From 736861ba4988d638b20d816063055c47b34700d8 Mon Sep 17 00:00:00 2001 From: Julien Herzen Date: Sun, 3 Apr 2022 22:23:53 +0200 Subject: [PATCH 1/8] added transfer learning example notebook --- examples/14-transfer-learning.ipynb | 2312 +++++++++++++++++++++++++++ 1 file changed, 2312 insertions(+) create mode 100644 examples/14-transfer-learning.ipynb diff --git a/examples/14-transfer-learning.ipynb b/examples/14-transfer-learning.ipynb new file mode 100644 index 0000000000..05306ee6a1 --- /dev/null +++ b/examples/14-transfer-learning.ipynb @@ -0,0 +1,2312 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e6c4236f", + "metadata": {}, + "source": [ + "# Transfer Learning for Time Series Forecasting with Darts\n", + "\n", + "The goal of this notebook is to explore transfer learning for time series forecasting. The notebook is 100% self-contained -- i.e., it also contains the necessary commands to install dependencies and download the datasets being used.\n", + "\n", + "Depending on what constitutes a \" learning task\", what we call transfer learning here can even be seen under the angle of meta-learning (or \"learning to learn\"), where models can adapt themselves to new tasks (e.g. forecasting a new time series) at inference time without further training [1].\n", + "\n", + "The notebook contains the following parts:\n", + "\n", + "* **Part 0:** Initial setup - imports, functions to download data, etc.\n", + "* **Part 1:** Forecasting passenger counts series for 300 airlines (`air` dataset). We will train one model per series.\n", + "* **Part 2:** Using \"global\" models - i.e., models trained on all 300 series simultaneously.\n", + "* **Part 3:** We will try some transfer learning, and see what happens if we train some global models on one (big) dataset (`m4` dataset) and use them on another dataset.\n", + "* **Part 4:** We will reuse our pre-trained model(s) of Part 3 on another new dataset (`m3` dataset) and see how it compares to models specifically trained on this dataset.\n", + "\n", + "The compute durations written for the different models have been obtained by running the notebook on a i9-10900K CPU, with an RTX 2080s GPU, with Python 3.9.7 and Darts 0.18.0.\n", + "\n", + "## Part 0: Setup\n", + "First, we need to have the right libraries and make the right imports. For the deep learning models, it helps to have a GPU, but this is not mandatory.\n", + "\n", + "The following two cells need to be run only once. They install the dependencies and download all the required datasets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "665504d8", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install darts==0.18.0 &> /dev/null\n", + "!pip install xarray==0.18.2 &> /dev/null # required to read pickle files\n", + "!pip install xlrd==2.0.1 &> /dev/null" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97f3449a", + "metadata": {}, + "outputs": [], + "source": [ + "# Execute this cell once to download all three datasets\n", + "# Some datasets are are in pickle for simplicity and speed.\n", + "!curl -L https://github.com/unit8co/amld2022-forecasting-and-metalearning/blob/main/data/m3_dataset.xls\\?raw\\=true -o m3_dataset.xls\n", + "!curl -L https://github.com/unit8co/amld2022-forecasting-and-metalearning/blob/main/data/passengers.pkl\\?raw\\=true -o passengers.pkl\n", + "!curl -L https://github.com/unit8co/amld2022-forecasting-and-metalearning/blob/main/data/m4_monthly_scaled.pkl\\?raw\\=true -o m4_monthly_scaled.pkl" + ] + }, + { + "cell_type": "markdown", + "id": "06d36ec5", + "metadata": {}, + "source": [ + "And now we import everything. Don't be afraid, we will uncover what these imports mean through the notebook :)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4439a58a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2022-04-03 21:35:44,587] WARNING | darts.models | Support for AutoARIMA, BATS and TBATS is not available.To enable it, install u8darts[pmdarima] or u8darts[all].\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import os\n", + "import time\n", + "import random\n", + "import pandas as pd\n", + "import pickle\n", + "import numpy as np\n", + "import requests\n", + "import tqdm.notebook as nbtqdm\n", + "from tqdm.auto import tqdm\n", + "from datetime import datetime\n", + "from itertools import product\n", + "import torch\n", + "from torch import nn\n", + "from typing import List, Tuple, Dict\n", + "from sklearn.preprocessing import MinMaxScaler, MaxAbsScaler\n", + "from sklearn.linear_model import Ridge\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from pytorch_lightning.callbacks import Callback, EarlyStopping\n", + "\n", + "from darts import TimeSeries\n", + "from darts.utils.losses import SmapeLoss\n", + "from darts.dataprocessing.transformers import Scaler\n", + "from darts.metrics import smape, mase, mape\n", + "from darts.utils.data import HorizonBasedDataset\n", + "from darts.utils.utils import SeasonalityMode, TrendMode, ModelMode\n", + "from darts.models import *" + ] + }, + { + "cell_type": "markdown", + "id": "c7ac4e58", + "metadata": {}, + "source": [ + "We define the forecast horizon here - for all of the (monthly) time series used in this notebook, we'll be interested in forecasting 18 months in advance. We pick 18 months as this is what is used in the M3/M4 competitions for monthly series." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7f138ef3", + "metadata": {}, + "outputs": [], + "source": [ + "HORIZON = 18" + ] + }, + { + "cell_type": "markdown", + "id": "1ae27064", + "metadata": {}, + "source": [ + "### Datasets loading methods\n", + "Here, we define some helper methods to load the three datasets we'll be playing with: `air`, `m3` and `m4`. \n", + "\n", + "All the methods below return two list of `TimeSeries`: one list of training series and one list of \"test\" series (of length `HORIZON`).\n", + "\n", + "For convenience, all the series are already scaled here, by multiplying each of them by a constant so that the largest value is 1. Such scaling is necessary for many models to work correctly (esp. deep learning models). It does not affect the sMAPE values, so we can evaluate the accuracy of our algorithms on the scaled series. In a real application, we would have to keep the Darts `Scaler` objects somewhere in order to inverse-scale the forecasts.\n", + "\n", + "If you are interested in seeing an example of how creating and scaling `TimeSeries` is done, you can inspect the function `load_m3()`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "979d3133", + "metadata": {}, + "outputs": [], + "source": [ + "def load_m3() -> Tuple[List[TimeSeries], List[TimeSeries]]:\n", + " print(\"building M3 TimeSeries...\")\n", + "\n", + " # Read DataFrame\n", + " df_m3 = pd.read_excel(\"m3_dataset.xls\", \"M3Month\")\n", + "\n", + " # Build TimeSeries\n", + " m3_series = []\n", + " for row in tqdm(df_m3.iterrows()):\n", + " s = row[1]\n", + " start_year = int(s[\"Starting Year\"])\n", + " start_month = int(s[\"Starting Month\"])\n", + " values_series = s[6:].dropna()\n", + " if start_month == 0:\n", + " continue\n", + "\n", + " start_date = datetime(year=start_year, month=start_month, day=1)\n", + " time_axis = pd.date_range(start_date, periods=len(values_series), freq=\"M\")\n", + " series = TimeSeries.from_times_and_values(\n", + " time_axis, values_series.values\n", + " ).astype(np.float32)\n", + " m3_series.append(series)\n", + "\n", + " print(\"\\nThere are {} monthly series in the M3 dataset\".format(len(m3_series)))\n", + "\n", + " # Split train/test\n", + " print(\"splitting train/test...\")\n", + " m3_train = [s[:-HORIZON] for s in m3_series]\n", + " m3_test = [s[-HORIZON:] for s in m3_series]\n", + "\n", + " # Scale so that the largest value is 1\n", + " print(\"scaling...\")\n", + " scaler_m3 = Scaler(scaler=MaxAbsScaler())\n", + " m3_train_scaled: List[TimeSeries] = scaler_m3.fit_transform(m3_train)\n", + " m3_test_scaled: List[TimeSeries] = scaler_m3.transform(m3_test)\n", + "\n", + " print(\n", + " \"done. There are {} series, with average training length {}\".format(\n", + " len(m3_train_scaled), np.mean([len(s) for s in m3_train_scaled])\n", + " )\n", + " )\n", + " return m3_train_scaled, m3_test_scaled\n", + "\n", + "\n", + "def load_air() -> Tuple[List[TimeSeries], List[TimeSeries]]:\n", + " # load TimeSeries\n", + " print(\"loading air TimeSeries...\")\n", + " with open(\"passengers.pkl\", \"rb\") as f:\n", + " all_air_series = pickle.load(f)\n", + "\n", + " # Split train/test\n", + " print(\"splitting train/test...\")\n", + " air_train = [s[:-HORIZON] for s in all_air_series]\n", + " air_test = [s[-HORIZON:] for s in all_air_series]\n", + "\n", + " # Scale so that the largest value is 1\n", + " print(\"scaling series...\")\n", + " scaler_air = Scaler(scaler=MaxAbsScaler())\n", + " air_train_scaled: List[TimeSeries] = scaler_air.fit_transform(air_train)\n", + " air_test_scaled: List[TimeSeries] = scaler_air.transform(air_test)\n", + "\n", + " print(\n", + " \"done. There are {} series, with average training length {}\".format(\n", + " len(air_train_scaled), np.mean([len(s) for s in air_train_scaled])\n", + " )\n", + " )\n", + " return air_train_scaled, air_test_scaled\n", + "\n", + "\n", + "def load_m4() -> Tuple[List[TimeSeries], List[TimeSeries]]:\n", + " # load TimeSeries - the splitting and scaling has already been done\n", + " print(\"loading M4 TimeSeries...\")\n", + " with open(\"m4_monthly_scaled.pkl\", \"rb\") as f:\n", + " m4_series = pickle.load(f)\n", + "\n", + " # filter and keep only series that contain at least 48 training points\n", + " m4_series = list(filter(lambda t: len(t[0]) >= 48, m4_series))\n", + "\n", + " m4_train_scaled, m4_test_scaled = zip(*m4_series)\n", + "\n", + " print(\n", + " \"done. There are {} series, with average training length {}\".format(\n", + " len(m4_train_scaled), np.mean([len(s) for s in m4_train_scaled])\n", + " )\n", + " )\n", + " return m4_train_scaled, m4_test_scaled" + ] + }, + { + "cell_type": "markdown", + "id": "cdd61bad", + "metadata": {}, + "source": [ + "Finally, we define a handy function to tell us how good a bunch of forecasted series are:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d70ca827", + "metadata": {}, + "outputs": [], + "source": [ + "def eval_forecasts(\n", + " pred_series: List[TimeSeries], test_series: List[TimeSeries]\n", + ") -> List[float]:\n", + "\n", + " print(\"computing sMAPEs...\")\n", + " smapes = smape(test_series, pred_series)\n", + " plt.figure()\n", + " plt.hist(smapes, bins=50)\n", + " plt.ylabel(\"Count\")\n", + " plt.xlabel(\"sMAPE\")\n", + " plt.title(\"Median sMAPE: %.3f\" % np.median(smapes))\n", + " plt.show()\n", + " plt.close()\n", + " return smapes" + ] + }, + { + "cell_type": "markdown", + "id": "11277911", + "metadata": {}, + "source": [ + "## Part 1: Local models on the `air` dataset\n", + "\n", + "### Inspecting Data\n", + "\n", + "The `air` dataset contains the number of air passengers that flew in or out of the USA per carrier (or airline company) from the year 2000 until 2019.\n", + "\n", + "First, we can load the train and test series by calling `load_air()` function that we have defined above." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d7e23729", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading air TimeSeries...\n", + "splitting train/test...\n", + "scaling series...\n", + "done. There are 301 series, with average training length 136.83388704318938\n" + ] + } + ], + "source": [ + "air_train, air_test = load_air()" + ] + }, + { + "cell_type": "markdown", + "id": "e3e55fcb", + "metadata": {}, + "source": [ + "It's a good idea to start by visualising a few of the series to get a sense of what they look like. We can plot a series by calling `series.plot()`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "082ed526", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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4fD7Ky8tDbrtjxw4AcnJyzG3q6uoA/3nbuHEjixcv5qqrrgoQ+rbwO4qGeMXSu3fvkOuciHgloM9eHmDtPfJn4DnDMJbiz5c3KUhWSs0FdAtNq1un0tLSZg9ISxKvWPTkxGeddRa9e/dmzJgxlJSUcODAAVNADx06FPDdpaWlnDp1ivLycnr16sWUKVMChjjVdOrUKaiza+7vGDhwYMB66+czMjKora3l+PHj5jbBjsuxY8dYv349aWlptG/fnurqanJycuJ+LuN1jnRq6KqrrqJ3794MHDiQ1atXc/LkSbM66OTJk03OkU51dOnSJWRcehq9Xr16sXfvXqqrq+nRo4cp7lZ0/j0/Pz9gf9nZ2Zw8eZIFCxbw29/+lnbt2vGLX/wiIJZU/x1FQ2vE4iSdsgq/ywaYCqzUK5RSJ5RStyml9Pp3XY5PiAL71GojRowA/BUq2qWXlpY26WCjB2DKy8sLKuAQmArRzszeuNmjR4+A982lU84880wgfMPmypUrqa2tZcyYMfTv3x9wPrBTIqJLPqdNmwZgplP27NnTbDpF34RDpVKg8Xh3797dvAZC1eEHS6dAY158+/btQNseGiHRCSviSqkvgQOGYawAhgELDcN4HsAwjNGGYSw3DOMjYKVSamdcoxUcocVN/4B14+bGjRtNwa2pqWmSy9Yi7kQgALO8zf4DP+OMMwLeN9ewOXjw4KD7sPPxxx8DMHHiRLp29Wf0klnE9XnQx1CLeGFhoSnUsYp4t27d6NKlCxD6WIUTcd0ALvnxxMVRiWGQ0sE7G5Z/CUxwNyQhFmprazl69Cgej8f8YepSwaKiooA5LUtLS81Hb2gU8ezs7JD7twrwWWedxRdffNFEgHVOVWPfn3UfQ4YMYdGiRWFFfPny5QBMmDCBbdu2Ack9QJP9aUk/XVgrcCorK/H5fAFPRZGIeH5+vine0Yq4zu/qFJ2QeEhnnxTDOniV7kQyYMAAwN94aB33xF5mqAXCqYgPGzYMwBRVjb3h056aycrKMitf9A2mrKysifhrjh49ytq1a0lPT2f8+PFh3WWiU19f30Q89dPL1q1bA7azd7jRYtq+ffuQ+9c52YKCgqiduH2cHHHiiYuIeIqhHZ7+8YL/B9mtWzdOnToVkEKxt6JH6sR1PvfTTz8NqE6xi7gdj8dj7qdHjx506dIFn88XUmg2bNhAXV0do0aNIjc3N+nTKUeOHMHn85GXl2fezLQTt2NPqThx4jfccAPz58/ngQceiDmdorv+i4gnLiLiKYb9MV1jz1NDUyceqYiPHj2a008/ncOHD5tVDhBexK37yc/PN7v3WwfWsqIbPbXD1MKUrOkUe5sF+J21vUEYohPxrKwsZsyYQdeuXWMWcY2kUxIXEfEUQ/9YrU4cGlMqVmJx4h07djTTGxA4lZfuwfmTn/yEDz/8MOh+zjjjDDIzM+nfv78p4lpQ7NhnsXEjnbJ06VKeeuqpVukwFMmNNhoRt6KPlR661o5TERcnnriIiKcYTgRC1wtH48R1o5kW3mAirp34I488wqWXXkow3nzzTTZs2MBpp51mCkgoJx4PEf/JT37CAw88wObNm6PeRzD27dtnzlgUilDnKNiNNlYR120O69atC7peRDz5ERFPMYI9qkOgQOgGyVjSKfpH35yIB+uir8nPzzfLCyN14rHmxL1erzm4lO4B6Rbjxo1jzJgxIYeRhZZ14hMmTADgX//6V0C/gPLycj7//POwDZsaSackLiLiKUawhk0IFIixY8cCsaVTtPAOHz6c7OxsSkpKzAGvnIi4lXA58VBOPNqc+K5du8xKGDe7SOuhC8rKypqNzYkT16WfsYr4gAED6NOnD2VlZWzatMlcPmnSJM4//3xzfBv7sMOROvG6ujqWLFkS8hwK8UNEPMVw4sQHDx5MTk4OR48eDfjROensYxfx9PR00xlXVVXh8/nMnLi1Jr05WjqdoscLgcCp5mLFGn9zMxU5EXHdCSiUiDdXYmjF4/FwySWXAJhDDAN89dVXAdvFmk559913mTp1asiB1YT4ISKeYoRy4n379jXL2Xr27GnOCPPcc8+Z2zhx4iNHjiQzM5Nvfetb5jJr124t4JmZmWadejhaOp1iHafbTRG3Cm5zsYVqfLY+LekZ5+0irtMaTp04NKZUdIepYDgR8eYagXV9ux7yWGg5RMRTjFBOPDMzk759+wJ+EZ8zZw4Ajz76qPnDc9LZZ/jw4ZSXl/Of//mf5jItwpWVlRGnUiByJ67/toqKiqgmWI6XiFuFuzkRD+XE+/TpYzY66zFlYk2nAKYTX7lyZchtwol4fX19s7Mp6ZEXi4uLHccluIOIeIoRyokDXHnllXTr1o3Ro0dz6aWXcvvtt+P1evnLX/4COHPi4E+pWHthajGqqqqKSsQjdeIZGRnk5uYGTC0WCa3txEOJeHp6Ovfddx8zZ840G33dEPGBAweSnp7O/v37zXNsnRkp2DjxwYb4bS6logdW2717N7W1tY5jE2JHRDzFCCUQAP/7v//L/v376d69O+AfTAoaXZRTEbdjTafEIuLBnLjX66Wqqoq0tLSAvymWlEqwnLjP56OwsDBk138nxCriAE888QQvvfSSeRN2Q8TT0tLMSZD1ubY2ZAarpAk2i1NzIq73W1dXR1FRES+88ELS9qhNNkTEU4xQ6RSNdbYeLeba6UYr4sHSKU4bNaHxUT6Yq9adVPLz8wNy7NbvjASfz8c333xjvt+/fz91dXUsXLiQIUOG8N///d8R7c+KGyKuCTWEbDQiDo29XfVNK9jEHFaCiXhzZYZaxAF++ctfMmvWLJ5++umIYhSiQ0Q8RfjHP/7BgAEDzAamYOkUO1rEdbf2WJ14rOmUYE7cnkrRaJGJtH553759nDhxgm7dutG9e3fq6+s5cOAAW7ZsATD/j4ZEFvFevXoBjSKuz3UoInXiOp0C8P777wOxHUvBOSLiKcJvf/vbgEYlJxMJa2F0S8RjbdgM5sRDibgusYtUxHUqZeDAgQHCpsUylvFYWlLEnZYYavTfWlpaitfrDZs2ikTEjx8/HnDu9L6lkbNlEBFPEey9/Zw4NWs6xefzuZJO0RUMierEdYeXwYMHB4i4Ft2WEPFQJYZW9AiHFRUVZkqpvr4+6nNkTadYXfgZZ5zBM88802R7J+mUI0eOMGPGDF544YWg3yki3jKIiKcIoaZTa46srCxyc3Opra3lyJEjjjr7BCPWdEpzJYZuO3FdTjlixIgAYWspEa+traW6upq0tLRmJ3nOzMxk4sSJ+Hw+3nrrLaCxJ2x2dnbE59t6w9JuXk/O/Mtf/rLJ9tbYdCOy3Ym/+uqrvPbaa/z85z8HaNIvoLKyMuI2CyFyRMRTBC3AhmHw2muvOf6cduNlZWUJkU6xdig5dOiQOZiUWyK+ceNGwC/isTrxb775hvPPP58HH3yQI0eOOBLxYJN2hOL6668H4I033gCc9agNhTWd4uQ8W524/qxdxO1xDB8+vMl+SkpKIo5ViAwR8RRBi+djjz3G9773Pcef0+JYXl4eczolWieemZlJ+/btqa+vN4WisrKS/v3781//9V8BcWq0iEcyup7P5wtw4lZhs+bEnQ5Pu2jRIj7//HOefPJJzjvvvIDGvWAivmLFCu644w7AWZvFNddcQ3p6OkuXLqW8vDzqRk0ITKc42Y81564/a79h2m9C5557rrlMVydJSiX+iIinCNGIJwQ6cSc9NoMRqxOHpo2bu3fvDhANN5y4Fuv8/Hx69uxp1k7v37/fFN2amhrH+7QOY1tYWMj69evN98FE/LHHHuPNN98EcNTTtHv37kyYMIHa2lr++c9/uuLErTnx5s5zWlqa+bSgJ3G23zDtk3/069ePYcOGkZWVxbe//W1ARLwlEBFPEaJ10VrEY3Hiboi4vXHTOrfktddey/Tp0wO2j6ZhU7vw4cOH4/F46NmzJxAo4uC8A5EWcX2DsTr46urqJp1orEP/jhgxwtF36DFqiouLYxLxvLw8cnJyqK6uNielDrefF154gT/96U/mjEN2EbeXKZ5++um8/fbbrF69mgsuuADwz+sqxBcR8RTBDSfeWukUaOrEtWBMmjSJhQsXmnFqonHi1lQKYIr4nj17zGF0wVle3OfzmSJ+7bXXBqzTf7u9UU83Ft5zzz08++yzjmLWN4jDhw+b5yfS8kLwN3zrtIgediDceb7++uu54447Qt4w7SLes2dP+vfvz+jRo805Q8WJxx8R8RQhWvF0Iyeek5NDZmYmJ0+eNEXYLSceqoIjmpy4XcS1w9TOVONExPft20dVVRVdu3bl4osvNpenp6eb6Qe7o9fO/Pvf/37IiZHtBBPxaJw4NKZUtIg73Y8W8XBOXI+8CI3D6oqIx58MJxsZhjEHGAcUA7cppbwNy3OA14FOQC1wk1LqQHxCFZoj1nSK1YlHKsAej4fOnTtz6NAhUxDdEvFg9coQnRPXHX30MLxZWVl06dKlidg6EXHtwocOHcrZZ59tLu/SpUvI8c6tw/Q6xdqrNpaGTWgq4k6vlVA3TH293HjjjVx//fXmcQXEibcgYZ24YRijgN5KqfFAIXCdZfV0YKNS6hLgReCH8QhSCE+s6ZSDBw9SX19PRkaGORxqJGgRPnDAfw+PZOwUCJ1OCefEIxFx3TPVOqu8TqlYaU7E9+zZw4ABA7jmmmuApiLeuXPnkCKunXgkx8ZNJ67/Vl3255YTP+ecc8zjocnPzyc7O5uqqqqA9g3BfZykU8YBSxpevw9caFm3A9BWqQtw2L3QhEiINZ2ix9SIViB046YW8ZZKp0Qj4tZKl0hF/J133qG4uNiMb+jQoeTm5poutzkRj9WJxyri+hzpUkinTjxUTtza+ciOx+MxOwlJh5/44kTEuwB6YIQqoKtl3XZgqGEYm4AfAa+6G55g5e233+b+++8PWp4WazpFi3ikn9dogYg1naJ/8Nr1hUqnhHKHoaipqeHIkSOkp6cH1Gg7EXGfz8dzzz3H2rVrUUoFrBs6dCiA6cadiHhrOXEdlxZxp/sJl04Jdc2EmztVcAcnz82V+HPeAHmA9Qq/BfhUKfWoYRjXAQ8DD1g/bBjGLGAWwOzZs5kyZUqsMceE1+t1dXLcWAgXy+bNm+nZs6fpaO6//362bt3KlClTTPHQaFd0+PDhiCZK0I/4Ot+amZkZ1fHRwmQdmzyS/WjRLyoqwuv1BoxPHWw/2glXVlY6+h4tXF27dg0YNjU3N9d8nZaWRn19Pbt37zb36fV6Wbx4MbNnzwYwJ2u48sorOXLkCAMGDKC0tNScNSkrK8tMR5WUlATEZj1HTvH5fLRr147jx4+bN5dQx8TJvqCxRr22ttbRfrQDr6ioCDguOp7jx48H3Y8W/+3btzvq3BQtyfSbjhZdWRQMJyK+CrgHeAmYCljnePLQmEI5jF/kA1BKzQXmNrx11hUujpSWljZ7QFqS5mIpLCxk+vTpXH755bz99ttAo0NMS0sL+FxdXZ05m0r//v0jGldDi4R2iR06dIjq+OiOM1qgTjvttIj2oxvFjhw5EjA/Z69evYLuRx+L2tpaysrKOHjwIJdeemnI/eu4evToEbA/PQ0a+OchLSkp4dSpU+Y2paWlAUJfWFhIeno6CxYsCCj1+9a3vsVLL71Ev379TKE/ePBgwHfpc1RQUBCRqHXv3p3S0lLzRhTpsdXYB0nr3r27o/3oY+f1esnIyKBHjx6Ulpaa5+j0008Pup/TTjsN8BuDeP7mkuU3HS/CplOUUl8CBwzDWAEMAxYahvF8w+pXgSsMw1gOPAb8Lk5xtjmUUtTV1fHpp5/i8/mor683R7OzP6bHMjCSx+MJKHeLNZ2iHX2k6RT7zDOR5MS/+93vMm3atGY76QTLh0NgOkWLnH0/9unGhg0b1qRWe+bMmTz00EPcc889GIYBwNq1awO20U89keTErTHH2m5hHzUx0uqUjRs30rNnT5YtWwaET6eEGk5XcBdHZQhKqftsi+5sWF4FTHM7KAG2bdsG+AXlwIEDtGvXznwMtv8oom3U1Jx99tnm98Uq4pqWEvFjx45RWVlJXV0dZWVlIYd31SJu7zRkFfGBAwfy8ccfN8mJ27uXa5G2kpeXx2OPPQY0Tni8ZcsWqqurzb8hmpy4NWb9mO6WiEdanaJZsWIFgwcPbrZhE0TEWwrp7JOgaFEFf27cmkcN5cRjEXFNtCKuRVgTaSxaTA8cOEBdXZ3jhs3y8nJTHJtr5NTHL5yI631asc/yrrvChyI7O5sRI0ZQX1/PunXrAH8eWk+WEGkJp3bisYq4/UYbaXWKHadOvKqqigULFrB48WJngQoRISKeoGzfvt18HU7Eo61M0Zx11lnm62j3Yc8DRiri7dq1o1u3bqajDufEtZBZUx3NlRs6deLQVMStTrx3797m4E7NoYVep1SsqZRIU15axHWVRzTd7iF6J56bm8v5559vvtdxhOscpkV89+7dfP/732fmzJmRhiw4QEQ8AfH5fM068XikUzStJeLQ6OYPHjwY1olnZmY2yS0358RD5cTz8/PNyaP79etHeno61dXVAe5bv7755pvZs2ePWYnSHKFEPNJUCjS98YwaNSrifYBftK3f7/Rcp6WlsWrVKubO9dcn2EU8XInh5s2bqauro6KiIuy0cELkiIgnIPv37w/o5bZp06a4OvFEE/EDBw6EdeLQ1JE6EXG7IKalpZluPD8/3/w7rJMZRHOTtIt4NB19NNYbT6dOnTjnnHMi3gc0Do+giSQtY/2sLmF1mk7Rk3dDZGPdCM4QEU9AtAvXAynFOyeuS8GAgNH8IqFLly4BP2a3nHi8RRzgySef5N5772XgwIFmeWBhYaG5PpoGSV0yWVxcTH19fdSNmvaYL7nkkqiGRdBYUyqR5tbtnXecirjuxQvRX19CaETEExAt4hMnTiQ3N5fDhw+zZcsWc73b6RRrjjba6bSsQ51CdGJlFfFwA2AFWxdNwyb4RxV8+umn8Xg8QUU8muPbrl07OnbsSH19PUePHo26vBACnfikSZMi/rwVqxOP9KnLPheq0+oUKzKOivuIiCcgWsTPPvts09F98skn5nq30ylWYpkTUT85gHvplOZE3IkT93q9lJSUhMyJ23HLiUPjBMPWCppobm5uirgbTjzSdIoVceLuIyKegHzzzTeAvzehHvvaOkOK2+kUgKlTpwIwefLkqPdhdeKxiHhJSQk+ny+gC3swnIj4gw8+SP/+/V0R8Uj/JquIx+LE9eBaEHwy4kiwingsk3+A8+oUK+LE3Sf65JoQN3TPvN69ewetRKioqMDn85lpEDdE/Pe//z1LlizhxhtvjHofbom4HoO6uXw4NBXxYCWG8+bNC3gfTkS1iG/ZssUca0Qf31icuP7eaJx4fn4+q1at4tSpU00mJ46UaBs2ITCdYs3zhzrXWvStiBN3H3HiCYjutXj66acHFfHa2toAwXIjndKpUyd+9rOfBTRyRoqbThwiF/FgTnzMmDERxdCzZ086depERUWF6d5b24kDXHDBBQwaNCiqz1qJxYl36NCB9PR0Tpw4YR7rrKyskHXv2dnZTY6ZOHH3ERFPMHw+X4CIjxw5MmC9/lFYUypuOHE3cEvENc3lw8GZiFs76jz00ENhY/B4PGY7hE6puOHEY8mJu0ksOXGPx2O6cT2OT7gbgT2lIiLuPiLiCYb+wefl5dG+fXs6d+5MQUEBEDhYVSqKePv27c2/FcI7cSfVKfopZfXq1ebYJuGw58XdaNiM1Ym7RSzVKdCYUtHVPpGKuKRT3EdEPMGwunCNTql06dKF/Px8AG699Va+/e1vU1dX52p1SizEWp0CMHr0aPO1m048kmOjJ/ndtWtXwD4i/Zv0uUoVJw6Nee5oRVycuPuIiCcYulHTWpGgRbxbt27mj3DdunW89957FBUVJYwTt45DEq1YWXsjupETj2byZy2++mnHTSeeKCKenp4e01yqWsTDHVdx4vFHqlMSjOacuFXENUeOHEkYEc/MzOSDDz6gvr4+6ioKqxN3U8QjceJW8YXonXiwnHiipFOiHQlRp1N0o6848dZHRDzBCCbi06dPZ8aMGVx//fUsX748YPuKioqESacAXHbZZTF93urEWyudYhfx1u7s4ybaBER7rcSaThEn7j4i4glGsHRK+/btmT9/PgBffvllwPYVFRUJ48TdwDpCYLjJBLTId+zYkerq6qB14m448UQoMXSLfv36MXnyZIYNGxbV5yOtTtGi36NHj4CeuIJ7SE48wQjmxK3Y3Wmqibi15tg6pnowtBPXwu9WTjxUOiUVnHh6ejpLly7lD3/4Q1Sfj9SJ60qfcePGAeLE44GIeIIRTsQPHjwY8D7R0iluoH/wEyZMaHY7PZiVntTCLuI+ny+qG5xbTlynLhIpJx4rkTZs3nrrrWzatIl7770X8OfEN23aZE76LMSOiHiCESydYuUnP/kJ3bp1o0ePHkDqOXGAuXPn8sILL4St6546dSpz587ld7/zz8997Ngxs6s8BOayI2lozcvLw+PxmHN3RuvEc3JyyMnJoaamxkwNtbYTj5VIGzY9Hg9Dhw41P1dcXMyYMWP4zne+E99A2xAi4gmEvbdmMM444wwOHTrEo48+CqSmiHfu3Jnbb7+d3NzcZrfLzMzkjjvu4IwzzjAnkrb20IwmlQL+lIN1AoRYUiHa1esxtVPFietBsJw+/elzefDgQWpqasxB3toK999/P0888URcJo0WEU8gKisrOXnyJLm5uWHL6/SjeiqmU6JBtxVYUyqxHBctvrHeJPV+dPog2Z24fVArp8fWfj23pdy41+vlf/7nf/j1r38d04QeoRARTyCKioqAwJ6PoXBLZFKFYCIeTXmhRt8kKysrXXHiWsST3YnrtIgmUieuOXXqVMA8pqnMtm3b8Hq9DBgwIKw5iwYR8QRCz8c4duzYsNtanbiIePyceGVlpThxC9E68Xbt2jW5gbUVN75x40YAc24At3Hk7Q3DmAOMA4qB25RS3obl1wA/a9jsDOC/lVLR1S4Jpoife+65Ybe1irh+RJN0CkGH6I1FfN124qkm4pEc29zcXLPiB/ztDXqIg1RGi3isE3qEIqwTNwxjFNBbKTUeKASu0+uUUm8qpSYopSYARcCiuETZRlizZg0QuYiLE3c/nRJMxKM5vtYyQ0j+dEq/fv3MyiiI7Ni21bx4q4s4fge+pOH1+8CF9g0Mw+gJZCmlop+gsY1z9OhRNm/eTGZmZtCJIOxYqwS0+2zLTlx3/IlnOiUaF213rsnuxNPT0/ne975nvo/k2Nrz4nquzlQnEUS8C6CPdhXQNcg21wIL3QqqLbJu3Tp8Ph8jR4509MNIT08nLy8Pn89ndgASJx6f6pRY0il2EU92Jw4wY8YM83Ukx8TuxNuCiB87doyioiIyMjI4++yz4/IdTnLilYBuks4DyoNscx3wg2AfNgxjFjALYPbs2UyZMiXyKB0wf/585s6dy9/+9jf69esXcjuv10tpaWlcYogUayzvvfceAMOGDXMcX6dOnaiqqjJrdsvLy6MW8kQ9Lk7R3fVfeOEFDh8+zJVXXmnuw+fzRb2/Q4cO4fP5SE9Pj6qXYX19fcD7Y8eORX2cE+UcWce32bJli+OY7IJfUlLiyt+TKMcFmsby1Vdf4fP5GDhwoNlBKhqsE67YcSLiq4B7gJeAqcBK60rDMHrQTCpFKTUXmNvw1hdsm1jx+Xz86U9/oqioiCVLlvAf//EfIbctLS1t9oC0JDqWtWvX8uyzzwLwne98x3F83bp1Y/fu3eb7AQMGRN1QlIjHJRJ0F/wlS5awZMkSfvCDH5jOr3PnzhHvb+DAgUBj9/J27dpFdXz0TEya0047LerjnEjn6PHHH2fOnDnMnj3bcUz2azMjI8OVvyeRjos9lg8//BDwD7EcrxjDplOUUl8CBwzDWAEMAxYahvG8ZZNWT6Vs2bLFrLF+//33WzOUiKmtreW6667jxIkT3HbbbVxzzTWOP2sdW7xPnz5mCqAtYh8YTHecgtjSKbGmqlItJ6659dZbqaqqYujQoY4/Y8+Jt4WGzZ07dwK4Msl1KByVGCql7rMtutOy7o+uRhQFb7/9tvl6zZo1lJWVJU3pUllZGbt27aJz58788Y9/DDlzeDCsIn7ppZdG9NlUwy7iVVVVrpQY6kfgaMU3FXPimkivN/1klJmZidfrbRM5cZ2CCzWMhhukRGcfLeIdOnSgvr6epUuXtnJEztHjK3fu3DliobCLeFvGPrqjtaokFieu3WK0Ttw+KUKqOPFouOGGGxg3bhwzZ84E2oYT1yJunbrQbZJexMvKyli9ejVZWVn88pe/BJIrpaKrKaLpjmt1n5MnT3YtpmRED0ersTrxWERcI048di655BJWrlxp9oNoS05cRLwZ1q5di8/n49xzz+Xyyy8Hms5+k8hoJx6NiG/dutV8Hc+LJBm46667+OMf/2iOQR5rTjwzMzPgSSdaJ24fa6QtO3GNPiZtScQlndIMSikADMMwhVBPhZUMxCLid911FwCzZs1yNaZkJDs7mx/96EcMGDAA8DvxWHuyWm+M0YpvRkZGwBNTW3biGt3AmerpFJ/PZ4q4tZer2yT9HJtWEddjiNTW1rZmSBGhRTzcpMDBuPLKK9m4caM5BZbQmIOO1YmD/4e3ZcsWILaOVHl5eWbaTJx423HiesiGTp06NZnU202S3onrQaOSVcRjyYl7PB6GDRtGenq622ElLdbhCNwQcU0s4mvNi4uItx0nrid4iXeqM6lFfO/evezdu5dOnTpx5plnJqWIx5JOEZoSzIlH66LjIeKSTmk7TrwlGjUhyUX8iy++APzjb6elpYmICwFOPJYSQwj88cWaTtGIE29exPfs2WOmsJIdEXEHWPPhQFKLeDQ5caEpbufENeLE3cOaTrFObA1w2WWXMWrUqKQX8uPHj0s6xQmFhYUAjBw5EkhOEY8lJy40RYu42zlxceLukZWVZfbatE5sXVZWxpYtW/B6vTz88MOtGGFsfPHFF3Ts2JF7770XEBFvFj1jth6sSDfwJZOISzrFXbRgxjqtGrhTYmiNCcSJg79B3urG9+zZw9q1a9mwYYO5zcKFC82ihWTj7bffDnjCiGeNOKSIiJ9xxhlAcjpxSae4izjx5MCaF584cSLnnnsuf/rTn4DGY/3UU0+1WnyxYO0kBuLEQ1JZWUl5eTnt27fntNNOA5JTxCWd4i5WJx6riOvrChqvrWiwjp8iTtyP1Ynv2LEDgNdffx2AX/ziF3g8Ht566y1zKOBk4sSJEwHv49nRB5JYxK0uXI+mFquIV1RUMHbsWB555BF3gnSApFPcxerw9I8pWhG3umZ9nqJBnHhTmqtQufzyy5k6dSper5dXX33Vle/bunVrizWWWifr7tSpk5nujRcpIeIanROvq6tr0urthMWLF7Nu3Tp+//vfm1NyxRsRcXfJyMigY8eO+Hw+cxhZN6atq6ioiPqzkhNvSnMiPnLkSG677TYA/vKXv8T8XV988QWDBw9m3LhxAQ2p8UKbhwcffJDCwsIm4+e4TdKLuPUu5/F4AoQ8Uj7++GPAL6yfffaZC1GGR3Li7qPTFwcOHADcmUDaDRHPyMho02O+W9HpFPuUZQUFBeTl5XHVVVeRnZ3Nhg0bYuoUtH//fq6++mrAn2LbunUr69evZ9u2bVHvMxxaxHv16hX3Rk1IARG3OnGILaWyfPly8/UHH3wQdWyLFi3i5ptvdtStWHLi7qNFU89vmSgiLqmURnTj365duwKWn3POOYD/6alPnz4AUc+fWVZWxmWXXcaePXvMZZ988gljxoyJ26TF0JhOied4KVaSTsR37NjBbbfdxqJFiwD3RHzXrl3mjQFiE/FHH32UV1991VE+T9Ip7mOfiCGWdEpBQQHQ2BchGrSISyqlET1ee3FxMeCfXnDOnDnMmTPH3EaLuFWEI+Gmm27i66+/ZvDgweZIn3/961/N9dGkXJ2gnXhOTk5c9m8n6UR83rx5zJs3z3xUdkvEdSplypQpZGdns27duiazxTjh5MmTbNq0CcC80TSHiLj72CdiiMWJv/766/z61782J7KOhp49e9K5c+cm12pbRot4SYl/fvX8/Hzuv//+gMk99MTC0TjxkydPsmzZMtLS0li6dCkTJ04EAucaiKWxujnEiYehqqoq4L19NvFoRVynUqZPn875558PNI7NEgkbN240v3vZsmVN8nl1dXUsW7aM48ePU1dX1+J37baAm068d+/ePP7443Tv3j3qfeTk5FBYWBiQrmvr6HSKFnH7JMoQmxPfsmULdXV1DBo0iN69ezN8+PAm29i1xC3EiYdB5znB3/prd1nRivinn34KwMUXX2zWB0dzktetW2e+9nq9/POf/wxYP3/+fC699FKeeOIJ82R36NCBtLSkOxUJi9WJ9+rVKyGG6u3Ro0fcqxSSCe3Ed+/eDTSdAQlic+Jff/01ACNGjAD80/fZa/0rKysj3q+mrq6OoqKioOvEiYdBC99f/vIXnnjiiSbroxHxAwcOsGPHDjp06MCoUaPMCyoWEddPCG+99Rbl5eXccMMNLF26lNWrVwP+hllp1IwPVgf00ksvtWIkQii0iOtZuNx24hs3bgQaRbxdu3ZB52GNlieeeIIzzzyTxYsXN1knTjwM4XrhRSPiq1atAuC8884jIyPDdHLRlDZpEb/jjjsAKCoq4rnnnuP1119nypQp5sVVUVEhIh4nZs6cyaRJk1iyZEmbn0A6UbFPRB3MicdSnaKduDWNYk+pxCLiegRV63gvGu3ERcRDEO4uF42I61TKRRddBEQ/aL3X6zVPqhaP8vLygFj0xVVeXm6ebKkRd5fRo0ezbNkypkyZ0tqhCCGwi3gwJ67TKdE4cXs6BeDCCy8M2CaWdIoujdQFFla0RiVUOsUwjDmGYawwDONlwzAybetmGIbxkWEYyw3DuCA+YTYSrit1NCK+cuVKoPEkRyvihYWFnDp1igEDBpiVCOXl5QG5uPLycsDvxLWIixMX2hr2QaKCOfEePXqQnp7OoUOHIuppWVFRQWlpKTk5OQEVQT/+8Y/56KOPuP3224HInXhRURHf//732bVrl5nLD1bBlnBO3DCMUUBvpdR4oBC4zrKuF/AdYLJSaoJSKu7dHHU6xS0nfuLECdatW0daWppZlRKtiOtUyahRo8yLtLKykrKysibblpeXSzpFaLPk5OQEVA0Fc+Lp6elmj8e9e/c63rf+Hdrnn83IyGDixInk5+cDkTvxJ598kldeeYWnn37a/E0nixMfByxpeP0+YH0mmQacAj5scOlxVyO30ymbN2/G6/UyePBgU7xjFfHhw4eTkZFBbm4u9fX1ZocGK5ITF9oyHo8nIKUSqnInmry47lI/ZMiQoOutU/hFgn5if//9981ldhH3+XwJ2bDZBdBqVgVYk1k9gG7AFOAzYLar0QXBrYbN8vJyjh49ytatW4HAEx5tdYru5DNs2DCgMe8XrBSpvr7evAAkJy60RawiHsyJQ3R5cT1EQqjafusUfk45fPiwOQqiHjoXmop4TU0N9fX1ZGZmxjR8cSQ4+ZZKQN8m84By27qPlVI+wzCWAQ/ZP2wYxixgFsDs2bNjbmzS45EcOXIk6N1Z15Hv3bs36Hqv10tJSQkjR46kpqaGO++8E/BfLHp7nX87fPhwRA5A9wbr3r07paWlpsMOVU+qL0yPxxP1+BBu4fV6Wz0GjcQSnFSLxfoEWlNTE3R/WnA3bdoU8vvssYT7XenB8fbv3+/4b1iyZEnQ5WVlZaxbt46NGzcyadIksxdodna2q+dK38yC4UTEVwH3AC8BU4GVlnUrgfsaXo8GvsGGUmouMLfhbcyDFei60v79+wf9w3QeqmvXrkHXl5aWkpWVZaZKVqxYAfgnW9bb63UnT55s9uBZOXbsGLt27SIjI4Px48fTrl07evTowaZNm8zHq0svvZRjx45x+PBhtm/fbrZw9+rVy/H3xIvS0tJWj0EjsQQn1WKxTpYwYMCAoPvTo5TW1NSE/D57LFqk+/btG/QzAwYMAPxmzenfoOfztePz+XjggQdYunQpb7zxhrnvDh06tNi5CptOUUp9CRwwDGMFMAxYaBjG8w3rNgC7DcNYDtwGRD/AhEPcSKfoXDQ01nUPHjzYXBZNTnzLli34fD7OPvtsc7Q6exnVvHnzWLVqlXly9YXRt29fx98jCKmCk5y4LhCIZBRJnQa1D7+gsU7h5xRdhtytW7cm6/RwCm+88UbYwot44Chpo5S6z7boTsu6f3c1ojC40bBpFXGNdWjKaETc2qipsZdR6ff2YThFxIW2iJOceCwibh8ITWOdws8Jx48fRymFx+Nh5syZ/P73vwf81TN1dXWm1rz33nvccsstQMtVpkCSdfaxtvy65cTBPxu11Ql06NABj8fDsWPHHE8uYS1r0lgv0szMzIBUj/57oLEFXhDaEk6cuN4mEhHX4hxKxCN14p988gk1NTWMHTuWcePGmcvtPUCrq6tZtmwZ0LJOPKlE3Ov14vP5yMjICNnyG42I2weIT0tLC5jI1Qk6NTJ06FBzmfUi7dKlizmri92hixMX2iKROHHdSc4J4dIpkZYY6kbNqVOnBvy+DcNosq0eflpEPARO6i+jEXFrPlwT6YkONl2cVaytF6z1dXZ2ttn5QBDaEvr3kZWVFXLWo2jSKeGceMeOHUlLS+PYsWNmoURzaBG/7LLLGDRoEH369GHIkCGceeaZ5jZ6vHKtA5JOCUG4Rk1wJuL2weC/9a1vNdnGWitunb06GPX19UGni7M78WCv+/btK/MuCm0S/fsI5cIhPjlxj8fjeJC73bt3s3nzZjp27Mj5559PZmYmmzdvZu3ateaQ1YA5j6dGnHgI3HbiM2bMYPHixWZjhBUt4ldffTU9e/Zsdpafffv2cerUKbp16xaQ2wsl4tblkkoR2ir6d9DcOOvWJ+La2lpzEolQ1NfXm8IcSsTBeYcfPZ3bpEmTzKeF3NxcOnToEFAief755wf8xsWJhyBcoyZEJuL5+flcfvnlQScN0BfWzp07OXr0KJs3bw65v2CpFAjtvu1OXBDaIkOGDGHQoEFcfvnlIbdJT083xfihhx6if//+vPvuuyG3r66upr6+ng4dOjTbY9JJuvTtt9/mN7/5DQA/+tGPmqy3ivjgwYMDZhkTJx4CJzWYkYh4c2OW2N3BoUOHQm6re2Ta51AMlQe3vpbKFKGt0qFDB7Zu3Rp2/lL9e9G5ad39PRjhUikaJ0787rvvxufz8fjjjzN9+vQm6wsKCkhPT2fQoEF06tQpQMRb0om3TOd+l3A7ndLcmCX2i+Dw4cNNttm1axcvvfSSOaKZOHFBiAwn7UFdunRh586d5tNwcxVj4SpTNOGceFVVFbt27SInJ4cHH3ww6Dbdu3dn6dKl9OzZE6DVnHhSibjbDZvNibgTJ37PPfewcOFC873diXfs2JGMjAxqa2slJy4IUaJ/O3pMo+ZmqQ9XmaLRIv/aa68xevRos7u8Rj9dDxw4sNn5bydMmGC+bi0nnlTplJZ04nYRD+bE9QiIGruIW4fbtIp4p06dTAciIi4IzWPvV+HEiYcTcd0p77XXXuOKK65osl6PVGgtIwyH9UYgOfEQuN2wGasTt3cSsqdToPECtLrvtLQ0CgoKyMrKCrh7C4LQlGhEPFw65d5772Xp0qV07tyZzZs3B4w06vP5ohJxceIOaM2GzcOHD1NfX2/eSKz7AX+HhV69ejXZjx7syr7u3Xff5fXXX2+2RlYQhMhE3Gk6xePxMHnyZHNo7A8++ACAO++8k4EDB7J27VoguDELRUFBgfm6OaPpNkkl4m6lU5zkxO3fcejQIa644goGDBhgdv7RIt6pUyfuvvvuoLmzZ599lnnz5jFmzJiA5cOGDWPs2LEhv18QBD/20UDdSKdopk2bBjTO1jN37lx27txpdp+PxIlbjZ/T4TrcIKkaNlsynWIfq+HAgQNs2bKFmpoadu7cybBhw8ybwbJly4KOowD+sVSs4y0IghAZdifupGEzXDpFo0X8o48+CjoZcyQibqW5zoFuk1RO3O10SnMifvXVV5OZmWl2p92/fz81NTUAZkmhk/0IghAb8WjY1PTq1YuRI0dy7NgxPvzww4B1mZmZERce3HjjjQDceuutEX0uFpJKxKNJp3z44Yf8+te/NqdtA2c58T59+lBeXs4//vGPJo0UulJFRFwQ4k88RRwax0767LPPApafccYZQXtzN8dTTz3F0aNHmxQ9xJOkEvFo6sRnz57NE088wSeffGJu41R8O3bsiMfjaTLhqjhxQWg5omnYdJpOgcaigw0bNgQsj6RRU+PxeJo1h/EgqUQ8UideXl7Otm3bgMCabicNm1bCiXhLnzRBaEvYRfz48eMhJ2uJxonrCjIt4oMHD2bKlCnMnj07mnBbnJRu2NRlQoAp5j6fz6wucVrLaZ9Xr6ysjJqaGrxeL+np6SHHQhYEIXas1Sl6SrTq6uomQl1WVsamTZuAwMGpwqGduJ4uceTIkbz22muxht1iJJWIR9qw+fnnn5vLtYifPHkSn89Hdna243xXMCduTaXIeOCCED86derEwIEDadeuHZWVlezbty+oiD/22GMcOXKEKVOmMGjQIMf7t/fhCDYZciKT0umUNWvWmMu1iGsXHkkKxH5SDx8+LPlwQWgh0tLS2LBhA+vWrQs5beKuXbv4v//7PzweD08//XRE+9fpFE2yzbSVlE7cSTrF6/UGOPFvvvkGr9cblfiGc+KCIMQXnfoMJeJr1qzB6/Uybdo0Ro0aFdG+u3fvbqZpQJx43Ni8ebPZAceJE9+xYweHDx+mW7duFBQUUFdXxzfffGM68UjEV59U/b1WEZdGTUFoOUKJuJ7NRw8LGwnp6ekBnxMRjwMvv/wy55xzDitXrgScOXE9cPyIESPMms1t27ZFJeK61Gj8+PGAX8QjrXARBCF2wol4c1O9NYc1L56S6RTDMOYA44Bi4DallLdh+QTgZaAIqFNKTY5HkEOHDg3orOPEieuTmpubS79+/ViyZAnbtm0ze2BF4qAnTJjAe++9x+jRo+nVqxfl5eXmRSQiLggth/7d2rve699jtAPK9e7d26xmSzknbhjGKKC3Umo8UAhcZ9vkNaXUhHgJOMDYsWN55JFHzPdORFyPg5Cdnc1ZZ50FQGFhYVROPC0tjenTp3P66aeTl5dHfX09e/fujXg/giDERks48ZQTcfwOfEnD6/eBC23rv2sYxgrDMH7mamQ2HnzwQSZNmkTPnj2bHYPbPjlqTk6OOVrgxx9/HHODpH7U0jWlIuKC0HKEEvFYnXgyp1OciHgX4EjD6yrAOi6kAs4GJgPTDMOI29iqGRkZfPjhh+zatavZVEgwET/vvPPo2rUrRUVFvPfee0D0d1t9gktKSgBp2BSElkSLdHl5ufkbhNiduC4zzMrKSjpj5iQnXgnoI5MHmGO0KqXMxJRhGO8Ao4AvrB82DGMWMAv845joQdjjhX326rq6Ovbv38/FF1/MokWLTBGfPHkypaWlEe9fn+Dt27cD/h6g0ewH/GWQ0X7WbSSW4EgswWmtWHTb2JNPPsmTTz7JO++8w/Dhw82Zt2pqaqKKS/e67tKli5kqjYZ4HRd7LbsVJyK+CrgHeAmYCqzUKwzD6KSU0i79IuBP9g8rpeYCcxve+pyFHD32EqNu3brRu3dvrr/+enOg92HDhnH11VdH1dNSH8wDBw4A/u69zR3g5igtLY36s24jsQRHYglOa8XSp0+fgPe7du3inHPOMYeJHjBgQFRxXXjhhXg8Hs4666yY/q7WOC5h0ylKqS+BA4ZhrACGAQsNw3i+YfX3DMNYYxjGKqBUKfWv+IXqjGDpFPAP/q5n3rnrrrui7ipvT6ck26OXICQz9vSlNlOx5sQLCgpYtWoV8+fPjy3AVsBRiaFS6j7bojsblv8Z+LPbQcVCKBHv2rUrt99+O6tWrWLmzJlR71/n0vVjneTEBaHlsIv0/v37gdhz4gDnn39+9IG1IknV7d4JdhG3dgx6/vnnKS0tjelE2xtExYkLQssRSsRjdeLJTFL02IyEUE7cLUaOHBnwXkRcEFqOeDrxZEVEPEIMwyArK8t8LyIuCC2HPX25f/9+vF4vJ0+eJC0tzfXfezKQ8iLe3Dgr0ZCVlWXOyQci4oLQktg74hw4cMDsgt+pU6c2ObZ/yot4PO7MF110kflaGjYFoeXIz8/no48+4uuvv6Zjx46cOnWKffv2AW0zHw5toGEz3iIuTlwQWpaJEycC/j4hO3bs4JtvvgHaZj4cxIlHxbhx4+K6f0EQwqM79u3cuRMQJ54yxDsnDv6uuTNmzGDbtm1N5ucTBKFlsIt4W3XiKS/i8XLKydizSxBSCRFxP5JOEQQhKenRoweAmRNvq+mUlBfxeKRTBEFofbQTLysrA8SJpwzixAWhbWAfsVSceIogTlwQ2gann356wHtx4ilCenq6+TojI6OJqAuCkBqMGDGCAQMGmO/FiacIVhGXVIogpC7t2rXjySefNN9nZma2YjStR8qJeFpamjn5g4i4IKQ2119/PXl5eQAMGTKklaNpHVIy15CRkUFNTY2IuCCkOB6Ph9WrV7N3714uuOCC1g6nVUg5Jw6NjZvSqCkIqU9ubi6TJk1qkyMYQoqLuDhxQRBSHRFxQRCEJCalRVzSKYIgpDopLeLixAVBSHVExAVBEJKYlBZxSacIgpDqOKoTNwxjDjAOKAZuU0p5bet/BVynlDJcjzAKxIkLgtBWCOvEDcMYBfRWSo0HCoHrbOtzgRHxCS86RMQFQWgrOEmnjAOWNLx+H7jQtv5nwP9zM6hYkXSKIAhtBSci3gU40vC6CuiqVxiGkQeMUEp9FofYokacuCAIbQUnOfFKQA/UmweUW9b9HHi2uQ8bhjELmAUwe/ZspkyZEnGQkVJfXw9ATU0NpaWlAeu8Xm+TZa2FxBIciSU4Ektw2kIsvXv3DrnOiYivAu4BXgKmAist684ELjAMA2CQYRi/Vkr91vphpdRcYG7DW5/zsKOnffv2gH/mD/sfX1pa2uwBaUkkluBILMGRWILT1mMJm05RSn0JHDAMYwUwDFhoGMbzDetmKqWmKaWmAdvtAt5aSE5cEIS2gqMSQ6XUfbZFdwbZJiHKC0Fy4oIgtB1SurOPiLggCKlOSoq4nmuvS5curRyJIAhCfEnJmX0ee+wxLrzwQiZMmNDaoQiCIMSVlBTxIUOGtNn59gRBaFukZDpFEAShrSAiLgiCkMSIiAuCICQxIuKCIAhJjIi4IAhCEiMiLgiCkMSIiAuCICQxHp+vRQYWFARBEOKAOHFBEIQkRkRcEAQhiRERFwRBSGJExAVBEJIYEXFBEIQkRkRcEAQhiRERFwRBSGJSVsQNw2jf8L+nleMoSIQ4GmI4LxHiADAMo19rx6AxDKNDw/+tfmwS5bptiEGu3SAk0rULKdjZxzCMq4HvA7uBp5VSe1spjvbAU0Bf4DqllLc14miIZRTwB2A18IhSqqYVY5kGzAZOAfOB95VS1a0Uy2XAHcBeYE5rXSsNsVxNAly3DbHItRs8loS5dq2klBM3DOMK4AfAHKASeKBheYvfwZVSx4EaILchptZ0NOOBJ5RSvwLOaKUYMAwjHfgRMBf4DWAAHVrxuNwE/BnYCPzIMIzxrRFEIl23kHDX7kXItdssST89W4NrOBvYDPwLWKeU2msYxjZggWEYpymlDrZQHGcBxUqpSsMwMoEdwD+AnxqG8b5Sale847DFskspVQ54gWmGYfwK2GcYxlrgHaVUUQvGUgzUAlvwO98SYDSQA2TiF414x5IF5DScnx7ALmAN8DFwAzDWMIyilnDBDbF0aDg/W4A7lFIHW/q6tcSij0s7/NdLa127ZiwNi07SeteuNZa+wNe00rXbHEntxA3DGA2sA24FFgE1DQLuAToBO1tIwK1xLDAMI7vhEXQw0Bn/j+FOwzD6tnAsrzRciB6gB3Af8GP8j4PfbsFYfgC8gl8cFuN3wOuBw/jTGT9pgVhuxH+jfxxAKXUAOB04r+ERfT2QDeS1YCyPNcRS1CDgHvzut0WuW1ss+rjUKKV8+K/dPFr22g2IpYH2+K/de2nZa1fH8lsApVQx8BFwMy187YYjqUUc6Af8h1LqZ8AG4OeGYWQ1XIQ5+J0fhmH0i/Njj47j58BXwC8bln8EfAEcw5/v/GlDPPE87tZYNgE/xC+kaUAfpVQVja443o/J1vOzGfiZUupTYAnwnFLq+8C7QDvDMNLiFYthGNlAB/w/Oo9hGFoE3gR+aBhGe6XURqAA6B+PGELEovOsGIaR3nDdtqeFrtsgx2WaZfXH+K+bFrl2g8RyecOqT4F0WvDaDXKOLgdQSn1MC1+7TkiqdEpDa/n9+A/eGvxO4WLgNeBp4HfAcPzCeRH+nNXvgHz8d0xXGiGcxGEYxlBgLHA3cAhYiP/REKVUvRtxOIhlDvDf+G8m7/g3N9oDl+N/XKZBOFoqlt8ZhjECf26zvWEY5+F3VxVuHhNbLIuBDUqpPzcszwFubEgRvGcYxsXA3YZhfIbfiae7GYeDWG42DGOJUqquYfMLiNN16zCWDxtiGQ/cBZQT/2s3WCw3GYbxgVJqvWEYy4BzGqqI4n3tNne91OPXl8x4XruRkjROvCHH/AiwB+gN/J9S6mVggGEY45RSh4GVwM8bPlIAjAO2K6VucasV2WEcq4E7gf8C5imlZiil7lFKPexGDBHEcqghll8opV7F36J+HrBKKfWbFo7Felzm4v8hPgqsUUr9NI6x9ASes6z+CDhIg8sCnrHEtUEp9W4Lx3II/9OSZgBwIS5ftxHEoo/Ls8BLLXTthjtHrwJv0DLXbqjjMssSy07idO1GQ8KXGBqGcS3QDVgK/FkpNalh+UvAB/hLsu5XSl3RsPxFpdStDZUGX1saSFo6jr8BP1ZKHWt4n+bWHTuKWOYBP1dKVTU8tteF2HVLxPIiMFspVW0YRjs3S8aaieUvwBal1DMNj70F+HOdnwMfKaU2GoaRoZSqbeVY/oE//1vk1nUbZSxrgCVKqS0N27XEtdvccflYKfV1C167zcWyVCm12e1rNxYSVsQNw+gOzMP/KLkff051EvCBUmpew+PPAuAS/HfOEuAyYLlS6pEEiOMj4DcuP/JJLNHF0g94HbhSKXXIMIyR+PPhJcBdSqmtCRDLbvwVKtsTIJZEOi4pHYsbJHI6xQc8r5Sagb+1eij+3NxwwzAGKaVK8AvCNPyNLouAZ9wU8BjjeNRNoZJYYoplF/6ngqmGYWTgb6v4pVJqUhx+kNHGMsFNAY8xlkQ6LqkeS8wkcsNmGf6WYJRShw3D6AkcBbbjr/74Ef5yrG+UUifwd9jYmMJxSCyxxdIF+KohZTIvTnFILBJLi5Ow6RRNQ04qD5ivlJresOx5/CWE7YBZSqkjbSUOiSW2WICjcXgSkFgkllYjkZ24lQzgU8MwxuJ/PP8rsE0pVdFG45BYJBaJJfViiYqEd+IAhmFMB94GlgGvKH/pWpuNQ2KRWCSW1IslWpLFiZcD/w78oZXLehIlDolFYpFYUi+WqEgWEV+jlPq8tYMgceIAiSUUEktwJJbgJFIsUZEU6RRBEAQhOIlcJy4IgiCEQURcEAQhiRERFwRBSGJExAVBEJKYZKlOEYSIMPzjpt+Pf7q8Fw3DuBV/9+n7lFLPtGpwguAi4sSFVKU98B/4p6kD+AS4Ef/kGIKQMogTF1IV1fD/JYZh+PAPI1qAf57RrYZhFOMfS/pv+Kcf+xT4f/gnrMgAfqCUet/wTxz8BP4bQAfgQ/zjxB9qwb9FEEIiTlxIVf694f8t+AU4WAqlQ8P/n+Gf9uuP+KfXOw14smHdg/hHtnsH+B9gOvCnuEQsCFEgIi6kKksa/j+olFpA8Hkq64Ff4B9LGuBlpdT/AnvxT5MGcEXD/3fiT890AKbEJWJBiAJJpwipipOuyCeUUjWGYXgb3lc1/F9H4ITJtfjFXE8NJuZHSBjkYhRSlSP4nfaZhmHcjD8fHg3v4jc7twD98A9XeqcrEQqCC4iICymJUsqLP7/dGfg7jS46Uv6rYT/j8Td8Tsdf6SIICYEMgCUIgpDEiBMXBEFIYkTEBUEQkhgRcUEQhCRGRFwQBCGJEREXBEFIYkTEBUEQkhgRcUEQhCRGRFwQBCGJ+f8BURKDdZLBWAMAAAAASUVORK5CYII=\n", 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ZM2ZQVVXl73L8asGCBWRmZtK/f3/ee+89QkNDuffee/n888/p1KkT77//PpMmTeLEiRP+LtVj2joTT0hIACTEhQkYM/Frr70WsM3Eg+HP6++//57Kyko2b97M73//e3+X4zfvvvsuy5cvJzIykk8++YT4+Hj7sssuu4x169bRr18/Nm3axJNPPum/Qj3MCHGZiTdNQjyIGCF+/vnnk5CQQH5+Pvv27fNzVW3nuPfF0qVL+eijj/xYjX9s2rSJxx9/HIBly5YxcuTIM+4zbNgwvvjiCwBeffVVjhw54tMavaGmpoa8vDwsFotLJ75yJCEuTMMI8d69e3PeeecBwdFSMQ5cuvjiiwG47bbbguLDyVkVFRXMmDGD6upq7rzzTubMmdPsfYcNG8a1115LdXU1zzzzjFfrKiws9HowHjlyBKvVSmJiIuHh4W6tQ0JcmIbRE+/VqxcTJ04EzL9xs6amxn5x3CVLlnDNNddQWlrKtddeS2VlpZ+r843169dz+PBhBg4cyEsvvdTq/Z988kksFgvLly+3bxRsq+zsbJYtW8bdd9/N5MmT6dGjBwkJCfTs2ZP9+/d75DmaYrRS+vXr5/Y6JMSFKVRXV1NUVERoaCjdunULmpl4dnY2lZWVpKSk0KVLF5YvX86AAQPYsmUL9913n7/L84mMjAzA1iZzZjZ61llnMXv2bGpqali0aFGbn//w4cMMHz6cO+64g6VLl/L9999z/PhxAOrq6uwfst5gfAh5IsSNmoONhHiQMGbhPXr0ICQkhOHDh9O5c2cOHDhg6t6o0Q/XNA2A2NhYPvroI8LDw/nHP/7BBx984M/yfMIIceM1cMYf/vAHQkNDeeutt9o8U87OzsZqtdK/f3+ef/55vvrqK3Jycpg/fz7w63vPGzw1Ew8LC+PEiRNBuXeThHiQMPrhxsaf0NBQxo8fD5i7pWKE+DnnnGO/bdSoUfa2wty5c73657y/Wa1W1q9fD7gW4oMGDeLGG2+krq6Op59+uk01HD58GLD9JfDAAw9w6aWX0q9fP3r37g2ceXyCJ3kixENCQuy/F96s1V8kxIOEYz/cEAwtFWOjpmOIA9xxxx3MnDmTsrIybr755qA9xcCuXbs4efIkffr0sYemsx5//HHCwsJ455132L17t9s1GCHe+LD3nj17AoEf4oD9tcvLy2tzTYFGQjxINJ6JA6bfuFlZWcn27dsJCQk544ouFouFf/zjH/Ts2ZO1a9fy8ssv+6dILzNaKePGjXP5sQMGDOCWW27BarXy1FNPuV2D0Y5rHOLGe80X7ZT+/fu3aT1GrRLiImAZIW7MjsD253dkZCTZ2dmm3DKflZVFbW0tQ4YMITo6+ozlXbt25fXXXwfg0UcfZefOnb4u0evaEuIAjz32GB06dGDFihVuvz7+molbrVb7aWiTkpLatC6ZiYuA19RMPCIigjFjxgCwdu1av9TVFo03ajZl2rRp3HrrrVRVVTFnzhxqamp8VZ5PtDXEk5KSuP3229F13e3ZeHMh7u2Z+NGjR6mpqSEhIYGoqKg2rUtCXAS8pnriYO6WSnP98MZeeuklkpKSUErx7LPP+qI0nygsLGT37t107NjR5cuSOXrooYcA+PLLL12+bJ+u682GePfu3QHbrnve2CbhqX44SIgLE2iqnQLm3rjZ1J4pTencuTNvvvkmAE899ZRX91v2JWOvlHPOOYcOHTq4vZ6kpCRSUlI4ffo027Ztc+mxxcXFlJeX06lTJ2JjYxssCw8Pp1u3blitVgoKCtyurzkS4s6REA8STbVTAMaMGUNoaChbtmyhtLTUH6W5pbS0lF27dtGhQweGDx/e6v0nT57M3XffTW1tLTfddFNQHM3Z1laKI2N3U1fbas3Nwg3e7ItLiDtHQjwIWK1Wjh07Bpw5E4+JiWH06NHU1dXZZ3ZmkJmZia7rjBgxgoiICKce8+yzzzJo0CB27NjBwoULvVyh93kyxCdMmADAunXrXHpcayHuzb64p/ZMAQlxNE17TtO0NZqmvaNpWodGy36nadp/NE37QdO0sd4pU7SkqKiI2tpa4uPjmww8oy9uhIIZOLNRs7GoqCjef/99wsPDWbZsGStWrPBWeV5XU1PDpk2bABg7tu2/Vo4zcVdOTxwsM/H4+HjCw8MpKSnh9OnTbV5fIGk1xDVNGwEkKqUmAruAGQ7LegNXAhcopSYppcwz1QsizfXDDYMGDQIw1eH3zm7UbMzxaM558+a16SAXf8rKyqKyspLU1FT7uT/aIi0tja5du5KXl2cPR2cEwkzcEyFusVh8coSpPzgzEx8HrKr//htgvMOyi4Eq4N/1s/QYD9cnnNBcP9zQo0cPAHvLxQyc3ajZlDvuuINZs2ZRVlbGtddea8rLlRl/NRkz6LayWCz2towrfXF/zcR1XffIya8cBWtLxZkQjwNO1X9fAsQ7LOsBdAOmAOuBBR6tTjgl2EK8sLCQAwcOEBUVxVlnneXy4y0WC6+99hqDBg1i27Zt3HPPPV6o0rs82Q83uNMX99dMvKioiPLycjp37kyXLl08ss5gnYmHOXGfk0Dn+u9jgRONln2vlNI1TfsOeKzxgzVNmwfMA9v1AadMmeJ2sTU1NeTm5rr9eLNrbvxGyyAmJqbJ5UYPNC8vzxSv3/fffw/A0KFDG3zwuPrzX7p0KVdccQWvv/46w4YN4+qrr/Z4rd5i7Nc/cOBA+5jb+v5PTU0FbK+vs+sxZsPh4eFNPiYszBYhBw8e9Oh7y9gVsnfv3h4bf6dOnQD4+eefTfF74Kil64s6E+IZwH3AP4GpgOPH+Drggfrv04FfGj9YKfUa8Fr9f9t0wcfc3Fy3L5YaDJobf3l5OWDrfTe13JjJFBYW0rt3bywWi1frbCsjOMaNG9dgPK7+/BMTE/nrX//K/PnzeeSRR5gyZQppaWmeLtfjDh8+zNGjR+nSpQvnn38+ISG2P5jb+v7v1q0bERER7Nmzh6ioKOLi4lq8v9Vqtc9aNU1r8qjJYcOGAXDixAmP/m5u3LgRgJSUFPt62zp+40Ps9OnTQZUjrbZTlFJZwDFN09YAZwMrNU17tX7ZNuCwpmk/ALcCf/NeqaI5rbVToqOjiYmJoaqqilOnTjV5n0DSln54Y7fffjvXXXcdp0+fduqqOIHAaKWMHTvWHuCeEBERgaZp6Lru1O6mBQUFVFdXEx8f3+xh746nePXkRbk9uXuhIVh74s7MxFFKPdDopvkOy/7HoxUJl7UW4mDri5eVlZGfn3/GkXeBxpMhbrFYuOWWW1ixYoVp9lTxRj/cMGHCBNatW8fatWu59NJLW7xva/1wsLUoOnbsSHl5OWVlZfaWRVt5cs8UQ7CGuBzsEwSaO2+KI7Ns3MzNzbW3ElJSUjyyTmM9Zrm4sjdD3NjbxZmNm86EuONV6D25wdDTe6aAhLgIYK3tJw7mCXHHg3w81bvv27cvHTp0IDc31779IFCdPn2aLVu2EBISwrnnnuvx9RsfDJs2baK6urrF+zoT4vDr+86Te6jITNx5EuImV1payunTp+nYsSOdO3du9n5mCXHjIB9XjtRsTVhYGMnJyQD88ssZ294DilKKuro6RowYQUyM5w+76Nq1K2eddRaVlZVs3ry5xfsaB4f16dOnxft5YybujRCPjY2lY8eOlJWVmeo8Qq2REDc5x354SzNXs4W4J/rhjoyWyt69ez26Xk/zZivFYOwv3tpBP/6aiZeWllJcXExkZKT9dLee4HjUZjDNxiXETc6ZfjiYI8R1XSczMxOA0aNHe3TdZumLG3uNeOJ8Kc1x9qAfZ0Pc0zNxx1m4p3eHlRAXAceZfjiYI8QPHz5MYWEhXbt2bfPluBozQ4g77vrnzRB33LjZ0m6Brs7EvRHiniYhLgKOM7sXgnc2Pnma0UoZPXq0x2dgZgjxX375hcLCQhISEuw9fG8YMGAAPXv2pKCgoNn2Ul1dnf2oRmd74p56b3ljzxSDhLgIOM6GuBlm4t5qpcCvZ3IM5BDfsGEDYLuQhzePqrVYLK1eJCI/P5+6ujq6d+/e6vncZSbuXxLiJudOO8WTR9Z5kjdDvF+/foSGhnL48OGAveqPEeLebKUYWuuLO9tKAc/PxL0Z4katEuIiYDi7YTMmJoaoqCgqKysDcvcqb27UBOjQoQP9+/dH13UOHDjg8fV7guNM3Ntam4m7EuIJCQlYLBYKCgqoqalpc22+mIkH05kMJcRNztl2CgR2S8Vxo6Y3fnkhsPviFRUVZGVlERIS4tF95JuTnp5OVFQUe/bsaXIG7UqIh4WF2XcFPH78eJtr88Z5UwzSThEBJ1hC3HEW7q1+cCCHeGZmJrW1tQwdOtRj5x9pSYcOHfjtb38LwJdffnnGcldCHDzXF6+srCQ/P5+wsDB74HqSY4gHalvRVRLiJlZdXU1RUREhISFOXcLLLCHuLYF8wI8vWymGK6+8EoB//etfZyxzNcQ91Rc/dOgQYNsjJjQ0tE3rakqnTp2IiYmhoqKCkpISj6/fHyTETcwI4x49ejj1hpcQD9yZuC83ahouv/xyLBYL3377LWVlZQ2WuRvibZ2Jb9++HfBOP9wQbC0VCXETc6WVAr+GeKDtK+7tjZqGQA1xx4N8fDkT79mzJ2PGjKGqqopVq1Y1WOZuO6Ut762srCzmzp0LwHnnnef2elojIS4ChqshbvyiBdpM/MiRIxQUFBAfH+/VGVhycjIhISEcPHiw1TP4+dKRI0fIy8ujS5cu9qvP+IrRUvnss8/st1VXV5Ofn09ISIjTfem2zsSzs7OZMmUKJ0+eZPr06Tz++ONurccZEuIiYDi7j7ghUNspvtioCbYr2yQlJWG1Wu1HBQYCo5Xym9/8xqNX8nGGEeJffvkltbW1wK8b/Xr16mW/hmZr2jIT37t3LxdeeCGFhYVccsklrFixgg4dOri8HmdJiIuA4ew+4gYzhLi3BWJLxR/9cENaWhqpqamcOHHCvs+4q60UcH8mnpOTwwUXXEB+fj6TJ09m5cqVrR4h2lYS4iJguNsTlxAPzBD3ZT/cUeOWijsh7s5MPDc3l8mTJ3P48GHGjx/P559/TseOHZ1+vLskxEXACIYQ13W9wYmvvC3QQry6utr+IeaNK/k4wzHEdV23XwzCnRB39oLJFRUVXHHFFRw4cIBzzjmH//u//yM6OtqN6l0nIS4ChvEmdLYn3qlTJyIjI+0XtQ0ExkbNuLg4rxyh11ighXhWVhZVVVWkpaURFxfnlxrGjBlD9+7dOXDgANu3b7fPxFs7e6GjmJgYYmJiqKqqanX/a13XueOOO9i8eTMDBgzg66+/bvGqVJ4WbOdPkRA3qcrKSnbs2IHFYnF6jwaLxRJws3FfbdQ0BNoBP/5upQCEhoZy+eWXA7bZuDvtFHC+L/7KK6/w9ttv07FjRz755BO6du3qRtXucwxxZ4/arKmp8ch5YbxBQtyk1q9fT1VVFcOHD3fplyDQ9hU3QtwX5wsB27m0wbZBLRB+Kf25UdORY0vF3RB3pi++du1a7r33XgCWL1/OiBEj3Ki2baKjo4mNjaWmpoaioqJW73/69Gn69evHBRdcQF1dnQ8qdI2EuEn98MMPAEyaNMmlxwXavuK+3KgJ0LFjR/r06UNtba39EG9/CoSZOMCFF15IVFQUmZmZ/Pzzz4DnZ+J5eXnMnDmT2tpaFi5cyHXXXde2otvAlbMZZmdnc/ToUdasWcObb77p7dJcJiFuUu6GeCC1U3x1pGZjgdIXP3bsGAcOHCA6Opqzzz7br7V07NiRiy66CLBtdAwLC7O/V5zV0kmwqqurmTlzJvn5+UyaNInnn3++7UW3gSsbN3/55Rf7948++iinTp3yWl3ukBA3oYqKCjZs2IDFYnH58ORACvHc3FyOHz/us42ahkC5yo8xCz/33HO9crInV1111VX27xMTE12uqaWTYC1cuJCMjAz69OnDBx984PRBRN7iSog7nn/++PHjLF682Gt1uUNC3IQ2bNhAdXU1I0aMID4+3qXHBlKI+3qjpiFQZuKB0koxXHbZZfYjRl1tpUDzM/GtW7fyyiuvEB4ezsqVK+3nHvcnd2bic+bMAeDFF18MqAuLSIibkLutFAjcEPclf4W4cVWhf/7zn9x+++28/vrrgP83ahq6detmv2ybOyHe3Ez82WefBeCOO+7w277wjbkT4rNmzeKGG26gurqaBx980Kv1ucK/f9MIt0iIt42vQlzXdXbv3s0PP/zA6tWrWb16tf0K8obExESvnrHPVddffz2rV69262fS1Ex8//79fPjhh4SFhXH//fd7rM62cqedMmDAABYvXszKlSv5+OOPWb16dUD87CTETcaxHz5x4kSXHx8ouxj6a6MmwMCBAwHbDKuurs5j/Wir1Up2djarV6/mxx9/ZPXq1Wd8WMbHxzNhwgQmTpzIxIkTGTVqlFdP9uSq22+/ndGjRzNs2DCXH9vUTPyFF17AarVy0003uTW79xZnQ9zYi8lisdCvXz8iIyN56KGHePLJJ1m4cCE//fSTz09adgZd13351SZHjhxp6ypM7ciRI/p3332nA3p6erpb6yguLtYBPTo62sPVuebQoUM6oMfHx+tWq9Wpx3jy59+rVy8d0A8cOOD2OqqqqvSMjAz9ueee06dNm6bHxcXpQIOvHj166Ndee63+97//Xd+xY4deV1fn9vMF+vu/rq5ODw0N1QG9srJSz8vL08PDw3WLxaLv3Lmzzev35PgPHDigA3qfPn1avN8vv/yiA3piYqL9ttOnT+t9+vTRAf3NN9/0WE2taDZXZSZuMkYrxbg+oqtiY2MJDw/n9OnTnD592mfnq2jsp59+AmwH+fhyo6YhJSWFo0ePsm/fPqf3jCkrK2PDhg2sXr2aNWvWsHHjRioqKhrcp2/fvkycOJHzzz+f888/n9TUVL+Mzx9CQkLo0aMHeXl5HDt2jFdeeYXq6mqmT5/OWWed5e/yGnDcp91qtTY7mzb64cZBYgBRUVEsXryYG2+8kUceeYQZM2YQExPj/aKbISFuMm3ph4Pt0PuePXty6NAhjh071uDN6UtGiJ9zzjl+ef6UlBTWrFnDvn37uPDCC5u8z8mTJ1m7di0//vgjP/74I5s3bz7jiL20tDTOO+88e3vEmxe1MINevXqRl5fHrl27eOWVVwB4+OGH/VzVmSIiIoiPj+fEiRMUFBQ0u0+8EeLJyckNbp89ezYvv/wymZmZfPXVV8yaNcvrNTdHQtxEKioq2Lhxo9v9cEOPHj0kxJvYuFlYWMjatWvtPe0tW7Y0OLdGaGgomqbZA3vChAkkJCT4vPZAZmzcfOKJJygtLWXy5MkBs0dKY3369OHEiRPk5OQ0G+KOGzUdhYSEcMEFF5CZmcnu3bu9XmtLnApxTdOeA8YBOcCtSqmaRssfBmYopXxzAox2avPmzVRXVzNy5Mg2nfHO33uo6A6nn/VXiBsH/Hz33XfMnz+fNWvW2A83N3To0IFzzz2X888/n/POO49x48bRqVMnf5RrGkabYuPGjQA88sgj/iynRUOHDmXbtm1s27aN3/zmN03ep6l2iiFQDhprNcQ1TRsBJCqlJmqa9igwA1jhsLwT4PqmbOGyjIwMwP1WisHfIb5v3z5KSkro1auX09dw9DRjJp6VlUVWVhYAkZGRjBkzxt7THjt2LFFRUX6pz6wcT4usaRoXXHCBH6tp2YgRI3jvvffYunVrs/dprp0Cv4a4v8+I6cxMfBxgXAr7G+AWHEIcuAdYCvzNs6WJxowrops9xP3dSgEYPnw4c+bMoaioyN4eGT16NOHh4X6rKRg4XqDk4YcfDuiNuunp6QD2D/GmNNdOAXOFeBxg7L1fAtiP89Y0LRYYppRa5KtTibZX5eXlbNmyxa3zpTTm733FAyHEQ0NDeeutt/z2/MEqKSkJgMGDBzN9+nQ/V9My4zS427Zta3IPldLSUgoLC4mMjGzywiu9evUiOjqaoqIiiouL/XZRD2dC/CRgXHYjFjjhsOxeWpmBa5o2D5gHsGDBAqZMmeJykYaampozjnhrL9auXUtNTQ3Dhg2z7x7oLuPgkpycHL+8nuvWrQNsf6K68vzt+ecP5hj/sGHDeOCBB5g6darLF01ujTfGn5CQQEFBARs2bDhjz6KdO3cCtg2gzY2lX79+7Ny5k3Xr1jFy5EiP1uYoMTGx2WXOhHgGcB/wT2AqsM5hWQowtn4WPkjTtEeVUs84Plgp9RrwWv1/nbuMRjNyc3NbHEww27FjBwBTpkxp82tg7LN76tQpn7+etbW1ZGdnAzB16lS6devm9GPb888fzDN+b51m1hvjHzlyJKtWreLYsWOMGzeuwTLjL8bU1NRmn3fIkCHs3LmTkpISv/1sWj1eVCmVBRzTNG0NcDawUtO0V+uX3aiUulgpdTGwt3GAC89p6/7hjvzZE//5558pLy8nOTnZpQAXwhuMlkpTGzdb2jPFEAiX+3NqF0Ol1AONbprfxH2kKe4l5eXlbNy4kZCQkDbtH27w59V9AqEfLoShpRA3Nmo2tWeKIRB2M5RT0ZrATz/9RE1NDWeffTZdunRp8/q6dOlCeHg4paWlZxw27m0S4iKQGCHe1B4qzszEA2EPFQlxEzDeIIMHD/bI+iwWi/3E/L6ejfv7IB8hHA0ePJiIiAhycnIoKSlpsExCXHiM8WYydt/yBH/0xauqqti6dSsWi4VRo0b57HmFaE6HDh3s1zfdtm2b/Xar1UpOTg7QcjulR48exMTEUFxcTFFRkVdrbY6EuAkYvTlvhLgv9xXftm0bNTU1pKWlyeHrImA01RfPz8+nsrKSbt26tfhetVgsTs3Gc3JyqK2t9VDFDUmIm0CwzMSlHy4CUVMh3tKRmo21FuK6rjN+/Hi6dOnCoUOH2lruGSTETcB4Q3nyyigS4kLYNBXiLZ0zpbHWQvzQoUPk5eURERFBnz592lruGSTEA1xZWRkFBQVEREQ0e7pMd/gjxGWjpghERohv377d3vJwZqOmobV9xY0T140dO9Yrl3KTEA9wxiy8f//+Hn0D+DrET58+zc6dOwkLC7P/0ggRCOLi4khKSqKystIexO60U5rbV9wI8cZHhHqKhHiAc2VG4ArjgB9fbdjcvHkzVquVYcOGERkZ6ZPnFMJZjVsq7rZTHC8iYpAQb+e8FeLGRlJj/d4m/XARyJoLcWd+7xISEujcuTMlJSUUFhY2WFZWVsbWrVsJDQ312ntfQjzAOXPorzv69+9PREQEeXl5nDp1yqPrboqEuAhkjiFeWVlJXl4eoaGhTu1M0NJuhj/99BN1dXWkp6d77aLkEuIBzlsz8dDQUFJTUwHYtWuXR9fdFGOjppx3XgQixxA/ePAguq6TlJREWJhzlyFuLsS93UoBCfGA562ZOPx6StrG15b0tOLiYvbt20dkZKT96DghAsnAgQOJjo4mLy+PTZs2Aa5NnCTERZN0XQ+KEDcuAjFy5Ej7BSmECCQhISEMHz4cgH/961+Aa79zTYW41Wq1X1JRQrydOnbsGBUVFcTHxxMbG+vx9fsixGtra3n00UcBuOyyy7z2PEK0ldFS+eabbwD3ZuKOuxnu3r2b4uJiEhMTPXqgXmMS4gHMW/1wgy9CfOnSpWzbto3k5GTuu+8+rz2PEG1lhHh5eTng2u+d4wE/xm6Gjgf5ePOC0RLiAczbIZ6amkpISAj79++nqqrK4+vPzc3l8ccfB+Cvf/0rHTt29PhzCOEpjQ9Cc6Wd0rVrV7p06UJpaSnHjx8HfNMPBwnxgObNfjhAZGQkycnJWK1Wr5wP+f7776esrIyrrrqKadOmeXz9QnjSsGHDGsyYXZk8NbWboYS48PpMHLzXUvn222/54IMP6NixIy+//LJH1y2EN8TExDBw4EAAOnXqRNeuXV16vGOIFxUVsWvXLiIiIhg5cqTHa3UkIR7AvD0TB++EeFVVFXfddRcATzzxBP369fPYuoXwJqOlkpyc7HIf2zHEN2zYANgObgsPD/dskY1IiAcws87E//znP7Nnzx7S0tJkY6YwlfT0dMC93znHEPfFroUGCfEAVVVVxZEjRwgJCfHoxSAaM0LcU0dtHjhwgKeffhqAv//9716fhQjhSddddx3Dhw9nzpw5Lj/WcTdDX/XDAZw7plT43KFDh+yH/nrzABkjxHfv3o3Vam3T6W51Xefuu++msrKS2bNnM3nyZE+VKYRPDBw4sMHFIVzR1NkMx44d67HamiMz8QDli1YKQGxsLL169aKiooKDBw+2aV0ff/wxX331FbGxsSxZssRDFQphDnFxccTHx3P69GnKy8tJSUmhe/fuXn9eCfEA5YuNmgZP9MVLSkr47//+bwCee+45evXq5ZHahDATYzYOvmmlgIR4wPLVTBw8E+KPPPII+fn5jBs3jttvv91TpQlhKhLiws5MM/H169ezbNkywsLCePXVV71yHUEhzEBCXNiZZSZeU1PDvHnz0HWdBx98kKFDh3q6PCFMwwjxzp07M2TIEJ88p4R4gPJXiDd1jcCWLFmyhB07dpCSksJjjz3mjfKEMI1x48YRGRnJ9OnTCQ0N9clzyi6GAai4uJiTJ08SFRVFQkKC15+vZ8+exMbGUlxczPHjx+nRo4dTj9u/fz9PPfUUAMuWLZMTXIl2r1+/fuTm5nrtUmxNkZl4ADL64QMGDPDqKSwNFovF5ZaKruvccccdVFZWcuONN3LBBRd4s0QhTCM+Pp6IiAifPZ+EeADy5UZNQ1paGuB8iL/33nv8+9//Jj4+nj//+c/eLE0I0QIJ8QDky364wZXD70+cOMHChQsBW0/cFy0fIUTTJMQDkD9m4q60Ux544AEKCgqYNGkSN998s5crE0K0xKkNm5qmPQeMA3KAW5VSNfW3Xw48BtQAmUqpe7xUZ7viz5l4ayH+448/8sYbbxAeHs6yZct80rMXQjSv1Zm4pmkjgESl1ERgFzDDYfFWYLxSagLQXdM0zTtlti/+CPHk5GQiIiI4cuQIpaWlTd6nqqqK+fPnA/Doo48yePBgn9UnhGiaM+2UccCq+u+/AcYbC5RSh5RStfX/rQasni2v/amrq7OfiKp///4+e97Q0FBSU1OB5vviixcvZvfu3aSlpfHQQw/5rDYhRPOcaafEAUfrvy8B4hvfQdO0c4DuSqnNTSybB8wDWLBgAVOmTHG72JqaGnJzc91+vBnk5eVRXV1NQkICJ0+e5OTJk/Zl3h5///792b59OxkZGfTu3bvBsn379rF48WIAFi1aRGFhodfqaE57+Pm3RMbffsefmJjY7DJnQvwk0Ln++1jghONCTdP6AC8D05t6sFLqNeC1+v+6djhgI7m5uS0OJhjs378fsJ3XuPFYvT3+UaNG8cUXX5Cfn9/geXRdZ/bs2VRXV3PbbbdxzTXXeK2GlrSHn39LZPzte/zNcaadkgFcWP/9VGCdsUDTtE7A+8B8pdRxz5fX/vijH25oauPm8ePHueWWW1i9ejUJCQk8//zzPq9LCNG8VkNcKZUFHNM0bQ1wNrBS07RX6xffCyQDSzVN+0HTtPO9VWh74Xi0pq85hnh1dTUvvvgigwYN4u233yYsLIxly5YRH39GN00I4UdO7WKolHqg0U3z629/Gnja00W1Z8ZM3Jf7iBtSU1MJCQlh//79DB8+nN27dwNw8cUX89JLL9mP6hRCBA452CfAbNmyBcAvgRkZGUlycjJ1dXXs3r2b1NRUvvrqK77++msJcCEClIR4ACkqKiI7O5uIiAhGjx7tlxpmzpxJ165dWbJkCdu3b+fSSy/1Sx1CCOfIqWgDSEZGBgDnnnuuT8+C5mjx4sX86U9/kiMxhTAJmYkHkDVr1gAwceJEv9YhAS6EeUiIB5C1a9cC/g9xIYR5SIgHiIqKCpRSWCwWxo4d6+9yhBAmISEeIDZu3EhNTQ0jRowgNjbW3+UIIUxCQjxASCtFCOEOCfEAYWzUnDBhgp8rEUKYiYR4AKitrbXvXighLoRwhYR4ANi2bRtlZWUMGDDgjFPACiFESyTEA0Cg7B8uhDAfCfEAIBs1hRDukhD3M13XZaOmEMJtEuJ+tm/fPo4dO0b37t3t17gUQghnSYj7mdFKmTBhgpyzRAjhMglxP5NWihCiLdrdqWhra2upqqqiqqqK2tpaEhIS/DoDlj1ThBBtEVQhvmfPHl566SW2bdtGRUUF5eXl9q+Kigqqqqqoq6tr8JjU1FTuvfde5syZQ1RUlE/rzc/PZ9++fURHR5Oenu7T5xZCBIegCPHNmzezePFiVq5cia7rLd43JCSEiIgIIiIiqKurY8+ePdx555089thj/Nd//Rd33XUXvXv3pry8nE2bNrFu3TrWrl3L3r17SU1NZfTo0YwePZpRo0bRt2/fNs3ijX742LFjCQsLih+FEMLHTJMc1dXVHDt2jOLiYoqKijhx4gSFhYV8/PHHrFq1CoDw8HDmzJnD7Nmz6dy5M1FRUfavjh07EhER0SAsa2tr+eSTT3jxxRfZuHEjf/rTn3jhhRcYMmQI2dnZ1NbWNqhh//79fP311/b/d+vWjZkzZ7J48WK3zjwo+4cLIdpM13VffrnlnXfe0YFmv6Kjo/X7779fP3LkiLtPoWdkZOgzZszQQ0JCdEAPCQnR09PT9QULFugrVqzQt27dqn/44Yf6ww8/rE+ZMkWPj4+3P39iYqL+1VdfufR85eXl+tlnn60D+nfffefUY9oyvmAg45fxt2PN5qpFb6X94OnPDHce9OWXX3LVVVfRpUsXEhIS6Nq1q/0rLS2NuXPnEh8f75ECDx06RE5ODiNHjqRTp07N3k/XdbZt28b8+fPZuHEjADfffDMvvvgicXFxLT7u008/5b777uPgwYPExsaSm5tLdHR0q7Xl5uaSmJjo+qCChIxfxt+Ox99837alhPfCl1tqa2t1q9UakJ/EtbW1+gsvvKBHRkbqgN6rVy/9448/1k+ePHnGfbOzs/ULL7zQPoMfPny4vn79eqefKxDH70syfhl/O9ZsrpqiJx4aGurvEpoVGhrK73//ey6//HJuvfVWMjIymDFjBgA9evRg0KBBpKamEhISwltvvUVtbS1xcXEsWrSIefPmyQZNIUSbSIJ4yODBg1m9ejVLly5l+fLl7N27l2PHjnHs2DH7BkyLxcL8+fNZtGgR3bp183PFQohgICHuQaGhodxzzz3cc889WK1Wjhw5wt69e9mzZw9Hjx5l+vTpjBw50t9lCiGCiIS4l4SEhJCUlERSUhIXXHCBv8sRQgQpOXeKEEKYmIS4EEKYmIS4EEKYmIS4EEKYmIS4EEKYmIS4EEKYmIS4EEKYmK9PgCWEEMKDZCYuhBAmJiEuhBAmJiEuhBAmJiEuhBAmJiEuhBAmJiEuhBAmJiEuhBAmFpAhrmla4F6Pzcs0TYuq/7f5C6MGMU3Tuvu7Bn/SNK1f/b/t8ucPoGnawPp/2+1r4IqAOdhH07RZwDSl1I3+rsUfNE27CrgBOAy8oJTK829FvqVp2hXA7cBpYCmQoZSy+rcq36n/8H4e6AvMUErV+Lkkn6t/D8wHViulnvN3PWYREDNxTdOGAdcBozRNu7X+tnYzG9c0bRpwC/AccBJ4qP72djET0TRtDLYPsMXAp8BFSilrexk/gFKqHKgGOmF7L7Sbnz+ApmmTgSexTWCe0zSto59LMg2/zcTrZx6pwD6lVFn9bYnASmCSUqpS0zSLUiow/lTwsPrxDwZ2AhFAjFIqT9O0WOB9YI5S6rg/a/Qmh/HvUUqd1jQtTClVq2laD+A14F7gSLDOSB3e/zlKqZOapnXA9pfINuC/gd8rpQ75s0Zvc3gP/Ax0wfbhNbr++xLgr8A6pVStn0o0Bb/MxDVNSwc2AzcDH2maFgmglMoFMrB9IhPEAZ7Or+P/F1BdH+AWoDNwIMgDPB3b+OcAKzVNi6wP8GTgFUAHHgAu8V+V3tPo5/9+/fhrgDRsAfYJMF/TtL7+qtHbGr0GnwDFQBawWSl1IfAutp//AP9UaB7+aqckAX9QSt2LbeZxr7FBD3gamKBpWldN02KNgA8yxvjv4dfxR9R/aHUEagE0TUsK0j+pHX/+W4GFmqaFAPnAnUqpq4BsIBaCsq3QePz319/+HyAT23aBG7DNyKl/bYKN4+9ANrAAWK+U+hOAUuoToA+2v1ZEC3xytfv6Le4PAl8Cm7D9cp4HfAC8ALwInAVkKqWKNU37DtiHrbXye6DSF3V6i5PjH4rtF3gCEK1p2otAV+AuoMwPZXuMk+MfpZRSQIWmad2AcfX3Nf1fZM6MX9O0IdhaCXcDBdje+5UAwbCBt5XX4Dls74GB2H4H0DStM7ZJpql/933B65/w9b2+J4AjQCLwilLqHSBZ07RxSqlCYB2/zjqSgFHAM0qpuUqpk96u0ZtcGP+99Q/phy3A9iql5hjbC8zKjZ//NGzbBDYqpf7ip7I9xsnxb8C2V8Zi4E2l1O+UUvcppR73W+Ee5MJ74J76+98E/B+wRSn1rZ/KNg2vbdjUNO1qoBvwLfD/KaUm19/+T+D/YduV7kGl1LT629/E9idVNRCmlKrwSmE+4sb431JK3axp2kRgexB8eLk8fmAu0B84ppQq9UPZHuPG+N/G1ko6Xf//ELPPwN18D9wCDAMOKqVK/FG32Xg8xDVNSwDexNYCyMe298Vk4P8ppd6s/7PqfeB84O/AQeAi4HvgySD409nd8f+glHrCP1V7Tlt+/kqpP/inas9pw/j/A/zR7O9/kN8BX/NGO0UHXlVK/Q5YBAzB1t8bqmnaIKXUQWxv2Iux/Qn9L2CJUuoPwfAGxv3xB8ub1+2fv5/q9TR3x2/6CYyD9v474FPe2LBZBKwCUEoVaprWEygF9mLbCv9f2A5o+KW+ZbKj/itYyPhl/O15/CCvgU95syduwbYFeoVS6pL6217FtgtdODBPKXXKK08eAGT8Mn7a8fhBXgNf8fYuhmHAWk3TRmP70+kNbEfoFXv5eQOFjF/G357HD/IaeJ1XD7vXNO0S4HPgO+Dd+t2K2g0Zv4yfdjx+kNfAF7w9Ez8B/A/wF6VUtZefKxDJ+GX87Xn8IK+B13k7xDcppTZ6+TkCmYxfxt+exw/yGnhdwJxPXAghhOuC8cQ6QgjRbkiICyGEiUmICyGEiUmICyGEifnkfOJC+Fr9RUYexHb5s7c0TbsZ20mZHlBKLfFrcUJ4kMzERbCKAv6A7fJfAD9iuxj3F/4qSAhvkJm4CFaq/t/zNU3TsZ3utB+2a3fu1jQtB9u5rt/Gdim0tcBSbBdpDgNuUUp9o2laOPAnbB8A0cC/sZ33u8CHYxGiWTITF8Hqf+r//RlbADfVQomu/3c9cCnwD2yXS+sOPFu/7BFsZ977AngZ28V7l3mlYiHcICEugtWq+n+PK6Xep+nrlFqBhdjOdQ3wjlLqr0AekFx/27T6f+dja89EA1O8UrEQbpB2ighWzhyKXKGUqtY0rab+/8blwOqAUIf71WIL87r6/8vkRwQMeTOKYHUK20w7RdO067H1w93xJbbJzhwgCdvpVOd7pEIhPEBCXAQlpVQNtv52F+B/+XUW7arF9euZiG3D5yXY9nQRIiDICbCEEMLEZCYuhBAmJiEuhBAmJiEuhBAmJiEuhBAmJiEuhBAmJiEuhBAmJiEuhBAmJiEuhBAm9v8DfJuatSNMJjQAAAAASUVORK5CYII=\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in [1, 20, 50, 100, 250]: # Feel free to plot a few other series\n", + " plt.figure()\n", + " air_train[i].plot()\n", + " plt.show()\n", + " plt.close()" + ] + }, + { + "cell_type": "markdown", + "id": "a648f063", + "metadata": {}, + "source": [ + "We can see that most series look quite different, and they even have different time axes! For example some series start in Jan 2001 and others in April 2010.\n", + "\n", + "Let's see what is the shortest train series available:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f7dd5d1a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "36" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min([len(s) for s in air_train])" + ] + }, + { + "cell_type": "markdown", + "id": "6f659cf6", + "metadata": {}, + "source": [ + "### A useful function to evaluate models\n", + "\n", + "Below, we write a small function that will make our life easier for quickly trying and comparing different local models. We loop through each serie, fit a model and then evaluate on our test dataset. \n", + "\n", + "> ⚠️ `tqdm` is optional and is only there to help display the training progress (as you will see it can take some time when training 300+ time series)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6b756c5e", + "metadata": {}, + "outputs": [], + "source": [ + "def eval_local_model(\n", + " train_series: List[TimeSeries], test_series: List[TimeSeries], model_cls, **kwargs\n", + ") -> Tuple[List[float], float]:\n", + " preds = []\n", + " start_time = time.time()\n", + " for series in tqdm(train_series):\n", + " model = model_cls(**kwargs)\n", + " model.fit(series)\n", + " pred = model.predict(n=HORIZON)\n", + " preds.append(pred)\n", + " elapsed_time = time.time() - start_time\n", + "\n", + " smapes = eval_forecasts(preds, test_series)\n", + " return smapes, elapsed_time" + ] + }, + { + "cell_type": "markdown", + "id": "2666471b", + "metadata": {}, + "source": [ + "### Building and evaluating models\n", + "\n", + "We can now try a first forecasting model on this dataset. As a first step, it is usually a good practice to see how a (very) naive model blindly repeating the last value of the training series performs. This can be done in Darts using a [NaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal) model:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "15c4a6a4", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e96b3501a66e403a95e3e504b181a801", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/301 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "naive1_smapes, naive1_time = eval_local_model(air_train, air_test, NaiveSeasonal, K=1)" + ] + }, + { + "cell_type": "markdown", + "id": "3a896d38", + "metadata": {}, + "source": [ + "So the most naive model gives us a median sMAPE of about 29.4.\n", + "\n", + "Can we do better with a \"less naive\" model exploiting the fact that most monthly series have a seasonality of 12?" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0917c7fa", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cb40300e1ef446899ab53b9f219ec882", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/301 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "naive12_smapes, naive12_time = eval_local_model(\n", + " air_train, air_test, NaiveSeasonal, K=12\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "52a48739", + "metadata": {}, + "source": [ + "This is better. Let's try ExponentialSmoothing (by default, for monthly series, it will use a seasonality of 12):" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b823be69", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f7ea09994fcb450e81dd8c46f42063af", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/301 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ets_smapes, ets_time = eval_local_model(air_train, air_test, ExponentialSmoothing)" + ] + }, + { + "cell_type": "markdown", + "id": "09fe5d63", + "metadata": {}, + "source": [ + "The median is better for with the naive seasonal. Another model that we can quickly is the `Theta` method which has won the M3 competition:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1337b1fe", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9d72a4c088734a8ebd97be3488541733", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/301 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "theta_smapes, theta_time = eval_local_model(air_train, air_test, Theta, theta=1.5)" + ] + }, + { + "cell_type": "markdown", + "id": "2d133a88", + "metadata": {}, + "source": [ + "And how about ARIMA?" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d9c1d1d3", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f8c871c2c5b1445da89905b1773963e4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/301 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "warnings.filterwarnings(\"ignore\") # ARIMA generates lots of warnings\n", + "arima_smapes, arima_time = eval_local_model(air_train, air_test, ARIMA, p=12, d=1, q=1)" + ] + }, + { + "cell_type": "markdown", + "id": "cccb821a", + "metadata": {}, + "source": [ + "Or the Kalman Filter? (in Darts, fitting Kalman filters uses the N4SID system identification algorithm)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b4418ffe", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f0bc9c41c7034ce8b1b0f1f33bf72f47", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/301 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "kf_smapes, kf_time = eval_local_model(air_train, air_test, KalmanForecaster, dim_x=12)" + ] + }, + { + "cell_type": "markdown", + "id": "bebae848", + "metadata": {}, + "source": [ + "### Comparing models\n", + "\n", + "Below, we define a function that will be useful to visualise how models compare to each other in terms of median sMAPE, and time required to obtain the forecasts." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8b8245e7", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_models(method_to_elapsed_times, method_to_smapes):\n", + " shapes = [\"o\", \"s\", \"*\"]\n", + " colors = [\"tab:blue\", \"tab:orange\", \"tab:green\", \"tab:red\", \"tab:purple\"]\n", + " styles = list(product(shapes, colors))\n", + "\n", + " plt.figure(figsize=(6, 6), dpi=100)\n", + " for i, method in enumerate(method_to_elapsed_times.keys()):\n", + " t = method_to_elapsed_times[method]\n", + " s = styles[i]\n", + " plt.semilogx(\n", + " [t],\n", + " [np.median(method_to_smapes[method])],\n", + " s[0],\n", + " color=s[1],\n", + " label=method,\n", + " markersize=13,\n", + " )\n", + " plt.xlabel(\"elapsed time [s]\")\n", + " plt.ylabel(\"median sMAPE over all series\")\n", + " plt.legend(bbox_to_anchor=(1.4, 1.0), frameon=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "432e7245", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "smapes = {\n", + " \"naive-last\": naive1_smapes,\n", + " \"naive-seasonal\": naive12_smapes,\n", + " \"Exponential Smoothing\": ets_smapes,\n", + " \"Theta\": theta_smapes,\n", + " \"ARIMA\": arima_smapes,\n", + " \"Kalman Filter\": kf_smapes,\n", + "}\n", + "\n", + "elapsed_times = {\n", + " \"naive-last\": naive1_time,\n", + " \"naive-seasonal\": naive12_time,\n", + " \"Exponential Smoothing\": ets_time,\n", + " \"Theta\": theta_time,\n", + " \"ARIMA\": arima_time,\n", + " \"Kalman Filter\": kf_time,\n", + "}\n", + "\n", + "plot_models(elapsed_times, smapes)" + ] + }, + { + "cell_type": "markdown", + "id": "44e366c3", + "metadata": {}, + "source": [ + "### Conclusions so far\n", + "ARIMA gives the best results, but it is also (by far) the most time-consuming model. The Theta method provides an interesting tradeoff, with good forecasting accuracy and about 50x faster than ARIMA. Can we maybe find a better compromise by considering *global* models - i.e., models that are trained only once, jointly on all time series?\n", + "\n", + "## Part 2: Global models on the `air` dataset\n", + "In this section we will use \"global models\" - that is, models that are fit on multiple series at once. Darts has essentially two kinds of global models:\n", + "* `RegressionModels` which are wrappers around sklearn-like regression models (Part 2.1).\n", + "* PyTorch-based models, which offer various deep learning models (Part 2.2).\n", + "\n", + "Both models can be trained on multiple series by \"tabularizing\" the data - i.e., taking many (input, output) sub-slices from all the training series, and training machine learning models in a supervised fashion to predict the output based on the input.\n", + "\n", + "We start by defining a function `eval_global_model()` which works similarly to `eval_local_model()`, but on global models." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d9a8d071", + "metadata": {}, + "outputs": [], + "source": [ + "def eval_global_model(\n", + " train_series: List[TimeSeries], test_series: List[TimeSeries], model_cls, **kwargs\n", + ") -> Tuple[List[float], float]:\n", + "\n", + " start_time = time.time()\n", + "\n", + " model = model_cls(**kwargs)\n", + " model.fit(train_series)\n", + " preds = model.predict(n=HORIZON, series=train_series)\n", + "\n", + " elapsed_time = time.time() - start_time\n", + "\n", + " smapes = eval_forecasts(preds, test_series)\n", + " return smapes, elapsed_time" + ] + }, + { + "cell_type": "markdown", + "id": "de77d4ae", + "metadata": {}, + "source": [ + "### Part 2.1: Using Darts `RegressionModel`s.\n", + "`RegressionModel` in Darts are forecasting models that can wrap around any \"scikit-learn compatible\" regression model to obtain forecasts. Compared to deep learning, they represent good \"go-to\" global models because they typically don't have many hyper-parameters and can be faster to train. In addition, Darts also offers some \"pre-packaged\" regression models such as `LinearRegressionModel` and `LightGBMModel`.\n", + "\n", + "We'll now use our function `eval_global_models()`. In the following cells, we will try using some regression models, for example:\n", + "* `LinearRegressionModel`\n", + "* `LightGBMModel`\n", + "* `RegressionModel(some_sklearn_model)`\n", + "\n", + "You can refer to [the API doc](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html) for how to use them.\n", + "\n", + "Important parameters are `lags` and `output_chunk_length`. They determine respectively the length of the lookback and \"lookforward\" windows used by the model, and they correspond to the lengths of the input/output subslices used for training. For instance `lags=24` and `output_chunk_length=12` mean that the model will consume the past 24 lags in order to predict the next 12. In our case, because the shortest training series has length 36, we must have `lags + output_chunk_length <= 36`. (Note that `lags` can also be a list of integers representing the individual lags to be consumed by the model instead of the window length)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d7fe3110", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computing sMAPEs...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lr_smapes, lr_time = eval_global_model(\n", + " air_train, air_test, LinearRegressionModel, lags=30, output_chunk_length=1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "abb1b101", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computing sMAPEs...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lgbm_smapes, lgbm_time = eval_global_model(\n", + " air_train, air_test, LightGBMModel, lags=35, output_chunk_length=1, objective=\"mape\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "ea006972", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computing sMAPEs...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "rf_smapes, rf_time = eval_global_model(\n", + " air_train, air_test, RandomForest, lags=30, output_chunk_length=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c8717b16", + "metadata": {}, + "source": [ + "### Part 2.2: Using deep learning\n", + "Below, we will train an N-BEATS model on our `air` dataset. Again, you can refer to [the API doc](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html) for documentation on the hyper-parameters.\n", + "The following hyper-parameters should be a good starting point, and training should take in the order of a minute or two if you're using a (somewhat slow) Colab GPU.\n", + "\n", + "During training, you can have a look at the [N-BEATS paper](https://arxiv.org/abs/1905.10437)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "66b06545", + "metadata": {}, + "outputs": [], + "source": [ + "### Possible N-BEATS hyper-parameters\n", + "\n", + "# Slicing hyper-params:\n", + "IN_LEN = 30\n", + "OUT_LEN = 4\n", + "\n", + "# Architecture hyper-params:\n", + "NUM_STACKS = 20\n", + "NUM_BLOCKS = 1\n", + "NUM_LAYERS = 2\n", + "LAYER_WIDTH = 136\n", + "COEFFS_DIM = 11\n", + "\n", + "# Training settings:\n", + "LR = 1e-3\n", + "BATCH_SIZE = 1024\n", + "MAX_SAMPLES_PER_TS = 10\n", + "NUM_EPOCHS = 10" + ] + }, + { + "cell_type": "markdown", + "id": "0a8180f8", + "metadata": {}, + "source": [ + "Let's now build, train and predict using an N-BEATS model:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "797f94fc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2022-04-03 21:40:29,804] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 53277 samples.\n", + "[2022-04-03 21:40:29,804] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 53277 samples.\n", + "[2022-04-03 21:40:29,819] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n", + "[2022-04-03 21:40:29,819] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n", + "GPU available: True, used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "\n", + " | Name | Type | Params\n", + "-----------------------------------------\n", + "0 | criterion | SmapeLoss | 0 \n", + "1 | stacks | ModuleList | 525 K \n", + "-----------------------------------------\n", + "523 K Trainable params\n", + "1.9 K Non-trainable params\n", + "525 K Total params\n", + "2.102 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2586b5e610774c77b40866677608f206", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a994c951d4de40a785ef3ddcdffed81b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: 53it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computing sMAPEs...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# reproducibility\n", + "np.random.seed(42)\n", + "torch.manual_seed(42)\n", + "\n", + "start_time = time.time()\n", + "\n", + "\n", + "nbeats_model_air = NBEATSModel(\n", + " input_chunk_length=IN_LEN,\n", + " output_chunk_length=OUT_LEN,\n", + " num_stacks=NUM_STACKS,\n", + " num_blocks=NUM_BLOCKS,\n", + " num_layers=NUM_LAYERS,\n", + " layer_widths=LAYER_WIDTH,\n", + " expansion_coefficient_dim=COEFFS_DIM,\n", + " loss_fn=SmapeLoss(),\n", + " batch_size=BATCH_SIZE,\n", + " # learning rate goes here\n", + " optimizer_kwargs={\"lr\": LR},\n", + " # remove this one if your notebook does run in a GPU environment:\n", + " pl_trainer_kwargs={\n", + " \"enable_progress_bar\": True,\n", + " \"accelerator\": \"gpu\",\n", + " \"gpus\": -1,\n", + " \"auto_select_gpus\": True,\n", + " },\n", + ")\n", + "\n", + "nbeats_model_air.fit(\n", + " air_train, num_loader_workers=4, epochs=NUM_EPOCHS # fill in series to train on\n", + ") # fill in number of epochs\n", + "\n", + "# get predictions\n", + "nb_preds = nbeats_model_air.predict(series=air_train, n=HORIZON)\n", + "nbeats_elapsed_time = time.time() - start_time\n", + "\n", + "nbeats_smapes = eval_forecasts(nb_preds, air_test)" + ] + }, + { + "cell_type": "markdown", + "id": "23c832ef", + "metadata": {}, + "source": [ + "Let's now look again at our errors -vs- time plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "99f5ce78", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "smapes_2 = {\n", + " **smapes,\n", + " **{\n", + " \"Linear Regression\": lr_smapes,\n", + " \"LGBM\": lgbm_smapes,\n", + " \"Random Forest\": rf_smapes,\n", + " \"NBeats\": nbeats_smapes,\n", + " },\n", + "}\n", + "\n", + "elapsed_times_2 = {\n", + " **elapsed_times,\n", + " **{\n", + " \"Linear Regression\": lr_time,\n", + " \"LGBM\": lgbm_time,\n", + " \"Random Forest\": rf_time,\n", + " \"NBeats\": nbeats_elapsed_time,\n", + " },\n", + "}\n", + "\n", + "plot_models(elapsed_times_2, smapes_2)" + ] + }, + { + "cell_type": "markdown", + "id": "c1ff22f9", + "metadata": {}, + "source": [ + "### Conclusions so far\n", + "So it looks like a linear regression model trained jointly on all series is now providing the best tradeoff between accuracy and speed (about 85x faster than ARIMA for similar accuracy). Linear regression is often the way to go!\n", + "\n", + "Our deep learning model N-BEATS is not doing great. Note that we haven't tried to tune it to this problem explicitely, doing so might have produced more accurate results. Instead of spending time tuning it though, in the next part we will see if it can do better by being trained on an entirely different dataset.\n", + "\n", + "## Part 3: Training an N-BEATS model on `m4` dataset and use it to forecast `air` dataset\n", + "Deep learning models often do better when trained on *large* datasets. Let's try to load all 48,000 monthly time series in the M4 dataset and train our model once more on this larger dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "5d08b317", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading M4 TimeSeries...\n", + "done. There are 47992 series, with average training length 216.32901316886148\n" + ] + } + ], + "source": [ + "m4_train, m4_test = load_m4()" + ] + }, + { + "cell_type": "markdown", + "id": "24cf841b", + "metadata": {}, + "source": [ + "We can start from the same hyper-parameters as before. \n", + "\n", + "With 48,000 M4 training series being on average ~200 time steps long, we would end up with ~10M training samples. With such a number of training samples, each epoch would take too long. So here, we'll limit the number of training samples used per series. This is done when calling `fit()` with the parameter `max_samples_per_ts`. We add a new hyper-parameter `MAX_SAMPLES_PER_TS` to capture this.\n", + "\n", + "Since the M4 training series are all slightly longer, we can also use a slightly longer `input_chunk_length`." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "d694e998", + "metadata": {}, + "outputs": [], + "source": [ + "# Slicing hyper-params:\n", + "IN_LEN = 36\n", + "OUT_LEN = 4\n", + "\n", + "# Architecture hyper-params:\n", + "NUM_STACKS = 20\n", + "NUM_BLOCKS = 1\n", + "NUM_LAYERS = 2\n", + "LAYER_WIDTH = 136\n", + "COEFFS_DIM = 11\n", + "\n", + "# Training settings:\n", + "LR = 1e-3\n", + "BATCH_SIZE = 1024\n", + "MAX_SAMPLES_PER_TS = (\n", + " 10 # <-- new parameter, limiting the number of training samples per series\n", + ")\n", + "NUM_EPOCHS = 5" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "7610c2c3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2022-04-03 21:41:03,920] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 479920 samples.\n", + "[2022-04-03 21:41:03,920] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 479920 samples.\n", + "[2022-04-03 21:41:03,934] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n", + "[2022-04-03 21:41:03,934] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n", + "GPU available: True, used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "\n", + " | Name | Type | Params\n", + "-----------------------------------------\n", + "0 | criterion | SmapeLoss | 0 \n", + "1 | stacks | ModuleList | 543 K \n", + "-----------------------------------------\n", + "541 K Trainable params\n", + "1.9 K Non-trainable params\n", + "543 K Total params\n", + "2.173 Total estimated model params size (MB)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f582a559a4d447a6862825b59c84cb23", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# reproducibility\n", + "np.random.seed(42)\n", + "torch.manual_seed(42)\n", + "\n", + "nbeats_model_m4 = NBEATSModel(\n", + " input_chunk_length=IN_LEN,\n", + " output_chunk_length=OUT_LEN,\n", + " batch_size=BATCH_SIZE,\n", + " num_stacks=NUM_STACKS,\n", + " num_blocks=NUM_BLOCKS,\n", + " num_layers=NUM_LAYERS,\n", + " layer_widths=LAYER_WIDTH,\n", + " expansion_coefficient_dim=COEFFS_DIM,\n", + " loss_fn=SmapeLoss(),\n", + " optimizer_kwargs={\"lr\": LR},\n", + " pl_trainer_kwargs={\n", + " \"enable_progress_bar\": True,\n", + " \"accelerator\": \"gpu\",\n", + " \"gpus\": -1,\n", + " \"auto_select_gpus\": True,\n", + " },\n", + ")\n", + "\n", + "# Train\n", + "nbeats_model_m4.fit(\n", + " m4_train,\n", + " num_loader_workers=4,\n", + " epochs=NUM_EPOCHS,\n", + " max_samples_per_ts=MAX_SAMPLES_PER_TS,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "2307875e", + "metadata": {}, + "source": [ + "We can now use our M4-trained model to get forecasts for the air passengers series. As we use the model in a \"meta learning\" (or transfer learning) way here, we will be timing only the inference part." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "d34081fe", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "265c80431a604e0c9b428ec4fef04b0a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: 469it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computing sMAPEs...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "start_time = time.time()\n", + "preds = nbeats_model_m4.predict(series=air_train, n=HORIZON) # get forecasts\n", + "nbeats_m4_elapsed_time = time.time() - start_time\n", + "\n", + "nbeats_m4_smapes = eval_forecasts(preds, air_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "34c3390e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "smapes_3 = {**smapes_2, **{\"N-BEATS (M4-trained)\": nbeats_m4_smapes}}\n", + "\n", + "elapsed_times_3 = {\n", + " **elapsed_times_2,\n", + " **{\"N-BEATS (M4-trained)\": nbeats_m4_elapsed_time},\n", + "}\n", + "\n", + "plot_models(elapsed_times_3, smapes_3)" + ] + }, + { + "cell_type": "markdown", + "id": "a250b0b1", + "metadata": {}, + "source": [ + "### Conclusions so far\n", + "Although it's not the absolute best in terms of accuracy, our N-BEATS model trained on `m4` reaches competitive accuracies. This is quite remarkable because this model has *not* been trained on *any* of the `air` series we've asked it to forecast! The forecasting step with N-BEATS is ~350x faster than the fit-predict step we needed with ARIMA, and about 4x faster than the fit-predict step of linear regression.\n", + "\n", + "Just for the fun, we can also inspect manually how this model does on another series -- for example, the monthly milk production series available in `darts.datasets`:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "a5cb0d90", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "50e7aa2e10304ccc92d1c817bd984752", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: 469it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from darts.datasets import MonthlyMilkDataset\n", + "\n", + "series = MonthlyMilkDataset().load().astype(np.float32)\n", + "train, val = series[:-24], series[-24:]\n", + "\n", + "scaler = Scaler(scaler=MaxAbsScaler())\n", + "train = scaler.fit_transform(train)\n", + "val = scaler.transform(val)\n", + "series = scaler.transform(series)\n", + "pred = nbeats_model_m4.predict(series=train, n=24)\n", + "\n", + "series.plot(label=\"actual\")\n", + "pred.plot(label=\"0-shot forecast\")" + ] + }, + { + "cell_type": "markdown", + "id": "865a17b4", + "metadata": {}, + "source": [ + "### Try training other global models on `m4` and applying on airline passengers\n", + "Let's now try to train other global models on the M4 dataset in order to see if we can get similar results. Below, we will train some `RegressionModel`s on the full `m4` dataset. This can be quite slow. To have faster training we could use e.g., `random.choices(m4_train, k=5000)` instead of `m4_train` to limit the size of the training set. We could also specify some small enough value for `max_samples_per_ts` in order to limit the number of training samples." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "481e14ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computing sMAPEs...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "random.seed(42)\n", + "\n", + "lr_model_m4 = LinearRegressionModel(lags=30, output_chunk_length=1)\n", + "lr_model_m4.fit(m4_train)\n", + "\n", + "tic = time.time()\n", + "preds = lr_model_m4.predict(n=HORIZON, series=air_train)\n", + "lr_time_transfer = time.time() - tic\n", + "\n", + "lr_smapes_transfer = eval_forecasts(preds, air_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "51114bab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computing sMAPEs...\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "random.seed(42)\n", + "\n", + "lgbm_model_m4 = LightGBMModel(lags=30, output_chunk_length=1, objective=\"mape\")\n", + "lgbm_model_m4.fit(m4_train)\n", + "\n", + "tic = time.time()\n", + "preds = lgbm_model_m4.predict(n=HORIZON, series=air_train)\n", + "lgbm_time_transfer = time.time() - tic\n", + "\n", + "lgbm_smapes_transfer = eval_forecasts(preds, air_test)" + ] + }, + { + "cell_type": "markdown", + "id": "693c60f6", + "metadata": {}, + "source": [ + "Finally, let's plot these new results as well:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "ae13d004", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "smapes_4 = {\n", + " **smapes_3,\n", + " **{\n", + " \"Linear Reg (M4-trained)\": lr_smapes_transfer,\n", + " \"LGBM (M4-trained)\": lgbm_smapes_transfer,\n", + " },\n", + "}\n", + "\n", + "elapsed_times_4 = {\n", + " **elapsed_times_3,\n", + " **{\n", + " \"Linear Reg (M4-trained)\": lr_time_transfer,\n", + " \"LGBM (M4-trained)\": lgbm_time_transfer,\n", + " },\n", + "}\n", + "\n", + "plot_models(elapsed_times_4, smapes_4)" + ] + }, + { + "cell_type": "markdown", + "id": "015b73f8", + "metadata": {}, + "source": [ + "Linear regression offers competitive performance too. It is somewhat slower probably only because the inference with N-BEATS is efficiently batched accross batches of time series and performed on GPU. " + ] + }, + { + "cell_type": "markdown", + "id": "6c7e092a", + "metadata": {}, + "source": [ + "## Part 4 and recap: Use the same model on M3 dataset\n", + "OK, now, were we lucky with the airline passengers dataset? Let's see by repeating the entire process on a new dataset :) You will see that it actually requires very few lines of code. As a new dataset, we will use `m3`, which contains about 1,400 monthly series from the M3 competition." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "6db212b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "building M3 TimeSeries...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b7528dd9cb56480887a37f09f3ab9bf8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "There are 1399 monthly series in the M3 dataset\n", + "splitting train/test...\n", + "scaling...\n", + "done. There are 1399 series, with average training length 100.30092923516797\n" + ] + } + ], + "source": [ + "m3_train, m3_test = load_m3()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "5eeabb1e", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "64492d7c4c4247a184924ad3e10775cc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1399 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "naive1_smapes_m3, naive1_time_m3 = eval_local_model(\n", + " m3_train, m3_test, NaiveSeasonal, K=1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "d6f44ec0", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "426b8699f02f4d438f72d2b3271a8370", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1399 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "naive12_smapes_m3, naive12_time_m3 = eval_local_model(\n", + " m3_train, m3_test, NaiveSeasonal, K=12\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "ba87b0d9", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "70f32963bf4146299dcfbbd432a21720", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1399 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ets_smapes_m3, ets_time_m3 = eval_local_model(m3_train, m3_test, ExponentialSmoothing)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "6e51a8ce", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0936d7d7f41e4f4cb7f21ec08691c002", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1399 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "theta_smapes_m3, theta_time_m3 = eval_local_model(m3_train, m3_test, Theta)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "8a7880b0", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cd6eab8ec03446c380385c741e704cfc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1399 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "warnings.filterwarnings(\"ignore\") # ARIMA generates lots of warnings\n", + "\n", + "# Note: using q=1 here generates errors for some series, so we use q=0\n", + "arima_smapes_m3, arima_time_m3 = eval_local_model(\n", + " m3_train, m3_test, ARIMA, p=12, d=1, q=0\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "81128793", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "320fc37b36c342c2b0450aeacd55e1c9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1399 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "kf_smapes_m3, kf_time_m3 = eval_local_model(\n", + " m3_train, m3_test, KalmanForecaster, dim_x=12\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "31b2dd03", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computing sMAPEs...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lr_smapes_m3, lr_time_m3 = eval_global_model(\n", + " m3_train, m3_test, LinearRegressionModel, lags=30, output_chunk_length=1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "d13624b2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computing sMAPEs...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lgbm_smapes_m3, lgbm_time_m3 = eval_global_model(\n", + " m3_train, m3_test, LightGBMModel, lags=35, output_chunk_length=1, objective=\"mape\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "c93ecb0e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "99dd0dde30e94395914c92164258f27f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: 469it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computing sMAPEs...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Get forecasts with our pre-trained N-BEATS\n", + "\n", + "start_time = time.time()\n", + "preds = nbeats_model_m4.predict(series=m3_train, n=HORIZON) # get forecasts\n", + "nbeats_m4_elapsed_time_m3 = time.time() - start_time\n", + "\n", + "nbeats_m4_smapes_m3 = eval_forecasts(preds, m3_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "710dde54", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computing sMAPEs...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Get forecasts with our pre-trained linear regression model\n", + "\n", + "start_time = time.time()\n", + "preds = lr_model_m4.predict(series=m3_train, n=HORIZON) # get forecasts\n", + "lr_m4_elapsed_time_m3 = time.time() - start_time\n", + "\n", + "lr_m4_smapes_m3 = eval_forecasts(preds, m3_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "e3af71c3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computing sMAPEs...\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Get forecasts with our pre-trained LightGBM model\n", + "\n", + "start_time = time.time()\n", + "preds = lgbm_model_m4.predict(series=m3_train, n=HORIZON) # get forecasts\n", + "lgbm_m4_elapsed_time_m3 = time.time() - start_time\n", + "\n", + "lgbm_m4_smapes_m3 = eval_forecasts(preds, m3_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "c19cb0cc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "smapes_m3 = {\n", + " \"naive-last\": naive1_smapes_m3,\n", + " \"naive-seasonal\": naive12_smapes_m3,\n", + " \"Exponential Smoothing\": ets_smapes_m3,\n", + " \"ARIMA\": arima_smapes_m3,\n", + " \"Theta\": theta_smapes_m3,\n", + " \"Kalman filter\": kf_smapes_m3,\n", + " \"Linear Regression\": lr_smapes_m3,\n", + " \"LGBM\": lgbm_smapes_m3,\n", + " \"N-BEATS (M4-trained)\": nbeats_m4_smapes_m3,\n", + " \"Linear Reg (M4-trained)\": lr_m4_smapes_m3,\n", + " \"LGBM (M4-trained)\": lgbm_m4_smapes_m3,\n", + "}\n", + "\n", + "times_m3 = {\n", + " \"naive-last\": naive1_time_m3,\n", + " \"naive-seasonal\": naive12_time_m3,\n", + " \"Exponential Smoothing\": ets_time_m3,\n", + " \"ARIMA\": arima_time_m3,\n", + " \"Theta\": theta_time_m3,\n", + " \"Kalman filter\": kf_time_m3,\n", + " \"Linear Regression\": lr_time_m3,\n", + " \"LGBM\": lgbm_time_m3,\n", + " \"N-BEATS (M4-trained)\": nbeats_m4_elapsed_time_m3,\n", + " \"Linear Reg (M4-trained)\": lr_m4_elapsed_time_m3,\n", + " \"LGBM (M4-trained)\": lgbm_m4_elapsed_time_m3,\n", + "}\n", + "\n", + "plot_models(times_m3, smapes_m3)" + ] + }, + { + "cell_type": "markdown", + "id": "458f442f", + "metadata": {}, + "source": [ + "Here too, the pre-trained N-BEATS model obtains reasonable accuracy, although not as good as the most accurate models. Note that two models out of the 3 most accurate (Exponential Smoothing and Kalman Filter) did not perform so well when used on the air passengers series. ARIMA performs best but is about 170x slower than N-BEATS, which didn't require any training and takes about 15 ms per time series to produce its forecasts. Recall that this N-BEATS model has *never* been trained on *any* of the series we're asking it to forecast.\n", + "\n", + "## Conclusions\n", + "Transfer learning and meta learning is definitely an interesting phenomenon that is at the moment under-explored in time series forecasting. When does it succeed? When does it fail? Can fine tuning help? When should it be used? Many of these questions still have to be explored but we hope to have shown that doing so is quite easy with Darts models.\n", + "\n", + "Now, which method is best for your case? As always, it depends. If you're dealing mostly with isolated series that have a sufficient history, classical methods such as ARIMA will get you a long way. Even on larger datasets, if compute power is not too much an issue, they can represent interesting out-of-the-box options. On the other hand if you're dealing with larger number of series, or series of higher dimensionalities, ML methods and global models will often be the way to go. They can capture patterns accross wide ranges of different time series, and are in general faster to run. Don't under-estimate linear regression based models in this category! If you have reasons to believe you need to capture more complex patterns, or if inference speed is *really* important for you, give deep learning methods a shot. N-BEATS has proved its worth for meta-learning [1], but this can potentially work with other models too.\n", + "\n", + "[1] Oreshkin et al., \"Meta-learning framework with applications to zero-shot time-series forecasting\", 2020, https://arxiv.org/abs/2002.02887" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6581927e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From dbb8a17a2ac10fe7f5735e30cd693daf5c41a855 Mon Sep 17 00:00:00 2001 From: Julien Herzen Date: Tue, 5 Apr 2022 11:58:16 +0200 Subject: [PATCH 2/8] small update to transfer learning notebook --- examples/14-transfer-learning.ipynb | 169 ++++++++++++++-------------- 1 file changed, 86 insertions(+), 83 deletions(-) diff --git a/examples/14-transfer-learning.ipynb b/examples/14-transfer-learning.ipynb index 05306ee6a1..a22751427b 100644 --- a/examples/14-transfer-learning.ipynb +++ b/examples/14-transfer-learning.ipynb @@ -2,16 +2,19 @@ "cells": [ { "cell_type": "markdown", - "id": "e6c4236f", + "id": "8e592c8f", "metadata": {}, "source": [ "# Transfer Learning for Time Series Forecasting with Darts\n", + "Authors: Julien Herzen, Florian Ravasi, Guillaume Raille, Gaël Grosch.\n", "\n", - "The goal of this notebook is to explore transfer learning for time series forecasting. The notebook is 100% self-contained -- i.e., it also contains the necessary commands to install dependencies and download the datasets being used.\n", + "## Overview\n", + "The goal of this notebook is to explore transfer learning for time series forecasting -- that is, training forecasting models on one time series dataset and using it on another. The notebook is 100% self-contained -- i.e., it also contains the necessary commands to install dependencies and download the datasets being used.\n", "\n", - "Depending on what constitutes a \" learning task\", what we call transfer learning here can even be seen under the angle of meta-learning (or \"learning to learn\"), where models can adapt themselves to new tasks (e.g. forecasting a new time series) at inference time without further training [1].\n", + "Depending on what constitutes a \"learning task\", what we call transfer learning here can also be seen under the angle of meta-learning (or \"learning to learn\"), where models can adapt themselves to new tasks (e.g. forecasting a new time series) at inference time without further training [1].\n", "\n", - "The notebook contains the following parts:\n", + "This notebook is an adaptation of a workshop on \"Forecasting and Meta-Learning\" that was given at the Applied Machine Learning Days conference in Lausanne, Switzerland, in March 2022.\n", + "It contains the following parts:\n", "\n", "* **Part 0:** Initial setup - imports, functions to download data, etc.\n", "* **Part 1:** Forecasting passenger counts series for 300 airlines (`air` dataset). We will train one model per series.\n", @@ -30,7 +33,7 @@ { "cell_type": "code", "execution_count": null, - "id": "665504d8", + "id": "e5e22f16", "metadata": {}, "outputs": [], "source": [ @@ -42,7 +45,7 @@ { "cell_type": "code", "execution_count": null, - "id": "97f3449a", + "id": "02db59b7", "metadata": {}, "outputs": [], "source": [ @@ -55,7 +58,7 @@ }, { "cell_type": "markdown", - "id": "06d36ec5", + "id": "2a9eae61", "metadata": {}, "source": [ "And now we import everything. Don't be afraid, we will uncover what these imports mean through the notebook :)" @@ -64,7 +67,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "4439a58a", + "id": "ee31f38c", "metadata": {}, "outputs": [ { @@ -113,7 +116,7 @@ }, { "cell_type": "markdown", - "id": "c7ac4e58", + "id": "6bf92eef", "metadata": {}, "source": [ "We define the forecast horizon here - for all of the (monthly) time series used in this notebook, we'll be interested in forecasting 18 months in advance. We pick 18 months as this is what is used in the M3/M4 competitions for monthly series." @@ -122,7 +125,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "7f138ef3", + "id": "13f08464", "metadata": {}, "outputs": [], "source": [ @@ -131,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "1ae27064", + "id": "55122050", "metadata": {}, "source": [ "### Datasets loading methods\n", @@ -147,7 +150,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "979d3133", + "id": "736237f9", "metadata": {}, "outputs": [], "source": [ @@ -241,7 +244,7 @@ }, { "cell_type": "markdown", - "id": "cdd61bad", + "id": "32d714ef", "metadata": {}, "source": [ "Finally, we define a handy function to tell us how good a bunch of forecasted series are:" @@ -250,7 +253,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "d70ca827", + "id": "039f14f3", "metadata": {}, "outputs": [], "source": [ @@ -272,7 +275,7 @@ }, { "cell_type": "markdown", - "id": "11277911", + "id": "d095cf90", "metadata": {}, "source": [ "## Part 1: Local models on the `air` dataset\n", @@ -287,7 +290,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "d7e23729", + "id": "0bea8553", "metadata": {}, "outputs": [ { @@ -307,7 +310,7 @@ }, { "cell_type": "markdown", - "id": "e3e55fcb", + "id": "c3a06633", "metadata": {}, "source": [ "It's a good idea to start by visualising a few of the series to get a sense of what they look like. We can plot a series by calling `series.plot()`." @@ -316,7 +319,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "082ed526", + "id": "58d1e7fb", "metadata": {}, "outputs": [ { @@ -390,7 +393,7 @@ }, { "cell_type": "markdown", - "id": "a648f063", + "id": "ab5f2476", "metadata": {}, "source": [ "We can see that most series look quite different, and they even have different time axes! For example some series start in Jan 2001 and others in April 2010.\n", @@ -401,7 +404,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "f7dd5d1a", + "id": "f1edcf3f", "metadata": {}, "outputs": [ { @@ -421,7 +424,7 @@ }, { "cell_type": "markdown", - "id": "6f659cf6", + "id": "b83ad8bc", "metadata": {}, "source": [ "### A useful function to evaluate models\n", @@ -434,7 +437,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "6b756c5e", + "id": "ca8de337", "metadata": {}, "outputs": [], "source": [ @@ -456,7 +459,7 @@ }, { "cell_type": "markdown", - "id": "2666471b", + "id": "1007319e", "metadata": {}, "source": [ "### Building and evaluating models\n", @@ -467,7 +470,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "15c4a6a4", + "id": "a4f9a902", "metadata": {}, "outputs": [ { @@ -510,7 +513,7 @@ }, { "cell_type": "markdown", - "id": "3a896d38", + "id": "3110d01b", "metadata": {}, "source": [ "So the most naive model gives us a median sMAPE of about 29.4.\n", @@ -521,7 +524,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "0917c7fa", + "id": "7ff123ad", "metadata": {}, "outputs": [ { @@ -566,7 +569,7 @@ }, { "cell_type": "markdown", - "id": "52a48739", + "id": "c552584e", "metadata": {}, "source": [ "This is better. Let's try ExponentialSmoothing (by default, for monthly series, it will use a seasonality of 12):" @@ -575,7 +578,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "b823be69", + "id": "1636ac00", "metadata": {}, "outputs": [ { @@ -618,7 +621,7 @@ }, { "cell_type": "markdown", - "id": "09fe5d63", + "id": "f16b20bf", "metadata": {}, "source": [ "The median is better for with the naive seasonal. Another model that we can quickly is the `Theta` method which has won the M3 competition:" @@ -627,7 +630,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "1337b1fe", + "id": "b7a66cd5", "metadata": {}, "outputs": [ { @@ -670,7 +673,7 @@ }, { "cell_type": "markdown", - "id": "2d133a88", + "id": "e0383cf5", "metadata": {}, "source": [ "And how about ARIMA?" @@ -679,7 +682,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "d9c1d1d3", + "id": "b1ae06e0", "metadata": {}, "outputs": [ { @@ -723,7 +726,7 @@ }, { "cell_type": "markdown", - "id": "cccb821a", + "id": "8fb73239", "metadata": {}, "source": [ "Or the Kalman Filter? (in Darts, fitting Kalman filters uses the N4SID system identification algorithm)" @@ -732,7 +735,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "b4418ffe", + "id": "097290ce", "metadata": {}, "outputs": [ { @@ -775,7 +778,7 @@ }, { "cell_type": "markdown", - "id": "bebae848", + "id": "e0b6d751", "metadata": {}, "source": [ "### Comparing models\n", @@ -786,7 +789,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "8b8245e7", + "id": "47941fda", "metadata": {}, "outputs": [], "source": [ @@ -815,7 +818,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "432e7245", + "id": "5770e2d3", "metadata": {}, "outputs": [ { @@ -855,7 +858,7 @@ }, { "cell_type": "markdown", - "id": "44e366c3", + "id": "90b65e35", "metadata": {}, "source": [ "### Conclusions so far\n", @@ -874,7 +877,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "d9a8d071", + "id": "9220183b", "metadata": {}, "outputs": [], "source": [ @@ -896,7 +899,7 @@ }, { "cell_type": "markdown", - "id": "de77d4ae", + "id": "9e37733e", "metadata": {}, "source": [ "### Part 2.1: Using Darts `RegressionModel`s.\n", @@ -915,7 +918,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "d7fe3110", + "id": "e42b7a80", "metadata": {}, "outputs": [ { @@ -947,7 +950,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "abb1b101", + "id": "5357e90f", "metadata": {}, "outputs": [ { @@ -979,7 +982,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "ea006972", + "id": "4575bb35", "metadata": {}, "outputs": [ { @@ -1010,7 +1013,7 @@ }, { "cell_type": "markdown", - "id": "c8717b16", + "id": "43152f33", "metadata": {}, "source": [ "### Part 2.2: Using deep learning\n", @@ -1023,7 +1026,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "66b06545", + "id": "44d34341", "metadata": {}, "outputs": [], "source": [ @@ -1049,7 +1052,7 @@ }, { "cell_type": "markdown", - "id": "0a8180f8", + "id": "b3d63f94", "metadata": {}, "source": [ "Let's now build, train and predict using an N-BEATS model:" @@ -1058,7 +1061,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "797f94fc", + "id": "bb94b98a", "metadata": {}, "outputs": [ { @@ -1182,7 +1185,7 @@ }, { "cell_type": "markdown", - "id": "23c832ef", + "id": "6939c645", "metadata": {}, "source": [ "Let's now look again at our errors -vs- time plot:" @@ -1191,7 +1194,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "99f5ce78", + "id": "29045449", "metadata": {}, "outputs": [ { @@ -1233,7 +1236,7 @@ }, { "cell_type": "markdown", - "id": "c1ff22f9", + "id": "0a990807", "metadata": {}, "source": [ "### Conclusions so far\n", @@ -1248,7 +1251,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "5d08b317", + "id": "b4792f8e", "metadata": {}, "outputs": [ { @@ -1266,7 +1269,7 @@ }, { "cell_type": "markdown", - "id": "24cf841b", + "id": "92d9c055", "metadata": {}, "source": [ "We can start from the same hyper-parameters as before. \n", @@ -1279,7 +1282,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "d694e998", + "id": "661fab7a", "metadata": {}, "outputs": [], "source": [ @@ -1306,7 +1309,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "7610c2c3", + "id": "14be0a9b", "metadata": {}, "outputs": [ { @@ -1393,7 +1396,7 @@ }, { "cell_type": "markdown", - "id": "2307875e", + "id": "eb3e61e4", "metadata": {}, "source": [ "We can now use our M4-trained model to get forecasts for the air passengers series. As we use the model in a \"meta learning\" (or transfer learning) way here, we will be timing only the inference part." @@ -1402,7 +1405,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "d34081fe", + "id": "5f063030", "metadata": {}, "outputs": [ { @@ -1457,7 +1460,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "34c3390e", + "id": "6d843983", "metadata": {}, "outputs": [ { @@ -1486,7 +1489,7 @@ }, { "cell_type": "markdown", - "id": "a250b0b1", + "id": "66045d59", "metadata": {}, "source": [ "### Conclusions so far\n", @@ -1498,7 +1501,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "a5cb0d90", + "id": "fe2a625f", "metadata": {}, "outputs": [ { @@ -1553,7 +1556,7 @@ }, { "cell_type": "markdown", - "id": "865a17b4", + "id": "70362753", "metadata": {}, "source": [ "### Try training other global models on `m4` and applying on airline passengers\n", @@ -1563,7 +1566,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "481e14ee", + "id": "f21547bd", "metadata": {}, "outputs": [ { @@ -1602,7 +1605,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "51114bab", + "id": "b80d9c6a", "metadata": {}, "outputs": [ { @@ -1640,7 +1643,7 @@ }, { "cell_type": "markdown", - "id": "693c60f6", + "id": "49491777", "metadata": {}, "source": [ "Finally, let's plot these new results as well:" @@ -1649,7 +1652,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "ae13d004", + "id": "bff4b500", "metadata": {}, "outputs": [ { @@ -1687,7 +1690,7 @@ }, { "cell_type": "markdown", - "id": "015b73f8", + "id": "cb3cbf69", "metadata": {}, "source": [ "Linear regression offers competitive performance too. It is somewhat slower probably only because the inference with N-BEATS is efficiently batched accross batches of time series and performed on GPU. " @@ -1695,7 +1698,7 @@ }, { "cell_type": "markdown", - "id": "6c7e092a", + "id": "30804181", "metadata": {}, "source": [ "## Part 4 and recap: Use the same model on M3 dataset\n", @@ -1705,7 +1708,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "6db212b1", + "id": "e8104bbb", "metadata": {}, "outputs": [ { @@ -1748,7 +1751,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "5eeabb1e", + "id": "6bfd1ef1", "metadata": {}, "outputs": [ { @@ -1794,7 +1797,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "d6f44ec0", + "id": "362091d7", "metadata": {}, "outputs": [ { @@ -1840,7 +1843,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "ba87b0d9", + "id": "3a06ca02", "metadata": {}, "outputs": [ { @@ -1884,7 +1887,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "6e51a8ce", + "id": "ca73df4a", "metadata": {}, "outputs": [ { @@ -1928,7 +1931,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "8a7880b0", + "id": "012f02c4", "metadata": {}, "outputs": [ { @@ -1977,7 +1980,7 @@ { "cell_type": "code", "execution_count": 39, - "id": "81128793", + "id": "92b287a9", "metadata": {}, "outputs": [ { @@ -2023,7 +2026,7 @@ { "cell_type": "code", "execution_count": 40, - "id": "31b2dd03", + "id": "0975c8e6", "metadata": {}, "outputs": [ { @@ -2055,7 +2058,7 @@ { "cell_type": "code", "execution_count": 41, - "id": "d13624b2", + "id": "9ca69825", "metadata": {}, "outputs": [ { @@ -2087,7 +2090,7 @@ { "cell_type": "code", "execution_count": 42, - "id": "c93ecb0e", + "id": "ebc9c74f", "metadata": {}, "outputs": [ { @@ -2144,7 +2147,7 @@ { "cell_type": "code", "execution_count": 43, - "id": "710dde54", + "id": "4043d4c5", "metadata": {}, "outputs": [ { @@ -2180,7 +2183,7 @@ { "cell_type": "code", "execution_count": 44, - "id": "e3af71c3", + "id": "60fd068a", "metadata": {}, "outputs": [ { @@ -2216,7 +2219,7 @@ { "cell_type": "code", "execution_count": 45, - "id": "c19cb0cc", + "id": "46654f35", "metadata": {}, "outputs": [ { @@ -2266,7 +2269,7 @@ }, { "cell_type": "markdown", - "id": "458f442f", + "id": "6ac5e72d", "metadata": {}, "source": [ "Here too, the pre-trained N-BEATS model obtains reasonable accuracy, although not as good as the most accurate models. Note that two models out of the 3 most accurate (Exponential Smoothing and Kalman Filter) did not perform so well when used on the air passengers series. ARIMA performs best but is about 170x slower than N-BEATS, which didn't require any training and takes about 15 ms per time series to produce its forecasts. Recall that this N-BEATS model has *never* been trained on *any* of the series we're asking it to forecast.\n", @@ -2282,7 +2285,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6581927e", + "id": "07434630", "metadata": {}, "outputs": [], "source": [] @@ -2290,7 +2293,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -2304,7 +2307,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.8.5" } }, "nbformat": 4, From d0be2e5a0cdf8f96926dc69fec1654e9c2fdecf3 Mon Sep 17 00:00:00 2001 From: Julien Herzen Date: Tue, 5 Apr 2022 21:19:01 +0200 Subject: [PATCH 3/8] list the notebook in examples list --- docs/source/userguide.rst | 9 ++ examples/14-transfer-learning.ipynb | 164 ++++++++++++++-------------- 2 files changed, 88 insertions(+), 85 deletions(-) diff --git a/docs/source/userguide.rst b/docs/source/userguide.rst index 6d880edfaf..55572eade8 100644 --- a/docs/source/userguide.rst +++ b/docs/source/userguide.rst @@ -6,6 +6,15 @@ You will find here some more detailed informations about Darts. **Note:** The user guide is not yet complete and still under construction. +TimeSeries objects +================== + +.. toctree:: + :maxdepth: 1 + + userguide/timeseries.md + + Neural Networks =============== diff --git a/examples/14-transfer-learning.ipynb b/examples/14-transfer-learning.ipynb index a22751427b..5c19df1994 100644 --- a/examples/14-transfer-learning.ipynb +++ b/examples/14-transfer-learning.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "8e592c8f", + "id": "df81a802", "metadata": {}, "source": [ "# Transfer Learning for Time Series Forecasting with Darts\n", @@ -33,7 +33,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e5e22f16", + "id": "a7d27a0b", "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,7 @@ { "cell_type": "code", "execution_count": null, - "id": "02db59b7", + "id": "5d79f0d5", "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "markdown", - "id": "2a9eae61", + "id": "fc4bbc42", "metadata": {}, "source": [ "And now we import everything. Don't be afraid, we will uncover what these imports mean through the notebook :)" @@ -67,7 +67,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "ee31f38c", + "id": "542873f0", "metadata": {}, "outputs": [ { @@ -116,7 +116,7 @@ }, { "cell_type": "markdown", - "id": "6bf92eef", + "id": "b5fce9a8", "metadata": {}, "source": [ "We define the forecast horizon here - for all of the (monthly) time series used in this notebook, we'll be interested in forecasting 18 months in advance. We pick 18 months as this is what is used in the M3/M4 competitions for monthly series." @@ -125,7 +125,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "13f08464", + "id": "13ccda4a", "metadata": {}, "outputs": [], "source": [ @@ -134,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "55122050", + "id": "beca9b9d", "metadata": {}, "source": [ "### Datasets loading methods\n", @@ -150,7 +150,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "736237f9", + "id": "8b06e100", "metadata": {}, "outputs": [], "source": [ @@ -244,7 +244,7 @@ }, { "cell_type": "markdown", - "id": "32d714ef", + "id": "8e0126a6", "metadata": {}, "source": [ "Finally, we define a handy function to tell us how good a bunch of forecasted series are:" @@ -253,7 +253,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "039f14f3", + "id": "245447de", "metadata": {}, "outputs": [], "source": [ @@ -275,7 +275,7 @@ }, { "cell_type": "markdown", - "id": "d095cf90", + "id": "dd44f934", "metadata": {}, "source": [ "## Part 1: Local models on the `air` dataset\n", @@ -290,7 +290,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "0bea8553", + "id": "0a42ecae", "metadata": {}, "outputs": [ { @@ -310,7 +310,7 @@ }, { "cell_type": "markdown", - "id": "c3a06633", + "id": "2d0ba6e1", "metadata": {}, "source": [ "It's a good idea to start by visualising a few of the series to get a sense of what they look like. We can plot a series by calling `series.plot()`." @@ -319,7 +319,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "58d1e7fb", + "id": "c7fda653", "metadata": {}, "outputs": [ { @@ -393,7 +393,7 @@ }, { "cell_type": "markdown", - "id": "ab5f2476", + "id": "cbcf0529", "metadata": {}, "source": [ "We can see that most series look quite different, and they even have different time axes! For example some series start in Jan 2001 and others in April 2010.\n", @@ -404,7 +404,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "f1edcf3f", + "id": "68e80230", "metadata": {}, "outputs": [ { @@ -424,7 +424,7 @@ }, { "cell_type": "markdown", - "id": "b83ad8bc", + "id": "8508453c", "metadata": {}, "source": [ "### A useful function to evaluate models\n", @@ -437,7 +437,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "ca8de337", + "id": "2dec4a7a", "metadata": {}, "outputs": [], "source": [ @@ -459,7 +459,7 @@ }, { "cell_type": "markdown", - "id": "1007319e", + "id": "3ee4291f", "metadata": {}, "source": [ "### Building and evaluating models\n", @@ -470,7 +470,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "a4f9a902", + "id": "f7bcdc13", "metadata": {}, "outputs": [ { @@ -513,7 +513,7 @@ }, { "cell_type": "markdown", - "id": "3110d01b", + "id": "edaa84ea", "metadata": {}, "source": [ "So the most naive model gives us a median sMAPE of about 29.4.\n", @@ -524,7 +524,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "7ff123ad", + "id": "b04746fe", "metadata": {}, "outputs": [ { @@ -569,7 +569,7 @@ }, { "cell_type": "markdown", - "id": "c552584e", + "id": "7cc55e73", "metadata": {}, "source": [ "This is better. Let's try ExponentialSmoothing (by default, for monthly series, it will use a seasonality of 12):" @@ -578,7 +578,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "1636ac00", + "id": "3f916795", "metadata": {}, "outputs": [ { @@ -621,7 +621,7 @@ }, { "cell_type": "markdown", - "id": "f16b20bf", + "id": "6d239048", "metadata": {}, "source": [ "The median is better for with the naive seasonal. Another model that we can quickly is the `Theta` method which has won the M3 competition:" @@ -630,7 +630,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "b7a66cd5", + "id": "bc314b6d", "metadata": {}, "outputs": [ { @@ -673,7 +673,7 @@ }, { "cell_type": "markdown", - "id": "e0383cf5", + "id": "589ba4aa", "metadata": {}, "source": [ "And how about ARIMA?" @@ -682,7 +682,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "b1ae06e0", + "id": "786370e8", "metadata": {}, "outputs": [ { @@ -726,7 +726,7 @@ }, { "cell_type": "markdown", - "id": "8fb73239", + "id": "5120f471", "metadata": {}, "source": [ "Or the Kalman Filter? (in Darts, fitting Kalman filters uses the N4SID system identification algorithm)" @@ -735,7 +735,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "097290ce", + "id": "ef194b1b", "metadata": {}, "outputs": [ { @@ -778,7 +778,7 @@ }, { "cell_type": "markdown", - "id": "e0b6d751", + "id": "4e0174e9", "metadata": {}, "source": [ "### Comparing models\n", @@ -789,7 +789,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "47941fda", + "id": "09374bce", "metadata": {}, "outputs": [], "source": [ @@ -818,7 +818,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "5770e2d3", + "id": "c8b1ca4b", "metadata": {}, "outputs": [ { @@ -858,7 +858,7 @@ }, { "cell_type": "markdown", - "id": "90b65e35", + "id": "00c8290f", "metadata": {}, "source": [ "### Conclusions so far\n", @@ -866,6 +866,7 @@ "\n", "## Part 2: Global models on the `air` dataset\n", "In this section we will use \"global models\" - that is, models that are fit on multiple series at once. Darts has essentially two kinds of global models:\n", + "\n", "* `RegressionModels` which are wrappers around sklearn-like regression models (Part 2.1).\n", "* PyTorch-based models, which offer various deep learning models (Part 2.2).\n", "\n", @@ -877,7 +878,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "9220183b", + "id": "8a9ab1bf", "metadata": {}, "outputs": [], "source": [ @@ -899,13 +900,14 @@ }, { "cell_type": "markdown", - "id": "9e37733e", + "id": "6a08f4bc", "metadata": {}, "source": [ "### Part 2.1: Using Darts `RegressionModel`s.\n", "`RegressionModel` in Darts are forecasting models that can wrap around any \"scikit-learn compatible\" regression model to obtain forecasts. Compared to deep learning, they represent good \"go-to\" global models because they typically don't have many hyper-parameters and can be faster to train. In addition, Darts also offers some \"pre-packaged\" regression models such as `LinearRegressionModel` and `LightGBMModel`.\n", "\n", "We'll now use our function `eval_global_models()`. In the following cells, we will try using some regression models, for example:\n", + "\n", "* `LinearRegressionModel`\n", "* `LightGBMModel`\n", "* `RegressionModel(some_sklearn_model)`\n", @@ -918,7 +920,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "e42b7a80", + "id": "adfc6570", "metadata": {}, "outputs": [ { @@ -950,7 +952,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "5357e90f", + "id": "49bcb69b", "metadata": {}, "outputs": [ { @@ -982,7 +984,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "4575bb35", + "id": "85588f5d", "metadata": {}, "outputs": [ { @@ -1013,7 +1015,7 @@ }, { "cell_type": "markdown", - "id": "43152f33", + "id": "9439078e", "metadata": {}, "source": [ "### Part 2.2: Using deep learning\n", @@ -1026,7 +1028,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "44d34341", + "id": "3a056468", "metadata": {}, "outputs": [], "source": [ @@ -1052,7 +1054,7 @@ }, { "cell_type": "markdown", - "id": "b3d63f94", + "id": "5a0f4cae", "metadata": {}, "source": [ "Let's now build, train and predict using an N-BEATS model:" @@ -1061,7 +1063,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "bb94b98a", + "id": "8773d2d5", "metadata": {}, "outputs": [ { @@ -1185,7 +1187,7 @@ }, { "cell_type": "markdown", - "id": "6939c645", + "id": "56bc6705", "metadata": {}, "source": [ "Let's now look again at our errors -vs- time plot:" @@ -1194,7 +1196,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "29045449", + "id": "4bedad61", "metadata": {}, "outputs": [ { @@ -1236,7 +1238,7 @@ }, { "cell_type": "markdown", - "id": "0a990807", + "id": "ede6754e", "metadata": {}, "source": [ "### Conclusions so far\n", @@ -1251,7 +1253,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "b4792f8e", + "id": "8e663f5e", "metadata": {}, "outputs": [ { @@ -1269,7 +1271,7 @@ }, { "cell_type": "markdown", - "id": "92d9c055", + "id": "89f1c7fb", "metadata": {}, "source": [ "We can start from the same hyper-parameters as before. \n", @@ -1282,7 +1284,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "661fab7a", + "id": "1593bb60", "metadata": {}, "outputs": [], "source": [ @@ -1309,7 +1311,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "14be0a9b", + "id": "20d305c4", "metadata": {}, "outputs": [ { @@ -1396,7 +1398,7 @@ }, { "cell_type": "markdown", - "id": "eb3e61e4", + "id": "305472e4", "metadata": {}, "source": [ "We can now use our M4-trained model to get forecasts for the air passengers series. As we use the model in a \"meta learning\" (or transfer learning) way here, we will be timing only the inference part." @@ -1405,7 +1407,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "5f063030", + "id": "e3b3cd5c", "metadata": {}, "outputs": [ { @@ -1460,7 +1462,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "6d843983", + "id": "365f5c70", "metadata": {}, "outputs": [ { @@ -1489,7 +1491,7 @@ }, { "cell_type": "markdown", - "id": "66045d59", + "id": "6cc6488a", "metadata": {}, "source": [ "### Conclusions so far\n", @@ -1501,7 +1503,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "fe2a625f", + "id": "2ae15a9f", "metadata": {}, "outputs": [ { @@ -1556,7 +1558,7 @@ }, { "cell_type": "markdown", - "id": "70362753", + "id": "34319272", "metadata": {}, "source": [ "### Try training other global models on `m4` and applying on airline passengers\n", @@ -1566,7 +1568,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "f21547bd", + "id": "c10b05a2", "metadata": {}, "outputs": [ { @@ -1605,7 +1607,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "b80d9c6a", + "id": "2b66ad8a", "metadata": {}, "outputs": [ { @@ -1643,7 +1645,7 @@ }, { "cell_type": "markdown", - "id": "49491777", + "id": "11f14ae4", "metadata": {}, "source": [ "Finally, let's plot these new results as well:" @@ -1652,7 +1654,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "bff4b500", + "id": "04305bd9", "metadata": {}, "outputs": [ { @@ -1690,7 +1692,7 @@ }, { "cell_type": "markdown", - "id": "cb3cbf69", + "id": "a5e1d2d0", "metadata": {}, "source": [ "Linear regression offers competitive performance too. It is somewhat slower probably only because the inference with N-BEATS is efficiently batched accross batches of time series and performed on GPU. " @@ -1698,7 +1700,7 @@ }, { "cell_type": "markdown", - "id": "30804181", + "id": "ac7cd421", "metadata": {}, "source": [ "## Part 4 and recap: Use the same model on M3 dataset\n", @@ -1708,7 +1710,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "e8104bbb", + "id": "5456e279", "metadata": {}, "outputs": [ { @@ -1751,7 +1753,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "6bfd1ef1", + "id": "68e3ecc8", "metadata": {}, "outputs": [ { @@ -1797,7 +1799,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "362091d7", + "id": "9139869a", "metadata": {}, "outputs": [ { @@ -1843,7 +1845,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "3a06ca02", + "id": "b79df289", "metadata": {}, "outputs": [ { @@ -1887,7 +1889,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "ca73df4a", + "id": "5317d684", "metadata": {}, "outputs": [ { @@ -1931,7 +1933,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "012f02c4", + "id": "6aef03b9", "metadata": {}, "outputs": [ { @@ -1980,7 +1982,7 @@ { "cell_type": "code", "execution_count": 39, - "id": "92b287a9", + "id": "79e64ef2", "metadata": {}, "outputs": [ { @@ -2026,7 +2028,7 @@ { "cell_type": "code", "execution_count": 40, - "id": "0975c8e6", + "id": "d5b50979", "metadata": {}, "outputs": [ { @@ -2058,7 +2060,7 @@ { "cell_type": "code", "execution_count": 41, - "id": "9ca69825", + "id": "f25530c0", "metadata": {}, "outputs": [ { @@ -2090,7 +2092,7 @@ { "cell_type": "code", "execution_count": 42, - "id": "ebc9c74f", + "id": "bb31e64d", "metadata": {}, "outputs": [ { @@ -2147,7 +2149,7 @@ { "cell_type": "code", "execution_count": 43, - "id": "4043d4c5", + "id": "46111129", "metadata": {}, "outputs": [ { @@ -2183,7 +2185,7 @@ { "cell_type": "code", "execution_count": 44, - "id": "60fd068a", + "id": "4bbe1ce7", "metadata": {}, "outputs": [ { @@ -2219,7 +2221,7 @@ { "cell_type": "code", "execution_count": 45, - "id": "46654f35", + "id": "317573cb", "metadata": {}, "outputs": [ { @@ -2269,7 +2271,7 @@ }, { "cell_type": "markdown", - "id": "6ac5e72d", + "id": "57941009", "metadata": {}, "source": [ "Here too, the pre-trained N-BEATS model obtains reasonable accuracy, although not as good as the most accurate models. Note that two models out of the 3 most accurate (Exponential Smoothing and Kalman Filter) did not perform so well when used on the air passengers series. ARIMA performs best but is about 170x slower than N-BEATS, which didn't require any training and takes about 15 ms per time series to produce its forecasts. Recall that this N-BEATS model has *never* been trained on *any* of the series we're asking it to forecast.\n", @@ -2281,14 +2283,6 @@ "\n", "[1] Oreshkin et al., \"Meta-learning framework with applications to zero-shot time-series forecasting\", 2020, https://arxiv.org/abs/2002.02887" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "07434630", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From f8b37e32a3d8d4d4030d2fd0a541e85003617c9c Mon Sep 17 00:00:00 2001 From: Julien Herzen Date: Thu, 7 Apr 2022 12:55:40 +0200 Subject: [PATCH 4/8] add example to list of examples --- docs/source/examples.rst | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/docs/source/examples.rst b/docs/source/examples.rst index 20d023dd6f..8228854a21 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -29,6 +29,19 @@ Data processing example notebook: examples/02-data-processing.ipynb +Transfer Learning Tutorial +========================== + +A self-contained notebook showcasing examples of training some bigger models +on large datasets of time series, and using such models to forecast new time +series that have not been seen during training: + +.. toctree:: + :maxdepth: 1 + + examples/14-transfer-learning.ipynb + + Fast Fourier Transform ====================== From 118aaf1dce659af538dafda8b1669828ecee3a14 Mon Sep 17 00:00:00 2001 From: Julien Herzen Date: Thu, 7 Apr 2022 12:57:00 +0200 Subject: [PATCH 5/8] revert change to userguide.rst --- docs/source/userguide.rst | 9 --------- 1 file changed, 9 deletions(-) diff --git a/docs/source/userguide.rst b/docs/source/userguide.rst index 55572eade8..6d880edfaf 100644 --- a/docs/source/userguide.rst +++ b/docs/source/userguide.rst @@ -6,15 +6,6 @@ You will find here some more detailed informations about Darts. **Note:** The user guide is not yet complete and still under construction. -TimeSeries objects -================== - -.. toctree:: - :maxdepth: 1 - - userguide/timeseries.md - - Neural Networks =============== From e358907c028e00673f5e820053e0be902703098b Mon Sep 17 00:00:00 2001 From: Julien Herzen Date: Thu, 7 Apr 2022 16:41:17 +0200 Subject: [PATCH 6/8] remove a few imports --- examples/14-transfer-learning.ipynb | 175 +++++++++++++--------------- 1 file changed, 81 insertions(+), 94 deletions(-) diff --git a/examples/14-transfer-learning.ipynb b/examples/14-transfer-learning.ipynb index 5c19df1994..9812b2bccc 100644 --- a/examples/14-transfer-learning.ipynb +++ b/examples/14-transfer-learning.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "df81a802", + "id": "09ec0621", "metadata": {}, "source": [ "# Transfer Learning for Time Series Forecasting with Darts\n", @@ -33,7 +33,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a7d27a0b", + "id": "9738950f", "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5d79f0d5", + "id": "119889ec", "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "markdown", - "id": "fc4bbc42", + "id": "197de730", "metadata": {}, "source": [ "And now we import everything. Don't be afraid, we will uncover what these imports mean through the notebook :)" @@ -66,18 +66,10 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "542873f0", + "execution_count": 4, + "id": "f6208112", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2022-04-03 21:35:44,587] WARNING | darts.models | Support for AutoARIMA, BATS and TBATS is not available.To enable it, install u8darts[pmdarima] or u8darts[all].\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "\n", @@ -91,32 +83,27 @@ "import pandas as pd\n", "import pickle\n", "import numpy as np\n", - "import requests\n", - "import tqdm.notebook as nbtqdm\n", "from tqdm.auto import tqdm\n", "from datetime import datetime\n", "from itertools import product\n", "import torch\n", "from torch import nn\n", "from typing import List, Tuple, Dict\n", - "from sklearn.preprocessing import MinMaxScaler, MaxAbsScaler\n", + "from sklearn.preprocessing import MaxAbsScaler\n", "from sklearn.linear_model import Ridge\n", "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from pytorch_lightning.callbacks import Callback, EarlyStopping\n", "\n", "from darts import TimeSeries\n", "from darts.utils.losses import SmapeLoss\n", "from darts.dataprocessing.transformers import Scaler\n", - "from darts.metrics import smape, mase, mape\n", - "from darts.utils.data import HorizonBasedDataset\n", + "from darts.metrics import smape\n", "from darts.utils.utils import SeasonalityMode, TrendMode, ModelMode\n", "from darts.models import *" ] }, { "cell_type": "markdown", - "id": "b5fce9a8", + "id": "d3c37690", "metadata": {}, "source": [ "We define the forecast horizon here - for all of the (monthly) time series used in this notebook, we'll be interested in forecasting 18 months in advance. We pick 18 months as this is what is used in the M3/M4 competitions for monthly series." @@ -125,7 +112,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "13ccda4a", + "id": "49d0d535", "metadata": {}, "outputs": [], "source": [ @@ -134,7 +121,7 @@ }, { "cell_type": "markdown", - "id": "beca9b9d", + "id": "b88f2896", "metadata": {}, "source": [ "### Datasets loading methods\n", @@ -150,7 +137,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "8b06e100", + "id": "bcce2484", "metadata": {}, "outputs": [], "source": [ @@ -244,7 +231,7 @@ }, { "cell_type": "markdown", - "id": "8e0126a6", + "id": "84c326b9", "metadata": {}, "source": [ "Finally, we define a handy function to tell us how good a bunch of forecasted series are:" @@ -253,7 +240,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "245447de", + "id": "77a78e20", "metadata": {}, "outputs": [], "source": [ @@ -275,7 +262,7 @@ }, { "cell_type": "markdown", - "id": "dd44f934", + "id": "0c9fd92d", "metadata": {}, "source": [ "## Part 1: Local models on the `air` dataset\n", @@ -290,7 +277,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "0a42ecae", + "id": "bf4f7907", "metadata": {}, "outputs": [ { @@ -310,7 +297,7 @@ }, { "cell_type": "markdown", - "id": "2d0ba6e1", + "id": "b60309b7", "metadata": {}, "source": [ "It's a good idea to start by visualising a few of the series to get a sense of what they look like. We can plot a series by calling `series.plot()`." @@ -319,7 +306,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "c7fda653", + "id": "d73caf5d", "metadata": {}, "outputs": [ { @@ -393,7 +380,7 @@ }, { "cell_type": "markdown", - "id": "cbcf0529", + "id": "6d058dab", "metadata": {}, "source": [ "We can see that most series look quite different, and they even have different time axes! For example some series start in Jan 2001 and others in April 2010.\n", @@ -404,7 +391,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "68e80230", + "id": "7269aa98", "metadata": {}, "outputs": [ { @@ -424,7 +411,7 @@ }, { "cell_type": "markdown", - "id": "8508453c", + "id": "02f13136", "metadata": {}, "source": [ "### A useful function to evaluate models\n", @@ -437,7 +424,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "2dec4a7a", + "id": "b26ebd2b", "metadata": {}, "outputs": [], "source": [ @@ -459,7 +446,7 @@ }, { "cell_type": "markdown", - "id": "3ee4291f", + "id": "69307302", "metadata": {}, "source": [ "### Building and evaluating models\n", @@ -470,7 +457,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "f7bcdc13", + "id": "f09986b1", "metadata": {}, "outputs": [ { @@ -513,7 +500,7 @@ }, { "cell_type": "markdown", - "id": "edaa84ea", + "id": "b23eab99", "metadata": {}, "source": [ "So the most naive model gives us a median sMAPE of about 29.4.\n", @@ -524,7 +511,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "b04746fe", + "id": "715daf4b", "metadata": {}, "outputs": [ { @@ -569,7 +556,7 @@ }, { "cell_type": "markdown", - "id": "7cc55e73", + "id": "42edf9a1", "metadata": {}, "source": [ "This is better. Let's try ExponentialSmoothing (by default, for monthly series, it will use a seasonality of 12):" @@ -578,7 +565,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "3f916795", + "id": "cf4295b2", "metadata": {}, "outputs": [ { @@ -621,7 +608,7 @@ }, { "cell_type": "markdown", - "id": "6d239048", + "id": "e46c8781", "metadata": {}, "source": [ "The median is better for with the naive seasonal. Another model that we can quickly is the `Theta` method which has won the M3 competition:" @@ -630,7 +617,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "bc314b6d", + "id": "ac8ee96a", "metadata": {}, "outputs": [ { @@ -673,7 +660,7 @@ }, { "cell_type": "markdown", - "id": "589ba4aa", + "id": "76fb6d09", "metadata": {}, "source": [ "And how about ARIMA?" @@ -682,7 +669,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "786370e8", + "id": "c5771d7d", "metadata": {}, "outputs": [ { @@ -726,7 +713,7 @@ }, { "cell_type": "markdown", - "id": "5120f471", + "id": "84a3526c", "metadata": {}, "source": [ "Or the Kalman Filter? (in Darts, fitting Kalman filters uses the N4SID system identification algorithm)" @@ -735,7 +722,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "ef194b1b", + "id": "e646f145", "metadata": {}, "outputs": [ { @@ -778,7 +765,7 @@ }, { "cell_type": "markdown", - "id": "4e0174e9", + "id": "fcfcf3fd", "metadata": {}, "source": [ "### Comparing models\n", @@ -789,7 +776,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "09374bce", + "id": "959c7c74", "metadata": {}, "outputs": [], "source": [ @@ -818,7 +805,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "c8b1ca4b", + "id": "22c499d4", "metadata": {}, "outputs": [ { @@ -858,7 +845,7 @@ }, { "cell_type": "markdown", - "id": "00c8290f", + "id": "23d1b89b", "metadata": {}, "source": [ "### Conclusions so far\n", @@ -878,7 +865,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "8a9ab1bf", + "id": "cae8f2de", "metadata": {}, "outputs": [], "source": [ @@ -900,7 +887,7 @@ }, { "cell_type": "markdown", - "id": "6a08f4bc", + "id": "863dd0df", "metadata": {}, "source": [ "### Part 2.1: Using Darts `RegressionModel`s.\n", @@ -920,7 +907,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "adfc6570", + "id": "c169a7cf", "metadata": {}, "outputs": [ { @@ -952,7 +939,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "49bcb69b", + "id": "ab11f68b", "metadata": {}, "outputs": [ { @@ -984,7 +971,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "85588f5d", + "id": "ce17d5b4", "metadata": {}, "outputs": [ { @@ -1015,7 +1002,7 @@ }, { "cell_type": "markdown", - "id": "9439078e", + "id": "45b38b30", "metadata": {}, "source": [ "### Part 2.2: Using deep learning\n", @@ -1028,7 +1015,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "3a056468", + "id": "ac4d360f", "metadata": {}, "outputs": [], "source": [ @@ -1054,7 +1041,7 @@ }, { "cell_type": "markdown", - "id": "5a0f4cae", + "id": "0fadf994", "metadata": {}, "source": [ "Let's now build, train and predict using an N-BEATS model:" @@ -1063,7 +1050,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "8773d2d5", + "id": "d7c73c3b", "metadata": {}, "outputs": [ { @@ -1187,7 +1174,7 @@ }, { "cell_type": "markdown", - "id": "56bc6705", + "id": "d553d77f", "metadata": {}, "source": [ "Let's now look again at our errors -vs- time plot:" @@ -1196,7 +1183,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "4bedad61", + "id": "5324b386", "metadata": {}, "outputs": [ { @@ -1238,7 +1225,7 @@ }, { "cell_type": "markdown", - "id": "ede6754e", + "id": "0855a8bf", "metadata": {}, "source": [ "### Conclusions so far\n", @@ -1253,7 +1240,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "8e663f5e", + "id": "2f1f0207", "metadata": {}, "outputs": [ { @@ -1271,7 +1258,7 @@ }, { "cell_type": "markdown", - "id": "89f1c7fb", + "id": "c96d0412", "metadata": {}, "source": [ "We can start from the same hyper-parameters as before. \n", @@ -1284,7 +1271,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "1593bb60", + "id": "9c4d55fb", "metadata": {}, "outputs": [], "source": [ @@ -1311,7 +1298,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "20d305c4", + "id": "90d16347", "metadata": {}, "outputs": [ { @@ -1398,7 +1385,7 @@ }, { "cell_type": "markdown", - "id": "305472e4", + "id": "cb7ef8d4", "metadata": {}, "source": [ "We can now use our M4-trained model to get forecasts for the air passengers series. As we use the model in a \"meta learning\" (or transfer learning) way here, we will be timing only the inference part." @@ -1407,7 +1394,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "e3b3cd5c", + "id": "2ec60b04", "metadata": {}, "outputs": [ { @@ -1462,7 +1449,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "365f5c70", + "id": "a42e2812", "metadata": {}, "outputs": [ { @@ -1491,7 +1478,7 @@ }, { "cell_type": "markdown", - "id": "6cc6488a", + "id": "a094d433", "metadata": {}, "source": [ "### Conclusions so far\n", @@ -1503,7 +1490,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "2ae15a9f", + "id": "40f4f7a4", "metadata": {}, "outputs": [ { @@ -1558,7 +1545,7 @@ }, { "cell_type": "markdown", - "id": "34319272", + "id": "6c50d008", "metadata": {}, "source": [ "### Try training other global models on `m4` and applying on airline passengers\n", @@ -1568,7 +1555,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "c10b05a2", + "id": "9064dc97", "metadata": {}, "outputs": [ { @@ -1607,7 +1594,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "2b66ad8a", + "id": "e2cc4bc4", "metadata": {}, "outputs": [ { @@ -1645,7 +1632,7 @@ }, { "cell_type": "markdown", - "id": "11f14ae4", + "id": "cf2c1f84", "metadata": {}, "source": [ "Finally, let's plot these new results as well:" @@ -1654,7 +1641,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "04305bd9", + "id": "2667bc7c", "metadata": {}, "outputs": [ { @@ -1692,7 +1679,7 @@ }, { "cell_type": "markdown", - "id": "a5e1d2d0", + "id": "ef93235d", "metadata": {}, "source": [ "Linear regression offers competitive performance too. It is somewhat slower probably only because the inference with N-BEATS is efficiently batched accross batches of time series and performed on GPU. " @@ -1700,7 +1687,7 @@ }, { "cell_type": "markdown", - "id": "ac7cd421", + "id": "c156626b", "metadata": {}, "source": [ "## Part 4 and recap: Use the same model on M3 dataset\n", @@ -1710,7 +1697,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "5456e279", + "id": "09a02763", "metadata": {}, "outputs": [ { @@ -1753,7 +1740,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "68e3ecc8", + "id": "5c367704", "metadata": {}, "outputs": [ { @@ -1799,7 +1786,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "9139869a", + "id": "edebec11", "metadata": {}, "outputs": [ { @@ -1845,7 +1832,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "b79df289", + "id": "aa136b53", "metadata": {}, "outputs": [ { @@ -1889,7 +1876,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "5317d684", + "id": "79aaf2fb", "metadata": {}, "outputs": [ { @@ -1933,7 +1920,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "6aef03b9", + "id": "5186afa4", "metadata": {}, "outputs": [ { @@ -1982,7 +1969,7 @@ { "cell_type": "code", "execution_count": 39, - "id": "79e64ef2", + "id": "a0dfe336", "metadata": {}, "outputs": [ { @@ -2028,7 +2015,7 @@ { "cell_type": "code", "execution_count": 40, - "id": "d5b50979", + "id": "d0cc81af", "metadata": {}, "outputs": [ { @@ -2060,7 +2047,7 @@ { "cell_type": "code", "execution_count": 41, - "id": "f25530c0", + "id": "b45adf97", "metadata": {}, "outputs": [ { @@ -2092,7 +2079,7 @@ { "cell_type": "code", "execution_count": 42, - "id": "bb31e64d", + "id": "ae2da8e5", "metadata": {}, "outputs": [ { @@ -2149,7 +2136,7 @@ { "cell_type": "code", "execution_count": 43, - "id": "46111129", + "id": "fd6febdf", "metadata": {}, "outputs": [ { @@ -2185,7 +2172,7 @@ { "cell_type": "code", "execution_count": 44, - "id": "4bbe1ce7", + "id": "f2cd6302", "metadata": {}, "outputs": [ { @@ -2221,7 +2208,7 @@ { "cell_type": "code", "execution_count": 45, - "id": "317573cb", + "id": "e766f941", "metadata": {}, "outputs": [ { @@ -2271,7 +2258,7 @@ }, { "cell_type": "markdown", - "id": "57941009", + "id": "329154b2", "metadata": {}, "source": [ "Here too, the pre-trained N-BEATS model obtains reasonable accuracy, although not as good as the most accurate models. Note that two models out of the 3 most accurate (Exponential Smoothing and Kalman Filter) did not perform so well when used on the air passengers series. ARIMA performs best but is about 170x slower than N-BEATS, which didn't require any training and takes about 15 ms per time series to produce its forecasts. Recall that this N-BEATS model has *never* been trained on *any* of the series we're asking it to forecast.\n", From 178d4aca8f7ca36fbc5243ac445c17e85b13eed1 Mon Sep 17 00:00:00 2001 From: Julien Herzen Date: Thu, 7 Apr 2022 20:58:19 +0200 Subject: [PATCH 7/8] fixed typos --- examples/14-transfer-learning.ipynb | 151 +++++++++++----------------- 1 file changed, 56 insertions(+), 95 deletions(-) diff --git a/examples/14-transfer-learning.ipynb b/examples/14-transfer-learning.ipynb index 9812b2bccc..536d12827f 100644 --- a/examples/14-transfer-learning.ipynb +++ b/examples/14-transfer-learning.ipynb @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "id": "f6208112", "metadata": {}, "outputs": [], @@ -310,58 +310,21 @@ "metadata": {}, "outputs": [ { - "data": { - "image/png": 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\n", 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\n", 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4fD7Ky8tDbrtjxw4AcnJyzG3q6uoA/3nbuHEjixcv5qqrrgoQ+rbwO4qGeMXSu3fvkOuciHgloM9eHmDtPfJn4DnDMJbiz5c3KUhWSs0FdAtNq1un0tLSZg9ISxKvWPTkxGeddRa9e/dmzJgxlJSUcODAAVNADx06FPDdpaWlnDp1ivLycnr16sWUKVMChjjVdOrUKaiza+7vGDhwYMB66+czMjKora3l+PHj5jbBjsuxY8dYv349aWlptG/fnurqanJycuJ+LuN1jnRq6KqrrqJ3794MHDiQ1atXc/LkSbM66OTJk03OkU51dOnSJWRcehq9Xr16sXfvXqqrq+nRo4cp7lZ0/j0/Pz9gf9nZ2Zw8eZIFCxbw29/+lnbt2vGLX/wiIJZU/x1FQ2vE4iSdsgq/ywaYCqzUK5RSJ5RStyml9Pp3XY5PiAL71GojRowA/BUq2qWXlpY26WCjB2DKy8sLKuAQmArRzszeuNmjR4+A982lU84880wgfMPmypUrqa2tZcyYMfTv3x9wPrBTIqJLPqdNmwZgplP27NnTbDpF34RDpVKg8Xh3797dvAZC1eEHS6dAY158+/btQNseGiHRCSviSqkvgQOGYawAhgELDcN4HsAwjNGGYSw3DOMjYKVSamdcoxUcocVN/4B14+bGjRtNwa2pqWmSy9Yi7kQgALO8zf4DP+OMMwLeN9ewOXjw4KD7sPPxxx8DMHHiRLp29Wf0klnE9XnQx1CLeGFhoSnUsYp4t27d6NKlCxD6WIUTcd0ALvnxxMVRiWGQ0sE7G5Z/CUxwNyQhFmprazl69Cgej8f8YepSwaKiooA5LUtLS81Hb2gU8ezs7JD7twrwWWedxRdffNFEgHVOVWPfn3UfQ4YMYdGiRWFFfPny5QBMmDCBbdu2Ack9QJP9aUk/XVgrcCorK/H5fAFPRZGIeH5+vine0Yq4zu/qFJ2QeEhnnxTDOniV7kQyYMAAwN94aB33xF5mqAXCqYgPGzYMwBRVjb3h056aycrKMitf9A2mrKysifhrjh49ytq1a0lPT2f8+PFh3WWiU19f30Q89dPL1q1bA7azd7jRYtq+ffuQ+9c52YKCgqiduH2cHHHiiYuIeIqhHZ7+8YL/B9mtWzdOnToVkEKxt6JH6sR1PvfTTz8NqE6xi7gdj8dj7qdHjx506dIFn88XUmg2bNhAXV0do0aNIjc3N+nTKUeOHMHn85GXl2fezLQTt2NPqThx4jfccAPz58/ngQceiDmdorv+i4gnLiLiKYb9MV1jz1NDUyceqYiPHj2a008/ncOHD5tVDhBexK37yc/PN7v3WwfWsqIbPbXD1MKUrOkUe5sF+J21vUEYohPxrKwsZsyYQdeuXWMWcY2kUxIXEfEUQ/9YrU4cGlMqVmJx4h07djTTGxA4lZfuwfmTn/yEDz/8MOh+zjjjDDIzM+nfv78p4lpQ7NhnsXEjnbJ06VKeeuqpVukwFMmNNhoRt6KPlR661o5TERcnnriIiKcYTgRC1wtH48R1o5kW3mAirp34I488wqWXXkow3nzzTTZs2MBpp51mCkgoJx4PEf/JT37CAw88wObNm6PeRzD27dtnzlgUilDnKNiNNlYR120O69atC7peRDz5ERFPMYI9qkOgQOgGyVjSKfpH35yIB+uir8nPzzfLCyN14rHmxL1erzm4lO4B6Rbjxo1jzJgxIYeRhZZ14hMmTADgX//6V0C/gPLycj7//POwDZsaSackLiLiKUawhk0IFIixY8cCsaVTtPAOHz6c7OxsSkpKzAGvnIi4lXA58VBOPNqc+K5du8xKGDe7SOuhC8rKypqNzYkT16WfsYr4gAED6NOnD2VlZWzatMlcPmnSJM4//3xzfBv7sMOROvG6ujqWLFkS8hwK8UNEPMVw4sQHDx5MTk4OR48eDfjROensYxfx9PR00xlXVVXh8/nMnLi1Jr05WjqdoscLgcCp5mLFGn9zMxU5EXHdCSiUiDdXYmjF4/FwySWXAJhDDAN89dVXAdvFmk559913mTp1asiB1YT4ISKeYoRy4n379jXL2Xr27GnOCPPcc8+Z2zhx4iNHjiQzM5Nvfetb5jJr124t4JmZmWadejhaOp1iHafbTRG3Cm5zsYVqfLY+LekZ5+0irtMaTp04NKZUdIepYDgR8eYagXV9ux7yWGg5RMRTjFBOPDMzk759+wJ+EZ8zZw4Ajz76qPnDc9LZZ/jw4ZSXl/Of//mf5jItwpWVlRGnUiByJ67/toqKiqgmWI6XiFuFuzkRD+XE+/TpYzY66zFlYk2nAKYTX7lyZchtwol4fX19s7Mp6ZEXi4uLHccluIOIeIoRyokDXHnllXTr1o3Ro0dz6aWXcvvtt+P1evnLX/4COHPi4E+pWHthajGqqqqKSsQjdeIZGRnk5uYGTC0WCa3txEOJeHp6Ovfddx8zZ840G33dEPGBAweSnp7O/v37zXNsnRkp2DjxwYb4bS6logdW2717N7W1tY5jE2JHRDzFCCUQAP/7v//L/v376d69O+AfTAoaXZRTEbdjTafEIuLBnLjX66Wqqoq0tLSAvymWlEqwnLjP56OwsDBk138nxCriAE888QQvvfSSeRN2Q8TT0tLMSZD1ubY2ZAarpAk2i1NzIq73W1dXR1FRES+88ELS9qhNNkTEU4xQ6RSNdbYeLeba6UYr4sHSKU4bNaHxUT6Yq9adVPLz8wNy7NbvjASfz8c333xjvt+/fz91dXUsXLiQIUOG8N///d8R7c+KGyKuCTWEbDQiDo29XfVNK9jEHFaCiXhzZYZaxAF++ctfMmvWLJ5++umIYhSiQ0Q8RfjHP/7BgAEDzAamYOkUO1rEdbf2WJ14rOmUYE7cnkrRaJGJtH553759nDhxgm7dutG9e3fq6+s5cOAAW7ZsATD/j4ZEFvFevXoBjSKuz3UoInXiOp0C8P777wOxHUvBOSLiKcJvf/vbgEYlJxMJa2F0S8RjbdgM5sRDibgusYtUxHUqZeDAgQHCpsUylvFYWlLEnZYYavTfWlpaitfrDZs2ikTEjx8/HnDu9L6lkbNlEBFPEey9/Zw4NWs6xefzuZJO0RUMierEdYeXwYMHB4i4Ft2WEPFQJYZW9AiHFRUVZkqpvr4+6nNkTadYXfgZZ5zBM88802R7J+mUI0eOMGPGDF544YWg3yki3jKIiKcIoaZTa46srCxyc3Opra3lyJEjjjr7BCPWdEpzJYZuO3FdTjlixIgAYWspEa+traW6upq0tLRmJ3nOzMxk4sSJ+Hw+3nrrLaCxJ2x2dnbE59t6w9JuXk/O/Mtf/rLJ9tbYdCOy3Ym/+uqrvPbaa/z85z8HaNIvoLKyMuI2CyFyRMRTBC3AhmHw2muvOf6cduNlZWUJkU6xdig5dOiQOZiUWyK+ceNGwC/isTrxb775hvPPP58HH3yQI0eOOBLxYJN2hOL6668H4I033gCc9agNhTWd4uQ8W524/qxdxO1xDB8+vMl+SkpKIo5ViAwR8RRBi+djjz3G9773Pcef0+JYXl4eczolWieemZlJ+/btqa+vN4WisrKS/v3781//9V8BcWq0iEcyup7P5wtw4lZhs+bEnQ5Pu2jRIj7//HOefPJJzjvvvIDGvWAivmLFCu644w7AWZvFNddcQ3p6OkuXLqW8vDzqRk0ITKc42Y81564/a79h2m9C5557rrlMVydJSiX+iIinCNGIJwQ6cSc9NoMRqxOHpo2bu3fvDhANN5y4Fuv8/Hx69uxp1k7v37/fFN2amhrH+7QOY1tYWMj69evN98FE/LHHHuPNN98EcNTTtHv37kyYMIHa2lr++c9/uuLErTnx5s5zWlqa+bSgJ3G23zDtk3/069ePYcOGkZWVxbe//W1ARLwlEBFPEaJ10VrEY3Hiboi4vXHTOrfktddey/Tp0wO2j6ZhU7vw4cOH4/F46NmzJxAo4uC8A5EWcX2DsTr46urqJp1orEP/jhgxwtF36DFqiouLYxLxvLw8cnJyqK6uNielDrefF154gT/96U/mjEN2EbeXKZ5++um8/fbbrF69mgsuuADwz+sqxBcR8RTBDSfeWukUaOrEtWBMmjSJhQsXmnFqonHi1lQKYIr4nj17zGF0wVle3OfzmSJ+7bXXBqzTf7u9UU83Ft5zzz08++yzjmLWN4jDhw+b5yfS8kLwN3zrtIgediDceb7++uu54447Qt4w7SLes2dP+vfvz+jRo805Q8WJxx8R8RQhWvF0Iyeek5NDZmYmJ0+eNEXYLSceqoIjmpy4XcS1w9TOVONExPft20dVVRVdu3bl4osvNpenp6eb6Qe7o9fO/Pvf/37IiZHtBBPxaJw4NKZUtIg73Y8W8XBOXI+8CI3D6oqIx58MJxsZhjEHGAcUA7cppbwNy3OA14FOQC1wk1LqQHxCFZoj1nSK1YlHKsAej4fOnTtz6NAhUxDdEvFg9coQnRPXHX30MLxZWVl06dKlidg6EXHtwocOHcrZZ59tLu/SpUvI8c6tw/Q6xdqrNpaGTWgq4k6vlVA3TH293HjjjVx//fXmcQXEibcgYZ24YRijgN5KqfFAIXCdZfV0YKNS6hLgReCH8QhSCE+s6ZSDBw9SX19PRkaGORxqJGgRPnDAfw+PZOwUCJ1OCefEIxFx3TPVOqu8TqlYaU7E9+zZw4ABA7jmmmuApiLeuXPnkCKunXgkx8ZNJ67/Vl3255YTP+ecc8zjocnPzyc7O5uqqqqA9g3BfZykU8YBSxpevw9caFm3A9BWqQtw2L3QhEiINZ2ix9SIViB046YW8ZZKp0Qj4tZKl0hF/J133qG4uNiMb+jQoeTm5poutzkRj9WJxyri+hzpUkinTjxUTtza+ciOx+MxOwlJh5/44kTEuwB6YIQqoKtl3XZgqGEYm4AfAa+6G55g5e233+b+++8PWp4WazpFi3ikn9dogYg1naJ/8Nr1hUqnhHKHoaipqeHIkSOkp6cH1Gg7EXGfz8dzzz3H2rVrUUoFrBs6dCiA6cadiHhrOXEdlxZxp/sJl04Jdc2EmztVcAcnz82V+HPeAHmA9Qq/BfhUKfWoYRjXAQ8DD1g/bBjGLGAWwOzZs5kyZUqsMceE1+t1dXLcWAgXy+bNm+nZs6fpaO6//362bt3KlClTTPHQaFd0+PDhiCZK0I/4Ot+amZkZ1fHRwmQdmzyS/WjRLyoqwuv1BoxPHWw/2glXVlY6+h4tXF27dg0YNjU3N9d8nZaWRn19Pbt37zb36fV6Wbx4MbNnzwYwJ2u48sorOXLkCAMGDKC0tNScNSkrK8tMR5WUlATEZj1HTvH5fLRr147jx4+bN5dQx8TJvqCxRr22ttbRfrQDr6ioCDguOp7jx48H3Y8W/+3btzvq3BQtyfSbjhZdWRQMJyK+CrgHeAmYCljnePLQmEI5jF/kA1BKzQXmNrx11hUujpSWljZ7QFqS5mIpLCxk+vTpXH755bz99ttAo0NMS0sL+FxdXZ05m0r//v0jGldDi4R2iR06dIjq+OiOM1qgTjvttIj2oxvFjhw5EjA/Z69evYLuRx+L2tpaysrKOHjwIJdeemnI/eu4evToEbA/PQ0a+OchLSkp4dSpU+Y2paWlAUJfWFhIeno6CxYsCCj1+9a3vsVLL71Ev379TKE/ePBgwHfpc1RQUBCRqHXv3p3S0lLzRhTpsdXYB0nr3r27o/3oY+f1esnIyKBHjx6Ulpaa5+j0008Pup/TTjsN8BuDeP7mkuU3HS/CplOUUl8CBwzDWAEMAxYahvF8w+pXgSsMw1gOPAb8Lk5xtjmUUtTV1fHpp5/i8/mor683R7OzP6bHMjCSx+MJKHeLNZ2iHX2k6RT7zDOR5MS/+93vMm3atGY76QTLh0NgOkWLnH0/9unGhg0b1qRWe+bMmTz00EPcc889GIYBwNq1awO20U89keTErTHH2m5hHzUx0uqUjRs30rNnT5YtWwaET6eEGk5XcBdHZQhKqftsi+5sWF4FTHM7KAG2bdsG+AXlwIEDtGvXznwMtv8oom3U1Jx99tnm98Uq4pqWEvFjx45RWVlJXV0dZWVlIYd31SJu7zRkFfGBAwfy8ccfN8mJ27uXa5G2kpeXx2OPPQY0Tni8ZcsWqqurzb8hmpy4NWb9mO6WiEdanaJZsWIFgwcPbrZhE0TEWwrp7JOgaFEFf27cmkcN5cRjEXFNtCKuRVgTaSxaTA8cOEBdXZ3jhs3y8nJTHJtr5NTHL5yI631asc/yrrvChyI7O5sRI0ZQX1/PunXrAH8eWk+WEGkJp3bisYq4/UYbaXWKHadOvKqqigULFrB48WJngQoRISKeoGzfvt18HU7Eo61M0Zx11lnm62j3Yc8DRiri7dq1o1u3bqajDufEtZBZUx3NlRs6deLQVMStTrx3797m4E7NoYVep1SsqZRIU15axHWVRzTd7iF6J56bm8v5559vvtdxhOscpkV89+7dfP/732fmzJmRhiw4QEQ8AfH5fM068XikUzStJeLQ6OYPHjwY1olnZmY2yS0358RD5cTz8/PNyaP79etHeno61dXVAe5bv7755pvZs2ePWYnSHKFEPNJUCjS98YwaNSrifYBftK3f7/Rcp6WlsWrVKubO9dcn2EU8XInh5s2bqauro6KiIuy0cELkiIgnIPv37w/o5bZp06a4OvFEE/EDBw6EdeLQ1JE6EXG7IKalpZluPD8/3/w7rJMZRHOTtIt4NB19NNYbT6dOnTjnnHMi3gc0Do+giSQtY/2sLmF1mk7Rk3dDZGPdCM4QEU9AtAvXAynFOyeuS8GAgNH8IqFLly4BP2a3nHi8RRzgySef5N5772XgwIFmeWBhYaG5PpoGSV0yWVxcTH19fdSNmvaYL7nkkqiGRdBYUyqR5tbtnXecirjuxQvRX19CaETEExAt4hMnTiQ3N5fDhw+zZcsWc73b6RRrjjba6bSsQ51CdGJlFfFwA2AFWxdNwyb4RxV8+umn8Xg8QUU8muPbrl07OnbsSH19PUePHo26vBACnfikSZMi/rwVqxOP9KnLPheq0+oUKzKOivuIiCcgWsTPPvts09F98skn5nq30ylWYpkTUT85gHvplOZE3IkT93q9lJSUhMyJ23HLiUPjBMPWCppobm5uirgbTjzSdIoVceLuIyKegHzzzTeAvzehHvvaOkOK2+kUgKlTpwIwefLkqPdhdeKxiHhJSQk+ny+gC3swnIj4gw8+SP/+/V0R8Uj/JquIx+LE9eBaEHwy4kiwingsk3+A8+oUK+LE3Sf65JoQN3TPvN69ewetRKioqMDn85lpEDdE/Pe//z1LlizhxhtvjHofbom4HoO6uXw4NBXxYCWG8+bNC3gfTkS1iG/ZssUca0Qf31icuP7eaJx4fn4+q1at4tSpU00mJ46UaBs2ITCdYs3zhzrXWvStiBN3H3HiCYjutXj66acHFfHa2toAwXIjndKpUyd+9rOfBTRyRoqbThwiF/FgTnzMmDERxdCzZ086depERUWF6d5b24kDXHDBBQwaNCiqz1qJxYl36NCB9PR0Tpw4YR7rrKyskHXv2dnZTY6ZOHH3ERFPMHw+X4CIjxw5MmC9/lFYUypuOHE3cEvENc3lw8GZiFs76jz00ENhY/B4PGY7hE6puOHEY8mJu0ksOXGPx2O6cT2OT7gbgT2lIiLuPiLiCYb+wefl5dG+fXs6d+5MQUEBEDhYVSqKePv27c2/FcI7cSfVKfopZfXq1ebYJuGw58XdaNiM1Ym7RSzVKdCYUtHVPpGKuKRT3EdEPMGwunCNTql06dKF/Px8AG699Va+/e1vU1dX52p1SizEWp0CMHr0aPO1m048kmOjJ/ndtWtXwD4i/Zv0uUoVJw6Nee5oRVycuPuIiCcYulHTWpGgRbxbt27mj3DdunW89957FBUVJYwTt45DEq1YWXsjupETj2byZy2++mnHTSeeKCKenp4e01yqWsTDHVdx4vFHqlMSjOacuFXENUeOHEkYEc/MzOSDDz6gvr4+6ioKqxN3U8QjceJW8YXonXiwnHiipFOiHQlRp1N0o6848dZHRDzBCCbi06dPZ8aMGVx//fUsX748YPuKioqESacAXHbZZTF93urEWyudYhfx1u7s4ybaBER7rcSaThEn7j4i4glGsHRK+/btmT9/PgBffvllwPYVFRUJ48TdwDpCYLjJBLTId+zYkerq6qB14m448UQoMXSLfv36MXnyZIYNGxbV5yOtTtGi36NHj4CeuIJ7SE48wQjmxK3Y3Wmqibi15tg6pnowtBPXwu9WTjxUOiUVnHh6ejpLly7lD3/4Q1Sfj9SJ60qfcePGAeLE44GIeIIRTsQPHjwY8D7R0iluoH/wEyZMaHY7PZiVntTCLuI+ny+qG5xbTlynLhIpJx4rkTZs3nrrrWzatIl7770X8OfEN23aZE76LMSOiHiCESydYuUnP/kJ3bp1o0ePHkDqOXGAuXPn8sILL4St6546dSpz587ld7/zz8997Ngxs6s8BOayI2lozcvLw+PxmHN3RuvEc3JyyMnJoaamxkwNtbYTj5VIGzY9Hg9Dhw41P1dcXMyYMWP4zne+E99A2xAi4gmEvbdmMM444wwOHTrEo48+CqSmiHfu3Jnbb7+d3NzcZrfLzMzkjjvu4IwzzjAnkrb20IwmlQL+lIN1AoRYUiHa1esxtVPFietBsJw+/elzefDgQWpqasxB3toK999/P0888URcJo0WEU8gKisrOXnyJLm5uWHL6/SjeiqmU6JBtxVYUyqxHBctvrHeJPV+dPog2Z24fVArp8fWfj23pdy41+vlf/7nf/j1r38d04QeoRARTyCKioqAwJ6PoXBLZFKFYCIeTXmhRt8kKysrXXHiWsST3YnrtIgmUieuOXXqVMA8pqnMtm3b8Hq9DBgwIKw5iwYR8QRCz8c4duzYsNtanbiIePyceGVlpThxC9E68Xbt2jW5gbUVN75x40YAc24At3Hk7Q3DmAOMA4qB25RS3obl1wA/a9jsDOC/lVLR1S4Jpoife+65Ybe1irh+RJN0CkGH6I1FfN124qkm4pEc29zcXLPiB/ztDXqIg1RGi3isE3qEIqwTNwxjFNBbKTUeKASu0+uUUm8qpSYopSYARcCiuETZRlizZg0QuYiLE3c/nRJMxKM5vtYyQ0j+dEq/fv3MyiiI7Ni21bx4q4s4fge+pOH1+8CF9g0Mw+gJZCmlop+gsY1z9OhRNm/eTGZmZtCJIOxYqwS0+2zLTlx3/IlnOiUaF213rsnuxNPT0/ne975nvo/k2Nrz4nquzlQnEUS8C6CPdhXQNcg21wIL3QqqLbJu3Tp8Ph8jR4509MNIT08nLy8Pn89ndgASJx6f6pRY0il2EU92Jw4wY8YM83Ukx8TuxNuCiB87doyioiIyMjI4++yz4/IdTnLilYBuks4DyoNscx3wg2AfNgxjFjALYPbs2UyZMiXyKB0wf/585s6dy9/+9jf69esXcjuv10tpaWlcYogUayzvvfceAMOGDXMcX6dOnaiqqjJrdsvLy6MW8kQ9Lk7R3fVfeOEFDh8+zJVXXmnuw+fzRb2/Q4cO4fP5SE9Pj6qXYX19fcD7Y8eORX2cE+UcWce32bJli+OY7IJfUlLiyt+TKMcFmsby1Vdf4fP5GDhwoNlBKhqsE67YcSLiq4B7gJeAqcBK60rDMHrQTCpFKTUXmNvw1hdsm1jx+Xz86U9/oqioiCVLlvAf//EfIbctLS1t9oC0JDqWtWvX8uyzzwLwne98x3F83bp1Y/fu3eb7AQMGRN1QlIjHJRJ0F/wlS5awZMkSfvCDH5jOr3PnzhHvb+DAgUBj9/J27dpFdXz0TEya0047LerjnEjn6PHHH2fOnDnMnj3bcUz2azMjI8OVvyeRjos9lg8//BDwD7EcrxjDplOUUl8CBwzDWAEMAxYahvG8ZZNWT6Vs2bLFrLF+//33WzOUiKmtreW6667jxIkT3HbbbVxzzTWOP2sdW7xPnz5mCqAtYh8YTHecgtjSKbGmqlItJ6659dZbqaqqYujQoY4/Y8+Jt4WGzZ07dwK4Msl1KByVGCql7rMtutOy7o+uRhQFb7/9tvl6zZo1lJWVJU3pUllZGbt27aJz58788Y9/DDlzeDCsIn7ppZdG9NlUwy7iVVVVrpQY6kfgaMU3FXPimkivN/1klJmZidfrbRM5cZ2CCzWMhhukRGcfLeIdOnSgvr6epUuXtnJEztHjK3fu3DliobCLeFvGPrqjtaokFieu3WK0Ttw+KUKqOPFouOGGGxg3bhwzZ84E2oYT1yJunbrQbZJexMvKyli9ejVZWVn88pe/BJIrpaKrKaLpjmt1n5MnT3YtpmRED0ersTrxWERcI048di655BJWrlxp9oNoS05cRLwZ1q5di8/n49xzz+Xyyy8Hms5+k8hoJx6NiG/dutV8Hc+LJBm46667+OMf/2iOQR5rTjwzMzPgSSdaJ24fa6QtO3GNPiZtScQlndIMSikADMMwhVBPhZUMxCLid911FwCzZs1yNaZkJDs7mx/96EcMGDAA8DvxWHuyWm+M0YpvRkZGwBNTW3biGt3AmerpFJ/PZ4q4tZer2yT9HJtWEddjiNTW1rZmSBGhRTzcpMDBuPLKK9m4caM5BZbQmIOO1YmD/4e3ZcsWILaOVHl5eWbaTJx423HiesiGTp06NZnU202S3onrQaOSVcRjyYl7PB6GDRtGenq622ElLdbhCNwQcU0s4mvNi4uItx0nrid4iXeqM6lFfO/evezdu5dOnTpx5plnJqWIx5JOEZoSzIlH66LjIeKSTmk7TrwlGjUhyUX8iy++APzjb6elpYmICwFOPJYSQwj88cWaTtGIE29exPfs2WOmsJIdEXEHWPPhQFKLeDQ5caEpbufENeLE3cOaTrFObA1w2WWXMWrUqKQX8uPHj0s6xQmFhYUAjBw5EkhOEY8lJy40RYu42zlxceLukZWVZfbatE5sXVZWxpYtW/B6vTz88MOtGGFsfPHFF3Ts2JF7770XEBFvFj1jth6sSDfwJZOISzrFXbRgxjqtGrhTYmiNCcSJg79B3urG9+zZw9q1a9mwYYO5zcKFC82ihWTj7bffDnjCiGeNOKSIiJ9xxhlAcjpxSae4izjx5MCaF584cSLnnnsuf/rTn4DGY/3UU0+1WnyxYO0kBuLEQ1JZWUl5eTnt27fntNNOA5JTxCWd4i5WJx6riOvrChqvrWiwjp8iTtyP1Ynv2LEDgNdffx2AX/ziF3g8Ht566y1zKOBk4sSJEwHv49nRB5JYxK0uXI+mFquIV1RUMHbsWB555BF3gnSApFPcxerw9I8pWhG3umZ9nqJBnHhTmqtQufzyy5k6dSper5dXX33Vle/bunVrizWWWifr7tSpk5nujRcpIeIanROvq6tr0urthMWLF7Nu3Tp+//vfm1NyxRsRcXfJyMigY8eO+Hw+cxhZN6atq6ioiPqzkhNvSnMiPnLkSG677TYA/vKXv8T8XV988QWDBw9m3LhxAQ2p8UKbhwcffJDCwsIm4+e4TdKLuPUu5/F4AoQ8Uj7++GPAL6yfffaZC1GGR3Li7qPTFwcOHADcmUDaDRHPyMho02O+W9HpFPuUZQUFBeTl5XHVVVeRnZ3Nhg0bYuoUtH//fq6++mrAn2LbunUr69evZ9u2bVHvMxxaxHv16hX3Rk1IARG3OnGILaWyfPly8/UHH3wQdWyLFi3i5ptvdtStWHLi7qNFU89vmSgiLqmURnTj365duwKWn3POOYD/6alPnz4AUc+fWVZWxmWXXcaePXvMZZ988gljxoyJ26TF0JhOied4KVaSTsR37NjBbbfdxqJFiwD3RHzXrl3mjQFiE/FHH32UV1991VE+T9Ip7mOfiCGWdEpBQQHQ2BchGrSISyqlET1ee3FxMeCfXnDOnDnMmTPH3EaLuFWEI+Gmm27i66+/ZvDgweZIn3/961/N9dGkXJ2gnXhOTk5c9m8n6UR83rx5zJs3z3xUdkvEdSplypQpZGdns27duiazxTjh5MmTbNq0CcC80TSHiLj72CdiiMWJv/766/z61782J7KOhp49e9K5c+cm12pbRot4SYl/fvX8/Hzuv//+gMk99MTC0TjxkydPsmzZMtLS0li6dCkTJ04EAucaiKWxujnEiYehqqoq4L19NvFoRVynUqZPn875558PNI7NEgkbN240v3vZsmVN8nl1dXUsW7aM48ePU1dX1+J37baAm068d+/ePP7443Tv3j3qfeTk5FBYWBiQrmvr6HSKFnH7JMoQmxPfsmULdXV1DBo0iN69ezN8+PAm29i1xC3EiYdB5znB3/prd1nRivinn34KwMUXX2zWB0dzktetW2e+9nq9/POf/wxYP3/+fC699FKeeOIJ82R36NCBtLSkOxUJi9WJ9+rVKyGG6u3Ro0fcqxSSCe3Ed+/eDTSdAQlic+Jff/01ACNGjAD80/fZa/0rKysj3q+mrq6OoqKioOvEiYdBC99f/vIXnnjiiSbroxHxAwcOsGPHDjp06MCoUaPMCyoWEddPCG+99Rbl5eXccMMNLF26lNWrVwP+hllp1IwPVgf00ksvtWIkQii0iOtZuNx24hs3bgQaRbxdu3ZB52GNlieeeIIzzzyTxYsXN1knTjwM4XrhRSPiq1atAuC8884jIyPDdHLRlDZpEb/jjjsAKCoq4rnnnuP1119nypQp5sVVUVEhIh4nZs6cyaRJk1iyZEmbn0A6UbFPRB3MicdSnaKduDWNYk+pxCLiegRV63gvGu3ERcRDEO4uF42I61TKRRddBEQ/aL3X6zVPqhaP8vLygFj0xVVeXm6ebKkRd5fRo0ezbNkypkyZ0tqhCCGwi3gwJ67TKdE4cXs6BeDCCy8M2CaWdIoujdQFFla0RiVUOsUwjDmGYawwDONlwzAybetmGIbxkWEYyw3DuCA+YTYSrit1NCK+cuVKoPEkRyvihYWFnDp1igEDBpiVCOXl5QG5uPLycsDvxLWIixMX2hr2QaKCOfEePXqQnp7OoUOHIuppWVFRQWlpKTk5OQEVQT/+8Y/56KOPuP3224HInXhRURHf//732bVrl5nLD1bBlnBO3DCMUUBvpdR4oBC4zrKuF/AdYLJSaoJSKu7dHHU6xS0nfuLECdatW0daWppZlRKtiOtUyahRo8yLtLKykrKysibblpeXSzpFaLPk5OQEVA0Fc+Lp6elmj8e9e/c63rf+Hdrnn83IyGDixInk5+cDkTvxJ598kldeeYWnn37a/E0nixMfByxpeP0+YH0mmQacAj5scOlxVyO30ymbN2/G6/UyePBgU7xjFfHhw4eTkZFBbm4u9fX1ZocGK5ITF9oyHo8nIKUSqnInmry47lI/ZMiQoOutU/hFgn5if//9981ldhH3+XwJ2bDZBdBqVgVYk1k9gG7AFOAzYLar0QXBrYbN8vJyjh49ytatW4HAEx5tdYru5DNs2DCgMe8XrBSpvr7evAAkJy60RawiHsyJQ3R5cT1EQqjafusUfk45fPiwOQqiHjoXmop4TU0N9fX1ZGZmxjR8cSQ4+ZZKQN8m84By27qPlVI+wzCWAQ/ZP2wYxixgFsDs2bNjbmzS45EcOXIk6N1Z15Hv3bs36Hqv10tJSQkjR46kpqaGO++8E/BfLHp7nX87fPhwRA5A9wbr3r07paWlpsMOVU+qL0yPxxP1+BBu4fV6Wz0GjcQSnFSLxfoEWlNTE3R/WnA3bdoU8vvssYT7XenB8fbv3+/4b1iyZEnQ5WVlZaxbt46NGzcyadIksxdodna2q+dK38yC4UTEVwH3AC8BU4GVlnUrgfsaXo8GvsGGUmouMLfhbcyDFei60v79+wf9w3QeqmvXrkHXl5aWkpWVZaZKVqxYAfgnW9bb63UnT55s9uBZOXbsGLt27SIjI4Px48fTrl07evTowaZNm8zHq0svvZRjx45x+PBhtm/fbrZw9+rVy/H3xIvS0tJWj0EjsQQn1WKxTpYwYMCAoPvTo5TW1NSE/D57LFqk+/btG/QzAwYMAPxmzenfoOfztePz+XjggQdYunQpb7zxhrnvDh06tNi5CptOUUp9CRwwDGMFMAxYaBjG8w3rNgC7DcNYDtwGRD/AhEPcSKfoXDQ01nUPHjzYXBZNTnzLli34fD7OPvtsc7Q6exnVvHnzWLVqlXly9YXRt29fx98jCKmCk5y4LhCIZBRJnQa1D7+gsU7h5xRdhtytW7cm6/RwCm+88UbYwot44Chpo5S6z7boTsu6f3c1ojC40bBpFXGNdWjKaETc2qipsZdR6ff2YThFxIW2iJOceCwibh8ITWOdws8Jx48fRymFx+Nh5syZ/P73vwf81TN1dXWm1rz33nvccsstQMtVpkCSdfaxtvy65cTBPxu11Ql06NABj8fDsWPHHE8uYS1r0lgv0szMzIBUj/57oLEFXhDaEk6cuN4mEhHX4hxKxCN14p988gk1NTWMHTuWcePGmcvtPUCrq6tZtmwZ0LJOPKlE3Ov14vP5yMjICNnyG42I2weIT0tLC5jI1Qk6NTJ06FBzmfUi7dKlizmri92hixMX2iKROHHdSc4J4dIpkZYY6kbNqVOnBvy+DcNosq0eflpEPARO6i+jEXFrPlwT6YkONl2cVaytF6z1dXZ2ttn5QBDaEvr3kZWVFXLWo2jSKeGceMeOHUlLS+PYsWNmoURzaBG/7LLLGDRoEH369GHIkCGceeaZ5jZ6vHKtA5JOCUG4Rk1wJuL2weC/9a1vNdnGWitunb06GPX19UGni7M78WCv+/btK/MuCm0S/fsI5cIhPjlxj8fjeJC73bt3s3nzZjp27Mj5559PZmYmmzdvZu3ateaQ1YA5j6dGnHgI3HbiM2bMYPHixWZjhBUt4ldffTU9e/Zsdpafffv2cerUKbp16xaQ2wsl4tblkkoR2ir6d9DcOOvWJ+La2lpzEolQ1NfXm8IcSsTBeYcfPZ3bpEmTzKeF3NxcOnToEFAief755wf8xsWJhyBcoyZEJuL5+flcfvnlQScN0BfWzp07OXr0KJs3bw65v2CpFAjtvu1OXBDaIkOGDGHQoEFcfvnlIbdJT083xfihhx6if//+vPvuuyG3r66upr6+ng4dOjTbY9JJuvTtt9/mN7/5DQA/+tGPmqy3ivjgwYMDZhkTJx4CJzWYkYh4c2OW2N3BoUOHQm6re2Ta51AMlQe3vpbKFKGt0qFDB7Zu3Rp2/lL9e9G5ad39PRjhUikaJ0787rvvxufz8fjjjzN9+vQm6wsKCkhPT2fQoEF06tQpQMRb0om3TOd+l3A7ndLcmCX2i+Dw4cNNttm1axcvvfSSOaKZOHFBiAwn7UFdunRh586d5tNwcxVj4SpTNOGceFVVFbt27SInJ4cHH3ww6Dbdu3dn6dKl9OzZE6DVnHhSibjbDZvNibgTJ37PPfewcOFC873diXfs2JGMjAxqa2slJy4IUaJ/O3pMo+ZmqQ9XmaLRIv/aa68xevRos7u8Rj9dDxw4sNn5bydMmGC+bi0nnlTplJZ04nYRD+bE9QiIGruIW4fbtIp4p06dTAciIi4IzWPvV+HEiYcTcd0p77XXXuOKK65osl6PVGgtIwyH9UYgOfEQuN2wGasTt3cSsqdToPECtLrvtLQ0CgoKyMrKCrh7C4LQlGhEPFw65d5772Xp0qV07tyZzZs3B4w06vP5ohJxceIOaM2GzcOHD1NfX2/eSKz7AX+HhV69ejXZjx7syr7u3Xff5fXXX2+2RlYQhMhE3Gk6xePxMHnyZHNo7A8++ACAO++8k4EDB7J27VoguDELRUFBgfm6OaPpNkkl4m6lU5zkxO3fcejQIa644goGDBhgdv7RIt6pUyfuvvvuoLmzZ599lnnz5jFmzJiA5cOGDWPs2LEhv18QBD/20UDdSKdopk2bBjTO1jN37lx27txpdp+PxIlbjZ/T4TrcIKkaNlsynWIfq+HAgQNs2bKFmpoadu7cybBhw8ybwbJly4KOowD+sVSs4y0IghAZdifupGEzXDpFo0X8o48+CjoZcyQibqW5zoFuk1RO3O10SnMifvXVV5OZmWl2p92/fz81NTUAZkmhk/0IghAb8WjY1PTq1YuRI0dy7NgxPvzww4B1mZmZERce3HjjjQDceuutEX0uFpJKxKNJp3z44Yf8+te/NqdtA2c58T59+lBeXs4//vGPJo0UulJFRFwQ4k88RRwax0767LPPApafccYZQXtzN8dTTz3F0aNHmxQ9xJOkEvFo6sRnz57NE088wSeffGJu41R8O3bsiMfjaTLhqjhxQWg5omnYdJpOgcaigw0bNgQsj6RRU+PxeJo1h/EgqUQ8UideXl7Otm3bgMCabicNm1bCiXhLnzRBaEvYRfz48eMhJ2uJxonrCjIt4oMHD2bKlCnMnj07mnBbnJRu2NRlQoAp5j6fz6wucVrLaZ9Xr6ysjJqaGrxeL+np6SHHQhYEIXas1Sl6SrTq6uomQl1WVsamTZuAwMGpwqGduJ4uceTIkbz22muxht1iJJWIR9qw+fnnn5vLtYifPHkSn89Hdna243xXMCduTaXIeOCCED86derEwIEDadeuHZWVlezbty+oiD/22GMcOXKEKVOmMGjQIMf7t/fhCDYZciKT0umUNWvWmMu1iGsXHkkKxH5SDx8+LPlwQWgh0tLS2LBhA+vWrQs5beKuXbv4v//7PzweD08//XRE+9fpFE2yzbSVlE7cSTrF6/UGOPFvvvkGr9cblfiGc+KCIMQXnfoMJeJr1qzB6/Uybdo0Ro0aFdG+u3fvbqZpQJx43Ni8ebPZAceJE9+xYweHDx+mW7duFBQUUFdXxzfffGM68UjEV59U/b1WEZdGTUFoOUKJuJ7NRw8LGwnp6ekBnxMRjwMvv/wy55xzDitXrgScOXE9cPyIESPMms1t27ZFJeK61Gj8+PGAX8QjrXARBCF2wol4c1O9NYc1L56S6RTDMOYA44Bi4DallLdh+QTgZaAIqFNKTY5HkEOHDg3orOPEieuTmpubS79+/ViyZAnbtm0ze2BF4qAnTJjAe++9x+jRo+nVqxfl5eXmRSQiLggth/7d2rve699jtAPK9e7d26xmSzknbhjGKKC3Umo8UAhcZ9vkNaXUhHgJOMDYsWN55JFHzPdORFyPg5Cdnc1ZZ50FQGFhYVROPC0tjenTp3P66aeTl5dHfX09e/fujXg/giDERks48ZQTcfwOfEnD6/eBC23rv2sYxgrDMH7mamQ2HnzwQSZNmkTPnj2bHYPbPjlqTk6OOVrgxx9/HHODpH7U0jWlIuKC0HKEEvFYnXgyp1OciHgX4EjD6yrAOi6kAs4GJgPTDMOI29iqGRkZfPjhh+zatavZVEgwET/vvPPo2rUrRUVFvPfee0D0d1t9gktKSgBp2BSElkSLdHl5ufkbhNiduC4zzMrKSjpj5iQnXgnoI5MHmGO0KqXMxJRhGO8Ao4AvrB82DGMWMAv845joQdjjhX326rq6Ovbv38/FF1/MokWLTBGfPHkypaWlEe9fn+Dt27cD/h6g0ewH/GWQ0X7WbSSW4EgswWmtWHTb2JNPPsmTTz7JO++8w/Dhw82Zt2pqaqKKS/e67tKli5kqjYZ4HRd7LbsVJyK+CrgHeAmYCqzUKwzD6KSU0i79IuBP9g8rpeYCcxve+pyFHD32EqNu3brRu3dvrr/+enOg92HDhnH11VdH1dNSH8wDBw4A/u69zR3g5igtLY36s24jsQRHYglOa8XSp0+fgPe7du3inHPOMYeJHjBgQFRxXXjhhXg8Hs4666yY/q7WOC5h0ylKqS+BA4ZhrACGAQsNw3i+YfX3DMNYYxjGKqBUKfWv+IXqjGDpFPAP/q5n3rnrrrui7ipvT6ck26OXICQz9vSlNlOx5sQLCgpYtWoV8+fPjy3AVsBRiaFS6j7bojsblv8Z+LPbQcVCKBHv2rUrt99+O6tWrWLmzJlR71/n0vVjneTEBaHlsIv0/v37gdhz4gDnn39+9IG1IknV7d4JdhG3dgx6/vnnKS0tjelE2xtExYkLQssRSsRjdeLJTFL02IyEUE7cLUaOHBnwXkRcEFqOeDrxZEVEPEIMwyArK8t8LyIuCC2HPX25f/9+vF4vJ0+eJC0tzfXfezKQ8iLe3Dgr0ZCVlWXOyQci4oLQktg74hw4cMDsgt+pU6c2ObZ/yot4PO7MF110kflaGjYFoeXIz8/no48+4uuvv6Zjx46cOnWKffv2AW0zHw5toGEz3iIuTlwQWpaJEycC/j4hO3bs4JtvvgHaZj4cxIlHxbhx4+K6f0EQwqM79u3cuRMQJ54yxDsnDv6uuTNmzGDbtm1N5ucTBKFlsIt4W3XiKS/i8XLKydizSxBSCRFxP5JOEQQhKenRoweAmRNvq+mUlBfxeKRTBEFofbQTLysrA8SJpwzixAWhbWAfsVSceIogTlwQ2gann356wHtx4ilCenq6+TojI6OJqAuCkBqMGDGCAQMGmO/FiacIVhGXVIogpC7t2rXjySefNN9nZma2YjStR8qJeFpamjn5g4i4IKQ2119/PXl5eQAMGTKklaNpHVIy15CRkUFNTY2IuCCkOB6Ph9WrV7N3714uuOCC1g6nVUg5Jw6NjZvSqCkIqU9ubi6TJk1qkyMYQoqLuDhxQRBSHRFxQRCEJCalRVzSKYIgpDopLeLixAVBSHVExAVBEJKYlBZxSacIgpDqOKoTNwxjDjAOKAZuU0p5bet/BVynlDJcjzAKxIkLgtBWCOvEDcMYBfRWSo0HCoHrbOtzgRHxCS86RMQFQWgrOEmnjAOWNLx+H7jQtv5nwP9zM6hYkXSKIAhtBSci3gU40vC6CuiqVxiGkQeMUEp9FofYokacuCAIbQUnOfFKQA/UmweUW9b9HHi2uQ8bhjELmAUwe/ZspkyZEnGQkVJfXw9ATU0NpaWlAeu8Xm+TZa2FxBIciSU4Ektw2kIsvXv3DrnOiYivAu4BXgKmAist684ELjAMA2CQYRi/Vkr91vphpdRcYG7DW5/zsKOnffv2gH/mD/sfX1pa2uwBaUkkluBILMGRWILT1mMJm05RSn0JHDAMYwUwDFhoGMbzDetmKqWmKaWmAdvtAt5aSE5cEIS2gqMSQ6XUfbZFdwbZJiHKC0Fy4oIgtB1SurOPiLggCKlOSoq4nmuvS5curRyJIAhCfEnJmX0ee+wxLrzwQiZMmNDaoQiCIMSVlBTxIUOGtNn59gRBaFukZDpFEAShrSAiLgiCkMSIiAuCICQxIuKCIAhJjIi4IAhCEiMiLgiCkMSIiAuCICQxHp+vRQYWFARBEOKAOHFBEIQkRkRcEAQhiRERFwRBSGJExAVBEJIYEXFBEIQkRkRcEAQhiRERFwRBSGJSVsQNw2jf8L+nleMoSIQ4GmI4LxHiADAMo19rx6AxDKNDw/+tfmwS5bptiEGu3SAk0rULKdjZxzCMq4HvA7uBp5VSe1spjvbAU0Bf4DqllLc14miIZRTwB2A18IhSqqYVY5kGzAZOAfOB95VS1a0Uy2XAHcBeYE5rXSsNsVxNAly3DbHItRs8loS5dq2klBM3DOMK4AfAHKASeKBheYvfwZVSx4EaILchptZ0NOOBJ5RSvwLOaKUYMAwjHfgRMBf4DWAAHVrxuNwE/BnYCPzIMIzxrRFEIl23kHDX7kXItdssST89W4NrOBvYDPwLWKeU2msYxjZggWEYpymlDrZQHGcBxUqpSsMwMoEdwD+AnxqG8b5Sale847DFskspVQ54gWmGYfwK2GcYxlrgHaVUUQvGUgzUAlvwO98SYDSQA2TiF414x5IF5DScnx7ALmAN8DFwAzDWMIyilnDBDbF0aDg/W4A7lFIHW/q6tcSij0s7/NdLa127ZiwNi07SeteuNZa+wNe00rXbHEntxA3DGA2sA24FFgE1DQLuAToBO1tIwK1xLDAMI7vhEXQw0Bn/j+FOwzD6tnAsrzRciB6gB3Af8GP8j4PfbsFYfgC8gl8cFuN3wOuBw/jTGT9pgVhuxH+jfxxAKXUAOB04r+ERfT2QDeS1YCyPNcRS1CDgHvzut0WuW1ss+rjUKKV8+K/dPFr22g2IpYH2+K/de2nZa1fH8lsApVQx8BFwMy187YYjqUUc6Af8h1LqZ8AG4OeGYWQ1XIQ5+J0fhmH0i/Njj47j58BXwC8bln8EfAEcw5/v/GlDPPE87tZYNgE/xC+kaUAfpVQVja443o/J1vOzGfiZUupTYAnwnFLq+8C7QDvDMNLiFYthGNlAB/w/Oo9hGFoE3gR+aBhGe6XURqAA6B+PGELEovOsGIaR3nDdtqeFrtsgx2WaZfXH+K+bFrl2g8RyecOqT4F0WvDaDXKOLgdQSn1MC1+7TkiqdEpDa/n9+A/eGvxO4WLgNeBp4HfAcPzCeRH+nNXvgHz8d0xXGiGcxGEYxlBgLHA3cAhYiP/REKVUvRtxOIhlDvDf+G8m7/g3N9oDl+N/XKZBOFoqlt8ZhjECf26zvWEY5+F3VxVuHhNbLIuBDUqpPzcszwFubEgRvGcYxsXA3YZhfIbfiae7GYeDWG42DGOJUqquYfMLiNN16zCWDxtiGQ/cBZQT/2s3WCw3GYbxgVJqvWEYy4BzGqqI4n3tNne91OPXl8x4XruRkjROvCHH/AiwB+gN/J9S6mVggGEY45RSh4GVwM8bPlIAjAO2K6VucasV2WEcq4E7gf8C5imlZiil7lFKPexGDBHEcqghll8opV7F36J+HrBKKfWbFo7Felzm4v8hPgqsUUr9NI6x9ASes6z+CDhIg8sCnrHEtUEp9W4Lx3II/9OSZgBwIS5ftxHEoo/Ls8BLLXTthjtHrwJv0DLXbqjjMssSy07idO1GQ8KXGBqGcS3QDVgK/FkpNalh+UvAB/hLsu5XSl3RsPxFpdStDZUGX1saSFo6jr8BP1ZKHWt4n+bWHTuKWOYBP1dKVTU8tteF2HVLxPIiMFspVW0YRjs3S8aaieUvwBal1DMNj70F+HOdnwMfKaU2GoaRoZSqbeVY/oE//1vk1nUbZSxrgCVKqS0N27XEtdvccflYKfV1C167zcWyVCm12e1rNxYSVsQNw+gOzMP/KLkff051EvCBUmpew+PPAuAS/HfOEuAyYLlS6pEEiOMj4DcuP/JJLNHF0g94HbhSKXXIMIyR+PPhJcBdSqmtCRDLbvwVKtsTIJZEOi4pHYsbJHI6xQc8r5Sagb+1eij+3NxwwzAGKaVK8AvCNPyNLouAZ9wU8BjjeNRNoZJYYoplF/6ngqmGYWTgb6v4pVJqUhx+kNHGMsFNAY8xlkQ6LqkeS8wkcsNmGf6WYJRShw3D6AkcBbbjr/74Ef5yrG+UUifwd9jYmMJxSCyxxdIF+KohZTIvTnFILBJLi5Ow6RRNQ04qD5ivlJresOx5/CWE7YBZSqkjbSUOiSW2WICjcXgSkFgkllYjkZ24lQzgU8MwxuJ/PP8rsE0pVdFG45BYJBaJJfViiYqEd+IAhmFMB94GlgGvKH/pWpuNQ2KRWCSW1IslWpLFiZcD/w78oZXLehIlDolFYpFYUi+WqEgWEV+jlPq8tYMgceIAiSUUEktwJJbgJFIsUZEU6RRBEAQhOIlcJy4IgiCEQURcEAQhiRERFwRBSGJExAVBEJKYZKlOEYSIMPzjpt+Pf7q8Fw3DuBV/9+n7lFLPtGpwguAi4sSFVKU98B/4p6kD+AS4Ef/kGIKQMogTF1IV1fD/JYZh+PAPI1qAf57RrYZhFOMfS/pv+Kcf+xT4f/gnrMgAfqCUet/wTxz8BP4bQAfgQ/zjxB9qwb9FEEIiTlxIVf694f8t+AU4WAqlQ8P/n+Gf9uuP+KfXOw14smHdg/hHtnsH+B9gOvCnuEQsCFEgIi6kKksa/j+olFpA8Hkq64Ff4B9LGuBlpdT/AnvxT5MGcEXD/3fiT890AKbEJWJBiAJJpwipipOuyCeUUjWGYXgb3lc1/F9H4ITJtfjFXE8NJuZHSBjkYhRSlSP4nfaZhmHcjD8fHg3v4jc7twD98A9XeqcrEQqCC4iICymJUsqLP7/dGfg7jS46Uv6rYT/j8Td8Tsdf6SIICYEMgCUIgpDEiBMXBEFIYkTEBUEQkhgRcUEQhCRGRFwQBCGJEREXBEFIYkTEBUEQkhgRcUEQhCRGRFwQBCGJ+f8BURKDdZLBWAMAAAAASUVORK5CYII=\n", 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vk4ZNteBFucYpW7b0+slSP28dDbuXUn7f8tS1ptd+4GpFBYDunZI+hd6w6aR3ilXEi6mLoWrYrKuro7Ozs2xFXDVqQumft3qwTxx075T0KQUnrnqmqEw63w2b6Thx9bvlKuKqURO0iJclShj0YJ/UKfbeKWYnPmrUKCBWxMPhMNdffz2//e1vMys8SX1uOHEoXxHXTrzMsWbiOk5xjtpHuY5TAoGA0WiZiGROvLu72xDxcePGAbEi/uGHH/Kb3/yG22+/PePa4+GWE1ciXq4jNs1OXF3oSxUt4nGwc+I6TklOvpy4k22Gw2FbJ26eAEvFKWPHjgViGza3b98OpL5Yg1PcGnavnbh24mWN7p2SPvlq2ITk309PTw/BYJCqqipjJR9FvDhlzJgxQMSJq37kagxBtmY2dGsCrHIW8Z6eHuNiC6V/3moRj4PVies4xTmF7MTtXDjEF/HBgwdTV1cXs8KOEofu7m5H8U2qmGdZzMSJl3PD5rZt2wiFQowePRqInLe5nBM+12gRj4OdE9dxSnLyKeLJLrJ2eTjE753S2NjIoEGRWSZULm52eNnIWnUmnjkqSjnmmGOorKwkHA6X9MVMi3gcdO+U9MlnnOK2E+/Xrx8DBw4EenNxs4i7Han4/X66u7vxer3U1NTo3ilpoho1J06cSG1tLVDajZtaxONg1ztFO/HkqJMlEAgQCoVytj1wz4nHE3HlxFUmDu6LuLk+j8ejR2ymiXLikyZNKgsDpkU8DtqJp0c4HE4po3YDtzPx7u5uI06xing4HM6qE7cuHaedeHpoES9Bdu7cyW233eZ45J3unZIeai5uRS5EPJXeKYmcuLmLoXLijY2NMSK+b9++mL8p2yKuR2ymhzlOKYdztyxE/K677uKWW27hkUcecfR+u7lTdJySGOuJUoxO3BqnmBs2zVEKZF/ElZvu7Ox0HE0p0VZZcLk1bAaDQWPyqwkTJmgRLxXU7dW+ffscvV878fSwNh7lWsTd7p1ibdg0RynWbbuBVcS9Xm+MkDuh3OOUnTt34vf7GTlyJPX19bphs1TYtm0bEJkc3gl6oeT0yLWI+/3+GKfphhPv6uoyRLyhoSEmTrGKeLYaNs31pZqLl3ucogzbxIkTgfI4d0texMPhMJ988gngXMQTxSmlPGggU3It4tbtueHEVQNmfX09Pp8vpyJudeKQei5e7k5c5eGTJk0CykPEHc0nXswcOHDAcFbt7e2OfsfqxL1eL5WVlfj9fnp6eoznNbHkWsStIuqGE1fLt6mVfsyZuMql6+vr6ejoyImIayeeGuaeKVAeIl7yTly5cEjfiZsfl/LBkCnF4sTjibi6MCsRV8ucxcvEp0yZAhSmE1eiXV1djcfjIRwO56S/fqGg1ngdPnw4UB7nrRbxOFiduPmx7qFiT75F3KkTTxSnWJ14vDjl2GOPBXIr4k6duIpTqqqqjPVOy8mNq2NC7TfdsFkCqEZNcB6naCeeHvmOUzJx4krEVUOpVcT37t3Lnj178Hq9xq16LuOUVJ14ZWWlMVNjOYm4+k6UeJfDeVvyIp6qEw8Gg/j9fjweT8x0peVwMGRKKThxhYpTBgwYAMDhw4cJh8OMHDnSEPhCd+LlKOLqmFANu+Vw3pa8iJud+KFDh5Lmg+Y+4h6Px3hexynJybeIu+HEFUqoKysrDUGHyBzjqfbddkq2nHg5DfjRTrwEMTvxcDhs9FSxQ33Z6iBQlMPBkCm5HrGZbu8UJ05ciTj0RioAo0ePNkTc7Zw1XhfIVJ24WcTLORPXTryEUCKuRDlZpBIvDzf/XMoHQ6aUohOHWBEfM2aMcSxly4nHG+yTaj/xco1TrE5cN2wWOYcPH6atrY2amhomTJgAJBdxOyeu45Tk5KthU8VeybbnZAIshTlCsYp4LuOUTJx4OYu4duIlgnLhY8eONU7EZD1U3HbiBw4c4PLLL2fZsmUp/V4xki8nrhofk303Tgb7KJzEKYlE/LXXXuNLX/pSzCLLyXAjE1dOvFxFXMcpJYZq1Bw7dqxxoqfrxNM9GJYsWcLTTz/Nb37zm5R+rxhRJ5DP5wNyJ+JKZDNx4olEXI3aBOdO/KabbuKpp55iyZIlCWsy46YTN8cpumFTi3jRopz4uHHj6N+/P5B+Jp5unKLmckhlTuhixeqMcxWnKBFPdKIGAgFj6bN40ybYdTE0fz44E/GPPvqIN998E0htQQe3e6eUW8OmeVESq4jrTLxIMccpSliSxSluO3E1t3EqJ3OxkmsRT2V71qXPrPh8vpjn48UptbW1DBo0KKmIP/7448bjVHJz3U88M8wjrb3eiLSp81g78SIlnTjF7Uxci3j2t+fEiSfKwyHSOGp24/FEfMyYMXg8noQiHg6Heeyxx/rUmIxwOJxwKlonTjwYDBrjIHw+X9mJuDUPBx2nFD3pxClu905RcUo5ibja17mOU5w6cTvMIm6OU1QmPnr0aICEIr5hwwbef//9PjUmo6uri1AoRE1NjRGDQGpO3BylmEccl4uIW/Nw0CJe9MRz4rnsndLR0UFra6vxuNRR+yZXIu6mE4fYboZmJz579mwuuOACbrzxRiBx32PlwtXx4lTE7epLxYmbGzWBsmvYtHYvBC3iRc3Ro0fZu3cvlZWVjBgxIuPeKeoET+VgUFEKlIeI5ztOcdOJW3unLF26lIsuugjonea1p6cnRiDD4bCRh19++eUxNSbDbjRpuk4cKLuGTWujJuiGzaJGTRs6evRofD5fxr1T1M+pCJNZxFNZ7LZYKeTeKU6cuBJx89qW8TDn4mZxWLNmDZ988gmjRo1i9uzZMTUmw07EU3Hi5kZNoGzjlHJz4o5W9hFC3AGcCWwDrpFS+i2v/x9gnpRSuF5hmpijFCAvvVNUHq7o7OxM6ASLnVJx4o2NjXF7sJipq6szVvdR+bmKUubPn29cLNx04uFwOGFdVidebiIer2FT904BhBBTgWYp5UzgA2Ce5fVG4KTslJc+5kZNIOPeKZnGKVD6kUqpOHFzlGJHvMbNFStWAJEoJdWh+cp0mPukQyQSqampIRQKJT1+zKM1zf+Xi4jrhk17zgSWRx8vA86yvP5t4NduFuUGVieeae+UdOIUqxPXIp6/7aXixNMVcTXE3jxJllMnvnjxYgAjhjGjlhrbs2dPws+wNmyqTLxcGjbLtYuhkzhlILA7+rgdMMYgCyH6AydJKX8uRPwkRQixAFgAsHDhwrgHqd/vp6WlJbXKk6C6efXv35+WlhbjS2xvb2fnzp22t6Vqea6urq6YutTJeuDAAce1btq0CYicTIFAgC1btriyyHI29lem+P1+QyiDwSAQmYAsm3WqaYXVd+v3+9mxY4cx0EM919LSYtQRCoVsa1LHRHV1ddK6lcvdtm1bn7u8zs5Ow/kfPHgw7meZv8Pu7m6WLl0KwOmnn97n/YMHD2bbtm2sX7++j7kws3PnTuPvaGlpMUR97969jr6HQjyuwHld6j3hcNh4rC5gR48eTXjeZ7MuN2hubrZ9zYmIHwSUNekPmGf0uQm4J9EvSynvB+6P/hiO956WlpaERabDvn37AJg2bZrx2TU1NXR1dcWMurOi5v0YMWIElZWVxu+OGDECiJwgTmoNBoPs2LEDgGOOOYb33nuPhoYGV/7ObOyvTGlpaTGc8MSJE4HIyZTNOlV8MH78eOO7HTx4cIzQqX2lXOmIESNsa1IuffDgwUnrVnd26jvt7u6mq6uLiooKJk2aZFxYAoFA3M8yf4fLli3jyJEjnHzyyZx55pl93jt27FiklLafpVBOva6ujubm5j41JqMQjytwXpcySNbvz+fzEQwGGTZsWMxqXbmqK9s4iVNWA+dFH88BVplemwTcIoRYBkwWQvyby/WljYpTVCYOziIV88o+ZlKNU5QTGj58OEOGDAFKO04Jh8N56ydeW1ubtM0iW5m4qkE1mPfv3x+Px5NSnPLMM88AcMkll8R9XRmIXbt2Jfwc3bDZt4uh+Wd1bPT09DB9+nSuvfba3BaYJZKKuJRyHdAqhHgVOAFYJIT4XfS1/yWlnCulnAt8JKW8LavVOqSnp4ddu3bh9XpjrpROeqjYHQipZmuqUXPChAkpz39RjKj9UlVVZey7XIl4XV1d0otsqr1TkmFdGEIdU+oYc9qwGQqFePbZZwG49NJL475n5MiRAOzevTvu6wo7ES+XTDxeF0Poe+5u3bqVt956y9jvxY6jLoZSyu9bnupzCSuk7oU7duwgHA4zatSomNsnJz1U7Jx4qsPuVaPmxIkTjdv+UhZxtV/MrjibIh4MBo3Pr6mpyZsTV8Khjil1F+LUib/++uu0trYyduxYpk6dGvc9Tp24tZ94uQ72SSbi6rsqlZlFS3Kwj7V7ocJJnOLWsPt4TrxUDpp4mHv15ELEzdvzeDxJt5nt3inqmErViZujFLtGt0ydeLmIeLwuhtB31Kb6rjo6OkpiAF5Ji7jqXqhwEqe4NdjH7MTLKU4xu+Jsiniqk/9n24lb45Sqqio8Hg9+v982zgiHw4aI20Up0OvEk4m4kxGb69evZ/369Qk/p1hJ1YmD+0vs5YOSFPF4jZrgLE5xa1GIcs3Ec+XErW0XTp14IhFX4t3U1JR0+8niFLuh+Wa2b9/O5s2bGTRoEGedZR1+0Yty4pk2bIZCIc455xzOOeecknCgVuycuLVh03z+l8I56SgTLzbsnHgqvVPccuLlLuLJhoqnS6prKR46dAhI7LJvuukmBg8ebExelYhkTly9xzo034zqBjtu3LiY6WetDBo0iKqqKtrb2+ns7LTtHms3AZa6E+js7OTAgQNAZP8luqAVI+k48SNHjjBs2LDcFJglStqJpxOnuJGJHzx4kLa2Nmpraxk+fHjZibjP58Pn8xEOh7PWM8LqupI5cTUwKJGIT5o0iZ/85CeuZOLm2uycuLlbYiI8Ho8xajNRpJIsTjG3yaj9UUoky8TVMaouZFAa7VQlKeJ2DZup9E7JZFEIc5Ti8XjKTsQh/UU0nGK3lmIyJ+6k+6ATksUp8d5jRYm4k4uGk8bNZHGK+fgrBfGykqyLobVhE0pjP5SciAcCAWP4sVqJReF275QlS5ZwySWX9Pk8JeJq5KLqEaFFPH3WrVvHxRdfbEwxbL11TrS9cDhsOE+3Rdw62CcbThycdTNMxYmXgnhZSTdOKXZKTsR37dpFIBBgxIgRfYQ4k94pFRUVeL1egsGgERH86le/4tlnnzVmr1Ooi8iYMWOA1Be7LUayLeI/+9nPWLx4MY888giQWu8UNZd7XV1dwuw5FayDfeLFKcmcuLo7cCLibjjxUhfxdBo2S2E/lJyI2zVqQvI4JRQKGaJjnagqXl/kvXv3Ar2z1ynUz6qXQzn0EzcP9gF3Rdzv9/PSSy8BvSKWSu8Ut6MUcBanZMOJpyLi1obNUs/EtRMvEewaNSF5nGIeARivR4X1YFDrZ1pFXDWcqAV2tRPPjLVr1xpCbBVxJ71TnPRMSRWnvVPM77GSiog76WZY7nFKuTZsllwXQ7tGTUgep9jl4QrzwRAKhYwuYuaDAnpFXU3wX4oi3tPTw6FDhxg8eDCQXRF/4YUXjMdqpr5UeqfkQsTTiVNSadhMx4lnQ8Q/+eQT47j3er2cdNJJrs4MmAnJnHi8hs1SOCdLzolnEqfY5eEKs1C0tbUZAybsRLxUnbjf72fWrFmMGjXKEJVciXg6TtztRk3zdnMdp7jlxNOJU6SUjBs3jhkzZjBjxgymT5/O1VdfnfLnZAsnTjwcDus4pdCxG60JETH1+Xx0dnbGnU8iFSeu8nAoPxH/xS9+wZo1a+ju7ubdd98F+k4c5paI79+/nzfffNPId/fs2UM4HC4oJx4KheL2Q89GnJKOE1eZeKZdDN977z0g0tZz0kmRFRnfeOONlD8nG1gnRDNjbtjs6uoyLnagRbwgSeTEPR6PccLEi1SSOXGziKs8HMpLxNetW8fPfvYz42flDLPlxP/2t78RDoeZNWsWdXV1dHZ2cvjw4ZT6iWdbxA8dOkQ4HKZfv37GoiLm2uyceCq9U5qamqioqODAgQO2n2fXsOlWnKLOmSuuuILVq1cDvTOG5hvrhGhmzMeG9S5ci3iBEQqFEoo4JI5UkjlxszCZnXiyhk2raytWenp6uOqqqwgEAsbflO04RUUpc+bMiRm1mEo/8WzHKfGiFOt74pGKE/d6vUakYrfWZrbjFHOG39DQwMCBA+nq6jKWNMwndgN9QIt4UdHa2kpPTw+DBw+2nRciUQ+VVJy4XZwSCoWMn9UFw+fzxXVlzz77LA888ICTP60guP3221m/fj0TJkzg5ptvBrIr4uFw2BDxuXPnxoiYXT/xXMcpR48ejdszxVybG5k4JM/Fs92waa1XjYNQA7DyiV2jJsQ2bFrvmrWIFxjJXDgk7qFityCEwomIHzp0iFAoRGNjY0yrfby+4t/85jdZsGBBTDRTyNx7770A/P73v2fy5MlAduOUDRs2sGfPHpqbmznhhBNiemjY9RPPVZxSWVmJ1+vF7/cbTjRdJ+60rmS5uHLiuRJxNSK6EETcrlET4jtxdbeiRbzASNSoqXASpzjpnWIW8YMHDxoxiTUPV1hzcfPJv3nzZtt6C4UDBw6wf/9+6uvr+exnP9uny1s2BvsoF37++efHTAK1Z88e294pieIUN0XcPNWs2gdWJ55IxP1+P52dnXi93oQLVZhJ1s1QibV1ZZ94g33SES9rhq+cuFoQPJ8kcuLmhk113qtlG0uhnaqkRNyJE3cSpzhx4mb3HA6HjQPcmocrrCL+6aefGq+paWsLGfMiFx6Pp8/gk2w4cXMeDrEiZtc7JZETdzMTh17BUPsglThFiWi/fv0cT9WbaZxiFqxMMvFCjFNSdeKjRo0CtBMvOBKN1lQkilOSOXG7OAV6HbhTJ25uDFITZhUy6m5h0qRJQKygmle6d0vEjxw5wmuvvYbH4+G8887rs81UnHg24hTztpWophKnKBF1moeD8zglWyM2rfFPIcUpTjPxVEU81Z43+eipU1Iinmi0pkKJuLVHCSR34vHiFCXOyoFbR2sqit2JKxFXMzM2NjbS0NBAV1cX7e3trov4ypUr6enp4dRTTzXmoDHHKek48WyJuF2cksiJpyPihdqw6TROee655xg1alRW+pY7deLqPHUi4r///e8ZMWKEMRYiGXv27GHMmDHceuutKdWeKSUp4omc+DHHHAPQZ+ZBSG+wz5QpU4C+Im514tbpaIvNiasLjXLiECsqbou4NUoxby9ew6aTTDzXcUoiJ55KH3GFmw2b+YhTnnnmGVpaWnjooYdS3nYyUu1i6ETEn3rqKVpbW3nttdcc1bB27Vp27tzJk08+mUrpGVMyIh4Ohx2J+IUXXkhtbS1r1qwx4heF0y6GBw4c4PDhw1RVVTF+/HjjOUgvTikmJ24WcbOouD1iM56IJ2rYzKcTt4tTnDjxVGpy6sSz1bBpFfGRI0fi9XrZvXt3zChIO1Tda9asSXnbyUi1YXPYsGH4fD56enpsa1fnpbVboh2qnezjjz/OaaxSMiLe1tZGR0cH/fr16+OIzDQ0NPDFL34RgMcffzzmNaeDfdTt49ChQw2xVl+004ZNs4i3trYWfCu5uWFTkS0nvnXrVj766CP69+/Pqaeeajw/ZMgQvF4v+/fvNwQlX/3EIXmc4rYTHzJkCD6fj7a2trh/ZypxSmdnJ8Fg0PG2g8EgHR0deDwe466yoqKCkSNHEg6HaWlpSfoZaj9t2LDB9QbFVBs2Bw4cmHCxlkAgwNatW4HURfzo0aMJp0dwm5IRcXVLl8iFK6644goAHnvssZjnnTpxta2hQ4ca2bfThk118JozcSjsSKWjo4Pdu3dTVVVl3IZCbLzhpogrF37eeefFLOLg8/mMRW3VCeMkE892nKIu/qmIuDoOUhFxr9ebcK3NRA2bfr+fnp4efD6fcSza9V+Ph/lC6PX2ykYqubhy4qFQyPVcPNWGzQEDBhgiHu+CsmPHDuMOJlURh9zeXZeMiKsoRR1UiZg7dy4DBgxgw4YNbNy40XjeaSYeT8RTbdi0DlUuZBFXB+T48eNj5gaJF6e4KeLmKEWhREyRrHeK2UG6vbq79WKfTpySiohD4r7iiZy4Ou7q6+uNi1kqubjdwCSnPVQCgYAxhS24H6mk6sTNIh7PiZvHbsTrBBEPs4jncuxHyYh4Kk68urqayy67DIiNVJxORWuOU+xE3GmconrSFHIuHq9RE3oFZefOnYYLzDQTN6/iE0/E1TYVyZy4clmNjY0xDtINrK4v23EKJBZxa8OmORNX+6GhoSGhA7XDbooAp42bra2tMTmxmkDLLZw4cXPvFHOcEm8/mEVYO/EckYoTh9hIRR1cTp24et+wYcP6ZOKpirjKfAvZiVu7FyqUE1fZoXlFpHRFfM2aNRw+fJjjjjsu7ndpduJVVVXGnYGdE1f72+0oBZKLuNmJWxu60h1FmmgSLGvDptmJm0Vc7Qs3RTxZnKKilCFDhgCRnhxuTgaXyIlXVlbi8XgIBALGedq/f/+EIm4WYacibh47op24DYkaYpQTcCri55xzDsOGDWPz5s28+eabgPNMXBEvE7dr2LTeuqlMXIl4MTtxdQEy77d0RTxRlGLept32rE48G0PuFVYRtwpcZWUlFRUVBIPBPvPXpxunJMrEE80nrrZnduLpxCnWep3GKareGTNmMGLECNra2ti0aZPj7ScjURdDj8djHCtqweyqqqqEa99m6sS1iFt47rnnmDp1Krfddpvte1KJUyDSSPblL38Z6G3gdNo7ReFGnHLaaacBxeHErSKunLj62/Mp4sp99vT0xLhe8/B2tzELRk1NTZ/jw1yjNRfPNE6J58StDZsej8e4U1FZcL7iFOXER44cyZlnngm4m4snilMg9pxWd0xOnbiTTFwNelNs3rw5Z90Mi0LEfT4fGzZsSNiJPtU4BeBLX/oSAMuXLwcyc+Jqsv6uri6qqqr6fIZZxNX6lD6fj2nTpgGRKQNS6fKVS+zilMbGxpiTJlMR7+rq4u2338br9fKZz3wm7nvMcYp5216v1xAv8zaz1TPFun07MbbLxdPpnQKpNWyaH5tFPJ04xe6i4zROUfWOGDGCM844A3A3F08Up0BqIh4Oh2NE/PDhw0ZPFTtUlDJy5EgaGhpob2933CCaKUUh4ueeey4DBw7kvffeM5aIMqMmpKqoqOjT8JWIGTNmUFVVxXvvvUd7e7vjTFxhzcTNLtw6qZFZxFWU0tTURENDA8OHD6enp8dRX9tc093dzY4dO/B6vX2mM/B4PEnjjVRE/IMPPiAUCjFp0iRbR2W3Pbtt5sqJ241NsBPxdJ14KnEK9DZuxnPibvROGTRoELW1tbS3t9suQA7F5cTViODBgwcbJs1uXV6FilKGDRtm3LHmKiJ1JOJCiDuEEK8KIR4WQlSanr9ICPG6EOI1IcR/ZavIyspKozfJX/7ylz6vKxcwatSomC5wyaipqeGUU04hHA7z+uuvO+6dohg6dCj9+/fH4/HQ3t5udKGyRikQ209cibhaKX7ChAlAYUYq27ZtIxQKMWbMGMPpmlGRCmQu4uoCfcIJJ9i+x+zEEy2Iq8i3iNvFKZl2MXQSp0CvoKvIq76+3tU4xePxOHLjZid+yimnUFVVxcaNG5OKo1NSceJKmO26GJrjQ2tcakc8Ec9VLp5UxIUQU4FmKeVM4ANgnunl9cBZUsqzgaFCCJGdMjHy6yeffLJP1pRqo6YZ5QpWr16dshNXIwjVga16aSQS8Y6ODiMPVyKuYopCbNy0a9RUmJ2xef/EizaS4UTEzduzuq5cO3GzYKQap6Tb4Goe7GSN3xLFKUqE3M7EwVmkokR85MiRVFdXM336dMM8uUEyJ27+rpI5cXN8qM7lZNGIWcRzfT47ceJnAsujj5cBZ6kXpJTbpZQqLOoBsraA5DnnnENTUxPvv/9+n0gl1UZNMyqfW7NmTUqZ+IABAwyhUldr9aU5FXE1O18hO3G7Rk1FNpz4iSeeaPue2tpaQ0hSceLZzsRTceKhUCjti0t1dTWDBg0iGAzGjPoNh8OORdzNLobgrIeKilPURVidd6+++qrjGhLhZiZuNi4l4cSBgcCh6ON2oI9CCSFmAEOllG+5WFsM5kjF2sCZTqOmQjnxtWvXGrdVTnqnKEcEvaKtvnzraE2IvXUrRidubdRUuJmJO3Hi0BupOMnEc9XFMJVM/PDhw4TDYRoaGlKK/xTxGjdVw5vP54sZ1JTIibvRxRCS91AJBALs3bsXj8djnDdz584FIvGoG704EnUxhPgibtfFMNM4RZ0ruRLxiuRv4SCgzoD+QMx9hRBiFHA3cGm8XxZCLAAWACxcuJDZs2f3eY/f73fUqPe5z32OBx54gEcffZQFCxYYjYcffPABEDlR02kcbG5upqWlpc/qPNa6zPndgAEDjNfUgfP+++8DkSjBWoe6QBw5csSIXaqrq2lpaTGc0QcffOCofqf7yw3UXMoDBw6Mu03rBU+9R+3Dzs5OR7UePXqULVu2UFFRQX19fcLfMd/pmN+nBHH79u3Ge9R3GgqFXN9n5pPf6/Um/PydO3carytX2tDQkFZNSljeffddwwgop19RURH3M5XIBAIBw7Hv3bs35r2JjitlPOI1wCc7fvfs2UMoFGLw4MExUzgPHjyYTZs28cILL3DSSSfZ/r1Ojnd1fh08eDDpez0eDy0tLUYbwr59+2J+x6wn6m5769atfT7XXJeaEbWystK4SH700UeuHXNqObl4OBHx1cB3gT8Bc4BV6gUhRCPwOHCtlHJvvF+WUt4P3B/9Me4lt6WlJWGRinnz5nHjjTfy8ccf09bWxsknnwxgNChOnTrV0edYmTlzZszw+wkTJlBXV9enLrNrGjVqlPGacoY7d+4EIs7EWocanaa6IUJkLpLm5mbjc3fs2OGofqf7yw3U33TqqafG3abZNQ8aNMh4j3mgiZNa33rrLcLhMJMnTzam97Vj3LhxrFmzhqamppjPVidP//79jeeVQxs9erTr+8yck9p9vorMamtrjdfNA8LSqWncuHG89tpr+P1+4/eVwaiuro75TGucM2rUKMNNh0KhmPcmOq7U70+cOLHPe9R5+Omnn8b9fdUI29zcHPP6/Pnzuffee3nppZcMZx4PJ8e7OqcmTpwY967I/NzYsWNpbm427iCCwaDx+eYprc844wzWrl0L9N1X1rqUWTjuuOOYPn061dXV7Nu3j379+mUlyjOTNE6RUq4DWoUQrwInAIuEEL+LvnwTMB74tRBipRDis9kqFCIu4/LLLwdiI5VMGjahN59TOGnYNMcpyhmpq3G8TNzr9RonlGoAUif4sGHDqKuro62trU9r/ebNm7n22mu5+uqrjX/f/e53jcd//etfU/hLU8M8HafK7a24Fac4jVKg96JpvXUuxN4p8eKURNGEE+LFKfHycPPP+YxTzN0LzaipLx5//HHD5ASDQX75y1/yt7/9zXFtkFrDprV3ivmO6tNPP6W9vZ3GxsaYLoapxClerzen7VxOnDhSyu9bnro2+vytQE7XIvryl7/Mfffdx+OPP86tt95KOBw2RNENEa+qqrKdKMks4kOHDjUeqy9a9RaIJ+IQyeCOHj1qXOnVrbDH4+G4447jzTff5Le//S3/+q//CkRuXefNm8f69etta1+8eDF79uzpc/JmSjAY5JprrsHv9zNp0iTbGQDdathMRcSPPfbYPtu222auRNxOkOM1bNr1uXaKeXEMRbzuhZCb3imqYXPnzp2EQqE+54+5e6GZM844gzFjxrB9+3ZWrVrFzJkzueuuu7j55psZMmQIu3btipmK2A4VEXm9XtvzwGnDprlR0+PxpCXi6vfff/99Nm/ezNSpU5P+DZlQFIN9zHzmM59hxIgRfPzxx0gp2bt3L93d3TQ1NaU91ei0adP6LC4QD/MJYhZxq2gnEnGgj4gDxpQCP/7xj3n77beN59avX8/48eN58MEHjX933XUXDz74IBMmTKCtrY2///3vjv9WJwSDQa688koefvhh6uvr+f3vf2/73v79+xv7LN4weL/f72iio1RE/KqrruL555/nxhtvjHk+nhPP1YjNQnXi1sE+6fQTD4VCCRfWqK2tpampCb/f32cBcbB34l6vl6985StAZOqLjRs38sMf/hCIRKRqNstkmF24dZCdIpGIm/uJW3tjORFxv99PW1sbXq/XuLvOZWeFohNx65wnmXQvVFRWVqK6uNt1UYLYod3xnLjdzwol4tbBPhCZK+T666/H7/fz9a9/ndWrVxvC/oc//CEmTpk/fz5XX321cTu6aNGilP7eRAQCAf7xH/+RP//5zzQ0NLBs2TLbIfAQuYtQJ6d535ldkZOlu1IR8aqqKubOneuon7g6QQtpsE+mIh7PiSeLU1TvlXS6GB45coRwOEx9fb2tM1ZuPF5fcTsnDr2Ryl/+8heuvPJKenp6jPPCumiLHcm6F0LqTlyJsBMRVxcutfISkNNuhkUn4tD7xT/xxBNGZptulKJQXQ0TOXHz6/EycYWdE1cHjUJdtRW//OUvmTJlChs3buScc84hGAzy7W9/m89+Nn5Tw7x5kXFXTz/9tCvzrvT09PCVr3yFRx991BDws88+O+nvqZPTSZe/eHR0dLB161YqKyuZPHlymtUnduLZEPHKykrjpE1lsE+6Q+4V8Zx4sjhFkU4m7qTedEV86tSpHHvssezfvx8pJaNHj+b5558HIgsrx1upyUqyPByci7jViTsZ7GONUoCcdjMsShE/9dRTmTBhArt27eLhhx8GMhdxlYsnuppDrzAlcuLJ4hSI3FFYT4q6ujoeeeQRYwHXSZMmcfvtt9vWMnXqVCZMmMDevXtZtWqV7fuc0NXVxWWXXcaiRYvo378/y5cv56yzzkr+i/TeJlsvgE5FXHXNnDJlSkbZfqJMPFs9BJRw5NKJp9OwqXCSiS9btowhQ4YYMZ2TDF8t26d6M5mxi1MgcienTBnAgw8+iBCCU045hUOHDjlquHfixOONrjX3E1d91dNx4vFEXDvxJHg8HiNLe+6554DM4hSI9EE/6aSTjEWU7bjssss4/fTTY7rBmUXb4/HYnpxmER88eHDc/G7GjBncddddNDc38/DDDyd0Fx6Px+itk0mk0tHRwYUXXshzzz1HU1MTK1as6NNjJxGXXnopo0ePZubMmTHPOxXxVKKURFideHd3Nz09PVRUVCS9w0qXefPmMXPmTNuJ17KRiffr14+amho6OjoMIU7FidfW1uL1eunq6oo7O9+9997L/v37eeaZZxzXm64TB/jGN77B5MmTueWWWzjvvPMA+3Vw45FsoA/0HhuNjY1GJOTz+aipqSEcDhsXWbdEfNy4cVRWVrJjxw7XF4W2UpQiDsRcvSFzJ97Y2MiGDRu44447Er7vvvvuY82aNTEnh9mJDxw40LZ3i1XE7fj2t7/Nzp07Of3005PWbY5U0lkppb29nTlz5vDSSy8xfPhw/v73v3PKKaek9BlXXHEFa9as6TNcPtcibt2eOUqxa/DKlAcffJBXXnnFduRlIhFPN+Ixzx6pBDJZw6aivr4+ZsV6q8D09PTw8ssvA71zAWUi4sFg0MjureujKpqbm9m0aRO33trb0W3+/PkALF26NGnsk0qcYr1jMu+HI0eO0NraGtPXvrGxEZ/PZ0whHY94Il5RUcExxxwD9N5pZouiFfETTzwx5qTP1IlnglXE7TCLuDUPT5cZM2YwevRodu7cmfIK4p9++innnXceq1atYvTo0bzyyisZC6mZfDtxleVme7BFIrIRp0Dfxk0ncUpNTY0h6nYivmrVKqMxOBURt4tT9u3bZ4zWjDcLph3qzq6rq8tYKMSOVBo2E4m4+nvHjx9vGDEn3QzjiTj0Hs/xps92k6IVcYh145k68UwwL8Jrl4eDcyeeCh6Px5hTJpVIZe/evcyaNQspJRMmTODVV1/NqFExHqmKeKKJr9LZXqJucbkiG3EK9M3FncQp5uPProfKsmXLjMdbtmwhHA5n5MQT5eHJUOf3H/7wB+655x7uueceFi9e3Od9bjjxjo4OI0qxDmzTIp5F1Jfc2NhoLMCaD7xer3Fw5FrEASMXT2UyoX/7t3/j3Xff5dhjj+WVV17Jyp2MExFvbW3lk08+obq62naSLadYnXg2e6Y4JZ4Tz7R3CvSdV9yJEzf3jrLroWJ2vWqytlQaNltaWmJ6SiXLwxMxb948KioqWL9+PTfeeCM33ngjF198sTGOQuHEiSshNndIgFgnrkZXahHPIRMmTGDRokU89dRTtjl0rlDinQ8RP+uss2hubuaTTz4x5npIRE9Pj+Han3rqqazNw+JExNX0CbNnz3Y0Oi+V7ZWyE7eu8KOceKoibnbie/fuZf369dTW1nLccccBkUjFyUWnurqaoUOHEgwGYxYMtk5BmwpDhgzh8ccf56qrrmLhwoUcf/zxAKxbty7mfU6c+Lnnnssdd9zBz372s5jnzfvBbsZOLeJZ5rLLLuP888/PdxnGF51IxM0nkVuZOETuBFRDkJPW/JdeeokDBw70aVdwGyciruq1NlSnQ7Fl4plcXOwaNq1xivnCmEzEX3nlFQBmzZplTG2wdetWxxcd5cbNkYp5MYh0uPzyy/n5z3/OPffcY/RIU7MMKpw48aqqKv7lX/7FuBAozN0Mkzlxu77idiI+ceJEqqqq2L59u3EsZoOiF/FCQX3RThs23XTi0CuCTz75ZNJFXZX7VSNfs0UyEd+2bRtr1qyhtrY2adfOdLZXCHGK1Yk7zZiTkWmcEi8TX7lyJRCZ69s8gZPTeuPl4pnEKVbUhcVOxBM5cTucOHHzOrpWAoEA+/fvx+Px9Il0KyoqjJo3btyYcm1O0SLuEk6ceDZFfPr06UyaNInW1lbjZIxHT0+P0f/3S1/6kqs1WDGL6rZt25g9e3bMlL/q8Re/+MU+o1nTwc6JF5KId3Z2EgwGqa6uTqm3hhW7OCVRw2aiTDwUChlOfM6cOcY4iFSceDwRz6Rh04oSRGuXPSdxih1qP7S3txuzkFqnQk4Up+zfv59wOExTU1PcOFA11mczUtEi7hKqb/W0adNs35NNETePfEsUqSxfvpz29nZOPvlk46TIFkrEOzs7+drXvsaLL77IN77xDWMUm6rzq1/9qqvbs2bihRSnuBGlgHsNm8qJv/XWW7S1tTF27FimTJmSlojH62aoHOi4ceNS+OviM3nyZDweD1u2bIm5u3MSp9ih9sMHH3yA3+9nxIgRfS4GiUT82WefBTD6hFvJRS6uRdwlvv/977N//37OOecc2/dko5+4GZUZLlq0yDbCyFWUAr2ieuedd7J69WogcsJdffXVvPvuu2zYsIEBAwYwZ84cV7ZXyL1TOjs7CYfDrl1YhgwZgsfjYd++fQQCAduGTbM7TNTFUPVKmTNnDh6PJ26ckmw/Wp14a2srmzdvpr6+3lg4IhNqamoYP348wWAwZnZAN5z4hg0bgPjz5tuJeDAY5M477wTghhtuiPv5WsSLCI/Hk1SYs+nEAY4//nhOPvlk2tvbY/r7Krq6ugznkO0oBXpFXC3x9vjjjzN8+HBee+01o1vk5ZdfHrN2qRvbK6TeKT6fz4g4uru7DUHMVMQrKioYOnQo4XCY1tZW24ZNp3GKWcSh1zlv377dEK9U4xQ1n89pp52Wcc8jheo1Y87F3XDi77zzDhB/LVm7SbCef/55Nm/ezPjx443j2YoW8RJDiXhFRUXWhCVRpLJ8+XIOHTrEtGnTmDJlSla2b8YszgsXLmT+/Pncf39kpb5NmzbF1OsGhdg7BWJzcbdEHHojleXLl/PRRx8B6cUp7e3trFmzBp/Px7nnngtE9uWIESMIBALG/PdORVzFKUrEnU6k5oR4jZtuOHE137pTJx4Oh/ntb38LwPe+9z3bi9T48eOpqamhpaWlz6pdbqFFPIeoE9du8is3UJHK4sWLYya7h8jUvZCbKAV6T6pjjjnGmJPmoosu4sorrwQijXOzZs1ybXvKie3atYuurq6CcOIQm4uruafdFPFrrrnGEBTrXY0TEV+xYgWBQIBTTjklRqitgpZMxEeOHInH42HXrl0EAgFDxJ1MZ+yUeCKuxDUTEVfEc+LxRHzlypWsX7+eIUOGcPXVV9t+vs/nM+4estVDxZ17HI0jxo0bx7e+9S1X8sFE2zj99NNZu3Ytzz33nCHYnZ2dRpSSKxH/+te/zpYtW/jRj34Uc4LdfffdhMNhLrjgAtuJo9Lh2GOPZcqUKWzatIkf/OAHBZGJQ6wTX7JkCQD/8A//kPHnfuc736Gzs9OIUhoaGvo0EjvpYqiiFOsFdfz48YYQV1dXJ429qqqqGDZsGHv27GHLli28+eabeL1eRxO5OcUq4ocPHzZ6Y6U6cRvQZzUwp05cmZIbbrghaYxzwgkn8Pbbb/Pee+8Z6xa4iRbxHOLxeLj33nuzvp358+ezdu1annzySUOwlyxZQkdHB6eeemrGw9udcsIJJxju38yAAQP44x//6Pr2KisreeSRRzjjjDP4z//8T0O0CiVO2bdvnyHiF198ccafO3v2bGbPnp3wPckG+xw+fNgYAWldfMTc1c5pn/bRo0ezZ88enn76aQKBANOmTXP1ImoW8XA4zLPPPktXVxdnn312WvMnWZ14IhFXmfj69et54YUXqKur4/rrr0+6jWzn4jpOKUHU9LTPPfec0fvAzZGRhcyMGTP40Y9+BGR3keRUUE7tscce4+jRo8Y0CbkgWZyybt06tm3bRlNTEyeddFLM75oFzek+VN0M1cXbzTwcIlFkU1MThw8fZteuXRkf1+Z9UldX12fUJUTcemVlJV1dXXR1dfHLX/7S2GaicSEKLeKalBk1ahRnnXUWXV1dLF26lIMHD/L888/j8XhyFqXkkx/84Aeceuqpxs/5FnHlxP/85z8Dub2QJotT1Hqvs2fP7hNtpevEoXd+E7dFHHp7qKxatYrly5fj8/nS7m1l3icTJkyI21Zlno727bff5oknnqCiooIFCxY42oYWcU1aKLF+4oknePrpp+np6WHWrFmujJwrdCoqKoxVkfr37593EVdO/MCBAxkJTjrYTUVrjRHi9dXPRMQVbjZqKlSkcttttxEIBDjvvPPSnsXUvB8SxYxKxG+55RaCwSBXXHGF47upcePGUVdXx+7duxOuEJQuOhMvUebNm8dNN93E888/b4zqc2tkZDEwZcoU1q1bx65du1zro5wu5kbdc889l6FDh9LS0pKTbSfLxBVz5szpszJUc3MzlZWV+P3+tER89OjRfUTdDZSIqwE6mdzZWJ24HUrEV6xYAUQG9znF6/Vyzz33MGTIkKwsE6ideIkycuRIZs6cSXd3N2vXrqWystJ2QEKpMnnyZGPB2nxi7r2Q6zaJZJk4wMknnxx3giqfz2fMM+9UxFUmDtlx4UDMdBHV1dVceumlaX+WUyduzr6/8IUv9Gk/SMY111zDRRddlNaApGRoES9hzPn33LlzE86wqMkeyolnKjjpYCfi1dXVhktPNO2BilTSceLZyMMhVsQvuOCCjOKympoaYy0CJ04c4Oabb057e9lAi3gJc/nllxsNNaXeK6WQUSL+hS98IaPpZ9PBTsTNiyUnEnElbE6FcsSIEYYoZkvEx40bZ/RZz/S49ng8RluBk0z8tNNOY+bMmRlt0210Jl7CDB8+nIULF7Ju3TpX+iVr0uOiiy7i+eefz4uDUyLu8Xj63MpfddVVbNy4MaEoXXbZZSxfvtzxJGWVlZV885vfZPfu3SlHDk7x+Xxce+21vPPOO1xwwQUZf978+fP56KOPEjrxSy65hOXLl3PnnXdmbbR1unicrsnoEnE31tLSkrN+s6mg63JOIdYEuq4VK1Zw7rnn0tjYmHR1mXLfV6mS47psrxw6TtFoShjlxK3DyzWlgxZxjaaEUSLuxspJmsJEi7hGU8KoBslszF+vKQx0w6ZGU8Icd9xx3HvvvcyYMSPfpWiyhCMRF0LcAZwJbAOukVL6o8/7gAeAycCbUsqbslOmRqNJB4/Hw7e+9a18l6HJIknjFCHEVKBZSjkT+ACYZ3r5QmBX9LV6IcQZ2SlTo9FoNPFwkomfCSyPPl4GnOXwNY1Go9FkGSdxykBgd/RxOzDI8tohm9cAEEIsABZAZJ3FeJPY+/3+nE0IlAq6LucUYk2g60qFQqwJdF1Awv7oTkT8IKDG3PYH2hy+BoCU8n7g/uiPerCPCxRiXYVYE+i6UqEQawJdVzKcxCmrgfOij+cAqxy+ptFoNJosk1TEpZTrgFYhxKvACcAiIcTvoi8vBcZEX+uSUq7JWqUajUaj6YOjLoZSSusM6NdGnw8AV7lck0aj0WgckusJsDQajUbjInrYvUaj0RQxWsQ1Go2miNEirtFoNEWMFnGNRqMpYrSIazQaTRGjRVyj0WiKGC3iGo1GU8TkVMSFEPXR/wtquejovOgFhRCiLvp/oe2rofmuwYoQYmz0/0LbVxOj/xdaXacVYE2fF0KMzHcdVoQQY/JdQzJyMthHCHE+8E/ALuAOKeWurG80CUKI+cCFUsr/le9azAghLgG+DuwA/r0Q9hWAEOKLRL7DDuDXwGopZSjPNdUBvwRGA/PUYiX5JrqvrgVekVLeke96FNG1Af4LWAv8SErZk+eSEEJ8HvhnItNYHyel3JbfiiIIIeYCC4Fu4DFgmZTySH6rik+unPhXgf8G3gWuE0LMzNF24yKEOAn4CnCKEOKa6HN5d+NCiAuBq4E7iMwQeXP0+by6JiHE6UQuLP8XeAY4X0oZynddUspOoAdoJLLfCmFffQ74MZEL8B1CiNp81mPhbOB2KeX/ASbkqwj1HQkh5hH53m4A/hP4Ur5qMhPVguuIzL76U0AQWfSmoO5eFFkRcSFEtRBiQPTxMGA78AbwR+AjYHqub52iNQ2M/rhTSnkpMBtYIISokVIG8/ElRetS87C/D/yTlPL/AXcDU4QQQ6WUOZ8bwbK/PpBSfllKuRpYCUwVQownx2u0Wo6rquj3tRn4EXCeEGJMAeyrvcDjwEIhxIvAI0KIzwohcr6erXl/RekC5gohVgA/FELcpCKfXNZEZB0CgNeix9X7wAqgNvp6zrHsq9HAO0SSg0+AaUAtUJmP2pLhuogLIa4ANgI/B5BStgIjgNOit29vAzVE5h/PCaaabo3WdEAI4YtGFauAn0Sfz6kAxKnrYynl3qg4NQJbpZR7c1mTTV0Ho8+PB+4lMi/894HP56EmdVz1RL+vY4kcS08D1wohRueqJpu63gU+BN6SUp4H/JnIfsqp87XWFaUOGAZ8D/gWkajggjzUpI6rPaaX64EBUspuIUSu2+pUXbdF69pG5KLyNSJ6tZ9IlHh9Lutyiqs7SwhRQ+TL+CfAI4RQB8gzwDeEEHXRg3wsMM7NbadQ09zoS+pv/zlwthCiSQjRP/r+XNelMjiiF5cwkRMuEH1uTK7uEhLsL4BW4Hop5SXAe0QvxNmuLUlNLwNvEcnqvw7cGP2drAtBnO/wC9GXXpJS3g4gpXwaGAVMyXY9NnV5THW9BviAUVLKdiILn6tjLJffofl4V3cofwPOEUKMzWVbi913KKV8mcjSk/dKKb9OZNrtKiGEt9BilYxv8aI9A/4FeA7YIKX87+jztcAVQohlUsq/CiE+A9wghFhDxIlnLYNOUtPXhBDLpZR+IURl1JW/ROS2fBERl9KVx7qC0befQSSH+w+giYgLyErDisO6QtEMulMIMYTI+qpvQHbuYBzU9LfovpoJ/G8iq0otIvrdZUsIHB7vHab39yNiGLJyTDms66tCiBeklG9Hj/V/EJGeYl8gctzn6ztcLqUMRM/DTiHEs8BJRCKMrOHwOwwBJwKVQojTiNy5HMh3Y348MnIrQohKInnkTmA4kVttxQoi+eA/RX++k0ir+LVEdtzSTLadQU37gG9Efw5Fb79PAW6TUn5TRQd5rgtgPJEW+4+klFdmq2U8lbqEEL7o3dVjwOtSyv/KY03quLoH+JOU8itSyu9KKX+YjZpSqEvtq0ohxD8CfwXellK+mMe6zOfho8BfgNOI9DD6aZ5qMvaVMlREjN3H2agnxboWRH9+FNhKJG59Q0p5YzZrS5e0uhgKIS4DBgMvAv8tpfxc9PnfA+9LKe+M3nKMJZIzvQ6skFK+K4SoiC4m4Spp1LQWeFFK+X405ul0u6Y063qdSL47DPg4ixeVdOpaChwFOqSUh+J/ck5regNYHm0YQwjhzYZTSnNfPUvkTnd/NLpwnTTrellK+U40tgvafXYOa1oLrIzWVJmtbqJp7qsXpZQbhRBVhdAd046UnLgQYogQYinwZeB4Iutr7hVCXB19y0+BeUKIIdHbs37A6cAlgB+M1YBcI4OaLgWC0ZpcF/AM6roMqJVSvpkNAc9wf/mklLvdFvAMaroYMETbbQHPcF9VRRuqXRfwDM/DHgC3BTzDfaVqcl3AM9xXSh8KVsAh9TglDPxOSvkVIg2CxxPJIU8UQkyWUm4HXgDmiEiDxXTgn6WUn5NSfuhm4S7VtClLNWVS1ywp5UcFWNc5WayrEI+rTOrK5r7KpK5CPQ8L8TvMdl2ukWrD5qdEWmyRUu4XQgwHDhPp+/3PRDrIDwTWRx33H1ystZhq0nUVf026ruKvqZDrco10M3EPke5lj0kpPx997ndEOsRXEWkYOCxz2O+6EGvSdRV/Tbqu4q+pkOtyg0y6GFYArwkhpgNzgQeBTVLKA65UVjo1ga6r2GsCXVex1wSFW1dGpD0BlohMXLMYeAn4s5TyYTcLS4dCrAl0XalQiDWBrisVCrEmKNy6MiUTJ94G/AD4rwJqvS3EmkDXlQqFWBPoulKhEGuCwq0rIzIR8TeklK+7Vok7FGJNoOtKhUKsCXRdqVCINUHh1pUROZlPXKPRaDTZQS/PptFoNEWMFnGNRqMpYrSIazQaTRGjRVyj0WiKmJwvGaXR5AIRWUT5X4BtUsqHhBBXERlS/X0p5Z15LU6jcRHtxDWlSh2RBYuviv78d+AKYEm+CtJosoF24ppSRUb//6wQIkxktZixRNYG/VAIsY3I/NJ/JLKs22vAr4mscF4BXC2lXCaEqAJuJ3IBqCeyjNi3pJT7cvi3aDS2aCeuKVV+EP3/fSICHC9CqY/+v4bIUmW/Bf4dGAr8IvravxKZ7W4JcDeRRY/vy0rFGk0aaBHXlCrLo//vlVI+Tvz1SUPAd4jMLw3wsJTyV8AuIsvjAVwY/f9aIvFMPTA7KxVrNGmg4xRNqeJkKPJRKWWPEEKtKKNW4QkSu5B3gIiYq9VwtPnRFAz6YNSUKoeIOO1JQoivEcnD02EpEbNzJTCGyBSm17pSoUbjAlrENSVJdL3GfwcGAI/Q66JT5f9GP2cmkYbPzxPp6aLRFAR6AiyNRqMpYrQT12g0miJGi7hGo9EUMVrENRqNpojRIq7RaDRFjBZxjUajKWK0iGs0Gk0Ro0Vco9Foihgt4hqNRlPE/H+m4pSvJPX71QAAAABJRU5ErkJggg==\n", 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\n", 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" + "
" ] }, "metadata": { @@ -371,11 +334,13 @@ } ], "source": [ - "for i in [1, 20, 50, 100, 250]: # Feel free to plot a few other series\n", - " plt.figure()\n", - " air_train[i].plot()\n", - " plt.show()\n", - " plt.close()" + "figure, ax = plt.subplots(3, 2, figsize=(10, 10), dpi=100)\n", + "\n", + "for i, idx in enumerate([1, 20, 50, 100, 250, 300]):\n", + " axis = ax[i % 3, i % 2]\n", + " air_train[idx].plot(ax=axis)\n", + " axis.legend(air_train[idx].components)\n", + " axis.set_title(\"\");" ] }, { @@ -463,7 +428,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e96b3501a66e403a95e3e504b181a801", + "model_id": "48a496f3bef0411b87a4f33328cd7ca2", "version_major": 2, "version_minor": 0 }, @@ -517,7 +482,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cb40300e1ef446899ab53b9f219ec882", + "model_id": "99e1248113684563a05663ed93fcc603", "version_major": 2, "version_minor": 0 }, @@ -571,7 +536,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f7ea09994fcb450e81dd8c46f42063af", + "model_id": "d8624f38fbf84cdbbd92aa8ad7e4ea69", "version_major": 2, "version_minor": 0 }, @@ -623,7 +588,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9d72a4c088734a8ebd97be3488541733", + "model_id": "ff496c1fac6f4b1589bb986edf9e5e83", "version_major": 2, "version_minor": 0 }, @@ -675,7 +640,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f8c871c2c5b1445da89905b1773963e4", + "model_id": "aa67824da19d4f64a21f9ebf9ef2a9b4", "version_major": 2, "version_minor": 0 }, @@ -728,7 +693,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f0bc9c41c7034ce8b1b0f1f33bf72f47", + "model_id": "a05084cf04f4477fb117ea4cb1321b25", "version_major": 2, "version_minor": 0 }, @@ -810,7 +775,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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dWzB+/Pjct99+O8W/zocffrjDZZddtvfLL79cPWzYsML/9//+X6cdO3bEBFKDBMfhjoB5Q/Wkyf5gdeFyucjLyyM7OxunU5+HJDLovJRIFK7zsrS0lA0bNpCbm0tiYmLNOxashb8PDt0L3/QlZHTzPVtW28MHDx7cvaqqimXLlq31tfXp0+fEoUOH7n/iiSe2HLn/c88913LixIlZe/fu/RY8I8R//OMfOxYVFX0DMHny5DbPPPNMm02bNq0Ez6hxv379en/11VerBgwYUHrHHXe0//zzz5svXrx4na/P9evXx3Xp0qXvt99+u7Jv375lR77mzJkzW9x88805mzZtWt6yZUuX/7aDBw86WrVq1f/tt9/+fsSIEcW+9iuuuCK7pKTE+fbbb2+o7uceM2ZM1s6dO2PffffdH3fs2BHTrl27/vPmzVt73nnnHThy35NOOqlH9+7dD86aNSvP13buued2KikpcS5atOgH8IwQ33LLLdumTZu2FWD//v3OtLS0Aa+99tq6yy67bP/xagDPRXWFhYUxCxcuXF/d/tGipKQkcd26dc0rKyvPHjhw4Jpj7Ru2dYiNMQnAk8AIoAWwGphgrV1ijEkEHgEu99Y4w1p7V7hqFRERiTjlR+WtOvZXVOcuevbsedD/eZs2bcoLCgriAN54442Uhx56qP369esTi4uLY6qqqhxlZWWOoqIiZ0pKiuvIvsaOHbtn8uTJHT/44INmw4cPL37uuefSe/bsWTJgwIBSgBUrViQtXbo0JTk5ecCRx65Zsybhs88+a3brrbdm+9rmzp277oILLtj/f//3f+WdOnXq84tf/GL/WWedVTh69Oh9KSkprlWrViWUlpY6L7jggm7+fVVUVDhOPPHEEt/zBx54IOOll15qvXXr1viysjJnRUWFo0ePHgcB2rZtW3XppZfuvuSSS7r9/Oc/33/GGWfsHzNmzN7s7OwKgPXr1ydec801Bf79Dxky5MBTTz3V1r+tX79+h97H1NRUV/Pmzau2b99+6IYIx6pBghPOG3PEAhuBU4HNwEjgbWNMDnA70Ac4EYgD5hljfrTWPhOeUkVERCJMfPMQ95dy/H2OIy4u7rDfzjocDlwuF2vXro2/4ooruo4ePbrgvvvu25KRkVH54YcfNp8wYUJOWVmZIyXl6NfOysqqPPnkk/e/+OKLrYYPH148d+7cVv5hsri4OGbYsGGFU6ZM2VzNsRVVVVUMHTr00EhvTk5OefPmzd2rVq1aPX/+/JR333037f777+/w4IMPdli2bNl3+/fvjwF47bXX1vkCrE9iYqILYMaMGS3//Oc/d5w0aVL+0KFDD6Slpbnuv//+dl999VUz377//ve/N3722Wc75s2blzZ37txWDz74YOZbb731/fDhw4sJ0JHvI3h+YxFoDVJ7YQvE1tpiYLJf02xjzKNAd+A8YLK1di+AMWYacCOgQCwiIgKQ0s5zQVywK0z4c8RAStvj7xekJUuWJLtcLmbMmJEfExMDwEsvvdTqOIcxatSoPZMmTTph4cKFezZv3pwwduzYPb5t/fr1K5k3b16L7t27l8XFVX832ZYtWx41bSIuLo6LLrqo6KKLLirav3//1latWvWfP39+ygUXXLA/Pj7evXHjxvjqpjsAfPbZZ80HDBhw4M477zwUzDdu3Jhw5H4///nPD/785z8/+MADD2zv379/D1+o79y5c+nnn3/efNy4cbv93pvmXbt2DXh0N9AapHYi5tbNxpiuQCvgB2+T/03WHUCvYxybABx2MixevJj4+Pgajohevk+Yvq8ikUDnpUSicJ2XLpcLt9t96FGjhFQ48VewZj6OOlxY53bEwIkXePrzvp7vIrBQ6d69e1llZaXj/vvvb3PppZfu++ijj5rPnDkz43jHjR49eu8dd9yRdfPNN2effPLJRTk5OYdGbm+99dadL7/8cusLLrig05133rm9devWVWvWrEmYNWtWq9mzZ2+MjT064syaNStt/fr1CcOGDStq3bp11euvv57mdrsdvXr1Km3ZsqXrt7/97fa77767o8vlcgwbNuzA3r17Yz7++OPmqampVePGjdvdtWvXsrlz56bPmTMntWvXrmXPPvts+ooVK5IzMzPLAdasWRM/ffr0jIsvvnhfVlZWxcqVKxPz8vISfv3rX+8GmDBhwvZrr722U//+/UvOPffc/XPmzGmxYMGClm+88cb3gb6Xx6tBghMRgdgYkwS8BDxgrS00xrwL3GqM+QzPlInxwLF+FfAH4F7/hmnTpjFy5Mj6KrnRy8/PD3cJIkfReSmRqKHPy6qqKqqqqqioqDjuxXyOAdcQv/rNOr2ew11F+YCxuMt/ylMJCaEdcBwyZMjBSZMm5U+fPr3d/fffn2mMOXDPPfdsvvnmm3OPdVzLli1dw4YNK3znnXdaTp06daP/tpycnIqPP/54za233nrC+eef362iosLRvn378jPOOGN/Te9bq1atqh577LGWjzzySIfy8nJHVlZW2VNPPfWjMaYUYOrUqVszMjIqH3300XYTJ05MSElJqerVq1fJXXfdtQ3g1ltvLfjmm2+Sx44d28nhcHDBBRfsGTNmTMEHH3yQBtCsWTPX999/n3jllVd23rdvX2xGRkbF2LFjC2677bYCgP/5n//Zt3Xr1vy//e1vbe++++6OmZmZ5dOmTdvwq1/9KuAJ3MerQYIT9lUmjDFxwOtAITDaWuv2BuRHgIuAcuBp4HprbbVLqdQwQlyoEeKjuVwu8vPz6dixo67ml4ih81IiUbjOy9LSUjZu3Hj8VSbAM6L7xMmwez2OIKZOuB0xkN4ZfrcU/EaFHQ5HrVeZEIk0jWKVCQBjjBN4Ec8SaVdba90A1tqDwM3eB8aY/wd8UVM/1toy4Kh5QlIzp9Op4CERR+elRKKGPi+dTicOh+PQ45gcDrjkafjnWVDponYrjjpwxMTBpc+A/t5JlAv3lImngPbAWdbaQxOgjDEnAFXADuBk4C7g/LBUKCIiEsk69Idfz4ZZo6CqIrCL7BwxEBPnOa59v3ovUSTShXMd4mzgN0ApsMsY49t0jreumUBrYD1wk7V2RTjqFBERiXidz4Br34O518Ou7z23c67uQjvfqhTpnT0jwwrDIkB4l13L4/CVJI6U1VC1iIiINHod+sNNX0De5/DF057bMfuPFjtioOcFMOh6yD7lsDnDItEu3FMmREREJFQcDsj5uedRWghFOzx3oItP8awznKiFCESqo0AsIiLSBBU5nRTExVLiSCA5NpYMp5O634tOpGlSIBYREWki3G43dodl9prZLNy0EJf7p5uKOB1ORmSNYFSPUZi2JuQ33xBpzBSIRUREmoDVu1dz56d3sqFwAzGOmMPCMIDL7eKDTR+wIG8BuWm5PDj0QXqm9wxTtSKRRQsPioiINHJLti5hzH/GkLc/D4CqGpZe87Xn7c9jzH/GsGTrkgarUSSSKRCLiIg0Yqt3r2bch+Moryo/alS4Ji63i/KqcsZ9OI7Vu1fXc4XRJTMzs8/kyZPbBLr/448/np6SktK/HktqcIH8TJdeemnOiBEjOjdQScelQCwiItJIud1ufv/J76lwVeCu1V3qwI2bClcFd356J2537Y6tzqWXXprjcDgGHvkYOnRo1zp3HoFqCn1ffvnldxMmTNgVyteaP39+85/97Gfd0tLS+iclJQ3Izs7ufckll+SUlpaGfSJ4bT8A+MyYMSN/9uzZG+uhpKBoDrGIiEgjZXdYNu7fGPTxLreLDYUbWLZjGaadOf4BxzF06ND9L7300gb/tqSkpLqn7UakQ4cO1dwRJXjLli1LvPTSS7uNHTt25/Tp0zclJye7v/vuu4R///vfLSsrQ/pSDSo9PT2AWyo2HI0Qi4iINFKz18wmxhFTpz5iHDHMWjMrJPXEx8e7srKyKv0fGRkZVQDz5s1LiYuLO+ndd99t7tv/7rvvbtuqVat++fn5sQCDBw/uPmbMmKwxY8ZkpaSk9G/ZsmW/8ePHd3C5fpoKUlBQEHPxxRfnpKam9k9KShpw2mmndV2xYkWCb7tv5HbOnDmpnTp16pWcnDxg6NChXfPy8uL8a3300Udbd+rUqVdCQsJJubm5vR588MEM37a1a9fGOxyOgTNnzmxx8sknd0tKShrQvXv3ngsXLmzm+1nGjx+fc+DAgRjfSPjEiRM7wNEjppMmTWrbrVu3nklJSQPatWvXd/To0VmFhYUB56/58+enpqenV/zjH//YPGjQoNJevXqVXXbZZftnz56d17x5c7f/zzxr1qy0nJyc3klJSQPOPvvsTkVFRc7p06enZ2Zm9klNTe0/duzYjv4h+njvJcDzzz/fokuXLr3i4+NPyszM7HPvvfe29W0bPHhw961bt8bfe++9HX3vg/+xx/ozOHLKxODBg7uPHTu24w033HBCWlpa/9atW/fzvac+X3/9deLAgQO7JyQknNS5c+deb7zxRorD4Rj44osvtgj0/ayJArGIiEgjVFRexMJNC2u8gC5QVe4qFm5aSFF5UYgqq96vfvWrouuuu27nddddl7t79+6Yzz77LOnhhx/OnD59+saOHTseSmlz5sxJj42NdS9evPi7+++/P3/GjBltH3vssda+7aNGjcpZvnx5s3/9618/fPjhh2vcbje/+tWvupaVlR2aPlBaWuqcMmVK2+eff37DggUL1m7dujV+3LhxJ/i2P/nkk60efPDBDvfee++Wb7/9duWkSZO2PPjgg5nTp09P96/5z3/+c+aECRN2LF26dHWnTp1Kr7766k4VFRWMGDHiwOTJk/ObN29elZeX921eXt6399577/bqfm6n0+meMmVK/jfffLNqxowZGz777LOUm2666YTq9q1Ou3btKnbt2hX3n//8p/mx9istLXX+7W9/a/Pyyy+vnzt37rqlS5emnHPOOZ3ffffdtLfeemvdU089teGVV17JeO6551oG+l5++umnydddd13niy++eI+1dtXvf//7rQ8//HCHxx9/PB3g7bff/qFt27YVt91221bf+xDon0F15syZk96sWbOqxYsXfzdp0qTNU6dObf/666+nAlRWVnLJJZd0TkxMdC1atOi7J554Iu+ee+7JDPR9PB5NmRAREWmECkoKAr6I7nhcbhcFBwtIia/brTsWLVrUIjk5eYB/2y233LLtwQcf3A4wderULR9//HHqmDFjsteuXZt06aWX7r7qqqsK/fdv165d+TPPPJPvdDrp169f2YoVK5KeeOKJtrfeeuuuFStWJHz44YctFixYsObMM88sBnjttdd+zM3N7fvyyy+3uPbaa/cCVFZWOp5++ulNvXr1KgO4/vrrd/71r389NNr4wAMPdPjLX/6y+eqrr94H0KNHj/JVq1bt+Oc//5kxbty43b79xo0bt2PUqFGFAPfdd99WY0yvlStXJg4YMKA0LS2tCiArK+uY8xbuueeenb7vu3fvXr579+6tEydOzAI2BfKeXnvttXsXLFiQdu6553Zv3bp1Rf/+/YvPOOOM/TfccMPuVq1aHToBKisrHTNmzDj0M59zzjl7X3/99fTt27d/m5aW5ho4cGDpE088UfTRRx+lXH/99XsDeS8feeSRtj/72c/2P/LII9sA+vbtW7Z69eqkxx9/vN0tt9yyu23btlUxMTHulJSUqiPfh+P9GVSnW7duB6dMmbINoE+fPmVPPfVUm/fffz/l4osv3v/GG2+k5ufnJ3z88cdrfa918ODBLRdffHG3QN7H41EgFhERaYRKKktC219F3fsbPHjw/hkzZhwW9DIyMg4FpcTERPdLL73048knn9yrffv2ZU899VT+kX2cdNJJxU7nT7/APuWUU4pnzJjRtrKykuXLlyfGxMS4zzjjjGLf9nbt2lXl5OSUrV69OtHvdVy+IAbQoUOHij179sQC7N+/35mfn5/wv//7v9kTJkzI9u1TVVXlaN68+WHD7SeddNKhNyUrK6sCYPv27bXKTm+88UbKQw891H79+vWJxcXFMVVVVY6ysjJHUVGRMyUl5bifaGJjY/n3v/+9ccOGDVveeeedlKVLlzafOnVq+8cff7z90qVLv8vOzq6o7mdu27ZtZWZmZnlaWtqh18jIyKjYtWtXHBDQe/nDDz8knnPOOfv86zn11FMPPPvss20qKyuJja35rTjWn0FNevbsedD/eZs2bcoLCgriAL777rvEdu3aVfgH79NOO634yD6CpUAsIiLSCCXHJoe2v7i695ecnOzq3bt32bH2+fjjj5sDFBYWxu7cuTMmNTU1NMPcfmJjYw+7kM/hcBxaScM3f/fRRx/NGzp0aPGxjouLi3P79wGe4BxoHWvXro2/4ooruo4ePbrgvvvu25KRkVH54YcfNp8wYUJOWVmZIyUl8BH53NzciptuumnPTTfdtKegoGBLt27dek+dOjXjscce21rTz1xdm/987Pp0rD+Dmvi/375jGqpezSEWERFphDKSM3A6QvPfuNPhJCMp4/g71tGqVasS/vSnP3WcMmXKxn79+hWPHj06t6rq8DnQX3/9dTP/50uWLGmWnZ1dFhsbS9++fUurqqocH3300aF9tm/fHrNx48aEXr16lQZSQ8eOHSszMjIqfvzxx4TevXuX+T969OhRHujPEh8f73a5XMcMx0uWLEl2uVzMmDEjf/jw4cV9+/Yt27p1a3ygr1GTjIyMqoyMjIri4uKgT4BA3ssuXbqU/ve//z1s7vLixYub5+TklPlGh+Pi4ty1+ZAQrBNPPLF0+/btcb4LMAE+/fTTZsc6pjYUiEVERBqhlPgURmSNCMkqE2dmnVnn+cMA5eXlzk2bNsX6P7Zt2xYLnouirrrqqtyhQ4cWjh8/fvdLL720ce3atUmTJk1q69/Htm3b4n/zm9+c8O233yY89dRTrZ577rk2N9xwww7wzCsdPnz4vhtvvDHnvffea75kyZKkkSNHdmrTpk3FlVdeuS/QOu+8886tf/vb39rdd999bZYvX57wxRdfJE2bNi39yFqOpXPnzmUlJSXON998M2Xbtm2xRUVFR2Wq7t27l1VWVjruv//+NqtXr47/+9//3mrmzJm1+uTxyCOPtL7qqquy5s6dm7pq1aoEa23ijTfemLl+/fqkCy+8cF9t+vIXyHt5xx137Pjvf/+bevvtt7dfvnx5wvTp09NnzpyZMW7cuEMXEGZmZpYtXry4+YYNG+J8f9b14aKLLtrfsWPHsiuvvDJ36dKlSQsWLGh27733ZsJPo/d1oUAsIiLSSI3qMSokq0yM6jEqJPV8+umnqdnZ2f38H6ecckp3gDvvvLP91q1b459//vk8gOzs7IqpU6fmPfTQQ5lLlixJ8vVxySWX7D548KDz1FNPPfGOO+7Iuu6663beeuuth250MWvWrI19+vQpvuyyy7oMGzash9vtZt68eesSEhICXu944sSJux577LG8l19+ubUxpteZZ57Z/aWXXmrdqVOnY0738HfmmWcWX3nllQVjx47t1KFDh3733ntvuyP3GTJkyMFJkyblT58+vd3AgQN7zZ49O/2ee+7ZHOhrgGcOdXFxccwtt9ySfdJJJ/U688wzu1trm7/44os/nHfeeQdq09eRjvdennrqqSXPPvvs+tdff73VwIEDez3wwAMdbr/99q233HLLoQsPJ0+evHXz5s0JPXr06NOhQ4d+dannWGJjY5k7d+76kpIS59ChQ0+84YYbcn7/+99vA0hKSqrzvApHKO5OE6Ga7A9WFy6Xi7y8PLKzs/G/aEEknHReSiQK13lZWlrKhg0byM3NJTEx8Zj7ut1uLnzzQvL25wW14oTT4SQ7NZs3L3zzyFG2ZbXuLAQGDx7cvXfv3iX//Oc/j7rYTuRICxYsaHbWWWf1WLly5Ur/C/h8SkpKEtetW9e8srLy7IEDB645Vl+6qE5ERKSRcjgcPDj0Qcb8ZwzlVeW1un2zAwdxzjgeGvpQSH7lLFLfXnjhhRYpKSmunj17ln733XcJt912W9ZJJ510oLowXFsaihEREWnEeqb3ZPqw6cTHxAd8kZ3T4SQ+Jp7pw6ZzYvqJ9VyhSGjs378/ZsKECVl9+/bt/dvf/ja3X79+xfPnz/8hFH1rhFhERKSRG9JhCC+c8wJ3fnonGwo3EOOIqXZusa89OzWbh4Y+FHFh+Isvvlgb7hokct188827b7755t3H37P2FIhFRESagJ7pPXnzwjdZtmMZs9bMYuGmhYfNK3Y6nIzIGsGoHqMY2HagpkmI+FEgFhERaSIcDgemncG0MxSVF1FwsICSihKS45LJSMoIydJqIk2RArGIiEgTlFwGmbvcuErcOJPdxLYB6nxLCJGmSYFYRESkiXC73ZR8+SV7X36FovffB//b3jqdpJx5Ji2vupLkQYM0ZULEjwKxiIhIE3Bw1Sq23n475T9ugJiYw8MwgMtF0cKFFL33HvGdcunwyCMk9eoVnmJFIoyWXRMREWnkij//nLwrr6J8Y56noaqGu9d528s35pF35VUUf/55A1UoEtkUiEVERBqxg6tWkX/j73CXlx89KlwTlwt3eTn5N/6Og6tW1W+BXvPmzUtxOBwDd+3aFdMgLyhSCwrEIiIijZTb7WbrbbfjrqgAd+B3qfMejLuykq233467tsceweFwDDzWY+LEiR3q9AJHvNaLL77YIlT9iYDmEIuIiDRaJV9+SfmGDcF3UFVF+Y8bOGgtyYMGBd1NXl7et77vZ86c2erhhx/usGrVqpW+trS0NNenn37aLPhCReqXRohFREQaqb0vv+K5gK4uYmLY8/IrdeoiKyur0vdIS0urqqbt0FyOzz77LLl3794nJiUlDRgwYECPb7/9NsG/r5deeqlFz549T0xISDjphBNO6HPrrbe2r6ioACAzM7MPwJgxYzo7HI6BvuerVq1KGD58eOf09PR+ycnJA3r37n3iG2+8oUWXJWAKxCIiIo1QVVGRZ2m1mi6gC7ijKooWLKCqqCg0hR3HPffck/nQQw/lL168+LuYmBj3Nddck+vb9u677za/8cYbc2688cadX3/99crp06fnzZ49u/Wdd97ZHuDLL7/8DmDatGkb8/LyvvU9379/v/Pss88ufPfdd9cuXbp09fDhwwtHjRrVdd26dVp5WQKiKRMiIiKNUOXOnYFfRHc8LheVBQXEpNT/oOrkyZO3nHfeeQcAbr/99u2jRo3qUlJS4khOTnZPnjy5w80337x93LhxuwF69uxZvm3bti2TJk06YcqUKds6dOhQCdCyZcuqrKysSl+fQ4YMOThkyJCDvufTpk3b+s4777R87bXX0u66666Cev+hpNFTIBYREWmEXCUloe2vuDik/dVk0KBBh4LrCSecUA6wZcuWuK5du5Z/9913SV999VXzadOmtT9Ul8vlKCsrcxQVFTlTUlKq/QRQWFjovP322zssXLgwraCgIK6qqspRVlbm3LRpU0J1+4scSYFYRESkEXImJ4e2v2YNc81bfHz8oSUtfHfLc3lHug8ePBhz2223bRk1atS+I49LTk6ucTj8xhtvPOHTTz9N/b//+7/NPXr0KEtOTnZddtllncvLy3U7PgmIArGIiEgjFNumDTidoZk24XQSm5FR937qqGfPniXff/99Yu/evctq2ic2NtZddcS8aWtt81GjRu0eM2bMPvCMGG/ZskXzhyVguqhORESkEYpJSSHlzDNDsspEylm/bJD5w8fzxz/+cevcuXPTb7311vbW2sSvvvoqccaMGS1vueWWQ+sYZ2Zmli9cuDB106ZNsQUFBTEAOTk5ZfPmzWv5+eefJy1ZsiTp4osv7uR2uzU6LAFTIBYREWmkWl51ZUhWmWh15ZWhKaiOLr300v2vvvrqDx999FHa0KFDT/zFL37R429/+1vb7Ozsct8+999/f/6nn36a2qlTp779+/fvCTB9+vT8tLS0yuHDh/e45JJLupx55pmFPXv2DO0ka2nSHHW9O00Ea7I/WF24XC7y8vLIzs7G6dTnIYkMOi8lEoXrvCwtLWXDhg3k5uaSmJh4zH3dbjc/nnce5Rvzgps6ERNDfHYWnebPPzSf12tZ7TsTiSwlJSWJ69ata15ZWXn2wIED1xxrX/3PIyIi0kg5HA46PPIIjrg4cNRyhoDDgSM2lsy//vXIMCwSdRSIRUREGrGkXr3o+OQTOOLjA59PHBODIz6ejk8+QWLPnvVboEgjoEAsIiLSyDU75RSyX3mZ+OwsT0NNwdjbHp+dRc6sV2h2yikNVKFIZNOyayIiIk1AUq9edJo/n4PWsuflVyhasODwecVOJym/PJNWV15JkjGaJiHiR4FYRESkiXA4HCQPGkTyoEFUFRVRWVCAq7gYZ7NmxGZkRMTSaiKRSIFYRESkCaqMTaI4qS0VziriEmJoFptAHVcsFmmyFIhFRESaCLfbzdZ1+1ixaDM/flOA22/GhMMJnfpn0Of0E+jQtYWmTIj4CWsgNsYkAE8CI4AWwGpggrV2iXfb34CLAAfwLnCjtbYoPNWKiIhEroJNRSz45yr2bS/B4XQcFoYB3C748ZtdrP+qgBbtkvnltb3IyNIUChEI/yoTscBG4FQ8gXgq8LYxpjlwM3AS0APIBdoAd4WjSBERkUiW/90e5jyyjMKdnpuzuV3V35vK1164s4Q5jywj/7s9DVajSCQL6wixtbYYmOzXNNsY8yjQHcgB3rXW7gYwxrwBnFVdP97R5AT/tsWLFxMfH18PVTduLu8Vx65g7mgkUk90XkokCtd56XK5cLvdhx7HU7CpiPlPLKeq0hXwPVrdLqhyu5j/xHIuue2ko0aKNZ1Cok1EzSE2xnQFWgE/ADOBKcaYNkAZcAnweg2H/gG4179h2rRpjBw5sh6rbdzy8/PDXYLIUXReSiRq6POyqqqKqqoqKioqjnvLaLfbzYJnV+GqRRj+6WBwVblY8OwqLrtrwGEhOCEh4RgHHtvChQubnXXWWT2GDh1auGjRoh987WvXro3v0aNHH9/z1NTUqm7duh38y1/+suXss88+4GufOHFih3feeafFmjVrVvueP/bYY+1PPfXU/Z9++uk6/9f605/+1Pa+++47YdCgQQe++OKLtf7b1q9fH9ejR48+OTk5ZevWrVsV9A8kUSFiArExJgl4CXjAWltojPkBKAC2e3dZAPyjhsMfAB71bxg/fnyhRoiP5nK5yM/Pp2PHjsf9h1akoei8lEgUrvOytLSUjRs3EhcXd9zfdG75fi+FOw8G/VpuFxTuPMjuTQfp0LVF0P34e/rpp1tfffXVO1999dXWGzdujMvJyanw3/7GG298P2DAgIM7duyInTx5cvvLL7+8y+rVq1d27NixsqY+MzIyKpYuXZqyfv36uM6dOx/q7+WXX27dvn378uqOeeqpp1qfe+65e7/44ovmH374YbNhw4YVh+QHlCYpIgKxMSYOeA3PyLBvCsUTQAWQhmeu89PAI8DEI4+31pbhGUWWADmdTgUPiTg6LyUSNfR56XQ6cTgchx7HsvLjLd4L6Go7PPwTh9PByo83k9mtZdB9+BQWFjrnzZvX6vPPP1+9c+fOuH/84x/pDz744Hb/fTIyMiqzsrIqs7KyKv/0pz9tnzdvXqtPPvmk2VVXXVVYU7+tWrWq7N27d8mMGTPSH3rooe0A77//frO9e/fGnnfeeXu///77JP/9XS4Xs2bNSp86deqmzMzM8qeffrq1ArEcS9j/5zHGOIEX8fyy52prre9vdT/geWttkbW2EHgBGB6mMkVERCJK2cFK79JqwYdh8Fxot/7rAsoO1jhAG7DnnnuuZW5ubmm/fv3Krrrqqt2vvPJK65rmYB84cMDx7LPPpgMkJCQc94cYO3bsrtmzZ7f2PX/mmWdaX3zxxXvi4+OPOnbevHkppaWlzgsvvHD/Nddcs/vtt99utX///rBnHolckXByPAW0By631vr/bbTA/xhjko0xzYDRwIpwFCgiIhJpiveVHbW0WrDcLigprPsvWl944YWMK664YjfAZZddVlhUVBTzzjvvHHbF3vDhw3skJycPSE1NPWnGjBlte/XqVXL++ecfd0nVK664ovDAgQMx//nPf5rv37/fOX/+/Fa//e1vd1W37zPPPNP6ggsu2BsbG8ugQYNKTzjhhLLnn3++7kPg0mSFNRAbY7KB3wCDgV3GmAPex1DgdiARyAc24VlF4rawFSsiIhJBKsqqQtpfeWnd+vv2228Tli9fnnzttdfuAYiLi+P888/f+8wzz7T23+/ZZ5/9cenSpauff/759VlZWWXPPffchkBGiBMSEtyXXHLJ7meffbb1888/3zInJ6f05JNPPmoC9a5du2IWLFjQcuzYsbt9bSNHjtw9c+bM1kfuK+IT7mXX8vDcdKMmlzVULSIiIo1JXEJob8Qcn1i3/p588smMqqoqR3Z2dj9fm9vtJj4+3rV79+5Dnefk5JT36dOnrE+fPmWVlZWOyy67rMuaNWtWJSUlHTcU//a3v9112mmnnbhmzZqk0aNH1zQ63KqsrMxxxhlnnOhfh8vlYvny5Ql9+/bVNUdylEiYMiEiIiK11KxFAo4Q/S/ucEJyWvBLrVVUVPDvf/87/d577928ZMmSVb7Hf//731UZGRkVzz77bKvqjhs7duzemJgY98MPP5wRyOsYY0q7dOly8Icffkj6zW9+U+1dRV588cXW119//Y4j6xg4cOCBf/zjHxollmpFxCoTIiIiUjsJSbF06p/Bj9/sqvMqE50HZJCQFHwkmD17dov9+/fHjBs3bld6evphcy/OO++8vS+88ELrCy+88KhVJJxOJ7/97W93Pvzwwx0mTpy4KyUl5bizohcvXvx9eXm5o3Xr1kfN8fj888+TVq9enfzSSy9tGDBgQKn/tssvv3zPX//61/bTpk3bEhcXF8yPKU2YRohFREQaqT6nnxCSVSb6nJ5Zpz6ee+651qeccsr+I8MwwMiRI/euWrUqed++fdXOyfjd7363u7Ky0vHQQw8FNEqcmprqqi4Mg2ft4c6dO5ceGYYBfv3rX+/ds2dP3L/+9a+0QF5HoosjkNtCNlJN9gerC5fLRV5eHtnZ2VrvVSKGzkuJROE6L0tLS9mwYQO5ubkkJiYec1+3280rf15K4c6SoFaccDghrU0yV9578pFrHi+rfW8ikaWkpCRx3bp1zSsrK88eOHDgmmPtq/95REREGimHw8Evr+2FM8Z57EvUqz0YnDFOfnltr+PeAESkqVMgFhERacQyslI473d9iYl1BnyRncMJMbFOzvtdXzKyUo5/gEgTp0AsIiLSyHU8sRWX3j6QtDbJgOdCuer42tPaJHPp7QPpeGK1iz+IRB2tMiEiItIEZGSlcOW9J7Pth32sWLSZ9V8XHDav2OGEzgMy6HN6Ju27tNA0CRE/CsQiIiKN1JZ9B9lbXH54Y7KTzHOzyBieSemBcirKXMQlOElsHk98Qgx7gD1b9x92SMtm8WS2SGq4wkUiTK0DsTHGAD2BRcB2YApwGvAtMM5ae9Q6gyIiIhJaW/YdZNhfF1FWGcTyEkdIiHXy4W2nKxRL1ApmhHgycBbQCbgWuMnb3hs4APwuNKWJiIhITfYWl4ckDAOUVbrYW1yuQCxRK5iL6voA26y1ecAI4CDwB6ASODeEtYmIiIiI1LtgAnEGnqkS4Jk68aW19iFgFdA2VIWJiIiIiDSEYAJxIZBjjBkCdAZWe9uT8UyZEBEREam1xx9/PD0lJaV/uOuorXnz5qU4HI6Bu3btioHG+3NEs2AC8VKgFbAYzxzkRcaYeKAj8GMIaxMREZFG4tJLL80ZMWJEZ/+25557rmVCQsJJ9957b6P/DfLatWvjHQ7HwCMfF154Ye6IESMO5OXlfduqVauq6o6dOHFihx49evRs6JolcMFcVHcbcALQBXgb+DeeVSb2AO+GrjQRERFprB599NHWd955Z9YjjzySN378+N3hridU3njjje8HDBhw0Pe8WbNm7sTERHdWVlZlfb92aWmpIzEx0V3frxONaj1CbK393lp7krU21Vp7lbXWZa1dZK3taK29tz6KFBERkcbj7rvvbnvXXXdlPfPMMz/6h+FJkya17datW8+kpKQB7dq16zt69OiswsLCGrOIb2R16tSp6e3bt++TnJw8YPTo0VmVlZXcfffdbVu3bt2vVatW/X7/+9+38z/ueK/jm9IwZ86c1E6dOvVKTk4eMHTo0K55eXlxx/vZMjIyKrOysg490tPTq46cMuHv8ccfT3/sscfar127Nsk3qvz444+nA+zatSvmiiuuyG7ZsmW/5s2bD/jZz37WbcmSJYeW+vD9/I8++mjrzMzMPsnJyScd/92XYAR9Yw5jzBnAz4C9wCtAC2CHtbYsNKWJiIhIY3PjjTdmvvDCC21effXVdRdeeGGR/zan0+meMmVKfrdu3crWrl2bMH78+KybbrrphJdeemlTTf3l5+cnvPfee2nz5s1bt3bt2oSxY8d2zsvLS+jcuXPpBx98sGbRokXN//d//zfnrLPOKho2bFhxoK9TWlrqnDJlStvnn39+g9Pp5Oqrr84dN27cCW+99daGUL4f11577Z6VK1cmffTRR2kffPDBWgDf1IoLLrigU2JiovvNN99c17Jly6rp06dnnHvuud3WrFmzsm3btlUAmzZtSnjjjTda/utf//ohNlb3U6svwdyYIwl4CxjmbVoK7AReA+4CHgpZdSIiItJofPLJJ2kffPBBizfffPP7Cy64oOjI7ffcc89O3/fdu3cv371799aJEydmATUGYpfLxSuvvLKxZcuWroEDB5Y++eSTRT/++GPiokWL1sXExNCvX7+yqVOntlu4cGGKLxAH8jqVlZWOp59+elOvXr3KAK6//vqdf/3rXzsc72ccPnx4D//bXr///vtrjrV/8+bN3c2bN3fFxMQcNq3ivffea75ixYpmO3fu/DYpKckNMGPGjM3vvvtuixdffLHlbbfdtgugoqLCMXv27A0dOnSo9ykZ0SyYjxr3AcOPaJsPlAPnoUAsIiISlbp163Zw7969sX/+8587/OIXv1iXlpZ22J1D3njjjZSHHnqo/fr16xOLi4tjqqqqHGVlZY6ioiJnSkpKtXcZyczMLG/ZsuWhbRkZGRUxMTHumJifZie0bt26cufOnYcyTSCvk5iY6PKFYYAOHTpU7Nmz57i56Nlnn/2xb9++pb7nnTt3Lv/ggw+aB/gWHfLVV18llZSUxKSnp/f3by8rK3OuX78+wa+ucoXh+hdMIB6J52YcQ4BvAKy1ZcaYPKBb6EoTERGRxqRdu3blc+fOXT9s2LDuw4YN67pw4cJ1vjC7du3a+CuuuKLr6NGjC+67774tGRkZlR9++GHzCRMm5JSVlTlSUlKq7TM2Nvawi8gcDke1bS6Xy1Gb16muD7f7+Ner5eTklPfu3bvO00MPHDgQ07p16wrfNAp/6enph1arSEpKCs3tCOWYgll2rQ3wvbV2+RHtFXjmEYuIiEiU6tatW/miRYvWFBQUxA0fPrzb3r17nQBLlixJdrlczJgxI3/48OHFffv2Ldu6dWt8qF+/oV6nNuLj410u1+G51hhTsnv37ri4uDh37969y/wf7du314hwAwsmEG8DuhljDq01aIzpD5wIbA1RXSIiItJIdenSpeKjjz5au3v37thhw4Z127Nnj7N79+5llZWVjvvvv7/N6tWr4//+97+3mjlzZkaoX7uhXqc2cnJyyjdv3pzw+eefJ23bti324MGDjgsvvHB/v379Dlx44YVd5s6dm7p27dr4999/v9m4ceMyP/nkk+Rw1huNggnEbwJJwErADQwAvgAc3m0iIiIS5Tp37lyxaNGitXv37o0dNmxYt+7du5dNmjQpf/r06e0GDhzYa/bs2en33HPP5lC/7pAhQw42xOvUxtVXX733tNNOKzz77LO7d+jQod/TTz/dyul0snDhwnU/+9nPim644Yac3r179x4zZkynTZs2xXfo0KEinPVGI0cg82X8GWNSgY+BfkdsWg6cZq3dH6La6koLV1fD5XKRl5dHdnY2Tmcwn4dEQk/npUSicJ2XpaWlbNiwgdzcXBITE2vcb+WWQn41fXHIXnfeuFPpnZnme7osZB2LhElJSUniunXrmldWVp49cODAY64GUuuL6qy1+40xg4ErgUHe5i+BWdba8tqXKyIiIiISPkGt8GytrQBmeh8iIiIiIo1WQIHYGPNP4Adr7f3e72vittZeF5rSREREpCYtm8WTEOukrLLuq3IlxDpp2SysCzGIhFWgI8Rjgf8C93u/r25+rsPbrkAsIiJSzzJbJPHhbaezt7jusxVbNosns0VSCKoSaZwCDcSf4FlVwve9LlgTERGpR4Fc9J7ZIklBVqQGbrfbN1h73F+jBBSIrbWnV/e9iIiIhJbvlsTl5eUkJSnsigSruLg42e12V+C5h8Yx1eqiOmNMHJ7bNu8G2llrNVIsIiISQrGxsSQnJ1NQUEBcXFxYliJ0uVw1r/cmEuHcbrejuLg4efv27fFVVVXPDhw4sOh4x9QqEFtrK4wx24F9CsMiIiKh53A4aN++PRs2bCAvLy8sNZSXlzcPywuLhIbb7XZXVFVVPYvn+rfjCmbZtWnA/caYX1prFwRxvIiIiBxDfHw8Xbt2pbw8PMv7r1q16uywvLBIaLiAbYGMDPsEE4jPBaqA/xhj1gI7+OkiO7e1dngQfYqIiIgfp9N5zDvV1afj3dVLpKkJJhD/wu/7Ht6Hj6ZRiIiIiEijEkwgfgEFXxERERFpImodiK21Y+uhDhERERGRsAhmhBgAY8wZwM+AvcArQAtgh7W2LDSliYiIiIjUv1oHYmNMEvAWMMzbtBTYCbwG3AU8FLLqRERERETqWTCrfd8HDAcc3gfAfKAcOC9EdYmIiIiINIhgAvFIPHer6+9r8E6TyAO6haYsEREREZGGEUwgbgN8b61dfkR7BZ55xCIiIiIijUYwgXgb0M0Y09nXYIzpD5wIbA1RXSIiIiIiDSKYVSbeBMYBK/GsRzwA+ALPfOI3A+3EGJMAPAmMwDOyvBqYYK1dYoz5BzDab/d4YK21tk8Q9YqIiIiI1CiYQPwn4DSgn/d5gvfrcuDeWr72RuBUYDOeuclvG2NyrLU3ADf4djTG/AfPahYiIiIiIiEVzI059htjBgO/BgZ7m78EZllry2vRTzEw2a9ptjHmUaA7sMzXaIxph2cU+eaa+vKONif4ty1evJj4+PhAy4kaLpfrsK8ikUDnpUSiaD4vnc5gZlSKNF4Ot7vud2E2xrS01u6tYx9dgRVAW2ttoV/7BOAya+3Pj3HsJI4Ynb788ssZOXJkXUoSERGJSrm5uY7j7yXSdARzY44xwOnAY3huyPE+0MsYsxk4z1q7Mog+k4CXgAf8w7DX/wAzjtPFA8Cj/g3jx48v1Ajx0VwuF/n5+XTs2FEjABIxdF5KJNJ5KRI9gplD/P/wTJWYCNwC9Pa2dwT+Alxcm86MMXF47nL3A4dPocAY0wvoCbx6rD686yDrltG14HQ69Q+8RBydlxKJdF6KNH3B/A3vBmyy1u4DTgF2AUOA/cDPatORMcYJvIhntYqrrbVHzt/4H2B+XadjiIiIiIjUJJhAnArs837fA1hmrV2KZ4S3ZS37egpoD1xura303+ANy1cBLwRRo4iIiIhIQIKZMrET6GmM+QOeaRL/8ra3AvYE2okxJhv4DVAK7DLG+DadY639FM885STgnSBqFBEREREJSDCBeD7wW+A+7/O3jDGtgBOATwLtxFqbh+dmHjVt/xBoHUR9IiIiIiIBCyYQ3wYcBLoAb1trFxtjBuG58G1eKIsTEREREalvwdyYoxjPChP+bV/iuQBORERERKRR0ToyIiIiIhLVFIhFREREJKopEIuIiIhIVFMgFhEREZGopkAsIiIiIlEtoFUmjDH3BNqhtXZy8OWIiIiIiDSsQJddmwS4A9xXgVhEREREGo1AA/EmAg/EIiIiIiKNRkCB2FqbU891iIiIiIiEhS6qExEREZGoFuhFdT8G2J/bWtu5DvWIiIiIiDSoQOcQ5wS4n+YZi4iIiEijEmgg/nO9ViEiIiIiEiaBXlSnQCwiIiIiTVKgI8RHMcbkAh2AGP92a+0ndS1KRERERKSh1DoQG2PaAW8Ag6rZ7A6mTxERERGRcAkmvD4IDA51ISIiIiIi4RDMOsRnAi7geu/z1cAfgD3AFSGqS0RERESkQQQTiDOAtdbaZ73PD1hrHwJ2AqNCVpmIiIiISAMIJhAXA5V+33cyxrTFE5TPClVhIiIiIiINIZhAvAXo6P3+eyAd2Aq0AvaFpiwRERERkYYRTCCeB+QZY3oBU71tDu9jWojqEhERERFpELVeZcJaeydwp/fpKmPMj8DJwHJr7cJQFiciIiIiUt/qvGawtfYz4LMQ1CIiIiIi0uCCmTIhIiIiItJkKBCLiIiISFRTIBYRERGRqKZALCIiIiJRrVYX1Rlj4oA1QCEw0FrrrpeqREREREQaSK1GiK21FUAKEKMwLCIiIiJNQTBTJp4Huhtjeoe4FhERERGRBhfMOsTtvF+/NMZ8BOwAfKPFbmvtdSGpTERERESkAQQTiEfjCcAO4Gx+CsMO7/cKxCIiIiLSaAQTiD/hpxAsIiIiItKo1ToQW2tPr4c6RERERETCIpgRYgCMMZ2Ak4ESa+2boStJRERERKTh1DoQG2NigKeAsXjmDS81xqTiWX3if62100NZoIiIiIhIfQpm2bU/ANd6j3V4214HKoELQlSXiIiIiEiDCCYQXwNUABf5Gqy1B4B84MTQlCUiIiIi0jCCCcQnAKuttW8d0V4EZNS9JBERERGRhhNMIN4F5Bpj0n0NxpgsPKPDBaEqTERERESkIQQTiN8DUoEV3uc9ga+AOODdENUlIiIiItIggll27Y/ACDxTJ8ATjgG2APfUpiNjTALwpLe/FsBqYIK1dol3+8nANKA3sBfPKhZzgqhZRERERKRawdyYY5sxpj8wDhjkbf4S+Lu1dlcQr78ROBXYDIwE3jbG5ADNgX8D1wML8QTm1Oo6EREREREJlsPtrt1dmI0xvwTet9bWy+2bjTFbgfPxhONya+2fAjgmAUjwb1u8eHFhfHx8aIoqLYSi7VBeDPHNIKUdJKaFpu8G5nK5yM/Pp2PHjjidwcyYEQk9nZcSiaL5vHQ6nY7j7yXSdAQTiF14RnNfAp631n4fqmKMMV3xzE1uC7wBLMazvFtrYAFwi7W2sJrjJgH3+rddfvnljBw5Mvhi3G4SdywjZc2rNNv0AQ6366dNDifFWcMp6nEFpW0HgkP/boiISNORm5ur/9gkqgQbiAF8B34JPAe8aq3dF2whxpgkYBHwjrX2z8aY74F44JfAVmAmsM9ae101x4Z2hHjbtzjmXo9j9zrczlgcrsqjdvG1u9O74r7kaWjfL7jXamDRPOIhkUvnpUSiaD4vNUIs0SaYi+q6AFd4H32BwXjmEj9mjHnLWjuqth0aY+KA14AfgMne5oPAy74RaGPM/cC86o631pYBZbV93Wqt/whmjYKqCoBqw7B/u2PPjzieOxt+PRs6nxGSEhqC0+mMun/gJfLpvJRIpPNSpOmr9d9wa+2P1toHrLX98aw9PAnYASQCl9e2P2OME3gRz4jz1X5zk1fy0yg0R3xfP7Z+4wnDlWXgrgrsGHeVZ/9ZozzHi4iIiEijEvRHXmNMRzwXv52PZ85vsJ4C2gOXW2v9h2OfB64xxnQyxiQDdwLz6/A6x+Z2w5zfeEeGa5u93Z7j5l7v6UdEREREGo1aT5kwxowDRgE/8zY58CTIj/DM861NX9nAb4BSYJcxxrfpHGvt+8aYx4DP+OmmH7fVtt6A5X0Gu9cFf7y7CnZ9D3mfQ87PQ1eXiIiIiNSrYOYQT/P7fh3wAvCitXZTbTuy1ubhCdQ1bX8ceLzWFQbji6fBGQs1zBkOiCPG048CsYiIiEijEUwg3g/MBmb67ijX6JUWwndvBz5vuCbuKvjuLU9/jXSdYhEREZFoE0wgbutd1aHpKNpe9zDs466Coh0KxCIiIiKNRDC3bi4zxgzFszyab9Lvl8C91tpPQ1lcgyk/EOL+ikLbn4iIiIjUm1qvMmGMORX4ADgNaOZ9nA4sNMacEtLqGkp88xD3lxLa/kRERESk3gSz7No9eEaWNwFPeh95eFaCuCd0pTWglHaeC+JCwREDKXVZhU5EREREGlIwc4gHA7uBftba/QDGmDRgPT8txda4JKbBiefDmnl1X2Wi5wWaPywiIiLSiAQzQpwI7PGFYQBrbSGwB0gIVWENbvD1dQvD4LmgbtD1oalHRERERBpEMCPE64EexpgpwCxv25VAF2B1qAprcNk/h9bdYPf64FaccMRAemfIbpzTqEVERESiVTAjxP/EczON/wWWeh/j8dyt7p8hq6yhORxwydMQE8cx7hVS08Ge4y59xtOPiIiIiDQawQTix/gp+Dr4KT3+07ut8erQH349G2ITAr/IzhHj2f/Xs6F9v3otT0RERERCL5h1iF3Ab4wx9wMDvc3LrLU/hrSycOl8Blz7Hsy9HnZ9X/PtnB0xnqkV6Z09I8MKwyIiIiKNUjBziAHwBuCmEYKP1KE/3PQF5H0OXzztuR2z/7xi32oSg673zBnWNAkRERGRRivoQNzkORyQ83PPo7TQczvm8iLPTTdS2mppNREREZEmQoE4EIlpCsAiIiIiTVQwF9WJiIiIiDQZCsQiIiIiEtVCFoiNMcnGmNRQ9SciIiIi0hACDsTGmD3GmHf9nv/TGPNHv10+wHP7ZhERERGRRqM2I8QtAP8R4LHAeUfso/XHRERERKRR0RxiEREREYlqCsQiIiIiEtVquw7xCcaYe2p4fkKIahIRERERaTC1DcSZwL3e791HPHd420REREREGo3aBmJdNCciIiIiTUptAnFuvVUhIiIiIhImAQdia21efRYiIiIiIhIOAQdiY0wMMAUY422aCdxqrXXVR2EiIiIiIg2hNsuu3QjcgucGHS28398Y+pJERERERBpObQLxtd6vm7wPh1+biIiIiEijVNuL6nYBPfAE6TygU30UJSIiIiLSUGozQpwGrLfWllprS4D1QGr9lCUiIiIi0jBquw5xijFmKJ7pEikAfs8BsNZ+ErryRERERETqV20D8YnAoiPa/J+7g+hTRERERCRsdKc6EREREYlqtQnE19RbFSIiIiIiYVKbO9XNPN4+xpjMupUjIiIiItKw6jzf1xiTCFwCjAVOB+Lr2qeIiIiISEMJOhAbY07BE4Ivx7P8mgPPRXUiIiIiIo1GrQKxd0rE1d5HF2+z70K7b4C5IatMRERERKQBBByIjTELgDPw3MzDF4JXAd2AWGvtSaEvT0RERESkftXmTnUjgBhgGzAFOMla2wcoqY/CREREREQaQm0CMXjmCLuBKqA89OWIiIiIiDSs2gRi3y2ZM4HbgRXGmK+ApJBXJSIiIiLSQAIOxNba04HOwGRgI555xP2BOABjzFJjzB0hr1BEREREpB7VapUJa+1GYBIwyRjzCzx3r7sUaAYMAgzwcKD9GWMSgCfxzE9uAawGJlhrlxhjxgLPAKV+h/S01m6qTc0iIiIiIscS9DrE1tqPgY+NMTfhWYt4LDA0iNffCJwKbAZGAm8bY3K82xdZa0cEW6OIiIiIyPHU+U511tpi4Hngeb8gW5tjJ/s1zTbGPAp0r00/3pHmBP+2xYsXEx+vm+YdyeVyHfZVJBLovJRIFM3npdNZ22vuRRo3h9sd2M3ljDH/L5D9rLUzgi3GGNMVWAG0BS4G/gaUATuAx621/6jhuEnAvf5tl19+OSNHjgy2FBERkaiVm5vrOP5eIk1HbQKxi+PfmtltrQ1q1NkYkwQsAt6x1v7ZGJPrfb1NeOYnvw6Ms9bOqebY6kaICzVCfDSXy0V+fj4dO3bUCIBEDJ2XEomi+bx0Op0KxBJVggmvIf9LYoyJA14DfsA7hcJau8Fvl6XGmMeBS4CjArG1tgzPSLIEyOl0Rt0/8BL5dF5KJNJ5KdL01TYQO/DckOPfeFaH2FzXAowxTuBFPKPBV1traxqFdlEPYVxEREREolttAnFvYBwwGrgSz4oQrwPTrbWL61DDU0B74CxrbaWv0RhzNrDMWltgjDkJuAW4rQ6vIyIiIiJylIDnEPsYY1KB64DfAZ28zcuBU6y1B2vZVzaeZddK8dwO2ucc4CLgf/CscbwFT/CeXovua/eDRQmXy0VeXh7Z2dn6FaBEDJ2XEomi/LzUb2QlqtR6DrG1dr8x5kmgGM9NOFKBvnhu4VyrQGytzaPmv3SfArfWtj4RERERkdqoVSD2juj+Ds8IcUtv83t4Rm/3hLg2EREREZF6F3AgNsa8AZwHOIEDwHTgb9baH+qnNBERERGR+lebEeILvF/LgU+ANsBkY4z/Pm5r7VUhqk1EREREpN7Vdg6xG4gDzq1mm8O7XYFYRERERBqN2gTiTWjlBhERERFpYgIOxNbanHqsQ0REREQkLIK5dfNhjDHJQHdgvbV2f91LEhERERFpOLUOxMaY2/DMIb4d2AssBtoCxcaYc+t41zoRERERkQYVzK13rgCGAN8B1wPt8FxQ1xy4J3SliYiIiIjUv2ACcSdgk7W2BDgZz22V2wO7gP6hK01EREREpP4FE4iTgRLv992Ar621O/CsQpESqsJERERERBpCMIF4K9DLGPMUnpHhb73tGUBBqAoTEREREWkIwQTif+G5GO96POsSv2aM6QCcACwPYW0iIiIiIvUumGXX/ghsA7oA86y1y40xfYD78dzSWURERESk0ah1ILbWuoDHj2hbAawIVVEiIiIiIg0lmCkTIiIiIiJNhgKxiIiIiEQ1BWIRERERiWoKxCIiIiIS1RSIRURERCSqBbPsGsaY4cBwoC3g8NvkttZeF4rCREREREQaQq0DsTHmj8DkajY58NyoQ4FYRERERBqNYEaIb8QTfiuAnUBlSCsSEREREWlAwQTiVKAA6Gmt3R3iekREREREGlQwF9W9hWd0eF9oSxERERERaXjBjBB/BVwOfGyM+RdHBGNr7QshqEtEREREpEEEE4j/iufiuSHehz83oEAsIiIiIo1GUMuucfhSa4G0i4iIiIhEpFoHYmutbuYhIiIiIk1GsCPEIiINYsu+g+wtLq9zPy2bxZPZIikEFYmISFMT7J3qzgFGAR2AGL9Nbmvt8FAUJiKyZd9Bhv11EWWVrjr3lRDr5MPbTlcoFhGRowRzp7qrqP7COd+d6kREQmJvcXlIwjBAWaWLvcXlCsQiInKUYOYDj8cTftd7vx4AtgN7gY9DV5qIiIiISP0LJhD3BPYAfbzPVwG98YTj50JUl4iIiIhIgwgmEMcCG621ZUAV0MxauxfYCtwbyuJEREREROpbMBfV7QFaer/fCfQyxjwJ9AAOhqowEREREZGGEMwI8XdAljEmA/jI28f/835dGsLaRERERETqXTAjxBOBHDxzhicCbYGTgeXADSGrTERERESkAQRzp7pvgW/9ms4MXTkiIiIiIg0roEBsjDkN2G+t/cb7fY2stZ+EpDIRERERkQYQ6AjxImAJ8HPv9zXdgMNdiz5FRERERMKuNuHVUcP3IiIiIiKNVqCBOBco8/teRERERKRJCCgQW2vzqvteRERERKSxC/Siun8G2J/bWntdoC9ujEkAngRGAC2A1cAEa+0Sv31iga+BJGttl0D7FhEREREJRKBTJsZS84V0Pg7vPgEHYu/rbwROBTYDI4G3jTE51toD3n1uBgqBpFr0KyIiIiISkEAD8SYOD8TtgAQ8t3EGaAVUAFtq8+LW2mJgsl/TbGPMo0B3YJkxpi2eu+DdDkyrqR/vSHOCf9vixYuJj4+vTTlRweVyHfZVJBLUdF6G+jx1uVw69yVg0fzvpdMZzI1sRRqvQOcQ5/i+N8ZcBLwEjLDWfuhtOwOYD9xVl2KMMV3xhOsfvE0PAfcDxcc59A/Avf4N06ZNY+TIkXUpp0nLz88PdwkiRznyvNxWcDCk/W/bto2Uyn0h7VOavmj89zI3V9fPS3QJZs3gh4CNvjAMYK39yBjzI/AXYHYwhRhjkvAE7QestYXGmCFAV+Aa4BfHOfwB4FH/hvHjxxdqhPhoLpeL/Px8OnbsqBEAiRg1nZexaQdJiM2jrLLuI3QJsU56dskms4VmX0lg9O+lSPQIJhBnAbHGmOuBud62i4EeeKZN1JoxJg54Dc/I8GRjjBN4HPidtdZtjDnm8dbaMn5aFk4C4HQ69Q+8RJwjz8uOrZrx4W2ns7e4vM59t2wWrzAsQdG/lyJNXzCB+FM8q0L8w/vwcXi31Yo3/L6IZ47y1d4AnAachOcCO4B4INUYsx3oZq3dH0TdItIIZbZIUpAVEZF6FcxH3t8AX+EJwP6Pr73bauspoD1wubW20ttWCGQC/b2P3wD53u+LgngNEREREZFq1XqE2Fq7CTDGmGFAL2/zKv85xYEyxmTjCbulwC6/qRHnWGs/9dtvD1Blrd1e29cQERERETkWh9t9vOWFa2aMaQ/EWmsj8RLc4H+wJszlcpGXl0d2drbmxEnE0HkpkSjKz0tHuAsQaUjBzCHGGDMa+D/gBGCpMeYBYALwV2vtOyGsT0RERESkXtU6EBtjLgVeOKL5KzxLo+0EFIhFREREpNEI5ndAd+GZjjDV12Ct3QJsBQaFpiwRERERkYYRTCDuCay11k48or0A6FD3kkREREREGk4wgbgUz5rAh441xiQAuUBJqAoTEREREWkIwQTiJXjWDfbNFT4BWAikereJiIiIiDQawQTiPwOVwJl45hJnAj/3tv0ldKWJiIiIiNS/Wgdia+1SYBjwCXDQ+/gYGOHdJiIiIiLSaAS1DrG19jPgjBDXIiIiIiLS4IK9MUcM0BloyxF3s7HWfhKCukREREREGkQwN+Y4BXgF6FjNZncwfYqIiIiIhEsw4fUJICvUhYiIiIiIhEMwgbgLsBe4HPgRz6iwiIiIiEijFEwg/hjoA3xira0McT0iIiIiIg0qmEB8HbAI+MoYswDY77/RWjs5BHWJiIiIiDSIYALx2XhWmHACvarZrkAsIiIiIo1GMIH4PiAm1IWIiESCovIiCkoKKKksITk2mYzkDFLiU8JdloiI1KNgAnFzYBtwGrDRWlsV2pJERBqW2+3G7rDMXjObhZsW4nK7Dm1zOpyMyBrBqB6jMG0NDofjGD2JiEhjFEwgfhYYDexUGBaRxm717tXc+emdbCjcQIwj5rAwDOByu/hg0wcsyFtAblouDw59kJ7pPcNUrYiI1IdgAnEGkAqsM8Ys5vCL6tzW2utCUpmISD1bsnUJ4z4cR4WrAoAqd/Wf8X3tefvzGPOfMUwfNp0hHYY0WJ0iIlK/ggnEo/GsPdwGuNiv3eFtVyAWkYi3evdqxn04jvKqctwBLqfucrsorypn3IfjeOGcFzRSLCLSRDiDOGaT95Hn973/cxGRiOZ2u/n9J7+nwlURcBg+dCxuKlwV3Pnpnbjdui+RiEhTUOsRYmttTj3UISLSYOwOy8b9G4M+3uV2saFwA8t2LMO0M6ErTEREwiKYEWIRkUZt9prZxDjqtnpkjCOGWWtmhagiEREJJwViEYkqReVFLNy0sMYL6AJV5a5i4aaFFJUXhagyEREJFwViEYkqBSUFRy2tFiyX20XBwYKQ9CUiIuGjQCwiUaWksiS0/VWEtj8REWl4CsQiElWSY5ND219caPsTEZGGp0AsIlElIzkDpyM0//Q5HU4ykjJC0peIiISPArGIRJWU+BRGZI0IySoTZ2adSUp8SogqExGRcFEgFpGoM6rHqJCsMjGqx6gQVSQiIuGkQCwiUce0NeSm5QY9dcLpcJKblsvAtgNDXJmIiISDArGIRB2Hw8GDQx8kzhmHA0ftjsVBnDOOh4Y+hMNRu2NFRCQyKRCLSFTqmd6T6cOmEx8TH/BIsdPhJD4mnunDpnNi+on1XKGIiDQUBWIRiVpDOgzhhXNeIDs1G6DGC+187dmp2bx4zosM6TCkwWoUEZH6FxvuAkREwqlnek/evPBNlu1Yxqw1s1i4aeFhd7JzOpyMyBrBqB6jGNh2oKZJiIg0QQrEIhL1HA4Hpp3BtDMUlRdRcLCAkooSkuOSyUjK0NJqIiJNnAKxiIiflPgUBWARkSijOcQiIiIiEtUUiEVEREQkqikQi4iIiEhUUyAWERERkaimi+pEQqiqqIjKnTtxlZTgTE4mtk0bYlJ0gZaIiEgkUyAWqSO3203Jl1+y9+VXKHr/fXD9tIYtTicpZ55Jy6uuJHnQIK1hKyIiEoHCGoiNMQnAk8AIoAWwGphgrV1ijLkQeAhoD5QAs4DbrbVVYSpX5CgHV61i6+23U/7jBoiJOTwMA7hcFC1cSNF77xHfKZcOjzxCUq9e4SlWREREqhXuOcSxwEbgVDyBeCrwtjGmOWCBodbaNKAn0A+4ISxVilSj+PPPybvyKso35nkaqmr4rOZtL9+YR96VV1H8+ecNVKGIiIgEIqwjxNbaYmCyX9NsY8yjQHdr7bIjdncBnRusOJFjOLhqFfk3/g53eTm43YEd5HLhLi8n/8bfkf3KyxopFhERiRARNYfYGNMVaAX84H1+KjAfSAUKgAk1HJcAJPi3LV68mPj4+HqttzFyeX+l7zryV/sSMLfbzZbbbsNdURF4GP7pYNwVFWy97XZy5r2tOcVeOi8lEkXzeel0hvsXyCINK2ICsTEmCXgJeMBaWwhgrV0MpBljcoH/AXbWcPgfgHv9G6ZNm8bIkSPrseLGLT8/P9wlNFqu5Suo2LCxDh24KN+wgY3z5+Ps0ydkdTUFOi8lEkXjeZmbmxvuEkQaVEQEYmNMHPAanpHhyUdut9ZuMMasAv4GVJdyHwAe9W8YP358oUaIj+ZyucjPz6djx44aAQjSlqlTKY+JqXnOcCBiYkj48EM6/OpXoSusEdN5KZFI56VI9Ah7IDbGOIEXATdwtbW2pt9BxwJdqttgrS0DyuqnwqbJ6XTqH/ggVBUVceD9hUevJlHrjqooWvA+7uJirVPsR+elRCKdlyJNXyT8DX8Kz9Jql1trK32NxpiRxpgs7/dd8UyL+DA8JYp4VO7cWfcw7ONyUVlQEJq+REREJGjhXoc4G/gNUArsMsb4Np0DdAceNca0BHbjmVJxdzjqFPFxlZSEtr/i4pD2JyIiIrUX7mXX8oCaLrP/FPhLA5YjclzO5OTQ9tesWUj7ExERkdqLhCkTIo1GbJs2EKq5hE4nsRkZoelLREREgqZALFILMSkppJx5puc2zXXqKIaUs36pC+pEREQigAKxSC21vOrKui25BlBVRasrrwxNQSIiIlInCsQitZQ8aBDxnXKDnzoRE0N8p1ySfrqIVERERMJIgViklhwOBx0eeQRHXBzU9tbLDgeO2Fgy//pX3bZZREQkQigQiwQhqVcvOj75BI74+MDnE8fE4IiPp+OTT5DYs2f9FigiIiIBUyAWCVKzU04h+5WXic/O8jTUFIy97fHZWeTMeoVmp5zSQBWKiIhIIMJ+62aRxiypVy86zZ/PQWvZ8/IrFC1YcPid7JxOUn55Jq2uvJIkYzRNQkREJAIpEIvUkcPhIHnQIJIHDaKqqIjKggJcxcU4mzUjNiNDS6uJiIhEOAVikRCKSUlRABYREWlkNIdYRERERKKaArGIiIiIRDUFYhERERGJagrEIiIiIhLVFIhFREREJKopEIuIiIhIVFMgFhEREZGopkAsIiIiIlFNgVhEREREopoCsYiIiIhENQViEREREYlqCsQiIiIiEtUUiEVEREQkqikQi4iIiEhUUyAWERERkaimQCwiIiIiUU2BWERERESimgKxiIiIiEQ1BWIRERERiWoKxCIiIiIS1RSIRURERCSqKRCLiIiISFRTIBYRERGRqKZALCIiIiJRTYFYRERERKKaArGIiIiIRDUFYhERERGJagrEIiIiIhLVFIhFREREJKopEIuIiIhIVFMgFhEREZGopkAsIiIiIlFNgVhEREREopoCsYiIiIhENQViEREREYlqseF8cWNMAvAkMAJoAawGJlhrlxhjxgK3AF2AvcCT1toHw1SqiIiIiDRR4R4hjgU2AqfiCcRTgbeNMc2BROBmIB34BTDWGHNlWKoUERERkSYrrCPE1tpiYLJf02xjzKNAd2vtP/zaNxpj5gJDgFeO7Mc70pzg37Z48WLi4+ProerGzeVyHfZVJBLovJRIFM3npdMZ7vEykYYV1kB8JGNMV6AV8EM1m08DXqrh0D8A9/o3TJs2jZEjR4a2wCYkPz8/3CWIHEXnpUSiSD0vK8pclBVVUlnhJjbOQUJKLHEJoQmyubm5IelHpLGImEBsjEnCE3gfsNYWHrFtIp6gPLOGwx8AHvVvGD9+fKFGiI/mcrnIz8+nY8eOGgGQiKHzUiJRJJ6XbrebbesKWfHxFjZ8W4Dbb/Da4YTcfhn0+UUm7bum4XA4wleoSCMTEYHYGBMHvIZnZHjyEduuAiYAp1lrD1Z3vLW2DCir7zqbEqfTGTH/wIv46LyUSBQp52XBpiIW/HMV+7aX4HA6DgvDAG4XbPh2Fz9+XUCLdsn88tpeZGSlhKdYkUYm7H/DjTFO4EXADVxtrXX7bbsQmAKcba3dEKYSRUREwir/uz3MeWQZhTtLAHC73NXu52sv3FnCnEeWkf/dngarUaQxC3sgBp4C2gOXW2srfY3GmOHAs8AF1tpV4SpOREQknAo2FTH/ieVUVbqOGhWuidsFVZUu5j+xnIJNRfVboEgTEO51iLOB3wClwC5jjG/TOcDdeJZi+9Cv/SVr7Q0NXKaIiEhYuN1uFjy7CleVy/N71FodDK4qFwv+uYor7z1Zc4pFjiHcy67lATX9DT2jIWsRERGJNFvX7WPfjpKgj3e7YN/2Erb9sI8OXVuGsDKRpiUSpkyIiIhINVYs2ozDWbeRXYfTwYpFm0NUkUjTpEAsIiISgcoOVvLjNwU1XkAXKLfLzfqvCyg7WHn8nUWilAKxiIhIBCreVxbwRXTH43ZBSaFWJxWpiQKxiIhIBKooqwppf+Wloe1PpClRIBYREYlAcQkxIe0vPjG0/Yk0JQrEIiIiEahZiwQcIfpf2uGE5LSE0HQm0gQpEIuIiESghKRYOvXPCMkqE50HtCEhKawrrYpENAViERGRCNXn9BNCsspEn9MzQ1SRSNOkQCwiIhKhOnRtQYt2yUFPnXA4oUW7ZNp3aRHSukSaGgViERGRCOVwOPjltb1wxjhrvq9rjQeDM8bJL6/tpds2ixyHArGIiEgEy8hK4bzf9SUm1hnwSLHDCTGxTs77XV8yslLqt0CRJkCBWEREJMJ1PLEVl94+kLQ2yQA1Xmjna09rk8yltw+k44mtGqxGkcZMl5yKiIg0AhlZKVx578ls+2EfKxZtZv3XBYfdyc7hhM4DMuhzeibtu7TQNAmRWlAgFhERaSQcDgcdurakQ9eWlB2spKSwjPLSKuITY0hOS9DSaiJB0t8cERGRRighKVYBWCRENIdYRERERKKaArGIiIiIRDUFYhERERGJagrEIiIiIhLVFIhFREREJKopEIuIiIhIVFMgFhEREZGopkAsIiIiIlHN4Xa7w12DiIiIiEjYaIRYRERERKKaArGIiIiIRDUFYhERERGJagrEIiIiIhLVFIhFREREJKopEIuIiIhIVFMgFhEREZGopkAsIiIiIlFNgVhEREREopoCsYiIiIhEtdhwFyCRwxjzMDAE+AH4jbW2KswlSRQzxiQAHwO9gf7W2h/CXJIIxphfAA8BlcCX1toJYS5JREJAI8QCgDGmH5BhrR0KbAR+Fd6KRKgALgT+He5CRPz8APzCWnsq0MYY0yfcBYlI3WmEWHxOAd71fv8ucAnwZvjKkWhnrXUBO4wx4S5F5BBr7Ra/p5WAfpMm0gQoEDdBxpgbgeuBPsD/WWsn+W3LAJ4HTgc2A7+z1n4AtAQ2eHcr9D4XCYkgz0mRelWX89IYMwBoba1d3YAli0g90ZSJpmkbMAmYU822vwPbgQzgduBfxphWwD4gzbtPGrC33quUaBLMOSlS34I6L40xbYDHgWsbpkwRqW8aIW6CrLVvABhjzvVvN8Y0By4COllrS4C3jDEr8MzT/BwYB7wKnAUsacCSpYkL8px8roHLlCgTzHlpjHkFeAX4X2vtjoatWETqi0aIo0tX4IC1drNf2wqgl7X2G2CvMeZToDMwLwz1SfSp8ZwEMMa8AfwSmGmMubzhy5ModazzcgyeKRZTjDGLjDFDwlGgiISWRoijS3Ng/xFt+4F0AGvtbQ1ekUS7452TFzV0QSIc47y01j4NPN3wJYlIfdIIcXQ5AKQe0ZbqbRcJB52TEol0XopEGQXi6LIOaG6MyfRr6w2sClM9IjonJRLpvBSJMg632x3uGiTEjDGxeKbDTAd2APcBFdbaKmPMa3iWVRsHDAdmAl2ttXvCVa80fTonJRLpvBQRH40QN013AweB3wB/9H7/P95tvwM6ALuBR4Er9A+8NACdkxKJdF6KCKARYhERERGJchohFhEREZGopkAsIiIiIlFNgVhEREREopoCsYiIiIhENQViEREREYlqCsQiIiIiEtUUiEVEREQkqikQi4iIiEhUUyAWERERkagWG+4CROTYjDFjgecArLWO8FZTO8aYScC9EFztxpjTgY+8T3OttRtDVVst61gE/MKvaYK1dmoAx43F+2fn9bG19vRQ1iYiInWnQCwiYWeMeR64mqMD435gqff7sgYuqzpbgM3AtgD3L8BT/wlAZn0VJSIidaNALCIRy1r7FfCzcNfh5xlr7aRAd7bWzgfm+4+Ui4hI5FEgFgkjY4wTGAdcD3QBDgILgTustRuOcdxVwHigE5AGHAC+AP5krf3Cu8/p/DTd4ELgdmAQnhHOO621//bu1xx4BPgV0AbPqOz3wAxr7UzvPmnAZOAioD2wE/gXcLe1tsS7jwP4C3AjEAO8CBQF8B5sBLK9T39hjHF7vz/D+/WwKRN+o8l5eELmfd734J/AH4AHgGuAQuB+a+0//F6rg3f/s4HW3vfiOeABa23l8WqtpvZ2wBRgGNAK2AusBv5qrX2ntv2JiEh46KI6kfD6GzAV6AX8AFQBlwGfG2PaHOO4QUAfYDewCkgCfgks9Ia0I/0LT9gtAzoDrxpjBni3TQZuADK8fRUBJ+MNpMaYeGARcIu3j++AdGAC8LY3CAPcDPwRTzAsAkZ6jzmer4Fd3u+L8EwxWIonmB9Le+AJ78+UgucDwjJ+CsMdgb8bY070/hzpwH+925t7f46O3p9/RgB1VucJ4EpvfyuBcuB0YHCQ/YmISBgoEIuEiTEmF08QBbjaWtsbyMEzatkOz8hxTf4OpFtru1tr+wO9ve0pwHnV7P+YtbY70B3Yh+fv/u+927p6v/7FWnuStbYTnuD7mLf910B/PGGvr7W2Hz9NYxjmfQDc4f26GMj1PjYf42cAwFp7MTDf+/Qra+3PvI+vjnNoPJ4PAd2AfG9bF6Af0BMo9f6cvovhbsYTgHcAnb0/x2XebWONMV2OV2s1fO/dDdbagdbaLDxzhWcH0ZeIiISJArFI+BjAN7o60ztVoAjPBVhw7LmzLYE3jTF7jDEuYJ3ftg7V7D8LwFq7nZ+mIPTxfn3b+/Uvxpg8Y8x7eML4Dm+7b7QzHvjeW+c3fn3/zBiT6lf3W9baSu9UivnUn73W2s+stS5gk7dtpbV2o7X2AJ5pHQBtvV8H+z3f6f053vC2OfCMiteW772baYz5wRgzDxgNbA2iLxERCRPNIRaJDN9w9CoKedXt6J3z+x7QAs8o6NdABT8FupjavLC1doYxZg1wAZ6QPBDPyOvl/DTyDJ4R4q+r6WJvbV4vhPynVFRW0+abi3zkcm9FeOb5HqkkiBr+CHwGnIXnvToNzwj96VQ/Ui8iIhFIgVgkfJbhCW0O4Hlr7TQ4dHHaqXjmwVanO54wDHCttXaWMeZnwJJjvNYVwHLvvOTTvW0rvK83GFhlrf3E+9zXVy/vvNsvvfvHAL/zTWUwxiTiCX0fWGv3G2M24xklPt8Y8xieEeVzA3wvfGG0WYD7B+NLPPVUAqN8axobY1KAi621rwfR58/xLBU339vXKDyj8aeFpGIREWkQCsQiYWKt/dEY8zTw/4CpxpjxeFaLyAZS8Vz8tbyaQ38EivGEx2eNMX/AM+f3WMYbYy7FMzc5DXABD3u33QJc4Q20e/DMwwXPmrt78AS8CUBf4EtjzHdAnLfOBDxzhfcBf8VzgeBQYIN3nxYBvRmwxvvVGGNWeH++M46xfzD+DvwGzxzftd6fIwXPvOI44IUg+nwQGGSMycfzAeZEb3t1f24iIhKhNIdYJLxuxBM2V+CZ+5sNbAQexbOyw1GstXvxTGdYjefvcDlw/nFe5zI8c4IT8QTqX/tdtDYf+BTPShV98EzDeBs411rrttaW4bkw7XE8F691wzOH2eKZMuCbazwdT0Dciyd0zwOmBfIm4FkybQ6eUNkbz/SPWk39OB5rbQGeednP4Vmdoxeen/lTPH8GwXgVz/uQiue924fngrpf17FcERFpQA632338vUSk0YmU2x43BX63bvbdqe4xa+2rARx3HvAnfrpTnW7dLCISgTRlQkQkcJneR/sA988guNUrRESkAWmEWKSJ0gixiIhIYBSIRURERCSq6aI6EREREYlqCsQiIiIiEtUUiEVEREQkqikQi4iIiEhUUyAWERERkaimQCwiIiIiUU2BWERERESimgKxiIiIiES1/w9/+5d7NRf8egAAAABJRU5ErkJggg==\n", 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" ] @@ -1057,10 +1022,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2022-04-03 21:40:29,804] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 53277 samples.\n", - "[2022-04-03 21:40:29,804] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 53277 samples.\n", - "[2022-04-03 21:40:29,819] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n", - "[2022-04-03 21:40:29,819] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n", + "[2022-04-07 17:04:05,386] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 53277 samples.\n", + "[2022-04-07 17:04:05,386] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 53277 samples.\n", + "[2022-04-07 17:04:05,403] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n", + "[2022-04-07 17:04:05,403] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n", "GPU available: True, used: True\n", "TPU available: False, using: 0 TPU cores\n", "IPU available: False, using: 0 IPUs\n", @@ -1080,7 +1045,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2586b5e610774c77b40866677608f206", + "model_id": "e3e2791cdc1a4499af7628ff155e7c26", "version_major": 2, "version_minor": 0 }, @@ -1101,7 +1066,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a994c951d4de40a785ef3ddcdffed81b", + "model_id": "950e38453a094cb885a6ae213b06017b", "version_major": 2, "version_minor": 0 }, @@ -1139,7 +1104,6 @@ "\n", "start_time = time.time()\n", "\n", - "\n", "nbeats_model_air = NBEATSModel(\n", " input_chunk_length=IN_LEN,\n", " output_chunk_length=OUT_LEN,\n", @@ -1150,7 +1114,6 @@ " expansion_coefficient_dim=COEFFS_DIM,\n", " loss_fn=SmapeLoss(),\n", " batch_size=BATCH_SIZE,\n", - " # learning rate goes here\n", " optimizer_kwargs={\"lr\": LR},\n", " # remove this one if your notebook does run in a GPU environment:\n", " pl_trainer_kwargs={\n", @@ -1161,9 +1124,7 @@ " },\n", ")\n", "\n", - "nbeats_model_air.fit(\n", - " air_train, num_loader_workers=4, epochs=NUM_EPOCHS # fill in series to train on\n", - ") # fill in number of epochs\n", + "nbeats_model_air.fit(air_train, num_loader_workers=4, epochs=NUM_EPOCHS)\n", "\n", "# get predictions\n", "nb_preds = nbeats_model_air.predict(series=air_train, n=HORIZON)\n", @@ -1188,7 +1149,7 @@ "outputs": [ { "data": { - "image/png": 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VPd6TVN/U1T2p0+mQlZWF+Ph4eHl5Vb5j3n7gvc7uO/ET24GQBPurHc4e3rlz50Sz2YwdO3bst5e1a9eubffu3Yvef//9E5fvP3/+/IAxY8bEFBQU7ARsLcQvvvhidHFx8d8AMGnSpNCPP/449NixY3sAW6txhw4dUv7888+M1NRU3bPPPhuxZcsW302bNh2013n48GHPVq1atd+5c+ee9u3b6y8/54IFC5o9+eSTcceOHdsVEBBgcdxWVlYmBQYGdvz+++8P9O7du8Refu+998aWlpYqvv/++6yKrnv48OExZ86c8Vi9evWR06dPK8PDwzuuXLly/x133HHh8n07derUJjExsWzJkiXZ9rLbb7+9RWlpqWL9+vWHAFsL8VNPPXVy5syZuQBQVFSk8Pf3T/3qq68O3nPPPUVXiwGwDaorLCxUrlu37nBF+zcVpaWlXgcPHvQ1mUy3pqWlZV5p3zqbh1gIoQbwAYDeAJoB2AtgtCzLW4UQXgDeAvCf8hjnyrL8Ql3FSkREVO8Y/pVvVbO+4mpXkZSUVOb4OjQ01JCXl+cJACtWrNBOnTo14vDhw14lJSVKs9ks6fV6qbi4WKHVai2X15Wenn5u0qRJ0T///LNPr169SubPnx+UlJRUmpqaqgOA3bt3e2/btk2r0WhSLz82MzNTvXnzZp+xY8fG2suWL19+sG/fvkX/+9//DC1atGh34403Ft1yyy2Fw4YNO6/Vai0ZGRlqnU6n6Nu3b4JjXUajUWrbtm2p/fUbb7wRsnjx4uDc3FyVXq9XGI1GqU2bNmUAEBYWZh44cGD+gAEDEq677rqiHj16FA0fPrwgNjbWCACHDx/2uv/++/Mc6+/ateuFOXPmhDmWdejQ4eL76OfnZ/H19TWfOnXq4mIHV4qBXFOXC3N4ADgK4HoAxwEMAvC9ECIOwDMA2gFoC8ATwEohxBFZlj+um1CJiIjqGZWvm+vTXn2fq/D09Lzk21lJkmCxWLB//37Vvffe23rYsGF5r7/++omQkBDTL7/84jt69Og4vV4vabX/PndMTIypS5cuRYsWLQrs1atXyfLlywMdk8mSkhJlz549C6dNm3a8gmONZrMZ3bt3v9jSGxcXZ/D19bVmZGTsXbVqlXb16tX+kydPjpwyZUrkjh079hUVFSkB4KuvvjpoT2DtvLy8LAAwd+7cgFdffTV64sSJOd27d7/g7+9vmTx5cviff/7pY9/366+/Prp58+bTK1eu9F++fHnglClTor777rsDvXr1KkEVXf4+ArZvLKoaAzmvzhJiWZZLAExyKFoqhJgOIBHAHQAmybJcAABCiJkAHgPAhJiIiAgAtOG2AXGuzjDhSFIC2rCr7+eirVu3aiwWC+bOnZujVCoBAIsXLw68ymEYPHjwuYkTJzZft27duePHj6vT09PP2bd16NChdOXKlc0SExP1np4VrxQbEBDwr24Tnp6e6NevX3G/fv2Ki4qKcgMDAzuuWrVK27dv3yKVSmU9evSoqqLuDgCwefNm39TU1Avjx4+/mJgfPXpUffl+1113Xdl1111X9sYbb5zq2LFjG3tS37JlS92WLVt8R40ale/w3vi2bt26yq27VY2BnFNvlm4WQrQGEAjgUHmR4yLrEoDkKxyrBnDJzbBp0yaoVKpKjiD7J037v0R1jfck1Td1dU9aLBZYrdaLj0qp/YC2dwKZqyBVY2CdVVICbfva6is/n30QmLskJibqTSaTNHny5NCBAwee//XXX30XLFgQcrXjhg0bVvDss8/GPPnkk7FdunQpjouLu9hyO3bs2DOfffZZcN++fVuMHz/+VHBwsDkzM1O9ZMmSwKVLlx718Ph3irNkyRL/w4cPq3v27FkcHBxs/uabb/ytVquUnJysCwgIsDzyyCOnXnrppWiLxSL17NnzQkFBgXLDhg2+fn5+5lGjRuW3bt1av3z58qBly5b5tW7dWj9v3ryg3bt3a6KiogwAkJmZqZo9e3ZI//79z8fExBj37NnjlZ2drb7vvvvyAWD06NGnRo4c2aJjx46lt99+e9GyZcuarVmzJmDFihUHqvpeXi0Gck29SIiFEN4AFgN4Q5blQiHEagBjhRCbYesy8TSAK30V8DyACY4FM2fOxKBBg2oq5EYjJyenrkMgugTvSapvavueNJvNMJvNMBqNVx3MJ6XeD9Xeb6t1PslqhiE1HVbDP/mUWu3eBseuXbuWTZw4MWf27NnhkydPjhJCXHjllVeOP/nkk/FXOi4gIMDSs2fPwh9++CFgxowZRx23xcXFGTds2JA5duzY5nfddVeC0WiUIiIiDD169Ciq7H0LDAw0v/POOwFvvfVWpMFgkGJiYvRz5sw5IoTQAcCMGTNyQ0JCTNOnTw8fM2aMWqvVmpOTk0tfeOGFkwAwduzYvL///luTnp7eQpIk9O3b99zw4cPzfv75Z38A8PHxsRw4cMBryJAhLc+fP+8REhJiTE9Pzxs3blweAPz3v/89n5ubm/Puu++GvfTSS9FRUVGGmTNnZt15551V7sB9tRjINXU+y4QQwhPANwAKAQyTZdlaniC/BaAfAAOAjwA8JMtyhVOpVNJCXMgW4spZLBbk5OQgOjqaI/qpXuA9SfVNXd2TOp0OR48evfosE4CtRff9LkD+YUgudJ2wSkogqCXw+DbAoVVYkiSnZ5kgqm8axCwTACCEUABYBNsUaSNkWbYCgCzLZQCeLH9ACPEwgD8qq0eWZT2Af/UToqtTKBRMPqhe4T1J9U1t35MKhQKSJF18XJEkAQM+Aj65BTBZ4NyMoxIkpScw8GOA/+eoiavrLhNzAEQAuEWW5YsdoIQQzQGYAZwG0AXACwDuqpMIiYiI6rPIjsB9S4ElgwGzsWqD7CQloPS0HRfRocZDJKrv6nIe4lgADwLQATgrhLBvuq08rgUAggEcBvCELMu76yJOIiKieq9lD2DkT8Dyh4CzB2zLOVc00M4+K0VQS1vLMJNhIgB1O+1aNi6dSeJyMbUVCxERUYMX2RF44g8gewvwx0e25ZgdW4slJZDUF7jmISC22yV9homaurruMkFERETuIklA3HW2h64QKD5tW4FOpbXNM+zFiQiIKsKEmIiIqBEqViiQ5+mBUkkNjYcHQhQKVH8tOqLGiQkxERFRI2G1WiGflrE0cynWHVsHi/WfRUUUkgK9Y3pjcJvBEGHC7YtvEDVkTIiJiIgagb35ezF+43hkFWZBKSkvSYYBwGK14OdjP2NN9hrE+8djSvcpSApKqqNoieoXTjxIRETUwG3N3YrhPw5HdlE2AMBcydRr9vLsomwM/3E4tuZurbUYieozJsREREQN2N78vRj1yygYzIZ/tQpXxmK1wGA2YNQvo7A3f28NR9i0REVFtZs0aVJoVfefNWtWkFar7ViDIdW6qlzTwIED43r37t2ylkK6KibEREREDZTVasVzvz0Ho8UIq1Or1AFWWGG0GDF+43hYrc4dW5GBAwfGSZKUdvmje/furatdeT1UWdK3ffv2faNHjz7rznOtWrXK99prr03w9/fv6O3tnRobG5syYMCAOJ1OV+cdwZ39AGA3d+7cnKVLlx6tgZBcwj7EREREDZR8WsbRoqMuH2+xWpBVmIUdp3dAhIurH3AV3bt3L1q8eHGWY5m3t3f1s+0GJDIysoIVUVy3Y8cOr4EDByakp6efmT179jGNRmPdt2+f+uuvvw4wmdx6qloVFBRUhSUVaw9biImIiBqopZlLoZSU1apDKSmxJHOJW+JRqVSWmJgYk+MjJCTEDAArV67Uenp6dlq9erWvff+XXnopLDAwsENOTo4HAHTu3Dlx+PDhMcOHD4/RarUdAwICOjz99NORFss/XUHy8vKU/fv3j/Pz8+vo7e2desMNN7TevXu32r7d3nK7bNkyvxYtWiRrNJrU7t27t87OzvZ0jHX69OnBLVq0SFar1Z3i4+OTp0yZEmLftn//fpUkSWkLFixo1qVLlwRvb+/UxMTEpHXr1vnYr+Xpp5+Ou3DhgtLeEj5mzJhI4N8tphMnTgxLSEhI8vb2Tg0PD28/bNiwmMLCwirnX6tWrfILCgoyfvjhh8evueYaXXJysv6ee+4pWrp0abavr6/V8ZqXLFniHxcXl+Lt7Z166623tiguLlbMnj07KCoqqp2fn1/H9PT0aMck+mrvJQB8+umnzVq1apWsUqk6RUVFtZswYUKYfVvnzp0Tc3NzVRMmTIi2vw+Ox17pZ3B5l4nOnTsnpqenRz/66KPN/f39OwYHB3ewv6d2f/31l1daWlqiWq3u1LJly+QVK1ZoJUlKW7RoUbOqvp+VYUJMRETUABUbirHu2LpKB9BVldlqxrpj61BsKHZTZBW78847ix944IEzDzzwQHx+fr5y8+bN3m+++WbU7Nmzj0ZHR1/M0pYtWxbk4eFh3bRp077JkyfnzJ07N+ydd94Jtm8fPHhw3K5du3y+/PLLQ7/88kum1WrFnXfe2Vqv11/sPqDT6RTTpk0L+/TTT7PWrFmzPzc3VzVq1Kjm9u0ffPBB4JQpUyInTJhwYufOnXsmTpx4YsqUKVGzZ88Ocoz51VdfjRo9evTpbdu27W3RooVuxIgRLYxGI3r37n1h0qRJOb6+vubs7Oyd2dnZOydMmHCqoutWKBTWadOm5fz9998Zc+fOzdq8ebP2iSeeaF7RvhUJDw83nj171vPHH3/0vdJ+Op1O8e6774Z+9tlnh5cvX35w27Zt2ttuu63l6tWr/b/77ruDc+bMyfr8889D5s+fH1DV93Ljxo2aBx54oGX//v3PybKc8dxzz+W++eabkbNmzQoCgO+///5QWFiYcdy4cbn296GqP4OKLFu2LMjHx8e8adOmfRMnTjw+Y8aMiG+++cYPAEwmEwYMGNDSy8vLsn79+n3vv/9+9iuvvBJV1ffxathlgoiIqAHKK82r8iC6q7FYLcgry4NWVb2lO9avX99Mo9GkOpY99dRTJ6dMmXIKAGbMmHFiw4YNfsOHD4/dv3+/98CBA/OHDh1a6Lh/eHi44eOPP85RKBTo0KGDfvfu3d7vv/9+2NixY8/u3r1b/csvvzRbs2ZNZp8+fUoA4KuvvjoSHx/f/rPPPms2cuTIAgAwmUzSRx99dCw5OVkPAA899NCZt99++2Jr4xtvvBH52muvHR8xYsR5AGjTpo0hIyPj9CeffBIyatSofPt+o0aNOj148OBCAHj99ddzhRDJe/bs8UpNTdX5+/ubASAmJuaK/RZeeeWVM/bniYmJhvz8/NwxY8bEADhWlfd05MiRBWvWrPG//fbbE4ODg40dO3Ys6dGjR9Gjjz6aHxgYePEGMJlM0ty5cy9e82233VbwzTffBJ06dWqnv7+/JS0tTff+++8X//rrr9qHHnqooCrv5VtvvRV27bXXFr311lsnAaB9+/b6vXv3es+aNSv8qaeeyg8LCzMrlUqrVqs1X/4+XO1nUJGEhISyadOmnQSAdu3a6efMmRO6du1abf/+/YtWrFjhl5OTo96wYcN++7nKyspO9O/fP6Eq7+PVMCEmIiJqgEpNpe6tz1j9+jp37lw0d+7cSxK9kJCQi4mSl5eXdfHixUe6dOmSHBERoZ8zZ07O5XV06tSpRKH45wvsbt26lcydOzfMZDJh165dXkql0tqjR48S+/bw8HBzXFycfu/evV4O57HYEzEAiIyMNJ47d84DAIqKihQ5OTnq//u//4sdPXp0rH0fs9ks+fr6XtLc3qlTp4tvSkxMjBEATp065VTutGLFCu3UqVMjDh8+7FVSUqI0m82SXq+XiouLFVqt9qqfaDw8PPD1118fzcrKOvHDDz9ot23b5jtjxoyIWbNmRWzbtm1fbGyssaJrDgsLM0VFRRn8/f0vniMkJMR49uxZTwBVei8PHTrkddttt513jOf666+/MG/evFCTyQQPj8rfiiv9DCqTlJRU5vg6NDTUkJeX5wkA+/bt8woPDzc6Jt433HBDyeV1uIoJMRERUQOk8dC4tz7P6ten0WgsKSkp+ivts2HDBl8AKCws9Dhz5ozSz8/PPc3cDjw8PC4ZyCdJ0sWZNOz9d6dPn57dvXv3kisd5+npaXWsA7AlzlWNY//+/ap777239bBhw/Jef/31EyEhIaZffvnFd/To0XF6vV7SaqveIh8fH2984oknzj3xxBPn8vLyTiQkJKTMmDEj5J133smt7JorKnPsj12TrvQzqIzj+20/prbiZR9iIiKiBihEEwKF5J4/4wpJgRDvkKvvWE0ZGRnql19+OXratGlHO3ToUDJs2LB4s/nSPtB//fWXj+PrrVu3+sTGxuo9PDzQvn17ndlsln799deL+5w6dUp59OhRdXJysq4qMURHR5tCQkKMR44cUaekpOgdH23atDFU9VpUKpXVYrFcMTneunWrxmKxYO7cuTm9evUqad++vT43N1dV1XNUJiQkxBwSEmIsKSlx+QaoynvZqlUr3e+//35J3+VNmzb5xsXF6e2tw56enlZnPiS4qm3btrpTp0552gdgAsDGjRt9rnSMM5gQExERNUBalRa9Y3q7ZZaJPjF9qt1/GAAMBoPi2LFjHo6PkydPegC2QVFDhw6N7969e+HTTz+dv3jx4qP79+/3njhxYphjHSdPnlQ9+OCDzXfu3KmeM2dO4Pz580MfffTR04CtX2mvXr3OP/bYY3E//fST79atW70HDRrUIjQ01DhkyJDzVY1z/Pjxue+++27466+/Hrpr1y71H3/84T1z5sygy2O5kpYtW+pLS0sV3377rfbkyZMexcXF/8qpEhMT9SaTSZo8eXLo3r17Ve+9917gggULnPrk8dZbbwUPHTo0Zvny5X4ZGRlqWZa9HnvssajDhw9733333eedqctRVd7LZ5999vTvv//u98wzz0Ts2rVLPXv27KAFCxaEjBo16uIAwqioKP2mTZt8s7KyPO0/65rQr1+/oujoaP2QIUPit23b5r1mzRqfCRMmRAH/tN5XBxNiIiKiBmpwm8FumWVicJvBboln48aNfrGxsR0cH926dUsEgPHjx0fk5uaqPv3002wAiI2NNc6YMSN76tSpUVu3bvW21zFgwID8srIyxfXXX9/22WefjXnggQfOjB079uJCF0uWLDnarl27knvuuadVz54921itVqxcufKgWq2u8nzHY8aMOfvOO+9kf/bZZ8FCiOQ+ffokLl68OLhFixZX7O7hqE+fPiVDhgzJS09PbxEZGdlhwoQJ4Zfv07Vr17KJEyfmzJ49OzwtLS156dKlQa+88srxqp4DsPWhLikpUT711FOxnTp1Su7Tp0+iLMu+ixYtOnTHHXdccKauy13tvbz++utL582bd/ibb74JTEtLS37jjTcin3nmmdynnnrq4sDDSZMm5R4/flzdpk2bdpGRkR2qE8+VeHh4YPny5YdLS0sV3bt3b/voo4/GPffccycBwNvbu9r9KiR3rE5TTzXaC3MHi8WC7OxsxMbGwnHwAlFd4T1J9U1d3ZM6nQ5ZWVmIj4+Hl5fXFfe1Wq24+9u7kV2U7dKMEwpJgVi/WHx797eXt7LtcLoyN+jcuXNiSkpK6SeffPKvwXZEl1uzZo3PLbfc0mbPnj17HAfw2ZWWlnodPHjQ12Qy3ZqWlpZ5pbo4qI6IiKiBkiQJU7pPwfAfh8NgNji1fLMECZ4KT0ztPtUtXzkT1bSFCxc202q1lqSkJN2+ffvU48aNi+nUqdOFipJhZ7EZhoiIqAFLCkrC7J6zoVKqqjzITiEpoFKqMLvnbLQNalvDERK5R1FRkXL06NEx7du3T3nkkUfiO3ToULJq1apD7qibLcREREQNXNfIrlh420KM3zgeWYVZUErKCvsW28tj/WIxtfvUepcM//HHH/vrOgaqv5588sn8J598Mv/qezqPCTEREVEjkBSUhG/v/hY7Tu/AkswlWHds3SX9ihWSAr1jemNwm8FIC0tjNwkiB0yIiYiIGglJkiDCBUS4QLGhGHlleSg1lkLjqUGId4hbplYjaoyYEBMRETVCGj0QddYKS6kVCo0VHqEAqr0kBFHjxISYiIiokbBarSjdvh0Fn32O4rVrAcdlbxUKaPv0QcDQIdBccw27TBA5YEJMRETUCJRlZCD3mWdgOJIFKJWXJsMAYLGgeN06FP/0E1Qt4hH51lvwTk6um2CJ6hlOu0ZERNTAlWzZguwhQ2E4mm0rMFeyel15ueFoNrKHDEXJli21FCFR/caEmIiIqAEry8hAzmOPw2ow/LtVuDIWC6wGA3IeexxlGRk1G2C5lStXaiVJSjt79qyyVk5I5AQmxERERA2U1WpF7rhnYDUaAWvVV6krPxhWkwm5zzwDq7PHXkaSpLQrPcaMGRNZrRNcdq5FixY1c1d9RAD7EBMRETVYpdu3w5CV5XoFZjMMR7JQJsvQXHONy9VkZ2fvtD9fsGBB4JtvvhmZkZGxx17m7+9v2bhxo4/rgRLVLLYQExERNVAFn31uG0BXHUolzn32ebWqiImJMdkf/v7+5grKLvbl2Lx5syYlJaWtt7d3ampqapudO3eqHetavHhxs6SkpLZqtbpT8+bN240dOzbCaDQCAKKiotoBwPDhw1tKkpRmf52RkaHu1atXy6CgoA4ajSY1JSWl7YoVKzjpMlUZE2IiIqIGyFxcbJtarbIBdFWuyIziNWtgLi52T2BX8corr0RNnTo1Z9OmTfuUSqX1/vvvj7dvW716te9jjz0W99hjj53566+/9syePTt76dKlwePHj48AgO3bt+8DgJkzZx7Nzs7eaX9dVFSkuPXWWwtXr169f9u2bXt79epVOHjw4NYHDx7kzMtUJewyQURE1ACZzpyp+iC6q7FYYMrLg1Jb842qkyZNOnHHHXdcAIBnnnnm1ODBg1uVlpZKGo3GOmnSpMgnn3zy1KhRo/IBICkpyXDy5MkTEydObD5t2rSTkZGRJgAICAgwx8TEmOx1du3ataxr165l9tczZ87M/eGHHwK++uor/xdeeCGvxi+KGjwmxERERA2QpbTUvfWVlLi1vspcc801FxPX5s2bGwDgxIkTnq1btzbs27fP+88///SdOXNmxMW4LBZJr9dLxcXFCq1WW+EngMLCQsUzzzwTuW7dOv+8vDxPs9ks6fV6xbFjx9QV7U90OSbEREREDZBCo3FvfT61M+ZNpVJdnNLCvlqepbylu6ysTDlu3LgTgwcPPn/5cRqNptLm8Mcee6z5xo0b/f73v/8db9OmjV6j0VjuueeelgaDgcvxUZUwISYiImqAPEJDAYXCPd0mFAp4hIRUv55qSkpKKj1w4IBXSkqKvrJ9PDw8rObL+k3Lsuw7ePDg/OHDh58HbC3GJ06cYP9hqjIOqiMiImqAlFottH36uGWWCe0tN9dK/+GrefHFF3OXL18eNHbs2AhZlr3+/PNPr7lz5wY89dRTF+cxjoqKMqxbt87v2LFjHnl5eUoAiIuL069cuTJgy5Yt3lu3bvXu379/C6vVytZhqjImxERERA1UwNAhbpllInDIEPcEVE0DBw4s+uKLLw79+uuv/t27d2974403tnn33XfDYmNjDfZ9Jk+enLNx40a/Fi1atO/YsWMSAMyePTvH39/f1KtXrzYDBgxo1adPn8KkpCT3drKmRk2q7uo09VijvTB3sFgsyM7ORmxsLBQKfi6iusd7kuqburondTodsrKyEB8fDy8vryvua7VaceSOO2A4mu1a1wmlEqrYGLRYtepif95yO5yvjKh+KS0t9Tp48KCvyWS6NS0tLfNK+/KvDhERUQMlSRIi33oLkqcnIDnZQ0CSIHl4IOrtty9PhomaHCbEREREDZh3cjKiP3gfkkpV9f7ESiUklQrRH7wPr6Skmg2QqAFgQkxERNTA+XTrhtjPP4MqNsZWUFliXF6uio1B3JLP4dOtWy1FSFS/cdo1IiKiRsA7ORktVq1CmSzj3Gefo3jNmkv7FSsU0N7cB4FDhsBbCHaTIHLAhJiIiKiRkCQJmmuugeaaa2AuLoYpLw+WkhIofHzgERJSL6ZWI6qPmBATERE1QiYPb5R4h8GoMMNTrYSPhxrVnLGYqNFiQkxERNRIWK1W5B48j93rj+PI33mwOvSYkBRAi44haHdTc0S2bsYuE0QO6jQhFkKoAXwAoDeAZgD2Ahgty/LW8m3vAugHQAKwGsBjsiwX1020RERE9VfesWKs+SQD50+VQlJIlyTDAGC1AEf+PovDf+ahWbgGN49MRkgMu1AQAXU/y4QHgKMAroctIZ4B4HshhC+AJwF0AtAGQDyAUAAv1EWQRERE9VnOvnNY9tYOFJ6xLc5mtVS8NpW9vPBMKZa9tQM5+87VWoxE9VmdthDLslwCYJJD0VIhxHQAiQDiAKyWZTkfAIQQKwDcUlE95a3JaseyTZs2QaVS1UDUjYOlfOSxxZWVjYhqAO9Jqm/q6p60WCywWq0XH1eTd6wYq97fBbPJUuU1Wq0WwGy1YNX7uzBgXKd/tRSzOwU1NfWqD7EQojWAQACHACwAME0IEQpAD2AAgG8qOfR5ABMcC2bOnIlBgwbVYLSNQ05OTl2HQHQJ3pNU39T2PWk2m2E2m2E0Gq+6ZLTVasWaeRmwOJEM/3MwYDFbsGZeBu55IfWSJFitVl/hwCtbt26dzy233NKme/fuhevXrz9kL9+/f7+qTZs27eyv/fz8zAkJCWWvvfbaiVtvvfWCvXzMmDGRP/zwQ7PMzMy99tfvvPNOxPXXX1+0cePGg47nevnll8Nef/315tdcc82FP/74Y7/jtsOHD3u2adOmXVxcnP7gwYMZLl8QNQn1JiEWQngDWAzgDVmWC4UQhwDkAThVvssaAB9WcvgbAKY7Fjz99NOFbCGunMViQU5ODqKjo6/6C5eoNvCepPqmru5JnU6Ho0ePwtPT86rfdJ44UIDCM2Uun8tqAQrPlCH/WBkiWzdzuR5HH330UfCIESPOfPHFF8FHjx71jIuLMzpuX7FixYHU1NSy06dPe0yaNCniP//5T6u9e/fuiY6ONlVWZ0hIiHHbtm3aw4cPe7Zs2fJifZ999llwRESEoaJj5syZE3z77bcX/PHHH76//PKLT8+ePUvccoHUKNWLhFgI4QngK9hahu1dKN4HYATgD1tf548AvAVgzOXHy7Ksh60VmZykUCiYfFC9wnuS6pvavicVCgUkSbr4uJI9G06UD6Bztnn4H5JCwp4NxxGVEOByHXaFhYWKlStXBm7ZsmXvmTNnPD/88MOgKVOmnHLcJyQkxBQTE2OKiYkxvfzyy6dWrlwZ+Ntvv/kMHTq0sLJ6AwMDTSkpKaVz584Nmjp16ikAWLt2rU9BQYHHHXfcUXDgwAFvx/0tFguWLFkSNGPGjGNRUVGGjz76KJgJMV1Jnf/VEUIoACyC7cueEbIs2/9XdwDwqSzLxbIsFwJYCKBXHYVJRERUr+jLTOVTq7meDAO2gXaH/8qDvqzSBtoqmz9/fkB8fLyuQ4cO+qFDh+Z//vnnwZX1wb5w4YI0b968IABQq9VXvYj09PSzS5cuDba//vjjj4P79+9/TqVS/evYlStXanU6neLuu+8uuv/++/O///77wKKiojrPeaj+qg83xxwAEQD+I8uy4/9GGcB/hRAaIYQPgGEAdtdFgERERPVNyXn9v6ZWc5XVApQWVv+L1oULF4bce++9+QBwzz33FBYXFyt/+OGHS0bs9erVq41Go0n18/PrNHfu3LDk5OTSu+6666pTqt57772FFy5cUP7444++RUVFilWrVgU+8sgjZyva9+OPPw7u27dvgYeHB6655hpd8+bN9Z9++mn1m8Cp0arThFgIEQvgQQCdAZwVQlwof3QH8AwALwA5AI7BNovEuDoLloiIqB4x6s1urc+gq159O3fuVO/atUszcuTIcwDg6emJu+66q+Djjz8Odtxv3rx5R7Zt27b3008/PRwTE6OfP39+VlVaiNVqtXXAgAH58+bNC/70008D4uLidF26dPlXB+qzZ88q16xZE5Cenp5vLxs0aFD+ggULgi/fl8iurqddy4Zt0Y3K3FNbsRARETUknmr3LsSs8qpefR988EGI2WyWYmNjO9jLrFYrVCqVJT8//2LlcXFxhnbt2unbtWunN5lM0j333NMqMzMzw9vb+6pJ8SOPPHL2hhtuaJuZmek9bNiwylqHA/V6vdSjR4+2jnFYLBbs2rVL3b59e445on+pD10miIiIyEk+zdSQ3PRXXFIAGn/Xp1ozGo34+uuvgyZMmHB869atGfbH77//nhESEmKcN29eYEXHpaenFyiVSuubb74ZUpXzCCF0rVq1Kjt06JD3gw8+WOGqIosWLQp+6KGHTl8eR1pa2oUPP/yQrcRUoXoxywQRERE5R+3tgRYdQ3Dk77PVnmWiZWoI1N6upwRLly5tVlRUpBw1atTZoKCgS/pe3HHHHQULFy4Mvvvuu/81i4RCocAjjzxy5s0334wcM2bMWa1We9Ve0Zs2bTpgMBik4ODgf/Xx2LJli/fevXs1ixcvzkpNTdU5bvvPf/5z7u23346YOXPmCU9PT1cukxoxthATERE1UO1uau6WWSba3RRVrTrmz58f3K1bt6LLk2EAGDRoUEFGRobm/PnzFfbJePzxx/NNJpM0derUKrUS+/n5WSpKhgHb3MMtW7bUXZ4MA8B9991XcO7cOc8vv/zSvyrnoaZFqsqykA1Uo70wd7BYLMjOzkZsbCznfKV6gfck1Td1dU/qdDpkZWUhPj4eXl5eV9zXarXi81e3ofBMqUszTkgKwD9UgyETulw+5/EO52sjql9KS0u9Dh486GsymW5NS0vLvNK+/KtDRETUQEmShJtHJkOhVFx5iHqFBwMKpQI3j0y+6gIgRI0dE2IiIqIGLCRGizsebw+lh6LKg+wkBaD0UOCOx9sjJEZ79QOIGjkmxERERA1cdNtADHwmDf6hGgC2gXIVsZf7h2ow8Jk0RLetcPIHoiaHs0wQERE1AiExWgyZ0AUnD53H7vXHcfivvEv6FUsKoGVqCNrdFIWIVs3YTYLIARNiIiKiBurE+TIUlBguLdQoEHV7DEJ6RUF3wQCj3gJPtQJeviqo1EqcA3Aut+iSQwJ8VIhq5l17gRPVM04nxEIIASAJwHoApwBMA3ADgJ0ARsmy/K95BomIiMi9TpwvQ8+310NvcmF6icuoPRT4ZdxNTIqpyXKlhXgSgFsAtAAwEsAT5eUpAC4AeNw9oREREVFlCkoMbkmGAUBvsqCgxMCEmJosVwbVtQNwUpblbAC9AZQBeB6ACcDtboyNiIiIiKjGuZIQh8DWVQKwdZ3YLsvyVAAZAMLcFRgRERERUW1wJSEuBBAnhOgKoCWAveXlGti6TBARERE5bdasWUFarbZjXcfhrJUrV2olSUo7e/asEmi419GUuZIQbwMQCGATbH2Q1wshVACiARxxY2xERETUQAwcODCud+/eLR3L5s+fH6BWqztNmDChwX+DvH//fpUkSWmXP+6+++743r17X8jOzt4ZGBhorujYMWPGRLZp0yaptmOmqnNlUN04AM0BtALwPYCvYZtl4hyA1e4LjYiIiBqq6dOnB48fPz7mrbfeyn766afz6zoed1mxYsWB1NTUMvtrHx8fq5eXlzUmJsZU0+fW6XSSl5eXtabP0xQ53UIsy/IBWZY7ybLsJ8vyUFmWLbIsr5dlOVqW5Qk1ESQRERE1HC+99FLYCy+8EPPxxx8fcUyGJ06cGJaQkJDk7e2dGh4e3n7YsGExhYWFleYi9pbVGTNmBEVERLTTaDSpw4YNizGZTHjppZfCgoODOwQGBnZ47rnnwh2Pu9p57F0ali1b5teiRYtkjUaT2r1799bZ2dmeV7u2kJAQU0xMzMVHUFCQ+fIuE45mzZoV9M4770Ts37/f296qPGvWrCAAOHv2rPLee++NDQgI6ODr65t67bXXJmzduvXiVB/2658+fXpwVFRUO41G0+nq7z65wuWFOYQQPQBcC6AAwOcAmgE4Lcuy3j2hERERUUPz2GOPRS1cuDD0iy++OHj33XcXO25TKBTWadOm5SQkJOj379+vfvrpp2OeeOKJ5osXLz5WWX05OTnqn376yX/lypUH9+/fr05PT2+ZnZ2tbtmype7nn3/OXL9+ve///d//xd1yyy3FPXv2LKnqeXQ6nWLatGlhn376aZZCocCIESPiR40a1fy7777Lcuf7MXLkyHN79uzx/vXXX/1//vnn/QBg71rRt2/fFl5eXtZvv/32YEBAgHn27Nkht99+e0JmZuaesLAwMwAcO3ZMvWLFioAvv/zykIcH11OrKa4szOEN4DsAPcuLtgE4A+ArAC8AmOq26IiIiKjB+O233/x//vnnZt9+++2Bvn37Fl++/ZVXXjljf56YmGjIz8/PHTNmTAyAShNii8WCzz///GhAQIAlLS1N98EHHxQfOXLEa/369QeVSiU6dOignzFjRvi6deu09oS4KucxmUzSRx99dCw5OVkPAA899NCZt99+O/Jq19irV682jster127NvNK+/v6+lp9fX0tSqXykm4VP/30k+/u3bt9zpw5s9Pb29sKAHPnzj2+evXqZosWLQoYN27cWQAwGo3S0qVLsyIjI2u8S0ZT5spHjdcB9LqsbBUAA4A7wISYiIioSUpISCgrKCjwePXVVyNvvPHGg/7+/pesHLJixQrt1KlTIw4fPuxVUlKiNJvNkl6vl4qLixVarbbCVUaioqIMAQEBF7eFhIQYlUqlVan8p3dCcHCw6cyZMxdzmqqcx8vLy2JPhgEgMjLSeO7cuavmRfPmzTvSvn17nf11y5YtDT///LNvFd+ii/7880/v0tJSZVBQUEfHcr1erzh8+LDaIS4Dk+Ga50pCPAi2xTi6AvgbAGRZ1gshsgEkuC80IiIiakjCw8MNy5cvP9yzZ8/Enj17tl63bt1BezK7f/9+1b333tt62LBhea+//vqJkJAQ0y+//OI7evToOL1eL2m12grr9PDwuGQQmSRJFZZZLBbJmfNUVIfVevXxanFxcYaUlJRqdw+9cOGCMjg42GjvRuEoKCjo4mwV3t7e7lmOkK7IlWnXQgEckGV512XlRtj6ERMREVETlZCQYFi/fn1mXl6eZ69evRIKCgoUALB161aNxWLB3Llzc3r16lXSvn17fW5ursrd56+t8zhDpVJZLJZL81ohRGl+fr6np6enNSUlRe/4iIiIYItwLXMlIT4JIEEIcXGuQSFERwBtAeS6KS4iIiJqoFq1amX89ddf9+fn53v07Nkz4dy5c4rExES9yWSSJk+eHLp3717Ve++9F7hgwYIQd5+7ts7jjLi4OMPx48fVW7Zs8T558qRHWVmZdPfddxd16NDhwt13391q+fLlfvv371etXbvWZ9SoUVG//fabpi7jbYpcSYi/BeANYA8AK4BUAH8AkMq3ERERURPXsmVL4/r16/cXFBR49OzZMyExMVE/ceLEnNmzZ4enpaUlL126NOiVV1457u7zdu3ataw2zuOMESNGFNxwww2Ft956a2JkZGSHjz76KFChUGDdunUHr7322uJHH300LiUlJWX48OEtjh07poqMjDTWZbxNkVSV/jKOhBB+ADYA6HDZpl0AbpBluchNsVUXJ66+AovFguzsbMTGxkKhcOVzEZF78Z6k+qau7kmdToesrCzEx8fDy8ur0v32nCjEnbM3ue28K0ddj5Qof/vLHW6rmKiOlJaWeh08eNDXZDLdmpaWdsXZQJweVCfLcpEQojOAIQCuKS/eDmCJLMsG58MlIiIiIqo7Ls3wLMuyEcCC8gcRERERUYNVpYRYCPEJgEOyLE8uf14ZqyzLD7gnNCIiIqpMgI8Kag8F9Kbqz8ql9lAgwKdOJ2IgqlNVbSFOB/A7gMnlzyvqnyuVlzMhJiIiqmFRzbzxy7ibUFBS/d6KAT4qRDXzdkNURA1TVRPi32CbVcL+nAPWiIiI6lgUziJKyndDTUEAot1QD1HDVKWEWJblmyp6TkRERHXkfA7wbhpgqvaiaYCHGnhyB9CMSTE1TU4NqhNCeMK2bHM+gHBZltlSTEREVBdK892TDAO2ekrzmRBTk+XUxIrls0ucApDHZJiIiIiIGgNXZhqfCSBRCHGzu4MhIiIiIqptrsxDfDsAM4AfhRD7AZzGP4PsrLIs93JXcERERNQ4SJKUtnDhwsP//e9/z9d1LE3JmDFjIn/44YdmmZmZe+s6lvrMlYT4RofnbcofduxGQURE1AQNHDgwrrCwULlu3brDFW3Pzs7eGRISYq7tuKpKkqQ0+3MfHx9LXFycbvz48SeHDRt2vg7DqrYJEyacevbZZ0/XdRz1nSsJ8UIw8SUiIiInxMTEmOo6BovFArPZDE9Pzwq3z5w582i/fv0Kz58/r5wxY0Zoenp6i4SEhH2dO3cuq6mYdDqd5OXlVWN5lb+/v8Xf37+mqm80nO5DLMtyuizL91f2qIkgiYiIqGGTJClt0aJFzQBg//79KkmS0hYsWNCsS5cuCd7e3qmJiYlJ69at83E85qeffvJNS0tL9PLy6hQeHt4+PT09uqio6GLu8t577wWmpKS09fHxSQ0ODu5w1113xZ84ceJiY9/KlSu1kiSlffnll37Jyclt1Wp1p59++klbWYwBAQHmmJgYU/v27fXTpk07YTabpTVr1lzc/9ChQ5633357C61W29Hf379jr169Wu7fv//iEn9GoxHp6enRWq22Y7NmzTo+9thjUQMGDIjr3bt3S/s+nTt3Thw+fHjMyJEjowMCAjrceOONrQFg+/btXjfccENrjUaTGhQU1KFfv37xJ0+evHgt8+fPD0hISEjy8vLq1KxZs47dunVLsL8XK1eu1LZr166tt7d3qlar7dipU6c2Bw4cUAG2LhNt2rRJstdjNpsxbty4iLCwsPYqlapTmzZtkr7++ms/+/aq/mwaG1cG1QEAhBA9hBDPCyEeFUL4CSFihBBqdwZHREREjderr74aNXr06NPbtm3b26JFC92IESNaGI1GAEBGRoa6f//+re++++6C7du3ZyxatOjwH3/84Tty5MgY+/FGo1F65ZVXTmzfvj3jiy++OHT8+HH1kCFD4i4/z8svv9z89ddfP/H3339nXHPNNaVXi8toNGL27NnBAKBSqawAoNfrpVtuuSXB19fX/PPPP+//9ddfM318fCy33npra51OJwHASy+9FL5ixYqg99577+ivv/6aWVRUpFy7dm2zy+tftmxZkEqlsq5fvz5z7ty52WfPnlXecsstie3bty/dvHnzvu++++5AXl6eR//+/VsAQHZ2tudDDz0UP3To0LM7d+7cs2bNmv19+/YtsFqtMBqNuO+++1p269atWJblvRs2bMi8//778yRJqvDaXn/99dA5c+aEvfbaaznbt2/P6NGjR+F9993Xavfu3ZfkcFf62TRGTneZEEJ4A/gOQM/yom0AzgD4CsALAKa6LToiIiJqtEaNGnV68ODBhQDw+uuv5wohkvfs2eOVmpqqmzRpUni/fv3OvfLKK2cAoF27dvp33nkn5/bbb08sLS3N1mg01v/7v/+7uExfUlKSYebMmcduvPHGtoWFhQp/f3+LfdvLL7+c279//6KrxfPQQw+1eOSRR6x6vV5hsVgQGRlpGDFixDkAmDdvXoDFYsHSpUuzFQpbe+KXX355NCAgoOMPP/ygHTBgQNG8efPCnnrqqZPDhw8/DwALFiw4FhMT86/+CrGxsboPP/zwuP31s88+G5GUlFT67rvvnrCXLVy48GirVq3a79q1S11UVKQ0m83Sfffddz4hIcEAAPZuHKdPn1ZeuHBB2bdv3/PJycl6AOjUqZOusmt87733wp944olTDz/8cAEAfPDBByc2bdrk9+abb4YtWrToWFV+Nld7HxsiV/oQvw7g8pkkVgEwALgDTIiJiIioCjp16nSxtTYmJsYIAKdOnfIAgIyMDM2BAwe8V6xYEWjfx2q1wmKxIDMzU92pUyfdxo0bNRMmTIjct2+fd1FRkYfFYsuBDx06pEpLS7uYuF133XUlVYln0qRJObfffnvRgQMH1M8++2z0tGnTjoWFhZkBYOfOnZqcnBwvX1/fVMdj9Hq94uDBg+r8/Hxlfn6+R9euXS+ey8PDAykpKaX2uOzat29/SSv17t27vbdt26bVaDSX1A0AmZmZ6gEDBhR17dq1uFOnTsndu3cv7N27d9Hw4cMLQkJCzGFhYeaBAwfmDxgwIOG6664r6tGjR9Hw4cMLYmNj/9Wce+7cOUVeXp5n9+7dLziWd+7c+UJGRoa3Y9mVfjaNkSsXNgi21eq6AvgbAGRZ1gshsgEkuC80IiIiasw8PT0vDiazf8VvNpslACgtLVUMGTIkb9y4cWcuP65Vq1aGoqIiRd++fVt37969aP78+VlhYWGmrKws1cCBA1vr9fpL+gtotVrL5XVUJCIiwpiSkqJPSUnRBwQEZA0YMKD1nj17MqKiokwXLlxQJCUllXz++edZFRzn1IBBjUZzSTwlJSXKnj17Fk6bNu345fvGxMQYPTw8sGnTpgPr1q3z+fHHH/3nzJkT+r///S9qy5Yt+9q0aWP4+uuvj27evPn0ypUr/ZcvXx44ZcqUqO++++5Ar169qvRBoCJX+tk0Rq70IQ4FcECW5V2XlRsBNKt2RERERNTkpaSklB44cMDbnqA6Pry8vKw7d+70On/+vMf06dOP33rrrRdSU1N17mzB7NGjR2lycnLpSy+9FAHYWkyzs7O9IiMjjZfHExQUZC5/mH7//feLg89MJhMyMjI0VztXhw4dSg8cOOCVmJj4r2v18/OzAIBCocDNN99c8s477+Tu3bt3r6enp3Xp0qUB9jquu+66sjfeeOPUX3/9ldm6deuyRYsWBV5+nsDAQEtISIhx48aNvo7lf/zxh29CQkKj7ApRVa4kxCcBJAghLo6YFEJ0BNAWQK6b4iIiIqIGpri4WLllyxZvx8ehQ4cqnuPsKl588cVTf/31l8/w4cNjtmzZ4r1792714sWLmw0fPjwGAFq2bGnw9PS0vv3222F79+5VffbZZ/5vvvlmpDuv56mnnjr9+eefh2RlZXk+/PDD5wICAky33XZbq9WrV/tmZmaqVq5cqU1PT48+fPiwJwA88MADp2fNmhW+ePHiZjt37lSPHDkypqioSFnZADe7sWPHniksLPTo27dviw0bNmgyMjLUy5Yt87vnnnviTCYTfvnlF5/x48eH//bbb5qDBw+qFi5cGFBQUOCRlJRUlpmZqXriiSei1q1b53PgwAHV8uXL/bKzs9Vt27atMMF98sknT7333nvhH330UcDOnTvVjz/+eFRmZqb3M88806TnKnblk9S3AEYB2APbfMSpAP4AIJVvq5LyGSk+ANAbtpblvQBGy7K8VQjxIYBhDrurAOyXZbmdC/ESERFRLfjjjz+01113XZJj2aBBg85+8cUX2c7W1aVLl7LVq1fvf+GFF6L69OnTxmq1Ijo6Wt+/f/9zABAZGWmaPXv20UmTJkXNnz8/NCkpqXTy5Mk5Q4cObeWu67nnnnuKxo8fr3/55ZcjFi9efOy3337L/L//+7/m9913X8vS0lJlaGio4frrry8OCAgwA8Drr79+6tSpU56PPfZYnEKhwNChQ/O6d+9epFQqrzjPcFxcnHHDhg2ZY8eObX7XXXclGI1GKSIiwtCjR48ihUKBZs2amTdv3qydO3duWElJiTIiIsIwceLEnEGDBhXl5OR4HDhwwGvIkCEtz58/7xESEmJMT0/PGzduXF5F53rxxRfPFBYWKl966aXoc+fOebRs2VK3ZMmSQ+3atdO7631riCSr1bm5oIUQfgA2AOhw2aZdAG6QZfmqozjL6/EBMBbApwCOw9Y3+V0AcbIsX7hs3x8BbJNleaIToXLxkCuwWCzIzs5GbGws7KNlieoS70mqb+rqntTpdMjKykJ8fDy8vLwq3zH3b2DujZVvd9bDG4DIjvZXO9xXcdNlNpvRsmXLlLvvvvvczJkz+S16LSstLfU6ePCgr8lkujUtLS3zSvs63UIsy3KREKIzgPsAdC4v3g5giSzLBifqKQEwyaFoqRBiOoBEOPxHFEKEw9aK/GRldZW3Nl8yf96mTZugUqkqOYLsI14vH/lKVFd4T1J9U1f3pMVigdVqvfionBXuHOFkhRUoP9/VvuKnih04cED1/fff+/Xp06dYp9MpZsyYEXrixAmVfeo2qr9c6nwuy7IRtiWcFwKAECLAmWS4IkKI1gACARy6bNN9AP6QZbnCtdHLPQ9ggmPBzJkzMWjQoOqE1CTk5OTUdQhEl+A9SfVNbd+TZrMZZrMZRqPxii3TktEEdzb7GI0mWA22P+VqNdfZcoVCobB+9tlnwRMnTmxutVql1q1bl3333XcHrjQvMNUPrizMMRzATQDegW1BjrUAkoUQxwHcIcvyHhfq9AawGMAbsiwXXrb5vwDmXqWKNwBMdyx4+umnC9lCXDmLxYKcnBxER0fz62mqF3hPUn1TV/ekTqfD0aNH4enpeeVvOv3DYPVQQzJVv+un1UMNT/8wgH83q6VVq1bGP//884pfzVP95EoL8cOwdZUYA+ApACnl5dEAXgPQ35nKhBCesK1ydwiXdqGAECIZQBKAL65UhyzLegBNujO4qxQKBZMPqld4T1J9U9v3pEKhgCRJFx+VahYDPLkDKM2vfJ8qkjRBQLPoatdD1FC5khAnADgmy/J5IUQ3AGcB3AXgJwDXOlOREEIBYBFsA+BGyLJ8eWep/wJYJctygQtxEhERNWonPTxQoK5+q26Ahwci3BAPUUPlSkLsB8C+1nUbADtkWd4mhDiEf1qLq2oOgAgAt8iyfMkqL+XJ8lBcYTAdERFRU3XywkncueJOGMzVGsIDAFApVVjZbyUifJkWU9PkSkJ8BkCSEOJ52LpJfFleHgigyqMohRCxAB4EoANwVghh33SbLMsbYeun7A3gBxdiJCIiatQK9AVuSYYBwGA2oEBfwISYmixXEuJVAB4B8Hr56++EEIEAmgP4raqVyLKcDVQ+Y4wsy78ACHYhPiIiIiKiKnMlIR4HoAxAKwDfy7K8SQhxDWwD31a6MzgiIiIioprmysIcJbDNMOFYth22AXBERERERA0K5zYiIiKiahs4cGBc7969W1a2ffPmzd533HFHi5CQkPYqlapTZGRkux49erT6/PPP/e2rAe7fv18lSVKa/eHp6dkpJiYm5dlnn41wXDFwzJgxkZIkpXXv3r315ed5+eWXwyRJSuvcuXNiTVwnNU5MiImIiKhGLV68uFmPHj3alpSUKObMmXP077//3rNy5cqDffv2Pf/qq69GnTt3Tum4/4oVKw5kZ2fvzMjI2PP888/nzpo1K2LmzJmXjCsKCQkxbtu2TXv48GFPx/LPPvssOCIiwj2jDanJcGnpZiIiIqKqKCoqUowaNSr2pptuKlyzZs1hx22dOnXSjR49+qxj6y8AhISEmGJiYkwAkJCQcG7RokXBf/75p8Zxn8DAQFNKSkrp3Llzg6ZOnXoKANauXetTUFDgcccddxQcOHDAu4YvjRoRthATERFRjVmxYoXf+fPnPZ577rlTle1zpZUAf/vtN82ePXs0Xbp0Kbl8W3p6+tmlS5debDn++OOPg/v3739OpVJdvtAX0RUxISYiIqIas3//fi8AaN++vc5etmHDBo1Go0m1P5YsWeLveEyvXr3aaDSaVE9Pz0433nhj2zvvvLPgySef/Nca1ffee2/hhQsXlD/++KNvUVGRYtWqVYGPPPLI2Zq/KmpsqtRlQgjxSlUrlGV5kuvhEBERUWPXuXPnsj/++GMvALRr1y7FZDJdsi7BvHnzjrRv315nMBikv//+2/vZZ5+Nefzxx03vv//+Ccf91Gq1dcCAAfnz5s0LPnjwYHFcXJyuS5cuZXPmzKnNy6FGoKp9iCcCqOrXD0yIiYiICACQkJCgA4Bdu3Z59erVqwQAvL29rSkpKfrKjomLizPYt3fq1El36NAh9Ztvvhn59ttv52o0mkvykUceeeTsDTfc0DYzM9N72LBhbB0ml1Q1IT6GqifERERERACAfv36Ffn7+5unTJkS3qtXr8NXP+LflEql1Ww2SzqdTro8IRZC6Fq1alV24MABzYMPPnjOPVFTU1OlhFiW5bgajoOIiIgauOLiYuWWLVsumd0hNDTUNGvWrKMPPPBAi5tuuqnVqFGjzrRt21ZXXFys/P777/0AW8LreExeXp7HsWPHPIxGo7Rjxw7vuXPnhnXp0qU4MDDw0ukoym3atOmAwWCQgoODzTV3ddSYcdo1IiIicos//vhDe9111yU5lg0aNOjsF198kR0XF5f5xhtvhD/88MPxhYWFSq1Way6fNu3I4MGDCx2P6devXwIAKJVKBAcHG3v27Fk4bdq0S/oPO/Lz86swUSaqqqoOqjtSxfqssixXukoNERERNU7Lli07CuBoZdtvuOGG0htuuOGK+URiYqLBarXuuNq5pk+fnjt9+vTcyrZ/8sknOVerg8hRVVuI46q4H/sZExEREVGDUtWE+NUajYKIiIiIqI5UdVAdE2IiIqJ6JEAdAJVSBYPZUO26VEoVAtQBboiKqGFyeVCdECIeQCQApWO5LMu/VTcoIiIiurII3wis7LcSBfqCatcVoA5AhG+EG6IiapicToiFEOEAVgC4poLNVlfqJCIiIucFF1nRrKD6w3c8AqyArxsCImqgXElepwDo7O5AiIiIqOqMubk4fOttsBqq32VCUqnQcvWP8IyMdENkRA2PwoVj+gCwAHio/PVeAM8DOAfgXjfFRURERFdgKihwSzIMAFaDAaaC6ne9IGqoXEmIQwDsl2V5XvnrC7IsTwVwBsBgt0VGRERERFQLXEmISwCYHJ63EEKEwZYo3+KuwIiIiIiIaoMrCfEJANHlzw8ACAKQCyAQwHn3hEVERERUdZIkpS1atKhZXcdBDZMrCfFKANlCiGQAM8rLpPLHTDfFRURERA3IwIED4yRJSpMkKc3Dw6NTVFRUu0cffbR5aWmpVNex1STH63Z87NmzR12XMfXu3btlXZ2/IXJ6lglZlscDGF/+MkMIcQRAFwC7ZFle587giIiIqOHo3r170eLFi7MMBoO0detWn8cffzxOkiTrBx98cKKuY6tJ9ut2LIuMjDRVtv+V6HQ6ycvLq/pz6ZFTXGkhvoQsy5tlWZ7OZJiIiKhpU6lUlpiYGFOrVq2M//3vf89369ateMOGDX727adOnVLedddd8aGhoe29vb1TExISkubMmRPoWEfnzp0T09PTox999NHm/v7+HYODgzuMGTPmkvngdu/erRZCJKrV6k4tW7ZM/uabb/xwmT/++MP72muvTfDy8urUrFmzjvfdd19sYWHhxbzH3oo6fvz48KCgoA5arbbjuHHjIoxGIx555JHm/v7+HcPCwtrPnDkzqKrX7fjw8LC1Oa5atcq3Xbt2bVUqVaeQkJD2jz/+eJTRaLzkeocPHx4zcuTI6ICAgA433nhjawDYvn271w033NBao9GkBgUFdejXr1/8yZMnLzZkzp8/PyAhISHJfn3dunVLKCoqUowZMyZy+fLlQT///HMze2v1ypUrtVX5+TVl1U6IiYiIiC63fft2rz///NPH09PzYmtnWVmZIjU1tXTFihUHd+zYkTFixIizTzzxRPyvv/6qcTx22bJlQT4+PuZNmzbtmzhx4vEZM2ZE2JNes9mMgQMHtlSpVNb169fve/fdd7NffPHFKMfji4qKFHfeeWdrf39/88aNG/cuXLjw8MaNG/1GjhwZ47jf77//7pebm6v65ZdfMl977bWcadOmRfbs2bN1QECAadOmTftGjBiRN27cuNjDhw97uvIeZGVled5zzz2tO3bsWLJt27a906dPP7ZkyZLg55577pIEf9myZUHl15M5d+7c7LNnzypvueWWxPbt25du3rx533fffXcgLy/Po3///i0AIDs72/Ohhx6KHzp06NmdO3fuWbNmzf6+ffsWWK1WTJgw4dTtt99e0L1796Ls7Oyd2dnZO3v37n3BlfibEq4qR0RERG6xfv36ZhqNJtVsNksGg0FSKBR46623jtm3x8fHGydNmnTa/jopKenMunXr/JYsWRLYo0ePUnt5QkJC2bRp004CQLt27fRz5swJXbt2rbZ///5F3377rd+RI0e81qxZszsuLs4IACUlJSf+85//tLYf/9FHHwXq9XrFV199leXn52cBoCsrKzs2ZMiQVjk5Ocejo6NNAODv72+aP3/+MaVSiQ4dOuhnzpwZXlZWppgyZcopAJg8efLJd999N/znn3/2bdmyZaUTNduv2/76xhtvLPzxxx+PTJ8+PTQ8PNywYMGCYwqFAqmpqboTJ054vvbaa83feuutXKVSCQCIjY3Vffjhh8ftxz/77LMRSUlJpe++++7FriYLFy482qpVq/a7du1SFxUVKc1ms3TfffedT0hIMABA586dy+z7enl5WfR6vRQTE+NSt42miAkxERERuUXnzp2L5s6de6y4uFjx9ttvh3l4eFjT09PP27ebTCY8//zzEd9++23A6dOnVUajUTIYDJK3t7fFsZ6kpKQyx9ehoaGGvLw8TwDIyMjwCg8PN9qTYQDo0aNHieP++/bt82rTpk1peTIMAOjdu/cFi8WCPXv2eEVHR18AgNatW5fZk1IACA4ONrVp0+biuT08PNCsWTPTmTNnrthCbL9u+2utVmsBgP3793ulpaWVKBT/fCF/4403Xnj++ecVR44cUbVu3doAAO3bty91rG/37t3e27Zt0zom2XaZmZnqAQMGFHXt2rW4U6dOyd27dy/s3bt30fDhwwtCQkLMV4qTKseEmIiIiNxCo9FYUlJS9ADw5ZdfHm3btm3SO++8Ezx69OizAPDKK6+Ef/zxx6H/+9//clJTU8u0Wq1l1KhR0QaD4ZKZKBy7WQCAJEmwWC7Jmd3Cw8PjX+dx5dyO1+0KjUZzyQlKSkqUPXv2LJw2bdrxy/eNiYkxenh4YNOmTQfWrVvn8+OPP/rPmTMn9H//+1/Uli1b9rVp08Y9yxc2MU71IRZCeAohDgsh/hRCNOppVIiIiMh1SqUSY8eOPTV58uTICxcuSADw+++/+/bp0+f8448/fq5r165lbdu21WdlZXk5U29ycrLu1KlTntnZ2RdbbdevX+/juE/btm11mZmZmqKioot5zrp163wVCgVSUlJ01b22qkpMTNTt2LHDxzGh3rBhg6+Pj4+lRYsWlSauHTp0KD1w4IBXYmKiPiUl5ZKHvdVboVDg5ptvLnnnnXdy9+7du9fT09O6dOnSAABQqVRWi8XCPM0JTiXEsiwbAWgBKGVZ5pQgREREVKmRI0eeUygUePPNN0MBoEWLFrqNGzf6rV271ufPP//0Gjp0aGx+fr5T31bffffdRXFxcfohQ4bEbd261Xv16tW+EyZMuGRQ3cMPP3xOrVZbBg0aFLd9+3av77//Xjtu3Ljofv365dv7D9eGMWPGnDl16pQqPT095q+//vJavHhxs6lTp0Y+/PDDpx27alxu7NixZwoLCz369u3bYsOGDZqMjAz1smXL/O655544k8mEX375xWf8+PHhv/32m+bgwYOqhQsXBhQUFHjYu5rExsbqMzMzvXfu3Kk+efKkh16vZ3J8Fa7MMvEpgEQhRIqbYyEiIqJGxNPTEw888MCZd999N7yoqEjxv//972RycnJpv379Evr06ZMYFhZm7NOnz3ln6lQqlVi+fPkhnU6nuPHGG9s++uijca+++uol8xxrtVrLypUrD54/f96je/fuSf/9739bXn/99cWffPLJscrqrQnx8fHGr7/++uBff/3l06VLl6TRo0fH3HfffWenTp2ae6Xj4uLijBs2bMg0m83SXXfdlSCESHrmmWei/f39zQqFAs2aNTNv3rxZ269fv9YpKSkpkyZNipo4cWLOoEGDigDgqaeeOhsfH6/r1q1bUmRkZIe1a9f61s4VN1yS1epcQ68QYiGAQQCsAH4FcLr8OQBYZVl+wK0Ruo4t2FdgsViQnZ2N2NhYOHb2J6orvCepvqmre1Kn0yErKwvx8fHw8qq8N0FZRgaODrzHbeeNW/Y1vJOT7S93uK1iojpSWlrqdfDgQV+TyXRrWlpa5pX2dWVQ3TDYkk0JwK34J/GUyp/Xl4SYiIiIiOiqXEmIfwNbX4mIiIiokXA6IZZl+aYaiIOIiIic4BEQAEmlgtVQ/Vm2JJUKHgEBboiKqGFyeR5iIUQLAF0AlMqy/K37QiIiIqKr8YyMRMvVP8JUUOkCalXmERAAz8jIq+9I1Eg5nRALIZQA5gBIh63f8DYhhB9ss0/8nyzLs90ZIBEREVVM5xUInVZb7Xq8vDxxxaXYiBo5V1qInwcw8rKybwB8DKAvACbERERENaz4nA6fvfI7zKbqr+Cm9FBg6KRroQ10ao0MokbDlXlk7gdgBNDPXiDL8gUAOQDauicsIiIiuhLdBaNbkmEAMJss0F0wuqUuoobIlYS4OYC9six/d1l5MYCQ6odERERERFR7XEmIzwKIF0IE2QuEEDGwtQ7nuSswIiIiIqLa4EpC/BMAPwC7y18nAfgTgCeA1W6Ki4iIiIioVriSEL8I4DiA8PLXfgACAeQCeMWZioQQaiHEJ0KIY0KIIiHE70KIrg7bu5SXXRBC5AghBroQLxEREdWwgQMHxkmSlPbCCy+EO5YvWrSomSRJaQCwcuVKrSRJafaHl5dXp1atWiW//fbbwTURT+/evVu6u15qnJxOiGVZPgmgI4BXAfxQ/ngVQKosy7lOVucB4CiA6wE0AzADwPdCCF8hRDiArwFMLN+WCuAvZ+MlIiKi2qFWq63vvvtueF5envJK++3cuXNPdnb2zr///ntPenp63nPPPRf77bffVn/+OCIXuTIP8c0A1sqy/Gp1Ty7LcgmASQ5FS4UQ0wEkAhgE4FNZlu3dMM6WPyqKSQ1A7Vi2adMmqFSq6oZooysEik8BhhJA5QNowwEvf/fUXUcsFssl/xLVNd6TVN/U1T1psVhgtVovPipzpW2ucDyfJEku1dGtW7eio0ePql9++eWIDz/88Hhl+0VGRpqCg4PNAPDSSy+dmTt3bqgsy5q77767GADMZjNeeuml8IULF4bk5+d7xsbG6saPH3/y/vvvLwAAk8mEIUOGxG7evNkvPz/fMzw83PDAAw+cefnll88AwJgxYyKXL18eVH4taQDw/fffH+jdu/eFhx9+OPrHH39sVlRU5BEUFGQcMWJE3htvvHHKpQumRsOVeYhXAzguhFgMW8J6wF3BCCFaw9b94hCAzgA2CSF2AwgGsAbAU7IsF1Zw6PMAJjgWzJw5E4MGDXI9GKsVXqd3QJv5BXyO/QzJ+s8vRKukQElMLxS3uRe6sDTAxV8c9UFOTk5dh0B0Cd6TVN/U9j1pNpthNpthNBqhUFT+Ra7R6N5p0oxGIwzly0Cr1eqr7F0xhUJhnThx4omHH364xTPPPHO6ZcuWVwzSYrFg+fLlfidPnlR169atxF7+wgsvhH/11VdBs2bNym7btq1u3bp12kcffTQ+NDTUeMcdd1wwm81SVFSUccmSJYdDQ0NNv/76q++YMWNiIyIijA8++GDBhAkTTu3fv9+ruLhYuXjx4iwACA0NNU+ePDl07dq1/osWLTrSokULQ1ZWluro0aNuaj2jhszVpZubA3gOwHNCiO0A5gP4Qpbl864GIoTwBrAYwBuyLBcKIaIA/BfAzbD1T14AYDqAByo4/I3ybRc9/fTThS63EJ/cCWn5Q5DyD8Kq8LgkGQYAyWqBT86v8M1eC2tQa1gHfAREdHDtXHXEYrEgJycH0dHRV/yFS1RbeE9SfVNX96ROp8PRo0fh6el5xW86PT3du7bc1c5XVcOHDz8/ffr00ueffz7yyy+/zK5on5iYmPYAYDAYJKvVKo0bN+7EbbfddgEAysrKpFmzZkWUt+iWAEBSUlL+5s2bfT/88MOQO+6444Jarba+8847F7tptmnT5tzWrVt9v/7664AHH3ywwN/f3+Ll5WXR6/VSTEyMyb7fsWPHVHFxcfqbb775gkKhQEJCgqHaF0yNgisJcSsA95Y/2sPWknsNgHeEEN/JsjzY2QqFEJ4AvoKtZdjehaIMwGf2FmghxGQAKys6XpZlPQC9s+et0OFfgSWDAbPtQ61kMVW4m71cOncE0vxbgfuWAi17uCWE2qRQKJh8UL3Ce5Lqm9q+JxUKBSRJuviojKvdGq5Un7vqfOONN47fddddiX/++efpiravXbs209/f36LT6aTNmzf7jB8/PiYwMND83HPP5WVkZKh1Op2ib9++CY7HGI1GqW3btqUO5whZvHhxcG5urkqv1yuMRqPUpk2bsivF9eCDD+bfeeedCS1atEi56aabCvv27Vs4YMCAIrdcNDVoTifEsiwfga1F9g0hRCJsifGjsM068R8ATiXEQggFgEUArABGyLJs7xS1p7zMzr2dpSqS+7ctGTbpq346qxkwWWzHjfwJiOxYgwESERHVf7fddtuF66+/vvDZZ5+NGjFiRP7l2xMTEw32PsRCCN22bdt8pk+fHvHcc8/lFRUVKQHgq6++OhgbG3tJlwsvLy8LAMydOzfg1VdfjZ44cWJO9+7dL/j7+1smT54c/ueff/pcKa7rr7++9MiRI7uWLVvm//PPP/uNGDGixdy5c4tWr159xH1XTw2Rq10mIISIBnBX+SOsGjHMARAB4BZZlh2bYz8FMLe8r/IpAOMBrKrGea7MagWWPVjeMuxs7m21Hbf8IeCJPxp0n2IiIiJ3ePPNN0907do1qXXr1rqr7atUKqHX6xUAkJqaWqZSqaxHjx5V3XHHHRcq2n/z5s2+qampF8aPH39xQbCjR49e0vFZpVJZLRbLv/4gBwYGWh566KGChx56qGDZsmUF99xzT+vTp08rw8LCzM5fJTUWrswyMQq2VuBry4sk2DLIX2Hr5+tMXbEAHgSgA3BWCGHfdJssy2uFEO8A2Ix/Fv0Y52y8VZa9Gcg/6PrxVjNw9gCQvQWIu859cRERETVAnTt3Luvbt2/+/Pnz/9Volpub61FaWirpdDrFpk2bfL755pug2267rQAAAgICLI888sipl156KdpisUg9e/a8UFBQoNywYYOvn5+fedSoUfmtW7fWL1++PGjZsmV+rVu31s+bNy9o9+7dmqioqIt9gmNjY/UbNmzw27lzpzo0NNQcGBhofuONN0IjIiKMnTt3LlUqldYvv/wyIDg42Ghvraamy5UW4pkOzw8CWAhgkSzLx5ytSJblbNgS6sq2zwIwy+kIXfHHR4DCA6ikz3CVSEpbPUyIiYiIMGXKlNxVq1YFXl7eoUOHFABQKpXW8PBw49ChQ/Peeuuti4PkZsyYkRsSEmKaPn16+JgxY9RardacnJxc+sILL5wEgLFjx+b9/fffmvT09BaSJKFv377nhg8fnvfzzz9fnBP1qaeeOrtx40Ztt27dkkpLSxXff//9Aa1Wa54xY0Z4dna2WqlUWtu1a1e6YsWKg0rlFadNpiZAcnYeQyHEeQBLASyQZXlrTQTlJlW/MF0hMDXe1spbXZISeC6r3s9TbLFYkJ2djdjYWA5gonqB9yTVN3V1T+p0OmRlZSE+Ph5eXl6V7pd3rBhfTt7utvMOeuEahMRcXBtjh9sqJqojpaWlXgcPHvQ1mUy3pqWlZV5pX1daiMPKZ3VoPIpPuScZBmz1FJ+u9wkxEREREdm4MsuEXgjRHbbp0eydfrcDmCDL8kZ3BldrDBX22a9GfcXurY+IiIiIaozT3wEJIa4H8DOAGwD4lD9uArBOCNHNrdHVFpWvm+vjcuxERFSzvHw9ofRwT1cOpYcCXr7uXeiDqCFxpcvEK+XHZQP4obzsdgCx5dtudU9otUgbbuv7664+xNrqzEJHRER0ddpALwyddC10F6q/hLOXrye0gZX3VyZq7FxJiDsDyAfQQZblIgAQQvgDOIx/pmJrWLz8gbZ3AZkrqz/LRFJf9h8mIqJqq8qgd22gFxNZokpYrVb71MCWq+3rynctXgDO2ZNhAJBluRDAOQDqSo+q7zo/VL1kGLC1MF/zkHviISKiJsk+BZjBYLjKnkR0JSUlJRqr1WoEcPJq+7rSQnwYQBshxDQAS8rLhgBoBWCvC/XVD7HXAcEJQP5h17pOSEogqCUQ2zC7URMRUf3g4eEBjUaDvLw8eHp61sk0hBaLhc3O1GBZrVappKREc+rUKZXZbJ6XlpZ21dkOXEmIPwHwFoD/K39cPH/5toZJkoABHwGf3AKYLHBu+WYJUHoCAz/mss1ERFQtkiQhIiICWVlZyM7OrpMYDAaDm0ebE9Uqq9VqNZrN5nkAJlflAFcS4ncAtAVwP/5ZZc6eDL/jQn31R2RH4L6lwJLBgNlYtZZiSWlLhu9bCkR0qPEQiYio8VOpVGjdunWddZvIyMhoeAPkif5hAXCyKi3Ddk6vVGcnhGgBIK385Q5Zlo+4VFHNce3CACD3b2D5Q8DZA5Uv52yflSI4wdYy3MCSYa4KRvUN70mqb5r4PcmvO6lJcaWFGABQngDXtyTYPSI7Ak/8AWRvAf74CNj33aWtxfbZJK55yNZnmN0kiIiIiBoslxPiRk+SgLjrbA9doW05ZkOxbdENbRinViMiIiJqJJgQV4WXPxNgIiIiokaqyXWKIiIiIiJyxISYiIiIiJo0tyXEQgiNEMLPXfUREREREdWGKifEQohzQojVDq8/EUK86LDLz7At30xERERE1GA400LcDIBjC3A6gDsu24fzjxERERFRg8I+xERERETUpDEhJiIiIqImzdl5iJsLIV6p5HVzN8VERERERFRrnE2IowBMKH9uvey1VF5GRERERNRgOJsQc9AcERERETUqziTE8TUWBRERERFRHalyQizLcnZNBkJEREREVBeqnBALIZQApgEYXl60AMBYWZYtNREYEREREVFtcGbatccAPAXbAh3Nyp8/5v6QiIiIiIhqjzMJ8cjyf4+VPySHMiIiIiKiBsnZQXVnAbSBLZHOBtCiJoIiIiIiIqotzrQQ+wM4LMuyTpblUgCHAfjVTFhERERERLXD2XmItUKI7rB1l9ACgMNrAIAsy7+5LzwiIiIioprlbELcFsD6y8ocX1tdqJOIiIiIqM5wpToiIiIiatKcSYjvr7EoiIiIiIjqiDMr1S242j5CiKjqhUNEREREVLuq3d9XCOEFYACAdAA3AVBVt04iIiIiotrickIshOgGWxL8H9imX5NgG1RHRERERNRgOJUQl3eJGFH+aFVebB9o9zeA5W6LjIiIiIioFlQ5IRZCrAHQA7bFPOxJcAaABAAesix3cn94REREREQ1y5mV6noDUAI4CWAagE6yLLcDUFoTgRERERER1QZnEmLA1kfYCsAMwOD+cIiIiIiIapczCbF9SeYoAM8A2C2E+BOAt9ujIiIiIiKqJVVOiGVZvglASwCTAByFrR9xRwCeACCE2CaEeNbtERIRERER1SCnZpmQZfkogIkAJgohboRt9bqBAHwAXANAAHizqvUJIdQAPoCtf3IzAHsBjJZleasQIh3AxwB0DockybJ8zJmYiYiIiIiuxOV5iGVZ3gBggxDiCdjmIk4H0N2F8x8FcD2A4wAGAfheCBFXvn29LMu9XY2RiIiIiOhqqr1SnSzLJQA+BfCpQyLrzLGTHIqWCiGmA0h0pp7ylma1Y9mmTZugUnHRvMpYLJZL/iWqa7wnqb5pyvekQuHsmHuihk2yWqu2uJwQ4uGq7CfL8lxXgxFCtAawG0AYgP4A3gWgB3AawCxZlj+s5LiJACY4lv3nP//BoEGDXA2FiIioyYqPj5euvhdR4+FMQmzB1Zdmtsqy7FKrsxDCG8B6AD/IsvyqECK+/HzHYOuf/A2AUbIsL6vg2IpaiAvZQlw5i8WCnJwcREdHsyWA6gXek1TfNOV7UqFQMCGmJsWV5NXt/0mEEJ4AvgJwCOVdKGRZznLYZZsQYhaAAQD+lRDLsqyHrSWZnKRQKJrcL3qq33hPUn3De5Ko8XM2IZZgW5Dja9hmhzhe3QCEEAoAi2BrDR4hy3JlrdAW1EAyTkRERERNmzMJcQqAUQCGARgC24wQ3wCYLcvypmrEMAdABIBbZFk22QuFELcC2CHLcp4QohOApwCMq8Z5iIiIiIj+pcp9iO2EEH4AHgDwOIAW5cW7AHSTZbnMybpiYZt2TQfbctB2twHoB+C/sM1xfAK2xHu2E9U7d2FNjMViQXZ2NmJjY/lVINULvCepvmni9yS/kaUmxek+xLIsFwkhPgBQAtsiHH4A2sO2hLNTCbEsy9mo/D/dRgBjnY2PiIiIiMgZTiXE5S26j8PWQhxQXvwTbK2359wcGxERERFRjatyQiyEWAHgDgAKABcAzAbwrizLh2omNCIiIiKimudMC3Hf8n8NAH4DEApgkhDCcR+rLMtD3RQbEREREVGNc7YPsRWAJ4DbK9gmlW9nQkxEREREDYYzCfExcOYGIiIiImpkqpwQy7IcV4NxEBERERHVCVeWbr6EEEIDIBHAYVmWi6ofEhERERFR7XE6IRZCjIOtD/EzAAoAbAIQBqBECHF7NVetIyIiIiKqVa4svXMvgK4A9gF4CEA4bAPqfAG84r7QiIiIiIhqnisJcQsAx2RZLgXQBbZllSMAnAXQ0X2hERERERHVPFcSYg2A0vLnCQD+kmX5NGyzUGjdFRgRERERUW1wJSHOBZAshJgDW8vwzvLyEAB57gqMiIiIiKg2uJIQfwnbYLyHYJuX+CshRCSA5gB2uTE2IiIiIqIa58q0ay8COAmgFYCVsizvEkK0AzAZtiWdiYiIiIgaDKcTYlmWLQBmXVa2G8BudwVFRERERFRbXOkyQURERETUaDAhJiIiIqImjQkxERERETVpTIiJiIiIqElzZZYJIqImpficDroLxmrX4+XrCW2glxsiIiIid3IpIRZC9ALQC0AYAMlhk1WW5QfcERgRUX1QfE6Hz175HWaTpdp1KT0UGDrpWibFRET1jNMJsRDiRQCTKtgkwbZQBxNiImo0dBeMbkmGAcBsskB3wciEmIionnGlhfgx2JJfI4AzAExujYiIiIiIqBa5khD7AcgDkCTLcr6b4yEiIiIiqlWuzDLxHWytw+fdGwoRERERUe1zpYX4TwD/AbBBCPElLkuMZVle6Ia4iIiIiIhqhSsJ8duwDZ7rWv5wZAXAhJiIiIiIGgxX5yGWnCwnIiIiIqqXnE6IZVnm6nZERERE1GhwpToiqvdOnC9DQYmh2vUE+KgQ1czbDREREVFj4upKdbcBGAwgEoDSYZNVluVe7giMiAiwJcM9314PvRsWx1B7KPDLuJuYFBMR0SVcWaluKCoeOGdfqY6IyG0KSgxuSYYBQG+yoKDEwISYiIgu4Up/4KdhS34Pl/97AcApAAUANrgvNCIiIiKimudKQpwE4ByAduWvMwCkwJYcz3dTXEREREREtcKVhNgDwFFZlvUAzAB8ZFkuAJALYII7gyMiIiIiqmmuDKo7ByCg/PkZAMlCiA8AtAFQ5q7AiIiIiIhqgystxPsAxAghQgD8Wl7Hw+X/bnNjbERERERENc6VFuIxAOJg6zM8BkAYgC4AdgF41G2RERERERHVAldWqtsJYKdDUR/3hUNEVL94+XpC6aGA2Q1Tvyk9FPDy9XRDVERE5E5VSoiFEDcAKJJl+e/y55WSZfk3t0RGRFQPaAO9MHTStdBdMFa7Li9fT2gDvdwQFRERuVNVW4jXA9gK4Lry55UtwGF1ok4iogZBG+jFRJaIqBFzJnmVKnlORERERNRgVTUhjgegd3hORERERNQoVCkhlmU5u6LnREREREQNXVUH1X1Sxfqssiw/UNWTCyHUAD4A0BtAMwB7AYyWZXmrwz4eAP4C4C3Lcquq1k1EREREVBVV7TKRjsoH0tlJ5ftUOSEuP/9RANcDOA5gEIDvhRBxsixfKN/nSQCFALydqJeIiIiIqEqqmhAfw6UJcTgANWzLOANAIAAjgBPOnFyW5RIAkxyKlgohpgNIBLBDCBEG2yp4zwCYWVk95S3NaseyTZs2QaVSORNOk2KxWC75l6iuVXZPuvsetVgsvO+pSpry70mFwpWFbIkarqr2IY6zPxdC9AOwGEBvWZZ/KS/rAWAVgBeqE4wQojVsyfWh8qKpACYDKLnKoc8DmOBYMHPmTAwaNKg64TQJOTk5dR0C0SUuvydP5pW5tf6TJ09Cazrv1jqpcWuKvyfj4zl+npoWV+YMngrgqD0ZBgBZln8VQhwB8BqApa4EIoTwhi3RfkOW5UIhRFcArQHcD+DGqxz+BoDpjgVPP/10IVuIK2exWJCTk4Po6Gi2BFC9UNk96eFfBrVHNvRuWClO7aFAUqtYRDVjDyy6Ov6eJGo6XEmIYwB4CCEeArC8vKw/gDawdZtwmhDCE8BXsLUMTxJCKADMAvC4LMtWIcQVj5dlWY9/poUjJygUCv6ip3rl8nsyOtAHv4y7CQUlhmrXHeCjYjJMTuPvSaLGz5WEeCNss0J8WP6wk8q3OaU8+V0EWx/lEeUJsD+ATrANsAMAFQA/IcQpAAmyLBe5EDcRNVBRzbyZyBIRUY1x5SPvgwD+hC0Bdnz8Vb7NWXMARAD4jyzLpvKyQgBRADqWPx4EkFP+vNiFcxARERERVcjpFmJZlo8BEEKIngCSy4szHPsUV5UQIha2ZFcH4KxD14jbZFne6LDfOQBmWZZPOXsOIiIiIqIrkazWq00vXDkhRAQAD1mW6+MQXNcvrAmwWCzIzs5GbGws+8ZRvcB7kuqbJn5PSnUdAFFtcqUPMYQQwwD8D0BzANuEEG8AGA3gbVmWf3BjfERERERENcrphFgIMRDAwsuK/4RtarQzAJgQExEREVGD4cp3QC/A1h1hhr1AluUTAHIBXOOesIiIiIiIaocrCXESgP2yLI+5rDwPQGT1QyIiIiIiqj2uJMQ62OYEvnisEEINIB5AqbsCIyIiIiKqDa4MqtsK4Bb801e4OYB1APwA/OimuIiIqB4y5ubCVFBQ7Xo8AgLgGckvFYmofnAlIX4VQC8AfWDrSxwFW1JsBPCa+0IjIqL6xJibi8O33garofrLaEsqFVqu/pFJMRHVC053mZBleRuAngB+A1BW/tgAoHf5NiIiaoRMBQVuSYYBwGowuKWlmYjIHVyah1iW5c0Aerg5FiIiIiKiWufqwhxKAC0BhOGy1WxkWf7NDXEREREREdUKVxbm6AbgcwDRFWy2ulInEREREVFdcSV5fR9AjLsDISIiIiKqC64kxK0AFAD4D4AjsLUKExERERE1SK4kxBsAtAPwmyzLJjfHQ0RERERUq1xJiB8AsB7An0KINQCKHDfKsjzJDXEREREREdUKVxLiW2GbYUIBILmC7UyIiYiIiKjBcCUhfh2A0t2BEBHVJ8WGYuSV5qHUVAqNhwYhmhBoVdq6DouIiGqAKwmxL4CTAG4AcFSWZbN7QyIiqhtWqxXyaRlLM5di3bF1sFgtF7cpJAV6x/TG4DaDIcIEJEm6Qk1ERNSQuJIQzwMwDMAZJsNE1Fjszd+L8RvHI6swC0pJeUkyDAAWqwU/H/sZa7LXIN4/HlO6T0FSUFIdRUtERO7kSkIcAsAPwEEhxCZcOqjOKsvyA26JjIiolmzN3YpRv4yC0WIEAJitFX/Wt5dnF2Vj+I/DMbvnbHSN7FprcRIRUc1wJSEeBtvcw6EA+juUS+XlTIiJqMHYm78Xo34ZBYPZAGsVp1W3WC0wmA0Y9csoLLxtIVuKiYgaOIULxxwrf2Q7PHd8TUTUIFitVjz323MwWoxVToYvHgsrjBYjxm8cD6uV6xMRETVkTrcQy7IcVwNxEBHVOvm0jKNFR10+3mK1IKswCztO74AIF+4LjIiIapUrLcRERI3C0sylUErVm0VSKSmxJHOJmyIiIqK6wISYiJqkYkMx1h1bV+kAuqoyW81Yd2wdig3Fboqs/vIICICkUrmlLkmlgkdAgFvqIiKqLlcG1RERNXh5pXn/mlrNVRarBXlleY1+4Q7PyEi0XP0jTAUF1a7LIyAAnpGRboiKiKj6mBBTo3bywkkU6Kv/xztAHYAI3wg3RET1Ramp1L31Gd1bX33lGRnJRJaIGh0mxNRonbxwEneuuBMGs6HadamUKqzst5JJcSOi8dC4tz5P99ZHRES1h32IqdEq0Be4JRkGAIPZ4JaWZqo/QjQhUEju+RWokBQI8Q5xS11ERFT7mBATUZOkVWnRO6a3W2aZ6BPTp9H3HyYiasyYEBNRkzW4zWC3zDIxuM1gN0VERER1gQkxETVZIkwg3j/e5a4TCkmBeP94pIWluTkyIiKqTUyIiajJkiQJU7pPgafCExIk546FBE+FJ6Z2nwpJcu5YIiKqX5gQE1GTlhSUhNk9Z0OlVFW5pVghKaBSqjC752y0DWpbwxESEVFNY0JMRE1e18iuWHjbQsT6xQJApQPt7OWxfrFYdNsidI3sWmsxEhFRzeE8xEREsLUUf3v3t9hxegeWZC7BumPrLlnJTiEp0DumNwa3GYy0sDR2kyAiakSYEBMRlZMkCSJcQIQLFBuKkVeWh1JjKTSeGoR4h3BqNSKiRooJMRFRBbQqLRNgIqImgn2IiYiIiKhJY0JMRERERE0aE2IiIiIiatKYEBMRERFRk8ZBdURuZC4uhunMGVhKS6HQaOARGgqllgOziIiI6jMmxETVZLVaUbp9Owo++xzFa9cCln/mroVCAW2fPggYOgSaa67h3LVERET1UJ0mxEIINYAPAPQG0AzAXgCjZVneKoS4G8BUABEASgEsAfCMLMvmOgqX6F/KMjKQ+8wzMBzJApTKS5NhALBYULxuHYp/+gmqFvGIfOsteCcn102wREREVKG67kPsAeAogOthS4hnAPheCOELQAbQXZZlfwBJADoAeLROoqQGKUAdAJVS5Za6VEoVAtQBl5SVbNmC7CFDYTiabSswV/JZrbzccDQb2UOGomTLFrfERERERO5Rpy3EsiyXAJjkULRUCDEdQKIsyzsu290CoGWtBUcNXoRvBFb2W4kCfUG16wpQByDCN+Li67KMDOQ89jisBgNgtVatEosFVoMBOY89jtjPP2NLMRERUT1Rr/oQCyFaAwgEcKj89fUAVgHwA5AHYHQlx6kBqB3LNm3aBJXKPa2DjZGl/Kt9y+Vf8TcyYZowhGnC3FKX/b2yWq04MW4crEZj1ZNhO6sVVqMRueOeQdzK79mn2EFTuSep4WjK96RCUddfIBPVrnqTEAshvAEsBvCGLMuFACDL8iYA/kKIeAD/BXCmksOfBzDBsWDmzJkYNGhQDUbcOOTk5NR1CA2OZdduGLOOVqMCCwxZWTi6ahUU7dq5La7Ggvck1TdN8Z6Mj4+v6xCIalW9SIiFEJ4AvoKtZXjS5dtlWc4SQmQAeBdARVnuGwCmOxY8/fTThWwhrpzFYkFOTg6io6PZEuCkEzNmwKBUVt5nuCqUSqh/+QWRd97pvsAaON6TVN/wniRqOuo8IRZCKAAsAmAFMEKW5cq+g/YA0KqiDbIs6wHoaybCxk2hUPAXvRPMxcW4sHbdv2eTcLoiM4rXrIW1pITzFF+G9yTVN7wniRq/+vA/fA5sU6v9R5Zlk71QCDFICBFT/rw1bN0ifqmbEIlsTGfOVD8ZtrNYYMrLc09dRERE5LK6noc4FsCDAHQAzgoh7JtuA5AIYLoQIgBAPmxdKl6qiziJ7Cylpe6tr6TErfURERGR8+p62rVsAJUNs98I4LVaDIfoqhQajXvr8/Fxa31ERETkvPrQZYKowfAIDQXc1ZdQoYBHSIh76iIiIiKXMSEmcoJSq4W2Tx/bMs3VqkgJ7S03c0AdERFRPcCEmMhJAUOHVG/KNQAwmxE4ZIh7AiIiIqJqYUJM5CTNNddA1SLe9a4TSiVULeLh/c8gUiIiIqpDTIiJnCRJEiLfeguSpyfg7NLLkgTJwwNRb7/NZZuJiIjqCSbERC7wTk5G9AfvQ1Kpqt6fWKmEpFIh+oP34ZWUVLMBEhERUZUxISZykU+3boj9/DOoYmNsBZUlxuXlqtgYxC35HD7dutVShERERFQVdb50M1FD5p2cjBarVqFMlnHus89RvGbNpSvZKRTQ3twHgUOGwFsIdpMgIiKqh5gQE1WTJEnQXHMNNNdcA3NxMUx5ebCUlEDh4wOPkBBOrUZERFTPMSEmciOlVssEmIiIqIFhH2IiIiIiatKYEBMRERFRk8aEmIiIiIiaNCbERERERNSkMSEmIiIioiaNCTERERERNWlMiImIiIioSWNCTERERERNGhNiIiIiImrSmBATERERUZPGhJiIiIiImjQmxERERETUpDEhJiIiIqImjQkxERERETVpTIiJiIiIqEljQkxERERETRoTYiIiIiJq0pgQExEREVGTxoSYiIiIiJo0JsRERERE1KQxISYiIiKiJo0JMRERERE1aUyIiYiIiKhJY0JMRERERE0aE2IiIiIiatKYEBMRERFRk+ZR1wEQOeV8DlCaX/16NEFAs+jq10NEREQNHhNiajjO5wDvpgEmffXr8lADT+5gUkxERETsMkENSGm+e5JhwFaPO1qaiYiIqMFjQkxERERETRoTYiIiIiJq0pgQExEREVGTxoSYiIiIiJo0JsRERERE1KQxISYiIiKiJq1O5yEWQqgBfACgN4BmAPYCGC3L8lYhRDqApwC0AlAA4ANZlqfUUahERERE1EjVdQuxB4CjAK6HLSGeAeB7IYQvAC8ATwIIAnAjgHQhxJA6iZKIiIiIGq06bSGWZbkEwCSHoqVCiOkAEmVZ/tCh/KgQYjmArgA+v7ye8pZmtWPZpk2boFKpaiDqxsFisVzyb4Ngtbr1E5zFagUa0vU3cg3ynqRGrSnfkwpFXbeXEdWuerV0sxCiNYBAAIcq2HwDgMWVHPo8gAmOBTNnzsSgQYPcG2AjlJOTU9chVJkq/ySi3FjfyZMnYTA0c2ON5A4N6Z6kpqG+3pNGvQX6YhNMRis8PCWotR7wVLsnkY2Pj3dLPUQNRb1JiIUQ3rAlvG/Islx42bYxsCXKCyo5/A0A0x0Lnn766UK2EFfOYrEgJycH0dHRDaclQHXerdVFREQAEbFurZNc1yDvSWrU6uM9abVacfJgIXZvOIGsnXmwOjReSwogvkMI2t0YhYjW/pAkqe4CJWpg6kVCLITwBPAVbC3Dky7bNhTAaAA3yLJcVtHxsizrAehrOs7GSKFQ1Jtf9Ffl5l/uCkkCGsq1NyEN6p6kJqG+3JN5x4qx5pMMnD9VCkkhXZIMA4DVAmTtPIsjf+WhWbgGN49MRkiMtm6CJWpg6vx/uBBCAWARACuAEbIsWx223Q1gGoBbZVnOqqMQiYiI6lTOvnNY9tYOFJ4pBQBYLdYK97OXF54pxbK3diBn37lai5GoIavzhBjAHAARAP4jy7LJXiiE6AVgHoC+sixn1FVwREREdSnvWDFWvb8LZpPlX63ClbFaALPJglXv70LeseKaDZCoEajreYhjATwIQAfgrBDCvuk2AC/BNhXbLw7li2VZfrSWwyQiIqoTVqsVa+ZlwGK22L5HdepgwGK2YM0nGRgyoQv7FBNdQV1Pu5YNoLL/oT1qMxYiIqL6JvfgeZw/Xery8VYLcP5UKU4eOo/I1gFujIyocakPXSaIqkYTBHior75fVXiobfUREdVju9cfh6SoXsuupJCwe/1xN0VE1DjVi1kmiKqkWTTw5A6gNL/6dWmCbPUREdVT+jITjvydV+V+w5WxWqw4/Fce9GUmqL35Z5+oIvyfQQ1Ls2gmskTUJJSc11c7GbazWoDSQj0TYqJKsMsEERFRPWTUm91an0Hn3vqIGhMmxERERPWQp1rp1vpUXu6tj6gxYUJMRERUD/k0U0Ny019pSQFo/N00KJmoEWJCTEREVA+pvT3QomOIW2aZaJkayv7DRFfAhJiIiKieandT80qXaa4qq8WKdjdFuSkiosaJCTEREVE9Fdm6GZqFa1zuOiEpgGbhGkS0aubWuIgaGybERERE9ZQkSbh5ZDIUSkXl67pWejCgUCpw88hkLttMdBVMiImIiOqxkBgt7ni8PZQeiiq3FEsKQOmhwB2Pt0dIjLZmAyRqBJgQExER1XPRbQMx8Jk0+IdqAKDSgXb2cv9QDQY+k4botoG1FiNRQ8Yhp0RERA1ASIwWQyZ0wclD57F7/XEc/uvSZZ0lBdAyNQTtbopCRKtm7CZB5AQmxERERA2EJEmIbB2AyNYB0JeZUFqoh0FnhspLCY2/mlOrEbmI/3OIiIgaILW3BxNgIjdhH2IiIiIiatKYEBMRERFRk8aEmIiIiIiaNCbERERERNSkMSEmIiIioiaNCTERERERNWlMiImIiIioSWNCTERERERNmmS1Wus6BiIiIiKiOsMWYiIiIiJq0pgQExEREVGTxoSYiIiIiJo0JsRERERE1KQxISYiIiKiJo0JMRERERE1aUyIiYiIiKhJY0JMRERERE0aE2IiIiIiatKYEBMRERFRk+ZR1wFQ/SKEeBNAVwCHADwoy7K5jkOiJkwIoQawAUAKgI6yLB+q45CIIIS4EcBUACYA22VZHl3HIRFRNbGFmC4SQnQAECLLcncARwHcWbcREcEI4G4AX9d1IEQODgG4UZbl6wGECiHa1XVARFQ9bCEmR90ArC5/vhrAAADf1l041NTJ/9/evcfYUdUBHP9WHmLsAyhFHiLybLAtD/mBNSoUSMCAKEQQ8AUSMEB4SCIoAWIDKEaB8rDEgFqqCViUiEBNSCCiCAj8eITyEggUKUJB2tJWEChd/zhn6bUsu9vb7t5l5/tJbubOmTNzfzN7tvnt6W/mZi4H5kdEp0OR3pGZz7esLgP8nzTpfc6EeJiKiOOBY4FJwA8zc2rLtnHAVcAUYB5wQmbeCmwAPFO7vVrXpTWizTEpDajVGZcRsQuwUWY+OoghSxoAlkwMXy8AU4Hretg2HXgRGAecBlwbERsCi4Axtc8YYOGAR6kmaWdMSgOtrXEZERsDlwJHD06YkgaSM8TDVGZeDxAR+7e2R8RI4CBg68x8DbghIuZQ6jTvBE4CZgH7AXcNYsga5tockzMGOUw1TDvjMiKuBq4GvpOZ8wc3YkkDwRni5tkOWJqZ81ra5gATMvNBYGFE3A5sA9zUgfjUPO85JgEi4npgX2BmRBw6+OGpoXobl9+klFhcGBG3RcSnOxGgpDXHGeLmGQksXqltMTAWIDO/O+gRqen6GpMHDXZAEr2My8y8Erhy8EOSNFCcIW6epcDoldpG13apExyTGoocl1KDmBA3z5PAyIjYvKVtIvBIh+KRHJMaihyXUoOM6Orq6nQMGgARsTalJOYyYD5wHvBWZr4dEb+jPFbtJGAfYCawXWYu6FS8Gv4ckxqKHJeSwBni4ews4HXgGODM+v4bddsJwGbAK8BFwGH+A69B4JjUUOS4lOQMsSRJkprNGWJJkiQ1mgmxJEmSGs2EWJIkSY1mQixJkqRGMyGWJElSo5kQS5IkqdFMiCVJktRoJsSSJElqNBNiSZIkNdranQ5AUu8i4ihgBkBmjuhsNKsmIqYCP4D2Yo+IKcCf6+pWmTl3TcW2inHcBuzZ0nRqZl7cj/2Oov7sqr9k5pQ1GZskafWZEEvquIi4CjiSdyeMi4G76/s3BjmsnjwPzANe6Gf/lynxfxTYfKCCkiStHhNiSUNWZt4PTO50HC1+kZlT+9s5M2cDs1tnyiVJQ48JsdRBEfEB4CTgWGBb4HXgFuD0zHyml/2+BpwCbA2MAZYC9wBnZ+Y9tc8UVpQbfAk4DdiNMsP5/cz8fe03Evgp8AVgY8qs7BPAFZk5s/YZA5wDHARsCrwEXAuclZmv1T4jgHOB44G1gN8AS/pxDeYCW9bVPSOiq77fqy7/r2SiZTb5WUqSeV69Br8CzgDOB74FvAr8KDN/3vJZm9X+nwc2qtdiBnB+Zi7rK9YeYt8EuBDYG9gQWAg8ClyQmX9a1eNJkjrDm+qkzvoZcDEwAXgKeBs4BLgzIjbuZb/dgEnAK8AjwIeAfYFbapK2smspye4bwDbArIjYpW47BzgOGFePtQT4FDUhjYh1gduAk+sxHgPGAqcCN9ZEGOBE4ExKYrgE+Erdpy8PAP+u75dQSgzupiTmvdkUuLye0yjKHwj3sSIZ3gKYHhE71PMYC/y9bh9Zz2OLev5X9CPOnlwOfLUe72HgTWAKsHubx5MkdYAJsdQhEbEVJREFODIzJwIfp8xabkKZOX4v04GxmTk+M3cGJtb2UcABPfSflpnjgfHAIsrv/vfqtu3q8tzM/GRmbk1JfKfV9iOAnSnJ3o6ZuRMryhj2ri+A0+vyb8BW9TWvl3MAIDMPBmbX1fszc3J93d/HrutS/gjYHniutm0L7AR8AvhvPc/um+FOpCTA84Ft6nkcUrcdFRHb9hVrD7qv3XGZuWtmfoxSK/zbNo4lSeoQE2KpcwLonl2dWUsFllBuwILea2c3AP4YEQsiYjnwZMu2zXrofw1AZr7IihKESXV5Y12eGxHPRsTNlGR8fm3vnu1cF3iixvlgy7EnR8TolrhvyMxltZRiNgNnYWbekZnLgX/Wtoczc25mLqWUdQB8pC53b1l/qZ7H9bVtBGVWfFV1X7uZEfFURNwEfB34VxvHkiR1iDXE0tDwIO9+isKzPXWsNb83A+tTZkEfAN5iRUK31qp8cGZeERGPA1+kJMm7UmZeD2XFzDOUGeIHejjEwlX5vDWotaRiWQ9t3bXIKz/ubQmlzndlr7URw5nAHcB+lGu1B2WGfgo9z9RLkoYgE2Kpc+6jJG0jgKsy8xJ45+a0z1LqYHsynpIMAxydmddExGTgrl4+6zDgoVqXPKW2zamftzvwSGb+ta53H2tCrbu9t/ZfCzihu5QhItajJH23ZubiiJhHmSU+MCKmUWaU9+/ntehORj/cz/7tuJcSzzLg8O5nGkfEKODgzPxDG8f8DOVRcbPrsQ6nzMbvsUYiliQNChNiqUMy8+mIuBL4NnBxRJxCeVrElsBoys1fD/Ww69PAfyjJ4y8j4gxKzW9vTomIL1Nqk8cAy4Gf1G0nA4fVhHYBpQ4XyjN3F1ASvFOBHYF7I+IxYJ0a5wcptcKLgAsoNwh+Dnim9lm/XxcDHq/LiIg59fz26qV/O6YDx1BqfP9Rz2MUpa54HeDXbRzzx8BuEfEc5Q+YHWp7Tz83SdIQZQ2x1FnHU5LNOZTa3y2BucBFlCc7vEtmLqSUMzxK+R1+Eziwj885hFITvB4loT6i5aa12cDtlCdVTKKUYdwI7J+ZXZn5BuXGtEspN69tT6lhTkrJQHet8WWUBHEhJem+CbikPxeB8si06yhJ5URK+ccqlX70JTNfptRlz6A8nWMC5Zxvp/wM2jGLch1GU67dIsoNdUesZriSpEE0oqurq+9ekt53hsrXHg8HLV/d3P1NddMyc1Y/9jsAOJsV31TnVzdL0hBkyYQk9d/m9bVpP/uPo72nV0iSBpEzxNIw5QyxJEn9Y0IsSZKkRvOmOkmSJDWaCbEkSZIazYRYkiRJjWZCLEmSpEYzIZYkSVKjmRBLkiSp0UyIJUmS1GgmxJIkSWq0/wG2ZZalWWHjeAAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ "
" ] @@ -1231,7 +1192,7 @@ "### Conclusions so far\n", "So it looks like a linear regression model trained jointly on all series is now providing the best tradeoff between accuracy and speed (about 85x faster than ARIMA for similar accuracy). Linear regression is often the way to go!\n", "\n", - "Our deep learning model N-BEATS is not doing great. Note that we haven't tried to tune it to this problem explicitely, doing so might have produced more accurate results. Instead of spending time tuning it though, in the next part we will see if it can do better by being trained on an entirely different dataset.\n", + "Our deep learning model N-BEATS is not doing great. Note that we haven't tried to tune it to this problem explicitly, doing so might have produced more accurate results. Instead of spending time tuning it though, in the next part we will see if it can do better by being trained on an entirely different dataset.\n", "\n", "## Part 3: Training an N-BEATS model on `m4` dataset and use it to forecast `air` dataset\n", "Deep learning models often do better when trained on *large* datasets. Let's try to load all 48,000 monthly time series in the M4 dataset and train our model once more on this larger dataset." @@ -1305,10 +1266,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2022-04-03 21:41:03,920] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 479920 samples.\n", - "[2022-04-03 21:41:03,920] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 479920 samples.\n", - "[2022-04-03 21:41:03,934] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n", - "[2022-04-03 21:41:03,934] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n", + "[2022-04-07 17:04:39,614] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 479920 samples.\n", + "[2022-04-07 17:04:39,614] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 479920 samples.\n", + "[2022-04-07 17:04:39,626] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n", + "[2022-04-07 17:04:39,626] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n", "GPU available: True, used: True\n", "TPU available: False, using: 0 TPU cores\n", "IPU available: False, using: 0 IPUs\n", @@ -1328,7 +1289,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f582a559a4d447a6862825b59c84cb23", + "model_id": "7db7b713577e434e9536d7f74b1cb2c6", "version_major": 2, "version_minor": 0 }, @@ -1342,7 +1303,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 26, @@ -1407,7 +1368,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "265c80431a604e0c9b428ec4fef04b0a", + "model_id": "c44763c7ef284e809a618fd71c4c051e", "version_major": 2, "version_minor": 0 }, @@ -1454,7 +1415,7 @@ "outputs": [ { "data": { - "image/png": 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RlJSEoKAg2Nraln1gWiLwRSfLXfjFg4B7qOnVoaqe3qlTpzCdTodDhw4lmsratGnTunv37llffvnl5ZLHL1261HnSpEn+GRkZRwFjC/Fbb73ll52dfQQAZsyY4bF48WKPixcvngCMrcbt2rWLPHz4cHxUVJT6tdde8967d6/D7t27z5jqPHfunHXLli3bHj169ETbtm3zS15z+fLlzV566aXAixcvHnN2dtYX3ZeXlye4uLi037Bhw+k+ffrkmMqHDh0akJubK9uwYUNSafc9atQo/+vXr1tt2rTp/LVr1+ReXl7t4+LiEh955JHbJY/t0KFDq7CwsLzVq1cnm8oefvjhFrm5ubIdO3acBYwtxC+//PKV+fPnpwJAVlaWzMnJKeqnn3468/jjj2dVFANgHFSXmZkp37p167nSjm8qcnNzbc+cOeOg1WofjI6OTijv2Dqbh1gURRsAXwHoA6AZgJMAJkqStE8URVsAnwB4ojDGWEmS3qyrWImIiOqdgrvyrWrWl13tKsLDw/OKvvbw8ChIS0uzBoD169erZs+e7X3u3DnbnJwcuU6nE/Lz84Xs7GyZSqXSl6wrJibm5owZM/y2bdtm37t375ylS5e6hoeH50ZFRakB4Pjx43b79+9XKZXKqJLnJiQk2OzZs8d+8uTJAaaydevWnenXr1/Whx9+WNCiRYs29913X9YDDzyQOXLkyFsqlUofHx9vo1arZf369QstWpdGoxFat26da3r90Ucfua9atcotNTVVkZ+fL9NoNEKrVq3yAMDT01M3ePDg9EGDBoXee++9WT179swaNWpURkBAgAYAzp07Z/v000+nFa2/S5cutxctWuRZtKxdu3Z33kdHR0e9g4OD7urVq3cWOygvBjJPXS7MYQXgAoBuAC4BGAJggyiKgQBeBdAGQGsA1gDiRFE8L0nS4roJlYiIqJ5ROFi4PlXFx1TA2tq62LezgiBAr9cjMTFRMXTo0JCRI0emffDBB5fd3d2127dvd5g4cWJgfn6+oFLdfW1/f39t586ds1auXOnSu3fvnHXr1rkUTSZzcnLkvXr1ypwzZ86lUs7V6HQ6dO/e/U5Lb2BgYIGDg4MhPj7+5MaNG1WbNm1ymjlzps+sWbN8Dh06dCorK0sOAD/99NMZUwJrYmtrqweA2NhY5/fee89v+vTpKd27d7/t5OSknzlzptfhw4ftTcf+/PPPF/bs2XMtLi7Oad26dS6zZs3y/e2330737t07B5VU8n0EjN9YVDYGqro6S4glScoBMKNI0RpRFOcCCAPwCIAZkiRlAIAoivMBjAfAhJiIiAgAVF7GAXHmzjBRlCAHVJ4VH2emffv2KfV6PWJjY1PkcjkAYNWqVS4VnIZhw4bdnD59evOtW7fevHTpkk1MTMxN07527drlxsXFNQsLC8u3ti59pVhnZ+e7uk1YW1tjwIAB2QMGDMjOyspKdXFxab9x40ZVv379shQKheHChQuK0ro7AMCePXscoqKibk+dOvVOYn7hwgWbksfde++9effee2/eRx99dLV9+/atTEl9cHCweu/evQ4TJkxIL/LeOISEhFS6dbeyMVDV1Julm0VRDAHgAuBsYVHRRdYFABHlnGsDoNjDsHv3bigUijLOINMnTdOfRHWNzyTVN3X1TOr1ehgMhjtbmWwcgdaPAgkbIVRjYJ1BkAOt+xnrK7yeaRCYpYSFheVrtVph5syZHoMHD771119/OSxfvty9ovNGjhyZ8dprr/m/9NJLAZ07d84ODAy803I7efLk6999951bv379WkydOvWqm5ubLiEhwWb16tUua9asuWBldXeKs3r1aqdz587Z9OrVK9vNzU33yy+/OBkMBiEiIkLt7Oysf/7556++/fbbfnq9XujVq9ftjIwM+c6dOx0cHR11EyZMSA8JCclft26d69q1ax1DQkLylyxZ4nr8+HGlr69vAQAkJCQoFi5c6D5w4MBb/v7+mhMnTtgmJyfbPPnkk+kAMHHixKtjxoxp0b59+9yHH344a+3atc02b97svH79+tOVfS8rioHMUy8SYlEU7QCsAvCRJEmZoihuAjBZFMU9MHaZeAVAeV8FvAFgWtGC+fPnY8iQITUVcqORkpJS1yEQFcNnkuqb2n4mdToddDodNBpNhYP5hKinoTj5a7WuJxh0KIiKgaHgv3zKxsayDY5dunTJmz59esrChQu9Zs6c6SuK4u1333330ksvvRRU3nnOzs76Xr16Zf7+++/O8+bNu1B0X2BgoGbnzp0JkydPbv7YY4+FajQawdvbu6Bnz55ZZb1vLi4uus8++8z5k08+8SkoKBD8/f3zFy1adF4URTUAzJs3L9Xd3V07d+5cr0mTJtmoVCpdRERE7ptvvnkFACZPnpx25MgRZUxMTAtBENCvX7+bo0aNStu2bZsTANjb2+tPnz5tO3z48OBbt25Zubu7a2JiYtKmTJmSBgBPPfXUrdTU1JTPP//c8+233/bz9fUtmD9/ftKjjz5a6Q7cFcVA5qnzWSZEUbQG8AuATAAjJUkyFCbInwAYAKAAwDcAxkqSVOpUKmW0EGeyhbhser0eKSkp8PPz44h+qhf4TFJ9U1fPpFqtxoULFyqeZQIwtuh+2RlIPwfBjK4TBkEOuAYDL+wHirQKC4JQ5VkmiOqbBjHLBACIoigDsBLGKdJGS5JkAABJkvIAvFS4QRTF5wAcKKseSZLyAdzVT4gqJpPJmHxQvcJnkuqb2n4mZTIZBEG4s5VLEIBB3wDfPgBo9ajajKMCBLk1MHgxwH9z1MTVdZeJRQC8ATwgSdKdDlCiKDYHoANwDUBnAG8CeKxOIiQiIqrPfNoDT64BVg8DdJrKDbIT5IDc2nied7saD5GovqvLeYgDADwLQA3ghiiKpl0PFca1HIAbgHMAXpQk6XhdxElERFTvBfcExvwJrBsL3DhtXM65tIF2plkpXIONLcNMhokA1O20a8koPpNESf61FQsREVGD59MeePEAkLwXOPCNcTnmoq3FghwI7wd0HAsEdC3WZ5ioqavrLhNERERkKYIABN5r3NSZQPY14wp0CpVxnmFbTkRAVBomxERERI1QtkyGNGsr5Ao2UFpZwV0mQ/XXoiNqnJgQExERNRIGgwHSNQlrEtZg68Wt0Bv+W1REJsjQx78PhrUaBtFTtPjiG0QNGRNiIiKiRuBk+klM3TUVSZlJkAvyYskwAOgNemy7uA2bkzcjyCkIs7rPQrhreB1FS1S/cOJBIiKiBm5f6j6M+mMUkrOSAQC6MqZeM5UnZyVj1B+jsC91X63FSFSfMSEmIiJqwE6mn8SE7RNQoCu4q1W4LHqDHgW6AkzYPgEn00/WcIRNi6+vb5sZM2Z4VPb4BQsWuKpUqvY1GFKtq8w9DR48OLBPnz7BtRRShZgQExERNVAGgwGv//06NHoNDFVapQ4wwACNXoOpu6bCYKjauaUZPHhwoCAI0SW37t27h1S78nqorKTv4MGDpyZOnHjDktfauHGjwz333BPq5OTU3s7OLiogICBy0KBBgWq1us47glf1A4BJbGxsypo1ay7UQEhmYR9iIiKiBkq6JuFC1gWzz9cb9EjKTMKha4cgeokVn1CB7t27Z61atSqpaJmdnV31s+0GxMfHp5QVUcx36NAh28GDB4fGxMRcX7hw4UWlUmk4deqUzc8//+ys1Vr0UrXK1dW1Eksq1h62EBMRETVQaxLWQC7Iq1WHXJBjdcJqi8SjUCj0/v7+2qKbu7u7DgDi4uJU1tbWHTZt2uRgOv7tt9/2dHFxaZeSkmIFAJ06dQobNWqU/6hRo/xVKlV7Z2fndq+88oqPXv9fV5C0tDT5wIEDAx0dHdvb2dlF9ejRI+T48eM2pv2mltu1a9c6tmjRIkKpVEZ17949JDk52bporHPnznVr0aJFhI2NTYegoKCIWbNmuZv2JSYmKgRBiF6+fHmzzp07h9rZ2UWFhYWFb9261d50L6+88krg7du35aaW8EmTJvkAd7eYTp8+3TM0NDTczs4uysvLq+3IkSP9MzMzK51/bdy40dHV1VXz9ddfX+rYsaM6IiIi//HHH89as2ZNsoODg6HoPa9evdopMDAw0s7OLurBBx9skZ2dLVu4cKGrr69vG0dHx/YxMTF+RZPoit5LAFi2bFmzli1bRigUig6+vr5tpk2b5mna16lTp7DU1FTFtGnT/EzvQ9Fzy/s7KNllolOnTmExMTF+48aNa+7k5NTezc2tnek9Nfn3339to6Ojw2xsbDoEBwdHrF+/XiUIQvTKlSubVfb9LAsTYiIiogYouyAbWy9uLXMAXWXpDDpsvbgV2QXZFoqsdI8++mj2M888c/2ZZ54JSk9Pl+/Zs8fu448/9l24cOEFPz+/O1na2rVrXa2srAy7d+8+NXPmzJTY2FjPzz77zM20f9iwYYHHjh2z//HHH89u3749wWAw4NFHHw3Jz8+/031ArVbL5syZ47ls2bKkzZs3J6ampiomTJjQ3LT/q6++cpk1a5bPtGnTLh89evTE9OnTL8+aNct34cKFrkVjfu+993wnTpx4bf/+/SdbtGihHj16dAuNRoM+ffrcnjFjRoqDg4MuOTn5aHJy8tFp06ZdLe2+ZTKZYc6cOSlHjhyJj42NTdqzZ4/qxRdfbF7asaXx8vLS3Lhxw/qPP/5wKO84tVot+/zzzz2+++67c+vWrTuzf/9+1UMPPRS8adMmp99+++3MokWLkr7//nv3pUuXOlf2vdy1a5fymWeeCR44cOBNSZLiX3/99dSPP/7YZ8GCBa4AsGHDhrOenp6aKVOmpJreh8r+HZRm7dq1rvb29rrdu3efmj59+qV58+Z5//LLL44AoNVqMWjQoGBbW1v9jh07Tn355ZfJ7777rm9l38eKsMsEERFRA5SWm1bpQXQV0Rv0SMtLg0pRvaU7duzY0UypVEYVLXv55ZevzJo16yoAzJs37/LOnTsdR40aFZCYmGg3ePDg9BEjRmQWPd7Ly6tg8eLFKTKZDO3atcs/fvy43Zdffuk5efLkG8ePH7fZvn17s82bNyf07ds3BwB++umn80FBQW2/++67ZmPGjMkAAK1WK3zzzTcXIyIi8gFg7Nix1z/99NM7rY0fffSRz/vvv39p9OjRtwCgVatWBfHx8de+/fZb9wkTJqSbjpswYcK1YcOGZQLABx98kCqKYsSJEydso6Ki1E5OTjoA8Pf3L7ffwrvvvnvd9HNYWFhBenp66qRJk/wBXKzMezpmzJiMzZs3Oz388MNhbm5umvbt2+f07Nkza9y4cekuLi53HgCtVivExsbeueeHHnoo45dffnG9evXqUScnJ310dLT6yy+/zP7rr79UY8eOzajMe/nJJ5943nPPPVmffPLJFQBo27Zt/smTJ+0WLFjg9fLLL6d7enrq5HK5QaVS6Uq+DxX9HZQmNDQ0b86cOVcAoE2bNvmLFi3y2LJli2rgwIFZ69evd0xJSbHZuXNnoulaeXl5lwcOHBhamfexIkyIiYiIGqBcba5l69NUv75OnTplxcbGFkv03N3d7yRKtra2hlWrVp3v3LlzhLe3d/6iRYtSStbRoUOHHJnsvy+wu3btmhMbG+up1Wpx7NgxW7lcbujZs2eOab+Xl5cuMDAw/+TJk7ZFrqM3JWIA4OPjo7l586YVAGRlZclSUlJs/u///i9g4sSJAaZjdDqd4ODgUKy5vUOHDnfeFH9/fw0AXL16tUq50/r161WzZ8/2PnfunG1OTo5cp9MJ+fn5QnZ2tkylUlX4icbKygo///zzhaSkpMu///67av/+/Q7z5s3zXrBggff+/ftPBQQEaEq7Z09PT62vr2+Bk5PTnWu4u7trbty4YQ2gUu/l2bNnbR966KFbRePp1q3b7SVLlnhotVpYWZX9VpT3d1CW8PDwvKKvPTw8CtLS0qwB4NSpU7ZeXl6aool3jx49ckrWYS4mxERERA2Q0kpp2fqsq1+fUqnUR0ZG5pd3zM6dOx0AIDMz0+r69etyR0dHyzRzF2FlZVVsIJ8gCHdm0jD13507d25y9+7dc8o7z9ra2lC0DsCYOFc2jsTERMXQoUNDRo4cmfbBBx9cdnd3127fvt1h4sSJgfn5+YJKVfkW+aCgIM2LL75488UXX7yZlpZ2OTQ0NHLevHnun332WWpZ91xaWdH+2DWpvL+DshR9v03n1Fa87ENMRETUALkr3SETLPNrXCbI4G7nXvGB1RQfH2/zzjvv+M2ZM+dCu3btckaOHBmk0xXvA/3vv//aF329b98++4CAgHwrKyu0bdtWrdPphL/++uvOMVevXpVfuHDBJiIiQl2ZGPz8/LTu7u6a8+fP20RGRuYX3Vq1alVQ2XtRKBQGvV5fbnK8b98+pV6vR2xsbErv3r1z2rZtm5+amqqo7DXK4u7urnN3d9fk5OSY/QBU5r1s2bKl+p9//inWd3n37t0OgYGB+abWYWtra0NVPiSYq3Xr1uqrV69amwZgAsCuXbvsyzunKpgQExERNUAqhQp9/PtYZJaJvv59q91/GAAKCgpkFy9etCq6XblyxQowDooaMWJEUPfu3TNfeeWV9FWrVl1ITEy0mz59umfROq5cuaJ49tlnmx89etRm0aJFLkuXLvUYN27cNcDYr7R37963xo8fH/jnn3867Nu3z27IkCEtPDw8NMOHD79V2TinTp2a+vnnn3t98MEHHseOHbM5cOCA3fz5811LxlKe4ODg/NzcXNmvv/6qunLlilV2dvZdOVVYWFi+VqsVZs6c6XHy5EnFF1984bJ8+fIqffL45JNP3EaMGOG/bt06x/j4eBtJkmzHjx/ve+7cObv+/fvfqkpdRVXmvXzttdeu/fPPP46vvvqq97Fjx2wWLlzounz5cvcJEybcGUDo6+ubv3v3boekpCRr0991TRgwYECWn59f/vDhw4P2799vt3nzZvtp06b5Av+13lcHE2IiIqIGalirYRaZZWJYq2EWiWfXrl2OAQEB7YpuXbt2DQOAqVOneqempiqWLVuWDAABAQGaefPmJc+ePdt33759dqY6Bg0alJ6Xlyfr1q1b69dee83/mWeeuT558uQ7C12sXr36Qps2bXIef/zxlr169WplMBgQFxd3xsbGptLzHU+aNOnGZ599lvzdd9+5iaIY0bdv37BVq1a5tWjRotzuHkX17ds3Z/jw4WkxMTEtfHx82k2bNs2r5DFdunTJmz59esrChQu9oqOjI9asWeP67rvvXqrsNQBjH+qcnBz5yy+/HNChQ4eIvn37hkmS5LBy5cqzjzzyyO2q1FVSRe9lt27dcpcsWXLul19+cYmOjo746KOPfF599dXUl19++c7AwxkzZqReunTJplWrVm18fHzaVSee8lhZWWHdunXncnNzZd27d289bty4wNdff/0KANjZ2VW7X4VgidVp6qlGe2OWoNfrkZycjICAABQdvEBUV/hMUn1TV8+kWq1GUlISgoKCYGtrW+6xBoMB/X/tj+SsZLNmnJAJMgQ4BuDX/r+WbGU7VOXKLKBTp05hkZGRud9+++1dg+2IStq8ebP9Aw880OrEiRMnig7gM8nNzbU9c+aMg1arfTA6OjqhvLo4qI6IiKiBEgQBs7rPwqg/RqFAV1Cl5ZsFCLCWWWN299kW+cqZqKatWLGimUql0oeHh6tPnTplM2XKFP8OHTrcLi0Zrio2wxARETVg4a7hWNhrIRRyRaUH2ckEGRRyBRb2WojWrq1rOEIiy8jKypJPnDjRv23btpHPP/98ULt27XI2btx41hJ1s4WYiIiogevi0wUrHlqBqbumIikzCXJBXmrfYlN5gGMAZnefXe+S4QMHDiTWdQxUf7300kvpL730UnrFR1YdE2IiIqJGINw1HL/2/xWHrh3C6oTV2Hpxa7F+xTJBhj7+fTCs1TBEe0azmwRREUyIiYiIGglBECB6iRC9RGQXZCMtLw25mlworZVwt3O3yNRqRI0RE2IiIqJGSJkP+N4wQJ9rgExpgJUHgGovCUHUODEhJiIiaiQMBgNyDx5ExnffI3vLFqDosrcyGVR9+8J5xHAoO3ZklwmiIpgQExERNQJ58fFIffVVFJxPAuTy4skwAOj1yN66Fdl//glFiyD4fPIJ7CIi6iZYonqG064RERE1cDl79yJ5+AgUXEg2FujKWL2usLzgQjKSh49Azt69tRQhUf3GhJiIiKgBy4uPR8r4F2AoKLi7Vbgsej0MBQVIGf8C8uLjazbAQnFxcSpBEKJv3Lghr5ULElUBE2IiIqIGymAwIHXKqzBoNICh8qvUFZ4Mg1aL1FdfhaGq55YgCEJ0edukSZN8qnWBEtdauXJlM0vVRwSwDzEREVGDlXvwIAqSksyvQKdDwfkk5EkSlB07ml1NcnLyUdPPy5cvd/n444994uPjT5jKnJyc9Lt27bI3P1CimsUWYiIiogYq47vvjQPoqkMux83vvq9WFf7+/lrT5uTkpCul7E5fjj179igjIyNb29nZRUVFRbU6evSoTdG6Vq1a1Sw8PLy1jY1Nh+bNm7eZPHmyt0ajAQD4+vq2AYBRo0YFC4IQbXodHx9v07t372BXV9d2SqUyKjIysvX69es56TJVGhNiIiKiBkiXnW2cWq2sAXSVrkiH7M2bocvOtkxgFXj33Xd9Z8+enbJ79+5Tcrnc8PTTTweZ9m3atMlh/PjxgePHj7/+77//nli4cGHymjVr3KZOneoNAAcPHjwFAPPnz7+QnJx81PQ6KytL9uCDD2Zu2rQpcf/+/Sd79+6dOWzYsJAzZ85w5mWqFHaZICIiaoC0169XfhBdRfR6aNPSIFfVfKPqjBkzLj/yyCO3AeDVV1+9OmzYsJa5ubmCUqk0zJgxw+ell166OmHChHQACA8PL7hy5crl6dOnN58zZ84VHx8fLQA4Ozvr/P39taY6u3TpktelS5c80+v58+en/v77784//fST05tvvplW4zdFDR4TYiIiogZIn5tr2fpycixaX1k6dux4J3Ft3rx5AQBcvnzZOiQkpODUqVN2hw8fdpg/f773nbj0eiE/P1/Izs6WqVSqUj8BZGZmyl599VWfrVu3OqWlpVnrdDohPz9fdvHiRZvSjicqiQkxERFRAyRTKi1bn33tjHlTKBR3prQwrZanL2zpzsvLk0+ZMuXysGHDbpU8T6lUltkcPn78+Oa7du1y/PDDDy+1atUqX6lU6h9//PHggoICLsdHlcKEmIiIqAGy8vAAZDLLdJuQyWDl7l79eqopPDw89/Tp07aRkZH5ZR1jZWVl0JXoNy1JksOwYcPSR40adQswthhfvnyZ/Yep0jiojoiIqAGSq1RQ9e1rkVkmVA/8r1b6D1fkrbfeSl23bp3r5MmTvSVJsj18+LBtbGys88svv3xnHmNfX9+CrVu3Ol68eNEqLS1NDgCBgYH5cXFxznv37rXbt2+f3cCBA1sYDAa2DlOlMSEmIiJqoJxHDLfILBMuw4dbJqBqGjx4cNYPP/xw9q+//nLq3r176/vuu6/V559/7hkQEFBgOmbmzJkpu3btcmzRokXb9u3bhwPAwoULU5ycnLS9e/duNWjQoJZ9+/bNDA8Pt2wna2rUhOquTlOPNdobswS9Xo/k5GQEBARAJuPnIqp7fCapvqmrZ1KtViMpKQlBQUGwtbUt91iDwYDzjzyCggvJ5nWdkMuhCPBHi40b7/TnLXSo6pUR1S+5ubm2Z86ccdBqtQ9GR0cnlHcsf+sQERE1UIIgwOeTTyBYWwNCFXsICAIEKyv4fvppyWSYqMlhQkxERNSA2UVEwO+rLyEoFJXvTyyXQ1Ao4PfVl7AND6/ZAIkaACbEREREDZx9164I+P47KAL8jQVlJcaF5YoAfwSu/h72XbvWUoRE9RunXSMiImoE7CIi0GLjRuRJEm5+9z2yN28u3q9YJoPqf33hMnw47ESR3SSIimBCTERE1EgIggBlx45QduwIXXY2tGlp0OfkQGZvDyt393oxtRpRfcSEmIiIqBHSWtkhx84TGpkO1jZy2FvZoJozFhM1WkyIiYiIGgmDwYDUM7dwfMclnD+SBkORHhOCDGjR3h1t7m8On5Bm7DJBVESdJsSiKNoA+ApAHwDNAJwEMFGSpH2F+z4HMACAAGATgPGSJGXXTbRERET1V9rFbGz+Nh63ruZCkAnFkmEAMOiB80du4NzhNDTzUuJ/YyLg7s8uFERA3c8yYQXgAoBuMCbE8wBsEEXRAcBLADoAaAUgCIAHgDfrIkgiIqL6LOXUTaz95BAyrxsXZzPoS1+bylSeeT0Xaz85hJRTN2stRqL6rE5biCVJygEwo0jRGlEU5wIIAxAIYJMkSekAIIriegAPlFZPYWuyTdGy3bt3Q6FQ1EDUjYO+cOSx3pyVjYhqAJ9Jqm/q6pnU6/UwGAx3toqkXczGxi+PQafVV3qNVoMe0Bn02PjlMQya0uGulmJ2p6Cmpl71IRZFMQSAC4CzAJYDmCOKogeAfACDAPxSxqlvAJhWtGD+/PkYMmRIDUbbOKSkpNR1CETF8Jmk+qa2n0mdTgedTgeNRlPhktEGgwGbl8RDX4Vk+L+TAb1Oj81L4vH4m1HFkmAbG5tyTizf1q1b7R944IFW3bt3z9yxY8dZU3liYqKiVatWbUyvHR0ddaGhoXnvv//+5QcffPC2qXzSpEk+v//+e7OEhISTptefffaZd7du3bJ27dp1pui13nnnHc8PPvigeceOHW8fOHAgsei+c+fOWbdq1apNYGBg/pkzZ+LNviFqEupNQiyKoh2AVQA+kiQpUxTFswDSAFwtPGQzgK/LOP0jAHOLFrzyyiuZbCEum16vR0pKCvz8/Cr8D5eoNvCZpPqmrp5JtVqNCxcuwNrausJvOi+fzkDm9Tyzr2XQA5nX85B+MQ8+Ic3Mrqeob775xm306NHXf/jhB7cLFy5YBwYGaoruX79+/emoqKi8a9euWc2YMcP7iSeeaHny5MkTfn5+2rLqdHd31+zfv1917tw56+Dg4Dv1fffdd27e3t4FpZ2zaNEit4cffjjjwIEDDtu3b7fv1atXjkVukBqlepEQi6JoDeAnGFuGTV0ovgSgAeAEY1/nbwB8AmBSyfMlScqHsRWZqkgmkzH5oHqFzyTVN7X9TMpkMgiCcGcrz4mdlwsH0FW1efg/gkzAiZ2X4BvqbHYdJpmZmbK4uDiXvXv3nrx+/br1119/7Tpr1qyrRY9xd3fX+vv7a/39/bXvvPPO1bi4OJe///7bfsSIEZll1evi4qKNjIzMjY2NdZ09e/ZVANiyZYt9RkaG1SOPPJJx+vRpu6LH6/V6rF692nXevHkXfX19C7755hs3JsRUnjr/rSOKogzAShi/7BktSZLpX3U7AMskScqWJCkTwAoAvesoTCIionolP09bOLWa+ckwYBxod+7fNOTnldlAW2lLly51DgoKUrdr1y5/xIgR6d9//71bWX2wb9++LSxZssQVAGxsbCq8iZiYmBtr1qxxM71evHix28CBA28qFIq7zo2Li1Op1WpZ//79s55++un0DRs2uGRlZdV5zkP1V314OBYB8AbwhCRJRf81SgCeEkVRKYqiPYCRAI7XRYBERET1Tc6t/LumVjOXQQ/kZlb/i9YVK1a4Dx06NB0AHn/88czs7Gz577//XmzEXu/evVsplcooR0fHDrGxsZ4RERG5jz32WIVTqg4dOjTz9u3b8j/++MMhKytLtnHjRpfnn3/+RmnHLl682K1fv34ZVlZW6Nixo7p58+b5y5Ytq34TODVadZoQi6IYAOBZAJ0A3BBF8Xbh1h3AqwBsAaQAuAjjLBJT6ixYIiKiekSTr7NofQXq6tV39OhRm2PHjinHjBlzEwCsra3x2GOPZSxevNit6HFLliw5v3///pPLli075+/vn7906dKkyrQQ29jYGAYNGpS+ZMkSt2XLljkHBgaqO3fufFcH6hs3bsg3b97sHBMTk24qGzJkSPry5cvdSh5LZFLX064lw7joRlker61YiIiIGhJrG8suxKywrV59X331lbtOpxMCAgLamcoMBgMUCoU+PT39TuWBgYEFbdq0yW/Tpk2+VqsVHn/88ZYJCQnxdnZ2FSbFzz///I0ePXq0TkhIsBs5cmRZrcMu+fn5Qs+ePVsXjUOv1+PYsWM2bdu25Zgjukt96DJBREREVWTfzAaChX6LCzJA6WT+VGsajQY///yz67Rp0y7t27cv3rT9888/8e7u7polS5a4lHZeTExMhlwuN3z88cfulbmOKIrqli1b5p09e9bu2WefLXVVkZUrV7qNHTv2Wsk4oqOjb3/99ddsJaZS1YtZJoiIiKhqbOys0KK9O84fuVHtWSaCo9xhY2d+SrBmzZpmWVlZ8gkTJtxwdXUt1vfikUceyVixYoVb//7975pFQiaT4fnnn7/+8ccf+0yaNOmGSqWqsFf07t27TxcUFAhubm539fHYu3ev3cmTJ5WrVq1KioqKUhfd98QTT9z89NNPvefPn3/Z2tranNukRowtxERERA1Um/ubW2SWiTb3+1arjqVLl7p17do1q2QyDABDhgzJiI+PV966davUPhkvvPBCularFWbPnl2pVmJHR0d9ackwYJx7ODg4WF0yGQaAJ598MuPmzZvWP/74o1NlrkNNi1CZZSEbqEZ7Y5ag1+uRnJyMgIAAzvlK9QKfSapv6uqZVKvVSEpKQlBQEGxtbcs91mAw4Pv39iPzeq5ZM04IMsDJQ4nh0zqXnPP4UNVrI6pfcnNzbc+cOeOg1WofjI6OTijvWP7WISIiaqAEQcD/xkRAJpeVP0S91JMBmVyG/42JqHABEKLGjgkxERFRA+bur8IjL7SF3EpW6UF2ggyQW8nwyAtt4e6vqvgEokaOCTEREVED59faBYNfjYaThxKAcaBcaUzlTh5KDH41Gn6tS538gajJ4SwTREREjYC7vwrDp3XGlbO3cHzHJZz7N61Yv2JBBgRHuaPN/b7wbtmM3SSIimBCTERE1EBdvpWHjJyC4oVKGXwf9od7b1+obxdAk6+HtY0Mtg4KKGzkuAngZmpWsVOc7RXwbWZXe4ET1TNVTohFURQBhAPYAeAqgDkAegA4CmCCJEl3zTNIRERElnX5Vh56fboD+VozppcowcZKhu1T7mdSTE2WOS3EMwA8AKAFgDEAXiwsjwRwG8ALlgmNiIiIypKRU2CRZBgA8rV6ZOQUMCGmJsucQXVtAFyRJCkZQB8AeQDeAKAF8LAFYyMiIiIiqnHmJMTuMHaVAIxdJw5KkjQbQDwAT0sFRkRERERUG8xJiDMBBIqi2AVAMICTheVKGLtMEBEREVXZggULXFUqVfu6jqOq4uLiVIIgRN+4cUMONNz7aMrMSYj3A3ABsBvGPsg7RFFUAPADcN6CsREREVEDMXjw4MA+ffoEFy1bunSps42NTYdp06Y1+G+QExMTFYIgRJfc+vfvH9SnT5/bycnJR11cXHSlnTtp0iSfVq1ahdd2zFR55gyqmwKgOYCWADYA+BnGWSZuAthkudCIiIiooZo7d67b1KlT/T/55JPkV155Jb2u47GU9evXn46Kisozvba3tzfY2toa/P39tTV9bbVaLdja2hpq+jpNUZVbiCVJOi1JUgdJkhwlSRohSZJekqQdkiT5SZI0rSaCJCIioobj7bff9nzzzTf9Fy9efL5oMjx9+nTP0NDQcDs7uygvL6+2I0eO9M/MzCwzFzG1rM6bN8/V29u7jVKpjBo5cqS/VqvF22+/7enm5tbOxcWl3euvv+5V9LyKrmPq0rB27VrHFi1aRCiVyqju3buHJCcnW1d0b+7u7lp/f/87m6urq65kl4miFixY4PrZZ595JyYm2plalRcsWOAKADdu3JAPHTo0wNnZuZ2Dg0PUPffcE7pv3747U32Y7n/u3Lluvr6+bZRKZYeK330yh9kLc4ii2BPAPQAyAHwPoBmAa5Ik5VsmNCIiImpoxo8f77tixQqPH3744Uz//v2zi+6TyWSGOXPmpISGhuYnJibavPLKK/4vvvhi81WrVl0sq76UlBSbP//80ykuLu5MYmKiTUxMTHBycrJNcHCwetu2bQk7duxw+L//+7/ABx54ILtXr145lb2OWq2WzZkzx3PZsmVJMpkMo0ePDpowYULz3377LcmS78eYMWNunjhxwu6vv/5y2rZtWyIAmLpW9OvXr4Wtra3h119/PePs7KxbuHCh+8MPPxyakJBwwtPTUwcAFy9etFm/fr3zjz/+eNbKiuup1RRzFuawA/AbgF6FRfsBXAfwE4A3Acy2WHRERETUYPz9999O27Zta/brr7+e7tevX3bJ/e++++51089hYWEF6enpqZMmTfIHUGZCrNfr8f33319wdnbWR0dHq7/66qvs8+fP2+7YseOMXC5Hu3bt8ufNm+e1detWlSkhrsx1tFqt8M0331yMiIjIB4CxY8de//TTT30qusfevXu3Krrs9ZYtWxLKO97BwcHg4OCgl8vlxbpV/Pnnnw7Hjx+3v379+lE7OzsDAMTGxl7atGlTs5UrVzpPmTLlBgBoNBphzZo1ST4+PjXeJaMpM+ejxgcAepco2wigAMAjYEJMRETUJIWGhuZlZGRYvffeez733XffGScnp2Irh6xfv141e/Zs73Pnztnm5OTIdTqdkJ+fL2RnZ8tUKlWpq4z4+voWODs739nn7u6ukcvlBrn8v94Jbm5u2uvXr9/JaSpzHVtbW70pGQYAHx8fzc2bNyvMi5YsWXK+bdu2atPr4ODggm3btjlU8i264/Dhw3a5ublyV1fX9kXL8/PzZefOnbMpElcBk+GaZ05CPATGxTi6ADgCAJIk5YuimAwg1HKhERERUUPi5eVVsG7dunO9evUK69WrV8jWrVvPmJLZxMRExdChQ0NGjhyZ9sEHH1x2d3fXbt++3WHixImB+fn5gkqlKrVOKyurYoPIBEEotUyv1wtVuU5pdRgMFY9XCwwMLIiMjKx299Dbt2/L3dzcNKZuFEW5urrema3Czs7OMssRUrnMmXbNA8BpSZKOlSjXwNiPmIiIiJqo0NDQgh07diSkpaVZ9+7dOzQjI0MGAPv27VPq9XrExsam9O7dO6dt27b5qampCktfv7auUxUKhUKv1xfPa0VRzE1PT7e2trY2REZG5hfdvL292SJcy8xJiK8ACBVF8c5cg6IotgfQGkCqheIiIiKiBqply5aav/76KzE9Pd2qV69eoTdv3pSFhYXla7VaYebMmR4nT55UfPHFFy7Lly93t/S1a+s6VREYGFhw6dIlm71799pduXLFKi8vT+jfv39Wu3btbvfv37/lunXrHBMTExVbtmyxnzBhgu/ff/+trMt4myJzEuJfAdgBOAHAACAKwAEAQuE+IiIiauKCg4M1O3bsSMzIyLDq1atXaFhYWP706dNTFi5c6BUdHR2xZs0a13ffffeSpa/bpUuXvNq4TlWMHj06o0ePHpkPPvhgmI+PT7tvvvnGRSaTYevWrWfuueee7HHjxgVGRkZGjho1qsXFixcVPj4+mrqMtykSKtNfpihRFB0B7ATQrsSuYwB6SJKUZaHYqosTV5dDr9cjOTkZAQEBkMnM+VxEZFl8Jqm+qatnUq1WIykpCUFBQbC1tS3zuBOXM/Howt0Wu27chG6I9HUyvTxksYqJ6khubq7tmTNnHLRa7YPR0dHlzgZS5UF1kiRliaLYCcBwAB0Liw8CWC1JUkHVwyUiIiIiqjtmzfAsSZIGwPLCjYiIiIiowapUQiyK4rcAzkqSNLPw57IYJEl6xjKhERERUVmc7RWwsZIhX1v9WblsrGRwtq/TiRiI6lRlW4hjAPwDYGbhz6X1zxUKy5kQExER1TDfZnbYPuV+ZORUv7eis70Cvs3sLBAVUcNU2YT4bxhnlTD9zAFrREREdcwXN+ArpFugJlcAfhaoh6hhqlRCLEnS/aX9TERERHXkVgrweTSgrfaiaYCVDfDSIaAZk2Jqmqo0qE4URWsYl21OB+AlSRJbiomIiOpCbrplkmHAWE9uOhNiarKqNLFi4ewSVwGkMRkmIiIiosbAnJnG5wMIE0Xxf5YOhoiIiIiotpkzD/HDAHQA/hBFMRHANfw3yM4gSVJvSwVHREREjYMgCNErVqw499RTT92q61iakkmTJvn8/vvvzRISEk7WdSz1mTkJ8X1Ffm5VuJmwGwUREVETNHjw4MDMzEz51q1bz5W2Pzk5+ai7u7uutuOqLEEQok0/29vb6wMDA9VTp069MnLkyFt1GFa1TZs27eprr712ra7jqO/MSYhXgIkvERERVYG/v7+2rmPQ6/XQ6XSwtrYudf/8+fMvDBgwIPPWrVvyefPmecTExLQIDQ091alTp7yaikmtVgu2trY1llc5OTnpnZycaqr6RqPKfYglSYqRJOnpsraaCJKIiIgaNkEQoleuXNkMABITExWCIEQvX768WefOnUPt7OyiwsLCwrdu3Wpf9Jw///zTITo6OszW1raDl5dX25iYGL+srKw7ucsXX3zhEhkZ2dre3j7Kzc2t3WOPPRZ0+fLlO419cXFxKkEQon/88UfHiIiI1jY2Nh3+/PNPVVkxOjs76/z9/bVt27bNnzNnzmWdTids3rz5zvFnz561fvjhh1uoVKr2Tk5O7Xv37h2cmJh4Z4k/jUaDmJgYP5VK1b5Zs2btx48f7zto0KDAPn36BJuO6dSpU9ioUaP8x4wZ4+fs7NzuvvvuCwGAgwcP2vbo0SNEqVRGubq6thswYEDQlStX7tzL0qVLnUNDQ8NtbW07NGvWrH3Xrl1DTe9FXFycqk2bNq3t7OyiVCpV+w4dOrQ6ffq0AjB2mWjVqlW4qR6dTocpU6Z4e3p6tlUoFB1atWoV/vPPPzua9lf276axMWdQHQBAFMWeoii+IYriOFEUHUVR9BdF0caSwREREVHj9d577/lOnDjx2v79+0+2aNFCPXr06BYajQYAEB8fbzNw4MCQ/v37Zxw8eDB+5cqV5w4cOOAwZswYf9P5Go1GePfddy8fPHgw/ocffjh76dIlm+HDhweWvM4777zT/IMPPrh85MiR+I4dO+ZWFJdGo8HChQvdAEChUBgAID8/X3jggQdCHRwcdNu2bUv866+/Euzt7fUPPvhgiFqtFgDg7bff9lq/fr3rF198ceGvv/5KyMrKkm/ZsqVZyfrXrl3rqlAoDDt27EiIjY1NvnHjhvyBBx4Ia9u2be6ePXtO/fbbb6fT0tKsBg4c2AIAkpOTrceOHRs0YsSIG0ePHj2xefPmxH79+mUYDAZoNBo8+eSTwV27ds2WJOnkzp07E55++uk0QRBKvbcPPvjAY9GiRZ7vv/9+ysGDB+N79uyZ+eSTT7Y8fvx4sRyuvL+bxqjKXSZEUbQD8BuAXoVF+wFcB/ATgDcBzLZYdERERNRoTZgw4dqwYcMyAeCDDz5IFUUx4sSJE7ZRUVHqGTNmeA0YMODmu+++ex0A2rRpk//ZZ5+lPPzww2G5ubnJSqXS8H//9393lukLDw8vmD9//sX77ruvdWZmpszJyUlv2vfOO++kDhw4MKuieMaOHdvi+eefN+Tn58v0ej18fHwKRo8efRMAlixZ4qzX67FmzZpkmczYnvjjjz9ecHZ2bv/777+rBg0alLVkyRLPl19++cqoUaNuAcDy5csv+vv739VfISAgQP31119fMr1+7bXXvMPDw3M///zzy6ayFStWXGjZsmXbY8eO2WRlZcl1Op3w5JNP3goNDS0AAFM3jmvXrslv374t79ev362IiIh8AOjQoYO6rHv84osvvF588cWrzz33XAYAfPXVV5d3797t+PHHH3uuXLnyYmX+bip6Hxsic/oQfwCg5EwSGwEUAHgETIiJiIioEjp06HCntdbf318DAFevXrUCgPj4eOXp06ft1q9f72I6xmAwQK/XIyEhwaZDhw7qXbt2KadNm+Zz6tQpu6ysLCu93pgDnz17VhEdHX0ncbv33ntzKhPPjBkzUh5++OGs06dP27z22mt+c+bMuejp6akDgKNHjypTUlJsHRwcooqek5+fLztz5oxNenq6PD093apLly53rmVlZYXIyMhcU1wmbdu2LdZKffz4cbv9+/erlEplsboBICEhwWbQoEFZXbp0ye7QoUNE9+7dM/v06ZM1atSoDHd3d52np6du8ODB6YMGDQq99957s3r27Jk1atSojICAgLuac2/evClLS0uz7t69++2i5Z06dbodHx9vV7SsvL+bxsicGxsC42p1XQAcAQBJkvJFUUwGEGq50IiIiKgxs7a2vjOYzPQVv06nEwAgNzdXNnz48LQpU6ZcL3ley5YtC7KysmT9+vUL6d69e9bSpUuTPD09tUlJSYrBgweH5OfnF+svoFKp9CXrKI23t7cmMjIyPzIyMt/Z2Tlp0KBBISdOnIj39fXV3r59WxYeHp7z/fffJ5VyXpUGDCqVymLx5OTkyHv16pU5Z86cSyWP9ff311hZWWH37t2nt27dav/HH384LVq0yOPDDz/03bt376lWrVoV/Pzzzxf27NlzLS4uzmndunUus2bN8v3tt99O9+7du1IfBEpT3t9NY2ROH2IPAKclSTpWolwDoFm1IyIiIqImLzIyMvf06dN2pgS16GZra2s4evSo7a1bt6zmzp176cEHH7wdFRWltmQLZs+ePXMjIiJy3377bW/A2GKanJxs6+PjoykZj6urq65w0/7zzz93Bp9ptVrEx8crK7pWu3btck+fPm0bFhZ21706OjrqAUAmk+F///tfzmeffZZ68uTJk9bW1oY1a9Y4m+q499578z766KOr//77b0JISEjeypUrXUpex8XFRe/u7q7ZtWuXQ9HyAwcOOISGhjbKrhCVZU5CfAVAqCiKd0ZMiqLYHkBrAKkWiouIiIgamOzsbPnevXvtim5nz54tfY6zCrz11ltX//33X/tRo0b579271+748eM2q1atajZq1Ch/AAgODi6wtrY2fPrpp54nT55UfPfdd04ff/yxjyXv5+WXX772/fffuyclJVk/99xzN52dnbUPPfRQy02bNjkkJCQo4uLiVDExMX7nzp2zBoBnnnnm2oIFC7xWrVrV7OjRozZjxozxz8rKkpc1wM1k8uTJ1zMzM6369evXYufOncr4+HibtWvXOj7++OOBWq0W27dvt586darX33//rTxz5oxixYoVzhkZGVbh4eF5CQkJihdffNF369at9qdPn1asW7fOMTk52aZ169alJrgvvfTS1S+++MLrm2++cT569KjNCy+84JuQkGD36quvNum5is35JPUrgAkATsA4H3EUgAMAhMJ9lVI4I8VXAPrA2LJ8EsBESZL2iaL4NYCRRQ5XAEiUJKmNGfESERFRLThw4IDq3nvvDS9aNmTIkBs//PBDclXr6ty5c96mTZsS33zzTd++ffu2MhgM8PPzyx84cOBNAPDx8dEuXLjwwowZM3yXLl3qER4enjtz5syUESNGtLTU/Tz++ONZU6dOzX/nnXe8V61adfHvv/9O+L//+7/mTz75ZHBubq7cw8OjoFu3btnOzs46APjggw+uXr161Xr8+PGBMpkMI0aMSOvevXuWXC4vd57hwMBAzc6dOxMmT57c/LHHHgvVaDSCt7d3Qc+ePbNkMhmaNWum27Nnjyo2NtYzJydH7u3tXTB9+vSUIUOGZKWkpFidPn3advjw4cG3bt2ycnd318TExKRNmTIlrbRrvfXWW9czMzPlb7/9tt/NmzetgoOD1atXrz7bpk2bfEu9bw2RYDBUbS5oURQdAewE0K7ErmMAekiSVOEozsJ67AFMBrAMwCUY+yZ/DiBQkqTbJY79A8B+SZKmVyFULh5SDr1ej+TkZAQEBMA0WpaoLvGZpPqmrp5JtVqNpKQkBAUFwdbWtuwDU48AsfeVvb+qntsJ+LQ3vTpkuYqbLp1Oh+Dg4Mj+/fvfnD9/Pr9Fr2W5ubm2Z86ccdBqtQ9GR0cnlHdslVuIJUnKEkWxE4AnAXQqLD4IYLUkSQVVqCcHwIwiRWtEUZwLIAxF/iGKougFYyvyS2XVVdjaXGz+vN27d0OhUJRxBplGvJYc+UpUV/hMUn1TV8+kXq+HwWC4s5XNAEuOcDLAABRer6Kv+Kl0p0+fVmzYsMGxb9++2Wq1WjZv3jyPy5cvK0xTt1H9ZVbnc0mSNDAu4bwCAERRdK5KMlwaURRDALgAOFti15MADkiSVOra6IXeADCtaMH8+fMxZMiQ6oTUJKSkpNR1CETF8Jmk+qa2n0mdTgedTgeNRlNuy7Sg0cKSzT4ajRaGAuOvchsbrrNlDplMZvjuu+/cpk+f3txgMAghISF5v/322+ny5gWm+sGchTlGAbgfwGcwLsixBUCEKIqXADwiSdIJM+q0A7AKwEeSJGWW2P0UgNgKqvgIwNyiBa+88komW4jLptfrkZKSAj8/P349TfUCn0mqb+rqmVSr1bhw4QKsra3L/6bTyRMGKxsI2up3/TRY2cDayRPg781qadmypebw4cPlfjVP9ZM5LcTPwdhVYhKAlwFEFpb7AXgfwMCqVCaKojWMq9ydRfEuFBBFMQJAOIAfyqtDkqR8AE26M7i5ZDIZkw+qV/hMUn1T28+kTCaDIAh3tjI18wdeOgTkppd9TCUJSlegmV+16yFqqMxJiEMBXJQk6ZYoil0B3ADwGIA/AdxTlYpEUZQBWAnjALjRkiSV7Cz1FICNkiRlmBEnERFRo3bFygoZNtVv1XW2soK3BeIhaqjMSYgdAZjWum4F4JAkSftFUTyL/1qLK2sRAG8AD0iSVGyVl8JkeQTKGUxHRETUVF25fQWPrn8UBbpqDeEBACjkCsQNiIO3A9NiaprMSYivAwgXRfENGLtJ/FhY7gKg0qMoRVEMAPAsADWAG6IomnY9JEnSLhj7KdsB+N2MGImIiBq1jPwMiyTDAFCgK0BGfgYTYmqyzEmINwJ4HsAHha9/E0XRBUBzAH9XthJJkpKBsmeMkSRpOwA3M+IjIiIiIqo0cxLiKQDyALQEsEGSpN2iKHaEceBbnCWDIyIiIiKqaeYszJED4wwTRcsOwjgAjoiIiIioQeHcRkRERFRtgwcPDuzTp09wWfv37Nlj98gjj7Rwd3dvq1AoOvj4+LTp2bNny++//97JtBpgYmKiQhCEaNNmbW3dwd/fP/K1117zLrpi4KRJk3wEQYju3r17SMnrvPPOO56CIER36tQprCbukxonJsRERERUo1atWtWsZ8+erXNycmSLFi26cOTIkRNxcXFn+vXrd+u9997zvXnzprzo8evXrz+dnJx8ND4+/sQbb7yRumDBAu/58+cXG1fk7u6u2b9/v+rcuXPWRcu/++47N29vb8uMNqQmw6ylm4mIiIgqIysrSzZhwoSA+++/P3Pz5s3niu7r0KGDeuLEiTeKtv4CgLu7u9bf318LAKGhoTdXrlzpdvjwYWXRY1xcXLSRkZG5sbGxrrNnz74KAFu2bLHPyMiweuSRRzJOnz5tV8O3Ro0IW4iJiIioxqxfv97x1q1bVq+//vrVso4pbyXAv//+W3nixAll586dc0rui4mJubFmzZo7LceLFy92Gzhw4E2FQlFyoS+icjEhJiIiohqTmJhoCwBt27ZVm8p27typVCqVUaZt9erVTkXP6d27dyulUhllbW3d4b777mv96KOPZrz00kt3rVE9dOjQzNu3b8v/+OMPh6ysLNnGjRtdnn/++Rs1f1fU2FSqy4Qoiu9WtkJJkmaYHw4RERE1dp06dco7cODASQBo06ZNpFarLbYuwZIlS863bdtWXVBQIBw5csTutdde83/hhRe0X3755eWix9nY2BgGDRqUvmTJErczZ85kBwYGqjt37py3aNGi2rwdagQq24d4OoDKfv3AhJiIiIgAAKGhoWoAOHbsmG3v3r1zAMDOzs4QGRmZX9Y5gYGBBab9HTp0UJ89e9bm448/9vn0009TlUplsXzk+eefv9GjR4/WCQkJdiNHjmTrMJmlsgnxRVQ+ISYiIiICAAwYMCDLyclJN2vWLK/evXufq/iMu8nlcoNOpxPUarVQMiEWRVHdsmXLvNOnTyufffbZm5aJmpqaSiXEkiQF1nAcRERE1MBlZ2fL9+7dW2x2Bw8PD+2CBQsuPPPMMy3uv//+lhMmTLjeunVrdXZ2tnzDhg2OgDHhLXpOWlqa1cWLF600Go1w6NAhu9jYWM/OnTtnu7i4FJ+OotDu3btPFxQUCG5ubrqauztqzDjtGhEREVnEgQMHVPfee2940bIhQ4bc+OGHH5IDAwMTPvroI6/nnnsuKDMzU65SqXSF06adHzZsWGbRcwYMGBAKAHK5HG5ubppevXplzpkzp1j/4aIcHR1LTZSJKquyg+rOV7I+gyRJZa5SQ0RERI3T2rVrLwC4UNb+Hj165Pbo0aPcfCIsLKzAYDAcquhac+fOTZ07d25qWfu//fbblIrqICqqsi3EgZU8jv2MiYiIiKhBqWxC/F6NRkFEREREVEcqO6iOCTEREVE94mzjDIVcgQJdQbXrUsgVcLZxtkBURA2T2YPqRFEMAuADQF60XJKkv6sbFBEREZXP28EbcQPikJGfUe26nG2c4e3gbYGoiBqmKifEoih6AVgPoGMpuw3m1ElERERV55ZlQLOM6g/fsXI2AA4WCIiogTIneZ0FoJOlAyEiIqLK06Sm4tyDD8FQUP0uE4JCgeBNf8Dax8cCkRE1PDIzzukLQA9gbOHrkwDeAHATwFALxUVERETl0GZkWCQZBgBDQQG0GdXvekHUUJmTELsDSJQkaUnh69uSJM0GcB3AMItFRkRERERUC8xJiHMAaIv83EIURU8YE+UHLBUYEREREVFtMCchvgzAr/Dn0wBcAaQCcAFwyzJhEREREVWeIAjRK1eubFbXcVDDZE5CHAcgWRTFCADzCsuEwm2+heIiIiKiBmTw4MGBgiBEC4IQbWVl1cHX17fNuHHjmufm5gp1HVtNKnrfRbcTJ07Y1GVMffr0Ca6r6zdEVZ5lQpKkqQCmFr6MF0XxPIDOAI5JkrTVksERERFRw9G9e/esVatWJRUUFAj79u2zf+GFFwIFQTB89dVXl+s6tppkuu+iZT4+Ptqyji+PWq0WbG1tqz+XHlWJOS3ExUiStEeSpLlMhomIiJo2hUKh9/f317Zs2VLz1FNP3eratWv2zp07HU37r169Kn/ssceCPDw82trZ2UWFhoaGL1q0yKVoHZ06dQqLiYnxGzduXHMnJ6f2bm5u7SZNmlRsPrjjx4/biKIYZmNj0yE4ODjil19+cUQJBw4csLvnnntCbW1tOzRr1qz9k08+GZCZmXkn7zG1ok6dOtXL1dW1nUqlaj9lyhRvjUaD559/vrmTk1N7T0/PtvPnz3et7H0X3aysjG2OGzdudGjTpk1rhULRwd3dve0LL7zgq9Foit3vqFGj/MeMGePn7Ozc7r777gsBgIMHD9r26NEjRKlURrm6urYbMGBA0JUrV+40ZC5dutQ5NDQ03HR/Xbt2Dc3KypJNmjTJZ926da7btm1rZmqtjouLU1Xm768pq3ZCTERERFTSwYMHbQ8fPmxvbW19p7UzLy9PFhUVlbt+/fozhw4dih89evSNF198Meivv/5SFj137dq1rvb29rrdu3efmj59+qV58+Z5m5JenU6HwYMHBysUCsOOHTtOff7558lvvfWWb9Hzs7KyZI8++miIk5OTbteuXSdXrFhxbteuXY5jxozxL3rcP//845iamqrYvn17wvvvv58yZ84cn169eoU4Oztrd+/efWr06NFpU6ZMCTh37py1Oe9BUlKS9eOPPx7Svn37nP3795+cO3fuxdWrV7u9/vrrxRL8tWvXuhbeT0JsbGzyjRs35A888EBY27Ztc/fs2XPqt99+O52WlmY1cODAFgCQnJxsPXbs2KARI0bcOHr06InNmzcn9uvXL8NgMGDatGlXH3744Yzu3btnJScnH01OTj7ap0+f2+bE35RwVTkiIiKyiB07djRTKpVROp1OKCgoEGQyGT755JOLpv1BQUGaGTNmXDO9Dg8Pv75161bH1atXu/Ts2TPXVB4aGpo3Z86cKwDQpk2b/EWLFnls2bJFNXDgwKxff/3V8fz587abN28+HhgYqAGAnJycy0888USI6fxvvvnGJT8/X/bTTz8lOTo66gGo8/LyLg4fPrxlSkrKJT8/Py0AODk5aZcuXXpRLpejXbt2+fPnz/fKy8uTzZo16yoAzJw588rnn3/utW3bNofg4OAyJ2o23bfp9X333Zf5xx9/nJ87d66Hl5dXwfLlyy/KZDJERUWpL1++bP3+++83/+STT1LlcjkAICAgQP31119fMp3/2muveYeHh+d+/vnnd7qarFix4kLLli3bHjt2zCYrK0uu0+mEJ5988lZoaGgBAHTq1CnPdKytra0+Pz9f8Pf3N6vbRlPEhJiIiIgsolOnTlmxsbEXs7OzZZ9++qmnlZWVISYm5pZpv1arxRtvvOH966+/Ol+7dk2h0WiEgoICwc7OTl+0nvDw8Lyirz08PArS0tKsASA+Pt7Wy8tLY0qGAaBnz545RY8/deqUbatWrXILk2EAQJ8+fW7r9XqcOHHC1s/P7zYAhISE5JmSUgBwc3PTtmrV6s61rays0KxZM+3169fLbSE23bfptUql0gNAYmKibXR0dI5M9t8X8vfdd9/tN954Q3b+/HlFSEhIAQC0bds2t2h9x48ft9u/f7+qaJJtkpCQYDNo0KCsLl26ZHfo0CGie/fumX369MkaNWpUhru7u668OKlsTIiJiIjIIpRKpT4yMjIfAH788ccLrVu3Dv/ss8/cJk6ceAMA3n33Xa/Fixd7fPjhhylRUVF5KpVKP2HCBL+CgoJiM1EU7WYBAIIgQK8vljNbhJWV1V3XMefaRe/bHEqlstgFcnJy5L169cqcM2fOpZLH+vv7a6ysrLB79+7TW7dutf/jjz+cFi1a5PHhhx/67t2791SrVq0ss3xhE1OlPsSiKFqLonhOFMXDoig26mlUiIiIyHxyuRyTJ0++OnPmTJ/bt28LAPDPP/849O3b99YLL7xws0uXLnmtW7fOT0pKsq1KvREREeqrV69aJycn32m13bFjh33RY1q3bq1OSEhQZmVl3clztm7d6iCTyRAZGamu7r1VVlhYmPrQoUP2RRPqnTt3Otjb2+tbtGhRZuLarl273NOnT9uGhYXlR0ZGFttMrd4ymQz/+9//cj777LPUkydPnrS2tjasWbPGGQAUCoVBr9czT6uCKiXEkiRpAKgAyCVJ4pQgREREVKYxY8bclMlk+Pjjjz0AoEWLFupdu3Y5btmyxf7w4cO2I0aMCEhPT6/St9X9+/fPCgwMzB8+fHjgvn377DZt2uQwbdq0YoPqnnvuuZs2Njb6IUOGBB48eNB2w4YNqilTpvgNGDAg3dR/uDZMmjTp+tWrVxUxMTH+//77r+2qVauazZ492+e55567VrSrRkmTJ0++npmZadWvX78WO3fuVMbHx9usXbvW8fHHHw/UarXYvn27/dSpU73+/vtv5ZkzZxQrVqxwzsjIsDJ1NQkICMhPSEiwO3r0qM2VK1es8vPzmRxXwJxZJpYBCBNFMdLCsRAREVEjYm1tjWeeeeb6559/7pWVlSX78MMPr0REROQOGDAgtG/fvmGenp6avn373qpKnXK5HOvWrTurVqtl9913X+tx48YFvvfee8XmOVapVPq4uLgzt27dsurevXv4U089FdytW7fsb7/99mJZ9daEoKAgzc8//3zm33//te/cuXP4xIkT/Z988skbs2fPTi3vvMDAQM3OnTsTdDqd8Nhjj4WKohj+6quv+jk5OelkMhmaNWum27Nnj2rAgAEhkZGRkTNmzPCdPn16ypAhQ7IA4OWXX74RFBSk7tq1a7iPj0+7LVu2ONTOHTdcgsFQtYZeURRXABgCwADgLwDXCn8GAIMkSc9YNELzsQW7HHq9HsnJyQgICEDRzv5EdYXPJNU3dfVMqtVqJCUlISgoCLa2ZfcmyIuPx4XBj1vsuoFrf4ZdRITp5SGLVUxUR3Jzc23PnDnjoNVqH4yOjk4o71hzBtWNhDHZFAA8iP8ST6Hw5/qSEBMRERERVcichPhvsPWViIiIiBqJKifEkiTdXwNxEBERURVYOTtDUChgKKj+LFuCQgErZ2cLREXUMJk9D7Eoii0AdAaQK0nSr5YLiYiIiCpi7eOD4E1/QJtR5gJqlWbl7AxrH5+KDyRqpKqcEIuiKAewCEAMjP2G94ui6Ajj7BP/J0nSQksGSERERKVT27pArVJVux5bW2uUuxQbUSNnTgvxGwDGlCj7BcBiAP0AMCEmIiKqYdk31fju3X+g01Z/BTe5lQwjZtwDlUuV1sggajTMmUfmaQAaAANMBZIk3QaQAqC1ZcIiIiKi8qhvayySDAOATquH+rbGInURNUTmJMTNAZyUJOm3EuXZANyrHxIRERERUe0xJyG+ASBIFEVXU4Eoiv4wtg6nWSowIiIiIqLaYE5C/CcARwDHC1+HAzgMwBrAJgvFRURERERUK8xJiN8CcAmAV+FrRwAuAFIBvFuVikRRtBFF8VtRFC+KopgliuI/oih2KbK/c2HZbVEUU0RRHGxGvERERFTDBg8eHCgIQvSbb77pVbR85cqVzQRBiAaAuLg4lSAI0abN1ta2Q8uWLSM+/fRTt5qIp0+fPsGWrpcapyonxJIkXQHQHsB7AH4v3N4DECVJUmoVq7MCcAFANwDNAMwDsEEURQdRFL0A/AxgeuG+KAD/VjVeIiIiqh02NjaGzz//3CstLU1e3nFHjx49kZycfPTIkSMnYmJi0l5//fWAX3/9tfrzxxGZyZx5iP8HYIskSe9V9+KSJOUAmFGkaI0oinMBhAEYAmCZJEmmbhg3CrfSYrIBYFO0bPfu3VAoFNUN0UidCWRfBQpyAIU9oPICbJ0sU3cd0ev1xf4kqmt8Jqm+qatnUq/Xw2Aw3NnKUt4+cxS9niAIZtXRtWvXrAsXLti888473l9//fWlso7z8fHRurm56QDg7bffvh4bG+shSZKyf//+2QCg0+nw9ttve61YscI9PT3dOiAgQD116tQrTz/9dAYAaLVaDB8+PGDPnj2O6enp1l5eXgXPPPPM9Xfeeec6AEyaNMln3bp1roX3Eg0AGzZsON2nT5/bzz33nN8ff/zRLCsry8rV1VUzevTotI8++uiqWTdMjYY58xBvAnBJFMVVMCaspy0VjCiKITB2vzgLoBOA3aIoHgfgBmAzgJclScos5dQ3AEwrWjB//nwMGTLE/GAMBtheOwRVwg+wv7gNguG//xANggw5/r2R3Woo1J7RgJn/cdQHKSkpdR0CUTF8Jqm+qe1nUqfTQafTQaPRQCYr+4tcjcay06RpNBoUFC4DbWNjU8HRpZPJZIbp06dffu6551q8+uqr14KDg8sNUq/XY926dY5XrlxRdO3aNcdU/uabb3r99NNPrgsWLEhu3bq1euvWrapx48YFeXh4aB555JHbOp1O8PX11axevfqch4eH9q+//nKYNGlSgLe3t+bZZ5/NmDZt2tXExETb7Oxs+apVq5IAwMPDQzdz5kyPLVu2OK1cufJ8ixYtCpKSkhQXLlywUOsZNWTmLt3cHMDrAF4XRfEggKUAfpAk6Za5gYiiaAdgFYCPJEnKFEXRF8BTAP4HY//k5QDmAnimlNM/Ktx3xyuvvJJpdgvxlaMQ1o2FkH4GBplVsWQYAASDHvYpf8EheQsMriEwDPoG8G5n3rXqiF6vR0pKCvz8/Mr9D5eotvCZpPqmrp5JtVqNCxcuwNrautxvOq2tLbu2XEXXq6xRo0bdmjt3bu4bb7zh8+OPPyaXdoy/v39bACgoKBAMBoMwZcqUyw899NBtAMjLyxMWLFjgXdiimwMA4eHh6Xv27HH4+uuv3R955JHbNjY2hs8+++xON81WrVrd3Ldvn8PPP//s/Oyzz2Y4OTnpbW1t9fn5+YK/v7/WdNzFixcVgYGB+f/73/9uy2QyhIaGFlT7hqlRMCchbglgaOHWFsaW3I4APhNF8TdJkoZVtUJRFK0B/ARjy7CpC0UegO9MLdCiKM4EEFfa+ZIk5QPIr+p1S3XuL2D1MEBn/FAr6LWlHmYqF26eh7D0QeDJNUBwT4uEUJtkMhmTD6pX+ExSfVPbz6RMJoMgCHe2spjbraG8+ixV50cffXTpscceCzt8+PC10vZv2bIlwcnJSa9Wq4U9e/bYT5061d/FxUX3+uuvp8XHx9uo1WpZv379Qoueo9FohNatW+cWuYb7qlWr3FJTUxX5+fkyjUYjtGrVKq+8uJ599tn0Rx99NLRFixaR999/f2a/fv0yBw0alGWRm6YGrcoJsSRJ52Fskf1IFMUwGBPjcTDOOvEEgColxKIoygCsBGAAMFqSJFOnqBOFZSaW7SxVmtQjxmRYm1/5yxl0gFZvPG/Mn4BP+xoMkIiIqP576KGHbnfr1i3ztdde8x09enR6yf1hYWEFpj7Eoiiq9+/fbz937lzv119/PS0rK0sOAD/99NOZgICAYl0ubG1t9QAQGxvr/N577/lNnz49pXv37rednJz0M2fO9Dp8+LB9eXF169Yt9/z588fWrl3rtG3bNsfRo0e3iI2Nzdq0adN5y909NUTmdpmAKIp+AB4r3DyrEcMiAN4AHpAkqWhz7DIAsYV9la8CmApgYzWuUz6DAVj7bGHLcFVzb4PxvHVjgRcPNOg+xURERJbw8ccfX+7SpUt4SEiIuqJj5XI58vPzZQAQFRWVp1AoDBcuXFA88sgjt0s7fs+ePQ5RUVG3p06demdBsAsXLhTr+KxQKAx6vf6uX8guLi76sWPHZowdOzZj7dq1GY8//njItWvX5J6enrqq3yU1FubMMjEBxlbgewqLBBgzyL9g7OdblboCADwLQA3ghiiKpl0PSZK0RRTFzwDswX+LfkyparyVlrwHSD9j/vkGHXDjNJC8Fwi813JxERERNUCdOnXK69evX/rSpUvvajRLTU21ys3NFdRqtWz37t32v/zyi+tDDz2UAQDOzs76559//urbb7/tp9frhV69et3OyMiQ79y508HR0VE3YcKE9JCQkPx169a5rl271jEkJCR/yZIlrsePH1f6+vre6RMcEBCQv3PnTsejR4/aeHh46FxcXHQfffSRh7e3t6ZTp065crnc8OOPPzq7ublpTK3V1HSZ00I8v8jPZwCsALBSkqSLVa1IkqRkGBPqsvYvALCgyhGa48A3gMwKKKPPcKUIcmM9TIiJiIgwa9as1I0bN7qULG/Xrl0kAMjlcoOXl5dmxIgRaZ988smdQXLz5s1LdXd3186dO9dr0qRJNiqVShcREZH75ptvXgGAyZMnpx05ckQZExPTQhAE9OvX7+aoUaPStm3bdmdO1JdffvnGrl27VF27dg3Pzc2Vbdiw4bRKpdLNmzfPKzk52UYulxvatGmTu379+jNyebnTJlMTIFR1HkNRFG8BWANguSRJ+2oiKAup/I2pM4HZQcZW3uoS5MDrSfV+nmK9Xo/k5GQEBARwABPVC3wmqb6pq2dSrVYjKSkJQUFBsLW1LfO4tIvZ+HHmQYtdd8ibHeHuf2dtjEMWq5iojuTm5tqeOXPGQavVPhgdHZ1Q3rHmtBB7Fs7q0HhkX7VMMgwY68m+Vu8TYiIiIiIyMmeWiXxRFLvDOD2aqdPvQQDTJEnaZcngak1BqX32q1FftmXrIyIiIqIaU+XvgERR7AZgG4AeAOwLt/sBbBVFsatFo6stCgcL18fl2ImIqGbZOlhDbmWZrhxyKxlsHSy70AdRQ2JOl4l3C89LBvB7YdnDAAIK9z1omdBqkcrL2PfXUn2IVdWZhY6IiKhiKhdbjJhxD9S3q7+Es62DNVQuZfdXJmrszEmIOwFIB9BOkqQsABBF0QnAOfw3FVvDYusEtH4MSIir/iwT4f3Yf5iIiGqFysWWiSyRBZjzXYstgJumZBgAJEnKBHATgE2ZZ9V3ncZWLxkGjC3MHcdaJh4iIqJqOnbpFp6M/QfHLt2q61CI6jVzWojPAWgliuIcAKsLy4YDaAngpKUCq3UB9wJuoUD6OfO6TghywDUYCGiY3aiJiKjxWXf4MvadT8e6w5fRtnmzug6HqN4yp4X4WxgX0/g/APsLt1dgnPf3W4tFVtsEARj0DSC3RjlrhZR1svG8wYu5bDMREdULer0BG44a17rYcDQVen3V1h0gakrMSYg/w3+Jr4D/ssdvC/c1XD7tgSfXAFY2xhbfyhDkxuOfXAN4t6vR8IiIiCrr0MUMpOcYVzJOzynA4YsZdRwRUf1V5YRYkiS9JEnPAggBMLRwC5EkaawkSQ3/42dwT2DMn8buD4BxOefSmBJm12Dgmc3G84iIiOqJjceuwEpmbLOSywTEHbtSxxE1bmq1WvD394/csmWLfV3HUh2DBw8O7NOnT3BNX2fSpEk+rVq1Cje9fuGFF3xHjx7tV9PXLYvZExhKknRekqSfCrfzlgyqzvm0B148AMT8DrR67O7WYtNsEjG/G49jyzAREdWBq5lqnLicWeq24WgqtIXdJHSF3SfKOvZqprrasQwePDhQEIToN99806to+cqVK5sJghBd3rm+vr5tBEGIFgQhWi6XR3t4eLQdMmRIQFpa2p1fwHFxcSrTMSW3ixcvFmu9OnfunLW1tXWHkJCQCFPZpEmTfMo637QBQGpqqtWIESP8vb292ygUig5ubm7tunXrFrJ58+ZyE91PP/3UvXnz5vl9+/bNMZWZ6t22bVuxc/Py8oRmzZq1FwQhOi4u7q7FC/Ly8oRWrVqFC4IQvXfvXrvyrlsysayu2NjYlDVr1lywVH2V9c4771xbu3at28mTJxW1fW3AvEF1TYMgAIH3Gjd1pnE55oJs46IbKk9OrUZERHVu0o9HsPdceqn7So5ouZlTgEcX7i712HuDXfHd2OrPnGpjY2P4/PPPvSZOnJjm7u5epRHqU6ZMSZ0wYUKaTqcTTpw4YfvSSy8FjB071n/9+vVJRY87evToiWbNmhWr29fXt9g0UYsWLXJ7+OGHMw4cOOCwfft2+169euVMmzbt6v/93/9dNx3TqVOn8KeeeiptwoQJaUXP7devX7BGoxFiY2MvhIWF5V++fNlq8+bNjmlpaWXmTHq9HosXL/Z48803L5fc5+XlVbBkyRK33r1730mUV65c2UypVOoyMzNL7Z/5wgsvNPf09CxITEwsNxmuivz8fMHGxqbCb/JdXV0tsChD1Xl7e2u7d++eOX/+fI9FixZdqu3rW2aJm8bO1glwDwV8o41/MhkmIqJ6YETnADjalp6nlcx8ysqEHG2tMLxzgEXi6dq1a5abm5vmnXfe8a7quSqVSufv768NCgrSPPbYY9lDhw5NP3HihLLkcT4+Plp/f/9im1z+X16p1+uxevVq11GjRqUPHDjw5jfffOMGAE5OTvoS5xhM1zRtN27ckB86dMhh5syZlx577LHs0NDQgp49e+Z+9NFHV0eMGJFZVuy7d+9WpqSk2DzxxBN3HTNkyJD0DRs2ON++ffvOZ5Tly5e7DRkypNRPMj/++KPjzp07HefOnVthUrhgwQLXzz77zDsxMdHO1Bq9YMECV8DYOj179mz3Xr16tbSzs4t64403vLVaLYYMGRLg6+vbxtbWtkNgYGDk+++/71G0zpJdJjp16hQWExPjN27cuOZOTk7t3dzc2k2aNMmn6Dk3btyQDx06NMDZ2bmdg4ND1D333BO6b9++Ysn8m2++6eXq6trO3t4+asiQIQFqtfquWQgeeeSRzF9//dWlovuuCUyIiYiIGqhH2npj+5T78UCEcYXUys5zZDrugQhPbJ9yPx5pW+X8tVQymcwwffr0y8uWLfM4d+6c2WtBJyUlWW/atMkpKioqp+Kji4uLi1Op1WpZ//79s55++un0DRs2uGRlZVUq33FyctIplUr9unXrnPPy8io9bdRff/3lEBAQoHZ2dtaX3CeKYq6Pj0/BihUrnAHgzJkzCkmSVM8888xdCXFKSorVhAkTApctW5bk4OBwV10ljRkz5ubYsWOvtWzZUp2cnHw0OTn56JgxY26a9n/88cc+/fv3zzh06FD8+PHjb+h0OsHX11ezevXqc0eOHDnx+uuvp86cOdN38eLFzuVdZ+3ata729va63bt3n5o+ffqlefPmef/yyy+Opv39+vVrkZaWZv3rr7+e2bdv38m2bdvmPvzww6HXrl2TA8DixYud58yZ4/P2229f3rt370kvLy/NihUrPEpe59577825du2adWJiYq13m2BCTERE1IC5Odjg65HRWPhkFFS2VpDLys/j5IIAla0VPh8ehUVPiXBzsOyaWqNGjbrVqlWr3DfeeMOn4qP/88EHHzRXKpVRtra2HVq0aNFWEAR89dVXKSWP8/f3b6tUKqNMW8uWLSOK7l+8eLFbv379MqysrNCxY0d18+bN85ctW1ZuwmdibW2NL774Iunnn392dXZ2jurQoUOrl156yXf//v3ldl1ITk628fT0LHMN7ZEjR95YsWKFGwAsWrTI9b777sv09vYu1s1Dr9djxIgRQaNHj07r0aNHbmXidXBwMDg4OOjlcrnB1Mrt4OBw58uAAQMG3HzllVfSw8PDC0JCQgpsbGwMn332WWqPHj1yW7VqVTB+/PibTzzxRPrPP/9c7vsTGhqaN2fOnCtt2rTJf+mll9IjIiJyt2zZogKAP//80+H48eP2GzduPNejR4/cNm3a5MfGxl5SqVS6lStXOgPAF1984TlkyJAbEydOvNGuXbv8BQsWpAYHB+eVvE5AQEABAJw9e7bhJsSiKCpFUXSs+EgiIiKyJEEQ8Fg7H2yfcj/uDXYt99h7W7pi+5T78WjbKuWrVfLRRx9dWrdundvhw4fvWle6aDI7fPhwf1P5uHHjrh44cODkwYMH43/99dfTAPC///0vRKstvorsli1bEg4cOHDStP3+++9nTPtu3Lgh37x5s3NMTMyd1tchQ4akL1++3K2yscfExNy6cuXK0dWrV5/t3bt35p49e1T33ntvuKkrQmnUarXMxsamzBbdsWPHph85csTh5MmTijVr1rg9/fTTN0oeM3PmTI+cnBzZzJkzy5wOpKz3riyiKN7Vwv7RRx+5R0REtHZ2dm6nVCqjVq9e7Xb58uVyPxWFh4cXS149PDwK0tLSrAHg8OHDdrm5uXJXV9f2ReO7fPmyzblz52wA4Ny5c7adO3cuFktpsdnb2xsAICcnp9YbbCs9qE4UxZsADkiS9GDh628BnJMk6cPCQ7YB6FiVOomIiMhy3Bxs0MLdAXvPpd+ZYaIouUxAC3cHi7cKl/TQQw/d7tatW+Zrr73mO3r06GJdAw4cOHBnVVtnZ+c7A7jc3Ny0kZGR+QDQpk2bfHt7+5Q+ffq0iouLUw0YMCDbdFxYWFiBm5tbqQO/Fi9e7JKfny/07NmztanMYDBAr9fj2LFjNm3bts2vTPxKpdIwcODArIEDB2YBuDJ06NCAWbNm+bz88sul9vt1dXXVnjp1qsxWZC8vL13Pnj1vPf3004EFBQXCE088kXnr1q1iA+p27NihOnLkiIOtrW2xGTl69OgR3q9fv/R169ZdKOu9K0vJbhexsbHO7733nt/06dNTunfvftvJyUk/c+ZMr8OHD5c7g4a1tXWxh0kQBOj1xqpv374td3Nz02zbti2x5HlVHaB3/fp1OQB4eXlpKzrW0qqSvDYDULQFOAbAPwA+LFLGZdqIiIjqiGl1utKSYeC/6dfefTQcsgq6VlTXxx9/fLlLly7hISEhxeZ0MyW9FbGysjIAQG5ubqVbC1euXOk2duzYa2PHji3WAjt+/PiAr7/+2u3LL7+8axaIymjdurV68+bNzcra36FDh9wVK1a46/V6yGSlh/v000/fGDp0aMj48eOvWlndnX599dVXKbdu3boTX0pKimLw4MEhixcvPte9e/ccoPT3TqFQ6E3JaUX27NnjEBUVdXvq1Kl3Zta4cOFCtT4diaKYO3v2bGtra2tDWFhYQWnHBAcHq/fv32//0ksv3flAcejQobuS8MOHD9tZWVkZOnTocFd3iprG1lwiIqJGoujqdDIBkAkChnb0ww8HU6A3GKA3GFetO3QxAx0Da3Ywf6dOnfL69euXvnTpUs/KHJ+dnS2/ePGilcFgEM6fP2/9+uuv+zk7O2t79ep1u+hxqampVrm5ucWyeU9PT92hQ4dsT548qVy1alVSVFRUsST8iSeeuPnpp596z58//7K1ddlj/a5evSofMGBA8KhRo25ER0fnOTk56fbu3Wv/+eefe/Xt2/dWWec99NBDWc8995zs0KFDth07dix1UufHH388KzU19WhZLbshISHFkklHR0c9AISGhuYHBweX2T85MDCw4NKlSzZ79+61CwoK0jRr1kxnZ2dX6ieikJCQ/HXr1rmuXbvWMSQkJH/JkiWux48fV/r6+paayFZG//79s9q1a3e7f//+LWfOnHkpIiJCffHiRevffvut2RNPPJHRo0eP3BdeeOHahAkTgkRRzOnZs+ftpUuXup49e9auefPmxRL8nTt3qkRRvF20H3Rt4aA6IiKiRmJj4Wp0cpkAZ6UCP47rgg8HtsGP47rAWam4M+BuYy2tWjdr1qzUyrZefvrppz4BAQHtAgMD2w4ePDhEqVTq4uLiTnt5eRVLINu1axcZEBDQrui2e/du5aJFi9yCg4PVJZNhAHjyySczbt68af3jjz+WO2+qk5OTPjo6OueLL77w7NOnT1hUVFTE+++/7zNixIi0b7/99mJZ53l5een+97//3Vq2bFmZ/YxlMhm8vb21tra2Fk32Ro8endGjR4/MBx98MMzHx6fdN998U+YnncmTJ6c98MADt2JiYlp069atdXp6utWoUaPSyjq+MmQyGbZu3XrmnnvuyR43blxgZGRk5KhRo1pcvHhR4ePjowGAsWPHZvzf//1f6vTp05t36dIl/OLFi4qnnnrqesm6fvnlF+enn366WvGYSzAYKvf3IoqiHsAlAIsLi6aXeD0WgI8kSaVOMl0HGv4y0jVIr9cjOTkZAQEBZX69Q1Sb+ExSfVNXz6RarUZSUhKCgoJga3vXmLQy6fUGdPxwK9JzChDl1wyLRkXDQ/Xf+dez1Hhu5SEcSbkFV3sFDr7Vp7xuE4eqdxdNz/79++0efvjh0PPnzx93cnKq3KcAuuPHH390fOONN/wSEhLiy2vFr4rc3FzbM2fOOGi12gejo6MTyju2ql0mfAFMK/zZUOK1ACahREREdUKt1SHIzR4PRXrh3ccioLAqnsR7ONrix+e74L0N8Ui8lg21Vgelgj0nLaVz585506ZNu5SYmGjTqVOnWu8D29Dl5OTIv/nmmwuWSoarqqr/EjhojoiIqB5SKqzw07guEISyf1UrrGT4cGAbGAyGco8j85Q1CwVV7Omnn86oy+tXJSEOqrEoiIiIqNoqm+QyGSYqrtIJsSRJyTUZCBERERFRXajKwhxyAHMAjCosWg5gsiRJ7DhORERkYZUd9E5EpTMYDKbxbRXmqlUZNjsewMswLtDRrPDn8VUPj4iIiMoilxsnayooMHtqWCICkJOTozQYDBoAFc4zWJU+xGMK/zTNw+dfWPZF1cIjIiKislhZWUGpVCItLQ3W1tZ1Mg2hXq+v/HxvRPWMwWAQcnJylFevXlXodLol0dHR2RWdU9VBdTcAtIKxZTkZQAvzQiUiIqLSCIIAb29vJCUlITm5bobvFBQUONTJhYksw2AwGDQ6nW4JgJmVOaEqCbETgP2SJKkBQBTFcwA6Vj1GIiIiKo9CoUBISEiddZuIj49/sE4uTGQZegBXKtMybFLVeYhVoih2h3E+YhUAFHkNAJAk6e8q1klEREQlyGSyKq1UZ0kVrepF1NhUNSFuDWBHibKirw1m1ElEREREVGe4Uh0RERERNWlVSYifrrEoiIiIiIjqSFVWqlte0TGiKPpWLxwiIiIiotpV7f6+oijaAhgEIAbA/QAU1a2TiIiIiKi2mJ0Qi6LYFcYk+AkAjjD2L+Y6k0RERETUoFQpIS7sEjG6cGtZWGwaaHcEwDqLRUZEREREVAsqnRCLorgZQE8YV6kzJcHxAEIBWEmS1MHy4RERERER1ayqLJDeB4AcwBUAcwB0kCSpDYDcmgiMiIiIiKg2VCUhBox9hA0AdADqZj1JIiIiIiILqkpCbFqS2RfAqwCOi6J4GICdxaMiIiIiIqollU6IJUm6H0AwgBkALsDYj7g9AGsAEEVxvyiKr1k8QiIiIiKiGlSlWSYkSboAYDqA6aIo3gfj6nWDAdgD6AhABPBxZesTRdEGwFcw9k9uBuAkgImSJO0TRTEGwGIA6iKnhEuSdLEqMRMRERERlcfseYglSdoJYKcoii/COBdxDIDuZlz/AoBuAC4BGAJggyiKgYX7d0iS1MfcGImIiIiIKlLtleokScoBsAzAsiKJbFXOnVGkaI0oinMBhFWlnsKWZpuiZbt374ZCwUXzyqLX64v9SVTX+ExSfdOUn0mZrKpj7okaNsFgqNzicqIoPleZ4yRJijU3GFEUQwAcB+AJYCCAzwHkA7gGYIEkSV+Xcd50ANOKlj3xxBMYMmSIuaEQERE1WUFBQULFRxE1HlVJiPWoeGlmgyRJZrU6i6JoB2AHgN8lSXpPFMWgwutdhLF/8i8AJkiStLaUc0trIc5kC3HZ9Ho9UlJS4Ofnx5YAqhf4TFJ905SfSZlMxoSYmhRzkleL/yMRRdEawE8AzqKwC4UkSUlFDtkviuICAIMA3JUQS5KUD2NLMlWRTCZrcv/RU/3GZ5LqGz6TRI1fVRNiAcYFOX6GcXaIS9UNQBRFGYCVMLYGj5YkqaxWaD1qIBknIiIioqatKglxJIAJAEYCGA7jjBC/AFgoSdLuasSwCIA3gAckSdKaCkVRfBDAIUmS0kRR7ADgZQBTqnEdIiIiIqK7VLoPsYkoio4AngHwAoAWhcXHAHSVJCmvinUFwDjtmhrG5aBNHgIwAMBTMM5xfBnGxHthFaqv2o01MXq9HsnJyQgICOBXgVQv8Jmk+qaJP5P8RpaalCr3IZYkKUsUxa8A5MC4CIcjgLYwLuFcpYRYkqRklP2PbheAyVWNj4iIiIioKqqUEBe26L4AYwuxc2HxnzC23t60cGxERERERDWu0gmxKIrrATwCQAbgNoCFAD6XJOlszYRGRERERFTzqtJC3K/wzwIAfwPwADBDFMWixxgkSRphodiIiIiIiGpcVfsQGwBYA3i4lH1C4X4mxERERETUYFQlIb4IztxARERERI1MpRNiSZICazAOIiIiIqI6Yc7SzcWIoqgEEAbgnCRJWdUPiYiIiIio9lQ5IRZFcQqMfYhfBZABYDcATwA5oig+XM1V64iIiIiIapU5S+8MBdAFwCkAYwF4wTigzgHAu5YLjYiIiIio5pmTELcAcFGSpFwAnWFcVtkbwA0A7S0XGhERERFRzTMnIVYCyC38ORTAv5IkXYNxFgqVpQIjIiIiIqoN5iTEqQAiRFFcBGPL8NHCcncAaZYKjIiIiIioNpiTEP8I42C8sTDOS/yTKIo+AJoDOGbB2IiIiIiIapw50669BeAKgJYA4iRJOiaKYhsAM2Fc0pmIiIiIqMGockIsSZIewIISZccBHLdUUEREREREtcWcLhNERERERI0GE2IiIiIiatKYEBMRERFRk8aEmIiIiIiaNHNmmSAialKyb6qhvq2pdj22DtZQudhaICIiIrIksxJiURR7A+gNwBOAUGSXQZKkZywRGBFRfZB9U43v3v0HOq2+2nXJrWQYMeMeJsVERPVMlRNiURTfAjCjlF0CjAt1MCEmokZDfVtjkWQYAHRaPdS3NUyIiYjqGXNaiMfDmPxqAFwHoLVoREREREREtcichNgRQBqAcEmS0i0cDxERERFRrTJnlonfYGwdvmXZUIiIiIiIap85LcSHATwBYKcoij+iRGIsSdIKC8RFRERERFQrzEmIP4Vx8FyXwq0oAwAmxERERETUYJg7D7FQxXIiIiIionqpygmxJElc3Y6IiIiIGg2uVEdE9d7lW3nIyCmodj3O9gr4NrOzQERERNSYmLtS3UMAhgHwASAvsssgSVJvSwRGRAQYk+Fen+5AvgUWx7CxkmH7lPuZFBMRUTHmrFQ3AqUPnDOtVEdEZDEZOQUWSYYBIF+rR0ZOARNiIiIqxpz+wK/AmPyeK/zzNoCrADIA7LRcaERERERENc+chDgcwE0AbQpfxwOIhDE5XmqhuIiIiIiIaoU5CbEVgAuSJOUD0AGwlyQpA0AqgGmWDI6IiIiIqKaZM6juJgDnwp+vA4gQRfErAK0A5FkqMCIiIiKi2mBOC/EpAP6iKLoD+KuwjucK/9xvwdiIiIiIiGqcOS3EkwAEwthneBIATwCdARwDMM5ikRERERER1QJzVqo7CuBokaK+lguHiKh+sXWwhtxKBp0Fpn6TW8lg62BtgaiIiMiSKpUQi6LYA0CWJElHCn8ukyRJf1skMiKiekDlYosRM+6B+ram2nXZOlhD5WJrgaiIiMiSKttCvAPAPgD3Fv5c1gIchirUSUTUIKhcbJnIEhE1YlVJXoUyfiYiIiIiarAqmxAHAcgv8jMRERERUaNQqYRYkqTk0n4mIiIiImroKjuo7ttK1meQJOmZyl5cFEUbAF8B6AOgGYCTACZKkrSvyDFWAP4FYCdJUsvK1k1EREREVBmV7TIRg7IH0pkIhcdUOiEuvP4FAN0AXAIwBMAGURQDJUm6XXjMSwAyAdhVoV4iIiIiokqpbEJ8EcUTYi8ANjAu4wwALgA0AC5X5eKSJOUAmFGkaI0oinMBhAE4JIqiJ4yr4L0KYH5Z9RS2NNsULdu9ezcUCkVVwmlS9Hp9sT+J6lpZz6Sln1G9Xs/nniqlKf8/KZOZs5AtUcNV2T7EgaafRVEcAGAVgD6SJG0vLOsJYCOAN6sTjCiKITAm12cLi2YDmAkgp4JT3wAwrWjB/PnzMWTIkOqE0ySkpKTUdQhExZR8Jq+k5Vm0/itXrkClvWXROqlxa4r/TwYFcfw8NS3mzBk8G8AFUzIMAJIk/SWK4nkA7wNYY04goijawZhofyRJUqYoil0AhAB4GsB9FZz+EYC5RQteeeWVTLYQl02v1yMlJQV+fn5sCaB6oaxn0sopDzZWyci3wEpxNlYyhLcMgG8z9sCiivH/SaKmw5yE2B+AlSiKYwGsKywbCKAVjN0mqkwURWsAP8HYMjxDFEUZgAUAXpAkySCKYrnnS5KUj/+mhaMqkMlk/I+e6pWSz6Sfiz22T7kfGTkF1a7b2V7BZJiqjP9PEjV+5iTEu2CcFeLrws1EKNxXJYXJ70oY+yiPLkyAnQB0gHGAHQAoADiKongVQKgkSVlmxE1EDZRvMzsmskREVGPM+cj7LIDDMCbARbd/C/dV1SIA3gCekCRJW1iWCcAXQPvC7VkAKYU/Z5txDSIiIiKiUlW5hViSpIsARFEUewGIKCyOL9qnuLJEUQyAMdlVA7hRpGvEQ5Ik7Spy3E0AOkmSrlb1GkRERERE5REMhoqmFy6bKIreAKwkSaqPQ3DNv7EmQK/XIzk5GQEBAewbR/UCn0mqb5r4MynUdQBEtcmcPsQQRXEkgA8BNAewXxTFjwBMBPCpJEm/WzA+IiIiIqIaVeWEWBTFwQBWlCg+DOPUaNcBMCEmIiIiogbDnO+A3oSxO8I8U4EkSZcBpALoaJmwiIiIiIhqhzkJcTiAREmSJpUoTwPgU/2QiIiIiIhqjzkJsRrGOYHvnCuKog2AIAC5lgqMiIiIiKg2mDOobh+AB/BfX+HmALYCcATwh4XiIiKiekiTmgptRka167Fydoa1D79UJKL6wZyE+D0AvQH0hbEvsS+MSbEGwPuWC42IiOoTTWoqzj34EAwF1V9GW1AoELzpDybFRFQvVLnLhCRJ+wH0AvA3gLzCbSeAPoX7iIioEdJmZFgkGQYAQ0GBRVqaiYgswax5iCVJ2gOgp4VjISIiIiKqdeYuzCEHEAzAEyVWs5Ek6W8LxEVEREREVCvMWZijK4DvAfiVsttgTp1ERERERHXFnOT1SwD+lg6EiIiIiKgumJMQtwSQAeAJAOdhbBUmIiIiImqQzEmIdwJoA+BvSZK0Fo6HiIiIiKhWmZMQPwNgB4DDoihuBpBVdKckSTMsEBcRERERUa0wJyF+EMYZJmQAIkrZz4SYiIiIiBoMcxLiDwDILR0IEVF9kl2QjbTcNORqc6G0UsJd6Q6VQlXXYRERUQ0wJyF2AHAFQA8AFyRJ0lk2JCKiumEwGCBdk7AmYQ22XtwKvUF/Z59MkKGPfx8MazUMoqcIQRDKqYmIiBoScxLiJQBGArjOZJiIGouT6ScxdddUJGUmQS7IiyXDAKA36LHt4jZsTt6MIKcgzOo+C+Gu4XUULRERWZI5CbE7AEcAZ0RR3I3ig+oMkiQ9Y5HIiIhqyb7UfZiwfQI0eg0AQGco/bO+qTw5Kxmj/hiFhb0WootPl1qLk4iIaoY5CfFIGOce9gAwsEi5UFjOhJiIGoyT6ScxYfsEFOgKYKjktOp6gx4FugJM2D4BKx5awZZiIqIGTmbGORcLt+QiPxd9TUTUIBgMBrz+9+vQ6DWVTobvnAsDNHoNpu6aCoOB6xMRETVkVW4hliQpsAbiICKqddI1CReyLph9vt6gR1JmEg5dOwTRS7RcYEREVKvMaSEmImoU1iSsgVyo3iySckGO1QmrLRQRERHVBSbERNQkZRdkY+vFrWUOoKssnUGHrRe3Irsg20KR1V9Wzs4QFAqL1CUoFLBydrZIXURE1WXOoDoiogYvLTftrqnVzKU36JGWl9boF+6w9vFB8KY/oM3IqHZdVs7OsPbxsUBURETVx4SYGrUrt68gI7/6v7ydbZzh7eBtgYiovsjV5lq2Po1l66uvrH18mMgSUaPDhJgarSu3r+DR9Y+iQFdQ7boUcgXiBsQxKW5ElFZKy9Znbdn6iIio9rAPMTVaGfkZFkmGAaBAV2CRlmaqP9yV7pAJlvkvUCbI4G7nbpG6iIio9jEhJqImSaVQoY9/H4vMMtHXv2+j7z9MRNSYMSEmoiZrWKthFpllYlirYRaKiIiI6gITYiJqskRPEUFOQWZ3nZAJMgQ5BSHaM9rCkRERUW1iQkxETZYgCJjVfRasZdYQIFTtXAiwllljdvfZEISqnUtERPULE2IiatLCXcOxsNdCKOSKSrcUywQZFHIFFvZaiNaurWs4QiIiqmlMiImoyevi0wUrHlqBAMcAAChzoJ2pPMAxACsfWokuPl1qLUYiIqo5nIeYiAjGluJf+/+KQ9cOYXXCamy9uLXYSnYyQYY+/n0wrNUwRHtGs5sEEVEjwoSYiKiQIAgQvUSIXiKyC7KRlpeGXE0ulNZKuNu5c2o1IqJGigkxEVEpVAoVE2AioiaCfYiJiIiIqEljQkxERERETRoTYiIiIiJq0pgQExEREVGTxkF1RBaky86G9vp16HNzIVMqYeXhAbmKA7OIiIjqMybERNVkMBiQe/AgMr77HtlbtgD6/+auhUwGVd++cB4xHMqOHTl3LRERUT1UpwmxKIo2AL4C0AdAMwAngf9v7/6j5Krr+48/Z3azm2yyJJuwgU1M0gRSjoaAyCcGFCS2FVvBKlWxBK2Ugy1w0JQe0m892oMHa2kLIlTFX/UHVVGwWtuQtlBKsabQ1k/4kRjtt0aSsIEAISRhyY/d7M70j3sXhrCb7I/ZvTN7n49zcnbn3juffc/ks8lrP/u+93J1jPHBEMI7gL8AOoD9wLeB1THGvozKlV7hwKZNPLl6NT2PbYGGhpeHYYBSia5776Xr7rtpWrSQOTfcwJQlS7IpVpIkDSjrHuJGYCtwFkkgvhlYE0KYBkTg7BjjdOA1wKnA5ZlUqbrU1txGU0NTVcZqamiirbntZdv2PfAA21ZeTM/WbcmGvkF+Vku392zdxraVF7PvgQeqUpMkSaqOTFeIY4z7gOsqNn0nhHATcFKMcf1hh5eAE8atONW9jmkd3PXOu9jdvXvUY7U1t9ExrePFxwc2baLziisp9/RAuTy0QUolyj09dF5xJQtu/5YrxZIk1Yia6iEOISwGZgKb08dnAWuBY4CdwNWDPK8ZaK7ctm7dOpqaqrM6OBGV0l/tlw7/Ff8Ec1zLcRzXclxVxup/r8rlMk9ccw3lQ4eGHob7lcuUDx3iyWtW80t3rbGnuEJe5qTqR57nZLGY9S+QpfFVM4E4hDAF+CZwfYxxL0CMcR0wPYSwEHg/8MwgT/8IcG3lhltuuYULL7xwDCueGDo7O7Muoe6UNmzk0JatoxigRM+WLWxdu5bi0qVVq2uicE6q1uRxTi5cuDDrEqRxVROBOIQwCfguycrwdYfvjzFuCSFsAj4LDJRyrwduqtywatWqva4QD65UKtHZ2cm8efNcCRimJ26+mZ6GhsF7hoeioYHm++5jzvnnV6+wOuecVK1xTkr5kXkgDiEUgW8AZeADMcbBfgfdCJw40I4YYzfQPTYVTmzFYtF/6Iehr6uLF/7l3ldeTWLYA/XRdc+/UN63z+sUH8Y5qVrjnJQmvlr4Dv8iyaXV3hNj7O3fGEK4MIQwP/18MUlbxH3ZlCglep95ZvRhuF+pRO/OndUZS5IkjVjW1yFeAFwGHASeDSH07/oN4CTgphBCG7CLpKXiY1nUKfUr7d9f3fH27avqeJIkafiyvuzaNmCw0+x/BHxiHMuRjqrY0lLd8aZOrep4kiRp+GqhZUKqG42zZ0O1egmLRRrb26szliRJGjED8RBt2L6Hi770n2zYvifrUpShhtZWWt/yluQ2zaMaqIHWt57rCXWSJNUAA/EQff+hJ3jwsV18/6Ensi5FGWu7eOXoLrkG0NfHzJUrq1OQJEkaFQPxEJRKZdY8+iQAax59klJpmHcn04TSsmwZTYsWjrx1oqGBpkULmfLSSaSSJClDBuIhWP/4bnbt6wFg174eHnp8d8YVKUuFQoE5N9xAYdIkGO6tlwsFCo2NzL3xRm/bLElSjTAQD8HaDTtoLCbhpaFY4K4NOzKuSFmbsmQJ8z5/K4WmpqH3Ezc0UGhqYt7nb2Xya14ztgVKkqQhy/xOdbXiqb0HefaFgW92t+bRJ+lN2yT60vaJd5/+qgGPPXZaM8dPnzxmdap2TH3DG1hw+7d4cvVqeh7bkgTjgXqL0+1NC+Yz98YbDcOSJNUYA3HqD+98hAd+sWvAfYf/Yvu5fT2c/5l1Ax77xhNm8a0PnlHl6lSrpixZwqK1azkQI89963a67rnn5XeyKxZpPfctzFy5kikh2CYhSVINMhCnLl6+gJ88sZfnD/a+Yt/hp9ANdkrdMZMbWbl8QdVrU20rFAq0LFtGy7Jl9HV10btzJ6V9+yhOnUpje7uXVpMkqcYZiFPnndLB8kUz+ejfbeTuTU9TYPDgW6n/uLcuOY5PXrCUY6c1j22hqmkNra0GYEmS6own1VU4dlozX3jf6XzmotNondxIQ/HIv95uKBRondzIZ1eexhffHwzDkiRJdchAfJhCocDbT53Dfdes4I0nzDrisW88cRb3XbOC80+ZM07VSZIkqdoMxIM4dlozi9qnvXi5tcM1FAssap/mqrAkSVKdMxAPov/udL2D3JWu//Jr3rVOkiSpvhmIB1F5d7piARqLBS5ePp/GYoH+ReNd+3pY713rJEmS6pqBeBBr07vRNRQLtLU0ceflZ/LJC5Zy5+Vn0tbS9OIJd2u9a50kSVJdMxAPoL9dAuCUudP5pz84m9fNbwPgdfPb+KdVZ7N07nQA2yYkSZLqnIF4AAd7+1h47FTet3w+d/z+mcxuffmtmGcfM5k7f/9MLl4+n4XtUznYO8DteiVJklQXvDHHAFqaGvnu5Wce8Ta7TY1FPnnBUsrlsrfjlSRJqmOuEA9iqCHXMCxJklTfDMSSJEnKNQOxJEmScs1ALEmSpFwzEEuSJCnXDMSSJEnKNQOxJEmScs1ALEmSpFwzEEuSJCnXDMSSJEnKNQOxJEmScs1ALEmSpFwzEEuSJCnXDMSSJEnKNQOxJEmScs1ALEmSpFwzEEuSJCnXGrMuQBqWPZ2wf9fox2mZBTPmjX4cSZJU9wzEqh97OuGzp0Nv9+jHamyGq9YbiiVJki0TqiP7d1UnDEMyTjVWmiVJUt0zEEuSJCnXDMSSJEnKNQOxJEmScs1ALEmSpFwzEEuSJCnXDMSSJEnKtUyvQxxCaAY+D/waMAP4KXB1jPHBEMIlwIeBE4HdwOdjjH+eUamSJEmaoLJeIW4EtgJnkQTim4E1IYRpwGTgKmAWcA5wSQhhZSZVSpIkacLKdIU4xrgPuK5i03dCCDcBJ8UYv1CxfWsI4fvAmcDth4+TrjQ3V25bt24dTU1NY1D1xFAqlV72sS6Uy1X9Ca5ULkM9vf4Jri7npCa0PM/JYjHr9TJpfNXUrZtDCIuBmcDmAXa/CfjmIE/9CHBt5YZbbrmFCy+8sLoFTkCdnZ1ZlzBkTbt2MLeK4+3YsYOenhlVHFHVUE9zUvlQq3PyUHeJ7q5eeg+VaZxUoLm1kUnN1QmyCxcurMo4Ur2omUAcQphCEnivjzHuPWzfH5IE5dsGefr1wE2VG1atWrXXFeLBlUolOjs7mTdvXv2sBDTtqepwHR0d0LGgqmNq5OpyTmpCq8U5WS6X2fHzvWz84RNseXQn5YrF60IRFp7aztJz5tKxeDqFQiG7QqU6UxOBOIQwCfguycrwdYftuxi4GnhTjPHAQM+PMXYD3WNd50RULBZr5h/6o6ryP+7FQgHq5bXnSF3NSeVCrczJnY93cc9XN7Hnqf0UioWXhWGAcgm2PPosjz28kxnHt3DupUton9+aTbFSncn8OzyEUAS+AZSBD8QYyxX73gF8Cvj1GOOWjEqUJClTnT97ju/dsJ69z+wHoFwqD3hc//a9z+znezesp/Nnz41bjVI9yzwQA18EOoD3xBh7+zeGEH4V+ArwmzHGTVkVJ0lSlnY+3sXaWzfQ11t6xarwYMol6OstsfbWDex8vGtsC5QmgKyvQ7wAuAw4CDwbQujf9RvAx0guxXZfxfZvxhgvH+cyJUnKRLlc5p6vbKLUV0p+jzqsJ0Opr8Q9X93EymuX21MsHUHWl13bBgz2Hfrm8axFkqRa8+TP97Dn6f0jfn65BHue2s+OzXuYs7itipVJE0sttExIQ9MyCxqbj37cUDQ2J+NJUg3beP92CsXRrewWigU23r+9ShVJE1NNXGVCGpIZ8+Cq9bB/1+jHapmVjCdJNar7QC+PPbJzyH3DgymXyvzi4Z10H+ileYr/7UsD8TtD9WXGPIOspFzYt6d71GG4X7kE+/d2G4ilQdgyIUlSDTrU3VfV8XoOVnc8aSIxEEuSVIMmNTdUdbymydUdT5pIDMSSJNWgqTOaKVTpf+lCEVqmV+mkZGkCMhBLklSDmqc0sui17VW5ysQJp822f1g6AgOxJEk1aumKVw16m+ahKpfKLF0xt0oVSROTgViSpBo1Z/EMZhzfMuLWiUIRZhzfQseJM6palzTRGIglSapRhUKBcy9dQrGhOPh9XQd9MhQbipx76RJv2ywdhYFYkqQa1j6/lfOuPIWGxuKQV4oLRWhoLHLelafQPr91bAuUJgADsSRJNW7eq2fyrtWnM312C8CgJ9r1b58+u4V3rT6dea+eOW41SvXMU04lSaoD7fNbWXntcnZs3sPG+7fzi4dfflvnQhFOOK2dpSvm0nHiDNskpGEwEEuSVCcKhQJzFrcxZ3Eb3Qd62b+3m56DfTRNbqBlerOXVpNGyO8cSZLqUPOURgOwVCX2EEuSJCnXDMSSJEnKNQOxJEmScs1ALEmSpFwzEEuSJCnXDMSSJEnKNQOxJEmScs1ALEmSpFwrlMvlrGuQJEmSMuMKsSRJknLNQCxJkqRcMxBLkiQp1wzEkiRJyjUDsSRJknLNQCxJkqRcMxBLkiQp1wzEkiRJyjUDsSRJknLNQCxJkqRca8y6ANWWEMJfAmcCm4HLYox9GZekHAshNAM/BE4GXhtj3JxxSRIhhHOAvwB6gR/HGK/OuCRJo+QKsV4UQjgVaI8xng1sBc7PtiKJQ8A7gL/NuhCpwmbgnBjjWcDsEMLSrAuSNDquEKvSG4B/Tj//Z+C3gL/PrhzlXYyxBDwdQsi6FOlFMcYnKh72Av4mTapzBuIJKoRwBfBBYCnwyRjjxyv2tQNfB1YA24ErY4z/CrQBW9LD9qaPpaoY4ZyUxtRo5mUI4TTg2BjjT8exZEljwJaJiWsH8HHgewPs+xzwFNAOrAbuDCHMBPYA09NjpgO7x7xK5clI5qQ01kY0L0MIs4G/Ai4dnzIljSVXiCeoGOMPAEIIb6vcHkKYBrwTWBRj3A/8QwhhI0mf5gPAh4A7gLcCD45jyZrgRjgnvzbOZSpnRjIvQwi3A7cDfxBjfHp8K5Y0Flwhzp/FwAsxxu0V2zYCS2KMjwC7Qwg/Ak4A7sqgPuXPoHMSIITwA+Bc4LYQwnvGvzzl1JHm5e+QtFh8KoRwfwjhzCwKlFQ9rhDnzzTg+cO2PQ/MAogxXjPuFSnvjjYn3zneBUkcYV7GGL8MfHn8S5I0Vlwhzp8XgGMO23ZMul3KgnNStch5KeWIgTh/fg5MCyHMrdh2MrApo3ok56RqkfNSypFCuVzOugaNgRBCI0lLzGeAp4E/BQ7FGPtCCN8luazah4BfBW4DFscYn8uqXk18zknVIuelJHCFeCL7GHAAuAz4aPr5+9N9VwJzgF3ATcB7/Qde48A5qVrkvJTkCrEkSZLyzRViSZIk5ZqBWJIkSblmIJYkSVKuGYglSZKUawZiSZIk5ZqBWJIkSblmIJYkSVKuGYglSZKUawZiSZIk5Vpj1gVIOrIQwiXA1wBijIVsqxmeEMLHgWthZLWHEFYA/5Y+XBhj3Fqt2oZZx/3AORWbro4x3jyE511C+neX+mGMcUU1a5MkjZ6BWFLmQghfBz7AKwPj88B/pZ93j3NZA3kC2A7sGOLxO0nqfxUwd6yKkiSNjoFYUs2KMT4EnJF1HRX+Osb48aEeHGNcC6ytXCmXJNUeA7GUoRBCEfgQ8EHgROAAcC/wRzHGLUd43sXAKmARMB14Afhv4E9ijP+dHrOCl9oN3gGsBpaRrHD+cYzxb9PjpgE3AOcDs0lWZf8X+FKM8bb0mOnAdcA7gQ7gGeBO4GMxxv3pMQXgE8AVQAPwDaBrCO/BVmBB+vCcEEI5/fzN6ceXtUxUrCZvIwmZf5q+B18FPgJcD/wusBf4sxjjFyq+1pz0+F8Hjk3fi68B18cYe49W6wC1Hw98CvgVYCawG/gpcGOM8R+HO54kKRueVCdl67PAzcASYDPQB7wbeCCEMPsIz1sGLAV2AZuAKcC5wL1pSDvcnSRhtxs4AbgjhHBauu864HKgPR2rC1hOGkhDCE3A/cCH0zF+BswCrgbWpEEY4CrgoyTBsAu4MH3O0TwMPJt+3kXSYvBfJMH8SDqAW9PX1EryA8J6XgrD84DPhRBenb6OWcB/pvunpa9jXvr6vzSEOgdyK7AyHe8nQA+wAnj9CMeTJGXAQCxlJISwkCSIAnwgxngy8Eskq5bHk6wcD+ZzwKwY40kxxtcCJ6fbW4HzBjj+0zHGk4CTgD0k3/v/L923OP34iRjj62KMi0iC76fT7RcBryUJe6fEGE/lpTaGX0n/APxR+nEdsDD9s/0IrwGAGOMFwNr04UMxxjPSPw8d5alNJD8E/DLQmW47ETgVeA1wMH2d/SfDXUUSgJ8GTkhfx7vTfZeEEE48Wq0D6H/vLo8xnh5jnE/SK/ydEYwlScqIgVjKTgD6V1dvS1sFukhOwIIj9862AX8fQnguhFACfl6xb84Ax38bIMb4FC+1ICxNP65JP34ihLAthHA3SRh/Ot3ev9rZBPxvWucjFWOfEUI4pqLuf4gx9qatFGsZO7tjjP8RYywBj6fbfhJj3BpjfIGkrQPguPTj6yseP5O+jh+k2wokq+LD1f/e3RZC2BxCuAt4H/DkCMaSJGXEHmKpNjzCK6+isG2gA9Oe37uBGSSroA8Dh3gp0DUM5wvHGL8UQvgf4DdJQvLpJCuv7+GllWdIVogfHmCI3cP5elVU2VLRO8C2/l7kwy/31kXS53u4/SOo4aPAfwBvJXmv3kSyQr+CgVfqJUk1yEAsZWc9SWgrAF+PMd4CL56cdhZJH+xATiIJwwCXxhi/HUI4A3jwCF/rvcCGtC95RbptY/r1Xg9sijH+e/q4f6wlad/tj9PjG4Ar+1sZQgiTSULfv8YYnw8hbCdZJX57COHTJCvKbxvie9EfRqcO8fiR+DFJPb3Ab/df0ziE0ApcEGP8uxGM+UaSS8WtTcf6bZLV+DdVpWJJ0rgwEEsZiTE+FkL4MvB7wM0hhFUkV4tYABxDcvLXhgGe+hiwjyQ8fiWE8BGSnt8jWRVCeBdJb/J0oAT8Zbrvw8B700D7HEkfLiTX3H2OJOBdDZwC/DiE8DNgUlpnM0mv8B7gRpITBM8GtqTHzBjSmwH/k34MIYSN6et78xGOH4nPAZeR9Pj+//R1tJL0FU8C/mYEY/45sCyE0EnyA8yr0+0D/b1JkmqUPcRStq4gCZsbSXp/FwBbgZtIruzwCjHG3STtDD8l+R7uAd5+lK/zbpKe4MkkgfqiipPW1gI/IrlSxVKSNow1wNtijOUYYzfJiWl/RXLy2i+T9DBHkpaB/l7jz5AExN0kofsu4JahvAkkl0z7HkmoPJmk/WNYrR9HE2PcSdKX/TWSq3MsIXnNPyL5OxiJO0jeh2NI3rs9JCfUXTTKciVJ46hQLpePfpSkulMrtz2eCCpu3dx/p7pPxxjvGMLzzgP+hJfuVOetmyWpBtkyIUlDNzf90zHE49sZ2dUrJEnjyBViaYJyhViSpKExEEuSJCnXPKlOkiRJuWYgliRJUq4ZiCVJkpRrBmJJkiTlmoFYkiRJuWYgliRJUq4ZiCVJkpRrBmJJkiTl2v8BW3u6+bRb/BMAAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -1503,7 +1464,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "50e7aa2e10304ccc92d1c817bd984752", + "model_id": "183a8719199e428098d6a8fc8624ef93", "version_major": 2, "version_minor": 0 }, @@ -1646,7 +1607,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1710,7 +1671,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b7528dd9cb56480887a37f09f3ab9bf8", + "model_id": "bc0f5def73af4e8aae58f1fdd6eb0edc", "version_major": 2, "version_minor": 0 }, @@ -1746,7 +1707,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "64492d7c4c4247a184924ad3e10775cc", + "model_id": "2b182fceabd24df2b54189aff50f6793", "version_major": 2, "version_minor": 0 }, @@ -1792,7 +1753,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "426b8699f02f4d438f72d2b3271a8370", + "model_id": "49228cd52eb849dba2318bd6627f4916", "version_major": 2, "version_minor": 0 }, @@ -1838,7 +1799,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "70f32963bf4146299dcfbbd432a21720", + "model_id": "8e1a94f8b393461dbf876fb36e8bf0d4", "version_major": 2, "version_minor": 0 }, @@ -1882,7 +1843,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0936d7d7f41e4f4cb7f21ec08691c002", + "model_id": "64ddb410426444269d02937d414479d0", "version_major": 2, "version_minor": 0 }, @@ -1926,7 +1887,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cd6eab8ec03446c380385c741e704cfc", + "model_id": "e4a654e9f89f47bf86d07e1e4097aea1", "version_major": 2, "version_minor": 0 }, @@ -1975,7 +1936,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "320fc37b36c342c2b0450aeacd55e1c9", + "model_id": "905d3d78ba4140dbacd0f86dcf03408f", "version_major": 2, "version_minor": 0 }, @@ -2092,7 +2053,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "99dd0dde30e94395914c92164258f27f", + "model_id": "9afcb447a9024770bc0ccd86ceca1be5", "version_major": 2, "version_minor": 0 }, @@ -2213,7 +2174,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -2274,7 +2235,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2288,7 +2249,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" } }, "nbformat": 4, From 03c8fd3ce29c8f1c1a336145857840941d09aa56 Mon Sep 17 00:00:00 2001 From: Julien Herzen Date: Fri, 8 Apr 2022 17:14:32 +0200 Subject: [PATCH 8/8] fix typo --- examples/14-transfer-learning.ipynb | 170 +++++++++++++++------------- 1 file changed, 89 insertions(+), 81 deletions(-) diff --git a/examples/14-transfer-learning.ipynb b/examples/14-transfer-learning.ipynb index 536d12827f..717f8f28c0 100644 --- a/examples/14-transfer-learning.ipynb +++ b/examples/14-transfer-learning.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "09ec0621", + "id": "1378d2bf", "metadata": {}, "source": [ "# Transfer Learning for Time Series Forecasting with Darts\n", @@ -33,7 +33,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9738950f", + "id": "bf46dca7", "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,7 @@ { "cell_type": "code", "execution_count": null, - "id": "119889ec", + "id": "9ca559b4", "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "markdown", - "id": "197de730", + "id": "8a027958", "metadata": {}, "source": [ "And now we import everything. Don't be afraid, we will uncover what these imports mean through the notebook :)" @@ -67,7 +67,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "f6208112", + "id": "c9689b89", "metadata": {}, "outputs": [], "source": [ @@ -103,7 +103,7 @@ }, { "cell_type": "markdown", - "id": "d3c37690", + "id": "d5756350", "metadata": {}, "source": [ "We define the forecast horizon here - for all of the (monthly) time series used in this notebook, we'll be interested in forecasting 18 months in advance. We pick 18 months as this is what is used in the M3/M4 competitions for monthly series." @@ -112,7 +112,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "49d0d535", + "id": "826351e8", "metadata": {}, "outputs": [], "source": [ @@ -121,7 +121,7 @@ }, { "cell_type": "markdown", - "id": "b88f2896", + "id": "1ab36bc6", "metadata": {}, "source": [ "### Datasets loading methods\n", @@ -137,7 +137,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "bcce2484", + "id": "ff0a526b", "metadata": {}, "outputs": [], "source": [ @@ -231,7 +231,7 @@ }, { "cell_type": "markdown", - "id": "84c326b9", + "id": "79fe37fa", "metadata": {}, "source": [ "Finally, we define a handy function to tell us how good a bunch of forecasted series are:" @@ -240,7 +240,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "77a78e20", + "id": "a69b6b25", "metadata": {}, "outputs": [], "source": [ @@ -262,7 +262,7 @@ }, { "cell_type": "markdown", - "id": "0c9fd92d", + "id": "58a59b4f", "metadata": {}, "source": [ "## Part 1: Local models on the `air` dataset\n", @@ -277,7 +277,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "bf4f7907", + "id": "bca44bbe", "metadata": {}, "outputs": [ { @@ -297,7 +297,7 @@ }, { "cell_type": "markdown", - "id": "b60309b7", + "id": "db486bff", "metadata": {}, "source": [ "It's a good idea to start by visualising a few of the series to get a sense of what they look like. We can plot a series by calling `series.plot()`." @@ -306,7 +306,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "d73caf5d", + "id": "144c3c14", "metadata": {}, "outputs": [ { @@ -345,7 +345,7 @@ }, { "cell_type": "markdown", - "id": "6d058dab", + "id": "74829d15", "metadata": {}, "source": [ "We can see that most series look quite different, and they even have different time axes! For example some series start in Jan 2001 and others in April 2010.\n", @@ -356,7 +356,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "7269aa98", + "id": "4564171e", "metadata": {}, "outputs": [ { @@ -376,7 +376,7 @@ }, { "cell_type": "markdown", - "id": "02f13136", + "id": "4db3ddac", "metadata": {}, "source": [ "### A useful function to evaluate models\n", @@ -389,7 +389,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "b26ebd2b", + "id": "4e247f54", "metadata": {}, "outputs": [], "source": [ @@ -411,7 +411,7 @@ }, { "cell_type": "markdown", - "id": "69307302", + "id": "a176ed58", "metadata": {}, "source": [ "### Building and evaluating models\n", @@ -422,7 +422,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "f09986b1", + "id": "82585d9f", "metadata": {}, "outputs": [ { @@ -465,7 +465,7 @@ }, { "cell_type": "markdown", - "id": "b23eab99", + "id": "5ed7039d", "metadata": {}, "source": [ "So the most naive model gives us a median sMAPE of about 29.4.\n", @@ -476,7 +476,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "715daf4b", + "id": "32ad3737", "metadata": {}, "outputs": [ { @@ -521,7 +521,7 @@ }, { "cell_type": "markdown", - "id": "42edf9a1", + "id": "cdafd472", "metadata": {}, "source": [ "This is better. Let's try ExponentialSmoothing (by default, for monthly series, it will use a seasonality of 12):" @@ -530,7 +530,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "cf4295b2", + "id": "e6afdf37", "metadata": {}, "outputs": [ { @@ -573,7 +573,7 @@ }, { "cell_type": "markdown", - "id": "e46c8781", + "id": "f3a33bfc", "metadata": {}, "source": [ "The median is better for with the naive seasonal. Another model that we can quickly is the `Theta` method which has won the M3 competition:" @@ -582,7 +582,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "ac8ee96a", + "id": "e38a16c8", "metadata": {}, "outputs": [ { @@ -625,7 +625,7 @@ }, { "cell_type": "markdown", - "id": "76fb6d09", + "id": "b866091a", "metadata": {}, "source": [ "And how about ARIMA?" @@ -634,7 +634,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "c5771d7d", + "id": "033ab873", "metadata": {}, "outputs": [ { @@ -678,7 +678,7 @@ }, { "cell_type": "markdown", - "id": "84a3526c", + "id": "f431b961", "metadata": {}, "source": [ "Or the Kalman Filter? (in Darts, fitting Kalman filters uses the N4SID system identification algorithm)" @@ -687,7 +687,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "e646f145", + "id": "3c82dbfb", "metadata": {}, "outputs": [ { @@ -730,7 +730,7 @@ }, { "cell_type": "markdown", - "id": "fcfcf3fd", + "id": "cdb1a576", "metadata": {}, "source": [ "### Comparing models\n", @@ -741,7 +741,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "959c7c74", + "id": "d47e85f1", "metadata": {}, "outputs": [], "source": [ @@ -770,7 +770,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "22c499d4", + "id": "ef4336ef", "metadata": {}, "outputs": [ { @@ -810,7 +810,7 @@ }, { "cell_type": "markdown", - "id": "23d1b89b", + "id": "e8df1cd5", "metadata": {}, "source": [ "### Conclusions so far\n", @@ -830,7 +830,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "cae8f2de", + "id": "ea804331", "metadata": {}, "outputs": [], "source": [ @@ -852,7 +852,7 @@ }, { "cell_type": "markdown", - "id": "863dd0df", + "id": "76969990", "metadata": {}, "source": [ "### Part 2.1: Using Darts `RegressionModel`s.\n", @@ -872,7 +872,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "c169a7cf", + "id": "2c09d538", "metadata": {}, "outputs": [ { @@ -904,7 +904,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "ab11f68b", + "id": "d491dac0", "metadata": {}, "outputs": [ { @@ -936,7 +936,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "ce17d5b4", + "id": "c56f3f5d", "metadata": {}, "outputs": [ { @@ -967,7 +967,7 @@ }, { "cell_type": "markdown", - "id": "45b38b30", + "id": "c7d1f2b3", "metadata": {}, "source": [ "### Part 2.2: Using deep learning\n", @@ -980,7 +980,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "ac4d360f", + "id": "17ca9d9d", "metadata": {}, "outputs": [], "source": [ @@ -1006,7 +1006,7 @@ }, { "cell_type": "markdown", - "id": "0fadf994", + "id": "731992cd", "metadata": {}, "source": [ "Let's now build, train and predict using an N-BEATS model:" @@ -1015,7 +1015,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "d7c73c3b", + "id": "c9de4c1b", "metadata": {}, "outputs": [ { @@ -1135,7 +1135,7 @@ }, { "cell_type": "markdown", - "id": "d553d77f", + "id": "bcf484aa", "metadata": {}, "source": [ "Let's now look again at our errors -vs- time plot:" @@ -1144,7 +1144,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "5324b386", + "id": "51a1a199", "metadata": {}, "outputs": [ { @@ -1186,7 +1186,7 @@ }, { "cell_type": "markdown", - "id": "0855a8bf", + "id": "83cae14c", "metadata": {}, "source": [ "### Conclusions so far\n", @@ -1201,7 +1201,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "2f1f0207", + "id": "1a48ae31", "metadata": {}, "outputs": [ { @@ -1219,7 +1219,7 @@ }, { "cell_type": "markdown", - "id": "c96d0412", + "id": "f7df8178", "metadata": {}, "source": [ "We can start from the same hyper-parameters as before. \n", @@ -1232,7 +1232,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "9c4d55fb", + "id": "83b26117", "metadata": {}, "outputs": [], "source": [ @@ -1259,7 +1259,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "90d16347", + "id": "9564e32f", "metadata": {}, "outputs": [ { @@ -1346,7 +1346,7 @@ }, { "cell_type": "markdown", - "id": "cb7ef8d4", + "id": "6e684e38", "metadata": {}, "source": [ "We can now use our M4-trained model to get forecasts for the air passengers series. As we use the model in a \"meta learning\" (or transfer learning) way here, we will be timing only the inference part." @@ -1355,7 +1355,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "2ec60b04", + "id": "1ab409a6", "metadata": {}, "outputs": [ { @@ -1410,7 +1410,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "a42e2812", + "id": "4b22aba8", "metadata": {}, "outputs": [ { @@ -1439,7 +1439,7 @@ }, { "cell_type": "markdown", - "id": "a094d433", + "id": "1fc609d5", "metadata": {}, "source": [ "### Conclusions so far\n", @@ -1451,7 +1451,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "40f4f7a4", + "id": "74f643d5", "metadata": {}, "outputs": [ { @@ -1506,7 +1506,7 @@ }, { "cell_type": "markdown", - "id": "6c50d008", + "id": "b779dd8e", "metadata": {}, "source": [ "### Try training other global models on `m4` and applying on airline passengers\n", @@ -1516,7 +1516,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "9064dc97", + "id": "f25d6af3", "metadata": {}, "outputs": [ { @@ -1555,7 +1555,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "e2cc4bc4", + "id": "4e1a8fc5", "metadata": {}, "outputs": [ { @@ -1593,7 +1593,7 @@ }, { "cell_type": "markdown", - "id": "cf2c1f84", + "id": "2b7fc449", "metadata": {}, "source": [ "Finally, let's plot these new results as well:" @@ -1602,7 +1602,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "2667bc7c", + "id": "ae5b3355", "metadata": {}, "outputs": [ { @@ -1640,15 +1640,15 @@ }, { "cell_type": "markdown", - "id": "ef93235d", + "id": "b8b722da", "metadata": {}, "source": [ - "Linear regression offers competitive performance too. It is somewhat slower probably only because the inference with N-BEATS is efficiently batched accross batches of time series and performed on GPU. " + "Linear regression offers competitive performance too. It is somewhat slower probably only because the inference with N-BEATS is efficiently batched across batches of time series and performed on GPU. " ] }, { "cell_type": "markdown", - "id": "c156626b", + "id": "9ef9f3be", "metadata": {}, "source": [ "## Part 4 and recap: Use the same model on M3 dataset\n", @@ -1658,7 +1658,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "09a02763", + "id": "fd30aebf", "metadata": {}, "outputs": [ { @@ -1701,7 +1701,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "5c367704", + "id": "ab72e092", "metadata": {}, "outputs": [ { @@ -1747,7 +1747,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "edebec11", + "id": "f3ac97e8", "metadata": {}, "outputs": [ { @@ -1793,7 +1793,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "aa136b53", + "id": "251dd9c1", "metadata": {}, "outputs": [ { @@ -1837,7 +1837,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "79aaf2fb", + "id": "93469d07", "metadata": {}, "outputs": [ { @@ -1881,7 +1881,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "5186afa4", + "id": "b9edb800", "metadata": {}, "outputs": [ { @@ -1930,7 +1930,7 @@ { "cell_type": "code", "execution_count": 39, - "id": "a0dfe336", + "id": "76e56c4d", "metadata": {}, "outputs": [ { @@ -1976,7 +1976,7 @@ { "cell_type": "code", "execution_count": 40, - "id": "d0cc81af", + "id": "bb2eff2e", "metadata": {}, "outputs": [ { @@ -2008,7 +2008,7 @@ { "cell_type": "code", "execution_count": 41, - "id": "b45adf97", + "id": "39a81a9b", "metadata": {}, "outputs": [ { @@ -2040,7 +2040,7 @@ { "cell_type": "code", "execution_count": 42, - "id": "ae2da8e5", + "id": "355f8190", "metadata": {}, "outputs": [ { @@ -2097,7 +2097,7 @@ { "cell_type": "code", "execution_count": 43, - "id": "fd6febdf", + "id": "3658e9ef", "metadata": {}, "outputs": [ { @@ -2133,7 +2133,7 @@ { "cell_type": "code", "execution_count": 44, - "id": "f2cd6302", + "id": "3b984116", "metadata": {}, "outputs": [ { @@ -2169,7 +2169,7 @@ { "cell_type": "code", "execution_count": 45, - "id": "e766f941", + "id": "cc711d2d", "metadata": {}, "outputs": [ { @@ -2219,7 +2219,7 @@ }, { "cell_type": "markdown", - "id": "329154b2", + "id": "58ec6ec8", "metadata": {}, "source": [ "Here too, the pre-trained N-BEATS model obtains reasonable accuracy, although not as good as the most accurate models. Note that two models out of the 3 most accurate (Exponential Smoothing and Kalman Filter) did not perform so well when used on the air passengers series. ARIMA performs best but is about 170x slower than N-BEATS, which didn't require any training and takes about 15 ms per time series to produce its forecasts. Recall that this N-BEATS model has *never* been trained on *any* of the series we're asking it to forecast.\n", @@ -2227,15 +2227,23 @@ "## Conclusions\n", "Transfer learning and meta learning is definitely an interesting phenomenon that is at the moment under-explored in time series forecasting. When does it succeed? When does it fail? Can fine tuning help? When should it be used? Many of these questions still have to be explored but we hope to have shown that doing so is quite easy with Darts models.\n", "\n", - "Now, which method is best for your case? As always, it depends. If you're dealing mostly with isolated series that have a sufficient history, classical methods such as ARIMA will get you a long way. Even on larger datasets, if compute power is not too much an issue, they can represent interesting out-of-the-box options. On the other hand if you're dealing with larger number of series, or series of higher dimensionalities, ML methods and global models will often be the way to go. They can capture patterns accross wide ranges of different time series, and are in general faster to run. Don't under-estimate linear regression based models in this category! If you have reasons to believe you need to capture more complex patterns, or if inference speed is *really* important for you, give deep learning methods a shot. N-BEATS has proved its worth for meta-learning [1], but this can potentially work with other models too.\n", + "Now, which method is best for your case? As always, it depends. If you're dealing mostly with isolated series that have a sufficient history, classical methods such as ARIMA will get you a long way. Even on larger datasets, if compute power is not too much an issue, they can represent interesting out-of-the-box options. On the other hand if you're dealing with larger number of series, or series of higher dimensionalities, ML methods and global models will often be the way to go. They can capture patterns across wide ranges of different time series, and are in general faster to run. Don't under-estimate linear regression based models in this category! If you have reasons to believe you need to capture more complex patterns, or if inference speed is *really* important for you, give deep learning methods a shot. N-BEATS has proved its worth for meta-learning [1], but this can potentially work with other models too.\n", "\n", "[1] Oreshkin et al., \"Meta-learning framework with applications to zero-shot time-series forecasting\", 2020, https://arxiv.org/abs/2002.02887" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "006e1900", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -2249,7 +2257,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.8.5" } }, "nbformat": 4,