07KdC87nWvyz6jDjnkkLj99tvjPe95T7bP448/HuXl5bFs2bI49dRT3WBekRadVti2bVtUVlZmXT0ablzv3tl6+o8N9kTqPpS+hB555JFx3nnnRVVVlRtKh3rqqaeyCZ4bf4YNHDgw+wOOzzA6wtKlS7Mvqccee2xccMEF8de//tWNpU1SsEkOOuig7DF970qtPI0/t1LX7rKyMp9btJqg0wrV1dWxffv27K+fjaX19OUB2it90Ux/UV+8eHHcdNNN2RfS008/PZ577jk3lQ5T/znlM4zOkLqt/cd//EcsWbIkrrvuuuwv8m9+85uz/29Ca6ReMh/96EfjtNNOixNOOKHhc6tfv35xwAEHNNnXdy/aYq827Q10qPRloN5JJ52UBZ/DDz88/vM//zPOP/98dxsoeo27Pp544onZZ9lRRx2VtfK84Q1vKOi10T2ksTq/+93vjFGlw2nRaYXS0tLo06fPLpU+0vqQIUM6/l+FHiv95erVr351PPHEE4W+FHKk/nPKZxhdIXXDTf/f9DlGa0yfPj3uvvvuuP/+++NVr3pVk8+tNHQgjf9qzHcv2kLQaYXUdDpq1KisWb5xM2taHzduXJtuOOzO888/H3/605+yEsDQUY444ojsS0Pjz7DNmzdn1dd8htHR/vznP2djdHyOsTupSEoKOam4xc9+9rPsc6qx9L2rb9++TT63UvnpNI7V5xatpetaK6XS0lOmTInRo0fHmDFjYt68eVkJxKlTp7b6ZsPOUgWsND9F6q6Wymam8uWp9fDcc891s2hzSG78F/Q03itV80sDe9Pg3dT//dprr41jjjkm+0Jx5ZVXZkUwGlfPgra+t9Jy9dVXZ/ObpDCd/lDziU98Io4++uisfDnsrrtaqqj2wx/+MJtLp34sYSqUkub6So+pC3f6/pXeZ/vvv39W6TaFHBXXaLU6Wu3GG2+sKysrq+vXr1/dmDFj6h5++GF3jz0yceLEukMPPTR7Tw0dOjRbf+KJJ9xV2uz++++vSx/pOy9TpkzJnq+tra278sor6wYPHlzXv3//uje84Q11q1atcqfZo/dWTU1N3VlnnVV3yCGH1PXt27fu8MMPr5s2bVrd2rVr3Vl2q7n3VFq+8Y1vNOzzwgsv1F144YV1Bx54YF1JSUndO9/5zro1a9a4s7SaeXQAAIDcMUYHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAADIHUEHAACIvPn/YmxgMm/en9YAAAAASUVORK5CYII=", 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", 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", 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GSzOfpuHxUjmqoX9NmvgojcmdpLG40xjiaVS2hqZraXjqVNpLM7MCAEBbUugopuDR1VLzuNTvJfXNSSMXp8EJbrzxxjjllFOip2t30EmDCmzcuDHmzZuXhZZRo0ZlT24aGzxJ23acUye1EXzmmWeyCYg+8YlPZAMRpI5NqW0eAADQcWl46lTBoZMGI0gjLjTMftpc6nDVXOqQlRYAAIBuOWEoAABAdyfoAADQLWzfvr3Qp0COXgsdaroGAACdJY3smyaVTFOPHHDAAdn6S014ST7V1dVlU9o89dRT2WsivRY6StABAKCg0gfaNF9KGtQqhR0oKSmJsrKy7LXRUYIOAAAFl765Tx9sX3zxxdi2bVuhT4cCSvMBpXk4d7eqJ+gAANAtpA+2e+21V7bA7jIYAQAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDtGXQMoIlVV9T9LSyPKygp9NgDQfQk6AEUgBZuSkogpU+rX0+8p9Ag7ANA6TdcAikAKNCnYVFZGVFRE1NZG1NQU+qwAoPtS0QEoorCjggMAu0ZFBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyJ0OBZ1FixbFyJEjY8CAATFmzJhYtWpVm/ued9550atXrxbLkUceuTvnDQAA0HlBZ/ny5TFr1qyYO3durFmzJiZMmBCTJk2K6urqVve/+uqrY926dY3L2rVrY7/99ot3vetd7X1oAACAPRN0FixYENOmTYvp06dHeXl5LFy4MIYPHx6LFy9udf/BgwfH0KFDG5d77703/vGPf8T73//+9j40AABA5wedrVu3RmVlZUycOLHJ9rS+evXqXbqP6667Lk455ZQYMWJEm/ts2bIlNm/e3GQBAADYI0GnpqYmtm3bFkOGDGmyPa2vX7/+JY9PTdd+9rOfZdWgnZk/f35WCWpYUsUIIG9Si9+qqkKfBQDkU4cGI0iDCeyorq6uxbbWLF26NPbdd98488wzd7rfnDlzYtOmTY1L6tcDkLeQU14eMWVKRElJRGlpoc8IAPKlb3t2Li0tjT59+rSo3mzYsKFFlae5FIauv/76mDp1avTr12+n+/bv3z9bAPKqpiaitjaioiJiwoSIsrJCnxEA9OCKTgooaTjplStXNtme1sePH7/TY++8887485//nA1kAEC9VNURcgCgwBWdZPbs2VlVZuzYsTFu3LhYsmRJNrT0jBkzGpudPfHEE7Fs2bIWgxAcd9xxMWrUqM47ewAAgM4IOpMnT46NGzfGvHnzssEFUnBZsWJF4yhqaVvzOXVSP5sbb7wxm1MHAACg2wWdZObMmdnS1oADzaWR02pTY3QAAIDuOuoaAABAdyboAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoABSpqqqI6upCnwUAdE+CDkCRKS2NKCmJmDIlorxc2AGA1gg6AEWmrKy+mlNREVFbG1FTU+gzAoDuR9ABKNKwk6o5AEDrBB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3OhR0Fi1aFCNHjowBAwbEmDFjYtWqVTvdf8uWLTF37twYMWJE9O/fPw455JC4/vrrO3rOAAAAO9U32mn58uUxa9asLOyccMIJcc0118SkSZPiwQcfjLKyslaPefe73x1PPvlkXHfddfHqV786NmzYEC+++GJ7HxoAAGDPBJ0FCxbEtGnTYvr06dn6woUL47bbbovFixfH/PnzW+z/3//933HnnXfGI488Evvtt1+27eCDD27vwwIAAOyZpmtbt26NysrKmDhxYpPtaX316tWtHnPLLbfE2LFj46tf/Wq84hWviMMOOywuuuiieP7553fa1G3z5s1NFoA8qK6OuO++iKqqQp8JAORbuyo6NTU1sW3bthgyZEiT7Wl9/fr1rR6TKjl333131p/nxz/+cXYfM2fOjL///e9t9tNJlaFLL720PacGUBQhp7w8ora2fr2kJKK0tNBnBQD51KHBCHr16tVkva6ursW2Btu3b89u+973vhevf/3r47TTTsuavy1durTNqs6cOXNi06ZNjcvatWs7cpoA3UpNTX3IqaiIqKysr+q00bURAOjKik5paWn06dOnRfUmDS7QvMrTYNiwYVmTtcGDBzduKy8vz8LRX//61zj00ENbHJNGZksLQB6lqs7o0YU+CwDIt3ZVdPr165cNJ71y5com29P6+PHjWz0mjcz2t7/9LZ599tnGbX/605+id+/e8cpXvrKj5w0AANB5Tddmz54d1157bda/pqqqKi688MKorq6OGTNmNDY7O+eccxr3P/vss2P//feP97///dkQ1HfddVd88pOfjA984AMxcODA9j48AABA5w8vPXny5Ni4cWPMmzcv1q1bF6NGjYoVK1Zkk4EmaVsKPg1e9rKXZRWfj370o9noayn0pHl1LrvssvY+NAAAwC7pVZc6y3RzaXjp1McnDUwwaNCgQp8OQIekYaXHjKkfiKAz+uh09v0BQDHY1WzQoVHXAAAAujNBBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyB1BBwAAyJ2+hT4BAHZPVVX9z9LSiLIyzyYAJIIOQJFKwaakJGLKlPr19HsKPcIOAGi6BlC0UqBJwaayMqKiIqK2NqKmptBnBQDdg4oOQJGHHRUcAGjJYAQAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDudCjoLFq0KEaOHBkDBgyIMWPGxKpVq9rc94477ohevXq1WP74xz/uznkDAAB0XtBZvnx5zJo1K+bOnRtr1qyJCRMmxKRJk6K6unqnxz300EOxbt26xuXQQw9t70MDAADsmaCzYMGCmDZtWkyfPj3Ky8tj4cKFMXz48Fi8ePFOjzvwwANj6NChjUufPn3a+9AAAACdH3S2bt0alZWVMXHixCbb0/rq1at3euyxxx4bw4YNi5NPPjl+8Ytf7HTfLVu2xObNm5ssAAAAeyTo1NTUxLZt22LIkCFNtqf19evXt3pMCjdLliyJG2+8MW666aY4/PDDs7Bz1113tfk48+fPj8GDBzcuqWIEAACwq/pGB6TBBHZUV1fXYluDFGzS0mDcuHGxdu3auOKKK+LEE09s9Zg5c+bE7NmzG9dTRUfYAQAA9khFp7S0NOtb07x6s2HDhhZVnp05/vjj4+GHH27z9v79+8egQYOaLAAAAHsk6PTr1y8bTnrlypVNtqf18ePH7/L9pNHaUpM2AACAbtF0LTUpmzp1aowdOzZrhpb636ShpWfMmNHY7OyJJ56IZcuWZetpVLaDDz44jjzyyGwwg4qKiqy/TloAAAC6RdCZPHlybNy4MebNm5fNhzNq1KhYsWJFjBgxIrs9bdtxTp0Ubi666KIs/AwcODALPLfeemucdtppnfuXAHRj6X+LVVWFPgsA6Dl61aWRBLq5NBhBGn1t06ZN+usARRlyyssjamsjSkrqA09ZWec+xn33RYwZE1FZGTF6dOfeNwAUYzZo94ShALRPTU19yKmo2DMhBwBoSdAB6CKpqiPkAEDXEHQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAcqSqKqK6utBnAQCF17fQJwDQY6VEUlPT+m2lpRFlZbt8V2n3kpKIKVPqf6bA047DASB3BB2AQoWc8vKI2trWb29nWkm7pd1XraoPOyk/CToA9GSCDkAhpCSSQk5FRX3g2VFKLB1IK2nX5ncFAD2VoANQSCmZjB7tGgBAJzMYAQAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDsdCjqLFi2KkSNHxoABA2LMmDGxatWqXTrunnvuib59+8ZrX/vajjwsQPGpro6oqqr/Pf287776pWEbALBH9G3vAcuXL49Zs2ZlYeeEE06Ia665JiZNmhQPPvhglJWVtXncpk2b4pxzzomTTz45nnzyyd09b4DiCDnl5RG1h0fEfRFT3hcRa/51e0lJRGlpIc8QAHKr3UFnwYIFMW3atJg+fXq2vnDhwrjtttti8eLFMX/+/DaPO//88+Pss8+OPn36xM0337x7Zw1QDGpqImprI754WcTnI6LiexHlz//r9hRydvIFUZtVn5c6DgBoX9DZunVrVFZWxmc+85km2ydOnBirV69u87jvfOc78Ze//CUqKirisssue8nH2bJlS7Y02Lx5s0sFFK+RI+t/purO6F3YPwWZVO2ZMqX129NtKQQJOwDQOUGnpqYmtm3bFkOGDGmyPa2vX7++1WMefvjhLBilfjypf86uSJWhSy+9tD2nBpAfKcCkIJMqQs2l7SkApdsEHQDovKZrSa9evZqs19XVtdiWpFCUmqul0HLYYYft8v3PmTMnZs+e3aSiM3z48I6cKkBxSiFGkAGArgk6paWlWR+b5tWbDRs2tKjyJM8880zce++9sWbNmrjggguybdu3b8+CUaru3H777fGmN72pxXH9+/fPFgAAgD0+vHS/fv2y4aRXrlzZZHtaHz9+fIv9Bw0aFA888EDcf//9jcuMGTPi8MMPz34/7rjjOnTSAAAAndp0LTUpmzp1aowdOzbGjRsXS5Ysierq6izANDQ7e+KJJ2LZsmXRu3fvGDVqVJPjDzzwwGz+nebbAQAAChZ0Jk+eHBs3box58+bFunXrssCyYsWKGDFiRHZ72paCDwAAQFENRjBz5sxsac3SpUt3euwll1ySLQAAAN2ijw4AAEAxEHQAAIDcEXQAAIDcEXQAAIDc6dBgBAC8tOp1e0VNHBtVjw7wdAFAFxN0APaANMp++b8fEbVxX8TnI0pKIkpLPdUA0FU0XQPYA2pqImpf6BMV8b6orKiKqqqIsjJPNQB0FRUdgD2oPKpidPnzEUIOAHQpFR0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3BB0AACB3+hb6BADofFVV9T9LSyPKyjzDAPQ8gg5AjqRgU1ISMWVK/Xr6PYUeYQeAnkbTNYAcSYEmBZvKyoiKioja2oiamkKfFQB0PRUdgGJum9ZcaWmUlZWp4ADQ4wk6AMXcNq05bdUAICPoABRj27TW2qOl7SkApdt0ygGghxN0AIpNCjGCDADslMEIAACA3BF0AACA3NF0DWB3VVe37DNTNTAiyj23AFAggg7A7oac8vL6CWuaODYi7osYMLB+pDQAoPs3XVu0aFGMHDkyBgwYEGPGjIlVq1a1ue/dd98dJ5xwQuy///4xcODAeM1rXhNXXXXV7pwzQPeRKjkp5KTZOdMsnQ1Lxffqb//RjwwcAADFUNFZvnx5zJo1Kws7KcBcc801MWnSpHjwwQezSeqa23vvveOCCy6Io48+Ovs9BZ/zzz8/+/1DH/pQZ/0dAIWVqjqjR7fcPmxYIc4GAHq8dld0FixYENOmTYvp06dHeXl5LFy4MIYPHx6LFy9udf9jjz023vve98aRRx4ZBx98cEyZMiVOPfXUnVaBAAAAuizobN26NSorK2PixIlNtqf11atX79J9rFmzJtv3pJNOanOfLVu2xObNm5ssAAAAeyTo1NTUxLZt22LIkCFNtqf19evX7/TYV77yldG/f/8YO3ZsfOQjH8kqQm2ZP39+DB48uHFJFSMAAIA9OhhBr169mqzX1dW12NZcaqp27733xre+9a2sudv3v//9NvedM2dObNq0qXFZu3ZtR04TAADoodo1GEFpaWn06dOnRfVmw4YNLao8zaVR2pKjjjoqnnzyybjkkkuyvjutSZWftAAAAOzxik6/fv2y4aRXrlzZZHtaHz9+/C7fT6oApX44AAAA3WJ46dmzZ8fUqVOzvjbjxo2LJUuWRHV1dcyYMaOx2dkTTzwRy5Yty9a/+c1vZsNOp/lzkjS89BVXXBEf/ehHO/tvAQAA6FjQmTx5cmzcuDHmzZsX69ati1GjRsWKFStixIgR2e1pWwo+DbZv356Fn0cffTT69u0bhxxySHz5y1/O5tIBAADoFkEnmTlzZra0ZunSpU3WU+VG9QYAAOj2o64BAAB0Z4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOQCdLI+xXVXlaAaDohpcGoO2QU14eUVsbUVISUVrqmQKAQlDRAehENTX1Iaeior6qU1bm6QWAQhB0APaAVNURcgCgcAQdAAAgdwQdAAAgdwQdAAAgdwQdAAAgdwQdAAAgdwQdAAAgdwQdAAAgdwQdAAAgdwQdgJyrqoqori70WQBA1+rbxY8HQFckm4goXbdXlAw4IqZM6RMlA7ZF1f+uj7Jxr/D8A9AjqOgA5EVpaURJScSUKRFjxkTZW4+OqhdGRkW8L2pf6BM1b3q30g4APYaKDkBelJXVV3Nqav61KSLKqwZGTImIF56vvy3tBwA5J+gA5EkKMYIMAGi6BgAA5I8+OgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO6YMBRgV1RXR9TUtNxeVeX5A4BuSNAB2JWQU14eUVvb+u0lJRGlpZ5HACj2pmuLFi2KkSNHxoABA2LMmDGxatWqNve96aab4s1vfnMccMABMWjQoBg3blzcdtttu3POAF0rVXJSyKmoiKisbLmkqk5ZmasCAMUcdJYvXx6zZs2KuXPnxpo1a2LChAkxadKkqE7feLbirrvuyoLOihUrorKyMt74xjfG2972tuxYgKKSqjqjR7dchBwAKP6gs2DBgpg2bVpMnz49ysvLY+HChTF8+PBYvHhxq/un2z/1qU/F6173ujj00EPj8ssvz37+5Cc/6YzzBwAA2L2gs3Xr1qwqM3HixCbb0/rq1at36T62b98ezzzzTOy3335t7rNly5bYvHlzkwUAAGCPBJ2amprYtm1bDBkypMn2tL5+/fpduo8rr7wynnvuuXj3u9/d5j7z58+PwYMHNy6pYgQAALBHByPo1atXk/W6uroW21rz/e9/Py655JKsn8+BBx7Y5n5z5syJTZs2NS5r167tyGkCAAA9VLuGly4tLY0+ffq0qN5s2LChRZWnuRRuUt+eH/7wh3HKKafsdN/+/ftnC0CxTbNjWh0AKMKKTr9+/bLhpFeuXNlke1ofP378Tis55513Xtxwww1x+umnd/xsAbrxNDtjxkRMmWJaHQAoyglDZ8+eHVOnTo2xY8dmc+IsWbIkG1p6xowZjc3OnnjiiVi2bFljyDnnnHPi6quvjuOPP76xGjRw4MCs/w1AnqbZSYEnzR1qxGkAKLKgM3ny5Ni4cWPMmzcv1q1bF6NGjcrmyBkxYkR2e9q245w611xzTbz44ovxkY98JFsanHvuubF06dLO+jsAus00OwBAEQadZObMmdnSmubh5Y477ujYmQEAAHTlqGsAAADdmaADAADkjqADAADkjqADAADkjqADAADkTodGXQOg+FRFecSK9VG67oEoG/bPpjea/AeAnBF0AHIuZZiSgdtjyvPfi/h8REk8l4Weslj7r51KSiKqqsx0CkBuaLoGkHNlZRFVf+wdlT9dFxVffDRqY++oqbgtorKyfqmoiKitjaipKfSpAkCnUdEB6CFhp6xsWMSwyKo6UV4eMbrQZwUAe46KDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDt9C30CAN1GdXVETU3L7VVVhTgbAGA3CDoADSGnvDyitrb156OkJKK0NN/PVVuBLv3dZWVdfTYAsFsEHYAkVXJSyKmoqA88u/hhP+Wjoi/4pL8tBbkpU1q/Pd2W/khhB4AiIugA7CiFnNGj210EKuqCTwowKci01WwvBaB0m6ADQBERdAA6oQg0YUKR54B08kX9BwBAU0ZdA9hNqaojIwBA9yLoAAAAuSPoAAAAuSPoAPRAaYyBNJgCAOSVoAPQg+w4knTqWyTsAJBXHQo6ixYtipEjR8aAAQNizJgxsWrVqjb3XbduXZx99tlx+OGHR+/evWPWrFm7c74AdMJI0mmkuDRiXGsjSgNAjww6y5cvz8LK3LlzY82aNTFhwoSYNGlSVLfxteCWLVvigAMOyPY/5phjOuOcAdjNsNPanKgA0KODzoIFC2LatGkxffr0KC8vj4ULF8bw4cNj8eLFre5/8MEHx9VXXx3nnHNODB48eJceI4WjzZs3N1kAAAD2SNDZunVrVFZWxsSJE5tsT+urV6+OzjJ//vwsFDUsKUgBAADsqr7tmwW8JrZt2xZDhgxpsj2tr1+/PjrLnDlzYvbs2Y3rqaIj7AAUUOrY09boBmZLBaDYg06DXr16NVmvq6trsW139O/fP1sA6EbDtLUm3ZZCkLADQDEHndLS0ujTp0+L6s2GDRtaVHkAyNEwba0Nz5a2pwCUbhN0ACjmoNOvX79sOOmVK1fGO97xjsbtaf2MM87YE+cHQKGlECPIAJD3pmup78zUqVNj7NixMW7cuFiyZEk2tPSMGTMa+9c88cQTsWzZssZj7r///uzns88+G0899VS2nkLTEUcc0Zl/CwAAQMeCzuTJk2Pjxo0xb968bDLQUaNGxYoVK2LEiBHZ7Wlb8zl1jj322Mbf06htN9xwQ7b/Y4891t6HBwAA2DODEcycOTNbWrN06dIW29JgBQDdQvoipq3+JgBAzw46AEUbcsrLI2pr2x5BLI0yBgAUPUEH6DlSJSeFnIqK+sDTwTlhGopCikAA0H0JOkDPk0LO6NGdUhRSBDKZKADdk6ADsBtFoV0sAuWTyUQB6MYEHYCuLQrlh8lEAejGBB2AHqyhn1GHK1MmEwWgmxJ0AHqg5q3O0u8p9PTYZngA5E7vQp8AAIVrdVZZWd/fKPU7am16IQAoVio6AD2UVmcA5JmgA8Ce09ZkQz16uDoAuoKgA0DnM/Q0AAUm6ADQ+Qw9DUCBCToA7Bk6AQFQQEZdA9hF1dVtdzkBALoXFR0gn4mktbGSdyOlpLssL68fhjnNOZO6oLCbDFQAwB4k6AD5smMiaU0HU0rKTeku05wzEyYYMGy3GKgAgC4g6AD5smMiSYGnk4c1TneZ11GRU4GlS0Z9NlABAF1A0AHyKSWS0aMLfRZFV2BJP1Pg6ZKwk9fECEC3IOgA9HANBZZVq+rDTiqKFTyD6L8DwG4SdADIgk1rLf26nP47AHQSQQeA7kP/HQA6iaADQPfyUv13NGsDYBcIOgC7MCWPiUK7Ac3aAGgHQQcoTntgUtCXmpKnp0wU2vAUdslQ053drC2NqLAHhhUHoPgIOkDx2UOTgr7UlDx5/6zcvGDSZUNNd0azNtUeAJoRdIDis4cnBe2pU/LsWDBpKJB0i6Gmd4VqDwDNCDpAcTVN27FtVU9JIF2oqOfxVO0BYAeCDlB8TdO6oMNMOgUDEOTE7lR7kry3WQTIKUEHKL6maXv4w+eOOaunDEDQlpQDcvE5v6PVnm7bWQmAlyLoAN135LQCNU3bMWdNmNAzP9/u+Pk/15/zd1btSYzmBlC0BB0g1yOn7c6cOen0cvnhvh2f/1OLrqIalKCzOybtymhuN90UccABrR+b2ycNoPsTdIDCVW26cOS0lzrFdDpnndXz5szZmfT0N1yabju3TiErPk89Vf+iectbWj9WCAIoqF51dXV17T1o0aJF8bWvfS3WrVsXRx55ZCxcuDAmpPYdbbjzzjtj9uzZ8Yc//CEOOuig+NSnPhUzZszY5cfbvHlzDB48ODZt2hSDBg1q7+kCe3oUtLY0fBDcWdWmwG2imvfHafhyvsd9oG/HpKnpOerJ1a5del/symu/rUrQznhhAsSuZoN2V3SWL18es2bNysLOCSecENdcc01MmjQpHnzwwShr5V+9Rx99NE477bT44Ac/GBUVFXHPPffEzJkz44ADDoh3vvOdLhXkIbDsTPpA99//XdCmPS81UnVP74+zqwWNHQsYQuEOT1BbL5qOVoJ2pqMBqaMEK6AnVXSOO+64GD16dCxevLhxW3l5eZx55pkxf/78Fvt/+tOfjltuuSWqdhinNVVzfve738Uvf/nLVh9jy5Yt2dIgpbUUotauXds9Kjrr19cvFI31NX1j/ca9Cn0ahff00xGf/1zElhfaf2z/ARFfvCxi333bd9y+gyOGDI1CSZ8zU/eK559ve5+BAyN++9uI4cO78syK09q1EQ891PQ5Tc9fCordoanf0KH1S1E8kRs3dv6LubN1p4sLFN7Q7vE/2VTRGT58eDz99NNZZadNde2wZcuWuj59+tTddNNNTbZ/7GMfqzvxxBNbPWbChAnZ7TtKx/ft27du69atrR5z8cUXp/Bl8Rx4DXgNeA14DXgNeA14DXgNeA14DdS19hysXbt2p9mlXU3XampqYtu2bTFkyJAm29P6+jYqHGl7a/u/+OKL2f0NGzasxTFz5szJ+vQ02L59e/z973+P/fffP3r16tWeU+6xGpJut6mC0elc457Bdc4/1zj/XOOewXXuOqlB2jPPPJP1/e/0Udeah430YDsLIK3t39r2Bv3798+WHe3b3uYyZFLIEXTyzTXuGVzn/HON88817hlc566x0yZr/1/v9txhaWlp9OnTp0X1ZsOGDS2qNg2GDh3a6v59+/bNKjQAAACdrV1Bp1+/fjFmzJhYuXJlk+1pffz48a0eM27cuBb733777TF27NjYay+dwwEAgAIHnST1nbn22mvj+uuvz0ZSu/DCC6O6urpxXpzUv+acc85p3D9tf/zxx7Pj0v7puOuuuy4uuuiizv1LaCI1/bv44otbNAEkP1zjnsF1zj/XOP9c457Bdc7RhKFf/epXswlDR40aFVdddVWceOKJ2W3nnXdePPbYY3HHHXc0mTA0BaKGCUPTkNPtmTAUAABgjwcdAACAXDVdAwAA6O4EHQAAIHcEHQAAIHcEHQAAIHcEnRz60pe+lM1rVFJSEvvuu2+r+6Qhwd/2trfF3nvvnU0E+7GPfSy2bt3a5edK5zj44IOjV69eTZbPfOYznt4il0a4HDlyZAwYMCCbw2zVqlWFPiU6ySWXXNLiPZsm2Ka43XXXXdm/rWmE2XRNb7755ia3p/Gf0rVPtw8cODDe8IY3ZCPSkp9rnEYfbv7ePv744wt2vj2doJNDKbC8613vig9/+MOt3r5t27Y4/fTT47nnnou77747fvCDH8SNN94Yn/jEJ7r8XOk88+bNy4Z8b1g+97nPeXqL2PLly2PWrFkxd+7cWLNmTUyYMCEmTZqUfUlBPhx55JFN3rMPPPBAoU+J3ZT+XT3mmGPiG9/4Rqu3p6k5FixYkN3+29/+Ngu3b37zm+OZZ57x3OfkGidvectbmry3V6xY0aXnyL/03eF3cuLSSy/Nfi5durTV22+//fZ48MEHY+3atdk3EsmVV16ZfQuRqkGDBg3q0vOlc+yzzz6+Ec6R9GFo2rRpMX369Gx94cKFcdttt8XixYtj/vz5hT49OkHfvn29Z3MmfRmRltakak56H6cvL84666xs23e/+90YMmRI3HDDDXH++ed38dnS2dd4x4lDVWi7BxWdHuiXv/xlNtFrQ8hJTj311NiyZUtUVlYW9NzouK985Sux//77x2tf+9ossGqKWLzStUvvxYkTJzbZntZXr15dsPOicz388MPZ/4dT88T3vOc98cgjj3iKc+zRRx+N9evXN3lfpw/EJ510kvd1ztxxxx1x4IEHxmGHHRYf/OAHY8OGDYU+pR5LRacHSv+jTd8g7ejlL3959OvXL7uN4vPxj388Ro8enV3H3/zmNzFnzpzsH9Vrr7220KdGB9TU1GRNTJu/T9O692g+HHfccbFs2bLsg9CTTz4Zl112Wda3MvXXSF9YkD8N793W3tePP/54gc6KzpaqPan7wIgRI7J/hz//+c/Hm970puzLqxRs6VoqOkXccbX5cu+99+7y/aX9Wyurt7ad7n/NL7zwwuxbwaOPPjpr6vStb30rrrvuuti4caPLV8Savx+9R/P1Yeid73xnHHXUUXHKKafErbfe2tiUiXzzvs63yZMnZ/2gU8uZNGjBz372s/jTn/7U+B6na6noFIkLLrgga9rwUiNv7YrUbvTXv/51k23/+Mc/4p///GeLb5oozmveMMLLn//8Z98OF6E0EmKfPn1aVG9S8wfv0XxKI2Cm0JOas5FPDX020vt62LBhjdu9r/MtXetU3fHeLgxBp4g++KSlM4wbNy7rw5FGAmn4n20aoCCVVNMQthT/NU+jdCU7/mNK8UjNSNN7ceXKlfGOd7yjcXtaP+OMMwp6buwZqY9kVVVVNroe+ZT6YqWwk97Hxx57bGN/vDvvvDPrY0k+pZYVafAn/x4XhqCTQ2n42b///e/Zz9TO//7778+2v/rVr46XvexlWUfII444IqZOnRpf+9rXsn0vuuiirMOcEdeKc3CJX/3qV/HGN74xBg8enA1Zmpqyvf3tb4+ysrJCnx4dNHv27Ow9Onbs2OzLiSVLlmTv6RkzZnhOcyD9Pzc1a0nv0fSNfuqjs3nz5jj33HMLfWrshmeffTarpDdIfTTSv8H77bdfdq3TkPGXX355HHroodmSfk9z3p199tme9xxc47SkZuepWWoKNo899lh89rOfzb603PFLK7pQHblz7rnn1qVL23z5xS9+0bjP448/Xnf66afXDRw4sG6//faru+CCC+peeOGFgp43HVNZWVl33HHH1Q0ePLhuwIABdYcffnjdxRdfXPfcc895SovcN7/5zboRI0bU9evXr2706NF1d955Z6FPiU4yefLkumHDhtXttddedQcddFDdWWedVfeHP/zB81vk0r+zrf37m/5dTrZv3579/3no0KF1/fv3rzvxxBPrHnjggUKfNp10jWtra+smTpxYd8ABB2Tv7bKysmx7dXW157hAeqX/dGWwAgAA2NOMugYAAOSOoAMAAOSOoAMAAOSOoAMAAOSOoAMAAOSOoAMAAOSOoAMAAOSOoAMAAOSOoAMAAOSOoAMAAOSOoAMAAETe/D+dgio2faZ3GgAAAABJRU5ErkJggg==", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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", + "image/png": 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", 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CDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuCDgAAkDuNDjqPP/54nHDCCdG/f//o0KFD3HPPPdvd/xe/+EV88pOfjF69ekXPnj1j1KhR8fDDD+/MNQMAADRt0NmwYUMMGTIkZs2a1eBglILOgw8+GIsXL46jjjoqC0rPPfdcY08NAADQIJ2jkY499thsaaiZM2fWWL/iiivil7/8Zdx3331xyCGHNPb0AAAATR90dtbWrVtj/fr1seeee9a7z6ZNm7Kl0rp161ro6gAAgDxo8WIEV111Vbz++uvx2c9+tt59ZsyYEbvttlvVMnDgwBa9RgAAoG1r0aAzd+7cmD59etx5553Ru3fvevebMmVKrF27tmpZsWJFS14mAADQxrVY17U77rgjzjrrrPjZz34Wo0eP3u6+3bp1yxYAAICCbdG5/fbbY+LEidm/xx9/fEucEgAAaMca3aKTxtc8//zzVesvvfRSLF26NCsuUFJSknU7e+WVV+LWW2+t6q42YcKEuO666+Kwww6LVatWZdt79OiRjb8BAABoao1u0Xn22WezstCVpaEnT56c/X/q1KnZ+sqVK6OsrKxq/5tuuinefvvt+NKXvhT9+vWrWs4///ymfB0AAAA73qJz5JFHRkVFRb2Pz5kzp8b6ggULGnsKAACAtlVeGgAAoLkJOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO4IOgAAQO50bu0L4F2lpXW/G8XFESUl3ikAAGgoQacApCBTVBQxblzdj6fHUggSdgAAoGEEnQKQAkwKMuXltR9L21MASo8JOgAA0DCCToFIIUaQAQCApqEYAQAAkDuCDgAAkDuCDgAAkDuCDgAAkDuNDjqPP/54nHDCCdG/f//o0KFD3HPPPe95zIIFC2LYsGHRrVu3+OAHPxhz5szZ0esFAABo+qCzYcOGGDJkSMyaNatB+7/00ktx/PHHx1FHHRVLly6NCy64IM4666x4+OGHG3tqAACA5ikvfeyxx2ZLQ82ePTv23nvvuPrqq7P1wYMHx5NPPhnXXnttjBkzps5jNm3alC2V1q1b19jLBAAA2rFmH6OzcOHCGD16dI1tKeCk7fWZMWNG7LbbblXLwIEDm/syAQCAHGn2oLNq1aro06dPjW1pPbXSvPHGG3UeM2XKlFi7dm3VsmLFiua+TAAAoD13XWsJqWhBWgAAAAqyRadv376xevXqGtvSes+ePaNHjx7NfXoAAKAdavagM2rUqJg/f36NbY888ki2HQAAoCCCzuuvv56ViU5LZfno9P+ysrKq8TXjx4+v2v+cc86JF198Mb761a/GsmXL4sYbb4w777wzLrzwwqZ8HQAAADsedJ599tk45JBDsiWZPHly9v+pU6dm6ytXrqwKPUkqLf3AAw9krThp/p1UZvqHP/xhvaWlAQAAWrwYwZFHHhkVFRX1Pj5nzpw6j3nuuecaf3UAAACFOEYHAACgpQk6AABA7hTkPDrtWhrfVF7+7nppKsE9OA1+ioh+rXllAADQZgg6hRZyBg+O2Lix2sZU9GFJxCmnRCy/PaKkpBUvEAAA2gZBp5CklpwUcm677Z3AU9miMy4i3nzjnccFHQAAeE+CTiFKIWfYsNa+CgAAaLMUIwAAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHJH0AEAAHKnc2tfAI1QWlpzvbg4oqTEWwgAANsQdNqK7j0ixo2rua2o6J3wI+wAAEANgk5bcdddEf1WvrueAk4KPuXlgg4AAGxD0Gkr+vWLGNavta8CAADaBMUIAACA3BF0AACA3BF0AACA3BF0AACA3BF0AACA3BF0AACA3BF0AACA3BF0AACA3BF0AACA3BF0AACA3BF0AACA3NmhoDNr1qwYNGhQdO/ePQ477LBYtGjRdvefOXNmHHDAAdGjR48YOHBgXHjhhfHmm2/u6DUDAABsV+dopHnz5sXkyZNj9uzZWchJIWbMmDGxfPny6N27d639586dG5dccknccsst8bGPfSz+9Kc/xRlnnBEdOnSIa665JtqtsrKI8vKa20pLW+tqAACgfQedFE7OPvvsmDhxYraeAs8DDzyQBZkUaLb11FNPxeGHHx6f//zns/XUEnTqqafGM888E+065AweHLFxY+3Hiooiiotb46oAAKB9dl3bvHlzLF68OEaPHv3uE3TsmK0vXLiwzmNSK046prJ724svvhgPPvhgHHfccfWeZ9OmTbFu3boaS66klpwUcm67LWLx4ppLatUpKWntKwQAgPbTolNeXh5btmyJPn361Nie1pctW1bnMaklJx33L//yL1FRURFvv/12nHPOOfG1r32t3vPMmDEjpk+fHrmXWnWGDWvtqwAAgNxp9qprCxYsiCuuuCJuvPHGWLJkSfziF7/Iurpdfvnl9R4zZcqUWLt2bdWyYsWK5r5MAACgvbboFBcXR6dOnWL16tU1tqf1vn371nnMpZdeGqeffnqcddZZ2fpBBx0UGzZsiC9+8Yvx9a9/Pev6tq1u3bplCwAAQLO36HTt2jWGDx8e8+fPr9q2devWbH3UqFF1HrNx48ZaYSaFpSR1ZQMAAGj1qmuptPSECRNixIgRMXLkyKy8dGqhqazCNn78+BgwYEA2ziY54YQTskpthxxySFaO+vnnn89aedL2ysADAADQqkFn7NixsWbNmpg6dWqsWrUqhg4dGg899FBVgYKysrIaLTjf+MY3sjlz0r+vvPJK9OrVKws53/72t5v0hQAAAOxw0EkmTZqULfUVH6iuc+fOMW3atGwBAADIRdU1AACAliboAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAuSPoAAAAudO5tS+AnVRaWntbcXFESYm3FgCAdkvQaatSmCkqihg3rvZjaXsKQMIOAADtlKDTVqUQk8JMeXnN7WlbCj9pu6ADAEA7Jei0ZSnICDMAAFCLYgQAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDu7FDQmTVrVgwaNCi6d+8ehx12WCxatGi7+7/22mvxpS99Kfr16xfdunWL/fffPx588MEdvWYAAIDt6hyNNG/evJg8eXLMnj07CzkzZ86MMWPGxPLly6N379619t+8eXN88pOfzB676667YsCAAfGXv/wldt9998aeGgAAoHmCzjXXXBNnn312TJw4MVtPgeeBBx6IW265JS655JJa+6ft//jHP+Kpp56KLl26ZNtSaxAAAEBBdF1LrTOLFy+O0aNHv/sEHTtm6wsXLqzzmHvvvTdGjRqVdV3r06dPHHjggXHFFVfEli1b6j3Ppk2bYt26dTUWAACAZmnRKS8vzwJKCizVpfVly5bVecyLL74Yjz76aJx22mnZuJznn38+zjvvvHjrrbdi2rRpdR4zY8aMmD59erRFZWXpfaq9vbR05563vuOLiyNKSnbuuQEAINp717XG2rp1azY+56abbopOnTrF8OHD45VXXonvfe979QadKVOmZOOAKqUWnYEDB0ZbCDmDB0ds3Fj340VF7wSTqCMI1Sftn44bN67+50whSNgBAIAdDDrFxcVZWFm9enWN7Wm9b9++dR6TKq2lsTnpuEqDBw+OVatWZV3hunbtWuuYVJktLW1NaslJIee2294JPPW2vjQi6KT9U5Cpr5UoBaD0mKADAAA7GHRSKEktMvPnz4+TTjqpqsUmrU+aNKnOYw4//PCYO3dutl8az5P86U9/ygJQXSEnD1LIGTas6Z4vhRhBBgAAmnEendSl7Oabb46f/OQnUVpaGueee25s2LChqgrb+PHjs65nldLjqera+eefnwWcVKEtFSNIxQkAAAAKYozO2LFjY82aNTF16tSs+9nQoUPjoYceqipQUFZWVtVyk6SxNQ8//HBceOGFcfDBB2fz6KTQc/HFFzftKwEAANiZYgSpm1p9XdUWLFhQa1sqL/3000/vyKkAAACav+saAABAoRN0AACA3BF0AACA3BF0AACA3BF0AACA3BF0AACA3BF0AACA3NmheXRoA0pLa64XF0eUlLTW1QAAQIsSdPImBZqioohx42puT9tS+BF2AABoBwSdvElBJgWa8vJ3t6X1FHzSNkEHAIB2QNDJoxRmBBoAANoxxQgAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDcEXQAAIDc6dzaF9AulJVFlJe/u15a2ppXAwAAuSfotETIGTw4YuPGmtuLiiKKi5v99AAA0B4JOs0tteSkkHPbbe8Enkop5JSUNPvpAQCgPdqhMTqzZs2KQYMGRffu3eOwww6LRYsWNei4O+64Izp06BAnnXRStDsp5Awb9u4i5AAAQOEEnXnz5sXkyZNj2rRpsWTJkhgyZEiMGTMmXn311e0e9/LLL8dFF10URxxxxM5cLwAAQNMHnWuuuSbOPvvsmDhxYnz4wx+O2bNnR1FRUdxyyy31HrNly5Y47bTTYvr06bHPPvs09pQAAADNN0Zn8+bNsXjx4pgyZUrVto4dO8bo0aNj4cKF9R73zW9+M3r37h1nnnlmPPHEE+95nk2bNmVLpXXr1jXmMqlPXdXejBUCAKC9B53y8vKsdaZPnz41tqf1ZcuW1XnMk08+GT/60Y9i6dKlDT7PjBkzstYfmkgKM6nK27hxtR9L21MAMmYIAIAcadYJQ9evXx+nn3563HzzzVHciFLKqcVo7dq1VcuKFSua8zLzL4WYFGYWL665pEpwqSJc9Tl+AACgvbXopLDSqVOnWL16dY3tab1v37619n/hhReyIgQnnHBC1batW7e+c+LOnWP58uWx77771jquW7du2UIThx2tNgAAtBONatHp2rVrDB8+PObPn18juKT1UaNG1dr/Qx/6UPzud7/Luq1VLieeeGIcddRR2f8HDhzYNK8CAABgZyYMTaWlJ0yYECNGjIiRI0fGzJkzY8OGDVkVtmT8+PExYMCAbJxNmmfnwAMPrHH87rvvnv277XYAAIBWCzpjx46NNWvWxNSpU2PVqlUxdOjQeOihh6oKFJSVlWWV2AAAANpM0EkmTZqULXVZsGDBdo+dM2fOjpwSAACgwTS9AAAAuSPoAAAAuSPoAAAAuSPoAAAAubNDxQgoLKWldW8vLjZHKAAA7ZOg04alIFNUFDFuXN2Pp8dSCCopaekrAwCA1iXotGEpwKQgU15e+7G0PQWg9JigAwBAeyPotHEpxAgyAABQk2IEAABA7gg6AABA7gg6AABA7gg6AABA7gg6AABA7gg6AABA7gg6AABA7gg6AABA7gg6AABA7nRu7QugAJSW1lwvLo4oKWmtqwEAgJ0m6LRnKdAUFUWMG1dze9qWwo+wAwBAGyXotGcpyKRAU17+7ra0noJP2iboAADQRgk67V0KMwINAAA5oxgBAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO51b+wIoUKWltbcVF0eUlLTG1QAAQPO36MyaNSsGDRoU3bt3j8MOOywWLVpU774333xzHHHEEbHHHntky+jRo7e7P60shZmioohx4yKGD6+5DB4cUVbW2lcIAABN36Izb968mDx5csyePTsLOTNnzowxY8bE8uXLo3fv3rX2X7BgQZx66qnxsY99LAtGV155ZRxzzDHxhz/8IQYMGNDY09MEDTPbbZxJG9NB5eW1nyiFn7Rdqw4AAHkLOtdcc02cffbZMXHixGw9BZ4HHnggbrnllrjkkktq7f/Tn/60xvoPf/jD+PnPfx7z58+P8ePH78y108CGmbqkx1J2qTfsCDMAALSXoLN58+ZYvHhxTJkypWpbx44ds+5oCxcubNBzbNy4Md56663Yc889691n06ZN2VJp3bp1jblMttMwk2icAQAg7xoVdMrLy2PLli3Rp0+fGtvT+rJlyxr0HBdffHH0798/C0f1mTFjRkyfPr0xl0YdNMwAANBetWh56e985ztxxx13xN13352N16lPajFau3Zt1bJixYqWvEwAAKA9tegUFxdHp06dYvXq1TW2p/W+fftu99irrroqCzq/+tWv4uCDD97uvt26dcsWAACAZm/R6dq1awwfPjwrJFBp69at2fqoUaPqPe673/1uXH755fHQQw/FiBEjduhCAQAAmq3qWiotPWHChCywjBw5MisvvWHDhqoqbKmSWiobncbZJKmc9NSpU2Pu3LnZ3DurVq3Ktr/vfe/LFgAAgFYPOmPHjo01a9Zk4SWFlqFDh2YtNZUFCsrKyrJKbJV+8IMfZNXaTjnllBrPM23atLjsssua4jUAAADsXNBJJk2alC11SROEVvfyyy/vyCkAAADaRtU1AACAliDoAAAAubNDXddox0pLa28rLn5ndlIAACgQgg4Nk8JMUVHEuHG1H0vbUwASdgAAKBCCDg2TQkwKM+XlNbenbSn8pO2CDgAABULQoeFSkBFmAABoAxQjAAAAckfQAQAAckfQAQAAcscYnXasrkrRiWrRAAC0dYJOO7S9StGJatEAALR1gk47VF+l6ES1aAAA8kDQaaeavFL0tv3g9H8DAKAVCTo0Tz84/d8AAGhFgg5N3w9O/zcAAFqZoEMB9oMDAICdYx4dAAAgd7ToNKWVKyOi3z8H5r+x/clqAACAZiPoNJWysohTTo2IX0eMOy0inqs5MD8N2gcAAFqEoNNU0mD8N//ZinPbTyMG//P/7bnUcl2tWe31vQAAoEUJOs1h8OCIYdGm1dfjrkE5pb6S04my0wAAtABBhwZnlAbnlLpKTifKTgMA0EIEHRqUURqdU5ScBgCgFQk61CKjAADQ1gk6tP4AIAUKAABoYoIOrT8ASIECAACamKBD6w4AUqAAAIBmIOjQsqWnDQACAKAFCDq0bOnp+phcFACAJiTo0PKlp6szuSgAAM1A0KFRmrznmclFAQBoBoIOrT9+Z3vpSSlqAAB2gKBDYY7fUYoaAICdIOhQmON3tleK+oknIgYPrrm/SUcBAKhG0KHFxu80ulvbtk+ocAEAAA0k6NB2urW9V+GCbVt6tPIAALRbgg4F0a2trt5odeaVupqNtjee5xe/iOjVaztPCABAHgk6O6CsrI4v7aU9ojTq+aZOvd3a3qu1Z3t5pfL4krqS1Jo1ESefHPGpTzX8CQUgAIDc6FBRUVHR2INmzZoV3/ve92LVqlUxZMiQuOGGG2LkyJH17v+zn/0sLr300nj55Zdjv/32iyuvvDKOO+64Bp9v3bp1sdtuu8XatWujZ8+e0dohJ7U8bNxY9+NF3bdE6fJOGgwa+Z7W1dpTPa/U+35vLwRteiVKuq1u+BNu78lqPblWIQCA1tDQbNDooDNv3rwYP358zJ49Ow477LCYOXNmFmSWL18evXv3rrX/U089FR//+MdjxowZ8elPfzrmzp2bBZ0lS5bEgQce2KQvpiUsWRIxfHjEbV2/EIM3L635YPceUfzonVEyakBrXV67CkI7HIJWrox47bWa2/7f/4v4ylci3nyjYRfVvUfE974XscceVZuKd387Svq9FTtFgAIAaJ2gk8LNoYceGt///vez9a1bt8bAgQPjy1/+clxyySW19h87dmxs2LAh7r///qptH/3oR2Po0KFZWKrLpk2bsqVSehGpe9KKFStaPegsXRrxiU9E/G8cEUNvmhRxwAHvPrjXXhEDB7bm5bU7K1ZE/P3vtbenYJS6w73RwNzSFHrEhrgtxkVx1NM81RDdukdc/q2I3XdvyksDoL1K303SH9FgJ/Xt+85SKEEn5Y/XXnstCzz1qmiETZs2VXTq1Kni7rvvrrF9/PjxFSeeeGKdxwwcOLDi2muvrbFt6tSpFQcffHC955k2bVoKXxbvgXvAPeAecA+4B9wD7gH3gHvAPVBR13uwYsWK7WaXRhUjKC8vjy1btkSfPn1qbE/ry5Ytq/OYNI6nrv3T9vpMmTIlJk+eXLWeWo3+8Y9/xF577RUdOnSI1k6PhdCyBNW5NylU7k0KkfuSQuXebJjUIW39+vXRv3//tld1rVu3btlS3e4F1JUnhRxBh0Lk3qRQuTcpRO5LCpV7871tt8vaP3WMRiguLo5OnTrF6tU1K1ml9b71dNpL2xuzPwAAwM5qVNDp2rVrDB8+PObPn1+jW1laHzVqVJ3HpO3V908eeeSRevcHAADYWY3uupbGzkyYMCFGjBiRzZ2TykunqmoTJ07MHk+lpwcMGJCVk07OP//8+MQnPhFXX311HH/88XHHHXfEs88+GzfddFO0Nak73bRp02p1q4PW5t6kULk3KUTuSwqVe7MAJgxNpaUrJwxNZaKvv/76rOx0cuSRR8agQYNizpw5VfuneXa+8Y1vVE0Y+t3vfrdRE4YCAAA0e9ABAADIzRgdAACAtkDQAQAAckfQAQAAckfQAQAAckfQaYRZs2ZlFeW6d++eVZlbtGhR8/1k4D1cdtll0aFDhxrLhz70Ie8bLe7xxx+PE044Ifr375/dh/fcc0+Nx1PNm6lTp0a/fv2iR48eMXr06Pjzn//sJ0Wr35tnnHFGrc/RT33qU34yNKs0Bcuhhx4au+66a/Tu3TtOOumkWL58eY193nzzzfjSl74Ue+21V7zvfe+Lz3zmM7F69Wo/mUYSdBpo3rx52RxCaR6dJUuWxJAhQ2LMmDHx6quvNvY9hybzkY98JFauXFm1PPnkk95dWlyaSy19JqY/BtUlTSmQpiGYPXt2PPPMM7HLLrtkn5/pFzm05r2ZpGBT/XP09ttv90OhWf3v//5vFmKefvrpeOSRR+Ktt96KY445JrtfK1144YVx3333ZVO0pP3/9re/xcknn+wn00jKSzdQasFJ6TvNIZRs3bo1Bg4cGF/+8pfjkksuaez7Dk3SopP+Orl06VLvJgUj/UX87rvvzv5CWdmak/6a/p//+Z9x0UUXZdvWrl0bffr0yeZb+9znPtfKV0x7vTcrW3Ree+21Wi090JLWrFmTteykQPPxj388+4zs1atXzJ07N0455ZRsn2XLlsXgwYNj4cKF8dGPftQPqIG06DTA5s2bY/HixVl3i6o3rmPHbD3dcNBaUvef9CVyn332idNOOy3Kysr8MCgoL730Uja5dPXPz9122y3745HPTwrBggULsi+ZBxxwQJx77rnx97//vbUviXYmBZtkzz33zP5N3zlTK0/1z83UNb2kpMTnZiMJOg1QXl4eW7Zsyf4CWV1aT7/AoTWkL4rpL+IPPfRQ/OAHP8i+UB5xxBGxfv16PxAKRuVnpM9PClHqtnbrrbfG/Pnz48orr8z+on7sscdmv/OhJaQeQhdccEEcfvjhceCBB1Z9bnbt2jV23333Gvv63tl4nXfgGKAApF/GlQ4++OAs+HzgAx+IO++8M84888xWvTaAtqB618mDDjoo+yzdd999s1aeo48+ulWvjfYhjdX5/e9/b4xtM9Gi0wDFxcXRqVOnWtUu0nrfvn2b62cDjZL+8rP//vvH888/752jYFR+Rvr8pC1I3YDT73yfo7SESZMmxf333x+PPfZYvP/976/xuZmGTaTxY9X53tl4gk4DpObD4cOHZ03b1Zsa0/qoUaN24G2Hpvf666/HCy+8kJXwhUKx9957Z7+0q39+rlu3Lqu+5vOTQvPXv/41G6Pjc5TmlIq0pJCTimM8+uij2edkdek7Z5cuXWp8bqby02kcrs/NxtF1rYFSaekJEybEiBEjYuTIkTFz5sysDODEiRMb+ZZD00gVrNL8EKm7Wio7mUqfp5bHU0891VtMi4fs6n8BT+PFUjXANLA2DZ5N/c+/9a1vxX777Zf9Qr/00kuzIhrVq19BS9+baZk+fXo2P0kK4+kPRV/96lfjgx/8YFb+HJqzu1qqqPbLX/4ym0uncixjKtSS5hpL/6Yu6Om7Z7pPe/bsmVX5TSFHxbVGqqDBbrjhhoqSkpKKrl27VowcObLi6aef9u7RasaOHVvRr1+/7H4cMGBAtv7888/7idDiHnvssYr062TbZcKECdnjW7durbj00ksr+vTpU9GtW7eKo48+umL58uV+UrTqvblx48aKY445pqJXr14VXbp0qfjABz5QcfbZZ1esWrXKT4ZmVdc9mZYf//jHVfu88cYbFeedd17FHnvsUVFUVFTx7//+7xUrV670k2kk8+gAAAC5Y4wOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAACQO4IOAAAQefP/AcVHFcL1ynb9AAAAAElFTkSuQmCC", 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", 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", 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", 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Zcdrp/50tXtoaRx55ZHYuab7S888/Hw888EBcfvnlZXm/aUYAAEBFq5t4XzSpbCy1Mlv/eZ5PZVo9I155JaL7shavh3PsscdmrZ2HDx8eXbp0yUJOaq+cPuCnNszf+c53siYEe+yxR/Yh/+CDD1577Ac/+MEswHzrW9/K5u+k41NL52uv3WeDU0xzX9K+qe1zCippon/6N4Wo1MksjZqkEZ/U1W3GjBlNXoKePXvGgw8+mJ1rao6Q7n/pS1+KiRMnrt0nNVJIoz4nnXpiRKeN4rRTx8anP/3paI3UcOFXv/pVnHzyybH77rtn3de+973vZc9Vap0K6xfeVaCUclM3irfeeiurcQQAID/SB/RXXnkltt1223qT1tN8/TS5Pi0aWi49e6yKWX9aGoO2bWCiSwo5aUJMiZfmSdIAUspb6fW3dnpNakHdv3//bGSoGJb+892Y9crGMXjbd2OTDzY9p6jU74nWZAMjOgAAVKRBg9aM5ixaVOSytJQgBgxoMLT06ds5Bm1fnj+s17WQbmzefiMDSPUsW7YsJk+enDU9SKNBqXTud7/7XTz00ENR6wQdAAAqOuykW9EsXb2mLG1w14hNyjM60dSoTUNL87RmACmVx6V5L2nR0DQKkhoJ/PznP4+RI0dGrRN0AACgRGEmbXvppaZHbT7wgbZXxKX21GkEhw0JOgAAUOIStO23b7gMrQTTfvg3QQcAANohjdq0twSN4hN0AACoCKsbGxKpEinktLZDGqV7Lwg6AAB0qLSoZufOneMf//hHbLnlltn95ha8bNFaOQ1Nmkl1ZnX/dmmghXQblOBLVpTlK9IL7JT92+W9dn5fmpFWvlmxYkUsXLgwe0+k90JbCToAAHSo9IE2rZcyf/78LOy0umZsfWmlzYUL06fmho9LISrVkjXXu7kVmSq1wO7aNYW2yJ0VS1fGokVdo2usjG6Lu5blOdMCpoMGDcreG20l6AAA0OHSX+7TB9v3338/VqWg0pwUiA49dM26OA3ZeOOIq6+O2HzzDR9L29I6OkWSGhGcfHLEz38eseOOkTt//dVLcfK3to2fX/ZS7HjQtiV/vrQe0EYbbdTuUT1BBwCAipA+2Hbt2jW7NWvx4ojZsyNuuy1i8OANH+/Tp8gL8ETMndvw4qXpNF57bc1AUUPNCKpdpxWd47XXemT/9qiiFyjoAABQvVLI2WOPkj9NCjnpqZYta/jxnj3XZCsqh6ADAADNSCM5KeSUcQCJdhJ0AACgmfK0WbPKOoBEEQg6AACgPC13BB0AAKp3iKWIlKfli6ADAEBl6qAOAMrT8kHQAQCgMpVoiKWMg0R0IEEHAIDKVsQhFm2ia4egAwBAzTAPp3YIOgAAdKwOqCUzDyf/BB0AADpOiWrJzMNB0AEAIFe1ZObhkAg6AACUXnNDLEWsJTMPh0TQAQCgtKyHQwcQdAAAKC3r4dABBB0AAMrDejiUkaADAEDVMQ+H5gg6AABULevh0BhBBwCA9ndPa0oJF/6Exgg6AAAUp3taUyz8SZkJOgAAFGdiTFMs/EmZCToAAFTkxBgNB2iPzq094LHHHouDDjooBgwYEJ06dYr77ruv2WMeffTR2GOPPaJ79+7x0Y9+NG655Za2ni8AADWaq9a/DRrU0WdGrkZ0li5dGkOGDImvfOUrceihhza7/yuvvBIHHnhgnHzyyXH77bfHtGnT4oQTToitttoqRo0a1dbzBgCg3A0HNBUgz0Hn85//fHZrqcmTJ8e2224bV1xxRXZ/8ODB8fvf/z6uvPJKQQcAoNoaDrSxqQDkbo7O9OnTY+TIkfW2pZGcb3zjG40es3z58uxWZ8mSJSU9RwAAOm5ijAEkqjLoLFiwIPr161dvW7qfwsu7774bG2+88QbHTJgwIS644IJSnxoAQO1qLl2UqeGAASRqquvaWWedFePGjVt7P4WigQMHdug5AQDkRgeki6ZyVZkHkKgRJQ86/fv3jzfeeKPetnR/s802a3A0J0nd2dINAIDqL09rSa4aMUKgocqCzl577RUPPPBAvW0PPfRQth0AgA5kPRxyrNVB55133okXX3yxXvvoZ599NrbYYosYNGhQVnY2b968+PGPf5w9ntpKX3PNNfHtb387a0n98MMPx09/+tOYMmVKcV8JAAAVrUy5Ctq2YOhTTz0Vu+++e3ZL0lya9N/jx4/P7s+fPz/mpvHJf0utpVOoSaM4af2d1Gb6xhtv1FoaAAConBGd/fbbLwqFQqOP33LLLQ0e88wzz7T+7AAAaDt9m6lhFdl1DQCAdtK3mRon6AAA5JGFP6lxgg4AQJ5Z+JMaJegAAFCNA0jQJEEHACBvzQaSWbOiI2ghTaUQdAAA8thsIOnZc81QCtQgQQcAII+1Yu2oF2tqoKgxHTSABI0SdAAAqlmRa8VaMlDUGANIVBJBBwCAVg0UNUbDASqJoAMAUMkaqyMrca2YpgJUO0EHAKBSNVdHplYMGiXoAABUKovTQJsJOgAAlU4dGbSaoAMAUIPzcDpo6g+UjaADAFBj83BM/aEWCDoAADU2D8fUH2qBoAMAkNN5OM2Vp5n6Q54JOgAAOaQ8jVon6AAAlEOZZ/8rT6PWCToAADkeXlGeRq0SdAAASs3wCpSdoAMAUC6GV6BsOpfvqQAAAMpD0AEAAHJH0AEAAHLHHB0AgCptId3Y05XwKaFqCDoAAFXYQrq5pyvBU0JVEXQAAKqwhXRzT1eCp4SqIugAAFRwC+nmquF0rIaGCToAABU6D6fM1XCQK4IOAECFJo8yV8NBrgg6AAAVnjyUp0HrCToAAB08MabMXamhJgg6AAAV3CbaPBxoG0EHAKCC20SbhwNtI+gAADREm2ioaoIOAFCbtImGXBN0AIDao0005J6gAwDUHm2iIfcEHQCgdlmgBnJL0AEAKBLr4UDlEHQAAIrAejhQWQQdAIAisB4OVBZBBwCgiEz7gcog6AAA+WXSDNQsQQcAyCeTZqCmCToAQD6ZNAM1TdABAPKtyJNmVMNBdRB0AABaSDUcVA9BBwCgFaM2y5ZF3HbbmoGi9fXpEzFokMsJVRt0Jk2aFJdddlksWLAghgwZEldffXUMHz680f2vuuqquO6662Lu3LnRp0+fOOyww2LChAnRo0eP9pw7AEDRa8laMmozYoRAA7kLOnfffXeMGzcuJk+eHHvuuWcWYkaNGhVz5syJvn37brD/HXfcEWeeeWbcdNNNsffee8cLL7wQX/7yl6NTp04xceLEYr0OAKAWlaCWTA8DqNGgk8LJiSeeGMcdd1x2PwWeKVOmZEEmBZr1Pfnkk7HPPvvEkUcemd3fZptt4ogjjog//vGPxTh/AKCWlTCVWPgTaijorFixImbOnBlnnXXW2m2dO3eOkSNHxvTp0xs8Jo3i3HbbbTFjxoysvO3ll1+OBx54II455phGn2f58uXZrc6SJUtac5oAQK2Vp7UhleieBvnWqqCzaNGiWLVqVfTr16/e9nR/9uzZDR6TRnLScZ/85CejUCjE+++/HyeffHJ85zvfafR50vydCy64oDWnBgDkVQnK03RPg/wrede1Rx99NC655JK49tprszk9L774Ypx22mlx0UUXxbnnntvgMWnEKM0DWndEZ+DAgaU+VQCgRsrTzMOB/GtV0Ekd07p06RJvvPFGve3pfv/+/Rs8JoWZVKZ2wgknZPd32WWXWLp0aZx00klx9tlnZ6Vv6+vevXt2AwBYqwTlaebhQH61Kuh069Ythg4dGtOmTYtDDjkk27Z69ers/tixYxs8ZtmyZRuEmRSWklTKBgBQCsrToLa1unQtlZSNGTMmhg0bljUXSO2l0whNXRe2Y489Nrbeeutsnk1y0EEHZZ3adt9997Wla2mUJ22vCzwAAMWmPA1qW6uDzujRo2PhwoUxfvz4bMHQ3XbbLaZOnbq2QUFaFHTdEZxzzjknWzMn/Ttv3rzYcssts5Bz8cUXF/eVAADVrURt0JSnQW3qVKiC+rHUjKBXr17x1ltvxWabbdbRpwMAdESdWQo8DTQdaCofHX10xMyZrZ7aA6zj6dtnxdCjB8fM22bFHkc10BCkQrNBybuuAQCUqs7MPBygMYIOAFA5WllnZh4O0BhBBwCo+Hk42kQDrSXoAADl0cY6M+VpQFsIOgBAebSxzkx5GtAWgg4AUF5t7PesTTTQGoIOAFAV6+EAtIagAwAUjwk1QIUQdACA4mnHhBoDQUAxCToAQOsVud+zgSCg2AQdAKB1SpBKdFYDik3QAQCiUlKJzmpAsQg6AECUK5WYhwOUi6ADAJSFeThAOQk6AEBZmIcDlJOgAwCUlXk4QDl0LsuzAAAAlJERHQCgqJ0DNBwAKoGgAwAUrXOAhgNApRB0AKCWNTX80oa1cjQcACqFoAMAtaolwy8jRrRp8U8NB4COJugAQK1qx/CLeThApRN0AKDWtXL4xTwcoBoIOgBAq5iHA1QDQQcAaFN5mnk4QCUTdAAg79owoUZ5GlDtBB0AyLM2JhblaUC1E3QAIM/amViUpwHVStABgFrQSGLJqtqeblVVG0BVEHQAoEaZhwPkmaADADXacMA8HCDPBB0AqPGhGfNwgDwSdACg2jUzNDN3eb9YtGjriPUGfMzDAfJM0AGAHK/gaR4OUKsEHQCoBtbDAWgVQQcAaqA8zTwcoNYIOgBQTZSnAbSIoAMAVU6baIANCToAkBPK0wD+H0EHAKrE3BgYi2ZtvMF2baIBNiToAEAVtJCe+/hrMThmxbKjN2nLmqAANUfQAYAqaCG9KHaPZfF/4rarFsXgERsmmhRyBg0q03kCVAFBBwCqoatAKlk7OrKQs96aoAA0QNABgEqbhxMpydQPOv9eDgeAFhJ0AKBCzJ3f1TwcgCIRdACgzOZOnxeLXl6ywfZZTyyJZbFJ3HbRKzH4gG03eNw8HICWE3QAoMwhZ/DevWNZbN3g4z1jaYz4bLcYZB4OQLsIOgBQRmkkJ4Wc2772RAzeZ4sNHu/zkc1i0F4NhyAAWk7QAYAOkELOHket11kNgKIRdACgPGt+Zma90sP1BiiDzm05aNKkSbHNNttEjx49Ys8994wZM2Y0uf/ixYvj1FNPja222iq6d+8eO+ywQzzwwANtPWcAqOw1P3daHUOHRoO3o8/dNpuH06f3+x19qgC51uoRnbvvvjvGjRsXkydPzkLOVVddFaNGjYo5c+ZE3759N9h/xYoVsf/++2eP3XPPPbH11lvHa6+9Fr179y7WawCAihm2WfTke7Hs3b3jtjgqaxXdkD49lsagXR4qw0kC1K5WB52JEyfGiSeeGMcdd1x2PwWeKVOmxE033RRnnnnmBvun7f/617/iySefjK5du2bb0mhQU5YvX57d6ixZsmELTgDo2GGbwRHLljXw4O4R8XQMvvrU2GPvRsrU9IkGqKygk0ZnZs6cGWedddbabZ07d46RI0fG9OnTGzzm/vvvj7322isrXfvlL38ZW265ZRx55JFxxhlnRJcuXRo8ZsKECXHBBRe09rUAQHksWhRzl30wFl30s4htt91wDs65EbH33hFaRANUR9BZtGhRrFq1Kvr161dve7o/e/bsBo95+eWX4+GHH46jjjoqm5fz4osvximnnBIrV66M8847r8FjUpBK5XHrjugMHDiwNacKACUzd37XrCxt2bmbNPh4z55rBm0AyHHXtdWrV2fzc66//vpsBGfo0KExb968uOyyyxoNOqlhQboBQCVatHijWBabxG0XvRKDD6g/opOoTAOosqDTp0+fLKy88cYb9ban+/3792/wmNRpLc3NWbdMbfDgwbFgwYKsFK5bt25tPXcA6FCDt30v9lCeBlD9QSeFkjQiM23atDjkkEPWjtik+2PHjm3wmH322SfuuOOObL80nyd54YUXsgAk5ABQyWvexPz5aY2EDTbPeuJfJT8vAMpcupbmzowZMyaGDRsWw4cPz9pLL126dG0XtmOPPTZrIZ0aCiRf+9rX4pprronTTjstvv71r8ff/va3uOSSS+K///u/23nqAFDC5mmZrf5921C2Fs5HNvMtAMhL0Bk9enQsXLgwxo8fn5Wf7bbbbjF16tS1DQrmzp27duQmSU0EHnzwwfjmN78Zu+66axaCUuhJXdcAoCOlkZwUcm67bU3gqWfWrIijj4q46LsbdFZLUsgZtNfWZTtXAMrQjCCVqTVWqvboo49usC21l/7DH/7QlqcCgJJLIWfDuTbvRsQzEQf0j9hj/RQEQNR61zUAqHhp9CYLNutvA6BaCToA5N7c6fNi0ctLNtg+688rI2LXNSVqafRmfRbEAahagg4Aue6etvCvb8ahx/aOZbF1400FfnxlxMc23fBBC+IAVC1BB4Ccd0/rm4WZqd9+OLbcdatGmgrsW47TBKCMBB0Act89rc/Ro2LQ6Ps0FQCoIYIOADXQPe31jjkhADqMoANAfuieBsC/CToAVEVTgabMejwd0Ef3NADWEnQAqJKmAk3ps6Z72lXnRoz4cAMP94kYNKhIZwlANRB0AKiOpgLz50ccdljEe+st7PlvfXosjUH/5yGBBoCMoANAdTQVeHp+xHtPNpKCjNoAUJ+gA0DFzMPJegm0rbUaANQj6ABQUfNwem68OvrM/2vE0yvbkIIAYA1BB4CKmofT57D9YtAXXmj44J491zQWAIBmCDoAVNg8nBfMwwGg3QQdACqPeTgAtJOgA0DHNBzI/mO9VtHm4QBQJIIOAOVvOJAW9zx6VES83sCD5uEA0H6CDgBtGplpShqYabThwKxZWcgZdNsE6+EAUDKCDgBtHplpShqYGTEiYtCg9R9J5Wqvm4cDQEkJOgC0rRV0M/osnxeDFr0Rsf5okHk4AJSBoANA8ZugNTtJxzwcAEpL0AGg+Q5pxR4KSot+bljTBgBFI+gA1LiSDr5YDweADiLoANS4dg2+FH0oCACKQ9ABqBHNZRLzcADIE0EHoAbCzMKFEYceWuTyNPNwAKhggg5ADc21mTo1Yssti1yeZh4OABVI0AHIiZIMsGgTDUCVEnQAqkxZB1iUpwFQpQQdgCpSsgEW5WkA5IygA1BFlKcBQMsIOgAVqCQDLE190aJP7gGAjiXoANRCeVpLvuiIEQINALkh6ADUQnmapgIA1BhBB6BWytPa/EUBoPoIOgAl1FjuWLgw4tBDO6A8rU0t2QCg+gg6ACXSktwxdWrElltu+JjyNABoH0EHoERKNi1GeRoANEvQASixok6LUZ4GAC0i6AC0YxCl7MvM6J4GAC0i6AAUYRDlF7/YcK5NXaOzktA9DQCaJOgAtGDqS2Nzbeq6p/3nfxa50Vlz83AAgCYJOkDNaKr8rCXtnkeMaLhELWWPopa1mYcDAO0m6AA1obns0J52z2l7UefomIcDAO0m6ABR6+VnJW0q0NQwUmPqytPMwwGANhN0gNxoScVXY+VnHXJCTWnz5B4AIBF0gNyouIqv5k6oKWU/WQDIF0EHqEi5qviquBMCgPxrU9CZNGlSXHbZZbFgwYIYMmRIXH311TF8+PBmj7vrrrviiCOOiC9+8Ytx3333teWpgRoIM811QGuKii8AoE1B5+67745x48bF5MmTY88994yrrroqRo0aFXPmzIm+ffs2etyrr74ap59+eoxIBfJAzWvJfJrGOqB1SMVXU0NMyswAoPqDzsSJE+PEE0+M4447LrufAs+UKVPipptuijPPPLPBY1atWhVHHXVUXHDBBfH444/H4sWL23/mQK67oFVUdmhJKvvFLzZMZRb3BIDqCDorVqyImTNnxllnnbV2W+fOnWPkyJExffr0Ro+78MILs9Ge448/Pgs6zVm+fHl2q7NkyZLWnCZQISquC1opmgrU1dn95382fKxaOgCo/KCzaNGibHSmX79+9ban+7Nnz27wmN///vfxox/9KJ599tkWP8+ECROy0R+gOuRi1Ka5F9JUU4H0uLI2AKidrmtvv/12HHPMMXHDDTdEn1asB5FGjNI8oHVHdAYOHFiiswTaIzejNi15IY39HEsvruJfIADUllYFnRRWunTpEm+88Ua97el+//79N9j/pZdeypoQHHTQQWu3rV69es0Tb7RR1sBgu+222+C47t27Zzeg8lXc2jVR6y8EAGh10OnWrVsMHTo0pk2bFocccsja4JLujx07doP9d9ppp3juuefqbTvnnHOykZ4f/OAHRmmgwuRi7Zr2dkermBcCAJS1dC2VlI0ZMyaGDRuWrZ2T2ksvXbp0bRe2Y489Nrbeeutsnk2PHj3i4x//eL3je/funf27/nagPHK9dk1bu6MlOqQBQG0HndGjR8fChQtj/Pjx2YKhu+22W0ydOnVtg4K5c+dmndiA6gszFbd2TTG7HzTXHa2iEhsA0F6dCoVCISpcakbQq1eveOutt2KzzTbr6NOBitaeQY2qmIbSkheYAk9DL6S52ryquAAAUF5P3z4rhh49OGbeNiv2OKqBeawVmg1K2nUN6Jg5M7mYU1+KntW6owFAzRB0IKdzZqqipXPue1YDAB1F0IEK/zxfFXNmotZXGgUAKo2gA2Xg83wjF8WoDQBQIoIONam9S60Uu9NZTVZhWaATACghQYea056uZI1pT9vmmq/CskAnAFACgg41NzLT1EBCS5ZaaYwwAwBQOQQdam5kJs1zb2ogIT3e2pbOSc2PzLSl4QAAQIkIOlSt9ozMpBCUgklDanqplbYs3NPemr7GvhEAAO0g6NDh2rtYfVtGZmp69KUUC/c0RU0fANABBJ0qV4ruYZX22bq5ErTG1PTITLkX7mlKJb0RAYCaIehUcWBpT/ewYrdQLlVHsvaUoNU0C/cAADVO0KnySfXtmaNS7BbKpapeUoLWShbiBAAQdMpZLtaWP7K3JLA0tthkYwGhVC2US1W9VNMlaMXun93UcQAAOWJEp0yjLy0p32ptYGnuM2tTAUEL5Rrunw0AUAMEnTK3NG5L+VYpRjRqepSkVob6TF4CAGqYoNNGWhrnWDk7Q3TEUB8AQA0QdIrMSEmVa0lgSQFj/RCRl6E+AICcEHSgpYElBZyjj454/PGGH0sM9QEAVARBBxrSUGBJoytp9CWFndbOizH6AgBQVoIO+dXW5gCNSfubFwMAUBUEHSo/dDR1bGNaMsm/qdbMjTEyAwBQFQQdKneCf0uObUpjk/y1ZgYAyD1Bh+KMoDSlqfVgmprg39yxzWlqpEgJGgBArgk6FG8EpSmNrQfT3AT/po5tDyVoAAC5JuhUylyUto6iFPtrtmcEpS3n2dwE/6aOBQCARgg6bZVNWn+39RPgG5qL0t55KA1Nqm/uXJr7msUeQWmK0RUAAIpM0Gmt+fMjYquIo4+KiGdaPgG+ucUm2zKK0pJJ9Q2dS3OMoAAAUOUEndZavHhN0LnouxEH9G95SGjJYpNtGUUxqR4AADYg6LTVtttG7DG44xebVPYFAAAbEHTKSSgBAICy6FyepwEAACgfQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMidNgWdSZMmxTbbbBM9evSIPffcM2bMmNHovjfccEOMGDEiNt988+w2cuTIJvcHAAAoe9C5++67Y9y4cXHeeefF008/HUOGDIlRo0bFm2++2eD+jz76aBxxxBHxyCOPxPTp02PgwIHxuc99LubNm9fukwcAAChK0Jk4cWKceOKJcdxxx8XOO+8ckydPjp49e8ZNN93U4P633357nHLKKbHbbrvFTjvtFDfeeGOsXr06pk2b1tqnBgAAKH7QWbFiRcycOTMrP1v7BTp3zu6n0ZqWWLZsWaxcuTK22GKLRvdZvnx5LFmypN4NAACgJEFn0aJFsWrVqujXr1+97en+ggULWvQ1zjjjjBgwYEC9sLS+CRMmRK9evdbeUrkbAABARXZdu/TSS+Ouu+6Ke++9N2tk0Jizzjor3nrrrbW3119/vZynCQAAVLmNWrNznz59okuXLvHGG2/U257u9+/fv8ljL7/88izo/O53v4tdd921yX27d++e3QAAAEo+otOtW7cYOnRovUYCdY0F9tprr0aP+/73vx8XXXRRTJ06NYYNG9amEwUAACjJiE6SWkuPGTMmCyzDhw+Pq666KpYuXZp1YUuOPfbY2HrrrbN5Nsn3vve9GD9+fNxxxx3Z2jt1c3k+8IEPZDcAAIAODzqjR4+OhQsXZuElhZbUNjqN1NQ1KJg7d27Wia3Oddddl3VrO+yww+p9nbQOz/nnn1+M1wAAANC+oJOMHTs2uzW2QOi6Xn311bY8BQAAQHV0XQMAACgHQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMgdQQcAAMidNgWdSZMmxTbbbBM9evSIPffcM2bMmNHk/j/72c9ip512yvbfZZdd4oEHHmjr+QIAABQ/6Nx9990xbty4OO+88+Lpp5+OIUOGxKhRo+LNN99scP8nn3wyjjjiiDj++OPjmWeeiUMOOSS7/eUvf2ntUwMAALRIp0KhUIhWSCM4n/jEJ+Kaa67J7q9evToGDhwYX//61+PMM8/cYP/Ro0fH0qVL49e//vXabf/xH/8Ru+22W0yePLnB51i+fHl2q/PWW2/FoEGD4vXXX4/NNtssOtKzd8+JfU/aMf7n+jmx2+gdO/RcAACg1j7/LlmyJMsfixcvjl69ejW+Y6EVli9fXujSpUvh3nvvrbf92GOPLRx88MENHjNw4MDClVdeWW/b+PHjC7vuumujz3Peeeel8OXmGngPeA94D3gPeA94D3gPeA94D3gPFBq6Bq+//nqT2WWj1qSnRYsWxapVq6Jfv371tqf7s2fPbvCYBQsWNLh/2t6Ys846KyuPq5NGjf71r3/FBz/4wejUqVNUQoKshNGlPHJ9XeM88D52faud97DrW+28h/N9fVNB2ttvvx0DBgxocr9WBZ1y6d69e3ZbV+/evaOSpG+qoOP6VjPvYde42nkPu8bVznvYNa52m3Xg5+EmS9ba0oygT58+0aVLl3jjjTfqbU/3+/fv3+AxaXtr9gcAAGivVgWdbt26xdChQ2PatGn1ysrS/b322qvBY9L2dfdPHnrooUb3BwAAaK9Wl66luTNjxoyJYcOGxfDhw+Oqq67Kuqodd9xx2ePHHntsbL311jFhwoTs/mmnnRb77rtvXHHFFXHggQfGXXfdFU899VRcf/31UY1SSV1qrb1+aR2ub7XwHnaNq533sGtc7byHXeNq171KPg+3ur10klpLX3bZZVlDgdQm+oc//GHWdjrZb7/9ssVEb7nllnoLhp5zzjnx6quvxvbbbx/f//7344ADDijuKwEAAGhP0AEAAMjNHB0AAIBqIOgAAAC5I+gAAAC5I+gAAAC5I+g0YNKkSVnnuB49emTd5GbMmNHkRUxd5Xbaaads/1122SUeeOCBUn2/au763nDDDTFixIjYfPPNs9vIkSOb/X7Q+vdwndT+vVOnTnHIIYe4jEV6DyeLFy+OU089NbbaaqusFecOO+zg50SRr3Fa6mDHHXeMjTfeOAYOHBjf/OY347333vM+bsBjjz0WBx10UAwYMCD7//t9993X7HV69NFHY4899sjevx/96EfrdVal/df4F7/4Rey///6x5ZZbZqvMp7UGH3zwQZe2iO/hOk888URstNFGWddginuNly9fHmeffXZ8+MMfzn5WpJ/hN910U3QkQWc9d999d7ZWUOoN/vTTT8eQIUNi1KhR8eabbzZ4AZ988sk44ogj4vjjj49nnnkm+4CYbn/5y1/K8f3L/fVNv1zT9X3kkUdi+vTp2QeYz33uczFv3ryyn3ter3Gd1P799NNPz4Ilxbu+K1asyD7ApOt7zz33xJw5c7IAn9YbozjX+I477ogzzzwz23/WrFnxox/9KPsa3/nOd1ziBqS179I1TWGyJV555ZVsHbxPf/rT8eyzz8Y3vvGNOOGEE3wQL+I1Th8q08+J9IfSmTNnZtc6fchMnyto//Vd949Oab3Hz372sy5rCa7x4YcfHtOmTct+BqffdXfeeWf2B6gOldpL8/8MHz68cOqpp669v2rVqsKAAQMKEyZMaPAyHX744YUDDzyw3rY999yz8NWvftVlLcL1Xd/7779f2HTTTQu33nqr61vEa5yu695771248cYbC2PGjCl88YtfdH2LdH2vu+66wkc+8pHCihUrXNMSXeO072c+85l628aNG1fYZ599XPNmpI8B9957b5P7fPvb3y587GMfq7dt9OjRhVGjRrm+RbrGDdl5550LF1xwgWtcxOub3rfnnHNO4bzzzisMGTLEtS3iNf7Nb35T6NWrV+Gf//xnoZIY0VnvL6/pLympPKpO586ds/tpNKEhafu6+yfpL4+N7V/L2nJ917ds2bJYuXJlbLHFFiU809q7xhdeeGH07ds3G5mkuNf3/vvvz8pQUulav3794uMf/3hccsklsWrVKpe6SNd47733zo6pK297+eWXs7+MW5i6OPyeK7/Vq1fH22+/7XddEd18883Zz4Y08kvxpd91w4YNi+9///tZxUIq0U5VIu+++250pI069NkrzKJFi7IPH+nDyLrS/dmzZzd4zIIFCxrcP22n/dd3fWeccUZWL7p+uKTt1/j3v/99NsycSlIo/vVNv1gffvjhOOqoo7IP3y+++GKccsopWWD3C7c41/jII4/MjvvkJz+ZqhTi/fffj5NPPlnpWpE09ntuyZIl2YeYNC+K4rr88svjnXfeyUqBaL+//e1vWXnr448/ns3PofjS77r0eSLNq7z33nuzn8npd90///nPLGR2FCM6VI1LL700myyf/g+U/o9E+6W/GB5zzDHZnJE+ffq4pCX6y2waLbv++utj6NChMXr06Gyy5uTJk13vIklz+dIo2bXXXpvN6UkTu6dMmRIXXXSRa0zVSXPOLrjggvjpT3+a/eygfdIfTtIfQ9I1TaMMlO53XWpacPvtt8fw4cOzEfWJEyfGrbfe2qGjOmLtOtIHvS5dusQbb7xR7yKl+/3792/wAqbtrdm/lrXl+q77160UdH73u9/FrrvuWuIzrZ1r/NJLL2WT5NOk13V/WCXpr15pMuF2221XhjPP73s4dVrr2rVrdlydwYMHZ38lT2Va3bp1K/l55/0an3vuuVlgTxPkk9T9Mk2kPemkk7JQmUrfaLvGfs+l7mBGc4or/TEvvY9TN1eVC8X7g95TTz2VNXYYO3bs2t9zafQ3/Z777W9/G5/5zGeK9Gy1a6uttspK1nr16lXvd126zn//+99j++2375Dz8tN/HekDR/qLa+oYUSf9nyHdTzX2DUnb190/eeihhxrdv5a15fomqd4z/WV26tSpWf0nxbvGqS36c889l5Wt1d0OPvjgtd2VUpc72vce3meffbJytboAmbzwwgvZLwUhp/3v4bq5e+uHmbpguWYeLe3h91x5pA5Vxx13XPZv6nJHcaRAvv7vuVTamrqBpf9O7etpv/S77h//+EdWcrnu77r0s/lDH/pQdJiO7oZQae66665C9+7dC7fcckvh+eefL5x00kmF3r17FxYsWJA9fswxxxTOPPPMtfs/8cQThY022qhw+eWXF2bNmpV18ujatWvhueee68BXkZ/re+mllxa6detWuOeeewrz589fe3v77bc78FXk6xqvT9e14l7fuXPnZp0Cx44dW5gzZ07h17/+daFv376F7373u0X6judPa69x+rmbrvGdd95ZePnllwu//e1vC9ttt13WFZMNpZ+fzzzzTHZLHwMmTpyY/fdrr72WPZ6ubbrGddI17dmzZ+Fb3/pW9ntu0qRJhS5duhSmTp3q8hbpGt9+++3ZZ4l0bdf9Xbd48WLXuAjXd326rhX/Gqf9P/ShDxUOO+ywwl//+tfC//zP/xS23377wgknnFDoSIJOA66++urCoEGDsg/Yqc3pH/7wh7WP7bvvvtkHwXX99Kc/Leywww7Z/qkF55QpU0r/nauR6/vhD384+z/Y+rf0Q4riXOP1CTrFfQ8nTz75ZNZ2Pn14T62mL7744qylN8W5xitXriycf/75Wbjp0aNHYeDAgYVTTjml8H//7/91iRvwyCOPNPhzte6apn/TNV7/mN122y37fqT38M033+zaFvEap/9uan/a/x5el6BTmmuc/hAycuTIwsYbb5yFntTmf9myZYWO1Cn9T8eNJwEAABSfOToAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDuCDoAAEDkzf8PBbANwu2evTAAAAAASUVORK5CYII=", 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", 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", 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", 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" ] @@ -1265,6 +1274,60 @@ "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": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Exercise 3: Recreating the paper's comparison plots\n", + "# Using math proxies since the models aren't trained yet. \n", + "# We're focusing on the \"multiple plot\" formatting requested.\n", + "\n", + "x = np.linspace(0.4, 1.0, 100)\n", + "\n", + "# Mimicking the general shape of the curves in the paper\n", + "# Fig 5 is log-scale rejection; Fig 6 is significance improvement\n", + "rej_dnn, rej_snn, rej_bdt = np.exp(11*(1-x)), np.exp(9.5*(1-x)), np.exp(8*(1-x))\n", + "imp_dnn, imp_snn, imp_bdt = 1.3-2*(x-0.65)**2, 1.2-1.8*(x-0.65)**2, 1.1-1.5*(x-0.65)**2\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n", + "\n", + "# --- Figure 5: Background Rejection (Log Scale) ---\n", + "ax1.plot(x, rej_dnn, 'k-', label='DNN', linewidth=2)\n", + "ax1.plot(x, rej_snn, 'r--', label='SNN', linewidth=2)\n", + "ax1.plot(x, rej_bdt, 'b:', label='BDT', linewidth=2)\n", + "ax1.set_yscale('log')\n", + "ax1.set(xlabel='Signal Efficiency', ylabel='Background Rejection', xlim=(0.4, 1.0), ylim=(1, 10**4))\n", + "ax1.tick_params(direction='in', which='both', right=True, top=True)\n", + "ax1.legend(frameon=False)\n", + "\n", + "# --- Figure 6: Significance Improvement (Linear Scale) ---\n", + "ax2.plot(x, imp_dnn, 'k-', label='DNN', linewidth=2)\n", + "ax2.plot(x, imp_snn, 'r--', label='SNN', linewidth=2)\n", + "ax2.plot(x, imp_bdt, 'b:', label='BDT', linewidth=2)\n", + "ax2.set(xlabel='Signal Efficiency', ylabel='Significance Improvement', xlim=(0.4, 1.0), ylim=(0.8, 1.4))\n", + "ax2.tick_params(direction='in', right=True, top=True)\n", + "ax2.legend(frameon=False)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1283,6 +1346,141 @@ "Which observables appear to be best for separating signal from background?" ] }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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rq46h2bp1a4vHXgGIDQQjAOrwzf3hu/MeSLtm6Z3/WKbzJCjtMqLdWYJNpKdjU7RlKHDmbt95CeyC0lg6rkVZjcEJpOMyNKDRLkrawlKftljpeBG9496cwdTBWLXC+N771atXm999k2SqwDJo97RQfJNl6t9Wf5xBuDvcOm6l/uvGGh1DpHRQfrAubDrZppP5Bs8//PDDTW6R0M/OhRdeaPbTFhFfC4kmJ6gf5GviAf2MasCtrwUg/hGMAGhw4ax9uPUu5lNPPVWnL7eOEwgWpMQSnexQuxtpwKFdRAIvwjXTlo75UNqaEXihpBPs+Vorgs14rReTOqld/TEtOhDcd1GlA9kbQyeT8138aQaywExJOohbW1zUhAkTWq2blk4aOGbMGPn73//eIOD66quvTJIALYdeLOqYokBTp041P3UCvoceeqjOeKLKykqTIOGNN94wd7x1HEggDWC0FUlbjfRc1e/apQOmfUFOsExUsUK7uWl90mxlgS1keq6vv/56M1mjk2n90BYqfR/1e0BbLuqPc9KJCidNmhR0f1/GLP2c6PMC19WnnyNtldWxSg888ECD+qivrV0gH3vssVb66wDYyu7cwgDsnfQwmMmTJ/vnIMjPzzeT0aWlpZm5HHRytVBzfoSbZ6Spc180dtLDYELNM6K++OIL/2RxOt/IgAED/PM8+OY9CJxjxOeMM84w23WStkGDBpl5DS6//HKz7aGHHvLvf8QRR5iJ+I477jgzV4dv3TfffNPo8m/fvt1M4uibLLJfv37ePn36+F9D54fZs2dPq53rCy+80H9sfZ91ckudQFDff9+khDoJ3d/+9reg++tcIb65UXRSOq0z/fv3N/XGd8wFCxY02G/OnDl15u3o3r27ed1evXr5z50uWietNHa+E7smPVQ64Wf9OqLnSuugb64VrWPB/q5Q89tY1fNwfGXRMnTs2DHk4puwsKioyLw/uo/WCa0j+jk4+uijTR3V9fo41Dw2nTp18r+m7mtl8eLF3vT0dH+9O/HEE0290ElFfcf45S9/WWcf5hkBYhPZtAA0MGvWLJOaU+886t1wvbut6TfvuusufxanWKYtD9r9aebMmf60tXonVgfr611qvfMbbIzIwoULTYuJzgOiWYW0e4125/LN/aFjGnQ+CZ0jQ8cAaBcmnc9FWzk0valmFmosTXn67rvvyoMPPmjuJGvKU82cpK062lqid6q1paG1LFiwwPxdmhFMW1+0S5kOVtaWEE07q+lydSC5b/xGsLlCtNXpkUceMd3LdGyBllcHIusgfm0ZCJaqWI+px9fX1dY47X6mqYO1dU7PraZM1vdEB7LHOm3l0dTEmhVM64d+rvTv0ta4aCYkqK+8vNxyu3aZUlp2HTM2d+5cWbJkiRQVFZnvh5ycHJPCWrPv6ecgGH0/NXOW7mvVKuJz0UUXmVZIbWl77bXXzGdKxyZpfdJt2u3LN98IgNjm0YjE7kIAAOBmGqBoF7bbb7895sdlAUBTMGYEAAAb6fwsvvFZrZWQAABiBcEIAABR8MQTT8ibb75ZZ52mr9XJMDXlb25urkkiAABuwpgRAACiQAORn//852bODZ2fRXtJ67gLzT6mY3M0/XFqairvBQBXIRgBACAKxo8fbwKP9957z6RL1oQH2hqiyQGmTJlSZ14bAHALBrADAAAAsAVjRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYIsmel3Wempoa2bJli2RkZIjH47G7OHARr9crFRUVkpubKwkJ0bs/QJ2H2+q8ot7DDtR5uI23Cd/zBCMHaSCSl5cXjfcHCKqkpES6du0atbNDnYfb6ryi3sNO1Hm4TUkjvucJRg7SFhHfScvMzIz8uwMcVF5ebgJhXx2MFt/rfbmxRDKo84iiivJyOap79Ou8ot7DDtR5uE1FE77nCUYO8nXN0kCEYAR2iHb3QN/raSBCnYcd7OgSS72HnajzcBtPI77nGcAOAAAAwBYEIwAAAABsQTACAAAAwBaMGWmk4mKR0tK667KzRfLzI/CuAAAAAC5AMNLIQKR3b5HKyrrr09NFiooISBDbvN7aJRim3AGA+MB3PZyKblqNoC0iGogUFoqsXVu76O+6rn5rCQAAAIDGoWWkCbR1ZMCApuwBAAAAIBRaRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0YwA4AQBOESoWtSIcNp/Ie/C8Yj3iiXh7Ah5YRAAAAALYgGAEAAABgC4IRAAAAALYgGAEAAABgC4IRAAAAALYgGAEAAABgC1L7Ai7n9XrNEoyHPKVwIavUvWZ7iPSoihSpcHK9Dlm3yewLG9EyAgAAAMAWBCMAAAAAbEEwAgAAAMAWjBkBXKKqqsosPuXl5baWB4gG6j3chjqPWEPLSAsVFYmsW3doKS5unTcGaG0FBQWSlZXlX/Ly8jjJiHvUe7gNdR6xJiFeP4iaBWjixIkRe43sbJH0dJFx40QGDjy09O5NQAJnmjp1qpSVlfmXkpISu4sERBz1Hm5DnUesibtuWqtXr5Z58+bJCSecENHXyc+vbRUpLT20Th9rcKLrdDvgJCkpKWapTzM9hslkCsRdvW9u6l4gZr/rrVL7AjaKq2Bk165dcuWVV8rjjz8u9913X8RfTwMOgg4AAACgeeKqm9Ytt9wi5513npx11lmNGuClA3gDFwAAAADREzctI88995ysW7fOdNNq7LiS6dOnR7xcAAAAAOK4ZUQH4t5+++1SWFgoqampjdqHAV4AAACAveKiZWTt2rWyfft2GajprA6qrq6WVatWySOPPGK6ZCUmJrZ4UCMAAACA1hMXwciZZ54pn376aZ11P/vZz+TYY4+VX/7ylw0CEQAAAAD2i4tgJCMjQ4477rg669q2bSsdO3ZssB5AXTU1XrMEk5jg4XTBfbwt2MxHBg5V4/WaJRiv17rieqjXiKC4GDMCAAAAIPbERctIMCtWrLC7CAAAAABiuWVk/PjxdhcBAAAAgBuDkTfeeMPuIgAAAACI125ahx9+eND1Xq9Xdu7cGfXyAAAAAHBJMKJBxz//+U/JyspqsP6UU06xrVwAAAAA4jwY0ckKv//+eznhhBMabOvSpYstZQLcotrrNUswyVEvDWC/EJmu/bwWyX0Tye0LhzpQ7TVLMMlMxwa3ByOLFi2S5OTkkLOrx5qiokO/Z2eL5OfbWRoAAADAmRwxgF0nKNRZ0q+66iqJZRp4pKeLjBunrT21S+/eIsXFdpcMAAAAcB5HtIwoDUY2b94ssUxbQLRVpLS09rH+roGJPqZ1BAAAAHBoMKLOOussuemmm+RnP/uZtGvXzr++T58+Eis06CDwAAAAAGIsGHn88cfNz3/84x/+dR6PR77++msbSwUAAAAg7oORjRs3Wm5/++23SfULAAAAxAlHDGBvrFtvvdXuIgBxp/pgusdgC+BG1TVey8Vk9g2xeMMsgH31ukYOhFi8Yf4DIimmghGdBBEAAABAfIipYETHjwAAAACIDzEVjAAAAACIH44awB4O3bSA5quqqjKLT3l5OacTcY96D7ehziPWxFTLyIQJE+wuAhCzCgoKJCsry7/k5eXZXSQg4qj3cBvqPGKNx+ug5oa9e/fK3Llz5aOPPjK/+/z1r3+N+GvrXWK9QCsrK5PMzMw629atExk4UGTtWpEBAxp/zObuB3exqnuRvlumAcnXm7+XjBCv2y41phpPESO07nXuGPk6b1Xvv/0+9GtX7a+xPGaCxfDFpETre3wMfXQnJ9T5LzeVNvu7PoGKiwjWeUddaVx//fWSkZEhq1atkjvvvFPmz58vw4YNs7tYQFxISUkxS32k8YUb670VTXVqJSkhpjoVwGVC1fl9B7xmCSrcbWnyByGCHPWNqi0i2jKiEZTOKbJixQr57LPP7C4WAAAAgAhwVMtIWlqa+ZmUlCSVlZWmlWTz5s0S64qK6j7OzhbJz7erNAAAAIAzOCoY6dChg+zYsUNGjx4to0aNko4dO0pOTo7EKg060tNFxo2ru17XaYBCQAIAAAA3c1QwsnTpUklMTJQZM2bIwoULTWAyfvx4iVUabGjQUVp6aJ0+1uBE1xGMAAAAwM0SnJaOzjfT+pVXXmlS+f7+97+XWKYBh2bS8i29e9tdIgAAAMAZHBWMLF68uFHrAAAAAMQ+R3TTWrZsmbz++uuyZcsWmTJlin+95iYGEFn7qmvMEky4WYhIPY94tL/auuIneBwzPRfQaFXV1dLmQHXQbV5J5kzC3S0jbdq0kXbt2pnuWW3btvUvxx57LC0jAAAAQJxyRMvI8OHDzXLhhRdKv3797C4OAAAAALe0jPhkZ2ebgGTgwIH+SRDnzJkjjqGpsNatO7QUF9tdIgAAACBmOSoYueGGG+SSSy6RAwcOmMfHHXecPPHEE3YXS2Tr1tqf464U0UDJt2hqLAISAAAAIPaDkW3btsm4ceMkISHBPxO7LrbbubP254z7RNaurV0KC0UqK+tOIgIAAACg0RxwpX+IBh7egPQ9OulhTU3wLD+26N5dZAAThQAAAABxF4xceumlcuONN0pFRYXMnz9f5s6dK9ddd504mo4jCZSdzdTqiCn7DtRI1X4HBf1AFOh9r1Cpq/UzYaVNoqM6FQCNUrWvRpL3NS+NO+CaYOTOO++UZ599Vnbu3CmvvPKK3HbbbabbliNp0JGeLlK/fLpOAxSdeh0AAABAbAQj6oorrjCL42mwoUFH4JgRfazBia4LE4zQoAIAAAC3c1Qwoi0if/rTn+Srr77yZ9RSf/nLX8SRNOBoYgsIDSoAAACAA4MRTevbqVMnGTJkiCQmJko8amGDCgAAABA3HBWMbN26VZYvXy4xL0wfrGY0qAAAAABxx1HBSM+ePaWsrEyysrIkJtEHCwAAAIitYGTKlCnmZ0ZGhpx00kkyatQoSU1N9W+fNWuWxAT6YCEG7dlXLYn7qoNu84p1vkePeCJUKiCydE6rwHmtAu0N8XnwSWsTn92IEd8qqw5IQptD43ED1YTN7ct3PeI8GGnbtq35efTRR5slptEHCwAAAIidYOTee+81Pz/55BM54YQT6mzTdQAAAADij6Omkb3mmmsatQ4AAABA7HNEy0hpaals375d9u7dK0VFRf5+vDqYfffu3Y06RkFBgSxevFg+//xzSUtLk6FDh8rMmTPlmGOOEUdoxCyHTISISKqqqjKLT3l5OScccY96D7ehziPWOCIYeeaZZ2TOnDmyZcsWGT16tH+9ZtXyDW4PZ+XKlXLLLbfIySefbCZMvOuuu2TkyJHy2Wef+cekODXDFkm4EA0asE+fPp2TDVeh3sNtqPOINR5vqHQiNpgxY4bcc889Ibe//fbbcsoppzTqWN99950cfvjhJkgZNmxY2OfrXWINfrQ1JjMzs862dc8UycBxvWVtYZEMuLK3NFlxcfBZDteuFRkwoLFPQZyyqnuRvluWl5cnb6/fLO0ygr/uUV2sA/kEDxlW0HRa9zp3jHydt6r3W7/bGfK1N/2wx/KYh7VrE3JbuxTre3x8ZNzJCXX+Xx8Vh/yuPyY3w/KYbZIc1asfcVbnHdEy4mMViKhbb71V1q1b16hj6R+vOnToYH8zZiMybJGEC5GWkpJilvr2WKR7DJPZl2yPiNl6X+31miWYyjCpfdtb3MMjHTacWud3VO2Xfcn7gu5TXeOY+9JwIUcFI+E0thFHnzdp0iQ59dRT5bjjjnNuM2bgIJEgY0gAAACAeBZTwYinke3bEyZMMCmB33rrrZDPmTp1qglY6jdjRkWwQSIBY0gAAAAAN4ipYKQxtCvXyy+/LKtWrZKuXbs2uRnTlpnafQNE9DHBCAAAAFwibrpp6TYNRJYsWSIrVqyQ7t27i6MFGyQS2G2rKE1EmjFYHgAAAIgRjg1GNLjYtWuXZGRk1Ol+FYqm9V24cKG89NJLZp9t27aZ9ZqlSOcdcbSguX37ax4vka1bRSTHxsIBAAAAkeGoXG3XXXed7Ny5U/bt2ycnnniidO7cWebOnVtneyiPPvqoyaB1+umnS05Ojn95/vnnxfF83bY0j69vmXGf2VT04V7RBGK6aPpfAAAAIF44qmVk7dq10r59e/n73/8u/fv3lzfffFNOO+00ufnmm8Pu66DpUlql21b21k8lXXbLuHu6ixzMeMwYd0TC9r1Vkp60N+i2Gm9W7NzNAJrgQLXXLMHsqgqR6vqgmhpONWLPt7v3SJon+GVfqM+CT7hLLObPQUs46lrCF1Do4PPzzz/fTJKSkOCoIkZNfs5+KZLesnbGK2ayxcIZG6WyUqT0U+22BQAAAMQ+R7WMdOnSRW688Ub5xz/+IXfddZfs379fqqutJ5+KW9nZkp/+veTfc17dMSSXXCKy4VmybgEAACDmOarZ4ZlnnpFjjz1WnnvuOdNda/PmzXXmAnGV+uNICp+pXb93z6GUwAAAAEAMc1TLSHZ2ttxwww1mwsIPPvhAjj/+eLnmmmvEtYKl/wUAAADihKOCkXfeeUcuueQSk0VLx49899138sILL8iQIUPsLpqzBM5H4ksNTNACAACAGOOoYES7ZP3tb3+TU045xR+c3HHHHfLee+/ZXTTnSE2rNx8JabYAAAAQmxwVjOzdu9cfiKihQ4eadTik6IGXRLK/8z/OLv1c8ieOrR1HQusImmFzxV5Jq0kOuq0mTD5Hr9djuZ10j3Cq/QdqZN+B4Dl6t1Va/7vT3ds2QqUCIufL76skZU/w7/qze5GvGvZxVDCSnp4uy5cvl7POOss8XrFihVmHgEnaJ2bro0PnLPVoKZI8yafrFgAAAGKMo4KRP/zhDzJ27FhJSUkRj8cjVVVVsmjRIruL5ajkWoGJtPTxuHGJUpqaJ/l03QIAAECMcVQwsmXLFlmzZo18++23ZgC7zjvy/vvv210s5yfXeuEFkZyt9aMUum4BAADA0RwVjNxzzz3y0UcfSadOncxjDUh0nc7GDgs5OSIDchquD+y6RcYtAAAAOIyjgpH6tKtWTQ2Dqpo/wCQg65Y+1uCEQe4AAABwCEcFI5mZmaZb1qBBg8xjTembkZFhd7Ecr+HY9fzaAe2+ASZ024KFz7fvkTbpiUG3VddYZ9NKDr4b4Hh7DlRL0v7qoNu+2bnHct+Tu1p/LgAn+s+2CklOC1539+wL/lnw8aZ7w948BuIiGJk5c6ZceOGF0rdvX/O4qKhIlixZYnexYqoB5FAjSL7kD6g3wISMWwAAAHAQRwUjOtP6Z599Ju+++65/npH27dvbXawYzLBVb+y6ddRC1y0AAADYwlHBiDrssMNk9OjRdhcj9jNsNStqAQAAAFwcjCDKUQtdtwAAAGATgpE4FTarL123AAAAYDOCEbdm9bXquvXmmyK9e9c9KF25Yl5VVZVZfMrLy20tDxAN1Hu4DXUesYZgJM7UjzEsh4bU77pFa0lcKygokOnTpzdY/58t5ZKUGjytY9V+63l+UpITLLd7hHSPcGa9r9xbLQnJwet90bZKy2Me6G3xuQiX9ZePBGyq899sLpfEEN/1O3bvszxml6xUy+2eMPWezL+wYn0lgZik8cWAAbVLYANHo3bU6GXt2kNLYaFIZWXdFhTEpKlTp0pZWZl/KSkpsbtIQMRR7+E21HnEGlpGUBcD3eNWSkqKWQA3od7DbajziDUEIy7R7KRZdN0CAABAhBCMxLkWxxKNHejOIHcAAAA0EcFInGuV+Q4bM9Cd2dwBAADQRAQjLtCoWdpbI2UXKYEBAADQBAQjLtaiydcDIxyrvmCLF4t06tTMF0E0bPz6e0losyfotl1VByz3bZdq/RXiCZOvj3SPsMsPlVVSlXBo3p1An33zg+W+B2p6RKhUQORs+uY7SWizO+i2L3ZUWO7bq0s7y+0JCaR5R/MRjLhQq49JD9YX7LvvRMaOFTn33FZ6EQAAAMQbghEXapVxJMEOWn/HVn8RAAAAxBOCEZdqzHQiLe5RxZwlAAAAsEAwguglyGJsCQAAAAIQjMAyQVar9qhibAkAAAACEIwg7LCPQBHpusWkigAAAK5EMILoZNyywqSKtvruq/+KJzk16LbS8uCpT32yM9pYbifdI5zq0+/KJbXSG3TbhvWbLffdf2BAyG3BjwjYr3JLsXiSgn/Xv/JZb8t9z+jZ2XJ7cqJ1at9wnwvSvLsbwQianHEr4nMbMqkiAACAKxCMoEUNFlFpLWHgOwAAQFwiGIHzW0sY+A4AABCXCEYQG60lzR347iskkywCAAA4DsEIotJaEpF4wNZ+ZAAAAGgpghHETzIsW0fdAwAAoKkIRtDqbE2G1ZTWksWLRTp1imBhYsS3X4kkBk/R+97WHZa7du2Ybrk9K81j/dqJ1ps9Yr0/6SDRXP/38XZJSt0ddFvlJ29b7ru/+vzQdZK3BE71w5aQ3/Ur3/2v5a7fnd7TcnubJOvUvinJ1tutPjcJfNHHPYIRRIRjkmE1dvA7XbkAAACijmAEEdfUZFit3mARbvA7XbkAAABsQTCCqGhMMqxQDRYRaT1xTNMNAACAe8VVMDJ37lz57W9/K1u3bpW+ffvKnDlz5LTTTrO7WGhmgNLY1pNgmhQztKTppskvBgAAgLgLRp5//nmZOHGiCUhOOeUU+dOf/iSjRo2Szz77TPK5UIzZAKWxMUJ9jQlY6r3yweVgbDGgCS9OgAIAAODuYOTBBx+U6667Tn7+85+bx9oq8tprr8mjjz4qBQUFDZ5fVVVlFp+ysjLzs7y8vMFzd1Xu0i3mZ7DtiJz27WsXn6OOEvngA5Hvvw+9j8YP2uMqXMASSlqaSGGhBiX6wgEvnnaUyPwPRXbW1hVj506Re+4WOXdK3YOkpEqXR+6RLkdn1j7u0qV2CcJXp7xeb/MK3Ey+1/NW7wv5nL27KyyPURHm8+DZb/0Vk5gYJlsW2bTiUoVNdT7wNQ/s3R36ORafCbWrInS9L29bY7lvQgL5ttzICXXeql7X7Ktsdp1X5Yn7W5ZNy+JjQTYtF9R5bxyoqqryJiYmehcvXlxn/W233eYdNmxY0H3uvfdePTssnAPH1IGSkhJvNOnr2f03s7j7HES7zlPv7X/P3b5Q5+1/D1jEcXU+LlpGSktLpbq6Wjp37lxnvT7etm1b0H2mTp0qkyZN8j+uqamRH374QTp27CieeiG63rnOy8uTkpISycw8eKcbnJ9GCld/9K5BRUWF5ObmRrVW6etpmTIyMhrUeaeK9c8i5be3zsdqvXeCWK+7dp+34uJiU9+o8+6oc5RdmvQ9HxfBiE/9f1j0RIT6xyYlJcUsgdoH9gcKQj8MsfaBiCbOT/PPT1ZWlkRbQkKCdO3aVWJRrNc1ym9PnY/1eu8EsV537aL13a7zFut1PpbrnNvLntXI73nrTnwxIjs7WxITExu0gmzfvr1BawkAAAAAZ4iLYKRNmzYycOBAWbZsWZ31+njo0KG2lQsAAACAC7pp6fiPq666Sk466SQZMmSIzJs3z/TRvPHGG1t8bO3Ode+99zbo1gXOD/UnumL9s0j5Eative7ahfPmznNH2ZvGo6PYJU7oHCOzZs0ykx4ed9xx8tBDD8mwYcPsLhYAAACAeA9GAAAAAMSOuBgzAgAAACD2EIwAAAAAsAXBCAAAAABbEIwAAAAAsAXBCAAAAABbEIwAAAAAsAXBCAAAAABbEIwAAAAAsAXBCAAAAABbEIwAAAAAsAXBCAAAAABbEIwAAAAAsAXBCAAAAABbEIwAAAAAsAXBCAAAAABbJNnzss5TU1MjW7ZskYyMDPF4PHYXBy7i9XqloqJCcnNzJSEhevcHqPNwW51X1HvYgToPt/E24XueYOQgDUTy8vKi8f4AQZWUlEjXrl2jdnao83BbnVfUe9iJOg+3KWnE9zzByEHaImJO2n/WS+bB34FoKK+okLyj+/rrYNTrfEmJZGZmRvW14W7l5eXm5k+067zyveaXG0skg3qPKKkoL5ejulPn4R4VTajzBCMH+bpmaSDChRnsEO3ugf46n5lJnYct7OgS63tNDUT4rodd9c+O16TOw6l1ngHsAAAAAGxBMAIAAADAFnTTAhBScbFIaemhx9nZIvn5nDAAANA6CEYAhAxEevcWqaw8tC49XaSoiIAEAAC0DrppAQhKW0Q0ECksFFm7tvanPg5sKQEAAGgJWkYAWNLWkQEDOEkAAKD10TICAAAAwBYEIwAAAABsQTACAAAAwBYEIwAAAABsQTACAAAAwBYEIwAAAABsQWpfAEDcqqqqMotPeXm5reUBIo06j1hDywgAIG4VFBRIVlaWf8nLy7O7SEBEUecRawhGAABxa+rUqVJWVuZfSkpK7C4SEFHUecQaumkBAOJWSkqKWQC3oM4j1tAyAgAAAMAdwciqVatkzJgxkpubKx6PR1588cU6271er0ybNs1sT0tLk9NPP13Wr18f9riLFi2SPn36mDsC+nPJkiUR/CsAAAAAxFwwsnv3bunXr5888sgjQbfPmjVLHnzwQbN99erV0qVLFzn77LOloqIi5DHfffddufzyy+Wqq66Sjz/+2Py87LLL5P3334/gXwIAAAAgpsaMjBo1yizBaKvInDlz5K677pKxY8eadQsWLJDOnTvLwoUL5YYbbgi6n+6jAYsO2lL6c+XKlWb9s88+G3QfUt8BAAAA9nLUmJGNGzfKtm3bZOTIkf512u1q+PDh8s4771i2jATuo8455xzLfUh9B7fRAFznWAhcAAAA7OSoYEQDEaUtIYH0sW9bqP2aug+p7+A2BOAAAMBpHBWM+OjA9vrdt+qva+k+2uKSmZlZZwHiGQE4AABwGkfNM6KD1ZW2aOTk5PjXb9++vUHLR/396reChNsHcBtyzwMAAKdxVMtI9+7dTWCxbNky/7p9+/aZwehDhw4Nud+QIUPq7KNef/11y30AAAAAuKxlZNeuXfLll1/WGbT+0UcfSYcOHSQ/P18mTpwo999/v/Tq1css+nt6err89Kc/9e9z9dVXyxFHHGH6wKvbb79dhg0bJjNnzpQf//jH8tJLL8ny5cvlrbfeivafBwAAAMCpwciaNWtkxIgR/seTJk0yP8ePHy/z58+XKVOmyJ49e+Tmm2+WHTt2yKBBg0wrR0ZGhn+f4uJiSUg41KijLSDPPfec3H333XLPPfdIz5495fnnnzf7AgAAAHCmqAcjOqO6Di4PRQed6wzsuoSyYsWKBusuueQSswAAAACIDY4aMwIAAADAPQhGAAAAANiCYAQAAACALQhGAAAAANiCYARA42zdWvuzqEhT2nHWAABAixGMAAhPgw9ftrpxV4r07k1AAgAAWoxgBEB4paUie/fU/j7jPpHKytp1AAAALUAwAqBpunfnjAEAgFZBMAIAAADAFgQjAAAAAGxBMAIAAADAFgQjAAAAAGxBMAIAAADAFgQjAAAAAGxBMAIAAADAFgQjAAAAAGxBMAIAAADAFgQjAAAAAGxBMAIAAADAFo4LRrp16yYej6fBcssttwR9/ooVK4I+//PPP4962QEAAAA0XpI4zOrVq6W6utr/+N///recffbZcumll1rut2HDBsnMzPQ/7tSpU0TLCQAAACDOgpH6QcQDDzwgPXv2lOHDh1vud/jhh0v79u0b/TpVVVVm8SkvL29GaYHYQZ0HAABO47huWoH27dsnhYWFcu2115quV1b69+8vOTk5cuaZZ8obb7wR9tgFBQWSlZXlX/Ly8lqx5IDzUOcBAIDTODoYefHFF2Xnzp1yzTXXhHyOBiDz5s2TRYsWyeLFi+WYY44xAcmqVassjz116lQpKyvzLyUlJRH4CwDnoM4DAACncVw3rUBPPPGEjBo1SnJzc0M+R4MPXXyGDBliAovZs2fLsGHDQu6XkpJiFsAtqPMAAMBpHNsy8s0338jy5cvl5z//eZP3HTx4sHzxxRcRKRcAAACAOG8ZefLJJ82g9PPOO6/J+3744Yem+xYAwN1I3AC3oc4j1jiyZaSmpsYEI+PHj5ekpKQG/d6vvvpq/+M5c+aYsSXaErJ+/XqzXcePTJgwwYaSAwCchMQNcBvqPGKNI4MR7Z5VXFxssmjVt3XrVrMtMOPW5MmT5YQTTpDTTjtN3nrrLVm6dKmMHTs2yqUGADgNiRvgNtR5xBpHdtMaOXKkeL3eoNvmz59f5/GUKVPMAgBAfSRugNtQ5xFrHNkyAgAAACD+tVowsmTJktY6FAAAAAAXaHIw8sILL8jvfvc72bBhg3n82muvyYABA0wfRQAAAACISDAyadIkE3SsXr1aLrroIrnzzjvl8ssvl5/97Gfy73//uymHAgAAAOByTRrA/uqrr5o5PNq1a2eyWnXr1k3WrFkjxx9/fORKCAAAACAuNallJC0tzQQiSicVPProowlEAAAAAES+ZaSsrExeeeUV/+M9e/bUeTx69OjmlQIAAACA6zQpGMnPz5ff/va3/sd5eXn+xx6Ph2AEAAAAQGSCkTfeeKNRz3v77bfllFNOacqhAQAAALhMRCY9vPXWWyNxWAAAAABubRlpLK/XG4nDAnCSoqJDv2dnaz9OO0sDAABiUESCER0/AiBOtW8vkp4uMm7coXX6WIMTAhIAAGB3MAIgjuXk1AYepaW1j/V3DUz0McEIAABoArppAWg6DToIPAAAQDSDkSlTplhunzVrlvk5YcKElpUKAAAAQNxrUjat2bNny8qVK81M7G3btm2w+Fx33XWRKCsAAAAAt7aMLF++XJ588klZuHChXHbZZXLttddKz549I1c6AAAAAHGrSS0jZ5xxhjz99NOydu1aMxv7lVdeKSNGjJD3338/ciUEAAAAEJeaNelhZmamXHDBBfLjH/9YPv/8c7O0lmnTppnUwIFLly5dLPfRrmMDBw6U1NRU6dGjhzz22GOtVh4AAAAADuimVV1dLS+//LI88cQTsnHjRrnqqqtk3bp1kqOpPltR3759TZcwn8TExJDP1XKMHj1arr/+eiksLJS3335bbr75ZunUqZNcfPHFrVouIJZVVVWZxae8vNzW8gAAADQpGDniiCNM9ywdKzJs2DCzbseOHWZRffr0aZUzmpSUFLY1xEdbQbRMc+bMMY979+4ta9asMYPtCUaAQwoKCmT69OmcEgAAEJvBiHaD+u6772TmzJkmja/X6/Vv0+5UX3/9dasU6osvvpDc3FxJSUmRQYMGyf3332+6XwXz7rvvysiRI+usO+ecc0zrzf79+yU5OTnoftwlhttMnTpVJk2aVKdlJC8vz9YyAQAAd2tSMPLf//5XIk2Dj6eeekqOPvpo+fbbb+W+++6ToUOHyvr166Vjx44Nnr9t2zbp3LlznXX6+MCBA1JaWhqyC1nIu8TemtolKI914T1htgM20uBeFwAAgJgewB5Jo0aNMt2rjj/+eDnrrLNk6dKlZv2CBQtC7qOtMoF8LTb119e/S1xWVuZfSkpKWu1vAAAAANDKLSN20MkUNTDRrlvB6NgSbR0JtH37djPuJFhLig93iYFGKirS/3G6AACA+4IRHdtRVFQkp512WtDtQ4YMkb///e911r3++uty0kknhRwvAqARtm4VkRyRcVeKyIciqUNF9h6MTQJkb02WfE4oAACIh2Bk8uTJMmbMGJMhS1s4dMyIDrQdP368v3vV5s2bzbgSdeONN8ojjzxiBuZqel8d0K6D15999lmb/xIgxu3cWRuMzLhPZHQXya7qLOlniYwbV/dp6al9pEjyCEgAAEDsByObNm2SK664wgw+17lCBg8eLO+9954ceeSRZvvWrVuluLjY//zu3bvLK6+8InfccYf88Y9/NFm4Hn74YdL6Aq2le3eRAb1NsKGtIqWlhzbp43HjEqVUsglGAABA7Acjzz33nOX2+fPnN1g3fPhwM/kigMjKz69dAAAA4jIYsV2NV6Smppmpe1u6vYVILQwAAIAY4rjUvgAAAADcgWAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC1I7Vvfgf21SzAJYWK3cNvDpfb1tHC719uy148k0g4DAACgHlpGAAAAANiClhEAQNyqqqoyi095ebmt5QEijTqPWEPLCAAgbhUUFEhWVpZ/ycvLs7tIQERR5xFrCEYAAHFr6tSpUlZW5l9KSkrsLhIQUdR5xBq6aQEA4lZKSopZALegziPWEIwAaB1FRbU/s7NF8vM5qwAAICyCkXq8+3aLtypE77XEZMuT6UkMczo94VIDe1q2f9j0uS1MHdySfcNlHW4pUgfbKzVNZNy42t/T02sDEwISAAAQBmNGALTcCy+IrF0rUlgoUlkpUlrKWQUAAGHRMgKg5XJyRAbkcCYBAECT0DICAAAAwBYEIwAAAABskeDEyXpOPvlkycjIkMMPP1wuvPBC2bBhg+U+K1asEI/H02D5/PPPo1ZuIBZm5dXZpwMXAAAAOzkuGFm5cqXccsst8t5778myZcvkwIEDMnLkSNm9e3fYfTVo2bp1q3/p1atXVMoMxAJm5QUAAE7juAHs//jHP+o8fvLJJ00Lydq1a2XYsGGW++rz2rdv36LX927+Srzt0oNvbBfm2Clp1tuTU623J4V5O8KkFg6XurdFqYdbmna4pal5wx0/XOrglqY9joPUwjor76RJk/yPtWUkLy/P1jIBTuT1xvVXAdDqasJ8aBL4YCCWgpH6ysrKzM8OHTqEfW7//v1l79690qdPH7n77rtlxIgRll1WdPGhywriHbPyAgAAp3FcN61AXq/X3Mk99dRT5bjjjgv5vJycHJk3b54sWrRIFi9eLMccc4yceeaZsmrVqpD70GUFAAAAsJejW0YmTJggn3zyibz11luWz9PgQxefIUOGSElJicyePTtk1y66rAAAAAD2cmzLyK233iovv/yyvPHGG9K1a9cm7z948GD54osvLLusZGZm1lkAAAAAuLhlRLtmaSCyZMkSk7K3e/fuzTrOhx9+aLpvAQAAAHAmxwUjmtZ34cKF8tJLL5m5RrZt22bWZ2VlSVpamr+L1ebNm+Wpp54yj+fMmSPdunWTvn37yr59+6SwsNCMH9EFAAAAgDM5Lhh59NFHzc/TTz+9QYrfa665xvyuc4gUFxf7t2kAMnnyZBOgaMCiQcnSpUtl9OjRTX79mhcWSE1K8BS6njAZvbydDrc+eIdO1tsP62i52ZMepitZ2zDb09pZb09uE3pbUkrL0hKHSc3rCZe2OCGxRccPm5o4IUz5E8L1aCSfJxAvqmuan9s3Mcx3DRlOEYuq9tdYbm+T5Nhe/4gBjuymFc78+fPrPJ4yZYpZAAAAAMQOQlkAAAAAtnBcywiAOFBUdOj37GyR/Hw7SwMAAByKYARA69HAIz1dZNy4Q+v0sQYnBCQAAKAeghEArUcDDg08SktrH+vvGpjoY4IRAABQD8EIgFbtlSWSL9nZ+cQeAAAgLIKReib/8Q1pEyJNa7+2FqlvRaR7qvX2vMxUy+2Hd6qdRyWUtMMzLLen5lmnBpbsMKmDO3ZsdtrhsNvDpB32ZBxmvX9qesvSHqe2td6ebJ262JNk/d5KUpjUxIkWHzWvdcrEWOuVVadnll0FA8LYd6DGLMFs2bHXct+MtNCf5/bp1t8FieT2hQNt2FJhub1HZ+t/Q6nWaAmCEQCt1iurQc8szi0AALBAMAKgxQEJw0EAAEBzMM8IAAAAAFvQMgIgeiPcmXMEAAAEIBgBEL0R7sw5AgAAAtBNC0DkR7ivXStSWChSWVl3tDsAAHA1WkbqubXX4ZKRGDxGa5NsHbu1a2ed0jGprXX62KQs69S+yR2tU/tKsvXb6UkK83bv3h16W6p12SQxUVrCe2B/i1L/esMc35MQJu4Ons350PHD5C30eBKan9o33jHCHQ608MNiSW0b/Dv1l7f+znLft5bcH3JbhzAp4AG75J8+WTyJwevn+y8/YLlvcojrIqA1uPgKCUD0JkIUyd6aTKpfAABQB8EIgOhMhJjaR4okT/IZzA4AAA4iGAEQpYkQE6U0NU/yAwezL14s0qlT7WMybQEA4DoEIwCiNkyk6IGXRLK/E9mxQ+QXv5Dsc6+XfCmp3UimLQAAXIdgBED0um5NzNZHB9e+Lemp1bL4t19Lp51fiNxzt2QvWSv5p9XLtkWLCQAAcYtg5CCvtzYf067qmpAnq02Cdc6mmgPW2SaSDlRbb99/wHJ78j7r7Z5wGZ/27rPcLkmh/3bZU2W9b4J1JjFJ2mO93fpPE09CSouqsscbpqrvs35vJDlMtq/EMOe2TejzV15RUacORkpVVZVZfMrKympfv7w86PN3Ve7SreZnqOc0Vvv2Ih98IPL994fWaTcu7bF17q2dRUSXFZI2cbcUyjjJloCAJCVVZMZ9tQeBSMeOtQFaDOjSpXapz1efIl3ng/G95t7du0I/p9r687yrIvTnIdxHJczXNOJUhQPqvFW9tqrTqrytxfUB0MI67/Ha8clwoE2bNkleXp7dxYCLffXVV9KjR4+IHX/atGkyffr0iB0faKqSkhLp2rVrVE8c3/WwE3UeblPSiO95gpGDampqZMuWLZKRkRG2hQEN73JqIKcVLjMzk9PTRNpCkZ+fLzt27JD2Ebz7X79lROv8Dz/8IB07dvTXed7LyOHcHqL3wCoqKiQ3N1cSws0B1Mr4rm8e6m/LzltxcbH5nqXOu6POUXZp0vc83bQO0hMV7Tt08Ua/LGLtC8NJIn1RlpKSYpZAoYIf3svI4dzWysrKEjvwXd8y1N/m13e7/n2M9Tofy3XO7WXPauT3PFNqAgAAALAFwQgAAAAAWxCMoMW068+9997boAsQYu/8Oaks8YZzi1hG/eW8Uefc8XlJsaHsDGAHAAAAYAtaRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC2S7HlZ56mpqZEtW7ZIRkaGeDweu4sDF/F6vVJRUSG5ubmSkBC9+wPUebitzivqPexAnYfbeJvwPU8wcpAGInl5edF4f4CgSkpKpGvXrlE7O9R5uK3OK+o94r3OV1VVmcVn8+bN0qdPn4i+JtCSOk8wcpC2iJiT9p/1knnwdyAayisqJO/ovv46GC3Uebitztep9yUlkpmZGfXXhzuVl5ebG57RqPMFBQUyffr0Buu/3FgiGdR5RElFebkc1b1xdZ5g5CBf1ywNRPgHCnaIdvdA6jzsZkeXWH+9z8zkux621b9Imjp1qkyaNKlhIESdh0PrPMEIAABAnEhJSTELECvIpgUAAADAFrSMAADQCMXFIqWlhx5nZ4vk53PqAKAlCEYAAGhEINK7t0hl5aF16ekiRUUEJADQEnTTAgAgDG0R0UCksFBk7dran/o4sKUEANB0tIwAABCma5a2gChtHRkwgNMFAK2FYAQAgEZ0zdJuWTpOBADQeghGAAAI0zVLgxIGrANA6yMYAQDAAl2zACByGMAOAAAAwBYEIwAAAABsQTACAAAAwBYEIwAAAABsQTACAAAAwBYEIwAAAABsQWpfAEDcqqqqMotPeXm5reUBANRFywgAIG4VFBRIVlaWf8nLy7O7SACAAAQjAIC4NXXqVCkrK/MvJSUldhcJABCAbloAgLiVkpJiFgCAM9EyAgAAAMAWBCMAAAAAbEEwAgAAAMAWBCMAAAAAbEEwAgAAAMAWBCMAAAAAbEEwAgAAAMAWBCMAAAAAbBETwciqVatkzJgxkpubKx6PR1588cU6271er0ybNs1sT0tLk9NPP13Wr19vW3kBAAAAxEkwsnv3bunXr5888sgjQbfPmjVLHnzwQbN99erV0qVLFzn77LOloqIi6mUFAAAA0DhJEgNGjRpllmC0VWTOnDly1113ydixY826BQsWSOfOnWXhwoVyww03BN2vqqrKLD7l5eURKj3gDNR5AADgNDHRMmJl48aNsm3bNhk5cqR/XUpKigwfPlzeeeedkPsVFBRIVlaWf8nLy4tSiQF7UOcBAIDTxHwwooGI0paQQPrYty2YqVOnSllZmX8pKSmJeFkBO1HnAQCA08REN63G0IHt9btv1V8XSFtPdAHcgjoPAPGPLrmINTHfMqKD1VX9VpDt27c3aC0BAACIZ3TJRayJ+WCke/fuJiBZtmyZf92+fftk5cqVMnToUFvLBgAAEE10yUWsiYluWrt27ZIvv/yyzqD1jz76SDp06CD5+fkyceJEuf/++6VXr15m0d/T09Plpz/9qa3lBgAAiCa65CLWxEQwsmbNGhkxYoT/8aRJk8zP8ePHy/z582XKlCmyZ88eufnmm2XHjh0yaNAgef311yUjI8PGUgMAAACI+WBEZ1TXAemh6EB1nYFdFwAAAACxIebHjAAAAACITQQjAAAAAGxBMAIAAADAFgQjAAAAAGxBMAIAAADAFgQjAAAAAGxBMAIAAADAFjExzwgAAM1RVVVlFp/y8nJOJAA4CC0jAIC4VVBQIFlZWf4lLy/P7iIBAAIQjAAA4tbUqVOlrKzMv5SUlNhdJABAALppAQDiVkpKilla3datIpIjUlQkIntEsrNF8vNb/3UAIM7Z2jKyY8cOO18eAICmKy4WueSS2t/HXSkycKBI79616wEAzgpGPv74YznmmGMkLS1NLr74YiktLfVvO/PMMyP98gAAtC79d2zvntrfC58RKSwUqaysXQ8AcFYwcvvtt8uDDz4omzZtkj59+shpp50mmzdvNtu8Xm+kXx4AgMjRFhFdAADOHDOiaRTPO+888/uMGTNMK8kZZ5why5cvF4/HE+mXBwAAAODWYKSyslJqamokIaG2EWbcuHGSnJxsumgF5n4HAAAA4C4R76Z1yimnyCuvvFJn3eWXXy733XefbDXZSAAAAAC4UcSDkSeeeELOP//8Busvu+wy2bdvn//x22+/HemiAAAAAHAQx0x6eOutt9pdBAAAAABuDEbIrAUAAAC4i2OCETJrAQAAAO7imGAEAAAAgLs4JhihmxYAAADgLrYGI4GpfSdMmGBnUQAAAADEYzDy7bffytq1a+XAgQPm8XfffScTJ06U3r17+59z3XXXRaMoAAAAANwSjMyfP1+OPPJIOe+886R///7y4osvSq9evWTTpk2yZs2aSL88AAAAAIdKivQLzJ492wQdxx13nKxcuVLOOuss+fOf/yzjx4+P9EsDAAAAcHPLSFJSkglE1PDhw6Vbt24EIgAAAAAi3zKi9uzZ48+W1bZt2zqP09PTeRsAAM6lyVbWHUq4IkVFdpYGsFRVVWUWn/Lycs4Y3B2MfPLJJ9KuXbs6qXt9j3Wiw+rq6kgXAQCA5rvkEpG979RdlzpUZC8nFc5TUFAg06dPt7sYgHOCkZqamki/BAAAkbN3j0hhoUhABkjZmiNyPicdzjN16lSZNGlSnZaRvLw8W8sE2BqM6DgRHbgOAEDM0kBkwIBDj9fZWRggtJSUFLMAsSLiA9jpqwgAAADAlpYRHRsSOGC9PgawAwAAAO5kywB2HbjOAHYAAADA3SLeTatfv34mY5YOZPctvsdk0gIAAADcK+LBiLaCAAAAAEDUg5GcnJxGPe/tt9+OdFEAAC6jk79pIpXABQDgomBk6dKljXrerbfeGumiAABcOAFcVlaWf2G+BQBwWTDSWKGybQEA0JIJ4MrKyvxLSUkJJxMA3JRNq7EYWwIAaG1MAAcAzuaYYAQAgJhWVFT3cXa2SH6+XaUBgJgQN920pk2bZlpXApcuXbq0WvkAAAhKg470dJFx40QGDjy09O4tUlzMSQMAO1tGpkyZYrl91qxZ5ueECRNa/Fp9+/aV5cuX+x8nJia2+JgAAFjS1g9tFSktPbROH2twoutoHQEA+4KR2bNny8knnyznnnuuJCSEboi57rrrWvxaSUlJjW4N0XSPuviQ7hHxjjoPRLJnVr5kZ+cTdwCA04IRbal48sknZeHChXLZZZfJtddeKz179ozIa33xxReSm5trBiwOGjRI7r//funRo0fIdI/Tp0+PSDkAJ6LOA5HpmeWjjzU4oSEEABw0ZuSMM86Qp59+WtauXSv5+fly5ZVXyogRI+T9999v1dfR4OOpp56S1157TR5//HHZtm2bDB06VL7//vugzyfdI9yGOg+0fs+stWtrl8JCkcrKuj21AAAOyqaVmZkpF1xwgfzwww/y8MMPy+eff24CiNYyatQo/+/HH3+8DBkyxLTALFiwQCZNmtTg+aR7hNtQ54HWD0hoBQEAh7eMVFdXy5IlS+T888+Xs846y2S5WrdunYwfPz6ir9u2bVsTlGjXLQAAAAAubBk54ogjTPcsHSsybNgws27Hjh1mUX369InYYN2ioiI57bTTInJ812lh6uWwPB57y9fS10dwvC8AAMDOYCQ1NVW+++47mTlzpknjGzifiLaSfP31163yOpMnT5YxY8aYwGf79u1y3333mQxZkW6BAQAAAODQYOS///2vRMOmTZvkiiuukNLSUunUqZMMHjxY3nvvPTnyyCOj8voAAAAAHDqAPdKee+45u4sAAAAAwEkD2AEAAAAgGIIRAAAAALYgGAEAAABgi7gZMxITIp0eN3wBxLHsPjdhXz9M6l+3pgb21rTsvIY7b2HfFpeedwAA4gQtIwAAAABsQTACAAAAwBYEIwAAAABsQTACAAAAwBYEIwAAAABsQTACAAAAwBak9g2WqjRcutLmpjFtcfpab2ynz41kCtgWp3gNs39CuLg9TlPM1lRbbz+wv2XvS2JSC09rnJ53AGimqqoqs/iUl5dzLuFotIwAAADEiYKCAsnKyvIveXl5dhcJsEQwAgAAECemTp0qZWVl/qWkpMTuIgGW6KYFAAAQJ1JSUswCxAqCEQBA3KL/PAA4G920AABxi/7zAOBsBCMAgLhF/3kAcDa6adW3r0pk395mpu61TgnsrQmXMjjM8Vu6fyRT/7Y0ta4noUXbPeFePyGxZdsl3P4tSEFrd8plTd8bIoWv94etYfY9YLnZ065DZFP/tjQlNOIe/eeB8A5Ue1uU3d4TJs06X8WwQssIAAAAAFvQMgIAQKQUFdV9nJ0tkp/P+QaAgwhGAAA4qLhYpLQ0eBzRJBp0pKeLjBtXd72u0wMTkACAQTACAMDBQKR3b5HKykOnIz21WrL3HoxOmkKDDQ06fJGN0scanOg6ghEAMAhGAACQ2hhBA5HCwtqgRGVv/Uzyz2/mDNYacBB0AIAlghEAAAJoIDJgwMEH6/ZzbgAggghG6ql+60WpTk8Lfraqg6c/9TtgneY0VPrUQ+9GC9+OcPuntW1+GtXkNpa7elLDHDslxDn1aZNqffww+3tT0sMcP8X6+MnWrx82hWxiuO3J4lTVa5dJddvg569y5kOW+6ZPvNFye0L/EZbbPcnW70tY5IsEgEb573e7pd3e4Gnqa2qs/w3L62j9b2xyUstS/8LdSO0LAAAAwBYEIwAAAABsQTACAAAAwBYEIwAAAABswQB2AAACmdkO9wT8DgCIFIIRAADU1q0ikiMy7koR+bDurOk6ozoAoNURjNRz57gZ0iZECrqcNsFT4vkcm26d/rZnqnUa086HW6fO69Cjo+X2lCMOs9zu6drVcrt0sDj+EUda7uoNl/o3KdN6e1q7lqUlDpOa1xMutW6i9XsrCWE+KgkJzU9Ba3N62jsvmBSyzvdOtz5vE36V3rLUvVbppGuPEGY70Ip27qwNRmbcJzK6y6H1GogweSFi3LBL7xVPYvB/q5944leW+3btaJ1e30Ovf7QAwQgAAIG6dxcZcHAKdgBARBGMAADQSnxDTCwbU+qPQ6HlBYCLEYwAANBCGk/o0JJx42of6+8ac9QJSOo/ySfokwHAHQhGAABoIY0jNJ4oLa39qfGG/l4nvgh8kk/IJwOAOxCMAADQCjSWCBtPNOpJAOAeTHoIAAAAwBa0jNTzy345khEizevhp/SyPJmefidan+0w6XE9ncNsbx8mz31qmPS3SdZpVj1WaVYTW5ratgWpb2ufEGZ7uN1JERvK7IkjJTMleApfz+iLLE9r4vHDrM97m9SW1QsAQKu4c8ZNkto2I+i2M4/qbLlvSpJ1+nv+iUVLEIwAAGC3wAxbZNcC4CIEIwAA2CVYhi2yawFwEYIRAADsauyon2HLl13rzTdFegdMvEhrCYA4RTACAICdjR2BGbaYiwSAyxCMAADQihrb2BG00cNqLpJQBwh5MABwPoKRg7xer/m5q7o65MlK3bff8mR69lRZn+3de6z337XbentimMxE+2v/hpCS9lkfPyGS2bTCZLNycTat8oqKOnUwWnyvV14Vul57dldaHiOxvLbsIbWxrnNk03KnaNb5qqoqs/iUlZXVlqG8vMFzd1Xu0i3mZ7DtTdG+fe2iUlJE0tIaTrzuo9sKC2vjiIN7H1wOatNOJOVHIuNmhX/hlFSRGfcdenGX6NJxv3TJPmB3MUS6dKld6vHVp2h/zwe+ZpWp38FVhKvv+4NnXIyBf2Jhk4om1HmP145PhgNt2rRJ8vLy7C4GXOyrr76SHj16RO31qPOwW0lJiXTt2jWirzFt2jSZPn16RF8DcFKdr4/veji9zhOMHFRTUyNbtmyRjIwM8RDiN4ne8dFATitcZmZmsyusW+md2vz8fNmxY4e0j+LdTLfXeeqtfedD74FVVFRIbm6uJIRrVW3llhGt9z/88IN07NixQb2P1zoRr39XLP1t0azz8fJdHyvvbTCUXZpU5+mmdZCeqGjfrYg3+mURa18YThLtf6Co87Wot/acj6ysLImGlJQUswQKF/THa52I178rVv62aNX5ePuuj4X3NhS3lz2rkXWe6Y8BAAAA2IJgBAAAAIAtCEbQYtoF4t57723QFQKcPyej3nI+3FIn4vXvive/ze1i+b2l7E3DAHYAAAAAtqBlBAAAAIAtCEYAAAAA2IJgBAAAAIAtCEYAAAAA2IJgBAAAAIAtCEYAAAAA2IJgBAAAAIAtCEYAAAAA2IJgBAAAAIAtCEYAAAAA2IJgBAAAAIAtCEYAAAAA2IJgBAAAAIAtCEYAAAAA2IJgBAAAAIAtkux5WeepqamRLVu2SEZGhng8HruLAxfxer1SUVEhubm5kpAQvfsD1Hm4rc4r6j3sQJ2H23ib8D1PMHKQBiJ5eXnReH+AoEpKSqRr165ROzvUebitzivqPexEnYfblDTie55g5CBtETEn7T/rJfPg70A0lFdUSN7Rff11MFqo83BbnVfUe7i2zpeUSGZmZtRfH+5UXl5ubvI3ps4TjBzk65qlgQgfVtgh2t0DqfOwmx1dYqn3cG2dz8zk+gaOrPMMYAcAAABgC4IRAAAAALYgGAEAAABgC8aMAAAAwK+4WKS0tO4Jyc4Wyc/nJKH1EYwAAADAH4j07i1SWVn3hKSnixQVEZCg9dFNCwAAAIa2iGggUlgosnZt7aK/67r6rSVAa6BlBAAAAHVo68iAAZwURB4tIwAAAABsQTACAAAAwBYEIwAAAKi1dWvtTx2trqPZgQhjzAgAAECcqKqqMotPeXl543fW4OOSK0TkbZFxV4qkb6gNSoScvogcWkYAAADiREFBgWRlZfmXvLy8xu+s6bL27qn9fcZ9pNBCVBCMAAAAxImpU6dKWVmZfykpKWnegbp3b+2iAUHRTQsAACBOpKSkmKXVmG5aaZrs9+B4kpzWOzZAywgAAAAaaN++dtr1ceNqx4+oSy5hUDtaHS0jAAAAqCsnp7ZVRMeRFKWJjJPa8ST6OJ8B7Wg9BCMAAABoSIMOAg9EGAPYAQAAALgjGFm1apWMGTNGcnNzxePxyIsvvlhnu9frlWnTppntaWlpcvrpp8v69evDHnfRokXSp08fM2hLfy5ZsiSCfwUAAACAmAtGdu/eLf369ZNHHnkk6PZZs2bJgw8+aLavXr1aunTpImeffbZUVFSEPOa7774rl19+uVx11VXy8ccfm5+XXXaZvP/++xH8SwAAAADE1JiRUaNGmSUYbRWZM2eO3HXXXTJ27FizbsGCBdK5c2dZuHCh3HDDDUH30300YNHc2kp/rly50qx/9tlnI/jXAAAAAIiLMSMbN26Ubdu2yciRI/3rtNvV8OHD5Z133rFsGQncR51zzjmW+1RVVUl5eXmdBYhn1Hm4EfUeAJzNUcGIBiJKW0IC6WPftlD7NXWfgoICycrK8i95eXktLj/gZNR5uBH1HgCczVHBiI8ObK/ffav+upbuo125ysrK/EtJSUkLSw04G3UebkS9BwBnc9Q8IzpYXWmLRo5OtnPQ9u3bG7R81N+vfitIuH20+5cugFtQ5+FG1HsAcDZHtYx0797dBBbLli3zr9u3b58ZjD506NCQ+w0ZMqTOPur111+33AcAAACAy1pGdu3aJV9++WWdQesfffSRdOjQQfLz82XixIly//33S69evcyiv6enp8tPf/pT/z5XX321HHHEEaYvsLr99ttl2LBhMnPmTPnxj38sL730kixfvlzeeuutaP95AAAAAJwajKxZs0ZGjBjhfzxp0iTzc/z48TJ//nyZMmWK7NmzR26++WbZsWOHDBo0yLRyZGRk+PcpLi6WhIRDjTraAvLcc8/J3XffLffcc4/07NlTnn/+ebMvAAAAAGeKejCiM6rr4PJQdNC5zsCuSygrVqxosO6SSy4xCwAAAIDY4KgxIwAAAADcg2AEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAADYgmAEAAAAgC0IRgAAAAC4YwZ2AAAAOENxsUhp6cEHRWlSJL1tLhHchmAEAADApYFI794ilZW+NRqIPCPpqdWSnZ1ob+HgGnTTAgAAcCFtEdFApLBQZO1akbWFRbJWBkjRC59Jfr7dpYNb0DICAAAQJ6qqqsziU15eHnYfbR0ZMEB/2yMiH4rk7I9sIYEAtIwAAADEiYKCAsnKyvIveXl5dhcJsEQwAgAAECemTp0qZWVl/qWkpMTuIgGW6KYFAAAQJ1JSUswCxApaRgAAAADYgmAEAAAAgC0cF4x069ZNPB5Pg+WWW24J+vwVK1YEff7nn38e9bIDAAAAiOExI6tXr5bq6mr/43//+99y9tlny6WXXmq534YNGyQzM9P/uFOnThEtJwAAAIA4C0bqBxEPPPCA9OzZU4YPH2653+GHHy7t27ePcOkAAAAAxG0wEmjfvn1SWFgokyZNMl2vrPTv31/27t0rffr0kbvvvltGjBjR6pMCAbGMOg83ot4DgLM5bsxIoBdffFF27twp11xzTcjn5OTkyLx582TRokWyePFiOeaYY+TMM8+UVatWWR6bSYHgNtR5uBH1HgCczdHByBNPPCGjRo2S3NzckM/R4OP666+XAQMGyJAhQ2Tu3Lly3nnnyezZsy2PzaRAcBvqPNyIeg8AzubYblrffPONLF++3LR2NNXgwYNN9y4rTAoEt6HOw42o9wDgbI5tGXnyySfNoHRt5WiqDz/80HTfAgAAAOBcjmwZqampMcHI+PHjJSkpqUGT++bNm+Wpp54yj+fMmWPmJunbt69/wLuOH9EFAAAAgHM5MhjR7lnFxcVy7bXXNti2detWs81HA5DJkyebACUtLc0EJUuXLpXRo0dHudQAAAAAYj4YGTlypHi93qDb5s+fX+fxlClTzAIAAAAgtjh2zAgAAACA+EYwAgAAACB2g5GXXnrJzIAOAAAAABEJRt5//30zQDwjI0P+3//7f7Jx40Yz0eCtt94qd9xxR1MOBQAAAMDlmhSMTJw4USZMmCCrV6+WvLw8GTRokJxwwgmyYcMGufrqqyNXSgAAAADuDkb27NkjN910kxx77LFyzz33SGJiosydO9ek1AUAAACAiAUjCQl1n66znGtAAgAAAAARnWdEu2P96Ec/Cvn4gw8+aHIBAAAAALhTk4KRV155JXIlAQAAAOAqTQpGhg8f3qjnXXXVVfL00083t0wAAAAAXCAikx6uX78+EocFAAAAEEeYgR0AAACALQhGAAAAADh/zAgAAABcrKio9md2tkh+vt2lgduDka+//lpefvll6dmzp4wZM8a/nrlHAAAA4kxqmsi4cbW/p6fXBiYEJIhmN62zzz5bPvroI/P7li1b5KSTTpLXXntNfvGLX8jMmTP9z1u9enVLywUAAIAmqqqqkvLy8jpLq3nhBZG1a0UKC0UqK0VKS3l/EN1gZPPmzXLiiSea3xcuXGhS/b766qvy7rvvyjPPPCNxwesNvQAAADhYQUGBZGVl+Ze8vLzWO3hOjsiAASK9e7feMeF6TQpGUlNT/b+/8847Mnr0aPP7YYcdJklJDD8BAACw09SpU6WsrMy/lJSU8IYgfoKRhIQE2bRpk+zevVtWrlxZZxLESm2uawXTpk0Tj8dTZ+nSpYvlPlqWgQMHmmCpR48e8thjj7VKWQAAAGJJSkqKZGZm1lkAJ2tSc8avf/1rc9GfnJwsI0aMkKOPPtrfStKtW7dWK1Tfvn1l+fLljRoQv3HjRtNCc/3110thYaG8/fbbcvPNN0unTp3k4osvbrUyAQAAALAxGBk7dqyccsopsnXrVunXr59/vQYi8+bNa71CJSWFbQ3x0VaQ/Px8mTNnjnncu3dvWbNmjcyePZtgBAAAAHCwJg/06Ny5s1kC5ebmtmaZ5IsvvjDH1KbGQYMGyf3332+6XwWjg+dHjhxZZ90555wjTzzxhOzfv9+04oTKNqGLT6tmmwAciDoPN6LeA4CzOW4Gdg0+nnrqKZMy+PHHH5dt27bJ0KFD5fvvvw/6fN1ePzjSxwcOHJBSi5RzEc02ATgQdR5uRL0HAGdzXDAyatQo073q+OOPl7POOkuWLl1q1i9YsCDkPjrIPZD3YBre+usbl23CG3rxtnABbOTKDCt8Jl3PlfUeAGKI4/Pxtm3b1gQm2nUrGB1boq0jgbZv327GnXTs2DHkcbULmC6AW1Dn4UbUewBwNse1jATr71tUVCQ5OtFOEEOGDJFly5bVWff666+b2eFDjRcBAAAAYD/HBSOTJ08284Zoyt73339fLrnkEjO4fPz48f4m96uvvtr//BtvvFG++eYbmTRpkgla/vKXv5jB63ocAAAAAM7luG5aOqniFVdcYQaf61whgwcPlvfee0+OPPJIs13TChcXF/uf3717d3nllVfkjjvukD/+8Y8mC9fDDz9MWl8AAADA4RwXjDz33HOW2+fPn99gnc4Ev27dugiWCgAAAEDcd9MCAAAA4A6OaxmxXY1XpKYm+DaLVMGN2h7p7L7hXh+INWFTYofZ7uF+CwAATsa/1AAAAABsQTACAAAAwBYEIwAAAABsQTACAAAAwBYEIwAAAABsQTACAAAAwBak9q3vwP7aJZiEMLFbuO3iaeH+LU2DamPqX9IOIxhviDTaPjXV1tsTkzmvcF69jeT3MN+lAOIMLSMAAAAAbEEwAgAAAMAWBCMAAAAAbEEwAgAAAMAWBCMAAAAAbEEwAgAAAMAWpPatx7tvt3irEpqVRtSTkNiy1L2ecNs9Ed7egtg03LGdnHbYvLzNrx+v9ldZbw+VRtsnrV2rFgdolHAppa22t/R7Nux3qfVmvssAxBpaRgAAAADYgpYRAACAOFFVVWUWn/Ly8lY7dlGR75c0Eekv2W9+I/n6ODtbJN/8BsR+y0hBQYGcfPLJkpGRIYcffrhceOGFsmHDBst9VqxYIR6Pp8Hy+eefR63cAAAATriOysrK8i95eXktPqbGGunpIuPGiQwcKDJwXG8ZKOuk98SRUjzwQpHevUWKi1ul/HAfxwUjK1eulFtuuUXee+89WbZsmRw4cEBGjhwpu3fvDruvBi1bt271L7169YpKmQEAAJxg6tSpUlZW5l9KSkpafExt9NBWkbVrDy2Fc0qlUtpK6YzHRCorRUpLW6X8cB/HddP6xz/+Uefxk08+aVpI1q5dK8OGDbPcV5/Xvn37CJcQAADAmVJSUszS2jQgqdsTK7v2R/furf5acBfHBSP1aVSvOnToEPa5/fv3l71790qfPn3k7rvvlhEjRtjSpxJwIuo83Ih6DwDO5uhgxOv1yqRJk+TUU0+V4447LuTzcnJyZN68eTJw4EDzD8/TTz8tZ555phlLEqo1RftUTp8+veFrbvpSvO3Sg79Qaoj1/u1tW7Y9yTp1sIRLHRwmZaQnMdzbbZFSMmza4nCpcVuYOjdcWuRwxw+bureF22MgNXCoOt8S3rLt1k+orLDc7OnSo1XLAzS63mu68VApx/fttTyR3n17Qm7zJLWxfhOSw2wPk0I+/HchAMQWR3+rTZgwQT755BN59tlnLZ93zDHHyPXXXy8DBgyQIUOGyNy5c+W8886T2bNnR7VPJeBk1Hm4EfUeAJzNsS0jt956q7z88suyatUq6dq1a5P3Hzx4sBQWFka9TyXgVNR5uBH1HgCcLcmJXbM0EFmyZInpZtW9mQOjPvzwQ9N9CwAAAIAzOS4Y0bS+CxculJdeesnMNbJt2zazXnNlp6Wl+ZvdN2/eLE899ZR5PGfOHOnWrZv07dtX9u3bZ1pEFi1aZBYAAAAAzuS4YOTRRx81P08//fQGKX6vueYa87vOIVIcMLmOBiCTJ082AYoGLBqULF26VEaPHh3l0gMAAACI6W5a4cyfP7/O4ylTppgFAAAAQOxwXDBitwNPPioH2gQ/LQnZh1nu6zn8cOuDtw8zV0p76+NLuyzr18+w3t+b3s76+MmpoY/dJvQ2I9z2cOkow6U19oRLLWy93RMuNXG4tMfhtodNu+z81L/BVL/ypPUT/vul5ebEG/83Ls8L4sD+fSL7D801Faj64xXW++74LuSmhH6nWe7q6ZTXsu8SAIgzjk7tCwAAACB+EYwAAAAAsAXdtAAAANysqEhE9hz8CUQXwQgAAIAbbd0qIjki467UGdpq16Wni2Rn210yuAjBCAAAgBvt3FkbjMy4T2R0l9p1Gojk5zf9WIGtKs09BlyJYAQAAMDNuncXGdC7efu2b1/bmjJu3KF1+liDEwISNALBSD1Tnv5A2kjwdKO9063Tzx6ZYr29a0oby+2HHZZiub1jR+v0ucnZmZbbU/I6Wm6Xtm2bn7Y4K0xa4gzrskmGddpiCZO2OFxaY8kMk1Y5Jd16e5L1e+dJsn7vJNli/5pqsdPWc8+UXYnB04n+77rNlvs+ttM6tS9pSuFUNRvel5q2wT/3t5xxo+W+j0wYHnKb58wrWvRdAsScnJzawKO0tPax/q6BiT4mGEEjEIwAAACg+TToIPBAM5HaFwAAAIAtaBkBAABAszQYt855RBMRjAAAAKBJNPAIOm79r8kEJGgSghEAAAA0iQ4RCTpufWdSbTDiazIhzS/CIBgBAABA64xbr5/qlzS/CINgpJ7f/ekOyUwLkUI3KczpSguTHjZE+tRD+4dOrWukpFlu9iSntmh/q/SzntS2zU9d25h0luHOTYL1ufckhnlvEsIdP6FFrx92f09C88sWYVlHdpDM5OB/32P/WmG9c3KYlMaAQ3lLt4m3Mvh34h8XTLHcN/GSWyNUKiDOUv2S5heNQDACAACA1kOqXzQBwQgAAABaPbuWFKVJtuRJfoOUW+TcwiEEIwAAAGj17FoivSVdiqRoXG/Jl5LaVfqkxYtFOnUiMIFBMAIAAIBWza6laoeMtJU3Z6yU3t33iuzYIfKLX0j2udfXBicEJiAYAQAAQCSGivhbS+7pHvCstyU9tVoW3/2hdLrvdpFzp9auTk0T+e1vRQ477ND+7Q9Ifs5+WlDiHC0jB3m9XvOzfE9V6LMVLmOT19OyjFE1YTIyHagtYyie5Brr/feH2Z68P/Sx93sjnE0rIbLZtDyRzqblaXY2rfKKijp1MFp8r1ex/0DI5xwory1bSMn7WrtYcAG76nzga5ZX7g39pD17rb+uystbu1iIc9Gs81VVVWbxKSsrqy1DkHq7q3KXbjE/g21vKc3y+8EHIt9/f2idtpxoV65z7z5aRF49tEE/dvUS1aXJbimUcZKdsktkxn21B3SLjh1rgzCH69KldqnPV58aU+c9Xjv+NXCgTZs2SV5ent3FgIt99dVX0qNHj6i9HnUedispKZGuXbtG9TWp94j3Oj9t2jSZPn16RF8DaM06TzByUE1NjWzZskUyMjLE4wlzlxsNol8N5LTCZWZmcnaaSO9a5efny44dO6R9FO/6uL3OU2/tOx96D6yiokJyc3MlIVyrZCtzW713Wz136t8bzTpfv2VE6/wPP/wgHTt2bFDnnXq+YhnntOl1nm5aB+mJivYdunijX2R8mTVftC/KqPO1qLf2nI+srCyxg1vrvdvquRP/3mjV+ZSUFLMECnejy4nnK9ZxTqXRdT66Vz8AAAAAcBDBCAAAAABbEIygxbQ5+N57723QLAzOn5NRbzkfbuC2eu62v7elOF+cUydgADsAAAAAW9AyAgAAAMAWBCMAAAAAbEEwAgAAAMAWBCMAAAAAbEEwAgAAAMAWBCNolLlz50r37t0lNTVVBg4cKG+++WbI565YsUI8Hk+D5fPPP3fd2V61apWMGTNGcnNzzTl48cUXw+6zcuVKc471XPfo0UMee+yxqJTVLaZNm9agbnbp0kXcIlyd9Hq95hzp9rS0NDn99NNl/fr1tpUXTRfvdZw6HP1/10EdjCSCEYT1/PPPy8SJE+Wuu+6SDz/8UE477TQZNWqUFBcXW+63YcMG2bp1q3/p1auX68727t27pV+/fvLII4806vkbN26U0aNHm3Os5/rXv/613HbbbbJo0aKIl9VN+vbtW6dufvrpp+IW4erkrFmz5MEHHzTbV69ebS5izz77bKmoqIh6WdF88VzHqcP2/bsO6mBEeIEwfvSjH3lvvPHGOuuOPfZY769+9augz3/jjTe8WrV27NjBuQ2g52TJkiWW52TKlCnm3Aa64YYbvIMHD+ZctpJ7773X269fP85nkDpZU1Pj7dKli/eBBx7wr9u7d683KyvL+9hjj3HOYoSb6jh1ODr/roM6GEm0jMDSvn37ZO3atTJy5Mg66/XxO++8Y7lv//79JScnR84880x54403ONON8O677zY41+ecc46sWbNG9u/fzzlsJV988YXphqRdFH7yk5/I119/zbk92DK3bdu2OnVQZ2gePnx42M87nMWtdZw6HNl/10EdjASCEVgqLS2V6upq6dy5c531+lgvWoLRAGTevHmma9HixYvlmGOOMQGJ9vOFNT2nwc71gQMHzHuBlhs0aJA89dRT8tprr8njjz9uzvnQoUPl+++/d/3p9X2mm/J5h/O4uY5ThyPz7zqog5GUFNGjI27oAMhA2jpef52PBh+6+AwZMkRKSkpk9uzZMmzYsIiXNR7PdbD1aB7tF+1z/PHHm/rZs2dPWbBggUyaNInT2sTPO5yHOk4dbgw+55HF+W08WkZgKTs7WxITExvcLdm+fXuDuypWBg8ebLoNwJoOFg52rpOSkqRjx46cvgho27atCUqon7X1T7X08w5ncVMdpw5H7991UAdbC8EILLVp08ak/Fu2bFmd9fpYm/0bS7N1aPctWNO79PXP9euvvy4nnXSSJCcnc/oioKqqSoqKiqifImZ8gV7MBdZB7V+u6aab8nmHs7ipjlOHo/fvOqiDrYVuWghLu65cddVV5oJYL5Z1PIim/7vxxhvN9qlTp8rmzZtNH2U1Z84c6datm0ktqRcyhYWFZvyIG9PT7tq1S7788ss6gys/+ugj6dChg+Tn5zc4d3pONaWqnvPrr7/eDGh/4okn5Nlnn7Xxr4gvkydPNvNs6PnXO4H33XeflJeXy/jx48UNwtVJTfd5//33m1Tcuujv6enp8tOf/tTWcqPx4r2OU4cj/+86qINRFdFcXYgbf/zjH71HHnmkt02bNt4BAwZ4V65c6d82fvx47/Dhw/2PZ86c6e3Zs6c3NTXVe9hhh3lPPfVU79KlS71u5EtzXH/Rcxbs3KkVK1Z4+/fvb851t27dvI8++qhNpY9Pl19+uTcnJ8ebnJzszc3N9Y4dO9a7fv16r1uEq5Oa3ldTw2qK35SUFO+wYcO8n376qd3FRhPEex2nDkf+33VQB6PJo/+LbvgDAAAAAIwZAQAAAGATBrADAAAAsAXBCAAAAABbEIwAAAAAsAXBCAAAAABbEIwAAAAAsAXBCAAAAABbEIwAAAAAsAXBCCzNmTNHtm/f7n88bdo0mTx5cpPP2ooVK+T1119v1bO9a9cuOeeccyQ7O9ssQGvo1q2bHH744bJ//37/un/961/i8Xgs6/7pp58uPXr0kBNPPFGOOeYYueOOO6SmpoY3BTFXn0ePHm3qsS76vBNOOMH8ftppp5ntvnX9+vWTPn36yJNPPhm2HNdee635XOhxhg0bJh999FEr/pVA8y1evFgGDhxo6mbv3r3lzDPPDPndrdcy6enp5rla/wcNGiTvvfcep7+Fklp6AMR/MHLWWWeZf8xaQj/AGjyMHDmyVcp14MABSU5OlilTpkjHjh1NGYHWkp+fLy+//LJcfPHF5vFf/vIXOemkk8Lu9/DDD8v5558vFRUV0r9/fxkyZIhcdtllvDGIqfr8yiuv+H/XwOOdd96Rdu3a1XmOb92nn35qjnXuuedKTk5OyGNeeOGFMm/ePElKSpL/+7//M5+L//znP63y9wHNtW3bNrnxxhtl9erVcuSRR5p169atM/U+FA3A16xZY36fO3euCbQ/++wz3oQWoGUkDrz77rvmjpVG6Xq36qWXXjIfFL0Q0sc/+tGP5O233zbP/e6770xAcPzxx5ttP/vZz0Ie93//939ly5Ytcskll5i7AL47WbpuzJgx5gN5xhlnyA8//ODfZ/bs2eb1BgwYYO6ulZSUmP0ee+wxeeqpp8xx9LgaTGirhv4j1rdvX7nyyiulsrIy7B2+3/zmNzJixAgZP368pKSkmDsY7du3b7VzCSj9x0Uv2FRZWZm586UXW42VkZFh7rR98803nFDEfH22ov+W6Hfwpk2bLJ93wQUXmEBEDR482Hw2aDmE3bZu3Wrqpd7U9NHrF6tgJJBej/A933IEIzFOA4GLLrpIZs6cKR9//LG58NcgZOzYsaZL1SeffCIPPvigCSh2794thYWF5qJe72bptt/97nchj/0///M/kpubKy+88II5rgYS6v3335cFCxaYOwHaYvKnP/3JrF+4cKG506XBkd5ZuOKKK2TChAlmP73zcPXVV5vj6HETExPN8zVo+ve//y2ZmZnmDkM4xcXFpovBM88804pnEahLu5F8/fXXsnnzZnn22Wfl0ksvNXW2Kf/A6edRW0mAWK/PVlauXGm6yerNsMb6/e9/b25WJSRwCQJ7ab3VayZtPdRrqd/+9rfmc9JYen30k5/8JKJldAO6acU4vfDXFoqhQ4eax/rl/u2330qbNm1My4M69dRTTdCgwYfekXrooYfkzjvvlOHDh/uf0xSjRo2SDh06mN/1Q6yBjXrxxRdNcKF3hFV1dXXIf/C8Xq8px9KlS00rid6t038ww9GWnMbesQBa4qqrrjJBt9ZrDX4bEwDfdttt8qtf/Uo2bNhgAnHtfwzEan22ov/maGv2xo0b5W9/+5v5N6cx9IbYX//6V3nzzTdb9PpAa9BrpkWLFsnnn39uAutXX33V9MDQa5mjjjoq6D56I1ZvsmoXL71+0Ru0aBluS8QhvdAPdsGu6zR40NYJHXSlH8CTTz7ZBA1NkZqa6v9dgw39MPpe9+677zbH10WDlFCDFLVVRD/4q1atMs/TgZR79+4N+9r1+y0DkXLNNdeYMSBa33v16tWoffT52tK3du1a0y1G/2EDYrU+W9ExI19++aU88cQT5iaR3gQL5/nnn5fp06fLsmXLWjwOEWhNxx57rNxwww0mWNebtjrGKhS9AazXNtoNXVtTtJu5Xv+g+QhGYpzenSoqKjL/MCjtg9ulSxepqqoy3ZmUbtOMWNq3V+9i6QW9Dh78wx/+YLpV6cDyULT7lLZaNIb2CdauVr4xJJq95cMPPwx6nB07dpg+mtq3Xgf7zp8/v0XnAWht2kWxoKDAdIFsKh2PNWPGDPn1r3/NP1KI+focLsjRsXv333+/5fO0NURvVi1fvtx0iQGcQLtk+cbU+q5N9DqpZ8+eYffVJDra5VDHS2kQg+ajm1aMO+yww2TJkiWm25Ve1Gvrh14EaauHdhnRcSJ6J0yb0du2bWuyWukYEm3R0BYR7R+ZlZUV8vh6DL3rpanswgUM2g3g+++/NylOtRzaYnLdddeZrEJ69+Dpp582TZs6nuX22283A+31DsMRRxxhBuA3pZ9m4EAz7Z+vXyBdu3Y1g8n0dYDWYJXgIZybbrpJHnnkEZM20pfFCIjV+mxFAxztnqvZDfX7PBi9e6w3yn784x/71/3zn/+sM3AYiDa9TtGkOhqA6HWOPtYEOYH11Iruo926dIyuZoyjG3nzeLy0LQEAAACwAd20AAAAANiCblqQP//5z6Y7SX06psQ3464bywLUR/1EPGnt+sznA/FC50DzJefx0TnRmFYgMuimBQA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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Exercise 4.1 Part a: Custom Pair Plot Function\n", + "def my_pairplot(dataframe, features, title_suffix=\"Features\"):\n", + " \"\"\"\n", + " Creates a pair plot grid without using seaborn.\n", + " Diagonals: 1D histograms showing overlapping class distributions.\n", + " Off-diagonals: 2D histograms to visualize correlations between features.\n", + " \"\"\"\n", + " n = len(features)\n", + " fig, axes = plt.subplots(n, n, figsize=(n*2, n*2))\n", + " fig.suptitle(f'Pair Plots of SUSY {title_suffix}', fontsize=16)\n", + "\n", + " # Pre-split data to avoid repeated filtering inside the loops\n", + " sig = dataframe[dataframe['signal'] == 1]\n", + " bkg = dataframe[dataframe['signal'] == 0]\n", + "\n", + " for i in range(n):\n", + " for j in range(n):\n", + " ax = axes[i, j]\n", + " \n", + " if i == j: # Diagonal: 1D Histograms\n", + " ax.hist(sig[features[i]], bins=30, histtype='step', color='red', density=True)\n", + " ax.hist(bkg[features[i]], bins=30, histtype='step', color='blue', density=True)\n", + " elif i > j: # Lower triangle: 2D Histogram for Signal\n", + " ax.hist2d(sig[features[j]], sig[features[i]], bins=20, cmap='Reds')\n", + " else: # Upper triangle: 2D Histogram for Background\n", + " ax.hist2d(bkg[features[j]], bkg[features[i]], bins=20, cmap='Blues')\n", + "\n", + " # Formatting to keep the grid clean\n", + " if i < n - 1: ax.set_xticklabels([])\n", + " if j > 0: ax.set_yticklabels([])\n", + " if i == n - 1: ax.set_xlabel(features[j], fontsize=8)\n", + " if j == 0: ax.set_ylabel(features[i], fontsize=8)\n", + "\n", + " plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n", + " plt.show()\n", + "\n", + "# Running the function on a subset of features to keep it readable\n", + "my_pairplot(df, RawNames[:4], \"Low-Level\")\n", + "my_pairplot(df, list(FeatureNames)[:4], \"High-Level\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"\\nObservation on Speed:\\nCreating these plots is slow because standard Pandas indexing inside a nested loop \\nforces a full lookup for every single subplot (N^2 times). \\n\\nModification made: \\nTo speed up the function in Part A, I pre-split the dataframe into 'sig' and 'bkg' \\nobjects before entering the loops. This prevents the code from having to filter \\nthe entire 500,000-row dataframe at every single iteration.\\n\\nProposed faster method:\\nA much faster way to create these histograms would be to use 'np.histogram2d' to \\ncalculate the frequency matrices mathematically first, then use 'ax.imshow()' \\nto render them as images. This decouples the math from the plotting and avoids \\nmatplotlib's overhead of binning millions of points on the fly for every subplot.\\n\"" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Exercise 4.1 Part b: Speed Optimization Reasoning\n", + "\"\"\"\n", + "Observation on Speed:\n", + "Creating these plots is slow because standard Pandas indexing inside a nested loop \n", + "forces a full lookup for every single subplot (N^2 times). \n", + "\n", + "Modification made: \n", + "To speed up the function in Part A, I pre-split the dataframe into 'sig' and 'bkg' \n", + "objects before entering the loops. This prevents the code from having to filter \n", + "the entire 500,000-row dataframe at every single iteration.\n", + "\n", + "Proposed faster method:\n", + "A much faster way to create these histograms would be to use 'np.histogram2d' to \n", + "calculate the frequency matrices mathematically first, then use 'ax.imshow()' \n", + "to render them as images. This decouples the math from the plotting and avoids \n", + "matplotlib's overhead of binning millions of points on the fly for every subplot.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"\\nAnalysis:\\nBased on the distributions, the high-level features (FeatureNames) like M_R and MT2 \\nappear to be the best for separating signal from background. \\n\\nIn the low-level plots (RawNames), the signal and background distributions are \\nalmost perfectly overlapping (especially in variables like l_1_eta or l_1_phi), \\nmaking them poor classifiers on their own. However, the high-level engineered \\nfeatures show a clear 'shift' or difference in shape between the two classes, \\nwhich provides the model with a much stronger signal to learn from.\\n\"" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Exercise 4.1 Part c: Analysis of Observables\n", + "\"\"\"\n", + "Analysis:\n", + "Based on the distributions, the high-level features (FeatureNames) like M_R and MT2 \n", + "appear to be the best for separating signal from background. \n", + "\n", + "In the low-level plots (RawNames), the signal and background distributions are \n", + "almost perfectly overlapping (especially in variables like l_1_eta or l_1_phi), \n", + "making them poor classifiers on their own. However, the high-level engineered \n", + "features show a clear 'shift' or difference in shape between the two classes, \n", + "which provides the model with a much stronger signal to learn from.\n", + "\"\"\"" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1302,6 +1500,208 @@ "Write a function that takes a dataset and appropriate arguments and performs steps b and c. " ] }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting tabulate\n", + " Downloading tabulate-0.10.0-py3-none-any.whl.metadata (40 kB)\n", + "Downloading tabulate-0.10.0-py3-none-any.whl (39 kB)\n", + "Installing collected packages: tabulate\n", + "Successfully installed tabulate-0.10.0\n" + ] + } + ], + "source": [ + "!pip install tabulate" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Low-Level Features ---\n", + "Covariance Matrix:\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Variable l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi MET MET_phi
l_1_pT 0.467 -0 0 0.305 -0 0.001 0.228 -0.001
l_1_eta -0 1.004 -0.001 -0 0.408 -0.001-0.002 -0.001
l_1_phi 0 -0.001 1.004 0.001 0 -0.267 0.001 -0.185
l_2_pT 0.305 -0 0.001 0.425 -0.001 0 0.079 -0.002
l_2_eta -0 0.408 0 -0.001 1.006 0 0 -0
l_2_phi 0.001 -0.001 -0.267 0 0 1.004-0 -0.035
MET 0.228 -0.002 0.001 0.079 0 -0 0.762 -0.003
MET_phi -0.001 -0.001 -0.185 -0.002 -0 -0.035-0.003 1.003
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation Matrix:\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Variable l_1_pT l_1_eta l_1_phi l_2_pT l_2_eta l_2_phi MET MET_phi
l_1_pT 1 -0.001 0 0.684 -0.001 0.001 0.383 -0.001
l_1_eta -0.001 1 -0.001 -0 0.406 -0.001-0.002 -0.001
l_1_phi 0 -0.001 1 0.002 0 -0.266 0.001 -0.184
l_2_pT 0.684 -0 0.002 1 -0.001 0 0.14 -0.002
l_2_eta -0.001 0.406 0 -0.001 1 0 0 -0
l_2_phi 0.001 -0.001 -0.266 0 0 1 -0 -0.035
MET 0.383 -0.002 0.001 0.14 0 -0 1 -0.003
MET_phi -0.001 -0.001 -0.184 -0.002 -0 -0.035-0.003 1
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- High-Level Features ---\n", + "Covariance Matrix:\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Variable cos_theta_r1 M_R M_TR_2 S_R dPhi_r_b M_Delta_R R MET_rel axial_MET MT2
cos_theta_r1 0.039-0.014 0.052-0.01 0.009 0.039 0.058 0.055 -0.054 0.045
M_R -0.014 0.392 0.21 0.38 -0.029 0.074-0.113 0.044 0.017-0.037
M_TR_2 0.052 0.21 0.338 0.228 0.058 0.242 0.104 0.302 -0.185 0.189
S_R -0.01 0.38 0.228 0.382 -0.003 0.096-0.083 0.082 -0.041-0.011
dPhi_r_b 0.009-0.029 0.058-0.003 0.19 0.042 0.087 0.146 -0.025 0.021
M_Delta_R 0.039 0.074 0.242 0.096 0.042 0.389 0.165 0.415 -0.233 0.433
R 0.058-0.113 0.104-0.083 0.087 0.165 0.222 0.249 -0.181 0.232
MET_rel 0.055 0.044 0.302 0.082 0.146 0.415 0.249 0.79 -0.12 0.409
axial_MET -0.054 0.017 -0.185-0.041 -0.025 -0.233-0.181 -0.12 1.005-0.461
MT2 0.045-0.037 0.189-0.011 0.021 0.433 0.232 0.409 -0.461 0.738
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation Matrix:\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Variable cos_theta_r1 M_R M_TR_2 S_R dPhi_r_b M_Delta_R R MET_rel axial_MET MT2
cos_theta_r1 1 -0.116 0.451-0.085 0.106 0.319 0.627 0.316 -0.272 0.264
M_R -0.116 1 0.577 0.981 -0.106 0.189-0.383 0.078 0.027-0.068
M_TR_2 0.451 0.577 1 0.635 0.229 0.668 0.38 0.584 -0.317 0.379
S_R -0.085 0.981 0.635 1 -0.013 0.249-0.287 0.15 -0.067-0.021
dPhi_r_b 0.106-0.106 0.229-0.013 1 0.155 0.424 0.378 -0.057 0.056
M_Delta_R 0.319 0.189 0.668 0.249 0.155 1 0.564 0.748 -0.373 0.809
R 0.627-0.383 0.38 -0.287 0.424 0.564 1 0.595 -0.383 0.574
MET_rel 0.316 0.078 0.584 0.15 0.378 0.748 0.595 1 -0.134 0.535
axial_MET -0.272 0.027 -0.317-0.067 -0.057 -0.373-0.383 -0.134 1 -0.535
MT2 0.264-0.068 0.379-0.021 0.056 0.809 0.574 0.535 -0.535 1
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from IPython.display import HTML, display\n", + "import tabulate\n", + "\n", + "# Exercise 4.2 Part d: Function to handle the math and the table formatting\n", + "def make_stats_table(data, features, title):\n", + " # Part b: Using numpy for the actual math\n", + " # Need rowvar=False so it doesn't try to treat 500k rows as variables\n", + " vals = data[features].values\n", + " cov_mat = np.cov(vals, rowvar=False)\n", + " corr_mat = np.corrcoef(vals, rowvar=False)\n", + " \n", + " # Part c: Clean up the look with tabulate\n", + " # Rounding to 3 decimals so the table isn't a mess of long floats\n", + " headers = [\"Variable\"] + features\n", + " \n", + " # Building the rows for the tables\n", + " cov_rows = [[features[i]] + list(np.round(cov_mat[i], 3)) for i in range(len(features))]\n", + " corr_rows = [[features[i]] + list(np.round(corr_mat[i], 3)) for i in range(len(features))]\n", + " \n", + " # Printing everything out as HTML so it embeds nicely in the cell output\n", + " print(f\"\\n--- {title} ---\")\n", + " print(\"Covariance Matrix:\")\n", + " display(HTML(tabulate.tabulate(cov_rows, tablefmt='html', headers=headers)))\n", + " \n", + " print(\"Correlation Matrix:\")\n", + " display(HTML(tabulate.tabulate(corr_rows, tablefmt='html', headers=headers)))\n", + "\n", + "# Running it for both sets of features\n", + "make_stats_table(df, RawNames, \"Low-Level Features\")\n", + "make_stats_table(df, list(FeatureNames), \"High-Level Features\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1444,7 +1844,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "ds", "language": "python", "name": "python3" }, @@ -1458,7 +1858,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.12" + "version": "3.11.14" } }, "nbformat": 4, diff --git a/Lectures/.DS_Store b/Lectures/.DS_Store new file mode 100644 index 000000000..6ffb5a0dd Binary files /dev/null and b/Lectures/.DS_Store differ diff --git a/Lectures/Lecture.16/Lecture.16.pdf b/Lectures/Lecture.16/Lecture.16.pdf deleted file mode 100644 index d22cfc46c..000000000 Binary files a/Lectures/Lecture.16/Lecture.16.pdf and /dev/null differ