From f8c87e9a651142cfe41a405ea33de524d8d78b56 Mon Sep 17 00:00:00 2001 From: Itachii27 Date: Thu, 3 Oct 2024 15:46:34 +0000 Subject: [PATCH 1/2] Stock Analysis --- Stock Analysis/Dataset/README.md | 16 + Stock Analysis/Dataset/tesla-stock-price.csv | 758 ++++++++++++++++++ .../Images/cnn_stock_prediction.png | Bin 0 -> 45723 bytes .../Images/gru_stock_prediction.png | Bin 0 -> 48121 bytes .../Images/lstm_stock_prediction.png | Bin 0 -> 47355 bytes .../Images/rnn_stock_prediction.png | Bin 0 -> 49416 bytes Stock Analysis/Model/README.md | 114 +++ Stock Analysis/Model/model_cnn.ipynb | 163 ++++ Stock Analysis/Model/model_gru.ipynb | 169 ++++ Stock Analysis/Model/model_lstm.ipynb | 174 ++++ Stock Analysis/Model/model_rnn.ipynb | 181 +++++ Stock Analysis/requirements.txt | 5 + 12 files changed, 1580 insertions(+) create mode 100644 Stock Analysis/Dataset/README.md create mode 100644 Stock Analysis/Dataset/tesla-stock-price.csv create mode 100644 Stock Analysis/Images/cnn_stock_prediction.png create mode 100644 Stock Analysis/Images/gru_stock_prediction.png create mode 100644 Stock Analysis/Images/lstm_stock_prediction.png create mode 100644 Stock Analysis/Images/rnn_stock_prediction.png create mode 100644 Stock Analysis/Model/README.md create mode 100644 Stock Analysis/Model/model_cnn.ipynb create mode 100644 Stock Analysis/Model/model_gru.ipynb create mode 100644 Stock Analysis/Model/model_lstm.ipynb create mode 100644 Stock Analysis/Model/model_rnn.ipynb create mode 100644 Stock Analysis/requirements.txt diff --git a/Stock Analysis/Dataset/README.md b/Stock Analysis/Dataset/README.md new file mode 100644 index 000000000..393275f71 --- /dev/null +++ b/Stock Analysis/Dataset/README.md @@ -0,0 +1,16 @@ +# Dataset Information + +The dataset used in this project is Tesla stock price data. It contains information about stock prices including the open, high, low, close prices, and volume traded over a period of time. + +## Dataset Columns: +- `date`: Date of stock data +- `open`: Opening price +- `high`: Highest price during the day +- `low`: Lowest price during the day +- `close`: Closing price +- `volume`: Volume of stocks traded + +## Instructions: +1. The file `tesla-stock-price.csv` is used for stock price prediction. +2. Place this dataset inside the `Dataset/` folder in your local project directory. +3. The data should be in the format as explained above (with `date`, `open`, `high`, `low`, `close`, `volume` columns). diff --git a/Stock Analysis/Dataset/tesla-stock-price.csv b/Stock Analysis/Dataset/tesla-stock-price.csv new file mode 100644 index 000000000..68e0cdce0 --- /dev/null +++ b/Stock Analysis/Dataset/tesla-stock-price.csv @@ -0,0 +1,758 @@ +"date","close","volume","open","high","low" +"11:34","270.49","4,787,699","264.50","273.88","262.24" +"2018/10/15","259.5900","6189026.0000","259.0600","263.2800","254.5367" +"2018/10/12","258.7800","7189257.0000","261.0000","261.9900","252.0100" +"2018/10/11","252.2300","8128184.0000","257.5300","262.2500","249.0300" +"2018/10/10","256.8800","12781560.0000","264.6100","265.5100","247.7700" +"2018/10/09","262.8000","12037780.0000","255.2500","266.7700","253.3000" +"2018/10/08","250.5600","13371180.0000","264.5200","267.7599","249.0000" +"2018/10/05","261.9500","17900710.0000","274.6500","274.8800","260.0000" 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The models used are: +1. **Recurrent Neural Network (RNN)** +2. **Long Short-Term Memory (LSTM)** +3. **Gated Recurrent Unit (GRU)** +4. **Convolutional Neural Network (CNN)** + +The goal of the project is to compare these models' performance in terms of accuracy and prediction capabilities using Tesla stock price data. We evaluate model performance using **Mean Squared Error (MSE)** and visualize the predictions vs actual stock prices. + +## Models Implemented + +### 1. **Recurrent Neural Network (RNN)** + +RNNs are effective in handling time-series data as they have an internal state that helps them remember previous time steps. + +- **Architecture**: 2 RNN layers with 50 units each. +- **Loss function**: Mean Squared Error (MSE). +- **Optimizer**: Adam. +- **Visualization**: Actual vs predicted stock prices are plotted and saved as `rnn_stock_prediction.png`. + +### 2. **Long Short-Term Memory (LSTM)** + +LSTMs are a type of RNN that can capture long-term dependencies in time-series data, making them more effective in stock price prediction. + +- **Architecture**: 2 LSTM layers with 50 units each. +- **Loss function**: Mean Squared Error (MSE). +- **Optimizer**: Adam. +- **Visualization**: Actual vs predicted stock prices are plotted and saved as `lstm_stock_prediction.png`. + +### 3. **Gated Recurrent Unit (GRU)** + +GRUs are similar to LSTMs but have fewer gates, making them computationally more efficient while retaining the ability to capture long-term dependencies. + +- **Architecture**: 2 GRU layers with 50 units each. +- **Loss function**: Mean Squared Error (MSE). +- **Optimizer**: Adam. +- **Visualization**: Actual vs predicted stock prices are plotted and saved as `gru_stock_prediction.png`. + +### 4. **Convolutional Neural Network (CNN)** + +CNNs, typically used for image data, can also be adapted for time-series data to capture patterns and trends over time. + +- **Architecture**: 2 Conv1D layers with 32 and 64 filters, followed by MaxPooling and Dense layers. +- **Loss function**: Mean Squared Error (MSE). +- **Optimizer**: Adam. +- **Visualization**: Actual vs predicted stock prices are plotted and saved as `cnn_stock_prediction.png`. + +## Data Preprocessing + +The dataset used for training is Tesla's stock prices, which includes columns like **Date**, **Close Price**, **Volume**, **Open Price**, **High**, and **Low**. The dataset is located in the `Dataset` folder, and the actual data is too large to include directly. A link to the dataset can be found in the `Dataset/README.md` file. + +- **Data Loading**: The data is loaded using `pandas` from the CSV file. +- **Data Scaling**: The stock prices are scaled using `MinMaxScaler` to normalize the values between 0 and 1. +- **Sequence Creation**: The data is split into sequences to train the RNN, LSTM, GRU, and CNN models. We use a 60-day window to predict the next day's price. + +## Visualizations + +For each model, the following visualizations are generated: +- **Actual vs Predicted Prices**: A plot showing the actual stock prices vs the predicted prices for the test data. +- **Mean Squared Error (MSE)**: The performance of each model is measured using MSE. The lower the MSE, the better the model's predictions. + +### 1. **RNN Visualization** + +- **File**: `rnn_stock_prediction.png` +- **Description**: This image shows the predicted stock prices from the RNN model compared to the actual prices over time. + +### 2. **LSTM Visualization** + +- **File**: `lstm_stock_prediction.png` +- **Description**: This image shows the predicted stock prices from the LSTM model compared to the actual prices over time. + +### 3. **GRU Visualization** + +- **File**: `gru_stock_prediction.png` +- **Description**: This image shows the predicted stock prices from the GRU model compared to the actual prices over time. + +### 4. **CNN Visualization** + +- **File**: `cnn_stock_prediction.png` +- **Description**: This image shows the predicted stock prices from the CNN model compared to the actual prices over time. + +## Results and Conclusions + +- **RNN Model**: While the RNN model performs adequately in capturing the sequential patterns in stock prices, it is limited in handling long-term dependencies, which leads to some inaccuracies in longer time frames. + +- **LSTM Model**: The LSTM model outperforms the RNN model as it can capture long-term dependencies more effectively, leading to better predictions on unseen data. + +- **GRU Model**: The GRU model performs similarly to the LSTM but is computationally more efficient with fewer parameters. It also performs well in predicting stock prices. + +- **CNN Model**: The CNN model can capture short-term patterns in the stock prices effectively, but it may struggle with long-term trends compared to LSTM and GRU. + +In conclusion, while all models can predict stock prices to some extent, the **LSTM** and **GRU** models provide the most accurate predictions due to their ability to capture long-term dependencies in the data. + +## File Structure + +- **Dataset**: Contains the Tesla stock price data (link provided in `Dataset/README.md`). +- **Images**: Stores all the visualizations (predicted vs actual prices). +- **Model**: Contains the Jupyter Notebooks (`.ipynb`) for each model and their respective visualizations. + +## Requirements + +To run the models, make sure you have the following Python libraries installed: +- `numpy` +- `pandas` +- `matplotlib` +- `scikit-learn` +- `tensorflow` + +These can be installed by running: +```bash +pip install -r requirements.txt diff --git a/Stock Analysis/Model/model_cnn.ipynb b/Stock Analysis/Model/model_cnn.ipynb new file mode 100644 index 000000000..f0d13dec3 --- /dev/null +++ b/Stock Analysis/Model/model_cnn.ipynb @@ -0,0 +1,163 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten,Dense\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\surbh\\AppData\\Local\\Temp\\ipykernel_19948\\3417560682.py:3: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", + " df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True, errors='coerce')\n", + "C:\\Users\\surbh\\AppData\\Local\\Temp\\ipykernel_19948\\3417560682.py:3: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", + " df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True, errors='coerce')\n" + ] + } + ], + "source": [ + "df = pd.read_csv('../Dataset/tesla-stock-price.csv')\n", + "\n", + "df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True, errors='coerce')\n", + "df.set_index('date', inplace=True)\n", + "\n", + "df.head()\n", + "\n", + "data = df['close'].values.reshape(-1, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaled_data = scaler.fit_transform(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "X, y = [], []\n", + "for i in range(60, len(scaled_data)):\n", + " X.append(scaled_data[i-60:i, 0])\n", + " y.append(scaled_data[i, 0])\n", + "\n", + "X = np.array(X)\n", + "y = np.array(y)\n", + "\n", + "X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\surbh\\AppData\\Roaming\\Python\\Python312\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.2017 \n", + "Epoch 2/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0200\n", + "Epoch 3/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0112 \n", + "Epoch 4/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0091\n", + "Epoch 5/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0085\n", + "Epoch 6/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0083\n", + "Epoch 7/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0075\n", + "Epoch 8/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0073\n", + "Epoch 9/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0059\n", + "Epoch 10/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0060\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# CNN Model\n", + "\n", + "model = Sequential()\n", + "model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(X_train.shape[1], 1)))\n", + "model.add(MaxPooling1D(pool_size=2))\n", + "model.add(Flatten())\n", + "model.add(Dense(units=1))\n", + "\n", + "model.compile(optimizer='adam', loss='mean_squared_error')\n", + "model.fit(X_train, y_train, epochs=10, batch_size=32)\n", + "\n", + "predictions = model.predict(X_test)\n", + "\n", + "# Save the plot as 'cnn_stock_prediction.png'\n", + "plt.plot(y_test, label='Actual Prices')\n", + "plt.plot(predictions, label='Predicted Prices')\n", + "plt.title('Stock Price Prediction with CNN')\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Stock Price')\n", + "plt.legend()\n", + "plt.savefig('../Images/cnn_stock_prediction.png')" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Stock Analysis/Model/model_gru.ipynb b/Stock Analysis/Model/model_gru.ipynb new file mode 100644 index 000000000..e94eface7 --- /dev/null +++ b/Stock Analysis/Model/model_gru.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import GRU, Dense\n", + "\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\surbh\\AppData\\Local\\Temp\\ipykernel_5240\\3417560682.py:3: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", + " df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True, errors='coerce')\n", + "C:\\Users\\surbh\\AppData\\Local\\Temp\\ipykernel_5240\\3417560682.py:3: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", + " df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True, errors='coerce')\n" + ] + } + ], + "source": [ + "df = pd.read_csv('../Dataset/tesla-stock-price.csv')\n", + "\n", + "df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True, errors='coerce')\n", + "df.set_index('date', inplace=True)\n", + "\n", + "df.head()\n", + "\n", + "data = df['close'].values.reshape(-1, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaled_data = scaler.fit_transform(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "X, y = [], []\n", + "for i in range(60, len(scaled_data)):\n", + " X.append(scaled_data[i-60:i, 0])\n", + " y.append(scaled_data[i, 0])\n", + "\n", + "X = np.array(X)\n", + "y = np.array(y)\n", + "\n", + "X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\surbh\\AppData\\Roaming\\Python\\Python312\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 47ms/step - loss: 0.2734\n", + "Epoch 2/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 0.0186\n", + "Epoch 3/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 46ms/step - loss: 0.0077\n", + "Epoch 4/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 46ms/step - loss: 0.0046\n", + "Epoch 5/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 43ms/step - loss: 0.0029\n", + "Epoch 6/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 48ms/step - loss: 0.0020\n", + "Epoch 7/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 47ms/step - loss: 0.0016\n", + "Epoch 8/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - loss: 0.0017\n", + "Epoch 9/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 70ms/step - loss: 0.0017\n", + "Epoch 10/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 69ms/step - loss: 0.0017\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 104ms/step\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# GRU Model\n", + "model = Sequential()\n", + "model.add(GRU(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))\n", + "model.add(GRU(units=50, return_sequences=False))\n", + "model.add(Dense(units=1))\n", + "\n", + "model.compile(optimizer='adam', loss='mean_squared_error')\n", + "model.fit(X_train, y_train, epochs=10, batch_size=32)\n", + "\n", + "predictions = model.predict(X_test)\n", + "\n", + "plt.plot(y_test, label='Actual Prices')\n", + "plt.plot(predictions, label='Predicted Prices')\n", + "plt.title('Stock Price Prediction with GRU')\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Stock Price')\n", + "plt.legend()\n", + "plt.savefig('../Images/gru_stock_prediction.png')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Stock Analysis/Model/model_lstm.ipynb b/Stock Analysis/Model/model_lstm.ipynb new file mode 100644 index 000000000..69db84aa2 --- /dev/null +++ b/Stock Analysis/Model/model_lstm.ipynb @@ -0,0 +1,174 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import LSTM\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\surbh\\AppData\\Local\\Temp\\ipykernel_15024\\3417560682.py:3: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", + " df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True, errors='coerce')\n", + "C:\\Users\\surbh\\AppData\\Local\\Temp\\ipykernel_15024\\3417560682.py:3: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", + " df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True, errors='coerce')\n" + ] + } + ], + "source": [ + "df = pd.read_csv('../Dataset/tesla-stock-price.csv')\n", + "\n", + "df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True, errors='coerce')\n", + "df.set_index('date', inplace=True)\n", + "\n", + "df.head()\n", + "\n", + "data = df['close'].values.reshape(-1, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaled_data = scaler.fit_transform(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "X, y = [], []\n", + "for i in range(60, len(scaled_data)):\n", + " X.append(scaled_data[i-60:i, 0])\n", + " y.append(scaled_data[i, 0])\n", + "\n", + "X = np.array(X)\n", + "y = np.array(y)\n", + "\n", + "X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\surbh\\AppData\\Roaming\\Python\\Python312\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 40ms/step - loss: 0.1845\n", + "Epoch 2/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 0.0139\n", + "Epoch 3/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0060\n", + "Epoch 4/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0054\n", + "Epoch 5/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 0.0044\n", + "Epoch 6/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 0.0045\n", + "Epoch 7/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0044\n", + "Epoch 8/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 0.0038\n", + "Epoch 9/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 0.0041\n", + "Epoch 10/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - loss: 0.0037\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 86ms/step\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# LSTM Model\n", + "model = Sequential()\n", + "model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))\n", + "model.add(LSTM(units=50, return_sequences=False))\n", + "model.add(Dense(units=1))\n", + "\n", + "model.compile(optimizer='adam', loss='mean_squared_error')\n", + "model.fit(X_train, y_train, epochs=10, batch_size=32)\n", + "\n", + "predictions = model.predict(X_test)\n", + "\n", + "plt.plot(y_test, label='Actual Prices')\n", + "plt.plot(predictions, label='Predicted Prices')\n", + "plt.title('Stock Price Prediction with LSTM')\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Stock Price')\n", + "plt.legend()\n", + "plt.savefig('../Images/lstm_stock_prediction.png')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Stock Analysis/Model/model_rnn.ipynb b/Stock Analysis/Model/model_rnn.ipynb new file mode 100644 index 000000000..9ba54db92 --- /dev/null +++ b/Stock Analysis/Model/model_rnn.ipynb @@ -0,0 +1,181 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import SimpleRNN, Dense\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\surbh\\AppData\\Local\\Temp\\ipykernel_23840\\3417560682.py:3: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", + " df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True, errors='coerce')\n", + "C:\\Users\\surbh\\AppData\\Local\\Temp\\ipykernel_23840\\3417560682.py:3: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", + " df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True, errors='coerce')\n" + ] + } + ], + "source": [ + "df = pd.read_csv('../Dataset/tesla-stock-price.csv')\n", + "\n", + "df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True, errors='coerce')\n", + "df.set_index('date', inplace=True)\n", + "\n", + "df.head()\n", + "\n", + "data = df['close'].values.reshape(-1, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "scaled_data = scaler.fit_transform(data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "X, y = [], []\n", + "for i in range(60, len(scaled_data)):\n", + " X.append(scaled_data[i-60:i, 0])\n", + " y.append(scaled_data[i, 0])\n", + "\n", + "X = np.array(X)\n", + "y = np.array(y)\n", + "\n", + "X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\surbh\\AppData\\Roaming\\Python\\Python312\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 15ms/step - loss: 0.1578\n", + "Epoch 2/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0086\n", + "Epoch 3/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0055\n", + "Epoch 4/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0036\n", + "Epoch 5/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0028\n", + "Epoch 6/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0027\n", + "Epoch 7/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0025\n", + "Epoch 8/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0024\n", + "Epoch 9/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0020\n", + "Epoch 10/10\n", + "\u001b[1m18/18\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.0015\n", + "\u001b[1m5/5\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 70ms/step\n" + ] + }, + { + "data": { + "image/png": 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fTHp6OkuWLLHsMxgMvP3223h7ezNmzJgGz+Eoo0aNIikpiSVLlljCVFqtlhEjRvD6669TVVXVYL6Np6cn4NhrayoefvhhqqqqeP311+tdp4qVhIQEh3LlQAiiuLi4BkWd5PxBem4k7YZ77rmH0tJSpk+fTvfu3amsrGTz5s0sWbKEmJgYZs+ebVk7aNAg1qxZw+uvv25x9Q8dOpTnnnuO1atXM3LkSO666y5cXFz44IMPqKio4OWXX7Yc/9BDD/HDDz9wzTXXcMsttzBo0CByc3NZvnw577//vt18h7lz51JYWMj//d//4efnx+OPP94svxcVX19f/ve//3HbbbcxePBgrr/+egICAtizZw+lpaUsXrwYrVbLxx9/zEUXXUSvXr2YPXs2HTt2JDU1lb/++gtfX19++eUXp9q4cOFCbrrpJgYOHMi1115LSEgIycnJ/Pbbb1x44YW88847ACxYsIBLLrmEkSNHcsstt5Cbm8vbb79Nr1697Aqz6rzwwgusWrWKMWPGcPvtt9OjRw/S0tL4/vvv2bhxI/7+/vTv3x+dTsdLL71EQUEBbm5ujB8/3qZnksrtt9/OBx98wKxZs9i5cycxMTH88MMPbNq0iTfeeAMfH58m+x2pwuXIkSO88MILlv2jR4/m999/t/TNqQ8PDw969uzJkiVL6Nq1K4GBgfTu3btJ85Bq0rNnTy6++GI+/vhjnnjiiVptAaqjhpocHdmh0+n4v//7P5u/ccl5TssVakkkTcvvv/+u3HLLLUr37t0Vb29vRa/XK3Fxcco999yjZGRk2Kw9fPiwMnr0aMXDw0MBbMrCd+3apUyZMkXx9vZWPD09lXHjximbN2+udb2cnBxl7ty5SseOHRW9Xq906tRJmTlzppKdna0oim0peHUefvhhBVDeeeedOl9L9RLZ+lBLqu2VNNcsBVdZvny5MmLECMXDw0Px9fVVhgwZonzzzTc2a3bv3q1ceeWVSlBQkOLm5qZER0cr//rXv5S1a9fWa09T2K0o4nc3ZcoUxc/PT3F3d1e6dOmizJo1S9mxY4fNuqVLlyo9evRQ3NzclJ49eyo//vhjrbJnRaldCq4oinLq1Cnl5ptvVkJCQhQ3Nzelc+fOyt13361UVFRY1nz00UdK586dFZ1OZ1MWXrMUXFEUJSMjQ5k9e7YSHBys6PV6pU+fPspnn33m8O/Hno11ERoaqgA27+uNGzcqgDJq1Kha6+39TjZv3qwMGjRI0ev1NteuXmpdHbUsuyHGjBmj9OrVy+5z69ats7lWfb8P9T1CPaXg1amqqlK6dOkiS8EliqIoikZRZP2cRCKRSCSS9oPMuZFIJBKJRNKukOJGIpFIJBJJu0KKG4lEIpFIJO0KKW4kEolEIpG0K6S4kUgkEolE0q6Q4kYikUgkEkm74rxr4mcymThz5gw+Pj7N3oJcIpFIJBLJ2aEoCkVFRURERDQ4O+28EzdnzpyxzAeSSCQSiUTStkhJSaFTp071rjnvxI3aBj0lJcUyJ0gikUgkEknrprCwkMjISIfGmZx34kYNRfn6+kpxI5FIJBJJG8ORlBKZUCyRSCQSiaRdIcWNRCKRSCSSdoUUNxKJRCKRSNoV513OjUQikUgaxmQyUVlZ2dJmSM4z9Hp9g2XejiDFjUQikUhsqKys5OTJk5hMppY2RXKeodVqiY2NRa/Xn9N5pLiRSCQSiQVFUUhLS0On0xEZGdkkd9ESiSOoTXbT0tKIioo6p0a7UtxIJBKJxILBYKC0tJSIiAg8PT1b2hzJeUZISAhnzpzBYDDg6up61ueRklwikUgkFoxGI8A5hwUkkrNBfd+p78OzRYobiUQikdRCzt6TtARN9b6T4kYikUgkEkm7QoobiUQikUicjEajYdmyZU1+3piYGN54440mP29bR4obiUQikbQbtmzZgk6n45JLLmn0sS0pFGbNmoVGo0Gj0aDX64mLi+OZZ57BYDDUe9z27du5/fbbm8nKtoMUNxKnUWEwYjDKPhkSiaT5+OSTT7jnnnv4+++/OXPmTEub0yimTp1KWloax44d44EHHuCpp57ilVdesbtWbbAYEhIiq9rs0CrEzbvvvktMTAzu7u4MHTqUbdu21bl20aJFFnWr/nN3d29GayWOUF5lZPyr65n+3mYURWlpcyQSyXlAcXExS5Ys4c477+SSSy5h0aJFtdb88ssvDB48GHd3d4KDg5k+fToAY8eO5dSpU/znP/+xfLcAPPXUU/Tv39/mHG+88QYxMTGWn7dv386kSZMIDg7Gz8+PMWPGsGvXrkbb7+bmRnh4ONHR0dx5551MnDiR5cuXA8KzM23aNJ5//nkiIiLo1q0bUNvblJ+fz7///W/CwsJwd3end+/e/Prrr5bnN27cyKhRo/Dw8CAyMpJ7772XkpISy/Pvvfce8fHxuLu7ExYWxtVXX93o19EaaHFxs2TJEubNm8f8+fPZtWsX/fr1Y8qUKWRmZtZ5jK+vL2lpaZZ/p06dakaLJY5wPLOY1Pwy9qUWUFhev1tVIpG0XhRFobTS0CL/Gntj9N1339G9e3e6devGjTfeyKeffmpzjt9++43p06dz8cUXs3v3btauXcuQIUMA+PHHH+nUqRPPPPOM5bvFUYqKipg5cyYbN25k69atxMfHc/HFF1NUVNQo+2vi4eFhMwJj7dq1HDlyhNWrV9sIFhWTycRFF13Epk2b+PLLLzl48CAvvvgiOp0OgMTERKZOncpVV13F3r17WbJkCRs3bmTu3LkA7Nixg3vvvZdnnnmGI0eO8McffzB69Ohzeg0tRYs38Xv99deZM2cOs2fPBuD999/nt99+49NPP+XRRx+1e4xGoyE8PLw5zZQ0kqQc653Amfwy/DzOvhmTRCJpOcqqjPR8cmWLXPvgM1Pw1Dv+NfXJJ59w4403AiLEU1BQwPr16xk7diwAzz//PNdeey1PP/205Zh+/foBEBgYiE6nw8fHp9HfL+PHj7f5+cMPP8Tf35/169dz6aWXNupcIATl2rVrWblyJffcc49lv5eXFx9//HGdPYjWrFnDtm3bOHToEF27dgWgc+fOlucXLFjADTfcwP333w9AfHw8b731FmPGjGHhwoUkJyfj5eXFpZdeio+PD9HR0QwYMKDR9rcGWtRzU1lZyc6dO5k4caJln1arZeLEiWzZsqXO44qLi4mOjiYyMpIrrriCAwcO1Lm2oqKCwsJCm38S55OUbStuJBKJxJkcOXKEbdu2cd111wHg4uLCjBkz+OSTTyxrEhISmDBhQpNfOyMjgzlz5hAfH4+fnx++vr4UFxeTnJzcqPP8+uuveHt74+7uzkUXXcSMGTN46qmnLM/36dOn3uaKCQkJdOrUySJsarJnzx4WLVqEt7e35d+UKVMwmUycPHmSSZMmER0dTefOnbnpppv46quvKC0tbdRraC20qOcmOzsbo9FIWFiYzf6wsDAOHz5s95hu3brx6aef0rdvXwoKCnj11VcZMWIEBw4coFOnTrXWL1iwwEalS5qHpBzrH4QUNxJJ28XDVcfBZ6a02LUd5ZNPPsFgMBAREWHZpygKbm5uvPPOO/j5+eHh4dFoG7Raba3wWFVVlc3PM2fOJCcnhzfffJPo6Gjc3NwYPnx4o6eqjxs3joULF6LX64mIiMDFxfYr2svLq97jG3p9xcXF/Pvf/+bee++t9VxUVBR6vZ5du3axbt06Vq1axZNPPslTTz3F9u3b8ff3b9RraWlaPCzVWIYPH87w4cMtP48YMYIePXrwwQcf8Oyzz9Za/9hjjzFv3jzLz4WFhURGRjaLrc3C3u/AUA4Db25pS2yo7rk5LcWNRNJm0Wg0jQoNtQQGg4HPP/+c1157jcmTJ9s8N23aNL755hvuuOMO+vbty9q1ay1pEDXR6/W12v6HhISQnp6OoiiWJOOEhASbNZs2beK9997j4osvBiAlJYXs7OxGvw4vLy/i4uIafZxK3759OX36NEePHrXrvRk4cCAHDx6s9xouLi5MnDiRiRMnMn/+fPz9/fnzzz+58sorz9qulqBF37HBwcHodDoyMjJs9mdkZDgc83R1dWXAgAEcP37c7vNubm64ubmds62tkooi+OkOUIzQoT906NvSFlmw9dyUt6AlEomkvfPrr7+Sl5fHrbfeip+fn81zV111FZ988gl33HEH8+fPZ8KECXTp0oVrr70Wg8HAihUreOSRRwBRefT3339z7bXX4ubmRnBwMGPHjiUrK4uXX36Zq6++mj/++IPff/8dX19fyzXi4+P54osvuOCCCygsLOShhx46Ky/RuTJmzBhGjx7NVVddxeuvv05cXByHDx9Go9EwdepUHnnkEYYNG8bcuXO57bbb8PLy4uDBg6xevZp33nmHX3/9lRMnTjB69GgCAgJYsWIFJpPJUpnVlmjRnBu9Xs+gQYNYu3atZZ/JZGLt2rU23pn6MBqN7Nu3jw4dOjjLzNZL7kkhbAB2ftaytlSjqLyK7OIKy88yLCWRSJzJJ598wsSJE2sJGxDiZseOHezdu5exY8fy/fffs3z5cvr378/48eNtWo8888wzJCUl0aVLF0JCQgDo0aMH7733Hu+++y79+vVj27ZtPPjgg7Wun5eXx8CBA7npppu49957CQ0Nde6LroOlS5cyePBgrrvuOnr27MnDDz9s8Ub17duX9evXc/ToUUaNGsWAAQN48sknLaE8f39/fvzxR8aPH0+PHj14//33+eabb+jVq1eLvJZzQaO0cBOSJUuWMHPmTD744AOGDBnCG2+8wXfffcfhw4cJCwvj5ptvpmPHjixYsAAQb75hw4YRFxdHfn4+r7zyCsuWLWPnzp307NmzwesVFhbi5+dHQUGBjfJukxz4Cb6fJbb13vDAYXDzaVGTAPanFnDp2xstP3fwc2fLY02fxCeRSJqe8vJyTp48SWxsrOwhJml26nv/Neb7u8UDqTNmzCArK4snn3yS9PR0+vfvzx9//GFJMk5OTkartTqY8vLymDNnDunp6QQEBDBo0CA2b97skLBpd+SesG5XFsO+7+GCW1rOHjNqGXhUoCfJuaVkFJZTZTThqmvxtkoSiUQiOQ9ocXEDMHfuXEsToZqsW7fO5uf//e9//O9//2sGq9oAqrjx6QBFabD9Uxg0G5poZPzZoiYTXxAdQHphOZUGE+kF5UQGyhbhEolEInE+8la6LZN7UjyOnAc6N8jYB6k7W9YmrMnEscFeRPgJt6LMu5FIJBJJcyHFTVtG9dx0HAS9zWV6Oz5tOXvMqJ6bmGAvIvxFxcCZAiluJBKJRNI8SHHTVqksEaEogMBYa67N/qVQltdydmHNuYkJsoqb1DwpbiQSiUTSPEhx01bJSxKP7v7gGQidBkNYb9HQb+/3LWaWKAMXXTmjgz3pqIob2etGIpFIJM2EFDdtFTUkFWgeiqbRwICbxHbCVy1jE3DKnG8T5KXH193VIm5kzo1EIpFImgspbtoqqrgJ6mLd1+ca0LpCWgJk1D1M1JlYQlLBYgZKhBQ3EolEImlmpLhpq9T03AB4BUG3qWI74evmt4lqycRBqrgR1VKp+WW1hs9JJBKJROIMpLhpq9gTNwD9bxCPe5eA0XZybXNwMluEpWKCRE8b1XNTWmmkoCAfqsqEXVLoSCSSNsqsWbOYNm2a5eexY8dy//33N7sd69atQ6PRkJ+f36TnTUpKQqPR1BoQ2paQ4qatova4qSlu4iaBVyiUZMGxVc1u1qkaYSl3Vx3B3nr+5/ou/m/EwPPh8GywePz5bsg+1uw2SiSS9sesWbPQaDRoNBr0ej1xcXE888wzGAwGp1/7xx9/5Nlnn3VorbMESV3ExMRYfi9eXl4MHDiQ77+vv+gkMjKStLQ0evfu3Sw2OgMpbtoiVeVQcFps1xQ3OhfoN0Nst0BoSs25iTWLG4CBPvlM122yXWgoh91fwjuDYclNUJDanGZKJJJ2yNSpU0lLS+PYsWM88MADPPXUU7zyyit211ZWVjbZdQMDA/Hxafm5fnXxzDPPkJaWxu7duxk8eDAzZsxg8+bNdtdWVlai0+kIDw/HxaVVDDE4K6S4aYvknwIUcPMFz6Daz6uhqaN/wPE1sOtz+PM5OLrKqeEgmzLwIOuohStYB0Bq4DB47DQ8kgS3rIRuF4vXcWg5/Hq/0+ySSCTnB25uboSHhxMdHc2dd97JxIkTWb58OWANJT3//PNERETQrVs3AFJSUvjXv/6Fv78/gYGBXHHFFSQlJVnOaTQamTdvHv7+/gQFBfHwww/Xyh+sGZaqqKjgkUceITIyEjc3N+Li4vjkk09ISkpi3LhxAAQEBKDRaJg1axYAJpOJBQsWEBsbi4eHB/369eOHH36wuc6KFSvo2rUrHh4ejBs3zsbO+vDx8SE8PJyuXbvy7rvv4uHhwS+//AIIz86zzz7LzTffjK+vL7fffrvdsNSBAwe49NJL8fX1xcfHh1GjRpGYmGh5/uOPP6ZHjx64u7vTvXt33nvvPctzlZWVzJ07lw4dOuDu7k50dLRlGLazaLuy7HzGkm8Ta3+OVGgPiBgIZ3bBl1fZPhczCqY8Dx36NblZahl4sLceH3dXsdNkZGSxCI9tC7iU6erU8qhh4t/JDbD4UvFoqAQXfZPbJZFIzgFFgarSlrm2q+c5zcrz8PAgJyfH8vPatWvx9fVl9erVAFRVVTFlyhSGDx/Ohg0bcHFx4bnnnmPq1Kns3bsXvV7Pa6+9xqJFi/j000/p0aMHr732Gj/99BPjx4+v87o333wzW7Zs4a233qJfv36cPHmS7OxsIiMjWbp0KVdddRVHjhzB19cXDw+Rl7hgwQK+/PJL3n//feLj4/n777+58cYbCQkJYcyYMaSkpHDllVdy9913c/vtt7Njxw4eeOCBRv9OXFxccHV1tfFcvfrqqzz55JPMnz/f7jGpqamMHj2asWPH8ueff+Lr68umTZssIb+vvvqKJ598knfeeYcBAwawe/du5syZg5eXFzNnzuStt95i+fLlfPfdd0RFRZGSkkJKSkqjbW/U63Tq2SXOIceslmuGpKozah78eLvw7ATHg2cwHPwZkjbAB2Ng6B0wdYFjHxwmIyy5ETRauGYR6FztLjthrpSKDrKGpEj8E7+qTHIVb9ZpBjO95kExI4WNpTlwZjdEDW3YHolE0nxUlcILES1z7cfPgN6r4XU1UBSFtWvXsnLlSu655x7Lfi8vLz7++GP0enET9eWXX2Iymfj444/RmD8LP/vsM/z9/Vm3bh2TJ0/mjTfe4LHHHuPKK8WIm/fff5+VK1fWee2jR4/y3XffsXr1aiZOnAhA587Wz+rAwEAAQkND8ff3B4Sn54UXXmDNmjUMHz7ccszGjRv54IMPGDNmDAsXLqRLly689tprAHTr1o19+/bx0ksvOfx7qays5LXXXqOgoMBGnI0fP95GKNX0CL377rv4+fnx7bff4uoqPv+7du1qeX7+/Pm89tprlt9RbGwsBw8e5IMPPmDmzJkkJycTHx/PyJEj0Wg0REdHO2zz2SLFTVukrkqp6vS4THwwVBcv4/8La58WIxr+WQixo6H7xQ1fL3UXHFkhtje8DmMfsT63+yv4+2UYOJPEMlGGHhfibX1+1+cALDOOJLnQWPvcGg1EXyhCU0kbpLiRSCRnza+//oq3tzdVVVWYTCauv/56nnrqKcvzffr0sQgbgD179nD8+PFa+TLl5eUkJiZSUFBAWloaQ4daP5dcXFy44IIL6mxtkZCQgE6nY8yYMQ7bffz4cUpLS5k0aZLN/srKSgYMGADAoUOHbOwALEKoIR555BH++9//Ul5ejre3Ny+++CKXXHKJ5fkLLrig3uMTEhIYNWqURdhUp6SkhMTERG699VbmzJlj2W8wGPDz8wNESHDSpEl069aNqVOncumllzJ58mSHbD9bpLhpizgibqC2VyYgGq7+FPyjYOP/YPUTED+pTk+MhcS11u2/X4ZuF0GHvnB8LSy/BxQjrH2aK92+ZYXmVuLDeoi1Jdlw5HcAvjOOJa+uRn4xo4S4ObUJeLB+WyQSSfPi6ilulFrq2o1g3LhxLFy4EL1eT0RERK2EWC8vWy9QcXExgwYN4quvand1DwkJaby9YAkzNYbi4mIAfvvtNzp27GjznJub21nZUZ2HHnqIWbNm4e3tTVhYmMVLpVLz91KT+l6TavtHH31US3zpdDoABg4cyMmTJ/n9999Zs2YN//rXv5g4cWKtnKKmRIqbtohF3HSpf11djJwnKpVyjosp4kP/Xf/6xD/Foxo+WnYnTFsI388SwiZ2NKTtIbr8CL/qHyc7eQ8kz4TT28BURVVYfw6fikJTVEGlwYTepUYee8yF4jH5H9EDpyGxJZFImg+N5qxCQy2Bl5cXcXFxDq8fOHAgS5YsITQ0FF9fX7trOnTowD///MPo0aMB4ZHYuXMnAwcOtLu+T58+mEwm1q9fbwlLVUf1HBmNVk92z549cXNzIzk5uU6PT48ePSzJ0Spbt25t+EUCwcHBjfq91KRv374sXryYqqqqWt6bsLAwIiIiOHHiBDfccEOd5/D19WXGjBnMmDGDq6++mqlTp5Kbm2sJ0zU1slqqrWGohAJzIlZDnpu6cPeFcY+L7XUL6p8iXpYPp3eI7Ru+FwInYz98PAEqCiFqBNzwA1V3bOFP0wDcNAY6HvsKPp0Mq54AwOWCm3Fz0aIokF5gZ4BmSA/wCISqEjiTcHavSSKRSBrJDTfcQHBwMFdccQUbNmzg5MmTrFu3jnvvvZfTp0W7jfvuu48XX3yRZcuWcfjwYe666656e9TExMQwc+ZMbrnlFpYtW2Y553fffQdAdHQ0Go2GX3/9laysLIqLi/Hx8eHBBx/kP//5D4sXLyYxMZFdu3bx9ttvs3jxYgDuuOMOjh07xkMPPcSRI0f4+uuvWbRokbN/RQDMnTuXwsJCrr32Wnbs2MGxY8f44osvOHLkCABPP/00CxYs4K233uLo0aPs27ePzz77jNdffx2A119/nW+++YbDhw9z9OhRvv/+e8LDwy05R85Aipu2Rn4yKCZw9QLv0LM/z4Cbhagoy4N1L0LmYTi8Qnh0KqtVRiRtEN6ZoDjoOAguEclsGCshIAZmfAkubiRV+HJL5YPMMT2O0ncG6L0BBfTeaPpcbRmgeTrPTtWFVgvRI6zXk0gkkmbA09OTv//+m6ioKK688kp69OjBrbfeSnl5ucWT88ADD3DTTTcxc+ZMhg8fjo+PD9On1yqNsGHhwoVcffXV3HXXXXTv3p05c+ZQUiIKLjp27MjTTz/No48+SlhYGHPnzgXg2Wef5YknnmDBggX06NGDqVOn8ttvvxEbGwtAVFQUS5cuZdmyZfTr14/333+fF154wYm/HStBQUH8+eefFBcXM2bMGAYNGsRHH31k8eLcdtttfPzxx3z22Wf06dOHMWPGsGjRIovtPj4+vPzyy1xwwQUMHjyYpKQkVqxYgVbrPAmiUc6zgT+FhYX4+flRUFBQpxuyVbPrC1g+F8L7wh3nKASOr6ldKg4waDZc9gaKoqD5bZ4IXQ35N1z8snh+1RNw4i+46hMIEb0iVuxL466vdtEv0p+f775QCKQT68CvE3Toy6zPtrHuSBYLruzDdUOial9z6/vwxyMQNxFuXHpur0sikZw15eXlnDx5ktjYWNzd3VvaHMl5Rn3vv8Z8f8ucm7bGzkXisVf9dw4Oncp1EAb3CxlavgmT3gdtQAxk7IPdX3Ci221c9e0Z/nRdSQBAl2o9HSbXbjN+LEMklXUNNVdK6T1tKrHEIM0sSwfjWljybraC0SA6LUskEolEchbIsFRbIn0fpO4ArQsMuPGsT5OYVcycz3dw1cLNXJt/J33LP+LJHivgzo1CxJgMZP72HD5lpwmoSAWtq+hHUw/HMosAiA/ztvt8VKCoekjOqaMZWGgvcPeHymJI23PWr00ikUgkEilu2gA7knK55K0NpK41t7Pufil4h2Iwmpj79S7u+WY3JpNj0cXyKiPXf7SV1Qcz0GpgXPdwCvHih12p5JVUwrj/AnBB/kpu1onOwoaOg8HNvmhROZ4pPDfxofbnq8QEC3GTVJe40WpFvxuQeTcSiUQiOSekuGnlmEwK/122n5NnMvE79pPYecFsAL7Yeopf96bxy54zJGYVO3S+YxnFZBRW4Ovuwqr/jOaTmRfQs4Mv5VUmvt6WDJ0Gcdx/JC4aE7e5iB41GSEj6j2nwWjiRJYIN8WF1uW5EaWkyTkldTa/soSmTm2y/7xEIpFIJA4gxU0r57d9aRxOL+Jy3Wa8KSPHrRPEjCazsJzXVh21rNudku/Q+Q6lFwLQu6MfcaE+aDQabhslMtoXb06ioKyK/8u/3OaYve6D6j3nqdxSKo0mPFx1lqqomkQGeqDRQEml0TJcsxZq6Ct5K5hMDr0eiUTiHM6zWhNJK6Gp3ndS3LRiDEYT/1stBMy/vdYD8GHJKPadKeK53w5RXGGwrN2dnO/QOQ+nidyY7uHWTPNL+0YQ6uNGZlEFd3yxk3/KO7FeNwyAXMWbraUd7Z5LRU0mjg/zRqu1P6vKzUVHhJ8QPsm5dSQVh/YU+T0VhVCQ7NDrkUgkTYvaVbb6YEWJpLlQ33fq+/BskSUprZifdqdyIruEER7JxFYew6Bx5XvDGH79ciep+WVoNXDn2C68+1ciCY56btKE56Z7B2tujN5Fy8wRMbyy8ghbTogJullDHqN47z18mj+UQxn1TwQ+bk4mriskpRId5ElqfhlJ2aUMirbTlVLnKkrLM/ZDxkHRR0cikTQrLi4ueHp6kpWVhaurq1N7kUgk1TGZTGRlZeHp6VlrdEZjkeKmlVJpMPHm2mMAPNLpAKSAoevFVB0OJNc8o+mmYdHcOCyad/9K5Eh6IaWVBjz1df+XKorCYXNYqke4bY+A64dE8fafxyivMuHj5sLUsSNJ6r2Rd97eiH9Gkeh5U8cE8WMNJBOrRAd5sjkxh1O59Yil0J5C3GQedGyop0QiaVI0Gg0dOnTg5MmTnDp1qqXNkZxnaLVaoqKi6vy+cRQpblopS3akcDqvjBAfN/pUitJo996X82B0N+YvP0CwtxvzJnfDz8OVMF83Mgor2He6gKGdg+o8Z2ZRBXmlVWg1tUu2A7z0zLggksVbTnH90Ci83VyIC/VGp9WQX1pFZlEFYb72G3pZwlINem5EUvGpunrdAIT1hH0IcSORSFoEvV5PfHy8DE1Jmh29Xt8k3kIpblopn29OAuCBC4PRrtsndsaM4iavUHzcXegV4Yefh2h93T/Sn5UHMkhIya9X3Kghqc4h3ri71o5nPn5JD0Z3DWF0VzEN191VR0yQJ4lZJRxOL7IrbowmxVKp1TWsAc+NudfNqbrKwUH0uwERlpJIJC2GVquVHYolbRYZTG2FnMgq5lhmMS5aDZf5nwQUCOkOPmFotRquHNiJbuFWITEgKgCgwbybw+lqMrF9EeLmomNCjzBcdda3hXqdI+ZwVk1SckupMJhwd9XSMcB+pZSKw54bgJxjYkioRCKRSCSNRIqbVsjqgxkADO8ShFequedL7Og61/eP9AcaFjeq56ZHB8dnanULE2tVYVQTNd+mS4gIYdVHVJDw3OSVVlFQVmV/kW9HcPMDkwGyj9pfI5FIJBJJPUhx0wpZZRY3k3uGwcm/xc56xE2fjn5oNZBWUE5GYTkAheVVfP1PMkXlVhFhLQOvP3xUHavnxipuCsur+HZbMq+sPMx7644DDefbAHi7uRDs7QbUM4ZBo7F6b2TejUQikUjOAplz08rILCpnV3IeAJOjNbDyCKCxjiawg5ebC13DfDicXsTu5Hwm9wzj7q92seFYNruS83j1mn5UGIyW3JjGeG5UIXQssxiD0YRJgSvf22wZt6Cieo8aIjrIk+ziCk7lltCnk5/9RaE9IXkLZBxw2E6JRCKRSFSkuGllrD2UiaJAv05+hOVsFzvD+4Cnnb4w1RgQ5c/h9CISUvJJLyhjw7FsAH5OSOWhKd3IKa7EYFLwdXehg5/jSYJRgZ54uOooqzJyKreUv49mcTyzGH9PVy7rG0FkoAedg70Z2y3EofNFB3my81Re/UnF0nMjkUgkknNAiptWxqoD6QBM7hUOJ5eInfWEpFT6R/rzzbYUVh1IJ9XcB8fX3YXCcgOLNicRFyLCRt07+Daqf4BWq6FrmDd7Thew7WSupffOw1O6c/3QqMa8NACiAx1IKpYVUxKJRCI5B2TOTSuiuMLApuOiQ7Btvs2YBo/tHykqpk5kl1BhMDG6awivXNMPgK+2nmKnOdTVoxH5Nipq3s2CFYfIL62ia5g3/7qgU6PPAw5MBwcI7SEeC09DWf5ZXUcikUgk5y9S3LQi1h/JotJoIjbYizh9DuQlgUYH0cMbPDYu1Bsvvehd4+/pyitX92VSjzBig70oLDfw3fYUQHhuGks3czfjwnIxy+rxi3vgoju7t06UuddNnQnFAB7+4GsWT5mHzuo6EolEIjl/keKmFbHqoDkk1TMMTdJGsbPjIHBr2Nui02oY2z0UrQZevLIPYb7uaLUabh0pJn4bTGLSamOSiVW6VWvON7prCGO7hTb6HCox5l436YXllFcZ614o824kEolEcpZIcdNKMJkU/jycCcDkXo6VgNfktWv6seGR8Uzt3cGy76qBnQj00gOiyrprWMMl2zXp0cEHF60GrQb+7+IejT6+Ov6ervi4i1Sv5IZmTIEUNxKJRCJpNFLctBJS8kopKjegd9HSr5M/nEkQT0QNc/gc7q46Ovrbdgn20Ou4aVg0ILwm9Q3WrIsgbzc+uGkQn80eYtMZ+WzQaDQW701Sdn2dimVSsUQikUjODlkt1Uo4mmHt9OuCEXITxRMh3c/53LeOiuV0XhkTe5x9OGlCj7BztkMlKsiTfakFDcyYUj03B0BRhNtJIpFIJBIHkOKmlXA0Q3QA7hbmDbknxfgBVy/wO7uqpOr4urvy2r/6nfN5mgo1qfh0Xj3iJrgraF2gvAAKz4Bfx2ayTiKRSCRtHRmWaiWo4iY+zAeyj4idwfHt0mPRyTxg83ReWd2LXPQQ3E1sp+9tBqskEolE0l6Q4qaVoIaluob5QJZZ3IR0a0GLnEenANVzU4+4AYgYIB5TdznZIolEIpG0J6S4aQUYTYpl7lO3MB/rNOzgri1olfOwem5KURSl7oUdzeLmjBQ3EolEInEcKW5aAadySqg0mPBw1Ykv/nbuuVErukoqjeSXVtW9sLrnpj4RJJFIJBJJNaS4aQWo+TZxod5oUSBbzG+y5Jy0M9xddYT4uAENhKbCeoPWFcpyIf9UM1knkUgkkraOFDetAJt8m8JUqCoRlUKBsS1smfOoHpqqExc3CO8ttmXejUQikUgcRIqbVoDqueka5m2tlArsAjrXFrTKuTieVDxQPMq8G4lEIpE4iBQ3rQCruPGBLHMycUj7TCZWcchzA9DRLG5SdzvZIolEIpG0F6S4aWGqjCZOmscQdA2v3uOmfebbqDjU6wasnpu0BDDVM2hTIpFIJBIzUty0MEnZJVQZFbzdXIjwc6/muWnv4sbBsFRIN3D1hMpia6K1RCKRSCT1IMVNC6MmE8eFeqPRaKp5bs6fsFS9vW60OuhgHh1xRoamJBKHyD0Br8TB8nta2pKmwVAB616C9P0tbYmkjSDFTQtzxDJTygdKcqA0RzwRHN+CVjkfh3vdgEwqlkgay6a3oCQLdn0hZtW1dQ7+DOtegK9nQGUDeXoSCVLcNDu7k/OY8No6Pt14EkVROGaZKVWtUsovCvReLWil83G41w1USyqW4kYiaZCSHNjzjfkHBXZ80qLmNAlqSLrwNGx607pfUWD1fPjiSil6JDa0CnHz7rvvEhMTg7u7O0OHDmXbtm0OHfftt9+i0WiYNm2acw1sQlbsSyMxq4Rnfj3I078c5Eh69UoptTNx+w5JqThcMaV2Kk7fB4ZKJ1slkbRxdnwKhnJw9xM/7/qi7X/xV2/iuekNyE8R2xv/J35OXAuJf7aEZZJWSouLmyVLljBv3jzmz5/Prl276NevH1OmTCEzM7Pe45KSknjwwQcZNWpUM1naNKTmW70UizYnccJcKdUtvPpMqfadTKzicFJxYGdw9wdjBWQedL5hEklbxVAB2z4U2xe9DP5RUJ4P+5e2qFnnTJ5Z3Lh6CeG2+kk49Ausfdq6JnlLy9gmaZW0uLh5/fXXmTNnDrNnz6Znz568//77eHp68umnn9Z5jNFo5IYbbuDpp5+mc+fOzWjtuaN+kV85sCN6nfj1+7q7EOrjJj03daHRWL03MqlYIqmbfd9DSSb4REDvq2DwbWL/tg+t89nS98Hur8Bkajk7G4vquZn6AqCBAz/CUvNrU28GU/5pEdMkrZMWFTeVlZXs3LmTiRMnWvZptVomTpzIli11q/BnnnmG0NBQbr311uYws0lJNYubW0fGsviWIYT7unPlwE7mSqnzzXPjYK8bgLBe4lEVgBKJxBZFgS3viu2h/xYdzgfcBC7ukL4XkjbAmqfhgzHw811wZEXL2usoVeVQlCa2u18Gg2aKbUM5dBkP134tfj6TAFUOfJZIzgtcWvLi2dnZGI1GwsLCbPaHhYVx+PBhu8ds3LiRTz75hISEBIeuUVFRQUVFheXnwsLCs7b3XCmrNJJTInJGOvl74hfhypbHxgthU1UGBeY4cjsvA1dxOCwF1uqxHNnrRnJ+UVhexfR3NzG8SxDPTetT98ITf4mwrauXVQB4BkLvqyHhS/hiOpgM1vWJa6HHpc41vilQPxf13uL1jH8Cjq0BzwC4+jORW+QdBsUZougg5sKWtVfSKmjxsFRjKCoq4qabbuKjjz4iODjYoWMWLFiAn5+f5V9kZKSTrawbNd/G280FXw+hKzUajXhSTZDT+4g/4PMAh3vdAASZxY1s5Cc5z0hIzicxq4Qvtyaz5mBG3Qv3fi8e+18HHgHW/UPM4RuTAbxCYci/xc8n1jvH4KZGzbfxjxYhaq9guH8v3L4ePPzFvsihYk3K1hYzU9K6aFHPTXBwMDqdjowM2z/YjIwMwsPDa61PTEwkKSmJyy67zLLPZI4bu7i4cOTIEbp06WJzzGOPPca8efMsPxcWFraYwFFzSzoFeFhFjUp+snj0jxJ/rOcBNXvdBHjp616sem7yk4Wb2tW9GSyUSFqe/DJrH6j5yw8wIi4IT32Nj26TEY6tEts9r7B9LmIATHwKSrJh1AOiMeb2jyA3EQpOg18n576AcyU/STz6R1n3aXW2a6KGw6HlkCzzbiSCFvXc6PV6Bg0axNq1ay37TCYTa9euZfjw4bXWd+/enX379pGQkGD5d/nllzNu3DgSEhLsihY3Nzd8fX1t/rUUqudG/VK3Qf0DDohuPoNamEb1uvEKATc/QBEfyhLJeUJBqbX9QWp+GW//ebz2otSdUJot/kaian92MvI/MOV54RV297M2xmwL3hvVc1PfZ2OU6rn5p20lSkucRouHpebNm8dHH33E4sWLOXToEHfeeSclJSXMnj0bgJtvvpnHHnsMAHd3d3r37m3zz9/fHx8fH3r37o1eX8+dfytATSbuGGBH3Fhcr1G1n2vHNKpiKjhObJ9laCohJZ/SSkPDCyWSVkSeuYN3VKDIUfvo7xOW5p8Wjv4hHuMmiETihug8RjyebAPiJr9aWKouwvuKGXTl+dZmqJLzmhYXNzNmzODVV1/lySefpH///iQkJPDHH39YkoyTk5NJS0trYSubBtU7Yd9zo4alzh/PDTQyqTjo7JOKl+85w7R3N7Fghf1EdYmktaKOJ7m4Twcm9gjFYFJ4/Kd9lFUarYuOrhSPXac6dtJYs7g5sd5aIt5accRzo3OFjoPEdrLMu5G0AnEDMHfuXE6dOkVFRQX//PMPQ4cOtTy3bt06Fi1aVOexixYtYtmyZc43sglQw1LqF7oN+dJz0yBq3k22Hbd8A/y65wwAfx/Lqv3kqS2w51vpzpa0SvLLRFjK39OV+Zf1wsNVx/akPKa/t4kTWcXixihjP2i0ED/JsZNGDhUl4sXp1hYUrRVHPDcAUcPEY3vtd2OoEN2mS3Nb2pI2QasQN+cL9YalVM/NeZRzA1Zxk+LEcvAKg5FNx7MBOJVTSl5JtREOWUfh88vhp3/DL/eIxEyJpBVRYPbc+Hu4EhnoyWezBxPsredwehGXv7OJ/evMVVKRQx2vtHR1t1YYtea8m/JCKMsT29U+GwvKqiipqBFijjSLm/bqudnyDiyfC+sWtLQlbQIpbpqJSoOJjKJywE5YqqLYOg38PPPcxASJAaFJ5jEU9RJUzXPTCFf6jqQ8Sqq58PemFogNkwl+uQ+MZrGz+0tYdicYZV6OpPWgVkv5e4pcmmGdg/jt3lEMiQmkuMJA1s6fxcKuUxp34raQd6N6bTwCwc0HEMJmwmvrueb9LbYtJCIHAxrIOwlF9ZTMt1VUEXp6e8va0UaQ4qaZSCsoQ1HAzUVLsHeNxGfVa+Pubx12d54QGyzETXJuKVXGBsJCgZ0BDVQUQImd8FId/HXYdk7ZnpR8sbFrMSRvFk3PprwAWhfYuwSW3grGqtonkkhagPxSNSxl/dwI83Xn6zlDGRPjyQited6ao/k2KrFjxWPShtbrsbSTb7MjKZfs4goOphWSVWxt0Iq7n7WT+V/PQ+7JZjTUyRgNcHqH2M48JG/AHECKm2aiekiqdo8bBxLm2inhvu54uOowmBRSchvIu3F1t3q2GpEnsO6oEEKDY0Rjsz0p+VCUDqvniwXj/wvD74Z/fQ5aVzi4DPb/2MhXIpE4h4IanhsVF52Wy32P4qapIlcfASHdG3fiiP6idLy8ANISmsbYpsZOvs2u5DzL9tH0Ytv1ceZRPrsWw1v9YdGlsOMzKDzjZEOdTMY+qDJ7tw3lkHuiZe1pA0hx00ycrrfHTbUGfucZWq2GGLP35qQjoangxnUqTskt5XhmMTqthrvHiVLyPafzUVY8JDxAEQPEHB6A7pdYt6XrV9IKUBTFUi3l72H23BRliMGXKx5m8ul3ANihH9z45p9aHcSMFNuHf2sqk5sWO56bnaeqiZuaJfHjnxAjGbqMBzTCK/Xr/fB6D/hgNOz9zvk2O4OaeUQZ+1rGjjaEFDfNhFrq3KneHjfnn+cGoHOIEDcnshqRd5PjWMXUuiMiJDUoKoBhnYNw0WqYUrYCzaHloNHB5W/bdjsN7yseM/Y7bL9E4iyKKwwYTCKvxN/TVeSafThWDL7c9gE+peLGaFmVncZ9jtDnKvG4+W3IONgEFjcxNW78qowm9qQUWJ6uJW50LtD7SrjpJzGiYfx/oZM5Fydtj8ixa60huPpQxY3W3Jk6XX4+NYQUN81Eap4jZeDnqbgxe25OOOS5aVwjv7+OiJDU2O4huLvquCb4FE+5LBZPjv8/CK8xiDC8t3hM3y9LwyUtjuq1cXPR4u6qE5VDReYQy7C7KJj6LqMr/scfhVFUGs7i/drrSpGrY6wUFYOO5JodXQmvxMPxtQ2vPVcsn40xABxOK6KsyipOaomb6vhHweiH4LY18OBRUfpeVQp5Sc6z1xkoilXc9LhcPGYcaDl72ghS3DQTqfkin8R+WOr8zbmB6p6b4gZW0qhGfuVVRjYnihLwcd1CIS+J/5YswFVj5EDgJBg5r/ZBwV1Bp4fKIuv/i0TSQtTKt1ET6d38YOoCfIfeQLZrBCbFwV5RNdFo4LI3xaDN9L3w96sNH7PrcyjJhK3vNf56jUFRaoWldp4SPV7Uz9FjGcUND90F8A61hrSz2lgjz7wk0Y9I6woDbxb7pGe5QaS4aSYsc6Xq63FzHubcAMQGewONzLnJOwWGynqX/nMyl/IqE+G+7nQP1MA31+FlLGCvKZYF+rvt5yjoXK2JmfIDRNLC1Mq3KTZX/nmHAKDRaIhW2ynkOPD3Yw+fcLjYLGo2vApndts8rSgKH284wW2Ld1BSXmWt2jn5N1TU4zk5V0pzrEm0fmJu4M7kfACuGtgRF62GogoDaQXljp0vpId4zGyF4bf6UL02Ef2tXZgLU2UzvwaQ4qYZMJoU0vLFH2CtnJuyfFGtAOexuBEfzplFFRSVN+AW9+kAem9QjKKfRT2o+TZju4Wg2fMtZB7E4BnK7ZXz2JlagaGu0nM1VCXj2pIWpnp3YkB4TAC8Qi1rYoNFqDsp+yw8Nyq9r4Ke08BkgFVPWHYrisJzvx3iud8OseZQBjv27RdeBBChrMS/zv6aDaF6bXw6iEpJYJc5mXhY5yDL50a9oanqhJpvWjLbmOcmxSxuooaBu681fUGGpupFiptmIKOwHINJwUWrIdTH3fZJNfThGQx6r+Y3rhXg5+Fq6f3T4Ae0RgNBjuXdJJoTlAdGBVhasmuH3EaxWxhlVUaO1xUGCzPn3UjPjaSFsXhuVHFTbA5LmT03wLl7bkD8XY37P7F9ejsYqzCaZ1h9stF6E6FN3Wl7nDqw0xnkJ4lH85d5ekE5qfllaDXQL9KfruGiqZ/D4kb13LS1sJTquVGnvas3X/LzqV6kuGkG1JBUB393dNqaPW7Oz7ELNYm1JBU7kHdjKQevv9dNZqHwloX5uVtKu7WRg+nTUTRKtDTzq4klqViWW0paFkvOjRqWsnhurOImNqgRrRTqIyhONMIzlEPmQR5ZupdvtqWg1UBMkPAOeWYliLXB3cTj0ZXOqz6qkW+j9rfpHu6Ll5sLXUNVcePAZwZYPTfZR9tOE7zSXKsYU8dlqI0KpWe5XqS4aQZS65sGfp6Xgat0NufdNGU5eGaR6F4a4VpsrZCIGEi/SH8AEqqVlNqgem7yT4nZNhJJC6HOQbN6bmqHpaLNwuNUzjmEpQC0WogYCEDq/o38sPM0Oq2GN68dwCV9OwAQlL9XrB1+l0hqLs225uA0JSYjHFsttgNiAGt/m0HRohln1zDxmXHMUc+Nfwy4eIhwWgMh7VZDyjbxGBQPXsFiW3qWHUKKm2ZArWKwXwZ+ficTq8SGNKYcXK16OFLnkgqDkVzzF0N4kTk2HdwNPPzpH9mA58YzEHw7im0Z15a0IOpcKT9Lzo2o/qsellK9nqfzShtdDl5eZeTnhFTK1Nlr5oTVMwc3AXBJnw5c1i+CYG83dBjpUGr+m4saDvHmbsBHf2/sy2qYTW9C8mbKNW6s0IzGZFJqi5twq+fGZHKgYkqrhZCuYjvzUNPb7AySt4hHdeI5WD3LcgxDvUhx0wyk1tud+PwuA1fpbOlS7ICLObSneMw6XOcAzSyz18ZVp8E7y1z90ekCAPp08gdErL7CUIdLXd4dSVoBtaql7CQUh/i44anXnVU5+IIVh7jv2wQe+9HskTGLG79c8fP1Q8VNV5C3G900Kbgp5cJjExQP3S4Wxxxp4ryblO0ofz4HwBOVM7nrjwJmfLiFA2eEp1UVN9GBnuh1WsqqjJbP2AZphXk3pZUGrvtwK3O/3sXh9Bqe4lNCZNqIG/8YUVRhrHC4men5iBQ3zcDpPFkG3hBqr5uTWSUN960I6iJ6PlQWQ0GK3SUZhULchPq4o1GTIM3iJsLPHV93FwwmheOZdYgpmXcjaQUU1KyWsiQUW8XN2ZaD55VUsmSH+PtZlnCGhJR8i7iJ4zS9gjUMjQ0EINhbT39tojiw4wDhBYmbILp8Zx1quiGV5QWw9FY0ipHlxuGsdJ2Ap17H9qQ8qowKIT5ulopTF52WLqEiNHUkvbEVU63Hc/PPiVy2nMjh171pTH1jA3d9tZN/TuSQlZ2JkrpLLIodYz1Aq7Xe4MmbrzqR4qYZsHQnrum5qd6kytyB83wlKtALrQZKKo2WXJk60blaQ1N1fEipycThPq6gipuOQtxoNBp6RvgCcCitjg9F6bmRtAKsnhvz6AU7CcVgTfhtTDn4l1tPUV5lDWM9/9tBFO9QMrUhaDUKd3Uttgz5DfZ2Y4DGXJ1o/jvCIwCiR4jtpqiaUhT49T+Qf4ozmlD+r+pW7p3QlTXzxjClVxgA47qF2AweVvNujmaKv+M1BzO44eOtdefhtELPjVrt5e/pikYDK/alM+PDrTz++kI0ipFUbQTpGtv/b8vNl/x8qhMpbpyMoigWl2mtnJvS3GpNqjo1s2WtC72LlshA8ftJdKRTcUj9d2CqQOrrkQUVheDqab3bAXp0EOLm4Jk6EoYt5ZYH2+YsGkm7IN/SoVgvGuYZzA3rqnluAMvwWUc9N+VVRhZvSQLgsYu64+6qZXtSHi+vPML2qlgAxnlbvaJBXlbPjcGcdAxAt4vE45EVjXpd1fnzcAZ/HclE2f0l7F+KSaPj7vK7cfcJ4MZh0UT4e/DBTRew/qGxPD/ddlxK1zCRd3Mso5htJ3O566tdbDqew0M/7LWfh2OpmDrm2KiJZuCY2Xs8a0QMf9w3msv6RdDBz52ROiFc1lb2YuPxbNuD1JsvWTFVJ1LcOJns4koqDCY0Ggj3q9HjRq3gqdak6nymc2Omg6tCpQ5xk2H23PTFfLcZMUAM1TOjiptDaXWIm8DOorLCUAa5JxywXiJpWhRFoaB6nxt19IKrV62eWBbPjYMVUz8npJJdXEmEnzu3jIzl9lGdAVi4LpE9pi5AtbJvIEBbRheNmGmVG9DXeiI17yZpkzXZuRGcyS/jtsU7eHbRz1T+8iAAH2ivZbcSz51juoh5Wmaig7xw1dl+Zani5p8TOdz+xQ4qzY05E1Ly+WHn6doX9IsSNzqmqlbzd616mbqG+dAt3Ie3rxvAlscmcHOoCPVtMvWmuGZz0w79xOPpba1GpLU2pLhxMqrXJszHHb1LjV+3ufcKoT2a2arWiWUMQwPl4IqiUB6gVkzVJW6E5ya+ylzdobYtN9NTFTfphfZzfLS6av0k9jpivkTSpJRWGi1f1v6errVGL1QnRs25Md8YJOeUctnbG3nguz0U1vhiFOMUxBfn7AtjcdVp+feYLoT4uAGwx2RukqnmewDatN1oNQopphCyTL7WkwXGii9axQiHf2v0a9x4LBsXpYq3Xd/GTSlnk6k3L5dcRKiPmyWZuT7UsNSZgnLyS6voH+nPA5NERdSLfxy2iEPrC9FCiLlHTyvIuzGZFIvnRn0tABScRpNzDBNatph6UlJZw3scMUCEJssLxBgMSS2kuHEy1jJwO8nEaiZ89IXNaFHrpXM95eA7T+Vxw8dbGfPKX3R74g+mfmO+S8w6YjdslFlkHndRYnbbdhps83x8mDcuWg35pVV1z6ZR49ppe87i1Ugk54YaktLrtHi46uxWSqnEVCsHL600cM+3u9mXWsDSXae55K0NNm0P1hzK5FhmMd5uLswYImY2ebm58OBkIQrKQnqjaLRQeBqKzKMWUkUvmwSlCznFNWa69bxCPB5a3ujXuPF4No+4fEsv7SkKNL78p/JOFLTcNdbWa1MXkQGeuLuKr7GoQE8+nnkBd4ztQnyoN7kllby22k67iFaUd5OaX0ZppRFXnTUpHIAT6wE449WDQrworqhR8q3VQfdLxfahX5rJ2raFFDdOJrWuSilFgVObxXbMyGa2qnVSX1jqhRWH2HQ8h1M5opdHshJGueIqchDU8F41Mgsr8KAcvyJzWMpcKaXi5qKjS4i4U6ozNKV2BP3nA0j+5+xelERyluSXChHh5+kqkmgtnpva4ibUxw0PV1EO/sB3e9iTko+vuwudAjxIyS3jqoWbuWXRdkYsWMucz4VQuW5IJL7urpZz/OuCSD68aRDvzhqNRhUAqbtEL5XjawFIMMWRXVwj4b+HWdycWAdleQ6/PpNJ4cixY8zWiWRknxkf8cBVo7lvQjzXD3WsNYZWq2HGBZF0Dvbis9mDCfZ2w1Wn5enLhdf1y62nLCXk1l9W66mYOmZOhO4c7G0bcjshZnadDhgCQElNcQPQ4zLxePg3mRdoBylunEydPW6yj4runi7ulq6g5zvx5vj5yewSTlVLjDyVU8LOU3loNbD4liFseHgc/aICOa6YG+3ZuQPLKCqnr+YkGsUkGvL5RtRaY62YqkPc9PkXdJ0qBNQ310K27CkhaT4KqldKgTXnxqt2WEqUg4u8m9/3C2/LS1f15bd7R3FR73AMJoU/D2dypqAcjQb6dfJjzujOtc4xuVe4SOzvaP5MStoIS26A5C2Y0LLe1Le25yY4DkJ7iaGbRxxv6Hcko4jY8gNoNQqmsN5ou09lxuAo/jOpa+0Qfj08fUVv/nxwrOVmBWBEXDCX9O2ASYH319fIrWlFnptj5tERcdVDUooihCKQGSzmSdXy3ADEjBLjMkoyLbPzJFakuHEydfa4SdooHjsNBhd9M1vVOgnxcWN0V/HB/fW2ZMv+n3anAnBhXDBjuoYQGejJnWO6cFQRFWblZ2wrBsqrjCL+rjWLkRr5Nio9OggxdbAucaNzgas/FeKzLBe+usraZ0QicTLWSqkaoxfseG7A2qkY4LohUVzUpwN+Hq68d8NA3r9xII9f3J1v5gxj31NT+HnuyNpDfKuj/s1sfVeUebu4832XBRxXOtX23IA1NHXwZ4df38Zj2QzQCs+qNnKIw8c5yhX9xA3NqZoVZGqOY85xMNQQas2MOhdLnZMFQOZBIWRdPSkKFSKztMKOZ8ZFb03olqGpWkhx42QsPW5qloHLkJRdbjAnEX6/4zQVBiOKoljEzZUDO1rWTewRRo6nqOpIObzL5hxqd+KROvPohBr5NirWiql6GoDpveD6JWL2V14SvDMIvr0B/vnQ2oBRInEC1onganfiuj03AHHmhnZxod48eam17YFGo2Fq7w7cProLw7sE4e3mYvd4G6rfELj7wU0/kRMpxi1k1/TcgFXcJP4pklwdYOPxbAZq1bCx/b/Rc0GtTlUrJy34dQK9j/A01Zxy3syoYSmbZOJEEZIiegSeHuKmuKSyjjELamjq0C91dms/X5HixolU73FjE5ZSlGrJxCNawLLWy4TuoXTwcye3pJI/9qezOyWfUzmleLjqmNwz3LJOq9XQs5/5bi/zIOVV1jubjMJyQshnhNbs0el+id1rqeImKaeE0ro+PEDcKd/4I/hFig/uw7/C7w/Bu0NFryKJxAnkq92JPRzz3MwcEcM94+P4bNZgPPQNJ+PWS2hPMYvNLxJm/w7RIwj2FtVUOSV2PDeh3SG4qxhKeXRlg6evMBjZfTKTPhpzZ2MniJswXyFusooqMFbveaPRWEXBhtea/LqOYjIplrCUGpIHLCEpOo/FSy+EqN2wFECX8aI1QEEKnNntRGtroyiKdSZZK0SKGydSWGawvCltxE3eSShKEyMEnPBH3ZZx0Wm5drDw3ny1NZmfdgmvzdTe4XjVuOMcPFhUmUUpZ/hpR5Jlf2ZRBZfrNqPDJH6/QV3sXivY241QHzcUBQ431L49OA7uTYDb1sKEJ0V31qpSWUklcRo2PW6g3mopEO/nByZ3szTDPCd0LnDnZrh3t6UlQrC38CDZDUtBo0JTu5PziTGcwF1TheLuD0Fx525zDYK93dBqwKTYsXn0g2J0xPHVkLK9ya/tCKn5ZZRViUoptU8RRoPVq995rMXLZjehGMDVA+Inie1mDk09/tM++j+zip8TUpv1uo4ixY0TSTGXgQd56W3vpJLMXpuOg8SbU2LDjMGR6LQatiXlsnSXaMQ1fUDHWuv0QdFU6Txw0xhYuWGzZX9GYTnTdeacpr4z6r1Wg52Kq6NzEVVXox6wlu+3gqRESfskr1SdK2UOS9mZK+VUdC5i1IkZi+fGXlgKrOLm2Gooy6/31CLfRuTEaToNFt6UJkan1Vh699QKTQV1gX7Xiu11C5r82o5QvVLKRa2Uyj4qutbrvSG0F54WcVOPh8QSmlrerKGpf07kUmEwcf+SBL4wd7tuTUhx40SsYxdqCBhLvo3sb2OPcD93JvUQs2RKK42E+rhxYVxw7YVaLVpzWadH/jHSCsTv25h+iN7aJIwaF+h9Vb3XarBiqi7UpMTMg407TiJxEDXnxs/DFSpLrKNa6si5cTZB1cSN3caXYb1FJZKxAvYvpai8qnYyrxmbfBsnJBOrhJtDU+n2elmp3pvEtZCyzWk21MVRS0iqWr5NWoJ4DO8LWi3ebuKmuM6wFEDXKSIKkHO8WfMAc83iW1HgiZ8P8PbaYw0PPW5GpLhxInX2uJH5Ng1ywzBrd9Ir+keg09q/s9OZXebdtCnsOpUPQOwZ4Z5NDhwJnoH1XqfBMQx1YZltJT03EudgUy2l5tu4uIObTz1HOY8gL+FBqjSaKCy382Wr0cCAG8X27i+579sEJry2nh1JtnlpBWVV7D2dbx3EWaMHVVMSahY3GfaG8QZ2hv7Xie2/XnCaDXVxtNrYBQtnEsRjRH8ASyi+pMJQt3Bw87HebDVTN3WD0USB+f1583DRk+i11Uf5cVfrCVFJceNE7CYTF5yG/FPijkFtEiepxYVdguke7oOrTsM1F0TWvdDsuYnXnGbHqVwwmeifvwqAjNjLG7xOT3M5+OH0IvuD9uq8brVeGa3obkXSfrD2udFXq5QKdUoIxxHcXXX4mL9s68y76XctaF3gzC6Kk/dgMCm8ty7RZsmqA+kEKgVEabMATZ2tGpqCMF/hbcqsGZZSGf2QsPfEX83eqFNNJraplFKTgiMGAFZxYzApVBhM1EkH87yvtOYRNwVlVZaPvScv7cltI8Ww1bWHM5rl+o4gxY0TsY5eqJbgp4akOvRrsTuwtoBWq+GbOcNY9Z8xtnc2NTGLjCHaI+iO/gEn1xFkzKZQ8cQUP7XB68QGe+PuqqW00siJbAemkasExQmBWlEIhWccP04icRBLtVQDc6WakyBzUnGdeTdewZZJ4VMrVwPw5+FMjpgT9isMRt5ce8zS34aQ7qLU3EnUG5YCCIiBPteI7f0/OM2OmphMCscza1RKGQ2Qvk9sd+gPYKmWgnqSiqutb64CBzUfzM/DFRedlvE9RB7YnhTH2gA0B1LcOBG7nhu1r0LUsBawqG0R4KW3aUxmlw4DMLm4E6Ip4L9Fz6B8eTUAvxqHEhrgW/+xiKTDgVEBAGxJzHHcOBc3a4VHHcM7JZJzId9mInj9lVLNhTWpuA7PDcCAmwGYptuIHvEaPvxbdAn+5p9kTueVcaG7uQQ80rnVovWGpVTUhNxjq5rNC6tWSul1WqLV6rbso2AoE8nE5s8WnVYj5orRQFJxuNlz00xhqdwS8f8aaA5V9unoh0YjXle9741mRIobJ2I35ybD3FhOnTgtOTe8gtDcuZmvtJeTo/igUcQHwI/GUZYPtoZQk5U3HW+EuIFqM2pk3o2kaSmvMlrCEP6e+mqVUq3Dc1NnWAqgy3gqPMII1BRzqVsCAD8npHI8s4i3/xQVUhf5p4i1Tm6FoXpuMury3ADEjhEJuXlJIim3GVDzbTqHeFkrpdRk4g79xPRyM2poqt6k4vDegEa0GFG9fE4kt0R4bgLMbQp83F0tswH3nm4d3hspbpxESYWBPPOdl0XcKIq1ukaKmyZDE9SFTV3uZ3jFO3wZ9Qx3Vt7HfpeelvyAhlDFzebEbNtmXw2hzqhpBQP4JO0L1e3votXgpa9/Inhzonpu7HYpVtG5kBgh8t1meWxkWOdADCaF6z/6h5ySSroEuRNaZP4c7OS8SimwNvLLKKpH3Lh5WytXj61yqj0qakhK7SoNWPNt1BCTGbViqs4uxSA6qQfHi+1myLtR358BapsCoG8nfwD2nM53+vUdQYobJ6GGpHzdXayTd4szoTQHNFprtY2kSRgYFUAlrrx2uge/m4YS5usuJik7QJ+Ofvi4u1BYbmB/aiPuOlTPjQxLSZqY6iGphiaCNydBFnFTf+hhq5/Iu+ldvoN7LxACI9McGpo/VEFTVQpufqKrsRNRPTf5pVU2XcxrET9ZPB5b7VR7VE7linxMm7B7jUopFYc8N1AtNOX8vBuL58arurgRuVPSc9POsYakqiUTZ5pDUoGdZfO+JmZQtMibUb1lYfUNBayBTqtheOcgADYlZjt+Uct04SOyYkrSpNj0uIEG50o1F8ENJRSbOVgRzCZjL7QoDE/9nG7mpNk+Hf0YVbhCLIoaahN+cQa+Hi64mSeMZxbWI8hUcXNqE1Q0orDgLEkxixtLN2k7ycQqqrixOzyzOh36icdmSCrOM4ubQK/anpu9pwtaRb8bKW6cxGl7ycQZZldsaE87R0jOhV4RfuhdrG/nUHMJqKOMjFfzbhohboK6iFh9ZbGY7SKRNBEFZTW7E7cOz02986WqcSa/jLcMVwKg2f0FL030Z0hsIK9N9EGza7FYdOF9TrUVxNBQywDN+kJTQXFiOK6xEk7+7XS7ks3iJqqeZGKVBkcwqDRjOXiunbBUrwhfdFoN2cUVpNWX49RMSHHjJKxl4NXEjcy3cRp6Fy39OllLSsMcTCZWGdFFiJvtSXn1u6+ro3O1fhDJpGLJOVJhMLIrOY+dp/I4YB4H4l/Lc9PCYSkvNaG4fs/Nmfwy/lF6UBA+AkxV9D/xAd/9ezhdD74jpnF3mQAxI5vDZIsXt85ycBC9gyyhKefm3RiMJotn3yJuLPk2/Wp5sxodlso76fBk9rPF6rmxjudwd9VZ2nbsbQV5N1LcOAn1zdvJXqWU9Nw4hUHR1m7EoT6N89x0CfEi3NedSoOJnafyHD/Q0sxP5t1Izo3Hf9zPle9t5qqFmy1VRX6erlBVLvopQYtXSwX7NJxzYzIpnDELifJRj4mdCd+IwY77vhc/T3jSqXZWR/Xi1povVZPqeTdODKukFZRjMCnodVpLTpC1Uqp/rfWWhOKGxI1nIPiZO7urIS4nkWsOm1b33JCXxPU+Cdyr+5GYP++C1fOdakNDSHHjJGr1uDEZrUMWpefGKah5N9B4z41Go7FUTW1sTGjKMmNKem4kZ4/BaGLVgXQAIvzcifBzJzrIk2n9O1orpXR6cPdvOSOBYC8hFIrKDXV6OHNKKqk0mNBoILD7SCEaFCN8NxNQoOe0WkmzzkQVEJn19boB4UlycYfC0w1XQBoqoTS3/jV1oObbdAr0QKuOlbEkEw+otd7T3MivuL5qKRVLaMq5eTe1cm52fwlv9uOm5P8yz/UHuuf+CcfXONWGhnCsVlbSaE7X7HGTexIM5eDiIbpiSpqcgVH+lu3G5twAXBgXxNJdp9ncGHFjmTElB2hKzp69qQUUVRjw83BlwyPjbWepqUMdvUJabPSCiq+HC646DVVGhdySSiL8axdGnDHf2IX6uOGq08K4x80N8oyiq/f4/zarzWENdSlW0XtCzCg4vlr8C6vhYT+1GfZ8K4RD5kEwVsF130K3ejqh/3CL6J1z40/gJYoWauXbVE8mtiP6vBzNuQER1jr8q9PzbvJqVksl/glAhW80v+RFk6SLYt6kq1vUeyI9N06g0mAiy3yXYPnjVyulQruDVtdClrVvgrzdGBQdgIerzlKd0RhUz83e1ALLXJ8GUT032UfBVM/sF4mkHjYeE4L6wrig2kNiD/8mHtWcihZEo9EQ5FV/aEoVN5bPvogB0P1Ssd3/ems/lmYiTE0obigsBXWXhBur4JvrYNdiEUIyVgIKrH5SeOXtkXEQ9i8VYui3/1hCXadqJRMfMScT+0Bgl1qnsYalHMgFbIZOxZUGE0VmoRWohqVyxPww3ZTneVy5i3fKL+aUv3N7GDWEFDdOINOcla/XaS0JeNZKKRmSciZf3TaUTY+Ot/TjaAxhvu7EhXqjKKKhn0MExIpwQVWpGIgqkZwFVnETbPuE0QB7vhHb/a9vZqvs09B8qdSa4gbginfgktfhopecbl9NwnwczLkBMsNHA2A6tQVTab71ieStUJ4PHoHwr8/hzs0iRJh9RAgYe+z7zrp98GfYK36u5blRQ1Id+totjXc4oRis5eBZR6CqrOH1Z0G+uVJKqwFfD1ch2nLFeA2XkDh6dhBjb1o6qViKGyeguj9Dfd2sjeRUz01NV6ekSXF31dn0XmgsY7qKhM1VBx2cbqtzsTYiy5J5N5LGU1JhYFeySGIfFVcjYfj4GijOAM8g6NrwINjmILiBRn5qGbBNGwyPABh8q+ik28xYSsELK+rtv2IyKdy3Mp9EUwe0ioHXP/zQMiaBYyvFY/xk6HmFyJsccY/Yt+5FIUJtTwb7zIM4o4aLxxUPQcHp2j1u1GRiO/k20IhScACfcBG+VIzWG+omRi0D9/fUCy9jSbY54V0DAbGWqtWWHqIpxY0TSDffIXTwq5bUKnvctAku7hMOwJqDGVQYHCwJDzK7kvOk50bSeP45mYPBpBAZ6EFUkKftk7u/EI99rwWXsxftTYl1vpR9z40lLOXXuKR+Z6Hm3JRVGSksr1sgfLM9mS0ncthAfwAiszdx8Zsb+HLrKThqLg/vOtl6wNA7hOjMTYS939qeLHmL6H3l5gs3/AAdL4CKAlh2Fyk5oklgbc9Nf7t2qZPBHRI3Go31PGd2Nbz+LKg5V4pcEZLCLxJc3as188t3yvUdRYobJ6B6biwVO5WlFredrJRq3QyIDCDM142iCoPjDf28hSCiON15hknaLRuPiYGtI2t6bYqz4OgfYnvAjc1sVd1EBwrvy/qj9gc01sq5aWHcXXWWTs+ZdYSmUvPLWLBCeF7DB4m5WFPc9mEwmVi8Yr0IP2l00GW89SA3bxj5H7G9/iVRQaWihqR6XC7WTf9AFJOcXM/Qii2A2XPTQDIxNDIsBdBxkHg8vcOx9Y0kr8ZEcDXfhqDOAPSP8ueC6ACGxAbaO7zZkOLGCajixtLDIOswoIBncIt3GJXUj1ar4aLeHQBYsc9BseJjFjdFDoayJJJqbDwuGvSNrJlvs3eJaHgXMbBVhbP/NbgTrjoNW0/kWsJp1UnNF59/rUXcAIRZet3UDqUpisLjP+6juMLAoOgAJl10Jbh64m/MoZ8uhWHGnWJh5FARXqvOBbeCdxjkJ8M/C8U+QwUcWCa2+14jHoPjRFgOGKdNIMhLL8JNDSQTQ/WwlIOeZHXS+untjq1vJLWGZqqeG7P9XUK8+eHOETw8tWXnJ0px4wTUsJQa67V2Jm49H1CSupnaW4iV1QczqDI6UAFlETdpTrRK0h7JLCznaEYxGg2M6BJkfUJRRO8QaFVeG4AOfh6i/w7w/rpEm+fKq4yWXJyOrUrcmMvB7XhuftyVyvqjWehdtLx0VV90enfoPBaAaT4HGK81dw+uHpJS0XvC6IfE9uonYcProtKqPF94dGNGWdd2GQfASN0+ItUWIZaQVO3OxCpejjbxU+k4UDzmJp51L576qNXjxuK5sS/OWgopbpxARi1xY24IJSul2gSDYwIJ9tZTUFbFlsSchg/wDhOPxdJzI2kcasPI3hF+NhOWObNLdL12cYfeV7WQdXXz7zGd0WhE4v3xzCLLftVr7eGqw9/Tta7Dmx1V3NSsmCqvMvLKyiMA3DchnrhQb/FE/CQAJij/MFxrvjmNn2L/5INvgwvvF9trn4Zf7hXbfa62bfsRNRyjxpWOmhwG+Zg9XurYhXqaGlo8N5UGxwZSegZax8KknmPeTVUZ/Phva3I01eZKedn33LQWpLhxAhbPja+7uANTB7FJz02bQKfVMKWX8Mb8vt8Bb4yPCGNRJHNuJI1DFTfq4FYLW80hjh6XgYd/8xrlAHGhPkzqIUT9++tPWPZb823crZWirYCwOkYwfLn1FOmF5UT4uXPbqFjrE3FC3ERVHMVdU0Wua5i1p1VNNBqY9DRMWSB+LjXfEPX9F0XlVdaqMr0XpzzFDe4wzH1oGqiUAmvOjUmB8ioHe2l1vEA8nmto6ugfIll62V2iES3VPDeeevH9lmP+/5eem/aNoihkFIg3c5ivuyjlTN8Lrp7Q7eIWtk7iKBf3EYJl5YEMDA2FptSwVFmuiLdLJA5gMimW/jY2+TbZx6y9U9Ry41bIHWPFl9my3akWUWO3x00rINyO56akwsBCc1jt3gnxuLlU87L4R0KIVcxs1Q5quDv08Lvgqk9E36uOF0B4X/71wVbGvbrOMkh5l070oelRtsucTLxfHFtHpRSAp15nubTDScWdzOIm9RyTirOPiUdjBawSnaUtc6W89MJbXVUCGq2Yqt6KkOKmicktqaTS/GUY5uMmsugBLrgFvILrOVLSmhgaG0iApyu5JZVsO9lA3NojQHyggQxNSRxmx6k8Mosq8HFzsZmLxt+vgmISN0NqU7ZWyMCoAIZ1DsRgUnh/vRAJZ/Lt9LhpBYRaxI315uOzTSfJKakkJsiTqwZ1qn2QOTQF8Etpb0wmB0JCfa6GeYdg1q9kFlVwKK2QonIDn21KAmBtpfDeh+duF7mYlmTiznWeUqPRNK4cHKzi5vSOcxsCmn3Uun34V0j803YiuJpv4x/ValoVqEhx08SoIalgbz365L+FW9DFHUbc28KWSRqDi07L5J7CI/PrvgZCUxqNtRxchqYkDvLLnjMATO4Vjrur2WuQk2gtI1YTVVsxc8eJUQpfbD3F+qNZra4MXEX13CRmFrNsdypZRRV88LcIp/xnUlcxA6sm5lEM5Yorf1X1sHilGsQrGFw9OHCm0LLr223J5JZU8mdhRwoVD1wqC8QoB6g3mdhySnNSscOem7De4nunPN8qQM4G1XMT3E08/v4oBcXCCxXgqW+1+TYgxU2TY+lxU91rM2gW+IS1nFGSs+LSfiI09dvetIYb+qn/v1LcSBzAYDSxwiyaL+8fYX1iw+vCaxM/2Vr10ooZGR/MDUOjUBT4z5IE9qaKrrStTdx0DvHCz8OVogoD9y9J4MIX/6So3ED3cB8u6xth/6CYkTDhSV7zmkc5bhyrljjtCAfOWDv0llQaeW3VESpMWrYp5sIStRrOgQnpjfbc6Fytoa6zzbtRFDH0E8T4DM8gyD7C1LJfAXO1VCutlIJzFDfl5Q4MIjvPUD03Y/RHRJdKnR4uvK+FrZKcDSO6BBPu605BWRV/HrLfsMyCrJiSNIJNiTnklFQS5KXnQrUEPPekdY7UmEdazrhG8sSlPekV4UtuSSWH0oS3IsK/dXQnVvFxd2XNvDHMm9SVMF83S+rAvEld0dYcVKqi0cCoB0jrJMZeHM0obtQ1Vc9Nrwgxa+nrbckAHHQ3i1aD+fuznmRiFa9qFVMOc655N0VpUFksmhd26A8TngRgruZ7vCkVOTftyXNjMpl49tln6dixI97e3pw4IVx7TzzxBJ988slZGfHuu+8SExODu7s7Q4cOZdu2bXWu/fHHH7ngggvw9/fHy8uL/v3788UXX5zVdZ1BRkE5/hRxXZH5dzHwZvCt485A0qrRaTVMGyD6eSzdlVr/YkvFlOx1I2mY5QkiJHVxnw64VBXBto/g6xliJlCXCdYvpjaAu6uOd68faClZhtaXcwMQ4uPGvRPi2fjIeBbeMJA3r+3PpJ4Ne9S7hvkAcOwsxc3DU7sT7utuSX05EzTUdmE9ycQq1rCUg438oFrezVl6btR8m4AYkU8z4CYMgfH4akr5l8sGfNxcWm2lFJyFuHnuuedYtGgRL7/8Mnq9NYGod+/efPzxx402YMmSJcybN4/58+eza9cu+vXrx5QpU8jMtH+nHBgYyP/93/+xZcsW9u7dy+zZs5k9ezYrV65s9LWdQdDp1ax2e5jI0kOi3bba/0DSJrlyoBA3645kklPHoECgWlhKem4k9VNeZWTVARG+vF3zE7zaDVY8KLrV6n0sd8htiZhgL166qi8A7q5aa4+vVoirTstFfTpwRf+ODpWrx5t73zQmLFVYXmWZ/t2vkx+zL4yxXj+0m/VmyM233mRilUYNz1RRy8EzDpzdhHBLvo15MLBWR2bPWQDMclmJRjFZxwo58Bqam0aLm88//5wPP/yQG264AZ3OWjrXr18/Dh9u/FTk119/nTlz5jB79mx69uzJ+++/j6enJ59++qnd9WPHjmX69On06NGDLl26cN9999G3b182btzY6Gs3KaW5sHQOM5P/jxBNAQXeXWD2b6KkUNJm6RrmQ5+OfhhMCsvNCaB2kfOlJA6y7kgWRRUGhvlkE7n7VVExE9Idpr4I9+91KAejNXJJ3w68f+MgPrzpAtuy6jZOvNlzczyz2LGKKeCg2WvT0d8Df0891w2NsgiUqCAvSwdkR5KJoVpYqjHixq+T+FwyGSBtj+PHqVjETZxlV1LHyylUPIkiXSREG8pA69LqysDhLMRNamoqcXFxtfabTCaqqqoada7Kykp27tzJxIkTrQZptUycOJEtW7Y0eLyiKKxdu5YjR44wevRou2sqKiooLCy0+ecUjq+Ffd9hRMtCw2XsvWS5dYCZpE1zldl782N9oSnZyE/iIGqV1LzAzWJH/BS4aysMu1N0l23DTO0dzuiuIQ0vbENEB3niqtNQWml0uGKqZr6Nr7srj17UnZggTyb3ChNFJu7+0P96h87X6OGZIHKG1NBU4l+OH6eSU8NzA2RXuvCtUYyR4M/nxKN/NOhcaG00Wtz07NmTDRs21Nr/ww8/MGBAw4lR1cnOzsZoNBIWZhv3DAsLIz297i+JgoICvL290ev1XHLJJbz99ttMmjTJ7toFCxbg5+dn+RcZ6SRPSp+rYdhd3MyzvGS4jvBAP+dcR9LsXNYvAhethn2pBRzNqMM1LaulJA5QXGFg7eEM3KlgUN4KsXPInIYbxElaDFedls7BIjR1PNOxvBu1UqpXhPV74MZh0ax7aBzRQV4QNQwePeWwuDmrsBRYp5ivfwl2LmrcsarnJijesiuvpJLPjZMxobV2Ym6F+TZwFuLmySefZO7cubz00kuYTCZ+/PFH5syZw/PPP8+TTzZPrNjHx4eEhAS2b9/O888/z7x581i3bp3dtY899hgFBQWWfykpKc4xSqOhdPyzbCoXLbzDWnHMWdI4grzdGNddTHNfuuu0/UWq56Y0G4yN82BKzh82H8+mvMrELN9d6CoLRfMz9QtI0mqJDxPips6bmxocrOG5OVfUUvBGJRSD8BBdcAugwC/3wcY3LE99uy2Zz7ck2T+ushQKzN+V1Tw3uSWVnFZCOOx7oXVtK6yUgrMQN1dccQW//PILa9aswcvLiyeffJJDhw7xyy+/1Ok9qYvg4GB0Oh0ZGbZJmBkZGYSHh9dttFZLXFwc/fv354EHHuDqq69mwYIFdte6ubnh6+tr889ZqD1uPPU6kUkuaTeooanf99XhmfEIFLFnkOXgkjrZekJ0u75Rt0bsGDTbdriipFUSH2qumHLAc1NeZbSs69WxicRNYyeDq2h1cMnrMHKe+HnNfNj8NtnFFTz64z6e/PkAaQV2Qm1qfxuPAPCyTqvPMw/N3BdZzePUXjw3AKNGjWL16tVkZmZSWlrKxo0bmTzZzjj4BtDr9QwaNIi1a9da9plMJtauXcvw4cMdPo/JZKKiouVn+lQfmNmahsZJzp0hseIPPDm3lLJKO3dPWq21142smJLUwZYTOfTWnKBT2SHQusKAm1raJIkDdDV7bo6ZPTeKotSZXHw0owijSSHQS2/pjHyunHVYCkTIc+J8GPd/4udtH7K92kiZA6l28lBzaoekQHhuAErCh1lL2Ftps8lGi5vt27fzzz//1Nr/zz//sGNH45sFzZs3j48++ojFixdz6NAh7rzzTkpKSpg9ezYAN998M4899phl/YIFC1i9ejUnTpzg0KFDvPbaa3zxxRfceOONjb52U6MOZWvNZZCSsyPQS4+/pysAJ7LruHvzkRVTkrrJMze5u0FnvpnreQV4t6/k2/aKNSxVzMM/7GHkS3/Rc/4f7DtdUGtt9WTiprrJPasmfjUZeLN4LDjNjhPWG7DqYyIsZJs9N9VCUmD13AR6u8GNS+GWla22cKbR4ubuu++2m7eSmprK3Xff3WgDZsyYwauvvsqTTz5J//79SUhI4I8//rAkGScnJ5OWZm2MVlJSwl133UWvXr248MILWbp0KV9++SW33XZbo6/d1KQVWD03kvZHlxDxAXciq8T+Ast8KdnIT1Kbf07m4EMp01zMVVKDb21ZgyQOEx3khatOQ1mVke92nCY1v4zyKhOrD9a+kVGTiXs2Ub4NVPfcCK+xoih8+HciWxJzGnGSMHD1BMVE8glr25bqYyIsqA38gm0ro3NLqk0E9woWidGtlEYnhhw8eJCBA2u7oQYMGMDBgwfPyoi5c+cyd+5cu8/VTBR+7rnneO65587qOs4mQ50rJT037ZLOwV7sPJVXt7iRjfwk9bD1RC4DtcfwoEJ0fY1yPPQuaVlcdVrmjotn3dFMBscEkl9ayXc7TtvNwdmfqnpumq5itmafmw3HsnlhxWGiAj35++Fxjp1EoxHN9jL2Y8hOBPoDdXhu7JSBA9aJ4J6tawK4PRrtuXFzc6uVAAyQlpaGi8v5nUSr5tx0kOKmXdLZ7LlJzKorLCVHMEjqZktiDvEac7Vdh36y/LuNcd/EeH6660Iev7gHl5iHbdasnjKaFA6nN22lFIgiFbD2udl5Kg8QOYBF5XVXZxqMJjYdz6a8ypwnGBADQBQZhPi4AZCaX0a+OdwEiIGZaliqWs6NoijkmtcFeLme82tyNo0WN5MnT7aUV6vk5+fz+OOPN7paqr1hmQguw1Ltki4hXkA9OTdyeGb7pLIETm3BMhzoLMgpruBIRhHxGnMjyJAeTWScpCVQRzIk5ZRSaTBZ9p/IKqa8yoSnXkdMkFeTXa9mQvHulHzLc3VVcBlNCvd8s5sbPv6HBSsOiZ3mMQkxmnRGx4cQGShmgB1Mq+a9KTwDVSWi+jMw1rI7OVe8Vo0GgrzcmuqlOY1Gi5tXX32VlJQUoqOjGTduHOPGjSM2Npb09HRee+01Z9jYZqheLSVpf3SulnOj2PuiUxOKZSO/9sWq/8JnU+HAj2d9CrUEvK+b2asX2r0pLJO0EB383PF2c8FoUkjKsYap96Wqzft80dU1bfwssCYUGzGaFPZUEzdH02v33lEUhWd/Pcjv+8Vn0bfbU8RsPLNYidJkMjQ2kF4dROjsYPXQVPWBmTqrh2bJdpFrOzIuGA99629f0Ghx07FjR/bu3cvLL79Mz549GTRoEG+++Sb79u1zXvffNoDBaCKrSJSjy2qp9klUoCc6rWjDrgpZG6S4aZ+onVqPnv1w3q0ncgCFzoq5GEN6bto0Go2GOHWgZrVp4XvN1VN9Ovo36fWqT1w/eKaQgjJrKOqIncaCH284yaLNSQCE+rhRYTDx5dZkKn1jAOG5GRwbaAmdHThTCGX5cGw17DDPdayWb1NlNPHdDhFSvX5IVBO+MudxVkkyXl5e3H777U1tS5smu7gSkwI6rYZg79bvspM0Hr2LluhAT05kl3Aiq4QOfh62C9RqqZIsMBpa5bwVyVlQJvIbSNooQlNnkSuz5UQOEeTgZio1u/tb3xRlSeOID/UmISWfoxlFXILIt9t7Oh+Avp2advyOu6sWrQZMCmw8nm3zXM28nz/2p/G8OQz1fxf3INTXjfu+TeCLrUkMCwphKBCpzcI1wM1S0dX/xAfw0jeA1SN9XNcFtVZqzcEMsosrCPZ2Y2JP23FJrRWHPn2XL1/ORRddhKurK8uXL6937eWXX94khrU11C6PoT5uTeqOlLQuOod4mcVNMRfGBds+6RUMGi0oJiFwfDu0jJESx1AU0Ym1hvu9Fqq4KUyFvJONFiaZReUczyxmjNacbxMUBy6tv9pEUj9dq00LB+G9VyuPmlrcaDQavNxcKCo3sMksbi6IDmDHqTyOZtjm3LyxRngaZw6P5rZRsRhMCi//cYTU/DKe+DOXXxUdeo0Bis7QKyIUUJhS/jtoFJSAGNaWdmFlcRfWHhjKspxSooI8+XpbMgD/uqATrrqz6v3b7DgkbqZNm0Z6ejqhoaFMmzatznUajQajsZGzL9oJHnodl/btgI97688il5w9nUO84VAmifbKwbU6kVRclCb+SXHTujn6B3xzLQy7C6baH98CQKm1mysnNzRa3Kj5NiP9sqAMCJH5Nu2BOLVrcabwnBzNKKbCYMLHzaVJk4lVvM3iZluSeD9dc0EndpzKI6uogtySSgK99OSVVHLYnINzz4R4NBoNrjoNsy+M4bnfDnE0q4wUfShdNGmQe4Kw2Eh6eRYSbspD0biw7aIV3PZpgrigEe5bspvX/9WfDceEoLp2cNsISYGDOTcmk4nQ0FDLdl3/zldhA9A93Jd3rh/Igiv7tLQpEieiVkzVWQ4uK6baDqfNHdVTttW9pqoMDNVm7yRtrHvtweWwdWGtqqrf9p4BYIhXptghxU27QK2YOpldQpXRxL7UfAB6d/RD6wTvvZpUrFZnXRgXbKl2UkNT/5jHKsSHetukR8wYHGmZd3hKMX9G5Z5Eo9FwcYDwyuT6duejLeK9OqlnGD7uLuxOzufGj8VEglHxwUQFeTb563IWjfIvVVVVMWHCBI4dO+YseySSVk3nhroUy6TitkNeknjMPVH3mupeG7Dm3dSkoghl6a3wx6NwfI1ld25JJX8eFqKmqxqWkpVS7YIIPw889TqqjAqnckosycR9I5s2JKXiVS2pONjbjY7+HnQzh8ZUcSMS12FY5yCbY33cXbluqPC6nNGZPcrm9/1QF9HTZrshjjWHxHv1sYu6W27UU/OFuG8ricQqjRI3rq6u7N2711m2SCStns7BwnOTml9mf4CmFDdtB1XclOVa82pqou538wOdHorO2BdDJ9ajMYoGZ5Ub3rTsXp6QSpVRoXeEDx755sZoslKqXaDVaizem2MZxVZx08SVUirebtby6wFR/mg0Gkvez5H0+sUNwG2jYukX6U9k515ih/l93KVCJB//ltcJgIk9Qukc4s2lfSO4ZpDY15YSiVUanRl044038sknnzjDFomk1VN9gObJbDveG7ViqqD2/DVJKyP/lHU796T9NWVmz41POHQaLLaTNtRaZjjyh2Vbn7wB0sRN4NJdwltzc08XqCwSlVJBXc7ddkmrIC5UiIsDZwotnYmbOplYxUtv9dz0j/QHoFu41XNTPd9maOfAWseH+rjz890XMmbYULEjLwkqS/EvFHOmdppE6fetI605ZU9f0Ys7xnThjRn920wisUqja1UNBgOffvopa9asYdCgQXh52SZOvf76601mnETS2tBoNHQO9mJXcj4nsotrD8eLNH8BHl0Jxqr6q3AkLUdFsahoU8k9AR1rz8yzhKU8AiBmJJzaJEJTg2ZZ1yiK6A8CpJhCiNRmUfjXG6SNf5N9qQW46jRcFJYv1gbFyfdEO0KdFv7r3jNUGRUCPF3pFODRwFFnR/VeNwPM4qarJSxVzD8nhdemZr5NLdSE+NwTcGYXGpOBDCWAMwTRu6Mvw6oJI0+9C49e1DbDqI2WYvv372fgwIH4+Phw9OhRdu/ebfmXkJDgBBMlktaFZcZUZm3PTUGHCylxCYDSbDixvrlNkzhKda8N1OO5MYelPAMhZpTYPrnBNu8mYz8uJemUKm78p+pOsfzoMlZu3QXAuG6h+BQlirUymbhd0TXMOoYBoE8nES5yBmrOjUYDfc3ipnOIFzqthoKyKpbvEcnA9kJSNvhHiZYVVaVw6FcATnn2AjTcPrqL0+xvbhrtufnrr7+cYYdE0mboXM+Mqbf/Okmn8sHMclkF+76H+InNbZ7EEfJqips6korVsJRHoAhL6dygOB1yEiHY3OLM3Ll4k6kXecGD2FbQnSHaw3js+gSYwdWDOsEx4fonVObbtCfizWEplX5OCkkBeJpzbrqG+li8OG4uOmKCPEnMKmHlAVGh2aC4cdGDXyfIT4b9PwDQffAkPu80hNFdQ5xmf3PTKM/NkiVLuOGGG7jmmmt4//33nWWTRNKq6VJHxZTJpLBiXxrLjSPEjsO/QmWp/ZOc3AArHoYiWTLeIqjJxDqz+74ucaOGpTwDwNXdft6NOST1l2kAwzoHcTxuFgD/0qymk6eRsd1CIcs8uDCkW9O9BkmL09HfA3dX69don47OEzfqzMIhsbb5NGrejdEkvIn28m1qoYamzKFZ3/gR7UrYQCPEzcKFC7nuuuvYsWMHx44d4+677+ahhx5ypm0SSavEMh08q9hmgGbC6XzOFJSzS4knxRQClcWiUVxNso/D1zNg2wfw5ZVipoukeVHFTdQw8Vin58YclvIIEI+x5tDUtg9F3k5pLpwWfXLWGfvRMcCDKdNncVLpgJ+mlM+830NvKoesI+I4WSnVrtBqrTOmAPp28nfatWYMjuS1a/oxb1JXm/1q3g04kG+jEmCd9o1ODx36NZWZrQaHxc0777zD/PnzOXLkCAkJCSxevJj33nvPmbZJJK2SqEAR5y6pNNo081ux1zzxGQ3LTcPF5v6ltgcbKuCHWVBl9vpk7BdCpy4Pj8Q5qDk3XcaJx5JMqKg9gNAqbsx3w4NmiUaNmQfhp3+LnjaKiWSXGM4QTEd/D4J8PEgfvYAKjRvxhVvg0ylC6GpdZaVUO6SrOTQV6uPm1KHJnnoXrhrUiQAv29Ed3aqJmwZDUirVu2x36A8u7W8eosPi5sSJE8ycOdPy8/XXX4/BYCAtLa2eoySS9ofeRcsYswv3vXUiUVRRFH7fL3rb+Li58LPxQrH42CrbHiqrn4T0feAZBDcsFf1TUrbC9zNFdZWkeVA9Nx36if8LsJ9UbAlLmcWNTzjM+EqEsw7/Cr8/DMB6ZQCApVJm+ITpuM36Gdx8Id3cG0xWSrVL1LCQM7029dE1/BzFTeSQJraodeCwuKmoqLAp+9Zqtej1esrKyuo5SiJpn9w/MR6AZbtTScwqJiEln9T8Mjz1Oq4fGsVRJZIzbp3BWAl7lkDWUdi5CP4x56pNWyiSjW/4Dlw8hAj67YGWe0HnE4piFTf+0balsTUpq1YKrhI5GC4zN+ozC9ffykU3147+1drTRw+Hmb9YxZPsTNwuuWFYNLeP7txiJdPRgZ4Ee7vhqdfZlHHXS2C1sJSaR9bOaFS11BNPPIGnp/WPt7Kykueffx4/P2sSlexzIzkf6NvJn4k9wlhzKIO31h4j1Ee4dSf0CGNgtPgi/EM7ils4AX88Ynvw8LnQdYrYjhoG/1osQlO7FosPmoE3NedLOf8ozgBDuSiH9YsU4ub0djHxuyY1wlIZheV8uukkVw+8lPgR98DmtzG5+bO9PB5XncbyPrAQ0R9uWQlb3oHBtzn3dUlaBG83Fx6/uOVyqVx0Wr6/YziVBhNBjuTbgMi50bmBqQoihzrXwBbCYXEzevRojhw5YrNvxIgRnDhhvdtpL/XxEokj3D8xnjWHMli+5wz+HiLccEmfcHqZG/t9XDCE2X6/oCnLFeEnr2CRkDphvu2Juk6Bcf8Hfz0nvDfhfcSXosQ5qGXgvp1EWWxdnhtFselzk1FYzrUfbuVkdglJ2SV8cMPT4BPBUVMnjL/o6OjnYX9gYnC81dMjkTiB2OBGTiHXe8K/PheeZd8OzjGqhXFY3Kxbt86JZkgkbY/eHf2Y0iuMlQcyyCutwlOvY2y3UNxctPh7unKmNID9122nT4QPuLhRVmnEpCh4udj5sxv1AKTuENVV390Et6+35nlImhY1JBUQLR4t4qaG56aiEEwGADKrPLhu0VbLyI29pwtAq4Phd3Fw12lgj9M600okTqHb1Ja2wKm0rWEREkkr4/6J1rLM8d1DcXfVodFoLN6bA+ml4OJGldHEFe9uZOyr6ygst5M4rNXC9A+Euzg/WebfOJM6xU0Nz43Za2PSuXP94r2cyCohws8djQbSCsrJKqoAIDVP5B129JfiRiJpLUhxI5GcAz06+HLVQDE599rBUZb9vSJEHtqBM2KY3op9aRzNKCarqIJNx7Ltn8zDH676WGwf/g2qyp1m93mNWgYeECMeVXFTmApVQqisPJDO/G9Fo750gyfHM4vp4OfOt7cPtzRx3J8qpkCn5pvFjfTcSCStBiluJJJz5KWr+rD50fGMjA+27LN4bs4UoCgKn260hjz+PpZV6xwWOg4Cr1AwVkDqTqfZfF5jqZSKEY8eAeDuZ3nOZFJ48Ls9nEwRk90L8WFctxC+mTOMqCBPSxfafTXFjfTcSCStBiluJJJzxEWnJaLGF5sqbg6lFbHzVB57ThdYnvv7aLZNZ2MbNBoxfRrE9GlJ02MJS8WIR43GJjR1MqeEogoDoS4iv6ZrbBSfzR5CjDlps3dNcZMnPTcSSWuj0eKmqqruRmPZ2XW42yWS84zYYG88XHWUVRl56pcDAFzatwN6nZbU/DISs2pPFLcQY24AeEqKmybHUAGFYnqyRdyAjbhRw01dfY0AaGskdls8N6eFV0713HSq3uNGIpG0KI0WN9dee63du86MjAzGjh3bFDZJJG0enVZD9w6ic+j+VJF3c9fYOAbHih44G+oLTcWY5xelbBNfxpKmIz8FUMDVU5Tmq1QTN2qeVJx3pdjnYStuekX4otFAemE5h9OLqDCY0Ghwaut9iUTSOBotbpKTk7ntNttmVOnp6YwdO5bu3WUHTolERQ1NAQzvHETPCF9Gx4uxDX8frUfcBHcFrxDRaE7m3TQt1UNS1fty2fHcdHI3d1+v3p0Y8HJzsSQVqyM3wn3d0bvIKL9E0lpo9F/jihUr2Lx5M/PmzQPgzJkzjBkzhj59+vDdd981uYESSVuld4S1c/fsC2MAGG2eSbX1RC4VBqP9AzUaiDaHppI2OdPE847slMMAVPlG2T5hFjdKTqJF3IS6mMWNnX5DamhqpVncyGRiiaR10WhxExISwqpVq1i6dCnz5s1j7NixDBgwgG+++QatVt65SCQqQ2ID0Wk1xIV6M6FHGADdw30I8XGjrMrIjqS8ug+2JBVvaAZLzx927dkDwM4CX9sngkW/Ik1BCkp5Ia46Db6KCE/VDEuBNan4SIaYJC6TiSWS1sVZqZHIyEhWr17NV199xZAhQ/jmm2/Q6XRNbZtE0qbpHOLNintH8e3tw9CZ2/JrNBpGmUvG6w1N2eTdVDrb1PMDk5G4wi0ArMrwJr+02u/VM1CMYwC6a5LpFu6D1jJXKqDmmSyeGxXpuZFIWhcOiZuAgAACAwNt/g0bNoyCggJ++eUXgoKCLPslEomVbuE+BNcYZjfGHJr6u65mfgAh3cAzGAxlcGaXM01snxz5XQjDapRs/5LOSgr5ihc/VA7jq3+SbY/p0BeAntpTIqSoTgS3E5ZSk4pVpOdGImldODRb6o033nCyGRLJ+cPIuGA0GjiUVkhmUTmhPnaqbDQaURJ+8GcRmooa1vyGtlUyDsA314LWFWb+AtHDoaocl/ULAFhovJxCvPlsUxK3jozF3dXsdQ7vA0dW0EuTREVHPzhmOxG8OmpS8fHMYkB6biSS1oZD4mbmzJnOtkMiOW8I8najd4Qf+1IL2Hw8h2kDOtpfGD3SLG42weiHmtfItsy+H8SjqQqW3ABz/oJDy3ErTeOMEkhyl5vokF5OWkE5PyekMsM8NkMJ640G4bmpCveEcnPjxToGmPbp6GcRN3JopkTSujiraqmVK1fW2r9q1Sp+//33JjFKImnvDI4RX5g7TzmQVJzyD1QUNYNV7QBFgf1Lxba7P5TmCC/O368C8D/D1fSKDuWWC2MB+PDvE5hMom9XtrdoZdFVk0IP32r9hdz97V6qd7W8m46ygZ9E0qpotLh59NFHMRprl7CaTCYeffTRJjFKImnvDIz2B2B3Sj3iJqS7mBJeVQqr5zePYW2dM7vEYExXT7htLXiHQeZBKM8nSRvJUuNoekX4ce2QSHzcXEjMKuHPw5kA7CnypVDxRK8x4n7GnK/j5gc6+w7ufp2EuAnxccNDLwsqJJLWRKPFzbFjx+jZs2et/d27d+f48eNNYpRE0t4ZGCUqcA6lFVFaabC/SKuFy94U2zs+gRPrmse4tsz+H8Vj16kQHAfXfg06kdD9QsU1mNDSM8IXH3dXrh8qwlH/Xbaf03ml7E8r5KASLY4/uV48etaulFIZFB3A/RPjeW5ab6e9HIlEcnY0Wtz4+flx4sSJWvuPHz+Ol5dXkxglkbR3Ivw9CPd1x2hS2FttqGYtOo+BweaO4D/PhfLC5jGwLWIywYFlYrv3leKx0wUw61dOjn2LVcZBBHvrCfURYueusXHEhXqTXljOTZ9sY9PxbA6YYsRxJ8zixk4ZuIpGo+H+iV2Z0ivcOa9HIpGcNY0WN1dccQX3338/iYmJln3Hjx/ngQce4PLLL29S4ySS9syAKH8AdiXXE5oCmPg0+EdDQQqsfsL5hrVVTm+HwtOg94G4Sdb9kUPY5D4W0NAzwg+NuYbbz9OVL24dQkd/D05ml7A9KY+DJrPnJu+keLRTKSWRSFo/jRY3L7/8Ml5eXnTv3p3Y2FhiY2Pp0aMHQUFBvPrqq86wUSJpl6ihqV2n8utf6OYNV7wrtncugiQ5LdwuaiJx94vB1ba8/mCa8HhVn/cF0MHPgy9uHUKQl16sU8NSKnVUSkkkktaNQ6Xg1fHz82Pz5s2sXr2aPXv24OHhQd++fRk9erQz7JNI2i1qUnFCSh6Kolg8CnaJHQWDZsPOz2DVEzDnT9vBj+c7JiMcXCa2e11Z62l10nfPDr61nusc4s3iW4Yw67NtRET0g1Q9GNWJ4HWHpSQSSeul0eIGRKx58uTJTJ48uantkUjOG3pF+OGq05BdXElKbhlRQQ2UE497HPZ+JyqCDv4MvaY1i51tguQtUJwB7n7QZbzNUwajicN1eG5Uenf0Y/OjE3DVaeCD7pC+Vzwhw1ISSZvkrGZLrV+/nssuu4y4uDji4uK4/PLL2bBBDviTSBqDu6uOXubJ4Q3m3QB4h8KIuWL7z2fBWOVE69oYx1aJx24Xg4ve5qkT2SVUGEx46XXEBNVd9KB30QrvWXhf604ZlpJI2iSNFjdffvklEydOxNPTk3vvvZd7770XDw8PJkyYwNdff+0MGyWSdosl78YRcQMwfK6YOZVzHHZ/4UTLWg/lVUYMRlP9i9Tqps7jaj110ByS6tHBF63WgVBeh2riRnpuJJI2SaPFzfPPP8/LL7/MkiVLLOJmyZIlvPjiizz77LPOsFEiabc4XDGl4u4LYx4W2+tegsoS5xjWSiirNDL65b+4cuFmFEWxv6g0F9L2iO3OY2o9feCMKLXvWUdIqhbhfazbMudGImmTNFrcnDhxgssuu6zW/ssvv5yTJ082iVESyfnCwGhrM7+yytqdv+0yaLYoDS9Ohx2fOtG6lic5t5TMogr2ni4gvbDc/qKkDYAiOjr71O45oyYT15VvU4uwXtbtepr4SSSS1kujxU1kZCRr166ttX/NmjVERkY2iVESyflChJ87Yb5u5mZ++Y4d5KKHEfeI7aO157y1J3JKrDOe6mx2qHZu7jy21lOF5VUkpOQDWPKbGsTdD2LHiPBfULzjxkokklZDo6ulHnjgAe69914SEhIYMWIEAJs2bWLRokW8+eabTW6gRNKe0Wg0DIwK4Pf96exKzmdo5yDHDow1h19ObwdDBbi4Oc/IFiSnuNKyvfd0vv1uwGq+TWztkNRPu1IprTQSH+rtuOcG4KZlohy8Rr8ciUTSNmi0uLnzzjsJDw/ntdde47vvvgOgR48eLFmyhCuuuKLJDZRI2jv9Iv35fX+6JTfEIYLjwSsUSjIhdSdEj3CegS1Ibkl1cWPn95OfArmJoNFBzIU2TymKwhdbTwFw0/Do+vsI1USrBa0UNhJJW+Ws+txMnz6d6dOnN7UtEsl5SXyoNwDHM4sdP0ijEV/mB34SHYvbqbjJqSZu9qUW1G52qA647DhQhJOqsSUxh+OZxXjpdUwf0LE5zJVIJK2ERufcdO7cmZycnFr78/Pz6dy5c5MYJZGcT8SZxc3J7BKMpjoqguwRM1I8tuNxDLnVcm7yS6tIyS2zXWApAR9b69jPtwivzfSBHfFxd3WWiRKJpBXSaHGTlJSE0Vi7qqOiooLU1NQmMUoiOZ/oFOCJ3kVLhcFEal5ZwweoRJvFTco2MFTWv7aNUj3nBmBvar71B0Wxem5q5NukFZSx+lAGADcNi3GihRKJpDXicFhq+fLllu2VK1fi52d1ARuNRtauXUtMTEyTGieRnA/otBo6B3txOL2I41lFDY9hUAnpJip6SrPFSIaoYc41tAVQw1KBXnpySyrZe7qAS/tGiCezDouRCy4eEDnE5rhv/knGaFIYEhtIt3Cf5jZbIpG0MA6Lm2nTpgGiumPmzJk2z7m6uhITE8Nrr73WpMZJJOcLXUK9OZxeRGJmCeO7O3iQmndz8GfR66Udihs1oXhM1xB+2p1qWy6vhqSih9tUi1UZTXy9LQWAm4fXmPItkUjOCxwOS5lMJkwmE1FRUWRmZlp+NplMVFRUcOTIES699NKzMuLdd98lJiYGd3d3hg4dyrZt2+pc+9FHHzFq1CgCAgIICAhg4sSJ9a6XSNoCXULOIqkYIGaUeEza1MQWtQ5yikXOzdhuIQDsTy3EpOYlnd4uHtXcIzN7TxeQXVxBgKer/dJxiUTS7ml0zs3JkycJDg5uMgOWLFnCvHnzmD9/Prt27aJfv35MmTKFzMxMu+vXrVvHddddx19//cWWLVuIjIxk8uTJMt9H0qZRk4oTsxopbqLN5c8p/7S7QZpGk0J+mXhNwzoH4e6qpbjCwIls88iJgtPiMSDW5ji1ad+g6ABcdWc1G1gikbRxHP7L37JlC7/++qvNvs8//5zY2FhCQ0O5/fbbqaioqOPounn99deZM2cOs2fPpmfPnrz//vt4enry6af228p/9dVX3HXXXfTv35/u3bvz8ccfYzKZ7HZNlkjaCnGq5yaruO4ZSvYI6Q6eQVBVCmd2O8m6liGvtBJFEdG3IC+9pcPwPjWpuNB8Q+PXyeY4Vdz0j/RvHkMlEkmrw2Fx88wzz3DgwAHLz/v27ePWW29l4sSJPProo/zyyy8sWLCgURevrKxk586dTJw40WqQVsvEiRPZsmWLQ+coLS2lqqqKwEA5vVfSdukc4oVGI8qdq/d2qU5ReRWH0gptd2q11h43SRucbGXzoubb+Hu44qLT0reTEDd7UgrAZITCM2Khr20Pm4QUMYS0f6ScCyWRnK84LG4SEhKYMGGC5edvv/2WoUOH8tFHHzFv3jzeeustS8diR8nOzsZoNBIWFmazPywsjPT0dIfO8cgjjxAREWEjkKpTUVFBYWGhzT+JpLXh7qqjU4AHAIl15N08unQfF725gS/NXXctqHk3e7+D/GRnmtmsZJvzbQK99AAWcbMvtUBUSSlG0Zm42rDMnOIKSy+cvpEOzpKSSCTtDofFTV5eno0IWb9+PRdddJHl58GDB5OSktK01jXAiy++yLfffstPP/2Eu7v9VukLFizA7//bu/P4qKr7b+Cf2bNM9pAJCQlBiKIQIGwxqD9sTaGWanFFSoFSf7ZagiB9rGIF+pSfAi4VrRQqr4e2z1MX5FVXqlhklYosCYsIArIlECYrSSZ7MnOeP+7cm8yahExmkpnP+/XK607uPZk5974gfPme7zknJkb54uae1Fd1HJpyZrMJfHGmHADwhy0ncKKkQ5A+fBpgiJamRv95EnD4TWkNmH5OztwkREozoUYNigUAfFNSg7Zqe71N1EBArVF+5qh9NtXQAZGI5sJ9RCGry8GNyWTC+fPnAUjDSYWFhbj55vappxaLBTpd936ZJCYmQqPRoLS01OF8aWkpkpO9z3J46aWXsGrVKvz73//GqFGjPLZbsmQJampqlC9/B2BEXeVtxtSFynrUNrUBAFrabMh/uxD1zdL3iBkE/Go3kHYz0GIBPvw18GG+3/rdW5TgxihlboYkRCLKoEVTqw1fy0PkMU5DUkXVADgkRRTquhzc/OhHP8LTTz+NL774AkuWLEFERARuu+025fqxY8cwdOjQbn24Xq/HuHHjHIqB5eLg3Nxcjz/3wgsvYMWKFdi6dSvGjx/v9TMMBgOio6Mdvoj6ovYZU/Uu1+SMxPUmI5Kjw3CuvB5LPzze3iD+OmDeJ0De7wGogCP/AGqv9H6ne5G8OrE8LKVWq/DQRCnzuu2rQqmRU73NYbmYOD3WL30kor6py8HNihUroNVqMXnyZGzYsAEbNmyAXq9Xrm/cuBFTpkzpdgcWL16MDRs24O9//ztOnjyJxx57DPX19Zg3bx4AYM6cOViyZInSfvXq1Vi6dCk2btyIjIwMmM1mmM1m1NV1cwotUR+jBDduMjdHi6UdsW8ZlojXZmZDrQLeK7yMj46WtDdSa4BbnwBi7UOvVy/0dpd7VaV9X6mEyPbfM09OHY5xg+MQb5WG6FqNA5VrNpvAUXtwk82ZUkQhrcsrFCcmJmLPnj2oqamB0WiERqNxuL5582YYjcZud2DGjBkoLy/HsmXLYDabMWbMGGzdulWp7ykqKoJa3R6DrVu3Di0tLbj//vsd3mf58uX4/e9/3+3PJ+or5GGpy9WNaGhpQ4S+/a9nx+nNE4fEI/97w/Daju+wftdZ3DVqoONO2bGDpcLi6ovS6r39VFW9Y+YGAPRaNf48ayy+fqUaEMDH51W4x75T+Hn70J1Bq+aWC0QhrsvBjazjnlId9WQqdn5+PvLz3dcI7Nq1y+H7CxcuXPPnEPVlcZF6JETqUVnfgnPl9RiZKv1da2mzKQXEo+1Ftb+4dQjW7zmHE1dqcfRSjeOaLnGDpWnhV51mVfUz8rBUgtHgcN4UHYbIhEagAvjskhb1X13E7NwMpd5mZGoMF+8jCnH8DUDUhwxNci0q/tZcixarDTHhOgy2b6oZG6HHj7OkIZk3naeGx2ZIx+r+Hdy0z5bSu1wzNksrmF8RCfiff53EKbOFi/cRkYLBDVEfIg9NddyGQa4jGZ0W6zD89NOcdADAx8dKUNPYYeuFOPtmkf09cyMPSxmdghtrG1AnrYM1ZOj1aG6zYcHbhTh4oQoAgxsiYnBD1KcMc5O5OWIvJh4zyHFIeNzgOFxvMqKp1YYPDnfYWy3WHtz048yN1SZwtcG15gYAYLkCCBug1mHpjMlINBpwurQO35otABjcEBGDG6I+JdMe3By6eBUNLdI6NvI08NFO/2irVCrMypECmTf3X2zfk0rO3NRe7rebaVbb95UCgPgIp+BG3lMqeiASo8Lx8oOjlUuJRr2y0jMRhS4GN0R9yMQh8RgUF45ySzP+vPMsaptalSEq5+AGAKZnpyJMp8bp0joUXJT2VILRBGjDpOxGTf9ctFLZVypC2lfKgbwbeLS0Yebk6wfgv2+VdgbPGZLgOHOMiEISgxuiPiRMp8Gz024CALyx5xz+dewKhAAGxYUj0WnWEADEhOtw16gUAMBb++37SqlUQKxUj9Nf624q6jwMSQEddgNvX8BvyY9uxPqfjcPv7x7hj+4RUR/H4Iaoj5k6woTbMhPRYrXh9x9J2wy4y9rI7hkr/SO//3xV+8l+XnfjbaaUu93ANWoVfjgyGQOiXANAIgo9DG6I+hiVSoXld42AVq1Cc5sNADDGvr6NO5lJ0oJ1JTWNaG6zSif7+YypKmV1YjfBijwsFTPIjz0iov6EwQ1RHzQsyYhf2OtIAO+Zm0SjHhF6DYQALl1tlE7288yNx2ngQIeC4lTXa0REYHBD1Gct+P4wDE6IQHJ0GLJS3a8MDkiZnsEJkQCAi5X2TTf7eeZGWZ3Y3bBUjWvNDRFRR93efoGI/CMqTIdPF94GFVQI12u8ts1IiMDJK7W4UNEgnejnmRt3+0oBANqagXppdWJmbojIE2ZuiPqwCL2208AGgOfMTX050FLv/oeuXgCaXXcg7wuUHcGdZ4jJxcTaMCAiwc+9IqL+gsENURCQ95y6WGXP3ITHAQb7UFZ1kesPlBwGXhsLfOR+w9pA8zhbSpkplSJNeScicoPBDVEQUIKbyob2k3Fe1ro5+TEgrMDFL/3Qu+6r9LTODYuJiagLGNwQBYEM+7BUcVUD2qzS9HGvdTfndkvHulLPw1YBYuuwr5RL5obTwImoCxjcEAWB5Ogw6LVqtNkErtQ0SSfjMqSjc+amqQYoKWz/vuq8X/rYVdWNrbDJ22Qxc0NE14DBDVEQUKtVSI+XhqYuyEXFnjI3F/4j7Tslu9q3ght5Ab+YcB10LvtKycFNip97RUT9CYMboiCRkSAHN/a6G09r3Zzf7fh9H8vcVHhb46aWw1JE1DkGN0RBIj1eqrspUqaDZ0jH6ouAEO0N5XqbhEzp2OcyN942zewwW4qIyAMGN0RBIiPRKXMj7wzeXAs0XpVeW0qB8pPS67FzpGMfy9zINUOm6DDHC23NQEOl9Jo1N0TkBYMboiDhspCfLhwwmqTXVy9Ix/N7pGNyFpA6zn6tbwU3l65Kwdmg+HDHCxazdNQYpHV8iIg8YHBDFCTkmpuiqgbY5OlGA4ZLxx0rAGsrcH6X9P11twPx9o05q4ula32EvPnnoLgIxwuWK9IxKpkL+BGRVwxuiIJESmw4NGoVmlptKLNIM47wgz8Augjg7A7gk/8FnLNnbobcDhiTpW0MhBWoKQ5Ut10U21dZHhTnlLlhvQ0RdRGDG6IgodOolYBAmQ6eMga47/8AUAEFfwNqigC1DhicC6jV7UXHfaTuRgiBy/bMTZrHzM1AP/eKiPobBjdEQcSl7gYAhv8I+OHK9u8HTQD0UjvE2Yem+kjdTU1jKyzNbQCYuSGia8fghiiIDI53s8cUAOQ8CuQ8Jr0ecU/7ebnupo9kbuR6m0SjAWE6p93Qmbkhoi7SBroDROQ7bjfQBKQC3DtXAZPyHadRK5mbC/7pYCfkmVJpzjOlgPbZUtEMbojIOwY3REFE3kDzQqWHzTCdV/ZVMjfnerFXXVdc5WGmFNA+LMXMDRF1gsNSREFEztwUVTZAdFyV2JOOmZuutO9lyho3zvU2QnBYioi6jMENURBJi4+AXqOGpbkNn31j7vwHYtMBlRpobQDqSnu/g5245GmmVONVoM2+2zmDGyLqBIMboiASptPgkf+SsjHPfvANqhtavP+AVg9E24eq+kBRcbGnzI2ctQmPB3RO2zIQETlhcEMUZBZ8PxPDkoyoqGvGHz4+oZwXQuBqvZtgJz5DOgZ4OrgQoj1zE++Uuam1BzecBk5EXcDghijIhOk0eOH+UVCpgPcOX8a2E6X4+GgJfrL2P8hesQ3vHnRajTiub0wHv9rQioYWKwAgJdYpO2NhMTERdR2DG6IgNDY9Dg/fIgUtj/zfQ1jw9mEcu1QDADh4ocqxcXzfWMhP3nbBFG2AQeu0xo2SuWFwQ0SdY3BDFKR+M+UGZTPNhEg9bstMBACYa5scG8ZfJx0DnLnxWEwMcKYUEXUL17khClLheg3e/VUujl6qwW2ZiTh04Sq+OFOBUufgpo9sweBxGjjA4IaIuoWZG6IglhQdhh/cZEKYToPkGAMAwFzjnLmxBzcNlUBduZ972K59ppSXBfxYUExEXcDghihEmKKlIt3apjY02gt3AQCGKGDAjdLron0B6JmkfaYUMzdE1DMMbohChNGgRYReKtR1qbsZnCsd/RjcnDJb8K9jV5SVlOWCYpfMTVsLUG/PKDFzQ0RdwOCGKESoVCok27M3LkNTg2+Rjhe/9Ft/Fr97BPPfKsQ7B4sd1rhxqbmps6+0rNEDEQl+6x8R9V8MbohCiDw05VJUnG7P3JiPAc0Wv/SlyL5z+fOfnMSJK7VobrNBrQIGxjgFN/I08KhkaXdzIqJOMLghCiHJMfbMjXNwE5Mq7TMlbEDxgV7vR2OLFZbmNgCApakNC985IvUvOgx6rdOvJdbbEFE3MbghCiEmT8NSAJA+STr6YWiqoq4ZAKBRq6BRq/BdWR0ADzOlGNwQUTcxuCEKIcnR0nRwl2EpABhsD278UFRcbg9uBsaE4b9vG6KcH+RuphSngRNRNzG4IQohHoelgPbg5tIhoK25V/tRbpHeP9FowKI7rke6faPMdOcNMwFmboio2xjcEIUQpaDY3bBUwjAgcgBgbQZKDvdqP+TgZkCUAeF6Dd6YMw4/zUnHTyemuzbmjuBE1E0MbohCiJy5KbM0w2YTjhdVKiD9Zul1L9fdyDU3A6KkYbLhydF4/p4sJEWHuTbmjuBE1E0MbohCyACjAWoV0GYTqKh3M/Tkp6LijsNSXgnBHcGJqNsY3BCFEK1GrQQUpTVughu57qZ4P2Czul73kY7DUl41VQNt0uJ+zNwQUVcxuCEKMV6LipOzAH0U0FwLXC7otT4ow1KdZW7k2p/oVEDnZiYVEZEbDG6IQoyy1o274EatAW74ofR665Jey96UKzU3eu8Nz+6Ujtd9r1f6QUTBicENUYhJ9jZjCgB+8AfAEA1cPgQceMPnny+EaB+WMropIO5IDm6GMrghoq5jcEMUYrwOSwHSlOsf/G/p9fYVwNWLPv38+hYrmlptAIBEb5mbujKg9Gvp9ZDJPu0DEQU3BjdEISYpyssqxbKxP5d2Cm+tB7Y8Ic1a8hE5axOp1yBCr/Xc8Nxu6Zg8CjAO8NnnE1HwY3BDFGLkzI3X4EatBu56FdAYgLPbgRMf+uzzuzxT6uwO6cghKSLqJgY3RCEm2dvmmR0lZgITHpZen9/ts893XsDPLSGAcywmJqJrE/DgZu3atcjIyEBYWBhycnJw4MABj22/+eYb3HfffcjIyIBKpcKaNWv811GiIGGyZ25qm9rQ2OI6G8pmE+3nk26UjtVFPvv8Li3gV35K2lNKGwak5/rss4koNAQ0uNm0aRMWL16M5cuXo7CwEKNHj8bUqVNRVlbmtn1DQwOuu+46rFq1CsnJyX7uLVFwiDJoEaHXAHAtKhZC4Ff/KMDoP/wbxVUNQKx9rycfBjddytzIQ1KDJwG6TmZUERE5CWhw88c//hGPPPII5s2bh5tuugnr169HREQENm7c6Lb9hAkT8OKLL+Khhx6CwdDJeD0RuaVSqTwOTX14pATbTpSipc2Go5eqHYMbHxUVdylzwyEpIuqBgAU3LS0tKCgoQF5eXntn1Grk5eVh3759Pvuc5uZm1NbWOnwRhTpld/AOmZuahlb8z79OKN9X1rUA0YMAqIC2JqC+3Cef3WlBcVszcGGv9JrFxER0DQIW3FRUVMBqtcJkMjmcN5lMMJvNPvuclStXIiYmRvlKS0vz2XsT9Vfu1rp58d/foqKuRfm+oq4Z0OqldW8Anw1Ndbr1wqWDQGsDEJkEJI3wyWcSUWgJeEFxb1uyZAlqamqUr+Li4kB3iSjgTE7DUkeKq/Hmfil4mTQ0AQDaAx1laMo3i/kpw1KeMjdm+8J96TnSlHQiom7ysoJW70pMTIRGo0FpaanD+dLSUp8WCxsMBtbnEDlJjpb+Tuz9rgIL3zmMfWcrIQRwb3Yqxg6Ow5dnK1Fpz7AgNh0o2ueTzI0QQgmaPA5LVZ2XjvHX9fjziCg0Bey/RXq9HuPGjcP27duVczabDdu3b0duLqd+EvWmgbHSDtvfldXhwyMlKLM0IynKgGem3YhEo7QlQmW9c+am58FNbWMbWqz2rReMHrZeuGoPbuKG9PjziCg0BSxzAwCLFy/G3LlzMX78eEycOBFr1qxBfX095s2bBwCYM2cOUlNTsXLlSgBSEfKJEyeU15cvX8aRI0dgNBoxbNiwgN0HUX9z+w0DMGN8GlptNgxLMiIzKQoTM+IRE6FDgr0WpqJj5gbwSXBTXicNg0WHaWHQatw3UjI3DG6I6NoENLiZMWMGysvLsWzZMpjNZowZMwZbt25VioyLioqg7jDmXlJSguzsbOX7l156CS+99BImT56MXbt2+bv7RP2WQavB6vtHub0mT9GudKm56XlwU9bZTCmbtb22h5kbIrpGAQ1uACA/Px/5+flurzkHLBkZGRA+3MCPiFwl2IeL6prb0NRqRVjsYOmCvNaNSnXN791pvU1tCWBtAdQ6IGbQNX8OEYU2TkUgIgdRBi30GulXQ2V9CxCdCqjUPlnrptMF/OR6m9h0QO1h2IqIqBMMbojIgUqlUrI3FRb7WjdR9rVurvZsOninC/ix3oaIfIDBDRG5UOpu6p2LinsW3HS6r5QyUyqjR59DRKGNwQ0RuVAyNz4uKu58WOqCdGQxMRH1AIMbInKRENk7M6Y4LEVE/sDghohcJCqZG9+uddPpvlJcwI+IfIDBDRG5aF/rxkNw09YM/PVHwP+7V1qbpgusNqGseuw2c9NQBTTVSK9Zc0NEPcDghohcJHjagqGmWFrr5tgm4OJ/gLPbgZMfdek9rza0wGqT1qmKj3Sz9YKctTEmA/qIHvWfiEIbgxsiciFvwSDXyDisdWO5Auxd09547xop4OlElT1Qio3QQadx86uH9TZE5CMMbojIhcvmmR3Xutm3Fqg6C4TFAtpw4MoR4PzuTt9TDm7cZm0A1tsQkc8wuCEiF3LNTVV9C2z2oSRlaGr/eumY8ytg7Gzp9d5XOn1PJbiJ8BDcVF2QjszcEFEPMbghIhdx9gDEahOoaWyVTsrBja1NythM/BWQmw+oNMC5XUDJYa/vycwNEfkLgxsicqHXqhETrgPgZjo4AIybC0QmAHGDgZH3Sec61uG4IQc3crGyawPW3BCRbzC4ISK3Ej2tUqzWArnz2xveukg6nvzI695TcnAT525YqrURsJRIr5m5IaIeYnBDRG4lOO8vNewOKcC5dbFjFsc0Ahg4BhA2wPy1x/fzOiwlB0X6KCAi3hfdJ6IQpg10B4iob1JmTMmZm+gUYJGH4CU2XZo1VVvi8f28BzfykFQGoFJdY4+JiCTM3BCRW/L+UkrNjTfRqdKx9rLHJl6DmyoWExOR7zC4ISK35OngSs2NN9H2NXC6kLmRgyYH8m7gLCYmIh9gcENEbilbMHQpc+M9uBFCoKrBXlAcqXNtYLkiHWPSut1PIiJnDG6IyC2XVYq9kYMbi/vgpr7FipY2GwAPmZu6MuloTOp2P4mInDG4ISK3EozdqbnpkLlxs89UlX1oK0ynRrhe4/rzdaXS0Wi6pr4SEXXE4IaI3JJrbiq7UnMTNVA6tjUBjVddLstDUm6zNgAzN0TkUwxuiMgtueamrrkNTa1W7421BiBygPTazYypKvtaOW7rbZotQGu99DqSwQ0R9RyDGyJyK8qghV4j/Yro0tCUnL1xU1RcVS/tTxXvrd5GbwQMxmvqKxFRRwxuiMgtlUrVYcZUV4qK5bVu3AU3UnCU4G6NG6XehlkbIvINBjdE5FGi8xYM3niZDl7pbV8pFhMTkY8xuCEijxKcN8/0xktwc9XbjuAsJiYiH2NwQ0Qe+WoLBq87gjNzQ0Q+xuCGiDxKiw8HAJwoqe28cbS3gmIv+0qx5oaIfIzBDRF5dFtmIgDgizMVaLPavDeWMzfyVgodVHVpWIqZGyLyDQY3ROTRmLQ4xITrUNPYiqOXarw3lqeCN9cCTY6ZHhYUE5E/MbghIo80apWSvdl9qsx7Y4MRCIuRXnfI3rRabbA0tQHwNBWcBcVE5FsMbojIq9tvkIKOXafLO2/spqhYnimlVgEx4U4rFNtsHJYiIp9jcENEXv3X9VLm5tilms5nTblZpVjeVyouQg+1WuXYvrEKEPatHeTtG4iIeojBDRF5lRQVhhEp0QCAPZ1lb9ysdSPvCO51plREAqBxs+8UEdE1YHBDRJ26/QYpq7LrVGfBjesWDEoxsddp4BySIiLfYXBDRJ2S6272nCmH1SY8N3STublqH5ZiMTER+QuDGyLqVHZaLKLDtKhuaMXRS9WeG7rL3NQxc0NE/sXghog6pdWocVumNDT1+YlSzw2VVYo7zJZi5oaI/IzBDRF1iVx38+ddZzFn4wEculDl2kgelmqsAlobAbTX3HjfeoGZGyLyHQY3RNQl07NT8dCENGjUKuw5XY771+/D/DcLIUSHGpywWEAXIb22L+TXpdlSDG6IyIcY3BBRl+g0aqy6bxR2/uZ2zJyYDgD419dXUGEPXgAAKpVLUXFLXRXUsHkIbjgsRUS+x+CGiLolPSECK+/NQkaClKE5XWpxbCAHN3tfAdbejH/WzsQK7V+5rxQR+Q2DGyK6JtebogAAp8xOwU2UPbj57nOg/CQA4H7NbiRq6x3btTUDjVel1wxuiMiHGNwQ0TW5IVkKblwyN6MfAhJvAEbcg4Yfr8NJWxoMqjYknNvi2K7eviCgWgeEx/mhx0QUKhjcENE1kYObb50zN0O/B+QfAB74G0ozfoLN1tsBALqv33Zs13FISuW05xQRUQ9oA90BIuqfbrAPS50ptcBmE8qmmMcv1+DV7WeQFheBmHAdPrROwjO6t6AtKQTKvgWShktvwGJiIuolDG6I6JpkJEZCp1GhvsWKy9WNSIuXCozX7vwO2xwW+otBgX48clr2A0ffAn7wB+k0i4mJqJdwWIqIrolOo8bQAUYA7XU3NpvAvnOVAIBpowYiKzUGYTo1KjPvl37o2LuAzSq9ZuaGiHoJMzdEdM1uSI7Ct2YLTpVacMeNJpw016K6oRWReg3WzBgDnUYNIQRU1lbg3EppYb+zO4HMPGZuiKjXMHNDRNfMeTr4vrNS1mbikHjoNNKvF5VKBWj1QNYD0g/tXwfUXAIsZul7Zm6IyMcY3BDRNbvBQ3CTOzTBtfGYn0rH7z4HXhkBnPpE+p6ZGyLyMQY3RHTN5Ong58rr0dRqxf7z0maak4YmujZOGQNMeQ5IGQuo1ICwSecTM/3UWyIKFay5IaJrlhobjki9BvUtVnx8tAR1zW2ICdfhxoHR7n9gUr701VQLFH0FaHRA0o3+7TQRBT0GN0R0zdRqFTJNUThSXI2/fXkBAJAzJB4adSeL8oVFA9dP6f0OElFI4rAUEfXIcPvQ1DcltQCASe7qbYiI/KhPBDdr165FRkYGwsLCkJOTgwMHDnhtv3nzZgwfPhxhYWHIysrCJ5984qeeEpEzecaUbNIwN/U2RER+FPDgZtOmTVi8eDGWL1+OwsJCjB49GlOnTkVZWZnb9l9++SVmzpyJhx9+GIcPH8b06dMxffp0HD9+3M89JyKgvagYABKNemQmGQPYGyIiQCWEEIHsQE5ODiZMmIDXX38dAGCz2ZCWloYFCxbg6aefdmk/Y8YM1NfXY8uW9h2Gb775ZowZMwbr16/v9PNqa2sRExODmpoaREd7KHokoi4rtzRjwnOfAwB+PGogXv/p2AD3iIiCUXf+/Q5o5qalpQUFBQXIy8tTzqnVauTl5WHfvn1uf2bfvn0O7QFg6tSpHts3NzejtrbW4YuIfGdAlAEJkXoAHqaAExH5WUCDm4qKClitVphMjot4mUwmmM1mtz9jNpu71X7lypWIiYlRvtLS0nzTeSJSzJ2UgdGDYnDnyORAd4WIKPA1N71tyZIlqKmpUb6Ki4sD3SWioPP4HZn4MP9WxNkzOEREgRTQdW4SExOh0WhQWlrqcL60tBTJye7/B5icnNyt9gaDAQaDwTcdJiIioj4voJkbvV6PcePGYfv27co5m82G7du3Izc31+3P5ObmOrQHgG3btnlsT0RERKEl4CsUL168GHPnzsX48eMxceJErFmzBvX19Zg3bx4AYM6cOUhNTcXKlSsBAAsXLsTkyZPx8ssvY9q0aXjnnXdw6NAhvPHGG4G8DSIiIuojAh7czJgxA+Xl5Vi2bBnMZjPGjBmDrVu3KkXDRUVFUKvbE0yTJk3CW2+9hWeffRbPPPMMMjMz8cEHH2DkyJGBugUiIiLqQwK+zo2/cZ0bIiKi/qffrHNDRERE5GsMboiIiCioMLghIiKioMLghoiIiIIKgxsiIiIKKgxuiIiIKKgwuCEiIqKgwuCGiIiIggqDGyIiIgoqAd9+wd/kBZlra2sD3BMiIiLqKvnf7a5srBBywY3FYgEApKWlBbgnRERE1F0WiwUxMTFe24Tc3lI2mw0lJSWIioqCSqXy6XvX1tYiLS0NxcXFIbtvFZ8Bn0Go3z/AZwDwGQB8BoBvn4EQAhaLBSkpKQ4barsTcpkbtVqNQYMG9epnREdHh+wfZBmfAZ9BqN8/wGcA8BkAfAaA755BZxkbGQuKiYiIKKgwuCEiIqKgwuDGhwwGA5YvXw6DwRDorgQMnwGfQajfP8BnAPAZAHwGQOCeQcgVFBMREVFwY+aGiIiIggqDGyIiIgoqDG6IiIgoqDC4ISIioqDC4MZH1q5di4yMDISFhSEnJwcHDhwIdJd6zcqVKzFhwgRERUUhKSkJ06dPx6lTpxzaNDU1Yf78+UhISIDRaMR9992H0tLSAPW4961atQoqlQqLFi1SzoXCM7h8+TJ+9rOfISEhAeHh4cjKysKhQ4eU60IILFu2DAMHDkR4eDjy8vJw5syZAPbYt6xWK5YuXYohQ4YgPDwcQ4cOxYoVKxz2vgm2Z7Bnzx7cddddSElJgUqlwgcffOBwvSv3W1VVhVmzZiE6OhqxsbF4+OGHUVdX58e7uHbe7r+1tRVPPfUUsrKyEBkZiZSUFMyZMwclJSUO79Gf7x/o/M9AR48++ihUKhXWrFnjcL63nwGDGx/YtGkTFi9ejOXLl6OwsBCjR4/G1KlTUVZWFuiu9Yrdu3dj/vz5+Oqrr7Bt2za0trZiypQpqK+vV9o88cQT+Pjjj7F582bs3r0bJSUluPfeewPY695z8OBB/OUvf8GoUaMczgf7M7h69SpuueUW6HQ6fPrppzhx4gRefvllxMXFKW1eeOEFvPbaa1i/fj3279+PyMhITJ06FU1NTQHsue+sXr0a69atw+uvv46TJ09i9erVeOGFF/CnP/1JaRNsz6C+vh6jR4/G2rVr3V7vyv3OmjUL33zzDbZt24YtW7Zgz549+OUvf+mvW+gRb/ff0NCAwsJCLF26FIWFhXjvvfdw6tQp3H333Q7t+vP9A53/GZC9//77+Oqrr5CSkuJyrdefgaAemzhxopg/f77yvdVqFSkpKWLlypUB7JX/lJWVCQBi9+7dQgghqqurhU6nE5s3b1banDx5UgAQ+/btC1Q3e4XFYhGZmZli27ZtYvLkyWLhwoVCiNB4Bk899ZS49dZbPV632WwiOTlZvPjii8q56upqYTAYxNtvv+2PLva6adOmiV/84hcO5+69914xa9YsIUTwPwMA4v3331e+78r9njhxQgAQBw8eVNp8+umnQqVSicuXL/ut777gfP/uHDhwQAAQFy9eFEIE1/0L4fkZXLp0SaSmporjx4+LwYMHi1deeUW55o9nwMxND7W0tKCgoAB5eXnKObVajby8POzbty+APfOfmpoaAEB8fDwAoKCgAK2trQ7PZPjw4UhPTw+6ZzJ//nxMmzbN4V6B0HgGH330EcaPH48HHngASUlJyM7OxoYNG5Tr58+fh9lsdngGMTExyMnJCZpnMGnSJGzfvh2nT58GABw9ehR79+7FnXfeCSA0nkFHXbnfffv2ITY2FuPHj1fa5OXlQa1WY//+/X7vc2+rqamBSqVCbGwsgNC4f5vNhtmzZ+PJJ5/EiBEjXK774xmE3MaZvlZRUQGr1QqTyeRw3mQy4dtvvw1Qr/zHZrNh0aJFuOWWWzBy5EgAgNlshl6vV/4yy0wmE8xmcwB62TveeecdFBYW4uDBgy7XQuEZnDt3DuvWrcPixYvxzDPP4ODBg3j88ceh1+sxd+5c5T7d/d0Ilmfw9NNPo7a2FsOHD4dGo4HVasVzzz2HWbNmAUBIPIOOunK/ZrMZSUlJDte1Wi3i4+OD7pk0NTXhqaeewsyZM5VNI0Ph/levXg2tVovHH3/c7XV/PAMGN9Qj8+fPx/Hjx7F3795Ad8WviouLsXDhQmzbtg1hYWGB7k5A2Gw2jB8/Hs8//zwAIDs7G8ePH8f69esxd+7cAPfOP9599128+eabeOuttzBixAgcOXIEixYtQkpKSsg8A3KvtbUVDz74IIQQWLduXaC74zcFBQV49dVXUVhYCJVKFbB+cFiqhxITE6HRaFxmwZSWliI5OTlAvfKP/Px8bNmyBTt37sSgQYOU88nJyWhpaUF1dbVD+2B6JgUFBSgrK8PYsWOh1Wqh1Wqxe/duvPbaa9BqtTCZTEH/DAYOHIibbrrJ4dyNN96IoqIiAFDuM5j/bjz55JN4+umn8dBDDyErKwuzZ8/GE088gZUrVwIIjWfQUVfuNzk52WWyRVtbG6qqqoLmmciBzcWLF7Ft2zYlawME//1/8cUXKCsrQ3p6uvK78eLFi/jNb36DjIwMAP55Bgxuekiv12PcuHHYvn27cs5ms2H79u3Izc0NYM96jxAC+fn5eP/997Fjxw4MGTLE4fq4ceOg0+kcnsmpU6dQVFQUNM/kjjvuwNdff40jR44oX+PHj8esWbOU18H+DG655RaXJQBOnz6NwYMHAwCGDBmC5ORkh2dQW1uL/fv3B80zaGhogFrt+GtUo9HAZrMBCI1n0FFX7jc3NxfV1dUoKChQ2uzYsQM2mw05OTl+77OvyYHNmTNn8PnnnyMhIcHherDf/+zZs3Hs2DGH340pKSl48skn8dlnnwHw0zPwSVlyiHvnnXeEwWAQf/vb38SJEyfEL3/5SxEbGyvMZnOgu9YrHnvsMRETEyN27dolrly5onw1NDQobR599FGRnp4uduzYIQ4dOiRyc3NFbm5uAHvd+zrOlhIi+J/BgQMHhFarFc8995w4c+aMePPNN0VERIT4xz/+obRZtWqViI2NFR9++KE4duyY+MlPfiKGDBkiGhsbA9hz35k7d65ITU0VW7ZsEefPnxfvvfeeSExMFL/97W+VNsH2DCwWizh8+LA4fPiwACD++Mc/isOHDyuzgbpyvz/84Q9Fdna22L9/v9i7d6/IzMwUM2fODNQtdYu3+29paRF33323GDRokDhy5IjD78fm5mblPfrz/QvR+Z8BZ86zpYTo/WfA4MZH/vSnP4n09HSh1+vFxIkTxVdffRXoLvUaAG6//vrXvyptGhsbxa9//WsRFxcnIiIixD333COuXLkSuE77gXNwEwrP4OOPPxYjR44UBoNBDB8+XLzxxhsO1202m1i6dKkwmUzCYDCIO+64Q5w6dSpAvfW92tpasXDhQpGeni7CwsLEddddJ373u985/EMWbM9g586dbv/+z507VwjRtfutrKwUM2fOFEajUURHR4t58+YJi8USgLvpPm/3f/78eY+/H3fu3Km8R3++fyE6/zPgzF1w09vPQCVEh6U0iYiIiPo51twQERFRUGFwQ0REREGFwQ0REREFFQY3REREFFQY3BAREVFQYXBDREREQYXBDREREQUVBjdE1K/8/Oc/x/Tp0wPdDSLqw7grOBH1GZ3tIrx8+XK8+uqr4NqjROQNgxsi6jOuXLmivN60aROWLVvmsDmn0WiE0WgMRNeIqB/hsBQR9RnJycnKV0xMDFQqlcM5o9HoMix1++23Y8GCBVi0aBHi4uJgMpmwYcMG1NfXY968eYiKisKwYcPw6aefOnzW8ePHceedd8JoNMJkMmH27NmoqKjw8x0TUW9gcENE/d7f//53JCYm4sCBA1iwYAEee+wxPPDAA5g0aRIKCwsxZcoUzJ49Gw0NDQCA6upqfP/730d2djYOHTqErVu3orS0FA8++GCA74SIfIHBDRH1e6NHj8azzz6LzMxMLFmyBGFhYUhMTMQjjzyCzMxMLFu2DJWVlTh27BgA4PXXX0d2djaef/55DB8+HNnZ2di4cSN27tyJ06dPB/huiKinWHNDRP3eqFGjlNcajQYJCQnIyspSzplMJgBAWVkZAODo0aPYuXOn2/qds2fP4vrrr+/lHhNRb2JwQ0T9nk6nc/hepVI5nJNnYdlsNgBAXV0d7rrrLqxevdrlvQYOHNiLPSUif2BwQ0QhZ+zYsfjnP/+JjIwMaLX8NUgUbFhzQ0QhZ/78+aiqqsLMmTNx8OBBnD17Fp999hnmzZsHq9Ua6O4RUQ8xuCGikJOSkoL//Oc/sFqtmDJlCrKysrBo0SLExsZCreavRaL+TiW41CcREREFEf4XhYiIiIIKgxsiIiIKKgxuiIiIKKgwuCEiIqKgwuCGiIiIggqDGyIiIgoqDG6IiIgoqDC4ISIioqDC4IaIiIiCCoMbIiIiCioMboiIiCioMLghIiKioPL/AQ6c7MMKUA/0AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error: 0.00173541318763599\n" + ] + } + ], + "source": [ + "# RNN Model\n", + "model = Sequential()\n", + "model.add(SimpleRNN(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))\n", + "model.add(SimpleRNN(units=50, return_sequences=False))\n", + "model.add(Dense(units=1))\n", + "\n", + "model.compile(optimizer='adam', loss='mean_squared_error')\n", + "model.fit(X_train, y_train, epochs=10, batch_size=32)\n", + "\n", + "predictions = model.predict(X_test)\n", + "\n", + "# Visualization \n", + "plt.plot(y_test, label='Actual Prices')\n", + "plt.plot(predictions, label='Predicted Prices')\n", + "plt.title('Stock Price Prediction with RNN')\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Stock Price')\n", + "plt.legend()\n", + "plt.savefig('../Images/rnn_stock_prediction.png') \n", + "plt.show()\n", + "\n", + "from sklearn.metrics import mean_squared_error\n", + "mse = mean_squared_error(y_test, predictions)\n", + "print(f\"Mean Squared Error: {mse}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Stock Analysis/requirements.txt b/Stock Analysis/requirements.txt new file mode 100644 index 000000000..b2c59951d --- /dev/null +++ b/Stock Analysis/requirements.txt @@ -0,0 +1,5 @@ +numpy +pandas +matplotlib +tensorflow +scikit-learn From de5b7f1892ea7c8af1154d8527326a7bd6da8301 Mon Sep 17 00:00:00 2001 From: Itachii27 <101548946+Itachii27@users.noreply.github.com> Date: Sat, 5 Oct 2024 23:04:22 +0530 Subject: [PATCH 2/2] Update README.md --- Stock Analysis/Model/README.md | 133 +++++++++++---------------------- 1 file changed, 42 insertions(+), 91 deletions(-) diff --git a/Stock Analysis/Model/README.md b/Stock Analysis/Model/README.md index 325899220..4631ee0e8 100644 --- a/Stock Analysis/Model/README.md +++ b/Stock Analysis/Model/README.md @@ -1,114 +1,65 @@ -# Stock Price Prediction Models +## **Stock Analysis** -## Overview +### ๐ŸŽฏ **Goal** -This project applies four deep learning architectures to predict Tesla's stock prices. The models used are: -1. **Recurrent Neural Network (RNN)** -2. **Long Short-Term Memory (LSTM)** -3. **Gated Recurrent Unit (GRU)** -4. **Convolutional Neural Network (CNN)** +The main goal of this project is to implement various machine learning models to predict stock prices based on historical data. The purpose is to evaluate which model provides the most accurate predictions for stock price movements. -The goal of the project is to compare these models' performance in terms of accuracy and prediction capabilities using Tesla stock price data. We evaluate model performance using **Mean Squared Error (MSE)** and visualize the predictions vs actual stock prices. +### ๐Ÿงต **Dataset** -## Models Implemented +The dataset used for this project can be found [here](https://github.com/plotly/datasets/blob/master/tesla-stock-price.csv). -### 1. **Recurrent Neural Network (RNN)** +### ๐Ÿงพ **Description** -RNNs are effective in handling time-series data as they have an internal state that helps them remember previous time steps. +This project explores the effectiveness of different neural network architectures in predicting stock prices. The models implemented include RNN, LSTM, GRU, and CNN, each chosen for their strengths in handling sequential data and capturing patterns over time. -- **Architecture**: 2 RNN layers with 50 units each. -- **Loss function**: Mean Squared Error (MSE). -- **Optimizer**: Adam. -- **Visualization**: Actual vs predicted stock prices are plotted and saved as `rnn_stock_prediction.png`. +### ๐Ÿงฎ **What I had done!** -### 2. **Long Short-Term Memory (LSTM)** +- Preprocessed the dataset for training and testing. +- Implemented four different neural network models: RNN, LSTM, GRU, and CNN. +- Trained each model using the training dataset and evaluated their performance on the test dataset. +- Visualized the predicted prices against actual prices for each model. -LSTMs are a type of RNN that can capture long-term dependencies in time-series data, making them more effective in stock price prediction. +### ๐Ÿš€ **Models Implemented** -- **Architecture**: 2 LSTM layers with 50 units each. -- **Loss function**: Mean Squared Error (MSE). -- **Optimizer**: Adam. -- **Visualization**: Actual vs predicted stock prices are plotted and saved as `lstm_stock_prediction.png`. +- **Recurrent Neural Network (RNN)**: Chosen for its ability to process sequences and capture dependencies. +- **Long Short-Term Memory Network (LSTM)**: Selected for its capability to handle long-term dependencies in the data. +- **Gated Recurrent Unit (GRU)**: Used due to its simpler architecture compared to LSTM while still retaining the ability to capture dependencies. +- **Convolutional Neural Network (CNN)**: Included for its effectiveness in detecting patterns in sequential data. -### 3. **Gated Recurrent Unit (GRU)** +### ๐Ÿ“š **Libraries Needed** -GRUs are similar to LSTMs but have fewer gates, making them computationally more efficient while retaining the ability to capture long-term dependencies. - -- **Architecture**: 2 GRU layers with 50 units each. -- **Loss function**: Mean Squared Error (MSE). -- **Optimizer**: Adam. -- **Visualization**: Actual vs predicted stock prices are plotted and saved as `gru_stock_prediction.png`. - -### 4. **Convolutional Neural Network (CNN)** - -CNNs, typically used for image data, can also be adapted for time-series data to capture patterns and trends over time. - -- **Architecture**: 2 Conv1D layers with 32 and 64 filters, followed by MaxPooling and Dense layers. -- **Loss function**: Mean Squared Error (MSE). -- **Optimizer**: Adam. -- **Visualization**: Actual vs predicted stock prices are plotted and saved as `cnn_stock_prediction.png`. - -## Data Preprocessing - -The dataset used for training is Tesla's stock prices, which includes columns like **Date**, **Close Price**, **Volume**, **Open Price**, **High**, and **Low**. The dataset is located in the `Dataset` folder, and the actual data is too large to include directly. A link to the dataset can be found in the `Dataset/README.md` file. - -- **Data Loading**: The data is loaded using `pandas` from the CSV file. -- **Data Scaling**: The stock prices are scaled using `MinMaxScaler` to normalize the values between 0 and 1. -- **Sequence Creation**: The data is split into sequences to train the RNN, LSTM, GRU, and CNN models. We use a 60-day window to predict the next day's price. - -## Visualizations - -For each model, the following visualizations are generated: -- **Actual vs Predicted Prices**: A plot showing the actual stock prices vs the predicted prices for the test data. -- **Mean Squared Error (MSE)**: The performance of each model is measured using MSE. The lower the MSE, the better the model's predictions. - -### 1. **RNN Visualization** - -- **File**: `rnn_stock_prediction.png` -- **Description**: This image shows the predicted stock prices from the RNN model compared to the actual prices over time. - -### 2. **LSTM Visualization** - -- **File**: `lstm_stock_prediction.png` -- **Description**: This image shows the predicted stock prices from the LSTM model compared to the actual prices over time. - -### 3. **GRU Visualization** - -- **File**: `gru_stock_prediction.png` -- **Description**: This image shows the predicted stock prices from the GRU model compared to the actual prices over time. +- `numpy` +- `pandas` +- `matplotlib` +- `seaborn` +- `tensorflow` +- `sklearn` -### 4. **CNN Visualization** +### ๐Ÿ“Š **Exploratory Data Analysis Results** -- **File**: `cnn_stock_prediction.png` -- **Description**: This image shows the predicted stock prices from the CNN model compared to the actual prices over time. +#### RNN Visualization +![RNN Visualization](../Images/rnn_stock_prediction.png) -## Results and Conclusions +#### LSTM Visualization +![LSTM Visualization](../Images/lstm_stock_prediction.png) -- **RNN Model**: While the RNN model performs adequately in capturing the sequential patterns in stock prices, it is limited in handling long-term dependencies, which leads to some inaccuracies in longer time frames. - -- **LSTM Model**: The LSTM model outperforms the RNN model as it can capture long-term dependencies more effectively, leading to better predictions on unseen data. - -- **GRU Model**: The GRU model performs similarly to the LSTM but is computationally more efficient with fewer parameters. It also performs well in predicting stock prices. +#### GRU Visualization +![GRU Visualization](../Images/gru_stock_prediction.png) -- **CNN Model**: The CNN model can capture short-term patterns in the stock prices effectively, but it may struggle with long-term trends compared to LSTM and GRU. +#### CNN Visualization +![CNN Visualization](../Images/cnn_stock_prediction.png) -In conclusion, while all models can predict stock prices to some extent, the **LSTM** and **GRU** models provide the most accurate predictions due to their ability to capture long-term dependencies in the data. +### ๐Ÿ“ˆ **Performance of the Models based on the Accuracy Scores** -## File Structure +- **RNN**: MSE score: 0.00173541318763599 +- **LSTM**: MSE score: 0.004513041744798277 +- **GRU**: MSE score: 0.0015398919282756725 +- **CNN**: MSE score: 0.010369056334420581 -- **Dataset**: Contains the Tesla stock price data (link provided in `Dataset/README.md`). -- **Images**: Stores all the visualizations (predicted vs actual prices). -- **Model**: Contains the Jupyter Notebooks (`.ipynb`) for each model and their respective visualizations. +### ๐Ÿ“ข **Conclusion** -## Requirements +In conclusion, the GRU model provided the most accurate predictions with the lowest Mean Squared Error (MSE). While all models are capable of predicting stock prices, the GRU and RNN models excelled in capturing long-term dependencies effectively. -To run the models, make sure you have the following Python libraries installed: -- `numpy` -- `pandas` -- `matplotlib` -- `scikit-learn` -- `tensorflow` +### โœ’๏ธ **Your Signature** -These can be installed by running: -```bash -pip install -r requirements.txt +Surbhi Bahukhandi