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1_nlp_fomc_fundamentals.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "1_NLP_FOMC_Fundamentals.ipynb",
"provenance": [],
"collapsed_sections": [
"A8HwOgfF8DiU"
],
"authorship_tag": "ABX9TyPbOrgt5VuvMo3hcNz78Xhl",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/theo-dim/ebb4da487f5546ce234077915048eb43/1_nlp_fomc_fundamentals.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BkLI-RllmIh3"
},
"source": [
"# **Predicting interest rates from Federal Reserve documents**\n",
"## Preliminary Analysis (Vol. 1)\n",
"FE 690: Machine Learning in Finance \\\\\n",
"Author: Theo Dimitrasopoulos \\\\\n",
"Advisor: Zachary Feinstein \\\\"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4kFm9MIi2aIQ"
},
"source": [
"## **Setup**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WDlVsQUeZ-Rf"
},
"source": [
"### Environment"
]
},
{
"cell_type": "code",
"metadata": {
"id": "pKK47tV77YEB"
},
"source": [
"## Update System\r\n",
"#!apt-get update\r\n",
"#!apt-get upgrade\r\n",
"#!apt-get autoremove\r\n",
"\r\n",
"## Install Packages\r\n",
"#!apt-get install tree\r\n",
"#!apt install texlive-fonts-recommended texlive-fonts-extra cm-super dvipng\r\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yVaJw2K6mNdA"
},
"source": [
"# -*- coding: utf-8 -*-"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "wndaQSUFZ-Rg",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "75476cbc-44e4-4bfe-fd59-f560c27afc8c"
},
"source": [
"# ENVIRONMENT CHECK:\n",
"import sys, os, inspect, site, pprint\n",
"\n",
"# Check whether in Colab:\n",
"IN_COLAB = 'google.colab' in sys.modules\n",
"if IN_COLAB == True:\n",
" print('YES, this is a Google Colaboratory environment.')\n",
"else:\n",
" print('NO, this is not a Google Colaboratory environment.')\n",
"print(' ')\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"YES, this is a Google Colaboratory environment.\n",
" \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "84AZLcuZcl8S"
},
"source": [
"# TIKA Java Server\r\n",
"os.environ['TIKA_SERVER_JAR'] = 'https://repo1.maven.org/maven2/org/apache/tika/tika-server/1.19/tika-server-1.19.jar'\r\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8z5XTLqMl8J2",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b9263d25-abf1-45ee-d696-44016d380a22"
},
"source": [
"# Python Installation Files:\r\n",
"stdlib = os.path.dirname(inspect.getfile(os))\r\n",
"python_version = !python --version\r\n",
"print('Python Standard Library is located in:\\n' + stdlib)\r\n",
"print(' ')\r\n",
"print('This environment is using {}'.format(str(python_version[0])))\r\n",
"print(' ')\r\n",
"print('Local system packages are located in:')\r\n",
"pprint.pprint(site.getsitepackages())\r\n",
"print(' ')\r\n",
"print('Local user packages are located in:\\n' + site.getusersitepackages())\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Python Standard Library is located in:\n",
"/usr/lib/python3.6\n",
" \n",
"This environment is using Python 3.6.9\n",
" \n",
"Local system packages are located in:\n",
"['/usr/local/lib/python3.6/dist-packages',\n",
" '/usr/lib/python3/dist-packages',\n",
" '/usr/lib/python3.6/dist-packages']\n",
" \n",
"Local user packages are located in:\n",
"/root/.local/lib/python3.6/site-packages\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "wZ4_r3UackEj"
},
"source": [
"## Installed packages:\r\n",
"#!pip list -v\r\n",
"#!pip list --user -v\r\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "1IHC8RsIfdqy"
},
"source": [
"### Mount Google Drive"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ek5xugZfrjRb",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ba1174e9-763f-4491-9a22-bb7d62fd00c2"
},
"source": [
"# Mount Google Drive:\n",
"if IN_COLAB:\n",
" from google.colab import drive\n",
" drive.mount('/content/drive', force_remount=True)\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Mounted at /content/drive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qEyedHZyfgtS"
},
"source": [
"### System Environment Variables"
]
},
{
"cell_type": "code",
"metadata": {
"id": "0zZeVbN9omKg"
},
"source": [
"if IN_COLAB:\n",
" employment_data_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/MarketData/Employment/'\n",
" cpi_data_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/MarketData/CPI/'\n",
" fed_rates_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/MarketData/FEDRates/'\n",
" fx_rates_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/MarketData/FXRates/'\n",
" gdp_data_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/MarketData/GDP/'\n",
" ism_data_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/MarketData/ISM/'\n",
" sales_data_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/MarketData/Sales/'\n",
" treasury_data_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/MarketData/Treasury/'\n",
" mkt_data_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/MarketData/'\n",
" fomc_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/FOMC/'\n",
" preprocessed_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/preprocessed/'\n",
" train_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/train_data/'\n",
" output_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/result/'\n",
" keyword_lm_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/LoughranMcDonald/'\n",
" glove_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/GloVe/'\n",
" model_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/models/'\n",
" graph_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/graphs/'\n",
" final_graph_dir = '/content/drive/My Drive/Colab Notebooks/proj2/report/graphs/'\n",
" img_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/img/'\n",
" root_dir = '/content/drive/My Drive/Colab Notebooks/proj2/'\n",
" src_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/'\n",
" data_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/'\n",
" bak_dir = '/content/drive/My Drive/Colab Notebooks/proj2/archive/'\n",
" tmp_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/archive/'\n",
" ts_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/time_series/'\n",
" config_dir = '/content/drive/My Drive/Colab Notebooks/proj2/src/data/settings/'\n",
"\n",
"else:\n",
" employment_data_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/MarketData/Employment/'\n",
" cpi_data_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/MarketData/CPI/'\n",
" fed_rates_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/MarketData/FEDRates/'\n",
" fx_rates_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/MarketData/FXRates/'\n",
" gdp_data_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/MarketData/GDP/'\n",
" ism_data_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/MarketData/ISM/'\n",
" sales_data_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/MarketData/Sales/'\n",
" treasury_data_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/MarketData/Treasury/'\n",
" fomc_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/FOMC/'\n",
" preprocessed_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/preprocessed/'\n",
" train_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/train_data/'\n",
" output_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/result/'\n",
" keyword_lm_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/LoughranMcDonald/'\n",
" glove_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/GloVe/'\n",
" model_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/models/'\n",
" graph_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/graphs/'\n",
" final_graph_dir ='C:/Users/theon/GDrive/Colab Notebooks/proj2/report/graphs/'\n",
" img_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/img/'\n",
" root_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/'\n",
" src_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/'\n",
" data_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/'\n",
" bak_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/archive/'\n",
" tmp_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/archive/'\n",
" ts_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/time_series/'\n",
" config_dir = 'C:/Users/theon/GDrive/Colab Notebooks/proj2/src/data/settings/'\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "zYf4wLMc2y8y"
},
"source": [
"### Packages"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qgVpJseB8F32"
},
"source": [
"#### Uninstall/Install Packages"
]
},
{
"cell_type": "code",
"metadata": {
"id": "skbUZfszq9WD"
},
"source": [
"# Note: The runtime will be restarted at the end of this block execution.\n",
"# Feel free to restart again using Runtime -> Restart Runtime\n",
"\n",
"#if IN_COLAB:\n",
"# # Uninstall existing versions:\n",
"# #!pip uninstall bs4 -y\n",
"# !pip uninstall textract -y\n",
"# !pip uninstall numpy -y\n",
"# !pip uninstall pandas -y\n",
"# !pip uninstall matplotlib -y\n",
"# #!pip uninstall requests -y\n",
"# #!pip uninstall tika -y\n",
"# !pip uninstall tqdm -y\n",
"# #!pip uninstall pytz -y\n",
"# !pip uninstall ipython-autotime -y\n",
"# !pip uninstall nltk -y\n",
"# #!pip uninstall quandl -y\n",
"# #!pip uninstall scikit-plot -y\n",
"# !pip uninstall seaborn -y\n",
"# #!pip uninstall sklearn -y\n",
"# #!pip uninstall torch -y\n",
"# #!pip uninstall transformers -y\n",
"# #!pip uninstall wordcloud -y\n",
"# #!pip uninstall xgboost -y\n",
"#\n",
"# # Install packages:\n",
"# #!pip install bs4==0.0.1\n",
"# !pip install textract==1.6.3\n",
"# !pip install numpy==1.19.4\n",
"# !pip install pandas==1.1.4\n",
"# !pip install matplotlib\n",
"# #!pip install requests==2.24.0\n",
"# #!pip install tika\n",
"# !pip install tqdm==4.51.0\n",
"# #!pip install pytz\n",
"# !pip install ipython-autotime\n",
"# !pip install nltk==3.5\n",
"# #!pip install quandl==3.5.3\n",
"# #!pip install scikit-plot==0.3.7\n",
"# !pip install seaborn==0.11.0\n",
"# #!pip install sklearn==0.0\n",
"# #!pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 -f https://download.pytorch.org/whl/torch_stable.html\n",
"# #!pip install transformers==3.5.0\n",
"# #!pip install wordcloud==1.8.0\n",
"# #!pip install xgboost==1.2.1\n",
"# os.kill(os.getpid(), 9)\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "S2Z0H8zUPQE6"
},
"source": [
"#### Inspect Packages"
]
},
{
"cell_type": "code",
"metadata": {
"id": "urMcNI8vPUHB"
},
"source": [
"# Previous & new installed package list\r\n",
"#!pip list -v\r\n",
"#!pip list --user -v\r\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "DfgwQISU8C0u"
},
"source": [
"#### Import Packages"
]
},
{
"cell_type": "code",
"metadata": {
"id": "pYiY1suXzjKg"
},
"source": [
"## Core Libraries\n",
"import sys\n",
"import os\n",
"import io\n",
"import inspect\n",
"import site\n",
"import re\n",
"import random\n",
"from collections import defaultdict, Counter\n",
"\n",
"## Import Time Libraries\n",
"import time\n",
"import datetime as dt\n",
"from datetime import date, datetime, timedelta\n",
"from dateutil.relativedelta import *\n",
"#from pytz import timezone\n",
"#from pytz import all_timezones_set\n",
"#import pytz\n",
"\n",
"## Import Data Management Libraries\n",
"import json\n",
"import pickle\n",
"import requests\n",
"from lxml import etree\n",
"from bs4 import BeautifulSoup\n",
"import textract\n",
"#import tika\n",
"#from tika import parser\n",
"#import urllib.request\n",
"#import zipfile\n",
"#import quandl\n",
"#import pandas_datareader.data as web\n",
"\n",
"## Import Data Science Libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"import scipy\n",
"import scipy.stats as ss\n",
"from scipy.stats import kurtosis,skew,norm\n",
"from scipy.optimize import minimize, least_squares\n",
"#import statsmodels.api as smf\n",
"\n",
"## Import Optimization Libraries\n",
"#from cvxopt import matrix, solvers\n",
"\n",
"## Import Financial Libraries\n",
"#import zipline\n",
"#import pyfolio as pf\n",
"#import alphalens\n",
"#import empyrical\n",
"#import mlfinlab\n",
"\n",
"## Import Visualization Libraries\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.dates as mdates\n",
"import matplotlib.transforms as mtransforms\n",
"from matplotlib.ticker import FuncFormatter\n",
"import matplotlib.ticker as ticker\n",
"#from mpl_toolkits.mplot3d import Axes3D\n",
"import seaborn as sns; sns.set()\n",
"\n",
"## Import Pytorch Libraries\n",
"#import torch\n",
"#from torch import nn, optim\n",
"#import torch.nn.functional as F\n",
"#from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset)\n",
"#from torch.autograd import Variable\n",
"#from torch.optim import Adam, AdamW\n",
"\n",
"## Import Scikit-Learn Libraries\n",
"#from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
"#from sklearn.metrics import accuracy_score, f1_score, plot_confusion_matrix\n",
"#from sklearn.preprocessing import normalize\n",
"#from sklearn.pipeline import Pipeline, FeatureUnion\n",
"#from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\n",
"#from sklearn.linear_model import LinearRegression, LogisticRegression, Perceptron, SGDClassifier\n",
"#from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
"#from sklearn.neighbors import KNeighborsClassifier, KernelDensity\n",
"#from sklearn.naive_bayes import GaussianNB\n",
"#from sklearn.tree import DecisionTreeClassifier\n",
"#from sklearn.neural_network import MLPClassifier\n",
"#from sklearn.svm import SVC, LinearSVC\n",
"#from sklearn import model_selection\n",
"#from sklearn.model_selection import GridSearchCV, cross_val_score, cross_validate, StratifiedKFold, learning_curve, RandomizedSearchCV, RepeatedStratifiedKFold\n",
"#from sklearn.impute import SimpleImputer\n",
"#import scikitplot as skplt\n",
"\n",
"## Import XGBoost Libraries\n",
"#import xgboost as xgb\n",
"\n",
"## Import nltk Libraries\n",
"import nltk\n",
"from nltk.corpus import stopwords\n",
"from nltk.util import ngrams\n",
"from nltk.tokenize import word_tokenize, sent_tokenize\n",
"\n",
"## Import Miscellaneous Utilities\n",
"import pprint\n",
"import codecs\n",
"import threading\n",
"import logging\n",
"from tqdm import tqdm\n",
"#from tqdm import tqdm_notebook as tqdm\n",
"from abc import ABCMeta, abstractmethod\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "v-0LaEPEAO9f"
},
"source": [
"### Runtime Type"
]
},
{
"cell_type": "code",
"metadata": {
"id": "QojZiqKbhCbB"
},
"source": [
"## Use TPU\r\n",
"#if IN_COLAB:\r\n",
"# assert os.environ['COLAB_TPU_ADDR'], 'Select TPU: Runtime > Change runtime type > Hardware accelerator'\r\n",
"# VERSION = \"20200220\"\r\n",
"# !curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py\r\n",
"# !python pytorch-xla-env-setup.py --version $VERSION\r\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "9p61Q8h8hn8Q"
},
"source": [
"## Use GPU Runtime:\n",
"#if IN_COLAB:\n",
"# if torch.cuda.is_available():\n",
"# torch.cuda.get_device_name(0)\n",
"# gpu_info = !nvidia-smi\n",
"# gpu_info = '\\n'.join(gpu_info)\n",
"# print(gpu_info)\n",
"# else:\n",
"# print('Select the Runtime > \"Change runtime type\" menu to enable a GPU accelerator, and then re-execute this cell.')\n",
"# os.kill(os.getpid(), 9) \n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "4YXab6-a7_Tx"
},
"source": [
"### Settings"
]
},
{
"cell_type": "code",
"metadata": {
"id": "xUEvP2ONZHZA",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "43e0e640-cc9e-490a-cef6-4537c728aae6"
},
"source": [
"# General\r\n",
"import warnings\r\n",
"warnings.filterwarnings('ignore')\r\n",
"%matplotlib inline\r\n",
"get_ipython().run_line_magic('matplotlib', 'inline')\r\n",
"\r\n",
"# Get Execution Time on Every Cell:\r\n",
"%load_ext autotime\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 1.95 ms (started: 2021-02-18 14:18:42 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iaqPH0Sr3KHm",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6e853298-c3a4-4796-d58c-d620ad63733a"
},
"source": [
"# Set Random Seed\n",
"random.seed(42)\n",
"np.random.seed(42)\n",
"rand_seed = 42\n",
"#torch.manual_seed(42)\n",
"#torch.cuda.manual_seed(42)\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 2.18 ms (started: 2021-02-18 14:18:42 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "4mPiavwCVid1",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "fbe2f3a1-272f-440a-fd81-8f63f75c4eb7"
},
"source": [
"# Set pandas display preferences\r\n",
"pd.options.display.max_rows = 50\r\n",
"pd.options.display.max_seq_items = 100\r\n",
"pd.set_option('display.max_colwidth', 300)\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 6.8 ms (started: 2021-02-18 14:18:42 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jL2U6JHiVk2E",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ea5e8f39-9d2a-44aa-f8c9-48d00e953330"
},
"source": [
"# Finalize nltk setup\r\n",
"nltk.download('stopwords')\r\n",
"nltk.download('punkt')\r\n",
"nltk.download('wordnet')\r\n",
"stop = set(stopwords.words('english'))\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Unzipping corpora/stopwords.zip.\n",
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
"[nltk_data] Unzipping tokenizers/punkt.zip.\n",
"[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
"[nltk_data] Unzipping corpora/wordnet.zip.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"time: 1.84 s (started: 2021-02-18 14:18:42 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "l5EgaGDuH82w",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "fa98e3b5-9dd8-4bbc-e402-967a4be0af8e"
},
"source": [
"# Test pprint\r\n",
"pprint.pprint(sys.path)\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"['',\n",
" '/env/python',\n",
" '/usr/lib/python36.zip',\n",
" '/usr/lib/python3.6',\n",
" '/usr/lib/python3.6/lib-dynload',\n",
" '/usr/local/lib/python3.6/dist-packages',\n",
" '/usr/lib/python3/dist-packages',\n",
" '/usr/local/lib/python3.6/dist-packages/IPython/extensions',\n",
" '/root/.ipython']\n",
"time: 3.01 ms (started: 2021-02-18 14:18:44 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cZbRv0az8WhI"
},
"source": [
"### Plotting"
]
},
{
"cell_type": "code",
"metadata": {
"id": "BA7NP3bVjM5x",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e4769f16-6108-4042-a113-ebe651d54c9d"
},
"source": [
"#@title Reset Plotting Configuration { run: \"auto\", vertical-output: true, form-width: \"50%\" }\n",
"#@markdown ##### *Move the slider to the right (i.e. value = 1) to reset the plotting style to the default values*\n",
"Reset = 0 #@param {type:\"slider\", min:0, max:1, step:1}\n",
"\n",
"if Reset:\n",
" print('Initiating factory reset...')\n",
" # MatPlotLib Reset\n",
" mpl.rcParams.update(mpl.rcParamsDefault)\n",
" # Seaborn Reset\n",
" sns.set_style()\n",
" sns.set_context()\n",
" sns.set_palette(palette = None)\n",
" print('Factory reset complete.')\n",
"else:\n",
" pass"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 4.8 ms (started: 2021-02-18 14:18:44 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "KejuTyRrpG7C",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "10f595bd-9a40-43d4-f3fc-3d73c1fbb4fa"
},
"source": [
"#@title **Seaborn/Matplotlib Configuration** { run: \"auto\", vertical-output: true, form-width: \"35%\", display-mode: \"form\" }\n",
"#@markdown ---\n",
"#@markdown #### **1. Color Styles**\n",
"\n",
"#@markdown **1.1 Seaborn**\n",
"# User Input\n",
"sns_style = None #@param [\"white\", \"whitegrid\", \"dark\", \"darkgrid\", \"ticks\"] {type:\"string\", allow-input: true}\n",
"sns_context = \"paper\" #@param [\"paper\", \"talk\", \"poster\"] {type:\"string\", allow-input: true}\n",
"sns_palette = None #@param [\"deep\", \"deep6\", \"muted\", \"muted6\", \"pastel\", \"pastel6\", \"bright\", \"bright6\", \"dark\", \"dark6\", \"colorblind\", \"colorblind6\"] {type:\"string\", allow-input: true}\n",
"\n",
"# Configure Seaborn Style\n",
"sns.set_style()\n",
"sns.set_style(sns_style)\n",
"\n",
"# Configure Context Style\n",
"sns.set_context()\n",
"sns.set_context(sns_context)\n",
"\n",
"# Configure Color Palette\n",
"sns.color_palette(sns_palette, as_cmap=True)\n",
"sns.set_palette(sns_palette)\n",
"\n",
"#@markdown **1.2 Matplotlib**\n",
"# User Input\n",
"plt_style = \"classic\" #@param [\"\\\"seaborn-ticks\\\"\", \"\\\"ggplot\\\"\", \"\\\"dark_background\\\"\", \"\\\"bmh\\\"\", \"\\\"seaborn-poster\\\"\", \"\\\"seaborn-notebook\\\"\", \"\\\"fast\\\"\", \"\\\"seaborn\\\"\", \"\\\"classic\\\"\", \"\\\"Solarize_Light2\\\"\", \"\\\"seaborn-dark\\\"\", \"\\\"seaborn-pastel\\\"\", \"\\\"seaborn-muted\\\"\", \"\\\"_classic_test\\\"\", \"\\\"seaborn-paper\\\"\", \"\\\"seaborn-colorblind\\\"\", \"\\\"seaborn-bright\\\"\", \"\\\"seaborn-talk\\\"\", \"\\\"seaborn-dark-palette\\\"\", \"\\\"tableau-colorblind10\\\"\", \"\\\"seaborn-darkgrid\\\"\", \"\\\"seaborn-whitegrid\\\"\", \"\\\"fivethirtyeight\\\"\", \"\\\"grayscale\\\"\", \"\\\"seaborn-white\\\"\", \"\\\"seaborn-deep\\\"\"] {type:\"raw\", allow-input: true}\n",
"\n",
"# Configure Matplotlib Style\n",
"plt.style.use(plt_style)\n",
"\n",
"#@markdown #### **2. Layout**\n",
"\n",
"#@markdown **2.1 Figure Dimensions**\n",
"# User Input\n",
"fig_width = 15 #@param {type:'number'}\n",
"fig_height = 5.5#@param {type:'number'}\n",
"\n",
"#@markdown **2.2 Font**\n",
"# User Input\n",
"font_name = \"MesloLGS NF\" #@param {type:\"string\"}\n",
"font_size = 12 #@param {type:\"number\"}\n",
"font_weight = \"normal\" #@param [\"100\", \"200\", \"300\", \"400\", \"500\", \"600\", \"700\", \"800\", \"900\", \"\\\"lighter\\\"\", \"\\\"normal\\\"\", \"\\\"bold\\\"\", \"\\\"bolder\\\"\"] {type:\"raw\", allow-input: true}\n",
"font_size_title = 16 #@param {type:\"raw\"}\n",
"font_size_subtitle = 12.5 #@param {type:\"raw\"}\n",
"font_size_legend = 11 #@param {type:\"raw\"}\n",
"font_size_labels = 12 #@param {type:\"raw\"}\n",
"font_size_xtick = 10 #@param {type:\"raw\"}\n",
"font_size_ytick = 10 #@param {type:\"raw\"}\n",
"font_size_notes = 9 #@param {type:\"raw\"}\n",
"\n",
"#@markdown **2.3 Title**\n",
"# User Input\n",
"title_loc = \"left\" #@param [\"left\", \"right\", \"best\"]\n",
"\n",
"#@markdown **2.5 Ticks**\n",
"# User Input\n",
"tick_major_size = 5.0#@param {type:\"number\"}\n",
"tick_minor_size = 0.0 #@param {type:\"number\"}\n",
"\n",
"#@markdown **2.6 Plot Lines**\n",
"# User Input\n",
"line_cstyle = \"round\" #@param [\"miter\", \"round\", \"bevel\"]\n",
"line_width = 1.0 #@param {type:\"number\"}\n",
"line_marker_size = 0.1 #@param {type:\"number\"}\n",
"\n",
"#@markdown **3. Other**\n",
"\n",
"#@markdown **3.1 Grid**\n",
"# User Input\n",
"toggle_grid = True #@param [\"True\", \"False\"] {type:\"raw\"}\n",
"\n",
"#@markdown ##### **3.2 LaTeX**\n",
"# User Input\n",
"toggle_latex = False #@param [\"True\", \"False\"] {type:\"raw\"}\n",
"\n",
"#@markdown ---\n",
"#@markdown *Feel free to take a look under the hood and improve the plotting procedure. The next step is to create a universal plotting/save function.*\n",
"####################################################################################################\n",
"\n",
"# Configure RCParams\n",
"sns.set(\n",
" font = font_name,\n",
" rc =\n",
" {\n",
" #LaTeX__________________________________________\n",
" 'text.usetex' : toggle_latex,\n",
" 'figure.figsize' : (fig_width, fig_height),\n",
" 'figure.titlesize' : font_size_subtitle,\n",
" #Fonts__________________________________________\n",
" 'font.family' : 'sans-serif',\n",
" 'font.sans-serif' : ['Helvetica'],\n",
" 'font.size' : font_size,\n",
" 'font.weight' : font_weight,\n",
" #Axes___________________________________________\n",
" 'axes.titlesize' : font_size_subtitle,\n",
" 'axes.labelsize' : font_size_labels,\n",
" 'axes.axisbelow' : True,\n",
" 'axes.grid' : toggle_grid,\n",
" 'axes.spines.right' : True,\n",
" 'axes.spines.top' : False, \n",
" #Lineplots______________________________________\n",
" 'lines.solid_capstyle': line_cstyle,\n",
" 'lines.linewidth' : line_width,\n",
" 'lines.markersize' : line_marker_size,\n",
" #Ticks__________________________________________\n",
" 'xtick.labelsize' : font_size_xtick,\n",
" 'xtick.major.size' : tick_major_size,\n",
" 'xtick.minor.size' : tick_minor_size,\n",
" 'xtick.direction' : 'out',\n",
" 'xtick.top' : False,\n",
" 'xtick.bottom' : True,\n",
" 'ytick.labelsize' : font_size_ytick,\n",
" 'ytick.major.size' : tick_major_size,\n",
" 'ytick.minor.size' : tick_minor_size,\n",
" 'ytick.direction' : 'out',\n",
" 'ytick.left' : True,\n",
" 'ytick.right' : False\n",
" }\n",
" )\n",
"####################################################################################################\n",
"\n",
"# Snippets\n",
"#stx_font = {'fontname':'STIXGeneral'}\n",
"#helv_font = {'fontname':'Helvetica'}\n",
"####################################################################################################\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 68 ms (started: 2021-02-18 14:18:44 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "A8HwOgfF8DiU"
},
"source": [
"#### sns/plt Documentation"
]
},
{
"cell_type": "code",
"metadata": {
"id": "XoB6Yuwf7XT6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 72
},
"outputId": "f0338e7f-1ae1-4ba0-fbf6-ec0bd4ec6ea8"
},
"source": [
"'''\r\n",
"############################################################################\r\n",
"# MATPLOTLIBRC FORMAT #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"# For the color values below, you can either use\r\n",
"# - a matplotlib color string, such as r, k, or b\r\n",
"# - an rgb tuple, such as (1.0, 0.5, 0.0)\r\n",
"# - a hex string, such as ff00ff\r\n",
"# - a scalar grayscale intensity such as 0.75\r\n",
"# - a legal html color name, e.g., red, blue, darkslategray\r\n",
"#\r\n",
"#Sections:\r\n",
"# - BACKENDS\r\n",
"# - LINES\r\n",
"# - PATCHES\r\n",
"# - HATCHES\r\n",
"# - BOXPLOT\r\n",
"# - FONT\r\n",
"# - TEXT\r\n",
"# - LaTeX\r\n",
"# - AXES\r\n",
"# - DATES\r\n",
"# - TICKS\r\n",
"# - GRIDS\r\n",
"# - LEGEND\r\n",
"# - FIGURE\r\n",
"# - IMAGES\r\n",
"# - CONTOUR PLOTS\r\n",
"# - ERRORBAR PLOTS\r\n",
"# - HISTOGRAM PLOTS\r\n",
"# - SCATTER PLOTS\r\n",
"# - AGG RENDERING\r\n",
"# - PATHS\r\n",
"# - SAVING FIGURES\r\n",
"# - INTERACTIVE KEYMAPS\r\n",
"# - ANIMATION\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# LINES #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"# See https://matplotlib.org/api/artist_api.html#module-matplotlib.lines\r\n",
"#\r\n",
"#lines.linewidth: 1.5 # line width in points\r\n",
"#lines.linestyle: - # solid line\r\n",
"#lines.color: C0 # has no affect on plot(); see axes.prop_cycle\r\n",
"#lines.marker: None # the default marker\r\n",
"#lines.markerfacecolor: auto # the default marker face color\r\n",
"#lines.markeredgecolor: auto # the default marker edge color\r\n",
"#lines.markeredgewidth: 1.0 # the line width around the marker symbol\r\n",
"#lines.markersize: 6 # marker size, in points\r\n",
"#lines.dash_joinstyle: round # {miter, round, bevel}\r\n",
"#lines.dash_capstyle: butt # {butt, round, projecting}\r\n",
"#lines.solid_joinstyle: round # {miter, round, bevel}\r\n",
"#lines.solid_capstyle: projecting # {butt, round, projecting}\r\n",
"#lines.antialiased: True # render lines in antialiased (no jaggies)\r\n",
"#\r\n",
"#lines.dashed_pattern: 3.7, 1.6 # 1/3 standard dash patterns, scaled by the linewidth.\r\n",
"#lines.dashdot_pattern: 6.4, 1.6, 1, 1.6 # 2/3 standard dash patterns, scaled by the linewidth.\r\n",
"#lines.dotted_pattern: 1, 1.65 # 3/3 standard dash patterns, scaled by the linewidth.\r\n",
"#lines.scale_dashes: True # enable scaling by the linewidth\r\n",
"#markers.fillstyle: full # {full, left, right, bottom, top, none}\r\n",
"#pcolor.shading : flat # No clue\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# PATCHES #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"##Patches are graphical objects that fill 2D space, like polygons or circles.\r\n",
"##See https://matplotlib.org/api/artist_api.html#module-matplotlib.patches\r\n",
"#\r\n",
"#patch.linewidth: 1 # edge width in points.\r\n",
"#patch.facecolor: C0\r\n",
"#patch.edgecolor: black # if forced, or patch is not filled\r\n",
"#patch.force_edgecolor: False # True to always use edgecolor\r\n",
"#patch.antialiased: True # render patches in antialiased (no jaggies)\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# HATCHES #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"#hatch.color: black\r\n",
"#hatch.linewidth: 1.0\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# BOXPLOT #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"#boxplot.notch: False\r\n",
"#boxplot.vertical: True\r\n",
"#boxplot.whiskers: 1.5\r\n",
"#boxplot.bootstrap: None\r\n",
"#boxplot.patchartist: False\r\n",
"#boxplot.showmeans: False\r\n",
"#boxplot.showcaps: True\r\n",
"#boxplot.showbox: True\r\n",
"#boxplot.showfliers: True\r\n",
"#boxplot.meanline: False\r\n",
"#\r\n",
"#boxplot.flierprops.color: black\r\n",
"#boxplot.flierprops.marker: o\r\n",
"#boxplot.flierprops.markerfacecolor: none\r\n",
"#boxplot.flierprops.markeredgecolor: black\r\n",
"#boxplot.flierprops.markeredgewidth: 1.0\r\n",
"#boxplot.flierprops.markersize: 6\r\n",
"#boxplot.flierprops.linestyle: none\r\n",
"#boxplot.flierprops.linewidth: 1.0\r\n",
"#\r\n",
"#boxplot.boxprops.color: black\r\n",
"#boxplot.boxprops.linewidth: 1.0\r\n",
"#boxplot.boxprops.linestyle: -\r\n",
"#\r\n",
"#boxplot.whiskerprops.color: black\r\n",
"#boxplot.whiskerprops.linewidth: 1.0\r\n",
"#boxplot.whiskerprops.linestyle: -\r\n",
"#\r\n",
"#boxplot.capprops.color: black\r\n",
"#boxplot.capprops.linewidth: 1.0\r\n",
"#boxplot.capprops.linestyle: -\r\n",
"#\r\n",
"#boxplot.medianprops.color: C1\r\n",
"#boxplot.medianprops.linewidth: 1.0\r\n",
"#boxplot.medianprops.linestyle: -\r\n",
"#\r\n",
"#boxplot.meanprops.color: C2\r\n",
"#boxplot.meanprops.marker: ^\r\n",
"#boxplot.meanprops.markerfacecolor: C2\r\n",
"#boxplot.meanprops.markeredgecolor: C2\r\n",
"#boxplot.meanprops.markersize: 6\r\n",
"#boxplot.meanprops.linestyle: --\r\n",
"#boxplot.meanprops.linewidth: 1.0\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# FONT #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"# The font properties used by `text.Text`.\r\n",
"# See https://matplotlib.org/api/font_manager_api.html\r\n",
"#\r\n",
"#The 6 font properties used for font matching with their default values are:\r\n",
"# - font.family: sans-serif\r\n",
"# - font.style: normal\r\n",
"# - font.variant: normal\r\n",
"# - font.weight: normal\r\n",
"# - font.stretch: normal\r\n",
"# - font.size: 10.0\r\n",
"#\r\n",
"#* The font.family property has five values (each of these font families has a default list of font names in decreasing order of priority associated with them. When text.usetex is False, font.family may also be one or more concrete font names):\r\n",
"# - 'serif' (DejaVu Serif, Bitstream Vera Serif, Computer Modern Roman, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif)\r\n",
"# - 'sans-serif' (DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif)\r\n",
"# - 'cursive' (Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, cursive)\r\n",
"# - 'fantasy' (Comic Neue, Comic Sans MS, Chicago, Charcoal, ImpactWestern, Humor Sans, xkcd, fantasy)\r\n",
"# - 'monospace' (DejaVu Sans Mono, Bitstream Vera Sans Mono, Computer Modern Typewriter, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace)\r\n",
"#\r\n",
"#* The font.style property has three values (the oblique style will be used for italic, if it is not present):\r\n",
"# - normal (or roman)\r\n",
"# - italic\r\n",
"# - oblique\r\n",
"#\r\n",
"#* The font.variant property has two values (for TrueType fonts, which are scalable fonts, small-caps is equivalent to using a font size of 'smaller', or about 83%% of the current font size):\r\n",
"# - normal\r\n",
"# - small-caps.\r\n",
"#\r\n",
"#* The font.weight property has effectively 13 values (Normal is the same as 400, and bold is 700. bolder and lighter are relative values with respect to the current weight):\r\n",
"# - normal\r\n",
"# - bold\r\n",
"# - bolder\r\n",
"# - lighter\r\n",
"# - 100\r\n",
"# - 200\r\n",
"# - 300\r\n",
"# - 400\r\n",
"# - 500\r\n",
"# - 600\r\n",
"# - 700\r\n",
"# - 800\r\n",
"# - 900#\r\n",
"#\r\n",
"#* The font.stretch property has 11 values (This property is NOT currently implemented)\r\n",
"# - ultra-condensed\r\n",
"# - extra-condensed\r\n",
"# - condensed\r\n",
"# - semi-condensed\r\n",
"# - normal\r\n",
"# - semi-expanded\r\n",
"# - expanded\r\n",
"# - extra-expanded\r\n",
"# - ultra-expanded\r\n",
"# - wider\r\n",
"# - narrower#\r\n",
"#\r\n",
"#* The font.size property is the default font size for text, given in pts. 10 pt is the standard value.\r\n",
"#---#\r\n",
"#\r\n",
"# Note:\r\n",
"# - font.size controls default text sizes i.e. NOT THESE,\r\n",
"# - tick labels\r\n",
"# - axes\r\n",
"# - labels\r\n",
"# - title\r\n",
"# - other special text sizes#\r\n",
"#\r\n",
"# - Special text sizes can be defined relative to font.size, using the following values:\r\n",
"# - xx-small\r\n",
"# - x-small\r\n",
"# - small\r\n",
"# - medium\r\n",
"# - large\r\n",
"# - x-large\r\n",
"# - xx-large\r\n",
"# - larger\r\n",
"# - smaller\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# TEXT #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"# The text properties used by `text.Text`.\r\n",
"# See https://matplotlib.org/api/artist_api.html#module-matplotlib.text\r\n",
"#text.color: black\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# LaTeX #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"# See https://matplotlib.org/tutorials/text/usetex.html\r\n",
"#text.usetex: False\r\n",
"## use latex for all text handling. The following fonts are supported through the usual rc parameter settings: new century schoolbook, bookman, times, palatino, zapf chancery, charter, serif, sans-serif, helvetica, avant garde, courier, monospace, computer modern roman, computer modern sans serif, computer modern typewriter If another font is desired which can loaded using the LaTeX \\usepackage command, please inquire at the matplotlib mailing list\r\n",
"#text.latex.preamble:\r\n",
"## IMPROPER USE OF THIS FEATURE WILL LEAD TO LATEX FAILURES AND IS THEREFORE UNSUPPORTED. PLEASE DO NOT ASK FOR HELP IF THIS FEATURE DOES NOT DO WHAT YOU EXPECT IT TO. text.latex.preamble is a single line of LaTeX code that will be passed on to the LaTeX system. It may contain any code that is valid for the LaTeX \"preamble\", i.e. between the \"\\documentclass\" and \"\\begin{document}\" statements. Note that it has to be put on a single line, which may become quite long. The following packages are always loaded with usetex, so beware of package collisions: color, geometry, graphicx, type1cm, textcomp. Adobe Postscript (PSSNFS) font packages may also be loaded, depending on your font settings.\r\n",
"#\r\n",
"#'''\r\n",
"#FreeType hinting flag (\"foo\" corresponds to FT_LOAD_FOO); may be one of the\r\n",
"#following (Proprietary Matplotlib-specific synonyms are given in parentheses,\r\n",
"#but their use is discouraged):\r\n",
"# - default # Use the font's native hinter if possible, else FreeType's auto-hinter. (\"either\" is a synonym).\r\n",
"# - no_autohint # Use the font's native hinter if possible, else don't hint. (\"native\" is a synonym.)\r\n",
"# - force_autohint # Use FreeType's auto-hinter. (\"auto\" is a synonym.)\r\n",
"# - no_hinting # Disable hinting. (\"none\" is a synonym.)\r\n",
"#'''\r\n",
"#text.hinting: force_autohint\r\n",
"#text.hinting_factor: 8 # Specifies the amount of softness for hinting in the horizontal direction. A value of 1 will hint to full pixels. A value of 2 will hint to half pixels etc.\r\n",
"#text.kerning_factor : 0 # Specifies the scaling factor for kerning values. This is provided solely to allow old test images to remain unchanged. Set to 6 to obtain previous behavior. Values other than 0 or 6 have no defined meaning.\r\n",
"#text.antialiased: True #If True (default), the text will be antialiased. This only affects the Agg backend.\r\n",
"#\r\n",
"#'''\r\n",
"#The following settings allow you to select the fonts in math mode.\r\n",
"#'''\r\n",
"#mathtext.fontset: dejavusans #Should be 'dejavusans' (default), 'dejavuserif', 'cm' (Computer Modern), 'stix', 'stixsans', 'custom' (unsupported, may go away in the future)\r\n",
"#\r\n",
"#'''\r\n",
"#\"mathtext.fontset: custom\" is defined by the mathtext.bf, .cal, .it settings which map a TeX font name to a fontconfig font pattern. (These settings are not used for other font sets.)\r\n",
"#'''\r\n",
"#mathtext.bf: sans:bold\r\n",
"#mathtext.cal: cursive\r\n",
"#mathtext.it: sans:italic\r\n",
"#mathtext.rm: sans\r\n",
"#mathtext.sf: sans\r\n",
"#mathtext.tt: monospace\r\n",
"#mathtext.fallback: cm # Select fallback font from ['cm' (Computer Modern), 'stix' 'stixsans'] when a symbol can not be found in one of the custom math fonts. Select 'None' to not perform fallback and replace the missing character by a dummy symbol.\r\n",
"#mathtext.default: it # The default font to use for math. Can be any of the LaTeX font names, including the special name \"regular\" for the same font used in regular text.\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# AXES #\r\n",
"###########################################################################\r\n",
"'''\r\n",
"#\r\n",
"# Following are default face and edge colors, default tick sizes, default fontsizes for ticklabels, and so on.\r\n",
"# See https://matplotlib.org/api/axes_api.html#module-matplotlib.axes\r\n",
"#axes.facecolor: white # axes background color\r\n",
"#axes.edgecolor: black # axes edge color\r\n",
"#axes.linewidth: 0.8 # edge linewidth\r\n",
"#axes.grid: False # display grid or not\r\n",
"#axes.grid.axis: both # which axis the grid should apply to\r\n",
"#axes.grid.which: major # gridlines at {major, minor, both} ticks\r\n",
"#axes.titlelocation: center # alignment of the title: {left, right, center}\r\n",
"#axes.titlesize: large # fontsize of the axes title\r\n",
"#axes.titleweight: normal # font weight of title\r\n",
"#axes.titlecolor: auto # color of the axes title, auto falls back to text.color as default value\r\n",
"#axes.titley: None # position title (axes relative units). None implies auto\r\n",
"#axes.titlepad: 6.0 # pad between axes and title in points\r\n",
"#axes.labelsize: medium # fontsize of the x any y labels\r\n",
"#axes.labelpad: 4.0 # space between label and axis\r\n",
"#axes.labelweight: normal # weight of the x and y labels\r\n",
"#axes.labelcolor: black # label color\r\n",
"#axes.axisbelow: line # draw axis gridlines and ticks. below patches (True). above patches but below lines ('line'). above all (False)\r\n",
"#axes.formatter.limits: -5, 6 # use scientific notation if log10 of the axis range is smaller than the first or larger than the second\r\n",
"#axes.formatter.use_locale: False # When True, format tick labels according to the user's locale. For example, use ',' as a decimal separator in the fr_FR locale.\r\n",
"#axes.formatter.use_mathtext: False # When True, use mathtext for scientific notation.\r\n",
"#axes.formatter.min_exponent: 0 # minimum exponent to format in scientific notation\r\n",
"#axes.formatter.useoffset: True # If True, the tick label formatter will default to labeling ticks relative to an offset when the data range is small compared to the minimum absolute value of the data.\r\n",
"#axes.formatter.offset_threshold: 4 # When useoffset is True, the offset will be used when it can remove at least this number of significant digits from tick labels.\r\n",
"#axes.spines.left: True # display axis spines to the left\r\n",
"#axes.spines.bottom: True # display axis spines to the bottom\r\n",
"#axes.spines.top: True # display axis spines to the top\r\n",
"#axes.spines.right: True # display axis spines to the right\r\n",
"#axes.unicode_minus: True # use Unicode for the minus symbol rather than hyphen. See https://en.wikipedia.org/wiki/Plus_and_minus_signs#Character_codes\r\n",
"#axes.prop_cycle: cycler('color', ['1f77b4', 'ff7f0e', '2ca02c', 'd62728', '9467bd', '8c564b', 'e377c2', '7f7f7f', 'bcbd22', '17becf']) # color cycle for plot lines as list of string colorspecs: single letter, long name, or web-style hex As opposed to all other paramters in this file, the color values must be enclosed in quotes for this parameter, e.g. '1f77b4', instead of 1f77b4. See also https://matplotlib.org/tutorials/intermediate/color_cycle.html for more details on prop_cycle usage.\r\n",
"#axes.autolimit_mode: data # How to scale axes limits to the data. \"data\" to use data limits, plus some margin or \"round_numbers\" move to the nearest \"round\" number\r\n",
"#axes.xmargin: .05 # x margin. See `axes.Axes.margins`\r\n",
"#axes.ymargin: .05 # y margin. See `axes.Axes.margins`\r\n",
"#polaraxes.grid: True # display grid on polar axes\r\n",
"#axes3d.grid: True # display grid on 3d axes\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# AXIS #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"#xaxis.labellocation: center # alignment of the xaxis label: {left, right, center}\r\n",
"#yaxis.labellocation: center # alignment of the yaxis label: {bottom, top, center}\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# DATES #\r\n",
"############################################################################\r\n",
"'''\r\n",
"\r\n",
"## The reference date for Matplotlib's internal date representation\r\n",
"##See https://matplotlib.org/examples/ticks_and_spines/date_precision_and_epochs.py\r\n",
"#\r\n",
"#These control the default format strings used in AutoDateFormatter. Any valid format datetime format string can be used (see the python `datetime` for details). For example, by using:\r\n",
"# - '%%x' will use the locale date representation\r\n",
"# - '%%X' will use the locale time representation\r\n",
"# - '%%c' will use the full locale datetime representation\r\n",
"#\r\n",
"#These values map to the scales:\r\n",
"# {'year': 365, 'month': 30, 'day': 1, 'hour': 1/24, 'minute': 1 / (24 # 60)}\r\n",
"#date.autoformatter.year: %Y\r\n",
"#date.autoformatter.month: %Y-%m\r\n",
"#date.autoformatter.day: %Y-%m-%d\r\n",
"#date.autoformatter.hour: %m-%d %H\r\n",
"#date.autoformatter.minute: %d %H:%M\r\n",
"#date.autoformatter.second: %H:%M:%S\r\n",
"#date.autoformatter.microsecond: %M:%S.%f\r\n",
"#date.epoch: 1970-01-01T00:00:00\r\n",
"'''\r\n",
"############################################################################\r\n",
"# TICKS #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"# See https://matplotlib.org/api/axis_api.html#matplotlib.axis.Tick\r\n",
"#\r\n",
"#xtick.top: False # draw ticks on the top side\r\n",
"#xtick.bottom: True # draw ticks on the bottom side\r\n",
"#xtick.labeltop: False # draw label on the top\r\n",
"#xtick.labelbottom: True # draw label on the bottom\r\n",
"#xtick.major.size: 3.5 # major tick size in points\r\n",
"#xtick.minor.size: 2 # minor tick size in points\r\n",
"#xtick.major.width: 0.8 # major tick width in points\r\n",
"#xtick.minor.width: 0.6 # minor tick width in points\r\n",
"#xtick.major.pad: 3.5 # distance to major tick label in points\r\n",
"#xtick.minor.pad: 3.4 # distance to the minor tick label in points\r\n",
"#xtick.color: black # color of the tick labels\r\n",
"#xtick.labelsize: medium # fontsize of the tick labels\r\n",
"#xtick.direction: out # direction: {in, out, inout}\r\n",
"#xtick.minor.visible: False # visibility of minor ticks on x-axis\r\n",
"#xtick.major.top: True # draw x axis top major ticks\r\n",
"#xtick.major.bottom: True # draw x axis bottom major ticks\r\n",
"#xtick.minor.top: True # draw x axis top minor ticks\r\n",
"#xtick.minor.bottom: True # draw x axis bottom minor ticks\r\n",
"#xtick.alignment: center # alignment of xticks\r\n",
"#\r\n",
"#ytick.left: True # draw ticks on the left side\r\n",
"#ytick.right: False # draw ticks on the right side\r\n",
"#ytick.labelleft: True # draw tick labels on the left side\r\n",
"#ytick.labelright: False # draw tick labels on the right side\r\n",
"#ytick.major.size: 3.5 # major tick size in points\r\n",
"#ytick.minor.size: 2 # minor tick size in points\r\n",
"#ytick.major.width: 0.8 # major tick width in points\r\n",
"#ytick.minor.width: 0.6 # minor tick width in points\r\n",
"#ytick.major.pad: 3.5 # distance to major tick label in points\r\n",
"#ytick.minor.pad: 3.4 # distance to the minor tick label in points\r\n",
"#ytick.color: black # color of the tick labels\r\n",
"#ytick.labelsize: medium # fontsize of the tick labels\r\n",
"#ytick.direction: out # direction: {in, out, inout}\r\n",
"#ytick.minor.visible: False # visibility of minor ticks on y-axis\r\n",
"#ytick.major.left: True # draw y axis left major ticks\r\n",
"#ytick.major.right: True # draw y axis right major ticks\r\n",
"#ytick.minor.left: True # draw y axis left minor ticks\r\n",
"#ytick.minor.right: True # draw y axis right minor ticks\r\n",
"#ytick.alignment: center_baseline # alignment of yticks\r\n",
"'''\r\n",
"############################################################################\r\n",
"# GRIDS #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#grid.color: b0b0b0 # grid color\r\n",
"#grid.linestyle: - # solid\r\n",
"#grid.linewidth: 0.8 # in points\r\n",
"#grid.alpha: 1.0 # transparency, between 0.0 and 1.0\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# LEGEND #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"#legend.loc: best\r\n",
"#legend.frameon: True # if True, draw the legend on a background patch\r\n",
"#legend.framealpha: 0.8 # legend patch transparency\r\n",
"#legend.facecolor: inherit # inherit from axes.facecolor; or color spec\r\n",
"#legend.edgecolor: 0.8 # background patch boundary color\r\n",
"#legend.fancybox: True # if True, use a rounded box for the legend background, else a rectangle\r\n",
"#legend.shadow: False # if True, give background a shadow effect\r\n",
"#legend.numpoints: 1 # the number of marker points in the legend line\r\n",
"#legend.scatterpoints: 1 # number of scatter points\r\n",
"#legend.markerscale: 1.0 # the relative size of legend markers vs. original\r\n",
"#legend.fontsize: medium\r\n",
"#legend.title_fontsize: None # None sets to the same as the default axes.\r\n",
"#\r\n",
"## Dimensions as fraction of fontsize:\r\n",
"#legend.borderpad: 0.4 # border whitespace\r\n",
"#legend.labelspacing: 0.5 # the vertical space between the legend entries\r\n",
"#legend.handlelength: 2.0 # the length of the legend lines\r\n",
"#legend.handleheight: 0.7 # the height of the legend handle\r\n",
"#legend.handletextpad: 0.8 # the space between the legend line and legend text\r\n",
"#legend.borderaxespad: 0.5 # the border between the axes and legend edge\r\n",
"#legend.columnspacing: 2.0 # column separation\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# FIGURE #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"# See https://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure\r\n",
"#\r\n",
"#figure.titlesize: large # size of the figure title (``Figure.suptitle()``)\r\n",
"#figure.titleweight: normal # weight of the figure title\r\n",
"#figure.figsize: 6.4, 4.8 # figure size in inches\r\n",
"#figure.dpi: 100 # figure dots per inch\r\n",
"#figure.facecolor: white # figure facecolor\r\n",
"#figure.edgecolor: white # figure edgecolor\r\n",
"#figure.frameon: True # enable figure frame\r\n",
"#figure.max_open_warning: 20 # The maximum number of figures to open through the pyplot interface before emitting a warning. If less than one this feature is disabled.\r\n",
"#figure.raise_window : True # Raise the GUI window to front when show() is called.\r\n",
"#\r\n",
"## The figure subplot parameters. All dimensions are a fraction of the figure width and height:\r\n",
"#figure.subplot.left: 0.125 # the left side of the subplots of the figure\r\n",
"#figure.subplot.right: 0.9 # the right side of the subplots of the figure\r\n",
"#figure.subplot.bottom: 0.11 # the bottom of the subplots of the figure\r\n",
"#figure.subplot.top: 0.88 # the top of the subplots of the figure\r\n",
"#figure.subplot.wspace: 0.2 # the amount of width reserved for space between subplots expressed as a fraction of the average axis width\r\n",
"#figure.subplot.hspace: 0.2 # the amount of height reserved for space between subplots, expressed as a fraction of the average axis height\r\n",
"#\r\n",
"## Figure layout\r\n",
"#figure.autolayout: False # When True, automatically adjust subplot parameters to make the plot fit the figure using `tight_layout`\r\n",
"#figure.constrained_layout.use: False # When True, automatically make plot elements fit on the figure. (Not compatible with `autolayout`, above).\r\n",
"#figure.constrained_layout.h_pad: 0.04167 # Padding around axes objects. Float representing\r\n",
"#figure.constrained_layout.w_pad: 0.04167 # inches. Default is 3./72. inches (3 pts)\r\n",
"#figure.constrained_layout.hspace: 0.02 # Space between subplot groups. Float representing\r\n",
"#figure.constrained_layout.wspace: 0.02 # a fraction of the subplot widths being separated.\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# IMAGES #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"#image.aspect: equal # {equal, auto} or a number\r\n",
"#image.interpolation: antialiased # see help(imshow) for options\r\n",
"#image.cmap: viridis # A colormap name, gray etc...\r\n",
"#image.lut: 256 # the size of the colormap lookup table\r\n",
"#image.origin: upper # {lower, upper}\r\n",
"#image.resample: True # Resample image\r\n",
"#image.composite_image: True # When True, all the images on a set of axes are combined into a single composite image before saving a figure as a vector graphics file, such as a PDF.\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# CONTOUR PLOTS #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"#contour.negative_linestyle: dashed # string or on-off ink sequence\r\n",
"#contour.corner_mask: True # {True, False, legacy}\r\n",
"#contour.linewidth: None # {float, None} Size of the contour linewidths. If set to None, it falls back to `line.linewidth`.\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# ERRORBAR PLOTS #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"#errorbar.capsize: 0 # length of end cap on error bars in pixels\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# HISTOGRAM PLOTS #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"#hist.bins: 10 # The default number of histogram bins or 'auto'.\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# SCATTER PLOTS #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#scatter.marker: o # The default marker type for scatter plots.\r\n",
"#scatter.edgecolors: face # The default edge colors for scatter plots.\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# AGG RENDERING #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#agg.path.chunksize: 0 # 0 to disable; values in the range 10000 to 100000 can improve speed slightly and prevent an Agg rendering failure when plotting very large data sets, especially if they are very gappy. It may cause minor artifacts, though. A value of 20000 is probably a good starting point. (Warning: EXPERIMENTAL, 2008/10/10)\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# PATHS #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"#path.simplify: True # When True, simplify paths by removing \"invisible\" points to reduce file size and increase rendering speed\r\n",
"#path.simplify_threshold: 0.111111111111 # The threshold of similarity below which vertices will be removed in the simplification process.\r\n",
"#path.snap: True # When True, rectilinear axis-aligned paths will be snapped to the nearest pixel when certain criteria are met. When False, paths will never be snapped.\r\n",
"#path.sketch: None # May be None, or a 3-tuple of the form (scale, length, randomness) where scale is the amplitude of the wiggle perpendicular to the line (in pixels), length is the length of the wiggle along the line (in pixels) and randomness is the factor by which the length is randomly scaled.\r\n",
"#path.effects:\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# SAVING FIGURES #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"# The default savefig params can be different from the display params.\r\n",
"#savefig.dpi: figure # figure dots per inch or 'figure'\r\n",
"#savefig.facecolor: auto # figure facecolor when saving\r\n",
"#savefig.edgecolor: auto # figure edgecolor when saving\r\n",
"#savefig.format: png # {png, ps, pdf, svg}\r\n",
"#savefig.bbox: standard # {tight, standard} 'tight' is incompatible with pipe-based animation backends (e.g. 'ffmpeg') but will work with those based on temporary files (e.g. 'ffmpeg_file')\r\n",
"#savefig.pad_inches: 0.1 # Padding to be used when bbox is set to 'tight'\r\n",
"#savefig.directory: ~ # default directory in savefig dialog box, leave empty to always use current working directory\r\n",
"#savefig.transparent: False # setting that controls whether figures are saved with a transparent background by default\r\n",
"#savefig.orientation: portrait # Orientation of saved figure\r\n",
"#\r\n",
"## tk backend params\r\n",
"#tk.window_focus: False # Maintain shell focus for TkAgg\r\n",
"#\r\n",
"## ps backend params\r\n",
"#ps.papersize: letter # {auto, letter, legal, ledger, A0-A10, B0-B10}\r\n",
"#ps.useafm: False # use of afm fonts, results in small files\r\n",
"#ps.usedistiller: False # {ghostscript, xpdf, None} Experimental: may produce smaller files. xpdf intended for production of publication quality files, but requires ghostscript, xpdf and ps2eps\r\n",
"#ps.distiller.res: 6000 # dpi\r\n",
"#ps.fonttype: 3 # Output Type 3 (Type3) or Type 42 (TrueType)\r\n",
"#\r\n",
"## PDF backend params\r\n",
"#pdf.compression: 6 # integer from 0 to 9. 0 disables compression (good for debugging)\r\n",
"#pdf.fonttype: 3 # Output Type 3 (Type3) or Type 42 (TrueType)\r\n",
"#pdf.use14corefonts : False\r\n",
"#pdf.inheritcolor: False\r\n",
"#\r\n",
"## SVG backend params\r\n",
"#svg.image_inline: True # Write raster image data directly into the SVG file\r\n",
"#svg.fonttype: path # How to handle SVG fonts:\r\n",
"# # - path: Embed characters as paths -- supported by most SVG renderers\r\n",
"# # - None: Assume fonts are installed on the machine where the SVG will be viewed.\r\n",
"#svg.hashsalt: None # If not None, use this string as hash salt instead of uuid4\r\n",
"#\r\n",
"## pgf parameter\r\n",
"### See https://matplotlib.org/tutorials/text/pgf.html for more information.\r\n",
"#pgf.rcfonts: True\r\n",
"#pgf.preamble: # See text.latex.preamble for documentation\r\n",
"#pgf.texsystem: xelatex\r\n",
"#\r\n",
"## docstring params\r\n",
"#docstring.hardcopy: False # set this when you want to generate hardcopy docstring\r\n",
"#\r\n",
"'''\r\n",
"############################################################################\r\n",
"# INTERACTIVE KEYMAPS #\r\n",
"############################################################################\r\n",
"'''\r\n",
"# Event keys to interact with figures/plots via keyboard.\r\n",
"# See https://matplotlib.org/users/navigation_toolbar.html for more details on interactive navigation. Customize these settings according to your needs.\r\n",
"# Leave the field(s) empty if you don't need a key-map. (i.e., fullscreen : '')\r\n",
"#\r\n",
"#keymap.fullscreen: f, ctrl+f # toggling\r\n",
"#keymap.home: h, r, home # home or reset mnemonic\r\n",
"#keymap.back: left, c, backspace, MouseButton.BACK # forward / backward keys\r\n",
"#keymap.forward: right, v, MouseButton.FORWARD # for quick navigation\r\n",
"#keymap.pan: p # pan mnemonic\r\n",
"#keymap.zoom: o # zoom mnemonic\r\n",
"#keymap.save: s, ctrl+s # saving current figure\r\n",
"#keymap.help: f1 # display help about active tools\r\n",
"#keymap.quit: ctrl+w, cmd+w, q # close the current figure\r\n",
"#keymap.quit_all: # close all figures\r\n",
"#keymap.grid: g # switching on/off major grids in current axes\r\n",
"#keymap.grid_minor: G # switching on/off minor grids in current axes\r\n",
"#keymap.yscale: l # toggle scaling of y-axes ('log'/'linear')\r\n",
"#keymap.xscale: k, L # toggle scaling of x-axes ('log'/'linear')\r\n",
"#keymap.copy: ctrl+c, cmd+c # Copy figure to clipboard\r\n",
"'''\r\n",
"############################################################################\r\n",
"# ANIMATION #\r\n",
"############################################################################\r\n",
"'''\r\n",
"#\r\n",
"#animation.html: none # How to display the animation as HTML in the IPython notebook. 'html5' uses HTML5 video tag and 'jshtml' creates a Javascript animation\r\n",
"#animation.writer: ffmpeg # MovieWriter 'backend' to use\r\n",
"#animation.codec: h264 # Codec to use for writing movie\r\n",
"#animation.bitrate: -1 # Controls size/quality tradeoff for movie. -1 implies let utility auto-determine.\r\n",
"#animation.frame_format: png # Controls frame format used by temp files\r\n",
"#animation.ffmpeg_path: ffmpeg # Path to ffmpeg binary. Without full path, $PATH is searched\r\n",
"#animation.ffmpeg_args: # Additional arguments to pass to ffmpeg\r\n",
"#animation.convert_path: convert # Path to ImageMagick's convert binary. On Windows use the full path since convert is also the name of a system tool\r\n",
"#animation.convert_args: # Additional arguments to pass to convert\r\n",
"#animation.embed_limit: 20.0 # Limit, in MB, of size of base64 encoded animation in HTML (i.e. IPython notebook)\r\n",
"#mpl_toolkits.legacy_colorbar: True\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'\\n############################################################################\\n# ANIMATION #\\n############################################################################\\n'"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
},
{
"output_type": "stream",
"text": [
"time: 231 ms (started: 2021-02-18 14:18:44 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "UX2a9SKkRMdg",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 128
},
"outputId": "fe1d1807-33b7-481c-a4d7-d23ce23cfd94"
},
"source": [
"# Install Gadfly style for MatPlotLib\r\n",
"mpl.get_configdir()\r\n",
"!mkdir '/root/.config/matplotlib/stylelib/'\r\n",
"!cp '/content/drive/My Drive/Colab Notebooks/proj2/src/data/settings/.config/matplotlib/gadfly.mplstyle' '/root/.config/matplotlib/stylelib/'\r\n",
"%ls -al '/root/.config/matplotlib/stylelib'\r\n",
"mpl.matplotlib_fname()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"total 44\n",
"drwxr-xr-x 2 root root 4096 Feb 18 14:18 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
"drwxr-xr-x 1 root root 4096 Feb 18 14:18 \u001b[01;34m..\u001b[0m/\n",
"-rw------- 1 root root 33574 Feb 18 14:18 gadfly.mplstyle\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/matplotlibrc'"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
},
{
"output_type": "stream",
"text": [
"time: 1.77 s (started: 2021-02-18 14:18:44 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9Uk91DdjkYY2"
},
"source": [
"## **Functions**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "r7KbvmF0kduf"
},
"source": [
"### Simple Save to .pickle"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uUmIItEikcsR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4ac82288-f008-46af-8c87-e28f11e78e92"
},
"source": [
"def pickle_dump(df, filename='no-filename.pickle'):\r\n",
" if filename:\r\n",
" with open(filename, \"wb\") as output_file:\r\n",
" pickle.dump(df, output_file)\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 3.31 ms (started: 2021-02-18 14:18:46 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Mb6zT4iLmCwQ"
},
"source": [
"### Save df to .pickle + .csv"
]
},
{
"cell_type": "code",
"metadata": {
"id": "VdlVAUU_l885",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c85db6c1-2447-4727-a705-0491180ff64b"
},
"source": [
"if IN_COLAB:\r\n",
" def save_data(df, file_name, dir_name=train_dir, index_csv=True):\r\n",
" if not os.path.exists(dir_name):\r\n",
" os.mkdir(dir_name)\r\n",
" # Save results to a .picke file\r\n",
" file = open(dir_name + file_name + '.pickle', 'wb')\r\n",
" pickle.dump(df, file)\r\n",
" file.close()\r\n",
" print('Successfully saved {}.pickle. in {}'.format(file_name, dir_name + file_name + '.pickle'))\r\n",
" # Save results to a .csv file\r\n",
" df.to_csv(dir_name + file_name + '.csv', index=index_csv)\r\n",
" print('Successfully saved {}.csv. in {}'.format(file_name, dir_name + file_name + '.csv'))\r\n",
"\r\n",
"else:\r\n",
" def save_data(df, file_name, dir_name=train_dir, index_csv=True):\r\n",
" # Save results to a .picke file\r\n",
" file = open(dir_name + file_name + '.pickle', 'wb')\r\n",
" pickle.dump(df, file)\r\n",
" file.close()\r\n",
" print('Successfully saved {}.pickle. in {}'.format(file_name, dir_name + file_name + '.pickle'))\r\n",
" # Save results to a .csv file\r\n",
" df.to_csv(dir_name + file_name + '.csv', index=index_csv)\r\n",
" print('Successfully saved {}.csv. in {}'.format(file_name, dir_name + file_name + '.csv'))\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 19.3 ms (started: 2021-02-18 14:18:46 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B17I_zNuAXJJ"
},
"source": [
"### Logger"
]
},
{
"cell_type": "code",
"metadata": {
"id": "9v6gt8lI3DeM",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a6497c08-1f93-44fc-df46-70e2c72c7c06"
},
"source": [
"# Set logger\n",
"logger = logging.getLogger('mylogger')\n",
"logger.setLevel(logging.DEBUG)\n",
"timestamp = time.strftime(\"%Y.%m.%d_%H.%M.%S\", time.localtime())\n",
"fh = logging.FileHandler('log_model.txt')\n",
"fh.setLevel(logging.DEBUG)\n",
"ch = logging.StreamHandler()\n",
"ch.setLevel(logging.DEBUG)\n",
"formatter = logging.Formatter('[%(asctime)s][%(levelname)s] ## %(message)s')\n",
"fh.setFormatter(formatter)\n",
"ch.setFormatter(formatter)\n",
"logger.addHandler(fh)\n",
"logger.addHandler(ch)\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 11.6 ms (started: 2021-02-18 14:18:46 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bORnHvk9GV6X"
},
"source": [
"### Graph Plotter"
]
},
{
"cell_type": "code",
"metadata": {
"id": "NP7jwB8XwDSw",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9c1ed9bf-4393-40f4-be86-5ac90961696f"
},
"source": [
"# Graph plotter:\r\n",
"'''\r\n",
"graph_tool(\r\n",
" 'type', # Plot type: sns_lineplot, sns_scatter, plt_lineplot\r\n",
" 'time_series', # Set to True when plotting time on the x-axis) to format the X-Tick labels (year only)\r\n",
" 'y_unit', # Unit of measurement on y-axis. Choose between 'money' and 'percentage'\r\n",
" 'trim_size', # Define the total number of x-ticks to decongest the graph\r\n",
" 'x_data', # X Data\r\n",
" 'y_data', # Y Data\r\n",
" 'plot_color', # Plot color\r\n",
" 'line_width', # Line width\r\n",
" 'fig_title', # Graph title\r\n",
" 'fig_subtitle', # Graph subtitle\r\n",
" 'x_label', # X-Axis label\r\n",
" 'y_label', # Y-Axis label \r\n",
" 'plot_label', # Plot label for the legend\r\n",
" 'fig_name', # Graph filename (e.g. fig_name.png, where .png is auto-appended)\r\n",
" 'save_loc'=graph_dir) # Save location is fixed at graph_dir, but can be modified.\r\n",
"'''\r\n",
"\r\n",
"def graph_tool(type, time_series, y_unit, trim_size, x_data, y_data, plot_color, line_width, line_stylize, fig_title, fig_subtitle, x_label, y_label, plot_label, fig_name, save_loc=graph_dir):\r\n",
"\r\n",
" # Initialize figure container\r\n",
" print('Plot Dimensions: ' + str(fig_width) + '\" x ' + str(fig_height) + '\\'')\r\n",
" fig, ax = plt.subplots()\r\n",
"\r\n",
" # Title\r\n",
" print('Title: ' + '\\'' + str(fig_title) + '\\'')\r\n",
" plt.suptitle(str(fig_title), horizontalalignment = 'left', x = fig.subplotpars.left, y = 0.99, fontsize = font_size_title, fontweight = \"bold\")\r\n",
"\r\n",
" # Subtitle\r\n",
" print('Subtitle: ' + '\\'' + str(fig_subtitle) + '\\'')\r\n",
" plt.title(str(fig_subtitle), loc = title_loc, y = 1.0, fontsize = font_size_subtitle)\r\n",
"\r\n",
" ## Sample datastreams\r\n",
" #print('\\nX-Datastream sample:\\n' + str(x_data[:10]))\r\n",
" #print('\\nY-Datastream sample:\\n' + str(y_data[:10]))\r\n",
"\r\n",
" ## Mask NaN/NA values in graph\r\n",
" #x_mask = np.isfinite(x_data)\r\n",
" #y_mask = np.isfinite(y_data)\r\n",
"\r\n",
" # Main plots\r\n",
" if type == 'plt_lineplot':\r\n",
" ax.plot(\r\n",
" x_data, \r\n",
" y_data, \r\n",
" color = plot_color, \r\n",
" linewidth = line_width, \r\n",
" linestyle = line_stylize, \r\n",
" label = str(plot_label)\r\n",
" )\r\n",
" elif type == 'sns_lineplot':\r\n",
" ax = sns.lineplot(\r\n",
" data = y_data, \r\n",
" color = plot_color, \r\n",
" linewidth = line_width, \r\n",
" linestyle = line_stylize, \r\n",
" dashes = False, \r\n",
" label = str(plot_label)\r\n",
" )\r\n",
" elif type == 'sns_scatter':\r\n",
" print('Additionaly, please provide the following parameters: \\n')\r\n",
" #marker_size = input('Marker size: ') # Add this to sns.scatterplot if you are debugging: s = marker_size,)\r\n",
" marker_shape = input('Marker shape: ')\r\n",
" ax = sns.scatterplot(\r\n",
" data = y_data, \r\n",
" color = plot_color, \r\n",
" linewidth = line_width, \r\n",
" linestyle = line_stylize, \r\n",
" marker = str(marker_shape), \r\n",
" label = str(plot_label)\r\n",
" )\r\n",
" else:\r\n",
" print('The tool currently supports the following: Seaborn Lineplot (sns_lineplot), Seaborn Scatterplot (sns_scatter), Matplotlib Lineplot (plt_lineplot).')\r\n",
"\r\n",
" # Set a tick for every integer multiple of a base within the view interval.\r\n",
" ax.xaxis.set_major_locator(ticker.MultipleLocator(2000))\r\n",
"\r\n",
" # Set legend\r\n",
" ax.legend(prop = {'size': font_size_legend}, loc=\"best\", borderaxespad=0, frameon=0)\r\n",
"\r\n",
" # Axis labels\r\n",
" if x_label == '':\r\n",
" print('X-Axis Label: ' + 'None given')\r\n",
" ax.set_xlabel(\r\n",
" str(x_label), \r\n",
" color = 'white', \r\n",
" fontweight = \"bold\"\r\n",
" )\r\n",
" print('Y-Axis Label: ' + '\\'' + str(y_label) + '\\'')\r\n",
" ax.set_ylabel(\r\n",
" str(y_label), \r\n",
" fontweight = \"bold\"\r\n",
" )\r\n",
" elif y_label == '':\r\n",
" print('X-Axis Label: ' + '\\'' + str(x_label) + '\\'')\r\n",
" ax.set_xlabel(\r\n",
" str(x_label), \r\n",
" fontweight = \"bold\"\r\n",
" )\r\n",
" print('Y-Axis Label: ' + 'None given')\r\n",
" ax.set_ylabel(\r\n",
" str(y_label), \r\n",
" color = 'white', \r\n",
" fontweight = \"bold\"\r\n",
" )\r\n",
" elif x_label == '' and y_label == '':\r\n",
" print('X-Axis Label: ' + 'None given') \r\n",
" ax.set_xlabel(\r\n",
" str(x_label), \r\n",
" color = 'white', \r\n",
" fontweight = \"bold\"\r\n",
" )\r\n",
" print('Y-Axis Label: ' + 'None given')\r\n",
" ax.set_ylabel(\r\n",
" str(y_label), \r\n",
" color = 'white', \r\n",
" fontweight = \"bold\"\r\n",
" )\r\n",
" else: \r\n",
" print('X-Axis Label: ' + '\\'' + str(x_label) + '\\'')\r\n",
" ax.set_xlabel(\r\n",
" str(x_label), \r\n",
" fontweight = \"bold\"\r\n",
" )\r\n",
" print('Y-Axis Label: ' + '\\'' + str(y_label) + '\\'')\r\n",
" ax.set_ylabel(\r\n",
" str(y_label), \r\n",
" fontweight = \"bold\"\r\n",
" )\r\n",
"\r\n",
" # Formatting\r\n",
" if time_series == True:\r\n",
" years = mdates.YearLocator() \r\n",
" months = mdates.MonthLocator() \r\n",
" years_fmt = mdates.DateFormatter('%Y')\r\n",
" ax.xaxis.set_major_locator(years)\r\n",
" ax.xaxis.set_major_formatter(years_fmt)\r\n",
" #datemin = np.datetime64(eff_fedrates_fred_redux.index[0], 'Y')\r\n",
" #datemax = np.datetime64(eff_fedrates_fred_redux.index[-1], 'Y') + np.timedelta64(1, 'Y')\r\n",
" #ax.set_xlim(datemin, datemax)\r\n",
" ax.format_xdata = mdates.DateFormatter('%Y')\r\n",
" else:\r\n",
" pass\r\n",
"\r\n",
" # Credits\r\n",
" ax.text(\r\n",
" x=0.0, \r\n",
" y=-0.12, \r\n",
" s='Source: Federal Reserve System Data Archives. Python scripts are provided under an MIT License.', \r\n",
" fontsize = font_size_notes, \r\n",
" fontstyle = 'italic', \r\n",
" c = \"grey\", \r\n",
" transform = ax.transAxes\r\n",
" )\r\n",
"\r\n",
" if trim_size != 0:\r\n",
" ## Reduce number of xticks (hide labels in a loop OR using plt.MaxNLocator())\r\n",
" #every_nth = trim_size\r\n",
" #for n, label in enumerate(ax.xaxis.get_ticklabels()):\r\n",
" # if n % every_nth != 0:\r\n",
" # label.set_visible(False)\r\n",
" #print('Displaying values every ' + str(trim_size) + ' tick on the x-axis.')\r\n",
" ax.xaxis.set_major_locator(plt.MaxNLocator(trim_size))\r\n",
" print('Displaying a total of ' + str(trim_size) + ' ticks on the x-axis.')\r\n",
" else:\r\n",
" print('All x-axis tick labels are shown.')\r\n",
"\r\n",
" if y_unit == 'money':\r\n",
" # Convert y-ticks to money\r\n",
" def money(y, ytick_pos):\r\n",
" return '%1.1f' % (y * 1e-3)\r\n",
" formatter_yticks = FuncFormatter(money)\r\n",
" ax.yaxis.set_major_formatter(formatter_yticks)\r\n",
" elif y_unit == 'percentage':\r\n",
" y_ticks = ['{:,.1f}'.format(y) + '%' for y in ax.get_yticks()]\r\n",
" ax.set_yticklabels(y_ticks)\r\n",
" elif y_unit == 'binary':\r\n",
" y_ticks = ['{:,.0f}'.format(y) + '%' for y in ax.get_yticks()]\r\n",
" ax.set_yticklabels(y_ticks)\r\n",
" else:\r\n",
" print('Please choose between \\'percentage'' and \\'Trillions\\'.')\r\n",
"\r\n",
" # Grid\r\n",
" ax.grid(\r\n",
" color = 'black', \r\n",
" alpha = 0.5, \r\n",
" linestyle = '-.', \r\n",
" linewidth=0.2\r\n",
" )\r\n",
"\r\n",
" # Finalize graph\r\n",
" handles, labels = ax.get_legend_handles_labels()\r\n",
"\r\n",
" ## Show Inline\r\n",
" #plt.show()\r\n",
" \r\n",
" # Save Graphics\r\n",
" fig.savefig(graph_dir + fig_name + '.png')\r\n",
" print('Figure saved as ' + '\\'' + str(fig_name) + '\\'' + ' in: ' + graph_dir + str(fig_name) + '.png')\r\n",
" return"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 182 ms (started: 2021-02-18 14:18:46 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vom0HveHBJTD"
},
"source": [
"### String Processing Utility"
]
},
{
"cell_type": "code",
"metadata": {
"id": "QwF1y1OWBMJ-",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"outputId": "408c6722-70c8-4509-f874-2c7ec5db13be"
},
"source": [
"######_STRING_WORKSHOP_######_STRING_WORKSHOP_######_STRING_WORKSHOP_######_STRING_WORKSHOP_######\r\n",
"# Paste scripts here for manipulation\r\n",
"\r\n",
"\r\n",
"######_STRING_WORKSHOP_######_STRING_WORKSHOP_######_STRING_WORKSHOP_######_STRING_WORKSHOP_######\r\n",
"\r\n",
"\r\n",
"######_PROCESSING_######_PROCESSING_######_PROCESSING_######_PROCESSING_######\r\n",
"import re\r\n",
"re.sub(' +', ' ', 'PASTE STRING HERE AS IS. ')\r\n",
"######_PROCESSING_######_PROCESSING_######_PROCESSING_######_PROCESSING_######\r\n",
"\r\n",
"\r\n",
"######_BACKUP_COMMANDS_######_BACKUP_COMMANDS_######_BACKUP_COMMANDS_######_BACKUP_COMMANDS_######\r\n",
"# |_EXTRACTED ENVIRONMENTS:\r\n",
"# |__FE630 CONDA\r\n",
"# |___LATEST VERSION: conda install _tflow_select absl-py argon2-cffi arrow astor async_generator attrs backcall backports backports.functools_lru_cache binaryornot blas bleach brotlipy ca-certificates cached-property certifi cffi chardet click colorama cookiecutter cryptography cudatoolkit cycler decorator defusedxml entrypoint filelock freetype google-pasta grpcio h5py hdf5 icu idna importlib-metadata importlib_metadata intel-openmp ipykernel ipywidgets jedi jinja2 jinja2-time joblib jpeg json5 jsonschema jupyter jupyter_client jupyter_console jupyter_core jupyterlab jupyterlab_pygments jupyterlab_server keras keras-applications kiwisolver krb5 libblas libcblas libclang libcurl libgpuarray libiconv liblapack liblapacke libpng libprotobuf libsodium libssh2 libtiff libuv libxml2 libxslt lxml lz4-c m2w64-gcc-libgfortran m2w64-gcc-libs m2w64-gcc-libs-core m2w64-gmp m2w64-libwinpthread-git mako markdown markupsafe matplotlib matplotlib-base mistune mkl msys2-conda-epoch nbclient nbconvert nbformat nest-asyncio ninja notebook numpy olefile openssl packaging pandas pandoc pandocfilters parso patsy pickle5 pickleshare pillow pip poyo prometheus_client prompt-toolkit prompt_toolkit protobuf pycparser pygments pygpu pyopenssl pyparsing pyqt pyreadline pyrsistent pysocks python python-dateutil python-slugify python_abi pytorch pytz pywin32 pywinpty pyzmq qt qtconsole qtpy requests sacremoses scikit-learn scikit-plot send2trash setuptools sqlite tensorflow tensorflow-base tensorflow-estimatortensorflow-gpu termcolor terminado testpath text-unidecode theano threadpoolctl tika tk torchaudio torchvision tornado traitlets transformers typing_extensions unidecode vc vs2015_runtime vs2017_win-64 vswhere wcwidth webencodings wheel whichcraft widgetsnbextension win_inet_pton wincertstore winpty wrapt xz yaml zeromq zipp zlib zstd\r\n",
"# |___SPEC'D VERSION: conda install _tflow_select=2.1.0=gpu absl-py=0.11.0=py37h03978a9_0 argon2-cffi=20.1.0=py37hcc03f2d_2 arrow=0.17.0=py37hc8dfbb8_1 astor=0.8.1=pyh9f0ad1d_0 async_generator=1.10=py_0 attrs=20.3.0=pyhd3deb0d_0 backcall=0.2.0=pyh9f0ad1d_0 backports=1.0=py_2 backports.functools_lru_cache=1.6.1=py_0 binaryornot=0.4.4=py_1 blas=2.21=mkl bleach=3.2.1=pyh9f0ad1d_0 brotlipy=0.7.0=py37h0013d47_1001 ca-certificates=2020.11.8=h5b45459_0 cached-property=1.5.1=py_0 certifi=2020.11.8=py37h03978a9_0 cffi=1.14.4=py37hd8e9650_1 chardet=3.0.4=py37hf50a25e_1008 click=7.1.2=pyh9f0ad1d_0 colorama=0.4.4=pyh9f0ad1d_0 cookiecutter=1.7.2=pyh9f0ad1d_0 cryptography=3.2.1=py37hd8e9650_0 cudatoolkit=11.0.3=h3f58a73_6 cycler=0.10.0=py_2 decorator=4.4.2=py_0 defusedxml=0.6.0=py_0 entrypoints=0.3=pyhd8ed1ab_1003 filelock=3.0.12=pyh9f0ad1d_0 freetype=2.10.4=h546665d_0 google-pasta=0.2.0=pyh8c360ce_0 grpcio=1.34.0=py37h04d2302_0 h5py=3.1.0=nompi_py37h19fda09_100 hdf5=1.10.6=nompi_h5268f04_1112 icu=67.1=h33f27b4_0 idna=2.10=pyh9f0ad1d_0 importlib-metadata=3.1.1=pyhd8ed1ab_0 importlib_metadata=3.1.1=hd8ed1ab_0 intel-openmp=2020.3=h57928b3_311 ipykernel=5.3.4=py37h7b7c402_1 ipywidgets=7.5.1=pyh9f0ad1d_1 jedi=0.17.2=py37h03978a9_1 jinja2=2.11.2=pyh9f0ad1d_0 jinja2-time=0.2.0=py_2 joblib=0.17.0=py_0 jpeg=9d=h8ffe710_0 json5=0.9.5=pyh9f0ad1d_0 jsonschema=3.2.0=py_2 jupyter=1.0.0=py_2 jupyter_client=6.1.7=py_0 jupyter_console=6.2.0=py_0 jupyter_core=4.7.0=py37h03978a9_0 jupyterlab=2.2.9=py_0 jupyterlab_pygments=0.1.2=pyh9f0ad1d_0 jupyterlab_server=1.2.0=py_0 keras=2.3.1=py37h21ff451_0 keras-applications=1.0.8=py_1 kiwisolver=1.3.1=py37h8c56517_0 krb5=1.17.2=hbae68bd_0 libblas=3.8.0=21_mkl libcblas=3.8.0=21_mkl libclang=10.0.1=default_hf44288c_1 libcurl=7.71.1=h4b64cdc_8 libgpuarray=0.7.6=hfa6e2cd_1003 libiconv=1.16=he774522_0 liblapack=3.8.0=21_mkl liblapacke=3.8.0=21_mkl libpng=1.6.37=h1d00b33_2 libprotobuf=3.13.0.1=h200bbdf_0 libsodium=1.0.18=h8d14728_1 libssh2=1.9.0=hb06d900_5 libtiff=4.1.0=hc10be44_6 libuv=1.40.0=he774522_0 libxml2=2.9.10=h1006b36_2 libxslt=1.1.33=h579f668_1 lxml=4.6.2=py37hd07aab1_0 lz4-c=1.9.2=h62dcd97_2 m2w64-gcc-libgfortran=5.3.0=6 m2w64-gcc-libs=5.3.0=7 m2w64-gcc-libs-core=5.3.0=7 m2w64-gmp=6.1.0=2 m2w64-libwinpthread-git=5.0.0.4634.697f757=2 mako=1.1.3=pyh9f0ad1d_0 markdown=3.3.3=pyh9f0ad1d_0 markupsafe=1.1.1=py37h0013d47_2 matplotlib=3.3.3=py37h03978a9_0 matplotlib-base=3.3.3=py37h3379fd5_0 mistune=0.8.4=py37hcc03f2d_1002 mkl=2020.4=hb70f87d_311 msys2-conda-epoch=20160418=1 nbclient=0.5.1=py_0 nbconvert=6.0.7=py37h03978a9_3 nbformat=5.0.8=py_0 nest-asyncio=1.4.3=pyhd8ed1ab_0 ninja=1.10.2=h5362a0b_0 notebook=6.1.5=py37h03978a9_0 numpy=1.19.4=py37hd20adf4_1 olefile=0.46=pyh9f0ad1d_1 openssl=1.1.1h=he774522_0 packaging=20.4=pyh9f0ad1d_0 pandas=1.1.4=py37h08fd248_0 pandoc=2.11.2=h8ffe710_0 pandocfilters=1.4.2=py_1 parso=0.7.1=pyh9f0ad1d_0 patsy=0.5.1=py_0 pickle5=0.0.11=py37h4ab8f01_0 pickleshare=0.7.5=py_1003 pillow=8.0.1=py37hdec93a9_0 pip=20.3.1=pyhd8ed1ab_0 poyo=0.5.0=py_0 prometheus_client=0.9.0=pyhd3deb0d_0 prompt-toolkit=3.0.8=pyha770c72_0 prompt_toolkit=3.0.8=hd8ed1ab_0 protobuf=3.13.0.1=py37hf2a7229_1 pycparser=2.20=pyh9f0ad1d_2 pygments=2.7.2=py_0 pygpu=0.7.6=py37h3a3b6f7_1002 pyopenssl=20.0.0=pyhd8ed1ab_0 pyparsing=2.4.7=pyh9f0ad1d_0 pyqt=5.12.3=py37h1834ac0_4 pyreadline=2.1=py37hc8dfbb8_1002 pyrsistent=0.17.3=py37hcc03f2d_1 pysocks=1.7.1=py37hf50a25e_2 python=3.7.8=h7840368_3_cpython python-dateutil=2.8.1=py_0 python-slugify=4.0.1=pyh9f0ad1d_0 python_abi=3.7=1_cp37m pytorch=1.7.0=py3.7_cuda110_cudnn8_0 pytz=2020.4=pyhd8ed1ab_0 pywin32=228=py37h4ab8f01_0 pywinpty=0.5.7=py37hc8dfbb8_1 pyzmq=20.0.0=py37h0d95fc2_1 qt=5.12.9=hb2cf2c5_0 qtconsole=5.0.1=pyhd8ed1ab_0 qtpy=1.9.0=py_0 requests=2.25.0=pyhd3deb0d_0 sacremoses=0.0.43=pyh9f0ad1d_0 scikit-learn=0.23.2=py37heb15398_3 scikit-plot=0.3.7=py_1 send2trash=1.5.0=py_0 setuptools=49.6.0=py37hf50a25e_2 sqlite=3.34.0=h8ffe710_0 tensorflow=1.14.0=h1f41ff6_0 tensorflow-base=1.14.0=py37hc8dfbb8_0 tensorflow-estimator=1.14.0=py37h5ca1d4c_0 tensorflow-gpu=1.14.0=h0d30ee6_0 termcolor=1.1.0=py_2 terminado=0.9.1=py37h03978a9_1 testpath=0.4.4=py_0 text-unidecode=1.3=py_0 theano=1.0.5=py37hf2a7229_1 threadpoolctl=2.1.0=pyh5ca1d4c_0 tika=1.24=pyh9f0ad1d_0 tk=8.6.10=he774522_1 torchaudio=0.7.0=py37 torchvision=0.8.1=py37_cu110 tornado=6.1=py37hcc03f2d_0 traitlets=5.0.5=py_0 transformers=4.0.0=pyhd8ed1ab_0 typing_extensions=3.7.4.3=py_0 unidecode=1.1.1=py_0 vc=14.1=h869be7e_1 vs2015_runtime=14.16.27012=h30e32a0_2 vs2017_win-64=19.16.27038=h2e3bad8_2 vswhere=2.7.1=h21ff451_0 wcwidth=0.2.5=pyh9f0ad1d_2 webencodings=0.5.1=py_1 wheel=0.36.0=pyhd3deb0d_0 whichcraft=0.6.1=py_0 widgetsnbextension=3.5.1=py37h03978a9_4 win_inet_pton=1.1.0=py37hc8dfbb8_1 wincertstore=0.2=py37hc8dfbb8_1005 winpty=0.4.3=4 wrapt=1.12.1=py37hcc03f2d_2 xz=5.2.5=h62dcd97_1 yaml=0.2.5=he774522_0 zeromq=4.3.3=h0e60522_3 zipp=3.4.0=py_0 zlib=1.2.11=h62dcd97_1010 zstd=1.4.5=h1f3a1b7_2\r\n",
"# |__FE690 CONDA\r\n",
"# |___LATEST VERSION: conda install _tflow_select absl-py argon2-cffi arrow astor async_generator attrs backcall backports backports.functools_lru_cache binaryornot blas bleach brotlipy ca-certificates cached-property certifi cffi chardet click colorama cookiecutter cryptography cudatoolkit cycler decorator defusedxml entrypoint filelock freetype google-pasta grpcio h5py hdf5 icu idna importlib-metadata importlib_metadata intel-openmp ipykernel ipywidgets jedi jinja2 jinja2-time joblib jpeg json5 jsonschema jupyter jupyter_client jupyter_console jupyter_core jupyterlab jupyterlab_pygments jupyterlab_server keras keras-applications kiwisolver krb5 libblas libcblas libclang libcurl libgpuarray libiconv liblapack liblapacke libpng libprotobuf libsodium libssh2 libtiff libuv libxml2 libxslt lxml lz4-c m2w64-gcc-libgfortran m2w64-gcc-libs m2w64-gcc-libs-core m2w64-gmp m2w64-libwinpthread-git mako markdown markupsafe matplotlib matplotlib-base mistune mkl msys2-conda-epoch nbclient nbconvert nbformat nest-asyncio ninja notebook numpy olefile openssl packaging pandas pandoc pandocfilters parso patsy pickle5 pickleshare pillow pip poyo prometheus_client prompt-toolkit prompt_toolkit protobuf pycparser pygments pygpu pyopenssl pyparsing pyqt pyreadline pyrsistent pysocks python python-dateutil python-slugify python_abi pytorch pytz pywin32 pywinpty pyzmq qt qtconsole qtpy requests sacremoses scikit-learn scikit-plot send2trash setuptools sqlite tensorflow tensorflow-base tensorflow-estimatortensorflow-gpu termcolor terminado testpath text-unidecode theano threadpoolctl tika tk torchaudio torchvision tornado traitlets transformers typing_extensions unidecode vc vs2015_runtime vs2017_win-64 vswhere wcwidth webencodings wheel whichcraft widgetsnbextension win_inet_pton wincertstore winpty wrapt xz yaml zeromq zipp zlib zstd\r\n",
"# |___SPEC'D VERSION: conda install _tflow_select=2.1.0=gpu absl-py=0.11.0=py37h03978a9_0 argon2-cffi=20.1.0=py37hcc03f2d_2 arrow=0.17.0=py37hc8dfbb8_1 astor=0.8.1=pyh9f0ad1d_0 async_generator=1.10=py_0 attrs=20.3.0=pyhd3deb0d_0 backcall=0.2.0=pyh9f0ad1d_0 backports=1.0=py_2 backports.functools_lru_cache=1.6.1=py_0 binaryornot=0.4.4=py_1 blas=2.21=mkl bleach=3.2.1=pyh9f0ad1d_0 brotlipy=0.7.0=py37h0013d47_1001 ca-certificates=2020.11.8=h5b45459_0 cached-property=1.5.1=py_0 certifi=2020.11.8=py37h03978a9_0 cffi=1.14.4=py37hd8e9650_1 chardet=3.0.4=py37hf50a25e_1008 click=7.1.2=pyh9f0ad1d_0 colorama=0.4.4=pyh9f0ad1d_0 cookiecutter=1.7.2=pyh9f0ad1d_0 cryptography=3.2.1=py37hd8e9650_0 cudatoolkit=11.0.3=h3f58a73_6 cycler=0.10.0=py_2 decorator=4.4.2=py_0 defusedxml=0.6.0=py_0 entrypoints=0.3=pyhd8ed1ab_1003 filelock=3.0.12=pyh9f0ad1d_0 freetype=2.10.4=h546665d_0 google-pasta=0.2.0=pyh8c360ce_0 grpcio=1.34.0=py37h04d2302_0 h5py=3.1.0=nompi_py37h19fda09_100 hdf5=1.10.6=nompi_h5268f04_1112 icu=67.1=h33f27b4_0 idna=2.10=pyh9f0ad1d_0 importlib-metadata=3.1.1=pyhd8ed1ab_0 importlib_metadata=3.1.1=hd8ed1ab_0 intel-openmp=2020.3=h57928b3_311 ipykernel=5.3.4=py37h7b7c402_1 ipywidgets=7.5.1=pyh9f0ad1d_1 jedi=0.17.2=py37h03978a9_1 jinja2=2.11.2=pyh9f0ad1d_0 jinja2-time=0.2.0=py_2 joblib=0.17.0=py_0 jpeg=9d=h8ffe710_0 json5=0.9.5=pyh9f0ad1d_0 jsonschema=3.2.0=py_2 jupyter=1.0.0=py_2 jupyter_client=6.1.7=py_0 jupyter_console=6.2.0=py_0 jupyter_core=4.7.0=py37h03978a9_0 jupyterlab=2.2.9=py_0 jupyterlab_pygments=0.1.2=pyh9f0ad1d_0 jupyterlab_server=1.2.0=py_0 keras=2.3.1=py37h21ff451_0 keras-applications=1.0.8=py_1 kiwisolver=1.3.1=py37h8c56517_0 krb5=1.17.2=hbae68bd_0 libblas=3.8.0=21_mkl libcblas=3.8.0=21_mkl libclang=10.0.1=default_hf44288c_1 libcurl=7.71.1=h4b64cdc_8 libgpuarray=0.7.6=hfa6e2cd_1003 libiconv=1.16=he774522_0 liblapack=3.8.0=21_mkl liblapacke=3.8.0=21_mkl libpng=1.6.37=h1d00b33_2 libprotobuf=3.13.0.1=h200bbdf_0 libsodium=1.0.18=h8d14728_1 libssh2=1.9.0=hb06d900_5 libtiff=4.1.0=hc10be44_6 libuv=1.40.0=he774522_0 libxml2=2.9.10=h1006b36_2 libxslt=1.1.33=h579f668_1 lxml=4.6.2=py37hd07aab1_0 lz4-c=1.9.2=h62dcd97_2 m2w64-gcc-libgfortran=5.3.0=6 m2w64-gcc-libs=5.3.0=7 m2w64-gcc-libs-core=5.3.0=7 m2w64-gmp=6.1.0=2 m2w64-libwinpthread-git=5.0.0.4634.697f757=2 mako=1.1.3=pyh9f0ad1d_0 markdown=3.3.3=pyh9f0ad1d_0 markupsafe=1.1.1=py37h0013d47_2 matplotlib=3.3.3=py37h03978a9_0 matplotlib-base=3.3.3=py37h3379fd5_0 mistune=0.8.4=py37hcc03f2d_1002 mkl=2020.4=hb70f87d_311 msys2-conda-epoch=20160418=1 nbclient=0.5.1=py_0 nbconvert=6.0.7=py37h03978a9_3 nbformat=5.0.8=py_0 nest-asyncio=1.4.3=pyhd8ed1ab_0 ninja=1.10.2=h5362a0b_0 notebook=6.1.5=py37h03978a9_0 numpy=1.19.4=py37hd20adf4_1 olefile=0.46=pyh9f0ad1d_1 openssl=1.1.1h=he774522_0 packaging=20.4=pyh9f0ad1d_0 pandas=1.1.4=py37h08fd248_0 pandoc=2.11.2=h8ffe710_0 pandocfilters=1.4.2=py_1 parso=0.7.1=pyh9f0ad1d_0 patsy=0.5.1=py_0 pickle5=0.0.11=py37h4ab8f01_0 pickleshare=0.7.5=py_1003 pillow=8.0.1=py37hdec93a9_0 pip=20.3.1=pyhd8ed1ab_0 poyo=0.5.0=py_0 prometheus_client=0.9.0=pyhd3deb0d_0 prompt-toolkit=3.0.8=pyha770c72_0 prompt_toolkit=3.0.8=hd8ed1ab_0 protobuf=3.13.0.1=py37hf2a7229_1 pycparser=2.20=pyh9f0ad1d_2 pygments=2.7.2=py_0 pygpu=0.7.6=py37h3a3b6f7_1002 pyopenssl=20.0.0=pyhd8ed1ab_0 pyparsing=2.4.7=pyh9f0ad1d_0 pyqt=5.12.3=py37h1834ac0_4 pyreadline=2.1=py37hc8dfbb8_1002 pyrsistent=0.17.3=py37hcc03f2d_1 pysocks=1.7.1=py37hf50a25e_2 python=3.7.8=h7840368_3_cpython python-dateutil=2.8.1=py_0 python-slugify=4.0.1=pyh9f0ad1d_0 python_abi=3.7=1_cp37m pytorch=1.7.0=py3.7_cuda110_cudnn8_0 pytz=2020.4=pyhd8ed1ab_0 pywin32=228=py37h4ab8f01_0 pywinpty=0.5.7=py37hc8dfbb8_1 pyzmq=20.0.0=py37h0d95fc2_1 qt=5.12.9=hb2cf2c5_0 qtconsole=5.0.1=pyhd8ed1ab_0 qtpy=1.9.0=py_0 requests=2.25.0=pyhd3deb0d_0 sacremoses=0.0.43=pyh9f0ad1d_0 scikit-learn=0.23.2=py37heb15398_3 scikit-plot=0.3.7=py_1 send2trash=1.5.0=py_0 setuptools=49.6.0=py37hf50a25e_2 sqlite=3.34.0=h8ffe710_0 tensorflow=1.14.0=h1f41ff6_0 tensorflow-base=1.14.0=py37hc8dfbb8_0 tensorflow-estimator=1.14.0=py37h5ca1d4c_0 tensorflow-gpu=1.14.0=h0d30ee6_0 termcolor=1.1.0=py_2 terminado=0.9.1=py37h03978a9_1 testpath=0.4.4=py_0 text-unidecode=1.3=py_0 theano=1.0.5=py37hf2a7229_1 threadpoolctl=2.1.0=pyh5ca1d4c_0 tika=1.24=pyh9f0ad1d_0 tk=8.6.10=he774522_1 torchaudio=0.7.0=py37 torchvision=0.8.1=py37_cu110 tornado=6.1=py37hcc03f2d_0 traitlets=5.0.5=py_0 transformers=4.0.0=pyhd8ed1ab_0 typing_extensions=3.7.4.3=py_0 unidecode=1.1.1=py_0 vc=14.1=h869be7e_1 vs2015_runtime=14.16.27012=h30e32a0_2 vs2017_win-64=19.16.27038=h2e3bad8_2 vswhere=2.7.1=h21ff451_0 wcwidth=0.2.5=pyh9f0ad1d_2 webencodings=0.5.1=py_1 wheel=0.36.0=pyhd3deb0d_0 whichcraft=0.6.1=py_0 widgetsnbextension=3.5.1=py37h03978a9_4 win_inet_pton=1.1.0=py37hc8dfbb8_1 wincertstore=0.2=py37hc8dfbb8_1005 winpty=0.4.3=4 wrapt=1.12.1=py37hcc03f2d_2 xz=5.2.5=h62dcd97_1 yaml=0.2.5=he774522_0 zeromq=4.3.3=h0e60522_3 zipp=3.4.0=py_0 zlib=1.2.11=h62dcd97_1010 zstd=1.4.5=h1f3a1b7_2\r\n",
"# |__FE630 PIP\r\n",
"# |___LATEST VERSION: pip install --upgrade absl-py analytics-python ansi2html astunparse brotli clickclick connexion cvxopt cvxpy dash dash-bootstrap-components dash-core-components dash-cytoscape dash-html-components dash-renderer dash-table ecos flask flask-compress future gast google-auth grpcio inflection intel-openmp itsdangerous joblib jupyter-dash keras keras-preprocessing llvmlite matplotlib mkl multitasking networkx numba numpy openapi-spec-validator opt-einsum osqp pandas patsy plotly pot protobuf pyasn1-modules pyportfolioopt pyqt5-sip pyqtwebengine pytz pyyaml retrying scikit-learn scipy scs statsmodels tensorflow termcolor threadpoolctl werkzeug yfinance zmq\r\n",
"# |___SPEC'D VERSION: pip install --upgrade absl-py==0.10.0 analytics-python==1.2.9 ansi2html==1.5.2 astunparse==1.6.3 brotli==1.0.9 clickclick==1.2.2 connexion==2.6.0 cvxopt==1.2.5 cvxpy==1.1.1 dash==1.14.0 dash-bootstrap-components==0.10.3 dash-core-components==1.10.2 dash-cytoscape==0.2.0 dash-html-components==1.0.3 dash-renderer==1.6.0 dash-table==4.9.0 ecos==2.0.7.post1 flask==1.1.2 flask-compress==1.5.0 future==0.18.2 gast==0.3.3 google-auth==1.21.2 grpcio==1.32.0 inflection==0.5.1 intel-openmp==2019.0 itsdangerous==1.1.0 joblib==0.16.0 jupyter-dash==0.3.1 keras==2.4.3 keras-preprocessing==1.1.2 llvmlite==0.32.1 matplotlib==3.2.1 mkl==2019.0 multitasking==0.0.9 networkx==2.4 numba==0.49.1 numpy==1.18.5 openapi-spec-validator==0.2.9 opt-einsum==3.3.0 osqp==0.6.1 pandas==1.0.4 patsy==0.5.1 plotly==4.10.0 pot==0.7.0 protobuf==3.13.0 pyasn1-modules==0.2.8 pyportfolioopt==1.2.5 pyqt5-sip==4.19.18 pyqtwebengine==5.12.1 pytz==2020.1 pyyaml==5.3.1 retrying==1.3.3 scikit-learn==0.23.1 scipy==1.4.1 scs==2.1.2 statsmodels==0.11.1 tensorflow==2.2.0 termcolor==1.1.0 threadpoolctl==2.1.0 werkzeug==0.16.1 yfinance==0.1.54 zmq==0.0.0\r\n",
"# |__FE690 PIP\r\n",
"# |___LATEST VERSION: pip install --upgrade argcomplete astunparse cython docx2txt ebooklib extract-msg future gast googlefinance hmmlearn imapclient inflection keras-preprocessing mllib more-itertools multitasking nltk opt-einsum pandas-datareader pdf2text pdfminer pdfminer-six pycryptodome pygad pyqt5-sip pyqtchart pyqtwebengine python-pptx pyyaml quandl regex scipy seaborn sentencepiece six sortedcontainers soupsieve speechrecognition statsmodels tensorboard tensorflow-hub tokenizers tzlocal urllib3 vadersentiment werkzeug xgboost xlrd xlsxwriter\r\n",
"# |___SPEC'D VERSION: pip install --upgrade argcomplete==1.10.0 astunparse==1.6.3 cython==0.29.17 docx2txt==0.8 ebooklib==0.17.1 extract-msg==0.23.1 future==0.18.2 gast==0.2.2 googlefinance==0.7 hmmlearn==0.2.4 imapclient==2.1.0 inflection==0.5.1 keras-preprocessing==1.1.2 mllib==1.0.0a2 more-itertools==8.5.0 multitasking==0.0.9 nltk==3.5 opt-einsum==3.3.0 pandas-datareader==0.9.0 pdf2text==1.0.0 pdfminer==20191125 pdfminer-six==20181108 pycryptodome==3.9.8 pygad==2.8.1 pyqt5-sip==4.19.18 pyqtchart==5.12 pyqtwebengine==5.12.1 python-pptx==0.6.18 pyyaml==5.3.1 quandl==3.5.2 regex==2020.9.27 scipy==1.4.1 seaborn==0.11.0 sentencepiece==0.1.91 six==1.12.0 sortedcontainers==2.2.2 soupsieve==2.0.1 speechrecognition==3.8.1 statsmodels==0.11.1 tensorboard==2.0.2 tensorflow-hub==0.9.0 tokenizers==0.8.1rc2 tzlocal==1.5.1 urllib3==1.25.9 vadersentiment==3.3.2 werkzeug==0.16.0 xgboost==1.2.0 xlrd==1.2.0 xlsxwriter==1.3.7\r\n",
"\r\n",
"######_BACKUP_COMMANDS_######_BACKUP_COMMANDS_######_BACKUP_COMMANDS_######_BACKUP_COMMANDS_######\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'PASTE STRING HERE AS IS. '"
]
},
"metadata": {
"tags": []
},
"execution_count": 29
},
{
"output_type": "stream",
"text": [
"time: 20.2 ms (started: 2021-02-18 14:18:46 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "42WZnhJ50_dU"
},
"source": [
"## **Load preprocessed data**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "bn0b9-V2qGeR",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "94548eb1-62b8-45d2-83c2-d04e95a25da9"
},
"source": [
"#@title **Load Documents By Type** { run: \"auto\", vertical-output: true, form-width: \"50%\", display-mode: \"form\" }\n",
"#@markdown Select document type:\n",
"doc_type = \"minutes\" #@param [\"meeting_script\", \"minutes\", \"presconf_script\", \"speech\", \"statement\", \"testimony\"]\n",
"print('Document type set to ' + '\\'' + str(doc_type) + '\\'.')\n",
"\n",
"# Load text data\n",
"file = open(fomc_dir + doc_type + '.pickle', 'rb')\n",
"data_df = pickle.load(file)\n",
"file.close()\n",
"\n",
"print('\\nText data from ' + str(doc_type) + ' documents successfully loaded.')\n",
"data_df\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Document type set to 'minutes'.\n",
"\n",
"Text data from minutes documents successfully loaded.\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>contents</th>\n",
" <th>speaker</th>\n",
" <th>title</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1993-02-03</td>\n",
" <td>A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System in \\r\\n Washington, D.C., on Tuesday, February 2, 1993, at 2:30 p.m. and was \\r\\n continued on Wednesday, February 3, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPR...</td>\n",
" <td>Alan Greenspan</td>\n",
" <td>FOMC Meeting Minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1993-03-23</td>\n",
" <td>A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System in \\r\\n Washington, D.C., on Tuesday, March 23, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[SECTION]\\n\\nMr. Greenspan, Chairman \\r\\n Mr. Corrig...</td>\n",
" <td>Alan Greenspan</td>\n",
" <td>FOMC Meeting Minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1993-05-18</td>\n",
" <td>A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System \\r\\n in Washington, D.C., on Tuesday, May 18, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[SECTION]\\n\\nMr. Greenspan, Chairman \\r\\n Mr. Corrigan, ...</td>\n",
" <td>Alan Greenspan</td>\n",
" <td>FOMC Meeting Minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1993-07-07</td>\n",
" <td>A meeting of the Federal Open Market Committee was held in the \\r\\n offices of the Board of Governors of the Federal Reserve System in \\r\\n Washington, D.C., on Tuesday, July 6, 1993, at 2:30 p.m. and continued \\r\\n on Wednesday, July 7, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[S...</td>\n",
" <td>Alan Greenspan</td>\n",
" <td>FOMC Meeting Minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1993-08-17</td>\n",
" <td>A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System \\r\\n in Washington, D.C., on Tuesday, August 17, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[SECTION]\\n\\nMr. Greenspan, Chairman\\r\\n Mr. McDonough, V...</td>\n",
" <td>Alan Greenspan</td>\n",
" <td>FOMC Meeting Minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>218</th>\n",
" <td>2020-06-10</td>\n",
" <td>The Federal Reserve, the central bank of the United States, provides the nation with a safe, flexible, and stable monetary and financial system.\\n\\n[SECTION]\\n\\nFederal Open Market Committee\\n\\n[SECTION]\\n\\nMonetary Policy Principles and Practice\\n\\n[SECTION]\\n\\nPolicy Implementation\\n\\n[SECTION...</td>\n",
" <td>Jerome Powell</td>\n",
" <td>FOMC Meeting Minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>219</th>\n",
" <td>2020-07-29</td>\n",
" <td>The Federal Reserve, the central bank of the United States, provides the nation with a safe, flexible, and stable monetary and financial system.\\n\\n[SECTION]\\n\\nFederal Open Market Committee\\n\\n[SECTION]\\n\\nMonetary Policy Principles and Practice\\n\\n[SECTION]\\n\\nPolicy Implementation\\n\\n[SECTION...</td>\n",
" <td>Jerome Powell</td>\n",
" <td>FOMC Meeting Minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>220</th>\n",
" <td>2020-09-16</td>\n",
" <td>The Federal Reserve, the central bank of the United States, provides the nation with a safe, flexible, and stable monetary and financial system.\\n\\n[SECTION]\\n\\nFederal Open Market Committee\\n\\n[SECTION]\\n\\nMonetary Policy Principles and Practice\\n\\n[SECTION]\\n\\nPolicy Implementation\\n\\n[SECTION...</td>\n",
" <td>Jerome Powell</td>\n",
" <td>FOMC Meeting Minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>221</th>\n",
" <td>2020-11-05</td>\n",
" <td>The Federal Reserve, the central bank of the United States, provides the nation with a safe, flexible, and stable monetary and financial system.\\n\\n[SECTION]\\n\\nFederal Open Market Committee\\n\\n[SECTION]\\n\\nMonetary Policy Principles and Practice\\n\\n[SECTION]\\n\\nPolicy Implementation\\n\\n[SECTION...</td>\n",
" <td>Jerome Powell</td>\n",
" <td>FOMC Meeting Minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>222</th>\n",
" <td>2020-12-16</td>\n",
" <td>The Federal Reserve, the central bank of the United States, provides the nation with a safe, flexible, and stable monetary and financial system.\\n\\n[SECTION]\\n\\nFederal Open Market Committee\\n\\n[SECTION]\\n\\nMonetary Policy Principles and Practice\\n\\n[SECTION]\\n\\nPolicy Implementation\\n\\n[SECTION...</td>\n",
" <td>Jerome Powell</td>\n",
" <td>FOMC Meeting Minutes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>223 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" date ... title\n",
"0 1993-02-03 ... FOMC Meeting Minutes\n",
"1 1993-03-23 ... FOMC Meeting Minutes\n",
"2 1993-05-18 ... FOMC Meeting Minutes\n",
"3 1993-07-07 ... FOMC Meeting Minutes\n",
"4 1993-08-17 ... FOMC Meeting Minutes\n",
".. ... ... ...\n",
"218 2020-06-10 ... FOMC Meeting Minutes\n",
"219 2020-07-29 ... FOMC Meeting Minutes\n",
"220 2020-09-16 ... FOMC Meeting Minutes\n",
"221 2020-11-05 ... FOMC Meeting Minutes\n",
"222 2020-12-16 ... FOMC Meeting Minutes\n",
"\n",
"[223 rows x 4 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 33
},
{
"output_type": "stream",
"text": [
"time: 77.9 ms (started: 2021-02-18 14:53:30 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EcCUICTgHzI1"
},
"source": [
"### Modify Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "wMcb2XOQmIh5",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ada5b656-5f47-44db-e61b-fb348b7b35d0"
},
"source": [
"data_df['text'] = data_df['contents'].apply(lambda x: x.replace('\\n\\n[SECTION]\\n\\n', '').replace('\\n', ' ').replace('\\r', ' ').strip())\n",
"data_df.drop(columns=['title'], axis=1, inplace=True)\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 47.9 ms (started: 2021-02-18 14:53:31 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Q9W1mVLOmIh5",
"scrolled": false,
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "561b6881-a526-4b73-85cf-92d769b2d36e"
},
"source": [
"print('Date: ', data_df.iloc[-1]['date'].strftime('%Y-%m-%d'))\n",
"print('Speaker: ', data_df.iloc[-1]['speaker'])\n",
"print('Text: \\n\\n', data_df.iloc[-1]['contents'].replace('\\n[SECTION]\\n', '')[1192:])\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Date: 2020-12-16\n",
"Speaker: Jerome Powell\n",
"Text: \n",
"\n",
" y videoconference on Tuesday, December 15, 2020, at 1:00 p.m. and continued on Wednesday, December 16, 2020, at 9:00 a.m.1\n",
"\n",
"PRESENT:\n",
"Jerome H. Powell, Chair\n",
"John C. Williams, Vice Chair\n",
"Michelle W. Bowman\n",
"Lael Brainard\n",
"Richard H. Clarida\n",
"Patrick Harker\n",
"Robert S. Kaplan\n",
"Neel Kashkari\n",
"Loretta J. Mester\n",
"Randal K. Quarles\n",
"\n",
"Thomas I. Barkin, Raphael W. Bostic, Mary C. Daly, Charles L. Evans, and Helen E. Mucciolo,2 Alternate Members of the Federal Open Market Committee\n",
"\n",
"James Bullard, Esther L. George, and Eric Rosengren, Presidents of the Federal Reserve Banks of St. Louis, Kansas City, and Boston, respectively\n",
"\n",
"James A. Clouse, Secretary\n",
"Matthew M. Luecke, Deputy Secretary\n",
"Michelle A. Smith, Assistant Secretary\n",
"Mark E. Van Der Weide, General Counsel\n",
"Michael Held, Deputy General Counsel\n",
"Trevor A. Reeve, Economist\n",
"Stacey Tevlin, Economist\n",
"Beth Anne Wilson, Economist\n",
"\n",
"Shaghil Ahmed, Michael Dotsey, Rochelle M. Edge, Marc Giannoni, William Wascher, and Mark L.J. Wright, Associate Economists\n",
"\n",
"Lorie K. Logan, Manager, System Open Market Account\n",
"\n",
"Ann E. Misback,3 Secretary, Office of the Secretary, Board of Governors\n",
"\n",
"Matthew J. Eichner,4 Director, Division of Reserve Bank Operations and Payment Systems, Board of Governors; Michael S. Gibson, Director, Division of Supervision and Regulation, Board of Governors; Andreas Lehnert, Director, Division of Financial Stability, Board of Governors\n",
"\n",
"Margie Shanks, Deputy Secretary, Office of the Secretary, Board of Governors\n",
"\n",
"Sally Davies and Brian M. Doyle, Deputy Directors, Division of International Finance, Board of Governors; Michael T. Kiley, Deputy Director, Division of Financial Stability, Board of Governors\n",
"\n",
"Jon Faust, Senior Special Adviser to the Chair, Division of Board Members, Board of Governors\n",
"\n",
"Joshua Gallin, Special Adviser to the Chair, Division of Board Members, Board of Governors\n",
"\n",
"William F. Bassett, Antulio N. Bomfim, Wendy E. Dunn, Burcu Duygan-Bump, Kurt F. Lewis, Ellen E. Meade, and Chiara Scotti, Special Advisers to the Board, Division of Board Members, Board of Governors\n",
"\n",
"Linda Robertson, Assistant to the Board, Division of Board Members, Board of Governors\n",
"\n",
"Eric M. Engen and John J. Stevens, Senior Associate Directors, Division of Research and Statistics, Board of Governors\n",
"\n",
"Jane E. Ihrig, Don H. Kim, and Edward Nelson, Senior Advisers, Division of Monetary Affairs, Board of Governors; Brett Berger,4 Senior Adviser, Division of International Finance, Board of Governors\n",
"\n",
"Elizabeth K. Kiser, Associate Director, Division of Research and Statistics, Board of Governors\n",
"\n",
"Eric C. Engstrom, Deputy Associate Director, Division of Monetary Affairs, Board of Governors; Norman J. Morin, Karen M. Pence, and John M. Roberts, Deputy Associate Directors, Division of Research and Statistics, Board of Governors; Jeffrey D. Walker,4 Deputy Associate Director, Division of Reserve Bank Operations and Payment Systems, Board of Governors\n",
"\n",
"Brian J. Bonis and Dan Li, Assistant Directors, Division of Monetary Affairs, Board of Governors\n",
"\n",
"Penelope A. Beattie,3 Section Chief, Office of the Secretary, Board of Governors; Lubomir Petrasek, Section Chief, Division of Monetary Affairs, Board of Governors\n",
"\n",
"David H. Small, Project Manager, Division of Monetary Affairs, Board of Governors\n",
"\n",
"Heather A. Wiggins,4 Group Manager, Division of Monetary Affairs, Board of Governors\n",
"\n",
"Michele Cavallo and Erin E. Ferris, Principal Economists, Division of Monetary Affairs, Board of Governors\n",
"\n",
"Kyungmin Kim4 and Arsenios Skaperdas,4 Senior Economists, Division of Monetary Affairs, Board of Governors\n",
"\n",
"Courtney Demartini,4 Lead Financial Institution and Policy Analyst, Division of Monetary Affairs, Board of Governors\n",
"\n",
"Randall A. Williams, Lead Information Manager, Division of Monetary Affairs, Board of Governors\n",
"\n",
"Becky C. Bareford, First Vice President, Federal Reserve Bank of Richmond\n",
"\n",
"Kartik B. Athreya, Joseph W. Gruber, Sylvain Leduc, Anna Paulson, Daleep Singh, and Christopher J. Waller, Executive Vice Presidents, Federal Reserve Banks of Richmond, Kansas City, San Francisco, Chicago, New York, and St. Louis, respectively\n",
"\n",
"Todd E. Clark, Senior Vice President, Federal Reserve Bank of Cleveland\n",
"\n",
"Jonathan P. McCarthy, Matthew Nemeth,4 Giovanni Olivei, Rania Perry,4 Matthew D. Raskin,4 Jonathan L. Willis, and Patricia Zobel, Vice Presidents, Federal Reserve Banks of New York, New York, Boston, New York, New York, Atlanta, and New York, respectively\n",
"\n",
"Robert Lerman,4 Assistant Vice President, Federal Reserve Bank of New York\n",
"\n",
"Lisa Stowe,4 Markets Officer, Federal Reserve Bank of New York\n",
"\n",
"Developments in Financial Markets and Open Market Operations\n",
"The manager of the System Open Market Account (SOMA) turned first to a discussion of financial market developments. Market sentiment improved over the period, as reduced uncertainty related to the U.S. election and positive vaccine news outweighed the anticipated effect of the ongoing surge in the pandemic. U.S. equity price indexes reached all-time highs, with the largest gains registered in sectors that have underperformed during the pandemic. Corporate credit spreads tightened, most notably among lower-rated firms and in sectors most affected by social distancing measures resulting from the pandemic. Longer-term Treasury yields rose modestly, driven by increases in inflation compensation. The positive vaccine news also supported risk sentiment abroad, leading many global equity price indexes to advance and the U.S. dollar to depreciate further.\n",
"\n",
"Market participants had highlighted that uncertainty nevertheless remained high and had pointed to several prominent risks to the economic outlook. These risks included the possibility that the vaccine rollout might not proceed as smoothly as anticipated, the potential for adverse developments in negotiations concerning the United Kingdom's withdrawal from the European Union, and the potential for deterioration in already strained sectors, such as those involving small businesses and certain segments of commercial real estate (CRE).\n",
"\n",
"With regard to market expectations concerning the policy outlook, responses to the Open Market Desk surveys of dealers and market participants suggested that views on the most likely timing of the next increase in the target range for the federal funds rate coalesced further around the first half of 2024. Survey responses continued to indicate median expectations of headline personal consumption expenditures (PCE) inflation above 2 percent and an unemployment rate of around 4 percent at the time of the first increase in the target range for the federal funds rate. A majority of Desk survey respondents indicated that they expected the Committee to revise its guidance on asset purchases at the current meeting, with many noting that they anticipated the announcement of some form of qualitative, outcome-based guidance tied to inflation, the unemployment rate, or both. Median Desk survey responses continued to suggest expectations that purchases would begin to slow in the first half of 2022 and cease altogether in 2023.\n",
"\n",
"The size of the Federal Reserve's balance sheet increased to around $7.3 trillion over the intermeeting period, driven by growth in securities holdings. The Desk conducted purchases to increase holdings of Treasury securities and agency mortgage-backed securities (MBS) at the minimum pace directed by the FOMC, as markets for these securities continued to function smoothly. News that CARES Act (Coronavirus Aid, Relief, and Economic Security Act) funding would not be available to support new activity in section 13(3) facilities after the end of the year had only a modest effect on financial markets. New activity remained limited across most Federal Reserve funding operations and section 13(3) facilities, although the Municipal Liquidity Facility and the Main Street Lending Program saw growing usage over the period, with more take-up expected before their scheduled year-end termination.\n",
"\n",
"The manager discussed a proposal to extend the temporary U.S. dollar liquidity swap arrangements as well as the temporary FIMA (Foreign and International Monetary Authorities) Repo Facility through September 2021. The path to a complete economic recovery remained uncertain across the globe, particularly for many emerging market countries, underscoring the need for backstops that could address potential market stresses and prevent spillovers from reemerging. Keeping these arrangements in place would contribute to sustaining improvements in global dollar funding markets and to the continued smooth functioning of the U.S. Treasury securities market. Under the proposal, provided that the Committee had no objections, the Chair would approve the extension of the temporary liquidity swap lines following the meeting. The extensions of the swap and FIMA repurchase agreement (repo) arrangements would be announced following this meeting.\n",
"\n",
"Market participants generally anticipated calm money market conditions through year-end, and the premiums paid for dollar funding crossing year-end generally were below those observed in recent years. Money market futures also indicated expectations of short-term rates moving down modestly in coming months, in light of anticipated further increases in aggregate reserve balances and a moderation in Treasury bill supply. The manager anticipated that administered rates and the overnight reverse repo program would be effective tools for maintaining control of overnight money market rates.\n",
"\n",
"By unanimous vote, the Committee voted to approve a resolution that extended through September 30, 2021, the expiration of a temporary repo facility for foreign and international monetary authorities (FIMA Repo Facility).5\n",
"\n",
"Secretary's note: The Chair subsequently provided approval to the Desk, following the procedures in the Authorization for Foreign Currency Operations, to extend the expiration of the temporary U.S. dollar liquidity swap lines through September 30, 2021.\n",
"\n",
"By unanimous vote, the Committee ratified the Desk's domestic transactions over the intermeeting period. There were no intervention operations in foreign currencies for the System's account during the intermeeting period.\n",
"\n",
"Staff Review of the Economic Situation\n",
"The COVID-19 pandemic and the measures undertaken to contain its spread continued to affect economic activity in the United States and abroad. The information available at the time of the December 15–16 meeting suggested that U.S. real gross domestic product (GDP) was continuing to recover in the fourth quarter, but at a more moderate pace than its rapid third-quarter rate, and that the level of real GDP remained well below its level at the start of 2020. Labor market conditions improved further over October and November, although employment continued to be well below its level at the beginning of the year. Consumer price inflation through October—as measured by the 12‑month percentage change in the PCE price index—remained notably below the rates seen in early 2020.\n",
"\n",
"Total nonfarm payroll employment continued to increase solidly over October and November, though the rate of monthly job gains was more moderate than the substantial third‑quarter pace. Through November, payroll employment had regained somewhat more than half of the losses seen at the onset of the pandemic. The unemployment rate moved down further and stood at 6.7 percent in November. The unemployment rates for African Americans and Hispanics each declined but remained well above the national average. Both the labor force participation rate and the employment-to-population ratio in November were above their levels of two months earlier. The four-week moving average of initial claims for unemployment insurance was only slightly lower in early December than it had been in late October. Weekly estimates of private-sector payrolls constructed by Federal Reserve Board staff using data provided by the payroll processor ADP suggested that the four-week average of private employment gains in early December was lower than it was in mid-November. Both the 12‑month change in average hourly earnings for all employees through November and the four‑quarter change in total labor compensation per hour in the business sector through the third quarter continued to be dominated by changes in the composition of the workforce. The substantial employment losses over the past year were most significant among lower-wage workers—a situation that had led to outsized increases in these average measures of earnings and compensation that were not indicative of tight labor market conditions.\n",
"\n",
"Total PCE price inflation was 1.2 percent over the 12 months ending in October, and it continued to be held down by relatively weak aggregate demand and the declines in consumer energy prices seen earlier in 2020. Core PCE price inflation, which excludes changes in consumer energy prices and many consumer food prices, was 1.4 percent over the same period, while the trimmed mean measure of 12‑month PCE price inflation constructed by the Federal Reserve Bank of Dallas was 1.7 percent in October. In November, the 12-month change in the consumer price index (CPI) was 1.2 percent, while core CPI inflation was 1.6 percent over the same period. The latest readings on survey-based measures of longer-run inflation expectations edged up, though each remained within the range in which it has fluctuated in recent years; in November and early December, the University of Michigan Surveys of Consumers measure for the next 5 to 10 years was slightly above its level in October, while the 3‑year‑ahead measure produced by the Federal Reserve Bank of New York rose a bit in November.\n",
"\n",
"Real PCE rose strongly in October, though at a more moderate pace than in the third quarter. Real disposable personal income declined in October, reflecting a large reduction in government transfer payments, even though wage and salary income continued to climb. As a result, the personal saving rate moved lower, though it continued to be notably above its 2019 average. In November, the components of the nominal retail sales data used to estimate PCE, along with the rate of light motor vehicle sales, stepped down, possibly reflecting the effects on consumer spending of renewed social-distancing measures and concerns about the resurgent pandemic. Consumer sentiment, as measured by both the Michigan survey and the Conference Board, moved somewhat lower, on net, since October, although both indexes were still above their April troughs.\n",
"\n",
"Housing-sector activity advanced further, on balance, in October, supported in part by low interest rates. Starts and construction permits for single-family homes continued to rise, while starts of multi-family units moved sideways. Sales of existing homes increased solidly, though new home sales were roughly flat.\n",
"\n",
"Business fixed investment appeared to be expanding further, on net, in the fourth quarter following an outsized third-quarter increase. Nominal shipments of nondefense capital goods excluding aircraft rose strongly in October, and new orders for these capital goods continued to advance. By contrast, nominal spending on nonresidential structures outside of the drilling and mining sector declined further in October. The number of crude oil and natural gas rigs in operation—an indicator of business spending on structures in the drilling and mining sector—continued to move up somewhat through early December, although the number of rigs in operation was still subdued, reflecting the effect of low oil prices on drilling investment.\n",
"\n",
"Industrial production rose strongly over October and November, led by gains in manufacturing output, but production was still below its February pre-pandemic level. The pickup in the production of motor vehicles and related parts was particularly strong in November. Output in the mining sector—which includes crude oil and natural gas drilling and extraction—increased, on net, over October and November.\n",
"\n",
"Total real government purchases appeared to be declining moderately, on balance, in the fourth quarter. Federal defense spending continued to rise in October and November, although federal employment declined with the layoff of temporary census workers. State and local government payrolls decreased in October and November, and nominal state and local construction expenditures in October were somewhat below their third-quarter level.\n",
"\n",
"The nominal U.S. international trade deficit widened in October. Both imports and exports continued to rebound from their collapse in the first half of the year. Goods imports in October rose above their January level after several months of strong growth. Goods exports, however, had only recovered three-fourths of their decline since January despite brisk growth in agricultural exports. Services trade remained depressed, driven by the continued suspension of most international travel.\n",
"\n",
"After a strong rebound in the third quarter, foreign economic growth appeared to slow sharply in recent months. The resurgence of coronavirus infections in Europe and Canada prompted governments to reintroduce social-distancing restrictions, leading to a fall in measures of mobility and services activity. Even so, with restrictions less severe and more targeted than in the spring, the hit to economic activity looked to be more limited. Economic growth appeared to hold up better in several emerging Asian economies. In these economies, effective virus control was supporting domestic demand, while strong external demand boosted exports. Inflationary pressures remained subdued in most foreign economies amid substantial economic slack.\n",
"\n",
"Staff Review of the Financial Situation\n",
"Financial market sentiment improved over the intermeeting period, boosted by news of forthcoming COVID-19 vaccines and reduced uncertainty following the U.S. election that outweighed concerns regarding the continued rise in COVID-19 cases and the potential effects of ensuing restrictions. Corporate bond spreads narrowed, and major global equity price indexes rose on net. The prospect of additional fiscal stimulus likely contributed to a steeper U.S. Treasury yield curve, increased inflation compensation, and broad dollar depreciation. Financing conditions for businesses able to access capital markets and households possessing high credit scores remained accommodative and eased a bit further in some sectors, but conditions for borrowers dependent on bank financing remained tight.\n",
"\n",
"Yields on 2-year nominal Treasury securities were little changed since the November FOMC meeting, while 10- and 30-year yields rose moderately. Market participants attributed the increases in longer-term yields primarily to greater optimism about the economic outlook, due to the forthcoming availability of effective vaccines and renewed fiscal stimulus negotiations. Near-dated option-implied volatility on the 10-year Treasury futures contract declined to historic lows. The rise in longer-term Treasury yields was concentrated in inflation compensation. The 5-year and 5-to-10-year measures of inflation compensation based on Treasury Inflation Protected Securities rose above their pre-pandemic levels.\n",
"\n",
"The expected path of the federal funds rate, based on a straight read of overnight index swap rates, remained close to the effective lower bound through mid-2023. Survey-based measures indicated that market expectations regarding the federal funds rate target range did not show a tightening until 2024.\n",
"\n",
"Broad stock price indexes increased over the intermeeting period, led by steep stock price gains in cyclical sectors and buoyed by the prospect of successful vaccines and lower post‑election uncertainty. One-month S&P 500 option-implied volatility—the VIX—declined, reversing a pre-election increase. Consistent with the optimism driving stock prices, spreads of corporate bond yields over comparable-maturity Treasury yields narrowed markedly across the credit spectrum, most notably for debt securities of the lowest credit quality firms.\n",
"\n",
"Conditions in short-term funding markets remained stable over the intermeeting period. Spreads on commercial paper (CP) and negotiable certificates of deposit across different tenors were little changed, on net, and remained around pre-pandemic levels despite continued outflows from prime money market funds (MMFs) and the coming year-end. CP issuance was robust over the intermeeting period across the different tenors. With the yields of prime MMFs approaching those of government MMFs, assets under management (AUM) of prime MMFs declined moderately, while AUM of government MMFs were little changed. Net yields of prime and government MMFs both remained near historically low levels.\n",
"\n",
"The intermeeting averages of the effective federal funds rate and the Secured Overnight Financing Rate remained unchanged from the previous intermeeting period averages, at 9 basis points and 8 basis points, respectively. Term and forward repo market quotes indicated muted year-end funding pressure amid ample liquidity conditions. The Federal Reserve maintained its pace of purchases of Treasury securities and agency MBS, and Federal Reserve repos outstanding remained at zero over the intermeeting period.\n",
"\n",
"Investor sentiment abroad improved over the intermeeting period, as favorable news on COVID-19 vaccines and the resolution of uncertainty regarding the U.S. election apparently outweighed concerns about another surge in COVID-19 cases and the resulting adoption of tighter social-distancing restrictions in many countries. On balance, prices of global risky assets increased notably, implied volatility dropped sharply, and the dollar depreciated against most currencies. Most advanced foreign economy sovereign yields were little changed, on net, as policymakers in several countries announced additional actions aimed at maintaining accommodative financial conditions.\n",
"\n",
"Financing conditions in capital markets continued to be broadly accommodative, supported by low interest rates and high equity valuations. With historically low corporate bond yields, gross issuance of both investment- and speculative-grade bonds remained solid in October. Much of the recent issuance was intended to refinance existing debt. Gross institutional leveraged loan issuance increased substantially in October for both new loans and refinancing. Seasoned equity offerings in October and November were similar to the typical volumes observed in previous years, though equity raised through initial public offerings moderated somewhat from the robust rate of issuance in September. Commercial and industrial (C&I) loans outstanding on banks' balance sheets contracted in October and November, reflecting the continued paydown of loan balances and the start of Paycheck Protection Program loan forgiveness activity.\n",
"\n",
"The credit quality of nonfinancial corporations continued to show signs of stabilization. Although the volume of nonfinancial corporate bond downgrades outpaced upgrades somewhat in October and November, nonfinancial corporate bond defaults continued to decline. The rate of leveraged loan defaults was largely unchanged in October, albeit at somewhat elevated levels. Market indicators of future default expectations for corporate bonds fell slightly but remained above their pre-pandemic levels. In the municipal bond market, financing conditions remained accommodative. Issuance of state and local government debt moderated in November after all-time high issuance in October, and market-based measures of state credit quality were little changed on net.\n",
"\n",
"Financing conditions for small businesses remained tight, although some indicators suggested that they might have improved a bit. Data provided by the Federal Reserve Small Business Lending Survey showed that standards for small businesses tightened, on net, over the third quarter, consistent with the most recent Senior Loan Officer Opinion Survey on Bank Lending Practices. Small business loan originations ticked up in October to a level near that seen in the same period last year. Short‑term delinquencies and defaults remained relatively elevated but significantly lower than the levels observed following the financial crisis. In light of the uncertain outlook, small business owners' assessments of the risk of permanent closures remained elevated in most sectors, according to the Census Small Business Pulse Survey.\n",
"\n",
"In the CRE market, financing conditions remained accommodative, on net, over the intermeeting period. Agency commercial mortgage-backed security (CMBS) spreads remained narrow amid strong issuance in October, while non-agency CMBS spreads ticked down. Triple-B-rated non-agency CMBS spreads came down substantially from their highs in the spring, although they remained elevated relative to pre-pandemic levels. Non-agency issuance picked up in October, nearing pre-pandemic levels. CRE bank loan growth in October and November remained weak, consistent with tightened bank lending standards.\n",
"\n",
"In the residential mortgage market, financing conditions remained highly accommodative for borrowers accessing government-backed loans. Mortgage rates remained near historic lows, supporting robust loan originations. Credit continued to flow to higher-score borrowers who met standard conforming loan criteria while remaining tight for lower-score borrowers and for nonstandard mortgage products. The credit quality of mortgages was little changed, as the fraction of mortgages in forbearance held fairly steady, and the rate of transition into mortgage delinquency remained at pre-pandemic levels.\n",
"\n",
"Financing conditions in consumer credit markets remained generally accommodative for borrowers with relatively strong credit. Credit card balances and average credit limits on existing accounts contracted, on net, for all types of borrowers. However, auto loan balances continued to increase for higher-quality borrowers, and loan rates remained well below pre-pandemic levels. Conditions in the asset-backed securities market remained stable over the intermeeting period.\n",
"\n",
"Staff Economic Outlook\n",
"In the U.S. economic projection prepared by the staff for the December FOMC meeting, real GDP growth was revised up and the unemployment rate revised down for the fourth quarter relative to the November meeting forecast. These revisions reflected incoming data that were, on balance, better than expected, although the recent resurgence of the pandemic and increased social-distancing restrictions in many states and localities were expected to weigh on economic activity in the coming months. As a result, the staff expected that real GDP growth would temporarily weaken in the first quarter of 2021, and the slowing seen in some of the most recent high-frequency indicators of spending and employment appeared consistent with that forecast. The inflation forecast for the rest of 2020 was revised down slightly in response to incoming data, and inflation was projected to finish the year at a relatively subdued level, reflecting substantial margins of labor- and product-market slack in the economy and the large declines in consumer energy prices seen earlier in 2020.\n",
"\n",
"Primarily in response to the recent favorable news on the development of COVID-19 vaccines, the staff revised up its projection of real GDP growth for 2021 as a whole, as social-distancing measures were expected to ease more quickly than previously assumed. With monetary policy assumed to remain highly accommodative, the staff continued to project that real GDP growth over the medium term would be well above the rate of potential output growth, leading to a considerable further decline in the unemployment rate. The resulting take‑up of labor- and product-market slack was expected to lead to gradually increasing inflation, and, for some time in the years beyond 2023, inflation was projected to overshoot 2 percent by a moderate amount, as monetary policy remained accommodative.\n",
"\n",
"The staff observed that the uncertainty related to the future course of the pandemic, the measures to control it, and the associated economic effects remained elevated. In addition, the staff continued to judge the risks to the economic outlook as being tilted to the downside. The recent sharp resurgence in the pandemic suggested that the near-term risks had risen, while the recent favorable developments regarding vaccines pointed to some reduction in the downside risks over the medium term.\n",
"\n",
"Participants' Views on Current Conditions and the Economic Outlook\n",
"In conjunction with this FOMC meeting, participants submitted their projections of the most likely outcomes for real GDP growth, the unemployment rate, and inflation for each year from 2020 through 2023 and over the longer run, based on their individual assessments of appropriate monetary policy, including the path of the federal funds rate. The longer-run projections represented each participant's assessment of the rate to which each variable would be expected to converge, over time, under appropriate monetary policy and in the absence of further shocks to the economy. A Summary of Economic Projections (SEP) was released to the public following the conclusion of the meeting.\n",
"\n",
"Participants noted that the COVID-19 pandemic was causing tremendous human and economic hardship across the United States and around the world. Economic activity and employment had continued to recover but remained well below their levels at the beginning of the year. Weaker demand and earlier declines in oil prices had been holding down consumer price inflation. Overall financial conditions remained accommodative, in part reflecting policy measures to support the economy and the flow of credit to U.S. households and businesses. Participants agreed that the path of the economy would depend on the course of the virus and that the ongoing public health crisis would continue to weigh on economic activity, employment, and inflation in the near term and posed considerable risks to the economic outlook over the medium term.\n",
"\n",
"Participants observed that the economy continued to show resilience in the face of the pandemic, though it was still far from having attained conditions consistent with the Committee's dual mandate. They noted that the economic recovery thus far had been stronger than anticipated—suggesting greater momentum in economic activity than had been previously thought—but viewed the more recent indicators as signaling that the pace of recovery had slowed. With the pandemic worsening across the country, the expansion was expected to slow even further in coming months. Nevertheless, the positive vaccine news received over the intermeeting period was viewed as favorable for the medium-term economic outlook.\n",
"\n",
"Participants noted that household spending on goods, especially durables, had been strong. Participants commented that the rebound in consumer spending was due, in part, to fiscal programs such as federal stimulus payments and expanded unemployment benefits. These measures had provided essential support to many households. The support to incomes provided by fiscal programs, combined with reduced spending by households on some services, had contributed to a historically large increase in aggregate household savings. Participants also observed that residential investment and home sales remained robust. Accommodative monetary policy was viewed as having provided support to interest rate sensitive expenditure categories, including residential investment and consumer durables spending. Participants regarded the positive news on vaccine development as further strengthening the medium-term outlook for household spending. However, participants saw increased challenges for the economy in the coming months, as the ongoing surge of COVID-19 cases and the related mandatory and voluntary measures prompted greater social distancing and damped spending, especially on services requiring in-person contact. Several participants pointed out that readings on high-frequency economic indicators, such as individual mobility indexes and online restaurant reservation data, might already be registering the effects of the recent rise in virus cases. Various participants noted that low-income households were particularly hard hit by the effects of the resurgent virus, and that—with the looming expiration of the expanded unemployment benefits, eviction moratoria, and loan forbearance programs—their situations could deteriorate significantly if additional relief and support did not materialize.\n",
"\n",
"With respect to the business sector, participants observed that business equipment investment had picked up further, with strong readings registered on new orders and shipments. A couple of participants remarked that the very low levels of inventories would likely be a factor supporting increases in production as demand continued to recover. Participants noted that the economic recovery had been uneven across firms and industries. Though many business contacts, particularly those in the durable goods or housing sectors, reported progress in adapting to the pandemic and improved business practices, others—especially those closely linked to the leisure, travel, and hospitality industries—were still struggling, and their problems were intensifying because of the resurgence of the virus. Furthermore, while larger firms were generally seen as recovering reasonably well, conditions remained worrisome for small businesses. A number of participants noted that many small businesses were in especially vulnerable positions and that further fiscal policy support would help such businesses weather the ongoing surge in the pandemic, especially over the coming months. Looking further ahead, participants observed that continuing positive developments on the vaccine front could further support business investment by helping reduce stresses in pandemic-sensitive industries and by boosting confidence.\n",
"\n",
"Participants remarked that labor market conditions generally had continued to improve, but they were still a long way from those consistent with the Committee's maximum employment goal. Although the pace of employment gains had moderated in recent months, the overall recovery in employment thus far had been faster than anticipated, with a little more than half of the 22 million jobs lost over March and April having been regained. The unemployment rate had declined further, although several participants underlined the fact that the labor force participation rate remained below its pre-pandemic level—likely reflecting, in part, health concerns and additional childcare responsibilities associated with online schooling. Participants assessed that the ongoing surge in COVID-19 infections would be particularly challenging for the labor market in coming months, but they indicated that they expected employment to continue to recover over the medium term. Participants stressed that the burdens of the economic downturn had fallen unequally on different groups; in particular, high rates of job losses had been especially prevalent among lower-wage workers and among African Americans and Hispanics. Some participants expressed the concern that the longer the pandemic continued, the more lasting damage to the labor market there could be. They noted that the number of unemployed workers who had been permanently laid off had increased notably in recent months and that those workers historically often required a longer time to find a new job than those temporarily laid off. In light of these considerations, several participants assessed that improvements in the labor market were lagging that of economic activity, and they indicated that they had not revised their projections of labor market variables to the same extent as their revision of the outlook for economic activity.\n",
"\n",
"In their comments about inflation, participants noted that increases in consumer prices had been soft of late, as prices of products in those categories most affected by social distancing—such as hotel accommodations and air travel—continued to be depressed and increases in rents remained low. These patterns were expected to continue in the near term as pandemic concerns intensified over the winter. However, participants generally saw these downward pressures on inflation starting to abate next year, with widespread distribution of vaccines reducing social-distancing concerns and spurring economic activity. A couple of participants suggested that, as a result of ongoing technology-enabled disruption to business models and practices or lasting pandemic-induced restraint on firms' pricing power, downward pressure on inflation could persist. Several participants noted a pickup in market-based measures of inflation compensation. Participants expected that, with continued monetary policy support, inflation would rise over time. In their SEP submissions, seven participants—five more than in the September SEP—expected overall inflation to be above the Committee's 2 percent longer-run objective in 2023.\n",
"\n",
"Participants noted that overall financial conditions were accommodative, in part reflecting policy measures to support the economy and the flow of credit to households and businesses. However, participants underlined important differences in credit availability across borrowers. Financing conditions eased further for large corporations that were able to access capital markets, as equity prices rose and corporate credit spreads continued to narrow, but smaller firms and some households reliant on bank lending continued to face tight lending standards. Participants noted that the financing conditions for small businesses were especially strained, with a few participants pointing out that a sizable fraction of small businesses had permanently closed or were in the process of transitioning to closure. A couple of participants observed that aggregate banking data had not indicated a significant increase in loan delinquencies for C&I loans thus far, though this development could be partly due to the CARES Act provisions that provided relief to many troubled borrowers or to the fact that many small businesses had gone out of business without declaring bankruptcy or defaulting on loans. Some participants noted the important role played by the various section 13(3) facilities implemented in 2020 in serving as temporary backstops to key credit markets and in helping to restore and maintain the flow of credit to households, businesses, and communities. These participants also mentioned the announcement that CARES Act funding to support new activity in many of these facilities would not be available after December 31, and a number noted that they saw downside risks associated with this development.\n",
"\n",
"Participants continued to see the uncertainty surrounding the economic outlook as elevated, with the path of the economy highly dependent on the course of the virus. The positive vaccine news was seen as reducing downside risks over the medium term, and a number of participants saw risks to economic activity as more balanced than earlier. Still, participants saw significant uncertainties regarding how quickly the deployment of vaccines would proceed as well as how different members of the public would respond to the availability of vaccines. Participants cited several downside risks that could threaten the economic recovery. These risks included the possibility of significant additional fiscal policy support not materializing in a timely manner, the potential for further adverse pandemic developments—which could lead to more-stringent restrictions, more-severe business failures, and more permanent job losses—and the chance that trade negotiations between the United Kingdom and the European Union would not be concluded successfully before the December 31 deadline. As upside risks, participants mentioned the prospect that the release of pent-up demand, spurred by wider-scale vaccinations and easing of social distancing, could boost spending and bring individuals back to the labor force more quickly than currently expected as well as the possibility that fiscal policy developments could see measures that were larger than expected in amount or economic impact. Regarding inflation, participants generally viewed the risks as having become more balanced than they were earlier in the year, though most still viewed the risks as being weighted to the downside. As an upside risk to inflation, a few participants noted the potential for a stronger-than-expected recovery, coupled with the possible emergence of pandemic-related supply constraints, to boost inflation.\n",
"\n",
"In their consideration of monetary policy at this meeting, participants reaffirmed the Federal Reserve's commitment to using its full range of tools to support the U.S. economy during this challenging time, thereby promoting the Committee's statutory goals of maximum employment and price stability. Participants agreed that the path of the economy would depend significantly on the course of the virus and that the ongoing public health crisis would continue to weigh on economic activity, employment, and inflation in the near term. Participants noted that, with the pandemic worsening across the country, the expansion would likely slow in coming months. In contrast, for the medium term, participants commented that positive vaccine news had improved the economic outlook. That said, participants agreed that the path ahead remained highly uncertain and that the economy remained far from the Committee's longer-run goals. In light of this assessment, all participants judged that maintaining an accommodative stance of monetary policy was essential to foster economic recovery and to achieve an average inflation rate of 2 percent over time.\n",
"\n",
"All participants supported enhancing the Committee's guidance on asset purchases at this meeting and, in particular, adopting qualitative, outcome-based guidance indicating that increases in asset holdings would continue, with purchases of Treasury securities of at least $80 billion per month and of agency MBS of at least $40 billion per month, until substantial further progress has been made toward reaching the Committee's maximum employment and price stability goals. In their discussions of this change, participants noted that the new guidance regarding balance sheet policy brought the statement's references to purchases into better alignment with the Committee's outcome-based guidance on the federal funds rate, offered more clarity about the role played by the asset purchase program in providing accommodation to meet the Committee's economic goals, and underscored the responsiveness of balance sheet policy to unanticipated economic developments. A few participants stressed that all of the Committee's policy tools were now well positioned to respond to the evolution of the economy. For example, if progress toward the Committee's goals proved slower than anticipated, the new guidance relayed the Committee's intention to respond by increasing monetary policy accommodation through maintaining the current level of the target range of the federal funds rate for longer and raising the expected path of the Federal Reserve's balance sheet. A couple of participants remarked that, against this background, it was important to convey to the public that the federal funds rate remained the Committee's primary policy tool.\n",
"\n",
"A number of participants discussed considerations related to determining the eventual attainment of \"substantial further progress\" toward reaching the Committee's maximum employment and price stability goals. Participants commented that this judgment would be broad, qualitative, and not based on specific numerical criteria or thresholds. Various participants noted the importance of the Committee clearly communicating its assessment of actual and expected progress toward its longer-run goals well in advance of the time when it could be judged substantial enough to warrant a change in the pace of purchases.\n",
"\n",
"Regarding the decisions on the pace and composition of the Committee's asset purchases, all participants judged that it would be appropriate to continue those purchases at least at the current pace, and nearly all favored maintaining the current composition of purchases, although a couple of participants indicated that they were open to weighting purchases of Treasury securities toward longer maturities. Participants generally judged that the asset purchase program as structured was providing very significant policy accommodation. Some participants noted that the Committee could consider future adjustments to its asset purchases—such as increasing the pace of securities purchases or weighting purchases of Treasury securities toward those that had longer remaining maturities—if such adjustments were deemed appropriate to support the attainment of the Committee's objectives. A few participants underlined the importance of continuing to evaluate the balance of costs and risks associated with asset purchases against the benefits arising from purchases.\n",
"\n",
"Participants shared their views on the appropriate evolution of asset purchases once substantial further progress had been made toward the Committee's maximum employment and price stability goals. A number of participants noted that, once such progress had been attained, a gradual tapering of purchases could begin and the process thereafter could generally follow a sequence similar to the one implemented during the large-scale purchase program in 2013 and 2014.\n",
"\n",
"Committee Policy Action\n",
"In their discussion of monetary policy for this meeting, members agreed that the COVID-19 pandemic was causing tremendous human and economic hardship across the United States and around the world. They noted that economic activity and employment had continued to recover but remained well below their levels at the beginning of the year and that weaker demand and earlier declines in oil prices had been holding down consumer price inflation. Overall financial conditions remained accommodative, in part reflecting policy measures to support the economy and the flow of credit to U.S. households and businesses. Members agreed that the Federal Reserve was committed to using its full range of tools to support the U.S. economy in this challenging time, thereby promoting its maximum employment and price stability goals. Members also stated that the path of the economy would depend significantly on the course of the virus. In addition, members agreed that the ongoing public health crisis would continue to weigh on economic activity, employment, and inflation in the near term and was posing considerable risks to the economic outlook over the medium term.\n",
"\n",
"All members reaffirmed that, in accordance with the Committee's goals to achieve maximum employment and inflation at the rate of 2 percent over the longer run and with inflation running persistently below this longer-run goal, they would aim to achieve inflation moderately above 2 percent for some time so that inflation averages 2 percent over time and longer-term inflation expectations remain well anchored at 2 percent. Members expected to maintain an accommodative stance of monetary policy until those outcomes were achieved.\n",
"\n",
"All members agreed to maintain the target range for the federal funds rate at 0 to 1/4 percent, and they expected that it would be appropriate to maintain this target range until labor market conditions had reached levels consistent with the Committee's assessments of maximum employment and inflation had risen to 2 percent and was on track to moderately exceed 2 percent for some time.\n",
"\n",
"In addition, members agreed that it would be appropriate for the Federal Reserve to continue to increase its holdings of Treasury securities by at least $80 billion per month and agency MBS by at least $40 billion per month until substantial further progress had been made toward the Committee's maximum employment and price stability goals. They judged that these asset purchases would help foster smooth market functioning and accommodative financial conditions, thereby supporting the flow of credit to households and businesses.\n",
"\n",
"Members agreed that, in assessing the appropriate stance of monetary policy, they would continue to monitor the implications of incoming information for the economic outlook and that they would be prepared to adjust the stance of monetary policy as appropriate in the event that risks emerged that could impede the attainment of the Committee's goals. Members also agreed that, in assessing the appropriate stance of monetary policy, they would take into account a wide range of information, including readings on public health, labor market conditions, inflation pressures and inflation expectations, and financial and international developments.\n",
"\n",
"At the conclusion of the discussion, the Committee voted to authorize and direct the Federal Reserve Bank of New York, until instructed otherwise, to execute transactions in the SOMA in accordance with the following domestic policy directive, for release at 2:00 p.m.:\n",
"\n",
"\"Effective December 17, 2020, the Federal Open Market Committee directs the Desk to:\n",
"\n",
"The vote also encompassed approval of a statement for release.6 The following statement was released at 2:00 p.m.:\n",
"\n",
"\"The Federal Reserve is committed to using its full range of tools to support the U.S. economy in this challenging time, thereby promoting its maximum employment and price stability goals.\n",
"\n",
"The COVID-19 pandemic is causing tremendous human and economic hardship across the United States and around the world. Economic activity and employment have continued to recover but remain well below their levels at the beginning of the year. Weaker demand and earlier declines in oil prices have been holding down consumer price inflation. Overall financial conditions remain accommodative, in part reflecting policy measures to support the economy and the flow of credit to U.S. households and businesses.\n",
"\n",
"The path of the economy will depend significantly on the course of the virus. The ongoing public health crisis will continue to weigh on economic activity, employment, and inflation in the near term, and poses considerable risks to the economic outlook over the medium term.\n",
"\n",
"The Committee seeks to achieve maximum employment and inflation at the rate of 2 percent over the longer run. With inflation running persistently below this longer-run goal, the Committee will aim to achieve inflation moderately above 2 percent for some time so that inflation averages 2 percent over time and longer-term inflation expectations remain well anchored at 2 percent. The Committee expects to maintain an accommodative stance of monetary policy until these outcomes are achieved. The Committee decided to keep the target range for the federal funds rate at 0 to 1/4 percent and expects it will be appropriate to maintain this target range until labor market conditions have reached levels consistent with the Committee's assessments of maximum employment and inflation has risen to 2 percent and is on track to moderately exceed 2 percent for some time. In addition, the Federal Reserve will continue to increase its holdings of Treasury securities by at least $80 billion per month and of agency mortgage-backed securities by at least $40 billion per month until substantial further progress has been made toward the Committee's maximum employment and price stability goals. These asset purchases help foster smooth market functioning and accommodative financial conditions, thereby supporting the flow of credit to households and businesses.\n",
"\n",
"In assessing the appropriate stance of monetary policy, the Committee will continue to monitor the implications of incoming information for the economic outlook. The Committee would be prepared to adjust the stance of monetary policy as appropriate if risks emerge that could impede the attainment of the Committee's goals. The Committee's assessments will take into account a wide range of information, including readings on public health, labor market conditions, inflation pressures and inflation expectations, and financial and international developments.\"\n",
"\n",
"Voting for this action: Jerome H. Powell, John C. Williams, Michelle W. Bowman, Lael Brainard, Richard H. Clarida, Patrick Harker, Robert S. Kaplan, Neel Kashkari, Loretta J. Mester, and Randal K. Quarles.\n",
"\n",
"Voting against this action: None.\n",
"\n",
"Consistent with the Committee's decision to leave the target range for the federal funds rate unchanged, the Board of Governors voted unanimously to leave the interest rates on required and excess reserve balances at 0.10 percent. The Board of Governors also voted unanimously to approve establishment of the primary credit rate at the existing level of 0.25 percent, effective December 17, 2020.\n",
"\n",
"It was agreed that the next meeting of the Committee would be held on Tuesday–Wednesday, January 26–27, 2021. The meeting adjourned at 10:05 a.m. on December 16, 2020.\n",
"\n",
"Notation Vote\n",
"By notation vote completed on November 24, 2020, the Committee unanimously approved the minutes of the Committee meeting held on November 4–5, 2020.\n",
"\n",
"_______________________\n",
"\n",
"James A. Clouse\n",
"Secretary\n",
"\n",
"1. The Federal Open Market Committee is referenced as the \"FOMC\" and the \"Committee\" in these minutes. Return to text\n",
"\n",
"2. Elected as an Alternate by the Federal Reserve Bank of New York, effective November 11, 2020. Return to text\n",
"\n",
"3. Attended Tuesday's session only. Return to text\n",
"\n",
"4. Attended through the discussion of developments in financial markets and open market operations. Return to text\n",
"\n",
"5. The approved FIMA Desk Resolution, which updates the July 2020 resolution with a new expiration date, is available along with other Committee organizational documents at https://www.federalreserve.gov/monetarypolicy/rules_authorizations.htm. Return to text\n",
"\n",
"6. The statement approved at the meeting included a drafting error. By notation vote shortly after the meeting concluded, the Committee unanimously approved a corrected version of the statement for release at 2:00 p.m. Return to text\n",
"\n",
"Board of Governors of the Federal Reserve System\n",
"\n",
"20th Street and Constitution Avenue N.W., Washington, DC 20551\n",
"time: 8.24 ms (started: 2021-02-18 14:53:31 +00:00)\n"
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"metadata": {
"id": "u7IS-QPvhqC7"
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"source": [
"## **Loughran-McDonald Financial Dictionary**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1y9LsE4EmIh5"
},
"source": [
"### Build Dictionary\r\n",
"*Build a custom dictionary based on Loughran and McDonald's Dictionary of Financial Terms.*"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KStCIFDdiWBB"
},
"source": [
"#### Maintenance"
]
},
{
"cell_type": "code",
"metadata": {
"id": "BwJbv26CmIh5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 690
},
"outputId": "685c90e0-7f86-45bf-c037-2527623d276a"
},
"source": [
"data_df['Index'] = range(0, len(data_df))\n",
"data_df.set_index(['date'], inplace=True)\n",
"data_df.head()\n"
],
"execution_count": null,
"outputs": [
{
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"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>contents</th>\n",
" <th>speaker</th>\n",
" <th>text</th>\n",
" <th>Index</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1993-02-03</th>\n",
" <td>A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System in \\r\\n Washington, D.C., on Tuesday, February 2, 1993, at 2:30 p.m. and was \\r\\n continued on Wednesday, February 3, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPR...</td>\n",
" <td>Alan Greenspan</td>\n",
" <td>A meeting of the Federal Open Market Committee was held in the offices of the Board of Governors of the Federal Reserve System in Washington, D.C., on Tuesday, February 2, 1993, at 2:30 p.m. and was continued on Wednesday, February 3, 1993, at 9:00 a.m.PRESENT:Mr. Greenspan, Ch...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1993-03-23</th>\n",
" <td>A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System in \\r\\n Washington, D.C., on Tuesday, March 23, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[SECTION]\\n\\nMr. Greenspan, Chairman \\r\\n Mr. Corrig...</td>\n",
" <td>Alan Greenspan</td>\n",
" <td>A meeting of the Federal Open Market Committee was held in the offices of the Board of Governors of the Federal Reserve System in Washington, D.C., on Tuesday, March 23, 1993, at 9:00 a.m.PRESENT:Mr. Greenspan, Chairman Mr. Corrigan, Vice Chairman Mr. Angell ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1993-05-18</th>\n",
" <td>A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System \\r\\n in Washington, D.C., on Tuesday, May 18, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[SECTION]\\n\\nMr. Greenspan, Chairman \\r\\n Mr. Corrigan, ...</td>\n",
" <td>Alan Greenspan</td>\n",
" <td>A meeting of the Federal Open Market Committee was held in the offices of the Board of Governors of the Federal Reserve System in Washington, D.C., on Tuesday, May 18, 1993, at 9:00 a.m.PRESENT:Mr. Greenspan, Chairman Mr. Corrigan, Vice Chairman Mr. Angell ...</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1993-07-07</th>\n",
" <td>A meeting of the Federal Open Market Committee was held in the \\r\\n offices of the Board of Governors of the Federal Reserve System in \\r\\n Washington, D.C., on Tuesday, July 6, 1993, at 2:30 p.m. and continued \\r\\n on Wednesday, July 7, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[S...</td>\n",
" <td>Alan Greenspan</td>\n",
" <td>A meeting of the Federal Open Market Committee was held in the offices of the Board of Governors of the Federal Reserve System in Washington, D.C., on Tuesday, July 6, 1993, at 2:30 p.m. and continued on Wednesday, July 7, 1993, at 9:00 a.m.PRESENT:Mr. Greenspan. Chairman ...</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1993-08-17</th>\n",
" <td>A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System \\r\\n in Washington, D.C., on Tuesday, August 17, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[SECTION]\\n\\nMr. Greenspan, Chairman\\r\\n Mr. McDonough, V...</td>\n",
" <td>Alan Greenspan</td>\n",
" <td>A meeting of the Federal Open Market Committee was held in the offices of the Board of Governors of the Federal Reserve System in Washington, D.C., on Tuesday, August 17, 1993, at 9:00 a.m.PRESENT:Mr. Greenspan, Chairman Mr. McDonough, Vice Chairman Mr. Angell Mr...</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" contents ... Index\n",
"date ... \n",
"1993-02-03 A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System in \\r\\n Washington, D.C., on Tuesday, February 2, 1993, at 2:30 p.m. and was \\r\\n continued on Wednesday, February 3, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPR... ... 0\n",
"1993-03-23 A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System in \\r\\n Washington, D.C., on Tuesday, March 23, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[SECTION]\\n\\nMr. Greenspan, Chairman \\r\\n Mr. Corrig... ... 1\n",
"1993-05-18 A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System \\r\\n in Washington, D.C., on Tuesday, May 18, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[SECTION]\\n\\nMr. Greenspan, Chairman \\r\\n Mr. Corrigan, ... ... 2\n",
"1993-07-07 A meeting of the Federal Open Market Committee was held in the \\r\\n offices of the Board of Governors of the Federal Reserve System in \\r\\n Washington, D.C., on Tuesday, July 6, 1993, at 2:30 p.m. and continued \\r\\n on Wednesday, July 7, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[S... ... 3\n",
"1993-08-17 A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System \\r\\n in Washington, D.C., on Tuesday, August 17, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[SECTION]\\n\\nMr. Greenspan, Chairman\\r\\n Mr. McDonough, V... ... 4\n",
"\n",
"[5 rows x 4 columns]"
]
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"tags": []
},
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{
"output_type": "stream",
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"time: 33.6 ms (started: 2021-02-18 14:53:31 +00:00)\n"
],
"name": "stdout"
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]
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"cell_type": "markdown",
"metadata": {
"id": "TimdNUb_iZbP"
},
"source": [
"#### Import Dictionary"
]
},
{
"cell_type": "code",
"metadata": {
"id": "qxd8QnWemIh5",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0fa6f5fe-b351-4f4f-9594-8a5b23a3ba02"
},
"source": [
"import re\n",
"\n",
"# Loughran and McDonald Sentiment Word Lists (https://sraf.nd.edu/textual-analysis/resources/)\n",
"lmdict = {'Negative': ['abandon', 'abandoned', 'abandoning', 'abandonment', 'abandonments', 'abandons', 'abdicated',\n",
" 'abdicates', 'abdicating', 'abdication', 'abdications', 'aberrant', 'aberration', 'aberrational',\n",
" 'aberrations', 'abetting', 'abnormal', 'abnormalities', 'abnormality', 'abnormally', 'abolish',\n",
" 'abolished', 'abolishes', 'abolishing', 'abrogate', 'abrogated', 'abrogates', 'abrogating',\n",
" 'abrogation', 'abrogations', 'abrupt', 'abruptly', 'abruptness', 'absence', 'absences',\n",
" 'absenteeism', 'abuse', 'abused', 'abuses', 'abusing', 'abusive', 'abusively', 'abusiveness',\n",
" 'accident', 'accidental', 'accidentally', 'accidents', 'accusation', 'accusations', 'accuse',\n",
" 'accused', 'accuses', 'accusing', 'acquiesce', 'acquiesced', 'acquiesces', 'acquiescing',\n",
" 'acquit', 'acquits', 'acquittal', 'acquittals', 'acquitted', 'acquitting', 'adulterate',\n",
" 'adulterated', 'adulterating', 'adulteration', 'adulterations', 'adversarial', 'adversaries',\n",
" 'adversary', 'adverse', 'adversely', 'adversities', 'adversity', 'aftermath', 'aftermaths',\n",
" 'against', 'aggravate', 'aggravated', 'aggravates', 'aggravating', 'aggravation', 'aggravations',\n",
" 'alerted', 'alerting', 'alienate', 'alienated', 'alienates', 'alienating', 'alienation',\n",
" 'alienations', 'allegation', 'allegations', 'allege', 'alleged', 'allegedly', 'alleges',\n",
" 'alleging', 'annoy', 'annoyance', 'annoyances', 'annoyed', 'annoying', 'annoys', 'annul',\n",
" 'annulled', 'annulling', 'annulment', 'annulments', 'annuls', 'anomalies', 'anomalous',\n",
" 'anomalously', 'anomaly', 'anticompetitive', 'antitrust', 'argue', 'argued', 'arguing',\n",
" 'argument', 'argumentative', 'arguments', 'arrearage', 'arrearages', 'arrears', 'arrest',\n",
" 'arrested', 'arrests', 'artificially', 'assault', 'assaulted', 'assaulting', 'assaults',\n",
" 'assertions', 'attrition', 'aversely', 'backdating', 'bad', 'bail', 'bailout', 'balk', 'balked',\n",
" 'bankrupt', 'bankruptcies', 'bankruptcy', 'bankrupted', 'bankrupting', 'bankrupts', 'bans',\n",
" 'barred', 'barrier', 'barriers', 'bottleneck', 'bottlenecks', 'boycott', 'boycotted',\n",
" 'boycotting', 'boycotts', 'breach', 'breached', 'breaches', 'breaching', 'break', 'breakage',\n",
" 'breakages', 'breakdown', 'breakdowns', 'breaking', 'breaks', 'bribe', 'bribed', 'briberies',\n",
" 'bribery', 'bribes', 'bribing', 'bridge', 'broken', 'burden', 'burdened', 'burdening', 'burdens',\n",
" 'burdensome', 'burned', 'calamities', 'calamitous', 'calamity', 'cancel', 'canceled',\n",
" 'canceling', 'cancellation', 'cancellations', 'cancelled', 'cancelling', 'cancels', 'careless',\n",
" 'carelessly', 'carelessness', 'catastrophe', 'catastrophes', 'catastrophic', 'catastrophically',\n",
" 'caution', 'cautionary', 'cautioned', 'cautioning', 'cautions', 'cease', 'ceased', 'ceases',\n",
" 'ceasing', 'censure', 'censured', 'censures', 'censuring', 'challenge', 'challenged',\n",
" 'challenges', 'challenging', 'chargeoffs', 'circumvent', 'circumvented', 'circumventing',\n",
" 'circumvention', 'circumventions', 'circumvents', 'claiming', 'claims', 'clawback', 'closed',\n",
" 'closeout', 'closeouts', 'closing', 'closings', 'closure', 'closures', 'coerce', 'coerced',\n",
" 'coerces', 'coercing', 'coercion', 'coercive', 'collapse', 'collapsed', 'collapses',\n",
" 'collapsing', 'collision', 'collisions', 'collude', 'colluded', 'colludes', 'colluding',\n",
" 'collusion', 'collusions', 'collusive', 'complain', 'complained', 'complaining', 'complains',\n",
" 'complaint', 'complaints', 'complicate', 'complicated', 'complicates', 'complicating',\n",
" 'complication', 'complications', 'compulsion', 'concealed', 'concealing', 'concede', 'conceded',\n",
" 'concedes', 'conceding', 'concern', 'concerned', 'concerns', 'conciliating', 'conciliation',\n",
" 'conciliations', 'condemn', 'condemnation', 'condemnations', 'condemned', 'condemning',\n",
" 'condemns', 'condone', 'condoned', 'confess', 'confessed', 'confesses', 'confessing',\n",
" 'confession', 'confine', 'confined', 'confinement', 'confinements', 'confines', 'confining',\n",
" 'confiscate', 'confiscated', 'confiscates', 'confiscating', 'confiscation', 'confiscations',\n",
" 'conflict', 'conflicted', 'conflicting', 'conflicts', 'confront', 'confrontation',\n",
" 'confrontational', 'confrontations', 'confronted', 'confronting', 'confronts', 'confuse',\n",
" 'confused', 'confuses', 'confusing', 'confusingly', 'confusion', 'conspiracies', 'conspiracy',\n",
" 'conspirator', 'conspiratorial', 'conspirators', 'conspire', 'conspired', 'conspires',\n",
" 'conspiring', 'contempt', 'contend', 'contended', 'contending', 'contends', 'contention',\n",
" 'contentions', 'contentious', 'contentiously', 'contested', 'contesting', 'contraction',\n",
" 'contractions', 'contradict', 'contradicted', 'contradicting', 'contradiction', 'contradictions',\n",
" 'contradictory', 'contradicts', 'contrary', 'controversial', 'controversies', 'controversy',\n",
" 'convict', 'convicted', 'convicting', 'conviction', 'convictions', 'corrected', 'correcting',\n",
" 'correction', 'corrections', 'corrects', 'corrupt', 'corrupted', 'corrupting', 'corruption',\n",
" 'corruptions', 'corruptly', 'corruptness', 'costly', 'counterclaim', 'counterclaimed',\n",
" 'counterclaiming', 'counterclaims', 'counterfeit', 'counterfeited', 'counterfeiter',\n",
" 'counterfeiters', 'counterfeiting', 'counterfeits', 'countermeasure', 'countermeasures', 'crime',\n",
" 'crimes', 'criminal', 'criminally', 'criminals', 'crises', 'crisis', 'critical', 'critically',\n",
" 'criticism', 'criticisms', 'criticize', 'criticized', 'criticizes', 'criticizing', 'crucial',\n",
" 'crucially', 'culpability', 'culpable', 'culpably', 'cumbersome', 'curtail', 'curtailed',\n",
" 'curtailing', 'curtailment', 'curtailments', 'curtails', 'cut', 'cutback', 'cutbacks',\n",
" 'cyberattack', 'cyberattacks', 'cyberbullying', 'cybercrime', 'cybercrimes', 'cybercriminal',\n",
" 'cybercriminals', 'damage', 'damaged', 'damages', 'damaging', 'dampen', 'dampened', 'danger',\n",
" 'dangerous', 'dangerously', 'dangers', 'deadlock', 'deadlocked', 'deadlocking', 'deadlocks',\n",
" 'deadweight', 'deadweights', 'debarment', 'debarments', 'debarred', 'deceased', 'deceit',\n",
" 'deceitful', 'deceitfulness', 'deceive', 'deceived', 'deceives', 'deceiving', 'deception',\n",
" 'deceptions', 'deceptive', 'deceptively', 'decline', 'declined', 'declines', 'declining',\n",
" 'deface', 'defaced', 'defacement', 'defamation', 'defamations', 'defamatory', 'defame',\n",
" 'defamed', 'defames', 'defaming', 'default', 'defaulted', 'defaulting', 'defaults', 'defeat',\n",
" 'defeated', 'defeating', 'defeats', 'defect', 'defective', 'defects', 'defend', 'defendant',\n",
" 'defendants', 'defended', 'defending', 'defends', 'defensive', 'defer', 'deficiencies',\n",
" 'deficiency', 'deficient', 'deficit', 'deficits', 'defraud', 'defrauded', 'defrauding',\n",
" 'defrauds', 'defunct', 'degradation', 'degradations', 'degrade', 'degraded', 'degrades',\n",
" 'degrading', 'delay', 'delayed', 'delaying', 'delays', 'deleterious', 'deliberate',\n",
" 'deliberated', 'deliberately', 'delinquencies', 'delinquency', 'delinquent', 'delinquently',\n",
" 'delinquents', 'delist', 'delisted', 'delisting', 'delists', 'demise', 'demised', 'demises',\n",
" 'demising', 'demolish', 'demolished', 'demolishes', 'demolishing', 'demolition', 'demolitions',\n",
" 'demote', 'demoted', 'demotes', 'demoting', 'demotion', 'demotions', 'denial', 'denials',\n",
" 'denied', 'denies', 'denigrate', 'denigrated', 'denigrates', 'denigrating', 'denigration',\n",
" 'deny', 'denying', 'deplete', 'depleted', 'depletes', 'depleting', 'depletion', 'depletions',\n",
" 'deprecation', 'depress', 'depressed', 'depresses', 'depressing', 'deprivation', 'deprive',\n",
" 'deprived', 'deprives', 'depriving', 'derelict', 'dereliction', 'derogatory', 'destabilization',\n",
" 'destabilize', 'destabilized', 'destabilizing', 'destroy', 'destroyed', 'destroying', 'destroys',\n",
" 'destruction', 'destructive', 'detain', 'detained', 'detention', 'detentions', 'deter',\n",
" 'deteriorate', 'deteriorated', 'deteriorates', 'deteriorating', 'deterioration',\n",
" 'deteriorations', 'deterred', 'deterrence', 'deterrences', 'deterrent', 'deterrents',\n",
" 'deterring', 'deters', 'detract', 'detracted', 'detracting', 'detriment', 'detrimental',\n",
" 'detrimentally', 'detriments', 'devalue', 'devalued', 'devalues', 'devaluing', 'devastate',\n",
" 'devastated', 'devastating', 'devastation', 'deviate', 'deviated', 'deviates', 'deviating',\n",
" 'deviation', 'deviations', 'devolve', 'devolved', 'devolves', 'devolving', 'difficult',\n",
" 'difficulties', 'difficultly', 'difficulty', 'diminish', 'diminished', 'diminishes',\n",
" 'diminishing', 'diminution', 'disadvantage', 'disadvantaged', 'disadvantageous', 'disadvantages',\n",
" 'disaffiliation', 'disagree', 'disagreeable', 'disagreed', 'disagreeing', 'disagreement',\n",
" 'disagreements', 'disagrees', 'disallow', 'disallowance', 'disallowances', 'disallowed',\n",
" 'disallowing', 'disallows', 'disappear', 'disappearance', 'disappearances', 'disappeared',\n",
" 'disappearing', 'disappears', 'disappoint', 'disappointed', 'disappointing', 'disappointingly',\n",
" 'disappointment', 'disappointments', 'disappoints', 'disapproval', 'disapprovals', 'disapprove',\n",
" 'disapproved', 'disapproves', 'disapproving', 'disassociates', 'disassociating',\n",
" 'disassociation', 'disassociations', 'disaster', 'disasters', 'disastrous', 'disastrously',\n",
" 'disavow', 'disavowal', 'disavowed', 'disavowing', 'disavows', 'disciplinary', 'disclaim',\n",
" 'disclaimed', 'disclaimer', 'disclaimers', 'disclaiming', 'disclaims', 'disclose', 'disclosed',\n",
" 'discloses', 'disclosing', 'discontinuance', 'discontinuances', 'discontinuation',\n",
" 'discontinuations', 'discontinue', 'discontinued', 'discontinues', 'discontinuing', 'discourage',\n",
" 'discouraged', 'discourages', 'discouraging', 'discredit', 'discredited', 'discrediting',\n",
" 'discredits', 'discrepancies', 'discrepancy', 'disfavor', 'disfavored', 'disfavoring',\n",
" 'disfavors', 'disgorge', 'disgorged', 'disgorgement', 'disgorgements', 'disgorges', 'disgorging',\n",
" 'disgrace', 'disgraceful', 'disgracefully', 'dishonest', 'dishonestly', 'dishonesty', 'dishonor',\n",
" 'dishonorable', 'dishonorably', 'dishonored', 'dishonoring', 'dishonors', 'disincentives',\n",
" 'disinterested', 'disinterestedly', 'disinterestedness', 'disloyal', 'disloyally', 'disloyalty',\n",
" 'dismal', 'dismally', 'dismiss', 'dismissal', 'dismissals', 'dismissed', 'dismisses',\n",
" 'dismissing', 'disorderly', 'disparage', 'disparaged', 'disparagement', 'disparagements',\n",
" 'disparages', 'disparaging', 'disparagingly', 'disparities', 'disparity', 'displace',\n",
" 'displaced', 'displacement', 'displacements', 'displaces', 'displacing', 'dispose', 'dispossess',\n",
" 'dispossessed', 'dispossesses', 'dispossessing', 'disproportion', 'disproportional',\n",
" 'disproportionate', 'disproportionately', 'dispute', 'disputed', 'disputes', 'disputing',\n",
" 'disqualification', 'disqualifications', 'disqualified', 'disqualifies', 'disqualify',\n",
" 'disqualifying', 'disregard', 'disregarded', 'disregarding', 'disregards', 'disreputable',\n",
" 'disrepute', 'disrupt', 'disrupted', 'disrupting', 'disruption', 'disruptions', 'disruptive',\n",
" 'disrupts', 'dissatisfaction', 'dissatisfied', 'dissent', 'dissented', 'dissenter', 'dissenters',\n",
" 'dissenting', 'dissents', 'dissident', 'dissidents', 'dissolution', 'dissolutions', 'distort',\n",
" 'distorted', 'distorting', 'distortion', 'distortions', 'distorts', 'distract', 'distracted',\n",
" 'distracting', 'distraction', 'distractions', 'distracts', 'distress', 'distressed', 'disturb',\n",
" 'disturbance', 'disturbances', 'disturbed', 'disturbing', 'disturbs', 'diversion', 'divert',\n",
" 'diverted', 'diverting', 'diverts', 'divest', 'divested', 'divesting', 'divestiture',\n",
" 'divestitures', 'divestment', 'divestments', 'divests', 'divorce', 'divorced', 'divulge',\n",
" 'divulged', 'divulges', 'divulging', 'doubt', 'doubted', 'doubtful', 'doubts', 'downgrade',\n",
" 'downgraded', 'downgrades', 'downgrading', 'downsize', 'downsized', 'downsizes', 'downsizing',\n",
" 'downsizings', 'downtime', 'downtimes', 'downturn', 'downturns', 'downward', 'downwards', 'drag',\n",
" 'drastic', 'drastically', 'drawback', 'drawbacks', 'drop', 'dropped', 'drought', 'droughts', 'duress',\n",
" 'dysfunction', 'dysfunctional', 'dysfunctions', 'easing', 'egregious', 'egregiously', 'embargo',\n",
" 'embargoed', 'embargoes', 'embargoing', 'embarrass', 'embarrassed', 'embarrasses',\n",
" 'embarrassing', 'embarrassment', 'embarrassments', 'embezzle', 'embezzled', 'embezzlement',\n",
" 'embezzlements', 'embezzler', 'embezzles', 'embezzling', 'encroach', 'encroached', 'encroaches',\n",
" 'encroaching', 'encroachment', 'encroachments', 'encumber', 'encumbered', 'encumbering',\n",
" 'encumbers', 'encumbrance', 'encumbrances', 'endanger', 'endangered', 'endangering',\n",
" 'endangerment', 'endangers', 'enjoin', 'enjoined', 'enjoining', 'enjoins', 'erode', 'eroded',\n",
" 'erodes', 'eroding', 'erosion', 'erratic', 'erratically', 'erred', 'erring', 'erroneous',\n",
" 'erroneously', 'error', 'errors', 'errs', 'escalate', 'escalated', 'escalates', 'escalating',\n",
" 'evade', 'evaded', 'evades', 'evading', 'evasion', 'evasions', 'evasive', 'evict', 'evicted',\n",
" 'evicting', 'eviction', 'evictions', 'evicts', 'exacerbate', 'exacerbated', 'exacerbates',\n",
" 'exacerbating', 'exacerbation', 'exacerbations', 'exaggerate', 'exaggerated', 'exaggerates',\n",
" 'exaggerating', 'exaggeration', 'excessive', 'excessively', 'exculpate', 'exculpated',\n",
" 'exculpates', 'exculpating', 'exculpation', 'exculpations', 'exculpatory', 'exonerate',\n",
" 'exonerated', 'exonerates', 'exonerating', 'exoneration', 'exonerations', 'exploit',\n",
" 'exploitation', 'exploitations', 'exploitative', 'exploited', 'exploiting', 'exploits', 'expose',\n",
" 'exposed', 'exposes', 'exposing', 'expropriate', 'expropriated', 'expropriates', 'expropriating',\n",
" 'expropriation', 'expropriations', 'expulsion', 'expulsions', 'extenuating', 'fail', 'failed',\n",
" 'failing', 'failings', 'fails', 'failure', 'failures', 'fallout', 'false', 'falsely',\n",
" 'falsification', 'falsifications', 'falsified', 'falsifies', 'falsify', 'falsifying', 'falsity',\n",
" 'fatalities', 'fatality', 'fatally', 'fault', 'faulted', 'faults', 'faulty', 'fear', 'fears',\n",
" 'felonies', 'felonious', 'felony', 'fictitious', 'fined', 'fines', 'fired', 'firing', 'flaw',\n",
" 'flawed', 'flaws', 'forbid', 'forbidden', 'forbidding', 'forbids', 'force', 'forced', 'forcing',\n",
" 'foreclose', 'foreclosed', 'forecloses', 'foreclosing', 'foreclosure', 'foreclosures', 'forego',\n",
" 'foregoes', 'foregone', 'forestall', 'forestalled', 'forestalling', 'forestalls', 'forfeit',\n",
" 'forfeited', 'forfeiting', 'forfeits', 'forfeiture', 'forfeitures', 'forgers', 'forgery',\n",
" 'fraud', 'frauds', 'fraudulence', 'fraudulent', 'fraudulently', 'frivolous', 'frivolously',\n",
" 'frustrate', 'frustrated', 'frustrates', 'frustrating', 'frustratingly', 'frustration',\n",
" 'frustrations', 'fugitive', 'fugitives', 'gratuitous', 'gratuitously', 'grievance', 'grievances',\n",
" 'grossly', 'groundless', 'guilty', 'halt', 'halted', 'hamper', 'hampered', 'hampering',\n",
" 'hampers', 'harass', 'harassed', 'harassing', 'harassment', 'hardship', 'hardships', 'harm',\n",
" 'harmed', 'harmful', 'harmfully', 'harming', 'harms', 'harsh', 'harsher', 'harshest', 'harshly',\n",
" 'harshness', 'hazard', 'hazardous', 'hazards', 'hinder', 'hindered', 'hindering', 'hinders',\n",
" 'hindrance', 'hindrances', 'hostile', 'hostility', 'hurt', 'hurting', 'idle', 'idled', 'idling',\n",
" 'ignore', 'ignored', 'ignores', 'ignoring', 'ill', 'illegal', 'illegalities', 'illegality',\n",
" 'illegally', 'illegible', 'illicit', 'illicitly', 'illiquid', 'illiquidity', 'imbalance',\n",
" 'imbalances', 'immature', 'immoral', 'impair', 'impaired', 'impairing', 'impairment',\n",
" 'impairments', 'impairs', 'impasse', 'impasses', 'impede', 'impeded', 'impedes', 'impediment',\n",
" 'impediments', 'impeding', 'impending', 'imperative', 'imperfection', 'imperfections', 'imperil',\n",
" 'impermissible', 'implicate', 'implicated', 'implicates', 'implicating', 'impossibility',\n",
" 'impossible', 'impound', 'impounded', 'impounding', 'impounds', 'impracticable', 'impractical',\n",
" 'impracticalities', 'impracticality', 'imprisonment', 'improper', 'improperly', 'improprieties',\n",
" 'impropriety', 'imprudent', 'imprudently', 'inability', 'inaccessible', 'inaccuracies',\n",
" 'inaccuracy', 'inaccurate', 'inaccurately', 'inaction', 'inactions', 'inactivate', 'inactivated',\n",
" 'inactivates', 'inactivating', 'inactivation', 'inactivations', 'inactivity', 'inadequacies',\n",
" 'inadequacy', 'inadequate', 'inadequately', 'inadvertent', 'inadvertently', 'inadvisability',\n",
" 'inadvisable', 'inappropriate', 'inappropriately', 'inattention', 'incapable', 'incapacitated',\n",
" 'incapacity', 'incarcerate', 'incarcerated', 'incarcerates', 'incarcerating', 'incarceration',\n",
" 'incarcerations', 'incidence', 'incidences', 'incident', 'incidents', 'incompatibilities',\n",
" 'incompatibility', 'incompatible', 'incompetence', 'incompetency', 'incompetent',\n",
" 'incompetently', 'incompetents', 'incomplete', 'incompletely', 'incompleteness', 'inconclusive',\n",
" 'inconsistencies', 'inconsistency', 'inconsistent', 'inconsistently', 'inconvenience',\n",
" 'inconveniences', 'inconvenient', 'incorrect', 'incorrectly', 'incorrectness', 'indecency',\n",
" 'indecent', 'indefeasible', 'indefeasibly', 'indict', 'indictable', 'indicted', 'indicting',\n",
" 'indictment', 'indictments', 'ineffective', 'ineffectively', 'ineffectiveness', 'inefficiencies',\n",
" 'inefficiency', 'inefficient', 'inefficiently', 'ineligibility', 'ineligible', 'inequitable',\n",
" 'inequitably', 'inequities', 'inequity', 'inevitable', 'inexperience', 'inexperienced',\n",
" 'inferior', 'inflicted', 'infraction', 'infractions', 'infringe', 'infringed', 'infringement',\n",
" 'infringements', 'infringes', 'infringing', 'inhibited', 'inimical', 'injunction', 'injunctions',\n",
" 'injure', 'injured', 'injures', 'injuries', 'injuring', 'injurious', 'injury', 'inordinate',\n",
" 'inordinately', 'inquiry', 'insecure', 'insensitive', 'insolvencies', 'insolvency', 'insolvent',\n",
" 'instability', 'insubordination', 'insufficiency', 'insufficient', 'insufficiently',\n",
" 'insurrection', 'insurrections', 'intentional', 'interfere', 'interfered', 'interference',\n",
" 'interferences', 'interferes', 'interfering', 'intermittent', 'intermittently', 'interrupt',\n",
" 'interrupted', 'interrupting', 'interruption', 'interruptions', 'interrupts', 'intimidation',\n",
" 'intrusion', 'invalid', 'invalidate', 'invalidated', 'invalidates', 'invalidating',\n",
" 'invalidation', 'invalidity', 'investigate', 'investigated', 'investigates', 'investigating',\n",
" 'investigation', 'investigations', 'involuntarily', 'involuntary', 'irreconcilable',\n",
" 'irreconcilably', 'irrecoverable', 'irrecoverably', 'irregular', 'irregularities',\n",
" 'irregularity', 'irregularly', 'irreparable', 'irreparably', 'irreversible', 'jeopardize',\n",
" 'jeopardized', 'justifiable', 'kickback', 'kickbacks', 'knowingly', 'lack', 'lacked', 'lacking',\n",
" 'lackluster', 'lacks', 'lag', 'lagged', 'lagging', 'lags', 'lapse', 'lapsed', 'lapses',\n",
" 'lapsing', 'late', 'laundering', 'layoff', 'layoffs', 'lie', 'limitation', 'limitations',\n",
" 'lingering', 'liquidate', 'liquidated', 'liquidates', 'liquidating', 'liquidation',\n",
" 'liquidations', 'liquidator', 'liquidators', 'litigant', 'litigants', 'litigate', 'litigated',\n",
" 'litigates', 'litigating', 'litigation', 'litigations', 'lockout', 'lockouts', 'lose', 'loses',\n",
" 'losing', 'loss', 'losses', 'lost', 'lying', 'malfeasance', 'malfunction', 'malfunctioned',\n",
" 'malfunctioning', 'malfunctions', 'malice', 'malicious', 'maliciously', 'malpractice',\n",
" 'manipulate', 'manipulated', 'manipulates', 'manipulating', 'manipulation', 'manipulations',\n",
" 'manipulative', 'markdown', 'markdowns', 'misapplication', 'misapplications', 'misapplied',\n",
" 'misapplies', 'misapply', 'misapplying', 'misappropriate', 'misappropriated', 'misappropriates',\n",
" 'misappropriating', 'misappropriation', 'misappropriations', 'misbranded', 'miscalculate',\n",
" 'miscalculated', 'miscalculates', 'miscalculating', 'miscalculation', 'miscalculations',\n",
" 'mischaracterization', 'mischief', 'misclassification', 'misclassifications', 'misclassified',\n",
" 'misclassify', 'miscommunication', 'misconduct', 'misdated', 'misdemeanor', 'misdemeanors',\n",
" 'misdirected', 'mishandle', 'mishandled', 'mishandles', 'mishandling', 'misinform',\n",
" 'misinformation', 'misinformed', 'misinforming', 'misinforms', 'misinterpret',\n",
" 'misinterpretation', 'misinterpretations', 'misinterpreted', 'misinterpreting', 'misinterprets',\n",
" 'misjudge', 'misjudged', 'misjudges', 'misjudging', 'misjudgment', 'misjudgments', 'mislabel',\n",
" 'mislabeled', 'mislabeling', 'mislabelled', 'mislabels', 'mislead', 'misleading', 'misleadingly',\n",
" 'misleads', 'misled', 'mismanage', 'mismanaged', 'mismanagement', 'mismanages', 'mismanaging',\n",
" 'mismatch', 'mismatched', 'mismatches', 'mismatching', 'misplaced', 'misprice', 'mispricing',\n",
" 'mispricings', 'misrepresent', 'misrepresentation', 'misrepresentations', 'misrepresented',\n",
" 'misrepresenting', 'misrepresents', 'miss', 'missed', 'misses', 'misstate', 'misstated',\n",
" 'misstatement', 'misstatements', 'misstates', 'misstating', 'misstep', 'missteps', 'mistake',\n",
" 'mistaken', 'mistakenly', 'mistakes', 'mistaking', 'mistrial', 'mistrials', 'misunderstand',\n",
" 'misunderstanding', 'misunderstandings', 'misunderstood', 'misuse', 'misused', 'misuses',\n",
" 'misusing', 'monopolistic', 'monopolists', 'monopolization', 'monopolize', 'monopolized',\n",
" 'monopolizes', 'monopolizing', 'monopoly', 'moratoria', 'moratorium', 'moratoriums',\n",
" 'mothballed', 'mothballing', 'negative', 'negatively', 'negatives', 'neglect', 'neglected',\n",
" 'neglectful', 'neglecting', 'neglects', 'negligence', 'negligences', 'negligent', 'negligently',\n",
" 'nonattainment', 'noncompetitive', 'noncompliance', 'noncompliances', 'noncompliant',\n",
" 'noncomplying', 'nonconforming', 'nonconformities', 'nonconformity', 'nondisclosure',\n",
" 'nonfunctional', 'nonpayment', 'nonpayments', 'nonperformance', 'nonperformances',\n",
" 'nonperforming', 'nonproducing', 'nonproductive', 'nonrecoverable', 'nonrenewal', 'nuisance',\n",
" 'nuisances', 'nullification', 'nullifications', 'nullified', 'nullifies', 'nullify',\n",
" 'nullifying', 'objected', 'objecting', 'objection', 'objectionable', 'objectionably',\n",
" 'objections', 'obscene', 'obscenity', 'obsolescence', 'obsolete', 'obstacle', 'obstacles',\n",
" 'obstruct', 'obstructed', 'obstructing', 'obstruction', 'obstructions', 'offence', 'offences',\n",
" 'offend', 'offended', 'offender', 'offenders', 'offending', 'offends', 'omission', 'omissions',\n",
" 'omit', 'omits', 'omitted', 'omitting', 'onerous', 'opportunistic', 'opportunistically',\n",
" 'oppose', 'opposed', 'opposes', 'opposing', 'opposition', 'oppositions', 'outage', 'outages',\n",
" 'outdated', 'outmoded', 'overage', 'overages', 'overbuild', 'overbuilding', 'overbuilds',\n",
" 'overbuilt', 'overburden', 'overburdened', 'overburdening', 'overcapacities', 'overcapacity',\n",
" 'overcharge', 'overcharged', 'overcharges', 'overcharging', 'overcome', 'overcomes',\n",
" 'overcoming', 'overdue', 'overestimate', 'overestimated', 'overestimates', 'overestimating',\n",
" 'overestimation', 'overestimations', 'overload', 'overloaded', 'overloading', 'overloads',\n",
" 'overlook', 'overlooked', 'overlooking', 'overlooks', 'overpaid', 'overpayment', 'overpayments',\n",
" 'overproduced', 'overproduces', 'overproducing', 'overproduction', 'overrun', 'overrunning',\n",
" 'overruns', 'overshadow', 'overshadowed', 'overshadowing', 'overshadows', 'overstate',\n",
" 'overstated', 'overstatement', 'overstatements', 'overstates', 'overstating', 'oversupplied',\n",
" 'oversupplies', 'oversupply', 'oversupplying', 'overtly', 'overturn', 'overturned',\n",
" 'overturning', 'overturns', 'overvalue', 'overvalued', 'overvaluing', 'panic', 'panics',\n",
" 'penalize', 'penalized', 'penalizes', 'penalizing', 'penalties', 'penalty', 'peril', 'perils',\n",
" 'perjury', 'perpetrate', 'perpetrated', 'perpetrates', 'perpetrating', 'perpetration', 'persist',\n",
" 'persisted', 'persistence', 'persistent', 'persistently', 'persisting', 'persists', 'pervasive',\n",
" 'pervasively', 'pervasiveness', 'petty', 'picket', 'picketed', 'picketing', 'plaintiff',\n",
" 'plaintiffs', 'plea', 'plead', 'pleaded', 'pleading', 'pleadings', 'pleads', 'pleas', 'pled',\n",
" 'poor', 'poorly', 'poses', 'posing', 'postpone', 'postponed', 'postponement', 'postponements',\n",
" 'postpones', 'postponing', 'precipitated', 'precipitous', 'precipitously', 'preclude',\n",
" 'precluded', 'precludes', 'precluding', 'predatory', 'prejudice', 'prejudiced', 'prejudices',\n",
" 'prejudicial', 'prejudicing', 'premature', 'prematurely', 'pressing', 'pretrial', 'preventing',\n",
" 'prevention', 'prevents', 'problem', 'problematic', 'problematical', 'problems', 'prolong',\n",
" 'prolongation', 'prolongations', 'prolonged', 'prolonging', 'prolongs', 'prone', 'prosecute',\n",
" 'prosecuted', 'prosecutes', 'prosecuting', 'prosecution', 'prosecutions', 'protest', 'protested',\n",
" 'protester', 'protesters', 'protesting', 'protestor', 'protestors', 'protests', 'protracted',\n",
" 'protraction', 'provoke', 'provoked', 'provokes', 'provoking', 'punished', 'punishes',\n",
" 'punishing', 'punishment', 'punishments', 'punitive', 'purport', 'purported', 'purportedly',\n",
" 'purporting', 'purports', 'question', 'questionable', 'questionably', 'questioned',\n",
" 'questioning', 'questions', 'quit', 'quitting', 'racketeer', 'racketeering', 'rationalization',\n",
" 'rationalizations', 'rationalize', 'rationalized', 'rationalizes', 'rationalizing',\n",
" 'reassessment', 'reassessments', 'reassign', 'reassigned', 'reassigning', 'reassignment',\n",
" 'reassignments', 'reassigns', 'recall', 'recalled', 'recalling', 'recalls', 'recession',\n",
" 'recessionary', 'recessions', 'reckless', 'recklessly', 'recklessness', 'redact', 'redacted',\n",
" 'redacting', 'redaction', 'redactions', 'redefault', 'redefaulted', 'redefaults', 'redress',\n",
" 'redressed', 'redresses', 'redressing', 'refusal', 'refusals', 'refuse', 'refused', 'refuses',\n",
" 'refusing', 'reject', 'rejected', 'rejecting', 'rejection', 'rejections', 'rejects',\n",
" 'relinquish', 'relinquished', 'relinquishes', 'relinquishing', 'relinquishment',\n",
" 'relinquishments', 'reluctance', 'reluctant', 'renegotiate', 'renegotiated', 'renegotiates',\n",
" 'renegotiating', 'renegotiation', 'renegotiations', 'renounce', 'renounced', 'renouncement',\n",
" 'renouncements', 'renounces', 'renouncing', 'reparation', 'reparations', 'repossessed',\n",
" 'repossesses', 'repossessing', 'repossession', 'repossessions', 'repudiate', 'repudiated',\n",
" 'repudiates', 'repudiating', 'repudiation', 'repudiations', 'resign', 'resignation',\n",
" 'resignations', 'resigned', 'resigning', 'resigns', 'restate', 'restated', 'restatement',\n",
" 'restatements', 'restates', 'restating', 'restructure', 'restructured', 'restructures',\n",
" 'restructuring', 'restructurings', 'retaliate', 'retaliated', 'retaliates', 'retaliating',\n",
" 'retaliation', 'retaliations', 'retaliatory', 'retribution', 'retributions', 'revocation',\n",
" 'revocations', 'revoke', 'revoked', 'revokes', 'revoking', 'ridicule', 'ridiculed', 'ridicules',\n",
" 'ridiculing', 'riskier', 'riskiest', 'risky', 'sabotage', 'sacrifice', 'sacrificed',\n",
" 'sacrifices', 'sacrificial', 'sacrificing', 'scandalous', 'scandals', 'scrutinize',\n",
" 'scrutinized', 'scrutinizes', 'scrutinizing', 'scrutiny', 'secrecy', 'seize', 'seized', 'seizes',\n",
" 'seizing', 'sentenced', 'sentencing', 'serious', 'seriously', 'seriousness', 'setback',\n",
" 'setbacks', 'sever', 'severe', 'severed', 'severely', 'severities', 'severity', 'sharply',\n",
" 'shocked', 'shortage', 'shortages', 'shortfall', 'shortfalls', 'shrinkage', 'shrinkages', 'shut',\n",
" 'shutdown', 'shutdowns', 'shuts', 'shutting', 'slander', 'slandered', 'slanderous', 'slanders',\n",
" 'slippage', 'slippages', 'slow', 'slowdown', 'slowdowns', 'slowed', 'slower', 'slowest',\n",
" 'slowing', 'slowly', 'slowness', 'sluggish', 'sluggishly', 'sluggishness', 'solvencies',\n",
" 'solvency', 'spam', 'spammers', 'spamming', 'staggering', 'stagnant', 'stagnate', 'stagnated',\n",
" 'stagnates', 'stagnating', 'stagnation', 'standstill', 'standstills', 'stolen', 'stoppage',\n",
" 'stoppages', 'stopped', 'stopping', 'stops', 'strain', 'strained', 'straining', 'strains',\n",
" 'stress', 'stressed', 'stresses', 'stressful', 'stressing', 'stringent', 'strong', 'subjected',\n",
" 'subjecting', 'subjection', 'subpoena', 'subpoenaed', 'subpoenas', 'substandard', 'sue', 'sued',\n",
" 'sues', 'suffer', 'suffered', 'suffering', 'suffers', 'suing', 'summoned', 'summoning',\n",
" 'summons', 'summonses', 'susceptibility', 'susceptible', 'suspect', 'suspected', 'suspects',\n",
" 'suspend', 'suspended', 'suspending', 'suspends', 'suspension', 'suspensions', 'suspicion',\n",
" 'suspicions', 'suspicious', 'suspiciously', 'taint', 'tainted', 'tainting', 'taints', 'tampered',\n",
" 'tense', 'terminate', 'terminated', 'terminates', 'terminating', 'termination', 'terminations',\n",
" 'testify', 'testifying', 'threat', 'threaten', 'threatened', 'threatening', 'threatens',\n",
" 'threats', 'tightening', 'tolerate', 'tolerated', 'tolerates', 'tolerating', 'toleration',\n",
" 'tortuous', 'tortuously', 'tragedies', 'tragedy', 'tragic', 'tragically', 'traumatic', 'trouble',\n",
" 'troubled', 'troubles', 'turbulence', 'turmoil', 'unable', 'unacceptable', 'unacceptably',\n",
" 'unaccounted', 'unannounced', 'unanticipated', 'unapproved', 'unattractive', 'unauthorized',\n",
" 'unavailability', 'unavailable', 'unavoidable', 'unavoidably', 'unaware', 'uncollectable',\n",
" 'uncollected', 'uncollectibility', 'uncollectible', 'uncollectibles', 'uncompetitive',\n",
" 'uncompleted', 'unconscionable', 'unconscionably', 'uncontrollable', 'uncontrollably',\n",
" 'uncontrolled', 'uncorrected', 'uncover', 'uncovered', 'uncovering', 'uncovers', 'undeliverable',\n",
" 'undelivered', 'undercapitalized', 'undercut', 'undercuts', 'undercutting', 'underestimate',\n",
" 'underestimated', 'underestimates', 'underestimating', 'underestimation', 'underfunded',\n",
" 'underinsured', 'undermine', 'undermined', 'undermines', 'undermining', 'underpaid',\n",
" 'underpayment', 'underpayments', 'underpays', 'underperform', 'underperformance',\n",
" 'underperformed', 'underperforming', 'underperforms', 'underproduced', 'underproduction',\n",
" 'underreporting', 'understate', 'understated', 'understatement', 'understatements',\n",
" 'understates', 'understating', 'underutilization', 'underutilized', 'undesirable', 'undesired',\n",
" 'undetected', 'undetermined', 'undisclosed', 'undocumented', 'undue', 'unduly', 'uneconomic',\n",
" 'uneconomical', 'uneconomically', 'unemployed', 'unemployment', 'unethical', 'unethically',\n",
" 'unexcused', 'unexpected', 'unexpectedly', 'unfair', 'unfairly', 'unfavorability', 'unfavorable',\n",
" 'unfavorably', 'unfavourable', 'unfeasible', 'unfit', 'unfitness', 'unforeseeable', 'unforeseen',\n",
" 'unforseen', 'unfortunate', 'unfortunately', 'unfounded', 'unfriendly', 'unfulfilled',\n",
" 'unfunded', 'uninsured', 'unintended', 'unintentional', 'unintentionally', 'unjust',\n",
" 'unjustifiable', 'unjustifiably', 'unjustified', 'unjustly', 'unknowing', 'unknowingly',\n",
" 'unlawful', 'unlawfully', 'unlicensed', 'unliquidated', 'unmarketable', 'unmerchantable',\n",
" 'unmeritorious', 'unnecessarily', 'unnecessary', 'unneeded', 'unobtainable', 'unoccupied',\n",
" 'unpaid', 'unperformed', 'unplanned', 'unpopular', 'unpredictability', 'unpredictable',\n",
" 'unpredictably', 'unpredicted', 'unproductive', 'unprofitability', 'unprofitable', 'unqualified',\n",
" 'unrealistic', 'unreasonable', 'unreasonableness', 'unreasonably', 'unreceptive',\n",
" 'unrecoverable', 'unrecovered', 'unreimbursed', 'unreliable', 'unremedied', 'unreported',\n",
" 'unresolved', 'unrest', 'unsafe', 'unsalable', 'unsaleable', 'unsatisfactory', 'unsatisfied',\n",
" 'unsavory', 'unscheduled', 'unsellable', 'unsold', 'unsound', 'unstabilized', 'unstable',\n",
" 'unsubstantiated', 'unsuccessful', 'unsuccessfully', 'unsuitability', 'unsuitable', 'unsuitably',\n",
" 'unsuited', 'unsure', 'unsuspected', 'unsuspecting', 'unsustainable', 'untenable', 'untimely',\n",
" 'untrusted', 'untruth', 'untruthful', 'untruthfully', 'untruthfulness', 'untruths', 'unusable',\n",
" 'unwanted', 'unwarranted', 'unwelcome', 'unwilling', 'unwillingness', 'upset', 'urgency',\n",
" 'urgent', 'usurious', 'usurp', 'usurped', 'usurping', 'usurps', 'usury', 'vandalism', 'verdict',\n",
" 'verdicts', 'vetoed', 'victims', 'violate', 'violated', 'violates', 'violating', 'violation',\n",
" 'violations', 'violative', 'violator', 'violators', 'violence', 'violent', 'violently',\n",
" 'vitiate', 'vitiated', 'vitiates', 'vitiating', 'vitiation', 'voided', 'voiding', 'volatile',\n",
" 'volatility', 'vulnerabilities', 'vulnerability', 'vulnerable', 'vulnerably', 'warn', 'warned',\n",
" 'warning', 'warnings', 'warns', 'wasted', 'wasteful', 'wasting', 'weak', 'weaken', 'weakened',\n",
" 'weakening', 'weakens', 'weaker', 'weakest', 'weakly', 'weakness', 'weaknesses', 'willfully',\n",
" 'worries', 'worry', 'worrying', 'worse', 'worsen', 'worsened', 'worsening', 'worsens', 'worst',\n",
" 'worthless', 'writedown', 'writedowns', 'writeoff', 'writeoffs', 'wrong', 'wrongdoing',\n",
" 'wrongdoings', 'wrongful', 'wrongfully', 'wrongly',\n",
" 'negative', 'negatives', 'fail', 'fails', 'failing', 'failure', 'weak', 'weakness', 'weaknesses',\n",
" 'difficult', 'difficulty', 'hurdle', 'hurdles', 'obstacle', 'obstacles', 'slump', 'slumps',\n",
" 'slumping', 'slumped', 'uncertain', 'uncertainty', 'unsettled', 'unfavorable', 'downturn',\n",
" 'depressed', 'disappoint', 'disappoints', 'disappointing', 'disappointed', 'disappointment',\n",
" 'risk', 'risks', 'risky', 'threat', 'threats', 'penalty', 'penalties', 'down', 'decrease',\n",
" 'decreases', 'decreasing', 'decreased', 'decline', 'declines', 'declining', 'declined', 'fall',\n",
" 'falls', 'falling', 'fell', 'fallen', 'drop', 'drops', 'dropping', 'dropped', 'deteriorate',\n",
" 'deteriorates', 'deteriorating', 'deteriorated', 'worsen', 'worsens', 'worsening', 'weaken',\n",
" 'weakens', 'weakening', 'weakened', 'worse', 'worst', 'low', 'lower', 'lowest', 'less', 'least',\n",
" 'smaller', 'smallest', 'shrink', 'shrinks', 'shrinking', 'shrunk', 'below', 'under', 'challenge',\n",
" 'challenges', 'challenging', 'challenged'\n",
" ],\n",
" 'Positive': ['able', 'abundance', 'abundant', 'acclaimed', 'accomplish', 'accomplished', 'accomplishes',\n",
" 'accomplishing', 'accomplishment', 'accomplishments', 'achieve', 'achieved', 'achievement',\n",
" 'achievements', 'achieves', 'achieving', 'adequately', 'advancement', 'advancements', 'advances',\n",
" 'advancing', 'advantage', 'advantaged', 'advantageous', 'advantageously', 'advantages',\n",
" 'alliance', 'alliances', 'assure', 'assured', 'assures', 'assuring', 'attain', 'attained',\n",
" 'attaining', 'attainment', 'attainments', 'attains', 'attractive', 'attractiveness', 'beautiful',\n",
" 'beautifully', 'beneficial', 'beneficially', 'benefit', 'benefited', 'benefiting', 'benefitted',\n",
" 'benefitting', 'best', 'better', 'bolstered', 'bolstering', 'bolsters', 'boom', 'booming',\n",
" 'boost', 'boosted', 'breakthrough', 'breakthroughs', 'brilliant', 'charitable', 'collaborate',\n",
" 'collaborated', 'collaborates', 'collaborating', 'collaboration', 'collaborations',\n",
" 'collaborative', 'collaborator', 'collaborators', 'compliment', 'complimentary', 'complimented',\n",
" 'complimenting', 'compliments', 'conclusive', 'conclusively', 'conducive', 'confident',\n",
" 'constructive', 'constructively', 'courteous', 'creative', 'creatively', 'creativeness',\n",
" 'creativity', 'delight', 'delighted', 'delightful', 'delightfully', 'delighting', 'delights',\n",
" 'dependability', 'dependable', 'desirable', 'desired', 'despite', 'destined', 'diligent',\n",
" 'diligently', 'distinction', 'distinctions', 'distinctive', 'distinctively', 'distinctiveness',\n",
" 'dream', 'easier', 'easily', 'easy', 'effective', 'efficiencies', 'efficiency', 'efficient',\n",
" 'efficiently', 'empower', 'empowered', 'empowering', 'empowers', 'enable', 'enabled', 'enables',\n",
" 'enabling', 'encouraged', 'encouragement', 'encourages', 'encouraging', 'enhance', 'enhanced',\n",
" 'enhancement', 'enhancements', 'enhances', 'enhancing', 'enjoy', 'enjoyable', 'enjoyably',\n",
" 'enjoyed', 'enjoying', 'enjoyment', 'enjoys', 'enthusiasm', 'enthusiastic', 'enthusiastically',\n",
" 'excellence', 'excellent', 'excelling', 'excels', 'exceptional', 'exceptionally', 'excited',\n",
" 'excitement', 'exciting', 'exclusive', 'exclusively', 'exclusiveness', 'exclusives',\n",
" 'exclusivity', 'exemplary', 'fantastic', 'favorable', 'favorably', 'favored', 'favoring',\n",
" 'favorite', 'favorites', 'friendly', 'gain', 'gained', 'gaining', 'gains', 'good', 'great',\n",
" 'greater', 'greatest', 'greatly', 'greatness', 'happiest', 'happily', 'happiness', 'happy',\n",
" 'highest', 'honor', 'honorable', 'honored', 'honoring', 'honors', 'ideal', 'impress',\n",
" 'impressed', 'impresses', 'impressing', 'impressive', 'impressively', 'improve', 'improved',\n",
" 'improvement', 'improvements', 'improves', 'improving', 'incredible', 'incredibly',\n",
" 'influential', 'informative', 'ingenuity', 'innovate', 'innovated', 'innovates', 'innovating',\n",
" 'innovation', 'innovations', 'innovative', 'innovativeness', 'innovator', 'innovators',\n",
" 'insightful', 'inspiration', 'inspirational', 'integrity', 'invent', 'invented', 'inventing',\n",
" 'invention', 'inventions', 'inventive', 'inventiveness', 'inventor', 'inventors', 'leadership',\n",
" 'leading', 'loyal', 'lucrative', 'meritorious', 'opportunities', 'opportunity', 'optimistic',\n",
" 'outperform', 'outperformed', 'outperforming', 'outperforms', 'perfect', 'perfected',\n",
" 'perfectly', 'perfects', 'pleasant', 'pleasantly', 'pleased', 'pleasure', 'plentiful', 'popular',\n",
" 'popularity', 'positive', 'positively', 'preeminence', 'preeminent', 'premier', 'premiere',\n",
" 'prestige', 'prestigious', 'proactive', 'proactively', 'proficiency', 'proficient',\n",
" 'proficiently', 'profitability', 'profitable', 'profitably', 'progress', 'progressed',\n",
" 'progresses', 'progressing', 'prospered', 'prospering', 'prosperity', 'prosperous', 'prospers',\n",
" 'rebound', 'rebounded', 'rebounding', 'receptive', 'regain', 'regained', 'regaining', 'resolve',\n",
" 'revolutionize', 'revolutionized', 'revolutionizes', 'revolutionizing', 'reward', 'rewarded',\n",
" 'rewarding', 'rewards', 'satisfaction', 'satisfactorily', 'satisfactory', 'satisfied',\n",
" 'satisfies', 'satisfy', 'satisfying', 'smooth', 'smoothing', 'smoothly', 'smooths', 'solves',\n",
" 'solving', 'spectacular', 'spectacularly', 'stability', 'stabilization', 'stabilizations',\n",
" 'stabilize', 'stabilized', 'stabilizes', 'stabilizing', 'stable', 'strength', 'strengthen',\n",
" 'strengthened', 'strengthening', 'strengthens', 'strengths', 'strong', 'stronger', 'strongest',\n",
" 'succeed', 'succeeded', 'succeeding', 'succeeds', 'success', 'successes', 'successful',\n",
" 'successfully', 'superior', 'surpass', 'surpassed', 'surpasses', 'surpassing', 'sustainable', 'transparency',\n",
" 'tremendous', 'tremendously', 'unmatched', 'unparalleled', 'unsurpassed', 'upturn', 'upturns',\n",
" 'valuable', 'versatile', 'versatility', 'vibrancy', 'vibrant', 'win', 'winner', 'winners', 'winning', 'worthy',\n",
" 'positive', 'positives', 'success', 'successes', 'successful', 'succeed', 'succeeds',\n",
" 'succeeding', 'succeeded', 'accomplish', 'accomplishes', 'accomplishing', 'accomplished',\n",
" 'accomplishment', 'accomplishments', 'strong', 'strength', 'strengths', 'certain', 'certainty',\n",
" 'definite', 'solid', 'excellent', 'good', 'leading', 'achieve', 'achieves', 'achieved',\n",
" 'achieving', 'achievement', 'achievements', 'progress', 'progressing', 'deliver', 'delivers',\n",
" 'delivered', 'delivering', 'leader', 'leading', 'pleased', 'reward', 'rewards', 'rewarding',\n",
" 'rewarded', 'opportunity', 'opportunities', 'enjoy', 'enjoys', 'enjoying', 'enjoyed',\n",
" 'encouraged', 'encouraging', 'up', 'increase', 'increases', 'increasing', 'increased', 'rise',\n",
" 'rises', 'rising', 'rose', 'risen', 'improve', 'improves', 'improving', 'improved', 'improvement',\n",
" 'improvements', 'strengthen', 'strengthens', 'strengthening', 'strengthened', 'stronger',\n",
" 'strongest', 'better', 'best', 'more', 'most', 'above', 'record', 'high', 'higher', 'highest',\n",
" 'greater', 'greatest', 'larger', 'largest', 'grow', 'grows', 'growing', 'grew', 'grown', 'growth',\n",
" 'expand', 'expands', 'expanding', 'expanded', 'expansion', 'exceed', 'exceeds', 'exceeded',\n",
" 'exceeding', 'beat', 'beats', 'beating']\n",
" }\n",
"\n",
"negate = [\"aint\", \"arent\", \"cannot\", \"cant\", \"couldnt\", \"darent\", \"didnt\", \"doesnt\", \"ain't\", \"aren't\", \"can't\",\n",
" \"couldn't\", \"daren't\", \"didn't\", \"doesn't\", \"dont\", \"hadnt\", \"hasnt\", \"havent\", \"isnt\", \"mightnt\", \"mustnt\",\n",
" \"neither\", \"don't\", \"hadn't\", \"hasn't\", \"haven't\", \"isn't\", \"mightn't\", \"mustn't\", \"neednt\", \"needn't\",\n",
" \"never\", \"none\", \"nope\", \"nor\", \"not\", \"nothing\", \"nowhere\", \"oughtnt\", \"shant\", \"shouldnt\", \"wasnt\",\n",
" \"werent\", \"oughtn't\", \"shan't\", \"shouldn't\", \"wasn't\", \"weren't\", \"without\", \"wont\", \"wouldnt\", \"won't\",\n",
" \"wouldn't\", \"rarely\", \"seldom\", \"despite\", \"no\", \"nobody\"]\n",
"\n",
"lmdict"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'Negative': ['abandon',\n",
" 'abandoned',\n",
" 'abandoning',\n",
" 'abandonment',\n",
" 'abandonments',\n",
" 'abandons',\n",
" 'abdicated',\n",
" 'abdicates',\n",
" 'abdicating',\n",
" 'abdication',\n",
" 'abdications',\n",
" 'aberrant',\n",
" 'aberration',\n",
" 'aberrational',\n",
" 'aberrations',\n",
" 'abetting',\n",
" 'abnormal',\n",
" 'abnormalities',\n",
" 'abnormality',\n",
" 'abnormally',\n",
" 'abolish',\n",
" 'abolished',\n",
" 'abolishes',\n",
" 'abolishing',\n",
" 'abrogate',\n",
" 'abrogated',\n",
" 'abrogates',\n",
" 'abrogating',\n",
" 'abrogation',\n",
" 'abrogations',\n",
" 'abrupt',\n",
" 'abruptly',\n",
" 'abruptness',\n",
" 'absence',\n",
" 'absences',\n",
" 'absenteeism',\n",
" 'abuse',\n",
" 'abused',\n",
" 'abuses',\n",
" 'abusing',\n",
" 'abusive',\n",
" 'abusively',\n",
" 'abusiveness',\n",
" 'accident',\n",
" 'accidental',\n",
" 'accidentally',\n",
" 'accidents',\n",
" 'accusation',\n",
" 'accusations',\n",
" 'accuse',\n",
" 'accused',\n",
" 'accuses',\n",
" 'accusing',\n",
" 'acquiesce',\n",
" 'acquiesced',\n",
" 'acquiesces',\n",
" 'acquiescing',\n",
" 'acquit',\n",
" 'acquits',\n",
" 'acquittal',\n",
" 'acquittals',\n",
" 'acquitted',\n",
" 'acquitting',\n",
" 'adulterate',\n",
" 'adulterated',\n",
" 'adulterating',\n",
" 'adulteration',\n",
" 'adulterations',\n",
" 'adversarial',\n",
" 'adversaries',\n",
" 'adversary',\n",
" 'adverse',\n",
" 'adversely',\n",
" 'adversities',\n",
" 'adversity',\n",
" 'aftermath',\n",
" 'aftermaths',\n",
" 'against',\n",
" 'aggravate',\n",
" 'aggravated',\n",
" 'aggravates',\n",
" 'aggravating',\n",
" 'aggravation',\n",
" 'aggravations',\n",
" 'alerted',\n",
" 'alerting',\n",
" 'alienate',\n",
" 'alienated',\n",
" 'alienates',\n",
" 'alienating',\n",
" 'alienation',\n",
" 'alienations',\n",
" 'allegation',\n",
" 'allegations',\n",
" 'allege',\n",
" 'alleged',\n",
" 'allegedly',\n",
" 'alleges',\n",
" 'alleging',\n",
" 'annoy',\n",
" 'annoyance',\n",
" 'annoyances',\n",
" 'annoyed',\n",
" 'annoying',\n",
" 'annoys',\n",
" 'annul',\n",
" 'annulled',\n",
" 'annulling',\n",
" 'annulment',\n",
" 'annulments',\n",
" 'annuls',\n",
" 'anomalies',\n",
" 'anomalous',\n",
" 'anomalously',\n",
" 'anomaly',\n",
" 'anticompetitive',\n",
" 'antitrust',\n",
" 'argue',\n",
" 'argued',\n",
" 'arguing',\n",
" 'argument',\n",
" 'argumentative',\n",
" 'arguments',\n",
" 'arrearage',\n",
" 'arrearages',\n",
" 'arrears',\n",
" 'arrest',\n",
" 'arrested',\n",
" 'arrests',\n",
" 'artificially',\n",
" 'assault',\n",
" 'assaulted',\n",
" 'assaulting',\n",
" 'assaults',\n",
" 'assertions',\n",
" 'attrition',\n",
" 'aversely',\n",
" 'backdating',\n",
" 'bad',\n",
" 'bail',\n",
" 'bailout',\n",
" 'balk',\n",
" 'balked',\n",
" 'bankrupt',\n",
" 'bankruptcies',\n",
" 'bankruptcy',\n",
" 'bankrupted',\n",
" 'bankrupting',\n",
" 'bankrupts',\n",
" 'bans',\n",
" 'barred',\n",
" 'barrier',\n",
" 'barriers',\n",
" 'bottleneck',\n",
" 'bottlenecks',\n",
" 'boycott',\n",
" 'boycotted',\n",
" 'boycotting',\n",
" 'boycotts',\n",
" 'breach',\n",
" 'breached',\n",
" 'breaches',\n",
" 'breaching',\n",
" 'break',\n",
" 'breakage',\n",
" 'breakages',\n",
" 'breakdown',\n",
" 'breakdowns',\n",
" 'breaking',\n",
" 'breaks',\n",
" 'bribe',\n",
" 'bribed',\n",
" 'briberies',\n",
" 'bribery',\n",
" 'bribes',\n",
" 'bribing',\n",
" 'bridge',\n",
" 'broken',\n",
" 'burden',\n",
" 'burdened',\n",
" 'burdening',\n",
" 'burdens',\n",
" 'burdensome',\n",
" 'burned',\n",
" 'calamities',\n",
" 'calamitous',\n",
" 'calamity',\n",
" 'cancel',\n",
" 'canceled',\n",
" 'canceling',\n",
" 'cancellation',\n",
" 'cancellations',\n",
" 'cancelled',\n",
" 'cancelling',\n",
" 'cancels',\n",
" 'careless',\n",
" 'carelessly',\n",
" 'carelessness',\n",
" 'catastrophe',\n",
" 'catastrophes',\n",
" 'catastrophic',\n",
" 'catastrophically',\n",
" 'caution',\n",
" 'cautionary',\n",
" 'cautioned',\n",
" 'cautioning',\n",
" 'cautions',\n",
" 'cease',\n",
" 'ceased',\n",
" 'ceases',\n",
" 'ceasing',\n",
" 'censure',\n",
" 'censured',\n",
" 'censures',\n",
" 'censuring',\n",
" 'challenge',\n",
" 'challenged',\n",
" 'challenges',\n",
" 'challenging',\n",
" 'chargeoffs',\n",
" 'circumvent',\n",
" 'circumvented',\n",
" 'circumventing',\n",
" 'circumvention',\n",
" 'circumventions',\n",
" 'circumvents',\n",
" 'claiming',\n",
" 'claims',\n",
" 'clawback',\n",
" 'closed',\n",
" 'closeout',\n",
" 'closeouts',\n",
" 'closing',\n",
" 'closings',\n",
" 'closure',\n",
" 'closures',\n",
" 'coerce',\n",
" 'coerced',\n",
" 'coerces',\n",
" 'coercing',\n",
" 'coercion',\n",
" 'coercive',\n",
" 'collapse',\n",
" 'collapsed',\n",
" 'collapses',\n",
" 'collapsing',\n",
" 'collision',\n",
" 'collisions',\n",
" 'collude',\n",
" 'colluded',\n",
" 'colludes',\n",
" 'colluding',\n",
" 'collusion',\n",
" 'collusions',\n",
" 'collusive',\n",
" 'complain',\n",
" 'complained',\n",
" 'complaining',\n",
" 'complains',\n",
" 'complaint',\n",
" 'complaints',\n",
" 'complicate',\n",
" 'complicated',\n",
" 'complicates',\n",
" 'complicating',\n",
" 'complication',\n",
" 'complications',\n",
" 'compulsion',\n",
" 'concealed',\n",
" 'concealing',\n",
" 'concede',\n",
" 'conceded',\n",
" 'concedes',\n",
" 'conceding',\n",
" 'concern',\n",
" 'concerned',\n",
" 'concerns',\n",
" 'conciliating',\n",
" 'conciliation',\n",
" 'conciliations',\n",
" 'condemn',\n",
" 'condemnation',\n",
" 'condemnations',\n",
" 'condemned',\n",
" 'condemning',\n",
" 'condemns',\n",
" 'condone',\n",
" 'condoned',\n",
" 'confess',\n",
" 'confessed',\n",
" 'confesses',\n",
" 'confessing',\n",
" 'confession',\n",
" 'confine',\n",
" 'confined',\n",
" 'confinement',\n",
" 'confinements',\n",
" 'confines',\n",
" 'confining',\n",
" 'confiscate',\n",
" 'confiscated',\n",
" 'confiscates',\n",
" 'confiscating',\n",
" 'confiscation',\n",
" 'confiscations',\n",
" 'conflict',\n",
" 'conflicted',\n",
" 'conflicting',\n",
" 'conflicts',\n",
" 'confront',\n",
" 'confrontation',\n",
" 'confrontational',\n",
" 'confrontations',\n",
" 'confronted',\n",
" 'confronting',\n",
" 'confronts',\n",
" 'confuse',\n",
" 'confused',\n",
" 'confuses',\n",
" 'confusing',\n",
" 'confusingly',\n",
" 'confusion',\n",
" 'conspiracies',\n",
" 'conspiracy',\n",
" 'conspirator',\n",
" 'conspiratorial',\n",
" 'conspirators',\n",
" 'conspire',\n",
" 'conspired',\n",
" 'conspires',\n",
" 'conspiring',\n",
" 'contempt',\n",
" 'contend',\n",
" 'contended',\n",
" 'contending',\n",
" 'contends',\n",
" 'contention',\n",
" 'contentions',\n",
" 'contentious',\n",
" 'contentiously',\n",
" 'contested',\n",
" 'contesting',\n",
" 'contraction',\n",
" 'contractions',\n",
" 'contradict',\n",
" 'contradicted',\n",
" 'contradicting',\n",
" 'contradiction',\n",
" 'contradictions',\n",
" 'contradictory',\n",
" 'contradicts',\n",
" 'contrary',\n",
" 'controversial',\n",
" 'controversies',\n",
" 'controversy',\n",
" 'convict',\n",
" 'convicted',\n",
" 'convicting',\n",
" 'conviction',\n",
" 'convictions',\n",
" 'corrected',\n",
" 'correcting',\n",
" 'correction',\n",
" 'corrections',\n",
" 'corrects',\n",
" 'corrupt',\n",
" 'corrupted',\n",
" 'corrupting',\n",
" 'corruption',\n",
" 'corruptions',\n",
" 'corruptly',\n",
" 'corruptness',\n",
" 'costly',\n",
" 'counterclaim',\n",
" 'counterclaimed',\n",
" 'counterclaiming',\n",
" 'counterclaims',\n",
" 'counterfeit',\n",
" 'counterfeited',\n",
" 'counterfeiter',\n",
" 'counterfeiters',\n",
" 'counterfeiting',\n",
" 'counterfeits',\n",
" 'countermeasure',\n",
" 'countermeasures',\n",
" 'crime',\n",
" 'crimes',\n",
" 'criminal',\n",
" 'criminally',\n",
" 'criminals',\n",
" 'crises',\n",
" 'crisis',\n",
" 'critical',\n",
" 'critically',\n",
" 'criticism',\n",
" 'criticisms',\n",
" 'criticize',\n",
" 'criticized',\n",
" 'criticizes',\n",
" 'criticizing',\n",
" 'crucial',\n",
" 'crucially',\n",
" 'culpability',\n",
" 'culpable',\n",
" 'culpably',\n",
" 'cumbersome',\n",
" 'curtail',\n",
" 'curtailed',\n",
" 'curtailing',\n",
" 'curtailment',\n",
" 'curtailments',\n",
" 'curtails',\n",
" 'cut',\n",
" 'cutback',\n",
" 'cutbacks',\n",
" 'cyberattack',\n",
" 'cyberattacks',\n",
" 'cyberbullying',\n",
" 'cybercrime',\n",
" 'cybercrimes',\n",
" 'cybercriminal',\n",
" 'cybercriminals',\n",
" 'damage',\n",
" 'damaged',\n",
" 'damages',\n",
" 'damaging',\n",
" 'dampen',\n",
" 'dampened',\n",
" 'danger',\n",
" 'dangerous',\n",
" 'dangerously',\n",
" 'dangers',\n",
" 'deadlock',\n",
" 'deadlocked',\n",
" 'deadlocking',\n",
" 'deadlocks',\n",
" 'deadweight',\n",
" 'deadweights',\n",
" 'debarment',\n",
" 'debarments',\n",
" 'debarred',\n",
" 'deceased',\n",
" 'deceit',\n",
" 'deceitful',\n",
" 'deceitfulness',\n",
" 'deceive',\n",
" 'deceived',\n",
" 'deceives',\n",
" 'deceiving',\n",
" 'deception',\n",
" 'deceptions',\n",
" 'deceptive',\n",
" 'deceptively',\n",
" 'decline',\n",
" 'declined',\n",
" 'declines',\n",
" 'declining',\n",
" 'deface',\n",
" 'defaced',\n",
" 'defacement',\n",
" 'defamation',\n",
" 'defamations',\n",
" 'defamatory',\n",
" 'defame',\n",
" 'defamed',\n",
" 'defames',\n",
" 'defaming',\n",
" 'default',\n",
" 'defaulted',\n",
" 'defaulting',\n",
" 'defaults',\n",
" 'defeat',\n",
" 'defeated',\n",
" 'defeating',\n",
" 'defeats',\n",
" 'defect',\n",
" 'defective',\n",
" 'defects',\n",
" 'defend',\n",
" 'defendant',\n",
" 'defendants',\n",
" 'defended',\n",
" 'defending',\n",
" 'defends',\n",
" 'defensive',\n",
" 'defer',\n",
" 'deficiencies',\n",
" 'deficiency',\n",
" 'deficient',\n",
" 'deficit',\n",
" 'deficits',\n",
" 'defraud',\n",
" 'defrauded',\n",
" 'defrauding',\n",
" 'defrauds',\n",
" 'defunct',\n",
" 'degradation',\n",
" 'degradations',\n",
" 'degrade',\n",
" 'degraded',\n",
" 'degrades',\n",
" 'degrading',\n",
" 'delay',\n",
" 'delayed',\n",
" 'delaying',\n",
" 'delays',\n",
" 'deleterious',\n",
" 'deliberate',\n",
" 'deliberated',\n",
" 'deliberately',\n",
" 'delinquencies',\n",
" 'delinquency',\n",
" 'delinquent',\n",
" 'delinquently',\n",
" 'delinquents',\n",
" 'delist',\n",
" 'delisted',\n",
" 'delisting',\n",
" 'delists',\n",
" 'demise',\n",
" 'demised',\n",
" 'demises',\n",
" 'demising',\n",
" 'demolish',\n",
" 'demolished',\n",
" 'demolishes',\n",
" 'demolishing',\n",
" 'demolition',\n",
" 'demolitions',\n",
" 'demote',\n",
" 'demoted',\n",
" 'demotes',\n",
" 'demoting',\n",
" 'demotion',\n",
" 'demotions',\n",
" 'denial',\n",
" 'denials',\n",
" 'denied',\n",
" 'denies',\n",
" 'denigrate',\n",
" 'denigrated',\n",
" 'denigrates',\n",
" 'denigrating',\n",
" 'denigration',\n",
" 'deny',\n",
" 'denying',\n",
" 'deplete',\n",
" 'depleted',\n",
" 'depletes',\n",
" 'depleting',\n",
" 'depletion',\n",
" 'depletions',\n",
" 'deprecation',\n",
" 'depress',\n",
" 'depressed',\n",
" 'depresses',\n",
" 'depressing',\n",
" 'deprivation',\n",
" 'deprive',\n",
" 'deprived',\n",
" 'deprives',\n",
" 'depriving',\n",
" 'derelict',\n",
" 'dereliction',\n",
" 'derogatory',\n",
" 'destabilization',\n",
" 'destabilize',\n",
" 'destabilized',\n",
" 'destabilizing',\n",
" 'destroy',\n",
" 'destroyed',\n",
" 'destroying',\n",
" 'destroys',\n",
" 'destruction',\n",
" 'destructive',\n",
" 'detain',\n",
" 'detained',\n",
" 'detention',\n",
" 'detentions',\n",
" 'deter',\n",
" 'deteriorate',\n",
" 'deteriorated',\n",
" 'deteriorates',\n",
" 'deteriorating',\n",
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" 'grow',\n",
" 'grows',\n",
" 'growing',\n",
" 'grew',\n",
" 'grown',\n",
" 'growth',\n",
" 'expand',\n",
" 'expands',\n",
" 'expanding',\n",
" 'expanded',\n",
" 'expansion',\n",
" 'exceed',\n",
" 'exceeds',\n",
" 'exceeded',\n",
" 'exceeding',\n",
" 'beat',\n",
" 'beats',\n",
" 'beating']}"
]
},
"metadata": {
"tags": []
},
"execution_count": 37
},
{
"output_type": "stream",
"text": [
"time: 5.42 s (started: 2021-02-18 14:53:31 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "n784Uho3ifrw"
},
"source": [
"#### Helper Functions"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ZoEXe4wwmIh5",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4276059d-2f7a-4ae9-f1d9-f29d0c4a91b2"
},
"source": [
"def negated(word):\n",
" if word.lower() in negate:\n",
" return True\n",
" else:\n",
" return False\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 2 ms (started: 2021-02-18 14:53:36 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "U289pkQ2mIh5",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "756106a6-0a50-4ab2-b036-0d39937443d8"
},
"source": [
"def tone_counter(dict, article):\n",
" pos_count = 0\n",
" neg_count = 0\n",
" \n",
" pos_words = []\n",
" neg_words = []\n",
" \n",
" input_words = re.findall(r'\\b([a-zA-Z]+n\\'t|[a-zA-Z]+\\'s|[a-zA-Z]+)\\b', article.lower())\n",
" \n",
" word_count = len(input_words)\n",
" \n",
" for i in range(0, word_count):\n",
" if input_words[i] in dict['Negative']:\n",
" neg_count += 1\n",
" neg_words.append(input_words[i])\n",
" if input_words[i] in dict['Positive']:\n",
" if i >= 3:\n",
" if negated(input_words[i - 1]) or negated(input_words[i - 2]) or negated(input_words[i - 3]):\n",
" neg_count += 1\n",
" neg_words.append(input_words[i] + ' (with negation)')\n",
" else:\n",
" pos_count += 1\n",
" pos_words.append(input_words[i])\n",
" elif i == 2:\n",
" if negated(input_words[i - 1]) or negated(input_words[i - 2]):\n",
" neg_count += 1\n",
" neg_words.append(input_words[i] + ' (with negation)')\n",
" else:\n",
" pos_count += 1\n",
" pos_words.append(input_words[i])\n",
" elif i == 1:\n",
" if negated(input_words[i - 1]):\n",
" neg_count += 1\n",
" neg_words.append(input_words[i] + ' (with negation)')\n",
" else:\n",
" pos_count += 1\n",
" pos_words.append(input_words[i])\n",
" elif i == 0:\n",
" pos_count += 1\n",
" pos_words.append(input_words[i])\n",
" \n",
" results = [word_count, pos_count, neg_count, pos_words, neg_words]\n",
" \n",
" return results\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 29 ms (started: 2021-02-18 14:53:36 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_j5amPVFikbb"
},
"source": [
"#### Preprocess Dictionary\r\n",
"##### *Longest waiting time block*\r\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Sc2i925CmIh5",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7d5c57f5-1de5-41b9-ad63-b80e51255c63"
},
"source": [
"print(len(data_df))\n",
"temp = [tone_counter(lmdict,x) for x in data_df.text]\n",
"temp = pd.DataFrame(temp)\n",
"\n",
"data_df['wordcount'] = temp.iloc[:,0].values\n",
"data_df['NPositiveWords'] = temp.iloc[:,1].values\n",
"data_df['NNegativeWords'] = temp.iloc[:,2].values\n",
"\n",
"# Normalize by word count\n",
"data_df['sentiment'] = (data_df['NPositiveWords'] - data_df['NNegativeWords']) / data_df['wordcount'] * 100\n",
"data_df['Poswords'] = temp.iloc[:,3].values\n",
"data_df['Negwords'] = temp.iloc[:,4].values\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"223\n",
"time: 50.9 s (started: 2021-02-18 14:53:36 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "LbU-FxFqmIh5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 624
},
"outputId": "a024bada-3d39-4e2f-f171-ff99d595ad8e"
},
"source": [
"# Normalize sentiment scores\r\n",
"temp.head()\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <td>258</td>\n",
" <td>[pleasure, satisfactory, above, satisfactory, opportunity, certain, most, more, up, above, effective, effective, stabilization, deliver, above, exceptional, up, above, achieve, stabilization, exceeding, most, exceeding, exceeding, most, exceeding, larger, growth, rose, strength, increased, despi...</td>\n",
" <td>[low, discontinuance, cease, below, questions, question, below, unable, below, increased (with negation), decreased, under, exceed (with negation), under, under, less, limitations, under, under, under, below, under, below, liquidated, delay, exceeding (with negation), disregarding, under, under,...</td>\n",
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" <td>[low, below, slowed, strong, sluggish, diminished, disappointing, strong, losses, declines, decline, unemployment, sluggishness, decline, slowly, down, weak, sluggish, decline, decrease, lackluster, strong, depressed, weakness, down, declined, depressing, unoccupied, declines, fall, strong, fell...</td>\n",
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" <td>[low, absence, below, slow, slowed, declined, lower, smaller, unemployment, decline, declined, sluggishly, weakness, fallen, late, below, depressed, decline, declining, down, sharply, low, slower, fell, low, slowed, declined, deficit, declining, deficit, weakness, declines, lower, unexpectedly, ...</td>\n",
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" <td>[low, below, fallen, decline, lost, slower, unemployment, dropped, declines, cutback, strong, declined, down, strong, depressed, down, strong, declined, slowing, strong, lower, decline, fell, lowest, lower, deficit, smaller, deficits, deficit, declines, drop, decline, declined, lower, fell, lowe...</td>\n",
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"</table>\n",
"</div>"
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" 0 ... 4\n",
"0 10214 ... [low, discontinuance, cease, below, questions, question, below, unable, below, increased (with negation), decreased, under, exceed (with negation), under, under, less, limitations, under, under, under, below, under, below, liquidated, delay, exceeding (with negation), disregarding, under, under,...\n",
"1 5774 ... [low, below, strong, less, less, unemployment, down, sharply, strong, depressed, claims, unemployment, unemployment, lower, fell, sharply, weakened, late, fell, decline, slowdown, low, dropped, lowest, down, decline, forcing, cutbacks, unprofitable, fell, lie, down, persisting, declines, downwar...\n",
"2 4982 ... [low, below, slowed, strong, sluggish, diminished, disappointing, strong, losses, declines, decline, unemployment, sluggishness, decline, slowly, down, weak, sluggish, decline, decrease, lackluster, strong, depressed, weakness, down, declined, depressing, unoccupied, declines, fall, strong, fell...\n",
"3 7047 ... [low, absence, below, slow, slowed, declined, lower, smaller, unemployment, decline, declined, sluggishly, weakness, fallen, late, below, depressed, decline, declining, down, sharply, low, slower, fell, low, slowed, declined, deficit, declining, deficit, weakness, declines, lower, unexpectedly, ...\n",
"4 4664 ... [low, below, fallen, decline, lost, slower, unemployment, dropped, declines, cutback, strong, declined, down, strong, depressed, down, strong, declined, slowing, strong, lower, decline, fell, lowest, lower, deficit, smaller, deficits, deficit, declines, drop, decline, declined, lower, fell, lowe...\n",
"\n",
"[5 rows x 5 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 41
},
{
"output_type": "stream",
"text": [
"time: 29.5 ms (started: 2021-02-18 14:54:27 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-3CPfoFKmIh5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "c4cc2e3c-513f-4dda-8ce6-bf17d9491d1e"
},
"source": [
"# Text Data\r\n",
"data_df.head()\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>contents</th>\n",
" <th>speaker</th>\n",
" <th>text</th>\n",
" <th>Index</th>\n",
" <th>wordcount</th>\n",
" <th>NPositiveWords</th>\n",
" <th>NNegativeWords</th>\n",
" <th>sentiment</th>\n",
" <th>Poswords</th>\n",
" <th>Negwords</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
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" <th>1993-02-03</th>\n",
" <td>A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System in \\r\\n Washington, D.C., on Tuesday, February 2, 1993, at 2:30 p.m. and was \\r\\n continued on Wednesday, February 3, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPR...</td>\n",
" <td>Alan Greenspan</td>\n",
" <td>A meeting of the Federal Open Market Committee was held in the offices of the Board of Governors of the Federal Reserve System in Washington, D.C., on Tuesday, February 2, 1993, at 2:30 p.m. and was continued on Wednesday, February 3, 1993, at 9:00 a.m.PRESENT:Mr. Greenspan, Ch...</td>\n",
" <td>0</td>\n",
" <td>10214</td>\n",
" <td>319</td>\n",
" <td>258</td>\n",
" <td>0.597220</td>\n",
" <td>[pleasure, satisfactory, above, satisfactory, opportunity, certain, most, more, up, above, effective, effective, stabilization, deliver, above, exceptional, up, above, achieve, stabilization, exceeding, most, exceeding, exceeding, most, exceeding, larger, growth, rose, strength, increased, despi...</td>\n",
" <td>[low, discontinuance, cease, below, questions, question, below, unable, below, increased (with negation), decreased, under, exceed (with negation), under, under, less, limitations, under, under, under, below, under, below, liquidated, delay, exceeding (with negation), disregarding, under, under,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1993-03-23</th>\n",
" <td>A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System in \\r\\n Washington, D.C., on Tuesday, March 23, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[SECTION]\\n\\nMr. Greenspan, Chairman \\r\\n Mr. Corrig...</td>\n",
" <td>Alan Greenspan</td>\n",
" <td>A meeting of the Federal Open Market Committee was held in the offices of the Board of Governors of the Federal Reserve System in Washington, D.C., on Tuesday, March 23, 1993, at 9:00 a.m.PRESENT:Mr. Greenspan, Chairman Mr. Corrigan, Vice Chairman Mr. Angell ...</td>\n",
" <td>1</td>\n",
" <td>5774</td>\n",
" <td>221</td>\n",
" <td>196</td>\n",
" <td>0.432975</td>\n",
" <td>[record, record, expanding, more, increasing, strong, rising, solid, gains, strengthened, increases, advances, larger, rose, advances, strong, gains, growth, rise, increases, high, up, increases, increased, gains, rose, increases, gains, up, high, rise, solid, gain, increase, above, advances, mo...</td>\n",
" <td>[low, below, strong, less, less, unemployment, down, sharply, strong, depressed, claims, unemployment, unemployment, lower, fell, sharply, weakened, late, fell, decline, slowdown, low, dropped, lowest, down, decline, forcing, cutbacks, unprofitable, fell, lie, down, persisting, declines, downwar...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1993-05-18</th>\n",
" <td>A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System \\r\\n in Washington, D.C., on Tuesday, May 18, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[SECTION]\\n\\nMr. Greenspan, Chairman \\r\\n Mr. Corrigan, ...</td>\n",
" <td>Alan Greenspan</td>\n",
" <td>A meeting of the Federal Open Market Committee was held in the offices of the Board of Governors of the Federal Reserve System in Washington, D.C., on Tuesday, May 18, 1993, at 9:00 a.m.PRESENT:Mr. Greenspan, Chairman Mr. Corrigan, Vice Chairman Mr. Angell ...</td>\n",
" <td>2</td>\n",
" <td>4982</td>\n",
" <td>192</td>\n",
" <td>160</td>\n",
" <td>0.642312</td>\n",
" <td>[expansion, strong, gains, growth, despite, increases, larger, rose, increases, strong, gains, solid, gains, grew, more, strength, increased, gains, strong, increases, up, rebounded, rise, increase, strength, up, expansion, up, increased, risen, expanded, strong, increased, up, rose, growth, ros...</td>\n",
" <td>[low, below, slowed, strong, sluggish, diminished, disappointing, strong, losses, declines, decline, unemployment, sluggishness, decline, slowly, down, weak, sluggish, decline, decrease, lackluster, strong, depressed, weakness, down, declined, depressing, unoccupied, declines, fall, strong, fell...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1993-07-07</th>\n",
" <td>A meeting of the Federal Open Market Committee was held in the \\r\\n offices of the Board of Governors of the Federal Reserve System in \\r\\n Washington, D.C., on Tuesday, July 6, 1993, at 2:30 p.m. and continued \\r\\n on Wednesday, July 7, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[S...</td>\n",
" <td>Alan Greenspan</td>\n",
" <td>A meeting of the Federal Open Market Committee was held in the offices of the Board of Governors of the Federal Reserve System in Washington, D.C., on Tuesday, July 6, 1993, at 2:30 p.m. and continued on Wednesday, July 7, 1993, at 9:00 a.m.PRESENT:Mr. Greenspan. Chairman ...</td>\n",
" <td>3</td>\n",
" <td>7047</td>\n",
" <td>276</td>\n",
" <td>182</td>\n",
" <td>1.333901</td>\n",
" <td>[growth, growth, expansion, up, strength, more, expansion, risen, gains, increase, rising, gains, up, increased, expand, higher, rebound, increased, highest, increased, high, increased, most, rise, above, improved, increase, up, rose, rise, increase, up, higher, above, rebounding, high, above, i...</td>\n",
" <td>[low, absence, below, slow, slowed, declined, lower, smaller, unemployment, decline, declined, sluggishly, weakness, fallen, late, below, depressed, decline, declining, down, sharply, low, slower, fell, low, slowed, declined, deficit, declining, deficit, weakness, declines, lower, unexpectedly, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1993-08-17</th>\n",
" <td>A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System \\r\\n in Washington, D.C., on Tuesday, August 17, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[SECTION]\\n\\nMr. Greenspan, Chairman\\r\\n Mr. McDonough, V...</td>\n",
" <td>Alan Greenspan</td>\n",
" <td>A meeting of the Federal Open Market Committee was held in the offices of the Board of Governors of the Federal Reserve System in Washington, D.C., on Tuesday, August 17, 1993, at 9:00 a.m.PRESENT:Mr. Greenspan, Chairman Mr. McDonough, Vice Chairman Mr. Angell Mr...</td>\n",
" <td>4</td>\n",
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" <td>190</td>\n",
" <td>148</td>\n",
" <td>0.900515</td>\n",
" <td>[expanding, expansion, rising, gain, expanded, increase, up, more, rose, strong, gain, rebounded, higher, gain, increased, rise, strong, up, positive, improved, increased, strong, solid, increases, rise, growth, largest, up, strong, expanded, expanded, increased, rose, more, high, larger, rose, ...</td>\n",
" <td>[low, below, fallen, decline, lost, slower, unemployment, dropped, declines, cutback, strong, declined, down, strong, depressed, down, strong, declined, slowing, strong, lower, decline, fell, lowest, lower, deficit, smaller, deficits, deficit, declines, drop, decline, declined, lower, fell, lowe...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"1993-02-03 A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System in \\r\\n Washington, D.C., on Tuesday, February 2, 1993, at 2:30 p.m. and was \\r\\n continued on Wednesday, February 3, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPR... ... [low, discontinuance, cease, below, questions, question, below, unable, below, increased (with negation), decreased, under, exceed (with negation), under, under, less, limitations, under, under, under, below, under, below, liquidated, delay, exceeding (with negation), disregarding, under, under,...\n",
"1993-03-23 A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System in \\r\\n Washington, D.C., on Tuesday, March 23, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[SECTION]\\n\\nMr. Greenspan, Chairman \\r\\n Mr. Corrig... ... [low, below, strong, less, less, unemployment, down, sharply, strong, depressed, claims, unemployment, unemployment, lower, fell, sharply, weakened, late, fell, decline, slowdown, low, dropped, lowest, down, decline, forcing, cutbacks, unprofitable, fell, lie, down, persisting, declines, downwar...\n",
"1993-05-18 A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System \\r\\n in Washington, D.C., on Tuesday, May 18, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[SECTION]\\n\\nMr. Greenspan, Chairman \\r\\n Mr. Corrigan, ... ... [low, below, slowed, strong, sluggish, diminished, disappointing, strong, losses, declines, decline, unemployment, sluggishness, decline, slowly, down, weak, sluggish, decline, decrease, lackluster, strong, depressed, weakness, down, declined, depressing, unoccupied, declines, fall, strong, fell...\n",
"1993-07-07 A meeting of the Federal Open Market Committee was held in the \\r\\n offices of the Board of Governors of the Federal Reserve System in \\r\\n Washington, D.C., on Tuesday, July 6, 1993, at 2:30 p.m. and continued \\r\\n on Wednesday, July 7, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[S... ... [low, absence, below, slow, slowed, declined, lower, smaller, unemployment, decline, declined, sluggishly, weakness, fallen, late, below, depressed, decline, declining, down, sharply, low, slower, fell, low, slowed, declined, deficit, declining, deficit, weakness, declines, lower, unexpectedly, ...\n",
"1993-08-17 A meeting of the Federal Open Market Committee was held in \\r\\n the offices of the Board of Governors of the Federal Reserve System \\r\\n in Washington, D.C., on Tuesday, August 17, 1993, at 9:00 a.m.\\n\\n[SECTION]\\n\\nPRESENT:\\n\\n[SECTION]\\n\\nMr. Greenspan, Chairman\\r\\n Mr. McDonough, V... ... [low, below, fallen, decline, lost, slower, unemployment, dropped, declines, cutback, strong, declined, down, strong, depressed, down, strong, declined, slowing, strong, lower, decline, fell, lowest, lower, deficit, smaller, deficits, deficit, declines, drop, decline, declined, lower, fell, lowe...\n",
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},
"metadata": {
"tags": []
},
"execution_count": 42
},
{
"output_type": "stream",
"text": [
"time: 53.9 ms (started: 2021-02-18 14:54:27 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kIZUjp_jmIh5"
},
"source": [
"### Sentiment analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fIjPigOHmIh5"
},
"source": [
"#### Plot positive and negative word counts"
]
},
{
"cell_type": "code",
"metadata": {
"id": "eQQ99tGJmIh5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 567
},
"outputId": "513d438d-37d8-4399-992e-3d66ab27616a"
},
"source": [
"NetSentiment = data_df['NPositiveWords'] - data_df['NNegativeWords']\n",
"\n",
"# Initialize figure container\n",
"fig, ax = plt.subplots()\n",
"\n",
"# Supertitle\n",
"# USEFUL for plt.suptitle(). Use x=subplotpars.left to align a supertitle with the subtitles/subplots.\n",
"plt.suptitle('Sentiment Based on Word Counting ' + '({})'.format(doc_type), horizontalalignment = 'left', x = fig.subplotpars.left, y = 0.99, fontsize = font_size_title, fontweight = \"bold\")\n",
"\n",
"# Main plots\n",
"ax.plot(data_df.index, data_df['NPositiveWords'], label = 'Positive Words')\n",
"ax.plot(data_df.index, data_df['NNegativeWords']*-1, label = 'Negative Words')\n",
"ax.plot(data_df.index, NetSentiment, c='grey', label = 'Net Sentiment')\n",
"ax.legend(prop = {'size': font_size_legend}, loc=\"best\", borderaxespad=0, frameon=0)\n",
"ax.set_ylabel('Word Count', fontweight = \"bold\")\n",
"#ax.set_xlabel('Time (Years)', fontweight = \"bold\")\n",
"ax.fill_between(data_df.index, NetSentiment, where=(NetSentiment > 0), alpha=0.3, interpolate=True)\n",
"ax.fill_between(data_df.index, NetSentiment, where=(NetSentiment <= 0), alpha=0.3, interpolate=True)\n",
"\n",
"# Title\n",
"plt.title('The counts are highly correlated; broad variation in the counts between doc_types is likely an influencing factor', loc = 'left', y = 1.0, fontsize = font_size_subtitle)\n",
"\n",
"# Source\n",
"ax.text(x=0.0, y=-0.12, s='Source: Federal Reserve System Data Archives. Python scripts are provided under an MIT License.', fontsize = font_size_notes, c = \"grey\", transform=ax.transAxes)\n",
"\n",
"# Formatting\n",
"years = mdates.YearLocator() \n",
"months = mdates.MonthLocator() \n",
"years_fmt = mdates.DateFormatter('%Y')\n",
"ax.xaxis.set_major_locator(years)\n",
"ax.xaxis.set_major_formatter(years_fmt)\n",
"#datemin = np.datetime64(eff_fedrate_data_trunc.index[0], 'Y')\n",
"#datemax = np.datetime64(eff_fedrate_data_trunc.index[-1], 'Y') + np.timedelta64(1, 'Y')\n",
"#ax.set_xlim(datemin, datemax)\n",
"ax.format_xdata = mdates.DateFormatter('%Y')\n",
"\n",
"## USEFUL Reduce number of ticks (hide labels in a loop)\n",
"#every_nth = 3\n",
"#for n, label in enumerate(ax.xaxis.get_ticklabels()):\n",
"# if n % every_nth != 0:\n",
"# label.set_visible(False)\n",
"\n",
"## USEFUL Reduce number of ticks (set total number)\n",
"ax.xaxis.set_major_locator(plt.MaxNLocator(20))\n",
"\n",
"# Grid\n",
"ax.grid(color = 'black', alpha = 0.5, linestyle = '-.', linewidth=0.2)\n",
"\n",
"# Finalize\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"#fig.autofmt_xdate()\n",
"\n",
"# Show Inline\n",
"plt.show()\n",
"\n",
"# Save Graphics\n",
"fig.savefig(graph_dir + 'sentiment_pos_neg_word_count_' + doc_type + '.png')\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
"findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
"findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
"findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
"findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
"findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 1200x440 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"time: 713 ms (started: 2021-02-18 14:54:27 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nPEtDpXv_fRq",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "957d76d9-4255-4869-869b-74e3e91af05b"
},
"source": [
"######################################################################################################################################################################################################################################################"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 2.39 ms (started: 2021-02-18 14:54:28 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K4xkT2PpitrQ"
},
"source": [
"#### Net Sentiment"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FeP-R2yUmIh5"
},
"source": [
"Positive and negative word counts are highly correlated, but the Net Sentiment provides a clearer picture. The correlation potentially stems from the highly varied total word counts across documents in the selected document category.\r\n",
"\r\n",
"*Net Sentiment* is defined as,\r\n",
"\r\n",
"\\begin{equation}\r\n",
"S_{net}=n_{\\text{positive words}} - n_{\\text{negative words}}\r\n",
"\\end{equation}"
]
},
{
"cell_type": "code",
"metadata": {
"id": "r_qDvEJ2mIh5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 473
},
"outputId": "2994df17-b03e-48ca-a765-2af559c94ce9"
},
"source": [
"NetSentiment = data_df['NPositiveWords'] - data_df['NNegativeWords']\n",
"\n",
"# Initialize figure container\n",
"fig, ax = plt.subplots()\n",
"\n",
"# Supertitle\n",
"plt.suptitle('Net Sentiment Using a Bag-of-Words Model ' + '({})'.format(doc_type), horizontalalignment = 'left', x = fig.subplotpars.left, y = 0.99, fontsize = font_size_title, fontweight = \"bold\")\n",
"\n",
"# Main plots\n",
"ax.plot(data_df.index, NetSentiment, c = 'blue', label = 'Net Sentiment')\n",
"ax.legend(prop = {'size': font_size_legend}, loc=\"best\", borderaxespad=0, frameon=0)\n",
"ax.set_ylabel('Net Sentiment (%)', fontweight = \"bold\")\n",
"ax.set_xlabel('Time (Yrs)', fontweight = \"bold\")\n",
"\n",
"# Title\n",
"plt.title('Positive and negative word counts are highly correlated, but the Net Sentiment provides a clearer picture', loc = title_loc, y = 1.0, fontsize = font_size_subtitle)\n",
"# Source\n",
"ax.text(x=0.0, y=-0.15, s='Source: Federal Reserve System Data Archives. Python scripts are provided under an MIT License.', fontsize = font_size_notes, c = \"grey\", transform=ax.transAxes)\n",
"\n",
"# Formatting\n",
"datemin = np.datetime64(data_df.index[0], 'Y')\n",
"datemax = np.datetime64(data_df.index[-1], 'Y') + np.timedelta64(1, 'Y')\n",
"ax.set_xlim(datemin, datemax)\n",
"ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"\n",
"# Grid\n",
"ax.grid(color = 'black', alpha = 0.5, linestyle = '-.', linewidth=0.2)\n",
"\n",
"# Finalize\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"\n",
"# Show inline\n",
"plt.show()\n",
"\n",
"# Save Graphics\n",
"fig.savefig(graph_dir + 'sentiment_bow_' + doc_type + '.png')\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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6dAinTp2CQqHABx98INzXqmRkZGDJkiUICgpCTEwMrKys8NlnnwkGu6urK9566y04Oztj06ZNSElJ0TjGtWvXBIO9Z8+emDVrFmQyGdLT03Hnzh0hFcAYgoOD0a5dOzx8+BB79uxBz549cfz4cQDAxIkTdea1r1+/XjDYnZ2dsXjxYnh6euLHH3/EtWvXUFxcjLfffhtnzpyBjY0NduzYIRjsNjY2WLx4MZo3b46ffvpJ6G9VMeYdYgopKSmCkdqkSRODxr9SqcSGDRsQExMjjHP89S1fvhyOjo748MMPUVpaitjYWFy5cgUDBgwQ3Z7k5GQMGDAAU6ZMwZUrV4TFk8OHD2PZsmUmLZSOHz8evXr1UkuPWb9+vdB3/P39UV5ejsWLFwv3YtSoURg9ejTy8/OxYcMGxMfH4/fff0fv3r0xbtw4hIeHCwZ769atsWDBAtjZ2SEjIwOPHj3C1atXNdrRvHlzyGQyKBQKREVFgWVZi0kPoVAoFEr9gxrtBpgwYQK+//57JCUlYd26dWqGqCoRERG4f/8+AG6CNnfuXMEonDRpEh4+fAiA8+AtWLAAgYGBat4jKysro8o5ffrpp5g3bx6io6OFbapegh07dmD79u2C1+zgwYPCfiNHjhTCWrt3747g4GDcvXsX+fn5OH36NCZOnKhxvoULF2LatGkAOG8Qr7zMe2v9/Pw0jGBTy1MNGzYMr776KgDg77//Fu65l5cX1qxZA6lUipKSEsFoV/UYq17nCy+8IAhqhYaG4ty5c4J37saNG+jVq5fGuVesWIFnn30WAOeJ+eWXX9TOUd3fzRTs7e0hlUqhVCrh4OAAX19fPPPMM7C2tgYAvPXWW0Yfc9CgQUKYrqurK1544QUA6vfy1KlTwr+HDx8ueJJ69+6Nu3fvIj09XfT5WrZsiQcPHmDfvn14/Pgx8vPz1XK2Aah57qqDra2thte2b9+++OGHH7B3714kJSWhpKQEhBDhc6VSiQcPHiAkJARXrlwRwqPt7OywceNGYSErOzsb33//vcY54+LikJWVpbbN2toa7du3R3h4OP79919h+4YNG4Qwajc3N8yePRsA8NdffyE/P99gX0pJSdFq4PL7dOnSRdC0uHXrFtq2bSv8rp07d4azszMALtIgOjpaLZ89ODhYiNbhw+wBYNasWZgzZw4AoE+fPrh//z5SUlJQWlqK06dPY/r06RrteeeddwTDqU+fPmBZVk1r46233hKEylq2bIlhw4ZpHEPVI92oUSP4+/ujWbNmkEqlGD9+vMb+Ypk2bRqWLVuG69evY82aNSgpKYFUKsXkyZOxYcMGjf0JIUKoM8AZ1/w42bVrVwwYMADFxcXIyMjAP//8g5CQEPz555/C/rNnz8bMmTMBcL/PgAEDUFJSonYOY98hpqAa6cE/B/pYsWIF+vXrB4CLgOL71YwZM4Tf9sSJE4Joour4IQY3Nzds3LgR1tbWGDBgAA4ePIiSkhIoFAokJSWZJADbpEkTDeG7du3aqaUtnT9/HqmpqQAAb29vTJ48GQDX30ePHi0ozx84cADjxo1Tew5dXV3h5+cHX19fyOVyPP/88zrb4uzsjOzsbCiVSjx58gRubm5GXw+FQqFQKAA12g0il8vx+uuv491338XNmzdx+fJlrftFRUUJ/y4tLRUmaFVJS0tDQUGBzvBIsTRv3hzHjh3DhQsXcOnSJdy9exeRkZGCOF1RURFWrFgheJBU27d//37s379f63EjIyO1blcta+fq6ir8Oy8vr1rXoQ0+fxxQn1i2a9dOCL3U1QbVRYzNmzdj8+bNWs8RERGh1WhXvU4XFxfh3/n5+UZcgW5UPeKqRqO2v3mvjLW1NZ5//nkcPHgQERERGDduHCQSCby9vdGhQweMGzdOmFiLRVUxWvU6Ve+l6gScz30GuD4RHBxslNjh+vXr8e233+rdx5RniReiKykpweHDh3HixAnExsZi/vz5OHPmDBo1aoTS0lJMmTJFLUxZG/xvrBoC27JlS7XIE9X7oMp3332nkYfO57zz4ngAZ6So5j2rHo9lWcTFxRnUzjh48KBGzi7AhcMD2o12gFtg4o1yFxcXPHnyBLdu3VLLZ1cNr1dtt2o7ZTIZgoODhYUD1f1UqaqYnZOTo5Yr3qlTJ+HfLVq0gLOzs8Yz0LVrV7Rq1QqRkZE4fvw4jh8/DrlcDj8/P3Tt2hXTpk3DM888o/X8+hg+fDg+//xz5ObmCmkbgwYN0qlynpOTo7ZYp9p2JycnYVEK4O5HSEiIWv9RzWd2dnaGv7+/2kIOUPvvkKrjjTaqtps32lXHaNWx2NhxsmPHjsLio0QigZOTk7CYYa4xVxuq9zo1NVWnaCX/PvT19UXv3r3xzz//4OrVqxgxYgSkUimaN2+OTp06YfLkyVpz1sXcYwqFQqFQxEBjtUQwZswYQWBHNRTaVLTlbpqCTCbDkCFD8PHHH+PYsWP4+++/MWnSJOHzyMhIIey1um1TNVyMyVk0BdXJqGo4YXUnqarouk7VRQJVb6e5Jl+q4kq8gjNP1Xxn1dDQTz75BF9++SVGjBiBgIAAyOVyJCcn4+TJk5g7dy7Onj1rVDvE/J6qCwxVw++NuR8VFRX48ccfhb9HjRqF7du3Y+/evWrh8KbcY16Irl+/fvjiiy9gZ2cHgPt9+RrnZ86cEQx2Ozs7fPDBB9i1axf27t2rlvuuWj6RpzrXXVeoGt73798XQrHbt28PKysrMAwjGJ36jPbq4uHhUe1jWFtb4+eff8bSpUsxcOBA+Pj4gGVZREZG4ueff8bkyZO1Rh2IOS7v5efhI4nqA6a+Q1Q9vWIWyYwdi43tH1W9/WLGXNUInapjqLnh7zPDMPjuu+/w0UcfYejQofD394dEIkFcXBwOHz6MadOmaWgdAJULDxKJRG1xlEKhUCgUY6GedhFIpVIsXLgQ//vf/4QQxaq0bNlS+LeTkxMuXbqkVRisuLhYMCxUJ0HaDAZdsCyL8+fPIyQkRM2ocHNzw9SpU9W86PzEp2XLloIXevHixZg3b57GccvLy6tV6qlqvl5d5PC1bNkSjx49AgB89dVXGDlypMY+qr+BKZj6uwFAq1atBEG027dvC0JhANSE1AD1Z0oikWD06NFCvrlSqcTOnTvx5ZdfAuBCVIcMGWL8xejB19dX8KKqTkgrKiq0TlB18eTJEzUj46OPPhIWL6p6p80Jb5TwYbAAlyfMh3IXFhZqDfFX9YRHR0ejqKhIaC+fklGVNWvW6FR1V615npOTg/j4eEHIT1UATiKRiCqRtWDBAr3h0W3btoWdnR2Ki4tRVlYmhLmrGuRdu3bF+fPncebMGeG3kUqlah5kf39/hIWFCe3kc5X5VAJt16dK1bHEzc0N9vb2wkLi3bt3hWc8NjZWqxFJCIGjoyNmz54tpBGUlJQIueMFBQW4ePEipkyZovN+6GLKlCnYsWMHlEolWrZsqTXyhsfV1VWITgC4+8F7+AsKCtQifPj74ePjI3hq7927J9y//Px8rVEfprxDjKVJkyZwdHREQUEBUlJSUF5eXm1h1NrAyclJMIDT09OF/sOH5WuDYRjh/Vd1AUD1Xrdo0QInTpzQ+q7i+wYhBNbW1pg8ebIQSl9eXo4vvvgCu3fvRkVFBU6fPq1WbjMhIUGo4PLMM8/QfHYKhUKhVAtqtItk2LBh+O6774QQ1KoEBgYK4kb5+fmC2rmHhweysrKQkJCAv//+G76+voLIlerKe0ZGBo4cOYJmzZrBxsZGrZRSVViWxWuvvYZmzZrh2WefRfv27eHu7o6cnBz89NNPwn6tWrUSDMJx48YJOZabNm1CWVkZOnbsCJZlkZKSgkePHuHcuXM4cOCAySXLnJ2d1SZKP/74I4KDg8EwjFk9ePoYN26cYLR//PHHSEtLQ5s2bVBaWorU1FTcuXMH58+f12l8icHU3w3g6iJv27YNhBAUFBRg4sSJGDp0KPLy8tQM2Hbt2qmVTxo6dCgGDBiAoKAgeHp6QqlUquUil5WVmXw9unjuuecEIbJjx47Bx8cHbdu2xcGDB43KZ3d3d4etra0Q9vrNN99g0KBB+L//+z8NRXljiYiIEDQODh8+rLY4wE/MVZ/nq1ev4siRI3B0dMSOHTu0Goq9e/cWyn4VFRVh0aJFePHFFxETE4Pdu3cb3cbWrVujTZs2ggH85ptv4rXXXhOE6HhCQkJMVs9XRSaToUOHDoI4Fn9PVPPi+X+r3q/AwEC16I6xY8cKbd65cyecnZ3RokULHDlyRCgpaGNjg9DQUFHtkkgkGDp0qLCIsHbtWjAMAycnJ51pLPfv38cHH3wgeDcbNWqEvLw8NSPZ1Ge/adOmWLlyJdLT0w3qUkgkEowZM0YYX9euXQtCCDw8PPDTTz8J99HDwwO9e/cGADz77LOC0b5jxw64uLigefPm2LVrl0Y+O2DaO8RYJBKJsGBTUVGB8PBwUaVM6xo/Pz8h33/VqlWYNGkSrl+/rlZ+syouLi6CJ/7nn3/GoEGDwDAMgoOD0adPH3h5eSEtLQ0xMTGYP38+xo0bB2dnZ6SnpyMuLg4XL17E4MGD8cYbbyArKwtTpkzBs88+i4CAAHh6eqKkpERtEb/qc8i3F+C0YygUCoVCqQ7UaBcJwzBYuHAhXn/9dZ37rF27FjNmzEBGRgbu3Lmj5kXj8fHxEf7dokULYeKgVCqxdOlSYR8x+cJJSUkaJdx4ZDIZ3nnnHeHvQYMGYfbs2dixYwfKy8t1TpKrg729PTp27Chc9+effw6A8+BVzd+sKaZMmYKbN2/ijz/+QF5enuCJNifV+d0CAwOxcOFCrF+/HgCX/6paxg7gJpuffvqp2rb09HRBWVkb+sSQTGX06NE4dOgQrl27BoVCIaSGyOVytcgNQ0gkEkydOhU//PADAE6hnjd+u3fvrnfibQhdZaG6dOmCgQMHAgAGDhyIpk2bIjk5Gfn5+cLv5eHhgRYtWmjkZDs4OODdd98VSr5dunRJiIJQNb6N4YsvvsDMmTORk5ODsLAwDU+5j48PVqxYYfRxddG1a1c1RWuJRKLmRQ8KCoKNjQ1KS0uFbVUX1l588UVcvXoV58+fR2lpqUYkgUwmwyeffGJUxYRFixbh0qVLyMnJQU5OjlBj293dXfAAq0IIEcriacPe3l4jd94YeAFGMbz55pu4ffs2Hjx4gCdPnuDDDz9U+9zOzg5r166FjY0NAE587sSJE4iLi0NJSYnQp+3s7NC4cWOtC1/GvkNMYcSIEcJi3KVLl+qF0f7iiy8KRvDjx4/x8ccfA+DSY3Q9G3369BE0XX744Qdh/Ll48SK8vLzw9ddfY968eUK0xsWLFzWOoVoCNTExUThGVWQymUbpQ9XjjRgxQuylUigUCoWiFRqvZQRDhgxRC3+rir+/P44dO4b58+cjMDAQdnZ2sLa2RtOmTdGrVy+8++67arVcpVIpNm3ahG7duhlVY1smk+H777/H3Llz0blzZzRt2hS2traQy+Vo2rQpRo4ciV9//RX9+/dX+97SpUuxfft2DB48GB4eHpDJZHBxcUFAQAAmTZqEbdu2wdvb2/gbo8IXX3yBAQMGqOVu1yZSqRTr1q3DV199hT59+sDFxQUymQzu7u4ICgrCzJkzsWvXrmqfw5Tfjee1117Djh07MGTIEHh4eEAul8PGxgbPPPMMZs6ciaNHj2qoJi9ZsgQhISFo2rQp7OzsIJVK4ebmhn79+mHbtm2C4r05kUgk2LJlC6ZPnw53d3dYW1ujU6dO2Llzp1oUgJh78L///Q8LFixA8+bNYW1tjcDAQKxduxZjx441S1sZhoGDgwM6dOiAt956Czt27BBy9e3s7PDTTz9h8ODBcHFxgaOjI0JCQrBv3z6dJdQmTJiAr7/+WtAPaNq0KRYtWiRUNRB73TwBAQE4evQoZs6cCT8/P1hZWcHa2hqtWrXC/PnzcfDgQbOWC6xqgD/zzDNqXny5XK4mJgZoiuzJZDJ8++23WLVqFbp06QIHBwfIZDJ4eHhg+PDh+OWXX4R0DbF4e3sLXk87Ozs4ODhg8ODB+Pnnn7VGGfj4+GD+/Pno3r07PD09YWVlBblcjiZNmmD06NH47bff0LRpU6PaYCr29vbYt28f3nnnHbRr1w52dnbCszF+/HgcOnRITeDRwcEBe/bswejRo+Hk5ARbW1v07NkTu3fvFsK7q2LsO8QUQkND4e7uDgBCeUxLZ8yYMVi8eDG8vb0hl8sREBCANWvWYNasWTq/8/7772P48OFwcXHRmvbVpUsXHDt2DDNmzECLFi1gY2MDW1tb+Pj4YODAgfjoo48wdepUAFx4/ptvvom+ffuiSZMmsLGxgUwmg6enJ5599lns3btXbfGjrKxM0NQIDAzUKWBJoVAoFIpYGFIflJUoFEqdQAjRmPCWl5djyJAhgqdw8+bNZs+nr2u0XTfARY/w0S1DhgypkYgVCqWm+fHHH4UQ+59++kltsYFSfX777Td88MEHALh0tOpEg1AoFAqFAtDweAqFoofly5ejWbNm6NGjB7y8vJCSkoJt27YJBnujRo3Qp0+fOm6l+Tl//jxOnjyJ0aNHw9/fH2VlZbhw4YJaisKYMWPqsIUUiulMnToVe/fuRUJCArZu3UqNdjPCsiy+//57AFyaCjXYKRQKhWIOqNFOoVB0kp2djV9//VXrZ3Z2dvjyyy9NShGwdFiWxe+//47ff/9d6+eTJk2qkZQECqU2sLKyEqWbQjEeiUSC06dP13UzKBQKhfKUQY12CoWik9DQUBQXFyMqKgpPnjyBTCYT8mtnzpyJ5s2b13UTa4SWLVti1KhRuH//PjIzM1FRUQFXV1e0a9cOEydOVBOoolAoFAqFQqFQahKa006hUCgUCoVCoVAoFIqFQtXjKRQKhUKhUCgUCoVCsVCo0U6hUCgUCoVCoVAoFIqFQo12CoVCoVAoFAqFQqFQLBRqtFMoFAqFQqFQKBQKhWKhUKO9Frl27RoCAwORn5+vd7+QkBD8+OOPtdMoM7Nx48Z6X786MDAQZ8+eretm1AjLli3Da6+9VtfNeOqYPn06Pv30U737GPtciRkvnob+psqhQ4fQtWvXOju/sWOv2DG9JrGENjwN1PWzV98QM+bVt7nM0/oMPK3XRaE0NKjRXoVly5YhMDAQgYGBaNeuHYYOHYpNmzZBoVBU+9idOnXClStX4OjoCED3QHrgwAFMmjSp2uej6EeXwXPlyhX079+/DlpE4REzIaxv0OeqZqhvhoE2xC6m1Wa/2LhxIwIDA7F8+XK17WFhYQgMDERSUpLoY4ltd2JiIpYsWYK+ffuiffv26N+/P1599VVER0cb3X59aHtmhg8fbjH11evD+Ldx40a8+eabdd0MigiMfbafhjGVQnkaoXXatdCvXz+sXr0a5eXluHjxIj7++GPI5XLMmzevWse1srKCh4eHwf3c3NyqdR5K9RDzG1k6FRUVkMvldd2MekdN3ren4bkSQ3l5OaysrNS2KZVKMAwDiYSuE9cnrK2tcfDgQcyePRt+fn41eq6KigrMnj0b/v7+2LRpEzw8PJCWloZLly6hoKCgRs8NADY2NrCxsanx89Q15hrjXFxcqt+YBoipY2F1xtC6era1vQsoFIrp0BmUFnjjumnTppg6dSp69+6Nv/76CwCQl5eHd955B926dUOHDh0wd+5cxMXFCd9NTk7G/Pnz0a1bN3Ts2BEjRozAxYsXAaiHMV67dg3vvvsuCgoKBM/+xo0bAaivci5ZsgSLFi1Sa19FRQV69OiBI0eOAABYlsXWrVsREhKC4OBgjB49GqdOndJ7jUeOHMG4cePQqVMn9OnTB0uWLEF2drbwOd/Wq1evYty4cejQoQMmT56MmJgYteNs27YNvXv3RqdOnfDee++hrKxM73nFHvfs2bMYO3Ys2rdvj8GDB2tEO0RHR2PKlClo3749hg8fjn/++Ucj/PjLL79EaGgoOnTogMGDB2PdunWoqKgAwEU5bNq0CeHh4cL9P3ToEAD1MObJkyfjyy+/VGtbTk4OgoKCcOPGDQDci+nzzz9Hv3790LFjR0ycOBHXrl3TeQ8+//xztQWgH3/8EYGBgbh06ZKwbejQofjtt98AcL/vpk2b0L9/f7Rr1w5jxoxR2zcpKQmBgYE4efIkpk2bhvbt2+P333+HUqnE6tWr0bVrV/To0QNffPEFCCF6fx8AuHXrFqZPn44OHTqgW7dumDNnDvLy8oRrXbVqFXr16oX27dtjypQpuH//vvBdbdEjZ8+eRWBgoPA3H+Fw5MgRhISEoEuXLvjf//6HwsJCAJzX8fr169i1a5fw2yQlJSEvLw9LlixBz549ERwcjGeffRYHDx7UeR2XLl3ClClThOufN28eEhISDN43APjtt98wbNgwtG/fHs899xz27t1r8L4RQvDFF1+ge/fu6NOnj9Cfeao+n7dv38aYMWPQvn17jBs3TrhPYWFhat979OiR3r7Cc+PGDQQFBSEzM1Nt+6effoqpU6fqbHd+fj6WL1+O3r17o3379hg5ciTOnz8vfH769GmMGDEC7dq1Q0hICHbs2KH2/ZCQEGzevBnvvPMOOnfujOXLlwvPwblz5zB8+HC0b98eKSkpRveVhIQEvPrqq8IYM378ePzzzz/C59OnT0dycjJWr14tPCs8N2/exNSpUxEcHIwBAwZg1apVKC4uFj7Pzs7G/PnzERwcjJCQEBw7dkxnOwxx+/ZtjBo1Cu3bt8cLL7yAiIgI4TNtET0//vgjQkJChM8PHz6Mc+fOCdeg7Z7o6hc8hp4TQ2OqNvz9/dGjRw988803eveLiIjA3Llz0alTJ/Tu3Rtvv/02cnJyRLWbJyoqCgkJCVixYgU6duyIpk2bCmNDx44dhf1SU1Px5ptvomvXrujevTteffVVtePxUQs//PAD+vbtix49euCjjz4Sxn5dz0zVsYv/3Q4cOICBAweiU6dOWLlyJZRKJbZv344+ffqgV69e2LJli9p15Ofn4/3330fPnj3RuXNnzJgxA+Hh4RrHNXb80wbf9xYvXoyOHTuiX79+GmNVYGAg9u3bh/nz56Njx4747rvvAAD79u3DkCFD0K5dO4SGhgrzCUDcvKNqNICY/mTo3oSHh2P69Ono1KkTOnfujHHjxuHBgwdarx0Adu7ciVGjRqFjx44YMGAAVq5ciaKiIp37823QN95VxVC/MdQGU8dCXd+rCj+nunDhgs4xSNt7+a+//sL48ePRvn179OjRA6+//joA3f3D0DgGVPa9LVu2oG/fvnjuuecAGO6zFApFJISixtKlS8mrr76qtm3+/Plk7Nixwr+HDRtGbty4QcLCwsjs2bPJ0KFDSXl5OSGEkFdeeYXMmjWLhIeHk4SEBPLXX3+R69evE0II+b//+z8SEBBA8vLySFlZGfnxxx9J586dSUZGBsnIyCCFhYWEEEIGDRpEdu7cSQgh5Pz58yQ4OFj4jBBC/vrrLxIcHEwKCgoIIYR8++235LnnniOXLl0iCQkJ5ODBg6Rdu3bk2rVrOq/zt99+IxcuXCAJCQnkzp07ZNKkSWTu3LnC53xbJ06cSK5du0YiIyPJ1KlTyaRJk4R9Tpw4Qdq1a0d+/fVXEh0dTb7++mvSqVMnMnr0aJ3nFXPcGzdukM6dO5NDhw6RhIQEcuXKFTJo0CCyceNGQgghCoWChIaGklmzZpGwsDBy48YNMmHCBBIQEEDOnDkjHGfz5s3k1q1bJDExkZw7d4707t2bbNu2jRBCSElJCVmzZg0ZMWKEcP9LSkoIIUTtOHv27CEDBw4kLMsKx929e7fatvfff59MmjSJ3Lhxg8THx5Pvv/+etGvXjsTGxmq9B+fOnSNdunQhCoWCEELIa6+9Rnr06EG+/PJLQgghaWlpJCAggMTFxRFCCNm5cyfp3LkzOX78OImOjiZffPEFCQoKEo6fmJhIAgICyKBBg8jp06dJQkICSU9PJ9u2bSPdunUjp0+fJlFRUeS9994jnTp10ni+Vfn3339Ju3btyIoVK0hYWBiJiIggu3fvJtnZ2YQQQj755BPSt29fcuHCBRIZGUmWLl1KunXrRnJzcwkhhBw8eJB06dJF7ZhnzpwhAQEBwt8bNmwgHTt2JG+88QZ5/PgxuXHjBunTpw/5+uuvCSGE5Ofnk0mTJpEPPvhA+G0UCgX56KOPyJgxY8j9+/dJYmIi+fvvv8m5c+d0XsupU6fI6dOnSVxcHPn333/JvHnzyMiRI4lSqdR7344ePUr69OkjbDt9+jTp3r07OXTokM5zTZs2jXTu3Jls3LiRxMbGksOHD5PAwEBy5coVYR/V56qgoIB0796dvPXWWyQyMpJcuHCBPPvssyQgIID8+++/hBBxfWXDhg1q/e3ZZ58l27dvF/4uLy8nPXr0IAcOHNDabqVSSV544QUyYsQIcuXKFWHMunDhAiGEkAcPHpDWrVuTTZs2kZiYGHLw4EESHBxMDh48KBxj0KBBpHPnzuSHH34g8fHxJD4+nhw8eJAEBQWRSZMmkVu3bpHo6GhSXFxssK9UfX7CwsLIzz//TB4/fkxiY2PJN998Q9q3b0+Sk5MJIYTk5uaS/v37k02bNgnPCiGExMfHk44dO5KdO3eS2NhYcuvWLfL888+TZcuWCceeO3cuGT16NLlz5w558OABmTRpEgkODhbGXjHwv9GwYcPIlStXSHh4OJk3bx4ZNGiQ8E6o+hsRwvXpQYMGEUIIKSwsJG+++SaZM2eOcA1lZWUa59LVL8wxpmqDb/fDhw9J69atyf379wkh3BgREBBAEhMTCSGE5OXlkZ49e5KvvvqKREVFkUePHpFZs2aR6dOn6213VdLS0kjr1q3JDz/8oPVzQrjnediwYeTdd98l4eHhJCoqiixevJiEhoYK92zp0qWkc+fOZPny5SQqKor89ddfpEOHDmT//v2EEN3PTNVnjx+nFixYQCIjI8m5c+dIUFAQmT17Nvnkk09IdHQ0OXDgAAkICCB3794VvvfSSy+RefPmkfv375PY2FiyZs0a0r17d2GMNHX808agQYNIp06dyNatW0lMTAzZtWsXadOmjca406tXL3LgwAGSkJBAkpOTyZ9//kmCgoLInj17SExMDNmxYwdp06YNuXr1KiFE3Lxj2rRpZNWqVcLnYvqToXszYsQI8tZbb5GoqCgSGxtLTp48ScLCwrReOyFcP7p69SpJTEwk//zzDwkNDSUrVqzQub+h8a7qMyCm3xhqQ3XGQm3fq4qYMajqdZ0/f560adOGrF+/nkRFRZGwsDDy3XffEUJ09w9D4xghXN/r2LEjefvtt0lERASJiIgQ1WcpFIo4qNFeBVWjnWVZ8vfff5N27dqRNWvWkNjYWBIQEEBu3bol7J+Tk0OCg4PJyZMnCSGEjBw5UudESNVoJ0S7gUOIutFeUVFBevToQQ4fPix8vnjxYrJo0SJCCCFlZWWkQ4cO5Pbt22rHeO+998jixYtFX/f9+/dJQECA8JLm2/rPP/8I+1y4cIEEBASQ0tJSQgghkyZNIitXrlQ7zsSJE0UZ7fqOO3PmTOEFwnPkyBHSp08fQgghFy9eJG3bthVeJoQQ8vfff2sY7VX5/vvvhcUXQrS/hAhRN66ys7NJ27ZtyY0bN4TPJ02aJBjYycnJpE2bNiQtLU3tGDNnziRfffWV1nbk5eUJk2CWZUn37t3J1q1bycSJEwkhhBw9epT069dP2L9v375ky5YtascYP368cO954/PHH39U26dPnz5qBlxFRQXp37+/XqN98eLFZPLkyVo/KyoqIkFBQeTYsWPCtvLyctK3b1/hPGKN9g4dOgiTP0II+fzzz4XrJ0RzQkgIIfPmzVMzuowlOzubBAQEkMePHxNCdN+3IUOGkN9//11t2+bNm9WMoKpMmzaNTJkyRW3b+PHjheeEEPXnat++faR79+7CM08IIb/++qtWo11fX6n6DG/bto0MGzZM+Pv06dOkY8eOpKioSGu7L1++TFq3bk1iYmK0fr548WIya9YstW2ff/45GT58uPD3oEGDyGuvvaa2z8GDB0lAQIDahFtMX9E1JqoyYsQIsnv3brXzVzW033vvPfLhhx+qbbtx4wZp3bo1KS0tJTExMSQgIIDcu3dP+DwqKooEBASYZLSfOHFC2Jabm0uCg4OFbWInu/r6JY+2fmGOMVUbqu3+3//+R2bMmEEI0TTaN2/eTGbPnq323dTUVBIQECA8V9rarY09e/aQDh06kE6dOpHp06eTTZs2kYSEBLU2h4aGqi2ilpWVkeDgYHL58mVCCHcvBw0apGboLly4UHhnEqL9mdFmtFcdp2bPnk0GDRokLPwRQkhoaCjZunUrIaTSyKtqjAwZMoT88ssvOo8rZvzTxqBBg8icOXPUti1atEhtAT4gIIB8+umnavvwiwKqLFy4kLz88suEEMPzjqptFNOfxNybTp066V0cNcQff/xBunfvrvNzQ+Nd1WfAlH5TtQ3VGQurfk8bYsagqtc1adIksmTJEp3H1NY/xI5jvXv3VvuNxfRZCoUiDprTroULFy6gU6dOqKioACEEI0eOxIIFC3D16lXIZDJ06NBB2NfV1RX+/v6CUM6MGTOwcuVKXLlyBb1798azzz6L1q1bm9wWmUyGYcOG4ffff8fzzz+P4uJinDt3Dl9//TUAID4+HiUlJZg9e7ba9yoqKtCmTRudx3348KEQHp6XlyeETaempuKZZ54R9lMNN+VzcrOzs9GkSRNER0dj8uTJasft2LGj3nBXMccNDw/H7du3hTA+gMvnKisrQ0lJCWJjY+Hl5aWWIxwcHKxxjpMnT2LXrl1ITExEcXExFAoFHBwcDLZNFTc3N/Tp0wfHjh1D165dkZiYiDt37uCjjz4CwIWFKpVKIQyMp7y8XGfOn5OTE1q3bo3r169DLpdDLpfjhRdewIYNG1BUVIQbN26gW7duAIDCwkJkZGSgc+fOasfo3LmzWlghALRr1074d0FBATIzM9WeVZlMhnbt2ukNkQ8LC9O4Fp6EhARUVFSotUUulyM4ONhooaimTZuq/Raenp5q6RnamDJlChYuXIh///0Xffr0wZAhQzTuiypxcXHYsGED7t27h9zcXLVnPCAgQNhP9b4VFxcjISEB77//Pj788ENhu0KhEAQkdaH6TAPcc63rmmJjYxEYGAhra2thW/v27Q0et2pfqcq4ceOwfv163L17Fx07dsShQ4cwbNgw2NnZaT12WFgYvLy84O/vr/XzmJgYDB48WG1b586dsWvXLiiVSkilUgDq95BHLpertd2UvlJUVIRNmzbhwoULyMzMhFKpRGlpqdYwUVXCw8Px+PFjId0B4NIXWJZFUlISYmNjhf7A07JlSzg5Oek9ri5Uw7ddXFzg7++vM42hpqjOmGpra6v32IsWLcLw4cNx5coVuLu7q30WHh6Oa9euoVOnThrfS0hI0PlsaePFF1/EmDFjcP36ddy7dw+nTp3C1q1bsWXLFvTp0wfh4eFISEjQ6PdlZWVqqS/PPPOM8Gzy90M1XFgsVcepRo0aQSqVquUVN2rUSOjnjx8/RnFxMXr06KF2nNLSUrX2mTL+6UL12eP//umnn9S2Ve2fMTExGmK3fL8GDM87qhIdHW2wP4m5N7NmzcIHH3yAo0ePonfv3njuuefg4+Oj89r/+ecfbN26FTExMSgsLDT4TBsa76oipt+IaYOpY2HV7+nDmDEoLCwMEydOFHVcYwkICFDLYxfbZykUimGo0a6FHj16YOXKlZDL5fD09IRMJv42TZw4EX379sWFCxfw999/Y9u2bVi6dCmmT59ucntGjRqF6dOnIzs7G3///Tesra3Rr18/ABByNLdu3YrGjRurfU+XAEhxcTHmzJmDvn37Yu3atXB1dUVqairmzJkj5P3xqF47wzAAuBzr6qLvuMXFxViwYAGeffZZje+pGjn6uHPnDt566y0sWLAAffv2haOjI06cOIGdO3ca3dZRo0Zh1apV+PDDD3H8+HEEBAQIL9Li4mJIpVIcPHhQbZIIQKehBADdu3fH9evXYWVlhW7dusHFxQUtW7bErVu3cP36dY1FGDHoO59YqitWI5FINBYFqj5TALT2KX2LCQAwYMAAnD9/HhcvXsTff/+Nl156CS+++CKWLl2qdf/58+ejadOmWLVqFTw9PcGyLEaOHKnRHtX7xvenTz75RG3Bg782fVS9JoZhRGkIGMKYPuju7o5Bgwbh0KFDaNasGS5fvixMxLVhLnEibRNkGxsbob2AaX3l888/xz///IOlS5fCx8cHNjY2WLhwodZnSpXi4mJMnjxZ67jr7e2N2NhYMZdlFrQ9B+aoRlKVmhxTfXx8MHHiRHz11VcaqubFxcUYNGgQ3nrrLY3vmSK+6ODggJCQEISEhGDRokWYM2eOYLQXFxcjKCgIa9eu1fieqoCrufqituNo28bf56KiInh4eGD37t0ax1Jd9DNl/KsOprwb9M07TEHMvVmwYAFGjhyJixcv4tKlS9iwYQO++eYbDB06VOM7SUlJmDdvHqZMmYL//e9/cHZ2xq1bt/D++++joqJC55hkDIb6jdg2mDoWVv2euTBl3Bc7jlW972L7LIVCMQw12rVga2sLX19fje0tW7aEQqHAvXv3hFXD3NxcxMbGqnmnvb29MWXKFEyZMgVfffUVfv31V62TR7lcDqVSabA9nTt3hpeXF06ePIlLly7hueeeE9RfW7ZsCSsrK6SkpKB79+6iri8mJgZPnjzBW2+9BW9vbwCc591YWrZsiXv37uH5558Xtt27d8/o41Slbdu2iI2N1fobAJw4UlpaGrKystCoUSMA0BCruXPnDpo0aYJXX31V2FbVOyeXy0UtQAwePBjLly/H5cuXcfz4cTUxljZt2kCpVCInJ8eoOqjdunUTXtj8RKh79+44ceIE4uLihN/SwcEBnp6euH37ttrve/v2ba3RBTyOjo7w8PDAvXv3BK+9QqHAo0eP0LZtW53f40UCFy5cqPGZj48P5HI5bt++jaZNmwLgDPIHDx5g5syZALjIk6KiIhQXFwuTj6oRAWLQ9du4ublh7NixGDt2LH755Rd88cUXWo12vl+uWrVK+F1u3rxp8LyNGjWCp6cnEhMTMXr0aKPbLRZ/f38cO3ZMTV1Xn+CSMUyYMAFLlixB48aN0bx5c3Tp0kXnvoGBgUhLS0NsbKxW71OLFi1w+/ZttW23b9+Gn5+fxmTTEKb0lTt37mDs2LHCpL2oqAjJyclq+2h7Vtq2bYuoqCidY0iLFi2gUCjw8OFDoR/FxMSYXOv87t27QuRDXl4e4uLi0KJFCwDcM5uVlQVCiDABryo2KHYsErtfVQyNqWJ4/fXXMXToUJw4cUJte1BQEE6fPo2mTZvqXOA2td0Mw6g9g0FBQfjjjz/g7u5udNSUOdpjiKCgIGRlZUEqlaJZs2YmH8eY9lV95967dw8tW7bU+x3+no4dO1bYdvv2bbV5jL55h7bjGepPYu+Nv78//P398dJLL2Hx4sU4ePCgVqP90aNHIIRg2bJlwoLqH3/8ofe6DY13VTHUb0xpA2D6vEEf+sagqgQEBODq1asYP3681s+1PX9ixjFtmKvPUigUqh5vFH5+fhg8eDA+/PBD3Lx5E+Hh4Xj77bfRuHFjIYT0008/xeXLl5GYmIhHjx7h2rVrOl+gTZs2RXFxMa5evYqcnByUlJToPPfIkSPxyy+/4J9//sGoUaOE7Q4ODpg9ezZWr16Nw4cPIyEhAY8ePcLu3btx+PBhrcdq0qQJ5HI5du/ejcTERJw7dw7ffvut0fdjxowZOHjwIA4ePIjY2Fhs2LABkZGRRh+nKq+//jqOHj2KTZs2ITIyEtHR0Thx4oSgYNynTx80b94cS5cuRXh4OG7duoV169apHcPX1xepqak4ceIEEhISsGvXLjXlboC7/0lJSQgLC0NOTg7Ky8u1tsfOzg6DBw/G+vXrER0djZEjRwqf+fv7Y9SoUXjnnXfw559/IjExEffv38fWrVtx4cIFndfYrVs3FBUV4cKFC4Ix3qNHD/z+++/w8PBQm1DMmTMH27dvx8mTJxETE4O1a9ciPDwcM2bM0HsfZ8yYge3bt+Ps2bOIjo7GRx99ZNAoeeWVV/DgwQOsXLkS4eHhiI6Oxr59+5CTkwM7OztMmTIFX3zxBS5duoSoqCh8+OGHKC0txYQJEwAAHTp0gK2tLb7++mskJCTg999/F1T5jaFp06a4d+8ekpKSkJOTA5ZlsX79epw9exbx8fGIjIzEhQsXdPYtZ2dnuLi4YP/+/YiPj8fVq1exZs0aUedeuHAhtm3bhl27diE2NhaPHz/GwYMHTYrS0MWoUaNACMGHH36I6OhoXL58WVBlr65npV+/fnBwcMCWLVswbtw4vft2794dXbt2xcKFC/H3338jMTFR8HIBwOzZs3H16lVs3rwZsbGxOHz4MPbu3WtSJIgpfcXX1xdnzpxBWFgYwsPDsWTJEo3JZNOmTXHjxg2kp6cLiuUvv/wy7ty5g48//hhhYWGIi4vD2bNn8fHHHwPgjIx+/fphxYoVuHfvHh4+fIgPPvjA5MiDb7/9FlevXkVERASWLVsGV1dXDBkyBADXr3NycrB9+3YkJCRg7969uHz5ssY1PH78GDExMcjJydEZSaCtX4jB0JgqhkaNGuGll17S8JROnToVeXl5WLx4Me7fv4+EhARcvnwZ7777rrAoLabdYWFhePXVV3Hq1ClERUUhPj4ev/32Gw4ePCi8X0eNGgVXV1e8+uqruHnzJhITE3Ht2jWsWrUKaWlpoq9F2zNjDnr37o2OHTvi9ddfx5UrV5CUlITbt2/jm2++MWpRzpjf+fbt29i+fTtiY2Oxd+9enDp1yuC7Ye7cuTh8+DD27duHuLg47Ny5E2fOnNHo17rmHVUR058M3ZvS0lJ8/PHHuHbtGpKTk3Hr1i08ePBA5xjv6+uLiooKYR5z5MgR/PLLL3qv29B4VxVD/caUNgCmzxv0oW8Mqsobb7yBEydOYMOGDYiOjsbjx4+xbds24XNt/UPMOKYNc/VZCoVCjXajWb16NYKCgjB//nxMmjQJhBBs27ZNWIFmWRYff/wxhg8fjrlz58LPzw8rVqzQeqzOnTtj8uTJWLRoEXr16oXvv/9e53lHjx6NqKgoNG7cWMNztmjRIrz22mvYunWrcN4LFy7oXM12c3PDmjVrcOrUKQwfPhzbt2/XGWKsj+HDh+O1117Dl19+iXHjxiElJQVTpkwx+jhV6devH7777jtcuXIFEyZMwAsvvIAff/xR8O5KpVJs3rwZxcXFmDBhAj744APMnz8fQGWo5+DBgzFz5kx8/PHHGDNmDO7cuaPmdQeA0NBQ9OvXDzNmzECvXr1w/PhxnW0aNWoUwsPD0bVrV41c4tWrV+P555/HmjVrMGzYMLz22mt48OCBEMWgDWdnZwQEBMDNzU2YlHTt2hUsy2pETMyYMQOzZs3CmjVrMHr0aFy+fBnffvutwbrJs2fPxujRo7F06VJMnjwZ9vb2Wj0Wqvj7+2PHjh0IDw/HxIkTMXnyZJw7d07woL311lsIDQ3FO++8g7FjxyI+Ph7ff/89nJ2dAXC5dF9++SUuXbqEUaNG4cSJE1iwYIHec+pqu1QqxYgRI9CrVy+kpKRALpfj66+/xujRozFt2jRIJBKdOZYSiQTffPMNHj16hJEjR2L16tV45513RJ174sSJWLVqFQ4dOiSEiB4+fLhanrOq8EZ1WFgYxowZg2+++UYouVPdurYSiQRjx44Fy7JqUTC62LhxI9q1a4fFixdjxIgRWLt2rWAkBAUFYd26dTh58iRGjRqFDRs2YOHChQYXA3RhbF9ZtmwZnJycMHnyZMyfPx/9+vVDUFCQ2j4LFy5EcnIyhgwZgl69egEAWrdujd27dyMuLg5Tp07F2LFjsWHDBnh6eqq1xdPTE9OmTcOCBQvwwgsvaORrL1u2TFRq05IlS/Dpp59i3LhxyMrKwpYtW4TfsWXLllixYgX27duHMWPG4P79+xrG0QsvvAB/f3+MHz8evXr10ohu4NHWL8RgaEwVy5w5czRCrRs3boyff/4ZLMtizpw5GDVqFD777DM4OjoK3kcx7W7cuDGaNm2KzZs3Y+LEiRg7dix27dqFBQsWCGO3ra0t9uzZgyZNmuCNN97A8OHD8f7776OsrMwoL562Z8YcMAyDbdu2oVu3bnj33Xfx3HPPYfHixUhOThaiwsRgzO88a9YsPHz4EGPHjsWWLVuwbNkyg2HsQ4YMwXvvvYcdO3YIhvlnn32mkW+ub95RFUP9ydC9kUgkePLkCZYuXYrQ0FAsWrQI/fv31xr1BXB9/N1338X27dsxcuRI/P7771i8eLHeNgL6x7uqGOo3praBv1/Gzhv0oW8MqkqPHj2wfv16/PXXXxgzZgxmzpyptqikrX+IGce0Ya4+S6FQAIbUZCIVhVJL3Lp1C1OnTsWZM2f0CtdQKJbKsWPH8N577+HmzZvVzjV/7733kJOToyagRDGeadOmoUePHiYtPFEoNU1ISAhmzJiBl156qa6bQqkjrl27hhkzZuDGjRsmC2lSKJT6gUXmtK9atQp//fUXkpOTceTIEUEFPSQkBHK5XJjQzps3D8OHDwfAKUUvW7YMubm5cHBwwJo1a9CqVas6uwZKzXLmzBnY2dnB19cXCQkJ+PTTT9G5c2dqsFPqDUeOHEGzZs3QuHFjPH78GGvXrsVzzz1XLYO9oKAAjx8/xvHjx7FlyxYztrbhUVBQgISEBGzdurWum0KhUCgUCqWBY5FGe2hoKObOnYupU6dqfLZu3TqtpcyWL1+OF154AePGjcOpU6ewbNkyHDx4sDaaS6kDioqKsHbtWqSkpMDV1RW9e/c2KcSfQqkrMjMzsWHDBmRmZsLDwwPPPfcc/ve//1XrmK+99hru37+PyZMno0+fPmZqacPE0dFRZ64rhUKhUCgUSm1i0eHxISEh2Lx5s5qnXfVvnuzsbAwdOhTXr1+HTCYDIQR9+/bFvn37qqWWS6FQKBQKhUKhUCgUSl1ikZ52ffBiUu3bt8dbb70FNzc3pKamwsPDQxDLYhgG3t7eSElJ0Wq0Z2RkIDMzU20bwzDw8vKidSMpFAqFQqFQKBQKhWIx1CujnVegrKiowLp167B06VJs377d6OPs378fmzZtUtvWtm1bHD58GLm5RVAozF+/lUKhUCgUCoVCoVAoFFVkMglcXe3171NLbTELfKktuVyOmTNnIjQ0FADg7e2NzMxMKBQKITw+NTVVozQXz6RJkxASEqK2rbpqzRQKhUKhUCgUCoVCoZibemO0FxcXQ6FQCCUtTpw4gbZt2wIA3N3dERQUhGPHjmHcuHE4ffo0GjdurDOf3dPTU61eL4VCoVAoFAqFQqFQKJaIRQrRLV++HBcuXEBWVhZcXFxgb2+PHTt2YMGCBVAqlQCAZs2a4f3330ezZs0AADExMXj33Xfx5MkT2NvbY/Xq1QgMDDT63DQ8nkKhUCgUCoVCoVAotYGY8HiLNNrrEmq0UygUCoVCoVAoFAqlNhBjtEtqqS0UCoVCoVAoFAqFQqHUOl9++Rl++GFrXTfDZKjRTqFQKBQKhUKhUCgUAMAbb7yCAQN6ICEhXtgWHx+Hvn27ivp+amoK+vbtirKyMp37KBQKfPvteowbNwJDh/bD2LHDsXz5u9VuOwD88MNWrFihfqy3334Pc+bMM8vxjaVv366Ij4+r1jGo0U6hUCgUCoVCoVAoFAF7ewd8//13NXb83bt34sGDe9iy5QecOXMZ27f/hM6dxS0KNESo0U6hUCgUCoVCoVAoFIHx41/A1at/IyIiXOvnFRUV2Lp1MyZOHI0RIwZj5cr3kZ+fDwCYP382AGDkyCEYOrQf/u///tH4fljYI/TrNxCNG3sBABo18sDzz48XPi8qKsSXX36GsWOHY/ToUHz99eeC5/727ZsYPToUBw78gjFjQjFy5FDs27cLAPD335exe/dOXLjwF4YO7YcJE0YBAD79dCW2bNmo9v1ff/0ZY8aEYvjwwfj99yMIDw/DSy9NRWjoAHzyyYdQKBRCe65du4o5c6bjuecGYvbsF3Hv3h3hszfeeAXbt2/BggXzMHRof7z66mykpaWq3Ys5c6Zh6NB+OHr0kLE/BQBqtFMoFAqFQqFQKBQKRQV3d3dMmDAJW7d+q/Xz777bhMePw7Ft2484dOgE5HI5vvnmi/8+2wEAOH78LM6cuYyePXtrfL9du2D8+uvP+O23XxAREQ6WVRcC/+yzj1BeXo49e37F3r0HkJSUiB9//F74PC/vCTIy0nHgwHF88cU32LbtWyQlJaJPn36YPn0WBg4MwZkzl3HgwO9a25+X9wS5uTk4cOA43ntvBb755gvs3LkNa9duwC+/HMG9e3fx119nAQCRkRH46KMPsHDhEpw8+RfmzJmPd999C0+ePBGOd+rUCfzvf2/j5MlzcHdvhO3bt6jdix9+2IMzZy5jzJhxYm6/BtRop1AoFAqFQqFQKBQLorQUKCio3n+lpdVrw9SpMxAW9gh3795W204IwdGjB7Fw4WK4urrB2toGc+fOx/nzZ4Xy3IZ48cWZeOmlObh48S+8+uocjBo1FHv3/gQAyM3NwZUrl/C//70Ne3sHODo6YubMOTh79k/h+wzD4OWXX4NcLkfbtu3g6+uHyMjHoq+NYRjMmTMPcrkcffv2h0wmx9Chz6FRo0ZwdXVFt249hSiDo0cPYeTIMejQoSMkEgn69OmHVq0CcPXqFeF4w4ePQosWz0Au546jK0LBVGRmPRqFQhHIywNefNEWR4+WQCqt69ZQKBQKhUKhUOoL3brZIz29ev5VLy8W9+8Xmfx9R0dHvPjiDGzdugnLli0Xtj95kovS0lLMnz9LbX+JRIKcnGxRx5ZKpXj++Ql4/vkJqKiowMWLf2HVqhV45pkAODo6gmVZjBs3QtifEKLmjXdycoZcLhf+tra2QUlJiehrc3JyhkxWaQrb2NjAzc1d7e+SkmIAQFpaCu7cuYVjxypD2xUKBbp27SH87e5e+V1j2yIGarRTKDXEvXtSXL8uQ34+4Opa162hUCgUCoVCodQXbtwoQkVF9Y6hYtOazIQJk/Dbb7/g778vC9ucnV1gbW2NnTv3wdu7icZ3+HxuscjlcgwZEoqff96DmJhoPPvsc5BKpfj99zNqhrlYGIYx+jv6aNzYC1Onzqgz9XmAhsdTKDXGw4dc98rPN+/AQaFQKBQKhUJ5urGxARwdq/efjU3122FtbYOXXpqLvXt/FLZJJBKMGTMOGzZ8jaysLABcSPvlyxcAAC4urpBIJEhOTtJ53F9/3Ydbt26gpKQELMvin3+uIDY2BkFB7eDu3gi9evXF+vVrkZ+fD0II0tPTtAraacPNzQ2pqakaefKmMnr0OBw7dhj37t0Fy7IoKyvF7ds3kZGRLrI97nrvhRio0U6h1BCPHnEx8dRop1AoFAqFQqHUV0aOHANHRye1bfPnL8Azz7TC66/PxdCh/TF//mz8++8jAFxo+cyZc7Bw4Xw899xAXLt2VeOY1tY2+PbbDRg7dhiGDw/Btm3f4p133kNwcEcAwAcfrIRMJsOsWVMRGjoQS5YsQGJigqj2Dho0BDKZDMOHD8YLL4yp3sUDCAxsjQ8+WIlvv12P4cMHY/z4Ufjllz1gWSLq+7Nnv4LPP/8Ezz03EMeOHTapDQwhRNzZGgi5uUVQKMyzKkNp2AwYYIewMCkOHy5Gnz7iRDkoFAqFQqFQKBRKw0Emk8DV1V7vPtTTTqHUAGVlQGSkBK6uBHl51NNOoVAoFAqFQqFQTIMa7RRKDRARIYGtLdC2rRL5+XXdGgqFQqFQKBQKhVJfoUY7hVIDPHokQVCQEs7OhOa0UygUCoVCoVAoFJOhRjuFUgM8eiRFUBALJycqREehNFQ+/NAa+/fTyqoUCoVCoVCqBzXaGzhlZUBmJjUqzc3DhxK0a8fC2ZnmtFMoDZX79yWIjqavWQqFQqFQKNWDziYaOHv3yjF/vhmKOFIECOE97Uo4OhIUFNR1i+qee/ckUFIBfUoDIz1dQhftKBQKhUKhVBtqtDdwwsMliIigj4E5SU5mUFAABAZSTzvALWJMmGCHGzekdd0UCqVWychgGnz/p1AoFAqFUn2otdbAiYyUID1dgsLCum7J08OjRxI88wwLW1vAyYkK0T15AuTlMcjJadj3gdKwKCoCCgup0U6hUCgUCqX6UKO9gRMZyT0CsbHmexRycoBPP7Uy2/HqG7wIHQAqRAcgIYF7tp48qdt2UCi1SXo61++p0U6hUCgUCqW6UKO9AZOXB2RkSODnx5rVaH/8WIpNm6xQUWG2Q9YrHj6UqBjt1NMeH889W7m5Dfs+UBoWGRncc5+fX8cNoVAoFAqFUu+hRnsDJjJSAg8PFp06KRETY75HobAQUCoZJCY2TCONF6ED8F+d9jpuUB3DG+1PnjTM54FiGZSXc/oKtUV6OgOZjFjcc19eDoSE2NHIFwqFQqFQ6hG0gGwDJjJSglatWPj7s2Y22rlJalycBC1aNCzJ8MJCLtWgXTvO0+7oyHnaCQEYy5q71xoJCdyF05x2Sl3Stas9iosZtG6tRGAgizZtWLRuzaJHDyXkcvOfLz2dgb8/i8REy1obj42V4OFDKeLjJXBxYeu6ORQKhUKhUERgWbMJSq3CG+0tWrCIiTGfQcUb7eYMua8v/PsvF73g6cm59JydCSoqGJSU1HHD6pD4eAlatmQtzuNIaTiUlQFpaRJs316CGTMq4OJCcOGCDDNm2OLo0ZpZu05PZxAQwKK0lEFpaY2cwiT4aiGpqbQ/UigUCoVSX6Ce9gZMZKQU/fop/jPazRseD3Ce9obGw4eVInQA4OjI/b+ggIGdXS3G5loQCQkSdOyoRGYmNRIodQMf5dG3r7pX/ZVXbJCaWjPjVHq6BAEBLE6c4MTobGwso//zRntamgRAw4qEolAoFAqlvtLwrKp6Qn4+8MsvNbumwnvaW7ZkkZUlQUGBeY7bkD3tjx5J0K5d5URYLgfs7BpurXalEkhMZNCxo5IK0VHqjKwsBi4uRCMM3sODICurZp7L9HQGzZoR2NpalhglXzEkLc1y2kShUCgUCkU/Dc+qqiccOCDHihU2NXb8sjIgLo5Bq1YsXF0BV1diNm97YSGXyxkX1/Amhf/+q+5pB3gF+TpqUB2TlsZAqQTataPh8ZS6Izubgbu7pqe7USNSYxEgGRkMGjdm4eREkJdXI6cwicePJWjTRkmNdgqFQqFQ6hHUaLdQjh2TIS+v5tSOY2MlsLEBmjThTmDOEPnCQqB9eyXi4yVgnxKdo2PHZNi+Xb9alVIJhIVJdBjtDXOCHB8vQdOmBB4ehArRUeqM7GwGbm6ag2lNeto5o53AxcVyIm2USiAqSoL+/ZX/hcdTKBQKhUKpD9C3tgWSns7g//5PCpZlUFRUM+eIjJTgmWdYSP57AsypIF9YyKB1axYVFU+H2FF+PrBsmTWOH9efrpCUxKC8HGjZsqrRjgZrtCckMPDxYeHiQlBczKCsrK5bRGmIcJ52zRVEDw+2Rjzt5eVAdrYEjRsTODnBYoz2hAQGLAv07Kl8KsZmCoVCoVAaCtRor0PKy4HiYs3tJ07I0LkzN8GsKWOPz2fnMa+nnYGrK0GzZqRO89rNFZL+1VfWkEor643rIipKAn9/ViNvlguPbZgT5Lg4CXx8CFxdOS8nDZGn1AXZ2QwaNaq98PjMTAYMQ9CoEYGzs+X0/4gIrpJD8+Ys0tMto00UCoVCoVAMQ432OuTbb60wf75m3vrvv8swdmwFHB1rbrIXEVGTRjtXn9zPj60zBfkLF6To08e+2seJiWGwc6cc69eXIjVVf+kmPnqhKs7ODTc8PiFBAl9fFlZWgL09oUY7pU7IytKe0+7hQZCdzZg9jSc9nVskkMlgYUa7FAEBLBo3JsjJkVhUKToKhUKhUCi6oUZ7HZKYyODPP2VISqqc0GVkMLh2TYqRIxU1OtmLitI02mNjzXOuwkIGDg5cyL25jmksO3fKkZ4uQUVF9Y6zYoUNJk2qwMCBSlhbA4mJurtM1egFHkfHhitEFx8vgY8Pd09cXQlVkKfUCTk5uoXolErG7M9leroEnp7c+SzLaOfGKA8PAqmUUG87hUKhUCj1BGq01yF8WObevZXx1FxovBLe3sQsxt7//Z8U8fHqEzOW1W605+RI8ORJ9c4H8EZ73XnaU1MZnD3L5Z9XR/zswgUprl6VYunSckgkgK8vq3EvVYmKop72qiQkMPD15e6Jiwsxy/NFoRiLLvV4OzsuAsTcIfLp6ZwIHcA995ayaBcZKUFgIKdl0rgxqbEa9RQKhUKhUMwLfWPXIZmZEowfr8DevXIoFNy233+XYfRo7o/qemgqKoBXXrHBl19aq21PSeEEwfz9Kw1MJyegUSPzhMgXFgIODgT+/nWT0753rxz9+yvh4GC6YrlCASxfbo233ioTcmF9fYnevHZdnnZLEqKqTUpKOI+jjw93/6innVJX6DLagZpRkFc12p2cLCMthBCu3FtAADdGeXlRTzuFQqFQKPUFarTXIZmZDCZNqoBMBpw5I0NmJqcaP3Ikb7RXT4ju5EkZCgsZ/PGHTE21OyJCAl9fAmt1Wx7+/uap1V5UxIXH8572mipbpw2FAtizR46ZM8vh7s7lq5rCTz/JUVHBYPbsyvh6X1/dkQO5uUBWlnZPu5MTQUFBw5scJyZKYGtLhDBhFxdqtFPqBl1CdEDNiNHxNdoBbhy3hEW7lBQGJSWV1S28vFiqIE+hUCgUSj3BIo32VatWISQkBIGBgQgLCxO2x8XFYfLkyQgNDcX48eMRGRkp6jNLJSuLgZcXwYsvVmDXLjlOnJChUydWqJ1e3freP/wgx5IlZXBxIbh4USpsj4qSICBAqbG/OcTolEqguJgLj/f1ZVFYyNRYHWRtnDsnBSHAkCFKNGpkmtFeWAh88YU1Pv64FFZWldv9/HSHx0dFSeDpycLZWfMzTj3e6GbUe/hyb8x/t8zV1TI8jpSGhVIJ5Obq87SzZh+jMjIkgqfdUnLaIyIk8POrXKz18iK0VjuFQqFQKPUEi3xjh4aGYt++fWjatKna9uXLl+OFF17A6dOn8fLLL2PZsmWiPrNECgs549bDg8XUqRW4dEmKHTvkGD260rNbncnew4cS3LsnxdSpFRg1SoFjxyrz5iMitHuEzWG083XlHRwI7O2Bxo1ZxMXV3oR11y4rTJvGRS+4uZkW9rp/vxw+PiyGDFFf2OBy2rXfn6oaAao4OzdMT3t8PBfRwWMoPJ4qWVNqgpwcBoQwcHOrPU97ejpjcUJ0ERHqi7Xe3gRpaXXfLgqFQqHULQcOyPDuu9aGd6TUKRZptHfr1g1eXl5q27Kzs/Hw4UOMHj0aAGfYp6WlIT4+Xu9nlkpmJgO5nMDFhZs8DR6sQHi4FKNGKYR9OE+7acffsUOOCRMq4OoKjB5doRYiz3natRvt1c1BLyzkJoH2/1Vb4xTka+cxS0xkcOGCFC++yC18mBIez7LAtm1WmDevXPAQ8/A57drC/XWVewMAR0fLCI+tbVSV4wFeiE77fcjLA4KCHKhQHcXsZGczsLMjsLXV/rmHR00J0fHh8ZYhRMcZ7ZX9sXFjlhrtFAqFQkF4uAR370oN70ipUyzSaNdGamoqPDw8IJNxquAMw8Db2xspKSl6P9NGRkYGHj16pPZfdHR0rV0L1wYJGjUigmG4YEE5pk8vF0LjAT6s2vhJVW4ucPCgHLNmccZrp06sWoi8Lk+7OQzswkJugiz9r+/7+ZFaU5Dfu1eOwYMVwj10dzdeiO7MGSmKiyGIAari48OiuJhBRobmMQ152huienx8PKNmtLu66v49MjIkKChg6EuDYnb05bMDvBCd+cYoluUWZauGx9emtoc2+HJvPJynvd5MASgUCoVSQ2RkSOgibj1AVtcNqAv279+PTZs2qW1r27YtDh8+XGttyMxk4OFROYvr1o1Ft25lavuYKkS3b58cHToo0a4dN0FjGGDUKAWOHpWjWzclsrK0G5g+PiyePGGQn88pnptCYSFXQonH359FRETNTwwVCs5oX7euMsba3Z3FvXvGGYHbtllhzpwKtVx2HltbTrwpPr5yQs4TGSnFSy9pLwrv5ERQWMhAqYSwmNEQSEiQYPLkysUPFxfo9LTzERF370oxcKCm3gKFYir6lOMB83vas7MZKBTq4fFKJYOiIsDBwWynMQpCgIgIKQIDK98xXE47t5hQNaqIQqFQKA2HjAwG6ekMWBaQ0LVci6XeGO3e3t7IzMyEQqGATCYDIQSpqalo0qQJHBwcdH6mjUmTJiEkJERtm42NTW1chkBmZuWkThemCNEplcDOnVZYvlx9AWDMmApMmGCHqVOlOgXTXFy4cyYkSASD31i4Gu2Vf/v5sfjzz5p/zGJiJMjLY9QMPmOF6B49kuDmTSm2bdOdXM0ryHfvXnl/ysuBuDhGp6fdyYn7nQsKuHvcECBEMzzezU13eDyf637nDn1bUMyLGKPdnEJ06ekMnJwqw/EdHbn/5+VxAp11QVYWg9xcRi3CytubixwqKDB9kZZCoVAo9Z+MDG6xWTVKjGJ51JsZsru7O4KCgnDs2DEAwOnTp9G4cWP4+vrq/Uwbnp6eCAoKUvuvZcuWtXYtgKanXRumCBidPy9FRQUwbJh6eHfHjixcXQm2b5drzWfnad6cRWKi6Y9FQYH6xNTfv3aE6JKTGTRpQtQ82W5uxhnt27ZZYcKECr0TfD8/zVrtcXESWFkBTZtq/569PSCVWoYYVW2Rm8st4Pj6que06xKiy8nh0ipoeDzF3Bgy2nkhOnOFr6uWewO46Jq6rtUeGSlBs2as2oKqoyNgZ0eQmlpvpgEUCoVCqQH4tE8aIm/ZWOTbevny5ejfvz/S0tIwZ84cDB06FADw0UcfYf/+/QgNDcW2bduwevVq4Tv6PrNEOKNdvzfbFCG6y5dlGDpUAblcfTsfIn/ypFynRxjgQuQTEkzvtIWFgKNj5ezXz49FdrakxoWYUlIkaNpU/brc3cV70PLygEOHZHjlFe0h7jzaFOQjIyVo2ZLVGVLEMJwnqyHltcfHS+Durm4kuLpyaQIVWm5xTg6DPn2USEvjQrQoFHNh2NPOoqSEESpfVBdOhE79fHWta/H4sab4KMNUhshTKBQKpWGiVHLRWM7OBKmp9H1gyVhkePzHH3+sdXuLFi2wf/9+oz+rLXhPjZj8wMxMRqfaOA8vRGdMzuHduxK88IKmiBrAqchv3mxlwGgn1fK0Vw2Pd3HhjLW4OAmCg8WF3JuS+8172lXhhejE3L9bt6Ro0oQgMFB/G319WVy4oN44fSJ0PI6ODUuMLiFBvdwbwBkuAJfXXjXKJCeHQfPmLFq1YnDnjgTPPUfz2inmITubQfv2uvunszMgl3PednOEr6enSzRSn7ixvNqHNpmqInQ8Xl4snaRRKBRKAyY7mwHLMggOVvwXeUXnX5aKRXra6ytbtsjxwQfi6hxmZEhEhMcDFRUMSkrEnV+pBO7dk6JjR+0drmNHFm3aKHV+DlTf015UpDnx9fNjRSvIZ2QwaNvWwegJbkoKo+Fpb9SIE4ASc6ybN6Xo3NnwQOXnp93TbmgBpq49bbVN1Xx2ALCx4cJxtYXI5+ZyizsdO7I0RJ5iVjj1eN39k2HMW6tdm6ddX7nD2iAiQqJ1QdLLiyA9nU4DKBQKpaGSkcHAxYXA15eWAbV06NvajMTFSTQMOl2IyWmvFDAT14kiI7ka4rq8xQwDXLxYjK5d9ee0JyRUx9Ourh4PGFdKLjGRE0w6e9a4IJDkZImGp93eHrC2FpfXfvu2FF26GDbafX25SW5xceU2MZ72uva01Tbx8er57DyurtqN9pwcLoS5Uycl7tyhRjvFfGRl6Q+PB8xb9q1qTjtgmqioOdHtaafhkBQKhdKQychg4OnJ/vc+oGahJUN/HTOSnc2IFj4Tox4vl3OeSbECZnfvStC+vRKyaiQ9+Phw6vGmijJVDY8HeE+7uGvgvT4nThh3ESkpDJo108zZdHMzPBknBLhzR5yn3cODwM6OCAsbhIjztDs5EdGLL08DCQkS+PhoPkScx1Fz/5wc5j9PuxL37pn+/FEoVTGU0w7UvKfd2Rl1JkSZl8eNqwEBmuOblxf1rFAoFEpDhjPaCby96SKupUONdjOSnc2IEj4rKgKKiw0L0QG8gry489+5I0WnTqaVauPx8WFRWMhoNazEwBntmuHxYj3t6emc8f3XXzLRaQGEaPe0A1xeu6GFlNhYToQqKMjwvWMYXoyOO2ZGBoPCQqBlS0NGe91N2msbQoB//9Xu2dPnaXdzIwgKYpGfz1QrRYNC4SGkMopDH+as1Z6eLtEqRFdX/T8iQgJPTxaurpqfeXsTpKXRaQCFQqE0VDIyJP8Z7XQR19Khb2szkpUlztOemclAJiOianYbE1apL59dLA4OgJub6SHyhYXQMNr9/YnonPb0dAb9+yvg4UFw8aK4MOm8PG4RpGpOO1ApRqePW7ekaN+ehbU4OQKhVjvAedmbN6+syayLhpTTHhfH4MkTBh06aD6LunJ7c3M5o93GBmjblua1U8xDXh6gUIgx2lmz1GonpNJroUpdGu2RkZrK8TyNG1P1eAqFQmnIZGRw6bo0PN7yob+OGcnOZlBYyKCsTP9+mZkMGjUiOkuEqSLWQ1teDjx8KEGnTtVXfWzenFTDaNceHp+SIhHlOefyQQmGD+fK04khOVkCR0cCR0fNz8R42m/fFhcaz+PrW1mrPTLScD47wKvHiz5Fveb6dSmCg1mtCxnaPO0sW2m0A0DHjjSvnWIesrMZWFkRjTGpKuYKjy8oAEpKNHPaXVzqTtPi8WOpTqPd25tFejoDtnoBWhQKhUKpp6iGx+fnm6/8KcX8UKPdTCiVEDy6hjy7YpTjecR6aMPCJLCx4bza1aU6CvLawuM9PQmcnAg6dnRA27b2aNvWHufPazfK+HJJI0ZU4PRpGRTaq9epoU05nqdRI8O12o012lUV5KOiDOezAw3L0379uhTdu2u/n9qM9rw8gGW5nHYA6NSJxd271R+a3njDBqdOUeO/IcPnsxsq+Wiu8Pj0dAlsbAicnNS38+U76wJdInQA52lXKhmzpQZQKBQKpX7BC9G5uRFYW9PoK0uGGu1mgqsHzsDOzrCRKEY5nkdsePydO1J06KAUXc9dH82bm16rvahIMzyeYYDz54vw22/F+OWXEvTqpcQ//+gy2jlPe9euLORygqtXDRtduvLZAU6ITp+nvbSUi1Aw1mjnhfXEetpNmbTn5qJeesBu3NBttGsLj8/JYWBtTWBvz/3NidFJoaxG0MjZs1L8+qscN25Qo70hk50tMRgaD4hb3BNDejrnsag6Dtd1eLyuiiI2Nlw6VHo6naRRKBRKQ4T3tDMMt5BLQ+QtF/rLmInsbAb29lx4iaFwbGOMdrFCdHfvmic0HuA97eYLjwe4hYDgYBbBwSx69VLi33/1Ge0sJBLguecUOHnSsIq8Pk+7ofD4Bw8kcHIi8PMTH6Hg68vdH5YVV+4N4NIcjFGPLykBund3QI8e9vj6ayukpNSPSfWTJ8DjxxJ066bL0w4NTzuvHM8bOoGBrHBvTaG8HPjwQxu0aqUUXYKR8nQiRjke4D3t1X9WtCnHA5x6fF1E2hQVcZUc9I1R3CStfowvFAqFUt+JirKs8ZYXogO4lCn6PrBc6IzWTPCTQ3d3VpTR7ukpzoUq1kN7544UHTuaxy3r48MiMdF84fFVadOGxb//aj56SiV3b/hJL5fXLjPobU5K0u1pNyREx4XGs0ZFKDRvTlBezqnOJyWJC4831tN+9qwM7u4Ey5eX4cYNKbp1s8eUKba4ft2yu+zNm1L4+RGd5Qx1edr5fHYAkMmA9u2VuHNH+7UqldAZqQEAP/wgh5UVwdtvl4sWQKQ8nRhjtOflMSgvr975+EXHqjg7axdgrGmioyVwcdHdHwGuVjtVkKdQKJSa5949CXr3dhBdBrmmKSnhFpQrjXbqabdk6C9jJrKyOO+5GOEz48LjDXtoioo476b5PO1ceLyxtbIJ4YSYDBvtSiQnSzQiCLKyGLBs5eDRt68SRUWMwfxmQznt+n4PY/PZAcDaGmjShODcORlcXAgaNTJ8o5ydCQoKxJ/j4EEZxo2rwKhRCvz8cwmuXy9C8+Ysli61MaqttY2+fHZAe067qggdD5fXrt0wP3dOiueft8OuXZpChZmZDNautcYnn5ShZUuWetobOFlZjKj+yeW9Vz9EPiNDs9wbwPX/wkJGlEaHOXn8mKvPrm9RknpWKBQKpeYhBPj4Y65MkaUYxpmZDKRSIszBuEVc+j6wVCzjqXkKyMriPDqGjETA+PB4Q0b7gwdSuLsTnd5mY2nWjEVxsbia86qUlXHllQwpNbu5cRPFsDB1oywjg4GLC1f2CwCsrIChQw2HyOvLaTe0iHLrlvFGO8CFyJ89K8Mzz4jz0js6cp48MQsheXmcp33cuAphW9OmBPPmlSMqSlKtXO+a5vp1qc7QeEC70Z6drWm061OQP3lShj59FFi+3FrD475mjRX69VOgf38lfH1Z5OUxyM018WIo9R6xnnaZjNO/qK4gm+7weG6bMQt35kBfuTceLy9Cc9opFAqlhjl/XoqHD6UIDFRajGGckcEtbEv/m0rRRVzLhhrtZqIyPN6wt8ZY9XhDYdVcPrtxId76sLPj6hYbqyBfWMjtb8jTDmgPkdcWWjp8uAJ//KHbaGdZIDVVt6fdzY2gpER7CYusLAYJCcaJ0PH4+bH45x+pqHx2gIuYqKhgUFpqeN8TJ2Ro04bFM8+o30c+795SwqqqUlHBpWkY8rQXFDCoqFyPQG5upXI8T+/eSty/L0FOjvr3FQrg1CkZli4tx6pVZZg920a4H/fvS3DggBwrV3I1F52cOJEt6m1vuIg12gHziNFxJSs1xwQ7O0Amq/0Qec7TbthotxSvD4VCoTyNsCzwySfWWLSoDK1asRZktEvU0qe8vWm6lCVDfxkzwYVhsgbVygFT1OP173P3rhQdO5rX/cqHyBtDYSEglVZ6yvXRtq1Si9Eu0ci97NlTichIKQoLtR8nK4tBeTkDb2/t99PVlUAi0f6b3L4twTPPKOHsbLi9VfH1JSgvZ0TlswPc7wiIE6M6eFCu5mXnkUqBZ55hER5umYroDx5IYGUFvUYC73FUXYjKydE0rLy9CYKCWJw7p75gc+2aFBIJ0K2bEtOmVWDCBAVmzLBFQQHw/vvWeOWVcjVRQT8/QvPaGzDGGO3mKPvGq8dXhWHqpuxjRITuGu08Xl6WM4GkUCiUp5EDB2TIz2cwe3bFf9FNljEv4ZXjebhF3Jp5HxACzJhhg/h4+r4xFct4ap4CVD3t+oTPiouBoiLjjHZDnvY7d6Rmy2fnMUVBnleOF+Px5zzt6santtBSDw+CRo1YhIVpb0tKCgM3NxZ2dtrPI5Fw3nZtvwkvQmcKvr7c91q1EnffrawAW1vDk/b0dAZXr0rx/PPak18DA1k8fmyZ3ZYTzFNCoqd5trbcfXjypHIbrx5flWefVeDPP9WN9pMnZRg2TCGEcq1cWYbGjQmefdYecXESvPmmupKYry/1tDdkjDfaq/esaFt45HF2Rq2WfSsr46JyDBntnGeFTqIoFAqlJigtBdasscayZWWwtrasvPGqRru3N1cCtCbSMMPCJDh1Sk7nZNWA3jkzoWq06/O0VxV9MIQh78yTJ0BsrMRsyvE8zZubFh4vJjQeANq2ZREeLlFThtflpWrbVtPA59GXz86j6zcxNZ8d4MLjAYgOjwf4BRj9+xw9KkPPnkqdkQOBgSwiIiyz2xoSoeNxcVHPa6+qHs8TGqrAX3/JBEVvQjijffjwygUNmQzYvr0E9vYEH39cpqGn4OfHWmw6AaVmIcT48PjqeNqTkxkUFHCaINqo7VrtMTESWFtzehj68PIiyMmRoKyslhpGoVAoDYgdO+RwcSEYP56buzRuzFqMjghntFe+s7y8CJRK4zWtxMDrUxUVWca110csc/ZfD+HV4w0J0WVmcqIP+ryRqjg5AaWljM4J1d27Uvj4sKInpmLx8SFGe9qLisTlswOcsVtaCrXScrryQdu00e9pNzQp1aYzwLJchEKXLqYZ7a1asejfXwEfH/H3nUt10P1sEMKHxuuWmA4M5BY7jOH2beMrARgLIeKN9qpidNrU4wEgOJiFvT3B1avcgs29exLk5zPo21f9HC4uwNmzxRg7VvO+UU97w6W4mBs73d3FLaxVNzx+5045QkMVcHXV/rmxZR+rCy9CZyjyiRMhshzPD4VCoTxN7N8vx5tvlgvzfksqs1nV025tDbi714wY3YkTvNFu9kM3GCzjqXkKUPW05+bqDi3JzBQvQgcYzoW+d8/8+ewA52k3PqfdsHI8j5UVZ/iqetDT07WXSwoK0sx/5+E87fon5dp0BqKiJCgv57z4puDoCBw4UAK5ZtUxnegr36dUAm+9ZY30dAajRmnms/O0bq1EVJREdOmo8nJg2DA7xMbW7IQ8IYFBdjYj6lmsarRrU48HuDSLoUMrQ+RPnpRh6FAFrK3Ft4vmtDdcsrO5qCYXF3H7V0eIrqQE2L3bCi+/rLvvurgYjrQxJ2JE6ABOK8PT03ImkRQKhfI0kZnJCCmVgGWFx2dmaqZ01YQ4aXw8g8ePJejSRYniYsu49voIfUubAaWyUkzL3Z2AEEZnXrsxInQAYGMD2NjoFqO7c0dSI0a7ry+LxERGLXzdEIWFDOztxV9bVQ8652nX/D63n1Srt1isp72q0X77tgTBwUqjjO7qosvTXlICzJ5tg+vXpThxolivMJ6vLxelITbkOyeHASFMjYueXL8uRXCwbm0BVVxcKlW0CdGuHs8TGqrA6dMyraHxYvD1ZZGczAgh9pSGQ3Y2858Qpbj9PTxYkz3thw7J4eXFok8f3WNxXXnaxeDtTcu+USgUirlhWc1oQi8vFkVFjE6B5dqkqqcd4N4H5va0nzwpQ9++SjRpwlJPezWgRrsZ4A0jd3dOOd3eXneIfEaGcUY7UFnjWxt370rRqZN589kBLg+yooJrr1gKC8WHxwN8rjr3CBKiveQbwKmRFxRwBnpVkpMlOsu98WgTB6yOCJ2paMtpzc0FJk60RXY2g2PHig0uQBirIM8/h8b8jqZgqD67Kqqe9vx8QKnUnXfcr58SmZkMTpyQIT5egpAQ44x2b28CuRxISqIGSUMjO5tLRRKLh4dpnnZCgO3b5Xj55Qq9oeicp732nkPO0y6uTzZuTGvzUigUirnJy+PmOKpGu5MT54yra287IZo57QAnRmfutvFOFzs7UE97NaBGuxnIzuY8zLa23N/6xOiM9bQDugWMnjwBUlIkCAoyv6edV7g0Jh/YmPB4gCv7xnva8/KAsjLtnnZbW6BFC+157WI87dp0BsLDa+a+6cPRkaCgoPJvQoDJk+3g5kbw228lOnNhqxIQIF6MrjaNdjH57IC6pz0nh4FMRnQ+N7a2wIABCrz/vjUGDlQa9XwBXPUAHx+Whsg3QLKyxIvQAZXjhDHRRQDwzz9SpKZKtJZpVKU21eMVCk6ITqyn3ZJyLCkUCuVpISeHgY0Ngb195TaGARo3rvsxNz+fm3fXdHh8RgaDmzelGDZMAXt7Qj3t1YC+pc1A1cmhvrJvmZmaq1qGcHYGCgo0jxceLoWXFys6Z9NYuLx2Yzzt4tXjAc7THh0tQUkJl89uZ6fbeNOmIK9UAmlpjMGcdk6ITv1Rj4yUGKX8bg6qLr7ExjJ49EiCLVtKhQUfMbRuLb7sW20Y7Xl53CKIWKPd1RVqRrubG9HroXz2WSVSUyUYPly/UaQLmtfeMDFGOR7gPO2EwOiSitu3yzFtWrnB1JDaDI/nK3+IFcqsiXBICkUfGRlcBNXKldYYOdIWAwaIyK2iUOoZvGZP1TmOl5f5vdnGkpEhga2t5rzb3O+DU6dk6NyZRePGBHZ2hHraqwGdyZqB7Gx177k2tXIeUzztuiZ74eEStG5dc4ansQryxobHe3sTODoCERESodybLuONq+uu3pb0dM4rpqs8Gk9VIbrsbAbZ2RI880ztGu1VheguXJChRw+l2gqsGIxRkOcXjzIyaqarK5XA6tXWCAhgtUZJaMPVtXJRS5dyvCpDhyrg6cni2WdNi4zw9aWe9oaIsUa7rS0we3YF3nvPWnS1hYQEBmfPyjBrluEFpdoMj3/8WIoWLVjIZOL2b9aMpSkklFqBEOD77+Xo3NkeX3xhhYICYPRoBcLCpFR7hPLUoaukrSWI0fHpulXn3eYOj+dC47l3pL09LflWHehM1gxo87TrDo83Tj0e4D20mtvDwyUIDKxJo904T3tBAQNHR/HHZ5jKEHld+ew8bdtqhscnJ3OGviExuaq/R1SUBF5eLJycxLfVHFQVortwQYoBA4w3RAMDlYiOFqcgn5XFQCIhNeJpLykB5s61wcWLMuzeXSL6e1XD4w0Z7Y0bEzx8WGRyWUM/Pxbx8fQl0dDIzpYY/cy8+24ZoqMl+O03cdbuzp1WCA1VoFkzw+fhFl+Nao7JREYa925o3pwYXS2EQjGW0lLgzTdtsG6dFQ4fLsbFi8X46qsyTJ/OTegtQZiLQjEn+oz2mhYINoQ2ETrAvOHx+fnA5ctSQUSY87Sb5dANEvqWNgPGGe2medq1qY4/fixBmzY1l5ft48Ma5WkvKjIuPB7gjPFHj6T/Ge26v9umjRKRkRK1lfiUFInBfHaAy1XNz2dQ8Z8zLCqq9kPjAf535P6tUAB//y3DwIHGCasBlQrysbGGf5vsbAYtWrBmN9qzsxlMmGCHjAwJTpwogp+f+N9dVYguJ0e3cry5oJ72homxnnaAK+X46adlWLnSGrm5+vctKgL27JHrLfOmii5tkppAbLk3Hh8fzrNSVlaDjaI0aFJTGTz/vB0iIyU4e7YY3bpVPp82NoBMRrSmAVLUIQT44w+Z6GggSt2SlaV98bhxY0sIj9eeruvtzaKgwDzq9mfOyNCqFQt/f+4eUE979aAzWTPAqRRXPvi6jPaSEs4bbc7w+Jr1tBsrRAejSr4BlWHvumq0q7bF2pozuHmSkw3nswMQVjn5kOyIiNoPjQe4STu/+HL7tgRWVgTt2hnfjkoFeXFGe5s25jXa4+IYjBhhh8aNWRw4UAw3N+O+r+ppFxMeX138/LjnmE5yGhamGO0AMHKkAh07svjkE2ud+7As8O23VmjenEWPHuIFGLUtvtYExpR7A7hoFrmcG1MpFHMTESHB0KF2CAxkcfhwMby81Pslw3ALZrXVP+ozf/whw8yZtlSDop7Al4OuiqWEx2vztLu6AtbW5mnfH3+ol+qlnvbqQY12M1B1ctioEavVaM/M5EKVjTVSnJ01X2aZmVxedk0a7f7+XJ6j2DwzY9XjgcrweF012nkkEk6ATTWvXayn3cqKW/jgdQbqytOuOim5cEGG/v2VomtIVyUwUJyCfE4Og9atWWRlGa+KrY2kJAbDh9th6FAFtm83TkCPx82NW4RSKitFWmoSHx8WxcWMyTW4KfUTU412hgFWry7FoUNyXLumWVrx2jUphg2zw759cnz6aZleEUVVnJyA0lIGpaVGN8koWJYzkowx2iUSoFkz4zRMKBSxnDolQ8eOLNatK4WNjfZ9HB0JCgvpGK0PhQL49FMrAJVirhTLRn9Oe12Hx0u0Gu28ur05QuTv35eiZ8/KhW3qaa8e9A1tBqqGx7u5aReiy8zk9pOKK7EtoC08PjxcAh8f1mgj2Rj4Gtdi89qNVY8H8J9BKcGjRxKDqvqqJeIA8Z52QF2Mri6U4wH18NiLF2UYMMD40HgesQryvKddqWR0VjQwhtOnZWjThsUnn5QZ/RzzuLhwz0heXu142u3suFA0mtfesDDVaAe4FJTFi8vx0ks2GDrUTvhv4EA7TJpki9BQBf7+uwi9eolPT3J25p/7mn0OU1K4MPcWLYwb47hqIXRKQDE/GRkM/P1ZvQtcVUuiUjTZt08OpZKb91CjvX6g22jnIiDrMgJQl6cd4ELkqxvNwbLc+6h588p3EVWPrx4itWUp+tCmHq/NQOLKvRnfQ7UJ0T1+XLNedoDzvvD5wC1bGp6cGqseDwAODtw5IiKkBtXH27ZlcfZs5SPLedrFGb58ykJpKaf4XFc57YWFDHJzufD4bdtM1yMIDFTi0CHD3Tcri0HTpiycnDgxukaNqveGuHZNij59qqejYGsL2Nhwee21kdMOVD7HqnmUlKeXsjIuFclUox0AXn+9HEFBSpSWqo/lXbsqRVdKUMXKipuw5OerRxWxLOfZEOuxV+XYMRlOnpSpeTAjIiTw92dhZWXcsYwVHqVQxJKRwaB9e/1jL2e01+7zN368Lb79ttSk/lzbFBUBX3xhhdWry7BunRU12usJuhaPvbwISkoY5Odz0bR1ga6cdoBz2lU3EiAjg9OSatKk8vppnfbqQZfVtbB7txwrVujOZ6xK1U7JG+1VV9BMUY4H1HOhecLCJGjduuZE6Hj8/IgowTPAtPB4gPOgAxBltJvqaW/UiPtNYmIksLExXCauJuA9badPy9CiBSsqtF8XAQEsoqIkgrieNli20pPt6Vn9vHZCgP/7P6noeuz6cHGpNNqrY1iJhc9rr0pREWiu+1MIv2hanSgOqRQYPFiJESMUav9VZ4Lv5ERw6pQMGzZY4Y03OC9+ixYOeO898e8bVW7ckOLIES7HteS/Ag6PH5sWSdS8OQ2Pp9QMXElXQ0Z77ea0EwJcuSJFZGT9eOa3brVC8+YEI0cq/isfWdctoohBVwqggwNnwNZliLw+TzunIF+9/piYyB3fWuX1ZmcH6mmvBvVjtKpl5HKCe/fE3RqlUlNoolEjgooKRlAK5zFFOR7gVqCrhlTWhqcd4PLaxShvs6xp6vEAJ0YHGDba27RRIiVFgidPgPJy7n6KNXzd3bm8bj6f3RSvVnWxtwckEoJjx+QmlXpTxdeXS7PQt6CSlwcoldyz6elZ/bJviYkMsrIYdOpUfaPd1ZUTo6ttT7sqYWESdO7sICpigVK/yMpi4OJiuBxkbdOuHYtff5Xh3j0uvWnBgnJs21aCvXvlJvXP1FQGixaVo7CQwfTptiguNr7cGw8Nj6fUFLpyZ1WpbU97aSlACFPnYmBiyMpisGmTFZYv5zQ0nJ0rK7BQLBt9ZW3rUoyO1xSqyfD4pCSJRjlU3tNOnSWmQd/QWvDzI6JLRHEedXWj3cEBsLLSVJDPyDDNaK8qREcIEB4uFYzdmsTPjxXlaefVIE0x2oOCWMjlhgX6XFyAJk1YhIVJkZrKQCqF6HQDPjy+rvLZAS781ckJuHhRalKpN1WkUqBVK/157dnZDGxsCOztYRaj/do1KYKDWdjbV+swADhPe04OUys57YBmrfaYGAYTJ9qiUSMWp05Ro/1pozr57DXJvn0luHy5GD/8UIp33inH6NEKPPusEn37KvHDD8avMKSlcak++/cXo7wcmDbNFvfvS40SoePhjHZqCFDMjyGhWYAXoqulBqHS22euetQ1yTffWKF3b6WgoeHqWnvlIymmU1HBzd11pSXWZdk3TpxYt01ijvD4xESJWj47wHnalUpaXtRULH+0qgP8/VmkpEiEcEN9ZGczsLcnairaDMMZiVXF6DhPu/GTKWdngqIiBor/7Ly0NK5+Ym2ULfPzYxEXZ3hQ4VVfTQmP79VLiVdfLRfl/eZLxKWkSODlJV7Ujxeiq0ujHeDCYwkBeveuvrc6MNCQ0S6BmxsBw/BGe/W6+7Vr5gmNB7hJR3KyBOXltWO0+/qyQnh8UhJXY37CBAU2bizF+fMyvWkGlPoHJ/pZf/QLXn+9HDt3WhlttKSlceOggwO3IMCyMNlo9/HhJml0MkUxJ8XFnL6EpXna+bza9HTLN35PnJBh3rzKMj7OzoTmtNcD+DQtXdGEXl4E6el1Y4ZlZHDRaNY6MrPMEQWQlMSgWTP1dxFfFpqWfTMNarRrwdOTwM5OXH5fVeV4Hs5IVP++qeHxTk7cd/hw+7AwCfz8iEnltozF358zdpQGbLXCQk5cTGaC07JRI4IPPhBXV65tWyX+/VdiVD47oO5pr4sa7TxOTgTduinNovpvSEFe1dtoDk/7jRtS0TWpDeHqShAdLYFUSuDkZJZD6sXPjzNI4uM5gz0kRIEVK8rQsSMLKyuCmzdNlMKnWBxlZcDmzVbVTkGpTXr3VsLfn8W+feK97YRwBoe3NzeeOTgAe/eWYMWKUpOisDw9CaysCK3VTjErmZkMpFLDkXS1ndNe6Wm37OedZbl+7uNT2addXGq+CgWl+uTkMHB0JDpFQRs3rrvweE4YW/d7wtubRXo6Y3Durw9t4fG2tgDDEFr2zUSo0a4FhuFzYA0/VFWV43l4I1GVzEzDeV3asLXl8uz5QTo8vHZE6ACudq9CYfjFZkq5N1PgPO1S0TXaeRo14iIfoqPr1tPu4kLMZkwEBCiNMNrZatUpf/KEe+7M5Wl3cQGioyVwdSUm16o3Bg8PbiFu9Gg7dOqkxBdfcLmBEgkQEqLE2bPUaH9a+PRTa1hbA4sWiVsItAQYhvO2f/edleioj9xcoKxMPezY3h54/fUKk3L5aa12Sk3ApwUaGudru+Qb7+mr61rZhsjKYqBUqvdzXsiVYtnoEqHj8fKqu/B4fSJ0AOdpZ1kgNtb09iUnMxrh8QxDxeiqg2WPVjoICQlBaGgoxowZgzFjxuDkyZMAgLi4OEyePBmhoaEYP348IiMjTT6H2FxuXZ72Ro20Ge2medq5XOhKBfnHj6Vo3bp2DE8rK24iZ+hemKocbyxt2rAID5cgMZErZSYWd3fOs1tWxkUP1BVr1pTh5ZfNY0wEBrKIjtatIK/6wqiup/3GDSlatCAmPb/acHUliI2tndB4gOtDzzzDomNHJTZuLFWbQA4ZolArJUipv1y4IMXu3XJ8+22JxYnQGWLECAWkUq6MmxhSUyVwciJm0ZjgoWJ0FHOTni7OWcHltNe+p93ShejS0zmxVr6kI4D/1OMtu90UTZHqqnAh6HUVHq+/X1pbAy++WIElS2xM8rYTwuW0V/W0A7TsW3Wot2/ndevW4ejRozh69CiGDx8OAFi+fDleeOEFnD59Gi+//DKWLVtm8vH9/cWJ0WVlMWjUSNMIrOppLy3lQr9MNXqcnCpDxzhPe+0Znlxeu/57UVBQO572Vq1YlJVx+dXGeNrd3QmUSga+vrpzeGqDgAAWjo7mOZavL5eOEBOj/bfJzq4UQPHwqF5O+7VrUvToUT3xPFU4ITpJrSjH8/zySwl++KFUI4Vj4EAFIiIkNCy4npOTAyxYYINPPilDy5aWJ0JnCKkUeO21cmzaZCVKWTc9nYGXl3nfA7RWO8XciBGhA7jw+NrMaS8uBuzsuPBkS1ay1tbPaU57/cCQICqX0153nnZD9sgnn5QhM5PBunXq8f2EAJ98YoVXXrHR8U2uelFhoWZOO0A97dWh3hrtVcnOzsbDhw8xevRoAEBoaCjS0tIQHx9v0vHEGKrcebV3yqpGe1YWA4mEmKxo7OzMrayybO2Ve+Px92cNhsgUFpqmHG8sVlac4R4eLkWTJuLPx3t06zI03txIJNz1REToNtpVc9pzc01X7OSMdvOlZPDGem152gEu+kWb5oKLC9CtmxLnzlFve32FEGDxYht07qzEiy/WX1XByZMrkJIiwbVrhtM1UlM5ETpz4uNDw+Mp5oULwzX83nV0JLWa015UxMDfn0VFBaMRFWlJpKVJNBY9XFwInjypm/ZQxKOv3BtQqR5fF4tGhsLjAS7dauvWUmzcaCW8kxQKYNEiG/z4oxX++Uf3eyoxUQJnZ6LVSUU97aZTb9/O77zzDkaNGoX33nsPOTk5SE1NhYeHB2T/zcoZhoG3tzdSUlI0vpuRkYFHjx6p/RcdHa22j7mN9owMrvOKVTuvChcez9XKLi8HWra0LE97bYXHA5V13Y0Jj7e354Ty6lKEriYIDOTSBbSh+my6uxMwjGZFAzGUlgJ37phPOR6oG6NdH0OG0Lx2S0WhAO7e1T/+/PyzDLduSfH112WiqlBYKra2wMiRFaJC5NPSGLMb7brC42/fNixGSqFogxO8EhceX7s57VwkmpNT3YmBiUFbP+fD4y05QoAixmgnqKhgBJX52kTsYlr79iyWLSvDq6/aID2dwezZNrh7V4Jjx4qRkSHRWfGEE6HTfnzqaTedemm079mzB7///jsOHToEV1dXLF261Kjv79+/H+PGjVP776233lLbx8+PRUJCZZk1XXDh8YaNdlPz2Xl4T/vjxxK0bMnqVKOsCfz9xea0184bpG1bbiAwxtPOl+Fr1erpmnnqK/um+sKQybjrNyWv/d49KZycCFq0MN/v6+JiWUb74MEKXLoko+WuLIzSUmDOHBuEhtrpXZnfts0Ky5eXWWRtdmMZPVqB33+XgTUwn0pNrVSONxfaarXn5gIjR9rhxg26qEUxHkO5szy1n9POhcd7ebEWXfYtLY1B48bq/dzFhUv3q8269hTjMRQeb28PrYtGV69Ka3whSYynneeVVyoQEMCiVy97ZGVJcORIMdq2ZeHkpDuNOClJU4SOh/O0W26fs2TqpdHepEkTAIBcLsfMmTNx8+ZNeHt7IzMzE4r/rGxCCFJTU4V9VZk0aRIOHTqk9t/atWvV9uHFEwzluarmDauiabSbphzP4+TEGe3h4bUnQsfDe9r1reoWFdVOeDzAlX2zsTE+1WDBgnKEhDxdRnvr1kq9nnbVZ9NUMTq+Prs5PZi8p702c9r10bYtC2dngv/7P2qYWAoFBcCUKbbIyJDA3h46Fw4rKoCoKAm6dn06+nafPkooFMD16/qfxfT0mgmPr1qr/cQJORQKRlQ1FQqlKunpYj3tQGkpF0lYGxQXM7Cz4/KKU1MtdyqsrZ87OnJls2heu2VjSD0egMaiESHAwoU2OH685tL1WBZIThZfgUkiATZuLMUrr5Tjt9+K4erKOcK41FntfUeXCB3ALZbROu2mYbkjlQ6Ki4uRzxcsB3DixAm0bdsW7u7uCAoKwrFjxwAAp0+fRuPGjeHr66txDE9PTwQFBan917JlS7V9ZDKgeXPDYnRiw+Or62l3cuJEWsLDazefHeDK3xUVMXpLhhUWMmZVMdZHnz5KrF9farQROXt2hdknuXVNmzasoIpflarPJme0G9/lzVmfnYf3tFuKZ5RhqIq8JZGZyWDsWDtYWwMHDhSjZUvdk4PYWAmkUk6Y8WlAJgOGD1fg6FH9z2JqqjiBL2Pw8CCwtlav1X74sAxWVjTXnWIaYgSvAM7TDtSeGF1REefx4xS8Ldf45Tzt6vdPIuG0WKjRbtkYUo8HNGu1R0cziI+X1GjIfFIStzjm5yfelvDwIFi2rFxtnq/PaE9K0i5CB3ARBtTTbhr17i2cnZ2NGTNmYNSoURg1ahSuX7+Ozz//HADw0UcfYf/+/QgNDcW2bduwevXqap3LUNk3pZLrlLo87SUlDIqKuJWze/c0xUSMgQ+Pr23leIDrYI0b669bX1tCdACX9zl2rPmUzOszzZoR2NoCkZHqz2lREVBSor7Ka4qnnWU5j5+5jXY7O8DamliMpx0ABg9W4tw56mmvK0pKgPPnpVi+3BpDh9qhZUsWu3aVwN4eaNGC1VklgV/INFQHuj4xerQCx4/L9OaRp6WZPzy+aq32jAwGV69KMWlSBTXaKUbDstwCXNXwbm3Y2AAyWe3ltfOedm9vFqmplmtAcDntmvePKshbPoZy2gFeQb5ybOUdBzVptEdFSeDnR6qdZuvvr9suSEqSoHlz6mk3N/XOrdS8eXMcOXJE62ctWrTA/v37zXYuQwJsOTkMCNG+kubqSiCREOTkMPjuOzlu35bik09Mf0qdnTn178hICdq0qf0wUH5FrXt37S9fLqf96RJ5qw8wDNC6NYuwMAnatau8/zk5XLUCVaPY05M12miPi2NQXMyJkZgThuFeVsboEtQ0/fsrkJhog/PnpRg06OkIta4v5OYCPXs6wN6eYNAgBVatKsPw4QrBEPf31220h4XV/kJmTaMaIt+rl+azWFHBGUM1ETlUKUanxO+/y9ClixI9eiixZ089K3xPqXOePAEqKsR52hlGtexbzb8Xiou5hWwPD4KwMMtcrFUodPdzTkG+7ox2pRImCys3FDhPu/53k5cXq+ZpP3tWhmbN2Bo32s0hyuzvz+L6de3vBepprxno0rke9K0iAZwInb09gY2WUoUSCSeytX69Fb77zgq//FKic9VJDE5OBPfucT+Xn1/tGzp+fvpTBWpTPZ6iTps2mnntfC6VqvfRFE97Xh4DZ2cCeQ3M1//6qwjBwZZjbDk4AF99VYq5c23x4AEdGmuTxEQJGIbg1q0ifPVVGUaOVKg9u5ynXfuzy0UfPV2LLHyIvC4VeT5VqTo6KbpQFaM7elSG559XwMeHID6+dvsEZ/DV6ikpZobToyCi5wa1KUanntNumQZEVhYDltWuCWCMp51lgZwc87ata1d7hIXR96Quios1ox21oRoeX1jIidBNmFBRo0Z7ZKR5jHY/P+0i1SUlQFaW7px5OzsqRGcqtMfpwVB4vC4ROh53d4L9++XYtatEUDw3FWdnLh+5VSu2TlY39eWuALUbHk9Rp00bVsNToE0AxdOT6NUl0EZxMQNb22o3USva6nfWNS+8oMCbb5Zj6lRbDRVtSs3B6y/o0qnQFx4fFlb74py1Aa8iry1EPjWVe/fUxGIaX6s9JYXBzZtSjBqlgK8v5w2qreoKLAuMGmWHgwfrXTAgRQWxInQ8XK32GmyQCkVFzH857azF5rSnpTFo1Eh7tSBXV/FG+/nzUowebWe2dj15wgmZUeFW3WRnc9GOzs7691MNj790SQZfXxadOtWspz06WmKWSkr+/ixSUiQoKVHfnpzMwMaG6Iywsben4fGmQo12Pfj5cd4FXarphoz2ceMU+P77Eq3hjcbi5MT9v7ZF6Hj8/Fi9npbaLPlGUYcz2jU97VXTNkwRoisp4VZFGxILFpRj+HAFpkyxRW5uXbemYZCTw+jVN2jRgkVmpkQj37WkBIiNZaq9KGqJ9OmjhFKpXUU+LU0Cb++a6ZfNm7NISJDg2DEZevVSwtOToHFjLv8xKal2jJs//5Ti8WNpndQvru/k5XEVPywBsbWgebha7bXlaa8UosvKYiwyqkObCB0Pp3Mk7jgpKRJEREjNtiDCzwXv3LGM58wS4d9phpxsqp72c+ekGDxYCVdXUuOe9pYtq//+8PQksLPTjMJKTOS87LoW4e3saHi8qVCjXQ++viyKixmdIcVZWfqVIf/3v3KEhponbNPJiTtPmzZ1MznlPO361eMt0XPaEGjdmkVyskTthazbaDduoCwp4UIIGxIMA3z2WRlatmQxc6atwXrZlOpjSLDHzY3L4awa7RMZKYGzM8yuom4J6FOR58Spas5oT0xkcOSIXBD85AXqaitEftMmK1hb154B9zTx++9yLFliXdfNAMAZ7cb0zcqc9pqHD4/nIwFMKYda06Sl6S7raExOe1YWt9/Dh+YxsuPjuXSmO3eoCaELQzXaefiSb0oll88+ZIgC7u6chlVNUFDAlRE0h6ddV9m3pCSJznx2gHraqwPtcXqwteWURXXlcmdlcaFLtYGzM9f5AwPrJnfTz49FTo5E58pubdZpp6jj7k7g6akeIq89PJ5bhCosFH/soiLA1rbh/a5SKbBlSylu3pTWmnexISNmgqMtRJ4ToVMaXf6xvjBmDKciX3XhSJeitDnw8eHCNR88kGDEiAqV7WytKMhfvy7Bv/9KMW6cotbym58mEhMZJCbqjhCsTTIyJEaHx9emp93OjksxadTIMsu+6evnphjtvC5SdYmPl6BnTyUiIjSjnygcYpTjAW7BWalkcPmyFHl5DHr2VMLNjas+VROGbVSUBO7uLNzczHM8bQ69pCQGzZvrfj9RT7vpUKPdAFxeu/aHS+xKmjlwdiZgGFJnnnYXF+4loWsBo6CAhsfXJVVD5LWVInR2BqysjPO2N0RPO4+dHdC8uXahFYp5ETPB0aYgHx7+dOaz8/TqpURBAYOoKPXrTk3V7YGrLp6eBDY2BAMGKOHqWrmdM9prfqK1ebMVZsyogLc3a9QCI4UjMVGCkhLdEYK1CRceb6zRXoMNUqGoqPLd5u1NkJpqeeN8erruSAVj6rRnZzNwdCS4f99cnnYG3bop0bQpwb17NEReG2KNdhsbTp9g7145+vdXwNqam2szTM1427nQePO9M7V52hMTJWjWTPe1U0+76VjeKGVh6FNNNxQeb04cHIDz54vh41N3hjGnpq95L8rLgfJyBvb2ddAoCgDOaFdVkNf2bDKM8XntxcUN09POY0iAkWIecnMNT3C0edo55fin12iXy4FOnZQaee01GR7PhzyOHaue5MsL1NUkUVEM/vpLhldeKYeDQ+0piT9N8AKa8fF1f+/qQ047wIuB1f39qkp6uu7FOS6nXVybMzMZ9O+vwP375vO0+/gQdOqkxO3b1GjXhjFOPS8vFidPyjBkCBdJK5VyizI1kdceFcUJWpsLf39Nx4a+cm8A1++op9006GzUAPpqtRsSojM3dS22pEtNn/eGUE973dG2rVLN056dLdFqBBmrIN+QPe0ANdprC66erWlGe11FH9UW3bsrceOG+sQ4PZ2Bt3fNXffBgyWYMEGhts3Xt+bD47dsscLzzyvQpAmBoyOo0W4CiYkSWFnV/AKLGIz3tNd+TjvAGU2WWPZNX3i8q6t4T2x2NoOQECWioiRmiV5JSJD8p3KupHntOtCWoqiLxo0JKioYDB5cOea6uRFkZ9eM0W6Ocm882px5ycn6Pe12dlz/ayiEh0sQGmqeiTTtbQbQ5V0Gat9or2t01a0vLORKWzRk466uad2ay2nn8xh1rfJ6erJGhsdTT7s+AUaKecjO1q8eD3BGu+pvkZ/PTQ7qSuejtujWTdPTnpoqqVHxvUaNNJV/azo8Pj2dwW+/yfHaa+UAuEVgmi9rHOXlXDnAbt2UtSYaqI+MDOOeUyen2vnNWZY32is97WlpdX+/qqIvosYYT3tWFoO2bZXw8CDVFqNTKrloDl9fFp07s1RBXgdiFqJ5vLwI2rRRqtU1N2ZRxhjM72lnkZRUWQ5UoQBSUsR42s3WBIsnNZXRaUcai+WNUhYG52k3TT3+aUO3p50LjX9axaDqAwEBLPLyIIT46XpheHgYl9Nek3Xa6wP6Fu0o5kNM/l+LFiyysyvFMMPDJWjc2HyCOpZK165KxMQwgtelqAjIz2dqrOSbLnx9OTHSmsozP3lShs6dlULkhKMjDY83lpQUBlIp0KNH3RvtZWVc2osxnnZuoabmf3M+n5Y32rmcdst61ioqgKws3YseLi5cTXtD1U1YttLB1KEDiwcPqvdcpKQwYFmgaVOC4GAl0tIYixTxq2vE5rQDQL9+CsyapZ6O5O5u/rJvSiUQE2NeT7uXF1cOlE/LSUtjQAj0vp/s7ICKCgbl5WZrhkVTUsKYbZGCzkYNoEs1vbyceyE1JE+7rvz+n36So0OHp9vbZenY2XG5Rf/+K0FFBSdQo+3ZNLbsW0Os064Kb7TTsm81ByHivBLOzoC7e2WIfFjY0y1Cx+PqCrRqxeLGDe6609MZWFkR0RNCc+HiwhnSNWUMPnggQZcule8RBwcaHq+L336Taa0rztdH9vOrHdFAfWRlMWAYYpRjo7bC4/nQXF6Hhy+7ZUlkZHD3z8NDt9FOCGOw9npeHqBUcuNr+/bKagvHJSRwoc9yOddHAwOpt10bxnjaJ05U4KWX1Dt0TYTHJyRwBrU5tbEkEnWHXlKSBN7e3POhC15LoqGI0RUXc7pf5likoEa7AbSpphMCvPeeNYKC2BoTA7JE/P1ZpKZKUFJSue2PP2Q4eFCOdetK665hFABA69ZcXju/Oqsrp904ITqmQYfHN29OUF4O6kmoQYqKuBeaGCPU358IRvvTLkKnimpeO1+7ubYjmximZsu+PXggRfv2lb8nJ0RXI6eq1ygUwOuv22ot35WYyJVa8vOrucUVsaSnc0aLTCb+O7UlRFdcDMhknIcQ4LyFlqYen5bGwMND9/2zt+euwVAIdVaWBLa2BPb2MIunPT6eC43noXnt2snKEu9p10ZNhMdHRUnQogVrVJ8Ug6rRnpioPzQegJBK21Dy2ktKuOs0xyIF7WkiqBoiu2OHHCdPyrBrVwmkDWiB0dOTwM6ucjKQksJg0SIbfPllaZ2q2lM4OAV5KXJyuPJ71taa+xjvaW/YQnTW1kCzZrTsW02Sk8NAKiVwcjK8r6oYHSdC1zAifLp1qzTaU1N1l4GqaWoqr72iAggLk6B9e1VPOw2P1wZf5uvxY83JR2KiBM2bE/j4sEhJqcwzrQuMFaEDai+nXVWEDuCM9oICxqIWifjFOV0wDOdQMpTXzofGMwzQoYMSjx9LqmU8cMrxqkY7SxXkq8Cy4iqi6MPNzfzh8eYu98ajqiCflKRfhA7gvPN2dg1HQZ7vb+a4XjoTFYGqgvylS1J88ok1fvyxxOCD+bTBMFxeY2ysBEol8PrrNggNVWDsWIXhL1NqnLZtuVrt+kqNGCtEV1zcsMPjAd1aDhTzkJPDidBJRNxi3mgnhDPyGoqnvVs3Je7elQpRHzWpHK8PMWXfCguBadNs8ddf4ifyERESyOXc5I/HwYGLwKhLw9MSefKE+79qiU8ezmjnIgDlciA5ue4mxcaK0AG152kvKlJ/r7m5EVhZGbegXdOIKevo7Gy4VntmZuV8wNubS6v591/T32fx8RL4+la2q3NnbmyiKWSV5OdXpiSYiru7+cPjo6PNK0LHo1plJymJi/YxhJ1dw6nVznvaqdFeS/BidDExDObOtcWaNaXo3r1hjlC8gvzGjVZITZXgs89oWLyl0Lo1i4gICTIydL8sPDy4km9iX7ANXYgOoAryNY0xuX+80Z6ZySAnR4KAgIYxDrdsyUU5PXggMeiBq0kMlX1TKoFXX7VFRIQEr79uIzpP+MEDCYKClGoLN3wJUeptV4cPmX38WHd4vETCRUXUpYimKZ52BwegrKzmF2qqetoZpvZC5BUK7j9DpKczaNxY//jm4kIMGu1ZWZX6NgwDBAezovLaS3VM7TijvbJdrVuzKC8HYmJoP+XJyWFgbU0EzQRTqInw+MhI84rQ8aga7byuhiHs7MxjxNYH+JRic0TyGDVCxcXF4e7du4iLiwMhDcf75ufH4sEDKaZPt8WUKRWYPLnhepb9/AiOHZNj3TorbN1aAgeHum4RhcffnwUhwO3bUr1Gu0LBiH4ZNHQhOoDWaq9pxJR74+GN9rAwLkSzoYw/DAN068bi+nUp0tIsNzx++XJrxMQw+PPPIoSEKPH66zaiFggfPlTPZwcAW1tAKqV57VXhx+6ICF2edu7Z8PGp27x2zmg3zkBwdKydhZriYkYQw+Jp3JjUinbJZ59Z4csvrQzuJ2ZxTozRXjXyLjhYaTCvPSxMgs6d7bWKHcbHM2rh8XI50L49DZFXha/RXh3dkZoIjzd3jXYef38WiYkMKirEe9obUtk3Pne/Vjzt0dHR+PDDD9GjRw8MGzYMU6ZMwbBhw9CzZ098+OGHiI6OrnYjLB1/f4L796Xw8SFYvrxhx+r5+7O4dUuKt98uQ4cODcPLVV+QybjSb1eu6Dba7e05D5bYMEDqaVfP16KYH2NK47RowSIvj8HVq1K0bdsw8tl5+Lz21NS6DY+Pj+fSE6qyfbschw/LsHdvCVxcgM8/L0VCggSbNxs2UB48UM9nB7iFCgeH2lETr0/k5jJo00aJlBSJmnK4QsHpHfATZkNRETVNerrxnnYbG0Aur/m8dm1pX97ebK2UfcvMlIjydIsLjzec067qaQfEedoTExlkZUnw99/q+xUVccJ2quHxABciTxXkK+GN9upg7pJvubncb1cTRnvTptwCRWIig+RkwzntAOdpbyhCdJU57dU/lt4R/bPPPsOYMWPw22+/IS8vD4QQ4b+8vDz89ttvGDNmDFavXl39llgw7dopMX16ObZubVjCc9ro31+B+fPL8eqrWpZgKXVOmzYs/v1XqveFYYwYHfW0V3raG1BwUa1iTHi8gwPg4cHi5ElZg8ln5+neXfmfp73uwuObN2dRXMwgK0t9/Dh9WopPP7XGrl0l8PPj2ubg8P/s3XeYVPX1P/D3nbad7Z1el85KkaYUlSqsUSNo7BqjxnxN1CSSxJJoMMWfGsWaqEQ0CRaqAoIgiqIC0osIC9sr2/vuzNzfHx/v7M5Ou1N3dvf9eh4fdWZ25s7unTv33HM+5wCvvtqEp5824OBBx6caZrP9TDvAWe32VFdLGDTIjMREs1WJfFGR+D0p85EHDDAjN7dr17S7G7RLUmDWtTc02DZYTUmRUVrq/4scTU2iTNmV0lIJKSm+Ko9vf55x40QzOkfl7wAsa6m3bLFuM56Xp0FEhO0YP9FBvpefHHfgzoVoR2JjZTQ1ST5b9332rAZJSWZER/vm+TrSasXx5ttvtWhqkpCerm5Ne2/JtAdsTftbb72F2NhY3HHHHXjzzTexd+9eHD9+HHv37sXq1atx5513IjY2Fm+99ZbXGxLMIiOB//f/WlR1N+7pBg2S8ac/tahqGkWBl5EhslXOgiB3mtE1NTHTPmCACFSCqUlRT+JuVmLwYDElobcF7ePHm1BVJSEvT9NlmfaICCAhQZTIV1UBu3dr8dxzBtx9dxheeKEZkyZZb1dmphkPPtiKu+8Oc1jmnpsroblZzHvujGPfbCldqTMyzFYd5JW1pMo4JzVNA/2pvNyzZRyBqK6wl2lPSQlMpr2pSUJ+vsblfq2+PN7583Quj+/XT0ZUlCiBd6S8XIPkZDO2btVZLW9Rxr11LvvOzDTh+HENm0b+oKJCY1Xd4ImYGBmS5Lt17dnZ/smyKwYNkrFnjw7x8WZVa/kjIuRemGn3c9D+6KOPYteuXfj1r3+NadOmIS4uDjqdDnFxcZg6dSoeeughfPrpp3jkkUe83hAi8t6oUeKg3PHKemfuZNobG9Gr57QDYm1tWhrXtfuLu6NxBg8Wj+1tQXtYmChtBdBla9oBYMAAGTfcEI4RI6Lw0EOhOH5cg+eea8aSJfZ7vdx3Xyv69TPjscfszKCEyLKPGGG2zMzuKCKCjeg6q66WEBMjY/hw60x75/nIItPeNccsWfZsTTugZNr9sFEdiDXt1relpARmTbtyAn/2rOO/TXOzOC66+pyrzbQnJrY/jyQBY8eacPSo48z4hQsSFi40oqVFwrfftm9nXp71uDfFwIGi6Zo3Xel7El9k2nU6MR3AVyXy/mpCpxg0yIw9e7Sqp2r1rkZ0Sqbd++dy+gm74YYboNfrnT6BTqfDDTfc4P2WEJHXRo4UB2VnXxhq10q1tQFGY++e065QpiaQ77l7gjN4sBk6nezXE5BgNXmyCVFRcpc24PvLX5rx6qtNOH26DgcONOBf/2pGVpbj5qwaDfDEEy14/3293ZMWsZ7d/t+S5fG2qqokxMSIygTroL29CR0ggvaaGsllJtYf6uqA5mb317QDgSmPt7+mXUZJSSDK4x13/1eUlUnQamWX2Vq1c9o7V96NG2fC0aOOX//CBQmpqTLmzTNiy5b2GKDzuDeFJIlsO5vRCe40V3XGl83o/NWETjFokPmH9ezqXkNk2v22OUGlqQkwGHwzl97tI1R1dTVWrFiBJUuW4IEHHkBpaanXG0FEvpGSIiMpyex05EZsrLovAuWA2tvXtAPsIO9P7gbtI0eakJFhPzPb082YYbQat9QVxo83Y/ZsE2Jj1f/MyJFm9Otnxief6GzuO3ZMa9OEThEZ6f+sa3dTVSUCAlEe3zlob983+vQRx3o1JfK+nrFdViYhNFSUYburTx+gttb/3eNt17SbUVIi+b13SVOTqNyy1/1foUyIcLUMMSbGefm0ySQCyM7B/6BBzvcLpXndokVGbNmis/xOcnM1GDjQ/s4yYYLz7H1vUlgoIS0tuIL2oiJ1DeI8NWiQ2C/UvkZERO/JtDc2imoXX1yAdvss9KmnnsLu3bthMBiwdetW/P73v/d6I4jINyQJ+OqrBowZ4/gsTO0XgZIR6O1r2gFR/seg3T/cXdN+xRUmbNrUSy7RdzJvngkbN3a/9y5JQFaWERs22AvaNQ6PV5GRLI/vrL083oTiYg1qasTt+fmSTemymhL5o0c1mDHDi4HSdpSWiiZ0noy8En0MAtGIznbkW2ur/ysTGhsljB9vctqMrrRUXbPJmBg4zbRXVkqQZdtMe3q62dK40B4lOz97thElJRK++05sa16e7T6m6NdPRmEhP6sAUFCgPuPsjC+D9uJi140NvaFczFH7vsPDe1emPSHBN433XJ6FFhYWWv3/oUOHsGbNGnzwwQdYsmQJDh486P1WEJHPuMpuxMaqa27S2AiEhMi9fmICwEy7v8iy+5l2ZRRYbyS6a3f1VngmK8uInTt1Vg24SksllJdLGDPGfqY9EKXS3Y3SAyIuTkxSUDK2+fm2mbQBA8zIyXF+3MrN1SA7W4PSUu9/z/X1wCuv6HHvvaHIzPRsJGNg1rTbVpBFRIhxc940/qqqgstmdo2NokFjxyaCnZWUqOsHEB3tfE17RYWYR9/5wntamozCQscTUZSO8+HhwJw57dl2R+XxAJCcrL7BbU9mNotJDmpmlbviq6DdaBSNIZXJEv7Qr58MrVZmpt0OJdMekPL4JUuWYO3atZb/T0hIwD//+U/85z//wf79+5GYmOj1RhBR4MTFqQ3a2TlewbFv/lFXJ/omqB35Rt3X8OFmDBpkxscft2fbjx/XYNAgx2v0IyN7z1ggtZRMOwBLB3mj0X6g0L+/6PTvjDLe6/Bhzy9KyjLw/PMGTJwYiXXr9Pjzn1vw6qtOZoo5EZg17baN6CRJXWM3Z1591YCnnrLfcFHR1CRh3DgTcnMlNDXZf4yaGe2A2N76egltDibwdp7RrkhPFxNRamttf0aWrX9OKZEvK5PQ1OQ4GE1Odj4yr7UV2Lmz52cAyssltLRITpcoquWroF25mOJJjwm19HrgxhvbVF+sE5n23hG0BzTT/tRTT+G5557DHXfcgdLSUtx///3YuXMn/vSnP6GyshIPPvig91tBRAGjvjye69kVAweaUVsr+axUjYSKCgk6nWdrX6n7ycoyYuPG9qDd2Xp2gOXxnRmNohxaaXKlNKMrKZFgNsNmHe2AAbLL8njlmObNnO3z5yX87W8GvPZaEz7+uBFXXmn0eCxsYNa02/9ui431LmgvKZGcVgnIsvheHTxYZLGzs+3/ktSMewNguXjjqETe3np2QFTrREaKbHtntbVAW1t7x/l584w4dUqDL7/UIjnZ7PBCflKSSAY4mv++f78WP/lJGMrLe/bnuaBAQmKiGaGh3j+X2gSLKyUlYj9w0Vfca3//e4vqtfwREb3ngmxTk9gnApJpnz9/Pj788EOEhobiyiuvRGFhIT799FO89957+OyzzzBv3jyvN4KIAkcpj3eVNWamvV1kJNCnj+yTElJqp5T6erL2lbqfrKw2fPqpzpLhc9Y5HlAa0XHnUCjBmRKsDR9uxnffaZCfr0Famu1JuZo17ZWVYn334cOeB+2HD2sxerQZs2aZvP4si4kB3j2HK2JNu+3t0dHwKmgvK9M4zR62tgImk8jyjxjhuBmdyLS7Lq8OCxNL2JS+Bp05yrQDjte1X7ggwWBor36JiQGmTzfhlVcMTptgJiSI8mhHJfJ5eRLMZgkffWTb16InKSiwnuLgjbg42VIJ443iYnUXgQIpIgK9ItNuNougXWTaA9SILj4+Hi+++CJ+//vf4y9/+Qt+/etfIyUlBTExMV5vABEFVlycDKPReUYAYKa9s+ho2e8ZIH9raQEefzwEmzcHx4lTZSVL43uTIUNkDBtmxrZtYv87dkzrcD07oDQlC9TWBb/qaiA0VLYEnBkZIvDrPKNd0b+/GQUFEkxOKlYrKiRceqkRR454vvznyBEtxo/3bA17Z4G4UNPYKDJ9nant9+JIWZnkNHuolMOHhckYPtxx0F5aqq48HnC+rr28XKxNt0dZ197ZhQsaJCRYX0hdtMiIw4e1DtezA4BWCyQmOr6wnZenQWiojE2bguO7x18cfRY94avy+JIS/65n90RvybQrn/nERN803nMZtMuyjPfeew9PPPEE2trasH79erS1teHKK6/Epk2bvN8CIgqoqChAp3P9ZdDUJCEsLLgO9F2pTx/HGY3uoKBAwtKl4XjjDX3QZDt8Nc+Wuo+sLCM2bdKjthbIyXGeaWcjOmtiRnv750XpIH/8uNZudq9vXxkmk/PmaBUVEmbNMqGmRkJ+vme/6yNHNBg/3jeBSlQUArKm3V6m3ds17WVlktPsYceJLMOHm5xk2jVITlZ3XHRW0m9vRrsiPd1sd7+wl51fuNAIAA47xyuSkx3Pus/P1+C669rw9dfaHl2xJjrH+y7T7qvy+OTkrh0V2ll4eO9oRKd85gM28u3JJ5/EI488gnfeeQePPvoo3nzzTbz++uu4//778dhjj+G+++7zeiOIKHCUhjuuvgxYHm8tOlp2Ol4nGDQ1we6FhU8/1eLyy8MxdqwJzz3X7HRGcCC52zmeur+lS9uwe7cWe/dqkZJitqydtYdr2q0pM9oVSgf5nTu1dhuE6fUicHc2k7uyUmThRo40e1QibzYDR4/6LtPep4//K5ocZdq9CdrNZpHZdh60t09kcVQe39Ag1vT7ItPurDzecabd9mdSU2VMn27EqFGug3Zn5fFTppgwaZIpaC4a+4Ovxr0Bvsy0a4Iy094bRr41NgIajYzY2AA1ovvoo48wY8YMHDp0CJMmTcJHH30EALjhhhuwadMmVPt7qCUR+ZyaK7gsj7fWHYL2J54IwbBhUZg8OQJ33BGK55834MknDbjttjA8/ngLnn66BWPGmJGdrYE5CC68M2jvfQYNEgHi3/8e4jTLDohMe28ooVSrc6YdECXyZ85oHWZB+/c3IzfX+SzvuDgZEyaYPGpGd+6cBKNRBKG+ELg17b4N2isqJJhMktNApONa+mHDzDh3TmPT+b20VIJeL6s+LsbEOF6H7yzTnpbmeE27vUD/gw+acOWVRqfbkpxsdphFz8/XoH9/+YdKm54btPu6PL6x0fGUAbXUTiMIpPBw36zxDnaiYlWZhBKgNe0ajQaSJEHq1GGkX79+WLNmjdcbQUSBpeYKLjPt1qKjHXfpDRbl5RJ++csW/P3vzRg/3oyjRzX49lstPvywEcuXixOuQYPMaGuDx6WwvsQ17b1TVlaby87xABvRddZx3JtCCZYdleS6akYnOoybMWGC2aOxb0oTOl91pvb3kgiTCWhpsV8e7033eCXD7CrTriw569dPNA48f976d15aKpqGqW3o549Mu71jslYLl9vkaOxba6sYSdi/vxlXXmnEvn09t0S+sNB35fHKZ93bEnm1jQ0DKSJCfA6Nzq8DdXvKpArl/Toaz/j111pVPUVcHqEXLVqEPXv2IDMzE/v378fixYut7u8cyBNR8IuNVRO02y8h7K26Q6a9rk5C374yZs824f/+rxX/+lcz1q9vwpgx7V/Yer0I3M+c6foS+YoKZtp7o6VLxZlax/3SnshI5yc6vY0ybaEjJWh3ND+7Xz/H5fGNjSITpGTajxzRul2B48smdED737ylxWdPaUXJhNv7bhMBsGfPW1YmISREVIY4OvlWsm4AoNGIbPvp09Z/m/37tRg+XP0fwdmFBqWpnD3p6TKKimynyDgaE6eGWNNuuy2FhRJ0OnF/crKMKVNM+PDDnpdtr60VSxscfRbdpdOJwN3bDvJqRwgGklLp0tNL5JXPvHK8cfR+f/zjMKe9RxQuz9r+8Ic/4PHHH8f111+Pxx9/HL/73e/c22IiCjpqMu0dTzAoMGstvVVbK6FPH9dfzsOGOe5cHEj2ghDq+fr3l/HnPzfj0kudp1kiI8W+wQ7ygsi0W982YoQZkiQjPd1xGbS9QAoQlS4ajYyYGFFmbzQCZ8+6d1w4fFiDCRN8F7Qrxy9/ZduVTLg/Mu39+5thMklobXX82h3L8jt3kJdl4P33dbj6avVXqRxdTG5rE/uL40y7Gc3NEqqqrG+/cEHMlPZESor98vj8fA3S08VafkBctNu4secF7fn5GkRFyYiO9t1zetuMrrFRVAgGX9Au/t3Tx74py0yVc2l7JfItLeJCpZrzbZefGo1Gg+XLl7u9oUQUvNSMtmlshE0pZm8WEyPj+PGu3grn6uqgOmgPhkw717T3Xj/9qevAJDxcNPGpq+OUAUBc5OrcDGziRBNef70ZBoP9n3FUBg20V7poNCLzO2aMGYcOaVRnek0mMbZv/HjfpcVDQgC9XkZdHZCQ4LOntWhoAAwGGTo7Z7/erGkvK9Ng4EAZZ86I786QENvHdCyPB8QFl5Mn2/82x49rkJensXRrVyMmRsaxY7a3KxflHS0/iogQP1tYqEFcXPvf21lJvSuOGtHl5Wmsei4sXmzEH/4QguLi4BtF5o2CAt+tZ1eoqYp0pqRE9EgItmVoOh0slSk9mTKpQqvtuI7f+m+hXKBUM63J6Vnbr371K5w9e9bpE2RnZ+OBBx5w+UKBkpOTg+XLl2P+/Pm45pprcObMma7eJKKgExvrep1UU5P9dX+9VXfJtEdFqc20u990ytdYHk/OSJIILthBXrDXiE6vh9MGYWlpYrSXvZLtzp+/zEyTWx3kz54Vs92HDfNdoCJJSjM6//zNOzaD60wJ2j2ZV19WJmHAAPF7cJQ97Fy9Nny4dXn8e+/psWiREZGR6l/X0YWGCxdE1ZWjizkAkJpq24zO26D9wgXb5nr5+ZJV0J6cLOPii3teiXxBgcbu6EVvxMd7Vx6vlMYH40pm0UE+CDfMhxob24NxEbTbPqa+XoxhtnehrzOnn5itW7di27ZtGDt2LC655BKMGDECUVFRqK+vx/fff48vv/wShw4dAgA888wz7r8bP3j00Udx3XXX4eqrr8a2bdvw8MMP44MPPujqzSIKKuoa0am78tdbdJc17VFRrh83fLjItMuybXOh9et12LNHi2ee8dOi0h+YzSyPJ9c4q71ddbX7FQcpKbKlDDouzvq+zpUuEyaY8MYbTqK8Tg4f1mDMGLPdrLU3IiP9Wx7vaCpKTIwoUxUlre49b3m5qIIID1cCEdvX6JxpHz7chOxsDUw/rC5Yt06HF15odut1Y2Lsfy+pCb7T05UqDLEBJpPzjvOuJCbKkCSRbe+4XCM3V4OMDOsLO0uXGvHBBzpVFTfdhVgG4NtMu7fl8cHYOV7RG2a1d0x+ORphWl8vITLSdaNHwEXQ/vzzz+Opp57C0aNHccxO/Y0sy0hLS8OKFSvUbb2fVVRU4Pjx43jjjTcAAPPnz8cTTzyB3NxcDBgwoIu3jih4qBv5xkx7R9HRCOpMu8kkDv5qyuOHDjWjulpCebmEpCTrx3/2mRbffef/LHxtLWAysXs8OSdG5XT1VgSHznPa1YiMFBcci4qsy6AB2wAtM9OE48dFplRNN/ijR7U+Xc+uEFVNPn9aAM4brEZHi9trahwH9o6UlUmYPVsJ2h29tvV36oABMmQZyM2VLB3+L7nEvd+no+7xImh3HkB2HvtWVSVBlj0/Jut0QEKCjNJS66A9P1+DK66wrgZZvNiI3/8+BEVFEtLSesZ3QEGBhPHjg6s8vrg4+DrHK3rDrPaOya+ICEeZdsnSv8UVp0H7vHnzMHfuXHz00UfYtm0bjh07hurqasTExGDMmDFYsGABFi9eDL2vZn14qbi4GImJidD9cNlXkiSkpqaiqKjIKmgvKytDeXm51c+GhoZiyJAhAd1eoq6i5ougc1agt3M2WicYKM261ATtkZFAerrIticlWZ8kHjqkDcjV78pKCQaDGIVC5EhUFMvjFfZGvqmhBGdjxljf3jnTPniwKKf+7jsNxo51faJ/+LAWN9/soOuaF/xZXeGsPF6nE69dVeX+WuuyMnEB1Fn2UBn/1PH1hgwRzeg2b9bj6quNblctOJrTriZjnp4uW/U2uXBBQkSE7NXF+vaxb+37T16ebUf1pCQZ06aZsHmzDj/7Wc/IthcWalzOsndXfLxs1ffAXSUlmqDtGxAR0Tsy7cqSGBG028u0Q9WyRkBFIzqdToesrCxkZWW5t6VBbO3atVi1apXVbaNGjcL69eu7aIuIAktNpp1z2q1FR4vmSGazaNoUbGprJUiS+iBY6SA/Y0Z70N7QAJw+rUFoqO+2S5aBjRt1WLrUaPV7UwKGYFxrR8EjIoLl8YCopKmpgUcN+dLSRKZdKYNWdA7sNBpRIn/okNZl0G40isZpEyb4PosXFeXP8ng4zaJ72kG+rEyDxETZaabd3kSWESPMOHxYi48+0mHzZvfTjjExMpqaxIi8jmti1ZTHp6WZ8dln7VVV3qxnV4igvf3319wsZs8PGGD7vEuXGvHuu/oeE7Tn5wdnI7px44Iz0+5ojXdPonSPBxxfpKirk1Sft/WoLhCpqakoLy+H0WiETqeDLMsoLi5GWlqa1eOWLVuGuXPnWt0W6suzVKIgFxsr1t01N8NhgNbxYEMiaJdlCXV18OlIF18RTejUrYsC2te1d3TsmBahoeKCTUMDfJIFz86WcNddYejbtwGTJrWfPLBzPKkRGSlz5BtEwC7Lnmfa7c0ArqyUbJrITZhgwuHDGtx8s/Pn/P57DSRJLLXxNfE39+eadsf3e1JR1dIist3JySJL7ai5lr2JLMOHm/Haawb072/GmDHu/y6V51NeXyFGt7kK2q0nC/gmaLceMVhYKObX29uWxYuNWLEiBIWFksORhd1Fc7O4cOPrRnRq+g85U1IiYd684Azae0OmvWPyy1l5vNpMexDmizwXHx+P0aNHY9OmTQCAjz/+GMnJyTbr2ZOSkjB69Girf1gaT72Jkq1xlm13dXLT20REAFpt8Dajq6tTt55dYW9W++HDGsycaYJWK+PCBd+8z337RCZn507ra8QM2kkNf2Zdu5PqahH8eHJMTk1VMu3W7E1vmDDBjEOHXPe0OHpUg3HjTJbZ274UFdU1a9oBpRu7e89ZVibm3cfHy07X6TrKtFdXS7j2WqNHVUcGg7i43vl7SV0jOnExx2xW/zOupKRYj33LzdWgXz+z3eq0hAQZ06eLEvnurqhILPdydaHEXfHx3q5pD97y+PamjT1Xx2Wmji5S1NdD9Zr2HhW0A8Af//hHrF27FvPnz8drr72Gp556qqs3iSjo6HRi7bOzLwNm2q1JUnB3kK+rU78uCrCfaT98WIvMTBPi430btPfrZ7YJ2jnujdTwZ9a1O1HGvXkS2HVuOKaorLRd95yZacJ332lcNog6fFjr86Zbij59umZNOyAuaLvbrbusTAS7Yhaz4+yhvT4xGRlmSJKMa67xvEQ8JsZ2my9c0Lhc056aKqO1VbKMFFPTvM6VpCRlTbuQn+88+5yVZcTGjcHRF8sbonO87POlc96Ux8syUFoa3I3o3CmPb24Gbr45FK2+b6PhNx2TX47eb12dpHrMY48L2gcPHoy1a9fi448/xrp16zBixIiu3iSioORqXTvXtNvq0wduBe0mEzya+euJ2lr3M+3FxRqr0uNDh0TQnpDg3WzYjvbt0+KBB1px9KjGKgPDTDupwfJ4wZPO8Qp3Mu3p6TJiY2UcP+789PDIES3Gj/d953jA32vanXeG9+TCbFmZxjKFw1n20F6mfdgwM/bubfCqPFxss/VtFRWus+ZhYUB8fPsFHX+sabfXhK6jRYuMOHJEg/z87n1hrqBA4/P17IA4T1OWMrqrulqMMOwpI9/y8zXYtk3vs4RCIHRMfjm6AN1ry+OJSD1na6Vkmd3j7XH3hO6ee0KxapX6ucfeUNa0q5WQICMurj3bXl0NnD+vwfjxZiQk+CbTXlEhITtbg4UL2zBunBmfftpeS8ugndRg93hBybR7Ij1dRnGxZHUB0WwWz9k5GytJokT+8GHruve8PAn//a8Ov/pVCGbMCMfJkxpMmeKvoN1/F2rUNKLzJNPeMWh3lD109NpDhnh3HBQl/e6XxwPW69r9saY9P1+D/v0dP2d8vIyZM4O3RL68XN2+IJrQ+f77TM1SRkeKizWIjJRVZ3EDzd2Rb8rFJW+WCwSaSH51QXn8yJEj8Ze//MXqttWrV+Paa69V+xREFESclV01N4umRyyPt+bO/ODvvtNgwwa93QZQ/uDumnbAel374cNa9O9vRny8/EPQ7v013f37NRg61Iy4OGDuXCN27Wo/MVMzkogoIoLl8YBY0+5ppj0tzYzGRskqG1tTA5hM9i+cKR3k29qAzZt1uPbaMEyfHoH//EePuDgZjz7agkOH6p0GY96IjJRRW+u/TLuzBpsxMZ5k2jsG7Y4b0dnLtPtC56C9pUV8H6g5vorJAu2Zdm+PySkp4oKv6YfrOSJod56BzsoyYtOm4CuRLy2VMGFCBHJzXe8P/sq063QiWeBJ5VtJiYTU1OAsjQfcz7Qr+6knFzC6ipryeJ/Nae9IlmXIneo88/PzceLECbVPQURBxFlGQTnpYHm8NXsZDUf+8Q8DQkMDtwbe3TXtgPW69iNHtJgwQZxpJSbKqjMMzuzfr7Vk4y6/3IgbbgiH0ShORKqqmGkn1/yZde1OvCmPj4wUv8fCQg1iYsRJfGWlhNBQ+43tMjNN+Ne/DJgwIQJhYcBNN7XhxRebrbqT+1OfPl038i0mxv2gQATt4vcaEeH4Yri/+sR0ntWuBHjqgnZzh0y7xutMe1KSDLNZwoULopt9bq7z8ngAWLiwDb/5TQjy8iS/XQjyxEcf6dDWJuHkSS0GDHA+f72wUMLMmf4JkD3tIF9SEryl8YAnmXaxn3anoF1dIzr1a9pdBu0rVqyw/PeXX35p+X9ZlvHpp59yVBpRN+Xsi6CpCZAk2afzunsCteXx589L+PBDHW6/vQ3nzgVmFZK7a9oBZT2lKIM9dEiDSZNEgJ2QIOO777zf7n37tLj+etFgKTPTDI1GxsGDGkyZYkZlpedBCPUekZHsHg+IoCwmxvOfV8a+jR4t/l9pVGavsd20aSZcdVUbFiwwYvZsk8+ba7kSFdV1jejcuTCrKCtrH50XHg4UFDi+GO6PiSzjxpmwZo0eDz7YCq1WZMxjY2XoVKTl0tNlnDwp/sBq1sG7YjAAcXFmlJaKdboXLjgvjweAuDjgkktM2LRJh/vuC56Z7Zs26RASIuP0aQ0WLnT+2Px8jV/K4wHX/YccKSnRBHXQLpaSuJ9p707l8U1NnTPt9ua0+7A8fv369diwYQMkSUJ2djY2bNiADRs2YOPGjaipqUFGRoZ774CIgoKzTLtSxudJp+KerE8fqCrbfOEFA5YuNWLcOJNNgyB/cXdNO6Bk2kXQLjrHixNPX3SPb2kRz6lk2rVaYPZsk6WLvL3O1USdsXu84O1Frs7N6Jz1lIiMBP72txbMnRv4gB3o+jXt7gftnRvROX5tf/SJufHGNtTVSXj/fXFsdacLvMi0S2htFU1WfTGyLDlZRkmJhPx8DcLDZVUXApYubQuqEvnSUgnffKPFDTe0ubyAbTKJgNIf5fGACNo9KY8vLg7ezvGAyDy7M/KtO2baO37mAzKnffLkyZg0aRJkWUZSUhImTZqESZMmYerUqbj22mtt1rkTUffg7OqtqxOb3kpNpr2wUMJ77+lx//2tAR0R586BXzFsmBk5ORIKCiQUF0uWbtAJCWavg/ajR0UTnI5Nli67zIidO3UwmVgeT+qIrGtXb0XXE5l2zz8v6enWY9+CuRGkmNPuz+7xju+PjnY/aC8vlyxLB5yPfPPPmvaQEODhh1vw17+GoLnZvYZy6eniYo4SFPpinxAd5EVH+H79zKou/i9caMSJExrk5ARHQPbRRzpMnmzCrFkml0F7aalYw5+W5r9MuyfZZTHuLTg/44DzC1z2FBVJGDTI3O0y7cpn3lF5vDsj31wWz6xZswYAMHfuXFx77bW499571W8tEQUt5+Xx/inj6+7UBOEvvmjAvHlGDB9uRnW1JmBBe20t3C6PT0+XERICrFunx7BhZssXhy+6x+/bp8XkySarE7Y5c0z4xS80OHNGA7M5eIMGCh6RkfZPdGS5d1UCVVd793lJTW3vEg4EdyPIqCigtVVCS4sISH2psVFkvByJjRXHeJNJVAe5IsvWa9odjXzz90SWa64x4uWXDXjjDZGtVvu3TU0VyyZKS8VFIb0Pkt3K2Le2Nucz2juKjQUuvdSETZv0+L//6/pB3Js26bB0qREZGSacPaux9GKxJz9fg+RkGQY/DYrxtDy+uFiDlBTna/G7kqMg1pGiIg0uucTYrTLtnUe+2cu0NzSo70ekuvBp165dDNiJehBn3eP9VcbX3dmbh9tRWZmEd97R45e/bP3h8e7NdfeGJ2vaNRpgyBAz3ntPhwkT2svolDnt3syYF0G7dWleQoKMCRPMeP99ncMmWEQdRUbKaGqSYOxw7tnaCkyeHIFPPlERVfUQ3ox8A5TRXtbNyoI3aBfb5Y917WJNu/M57QBUTwmpqwOamyWX5fEtLYDZ7L+L4RoN8MgjLXjuuRBkZ6tvKJeaKsNolPDddxrVJfWuJCeLNe15eRqXTeg6yspqw6ZNXT/6rbRUwr59Wlx5pREDBsjQaOC0AqCgwD/j3hSelseLRnTBWx7vTqa9vl6cS40ZY+42QbvRKC4+ulrT7k73eNVBe25uLm6//XZMnDgRI0eOtPwzatQotU9BREHEVfd4BlS2oqOdl22++qoeM2eaMHas2fL4hgbrgMNfxMg3939u2DAzTp/WIjOzfeZyQoI4kfN0Pb4sW3eO7+iyy4z44AMxOqo3ZUrJM0oA13GNc3a2BoWFEn72szBLE62eztvyeKURnSKYy+NDQgC93j/LIlx9t0VEiNdWGxiUlYk5zEqVkqN1uk1N4t/+vBg+e7YJY8ea8N//6lVfkAkJARITzTh6VOt1EzqFkmnPz5dcjnvraMECI06d0uD8+a79YvjwQx0mTTIhJUWGViu+I7/7zvEFwoIC9y5OuKtjVaQsA2fPSlZLXewxGsWyjdTU4PyMA+0NJ9UkB4qLNQgLkzF4cPcpj+/8mY+IEBf4Ol+AbmlxPoayI9Xfdr/73e+wd+9eNDQ0WMa/2RsDR0TdQ1yc/MOsXtv7/FnG15316eO4PL6qCnjjDQN++csWy23uZm28UVur/mptR8OHi5MNZdwbIL5cwsI8L5E/f15CbW37GvmOLrvMiMJCTdAGDBRclACrYzO606c1GD/ejHvuacWNN4ahtLR7nMR5ymQCqqvhVSM6kWnXWE6Qg7kRpCT5r4O8q34tkuTerPbSUg0SE9svQDrKHjY1+X+MqiQBjz7aAqPRvS7w6ekyjhzxXdCekiLWtOflue4c31FMjLjwsHlz1zak27xZh6ys9shqxAiz03XtItPu36D95EkNbr45FKNGRWD69Ej84hfOR/uUl0swm9srQIJRcrKMlhZ1yYHCQnEBwlmyyRdaW4F9+3xzIbjz6GRlWU7H44NyMVptebzqOpQTJ04gNjYWP/3pTxETEwOJKRKibi02VoYsS6iutj15a2z0T8Oc7q7zPNyOXn/dgMxMk1VJeGgoYDCIE0B/BqmyLMo03S2PB0QWQaeTMXp0+3ZLkrKuXYOhQ+1c1XFh3z4txo832x0ZOGGCGQkJZo57I1U0GlEiLwI4sc98950GI0aY8eCDrcjO1uDWW8Owbl1jjz1m1dYCsux9pr2xUfrhOBHc5fGAWNfu66C9tRVoa3Od1YqJcS/T3jEwCg+3n2lXlpz5uxv/+PFm/P73LZg6Vf1xOy3NjN27dRg92v1jvT1JSSLT3twMtzLtgOgi/9prhi5b166Uxr/6arPltowMM44dcxa0azBvnv/K6caPN/1QwWfC//1fK8LDgfnzw9HcDIdjeYuLxQQBf62z94XISBHIlpRoEBPjfD8pLpaQnm72eH2/Wl98ocVDD4Xi4EE7i8/d1NgoqnaUPhHKBeiGhvaljHV1EjQaWfV3l+qgPTk5GZMmTcLtt9/u1kYTUXAKDxcnEfYyLh2bZ1C76GjRZKitDTYNez77TIuf/MR6xqwkiUBalNT77/fZ2AiYTO6vaQeASy4x4s9/brH58k9IkFFe7tmX47599kvjARGEzZ5tQlvwjOOlICfGvrX//6lTGkydKpocPvtsM665Jhy//W0onn++2fGTdGNVVRIMBll1CaU9UVHiBLmwUIM+fcyoqAje8njAP1MDlAyXs0Z0gPOLs511bEIHKLOnbRslii7Sgfl933+/ewFvWpr4XvPVRZzkZDNKSkSm192y8QULjHjwwVCcOydh8ODA758ffii6xivTAAAgI8NkGadnT0GB++/THX37ynjllfZjmyyLC0vffqvFjBn2v2eDfUa7QhkP6Gp6eGGhxpJpr66G6kaR7ios1KClxfXj1Og8LUKrFefcHZvRiak/6puqqr7md9999+Gzzz7DoUOHYLJXT0tE3Y6jZnTMtNunBMX21rXn5moweLDtF3cgmtEpGSl357QD4gT1tttsI2hvZrU7C9oB4KGHWvCLX3R9h2DqHjrPaj99WouMDPFZCw0FHnusGbt399ymdMp6dm8KHCXJel17MK9pB/xTHt+5XNURd2a128u0m82SzYl/MH+npqWJz5Iv17SbzRIiImTExrr3s9HRYspIV81s37JFdI3vaMQIM86e1aDVzleWLIvu8f5sRNeZJAEzZpiwZ4/jY15xcXCvZ1ekpIgLPK4omXalQtTTfjuuFBZKMBp9c9yxtxSnczM6d5rQAW4E7b/+9a9RUVGBG264AWPGjGEjOqIeQKwPsr2dmXb7wsKAkBDbDvJNTeLK9oABtr+zQMxqr60VHZEdjaTxhKdj36qqgDNnNJg82XHQPniwjHHjgrerLQWXqKj2Ne1NTaJnwsiR7ftPUpLYV3tqi53qaskny0lSU8VM7pYWcaEv2MvjfT2rvbERCA2VXWbo3JnVXlamsQra7a1bBYK7T0x6utiuxETfbF9YmPgd9u+vbkZ7Z0uXtmHjxq7pIp+Xp7FcEFT06yfGuZ07ZxsyVVWJCzL+XNNuzyWXmPDll4535PPnNUHdOV6RkiKjrMx1KKpk2sPDxTmYv0rki4rsX5zxROdMO2A75q6+Hv4J2gHYNKBjIzqi7s3R+iB7BxsS7DWjy8vTIDxcttv0pb083n/q6tQ3MlErIcHsUdC+f78WgwfLPsvaEHUsjz9zRoM+fWBVvurttINgV1np3Xp2RXq6jKIiyXLMD+a+ElFR1tUVvtDYKLksjQecT1bprHOmPSwMkCTbWe3BPJElLU1svy+P2cnJZrea0HW0YIERZ89qcPZs4Htn2fsu1WhEw9bTp21DpoICDWJi2qcHBMqMGUYcPKi1O/e7thb43//0uPba4J3RrlDK410pLpaQliYuAjkbV+ytwkLJZ0v37F2oi4iwXuolMu3qn1P1paynnnpK/bMSUbfguDze+qSY2tnLnOfmShgwwH5WwdVsd1/wZEa7KwkJMnJz3e+a9PXXWlx8cfCfLFD3ERHRXip96pQGGRkmq89aRITIoFZU+Ca4DTa+y7SbUVQkoaJCQnS0bNOXI5j4Y027mNHu+nExMTLy8tQd+8rKJCQnWzfxDAtTSvHb/2bBnWkX2+/LyovkZNnjdd5RUcCcOUZs2qTHAw8EbhmVLIvvUnsXwJUO8llZ1reL0vjAZ7QHDJCRkiJj3z4t5syxrmp7800DRo40Ydq04F/KnJJixv79rpc2FRZqLBeX/Bm0i0y7ZNOTwhP2LtR1zrTX1blXHq86aP/Rj36k+kmJqHtwlFEQ6++C8wSjq0VH25Zt5uRoMGCA/S/uwGTaPZvR7kxCggiC3LV7t47r1cmnOpbHnz5tW76qTDsoL9dgyJDgP1F1V1WV5PbaYHvS0mQcPKgN+s7xgL/WtKtb9hUTI+PoUXXP2TnTDtgf+xbM1WspKTJmzzZagndfuPRSE4YM8fz5srKMeP55Q0CD9uZmwGi0/12akWHCt9/aBpf+HvfmiLKu/csvrYP2hgbglVf0ePHF7tGUMyVFdI93pr5enHMpvRf81UFelmHp+WE02jYbdpejTHvn8nh3qiTdSqPs3r0bt9xyC+bPn4+SkhKsWrUKR9Ue2Ygo6MTHO860c027ffYz7fbXszt6vK95OqPdGU/WtJeVSTh5UoNLL+15gRN1nY7l8d99p7UJ2gHvGicGO6URnbeURnTB3oQO8NeadvWZdjVBgckkRufZBu3W2TTx2sH7narTAe++2+TTEu/772/FlVd6XnE1f74R585p8P33fp6R14Gyv9kLojIyHJfH9+vXNX/XGTOM+OIL69zr22/r0bevbJN9D1bJyWI8oDPFxRqEhbU3NfRXpr26ur1ZpS/WtdtbjiOC9vb/d7c8XvWn4auvvsK9996Lb775Bnl5eYiLi8Nbb72Ft956S/2rEVFQcXTwa2qSgvYEo6vZa1KUm6vBwIH2r7YHpnu8ZzPanUlMdD8I+uwzLcaNMwd9Fo+6l45Z1+++s820A55XhnQHlZW+bUTXXTLtvl7T3tCgLnCOjVV3ofXCBTHWrPNa8IiI7pVpD0aRkcDcuUZs2hS4hnT19WKZjb3Z5iNGmHHunO04sPz8rsm0A8DMmSYcOaKxLCNpaQFefNGAX/2q1evS7kBJSTGjtNR5E9HCQtEJX3lP/sq0FxZqoNeLDfHFunZ7EyO8LY9XHbS/9NJL0Ol0GDJkCADAYDDgoosuwqFDh1S/GBEFF0fl8aKspws2qBsQ5e7Wt+XkSF1aHu+vNe2VlRoY3UiWfPqpDrNncz07+VZkpMhI1NWJzNaIEfaDdmbanUtPN6OuTkJOjgbx8cHdWdpfa9rVzLqPjlYXFBQUiIspISHWt4eHw6YRHSeyuC8rK7BBu7OKtfR0GWFhwNmz7WFTTY1YDjZ9etdktdPTZfTvL+Prr0XZ/v/+p0dMjIwFC7rPd3BSkozWVsnuFCOFMu5NERfnn0x7UZFkqZpobfX++R2Xx7f/v9/K40+fPo2FCxdixowZlttSUlJQUVGh+sWIKLg4umIpygh5gmFP53J3s1l0j3ecafd/I7q6OsmjGe3OKJk4tdlLs1lk2rtLWR51H5GR4kTn9GkNEhPNdrtc9+Ty+JIS35SzR0eLwPHECU23KI/3x5x2X2baX37ZgMWLbVNy9ta0B/Oc9mA1b54ROTkau2Xp/uCsN4wkiWx7x2155x09xowxYfz4rrsANnOmEXv26NDWBrzwggG//GUrNIFbUeC1yEgRtDpb166Me1O4M93BHYWF7b2JfJVpt21EZzunXc2FRIXqP63WzmDLwsJCRAZ6zgER+Yyz8nieYNjXuRFdWZmElhagb1/Ha9oDkWn39cg3g0FUCagNhE6c0KCxUcLEiQzaybciI0V5vKP17IAYUdgTy+NPntQgO1uDmTO9z55JkiiRP35c2w2Cdt8fN9WuK4+OBpqbJTQ1OX7Mvn0afPKJDr/9re3iV1Ee333WtAeryEjgsssCl2139T2akWGyBO0mE/DGGwbcdZeP5oN5aOZM0Yxu3TodtFpRndDdpKSYnY5965xp91fQXlwsljro9bJP1rTby7QrVWOK+nr3zt1UB+1Dhw7F9u3bcfjwYQDAypUr8cUXX1jK5Ymo+1EOfp3XEzU2Bu94mq7WeU17To64Chwaav/x9ua6+1ptre/XtAO2JccmE6xmjHa0e7cOl1xitLsekMgbyonOd99pMHKko6C9Z2ba33hDj6uvbkNcnG+eLy3NjOpq23XYwUasafftc7rTiA6ATe8ShSwDjz0Wip//vNXuaFR7jeh4IdwzgSyRr693/j2qjH0DgG3bdDCZgEWLujZInj7dhOPHNfj730Nw//0tsJNfDXopKc6b0XXOtPurPF4ZK6fXA21tviiPt7em3bY83i9r2u+88040NTXh2LFjAIA1a9YAAG6//XbVL0ZEwSU+XkZbm2RzciQa0XXNNgW7zuXxOTmSw9J48fhANKLz/Zp2QGQvOwZCq1frccUVEXavQu/ercWsWcyyk++1Z9rtr2cHPGucGOxqaoD339fj9tt9l81TZh13h0y7r8vjGxpg083ZHp1OvL6joH3zZh0KCiTce6/9dJz9kW+8EO6Jyy83Ii9PYwmW/cnVFBYRtIuo+J//1OO229qgC9ySe7uSk2UMH26GyQRce233y7IDYl27s/L4QGXai4rE6xgMvuoeb1tdY78RnfrnVP0pmDVrFp544gn069cPWq0W/fr1w+OPP45Zs2apfzUiCip9+gBare1VS55gONa5EZ2zcW+ACPIbGiS3Grq5yx9r2gHbjtz79mmRna3B669bDzBtaAC++UaLOXO650kDBTcl6yo6x9u/MNQT17T/7396jB5txrhxvlsz23HWcTCLihLNoDp36/aG2kw7ILLt9oL2lhbgT38KwYoVLQ7XojLT7juRkSJw37jR/9GxaOjq+P6MDDNyciQcOKDBoUNa3HRT4GbIO3PXXW148smWblvl5kmm3V6FqLeU19HrZZ+saXecaQ9AeTwA/PjHP8b27dtx/PhxbN++HcuWLXPnx4koyEiS7VVLkwloaXGvOUZvEhPTOdOucdg5Hmgvt+vccd6X/NE9HrAtOT50SIuf/7wV/+//hVjd/tVXWqSkyBg0KLgDAeqeIiNFQ8SyMvvj3gBl2oEEk8pij/PnJezaFby1pGazWDN7xx2+DQyUk99gH/mmlKi706fg7bf1aG52fL8768odzWp/4w09IiJkLFvm+AKlvZFvXNPuOaVE3tdBWmeuKtZSUmRERQErVoTi2mvbLHPDu9pNN7Vh8eLue8Hc2Zr2+npxftM5097SItl8xrwhy+0ZfZFpD1z3eL+UxwNAUVERduzYgQ0bNlj9Q0TdV2ysbHXAVJrvMNNuX+fyeGcz2gEgNBQwGPy7rr221r2xIWp1DNqrqsQFil/8ogXTpxvxl7+0X9b/9FMd5swxdpvZsNS9REXJMJlEkyBHFSXx8TLMZkl12eT69XrcfHMYjh0LzlbLu3drUV8PXHmlb0/GlUx7sAfter1YnuMsA9eR2Qw89FAIjhxxfCHG3Ux756kf9fXAM8+E4PHHna8dtj/yjZl2T112mRGFhRqcOuXfz2pdnfPvUUkSzeiOHNF2eQO6nkRk2u3/bYuKNAgLkxET035bdDQgSb4tka+okNDSInVY0+79czY22ia/OpfH19e7N6dddb3Jhg0b8Ic//AGmTpexJUnCVVddpfoFiSi4XHmlEc8+G4IrrmiEVtt+QOEJhn19+ohKhOZmEZDn5jqe0Q6IL/r2We3+OVGur/dPpj0xUcbJk+LL9PBhLQYMMCMuDnj88RbMmROB225rw+jRZuzercXvfhccpYLU8ygnNY7WswPieBURIS4yqWmyVlUlArh77gnF9u2NQdfD4/XXDbj55jafl7ymp8sICZH9spzG11JSZBQXa5CZ6Xp5QEMDYDZLyMmRcPHFjh4jqVrTDtjPtJ84oUVoqOxyrKWjNe3MtHsmIgK44gqRbR81yn/fM3V1EoYMcb6vjRxpRliY0emxiNyTnCw7zLQXFYlAumNCQKsFYmLEMbzz1J6nnjIgK8uIUaPc+/sUFUmIjZURHi6SLP7qHt8x097WJqZUuHMsVn3Z6vnnn4fRaIQsy1b/mM3ccYm6s1/+shXV1RLeeEOsU25qAvR6cbWRbEVHi4NwTY2EhgagvNz5mnbxM/5rRtfa6v6BX62EBBnl5e1Be2amOFkdPFjG7be34ZFHQpCfL+HcOQ0uuaT7ludRcFOyFY5K4xWdezA4U1kp4c47WxEdDTz+eIi3m+hT589L+OwzLW65xffZvFGjzPj448ZuURWTmur4ZL4zZf15To7j01p3y+M7r2k/d07C0KGuz3ntrWl3J8tPtpYuNWLjRr1fS+TFyDfnj/n1r1uxapWTNRjktpQUUVFjL5wUQbvtHY7GFb/7rh4HD7q/7El0jhevo9fDJz2IGhpsP/ORke1r2pUG0GovJAJuBO1VVVWYOnUqDhw4gO+++87qHyLqvsLCgKefbsbKlSEoKJBYxueCXi9O/GprJeTmahAZKbssNe1cUu9Lyixjf2TaOzb3OnRIgwkT2jNMDzzQgu++02DFilBMnGhy2sCHyBtarfjMOWpCp3Bn7FtVlYTERBkvv9yEDz7QY+vWLm4D3cHq1QYsXGhESorvP9OSBLezUF3F1fzmjpTjq/OgXX3gHBtrG7RnZ2tcZmIBJdPO5q6+dNllRhQXSzhxwn8l8mqmsCQlyXbH/JHnkpNlGI2S3QuuRUUay8SLjux1kG9tFevSy8vdP9dSMvqAOMfz35p2sVRGjM+VIEmyW/2jVO/9l156KSRJQqQ7vemJqFu45BITliwx4uGHQ9kwRwUxq13pHG92mbVqL4/3vdpaURkR4odkYccgSGTa209Yo6KA3/2uFdu361yWixJ5a+JEEyZP9m3QHhcno39/GX/7WzN+9asQ1QGiPzU2Av/5jx533ME1s6JsVt1pqrqgXf13W3S07Zz2s2fVBe32G9HxYrg3wsOB+fP9O7Pd1Zp28o+wMHFOZa9/haNMu71Z7UVFEsxmCWVl7h/HCwslq0y7P7vHA+JYpIx7c6fqSfXe/+ijj2LRokXIysrCiBEjoP2hC4ckSVi5cqX6VySioPT4482YMSMCa9fqeXLhQnS0kml3vp694+M7NzXyFWU9uz/KXRMSRCnX+fMSSksljB1rHTRdf30bvvpKi6wsBhjkXx980OTyMQkJZtVBe2WlWMMIANdcY8TOnTrcd18o3n23CZou7E339ddaxMTIuPhiXghLTZWxf7/68nidTkZuruPHe5tpP3dOgxtucH2s69yITpaZafeFJUuMeOKJEKxY0eqX7zsxOpV/o66glMiPGWN9e1GRBuPH29aq28u05+eLA7dnmfb2ySS+WNMuy/YvEirHn4YGye3O8YAbmfaXX34ZNTU1+P7777F582Zs2LAB69evx/r16916QSIKTnFxwBNPtGD1agNPLlzo00eUu+fkaDBwoOvflX8z7f5Zzw6IL0aNRsbOnToMH25G50IrrRZ48cVmDB3K/YW6nruZdiVoB4C//rUZOTkavPxy1zbzyMsT2dzusObc35QTeTVqa0XPgwsXNJa1oh3JsmhW504juo5Bu8kEnD+vUbmm3TrT3twMyDLXtHvrssuMKC2VcPy4f66q+fO7lJxzVFXjzpr2ggLx/56Xx3fMtHt3AG5tFY0xOyfAdDogNFQ0o3N3RjvgRqZ9/fr1kCQJQ4cORUzH3vtE1GNcc40R775rRF0dzxidiYkR5Zi5uRrMm+e6Y4m/17T7Yz07IILyuDgZO3boMGFC91gHS71XfLyMs2ddn9CbTEBNDayC9qgo4JVXmnDNNeG45BITxo3rmv09P19Cv378rAHt3ePVqK4WVU95eRqcP6/B2LHWv8OWFnES7c7It46ZvIICsQ61Xz/Xx9rOjeg4RtU3wsKABQuM2LhRh7FjfdtF3mz23xQWci0lxbbpZHm5hLNnNRg92n55/PffWx8b8vI0SE01e5xpT08Xf3sxp93tp7Di7DMvOshLP4x7c+95VQftYWFhuOyyy/C3v/3NvVcgom5DkoAXXmjG6dPBObs4WCiZdvXl8fDbell/zWhXJCbK2LtXi8cfb/HbaxD5gtru8dXVEmTZOtMOAJMmmfF//9eKu+8OxY4djW41CPKVggL7J6m9UUqKOM6KMlPnj62pkRATI2PgQDNycmyDdiXz7U73+I4XWrOzxZQQNVNVlEy7LIvv1KYm8TyhoapempxYutSIxx8Pwe9/79sSeaU6g0F717DXdPKjj3S46CKT3UZ0cXH2y+MvusiEL75wr++B2Swa2KWmKpl22es17cryGHvHLWVWu1/L4++8806cOHEClZWVbr2AL73wwguYOnUqsrKykJWVhQcffNByn9lsxhNPPIHLL78cV1xxBd5+++0u206i7iw5Wcall3I9pTPR0aI0Ky9Pg4EDXZ9g+7M83t/r8BISZLS0SJZxb0TBSm15fFUVEBIi2z2h+uUvW5GQIOPRR7tmDFxenoaZ9h/Ex8vQ6+03qOqspkZCdDQsQXtnykm02n4tImgXVRmAWM+upgkdIE7UZVlCc3P7a4eHy13aK6GnmDvXiPJyCceO+faXWVvrfidv8p3kZNvP+caNOmRl2a9ktB+0S5g40YSaGgktbuQYyssltLW1d48XmXbvzteamkQZvL3PvDKrXTSi81N5/FtvvYWSkhJceumlSEhIsGpE98knn7j1ot5YsmQJfv/739vcvmnTJpw9exYff/wx6urq8KMf/QgXX3wxhg0bFrBtI6LeITpaxuHDWhiNQN++rg+6/mxEJ8bU+Oe5AREI6fUys38U9MSIQtcn80oTOnuZOq0WeOmlZsyZE4G5c01YvNgHA3vdUFAgoW9fftYAQKNpX+s6aJDzi4Y1NRKGDTNDp5ORk2P7hxUzk9UHzjExMmRZQm0tEBurvnM80LFDtISwMJlN6HwoNLS9RH7cON+VyCudvHlhpWukpMgoLW3/5ZeWSvjmGy1efrnZ7uPtr2nXYPx4MzQacfFWKXd3pahIQkKC2VIJ44vu8c6mRUREiKUYnpTHq949i4qKYDabYTQaUVJSgsLCQss/wWDLli247rrroNVqERMTg4ULF+LDDz/s6s0ioh4oOlrG0aNiDZTBoO7x/mxE58+Svvh4GaNGmf0yUo7IlxITRfMwVydcnZvQdda3r4y//70ZDzwQiqKiwPX3aG4GSks16N+fAZ7C3lpXe0SmXcbAgbKDTLt7o0wjIkSZrNKMTu2MdkAElpLU3oyO4958KyurDRs36iH78GMimtDxc9dVkpOty+M//FCHyZNNSEmx/zfp3D3eaBTB98CBZsTFyW6NfSsstJ4F74vu8Up1jT0d17T7rRHdfffd59YT+8u2bdvwzTffICYmBvfeey+mTp0KACguLkZaWprlcenp6Thy5Ijd5ygrK0N5ebnVbaGhoRgyZIj/NpyIeozoaJHRu+QSdVk4ZQ28P9TViWy4v0yebLLbvZUo2MTFic9BZaWE5GTHnwllRrszV11lxJYtRjz/vAF/+Utg+jkUFUkwGGQkJjJ4UKSkmFFcrK5PQUyMjKQkGbm59svj3eneLkniOC+Cdtmt8nhJ6jj2jZl2X5s924SqKglHjmh81iC1vp7r2btSSooItE0mUe3krDQeEMf62loJRqPoyK5cXE1NFcdPd5rRde5Q74tMu7PPfHt5PJxePLYnqIL2ZcuWIScnx+59GzZswPLly3H33XdDr9fj22+/xX333Yf3338f6enpbr3O2rVrsWrVKqvbRo0axfF1RKSKUo6upgkdIBrR+bN7/KBB/guqf/SjwJYHE3nKYBCBVnm586C944x2Z268sQ333BOKP/+5BT+sCPSrvDwN+vbl2ueOUlPtj4LqrLYWP2TazSgokNDaCqsqqMZG9ePeFMqs9qYmsWxBbdAOiKx+Q4Py2hz35kvtJfJ6TJjgmwtqtbXulyqT7yQnyzCZJFy4IMFsBg4c0OKf/7RfGg+0B7tVVRISE2Xk54vKR50OPwTtGgDq+vDYZtq9X9Pu7DPf3ohOcruqymnQvn//fqSkpKBfv37Yv3+/w8dNnjzZrRd1ZO3ataofO3HiRIwaNQrHjx9Heno6UlNTUVRUhMzMTABAYWEhUlNT7f7ssmXLMHfuXKvbQtnWk4hUio4WB9oBA9QdcKOjRTmUclXYl/y9pp2oO1HTjM5Vebxi+nQTZBn45hstpk/3fyPGggIN17N3kpwsq5rLXV0tyuNTU8WJe0GBhMGD2//Gp05pVV9kVcTEiOc9f16D8HA4vRDUWXum3XnWjTyTldWGFStC8eijLT7pIi++R/k36iohIUBcnBmlpRK+/lqLqVNNTj9voaHiwlh70N7eC8STTHvHaRN6vWz57HpKTaa9vl5y+0Ki09PHm2++Gbfccgsefvhh3HTTTZDsfDIkScLJkyfdelFPlZSUICUlBQCQk5ODU6dOYfjw4QCABQsW4N1338WCBQtQV1eHrVu34pVXXrH7PElJSUhKSgrINhNRzxMTIw60ajrHA+1ld7W1QFycb7fF32vaibqThASzy7FvlZWuy+MBcYFt0SLR9CoQQXt+voT+/Rm0d5SaasYnn7guc6itFd3jtVqgf3/RQX7w4Pa/2Y4dWvz4x+5VDSmz2pX17O4EhxER7Wvam5q4pt3XZs0SXcIPHdLgoou8/8z4e3QquSaaTkrYsEGPZctc16d3bEaXn69Bv37i7+d+0K7BggXtxwaDAV43Dnb2mfdm5JvToF2WZcgdOj3Idro+2LvNX5599lmcOHECWq0WWq0Wjz76KAYNGgQAyMrKwrFjxzBv3jxIkoRbb70VI0aMCNi2EVHvoQTJajM3oaGiuUlNjbpgwR11dVyLR6QQHeRdZ9rVljpnZRlx992hWLnS/yXy+fkaDB3KoL2jlBQZxcXOM+0tLeIkWamAGjhQxvnz7eWxlZWi3Pa11xyX29qjzGqvq3OvNB6wzrS72wSPXAsJARYuFCXyF13kfYl8fT0vfne15GQZBw9qcfiwBm+95foCW8dmdCJoF5/RpCQzjh5Vf7AuKrLuNK/X+6I83vFnPjJSRkWF5odGdO49r9OgfefOnYj8YZHHzp073XtmP/jrX//q8D6tVovHHnssgFtDRL1VXJyM8HBZdaZdkjrOavftiYFYi8eTDSJAXXl8dbX6i2fTpokS+a+/1mLGDP9m2/PzJcyZw6C9IzEKSoIsw2GmW+nw3h60W89q37VLh1GjzEhNdX9Ne1WV5NZFHkV4ODPt/paV1YZf/zoUjz/ufYm86B7vm+0iz6SkyHjrLT2mTzepaq4rPp/iv/PzJUyf3l4er7Z7vMkElJRISE31dSM6Z5l2GfX1ns1pd3r5Mj09HdHR0QCA9evX4/z580hPT7f809zcjLNnz7r1gkRE3V1UFHDsWD1iYtT/jL+a0XFNO1G7hARZVXm82q69Oh2weLERmzb5uBmFHQUF7SWeJKSmmtHcLKG62vFjamvFPHRlLOXAgWbk5rbvAzt26HDFFe431FS6x7sz7k0hgnauafenSy81ob5ewrffet+5kSPful5KihkXLmhw1VXqPqtxcd6Xx5eWisZ3HS/oGQyyT7rHOx751l4e7+4+p3pPX7VqFfbs2WN129q1a3H33Xe79YJERD2Bu1flo6N9P/bNZGJZH1FHiYm+a0SnWLrUiA8/1MHkx0R7WxtQXCxZSjxJiIwUmSlnHeSrq9uz7IB1pt1oFJl2T4J20T0eOHfOs/L4hgalPJ7d4/3BYGgvkfcWl5l1veRkGVqtjEWL1EXMSiWMMqPduhGduvC2sFA0sus4acIXmfaGBmfd49sb0bk7scDluyoqKkJRUREAoL6+3vL/BQUFyM7OttucjoiIrLWXx/tOfX37cxORWNPu6oRNzZz2jjqWyPtLUZEEjUaUiJK11FQzSkocHztFE7qOQbuY1S7LwP79Wuj1MjIz3b8YEhMj1sZXVrqfae/YiI5r2v0nK6sNmzfrYPbyWlddHTPtXW3KFBPuuadVdbPeuDgRtJeUiGy5MrYtKUlUyLS2un6O4mKN1Xp2wDdr2l11j6+tldDU5H55vMt6r8suuwyA6BK/bt06rFu3zur++Ph4t16QiKg3Epl23z5nba0ESZKZxSH6gavy+MZGoLlZQmys+ufsWCLvr3Xt+fliVnAg5sF3NykpstOgXRn3pujf34zmZlH6un27DpdfboLGgwrqmBgZ33+vQVKS2e3KKuuRbxLL4/3kkktMaGyUcOCABlOmeB65i6DdhxtGbhs71oyxY1VE2j+IjZXx3Xcay7FT/0PBRXy8DEkSFVcd56/bU1goIS3Ner/xTXm84898RAQsa+59Xh7fsYO88t8d/7npppvcekEiot7IH5l2pXmOJyekRD2Rq0Z0VVXiQpcytlGtrCz/lsgXFLA03hERtDs+yNXUSFb9RUJDxRrVnBwNduzQelQaD4igXZbdL40HOo9845p2fzEYgEWL2rB5s3cl8lzT3v0o5fF5edbHTp1OZOHVrGsvKtLYBPYi0+7dtjmrromIkFFZKY5n7iZcXGba33rrLciyjFtuuQXz58/HT37yEwCiW3t6erplbjoRETnmjzXtogkdTzSIFPHxMurrpR8CJdv7Kyvb53m7QymR379fi6lTfR+55+WxCZ0jKSlmFBc7PnbW1NgeBwcONGP3bi3On9dg9mzPg3YAHgXtXNMeOEuXGvGrX4Xij39s8fgCNte0dz/x8aIRXX6+Bn37Wv/tkpLUBu0SMjOtj+cGA9DW5m15vPM57YAY/ebu/uoyaJ8yZQoA4KmnnsKQIUMwbtw4916BiIgQHQ2nJ56eqKtzv7yKqCeLixOlkRUVks2JHOB+EzqFVivWSjsr0/ZGQYHG0kiJrKWmytizx1kjOsmmcmLgQDPeekuPadNMHpc9K9l7z4J2ZtoD5ZJLTGhulrB/vxYXX+zZBTWuae9+YmNF0G6vSikhQd3Yt6oqCfHxnTPtsl8z7co6dk9G9aqeYfKjH/0IBw8exOrVq1GvdD/6wX333ef2CxMR9Sb+K4/niQaRQqsVGRhnQbs7Teg6El1//RO0d5wzTNZclcfX1orO0x0NHCjjwgUNrrjC87Nv7zLt7fsK57T7l14PLF7chk2bdB4F7S0tQEsL17R3N+3l8RpMmWK9CF1tB3lliWFHItPu3ba5mtMO+Dlof/nll/H888/bvY9BOxGRc/5qRMcZ7UTW4uMdr2t3Z0Z7Zx2zp75mr8SThJQU593jq6sljBhhHVgPHCj+39P17IBYGztpkgljx3pWHs857YGzdKkRv/hFKJ54wv0S+bo68XdieXz3Ehcnw2iUcOqU7dIitbPaxTlUYLvHK0tlPLlIpDpof+eddyDLMiIjI9GHZ4lERG6JjvZ9pp1r2olsJSQ4PmHztDwesA7EfMlkEmsr2YjOvpQUUepqNIpAurPaWtvy+IsuMuHqq9sweLB3x8ctWzy7SmM98o1r2v1t5kwT2tqAffvc7zlRWytKokNC/LRx5Bd9+gBarYyKCtulRYmJMk6ccH31xl4vA1/MaXf2mdfrgZAQ2b+Z9oaGBsyYMQP/+te/OJudiMhNffr4oxEd17QTdeZs7Js35fGi5NmbLbOv85xhspacLMNslnDhgmR3jn11tW3FUf/+Ml55pTlAW2irc6adc9r9SxnLuHGjzu2gvb5eXPxmaNO9SJIoka+ogM2s9aQk0YjSGVm2n2k3GLxf0+7qMx8R4VnQrrqIZPbs2QDAgJ2IyAPR0bAbtL/3ng6nT3vW8pZr2olsifJ4+58pb8rjIyL8k2nPz9cgNbV9zjBZMxiAhATHJfJi5FtwHQetG9FxTXsgZGUZsXmz+2MZa2slREb6Z5vIv2JjZaSmyjAYrG9XUx7f3Cy6xHe+4Ccy7f7rHg+I7xJP9jnVmfaYmBhs27YNt99+O8aNGwddhxolrmknInIuOlpGY6OEtjZYTs6/+kqL++4Lxe9+14oRI9y/tFtbKyE1NbhOVom6WkKCjNxc+0F7VZXnAV54uLqOxO7Kz2dpvCvJyTKKizWYMMH292Rv5FtXU0a+mc1KqWxwbV9PNH26CWYz8M03Wkyfrj5y5zKz7is2VrZbOZWUJKOszHkyRFmuaJtp925Ou5rPvKeZdtVB+3//+19IkoSvvvoKX331ldV9DNqJiJxTvhhqa8WIkZoa4N57Q5GS4rhpliv19cy0E3WWkCDj4EF/lMf7L9POJnTOpabaH7dnNos1ycGYaW9qEmWyABvRBYJOByxaJErk3Qnaa2u5zKy7iouT7WasExNFZ/mOSZLO6uqA0FDbLL1eL3u1pr35h1U5zj7zERGe7XOqg/a0tDS3n5yIiITQULFWqqYGiIsDfvObUGRkmHHxxSZ8952n5fHseEvUmb+6x/tr5FtBgYT+/Zlpd8ZRB/m6OkCWJURHB9dxMCJChixLqKoS28zy+MDIyjLiZz8LxcqVLdA6X9Jsoaxpp+6nb18ZSUm2fztl9vqFC46rER0tLxSZdgmyDI/6HDQ1uf7Mi0y7+8+tOmjftWuX+89OREQAxMFfmdX+/vta7NmjxaefNmLHDp3HmXZ7TVSIervERMdBu7dz2v0x8i0vT4OLLvJ8NFlvkJIio7DQ9uJmdbUErdazE2B/UjpHV1RIkCQZoaFduz29xbRpIsP+1VdazJypLtvONe3d15/+1GI3sNbrgbg4M8rLnQft9oahKZl5k8n+tApXGhvh8jM/bJgZgwa5f6HW7fTOgQMHsGnTJrdfiIiot4uJkXHsmBYPPxyK555rRnKyjIQEs8NO167U1Ukezfok6smUz5Tc6VzNZAJqahB0I98KCjRc0+5CSoqM4mLb331trciyB1uP5JAQQKMRUwzCwjzL2JH7dDrgyitFibxa9sZ+Ufeg08FhRYWrZnSOehno9eI2T9e1K03onH3mn3qqBVlZ7l+oVR2019fXY/ny5bjpppvw8MMPIycnByNHjsSqVavcflEiot4oOhp45JEQXHttG+bNE1mAhATP1rS3tYksE9fiEVmLj5fR1CTZjGerrpYgy97Mafd9pt1sFuXxDNqdS001o7TU9jhpb9xbMJAkcZGnvJxN6AItK8uIjz7SwagyJmLFWs/kKmh3Vh4PeD6rvbHRfyMeVQftL774Ig4fPgxZliHLMgYOHIiRI0diz549ftkwIqKepk8fGenpZjz2WIvlNmWmtNmNc/bSUgnXXBOG9HQzBg/myT5RR9HRgE5nezGsqgoICZEtpcvuUjqC+1J5uYTWVslmzjBZE5l221PWYBz3pggPF/sg17MH1tSpJkiSKJFXo66O5fE9UWKi8w7yjnoCKeXxra2eHeubmiSPv2NcUR2079y5ExkZGbj66qsttw0dOhS5ubl+2TAiop7mgQda8fbbTVYH9IQEGUajhJoadc+xd68Wc+eGo18/GVu3NiIiwj/bStRdaTT2m9EpTeg8LVX2x5r2vDwJyclmhIT49nl7mpQUGdXVkqUbuyIYx70pwsPFmnZm2gNLqwWWLFFfIs+Rbz2Tmky7s6Dd00x7U5P/pkWoDtrLysqQmZmJyA6Xo0JCQtCs9LYnIiKnLr7YhEGDrA/mERFi7MiFC84Px7IMvPiiHjfcEIZf/7oVq1Y1++1qLlF3Fx8v2/SKqK72vDQe8M+adrGenQGDK/HxMvR62aZEvro6+Ma9KSIixHGdmfbAc6dEniPfeqakJNdr2u31BNJoRKWWp2vaGxqCINOekpKCo0ePwmQS6zArKirw+eefIzU11T9bRkTUC0hSe4m8M6++qsc//2nAunWNuPXWNjY2InIiOVlGQYH1KU5lpeed4wFlTbt7S1lcyc9nEzo1NBrxNy0psf6bKo3ogpGSaeeM9sCbMsUEnQ748kvXJfLMtPdMiYlmjzLtgFjX3tbmaXl8EGTaZ82ahRMnTuB///sfAGDOnDkoKyvD7Nmz/bJhRES9RUKC8yvCALBrlw6/+EUrLrqIJ/hErsyaZcT27dblsVVV3mbaxc/6skQ+P59N6NQSQbtt9UTwBu3t3eMpsJQS+U2bXJfIcwpLz+S6PN7x1AC93vvu8f6gOmi/5557MGDAAJhMJsiyjNbWVvTr1w933XWXf7aMiKiXcNVB3mQCvv1WiylT1M2dJertrrzSiC++0KKqqv02b4N2pX+EL0vk8/M16Ns3OIPOYNOvnxk5OdanraIRXddsjytKIzquae8aS5eKEnlXa5NF0M6/UU/j6cg3QIx9C8bu8aoHGcbExGDTpk3Yvn07ioqKkJqainnz5iHU2fR4IiJyyVXQfvq0BiYTMHIkM3JEavTvL2PMGDO2btXhhhvEwlZvy+MNBrHW0ZeZ9oICCf3783OtRmamCXv3Wp+2BnsjOnaP7zpTpphgMABffKHFnDn2L3jLspjTzqC950lMlFFZKaGtrb25XEdi5Jv9nzUYgjPTrjpoB0TjuSVLlvhnS4iIeil7TbM6OnBAi4kTxRo9IlJnyZI2bN6stwTtVVWS1yMS28e+eX+SL8vMtLtj0iQTnn/eAFmGpadHdXXwjnwT0wa4pr2raDQi275pk85h0N7QAJjNwXvhhzyXkCBDliVUVEhISbH9+zpb067Xe76mvbGxC9e0nzlzBp999hkaf7i0/M033+Dqq6/GvHnzsHLlSrR6eimCiIgAAAkJZqeZ9v37tZg8maXxRO5YssSIzz/Xorpa/H9VlXeZdsC3Y98qKiQ0NUno25eZdjXGjTOjrk7C+fPtx8raWgTxmnbrf1PgLV3aho8+0jssda6rE/sS17T3PAaDmCzhqES+rs7xmnaDwfPu8V06p/1vf/sbHnzwQRgMBrS0tOCBBx7AqVOnkJeXhzVr1uAf//iHf7aMiKiXcFUez6CdyH0DBsgYNcqMbdtEiYoyp90bvhz7lp8vISHBzKBOpZAQYOxYMw4caO8IHuyN6Dr+mwJv0iQzwsJk7Nljv4t8ba3oOaB13WSeuqGkJMcd5F1n2j17zS7tHp+dnY2LLroIOp0O+/btQ0VFBUJDQ3HjjTciJCQE27dv98uGERH1FomJjoP2CxdEZmniRAbtRO5assSIzZvFgkZvG9EB7WPffKGggKXx7po0yYRvv22PsGprg7c8XrkYwzXtXUcpkd+40c6iZjjPtlL3l5goo6zM9njd3Ay0trpa0+5peXwXZtqrqqoss9i//fZbAMCPfvQj/OEPf8Dll1+OkpIS/2wZEVEv4WxO+4EDGgwfbg7aDslEwWzJkjZ89pkWNTW+KY8PD5fR0OCbbSsqkpCWxtJ4d0yaZLJk2puagJYWCX36dPFGORARIfY1rmnvWkuXtmHLFp3dcmfRjIx/n55KdJC3DXVra8X5lj8y7fX1kuWz72uqRr41NTUBAI4fPw5JkjBp0iQAQHh4OHTsjERE5JX4eNHl1Gi0vY+l8USeGzRIxogRZmzYoEdzs4TYWO+eLyLCd+XxxcUapKYyYHDHpEkmnDypQUND+4l38JfHd/GG9HITJ5oRGWm/RL6+Pngv+pD3HI19q6sDQkJkhITY/zlv1rTn5/uvT4nLoL1fv37YsWMHnnzySXz99dcAgIkTJwIACgoKEOvtNyARUS8XHy+6nFZW2n657N+vxaRJDNqJPLVkiRH//rcekiR7XUotyuN9s10lJRKDdjelp8tISpJx+LAW1dUio2VvnFMwiIgQ/2amvWtpNOIYYK9EvrZWQmQk/z49laOg3VWFhV4PGI3uX5yVZSA3V4MBA7oo0/7jH/8YTU1NeOedd2A0GjF9+nQkJyejvr4eBw8exMiRI/2yYUREvUVoqJgT23lde1sbcPiwFpMns4SWyFNLl7bh+HEtoqPhdcMpXzaiKy6WkJLCz7a7lBL5YG5CB7Rn2rmmvetlZdkvkeea9p7N0Zp20YTO8c/p9Z7NaS8vl9DYKKF/f/8c113Wtt90003Q6XTYs2cPUlJScN999wEQo+AuvfRSzm0nIvIBex3kjx/XICwMGDKEJ/ZEnho8WMbo0SafBNu+XNPO8njPTJpkwt69OowaZQryoF35d/BuY29x0UVm9Okj47PPtLjiivbKNa5p79mSk+13j3fWOR4A9HrZozXtubkSkpP9NxFE1YL066+/Htdff73VbZmZmcjMzPTLRhER9Tbx8bbN6JTSeI2q7iNE5MiSJUbs2OF9Dx5frWmXZaU8nhfk3DVpkgnPP2/A0qXMtJM6ktTeRb5j0F5f77iDOHV/yckySkpsT6Dq6uD0Yo3oHu/+64nSeP8d04PuVHD37t24+uqrMWbMGPz5z3+2us9sNuOJJ57A5ZdfjiuuuAJvv/22qvuIiIJdQoLZJtN+4ADXsxP5wl13teKZZ5q9fh5fjXyrqgKamyWkpARv0Bmsxo0zo65OwpEj2qAd9wYw0x5ssrLasHWrDi0t7be5yrhS95aSIqO6WsIP/dQtXGfagbY294/z/lzPDgRh0D5gwACsXLkSd9xxh819mzZtwtmzZ/Hxxx/jvffew+uvv44zZ864vI+IKNjZm9X+/fcajBzJoJ3IW5GRwMiR3mdAfFUeX1ysQVSUjMhI75+rtwkJAcaONWPnTh2io7t6axxrz7QzKAwGEyaYERsrY/fu9sYWtbXOM67UvcXHy9DpbNe1u1rT7mn3+F6XaR80aBAyMjLsjpLbsmULrrvuOmi1WsTExGDhwoX48MMPXd5HRBTsOq9pN5uB8+c1GDqU5bNEwcJXjehYGu+dSZNMyM7WBHV5vDKrmSPfgoMokW+z6iJfV8dMe0+m0QBJSbYl8q7+7p7Oac/NlXpX0O5McXEx0tLSLP+fnp6O4uJil/d1VlZWhhMnTlj9k52d7d+NJyJyonPQXlgoobUV6N+fJxREwSIiwjcj34qLNSyN94KybCiYg/aoKGDOHCNiY4N3G3ubrCwjtm3TofmHlTJ1dVzT3tOlpMgoLbXNtLte0+7+xdmcHP+Wx6vuyrJhwwYMGTIEY8eOtdxWUVGBqqoqDB06VPULLlu2DDk5OQ5fIzU1VfVzeWrt2rVYtWqV1W2jRo3C+vXr/f7aRET2xMfLuHCh/TpqdrY4+BsMXbhRRGTFV5n24mLOaPdGdwjadTpg7dom1w+kgBk3zoz4eBmffqrDwoVGlw3JqPtLTjajpKRz0A6MHOk8097sZguU5mZxMXbgQP9l2lUH7Q8//DBuueUWq6D91VdfxZo1a3Dq1CnVL7h27Vr3trCD1NRUFBUVWbrWFxYWWoJ8Z/d1tmzZMsydO9fqttDQUI+3i4jIW50z7dnZGo56IwoyvlrTXlIiIS2Nn29PpafLSE01B3XQTsGnvUReBO0c+dbzpaTIdoJ25+XxBoOM2lr3Ls7m52sQGiojOTlIG9GVlJT4ajtUWbBgAd59912YTCZUV1dj69atWLRokcv7OktKSsLo0aOt/hkyZEgg3woRkRUG7UTBz3eZdpbHe+v//b9mzJnDRp3knqwsIz7+WIemJjHyzVlDMur+RNBuu6bd2bIIT7rHK+vZJe+/HhxymWkfOXIkAECSJLz11lt46623rO6PiYnx6QZ99dVX+O1vf4v6+nrIsoyPP/4Yjz32GC677DJkZWXh2LFjmDdvHiRJwq233ooRI0YAgNP7iIiCXUKCjLo6CS0tojvy2bMaLF5s7OrNIqIOxJp2X5XH86KcNy6/nAE7uW/sWDMSE2Xs2KFDYyMz7T1dSooZX3yhtbrNdabd/Tnt/h73BqgI2mVZbIAkSZb/7shRNttT06ZNw+eff273Pq1Wi8cee8zt+4iIgl1cnAxJklFRISEtTUZ2NjvHEwUbXzWiE93jGSwQBZokiZnt77wjusgzaO/ZkpPtNaKDi+7xstvd40UTOv+es7kM2p966ikAwIoVKzBjxgxceeWVAESQnJ6ejokTJ/p1A4mIegOdDoiNFSXysbEyCgokBu1EQSY8HGhpkWA0is+sJ5qbgYoKlscTdZWlS414/nkDtFqZI/l6uORk90e+GQzuj3zLzZUwc6Z/q39cfuX86Ec/AiAau40fPx6XXHKJXzeIiKi3Uta163QahIeL+aJEFDzCw8VnsrERHq+FLSmRoNPJSEzk55uoK4wZY8agQTIqKyW/rkGmrpeSIprKNTQAERFAS4u48OpqTbu7I99yczX4yU88GO7uBtWN6O677z4kJCTgd7/7HW6//XZUVFRgw4YNDse3ERGRexISZJSXS5bSeJ5MEAUXJSvnzbr2khINkpNlaLxqBUxEnlJK5J1lW6lniIuTode3l8grXeFddY93J9Muy0Gypl1x8uRJXH/99WhpaYEkSQgNDcUf//hHLF68GE8++aQ/t5GIqFdISBBr2ouLJXaOJwpCOh0QEuLduvbiYoml8URd7MYbGbT3BpIksu2lpRoMHmxCXZ0Iyp1N+tbp3CuPLy+X0NgooX9//563qb7O++yzz6K1tRXJyckAgIiICFx00UXYv3+/3zaOiKg3Ucrjz57luDeiYBUeDjQ0eJ5pZ+d4oq7Xr5+Mn//cv+XMFBzEuvb2TLurizViTbv6Y3xuroSkJLPf+yOoDtqPHz+O+fPnY/78+Zbb0tPTUVZW5pcNIyLqbeLjZVy4oOGMdqIgFhEhexm0a9g5nogoQJKTzVbl8c7WswPKmnb1z5+bq8HAgf4/Z1MdtJtMJkRGRlrdVlVVBYPB4PONIiLqjTqvaSei4BMe7l15fEkJy+OJiAIlJaW9g7y6TLt7a9oDsZ4dcCNoHzhwID755BOcPXsWAPDvf/8bn376KQYNGuS3jSMi/r2gNQAAOlNJREFU6k0SEmScOaNBdbWEwYMZtBMFI5bHExF1HyJoF8fsujrnTegA97vHi6A9iDLtN954I6qrq/HVV18BAP7yl7/AZDLh+uuv99vGERH1JomJMvLyNEhJMaNTYRMRBQnvM+0apKUx005EFAi25fFq1rSrf/7cXCm4gvalS5fi7rvvRmhoKGRZRmhoKH76058iKyvLn9tHRNRrxMeLLxKWxhMFr4gIz0e+mc1KeTw/40REgWBbHu/88Xq97Paa9kCUx6se+QYAv/zlL/Hzn/8cVVVViI2NhV6v99d2ERH1OgkJ4kSepfFEwcubTHtFhYTWVomN6IiIAqRjebz67vHqnru5WSx5CqpGdAq9Xo+kpCQG7EREPhYdDeh0MjPtREHMmzXtJSUSYmJkhIX5eKOIiMiu5GQzGhok1NeLNe2uyuPdWdOen69BSAiQlBQEmfaRI0c6vV+SJJw8edJnG0RE1FtpNKIZHYN2ouAlMu2eBe1sQkdEFFgxMUBIiIzSUgm1tRJGjHB+DHane3xuroT+/c3QuJ0Gd5/LoF2WWcJFRBQob7/dhFGjeFJPFKwiImTU1noatGs47o2IKIAkCUhOFuvaa2slREerybQDsix+1hkxoz0wx3SXQfuKFStsbjt16hQ2bdoEs9kMnc6tZfFEROTEuHEM2ImCWXg4LOsj3cVMOxFR4KWkmFFSIqGuTkJUlPPHGgyALEswmQBXYW5OTmDGvQEqgvZbbrnF8t/l5eV45plnsHnzZpjNZlx22WX4zW9+49cNJCIiIgoW4eEyGho8+1nROZ6ZdiKiQFKa0alpRKfXi/tbW10H7bm5EmbONPlqM51SlSZvaWnBv/71L7z++utobGzEqFGj8PDDD2PKlCn+3j4iIiKioBEe7vnIt+JiDTIzjT7eIiIickYZ+1ZbC1Xd4wF1HeRzczX4yU/cGOruBZdB+4YNG/Dcc8+htLQUSUlJePTRR3HVVVcFYNOIiIiIgktEhOcj30pKWB5PRBRoyckyjh3ToK5OTaZd/Ft0kHf8WFkO3Ix2QEXQ/vDDD0OSJGg0GgwdOhSffPIJPvnkE8v9kiThhRde8OtGEhEREQUDbzPtnNFORBRYyclmbNmiQ3Oz6zXtStBudFEUdeGChMZG0T0+EFR3kTOZTNi7dy8A0VFekiTLv4mIiIh6A7Gm3f1zn8ZGoLqaa9qJiAItJUXGmTNiLpurTLtGA2i1MlpbnT9nRYWEiAgZ4eG+2krnXAbtkydPDsR2EBEREQU9Mafd/Z8rKpJgMMiIj2fQTkQUSCkpYlSnwSAjNNT14w0G12vaGxvF90GguAza16xZE4jtICIiIgp6ERGelcfv36/F+PFmaDR+2CgiInIoJUWUsLvKsivErHbna9obG6WAZdkBgF8dRERERCopI99kNxMsX3yhw8yZ7BxPRBRoUVHi2O1qPbvCYJBdZtqbmgKbaWfQTkRERKRSeDhgMkku1zt2JMvA3r1aTJ8emHm+RETUTpJEB3n3Mu3OHxPoTLvqRnREREREvZ2SWWlsBEJC1P1MTo6EsjIJkyczaCci6grJyWZLZ3hX9Hqgrc35MqhAr2lnpp2IiIhIJSWz4s669i+/1GHiRFNAszJERNQuJUVGVJS6INtgcN09nmvaiYiIiIKURuP+2LcvvtBixgxm2YmIuoooj1f3WJFpd/4YEbQHUfd4IiIiImrnztg3WQa+/FKLm25ycQZIRER+c+edrWhpUXex1WBQusc7FnQj34iIiIioXXi4+vL4c+ckVFdLmDiRmXYioq4yYIAMZyPcOlKfafd+u9RieTwRERGRGyIi1Gfav/hCh0mTTAgN9e82ERGRb6hb0w6EhbERHREREVFQCg+H6jXtX37J9exERN2Juu7xzLQTERERBS21a9qV9ewzZzJoJyLqLsSaduePaWriyDciIiKioCXK411n2r//XoO6OgmZmQzaiYi6C71e5pp2IiIiou5MbXn8l19qMXmyCSEhAdgoIiLyCTWZ9kB3j2fQTkREROQGteXxLI0nIup+dDrAaHS9pj0sLEAbhCAM2nfv3o2rr74aY8aMwZ///Ger+1544QVMnToVWVlZyMrKwoMPPmi5z2w244knnsDll1+OK664Am+//XagN52IiIh6ATWZdrMZ2LtXixkzjAHaKiIi8gW13eN79Zz2AQMGYOXKldi6dSsa7VzGXrJkCX7/+9/b3L5p0yacPXsWH3/8Merq6vCjH/0IF198MYYNGxaIzSYiIqJeIiJCxoULzoP2777ToKlJQmamOUBbRUREvqBmTntTUy9f0z5o0CBkZGRAp3PvesKWLVtw3XXXQavVIiYmBgsXLsSHH37op60kIiKi3kpNpv3LL7WYMsUEvT5AG0VERD4h1rSrGfnWizPtrmzbtg3ffPMNYmJicO+992Lq1KkAgOLiYqSlpVkel56ejiNHjth9jrKyMpSXl1vdFhoaiiFDhvhvw4mIiKhHULOm/eBBEbQTEVH3otcHXyO6gAfty5YtQ05Ojt37NmzYgNTUVIc/u3z5ctx9993Q6/X49ttvcd999+H9999Henq6W9uwdu1arFq1yuq2UaNGYf369W49DxEREfU+4eFwOfKtoEDC3LksjSci6m4MBhn19Y6P8SYT0NIS2PL4gAfta9eu9fhnExMTLf89ceJEjBo1CsePH0d6ejpSU1NRVFSEzMxMAEBhYaHDCwDLli3D3LlzrW4LDQ31eLuIiIio91Azp72wUIO+fQOXhSEiIt8Qa9odH+ObmsS/e3Sm3RslJSVISUkBAOTk5ODUqVMYPnw4AGDBggV49913sWDBAtTV1WHr1q145ZVX7D5PUlISkpKSArbdRERE1HOEh8toaHB8v9EIFBdLSE9npp2IqLtxNadd6WnSozPtrnz11Vf47W9/i/r6esiyjI8//hiPPfYYLrvsMjz77LM4ceIEtFottFotHn30UQwaNAgAkJWVhWPHjmHevHmQJAm33norRowY0cXvhoiIiHoaV+XxpaUSzGYgNZWZdiKi7kavl512j29sFI8JZKPRoAvap02bhs8//9zufX/9618d/pxWq8Vjjz3mr80iIiIiAuC6EV1BgQbJyYE9oSMiIt9wlWlvbJQQFha47QGCcOQbERERUTCLiHCeaS8slJCeziw7EVF35GpNe6A7xwMM2omIiIjcomTaZQfnbAUFGvTty/XsRETdkcEgO820NzUFtnM8wKCdiIiIyC3h4YAsS5YOwp0VFTHTTkTUXYlMu+P7mWknIiIiCnLKyZqjEnkx7o2ZdiKi7shgcFUeLzFoJyIiIgpmoaGARuN47FtBATPtRETdlV7PRnRERERE3ZokOR/7xkw7EVH3pdfLMBod39/UxPJ4IiIioqAXGSmjttY2aK+vB6qrJaSnM2gnIuqORKbdVXl8ADcIDNqJiIiI3DZihBknT9qeRhUWahAeLiM2tgs2ioiIvCbWtDu+n43oiIiIiLqBCRNMOHxYa3O7mNFuhuQ4SUNEREFMr3c+8o2ZdiIiIqJuYMIEMw4ftj2NKijQsAkdEVE3xkw7ERERUQ+QmWnC6dMamw7yhYUSm9AREXVjXNNORERE1AOkpspISJBx7Jh1iTwz7URE3ZvBIDPTTkRERNTdSRKQmWlbIq+saSciou5JzZx2ZtqJiIiIugF7zegKCjTo25eZdiKi7spgAMxmCSaT/ftF0M5MOxEREVHQy8w04dCh9qDdZAKKi5lpJyLqzvR68W9H2fbGRiAsjEE7ERERUdAbP96M8+c1qK4W/19eLqGtTUJaGjPtRETdlcEgjuGO1rU3NbE8noiIiKhbiI+X0b+/2VIiX1AgITHRjJCQLt4wIiLyWHum3X4HeTaiIyIiIupGOq5rLyzkenYiou5OCdodZdrZiI6IiIioG5kwwYRDh8TpVEEB17MTEXV3Wi2g1cpc005ERETUE2RmmnHkiMi0FxVxRjsRUU9gMNjPtLe1AUYjM+1ERERE3cb48SYUF0soLZVQUCChb19m2omIujudzv6a9sZG8e+ICGbaiYiIiLqFyEhg2DAzDh/WoLCQmXYiop7AYJBhNNre3tgoAvmwsMBuD4N2IiIiIi9MmGDGoUNaFBYy005E1BPo9fbntDc2AqGhMrTawG4Pg3YiIiIiL2RmmvDVV1pUVDDTTkTUE4g17fbK46WAZ9kBBu1EREREXpkwwYRvvtEiJERGQgKDdiKi7s5xpl0K+Ix2gEE7ERERkVdGjzZDowHS02VItokZIiLqZgwG2W73+MZGdEnQrgv4KxIRERH1IKGhwKhRZkRHM8tORNQTiEy7/fL4QI97Axi0ExEREXktM9NkNytDRETdj15vf047M+1ERERE3dRvf9sKk6mrt4KIiHzBYJAdrmnvikZ0DNqJiIiIvMQGdEREPYejTHtTU9dk2tmIjoiIiIiIiOgHBkNwrWln0E5ERERERET0A70+uLrHM2gnIiIiIiIi+oHItNvezkw7ERERERERURcTa9pty+ObmoCwMGbaiYiIiIiIiLqMs+7xnNMO4K233sK7774LSRJXNu68805kZWVZ7n/ppZewbt06AMDixYvxq1/9StV9RERERERERK44ntMucU47AAwbNgz//e9/ERUVheLiYlx11VXIzMxE//79sX//fnz00UfYtGkTdDodli9fjszMTMyePdvpfURERERERERqOCqPb2wEIiJYHo9p06YhKioKAJCamorExEQUFxcDALZs2YKsrCyEh4fDYDDgmmuuwYcffujyPiIiIiIiIiI1nGfaA789QRe0d7R3717U1NRg7NixAIDi4mKkpaVZ7u/bt68loHd2X2dlZWU4ceKE1T/Z2dl+fCdERERERETUHThe0941jegCXh6/bNky5OTk2L1vw4YNSE1NBQCcPn0aK1aswLPPPotwH1/OWLt2LVatWmV126hRo7B+/Xqfvg4RERERERF1L8GWaQ940L527VqXjzl79izuvvturFy5EpMmTbLcnpqaiqKiIsv/FxQUWIJ8Z/d1tmzZMsydO9fqttDQULfeBxEREREREfU8Yk67/TXtXdGILujK47Ozs3HXXXfhT3/6E2bMmGF134IFC7Bx40Y0NjaitbUVH3zwARYvXuzyvs6SkpIwevRoq3+GDBni9/dGREREREREwU2vl3t3pt2VJ598EnV1dXj66afx9NNPAwAeeughXHLJJbj44ouxcOFCLFmyBACwaNEizJkzBwCc3kdERERERESkhsi0W98my12XaZdkWQ78qwaxqqoGGI3mrt4MIiIiIiIi6gKrV+uxfbsO//lPk+W25magf/8onDxZj4QE34XQOp0GsbERTh8TdOXxRERERERERF3FXvf4xkbxb65pJyIiIiIiIupC9rrHNzZKkCQZYWGB3x4G7UREREREREQ/sNc9vrFRQlgYINk2lfc7Bu1EREREREREP7CXaW9q6prSeIBBOxEREREREZGFwWA78q2rxr0BDNqJiIiIiIiILPR6e+XxzLQTERERERERdTmDwX4jOmbaiYiIiIiIiLqYTmc78q2hgZl2IiIiIiIioi5nMABGo/VtTU1Sl4x7Axi0ExEREREREVlwTTsRERERERFRkGL3eCIiIiIiIqIgJTLt1reJoJ2ZdiIiIiIiIqIuZTAAJpMEk6n9NpbHExEREREREQUBvV78u2OJPBvREREREREREQWBsDAZGo2M/Pz2cJmZdiIiIiIiIqIgEBEB/OQnbXjiCYPlNjaiIyIiIiIiIgoSK1a0Yu9eHT79VAuAmXYiIiIiIiKioJGQIOM3v2nBI4+EoK1NrGlnpp2IiIiIiIgoSNx2m+hE98Yb+i7NtOu65FWJiIiIiIiIgpheDzzxRAt++tMw6HQyM+1EREREREREwWTOHBOmTzeislLDNe1EREREREREweaPf2xBaKiMmJiuCdolWZa75pWDVFVVA4xGc1dvBhEREREREQWJ2lqgTx/fP69Op0FsbITTxzDTTkREREREROSEPwJ2tRi0ExEREREREQUpBu1EREREREREQYpBOxEREREREVGQYtBOREREREREFKQYtBMREREREREFKQbtREREREREREGKQTsRERERERFRkGLQTkRERERERBSkGLQTERERERERBSkG7URERERERERBikE7ERERERERUZDSdfUGdPbWW2/h3XffhSRJAIA777wTWVlZAIB169bhz3/+M/r27QsA6NOnD9asWWP52Zdeegnr1q0DACxevBi/+tWvArz1RERERERERL4TdEH7sGHD8N///hdRUVEoLi7GVVddhczMTPTv3x8AcPHFF+Oll16y+bn9+/fjo48+wqZNm6DT6bB8+XJkZmZi9uzZAX4HRERERERERL4RdOXx06ZNQ1RUFAAgNTUViYmJKC4udvlzW7ZsQVZWFsLDw2EwGHDNNdfgww8/9PfmEhEREREREflN0GXaO9q7dy9qamowduxYy23ffvstsrKyEBYWhltuuQULFy4EABQXF2PixImWx/Xt2xdbtmyx+7xlZWUoLy+3ui0sLAyDBw+GVht01zGIiIiIiIioB1ITfwY8aF+2bBlycnLs3rdhwwakpqYCAE6fPo0VK1bg2WefRXh4OABg9uzZWLhwIcLCwpCdnY3bb78dqampmDBhglvbsHbtWqxatcrqtiuvvBL/7//9P/TpE+b2eyIiIiIiIiLyh4CnldeuXYtvvvnG7j9KwH727FncfffdWLlyJSZNmmT52bi4OISFiaB6yJAhmDVrFg4ePAhAlNIXFRVZHltQUGB5vs6WLVuGdevWWf1z5513oqamxl9vm9yUnZ2NH/3oR8jOzu7qTaEgwP2BOuM+QZ1xn6DOuE9QZ9wnqLPusk8EXS14dnY27rrrLvzpT3/CjBkzrO4rLS21/PeFCxfw9ddfY+TIkQCABQsWYOPGjWhsbERrays++OADLF682O5rJCUlYfTo0Vb/jBw5EtHR0f57Y+SW5uZmnDx5Es3NzV29KRQEuD9QZ9wnqDPuE9QZ9wnqjPsEddZd9omgW9P+5JNPoq6uDk8//TSefvppAMBDDz2ESy65BO+88w527twJnU4Hs9mMW265BdOmTQMgusovXLgQS5YsAQAsWrQIc+bM6bL3QUREREREROStoAva33zzTYf3PfDAA3jggQcc3n/ffffhvvvu88dmEREREREREQVc0JXHExEREREREZHAoJ2CUmJiIu677z4kJiZ29aZQEOD+QJ1xn6DOuE9QZ9wnqDPuE9RZd9knJFmW5a7eCCIiIiIiIiKyxUw7ERERERERUZBi0E5EREREREQUpBi0ExEREREREQUpBu0UEE8++STmzp2LESNG4NSpU5bbP//8c1x99dVYsmQJrrvuOnz33XeW+44ePYrly5dj6dKlyMrKwldffWX1nO+88w4WLlyIJUuWYOnSpWhpaQnY+yHvtLS04N5778X8+fOxdOlS3HbbbcjNzQUAVFRU4I477sC8efNw5ZVXYv/+/Zaf8/Q+Cn7+2CcUX331FUaOHInVq1cH6u2QD/hjnzh69Ciuu+46XHXVVVi4cCH++c9/Bvx9kec83SdeeeUVzJ8/HxkZGfjkk0+snnPFihWW51u+fDmOHj0a0PdE3vHHPiHLMl544QXMnz8fS5YswU033RTQ90Te8XSfcHYsaGpqwgMPPIArrrgC8+fPx7Zt2wL+viATBcC+ffvk4uJiec6cOfLJkydlWZbl6upqecqUKfL3338vy7Is79+/X168eLEsy7JsNpvlSy65RP7yyy9lWZblc+fOybNmzZKbmppkWZblHTt2yMuWLZNra2tlWZbliooK2Wg0BvptkYeam5vl3bt3y2azWZZlWV6zZo184403yrIsyw8//LD8/PPPy7Isy0eOHJEvueQSubW11av7KPj5Y5+QZVmura2Vr7nmGvlnP/uZ/OabbwbwHZG3/LFPLF26VP7kk09kWZblqqoqeerUqfKZM2cC+r7Ic57uE0eOHJHz8vLkG2+8Ud6xY4fVc37yySdyW1ubLMuyvGvXLnnOnDmBejvkA/7YJ1avXi3//Oc/l1taWmRZluWysrJAvR3yAU/3CWfHghdeeEH+7W9/K8uyLOfl5clTp06VKysrA/aeZFmWmWmngJg8eTJSUlKsbsvLy0NMTAyGDRsGAJg0aRKKiopw4sQJVFVVobKyEtOnTwcADBo0CFFRUfj8888BAK+//jruu+8+REVFAQDi4uKg1WoD+I7IGyEhIZg1axYkSQIAjB8/HoWFhQCAbdu2Yfny5QCAcePGISkpyXIl1NP7KPj5Y58AgD/96U+45557EBMTE8B3Q77gj31CkiTU1dUBEJkTvV6P6OjogL4v8pyn+8S4cePQr18/u8952WWXQafTWZ6vtLQURqPR32+FfMQf+8Trr7+OBx98EAaDAQCCfhQYWfN0n3B2LNi6davl5/r164cpU6bYVGj4G4N26jIDBw5EdXU1Dh48CADYuXMnGhoaUFhYiLi4OCQlJWHLli0AREnj+fPnLR+67OxsHD9+HMuXL8fVV1+Nt956q8veB3nvrbfewty5c1FVVYW2tjarL8j09HQUFRV5fB91T97uE4D4ctZoNLjssssCvv3ke77YJ5566in84x//wOzZszF//nw88MADPCHvxtTsE+4+36WXXmo5cafux9t9or6+HhUVFdi5cyd+/OMf48c//rHlXJS6J0/2ic7HgqKiIqSlpbn8OX/iUYm6TFRUFP7xj3/gmWeeQWNjIyZMmIChQ4daMuYvvfQS/v73v+O1117D0KFDMXHiRMt9RqMRBQUFeOedd1BTU4ObbroJ/fr1w5w5c7ryLZEHXnnlFeTl5WH16tVobm7u6s2hIOCLfaK8vBwvv/wy1qxZ4+Oto67gq+PEa6+9hgceeABLlixBfn4+brzxRowZMwZDhw714dZSIPj6u2Pjxo3YunUr3nnnHR9sHXUFX+wTRqMRRqMRzc3NeO+991BQUIDly5dj8ODByMjI8PEWk795sk8E67GAQTt1qalTp2Lq1KkAgNbWVsyYMcNy8pSRkYHXX3/d8tiFCxdaSunT0tKwePFiaLVaxMXF4dJLL8Xhw4cZtHczr7/+OrZv347Vq1cjLCwMYWFh0Ol0KC8vt1wJLSwsRFpaGmJjYz26j7oXX+0TJ06cQHl5Oa666ioAQFVVFXbt2oWqqir86le/6qq3Rx7w1T5RWVmJHTt24NlnnwUgShzHjx+PgwcPMmjvZtzZJ9TYsmULXnzxRaxevRoJCQn+3HTyE1/tEzExMQgPD0dWVhYAoG/fvrjoootw7NgxBu3djCf7hKNjQVpaGoqKipCUlGT5uZkzZwb0/bA8nrpUWVmZ5b9ffPFFTJ06FQMGDLC5791330VYWJglwL/yyiuxZ88eAEBzczP27dvHg2k38+abb+Kjjz7Cm2++iT59+lhuX7BgAf73v/8BEMsiSktLMXnyZK/uo+7Bl/vE7NmzsXfvXuzatQu7du3C/Pnzce+99zJg72Z8uU9ER0cjPDzcMomksrISR48etVwMpu7Bk33CmS1btuC5557Dm2++yQu93ZSv94krr7zS0kOpuroax44dw4gRI/yz8eQXnuwTzo4FHX8uPz8f+/btC/jSO0mWZTmgr0i90qOPPordu3fjwoULiImJQUREBHbs2IE//OEPOHDgAEwmEyZMmIBHHnnE8uFatWoVNm/eDFmWMXjwYDz22GNITU0FIMY5PProozh27BgkScK8efNw//33d+VbJDeUlJRg1qxZ6NevHyIiIgAABoMB7733Hi5cuIDf/OY3KCgogF6vxyOPPGK5WOPpfRT8/LFPdPTwww8jIyMDt956ayDfFnnBH/vE3r178fTTT1tKYH/84x/jtttu67L3SO7xdJ946aWX8L///Q+VlZWIiIhASEgINmzYgLi4OIwePRoJCQlWzSpXr16N2NjYrniL5CZ/7BNVVVVYsWIFCgoKAADXX389fvKTn3TZeyT3eLpPODsWNDY24ne/+x2OHz8OrVaL+++/H4sWLQro+2LQTkRERERERBSkWB5PREREREREFKQYtBMREREREREFKQbtREREREREREGKQTsRERERERFRkGLQTkRERERERBSkGLQTERERERERBSkG7URERD3Mww8/jBEjRuCmm27q0u148sknMWLECLzzzjsBe82//e1vGDFiBFavXh2w1yQiIvInzmknIiLqJubOnYvCwkKnj9m5cycOHTqEo0ePYsCAAbjxxhsDtHXWzp49i6VLlyIuLg67du1CfX09li5divLyclx88cX497//DUmSAADPP/88XnzxRWg0Gvz73//GlClTPH7dqqoqXHrppdDr9fjkk08QFxfnq7dERETUJXRdvQFERESkztVXX42amhoAwJdffons7GxERETgmmuusTwmMjISS5YswZIlS7pqMwEAb775JkwmE5YuXQqDwYC4uDg89dRT+OlPf4pvvvkG//73v3Hrrbfi6NGjePXVVwEAd911l8OA3Wg0QqvVWgJ9R2JjYzF37lxs27YN//vf/3Dvvff6/L0REREFEjPtRERE3dDDDz+M9evXIz09Hbt27bJ735QpU7BmzRoAwIgRIwAA9957L7799lscOnQIAwcOxB//+EcUFRXhH//4By5cuIApU6Zg5cqViI+PByAy16+88gp2796NkpISJCUl4fLLL8e9996LqKgou9tmMpkwbdo01NTUYPXq1Zg2bZrlvpUrV+Lf//43QkJC8M477+Chhx5CTk4Oxo0bh//+97/Q6XRYt24dVqxYAQD461//ihdeeAFFRUX45ptvcODAAbz44os4f/48jEYjEhMTMWLECLz00kuW13jvvffwhz/8ASNGjMCmTZt890snIiLqAlzTTkRE1Iu89tprSExMRN++ffH999/jrrvuwpNPPonJkydDo9Fg9+7dePbZZwEAzc3NWL58OVavXo2IiAhcddVVCAsLwxtvvIE77rgDZrPZ7mvk5uZaKgKUiwWKhx56CCNGjEBLSwtuuOEG5OTkIDw8HE8//TR0OtsCwD/+8Y+YNGkS5s2bB41Gg/vvvx/Hjx/HzJkzcdVVV2HQoEH45ptvrH5Gec3vv/8eLS0tXv/OiIiIuhLL44mIiHqR5cuX45FHHsHHH3+M//u//0NdXR2ef/55zJ8/H5GRkfj3v/+NI0eOAAC2b9+OnJwcaDQaTJgwAVqtFqNHj8bp06dx5MgRHDhwwG45e3V1teW/+/TpY3WfwWDAM888g2uuuQbNzc0AgEceeQQDBgywu73PPPMM5syZAwBobGyEyWSCVqvF7NmzkZGRgYEDB8JgMFj9jPKasiyjuroaycnJnv2yiIiIggCDdiIiol4kIyMDABAdHW1zm1Lu3tDQAAAoKioCAJjNZrsd4JX7O+v43HV1dYiNjbW6f+jQoZg3bx42bdqElJQUXH311Q63d/r06Zb/Dg8Px8qVK/Hyyy9byue1Wi1mzpyJZ555BpGRkQCA2tpay8/ExMQ4fG4iIqLugEE7ERFRL6LVagHAqqGbcltnqampAAC9Xo9PP/0UiYmJlvvOnz+PQYMG2f25gQMHok+fPqitrcWZM2fsZuOV13T02oqQkBCr/1+4cCGuuuoq1NfX49y5c/jtb3+Lzz77DBs3bsRPfvITAKIsHgCGDx9u8/NERETdDYN2IiIismvevHlYtWoV8vLycM011+DSSy9FS0sLsrOzceLECZw+fdruz2m1Wlx++eVYt24dvvzyS69GuHU2depUTJw4EampqTAajZZsf8fs/hdffAEAWLBggc9el4iIqKuwER0RERHZFRYWhrVr1+LWW29FWFgYNm3ahM8//xxarRY///nPnf7s7bffDq1Wi/Xr16Otrc1n2zR79mzk5uZi8+bN2LJlC1JSUvCLX/wCixcvBiC63e/atQsRERG4/vrrffa6REREXYUj34iIiMgvnnzySaxZswaPPfYYbrjhhoC85t/+9je8/vrrWLFiBW699daAvCYREZE/MWgnIiIiIiIiClIsjyciIiIiIiIKUgzaiYiIiIiIiIIUg3YiIiIiIiKiIMWgnYiIiIiIiChIMWgnIiIiIiIiClIM2omIiIiIiIiCFIN2IiIiIiIioiCl6+oNCDaff/45jh8/DkmSIEkSrrzySvTt27erN8ti9+7dOHjwIMLDwwEAQ4cOxeWXX67qZzds2IDhw4dj1KhRXm/H4cOHUVRUhEWLFtncvmPHDkRFRcFoNGLixImYNm2a16/nD21tbdi8eTNKS0shyzJCQ0Nx4403wmAwuPU8e/bswSWXXOLz7auursaLL76IhIQEGI1GGAwGTJ48GRMmTHD6cyUlJairq8OwYcPcfs2vv/4an3zyCR566CGEhoa69bMrV67E7373O5vbDxw4AL1ej/Hjx7u9Pe7605/+hKSkJJjNZiQmJuKqq66CXq+3+9jq6mrk5+dj7NixABzv011Jze8uJycHWq0W/fr1C+CW+d8777yDa665xmY/3L17NwwGA6ZPn676uRztm95avXo15s2bh7S0NJ8/txrPPfccoqOjcdttt1lue+WVV2A2m3HvvfciJycHe/fuxciRI/HNN98AAMrLy5GQkABJkmy+P5TH33DDDVavs2nTJkybNg2JiYmBeWNERERkhUF7B/n5+Thz5gzuuusu6HQ6NDY2wmQyef28ZrMZGo3vihqmTp3q1gmrpzzd7tGjR2PRokVobGzEqlWrMGrUKERHRwfN9im+/vprRERE4J577gEAXLhwwaPn81fQDgCxsbH42c9+BgCoqqrC2rVrIcsyMjMzHf5MSUkJioqKPArajx8/jvT0dJw6dcrua3jyO580aZLb2+EpnU6Hu+++GwCwbt06HDhwwOFFo+rqahw7dswStAcbs9ms6neXk5MDg8Hg06BdlmUAgCRJPnk+T/abn/zkJz557WDi6+8CAGhpaUFNTQ2io6NRXl5u9zGZmZmWz/Nzzz2HW265xXLhV42lS5f6ZFuJiIjIMwzaO6ivr0d4eDh0OvFr6XhSc+7cOezYsQNmsxlpaWlYvHgxdDodnnvuOdx1110IDw9HUVERtm/fjltvvRW7d+9GVVUVqqqqEB0djfnz5+Ojjz5CVVUVAGDx4sXo168fjh49im+++QYmkwnp6elYvHix2yd1ZrMZn3zyCXJzc2E0GjF58mRMmjQJsixj69atOHfuHPr06QOtVmv5GWVbW1tbER4ejqysLERFRWH16tVISUlBXl4exowZg/j4eOzZswcmkwlhYWG4+uqrERkZqWq7wsPDERcXh/r6ekRHR9t9r4DI4hQVFUGSJEyYMAHTpk1DZWUltmzZgsbGRuj1eixZsgQJCQnYsGEDdDodSkpK0K9fP5w6dQp33323JRv3wgsv4LbbboMkSfjoo49QU1MDAJg/fz769+9v8/fueDEhISEBAPDpp58iLCwMU6dOBQDs3LkTERERGD16NN5//320tLTAbDZj8eLFOHPmDIxGI1555RUkJSXh6quvdvg3XblyJSZNmoSzZ88iMjISl112GXbs2IGamhosWLAAI0aMcPr7jI2Nxfz587F9+3ZkZmaisLAQ27Ztg9FohE6nQ1ZWFmJjY/Hpp5/CaDQiLy8PM2fORGxsrM3jlPfaUWVlJVpbW7Fo0SLs2bPHcpJ/+PBhnDp1Cq2trZBlGTfccAO2bt2KoqIiAMCsWbMs1Rs7d+7EmTNnoNPpsHz5ckRGRloyo8OHD8f69evx05/+FIAImv/73//innvucbg/fvPNNzhw4AA0Gg0SExNx7bXXqtr3AKB///4oLS11+Pc8fvw4Lly4gFdeeQXjx49HWFgY6urq8Pbbb6OqqgoZGRm44oorAADHjh3DF198AVmWMWzYMMvtK1euxMUXX2zznjvKycnBtm3bAIgg+NZbb0VISAi++OILHDt2zCrj2fnz19raaskqr169GsnJycjNzYXZbEZWVhYiIiIsv5+jR49i4cKFqK+vx2effQaNRoOQkBCrLCwAtLa24n//+x+amppgNpsxZ84cZGRkoLq6Gm+//TbS09NRXFyMG264ASdOnMDJkydhNBqRkZGBOXPm2PyeV65ciYsuugjnzp1DZGQkrrnmGkRERNi8l5SUFJtjaE5ODg4dOoQf//jHlt+VkunteGz9/PPPceTIEURERCA6OhqpqamWfdbecaKqqgrr1q1Da2urw89VdXU1/vOf/+Dee+8FAOzduxetra2YPXs2Vq9ejfT0dOTk5KC5uRlLly7FgAED0NbWho0bN6K0tNRSAaPIzs7G7t27YTQaERcXh6ysLBgMBjz33HMYPXo0zp07hxkzZmDMmDGWnzl9+rTd4+vu3btRU1ODqqoq1NTUYOrUqbj44ovtvo/Ro0fjxIkTmD59Oo4fP44xY8bg6NGjdh/rqY4VBWfPnsXOnTshyzLCw8Nx8803o7W1FVu3bkVZWRnMZjNmzZqFjIwMHD58GKdPn0ZbW5vVZ8psNrt13CciIurtGLR3MGTIEHz22Wd44YUXMHjwYIwePRoDBw6E0WjExo0bcfPNNyM+Ph7r16/HgQMHLEGAI+Xl5bjtttug1+vx/vvvY8CAAVi2bBnMZjNaW1tRXl6OEydO4Pbbb4dWq8VHH32EY8eOYfz48di0aRMmTZpkt+zy66+/tpyUXX755aipqUFoaCh++tOfwmg04o033sCQIUNQUlKCiooK3HvvvWhoaMCLL76IzMxMmEwmbN26FcuXL7cEL7t27UJWVhYAwGQy4a677gIANDU14Y477oAkSTh48CC+/PJLzJ8/X9Xvs6amBkajEcnJyQ7fa2JiIurq6iwnzs3NzQCADz/8EIsXL0Z8fDwKCgrw0Ucf4ZZbbgEA1NbW4vbbb4dGo4HZbLZkhQsKChAdHY3IyEh88MEHmDp1Kvr374+amhq8/fbb+PnPf261fZmZmVizZg1OnTqFQYMGYfz48YiPj0dmZibWrl2LqVOnQpZlnDhxAnfeeScOHz6MIUOG4NJLL4XZbEZbWxsGDBiAffv2WbK7zv6mbW1tGDRoEObNm4e1a9di165duOmmm1BeXo4NGza4DNoBIDU1FRcuXAAgLjLcdttt0Gg0OHfuHHbt2oXrrrsOc+bMsSrzbmlpsfu4zo4fP47Ro0djwIAB2LBhA+rr6y0BaHFxMe655x6EhYVhx44dCAkJsVQoNDU1ARDLDfr27Wu5GHHw4EFceumlludPSEiAyWRCVVUVYmNjLa/nbH/84osvcP/990On01n2DTXMZjPOnj2LIUOGYNiwYXb/nikpKValwIcPH0ZJSQl+9rOfQafTYdWqVZgyZQo0Gg0++eQT3HXXXQgLC8OaNWvw3XffISMjw+V7BoCvvvoKixYtQv/+/dHa2gqdToczZ87g9OnTuPPOO6HX6y2/Q8D687d7926r52pra8Pdd9+N3NxcbNy4Effeey8mTZpkVS7+8ssv48Ybb0SfPn3s/s50Oh2WLVuGkJAQNDY24l//+pdl36uoqMBVV12Fvn37Ijs7G5WVlbjzzjsBAP/973+Rm5uLAQMG2GxTWloaFixYgM8++wyfffaZZd9T3ovRaMQLL7xgcwydMmUKNm/ebLk4oQSdHRUVFeHEiRO4++67YTab8eqrr1qCdkfHiW3btmHSpEkYP3489u3bp3q/6chsNuOnP/0pzpw5g88++ww333yzZbnCz3/+c5SWluLVV18FADQ2NuLzzz/HTTfdBIPBgC+++AJfffUVZs2aBUBcwFQqZjrq37+/w+PrhQsXcMstt6C1tRWrVq3CpEmTrC68KkaOHImNGzdi+vTp+P777y0XDv2hoaEBmzdvxq233orY2FjLfrtnzx4MGjQIWVlZaG5uxj//+U8MHjwYAOx+phoaGtw+7hMREfVmDNo7MBgMuOuuu5CXl4fz58/j/fffx+WXX46UlBTExMQgPj4eADB+/Hjs37/fZdA+fPhwy3ra8+fP46qrrgIAaDQahIaG4ujRoygqKsI///lPAIDRaERERAQA5+WIncvj3333XZSWluLkyZMARJBWWVmJ3NxcjBkzBhqNBlFRURg0aBAAcWJeVlaGNWvWABClsB2zg6NHj7b8d21tLd5//33U19fDZDIhJibG5e/xxIkTyM3NxYULF7Bo0SLodDqcP3/e7nsdPnw4qqqqsGXLFgwfPhxDhgxBa2sr8vPz8d5771mes+MyhVGjRlmqEcaMGYPPPvsMmZmZliAQEJURHUtFW1paLIGBIiUlBffffz+ys7Nx7tw5/POf/8Qdd9yBxMREhIeHo7i4GA0NDUhJSUF4eDjS09OxceNGmM1mZGRkICUlxea9O3qfAKDVajF06FAAQFJSErRaLbRaLZKTk1FdXe3y9wq0ly0D4kR3w4YNqKiogCRJDpdyqH3c8ePHsWzZMkiShJEjR+LkyZOYMmUKAHFBKywszPIer7nmGsvPKbdrtVoMHz4cgLi4cO7cOZvXULKCM2fOxIkTJ3Dttdc63R+Tk5Oxbt06ZGRkICMjw+XvR6l6AERAdNFFF0Gr1dr9e9ozaNAgS9VGYmIiampq0NjYiIEDB1r+jmPHjkVubi4yMjJUved+/fph+/btGDt2LEaOHIk+ffrg3LlzmDBhguX4oPwOld+RI0op/4ABA9DS0mI3KO/Xrx82btyIUaNGYeTIkTb3y7KMnTt3Ijc3F5Ikoa6uDg0NDQCAmJgYSw+P7OxsZGdnWwLT1tZWVFRU2ATtkiRZAu1x48Zh7dq1Nu/lwoULDo+hQ4cOxffff49Ro0bhzJkzlioGRV5eHjIyMiy/K+UCg7PjRH5+vuXC1Pjx4/HJJ584/J06ovzuUlNTLZ/P3NxcS8Y7OTkZycnJAICCggKUl5fjjTfesGxHx14ojv6mzo6vw4YNg06ng06nQ0REBBoaGtCnTx+b5wgPD0dYWBiOHz+OhIQEhz0cfKGgoAADBgxAbGwsgPb9Njs7G6dPn8bevXsBiM+hUuVk7zOVmJjo9nGfiIioN2PQ3olGo8HAgQMxcOBAJCcn48iRI3aDs46PVwKpjqWSAFw2NJNlGePHj1fdSM6ZhQsXWgJCxZkzZxy+blJSEu644w6793fc7q1bt2LatGkYMWIEcnJybDJ/9ihr2ouKirBmzRqMGDHC6Xu9++67cfbsWRw4cAAnTpzAggULEBoaasleO9u+vn37orKyEg0NDTh9+rQlyynLMu68807LUgdHDAYDRo4ciZEjR0KSJJw5cwaJiYnIzMzE4cOH0dDQYCkTHzBgAG677TZ8//332LBhA6ZNm2bTIMzZ+9RoNJY1wpIkWbZNkiSYzWan26koKSmxKuMfOHAgli1bhurqaqxevdruz6h5XGlpKSorKy2Bs8lkQmxsrCVoVxMIdHx/ShVEZ6NHj8Z7771n+X3Hx8ejtLTU4f54ww03IDc3F99//z327NmDe+65x+nykY5r2juy9/d09PMKNX8XNe955syZGDZsGM6cOYM33ngDN954o9PndLcRYmdXXnklCgoKcObMGbz22muWEnPFsWPH0NjYiLvuugtarRbPPfec5djV8e8syzJmzpzpdk+Cjuvg1byXMWPGYN++fQgLC0NaWhpCQkJUvY7SPNLRccLVevyOx27A9vit7AuO/q6dt2XIkCFWF7M6cvR7cHZ8dWdfHD16NLZs2WKpluoK1113nU0pe2Fhod33ERYW5vZxn4iIqDfjyLcOLly4gIqKCsv/l5SUIDo6GgkJCaiurkZlZSUA4OjRo5ZsU0xMjGVtr5LptmfQoEE4cOAAAFF22dzcjMGDB+PUqVOWLFdTU5PqjGtHQ4YMwYEDByxZiYqKCrS2tmLAgAE4ceIEzGYz6urqkJOTA0CUKTc0NCA/Px+ACNDKysrsPndLSwuioqIAAEeOHHFru9LS0jBu3Dh8/fXXDt9rY2MjZFnGqFGjMHfuXBQXFyMkJAQxMTE4ceIEAHFCXFJSYvc1JElCRkYGtm/fjoSEBEtwMmTIEEu3ZAB2fz4vL89S3mkymVBeXm7JdI0cORLZ2dkoLCzEkCFDAIg1sBEREZg4cSIuuugiFBcXAxAZZuV376u/qT3V1dXYvn27JZDu+Lc5fPiw5XEGgwGtra2W/3f0uI6OHz+OWbNm4Ze//CV++ctf4sEHH0RdXZ3dbR88eDD2799v+f+Opd2uxMXFQaPR4PPPP7dkHx3tj7Iso7a2FoMGDcLll1+O5uZmq/flDnt/z86/J0fS09ORm5uLxsZGmM1mHD9+3Cbb7ExlZSWSk5Mxc+ZMpKWl4cKFCxgyZAgOHz6MtrY2AOp/h8pnIi8vD6GhoQgNDYXBYEBLS4vV6/Xt2xdz5sxBREQEamtrrZ6jubkZ4eHh0Gq1OH/+vCUj2tnQoUNx+PBhy++otrbWsl93JMuy5dh37Ngxuw3xnB1DBwwYgOLiYhw8eNCmNF65/7vvvkNbWxtaWlrw/fffA4DT40S/fv1w/Phxy2vZo2SvGxsbYTQaLc/rzIABA3Ds2DEAQFlZGUpLSwGIi4d5eXmW96dUJbjizfG1o4yMDEyfPt3mwq2v9e3bF7m5uZbeLMp+O2TIEOzbt89yEUQ5Njri7XGfiIiot2GmvQOlmU5zczM0Gg3i4uKwZMkSS/Ou9957z9JESck+zZo1C5s2bbJkMx1ZsGABPvzwQxw6dAiSJFka0c2ZMwdr1qyBLMvQarVYtGgRYmJinK5p7+yiiy5CdXU1XnvtNciyjIiICCxbtgwZGRn/v727eUlti8M4/ui2MkQKAsMmKb0JRoMIhDCFnCQ4KKFm/Q39Jw11714GTQqMJAda0KwQHDRo4MSRTXIiJDRrUN5BuG+d9NzOuRyO597vZya4xc1ae7l/rrWfpXq9rkwmo5GREXu5pmEY2tra0sXFhR2qFolE5PP5Pn12PB7X6emphoeHFQgE7Ju1r4pGo7IsSysrK13P1eVyqVAo2Dd7iURCkpROp1UsFu2Qpk6QVTfz8/Pa39//MMuUTCZVKpWUzWb1+vqqyclJpVKpD8e1Wi0Vi0VJsgPGOktiDcNQIBCQ2+22Z3Y7IVmGYWhwcNB+3GFxcVGmacrv9yudTvds05/RarVkWZa95VskErG3fFteXtb5+blubm4+JMUHg0GVy2WZpqloNNrzfe9Vq9VPad2hUEjVavVTsFosFlOpVFImk5HT6VQ8Hu+6DLuXcDisq6sr7ezsSOrdH8fGxpTP5/X8/Kx2u61IJCK3261Go6Hb29sfSrTu1p7j4+NyOBwfgui68Xq9SiQSOjo6svvJV5bqd1QqFd3f38vhcMjn82l6etoOU9zb25NhGJqZmbH7/ve4XC5ZlqWXlxe7v8/NzSmXy6lWqymZTKpSqejx8VHtdlvBYNBewt2xsLCgk5MTZbNZTUxM9Az6mpqaUrPZ1OHhoaS3Pzk2NjbsxwQ6BgYG9PDwoOvra3k8nq5hgd8bQ51Op2ZnZ3V3d2dfU+/5/X6Fw2GZpimPx/NhTOw1TqytrSmfz6tcLvfMijAMQ/F4XAcHB/J6vV8KPFtaWlKhULC3Yux8F4/Ho/X1dZ2dndkz9qurq/bjAL382/G1Y2hoSNFo9KeO/Va9Xtfu7q79uhMSKL2dZyqVUi6Xs39rtre3FYvFdHl5KdM01W63NTo6+mnbuPeenp5+aNyv1WpqNBpdgxABAPg/cLTfrw8EIOmtiLcsS5ubm/94443+919oz9+9J3gvv2oPdAAAALxhph34RrPZ1PHxsUKh0B9b4OFvtCcAAAD+ZMy0AwAAAADQpwiiAwAAAACgT1G0AwAAAADQpyjaAQAAAADoUxTtAAAAAAD0KYp2AAAAAAD6FEU7AAAAAAB9iqIdAAAAAIA+9RfVhj6FmwqBUgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1200x440 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"time: 456 ms (started: 2021-02-18 14:54:28 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "KA0Huk8h_cX-",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6b69faa0-b3ae-493e-e74a-fa24f21b7e3b"
},
"source": [
"######################################################################################################################################################################################################################################################"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 908 µs (started: 2021-02-18 14:54:28 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "s6f4tSTPi3Ix"
},
"source": [
"#### Percent Change in in Net Sentiment\r\n",
"*Calculate and plot the first derivative of the net sentiment*"
]
},
{
"cell_type": "code",
"metadata": {
"id": "JRz4dognmIh5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 477
},
"outputId": "fba1ff38-e40f-46c0-aaa7-8bb3a3f890ad"
},
"source": [
"net_sentiment_deriv_1 = (NetSentiment.shift(1) - NetSentiment) / NetSentiment\n",
"\n",
"# Initialize figure container\n",
"fig, ax = plt.subplots(nrows=1, ncols=1,)\n",
"\n",
"# Supertitle\n",
"plt.suptitle('Change in Net Sentiment over time ' + '({})'.format(doc_type), horizontalalignment = 'left', x = fig.subplotpars.left, y = 0.99, fontsize = font_size_title, fontweight = \"bold\")\n",
"\n",
"# Main plots\n",
"ax.plot(data_df.index, net_sentiment_deriv_1, c = 'blue', label = 'Sentiment Change')\n",
"ax.legend(prop = {'size': font_size_legend}, loc=\"best\", borderaxespad=0, frameon=0)\n",
"ax.set_ylabel('Change in Net Sentiment (%)', fontweight = \"bold\")\n",
"ax.set_xlabel('Time (Yrs)', fontweight = \"bold\")\n",
"\n",
"# Title\n",
"plt.title('The first derivative of the Net Sentiment provides us with a granular view of its movements', loc = title_loc, y = 1.0, fontsize = font_size_subtitle)\n",
"# Source\n",
"ax.text(x=0.0, y=-0.15, s='Source: Federal Reserve System Data Archives. Python scripts are provided under an MIT License.', fontsize = font_size_notes, c = \"grey\", transform=ax.transAxes)\n",
"\n",
"# Formatting\n",
"datemin = np.datetime64(data_df.index[0], 'Y')\n",
"datemax = np.datetime64(data_df.index[-1], 'Y') + np.timedelta64(1, 'Y')\n",
"ax.set_xlim(datemin, datemax)\n",
"ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"\n",
"# Grid\n",
"ax.grid(color = 'black', alpha = 0.5, linestyle = '-.', linewidth=0.2)\n",
"\n",
"# Finalize\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"\n",
"# Show inline\n",
"plt.show()\n",
"\n",
"# Save Graphics\n",
"fig.savefig(graph_dir + 'sentiment_pct_change_' + doc_type + '.png')\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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FwcnJCdu2bZN7yOvVq4fPPvsMgDQ7Qp06dXDo0CHodDps27YNw4YNS7Ad7969k//OkSNHgutWqlRJTt+/f/8+Zs6cCUDa7//8809kz54defLkwaFDhwAk7biT2P6WFNWqVTOZiixPnjxxvjNr95+Y2SPFihWDh4cHcufODQDxZmTF/HxjZjQRERFR2spyQXnstO7g4GCrn6NSpUpyr4NCoUD27NkRGRkJAHLP1+vXr9GxY8dET3Ti6ymL2VMVc5xfaGio/HfMk8KYvZW5cuVCiRIlcPPmTZPnvH//vvx3VFQUevbsafa1X758iQ8fPiS7qrqTk5MckAOm7yO+950QtVqNQYMGwdvbG+fPn5cD/NjS+n0WLlwYO3fulNN0L1++jHv37slBSnh4OCZOnIjdu3fHad/GjRvlICe2e/fumV0ec9uIeVEp5raRUozjtwHTE/hy5crJww/ia0PMixTz58/H/Pnzzb7G3bt3zQbl8e0DSdl2YotZxLF8+fImQyli7ksx1+vYsaOcqr5nzx60bt1a/k6N9xsl9ztOjzTimJ9DzO86e/bsckBoDPKMQkND4eTkZPJ+jxw5giNHjph9jfjeb3yM6efxiW/7LF68OLJnzw4g+ftIWu5vMVm7/3Tq1An//vsv9Ho9hg8fDkD6TDw8PNCwYUN07doV9vb2Jo9N7PMlIiKitJHl0tcdHBxMUvbOnDlj9XPE7r2J2StpPMnx9fWVA/K8efNi6tSpWLt2LdatW2dyYhdz/GVMxhNKAPGOvTaOXTUnuSdbxvTc5LDkc7JW27ZtUbx4cQDAnDlzktw2o5R4n4D03po0aYLJkydj586dOHXqlEmqr7HwUkq0zZJtI6XEvGARcx7jlJwGL773GXP7SYltJ7lq1aolTyd28uRJBAUFyZX1c+bMmaTq1fG997x58ya9oUmUlO/a2u/Ckv0tZuCfWOCbFtuntcfi2MMxknLh1xrGz7RmzZrw9fVFjx49UKVKFeTKlQuhoaE4d+4cZs6caTbzIubnm9azaRAREdFHWa6nHJDGehsDulOnTuHgwYNme6bu3buHUqVKJek1Xrx4If/dpk0beY7gFy9emKTcJkfRokXl8ZfXrl1DgwYNAEgngY8ePYqzvpubm/x39uzZcfz48Tg9J4B0kufg4JAibUxpSqUSQ4cOxY8//iinoceWlPcZ84Q+vgsl5hgMBhw5cgReXl4mJ+a5c+dG165dTXpIjQGMm5ub3As2fPhwfPfdd3GeV6PRJHjRJTEx34+xnbGXpTY3NzfcuHEDAPDHH3+gVatWcdZJ7raW1O/NeGEHkPYdvV4vB1zGVHwAJrMEKBQKtGvXDgsWLIBWq8W4cePkXvtWrVqZpJAn9zu29ruPub41n0NKcXNzw/HjxwFIQ0QmT54cZx2DwSAXx0xIzAKbyUkRT0sxLwK8evVK/vvcuXPxXohI7Duzdv8RRRFlypTBuHHj5PufPHmCdu3ayUMuIiMjTY6FMX8n4itsSkRERKkvSwblPXv2xN69e+WAdtiwYfjqq69Qv3592Nvb4/nz5/Dz88O9e/fg5+eXpNeIORXT/v37UalSJRgMBsyfPz/FevqaNWsmv4dly5YhZ86cKFiwIFasWBFnPDkgjcEtV64crl+/jvfv38tVtPPly4c3b97g6dOnOHXqFIoWLYrp06enSBtTQ4sWLbBo0aJ4CxMl5X3GTI8OCgrC9u3bUahQIdjZ2cnjgc0xGAz4/vvvUahQITRt2hTly5dHnjx58O7dO6xatUper1SpUvKYzw4dOshFmXx8fBAdHS1vHy9evMCNGzfg5+eHLVu2JDqlV3xy5MgBQRDkbW3lypWoUKECBEGQx7antg4dOshBxeTJk/Hy5UuUKVMGUVFRCAwMxKVLl3DkyBGTINhaSf3eWrRogT///BNRUVEICAjAiBEj0L59ezx8+BArV66U12vbtm2c97Rw4UKIoigHoQDiFFRLi+84ppw5c8rFHbdv3w6FQgGlUonSpUunSKX6xLRr1w6rVq2CwWDApk2bYGtrizp16kCpVCIwMBB37tyBn58fZsyYYZIObk7MqdOuXbuW2k1PETGzr/z9/TFz5kzkyZMHy5Yti/cxMTOmzp8/j6NHj8LJyQn58uVD0aJFrd5/pk2bhmfPnqFOnTooUKAAnJyccPPmTfm3QBRFaDQak6DcWHtDEIQsPWc7ERFResuSQbmDgwOWLl2KoUOH4vLly9DpdPjnn3/wzz//mKzn6uqa5Ndo164dlixZgg8fPiAgIABDhw4FAJQsWRJ58uRJkaI6ffr0wd69e/HgwQNERETg119/BSD12ri6uspz48Y0a9Ys9OjRA0FBQbh06RIuXboUZ50iRYoku22pSRAEDB06FIMGDYp3HWvfZ4kSJeDi4oKXL19Cr9dj9OjR8jrGFOWEPH/+PM70V0YqlQo//fSTfLtRo0bo06cPli9fDo1GE+9Y0eQwzg9tfN8zZswAIGUaxK41kFq+/vprnD9/Hv/++y9CQ0Px+++/p/hrJPV7c3Z2xq+//ipPifbvv//KVdSNGjVqZDK7AiDVD6hevbrJ/OhlypSJU9E+Lb7jmOrUqSNnjmzfvh3bt28HIFX5Totgq3Tp0vD29sb06dNhMBiwevVqrF69OknP5enpiWLFiuHx48fw9/eP07ubEdWtW1dusyiKcjCeP39+ZM+e3WwdhM8++0yeTjEgIEDOpvjyyy8xdepUq/ef6OjoBMfze3l5mQwJefr0qTwTSe3atePUCyAiIqK0k+XGlBvlz58f69evx+zZs9G4cWPkz58fNjY2yJYtG0qVKoUuXbokK4hwdnbG2rVr5aq4OXPmRJs2bbB69WrY2dmlyHtwcHDAmjVr0K5dO2TPnh0ODg6oU6cO1q9fbzIOMuYJbfHixbFz504MGDAAHh4ecHBwgK2tLVxdXVGrVi14e3vLFxAysiZNmiQ4h7G171OpVMLHxwfVq1e3KgBQqVRYunQp+vXrhypVqsDV1RX29vZQq9VwdXVFq1atsGnTJtSvX9/kcaNHj8bff/+Nxo0bI1++fFCpVMiZMyfc3d3RuXNnLFmyRK7knVQzZ85EgwYN4OjomKznSSqlUok5c+bgjz/+QJ06dZAzZ06oVCrkyZMHnp6e6NmzZ5IDt5ivkZTvDZCGlWzcuBGtWrWCs7MzVCoVnJycULlyZUyePBkLFiwwO4Y4dq94fNOOpcV3bDRw4EB07twZefLkSdawh+To0aMH/vnnH3zxxRdwcXGBWq1G9uzZ4ebmhjZt2uCvv/6KM0+2OYIg4OuvvwYgpWfHF2RmJEqlEgsWLEDt2rVhZ2eHHDlyoHXr1ti8eXO849vd3NwwY8YMlCpVSp4uMvZzWrP/tGrVCl999RXc3d2RM2dOKJVKODg4oGzZshg6dGicGhwx5z83ft5ERESUPgSR5VczNVEU45yEv3v3Do0aNZLTFnfs2CFP00ZElNFFRESgWbNmCAoKQvXq1eX54ill6HQ6NG3aFAEBAShTpgy2bt2a5jUniIiI6CP+CmdygwcPxpo1a3D9+nUEBgbi9OnT+P777+WA3MPDw+w86EREGZWDg4OcyeLv75+sugMU1549e+ThTaNHj2ZATkRElM7YU57JtW3bFrdv3zZ7X+7cubFy5UoG5URERERERBlUliz09ilp3749Dhw4gMePH+P9+/dQq9UoWrQo6tevj549e3LuWSIiIiIiogyMPeVERERERERE6YQDyYiIiIiIiIjSCYNyIiIiIiIionTCoJyIiIiIiIgonTAoJyIiIiIiIkonDMrNOHv2LDw8PPD+/fsUf+558+ahdu3a8PDwwKFDhzBmzBh8//33Kf46ljK2I7m8vLywcuXK5DcoEen9eRk9ePAAnTp1Qvny5dG2bVuLH5ea21ZWsnXrVlSrVi29m5FpdO/eHVOnTk1wnbTah9ODpdtLSh0PM7vnz5/Dw8MDt27dSvPXzqjHyNi/3bFl1HYTEVHmkOWmREtszu7BgwejRo0aqfLaDx48gI+PD+bPn4+KFSsiR44cqFmzJpJbAN/Lyws9evRAr169UqahSbBlyxbY29un2PM9f/4cjRs3xvbt21GmTBl5+bhx45L9eaWEefPmwd7eHvv27YODg4PZdbp3747SpUtj3LhxadIeHx8fdO7cGZMnT5aX37p1C+3atYOfnx8KFSpk0XNZ2u5nz55hzpw5OHv2LEJDQ5ErVy54enpi5MiRcHNzS9b7icnc9t2yZUs0aNAgxV4jOdLye06qefPmQaXKcod7WeztZd68eTh06BB27NiRjq0icypXroyTJ08iW7Zs6d0Umbnf7thit3vr1q2YNm0azp8/n9bN/eRlhHMeIqKUluXO0k6ePCn/vXfvXsydOxf79u2Tlzk4OOD69eup8tpPnz4FADRu3BiCIAAAbGxsEnyMRqNJdJ30ZGxf7ty50+T1MsqJ2tOnT9GwYUO4urqmd1Nktra28PX1RZ8+fVCsWLFUfS2tVos+ffqgePHi8PHxQb58+fDy5UscP34cHz58SNXXBgA7OzvY2dml+uukN61WC7VaneznyZkzZ/Ibk4l9CttLSm0LaSWpv102NjbIly9fKrQo6cz9dseWEdtNRESZR5ZLX8+XL5/8L1u2bBAEwWSZo6OjvO6NGzfQoUMHVKxYEV26dMHDhw9NnuvQoUNo3749ypcvj8aNG8PHxwc6nc7s686bNw8DBgwAAJQuXVrusY+djt29e3dMnjwZU6dORc2aNdG3b1+Iooh58+ahYcOGKFeuHOrWrYspU6bI6wcEBGD69Onw8PBIMBPg8ePH+Oabb1C+fHm0bNkSp06dirNOYGAgfvjhB1SrVg01atTAwIED8fz5c/l+Y3sXLlyIunXronnz5gBMU19HjBiBYcOGmTyvVqtFzZo1sX37dgDA8ePH8fXXX6NatWqoWbMmvvvuO/nEB5BOfgCgXbt28PDwQPfu3eN8Xhs3bkTdunVhMBhMXmvgwIHw9vaWb1vzPQGAwWCAj48P6tevj3LlyqFt27Y4fvy4fL+Hhwdu3LiB+fPnw8PDA/PmzYvzHGPGjMG5c+ewevVq+XuJ+Tmm5LZlVLx4cdSsWROzZ89OcL27d++iX79+qFy5MmrXro1Ro0bh3bt3FrXb6P79+3j69CkmTpyISpUqwdXVFVWrVsWPP/6ISpUqyetZuj0tW7YMdevWRc2aNfHLL79Aq9UCiH/7jp2OPG/ePLRt2xZbtmxBw4YNUblyZUyaNAl6vR5///036tSpg1q1amHhwoUm7+P9+/cYN24cPvvsM1SpUgU9evTA7du34zzv9u3b4eXlJb/HsLAwqz4vQNpH5s+fj+HDh6NSpUqoV68e1q1bZ7KOh4cH1q9fjwEDBqBSpUpYtGgRAGD9+vVo0qQJypUrh2bNmsn7EWDZ/hY7ff3t27cYMGAAKlSoAC8vL+zcuTNOexP7bG7fvo3u3bujcuXKqFKlCjp06IBr166Zfe/m0qHfv38PDw8PnD17FgAQGhqKESNG4LPPPkOFChXQtGlT+Pr6mn2+I0eOoFq1atDr9QCkjBAPDw/MmjVLXmfcuHEYOXIkANPtZevWrfDx8cHt27fl72zr1q3y44KDgzFo0CBUrFgRTZs2hZ+fn9k2GG3fvh0dOnRA5cqVUadOHYwYMQJv375N8DFBQUH49ttv5c9/165dcYYPmNsW9Ho9xo4dCy8vL1SoUAHNmjXDqlWrTJ47sX3K+NyxU7CrVatm8jnEZM3rxv5tiOnRo0fw8PDAgwcPTJavXLkSTZo0AWA+Dfz8+fPo2rUrKlSogAYNGmDKlCmIiIgAAKxduxatWrWS1z106BA8PDzwzz//yMt69eqV4HHxzp076NGjBypUqICaNWtiwoQJCA8PBxD/b3dsMdt99uxZeHt748OHD/I2ZvydWLduHZo2bYry5cujdu3aGDp0aLztMm63R44cQbNmzVCxYkUMHToUkZGR2LZtG7y8vFC9enVMmTJF3hcAaV/66aefUL16dVSsWBH9+vXD48ePAQBhYWGoUKECjh07ZvJaBw8eROXKlREZGQnA8mP3okWLULt2bVSrVk3+nZoxYwZq1KiB+vXrx9mHU+s3ISAgAAMGDED16tVRqVIlfPHFF3HeIxFRRpblgnJrzJ49G2PGjIGvry+USiXGjh0r33f+/HmMHj0aPXr0wN69ezF58mRs3bpVPomOrU+fPpg+fToAqbc+Zo99bNu2bYNarcY///yDX375Bfv378fKlSvxyy+/4MCBA1iwYAHc3d0BSCcMLi4uGDp0aILPazAYMGTIEKjVamzevBm//PKLyQksIJ3I9+3bF46Ojli3bh3++ecfODg4oF+/ftBoNPJ6p0+fxqNHj7BixQosXrw4zmu1bt0aR44ckU9qjO85KipKPvGKjIxE79694evri5UrV0IQBAwaNEgOsDdv3gxAOlk7efKk2cC3efPmCAkJkU/qASAkJAQnTpxAmzZtAFj/PQHA6tWrsWLFCowePRo7d+5E3bp18f3338snNSdPnkSpUqXQp08fnDx5En369InzHOPGjUPlypXRqVMn+XspUKCAfH9KblsxjRgxAgcOHIg3OHr//j169uyJsmXLYsuWLVi6dCnevn0rB3WJtdsod+7cUCgU2L9/v8nJYEyWbk9nz57F06dPsWrVKvz222/Ytm0btm3bBsDy7RuQerOOHz+OpUuX4o8//sCWLVvw7bff4tWrV1izZg1GjhyJOXPm4MqVK/JjfvjhB7x9+xZ///03tm7dCk9PT/Ts2RMhISEmz+vn54dFixZh8eLF8Pf3x99//23V52W0bNkylC5dGtu2bcO3336LqVOnxrk45uPjg88//xy7du1Cx44dcfDgQUybNg29e/fGrl270KVLF4wdOxZnzpwBYNn+FtuYMWMQGBiI1atXY+7cuVi/fn2cQDKxz2bkyJFwcXHBli1bsHXrVvTv3z9ZPbl//fUXHjx4gL///ht79+7FpEmTkCtXLrPrVqtWDeHh4bh58yYA4Ny5c8iVKxfOnTsnr+Pv74+aNWvGeWzLli3Rp08flCpVSv7OWrZsKd/v4+ODFi1aYOfOnahfvz5Gjhxpsj3EptPp8MMPP2Dnzp2YP38+AgICMGbMmATf6+jRoxEUFIQ1a9Zg3rx52LRpk9lAPva2YDAY4OLigr/++gt79uzBoEGDMHv2bOzdu9fkcQntU0lh6esm9ttQvHhxlCtXDrt27TJZvnPnTpPAOqanT5+if//+aNq0KXbu3InZs2fjwoUL+PXXXwEA1atXx/379+ULi7G3Ba1Wi8uXL5vdFgAgIiICffv2RY4cObBlyxbMmTMH//33n/z81vx2G1WuXBljx46Fk5OT/Jg+ffrg2rVrmDp1KoYOHYp9+/Zh6dKlidY6iIqKwpo1azB79mwsXboUZ8+exeDBg3Hs2DEsWbIEM2fOxIYNG7B//375MWPGjMH169excOFCbNy4EaIo4ttvv4VWq4WTkxMaNmyI3bt3m7zOrl270KRJE9jb21t87D5z5gyCgoKwdu1ajBkzBvPmzcN3332HHDlyYNOmTejSpQsmTpyIly9fyt9Fav0mTJ48GRqNBmvXrsWuXbswcuTIeIeWERFlSGIW5uvrK1atWjXO8jNnzoju7u7if//9Jy87evSo6O7uLkZFRYmiKIo9e/YUFy1aZPK47du3i3Xq1In39Q4ePCi6u7ubLBs9erQ4cOBA+Xa3bt3Edu3amayzfPlysWnTpqJGozH7vI0aNRJXrFgR7+uKoiieOHFCLFu2rPjy5Ut52bFjx0R3d3fx4MGDcvubNWsmGgwGeZ3o6GixQoUK4okTJ+T21q5dW4yOjo63DVqtVqxZs6a4bds2+f7hw4eLw4YNi7d9b9++Fd3d3cU7d+6IoiiKz549E93d3cWbN2+arBf78xo4cKDo7e0t396wYYNYt25dUa/Xi6KYtO+pbt264sKFC02WdezYUZw0aZJ8u02bNuLcuXPjfQ5RlL7LKVOmmCxLrW1r7ty5Yps2bURRFMUff/xR7NGjhyiKonjz5k3R3d1dfPbsmSiKojh//nyxT58+Jo8NDAwU3d3dxYcPH8bbbnPWrl0rVqxYUaxcubLYvXt30cfHR3z69KlJmy3Znho1aiTqdDp5naFDh5psK+a279j77ty5c8WKFSuKHz58kJf16dNHbNSokbwtiKIoNmvWTFy8eLEoiqLo7+8vVqlSJc623KRJE3HDhg3xPu+MGTPEr776Sr5t6efVqFEjsW/fvibLhg0bJvbr10++7e7uLk6dOtVknc6dO4vjx483WTZ06FCxf//+oihatr/FbOPDhw9Fd3d38cqVK/L99+/fF93d3eXP2ZLPpnLlyuLWrVsTfd+iaH5/Dg0NFd3d3cUzZ86IoiiK3333nThmzBiLnk8URbF9+/bi0qVLRVEUxe+//15cuHCh6OnpKYaFhYkvX74U3d3dxUePHomiaH57Me4vMbm7u4uzZ8+Wb4eHh4vu7u7isWPHLG7X1atXRXd3dzEsLMzs/cbP+urVq/Kyx48fm3z+xrbE3hbM+eWXX8QhQ4bIty3Zp2Ie942qVq0q+vr6iqIY//E3sdc199sQ24oVK8QmTZrIt43b4/3790VR/HiMDA0NFUVRFMeOHStOmDDB5Dn8/f3F0qVLi1FRUaLBYBBr1Kgh/vvvv6IoimLbtm3FxYsXy8fL8+fPi56enmJERITZ9mzcuFGsXr26GB4eLi87evSoWLp0afH169eiKJr/7Y4tdrvNnV/s379frFKlisnxJCG+vr6iu7u7+OTJE3nZhAkTxIoVK5psX3369JE/o0ePHonu7u7ihQsX5PvfvXsnVqhQQdy7d6/8fipVqiR/Jh8+fBDLly8vb+fWHLtjH1+7du0q39bpdGKlSpXE3bt3W/281v4mtGrVSpw3b17iHyoRUQaV5caUWyNmmppxrNjbt29RsGBB3L59GxcvXjTpvdTr9YiOjkZkZGSyip55enqa3G7evDlWrVqFJk2aoF69emjQoAEaNWpkVeGmBw8ewMXFBfnz55eXVa5c2WSd27dv4+nTp6hSpYrJ8ujoaJPUcnd39wTHCqpUKrRo0QK7du1Cu3btEBERAT8/P/z555/yOo8fP8bcuXNx5coVBAcHy8XbAgMD5SwAS7Ru3RoTJkzApEmTYGNjg127duGLL76AQqGQ35M131NYWBiCgoLifAZVqlQxSd1NrtTctoYNG4aWLVvi5MmTyJMnj8l9t2/fxtmzZ+N894DUI1W8eHGL38M333yDtm3b4ty5c7hy5Qr27duHxYsXY+HChahTp47F21PJkiWhVCpNPo+7d+9a3A4jV1dXODk5ybfz5s0LpVIpbwvGZcYeyTt37iAiIiJOD1pUVJRJ+2I/r7Ozc6LpyfGJmdpvvB07DbhcuXImtx8+fIjOnTubLKtSpQpWr14NwLL9LaYHDx5ApVKZvI6bmxuyZ88u37bks+nduzfGjx+PHTt2oHbt2mjevDmKFCliwadg3tdff42hQ4fi5s2bqFOnDpo0aRJn24mpevXqOHfuHPr06YPz589j+PDh+Pfff3HhwgWEhobC2dk5SbUVYu6bDg4OcHJyknthzbl+/bqcDh8aGmpyLCtZsmSc9R89egSVSmVynC9atKjZ4mGxtwVASn/29fXFixcvEB0dDa1Wi9KlS5usk1L7lLWvm9hvAwB88cUXmDlzJi5fvoxKlSph165d8PT0jLc45O3bt3Hnzh2T3nVRFGEwGPD8+XO4ubnJ20Lt2rVx//59dO3aFUuXLsWDBw/g7++PcuXKxXvcfPDgATw8PEx6VatUqQKDwYBHjx4hb968ln5EiapduzYKFiwo/5bXq1cPn3/+eYLHdHt7e5P9Km/evHB1dTUZapc3b155GzXu3xUrVpTvz5UrF4oXLy4PG6hfvz7UajUOHz6ML774Avv374eTkxNq164NwPJzgZIlS8Y5vpYqVUq+rVQqkTNnTvl4mZq/CT169MCkSZNw8uRJ1K5dG02bNo2zfRIRZWQMyhMQM+g1FncxpldHRERgyJAhaNq0aZzH2draJut1Y/9AFyhQAPv27cN///2H//77D7/88guWLVuGNWvWpGjhn4iICHh6esZJawdgUsjNkgsOrVu3Rvfu3fH27VucOnUKtra2qFevnnz/gAED4OrqiilTpsDZ2RkGgwGtWrUyGfdoCS8vL4wfPx5Hjx5F+fLlcf78eZPx5Kn5PSVHam5bRYoUwVdffYU//vgjzjRYERERaNSokTzWNqakFClycnKCl5cXvLy8MGzYMPTt21cOyi3dnmJfXBIEIUkV9s09j7llxs85PDwc+fLlw5o1a+I8V8yCguYufiWlfZZKSsplYvubtSz5bIYMGYJWrVrh2LFjOH78OObOnYvZs2fj888/j/MY44l7zM8tdo2EBg0a4MiRIzh27BhOnTqFXr164ZtvvsHo0aPNtrFGjRrw9fXF7du3oVar4ebmhho1auDcuXN4//59kmfRiH1MjbnNxGZMfa5bty5mzZqFXLlyITAwEH379rX6WGZO7G1hz549mDFjBkaPHo3KlSvD0dERy5YtMxmSASS+T5nbxxKqWWHp61ry25AvXz589tln2L17NypVqoTdu3fj66+/jnf9iIgIdOnSRa4rEpNxqEiNGjWwadMmnD9/HmXLloWTkxOqVauGc+fOwd/fP9VmVLGWk5MTtm3bhnPnzuHkyZOYO3cufHx8sGXLFpOLYjFZe1yzhI2NDZo1ayZfxN69ezdatmwpP29yjt0JtS01fxO++uor1K1bF0ePHsWpU6ewZMkSjB492ux2Q0SUETEoT6KyZcvi0aNHKFq0aJq8np2dnRz8dO3aFS1atMDdu3fh6ekJtVqd6A+ym5sbXr58iaCgIDg7OwMALl++bLKOp6cn/v33X+TJk8ekZzApqlSpAhcXF+zduxfHjx9H8+bN5ZPd4OBgPHr0CFOmTJHH08WeNsa4bnzjlY1sbW3RtGlT7Nq1C0+ePEHx4sVNeqCs/Z6cnJzg7OyMixcvmpzIXbx4ERUqVLDoOWK+B2tOlIxSYtsaNGgQPv/8c+zZs8dkuaenJ/bv3w9XV9d4My2S2m5BEFCiRAlcvHhRfq2U2J6S2p7EeHp64s2bN1AqlRZPF2eONe2LHcRcuXIl0enjjJ9p+/bt5WUXL1406YVNaH8z93w6nQ7Xr1+Xt+mHDx+aFNay9LMpXrw4ihcvjl69emH48OHw9fU1G5QbT7hfv34tLzM3B3bu3LnRvn17tG/fHhs2bMDMmTPjDcqN48pXrlyJ6tWrAwBq1qyJJUuWIDQ01GytB6OU2qYePnyIkJAQjBw5Ug4QE5u9o3jx4tDpdLh586bcE/7kyROEhoYm+noXL15E5cqV8c0338jLYvYwWip37twICgqSbz9+/Fgu8JWar2vUunVr/P777/jiiy/w7NkzkzH9sZUtWxb3799P8HhYo0YNTJs2Dfv27ZOP2zVq1MDp06dx8eJF9O7dO97Hurm5Ydu2bYiIiJAvgly8eBEKhcKqzKHY1Gq12d8vlUqF2rVro3bt2hg8eDCqV6+OM2fOmL0ImxRubm7Q6XS4cuWK3CNt/M2Necxo3bo1+vTpg3v37uHMmTMmxSJT8lwgptT+TShQoAC+/vprfP311/jjjz+wadMmBuVElGmw0FsSDRo0CDt27ICPjw/u3buHBw8eYM+ePYlWvk6KrVu3YvPmzbh79y6ePXuGnTt3ws7ODgULFgQgpdf6+/vj1atX8aZZ1q5dG8WKFcOYMWNw+/ZtnD9/Pk5bW7dujVy5cmHgwIE4f/48nj17hrNnz2LKlClyoRZrtGrVChs2bMB///2H1q1by8tz5MiBnDlzYuPGjXjy5AlOnz6N3377zeSxefLkgZ2dHU6cOIE3b94kOM1W69atcfToUfj6+pq8DpC076lv375ysamHDx9i1qxZuH37Nnr06GHV+3d1dcWVK1fw/PlzvHv3zuIgICW2rbx586JXr15xejq7du2K0NBQDB8+HFevXsXTp09x4sQJeHt7yyeQlrT71q1bGDhwIPbt24f79+/jyZMn2Lx5M3x9feXK+Sm1PVmyfSdF7dq1UalSJQwaNAgnT57E8+fPcfHiRcyePTveQnnxtc/S7/nixYv4+++/8ejRI6xbtw779u1LdLvq168ftm3bhvXr1+Px48dYsWIFDh48GCfojG9/i61EiRKoV68eJk6ciCtXruD69esYP368yZRhiX02UVFRmDx5Ms6ePYuAgABcuHAB165di/cCg52dHSpVqoQlS5bgwYMHOHfuHObMmWOyzl9//YVDhw7hyZMnuHfvHo4ePZrgBYscOXLAw8MDu3btkgOxatWq4ebNm3j8+LEcqJvj6uqK58+f49atW3j37p1JkSlrFCxYEGq1GmvWrMGzZ8/g5+eHBQsWJPgYNzc31K5dGz///DOuXr2KmzdvYsKECbCzs4t3ui2jokWL4vr16zhx4gQePXqEOXPmWLWtGn322WdYt24dbt68iWvXrmHixIkJZl2l1Osaff755wgPD8ekSZNQs2ZNk2FVsfXv3x+XLl3C5MmTcevWLTx+/BiHDh3C5MmT5XU8PDyQI0cO7N69W94WatasiUOHDkGj0SQ4DKJ169awsbHBmDFjcPfuXZw5cwa//vor2rZtm6zUdVdXV0REROD06dN49+4dIiMjceTIEaxevRq3bt1CQEAAtm/fDoPBkKzgP7ZixYqhcePGmDBhAs6fP4/bt29j1KhRyJ8/v3xsBqThH3nz5sXIkSNRqFAhk3T3lD4XSOnnNfebMHXqVJw4cQLPnj3DjRs3cPbs2UQveBIRZSQMypOoXr16WLRoEU6ePIkvv/wSnTp1wsqVK1Nl3urs2bNj8+bN+Prrr9GmTRucPn0aixYtkisTDx06FAEBAWjSpAlq1apl9jkUCgV8fHwQFRWFL7/8EuPGjcOPP/5oso69vT3Wrl2LggULYvDgwWjZsiXGjRuH6OjoJF3VbtOmDe7fv4/8+fOjatWqJm2ZPXs2bty4gVatWmH69On46aefTB6rUqkwfvx4bNy4EfXq1TOZNi62zz77DDly5MCjR4/iBCNJ+Z569OiB3r1747fffkObNm1w4sQJLFiwwOrxqX369IFSqcQXX3yBWrVq4cWLFxY9LqW2rb59+8ZJf82fPz/++ecfGAwG9O3bF61bt8a0adOQLVs2OcXYknbnz58frq6umD9/Pr766iu0b98eq1evxpAhQzBw4EAAKbc9WbJ9J4UgCFiyZAmqV68Ob29vNG/eHMOHD0dAQIBVJ+PWfM+9e/fG9evX0b59eyxcuBBjxoxJNM28SZMmGDt2LJYvXy4H3tOmTYsz3ju+/c2c6dOnw9nZGd26dcOQIUPQqVMnk/oDiX02CoUCISEhGD16NJo1a4Zhw4ahfv36CU7vNG3aNOj1enTo0AHTpk2LM42bWq3Gn3/+iTZt2qBbt25QKBTxjos3ql69OvR6vRyI5cyZE25ubsiXLx9KlCgR7+OaNWuGevXqoUePHqhVq1acStSWyp07N3777Tfs27cPLVu2xN9//x1vz35MM2bMQJ48efDNN99g8ODB6NSpExwdHRMdntKlSxc0bdoUP/74Izp16oSQkBB07drV6naPHj0aBQoUwDfffIORI0eiT58+Cc7jnlKva+Tk5IRGjRrh9u3bCV5AAqRpyNasWYPHjx+ja9euaN++PebOnStnfAHS9lq1alX5f0AK1J2cnFCuXLkEh4TY29tj2bJlCAkJwZdffokffvgBtWrVwoQJE5L8/gApe6VLly4YNmwYatWqhaVLlyJbtmw4ePAgevbsiZYtW2LDhg34448/TMZhp4Tp06fD09MTAwYMQOfOnSGKIpYsWWJy4UUQBHzxxRdmv4OUPhdI6ec195tgMBgwefJktGzZEv369UOxYsUwceLEJLeViCitCWJqDo4kIiIAUv2DHj16oFevXundFMpgXr58iQYNGmDlypUpeuGJiIiIMocMOaY8OjoaP/74Ix48eABbW1vkyZMHkyZNQtGiRdG9e3cEBATIxYbat2/Pk1wiIso0Tp8+jYiICLi7u+P169f4/fff4erqmuic1URERPRpypBBOQB07twZ9evXhyAIWLt2LcaPHy+Pjx07diyaNGmSzi0kIiKynk6nw+zZs/Hs2TM4OjqicuXKmDVrVorOpkFERESZR4YMym1tbdGgQQP5dsWKFbF8+fJ0bBERUfIcPnw4vZtAGYRxjmoiIiIiIJOMKR81ahRy5MiB8ePHo3v37nj9+rU8L+2IESNQuHDhOI8JCgoymX4HkAqbuLi4mMyHSURERERERJReMnxQvmjRIhw5cgQrV66Evb09AgMDUaBAAYiiiHXr1mH9+vXYu3dvnMfNmzcPPj4+JsvKli2Lbdu2ITg4HDpdys97TERERERERBSTSqVArlyO8d6foYPyZcuWYc+ePVi5ciWyZ89udp3y5cvj+PHj8vRgRuZ6yu3s7ODm5sagnIiIiIiIiNJEYkF5hhxTDgArVqzAnj17sGLFCjkg1+l0CAkJkecQ3r9/P/LmzRsnIAcAZ2dnk3lMiYiIiIiIiDKaDNlTbpyztXDhwnB0lK4o2NjYYNWqVejWrRu0Wi0EQUCuXLng7e2N0qVLW/X87CknIiIiIiKitJCp09dTC4NyIiIiIiIiSguJBeWKNGwLERERERERZRAvX77E55/Xg0ajSe+mJMnFi+fRpk2z9G5GsmXYMeVERERERERZzbVrV7Bw4Tw8fHgfAODqWhj9+n2HWrXqJvu569athnXrtqBo0WIAABcXFxw8eCLZz5sUy5YtxtOnj/HLL9MTXO/CBX+sWrUMt2/fgkqlgqtrIbRv/yVatmydRi1NfQzKiYiIiIiIMoDw8DCMGjUMP/wwAp9/3hwGgwG3bt0AIKR309LFsWNHMGXKRAwaNBRTpsxEtmzZcOvWDaxfv5pBOREREREREaWsZ8+eQq/XoUWLVvKyihUrm6xz9uxpLFmyAAEBz1CwoCt++GGkvM7gwd+iYsXKuHr1Mm7fvoWSJUti4sSpcHEpgAED+gAA+vbtBkEQMHjwj6hR4zN89VUb+Pmdgq2tLQYP/hbly1fEtWtXcPv2TZQuXRa//vob1q5dhb17d8HJKRu8vSegSpVqAKSLCAsWzMV//52EXq9Hw4ZeGDRoGGxtbXHx4nlMmjQOPXr0xpo1K6DXG9C1a3d07doDp06dwJo1KyCKIj7/vB5y5MiJLVt2mbxPURQxd+4f6NmzD9q1+1JeXrZsOUyZMtNk3S1bNsR5DQC4ffsm5syZhcePH8LGxhb16zfC0KHDYWNjA0DKHBg1aiw2blyHt2/foE6d+vD2/hlqtRoAsHHjOvzzz1ro9Xp8800PrF+/BpMmTUWVKtUgiiI2blyH7du3IjQ0BJ6e5fDTT+Pg7Jzf6u+dY8qJiIiIiIgygMKFi8DGxgaTJo3DqVMnEBwcbHL/vXt38csv4zF06Ajs3XsYffsOgLf3SISEhMjr7Nu3Bz/+OAp79/ohT568+PvvhQCARYuWAwCWLVuLgwdPoG3bDmbbcPDgPowaNRa7dx+CwWDAt9/2RvHiJbB790G0b/8lZs6cJq87bdov0Gg0WLt2E9at24Lnz59h5cql8v2hoSEICnqFLVt2Y+bM2ViyZAGeP3+GOnXqoXv33mjY0AsHD56IE5ADwNOnT/Dq1Us0atQkwc8svtcAAIVCgUGDfsDu3YewZMlKXL58EVu2bDR5/NGjfli4cBk2bNiG69evYv/+vQAAf/8zWLNmBX7/fQ62bNmFwMAXCA39+Dn7+m7EgQP7MHu2D3btOgB399KYOHFsgm2ND4NyIiIiIiKiGKKigA8fkvcvKsr613V0dMLChcthZ2eHP/+cgbZtm2HIkO/kIHPHjq1o1aotKlasBIVCgTp16qFUKXecPn1Sfo6WLVujRImSUKvV+Pzz5rh797ZVbWjRohWKFi0GOzs71K/fEGq1Cq1atYVSqUTTps3x/PlTREREIDj4HU6ePI4ffxwFR0cnZMuWDT179sWhQwfk5xIEAf37fw+1Wo2yZcuhaNFiuHfvjkXtMAbAefPmS3C9hF7D3b00ypevCJVKBReXAmjTpj0uX75o8vju3XsjR46cyJUrN2rXrit/XgcP7kfz5q1QqpQHbG1t8e2338Ng+DiD1/btvujffyAKFCgIlUqFPn2+xe3bN/Hy5UuL3l9MTF8nIiIiIiKKoXp1R7x6lbz+SxcXA65eDbf6cUWKFMWYMRMAAC9fBmLmzKn49defsXjxCrx8+QKXLl3Azp1b5fV1Oh2qVasp386TJ4/8t62tHSIjI616/ZiPt7OzQ+7cps8HAJGREXj16iUMBgM6dPhCvl8URZPANXv2HHIquLXtyZEjJwDgzZvXcHUtFO96Cb3G06dP4OMzG7dv30JUVBT0eh1KlnSP9X7zmjz23bu3/3/dNybrOjo6wdHRSb4dGPgCEyeOhULxcby/ICjw+vUruLi4WPQejRiUExERERERxeDvHw6tNnnPESNOTDIXlwLo2LEzJk0aBwDIn98FXbv2QN++3yX/yZMpf34XKJVK7Np10CQotpQgJFy8rkiRosif3wVHj/rhm296JqmNf/zxG0qUKImJE6fA0dEJmzb9g0OH9lv02Lx58yIo6JV8Ozw8DOHhYfLt/PldMHKktzy+PjmYvk5ERERERBSDnR2QLVvy/tnZWf+6T548xvr1q/Hy5UuIoojg4HfYvXs7ypUrDwBo06YDdu7chitXLsNgMCA6OgoXL543CR4Tkjt3HgQEPLe+YWbkyZMXtWrVxV9/zcL79+8hiiJevXqJM2f+s7AtuREYGGjSsx6TIAgYOnQ4Vq1ajh07tuLDhw8QRRF37tzGzz97W/QaERHhcHR0hIODI54+fYIdO3wtfn+NGzfFvn17cP/+PWg0Gvz99yIoFB/D53btvsTffy+QP8/379/Dz++gxc8fE3vKiYiIiIiIMgAHBwfcvn0LmzdvwIcP7+Ho6ISqVatjxAgpCPXwKI3x4ydhwYK/8OTJY6hUKpQt64nhw8dY9Px9+nyLGTN+RXR0NL7//gdUr14z8QclYPz4Sfj774Xo3bsrPnz4AGdnZ7Rt2xGffVY70cc2atQEBw7sQ8uWjZE9e3Zs2rQjzjoNGnjB0dEJq1cvh4/PHKjVahQqVBgdOnxlUfsGDRqGmTOnYuPGdShVygONGjXBuXNnLHpszZq10LVrD4wYMeT/1dd7wtHRSa7c/uWXnaFQCPjpp2F4/fo1nJycUL16TTRu/LlFzx+TIIqiaPWjMrng4HDodOavyBARERERERHF9P79e7Rs6YWtW/dYPe2ZSqVArlyO8d7P9HUiIiIiIiKiWI4dOwyNRoOIiHDMm/cnypTxTNI85Ilh+joRERERERFRLHv27MS0ab9AEASULVsOkyZNTZXXYfo6ERERERERUSph+joRERERERFRBsWgnIiIiIiIiCidMCgnIiIiIiIiSicMyomIiIiIiIjSCYNyIiIiIiIionTCoJyIiIiIiIgonTAoJyIiIiIiIkonDMqJiChZXr0S0Lq1fXo3g4iIiChTYlBORETJ8vq1gMuXlendDCIiIqJMiUE5EREli1YLaDTp3QoiIiKizIlBORERJYtGI0AUBej16d0SIiIiosyHQTkRESWLViv9z95yIiIiIusxKCciomQxBuPG4JyIiIiILMegnIiIksUYlGs0Qvo2hIiIiCgTYlBORETJotVKwbhOl84NISIiIsqEGJQTEVGyfOwpT992EBEREWVGDMqJiChZjGPJOaaciIiIyHoMyomIKFmMY8k5ppyIiIjIegzKiYgoWdhTTkRERJR0DMqJiChZOCUaERERUdIxKCciomQxVl83/k9EREREllOldwPMiY6Oxo8//ogHDx7A1tYWefLkwaRJk1C0aFG8ffsWP/30E549ewYbGxtMnDgR1atXT+8mExFlWdHR0v+svk5ERERkvQzbU965c2fs27cPO3fuROPGjTF+/HgAwKxZs1CpUiUcOHAA06ZNw4gRI6BlziQRUbrhmHIiIiKipMuQQbmtrS0aNGgAQZBSIStWrIiAgAAAwL59+9ClSxcAQIUKFeDs7Ax/f/90aysRUVZnrLrOoJyIiIjIehkyfT221atXw8vLC8HBwdBqtciXL598n6urK168eBHnMUFBQXj9+rXJMjs7O7i5uaV6e4mIspKPPeUcU05ERERkrQwflC9atAhPnz7FypUrERUVZfHjNm7cCB8fH5NlZcuWxbZt21K6iUREWZpxLDnHlBMRERFZL0MH5cuWLcOBAwewcuVK2Nvbw97eHiqVCq9fv5Z7ywMCAlCwYME4j+3cuTO8vLxMltnZ2aVJu4mIshKOKSciIiJKugwblK9YsQJ79uzBihUrkD17dnl58+bNsWHDBgwZMgRXr17Fq1evzFZfd3Z2hrOzc1o2mYgoS/o4ppzp60RERETWypBB+cuXL/Hbb7+hcOHC6NGjBwDAxsYGmzdvxsiRI/HTTz+hadOmUKvV+P3336FWq9O5xUREWZexh5zp60RERETWy5BBuYuLC+7cuWP2vrx582L58uVp3CIiIoqPMRhn+joRERGR9TLklGhERJR5GNPXjf8TERERkeUYlBMRUbKwp5yIiIgo6RiUExFRsmi1gIODyKCciIiIKAkYlBMRUbJotQKDciIiIqIkYlBORETJotEADg4cU05ERESUFAzKiYgoWbRawNGRPeVEREREScGgnIiIkkWjEeDkxEJvREREREnBoJyIiJLlY08509eJiIiIrMWgnIiIkkUaUy7KU6MRERERkeUYlBMRUbJoNAIcHZm+TkRERJQUDMqJiChZNBoWeiMiIiJKKgblRESULNKYcnBMOREREVESMCgnIqIkE0UpfZ1jyomIiIiShkE5ERElmU4n/c/0dSIiIqKkYVBORERJZuwdZ/o6ERERUdIwKCcioiQz9o47ObGnnIiIiCgpGJQTEVGSaTRS77iDA6dEIyIiIkoKBuVERJRkxkDc3l6UA3QiIiIishyDciIiSrLoaMDGRoStLXvKiYiIiJLCqqBco9Hg9evX0HDeGyIiglTcTa0G1GqOKSciIiJKClViK4SEhGDDhg3Yt28f7t69C1EUAQDu7u5o0aIFunTpgpw5c6Z2O4mIKAPSaAAbG+kfg3IiIiIi6yUYlK9atQrz5s1DeHi4HIwb3blzB3fv3sXSpUsxdOhQ9OjRI1UbSkREGY9WK/WSq1TgmHIiIiKiJEgwKJ8+fTpsbGzQrFkz1K1bFx4eHsiePTvev3+Pu3fv4uTJk/Dz88P06dMZlBMRZUEajfD/nnKmrxMRERElRYJBed++fdG/f3+z6enly5dHx44dERwcjKVLl6ZW+4iIKAOTesqlfwzKiYiIiKyXYFA+atSoRJ8gV65cFq1HRESfHq0WsLUVYWMD6HQCDAZAwXk9iIiIiCyWaKG32DQaDZYsWYLr16+jVKlS+O677+Dk5JQabSMiogxOo5F6yVX//zWRgvT0bRMRERFRZmJ1UD5z5kxs2LAB2bJlw7Fjx/D8+XPMnj07NdpGREQZnFb7cUy5dJtBOREREZE1Ek0yDAsLM7l98uRJrFu3DqdPn0bjxo1x4sSJVGscERFlbNHRUvV1tVq6zXHlRERERNZJNChv1aoVTp48Kd92cnKCn58fjh07hkePHjF1nYgoCzMWerOxkW5zWjQiIiIi6yQalPfo0QODBg3Czz//jPDwcPTt2xfLly/Hd999h4cPH+Lbb79Ni3YSEVEGZJwSTamUbrOnnIiIiMg6iY4p79OnD+rXr4/Ro0ejdevWmDZtGvbs2YNbt27Bzc0NpUqVSot2EhFRBiT1lIsQBGlcuUaT3i0iIiIiylwsmrimZMmS2LRpEzp06ID+/ftj1apVaNCgAQNyIqIsTqP5mLquVkvTohERERGR5SwKyv/77z+sWrUKVapUwaZNm+Dv7482bdrg/Pnzqd0+IiLKwLRaQS7yZmMD9pQTERERWSnR9PWFCxdi7ty5EEURgiDghx9+gK+vL/766y/06tULXbt2xdixY9OirURElMHE7ClXqUSOKSciIiKyUqI95evWrUPFihWxe/dulClTBuvWrYONjQ1GjRqFNWvW4NixY2nRTiIiyoC02o9zlEs95UxfJyIiIrJGoj3l0dHRKFCgAEqWLAkXFxc8f/5cvq9y5crYsWNHijdqypQpOHz4MAICArB9+3aUKVMGAODl5QW1Wg07OzsAwHfffYeWLVum+OsTEZFljNXXAWlMOXvKiYiIiKyTaFBev3597NmzB2fPnkVwcDC++OILk/uNAXJKatasGfr164euXbvGuW/OnDlykE5EROnLOE85IPWYMygnIiIisk6iQfnkyZNRsGBB3L17Fx4eHvjuu+9SvVHVq1dP9dcgIqLki44GHB2l9HX2lBMRERFZL9Gg3NHRESNGjEiLtljkp59+AgCUL18eI0eORO7cuc2uFxQUhNevX5sss7Ozg5ubW6q3kYgoq5Cqr38MyjmmnIiIiMg6CRZ6mzVrFkJCQhJ8gvfv3+OPP/5IyTbFa+3atdi1axe2bt2KXLlyYfTo0fGuu3HjRnTo0MHk38iRI9OknUREWUXsecrZU05ERERknQR7ypcuXYo1a9agcePGqFu3Ljw8PJA9e3Z8+PABd+/exalTp3Do0CFERUWlSW96wYIFAQBqtRo9e/ZEs2bN4l23c+fO8PLyMlmWGuPfiYiyMmlMubH6OseUExEREVkrwaB89OjR8PHxwd69e/Hvv//GuV8URTg6OibYY51SIiIioNPpkD17dgDAnj17ULZs2XjXd3Z2hrOzc6q3i4goK4vbU870dSIiIiJrJBiU9+7dG+3atcM///yDffv24e7du/J9pUqVQvPmzfH111/HO647qX7++WccPXoUb968Qd++feHo6Ijly5djyJAh0Ov1AIBChQphxowZKfq6RERkHWlMufS3NKY8fdtDRERElNkIoiiKlq6s0WgQEhKCHDlywNbWNjXblaqCg8Oh0xnSuxlERJlex472+OorLbp00aFXLzvUrq3Ht98yh52IiIjISKVSIFcux/jvt+bJbGxsmBJOREQy03nKWeiNiIiIyFoJVl8nIiJKiFYrcEw5ERERUTIwKCcioiSLjkaMecpFjiknIiIishKDciIiSjKtlvOUExERESUHg3IiIkoyjUaINaac6etERERE1rA4KG/cuDEWLFhgsmzbtm344YcfUrxRRESUOcQs9MaeciIiIiLrWRyUBwQEIDQ01GTZjRs3cODAgRRvFBERZQ4aDWBjwzHlREREREmV6JRoPj4+8t9XrlyRb4uiiMOHD0Nt7CIhIqIsR6sV2FNORERElAwWBeWCIEAQBFy5cgVXrlyR7xNFEaVLl07VBhIRUcal0QC2ttLfNjbSGHMiIiIislyiQXnBggUBAIGBgXBwcECOHDmkB6pUcHV1xY8//pi6LSQiogxLGlP+MX1dp0vnBhERERFlMokG5YcPHwYAlC5dGl9++SW8vb1TvVFERJTxiaLUM26cEk3qKU/fNhERERFlNokG5Ua3b99OzXYQEVEmYxw/bhxTrlJxSjQiIiIia1kclL979w6zZ8/GlStXEB4eLi8XBAGHDh1KlcYREVHGZewVN1ZfZ085ERERkfUsDsrHjBmDEydOQBRFk+WCwF4RIqKsKHZPOceUExEREVnP4qDc398fdnZ26NChA3LmzMlgnIgoizNWWjcdU87fBiIiIiJrWByU58yZE7Vq1cKECRNSsz1ERJRJGHvKVaqP/3OeciIiIiLrKCxdsWfPnjh37hxevXqVmu0hIqJMQqORxpMbE6dsbESOKSciIiKyksU95atXr0ZgYCC8vLyQN29eqP7fNcJCb0REWZNWK8jjyQFpbDnHlBMRERFZx+Kg/MWLFwAAvV5v0lvOseVERFmTRgPY2n4s/skx5URERETWszgoHzx4cGq2g4iIMhmtFiY95RxTTkRERGQ9BuVERJQkGo0gV14HOKaciIiIKCksLvQGALdu3cLYsWPRp08fvH37Ftu3b8fjx49TqWlERJSRaTSIM6Zcq2X6OhEREZE1LO4pv3nzJr7++mtER0dDEATY2dnhl19+wRdffIEpU6akZhuJiCgD0mql3nEjGxumrxMRERFZy+Ke8tmzZ0Oj0SB//vwAAEdHR1SpUgX+/v6p1jgiIsq4NBoh1phykUE5ERERkZUsDsqvX7+OZs2aoVmzZvIyV1dXBAUFpUrDiIgoY5N6yj/eNlZfF8X4H0NEREREpiwOyvV6PZycnEyWBQcHwybmGRkREWUZ0pjyjxG4sdecc5UTERERWc7ioLxYsWI4dOgQ7t+/DwBYtWoVjhw5guLFi6da44iIKOOKPSWacXw5U9iJiIiILGdxUN6tWzeEhITg9OnTAIDffvsNer0eX3/9dao1joiIMi6NRoCt7cfbxgCdQTkRERGR5SwOytu0aYMBAwbAzs4OoijCzs4O/fv3R9u2bVOzfURElEFJPeVx09c1Gk6LRkRERGQpi6dEA4Bhw4Zh0KBBCA4ORq5cuaCOmbdIRERZSnS0aaE39pQTERERWc+qoDwqKgoPHz5EeHg4njx5Ii+vXr16ijeMiIgyNq3WdEo0hYLTohERERFZy+Kg/Pjx4xg1ahTev39vslwQBNy8eTPFG0ZERBmbRvOxuJuRjQ17yomIiIisYXFQPn36dISGhqZmW4iIKBOJXX0dAFQq45hyTlZOREREZAmLg/LAwECUK1cOU6ZMQbZs2VKzTURElAloNILJmHJA6jlnTzkRERGR5SwOyqtUqQJHR0eULl06NdtDRESZhFaLOEG5Ws30dSIiIiJrWByUT548GR07dsTAgQNRunRpKJVK+b7BgwenaKOmTJmCw4cPIyAgANu3b0eZMmUAAI8fP8aYMWMQHBwMJycn/PbbbyhVqlSKvjYREVlGowGcnEzT1KWgnFOiEREREVnK4qB8x44dCA0NxdGjR3H06FGT+1I6KG/WrBn69euHrl27miz/+eef0alTJ3To0AH79u3DmDFj4Ovrm6KvTUREltFqhTiF3tRqKVgnIiIiIssoLF1xzZo1AABHR0cUKFDA5F9Kq169OlxcXEyWvX37FtevX0ebNm0ASIH7y5cvTaZmIyKitCNVXzddxjHlRERERNaxuKdcFEV8/vnnmDdvXmq2J16BgYHIly8fVCqpyYIgoECBAnjx4gWKFi0aZ/2goCC8fv3aZJmdnR3c3NzSpL1ERJ86jQZQq831lDN9nYiIiMhSFgflX331Fc6ePQudTicHxhnZxo0b4ePjY7KsbNmy2LZtWzq1iIjo0xJfoTedLn3aQ0RERJQZWRxdX758GTdu3EC9evVQokQJudCbIAhYtWpVqjXQqECBAnj9+rV8UUAURQQGBqJgwYJm1+/cuTO8vLxMltnZ2aV6O4mIsgqNRogzT7laLXJMOREREVntwwcgq868bXFQfv78eQBAcHAwLly4IC8XhLRJU8yTJw88PT2xc+dOdOjQAfv370f+/PnNpq4DgLOzM5ydndOkbUREWZHUU26u+no6NYiIiIgypXv3FPjqK3tcvhye3k1JFxYH5e3atUuzAPznn3/G0aNH8ebNG/Tt2xeOjo44ePAgfvnlF3h7e2Px4sVwdHTE9OnT06Q9REQUlzSm3HSZjQ3HlBMREZF13rwR8OZN1j1/sDgo/+2331KzHSYmT55sdnmJEiWwcePGNGsHERHFT5oSzXSZWi1yTDkRERFZJTxcuqhvbmaXrCDBoPzFixdwcnJC9uzZ8eLFi3jXi29cNxERfbrMVV+XesrTqUFERESUKYWHS73kEREMyuNo3LgxevbsiTFjxsDLy8ts+rogCLh582aqNZCIiDImc9XXVSqpB52IiIjIUhER0v9hYQJy5hQTXvkTlGBQLooiRFE0uU1ERARIaWaxg3L2lBMREZG1jD3l0v9ZL+ZMMChfvXo1XFxc5L+JiIiMzBV645hyIiIistbHoDydG5JOEgzKa9SoIf/94sULuLm5oXz58vKyt2/fIjg4OPVaR0REGZZUjIVjyomIiCh5jMG4MTjPahSWrjhmzBjs3r3bZNnixYvRunXrFG8UERFlfFqtEKennGPKiYiIyFpZvafc4qDcnJcvX6ZUO4iIKJMxN22JjY3InnIiIiKyirHQW1btKU90nvIyZcoAkKqsr169Os7Y8pw5c6ZKw4iIKGPTauNOiaZWg2PKiYiIyCqmhd6ynkSDcmPFdUEQzFZfb9myZcq3ioiIMjRRlNLUzVdfz5o/qERERJQ0WT19PdGgfPr06QAAb29v1KlTB61atQIAKJVKuLq6omrVqqnbQiIiynC0Wul/82PK0749RERElHmFhwMqlYiwsKx5YT/RoLx9+/YAgICAAFSsWBH16tVL9UYREVHGZhw3Hrf6OseUExERkXXCwwU4O4tMX0/M4MGDERAQgIMHDyI8Vl5Bu3btUrpdRESUgRl7w2Onr6vVrL5ORERE1omIAPLlE5m+nhhfX19MnDgRer3eZLkgCAzKiYiyGOO4cXNjypm+TkRERNYIDxdQrJghy/aUWzwl2vz586HT6SCKosk/g8GQmu0jIqIMSKMBBEGEUmm6XK0WGZQTERGRVaT0dQN7yhPz9u1bVKpUCQsXLkSuXLlSs01ERJTBabVSr7gQ64K2Wg2OKSciIiKLiaJU6C1/fhHPnlncZ/xJsTgor1GjBuzt7RmQExERNBohTuV1wJi+njVTz4iIiMh6Gg2g0wn/H1OeNc8hLA7Ky5Urh8WLF+Pnn39G+fLloY5xNsYx5UREWYvUUy7GWc70dSIiIrKGMWXd2VlERET6tiW9WByUL1y4EIIgYPPmzdi8ebO8nIXeiIiyHo0m7hzlAAu9ERERkXUiIgQIgog8eThPuUVEMW6viLllRET0adNqhTiV1wFApfpYmZ2IiIgoMeHhAhwcgGzZOCVaom7fvp2a7SAiokyEPeVERESUEsLDAUdHEY6OWXdMudXl7QIDA3HhwoXUaAsREWUSWi1ga8sx5URERJQ84eECHB0BR0cgOlrIkucRFgflGo0Gw4YNg5eXF7p3744nT56gcePGWLduXWq2j4iIMqDo6ISqr6d9e4iIiChzitlTDiBLFnuzOChfvnw59u3bB1EUIYoiihYtCjs7O+zbty8120dERBmQVms+fZ1jyomIiMgaERECHBxEODhIt7NiCrvFQfmOHTtQuHBhNG3aVF5WunRpPHz4MFUaRkREGZdGY35KNBsbpq8TERGR5Yzp6woF4OCQNceVWxyUBwYGol69enBxcZGXOTk5ISwsLFUaRkREGVd8PeVqtRSwExEREVnCmL4O4P/F3tK5QenA4qA8b968uH//vnw7OjoaZ86cQf78+VOlYURElHFpNOanRLOxAQwGAXp92reJiIiIMh9jTzkgFXvLinOVWxyU16hRA/7+/tiyZQsAoEmTJnj69Clq1KiRao0jIqKMSeopj5u+rlJ9vJ+IiIgoMewptyIoHzp0KHLmzImIiAiIoojXr18jR44c+P7771OzfURElAFJY8rjLjeOM2dQTkRERJYwFnoDkGXnKldZuqKLiwt27dqFLVu24MWLFyhQoAA6duwIZ2fn1GwfERFlQFqt+SnRjMukCuxxe9KJiIiIYgoPF5AvnzEoz5rV1y0OygFpXPmAAQNSqy1ERJRJxF99Xfpfp0vjBhEREVGmxPR1C4Ly169fIzQ0FEWKFIGNjQ3u3buHuXPnIiwsDA0bNkTPnj3Top1ERJSBaLXm09eVSkAQRFZgJyIiIovELvTGnnIzfv75Z5w+fRpnzpyBTqfDwIEDERAQAFEUcebMGURHR+Pbb79Ni7YSEVEGER1tvvo6IAXrHFNORERElojZU+7klDV7yhMt9Hb//n1UrlwZdnZ2uHjxIp4/fw6lUolGjRpBEATs2LEjLdpJREQZSHzzlAPGucqz3lVuIiIisl54OAu9JRqUv337FoULFwYAnD9/HgDQunVrLFy4EE2aNMGzZ89St4VERJThxDemHJCWs6eciIiILBERwXnKEw3KDQYDdP+v2HPjxg0IgoCaNWsCAHLlygWFwuJZ1YiI6BMRX/V1QOopZ1BORERElmChNwvGlLu4uODw4cNYuXIl/vvvPwBAlSpVAAAvXrxAzpw5U7WB5nh5eUGtVsPOzg4A8N1336Fly5Zp3g4ioqwqvnnKAaavExERkeVY6M2CoLxVq1bw8fHBjBkzIIoiypcvjyJFikCj0eDKlSuoUKFCWrQzjjlz5qBMmTLp8tpERFmdNKbcfPo6e8qJiIjIEgYDEBERu6ecQXkcAwYMQFRUFE6cOAEXFxeMHTsWAHD58mUULlwYzZo1S/VGEhFRxhLflGgAx5QTERGRZSIjAVEUmL6e6AoqFUaOHImRI0eaLK9RowZ8fX1TrWGJ+emnnwAA5cuXx8iRI5E7d26T+4OCgvD69WuTZXZ2dnBzc0uzNhIRfao0Go4pJyIiouSJiJB6xR0cpNtMX89E1q5di4IFC0Kr1WLOnDkYPXo0/v77b5N1Nm7cCB8fH5NlZcuWxbZt29KyqUREnySppzz+9HWOKSciIqLEhIcDKpUoZ985OYmIiEjfNqWHTBmUFyxYEACgVqvRs2dPsyn0nTt3hpeXl8kyY2E4IiJKnujohAq9MX2diIiIEmcs8ib8/1o+x5RnEhEREdDpdMiePTsAYM+ePShbtmyc9ZydneHs7JzWzSMiyhISmhLNxkaqzk5ERESUkJjToQFS+npkpACdDlBlukg16TLdW3379i2GDBkCvV4PAChUqBBmzJiRzq0iIspapCnR4k9f1+my3lVuIiIiso7UUx4zKJf+jogA/t8HmyVYHJT7+/vDxcUFhQsXlpfpdDpotVrY29unSuPMKVy4MLZv355mr0dERHFJU6KZv08aU5627SEiIkqMr68KrVrpYGub3i0ho4gIQS7yBnws+BYeLiB7dvMX/z9FCktX7NGjB9auXWuybObMmahatWqKN4qIiDI2jUbgmHKiNPDhA+Dnp0zvZhBlegYDMHiwHfz9uT9lJLHT15VKwN4+602LZnFQLopxr1R8+PDB7HIiIvq0JdRTLo0pZ/o6UUo4cUKF0aNZqJYoud69E6DXC7hzx+Lwh9KAsdBbTFmx2Fui6euNGzeW//b19cWhQ4cASEH6y5cvkS1bttRrHRERZUiJjylP4wYRfaJCQoA3b+I/Od2zRwUXFwOqVjWkXaOIMqHXr6X9iEF5xhK7pxyQUtgZlMcSEBAAABAEAWFhYQgLCzO5v06dOqnTMiIiyrASqr6uVoscU06UQoKDBURECIiMBMyV8NmwQQ0PDz2qVuVOR5QQBuUZU+xCb4A0V3lWS19PNCgfPHgwAMDHxweVKlVC3bp1AQBKpRKurq5o3rx56raQiIgyHI0G8RbKUavBMeVEKSQ0VAok3r4VUKhQ3OyUN28E5MrFIIMoMa9fC3BwEHHnjgKi+HFebEpf4eGmhd4AaVo09pTHYgzKAwICUKdOHbRq1SrVG0VERBmbRiP1iJvDMeVEKSc4WNqX3rwxH5S/fi0gRw7ub0SJCQoSULOmHkeOqPD6tQBnZ9bFyggiIoDcuU2/C0dHEWFhWeu4ZvGl1enTp6N69eqYP38+JkyYgPfv38Pf3x9v3rxJzfYREVEGYzBI85DHX32dY8qJUkpIyMeecnPevBHktFwiit/r1wKKFjWgSBEDU9gzkPgLvaVPe9KLxVvk06dP0aZNG/j4+GDLli0wGAzo3bs3Fi1alJrtIyKiDMaYmh5/9XWOKSdKKcaecnOBd1iYNMdvUBCDcqLEvH6tQL58ItzdDbh7l0F5RmGu0FtWTF+3eIv8888/ERoaCof/J/3nzJkTVapUwZkzZ1KtcURElPEYg/L4qq+rVFIhOCJKvpAQAba2otmecmNV9jdvBOj1ad0yoszl9WsB+fKJ8PAw4PZtBuUZhblCb+wpT8CFCxdQv359dOzYUV5WtGhRBAYGpkrDiIgoYzL2gic8T3natYfoUxYSIqBECUO8QXnOnCL0egHv3vFCGFFCjOPIPTz0TF/PQCIiOE85YEVQHhERgQIFCsRZplBwoyYiykqMveDxjykXOaacKIWEhAhwczPgzZu451uvXytQqJAB2bOLHFdOlAipp9yA0qUNuHNHCZF13jKEiAjAwYHp6xZH1IULF8axY8fw8uVLAMChQ4dw+PBhFC5cONUaR0REGY9GAwiCCKXS/P3sKSdKGTod8P69gFKl4u8pz5tXRL58IseVEyXAYJD2l3z5RJQqZUBwMGsxZBTmCr1lxXnKLQ7Kv/zyS7x8+RIHDx4EAAwZMgRRUVHo0KFDqjWOiIgyHq1WCrzjm+OVY8qJUoZxjnKppzz+oNzZ2cAAgygBwcECdDopKHd0BCuwZyAs9CaxeGvs0qULWrduDVEU5X8tW7ZEly5dUrN9RESUwWg0QrzjyQFWXydKKSEhgK2tiEKFzBd6MxaucnZmTzlRQl6/FuDgIMo9sh4erMCeUcRX6C2rzVOusnhFlQq///47Ro4ciRcvXqBAgQJwcXFJzbYREVEGpNFIgUJ81OqPFdqJKOmCg6VCbnnzivH2lJcvb4BOBwQFMcAgio/xApaRh4eeFdgzAL0eiIriPOWAFT3lRvnz50flypUZkBMRZVEaTfyV1wEptZ3p60TJFxoqIFcuEXnzGhARISAiwvR+aYysAc7OLPRGlBBj5XUjDw+mr2cExmNaWhV6u3NHgXXrEjiBSUeJ9pQ3btw4wfsFQcChQ4dSrEFERJSxabVCvJXXAeOY8rRrD9GnythTnjMnoFRKKewxT16NvX8GgwHHj8dTeZGI5MrrRjErsMdXH4VSnzHwdnAwXZ5aPeWLF6vh76/EN99kvJOURIPygICABO8XuCUTZTh796pQsaIerq6c74NSXuI95RxTTpQSQkKkoFyhAHLlklLYCxf+eFw3FnoTRbCnnCgBsdPXS5UyIDQUCAoSkD8/z5XSS3g4YGcnQhUrInV0lOYvT0nR0cDu3WpERSFDXoxJNCjv0aNHnGVBQUE4cOAADAaDmUcQUXqbOtUGfftq0adPxrsSSJmfVH2dY8qJUpvUUy79nS+fabE3nQ54904KygUBLPSWCFEEHj0SUKIEA7CsKChIgUKFPsYtDg5A4cIibt9WIH9+fTq2LGszV+QNkHrKIyIE6PWId/pVax05ooSDg4jQUCFDXoxJNCgfO3as/Hd0dDT+/vtvbNmyBQaDAWXLlsWYMWNStYFEZB2tFnj0SIHAQJ6gUepIvPo6x5QT8OpVxjvpyWyMPeUAkCePabG3t28FiKIUlCuVwLt3Cmi1CWexZGU3byrQooUDHj4Mi9MrR5++168FVK5sejwqXVqqwN6gAYPy9GJujnJAmqccACIjASenlHmtbdvU6NhRC19fNR49yngXYyyucLB9+3Y0a9YMPj4+cHJywvTp0+Hr64saNWqkZvuynHfv0rsFlNk9fqyATicgMJAFTFLaf/8pERaW3q1If4md+KtUInvKs7iXLwVUruzIi4PJFBwsFXoDEKcC+5s3ArJnF2FrKwXsxmVk3vPnAqKiBDx+zM8oK4pd6A1gBXZrREYCtWo5pvgxJjw8bpE34OMY85Qq9hYeDuzfr0L79joULWrAkycZ7ziQ6JZ4/vx5fPnll/D29kZoaCgGDx6MAwcOoH379hxPnsKuXVOgTh0zl4uIrGCcd5Mnwynv++/tcPq0aR7V4MF22L8/axVY0mgSTl+3sQHHlGdxR44oodMJuHGDJ7zJERoau6f84+dpHE8OSBfJ8uQxfPIp7Ml5fy9eSJ/dnTtZ63hNktiF3gBWYLfGpUtKPHigwMWLKft5RUSY7ylXqaSx5inVEbJ/vwqFChng6WlA0aIinjzJeN97ogk83bp1gyAIUCgUaNiwIT58+IDZs2ebrOPt7Z1qDcxKzp5VIjLy0/5BpdR3/74CuXMb5BMQShnh4YjzmYaHA9u3q+DiYkCzZhkrDSo1JdZTLo0p57EsKztyRDq9uHlTiSZNrN83wsKAGTNsMXlydIYrxpOWYveUP3788RgUO8hwdhY/6aA8IEBAzZqOuH49TB5nbw3jhep79/jbmNWIonH6wLjp66zAbplz56SLWZcvK9G0acqd78TXUw4YK7ALAJI/DGrbNjXat9dBEICiRQ14+DDjHQcsHlVjMBiwb98+s/cxKE8Z58/z6i0l3717CtSrp8fBg6p0/aEJCwMaNXLEv/9GyL05mdmjR3EP4CdPKqHRCLh/P+Md3FNTdLQAW9v475fGlKddeyhj0euBY8dUaNxYh5s3k7ZvnDmjxOLFNujbV4NixZJ2/Hj4UIAgAMWLZ97jT0gIkCPHx57yCxdM09djHlvz5fu05yq/eFE63t66pUStWtYHBS9eKODgILJnNAsKCZEuFMcOykuVMiAsTLrgU6hQ5j1OpIVz55QoVsyAK1dSNlaJr9AbkHJzlQcHA4cPK/HLL1EApKD8yJGMF3MlemQqWLAgChQokOA/ShkZJSg/ckSJhQtZKSazundPgfr19YiIEPD+ffq14+RJJZ48UeDq1cxxAvTqlYBq1Ryhj+dcz9xV1UOHVChe3IAHDzLHe0wpUk95QtXXOaY8K7tyRQGDAejaVYtbt5IelAPA1atJ/1389Vdb9O9vDzETn2uHhHzsKY9d6M1cUB4U9Okeiy5dkt5bUi/0BAYKqFNHLw/xoqwjKEi6IBO7YJi9vRSYX7/ObSIhBgPg769E//4aXLmiSNFjanyF3oCUm6t89241ypUzyDMvSGPKM953nmiLDh8+nOi/T0V6/nAHBQl4+jT9NxCdDvD2tsOcObbQ6dK7NZnT/v2pXwwsvoBHFKWgvHJlPRwdxXRNYTemr16/njEuNiXm3Dklnj6VgglzYgfeogj4+anw7bcaPHqkiDeY/xRJY8rjv9+Yvp7UY+q9ewr4+WWO7YbiOnJEhfr1dShXTo/79xVJukBz+rQKOXKIuHw5accwUZSGhN26pcCBA5lzWxJF45Ro0o4Ue0q0168VJj1/n3r6+pUrSuTNa0hWUN6ggQ7378d/nM/KPnxI7xakntevhXgz9sqVM+DatdQ9RgQEpG8nSXLdvSsdxzt31uLNGwEvX6bccSY8HPH2lDs4pExP+bZtKrRv//GHqGhREa9eKRARkeynTlHpHwVmEPfvC2jSxCHdAvMLF5SwtU3/y/mbN0spz0qlKI8fIctFRAD9+9tj587Um2/lyhUFypRxMntQfPVKQHg44OZmQMGChnQt9nbkiApVq+ozTaGnxLb32EH5nTsKvH0roHNnLUQRePr00z0Zjk2rTXhKNON9Se0t//tvNSZPTiA/njK0I0eUaNRIj6JFRajVsHp4R2QkcPmyAt26aZOcKvnggYAPHwSMHq3B77/bZsre8vBwQK+3vKfc2fnTLfRmMEhjWTt10uHmTeu3CVGU0tdr1dJDqwWePfs0P6fkaNTIEf/992me95mrvG5Uvrwe166l7nnKt9/aw8cngSvZGdy5c0pUqaJH9uxScbzLl1NuO4mIEORK67GlRE/5y5cCzpxRol27jz2N+fKJcHAQ8exZxjo/zVitSUdFikhfzvnz1n0kT58mvTcopvPnFahYMX272jQa4I8/bDFyZDSaNtXh338z30SeN26kbFqNtU6cUCIqSsCpU6nz2UVHA0OG2OHDB2l8XWx37ypQpIgIe3vAxUVMt2nRHj0SEBAg4LvvNJkmKPf3ty4oP3RIiXr19HByAooVy1op7IlXX5fuS2pQfuSICrduKU16BSntbNigSvJn//69dJG5USMdFAqpkJK1KeyXLimRO7eItm21uHZNmaRj+pkzKlSpoke/fhoEBgqZcoaE4GABCoWIbNmk23nzGhAZKcgnqbELVzk7f7pjyh8+FKDRAB06aHH7tvU93e/fSyf/xYoZUKKEgSnssYSFAU+fKjLMMMqUZq7yulG5cgbcuJF67zswUIC/vzJTX/A4d06JGjWkGKViRQOuXEm5/SehnnInJzHZPeU7dqhQs6YeLi4fX8NY7C2jTYvGo9L/2dgArVppsW2b5WOp9XqgcWNHHD6c/B3t/HklqlZN33yq9evVsLMT0aGDDs2b67B/vypT9S68fCmgcWMH+Pun32Z94IAKnp56nDqVtBPJxMyaZQMbG6BDB53Zg+K9ewqUKiVtRwULinjxIn0OOEePqlCjhh41akjpq5GR6dIMi0VEIMGx76IoBeWC8PFL9fOTClkBUmZCVir2Zkn1deN61nr0SMCLFwKKFDHI44ozk7t3M3dqbHQ0MGqUHVatSlpdkePHVXBzM8DVVdpXypTRWx2UnzmjxGef6VGmjAHh4UjSidPZs0rUrKmHgwMwdGjG6i039twkJiREQI4cgOL/H1+OHFIWm/GCiRRoxE5f/zSPQ5cvK1GunAGlSxug0VifmfTihQJOTtIFjlKlGJTHZryonNDv4Jw5NlizJnPWG4q9r8RUrpwez54pEBycOq+9d68KxYoZcOmSMsOlS1vKNCjXp2hPeWoXetu+Xaq6HltGHFeesVqTztq312H7dpXFY6nv3FEgNFSQx84mlU4n/eBUq5Z+PeVRUcDs2TYYNUoDpRKoX1+PV6+ETFWldMsWFQwGAWfPpk8PvygCBw+q4O0djTdvBDx6lLIB8aVLCixZYoO5c6NQrZr5g+K9ewqULGkMyg0pOu7HGsb01QIFROTMKeL27Yy9HV25ooQygd+Yd+8EhIQIKFxY+uF4/1466TcG5SVLZq2gXKMREh1TblzPWkePSle1vbx0ceaEz+jevQPq13fA5s2ZL8vI6MoVBaKjBWzYoE5SEGvc943KlDHg1i3rvsfTp6Wg3NZW6mlPSrE3Y2APAD16aPHqVcboLX//HujUyR6dOtknGhjGHE8OSMG5MYXdOMVT3rwfrwBJhd4yVs9PSrl8WYlKlfSwsZGOt9ZuU4GBAgoWlD4rDw8D7t5N/20hNZ06pbSqN/PBAwVUKjHBfW3zZhVOncqcn1tCQXmuXEChQoZUq3+zZ49UeyZvXtFshmNG9+qVgCdPBDlGqVRJn6LF3lKz0Nvr1wIuXVKgeXNzQXnGm6s8Y7UmndWurYdSKVWNtsT580rY2Yk4ejR5O9nNmwqo1ZB7ONPDmjVq5MolonVracN1cAAaNNBh377McXIpisCmTWpUqKBPt7Hw164pEBYmoGFDPapW1adoCntUlJS2/uOPGpQta4j3oHjvngLu7tJ25OKSPoXetFrg5EkVGjWS5oP09Ey9H7uU4u+vTHD4yIMHAvLnN8DJSfrAjx2TegOLFJFulyyZtdLXU7On/MgRJRo21KN2bX2mS/c7dkwFtRr4/XdbaDRJe45Tp5RYvTr1e6MCAswHb2fPqtCkiQ6hoQLOnrXu8xdF6aJKo0YfT4CkoNzyfUOnk35bjQF1xYp6q1MlX74U8OyZgOrVpeewtweGDEn/3nKdTqo5UqCAiF69tBg0yC7BfSRm5XWjPHmknvLwcCAqKvaYchEfPgiZtjcuIZcuSUE5AJQta32xtxcvFChQQPqs3N0//Z7y+fNtrMp2uX9fgUaN9Hj8WIHQ0Lj3BwYKuHdPaXZq0MwgdlHE2MqV06dKBfY3b6SsmJYtdahVK+P9pllyPDx3TonSpQ3Inl26XbasAaGhAp4/T5kLgAmlrzs6AmFhpq/z2282JrU1EnLkiBLlyxvM1hMoViyT95RHRkbi5s2b8Pf3N/n3qVAqgXbtdBansJ8/r0TXrlo8eKCI9wTHEv7+UgEFRTptGxERUlrS6NEakzYYU9gzg+vXFXj6VIGff46Gv3/6jCs/cEA6GbWxAerU0afoFeXff7eBgwMweLB0pl+2rAHv3wtxitXcv2/aU54e6evGi1WenlI7pKA8Yx34YouZmmXOw4cKuLl9vGjm56dE48Yf13dzE7NYT3nCY8oFQZoWzdrANOYFnVq19Lh5U4GQkOS1NS35+anQr58WdnYi1q+3PrAOChLQv78dfv/dJlWOYRoN4OurQosWDqhc2cnsBeizZ5WoV0+HDh202LDBuuP/gwcCgoIEOaAGpKD82TOFxZWdr19XQKWSHgcAFSpYX1TozBklypY1yGOxgY+95el1oVkUgXHjbPHypYClSyMxblz0/+u4xJ9yEhJi2lMOAHnzSj3lQUEC1GoROXJ8vC93bhFK5ac3rlynk7aLSpWkbcLaCz2AFFQag3Jj+rq5fSwqChly/mJr3bqlsCqb4MEDBapX16NQIfOVyE+eVCJbNjHTBuVBQfEXegNSrwL7vn0qVKxoQMGCImrV0meoIVmPH0vTwCYWXMc+P7K3T9lib4kXevvYvrdvBfz5py22bbPsOH748MdhhrFZOqY8JMSYiWvRSyaLxXvXsWPH0KBBA3Ts2BE9evSQ//Xs2TM125fm2rfXYvduFaKiEl/X318JLy8dqlQx4NixpG+cFy6kb+r6ihVquLqKcdI7Pv9cj8uXFXj1Ku1/4LVaWJWGt3GjGq1b6/DZZ9L83OkRIB08qMLnn0ufoTEoT6kigEuXSmnrqv8fh+ztpbTOmJWJP3wAAgMVcHeXtqWCBUW8fJn2n4Oxp9N4gcfTM2NXYBdFaV+uXj3+I+6DBx+DclGU5idv0uTj/lKypAGvXlkeeGRmogjcuKFM8AQHkHrLdTrrjh0XLny8oJM/v4jixUWre2sTotMh1dIvDQZp2//8cx1Gj9bgzz9trKqlIIrAjz/aoWZNPd6/F1K8J2/XLhWqVHHEzJm26NBBix49NFi71vTCgcEgnYDVrKlHly5a7Nihtip18MgRFT77TG9ygpU3r4h8+SwPos6ckU4AjcePSpX0Vhd7i5m6bmRvbxxbnjoXPBKzdKkau3apsHZtJLJlA2xtgfnzo7BwoQ0uXDD/2cQflCvkyutCjF1MoZBS2D+1oPzOHQUUCsgXnD099Vb3lMdMXy9Z0oCwMJgd3rV3rwqdOztYfUEqIwkJAQICFFYVxLt/X/qNK19eb3Zc+YkTKnz5pRYhIQLevUvZ9qaFhNLXAaB8eUOqnKfs3q3CF19I6TC1aulx/rwS0dEp/jJJsmmTGgEBQqIznfj7x+20MGZrpoSEe8pN09eNNaN27Up8/9Trpd+k+INyKX09sd+D33+3xaBBdvjmG/tU3/Yt/kSnT5+O9+/fQxTFOP8+JZUrG5Anjwg/v4S/8HfvpBP1qlUNaNhQh6NHk34AP38+/YLysDDAx8cGo0dHm/y4A9KPe9Wq+jTvLQ8JkcbceXk5WHRSq9UCW7eq0LmzFjY20sEirVPYX70ScPWqQu49rVpVj5AQAQ8eJO/k6MULAb1728PbOxqlS5v+ulaqpDeZw/f+fQXy5DEgd27ptouLiOBgIc2LrB05Ypq+aqxsmtpXGXU6wNvbFvfvW/eZ378vzVVZvnxC6esKlCghvYFr15SIiBBMfqTy5BGRK5f4SaSwJ3ZIP3pUSmHs3Dnh3HS1Glb3lB89qkSDBh8Dstq1dfjvv5Q7/ixapEb79g7YsiXlj2nXrysQGSmlTH/xhQ7OziJWrLC8t3zVKjVu3FBg9uwofPaZHsePp9wxLDQUGDnSDt7eGpw+HY7+/bXo31+LvXtVJpkId+4ooNFIJ6gVKhhQpIgBu3db/lnF3veNrBlXbhxPHvOx1hZ7O3s2blAOAN27a/H6tZDmM4scOKDE1Km2WLMmUq5LAUjHxhEjNBg0yN7sxY/YY8qBj2PK37xRmJ13WRpXnvmPQzFdvqxEhQp6ue5HmTIGPHxoXQHRmOnr9vbSjDvmauZcuiRlLo4ZY5dpx0/fvi3N5x4dDYtSjI2FTEuWNKBixbg1HERR6ilv0UKHvHmlzz4zMdZfiK/6OiD9/t+9m7JFaUNDpRl5WrWSjomlSklD4FKqh/n27aR3mokisGWLGlOnRuPgQVW8PfjGIrg1a5oeT6UK7CnzPqwp9Obvr0STJjr4+ysTfe8XL0rbaZUq5r/3woWl2SwS6gAMCBCwZo0aO3ZEwtZWROPGjqlaTNriZ37x4gVKlSqFLVu24NChQ/Dz84Ofnx8OHTqUao1LD4Ig9ZYnlhpx4YISJUpIAXzDhjocP560oOPNG6mAQpUqaR+UP34soGdPe7i5GeDlZf71mzXTp0i6n5+fEjNmJD5H45MnAr74wgFOTlKPwLp1iZ/UHj6shK2tVBMAAGrUSJmg/MkTAX/+aWPR93rokAqVKxvkK7F2dkD16nqcPJn0zy4sDOjWzR5Nmujw3XdxA6BKlUzTh+7e/Zi6DkgnbzY2YprOVf7mjYBr1xRo2PDj9lSqlFQtN7Wnnli+XI21a9Xo2dPeqh5rf38FKlXSJzhGOmZP+aFDKjRooItT6OxTqcA+d64NBgywMxuciyIwa5YtBg7UwMkp4edRq0Wrx5QfPapCw4Yfg7patfQpVuzt6VMBs2bZYvjwaIwaZZfiPdF+firUr6+DWi31WHp7R2PuXBuLtsV79xSYNMkW8+ZFIWdOoH59HY4fT7nAcc4cW5Qrp0fXrlr5goeHhwGengb4+n7c8M+eVaJqVWlfEASgSxctNmyw7MJCdDTw33+mRd6MLE03FkWpp/6zzz5uA9YWewsNleq0xD6JBD72ls+alXa95RcvKjBggD3++ivK7AwrgwZpkC+fwWxvVUgIzPaUv30rxNvzJ1Vg/7R6yi9f/pi6DgAFCojInh1W7cMxe8oBafu/dy/u4y9fVqBXLw2mTIlG7972Vl/kzQhu3FCgQgUD3NwMFhVZfflSunhfvLgBFSrE7Sl/9EgKXGrU0KNECUOmS2EPDZWKjiaU3eXqKlXmT8mitAcOqODhYUCxYtLrCgLw2Wcp85sWFQV07WqP5cuTVn/E31+B4GAB3bppMWyYBuPG2UJvJgy4fFmJvHlFk4uJgLGnPGWyQa0p9HbunBKtWmlRvboee/Yk/Bvp5yddJI6viK+dHVCggAGPH8f/nf/5pw1atpSycFesiMKAARp89ZUDFi1KWiHUxFi89ZUrVw5lypRBuXLlUKhQIbi6usr/0trjx4/RpUsXNGvWDB07dsS9e/dS9Pk7dNDhwAEVwsLiX+f8eaVcRKZyZQN0OikYsdaFC1IglTNnEhubBDodMH++Gg0bOqJ4cQPWrYuM00tu1KKFFidOKBP8LAApc6BNG3uzJ167d6vQt6895s61SfCq7YULCrRo4YAGDfRYuTISI0ZoMG+eTaKpPps2qfHVVx9PNmvWTH5Q/u4d0KWLA/780wbLliV+0DtwQImmTU17iJJTqEqvBwYOtEeuXCJmzIibxQDEPSjev/+xyBsgBQZpPVf5sWNKeHoaTE4WbWykE6DULPb28qWA336zxerVkXB1FTFokJ3ZiymiKI21i/njYy41KyaDAXj0SAE3N+k9XbyoQJMmcdf/VIq9+fkpsXWrGhs3xv3BO3FCiXv3FOjbN/EucBsb63rKg4OlE+KYF3Rq15ZOEJM7LEAUgdGj7dCxoxZjxmjQp48G/frZJauqa2yHD5vWGfDy0qNUKQMWL074YuT798D339uhVy8t6tWTHl+/vjT8JanzvMf07JmA5cvVmDQp7nHkm2+0+Ocf06A8ZjD75Zc6nDuntOiC2tmzSuTIIcbJ6AGAsmUtmxbt3j0FIiIEVKxo+hzWFHvz91eiaFER+fObP2Pq1i1tessNBmDBAjU6dHCAt3c02rY1n0KpVALz5kVhwwZ1nJ7b4OD4C70Z09dj+zSDciUqV/64XQqCNNWeNSnsMXvKAcDdXR/n89bppEyoSpUM6NZNi27dNOja1UGegi6zuHVLgbJl9RZnqNy/r0DhwiLs7KQaDg8eKEzO+U6eVKFaNWlYSvHiYqbrKX/9WgF7ezHewA+Qtqny5fVmz1OWLlUnaZ+SUtfjnhemRFC+apUaz58rknwRYfNmNdq00cLWFvjuOw3evxdMfguMjOPJY/92JGe6yphE0ZL0dek1oqOlY0GNGnq0bq1LNCg/dCj+1HWjhMaVP3woYNMmNUaNkoIQQQC++06LLVsiMGeOTYpMhx2bxd9mkyZN8O+//2Lx4sU4ffp0uhZ6+/nnn9GpUyfs378f/fv3x5gxY1L0+T08DChRwoC9e+P/wmOmnKtUQL16SUthl54n7aquP3kioHlzB6xfr8aGDZGYNSvapFBMbCVLiihc2JDoe1u71ga3binRpYu9SdG7rVtVGDzYDosWRaJxY128Pd+7d6vQsaMDhg3TYNq0aCiVwBdf6JAjh5hgT01IiHQ1slOnj2ev1arp8fChYHF1xtiiooAePexRurQemzZFYupU2wR//KOipKrLxvHkRnXrJn1c+S+/2OLBAwHLlkXGO/VU6dIGREVBnnot5nRoRgUKpG2xt/jSVz09447Xun5dgfHjpYIdye3N//lnW3z+uQ6NGumxeHEkbt5UYvZs0w9OFIEJE2zRoIEj6tRxxNq1akRHSz86xgts5gQECNBqpYO39DyC2QN9UoPyrVtVmDfPBrt3q3D9uiLJVbtTglYrTQ83cWIUxo2zMykkKPWS22DAAI1JAa34WDum/PhxFUqXNpgEUwULSlfok3uRbedOFa5cUWDCBOnH1dtbg5w5RXh72yXreY1CQ6VjuZfXx+1CEICxYzVYuNAmzvy3Op108ePbb+1QrpwTsmcX4e398eqjp6cBdnYpM33O9Om2aNVKh/Ll4/7OtGunxf37CvmCcuygPF8+EY0b67BxY8IXJsPDgZkzbdCihc7sBURjcJDYsfDMGamnPvYxr0IFy1MlzY0njynm2PLkDqkRRWDZMjVmz5ZO0Iy/OS9fCujc2R7Ll9tg8+YI9O+f8NWVYsVEVKqkx6VLpscPc2PKP6avxxeUG5I8prx7d3v072+H9etV6VIk1JzoaCnzIfbsGFIFdtNtYuVKNSZNiptxEB4OhIYKsYLyuBXYjbeNM+GMH6+Bp6ceo0YlPOY2o7l5U4kyZaQ53S25GGYcTw5IF3Xy5xdNgtMTJ5SoW1f6/DNjT7kxqyS+zicjT09DnM61wEAB48bZWj2MMyxMOh8ypq4bffaZ1Glk6dTL5nz4IBVn/vZbDW7ftv43QqMBduxQ46uvpEbY2QG//BKNadNs8P696brxFcG1tZX2weSmsJ89q4SDA+LNYnBy+pi+fvWqAk5OItzcRHzxhTRlanzn+a9eCbh+XWE2cyumhKZF+/13W3TsqJU7ZIyqVTOgZk292Uyb5LL4GWfOnAmdToc5c+agT58+6Vbo7e3bt7h+/TratGkDAGjWrBlevnyJJ0+epOjrdOgQfxV2vR64eNF0HHjDhvpEp0bT6RCnxzmli7y9eCFg3jwbs0GORiNNx+LpacDhwxEJnrjE1Lx5wlOj6XRSsbg5c6Lw+ec6dO5sj+BgYMMGFYYPt8Py5ZFo3lyP7t21WL9eHedg5OurwpAhdli4MArffvvx5EWhAIYN02DuXJt4e4y2b1ejXDkDSpb8uNPkyiX94CblRN5gAAYPtoNeL2DBgijUrq3HgAEaDBxoF2/xv//+UyJXLhFly5qe4VWurEdYmPUFm1atUmPTJqkgUEIZFDY20o+IMYU95nRoRgULpl1PuTQdkvn01XLl9LhxQ2my7pgxtrh+XYH5821QubIj6td3SDDbJL4T+qNHlfDzU2HyZCmoyZULWLUqEj4+NjhwQCk/dsIEW+zdq8Lp02EYPjwaixerUa2aI+7fVyR4YezBAwWKFBHlQKFcOT1cXOI2JqH09UuXFGZ7ZW/dUmDYMDtcvqzAnDk2aNvWAe7uTujRww4rV6rjVNc3R+rdSZnv+OZNBWxsgIEDtWjbVosffviYcfDff0rcuqVEv36WXTWwtvr60aNKk15yo+ROjRYaKlW9njIlWt6fVCpg8eIoHDqkhI+POtk9YcePq1CqlAGurqbbRa1aelSrpoePj7TxhIRIJ1OVKzvixx/tULiwAQcORMDXNxK2Mc77FQqgXr34x5VbOuXV1asK7NmjMgn4Y3JyAtq00WH9ejWePxcQGCigalXT76BLFx02bVLHG8BGRkoXMW1sgIkTzb+Ou7sBoaHmC2vFdPq00mzaecWKely9atkFTmk8ecJnvN26afHmTfJ6y6OjgYED7bBggQ1u3VLA29sOZcs6oVIlR9Sv74h8+UQcORKeYAHJmMqVi5tNZG5KNNP09bjPndS5ysPDgf37VShc2IB162xQtaojWrRwSPeiVDdvKuDkBDkF2KhMGdNp0V69EvDLL7bYulUVZzt5+VKAra2I3LlNg/LYJ9WXL0vBvzHdVaGQesYuXMg8Y8sNBikFu2xZKSi3pCfVOJ7cSBpXrpCf79QppZzFkxmD8qCghIu8GZnrKd+yRQ1RFOItxhifI0ekfcnDw3QfLVvWAJUqeb/ZCxbYwN3dgO+/1+DxY+vrBh06pEL27KJJsN2ihQ5lyhjwyy+28j5vMCScSVixoj7Z4+PnzbNBr16aRKqvS38bO1AEQTq3rVjREO8x/MgRKePF3IXLmKSe8rjfxe3b0m/niBHmT2KKFEmdOc6tekZzRd7SutBbYGAg8uXLB9X/y1ALgoACBQrgxYsXJusFBQXhxo0bJv8ePHhg8eu0a6fF8ePmr8LcuqWAIMAkTa9hQynNL6E078mTbVGunBPGjrXFkycCdDopuI99EpSUjzQ4WOpdrVXLEdu3q9C4sUOc6W5mzJCC25kzo0xOABPTrJkehw7Ff2Xv339VUCik4H3GjGi4uRnQvLkjxo61w9q1kfJ4dS8vaWeKmfJx7ZoCI0bYYenSSLRoEfcF2raVxu5u3mx+x9u0SW3SS26U1HHlkyfb4to1JdasiYS9vbRsxAgN7O2BKVPMf2gHDqjQtGncHiJbW2lcuSXFYkRRuko/b54NJk60xYoVUShRIvENwXhQ1GqlFOvYPeVS+nra9HjcuKFAeLhgttc59rRohw4p8eCBAmvWROLQoQjcvx+Gdu10aN3aIU42RVCQgIkTbVG8uBOaN3fA/PlqPH0qvaeoKCkt2ds72qSH1dPTgDlzovD999KYQGNAvnVrBNzcRHTqpMOxYxGYNSsKo0ZpEjxwxxxPDsCk6npMJUtKBXBiBy8vXgho1szR7JX2adNs0a2bFsuWRcmfw549EahWzYBt21SoWdMRTZo4mB3rZXTihBKNGzti2rTk9/ydP/9xesbJk6Px9KkCS5dK38esWdKVeeNcpYlRqxOepzww8ON8ysb5rWOOJzeqVSvpxd5EUdqny5Y1oH170+cuUEDEkiVRWLfOBmXKOKFGDUcMGGCHJUvUOHBAiYsXFXj2TEjwszfy8zN/MQqQxpYvW2YDb29bVK7shKNHlfjjjyhcuhSOCRM0ZtO9ASmF3VxQfuCAEu7uTli5MuHea1GUfhP69dOgUKH4t++uXbXw9VXjxAlpPtfYtQKaNNEhPBxmL4xoNEDfvvaIihKwevXHY2ZsxrTXxHrt4ivQVqaMVC07sVTJqCipUJe5wD4me3vghx+kseVJ2WfevQO++soejx8r8O+/EViyJApnz4bj/v0PWLAgCkuXRmLBgiiLMkqMzM2TbL76ugFv3khBefzp69afLD56pEC2bCImTNBgz54I3L4dhhcvhGRP4SSK0hCNe/cU8nHbGpcuKVGxYtz02dhDIn791Rb16unw9m3cqUKNqesxn8Pd3YC3bxUm53jSXOimG0SJEgYEBioyzdzvz54JiIqSevvLlJF68xIbBhP7N658eb3cA3rrljSkxDh8oHhx6XcuM9V4ju8CVmzly0sXeozHfFEENm1SoW1b6y/M7N4dt5cckC70fPZZ4healy9Xm63DFBQkYNEiG4wfH40CBaRx8NbWstm8WaqkH3MKZEEAZsyI+n9dEUf89ZcNzp2Tzi2N09vGJhV7S3pgeuuWAidOKBPMJIo5T7m/v2lmcevW2nirsPv5JZ66DsSfvj5jhg2++UYbZyy9UbFiBjx9mvJBucVnOn5+fin+4qlp48aN8PHxMVlWtmxZbNu2zaLHFykiolIlA3btUqF3b9MNxt9fGt8Us3hA0aIiChUSsX+/Ch07xt0Qnj4VsHKlGrNnR+HAARXq1HFEzZrSya/xSpqLiwHZsokYNszO4sD51SsBa9eqsXChDapU0WPXrgiUL2/A2rVqdOtmj+HDNRgyRIMTJ5RYtswGBw5EWBWQA1I6uEIhve9ateKe7Cxbpkbv3hr581i0KArjxtmic2edycmRUimNYVyzxgZNm0bi7VsBvXrZY9gwjclYzJiUSmDYsGj88YctOnXSyVOCAVKl4KtXFVi3Lu4OXb26HqtXm8/7FkXpJHrTJjW0WulgJAhAZKSAS5cU2LMnwuRkR60GFiyIROPGjvDy0pkUxRNFaSq0mTPNd6Mbp0br00eL69cVWL9ejWPHpDGPnp56FCki4sIFBY4eVeHdOwG1aunx99+RZj9ncypV0mPjRjWePBGgUiHOyXfBgoZk9TICUvXW9evVCAhQoEULLRo21MPOTNbvkSMq1K0bN/UUkKawCQhQIDgYyJEDmDrVFsOGfUyDdnIChg/XoFo1PQYMsIO/vwLDh2uwdKkNVq9Wo25dPdavj8SjRwrs3KnC1Km2KF/egPz5pWqmvXrF3QbattXhyhUNmjZ1RM6cIrZujTDpbVEopAtOzZpJn3W2bCJsbaV5WGNmHMSco7xlS12c4M6oWDFpOEFgoGDSYzpjhrTDLVlig8aNdfJwkTNnlDh5Uolz5z5uO4Ig/QB6emowdKj0A1yunBNCQyFX1Y8tNFRA0aIG7Nihxs2bSixYEGlx4BxbzGE5Tk6Aj08UunSxh62tNNZy5UrLL8lLY8rNn4QHBAioWtURBoMAZ2cDChWSev/MBWS1a+sxfLji/+POPi5/+1YaVmAuawGQrvL//LMt9u1TYc+eCLOpi3Xr6nH6dDiCg6UT8osXlTh2TIV166T04LdvpQI0NWvq8dlnetSqpUPFigaTbVwUpblQfXzMHwMqVTLgyy+lccxbt0agcmXLosD69XUYNcoWYWGQA+WQEKmKeu/eWsycaYObNxWYOjXabJFCPz8lbtxQYMWKhL+zmjX1yJvXgJkzbc2eRNrYAJ076zBokB3attWhZUsdqlfXw2AAvv3WDm/eCNiyJSLRwn/GMcDxFRWVKkUrzF6ksLP7WOytWLH4T7QuX1Yie3bRogua3bpp8ddfNti71/zJc3wePhTQtasDSpfWY8GCSJMenuzZPxYctVa5cgZMmCBlAxi3VSkoN10vb14RUVECnj5VxFvoLSnp648eSTNMGF87Rw6gcWMd/PxUaNAg7nt6+1bAu3eCnOod27VrCgwcaIdnz6RZCdRqKS364kXrCjlcuaJEpUpxX790aQPevFEgKEgKwnfvVuHkyXD076/AmTNKFCny8Tt98cK0yBsg7VMFC0op7HnzSs9/+bISgwaZ9ozlyyfCyUn6XYgvOElpomg+880St24pUKqUdIwqWlSEWi39hsXusY3p/n0FBg78eH+FCnp51oWTJ6ULZcZjXokSBoSGCggOjv83KaNJbDo0o5IlDTAYpM+rVCkplf35cwXWro1EjRqOeP8eFv22RkRInTU7dpi/klOrlg6nT6swaFD8weipU0rs2qWGp6fB5Pg0Z44N6tXTyRk4Hh5SNoS54UnmhIRI56zjx8fdD0uWFHH8eAQOHVJi/nwbTJ1qi7p1dfEWwa1USY9Jk2xhMMAkwLeUj48NvvpKG2/9D0DqKY+IEOTpOgcM+PiZtWqlw9SptggOljIkjXQ66SL/wIGJX0kz11N+5YoCR46ocPZs/MeqIkUMSbrImBiLP8aYhd1i/0tLBQoUwOvXr6H7f7etKIoIDAxEwYIFTdbr3Lkztm7davJv1qxZVr1Whw5abN4ct8JezCJvMY0cGY0RI+zM9urOmGGLNm106NhRh8WLo/Dff+EoW9aA7t21cjCbPTtw8GAEbt9WoH17h3jL/RsMUkGtvn3tULWqI06fVmL58khs2hSJChWkH9Xu3bXYvj0Cq1ap0bOnHQYNssPkydFJOsgrlUDTpjqzaSLXrytw6ZIS33zzcUextwf+/DPabG9F165aHD6sxLNnAr77zg4VKujxww8J57h27KiDKEpjb40ePBDQpYs9+vXTmuyMRjVqSAWiYqach4VJvfpNmzpg6FA7lChhwGef6VGjhh5Vq+pRp44O27ZFmj2hK1FCxLRpURgyRDoJFUVp/vAxY2zx9q0Q74lYnTq6/483d0CrVg748EHAuHEaNGqkw5s3ArZuVSFXLuCvv6Jw924YNm6MxOefW35SZ5y+5PZtJdzcDHGqTMY3V/nBg8r/tXff8U3V+//AXydJZ7o3ZZRSaAste28ZspdFhl5REERF/Xod9zqu414HXP151XsRBVwoDnAwRAEBkSEKgswWBGxpGWW0dO8m5/z++Ji0aZI2SdOmLa/n43EfF5M0OSc5OTnvz+f9eb/NehRXV1kp+rXefrsX+vfX4vhxNQICFDzzjEjRXLTIE5s3a3DypMo44ynSjy1f3AYGAm3aiNZo69drUFAg4a67zH+Qhg3TY8eOEpw5o0afPj44d06FjRtL8MknpRg0SI+//KUSa9eWIjm5CHfeWQlJAt54o8xksKa6f/yjAvffX4H160vM0h9r8vYWs4I1Oy9Ub4f2979XWL0I9fAA2rZVTEatT55UYf16cbEYHKzg1lu9kZsrLrpeeskd999fUevFQmioArVaQWGh9ZN/QYGEDh1kfP99MSorgfHjvR1uxXfokGnmzoABetx9dwX+9jdP3HNPRa31J2oSa8ot37d3rxo9e8pISSnCqlWluOeeCqxYUWZxsKddOwUREQoOHqw6uDMyJIwd642hQ7XYscN80Km8HLj3Xk/s2KHB5s11f/aBgSKT5/HHK/Dpp6XYvbsEKSnFuHixCOvXl2D4cB0OH1ZhzhwvdO7sgyVL3I0p7ydPqlBQINU6O/vaa+V4770ymwNyQBxLbdsqJoNqzz3nic6dZbzwQjm+/74EBw+qMXOml1n6/dWrovfso4/WndkgScBtt+lw6ZLliuUA8PTT5XjllTLk5UmYO9cTXbtqMWmSN9LTVVi7tsSmC1VLRac2bdKYFfGzVinXlr64X3yhwaBB5rOqlnh6Vs2W63TiN2XTJg3eftvNai/aM2dUmDLFGzffrMP775dZTbl0RGysjJISGGd5y8qAkhLzmXJ/f0CjUXDpkrWgXKwpt3cms/rgo8GoUXr88IPlD+Tf/3bH5MleFjOxCguBBQu8MGaM7s8MoEKcOlWEixdVdi0VKS0Vg5c1Z68BEVRHRYlaJU8/7YkHHhAZIf3763HggOk2X75sWuTNoPq6csPa9ZoDAJIkAtHGLG62Zo24ZnBkNtqwnhyAcdKnthT28nJxzFX/7Lt3F+9LcbHoTz50aNWJ3McHCA1tXm3RbA3KNRpxnjJkrHzxhRsmT9YhKkqci48csW2CY/FiD8THy1YD5YED9di/X11rFlZmpgpTp1bir3/1RHq6+M5kZIhJuKefrrpmjo83L1hYm40bxZLPmuukDVQqYMwYPTZuLMX33xfjpZesr1+JixOddQx1jQDRgWfRIs86M0suXpSwcaPGbBCsJkMBuJQU8Ttb/fvZrp1YNlozC/HQITXc3BSL542aoqLENXL1JQBLlnhg3rzaBwvatVNw/rzzM0ZqnSl/6qmnMGTIEEycOBFPPfWUxcdIkoTFixc7d6tqERwcjISEBHzzzTdISkrC999/j/DwcERFRZk8LiwsDGFhYfV6rVtuqcQ774j2QP/9b9UF46FDaiQlmQcUM2boEBJSinvu8cK5cxX4298qIEkicN20SYN9+6pGXdq1U/Dii+YHe0SEgo0bS/DYY54YM8YbH31UajywsrNFdcRPPnFDYSFw222V2Lu3GNHRlo+KHj1kbN9ejP/7Py8MHizWdDsqKUmHefO8MH16pUll3Pffd8Ott1oOjC1p3VrBTTfpccst3vD0VLBqlfXK7wYajVhb/uab7pg+XYeTJ1WYNcsLt99eiX/8w/IXOjpagZ+fgrlzvZCfL9rOZWer0KqVjPvvr8Cdd1bafUE1e7YOO3ZoMHOmeM6CAgkTJ4pKjNbSNnv2lDF2rMgYuOWWSrvSGW0RFydDrwe2btVYDBYjIiwXehPr9EU7jJoOH1bhzjvF7Ojtt1fitdfKEBkpjrF//rMcx44ZZqvdcfmyyphaJEkKXnvNysJ7iBngI0fU+PhjN/z97+UWAzBADCRs3FiCq1clq2m3QUEi66L6YJAlajXwt7/ZvrA5KUmHl17ywN//XmE8Lmuut6tNx45iXblhZunFFz1w112ViI2V8eGHpbjnHk8kJXlj4cIKpKWpsGZN7bOYkgT4+orAG7D8XojRewUBAcBnn5XipZc8MHasFl9/XWJWxbo2WVkSzp83X1P8xBMVUBQJ995rXwU6d3fra8p/+km0DwsNVRAaqtRZ7NLQGu2mm/RITZUwfbo3JkzQoVcvPe65xwuLFlXgsccqoFKJ92PuXC8UF0v49tuSOteU1UajEUXGunWTce+9lZBlkWL9xhvu6N3bHfPmVUJRxPpvezOQbDF8uGiNNmaMCI6++06DPXuKIUkiaP/22xI89JAnxo71xsqVpUhPV+HLL0U2zk036S1mkFgyc2Yl3nnHzWpQ7u4OjBunx7hxeuh04j349Vc17rjD9nN/ly6ySW2SrVvVmD/fC2+8UVbn9xgQ2U//+Y8HFiyotBhgrV7ths2bNfj+e9vzjO+4oxL/+5872rf3gSSJlF83N+Czz9zwxRelxvMeIDKzkpK8MGdOJZ54osKmwN8eHh7i9ZOTxSxvfr54gZpBuSSJYm9Xr1pOXw8NVVBaKqGoCHb93qSmqhAdbfo9HDZMh4ULRVBQfWBLpwO++06Ddu1Ep4svvyw1DqYoisjmaNtWxj/+UVEjo1A2a5lpzfXrEu680wshIQqGD7c8ute5sx5LlnggO1syXtz376/H4sWm6VqWZsoB8ftpCMpTUkT6flSUpUH5xltHXVAgfjdKS0UaurVrC2tOnVIhMbFqXw3F3qZOtfz4c+dUf7aGqtrviAix/v7ECTV+/lmNv//d9Fo1Olq8H41ZpLg+srJUSEy0LRsmMVGPEydUmDRJTAStXCmuaXr31uO339QWs0aq27tXTHj88EOx1XNE167iuq3mZ1VdZqaEf/6zEmFhCu65xwvffluCV17xwNSpOpNsovh4Gbt3276868svNZgxw7bfhboGkQ11jY4dUyMmRgedTgyG792rwaOPlpvUeqpp+XJ3jB2rqzOryXCd/uOPGnTrJptdN06erMOmTW6YPVt8vrIs9vGmm/Q2zd6HhSnw9lZw4YLITNm/X/y2vf127ddm1Xuc1xa826vWTV6/fj2OHz9u/PeGDRtM/rd+/Xqb08Gd6V//+hfWrl2LsWPHYuXKlViyZEmDvE5QELBlSwkuXFAhKcnbWPE0Pd38wtVgxAiRQr52rRsWLfJEeblYizx3rvW1CTV5eoq00XvvrcC0ad546y033HuvJ3r00OLHH9V46qlyHD0q1iNaC8ir78Mnn5Ri+fKyel1EDBumx+OPl2P2bC/jj9j16xK+/toNCxbYF+zffXcF8vMlrFpVWmfKo8GMGZUoKxOzP7fc4o377qvEM89YvzCSJODFF8sxYIAe994rZr9Ony7EsWPFuO8++wNyw3O+9loZBg/WY8mSMqSkFOHNN8tr/WFycwOWLSvDnXc6PyAHRNCQmCjj228tB+WRkaLoT/U1ZRcuSPj1V5Haailo+vxzN4wercOvvxbjsccqTC5MJUkM9jz3XAX27StBWloRMjPFLMixY9YHiACRwr50qTs8PBRj1U9r3NzMU/Ebw+jROmRlScYZOUuzCLWpXoF9zx41Dh5U45FHxAWNuzvw3ntliImR8fDDYmmJLce/n1/tM+WFhRL8/MR7pVaLYlszZ1bW2Yqrpt9+Eyl7NWfDPTzEc9rbtlGjsbymXFFESqShmq8tDMXeTp0SM5XTp1fi5ZfLceutOnz7bQm+/NINd94pzk1Tp3rDwwP4+uv6BeSWqFRigOCLL0rx9dclOHNGhbffdjepuu5MhnXlBQXAo4964p//LDdZGqHVimNq9uxKjBunxeuvu/9Z3bcYn31mvXNDTeHhClJSim2eTRo8WI9HHqk9y6OmLl3E+ladTszkP/KIJ7p0sb32x8yZOgwbpsOUKd5maYP796vx7LMeeP/9MotBlTWensCmTSX48ccSnDtXhJ07S7B5cwn69dNj0qSqjJNTp1S45RYv3HWXaKnn7IDcQBR7E+ePvDwJWq1i8TMMDlZM/r86Pz/Aw8P+Ym9paZIxI8jA11dky/zwg+lF/88/q6FSAV99VYLMTFELxeCTT9ywb58ab79dZpb1YGndvCXnzkmYONEb4eEyvvqqxGorqy5dRKHT558vN/6m9+2rx5kzKpNsB2sz5Z06ycZZRrF2Xbb42YqZ8sapzfLaax7G33IxGGvZlSuWsyFOnhTt0Azi42tvR2jIBKu5vrh7dxkff+xmvMaorkOH5tUWLSur9h7l1RkKLv74oxoeHlXLUQxBeW3y84H/+z9PPPdcudWZaECcQ7t00VvNYDCcIyMjZTz3nGhluWCBF779VmM2QFJXJkR16ekSjhxRW23P6Ijqxd5efNEDWVkSgoNl5OZaP3ZzcsR54qGHbGut6u6uYPduyxnKkyZVYvdu8Rt57pyE6dO9/kxdt20SQZKq1pUrisgAuvfeijqXZoiK8c5PYa91eKVv375o166d8d9NRYcOHbB27dpGea3QULEW9eGHPTFunDfuuquyzr7inTvL2Ly5BHfe6YXRo72Rmamqc9SlJkkCFi2qRHy8jJde8sDQoXrs3l1c6xe9od1/fyXy8iTMmOGFTZtKsGGDG/r21RtTpWw1apQeJ04UWZ0ptcTdXbSxeeIJD7z6arnF1OeaLK3tr6/AQFjMcHClHj30OHRIbTEoDwsTxW2uXata57xunRtGjBAXw6dPm69F2rtXgxdeML+gskajsXxxWFNCgoy8PAlvvFFh83M3Ni8vsW7866/d0KNHOdLTxSyCtXXLNcXEiFaKsiwKjD38sOnJ3c1N1FwYPVqHpCTbjk9fX6XWPt0FBZLZgM9tt1Vi8mRvlJTA5gGo335To3dv5818WFtTnpYm1mrb03Vi4EAd/v53D9xyi1iy8thjVYFRQoKMbduKsWiRF4YM0WLWrEq8/nqZ1XVwztK7t4xPPy1FWpr1jI76GjJEhwULPPHgg57o1Em2mNmiUgGPP16B+fMrEBAAhwPGhgo0DaKiFKhUIgh4/nkPDBumx623VuLZZ237IVCrxTKAp58Gpk71xtdfl6BDBwUXLki4+24xYDF4sP3ruUUQX/X5qdXAf/5TjpdfVjBpkjdeeKEczz/vgbvvrsTjjzdsv8LExKoexpZ6lBsEBysICLAcsEtSVbG3mBjb3w9L6esAMHKkWFc+f37VsbdxowaTJ+vg5wesWFGKKVO8MXiwDt7eosvF6tWlFoOgrl0NFeat/34fPqzCHXd44dZbdfjnP8trne0aNUqHixdVmDKl6lwaEqKgY0cZBw+qjfVCLl+WrKavGyqwHztm2gu9uuhoGT//3MAnFIjlEatWueH770swfrwaBQXWZ+EmTfLG//2fyPozKCsTn2P1a7LOnWWsWmV9dM5aJli3bmIQfexYndln0KGD7YFgU2Br+jogvoP/7/+5Y+1aN8yYUVUMrVcvPV5/3d2k5kNNTz/tidhY2awOlSW19Xu/dk0EiOHh4jv+7ruiptGdd5pP7sXFiYJjNWuuADAOZF+7JmZ0k5NFQVJnDlb36KHHmjVuWL9eg08/dcO2bcWYN88LeXnWf1A+/NAdvXvrbUovB8R+HTggajPV1KGD+L4//LAnfvxRg9mzK7F6te0TfkDVuvLdu8Xyj48/ti1eM6Sw29phwxa1BuWrV6+2+O8bjacn8M47ZXj1VXe8+KIHbrut7i9ceLiC9etL8MQTnrj77kqHC2KMHKnHyJFNp+znk0+KWe4ZM7xRVgYsWeJYgGpPQG4wZ04lBg/WW13Te6MyrLGx9MPq5iYGljIzqwflGjz0UAXKy8WFSPWg/MIFkepva6E5ewwerMPDD5djwoSGmVV0lqQksY7rn/8sN6Z02lrExDBTvm6dBtevSxbbh2k0MKZa2cLXV6l1xqSgwHwmPzFRRmSkjG3bNJg2zbbXEstynPfZuLkpFmfK9+7VoG9fvV1pmdHRCjp1EgXTHnzQ/EkDAkRGkKF6d0MHmNXZUlTMUQEBYsZq796qtHVrbE0jdxW1WlxAPv20B9LSVNi1qxiKImY3srKqsj1qo1KJ3xxPTxGYr15dikce8cSkSTqbU/VtIUmiR3VgoIIHHvDCk0+W49FHGzYgB8T39r33qtrn+ftbfk9CQxWEhFj/HbS32FtBAZCdrTKbKQeA0aP1ePVVD5SWikFLnU7UG/nwQ5HW2727jCefLMd993nBw0PBokUVxvZZ5vunx9df155q+9BDnrjnnko88kjd73ffvjL69jVfMmVYV24Iyq2lr8fG6nHligr5+aId2oQJlo+hxlhTrijAM894YM6cSnTuLMPPT0F+vvXH5+ZKePVVdyQlVRoDkLNnVdBqYZJN07mzjPR0yWLQBpj2KK+ua1cZOp1k8bOMjrbeiqqpURQR5IaF2Xbd2KWLjOvXRbvEPXuqlpx27SqjqEhkylrKCNy0SYPt2+s+TxtER5u35DPIzBSDCIZBt6goBTt3FlscoAkLUxAYqODsWZVJkJueLuHWW70wapQeYWEywsMVJCZWOv36S3z/1Xj0UTVWrBA1mQICFKsz5SUlwHvvuWHZMutLHWvy8VGQm6uyOFMOiEzajz92x2eflTpUaDMqSjEu/XrwQds7zIhib849LzSPb1UToFKJgLR7dxlt29r25fb2BpYutf3Aaw4kCVi8uBwPPCDh0CE1xo5tvABLrQYDcgt699bDw0OxmmJd1atcFMXJyFBh3DgdkpPVOHpUhTvuqHrs3r1q9OolN0iqfVAQrNYAaEqGDRPrZvfvV5u1iqlLx44yLl6U8PLLHnjyyXK71wNa4udXexpjYSHMAhpJEuvj162zLSjX6UT65ssvOy8LxN3dcvq6vanrgNifH3+0XEHdwJBa3tI8+KBYK2/r8qemrHNnGWvWaLB+falxmURcnIxff1VbbTVYkySJ5RSengrGjNFi8GCdU4/b6h54oBJJSTqLs6wNISFBjwsXRJBoqUe5QXCwUutsV2iobFf6elqaCkFBljMAY2NFr99fflFj5EjRTUSjgUnv4nvvrcTevRqUl8NqX19ABDZ//GF5Vg8wtE5TY84cOxsv19Cvnx6rV7sDEIPP2dkqk2VYBkFBosXckSNqnDmjsjpz16GDyDyo3gnB2bZu1eDECRVWrhT77u9vfTBWlkXh2qAgsTbXkMGRkqJC586mg5JhYaLeSM2gzeCPP1QYNsz8u9e9u/h8LQXlhkGK2maNm4qCApGxZetMuVYrfsf9/GCyJtrDQxy/v/2mRnS06ful0wFPPumBJUvKbM6qi46WsX275RDs8mXz47VdO8vPK9ozi1T46p/vtm2i6OXq1fX7LtUlNlYUGL7vvgqMGSOOlYAAxepM+YYNGkREKFZbiFqi1SqIjpatLkFYtKgSixZVOnwsRkXJ+M9/3KFSAfPn236NGhXl/PT15pN/0kSMH6+zWpjhRqFSiXXSO3cWN9k05BtJTIyCQ4eKrQaArVrJxgq569ZpMG6cDj4+4kf3+HHTD3DPHo3FH+gbiUYjioesW6dBWprt68kBkSHj7S1+lOpaN28rX1/FWEzPksJCCb6+5j9WSUmV2LlTg7y8ul/j999VJu0ZnUGsKTfdblkWrV6qV/O1VVO/+GsokyfrMHFiy/hOTp9eiX//u9xkNqNfP9vXlRtIkhgkX7WqFO+/X9qgSxUaKyAHRJDVurXoUpGba1553SA8XKn14j801L415WJdsfWL/lGjRJFTAPjmG5G6Xj17SKUCPv64FGvXltZ6TRARoSA42Hq/+uPH1WjdWq53em3//nocPSoqKl+5IkGjsT6IERcn4+uv3RAWZv09DQpS4O+vNFixt4ICkfb/9NMVxoERPz8Yi/3VVFwMKIqEJUvKsGyZu/GzPnVKjS5dTM/hkiQK4ll7z62lr7dtq2Dz5mKLEyHR0VVt0Zq6rCwJnp6KXYMpkybpcM895sFZr16W15UfOaKCTifZnJUGGIoHWv58rWV2WCPWlZtu1/ffi2u9hqbRAPv2FZss7QkIgNWgPC1NhT597Mtm02phdZYcqGpr7KioKBnXr6vw179WWK1fYe3varZTqy8G5eQQlcq+yq7UsGqr/tiqlYLMTBVkWawnnz5dTF927y76BhuKvSmKmCkfNqzlzTbaKylJVPQ0tJqzlSQBU6aImTtH+nZa4uenoKDA+v2W1pQDYnYnIUHGt9/WHbEcOiTWUzpzkC0gQMFPP6lRXm0S89QpFcrKJJvXklHLMmyY3my9Zf/+9gflBhMm6JpNr2RbGYq91TZTfvfdFbVmB4g15fbNlFtKXTcYPVqH7ds1qKwUVderr+E20GhgtTWlgSSJGhAnTlj+vI8dUxlnaOujfXuR0nvsmBqXL6sQHq5YPbfFxsrYtEljsRd69e1uqBR2WQYefNATMTEybr+96rshzvuWP0ND4c8RI/QYPlyH114Tec4nT6os1vgRFdjN34CcHJEGb+03zloR2+bUFi0rS7QOtCdoe+qpCotLuawVe/vxRzGZYc/vZ/v2MnJyVBYHzS9dspzZYU1cnGzSFi0/H/jlFzXGjGmcwdxWrRST653aZspzciSbahBVFxEhW+3A4AyJiTKGDtXZ3aHKsKbcmZr+N4qI6qVVKwVXrkg4cECN0lIY29FERyvw9ISxYMvvv6tQXGy9s8CNpG9fPXx8FPz2m31BOSB6zjuyrskaW9aUW1uPe8stlVi3ru5VSsePq6wWOXLU3/9e/mc/ei9jz9K9e9UYOFDf4EXYqPno10+P48dN+8TeyBIS9EhOViMvz/pMuQiKrF/YGgq92cpakTeDwYP1uHxZwscfu8Hd3TR13V5du1qvwH78uNquNo7WSFLVuvLMTMtF3gxEf3ipzvZP0dHWg9D69Cp+4w13nDqlxvLlplkG/v6K1ZnywkJRmV+tBp55phxr1rjh7FkVTp2yHJR37my5MNsff6gQHu7YcrXG7t3uqEuXbK+8XpfevcWxW/Nc9eOPGrs7cPj7A8HBllvtWStMaE18vGlQvnOn6MZjLeW9oQUGWl9Tfv26hKAg+7brgw/KcOutDReUt2ql4OuvS+2uddWunYxLlyTonLhpTf8bRUT10qqV6FX+9ddihsMQEIm2J1XtLPbsEQGTrW2UWjKVCpg2TYya1jaD1Bj8/FBHSzTzNeUG06bpsH+/Gleu1D5NkJVluWVQfQQGipZJxcXAX/7ihaIi0Z98yJCWkYpNztGunUhpPnKEa6EAMZNsmCmv2Z7QVmFhoo+5reqaKddqRWuoxYs9MGWKeTVue1RVYDd39KjaKTPlgGlQXlsqcGysuK+2mXLAehB66pQKvXppHQrMd+xQY+lSd3z4YalZocbaMqSqn/M7dlQwe3YlHn/cA9euiTXlNRl6lddkLXXdFh06NFw6f31duCBh+XI3TJ7shYce8nTaLGu7dqKI2YkTVfudlyeKBBomO+zRvr3l9zAzU2V3+vrFi6LmAdB4qevWOHumvL7p6Q3FkM1w6ZLzNs6ub9SuXbtw1113YezYsbhy5QreeustYx9zImqaIiNFis2mTW5mbeK6d9cbe3Lv2aNxaK1vSzVrlg69e+tdXtVatESzfNJXFEP6uuUfuYgIBQMH6rFhQ+2z5Tk51lNl68PXF1izphQaDTBjhjd+/llttTIz3ZgkybF15S1VYqIep0+LNkaOfic7dxbPYUv2gaLUHZQDIoW9sFDClCn1q3KfmCgCxJqzS6LPsArdujlnENSwLCIzs/YBx/h4GVqt4nBQ/tVXGly6pKpz4LOmtDQJ99/vhddfL7NYp6iumfLq5/zHH6/A8eNqtG0rW6wcHR+vx9Wrpr3bATFT7uigc3S05VleV0lNlfDf/7pjzBhv9O+vxY4dGkyfrsPRo8V48knnFJiVJNEKs3oK+549GsTGyg4Naot15ZZnyu1JXw8JEd0YTp9WobIS2LFD06hFmGsKDKw9KLd3pryp0miANm0Up64rt/mZfvnlFyxatAgHDhzA+fPnERQUhI8//hgff/yx0zaGiJxPFHpTwcdHMSuW0b27jGPH1KisFD0tuZ68SlycjC1bXN+OUKSvW76vtBTQ6aRaW3iIKuy154vn5qJBgnJAzLKtXl2KkBAF7u5inTtRdfVZV97SREUp8PAQ3RCspa/XJTpaFDY7eLDu9/T6dQn5+RKio2v/Xk6YoMOYMTqr64xt1aGDaDH5xx+ml5/Hj6vRpk39i7wZJCTI0OlEanFts46hoQpSUorqHHy1VJhLUYANG8S51Z5U7qIiYN48L9x2W6XVNpS+vtYzpIqKTOuIhIUpeOSRCqvFsPz9RQHB06dNj4f6zZS7Nn1dUYDkZBVeecUdw4Z5Y/hwLQ4eVOPuuyuQklKEr74qxdy5lbXW23FEzXXlP/6odmiWHLC8JEKvF8UJ7ZkpB8Tg0u+/q7F/vxqenopL67bU1hLt+nX7Z8qbstraomVlSdDbeWjY/I16++23odFoEBMTAwBwd3dHr169cOTIEftekYgalWEENymp0iztsHt3UZV1/341vLwUs8qt5Hp+ftZnyg2319bjeeLESpw8qUJamvWZnIYevfb0BD74oBTbt5c4rQAetRz9+lkuoHQjUqnEuvKSEsdnyiUJGDJEj59+qvs9TUuTEB4u11mduk0bBZ98Ulrv769aLXpBV08BBkQKcLduzhsU1mhEAJWaWnfRLG/vup+vQwcZ2dkqkwHS335ToaBAwoABOrsC1LVr3eDmBjz3nPVifbXPlIvezdU99FAF3nnHegve+HgZJ0+abmN9gnJDQFmf9fT2UhTxnr/wgjv699di0iRvnD2rwiOPVODUqSJ88kkpZs/WNWh2W+/eehw+rDZuz86dGowY4distKVsg+xsCTqdZHNrNQNRgV2Fbds0GDOmfktM6isgQEF+vvnter0oLNhSZsqB2tuiTZzojV277Ptds/ljO336NMaPH4/Bgwcbb4uIiMD169ftekEialxeXsDAgTrMmmWedhgVpUCrBVascMfQoXoGTE1QbWvKCwokeHkptRZOCwgARo60Plsuy6J9SUP/ULq5tYxe2+R8zJ4wZUhndnSmHACGDNHhp5/qLvJoS+q6syUm6s0qsDuryFt1/fuLIN/eAMeSgAAgKMh0ZnPDBjdMnFiJuDj7Zo2zsiR07aqvtVp9bUG5pSVLda277dxZb1LsTa8XywXsLWRqEB0to6BAQk5Owy721etFFt/TT3ugZ08tZs3yxtWrKvzzn+U4daoI771Xhltu0TVaN6CePfW4dEnClSsSTp8WtR8GDHB8pjw93fT9y8yUEBIiw8PDvucyVGDfssW1qetA1Uy5XOPQysuToCgtKyi3VoE9PV1CerrK7u+HzWcRtYVa/5cuXYKPPc3/iMglNm4sRceO5idCSQK6ddNj2zYN1/o2UT4+tbXGgdX15NVNmKCzOmKbnw/IcsOsKSeyhWFWkwTnBOV6HDlSVfzJmroqrzeErl1lpKSYXn4eO+a8Im8GhqDc3lRga6KjFWPwrdcDGzdqMG2a7s9UbtsvvkVl/dofU3uhN8ttMGsj0pur3vPz5yUoChyu0O3jA4SFWe+1XR8VFcDOnWo89pgHunbVYsECT5SXA6+/XoaTJ4uwbFkZJkzQwcvL6S9dJx8f8V7+9psaP/6oxqBBerurdhtYyr6wtx2aQXy8jF9+USMrS3L5tVxgoAJZlszOPTk5YhLBlsyU5sJar/K9e8WIW3FxAwXlHTt2xLZt23D06FEAwOLFi/HTTz8Z09mJqHkyFLgZNoxF3poikb5uue2OtR7lNUVGKsjKsny6z82V4O4uMiaIXMUQQJGYSQbqF5S3aaOgbVsF+/fXnj6ZlqZCdHTjDsh17Spmyg3ntPx8MWvr7JnyPn30uOWWSqfMlAOm66gPHFBDrxeDH9YKdllTWw96A3sKvdmiSxcRSA4f7o0xY7wxd64X2reX6+wtX5uePWV89plz+luWlgJbtmjwwAOeSEjwwSOPeMLDA3jvvTKcOFGM//ynHCNHNo3uMGJduQo//uh46jogsi8CA00rsIsib/Z/D+Lj9SgvlzB8uM7lQa+vL6BWm68rd6QdWlPXrp2MjAzz7+neveK8W9egaE02fx0XLFiAe++9FydOnAAArF69GgBw99132/eKRNSk9OmjR8eOepf1tKTa+fmJUefiYpit+ywstN6jvLrgYAXXr1uvhhoYqDTJliN042BQXiUhQcZ//1ta74GyoUNFCvvo0dbf29RUldViYw0lPl5GYSFw8aKEtm0VY5E3ZxeAEkuzrK+ztleHDjJSU0UAtX69BpMm6aDRiPZg6ekqyDJsWgJWWw96g9qWLRUVwe7e2wkJMtavL0VhIVBWJqGsDHUW96vLv/9dhlGjvDFggB4zZ9p/DBUVAdu3a/Dddxrs2KFBWJiCSZMqsWZNCXr2lJvscrrevWV8/LEbTp1S4eWXrdcFsIVhXblhQKqubgHWBAQAEREyxoxx/XlUkqraokVFVe1LS6q8btCunYLsbBWKi2E8X8sy8NNPanTqpLd7ptzmoHz48OF48cUX8d577+HSpUuIjIzE/PnzMXz4cLtekIialrFj9Rg0yPVVxskyQyBeWCiZFfeprR1adSEhIgW+ogJmMw0trfAKNU+9e+vRrp0MT08eixoNcNtt9Q+UhwzRY9ky61OLiiJmqBt7Tbmnp+gPnpysRtu2Ohw7pnJ66npDiI6WsWOHBpWVwLffavDBByLgj4qSUVEh1gO3aVP38WtbUK6gpMTyObuwULK7QJskOX/gq3VrBW+/XYb5873QrVsJ4uPr3qbcXNFH+9tv3bB7txrR0TImTtThr38tQUKC3CwGh3v31uORRzzRpo3scKE8g/btZbOZckcL7n7ySSni4ppGfQ5/f5i1RWuJQXlIiAJvbwUXLqiMx//vv6tQUiJhyhQdiooaKCgHgBkzZmDGjBl2vQARNW2ShFpbapFraTSAt7cIqmuOoBcU1F553cDwQ5iTY17V9fp1ricn1/PxAQ4dKnb1ZrQogwbpcf/9qj9bHprff/WqhNJSERg0tsREUYF9/HjDevKmEUzUxpC+vnevGm5uVUGuu7tYLpCWpkKbNnUHvrm5dQfl/v7i/oICyaxNnCNryhvKyJF6LFxYgQULPLF1a4nFKv5Xr0rYskWDb7/V4Oef1UhIkDFpkg7//GeZxVo3TV2nTjJ8fBSMGKGr9yBCzaUPmZkSRo1y7LvQrVvT+Q5Z6lWek9Oy2qEB4vrZUIE9Pl7ctnevGgMG6BEQoODatQYKyg8ePGjxdrVajbZt2yI0NNSuFyYiItsY1pXXVFBgW/q6m5u4yMvONg/KOVNO1DKFhyvo1EnGzz9rMHGi+cx7aqoKbdooDheqqo/ERD1++UWsuzx2TI3bbjPvDtLUdOggIzdXwqpVbpg61bTtlCFgHzas7qA8Px91DoR6egIeHqLYW0iI6X2OrClvSH/7WwV+/VWNxx/3xDvvlEGSgAsXJHz3nUhNP3RIjT599Jg4UYc33ihr9l041Gpg1qxKTJpU/2yW6GjZuP4YEOnrjhR6a2os9SpvaT3KDdq1U/4s9ia++3v3ajB0qO7PZYcNFJTPmTMHUi1DQtOnT8cLL7wAVVNdBEJE1Ez5+lquwG7PjIm1deW5uZwpJ2qphgzRY98+tcWgXBR5c83sWteuMlaudEdeHpCe7vwibw3Bzw8ICZGxdasbtmwxzeqoXgSuNoYWlLYU8fPzMxR7qzlTblvXjcai0QDLl5dh5EhvPPSQJ86cUSE5WYVBg/RIStLhvffKEB7edLbXGZYsqd9acgNDv3dAHBuXL0to1arpfxfqYlhTXl1OjuSSrJyG1q6dbGyLptOJFn5PPFGOQ4fUdqev2x1BK4pi8X9ff/01Pv74Y3ufjoiI6mCt6I+tM+WA9aC8Ja7zIiJh8GA9fvrJcgV2V/QoN0hI0OPSJRX27NGgbVvnF3lrKNHRCtq1k9Grl+n7FhNjWwX2wkLRgtK2oBxWB2ObWjfi8HAF779fhpIS4O67K5CcXISvvirFvHmVLS4gd6boaNEZpagIyM6WUFlpvkytOQoMvDGqrwOmFdiPHFHB3V1BQoJY4lBs54osm4PyV199Fb6+vnj99dexefNmvP766/D398dLL72EJUuWwNPTExs2bLDv1YmIqE6+vorFoLyw0LY15QAQHCwjO5sz5UQ3kkGDdDhzRmVxbWNqqtToPcoNAgOBtm1lfPqpG7p1a/pF3gy6d9dj1qxKs7XEtvYqz82VoFYrNgXV/v61ZUg1vXP2gAF6fPBBGWbP1iEoyNVb0zwEBSnw9xdt0S5flhAcLLuk/7qziZZ+pre1xDXlgGFNuQin9+7VYPBgPVQqUY29wWbKP/zwQ9x8882YMGECOnTogAkTJmD06NH47LPPcMstt2Dq1KlIT0+368WJiKhuIn3d/HZbq68DokqotfT1ljh6TURAUJBoh7Vvn/lsuSsqr1eXmKjHrl3No8ibweLF5Xj88Qqz26OjZaSnq6CrY5mxoUe5LQXCqtLXqyhK00tfJ8dJUtWx42g7tKboxpopV3D+vAqKIoq8DR0qBhnFTHkDBeXnzp3D0aNHUfRnJ/SSkhKcOHEC586dAwCEhIRAUVrem01E5Gp+fs5ZU25pptzQp5yIWqYhQ/TYuFFj8v3X60VQ7qqZckBUYFcUqVm0Q6vOUkDdrp0CRRG912tj63pywPJsY3ExoChNc6acHGNYV56ZKbWIIm+A9TXlLTMol1FUJCEzU8LBg2oMHSpG5rRaBX+GzDazudBbp06dkJycjKFDhyIyMhKXL19GaWkpunbtCgD4/fffWYGdiKgB+PpaToOyd0352bPm47At9YeSiITbbqvEgw96IjFRi+7dZYwYoUNCggy9Hi6thN21qwjGm1tQbolGIwLztDQV2re3vj8iKLftOUXXDdPzvuF3oKm0RKP6i46Wce6chJAQtIgib4B5S7SKCjGJ0BLT1318xPLAr792Q0iIgg4dFOPtDZa+/o9//AOenp4oLS1FamoqSkpK4OXlhWeeeQYFBQWorKzE9OnT7dsTIiKqk7Xq67b2KQcsF3pTFKavE7V08fEyduwowbFjxbj77gqcO6fC4497olMnGW5urtuugQP1ePTR8haz/rhmz2lLbOlRbuDnB7P09cJCCd7eCtSWa/dRMySCchUuXWoZ7dAA85Zohn+31Ky8du0UfPKJG4YO1RszabRaBSUloqq+rWyeKe/Rowe+//57bNiwAZcvX0ZkZCSmTp2KsLAwAMCKFSvs2gEiIrKNSF83v72w0PaZcktryktKgPJypq8T3QjCwxXMmqXDrFk66PVAaalrt8ffH3jySfP12c2VLW3R7E1fv3rV9PkKCrievKUxBOWSJGP48JYzU159QOn6dXGt4u7uwo1qQFFRMo4cccPjj1e1yvPxUaAoEkpKYHO3BJuDcgAICwvDwoUL7dpQIiKqH0tpjOXlIqC2NY1RBOWmF3i5uRIkSYG/v7O2lIiaA7Xa9gtFsk10tIwdO2q/rDYUerOFpVoiTbXyOjkuOlrBlSsqyDLQunXL+Gz9/YHSUgmlpYCXV8tfJteunRhMGTasaumKViv+v7hYgo+Pbftuc1BeWlqKjz76CMeOHUNxtcZrkiTho48+svVpiIjITr6+5n3KDf9tT/p6bq4EnU6sfwQMqZRgKiQRUT3ZOlPepo1ts6GWCr3ZU9yTmoeQEAU+PgquXVMhMrJlzJQbskHy8yV4eSktth2aQfv2Cjp10iMiomof3dwADw/7epXbHJQ/88wz2Lx5MwCYVFmXbOnrQEREDrO0prygAHB3V+DpadtzGH4Qc3IkhIVV/Zup60RE9RcTI+P8eQmVlbC6Vj83F+ja1fGZ8qIi2DzrRs2DJIkBnePH1SZBXXPm5iaO09xcCRERSotth2aQlFSJQYPM+yFWtUVz8kz57t27oVKpMHToUAQEBDAYJyJqJCJ93fQ2e9MYPTzED8T166ZBeUv+oSQiaiytW4sCbBcuSMYKzDXl5Unw97e90BvT128M0dEyzp9XGVOeW4LqFdhbelDu7Q2L33mt1r4K7DYH5R4eHhg5ciReffVVm5+ciIjqz9Ka8oIC+9MYa1ZgZ1BOROQcajXQvr1IYe/QwXJbNHvWlIv0dUtBeb03lZqY6GgZZ860jNR1g+oV2G/Uaw17e5Xb3BLt1ltvxe+//46ysjJHtouIiBzk4wOUlUmoqFao2J4e5QY1K7Dn5jJ9nYjIWepaV25v9fWiIlEHxMCejhvUfAwYoMfQodb72zdHAQFVNRFa+ppya7Ra/Jm+bhubZ8oPHjyIs2fPYvjw4YiJiYHmz0pBLPRGRNSwDBdhhYVVP2yFhbYXeTMIDlaQnc2gnIioIURHK3UG5fZUXwfEuT4wEMZ/h4bynN3SjBypx8iRLS8oN8yUt/T0dWt8fJSGSV8/fPgwACA/P9/4b4CF3oiIGpqnJ+DmJnqVBweL20T6uv1Bec309bi4lpUyR0TkKh06yPjuO8uX1qWlIuPJ1haU3t6AWi2KvRkC+cJCCdHRPGdT0xcQULWm/EZOX2+Q6uvTpk1jAE5E5AKSJCqwi3Xl4odNpK/b9zzBwbLZTPmN+ENJRNQQaktfNwQotqavS5JIYRfF3qqCcq4pp+YgMNB0TfmNmL7u49NAhd7+/e9/O7RBRERUf76+ppV4HVlbGBysICOj6oKRLdGIiJynQwcZFy9KKC8XHS+qy8sT2U0am6+8RQX26sXeCgvB6uvULAQEVF1viKD8xsvwEC3RbH+8HacGQJZlZGRk4Pr16ya9yvv27WvP0xARkZ1qVmAvLISxtZmtWH2diKjhtGqlwN0dyMhQITbWNAixZz25Qc0K7GyJRs1FQIA45ktKgNLSG/NaQ6tVzNoa1sbmoDw1NRWLFi3C+fPnTW6XJAknT560fQvrYenSpfj0008RHh4OAOjYsSP+85//NMprExG5kq+vWFNuUFAgISbGvpFnVl8nImo4KpVob5WWJiE21vS+3FzbK68biMHYqv8uKmL6OjUPhjXlOTkSVCrF5loKLYmPD5CZ2QBB+auvvoqMjAyHNsqZJk+ejH/84x+u3gwiokZVc6bcsTXlVUF5ZaWYdbkRR6+JiBpKTIyMP/5QATCtpp2XJ2a+7eHnZz5T7uPDczY1fYY15YZlcmq1q7eo8dlb6M3mPuVHjx5FXFwcpk6dCkmSsGbNGkRGRnKmmoioEfj6okb6umNrynNyJMgyjAVYOFNOROQ8sbEyzp41j0Dqm76uKI61wiRyBcNMeXb2jTv432At0YqLi9G7d2+4u7sDALp164bBgwdj5cqVmDBhgv1b6qCtW7fiwIEDCAgIwKJFizBgwACLj7t27RqysrJMbvP09ERMTExjbCYRkVOJ9PXqM+X2F/wJDlYgyxJyc8X/tFrFrBgRERE5Li5Oxu7d5pfXeXmOpK9XFfgsKQH0eq4pp+YhMFBcs2Rl3bhBuVYLlJQ0QFDu7e2NyspKBAUFAQA++eQTHD161Kkp7bNmzUJ6errF+zZs2IDZs2fjvvvug5ubG3777Tc8+OCD+Oqrr9C6dWuzx69duxZvvfWWyW1dunTB+vXrnba9RESNxc/PdD24SF+374dOqwW8vcXzsB0aEZHzxcbKOH1aBUURbc0MHKnh4e+vID1dJLUaZtx8fJy2qUQNxrBU49w51Q17rSFmym1/vM1BeevWrXHp0iWMGDECiqJgyZIlAESxNWdZu3atzY/t3bs3unTpguTkZItB+axZszBy5EiT2zw9Peu9jUREruDrC2Rk1Exft/95DOvK2Q6NiMj5YmJklJQAly9LiIysOsfm5UmIirKvOKefX1WBz8JCwMtLgZubM7eWqGF4ewPu7grS0lQICbkxrzW0WqC4uAFmyu+//35kZmbipptuws0334zt27fD19cXTz/9tEMb6ogrV64gIiICAJCeno5Tp04htmZ5yz+FhYUhLCys0baNiKghVU9f1+lESpQjawuDgxVkZ0vIz2dQTkTkbJ6eQPv2Ck6fViEysqrYm5gpt++5RFAuzvss8kbNiSSJdeWpqSqMHKlz9ea4hFbbQGvKx4wZY/z30qVLUVRUBC8vL6gbsZzeG2+8gZSUFKjVaqjVajz33HOIjo5utNcnInIV04szcZsjF2iGmfKCAqavExE1hNhYPc6cUWHEiKqgPD/f/jXl1Qu9iR7lTt1MogYVGCiC8ltvvTGvNQzp6zWXslhjc1AOAKWlpTh37hyKa9R379u3r10b6ahXXnmlUV6HiKip8fOrWptUUCBBrVag1dr/PIagvKiIM+VERA0hLk7GmTOmDY4cW1MOk5lyFnmj5iQgQMHp0zfuBIBWC+h0EioqYFNRXZuD8t27d+Nvf/sbCg1TNH+SJAknT560e0OJiMh2Pj5VF2cFBWLGxJaR15oMQXlJCUzWOxIRkXPExsr46CPTxd+OVF/39a0+U852aNS8GAahgoNvzOPWkM1YVCTBw6Pu98DmPuVLlixBQUEBFEUx+x8RETWsmmsLHb04q17o7UYdvSYiakhiplwNwyWyXi8GUx1JXy8sBGSZa8qp+fH3F/9/o15reHoCKpWCGgnmVtk8U3758mV06tQJS5YsQUBAACRHpmiIiMghhvR1WXasR7lBSIiM7Gw1ysvB9HUiogbQsaOM/Hzg2jUJ4eEK8vPF7Y4E5bIsobiYa8qp+TEc7zdqUC5JIstRFHur+z2wOSjv1q0bWrVqhcTExPpsHxEROcDXV4GiiIszR3qUGxiqr+t0DMqJiBqCtzfQtq2CM2dUCA/XIy9PpK96edn3PD4+gCSJFHauKafm5kZPXwdEBXanzJQfPHjQ+O9x48bhlVdeQUxMDLp16waNpupPG6vQGxHRjcrHR/x/QYHkcI9yoCp9XZZv3NFrIqKGFhcn4/RpFYYO1SM3V6Su25tkqlIBfn74MyjnOZual4AABW5uivH65UYkKrDb9sWvNSifM2eOWZr6m2++afLfLPRGRNTw1Gpxci8srN+MSXCwgpwcCYrCmXIiooYSGyuCckAUeXP0fGuoJ1JUJKFdO56zqfkIDFQQHGz/YFRLUpW+Xrc6C71ZKuzGQm9ERI1PXJzVd025Ap1Ogl4v3dApZUREDSkuTm9si5abK8Hfvz5BOVuiUfMTFydj6FC9qzfDpZyWvv7DDz84Y3uIiMgJfH3FTHlBgeOzLj4+gLu7AkWBQ33OiYiobrGxVb3K8/MdP2f7+yvG9HUG5dScdOkiY9myMldvhkv5+CgoLnZC+nrr1q2dskFERFR/vr5Va8odTWOUJMNsuWN9zomIqG6xsTKuX1chO1v6c025Y89jSF+vT4FPInINb2/YHJTXmb6+bNky9O/fH6dPnza5PTU1Ff3798c777zj2FYSEZFd/PyqZsrrc3EWHKywYBARUQPy8QHatBGz5Xl59vcoN/D3FzPtRUVsiUbU3IhCb7Y9ts6gfMeOHWjdujXi4uJMbo+JiUF0dDS2bdvm0EYSEZF9fH0Na8rrH5SzyBsRUcMyFHszVF93hGGmvLBQgo8Pz9tEzYlW68SZ8gsXLiA+Pt7ifR07dsTFixft2zoiInKIYaa8sBAMyomImjjDuvL8/PoF5fn54JpyombInpZodQblFRUVKCkpsXhfUVERysvL7ds6IiJyiK8vqrVEc/x5QkMVVl4nImpgcXEiKM/NrV+ht2vXVNDpmL5O1NzYk75ea6E3AGjVqhV+/vlnXL9+HcHBwcbbs7KysG/fPkRERDi8oUREZDtfXwXnzqnqnb7+wAMV0OmcuGFERGQmNlaP06fd4e+v1GNNuYJLl8RMG9PXiZoXe9LX6wzKBw4ciDVr1mDmzJmYP38+2rZtiwsXLuD9999HUVERJk6cWO8NJiKiuok0RglFRfVLYwwP54UdEVFDi42Vce2aCoWFji8Z8vUFLl5UwcNDgYeHkzeQiBqUU2fKFyxYgI0bNyIzMxMvvvii8XZFUeDl5YX58+c7vKFERGQ7X18Fly9LUBS2xiEiaur8/YGICBlXrqjg7+/4THlhoYSQENnJW0dEDU2rtb1PeZ1rytu0aYPly5cjNDQUiqIY/xcWFoZ33nkHbdu2rfcGExFR3Xx9gcxMCZKkwMfH1VtDRER1iY0VwXR91pQD4HpyombIxwc2F3qrc6YcAPr3748dO3bg8OHDyM7ORkhICHr16gV3d/d6bSgREdnOz09BdrYKvr4KVHUOqRIRkavFxcnYu1eBn59jf2/IimLldaLmR8yU2/ZYm4JyAHB3d8eAAQMc3SYiIqonw8UZU9eJiJqH2FgZAQFweCC1aqac532i5sbHx4np60RE1DQYLsoYlBMRNQ99++rRq5fe4b83pK0zKCdqfnx8gNJSyaaONwzKiYiaCV6cERE1L126yPj881KH/16jESmwrCNC1PxoteJ6raSk7scyKCciaiaq0tddvCFERNRo/P0VDsYSNUPe3uL/bSn2xqCciKiZ8PAA3N15cUZEdCNhUE7UPKnVgLe3bevKGZQTETUjfn68OCMiupGI876rt4KIHKHVKigqqvtxDMqJiJoRX18WeiMiupG0bq2gVSvZ1ZtBRA7QamHTTLnNLdGIiMj1fH0d73dLRETNz7JlZQ63VCMi1/LxsW2mnEE5EVEzwrWFREQ3FrXa1VtARI4S6esSgNqv3RiUExE1I88+W442bRiUExERETV1Pj5MXycianF69OC6QiIiIqLmwNb0da5QISIiIiIiInIyrZYt0YiIiIiIiIhcwscHf64prx2DciIiIiIiIiInEzPldT+OQTkRERERERGRk9nap5xBOREREREREZGTVbVEqx2DciIiIiIiIiIn8/Fppunru3btQlJSEhITE/Hyyy+b3CfLMl588UWMHj0aN998Mz755BMXbSURERERERGRdbYWemtyfcqjoqKwePFibNmyBSUlJSb3ffPNN/jjjz/w/fffo7CwELfccgv69++PTp06uWhriYiIiIiIiMw120Jv0dHRiI+Ph0ZjPl6wefNmzJw5E2q1GgEBARg/fjy+/fZbF2wlERERERERkXU+PratKW9yM+W1uXz5MiIjI43/3bp1axw7dsziY69du4asrCyT2zw9PRETE9Og20hERERERERka/X1Rg/KZ82ahfT0dIv3bdiwAa1atXLK66xduxZvvfWWyW1dunTB+vXrnfL8RERERERERNYYCr0pSu2Pa/SgfO3atQ7/batWrZCZmYmePXsCAC5dumQ1iJ81axZGjhxpcpunp6fDr01ERERERERkq5AQBTffrG96QXl9jBs3Dl988QXGjRuHwsJCbNmyBcuXL7f42LCwMISFhTXyFhIREREREREBXl7A6tWlUKlqL+XW5ILyX375BU888QSKioqgKAq+//57PP/88xg1ahSmTp2KEydOYMyYMZAkCXPnzkVcXJyrN5mIiIiIiIjIIZKi1DWZ3vLk5hZDp5NdvRlERERERETUwmk0KgQGaq3e3+RaohERERERERHdKBiUExEREREREbkIg3IiIiIiIiIiF2FQTkREREREROQiDMqJiIiIiIiIXIRBOREREREREZGLMCgnIiIiIiIichEG5UREREREREQuwqCciIiIiIiIyEUYlBMRERERERG5CINyIiIiIiIiIhdhUE5ERERERETkIgzKiYiIiIiIiFyEQTkRERERERGRizAoJyIiIiIiInIRBuVERERERERELsKgnIiIiIiIiMhFGJQTERERERERuQiDciIiIiIiIiIXYVBORERERERE5CIMyomIiIiIiIhchEE5ERERERERkYswKCciIiIiIiJyEQblRERERERERC7CoJyIiIiIiIjIRRiUExEREREREbkIg3IiIiIiIiIiF2FQTkREREREROQiDMqJiIiIiIiIXIRBOREREREREZGLMCgnIiIiIiIichEG5UREREREREQuwqCciIiIiIiIyEUYlBMRERERERG5CINyIiIiIiIiIhdpckH5rl27kJSUhMTERLz88ssm9y1duhQDBgzA1KlTMXXqVDz22GMu2koiIiIiIiKi+tO4egNqioqKwuLFi7FlyxaUlJSY3T958mT84x//cMGWERERERERETlXk5spj46ORnx8PDSaJjdeQERERERERORUzS7y3bp1Kw4cOICAgAAsWrQIAwYMsPi4a9euISsry+Q2Ly8vdOjQAWp1kxuLICIiIiIiohaorviz0YPyWbNmIT093eJ9GzZsQKtWraz+7ezZs3HffffBzc0Nv/32Gx588EF89dVXaN26tdlj165di7feesvktkmTJuE///kP/Py86rUPRERERERERM7Q6FPGa9euxYEDByz+r7aAHABCQ0Ph5uYGAOjduze6dOmC5ORki4+dNWsW1q1bZ/K/BQsWID8/3+n7RI5JTU3FLbfcgtTUVFdvCjUBPB6oJh4TVBOPCaqJxwTVxGOCamoOx0SzSl+/cuUKIiIiAADp6ek4deoUYmNjLT42LCwMYWFhjbl5ZKeysjKcPHkSZWVlrt4UagJ4PFBNPCaoJh4TVBOPCaqJxwTV1ByOiSYXlP/yyy944oknUFRUBEVR8P333+P555/HqFGj8MYbbyAlJQVqtRpqtRrPPfccoqOjXb3JRERERERERA5pckH5wIEDsWfPHov3vfLKK428NUREREREREQNh2XIiYiIiIiIiFyEQTm5TGhoKB588EGEhoa6elOoCeDxQDXxmKCaeExQTTwmqCYeE1RTczgmJEVRFFdvBBEREREREdGNiDPlRERERERERC7CoJyIiIiIiIjIRRiUExEREREREbkIg3JympdeegkjR45EXFwcTp06Zbx9z549SEpKwuTJkzFz5kz8/vvvxvuOHz+O2bNnY8qUKZg6dSp++eUXk+f89NNPMX78eEyePBlTpkxBeXl5o+0P1U95eTkWLVqEsWPHYsqUKZg3bx4yMjIAANevX8f8+fMxZswYTJo0CQcPHjT+naP3UdPXEMeEwS+//ILOnTtj1apVjbU75AQNcUwcP34cM2fOxLRp0zB+/Hi8++67jb5f5DhHj4nly5dj7NixiI+Px44dO0ye86mnnjI+3+zZs3H8+PFG3Seqn4Y4JhRFwdKlSzF27FhMnjwZc+bMadR9Isc5ejzUdh4oLS3Fo48+iptvvhljx47F1q1bG32/oBA5ya+//qpcvnxZGTFihHLy5ElFURQlLy9P6devn3LmzBlFURTl4MGDysSJExVFURRZlpWhQ4cq+/btUxRFUdLS0pThw4crpaWliqIoyvbt25VZs2YpBQUFiqIoyvXr1xWdTtfYu0UOKisrU3bt2qXIsqwoiqKsXr1aueOOOxRFUZQnn3xS+d///qcoiqIcO3ZMGTp0qFJRUVGv+6jpa4hjQlEUpaCgQJk+fbpy7733Kh9++GEj7hHVV0McE1OmTFF27NihKIqi5ObmKgMGDFDOnj3bqPtFjnP0mDh27Jhy/vx55Y477lC2b99u8pw7duxQKisrFUVRlJ07dyojRoxorN0hJ2iIY2LVqlXKAw88oJSXlyuKoijXrl1rrN2henL0eKjtPLB06VLliSeeUBRFUc6fP68MGDBAycnJabR9UhRF4Uw5OU3fvn0RERFhctv58+cREBCATp06AQD69OmDzMxMpKSkIDc3Fzk5ORg0aBAAIDo6Gr6+vtizZw8A4P3338eDDz4IX19fAEBQUBDUanUj7hHVh4eHB4YPHw5JkgAA3bt3x6VLlwAAW7duxezZswEA3bp1Q1hYmHE009H7qOlriGMCAF544QXcf//9CAgIaMS9IWdoiGNCkiQUFhYCELMfbm5u8Pf3b9T9Isc5ekx069YNbdu2tfico0aNgkajMT7f1atXodPpGnpXyEka4ph4//338dhjj8Hd3R0AmnSrLDLl6PFQ23lgy5Ytxr9r27Yt+vXrZ5Zd0dAYlFODat++PfLy8nD48GEAwA8//IDi4mJcunQJQUFBCAsLw+bNmwGIlMNz584Zv1ipqalITk7G7NmzkZSUhI8//thl+0H19/HHH2PkyJHIzc1FZWWlyQ9g69atkZmZ6fB91DzV95gAxA+wSqXCqFGjGn37yfmccUwsWbIE//3vf3HTTTdh7NixePTRR3nB3YzZckzY+3zDhg0zXpxT81PfY6KoqAjXr1/HDz/8gBkzZmDGjBnGa1Fqfhw5HmqeBzIzMxEZGVnn3zUknpGoQfn6+uK///0vXn/9dZSUlKBHjx7o2LGjccb77bffxv/7f/8PK1euRMeOHdG7d2/jfTqdDhcvXsSnn36K/Px8zJkzB23btsWIESNcuUvkgOXLl+P8+fNYtWoVysrKXL051AQ445jIysrCO++8g9WrVzt568gVnHWeWLlyJR599FFMnjwZFy5cwB133IHExER07NjRiVtLjcHZvx0bN27Eli1b8Omnnzph68gVnHFM6HQ66HQ6lJWV4csvv8TFixcxe/ZsdOjQAfHx8U7eYmpIjhwPTfU8wKCcGtyAAQMwYMAAAEBFRQUGDx5svDiKj4/H+++/b3zs+PHjjanukZGRmDhxItRqNYKCgjBs2DAcPXqUQXkz8/7772Pbtm1YtWoVvLy84OXlBY1Gg6ysLONo5qVLlxAZGYnAwECH7qPmxVnHREpKCrKysjBt2jQAQG5uLnbu3Inc3Fw88sgjrto9coCzjomcnBxs374db7zxBgCRhti9e3ccPnyYQXkzY88xYYvNmzdj2bJlWLVqFUJCQhpy06mBOOuYCAgIgLe3N6ZOnQoAaNOmDXr16oUTJ04wKG9GHDkerJ0HIiMjkZmZibCwMOPfDRkypFH3h+nr1OCuXbtm/PeyZcswYMAAREVFmd33xRdfwMvLyxjAT5o0CXv37gUAlJWV4ddff+XJspn58MMP8d133+HDDz+En5+f8fZx48ZhzZo1AMSyhatXr6Jv3771uo+aB2ceEzfddBN+/vln7Ny5Ezt37sTYsWOxaNEiBuTNjDOPCX9/f3h7exs7eeTk5OD48ePGwV5qHhw5JmqzefNmvPnmm/jwww85kNtMOfuYmDRpkrGGUV5eHk6cOIG4uLiG2XhyOkeOh9rOA9X/7sKFC/j1118bfVmcpCiK0qivSC3Wc889h127diE7OxsBAQHQarXYvn07nnnmGRw6dAh6vR49evTAs88+a/wCvfXWW9i0aRMURUGHDh3w/PPPo1WrVgBEy4PnnnsOJ06cgCRJGDNmDB5++GFX7iLZ4cqVKxg+fDjatm0LrVYLAHB3d8eXX36J7Oxs/P3vf8fFixfh5uaGZ5991jgY4+h91PQ1xDFR3ZNPPon4+HjMnTu3MXeL6qEhjomff/4Zr732mjFFdcaMGZg3b57L9pHs4+gx8fbbb2PNmjXIycmBVquFh4cHNmzYgKCgICQkJCAkJMSkGOSqVasQGBjoil0kOzXEMZGbm4unnnoKFy9eBADcdttt+Mtf/uKyfSTbOXo81HYeKCkpwdNPP43k5GSo1Wo8/PDDmDBhQqPuF4NyIiIiIiIiIhdh+joRERERERGRizAoJyIiIiIiInIRBuVERERERERELsKgnIiIiIiIiMhFGJQTERERERERuQiDciIiIiIiIiIXYVBORETUDD355JOIi4vDnDlzXLodL730EuLi4vDpp5822mu++uqriIuLw6pVqxrtNYmIiBoK+5QTERE1ISNHjsSlS5dqfcwPP/yAI0eO4Pjx44iKisIdd9zRSFtn6o8//sCUKVMQFBSEnTt3oqioCFOmTEFWVhb69++Pjz76CJIkAQD+97//YdmyZVCpVPjoo4/Qr18/h183NzcXw4YNg5ubG3bs2IGgoCBn7RIREVGj07h6A4iIiKhKUlIS8vPzAQD79u1DamoqtFotpk+fbnyMj48PJk+ejMmTJ7tqMwEAH374IfR6PaZMmQJ3d3cEBQVhyZIluOeee3DgwAF89NFHmDt3Lo4fP44VK1YAABYuXGg1INfpdFCr1cZA3prAwECMHDkSW7duxZo1a7Bo0SKn7xsREVFj4Uw5ERFRE/Xkk09i/fr1aN26NXbu3Gnxvn79+mH16tUAgLi4OADAokWL8Ntvv+HIkSNo3749/vWvfyEzMxP//e9/kZ2djX79+mHx4sUIDg4GIGaely9fjl27duHKlSsICwvD6NGjsWjRIvj6+lrcNr1ej4EDByI/Px+rVq3CwIEDjfctXrwYH330ETw8PPDpp5/i8ccfR3p6Orp164bPP/8cGo0G69atw1NPPQUAeOWVV7B06VJkZmbiwIEDOHToEJYtW4Zz585Bp9MhNDQUcXFxePvtt42v8eWXX+KZZ55BXFwcvvnmG+e96URERI2Ma8qJiIhamJUrVyI0NBRt2rTBmTNnsHDhQrz00kvo27cvVCoVdu3ahTfeeAMAUFZWhtmzZ2PVqlXQarWYNm0avLy88MEHH2D+/PmQZdnia2RkZBhn9A2DAQaPP/444uLiUF5ejttvvx3p6enw9vbGa6+9Bo3GPEnvX//6F/r06YMxY8ZApVLh4YcfRnJyMoYMGYJp06YhOjoaBw4cMPkbw2ueOXMG5eXl9X7PiIiIXIXp60RERC3M7Nmz8eyzz+L777/H//3f/6GwsBD/+9//MHbsWPj4+OCjjz7CsWPHAADbtm1Deno6VCoVevToAbVajYSEBJw+fRrHjh3DoUOHLKab5+XlGf/t5+dncp+7uztef/11TJ8+HWVlZQCAZ599FlFRURa39/XXX8eIESMAACUlJdDr9VCr1bjpppsQHx+P9u3bw93d3eRvDK+pKAry8vIQHh7u2JtFRETkYgzKiYiIWpj4+HgAgL+/v9lthnT04uJiAEBmZiYAQJZlixXUDffXVP25CwsLERgYaHJ/x44dMWbMGHzzzTeIiIhAUlKS1e0dNGiQ8d/e3t5YvHgx3nnnHWN6u1qtxpAhQ/D666/Dx8cHAFBQUGD8m4CAAKvPTURE1NQxKCciImph1Go1AJgUTDPcVlOrVq0AAG5ubvjxxx8RGhpqvO/cuXOIjo62+Hft27eHn58fCgoKcPbsWYuz6YbXtPbaBh4eHib/PX78eEybNg1FRUVIS0vDE088gd27d2Pjxo34y1/+AkCkrQNAbGys2d8TERE1JwzKiYiIbmBjxozBW2+9hfPnz2P69OkYNmwYysvLkZqaipSUFJw+fdri36nVaowePRrr1q3Dvn376tXirKYBAwagd+/eaNWqFXQ6nXG2vvrs/E8//QQAGDdunNNel4iIyBVY6I2IiOgG5uXlhbVr12Lu3Lnw8vLCN998gz179kCtVuOBBx6o9W/vvvtuqNVqrF+/HpWVlU7bpptuugkZGRnYtGkTNm/ejIiICDz00EOYOHEiAFEtfufOndBqtbjtttuc9rpERESuwJZoRERE5LCXXnoJq1evxvPPP4/bb7+9UV7z1Vdfxfvvv4+nnnoKc+fObZTXJCIiaigMyomIiIiIiIhchOnrRERERERERC7CoJyIiIiIiIjIRRiUExEREREREbkIg3IiIiIiIiIiF2FQTkREREREROQiDMqJiIiIiIiIXIRBOREREREREZGLaFy9Aa6wZ88eJCcnQ5IkSJKESZMmoU2bNq7eLKNdu3bh8OHD8Pb2BgB07NgRo0ePtulvN2zYgNjYWHTp0qXe23H06FFkZmZiwoQJZrdv374dvr6+0Ol06N27NwYOHFjv12sIlZWV2LRpE65evQpFUeDp6Yk77rgD7u7udj3P3r17MXToUKdvX15eHpYtW4aQkBDodDq4u7ujb9++6NGjR61/d+XKFRQWFqJTp052v+b+/fuxY8cOPP744/D09LTrbxcvXoynn37a7PZDhw7Bzc0N3bt3t3t77PXCCy8gLCwMsiwjNDQU06ZNg5ubm8XH5uXl4cKFC+jatSsA68e0K9ny3qWnp0OtVqNt27aNuGUN79NPP8X06dPNjsNdu3bB3d0dgwYNsvm5rB2b9bVq1SqMGTMGkZGRTn9uW7z55pvw9/fHvHnzjLctX74csixj0aJFSE9Px88//4zOnTvjwIEDAICsrCyEhIRAkiSz3w/D42+//XaT1/nmm28wcOBAhIaGNs6OERERkdENF5RfuHABZ8+excKFC6HRaFBSUgK9Xl/v55VlGSqV8xIPBgwYYNcFqaMc3e6EhARMmDABJSUleOutt9ClSxf4+/s3me0z2L9/P7RaLe6//34AQHZ2tkPP11BBOQAEBgbi3nvvBQDk5uZi7dq1UBQFPXv2tPo3V65cQWZmpkNBeXJyMlq3bo1Tp05ZfA1H3vM+ffrYvR2O0mg0uO+++wAA69atw6FDh6wOCuXl5eHEiRPGoLypkWXZpvcuPT0d7u7uTg3KFUUBAEiS5JTnc+S4+ctf/uKU125KnP1bAADl5eXIz8+Hv78/srKyLD6mZ8+exu/zm2++ibvuuss4sGuLKVOmOGVbiYiIyH43XFBeVFQEb29vaDRi16tftKSlpWH79u2QZRmRkZGYOHEiNBoN3nzzTSxcuBDe3t7IzMzEtm3bMHfuXOzatQu5ubnIzc2Fv78/xo4di++++w65ubkAgIkTJ6Jt27Y4fvw4Dhw4AL1ej9atW2PixIl2X7TJsowdO3YgIyMDOp0Offv2RZ8+faAoCrZs2YK0tDT4+flBrVYb/8awrRUVFfD29sbUqVPh6+uLVatWISIiAufPn0diYiKCg4Oxd+9e6PV6eHl5ISkpCT4+PjZtl7e3N4KCglBUVAR/f3+L+wqIWZjMzExIkoQePXpg4MCByMnJwebNm1FSUgI3NzdMnjwZISEh2LBhAzQaDa5cuYK2bdvi1KlTuO+++4yzaUuXLsW8efMgSRK+++475OfnAwDGjh2Ldu3amX3e1QcLQkJCAAA//vgjvLy8MGDAAADADz/8AK1Wi4SEBHz11VcoLy+HLMuYOHEizp49C51Oh+XLlyMsLAxJSUlWP9PFixejT58++OOPP+Dj44NRo0Zh+/btyM/Px7hx4xAXF1fr+xkYGIixY8di27Zt6NmzJy5duoStW7dCp9NBo9Fg6tSpCAwMxI8//gidTofz589jyJAhCAwMNHucYV+ry8nJQUVFBSZMmIC9e/caL+KPHj2KU6dOoaKiAoqi4Pbbb8eWLVuQmZkJABg+fLgx++KHH37A2bNnodFoMHv2bPj4+BhnNmNjY7F+/Xrcc889AERQ/Pnnn+P++++3ejweOHAAhw4dgkqlQmhoKG699Vabjj0AaNeuHa5evWr180xOTkZ2djaWL1+O7t27w8vLC4WFhfjkk0+Qm5uL+Ph43HzzzQCAEydO4KeffoKiKOjUqZPx9sWLF6N///5m+1xdeno6tm7dCkAEuXPnzoWHhwd++uknnDhxwmTGsub3r6KiwjgrvGrVKoSHhyMjIwOyLGPq1KnQarXG9+f48eMYP348ioqKsHv3bqhUKnh4eJjMogJARUUF1qxZg9LSUsiyjBEjRiA+Ph55eXn45JNP0Lp1a1y+fBm33347UlJScPLkSeh0OsTHx2PEiBFm7/PixYvRq1cvpKWlwcfHB9OnT4dWqzXbl4iICLNzaHp6Oo4cOYIZM2YY3yvDTG31c+uePXtw7NgxaLVa+Pv7o1WrVsZj1tJ5Ijc3F+vWrUNFRYXV71VeXh4+++wzLFq0CADw888/o6KiAjfddBNWrVqF1q1bIz09HWVlZZgyZQqioqJQWVmJjRs34urVq8YMFoPU1FTs2rULOp0OQUFBmDp1Ktzd3fHmm28iISEBaWlpGDx4MBITE41/c/r0aYvn1127diE/Px+5ubnIz8/HgAED0L9/f4v7kZCQgJSUFAwaNAjJyclITEzE8ePHLT7WUdUzAv744w/88MMPUBQF3t7euPPOO1FRUYEtW7bg2rVrkGUZw4cPR3x8PI4ePYrTp0+jsrLS5Dsly7Jd530iIqIb2Q0XlMfExGD37t1YunQpOnTogISEBLRv3x46nQ4bN27EnXfeieDgYKxfvx6HDh0yXuRbk5WVhXnz5sHNzQ1fffUVoqKiMGvWLMiyjIqKCmRlZSElJQV333031Go1vvvuO5w4cQLdu3fHN998gz59+lhMi9y/f7/xomv06NHIz8+Hp6cn7rnnHuh0OnzwwQeIiYnBlStXcP36dSxatAjFxcVYtmwZevbsCb1ejy1btmD27NnG4GTnzp2YOnUqAECv12PhwoUAgNLSUsyfPx+SJOHw4cPYt28fxo4da9P7mZ+fD51Oh/DwcKv7GhoaisLCQuOFcVlZGQDg22+/xcSJExEcHIyLFy/iu+++w1133QUAKCgowN133w2VSgVZlo2zuhcvXoS/vz98fHzw9ddfY8CAAWjXrh3y8/PxySef4IEHHjDZvp49e2L16tU4deoUoqOj0b17dwQHB6Nnz55Yu3YtBgwYAEVRkJKSggULFuDo0aOIiYnBsGHDIMsyKisrERUVhV9//dU4O1vbZ1pZWYno6GiMGTMGa9euxc6dOzFnzhxkZWVhw4YNdQblANCqVStkZ2cDEIMI8+bNg0qlQlpaGnbu3ImZM2dixIgRJmnY5eXlFh9XU3JyMhISEhAVFYUNGzagqKjIGGBevnwZ999/P7y8vLB9+3Z4eHgYMwxKS0sBiOUAbdq0MQ42HD58GMOGDTM+f0hICPR6PXJzcxEYGGh8vdqOx59++gkPP/wwNBqN8diwhSzL+OOPPxATE4NOnTpZ/DwjIiJMUnWPHj2KK1eu4N5774VGo8Fbb72Ffv36QaVSYceOHVi4cCG8vLywevVq/P7774iPj69znwHgl19+wYQJE9CuXTtUVFRAo9Hg7NmzOH36NBYsWAA3NzfjewiYfv927dpl8lyVlZW47777kJGRgY0bN2LRokXo06ePSTr3O++8gzvuuAN+fn4W3zONRoNZs2bBw8MDJSUleO+994zH3vXr1zFt2jS0adMGqampyMnJwYIFCwAAn3/+OTIyMhAVFWW2TZGRkRg3bhx2796N3bt3G489w77odDosXbrU7Bzar18/bNq0yTj4YAgqq8vMzERKSgruu+8+yLKMFStWGINya+eJrVu3ok+fPujevTt+/fVXm4+b6mRZxj333IOzZ89i9+7duPPOO43LCR544AFcvXoVK1asAACUlJRgz549mDNnDtzd3fHTTz/hl19+wfDhwwGIAUpDxkt17dq1s3p+zc7Oxl133YWKigq89dZb6NOnj8nAqkHnzp2xceNGDBo0CGfOnDEODDaE4uJibNq0CXPnzkVgYKDxuN27dy+io6MxdepUlJWV4d1330WHDh0AwOJ3qri42O7zPhER0Y3qhgvK3d3dsXDhQpw/fx7nzp3DV199hdGjRyMiIgIBAQEIDg4GAHTv3h0HDx6sMyiPjY01rmc9d+4cpk2bBgBQqVTw9PTE8ePHkZmZiXfffRcAoNPpoNVqAdSeLlgzff2LL77A1atXcfLkSQAiCMvJyUFGRgYSExOhUqng6+uL6OhoAOLC+9q1a1i9ejUAkapafXYvISHB+O+CggJ89dVXKCoqgl6vR0BAQJ3vY0pKCjIyMpCdnY0JEyZAo9Hg3LlzFvc1NjYWubm52Lx5M2JjYxETE4OKigpcuHABX375pfE5qy8j6NKlizGbIDExEbt370bPnj2NQR4gMhuqp3KWl5cbL/wNIiIi8PDDDyM1NRVpaWl49913MX/+fISGhsLb2xuXL19GcXExIiIi4O3tjdatW2Pjxo2QZRnx8fGIiIgw23dr+wkAarUaHTt2BACEhYVBrVZDrVYjPDwceXl5db6vQFVaMSAuZDds2IDr169DkiSrSy1sfVxycjJmzZoFSZLQuXNnnDx5Ev369QMgBqy8vLyM+zh9+nTj3xluV6vViI2NBSAGD9LS0sxewzCrN2TIEKSkpODWW2+t9XgMDw/HunXrEB8fj/j4+DrfH0PWAiACnl69ekGtVlv8PC2Jjo42Zl2EhoYiPz8fJSUlaN++vfFz7Nq1KzIyMhAfH2/TPrdt2xbbtm1D165d0blzZ/j5+SEtLQ09evQwnh8M76HhPbLGkGofFRWF8vJyi0F327ZtsXHjRnTp0gWdO3c2u19RFPzwww/IyMiAJEkoLCxEcXExACAgIMBYQyM1NRWpqanGwLOiogLXr183C8olSTIG0t26dcPatWvN9iU7O9vqObRjx444c+YMunTpgrNnzxqzEAzOnz+P+Ph443tlGECo7Txx4cIF48BT9+7dsWPHDqvvqTWG965Vq1bG72dGRoZxxjo8PBzh4eEAgIsXLyIrKwsffPCBcTuq1yKx9pnWdn7t1KkTNBoNNBoNtFotiouL4efnZ/Yc3t7e8PLyQnJyMkJCQqzWUHCGixcvIioqCoGBgQCqjtvU1FScPn0aP//8MwDxPTRkKVn6ToWGhtp93iciIrpR3XBBOSAC5vbt26N9+/YIDw/HsWPHLAZf1R9vCJSqpzICqLNgmKIo6N69u82F2mozfvx4Y8BncPbsWauvGxYWhvnz51u8v/p2b9myBQMHDkRcXBzS09PNZu4sMawpz8zMxOrVqxEXF1frvt533334448/cOjQIaSkpGDcuHHw9PQ0zj7Xtn1t2rRBTk4OiouLcfr0aeMspaIoWLBggXEpgjXu7u7o3LkzOnfuDEmScPbsWYSGhqJnz544evQoiouLjWncUVFRmDdvHs6cOYMNGzZg4MCBZgW4attPlUplXKMrSZJx2yRJgizLtW6nwZUrV0zS7Nu3b49Zs2YhLy8Pq1atsvg3tjzu6tWryMnJMQbGer0egYGBxqDclgv96vtnyGKoKSEhAV9++aXx/Q4ODsbVq1etHo+33347MjIycObMGezduxf3339/rcs7qq8pr87S52nt7w1s+Vxs2echQ4agU6dOOHv2LD744APccccdtT6nvYUGa5o0aRIuXryIs2fPYuXKlcYUcIMTJ06gpKQECxcuhFqtxptvvmk8d1X/nBVFwZAhQ+yuCVB9Hbot+5KYmIhff/0VXl5eiIyMhIeHh02vYyjOaO08Udd6+OrnbsD8/G04Fqx9rjW3JSYmxmSwqjpr70Nt51d7jsWEhARs3rzZmO3kCjNnzjRLNb906ZLF/fDy8rL7vE9ERHSjuuFaomVnZ+P69evG/75y5Qr8/f0REhKCvLw85OTkAACOHz9unC0KCAgwrq01zFRbEh0djUOHDgEQaZFlZWXo0KEDTp06ZZylKi0ttXnGtLqYmBgcOnTIOKtw/fp1VFRUICoqCikpKZBlGYWFhUhPTwcg0oiLi4tx4cIFACIAu3btmsXnLi8vh6+vLwDg2LFjdm1XZGQkunXrhv3791vd15KSEiiKgi5dumDkyJG4fPkyPDw8EBAQgJSUFADigvfKlSsWX0OSJMTHx2Pbtm0ICQkxBh8xMTHGasMALP79+fPnjemXer0eWVlZxpmqzp07IzU1FZcuXUJMTAwAsQZVq9Wid+/e6NWrFy5fvgxAzBAb3ntnfaaW5OXlYdu2bcZAufpnc/ToUePj3N3dUVFRYfxva4+rLjk5GcOHD8df//pX/PWvf8Vjjz2GwsJCi9veoUMHHDx40Pjf1VOv6xIUFASVSoU9e/YYZw+tHY+KoqCgoADR0dEYPXo0ysrKTPbLHpY+z5rvkzWtW7dGRkYGSkpKIMsykpOTzWaLa5OTk4Pw8HAMGTIEkZGRyM7ORkxMDI4ePYrKykoAtr+Hhu/E+fPn4enpCU9PT7i7u6O8vNzk9dq0aYMRI0ZAq9WioKDA5DnKysrg7e0NtVqNc+fOGWc0a+rYsSOOHj1qfI8KCgqMx3V1iqIYz30nTpywWHCutnNoVFQULl++jMOHD5ulrhvu//3331FZWYny8nKcOXMGAGo9T7Rt2xbJycnG17LEMPtcUlICnU5nfN7aREVF4cSJEwCAa9eu4erVqwDE4OD58+eN+2fIKqhLfc6v1cXHx2PQoEFmA7PO1qZNG2RkZBhroxiO25iYGPz666/GQQ7DudGa+p73iYiIbiQ33Ey5oVhNWVkZVCoVgoKCMHnyZGNxrC+//NJYpMgwezR8+HB88803xtlIa8aNG4dvv/0WR44cgSRJxkJvI0aMwOrVq6EoCtRqNSZMmICAgIBa15TX1KtXL+Tl5WHlypVQFAVarRazZs1CfHw8zp07h7fffhv+/v7GdEq1Wo2ZM2diy5YtxqJl/fv3R1hYmNlzDx8+HF9++SW8vLzQvn1748WYrYYMGYIVK1Zg6NChFvdVo9Fg48aNxou5UaNGAQCSkpLw3XffGYsgGQpFWZKYmIh3333XZJZo/Pjx2Lx5M9555x3IsoyoqChMmjTJ5O9yc3Px3XffAYCxgJchZVWtVqN9+/bw9PQ0zswailCp1Wq4u7sblyP06tULy5cvR6tWrZCUlGT1M3VEbm4uVqxYYWyJ1r9/f2NLtEGDBmHDhg3Yu3evSaX16Oho7Nu3D8uXL8eQIUOsPq665ORks2rX8fHxSE5ONitcNmzYMGzevBlvv/02VCoVhg8fbjFN2pqEhARs374dDz/8MADrx2NwcDDWrVuH8vJyKIqC/v37w9PTE5mZmTh06JBdFaEtfZ7h4eGQJMmk0Jslvr6+GDVqFD766CPjcWJLKr3B/v37kZ6eDkmSEBYWho4dOxqLFa5cuRJqtRqdOnUyHvu10Wg0WLFiBfR6vfF4j4uLwxdffIHTp09j/Pjx2L9/P3JycqAoCqKjo40p1gbdunXD559/jnfeeQeRkZFWC2nFxMQgKysL77//PgAxiHHLLbcY0/gN3NzccOnSJezZswdardZiMb7azqEqlQqxsbE4evSo8TtVXatWrZCQkIDly5dDq9WanBOtnSfGjRuHdevWYd++fVZrNajVagwfPhzvvfcefH19bSoo1qdPH2zcuNHYqtCwLVqtFtOmTcPXX39tnHEfOXKkMV3fmvqeXw08PDwwZMgQh/62pnPnzuH11183/rehCB8g9nPSpEn44osvjL81c+bMwbBhw7B161YsX74ciqIgICDArK1adQUFBXad90+fPo3MzEyLhQaJiIhaOkmpnttHdANRFAUrVqzAjBkz6rywpqavJXyeru6JbU1D9QAnIiIiohtwppwIEBXUP/vsM8THxzfbAI6q8PMkIiIiouaKM+VERERERERELnLDFXojIiIiIiIiaioYlBMRERERERG5CINyIiIiIiIiIhdhUE5ERERERETkIgzKiYiIiIiIiFyEQTkRERERERGRizAoJyIiIiIiInKR/w8g27+aaYcC4QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1200x440 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"time: 524 ms (started: 2021-02-18 14:54:28 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vkYOFFTO_ndr",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "89d40c1b-6fee-4ec6-de6e-480088d0a55f"
},
"source": [
"######################################################################################################################################################################################################################################################"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 1.3 ms (started: 2021-02-18 14:54:29 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cdjesSoPjCoq"
},
"source": [
"#### Normalized Sentiment"
]
},
{
"cell_type": "code",
"metadata": {
"id": "l8zDFWDHmIh5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 478
},
"outputId": "dba299d5-3ced-463f-c105-04494202d232"
},
"source": [
"# Normalize data\n",
"NPositiveWordsNorm = data_df['NPositiveWords'] / data_df['wordcount'] * np.mean(data_df['wordcount'])\n",
"NNegativeWordsNorm = data_df['NNegativeWords'] / data_df['wordcount'] * np.mean(data_df['wordcount'])\n",
"NetSentimentNorm = (NPositiveWordsNorm - NNegativeWordsNorm)\n",
"\n",
"# Initialize figure container\n",
"fig, ax = plt.subplots()\n",
"\n",
"# Supertitle\n",
"plt.suptitle('Counts normalized by the number of words ' + '({})'.format(doc_type), horizontalalignment = 'left', x = fig.subplotpars.left, y = 0.99, fontsize = font_size_title, fontweight = \"bold\")\n",
"\n",
"# Main plots\n",
"ax.plot(data_df.index, NPositiveWordsNorm, label='Positive Words')\n",
"plt.plot(data_df.index, NNegativeWordsNorm, label='Negative Words')\n",
"ax.legend(prop = {'size': font_size_legend}, loc=\"best\", borderaxespad=0, frameon=0)\n",
"ax.set_ylabel('Normalized Word Count', fontweight = \"bold\")\n",
"ax.set_xlabel('Time (Yrs)', fontweight = \"bold\")\n",
"\n",
"# Title\n",
"plt.title('Normalizing the count by the number of words yields a more balanced metric by which we can reduce correlation', loc = title_loc, y = 1.0, fontsize = font_size_subtitle)\n",
"\n",
"# Source\n",
"ax.text(x=0.0, y=-0.15, s='Source: Federal Reserve System Data Archives. Python scripts are provided under an MIT License.', fontsize = font_size_notes, c = \"grey\", transform=ax.transAxes)\n",
"\n",
"# Formatting\n",
"import matplotlib.dates as mdates\n",
"years = mdates.YearLocator() \n",
"months = mdates.MonthLocator() \n",
"years_fmt = mdates.DateFormatter('%Y')\n",
"datemin = np.datetime64(data_df.index[0], 'Y')\n",
"datemax = np.datetime64(data_df.index[-1], 'Y') + np.timedelta64(1, 'Y')\n",
"ax.set_xlim(datemin, datemax)\n",
"ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')\n",
"\n",
"# Grid\n",
"ax.grid(color = 'black', alpha = 0.5, linestyle = '-.', linewidth=0.2)\n",
"\n",
"# Finalize\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"\n",
"# Show Inline\n",
"plt.show()\n",
"\n",
"# Save Graphics\n",
"fig.savefig(graph_dir + 'counts_normby_num_words_' + doc_type + '.png')\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1200x440 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"time: 487 ms (started: 2021-02-18 14:54:29 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7wQy_ZB9Ba18",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9311a4f3-f39e-44ec-ee0e-5ed0997ec11d"
},
"source": [
"######################################################################################################################################################################################################################################################"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 1.02 ms (started: 2021-02-18 14:54:29 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Lx3DHfq_ArNP",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 473
},
"outputId": "481908e1-9912-465a-951d-376d51e9abc4"
},
"source": [
"# Initialize figure container\r\n",
"fig, ax = plt.subplots()\r\n",
"\r\n",
"# Supertitle\r\n",
"plt.suptitle('Normalized Net Sentiment Using a Bag-of-Words Model ' + '({})'.format(doc_type), horizontalalignment = 'left', x = fig.subplotpars.left, y = 0.99, fontsize = font_size_title, fontweight = \"bold\")\r\n",
"\r\n",
"# Main plots\r\n",
"ax.plot(data_df.index, NetSentimentNorm, c = 'blue', label = 'Net Sentiment (Norm)')\r\n",
"ax.legend(prop = {'size': font_size_legend}, loc=\"best\", borderaxespad=0, frameon=0)\r\n",
"ax.set_ylabel('Normalized Word Count', fontweight = \"bold\")\r\n",
"ax.set_xlabel('Time (Yrs)', fontweight = \"bold\")\r\n",
"\r\n",
"# Title\r\n",
"plt.title('The Normalized Net Sentiment further reduces the correlation between positive and negative word totals', loc = title_loc, y = 1.0, fontsize = font_size_subtitle)\r\n",
"\r\n",
"# Source\r\n",
"ax.text(x=0.0, y=-0.15, s='Source: Federal Reserve System Data Archives. Python scripts are provided under an MIT License.', fontsize = font_size_notes, c = \"grey\", transform=ax.transAxes)\r\n",
"\r\n",
"# Formatting\r\n",
"import matplotlib.dates as mdates\r\n",
"years = mdates.YearLocator() \r\n",
"months = mdates.MonthLocator() \r\n",
"years_fmt = mdates.DateFormatter('%Y')\r\n",
"datemin = np.datetime64(data_df.index[0], 'Y')\r\n",
"datemax = np.datetime64(data_df.index[-1], 'Y') + np.timedelta64(1, 'Y')\r\n",
"ax.set_xlim(datemin, datemax)\r\n",
"ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')\r\n",
"\r\n",
"# Grid\r\n",
"ax.grid(color = 'black', alpha = 0.5, linestyle = '-.', linewidth=0.2)\r\n",
"\r\n",
"# Finalize\r\n",
"handles, labels = ax.get_legend_handles_labels()\r\n",
"\r\n",
"# Show Inline\r\n",
"plt.show()\r\n",
"\r\n",
"# Save Graphics\r\n",
"fig.savefig(graph_dir + 'net_sentiment_bow_' + doc_type + '.png')\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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