2876 lines
1.2 MiB
2876 lines
1.2 MiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Analisi e Previsione della Produzione di Olio d'Oliva\n",
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"\n",
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"Questo notebook esplora la relazione tra i dati meteorologici e la produzione annuale di olio d'oliva, con l'obiettivo di creare un modello predittivo."
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]
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},
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{
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"metadata": {
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"jupyter": {
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"is_executing": true
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},
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"ExecuteTime": {
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"start_time": "2024-10-25T20:14:08.562468Z"
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}
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},
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"cell_type": "code",
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"source": [
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"!pip uninstall -y tensorflow tensorflow-gpu keras tensorboard tensorflow-estimator\n",
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"!pip cache purge\n",
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"\n",
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"!pip install tensorflow --no-cache-dir\n",
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"!pip install tensorflow-macos --no-cache-dir\n",
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"!pip install tensorflow-metal --no-cache-dir\n",
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"\n",
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"import tensorflow as tf\n",
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"print(f\"Keras version: {tf.keras.__version__}\")\n",
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"print(f\"TensorFlow version: {tf.__version__}\")\n",
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"\n",
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"# GPU configuration\n",
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"gpus = tf.config.experimental.list_physical_devices('GPU')\n",
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"if gpus:\n",
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" try:\n",
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" for gpu in gpus:\n",
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" tf.config.experimental.set_memory_growth(gpu, True)\n",
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" logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
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" print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n",
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" except RuntimeError as e:\n",
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" print(e)"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Found existing installation: tensorflow 2.16.2\r\n",
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"Uninstalling tensorflow-2.16.2:\r\n",
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" Successfully uninstalled tensorflow-2.16.2\r\n",
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"\u001B[33mWARNING: Skipping tensorflow-gpu as it is not installed.\u001B[0m\u001B[33m\r\n",
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"\u001B[0mFound existing installation: keras 2.12.0\r\n",
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"Uninstalling keras-2.12.0:\r\n",
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" Successfully uninstalled keras-2.12.0\r\n",
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"Found existing installation: tensorboard 2.12.3\r\n",
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"Uninstalling tensorboard-2.12.3:\r\n",
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" Successfully uninstalled tensorboard-2.12.3\r\n",
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"Found existing installation: tensorflow-estimator 2.12.0\r\n",
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"Uninstalling tensorflow-estimator-2.12.0:\r\n",
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" Successfully uninstalled tensorflow-estimator-2.12.0\r\n",
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"Files removed: 40\r\n",
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"Collecting tensorflow\r\n",
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" Downloading tensorflow-2.16.2-cp310-cp310-macosx_10_15_x86_64.whl.metadata (4.1 kB)\r\n",
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"Requirement already satisfied: absl-py>=1.0.0 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (2.1.0)\r\n",
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"Requirement already satisfied: astunparse>=1.6.0 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (1.6.3)\r\n",
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"Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (24.3.25)\r\n",
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"Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (0.4.0)\r\n",
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"Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (0.2.0)\r\n",
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"Requirement already satisfied: h5py>=3.10.0 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (3.11.0)\r\n",
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"Requirement already satisfied: libclang>=13.0.0 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (18.1.1)\r\n",
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"Requirement already satisfied: ml-dtypes~=0.3.1 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (0.3.2)\r\n",
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"Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (3.3.0)\r\n",
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"Requirement already satisfied: packaging in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (24.1)\r\n",
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"Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (4.25.4)\r\n",
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"Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (2.32.3)\r\n",
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"Requirement already satisfied: setuptools in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (72.1.0)\r\n",
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"Requirement already satisfied: six>=1.12.0 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (1.16.0)\r\n",
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"Requirement already satisfied: termcolor>=1.1.0 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (2.4.0)\r\n",
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"Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (4.11.0)\r\n",
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"Requirement already satisfied: wrapt>=1.11.0 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (1.14.1)\r\n",
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"Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (1.66.1)\r\n",
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"Collecting tensorboard<2.17,>=2.16 (from tensorflow)\r\n",
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" Downloading tensorboard-2.16.2-py3-none-any.whl.metadata (1.6 kB)\r\n",
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"Requirement already satisfied: keras>=3.0.0 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (3.6.0)\r\n",
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"Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (0.37.1)\r\n",
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"Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorflow) (1.23.5)\r\n",
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"Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from astunparse>=1.6.0->tensorflow) (0.43.0)\r\n",
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"Requirement already satisfied: rich in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from keras>=3.0.0->tensorflow) (13.7.1)\r\n",
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"Requirement already satisfied: namex in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from keras>=3.0.0->tensorflow) (0.0.7)\r\n",
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"Requirement already satisfied: optree in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from keras>=3.0.0->tensorflow) (0.12.1)\r\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (3.3.2)\r\n",
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"Requirement already satisfied: idna<4,>=2.5 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (3.7)\r\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (2.2.2)\r\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorflow) (2024.8.30)\r\n",
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"Requirement already satisfied: markdown>=2.6.8 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (3.7)\r\n",
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"Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (0.7.2)\r\n",
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"Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (3.0.4)\r\n",
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"Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow) (2.1.3)\r\n",
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"Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from rich->keras>=3.0.0->tensorflow) (2.2.0)\r\n",
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"Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from rich->keras>=3.0.0->tensorflow) (2.15.1)\r\n",
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"Requirement already satisfied: mdurl~=0.1 in /usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow) (0.1.0)\r\n",
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"Downloading tensorflow-2.16.2-cp310-cp310-macosx_10_15_x86_64.whl (259.5 MB)\r\n",
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"\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m259.5/259.5 MB\u001B[0m \u001B[31m19.1 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m00:01\u001B[0m00:01\u001B[0m\r\n",
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"\u001B[?25hDownloading tensorboard-2.16.2-py3-none-any.whl (5.5 MB)\r\n",
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"\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m5.5/5.5 MB\u001B[0m \u001B[31m22.6 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0ma \u001B[36m0:00:01\u001B[0m\r\n",
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"\u001B[?25hInstalling collected packages: tensorboard, tensorflow\r\n"
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]
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}
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],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"# Test semplice per verificare che la GPU funzioni\n",
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"def test_gpu():\n",
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" print(\"TensorFlow version:\", tf.__version__)\n",
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" print(\"\\nDispositivi disponibili:\")\n",
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" print(tf.config.list_physical_devices())\n",
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"\n",
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" # Creiamo e moltiplichiamo due tensori sulla GPU\n",
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" with tf.device('/GPU:0'):\n",
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" a = tf.random.normal([10000, 10000])\n",
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" b = tf.random.normal([10000, 10000])\n",
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" c = tf.matmul(a, b)\n",
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"\n",
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" print(\"\\nShape del risultato:\", c.shape)\n",
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" print(\"Device del tensore:\", c.device)\n",
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" return \"Test completato con successo!\"\n",
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"\n",
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"test_gpu()"
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]
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-10-23T05:20:24.951279Z",
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"start_time": "2024-10-23T05:19:34.057560Z"
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}
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},
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"cell_type": "code",
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"source": [
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"!pip install numpy\n",
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"!pip install pandas\n",
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"\n",
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"!pip install keras\n",
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"!pip install scikit-learn\n",
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"!pip install matplotlib\n",
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"!pip install joblib\n",
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"!pip install pyarrow\n",
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"!pip install fastparquet\n",
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"!pip install scipy\n",
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"!pip install seaborn\n",
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"!pip install pysolar"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: numpy in /usr/local/anaconda3/lib/python3.12/site-packages (1.26.4)\r\n",
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"Requirement already satisfied: pandas in /usr/local/anaconda3/lib/python3.12/site-packages (2.2.2)\r\n",
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"Requirement already satisfied: numpy>=1.26.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from pandas) (1.26.4)\r\n",
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"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/anaconda3/lib/python3.12/site-packages (from pandas) (2.9.0.post0)\r\n",
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"Requirement already satisfied: pytz>=2020.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from pandas) (2024.1)\r\n",
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"Requirement already satisfied: tzdata>=2022.7 in /usr/local/anaconda3/lib/python3.12/site-packages (from pandas) (2023.3)\r\n",
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"Requirement already satisfied: six>=1.5 in /usr/local/anaconda3/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\r\n",
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"Requirement already satisfied: tensorflow in /usr/local/anaconda3/lib/python3.12/site-packages (2.16.2)\r\n",
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"Requirement already satisfied: absl-py>=1.0.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (2.1.0)\r\n",
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"Requirement already satisfied: astunparse>=1.6.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (1.6.3)\r\n",
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"Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (24.3.25)\r\n",
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"Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (0.6.0)\r\n",
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"Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (0.2.0)\r\n",
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"Requirement already satisfied: h5py>=3.10.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (3.11.0)\r\n",
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"Requirement already satisfied: libclang>=13.0.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (18.1.1)\r\n",
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"Requirement already satisfied: ml-dtypes~=0.3.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (0.3.2)\r\n",
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"Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (3.4.0)\r\n",
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"Requirement already satisfied: packaging in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (23.2)\r\n",
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"Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (3.20.3)\r\n",
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"Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (2.32.2)\r\n",
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"Requirement already satisfied: setuptools in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (69.5.1)\r\n",
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"Requirement already satisfied: six>=1.12.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (1.16.0)\r\n",
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"Requirement already satisfied: termcolor>=1.1.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (2.5.0)\r\n",
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"Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (4.11.0)\r\n",
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"Requirement already satisfied: wrapt>=1.11.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (1.14.1)\r\n",
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"Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (1.67.0)\r\n",
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"Requirement already satisfied: tensorboard<2.17,>=2.16 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (2.16.2)\r\n",
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"Requirement already satisfied: keras>=3.0.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (3.6.0)\r\n",
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"Requirement already satisfied: numpy<2.0.0,>=1.26.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (1.26.4)\r\n",
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"Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from astunparse>=1.6.0->tensorflow) (0.43.0)\r\n",
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"Requirement already satisfied: rich in /usr/local/anaconda3/lib/python3.12/site-packages (from keras>=3.0.0->tensorflow) (13.3.5)\r\n",
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"Requirement already satisfied: namex in /usr/local/anaconda3/lib/python3.12/site-packages (from keras>=3.0.0->tensorflow) (0.0.8)\r\n",
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"Requirement already satisfied: optree in /usr/local/anaconda3/lib/python3.12/site-packages (from keras>=3.0.0->tensorflow) (0.13.0)\r\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/anaconda3/lib/python3.12/site-packages (from requests<3,>=2.21.0->tensorflow) (2.0.4)\r\n",
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"Requirement already satisfied: idna<4,>=2.5 in /usr/local/anaconda3/lib/python3.12/site-packages (from requests<3,>=2.21.0->tensorflow) (3.7)\r\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from requests<3,>=2.21.0->tensorflow) (2.2.2)\r\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/anaconda3/lib/python3.12/site-packages (from requests<3,>=2.21.0->tensorflow) (2024.8.30)\r\n",
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"Requirement already satisfied: markdown>=2.6.8 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (3.4.1)\r\n",
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"Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (0.7.2)\r\n",
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"Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (3.0.3)\r\n",
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"Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow) (2.1.3)\r\n",
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"Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from rich->keras>=3.0.0->tensorflow) (2.2.0)\r\n",
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"Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from rich->keras>=3.0.0->tensorflow) (2.15.1)\r\n",
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"Requirement already satisfied: mdurl~=0.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from markdown-it-py<3.0.0,>=2.2.0->rich->keras>=3.0.0->tensorflow) (0.1.0)\r\n",
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"Requirement already satisfied: keras in /usr/local/anaconda3/lib/python3.12/site-packages (3.6.0)\r\n",
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"Requirement already satisfied: absl-py in /usr/local/anaconda3/lib/python3.12/site-packages (from keras) (2.1.0)\r\n",
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"Requirement already satisfied: numpy in /usr/local/anaconda3/lib/python3.12/site-packages (from keras) (1.26.4)\r\n",
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"Requirement already satisfied: rich in /usr/local/anaconda3/lib/python3.12/site-packages (from keras) (13.3.5)\r\n",
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"Requirement already satisfied: namex in /usr/local/anaconda3/lib/python3.12/site-packages (from keras) (0.0.8)\r\n",
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"Requirement already satisfied: h5py in /usr/local/anaconda3/lib/python3.12/site-packages (from keras) (3.11.0)\r\n",
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"Requirement already satisfied: optree in /usr/local/anaconda3/lib/python3.12/site-packages (from keras) (0.13.0)\r\n",
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"Requirement already satisfied: ml-dtypes in /usr/local/anaconda3/lib/python3.12/site-packages (from keras) (0.3.2)\r\n",
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"Requirement already satisfied: packaging in /usr/local/anaconda3/lib/python3.12/site-packages (from keras) (23.2)\r\n",
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"Requirement already satisfied: typing-extensions>=4.5.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from optree->keras) (4.11.0)\r\n",
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"Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from rich->keras) (2.2.0)\r\n",
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"Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from rich->keras) (2.15.1)\r\n",
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"Requirement already satisfied: mdurl~=0.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from markdown-it-py<3.0.0,>=2.2.0->rich->keras) (0.1.0)\r\n",
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"Requirement already satisfied: scikit-learn in /usr/local/anaconda3/lib/python3.12/site-packages (1.4.2)\r\n",
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"Requirement already satisfied: numpy>=1.19.5 in /usr/local/anaconda3/lib/python3.12/site-packages (from scikit-learn) (1.26.4)\r\n",
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"Requirement already satisfied: scipy>=1.6.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from scikit-learn) (1.13.1)\r\n",
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"Requirement already satisfied: joblib>=1.2.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from scikit-learn) (1.4.2)\r\n",
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"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from scikit-learn) (2.2.0)\r\n",
|
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"Requirement already satisfied: matplotlib in /usr/local/anaconda3/lib/python3.12/site-packages (3.9.2)\r\n",
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"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib) (1.2.0)\r\n",
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"Requirement already satisfied: cycler>=0.10 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib) (0.11.0)\r\n",
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"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib) (4.51.0)\r\n",
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"Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib) (1.4.4)\r\n",
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"Requirement already satisfied: numpy>=1.23 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib) (1.26.4)\r\n",
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"Requirement already satisfied: packaging>=20.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib) (23.2)\r\n",
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"Requirement already satisfied: pillow>=8 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib) (10.3.0)\r\n",
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"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib) (3.0.9)\r\n",
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"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib) (2.9.0.post0)\r\n",
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"Requirement already satisfied: six>=1.5 in /usr/local/anaconda3/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\r\n",
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"Requirement already satisfied: joblib in /usr/local/anaconda3/lib/python3.12/site-packages (1.4.2)\r\n",
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"Requirement already satisfied: pyarrow in /usr/local/anaconda3/lib/python3.12/site-packages (14.0.2)\r\n",
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"Requirement already satisfied: numpy>=1.16.6 in /usr/local/anaconda3/lib/python3.12/site-packages (from pyarrow) (1.26.4)\r\n",
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"Requirement already satisfied: fastparquet in /usr/local/anaconda3/lib/python3.12/site-packages (2024.5.0)\r\n",
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"Requirement already satisfied: pandas>=1.5.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from fastparquet) (2.2.2)\r\n",
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"Requirement already satisfied: numpy in /usr/local/anaconda3/lib/python3.12/site-packages (from fastparquet) (1.26.4)\r\n",
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"Requirement already satisfied: cramjam>=2.3 in /usr/local/anaconda3/lib/python3.12/site-packages (from fastparquet) (2.9.0)\r\n",
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"Requirement already satisfied: fsspec in /usr/local/anaconda3/lib/python3.12/site-packages (from fastparquet) (2024.3.1)\r\n",
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"Requirement already satisfied: packaging in /usr/local/anaconda3/lib/python3.12/site-packages (from fastparquet) (23.2)\r\n",
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"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/anaconda3/lib/python3.12/site-packages (from pandas>=1.5.0->fastparquet) (2.9.0.post0)\r\n",
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"Requirement already satisfied: pytz>=2020.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from pandas>=1.5.0->fastparquet) (2024.1)\r\n",
|
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"Requirement already satisfied: tzdata>=2022.7 in /usr/local/anaconda3/lib/python3.12/site-packages (from pandas>=1.5.0->fastparquet) (2023.3)\r\n",
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"Requirement already satisfied: six>=1.5 in /usr/local/anaconda3/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\r\n",
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"Requirement already satisfied: scipy in /usr/local/anaconda3/lib/python3.12/site-packages (1.13.1)\r\n",
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"Requirement already satisfied: numpy<2.3,>=1.22.4 in /usr/local/anaconda3/lib/python3.12/site-packages (from scipy) (1.26.4)\r\n",
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"Requirement already satisfied: seaborn in /usr/local/anaconda3/lib/python3.12/site-packages (0.13.2)\r\n",
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"Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/anaconda3/lib/python3.12/site-packages (from seaborn) (1.26.4)\r\n",
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"Requirement already satisfied: pandas>=1.2 in /usr/local/anaconda3/lib/python3.12/site-packages (from seaborn) (2.2.2)\r\n",
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"Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/anaconda3/lib/python3.12/site-packages (from seaborn) (3.9.2)\r\n",
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"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.0)\r\n",
|
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"Requirement already satisfied: cycler>=0.10 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\r\n",
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"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.51.0)\r\n",
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"Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.4)\r\n",
|
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"Requirement already satisfied: packaging>=20.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.2)\r\n",
|
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"Requirement already satisfied: pillow>=8 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.3.0)\r\n",
|
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"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.0.9)\r\n",
|
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"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\r\n",
|
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"Requirement already satisfied: pytz>=2020.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2024.1)\r\n",
|
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"Requirement already satisfied: tzdata>=2022.7 in /usr/local/anaconda3/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2023.3)\r\n",
|
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"Requirement already satisfied: six>=1.5 in /usr/local/anaconda3/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\r\n",
|
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"Requirement already satisfied: pysolar in /usr/local/anaconda3/lib/python3.12/site-packages (0.11)\r\n",
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"Requirement already satisfied: numpy in /usr/local/anaconda3/lib/python3.12/site-packages (from pysolar) (1.26.4)\r\n"
|
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]
|
|
}
|
|
],
|
|
"execution_count": 17
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-25T09:07:08.001373Z",
|
|
"start_time": "2024-10-25T09:07:07.994803Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"import pandas as pd\n",
|
|
"import numpy as np\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import seaborn as sns\n",
|
|
"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
|
|
"from sklearn.model_selection import train_test_split\n",
|
|
"from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
|
|
"\n",
|
|
"from tensorflow.keras.layers import Input, Dense, Dropout, Bidirectional, LSTM, LayerNormalization, Add, GlobalAveragePooling1D, Activation, BatchNormalization, MultiHeadAttention, MaxPooling1D\n",
|
|
"from tensorflow.keras.models import Model\n",
|
|
"from tensorflow.keras.regularizers import l2\n",
|
|
"from tensorflow.keras.optimizers import Adam\n",
|
|
"from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
|
|
"from datetime import datetime\n",
|
|
"import os\n",
|
|
"import json\n",
|
|
"import joblib\n",
|
|
"import re\n",
|
|
"import pyarrow as pa\n",
|
|
"import pyarrow.parquet as pq\n",
|
|
"\n",
|
|
"random_state_value = 42"
|
|
],
|
|
"outputs": [],
|
|
"execution_count": 3
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": "## 1. Caricamento e preparazione dei Dati Meteo"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-25T09:07:23.151838Z",
|
|
"start_time": "2024-10-25T09:07:11.878633Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"# Function to convert csv to parquet\n",
|
|
"def csv_to_parquet(csv_file, parquet_file, chunksize=100000):\n",
|
|
" writer = None\n",
|
|
"\n",
|
|
" for chunk in pd.read_csv(csv_file, chunksize=chunksize):\n",
|
|
" if writer is None:\n",
|
|
"\n",
|
|
" table = pa.Table.from_pandas(chunk)\n",
|
|
" writer = pq.ParquetWriter(parquet_file, table.schema)\n",
|
|
" else:\n",
|
|
" table = pa.Table.from_pandas(chunk)\n",
|
|
"\n",
|
|
" writer.write_table(table)\n",
|
|
"\n",
|
|
" if writer:\n",
|
|
" writer.close()\n",
|
|
"\n",
|
|
" print(f\"File conversion completed : {csv_file} -> {parquet_file}\")\n",
|
|
"\n",
|
|
"\n",
|
|
"def read_json_files(folder_path):\n",
|
|
" all_data = []\n",
|
|
"\n",
|
|
" file_list = sorted(os.listdir(folder_path))\n",
|
|
"\n",
|
|
" for filename in file_list:\n",
|
|
" if filename.endswith('.json'):\n",
|
|
" file_path = os.path.join(folder_path, filename)\n",
|
|
" try:\n",
|
|
" with open(file_path, 'r') as file:\n",
|
|
" data = json.load(file)\n",
|
|
" all_data.extend(data['days'])\n",
|
|
" except Exception as e:\n",
|
|
" print(f\"Error processing file '{filename}': {str(e)}\")\n",
|
|
"\n",
|
|
" return all_data\n",
|
|
"\n",
|
|
"\n",
|
|
"def create_weather_dataset(data):\n",
|
|
" dataset = []\n",
|
|
" seen_datetimes = set()\n",
|
|
"\n",
|
|
" for day in data:\n",
|
|
" date = day['datetime']\n",
|
|
" for hour in day['hours']:\n",
|
|
" datetime_str = f\"{date} {hour['datetime']}\"\n",
|
|
"\n",
|
|
" # Verifico se questo datetime è già stato visto\n",
|
|
" if datetime_str in seen_datetimes:\n",
|
|
" continue\n",
|
|
"\n",
|
|
" seen_datetimes.add(datetime_str)\n",
|
|
"\n",
|
|
" if isinstance(hour['preciptype'], list):\n",
|
|
" preciptype = \"__\".join(hour['preciptype'])\n",
|
|
" else:\n",
|
|
" preciptype = hour['preciptype'] if hour['preciptype'] else \"\"\n",
|
|
"\n",
|
|
" conditions = hour['conditions'].replace(', ', '__').replace(' ', '_').lower()\n",
|
|
"\n",
|
|
" row = {\n",
|
|
" 'datetime': datetime_str,\n",
|
|
" 'temp': hour['temp'],\n",
|
|
" 'feelslike': hour['feelslike'],\n",
|
|
" 'humidity': hour['humidity'],\n",
|
|
" 'dew': hour['dew'],\n",
|
|
" 'precip': hour['precip'],\n",
|
|
" 'snow': hour['snow'],\n",
|
|
" 'preciptype': preciptype.lower(),\n",
|
|
" 'windspeed': hour['windspeed'],\n",
|
|
" 'winddir': hour['winddir'],\n",
|
|
" 'pressure': hour['pressure'],\n",
|
|
" 'cloudcover': hour['cloudcover'],\n",
|
|
" 'visibility': hour['visibility'],\n",
|
|
" 'solarradiation': hour['solarradiation'],\n",
|
|
" 'solarenergy': hour['solarenergy'],\n",
|
|
" 'uvindex': hour['uvindex'],\n",
|
|
" 'conditions': conditions,\n",
|
|
" 'tempmax': day['tempmax'],\n",
|
|
" 'tempmin': day['tempmin'],\n",
|
|
" 'precipprob': day['precipprob'],\n",
|
|
" 'precipcover': day['precipcover']\n",
|
|
" }\n",
|
|
" dataset.append(row)\n",
|
|
"\n",
|
|
" dataset.sort(key=lambda x: datetime.strptime(x['datetime'], \"%Y-%m-%d %H:%M:%S\"))\n",
|
|
"\n",
|
|
" return pd.DataFrame(dataset)\n",
|
|
"\n",
|
|
"\n",
|
|
"folder_path = './data/weather'\n",
|
|
"raw_data = read_json_files(folder_path)\n",
|
|
"weather_data = create_weather_dataset(raw_data)\n",
|
|
"weather_data['datetime'] = pd.to_datetime(weather_data['datetime'], errors='coerce')\n",
|
|
"weather_data['date'] = weather_data['datetime'].dt.date\n",
|
|
"weather_data = weather_data.dropna(subset=['datetime'])\n",
|
|
"weather_data['datetime'] = pd.to_datetime(weather_data['datetime'])\n",
|
|
"weather_data['year'] = weather_data['datetime'].dt.year\n",
|
|
"weather_data['month'] = weather_data['datetime'].dt.month\n",
|
|
"weather_data['day'] = weather_data['datetime'].dt.day\n",
|
|
"weather_data.head()\n",
|
|
"\n",
|
|
"weather_data.to_parquet('./data/weather_data.parquet')"
|
|
],
|
|
"outputs": [],
|
|
"execution_count": 4
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"cell_type": "markdown",
|
|
"source": ""
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-25T14:44:58.286583Z",
|
|
"start_time": "2024-10-25T14:44:56.063431Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"# Crea le sequenze per LSTM\n",
|
|
"def create_sequences(timesteps, X, y=None):\n",
|
|
" \"\"\"\n",
|
|
" Crea sequenze temporali dai dati.\n",
|
|
" \n",
|
|
" Parameters:\n",
|
|
" -----------\n",
|
|
" X : array-like\n",
|
|
" Dati di input\n",
|
|
" timesteps : int\n",
|
|
" Numero di timestep per ogni sequenza\n",
|
|
" y : array-like, optional\n",
|
|
" Target values. Se None, crea sequenze solo per X\n",
|
|
" \n",
|
|
" Returns:\n",
|
|
" --------\n",
|
|
" tuple o array\n",
|
|
" Se y è fornito: (X_sequences, y_sequences)\n",
|
|
" Se y è None: X_sequences\n",
|
|
" \"\"\"\n",
|
|
" Xs = []\n",
|
|
" for i in range(len(X) - timesteps):\n",
|
|
" Xs.append(X[i:i + timesteps])\n",
|
|
"\n",
|
|
" if y is not None:\n",
|
|
" ys = []\n",
|
|
" for i in range(len(X) - timesteps):\n",
|
|
" ys.append(y[i + timesteps])\n",
|
|
" return np.array(Xs), np.array(ys)\n",
|
|
"\n",
|
|
" return np.array(Xs)\n",
|
|
"\n",
|
|
"\n",
|
|
"# Funzioni per costruire il modello LSTM avanzato\n",
|
|
"def create_residual_lstm_layer(x, units, dropout_rate, l2_reg=0.01):\n",
|
|
" residual = x\n",
|
|
" x = Bidirectional(LSTM(units, return_sequences=True, kernel_regularizer=l2(l2_reg)))(x)\n",
|
|
" x = LayerNormalization()(x)\n",
|
|
" x = Dropout(dropout_rate)(x)\n",
|
|
" # Adjust residual dimension\n",
|
|
" if int(residual.shape[-1]) != 2 * units:\n",
|
|
" residual = Dense(2 * units, activation='linear')(residual)\n",
|
|
" x = Add()([x, residual])\n",
|
|
" return x\n",
|
|
"\n",
|
|
"\n",
|
|
"def attention_block(x, units, num_heads=8):\n",
|
|
" attention = MultiHeadAttention(num_heads=num_heads, key_dim=units)(x, x)\n",
|
|
" x = Add()([x, attention])\n",
|
|
" x = LayerNormalization()(x)\n",
|
|
" return x\n",
|
|
"\n",
|
|
"\n",
|
|
"def build_advanced_model(input_shape, l2_lambda=0.005):\n",
|
|
" inputs = Input(shape=input_shape)\n",
|
|
" x = create_residual_lstm_layer(inputs, 64, 0.3, l2_lambda)\n",
|
|
" x = create_residual_lstm_layer(x, 32, 0.3, l2_lambda)\n",
|
|
" x = create_residual_lstm_layer(x, 16, 0.2, l2_lambda)\n",
|
|
" x = attention_block(x, 16, num_heads=8)\n",
|
|
" x = MaxPooling1D()(x)\n",
|
|
" x = Dense(32, kernel_regularizer=l2(l2_lambda))(x)\n",
|
|
" x = BatchNormalization()(x)\n",
|
|
" x = Activation('swish')(x)\n",
|
|
" x = Dropout(0.3)(x)\n",
|
|
" x = Dense(16, kernel_regularizer=l2(l2_lambda))(x)\n",
|
|
" x = BatchNormalization()(x)\n",
|
|
" x = Activation('swish')(x)\n",
|
|
" x = Dropout(0.2)(x)\n",
|
|
" outputs = Dense(1, kernel_regularizer=l2(l2_lambda))(x)\n",
|
|
" model = Model(inputs=inputs, outputs=outputs)\n",
|
|
" return model\n",
|
|
"\n",
|
|
"\n",
|
|
"def get_season(date):\n",
|
|
" month = date.month\n",
|
|
" day = date.day\n",
|
|
" if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n",
|
|
" return 'Winter'\n",
|
|
" elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n",
|
|
" return 'Spring'\n",
|
|
" elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n",
|
|
" return 'Summer'\n",
|
|
" elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n",
|
|
" return 'Autumn'\n",
|
|
" else:\n",
|
|
" return 'Unknown'\n",
|
|
"\n",
|
|
"\n",
|
|
"def get_time_period(hour):\n",
|
|
" if 5 <= hour < 12:\n",
|
|
" return 'Morning'\n",
|
|
" elif 12 <= hour < 17:\n",
|
|
" return 'Afternoon'\n",
|
|
" elif 17 <= hour < 21:\n",
|
|
" return 'Evening'\n",
|
|
" else:\n",
|
|
" return 'Night'\n",
|
|
"\n",
|
|
"\n",
|
|
"def add_time_features(df):\n",
|
|
" df['datetime'] = pd.to_datetime(df['datetime'])\n",
|
|
" df['timestamp'] = df['datetime'].astype(np.int64) // 10 ** 9\n",
|
|
" df['year'] = df['datetime'].dt.year\n",
|
|
" df['month'] = df['datetime'].dt.month\n",
|
|
" df['day'] = df['datetime'].dt.day\n",
|
|
" df['hour'] = df['datetime'].dt.hour\n",
|
|
" df['minute'] = df['datetime'].dt.minute\n",
|
|
" df['hour_sin'] = np.sin(df['hour'] * (2 * np.pi / 24))\n",
|
|
" df['hour_cos'] = np.cos(df['hour'] * (2 * np.pi / 24))\n",
|
|
" df['day_of_week'] = df['datetime'].dt.dayofweek\n",
|
|
" df['day_of_year'] = df['datetime'].dt.dayofyear\n",
|
|
" df['week_of_year'] = df['datetime'].dt.isocalendar().week.astype(int)\n",
|
|
" df['quarter'] = df['datetime'].dt.quarter\n",
|
|
" df['is_month_end'] = df['datetime'].dt.is_month_end.astype(int)\n",
|
|
" df['is_quarter_end'] = df['datetime'].dt.is_quarter_end.astype(int)\n",
|
|
" df['is_year_end'] = df['datetime'].dt.is_year_end.astype(int)\n",
|
|
" df['month_sin'] = np.sin(df['month'] * (2 * np.pi / 12))\n",
|
|
" df['month_cos'] = np.cos(df['month'] * (2 * np.pi / 12))\n",
|
|
" df['day_of_year_sin'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25))\n",
|
|
" df['day_of_year_cos'] = np.cos(df['day_of_year'] * (2 * np.pi / 365.25))\n",
|
|
" df['season'] = df['datetime'].apply(get_season)\n",
|
|
" df['time_period'] = df['hour'].apply(get_time_period)\n",
|
|
" return df\n",
|
|
"\n",
|
|
"\n",
|
|
"# Carica il dataset\n",
|
|
"weather_data = pd.read_parquet('./data/weather_data.parquet')\n",
|
|
"\n",
|
|
"# Aggiungi le caratteristiche temporali\n",
|
|
"weather_data = add_time_features(weather_data)\n",
|
|
"\n",
|
|
"# Encoding delle variabili categoriali\n",
|
|
"weather_data = pd.get_dummies(weather_data, columns=['season', 'time_period'], drop_first=True)\n",
|
|
"\n",
|
|
"weather_data.to_parquet('./data/weather_data_extended.parquet')\n",
|
|
"\n",
|
|
"# Dividi i dati in quelli dopo il 2010 e quelli prima del 2010\n",
|
|
"data_after_2010 = weather_data[weather_data['year'] >= 2010].copy()\n",
|
|
"data_before_2010 = weather_data[weather_data['year'] < 2010].copy()\n",
|
|
"\n",
|
|
"# Aggiorna le target variables se necessario\n",
|
|
"target_variables = ['solarradiation', 'solarenergy', 'uvindex']\n",
|
|
"\n",
|
|
"# Seleziona le features\n",
|
|
"features = [\n",
|
|
" 'temp', 'tempmin', 'tempmax', 'humidity', 'cloudcover', 'windspeed', 'pressure', 'visibility',\n",
|
|
" 'hour_sin', 'hour_cos', 'month_sin', 'month_cos', 'day_of_year_sin', 'day_of_year_cos',\n",
|
|
" ] + [col for col in weather_data.columns if 'season_' in col or 'time_period_' in col]\n",
|
|
"\n",
|
|
"# Prepara data_after_2010\n",
|
|
"data_after_2010 = data_after_2010.sort_values('datetime')\n",
|
|
"data_after_2010.set_index('datetime', inplace=True)\n",
|
|
"\n",
|
|
"# Interpola eventuali valori mancanti nelle variabili target\n",
|
|
"columns_to_interpolate = target_variables\n",
|
|
"for column in columns_to_interpolate:\n",
|
|
" data_after_2010[column] = data_after_2010[column].interpolate(method='time')\n",
|
|
"\n",
|
|
"# Rimuovi eventuali valori mancanti residui\n",
|
|
"data_after_2010.dropna(subset=features + target_variables, inplace=True)\n",
|
|
"\n",
|
|
"# Crea X e y\n",
|
|
"X = data_after_2010[features].values\n",
|
|
"y = data_after_2010[target_variables].values\n",
|
|
"\n",
|
|
"# Normalizza le features\n",
|
|
"scaler_X = MinMaxScaler()\n",
|
|
"X_scaled = scaler_X.fit_transform(X)\n",
|
|
"\n",
|
|
"\n",
|
|
"def prepare_multi_target_datasets(X_scaled, y, target_variables):\n",
|
|
" \"\"\"\n",
|
|
" Prepara dataset separati per ogni target variable e restituisce anche gli scaler\n",
|
|
" per un uso successivo in fase di predizione.\n",
|
|
" \n",
|
|
" Parameters:\n",
|
|
" -----------\n",
|
|
" X_scaled : numpy.ndarray\n",
|
|
" Features già scalate\n",
|
|
" y : numpy.ndarray\n",
|
|
" Target variables (matrice con una colonna per ogni target)\n",
|
|
" target_variables : list\n",
|
|
" Lista dei nomi delle variabili target\n",
|
|
" \n",
|
|
" Returns:\n",
|
|
" --------\n",
|
|
" tuple (dict, dict)\n",
|
|
" - Primo dict: contiene i dataset per ogni target\n",
|
|
" - Secondo dict: contiene gli scaler per ogni target\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" # Inizializza i dizionari per contenere i dataset e gli scaler\n",
|
|
" train_datasets = {}\n",
|
|
" scalers_dict = {}\n",
|
|
"\n",
|
|
" # Scala e splitta i dati per ogni target\n",
|
|
" for i, target in enumerate(target_variables):\n",
|
|
" # Scala il target corrente\n",
|
|
" scaler = MinMaxScaler()\n",
|
|
" y_scaled_current = scaler.fit_transform(y[:, i].reshape(-1, 1)).flatten()\n",
|
|
" scalers_dict[target] = scaler\n",
|
|
"\n",
|
|
" # Split dei dati per il target corrente\n",
|
|
" X_train_full, X_test, y_train_full, y_test = train_test_split(\n",
|
|
" X_scaled,\n",
|
|
" y_scaled_current,\n",
|
|
" test_size=0.2,\n",
|
|
" shuffle=False\n",
|
|
" )\n",
|
|
"\n",
|
|
" # Ulteriore split per validation\n",
|
|
" X_train, X_val, y_train, y_val = train_test_split(\n",
|
|
" X_train_full,\n",
|
|
" y_train_full,\n",
|
|
" test_size=0.2,\n",
|
|
" shuffle=False\n",
|
|
" )\n",
|
|
"\n",
|
|
" # Salva i dataset per questo target\n",
|
|
" train_datasets[target] = {\n",
|
|
" 'X_train': X_train,\n",
|
|
" 'X_val': X_val,\n",
|
|
" 'X_test': X_test,\n",
|
|
" 'y_train': y_train.reshape(-1, 1),\n",
|
|
" 'y_val': y_val.reshape(-1, 1),\n",
|
|
" 'y_test': y_test.reshape(-1, 1)\n",
|
|
" }\n",
|
|
"\n",
|
|
" return train_datasets, scalers_dict\n",
|
|
"\n",
|
|
"\n",
|
|
"datasets, scalers = prepare_multi_target_datasets(X_scaled, y, target_variables)"
|
|
],
|
|
"outputs": [],
|
|
"execution_count": 8
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-25T15:27:58.094588Z",
|
|
"start_time": "2024-10-25T14:45:01.366071Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"# numero di timesteps (utilizziamo le ultime 24 ore)\n",
|
|
"timesteps = 24\n",
|
|
"\n",
|
|
"# Costruisci il modello per ogni variabile target\n",
|
|
"models = {}\n",
|
|
"histories = {}\n",
|
|
"for i, target in enumerate(target_variables):\n",
|
|
" target_data = datasets[target]\n",
|
|
" target_scaler = scalers[target]\n",
|
|
"\n",
|
|
" X_train = target_data['X_train']\n",
|
|
" y_train = target_data['y_train']\n",
|
|
" X_val = target_data['X_val']\n",
|
|
" y_val = target_data['y_val']\n",
|
|
" X_test = target_data['X_test']\n",
|
|
" y_test = target_data['y_test']\n",
|
|
"\n",
|
|
" num_features = X_train.shape[1]\n",
|
|
"\n",
|
|
" X_train_seq, y_train_seq = create_sequences(timesteps, X_train, y_train)\n",
|
|
" X_val_seq, y_val_seq = create_sequences(timesteps, X_val, y_val)\n",
|
|
" X_test_seq, y_test_seq = create_sequences(timesteps, X_test, y_test)\n",
|
|
"\n",
|
|
" print(X_train_seq.shape, y_train_seq.shape)\n",
|
|
" print(X_val_seq.shape, y_val_seq.shape)\n",
|
|
" print(X_test_seq.shape, y_test_seq.shape)\n",
|
|
" \n",
|
|
" print(f\"Addestramento del modello per: {target}\")\n",
|
|
" model = build_advanced_model((timesteps, num_features), l2_lambda=0.001)\n",
|
|
" optimizer = Adam(learning_rate=0.001, clipnorm=1.0)\n",
|
|
" model.compile(optimizer=optimizer, loss='mean_squared_error')\n",
|
|
" early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
|
|
"\n",
|
|
" reduce_lr = ReduceLROnPlateau(\n",
|
|
" monitor='val_loss',\n",
|
|
" factor=0.5,\n",
|
|
" patience=5,\n",
|
|
" min_lr=1e-6\n",
|
|
" )\n",
|
|
" history = model.fit(\n",
|
|
" X_train_seq, y_train_seq[:, i],\n",
|
|
" validation_data=(X_val_seq, y_val_seq[:, i]),\n",
|
|
" epochs=50,\n",
|
|
" batch_size=180,\n",
|
|
" callbacks=[early_stopping, reduce_lr],\n",
|
|
" verbose=1\n",
|
|
" )\n",
|
|
" test_loss = model.evaluate(X_test_seq, y_test_seq[:, i])\n",
|
|
" print(f'Test MAE per {target}: {test_loss:.4f}')\n",
|
|
" models[target] = model\n",
|
|
" histories[target] = history\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(82972, 24, 21) (82972, 1)\n",
|
|
"(20725, 24, 21) (20725, 1)\n",
|
|
"(25913, 24, 21) (25913, 1)\n",
|
|
"Addestramento del modello per: solarradiation\n",
|
|
"Epoch 1/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m126s\u001B[0m 250ms/step - loss: 0.5388 - val_loss: 0.1221 - learning_rate: 0.0010\n",
|
|
"Epoch 2/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m91s\u001B[0m 198ms/step - loss: 0.1098 - val_loss: 0.0773 - learning_rate: 0.0010\n",
|
|
"Epoch 3/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m90s\u001B[0m 195ms/step - loss: 0.0750 - val_loss: 0.0659 - learning_rate: 0.0010\n",
|
|
"Epoch 4/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m83s\u001B[0m 181ms/step - loss: 0.0675 - val_loss: 0.0606 - learning_rate: 0.0010\n",
|
|
"Epoch 5/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m89s\u001B[0m 193ms/step - loss: 0.0626 - val_loss: 0.0594 - learning_rate: 0.0010\n",
|
|
"Epoch 6/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m81s\u001B[0m 177ms/step - loss: 0.0607 - val_loss: 0.0586 - learning_rate: 0.0010\n",
|
|
"Epoch 7/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m97s\u001B[0m 211ms/step - loss: 0.0601 - val_loss: 0.0587 - learning_rate: 0.0010\n",
|
|
"Epoch 8/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m102s\u001B[0m 222ms/step - loss: 0.0603 - val_loss: 0.0579 - learning_rate: 0.0010\n",
|
|
"Epoch 9/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m92s\u001B[0m 199ms/step - loss: 0.0603 - val_loss: 0.0577 - learning_rate: 0.0010\n",
|
|
"Epoch 10/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m92s\u001B[0m 200ms/step - loss: 0.0590 - val_loss: 0.0576 - learning_rate: 0.0010\n",
|
|
"Epoch 11/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m94s\u001B[0m 203ms/step - loss: 0.0596 - val_loss: 0.0576 - learning_rate: 0.0010\n",
|
|
"Epoch 12/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m95s\u001B[0m 207ms/step - loss: 0.0588 - val_loss: 0.0576 - learning_rate: 0.0010\n",
|
|
"Epoch 13/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m95s\u001B[0m 205ms/step - loss: 0.0594 - val_loss: 0.0575 - learning_rate: 0.0010\n",
|
|
"Epoch 14/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m99s\u001B[0m 215ms/step - loss: 0.0596 - val_loss: 0.0576 - learning_rate: 0.0010\n",
|
|
"Epoch 15/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m89s\u001B[0m 194ms/step - loss: 0.0600 - val_loss: 0.0575 - learning_rate: 0.0010\n",
|
|
"Epoch 16/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m90s\u001B[0m 196ms/step - loss: 0.0595 - val_loss: 0.0575 - learning_rate: 0.0010\n",
|
|
"Epoch 17/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m91s\u001B[0m 197ms/step - loss: 0.0599 - val_loss: 0.0575 - learning_rate: 0.0010\n",
|
|
"Epoch 18/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m88s\u001B[0m 190ms/step - loss: 0.0594 - val_loss: 0.0575 - learning_rate: 0.0010\n",
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"Epoch 19/50\n",
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"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m87s\u001B[0m 189ms/step - loss: 0.0590 - val_loss: 0.0575 - learning_rate: 5.0000e-04\n",
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"Epoch 20/50\n",
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"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m92s\u001B[0m 199ms/step - loss: 0.0592 - val_loss: 0.0575 - learning_rate: 5.0000e-04\n",
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"Epoch 21/50\n",
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"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m86s\u001B[0m 186ms/step - loss: 0.0595 - val_loss: 0.0575 - learning_rate: 5.0000e-04\n",
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"Epoch 22/50\n",
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"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m86s\u001B[0m 187ms/step - loss: 0.0595 - val_loss: 0.0575 - learning_rate: 5.0000e-04\n",
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"Epoch 23/50\n",
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"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m85s\u001B[0m 184ms/step - loss: 0.0591 - val_loss: 0.0575 - learning_rate: 5.0000e-04\n",
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"Epoch 24/50\n",
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"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m84s\u001B[0m 182ms/step - loss: 0.0599 - val_loss: 0.0575 - learning_rate: 2.5000e-04\n",
|
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"Epoch 25/50\n",
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"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m95s\u001B[0m 205ms/step - loss: 0.0596 - val_loss: 0.0575 - learning_rate: 2.5000e-04\n",
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"Epoch 26/50\n",
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"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m91s\u001B[0m 196ms/step - loss: 0.0595 - val_loss: 0.0575 - learning_rate: 2.5000e-04\n",
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"Epoch 27/50\n",
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"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m87s\u001B[0m 189ms/step - loss: 0.0590 - val_loss: 0.0575 - learning_rate: 2.5000e-04\n",
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"Epoch 28/50\n",
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"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m90s\u001B[0m 195ms/step - loss: 0.0598 - val_loss: 0.0575 - learning_rate: 2.5000e-04\n",
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"Epoch 29/50\n",
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"\u001B[1m 43/461\u001B[0m \u001B[32m━\u001B[0m\u001B[37m━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[1m1:15\u001B[0m 180ms/step - loss: 0.0578"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
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"\u001B[0;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)",
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"Cell \u001B[0;32mIn[9], line 40\u001B[0m\n\u001B[1;32m 32\u001B[0m early_stopping \u001B[38;5;241m=\u001B[39m EarlyStopping(monitor\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mval_loss\u001B[39m\u001B[38;5;124m'\u001B[39m, patience\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m10\u001B[39m, restore_best_weights\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[1;32m 34\u001B[0m reduce_lr \u001B[38;5;241m=\u001B[39m ReduceLROnPlateau(\n\u001B[1;32m 35\u001B[0m monitor\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mval_loss\u001B[39m\u001B[38;5;124m'\u001B[39m,\n\u001B[1;32m 36\u001B[0m factor\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0.5\u001B[39m,\n\u001B[1;32m 37\u001B[0m patience\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m5\u001B[39m,\n\u001B[1;32m 38\u001B[0m min_lr\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1e-6\u001B[39m\n\u001B[1;32m 39\u001B[0m )\n\u001B[0;32m---> 40\u001B[0m history \u001B[38;5;241m=\u001B[39m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 41\u001B[0m \u001B[43m \u001B[49m\u001B[43mX_train_seq\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_train_seq\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mi\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 42\u001B[0m \u001B[43m \u001B[49m\u001B[43mvalidation_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mX_val_seq\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_val_seq\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mi\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 43\u001B[0m \u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m50\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 44\u001B[0m \u001B[43m \u001B[49m\u001B[43mbatch_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m180\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 45\u001B[0m \u001B[43m \u001B[49m\u001B[43mcallbacks\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43mearly_stopping\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreduce_lr\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 46\u001B[0m \u001B[43m \u001B[49m\u001B[43mverbose\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\n\u001B[1;32m 47\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 48\u001B[0m test_loss \u001B[38;5;241m=\u001B[39m model\u001B[38;5;241m.\u001B[39mevaluate(X_test_seq, y_test_seq[:, i])\n\u001B[1;32m 49\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mTest MAE per \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtarget\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtest_loss\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124m.4f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n",
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"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:117\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 115\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m 116\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 117\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfn\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 118\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m 119\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n",
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"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py:318\u001B[0m, in \u001B[0;36mTensorFlowTrainer.fit\u001B[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001B[0m\n\u001B[1;32m 316\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m step, iterator \u001B[38;5;129;01min\u001B[39;00m epoch_iterator\u001B[38;5;241m.\u001B[39menumerate_epoch():\n\u001B[1;32m 317\u001B[0m callbacks\u001B[38;5;241m.\u001B[39mon_train_batch_begin(step)\n\u001B[0;32m--> 318\u001B[0m logs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtrain_function\u001B[49m\u001B[43m(\u001B[49m\u001B[43miterator\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 319\u001B[0m logs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_pythonify_logs(logs)\n\u001B[1;32m 320\u001B[0m callbacks\u001B[38;5;241m.\u001B[39mon_train_batch_end(step, logs)\n",
|
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"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py:150\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 148\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m 149\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 150\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfn\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 151\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m 152\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n",
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"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:833\u001B[0m, in \u001B[0;36mFunction.__call__\u001B[0;34m(self, *args, **kwds)\u001B[0m\n\u001B[1;32m 830\u001B[0m compiler \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mxla\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_jit_compile \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnonXla\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 832\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m OptionalXlaContext(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_jit_compile):\n\u001B[0;32m--> 833\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 835\u001B[0m new_tracing_count \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mexperimental_get_tracing_count()\n\u001B[1;32m 836\u001B[0m without_tracing \u001B[38;5;241m=\u001B[39m (tracing_count \u001B[38;5;241m==\u001B[39m new_tracing_count)\n",
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"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:878\u001B[0m, in \u001B[0;36mFunction._call\u001B[0;34m(self, *args, **kwds)\u001B[0m\n\u001B[1;32m 875\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lock\u001B[38;5;241m.\u001B[39mrelease()\n\u001B[1;32m 876\u001B[0m \u001B[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001B[39;00m\n\u001B[1;32m 877\u001B[0m \u001B[38;5;66;03m# run the first trace but we should fail if variables are created.\u001B[39;00m\n\u001B[0;32m--> 878\u001B[0m results \u001B[38;5;241m=\u001B[39m \u001B[43mtracing_compilation\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall_function\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 879\u001B[0m \u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_variable_creation_config\u001B[49m\n\u001B[1;32m 880\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 881\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_created_variables:\n\u001B[1;32m 882\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCreating variables on a non-first call to a function\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 883\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m decorated with tf.function.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
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"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001B[0m, in \u001B[0;36mcall_function\u001B[0;34m(args, kwargs, tracing_options)\u001B[0m\n\u001B[1;32m 137\u001B[0m bound_args \u001B[38;5;241m=\u001B[39m function\u001B[38;5;241m.\u001B[39mfunction_type\u001B[38;5;241m.\u001B[39mbind(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m 138\u001B[0m flat_inputs \u001B[38;5;241m=\u001B[39m function\u001B[38;5;241m.\u001B[39mfunction_type\u001B[38;5;241m.\u001B[39munpack_inputs(bound_args)\n\u001B[0;32m--> 139\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunction\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_flat\u001B[49m\u001B[43m(\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;66;43;03m# pylint: disable=protected-access\u001B[39;49;00m\n\u001B[1;32m 140\u001B[0m \u001B[43m \u001B[49m\u001B[43mflat_inputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcaptured_inputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfunction\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcaptured_inputs\u001B[49m\n\u001B[1;32m 141\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
|
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"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1322\u001B[0m, in \u001B[0;36mConcreteFunction._call_flat\u001B[0;34m(self, tensor_inputs, captured_inputs)\u001B[0m\n\u001B[1;32m 1318\u001B[0m possible_gradient_type \u001B[38;5;241m=\u001B[39m gradients_util\u001B[38;5;241m.\u001B[39mPossibleTapeGradientTypes(args)\n\u001B[1;32m 1319\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (possible_gradient_type \u001B[38;5;241m==\u001B[39m gradients_util\u001B[38;5;241m.\u001B[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001B[1;32m 1320\u001B[0m \u001B[38;5;129;01mand\u001B[39;00m executing_eagerly):\n\u001B[1;32m 1321\u001B[0m \u001B[38;5;66;03m# No tape is watching; skip to running the function.\u001B[39;00m\n\u001B[0;32m-> 1322\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_inference_function\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall_preflattened\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1323\u001B[0m forward_backward \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_select_forward_and_backward_functions(\n\u001B[1;32m 1324\u001B[0m args,\n\u001B[1;32m 1325\u001B[0m possible_gradient_type,\n\u001B[1;32m 1326\u001B[0m executing_eagerly)\n\u001B[1;32m 1327\u001B[0m forward_function, args_with_tangents \u001B[38;5;241m=\u001B[39m forward_backward\u001B[38;5;241m.\u001B[39mforward()\n",
|
|
"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216\u001B[0m, in \u001B[0;36mAtomicFunction.call_preflattened\u001B[0;34m(self, args)\u001B[0m\n\u001B[1;32m 214\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcall_preflattened\u001B[39m(\u001B[38;5;28mself\u001B[39m, args: Sequence[core\u001B[38;5;241m.\u001B[39mTensor]) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Any:\n\u001B[1;32m 215\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001B[39;00m\n\u001B[0;32m--> 216\u001B[0m flat_outputs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall_flat\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 217\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunction_type\u001B[38;5;241m.\u001B[39mpack_output(flat_outputs)\n",
|
|
"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:251\u001B[0m, in \u001B[0;36mAtomicFunction.call_flat\u001B[0;34m(self, *args)\u001B[0m\n\u001B[1;32m 249\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m record\u001B[38;5;241m.\u001B[39mstop_recording():\n\u001B[1;32m 250\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_bound_context\u001B[38;5;241m.\u001B[39mexecuting_eagerly():\n\u001B[0;32m--> 251\u001B[0m outputs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_bound_context\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall_function\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 252\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 253\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mlist\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 254\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mlen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfunction_type\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mflat_outputs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 255\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 256\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 257\u001B[0m outputs \u001B[38;5;241m=\u001B[39m make_call_op_in_graph(\n\u001B[1;32m 258\u001B[0m \u001B[38;5;28mself\u001B[39m,\n\u001B[1;32m 259\u001B[0m \u001B[38;5;28mlist\u001B[39m(args),\n\u001B[1;32m 260\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_bound_context\u001B[38;5;241m.\u001B[39mfunction_call_options\u001B[38;5;241m.\u001B[39mas_attrs(),\n\u001B[1;32m 261\u001B[0m )\n",
|
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"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/tensorflow/python/eager/context.py:1500\u001B[0m, in \u001B[0;36mContext.call_function\u001B[0;34m(self, name, tensor_inputs, num_outputs)\u001B[0m\n\u001B[1;32m 1498\u001B[0m cancellation_context \u001B[38;5;241m=\u001B[39m cancellation\u001B[38;5;241m.\u001B[39mcontext()\n\u001B[1;32m 1499\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m cancellation_context \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m-> 1500\u001B[0m outputs \u001B[38;5;241m=\u001B[39m \u001B[43mexecute\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 1501\u001B[0m \u001B[43m \u001B[49m\u001B[43mname\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdecode\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mutf-8\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1502\u001B[0m \u001B[43m \u001B[49m\u001B[43mnum_outputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnum_outputs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1503\u001B[0m \u001B[43m \u001B[49m\u001B[43minputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtensor_inputs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1504\u001B[0m \u001B[43m \u001B[49m\u001B[43mattrs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mattrs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1505\u001B[0m \u001B[43m \u001B[49m\u001B[43mctx\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1506\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1507\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 1508\u001B[0m outputs \u001B[38;5;241m=\u001B[39m execute\u001B[38;5;241m.\u001B[39mexecute_with_cancellation(\n\u001B[1;32m 1509\u001B[0m name\u001B[38;5;241m.\u001B[39mdecode(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mutf-8\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m 1510\u001B[0m num_outputs\u001B[38;5;241m=\u001B[39mnum_outputs,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 1514\u001B[0m cancellation_manager\u001B[38;5;241m=\u001B[39mcancellation_context,\n\u001B[1;32m 1515\u001B[0m )\n",
|
|
"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:53\u001B[0m, in \u001B[0;36mquick_execute\u001B[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001B[0m\n\u001B[1;32m 51\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 52\u001B[0m ctx\u001B[38;5;241m.\u001B[39mensure_initialized()\n\u001B[0;32m---> 53\u001B[0m tensors \u001B[38;5;241m=\u001B[39m \u001B[43mpywrap_tfe\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mTFE_Py_Execute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mctx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_handle\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdevice_name\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mop_name\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 54\u001B[0m \u001B[43m \u001B[49m\u001B[43minputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mattrs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnum_outputs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 55\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m core\u001B[38;5;241m.\u001B[39m_NotOkStatusException \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m 56\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m name \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
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"\u001B[0;31mKeyboardInterrupt\u001B[0m: "
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]
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}
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],
|
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"execution_count": 9
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},
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{
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"metadata": {},
|
|
"cell_type": "code",
|
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"outputs": [],
|
|
"execution_count": null,
|
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"source": [
|
|
"import joblib\n",
|
|
"import os\n",
|
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"\n",
|
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"\n",
|
|
"def save_models_and_scalers(models, scaler_X, scalers_y, target_variables, base_path='models'):\n",
|
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" \"\"\"\n",
|
|
" Salva i modelli e gli scaler nella cartella models.\n",
|
|
" \n",
|
|
" Parameters:\n",
|
|
" -----------\n",
|
|
" models : dict\n",
|
|
" Dizionario contenente i modelli per ogni variabile target\n",
|
|
" scaler_X : MinMaxScaler\n",
|
|
" Scaler unico per tutte le feature di input\n",
|
|
" scalers_y : dict\n",
|
|
" Dizionario contenente gli scaler per le variabili target\n",
|
|
" target_variables : list\n",
|
|
" Lista delle variabili target\n",
|
|
" base_path : str\n",
|
|
" Percorso base dove salvare i modelli (default: 'models')\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" # Crea la cartella se non esiste\n",
|
|
" os.makedirs(base_path, exist_ok=True)\n",
|
|
"\n",
|
|
" # Salva lo scaler X generale\n",
|
|
" scaler_x_path = os.path.join(base_path, 'scaler_x.joblib')\n",
|
|
" joblib.dump(scaler_X, scaler_x_path)\n",
|
|
"\n",
|
|
" # Salva i modelli e gli scaler Y per ogni variabile target\n",
|
|
" for target in target_variables:\n",
|
|
" # Crea una sottocartella per ogni target\n",
|
|
" target_path = os.path.join(base_path, target)\n",
|
|
" os.makedirs(target_path, exist_ok=True)\n",
|
|
"\n",
|
|
" # Salva il modello\n",
|
|
" model_path = os.path.join(target_path, 'model.joblib')\n",
|
|
" joblib.dump(models[target], model_path)\n",
|
|
"\n",
|
|
" # Salva lo scaler Y\n",
|
|
" scaler_y_path = os.path.join(target_path, 'scaler_y.joblib')\n",
|
|
" joblib.dump(scalers_y[target], scaler_y_path)\n",
|
|
"\n",
|
|
" # Salva la lista delle variabili target\n",
|
|
" target_vars_path = os.path.join(base_path, 'target_variables.joblib')\n",
|
|
" joblib.dump(target_variables, target_vars_path)\n",
|
|
"\n",
|
|
" print(f\"Modelli e scaler salvati in: {base_path}\")\n",
|
|
"\n",
|
|
"\n",
|
|
"def load_models_and_scalers(base_path='models'):\n",
|
|
" \"\"\"\n",
|
|
" Carica i modelli e gli scaler dalla cartella models.\n",
|
|
" \n",
|
|
" Parameters:\n",
|
|
" -----------\n",
|
|
" base_path : str\n",
|
|
" Percorso della cartella contenente i modelli salvati (default: 'models')\n",
|
|
" \n",
|
|
" Returns:\n",
|
|
" --------\n",
|
|
" tuple\n",
|
|
" (models, scaler_X, scalers_y, target_variables)\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" # Carica la lista delle variabili target\n",
|
|
" target_vars_path = os.path.join(base_path, 'target_variables.joblib')\n",
|
|
" target_variables = joblib.load(target_vars_path)\n",
|
|
"\n",
|
|
" # Carica lo scaler X generale\n",
|
|
" scaler_x_path = os.path.join(base_path, 'scaler_x.joblib')\n",
|
|
" scaler_X = joblib.load(scaler_x_path)\n",
|
|
"\n",
|
|
" # Inizializza i dizionari\n",
|
|
" models = {}\n",
|
|
" scalers_y = {}\n",
|
|
"\n",
|
|
" # Carica i modelli e gli scaler per ogni variabile target\n",
|
|
" for target in target_variables:\n",
|
|
" target_path = os.path.join(base_path, target)\n",
|
|
"\n",
|
|
" # Carica il modello\n",
|
|
" model_path = os.path.join(target_path, 'model.joblib')\n",
|
|
" models[target] = joblib.load(model_path)\n",
|
|
"\n",
|
|
" # Carica lo scaler Y\n",
|
|
" scaler_y_path = os.path.join(target_path, 'scaler_y.joblib')\n",
|
|
" scalers_y[target] = joblib.load(scaler_y_path)\n",
|
|
"\n",
|
|
" print(f\"Modelli e scaler caricati da: {base_path}\")\n",
|
|
" return models, scaler_X, scalers_y, target_variables\n",
|
|
"\n",
|
|
"\n",
|
|
"save_models_and_scalers(models, scaler_X, scalers_y, target_variables)"
|
|
]
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-24T16:14:44.770508Z",
|
|
"start_time": "2024-10-24T13:29:15.181470Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Addestramento del modello per: solarradiation\n",
|
|
"Epoch 1/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m128s\u001B[0m 256ms/step - loss: 0.5408 - val_loss: 0.1378 - learning_rate: 0.0010\n",
|
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"Epoch 2/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m95s\u001B[0m 207ms/step - loss: 0.1200 - val_loss: 0.0832 - learning_rate: 0.0010\n",
|
|
"Epoch 3/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m93s\u001B[0m 202ms/step - loss: 0.0805 - val_loss: 0.0689 - learning_rate: 0.0010\n",
|
|
"Epoch 4/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m85s\u001B[0m 184ms/step - loss: 0.0687 - val_loss: 0.0826 - learning_rate: 0.0010\n",
|
|
"Epoch 5/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m96s\u001B[0m 209ms/step - loss: 0.0646 - val_loss: 0.0619 - learning_rate: 0.0010\n",
|
|
"Epoch 6/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m94s\u001B[0m 203ms/step - loss: 0.0616 - val_loss: 0.0616 - learning_rate: 0.0010\n",
|
|
"Epoch 7/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m96s\u001B[0m 207ms/step - loss: 0.0606 - val_loss: 0.0588 - learning_rate: 0.0010\n",
|
|
"Epoch 8/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m93s\u001B[0m 202ms/step - loss: 0.0600 - val_loss: 0.0580 - learning_rate: 0.0010\n",
|
|
"Epoch 9/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m96s\u001B[0m 208ms/step - loss: 0.0593 - val_loss: 0.0579 - learning_rate: 0.0010\n",
|
|
"Epoch 10/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m96s\u001B[0m 209ms/step - loss: 0.0598 - val_loss: 0.0578 - learning_rate: 0.0010\n",
|
|
"Epoch 11/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m97s\u001B[0m 211ms/step - loss: 0.0594 - val_loss: 0.0576 - learning_rate: 0.0010\n",
|
|
"Epoch 12/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m92s\u001B[0m 201ms/step - loss: 0.0596 - val_loss: 0.0576 - learning_rate: 0.0010\n",
|
|
"Epoch 13/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m87s\u001B[0m 188ms/step - loss: 0.0601 - val_loss: 0.0576 - learning_rate: 0.0010\n",
|
|
"Epoch 14/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m88s\u001B[0m 191ms/step - loss: 0.0591 - val_loss: 0.0576 - learning_rate: 0.0010\n",
|
|
"Epoch 15/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m96s\u001B[0m 208ms/step - loss: 0.0600 - val_loss: 0.0575 - learning_rate: 0.0010\n",
|
|
"Epoch 16/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m103s\u001B[0m 224ms/step - loss: 0.0595 - val_loss: 0.0575 - learning_rate: 0.0010\n",
|
|
"Epoch 17/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m91s\u001B[0m 198ms/step - loss: 0.0591 - val_loss: 0.0575 - learning_rate: 0.0010\n",
|
|
"Epoch 18/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m88s\u001B[0m 191ms/step - loss: 0.0593 - val_loss: 0.0575 - learning_rate: 0.0010\n",
|
|
"Epoch 19/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m92s\u001B[0m 199ms/step - loss: 0.0595 - val_loss: 0.0575 - learning_rate: 0.0010\n",
|
|
"Epoch 20/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m90s\u001B[0m 196ms/step - loss: 0.0597 - val_loss: 0.0575 - learning_rate: 0.0010\n",
|
|
"Epoch 21/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m94s\u001B[0m 204ms/step - loss: 0.0597 - val_loss: 0.0575 - learning_rate: 0.0010\n",
|
|
"Epoch 22/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m90s\u001B[0m 196ms/step - loss: 0.0596 - val_loss: 0.0576 - learning_rate: 5.0000e-04\n",
|
|
"Epoch 23/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m100s\u001B[0m 216ms/step - loss: 0.0595 - val_loss: 0.0575 - learning_rate: 5.0000e-04\n",
|
|
"Epoch 24/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m95s\u001B[0m 205ms/step - loss: 0.0589 - val_loss: 0.0576 - learning_rate: 5.0000e-04\n",
|
|
"Epoch 25/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m91s\u001B[0m 197ms/step - loss: 0.0596 - val_loss: 0.0575 - learning_rate: 5.0000e-04\n",
|
|
"Epoch 26/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m89s\u001B[0m 194ms/step - loss: 0.0595 - val_loss: 0.0575 - learning_rate: 5.0000e-04\n",
|
|
"Epoch 27/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m91s\u001B[0m 198ms/step - loss: 0.0594 - val_loss: 0.0575 - learning_rate: 2.5000e-04\n",
|
|
"Epoch 28/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m98s\u001B[0m 213ms/step - loss: 0.0598 - val_loss: 0.0575 - learning_rate: 2.5000e-04\n",
|
|
"Epoch 29/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m107s\u001B[0m 232ms/step - loss: 0.0591 - val_loss: 0.0575 - learning_rate: 2.5000e-04\n",
|
|
"Epoch 30/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m86s\u001B[0m 187ms/step - loss: 0.0596 - val_loss: 0.0575 - learning_rate: 2.5000e-04\n",
|
|
"Epoch 31/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m95s\u001B[0m 205ms/step - loss: 0.0594 - val_loss: 0.0575 - learning_rate: 2.5000e-04\n",
|
|
"Epoch 32/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m97s\u001B[0m 211ms/step - loss: 0.0596 - val_loss: 0.0575 - learning_rate: 1.2500e-04\n",
|
|
"Epoch 33/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m92s\u001B[0m 200ms/step - loss: 0.0596 - val_loss: 0.0575 - learning_rate: 1.2500e-04\n",
|
|
"Epoch 34/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m88s\u001B[0m 191ms/step - loss: 0.0593 - val_loss: 0.0575 - learning_rate: 1.2500e-04\n",
|
|
"Epoch 35/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m94s\u001B[0m 204ms/step - loss: 0.0592 - val_loss: 0.0575 - learning_rate: 1.2500e-04\n",
|
|
"\u001B[1m810/810\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m37s\u001B[0m 45ms/step - loss: 0.0496\n",
|
|
"Test MAE per solarradiation: 0.0561\n",
|
|
"Addestramento del modello per: solarenergy\n",
|
|
"Epoch 1/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m95s\u001B[0m 185ms/step - loss: 0.4789 - val_loss: 0.1031 - learning_rate: 0.0010\n",
|
|
"Epoch 2/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m101s\u001B[0m 219ms/step - loss: 0.0913 - val_loss: 0.0683 - learning_rate: 0.0010\n",
|
|
"Epoch 3/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m94s\u001B[0m 203ms/step - loss: 0.0707 - val_loss: 0.0629 - learning_rate: 0.0010\n",
|
|
"Epoch 4/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m93s\u001B[0m 201ms/step - loss: 0.0629 - val_loss: 0.2182 - learning_rate: 0.0010\n",
|
|
"Epoch 5/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m90s\u001B[0m 196ms/step - loss: 0.0616 - val_loss: 0.0605 - learning_rate: 0.0010\n",
|
|
"Epoch 6/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m96s\u001B[0m 208ms/step - loss: 0.0608 - val_loss: 0.0588 - learning_rate: 0.0010\n",
|
|
"Epoch 7/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m86s\u001B[0m 187ms/step - loss: 0.0599 - val_loss: 0.0584 - learning_rate: 0.0010\n",
|
|
"Epoch 8/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m93s\u001B[0m 201ms/step - loss: 0.0603 - val_loss: 0.0582 - learning_rate: 0.0010\n",
|
|
"Epoch 9/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m82s\u001B[0m 179ms/step - loss: 0.0599 - val_loss: 0.0596 - learning_rate: 0.0010\n",
|
|
"Epoch 10/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m87s\u001B[0m 189ms/step - loss: 0.0606 - val_loss: 0.0579 - learning_rate: 0.0010\n",
|
|
"Epoch 11/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m87s\u001B[0m 189ms/step - loss: 0.0604 - val_loss: 0.0579 - learning_rate: 0.0010\n",
|
|
"Epoch 12/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m123s\u001B[0m 266ms/step - loss: 0.0600 - val_loss: 0.0579 - learning_rate: 0.0010\n",
|
|
"Epoch 13/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m96s\u001B[0m 208ms/step - loss: 0.0602 - val_loss: 0.0578 - learning_rate: 0.0010\n",
|
|
"Epoch 14/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m97s\u001B[0m 210ms/step - loss: 0.0601 - val_loss: 0.0578 - learning_rate: 0.0010\n",
|
|
"Epoch 15/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m94s\u001B[0m 204ms/step - loss: 0.0602 - val_loss: 0.0578 - learning_rate: 0.0010\n",
|
|
"Epoch 16/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m103s\u001B[0m 224ms/step - loss: 0.0598 - val_loss: 0.0578 - learning_rate: 0.0010\n",
|
|
"Epoch 17/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m92s\u001B[0m 198ms/step - loss: 0.0599 - val_loss: 0.0578 - learning_rate: 0.0010\n",
|
|
"Epoch 18/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m87s\u001B[0m 188ms/step - loss: 0.0599 - val_loss: 0.0578 - learning_rate: 0.0010\n",
|
|
"Epoch 19/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m100s\u001B[0m 216ms/step - loss: 0.0597 - val_loss: 0.0578 - learning_rate: 5.0000e-04\n",
|
|
"Epoch 20/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m96s\u001B[0m 208ms/step - loss: 0.0598 - val_loss: 0.0578 - learning_rate: 5.0000e-04\n",
|
|
"Epoch 21/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m97s\u001B[0m 210ms/step - loss: 0.0600 - val_loss: 0.0578 - learning_rate: 5.0000e-04\n",
|
|
"Epoch 22/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m103s\u001B[0m 224ms/step - loss: 0.0596 - val_loss: 0.0578 - learning_rate: 5.0000e-04\n",
|
|
"Epoch 23/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m91s\u001B[0m 198ms/step - loss: 0.0598 - val_loss: 0.0578 - learning_rate: 5.0000e-04\n",
|
|
"Epoch 24/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m95s\u001B[0m 206ms/step - loss: 0.0599 - val_loss: 0.0578 - learning_rate: 2.5000e-04\n",
|
|
"Epoch 25/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m92s\u001B[0m 200ms/step - loss: 0.0598 - val_loss: 0.0578 - learning_rate: 2.5000e-04\n",
|
|
"Epoch 26/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m92s\u001B[0m 199ms/step - loss: 0.0602 - val_loss: 0.0578 - learning_rate: 2.5000e-04\n",
|
|
"Epoch 27/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m95s\u001B[0m 206ms/step - loss: 0.0595 - val_loss: 0.0578 - learning_rate: 2.5000e-04\n",
|
|
"Epoch 28/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m97s\u001B[0m 210ms/step - loss: 0.0601 - val_loss: 0.0578 - learning_rate: 2.5000e-04\n",
|
|
"Epoch 29/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m98s\u001B[0m 213ms/step - loss: 0.0602 - val_loss: 0.0578 - learning_rate: 1.2500e-04\n",
|
|
"Epoch 30/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m101s\u001B[0m 220ms/step - loss: 0.0592 - val_loss: 0.0578 - learning_rate: 1.2500e-04\n",
|
|
"Epoch 31/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m93s\u001B[0m 202ms/step - loss: 0.0596 - val_loss: 0.0578 - learning_rate: 1.2500e-04\n",
|
|
"Epoch 32/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m97s\u001B[0m 210ms/step - loss: 0.0599 - val_loss: 0.0578 - learning_rate: 1.2500e-04\n",
|
|
"Epoch 33/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m98s\u001B[0m 213ms/step - loss: 0.0604 - val_loss: 0.0578 - learning_rate: 1.2500e-04\n",
|
|
"Epoch 34/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m101s\u001B[0m 218ms/step - loss: 0.0596 - val_loss: 0.0578 - learning_rate: 6.2500e-05\n",
|
|
"\u001B[1m810/810\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m30s\u001B[0m 37ms/step - loss: 0.0498\n",
|
|
"Test MAE per solarenergy: 0.0563\n",
|
|
"Addestramento del modello per: uvindex\n",
|
|
"Epoch 1/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m114s\u001B[0m 227ms/step - loss: 0.6634 - val_loss: 0.1552 - learning_rate: 0.0010\n",
|
|
"Epoch 2/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m109s\u001B[0m 237ms/step - loss: 0.1404 - val_loss: 0.1063 - learning_rate: 0.0010\n",
|
|
"Epoch 3/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m106s\u001B[0m 231ms/step - loss: 0.0984 - val_loss: 0.0979 - learning_rate: 0.0010\n",
|
|
"Epoch 4/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m100s\u001B[0m 216ms/step - loss: 0.0871 - val_loss: 0.0865 - learning_rate: 0.0010\n",
|
|
"Epoch 5/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m104s\u001B[0m 226ms/step - loss: 0.0821 - val_loss: 0.0823 - learning_rate: 0.0010\n",
|
|
"Epoch 6/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m99s\u001B[0m 214ms/step - loss: 0.0766 - val_loss: 0.0819 - learning_rate: 0.0010\n",
|
|
"Epoch 7/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m104s\u001B[0m 225ms/step - loss: 0.0757 - val_loss: 0.0741 - learning_rate: 0.0010\n",
|
|
"Epoch 8/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m101s\u001B[0m 219ms/step - loss: 0.0754 - val_loss: 0.0735 - learning_rate: 0.0010\n",
|
|
"Epoch 9/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m99s\u001B[0m 214ms/step - loss: 0.0748 - val_loss: 0.0736 - learning_rate: 0.0010\n",
|
|
"Epoch 10/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m105s\u001B[0m 227ms/step - loss: 0.0748 - val_loss: 0.0732 - learning_rate: 0.0010\n",
|
|
"Epoch 11/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m103s\u001B[0m 224ms/step - loss: 0.0748 - val_loss: 0.0726 - learning_rate: 0.0010\n",
|
|
"Epoch 12/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m104s\u001B[0m 226ms/step - loss: 0.0747 - val_loss: 0.0724 - learning_rate: 0.0010\n",
|
|
"Epoch 13/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m110s\u001B[0m 239ms/step - loss: 0.0747 - val_loss: 0.0726 - learning_rate: 0.0010\n",
|
|
"Epoch 14/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m106s\u001B[0m 231ms/step - loss: 0.0739 - val_loss: 0.0724 - learning_rate: 0.0010\n",
|
|
"Epoch 15/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m100s\u001B[0m 217ms/step - loss: 0.0746 - val_loss: 0.0723 - learning_rate: 0.0010\n",
|
|
"Epoch 16/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m97s\u001B[0m 211ms/step - loss: 0.0746 - val_loss: 0.0724 - learning_rate: 0.0010\n",
|
|
"Epoch 17/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m100s\u001B[0m 218ms/step - loss: 0.0747 - val_loss: 0.0723 - learning_rate: 0.0010\n",
|
|
"Epoch 18/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m109s\u001B[0m 235ms/step - loss: 0.0749 - val_loss: 0.0723 - learning_rate: 0.0010\n",
|
|
"Epoch 19/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m112s\u001B[0m 244ms/step - loss: 0.0745 - val_loss: 0.0723 - learning_rate: 0.0010\n",
|
|
"Epoch 20/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m106s\u001B[0m 229ms/step - loss: 0.0743 - val_loss: 0.0723 - learning_rate: 0.0010\n",
|
|
"Epoch 21/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m105s\u001B[0m 227ms/step - loss: 0.0746 - val_loss: 0.0724 - learning_rate: 5.0000e-04\n",
|
|
"Epoch 22/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m100s\u001B[0m 218ms/step - loss: 0.0744 - val_loss: 0.0723 - learning_rate: 5.0000e-04\n",
|
|
"Epoch 23/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m102s\u001B[0m 222ms/step - loss: 0.0749 - val_loss: 0.0723 - learning_rate: 5.0000e-04\n",
|
|
"Epoch 24/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m100s\u001B[0m 217ms/step - loss: 0.0735 - val_loss: 0.0723 - learning_rate: 5.0000e-04\n",
|
|
"Epoch 25/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m101s\u001B[0m 219ms/step - loss: 0.0735 - val_loss: 0.0723 - learning_rate: 5.0000e-04\n",
|
|
"Epoch 26/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m98s\u001B[0m 212ms/step - loss: 0.0743 - val_loss: 0.0723 - learning_rate: 2.5000e-04\n",
|
|
"Epoch 27/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m103s\u001B[0m 224ms/step - loss: 0.0741 - val_loss: 0.0723 - learning_rate: 2.5000e-04\n",
|
|
"Epoch 28/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m102s\u001B[0m 222ms/step - loss: 0.0742 - val_loss: 0.0723 - learning_rate: 2.5000e-04\n",
|
|
"Epoch 29/50\n",
|
|
"\u001B[1m461/461\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m109s\u001B[0m 236ms/step - loss: 0.0745 - val_loss: 0.0723 - learning_rate: 2.5000e-04\n",
|
|
"\u001B[1m810/810\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m36s\u001B[0m 45ms/step - loss: 0.0623\n",
|
|
"Test MAE per uvindex: 0.0702\n",
|
|
"Previsione di solarradiation per data_before_2010\n",
|
|
"\u001B[1m7122/7122\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m261s\u001B[0m 37ms/step\n"
|
|
]
|
|
},
|
|
{
|
|
"ename": "ValueError",
|
|
"evalue": "Found array with dim 3. None expected <= 2.",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
|
|
"\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)",
|
|
"Cell \u001B[0;32mIn[64], line 233\u001B[0m\n\u001B[1;32m 231\u001B[0m \u001B[38;5;66;03m# Ricostruisci i valori originali\u001B[39;00m\n\u001B[1;32m 232\u001B[0m scaler \u001B[38;5;241m=\u001B[39m scalers_y[target]\n\u001B[0;32m--> 233\u001B[0m y_pred \u001B[38;5;241m=\u001B[39m \u001B[43mscaler\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43minverse_transform\u001B[49m\u001B[43m(\u001B[49m\u001B[43my_pred_scaled\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 235\u001B[0m \u001B[38;5;66;03m# Allinea le previsioni con le date corrette\u001B[39;00m\n\u001B[1;32m 236\u001B[0m dates \u001B[38;5;241m=\u001B[39m data_before_2010\u001B[38;5;241m.\u001B[39mindex[timesteps:]\n",
|
|
"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:566\u001B[0m, in \u001B[0;36mMinMaxScaler.inverse_transform\u001B[0;34m(self, X)\u001B[0m\n\u001B[1;32m 562\u001B[0m check_is_fitted(\u001B[38;5;28mself\u001B[39m)\n\u001B[1;32m 564\u001B[0m xp, _ \u001B[38;5;241m=\u001B[39m get_namespace(X)\n\u001B[0;32m--> 566\u001B[0m X \u001B[38;5;241m=\u001B[39m \u001B[43mcheck_array\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 567\u001B[0m \u001B[43m \u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 568\u001B[0m \u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 569\u001B[0m \u001B[43m \u001B[49m\u001B[43mdtype\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_array_api\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msupported_float_dtypes\u001B[49m\u001B[43m(\u001B[49m\u001B[43mxp\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 570\u001B[0m \u001B[43m \u001B[49m\u001B[43mforce_writeable\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[1;32m 571\u001B[0m \u001B[43m \u001B[49m\u001B[43mforce_all_finite\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mallow-nan\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 572\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 574\u001B[0m X \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmin_\n\u001B[1;32m 575\u001B[0m X \u001B[38;5;241m/\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mscale_\n",
|
|
"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/sklearn/utils/validation.py:1058\u001B[0m, in \u001B[0;36mcheck_array\u001B[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001B[0m\n\u001B[1;32m 1053\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m 1054\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdtype=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mnumeric\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m is not compatible with arrays of bytes/strings.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 1055\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mConvert your data to numeric values explicitly instead.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 1056\u001B[0m )\n\u001B[1;32m 1057\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m allow_nd \u001B[38;5;129;01mand\u001B[39;00m array\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m3\u001B[39m:\n\u001B[0;32m-> 1058\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m 1059\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFound array with dim \u001B[39m\u001B[38;5;132;01m%d\u001B[39;00m\u001B[38;5;124m. \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m expected <= 2.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 1060\u001B[0m \u001B[38;5;241m%\u001B[39m (array\u001B[38;5;241m.\u001B[39mndim, estimator_name)\n\u001B[1;32m 1061\u001B[0m )\n\u001B[1;32m 1063\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m force_all_finite:\n\u001B[1;32m 1064\u001B[0m _assert_all_finite(\n\u001B[1;32m 1065\u001B[0m array,\n\u001B[1;32m 1066\u001B[0m input_name\u001B[38;5;241m=\u001B[39minput_name,\n\u001B[1;32m 1067\u001B[0m estimator_name\u001B[38;5;241m=\u001B[39mestimator_name,\n\u001B[1;32m 1068\u001B[0m allow_nan\u001B[38;5;241m=\u001B[39mforce_all_finite \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mallow-nan\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 1069\u001B[0m )\n",
|
|
"\u001B[0;31mValueError\u001B[0m: Found array with dim 3. None expected <= 2."
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 64,
|
|
"source": [
|
|
"# Previsione delle variabili mancanti per data_before_2010\n",
|
|
"# Prepara data_before_2010\n",
|
|
"data_before_2010 = data_before_2010.sort_values('datetime')\n",
|
|
"data_before_2010.set_index('datetime', inplace=True)\n",
|
|
"\n",
|
|
"# Assicurati che le features non abbiano valori mancanti\n",
|
|
"data_before_2010[features] = data_before_2010[features].ffill()\n",
|
|
"data_before_2010[features] = data_before_2010[features].bfill()"
|
|
]
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-24T18:50:48.087413Z",
|
|
"start_time": "2024-10-24T18:47:52.511763Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"# Crea X per data_before_2010\n",
|
|
"X_before = data_before_2010[features].values\n",
|
|
"X_before_scaled = scaler_X.transform(X_before)\n",
|
|
"\n",
|
|
"# Crea le sequenze per LSTM\n",
|
|
"X_before_seq = create_sequences(timesteps, X_before_scaled)\n",
|
|
"\n",
|
|
"# Prevedi le variabili mancanti\n",
|
|
"for i, target in enumerate(target_variables):\n",
|
|
" print(f\"Previsione di {target} per data_before_2010\")\n",
|
|
" y_pred_scaled = models[target].predict(X_before_seq)\n",
|
|
" # Ricostruisci i valori originali\n",
|
|
" scaler = scalers_y[target]\n",
|
|
" y_pred = scaler.inverse_transform(y_pred_scaled)\n",
|
|
"\n",
|
|
" # Allinea le previsioni con le date corrette\n",
|
|
" dates = data_before_2010.index[timesteps:]\n",
|
|
" data_before_2010.loc[dates, target] = y_pred\n",
|
|
"\n",
|
|
"# Gestisci eventuali valori iniziali mancanti\n",
|
|
"data_before_2010[target_variables] = data_before_2010[target_variables].bfill()\n",
|
|
"\n",
|
|
"# Combina data_before_2010 e data_after_2010\n",
|
|
"weather_data_complete = pd.concat([data_before_2010, data_after_2010], axis=0)\n",
|
|
"weather_data_complete = weather_data_complete.sort_index()\n",
|
|
"\n",
|
|
"# Salva il dataset completo\n",
|
|
"weather_data_complete.reset_index(inplace=True)\n",
|
|
"weather_data_complete.to_parquet('./data/weather_data_complete.parquet', index=False)\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Previsione di solarradiation per data_before_2010\n",
|
|
"\u001B[1m7122/7122\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m172s\u001B[0m 24ms/step\n"
|
|
]
|
|
},
|
|
{
|
|
"ename": "ValueError",
|
|
"evalue": "Found array with dim 3. None expected <= 2.",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
|
|
"\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)",
|
|
"Cell \u001B[0;32mIn[65], line 17\u001B[0m\n\u001B[1;32m 15\u001B[0m \u001B[38;5;66;03m# Ricostruisci i valori originali\u001B[39;00m\n\u001B[1;32m 16\u001B[0m scaler \u001B[38;5;241m=\u001B[39m scalers_y[target]\n\u001B[0;32m---> 17\u001B[0m y_pred \u001B[38;5;241m=\u001B[39m \u001B[43mscaler\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43minverse_transform\u001B[49m\u001B[43m(\u001B[49m\u001B[43my_pred_scaled\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 19\u001B[0m \u001B[38;5;66;03m# Allinea le previsioni con le date corrette\u001B[39;00m\n\u001B[1;32m 20\u001B[0m dates \u001B[38;5;241m=\u001B[39m data_before_2010\u001B[38;5;241m.\u001B[39mindex[timesteps:]\n",
|
|
"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:566\u001B[0m, in \u001B[0;36mMinMaxScaler.inverse_transform\u001B[0;34m(self, X)\u001B[0m\n\u001B[1;32m 562\u001B[0m check_is_fitted(\u001B[38;5;28mself\u001B[39m)\n\u001B[1;32m 564\u001B[0m xp, _ \u001B[38;5;241m=\u001B[39m get_namespace(X)\n\u001B[0;32m--> 566\u001B[0m X \u001B[38;5;241m=\u001B[39m \u001B[43mcheck_array\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 567\u001B[0m \u001B[43m \u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 568\u001B[0m \u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 569\u001B[0m \u001B[43m \u001B[49m\u001B[43mdtype\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_array_api\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msupported_float_dtypes\u001B[49m\u001B[43m(\u001B[49m\u001B[43mxp\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 570\u001B[0m \u001B[43m \u001B[49m\u001B[43mforce_writeable\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[1;32m 571\u001B[0m \u001B[43m \u001B[49m\u001B[43mforce_all_finite\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mallow-nan\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 572\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 574\u001B[0m X \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmin_\n\u001B[1;32m 575\u001B[0m X \u001B[38;5;241m/\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mscale_\n",
|
|
"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/sklearn/utils/validation.py:1058\u001B[0m, in \u001B[0;36mcheck_array\u001B[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001B[0m\n\u001B[1;32m 1053\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m 1054\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdtype=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mnumeric\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m is not compatible with arrays of bytes/strings.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 1055\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mConvert your data to numeric values explicitly instead.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 1056\u001B[0m )\n\u001B[1;32m 1057\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m allow_nd \u001B[38;5;129;01mand\u001B[39;00m array\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m3\u001B[39m:\n\u001B[0;32m-> 1058\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m 1059\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFound array with dim \u001B[39m\u001B[38;5;132;01m%d\u001B[39;00m\u001B[38;5;124m. \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m expected <= 2.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 1060\u001B[0m \u001B[38;5;241m%\u001B[39m (array\u001B[38;5;241m.\u001B[39mndim, estimator_name)\n\u001B[1;32m 1061\u001B[0m )\n\u001B[1;32m 1063\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m force_all_finite:\n\u001B[1;32m 1064\u001B[0m _assert_all_finite(\n\u001B[1;32m 1065\u001B[0m array,\n\u001B[1;32m 1066\u001B[0m input_name\u001B[38;5;241m=\u001B[39minput_name,\n\u001B[1;32m 1067\u001B[0m estimator_name\u001B[38;5;241m=\u001B[39mestimator_name,\n\u001B[1;32m 1068\u001B[0m allow_nan\u001B[38;5;241m=\u001B[39mforce_all_finite \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mallow-nan\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 1069\u001B[0m )\n",
|
|
"\u001B[0;31mValueError\u001B[0m: Found array with dim 3. None expected <= 2."
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 65
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 2. Esplorazione dei Dati Meteo"
|
|
]
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-23T06:10:46.688323Z",
|
|
"start_time": "2024-10-23T06:10:46.586185Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": "weather_data = pd.read_parquet('./data/weather_data_complete.parquet')",
|
|
"outputs": [],
|
|
"execution_count": 21
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-23T06:10:50.718574Z",
|
|
"start_time": "2024-10-23T06:10:46.901554Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"# Visualizzazione delle tendenze temporali\n",
|
|
"fig, axes = plt.subplots(5, 1, figsize=(15, 20))\n",
|
|
"weather_data.set_index('date')['temp'].plot(ax=axes[0], title='Temperatura Media Giornaliera')\n",
|
|
"weather_data.set_index('date')['humidity'].plot(ax=axes[1], title='Umidità Media Giornaliera')\n",
|
|
"weather_data.set_index('date')['solarradiation'].plot(ax=axes[2], title='Radiazione Solare Giornaliera')\n",
|
|
"weather_data.set_index('date')['solarenergy'].plot(ax=axes[3], title='Radiazione Solare Giornaliera')\n",
|
|
"weather_data.set_index('date')['precip'].plot(ax=axes[4], title='Precipitazioni Giornaliere')\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 1500x2000 with 5 Axes>"
|
|
],
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lOM4jh8rHixs3CuFRKXSt6zOw5vs1m0I/t4KCmuddGk1ZAN0R4qzl79UYxW6ffPfdd9i5cydat26N1q1b4/vvv8f333+P1q1bo1mzZjh27FiV56enp6Np06Zmt6PVOu7/5GZq20rkbwsP8DT6niv6J3tvdLcr17ZrtFPpb74yzEKq0r6sW7eckjv0ce0aonfLOhb9/oK93fDV/UkWt73l8Y7w93RV/ACm830qtN8x2g9H5mCfxeTOEWgUVHNWjlJj4v424fI3Uo0U7218+4aIDrUud6lGezugZM+/Q60WmNmvBe5sFoLF98bXeI/V9/tx9ew7Fz9Q9j4VW8WtRY1800LGmRao7VN15d2o1uE6j4M6/21HjP0GlOqDobb05b23tH9Gr7EE7P90pTqSU+X3ruvaQunvXtS1b+U+iCDqPVduW/8TgM/uiVeqOxax6uOS4PO25lcr8lxL335f0T4o2xwA4M6mtW63X+0Niz7vVa4+j0bvfkd0rFOp/5lCsSD6Tz/9hD179mDXrl3YtWsX+vbti759+2LXrl1ITU1FZmYmFi9ejKKiImzfvh3r1q3D4MGDleoeWcEWcqJJTdRb1vm7dryP36ZF1jJ/hQ2RLo627w31c8eKMW10PqbEJ2GrH7ehGZVUU1iAJ2b2a4nouuovmiY3X3f7XHFgiq8esPyGtyhy76PUep9A6QCzkrr9l9N9XLuGgnsigK0edB1IbD37PU5qJdjjWTOEHfn4C4gJWIf4qDNtLVA2IYbUQ9HCovoEBgZi4cKF+Omnn5CSkoLp06dj+vTpaNu2reiukYVe7RUlextynFslhdv/rDKyT5+OiFO0PW8V5ULsZkHhMCK1k+oQ5+ehnt8q2ZamSt/0rTboRc768jMzB3Xlc1JRxcb8VZajnaz3Vv+WSJvaGfX85a15ZI7HOjUW3QVSAbXeVFOT6iurzOGlZ4UNycfc2JKS9/mCVFqXzlEJu7KaOXNmlf+OiYnB8uXLBfWGpFZ5p5IaVQsbjmRIun25Zp3U9pEmt3ZkiJ3ONrbH9fsqF+TtiuybuovYVqbUTKxn74zE/gvXcAcD1w4lpq4f/rmoTHFBS1hSPFDtRiTWt3obqVG10L9VqAS9uc3V2f4+a0OqL+klx9E3ug6+P3jZ7Nf56ylMKre4+n5Y/+9l5BaIyaNL9m9y58YYJSC1GRERkVqoYiY6SUOt17X6chVaQ+1BanteWkrKquWtXNFUUwyJr4f/691cVWPcDuOnqrPw3niEB6hnJly58q/ew1X/6Uw9fw8MiaurTIckJMW4fqNvC7g6S3OqNyYlHL1a1Ebz2j4Vf9M3O3JAjLSBe3JMom9fhHjb1swvDYBHOjYS3Q3FGbrPVcdXXedQpE6Ng8SsHiEiItvDILodkepCuVxjE5ejdo8qm5EaVenC2prlSyaRKWrmpljRBiJpcbIkOZoGgZ4m5YX/7qFkPNvd/ELlVNUjHRvj/3o3r/KZf/1AEibpCNqFqSj1AJHS2jYMrPi37OfDlfGOcg0jEuujTYMASbdpKx/zJ8NiRXfBbE1CxASzX+jBcwR7JPrayJJ9hbuLEz4eanu/Xfuknp29UsedIfG2N+lIBEYMSa+E+qblB6/v74lfJ7XD5/clyNwj+T3coZHoLpARXSODRXdBMfe1DhPdBSLVCvJynPy/oi8E9RXY8nB11jkbnff05PHugGjRXcALqfIFm/RdIyq90rL8YtXccfzXEx2x5sE2aF6n0qQS9VyD2zxL9oNOGg3mDY2Fl6vj5RdOCg8Q3QWzLX+gtZB29d2QF33sJcfTrWkIWkt8448s42jH71d7RSEiWN3ZHtSCQXQ7IvI47+fhChc96R1En3+YcwJkbOluedCmcxPHCeSqTceIoCoVqmubWEm7d8vacnXJZObk1v15Yls83iVCxt6QKHIGoeQkel8umruO1V4tQ31Nfn1sPT+L23b0z57KdFHBTeSBscrPUqqt0pQc1X+Xrs5OCAvwFNIXoOwmhJT7iue6R0q4NWUFVstLzyJ9ZIiDxcpsWr/oOnizbwvR3TAJx5VlGgSKO446Mke82WwpBtHJZDwBBZY/kIT3B7USl++VkRQAVXfy9yaZNlu7Z3PxQXRz2GsV7vpM84CoOj74clSi1duJsyIoaw+UniF2T1J9tKg0wxQA3h3Q0ujrGgV54rdH22PBiDiz2nsgmcXb1MCUlEFq1bSWuBlF97eRbiWV7X4Dts3Vqeploq3Myt3yeEf88kg70d2waeb+5ji5SHrvDRS/8siQJoLqk710VxS6R9WSvZ1wgTdEq2voYEHlun7ib5zbyOGOBGEQnbD5sfZGn/NYp8bwdnNRoDemC/X1QKMg6Q8qX49O0vtYoJcbOjQOgpMNX1TbG1Pz2LdvHIRNk9ojbWpnmXskzqqxbSTfplQBpO8eSsaGie0wkAUHAQCeEtztn9SpsQQ9kU7P5vJf1FjD1FUr+vi4u2DJfbdvfgyICUUtH9NO9H3cXcw+bkh9I23Ng23w2T3xFr1W0bzOKmPOCiK1GS3wRsxjne1rJZUtnPZJ3UV9aZzUILlRkN7H3K2sb6RvZa21pnS1r99EZa/cFSXZtvpE1wEAjGvXQLJtVjfKBtIldlL5jYmH2sr3/YgWGeKNhhIUm00IMy01rjHhDhZEF8UGDvOkEgyiEzxcjAdz7lfhjDhnJw2+Hi197ryIYG/8MD5F8u3KhTt80/l6qOtGkNTUvvwtwIFyWEvB0LK6Do2DVLc6SIobA5YyJcA1f7h5M8HtTViAp1UpZRyV6DBi+UX4sPh6gnsiv+q/YyluYCi1+kkNKxZU0AXFLJZh0oDc7k0Ks5k0FPro+0lKeX79Us9mWDsuGX2j5Zt08YiOgtgEPN2tCQDgyTvK/n9Aq7LvQNekNQ8bSP1g6REkuWGA1W33aVkbcfXNP+dypP04ka1iEJ30soWduL3OCI+pa3qeXUej1drG2KSqyr8zXUUISbeOEfpn2pH5wgI88aAdz5xSE+6ipXN3bCjWjUvGU/8FNxyJFDcw+rZSfvUTb1bJz0tlq2NNVdcBzoFaWXkN46TRoK6f/X9OajQsoT7SpnbGPYn1AQBdm4bgm9GtsXSU/lXapFt8fWlmoQO2k0pLKq46ahDZmvAA29uHuShdxd2G2f4IJZNEh/oibWpn9FAgh5g5OjRWZ5BIdJD24Q6NZG+Du0n7Iur8qm2jQLOen6qyfZAIpv72DO2H5Fpm7+Nu+cwi0ftNtXLEVChqWyVh60L9PFQx09kWWXNNaOlLfdxvB3gTJVrOT9aR8udTx4pCt9Z0Q6rUEHITsaca27YBfn+sg8WvtzYFkD1rHOxlcupMuo2HbMtN6ap70sCrvaRLGyUFQ+f3qx9MtrkUsu0MpEmjqrhHdBAib2Cq+Rjy88S2WOCgS/x5cLcNas5JCgD9zZzlZ00gSN9MDA5ldXDEYLFIKRIsN5ZLmIQFuaoXcyUHpPAJixQrptaOS8a7A1oqUgDP0Sl9nhQqaJZ089r2uy/sEVULyx+wfLazswbwcLU8rKGvCHOQN9MQkmV4Tmy5eiooLAoYTqsJyHdqYqxdU0SHmrciKG1qZzjLVA/EHjGITrKwlZ9gkJcbdxikOmq6UDKWk7ZrpLoLHzmS8mJc9sqRb/zF68irOXdIDO5RqF6J2m/mkTyUXlKtlqDDUAvyz1c/Vtb180CXyJAq72hwXF0re0Zyu7+N+mpAOYIZfVugSYjuQLbcIoK99E7wmDUwWuHekJSsTS0ESFs0l0yk5/eodFqbHs1roWNEEB7vorsotL5JHknhlq8aitOTDs6Rr4HUiEF0O2JK4E1tl8HcIeim73NR2/enBvaYJ86WbuzYQ946Eaz9hnWNe3cJvgtDQSxzZzWoka3uLiZ1aozHOjXG1DtuL3HVaDRws+A7f/muZlJ2TScpP2elCkNSTWMdtIZA+XHN2Dj2Vl3aIo2s50Si959KtP9Y58YKtGK7TPsObOcc1piIYOUD+yHebqpLW2GrHuvcGOPbN0RtHzeLt6GGCSqcyKA8bzcXuDo7YfbdrXBf6zCdz9FXm29a96YWt6uG8UbGMfphR95QacV38bt98T3QRXSeTEtOcdX5SZKc1JiHN9aCavdS0JUv3Nfd/OJm5vyO9C3pE/FbfPbOpgaDamE2WETHVNbkwJWCp6sz7k8OR4NA69Ok2FrBtiAvyy9+pdCliWOutkmb2hkh3mI/e7V6qWcztA73x7h2DSv+5uchvtCl1Idra2+S925ZW6KeGFe+euDHh9safa41swTJPrioeALI+gkp6N2SgTQpeLu5YFy7hogQtMrBFukLGDuCygWrhyWYvyINAMamhKNhkJdUXSKVUu8RhMxW29cdoyVc2u3tJv8FgfrCcw5EhcFRUwV6MUehI0sMC8DcITGKt/umiTcqPxkWK0l7DYO80K+Vei6kfD1cMNFA0eM7moZI0o6+3KSGmJoGwtKgoK5Cmba7B7Utog9V9yQ57gUl6davVSg+HhYHf8/b5yKdVHCzRcqfSoi3G7o3szyf+zv9WyqaBuGZOyOx88lOJu3jPxoizTFaacMTLQvqUFWTVb7aQI2TV9SulpGZ5klmTlxLddBaFmsebKP634ec3F2csGJ0a3wzujU8JchLLiV9K0+jVJQC1pEwiE56ZzTeHWtewUBDXuwp//JxUpapAUWpaQH0iFJudpM9sGYWrVpP5VMaBorugl5J4QGSbKd1eAAmd47AuwNa1sw9r+BU9EX3xuv8e6ivO94dcDtfqL5ljeaqHJgyxJKPQI4VQPryF9oTUbmqV4xuLdm2XJ0tew+x9fzwdv+W+Op+y4veieJjwUoZXaoHdZrVlmdWn75diK4/uyic9sxYa056nmGrK/jWT0iBm4vll4m1fNwUDwaa2p7IlHnWpJWo7++pXM52Cb+7tKmdJduWPpYco8onnj1ZKU0a2Z7OTYKNBjzN+c2vebANWtWV5rxOTTWuTBEW4Kmamzii0rU2CvZC42D1zSSPr6/7+uXLUYk6/66Sr9FuMYhOeoUFeGLZ/YlYPyHF6m31bxUKC69fbZaxGTxSHRvKc2e1aRAg0RbVT+kLoJb/5YJ+SsUn2oYKgNb2UUeVc1un7wRGl2X36z6pAQB/D9NXUri5OKFLZIhkgXljquddHN+uod6Libta1FbNfsfUPYIcJ5UdIoKMNCpdW7ZyUuxRLfhmSXG2ad0j0UjCCxlrPro7moYg0oIVEiJ5uzmjXSN5bjY2CDCcVmiklbP3h8TVhYuTxuCyctN/CxoMYUFPs6klkGJvQnzcK3LpW1KYXQ1pg1RJY/6N/EmdGuPPyR0qJmVse6Ij5gxuZXM3xn97tD1+MiGNkT1w0xFMMHT9Y4kwI8c3UyWG+yPEgusvXaPYHut/GaLvlyxnbnh3K24aV2HlsVPfO+QhWV0YRCeDmtbyQS07D8DpisdKsZ+qr1B+4Lp+Hvj9sQ5mp7fgvth0D7VtgC2Pd8TwxPqiu2Kz5BxvSi2585CoHUMrAx7Uk3PclM8vwoSA4xN6KsybY9sTHfHJsFhM6uS4Sz5FsZX9dvXlpZ2bBOORjo3M2oZUvzdd7mwmTeohJVgyw3Lu4BhsfqyDsECor4d1392z3Zviz8kdDAYzzAkqMD+pbRmbIv1s6ye7lk2CuL+NdOmZeja3LOXDb4+2x5bHO9rt9ZW3jvoxSjF31nvl44yLsxPaNgqyudUjPu4uCHaA+hV3NA0xqeaBWkgVjBdNxCRIEacuUjUpxXZYSFb9GER3EFLfpRVNyn1r8zq+iKnra1XuR2tVXyauL++VPl5uzpKlUjCVyOWwIkh2h9pM+n66nq6Osfs2dDE9vUdTRIf64hEZgrkv32VaCipLT3R0/VwDPF3xsIGc4/r8b3gc7m8Tji4mzGobHFcXHY3NnDbCxdmpYma8p6sTYur6omktb4T6SRcQGN++ofEnKcm+DqEmeb13c5OeF2virD01HDE+GNQK/9c7CvX9bau4qrnaNAwQ3QWrqaHwnxrGrCOqHHwaZUL6ElMucbpH1cKvk9rhsc41byQnWJjm67XezS2aAazRaODu4oRSG7w2M3QuPKNPc7So44Np3Zsq2CNyBP4eLni7f0v4mbGS01HJtQINgPBzJxvcZZKdEn+GSoqyl8CblAFcZycNFt6bgDf7icnxDZQV7KsswNNVZwG/uhIGqayNuVsbiCP9fE3IY7vkPv3pQqpT6v5Kdxlmd07sqD9APiCmLhaPTLC4WKQhfaND8cGgVujQOAh1fN0xU+D+ATD8HSaE+eMxEwsBebg6Y/bdrSTqVVkw4LN74vHlqERJb+TpSgfRKtS2lljbspn9WqBHpVmWJQauXEL9PPDdQ8l6H4+p6ytp3yylBdC+cRB6tahj9xdicu/ylZ7hPi21LCj3cAeV3VwTbM2DbWr8Tc6hHaZQAKXye5CyaJq+AFw9C9+XRqNxiBnAQFkxzqRwfwyM0V8vq0fz2lhyX6LFnyeJ1SRE7IodtaQHVDtjE2bk/P0tf8D2asPIqXkdsTnvhyew2LQo9hFRJZNN7NgYLRT4wVfOEVt+qaXrxF7XhZjcBV7aNw5E42AvvCRzsVNrLtI1Gg3Gtm2AtpWKJ26Y2E7W5e3mcnV2Mnt5PpmmPGBgSCMzlqfbS8BIqbdRnnO0feMgvD+oFb4fn4I79axUEVVwUU258TQajaQBdA3KVtdUZ02RO3NsmNgOXira14pwZ7NaVY7P53IKDD7f0EXbGwoUoW4Q6Iktj3c0+Bx72Q+ay1BecVuR0jAQWx7viAfb3g6iq+3rNGVVkqvEs+uVShfwv+Fx6N+qDh7XkQ4sob6em5syHKRa/xdk85c4L7gSh9Py+kWVqW0m+os99F8XjWoTjk+GxVW5DuFkGul56zj3cRRD4+0nIGjqCj1TVd5TvHJXlMHnyrk/U1McQg36Reu/qagER8sKoCYMojuYEG83LLkv0Wgev6V6Kv2ayseEmbSGSBngrpx/+PnUpnirX0t8M7o1+rWSe8dn+OTYUKErXbvEAC9xS9i+Gd3a4ExDfbYaCWpYouanqq6LEClwFo/tGiD7fqVMMwln51VW/aZA5ybmFz5Ts0ZBXogONTwz2svNGasfbIP3B0k3Y99c/XQEXKRiSdzG3FQ9lZf8l8/+tDRlgikaBHpanHLrof/qEAy20+KTugKftkhESjUpzi6cNBrc3yYMA2NCEa5QrRypJYT548WeUfD3rHkeGhGiXMHdp+5ogsmdG+MLK69RRNAV+FdZDF1S5QF2S/PGiyTyexGVOtLWyR1KNOfG3YLhcWbXKTNH9dXrcikfi3JdbzgKEVkg+sp4DUFlWOKbdJJjh6nrpETfQa9fq1D8389HJWm3c5NgTGjfEC1DfdG+sXpmTaipL8Y0NqFgIekW7O2Gq/mFortBCpjQviEealc2U1LXDPUXezSDh6sTnJ2sm7ndINDT5NmlHwxqhcdXH7CondVj2yA8ULqZjm7OGhSWiI0aOGk0WHRvPFbvv4iZG9P1Pi/Y2w0djOyjm0gUPOrVojZ+PHQFQFnqgudTm5p8DFbq0ywqKTXr+a7OTvjq/iSUaLUVqwri6usOog+ICcV3/1yyuG99ouvgUSvqIoxv3xB9ous4zM3LBcPjsO10NhZuPyNbG+XnexqYN0bN2Sva0vyr8jzc3+w9L9k2m9aquf+RavK36Ly3+vi4u2BUm3BVrcSyhj0H0Wf0aYHtp7LQzoauddTAmjHxf72j8NL6I9J1RkFBVkwUq+PrjjPZN816TW0fN1zJM/3arPrNjdbh/th1Nlfnc+NlnDBgCkvTrVVfgbnkvgQs33MeY1MaSNEts9jJLh4AFK9Z9+OEFAR7u+HBr/5WtF1Hw9udDqr1f0XhAPmrLhs6H9C1X5G6OxqNBg+1a2hTQWslKLFLt+PrA5MFeLpUpAdJNHBi1TDQEwNahWLB8DilumYSe7lYlUvlJfzGPqv+MaHo0bw2AOvqG6wa2wYBOmYE6mLNfk/KADoAPJBsvECcEjQaDQbHWb5sePHIBDzSsVHFaqJwIykVjP2Enq+UvsnbzRktQ33horIlmsUm3PyYM7gV/D1c8Fb/lgCAyFrekuYz1ueVu6L01kWY1j3S6Os1Gg3CAjwtvtCxtTyu8WH+CNSx/zCU6s/S0fjduGS82isKk02s2WAORz42tarri2X318xNK1V6sU+GxUqyHaDmNcbYtg3wvrG6HDZ88uhhxkxiSwqTm5PKz1yWFkrXxcvNGd2a1YKnQukfHHh3UKFXizpWBaNFsuScZ87gVujVojYmmpBatHpguUUd62q1DE2ob/Jz/6ey6zpTRQR74/nUZgj1q3pT1VXuwBF03wgYGBNqE4eG6rPAzT5X0XMnTdefq6cNGpMSjhAfd7NvpMi5UtReMYjuoPq1CsWMPs3x3UPJ0Gg02PxYeyF52EwpoCiCriWrclt0b7yi7UlRGMzbTZ3fn9p8PjIBD3doaDDXeViAJ6b3bCZ8BoM1bOHkRi00Gk2Ni53qS/0rzwppaSQFiaNTMqAWHeqLMSkN4PJffmNd136V04gZElfPDx6uznitd3NEhnjjBQM5aQHl8x+W3+zpZEJan7aNgrDhkXbo1tT0AsPmBJwskRAWIOv2AWCSFbPgrfF0N+M3CMyxeGSCpNsDgLp+Hujdso7QFAX69g1P3tEE7w2MNmM71v/2pM6JLpcQH+mK2AdVu8E1sUOjKnWTdJEymGsKKfeqvVrWNvm5pRa8zc5NgvDsndL+9suZOwtaTTPpjf0806Z2VqYjClgxurXexyKtWCEnqsaPpdo2CsL/9W6ut2CwWkgRoJT7XMkc3z5ofnpXS4T43D52LB2ViOe6N5X1QlOK64gPBrVSpFbJuHYN8GbfFjXOt0299qjuTQVqF9kb9fwiSRIDDFRNr8zZSYMezWtXLF/2drs9W1YuunZOuorHqYGICx1dM/aCvNV1YlD9hFnKwFVMXd1FWF4wocimNZw0xovZGptpWl3lgKcGZbMcH2zb0OCJnrxBQGWudNRyQdVMxzJ3c1j6PgbGmJdTed24FAyNr4fx7RoiNaoWZvx3EvPkHU1wb1J9NKt9+320quuHuUNisObBNpZ1zg6pZbzpupn49n+zsasLrhRQujs2tKLo5l0tauOrB5LQwMjs/8SwACSG+SuWv3vFmDb4fnyKySm9zAk0fnlfIjZOam9p10zSMEj+i5nK9zUmtG+o/4kSc3fR/1lbEvBVesmxteL1pAfSpV2jwBp/83Z1NunmkJR6t6yD+Pp+Ft0MaxDoibb/vY97Em/PgvxIhty7alsJowam3LQaElfXrN+RJYcwjUaDIZUKMMo1GUnqEfBiz2ZV+mppsMeeWFpIs1GwFxrpObY9ZWCcrnmwDaZ0tY8aGdZysnYfp/AJ6JSuTRAR7IU3Bxnf38u9966t0G/X09UZa8clY/2EFDSr7aOaIpr1DKwklmpCavdmhmtJhAV4ontULUlu7gNVr03INAyi25mwAE+dd9vVGqx2ZN5uzmgd7o+4en6o7aN759W0lvlL4d/RE7xRu8oBw8pSZS5KtPXxjlUuSHVpZGZO+BGJ0lWYl+s0zZp0InJ7q39LLLs/ET9PbGvR610EzfYz9yTEzcUJz9wZiXHtG+KNvi1Q978lk/ck1seUrk1qnBylNAw0OMNBX0oLcyg9A1CXO/6bWRFT9/bNqPHtlAtOmiu5YYDJz503NAZdI4OxdFQink9tZvbFiLOTBvOHx5XNyFGAu4uTbMEOb3dnuyuiNsSKNEEiGUrlootcObPNuSBsHOSFrx6omdJEl4a6UmCY0JS+m/uWcndxwoIR8RXFbM01e2A0Vo5pXZEWDAC8Ba/o9DHSfuVYU+V9yeu9m8vVJVmEyVEU1opA3OKRCWjfOBCf3iMuVYQ55wv9W4Vi46R2WDGmNUYnh2N6D2WOYboYT4mjzHnQIx0bYaqRSTzm8vfU/3sMC/CEm42shjFX5UOHKdlGHu8iZgWZSXQcB+v5e+CbMa1xT7Ly+clFquvngVoSroySQnMrUwGZ4mkLVxtZElIXkYnCHtjnnpQqDGgViqa1vPFCquEl4kpIrJSHvZw5xUbtjUajwbyhsVgwIk7vhWNkiPm5D7uasZSelE+PIEbN96hUEM5UlVOZ+Hu4oGktHwR58c64OfTdPGiuQF5qSySF15xJ6u3mjEGxZbOs3x4QjYc7NMT6CSkYp+AMX3NnKGk0GoP1DiqLCPbGOwOiZSnebWt0Hf/NDeaqgT0cQcydzfRg2wYYmdIA84ZKPwvaHNakLjBFfTkCp7B85ZmLs5PuGwKVKH1O80CbcJPrAswZfHu8NDGwYkwtq4zULDrUFx8MikFEsLy/ASk5aTRoFOSFSZ0aC0ub+Vz3SHQ0kk5ICf8bHqfzBpS1Q9+WfztS7blMGVv1/S1fpVY99aIpUqPknRBWmaXHF0tznTexIFbhSCxJk2SslkT13/nguLoIC/DAXS3q6H4BSY5BdDvXo3ktLLs/yaQCcW/0bQE/DxfJZwcU/5f0r5HERerUyNCS99GViuqV3/XTaDQGL15TGgbi1V5R+HJUonSdlJCQu5c2cIbY2YIl4kpd9r7Yoxnuax2Gdo0CMa6d2BkNomehWjJz++W7mqGLwikArOWmktm+ppxItm8cVBEECvF2w4NtG8o2C0VXAO6OpiE6UxkYm3Endzo0c9T1c0dygwC8ZiMzPXtWWm0k1dLUcpUPF7X0rPhyRNZ+yp5uzphxdwySG9ZMk6KUumbOhp99t+n5z22Zi8JpeXw9XDBvaCwiTFixZ2paKFul/rNT9ai8b6686kxOrer6YXBcPcmPM5ZgIT/TBPwXEJfj84qvX7bSqGGgp1n7Jo1GY/ZvXYki69ao5+eO9wcZKfisx//1so1zTalU3n2oYFcCoGxi3OqxbZh5QkHquLImVWhV1w8bH2mHAWbm9TXG3U6Xjukyf1hsxb+rBwiN7Wh1zsrXaNC7ZR1ZDr7G9vtf3Ge8yFiP5rXRI6qWbEWObNHkzo2FFFw19d5C/5hQPN4lAhqNxqqZGFITcVHz8dBY40+qpm90KN6tVIxObfd05gy27CS4nJxjV8TMM0N0zaJtZWEB1+p5SCsHlAIUft8Bnq74aGgs7mpheoE7kdo0kDcQ++mIOLRpEIDZd9/+bdh7MM9clW/y2wq//2ZxppiYTqljRDC+H59y+w8S77vlPhTo3Y+o5CBU/RCuktiCSdQSCDGWWtBeKZVjmQwzJzXd8ATjqcukSPu1eGQ8JrRviOlGiq6X55BuEOgJDyOzeMvN7NcS49s3xEdDY2WrLSCCJddT341LQSsL05dVLx5t71RyuKih+vdu6orXB2zw/E8NHCe6SSaRI5BlKD+bXKf+j3cRUzglsFL6CXODUZU/C7Wc0Bvj4qTBjL4tqhQ5kkqLOj5ICPOHu4t5S5pEs3S2hBpmxqiFvo/CkvRGhjQK9sKjneTJiyhqNkCgFSlwXu/dXNaAb6CXuoLoLk7SnQLV8XVH2tTO2PZER/w5uQM8XJ0x++5oJIb546W7xKdTq8zYrHqR9RJM2Qt2aGzeUvy4+v6YNzQWTSqtPOjdsg4e7xKBxffGm9dBO5UicEZ5ZZYcBdubMR5Er36yxP/1jkJimD9e7RVl9Llx9XUHQWLr6Q+OPN1N2pzMpjL0XauxyK0Sp5rGityTacqLy8udHmzukBiMTAqDv5kr0fpEy5dywdJx6uykwcQOjUx+frdmxlOHSvEzru/viYfaNTQ6CWNs2wZ4q39LfDYiHm/3a4kGgZ54q1KNsEAdrw/2dsO4dg1Rx9dd6LVkqB3eRJJtxa4qDg3mdcLcm0n6hqK5vydTr2+TTUzHRlXZ3tkk2RVdhXqkOOje1zrM+o1ITBX7/UqMfc6m3sk3RGviWcnDHWrmO/58ZEKVmf3WsCbtzICYUJOet25cMv43PM7iO/nyjg/lzg69rBg3/aLrIK6eH+LqVb0RsXRUIl7tFSVLoOfu2FDU8XXHMAtvBOn7HdXz95AtQC8HDYCeKpi5rLb9pLlcnJ0q9p0dI4Ixf3ic4is+rLkYXHxvPNY8mCxdZ6xQvt+unM5n4T3xVS6MLeXspMF9rcMQLXHhSCVZkmfTUuamTlGEtW9fz+urXPDKdOg0JUg8b2gM3h/UCr1a1MH84XEGi0qX01fk01Ce9GEJ6pv93KN5LTQK8sRQGSZoOJr5w2OFp+7TRc66IEvuS8Rvj7ZHbQtTwZn6s09pGIgnukbA1cwV1yLPc/S9twntG0py3SeKq7MTujUNQYCXK6Lq+GDV2DboVqlGWM8WtdHUQB0GkRaMiJP0ekEN59FypSvqIVFueVPSj0nlzX4t0DUyuMpN/Op55I3d8LurRW2z64QZK/pdTm2rhG2F/axdIdXSF1R7f1ArVezoSbdGQV4YkxKuSCqCmLp+2HQss8rfKmZmWzk9wNPVqUZO+U4RQfjzRJbe17QM9UXOzSJ8M7q1yTPXQv08EOpneaAhyYI7wYNi62L1/osWtymHEB83nMm+adJzI4K9cOhyXsV/v3SX7pl2zWr7yHbB5efhinXjkqHRaPDN3xck3fYDyeGY++dJSbdpT6Sc+WPuRay9kPIzbNswEC1DfYWvipl6RxP8e+k6OkaUzWQakVgfp7NuoHOTYMQYmFGrBoZW3qmZh6vh3090qC9e7NnM5BlVNYel9GOqfIseRo7R5hYRtLS4mjlMWakod5ojNfNwdcY3o1vLui+Kqu2DI1fyUN/fQ9EbUkpLDAtAYlgAFmw7Y/S5Sk7G1VV7xBJ3taiNTUczqqQidXbSmBxAklurur44cPF6jb/pMyjWtIk7UmvbyLz9jdpWARvj4qTBsvuT8OOhywjTObHBvDck5fsP9fPg9YKJpLrRExHshRNXb1T5m6nHAXMPS2EBnnhnQDTe3ZSOr/eWXWf2bF4b87acMun1A1qFYnpP6Ve0dowIQseIIJNu0FNNjnnV6UBE5ql69s5ItG0UiOH/5fhLDA+o8ri5S7JtnoKBiaRw43eA3UwIOj3SsTHuTZJnVr+cyxkrG5vSoMYBwtgssE9HxGHNg20kWfpt7Gv/7qFkvHxXM4tmXDUy8U663IUly2eMmvubfqRjIxl6Yz7RQUMpVR6zM/qUFfuxtYsdS8i9bNvezRoYjTlDYlTxWxiRWB//17t5xexZdxcnvHRXFLo2Nb58XDQ1fH6WKD8mGjrm9W8ViqRq53GqYOAz9/NwQV0jN7dHJ4ebHWgn09WzcBWD3L+lWQOjMap1GOZZUBtFH60W6BRhW4XHKzP3VEGqcwtrvunXejfH74910FkofuB/AWmlCpcas2J0azyf2hQD9dQee7FHM4xr38ikbY1qXZbHuGuk/vH208Ntze6jvevVoo7Om/HmDOX/U3nRdhs9DVGUNRNvpLoBWJmIVRLdmoZgcBxXe1mKQXQ79d7AaExLbYrIEHFLl4bE18OcwTHw/O+uYfdmIRjQSswddjWovMuVO6j13kDjxQXdXZxwf5tw3JskZilvbZ+qJ7w9m5uWSkLueGCrur5wdXZSLCdnPX8P9I0O1XtQluJYHRnijbtjQzG+fc20OVJYNbYNZt8djb6tqt4YGRxnuEixiAKspjAnd7faAtSNghypYOLtD1+j0dhMIU016ixX/spqjKVNcoSLv24y3QyQ4vxq/vA4xNT1w6cj4iTokfmk/v515cGtblKnxlWKztoaUw5B1qRZs9brfZqja2SwsDGlTx1fd0zuEmFxkF+f9o0tW0EgVRBFqrz/pvwW1XL646InINYxIhirx7bB/OHyjj1Tg2qNgr1wd2xdvemVkhr4m7yt/jGhWDmmNd7spz+9mRzBPkdV+Zow2sIC9OX0rfC+479zg3sUTrGlK7WuOXSNMpGrQ9s2CsT7gwwf061Z3fxIx0YI9XXHYxKm4BmpwjTEZBiD6HaqU5NgDIo1HMBSmkajwXOpTdE63B8jZZrdDMh3gWqtTpVmOhkr7Gat6kUNX9Nx11yj0eCxzo0xpav4olKNgrz0509TOEqpZIX2xibMJH+qWySCvd10FpyqnHPe2Kf0fGozjGtXNYguVcAi2NsNHSOC4aTRVClKWPm3qOQMTd0ndMbb/2hIDOLr++GdAdbnXLY1nIlJSniss+3UCZBLvFS5Qqvt0sxJQ6Iv0BYd6ouF98Yjrr48+UzVRN+npURqjy4GZo/K4aF2DRBT1w/TUpvK1oa+z62unwfeGRBt+2PKhFNRLbQWn+ssGZkAAHjKyuKiwV5lKeo2PNLOqu3Yi/BAT9kDem/1b4k6vu4Vq/8qm9Sx7Jin7wZyuBWpFBoGeak6UK7entVk7FJzZr8WCPR0xcsWFoqv/Fmsn5BS8e/KN8/e6tcCvz3aHlEKr6ycMzhG8m2aWk+sstHJ4Va3O6F9Q8wZHGN0ZfTwhHp4tFNjfHlfosHn6RLq54F141Nwv47+mhqyqFOtmKwp2QGk5qhpMKXCT48U5eKkwcfD4vBE1wjZ2lBqBrG5RBYwU+sMze8eSsaXoxJRW+HK5GqZPWPKja5GQV74cUIK7km0fGaCkgFsz0oz3tT0U/xmdOuKf3u4OGGBjplJyQ0DsWBEPCKC1Vl8SA7tGwfiiS4ReKVXWT56c2bhW0LqWX9kWyrvi3Tth+tZUVfCVli7WxwcVxcNAz1rFNhqacbsuF4tb68a+rHSBb0tUtFhxmR1fN0xs18LxdoL8HTFwnvjVTW5JtRPuvM+ta0Is0T5bGpLzof/r3elejIaDUL9PBSpZ2SvzB1PLUN98f34FPTQsaK2dYMAbH6sPZ6+M1LnaxNNSL9pFiOdj1VxMW01XTNUF13XDz9PbIu+0WXBYWt2OZWDl5Un2Gk0yuby9/NwwZ+TO1iUE/vVXrprWAFlM9vNXRGzdFSioilNXJ2d8EByuOI3LMqZuvpeH1MnY6Y0DND72J3N1Dnp1FYwiE5C6d4FVD2Kers5Y/Nj7ZXojqQ8jRTqUrPvHkpWpJ16/h6IkqlgZDljhxmROWxNnUBiq3l2jc3ok2rZsSkqnyTOHx4n3WxQGxfi7YaRrcPg51F2wf1CalO0aRCA2XdHS95WQpi/JOmJ5KQv8BAssL6IPdH39X8yLBZ3taiNp7pJtzIqMsRbkfGmdFHC57o3xYoxrWsU2GpuxsWgu4sT0qZ2RtrUzgjxUfYmtiF3WLCSUOW7FL3ubFZL581ce2EsGOTt5oJ145LtOm9z5fPbhoE6AlUSndv1aqFMjSEybu24mtdPakpdKFXQ0B5uXJnLVq/F9Hm4QyPJCnVay5r0KpWVX8uonb6UTkDN31bDIMtXq/Q3kOaPM9Gtw0+PxDLxIKymExBT9YlWd/53S04F7OWcST2zsaQ/IbOFmb6PdGyEIXF1hRRSIcNC/Twwb2gsOspQHC3E2w1D/iti07pBgOTbt8ajnRqjb3Qd9G5ZdXbInMGt0LZhIF7qqX/WDVkvKTwAr/VujkAv6W5WLL0/0e4uesvZ4/tqHOylaOFSvR+hjX+0df0N3xRZNbZNjb+90VfaWfGTOzdGA11B42pC/Txq3KCMr6/embLG6AosLhgeh0GxdXG3Beedz+iZvUzqZayQsZI0GsDHvSxIasrvkeyD0jf21ZRSSOmuyNncguFxGJ0cjuEK58gn09heZJLIRri7OCHU1x2Xrt8y6fmVd/xK5MaS+xrc09UJbgrONDaHlMuI7Z0cJ2NjUhpIvk19ejavhYExarlpQvck1UdsPT80reWNx1cfEN2dCr1b1kYtHTNy2zYKQttGtpUv3s/D8Kmdo8wgU2tqN0dUy8f4zZGmIfLfVK08O1qqdBcJ9f1wJa9Q0v6bs8y6+u+5TYNA9G5ZGwn1/TFjw7Eaz9cVTEuNqoUZvxxFfmGJ2X0tV/nnNqqNZbltWzcIwEyJA/omkXFXER/mb9bKt8pfp5pTbzi6J++QLy2pNaof3j+7Jx5L0s7hobaGz7ubmJnG0JR0ErZ0s9dBToskU3klsb+nK8akhGPRjrN6n79iTGscuZyHzemZ2Hg0U4ku6jU4Tvc1odLDddbAaEz99qDex809dpCy1BnhIodm7k7MXmazujo7YUL7hnggOVzxHOFy2PhI+ypBjHArq39XNySuLur5uaNPtLTLWG3onE8Rtv55vN6nhepmPStFjRcwThoNYur56V1CKnWXzSm2aOve6d8S8fX98EIPywpfkTIcZ0Te1iUyGA9IUDSsMkv2FZVnzJkye86UQNH84XFYNbZNRU5rKczsZ11x61d7NUffSsu4Tcmzq6t4udL6RdeBv4Bc3qYMpSBv4/1iIM48sfXKbhIMVM3qUNPdmxSm97FX7ipbuSZnIV9TaKBBRLA3XrkrymDe60kdG6l20pNStGbOLjD3+fo0keHmsanHRmtq0HSJDEHHiCBM7NAIAPBIR/1F42v7uKFRkBd6tqhdpW5WdeX7A7l5uKgjhU2ywGvTdTrSTpF5OBOdSEYv9GiKx1YdwCMdG5n0/IfaNZS3QwqqfkIWVdsH/VqFYt6WU2Ztp/ppSvl5y7Pdm0Kr1aoyUKiU3i3rYPbmE0afZ83JnpsVQUhzvhrH/RYdl5ebfCeyX96XiDl/nsCjnfSf2Nubrk1D0NWCnNJEcnPSaPBop8a4IzIYe89fwwe/Gz9uGWPKKilLjnzmHos0Gg2kuFcXEeyFE1dvWL+h/7g4aTB3SAwKi0tNmnXvq2BBO1vUoo4vJndujPoqTZnXtJY3jmXk467mtYw/WR+FlynNHxaLK3mFqktDaO2n0Ce6Du5sFqKafNPGGMrPbK5eLWrjx0NXJNuevVpyXwIOXLyO1Cgrfq+VlP/+9fl+fArOZt+Eh6sTHvzqb/RqWQftGwda3J6Lkwaz726l8zFDuxFd14Ut/svTH+rnge8eSja6mtJeVL6Rr2RB2VGtwxCqorRTtsoxRik5lAEx6imw07ZRELY+3tHh7/CXG5PSwOwguiG2EECXqliKLpUvjE2t1G2uhkFesmxXJCkvGMhyz9wZiav5/+LepDC8uP6wpNuOquODuUNiJd2mGknxu/fg8UlVtj7eEa7OGiS/96fR5zppgFIbmv4aXdcP0XX9KoLoLer44NDlPMG9sl8pDS0PklBNpqapebpbJP731ym81FO5VUGfDIvF3nPX0MGKwJi5Eur7Y/upbItf7+LspLoAulRMDaBXPht1toFrmnJ+7rpvzD3XvWlFEF2J1KS2qkUdX7So4yvZ9p7oEoFJK/8BoPtGcB1fd9T5b5X7jic7S9auuXTd/F48MqHi39buD2zodAguzk6YNzQGhSVaydLLVVd5JXbHiCD0bxVqUQF3qolBdFKt1Kha2HAkA/e11r9krro1D7YxuGRNLn2i66ClnoMhA+iOaemoRPx46ArGKpj/W59wPb8JuVMh+bi5oHWDAJSUlKK2CTlxrWFKMGlYfD1k5BeimZ2kgLJ1df08sOS+RACQPIhOprPHQJua0/gYi5NYes4QKCANhl4mXsn6e4jrs5/AtstVHguPd4nA46sP4N4k84qI2VLQwF65OmtQVKJF+0Zl+9JhCfUwNL6uohM9/Dxc0SXSuoLgXm63wwI+bsZDBKPahMHPw8UujyFK8XB1xoCYUBQUlVQEOW1BVB3dE4QqrzBUU8FJ0Wzo/oi8dHwOjly/pk0DefedQV63r72bhHgzgC4hBtFJtf6vVxTubxNm1kxeEQF04Hb+O1syOK5exb+DvFyRdaOo4r/lTLPQKMgTp7JuIjWqtmxtqEGz2j6yzkLXR9epSHx93YVJmtaSuX8aYN6QmLJ/ynySVMfXHRevGS7i+/SdkbL2Qa06NQnCPxevIdhb3hsZSquZ6onhJEvY4kzARzo2MriqqUNj2yoGK4UQH3e8OyAa3jIev+3BSz2b4UJuAVqGSjcLUArtGwdh82Pt4W1C8NJeWRpMER18XDsuBccz8pHcMKDib5Kc8ygcW3Jz1mDNg23K/m3CzTxXZycMia9n9Hlk2HQbq2NizvF17pAYLN11DtuMrFjwcHXmpDMrVN7dVN/1BHmJv2FcTg33VeRauU2OxXHP1Ej1XJyd0FzCpU7mquXjhoy8Qkm3+XiXCHzw+wm8LDDo3ie6DrxcnTGy0mynFWNa4+TVG9h4NBM3C0tkW1YEAMvuT0LOzSLU8ql50ePspEGJhWvTuzSrhd+PZljcr/IbCV0ibfsurZpu6GtgGyl3rBHq547jmdLlsZVS+Sc/qnUYwgM8kcAq71TNgJhQjG+vXC2OyrsDa1Z6t9AzC+7pbpH4eu95oUUSy2el6iPn7DxrZ6JaY2h8Paz4+4LZr2sd7o9dZ3MxON6y4oLmrqjqV6ngptooHUD3UknO5sFxdbH3XK7Zs+Q+uycOC7efxZSuETL1zDQh3m4IkeomteCbwaImI6mJlyuDuVJJaRiIlIaByL5RiOfWHcKec7k6n/d6n+ZoVssHXZoEo0mIF6Lq+KKlnuM82a7qN0obBNrX/saavXevFrWx/VQ2ejY3PsnQy4FvtqsFvwFSHbWE3D4aEotPtp7CpmOZkm3zvtZhGBJXV2ixmWHx9WrMwPLzcEVcfX/E6ZmxLCVXZyedAXQAmDc0Bi/+cBjPdr9d0d7UmUmLRrfBiXPZSM/Mx+/pV/HVnvN6n6vrILf8gST8ezkP7RoptyxVDfHlJ+9ogvd+Oy66G5JS8nMN8nLDrEkJKMwvUK5RM7k4O6G7RMWLLPFa7+aqLcbm6JSeAefq7IQRifWRf6sY9f2lv3gallAPwxLEzoxcMaY1tp3Mxlu/ptd4bGh8PYToOf7ZOncLZxHOHRKLrBuFes8LjImu64dZA6NRx8cd9325x6JtAMBjnRpjzp8n8XxqU+NPtnGf3ROPOX+cEHqzqbLnulv2mcfV98f7g3hz2BTlN6vIuOdTm+HZdf/ifhNz4KuJWhfkBXq5IT7Mv0YQ/ZGOjTCmUtrLdwdGK901AICLkz3dOFHBxSXVYGxS2f/1bo6SUq3Bul1Pd4vEgYvX0KWJuAkTVMae9hhkg6Jqqzc3ceNgL7zVv6Xk2xUVQF8wPA6v3BUlbAmzKcUcE8MC8MOEtuhc6eDg7KTB4nvjjb7WyUmDAC9XJIUHVLkwNDUIH+jlhg6NgxwuN9s9iYbzr0o2u0qPiR0aAbD8IrqcyAuH+PAARISod18m2l0taiOmnp9JzxW9LJ/kN/WOJnjJBlOg6VXtkFHf37NKioPK6VWecdCUUtWVLy/vHBkMZyeNxQH0cp2bBCMixLoi2Pcnh2PbEx0VmUwgWmw9PywYES9pYTtbFino+K3k2ea8oVULbb9/dysFW7ct4YGeWHZ/Eu5qYXtpJyufCtvC5YxaJlg0N3PWe6CKUqTYEk8JYiAfDFLPvsvJjJWFo5PDEerrjnHt9NdKMxYrGZZQD//Xu7lJMZXqLJ3oQLrx0yShQnzca5y82sJB3xbFh/mjT3Qdxdvt+t/y8nHtLE8ZEF3Xr6LCdLdmpi33HdU6DA0CPTE4zrIl4o7unf4t8XCHhmhTqbK3HMa2bYCtj3esUkGc7EfrcNMCUu/f3Qp9WtbG2LbiC/HaO5VOVLM7K8e0xifDYlHbwI2h5Q8kIaG+aTeY7MnyB5Iw++5oDIqV7vjs4qSBr7uLRReXFduwJseQIFLVgrC20HhzATVgpDI4vh4mdWyExSMTRHdFNtVnQXaIcLy6EdYKD1BHwNda1q64NfU63Zau583tauvwADm6YZXhCfXg5+GCexKrrsZTuvCvoSPSSDMLZ+vSvlpO/rp+us+xlJit7aTRYN24ZJOeG+zthrXjkjG+fSN5O1XNlK4RiKvnZ3TSHJmH6VxIuAUj4vDH8at4+ccjortCMnhngDRL8+YNicGt4lKTZ/JP7hKByV3E5slUktQzsbs2DUFXC6p4W9IPFhMSR84Z/OEBHiav5ukQEcSLejKZLdRaaBjkhYZBhmdHNwnxxpi2DbB31QGL2oiq7YNDl/PgoYJ9qDn7kkAvN3SMkPYCV6PR4KeH26JUq0WnD7dKsk0PF3XkDFdCWIAnltyXYHFNHFue5ebipMHoFN7AJcNGJNbHO5tsP/3hh4NjAABtZv0huCe2S6PRoFvTEElTvlrrqW6RmNK1SZUbya0bBKhqBZwc6exa1PHFxWu3avy9PDXQnnM5mPD1fsnbLRfqZ/rNNRHnrvcmheHepDDF27V3tnvGQ3bDx90FvVsqP0OazCcyZYZGoxGaS57MZwNxLpLR6geT4efBJa8kvaTwAMTU9cOAGPUWiVTC2/1bYnBcXXx+n/3OoK2ub3Qd1Pf3wJ06VqW5uThJep4QHuiJB5LDMblzY8m2qWYt6viirhkBAbIt3f/7zdyrZzaopxvPsQ2xZpWLIzKlQKK9+ULPsbhfq1D4ebjItiK89n/B6epjdGRSffi4q3fOrNS/KF1xisSwAIlbIeJMdCIiIrvEmxhkr1ycNFhoQq0MW2Bs5q+3gcBWqJ+H1fUkbM3Ld0VBq9WaNKMr0MJZ1ZU92km+ALq1ueDJ/sg5V+XVXs0xNOEaYuvVTLPWp2VtxdM+kH2rcaNTrVVPJdK2USCa66kzEeDpil8mtpP8RkygpyuevjPS6Ko3EcIDPHA2pwD9Wjn2ZAeyTwyiE9k5H3fOLFErF4VmtdT154U6kT2y82tSh6CvuGPP5rXw8+EMdGSaoxqMBdDfv7sVlqSdxYs9mynUI8vU8y9LeeXvwcsxqknqM0Q3F6cqszJTo2phw5EMDE+oh6e6qSflA9m2z+6Jx/WCYrPSXIimxKmUHCsZEsP9kRpVS/LtSmH1g8m4VlBUY0Xqmgfb4O7P0gT1ikgaiqZzOXz4MMaMGYPk5GR06NABzzzzDLKysgAA+/btw9ChQ5GQkIBu3bphxYoVSnaNyO58OiIOGgALRsSL7gpVMzYlHNGhvujVQt6ljj+MT8GqsW2EpdQI9HIT0i45BgaQyV7oCpS/3qcF0qZ2ton879UFeYtN49QhIgjzh8chLMBTaD9M0a1pCJKsKFIXHqj+90iWibSy2KsxL98VhU+GxeIJB6ofZA1b3BeLEFvPz+QaNzyPk5dG8ltx5tF1/SnFcXlsSrjFr2XKMpKCYkH0goICPPTQQ0hISMCWLVvw/fffIycnB88//zxyc3Mxfvx4DBw4EGlpaZgxYwbefPNN7N8vXxEAInsXV98fO6d2RmSIvCfhZL6JHRtj8cgEWXK8JzcoW47r4+6M2r7uaCDwAjvE2/6D6Lyksl2VZwW5OrNEDMnEgYIEb/ZtgYExoRjA5duKCfJyw9ejk7BuXLLQfpTn+m0qc+DXkTjJHLR1d3FCUngAXHj8M0mvFrUREeyFsR0co0aCEmz58KiGvkeH6l7JZu8mdrT8Nzgkrq7Ov/MeGZlDsfWDFy5cQPPmzTFp0iQ4OzvDzc0Nw4cPxzPPPINffvkFAQEBGDlyJACgXbt26NevH5YuXYrY2FilukgqwaAvkfnc/rsIerxLBBoGeaJrZM2ia0R0m5ebM8ICPFBUojWal5pIKg10zMK6O7YutpzIQnx9P1nafHdAS7z601G81ru5LNsv1z2qFrqrdGm5PYsIFn/e3KquH9aNS0awA9w8J8fk6eqMb8a0RkiILzIzr4vujk7ulW6IKJUy0lQhdl4DQsSn7e6iwhtgZiwvEFH0VM6bhhHB6stNT/JQbORGRETg008/rfK3n3/+GdHR0Th27BiaNauatzAyMhIrV640ux3eRRKn/LO39DtY/kASzuXcRHxYzYI3+toCqh605P7+9W1fxLjTaJRvt8rnbqRtJftm7tjzdL19ALX1fca4dg1wLrcAMfV8odEA3u7OGNk6zOrtWvy5mDFGpFT5lE3JfYI57SjVF193F1y/VYyEMH/jbcq4H1Hi/Vq7T/72IbGzN61R/T2a+p7N2Y+TceYeFxuHeOH9u6MR4uNW8fwukcFY82Ab1PVzl+U76do0BF0igyVLR6D7mKvleHJwdf2VWSZv7fWGWrSq64vvD14GoPu92Pr7s0dqH3uB3q548o4IuDo5wdNAUerqzHk/GgPPN7SdQbGhePvX9BrPV9tnaWp/avtUvWEo93vRN/YMtino8zXW5jsDWmLh9jP4v95RkvbPlO/A0OOW9uW3R9ujoLgEAV72OSFH7fs9KZn6HoVUstFqtXj//ffx22+/4csvv8SSJUvg6Vl1Zo6Hhwdu3Lhh9raDgx1zWYuaWPodhISY/rrKz3V2cdb5dzlUbddJ59+V4u3toXi7Lma8ZxGfialjLyTEFxO6RCDA001IP6X0woAYWbZr6efi5nb7sKLkZ1s5NYeLq3L7BNf/LlJMGXtKfR5p07sj/1Yxgk2Y9ePh7ipLv1Y+3E729+vi6lylDXf32yevtv67toSp7znsZonZryH9LheWVvxb1+ep61xhoI7n2eJ3UXm/52kHx1OyLbZ+zTeuWzP4+XoiuXFgxW8nMq+o4nH+ntRLzWNvcs8WZj2/XoiPWWPNzc1F7/ONbWdoUhhW7D5X8d++vspfy+ri6Wn++eOApHB8vfdCxX8b+lykVHns+fgY/vz8/TwV/3ydnJ2Mj4MQXwxtJ01aJPdKs9lNGcv6HtdoNBZ/Vo6y9lvN+z2lKR5Ez8vLw7Rp03Dw4EF8+eWXiIqKgqenJ65fr7osqqCgAN7e5i9PvHr1OotUCKLRlP24lPgOKi+jKyku0fl3+dst1fl3peTnFyjebrEZ71nJvlky9sa1KZutrdYlmaJZ+rkUFhZbvQ1LlJTe/uKLi5TbJxQVlrVlythTeqxlFhQafU7BrSJZ+nX92k3Z329xUUmVNm7duh18cITfdfXZEqa+53oeThidHI76AR4O8TnJLTfn9oQPXZ+n6HMFOVQ+5pa7cbPQbt4fqZuS1xty69GkrI5N+W+nkY8rJndujIZBXvw9qZA9jb2XejZD2pkcdG3ob9ZYKywq1vt8Y9uZ2C4c2uISrNx3EQBw/bry17K6xNa+HXMytT/Xrt2s8t+Fhfo/FynoOu7m5Rn+/NxKSxT/fEtLShVt89atYkzv0RR7zuWibX3jqZb0Pa7ValUxFtXInvZ7xpS/V2MUDaKfOXMG48aNQ7169bBy5UoEBZVVbm7WrBm2bt1a5bnp6elo2rSp2W1otaz0LJoS30Hl7Wv1/F3udk35u5xEjPUqn7uRth3lM7FXln6OcfX88Hv6Vau2ISWl+mDK2FPD51GDTL8ZpX6Latoni2b6e9ZgUqfGZr6G9FH7cVFO1d+Pvb0/Ujd7Pecb1SYcgH2+N3thD2OvX6tQ9PuvCLRZ78XAeze2HV93VzzbvWlFEN3T1VkVn2PnJsH4cHArRIZ4W9UfEee9utp8f1ArXL5WgGa1fIRdjytpQExdDIipa1Lbhh5Xw1hUM3vY70lFsWoEubm5eOCBB5CYmIjPPvusIoAOAKmpqcjMzMTixYtRVFSE7du3Y926dRg8eLBS3SMiIgnck1gf07pHYtXYNqK7QkREREREKvJ0tyboE10HHSOCjD9ZARqNBu0aBaGWjRQ/bd84EBoAqXoKeXdoHIRBcfWU7ZQgjpCnm9RHsZnoq1evxoULF/Djjz/ip59+qvLY3r17sXDhQsyYMQMffvghgoKCMH36dLRt21ap7hERkQRcnJ0c5sSNiIiIiIhMNyyhvugu2LT3726FohIt3FwUmw+rOpEh3kjPzK9YuWOpUa3D8MWuc5jStYlEPSNHoFgQfcyYMRgzZozex2NiYrB8+XKlukNEFuEaHiIyztvd2fiTyGrcI6vfvUn1MWPDMXRuEiy6K0RERFYJ9HI1/iQHEFfPT1jbGo0Gbi7qmoJ9V4va+OnQFYxt20CR9r56IEmS7TzWuTHuaxOGIC83SbZHjkHxwqJEtialYQB2nM5By1BWJDbk69FJGL54t+hukAWcnTRVCnOS45D6W38htSky8wsREWx+YXBTjW3bAAu3n8GUrhGytUEklQExoYir74/wQE/RXSEiIrLIW/1bYu0/l/DofzVVHJ2Ls+POAtfl1V5RmNC+IcICbOtcR6PRMIBOZmMQnciIGX1aYP2hK+jZXHfeMSojZ9CM5LV0VCKmrTuEk1k3RHfF5ni4csZ1ZQNj68rexsQOjfBgSgOHXsZKtkOj0aBxsJfobhAREVmsW9MQdGsaIrobqtXEwa+DnTQamwugE1mKV6BERvh7uuKexPo17lL2a1UHABDszbuXZNuahHjjmTsjRXfDKl3+S5VQz99DkfZevqsZmtXyxtPdmENPBAbQiYiIiEg0dxcnjGvfUHQ3iEghnIlOZKFRbcIxMKYufD34Myrn7+GC3IJi0d0gB/RY58aIquOD9o2DMPePE/jn4nVZ2+sbHYq+0aGsCq8io9qE4+fDGejTsrborhA5HC0zghEROZx3B0Tj1Z+O4NVeUaK7IsykTo3hyZWpRA6D0T8iK4gMoCeFBQhrm+yP1sZLFHq4OqN/q1AAwBNdm8DF2Qn9ousI7hUpKaq2D/6Y3AEenKVOCgriajQALHJLROSIukQG49dJ7aDhrBIichAMohPZmN8f64ALuQWIrOXYudeI9AnwdMX0Hs1Ed4ME4EwgUlqItxveH9QK3hx7RETkgBhAJyJHwiA6kY3xcnNmAJ0k5+fhKroLREQ2qUPjINFdICIiIiI9XrnLcVMOkbQYRCciIkTV9hHdBSIiIiIiIiJJbJrUHiezbiCmrq/orpCdYBCdiIiqYE5pIiIiIiIismW+Hi6IrecnuhtkRxgpISIiAMD49g0BANNSmwruif34ZFgs6vm544NBrUR3hexQqJ+H6C4QEREROZwmIV4AgK6RwYJ7QkRK4kx0shkaAFrRnSCDWFjGto1r1xAjEurD14OHBqkkhQfgu3EportBdmpcpwgcu5iLOyJDRHeFiIiIyGF8eV8i8gtL4O/JulJEjoQz0clmfDwsFqG+7ph9d7TorhDZLQbQiWyHp5szXr4rCp2acBYUERERkVJcnJ0YQCdyQIyWkM1ICg/AuvGc0SkSVwIQEREREREREZGj4Ux0IiIiB8UbY0QkBXcWpCYiIiIiO8czXrJ5zMJNREREpLypdzRBdKgv7m8TJrorRERERESyYhCdbN6jnRsDAHo2ryW4J0RERESOY0RifSwemQA/D+aFJSIiIiL7xpzoZPPaNQpC2tTOortBREREREREREREdogz0YmIiIiIiIiIiIiI9GAQnYiIiIiIiIiIiIhIDwbRiUgy7w5oCV93F7x8VzPRXSEiIiIiIiIiIpIEc6ITkWTi6vtj46R2cNJoRHeFiIiIiIiIiIhIEpyJTkSSYgCdiIiIiIiIiIjsCYPoRERERERERERERER6MIhORERERERERERERKQHg+hEZLI7IkMAAHX93AX3hIiIiIiIiIiISBksLEpEJru3dRgaBnkirp6/6K4QEREREREREREpgkF0IjKZi5MGXf6bjU5EdkCrFd0DIiIiIiIiItVjOhciIiIiIiIiIiIiIj0YRCciIiIiIiIiIiIi0oNBdCIiIiIiIiIiIiIiPRhEJyIiIiIiIiIiIiLSg0F0IiIiIiIiIiIiIiI9GEQnIiIiIiIiIiIiItKDQXQiIiIiIiIiIiIiIj0YRCciIiIiIiIiIiIi0oNBdCIiIiIiIiIiIiIiPRhEJyIiIiIiIiIiIiLSg0F0IiIiIiIiIiIiIiI9GEQnIiIiIiIiIiIiItKDQXQiIiIiIiIiIiIiIj0YRCciInIwA2JCAQCj2oQL7gkRERERERGR+rmI7gAREREpa3qPZni6WyTcXXgvnYiIiIiIiMgYXj0TERE5IAbQiYiIiIiIiEzDK2giIiIiIiIiIiIiIj0YRCciIiIiIiIiIiIi0oNBdCIiIiIiIiIiIiIiPRhEJyIiIiIiIiIiIiLSg0F0IiIiIiIiIiIiIiI9GEQnIiIiIiIiIiIiItKDQXQiIiIiIiIiIiIiIj0YRCciIiIiIiIiIiIi0oNBdCIiIiIiIiIiIiIiPRhEJyIiIiIiIiIiIiLSg0F0IiIiIiIiIiIiIiI9XER3QGoajegeOK7yz57fASmNY49E4dgjUTj2SBSOPRKJ449E4dgjUTj2SBRHGnumvkeNVqvVytsVIiIiIiIiIiIiIiLbxHQuRERERERERERERER6MIhORERERERERERERKQHg+hERERERERERERERHowiE5EREREREREREREpAeD6EREREREREREREREejCITkRERERERERERESkB4PoRERERERERERERER6MIhORERERERERERERKQHg+hkUFZWFlJTU7Fjx46Kv/3+++8YOHAgEhIS0L9/f2zYsKHisdLSUsyePRudO3dGUlIShg0bhp07d1Y8npmZiaioKCQkJFT8r1u3boq+J7IN5o49rVaLBQsWoFu3bkhMTMTo0aNx9OjRisdLSkrw1ltvoX379khISMDEiRNx5coVRd8T2Qapxx73e2TM4cOHMWbMGCQnJ6NDhw545plnkJWVBQDYt28fhg4dWjFuVqxYUeW1a9asQWpqKuLj4zFo0CDs3bu34jHu98gYucYe93tkCmvGX7lFixZh1KhRVf7GfR8ZI9fY476PjLF07Gm1Wnz00UcV1xv9+vXDTz/9VPE493tkjFxjz+H2e1oiPXbt2qXt3r27tlmzZtrt27drtVqt9sCBA9ro6GjtN998oy0qKtKmpaVpExISKh5funSptnfv3tpLly5pS0pKtIsWLdLGx8drCwoKtFqtVrtp0ybtHXfcIew9kW2wZOx9/vnn2uTkZO3u3bu1RUVF2iVLlmhTUlK0V69e1Wq1Wu2cOXO0/fr10164cEF7/fp17RNPPKEdN26csPdI6iTH2ON+jwy5efOmtkOHDtoPPvhAe+vWLW1WVpZ23Lhx2gkTJmhzcnK0ycnJ2i+//FJbVFSk/euvv7QJCQnaffv2abVarXb79u3ahIQE7a5du7SFhYXaRYsWaVNSUrQ3btzQarXc75Fhco497vfIGGvGn1ar1ebn52vffPNNbbNmzbT33XdflW1z30eGyDn2uO8jQ6wZe4sWLdJ269ZNm56eri0tLdX++uuv2piYmIrHud8jQ+Qce4623+NMdNJpzZo1eOqppzBlypQqf//xxx+RmJiIoUOHwsXFBa1bt0a/fv3w1VdfAQBOnDiB0tJSlJaWQqvVQqPRwMPDo+L1//zzD1q1aqXoeyHbYunY+/777zFq1CgkJibCxcUFo0aNQmBgYMVd0hUrVmDcuHGoW7cufHx88MILL+CPP/7A2bNnFX+PpE5yjT3u98iQCxcuoHnz5pg0aRLc3NwQGBiI4cOHIy0tDb/88gsCAgIwcuRIuLi4oF27dujXrx+WLl0KoGy/1qdPHyQlJcHV1RWjR49GYGAg1q9fX/E493ukj5xjj/s9Msaa8QcAAwYMQEZGBu65554a2+a+jwyRc+xx30eGWDP2rl27hkmTJqFJkybQaDTo1q0bmjRpgj179gDgfo8Mk3PsOdp+j0F00qljx47YsGEDevfuXeXvJSUl8PLyqvI3JycnnDhxAgAwYsQIFBQUoGvXroiJicH777+PDz/8EO7u7gDKfmCXLl1C37590bZtW4wbNw7p6enKvCmyCZaOPUOPX79+HZcuXUKzZs0qHgsJCYG/vz+OHDki0zshWyPH2AO43yPDIiIi8Omnn8LZ2bnibz///DOio6Nx7NixKvstAIiMjMThw4cBAOnp6Xof536PjJFr7AHc75Fx1ow/APjiiy8wa9YsBAcHV3ke931kjFxjD+C+jwyzZuxNnjwZgwYNqnjs+PHjOHbsGKKjo7nfI6PkGnuA4+33GEQnnWrVqgUXF5caf09NTcWWLVvw888/o7i4GLt378b69etx69YtAEBRURGSk5Px448/Ys+ePXjooYcwefJkZGRkAAD8/PyQlJSEJUuWYOPGjWjUqBHGjBmD69evK/r+SL0sHXs9e/bEF198gUOHDqGoqAhfffUVTp48iVu3biE/Px8AagQ6PTw8Kh4jkmPsAdzvkem0Wi1mz56N3377DS+88ALy8/Ph6elZ5TkeHh64ceMGABh8nPs9MoeUYw/gfo/MY+74A4DQ0FCd2+K+j8wh5dgDuO8j01ky9sqdPHkS48aNQ//+/dGmTRvu98gsUo49wPH2ewyik1kSExPx9ttvY+7cuejQoQM+++wzDBo0CH5+fgCAZ555Bp07d0ZERAQ8PDwwadIk+Pr6VqQ1mDVrFp599lkEBQXBx8cH06ZNQ35+Pnbt2iXybZENMDb2xo4di4EDB2LSpEm44447cOLECXTs2BF+fn4VB4WbN29W2WZBQQG8vb0Vfy9kW6wZewD3e2SavLw8TJ48GevWrcOXX36JqKgoeHp6oqCgoMrzKu+3DD3O/R6ZSuqxB3C/R6azZPwZwn0fmUrqsQdw30emsWbsbdq0CcOHD0ePHj0wY8YMANzvkemkHnuA4+33GEQns+Tk5KBp06ZYt24dduzYgXnz5uHixYsVOZAuXLiAwsLCKq9xcXGBq6sr8vLy8NZbb+H8+fMVj5WUlKC4uLhK3nQiXYyNvcuXL2PIkCHYtGkTtmzZgmeffRaHDx9Gq1at4O/vjzp16lRZVpSRkYGcnJwaS5eIqrNm7HG/R6Y4c+YMBg8ejLy8PKxcuRJRUVEAgGbNmuHYsWNVnpueno6mTZsCAJo2bar3ce73yBRyjD3u98hUlo4/Q7jvI1PIMfa47yNTWDP2PvroI0ydOhUvvvginnvuOWg0GgDc75Fp5Bh7jrjfYxCdzHL69GkMGzYMhw8fRnFxMdavX4/ffvsN9957LwCgW7du+Pjjj3H27FkUFRXh888/R0ZGBu644w74+Pjgr7/+wltvvYXr168jPz8fr732GsLCwtC6dWvB74zUztjY++GHH/DII48gOzsb+fn5mDVrFtzc3NCtWzcAwKBBgyrGZl5eHt544w0kJyejQYMGIt8W2QBrxh73e2RMbm4uHnjgASQmJuKzzz5DUFBQxWOpqanIzMzE4sWLUVRUhO3bt2PdunUYPHgwAGDIkCFYt24dtm/fjqKiIixevBhXr15FamoqAO73yDC5xh73e2QKa8afMdz3kSFyjT3u+8gYa8beokWLsGjRIixduhT9+vWrsW3u98gQucaeI+73NFqtViu6E6RuUVFRWLJkCVJSUgAAX331FRYsWIDs7GxERETgqaeeQrt27QCU5SGcPXs2fvnlF9y8eRNRUVF45plnEBsbCwA4f/483nzzTaSlpaGoqAgpKSmYPn066tevL+z9kXqZM/aKiorwxhtv4KeffkJRURFat26N6dOnIywsrOLxDz74AGvXrkV+fj5SUlLw2muv6SwKRCTl2ON+jwxZtGgRZs6cCU9Pz4pZHeX27t2Lf/75BzNmzMDRo0cRFBSERx55pEpxn++++w4ff/wxLl++jMjISEyfPh1xcXEAuN8jw+Qce9zvkTHWjr9yc+bMwc6dO/HFF19U/I37PjJEzrHHfR8ZYunY02q1aNOmDW7evAk3N7cqr5swYQIefvhh7vfIIDnHnqPt9xhEJyIiIiIiIiIiIiLSg+lciIiIiIiIiIiIiIj0YBCdiIiIiIiIiIiIiEgPBtGJiIiIiIiIiIiIiPRgEJ2IiIiIiIiIiIiISA8G0YmIiIiIiIiIiIiI9GAQnYiIiIiIiIiIiIhIDwbRiYiIiIiIiIiIiIj0YBCdiIiIiMiBnDp1SnQXiIiIiIhsCoPoREREREQ2bs6cORg1apTR523atAkPPvigAj0iIiIiIrIfDKITERERETmInJwcaLVa0d0gIiIiIrIpDKITEREREdmYPXv2YPDgwYiPj8eIESNw7tw5AIBWq8X//vc/9OvXD61bt0abNm0wdepUFBQUYMeOHXj55Zdx4cIFJCQk4PLlyygsLMQHH3yAO++8E8nJyRg3bhxOnz4t+N0REREREakLg+hERERERDYkOzsbEyZMQM+ePZGWloann34aGzduBAD8+OOPWLJkCebMmYNdu3Zh+fLl2LJlC9atW4eUlBS8+uqrqFevHvbu3Ys6depg9uzZ2Lx5MxYvXow///wTcXFxGDt2LG7duiX4XRIRERERqQeD6ERERERENmTz5s3w9PTEuHHj4OrqiqSkJAwePBgA0LlzZ6xcuRKNGjVCVlYWsrOzERAQgMuXL9fYjlarxfLly/Hkk08iPDwc7u7umDRpEoqKirB582aF3xURERERkXq5iO4AERERERGZ7vLly6hbty40Gk3F3xo0aIBDhw5Bq9Vi9uzZ+O233xAUFIQWLVqgqKhIZx70rKws3LhxA48//jicnG7PrSkqKsL58+cVeS9ERERERLaAQXQiIiIiIhsSGhqK8+fPo7S0tCL4fenSJQDAu+++iwsXLmDTpk3w8fEBAPTr10/ndgIDA+Hu7o6FCxciPj6+4u8nTpxAnTp15H0TREREREQ2hOlciIiIiIhsSLdu3aDVajFnzhwUFhbiwIEDWLFiBQAgLy8P7u7ucHZ2xq1bt7Bw4UIcPXoURUVFAAB3d3fcvHkTxcXFcHJywpAhQzBr1ixcunQJpaWlWLNmDfr27cviokRERERElWi0utZ2EhERERGRah0+fBivvPIKDh8+jIYNGyIuLg4nT57EG2+8gWnTpuHgwYPw8vJCUlISPDw8cO3aNXzyySe4cuUKxo4di/Pnz2P58uVo1KgR5syZg/Xr1yMnJwfh4eF47LHH0L17d9FvkYiIiIhINRhEJyIiIiIiIiIiIiLSg+lciIiIiIiIiIiIiIj0YBCdiIiIiIiIiIiIiEgPBtGJiIiIiIiIiIiIiPRgEJ2IiIiIiIiIiIiISA8G0YmIiIiIiIiIiIiI9GAQnYiIiIiIiIiIiIhIDwbRiYiIiIiIiIiIiIj0YBCdiIiIiIiIiIiIiEgPBtGJiIiIiIiIiIiIiPRgEJ2IiIiIiIiIiIiISA8G0YmIiIiIiIiIiIiI9GAQnYiIiIiIiIiIiIhIDwbRiYiIiIiIiIiIiIj0YBCdiIiIiIiIiIiIiEgPBtGJiIiIiIiIiIiIiPRgEJ2IiIiIiIiIiIiISA8G0YmIiIiIiIiIiIiI9GAQnYiIiIhMNmrUKERFRVX5X/PmzZGUlIShQ4fihx9+kKSdc+fOISoqCqtXrwYA7NixA1FRUdixY4ck2y/XrVs3PPfcc5Ju01rnz5/HCy+8gC5duqBVq1Zo27YtJkyYgO3bt5u9rdWrVyMqKgrnzp2ToafmOX78OF577TX07NkTcXFxSEpKwvDhw7F06VIUFRVVeW5UVBTmzJkjqKe6VR4r1ccnEREREdk3F9EdICIiIiLb0rJlS7z88ssV/11SUoJLly5h8eLFePLJJ+Hr64vOnTtL2mZ0dDS+/vprREZGSrrduXPnwsfHR9JtWiMjIwPDhw9H7dq1MWXKFNSrVw9ZWVlYsWIFRo8ejQ8//BA9evQQ3U2zrV+/HtOmTUNERATGjBmDxo0bo6CgAL///jvefPNN/PHHH/jkk0+g0WgAAF9//TVCQ0MF91q/2rVr4+uvv0aDBg1Ed4WIiIiIFMAgOhERERGZxcfHB/Hx8TX+3qVLF7Rr1w6rVq2SPIiur01rtWzZUvJtWuObb77BtWvX8OOPP8LX17fi76mpqRg6dCg++OADmwuiHz9+HNOmTUP79u0xZ84cuLjcvgTp0qULUlJSMHnyZPzwww/o27cvAMjyXUvJzc1N9X0kIiIiIukwnQsRERERScLNzQ2urq5V/lZQUIBZs2ahR48eaNWqFRITEzFmzBgcOnSoyvN++eUX9O/fH7Gxsbj77rtx+PDhKo/rSueyceNG3HvvvUhISECrVq1w11134csvv6x4XFfqmfL/lafhqJ7O5fr163jzzTfRvXt3xMTEoG/fvli5cmWVvnTr1g0ffvgh3nrrLbRv3x6xsbF48MEHcfLkySrP27VrF+677z7ExcUhOTkZzz77LLKysgx+hpmZmdBoNCgtLa3yd2dnZ0ydOhXDhg2r8vetW7fi3nvvRVJSElJSUjB16lRcvHjRYBsrVqzAoEGDEB8fj9jYWAwYMADr16+veHz16tVo2bIlVqxYgY4dO6Jz5844duxYxWc+aNAgxMTEoEOHDnj99ddx48YNg+19+umncHJywuuvv14lgF6uZ8+eGDhwIJycbl+aVE/ncuXKFUybNg1dunRBbGwshgwZgl9//bXKdqKiojB37lwMHjwYSUlJmDdvXsV72bdvH4YPH46YmBh07doVCxYsqPLac+fO4ZlnnkHHjh0RHR2Ndu3a4ZlnnkF2drbO96QrncuFCxfw5JNPIjk5GXFxcXjggQfw77//1njNokWL0KtXLyQnJ1e83thYJiIiIiKxOBOdiIiIiMyi1WpRXFxc8d/l6Vw++ugj5OfnY8CAARWPPfPMM0hLS8PUqVPRoEEDnDp1Ch988AGmTJmCH3/8ERqNBps2bcLkyZPRp08fPPXUUzh8+DCefvppg33YvHkzJk2ahPvvvx+PPfYYCgoK8OWXX+K1115Dy5YtkZiYiJdffhl5eXkVrykuLsZzzz2HoqIinTPlCwoKcO+99yIzMxOPPfYYwsPDsXHjRrzwwgvIzMzEww8/XPHcJUuWICkpCW+++SZyc3MxY8YMPPfcc/j6668BAGlpaRgzZgzatm2L999/H7m5ufjggw9w//33Y+XKlfDw8ND5vrp27Yply5Zh2LBhGDZsGNq1a4eoqCg4OzujQ4cO6NChQ8Vzv/vuOzzzzDPo3bs3JkyYgOzsbHz44YcYPnw41qxZg+Dg4BrbX7p0KV5//XU8+uijePbZZ5GTk4MFCxbg6aefRnx8POrVq1fxnX7yySd4/fXXkZWVhcjISKxbtw5PPfUU+vXrhyeeeALnz5/H7NmzkZ6ejkWLFlWkYqnu119/Rdu2bXX2p9xbb72l97HMzEwMGTIErq6umDJlCgIDA7F69WpMmjQJb7/9Nvr371/x3I8//hiPP/44oqKiEBoaioMHD6K0tBRPPPEERo8ejSeeeAIrV67Eu+++i+bNm6NTp064efMm7r//fgQGBuLll1+Gr68vdu/ejY8++gju7u547bXX9PatXFZWFkaMGAFPT0+8+OKL8PT0xOeff46RI0di5cqVaNKkScVzZ8+ejZdeegl+fn5o1aqVSWOZiIiIiMRiEJ2IiIiIzJKWlobo6Ogqf9NoNGjWrBk++OADdOvWDQBQWFiI/Px8vPjii+jduzcAIDk5Gfn5+Zg5cyYyMjJQu3ZtfPTRR4iOjsasWbMAoCLAXf7fuqSnp2PgwIF44YUXKv6WkJCAlJQUpKWlITExsUb+9FdeeQUZGRlYunQpQkJCamxz9erVOHr0KJYtW4akpCQAQKdOnVBcXIx58+ZhxIgRCAgIAAD4+flh3rx5cHZ2BgCcOXMGc+bMQXZ2NgIDAzFr1iw0btwY8+fPr3hOXFwc+vTpg1WrVmHkyJE631eXLl3w0ksv4b333sPbb78NoCyVTbt27TBixAh07NgRAFBaWop33nkH7du3x+zZsyten5iYiN69e2PhwoU6b0ScPXsWY8eOxaRJkyr+FhYWhkGDBmHPnj0VQXQAePjhh9G1a1cAZTdO3n33XXTq1AnvvvtuxXMaNWqE0aNH4/fff694bmW5ubnIzc1Fo0aNajxW+UYMUDaGyj+ryhYtWoSsrCz8+OOPCA8Pr/icRo8ejbfffht9+/atmMUeGxuL8ePHV7z24MGD0Gq1eOSRRzB06FAAQFJSEjZs2IDNmzejU6dOOHXqFEJDQzFz5syKHOdt27bFP//8g507d9bojy6ff/45cnJy8NVXX6F+/foAysZx79698cEHH+DDDz+seG6PHj0wZMiQiv9ev3690bFMRERERGIxiE5EREREZomOjsarr74KALh8+TI++OADFBUVYfbs2VVm3Lq5ueGzzz4DUJaO4/Tp0zhx4gR+++03AEBRUREKCgpw8OBBTJ48uUobvXr1MhhEf+ihhwAAN27cwJkzZ3Dy5En8888/FdutbtmyZfjqq68wa9YstGrVSuc2d+7cifr161cE0Mv1798fK1euxL59+9ClSxcAQExMTJWAb3kRzJs3b8LDwwP79u3Dgw8+WGXWfnh4OJo0aYKtW7fqDaIDwMiRIzFo0CBs2bIF27Ztw86dO7FhwwZs2LABY8aMwXPPPYeTJ08iIyMDTz75ZJXXNmjQAAkJCVXS3lRWnrrm+vXrOHXqFE6dOoVt27bp/NyaNWtW8e8TJ07g0qVLmDBhQpXgd5s2beDj44OtW7fqDKJXT0tT7vTp0zVyu9evXx+bNm2q8dydO3ciISGhIoBern///pg2bRpOnDhRccOkcp8rS0hIqPi3m5sbgoKCKtLQtGjRAsuWLUNpaSnOnj2LU6dO4dixYzhx4kSNQL8+27ZtQ4sWLVCnTp2K1zg5OaFz585Yu3ZtledW76O5Y5mIiIiIlMcgOhERERGZxdvbGzExMQDKgskJCQkYMGAAxo4dizVr1iAoKKjiuX/++SfeeOMNnDhxAt7e3oiKioK3tzeAstnNubm50Gq1VV4DALVr1zbYh6ysLLz88svYuHEjNBoNGjZsWBH81mq1VZ67Y8cOzJgxA+PHj68oXKlLbm6uzhnq5X+7du1axd88PT2rPKd8JnRpaSmuXbuG0tJSLFiwoEbubQBwd3c3+N7Kt5+amorU1FQAZUHnF154AYsWLcKgQYNw/fr1Kn2r3t/KubgrO3PmDF566SVs374dLi4uiIiIQFRUFICan1vl9Cs5OTkAgFdffbXiBkplV65c0dleYGAgvLy8cP78+Sp/r1u3bpVc8x999BGOHj2qcxu5ubkICwur8Xdd34uuzwNAjfQ5Tk5OVd7vokWLMH/+fGRnZyMkJATR0dHw9PSs+JyNycnJwenTp2us0Ch38+ZNvX00ZywTERERkRgMohMRERGRVYKDg/HSSy/hsccew4wZMypmkJ85cwaTJk3CnXfeifnz51ekyli6dCn+/PNPAEBAQACcnJyQmZlZZZvlQVt9nnrqKRw/fhyLFi1CYmIi3NzccPPmTaxYsaLK886ePYvJkyejY8eOmDJlisFt+vv74/Tp0zX+npGRAaAsIGwKb29vaDQajB49Gn369KnxePUAfLmSkhKkpqZi4MCBNWbmN2zYEC+88AIGDhyI9PT0isB39c+tvL+6+lpaWorx48fD1dUV33zzDVq2bAkXFxekp6fXmC1dnZ+fH4CyHPfJyck1Hvf399f72jvvvBObNm1CXl4efHx8AJTNBi+/EQOgIk2OLv7+/nrfJ2D696LPunXrMHPmTEydOhVDhgypuKHz+OOPV8wIN8bX1xfJycl45plndD7u5uam97WmjmUiIiIiEsdJdAeIiIiIyPb16NEDnTp1wvfff1+RSuTAgQO4desWJkyYUBFAB1ARQNdqtXB3d0dCQgJ++eWXKrNudaX1qGz37t3o2bMn2rZtWxGg/OOPPwDcTiGSl5eHiRMnIigoCLNmzaqYLa5PmzZtcP78eezevbvK39euXQtXV1fExsaa8lHAx8cHLVu2xIkTJxATE1Pxv6ZNm2Lu3Ll6U604Ozujdu3aWLVqFbKzs2s8fvLkSQBl6UAaN26MWrVqYd26dVWec/bsWfz9998682hnZ2fj5MmTGDJkCGJjY+HiUjafpvrnpktERASCg4Nx7ty5Ku8pNDQUs2bN0jvzHQAmTJiAkpISPP/88ygsLKzxeEFBAc6ePav39W3atMHevXtrPGft2rWoVasWGjZsqPe1pti9ezd8fX0xfvz4igB6fn4+du/ebfAzqSw5ORknT55E48aNq3w+a9euxYoVK3Tmeq/cvrGxTERERERicSY6EREREUni+eefR//+/fH6669jzZo1iI6OhouLC9555x2MHTsWhYWFWL16NTZv3gwAFTmpn3zySTzwwAN49NFHMXz4cJw6dQoff/yxwbZiY2Oxbt06REdHIzQ0FHv37sX8+fOh0WgqUmc89dRTOHv2LN577z2cOHGiSkAyKCioSmAfAAYNGoRly5bh0UcfxeTJkxEeHo5NmzZh1apVePTRRytmY5viySefxPjx4zF16lT0798fJSUlWLhwIfbt24eJEyfqfd306dMxatQoDBo0CPfffz9atGiB0tJSpKWlYfHixRgxYkRF/u8nn3wS06ZNw5QpUzBw4EBkZ2dj7ty58Pf3x5gxY2psOzg4GPXr18fSpUsRGhoKPz8/bNmyBZ9//jmAqilHqnN2dsaUKVPw0ksvwdnZGXfccQeuXbuGefPm4fLly3rTmABA06ZNMWvWLDz77LMYOHAghg0bhqioKBQXF2Pv3r1YuXIlMjMzK3KDVzdmzBisXbsWY8aMwaOPPorAwEB8++232L59O9544w2jN0eMiY2NxVdffYWZM2fijjvuwJUrV/DZZ58hMzPT4Az7ykaPHo3vvvsOo0ePxtixYxEYGIj169fjm2++wbRp04y2b2wsExEREZFYDKITERERkSQiIiIwatQoLFy4EF9++SVGjx6NWbNmYe7cuZg4cSL8/f0RHx+PL774AqNGjcKuXbsQFRWF1q1bY8GCBXjvvffw6KOPIiwsDG+88QYefvhhvW3NnDkTr732Gl577TUAQKNGjfDqq69i7dq12LVrFwBUFDB95JFHarz+7rvvxsyZM6v8zdPTE1988QVmzZqFDz/8EHl5eYiIiMCMGTMwZMgQsz6Ljh074rPPPsPcuXMxefJkuLq6Ijo6GosWLUJ8fLze17Vq1Qrffvst5s+fjy+//BIZGRlwdnZGZGQknn/++Sr9GDRoELy9vTF//nxMmjQJPj4+6NSpE5588knUqlVL5/bnzZuHGTNm4LnnnoObmxsiIyPx8ccf44033sCuXbswatQovX0bOnQovL298emnn+Lrr7+Gl5cXEhMT8e6779Yo+lld9+7dsXbtWnz11VdYuXIlzp8/D61Wi/DwcPTu3RsjRoxAo0aNdL62Vq1aFUVhZ8yYgaKiIjRv3hzz5s3DnXfeabBdU9x99904d+4cVq1ahWXLlqFOnTro0qUL7r33Xrz44otIT0+vuHGhT506dbB8+XLMmjULr7zyCm7duoVGjRqZNHZMGctEREREJJZGy2o1REREREREREREREQ6MSc6EREREREREREREZEeDKITEREREREREREREenBIDoRERERERERERERkR4MohMRERERERERERER6cEgOhERERERERERERGRHgyiExERERERERERERHpwSA6EREREREREREREZEeDKITEREREREREREREenhIroDUrt69Tq0WtG9cEwaDRAc7MvvgBTHsUeicOyRKBx7JArHHonE8UeicOyRKBx7JIojjb3y92qM3QXRtVrY/ZerdvwOSBSOPRKFY49E4dgjUTj2SCSOPxKFY49E4dgjUTj2bmM6FyIiIiIiIiIiIiIiPRhEJyIiIiIiIiIiIiLSg0F0IiIiIiIiIiIiIiI9GEQnIiIiIiIiIiIiItKDQXQiIiIiIiIiIiIiIj0YRCciIiIiIiIiIiIi0oNBdCIiIiIiIiIiIiIiPRhEJyIiIiIiIiIiIiLSg0F0IiIiIiIiIiIiIiI9GEQnIiIiIiIiIiIiItKDQXQiIiIiIiIiIiIiIj0YRCciIiIiIiIiIiKyYTcKSzB88S588PsJ0V2xSwyiExEREREREREREdmovFvF6DJnK05cvYEvd50T3R27xCA6ERERERERERERkY1atpuBc7kxiE5ERERERERERERkowpLtKK7YPcYRCciIiIiIiIiIiIi0oNBdCIiIiIiIiIiIiIiPRhEJyIiIiIiIiIistK0dYcwdtlelJQytQaJlXOjCOsOXMKNwhLRXbEbLqI7QEREREREREREZOs2Hs0AABy6fB2t6voJ7g05ssmr/8Ghy3nYfTYHr/RqLro7doEz0YmIiIiIiIiIiIjsxKHLeQCAjUczBffEfjCITkRERERERERERESkB4PoRERERERERERERER6MIhORERERERERERERKQHg+hERERERERERERERHowiE5EREREREREREREpAeD6EREREREREREREREejCITkRERERERERERGSjtFrRPbB/DKITERERERERERFZQcsoJpFdYxCdiIiIiIiIiIhs3umsG1i2+xxuFZcq3vbIL/Yo3iYRKcdFdAeIiIiIiIiIiIisNWTRLgBA7s0iTOzYWLF2i0u1OJaRr1h7RKQ8zkQnIiIiIiIiIiK7se/CNUXbu1VcUuW/T2fdVLR9oiVpZ0V3we4xiE5ERERERERERCSRV346IroLRCQxBtGJiIiIiIiIiIiIbMicP07i0ZX7UVzKorZKYE50IiIiIiIiIiIiIhtSnsJl28kswT1xDJyJTkREREREREREdqVUy9m55Bg4E10ZDKITEREREREREZHd2H/hGrrN/Qtr9l8U3RUishMMohMRERERERERkd0oKtEiv7AEb2w4JrorRKpx6VoBZ61bweIgelZWFlJTU7Fjx46Kv+3btw9Dhw5FQkICunXrhhUrVlR5zZo1a5Camor4+HgMGjQIe/furXispKQEb731Ftq3b4+EhARMnDgRV65csbR7RERERERERERERA5v15kc9FuwE498s090V2yWRUH03bt3Y/jw4Thz5kzF33JzczF+/HgMHDgQaWlpmDFjBt58803s378fALBjxw689tprmDlzJtLS0tC/f39MnDgRN2/eBAB8/PHH2Lp1K1atWoU///wTHh4emD59ugRvkYiIiIiIiIiIiEgZl64V4N1N6TibfVN0VwAAq/9LbbT3/DXBPbFdZgfR16xZg6eeegpTpkyp8vdffvkFAQEBGDlyJFxcXNCuXTv069cPS5cuBQCsWLECffr0QVJSElxdXTF69GgEBgZi/fr1FY+PGzcOdevWhY+PD1544QX88ccfOHv2rARvk4iIiIiIiMixFJdqseCv0/j7XK7orhARKWbZ7nP44/hVoX14+rt/8fXeC3ho+d9C+0HScTH3BR07dkS/fv3g4uJSJZB+7NgxNGvWrMpzIyMjsXLlSgBAeno6Bg8eXOPxw4cP4/r167h06VKV14eEhMDf3x9HjhxBeHi4yf3TaMx9RySV8s+e3wEpjWOPROHYI1E49kgUjj0SiePPfN/uv4j/bTuN/207jV1PdRbdHZvFsWfblPjedLXx06HL6NWyjiTb5dgz3f4L1zB78wkAELrfO3wlDwCQdaNI9u/PWAHd6u2b0h9HGnumvkezg+i1atXS+ff8/Hx4enpW+ZuHhwdu3Lhh9PH8/HwAgJeXV43Hyx8zVXCwr1nPJ+nxOyBROPZIFI49EoVjj0Th2CORbG38FRSVIOdGEUL9PRRrc8m2U/hh/0U0CLp9jV3k6oK6/p4GXkXG2NrYozIhIfJ/bx63imv87cX1RzCqc6Qk2zdn7J3KzEegtxv8PV0ladvW3LqYV/FvJb57UwQH++Do5Tw0DPaCh6uz5Nvfdipb72MaTdnn4O5+OwRszufC/d5tZgfR9fH09MT169er/K2goADe3t4VjxcUFNR4PDAwsCK4Xp4fXdfrTXX16nVoWWhWCI2m7MfF74CUxrFHonDskSgceyQKxx6JZMn4y7lZBB93F7g4iZtKN2DBTpzPLcDXo5PQJMS861tLvfTdQQDAjpNZFX97cFEaltyXoEj7ebeKcSwjH7V83PDL4QwMja8HXw/Jwg+Ks3TfdyrrBpbsPIuxbRsgLIA3METJyLgGjczTafN0BNEBIDPzus6/m8rcsXcm+yYGfZYGZw2wY6pjrj65dv12bNHaz18qjaeVpbKOreeHhffGK9q2Vlv2OdyqNEZN+Vwc6Zyv/L0aI9lRrFmzZti6dWuVv6Wnp6Np06YAgKZNm+LYsWM1Hu/cuTP8/f1Rp04dpKenV6R0ycjIQE5OTo0UMcZotbD7L1ft+B2QKBx7JArHHonCsUeicOyRSKaOv3M5N3H3Z2mIDPHGVw8kyd8xPc7nlk0m++1YJiKClQmi6/LvJeUCIV3n/FXlv/edv4b3B7VSpnEZmTr2cm4WYc4fJ7D2wGUAwJ5zuVjzYLLMvSN9dpzOQUrDQFnb0DcutFrg6e8OIu9WMeYNjbU4mG/q2Nt1JgcAUKIF/u+nI+jdsg6SwgMsatNmVfqc1Hausv/CNSF9qt6mOX3gOd9tZhcW1Sc1NRWZmZlYvHgxioqKsH37dqxbt64iD/qQIUOwbt06bN++HUVFRVi8eDGuXr2K1NRUAMCgQYPw8ccf4+zZs8jLy8Mbb7yB5ORkNGjQQKouEhERERERkYP49WgmACA907wUoVLJuVGE345lCmlbbbaezMKfChf5u3z9Fv44fhVarRZahSNAszcfrwigA8C5nAIDzya55euZJS6VawVFeh8rLtVic/pV7Dqbi9PZN/U+Tw5rD1zGw9/sV7RNInsm2Uz0wMBALFy4EDNmzMCHH36IoKAgTJ8+HW3btgUAtGvXDi+//DJeeeUVXL58GZGRkViwYAECAgIAAJMmTUJxcTFGjhyJ/Px8pKSk4P3335eqe0RERERERESKeXD53zijcNDMmPX/XkbXyBB4uUmfk9eYJ789iDSF0ksUFpei7/92AABe7NkMn+88ixZ1fPB6nxaKtH8sQ8yNG9JNzlsoH/5+Al/sOocXe5iQRYGzee1WQVEJZm4sy77xSq/mgntTlQPUBVWMVUH0I0eOVPnvmJgYLF++XO/zBwwYgAEDBuh8zNXVFU899RSeeuopa7pEREREREREVEVxqRY5NwoR4uOuWJvVA+ilKgigvfzjEfRsniVrMPnSNbGzrotLtejwwZaK/37t56MAyr4PkUH03WdzHC+thko8t+4Q0qbWkmXbX+w6BwB4b/NxWbZvi4pLSuHiLFniC9VbsvMs5vx5suK/n+jSBAFejlnU1d45zqgmIiIiIiIihzRu+d/oNX8HDl5SpshcZn5hjb99uu20Im3vOJVt8PGfD2fI2v74r/fpfWzvuVxZ2waAM9k3ZG/DEocv54nuApHsvkg7i3bvb8H8radEd0UxlQPoAKBV2ZKDguJS0V2wGwyiExERERERkd0pKCqp+PeBi2XB8+8PXJK93ZJSLXp9sr3G30u1wMdbT6FExinpBUUleHTVP7Jt3xQXr93S+9jZHHWltyEi6fx76To+/KMsoPzp9jOCe0MkPQbRiYiIiIiIyO6ICuJ8+89FvY8t3H4GbWf/KVvbhSWccXjokv4Z3/8qtBJBlw1H5F0BQI7tan4h1h24JDQf/9aTWcLarizvVonxJxFZgEF0IiIiIiIicggr913E7rM5srYxc2O6rNu3ZXJ/9gDw2Xb9aXMeWLpX9vaPXtEdxD946TrybhXL3j6pjFaZ1B7jlv+N//v5KFb8fUGR9tSquFSL1345KrobqsQCo9ZjEJ2IiIiIiIgcxsPf7BfdBdwscsyZkuv/vSK6C/jh4GVZtz/yiz16H7tj7l+4VlAka/ukLi/8cLji3+9tPq6zXoIUzuaILeibc7MI//tLmboPulwvKMafx6/y90WyYhCdiIiIiIiI7MoRlRdxLC5RV+E5R7J09zmh7Zfn5yfHsOlYZsW/t53KxovrDxt4tu1atENsDvRJK/fjyW8PYtIKsTUhyL4xiE5ERERERER25b4v9c8GBoB953MV6ok6/Z6eafxJMnl/8wlhbavBZwrm6tdqtZyZ+59imesFmJq15ZDAvPxyul5QM1VRm1l/YMnOsyhSoFbDof9unKZnissJT/aPQXQiIiIiIiKyG3vO5Rh9zkPL98nfERV76rt/hbUt50xwrVZrNK1FfqF8qXQuX79l9Dn7L1zD6awbsvWhsvd/P4E7P9qG346JuWmSnpGPdQcuQatQXnBDZv4qb62CGw6aoqncIT2rf+b8eRLL95yXte2cm+q7UaSGMU/SYxCdiIiIiIiI7Ma+89dMet6FXHE5hB9d9Q+2nLgqrH0AOHRZ3IzY1fvkKX44Y8Mxo8+Rs7jnd/9cNOl5SgVcl+0uC16+/7uY2f/3LNmN//v5KJLf+xPncm7K3t7Fa/p/09/9cwmlDhrY3H02B3+dzBLW/g//XsaPhy7LFlhOnbdN72PDFu3ClDUHKr77vFvFyLohXV56XTdtL+QWIPm9PyVrg9SDQXQiIiIiIiKyG+sOXDLpebeK5U8xoM+/l65jypqDkm/3hhmzrEXmDn5zozyzgr/7x/h3L2dR1yIHznWv1Wrxzd7zSDuTDQA4WC1tyd2fpcneB2NFg09nyR/IV6OHv9mPx1cfEDZj+3jmDby0/gi2Cgjkn8y6gS0nsvBHetlNyzvm/oWeH29H9o1CzNx4DL+nW3cz84nVB2r8bfSyv63aJqkXg+hERERERERkF87l3DSazqOcFvYX8Oy3YKfJz70u44xsNSsq0eLUVenTqZRqtTiaoe6CtnLacy4X72w6jkdW/INjGXkYvXSv4n2Qc5WBPbimI2+5ko5cEff7yKp2A+HdTcexat9FPPWddTczS+3vMEIGMIhOREREREREdiH7hukzLbefypaxJ6RmQxfvknybc/44ib9OqndMlZRqZc3TfL5SeqR7l+gu7LvWhJUCRHJY9XfVFFJX8ozXLyCqjkF0IiIiIiIicjg/HboiugtkR77cZXrB1FyF02oUlZRi0Gc78ehKcSl8AOC1X44Kbf+CgZzpStNqtShWeBrz+VzHTGcDAEcz8nFShhUollD6eyfpMIhOREREREREDufQZcdNvUHAj4cuC2t7s5V5mI3ZcuIqNhzJqPjvjLxCXLh2CzvP5EhaVNHW6MpfLcojK/aj3/92KFqb4UkZ6jAAZfUl0jPzZdm2lIbJsALFku+v3ew/8cthZW/i5t4swlkFivvaOwbRiYiIiIiIiMihvLT+iLC2V+27KNu2S7VaTFlzEM9/f0jn4wv+Oi1b26aQM6WMLdl1NheZ+YX4+3yuYm3KNQN61b4Lxp9EVbzww2FF2+s+bxtvHEuAQXQiIiIiIiIiK2m1Wqz4W1ww6UZhibC2ST2MxahFF5T999J12bYtunCmo7qar2x6IrUoKOI+19EwiE5EREREREQOafupLMm29duxTLz9a7pk2zNHfmExuszZKqTtysyZ6bpmv7SzsS0JaNnTrOiMvFt46Ku/8bORNBG5ggPNebccO/BoR0OuwtaT8qYnkkNhifVfxG/pmRL0RIyNldI9kekYRCciIiIiIiK7kFtg3ozI+RKmtjhuQdG6mxLNZDyskmX68/48afJz39hwTNK2LSmaebNIuXzUcvvg9xPYd+EaXv5RXJoaU/ykcC7o6jh7WHrHM9VRsNMcRSXW//Zt+YbIND3pnsgwBtGJiIiIiIjILkyRqXCeXORMbSHCF7vOCWt734VrwtpWA9FpWs7nFpj0vO8PSlvQNfdmEXafzUGpiRHNp747iNFL9+LiNdP6a6+kCCIDwOXrtyTZjqXyBI97ciwMohMREREREREJIHVA0VxDF6WZHHw0hjN8zZN1o1DS7f11Mtuk58mVO3/h9jMmP/fgRWlueBSXatF93jY8/M1+fH/AtN/SjtM5OHjpOvov2ClJH2zR5zvPov37W7DrTI7V2zqbfdP6DlnB0psBxzLyJe4JOQIG0YmIiIiIiMghiZ5FKTqIfirrJradMi34aszyPecl2Y6Ssm9KG8g2x+CFaULa3a+CGfunJQq8Xqg0+/2Hf8X+lkx1QwU3m+b+l3bpjQ1Hrd5WUanpQWw50p9sPGq7ecnJ9jCITkRERERERA4pI68Qp7NsL5+vlG5JFNT7aMspSbajpKe/+1eS7Vy3oFinyAKbolNg2GIObSldzZf25s2Ph0y/gWBO8V9T3JRpZYMptFqtsGLO5JgYRCciIiIiIiKHNWTRLkm28+fxq5Jsx5GcsqAYq5SkSulgaYFYraDKhHfM/QuXBOYEX5J2VljbanAuR9oUKC+tN72Y7NYTWZK2LdJv6ba9z2UKLNvDIDoRERERERGRlQ5dzrPodVLlJLdF09cfFt0FoU4IvImw4UiGZNty5DFsia/3XhDW9rZT9hNEt/XCzBl54tJJkWUYRCciIiIiIiISZO0/l0R3gaz0u4WrEPacy5W4J6ZLk6CoZLkNh6ULyFvKlsL4JZVTqijc8VX7Lkq6vYvXxNaVEKlyTn5bVCJxah9HwCA6ERERERER2bwbAnPzWmOLBOkVRL93S9NTSJXOwNJ0KlKxNC+zVIVlLclzLVVBWQC4IDA1TLm9Am9IOLL3fz9h8nPn/3UaBy+KL2wrlXxB+91rBUX4bPtpq9MC7Tgt3T7AUTCITkRERERERDZv8MI00V0QxppigfsuWB/Uenz1AYteJ1UakDPZ0uaYVopU6Si+cPAc47Zm07FM0V0QZuxXf0u2LdEzqUVlMZq5MR2fbD2N+77YY9V2iko4E91cDKITERERERGRTSsoKkFmvrj8skUlpcLaBqzLCCHFjGRLg+Fnc6SZwfzWRstmgtuLeVtOie6C2a4XFIvugl3YJWFaHiVIGfc+lSW2MLEo5asuRM2Ed2QMohMREREREZFNs2YmthRW/C2uUKCtuyRBKpDMfMfNy2yrlu85L7oLqvDKT0esWkmx8aj4fPREjoJBdCIiIiIiIiIrWBMEO3RZmpQetqqQKQUc0v+2nRbdBVXIzC/EYyv3W/x60fUQiBwJg+hERERERERk0w5eFBuItiY1xZU8cWlo7MXFa+JmoufeLBLWtqPTWpmU2trXS+WCFeP3x0NXJOwJERnCIDoRERERERHZtNm/HxfdBZulEdz+icx8wT2wznubOfZsleg0UNayNq+8RiP6109kWxhEJyIiIiIiIpt2PNMxC8xJQXQ6iKfX/iu0fWvlcCa6zfo9/aroLlhFZDFla6Rn5CPvFgvLku1hEJ2IiIiIiIjIClk2HEi9dJ1FOckx3SouFd0Fh3TPkt0Y+OlOq7ejkmw8FrPx7jskBtGJiIiIiIiIrLDrTI7oLhCZ7apEM5nnbTll0evUkpOclJdrZSoae3DpWgFKbDylkKNhEJ2IiIiIiIiIbNIvh8UWVvzrZLZVrxdZmPTP42LTmbz/+wmh7RNZo6jEupUMk1b+gyfWHJCoN5ZgAN9cDKITERERERERCWRtMEZ0eUCR7R+5YtuFSXvN327V662ZyTpjwzGr2rbWst3nhbb/yk9HUFAktiaALdt9Nkdo+1rBQeBv/r5g9Ta2n7LuJhwpi0F0IiIiIiIiIoEuXrMuL3mhlbmdD166bvFrtVotzuYUWNW+NaxNCXI8U2wQvqjEuv4zHYR1vvvnksWvvVZgu7UQpPDwN/tFd0GYYoG/O43ou6YOjEF0IiIiIiIiIoHe2ZRu8WsvXSvA9PWHrWr/4EXLg+ins29a1ba1lu0+Z9XrP912RqKekC0qtGIViOhaCKeybght35FlSVRPwBLZNxz75o1IDKITERERERERCWTNkv41+y9K2BPzWZuKxlpWTuS2eTeZjsRhPbP2X9FdsMpz6/7FDwcvi+6GzRE5C97RMYhOREREREREZKOyBRamlMrec7nC2t54NENY21IQ3f+ZG8XmVRfJ1n97Z7JvYssJccVlfz2aiVd+OiKsfSJzMYhOREREREREZKPsYWn/x1tOCm3/jOCUNNawMiW81VbtE7cSQvR83GsFxYJ7YL0paw7iqsDUJGSeC7ni6k+QxEH0gwcPYuTIkWjdujU6duyI119/HYWFZT/Gffv2YejQoUhISEC3bt2wYsWKKq9ds2YNUlNTER8fj0GDBmHv3r1Sdo2IiIiIiIhIp3+tKKzp6PZfuGb1NkRnJ/h6z3mxHbBC3i3bD+TaKtE3MKRi6zPqHclT3x0U3QWHJlkQvbS0FBMmTEDPnj2xc+dOrFy5Elu2bMGCBQuQm5uL8ePHY+DAgUhLS8OMGTPw5ptvYv/+skq+O3bswGuvvYaZM2ciLS0N/fv3x8SJE3Hzpu3eDSYiIiIiIiLbcN2KQGR+oWMHMWdutLwoqlqU2HA09KMtp0R3QZhzOWJjRmsPXBLavqNLz8gX3QXFOeJ7VhPJgui5ubnIyMhAaWkptP8dgJycnODp6YlffvkFAQEBGDlyJFxcXNCuXTv069cPS5cuBQCsWLECffr0QVJSElxdXTF69GgEBgZi/fr1UnWPiIiIiIiISKc3N1ie1/l/f52WsCeOaZ8Es9mtseloptD2RRdntVSx4H5PWSNuVu7szcdxnqk1hCkoKsGVPKahIWW5SLWhwMBAjB49Gm+99RbefvttlJSU4M4778To0aMxc+ZMNGvWrMrzIyMjsXLlSgBAeno6Bg8eXOPxw4cPm90Pjcby90DWKf/s+R2Q0jj2SBSOPRKFY49E4dgjkeQcf+dzCyze7uHLeZL0wdL2pfk8tBZtJ1fCNBAi9yvXbxUbbF/ufV9xqRZukkVnzJOZfwu1fNwteu3209lWty/6eKLRWNaHZbulTQGkrw9KHHc1Mm/faPsWtJ1fVCK0fSk/L2GfvZGx70jnfKa+R8l206WlpfDw8MCLL76IIUOG4PTp03j00Ufx4YcfIj8/H56enlWe7+HhgRs3bgCA0cfNERzsa/mbIEnwOyBROPZIFI49EoVjj0Th2COR5Bp/ISGWbXfPuVyh7btJEH19Z9NxTOrR3OzXHTqWYXXb5Sx9/1LQaExrX66xFxzsA293MVF0vwBvhAR4Gn+iDt6XrU8tkVmsRfNQP7Nf9/Hm41a3DQBeXu5Cx145Y32Q87gbGOgt9DOwpO0iV+lS+VjSfrZ0MXzz2tdAsoq6rh5uQvd7tkiyvfSGDRvw888/46effgIANG3aFJMmTcKMGTPQr18/XL9etVBLQUEBvL29AQCenp4oKCio8XhgYKDZ/bh69brdFHewNRpN2Y+L3wEpjWOPROHYI1E49kgUjj0SSe7xl5kptriope0XSpSTPSPjGjRmTjnMzZUukCXy89dqDbcv99i7ejUPN92cpd+wCTYfuICezWtb9Npr163//g+fyUaIi/lTXd/6yfzMBbrk598S/tsH9I8/JY672dn5yBS0EgKw7Le/bOdZoe2nzv5DTPsSjoHHl/+N9vV99e73Hemcr/y9GiPZz+TixYsoLKyaj8jFxQWurq5o1qwZtm7dWuWx9PR0NG3aFEBZwP3YsWM1Hu/cubPZ/dBq7adCsq3id0CicOyRKBx7JArHHonCsUciyTX+RI9pS9uXqt+/p2ehS2SwmY1L0zZgG5+/nGPPku1eK7A+nc6bG46hR5RlQXQpvv/8W8U28d2L7oOcx12tCe3LyZK2c29KV9BZ9Pcvsv1fDmegh5GbaDznu02ywqIdO3ZERkYGPvnkE5SUlODs2bP4+OOP0a9fP6SmpiIzMxOLFy9GUVERtm/fjnXr1lXkQR8yZAjWrVuH7du3o6ioCIsXL8bVq1eRmpoqVfeIiIiIiIiI9Dpx1frUFNYoLjU/SlGq1WJz+lVJ2v/3svmzMTPzHbuwn1aiyNL7v1uWmiS/UMKcEoLYw3twRNcLpAtiWyLnpmPve6SyX3BRZ1sjWRA9MjIS8+fPx6ZNm5CSkoL7778f3bp1w5QpUxAYGIiFCxfip59+QkpKCqZPn47p06ejbdu2AIB27drh5ZdfxiuvvILk5GT88MMPWLBgAQICAqTqHhEREREREdmhHImKW0pVINRS6w5cMvs1/wgOgLz9a7rQ9kXbdTZHku2s2W/+d0+O7eAlCVPQWHAvqLCkVLLmC4vN39baA5cla5/IVJJmPWrfvj3at2+v87GYmBgsX75c72sHDBiAAQMGSNkdIiIiIiIisnOp87ZJsp3T2dLl97ao/Szz25cykGWJG0ViZxFbMntfSq/+dFRo+2S9HAnTgijptZ+PiO6CZM7nFqBxsJfobhAZJdlMdCIiIiIiIiJbtXD7GaHtn86+IbR9W/TV7nNC27dkBq3a5N1y7HQqS9KkK1CplP0XruF4JvcXjsjM2s8kMQbRiQTbfTYHl6/fEt0NIiIiIiISaMuJLKHt/yFRbnVLncsxfyb+L4czJGnb0sBUtkSphIjMcTG3QHQXiBwSg+hEAu05l4OHv9mPvv/bIborRERERERkY6Qs7peeKbaw6t2fpZn9mqwb0hQX5OROy0hUV5WIyCYwiE4k0N5zuaK7QEQOSKvVIu+WbeZ/JCIiott++PeK6C4IdSVPmiB6YYltRoMvXbP9Fc2W1AIgImkU2ei+TxQG0YlImA9/P4En1xxAKacwkAM6cPEaZm8+jvxC5YPZz607hDvm/oXDl68r3jYRERFJZ8fpbNFdsBtpZ2zvs/x46ymh7ZdIcB23VHBee0C6FQ3WEF0kl0xTqtViwbbTorshmdX7L4rugk1hEJ2IhPli1zn8eSILf5/njHxyPGOW/Y1lu8/j4y2nFG9707FMAMBXe84r3jYREZE9yRGcE/uWHRS2VIu3f00X3QWznckWO4v7uXWHhLYvlX4qSK+arYJAvjm2nhRbw0FK5tzA+PVoJv73l/0E0ck8DKITkXBcQkSOnFrk5NUbortARERE/9GaObOWtY3sxykbTCtyNd+2Aq9qZavpfER67eejorsgmY7v/4l/L5m2QpdFXR0bg+hU4VpBEeZvPYXTWQzoiPDjocuiu+BwLl0rQEFRCQDgr5NZeP77Q8gVPJvIEW0+lok75v6FuX+eFN0VciCLdpzBlDUHUCJg6WxBUQkmfL0PT6w+ULEPUtIfx6/i1Z+OCGnb0RWXajH+6314d5PtzXaUyoXcAlwr4LGW1Gv53gtmPZ8zwclRmXvDyZAv0s5Kti1LFfK3LIzo84ISLTDrt+PC2pfyt0TyYhDdwWm1Wjyx+gCe/u4gnlt3CJ9uP4Mhi3aJ7pZDemn9EdFdcChnsm+i34KdGPDpTgDA46sPYMORDAZyFXQ8Mx/f7r+It/8LJn2+U/zJs6Mp1TrmSdsfx69i3pZT2HIiC2sPXFK8/W/2XsCec7nYejILb248pnj7U789iO8PXkanD7di09EMxdt3ZDtOZ2PvuVx8bWaQzl5cuX4LAz7diTs/2oaP/jzJGZSkSkt4PkKCiA4kmuuGhDfjP/xD/DVY2pkc0V0wmdRppLQQez3w0o+OHQvZaUNjz9ExiO7gruQVYuvJLGxOv1rloMG7sGTv/vovh1vWjaonIFfybL/Cva0Y8fluzNhwDBl5YoIofx6/inuX7MaxjDwh7avBT4euYPKqA6K7obip3x6s+Pe5HOWXblcuArf+3yuKt1/Zs3aSy9RWiFj5oM+lawW4cl3ZY97BSkulF+88i6e/+1fR9svdKi7FJ1tP4eDFa0LabzPrD7SZ9QcucEk4kU2TOh957k1x6Q0tOR9ywHkYqvGuwFnTcjD3eLhoxxmZeiJGfiFXh9oKBtEdnL4ZiKtYoZccyC+HxQax6LZp6/7FxG/2oVSBs/Invz2IYxn5mLzqANLOZJtVUMaebK8U0HVES9LOYfZmZS9EONtErFNZNxz2916uoKgE/RbsRJ//7RD6WfwjIIit1WqxaMcZfLb9DEYv+1vx9it/3uWr4ZSy73wu5m89haISTpYhkoI91fT5PwvyW6/42zFXVZF487acEt0FYTQybJOTaE3HILqDe2/zCZ1/Z15oMeb8cQLFvLBRxMVrt+92v/DDYYE9oco2Hs3ErrO5OKVgbYbM/EI8suIfh8hRXFKqxXPrxMz8rO7b/RexUyUB/GW7zwvd995wsNknWq0Wvx7NwFmJZ/CZ4r4v9mDool14bNU/iretFudzb+KXw7fT+Hyz97zA3ihrw5EM9Ph4Oz7bLm4Gm8hjzUPL9+HT7WfwjgMc74jIPHvP5Zr9GpH7UrI/J67mC21faIo5rRZLdp5F2hkx10aXFV6ZaMsYRHdgxSWl+O1Yps7HLvFHpAhNtfuIS9LOYeU+rgKQW96tYizb7ThBA1skYnnoKkG/PSVngW4/nY1fj+re7yvp30vXMWPDMUxa6biBzMq6z/tLsbbUkAP/j+NX8dy6Qxi0ME3xto9cKUvftEvQaoCsShdoKwXN4Bv4aRpe++X2jMP0DLEXrUp6/vtDkueRNZeoY01la/ZfErYUvqCoBG//mo7tp7KEtG+KTObqJyJS3PDFu02qCyBXKs7zAlOs/XH8Kub8eRKPrDB+bVQi/lTeoTGI7sAM/fZ+OHhZsX4oLe9WMSat2I/v/hF/EaOLiPy8wil8IFDTTaKp3x5Em1l/YNW+CygoKhEyM5PE2nMuFy+tP4xTV+WffX9LJUv1Kv8Gz2TfVEVgV6QiBc+Gr94Qv9Js/wUxeajVYMaG24VkP9l6SlxHHFDlFWhUthReySKGe87l4J1f07Fg2xms+PsCHnPAehym4qpU27Fs9zlJt3fo8nXjTyKhSrVapGfmy5J60pZOhzNkqiNmbEb03nO5uHfJHlnaFklkAJ/MwyA6OZwlaWex80wOXv/lmPEnC3A803FmhFHZXWcAmLkxHZ0+3IpBC9MqZkqS/TqfW/VmyY+HrmDCN/sE9Qa4WSR/OpGtJ7Ow8UhGjb8PXpimN7WYkvJuOVZKFVG0Wi2WpEkbdLBUZt4tfLP3vGI5bbNuVJ3dmltQ7HA3FHTl4laquPOjXPlSg5I38CZ8vR/f/H0BS9LOVvxN34pYR/fiesdOM6hEXRyp/Hy45nmNNZhiUv0+2XoK93y+G+/JUNjzikyBaTmIKoK7Od0+jxt/n3es80FbxiA6ORw1BUqO6VhCfavYdk4cSR6i0gxQWVBNCT8dqlnMNkvgDOEzCqyAeGL1AUz7/pDOz3j5HvHplcpvaJF8Ll4rwINfibtZVN34r/fhnU3H8davyuSH/j295hh78Ku/8ddJ9aa1kNrMjTU/a7lnlJ3OuoHHV/+jyH7OkKwbhRi+eJew9tWYouSZteqo0aE2G1WQdk2kawWmB+ccfSUbKW/RjrIbgV/vlT4l2xgBxa7JfBo5KnuSzWAQ3YHxnEO8jUelnb1gq7795xJKFMwLbYgSv4vM/ELkF4q5e19dcUkpDl++jmW7zyk2GxMAsm+o72IeAHrN34EDF+WfCXBBZUv27vtCuWWROWZcHJN9eWn9YfxT7fc167fjwnJyn80p+x1uPSE2iC16Nm6Bgmmerit4nCn3zNp/8ddJ8UWMP9t2BicUSNulzwmudCQT2VKx6y2C99+iib6JoJY0gVLRKp1jlEglOPJNxyC6gyosLsU9S3YbfI69HRRJvTYezahY3nviaj4KZE4tkSNwxm/2jUL0+mQ7us75S/iJLwCM+nIvRn25F7M3n8AbG5RLcbT2gHrrLoxZ9rfseWJV8NVTNfsu5OJ0lrgAlxJuFZfiyTU18xArFcTVdfNo+Z7zRs9H7IWuVCZA2Y3kvedyZW8/71Yx3tEx636DjjRLcigUdF5pLL+qUgodOM+1oWuK67yxqjov/HBIdBdMJkeBXFH7KktIfb1+UuCNPhKL1yaObdZvyqzKtAcMojuoHaezjS5rVSJHLlG5eVtOYcuJqxi+eLdsS9ky8wux43Q25vx5Uu9z5L7IPVwp37mI2Ye3ikvx76XrFQH89Eoz0xwppYAxWfnyBdFLtVrk29AsL6nJVYjIWmsPXMaQRfKnWjh4SVzRsD7zt+PQ5Zr5p59Z+y8OCy5mlntTfMFTuX33zyW9j43/Wv40N3P/PIlv/pZ++bmpHDmI7OgMne9cluCYoGSBVEdgzuzuszKkSbp8zfQx8c9F6Y9dPx2umXLPUbCwqXhqmGRFjkcNK/ZsBYPoDsqUXbO+GVMkjYvX1JXOQWm68n7/cLDspDVdpiXHff+3A4+u/Af/Gghi7T4r/2zAcjsF5D5/Ys0BPLB0L1YIDKQAjrt/+etkFrp8uBWbVFhMTY4LYV0+/F33TawcBwiiAsCU1TVngisl18CMz1Ff7lWwJzUNXpgmtH0lHBWUtqacva+0IPN9kXZO1htYWq0WT393EDM3yrvSTa7zRjJukAz77j9OiK1RctOBJzoYOk9QG1tKO2SOH3XUTVIj3hgnR8UgOunVb8FO0V2wa/pysJ7JdoyL3Lc3Kb9kSC1518ut2ndR8TbLb16s/Ltm20rOjs42klJHroItl64V4LPtpxXJe67L46sPKJp/uLrV+/TfPJEzL3rlGgD6VjnN/UP/ChEpdHj/Tzz/vfEl6nLPiNb3/tWw+mvGL0eFFR+U88L9XM5NPLnmAPadV+4mKZEtWLr7HJ5b969sK4TSM/OxOf2q7OcXE77eL+v2RTifK7YQriPLU0ndIhFET2gw54bYh3+ckLEn4vxkI0H0R1f+I7oLREIwiE56qS3gKKcdp7NVM0Mrt6AYBUUldruUa/zyv9H3fztkz3tuy7IUKLp5MusGSnWMsd1ncxTJGX9L0OyFCV/vwydbT8uWMkjtdpzO0fvYDRl/k13n/FXlv3XdI9l+Olu2AO6GIxkoLNGalPt5hsy1AUr07Ns/+F38xeC3/1zCaz8fEd0NyT3//SH8eSILDy2XP2VKdVqtFieu5uvc3zqKnJtFmPjNPjz41d+iu6JKos/3dp3NRe/5O3BER6onaylxLZF2RuwS9GKZ3mP+LZ4nk3FS34BeuF36HPPmuGTGSm1dq5pJOXIVCnfg0yWyEQyik0PRdRF78OI1PLryH0Vy4Zqq04db8ew62ynqY6pTWTew9/w1XL5+CxfNyHfoaJaknZNlu6dMKBb08Df7kfrxNmTKmLe6qKTUYG5gOV0QNO4uXSvAEBOWPDvyzaXL12+h1yfbZSnoddiM4ND+C/KuUigq0X11oG91ktL2KJjSqrplu+XZ910y8ruX60IQABZsO43hi3frLOjpKGb8chS7zubihEoL1sk9ieLolTw8t+5fnNaTMmvjUXWk9/r+X+kLfm81odZKeka+Secn+kwTfL7c9387hLa/WIbCmo5O6mKdclqy86zoLgijb59KlmF6FiLTMIhODmPVvgvoOmdrjVzQ3x0QE8wztlxNRNFJuRkrVrPxqPFZoo5irQzjMr9agNbQkk05c8O/tdF4MOnjLadka1+EuX+eNOlkPyNPTCoNJZWUavG3gbQacw0U/rV1Vw3MtL90/RZOXBWf11dkuqHZm8XPxpfagm1lAa6VJqTvkntG8hVB+5fN6cbzG7eZ9QfO5UgfECkoKjGaSkTOSRTFJaUY+cUe/Ho0E3vP6d7vmZJmSgrGRlepDDOqP9l62uhzXlx/GEMX77J4/IvO4Wxov66Ej+zsfEkNFu2wncC0qJWd9srQik1756irdInMxSA6GbTIjmY3zNyYjptF6jnRsNdiKFLpOmer6C4I9drPR2VvY9IKMbnsTLlxZUrQxZbom31cnRKpfAzp/tFfshedvXT9FpbtPq/3cVvJBWmJuz7ZbvDxKWsOKtQTUqPFMs8oPGPgRt6RK9Kn8jDXWzLM1hd9Hrt8r9gi3pUZW+l0THBxTq7gp3Lf7le+ZhARACzfo//8VE2uFciTetORU88RmYpBdDJoHmc3yMaU5ePvbkqXJbWBLcgvLBFW4G7aun/xtUwnUWo6NzGneI9U5EwTY0x6hunvV9TXJCJnc2W5BcV4287TThSreNaWXMX91ECr1QqbCW2Km0UlwmvBiDznuphreh5aucjx2zxmxn5fDmrK2fv5TsPpkkTnZqeqHPnbUPOxQgnGVu7aM+6GTPOeTCv3Vpmwao7I0TGI7qDUdoD6dv9F3P/lHmFBUxG2mJD/9uu9F/D1XmXuiIsOHugi6gbCxqOZePe340LaVgu5RkOv+eJyh5pTOFMr8PLVkfOiA0C2gTRDlqocnO7xseHZ4CIDSUUlWqzep56Zq1Laflps8T9jOn+4FaO+3CO6G+Sg8mTMy1/Onm/S2aP3HPw81JGZU4TZ3s4Zn/yWK/JMuSbPleFcGQB+OCh9bQx7lHVDns+fbAOD6A6qSGUz8WZsOIZDl/MwT+FcuMt2n8Oa/WJyopvqvAKzw85k38Qdc7di3hb7zUVMpESQorLiUi1e//kovj9o3j5m/l/Gc8jau8vXpQ32/FgpRYyxVUCiT4zfNKFmgC06ekV8vndjRM9alsv53JtoM+sPg885miE+nYtIebeKMW3dv9gsqB6Nsbzt5Hj26MmhT/bP1BSARSWl+Pkw60nZGxErhck8xSqcfEjKYRDdQf1vm3qCNAcv3V6ytuVElqI7pepFzDYcycCBi9cUa18tPt5yCjeLSlVXSEfqQJqjMyd/3kkZChxeuiY2XcDjqw8o2t4vh6/guwOX8OpP5uW3r7xPdFT7L9jfflh0qoRtp4yvfpJDYXEptp7Mwk07my1nSwZ+mmb0OQu2ncFOGVYLPLZSTO0Nc3267Qw2Hs3E02v/FdL+/7N33/FRVekfx7+hIx2xN1SKiigdUbGwoj8LyCKW1UXBtSMq9rbqLqK4iiggFhQQBUWagoIC0ksgCaETSEgCgZCQ3nvu7w9MSJtkJrkzZ5L5vF8vds3MnTnnzpy5c+e55zzPzC2HbQ/gL9kdo94T1umjVWF6efFexXj5OZW3rZIFvJ0vFKMH4DlJhmtz1RYE0X3UcS86kR45O7j4v5Oy8vT20hBjfXn9130aOWe724p1SJJ/ZGKVM8JMSs/J18H4DLcEfPbGuDbTbelelpTZ6ZkFzgeRp2+JsiWdzpygI/ou4MTFmdELzAVTdhx1bUZXXFrNTyJSsk/OeF5laHajJB1JztKMLYc9PhO/JorGjB2+8feOi8Ym05nkF1ouff7tNHHNQT23cLe+8TdX4NEdKYLqolE2B7xz8wu9Po1PkR+2VZ4v3N3m7zhmewB//MpQSSdSA6524jvI7tO+PS5OSgk4XDvGCgDAfbieao63Taj0VgTRfZQrM8LcXYSt7IFy+X7zy9JcyUXnrPiMXL30yx6NdjGQcTgpy6Oz82+cskn3fRvkllm7rlY83xhhZuYkTrjm0w01uqCUmVugiWvCNWldhH7cdlSRiVk29s41rqboeNpgwN/u+gT3fRukqRsi9eRPO219XmdYluVyOhtJ2hebrrVh9lx4+GKjdwTRD8ZnGml3x9EUHU0299mbT5Eql5lctWBn25kGU5TsP56uOUFHtN6J+jOSZPdp1rHUbN07M9DoeYyr+2T3qBsxZ7tL2ydkcLELrjGdDzzHUO0mdwuKSjbdBZ9mMlNIbVgNy6ol9yFNjXMIovsoVz4fds9Mqg0iE7NUaPMR+qNVYVoTluDy4wIOJ+uFn90zg7DQsrTnWKqiKgiwbI40PyMoLj3X7RdxKuOOFRsbnfxB7y1mB1W/sG1+4cn3bgIFspxm94+youcLOe75nMdvL9vvcjqbIi/+slf7Yr3/ZN4Z2XkF+mWX54PJoXHpeuTHHRo2I9DjbaN6IhIydftXW7TAUIHZJ37aaSyIb2er//xuW7mUfZ708eqDCk8wc+GsunZGpxrNxWuyoHdt5a7igrXF+D/N1hD535+hRtt3lycMTLrASabT/wGoHEF0VGnbkRQdMTiLzZRbPve39UusJgHZTRHuCWjPDY7WiDnbtd9AcM1ZJq+Ifr4x0vbn/Gm7mcBIdZm8iAFzFu08pqMpNT/ulyzoWR1PzK29P+Qsy9Lev2b0fLo23MhKjH0uptCqiyJrWSDz7WUhikvP1XgbCsweOJ6uRTtdu3iz7UiKUrPNpH6qS3GDtdWYNCG5tlLUHf7xbZCxtuvS++8peW48R84zfP7nzCzz3/aYTfvozu/1NEPHYeAghU19Vib1i5xCEB1O8fRMh2M2FyCsznK/5Kw8rfCC1DLukl9o6WNmB1fK9JImZiKYtf2Ia3nU7RKVZP6i5XsrQp0qRuhupk7m7PjOu2t6gB6aHawxi3Zr3cHqBdRQc3fPrF2z8PfF2nfh44Hvtum9FbVnpqTd534mVffbO8nF1GOVMXkOk1iN4mS7j9WNlUd1hTNpHe242O7IlPURbntuZ/wUXPWkF3eeplPLo3KmU/mYtsGNK5snrTO3iqu28DPdATexO6VoXUUQHU4ZMWe7Rz9Ug6dt1Zu/7bMliJiZW6D+kzZW67F/HjBXDNDd+k1cb7oLPiXwcLL+5+Ky0+qk/0HFqpOXtjpBADtk5hVUexYj7PHo3B01fo6o5BMBQXf+0AHcwa7ga66LM1mPpXpP0XtfVtPz/aTMXN3yub/Lj1vo4qoJd7M7rWNtE3A4ucpt3Hmx3fR354erqj5n3+Vi8dy6xuRkn+8CzRaDdiffPvKgKu7MW+9MEXIQRIcLPB1Q+iMkTodtmJFZV3LqovqSMnONnujN2hqlJ+ft1DwXU7nsjPbtk3M7/bLL9eKWJr34yx7TXfAarqajsEOEzSlAjqe79v3Ze8I6/XtpiK19qC16T1hX6y8izQk6osW17JhT0viV9sxeNzWbLSHDzAXQIt6wmqgmanoRZVcdmVEeerzqlAbG0136eKAfZk3fcthY2+48zptOZQTv5+fmqeiVrbQYMTvYbe3W1WLJdiOIDiNWh8br8bk7FF/FF2BoXN3JyRXjhiKVtUVGbr5+2Fb9ApU1sTYsQTd/7q9x1VjSnmzTsurJ1VySesCLc9VXJT49Rw9+X7Mv+Xf/qF5BSrvUdBYU6XjsMXld9T4/H6wMVe8J62zujef8XsN88rWZHReRZm45rMdsWFHgqpjUbE1cE66xyw/U2pmsdq2C+iPE9ZR4myNrPvv0v3/sr/Fz1ERoXO397sZJzqQqmbklygM9cWxnHblgUZG6lN7JXdwdyKvKFxsPueV5TQexF+zwrlUxnpZfUDvPXeqS137dp3eW+eZkmtqAIDqMeHnxXm07kqJbv6h8uafpnOS7bVymF+fiTER3cnbmjB0FrkLj0nXD5E3G8q9/uSlSUvVmIvsfck9BVxPCE6p3QepIcvV+xExZH6GjKTX7AfTL7pgaBaEsy9IP244qKCq5Wo9fYrhglR1MpaSxU1pO9YprzffxH0G13a4arsT5bEOkgqtR1+CZBbuUW4OZOBRlqplnFlSdh7kq7irG7qypGyKNti+dWIGHmnF19aAJ6XW4+CSpeatm96o5b2H6s2f6Ao6p4t5FtjqRymlTNdJk1hbeMAlqQ3iiftt73LYJfbAXQXR4NdOpWFxdgl9bPOdEsSDJngDc/bO21ejxNQ3E4oR7ZwZV63GrqpkbLSvPnlkk4fHV/4GwOTJJH68+qCd+2mlLX2qjxAxOvuC6DeEJmrHlcI1mgyVn5Wn0/F3VfnyUoTQJmyOT9Nve2n8BDeYcqkE6l5pcwCkptgarH7/xd8/s0tomMMpMcfGS0qu4iDx2udkVe3CfFfurXo1Wk2ONN5vvRBC9rl5AqC2cKTxcbYZj2FHVnEDmDodNpwxDhQii+5jEzFytNDy72xUUmbLf0ZSsWnXSdd+3QdoSWXdmhMM1/5gVVGleuMpwAcYsb5jJUROvLdlrW4FFY6q51HvMoj2auiFSP9YgDdeXGyNr7WqemqzCKjlk3qtGGjH4to9Wu1aA3JGafP/NMJiiZMlu76olYPp7zNcLV/oyd6VKqQ2cCWJuq8ZKM2fZdTET7uHuo3JNi2vb6dutZlOGoWIE0X3IhvAE3fK5v177dZ/prsCgIV8HmO6Cy55eUL3ZjNuOJNc4r36Ywbz8ziync0ZtXwqWkUt6hNpoo6GlnslZebacdK48EK+ldSClT03sr0FdhpRanGZg4prwaqfD2FriwkFtK2jsLWpzLYOa2nIo2ZZUeq8uqZ3n+v81XAulrE1M4gCAUuxMd1uR/Fo+CQd1H0F0H/LOMrOFlmqrmQYrj5cUn86sfFe9vbTmY/4fs4KMFodzNn99ZQZ+vtmGnqA2+nCVPbMaXbX/eLqxwpjvLNuvKdUs5ltWVcWv3W27G2daOaM6hSGLZNbyi1/VnUWeVosvHtghOTPPeD7ZmjJdEPa/v9fu8/WavnwhhlM5lhQeb24iBQDf5c0rIQ+7eUX7vODKV0H+ssu9NY/yC1mJgMrZGkRPTk7Wyy+/rL59+6p379566qmndPz4iR/RO3bs0N13363u3btrwIABmjdvXqnHLlq0SAMHDlS3bt00dOhQBQcH29k1+LCazmL9bEOk5gQdUYLhYMqcoOovq/dVMTXICVpSoE0zwqujOoXxYJ+afO5rGkiw4wS6pstdq5NKp/eEdfrnd9tqFICtCf/IulPsqLqrcEwrKLRqvBLh7WX7lV+DnOw1tSYswVjbtZVlWRr4+WYNnra1Vq+A+mx9pNH2Vx6oXi2SumL498HaG+M9gXQA5VU31SKcM8Pf8SQ+02mm3C2git/ddtXdcmTx7qpXoeYV1O33AJWzNYg+evRoZWZmasWKFVq9erXq16+vf//730pJSdFjjz2mIUOGKCAgQOPGjdP777+vnTtPFHvbsmWLxo4dq/HjxysgIECDBw/Wk08+qays2pO3uTao6dLqkNjqL+s2aakNBcImrgnXmEXVK6BhWZbCE2o+k8VEfvgfgo4qksIteumXvba8h76kugVJK2LyNCUoKrnaj90ZXbPljjuOmr+AMivAtbQo5JG0V00Ke5pkRzoKSQqs5ufPZMExv2rmoa9IVUUFvU1ciWLsE9YcNNiTmpkVEOXzAaIFO6ou7OdONfnuBeB+P9fhdGWLdrp3prMzvtp8yGFu7uqeG9UWx9PNTlw87sQkvHlOFL9F3WVbEH337t3asWOHxo8fr5YtW6p58+YaO3asXnzxRS1fvlytW7fWAw88oAYNGqhfv34aNGiQZs+eLUmaN2+ebr/9dvXs2VMNGzbUiBEj1KZNGy1dutSu7sEGyVm1d1aRHfZV8yLCNZ9u0L0zg2zujWfMDY7W3TMDq/34urKsPTOvQI/P3Wmk7XUHE4zPOPg+8IjR9nfXMBhdEzk1CArXdCb5n14wG3HdQe+Y1R1w2Exe2s83Rlbrcb4egLNLdT9BJgNwftWt5lqBxV5WZNEVptI52aU6Rf0ycwvqTPB3/MrqpQLLzivQi7/ssbk35iRWtaLCxotmgKtMDr+6PGnCWyYZO/odkZDh2zEZX2I6BoCK2RZE37lzpzp06KCffvpJAwcO1LXXXqsPPvhAp512mkJDQ9WpU6dS23fo0EEhISGSpLCwsErvR93iasVjk8u57eDLy30GfLbJdBdsY+oi0pqwBC3dazYY8enacKPtv7R4b7Uqpdvx42LqhkgbnqV6vGGWQ00KS9rpqXm7alVg+t3l3lUcD55z1HA+8IlrDuqFn/cYz+td21UnGD5q/k498ZM9F9xr0/GupOX77UnjFZno3GqSjeHuvdBbW1cD+YJdBidYeAtvOUcDAF9iWxA9JSVF+/fvV2RkpBYtWqSff/5ZsbGxeuWVV5SRkaGmTZuW2r5JkybKzDxxglTV/a7w8+NfRf82hNc8t+d//zigQsuqtJ2q3oP//rFft7hY5PCB77bVaN/tZLJ9u8aCJ9u2kzNtubN9Z/rgrtfhnd/3G/8MmHzvJelgQoaRfY/PyK3W2LPrNbhp6ib93xf+iknN9tn3vkhuQaGR9qvzutuZD746Y6/oXz0bXg+jx51qtP/lxkh9Uc0VBOXad7Ht7UdT9NuemqeQq277fn4n6qesO5igzZE1W71h8rjnTPvuHnvV+c7Zfcy+PN79J22UpcrPuR39C4u3J7BWnbZjbLqItHh3rFPj77lqplp01or9cZXvs1tbr/oY6G6e+KxVt327jvOutl/ydhPtl/z3q43fN6627e7BX1nbiZnuT/dhctw76kPJ2zzdtqf331uPO54eA/KS/Xd0e13854wGdr3YjRo1kiS98cYbaty4sZo3b67nnntO99xzj4YOHars7NInVdnZ2WrWrJkkqWnTphXe36ZNG5f7ceqpLaq5B3VXVGKmxiyyZ2nl9rhM3dLlzEq3qew9WOJEoYaywhMy9eGaCH0w7AqXHytJjRrZNszVrp258dWocQNj7Zvcb1f6UHLszdwYYaQP7mL6PTDdfpvWzVzuQx5CnkgAAQAASURBVLPmTWxp29WxV8SOY09K1omUSJ/7H9bUB3rW+Pmq4+P1kXrv712d2jYn332zJ089tblan9Ko6g39/Gpe1bUEk2O/0JKiswt0xbmtHW5T0djbdjhJv+44pvPbNq3gEa6pzv43zrZn5U7LVqe41H5adp6+rqQYl6tc3fdV6+z93mnWrEm1x98Xmw7VqO2WrZupUYPK59qUHXv5DRvWqM2SChs11Okt7TmGV0degWX8e69Zy1PUrLHr3yO9PlpnS/uu7v/x1GxN2+y5z58nfvMlZuZV2o8mTewb8xVp5eIx0G6mPwOVtl/P1tJuLrV/6qktFOXkagl3tO8JVbXdrFljY+0nF7q/8LDpsS+dOO9t0rB+mdtaaGmI+1NmVbb/zZunGG3f3daEJXjF+y9JjRs19HhfKjvu4QTboosdOnRQYWGh8vLy1LjxiYNqYeGJJXCXXnqp5syZU2r7sLAwdezYUZLUsWNHhYaGlrv/uuuuc7kfCQlpdv52rhP2H7HvQHcsPl3x8RV/cfn5nfhwueM9mBsYpbuvOEPt257i0uMKLUvLbMwp6mjfPSErK89Y+yb325k+VDT23lmy16N9cDfT74Hp9pOTMxTfyLXL7yt22VMYyNWxV+RgrH2vWVpmrtPvwYbwRH2wMlT/ubWzLW3P2XJYz/dv79S27syRmZCQrvxM9wYsKmJ67A+eslGBL5Y/H6ps7A2dal8qrersv10FMWdvilCXts4HUlNsTrvl6r5n23TxoEhGRna1x9+RpJoFeEZ9F6j3B11a4X2Oxl6iE8W4nNXnvT8rHPeeZPqzn5CQrqxG9avesISa1PEoKzompcoLKSWts7GguOT49S85/jzBUT8KCi396GLxbVelpmYaHYe/BR1W3wsqntTmiVy9le17Xr776y6Vbb/k2Nti83h3pn1Pqqrt/UeTjbWfVMPvN2ccOZZcLoDtaQkJ6Wr81zG45NjbdLDmGQaqUtnrn5bu/rR1Jsd+QaFl/Pu/SG5uvsf7Utlxr67HWYv2tSq2XcK9+uqrdd555+n1119XRkaGEhMTNXHiRN1000264447FB8fr5kzZyovL0/+/v5asmSJ7rrrLknSsGHDtGTJEvn7+ysvL08zZ85UQkKCBg4c6HI/LIt/Zf+l59g4M7CKtqp6D2pi2PRAl/f9YLy9X7LVef3tsio0vsZjIb+a+dmr05Yzla1dsSY0waWx5w7VeR3ibKow/vHqg9V+3+1gatxXp/3CQkv7YtK0zqaTTGf2text0/0PK8LGWUrrDyYqr8Byav+fW7hbx1JzbMvNK5l9713tg90rHcevCFVadr5Xjv+K7rO7DofJ487ykDiX2oy1+XvH1H57Q/sr9lf+2jvqn51Mjj1JOnA83Ss/957qx7gVoV47/ovu9wRHffi1Gqtr7WrbXWOurFHzdjls2xN1JSvddw/sf2Vj78Wf7Z+s40z7nnr/q2p70U73Fr6urG1HBTftNH1LlLHPXZHc/EKvO+6Z/Ox58vWvrH1P17vz9P5XdtzzhX/OsC2I3rBhQ3333XeqX7++brnlFt1yyy0688wz9d5776lNmzaaPn26fv/9d/Xt21dvvvmm3nzzTV111VWSpH79+untt9/WO++8oz59+ui3337TtGnT1Lp1a7u657Ny8gvdni/Qq3n2GOf1Zrl5xkxJ1SkEWZkXf9kj/0j3FpByh6fn2xPInBN01Jbn8QWbI5M0/Ptgo31wR0HS4CPJLm3vgd8Y5dTFQ+78Hcc0YrbZ8eSsrYeSdO0n6013Q6nZ9s0QnBN0xOlt75+1zbZ2Jc/8UK9LvOHVOpqSZdtz2T2eahs78/tXh7cXx10d5v6ZyN4s3wNFVyub7Z6c5YGZ6On2XpiF81ZVMtN/2V73H5sW7bBnNWtN/HnAvvo6sM/PO82PDZhlX7JoSWeccYYmTpxY4X1du3bVjz/+6PCxd955p+688047uwNJx2wq8IPaL7+g0C2BPU/adSxNV7Vva7obLrF7NYQrFu6I1t4YewqM1SarbV5ia1mW/DxaRaZixPPMOZRkX2CuOpwdg6//uk8eniBToTu/3mrbc01cE677e55r2/O54o1f9+mDwZcZaVuq2Ywjuy9k1xbxNq3+gnnrDybo+g7tHN6fnee+GhzO2BDu/okdYfGZXnve+8XGmtVdcMbKA/Ea2Pm0Cu+zc7WfI2/8FqIv773S7e2gvFcW71XACxWn9DqS7P74Rl6hB5ZaVCEn3ze/x71BZefdXNyA+ytyAPAK939X/RlVI2YH++wP8trs/ZVh+sXGmgC+KtnFPMt252XGCb58BNpmY20TOK+ymXDeqOSszay8mgcAek+wp0Bldbl6LA2KStYjP+5wU288L60atQV2H0t1Q0+qlp1XoN9snh2aUsVM4+iUuj9RyJtXYC7b5/7ZwHGGZ4Lvs7G2DYDaY/cxx5/9wCjPnZN7ovYEXEcQHS5ZeSBOu6LNnKCjZiISqj9jY09MmrEfZiXleWDpqDfLzDU36yop03dn932yNlxztzmfTick1vdm/3tELTmPPJJs/6z1NBvTo8B9TC5YsSxLI+dsN9cBST/YnHZs+pbDLm0/cU24re2b9u1W11Pwvb8i1A09qdrENeFaE+b+YnfwHomZTBiAGd6e6gm1n6Mi3UwqhEQQHS7aEJ6oh3/Y7vLjEjJytSUyyf4O+RiTVyNdbfqxufbPBtsU4dtjaKHBHGzfBzqfl9gdTAanlu49ro9WHzTXgb8sDzluugtwwqh59hV0dRWn9mb52VzW1pX3s9A6ccHbpNku5K93RrYNs+lrs6xqpCsxlXrKxPL2P0Lq/pJ6u48ptU2uJ6qXVsKOFT2wHxfs4G6O6qt4eoW3N6QTRXkE0eERd369VU8v2OXxdv0P1a2ga5QHcsDZ5Viq/Uswa9vMA7tn77syE3+pzcuqnWl6ye4Yvbx4r/E8pXV1dtTi3WaLvJlW4MTnPzuvwHgxyGg3HPucZWdBT7hu7UF7f9jvNRwUB5yVYuDY842/aysVUPtUllLBFziaDQuYZPnAlA1HF2n/3F/3L96iagTR4REmTgJi03L06dq6tbQ3v5pFTuxILzDzr2XF+QZTqoTGZRhrW5LeW3HApe1NLq2fbuDH5X//OKDVofHqP2mjx9suaZ3Ngawi+6tI05KdV6BCy6rW7EFUbYUTJ64zqpH+ALBLQoa9aa98OY2WtzB5IcPVItl2jz+Yl51v/nyC9AXmuJrSyk61rSYIYKeAw8laX8Hvya2Hkz3fmRI4L/QOBNHrOh8+7zmc5P6q7bXFLhtmRG+MSNTuY6nq98kGfb35UIXbFFqWgqKSlV6NYljOciYtkLt+SC7aSZFOX/bcot0O70vOzFP/SRs1cs52vbR4rwd75V3cmXIqNbvyFQavLN5r5OIRIDle+lsTnixehYodSzW3AjAjt8ClFW3/94W/7X0wOXFiY4T3FtX0lO1Hzdcj8uGfksYFGQzYveLD57KSt6RS4tNn0oerwkx3oZzPN0aa7gJEEB21zE6Kmhr10aoTeaG/3FRxEP2XXTF64qedGjE72G19OOzErPr7vg1yW/vOMp3SxFRe1LosvpKLMxv++rHvy+kXLMvS/uPmiqrW5VlTJn9GvfDzHiP5jmubIV8HmO5CnbNw5zEt3BFtuhtGbTZcT+gTgys6veWYbjovty8znZ4NvssbUlGbzGIanVJ7Usj6krRs86uTQBAdtcxaw4VEDsabTSdSHVsOJemtpfttf94xi3aXm3X6x1+FD90ZwD2eVnW+4eQs8zmxTRYBjXHDzLnkLJaPoXKPz92hR360v6BwkVpWEsFWzux6sptqAaw7mKBXl+xzevtfdpk79qHueX+l8zPBvCDmUefMDTZ7EcOdq5ucZcdqTlTPxohErxgDpuxg8hgMmeZg1bkkVTO7LKrBl49/3owgOuCC2hhE/3xDpFued0N4oiITPT/beWYtyXmcmWv/lWJnv0dj3FDYcEO42WXViRl55OX0csFuXnbOu1+5lCrS3bhTQaGlhTuidTA+Q+8uDzXWj7rI2YLSfD7cM3Mw4HByrStqXpe4q8aJK3w9rQV8l+lVtTCnspW1Y5e7ViMMqGsIoqNWqW5hTVPsLqh64LjrQfw9bkwvwQ9LzzKZKsO0pxfsqjQnOU6wo4hwbcRye7OW7I7R+yvDvCKVlt0+WWO2QPlXDtKnobx9VRR/ro4FO47pf396X17Usr6tJRMMXOVMQWl389br93OCjhhr2x0rHh225cQKVLhHiBuOqagdwhPM1pZLz8mvcBb2YdKVwgsQRK/jnMkfXZvMCTrq8L6d0amavC7cq66az9tu7zLYrzZFOr1tSKzv5mauq+LSffuHhL/h3LC1QYYbVkB4i8riGHU9b2qKF6SoqsxeN3/fTFxz0OF9lnViFry7LjLONhiokqTvAqI09o/9OuDDF1Gd4eyM/epYsOOY0rLdVzC9phbvitGU9RGmuwE3cfS7ZqKnLvBVEMhaHuK5ixs7vKC4qjeqrE4P3CM7r4DCjh5y45RNGl9BOre7pvtW/Rm/v5bYWZalwMPJRled4qQGpjsA93rh5z2mu+Ax//phuySpYf16euKa9kb7UsRU4OOxH7cr+GiqLj+rhZH2fd2WQ0kOi6/CvdaGeUchMlPctTokJjVbZ7Zs4pbndlotWPnirtyFdfniiDPmBB3VmBsurvC+30OOu5Q3u7YpsKTFu2O1eHesAl64znR3KrTnWKqaNqpvrP2gqGS1PaWRW9uIy8hRiyblfzYVFFqq53fyh64JppfW+3JBbU+ITMzUJWdwPm+KV5z/VKCurj7xFhUd0b/2P+zR172gFpz3utPCncf02sCOxX97etWpN738f4TE6d9LQ0x3A38hiI5aJyEjV6c2c/xj6VCi+5Yf/bgtWjdfcrrT2/+8K8ZtfalMUW7i3cfs/WFTVWoYT37ZrDoQp/XhiXr7/zp7rlEnPT1/l5F2LctSek6BT590/WLoM+epwV9oWapXSbDGXd0YNG2rPhh0qQZ0Os09DdQRb/xm7gT3mAeX13sTT86IRHmJmbkaMWe7sfb3H0/XEz/tdHs7WXnlf7znF1rqN3G9exuu4pjuTLF1d3todrDpLrhVWo73rULwjzRbp8aXzjIX7YrRkwYnZ6Vk5alV04albtsYnqgftzlenQ338HRttIlrwnXrpaerjZsvErsiweAKiLBaWJvOLr4+SczbkM4FtY7JKsW7jqU6nYvrWGq2kg3MRPeVlB+vLNmnX/fEakO4+aJT3uKtZfs14LNNHgkooLTFu2M90o65uY7S94Fm01rUBu7K31vV996a0HiNXkDNAFPcWXvE27mjkLUr9nsoZ++kteVTZxxLcf+Fq6rOI2dsOez2PsCsoxWMM9PH+1WhngvopPp4+oKEzPJBy1/3GJow4mG+8pu2MmUnBwVFJZvpyF8K6nj6RMAZBNEBFzmbi8udM+IrYzI3sImWjySX/nGxeLdvnFhW5Pd9x013wWdtO5JiugtGeaKgKaftjr20eK/pLvisQsvSiDo+E7cynghw7Yo2nxPZ1DHeG2dBw7NeXbLPdBfK8WQKn9mV1MMy5eedxzzWVkZO+XRuKw/4xqzUY4Yv0nqj0YZWOwM4iSA6gGorm1Ui2HAgMTuvQGP/MJsb1J12HUszsrpBOhHAtCzL6DI+VM3U+xPpgYuGPpylyOv58nvjNftuqCPrDro/rcTDf9W8qciuY+YD7CbV9UmBJnPNe5PkTN+dje2NM1/fXR7qsbbmBpu7iOB9rzxMvyc5Hs5L7utMZmCAYwTRgVokKtk3c96W5egLxRMBxD0xaR6ZdevIK4ZmnKZm5+uLTYf0f1/466fgaCN9QNWed2Mx6cw8x8UtPXHxyttPIxfuqNufi0wfL24K72Sq9oy3WO3BtBowJ+S4uZRRKdmshjApr8Dc2c9jc3cYa9u0lOx8r8nBHZ6QoX9+t00r98YaHQ+SNNTJFflAXUYQHXWOtwdaaioywUyaGG+yJqziPOiHnMxXXxMjZgfr79+YO4EwmTZkuv+J3Ksfrgoz1oeqMGvNfQ7GZ2rknIrTViR6YJact07GWLYvVtP9D+v9ld77ubCDpwtqlUVuVKC8JA+sTpsTdER5Bcw+NCnaA/n3Hfm/L/yNtS2ZrQUDszZGlF/p5MlUQqYmLpX16pJ9ColN1yOzAk13xSiTKWulEyvePY3ftd6JIDpqnWVV5H321kCLXRbt8lwePm8VFucdMwNw0v7jninuVpX8wrr9Qz/X8AyU3cd8t4CiI28t3a/PN0aa7kadd9uXW0r9nZ6Tr1u/8NeGcPenE0F5f+6L9Zrjfl3mDae0E9eE68dt3peXGr7Ll1IceOOeempFbtHknZJ2ezCFV5oXrMKwdOJ8B9IqD9cCSM/1ptedYLo3IYheh/22J9Ztz/2N/yG3PXdVJq2L8Mr8eJ4yJ+ioDlcy49oXzisLKtjJ0Ng0PbNgt4HenLQlMklvLQ0x2gdT/vndNtNdkCRtikgy3QW3MlWw2Nv58neCr5qw+qDiqdFgxPajKfrXt4F6YNY2FfrCSYdB3hIs/MXL0uZsPljxikR3Scosfazx9IxIUqqU9vqvniu2SuiqvHHL6279KXiv7HzPzgRPr6CoLyARRK+z9sSk6Z3f97vt+b/YeMjpK8HuCPp4x0+Kyvm58bTrrukBCjyc7Lbn93bfVDAzYdgXmw30pLSnF+yqcqUEUBMErCq2aGfdXqEzaV2Epm0yd/HaGyVmekcA3Rc/kUt2n5yk4ak0H6bTCfk6T6TLc8W/vvVsWr0vNpY+/v7vT88VlpSkL334+F/RMXalh2ekmuQtF9KKZOcVKDDKXGpJT16/8kS6LAC1D0H0Oig5M08jZlect9ZOSU7mwN3qY8Fey7I8Mjtu2T73rTRwljsvFDiraDZQCic6Ps9UgHlTmOd+zH26LsLhfQfqfGoFx+9vpA/M0P9qs/cGUUznqXQ3by+qmp1X4NFgvok0LjO2lL94DvN85cJuRpll/Yt2enZmPqutzNl6yOwKx3UeXnVRlex8s2kTJ6w+6NH24qnH4jVyDY89oAhB9DroGx//oXEsNVu9J6zTU/N2GWm/z8frdesX/pq/I9pI+75m/nZeZ1+y7UiydkZXvArG07nyitz/9ZaqN7JJZStQPDlT0MQPemIIZlX28md4eZC5pqZucHzxyrTFu2PUf9JGXf3JBo+1GRLr+SD6HyFx5W5byHmWcb+7efXd7/uOV1jMzdsvbME+sWlmg5i7jqVpx1HfmHntDG+bGe9uOV5QVNnUS57vBfte0hI3pip2xvF0z6+ANPF587XPeHUQRK+DPFU52FuXOO0z8OOuSMnUNWvCvGvmgDtFp2RrraH9PZqSbaTdsjw5G8t0GgOT6w8en7tT//phe4Undsd9dLZIoWUpM7fAY4WHJq0N1w2TN3qssFSR7wOPeLQ9Z3jq+9Yb5Hg4F2VVPFn/YHkFAVxvMfYP38xNG5WUpfdXhpnuhttFJWdr+xFzAbyqeOKzUVEKP19mIrAVGmd2pVtIrNmi5ntizLbvTYUlfen3rWTmorG3mLE1ynQXvMqmCDOF7JMz87TygOfOQ70ltuLNCKKj2sb+ccD47ABvk+VDAZWS7vx6q178ZY/pbhjV9+P1Hmvrls/9vaJivEmmlpMu3Ws+jVJZT8/fpesnb1SMh47H3wUeUXZ+ob7+K7DhyzMW8gp8Z98LvWtCEjykqs+3qcJbyV46kcNue2PS9OjcHV6bsmqjB4IKu4+l+vT3TFmHPXwBW5L8I82mNPGmz3tatuf7Mn6lZ3PwV8bXCnp/F3ByAoevxT28YbV3TKr3BHRNTSD9zx/uq3NYEW9b/eKNCKKjRozlaeNkukKFlqVfdtXtAns4IcLgD+qyn769Bmbo3Dhlkyau8WxeREl6e5lnT2ScEfBXipffDQT4s/IK1MeDF5Dg25KdrMViN0c/nGZsOVzni9rOq+JH9KpQM2m0fCUXdxFfLqwaGJWip+btJJD+l489nBMa0rHUk8HTxQbSSHlVXnSDH8McwzmxQ+N89zhsisnfu2VNN7Aqys/PTzuOVpzGFOYQREeNhPFl4lX+CDmu6Vs8u/TKVBoX0+J8NHVIRT7408yS+jlBR420uzzEvTlgq2IqkFiRHwy9B2X5eSDHUFx6TqmiRll5BRo1f6f7Gy4hJ79QmyMTfSqNTEkDP99srMBe2RQKUUlZmrohUv8zdPzzlA9XHfSqmWBFnlmw23QXfFpMarY2R3puaXtgVEqdr73gLNNFRk0HMk0oSl9nWZbeWGT22BOZ4Nmg4pYyhVV/3ePZorolmZgZHU1qC6NmejiuURbFTFERguh1kCeCCa7wxh9fdUFqBek89sV4Nm/bvtg0n03jcjSZce2r3vgtRHOCjujB77cp1cCy3oGfb/aaizhHUzy7rLzsMuLvA0+8D55IKXHbl1t0zacbigvbzguO9ngNjreXheiZBbvVf9JGr5qJ68nTjlwvKXSVnus7KbVGzTdTqL0ymQYuJGXk5qvQshSRkOnzs6IHTdvqUxcyolO84ztXMpPOruR3ry+vigiopLi7O5W8bvLBn55N7fJ0meN/dKq5z4KJc25vrQHnK7YdSVF+QaGx79w0L6pHAO9BEL0O8jNa9q+8WQHeVwzOXTz52ud7QcKqGVu8o9jTB16UK9DT4jNy9fXmQzruY3n6yiootDRpbbjH2pu4Jlz7YtM1a6uZ49vWQ8lG2jXtlcV7JZ1M5fDp2hPvgydTShSNswwDQbw/D5zcT0/XRThguLAczK2COZzk+RzMjhRalrGVGHkFlj5dG657Zgbq842RRvoAM3Yd857l9LuPeT6FnqmVf94m09BqiJKz/02vRPA0X8vBXhFT73hiZp7XTNgomsDiafd9G8SEUJRDEB1u5S0HXo/xrusXbheZ6B0/rOdtr9s5aSsydUOEft55TK8s3qsvNx3S7V9tMdYXEznRy/pwVZhM1HjM8ZJZsZI8vwzJwPF9Z3Sqlu2L1YApmxRoaEaYr5q0LsJ0F3zeG7+FSDqxwu/mqZv1n9+9r0aDuz3yww71n7TRWPtFwcQZhpeYw5x0ZiZ6nI/9mqxUsIH8yCv3x3m8zSK3fuFvrG1Iy0NOvvc/BZu7mHY83czFlOSsPH28xnOTtCpiefgI6GPhrGohiI4aqepD/YWbZupwMgXT8grNBk+DolI0bkWosSvzRbylLsKCHb53IaUsX8nb+NbS/crILdAYg3lJ31txwEiBoYqQr9G3FBRaGjRtq5Ky8vTrHs8VEx42PUBBUckea68i+4+nG50R/H2guZWVvjYnxZvNCfKdFbYl+XoKI0k+m5f/tV/3me6CUSZXf5v+2JVMH/nhKnNFjU0Wcc/JN/e557jrnQiio0aq+lwzU8d9NoQnKqVMnjYOs57jS2mKKkOKB9/mi+d22fmFWrTTXGGtIkdTsvWPr/x102ebTXfFoyatDTeSF7XsqhNTYz/kuJlj7qGkLD3xk2cL6Zb1z++2GW3/262lz2m/3BTpsbYtnfgx/eLPezT2D3MrEHLyC8sV2fU0U+kdEjNPtDtts7kLqMFHUoy1vToswVjbpm0IP1FE9+1lvrf6p6QDhr5/TJvhJZMmfJkvFjSG9yKIjhrLyM3nKpkhvjobxhv4RyZVvRFQh4XFZ3pdIWtPOOYluRH/vTREm8MTPFpg8Uiy+RRe83cc00cGZkPdMHmTx9usyIjZwUbbH+/DNUjK+iPEsykODiVlae3BBC3efWIFgqfr0uTkF+rGKRs1aNpWj7ZbpOinhqk6OLd87q8th8yd+yVl5mqXwdWHryze6zWrD32Ryfe+yAOGL2SaOun8avMh3WEwbaZJ3lCDTfK9WgBF/Hzxh1YtQBC9DolLz9GYRbvl78ETvJ93xeiGyZvU5+P15e7L86ZcwXUUF2VhCjlBzfKGehP7j6d7XSFrT/CCl16SlJjh+dnYw2YEVni7Jwu7SuZmY4PUWSaVPO5n5hZo6oZIj7Z/KDFTeQWW8UJ/q0PNzYh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LePcmj/64XYGHk/XYXM9WKfe05Kw8fRcQpbj0HNNdKfbfPw6Y7gLgMZZl6WB8BkWtvcDRlCzdOzNQi3dxIQ/2qc6koN3H0pSYaU+Bt8MuFjaUpA/+DFOhwclMX28+ZMvzWJalF3/eU63HmZSTb89vguoUiL3q43WKt/GccFNEklPbzQ46oreXhRh97WPT7D8X3nrYuf0vkpFrpiCmO+IuB+IynN52S2SS0nPy5R+ZqHwbKuu6cvz0j0yyvR7a9C1R1XpcWLzzr5m3yS8olH9korEx7MsIogPwOaEunGTAM1IMV3X31I+Ify8N0aR1EXp6/i7tjUnTpLXhnPwAHrRkT6zu+zZIz1cj0FMX5Ru8mPDRqoMKT8jU2OVcyPNFBYWWWy5mLQ+Jq9bj7py21Zb2f9933OXHRCRkatle1x9XVnVfz62Hk/XzzmM1bn/e9mitPZjg8uMW2tC2JOXkV2//xy0PtaX9G6dscvkxBZZ065dbbGl/9zHXakwt3Xtcn6wNt6VtSdpx1LXVpFM3RNrSbsmLF67GgyMTXb/oZZfVofG2Pt/rv+5zetunF+zS0/N3afSC3fp26+Eat/3GbyFOb7sjOlXv2vi9/+eBOKM1zmr6G7Y6F98kaZr/YY1esFtjFtXsfPZIsrnPQG1FEN2HLd0ba7oLgBFHk7NNdwFeJDuvQPfODNL4lfb8iKqMf+SJGTLhCZl6aHawvgs8oi822jMDDEDVin5oFX0WfdnUDRHq98kG7Y1Jc3tbqw7EaeScYB1NOfljLbuaAa/a5mhKlj5dG67jbph1WRvsjUnTmEW7FZFQuu7SPTMD9X9f+NseSK9OEFcyPx7tCKhl5VV/H+bYEIT6oZrPMTvwSI3blqo/q7i6Y8bbVCcl4pwg+4KP2wylZEzMzDPSbk1V54Kbnfb89d3/2x7Px4T+qObFzor8aqD/JdX0uz2pmuO3aBVfTVOhHoynJqKrCKL7qNC4dL1r01V3oLapaNlmek6+dkanGl9S6kuW7o3VCz/vMT4Te1VovCISM7Vghz0zoVwVXouXEtYFYfEZGj1/l+Iz7FnK76zkzDz9FBytlCwzP/4sy9KR5CwVWpbty2q9WXV/rNjlaEqWR4LWzpjx1/Lnyevsm4noyCtL9mn3sbRSMz4DDye7vV1v8ORPO/V94BG9+Itvrn54aHawNoQn6un5O0vdfjgpS6nZ+TpUjfQnAAAAJhBE91HHUn1zNoy3KRmvXRtWN2ZBVIfpoIYkjZgdrH/9sF3LDM8K8BU5+YV6e9l+rTuYoOn+NV9GWBMlZ8FFp9TtVQqmLhL5Ryaq94R1OuhFFwySMnN1PC1H//g2SP6HknTrF/4ebf/5n/fow1Vh+vdS55fA2unH4Gj9/ZsA9f14vfpP2qiNEYkea3vdwQRtDE+UZVn6LiBKH60Kq/ZyVld5+mJJWUO+DtBDs4N1JDlLmbkFenbhLlvSKNQW6Tn5+i4gSmtsXsZelfyCQgUfSdE9MwK1K9q1lAc1VXTOvS82vdTt+2PTNW97tNFc2J50PD1XswOPKK+gUPO3R5vuTp3kGyMJAABzCKKjmGWpVIAj10eW2Zr05aaTaRx8dYaSJGNBpJKKZkLZtaS0Mntj0vTO7/uNz8A2afHuk4XkZgW4/zWvTMn4hR3FdbzZq0ucz5dop9ELdkuSnl24u/i2+duj5R/pucBtSe+vCNXNn/vr9q/syUNaHbv+yl26OTJJb/62zy0FthzJLyjUx6sPlrrtAw+kM5JOBFFf+HmPnlu0W0v3HtekdRGaGxytt7zge8CTDsZn6sdtR7UpIknjVvjOysB9sematC5CLy3e67E2k7PydOOUTXps7g5FJGbq4R+2S5LWhMar94R1xUvaPeHzDRHa/tfS639+v03/+zNMf4T4zsX7T9aGa3bgEX3wZ5jprgCA96jbPz/qNB+5Do4SCKKj2N+/CdDi3SdzSmXm+s7ybm+RZri4oi+oKvemK5XNq+uh2cH6bU+sHvlhh9vb8lYmjy+WZZWa9brVC1IKvLZkr55ZsMvYTPGth5I88p6kZp9YdbLnWKo++DOsOLjuaXYVMXOVZVmKSc0u9z7/ERKnd37f75E+FBRa6vfJBo+0VVZ6Tr6+3nxy5cmuEkXQ1oebuaDiSXHpJy+UpGTleWz2va9btu94uXzX244kFwfyR8wO9lhfpm+J0qNzd5QqABd63HtW6HjC3jIz8gEAAGoLgugolmwoL6uvOpZaPm3Ev37c7vmO+JBxyw/o6k82KLpMOqN9sWby04bFZ9heUKu60nPy9cmacIUYei1K2uTmtBLPLdqtG6dsUnhChh6aHawV+08Wt4kyUKE8r9DSygPx2hyZpKOG0smMmr9LL/zs/oB2UdGz2HSzKTVMmbD6oAZN26pR83eVu++IB/ICp2bn6ZbPN7u9nYpYlqUbp2zS7KCTK08yfOxi/f2zthX/99jlBwz2xLdU9Nl6fO7OCrb0nJLfO8FHPVeMLyEjV1sPJcmyLMWn5+jA8boX0PaW8yo4L6+AqZzwPSWPVYU1nIpeG9OC1cY+AxJBdFRimwdP6j0pISNXd369VV9vPlT1xm5UUaAwIoHqyO70866YCm9/8HvPzUIr693lBxRw+MQP2uTMPIXGufcHbUhsmt5ZFqLkMnnoP1kbrtlBRzTcA69FoWVp9zHHOWmfXbjbrcUWN0UkSZLunRlUrsDf7wZy4pesqm7yfDIwyjPH/Ph0363JMTf4RB7gAEOrH77adEgpDlY8ubtWSkUzzU183kyZsPqgV0xWOJKcVefTVpX1k5fn3/ZknaI7vz5xEe+Jn3bq1i+36IHvtulQYt0595wTdERrDNYYiqlggoyz1h40WxvJ5G+QmLQc3TB5Y42f573l5lJjecNvuG+3Rpnugst6T1hnugvGVseWPC96ZsFuravmMaCg0FLfj9fb1S2PuXnqZk0ok1oQqA0IosOh0Do4O0WSZmw5rOiU7FL5yL2JrwSYkjJ9cyZqWUv3HtdT83apz8frNfDzzbp/1jbtd+Nnb/j3wfpt73GNmFM6WO7Jgo+T1kZU+SM3zlDxvwIfCy6ZsCfGzHfL1kNJiqxDwaLqKArim+ANAYbvy9S8sCxLo+fv0tvL3J+P/cdtR8vd5sk8+JK0OjRef/8mQGMW7lZ2nm+tAvBmhW7+3rEsSweOpyszt0A5f6W12Vbi4q07c8JX9J3qzotJE9eEu+25nTF5XYTR9mszO1YmmbwQ8cla88FARxfJUbnVHi507cgLP1evPtphD6xkdIeU7PwKz40Ab0cQHT7H22Nk3xousugJiZm5uvlz/3K3Z+YW6IiBdBreJigq2e1tmEobIqlUOgdPqyrneMkl9nCPT8v80Hzw+21uH/NhcRkaNX+X7p4R6NZ24J0KLUs/BUfr07WlA2wH4zPlfyhJS/eamRG/3IPHm5jUbL38Vw5w/0NJ6j/p5KzPHArJ12n+h5L0wHfb9I9ZQR5vO6yCOjMlV18BAADUJgTRAUMcxfLC3JzOwxs8Na/iXKSDp23R378JUJgHZ0VXhHyadZfJiwc44Xh6rj5YeXLJ9b7YdD3x0063FlbeejjJbc8N7/ft1ih9uCqs3O0mgoqmjP3DcQ72XcfcWwtjrcH0GpBWhJy4WBNt4PsvNL7un9MCAMwhtTo8za1B9M2bN+vuu+9Wjx49dM0112js2LHKzq74BG7t2rUaNGiQunXrpltvvVWrV692Z9cA40IrmJ0jSTn55r4JAg4nub2oY2Ripg7GV7ysv2gZ4mY396Eq3pC31h0qm4XNCQg8JSe/UImZ5T9jH/zpvlymCYbSA8E7/BBkbrlwXHqOV6R4iDNYzNcT6XIcqWr1EdxrnzPpu3iL6gw/0x2AT+I479usWv4lYr735ntQ27gtiJ6YmKjHH39c//jHPxQYGKhFixZp69at+uqrr8ptGxkZqdGjR+vZZ59VYGCgRo8ereeee06xsbHu6p7P47vmBJP5cbPzvSsfaV5BoZ6at0vPLtyt1Gz3BJGPJGc5TKeQX2L2dzZLy93C0cULyb05Ub3Jop0VF5d1N8uytCvacTFVlM7RC9QVryzeq1kBta/YW11hOtetr9d/8XTef19nOojt52e6BwBcxee2Zky9fLxt5rgtiN62bVtt2rRJQ4cOlZ+fn5KTk5WTk6O2bduW23bRokXq1auXbrrpJjVo0EC33Xabevfurblz57qre4Ak6e4Zgbpz2hYjBbbWH6x8tnVMarbWhiV47Op6XsHJdtJz3PN67K5kyXh6iYJCKXV0JrhpOQ7S1LgzjUZZJmf5RyVlGQtmrTwQr4d/2G6k7doiLj1Xe2LStDbMOwo8AXZwd6oUVO7XPWYn5EzyglUIVfkjxH01AUwWegQAALBbA3c+efPmzSVJ119/vWJjY9WrVy8NHTq03HZhYWHq1KlTqds6dOigkBDXl39yRcY5zrxOfn6uvZ5F29a29yA6NUf9J23Uo/3O12NXX6CgqBRd3O4UtTmlkVvbTcupOHC561iqth1J1uNzT+QNHz/oUt3U+TS39kUq/b6lZufpnNZN3NpGWZPWlS74ZnLsuTr2XeHsBRt3tF/2KYvaeG7Rbre3XWT/cecCSn5u6IezebHdMfZWOllA0J1jz9n2TRoxO1iS9NOInrqoXTO3tPHakr2V3Gu5bUaOMxdETX/nmn7/fXnsf7Y+Qk9fd6Hbnj+iipV3dXbsOfm87mo/qYLUVWVZbmxfUpWvwaaIJOOfPdPjr+xzV1dNiwVvOZSoq9qXn3DmjLyCwuLiwdVlyVK9/2fvvsOjqvI/jn8CoYQmTRHXTrMgEqoFXBvrWkBsq7uKwi42UBR727X9bKvIKoJrARTBsoAoIEgRpCiEFlogQIAQQkggvff7+wMTCWSSKffOmcm8X8/D85CZO/ecO/fMmbnfe873+PAmJGQU+FR+VmGJWkY08Oq1B33M+b8sLlWXd2pb4zY1tb1f9/q29spPOw/7dL1X7sOgq6nr9mtI79O8fr0kLd+dpgkr4716rTvvfU18/dy+vSRO1553kurX825HpTaupeXqWGpqe5uTfJ/J6ct7uHKPb2lY31y8Sxef2cqnNmAHb9+DqesSfSr3/xbu0P2XnKlep7f0aT9eq+U7OFhjfN5w9xgdDaJXWLhwobKysvTEE09o1KhR+vTTT6s8n5eXp4iIiCqPNW7cWPn5nqfaaNOmuU91DRUtDtX+3kY0aaS2bT1/PwP9HES4+HH2yaoEdT+rrUb+b7OaNw7Xlpeu8XPNfjdi+pbK/289nKc7Lj3b8TLzjgrqT153QJOG9ra9jObNXaezmLP199FiERENjba91q2bqW0L+28iSNKynbVfYDVt6t1nrzYtC6oG8CvK2HxMmhEnyq7w0DvL3dquNDzc9nq8ufj4hQWr07p1M9Xz8Id0bW2vUSP3vm5btWqqtg4Fj93h5Ln3RFZ5mK11iTjqpujina5Hupc3aqh2hj779evX0zdbUnRi80b6Sy/3L2bt/M518vxb4bVPfnSqfHc+z6bb/vI96Xrp5m7GynfyO7fcjdiOU+9/o4bu9b1Old+wYf1at6lXz97+7liNG9UelDTZ/lu2ahIw1xum+4GHZmxV/JvXe/XaN+Zt97n8NQdzdUO3U7x67X4bUmROWJWgd//S3avX9nLz96UrT3y/ze333om298yc7Yr34Xrvx63epyt8b9lejb72PK9fL0mP+fD+e/LeVyfb8i26l1dcpl8Ss3Vzj1O9ev27i1wv3O2p2vqg6tre/y30re3Xr1/Pp77vUR8/ezM3HdTMTQe9bgMN3fyer40370G6DWsuRSdm64H/bfb4+O1a66ZF8wi3jj3QY3z+5JcgeuPGjdW4cWM9+eSTuu2225SVlaUTTjih8vmIiIjjFhwtLCxU06aeBxLS0nLI9+2G7OzaRwoU5BcpNdX9achhYUc+XIF+Dqas2ufyuR82HrmTmFNY6tGx263sqCvOwoISv9Ql/6h0KqnZhY6UmZPj3iiRgoJio20vPT1X9YudSTuSnVX7RUZenmefPXdlZlYt21UZJtt+hbsmRmndE5cZKfvPY5fpiyE93BqR4m7bK3Ix8+RY6Rl5aiZzawIEwrmXjnxH2VmXAjfzEj87fZP+faNvF5KuRO+uOU3NgcwCvb1ghyTpyjNb1ro/J75znTz/h9wYoehU+eVuRHFNt/09qXlG6xB/IEPN3LzZ52nbK3BjBpZTx15U7F7f61T5xcW1H3t5ueXouc8tqL3/M9n2MjPylRrufhDMyesN0/2AL3XYedD30agJKTlel3/gcJ7P5e/2oXw71Fa209e6vhx7fLJv59902/el/IwM32/g7EvJ9roOu2wYCV7BVR2cbHtlZeXGz7/kfRsodvN73onyD9m47oepc5CdU/M1V7DE+OxQcay1cSyIvmHDBj333HOaPXu2GjY8MgKsuLhYDRo0OG7UeefOnRUTE1Plsbi4OHXt2tXjci2LRTPd4U7OaW/fS09fF5+er0mrE/T3vqfrzDZNPC/QA4dz3e/oAqUdWfJPXY4tw4kyPdmnP9qe0/updt8e1MHpso8c5/EFBUzbN1SPnYfzlJBe4FF/FAxtz51UQoF07k3UZcmuVOOffcnzvtKuOjv5nruTzsZk+wuEtm/ys5+eV6KmHo7mCoq25+Z+TZ7/0vJyzd6SrF6nt1R7B2bCzN9We85z0589k7/5jt2nad7WwfRvEFveO8PX8e6W7dRvlGA49kAs33jb9734KvWo7fm61vYCpQ7efg+ZLN+egt0rmzjr7xxbWLRLly4qLCzUmDFjVFxcrAMHDuitt97SrbfeWhlUrzBo0CCtWbNG8+bNU2lpqebNm6c1a9boxhtvdKp6Ie+TGkZjV/BX3qPbJq/T/O2HdNtn6xSXmqd/zYvVfh9z6rmS78aIIH8wsZBpTQpLyjTumJzkJv2y17fcasFu56Fcv5Tzfwt3qs+7K/xSFsybt925xePqklJ3ck/UUb7kVA12/lrE24T/LDP3/b7QwUUr64rcojK9smCnBn2yxtbPYFZBib4wtJg2AABAXeRYEL1p06b69NNPtWvXLl166aUaMmSILrnkEj333HOSpMjISM2ePVuS1KFDB40fP14fffSRevfurQkTJmjcuHE66yznFlgKdVkF9kx7sds/vtyo+dsPHbfQoR0sy1K6Gws8OWnlnjT1HrNcb/7kXm5mf5mydr9mbDpouhqVEjN9Wxwo2P3gxqgxb8Qdrhqc/35L9fkTS8rKletm+hFP1OUglV2cfI/K3AgOx6aYn87pBE/e1k/duMlcV93wcZRe+XGH6WoYsXx33b15u8jHxQ598fwPscbKDkbJ2fZNDX/5xx16f/le2/bnFDsWxQMAAPAHR3Oid+zYUZMmTar2uejo6Cp/9+/fX/3793eyOvDQ6vgM3XfJmX4tM/+3Edq+ru5enbeX7Nb0jUm279cTo2cdSVv0Q0xKLVv6x85DuRq3Yq+2JdfNwJm36mqs9/8W7nJru0v+s1KStGTkJWre2L6vibE/B85sB38rKnUvz/mKPek6o7Wzaa1q8uL8HfpmaC9b9+lO+rBj2X0zIduDm0LfbUnWA5eeaWv5weJwbrHmxKToX3/uYts+dx7K1b9/ilOuGzkrM/NL1LJJ7YsgOmF7So7+2LGNkbIBJ/wan2G6Cm5ZsSddN1/o3WKWAAAA/uTYSHQEvy0HnQ2s7k3L9+s0U9MB9EAzbvke3fnFBq2Oz1B2oX9mJvgrTQnsEZOcbev+vtpwwNb9BRN3UxSZ/ow4kcbr/xbu9Pg1dqeecTXrAs57eOYWbUrK1u7U2hf+umXyWkfq4K/0dIHKX9/xCF5vLHLvJrsT8gIk1aJ05Gaat0rKzC0Kbpf3fUjt+NGv8T6XfzDb7EzUYJ4xOdPHGcXBmsouI79YCwymDdudmqefdpqb7WWHhIwCxQTxgLpf95q7YZxvKEWvu4Oz3PG/6KSg7vtMIIgeokx94I/2l8/WBdw008IS534AB1LnVFpuacraRJfPO1XVL9a5LvNYT3wXoxd+2O5MRWqxPjFTY5buDrjc9fBeWl6x6SqErFQv3vtfQ3xdBCc88u0WI+V6kkbNqWBvAH39hpTsQrMp9OC+bzcfVKnNQeCfd6W6tV10Ypae/D4mIH5z3T01uvaNXLhlkjM3Af2pqLRcUfs8D0gdzC7Uz3FpPpd/KNfsb7WPfzWbzs3bz2Becal2Hc7zqeyPfbgJYsdaZnGp3tX/js/X65NVCT6X7607Pl+vkrLg/5ExdFq0Mr2YOWrauoRMo+XfNnmdkXLfXbrbtn2tScis0ykNnUAQHTjKEjd/8HtjnkN5rr0xMwhG5S/bnaYFsYeNXFT9a94Ofb3hgKawIFedMfjTNR5tX1JuZkTZ/Dq4+CcBTPOKS8uNjtQxaWHsIWUE4YVhXeDOOgwVDuXYlwv8aGEGpyHsTs3TgSz70xM6ZZXN6V+enL3N7W1/jkvzatZSIDloYz57kzYkep6jPiE9eNp5TSavMfu7v9jLYKwdN6B9mbG9J823AL4kbUnybvar6fXO6hJvBr2YZvf3VrCYtdnetezWJoTm++gtguiAn8zc5N2PEycCUOv2Z9b4fJ4buWtDwX4HFjh193wG0syFuqDQw2lvf5uywaMAUF1yMLvQ1mMP5imidkrIqD2dCezHwpbB4fqPo7Ryj+8jWY+1fLf9+3RHVkGJ7vh8veKDKLg4e6vZtFcLYoM7JQIAAKj7CKIDAW6ZAxeAtQXR96TlKz+AclTWJTlujha58dM1yvVgMUTYb30tn5PaWJalf3y1Uf+L9iwX/BovplLbZXdqvgZ9skbPG0qlVMHOXH+Boq6MUoRzxq/YG7I37yRpmgcp39xh8gZechB+3oNxKj9+Z3LWBQAAoYIgOmr0wg/bvc5RBnukOzC1Kreo9gD5bs67I9wNDh7MLrJ9qhY84+tNjCW7UrU5KVtvL/Esb930jUlu32xxR0JGgcYt3+vR8fy007nUVqEou7BEq0N0yinc99ma/Zq3LcVI2R+s2Ku3FptbXNJuc7Yma9g09/NbW5al1NzgC3zbaeMBexcTh38RQgcAwHkE0VGjBbGH9Y8vN9q+XwLzgc+X3HhwbX9m8Ezthm8Oe7lA1s9xabpy/K+21eOWSWs1Ze1+TVgZb9s+4Zk3FsWZrgKCxIEs+9OIuWPJrlTN2HRQSTaWn1lgbjbVKwt2ypMx/a8s2KlrP4rSTzvrTkqRUJ7VAAAA4ASC6KhVvgMLO/718/W27xP2qosLHJq2fHeaPjO8aBEQapxcMNpdm5M8X6jNDuWWpbcW79LcGLO5jj2xPcVMCo4dh3KVyQJlysi3b/bbyz/usG1fTpsbc2QGwCer9hmth+VR6N+15GwzN2NMCvW1ZIrL6l4atFBj1+cfoaOUm6WA3xFEBwAX7MwLn1tUqse/i/HoNYkOLGzqibqYlxqh558e5nevS6M3V+xO04xNB/XG4uAZCX/3VPdTcNhp5Z50DfhwlZGyA8lri+xL6cKiwuZ4m99868HgTenS590VtuznBcNrgniT0mlPWp5W7km3pfw9XswWtvOzvmiH57NB5m83kwarwocr99qyn8vH2TcL0VPupPqsy95fbs859Ke84lJdPNaefs9bdl4renNDYMpaBqfB/wiiA4ALy21c1NWb/NqJhlO/2LXIW2pukR6asdmWfQFOyywo0eytwTNyuyZZBtNpIDjtOky6PTsE66hg+owjqSxNenG+5zM4PB2kUZOf4zz/7fvhL/G2lf/cXM9vYvxrntlZL5OiCOTB/1butufGmS/snGm5P4OUpwgOBNEBF+yeFloWIIMbdxzKNV0FBIlEm3LjvrN0t6L2ZdqyL8BT3nS9ry7YaWsdDnmZHx9AcPr7VxtNV8GoQgdSQQKOC/GUQAhxNH/ALQTRARfmbLV3amB2YWDkWr3riw2mq4Ag4e3CmEebuHqfftppPic1zCgPgAvSEkN3MKMTs3Tv1xu9unGZlkfQPVTZubCnRJ5o+F9uUan6v/+L6WoAIcN0N2+6fADwJ4LogAuvLrR3JGKo/8DwNigUFhZmc03gL0Wl5frvL2YXaYNZoZwa4r5vNmnjgWyvblx6kxMXxwvG0bB2LuqdX1xmW55of1i3P0sJhqdzm7rpVpdsSDSzkDLgM645AAQBu7sq4i2eIYgOwC/sDAx4w+SI2HLLUq6Ni5QGi2IWJg15zHyBSR/9avYm3v+ik4yWH4xrUfz9S98Xlk3O9n40v+kgPgAAAAJXuOkKAE6zLEsv/BBruhowLNrgyKhHv92qVfEZHr+O8XDwVSCkUwFM2ZacY7T8j36NN1r+loNmj98bWYW+L2xp+uZdXAjPwAEAAKjLGImOOm/LwRwt3HHYq9eSS9QeiZkFem/ZHqN1KDQ4KtqbADpghz1p+aar4LWCIEzFUVewALU9gjGdTF1gRyDeFym5RUbLh+/yPZw9+JrNKSA9lZhp71oKoaqs3NKk1Qmmq+GxTUn2DBSKOZjt8WuSswv15OxtPpf9+qJdijf8m7X3mOVBde0fGwC/1T5ZZd/nJSHDs/O/LiHTtrIl6Z/zYpWR71762dJyy/Z+32Tb+3rDAWNlByOC6AhY/5oXa0tnUlLmffB0WVyaz+VXOGDzYmHB5F/zmAkAz+01/GM6eH5G1yCIDyKIrmPqHLt+TIfiiFzLsvTqgh16b9keFZNfGwhKj38f49H2321Jdqgm8LcPf4n3aHu7f6um5Hh+E+5f83bYUvbQLzd6/Jonv/c9gF7h/v9tsm1f3tqc5PmNBDtl5pe4ve3UdYkO1sQ9ds70fsLDtvTgdHtT1v24/ZDGLN3t1rbztqXY3u+v259p6/485U3fE6oIooeQwpIy/RCT4vYdNtPmbz9kPDelHXfWQaccCF7+0Z4f2P70hIcXsXZbsSfdaPmAzwyuE1Rabhkf2RJjIJ3L/sxCzd6aEhAXtzBjkZezHyX7bh56OwvJCuY7rzYyPSIWwcPuWXPBtp7Q3nT7PivpHgSQnZJbZHYGWUl5cJ3/uuZgtnsxC09udrgrx3DbC7a+xySC6CFk7M979NKPO2y/a+eksgAYivjJKrMLkwW7JTsP61CuuRs3BSVl+nJ9og4E4VTXsnJ72v/u1DzNjUmxZV/+dMjwzZeVu+2bieKNYM9nHpviWwDz30vibKoJTJi5MUnfbzU7OrPIwAVBaR24AM4tMpsOJdj9EADfty/O9+7GuelRmAAAAIGMIHoI+WnnkZExu1MZXeGJj381G0S3JJX6kJLGtKfnbDda/vgVezX25z16OwgDchtsmiIXqnl5S8stvTg/VrM2HzRdFa9sDcJFAY/2v+gkn14fCIEoU0wuhGyXTYaDcXnFBIK9ZdcNXASfyVH7TVcBAAAgYBFEB4LA6Flm0lokZwff6O1j2RWIDmbvL99rugpGLN5xWPO2HdLri3aZropXCGSFLlIJ+e7ycb+argIQkoJ8EhUAhCTSeQHuIYiOgEYM6YjV+zKMlPvKAntXnfaGwZS+dYYvNxL2G16XwBc5pCQIekk+LMgcczBb/4tmtXkAAAAAdQPxEbMIosMtphYa8GVxJvhubUImU+JDXLCmQkHdcOOna7x+7dAvN+rtJbttrA0AIBSUGh7FE2Vo8EwFE+tJeMv0rL2cQnuvkxKzgmvwSjC1FbsUlpRpyc7Djlwjl5SF9gjCTQdqH/hlWZaW7Kx7MaIlOw9rWVyaEjPN9AGZBeYX9g0WBNHhln0ZZvKob2WBI+OYlgsEL1+mZu46nGtjTYDQkFsUmmtQoO4wNXAmkJgOJjw0Y4vR8sf+bPYGtCeB2TcX25+yL+ag+9efD82091yNmrnVo+3tvuGzL52102rzfwt36uk52/XCD7G27/vuqRts36dT4g7n2b7P4V9vUkZ+cY3bLN+d7tiaa5589u22IPawnvg+RjdNXFvrtk7ca/n7Vxvt32kdRRAdAW1NQqZScopMVwMGHfAhnYNlWdrlwBc8EAr+NiV4fsijbnHiwsxfVu31PZ/9r3vTlcWIIBjC2AmYXti7oMT9m5HfbUm2vfxtKcEziKC0zN6bXvHpwTUS3oQFsUdGQa90YP2aLJtnNjgpLtWZ32qpeTUH0TckZjpSruRMcBp1D0F0BLzn5vp2pzGMpFFBLb2Wu9E18bXtILT5+kO2PACmcezPDP7FgeF/dnxt+nID9K9T1ttQg+D1yLdbfUplBAAAPMfimgBq42gQPTY2VsOGDVOfPn106aWX6qmnnlJ6evV37IYPH64LLrhAkZGRlf+WL1/uZPUQJEgpYB/TeQP9zZcFNWFeYZBPKd96MMd0FfT07G2mq+Azy4ubEd68pi4yeQ95W7L59u+Ne6ZFq9CDUZDHsuvGfV6xubQw6fmMggcAAKgOgzRDm2NB9MLCQg0fPlyRkZFauXKl5s6dq8zMTD333HPVbr9161ZNnDhR0dHRlf8uu+wyp6oXknwJKayON7vAjWm9xyzX2z/Fma6Gz6atSzRdBcAjgTCa21slNk+xDVVfrPW839pbB3J6ciPAjG3JOZpjOJWBaY9951lOXgAAACAUOBZET0pK0jnnnKORI0eqYcOGatWqlW6//XatXXt8ovz9+/crKytL5513nlPVgY/eX77XkRWo/cGuOMT/NiZ5/Vq7F33x1sxN3h8DQpPptuvLjf5gHyQQ9PW36QA+X7vf49fUhYXx/rNsj+kqeM2OWU87DOakDfUbYImkgYJPAuM3LwAAgN0cC6KfffbZ+vTTT1W/fv3KxxYsWKDzzz//uG23bNmipk2bavTo0brooot0ww03aMaMGU5VDV4qMDS1uKDEt4vZn+PSbKqJd+6csl4Xj11htA6+2HGIdDqh7KsNB7x+7cFs3wMxvuRVZqodgtmX673/7PnK11QiniwK58rutOBdXBTw1m6HFmqr60wO9GHWEFB3hPLHOZSPHfCEXxYWtSxLY8eO1dKlS/X8888f93xxcbG6d++u0aNHa8WKFXrmmWf02muvaf78+R6XFRbGP1f/sn1cJM+dMjzZzl/n9XBukU/H7Ws9dh6254LIjjaQlO35e/HA/zb7VKav6nlQznGP+168Le97MJfvbR2i9mXozi82+Fz2TRPXqrS83MhxSz60vRA+93a+/9KR7y4T5cen5xs/fl/ani/1KCgpC+q2Z/KzZ6dAfg/c3SbYjtt0+U98v83n8lfHV7/+lLu2HMzy7fj9cO6P3nbZ7lRdPu5Xn47Zl7r0edeZgTLu/vbpPcb+NcQKS93/3fXKjztsL39uTLLfPu/V2XIw2+2+zwmefN6SbBiwcrQxS+P81t9Vp8yy3C771snHZzjw1bNztxtpc0eLTcmpte3tcyBt4aiZWzw69x+u3Gt7HSQ3ynakVA/K90MbMFW+u/1eXf7njnDnTsERubm5evbZZxUTE6OpU6eqS5cux20zePBgDR48uPLvfv36afDgwZo/f76uvfZaj8pr06a5r1WGC61bN1PbFo1r3c6Jc9C2rff7bNTIvmbuSz0o2zsnnNDE7fKPbXtpNiyOVlCvvk5r3cTn/XjL5HvvSx1++HGnbeXXb9JIbZvX3vccq1kzz19zLG/bniQ1bOhb33NCS/fbvlN8Kb9x4wbG6tGyyPd0HLdNXqc9r1+nevU8/7W6aJs9ObXdOe6avnObNcv0qtwGDcN9+94t9L3vbd68sbH237Sp92U3adLItnoE+ve+q7b30/YUfevDLKZg7vdMl19aVq6H3/EtqPrg/7Yo/s3rvX59ixTvB4+0adNMYW5exVa0vyd8PF5XTLeDjLIwndcusPsASY6sIfHesr0afa25NK/ztx3Sh3f3dvm80/EGT9reO8vsDWQezC4y2vZLwsN1spvXXfvSC2wvv6i03Phn/9M1ifr8732qfa6i7T09N9b2ctPySzw6dm8G57mjZcumNdYjIqKhI+VWcOc9aNrUvt963tbB3+USZ/2do0H0hIQE3XvvvTrllFM0Y8YMtW7dutrtZsyYoaZNm1YJmBcXF6tRI88bZ1paDlNRHJKenqt6xa4vjMPCjny4ajsHqV6MDE9NzfH4NRWKi+yb4ulLPUyXPWbJbmNl+yIzK1+pqTV/Wbrb9rzx3592avQVHezdqQdMvve+1KHIxs9denqe6hV5HpTLzfV9dE5tx+6q7RWVlmuBjxeWWZn5Sk21LxDtDV/aX6ENgVRv6pFZUKL3FtuzEPSXv+zRn889yePX3TtlnS3l13Tc7vR73n4GSopLfTr3uTZ8/nNyCo31f3l53pedn2/fRWWg/uaore3943Pf2r/p771gLr/IpvUgfKlDdo73ga1/TFqjf99Yc/DUyd98RzPdDjIz85Ta0A9DHl0wffyBWH4gtr0iL34f21m+3TIy8hRRbiaFbAXTba+632DHtr1ih1JYmT526UgbSG3guu/LLyh2tHx33oO8PGduIHhSB3+V669+LxBUHGttHAuiZ2Vl6Z577tFFF12k1157TfXquc4ck5ubq3fffVdnnHGGzjnnHC1fvlxz587VxIkTPS7Xssjn5BR339vatvvzf6O8Ktt79v0ANdm2fC3bl9zWpo/b3fKd+PxbMn/8ppmug8l+3du2t8yGtRgC4fvM2/LXJmRo9lb7Rqd5Uo8X5+3QL3t9S2dQ4UBmYcB//mtqJ96fv0yVlFkK92IUviTF2rAoqC/tf2Nilk9lF5aUe122ne0lmNue02U7KZjLt6vuvuyntMz7Fy/ZlWr0N9+x+zfJ9G+AQDj+QC0/kNpeXeuDTbf7ijoYLb+GOlS8P05V0fSxV9TBdBs0LRCve02fl0DiWE70b7/9VklJSZo/f7569uypyMjIyn+SFBkZqdmzZ0uS7rnnHt1111166KGHFBkZqXfeeUdvvfWWevXq5VT1gKBTaMNCbQD8I5QXGtuYmKUR07cYK9/XfMCQSsstvf2T96P57RoN6621+zN9en2+Dwur+iNXZl1308Q1pqsAL338a7yembPdp338vCvVptoAAADYy7GR6MOGDdOwYcNcPh8dHV35/7CwMI0YMUIjRoxwqjpA0CsqLVfjBvVNV8PvHvjfZo267CwN6X2akfL/F52k0ZebS+diWZbb+UFRVZnZOF7IemGe/Xka4Z08H4LB324+qD+fe5IiTz3Bxhr5x8e/7vPp9aF7CywwJGbau1Ae/GNbco4+WZXg836enL1NE267QL1Pb2VDrQAAAOzj2Eh04FgxB7P1pwmrTFcDQej95c6s/u2O0nJL21PM5Ydb5+OIylA2NybZdBV8km1jXnlveTOiPiXH2TyBcE9qbpHGLPV+LQxJ+tqHNGDBrC5MJEnMtH/BtVCxxPBI6FU+zKbZb/C8H7Kx77cjJRQAAIDdCKLDfT6Ohh365UZlFNi/+Ent6sDVMIxKy/N+AZNyH6MxBCS9t92Gi3CTgaiFsYeMlV3h1/gM01WAlyasjPd5H94GE19ZsNPnsutCINtXvqSFem/ZHhtr4r5Ygzed7fL07G1Gyx81c6vXr432cT0ABA5f1jIKFXvT8o2WX+LQlMfeY5a7ve13W+wfMBJ3OM/2fbrrxk9J52XSh7/Em64CDCJ9sHsIooeI6z9abboKPlm047DpKhhXRkTBmGIfFsn6tw95hXFEscH8ytuSzQWEAuEjv+1g8AfEgtkT38V4/VqTecl9ufEY7Nbvty+IudSHBYrLDfQfpeWWhkyNrn1DALWaG1P74tyZRgYnBY6/fLbOaPmv2nDD2JXk7NrTWqXmOjPQ5r5vNjmyX7hnd6q5mxiTVvuekisUZOTXzb73U86/Wwiih4Bdh3N1KNfsBa2vd7Wem+vbIkV1wbebDpquQsh6evY2vTjfuzzPM308byU+BPBNsjONu8mRddmF5lKqLOTmYchbvtv7IGqwKza4qEF+sfef+w029lfuBFECSUZ+6N48AUwoNXG3DJUOZDnXR7szgMeXQT41yQmAdIKhrNDw4uyoXUkd7XudujFX1xBEDwF5ReanZdz5xQbTVQh6u1O9n7K4kem9Ppu3zUxqjf8yrU4v/bjDWNnBPJMgK8RHqIWyQzlFQX8T5vVFu5Tvw8Kovpix6aAmrvZtcVLABLvuX+/PIKc+AJhg4zikoFRbeDoQZuoitBFEh9tKfRgVlmD0x3iofxVJB3PMjGabtZnR875Kr6PTxTy1J83M1MZg/p1mIgVUWR0bmWHnjAp/um2y2SnudtmUZO4G8H9/IYiO0HXzpLWmqwAAABBwCKLDbXbm+fSnYA2CBLvM/BK9vmiX6WqgjgjWtDahJiCC6HT6yg/hhYECog0i6NGOPEfXCwAA6jqC6Agaa/ZlmK5CUCoqLdcsH/Nyl3sxojWUgzh1jS+zUOCbXMN5KUvKOfcIHuWWpdsNLzQXCMrKLW1OyjZdDSMsm2bgrPbiN2duUalWxafbUv4j326xZT/+RRQdAADUbQTRETTivFypOtTzZn26ap+iD/h2MX3HZ+sJpBoUl5qnIoOLzLyx2PMZBUkOLrYUSmZtPqhn52w3lt/8q/UHjJQbKGjHwSWnsFT7yOWsj1ftU2aIromwO8379WOO5s2irvd/s0mjZm61pfxf92aogMEIRrmzwJo3g0zsdCjHuUXgNh2ofQayk4vQFRteXDE1r/aFkp28WRmfbk9f5iSn+qhCN/ab4mDbN82da771+zMdK9+dBYt3Hc51rPxg4PQt479+vl5v/xRX7XfM9pQch0tHbQiiw22mp2l+sTbRbAWCVJQNI/j3pudr60E6bFP++vl6jZi+2Vj5s7emePya7Smh/ePKLu8v36vFOw/rQ0MLzBYavog17bstyUbLD/F7wB4j6Cg9P3e7Jq1OMF0NY8psSv315mLPF5XeedjetTu8ic8GQ+AtWFz7UVSt2/y43dlF52tb4PWeadGOlT386021buPOe+StJ76PcWzf7rjriw01Pu/04KLHv6v9+E1/3p1K23nTxNrXZLjhY+faXsxBszO53PntXVDiXPt7feHOWrf525SaPx/BLDMA1iOLS83T/zYm6ftqrkPunupcv7+FeI9bCKLDbTmFZtMKuDMioDqFpVxUI/h5M9ql95jl6j1muQO1gb85OdoMgc2u9BShYNS39owCDmYLdxw2XQUY9E10kukq2OL95XtNV8EtGxxeLyoxK3Rn1qyKN5vGM62W6053Rus6zfTMK6duInl7zW+X2EOhPRBoTozng6fqkowAmsm3co89KeLclcBsTrcQRIfb3lm629Fpe07xd+cDAHWJ6bzsoc5kKifTPF3cca9NqTy8tdqmfNgVQvn+SaimowEAd7EKAWCG6QwNMIsgOjwStS/TdBVClreddQAMlABggF2/756ds92mPQGeecOhqeJO+Wlnqukq1BkDJqwyXQUAAACgCoLoQJA47OUsgB02TUnzdNqiRTZfhLCwOjREYbUH6yo49alfspMUFaHoUK7ZKd2hzPSifgAAIATVcjFBhAGmEUQHgsTGA2YXOfn3T54tshXK09ABu63Yk17tCu2h4uk521Xo5qKRnqYAQWAL5XZv0t3TNpCPHx6pQ/eOAQAAqkUQHYBb9hpeAb62BX6OtXx3mkM1CU0EJs3beMD9BczqYuAxVHODf7v5oOkqGHXP1GhjZbt746Yu2p2ar8VBmJ7GzllwGxIzbduXN0zP6FvlYY7/mZvqxqKmR+NGEgATSstC8zcvEAwIogMICp+t2e/R9o9/F+NQTULTnK3JpqtgTKqHN3AkaW6M/e9XaZl7F/O9xyxX33dX6G9TNtheB/jf2J/3mK6CUbE2pSTzxqOzthor2xtvLrY3h/waD1I5OSG70Oziovd/s9lo+aaNmulZ+/91r//by8e/xju6/4dnbnF0//BeXGqe6SoYtzD2kLGyPR3cZKcShwPM7yzZ7ej+3XHxf1a6vImXZXjh7cTMAkf3P72WG7JfbzjgaPnu/Pb4cr2zdUBgI4iOOi05u9B0FWxl+oLSpK83HCBHq0Hx6c7+YApko7y4iDYRTJCq/rD1JvgP35kcubh+f6Z6j1lurPy6Zv1+92d/OMGTllRuWZq5qW7NWpi4OsF0FeCmX/Z4NmrdHdGJtX/+PlnlbBuJ2pdZ8wYOp7AhQ45r/7b5pmEw2nIwx1jZP8SkGCs7MdPZ63tP1wFzSrGLwTOr4s3e4J612dmBVd9vMTtwa7Xh9xeBjyB6CMgK4cBrblHdmood6kGx7wx/qXrqoM03cXKLSm3dX11lWZZWezgNvSa7DgfPaKe84rrV5wWCmGTPLlInR3k2a8ZOD/wvtEfO1jWmU0kkZJi9eVpYwo3zYPHKgh2273O/4fYHAAg9rO+B2hBEDwGfe5gGoyam8zMieOxOtT+HerDdELpp4lpb9xcsoww9DTrabemuVD3s4TR0wJWh06L16Lfut6cPf4l3rjI1SM8P7ZusgcDuC6+0PLPfeRvcGAkMSKybAgAAQgNB9BBg51Qv0+vrBMr0KtTuie/JSW73RWWwLBY5dJq5hQAl89McQ5npkbNO+WWv/akK7JZTyEwV08JsTr4Qe8jsDclgU1RariEGF6K121aDqRo8lUX/AwAAQgBB9DrO9MITdtubZjatwkZGZQWFuhrIA0JZKH+q6dNCk+mc7MHGiUUmn5mzzdgsjwXbzS0aCAAAgOMRRK/jMm0OopseaZJtuPxDuUVGyw916W7mhF/pwAJXCG1vBcACViEfRw3h41+bkGm6CkCt4lLNDnSodRFIL/y0M1Vjluy2fb/uCOEuDwAAICARRIdHDnsRRE6zcTHML9cfsG1f8FxJmdlFvjYnZbu1nVMLsJo+fpNCfT2EGR7ko9/j0IyZd5bGObJfBL58FowNeDsO5erbzcGxboVTRtXRtShSchhAgeNtS87R9w4veG86jeX6/ZkunzuQ5fzCr6U1/O7e5OY1gS9qWtz2r1PWO15+IEt0+Pxn17AOVnGp89dj/iijNq7SeP5zXqyfaxJaPlmVUOPzj82qm7914D6C6HDckl2ptu3L0yBmXQv8mR6JGiwjvO1sc0e7/bN1juwX9jKdO/6QQwGX+HTnL1gBeOfB/202XQUYNG75Htv3uaGGAGa12yd6tn2gS84prPH5xEyz34n3+GH9lx9izKb0eaCGfu35uc4H8ubXkNLIH/cXalrfKTGz5vZZ183a7OwNpPu/cd32ZmxKcrRsSXplwQ7Hy6jNUoeuZ1G7mlIirwiSeAicQxAdQSXUFww0fUvAdHDycK57I8xXO9RO9hv+wWz6JkqwmL01xWj5T83eZrR8wKS84tBcYDCnKDSPO5DsOJRrrOwpaxNt32dStmc3ZGsKOgWj2kYDBsJIUaflBnC/cjDb+d/EpvtVfxwjqre/hptk/kgvGwgzkHKLAnMWYpi9a6gHpDIuulEDgujwCP2JZ0wHneuajDq2UK6nPJlZUdMUXHgnoYZpvRWSswtVUFL3L+wD1fztZm+gmBQWIFc1dS2Qh+BQV39vmU7nAQAAgN85GkSPjY3VsGHD1KdPH1166aV66qmnlJ5e/fSHZcuWaeDAgerevbuuvfZaLV261MmqwUuB8FM+M99cINXTa7SFsYedqYibRtfBnF27DS9c5o5FOw4bzVcXn5Zf4xRcb2xLzrF1f8Ho8e9q/zz9hZQ/jiksrXlETmlZuf41z/z0W1MCI4RudjQwQldZHQ02z93qbMoEAAAAuM+xIHphYaGGDx+uyMhIrVy5UnPnzlVmZqaee+6547aNj4/Xww8/rEceeUTr1q3Tww8/rEcffVQpKaE7oixQbU7KrnGRFX8oLQ+eUZ52B3xTPJzaGyw5zD2xITHLdBVq9dzc7fqxhjyOTrvNgUDu3rR82/cZbJKyap/WG+qj0J1MuTVvW82fqU9W7XOsbAAw4bBDC6XbKYab7AAAIEQ4FkRPSkrSOeeco5EjR6phw4Zq1aqVbr/9dq1du/a4bWfNmqVevXrp6quvVnh4uK677jr17t1b33zzjVPVg5e2Jefo5knHn0P4x3sOLFzlKauOTplGYAuEUYbFZebrEOjcXbfAG8W1LCx9wI2bHE67xaHvR1oeQlFRLbNP/IXPX80CIXcwAACAPzgWRD/77LP16aefqn79+pWPLViwQOeff/5x28bFxalz585VHuvYsaNiY82lQ4B9AmWKuR2iD5gdBV1aSxDJaXtS83X9x1H61g+roruyLC50Vyo3GUc2HUR4bu52wzUAaudO3nwA7ikKgYUj7VBuWdpk+PepCTsP+ye9X2GJuZs5q/c5N7vLV+l+SK+5JSnb8TJqYnp2YXq+uZkok1bXvLCv02rq/w/nOn/jLtTHjKXWMAsqFGYnT1gRX23fb/L7QAqMQWXw08KilmVp7NixWrp0qZ5//vnjns/Ly1NERESVxxo3bqz8fM8/oGFh/Dv2nxNqKsvpOnhy7P+LtjfYO3PTQaPvvyWz5/7jVft0OLdYbyyOM9LuJClqX2atbc9Jpj5zkrRhf/XH7o/yLctsn3Mgq9Bo+RVMtj1X5R/9z6l0V6a/cyQprJZ6bDzg7MW20WOvoTynyzf5nedJHaIcCjaZbvfp+cUB+d5X95w/yw+Evj8Q+r2wMOnrDQc0/OtNzpTvznvvSMk1ly9JL/zgn8FW7/682+/t7Wim2nwFS5axshfvTJUMli+57vv84ZoPVxs7/x/+Eq/kbNe/vf3BVdlbD/ohhZThz90RlrG2d+1/Xbe95bvTHC/fdL/3/dZkTVgZf1zZ/d//xT8VOMrR5X+35aBfy6vpN19d/eeOcOfe/iNyc3P17LPPKiYmRlOnTlWXLl2O2yYiIkKFhVWnYRcWFqpp06Yel9emTXOv61oXrT3ozAJfbdu6fp+PPQeNIxraWnbr1s3UtkXjWrdLSMvXd1vsX5Dps/VJeuKa49txddbsz7K17Hr16tX43h/tV4dHbLtbD3+W7Y/Pv8njLlWYsfLDwtw7difT/dw1NVo/PnqZY/t3h8m2Jx3p/+rVc/0NP/Sd5Y6U6267a9askSPlS1KTJg1rrEdesbOjQ0x+9ps3j6i17eWFOTMuwuRxe1KH7x1aVNb08Y//NUHv/zWy1u3KHRqd5MnvPbst3pmqxs0j1KxR7ZcrxQ6OWm/QoL6xdpBZXF5r2bNjnFtDyp3jbpHs3GLCpj9/kvTTzlSN/VtPY+Wbfg9at26m8Pp+GXdXrbZtmyvMv5HL48o/lj/jDSbPf2F9c32fZPbYGxrs9ys0bdrouDqEStsz/d5L0vI96XrjL92N1qFhw/Aq78WWQ87PwDJ9rRsMHA2iJyQk6N5779Upp5yiGTNmqHXr1tVu17lzZ8XExFR5LC4uTl27dvW4zLS0nJCffnO0B6dtcGS/qanH3wEOCzvy4Tr2HBzKsHfKT3p6ruoV1z6FMCbBmVFpHyyN09Cep7i1bYzN0xDLysqrfe+r87dPo2wt+1ju1sMfZbtqe/4o259KSsqMlW9Z7h37y/OdCWRJUmxyjtH3XzLb9qQj33EmLibdfd9zHZxim59fXGM9nF6vwWTby8kpqLXtZWQ6k0om+VC2wmu4cXO0NQ6NBnfnvS8uLjVWtpPScwrdqkO5Q+3fk997Tuj64gKte6L2m6clDqa7M/ndm51fVGvZZQ4e+0+bEnXhH06ocZv4ZOdmAdXU/vzF3d8/TjHdB6Wm5br9HeBI+almfvccXX6Fo/s+E+X7W1ZmvlJTGxgrP1SvuSrk5v3e/4da2zP93ktSebn7cRenFBeXVqlDcZEzv3WPZvpa1yR3f184dls5KytL99xzj3r06KGJEye6DKBL0qBBg7RmzRrNmzdPpaWlmjdvntasWaMbb7zR43Iti39H/3NKTeU5XQdLYQF7/P4o3/Sxu1sPf5d99OP+Ltv0sfuj/JyiUrfKnuPgiDgpMN//isfX7890tnAX5QdSn+dkILu8lno4PRI9EL9zjn3OCZe9t1Lv/bzHreMfMX2LI3UoL7dqf+8dOv5A+M41WQd3214g9D9OMla24eMeMX1LreW/uTjOsfJNHLO79TBZtj/rYPocBFr5FY+ZKt+f7wFtz3D/E6Btz2TZ/u7/TX/3mKiDq/fc3d9iwf7PHY4F0b/99lslJSVp/vz56tmzpyIjIyv/SVJkZKRmz54tSerQoYPGjx+vjz76SL1799aECRM0btw4nXXWWU5VD0FseQgvLBnodh7KVe8xy/V/C3eargrgV3GH83T/N5tNV8O4t5fsdm7npn69hrjiMktfrEusdbt1CZmO1cHu9U08kZxdWPtGDqLZmxfK54DFXQEAAH7nWDqXYcOGadiwYS6fj46OrvJ3//791b9/f6eqgzpk4uoE3Xxh9elULMsyOuXvaDmF9k+3CZBDc2nc8r2SpO8dyEWPwBfKN09iDzmXEzZYLIw95Oj+QziOFfDHvjD2kJ53cJG/d5bu1i3dTzGSUmDgJ2u09nFzazFYAX/2AQAAgNBgbpUQwGaToxL05/+u1oEsZ/LCeirGwRyRtdmXbm8eeneVljNiKZSF8s2T9Q6Owj1aQUngfsaSspwdsRvKo0ED3XqbF9GuTlktC2cu253meB1M+HVvhukqGPfhyr1Gy68t3/yP2529gVib1Lxio+U76VCOc+tsBIvMgurXgcr1Q25cVK/UoYWcPZHCZ8NRrrp9f37u8kucTVMYyHKLSlVdisiCEH9PnFz/Be4jiA7H+Wvc2ISV8UrPL9EHy+P9VKL/uRNEKiwp062T1zlfGYM2JjofsAlEMck5XDQFKKdzwVdIdGjxSDt8F8I3UZzmzsWyP3Lyw4yh06K1Oj7ddDWMmRS1v8bno+IztC/Dub4xJrnmhcX+Oc+5WRjuhOpyi+puUGHCL/Gmq6Acw7+7BkxYdVwg/Yu1+3XFB7/6pwLVXHzcNHGNf8qWtOWg2YX9qvvd1f+9lX4rf2/a8QOjPvl1n274OMrxsk3fKgi0gOmmA1n++9zpSGzj4DEp5eZv88/1hivbavk+tMsVH/x63HobhSVluuz9X/xSvhRYg3diknN0xQe/1tkBI8GGIDocN8vvgZUA6vFs5s6RuRqxUpes9dOo30BU12+QIHgdcHgkeiirbRS25OzifjArJjlHD8/caroaASk2JUcPzdyiv36+3nRVHOHkYs3BoJRRd5KkJTsPV/n7/eVmZ2ckZvrv+/65udv9VlZ1qhskUVLmv8/lf6u5kfTxqn1+K9+kXYfzjJVdXQrVz9bUfEPXCQuOmen0ygKzqTPHr/Bf3/Pt5oNV/t5TzQ2lUDF1rf/bHlwjiA7H2T3FP684sO5KA/6UVsu07VCf+rycO/SOyA/wfpdAS2BMLwf8bechc0EWAAAAhBaC6Ag6BNERqGZvSdbfppgdDfeq4REKpj3+XYzpKtRJ90zbYLoKNc7EKfbjqLBQFSjrjSDwrI5P15Ykc+vAAAAAAP4QbroCgDdKyy2F1/NXtnXvbEvONV0FRwXDOfC3VxeaD2BvNbigLequ+PQCbT2Yra7tW1T7vD9SDoRyVoNASOmQnleiP5wQYax8V+9BXnEpN88MOpxbFNKpZljky1kMnAEAAIGEkehwzJPfx6j3mOWmq2HMhw4shpSUVajyAAimSNLgT9eQQiEA1eUFxmB2xYdhX250+Zx/UioERt+HwPLNhiSt3x+ai00HgsO5NacYqytc3cS55D/+W2CwOnNj6vaCziv3hO6CugAAIPAQRIdXHpqxucaFzlJzi/RznJncxHU9zLL5QGCMNE7JKdK+jMCb3r8njfyoqMMCtIMrKXf+hlqA3D90acehuj37KFDR54e2Yj/dzN+Xbub3Tm393ss/mp8Bh7otwL966zyT778Vwmc/UH5zBkg1KgVafZwUSscKzxBEh1ei9mVqTUKGy+czC0r9WJuqAuVLzyn+CFYFPBdZZErLynX7Z2Zzkpv0hcMrdx+7QjyA3z08Y4tj+w4LI3XW3vT8ah8PhHQPk1YnmK5CSCotK9dbP8X5pazyai6nk7IKHS830H/SpuebmYkwKzrRr+UVl1b97R1z0L8DWjYfzPFreUe75D8rjaYtSskpOm4myOBP1/it/EmrE4we/6YD5mZajV2657jHSsvK/TbT/B9fbTzusVXx/pmdsikpW73HLFdWQYlfyqtJYmaBHp6xRb/uTlWJH9cAyi06Pp6zNiHTb+UfK+6wfwdNHPv9VlrDAFKEFoLodVhcqrMdTWmAL+Q2Z2uK6So44vM1zgZKg1lhaWjfYHh/+V5H9//CvFhH9++rNftc39hD8PtqwwHTVahRhsELrcIS84Fkp32/JTDTVliW5Uj6Nk85tbBnUS3fqwUG294hw6lkuKA2dwNp9Deb/Fred8f0P0NrSG/mhB9ifr+mMZFKcW6M2WuqTcfMwj3ghxtYR5u/zdwgkvR8c78tYquZYfft5oMGavK7UX5eg+P/AmC9q+d/iNWq+Az97ZMov5Y7Yvpmv5ZXG3+vPXbsDYsft5vsB/03mOZgtn/712BEEL2OSsws0F8/r8Mjct0Ybj6/jo6ajdqXaboKLuX4MR/3gAmr1HvM8mrvksM5axMyAjIX/pfrEzXSwZHAMI+AlWv3fu18QIl3v3qB8r4kZjpz0XPfN5uOG4V7tCU7Ux0pF0cE+uzKUOmX84oD57emiXfc9G/twlKzN4pzA+j8m5ZVGFrvRSCs+3E4t8hIuSk5ZsoNVKGy7pg/ZzsEK4LoddS2ZHPT/uD8FN9dhwMz964/cwJn/jbqc962ujnjIFCNmL5F7zk84r0m0zcmVfu4v6YXfrnev1PIPZFd6Pxopfi06lNqhIJAzr1d3Wgxu7laWNFfAj2YWFdtS86pcfRXqJyWmm4k1HU1BZADcW0cAAAApxBEr6OWGVrUs0IoL0QiSTkO36X/25QNju4/mBw9CMofI6JGzdyisT/vdrycQPa1wbQa//ZT/ltXxv58fH5If6qpby0ocT7IM3JG9VM7QyHAaXK9hR8MT6WXpDLDJ9lV7mV/VMv0DQTTfqxhZl+oZOufsDLedBWMqan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|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"execution_count": 22
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": "## 3. Simulazione dei Dati di Produzione Annuale"
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-23T06:10:51.081621Z",
|
|
"start_time": "2024-10-23T06:10:51.044080Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"\n",
|
|
"# Esempio di utilizzo\n",
|
|
"olive_varieties = pd.read_csv('./data/variety_olive_oil_production.csv')\n",
|
|
"\n",
|
|
"\n",
|
|
"def add_olive_water_consumption_correlation(dataset):\n",
|
|
" # Dati simulati per il fabbisogno d'acqua e la correlazione con la temperatura\n",
|
|
" fabbisogno_acqua = {\n",
|
|
" \"Nocellara dell'Etna\": {\"Primavera\": 1200, \"Estate\": 2000, \"Autunno\": 1000, \"Inverno\": 500, \"Temperatura Ottimale\": 18, \"Resistenza\": \"Media\"},\n",
|
|
" \"Leccino\": {\"Primavera\": 1000, \"Estate\": 1800, \"Autunno\": 800, \"Inverno\": 400, \"Temperatura Ottimale\": 20, \"Resistenza\": \"Alta\"},\n",
|
|
" \"Frantoio\": {\"Primavera\": 1100, \"Estate\": 1900, \"Autunno\": 900, \"Inverno\": 450, \"Temperatura Ottimale\": 19, \"Resistenza\": \"Alta\"},\n",
|
|
" \"Coratina\": {\"Primavera\": 1300, \"Estate\": 2200, \"Autunno\": 1100, \"Inverno\": 550, \"Temperatura Ottimale\": 17, \"Resistenza\": \"Media\"},\n",
|
|
" \"Moraiolo\": {\"Primavera\": 1150, \"Estate\": 2100, \"Autunno\": 900, \"Inverno\": 480, \"Temperatura Ottimale\": 18, \"Resistenza\": \"Media\"},\n",
|
|
" \"Pendolino\": {\"Primavera\": 1050, \"Estate\": 1850, \"Autunno\": 850, \"Inverno\": 430, \"Temperatura Ottimale\": 20, \"Resistenza\": \"Alta\"},\n",
|
|
" \"Taggiasca\": {\"Primavera\": 1000, \"Estate\": 1750, \"Autunno\": 800, \"Inverno\": 400, \"Temperatura Ottimale\": 19, \"Resistenza\": \"Alta\"},\n",
|
|
" \"Canino\": {\"Primavera\": 1100, \"Estate\": 1900, \"Autunno\": 900, \"Inverno\": 450, \"Temperatura Ottimale\": 18, \"Resistenza\": \"Media\"},\n",
|
|
" \"Itrana\": {\"Primavera\": 1200, \"Estate\": 2000, \"Autunno\": 1000, \"Inverno\": 500, \"Temperatura Ottimale\": 17, \"Resistenza\": \"Media\"},\n",
|
|
" \"Ogliarola\": {\"Primavera\": 1150, \"Estate\": 1950, \"Autunno\": 900, \"Inverno\": 480, \"Temperatura Ottimale\": 18, \"Resistenza\": \"Media\"},\n",
|
|
" \"Biancolilla\": {\"Primavera\": 1050, \"Estate\": 1800, \"Autunno\": 850, \"Inverno\": 430, \"Temperatura Ottimale\": 19, \"Resistenza\": \"Alta\"}\n",
|
|
" }\n",
|
|
"\n",
|
|
" # Calcola il fabbisogno idrico annuale per ogni varietà\n",
|
|
" for varieta in fabbisogno_acqua:\n",
|
|
" fabbisogno_acqua[varieta][\"Annuale\"] = sum([fabbisogno_acqua[varieta][stagione] for stagione in [\"Primavera\", \"Estate\", \"Autunno\", \"Inverno\"]])\n",
|
|
"\n",
|
|
" # Aggiungiamo le nuove colonne al dataset\n",
|
|
" dataset[\"Fabbisogno Acqua Primavera (m³/ettaro)\"] = dataset[\"Varietà di Olive\"].apply(lambda x: fabbisogno_acqua[x][\"Primavera\"])\n",
|
|
" dataset[\"Fabbisogno Acqua Estate (m³/ettaro)\"] = dataset[\"Varietà di Olive\"].apply(lambda x: fabbisogno_acqua[x][\"Estate\"])\n",
|
|
" dataset[\"Fabbisogno Acqua Autunno (m³/ettaro)\"] = dataset[\"Varietà di Olive\"].apply(lambda x: fabbisogno_acqua[x][\"Autunno\"])\n",
|
|
" dataset[\"Fabbisogno Acqua Inverno (m³/ettaro)\"] = dataset[\"Varietà di Olive\"].apply(lambda x: fabbisogno_acqua[x][\"Inverno\"])\n",
|
|
" dataset[\"Fabbisogno Idrico Annuale (m³/ettaro)\"] = dataset[\"Varietà di Olive\"].apply(lambda x: fabbisogno_acqua[x][\"Annuale\"])\n",
|
|
" dataset[\"Temperatura Ottimale\"] = dataset[\"Varietà di Olive\"].apply(lambda x: fabbisogno_acqua[x][\"Temperatura Ottimale\"])\n",
|
|
" dataset[\"Resistenza alla Siccità\"] = dataset[\"Varietà di Olive\"].apply(lambda x: fabbisogno_acqua[x][\"Resistenza\"])\n",
|
|
"\n",
|
|
" return dataset\n",
|
|
"\n",
|
|
"\n",
|
|
"olive_varieties = add_olive_water_consumption_correlation(olive_varieties)\n",
|
|
"\n",
|
|
"olive_varieties.to_parquet(\"./data/olive_varieties.parquet\")"
|
|
],
|
|
"outputs": [],
|
|
"execution_count": 23
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-24T10:59:32.356335Z",
|
|
"start_time": "2024-10-24T10:59:32.229812Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"def preprocess_weather_data(weather_df):\n",
|
|
" # Calcola statistiche mensili per ogni anno\n",
|
|
" monthly_weather = weather_df.groupby(['year', 'month']).agg({\n",
|
|
" 'temp': ['mean', 'min', 'max'],\n",
|
|
" 'humidity': 'mean',\n",
|
|
" 'precip': 'sum',\n",
|
|
" 'windspeed': 'mean',\n",
|
|
" 'cloudcover': 'mean',\n",
|
|
" 'solarradiation': 'sum',\n",
|
|
" 'solarenergy': 'sum',\n",
|
|
" 'uvindex': 'max'\n",
|
|
" }).reset_index()\n",
|
|
"\n",
|
|
" monthly_weather.columns = ['year', 'month'] + [f'{col[0]}_{col[1]}' for col in monthly_weather.columns[2:]]\n",
|
|
" return monthly_weather\n",
|
|
"\n",
|
|
"\n",
|
|
"def get_growth_phase(month):\n",
|
|
" if month in [12, 1, 2]:\n",
|
|
" return 'dormancy'\n",
|
|
" elif month in [3, 4, 5]:\n",
|
|
" return 'flowering'\n",
|
|
" elif month in [6, 7, 8]:\n",
|
|
" return 'fruit_set'\n",
|
|
" else:\n",
|
|
" return 'ripening'\n",
|
|
"\n",
|
|
"\n",
|
|
"def calculate_weather_effect(row, optimal_temp):\n",
|
|
" # Effetti base\n",
|
|
" temp_effect = -0.1 * (row['temp_mean'] - optimal_temp) ** 2\n",
|
|
" rain_effect = -0.05 * (row['precip_sum'] - 600) ** 2 / 10000\n",
|
|
" sun_effect = 0.1 * row['solarenergy_sum'] / 1000\n",
|
|
"\n",
|
|
" # Fattori di scala basati sulla fase di crescita\n",
|
|
" if row['growth_phase'] == 'dormancy':\n",
|
|
" temp_scale = 0.5\n",
|
|
" rain_scale = 0.2\n",
|
|
" sun_scale = 0.1\n",
|
|
" elif row['growth_phase'] == 'flowering':\n",
|
|
" temp_scale = 2.0\n",
|
|
" rain_scale = 1.5\n",
|
|
" sun_scale = 1.0\n",
|
|
" elif row['growth_phase'] == 'fruit_set':\n",
|
|
" temp_scale = 1.5\n",
|
|
" rain_scale = 1.0\n",
|
|
" sun_scale = 0.8\n",
|
|
" else: # ripening\n",
|
|
" temp_scale = 1.0\n",
|
|
" rain_scale = 0.5\n",
|
|
" sun_scale = 1.2\n",
|
|
"\n",
|
|
" # Calcolo dell'effetto combinato\n",
|
|
" combined_effect = (\n",
|
|
" temp_scale * temp_effect +\n",
|
|
" rain_scale * rain_effect +\n",
|
|
" sun_scale * sun_effect\n",
|
|
" )\n",
|
|
"\n",
|
|
" # Aggiustamenti specifici per fase\n",
|
|
" if row['growth_phase'] == 'flowering':\n",
|
|
" combined_effect -= 0.5 * max(0, row['precip_sum'] - 50) # Penalità per pioggia eccessiva durante la fioritura\n",
|
|
" elif row['growth_phase'] == 'fruit_set':\n",
|
|
" combined_effect += 0.3 * max(0, row['temp_mean'] - (optimal_temp + 5)) # Bonus per temperature più alte durante la formazione dei frutti\n",
|
|
"\n",
|
|
" return combined_effect\n",
|
|
"\n",
|
|
"\n",
|
|
"def calculate_water_need(weather_data, base_need, optimal_temp):\n",
|
|
" # Calcola il fabbisogno idrico basato su temperatura e precipitazioni\n",
|
|
" temp_factor = 1 + 0.05 * (weather_data['temp_mean'] - optimal_temp) # Aumenta del 5% per ogni grado sopra l'ottimale\n",
|
|
" rain_factor = 1 - 0.001 * weather_data['precip_sum'] # Diminuisce leggermente con l'aumentare delle precipitazioni\n",
|
|
" return base_need * temp_factor * rain_factor\n",
|
|
"\n",
|
|
"\n",
|
|
"def clean_column_name(name):\n",
|
|
" # Rimuove caratteri speciali e spazi, converte in snake_case e abbrevia\n",
|
|
" name = re.sub(r'[^a-zA-Z0-9\\s]', '', name) # Rimuove caratteri speciali\n",
|
|
" name = name.lower().replace(' ', '_') # Converte in snake_case\n",
|
|
"\n",
|
|
" # Abbreviazioni comuni\n",
|
|
" abbreviations = {\n",
|
|
" 'production': 'prod',\n",
|
|
" 'percentage': 'pct',\n",
|
|
" 'hectare': 'ha',\n",
|
|
" 'tonnes': 't',\n",
|
|
" 'litres': 'l',\n",
|
|
" 'minimum': 'min',\n",
|
|
" 'maximum': 'max',\n",
|
|
" 'average': 'avg'\n",
|
|
" }\n",
|
|
"\n",
|
|
" for full, abbr in abbreviations.items():\n",
|
|
" name = name.replace(full, abbr)\n",
|
|
"\n",
|
|
" return name\n",
|
|
"\n",
|
|
"\n",
|
|
"def create_technique_mapping(olive_varieties, mapping_path='models/technique_mapping.joblib'):\n",
|
|
" # Estrai tutte le tecniche uniche dal dataset e convertile in lowercase\n",
|
|
" all_techniques = olive_varieties['Tecnica di Coltivazione'].str.lower().unique()\n",
|
|
"\n",
|
|
" # Crea il mapping partendo da 1\n",
|
|
" technique_mapping = {tech: i + 1 for i, tech in enumerate(sorted(all_techniques))}\n",
|
|
"\n",
|
|
" # Salva il mapping\n",
|
|
" os.makedirs(os.path.dirname(mapping_path), exist_ok=True)\n",
|
|
" joblib.dump(technique_mapping, mapping_path)\n",
|
|
"\n",
|
|
" return technique_mapping\n",
|
|
"\n",
|
|
"\n",
|
|
"def encode_techniques(df, mapping_path='models/technique_mapping.joblib'):\n",
|
|
" if not os.path.exists(mapping_path):\n",
|
|
" raise FileNotFoundError(f\"Mapping not found at {mapping_path}. Run create_technique_mapping first.\")\n",
|
|
"\n",
|
|
" technique_mapping = joblib.load(mapping_path)\n",
|
|
"\n",
|
|
" # Trova tutte le colonne delle tecniche\n",
|
|
" tech_columns = [col for col in df.columns if col.endswith('_tech')]\n",
|
|
"\n",
|
|
" # Applica il mapping a tutte le colonne delle tecniche\n",
|
|
" for col in tech_columns:\n",
|
|
" df[col] = df[col].str.lower().map(technique_mapping).fillna(0).astype(int)\n",
|
|
"\n",
|
|
" return df\n",
|
|
"\n",
|
|
"\n",
|
|
"def decode_techniques(df, mapping_path='models/technique_mapping.joblib'):\n",
|
|
" if not os.path.exists(mapping_path):\n",
|
|
" raise FileNotFoundError(f\"Mapping not found at {mapping_path}\")\n",
|
|
"\n",
|
|
" technique_mapping = joblib.load(mapping_path)\n",
|
|
" reverse_mapping = {v: k for k, v in technique_mapping.items()}\n",
|
|
" reverse_mapping[0] = '' # Aggiungi un mapping per 0 a stringa vuota\n",
|
|
"\n",
|
|
" # Trova tutte le colonne delle tecniche\n",
|
|
" tech_columns = [col for col in df.columns if col.endswith('_tech')]\n",
|
|
"\n",
|
|
" # Applica il reverse mapping a tutte le colonne delle tecniche\n",
|
|
" for col in tech_columns:\n",
|
|
" df[col] = df[col].map(reverse_mapping)\n",
|
|
"\n",
|
|
" return df\n",
|
|
"\n",
|
|
"\n",
|
|
"def decode_single_technique(technique_value, mapping_path='models/technique_mapping.joblib'):\n",
|
|
" if not os.path.exists(mapping_path):\n",
|
|
" raise FileNotFoundError(f\"Mapping not found at {mapping_path}\")\n",
|
|
"\n",
|
|
" technique_mapping = joblib.load(mapping_path)\n",
|
|
" reverse_mapping = {v: k for k, v in technique_mapping.items()}\n",
|
|
" reverse_mapping[0] = ''\n",
|
|
"\n",
|
|
" return reverse_mapping.get(technique_value, '')\n",
|
|
"\n",
|
|
"\n",
|
|
"def simulate_olive_production(weather_data, olive_varieties, num_simulations=5, random_seed=None):\n",
|
|
" \"\"\"\n",
|
|
" Simula la produzione di olive per diverse zone e varietà, considerando variazioni meteo specifiche per zona.\n",
|
|
" \n",
|
|
" Args:\n",
|
|
" weather_data: DataFrame con dati meteorologici storici\n",
|
|
" olive_varieties: DataFrame con informazioni sulle varietà di olive\n",
|
|
" num_simulations: Numero di simulazioni/zone da generare\n",
|
|
" random_seed: Seme per la riproducibilità dei risultati\n",
|
|
" \n",
|
|
" Returns:\n",
|
|
" DataFrame con i risultati delle simulazioni per tutte le zone\n",
|
|
" \"\"\"\n",
|
|
" if random_seed is not None:\n",
|
|
" np.random.seed(random_seed)\n",
|
|
"\n",
|
|
" create_technique_mapping(olive_varieties)\n",
|
|
" monthly_weather = preprocess_weather_data(weather_data)\n",
|
|
" all_results = []\n",
|
|
"\n",
|
|
" # Preparazione dati varietà\n",
|
|
" all_varieties = olive_varieties['Varietà di Olive'].unique()\n",
|
|
" variety_techniques = {\n",
|
|
" variety: olive_varieties[olive_varieties['Varietà di Olive'] == variety]['Tecnica di Coltivazione'].unique()\n",
|
|
" for variety in all_varieties\n",
|
|
" }\n",
|
|
"\n",
|
|
" # Per ogni simulazione (anno)\n",
|
|
" for sim in range(num_simulations):\n",
|
|
" # Seleziona anno di base per questa simulazione\n",
|
|
" selected_year = np.random.choice(monthly_weather['year'].unique())\n",
|
|
" base_weather = monthly_weather[monthly_weather['year'] == selected_year].copy()\n",
|
|
" base_weather.loc[:, 'growth_phase'] = base_weather['month'].apply(get_growth_phase)\n",
|
|
"\n",
|
|
" # Per ogni zona nella simulazione\n",
|
|
" for zone in range(num_simulations):\n",
|
|
" # Crea una copia dei dati meteo per questa zona specifica\n",
|
|
" zone_weather = base_weather.copy()\n",
|
|
"\n",
|
|
" # Genera variazioni meteorologiche specifiche per questa zona\n",
|
|
" zone_weather['temp_mean'] *= np.random.uniform(0.95, 1.05, len(zone_weather))\n",
|
|
" zone_weather['precip_sum'] *= np.random.uniform(0.9, 1.1, len(zone_weather))\n",
|
|
" zone_weather['solarenergy_sum'] *= np.random.uniform(0.95, 1.05, len(zone_weather))\n",
|
|
"\n",
|
|
" # Genera caratteristiche specifiche della zona\n",
|
|
" num_varieties = np.random.randint(1, 4) # 1-3 varietà per zona\n",
|
|
" selected_varieties = np.random.choice(all_varieties, size=num_varieties, replace=False)\n",
|
|
" hectares = np.random.uniform(1, 10) # Dimensione del terreno\n",
|
|
" percentages = np.random.dirichlet(np.ones(num_varieties)) # Distribuzione delle varietà\n",
|
|
"\n",
|
|
" # Inizializzazione contatori annuali\n",
|
|
" annual_production = 0\n",
|
|
" annual_min_oil = 0\n",
|
|
" annual_max_oil = 0\n",
|
|
" annual_avg_oil = 0\n",
|
|
" annual_water_need = 0\n",
|
|
"\n",
|
|
" # Inizializzazione dizionario dati varietà\n",
|
|
" variety_data = {clean_column_name(variety): {\n",
|
|
" 'tech': '',\n",
|
|
" 'pct': 0,\n",
|
|
" 'prod_t_ha': 0,\n",
|
|
" 'oil_prod_t_ha': 0,\n",
|
|
" 'oil_prod_l_ha': 0,\n",
|
|
" 'min_yield_pct': 0,\n",
|
|
" 'max_yield_pct': 0,\n",
|
|
" 'min_oil_prod_l_ha': 0,\n",
|
|
" 'max_oil_prod_l_ha': 0,\n",
|
|
" 'avg_oil_prod_l_ha': 0,\n",
|
|
" 'l_per_t': 0,\n",
|
|
" 'min_l_per_t': 0,\n",
|
|
" 'max_l_per_t': 0,\n",
|
|
" 'avg_l_per_t': 0,\n",
|
|
" 'olive_prod': 0,\n",
|
|
" 'min_oil_prod': 0,\n",
|
|
" 'max_oil_prod': 0,\n",
|
|
" 'avg_oil_prod': 0,\n",
|
|
" 'water_need': 0\n",
|
|
" } for variety in all_varieties}\n",
|
|
"\n",
|
|
" # Simula produzione per ogni varietà selezionata\n",
|
|
" for i, variety in enumerate(selected_varieties):\n",
|
|
" # Seleziona tecnica di coltivazione casuale per questa varietà\n",
|
|
" technique = np.random.choice(variety_techniques[variety])\n",
|
|
" percentage = percentages[i]\n",
|
|
"\n",
|
|
" # Ottieni informazioni specifiche della varietà\n",
|
|
" variety_info = olive_varieties[\n",
|
|
" (olive_varieties['Varietà di Olive'] == variety) &\n",
|
|
" (olive_varieties['Tecnica di Coltivazione'] == technique)\n",
|
|
" ].iloc[0]\n",
|
|
"\n",
|
|
" # Calcola produzione base con variabilità\n",
|
|
" base_production = variety_info['Produzione (tonnellate/ettaro)'] * 1000 * percentage * hectares / 12\n",
|
|
" base_production *= np.random.uniform(0.9, 1.1) # Aggiungi variabilità alla produzione base\n",
|
|
"\n",
|
|
" # Calcola effetti meteo sulla produzione\n",
|
|
" weather_effect = zone_weather.apply(\n",
|
|
" lambda row: calculate_weather_effect(row, variety_info['Temperatura Ottimale']),\n",
|
|
" axis=1\n",
|
|
" )\n",
|
|
" monthly_production = base_production * (1 + weather_effect / 10000)\n",
|
|
" monthly_production *= np.random.uniform(0.95, 1.05, len(zone_weather))\n",
|
|
"\n",
|
|
" # Calcola produzione annuale per questa varietà\n",
|
|
" annual_variety_production = monthly_production.sum()\n",
|
|
"\n",
|
|
" # Calcola rese di olio con variabilità\n",
|
|
" min_yield_factor = np.random.uniform(0.95, 1.05)\n",
|
|
" max_yield_factor = np.random.uniform(0.95, 1.05)\n",
|
|
" avg_yield_factor = (min_yield_factor + max_yield_factor) / 2\n",
|
|
"\n",
|
|
" min_oil_production = annual_variety_production * variety_info['Min Litri per Tonnellata'] / 1000 * min_yield_factor\n",
|
|
" max_oil_production = annual_variety_production * variety_info['Max Litri per Tonnellata'] / 1000 * max_yield_factor\n",
|
|
" avg_oil_production = annual_variety_production * variety_info['Media Litri per Tonnellata'] / 1000 * avg_yield_factor\n",
|
|
"\n",
|
|
" # Calcola fabbisogno idrico\n",
|
|
" base_water_need = (\n",
|
|
" variety_info['Fabbisogno Acqua Primavera (m³/ettaro)'] +\n",
|
|
" variety_info['Fabbisogno Acqua Estate (m³/ettaro)'] +\n",
|
|
" variety_info['Fabbisogno Acqua Autunno (m³/ettaro)'] +\n",
|
|
" variety_info['Fabbisogno Acqua Inverno (m³/ettaro)']\n",
|
|
" ) / 4 # Media stagionale\n",
|
|
"\n",
|
|
" monthly_water_need = zone_weather.apply(\n",
|
|
" lambda row: calculate_water_need(row, base_water_need, variety_info['Temperatura Ottimale']),\n",
|
|
" axis=1\n",
|
|
" )\n",
|
|
" monthly_water_need *= np.random.uniform(0.95, 1.05, len(monthly_water_need))\n",
|
|
" annual_variety_water_need = monthly_water_need.sum() * percentage * hectares\n",
|
|
"\n",
|
|
" # Aggiorna totali annuali\n",
|
|
" annual_production += annual_variety_production\n",
|
|
" annual_min_oil += min_oil_production\n",
|
|
" annual_max_oil += max_oil_production\n",
|
|
" annual_avg_oil += avg_oil_production\n",
|
|
" annual_water_need += annual_variety_water_need\n",
|
|
"\n",
|
|
" # Aggiorna dati varietà\n",
|
|
" clean_variety = clean_column_name(variety)\n",
|
|
" variety_data[clean_variety].update({\n",
|
|
" 'tech': clean_column_name(technique),\n",
|
|
" 'pct': percentage,\n",
|
|
" 'prod_t_ha': variety_info['Produzione (tonnellate/ettaro)'] * np.random.uniform(0.95, 1.05),\n",
|
|
" 'oil_prod_t_ha': variety_info['Produzione Olio (tonnellate/ettaro)'] * np.random.uniform(0.95, 1.05),\n",
|
|
" 'oil_prod_l_ha': variety_info['Produzione Olio (litri/ettaro)'] * np.random.uniform(0.95, 1.05),\n",
|
|
" 'min_yield_pct': variety_info['Min % Resa'] * min_yield_factor,\n",
|
|
" 'max_yield_pct': variety_info['Max % Resa'] * max_yield_factor,\n",
|
|
" 'min_oil_prod_l_ha': variety_info['Min Produzione Olio (litri/ettaro)'] * min_yield_factor,\n",
|
|
" 'max_oil_prod_l_ha': variety_info['Max Produzione Olio (litri/ettaro)'] * max_yield_factor,\n",
|
|
" 'avg_oil_prod_l_ha': variety_info['Media Produzione Olio (litri/ettaro)'] * avg_yield_factor,\n",
|
|
" 'l_per_t': variety_info['Litri per Tonnellata'] * np.random.uniform(0.98, 1.02),\n",
|
|
" 'min_l_per_t': variety_info['Min Litri per Tonnellata'] * min_yield_factor,\n",
|
|
" 'max_l_per_t': variety_info['Max Litri per Tonnellata'] * max_yield_factor,\n",
|
|
" 'avg_l_per_t': variety_info['Media Litri per Tonnellata'] * avg_yield_factor,\n",
|
|
" 'olive_prod': annual_variety_production,\n",
|
|
" 'min_oil_prod': min_oil_production,\n",
|
|
" 'max_oil_prod': max_oil_production,\n",
|
|
" 'avg_oil_prod': avg_oil_production,\n",
|
|
" 'water_need': annual_variety_water_need\n",
|
|
" })\n",
|
|
"\n",
|
|
" # Appiattisci i dati delle varietà per il DataFrame finale\n",
|
|
" flattened_variety_data = {\n",
|
|
" f'{variety}_{key}': value\n",
|
|
" for variety, data in variety_data.items()\n",
|
|
" for key, value in data.items()\n",
|
|
" }\n",
|
|
"\n",
|
|
" # Aggiungi il risultato con tutti i dati della zona\n",
|
|
" all_results.append({\n",
|
|
" 'simulation_id': sim + 1,\n",
|
|
" 'zone_id': zone + 1,\n",
|
|
" 'year': selected_year,\n",
|
|
" 'temp_mean': zone_weather['temp_mean'].mean(),\n",
|
|
" 'precip_sum': zone_weather['precip_sum'].sum(),\n",
|
|
" 'solar_energy_sum': zone_weather['solarenergy_sum'].sum(),\n",
|
|
" 'ha': hectares,\n",
|
|
" 'zone': f\"zone_{zone + 1}\",\n",
|
|
" 'olive_prod': annual_production,\n",
|
|
" 'min_oil_prod': annual_min_oil,\n",
|
|
" 'max_oil_prod': annual_max_oil,\n",
|
|
" 'avg_oil_prod': annual_avg_oil,\n",
|
|
" 'total_water_need': annual_water_need,\n",
|
|
" **flattened_variety_data\n",
|
|
" })\n",
|
|
"\n",
|
|
" # Crea DataFrame finale con tutti i risultati\n",
|
|
" df_results = pd.DataFrame(all_results)\n",
|
|
" return df_results\n",
|
|
"\n",
|
|
"\n",
|
|
"simulated_data = simulate_olive_production(weather_data, olive_varieties, 100, random_state_value)\n",
|
|
"\n",
|
|
"simulated_data.to_parquet(\"./data/simulated_data.parquet\")\n",
|
|
"\n",
|
|
"\n",
|
|
"# Funzione per visualizzare il mapping delle tecniche\n",
|
|
"def print_technique_mapping(mapping_path='models/technique_mapping.joblib'):\n",
|
|
" if not os.path.exists(mapping_path):\n",
|
|
" print(\"Mapping file not found.\")\n",
|
|
" return\n",
|
|
"\n",
|
|
" mapping = joblib.load(mapping_path)\n",
|
|
" print(\"Technique Mapping:\")\n",
|
|
" for technique, code in mapping.items():\n",
|
|
" print(f\"{technique}: {code}\")\n",
|
|
"\n",
|
|
"\n",
|
|
"# Visualizza il mapping delle tecniche\n",
|
|
"print_technique_mapping()"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'weather_data' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
|
|
"\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)",
|
|
"Cell \u001B[0;32mIn[55], line 350\u001B[0m\n\u001B[1;32m 346\u001B[0m df_results \u001B[38;5;241m=\u001B[39m pd\u001B[38;5;241m.\u001B[39mDataFrame(all_results)\n\u001B[1;32m 347\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m df_results\n\u001B[0;32m--> 350\u001B[0m simulated_data \u001B[38;5;241m=\u001B[39m simulate_olive_production(\u001B[43mweather_data\u001B[49m, olive_varieties, \u001B[38;5;241m100\u001B[39m, random_state_value)\n\u001B[1;32m 352\u001B[0m simulated_data\u001B[38;5;241m.\u001B[39mto_parquet(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m./data/simulated_data.parquet\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 355\u001B[0m \u001B[38;5;66;03m# Funzione per visualizzare il mapping delle tecniche\u001B[39;00m\n",
|
|
"\u001B[0;31mNameError\u001B[0m: name 'weather_data' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 55
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-23T06:10:54.639402Z",
|
|
"start_time": "2024-10-23T06:10:52.895228Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"simulated_data = pd.read_parquet(\"./data/simulated_data.parquet\")\n",
|
|
"\n",
|
|
"\n",
|
|
"def clean_column_names(df):\n",
|
|
" # Funzione per pulire i nomi delle colonne\n",
|
|
" new_columns = []\n",
|
|
" for col in df.columns:\n",
|
|
" # Usa regex per separare le varietà\n",
|
|
" varieties = re.findall(r'([a-z]+)_([a-z_]+)', col)\n",
|
|
" if varieties:\n",
|
|
" new_columns.append(f\"{varieties[0][0]}_{varieties[0][1]}\")\n",
|
|
" else:\n",
|
|
" new_columns.append(col)\n",
|
|
" return new_columns\n",
|
|
"\n",
|
|
"\n",
|
|
"def prepare_comparison_data(simulated_data, olive_varieties):\n",
|
|
" # Pulisci i nomi delle colonne\n",
|
|
" df = simulated_data.copy()\n",
|
|
"\n",
|
|
" df.columns = clean_column_names(df)\n",
|
|
" df = encode_techniques(df)\n",
|
|
"\n",
|
|
" all_varieties = olive_varieties['Varietà di Olive'].unique()\n",
|
|
" varieties = [clean_column_name(variety) for variety in all_varieties]\n",
|
|
" comparison_data = []\n",
|
|
"\n",
|
|
" for variety in varieties:\n",
|
|
" olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n",
|
|
" oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n",
|
|
" tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n",
|
|
" water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n",
|
|
"\n",
|
|
" if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n",
|
|
" variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n",
|
|
" variety_data = variety_data[variety_data[tech_col] != 0] # Esclude le righe dove la tecnica è 0\n",
|
|
"\n",
|
|
" if not variety_data.empty:\n",
|
|
" avg_olive_prod = pd.to_numeric(variety_data[olive_prod_col], errors='coerce').mean()\n",
|
|
" avg_oil_prod = pd.to_numeric(variety_data[oil_prod_col], errors='coerce').mean()\n",
|
|
" avg_water_need = pd.to_numeric(variety_data[water_need_col], errors='coerce').mean()\n",
|
|
" efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n",
|
|
" water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n",
|
|
"\n",
|
|
" comparison_data.append({\n",
|
|
" 'Variety': variety,\n",
|
|
" 'Avg Olive Production (kg/ha)': avg_olive_prod,\n",
|
|
" 'Avg Oil Production (L/ha)': avg_oil_prod,\n",
|
|
" 'Avg Water Need (m³/ha)': avg_water_need,\n",
|
|
" 'Oil Efficiency (L/kg)': efficiency,\n",
|
|
" 'Water Efficiency (L oil/m³ water)': water_efficiency\n",
|
|
" })\n",
|
|
"\n",
|
|
" return pd.DataFrame(comparison_data)\n",
|
|
"\n",
|
|
"\n",
|
|
"def plot_variety_comparison(comparison_data, metric):\n",
|
|
" plt.figure(figsize=(12, 6))\n",
|
|
" bars = plt.bar(comparison_data['Variety'], comparison_data[metric])\n",
|
|
" plt.title(f'Comparison of {metric} across Olive Varieties')\n",
|
|
" plt.xlabel('Variety')\n",
|
|
" plt.ylabel(metric)\n",
|
|
" plt.xticks(rotation=45, ha='right')\n",
|
|
"\n",
|
|
" for bar in bars:\n",
|
|
" height = bar.get_height()\n",
|
|
" plt.text(bar.get_x() + bar.get_width() / 2., height,\n",
|
|
" f'{height:.2f}',\n",
|
|
" ha='center', va='bottom')\n",
|
|
"\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"\n",
|
|
"def plot_efficiency_vs_production(comparison_data):\n",
|
|
" plt.figure(figsize=(10, 6))\n",
|
|
"\n",
|
|
" plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n",
|
|
" comparison_data['Oil Efficiency (L/kg)'],\n",
|
|
" s=100)\n",
|
|
"\n",
|
|
" for i, row in comparison_data.iterrows():\n",
|
|
" plt.annotate(row['Variety'],\n",
|
|
" (row['Avg Olive Production (kg/ha)'], row['Oil Efficiency (L/kg)']),\n",
|
|
" xytext=(5, 5), textcoords='offset points')\n",
|
|
"\n",
|
|
" plt.title('Oil Efficiency vs Olive Production by Variety')\n",
|
|
" plt.xlabel('Average Olive Production (kg/ha)')\n",
|
|
" plt.ylabel('Oil Efficiency (L oil / kg olives)')\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"\n",
|
|
"def plot_water_efficiency_vs_production(comparison_data):\n",
|
|
" plt.figure(figsize=(10, 6))\n",
|
|
"\n",
|
|
" plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n",
|
|
" comparison_data['Water Efficiency (L oil/m³ water)'],\n",
|
|
" s=100)\n",
|
|
"\n",
|
|
" for i, row in comparison_data.iterrows():\n",
|
|
" plt.annotate(row['Variety'],\n",
|
|
" (row['Avg Olive Production (kg/ha)'], row['Water Efficiency (L oil/m³ water)']),\n",
|
|
" xytext=(5, 5), textcoords='offset points')\n",
|
|
"\n",
|
|
" plt.title('Water Efficiency vs Olive Production by Variety')\n",
|
|
" plt.xlabel('Average Olive Production (kg/ha)')\n",
|
|
" plt.ylabel('Water Efficiency (L oil / m³ water)')\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"\n",
|
|
"def plot_water_need_vs_oil_production(comparison_data):\n",
|
|
" plt.figure(figsize=(10, 6))\n",
|
|
"\n",
|
|
" plt.scatter(comparison_data['Avg Water Need (m³/ha)'],\n",
|
|
" comparison_data['Avg Oil Production (L/ha)'],\n",
|
|
" s=100)\n",
|
|
"\n",
|
|
" for i, row in comparison_data.iterrows():\n",
|
|
" plt.annotate(row['Variety'],\n",
|
|
" (row['Avg Water Need (m³/ha)'], row['Avg Oil Production (L/ha)']),\n",
|
|
" xytext=(5, 5), textcoords='offset points')\n",
|
|
"\n",
|
|
" plt.title('Oil Production vs Water Need by Variety')\n",
|
|
" plt.xlabel('Average Water Need (m³/ha)')\n",
|
|
" plt.ylabel('Average Oil Production (L/ha)')\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"\n",
|
|
"def analyze_by_technique(simulated_data, olive_varieties):\n",
|
|
" # Pulisci i nomi delle colonne\n",
|
|
" df = simulated_data.copy()\n",
|
|
"\n",
|
|
" df.columns = clean_column_names(df)\n",
|
|
" df = encode_techniques(df)\n",
|
|
" all_varieties = olive_varieties['Varietà di Olive'].unique()\n",
|
|
" varieties = [clean_column_name(variety) for variety in all_varieties]\n",
|
|
"\n",
|
|
" technique_data = []\n",
|
|
"\n",
|
|
" for variety in varieties:\n",
|
|
" olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n",
|
|
" oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n",
|
|
" tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n",
|
|
" water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n",
|
|
"\n",
|
|
" if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n",
|
|
" variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n",
|
|
" variety_data = variety_data[variety_data[tech_col] != 0]\n",
|
|
"\n",
|
|
" if not variety_data.empty:\n",
|
|
" for tech in variety_data[tech_col].unique():\n",
|
|
" tech_data = variety_data[variety_data[tech_col] == tech]\n",
|
|
"\n",
|
|
" avg_olive_prod = pd.to_numeric(tech_data[olive_prod_col], errors='coerce').mean()\n",
|
|
" avg_oil_prod = pd.to_numeric(tech_data[oil_prod_col], errors='coerce').mean()\n",
|
|
" avg_water_need = pd.to_numeric(tech_data[water_need_col], errors='coerce').mean()\n",
|
|
"\n",
|
|
" efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n",
|
|
" water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n",
|
|
"\n",
|
|
" technique_data.append({\n",
|
|
" 'Variety': variety,\n",
|
|
" 'Technique': tech,\n",
|
|
" 'Technique String': decode_single_technique(tech),\n",
|
|
" 'Avg Olive Production (kg/ha)': avg_olive_prod,\n",
|
|
" 'Avg Oil Production (L/ha)': avg_oil_prod,\n",
|
|
" 'Avg Water Need (m³/ha)': avg_water_need,\n",
|
|
" 'Oil Efficiency (L/kg)': efficiency,\n",
|
|
" 'Water Efficiency (L oil/m³ water)': water_efficiency\n",
|
|
" })\n",
|
|
"\n",
|
|
" return pd.DataFrame(technique_data)\n",
|
|
"\n",
|
|
"\n",
|
|
"# Esecuzione dell'analisi\n",
|
|
"comparison_data = prepare_comparison_data(simulated_data, olive_varieties)\n",
|
|
"\n",
|
|
"# Genera i grafici\n",
|
|
"plot_variety_comparison(comparison_data, 'Avg Olive Production (kg/ha)')\n",
|
|
"plot_variety_comparison(comparison_data, 'Avg Oil Production (L/ha)')\n",
|
|
"plot_variety_comparison(comparison_data, 'Avg Water Need (m³/ha)')\n",
|
|
"plot_variety_comparison(comparison_data, 'Oil Efficiency (L/kg)')\n",
|
|
"plot_variety_comparison(comparison_data, 'Water Efficiency (L oil/m³ water)')\n",
|
|
"plot_efficiency_vs_production(comparison_data)\n",
|
|
"plot_water_efficiency_vs_production(comparison_data)\n",
|
|
"plot_water_need_vs_oil_production(comparison_data)\n",
|
|
"\n",
|
|
"# Analisi per tecnica\n",
|
|
"technique_data = analyze_by_technique(simulated_data, olive_varieties)\n",
|
|
"\n",
|
|
"print(technique_data)\n",
|
|
"\n",
|
|
"# Stampa un sommario statistico\n",
|
|
"print(\"Comparison by Variety:\")\n",
|
|
"print(comparison_data.set_index('Variety'))\n",
|
|
"print(\"\\nBest Varieties by Water Efficiency:\")\n",
|
|
"print(comparison_data.sort_values('Water Efficiency (L oil/m³ water)', ascending=False).head())"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 1200x600 with 1 Axes>"
|
|
],
|
|
"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"text/plain": [
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"<Figure size 1200x600 with 1 Axes>"
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],
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"image/png": 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PH+DR2On48eMcPnyY2rVrY7VacXJyIn/+/OzatYu4uDg8PDzsXLVkBjqTyDOTkpKC1WolPj4eb29vtm7dyoULF8ifPz9NmjShfPnyrFmzhtDQ0N8dq4uepJe+ceLNmzdxcHCgV69eeHp6MmHCBE6cOEGbNm347LPPmDNnDosXL7Z3yZLJbdiwgVWrVnHlyhW2bt2KxWLh/PnzHDp0CAAXFxfMZjNZs2bFx8cHV1dXnJycFEjJUy1atMj2nVq/fj0ffPABjo6ONGvWDIDPP/+cunXr0r17d3766SfbcQqk5GmMRiO3bt0iICCAgIAAli9fzuzZs/nuu++4ePEijRs3ZuzYsbz33nt8/PHHrF27FkdHR1JTUxVIye+YTCbKlStHjhw5GDNmDDExMQwZMoRatWoxZswYdu/eTY0aNZg6dSpr1qxh3rx5GY7X2FweV716dfr06cOMGTM4efIkBQsWpEaNGuzdu5fw8HDbeSgqKopXX31V5yWxUaNz+dc9ficlOTkZJycn2rdvz9mzZwkJCaFQoUJcv36duXPnYrVaGTVqlE5U8kTpGyem9fO5ceMG69ev58qVK6xcuZLr168zaNAg3njjDVauXEmVKlUoUKCAvUuXTGzt2rWsXr2aGzdu8M477xAUFMSqVasIDQ2ldevWtiBh7dq1LFmyhNDQUO3cKH9oz549jBo1ioSEBOrWrcvIkSM5c+YMPXv25JVXXmHVqlUA9O7dm2PHjrFjxw6yZMli56ols/vpp58IDQ1lzpw5HD58mKFDh+Lm5kZYWBhNmjSxzWZJo1325EnSj8137drF6tWrcXR0ZPTo0bi5uTF9+nR27NjB0KFDqVWrFseOHeONN95QPzJ5ovTfp9OnTzN27FhcXFwYM2YMN27cYNKkSbabetmyZWPVqlWEhIRQvHhxO1cumYVCKflXpQ2GLl++zO7du0lNTSV79uw0bdoUs9lMt27dOH36NMuWLePYsWO4u7tTrVo1DAaDpprLU1mtVlq1aoWHhwfjxo0jMjKSokWLkpqayg8//MCuXbs4efIkQUFBlCxZ0t7lSiaW/jzTsGFDLl++TIcOHWjWrBkWi4UlS5awfv16PDw88PLy4vjx48yZM0ffK3mq9N+p+vXrc+nSJTp06EDz5s1xd3cnPDycnj17kidPHpYtWwY8umucJ08ee5YtmdTjN/YuXbpEs2bNcHZ2JleuXBQpUoTx48ezbds2tm3bxrRp0xRuyh9K+06lDywPHDjAvHnzyJIliy2YmjlzJkuWLCEkJMS2a7F2bpTHPek7cfToURYsWEBiYiLjx48nNjaWb7/9loMHD/L666/TqFEjihQpYqeKJTNSKCX/urNnz9KuXTv+7//+DwcHB77//ntKlizJ7NmzMZvNBAQEcOTIEXx8fNi4cSMmk0mBlPyhixcvMmLECBYuXIiTkxPwaAbemjVryJs3L7ly5WLDhg107tyZfPny2blayazSBuQWi4XU1FT2799PVFQU69ev56233qJt27bkyJGD06dPs3fvXvLmzUvlypXx9fW1d+mSSaV9p9KuYQcPHuThw4d8/vnnfPjhh7Ru3RpPT0/OnTtHu3btKFy4MCEhIfYuWzKptO/T9evXOX36NK6urlStWpVTp05x6dIlvLy8KFOmDACBgYFYrVYmT55s36IlU0v7TkVERLBp0ybu37/PK6+8Qo8ePfjll19YsGABzs7OjBo1CldXVzZs2EDz5s01206eKP04avjw4RgMBt544w38/Pz49ddfmTt3LsnJyQwbNozXXnsNs9mM0WjUbzz5HYVS8q8xm82kpKTQrVs3KlSoQJcuXUhOTuaTTz6hcuXKdO3albx58wJw7NgxSpcubTuxaZ26pPf48oMLFy7QuHFjpk+fTvXq1W0/ACdPnkx4eDjz588nKSkJZ2dnO1YtmVn68GDnzp2YTCZq1aoFQEhICFu3bqVatWoEBAQQHR2Nl5eXnSuWzC7tbrHFYmHdunUYDAbq16+Pk5MTmzZtYtq0aXz88cd069aNixcvYrFYyJkzJz4+PvYuXTKhtLFQeHg4bdu2pUCBAhw/fpyBAwfSrl07AHbv3s2uXbuIj48nIiKCjRs34ujoqBt78ofOnj1LmzZtqF+/Pg8fPuT06dMkJyezdOlSzp8/z7x584iPj2f27NnkzJkT0DJQ+b2084zFYqFhw4Z4eHjYer1+9NFHdOzYkRMnTrBo0SJu3LjB+PHjNTtKnkrzL+UfFR8fz88//0zNmjUxmUyYzWaSk5P56KOPAGjSpAlly5Zl0KBBNG/enIEDB1KlShXKli0L6KInv5e+qfnx48eJj48nf/781KxZkz179uDl5WW7yGXJkoX8+fMDKJCSp7JarbYAvFGjRpjNZi5fvsybb77JggULaNu2LSaTiY0bN3LgwAGuXbvGxo0btbxKnspisdgCqfr16+Pg4MDdu3dtyz8bNGhgC84PHTrE3bt3Wb58uXYdkqcyGo1cvnyZHj160Lt3b9577z1mzJjBhAkTSE1NpWPHjhgMBoxGI0WLFmXq1Kk4ODhoeZU8VdrN4gkTJtC6dWu6dOkCPNopNCAggD59+hAaGsrdu3c5ceIE2bNntx2rsbk8Li347t+/P8WLFycoKIjz588zfPhwdu3aBUDnzp1p27YtK1as0CYe8od01ZJ/1Pbt2xk2bBhBQUE0aNAAeNQrY926dRw+fJgiRYoQFBTErVu3sFgsFCxYMMPxuuhJelar1fZDz8/PDxcXFy5fvsyKFSuoXbs2y5YtY+bMmRQsWBBnZ2dbA2qRp0k/E3Ps2LGUKlWK4cOHExERQc+ePWnfvj2LFi2iVatWeHp6cvHiRWrWrKlASv6Q0WjEarXSsWNHypQpw5gxY9i8eTNjxoyhcePGrF+/nvr165MnTx5OnTrFu+++q0BKfic5ORnAtix9z549lC9fnqZNm3Lz5k2Sk5Np1qwZkydPJkeOHPj7+/Pee+/ZjjebzQqkJIOEhAS2bduGv78/JpOJhw8f8uDBAypVqgQ82hk7e/bsdO3alREjRnDz5k3q1KlDnTp1gN/3NBN5PPiOiIhg+vTpACxbtoxSpUphsVhYtmwZV69eZdiwYYwfP952XhN5Ep1l5B+RmJjIrVu38Pf3p0+fPgwaNIh169bh5OREhw4dWLt2LYmJiXzxxRcATJs2jZw5c5I7d247Vy6ZWdpdmICAAHx9fVm+fDmrV6/Gx8eH2rVrU7duXUqWLMnRo0e5efMmS5YsoVixYnauWjKztPBg1qxZRERE0LRpU5ycnChatCgLFizgxo0bdOzYkaSkJGrVqkXHjh0pXLiwvcuWTMpsNtv+HRERQVJSEsOHDwfg+PHjfPrppxgMBlq0aMHly5epUqUK7du3p1ChQvYqWTKplJQUPvzwQ8LCwmyPnTlzhldeeYXU1FS6du1Knjx56NevH97e3gwfPpzZs2dneA/d2JPH7d69mzFjxjB37lwAcuTIQUJCAt988w0Ajo6OAOTLlw93d3csFkuG4xVISXpps4LNZjPDhw/nxo0beHt7k5yczOjRozlx4gT/+c9/qF+/Pm5ubkRFRXHnzh0FUvKndDtF/mdWq5WpU6eyfft2Vq1aRefOnbFYLAwdOpQsWbLg7+9PVFQUW7ZsoWnTpri5uREbG8uyZcswGo26CyN/6MGDB6SkpNCrVy8A8uTJQ2pqKtu3bycmJobOnTvTvn17DAaDBuTyVOnPMykpKZw/f57Dhw/zzTffULRoURwdHfHx8SE0NJQGDRrQu3dv5syZo74s8lTpG7weOHCA+Ph4YmNjcXBwYNCgQdy4cYP58+djsVhYvHgx3bp1Y82aNbi4uNi7dMmEHB0dmTFjBgUKFGDVqlU0bdqUHj16YDQaWbRoER4eHvTr1w+ASpUqUaNGDVsfPJGn+fDDD4mOjiYkJARnZ2fatm2Lv78/O3bsYMGCBbRr1w6TycTcuXPJnj07np6e9i5ZMqn046gBAwZgNpvx9PRkwoQJJCYmEh4ebpsxdeDAAUqWLMmIESPIkSOHPcuW54RCKfmfGQwG6tSpw40bN+jWrRuzZs2iS5cuWK1WAgMDMRqNdOvWjU8++YSDBw/i5eXFW2+9hclkUu8D+Z3HG7SazWZOnjzJ999/T4ECBWzfF4vFws6dO2nXrp3uwMgfSn+euX37Nrlz52bSpEmMHz+eo0ePsmnTJurVq4ezszPe3t5s3ryZlJQUO1ctmZnFYrE1ym/evDklS5Zk2LBhFCxYkLCwMM6ePcuSJUtwcnLCycmJzz//nLfffluBlDxRSkoKjo6OlCpVim3btjFy5EgSEhJo3bo1JpOJqKgoW3+fwMBArl69yueff47RaNQ4Sp4oLTS/du0av/32G46OjkyePBmTyUSbNm2IiYlhy5YtLF68mNdff53Y2FhWrFhha1ytm8XyuLTvRP/+/bl+/TqjRo0CwNXVFbPZjMViYfr06RQsWJCQkBCWL1+uQEr+Ml3F5H+SFiCUKVOGjh07MmfOHLp160ZwcDBdu3YFoG/fviQmJtKoUSNbE2pQ7wP5vccb3cfHx+Pu7k6zZs34/vvv8fX1pUaNGgA8fPgQb29vO1Uqz4u084zFYqFr165cu3aNkiVL0qdPHwYPHszo0aPZtGkTBoOBTz75BGdnZ+20J38qbXA+depU3NzcGDZsGACFChVi//793L59m/v377Nu3TrWrFnDypUrbbvNiqRntVpxdHTk+vXrXL58mY8//pjExESGDh0KQLt27ahatSpdu3YlIiKCxMRE1q9fb5tprnGUPInJZCIyMpJPP/2U7t27U6dOHc6cOUNISAgWi4WePXvSrFkz9u3bh7e3NxUqVNDNYnmix28WOzs7c+LECbZv346npyfZsmXDycmJmjVr8ttvv/Hjjz8SGhqqnfbkv6KzjvxtaXdS0oKEN954g8DAQCZMmJAhmDIajfznP/8hV65cVK9e3Xa8llpJeumXwowYMYKYmBgePnxI8+bNef/997l58yYzZ85k48aNeHp6snnzZhYtWqRZUvKH0r5TjRs3Jn/+/LRv357x48djNpvp06cPw4YNY9y4cYSGhuLg4GDboEHkadIG6IcOHeLYsWOcOHGCU6dOUbJkSQAqVqxIzpw5CQgIID4+ngULFlCgQAH7Fi2ZUtp17/79+0ycOJHDhw8TFBSEn58fVqvVFna2a9eOrVu3Eh0dTfny5RUeyF/y448/UrZsWVq1agVAlSpV8PHxISgoCAcHB1q0aEGjRo1sr9fNYnlc+vNMcnIyTk5OjB07lty5c7NlyxZee+013nvvPbJly0bbtm1xcnIiPj4eV1dXO1cuzxudeeRvSRtIRUREsGnTJhISEnjllVfo0qULAwcOZNKkSXTv3p1Zs2YREBCAh4cHb7/9tr3LlkwsLTxo0qQJvr6+1K1bl6ioKLp3786CBQvo1q0bp06d4ttvvyV79uwsXbpUDajlqdLf2Zs0aRLFihWzhVH79+/nhx9+ICEhgeHDhzNkyBAmT55MhQoV7Fy1ZGZp172071XFihXp378/EydOJCgoiJEjR1KoUCGcnZ1ZtWoVV65cIXfu3OTKlcvOlUtmZLVaMZlMhIWFMWTIEFtvu759+zJ58mT8/f0BGDlyJHFxcXTv3t12rMID+Svu3bvHvXv3bH+7uLhQvnx5nJ2dGTNmDK6urjRs2ND2vG4WS3rpZ5oPHz6ce/fu8fDhQz7++GNbn9fg4GAMBgM1a9YkW7ZsAAqk5G/RgmH5W0wmE+fPn6d58+YkJCTg4uLCrl27+Pjjj8mTJw89e/bE29ubZs2aERUVRcOGDXFwcCA1NdXepUsms3v3btu/v/nmG7JkycLkyZOpXbs2ly5dokSJEnh5eRETE0PdunWZPHkyXbt2VSAlT2U2mzNMNb9165Zt6fDgwYPx8vJi/vz57Nmzh8DAQPbv38/AgQPJly+fvUqWTC79TM6lS5cyc+ZM5s2bR7FixQgMDCRr1qyMGzeOixcvAuDm5kaxYsUUSMlTGQwGoqOj6d+/P40bN2b8+PF89913NGzYkCFDhvDDDz/g7+/PgAED2L9/P1ar1XaswgN5XNpOoMnJySQmJgJQq1Ytzpw5Q2hoqO11np6evPHGG4wePZp69erZpVZ5PqSfaR4bG4ufnx+vvfYa27ZtY/To0fTq1Yv333+f8ePHs2/fvgznKJH/lm6zyH8trQHwzJkzadKkCX369CE1NZW9e/dSpkwZYmJiKFasGN27d+ebb77JMCjXnT1J7+TJk3Tv3p0uXbrQq1cv4uLiyJo1KwD9+vXj7NmzbNy4kS+//JJDhw5lGFiJPMm9e/ds21p36tSJJk2a8MYbb/D2228za9YswsLCGDduHKmpqZQuXZo8efJQtGhRe5ctmVjajJa0wbm3tzdGo5GrV6+yevVqli5dSkBAAHPnzmXw4MFMnDgRX19fe5ctz4HY2FiMRiNVq1a1PTZ48GBu3LjBsGHD+Pzzz2nTpg2tW7fGYDD8rreLCPz/jRfOnTvHl19+yf3792nUqBEfffQRvXv3Jjg4mIiICMqXL8+3335LZGSkren54708ReD/zzZfvXo1Hh4eTJs2DYCaNWuydu1a1qxZw/HjxxkwYAAApUqV0rlJ/ieaKSV/SXx8PIsXLwYebVtsNBqJjY3lnXfeAaBx48a8/vrrjBw5km7durF7925KlChB3759bRc9kceVKlWKGTNmsHDhQr788kvy58/Pjz/+SPv27bl27Rpr167FwcGBy5cvU6ZMGXuXK5lcly5d+O233zCbzSxdupQcOXLwwQcf0Lx5c4oUKcK1a9do2bIlDg4OrFu3Dnd3dwYPHqwtsOUPpQ20R40aRb58+Zg1axYzZsxg6dKlFCxYkM6dO1OqVCk+/fRTfHx8cHR0tHPFkllZLBYAYmJiSExMxMHBgaSkJG7evAk8muUCULlyZcxmMwMHDiQ8PByDwUBqaqp+9MnvWK1WjEYjERERtGrVCicnJ1555RUCAwNZt24dLVq04PPPP+fXX3/l66+/xmAwsHbtWlvQrkBK0kv7vZZ2rnnw4AFxcXG23fUAGjZsyP379/nuu+8AGDBgQIaNrET+Dk1bkb/k+PHjTJgwgaioKAYOHGhrsrlkyRImTZpEiRIlGD9+PCkpKZjN5t8tg9FFT57mgw8+4IsvvrDthtahQwfmzp3LvHnzMBqNLF++nO+//54VK1bYu1TJxBYvXkxYWBjVq1enadOmnD9/nh49egCPdkpLSkriwYMHbNiwgcOHD7N3715CQ0O1vEqe6vEZBHfv3uX9998HHoUHbm5uDBw4kA4dOvDbb79RvXp1KlSogJubm71KlkwsbXOYu3fvMmvWLIoUKULz5s0pV64cQ4YMYfHixbaA/MyZMwwdOpSdO3cyePBgNm7cqJnm8kQGg4H79++zceNGAgICaNu2LQBly5Zl2LBhtl6d7777bobvkBrly+PSrnlWq5Xdu3eTL18+kpKScHJy4tKlSxQsWBB4tOqlQoUKtiBKMzjln6Czkfwpq9VK1apVmTJlCgMGDMDZ2ZnevXvTqVMnRo0ahcFgYNWqVQAMHz4cV1dXXn/9dTtXLc+T2rVrM3XqVPr160fdunUZMmQI/fr1o2jRoiQlJRESEkKhQoXsXaZkUikpKbi4uJA/f37q1q2Lu7s7BQsWZPPmzfj7++Pq6oqDgwOdO3dm27ZtmM1mli9frvOUPFXaDzar1covv/xC9uzZiYqKYu/evdSvX9+262fevHlxd3fHaHw08VyBlDxJWiB18eJFFi9ezO7du9m3bx/ZsmVj6NChDBgwgJYtW9quedeuXWP06NF4eHgwY8YMUlJSNANPfsdqtZKYmMigQYP49ddfadq0qe25Fi1aYLVaGT16NDExMXTo0CHDcQqk5HHpl6nfuXOH3LlzU7RoUY4ePcrMmTNp3LgxxYsXZ8eOHezdu5eAgAAABVLyjzBY1ZVM/kBaan727FkWLlxIWFgYZ8+epVOnTvTt25f169ezYsUKHjx4QP78+W0BgqOjo20QJvJX7d69m169ejFs2DCqVauGq6srRqORnDlz2rs0yeQuX75MvXr1yJkzJ9OnTydv3ry0aNGC1157jVmzZuHs7Axga8SpQZQ8TdpdX4vFgp+fn23pVNWqVTly5Ag1atSga9euAKxevZpFixaxbNkycufObefKJTOLiIjA39+fbt26UaBAAb766isePHhA3bp1adCgAV999RXR0dGkpKTQokULHB0dmT17NocPHyY4OBgXFxd7fwTJJB6fmXLgwAGmT59OtmzZ6NatW4Z2B/PmzWPv3r0sX75c1z15ovSzgleuXMmJEycYP348c+bMITIykpSUFMLCwsiRIwdxcXE4ODgwduxYSpYsaefK5UWiUEr+1JUrV2jSpAndu3enRIkSREREMGbMGFq2bEm/fv1ITk7mhx9+wMvLi6JFi9qW9ukujPwdO3fupGfPnvTu3dt2F0bkz1y8eJHg4GASExOxWCw0b96c/Pnz0759ewoXLsz06dNtwZTIXxEQEICrqytTpkzh3r17ZMmShUWLFvHzzz9z9epVypYty9GjR5k9e7YG5/KnZs+eTVRUFCNHjgQezZ6aPHkyP/74Ix06dKB27do4Ojpy4sQJwsPDiYiIYN26dSxdupRixYrZt3jJNNIChAcPHvDw4UOcnJzIkycP4eHhjBw5El9fX5o2bUrZsmVtx6SFWFpmJY9Lv7PsN998w/HjxylTpgx169YFYP78+Zw+fZps2bJRp04dChQogLOzM+7u7nauXF40CqXkT23ZsoX169dn2Pns0KFDtGnThq5du9K9e/cMr9cMKflf7dmzB19fXy3Zk//arVu3GDp0KCaTidatW5MvXz4aN25M1apVmT59ur3Lk+dEfHw83bt3p1+/fpQsWZLk5GScnJzYtWsXkZGRWK1W8ufPT8mSJfHx8bF3ufIcGD9+PKdPnyYkJMQ2KyEmJoa6devi5eVFs2bNaNiwIWvXrmXfvn04OzvTsWNHLTMWm7TxdXh4OP379ydbtmxERUVRqVIlOnfuzIMHDwgKCsLX15eGDRtSqVIl27EKpORxad8ni8VCnTp1cHJy4ty5c7aJCHny5AEgJCSE7du3U7VqVfr06WPnquVFpeRA/lR8fDzR0dG2v1NTUylevDjFihVj1qxZLFu2LMPrFUjJ/6pmzZoKpORv8fDwYPjw4VgsFpYvX05ERAQbNmygb9++9i5NniOJiYmcOHGCEydOANhm/t6/f58ffviBjh078uGHHyqQkidK28EqNTXV9tibb74JwC+//GJbRpw9e3YqV66Mu7s7W7du5d69e/j7+zNjxgyCgoIUSEkGRqORmzdvEhAQQP369VmxYgV9+/Zl27Zt7N+/n9KlSzNw4ECOHTvGkSNHMhyrQEoel/Z7bc+ePZQrV46tW7cyZMgQ9u/fz6ZNm7h16xYAbdu2pWHDhjRp0sSe5coLTumB/Klq1apx5coVZsyYATwanGfNmpWiRYsybty4DI0VRUTszcfHh2HDhnHv3j02b97Mq6++iq+vr73LkueIu7s7bdu2ZeXKlezbt882eE9OTiZr1qwkJSXZuULJrNKWw5w/f55BgwYxYcIE9u3bR61atXB2dmbevHmEhIRw/vx5+vXrR1JSElOnTuXcuXNs3rzZ9j7atVjSs1gsAFy4cIESJUrQoUMHrFYroaGhNGjQgEqVKhESEsKbb77JF198QadOnexcsTwP2rRpw/Tp06lcuTIArVq1ol27dqxYsYKtW7dy8+ZNAJo3b463t7c9S5UXnJr+yB+yWCzky5ePzz//nAEDBhAZGUmxYsU4deoU586dY+zYseohJSKZjo+PD1988QUmk0kNguVvadq0Kbdv3+Y///kPlStXxs3NjV27drFw4UKyZMli7/IkE0oLpM6cOUOrVq147733OHDgAMePHwdg1qxZTJ8+na+//ppt27aRLVs2vvzyS7JkyULVqlXJmzevfT+AZDppS6zSgvGUlBQiIyO5dOkSffr0oUCBAowePZopU6Zw+vRp2rZtyxtvvAFkbGAt8iSffvopw4cP5+DBgzRo0AB4FEwZjUYmTZqEg4MDrVq10vdI/nXqKSV/2a+//srs2bPJnj07zs7OjBgxQrvsiYjICys2Npb9+/fz/fff4+PjQ+3atbW0WH7nzp07vPrqqwBER0fTpk0b/Pz8aN26NZs2bWLx4sVkzZqVTp06Ub16dQBu3Lhhaxi8atUqZs6cycqVK8mfP789P4pkImmh0qVLl/j6668xmUzEx8cTERHBr7/+yptvvsm0adMA6NGjB76+vgQGBtq3aHnu7N69m969e9OhQwd69+5te3z16tVUrlyZAgUK2K02eXkolJK/JP0W2ekDKM2QEhERkZdVbGwsc+fOpWnTpjg7O3PixAm+/PJL1qxZQ0xMDEOGDKFkyZIcOnSIqKgoatWqRa9evYiMjGTOnDmcPn2ahIQEpk2bRqlSpez9cSSTSBt3nz17liZNmvD222+TkJBAWFgYMTExvP7669SqVQsfHx++//57zp07x4YNGzQml79l586d9O3bl4CAgN9tYCXyLOjMJX9JWoPExzNMXfxERETkZRUdHc3OnTs5ffo0169fp2XLluTNm5d79+7RsWNHihYtSkBAAADbt2/n2rVrAHh5edGiRQssFguenp54eHjY82NIJmMwGLh37x4//fQTXbt2pVOnTsTFxXH8+HH69etHQkICSUlJ7Ny5E09PT9avX4+Dg4OW7Mnf8sEHHzBt2jS6d++Oo6MjnTt3tndJ8pLRTKmX2H+77C79drLXr18nd+7cODo6/lvliYiIiGR63333HT169KBgwYLMnz+fV155hZMnTzJx4kRWrVoFQGBgIAULFqRLly7aCU3+VFxcHLVr1yYxMZEhQ4bQsGFDW+C0cOFCDhw4wPTp03F2drbdIFYgJf+rPXv24Ovrq2Xq8sypEdBLymw2YzQauXLlCkuXLmX//v3cuXPnqa9PH0iFhIQwfPhw7T4kIiIiLz0fHx969OiB1WolKCiIK1euYDQaiY6OZu7cufTr14/z58/TqVMnWysEkT/i5ubG8OHDMRqNnDlzBvj/qxZy586NwWDAxcUlw4oFBVLyv6pZs6YCKbELrb16CVksFkwmE+Hh4bRt25b8+fNz5coVmjRpgr+/P/ny5cvw+vSB1PLlywkODmbhwoVkzZrVHuWLiIiIZBqFCxemcOHC1KxZk169ejF79mzq1avHhx9+yK5du/D19WXt2rVaXiX/lQ8++ACDwUDv3r3Jmzcv/v7+uLm5sX//frJly6ZNhkTkhaHley+pq1ev0qZNG9q0aUPr1q0ZNmwYR44coVatWjRr1gxPT08g41Tg5cuXM336dEJCQihRooQ9yxcRERHJdM6dO0f37t2pWLEiZ8+epVy5cgwYMACj0ajNYeRv2bFjB/369cPd3Z1q1apx8eJFlixZgpOTU4YbxyIizytF7C+R9NPFf/rpJ8qUKUPr1q25ffs2KSkplCpVim3btrFixQpOnjwJ8LtAavHixQqkRERERJ6gSJEizJo1C7PZjKenJ4GBgRiNRiwWiwIp+Vtq167NrFmzePjwIYmJiaxatQonJydSU1MVSInIC0Gh1Esiral5ZGQkp06dIjU1leTkZJKSkmjfvj3u7u5MnDiRPHnysHXrVjZv3mw7dvny5UydOpWQkBBKlixpx08hIiIikrkVKVKEUaNGMX36dBwcHEhNTdVSK/mf1KhRgy+++IKdO3cye/ZsQDtgi8iLQ2ezl0DaErybN2/y6aefMmDAAGrUqMFHH33EqlWryJ07NwMGDADAw8ODOnXq0KpVKwBOnz7NypUrCQ0N1QwpERERkb/AyckJeNSXU+GB/BNq1arF1KlT6dGjByaTic6dO9u7JBGRf4Suki8Bk8nExYsXWbx4MQ0bNqRx48a259KmlF+5coXg4GDu3btHy5YtbXf0SpQoQWhoKLly5bJX+SIiIiLPJS2vkn/S+++/z+zZs/H19bV3KSIi/xiFUi+Js2fPsnbtWkqUKEFUVBR58uQBIHv27ERHR9OzZ09MJhMrV67EZDLZ+k8ZjUYFUiIiIiIimUDNmjXtXYKIyD9Ku++9oNKW7KX1MTAajXz99df06dOHXr160bZtW1xcXIBHO8VYLBYKFy5sO0ZTzUVERERERETk36Tk4QWUFkhduHCB5cuXExMTQ69evahTpw5JSUkMGjQIo9FIy5YtcXNzo0iRIhmOVSAlIiIiIiIiIv82bQXygrFYLJhMJs6cOcOnn35KfHw8YWFhtGzZkvDwcBo0aEBQUBAzZszgyy+/JDk5OcPxJpPJTpWLiIiIiIiIyMtEy/deQLdv36Zz5840b94cPz8/9u7dy/Dhw7FarcyfP59ixYqxdu1a1q9fz8qVK9WEU0RERERERESeOc2UegEkJiZy7NgxUlNTAbh79y5OTk74+fkRGxvL6tWr6du3L4UKFaJnz5589dVX+Pv7s2rVKgwGA8olRURERERERORZUyj1Apg0aRIDBgzg559/JiUlhejoaLy9vYmNjaVdu3a8+uqrNGjQgFKlSnH37l127NiRIYjSTCkREREREREReda0fO85lpycjKOjI2azmR49enD37l369OlDlSpVSE5O5vTp08ycOZOFCxcCEBgYyLvvvkudOnUwGo1YrVYFUiIiIiIiIiJiF5op9ZyyWq20atWKgIAATCYTs2bNImfOnEyaNIn9+/djNBqJi4vj4MGDfP311/Ts2ZPw8HBq166N0WjEYrEokBIRERERERERu9FMqefYiRMnaNeuHTVr1mTixIlYLBa6du3KvXv36Nu3L1WqVKFPnz7cuXMHFxcXgoODbTOrtMueiIiIiIiIiNiTQqnnVGpqKg4ODpw+fZpmzZrx/vvv88UXX2CxWOjSpQt3795l0KBBVKxYkbi4OFxdXTEYDLbjRERERERERETsScv3njMWiwUABwcHLBYLJUqUYMWKFXz77bcEBgZiNBqZM2cOuXPnZuDAgZw4cQI3NzfbLnsKpEREREREREQkM9BMqedI2rK7yMhI9u7dy/Xr16lSpQrvvvsuFy5cwM/Pj1q1ajFx4kTMZjOTJ08mMDBQS/VEREREREREJNNRKPWcOXv2LJ999hnVqlXD2dmZb7/9lrfeeotJkyYRFhZGixYtqFChAnPnzrU1MlcPKRERERERERHJbLR87zmSkJDAtGnTaN++PUFBQYwaNQoALy8vbt++TfHixVm8eDFJSUmkzxoVSImIiIiIiIhIZqNQ6jliNBq5d+8eZcqUwWKx0KBBA6pWrUqnTp3w8/Pj8OHDvPnmm4SGhmI0Gm39p0REREREREREMhuFUpmY2WzO8LeTkxMGg4HTp0/TtGlTChUqxMSJEwHIlSsXnp6eALZZUkaj/veKiIiIiIiISOak1CKTSusDdeHCBSZNmkRAQAARERHUqVOHMWPG4OLiwuTJkwEYMWIErq6utlAqrZeUiIiIiIiIiEhm5WDvAuTJTCYTZ8+epVWrVtSpUwdfX1/i4+Np3bo1sbGxhIaG0qZNGwwGA3FxcaxYscK2ZE8zpEREREREREQks9Pue5lUUlIS/fv3p1SpUnTq1Mn2eHh4uK1x+eXLlzGZTLzzzjuYTCZSU1NxcFDOKCIiIiIiIiKZnxKMTMrZ2Zno6GicnZ0zPH7nzh0GDx7Mhg0bKFKkiO1xs9msQEpEREREREREnhta55VJPL5TXnJyMjly5ODKlSvExsbaHvfy8qJIkSJkzZo1w+vTZk+JiIiIiIiIiDwPNLUmE0hrah4VFcXFixcxm80UKVKETp060bx5c1555RVq1qxJ8eLFmT9/PgaDgSxZsti7bBERERERERGRv009pewsrTF5eHg4PXr0IF++fFy7dg2LxcL48eMxGAwMHz6clJQUPDw8SElJYdmyZTg6OqqpuYiIiIiIiIg8txRKZQJRUVE0a9aMNm3a0Lp1ayIjI9m+fTtz585l5cqVeHh4cP36dcxmM2+++SZGo1FNzUVERERERETkuaZQyo6sVisGg4HDhw8zZ84cFi5caHvu4cOHjBw5EldXV0aPHo3BYLA9pxlSIiIiIiIiIvK8U7JhB2azOcPfSUlJHD16lDNnztiez5YtGx4eHsTHx2cIpAAFUiIiIiIiIiLy3NP6r2csran5xYsXWbFiBe7u7ri7u1O9enW2bt1K1qxZ8fb2BuDGjRt4eXnZuWIRERERERERkX+elu89Q2nL9c6dO0fTpk0pV64c0dHRZM2aldTUVHLmzEl8fDzFixfnypUrREREsHHjRvWOEhEREREREZEXjkKpZ+zevXvs3LmTxMRE2rZty4kTJ/jqq684c+YM3t7eFClShN9++w1vb2969OiBg4ODbXaViIiIiIiIiMiLQlNwnqG4uDjq1auH0Whk8ODBAJQuXdq2m97p06d55513aNOmje0YBVIiIiIiIiIi8iJSx+xnyM3NjWHDhpGQkMCRI0dsj5cqVQo/Pz88PT3Zt28f8GipH6BASkREREREREReSFq+Zwe7du2iT58+dOzYkV69etkev3TpEj4+PtpdT0REREREREReeAql7GTnzp307duXzp0706NHjwzPWSwWBVMiIiIiIiIi8kJT8mEnH3zwAVOnTiU4OJi1a9dmeE6BlIiIiIiIiIi86DRTys5++eUXypUrh4ODes6LiIiIiIiIyMtDoVQmkZqaqmBKRERERERERF4aCqVEREREREREROSZU/MiERERERERERF55hRKiYiIiIiIiIjIM6dQSkREREREREREnjmFUiIiIiIiIiIi8swplBIRERERERERkWdOoZSIiIjICywyMtLeJYiIiIg8kUIpERERkX/RZ599Rvfu3Z/43Jo1a6hatSrJycl/+f2+/PJLOnTo8Jdeu2fPHtq3b/+X31tERETkWXKwdwEiIiIiL7JWrVrRvXt3bt++Te7cuTM8t3LlSpo2bYqTk9Nffr+AgIC//Nr79+9jtVr/8utFREREniXNlBIRERH5F1WvXh0vLy82btyY4fHjx49z7tw53nnnHTp37kyNGjUoXbo0devW5bvvvgPg6tWrFC1alKCgICpWrMioUaOYOXMmrVq1sr3PTz/9hJ+fHxUqVOCjjz5iy5YtABw8eJARI0Zw/fp1ypYty9GjRylevDg3b960Hfvbb79RpkwZYmNjn8F/CREREZGMFEqJiIiI/IuMRiPNmzdn7dq1GWYtrVy5kg8//JAhQ4bw+uuvs2vXLg4fPky1atUYOXJkhveIi4vjxx9/pE+fPhkeDw8Pp0uXLnTq1ImDBw8yZswYxo0bx759+6hcuTKjRo3Cy8uLY8eOUa5cOQoWLGgLrQA2bdpE7dq1yZo167/630BERETkSRRKiYiIiPzL/Pz8uHPnDj///DPwaFnd119/TevWrZk7dy49evTAarVy7do1smfPzq1btzIc36BBA5ycnMiePXuGx1etWsV7773HBx98gMlkoly5cjRp0oTly5c/sY5GjRrZQqmUlBS2bdtG48aN/4VPLCIiIvLn1FNKRERE5F+WLVs26tWrx9q1a6lSpQrr16+nRIkSlC5dml27dtG1a1du375NoUKFcHd3/10fqDx58jzxfa9du8bPP/9MhQoVbI+ZzWby58//xNfXr1+fKVOmcPr0aa5evUq2bNmoWLHiP/dBRURERP4LCqVEREREnoFWrVrRsGFDoqOjWbNmDT179uTWrVv06tWLWbNmUbNmTQB27NjBzp07MxxrMBie+J558+alYcOGjB492vZYVFTUU5ubv/rqq/zf//0f27dv5+rVqzRq1Oip7y0iIiLyb9PyPREREZFnoHDhwpQvX56goCASEhL44IMPiIuLw2w24+LiAsD58+cJDg4GIDk5+U/f08/Pj23btrF//34sFguRkZG0bNmSRYsWAeDs7ExCQgKpqam2Yxo3bsyuXbv46aefaNiw4b/wSUVERET+GoVSIiIiIs9Iy5Yt2bRpE82aNcPR0ZGCBQsyYMAA+vfvT/ny5enVqxeNGzfG0dGRs2fP/un7vfnmm0yZMoUpU6ZQsWJFWrZsSc2aNenXrx8AFStWJFeuXFSsWJEzZ84AUKNGDeLi4ihdujSenp7/6ucVERER+SMG69Pmd4uIiIjIC6lhw4Z07NiRunXr2rsUEREReYmpp5SIiIjISyIiIoKDBw9y+/ZtatWqZe9yRERE5CWnUEpERETkJTFs2DAuXLhAUFAQTk5O9i5HREREXnJaviciIiIiIiIiIs+cGp2LiIiIiIiIiMgzp1BKRERERERERESeOYVSIiIiIiIiIiLyzCmUEhERERERERGRZ06hlIiIiIiIiIiIPHMKpURERERERERE5JlTKCUiIiIiIiIiIs+cQikREREREREREXnmFEqJiIiIiIiIiMgz9/8A1SsUX+mFU7wAAAAASUVORK5CYII="
|
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},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
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},
|
|
{
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"data": {
|
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"text/plain": [
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"<Figure size 1200x600 with 1 Axes>"
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],
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
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"<Figure size 1200x600 with 1 Axes>"
|
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],
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
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"<Figure size 1200x600 with 1 Axes>"
|
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],
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"text/plain": [
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"<Figure size 1000x600 with 1 Axes>"
|
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],
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
|
"text/plain": [
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"<Figure size 1000x600 with 1 Axes>"
|
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],
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
|
"text/plain": [
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"<Figure size 1000x600 with 1 Axes>"
|
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],
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" Variety Technique Technique String \\\n",
|
|
"0 nocellara_delletna 1 intensiva \n",
|
|
"1 nocellara_delletna 2 superintensiva \n",
|
|
"2 nocellara_delletna 3 tradizionale \n",
|
|
"3 leccino 2 superintensiva \n",
|
|
"4 leccino 1 intensiva \n",
|
|
"5 leccino 3 tradizionale \n",
|
|
"6 frantoio 2 superintensiva \n",
|
|
"7 frantoio 1 intensiva \n",
|
|
"8 frantoio 3 tradizionale \n",
|
|
"9 coratina 3 tradizionale \n",
|
|
"10 coratina 2 superintensiva \n",
|
|
"11 coratina 1 intensiva \n",
|
|
"12 taggiasca 1 intensiva \n",
|
|
"13 taggiasca 2 superintensiva \n",
|
|
"14 taggiasca 3 tradizionale \n",
|
|
"15 pendolino 2 superintensiva \n",
|
|
"16 pendolino 1 intensiva \n",
|
|
"17 pendolino 3 tradizionale \n",
|
|
"18 moraiolo 1 intensiva \n",
|
|
"19 moraiolo 2 superintensiva \n",
|
|
"20 moraiolo 3 tradizionale \n",
|
|
"\n",
|
|
" Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n",
|
|
"0 13484.387297 2938.696221 \n",
|
|
"1 20366.973335 4485.937492 \n",
|
|
"2 7925.814903 1778.308342 \n",
|
|
"3 15997.073399 3121.799469 \n",
|
|
"4 9025.145010 1773.563999 \n",
|
|
"5 10108.938875 1997.846706 \n",
|
|
"6 24265.376105 5969.363987 \n",
|
|
"7 24967.326713 6123.112604 \n",
|
|
"8 10351.946892 2525.767703 \n",
|
|
"9 12170.662614 3084.580532 \n",
|
|
"10 19291.711929 4912.670225 \n",
|
|
"11 23042.166618 5897.571654 \n",
|
|
"12 8659.152131 1730.266104 \n",
|
|
"13 18886.132975 3872.392231 \n",
|
|
"14 5414.742282 1086.460741 \n",
|
|
"15 16312.507500 2931.346981 \n",
|
|
"16 14916.874917 2689.451894 \n",
|
|
"17 12279.430682 2203.132618 \n",
|
|
"18 10384.336363 2266.477978 \n",
|
|
"19 34375.209178 7479.435940 \n",
|
|
"20 10014.347057 2172.626249 \n",
|
|
"\n",
|
|
" Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n",
|
|
"0 33768.971526 0.217933 \n",
|
|
"1 36053.911751 0.220255 \n",
|
|
"2 26406.432054 0.224369 \n",
|
|
"3 20134.892898 0.195148 \n",
|
|
"4 13425.362086 0.196514 \n",
|
|
"5 22895.581275 0.197632 \n",
|
|
"6 28810.421723 0.246003 \n",
|
|
"7 34101.902697 0.245245 \n",
|
|
"8 22178.839690 0.243990 \n",
|
|
"9 38177.969803 0.253444 \n",
|
|
"10 37446.029717 0.254652 \n",
|
|
"11 53775.708012 0.255947 \n",
|
|
"12 20269.923048 0.199819 \n",
|
|
"13 28991.283481 0.205039 \n",
|
|
"14 20372.596517 0.200649 \n",
|
|
"15 21910.021485 0.179699 \n",
|
|
"16 29764.139976 0.180296 \n",
|
|
"17 27553.909208 0.179417 \n",
|
|
"18 23823.097838 0.218259 \n",
|
|
"19 59327.838908 0.217582 \n",
|
|
"20 37369.133353 0.216951 \n",
|
|
"\n",
|
|
" Water Efficiency (L oil/m³ water) \n",
|
|
"0 0.087024 \n",
|
|
"1 0.124423 \n",
|
|
"2 0.067344 \n",
|
|
"3 0.155044 \n",
|
|
"4 0.132105 \n",
|
|
"5 0.087259 \n",
|
|
"6 0.207195 \n",
|
|
"7 0.179553 \n",
|
|
"8 0.113882 \n",
|
|
"9 0.080795 \n",
|
|
"10 0.131193 \n",
|
|
"11 0.109670 \n",
|
|
"12 0.085361 \n",
|
|
"13 0.133571 \n",
|
|
"14 0.053330 \n",
|
|
"15 0.133790 \n",
|
|
"16 0.090359 \n",
|
|
"17 0.079957 \n",
|
|
"18 0.095138 \n",
|
|
"19 0.126070 \n",
|
|
"20 0.058140 \n",
|
|
"Comparison by Variety:\n",
|
|
" Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n",
|
|
"Variety \n",
|
|
"nocellara_delletna 16284.306578 3578.425947 \n",
|
|
"leccino 11690.863663 2292.609987 \n",
|
|
"frantoio 20596.625089 5056.518126 \n",
|
|
"coratina 18446.388159 4704.216038 \n",
|
|
"taggiasca 9980.436088 2022.517414 \n",
|
|
"pendolino 14761.447671 2654.172853 \n",
|
|
"moraiolo 16231.746411 3531.590204 \n",
|
|
"\n",
|
|
" Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n",
|
|
"Variety \n",
|
|
"nocellara_delletna 34061.917853 0.219747 \n",
|
|
"leccino 18024.908377 0.196103 \n",
|
|
"frantoio 28258.273811 0.245502 \n",
|
|
"coratina 44003.305214 0.255021 \n",
|
|
"taggiasca 22640.111000 0.202648 \n",
|
|
"pendolino 25766.594689 0.179804 \n",
|
|
"moraiolo 38202.360033 0.217573 \n",
|
|
"\n",
|
|
" Water Efficiency (L oil/m³ water) \n",
|
|
"Variety \n",
|
|
"nocellara_delletna 0.105057 \n",
|
|
"leccino 0.127191 \n",
|
|
"frantoio 0.178939 \n",
|
|
"coratina 0.106906 \n",
|
|
"taggiasca 0.089333 \n",
|
|
"pendolino 0.103008 \n",
|
|
"moraiolo 0.092444 \n",
|
|
"\n",
|
|
"Best Varieties by Water Efficiency:\n",
|
|
" Variety Avg Olive Production (kg/ha) \\\n",
|
|
"2 frantoio 20596.625089 \n",
|
|
"1 leccino 11690.863663 \n",
|
|
"3 coratina 18446.388159 \n",
|
|
"0 nocellara_delletna 16284.306578 \n",
|
|
"5 pendolino 14761.447671 \n",
|
|
"\n",
|
|
" Avg Oil Production (L/ha) Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n",
|
|
"2 5056.518126 28258.273811 0.245502 \n",
|
|
"1 2292.609987 18024.908377 0.196103 \n",
|
|
"3 4704.216038 44003.305214 0.255021 \n",
|
|
"0 3578.425947 34061.917853 0.219747 \n",
|
|
"5 2654.172853 25766.594689 0.179804 \n",
|
|
"\n",
|
|
" Water Efficiency (L oil/m³ water) \n",
|
|
"2 0.178939 \n",
|
|
"1 0.127191 \n",
|
|
"3 0.106906 \n",
|
|
"0 0.105057 \n",
|
|
"5 0.103008 \n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 25
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 4. Analisi della Relazione tra Meteo e Produzione"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-23T06:10:55.903873Z",
|
|
"start_time": "2024-10-23T06:10:54.655058Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"def get_full_data(simulated_data, olive_varieties):\n",
|
|
" # Assumiamo che simulated_data contenga già tutti i dati necessari\n",
|
|
" # Includiamo solo le colonne rilevanti\n",
|
|
" relevant_columns = ['year', 'temp_mean', 'precip_sum', 'solar_energy_sum', 'ha', 'zone', 'olive_prod']\n",
|
|
"\n",
|
|
" # Aggiungiamo le colonne specifiche per varietà\n",
|
|
" all_varieties = olive_varieties['Varietà di Olive'].unique()\n",
|
|
" varieties = [clean_column_name(variety) for variety in all_varieties]\n",
|
|
" for variety in varieties:\n",
|
|
" relevant_columns.extend([f'{variety}_olive_prod', f'{variety}_tech'])\n",
|
|
"\n",
|
|
" return simulated_data[relevant_columns].copy()\n",
|
|
"\n",
|
|
"\n",
|
|
"def analyze_correlations(full_data, variety):\n",
|
|
" # Filtra i dati per la varietà specifica\n",
|
|
" variety_data = full_data[[col for col in full_data.columns if not col.startswith('_') or col.startswith(f'{variety}_')]]\n",
|
|
"\n",
|
|
" # Rinomina le colonne per chiarezza\n",
|
|
" variety_data = variety_data.rename(columns={\n",
|
|
" f'{variety}_olive_prod': 'olive_production',\n",
|
|
" f'{variety}_tech': 'technique'\n",
|
|
" })\n",
|
|
"\n",
|
|
" # Matrice di correlazione\n",
|
|
" plt.figure(figsize=(12, 10))\n",
|
|
" corr_matrix = variety_data[['temp_mean', 'precip_sum', 'solar_energy_sum', 'olive_production']].corr()\n",
|
|
" sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
|
|
" plt.title(f'Matrice di Correlazione - {variety}')\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
" # Scatter plots\n",
|
|
" fig, axes = plt.subplots(2, 2, figsize=(20, 20))\n",
|
|
" fig.suptitle(f'Relazione tra Fattori Meteorologici e Produzione di Olive - {variety}', fontsize=16)\n",
|
|
"\n",
|
|
" for ax, var in zip(axes.flat, ['temp_mean', 'precip_sum', 'solar_energy_sum', 'ha']):\n",
|
|
" sns.scatterplot(data=variety_data, x=var, y='olive_production', hue='technique', ax=ax)\n",
|
|
" ax.set_title(f'{var.capitalize()} vs Produzione Olive')\n",
|
|
" ax.set_xlabel(var.capitalize())\n",
|
|
" ax.set_ylabel('Produzione Olive (kg/ettaro)')\n",
|
|
"\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"\n",
|
|
"# Uso delle funzioni\n",
|
|
"full_data = get_full_data(simulated_data, olive_varieties)\n",
|
|
"\n",
|
|
"# Assumiamo che 'selected_variety' sia definito altrove nel codice\n",
|
|
"# Per esempio:\n",
|
|
"selected_variety = 'nocellara_delletna'\n",
|
|
"\n",
|
|
"analyze_correlations(full_data, selected_variety)"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 1200x1000 with 2 Axes>"
|
|
],
|
|
"image/png": 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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 2000x2000 with 4 Axes>"
|
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],
|
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"image/png": 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SCJQYF5E9gg8/TdObxp3etV5XbDv6zmPZM1mWgVFUTMAMxJ0nKykL2DVX3Lou5JeE407PLQrtom8WEREREZGdwXVdsgKZcaebhkmqP5WAkbIboxIRkUTg85kUfzqL0p9/jpoW/OZrmq9azAH1tj7+L68ohKGhB0VEEoIS4yKyR0gjk8u7XxFzWsPUhrTO0nPj9ia+cBBz+iyOb3ZUzOkGBgMb9ycc3jUXO6RYBoe0qRd3+tAujUHlSURERERkj+U4Lp3rdsFn+GJOP6TZIdRPboAZjj1dREQkHl9xAXmvvhp/+tQpHHVARvnffVrm7LI+LBER2b2UGBeRPUI47NCjTm9u6nsTmRXuCuhevzsTjphIqp1Vi9FJTRmuS3j2V5yTcwz7Z+4fNf3mLteQZafuugBshzGHtSp/JlRFzeuk0qt5tkYgEBERERHZw6W6mdw18G5MI7L7qml6Uy7vcTkZbjaurncVEZGacgEnfr+Qazv4ttwgfmibetRL8++euEREZJfTZbUissfwhZMZ0mgo/Y8ZSEEon4AVIM3IxG+n4Ki3Y68S8ieTMex41l98Hf+9658sb1HKZ/nfUt+XzeFZfcleX4phZYC96/Zrjt/kzUsP4r73F/PxonUELJORvZpxwcCWpLKrBnEXEREREZGdxbB99Mzuy5vHv8WHKz5gdcFqDmxyIO1z2pPp1iMUtms7RBER2QuFU9LIOOEESu+9N+Z0+5hhzFsb5JohbTmxexMClSTRRURk76LEuIjsUZwwpJBFClmwpY/DVQpzr2PbDml9+uFr2JCSS65jv0aNOLdtW5yi3wmv/5DMCRMp2IVJcQDXcannN7nzuI6UDnXx+y0CjoMTdlSiRERERET2EobtI4N6jNxvFKbpjTbmBF1CKCkuIiLbJ2y7pB91FHmvvELojz8ipiW1bUuDwwZyb1Im6T4DW0Ooi4gkFCXGRURklyhKzqDRo49R/MnH5L36KqFVq8gcfgLpRx1DYUoWu2PMQ9cF03VINaBeVgrr1+fv8u8UEREREZGdL6y7w0VEZCcqTM6i6aRJFMyYwea33sIwTTJHjiRt8OEUJmeQ4rrYYd1aISKSaJQYFxGRXcJ1oTApA+vY4dQfdATgEk5J9+4U19D4u5VlGRiGieM4OI62vYiIiIiIiIjs21zXpSA5C9+IU2lwzFDAIJSSToHt6Pl7IrLPqNhv7O4jffZKjIuIyC5l2w52IHXLH/vGj+uewrBcCs1NfLPuG/7MX0HXet3YP7MVqXamEuQiIiIiIiIiss8L2y7hQJr3h61h00Vk3xCr3/iAzFZARm2HtsspMS4iIpKADNPlt9JfueTDiymxS8rfb5LWhCePGE+6W0c37ouIiIiIiIiIiIjsQyrrN5541ERSjeyE7jc2azsAERER2fmKrM1c/vHfIyo3AKsKV3HnvP8Q9pXWUmQiIiIiIiIiIiIiUhsq6ze+Y/YdhK3E7jdWYlxERCTBGAasLFhBfig/5vQ5q+dQ5MSeJiIiIiIiIiIiIiKJxzDgz0r6jWevnk1hgvcbKzEuIiKSYAzDIC+4udJ5Sp3gbopGRERERERERERERGqb12+cV+k8wQTvN1ZiXEREJME4jssBWQfEnZ6dlE2albYbIxIRERERERERERGR2uQ4Lq2yWsWdnpOUQ5ovfTdGtPspMS4iIpKAMsxsjmxxZMxpV/S4knSyd29AIiIiIiIiIiIiIlKrMsxshjQfEnPaVb2uIo2s3RzR7qXEuIiISALyhZO5use1XNr1UjIDmQA0S2/GvQffy4AGh2CH3VqOUERERERERERERER2J184mWt6XsfFXS/Z2m+c0Yz7Drmfwc0H4yR4v7GvtgMQERGRXcMfTOPUlmdy3P7DsN0wPgKkupnYYae2QxMRERERERERERGRWuAPpnF6y9EM2/8Er9/YCJDmZpKVlMb6/PzaDm+XUmJcREQkgdlhSCZz698oKS4iIiIiIiIiIiKyL4vqNzb2jX5jJcZFEoThdyl28wGDVNJxwkZthyQiIiIiIiK7iOlzKaYAF9QGFBERERERqQYlxkX2cqZpUGjl8vwvz/Heb+9hGibDWg3j5DankBrOxnES+3kQIiIiIiIi+xLTNCjybeLlRS8xbfk0AI5teSyntzuDNDtHbUAREREREZE4lBgX2csVWrmcNWM0G0o2lL/31I9PMfP3mYw/YiLJwcxKPi0iIiIiIiJ7kyJfHufNPJe/Cv8qf+/5X55n5h8zeerIp0kOZtVidCIiIiIiInsus7YDEJHtZ/ng7WX/i0iKl/mz4E++WPUZPp8OcxERERERkURg+Qw+XDEzIileZk3RGt777T0sn4ZUFxERERERiUUZM5G9WLFbyPt/vB93+vTfplNK8W6MSERERERERHaVUop577f34k6f8ft7lFC4GyMSERERERHZeygxLrIXMwyTFCsl7vRkXzKWoScmiIiIiIiIJAITkxRf5W1AE2s3RiQiIiIiIrL3UGJcZC+W7KZxWrvT404/o90oTFuJcRHDMMgP5lNqFYDfru1wRERERES2i89J4vR2Z8Sdfka7UQTc5N0YkYiIiIjsTJYPgr5CQr4iPSZVZBdQxkxkL2bbDn0b9KNfo37M/WtuxLQhzYfQNrMdTsitpehE9gy2r4SlRcuYOH8CqwtX06luJ87rdD51zYZg624aEREREdl7OI5L55wuHNr0UGatnBUxbUCTgXSr2x1bbUARERGRvY5pGhRauby97G3e/30GASvAae1O46BGAwgE02s7PJGEocS4yF4uEErntn53sDx/Gf9b+hamaXFS6xE0S22BP5Ra2+GJ1CrXF+KdFVN56JsHy9/7I/8P3v/9fR4//AnapXTBtp1ajFBEREREpGb8oTRu7P0vzuwwmjeXTsF1XU5sPYLmaS0JhNJqOzwRERER2Q6FVi7nvH8264rXlb9325zb6NmgJ2MPuptASMlxkZ1BiXGRBBAIpdMhtRude3QHIBxycXWXgAhF5PPItw9HvW+7NrfNuZWJh08myc6ohchERERERLZfIJRG66RO3NitC+CqDSgiIiKyFzN9Li8vfjkiKV7m67Vf8+vmxXRJ64XjqL4nsqP0gAKRBOE4LqGgQyjo4Lr6gRQxDIPlm5dju7GfKb6yYCWF4YLdHJWIiIiIyM7hui6hoK02oIiIiMherogC3v1tetzpby6dgmGpvieyMygxLiIiCcrFMit/hrhhGLspFhEREREREREREZFoBgaWEb8f02f41I8pspMoMS4iIgnJdaFFRkv8pj/m9P2z9ifN1DDqIiIiIiIiIiIiUntSSWd4q+Fxp49sPRI7tPviEUlkSoyLiEjCSnUy+He/m6PeT7KSuP2gO0h20mshKhERERERERERERGPHYbhrU6keUbzqGmDmw2mefr+enSOyE7iq+0AREREdhnbon+9g3n52Jd5cdGL/LH5D7rX786JrUeQ6dbFsVWhFBERERERERERkdqVHMriicHjmbtmNu8sf4eAFeD0dmfQJrMdgVBabYcnkjCUGBcRkYRm2gEaGs255aBbyM3Px+8mYYddlBIXERERERERERGRPYHruiSHMjm8wTEc3HAQJham7cMJqRdTZGdSYlxERPYJASuAFQ5ga9ghERERERERERER2QOFww4mAQAc3dojstPpGeMiIiIiIiIiIiIiIiIiIpLQlBgXEREREREREREREREREZGEpsS4iIiIiIiIiIiIiIiIiIgkNCXGRUREREREREREREREREQkoSkxLiIiIiIiIiIiIiIiIiIiCU2JcRERERERERERERERERERSWhKjIuIiIiIiIiIiIiIiIiISEJTYlxERERERERERERERERERBKaEuMiIiIiIiIiIiIiIiIiIpLQlBgXEREREREREREREREREZGEpsS4iIiIiIiIiIiIiIiIiIgkNF9tB1AbNmzYwL///W/mzZuHZVkMGzaM66+/Hp9v794chgHJZpCAU4QLFFvZhMKx5/X7IMXehIFL0EyjxAngursuNtM0SKEAnxvEMSwcXzq+UB4uBiVmNkF7x2OqyfrvbAELkp2y2NMpcfzVit2yDFLczVhuGNvwU2xkYNvxPxjwOSTbmzFwKbUyKAlvf5lNsUIEnAJcDEL+HJLDGzCxcQwfhWZdwrZLKgWwOZ90w6XIzMbC2bKeEDRTq72PvH1TumXfGBRbOYTC21fgAj6XZDvP2wZmOraZTIa7HtMJ4WJS5K9PcWnly062bJKczV75szIJhk0MwyDFKMJPCSbgWn6csE2xmU2okn0Sz7ZlvsjIxrYdAHw+kxR7EyY2YSNAMek4juvFYBbhd0pwMCmxsnFcgxQnD4swNn6KjAwcp/J4LMsk1d2E6UYuPzK2Inyu9z3FVg4Aqc4mLMIY/mTMUAEAji+NsGtg2EEsw8EwLQgHCSXlQGmBV+Z9mZh2KT63FAeLYstb12QzSIBSLDeEi0HYtSgyt26HrbEU4qcU07VxfCmUOj5KnCRM0yTFzdtyfAQo2mY9tlfZd3rxmoQCOSSFcrEI4Ro+QvgoJjMizjLblr+wkUSKnYtlmRhOEAeLUiODoOsjzSzG7xQBDq6VhOHa4IQJk0ShkRWx/LLzV5JbhGmaGKaLES7BxaTYV4+ioLFd65lKgbdPcTEMMJwwji+ZoOOj2EnGMLbM43qxF5nZOI7rHQtl5cPMIVwhVr/PIMXOxTBcwlY6hu3tY9vwUWxkVnoOq0rk9k2jxA5s97Kqy2cZpDibMHEIG8kUk4ppGKQ4uZg4OL40TLsI3MrPe8lWiKQt59QSK4tguOb7rCqWZULBOrKMYsLVPB9s7/fsimMPys5/uVu2dxLFpO2SdRCRxOTV14rxu8XldaXtrVPK7pOo7W/w6lsZZj4+p9hr5/gbUFwaXYcs49X5trbDytrCO8KyTFIdr22xs3+3dyav7ZuPjxAYJi4mrmOXtw3j1aWSrTBJTn5E2y2yPu+1Pyw3RLKzGRcoNbMwsElyCsAwcK0UfEYpRrgEDIsSI4Mie9f2gVTk9xkk21vqm740r221pf4c9GWTFMr12uK+FHDCmE4IxwzgGj4su6i8zRYOxy9b+5qtbXpvf5fa8e/7iSxDu6aeXsayTDLIxXKDuFgEA9kEgrmwcT2ZvmQKnK3tzHjH7rZ9W/HOE/E+X94OrNDGs2O150wTDAPDCWO7W8tYef+NW4TrGhSbOVX2iViWQaaRh+WUePsk0ICCkpptu5q2pyzL3NpXYvgpInbbKGJ7GD6KtrTFy87HSW6hd/yZSYQdopYTuT19FJlb2/Jb91UhLgal/hz84ejv2jaeNLMIv1MIQNCXRWEogLsTTkhl51nv/OLHtlIJ2HlYODj+VIxQMS4mtunHcB0MJ7zT+4STLIckJw/T5wc7hOsS9zsCPkje0g8c9qVj2sWYro3tS8OwSzG3aY8mWTZp5Hn9f1YSJWY6hcHtr0uUnZstw8E0LQw3hOMaFFk5WHYpftfbXsVx6ps+n0mau6m836/UyKDY9u223xapfTXtw9qe81xldTzvfL4JY8sxboaKAKqVM9i2Thr2ZeELl61LBiW29/l4fdh7m93RZ5eoqqpX7K32/pbodrjyyitp2LAhn332GevXr+eSSy7h6aef5oILLqjt0Labz3TJDK3E/OQ/GEs/hEA6/t7nY3c7kzwnu/xH2TAgy9iE9e3zmPMnQbAAf6vDSTnsX2z2NyXs7PyTQrJZSmrez5gf3Ybx10LIbIrb9yIMyw+f3IW/5znQ40z48S3MOY9CaT7+AwaTcthN5AeaEXKqHthg6/rfibH0g7jrv7OVb8+vn8b85mkIFuFvcyQph97IZl+TSrdnmllI8l/zMT+5EzYsgXptSDrsX5Q06EWhkxoxr2kaZLEBc87jmAtfBjuEv8MwUgZczWarEXYNfpB8JmSGV2F+cjfG4vfAn0Kg+xkYrQbDmxdj5rQg85DrcXNaYv7vUlj9Lcn125N0wmPw41sYXz8FwUL8rbesZxXlxm/aZARXYn58B8ayjyE5E3+fC7G7nMEmO6vacRuGQbaxAXPeBMxvnwc7iH/4k2CA8cldsP5XjLqtSRtwBanN+pFL/agfassyyLLXYHz6AOZPb4Fp4e96Gu6BY6BwHeaHt2L8OQ/SGkCP0VCvNf7cFdidRtYo1mSzhNS8XyLKfGDgNQRbDgYXAss/xPz8fti8En+jriQdfiulWW1JKl6F+eFtGH98Aal1CfS7FFoNxnzrYli/COq1Jemwf1FcvwdF25SRMhlmAYHfP8L87L6tyx98C0VZ7SlxkkkySkkrWIb50a0YK+dDeiOS+l8OTXpgfPkwHHYjzH0Uvn8V7CBWu2Mx+1yI8d4NkLscep4N3U4n8OmdGD+87pXF9sdBz7Mw3r0WgkUEjrgDo94BsPRLjNQcmPMorPkRX9Z+BA6+luB+h5LvpHvnhvwlmB/egrH6G8hojNXzbHz125Natw2s+gFz1t3e99ZrR2DQvyip14MiJ6Xa+2JbSUaF79ywGE58kqQ/5mJ8/QwEC2D/Q/D1u5SA6xDM6UiBkxZZ/uaOx/zuBa/8tTsO+l/mdbB98xz89DY4YfxHjYXG3TDWL4bZ46BpL9j/EPjyEVjzA1ZmUwIHX0Np88HkO+n4TIfM0CrMT+/C6DQCclrCvAnw89sYQGqnE0ntfzkbjUbVrnx6ZfBnrCUzofmBMPtRWDkf0hpg9TwLX+OupGS1hII1mB/dCmt+hKxmBA6+Fpr0xJj+D4w/v4K0BgQO+jvhtseRZ2eSZW3G98OrmPOehB6j8dc5AGP2I7D+V6jbmsBh/6S0YR8Kt2y36vLOpblY8ydjfvMMhErwtzuG1IOv33IurdHiqi3Tyse/5F3ML/8LBX/hb9aP5KH3Yyz5wCsTR9yK+9sXGAtfijzvBZoStr3znmVCVngV5qz/w1g0HfzJ+LufhdPrfDa5dXZKBwdAullIYNU8mDUW/4al+Ou1JWnQvyiu3zPu+WC7v2flbKxPKx57/6akXrcd/p5MMx//kvcwv3jI295NepJ0+G0Uprei1EnaOSsgIgkrYIZJL/kd86PbMX7/3Ksr9b2EcMeTyLMzazs8qUQitr8BUqxS0gqWwIe3e/XqtAak9b2Q1A7D2ejUiWqD+kyXzPBKzI/uxvj1fQik4u95LnaPs7e7zerV/T/B/PxeyPsTGnYmcPitFGV1oMRJ3jkruhOkmUUkr9nS9t1Sb6TfRVC0EX9yNkaLAfDx/RiLpoEVwN/9TJy+l2CEijE/uQ/jl6lg+b22W9+LoXAt5vs3Yfz1HTTrR9JRd8K88Rg/vQWGSaDLydD1VIy3LoVgPu7IpzF+/wK+mgjFuaS0PJiUwf8iL6kFIWfXdotlWZvx/fQm5tzHoPuoyPrz0AdJxsX44iHodjqk1Yc5j22pg7XFPegyjHW/wI9vERhwFaH9h7DZydil8e7pytv0nz2E+dObYJj4u5xKar8x5Bn1ItpLlmmQZa+uUIYC+LuNwunzNza5dXdaPb1MplVI4I+PMT6/H+wgnPAoyT89grHwNXDCBNodiG08hQABAABJREFUQ86AKyhNagiOHfPYLcnpSHLpOsxZd2EseR8Cafh7nofd46yI84R37M/a8vkV0KATgcG3EM5pi2/Tr1gV23gDryPY3GuDZ1t5WN+9iFmwGg44DHf2oxirv8Wf0ZhA/ysJtzoSq2Sj13+zfBYkZ+HvexF251Pj9olkWEUkFfyG8fGdsGIORmo9kvteSHLHE8mlLnYVF/9YJt5++nhLe8oK4O9R1p6KvZ+2tlnugtzfoH57Aof9i5J63SP6C1LMYlJyf8T66DZY+xNkN8d/8HWE9zsYXygP86O7vPOxPwWry0j87Y4lYIcpqdOFIieVVLOI5A3fY318O6z7BbJb4D/kBoJNB1BCSoV+yA+h3yUEmvTA+Ox+WPdzhXn7U+CkA17/aHrBr/DhbRirvoaMxiT3Pp+ktseQb9Yj5G7/+ai8j3HWWO/8Uq8Nbv8rMHKXQ3oDLMf2zi+bV+Jr2Bmj/99h+Wf48/+qUf9rPKZpkOWuw5w7AfOAg+H3L2DhKzH7eMvb/wuexvz6aQgV4299BEafC2DzStxgEca8J2DTH9CgI4Ej78JIr4/x+cMYP04Bw8Rofzwpvc4hJb0BuW69GidpsqzN+H7e0g9dtB72OxAOvARz4atkZDaGzidhvHIW4OCPUd/MtPIJLJvpnb83r8Rs1BXr4H+QUqcteWaDXdK/LnuOmvZhbT3P3Yux6J0tdZ3ROL0vjNtvFF3H60Rg8K0UZ3Wk2E0my8rD9+MUzB9fh8Nvxv35He/4cML4Og4ndcBV5JmxcwYRddJVX8NxDxFYPANjS1+wv/3xpA68hrAvHf/yjzA/j93HvDfwtv1fW7b9tF1eF0g05f3LH94Ka37wchwHX0uw+SDyt/y27a0Mdx/b+7///jtHHnkkn376KQ0bNgRg+vTp3HvvvXz88cfVWsb69flVNloNA+rVy6jWvDvKMCAnvAJr4iAIR16S6TbuTnDkC2x2vApspplH4PVRGKu/jVyILxn7go/J9e23U+P1WQaZqz/Cem109MTuZ0BSJsx9ArdBR4wBV8CbF1X4cBL2+R+R629RaUyVr383gie9yGa3+knNmsgyN+F/5VSMtT9GTvCnYl/wMRvNpjE/F7BsMn56FvODf0dNc468i/z2ZxC0rfL3so2N+J8/Hjb9HjlzSg7h8z4il/rVjrmOu9rbVsGCyAn128HB18CUC72/hz8BuPDWJTD8MZj9mHcCjFjPFOzzPybX1yzmPjIMg5zgMqxJh3sNwwrcZn0Jnvg0m53qdWRmGbkEXjwBNi7z3mg/1Ku4zozehhxyHXaPs9kYjtzvOcZ6fJMPh6INW9+scwAcfjO8fh6429Rc2h4FzfrgLv2Y0mETyK9GrD7LJHPVTKzXz46a5vb9G27TXpgVyzlA1n64wx/DeG44ONu0Gg84DFofAe//q/wt5+h7yW97KsFtropPNUtImXMv5ldPRn23PfIZCvY7kvS1X2K9OJKoHdZhGBz+b3jx1K3buExyFpz8NLx4Cox8yosl97fIeVJy4KRJ8Mb5cNJE+OK/0P44ePe6qFicAy+juN8/SFn7FeZLp0ZNp8vJXjyvRp83nKEPsrnVSEJ2zRoZZedkZ9EMzJdO8d4c9gjMnwyrvomc2ZcMpz6Ps+YHCjucTYnjJ9vMxf/CCbG3zanPw/MjwA7Bfv2g/99hw1L44BZo1ge6nQbvXB0Vk9v3Ygr7XUNKaD3WpEFw0GXQajC8fi7k/xU5c3oDnHNnsMGp+lj3WQaZK9/HmjUWDr0BplwQvb/bDYUDL4FnjoteQK9zwTBh/qStsXYYjj3kP1hvX4Lx22fQ7lho3BU+uTvq484Rt5Pf8eyIc1hVssxcAi+dBOsXR04IpHvnUqNxtZdVXWlmIckf/wvzh9e2vnn4zfDHbPh1ZrXPe3Xcv7z9V5ofOV+9NgRPm0Kem7PDsSZZYdJ/fBrzw1ujpjlH/x/5bU+LOh9sj2QzRNr34zE/uSv6e7bz2CuTZhaRPOsWzIUvRU4wDOzTXiOvfv+EuOJUqrY768iSOEzTILvgJ6xnjo6qK7mtDqfk2HEU7MZkUXXKcdk8+7qd0f6G6rXBd0RNz02WZZCzcR7GCyOi6lluu2NwjrqHjXadiPfrOCu9dtiWu3nK52/YmdApL5PnZNco5hSzhNR592POfTxqmn3SU2xueiThHRjJZ2cJWDYZv7yA+f4/oyceegO0GuS1M0rytr5v+mD0m/DqWVCcG/mZnJZw5H/glTO9HTfqda8foXB95HyZTWDoA14b+se3vDpeRaYP57z3yU1pV+M7n6pbXtLNApLfvdxLcG5bf+59nld2FjwFnU+CrP3gi4eiF3L4zfD7l7DkA5zuoyk++N879aLIvU3MNj1AZlPCZ71Lrrv1uMthDb5JgyPLFkCdAwid8Rab3MhjdEckm6WkL3gI48tHvDdOexHevd5LWleUWgf37Gm4Xz/vXSxRUVIG7llTMZ4+BkLFEZPcRl0JnfwieU42KWYpqfMfwpz9SFQc7gmPYXzzXFR5dw4cg93nYnxTzsMIF8NBl8Jbl0Z/vuvpGFlN4LP7I99v3p/SEyZG9Ykk+Q0yNi7AeP7E6L6MVofjDn2A9XbdqO+pqA5rvHNj6ebICXVbETzjLfKcyPZUshUi7bvxmLNitFmO+y+bDxhByDbwW5D5xzuYb10UNZ/b/3KMUDF8NSFyQoOOMOAKnNICitqeTOrS/2FO/Xv09xx8HXQ9FfPJAV4/5P6HQpshEf025fMOvIbCnpcSIpnsdZ9v7Y+oqNOJOAdfy6ZAC+rUSa/xb17Assn4+XnMmTdFTxz8bwikw3vXR08b+gB89yL89T32eR+SG2i53b+1OcYGfM8c7d1sseBpWPV15AwV+ngzjU34XzkFY+1PkfP0Ph8CafDlw1vfMww44zWvb7JwXeT8GY1h+GO42S1Y7zSodqzpZgHJ712B8euMyAmmtaXP699g+eCI27zfGsA9YBClQx8j38kg1Swm9cuxGAueil74sIexmx9MLvV3Sz5A7araUdM+rJqe57w63oOYcx+N+m5nxCRCzQYQmHoxxvJP4LQX4L0bvQtJIhaSQ/j8j8h1vX7EiuUlx65QJz3lWfjg1uj+zgFXeBepbHuexOtj3txkSMToknuqOqzBilcXGPU/Njk73meXaMrKyqbcAjL+nIn1xjlR8zh9L6ao3zUU72EXSNSkDb7PPWP8119/JTs7u7xRDtCqVStWrVrF5s2bK/nknivZLMX8+D9RSWEAY/W3+Db8gmkamKaBb8PP0UlxgHAJ5sf/Idks3amxpTobsd67NvbEb1+E1od7Qzet/cmLP7t5hZhKMT+6lZQqYko2g5Ws/3f4Nv6Mae78K/VM08BauzA6KQ4QKsL89B5SrFDMz6baud7V8rGW+/HtpDpbG/8+n4m1/OPopDhAcS7mgkkErOr9ECVbIe9O4m2T4gDrFnk/0Dn7e39/dAc07Aj12oIdjk4OAYSKMT+9m2Qz9nqmmEWYH9wclRQHMP6ch2/TUgyj6n1jWSbWn19G/kj3/Rt8Mjb2B758GLNwDT7f1lNcwOd6V/Ft24Duc77XObFtUhxg8Qxo0BFj5QL8+b9TjVBJczZgvRedDAYwvpqAmVqXqAX1Phfj/X9HNyQBln0C2S28hswW5ke3RpSRMkl2Lub88TG/23rvOtLDa7CmXxOdJAXY8Cv8MTe6IgRe5eH71+HAMd6Vx9smxcHrsPp5qtfo+mqiNwrErHtixmLOfYyU8HrMd+OcG75/zUtOm9GJVfODm0lzNsb+XFXyV2O+e433//SG3vK3TYqDdy6Z+wRm4TpSnE1e+fv98/jbZuEr0P547+/e53n/fnaf929Z+YrB+OpJb0jpT+4EJ+xdAPHrzOikOEDBWowfXsPvq7oQpjkbvTLY+3yYdXfs/b3oHa8CbMUY5unrZ6DdMZGx/vwWVvE6jBVzvTe6n+Fd/BCD+clYUu3o8hmPZRlYqxZENygAggWYXzxIsm/nPxcjKbghMinuS/YuEPp1JmQ29Y7HeOe9WXeRbAZJ9oUxvnwoOikOsP5XrNXzsawd//1JtTfF7PgBMD+6Leb5YHukOJu834hY37Mjxx6QFNoQnRQHcF2s964lbSetg4gkplQKsGZcH7OuZCz9kEDhymrV02T3S8T2N0AGuRjvXR+znmUsehezcG1EGzTFCnkjIW2TFAcw1vyAtfb7GrdZk+1NmPOeiDnNmnH9Dv1u70ypTi7mx3fEnpj7G3z3UnRnZbtj4Yc3opPiZZ/ZsAQadYVWh8OSD6KT4gCbV3l1/Xrto5PiAE4Y4/2bSKGwpqtUbYGiVV5SHKLrz+2Oga+f9v7fZaQ3ylYsnz0APc8CwPz2OZJDe8Z+rQ0By9sGUW168O7Y/PVdfJbXB5BkOd7xsW3ZAti4DOuPL7zHFO0kqc4mjNlb9mHTnt7d2tsmxQGKNmJ8+xLmpuXR07qeivHp/0UlxQGMvxZirfsJ09wyLH+c8mLM/Df0PjfqffP71/DlLsFY+RX0uSB+G3XhS97F3tu0xY0/vsS/+beo39o0Z733nbH6MpZ+CAVrSLLi3zKeZNneCArbJosANizF+nNO1H5KsTdhfnZvzOWZM/9d3q5IdTZizrgh5nzG7HHQ9sjoCWt/8tp7X00kNbwOc2Z0ohvA/OIBjMI1W/she50Nn8ZpR335ICn2JtKc3Ph9ID++iVm4lhQ3RruyGio9z352H2Q3iz1t1t1ev0G4FPPj20gxazj+/RZ+n4H5y9vg2uALRCfFobyPN9UqxVrzXXRSHLybU2aPi3zvgEGw7OPopDhA/mrvzvS/vic5qfrHc6BodXRSHLxy/Nn93jG0Yan321K/PQDGso/xb6lvJodzYyfFAT65G3PDYpLN6H5QSQw17cPanvOcV8d7LHp+wPz4DgIFv3tJ8SY9YO0v0Ulx2JIzmEzAF1lXTTZDmJ/e49VJG3aC3N+j+zsNA1oOxJg/MfY2mHE9aU6M3+I9TJLPwZjzWNy6gLniy51aF0g0qfZGrBmxcxzmV0+SbG/avQHtZPvcni8sLCQlJXIY3rK/i4qiG6mxGEb1XjWZd0deSW7R1sZWDOZPb+L3W/j9FubP/4u/Xkve95a1E2OzgptjJ3nK5P7mDVsNXgKwaa9tYvqQgFtYxfoXYiyZGX/9f/TWf2dvd7/fxPzxjfjb89f3CTgFMT9rFq+LmcgHvEp40fqt30MJ5k/xv8dcNI0kJ796ZcUpqLSssOwTaNbb+//mlVCS73VK/PZpJes5I+4+CrjF3g91vM8umobfb1a9rc0wVsUEFnidUDEajACESzAK13hloywWOx/zl6nR82bt5w2JFc/q76BuK4zF71WrHJnBPChYE3tZruuV+dR6ke/XbQ2xLlgps3K+l7ArEyzELN4Q+b2mgbF+UewkKHhDZgfzvWH5YmnWB356M34MSz+C5v29MlLZPI06e/P4kmN3WAC4DsaGpVBSSWfo+kWQGaPxVpofte7VPieX5G2tLDbpDr99Hv/7l30MTXthFPxFwAx5QxNVtt779fH+70/xLrIoS5T6U2M34ABcF3PDYm/Youzm3vaoZPsaP71Nshv7nBJVBgvXeVdPr/81ftyrvvFGTIiKy/GO/5ScyPd/+3zr/K5T6fFnFq+r/rnUAuvH12IvCzB+fY+AXfV61+i3yTK87V5RvTaw6lvv/017wfJZ8WNa8j4Bp9A7ryx+N+581g+v4rfcHY+5aC2E41wkFiyM+M3YoVfBam/kg1hK8zGLN2739o55EUqZ3OVY4er9jumVGC+o/Rj02rtePqcwdidr2Xl52cf4fFXXKXfmqzrlWHZO+xv2nH1a9rJCBbE7RMv8/jlJSVvbDgGnoIo26+v4a1CGTdPA2PBrJXX/tZilm2r92DUMMIs2xK83Nu3hXYy8rf36wtJKRhRY+rE3T7M+Vcz3IRTHTyQbf8wmsJ19IFD5dL/f9EZaKlOx/pySA5tXe/vPtCBUEr8Otu1F7X8t9Oqye8C+3d2vgJOPuWha3P1p/vQGSXh9AElOPuai6fHn/fF1AkZw55RxE4zcZVsvtm/Wx2tPxrPoHWjWN/r9Kj5n/vi61ydR8bu2VbTBa4tvq3FX+GXLtkutEzuJUmbdIq+fZBvG4nej+kTMUJHXZxKHsewTkq3SuNsuyamqPfUafjMc+bmCVd6F5bGUbi7vL7BKN8W+uAa87VewxhvFclvLZ0GDDhjFG2MnU8C7sGbzn1u3tWFByaY489oYeSvwhfNj3/BS5q+F+PHafDU/z66vtI8RO872Kljr3aENW/pfi7fz2CzE/PlNaNyt0n4W7zuKYvevBNK9foxty3azPlX2QxlF60lyNlcrVu/cHL+Pk9Xfbb1h6Jdp3veXxb/0Q+8YWBcjqV9m8yoMOxjRH7krX1D75+Z97VXTPqyanudME4yNS+PX8dIbev2Q4PXhV/a78cs0kuytfS2wpU7665bcQLzjK7WelzCPF0P+X5jBvFrfF1W9kuzNmIvj1wWsH14lYIZqPc498QVgBfO834lYXBdj/WKvTbIHxLtt7NWxzz1jPDU1leLiyEZZ2d9padV7JmrdutUfEq8m8263zfleBSJOhc9IySEzc0tnRHIlQ4oH0vH7fdTL3Ikxr6/imaH+1K2d/YF077kuETGl4ff7K49pc75XkSuOfTWekZq9df13tuSc+NMCaQSS/NTLiBH76sq3ixVI3jrsQ6gEApWsfyCd5JRkktOqsd/yC71tHq9xEEiPbHhbAe9vfyXHRiCdQLxyk7cZfCkx74wAMFNyyMqqxjBwdii6wRLjbuIIVoDUtFRSk7bcaV0Uirjrupxheq94DcukDC/Rl1rNWKsq84G02Aku0xe/cRfjM5Y/KXpokE2VlwHD8sefGK6inCWlg10KgUq2QVK61+Dyp1a5f4xAWsyRBMoF0uI27CKOj5rYUGH9w6XlDcDY358O4VKsQArp6aleOahiXiB6vauzHUzL+7zlq3L7JqekkJxSxbqvC2z9bsOIX5FNSo+fbK14bi6LNTkz/npuw/LXYB85TpW/TUlJAZJinUt3xOptlhcu2XqOCJd4HUZxY0ojEPB7545AevyLQJKyychII8PcwesQgzX4zdgRpZX/VlqBGOed6lpT+aMofIEk6tXRkMP7kt1SR5bEsSkXLH/cxJGZmk12dvXacjuTynHVdkb7G3bftq7296z3VVrPMpIySE9PIb2s+bG5wKvnxRq5CzBS6pBZnbZGRZsrf66fb2fVD3bUXzFGKCoTr04eLqmirr6lrVBlnT6j8nqrLwmf30+9rO3bTlWWl+QK9Z+KcYRLvQtqwbtDsbJ22jaftVIyqVNn736m43YrClXRN5FBanoaqYE0KCiuvGwkZZCekUa6r5LyWRP5Fb6rqnK5pY8hStnn4lxIYqTU8fq2NlZx7oxV5sOlGGVtLqOKtkmcGGL236yzKu/LSM4i4PPFP8byiyrfVslZXnvKqtB9XVJVm2XLuW9tNfojY/VJBNK8O/tjja627eedLfUSq/LudSspDfxV9RWl4/N554Ia/+ZV0cdYaXxl5SWQtv19wqXG1n6RKs7dPp8vdl+qE9p6XqwoXOpt67jLzAAMAoFk6mVXM/aUSvofyvpRAJKyIo5VMzXbOwbWVnEOtvw7v3+9EqqP7mY17cMqqOI8l5QZfZ7bXMn84VKMsj7yKn9vonMGgYDf+0ywYEt9K8bxZQcr759kD6pnVqbKukCWVxeoqh62j7Kq+N2yktOpW3fvrZPuc4nxNm3asGnTJtavX0+9et5dm0uXLqVRo0ZkVLPTfcOG6j1jvG7djGrNu6MCVjrpPc/F/OKBmNPtzqeQu967ezGn86lY2w5Ls4XT8xwK7HSC67dv6J5Y0oxMkpv2wli5IHqiP9X7ISm7qrLtUZHPGAecHmdT4FQek99KJ6PXuZifx1v/U8vXf2fL6TYKK86wIk6v88i30wnF+O7MQB0CGY29YX+2ldWMoD+bzVs+ZxgG2X3+hrXondjf0+ci8oIp2MVVr6PfSiejzwWYH94We4Z2x8D/xnj/368v+JK8oe1OfNIbXjneejqx1zNgpZPe46y4Q/zZ7YdVa98YBmT3vgDrxylb3yxYC1nNIO/P6A9kNMZNa0BuITj53vItK4msfpdEP1/qt8+2DGEd40560/Lu1N6wFLv10dWKNc3MJKVJz9h3NQXSvM6RbYfPWfIhbvvjMH56K/ozhuENjVPxmb/ZLSj1Z5O/TTxZWS3xB9IgGGM4wCY9Cfkz8R0wCCPW1YTLZsGICRArBvCe+73gaeh6MiyKc6Vjl5O9z3c9BTYsg4adYw9FnZSBnbUfZpOeGL/HuJrYl+TdLR7rzvs6B1Dqi173qhgG1E2tg9tyIMZvn3vDKfa7COZFPyunfF2WfUJwv4Hk5wXJ7nMhVmXb5ue3vf9vWAoZjbw7qzcug3WLoXH32CMCBNIJZ++P2eVUbyg4w+c9XzDOFdZu34vILbZwCitf93Qzk+TG3bxnEZYNL7kt0/L2T6wRBJIyvH1Q8YIW08JtfhBG7hXe30UbKz3+goGc8nNYVQwDsnuci/XtizGnO70uYLOdRngnn8ezG3bFVzHJsv5X7/ERhumdF0ZM8Mp8rJh6eud31zDI7HNh3CH27F7nsim3cIfrAZlJdQikN4x9TGQ3j3k+2B5ZyfXwp9WLPRzpdh57ZbLrd8ZnBWJ2Prn7H0ox6RTtot9q2bPszjqyJI5kK520jiMwvn8leqJhYLc8bJfV92OpTjkum2dftzPa31C9NviOqOm5Kd2XQ1LrIVvvuKnItHBbDGBDhTLp96WT0et84j0axe52Ro3LcFZGc/zbXthcpnF3iq0MCveA39ZMfw6BzKbeiETb+v1L6HVO9HN5f57q1bHjtVs7nuB9JiXHe5RRrP4GgM4jokdBqsDtcioFTjql29O2qEZ5yW5xML6yCygq1p9DRV59OynDG2kqWAhp9WOPNJWzP+RvqQP6kgjXbc+mPWC/1gbLSiKz7yVYb10Yc7rd92LyClwcJx+fL43MPhdhvnt17Hl7X8CmvCCuu3MeJ5iV0Qx/cpZ3h/Hi97xnw/8aZ5SI3ufD189Fv//z29D1VCgbkn3bmLueTu76fLLSm+JPitGvAF4bb8PS6PfX/oR71FiMzx/wRslqMcAbgnpbVgAyG8dsd9htj406T2X5M/C3GwqxRqc0DNz9DyYvmEw4Tn+Vz0r32lMzbow53e51Pps2FUccZ1kp9fGn1o19cXLdVpT6sshfn0+6lUlyg47e8OjbSs7y2n2xLlBocxR8chd2UjZWvTaxR2FLycFNrYtRNoR83p/eYwhjjSSSkkMopQEhI4WUlgdHjiRRxgrg1m9PkZNCGjX/zfPOs028R0hETWwaf7S+smGY2dL/aqdtV5+waRpk970E8/Wz4cBLYe6TMecr6+NN6z4aa8HkyInhUm+flB1HZX6Z5j1K8c+vYn955xG4OQewqTSAXVS92LObVzg3b6vdsVtHIuly0tY+UsDef7B3DOa0xu9PiX0RS7M+uBhstjNi9pPuTGpX1Y6a9mFt13kuYz/8ZXWEWN/Regjm+zd5o+4ccWvs0XcAp8/F5AWTsYvzy8vLZied9F7neXXSX2fC0Xd79a6KSvK8G9Qq6WMuNveMemZlfNaWukCcx/zavS9g06YSXHf7HiORqMrKSomVQXLcPu00QpktyNvDykBN2uD73FDqLVu2pFevXowdO5aCggJWrFjBY489xsiRI6u9DNet3qsm8+7IqzRsYPc6D7dBp6hYnUNuoCS5Yfm8JcmNcA65PnqdGnTC7nU+pWFjp8ZWRBrO8Y9G33lnWnDsveWVJffAMbDy64iTrVu/A3afiygNVR5TMGxg9zwPt2Hn6PU/+DpKkhvvsm1fktoUp/9V0duzcTfsbqMIhmJ/roAc7JHPRF8N6U/FPukpCow65fM6jkswpx1O91HR37P/YYQPOJxw2KlWvMGwi93pFNwmvaKWRa9z4K/vvR/dtHpw1F1eUihY6FVA+/4t+vsbdcXuPjruepaGTZx+Y7wGwrb7ZvAtFAcaVCtux4FgZiucXudtXcCse2D4E9FXjvpTcIc9TCilIbbtli8jHHYI73cwbqsjIuf/9iXcg6+GzCaR7xsGHPkf+PpZnCF3UuSvV70y76ZjD4tV5n04Jz2Fk1Qn+grupR/A4Ju84bS3dfgt3jOsy04qgTTsk56i0MiO+u5CIxvnpKe9K7YrSsnBHvYo+W4W9tH3esPubLuuh17nNUBilDP26wd122x5Rtg66HJK9DwtBngNrq8mwP6H4i77BI64BZKzI+ez/NgjnyHfqIdz7ANeB1BELCYce793bPi2uTItkI49YhKFRtZ2Ha+k5Gz9znApLP8U+v89el0adIC2R2H3OJtCI8c7BjNb4fQ6N3re5v29i0hWbrkQYu4TXlk6+m6vo2veeG/fbtshZ/pwRj7tnQt6nI3bqCvMGed1ZLQ9Oupr3HbHEG7SN6JMx3sVkoF9whPwyzteozSj8Tbb2ICj7gZ/esy4GPpAZGeMYeCc8DglVg7OUVueG//lI946xjj+7JHPUEBOtfeL40BpenOcvpdEr3fTPtidTiIUqnq9a/oqNHKwh4+PHGfnm+fhqDu9Bm5l570eZxEMQyjkYnc4ETfGUIhO34sIprfAcXY81gLqYI98NnpYxEAa9klPU2hUf3tX+j1GzpbviXXsTaaA7Tv2ys5P9okTou9QSW+Ac8x9FDkpO30f67XnvqD2Y9Br73oVh/3Yh/4TsltEn2+PuZ8iq85uj6k65Vh2Tvsb9px9WvYqCCfBkXfGrGe5Qx8g6Iv8bQ6GwO42Crdxt6h1cwb8g5KUpjWOt9DIwR75dJy6/2MUuRm1fuy6LhQYdbw4Y9Qb6XEm1G2Nu9+BkdNWf4fbrA9uy0Oiy0L3Ud7F9YXrvCRUUjq0GhxdaNoP9ep0C1+Fw2J0Rtc5AGfg1ZSErV1WXoqsOjhD/+vNvG39efajcOx93v6bPc77f1RdLx2OHutNN0ycEydSsJPqfXvjy2vTD8BtHf1saKfzSYTqdS5vL4VCDuE2R+O2GBg9b89zCGa1xnF2XhujwMjBHfm0d/d//l9QvAk6Do8ud/sfgtO8P86h10cfuyu/xu19vtc23DbmgddQktK4wjH1TPRIA8nZuCc8Ct+9HPm+6cM+cTzFSY1wBt8MCybDwf+I2RZ3R0yITpAAzpD/xOwT2Wxn4g7+V8y+DPeYe3GSsglV0l8VCjuE2w3DbX5g9Hf2voBgxv5R7akCcrBPjtFmScqIaLMUkum197a9s9Py4458CndxjAub+lwAa370+k+s+tgnTowePc4KYJ/8LHZmy639sLMfgyPviB7p0Apgj3yWAqMORU4q7rEPQHqDqO3OsffiZDaj2Pb26faUv3h9jBz3oBdXjN8KBv0TvpqAW7+91/8aNrer/Nu2S7BBd9x2x3rDOg+4ImrTVuzjLU1tinPQ5dHbf9F7MGJiZNnesASSMnBj9JPQ7hjc9IY42S2r3S/qulBkZuMc93D08rL2g17nwvevev+uW1Q+4qZz7P0UWXW9smXk4Jw0ObpvL7Uu7uB/EW7QhWB495wXt6e86LVjr5r2YVV+njufYGaM85yRg33SU/HreL56OMfc613EVLQBOo2IjmX/wwi3HBRxbAAEwxXqpEUbvAsXu0b387rLP8M9aVJ0DKl1sIc9SpGbXuv7oqpXKOwQbnssbov+0du+13kEMw/YKX12ifgCKCID+4THY/dp78S+yF0Re3UYrluT2RPD+vXruf3225k7dy6maTJ8+HCuueYaLKuKoZnLP1+9O8br1cuo1rw7g2FAprEJa90PmD9OwU2pg9vtdEqSm1DkRFaMUs1ikktWYXz3EkbxRpyOI7AbdGazm71LYrUsk0xnHeZvs7znuNRr412Bt+xj3E0rcLuNwk6pj1WyEWPhSxhF63A6nojdoGu1Y/LWPw9r3fdb1j8Ht9sZMdd/Z0s1i0guXoXx3QsYJXk4nU8mXK8j+W5WpbH7TJcMZx3m4ncxVn+N26QXTpujybfqE7ajH4iQbhYQKFiBufAFCAdxup5GKLs1+U7lw9NuyzAgw8jDt+FnzB9e9zoSup7qDWP//eteEvSAwTj+FIxfZ2L+/jlO/fa4nU7CKMmDhS9hFOfidBqJXb9jlfvIMAwyjVysNQsxf34TN7U+btfTKUluVON9k24WEij809sGoWLcXudhpObAounec4Dqd4D2Qwkl1SUvFHvIlwxzM/5NyzAXvgSmhdNtFKGs/bHsEsw/52IumQFZTaHNkbi5v+M27EpxUkOKaxCrZRpkuuvLy7xbry1OxxEUWt5dMmn2OsyfpmCs/xW35SE4LQ8l32pAmr0ea/V8zMXTcTOa4HY9FfypGD9PxfjrW5ymvXHbHE2+UZ9wnJHfA2aYNHv9luUvxm15ME7Lw9hs1MN2XEzTINPdgLniC8ylH+Lm7I/b+SQwA5g/vgFtDveG71/4inf1dJeTIb2Rl+A1La+DKSXHSyx//5r3b9dTvE6brybh5rTA7TQSx0rCzPsD0zK9pPGqr3Hrd8DpMJwCX31CtrUllvVYf3zu3cWetR+0PRLHBSerJUa4GPOXaRhrvsNt1g+n9ZBK170yZefkjRsLyHDWY/3xBcayj3A7nYiR2cRb36IN3ugBdQ7ATq5HgVWXkL01ieeVvxWY370I4RLcLidDdguMvBVgl8DP08CxcXqdh5HR0Hsm2bJPvIsJOp/oPWP5j9lQry1OxxMpsBoSdKzy85dv/Q8YG5fDAYd6V5yXPfO92+mEs1qxya7+sV5eBlfOw8hqAuuXwO+fe0n7NkfiumBnH4Bhl2Iu+xjjjy9w67fH7TAc15eC8ceXmEtm4Ga3wO18MoX+BpQ6AVLMElKCa7xztT/Fu1tn2Sxv/zbuidP2mLjnsKqkGYUkFa/C/O4FCBZ459K67as8l+6IJDNIWngdxg+vY+Qux2l1OG6LgRjBQozvX8Wo1wrqtIIfp0DxprjnvUwzD9/GRZjfvwqBNJxuoyhNbUqhs/OG9S37zfD9Oh131Te4TXp7vxnm9h0T8fgth3R7PeYvUzHWLNx67Jn1Cds7tuwkM1Rhey/DaXU4zn792WzUw3H2ueroPmt315ElcZimQQYbsVZVqCt1OZWiQENKnCqGEN3JqlOOy+aRHW9/e8vY9XeM1/TcZFkG2e5ajD++9J71mNEEup5KaVI98u3oYQXL22Hrf8L84TXc5CzcbqMoSWlCkVPDYdS3CJj2lrbFmxjrF+G2GIiz/2FsNupj70G/rT7LJcNeh/nrDIxV86FRV2jeD3flt7gtB+IGsjA2LsH84VXwp+B0PYNg2n4Y2Pg3LcFc+DL4knC6nkEoozmWU4L1+6cYy2fh1mvv3Rmevxrju5e85F630yEpHWPuE17yrOc53qg1P7zu1fnbD8Vu1JNNbl22p0usJuUl2SwlNbQW4/tXvEdbdTjBe4bxqq9x2x2H0bAD7s/TMOwwtD/Gaz/8tRCa9vYuPv7pf7imD7fTSRT6GlDq7HMDP0bJMDfjz1uG+d3WNn0wozkFTvQ5N9PcjC93sVdP9yXjdDuDYFozCpydP/Sn37TJtNd4I8Ft+BV6jPYSfN+9jGGX4nYcjluvHZuNuhiuG/PYzTfrk+bmYq37CfPH172+ra5nUJLSOOI84bds0sPrMH9+C2Pdz7jNB+IcMIhCqwFp9lrM5Z9g/P45bv32OB2GU+irT9C2vP6rkjUYS97HaHGQdzf0b5/h1mmF2+kkivwNSHILsP76BvOXt3HTGuB2PZ3ipEYUOzGeXQ74fCbZzl/wxxzvYvr0RtDtNOzkuuSGq9eGjWxPpeJ0HUVpWrO47anoNsuBOK2PiGqzeO3idZjLPsL440vcBh1w2p9Aoa8+yW4B/g0/YfzwundXZPvjcTFwslqUt7F8Ft65a+kHGH/OwW3QBaf98RT46hN2zMh+2AYdMdod612Av2IOboNOW+ZtUN6vYJoG2azD/P0z71jPbAqdRxBOrsdmNwfXdbe7nrz1PPsexqoF0KQntBrkXegQyIADDvH6LDYsgRb9vYvyf56K0/ygGvW/VibdzCeQ/wfmpuVe38Oi6VC0YUu/c5eI74joSy3dDJ1O9C5iWPGV1yey5ANY+zNu84NwDjgc20wikLdky4UfLnQYBtnNsf0Z5NqVPOYyjrJzs/n9K15isNXh3qgeSz6Adsfimn6MuY/jpjfC7XoaRYEGlFQ4BgJmmAx7DcaPU7xt2qwPtDyEUFIdNtvpuy0XoHZV7alpH5Z3nlvslblAqvfbldqMgjjnuYBlk1Z+nv9ly+/EoPL+3WSzhNTgWoyFr2A07QGpdeHHN7bkDE4nlN0qImdQsbxAxTrp69DheO8muYWvQLgUp8sphHPaUkJqhd+qCn3MZn1seyd2RO1imeZm7zfmh1fBl7KlLrBf3G2/r6tYVkyjLMfxCcZvn+HWa4fT8UQKrfoEneq35XaXmrTB98nE+I7aExPjZUzTwOezAJdgsPIe7EDAAgzCYXu3dEhblollmbiui207MePc0Zhqsv472/bG7vOZmKaJ4ziEq8huGAb4/d73hEL2djXiy2y7rZKSfFiWiW07lJaGy2PLyUlj06YiQiFve27veu6sfVNxG5TFkJLix+ezcByHoqJgNY5PY8sy3C3b0Xu/rIyWjajkum75em+PimV+2+X4/RaGYWDbTkRlwrIMLMuK+ExNykhVy68sNr/fwrIMDMPbV64LoVAY2/YS6qYJ5pZnJZeUhPD5TMrKomkaUcsr2+emyZYr8GJvz7JYLMvAtt2IsrU96x7Ltufkbdc/ELCwLBPHAdu2435XrPJXtq1N08Bx3PJj0+czI/ZD2TyhkB1zO5RtL8syMM2y7VG9Mh3PtmW6sm1ccf9UVnZha/kKh+0tZWPH91GZ3f3bBLGPl4rvWZZRZUy74/enrBxv3lyMbe+c7R2Pd+waO22/VlTV+UkSmzpwZEfFqivtbkqM7357YmK8jN9v4vf7cF2X4uJQlfPvijrD3vLbWlbvBBcwYrYdtm2jVdV2K1vG1vm8NkxZmwZcbNurs/t8Jo7jUlIS2qF65vaUl637yN7SLthafy6b5jjOlm3hTStrt9Tm+W5Pte3+rupi/VhlaFfx+y38fq9/IhgMk5TkJz09mby8QoJBJ2reWMdudc8TFctOxTp7rDZeRWVtLtd1MQwjar6y769J2QsELJKS/LiuS1FRsMbHWLxzQGWq22aJtz229ll4bfl4y9naL+G1pePFHQzalc5bxrJMkpJ8uC4EgyFs21vZnVFPrtiHUnbuK+sHKFvP6rZxt4fXZxJ5AU9l31Gx/e/1XXjHg2kaUWXbMAwCAYtAwIdtOzt8Lgcq9Od4517b9o5br4+r6mNg6/HuEgyGd+sF32pX7Rlq0oe1Pee5eOf56On2ljpW7JxBrPKybTzxcg57Sz2zMtuz7fdVscpKVf3EewolxnexPTkxLrIzqRxLolBZlkSgciyJQOVYEoES47vfnpwYl32PyovUhMqLVJfKitSEyovUhMqLVNfeXFZq0gbf554xLiIiIiIiIiIiIiIiIiIi+xYlxkVEREREREREREREREREJKEpMS4iIiIiIiIiIiIiIiIiIglNiXEREREREREREREREREREUloSoyLiIiIiIiIiIiIiIiIiEhCU2JcREREREREREREREREREQSmhLjIiIiIiIiIiIiIiIiIiKS0JQYFxERERERERERERERERGRhKbEuIiIiIiIiIiIiIiIiIiIJDQlxkVEREREREREREREREREJKEpMS4iIiIiIiIiIiIiIiIiIglNiXEREREREREREREREREREUloSoyLiIiIiIiIiIiIiIiIiEhCU2JcREREREREREREREREREQSmhLjIiIiIiIiIiIiIiIiIiKS0JQYFxERERERERERERERERGRhKbEuIiIiIiIiIiIiIiIiIiIJDQlxkVEREREREREREREREREJKEpMS4iIiIiIiIiIiIiIiIiIglNiXEREREREREREREREREREUloSoyLiIiIiIiIiIiIiIiIiEhCU2JcREREREREREREREREREQSmhLjIiIiIiIiIiIiIiIiIiKS0JQYFxERERERERERERERERGRhKbEuIiIiIiIiIiIiIiIiIiIJDQlxkVEREREREREREREREREJKEpMS4iIiIiIiIiIiIiIiIiIglNiXEREREREREREREREREREUloSoyLiIiIiIiIiIiIiIiIiEhCU2JcREREREREREREREREREQSmhLjIiIiIiIiIiIiIiIiIiKS0JQYFxERERERERERERERERGRhKbEuIiIiIiIiIiIiIiIiIiIJDQlxkVEREREREREREREREREJKH5ajuAvZFhVH+e6swrsqdSOZZEobIsiUDlWBKByrEkguqUY5XxnWtXb0+dm6QmVF6kJlRepLpUVqQmVF6kJlRepLr25rJSk5gN13XdXReKiIiIiIiIiIiIiIiIiIhI7dJQ6iIiIiIiIiIiIiIiIiIiktCUGBcRERERERERERERERERkYSmxLiIiIiIiIiIiIiIiIiIiCQ0JcZFRERERERERERERERERCShKTEuIiIiIiIiIiIiIiIiIiIJTYlxERERERERERERERERERFJaEqMi4iIiIiIiIiIiIiIiIhIQlNiXEREREREREREREREREREEpoS4zvRxo0bGTJkCHPnzo2atnbtWvr378+UKVNqITKR6otVjm+55RY6d+5Mjx49yl+vvPJKLUYpUrlY5fiXX37h7LPPpkePHvTv35+77rqLcDhci1GKVG3bsnzzzTdHnIt79OhBhw4dOP/882s5UpH4Yp2T33nnHY455hh69uzJUUcdxUsvvVSLEYpULVY5njVrFsOHD6dHjx4MGzaMmTNn1mKEsr02bNjApZdeSu/evenXrx933nmn6oj7sF9++YVzzz2Xvn37MmDAAK677jo2btwIwHfffcfJJ59Mjx49GDx4MK+99lrEZ998802GDBlC9+7dGTFiBN98801trILsZrZtM3r0aG644Yby91RWZFubNm3iuuuuo1+/fvTp04dLL72UtWvXAiovEu3HH39k1KhR9O7dm4EDB/Kf//yHYDAIqLzIVrHaJztSPmzb5p577qF///706NGDSy65pPw8JXu3WGVlxowZnHDCCfTs2ZPBgwczbtw4HMcpn57wZcWVnWL+/PnuEUcc4bZt29adM2dOxDTbtt3Ro0e77du3d994441ailCkavHK8YknnuhOmTKlFiMTqb5Y5XjDhg1uv3793CeeeMINBoPuihUr3COPPNKdOHFiLUcrEl9ldYsyn332mdu3b1938eLFuzk6keqJVY4XLVrkduvWzf3mm29c13XdBQsWuJ06dXK/+uqrWoxUJL5Y5fiHH35wO3Xq5L766qtuKBRyv/rqK7dHjx5xz9ey5zrzzDPdq6++2i0qKnL/+OMPd+jQoe6ECRNqOyypBcXFxe6AAQPc//73v25paam7ceNG98ILL3Qvuugid9OmTW7fvn3d559/3g2FQu6XX37p9ujRw/3uu+9c13XdOXPmuD169HDnz5/vBoNB96mnnnL79evnFhUV1fJaya720EMPue3bt3evv/5613VdlRWJ6cwzz3THjBnj5uXlufn5+e5ll13m/u1vf1N5kSi2bbsDBgxwn3nmGde2bXf16tXuUUcd5Y4bN07lRcrFap/saPl45JFH3OOPP95dtWqVm5+f71555ZXuhRdeWGvrKDtHrLLy/fffu127dnU/+ugj17Ztd8mSJe6gQYPcSZMmua67b5QV3TG+E7z55ptcc801XHXVVTGnP/roozRq1IjGjRvv5shEqi9eOQ4GgyxevJjOnTvXUmQi1RevHL/11lu0bNmSiy66CL/fT7NmzZg8eTLHHHNMLUUqUrmq6hbgXfF5zTXXcNNNN9GmTZvdGJ1I9cQrx7/99hvhcBjHcXBdF8MwsCyLQCBQS5GKxBevHL/77rv07NmTk08+GZ/PR+/evTn++OM1+sFe5vfff2fevHlce+21pKSksN9++3HppZfywgsv1HZoUgtWrVpF+/btGTNmDIFAgJycHE499VS++uor3n//fbKzsxk1ahQ+n4+DDjqI448/vrysvPbaawwdOpRevXrh9/s555xzyMnJYfr06bW8VrIrzZ49m/fff58jjzyy/D2VFdnWDz/8wHfffcfdd99NZmYm6enp3HHHHVxzzTUqLxIlLy+PdevWlbeVAEzTJCUlReVFgPjtkx0tH6+99hoXXnghjRs3Jj09nZtuuolPP/2UFStW7PZ1lJ0jXllZuXIlp512GoMGDcI0TVq1asWQIUP46quvgH2jrCgxvhMMHDiQmTNncuyxx0ZNmzNnDu+88w633HJLLUQmUn3xyvEvv/xCOBzm4Ycfpn///hx11FGMHz8+YmgNkT1FvHK8cOFC2rZty80338yAAQM44ogjePvtt2nUqFEtRSpSucrqFmXuu+8+OnfuzLBhw3ZjZCLVF68cDxw4kO7du3P66afTqVMnTjvtNK644gq6du1aS5GKxBevHNu2TWpqasR7pmmybNmy3Rme7KBff/2V7OxsGjZsWP5eq1atWLVqFZs3b67FyKQ2HHDAAUycOBHLssrfmzFjBp06deLXX3+lbdu2EfO3bt2aX375BYAlS5ZUOl0Sz4YNG7jpppu4//77SUlJKX9fZUW2tXDhQlq3bs2rr77KkCFDGDhwIPfccw/169dXeZEoOTk5nHPOOdxzzz106dKFQw89lJYtW3LOOeeovAgQv32yI+UjPz+fv/76K2J6vXr1yMrKYtGiRbtoTWRXi1dWjjrqKG688cbyv0tKSvjkk0/o1KkTsG+UFSXGd4L69evj8/mi3t+wYQP//Oc/ue+++0hLS6uFyESqL145zs/Pp2/fvowePZpZs2Zx77338txzzzF58uRaiFKkcvHKcV5eHlOmTKFr16588sknjBs3jldeeYWnnnqqFqIUqVq8slxmxYoVvP3221x99dW7MSqRmolXjoPBIM2aNeOpp57iu+++48knn+SRRx7h888/r4UoRSoXrxwPGTKEzz//nBkzZhAOh1mwYAHTp0+ntLS0FqKU7VVYWBiR0ALK/y4qKqqNkGQP4bouDz74IB9//DE33XRTzLKSnJxcXk6qmi6JxXEcrr32Ws4991zat28fMU1lRbaVl5fHokWL+O2333jzzTd56623WLNmDddff73Ki0RxHIfk5GT+/e9/8+233zJt2jSWLl3Kww8/rPIiQPz2yY6Uj8LCQoCoC3+Tk5PLp8nep6q+RYCCggLGjBlDcnIy55xzDrBvlBUlxncR13W57rrrGD16tIaglr3agAEDePbZZ+nbty9+v5+uXbty9tlnaxge2asEAgG6dOnCyJEj8fv9tG/fnjPPPJN33323tkMT2S5vvPEGPXr0oEOHDrUdikiNPfLIIwQCAfr374/f7+ewww5j6NChvPLKK7Udmki19ezZk//7v/9j3LhxDBgwgEmTJjFixAgyMzNrOzSpgdTUVIqLiyPeK/tbF7fvuwoKCrj88suZOnUqzz//PO3atSMlJYWSkpKI+UpKSsrLSVXTJbE8+eSTBAIBRo8eHTVNZUW2Vfa4oJtuuon09HTq1avHlVdeyaxZs3BdV+VFIsycOZMZM2ZwxhlnEAgEaNOmDWPGjOGll17S+UUqtSPloywJum29WOUnsS1btozTTjuNcDjMs88+S3p6OrBvlBUlxneR1atXM2/ePB599FF69+5N7969WbVqFbfddhsXXXRRbYcnUm0ffPABL7/8csR7wWCQ5OTkWopIpOZatWpFMBiMeK/i85pE9jbvv/8+J5xwQm2HIbJdVq1aRSgUinjP5/Ph9/trKSKRmtu0aRNt2rRh6tSpzJ07l8cee4zVq1froui9TJs2bdi0aRPr168vf2/p0qU0atSIjIyMWoxMassff/zBSSedREFBAa+//jrt2rUDoG3btvz6668R8y5ZsoQ2bdoAXlmqbLoklv/973/MmzevvL9v2rRpTJs2jd69e6usSJTWrVvjOE5E/bfs8YQdOnRQeZEIq1evjuq/Kmsr6fwildmR8pGVlUXDhg1ZsmRJ+bR169axadOmqCG1JTHMmjWLk08+mYMPPphJkyaRlZVVPm1fKCtKjO8iTZo04fvvv2f+/PnlryZNmnDLLbfw5JNP1nZ4ItXmui533XUXs2fPxnVdvvnmG5599llOPfXU2g5NpNpOOukkFi9ezIQJE7Btm0WLFvH8888rsSh7pdzcXJYuXUqfPn1qOxSR7TJ48GCmT5/OZ599huu6zJs3j7fffpvjjz++tkMTqbbff/+dU045hV9++YVwOMz06dP5+OOPOeOMM2o7NKmBli1b0qtXL8aOHUtBQQErVqzgscceY+TIkbUdmtSCvLw8zj77bHr27MmkSZOoU6dO+bQhQ4awfv16nn76aUKhEHPmzGHq1KmcdNJJAIwcOZKpU6cyZ84cQqEQTz/9NBs2bGDIkCG1tTqyC7333nt8/fXX5f19xx13HMcddxzz589XWZEo/fv3Z7/99uOf//wnhYWFbNy4kQcffJAjjjiC4447TuVFIgwcOJB169bxxBNPYNs2K1as4PHHH+f444/X+UUqtaPlY8SIETz++OOsWLGCgoICxo4dS9++fWnevHltrpbsAt9++y1jxozhxhtv5Prrr48abn1fKCuVDzAvIvu8IUOGcOONN3LrrbeyZs0a6tWrx9///nclFGWv0qpVK55//nn+7//+j/Hjx5OcnMzpp58ec+g7kT3dn3/+CUDDhg1rORKR7XPyySdTUlLCf/7zH9atW0eTJk249dZbGTRoUG2HJlJt3bp147rrruPSSy8lNzeXAw44gCeeeEJ35OyFHn74YW6//XYOP/xwTNNk+PDhXHrppbUdltSCKVOmsGrVKt59913ee++9iGnffPMNkydP5s477+Thhx+mTp06/Otf/+LAAw8E4KCDDuKWW24pbze3bt2aCRMmkJ2dXQtrIrUpJydHZUUi+P1+nnvuOe6++26OOuooSktLGTx4MDfddBOZmZkqLxKhdevWPPnkkzz00ENMnDiRjIwMhg0bxpgxYwgEAiovEteO/v6MGTOGcDjMqFGjKCwspF+/fjz00EO1t0KyyzzxxBOEw2HuvPNO7rzzzvL3e/XqxcSJE/eJsmK4GkdWREREREREREREREREREQSmIZSFxERERERERERERERERGRhKbEuIiIiIiIiIiIiIiIiIiIJDQlxkVEREREREREREREREREJKEpMS4iIiIiIiIiIiIiIiIiIglNiXEREREREREREREREREREUloSoyLiIiIiIiIiIiIiIiIiEhCU2JcREREREREREREREREREQSmhLjIiIiIiIiIiIiIiIiIiKS0Hy1HYCIiIhU380338zUqVMBCIfDhEIhUlJSyqdPmDCB3r1711Z4IiIiIiIiIgln8ODBrFu3Dp/P6053XZf09HSOP/54rr32Wkxz591/9sQTTzB//nwmTpy405YpIiIiHsN1Xbe2gxAREZGamzJlCuPGjeOjjz6q7VBEREREREREEtbgwYO57LLLGDFiRPl7ixYt4pxzzuH000/n8ssvr8XoREREpLp0x7iIiEiC+OOPPxg7dizffPMNqampDBs2jDFjxhAIBJgyZQqvv/463bp144033sA0TcaMGUNSUhKPP/44mzdvZujQodx+++2A1+gfMWIEU6dOZe3atXTs2JHbbruN1q1bVxnHI488wpIlS0hOTmbmzJmkp6dz4403smzZMl544QXC4TDnnHMOl156KQDr16/n7rvvZvbs2RiGweDBg7nuuutIT08H4PXXX+fFF19k5cqVBINB+vbty1133UWdOnV45JFH+PXXXwkEAnzyySekpqZywgkncPXVV++6DS0iIiIiIiL7vHbt2tGnTx9++uknRo8eTdOmTZk7dy6u6zJt2jQ2btwYt40OMHXqVJ588klWrlxJo0aN+Pvf/86xxx7LI488wrx583juueeYMmUKL7/8Mu3bt2fatGmkpqZyxhlncMkll2AYRpUxvvjii0yePJlNmzbRuHFjzjrrLE4++WT+/PNPDj/8cD788EOaNWsGEPW9NelDEBER2VvoGeMiIiIJoKioiHPOOYc2bdrw6aef8uKLL/Lll1/yyCOPlM+zYMECGjZsyJw5c7j88su56667mDt3LtOnT+fpp5/m9ddf56uvviqf/5VXXuGhhx5i9uzZtGrViosvvphQKFSteGbMmMGgQYNYsGABw4YN4+qrr6agoIBZs2YxduxY/vvf/7Jy5Uocx+HSSy/FNE1mzJhRnoi/+eabAVi4cCH/+c9/uPXWW5k7dy7vvvsuv/32G88++2z5d73//vsMHDiQuXPncscddzBhwgS+/fbbnbNhRURERERERLYRCoWYO3cuc+bMYcCAAQB8+eWXvPzyy7z99tuYpllpG33u3Ln885//5Nprr2XBggXceOONXHfddSxZsiTqu7777jtSUlKYPXs2jz/+OM888wyvv/56lTGuWLGCu+66i/HjxzN//nyuu+467rjjDtauXVutdaxpH4KIiMjeQIlxERGRBPDJJ58QDAb5xz/+QVJSEo0bN+aKK67ghRdeKJ8nNTWVs88+G9M0GThwILZtc/7555OSkkKXLl1o0KABK1euLJ///PPPp0OHDiQnJ3PjjTeyevVqvv7662rF07p1a44++mgMw2DAgAHYts3FF1+M3+9n8ODBAKxatYoffviBH3/8kVtuuYX09HRycnK4/vrreeedd8jNzaVt27ZMmzaNrl27kpeXx9q1a6lTpw5r1qwp/66WLVsyfPhwLMvi0EMPpX79+vz22287Z8OKiIiIiIiIALfddhu9e/emd+/eHHTQQdxxxx2ce+65nHnmmQAccsghNGzYkMzMzCrb6G+99RZHHnkkhx56KKZpcsghh/Diiy/SsGHDqO/Nzs7mmmuuISkpiS5dunDqqafy9ttvVxmvZVm4rsvLL7/MggULOOigg/j2229p0KBBtda3pn0IIiIiewMNpS4iIpIAVq5cycaNG+nTp0/5e67rEgqF2LBhA+A1psuGWjNN79q4zMzM8vlN08RxnPK/W7RoUf7/lJQUsrOzWbduXbXiyc7OjlguQFZWVsTfjuPw559/Yts2hx56aMTnA4EAK1asoH379jz77LNMnTqV1NRU2rVrR0FBAa7rls9bv379iM/6/f6I9RARERERERHZUbfcckvEM8a3VTHhXFUbveyRZRV17do15nKbNm2K3+8v/7tx48bMmDGjynibNGnCc889x8SJE7n44ouxbZsRI0Zw7bXXVvlZqHkfgoiIyN5AiXEREZEE0KhRI5o3b857771X/l5BQQEbNmygTp06ANV6/lhFFe/KLiwsJDc3l8aNG1frs9X9rkaNGpGcnMzcuXOxLAuAYDDIihUraNGiBZMnT+aLL75g6tSp1KtXD4CLL764RushIiIiIiIisqtVbAdX1UZv3Lgxq1ativj85MmT6d69e9Ry165di+u65cv/888/adKkSZXxbNiwAdu2efTRR3Ech6+//prLL7+c/fffv3wkt4qPS8vNzY27PiIiIolCQ6mLiIgkgEGDBlFYWMjEiRMJBoNs3ryZ66+/nquuumq7G7NPPfUUv//+O8XFxdx1110ccMAB9OjRY6fG3bVrV1q0aMHdd99NYWEhJSUljB07lnPOOQfbtikoKMDn8+H3+wmHw/zvf//js88+q/azzkVERERERER2t6ra6CeeeCIzZ87k888/x3EcPvvsMx555BEyMjKilrVu3TrGjx9PKBRi4cKFvPbaa5x88slVxrBq1SrOO+88Zs+ejWma5cO05+TkULduXbKysnjnnXdwXZcff/wxIokvIiKSqHTHuIiISAJIT0/n6aef5u6772bixIk4jkO/fv14/PHHt3uZvXr1YsyYMaxatYo+ffowfvz48uHTdhafz8eTTz7JPffcw5FHHklpaSldu3blqaeeIikpifPOO4/FixczaNAgkpKS6NixI2eccQZz5szZqXGIiIiIiIiI7CxVtdF79erFPffcwz333MPKlStp2rQpDzzwAG3atIlKUNevX58///yTgQMHkpaWxhVXXMGxxx5bZQxdunTh5ptv5tZbb2Xt2rVkZGRwxhlncMwxx2AYBnfccQcPP/wwkyZNonPnzpxyyiksWLBgl2wPERGRPYXhVnxIp4iIiAgwePBgLrvsskqfnyYiIiIiIiIiu86UKVMYN24cH330UW2HIiIikhA0lLqIiIiIiIiIiIiIiIiIiCQ0DaUuIiIi1TZjxgxuuOGGuNN79erFxIkTd2NEIiIiIiIiIvueESNGsHz58rjTJ0yYQO/evXdjRCIiIns+DaUuIiIiIiIiIiIiIiIiIiIJTUOpi4iIiIiIiIiIiIiIiIhIQlNiXEREREREREREREREREREEpoS4yIiIiIiIiIiIiIiIiIiktCUGBcRERERERERERERERERkYSmxLiIiIiIiIiIiIiIiIiIiCQ0JcZFRERERERERERERERERCShKTEuIiIiIiIiIiIiIiIiIiIJTYlxERERERERERERERERERFJaEqMi4iIiIiIiIiIiIiIiIhIQlNiXEREREREREREREREREREEpoS4yIiIiIiIiIiIiIiIiIiktCUGBcRERERERERERERERERkYSmxLiIiIiIiIiIiIiIiIiIiCQ0JcZFRERERERERERERERERCShKTEuIiIsXryYq666igEDBtC5c2cGDhzIlVdeyU8//VTjZT3yyCO0a9duF0Qp+5KyclTx1bFjR/r168eYMWP49ddfd9l3//nnn7Rr144pU6bs1OXuqcdGYWEhjz32GMOGDaN79+707duX0047jVdeeYVwOBwx77brMHr0aEaPHr27QxYRERERkb3ADTfcwODBg+NO35PbE2qT7j5qk4qIyO7kq+0ARESkdv3666+ceuqpdO3alZtuuol69erx119/8fzzz3Pqqafy3HPP0b1799oOU/ZRr7zySvn/bdtm1apVPPjgg4waNYp33nmH+vXr12J0NXPyySdz8MEH13YYEVavXs25555Lbm4uo0ePplevXpSWlvLll19y5513Mm3aNB577DEyMjJifv6WW27ZzRGLiIiIiIjsPmqT7lpqk4qIyO6mxLiIyD7uqaeeIjs7m4kTJ+L3+8vfP+KIIzjmmGN47LHHGD9+fC1GKPuybS/K6NWrF40bN2bUqFG8+eab/O1vf6udwLZDo0aNaNSoUW2HUc51XS6//HKKi4t58803adKkSfm0ww47jGOOOYazzjqL22+/nXvvvTfmMlq3br27whUREREREdnt1CbdddQmFRGR2qCh1EVE9nHr168HvAZJRampqdx4440cc8wxEe9Pnz6dESNG0KNHDwYMGMDNN99MXl5e3OXbts348eM57rjj6Nq1K927d+e0005j9uzZ5fM88sgjDBkyhHHjxtGvXz+OOOIIcnNzq70Oixcv5qKLLqJnz5707NmTMWPGsGLFivLpc+fOpV27dsyePZvzzjuPbt260b9/f+65556IYbkcx2H8+PEMGTKEzp07c9RRR/Hcc89FfNfo0aO55ppruPzyy+nZs2d5I3jt2rVcddVV9O3blz59+nDzzTfz4IMPlg8bd88999C1a1fy8/Mjljd+/Hh69OhBUVFRtdd3+vTpDBs2jK5du3LggQdyzTXXsHbt2vLpgwcP5oYbboj4zJQpU2jXrh1//vkn4G3zo48+mg8++IDjjjuOLl26cMIJJ/DNN9/w7bffcvLJJ9O1a1eOO+64iH21rX//+98ceOCBUcOb3XvvvfTt25dgMEhpaSm33XYbhxxyCJ07d+boo49m8uTJ1V7fbXXu3BmAlStXlq9LrPJj2zYvvPACxx9/PF27duWwww7jvvvuo7S0NGJ577//fvn2PPHEE/nll18ippeVn7lz50a8X3HItrLtG+tVNk+sYeuqOp7K1u2TTz7h+OOPLy+Xb775ZsRyNm3axM0330z//v3p0qULp5xySqX7DWDWrFksXLiQa665JqIDokyPHj04++yzefvtt/njjz9iLqPiNjjvvPMYPnx41DxXXnklQ4cOLf97/vz5nHnmmXTr1o2+ffty/fXXs3HjxkpjFRERERGRxFed/oNtqU2qNqnapCIiUhNKjIuI7OMOO+wwVq1axWmnncYLL7zA0qVLy5PkRx99NCeeeGL5vI899hhXXXUV3bp14+GHH2bMmDHMmDGD0aNHU1JSEnP59913H48++iinnnoqEydO5Pbbbyc3N5crrrgiIhm8atUqZs6cyQMPPMCVV15JTk5OteJfvnw5p512Ghs2bODuu+/mzjvvZMWKFZx++uls2LAhYt5rrrmGXr168cQTT3D88cczefJkXn/99fLpt956Kw8//DDDhg3jiSee4Oijj2bs2LE8+uijEct599138fv9PProo5x11lkEg0HOPvtsvv76a/75z39y11138csvv0Q0tEeOHElpaSnvvfdexLLeeustjj76aFJTU6u1vgsWLOCaa67hyCOPZMKECdx4443MmTOHq6++ulqfr+ivv/7irrvu4uKLL+ahhx4iLy+Pyy+/nH/84x+ccsopPPDAAziOw1VXXRV3/55wwgnk5uZGNHhd12X69OkcffTRBAIB7rzzTmbNmsX111/PpEmTOPzww7nnnnu2+3lpy5cvB6B58+bl78UqPzfffDNjx45l8ODBPP7444waNYrnn3+eSy+9tLyMf/TRR1x++eW0adOGcePGccwxx3DttdfWOKbDDjuMV155JeJ12mmnAd5wdbFU93hat24dt99+O2eddRbjx4+nWbNm3HDDDSxduhSA0tJSzj77bD788EOuuuoqxo0bR6NGjbjgggsq7Yj47LPPME2TQYMGxZ2nrPPgo48+qnIbnHDCCfz8888sW7as/L3CwkI+/vhjTjjhBAC++uorzjnnHJKTk3nooYf45z//ybx58zjrrLPiljEREREREdm7hcPhmK9tL9Cvbv9BRWqTetQmVZtURESqR0Opi4js48444wzWrVvHpEmTuP322wHIyclh4MCBjB49mm7dugGQl5fH448/zsknnxzxDKe2bdsyatQopkyZwhlnnBG1/LI7qcuu4AVITk7m73//O4sWLaJHjx6A11C+/vrr6d+/f43iHzduHMnJyTz99NOkp6cDcNBBB3HEEUcwceJErr/++vJ5Tz75ZMaMGVM+zwcffMAnn3zCaaedxvLly3n11Vf5xz/+UX4X+MCBAzEMgyeffJIzzjijPFlvmiZ33HFHeTL79ddfZ9myZbzxxhvlV44feOCBHHHEEeXf3apVK3r06MH//ve/8kbpwoULWbp0afl2r44FCxaQlJTEhRdeSFJSEgDZ2dl8//33uK6LYRjVXlZxcTG33HILhxxyCABLly7l/vvv584772TkyJGAd8X+5ZdfzvLly+nQoUPUMnr16kWzZs2YPn16+bPKFixYwKpVq8obnvPmzaN///7lDdp+/fqRmpparYsfKl71X1JSwi+//MLYsWPJyMhg2LBhEfNVLD9Llizh9ddf58orr+SSSy4BYMCAATRo0IDrrruOTz/9lEMPPZRHH32UTp06cf/99wOUb4uyv6urTp061KlTp/zv+fPn88Ybb3DBBRdExFmmJsdTcXExd955JwcddBAALVu2ZNCgQcyaNYtWrVrxv//9j19++YVXX321/Hg95JBDGD16NPfddx9vvPFGzJj//PNPsrOzy4+bWMo6espGGqjMkCFDSE1NZfr06Vx22WUAzJw5k9LSUo4//njA2677778/Tz75JJZlAdCtWzeGDh3KG2+8wahRo6r8HhERERER2XusXLmSTp06xZ3et2/f8v9Xt/+gIrVJPWqTqk0qIiLVozvGRUSEK664gs8++4z777+fkSNHkp6eztSpUzn11FN55plnAPj2228JBoPljYkyvXv3pmnTplFDepW5//77Oeecc9i4cSPffPMNU6ZM4e233wYgFApFzNu2bdsaxz5nzhz69etHcnJy+VXn6enp9O7dmy+//DJi3m0b0Y0aNSq/6nzOnDm4rsvgwYMjrmAfPHgwpaWlLFiwoPxzzZo1i7jDe86cOey3337lSXGA9PT0qKueTzrpJObPn1/eoJsyZQrNmzend+/e1V7fPn36UFJSwvHHH8+DDz7IggULGDhwIJdddlmNkuJlevbsWf7/evXqAZHPUMvOzgZg8+bNMT9vGAbDhg1j5syZBINBAKZNm8Z+++1Hr169AK/T4bXXXuPCCy/kxRdfZOXKlYwZM6bSq8LLdOrUqfzVq1cvRo0aRWlpKY888gj169ePmLdi+Zk3bx5AVHkdOnQolmUxd+5cSkpK+PHHHzn88MMj5tn28QE1tWrVKi6//HIOOuiguHfy1/R4qrhPyp4JV1Z2Z8+eTf369enUqVN5ubVtm0GDBvHDDz/EfdSB67r4fJVfI1nV9IpSU1MZMmQI06dPL3/vnXfeoW/fvjRu3Jji4mK+++47Dj30UFzXLY91v/32o1WrVnzxxRfV/i4REREREdk71K9fn9dffz3ma9uEeU36D8qoTRpNbVK1SUVEJD7dMS4iIgBkZWVx3HHHcdxxxwHw008/cd1113HfffcxbNiw8oZMWfK0onr16kU9O7vM999/z2233cb3339PcnIyrVu3pmnTpkD0c81jLbsqmzZtYvr06RENnzIVr5YG70rzikzTLI9h06ZNABHPnapozZo1cePMzc2lbt26UZ/Zdr5jjz2WsWPH8vbbb3PBBRfw7rvvcvbZZ8dZs9h69OjB+PHjefrpp5k0aRJPPPEE9evX58ILL6zxsoCYV2Zvu52qMnz4cB577DE+/fRTDjvsMN57772I0QNuuukmGjVqxNtvv81tt91Wvh4333wzHTt2rHTZFYe69/v91K9fP+a2hsjtXVZet+2o8Pl85OTkkJ+fT15eHq7rRpWTBg0aVGOtYysqKuKSSy4hMzOTBx54ANOMfQ1iTY+nlJSU8v+XLbNi2V23bl3cuzDWrVtHVlZW1PtNmzbliy++oKSkJO4+X7FiBUDM573FMnz48PK7BRo0aMCXX35ZPiLC5s2bcRyHCRMmMGHChKjPlo2AICIiIiIiiSMQCNClS5eY09LS0iL+rkn/QUVqk26lNqnapCIiUjklxkVE9mFr1qzhpJNO4oorroh65lTHjh258sorGTNmDCtWrChvxKxfv55WrVpFzLtu3Tr222+/qOUXFBRwwQUX0K5dO6ZNm0arVq0wTZNZs2YxY8aMnbIOGRkZ9O/fn3PPPTdqWk2uLM7MzATgmWeeiWqcQ+WNsIYNG/L7779Hvb/tM87T0tI4+uijeffdd+nQoQObN29m+PDh1Y6xzMEHH8zBBx9McXExc+bM4dlnn2Xs2LF07969fNgy27YjPhPveWw7Q4sWLejevXv5s9dzc3MjhmoLBAJccsklXHLJJaxatYqPP/6Yxx57jKuvvpp333230mXH60CpSll5XbduHc2aNSt/PxQKkZubS05ODtnZ2Zimyfr16yM+W3aRRJmyO/Edx4l4v7CwMKKsuK7L9ddfz8qVK3nttdfIyMioMr6aHE/xZGRk0LJlS+67776Y0yuuf0WDBw/mxRdf5IMPPii/IGZb7733Xvm81XHggQfSsGFD3n33XRo2bIjP5+Ooo44CvPJvGAbnnHNOzAtQKna0iIiIiIjIvmVH+g/UJvWoTao2qYiIVE1DqYuI7MPq1auHz+fjxRdfpLS0NGr6smXLSEpKokWLFnTr1o1AIMDUqVMj5pk/fz6rVq2KGJK74uc3bdrEWWedRZs2bcqvKv7000+B6Ebd9ujbty9LliyhQ4cOdOnShS5dutC5c2eefvppZs6cWe3l9OnTB/Du/i5bTpcuXdi0aRMPPfRQVMN02xhWrFjBzz//XP5eaWlp+XpWNHLkSBYvXszkyZM58MADq33Vc5l77rmHkSNH4rouKSkpDBo0qPw56qtXrwa8u8D/+uuviM99/fXXNfqemho2bBiffvop06ZNo3v37rRs2RLwnsF21FFHMXnyZMC7wGDUqFEMHTo0Ksadqew5dduW13feeQfbtunVqxdJSUn06NGD999/P+Lug48++ijiM2V31ZdtX/Curl+6dGnEfA8//DAffvghDzzwAPvvv3+l8W3P8RRP3759Wb16NXXr1o0ou7Nnz2bixInlz03b1oABA+jVqxf33HMPK1eujJr+/fffM3HiRI499tjy/VkV0zQ57rjj+PDDD3nvvfc4/PDDy7dfeno6HTt2ZNmyZRFxtmnThnHjxsV9HIOIiIiIiCS+He0/UJtUbVJQm1RERKqmO8ZFRPZhlmVx6623MmbMGE466SRGjRpFq1atKC4u5osvvuCFF17giiuuKL+S+G9/+xvjxo3D7/dz+OGH8+eff/Lf//6X1q1bM2LEiKjl77///qSnp/PEE0/g8/nw+XzMmDGjfCiy4uLiHV6HSy+9lNNOO42LLrqI008/naSkJF555RU++OADHn744Wovp23btgwbNox///vfrFy5ks6dO7N8+XIefPBBmjVrVmkj7LjjjmP8+PGMGTOGK664gszMTCZPnsyGDRuiEt+9evXigAMOYN68eXGvpq7MQQcdxFNPPcUNN9zAsGHDCIVCTJw4kezsbA488EAABg0axJNPPskTTzxB9+7d+eSTT5g9e3aNv6smhg4dyl133cU777zDTTfdVP5+cnIynTp1Ki837dq1Y/ny5bz55pvlV23vCq1bt+bEE09k3LhxlJSU0K9fP37++WfGjRtHv379OPjggwH4xz/+wdlnn81ll13Gqaeeym+//cbjjz8esax27drRuHFjxo0bR0ZGBqZpMn78+IiryWfMmMHjjz/OqFGjqFu3Lt9++23EMio+jw28Z7fX9HiKZ8SIETz//POce+65XHzxxTRu3Jgvv/ySCRMmcOaZZ+L3+2N+zjRN7r//fv72t78xYsQIzj77bHr06IHjOHz55Ze88MILdOzYsXyoweoaPnw4kyZNwrKsqG35j3/8g7/97W9cffXVDBs2DNu2mTx5Mt999x2XXHJJjb5HREREREQSx472H6hNqjZpGbVJRUSkMkqMi4js4w477DBeffXV8udVb9y4kUAgQMeOHXnwwQc58sgjy+f9+9//Tr169Xj++ed57bXXyM7O5uijj+bKK6+MOeRURkYGjz32GP/3f//HFVdcQVpaGh06dOD555/nwgsvZP78+dUeDiue9u3b88ILL/Dggw9y3XXX4boubdu25dFHH+Xwww+v0bLuuusunnzySV5++WX++usv6taty7HHHsuVV14Z9wpn8IZsnzRpEnfeeSe33norPp+PYcOGkZOTw/Lly6PmP+yww1i3bh1Dhgyp8foecsgh3HfffUyePJnLLrsMwzDo1asXzz77LNnZ2QBcdNFFbNy4kcmTJxMKhTjssMO48847d2kjLzs7m0MPPZRZs2Zx7LHHRky7/fbbeeihh5g8eTLr1q2jbt26jBw5kiuuuGKXxQNw55130qJFC9544w0mTZpEgwYNGD16NGPGjCm/+6B3795MmDCBBx54gMsuu4xmzZoxduxYLr744vLlWJbFww8/zNixY/nHP/5BvXr1OPvss1m2bFn5/v34449xXZfnn3+e559/PiqWRYsWRb1X0+MpntTUVF544QXuv/9+7r33XvLz82natClXX3015513XqWfbdy4Ma+88govvfQS06ZNK7+av1WrVtxwww2MHDmyRo8kAO8ikw4dOrBmzRoGDBgQMW3gwIFMmjSJcePG/T979x0uV13nD/w9t6T3BBJAFCWgojSDIL1IQCChhAAqZdEVlIASlwSkrQiEIiBlERQFQZdFKdI7KIpICUVAlxYUjJCQAgnJTbtlfn/wy11jUHLJ3Nx7z7xez+OzueecmfOZ+XyfYT/znjmTb3zjG6mvr88nPvGJ/OQnP1nujRoAAKB6rOz7B2ZSM+lSZlIA/pVS+e+v0wIAtNlLL72UP//5z9lll11af/srSfbdd9/WT3UvVS6XM3r06GyxxRY5+eSTO6JcAAAAAACoOr4xDkCnUy6X09zc/J7H1dTUtH7KuiMtWLAgRx99dL74xS9m5MiRaW5uzm233ZY//elPmThxYpJk/vz5ufLKK/Pss8/mlVdeySWXXLLMfTQ3N+e9PqtWKpX+5TfXAQAAAACAd+cb4wB0Oo8++mgOOeS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rUjWUbaUmL9LluLJu+9t8S/Gdf6Aqv7LDHbtcba7aHiG3C/umnljm+hJWvWJLZ8s8fZm0aqFUuonsnc6SNX4PmYfPlz59TlYorpLtvi1/p9Nl5v5XZvbvpcY10uQjZc/8kfymapmXr5E+eihY6OYHSVNPkB46T+GKcTJ7/lxa8qrkRKUX/yitnC+VjFBo5o+VHP911fnFbcZojFRq1sh++VpZ8x6RDrhKztz7ZN69Kxh8Hren7Blnyln9qczYXWUe+IE0dEtp1E6yYmXSwqekt+6QmqqkkdNk73WhwsUjgt/80f8kSbHND1J0z5+ryh4h1/UVsVIqTi+TPnlF5sWrpdUfy5SOUmLXHys+bm8l5SjSsExmwRPSazdKdcsVGbKFQntfpMZB28sxaYVXvCfz5MXSF+9KiUFypp8uf+hWMnd+WwoXydnhVJkpx8qf/QdZb98h7fMr+XZEpmKMNPde6d27pXSj7LG7qWSPn8stHi2z5mPZT1woLX1TipUrNP0Mpbc5VtVuyUa1g46KWw2KLpsj+6lfSasWSCUjFN75+zKRYum/ZykydGuFD/2b9NYdMm/8XWquUXj0DIVn/Urmg/8F9V8yUqH9fyNTton0+P9r6Y9o8lGydzxVzsfPKDpmd/mRElmvXCfrjZslLy3nmH8q9ukLMq/eENTlJtNk7/1L+R8/K/P8/wXrdPsT5e54mppUpHjtfFmPXyiz5BUpWiZnh1PlTjlBVW5pr6wrdE0h9r97y7rjpv3MVTLv3b32uPl1xfb8hapDI+R24cKQErtWoQ/vlzX791LdcoWGbKHw3r9S06ApavBi3f4bolaz4tXvy378QmnZ21K8QqGdzlR6629m7Occy5e+/EDxJy7q0b5UR7Sc27zQ+twmuvbcJqIGRZa+JPvpS1qOtaFdz1dy7CzVekUd+g7H8lSSXNz6PGXqSSre8RT5r9wY7CtLRsg54HfSK9fLfPigZIxCWx6m+G7nq8oaqhJTJfutW2W9cp3UVCVnkx0UnnWZ6osmqNkL9/Ba2ji2ZVTiLpP95OUyH/x37W87RIk9fi6z9C2Zpy9tWbfWTmdIY3dXaPYfFJ76XdUXb5bz95XZ1Wv7I9cH/ZFNdgz6I0UTOtQfiVmNiq18W/Zzv5F2/ZE0/3HpnTuDvv+YmYrsc6m8xBDZ794pa8gk6bMXpbfukN24Rs7IaQrPulT1xZPU7IWDWN76u6xX/hbUz8hpQf0UT+z2+olbDYqnVsu8fbv05m1S4xpFRk5TZNYlqm1jfUnBeVm5tVpW7UqZ5XOl0pHShw/KvPNvWck6acyu0owzFXr/foWnHCe5KVmP/0L64h0pXilNPVEasoX0xK8U2ukspTY7WDXuV+eCwXq4TdYrfw3ON0duL+1yjjT3Pjk1SxX52v/J2I7M+mMBWx+p+K4/kRpWyHrsApnFL0uREjnTTpY79buq9so2aqyz2KpT+OMnZD13pVS9WKGKcYrsdq5M7RfSkxcrNGwbhfe5RA3lW29Uf6SrYlajYivekv3kL6Uv35eKhii08w+V2vxQ1bhFLfso+4Xfy7zzr+D8efyesnc6U6Ha5Upvuruq3RKV2rVylr8lK9Ugzf6D9MW7CicGydnpLKW3OlrVbutz9iKrXuEls2U/c6m0+mOFSkcpvPtP1bzp3qpbb99mjFGZVsp64WpZ79wRjF1M2EfRvX7Vqv/XWWV2tew3bpH16nXrbb+Xqr5oYreMJ2QTttIqalgYtLPxewZt+uVrgt9fNlrh3X6q5k33avX7e0LUala8aq7sl/4k7XS69Nbt0gcPtPSZ4/tcLC2bK+vpi9fre52n5ISvqdZtO7ZSu1bOu/+S9fKfpYZVQfte29+2LEvxps+DftSCxyRjK77VoYrt9hOtMcM6fOxt6ePOuU5m4qxgv/n+/ZKbkjNxv2BsILSJjFwVNy+W9cT/k/nkacmJyZlynOLTz2yzjx70H6tkv3ZTMN60dhzU7H5e0O94+1/Su3dJ6UY5Y/dQdJ+LVRsZrZQXJPhDVpbv3e7biu90VqvvTVj1inz+YnB8X7cN7PqTTh3fu0upXSNn7t2yXrpaql+p0NCtg/1SxWQ1edGcnwv2uRueG1wqv2SkrPXHkUfPUGTWpfKLhsl68+/Bduel5Rx9m6LzH5d549ZOjVcFbfj9oG+77G0pXrn2nO+YVud8tm1Ukl4m+6krZN6/X5IU2vIQxXb7WY+NRQf1v2ht/T/T0u686Wepyq/oUoI5bhoUW/6arCcvklbOk4qHKzTzx0pNPFA1ObbJ4NjzqKznfiPVfK5Q5XhF9rhAalgl8/B5UrRUzrRT5G1/YjBO8PiFLcfa8A6nyAzeTLr3NDmjdw7aeHTTljbeGbnOczccw4tbjYp9+YasJ38prfhQKh4WHIs2OM73R8bv77cmddJnn32mfffdV88995yGDh0qSXrooYd01VVX6emnn+7QMlaurM17otkYadCgYq1cGVw1Xp78VPaNe0puqtX7/MFbKHXMXar2yvIQJdqzfj3mu02hY0qsaoXv+qbMF++0LrAcud99QgonZF+/u5RqaF1eNlqpbz+oKr+im+KoUfie42SWvr5BHLbcEx9VVWKLNk9eY1aT4i9fKevV6zML97lI+vx1pfe+RGv8Qd0Sb0dU+Mtk37CnlKxrXVAyUqnjH+7yujNGKk8vkX3DHlK6aYMvHafksferxi/vE9tiqbVG4du/Ia3+uHWBE5V78jNabW/S7d8Zs5rXtoXrMsq8A36vmvFHKOV2rFNbYtUo/J8TZD5/tXWBseSe+Iiqirba6AHNcrNKzi2zpLovWxdEy5Q++Sk5g8bnvR7bU2JVK3z3sTLL3mpdYNlyv/OYquKb96u7fCSpzFqt0D8Okqo+a10Qigdt1wouWig3q+X8ff/gYpz1RYqlo/4u3XFUq3MZ7+h/qmro7l2evaDCbXvbr/bK211Gm23upKe0RkM6HVfby3xaazS408tcJ2x7KvnwNpnHfp5ZuMPJwbp487aWl/xBm8ns9mPpP6dKdlj61p3BtUL/OUlqWN368/FK6dC/SXccGQw+nPS4dP2ea+8k/4q3wylq2Ol8FVUOzrk9llpVCt15tMyXc4O6f/z/SWs+bf2mUEw6+nZp9h+lKd8OEqeeK71/n7To5dbvNUY6/GbpyV9Jaz5ZL+YKed99UmvMEJU3fyzrowelZ6/MiMefcbb87b4p6/nfSu/dk1HuffPfMql6mbu/k/ljJu0njZgiPfPr4O9R06WtD5O+eC+4y2zUdOm53wQDWOuzw/K/+4jM3w+RmmtaxzNhlpq+9qceH4AL2b5KFtwl66EfZRZuf7xkhaTh20iv3Sxl2Wfp+Aeke06SUo3S4ddL/zp27V3x6xk0STrsOmnNZ/Kf/+1X51BfuzK4WCBrXd4kPXlxS136E2bJ3+UcWX8/UBs2KH/0DDV/4ybVen2rY77uPH+g647+t9Q3+uD5UG6tlnPLvlLtF60LIiXB8bWTx6C41ajY8xfJeuv2jDLvkL+petMDlW7jTu7Osm1LZStekPXPozLK/In7q2n/P7Taz1V4S4Pjdg/3pTqizFqj0D8OzHJuE5P7vZdk5j0i69GfZnzOm/F91e9wjprctpOeOfso3/izNOdvwQVYxpKOvTvYzzauab2AxGB533lM5oGzZD6bnbFw97gHVVW6XZ8+pyw3K+TcuFfr3zZxX2mTHaSnL8v8wLTvBsemG/eV+827VF0xLWPwvNiqUeT+78pkHFssucf/T1Ul27S5ThzbqHTJI7L+c5L0jb8EybHlc1u/yXLkH3uXzJrPpHf/HSTGW32XkXvcf5Uum6Dwf0+V+fT5rOVVpdu3xLKxY0MhWyqt+0DmyV9mjcc77n5VlU3NmWwoC9XLWf52kHweNEF68c9BMnaD363j/xscm+8/M3MhE/aRRu8kPXWpvCnHq3HmL9TgxVRs1Sry4PdkPnk2Iy4ddr303P9J+18h/ft4qbn2q/KSEdLXfyv9+9vB+d96/JHTlDzs76rxunYRYdROKvH61bJm/yGzcPfzgyTERw9Lkryj71D1sD169e44xzYqXfywrHszLyDztvmmGne/WGG/UaHbsve/dMzt8t+8Q+m9LpLz+k0yFZtKD3w/Y1n+FgeraZ+rVOclJElh21Xx+7fKeuL/ZX7vzHNVv/2ZLXd0lllrFLrt4Nbn3VKwjzz5Ga22Rnbot67f9ovMxm2/XWFZRmX1H8i+ZT9ps69LgzcL2uQGNvz93c22LZUtf07WncdI3/yn9OCPpNplX71h/N7S2JnSE7/KjG3H76lh+nlqzHEBR5FVr+hjP5JZe/Fwq88e9z+ZonKZm/bP6I+oeLi8Ex/WKq9j44ItfdwDfic98lOpeknrN4QTck95Tko3BzmMDfqQqpyg5Lfuy5nDCMabjpf5/LXWBYffEPTvsvS13JOf0ZrQ6CC+1KJ2vzdsuyr+8DZZj12Q8f3ezueoftr32z2+d1R7+/2EVa/Yk+fLvH9fZixH/kPVI/bOul8qtmoV+e/JWc8N/CP/IfP4ha373ZYt/5h/Bhei1y6TDv6T9Mr1wX5wfZYt9zuPa01ss6xJZNu2VPblc7L+dUxGmT/pa2ra7w8t+5pys1LOTXtlHW9If/fJbh+LNmZt/d+wR5a+6kQlv3lvp3NnIUcqWXiPrAd/kFHmTTtJjTN+poYNLl6IWs1KvPo7WS//JXOBe14gLXkluBhv6Nbydz5b5t7TMt83YZ9gfOHpyyQ7JPekp7UmPKbT5w4dGcML2VLJZw/Iuv/0zN+43nG+K3oqJ9aZPnjfnEO2B82fP19lZWUtnXJJGj9+vJYuXaqampo2Ptl3xaym4A6rDZLikmRWfCB7xXuyrO6ZZggYyCzLyFk9LzMpLkleWtbHT8qa/bvMgRxJqloke9Fs2fbG73aNMXKqPs5MikuS58p67OeKqb7NZUTdquAKy2xe+L201aGy3r1ToV66cTViuzIv/yUzKS5JNZ/L/uQpOU7X1l3USgVXgW+YGJOk1R/L/vw12Xb+95G2bWR//mpmUlwKpix+7krF7Mz9/MYK2kKWCyQkWU9epIS3JmvZhoyRnJpPM5PikuR7sh/9qeLKUr+dEHKMrA/uz0wmSlJTlazXb8l6LOxLgu13YWZSXAq238cvbHf77Wts25K16MXME2pJSjXImv07Re2UHNvIWvBoZlJcCgbB3rsnuCt5Pdaj5yvhrc58fwdE7aSs53/Txrb/Srvbfsgxsj78b+4298YtCjudO4sP2vG9uZf55q2dXub64t4amWeyDCZL0us3S1sc3Ools/IjKdUklY2WtjgoGIhf8FhmJ1UK7ub/5Flp7B5Ssj4YLN722Iy3Wa/doKhblTNGyzKyV34QJMUHby5VL85MiktBsvXNfwSxFQ2V3viHVDw8M5EqBcnS568KBs1bxbxa5t1/qciqlVW/XHrxT1ljMnOukWXsrElxSbIaV8o8/ovsP2jeo9Lw7SR77UDZ4jlBvFsdGtzZ3lybOVAjSW5S5tkrpe2+mRnPgscVblye/fu6UcJbE1zhns2bt0mbHxBcoJBjn6UnL5J2O1fa9pggkbPhQIMUXD2/cr7kRL46h4oUS0XD2qjL37aqSzNmZpCAytJrNoteUqhusUz+D+XIohD7373FsS1Z8x7OTIpLUnONzKt/U9jpXKImml6TNSkuSdbjFyjhdu2Ym0vcWyPrkfOzlpn5jyjc9NWxMGqnZb34+x7vS3VEcG4zO8e5TaOsmiWynr4k62etOX9RrI1j4DpRKynr2Stan6cUDZGMHRyLJWniLGnew5lJcUmqXyEz9x4Zk2Wd+L7sR89XXH13GwvbCtrihr9tyreDC+KyeePW4Jxli4OC3+e3/n3GSKHaRZlJNSnojzz2U8VVm1m2noS3WtZjP5OKhwUvbJgUlyQvLfP876ShW2cmoaVg/T/5q+DYtGFSfF35oz/t1vqJ+2tk6r7IGY/1yPmK+dm/z7KM7MYVMsaX5v5H8r3MpLgkeWmpYWXWCwwlSQuekIZuJdlhWW/+veXxK6GGZZlJ8bVx6bn/C5IB797VOikuSdufID3/fxlJcUkyn78mp/oTmS4e/GNulayXsp8T6qU/S9t+q+VP69GfKt7N+8b2tLTDLKx3/qmoXy9rca59VIP05m0yoYic5tVBUvy532RdlvngvwonV7X8HXfXBPulbN/74h9a9m22bclaMiczKS4F+8jnr1K0k2MXwfa7eKO2366Iqy5Y154rbXO09FKWhJUk66U/dmjf3lUJf42sR38ibTItuNN2/aS4JG1/nPT877PH9up1bfa9ws0rsibFJclSUmbOXzOT4pJUu0xm3sNyOnDsbRmrKRoWnPdvmBSXpGS9zJJXZT1zWWZyWpJWLZD9xVtZcxjGKNjmN0yKl4+Rmmpy9rWsZy5T1GpW1Gpu53vfDKatdtfIynZhliTr5T/1aBvYUCS5KmtSXMq9XzJGCtUtyUyKS5Lvyzz768z+sufKPHdVMDtcYnDQp8067h2MV8VN9vGqRFvnfPMebjnnC9nBfizXeIP1XvePRUet5uC8LWtfdb7s5W93OncWd1fLeuKXWcus129SJEtbiblVsuZcm32BL14tbfft4P87nBTM4JDNgiekYVsHNzW4KVlPX6KYlWXMqw0dHcOLe6tlPZ55kYikVsf5/mrAJcbr6+sVi7W+kmHd3w0NWTpgWRjTN/6tiyXsN8p8/EzOeK0PHlAoZOc9Xv61XY/86/v/HMcKpvXKtW+IlgRT4OXcFu9VyEpvdByhkCWz8PHccSx5RSG/MefnLUsyVZ9lHVyWJDVVS5Yja95DCnv1vbJuw16trAW5f5P1wf0KqbmLy66T+fipnMu2P7hXztpZZ/LZvkJ2EEvOev34qeC3dON3Bm3h0zbbgtW0poPbhy2zMPd61tI35HgNG9dO/AZZ8x7M3U7mPyI1VuW1Htut55AlM//R3PW86KU2t9+++C9ku2233YVPKOzVKWSagyRzLp88H9x1u76qRbLTXdsPRbz6Ntuk/cF9CtkdaHMfPdBmmwu7tZ1rx16DrBwDE8EyH1bY7fq2bjWuDpLW2XhuMOhhb3Cnw6cvSMO3Daa19L3g71zWr6eFT0ljds58j+8Hxxllj9FxrK/W64jtgmXm/L7ngu9b9KI0bLK09I3c710+VyrfNONl89HDCqXrg4HCbAkXKVg3tcuCu6GyCRdlH+BZZ8VHUumor/7+fO3Fa8MmS9kGJtbJ1u7XWfJycO7Rg9uv1bQ6+0CYFLSFxjXS0rdyx7/4lWDq1BFTpGyD/+tULwnayzqDJn61jrJZ/l5wQcQ6FWO/ShRlYT55tsfXVVf+oXv631L+6zIf/8JqavO4ac1/TJFOHIMsy0grP2yjslbKSlZ3629w3PrsiZN19fr5ay3bbtirCx5lkev3dlNfqiP/QpYr+4P7csfduKbNY62pXdp+/Xr1Mh9vMGvC0K1bXzA0Yvs2j5Fm3sO5jyHL31PI3bhz7x5t336drPk5+tbZLpZeu25V87k0cpq04kM5G5wjOo4VTI+ay7K32+2PWKna4OLFDetiQ5+9IPltPMvA97InxddZPjejfqSur0/bbW77HOnLD3K2h1DIkmlYGSSMBk2SPnsp93IkqWpRG7/r/Zbjt1k1LxiL/KyN88oVHwYXE2Zr54M3b/NcwSx8PIi9K+us7ousCXdJwbbtr3fRUdUi2enu7Ye3989K1kj1K3L/9vovZb+fu/+lT56TRs8IEqzR0jbPYc2yt2TbJvjeptXBhanZeGmZ+uVBm7E92e//J/cyFz7ZqbELqYPbbw/s0xy/UWbJ2ov7PTf373dTLb+/J/7Zqdpg2xoxJUdfzLR5zm6qF2Vfrm2y31SzTqQ4aC85mI/+p4jV/lhcS785Z/wBywlnHvvWL//gvqw5jGC8Kct467Bt2uxrBeNoDQp7De1/r2PJalrTZn/R1H3RrfUu5WgPtpG+eDtnvKr5XHYq8xzQcazgkVa5LH9PKsvsL2vJq9LgLdYe++bk/LhZ9GLO8So7XZc90brus0uDc76wVydr3kM532d99HC3j32GvYY29y+52l2b22xzVTCjXTa+L7Pmk2DMdf3P1X7e+viyvubarxpFvLLtY+2XH3x1rP346SA32InY2z3PXTuGZyVrsl/AsO59q+bJsky3t//u2K46ov89xHIjxeNxNTa2Psit+zuRSHRoGZWVfWdKvMrKYqmmLjjRaViV9T2maLBKSrr/WWHoPn2pTaEdRW1Mb+umpEhJ9rsAJSleoeKShIpzDbp3RqKNaWVCcYVCIQ0qbaNdNbTT5ixHipQonkgoHu2F9lnXFOzHaj7PXh4rV1FxkYqcLkxZVFsfLLupOnt5fJCKi4P9f163Rd8Prs7MJVqqcCSsQcXdHGND29PP2ZFox6eCLWqjXTpRhcLhtttle5KWFGnjObLREsl2+v4+ta39SDjR/vbb13iuFG+j7qOlikQjikSLpFh5m+/L6IQaIycc0aCKLqyP2oa1235V9vL4oGCf3NaZc9KSomW5y6MliiXiisU6EV/SCuLKJdKFZa7vy3bO+ZxocMfP+mJl0qpVwUDQ2v1/TtGSr+opUpJz4MBeG3/O7TGxdvrfVGPb62Ndu4hVBFOTV47P/V47nP1Cn2ipLCcUTAvelnA89yC3WdurzXUhUbSkdZIkWhpMNZ5qaJ0wz/a5HANwdrxS5eUd6590WXvtJRSTwm3EEIpLbvqresx1DmSHW5+7tFfvdljBnP5r+d7aq+KzXOUvyUpUqqysh9cVuqQ7+t/SAO0vpZraPW5GYzFFE51YN9VlbRY74U6c93XEmkiw/8wxCGglKr7az9Wt3S/kSgbFK7uvL9Ue1w0GJXNx2n7esB0tan891tYHx4D1Ex2phtb7xnaPkWW5B/DtcHAOVdZHt51GL/tva69+w4kgcW45we8r3+D3tdVPdiIKhcJtr5NVa6c9TTUG9ZNLW+dKkuQlZeJtTP1vh+SEM2Pp8r5u9aq2Y8rxfS2qomsvIGynzVmh9s+H1p7X2LFSlZXF264TywmWFy0NZhBan5cOzkNynCdZiUEqLY3nXnZbku0cf9a/iNSYYN/Ylf5IV61rhzlY4Vjb+6hoiZSskylZO515G3VmxctVUbH2kRbL2/5eOxIP9m2e13a9RksViUQU6cTYRWlpov3tt6023FXV1cH5bKpBstve/7T8/p6wOhrUU6ox+7bcXmzR4tyxLW+jrci02xeLJ+KKh9puGy395lRD2/tOL926P7lhNIk2chiJLL8j1SAlRme+vk6kROFIKOhWZBtvWP97S+NSU3vbQKLb20DO/f6KNs4BjZETybFfamtczs4xphqKS14q8zxkQ22NV62OtnPOt7Zv2+S20+ZKFE8Ude9YdG3d2rGL7McTkxjS+dzZirbfb8eKM+u2qZ3HpK07B2pv7GH9cZhoSVAnJZ1YX+2d50ZKFIlGJLWzPcRKVVm5cY9+y2cfb8AlxidOnKiqqiqtXLlSgwYFO4qFCxdq2LBhKu7gAXvVqvw/38yYoOGsWlUrxy5S8bRTZD3366zvdbc+UmtWdv90M9h469djvtsUOqZ8s4Nk55jOxDMhafrpsh76cdZyd+pJqlrT2C11XT5hP9k5pnT1phynOq9IyTa2+9LYMIVi5dmn5Ru2jbRyntydzlR1g5FX1/P7D9uOq3TH02U9eHbWcneHU1VV3SzfzzLtUTscp0glO5yWc/oXd8rxqlpd1ye2xfLtjpf92k1Zy7wdTlONW6R0N+/PS2NDFYpXZL8KcNg2arJKVdfB7ywft7fsHCdv3nbfVp1X3Ga7bI8xRmXTz5CdY3YBd6ezZMfK816P7Smf+HXZT1yUtczb/sR2t9++xhipbPvvyM4xNau34+mqSRXJbU6pbMfTZH+Q4+63bY6SNliGP3E/NahYjV1YH46dUMmOp8nK9qxtBdv+mlVtT+8ftLnTZc9/LPsypp+l6saQvPqOx2dZRqXTz5Cd4644d6ezVN3odGqZ6yu2SxQZNCmYvnpDRUODK5E33EDG7xVMT1r/pTTtJGnykbmvvJ985FdTaE4/TXo1yz4rVq5UbJhCyn3eXL7lYbKf/7/gLuJvXCO9c2f279v2GOn9+4NnT869NzhG5epwb3GQlGVGBn/66ao3pUoYS6atdRMtD+4M33AqT0l+1RKZcXtJ2e5UcKLB5+vWTn1urOAO/NplwZ3kU78j5di3a7tvS3Pvy3zdcpQeMVVVPbwvKLJLFB26dXD3wIbilUGHe9SOuRcw5VjpnX9JNcukbY4JpoDLZujWUigqPX158PeXHwTrKFddbnmwtP52N+9R+dscLfPmPzLfayylN929x9dVZ607zx/ouqP/LfWNPnhvM8aobIfTZH+YfbYcd/qZqklG5TZ2vO2XlmyqUKQ4+35u1E5qtErU0I3bUswqVnyzr8tk+w12SOmh27Vsu46TUEmbfanvdltfqj3tndv4kRKZwZsF+/gNFQ1RMlypmnbWY0sf5Yn1+nRLXpV2/fFX+9IP/ivNODN4REe2OKZ/T+bBc7KWeZOPUoNfpKY+tm9cx7JslU4/U/aGU3+v+TS4U3hFltkNEoODY9P798nf6nDVK/P3lY3ZQ06u/sjkY1TvF6u5jXUSt4oVGzMzmM55l+/nnFZZ2xwVXABn2VnvPPaHbC1v091k5zjO+VsfqXq/uCX+jR0bSlhRxUZOyx3PVoe1+r4NVcQHy6r9Inh0w85nBY/LySbVIE2Y1foYvY4dlko3CS52j5QoVTxa1StrVbbJTnJyxBWcuz0RnO89dmHrsg//J219RPBYnSzcCft1eayzJFKh8PrnbusbNKnVnXpBf6SoS/2RropbxYptukv26ZDDRUqFS2VNPUn22//MvoBtvynNvU/ewX+W9dZt0rg9W8/cs44TVXrQFi374WKnTJHK8dKqhZnvLR6uZKhCNSuDu1TLppwo+42/Z/16b8fvqSbdsbGL9dt+6ZjdN2r77YqwU6yiKcfJeuVvwflsrt9fMqLl9/eEmFWk+MT9ZD56WNr/19JHG9xRu+rj4Hw6xzl7Kj5U1TliKxu8tRwnknUacX/1J9L2x8v8L/ux19/xNK2uScv32/7dQR/3dNn3fFc68PdBvy0LL1Iq7XCqrKcuzlrubvOtnNt1+YR9ZT9+Yev28ekL0pE35+xreTueptp0cL5ZvMMp7X5vsVOqyKCJ2admLxqqZLj72kB7+/3Sys0VynFxkD9+bzWqOOs5W9nomXLa7GNlmcFw8pHSBw9Kn78WPCZr9h+yxuxt/52c41Uxu0jxSfvLbNh2JckOKz1kG1WtrJVlWSqbfqasHDMLuDud1e1j0SG7OKj/py/N/p3bHNPp40nCLlZs+HbZHzkWK1cqPiJjmyyJDVY4MTj7RaBDtvjq0ZqfvSiN3zuYNn1DTkQqHdnyiEJv2imq9YqU6kT8HR3Dixhf0U12+GpWjfWtd5zvip7KiXWmDz7gplIfM2aMpk6dqssvv1x1dXVavHixrrnmGh1xxBEdXobv941/62JJpiR3u+Pkj5yWEas361I1hofkPVb+tV+P/Osf/xpCg+Xtf1XmjmHYNvImfV3pCV+TP27PzG1xlx+pOTFantdNcTiV8g7MHHz2h2wld/rZak5bbX6+zlTIPeqOYDB/ffFKae//J6/6C6UGbyfX9XtlvabTntJj95Y/Yd/MdbfTmUoWj+3yukulfKW3OFT+pjMzl737z9QUGynf7xvbYlNspLzdM58l5m86U+4WhyqV6v76aKstuIdcp3oVd3hZ9XalvAMzn9XmD95C3owftNsu2/vneb6SFVvI2/7EzO/Y4hClR0zvE/XY3r8Gp1LeAb/P/A1DJ8vd8fSNXk+9/c/zFOzfZmZ2pv2xeyg98etKpb2g/komyNvxtMz3Tdo/uCP4yw++erF0lNx9r1CjF+lSXKm0r/Tmh8gfs2vG93m7nd+y7bfb5spztblvKD1yeqf3k67rK1mxpbztT8jejofvuFH73jqVyj385sy7DENx6eCrMxKX/l7/LxgoSzcHU1U7keAO8i2/kRGftjo0uCu6eknQURuza+bdPU5U7tF3qM4K7pLKFWdjaIi8/a4IkjNL3wyS7BvadBepckLwvL9Xb5R2O096/75gsGjDO/0rx0s7niq913paR3/yUUoO3V6NKVve0K3lf+3KrOvGP/JWecaRDvxj5pX0RUPlTZglf/8rggHf9VmOdODvpDl/Df42Rv4Bvw+ezz37amm/y4OO7cwfZv6+TabJn3Js5jRpxpJ3+M2qN+U9vv3Wq0TuoddnXjEeikkH/iH4De/eLc3KMog0ZMsgsf/Ov6VPnpFG7yR/kyxJ9F3PlaIl8he9LP/rv/3q9Tf+nqMuJwQDuuvVpTdiqrxdzw2mYG+1roy8b1yrBrvn11VX/qF7+t9S/usyH/88z1eybJK8aadkro/NDlB61Eyl016nlllnKuQeeVv2/dxBf1KDl+jW39DoheXuc0nmtJ2WLe/wW1S33n4ulfKUnvC1IIGzge7uS7W/7ts+t0mVjpV72E1ZjyfuUberzlS0+x2plC93y8Pkb7rLej/UlebeJ3+/tRcRrRsg3fyAzHWy3fFKDdpK3t6/zLof9Xb9iRrTTt7bca5/rusrNXR7eVtvsC94+Rr5B/w2+3nMIdcEicxUo9w9fq7GdChjuQ12hbyDr81YJ/6gzeTNPFdNabvNuBq8uNyv/yG4c/XD/0l7Zl5c7Q/fTv60k4JjZI5zEnf3n6veqpB3yHXZy3f7aUb9SF1fnw1+kdySkdL+V2Z+X8U4ebtf0GZ7aLDK5FdOCi5EXPCUtEeW51sP3za403OnMzNnw7HstedD10l2WO7Rt6vOKl+77HK5h94QXAy3QVya8m3ppavll42WP3H/1uUfPiB/6onyh2yZEYp30J/UYLe/neX6V6cKuUfdnjkrTqxc2u+yry4MKB0ld9YVavSivbp9NHgJeQf84atn3a9jh+QedZvqVL52H/WjzHoas6tUPk7eLj9SvV0pd+we0oyzsp7Dekf+vdV+OOhH3JI5Y1a4aO2+rbxlH9kU30Terj/J+Hp/zG5Kb3awUp04Pkltb78aNKlD229X/jWnLLnTz5Y/ZKvg+fL7XZH99x95W6t11d3/Gr2o3FlXBPu6qkXSdse2jmHOtUE/Jss5u3vUHapT7tjqTJm8w28NttP1lY6SO3Ka/PH7SBMzx+L8aScpWTZRntd+37Slj7vlIcGFTRs+x1qSP2GW0kMmy936aPmjd8oo///t3Xl4FFW+//FPVXdngRC2hE1gVEJAVCAQQIEZJBJRQVDAHa565yI/hOtyFUUQEdAZ5DLO4MhF54I4Dl51nAFkU5wZZXEBRAEdVlEz7JAFQvbudNXvjyYtIXQngSTdNO/X8+RRuqu6T9X3nDqnzre6ykp7VsUxzQO3C0eTivNNpcXSke1S/2cqfJ7d5hp5r7pT7lJb7lK7ku9tWb4NnLV/X1Sl/r06f1Lg9wqMxrJu/1PFO6nEXyLvwFkqDHBcKjSbyLr11bOODew+j1U8X252pexeY6TdK33jkH8ulq5/tuL+bNFZ3p7/L+B8VZE3Wt70F8o/DkvyjflG/DTm83ptuZt1lXX1nRVj0eVueRI61/hctLvUlvfqu2W3OVv8p6o4ukW1P7PQbiDv0Fcr3unCGRNwLJhvNPb1Pa4zfm0e29h3vr3h1PPHt74l+/rnzt7XDvqNr6+VZF+SKm+XkXJ7qlf2SufwkgfJU2qpwI6TdctcKa5Z+YXO6Odro/6f7+dWhWHb1Vk8MmRlZWn69OnauHGjTNPUrbfeqieeeEIOh6PylSVlZYX+anXDkBISGvjLYhhSAyNXzhPf+67MiW0s+4ohKnImqsiu5HYnCJkz44gLQ4xZonqeTBm7lkmF2bKTb5SjeSdlu+vLsqQGZp5cef+SsXOZb7K9060qjmqmQvscb/MVsBxu1SvN8pWjIFN20g3yNu2ok3bDKtUnp2mrgZUlx48fS5k7fVefNuskyxUnd1QT5VvndzuUc+Hbd/t82+SMkX3FrSqOaa5C6/z3XQPzpFwnf/T9YiW6gewrhqo4KlGFVr2waov1zELFuDNl7HxfKsmT3XGwPPGXKc+q5HZ956GsLpg/fiJl7pTduqesS3oqz0yQ11u9HRJjlqheabaM3Suk/KOyk9JP1ctGNbZv65sFii46LGPHUsnyyO50m9z1W6vAjgubOFbGdxzJkrF7mVSQJbv9QJU26aC8KrbfcFTPLFJMyVFfXDyFsq+4RZ4GP6tQd+ubhYouPiJjxxLJ65Z9xVB5G7SWWVoo87vV0ol/yby8nzzNOuukmsiyzm+H+Np+hoxdy6WoBrI7/dT2qypQnTuf42RtfGYZh2koXlly7P9MOvCVb+Lx0r6+SZ7Mnb5f5tRrIrvTrXJHNZWrNM/3nK8TGVLb3r7nfns9Uv4Rac9q3+0kO94iFWVL36/xJc2jG8je85GM5Bt8FzQc+FJ2QkdZl6cp30xQqW1U2h5jzGJfO9j5vpSQJKPxZb4T8pJ8qV1/KaqBrPrNZTijZBz91pdATx7ou72wM0r6YY1UmOVLoDe61HdCefKAtGO5ZBrSlcNUUr+N8ry+fWoYhho58+QoyZH2feGbRGlxpXRZPxW4mslTaijeOOF7vuIPa3yTUj/rI2+rVOWqqRwOQ/HeozIOb/U9W7TBJVKHG6WiE75fr8W1lNVhkEpd8XIVHZOxc6mMek2lpOt9V3Q7Y6S9/5Dceb5fWzVtp5NGgmLtk3Ic+0bGD2ukhm1kJd+kQkdTlViV3Pq9hjgcpq8POLhRxoFNUuIVMi77uexju2VmrJN1SXepzTUyPHm+X+8XZkvJN8pumiz70BYZ+z6XEjvJuqy/bIdLzuPf+ZIJUXHSFYMlR5Qsd5E8ca3lUfSpMdRyX+yuvkNGdH3fr2JOHvI9D7P5Vb5bsu9aIbteU9kdh6jQlaA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|
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},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"execution_count": 26
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
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"## 5. Preparazione del Modello di Machine Learning"
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|
]
|
|
},
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{
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"cell_type": "code",
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"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-24T10:25:45.872651Z",
|
|
"start_time": "2024-10-24T10:25:45.859503Z"
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}
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|
},
|
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"source": [
|
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"def prepare_data(df, olive_varieties_df):\n",
|
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" # Crea una copia del DataFrame per evitare modifiche all'originale\n",
|
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" df = df.copy()\n",
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"\n",
|
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" # Ordina per zona e anno\n",
|
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" df = df.sort_values(['zone', 'year'])\n",
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"\n",
|
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" # Definisci le feature\n",
|
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" temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n",
|
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" static_features = ['ha'] # Feature statiche base\n",
|
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" target_features = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n",
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"\n",
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" # Ottieni le varietà pulite\n",
|
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" varieties = [clean_column_name(variety) for variety in olive_varieties_df['Varietà di Olive']]\n",
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"\n",
|
|
" # Crea la struttura delle feature per ogni varietà\n",
|
|
" variety_features = [\n",
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" 'tech', 'pct', 'prod_t_ha', 'oil_prod_t_ha', 'oil_prod_l_ha',\n",
|
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" 'min_yield_pct', 'max_yield_pct', 'min_oil_prod_l_ha', 'max_oil_prod_l_ha',\n",
|
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" 'avg_oil_prod_l_ha', 'l_per_t', 'min_l_per_t', 'max_l_per_t', 'avg_l_per_t'\n",
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" ]\n",
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"\n",
|
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" # Prepara dizionari per le nuove colonne\n",
|
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" new_columns = {}\n",
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|
"\n",
|
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" # Prepara le feature per ogni varietà\n",
|
|
" for variety in varieties:\n",
|
|
" # Feature esistenti\n",
|
|
" for feature in variety_features:\n",
|
|
" col_name = f\"{variety}_{feature}\"\n",
|
|
" if col_name in df.columns:\n",
|
|
" if feature != 'tech': # Non includere la colonna tech direttamente\n",
|
|
" static_features.append(col_name)\n",
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"\n",
|
|
" # Feature binarie per le tecniche di coltivazione\n",
|
|
" for technique in ['tradizionale', 'intensiva', 'superintensiva']:\n",
|
|
" col_name = f\"{variety}_{technique}\"\n",
|
|
" new_columns[col_name] = df[f\"{variety}_tech\"].notna() & (\n",
|
|
" df[f\"{variety}_tech\"].str.lower() == technique\n",
|
|
" ).fillna(False)\n",
|
|
" static_features.append(col_name)\n",
|
|
"\n",
|
|
" # Aggiungi tutte le nuove colonne in una volta sola\n",
|
|
" new_df = pd.concat([df] + [pd.Series(v, name=k) for k, v in new_columns.items()], axis=1)\n",
|
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"\n",
|
|
" #print(\"Temporal features:\", temporal_features)\n",
|
|
" #print(\"Static features:\", static_features)\n",
|
|
"\n",
|
|
" # Crea scalers separati per ogni tipo di dato\n",
|
|
" scaler_temporal = StandardScaler()\n",
|
|
" scaler_static = StandardScaler()\n",
|
|
" scaler_y = StandardScaler()\n",
|
|
"\n",
|
|
" # Prepara array per i dati\n",
|
|
" zones = new_df['zone'].unique()\n",
|
|
" years = new_df['year'].unique()\n",
|
|
" years.sort()\n",
|
|
"\n",
|
|
" # Inizializza arrays con le dimensioni corrette\n",
|
|
" X_temporal = np.zeros((len(zones), len(years), len(temporal_features)))\n",
|
|
" X_static = np.zeros((len(zones), len(static_features)))\n",
|
|
" y = np.zeros((len(zones), len(target_features)))\n",
|
|
"\n",
|
|
" # Popola gli arrays\n",
|
|
" for i, zone in enumerate(zones):\n",
|
|
" zone_data = new_df[new_df['zone'] == zone]\n",
|
|
" # Dati statici\n",
|
|
" X_static[i] = zone_data[static_features].iloc[0].values\n",
|
|
"\n",
|
|
" # Per i target, prendiamo solo l'ultimo anno disponibile per il training\n",
|
|
" # Questo simula una predizione per il prossimo anno\n",
|
|
" last_year_data = zone_data.iloc[-1]\n",
|
|
" y[i] = last_year_data[target_features].values\n",
|
|
"\n",
|
|
" #print(\"\\nShape prima dello split:\")\n",
|
|
" #print(\"X_temporal shape:\", X_temporal.shape)\n",
|
|
" #print(\"X_static shape:\", X_static.shape)\n",
|
|
" #print(\"y shape:\", y.shape)\n",
|
|
"\n",
|
|
" # Dividi i dati in train, validation e test\n",
|
|
" # Modifica: ora dividiamo per zone invece che per anni\n",
|
|
" n_zones = len(zones)\n",
|
|
" train_idx = int(n_zones * 0.7) # 70% per training\n",
|
|
" val_idx = int(n_zones * 0.85) # 15% per validation\n",
|
|
"\n",
|
|
" # Split dei dati\n",
|
|
" X_temporal_train = X_temporal[:train_idx]\n",
|
|
" X_temporal_val = X_temporal[train_idx:val_idx]\n",
|
|
" X_temporal_test = X_temporal[val_idx:]\n",
|
|
"\n",
|
|
" X_static_train = X_static[:train_idx]\n",
|
|
" X_static_val = X_static[train_idx:val_idx]\n",
|
|
" X_static_test = X_static[val_idx:]\n",
|
|
"\n",
|
|
" y_train = y[:train_idx]\n",
|
|
" y_val = y[train_idx:val_idx]\n",
|
|
" y_test = y[val_idx:]\n",
|
|
"\n",
|
|
" # Standardizzazione\n",
|
|
" X_temporal_train = scaler_temporal.fit_transform(X_temporal_train.reshape(-1, len(temporal_features))).reshape(X_temporal_train.shape)\n",
|
|
" X_temporal_val = scaler_temporal.transform(X_temporal_val.reshape(-1, len(temporal_features))).reshape(X_temporal_val.shape)\n",
|
|
" X_temporal_test = scaler_temporal.transform(X_temporal_test.reshape(-1, len(temporal_features))).reshape(X_temporal_test.shape)\n",
|
|
"\n",
|
|
" X_static_train = scaler_static.fit_transform(X_static_train)\n",
|
|
" X_static_val = scaler_static.transform(X_static_val)\n",
|
|
" X_static_test = scaler_static.transform(X_static_test)\n",
|
|
"\n",
|
|
" y_train = scaler_y.fit_transform(y_train)\n",
|
|
" y_val = scaler_y.transform(y_val)\n",
|
|
" y_test = scaler_y.transform(y_test)\n",
|
|
"\n",
|
|
" # Prepara i dizionari di input\n",
|
|
" train_data = {'temporal': X_temporal_train, 'static': X_static_train}\n",
|
|
" val_data = {'temporal': X_temporal_val, 'static': X_static_val}\n",
|
|
" test_data = {'temporal': X_temporal_test, 'static': X_static_test}\n",
|
|
"\n",
|
|
" #print(\"\\nShape dopo lo split:\")\n",
|
|
" #print(\"Train temporal shape:\", train_data['temporal'].shape)\n",
|
|
" #print(\"Train static shape:\", train_data['static'].shape)\n",
|
|
" #print(\"Train target shape:\", y_train.shape)\n",
|
|
"\n",
|
|
" return (train_data, y_train), (val_data, y_val), (test_data, y_test), (scaler_temporal, scaler_static, scaler_y)"
|
|
],
|
|
"outputs": [],
|
|
"execution_count": 53
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"cell_type": "markdown",
|
|
"source": "## Divisione train/validation/test:\n"
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-24T10:25:49.473595Z",
|
|
"start_time": "2024-10-24T10:25:49.199833Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"simulated_data = pd.read_parquet(\"./data/simulated_data.parquet\")\n",
|
|
"olive_varieties = pd.read_parquet(\"./data/olive_varieties.parquet\")\n",
|
|
"\n",
|
|
"(train_data, train_targets), (val_data, val_targets), (test_data, test_targets), scalers = prepare_data(simulated_data, olive_varieties)\n",
|
|
"\n",
|
|
"scaler_temporal, scaler_static, scaler_y = scalers\n",
|
|
"\n",
|
|
"print(\"Temporal data shape:\", train_data['temporal'].shape)\n",
|
|
"print(\"Static data shape:\", train_data['static'].shape)\n",
|
|
"print(\"Target shape:\", train_targets.shape)"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Temporal data shape: (70, 39, 3)\n",
|
|
"Static data shape: (70, 337)\n",
|
|
"Target shape: (70, 5)\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 54
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"cell_type": "markdown",
|
|
"source": "## OliveOilTransformer"
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-24T09:32:37.506903Z",
|
|
"start_time": "2024-10-24T09:32:36.905756Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"import tensorflow as tf\n",
|
|
"\n",
|
|
"\n",
|
|
"class PositionalEncoding(tf.keras.layers.Layer):\n",
|
|
" def __init__(self, position, d_model):\n",
|
|
" super(PositionalEncoding, self).__init__()\n",
|
|
" self.pos_encoding = self.positional_encoding(position, d_model)\n",
|
|
"\n",
|
|
" def get_angles(self, position, i, d_model):\n",
|
|
" angles = 1 / tf.pow(10000, (2 * (i // 2)) / tf.cast(d_model, tf.float32))\n",
|
|
" return position * angles\n",
|
|
"\n",
|
|
" def positional_encoding(self, position, d_model):\n",
|
|
" angle_rads = self.get_angles(\n",
|
|
" position=tf.range(position, dtype=tf.float32)[:, tf.newaxis],\n",
|
|
" i=tf.range(d_model, dtype=tf.float32)[tf.newaxis, :],\n",
|
|
" d_model=d_model)\n",
|
|
"\n",
|
|
" sines = tf.math.sin(angle_rads[:, 0::2])\n",
|
|
" cosines = tf.math.cos(angle_rads[:, 1::2])\n",
|
|
"\n",
|
|
" pos_encoding = tf.concat([sines, cosines], axis=-1)\n",
|
|
" pos_encoding = pos_encoding[tf.newaxis, ...]\n",
|
|
" return tf.cast(pos_encoding, tf.float32)\n",
|
|
"\n",
|
|
" def call(self, inputs):\n",
|
|
" return inputs + self.pos_encoding[:, :tf.shape(inputs)[1], :]\n",
|
|
"\n",
|
|
"\n",
|
|
"class TransformerBlock(tf.keras.layers.Layer):\n",
|
|
" def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):\n",
|
|
" super(TransformerBlock, self).__init__()\n",
|
|
" self.att = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)\n",
|
|
" self.ffn = tf.keras.Sequential([\n",
|
|
" tf.keras.layers.Dense(ff_dim, activation=\"relu\"),\n",
|
|
" tf.keras.layers.Dense(embed_dim),\n",
|
|
" ])\n",
|
|
" self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
|
|
" self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
|
|
" self.dropout1 = tf.keras.layers.Dropout(rate)\n",
|
|
" self.dropout2 = tf.keras.layers.Dropout(rate)\n",
|
|
"\n",
|
|
" def call(self, inputs, training):\n",
|
|
" attn_output = self.att(inputs, inputs)\n",
|
|
" attn_output = self.dropout1(attn_output, training=training)\n",
|
|
" out1 = self.layernorm1(inputs + attn_output)\n",
|
|
" ffn_output = self.ffn(out1)\n",
|
|
" ffn_output = self.dropout2(ffn_output, training=training)\n",
|
|
" return self.layernorm2(out1 + ffn_output)\n",
|
|
"\n",
|
|
"\n",
|
|
"class OliveOilTransformer(tf.keras.Model):\n",
|
|
" def __init__(self, input_shape, num_outputs, d_model=64, num_heads=8, ff_dim=64, num_transformer_blocks=4, mlp_units=[128, 64], dropout=0.1, mlp_dropout=0.1):\n",
|
|
" super(OliveOilTransformer, self).__init__()\n",
|
|
"\n",
|
|
" # Input shape dovrebbe essere (seq_length, num_features)\n",
|
|
" self.input_layer = tf.keras.layers.Input(shape=input_shape)\n",
|
|
"\n",
|
|
" # Feature projection\n",
|
|
" self.feature_projection = tf.keras.layers.Dense(d_model)\n",
|
|
"\n",
|
|
" # Positional encoding\n",
|
|
" self.pos_encoding = PositionalEncoding(position=input_shape[0], d_model=d_model)\n",
|
|
"\n",
|
|
" # Transformer blocks\n",
|
|
" self.transformer_blocks = []\n",
|
|
" for _ in range(num_transformer_blocks):\n",
|
|
" self.transformer_blocks.append(TransformerBlock(d_model, num_heads, ff_dim, dropout))\n",
|
|
"\n",
|
|
" # Output layers\n",
|
|
" self.global_average_pooling = tf.keras.layers.GlobalAveragePooling1D()\n",
|
|
" self.dropout = tf.keras.layers.Dropout(mlp_dropout)\n",
|
|
"\n",
|
|
" # MLP head\n",
|
|
" self.mlp_layers = []\n",
|
|
" for dim in mlp_units:\n",
|
|
" self.mlp_layers.append(tf.keras.layers.Dense(dim, activation=\"relu\"))\n",
|
|
" self.mlp_layers.append(tf.keras.layers.Dropout(mlp_dropout))\n",
|
|
"\n",
|
|
" self.final_layer = tf.keras.layers.Dense(num_outputs)\n",
|
|
"\n",
|
|
" # Build the model\n",
|
|
" inputs = tf.keras.layers.Input(shape=input_shape)\n",
|
|
" outputs = self.call(inputs)\n",
|
|
" self._model = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
|
|
"\n",
|
|
" def call(self, inputs, training=None):\n",
|
|
" x = inputs\n",
|
|
"\n",
|
|
" # Project features to d_model dimension\n",
|
|
" x = self.feature_projection(x)\n",
|
|
"\n",
|
|
" # Add positional encoding\n",
|
|
" x = self.pos_encoding(x)\n",
|
|
"\n",
|
|
" # Apply transformer blocks\n",
|
|
" for transformer_block in self.transformer_blocks:\n",
|
|
" x = transformer_block(x, training=training)\n",
|
|
"\n",
|
|
" # Global pooling\n",
|
|
" x = self.global_average_pooling(x)\n",
|
|
"\n",
|
|
" # Apply MLP layers\n",
|
|
" for layer in self.mlp_layers:\n",
|
|
" x = layer(x, training=training)\n",
|
|
"\n",
|
|
" # Final output\n",
|
|
" return self.final_layer(x)\n",
|
|
"\n",
|
|
" def model(self):\n",
|
|
" return self._model\n",
|
|
"\n",
|
|
"\n",
|
|
"# Costruisci il modello\n",
|
|
"model = OliveOilTransformer(\n",
|
|
" input_shape=(train_data.shape[1], train_data.shape[2]), # (seq_length, num_features)\n",
|
|
" num_outputs=train_targets.shape[-1] # numero di target\n",
|
|
")\n",
|
|
"\n",
|
|
"# Compila il modello\n",
|
|
"model.compile(\n",
|
|
" optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
|
|
" loss='mse',\n",
|
|
" metrics=['mae']\n",
|
|
")\n",
|
|
"\n",
|
|
"# Mostra il summary\n",
|
|
"model.model().summary()"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"\u001B[1mModel: \"functional_16\"\u001B[0m\n"
|
|
],
|
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"text/html": [
|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_16\"</span>\n",
|
|
"</pre>\n"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
|
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"┃\u001B[1m \u001B[0m\u001B[1mLayer (type) \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1mOutput Shape \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1m Param #\u001B[0m\u001B[1m \u001B[0m┃\n",
|
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"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
|
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"│ input_layer_20 (\u001B[38;5;33mInputLayer\u001B[0m) │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m34\u001B[0m, \u001B[38;5;34m4\u001B[0m) │ \u001B[38;5;34m0\u001B[0m │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
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"│ dense_219 (\u001B[38;5;33mDense\u001B[0m) │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m34\u001B[0m, \u001B[38;5;34m64\u001B[0m) │ \u001B[38;5;34m320\u001B[0m │\n",
|
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
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"│ positional_encoding_18 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m34\u001B[0m, \u001B[38;5;34m64\u001B[0m) │ \u001B[38;5;34m0\u001B[0m │\n",
|
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"│ (\u001B[38;5;33mPositionalEncoding\u001B[0m) │ │ │\n",
|
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
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"│ transformer_block_72 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m34\u001B[0m, \u001B[38;5;34m64\u001B[0m) │ \u001B[38;5;34m141,248\u001B[0m │\n",
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"│ (\u001B[38;5;33mTransformerBlock\u001B[0m) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ transformer_block_73 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m34\u001B[0m, \u001B[38;5;34m64\u001B[0m) │ \u001B[38;5;34m141,248\u001B[0m │\n",
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"│ (\u001B[38;5;33mTransformerBlock\u001B[0m) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ transformer_block_74 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m34\u001B[0m, \u001B[38;5;34m64\u001B[0m) │ \u001B[38;5;34m141,248\u001B[0m │\n",
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"│ (\u001B[38;5;33mTransformerBlock\u001B[0m) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ transformer_block_75 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m34\u001B[0m, \u001B[38;5;34m64\u001B[0m) │ \u001B[38;5;34m141,248\u001B[0m │\n",
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"│ (\u001B[38;5;33mTransformerBlock\u001B[0m) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ global_average_pooling1d_18 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m64\u001B[0m) │ \u001B[38;5;34m0\u001B[0m │\n",
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"│ (\u001B[38;5;33mGlobalAveragePooling1D\u001B[0m) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_228 (\u001B[38;5;33mDense\u001B[0m) │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m128\u001B[0m) │ \u001B[38;5;34m8,320\u001B[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dropout_201 (\u001B[38;5;33mDropout\u001B[0m) │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m128\u001B[0m) │ \u001B[38;5;34m0\u001B[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_229 (\u001B[38;5;33mDense\u001B[0m) │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m64\u001B[0m) │ \u001B[38;5;34m8,256\u001B[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dropout_202 (\u001B[38;5;33mDropout\u001B[0m) │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m64\u001B[0m) │ \u001B[38;5;34m0\u001B[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_230 (\u001B[38;5;33mDense\u001B[0m) │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m5\u001B[0m) │ \u001B[38;5;34m325\u001B[0m │\n",
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"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
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],
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
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"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
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"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
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"│ input_layer_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_219 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ positional_encoding_18 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
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"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">PositionalEncoding</span>) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ transformer_block_72 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">141,248</span> │\n",
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"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TransformerBlock</span>) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ transformer_block_73 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">141,248</span> │\n",
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"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TransformerBlock</span>) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ transformer_block_74 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">141,248</span> │\n",
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"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TransformerBlock</span>) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ transformer_block_75 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">141,248</span> │\n",
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"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TransformerBlock</span>) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ global_average_pooling1d_18 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
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"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling1D</span>) │ │ │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_228 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">8,320</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dropout_201 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_229 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dropout_202 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_230 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">325</span> │\n",
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"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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"</pre>\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"\u001B[1m Total params: \u001B[0m\u001B[38;5;34m582,213\u001B[0m (2.22 MB)\n"
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],
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">582,213</span> (2.22 MB)\n",
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"</pre>\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"\u001B[1m Trainable params: \u001B[0m\u001B[38;5;34m582,213\u001B[0m (2.22 MB)\n"
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],
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">582,213</span> (2.22 MB)\n",
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"</pre>\n"
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]
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},
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"data": {
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"\u001B[1m Non-trainable params: \u001B[0m\u001B[38;5;34m0\u001B[0m (0.00 B)\n"
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
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"</pre>\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"execution_count": 29
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "## Model Training"
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},
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{
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"metadata": {
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"ExecuteTime": {
|
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"end_time": "2024-10-24T09:33:43.625381Z",
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"start_time": "2024-10-24T09:33:34.088970Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# Addestramento\n",
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"history = model.fit(\n",
|
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" train_data,\n",
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" train_targets,\n",
|
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" validation_split=0.2,\n",
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" epochs=100,\n",
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" batch_size=32,\n",
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" callbacks=[\n",
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" EarlyStopping(\n",
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" monitor='val_loss',\n",
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" patience=10,\n",
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" restore_best_weights=True\n",
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" ),\n",
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" ReduceLROnPlateau(\n",
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" monitor='val_loss',\n",
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" factor=0.5,\n",
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" patience=5,\n",
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" min_lr=1e-6\n",
|
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" )\n",
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" ]\n",
|
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")"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/100\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/keras/src/layers/layer.py:372: UserWarning: `build()` was called on layer 'olive_oil_transformer_18', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.\n",
|
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" warnings.warn(\n",
|
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"2024-10-24 11:33:43.277232: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: INVALID_ARGUMENT: Incompatible shapes: [32,34,5] vs. [32,5]\n",
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"\t [[{{function_node __inference_one_step_on_data_37911}}{{node gradient_tape/compile_loss/mse/sub/BroadcastGradientArgs}}]]\n"
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]
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},
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{
|
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"ename": "InvalidArgumentError",
|
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"evalue": "Graph execution error:\n\nDetected at node gradient_tape/compile_loss/mse/sub/BroadcastGradientArgs defined at (most recent call last):\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/runpy.py\", line 86, in _run_code\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel_launcher.py\", line 17, in <module>\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/traitlets/config/application.py\", line 1075, in launch_instance\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel/kernelapp.py\", line 701, in start\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/tornado/platform/asyncio.py\", line 205, in start\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/asyncio/base_events.py\", line 1909, in _run_once\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/asyncio/events.py\", line 80, in _run\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 534, in dispatch_queue\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 523, in process_one\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 429, in dispatch_shell\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 767, in execute_request\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 429, in do_execute\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3075, in run_cell\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3130, in _run_cell\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3334, in run_cell_async\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3517, in run_ast_nodes\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3577, in run_code\n\n File \"/var/folders/j4/dltmqwjj1438ftthspk8_knm0000gn/T/ipykernel_2258/3746327179.py\", line 2, in <module>\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py\", line 318, in fit\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py\", line 121, in one_step_on_iterator\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py\", line 108, in one_step_on_data\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py\", line 70, in train_step\n\nIncompatible shapes: [32,34,5] vs. [32,5]\n\t [[{{node gradient_tape/compile_loss/mse/sub/BroadcastGradientArgs}}]] [Op:__inference_one_step_on_iterator_38382]",
|
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"output_type": "error",
|
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"traceback": [
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"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
|
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"\u001B[0;31mInvalidArgumentError\u001B[0m Traceback (most recent call last)",
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"Cell \u001B[0;32mIn[30], line 2\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[38;5;66;03m# Addestramento\u001B[39;00m\n\u001B[0;32m----> 2\u001B[0m history \u001B[38;5;241m=\u001B[39m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 3\u001B[0m \u001B[43m \u001B[49m\u001B[43mtrain_data\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 4\u001B[0m \u001B[43m \u001B[49m\u001B[43mtrain_targets\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 5\u001B[0m \u001B[43m \u001B[49m\u001B[43mvalidation_split\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0.2\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 6\u001B[0m \u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m100\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 7\u001B[0m \u001B[43m \u001B[49m\u001B[43mbatch_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m32\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 8\u001B[0m \u001B[43m \u001B[49m\u001B[43mcallbacks\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\n\u001B[1;32m 9\u001B[0m \u001B[43m \u001B[49m\u001B[43mtf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mkeras\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcallbacks\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mEarlyStopping\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 10\u001B[0m \u001B[43m \u001B[49m\u001B[43mmonitor\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mval_loss\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 11\u001B[0m \u001B[43m \u001B[49m\u001B[43mpatience\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m10\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 12\u001B[0m \u001B[43m \u001B[49m\u001B[43mrestore_best_weights\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\n\u001B[1;32m 13\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 14\u001B[0m \u001B[43m \u001B[49m\u001B[43mtf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mkeras\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcallbacks\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mReduceLROnPlateau\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 15\u001B[0m \u001B[43m \u001B[49m\u001B[43mmonitor\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mval_loss\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 16\u001B[0m \u001B[43m \u001B[49m\u001B[43mfactor\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0.5\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 17\u001B[0m \u001B[43m \u001B[49m\u001B[43mpatience\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m5\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 18\u001B[0m \u001B[43m \u001B[49m\u001B[43mmin_lr\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1e-6\u001B[39;49m\n\u001B[1;32m 19\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 20\u001B[0m \u001B[43m \u001B[49m\u001B[43m]\u001B[49m\n\u001B[1;32m 21\u001B[0m \u001B[43m)\u001B[49m\n",
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"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:122\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 119\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n\u001B[1;32m 120\u001B[0m \u001B[38;5;66;03m# To get the full stack trace, call:\u001B[39;00m\n\u001B[1;32m 121\u001B[0m \u001B[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001B[39;00m\n\u001B[0;32m--> 122\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m e\u001B[38;5;241m.\u001B[39mwith_traceback(filtered_tb) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m 123\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[1;32m 124\u001B[0m \u001B[38;5;28;01mdel\u001B[39;00m filtered_tb\n",
|
|
"File \u001B[0;32m/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:53\u001B[0m, in \u001B[0;36mquick_execute\u001B[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001B[0m\n\u001B[1;32m 51\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 52\u001B[0m ctx\u001B[38;5;241m.\u001B[39mensure_initialized()\n\u001B[0;32m---> 53\u001B[0m tensors \u001B[38;5;241m=\u001B[39m pywrap_tfe\u001B[38;5;241m.\u001B[39mTFE_Py_Execute(ctx\u001B[38;5;241m.\u001B[39m_handle, device_name, op_name,\n\u001B[1;32m 54\u001B[0m inputs, attrs, num_outputs)\n\u001B[1;32m 55\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m core\u001B[38;5;241m.\u001B[39m_NotOkStatusException \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m 56\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m name \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
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"\u001B[0;31mInvalidArgumentError\u001B[0m: Graph execution error:\n\nDetected at node gradient_tape/compile_loss/mse/sub/BroadcastGradientArgs defined at (most recent call last):\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/runpy.py\", line 86, in _run_code\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel_launcher.py\", line 17, in <module>\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/traitlets/config/application.py\", line 1075, in launch_instance\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel/kernelapp.py\", line 701, in start\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/tornado/platform/asyncio.py\", line 205, in start\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/asyncio/base_events.py\", line 1909, in _run_once\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/asyncio/events.py\", line 80, in _run\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 534, in dispatch_queue\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 523, in process_one\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 429, in dispatch_shell\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 767, in execute_request\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 429, in do_execute\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3075, in run_cell\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3130, in _run_cell\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3334, in run_cell_async\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3517, in run_ast_nodes\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3577, in run_code\n\n File \"/var/folders/j4/dltmqwjj1438ftthspk8_knm0000gn/T/ipykernel_2258/3746327179.py\", line 2, in <module>\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py\", line 318, in fit\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py\", line 121, in one_step_on_iterator\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py\", line 108, in one_step_on_data\n\n File \"/usr/local/anaconda3/envs/ml_env/lib/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py\", line 70, in train_step\n\nIncompatible shapes: [32,34,5] vs. [32,5]\n\t [[{{node gradient_tape/compile_loss/mse/sub/BroadcastGradientArgs}}]] [Op:__inference_one_step_on_iterator_38382]"
|
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]
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}
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|
],
|
|
"execution_count": 30
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"source": [
|
|
"next_year_production = model.predict(next_year_weather.mean().to_frame().T)[0]\n",
|
|
"print(f'Previsione produzione di olive per il prossimo anno: {next_year_production:.2f} kg/ettaro')"
|
|
],
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 8. Conclusioni e Prossimi Passi\n",
|
|
"\n",
|
|
"In questo notebook, abbiamo:\n",
|
|
"1. Caricato e analizzato i dati meteorologici\n",
|
|
"2. Simulato la produzione annuale di olive basata sui dati meteo\n",
|
|
"3. Esplorato le relazioni tra variabili meteorologiche e produzione di olive\n",
|
|
"4. Creato e valutato un modello di machine learning per prevedere la produzione\n",
|
|
"5. Utilizzato ARIMA per fare previsioni meteo\n",
|
|
"6. Previsto la produzione di olive per il prossimo anno\n",
|
|
"\n",
|
|
"Prossimi passi:\n",
|
|
"- Raccogliere dati reali sulla produzione di olive per sostituire i dati simulati\n",
|
|
"- Esplorare modelli più avanzati, come le reti neurali o i modelli di ensemble\n",
|
|
"- Incorporare altri fattori che potrebbero influenzare la produzione, come le pratiche agricole o l'età degli alberi\n",
|
|
"- Sviluppare una dashboard interattiva basata su questo modello"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.8.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|