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Created February 18, 2021 14:17
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2_NLP_FOMC_Preprocess_I.ipynb
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2_nlp_fomc_preprocess_i.ipynb
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"_view_module_version": "1.2.0",
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"_view_name": "StyleView",
"_model_name": "DescriptionStyleModel",
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"_view_module": "@jupyter-widgets/base",
"_model_module_version": "1.5.0",
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"_view_module_version": "1.2.0",
"_model_module": "@jupyter-widgets/controls"
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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/theo-dim/5fd1f6e7edfca3cb8a2f1d84c33a30ea/2_nlp_fomc_preprocess_i.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Kc6Xn7hAkC3V"
},
"source": [
"# **Predicting interest rates from Federal Reserve documents**\n",
"## Preprocessing I (Vol. 2)\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": "oZXueyCKIH9Y"
},
"source": [
"### Update Linux Packages"
]
},
{
"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": "markdown",
"metadata": {
"id": "ZmNZi28M2w6o"
},
"source": [
"### Environment"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-z1LUHSetAze",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "70314858-9b4b-4839-b605-2a846cfb930b"
},
"source": [
"# -*- coding: utf-8 -*-\n",
"\n",
"# ENVIRONMENT CHECK:\n",
"import sys, os, inspect, site, pprint\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",
"\n",
"# Python installation files:\n",
"stdlib = os.path.dirname(inspect.getfile(os))\n",
"python_version = !python --version\n",
"print('Python Standard Library is located in:\\n' + stdlib)\n",
"print(' ')\n",
"print('This environment is using {}'.format(str(python_version[0])))\n",
"print(' ')\n",
"print('Local system packages are located in:')\n",
"pprint.pprint(site.getsitepackages())\n",
"print(' ')\n",
"print('Local user packages are located in:\\n' + site.getusersitepackages())\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"YES, this is a Google Colaboratory environment.\n",
" \n",
"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": "markdown",
"metadata": {
"id": "WaEjpp0Dcgip"
},
"source": [
"### Default Packages"
]
},
{
"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": "487a0495-359d-4103-88b2-b4b4d55962f0"
},
"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: After the following pip install, need to restart the kernel.\n",
"# Required modules - written here so that it can setup a new instance in Colab. Not required to run every time.\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 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 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": [
"#!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": [
"# Python Libraries\n",
"import pprint\n",
"import os\n",
"import io\n",
"import re\n",
"import pickle\n",
"from tqdm import tqdm_notebook as tqdm\n",
"import logging\n",
"import random\n",
"from collections import defaultdict, Counter\n",
"import codecs\n",
"from lxml import etree\n",
"#import urllib.request\n",
"#import zipfile\n",
"\n",
"# Import Time Libraries\n",
"import datetime as dt\n",
"#from datetime import datetime, timedelta\n",
"from dateutil.relativedelta import *\n",
"#from pytz import timezone\n",
"#from pytz import all_timezones_set\n",
"#import pytz\n",
"import time\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",
"#from mpl_toolkits.mplot3d import Axes3D\n",
"import matplotlib.ticker as ticker\n",
"import seaborn as sns; sns.set()\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 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 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 XGBoost Libraries\n",
"#import xgboost as xgb\n",
"\n",
"## Import Miscellaneous Utilities\n",
"#import pandas_datareader.data as web\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "4YXab6-a7_Tx"
},
"source": [
"### Settings"
]
},
{
"cell_type": "code",
"metadata": {
"id": "X2X2acChVnSJ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "671a0b2e-dd53-4dfe-82ac-ffceeec67897"
},
"source": [
"#@title Choose Save Location { run: \"auto\", vertical-output: true, form-width: \"50%\", display-mode: \"form\" }\n",
"save_dir = \"Data Graphs (Drafts)\" #@param [\"Dictionary Data\", \"Employment Data\", \"CPI Data\", \"FED Rates Data\", \"GDP Data\", \"ISM Data\", \"Sales Data\", \"Treasury Data\", \"Market Data\", \"FOMC Data\", \"Preprocessed Data\", \"Training Data\", \"Output Data\", \"GloVe Data\", \"Model Backups\", \"Data Graphs (Drafts)\", \"Data Graphs (Final)\", \"Publication Graphics (Final)\", \"Project Root Directory\", \"Source Code Root Directory\", \"Data Repository\", \"Backups\", \"Temporary Files\", \"Time Series Data\", \"FOREX Data\"]\n",
"\n",
"# Directory Masterlist\n",
"dir_set = pd.DataFrame(\n",
" {\n",
" \"Directory Name\" : [\n",
" 'Employment Data',\n",
" 'CPI Data',\n",
" 'FED Rates Data',\n",
" 'GDP Data',\n",
" 'ISM Data',\n",
" 'Sales Data',\n",
" 'Treasury Data',\n",
" 'Market Data',\n",
" 'FOMC Data',\n",
" 'Preprocessed Data',\n",
" 'Training Data',\n",
" 'Output Data',\n",
" 'Dictionary Data',\n",
" 'GloVe Data',\n",
" 'Model Backups',\n",
" 'Data Graphs (Drafts)',\n",
" 'Data Graphs (Final)',\n",
" 'Publication Graphics (Final)',\n",
" 'Project Root Directory',\n",
" 'Source Code Root Directory',\n",
" 'Data Repository',\n",
" 'Backups',\n",
" 'Temporary Files',\n",
" 'Time Series Data',\n",
" 'FOREX Data'\n",
" ],\n",
" \"Variable Name\" : [\n",
" 'employment_data_dir',\n",
" 'cpi_data_dir',\n",
" 'fed_rates_dir',\n",
" 'gdp_data_dir',\n",
" 'ism_data_dir',\n",
" 'sales_data_dir',\n",
" 'treasury_data_dir',\n",
" 'market_data_dir',\n",
" 'fomc_dir',\n",
" 'preprocessed_dir',\n",
" 'train_dir',\n",
" 'output_dir',\n",
" 'keyword_lm_dir',\n",
" 'glove_dir',\n",
" 'model_dir',\n",
" 'graph_dir',\n",
" 'final_graph_dir',\n",
" 'img_dir',\n",
" 'root_dir',\n",
" 'src_dir',\n",
" 'data_dir',\n",
" 'bak_dir',\n",
" 'tmp_dir',\n",
" 'ts_dir',\n",
" 'fx_rates_dir'\n",
" ],\n",
" 'Directory Location' : [\n",
" employment_data_dir,\n",
" cpi_data_dir,\n",
" fed_rates_dir,\n",
" gdp_data_dir,\n",
" ism_data_dir,\n",
" sales_data_dir,\n",
" treasury_data_dir,\n",
" mkt_data_dir,\n",
" fomc_dir,\n",
" preprocessed_dir,\n",
" train_dir,\n",
" output_dir,\n",
" keyword_lm_dir,\n",
" glove_dir,\n",
" model_dir,\n",
" graph_dir,\n",
" final_graph_dir,\n",
" img_dir,\n",
" root_dir,\n",
" src_dir,\n",
" data_dir,\n",
" bak_dir,\n",
" tmp_dir,\n",
" ts_dir,\n",
" fx_rates_dir\n",
" ]\n",
" })\n",
"\n",
"# Map directory name to variable (df_new = df[df['Pid'] == 'p01'])\n",
"save_dir_VAR = dir_set[dir_set['Directory Name'] == str(save_dir)]['Variable Name'].reset_index(drop=True)[0]\n",
"save_dir_NAME = str(dir_set[dir_set['Directory Name'] == str(save_dir)]['Directory Name'].reset_index(drop=True)[0])\n",
"save_dir_PATH = dir_set[dir_set['Directory Name'] == str(save_dir)]['Directory Location'].reset_index(drop=True)[0]\n",
"print('Save directory configured successfully.\\n')\n",
"print('Folder: {} ({})\\nFilepath: {}'.format(save_dir_VAR, save_dir_NAME, save_dir_PATH))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Save directory configured successfully.\n",
"\n",
"Folder: graph_dir (Data Graphs (Drafts))\n",
"Filepath: /content/drive/My Drive/Colab Notebooks/proj2/src/data/graphs/\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3AM57wQGPGda"
},
"source": [
"#### General"
]
},
{
"cell_type": "code",
"metadata": {
"id": "9Suzx8xo77ui",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "46a8a082-7a39-44d3-9509-2e328c809c5d"
},
"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: 59.9 µs (started: 2021-01-30 20:06:27 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "HAMlfm6Fq9WH",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ab334d80-0699-454c-e702-3ad68250c2cc"
},
"source": [
"# Set pandas display preferences\n",
"pd.options.display.max_rows = 20\n",
"pd.options.display.max_seq_items = 50\n",
"pd.set_option('display.max_colwidth', 200)\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 1.51 ms (started: 2021-01-30 20:06:27 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "xUEvP2ONZHZA",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ae8d77a1-cba7-45c9-ae08-32e7b5c21eb7"
},
"source": [
"# Fiinalize 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",
"\r\n",
"# Test pprint\r\n",
"pprint.pprint(sys.path)\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": [
"['',\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: 1.03 s (started: 2021-01-30 20:06:27 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iaqPH0Sr3KHm",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6e228399-c394-4d46-92a8-4cba80e24ba4"
},
"source": [
"# Set Random Seed\n",
"random.seed(42)\n",
"np.random.seed(42)\n",
"#torch.manual_seed(42)\n",
"#torch.cuda.manual_seed(42)\n",
"rand_seed = 42\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 1.96 ms (started: 2021-01-30 20:06:28 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "v-0LaEPEAO9f"
},
"source": [
"#### Runtime Type"
]
},
{
"cell_type": "code",
"metadata": {
"id": "QojZiqKbhCbB",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3cbbc22b-6ff0-4b73-9b8a-f834e914dbd8"
},
"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": [
{
"output_type": "stream",
"text": [
"time: 1.15 ms (started: 2021-01-30 20:06:29 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9p61Q8h8hn8Q",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "5521f8fe-f79d-475c-b5c3-428a951291a9"
},
"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": [
{
"output_type": "stream",
"text": [
"time: 1.1 ms (started: 2021-01-30 20:06:29 +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": "8b421c0f-f4db-4a24-dcd9-e82a3f6f0c75"
},
"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: 6.26 ms (started: 2021-01-30 20:06:31 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cZbRv0az8WhI"
},
"source": [
"#### Plotting"
]
},
{
"cell_type": "code",
"metadata": {
"id": "BA7NP3bVjM5x",
"colab": {
"base_uri": "https://localhost:8080/"
},
"cellView": "form",
"outputId": "da36b7bd-0103-4127-a872-5d5181184f09"
},
"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: 3.84 ms (started: 2021-01-30 20:06:32 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "KejuTyRrpG7C",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b61c3f34-2677-4750-d9db-7ff61759a7cd"
},
"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: 45.2 ms (started: 2021-01-30 20:07:38 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mTlseZKpQN-5"
},
"source": [
"#### Other"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "A8HwOgfF8DiU"
},
"source": [
"##### MatPlotLibRC Template"
]
},
{
"cell_type": "code",
"metadata": {
"id": "XoB6Yuwf7XT6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"outputId": "e1910150-b538-4201-b8bf-f67fd3fc7691"
},
"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": 101
},
{
"output_type": "stream",
"text": [
"time: 200 ms (started: 2021-01-30 20:06:38 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "UX2a9SKkRMdg",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 109
},
"outputId": "3e61f5c6-7631-467e-86bd-cac6c687ac8e"
},
"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 Jan 30 20:06 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
"drwxr-xr-x 1 root root 4096 Jan 30 20:06 \u001b[01;34m..\u001b[0m/\n",
"-rw------- 1 root root 33574 Jan 30 20:06 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": 102
},
{
"output_type": "stream",
"text": [
"time: 2.85 s (started: 2021-01-30 20:06:38 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ibCRdt-9l3zG"
},
"source": [
"## **Function Definitions**"
]
},
{
"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": "a7bccc30-c5c7-42dd-e381-bfec33605d69"
},
"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: 10.6 ms (started: 2021-01-30 20:08:36 +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": "41389362-688c-4a3b-ce04-df36ebfc40eb"
},
"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: 150 ms (started: 2021-01-30 20:34:55 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5DS61eZ7fLt7"
},
"source": [
"## **FOMC Meeting Minutes**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0WZpDpSLfLuA"
},
"source": [
"### Calendar"
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "QcCnYObCfLuA",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 442
},
"outputId": "0dc9afd6-54ca-4a34-face-75e2f1a8a0b0"
},
"source": [
"# Pickle\n",
"file = open(fomc_dir + 'fomc_calendar_latest.pickle', 'rb')\n",
"fomc_cal = pickle.load(file)\n",
"file.close()\n",
"# Comma-Delimited\n",
"# fomc_cal = pd.read_csv(fomc_dir + 'fomc_cal.csv')\n",
"fomc_cal"
],
"execution_count": null,
"outputs": [
{
"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>unscheduled</th>\n",
" <th>forecast</th>\n",
" <th>confcall</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1980-01-09</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1980-02-05</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1980-02-22</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1980-03-07</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1980-03-18</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</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>441</th>\n",
" <td>2021-06-16</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>442</th>\n",
" <td>2021-07-28</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>443</th>\n",
" <td>2021-09-22</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>444</th>\n",
" <td>2021-11-03</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>445</th>\n",
" <td>2021-12-15</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>446 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" date unscheduled forecast confcall\n",
"0 1980-01-09 False False False\n",
"1 1980-02-05 False False False\n",
"2 1980-02-22 False False True\n",
"3 1980-03-07 False False True\n",
"4 1980-03-18 False False False\n",
".. ... ... ... ...\n",
"441 2021-06-16 False True False\n",
"442 2021-07-28 False False False\n",
"443 2021-09-22 False True False\n",
"444 2021-11-03 False False False\n",
"445 2021-12-15 False True False\n",
"\n",
"[446 rows x 4 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 119
},
{
"output_type": "stream",
"text": [
"time: 22.1 ms (started: 2021-01-30 20:08:54 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "wkmRrQxMfLuA",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 256
},
"outputId": "86131e6a-cd8b-4232-b63d-5a762f4a50d4"
},
"source": [
"fomc_cal = fomc_cal.loc[fomc_cal['date'] >= dt.datetime(1982, 9, 27)]\n",
"fomc_cal.set_index('date', inplace=True)\n",
"fomc_cal.head()"
],
"execution_count": null,
"outputs": [
{
"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>unscheduled</th>\n",
" <th>forecast</th>\n",
" <th>confcall</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1982-10-05</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982-11-16</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982-12-21</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983-01-14</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983-01-21</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" unscheduled forecast confcall\n",
"date \n",
"1982-10-05 False False False\n",
"1982-11-16 False False False\n",
"1982-12-21 False False False\n",
"1983-01-14 False False True\n",
"1983-01-21 False False True"
]
},
"metadata": {
"tags": []
},
"execution_count": 120
},
{
"output_type": "stream",
"text": [
"time: 13.8 ms (started: 2021-01-30 20:08:54 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TvjVzC4NfLuA"
},
"source": [
"### Add Chairpersons to Calendar"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ne3NPibZfLuA",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "25f5448d-6922-4d7b-981c-c94554f60ccb"
},
"source": [
"chairpersons = pd.DataFrame(\n",
" data=[[\"Volcker\", \"Paul\", dt.datetime(1979,8,6), dt.datetime(1987,8,10)],\n",
" [\"Greenspan\", \"Alan\", dt.datetime(1987,8,11), dt.datetime(2006,1,31)], \n",
" [\"Bernanke\", \"Ben\", dt.datetime(2006,2,1), dt.datetime(2014,1,31)], \n",
" [\"Yellen\", \"Janet\", dt.datetime(2014,2,3), dt.datetime(2018,2,3)],\n",
" [\"Powell\", \"Jerome\", dt.datetime(2018,2,5), dt.datetime(2022,2,5)]],\n",
" columns=[\"Surname\", \"FirstName\", \"FromDate\", \"ToDate\"])\n",
"chairpersons\n",
"\n",
"def get_chairperson(x):\n",
" if type(x) is str:\n",
" try:\n",
" x = dt.datetime.strftime(x, '%Y-%m-%d')\n",
" except:\n",
" return None\n",
" \n",
" chairperson = chairpersons.loc[chairpersons['FromDate'] <= x].loc[x <= chairpersons['ToDate']]\n",
" return list(chairperson.FirstName)[0] + \" \" + list(chairperson.Surname)[0]"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"time: 8.67 ms (started: 2021-01-30 20:08:54 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "pr8nb5LhfLuA",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 474
},
"outputId": "e3be8396-693e-433d-c999-0f9842cadcb7"
},
"source": [
"fomc_cal['ChairPerson'] = fomc_cal.index.map(get_chairperson)\n",
"fomc_cal"
],
"execution_count": null,
"outputs": [
{
"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>unscheduled</th>\n",
" <th>forecast</th>\n",
" <th>confcall</th>\n",
" <th>ChairPerson</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>1982-10-05</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Paul Volcker</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982-11-16</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Paul Volcker</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982-12-21</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Paul Volcker</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983-01-14</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>Paul Volcker</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983-01-21</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>Paul Volcker</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>2021-06-16</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>Jerome Powell</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-07-28</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Jerome Powell</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-09-22</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>Jerome Powell</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-11-03</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>Jerome Powell</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-12-15</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>Jerome Powell</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>403 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" unscheduled forecast confcall ChairPerson\n",
"date \n",
"1982-10-05 False False False Paul Volcker\n",
"1982-11-16 False False False Paul Volcker\n",
"1982-12-21 False False False Paul Volcker\n",
"1983-01-14 False False True Paul Volcker\n",
"1983-01-21 False False True Paul Volcker\n",
"... ... ... ... ...\n",
"2021-06-16 False True False Jerome Powell\n",
"2021-07-28 False False False Jerome Powell\n",
"2021-09-22 False True False Jerome Powell\n",
"2021-11-03 False False False Jerome Powell\n",
"2021-12-15 False True False Jerome Powell\n",
"\n",
"[403 rows x 4 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 122
},
{
"output_type": "stream",
"text": [
"time: 572 ms (started: 2021-01-30 20:08:54 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Pb9eyXsYfLuA"
},
"source": [
"## **Market Data**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9KgHSgR4fLuA"
},
"source": [
"### Target FED Rate\n",
"\n",
"Sources:\n",
"* Federal Reserve Bank of New York (https://nyfed.org/2L2O95Q). Data only from 2000 onwards.\n",
"* Federal Reserve Bank of St. Louis (https://bit.ly/3ta4A1q). Data from 1982 (PICK)."
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "mwZMzpZ6fLuA",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 474
},
"outputId": "12cadbca-cf98-452c-e460-7e4c48f1293e"
},
"source": [
"filename = fed_rates_dir + 'ffs-01172000-02092020.csv'\n",
"\n",
"fedrates_frb_ny = pd.read_csv(filename, header=0, names=('Date', 'Vol', 'EFFR', '1st','25th' ,'75th' ,'99th' ,'Target' ,'Low' ,'High','Std.' ))\n",
"fedrates_frb_ny.index = pd.to_datetime(fedrates_frb_ny.Date, format=\"%d/%m/%Y\")\n",
"fedrates_frb_ny.dropna(inplace=True, axis=0, subset=['Date', 'Target'])\n",
"fedrates_frb_ny.drop(columns=['Date', 'Vol', 'EFFR', '1st','25th' ,'75th' ,'99th','Low' ,'High','Std.'], inplace=True)\n",
"\n",
"fedrates_frb_ny['Low'] = None\n",
"fedrates_frb_ny['High'] = None\n",
"\n",
"for i in range(fedrates_frb_ny.shape[0]):\n",
" target = fedrates_frb_ny['Target'][i]\n",
" if '-' in target:\n",
" fedrates_frb_ny['Low'][i] = re.findall(r'([0-9.]+)-', target)[0]\n",
" fedrates_frb_ny['High'][i] = re.findall(r'-([0-9.]+)', target)[0]\n",
" else:\n",
" (fedrates_frb_ny['Low'][i], fedrates_frb_ny['High'][i]) = (target, target)\n",
"\n",
"fedrates_frb_ny"
],
"execution_count": null,
"outputs": [
{
"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>Target</th>\n",
" <th>Low</th>\n",
" <th>High</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2020-02-06</th>\n",
" <td>1.50-1.75</td>\n",
" <td>1.50</td>\n",
" <td>1.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-05</th>\n",
" <td>1.50-1.75</td>\n",
" <td>1.50</td>\n",
" <td>1.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-04</th>\n",
" <td>1.50-1.75</td>\n",
" <td>1.50</td>\n",
" <td>1.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-02-03</th>\n",
" <td>1.50-1.75</td>\n",
" <td>1.50</td>\n",
" <td>1.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-31</th>\n",
" <td>1.50-1.75</td>\n",
" <td>1.50</td>\n",
" <td>1.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-07-10</th>\n",
" <td>6.5</td>\n",
" <td>6.5</td>\n",
" <td>6.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-07-07</th>\n",
" <td>6.5</td>\n",
" <td>6.5</td>\n",
" <td>6.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-07-06</th>\n",
" <td>6.5</td>\n",
" <td>6.5</td>\n",
" <td>6.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-07-05</th>\n",
" <td>6.5</td>\n",
" <td>6.5</td>\n",
" <td>6.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-07-03</th>\n",
" <td>6.5</td>\n",
" <td>6.5</td>\n",
" <td>6.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4925 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" Target Low High\n",
"Date \n",
"2020-02-06 1.50-1.75 1.50 1.75\n",
"2020-02-05 1.50-1.75 1.50 1.75\n",
"2020-02-04 1.50-1.75 1.50 1.75\n",
"2020-02-03 1.50-1.75 1.50 1.75\n",
"2020-01-31 1.50-1.75 1.50 1.75\n",
"... ... ... ...\n",
"2000-07-10 6.5 6.5 6.5\n",
"2000-07-07 6.5 6.5 6.5\n",
"2000-07-06 6.5 6.5 6.5\n",
"2000-07-05 6.5 6.5 6.5\n",
"2000-07-03 6.5 6.5 6.5\n",
"\n",
"[4925 rows x 3 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 174
},
{
"output_type": "stream",
"text": [
"time: 850 ms (started: 2021-01-30 20:35:04 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "dO5m3sNES3Q9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 615
},
"outputId": "c8ce790a-544a-4a5f-d317-a1fa36980532"
},
"source": [
"# Plot Federal Funds Rate (NY Data)\r\n",
"graph_tool(\r\n",
" 'sns_lineplot', \r\n",
" True, \r\n",
" 'percentage', \r\n",
" 20, None, \r\n",
" fedrates_frb_ny['High'].apply(lambda x: float(x)), \r\n",
" 'black', 1.0, \r\n",
" 'solid', \r\n",
" 'Target Federal Funds Rate (FRB-NY)', \r\n",
" 'The Federal Reserve Bank of NY provides accurate data, but only dating back to 2000', \r\n",
" '', \r\n",
" 'Rate (%)', \r\n",
" 'Target Fed Rate', \r\n",
" 'target_fedrates_frb_ny'\r\n",
" )\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Plot Dimensions: 15\" x 5.5'\n",
"Title: 'Target Federal Funds Rate (FRB-NY)'\n",
"Subtitle: 'The Federal Reserve Bank of NY provides accurate data, but only dating back to 2000'\n",
"X-Axis Label: None given\n",
"Y-Axis Label: 'Rate (%)'\n",
"Displaying a total of 20 ticks on the x-axis.\n",
"Figure saved as 'target_fedrates_frb_ny' in: /content/drive/My Drive/Colab Notebooks/proj2/src/data/graphs/target_fedrates_frb_ny.png\n"
],
"name": "stdout"
},
{
"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: 723 ms (started: 2021-01-30 20:35:11 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "GQDgV3ImfLuD",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 474
},
"outputId": "d682da76-180e-44cc-d198-379937d71972"
},
"source": [
"filename_till08 = fed_rates_dir + 'DFEDTAR.csv'\n",
"filename_from08u = fed_rates_dir + 'DFEDTARU.csv'\n",
"filename_from08l = fed_rates_dir + 'DFEDTARL.csv'\n",
"\n",
"fedtar = pd.read_csv(filename_till08, names=['Date', 'Rate'], header=0)\n",
"fedtaru = pd.read_csv(filename_from08u, names=['Date', 'Rate'], header=0)\n",
"fedtarl = pd.read_csv(filename_from08l, names=['Date', 'Rate'], header=0)\n",
"fedrates_compiled_fred = pd.concat([fedtar, fedtarl], axis=0)\n",
"fedrates_compiled_fred.index = pd.to_datetime(fedrates_compiled_fred.Date, format=\"%Y-%m-%d\")\n",
"fedrates_compiled_fred.drop(columns=['Date'], inplace=True)\n",
"fedrates_compiled_fred['Rate'] = fedrates_compiled_fred['Rate'].map(lambda x: np.float(x))\n",
"fedrates_compiled_fred['diff'] = fedrates_compiled_fred['Rate'].diff()\n",
"\n",
"fedrates_compiled_fred"
],
"execution_count": null,
"outputs": [
{
"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>Rate</th>\n",
" <th>diff</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1982-09-27</th>\n",
" <td>10.25</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982-09-28</th>\n",
" <td>10.25</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982-09-29</th>\n",
" <td>10.25</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982-09-30</th>\n",
" <td>10.25</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982-10-01</th>\n",
" <td>10.00</td>\n",
" <td>-0.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-05-30</th>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-05-31</th>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-01</th>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-02</th>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-03</th>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>13765 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Rate diff\n",
"Date \n",
"1982-09-27 10.25 NaN\n",
"1982-09-28 10.25 0.00\n",
"1982-09-29 10.25 0.00\n",
"1982-09-30 10.25 0.00\n",
"1982-10-01 10.00 -0.25\n",
"... ... ...\n",
"2020-05-30 0.00 0.00\n",
"2020-05-31 0.00 0.00\n",
"2020-06-01 0.00 0.00\n",
"2020-06-02 0.00 0.00\n",
"2020-06-03 0.00 0.00\n",
"\n",
"[13765 rows x 2 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 176
},
{
"output_type": "stream",
"text": [
"time: 51 ms (started: 2021-01-30 20:35:15 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "LI1oqwEhIAcC",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 615
},
"outputId": "a7ee2ddb-e5ad-4f72-cd19-794abc1c660b"
},
"source": [
"# Plot Federal Funds Rate (St. Louis Data)\r\n",
"graph_tool(\r\n",
" 'sns_lineplot', \r\n",
" True, \r\n",
" 'percentage', \r\n",
" 20, \r\n",
" None, \r\n",
" fedrates_compiled_fred['Rate'].values, \r\n",
" 'black', \r\n",
" 1.0, \r\n",
" 'solid', \r\n",
" 'Target Federal Funds Rate (FRED)', \r\n",
" 'The Federal Reserve Bank of St. Louis provides data dating back to 1982', \r\n",
" '', \r\n",
" 'Rate (%)', \r\n",
" 'Target Fed Rate', \r\n",
" 'target_fedrates_fred'\r\n",
" )\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Plot Dimensions: 15\" x 5.5'\n",
"Title: 'Target Federal Funds Rate (FRED)'\n",
"Subtitle: 'The Federal Reserve Bank of St. Louis provides data dating back to 1982'\n",
"X-Axis Label: None given\n",
"Y-Axis Label: 'Rate (%)'\n",
"Displaying a total of 20 ticks on the x-axis.\n",
"Figure saved as 'target_fedrates_fred' in: /content/drive/My Drive/Colab Notebooks/proj2/src/data/graphs/target_fedrates_fred.png\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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pmlg+bin/dD7q9OanLlSoEAoWLKhJZjdu3IgaNWqk2S6jqePSe/8//p0WKFBAq3hbaiV4Y/q0xfPly5fw8PCAUqnUjBvPrY/f9wcPHiAuLk7TGn/9+vV09xEEARUqVNAatx8REYGOHTsiISEBR44cQWJiYrZb4/Pnz4/x48fj3LlzePDgAVJSUvDHH39o3vuPfz+NGjVC7969AaiLSb58+TLdY2b1t56Tz09GvLy8NEn848eP9dajIpVIJEK5cuUwY8YMzRz0Fy5cwJEjR7RmMdCn8PBwrSKIPj4+aR5AftwzIqMilERERESUc0zi9aR48eKan+/du4d169ahePHi2Lx5c5bdfTPSvHlz/Pbbb1AqlYiMjMSYMWPg7++Py5cva40Z/9iXX36pSUKCgoLw4sULVKhQAUlJSXj+/DmuXLmCo0ePaj0EKFCgAJ4+fQoA2LlzJ8RiMSQSCcqXL6/VnTo3vvzyS03MwcHBSE5ORvXq1aFSqfDs2TPcunULR44cwdatW1G8eHG0bNlS0z3/4sWLmDFjBho2bIgTJ06k6UoPqBOaTp06aYrn/fDDD/j2229RpkwZxMbG4tmzZ7h06RIePHiQZgaBjHz8O3337h0WL16MypUrIzQ0VGtstbE4OzujcOHCiImJAQCMGTMGbdq0waFDh7S6wedGw4YN4ejoiISEBCQkJGD48OGawnarV69Od58ZM2YgMjISPj4+cHd3h5OTE27fvo2kpCQA6iQ/JSUlR13qJRIJBg0ahFGjRgEADh8+jHv37sHLy0vr93P27Fns3LkTzs7OWLlyZYZDHD7uOn7p0iUcO3YMTk5OcHV1RalSpXL0+clInTp1NFPj3bhxA3Xr1s329euiRo0a8PHx0bSQL168OMMkPiIiIt1ieRUrVkz3wUTq5yY2NhYXL17Epk2bEBcXB0BdC+HTWQkA9bWmql27do6uiYiIiIgyxiReT6pUqYLq1avj6tWrkMvlCAoKAqDu5lulShWtL7a6KlGiBAYNGqTpirxr1y5N9+4KFSogLCwszT4BAQG4dOkSDhw4gPfv3+O3337L8jw+Pj64efMmAHUSn9pNfN26dXorcte0aVP069cPK1euREpKSpbdq+vVqwd/f3/s27cPALBmzRqsWbMGgLqHwuPHj9PsExgYiOvXr+PSpUt4/vy55nfwsdRid7pwcXFB69atNUl/akVuiUSCWrVqZVg53JB69uypqSqfOo2aSCRC2bJlteoO5JSTkxNGjx6tqSlw6tQpTSt/Ru97cnIyjh49qklYP+Xr65vpOO2stGnTBvPnz0dkZCQEQcDixYsxd+5cNGnSBB4eHoiKisKHDx8wZswYAOqq8mXKlNEU7ftYvXr1IBaLNVXsU3uEfPXVV5g+fXqOPj8Z8fPzw5w5c6BUKnHixAkMGDAgx8fKysCBAzVJ/I0bN3DixIl0azwsWbJEa8aLVDt37kxTiwEAtm/frlUwMlWxYsUwb948eHp6ai0XBAEnT54EoO45kl4MRERERJQ7YlMHYC3EYjEWL16Mjh07wsXFBY6Ojqhbty5CQkI0xd1yIjAwEBMnTkTJkiUhk8nw2WefYcqUKejYsaNmm49bOCUSCebNm4fff/8dPj4+KFCgAKRSKQoXLoxKlSrh66+/RkhIiNY5Bg8ejG7duqFw4cIGrWw9ZswYLF++HM2aNYOrqyukUikKFCgALy8vdOvWDcuWLYO7u7tm+1mzZmHw4MEoWrQo7Ozs4O3tnWEXZ0D9PqxZswY///wzatWqhXz58kEmk8HV1RXVqlXDoEGDNAmwrmbMmIHevXujaNGicHBwQLVq1bBs2TLUq1cvV+9FTg0cOBB9+/bVzCpQpUoVLFmyBK1bt9bbOQICAvDbb7+hXLlykMlkKFasGIYOHYrJkyenu33btm3RpUsXeHl5oUCBApBIJHB0dETFihURGBioefiRUxKJBP3799e8/ueff/Dw4UM4OjpizZo1aNasGQoUKABnZ2f4+vpi/fr16daYANTd5WfNmqW5tvTOld3PT0bc3d3RpEkTAOoHLp8W5tOnevXqaRUezM6UcVkRi8XImzcvSpUqhSZNmmDq1KnYv39/uoUOr1y5ohk+1KFDh1wXNCQiIiKitEQCSwmbtY/HwX8sMDAQoaGhANQF19Lr1kpEpnXr1i107twZgiBg6NChCAwMNHVIBjVq1Cjs2bMHefLkQWhoqN7rABARERERW+LN3oYNGxAUFISzZ8/i2bNnuHPnDn7//XdNAi8SidCuXTsTR0lE6alUqZLm8/n3338jPj7exBEZztOnTzXTXn7zzTdM4ImIiIgMhC3xZm716tX49ddf010nEonwww8/pFvpnYiIiIiIiKwPC9uZuRo1aqBVq1a4efMmYmJioFKp4OLigho1aqBHjx56KzxHRERERERE5o8t8UREREREREQWgmPiiYiIiIiIiCwEk3giIiIiIiIiC8EknoiIiIiIiMhCMIknIiIiIiIishBM4vXg/Pnz8Pb2xocPH0wdSpZ8fX2xevVqg5/n6dOn8Pb2RlhYmMHPRWkZ4vecmJiI77//HjVr1rSYv/fs6t27N6ZPn27qMLJNl3vQ9u3bjT6bxcKFC9GhQwejnjMz+orHWNc1duxYDBkyxKDnMLffEREREWWNSXwWvL29M/23cOFCg5174cKF6Z7zzJkzBjunMfXu3VtzTVWqVEGrVq2wdOlSWOuECR9fr7e3Nxo0aIDAwEBERUWZOjSd7NixA5cuXcLGjRtx6tQpODs7p9lGqVRi2bJlaN26NapWrYo6deqgS5cu2LJli2abnCbKxkhCFy5ciOHDhxv0HIZQo0aNDH8nlsbb2xuHDx82dRga6cXTr18/ozwMNVf6+h3dv38f33//PXx9feHt7Z3uexoXF4fp06ejadOmqFq1Krp3747r169rbRMfH4+goCB88cUXqFq1Ktq0aYMNGzZo1r979w5Tp05Fq1atULVqVTRp0gTTpk1DbGxsrq+BiIjIFDhPfBZOnTql+Xn//v1YsGAB/vnnH80yR0dH3Lx502DnL1euHFatWqW1LH/+/AY7X1ZSUlJgZ2ent+N17doVgYGBSElJwblz5zB58mQ4OzujR48eejtHduj7+j6Ver2CIODZs2eYMWMGfvrpJ6xfv95g59SXyMhIeHp6wsvLK8NtgoODsWnTJkyaNAmVK1dGfHw8bty4YTGt9gUKFDDq+fT192ZnZwdXV1c9RES6yJs3L/LmzWvqMCxeYmIiihcvjtatW+PXX39Nd5uJEyfi/v37mD17Ntzc3LB792707dsX+/fvR5EiRQAAM2fOxLlz5/Dbb7/Bw8MDp0+fxpQpU+Dm5oZmzZrh1atXePXqFcaMGYOyZcsiKioKv/zyC169eoUFCxYY85KJiIj0gi3xWXB1ddX8c3Z2hkgk0lr28Re5W7du4csvv0S1atXQvXt3PHz4UOtYhw8fRqdOnVClShU0a9YMwcHBUCgUmZ5fIpFonc/V1VXzpf/SpUvo0aMHqlatisaNG2PatGlISEjQ7BsTE4NBgwahatWq8PX1xe7du9Mc/8OHD5gwYQLq1auHmjVrok+fPrhz545mfWpXyy1btsDX1xdVq1YFAJw4cQIBAQGoVasW6tati++++w5PnjzJ9vvr4OAAV1dXeHh4oHPnzml6GqSkpGDWrFlo1KgRqlevji5duuD8+fOa9VFRURg0aBBq166N6tWrw9/fH8ePH9esv3fvHgYMGIAaNWqgQYMG+Omnn/DmzRvN+t69eyMoKAjTp09H3bp10b9/f/z4448YMWKEVpxyuRx169bFzp07AQAqlQpLly7VvCft27fXeriT1fW6ubmhevXq6NmzJ27fvq1Zr1QqMX78eM1xW7VqhTVr1mgdI7WL7YoVK9CwYUPUrVsXU6ZMgVwuz/C8W7ZsQa1atXD27NkMtwkNDYW/vz8qV64MX19frFy5Uut9WrlyJS5evAhvb2/07t073WP8+++/6NGjB/z8/FCiRAmUL18eXbp0Qf/+/TWxX7hwASEhIZoeCU+fPs3yfdPFs2fPMHjwYNSoUQM1a9bE8OHD8fr1a8369LomT58+XetaPu0lsG7dOrRs2RJVqlTR9JzISGpPgcOHD2v26d+/P54/f67ZJqPPU2axP3r0CN7e3ggPD9c63+rVq9G8eXMA6Xen3759O5o0aYJq1aph6NChePfuXZqYM7snCYKAhQsXokmTJqhcuTIaNmyIadOmZXj9ALBs2TI0aNAANWrUwPjx45GcnKy1/vr16+jbty/q1q2Lzz//HL169cKtW7c06319fQEAQ4cOhbe3t+b1kydPMHjwYM2xO3furFOPJEPF82kXdF0+k69evcLAgQM19+M9e/boPOwlODhYc4+ePHkyUlJSNOt0uRe/ePECI0eORJ06dVC9enV8+eWXuHbtWrrnevLkCZo1a4agoKB0e0Vl9J4AwPr169G8eXNUrlwZrVq10twvM1K1alWMGTMG/v7+6T7MSkpKwsGDB/HTTz+hdu3aKFWqFL7//nuUKlVK68HnlStX0LFjR9StWxfFixdHt27dUL58eU2LvZeXFxYuXAhfX1+ULFkS9evXx4gRI/Dvv/9m+X8wERGROWISr0dz587F2LFjsW3bNkgkEowfP16z7tKlSxgzZgz69OmD/fv3IygoCNu3b8eSJUtydK4nT57g22+/RcuWLbF7927MnTsX//33H6ZOnarZZuzYsXj+/DlCQkKwYMECrF+/HjExMVrHGT58OGJiYrB8+XJs374dlSpVwtdff631hf/JkycIDQ1FcHCw5ktZYmIi+vbti23btmH16tUQiUQYOnQoVCpVjq5HEARcunQJDx8+hEwm0ywPCgrClStXMHfuXOzevRutW7fGgAED8PjxY836lJQU/P3339izZw9GjRoFR0dHAOoHFF9//TUqVqyIrVu34q+//kJMTEyaBH3Hjh2QyWTYsGEDpkyZgnbt2uHo0aOIj4/XbHPq1CkkJSVpkqalS5di586dmDJlCvbt24dvvvkGP/30Ey5cuKDzNb979w4HDhzQJHKA+uFA0aJFMX/+fOzbtw9Dhw7F3LlzsX//fq19z58/jydPnmDNmjWYOXMmduzYgR07dqR7nuXLl2POnDlYuXIl6tevn+42N2/exIgRI9CmTRvs2bMHw4YNw/z587F9+3YA6qSla9eumm7bGQ0jcXFxwblz57QelHxswoQJqFGjBrp27YpTp07h1KlTcHd3z/K9yopKpcKQIUPw/v17rF27FqtWrUJkZCR++OGHHB/zxo0bmD59OgIDA/HPP//gr7/+yrI7f1JSEhYvXoxZs2Zhw4YN+PDhQ5oYPv08ZRX7Z599hsqVK2PPnj1ax9m9ezfatm2bbhzXrl3DhAkT0LNnT+zcuRN169bF4sWLtbbJ6p4UGhqK1atXY8qUKTh48CD+/PPPTHth7N+/HwsXLsQPP/yAbdu2wdXVNU0Pk/j4eHTs2BHr16/H5s2bUapUKQwcOBBxcXEAgK1btwIAfv31V5w6dUrzOiEhAY0bN8bq1auxY8cONGrUCIMGDcKzZ89MEk96svpMjhkzBq9evcLatWuxcOFCbN68Oc39OD1nz55FeHg41q5diz/++AOHDh3CokWLNOuzuhfHx8ejV69eePnyJf7880/s2rULAwYMSPdefefOHfTo0QNt27bF5MmTIRKJ0myT0Xty6NAhzJgxA3379sWePXvQvXt3jB8/HufOncvyGjOiUCigVCphb2+vtdze3h6XL1/WvK5Rowb+/fdfvHz5EoIg4Ny5c3j06BEaNmyY4bHj4uLg5OQEqZQdEomIyAIJpLNt27YJn3/+eZrl586dE7y8vIQzZ85olh07dkzw8vISkpKSBEEQhK+//lpYsmSJ1n47d+4UfHx8MjzfggULhPLlywvVq1fX/OvcubMgCIIwfvx4YdKkSVrbX7x4UShfvryQlJQkPHz4UPDy8hKuXbumWf/gwQPBy8tLWLVqlWb7mjVrCsnJyVrHad68ubBx40ZNDJUqVRJiYmIyfW9iYmIELy8v4e7du4IgCEJkZKTg5eUl3L59O8N9evXqJVSqVEmoXr26UKlSJcHLy0uoUqWK8N9//wmCIAhRUVFChQoVhBcvXmjt9/XXXwu///67IAiC0LZtW2HhwoXpHn/RokVCv379tJY9f/5c8PLyEh4+fKiJoWPHjlrbyOVyoW7dusKOHTs0y0aOHCmMGDFCEARBSE5OFqpVqyZcvnxZa7/x48cLI0eO1Ol6q1WrJnh5eQktW7YUIiMjM9xHEARhypQpwvfff695PWbMGKFp06aCQqHQLAsMDNTEJwiC0LRpU2HVqlXC7NmzBR8fH+HevXuZnmPkyJFC3759tZbNmjVLaNOmjeb1tGnThF69emV6nPv37wt+fn5C+fLlhbZt2wqTJk0Sjh07prVNr169hGnTpmV6nPRk9PkTBEE4deqUUKFCBeHZs2dasXz8GRgzZowwePBgrf0+vaaPYwsNDRVq1qwpxMbG6hyfl5eXcPXqVc2y1M9cagzpfZ50iX3VqlVC8+bNNetTP98PHjwQBOF/96D3798LgqD+fX777bda8Y0YMULr/cvqnrRy5UqhZcuWQkpKik7X361bN+GXX37RWtalSxehffv2Ge6jVCqFGjVqCP/++69mmZeXl3Do0KEsz+fv7y+sXbvWJPEsWLBA6zhZfSZT/w6uX7+uWf/48WOt+3F6xowZI9SpU0dISEjQLFu/fr1QvXp1QalUprvPp/fijRs3CjVq1BDevn2b7vap1/Lff/8JtWvXFlasWJFhPKnSe0+6desmTJw4UWtZYGBgmr/DjKTesz7VrVs3oVevXsKLFy8EhUIh7Ny5UyhfvrzQsmVLzTbJycnC6NGjBS8vL6FixYpCpUqVtO7fn4qJiRGaNGki/PHHHzrFRkREZG74CFqPvL29NT+njk+NiYlBsWLFcOfOHVy+fFmr5V2pVCI5ORmJiYnIkydPusf87LPPtFrQUrsc3rlzB3fv3tVqnRMEASqVCk+fPsWjR48glUpRuXJlzXpPT0/ky5dP8/ru3btISEhA3bp1tc6ZlJSk1R2zWLFiKFSokNY2jx8/xoIFC3Dt2jW8fftW0+3y+fPnmbbWfapdu3YYNGgQ3r9/j4ULF2q6EwPqrvBKpRKtW7fW2iclJUUzdrlPnz745ZdfcOrUKTRo0AAtW7ZE+fLlNe/R+fPnUaNGjTTnffLkCT777DMAQKVKlbTWSaVS+Pn5Yc+ePejYsSMSEhJw5MgR/PHHHwCAiIgIJCYmol+/flr7yeVyVKhQQafrBYDXr19j6dKl6N+/P7Zt2wYnJycA6i7c27Ztw7Nnz5CcnAy5XK65plRly5aFRCLRvHZ1dcW9e/e0tlm1ahUSExOxbds2lChRItO4Hj58iGbNmmktq1mzJkJCQqBUKrXOlZmyZcti7969uHnzJi5fvoxLly5h8ODB6NSpk0GrvoeHh6No0aJarfply5ZFvnz58PDhQ63eDrpq0KABihUrhubNm6NRo0Zo1KgRWrRokeFnFVD/7VSpUkXzOvUzFx4eronh08+TLrH7+/tj9uzZuHr1KqpXr449e/agUqVK8PT0zPD9SO01kqp69eo4efKk5nVW96TWrVtjzZo1mutv3LgxmjZtmmHLZXh4OLp3757mnB8Pf3n9+jXmzZuHCxcuICYmBiqVComJiZm2qAPq1uTg4GAcO3YM0dHRUCqVSEpKynQ/Q8aTnsw+k6n344/vNaVKldKpvom3t7fW31yNGjWQkJCA58+fw8PDI8t7cVhYGCpWrJhpvYfnz5+jX79+GDFiBL755ptsXrnaw4cP0a1bN61lqfeQ3Jg9ezbGjx+PL774AhKJBBUrVoS/v7/WsIe1a9fi6tWrWLx4MYoVK4ZLly5pxsQ3aNBA63hxcXH47rvv4OnpiWHDhuUqNiIiIlNhEq9HH3+5Te2GmNplMSEhAd9//z1atmyZZr9Puwp+TCaToVSpUmmWJyQkoHv37umOTXZ3d8ejR4+yjDc+Ph6urq5Yu3ZtmnUfV7lOL2kZNGgQPDw8MG3aNLi5uUGlUqFt27aZjstOj5OTk+b65s2bh5YtW6J69epo0KABEhISIJFINMMTPpbaZb5Lly5o2LAhjh07htOnT2PZsmUYM2YMevfujYSEBDRt2hSjRo1Kc96Pi4Cld33t2rVD7969ERMTg9OnT8Pe3h6NGjUCAE3dgaVLl2oKK6XKqkjZx9dbqlQpTJ8+HQ0bNsSBAwfQpUsX7Nu3D7NmzcKYMWNQo0YN5M2bFytWrEgzfvXTREokEqUZv1qrVi0cO3YMBw4cwMCBAzONS5/EYjGqVq2KqlWr4ptvvsGuXbswevRoDBo0KMuHCYaU3nuU2XhYJycn7NixAxcuXMCpU6ewYMECBAcHY+vWrVoPw7Irs4cAGXF1dUW9evWwd+9eVK9eHXv37kVAQECOYwCyvie5u7vjn3/+wZkzZ3DmzBlMmTIFK1aswNq1a7WGvGTHmDFj8O7dO0yYMAHFihWDnZ0dunXrluV9Y9asWThz5gzGjBmDkiVLwsHBAYGBgdm+3+grnvTo8pk0hKzuxQ4ODlkeo2DBgnBzc8O+ffvw1VdfaR4omoOSJUvi77//RkJCAuLi4uDm5oYRI0Zo7iVJSUmYO3cugoOD0aRJEwBA+fLlERYWhhUrVmgl8XFxcRgwYADy5s2LRYsW5fjvmIiIyNQ4Jt5IKlasiEePHqFUqVJp/onF2f81VKxYEQ8ePEj3eHZ2dihTpgwUCoVW5fyHDx9qFb6qVKkSXr9+DYlEkuYYn7a8f+zt27d49OgRBg8ejPr168PT0xPv37/P9jV8Km/evOjTpw9mzZoFQRBQoUIFKJVKvHnzJk18Hyfh7u7uCAgIQHBwMPr27YvNmzdrru/+/fvw8PBIs3/qQ4CM1KxZE0WLFsX+/fuxZ88etG7dWvOFz9PTE3Z2dnj27Fma42Z3fHfqw4mkpCQAwOXLl1GjRg307NkTFStWRKlSpXJUMBAAqlSpguXLl2PJkiVYsWJFptuWKVNGa4xpaiylS5fWuRU+I2XLlgWgHrsLqB9M5bR2QkY8PT3x4sULrSJyDx48wIcPHzSt1YUKFUJ0dLTWfmFhYZkeVyqVokGDBhg9ejR2796NqKioTMf4ZvSZy6jFXNfYAfWDpf379+PKlSuIjIxEmzZtMj3mp9NwffogSJd7koODA3x9fTFx4kSEhITgypUraXp8fHzOT8/x6evLly+jd+/eaNy4McqVKwc7Ozu8fftWaxuZTAalUqm17MqVK+jUqRNatGgBb29vuLi4ZDk1oyHjya7PPvsMCoVCq4hlRESETvfNu3fvau4PAHD16lU4OjrC3d1dp3uxt7c3wsLC0i1smMrBwQFLly6Fvb09+vfvr6kJkJH03pOM7iGpn//ccnR0hJubG96/f49Tp05peg4pFArI5fI04/clEonWQ5S4uDj0798fMpkMixcvzvThORERkbljEm8kQ4cOxa5duxAcHIz79+8jPDwc+/btw9y5c3N0vG+//RZXrlxBUFAQwsLC8PjxYxw+fBhBQUEA1F+oGjVqhJ9//hnXrl3DzZs3MXHiRK1WmQYNGqB69eoYOnQoTp06hadPn+Ly5cuYO3cubty4keG58+fPjwIFCmDTpk2IiIjA2bNnMXPmzBxdx6e6deuGx48fIzQ0FJ999hnatWuH0aNH4+DBg4iMjMT169exdOlSHDt2DIC6uvjJkycRGRmJW7du4fz585rEp0ePHnj//j1GjhyJ69ev48mTJzh58iTGjRun05fytm3bYuPGjThz5gzatWunWe7k5IR+/frh119/xY4dO/DkyRPcunULa9euzbC4XKqkpCRER0cjOjoad+7cwS+//AJ7e3v4+PgAULfO37x5EydPnsSjR48wb968TH8XWalZsyaWLVuG4ODgTKtg9+vXD2fPnsWiRYvw6NEj7NixA+vWrUszZCArgYGBWL16Na5du4aoqCicP38eQUFBKF26NMqUKQMA8PDwwLVr1/D06VO8efNGk9C3bt0ahw4dyvT4SqUSYWFhWv/Cw8PRoEEDeHl5YdSoUbh16xauX7+O0aNHo06dOpru7fXq1cPNmzexc+dOTRfk+/fvZ3iuo0ePIiQkBGFhYYiKitIUoUsdhpEemUyGqVOnaj5z48aNQ/Xq1TPtzq9L7ADQokULxMfH45dffkHdunXT9AL5WO/evXHy5EmsWLECjx8/xt9//63VlR7I+p60fft2bNmyBffu3UNkZCR2794NBwcHFCtWLN1z9unTB9u2bcO2bdvw6NGjdN/f0qVLY/fu3QgPD8e1a9cwatSoNC3FHh4eOHv2LKKjozUJaalSpXDo0CGEhYXhzp07+PHHH7N8EGTIeLLL09MTDRo0wOTJk3H9+nXcvn0bkyZNgoODQ7rF4z6WkpKCCRMm4MGDBzh+/DgWLlyIXr16QSwW63Qv9vf3h4uLC4YOHYr//vsPkZGRCA0NxZUrV7S2c3R0xNKlSyGRSPDtt99qFfb8VHrvyYABA7Bjxw6sX78ejx8/xqpVq3Do0KFM7yEpKSmaz3FKSgpevnyJsLAwREREaLY5efIkTpw4gcjISJw+fRp9+vRBmTJl8OWXXwJQ34/r1KmD3377DefPn0dkZCS2b9+OnTt3aoaUxMXFoV+/fkhISMD06dMRFxenuQ/n9gENERGRKTCJN5JGjRphyZIlOHXqFL766it07doVq1evhoeHR46OV758eaxduxaPHz9Gjx490KlTJyxYsABubm6abX799Ve4ubmhV69e+P7779G1a1cULlxYs14kEmHZsmWoXbs2xo0bh9atW2PkyJGIioqCi4tLhucWi8WYO3cubt26hbZt2+LXX3/F6NGjc3QdnypQoAA6dOiA4OBgqFQq/Prrr+jYsSNmzpwJPz8/DBkyBDdu3NC0eKtUKgQFBaFNmzYYMGAASpcujZ9//hkAUKRIEWzYsAEqlQr9+/dHu3btMGPGDDg7O+vU+6F9+/Z48OABihQpgs8//1xr3YgRIzBkyBAsXbpUc+5jx46hePHimR5z8+bNaNiwIRo2bIg+ffrg7du3WLZsmSbB7d69O1q2bIkffvgBXbt2xbt379CjR4+cvJUatWrVwrJlyzB//vx0h04A6l4L8+bNw/79+9GuXTssWLAAgYGBmi/KumrYsCH+/fdfDBo0CK1atcLYsWNRpkwZrFy5UtPduF+/fpBIJPD390f9+vU1448fPXqE2NjYTI+fkJCAjh07av0bNGgQRCIR/vzzT+TLlw+9evXCN998gxIlSmg9JGvUqBGGDBmC3377DV999ZWmMnlGnJ2dcejQIXz99ddo06YNNm7ciN9//x3lypXLcB8HBwd8++23+PHHHxEQEABHR8csH9TpEjugTlaaNm2KO3fuaD1USk/16tUxdepUhISEoEOHDjh16hQGDx6stU1W96R8+fJhy5YtCAgIQPv27XH27FksWbIEBQsWTPecbdq00by/X375JZ49e5amy//06dPx/v17dOrUCaNHj0bv3r217kmAuov7mTNn0KRJE3Tq1AmAeqaNfPnyoXv37hg0aBAaNWqUppaFMePJiVmzZqFw4cLo2bMnhg0bhq5duyJv3rxZtgjXr18fpUqVQs+ePTFixAj4+vri+++/B6DbvdjOzg4rV65E4cKFMXDgQLRr1w7Lli1Lt4dN3rx5sXz5cgiCgO+++05rytKPpfeeNG/eHOPHj8fKlSs1D0BnzJiRpubKx169eqX5HEdHR2PlypXo2LEjJk6cqNkmNjYWQUFB8PPzw5gxY/D5559jxYoVWl3h//jjD1SpUgWjRo2Cv78/li1bhh9++EHz+7516xauXbuGe/fuoUWLFpp7cMOGDbV6wBAREVkKkWCMQXtERFZu+/btmDFjBi5dumTqUMgCvHjxQjNtXkZTPxIRERGlx+gt8dOmTYOvr69mnB4AJCcnY8iQIWjVqhXat2+Pvn37anWn+9TRo0fRunVrtGzZEsOGDdOM34uMjESXLl3g7++vVXE5PDxcUxGciIjI2M6ePYsjR44gMjISly9fxg8//AAPDw/UqlXL1KERERGRhTF6Et+qVSusX78+TTfybt264Z9//sHu3bvRrFkzre50H4uPj8eECROwaNEiHDx4EG5ubli0aBEAYP369ejZsyd2796NHTt2IC4uDoIgYMaMGZgwYYLBr42IiCg9CoUCc+fORdu2bTFs2DAUKlQoV5X+iYiIyHYZfYq52rVrp1lmb2+Pxo0ba15Xq1YNK1euTHf/EydOoEKFClrFy/r164cxY8ZAKpUiMTERCoUCgiBALBZjw4YN8PHxMenUVkRk/b788sts1xAg29GoUSPNNJVEREREuWGW88SHhITA19c33XXPnz/XasX38PBAdHQ0FAoFevfujXHjxmHTpk3o168fYmNjERoamuEDgY+9evUqzfRTIpEIRYsWzXS6NSIiIiIiIiJjMbskfsmSJXjy5Emm02FlxM3NTWs+7MDAQIwdOxbnz5/Hhg0bYGdnh5EjR6ZbEX7Tpk0IDg7WWlaxYkXs2LEDb9/GQ6HQ77zWRERERERERJ+SSsUoWDBvxuuNGEuWVqxYgYMHD2L16tXIkydPutu4u7vj9OnTmtdRUVFwdXXVTF+VKjQ0FCVLlkSFChXg5+eHLVu24ObNm1iwYAFmzZqV5rjdunVL0/r/6XzBRERERERERKZkNkn8qlWrsG/fPqxatQr58uXLcLtGjRohKCgI4eHh8PT0xPr16+Hv76+1zYcPHxASEqJplU9KSoJYLIZYLM5w3ls3NzetOdaJiIiIiIiIzI3R54mfPHkyjh07htevX6NAgQLImzcv1q5di8aNG6NEiRLIm1fdbcDOzg5btmwBAMyfPx9ubm4ICAgAABw5cgS//fYblEolypUrh1mzZsHZ2VnrHH5+fpq5dzdv3oxVq1ZBJpNh+vTpqFKlSrZiZnd6IiIiIiIiMoasutMbPYm3REziiYiIiIiIyBiySuKNPk88ERERERERkakNHPgN9u/fY+owss1sxsQTERERERGRabRo0Ujzc3JyMqRSKSQSCQCgd+++6NOnn1HiGDZsIJo3b4mOHb/KcJuvvmqHN2/eQCL5X5v0okXL4eVVXm9x7N+/BzNnToW9vT1EIjFcXV3Rq9c38PNrq9P+ulxHTjGJJyIiIiIisnGHDp3U/Dxw4Dfo2LEz2rRpl61jqFQqiEQiiEQifYeXxowZv6FevQYGPUf58hWxbNlqCIKA06dPYsKEn1CpUmWULFnaoOfNCpN4IiIiIiIiStezZ1GYNWsa7t+/B5EIqFWrLn78caxmRrGvvmqHTp2+wpEjh/D48UNs3rwbERGPMHfubLx8+RJffNEEiYmJ8PQsi/79vwMAnD9/FsuW/YmoqEgUK+aB4cNHoVq1GvjzzwW4fv0qbt++iUWL5uOLL5pg0qSpOscql8uxcuUyHD4cioSEeNSuXQ8jR47RxHr4cCiWLv0TsbEf0LZtB52PKxKJ0LDhF3B2dsaDBw9QsmRpxMfHYcqUSbh9+yYUCgUqV66CUaPGo2jRohleR0zMa8yf/zuuXPkPMpkM/v7t0bfvtxCLszfKnWPiiYiIiIiIzEBSUhJiYz/k6l9SUpJeYxIEAT17fo2dOw9g3bptiIl5jb/+Wqy1zT//7MPUqTNx8OAJSCRijBs3Cn369MeBA/+ibt0GOH36hGbb+/fvYcqUiQgM/BH79/+L/v0HYdy4UXj37h2GDAlE1arVERg4EocOncxWAg8AS5YE4+7dO1i2bDW2b98HmUyGuXNnAwAiIh7j11+DMHr0eOzdewhOTk64ezdMp+OqVCocP/4v3r9/j+LFi///MgF+fv7YunUPtm/fh7x58+KPP2YCQLrXoVKpMHbsSHh4FMfWrXuwbNlqnDp1HHv37srWNQJsiSciIiIiIjILtWtXxcuXL3J1jKJF3XH9+l09RQR4eBSHh4c6cbWzs0O3bj2wfLl2Et+5c1fNNufOnUGJEiXRsmVrAEDLlq2xadM6zba7dm1H27YdUK1adQCAj08jlCvnhbNnT+k83hwAJk0aoxmzX7p0GSxevAK7dm3DX3+tRcGChQAAAwYMQrduHTFx4hT8++8h1KvXALVr1wWgHue/devGTM9x924YWrdugqSkJAiCgOHDf9SMu3d2dkbTps012/bq1RdDhgzI8Fh37tzGq1cvMXDgEIhEItjbu6Jbt57Yv38P2rfvpPN1A0ziiYiIiIiIzMLFi9chl6fk6hgymZ2eolF78yYG8+fPwbVrV5GQkABBUMHR0VFrmyJF3DU/v379Gm5uRbTWf/z6xYtnuHLlP+zevV2zTKFQoFatutmKa+rUWVpj4t++fYOkpCQMGtRXazuxWIw3b2Lw+nU0ihQpqlkukUjg4uKW6Tm8vStg2bLVSE5OwsKFc/Hff5fw1VfdAah7TSxY8DvOnz+L2NhYAEBiYgJSUlJgZ5f2d/D8+XO8e/cOfn5NNctUKiHNe6ULJvFERERERERmwMHBAQ4ODqYOQ8vSpYugUglYs2YD8ucvgJMnj+G3337V2ubjQnYuLi549eql1vpXr16ibNlyAIAiRYqiR48+mvHxn8ppUbz8+QvA3t4eq1ath7t7sTTrXVxc8eDBPc1rpVKJ169f6XRse3sHBAb+iICAL3Hy5DE0atQEGzf+jcePH2Hp0tVwcXHB/fv30LdvDwiCkO51FClSFK6ubti6NfdT2nFMvJURBAFRUU8REfEYERGPoVAoTB0SERERERFZqISEBOTJkwd58zrh9etobNjwd6bbN2jQEJGRT3DkyEEoFAocPhyqlTy3b/8ldu/egWvXrkKlUiE5OQmXL1/SJP6FChVCVFRUtuMUi8Xo0OFLLFjwB16/fg1A3Tp/8uQxAEDTps1x7twZ/PffRSgUCqxbt0bTgq4L9VCCnli5chkEQUBCQjzs7e3h7OyEDx8+YM2av7S2//Q6KlSoiAIFCmL16r+QmJgIlUqFyMgnuHLlv+xfa7b3ILN26dIF1KhREfXr10SdOtWwcOFcU4dEREREREQWqm/fb/HgwX34+TXFqFHD0ahRk0y3z5+/AKZPn42VK5fBz88XZ8+eRt26DTRdzL29y2PixF/w55/z0aZNM3Tu3A4bN/4NlUrdgt2lSwBOnTqO1q2bYtq0n7MV66BB36Ns2XIYOnQAWrT4AoMG9cPt27cAAKVLf4YxYyZh5sypaNu2Bd6/fw9v7wrZOn67dh3x6tVLnDx5HF279oBcLkfbti0wcOA3aYYDfHodEokEs2b9gcjIJ+jevRP8/Jpi8uSxiIl5na0YAEAkpLb3U4bevo2HQqEydRg6+fffw5g8eRxOnbqIsWN/hKNjXkyeHGTqsIiIiIiIyEZ9/XUAevTojVat2pg6FIsglYpRsGDeDNezJd7KKBRySCTqUgdSqZTd6YmIiIiIyKjOnz+LDx/eQ6FQYOfOrXj27Cnq1Klv6rCsBgvbWRmFQgmZTAYAkEplSElJNnFERERERERkS8LCbiEoaCJSUlJQvHgJzJjxGwoWLGjqsKwGk3gro1DIIZP9ryU+Pj7exBEREREREZEt+eabAfjmm4znTKfcYXd6KyOXa3enVyrZnZ6IiIiIiMhaMIm3MnK5/KPu9FLI5XITR0RERERERET6wiTeiixfvhi//farZvoGsViM7du3mDgqIiIiIiIi0hcm8Vbk/Plz8PFphOnTZwMAWrRoxZZ4IiIiIiIiK8Ik3orI5XJUqVIVZcuWAwC4uLgCAJRKpSnDIiIiIiIiIj1hEm9FPp4jHlBPMadezuJ2RERERERE1oBJvBX5uKgdAEilEs1yIiIiIiIisnxM4q2IUqnUSuJTf+Y0c0RERERERNaBSbwVkcvlkErTdqeXy5nEExERERERWQMm8Vbi1q2bePo08pMkXv3zwYMHTBUWERERERER6RGTeCsRErISzs7OqFathmaZRCJB164BmDbtZxNGRkRERERERPrCJN5KKJUqtGrVBp99VkazTCQS4bvvhkClUpkwMiIiIiIiItIXJvFWQhBUEIvT/jrFYgmTeCIiIiIiIivBJN5KKJXKDJJ4MZRKJvFERERERETWgEm8lVCpMmqJF7MlnoiIiIiIyEowibcSKpUKEokkzXKxWAxBYBJPRERERERkDZjEW4mMutNLJGIolUoTRERERERERET6xiTeSgiCCiJR2l+nSMTu9ERERERERNaCSbyVyKw7PZN4IiIiIiIi68Ak3koolSxsR0REREREZO2YxFsJdXV6UZrlEokEgiBAEAQTREVERERERET6xCTeSmTWnT51PREREREREVk2JvFWQqVSZljYTr2eSTwREREREZGlYxJvJdTd6dMfEw+A08wRERERERFZAampAyDdJSYmIiCgM+Lj49Ose/gwHH5+bdMsl8nUv+LLly+hQYOGBo+RiIiIiIiIDIdJvAX58OE9zpw5hb/+WgOJRPtXJxKJ0KjRF2n2KVCgINzdiyEi4jGTeCIiIiIiIgvHJN6CpFaYb9OmHaRS3X91Xl7e7E5PRERERERkBTgm3oKkJvEiUdqp5DIjkUhY2I6IiIiIiMgKMIm3IDlN4sViMZN4IiIiIiIiK8Ak3oIwiSciIiIiIrJtTOItSM6TeAlUKo6JJyIiIiIisnRM4i1IahKfXWyJJyIiIiIisg5M4i2IIAjZboUHmMQTERERERFZCybxFiSnSby6On3OWvGJiIiIiIjIfDCJtyA5b4kXcZ54IiIiIiIiK2D0JH7atGnw9fWFt7c3wsLCNMsfP36M7t27o1WrVujcuTPu37+f4TG2bNmCli1bonnz5pg4cSLkcjkA4MaNG+jQoQPatGmDHTt2aLY/e/YsJk+ebLiLMpKcJvEikRiCwO70REREREREls7oSXyrVq2wfv16eHh4aC2fPHkyunbtitDQUHz77bcYO3ZsuvtHRkZi/vz5WLduHQ4dOoTXr19j8+bNAIDly5dj4sSJ2Lp1K4KDgwEASUlJCA4OxqhRowx7YUaQu+70TOKJiIiIiIgsndGT+Nq1a6No0aJay2JiYnDz5k20b98egDrRf/HiBSIiItLsHxoaCl9fX7i6ukIkEiEgIAB79+4FAEilUiQmJiI5ORkSiQQAsHDhQvTp0wf58uUz8JUZQ84L27E7PRERERERkeWTmjoAAHj+/DlcXV0hlarDEYlEcHd3x7Nnz1CqVKk0237ciu/h4YHnz58DAIYOHYrJkycjMTERo0ePRlhYGCIjI/HTTz9lGcOrV68QHR2ttczBwQGenp65vTy9yU11+rdv3yAuLhZOTs4GiIyIiIiIiIiMwSySeH3x9PTEunXrAABKpRL9+vXD7NmzsXfvXoSGhsLJyQljx45F/vz50+y7adMmTRf8VBUrVsSOHTsQFxcHBwdHo1xDZgQBOUri3d2LYd68OQgLC8O2bbsNEBkREREREREZg1kk8e7u7oiOjoZCoYBUKoUgCHj+/DmKFSuW7rZPnjzRvI6KioK7u3ua7dasWYPWrVsjX758+PPPP7F7927s2rULa9asQWBgYJrtu3XrBl9fX61lDg4OAICUlBQzSeJz1hI/duxEVKxYCYsXB2e9MREREREREZkts5hirnDhwqhUqRJ271a3EoeGhqJIkSJputID6vHy//77L6KjoyEIAjZs2AB/f3+tbSIjI3H69Gl0794dCoUCSqUSIpEIYrEYCQkJ6cbg5uaGSpUqaf1L7UpvLkXhcprEA+oK9SoVx8UTERERERFZMqO3xE+ePBnHjh3D69ev0b9/f+TNmxeHDh3ClClTMG7cOCxduhR58+bFr7/+qtlnwoQJ8PX1RbNmzVCiRAkEBgYiICAAAFCnTh1069ZN6xzTp0/H+PHjIRKJ4OzsjLZt26Jdu3ZwdHTE3Llzsx2zuUzPJggCgJwl8WKxGCqVoN+AiIiIiIiIyKhEgjozpEzcufMQhQu7mjoM3LkThjZtmuPhw6hs73vgwD7MmTMTR46cNEBkREREREREpA9SqRgFC+bNcL1ZdKc3d+bSDT033eklEk4zZ4lOnTqBYcO+Q0jIKlOHQkREREREZoBJvA7MpRt6bpJ4sVhsNsMCSHd79uzEkSMHsWHDWlOHQkREREREZsAsqtObO3NJfnObxJtLgT5KX+/e3fDixQutZU+ePEbZsl5ISUkxUVRERERERGROmMTrwFySX3USn7N9RSJ2pzd3oaEHMGfOfBQu7KK1PD4+Dn/+udBEURERERERkTlhEq8Da+hOL5FIzOZhBKWVWl+yefOWKFbMQ2vdv/8ehkIhN0VYRERERERkZjgmXgfm0p0eYHd6a5XaS0IikaRZJ5PJoFAojB0SERERERGZISbxOlAqzSP55Zh465WaxItEaT+SUqmUSTwREREREQFgEq8TfbbEf/jwHlevXs5hHEzirVXq7ya9lnipVIq4uFgcPHgAYWG3jR0aERERERGZESbxOkgdr6wPCxfOQ8uWTXIRR06TeI6JN2f/606f9iNZvHgJlCxZCuPHj8GgQf2MHRoREREREZkRJvE6CA3dj4UL52H79i25PlZCQjwAYP7837F//95s7Zu7lngRPnz4gOPHj+ZofzIslUqdxIvFaT+S7u7FcPDgcfzxxwIkJycbOzQiIiIiIjIjTOJ18OHDB5w9ewqjRo3I9bHy5HEEAJw6dQITJozO1r65SeLLlPFEtWrVMXv2jBztT4aV2ktCLE7bnT4VC9wRERERERGTeB1MmPAzfvlleq67o8+bNwehofvx3XdDMGnSFCQlJWZr/9wk8YUKFcY33/Rnl3ozlVo8Mb0x8akkEinkck41R0RERERky5jE60giEee6wN2yZYvRoEFDBAT0hoNDHiQmJmVr/9wk8YC6ldd8psujj6WOiU+vO30qmYxV6omIiIiIbJ3U1AFYCpEo99Xd5XI5vv66PypWrISoqKdISIhHixaNdd4/Pj4OUmnOf2VisdhspssjbakPVzJriZdKZVAo2BJPRERERGTLmMTrSJ0AK3N1jJSUZNjZ2QEAihXzwKZNO5CQkJCtY3h4eOT4/Jxmznzp0hKvni8+d3+DRERERERk2ZjE60gfCXBKSgpkMhkAQCQSoWnTZvoITWcSCZN4c/X777MBZJ7EOzk5ITb2A7y8SuLkyQsoUqSoscIjIiIiIiIzwTHxOpJIcjfPulKphFKp1LTEm4L6QQRbcs1RTMxrDBs2ItNtSpQoicuXb0GhUCImJsY4gRERERERkVlhEq+j1BZSQRBytP/Jk8cBAM7O+fQWU3axO735UioV8PAonuV2xYuXgJ0dx8YTEREREdkqJvE6Sk3iczouPj4+HtWq1YCTk5M+w8oWsTh3vQnIcJKTk3XupaEucMcq9UREREREtohJvI5EIvVbldMkWKGQ56qyvD6wJd58yeVyTb2ErMhkMsjlTOKJiIiIiGwRk3gdpU79lfMkXmEWSXxuK+yTYYSF3YK9vb1O20okUiiVTOKJiIiIiGwRk3gdicUiADnvTp+dllZDURfny9mYfjIcQRDw5s0bFCuW9Zh4AJDJpJDLOSaeiIiIiMgWcYo5Hf2vsJ3ltsSLROIcx0+Gk5SUBADw9Cyr0/ZSqRQLFszFli0btZb7+DRCQEAvvcdHRERERETmg0m8jlKT+Jx2p5fLzWNMPLvTm5+kpEQAgIODg07bjx//M+7cua217MaN69i8eQOTeCIiIiIiK8ckXkdSqborfIcObdC//0D06vW1zvuuWvUXFiz4A9Wq1TBUeDqxs5Ph2bMorFy5HP36fWvSWAi4cycMI0YMQVJSMkQiEfLkyaPTfn5+/vDz89datm3bZqxevcIQYRIRERERkRnhmHgd5cmTB9u370XZsuVw+fKlbO178eJ51KvXAJMnBxkoOt1UrlwVX3zRFBcunDNpHKQWHv4AMTEx+Omncdi164CmeGJOSKVS9rIgIiIiIrIBTOKzoWHDL+Dl5Z3tZEmhkKNy5aooU8bTQJHpRiqVomnTZkz2zIRSqYCLiwv8/duhXr0GuToWK9YTEREREdkGJvHZlJO51uVyBaTSnLey6hMrm5sPhUIBsVg/fxcSiQQKBR/OEBERERFZOybx2aSepi17SbxCIdeMqTc1ttiaD33OWCCVStjDgoiIiIjIBjCJz6acVHg3h+nlUslkMrbEmwmVSqW3vws+nCEiIiIisg1M4rMpJ3Oty+UKyGTm0RIvk8nY7dpMKBSKXBWz+5i6Oz2TeCIiIiIia8ckPpvUY+IFnba9du0KFi6ch4iIR3pL1nJLIpHgxImjePjwgalDsXn6TOKlUinevInB9u1b9HI8IiIiIiIyT0zis0ki0b07/bJli7Ft22bUqlUHtWrVNnBkuvn881oAgK1bN5s4ElIqlXrrTu/lVR516zbATz/9oJfjERERERGReWISn03ZqU6fmJiIbt16YPHiv+DpWc7AkenG07Mc+vTpx/HTJrZt22Zs3rxeb9XpXV1dMWnSFCgUrHdARERERGTNmMRnU3aS+KSkRDg4OBg4ouyTSiWQy5nEm9LWrZtQpIg7Bg8eprdjSqVSjosnIiIiIrJy5lEy3YKIRGKoVOl3p3/48AGGDh2oKRwXHv4AHTt2NmZ4OmGFetNTKBRo27YD6tVroLdjpv5eBUGASCTS23GJiIiIiMh8MInPpszmib9//z6io6MxZcoMAIBIJMIXXzQxYnS6kUplSE5OMnUYNk2lUum92GHq+Hp9jrUnIiIiIiLzwm/62ZRZd/qkpES4urrB37+dkaPKHqlUirg4drs2JYVCofdEWyqVGezYRERERERkHvhNP5vEYjHi4+MREfE4zbqoqCjkyZPH+EFlk1QqRWzs+3TXvXv3Fu/fv0e+fPlQsGAhI0dmO/Q5vVwqmUz9cX706CEcHR1RoEAB5M9fQK/nICIiIiIi02ISn01FixbF5cuXUL9+zXTXd+0aYOSIsk8QVDhx4ni665o29cHz58/g5lYE16/fNXJktkOl0n+XdweHPHBxcUGzZg2hUqng7V0ex4+f0+s5iIiIiIjItJjEZ1Pz5q0QFRVj6jBypU6d+ti7d3e6696+fYvly1cjMHCIkaOyLQqFEhKJfj9+9vb2uH37IQDg3LmzGDJkgF6PT0REREREpscp5myQRCKBUpm2wr4gCEhKSoSTkzPnkTcwQ3Sn/5hUKuF0c0REREREVogt8TZIKpVqJfHnzp3BxYsXoFIpoVKp4OTkxCnoDCQ+Ph5r167Cq1cvIZUaLomXyWRQKPg7JCIiIiKyNkzibdCn0+TNmzcH7969RenSn6FPn34oUqQolEol5xs3gP/+u4jffpuJ1q3boEKFSgY7j0QiZUs8EREREZEVYhJvg8Ri7a7WiYmJ6N//O3Tp0h0A8PLlSwDqLt8ymcwkMVqr8+fPonTpz7Bo0TKDnkcmk0EuZxJPRERERGRtOCbeBkkkYq3u9ElJiXBw+N/UeKlV09mlXv9iYl6jWLFiBj+Pekw8f39ERERERNaGLfE2KLWwXUJCAnr0+AphYbeRN29ezXp7ezsAQNu2LTFgwHfo0aO3qUK1OnK5HJUqVTb4eeztHZCcnIwWLRpne9/Ro8ehRYvWBoiKiIiIiIhyi0m8DVKPiVfi9etonDt3BmvWbICPTyPNeicnZ2zfvherVv2Fy5f/YxKvRykpKZDJ7Ax+Hg+P4ti4cTsSExOztd+KFUtx/fo1JvFERERERGaKSbwNEoslUCpVSEpKgoNDHrRq5Zdmm4YNv8CpUyfw6tVLE0RoveRy4yTxIpEIvr7Ns73fkSMHOYyCiIiIiMiMMYm3QRKJBHK5HI8fP0SePA4ZbicWi7Wq2JPunj9/hpSUlDTL3717pxmuYI4kEu3pB4mIiIiIyLyYVRJ//PhxzJs3DyqVCkqlEv3790enTp3SbHf06FHMmjULKpUKXl5emDlzJpycnBAZGYmRI0ciISEB7dq1w6BBgwAA4eHh+O2337BkyRJjX5JZKliwIEQiEb75pieqVq2W4XafTkVHurlzJwxffFFXUyDwYyKRCD179jFBVLqRyaRsiSciIiIiMmNmk8QLgoCffvoJISEhKF++PJ4+fQo/Pz+0aNECTk5Omu3i4+MxYcIErF27Fp6enggKCsKiRYswZswYrF+/Hj179kS7du3Qpk0b9OrVC3nz5sWMGTPwyy+/mO7izEyRIkXx6NGzLLcTi8Vslc2BuLhYFC3qjuvX75o6lGxTt8RzajoiIiIiInNlVlPMiUQixMbGAgDi4uJQoEAB2Nlpdz0+ceIEKlSoAE9PTwBAjx49sG/fPgDqqdESExOhUCggCALEYjE2bNgAHx8flChRwrgXYwXYnT5nlEoVJBKJqcPIEfX88myJJyIiIiIyV2bTEi8SiTB37lwMGzYMjo6OeP/+PYKDg9Mk8c+fP4eHh4fmtYeHB6Kjo6FQKNC7d2+MGzcOmzZtQr9+/RAbG4vQ0FCsXLkyy/O/evUK0dHRWsscHBw0DwtskVgsgSCkn8QrFAqsWrUcSUnJ6a6vW7c+6tSpa8jwzJZKpbTYJF4qleLatStYuHBeuusbNPDB55/XNm5QRERERESkYTZJvEKhwOLFixEcHIzatWvj+vXrGDJkCHbv3o1ChQrpdAw3NzesWLFC8zowMBBjx47F+fPnsWHDBtjZ2WHkyJFaDwFSbdq0CcHBwVrLKlasiB07duTuwiyYujt9+kn8w4fh+PnnCWjfvmOadQ8ePMB//11EnTrrDByheVIoFBCLzaqTi858fZvjyZPHuHXrepp19+7dw40bV7Fs2WrjB0ZERERERADMKIkPCwvDq1evULu2upWvatWqKFKkCMLCwuDj46PZzt3dHadPn9a8joqKgqura5oiYqGhoShZsiQqVKgAPz8/bNmyBTdv3sSCBQswa9asNOfv1q0bfH19tZY5OGRcud0WZNadPiUlBc7OzliyJG0vhxUrluHo0cOGDs9sKZWW2xJfr14D1KvXIN11S5YE4/z5c0aOiIiIiIiIPmY2Sby7uztevXqF8PBweHp6IiIiApGRkfjss8+0tmvUqBGCgoI0261fvx7+/v5a23z48AEhISGaVvmkpCSIxWKIxWIkJCSke343Nze4ubkZ5uIslESScRKf2XznMpkMCoXtFkez5O70mZHJZCx6R0RERERkYmaTxLu4uGDq1KkYMWIERCIRBEHApEmTUKxYMcyfPx9ubm4ICAiAk5MTpk2bhqFDh0KpVKJcuXJpWtbnzJmDYcOGaVrSBw8ejM6dO0Mmk2H69OmmuDyLlFl1+pQUeZp6BanUxdFsN9mz5Jb4zEilLHpHRERERGRqZpPEA0Dbtm3Rtm3bNMuHDx+u9bpZs2Zo1qxZhscJCgrSet21a1d07dpVP0HaEJFInGFhO3VLvCzddRKJBAqF7SZ7SqUKYrE1JvFSm344Q0RERERkDiyz+hYZhUQiSbc7fXj4ffTu3R1OTs7p7mfr3emttyWec8gTEREREZmaWbXEk3kRi8WIj49HRMRjAICjY16IRCJcvHgBbm5u2Lp1V7r7SaUyrf1S5cuXDwUL6jbTgCVTj4m3vudjMpkMcXFxiI+PR968eU0dDhERERGRTWISTxkqUqQoLl48j/r1a0IQBM34eIlEAj+/thkm5EWKFEV4+H3Ur19Ts0wQBBQpUhRXr4YZJXZTUigUkEis76NVpEhR3Lp1A8OGfYdVq/42dThERERERDbJ+jIN0ptWrfwQFRUDAFCpVChatAAA4NGj55lOv1enTl1ERkZrLbt58wa+/NI/gz2sx4sXz3H58iXkyZPH1KHonY9PI/z++wJs27bF1KEQEREREdksJvGkE7FYjDZt2iE5OSnTBD6z/VUqwQCRmZc5c2Zh//496NfvW1OHYhC2XrSQiIiIiMjUmMSTzlavXpfjfTObrs6aJCUl4ttvB+GHH34ydSgGYetFC4mIiIiITM36qm+RWZJIJBlOV2dN1FPv2Zk6DIORSqVsiSciIiIiMiEm8WQUYrEo3enqrE1Kihx2djJTh2EwUqmMc8UTEREREZkQk3gyCpFIbBNJvPW3xHNMPBERERGRKTGJJ6OQSCRWPyZ+8uTxOHPmdI4K/1kKe3sHPHwYDj+/ZnjyJMLU4RARERER2Rwm8WQU6ur01t0Sf/DgAQwdGoh27TqYOhSDqVevAf76KwSRkU+YxBMRERERmQCTeDIKsVgMQRAgCNY5zdzBgwfw8GE4/P3bw8nJ2dThGIydnR1atfJD/vz5IZezWz0RERERkbExiSejkEgkAGC1SfzTp0/x+ee1UL58BVOHYhSsUk9EREREZBpM4skoRCL1n5q1jotPSUmGu7uHqcMwGqlUBoXCOn+XRERERETmTGrqAMg2iMXqJD4hIR4XL56Hm1sRVK1a3bRBAXj37i0uXDgHQJ2YfvFFE0il2f9YWPvUcp9ilXoiIiIiItNgEk9GIZGok/j9+/fihx+GwdXVDTdu3DNxVMCaNSuxaNF8FCtWHPfv38X27ftQt269bB/H2qeW+5S6JZ7zxRMRERERGRu705NRpLbE7927C+7uxcyiFffffw/j6NEj6No1AMeOnYGHR3HI5SnZOkZcXCwWLw7GmTOnYWdnb6BIzdPmzRtMHQIRERERkc1hEk9G4eycD19/3R/Ozs7o0qW7WUw3N3PmVCiVSvj5tQWQs2nwrly5jNmzZ8DV1QWtW/sZIkyz5O/fHkeOHEJcXJypQyEiIiIisilM4skoJBIJfvttLpYsWYmOHTtDpTJ9lXqFQomhQ4fDx6cRAHWM2U3i5fIUeHh4YMmSlWjRorUhwjRLffp8AwBQKtmlnoiIiIjImJjEk9HlpMXbEJRKpWasPpAaV/YqrisUCkiltlPQLpVEoi6nIZcziSciIiIiMiYm8WR05pPEKzTz1wM5i0suV0Ams70kPvWa2RJPRERERGRcTOLJ6MRiMQTBHJJ4JcTij5P47HenVyjkkEolWW9oZVIffsjlpi9QSERERERkS5jEk9GJxSIoldnrtm4I6u702i3xSmV2k3jb7E4vEokglUqZxBMRERERGRmTeDI6kcg8utOrVKpcdacXBAGvX0fbZHd6QN2lPjLyiVn8LomIiIiIbIVUl40SExNx+vRp3LhxA2/fvkXBggVRpUoV+Pj4IE+ePIaOkayM+YyJ1+5OL5FkL66jRw9j0qRx6No1wBDhmb3ixUvgq6/aY86c+ejTp6+pwyEiIiIisgmZJvGJiYlYsmQJNmzYgNjY2DTrnZ2dERAQgEGDBjGZJ52ZUxL/cXV6dQ8B3bv5x8bG4vPPayM4eKkhwjN7p09fwrfffpPuvYGIiIiIiAwj0yTez88PL1++hCAIKFq0KLy9veHs7IzY2Fjcu3cPz58/x9KlS7F7924cPXrUWDGThVMXthMgCAJEIpHJ4khvTHx2Hi7I5XJIpTp1ZrFaEomEFeqJiIiIiIwo0wzk/fv3+Oabb/DVV1/B09Mzzfrw8HBs3boVGzduNFiAZH3EYnXrtyAIAIDVq1cgLi4OEokEPXv2Rv78BYwSh0qlncRLJNmrTq9Q2Ob0ch+TyWQsbkdEREREZESZJvFHjhxBoUKFMlzv6emJMWPG4Ntvv9V7YGS9UpN4lUqFly9fYMyYkfjyy69w+PAhlCtXDi1atDZKHAqFEhLJ/z4C6ur0unenV1emt+2WeKlUCoWCLfFERERERMaSaQaSUQIfFxeHiIgIFC9eHPnz58800Sf6lEikTuKVSiXmzJmJ/PkLYMmSlWja1AcqlWC0OD5tSReLxVizZiVOnDimtV25cl4YPvzHNPuzOz0glcqYxBMRERERGVG2p5g7f/48mjZtiq+++gqNGzfGoUOHDBEXWbHUlvgPHz5g3boQTJ36K4Dsd2fPLYVCDqn0f93phw//Ec2bt4SnZ1nNP0dHRyxfviST/W27O71UKmF3eiIiIiIiI8p2M+L8+fPRpEkTVKtWDX/99RfmzJmDFi1aGCI2slKpifNXX7WDVCrVTNEmFouy1Z1dFzNmBOHo0SMAgNKlP8Py5asBALdu3URycjLs7Ow12zZu3BSNGzfV2v/mzRvYtWu71rI+fQLw/PkzREe/Qu3adfUar6Wxt3fA5s0bEBHxGKtW/W3qcIiIiIiIrF6WSfzKlSvRr18/zevXr18jKCgIZcuWxfXr13H48GGDBkjWp0CBgti6dTdiY2Ph4uKqaZlXV63Xb0v8yZPH0aBBQ5QqVRq//DJBs/zOndtwcXFB8eIlMt3fzs4OKSnaLc2HDv2DmTN/h4uLKypVqqzXeC3N4MHfo2TJUlrvLRERERERGU6W3elDQkLQo0cPPHnyBABQvXp19OnTB126dMHevXtRo0YNgwdJ1ueLL5rA378d6tatp1kmFuu/O71CoUCdOvXQqpUfkpKSNBXxk5KSULFilSynuFNXX0/RvBYEAUqlEi1btoa/fzuULv2ZXuO1NEWKFEHTps303oOCiIiIiIjSl2USv3fvXhQvXhwdOnTA2rVrMXbsWDRq1AiJiYlo0aIFpk+fbow4yQZktzq8LlKLzzk45AEA3L9/DxERj/HsWRTy5HHIcn91S3wKPnx4rzkeAK2q9rZOLBYbtZYBEREREZEtyzITcXJywuzZs3H48GH8/PPPOHToEGbMmIHixYsbIz6yIYZIBhUKOWQyKZydneHi4oImTepr1vXrl/XUiPny5UO+fPnRpUsHhIYe0yTxMhmT+FTGLkhIRERERGTLdK5O37x5c+zduxcFChRA+/btsXHjRkPGRTbIEMmgei53Gezs7HD79kM8e/ZG82/atFlZ7u/snA+rVv2Nt2/fAgCUSvV0arY+tdzH1LUMBM1QBSIiIiIiMpwsk/iHDx8iICAANWrUwNChQ/HDDz/gl19+wdy5c9G/f3+8ePHCGHGSDTBMS7z2XPA54eDggKSkJACAXJ6axNv21HIfSy1MyNZ4IiIiIiLDy7I5cfLkyQgLC0Pjxo1x/PhxTJ48GWvXrkXdunUxceJEtG3bFpcuXTJGrGTlRCL9JfEJCQkICVmJd+/e5Xr8uoNDHrx//w4LF85DfHwsALbEf0wk+l8SL5FITBwNkW06d+4sLl48n+P9a9eui3r16me9IREREZlclpnIrVu30K5dO0ydOhWjRo3CkSPqObeLFCmC5cuXY8uWLQYPkmyDRKK/JP7mzRv49depaN++E8qWLZurY332WRl07NgZt25dBwAMHDiYSfxHUhN3pVKZ614PRJQzixbNw4sXL+Dp6ZntfR8+DMe5c6dRrx7/PyciIrIEWWYiRYoUwZkzZ7B+/XpcunQJRYsW1VrfpUsXgwVHtkWf1elVKiVcXFyxcOGSXB/LyckJ8+f/qYeorJNYrJ6mj93piUxHqVSiZ88++Oab/tned/XqFQgN3W+AqIiIiMgQshwT/9NPP+HNmzcICgrC+/fvMXr0aGPERTZIn2PiVSqVZqw2GRbHxBOZXm6Gs3CGCSIiIsuSZUt8s2bNcPz4cURERKBUqVLIly+fMeIiGyST2WHBgj+wfv1aFC5cGOvXb81xIq5UKpnEG8n/knj99KIgouzLzT3PEEVFiYiIyHAyTeLfvHmDQoUKIV++fKhSpUqG2719+xYFCxbUe3BkWyZPDkJY2G28f/8OI0YMzdWXUrbEG49YrG79YxJAZDoqlcAknoiIyEZk+j9+8+bNMXv2bISHh6e7/smTJ/j999/RokULgwRHtqVMGU/4+7dDixatASBX4+NZKd142J2eyPQEIecPLpnEExERWZZMW+KdnJywatUqrFq1Cu7u7vDy8kK+fPkQGxuL+/fvIyoqCoIgwM3NzVjxkg3QR1KoUrE7vbGkvs9PnkQgISEBHh7FIRKJTBwVkW1hd3oiIiLbkWkSf+DAASxZsgQbN27Es2fP8OzZM631Tk5O6NGjB7777juDBkm2RSLRRxKv0sxfToYlk8ng5lYEbdo0h0KhwI4d++Dj08jUYRHZlNwMIWIST0REZFkyTeLz5s2LH3/8EYMHD8bp06dx48YNvHv3Dvnz50eVKlXQsGFDODo6GitWshGpX0QFITdJvMDu9EYikUhw8+Z9AICvb0PExsaaOCIi25Pb6vT6mt6TiIiIDC/L6vQA4OjoiBYtWnDsOxlFahKfmy+VrE5vGlKpBHK53NRhENmc3AwhEovFuXpoSkRERMbFLIfMjj6qnau7lnJctrFJpTIolQpTh0Fkc3IzhEgkYnd6IiIiS2JWSXxKSgqCgoLQsmVLtGvXDqNGjUp3uy1btqBly5Zo3rw5Jk6cqGn5u3HjBjp06IA2bdpgx44dmu3Pnj2LyZMnG+UaKPf+V9hOyPExBIHV6U1BJpOxJZ7IBHIzhEjdnZ5JPBERkaXQqTu9scyZMwcikQihoaEQiUSIjo5Os01kZCTmz5+PHTt2wMXFBYMHD8bmzZvRs2dPLF++HBMnTkSlSpXQrl07dOrUCUlJSQgODsbixYtNcEWUE/qoTq9UKlnYzgSkUikUCrbEExkbq9MTERHZDrPJchISErB161b88MMPmumpXF1d02wXGhoKX19fuLq6QiQSISAgAHv37gWgTiASExORnJysaZFYuHAh+vTpg3z58hnvYihXUn93KlXu5onnmHjjk0qlbIknMoHcDCESi0VM4omIiCyI2bTEP3nyBAUKFMCSJUtw5swZODg44Pvvv0f9+vW1tnv+/Dk8PDw0rz08PPD8+XMAwNChQzF58mQkJiZi9OjRCAsLQ2RkJH766acsz//q1as0Lf8ODg7w9PTUw9VRdujaEv/jj4G4fv1auuvevn2DEiVK6j02ypy9vQPmzv0Na9euBgDUq9cAU6f+atqgbMQ33/REVNTTLLeTSMSYO3cRKlSoaISoyFDOnDmFX36ZAOH/Rx09fPgAUqksR8eSyezw8OEDtGjRGN99NwRffdVNj5ESERGRvmUriV+/fj1WrlyJFy9e4J9//sGiRYvg7++Phg0b5joQpVKJqKgolC1bFqNGjcLt27fRt29f7Nu3Dy4uLjodw9PTE+vWrdMcr1+/fpg9ezb27t2L0NBQODk5YezYscifP3+afTdt2oTg4GCtZRUrVtQaW0/GkdoTI6sk/tChUAwYMAienmXTXV+2bDm9x0aZ++WXqQgLCwMAXL9+BaGh/5g4Ittx+HAopk6dCTe3IpluFxQ0CeHhD5jEW7jbt29CIpFg2LAfAKh7MPn4NMrRsRo0aIhly1Zj/foQXL16mUk8ERGRmdM5iT9w4ACCgoIAqJOsYsWK4eTJk4iLi9NLEu/u7g6xWIx27doBUCfQxYsXx71797SSeHd3dzx58kTzOioqCu7u7mmOt2bNGrRu3Rr58uXDn3/+id27d2PXrl1Ys2YNAgMD02zfrVs3+Pr6ai1zcHDI9XVRzugyb3FSUiJ8fZujcuUqRoqKslKmTFmUKaN+qJInTx7s27fHxBHZDqVSCV/f5ihVqnSm2y1aNB8KBYc8WDq5XAEPjxLw92+X62PZ29ujdes2OH/+LJKSEvUQHRERERmSzkn8ypUr4eTkBE9PT1y/fh1isRjVqlXDzZs39RJIoUKFUL9+fZw6dQqNGzdGZGQknj59mqY7e6tWrRAQEIDvv/8eLi4u2LBhA/z9/bW2iYyMxOnTp/HXX38hLi7u/4uciSAWi5GQkJDu+d3c3ODm5qaXa6HcE4vFePo0MtNtEhMTkScPH7SYK46PNy5d60Dw92Id5HI5pFL9zsDBAndERESWQeck/uHDh/Dz84OjoyOuX78OQF147s2bN3oLZsqUKRg/frymSn1QUBCKFCmCCRMmwNfXF82aNUOJEiUQGBiIgIAAAECdOnXQrZt217/p06dj/PjxEIlEcHZ2Rtu2bdGuXTs4Ojpi7ty5eouXDMfDozi6dOmQ6TZ58jiiUKHCRoqIsksmk7FSvZEIggBB0G2KMf5erINCIc/xGPiMSCSSXE3tSURERMahcxLv6OiI+Ph4ODo6apY9ePAABQoU0FswJUqUwNq1a9Msnz59utbrrl27omvXrhkeZ8mSJVqvv//+e3z//ff6CZKM4sKF9AvWkeXgdHPGk9p6qmtLPH8vlk+hUEAq1W9tWlapJyIisgw6fwMoX748Dh06hJIl1RW/hw4div/++w+NGzc2WHBEZLlkMhm7bRtJauIlEmWdxPP3Yh3USbx+W+JFInGupvYkIiIi49A5iR8yZAhOnz6NR48eAQCOHDkCiUSCgQMHGiw4IrJcEokU8fFxWLhwXpp1YrEYPXr0QsGChYwfmBVKTeJ16U4vkUhx9OhhxMfHZ7mtWCxG9+49Ubgwh62Ygxs3ruHYsaMAgAsXzqF8+Qp6PT7HxBMREVkGnZP4GjVqYNmyZVi5ciWioqJQrFgx9O3bFzVr1jRkfERkoUqXLo0OHb7ErVvX06w7evQISpYshXbtMq97QLpJnclBLBZluW2HDp1w8OCBdH8vnzp+/CiKFSuGTp2+ynWMlHsrVy7H5cuXUKFCRRQtWhQtW/rp9fjqMfFM4omIiMydzkn8xYsXUapUKaxcuVKzTKFQ/H+F8DwGCY6ILJezcz4sWLA43XVt2jSHUslx2fqSnTHxX37ZBV9+2UWn43bo4Meu92ZELpejQ4cvMXLkaIMcny3xREREliHrb3z/r0+fPvj777+1ls2ePRuff/653oMiIuvGcdn6JQi6d6fPDqlUpmnlJ9MzREX6j4nFYv6+iYiILIDOSbwgpJ12JjY2Nt3lRESZYXKoX6nvpS6F7bJDJuOc8uZEoVDqvSL9x0QiseaBEBEREZmvLL8NNGvWTPPztm3bcPjwYQDqpP7FixdwdnY2XHREZJWkUgmTQz3KTnf67JBKmcSbE7lcDpnMcEk854knIiKyDFl+G4iKigIAiEQixMXFIS4uTmu9j4+PYSIjIqvF7vT6lZp4GaY7PWsXmAuFQg6JxHBJvFgsYg8ZIiIiC5Dlt4Fhw4YBAIKDg1G9enU0bNgQgPrLooeHB1q3bm3YCInI6kilMkRHv0JExGMULFgQ+fLlN3VIFuXlyxdISkrSvI6JeQ1A/y3xMpkM0dHRiIh4DDs7O7i7F9Pr8TPz5k0MYmNjjXY+XRQu7AInJyejnCsuLhYxMTGfLIuDTGbYMfFxcbGIiHgMwLjXS0RERLrTOYmPioqCj48P2rZta/CgiMi6FS9eHPPn/465c39DpUpVcOTISVOHZDGePo1EzZqV0oyNLlKkqN5b4j08imPRovlYtGg+FAoFTp++hHLlvPR6joz4+NTCu3fvIBJlPW2eMahUKjRq1Bhbtuwyyvl69eqGc+fOaD2YEYlEGDZsuMHOWbSoO06fPon69WtCpVKhadNm2LBhm8HOR0RERDkjErJRmU6lUiEiIgIxMTFaBe1q165tkODMxdu38VAoWOyHSN9OnTqBkSO/x4UL10wdisW4f/8eWrT4Ao8fvzDqeStXLoeQkA2oWbOWUc7n4VEYp05dxGeflTHK+bKye/cOLF68EAcO/GuU8zVr1gijRo2Fn5+/Uc73qa1bN2Ht2tXYteuASc5PRERky6RSMQoWzJvxel0PFB4ejiFDhuDJkyday0UiEW7fvp3zCInIZkmlUigUHHOdHSqVSu9V6HWhLnJnvN+VQqEwaCX27JJIpFAojDdeXD2dnH57VmQHp5sjIiIyXzp/Q5o9ezYiIiIMGQsR2Rgm8dknCIJJupgbs8idUqmEIAgGHf+dXcaebk/9EMN01y8WizWzHhAREZF50bk55+rVq/D29kaHDh0gEomwceNGFCtWDL///rsh4yMiKyaTyaBQsEp9dqhUKr0XsNOFMZPY1Ac7hqzEnl1SqdSolfrV08mZLomXSCScM56IiMhM6fxNMD4+Hp9//jkKFiwIAKhatSp8fHywbNkygwVHRNZNIjFuF21roO5Ob4qWeKnRHrikPiww5Jzo2SWVGndaRFMPJxCJ2J2eiIjIXOmcxDs6OkIul2umm/n7779x9epVPHz40GDBEZF143zx2ScIAsRi03SnN9YDl9SHBeY0Jt7YQz/kcrlJr1/dnV7nurdERERkRDp/Q/Dw8EBUVBSaNm0KQRDw66+/AgDKli1rsOCIyLrZ29sjISEeLVo0BgAULFgQGzZs0/tUadZEEEzTEu/gYI9Jk8bi999nGfxcqcmyTGZn8HPpyt7eHs+fP8P48T9hxozf9Hbcy5cvYezYH/HpPDGvX0fD3t5eb+fJLolEwjHxNiwq6ikGDPja6DVLRCJg+vTZqF27rlHPS0RkaXRO4gcPHoxnz56hSZMmaNGiBQ4dOgRnZ2eMGzfOkPERkRUrVao0Nm7chsTEJMTHx2HYsO+QnJwMR0dHU4dmttQt8cYfE//HH8F4+DDcaOcrWLCgSZPYT1WrVgOjRo3Ftm2b9Xrcu3fvICVFjp9+0v6/1M5OhooVK+v1XNkhFovYnd6GRUY+QUTEY/z22zyjnnfOnJm4d+8uk3gioizonMS3bNlS8/PChQsRFxeHPHnysMWMiHJMJBLB17cFACAuLhYAWOguC+rWUeO3xFeoUBEVKlQ0+nnNhVQqRa1adbBx4zq9Hlcul6No0aLw92+n1+PmllgsZmE7GyaXy5EvXz6j/12GhKzkwyMiIh3kuDnHyckJ9+/fx7fffqvPeIjIRqVWImehu8yZqiWeUivU6zfBMHUBu4yIxexOb8tMVZOBUxsSEekmyzu0QqHA4sWLcevWLVSuXBmDBw/G27dvMXPmTOzfvx/CpwP5iIhyIHU6Lc4bnzlTVacndRKv70KMCoXcpPPBZ0QsZnV6W2aqv0vWYiAi0k2WSfysWbPw999/AwCOHz+O+Ph4HDp0CFFRURAEAUWKFDF4kERk/VKH5rA7febYEm86MplM7w+ZFAqlmbbEs0XUlpnq75LDOIiIdJPlHfrIkSMQBAHly5fHq1evsGbNGqhUKhQoUAADBw5Ez549jREnEVk5kUhk9Gm8LJFKxSTeVKRSmd4fMqlbPM0viWeLqG1TKOSQyYz/dykSsQcIEZEusrxDv3r1Cs2aNcOiRYsQGRmJFi1aoGTJkti4cSMKFSpkjBiJyEYoFAo8eRKBUqVKmzoUsyUIArvTm4hUKkVCQgIWLpyHwoULo0eP3jk6zvr1axETEwMAOHXqBNzczK9Hm1gsxrt377Bw4TzUq1ef1cJtwJ07YTh0KBQAcOvWdU2dEmPiwyMiIt1k2ZyjUChQvHhxAECJEiUAAF988QUTeCLSOxcXF9y+fdPUYZg1QVCxJd5ESpQoiS+/7IL//ruIESOGIiUlJdvHSE5OxogRQ/Hffxdx69Z1FCpUCG3amFdlegAoW9YLrVr5YefObfjzz4WmDoeMYN26EGzYsBa3bl0HAHTp0t3oMaiHcbDWEhFRVnR6zBoaGoqwsDDN68OHD+PevXsA1F1g16xZY5joiMim+Ph8AYWCXSkzoy4mypZ4U8ibNy/mz/8Tb97EYP/+PZDL5bCzs8vWMVKHi8yduxAFC5rvw3AXFxcEBy/F8uWLcerUSVOHQ0agUMjRqlUb/PzzVJPFIBaL2BJPRKQDnZL4ly9f4sWLF5rXL168wMuXL9mtk4j0SiKRQKnkmPjMqFQqiMW875pS6kwKOflbTR1Tn3oMcycWS1hozEbI5Qo4O5v271I9tSEf5BIRZSXLJL527drGiIOICDKZTO9TeFkbTjFneqlTb8nlOUnilVrHMHecas52KJUKzSwhpsJZEYiIdJNlEr927VpjxEFExCReB5xizvRSq8nnpFJ96j7mWJE+PUyqbIdcLjd5DxH+vRER6YbfBInIbEgkUrb6ZYEt8ab3vyQ++y3xqQ+pTN3iqStWC7cd5jDdoXpIFf8PICLKimU0BRCRTZDJpHjzJgYREY9RokRJm29xfv78WZoK6C9fvrD598XURCIRpFIpIiIeZzuRj4p6CplMZjEPYtTd6ZnEWyuVSoWnTyMhCAI+fPhg8mEeYrEYb9++QUTEY8hkMhQr5mHSeIiIzBWTeCIyG8WKFceMGVOwdu1qzJ0bjJ49+5g6JJO5f/8efHxqpdsy5uPTyAQR0ceKFy+Br75qn+N9LYVYLGZhOyu2ZctGfP/9IM19pmvXAJPG4+5eDPPmzcHq1SugUChw8OAxVK9e06QxERGZIybxRGQ2hg0bjmHDhqNfv96Ijf1g6nBMKj4+Dq6ubrh164GpQ6F0XLhwzdQhGAXHKFu32NgPaN3aHyEhG0wdCgBg9OjxGD16PACgbt3qiI2NNXFERETmiX0yicjsSKUSm58vXqlUsts8mRyr01s3hUJh8nHwGZFKpTmqO0FEZAv4DZGIzI5UKstR5W9rop4PnrdoMi22xFs3uVwBmcxck3j+P0BElBF+QyQis8MWGEClEiymgjlZL1ant25KpcLkxewyov5/gL1AiIjSwySeiMyOTMYWGJWK3enJ9NQt8UykrJVcbvpp5TIik0lt/v8BIqKM8BsiEZkdiUQCudzWW+I5HzyZnkjE7vTWTD03vHm2xEskUsjlTOKJiNLDJJ6IzI69vQM2b96Aa9eumDoUk1GpVOxOTyZnZyfD3bt30KJFYxw5ctDU4VAuxcXFon371mjRojFatGiMv/8Ogb29nanDSpeDgwNmzAhCixaN8c03PU0dDhGRWTHPPlREZNOGDg3EsWNHcP/+PVSrVsPU4ZgEq9OTOfjii6ZYunQVFi9eiFu3bqJZs5amDolyITo6Ghcvnsdff4VoltWs+bkJI8rYzJm/4969u3j16iUmTRpr6nCIiMwKk3giMjtFi7rDxcXVpqe2YnV6MgcODg5o3boN9uzZadOfR2uhUChgb28Pf/92pg4lS+XKeaFcOS9ERj7B2LE/mjocIiKzwm+IRGSWxGIxBEEwdRgmIwjsTk/mg1PNWQe5XA6JxLLab6RSKQRB4EMkIqKPMIknIrNk60mDUqmESMRbNJkHTjVnHRQKudnOC5+R1MJ7LHJHRPQ//IZIRGbJ1pN4lUpgd3oyG2KxmC2hVkChMN954TOS+tBBobDtGUuIiD7Gb4hEZJZsPWlgdXoyJyKRGIJguw/VrIVcroBMZllJfOpDB84ZT0T0P5bVp4qIbIattMTHx8dj7dpVkMu1W5nu3LkNsZjzxJN5kEjEUKlst0aFJXv58iW2bNkIQRAQEfHYIsfEA8CSJYuQN68TqlevgUaNGps4KiIi07KsOzkR2QxbGYN7/fpVzJw5Ha1b+6VZ17VrgAkiIkrL1nvGWLLDh0OxZEkwGjZsBADo0+cb0waUTfb29hgw4Ds8fvwQT548weHDoUziicjmMYknIrNkK913lUol3NzcsGTJSlOHQpQhW+kZY43kcjmqVq1msfcYkUiEGTN+AwBs27YZa9ZY5nUQEekTx8QTkVmylZY/jn0nS2ArPWOskUIht7hidhkRiUQ2PfUoEVEqs0zit23bBm9vbxw+fDjd9UePHkXr1q3RsmVLDBs2DHFxcQCAyMhIdOnSBf7+/liyZIlm+/DwcAwaNMgosRORfqhb/qz/y5pKpWIVejJ7IpEYKpX1P1SzRuqK9NbR8ZI9QoiI1Mzum+PTp0+xZcsWVK9ePd318fHxmDBhAhYtWoSDBw/Czc0NixYtAgCsX78ePXv2xO7du7Fjxw7ExcVBEATMmDEDEyZMMOJVEFFuqQtpWf+XNZVKySSezB6TJ8ulrkhvHUm8SCTi3yEREcwsiVepVJg4cSImTpwIOzu7dLc5ceIEKlSoAE9PTwBAjx49sG/fPgDqCqaJiYlQKBQQBPUcyxs2bICPjw9KlChhtOsgotyzpe70TOLJ3LE7veVSKOQWV5E+I+p7pfX30CIiyopZ3dVXrVqFmjVronLlyhlu8/z5c3h4eGhee3h4IDo6GgqFAr1798a4ceOwadMm9OvXD7GxsQgNDcXKlVkXQXn16hWio6O1ljk4OGgeFhCRcYlEYrx5E4OIiMfprndxcUXevHmNG5QBqJN4jokn8yYWi/Hu3TtERDyGWCxG8eIlIBJxCkRz9uLFcyQnJyMm5rXFzQ2fMbbEExEBZpTE37t3DwcPHsTff/+d42O4ublhxYoVmteBgYEYO3Yszp8/jw0bNsDOzg4jR47UegiQatOmTQgODtZaVrFiRezYsSPH8RBRzhUr5oHFixdi2bI/06xTqVRo2dIPISEbTBCZfqlUAlviyewVLeqORYvmY/fuHVAoFFi/fguaN29l6rAoA48ePUTdutU1Y+GHD//RxBHph1gsZmE7IiKYURJ/6dIlREVFoVUr9ZeC6OhoPHjwAK9evUKPHj0027m7u+P06dOa11FRUXB1dU1TtCU0NBQlS5ZEhQoV4Ofnhy1btuDmzZtYsGABZs2aleb83bp1g6+vr9YyBwcHfV4iEWXDpElTMGnSlHTXbdy4Dps3W34CD6inmGMST+auf/+B6N9/IADA378FYmNjTRwRZSYuLhaFChXCnTuPTR2KXqnHxDOJJyIymyS+R48eWsl679698fXXX6N58+Za2zVq1AhBQUEIDw+Hp6cn1q9fD39/f61tPnz4gJCQEE2rfFJSEsRiMcRiMRISEtI9v5ubG9zc3PR8VURkCFKpFHK53NRh6IW6Oz27JZPlkEqlUCgUpg6DMqFQKKxmHPzH2BJPRKRmEXf4+fPnw83NDQEBAXBycsK0adMwdOhQKJVKlCtXLk3L+pw5czBs2DBNS/rgwYPRuXNnyGQyTJ8+3RSXQER6JJPJrCaJEASOiSfLIpVaz+fPWqkr0lvLOPj/YXV6IiI1s03i165dq/l5+PDhWuuaNWuGZs2aZbhvUFCQ1uuuXbuia9eu+g2QiEzGmpIIdqcnSyOVSqzm82etlErrmRv+Y2KxiC3xREQwsynmiIh0YX3d6XkrJsshk8ms5vNnreRyuZUm8WIIAlviiYj4zZGILI5MJoVCYR1JBJN4sjQSifV8/qyVQmGdSbxIxJZ4IiLAjLvTExFlxN7eARERj9GiRWOt5RKJGPPm/Yny5SuYKDLdvXkTgz59AvDixXOUKvWZqcMh0pmDgz0WLVqALVs2AVDPGhMSstHEUREA9O3bC0+fRuL9+3dwcnI2dTh6JxKJOSaeiAhM4onIAtWuXRd//RWSZlzuL79MwKNHDy0iiX/+/Dlu3ryBRYuWoVw5L1OHQ6SzceMm49atmwCAmJjXGD36BxNHRKkOHfoHU6fOhJtbEZQubX0PB1nYjohIjUk8EVkcOzs7tGrll2b5/PlzoFQqTRBR9ikUcjg65oG/fztTh0KULaVLf6ZJEJ8/fwaVSsVhIWZCLpejefOWKFGipKlDMQhOMUdEpMb/cYnIalhS0SNrnceZbEvq3zCr1ZueSqWCIAhWORY+FVviiYjUmMQTkdUQiyUW8wXPWudxJtsik6kTRlarN73U34FUar33FbbEExGpMYknIqshFostpju9UqmARCIxdRhEuZLa6qtUsiXe1FJ7Q6Q+WLFG6pZ4JvFEREziichqiMWWU7lYLpezJZ4sXmqrr1zOJN7UUqf9s/aWeIBJPBGR9T6uJSKbYylJ/Natm3Dq1AmrHrtKtiH1b3j58j9Rr54PmjTxNXFEtufEiWO4du0qEhLiAcDK7ysiPHkSgQ8f3iNfvvymDoaIyGTYEk9EVkMisYwx8SNHfo/Xr6PRo0dvU4dClCtSqRQDBw7GyZMnMHv2DFOHY5N+/30WDhzYi0ePwjFgwHewt7c3dUgGU7ZsOQDAxYvnTRwJEZFpWfPjWiKyMSKRZbTEy+VyTJ8+G6VKlTZ1KES5IhKJMG3aLOzevQOLFy80dTg2KSUlBd99NwQdO3Y2dSgG5+LigooVK1vEfZ6IyJDYEk9EVkMsNv/phwRBgFKp5Hh4siqWNDOEtVEqFVY9Dv5T6mFTHBdPRLaNSTwRWQ1LqE6fWkHalr50k/VTf/aYxJuCrU1XaQn3eSIiQ2MST0RWwxLGxP9vLmdOL0fWw1KKSlojhUJuU/cTiYR/a0RETOKJyGpYQiKROg2ULbWckfVjYmU6CoXtdacXBP6tEZFtY2E7IrIaIpEYr19HIyLisWaZu3sx2NnZmS6o//f69WvEx8fh/ft3ANidnqyL+gEauzgbi0qlwtOnkRAEAUlJSTb1UFAkYnd6IiIm8URkNTw8PDB//u+YP/93AIBSqcTw4T9i/PjJJo1LqVSiRo0KUCgUEIlEcHV1M4sHC0T6YikzQ1iLzZs3IDBwMKRSKWQyGQoXdjF1SEZjCT2uiIgMjd3pichq/PrrHDx79kbzb8iQQMTGfjB1WEhJSUFycjJu3nyAZ8/e4NatB5BIbGcMK1k/S6hHYU3i4mLh59cWz569QUTES3h7lzd1SEbDvzUiIibxRGTFpFIp5HKFqcPQjIO3peJTZFtYMdy45HIFpFLb7EzJvzUiIibxRGTFpFIplEpzSOI5rRxZN3ZxNi6FQgGZzHaTeEHgPPFEZNuYxBOR1ZLJZJop3UwptTeALRWfItsikUiYWBmRQiGHRGK7STwfGBGRrWMST0RWSyqVarqym9L/utPb5pdusn6sGG5ccrncZh8Ksjs9ERGTeCKyYlKpzCzGxMvlcojFYojFvOWSdRKLRWwdNSKFQsGWeCIiG2ab/wMQkU2wt7fH8eNH0aJFY5PGkZycBHt7e5PGQGRIdnZ2iI5+hRkzgkw+paM1evToIYYMGQCFQt0C/exZFDp06GTiqExDJrPDvHlz8PffawAAY8aMR/PmrUwcFRGRcTGJJyKr9dVXXVG0qLupwwAAuLi4mjoEIoOpVKkKhg4djtOnT5o6FKv0+PEjPHv2DDNm/KZZVrPm5yaMyHR++WUqwsLCAADLlv2JmzdvMIknIpvDJJ6IrFb+/AXg79/O1GEQWT2pVIoqVari1Knjpg7FKikUchQoUJD3MwBlypRFmTJlAQD79+9hQUUiskkcoElERES5JhJxrLKhKBRKFsZMB8fHE5GtYhJPREREuSaRSKBSsVXUEBQKuc3OC58ZkUjElngisklM4omIiCjXOPWX4dhyNfrMsCWeiGwVk3giIiLKNU4zZzi2PC98ZkQi/s0RkW3iY10iIiLKNYlEAkFgQqUvgiBg7drVeP/+Pa5du8KW+HSIxWIA7E5PRLaHLfFERESUayIRu9PrU1xcLEaNGo6rVy9DKpXgq6+6mjokMyRiHQYiskl8rEtERES5xvHJ+pWSIgcALFy4BI6OjiaOxjyJxWIWtiMim8SWeCIiIso1dXV6JvH6olAoAIBj4TMhEoF/c0Rkk5jEExERUa6xOr1+KRTqlnjOD58xtsQTka1iEk9ERES5xoRKv+RyOSQSCUQikalDMVusTk9EtopJPBEREeWaWMzu9PqkVCrYlT4LfHBERLaKfbSIiIgo18RiEZKTkxER8Rh58jjCzc3N1CFZpGfPoiCXy/HkyRNIpUziM8NiikRkq5jEExERUa4VLlwY8fFxqFevBlQqFe7efYwCBQqaOiyLcvXqZbRs2UQzDr5s2XImjsjcicB54onIFjGJJyIiolwrU6YsHj9+AaVSCXf3gkhOTjZ1SBYnNjYWn31WBufPXzV1KBZBLBZrCgASEdkSjoknIiIivRGL1V8tWKk+++RyOavRZ4NIJOKYeCKySUziiYiISG9Sq6lzrHL2KZUKjoPPBo6JJyJbxSSeiIiI9EoiYaX6nJDLWZE+OzjFHBHZKibxREREpFdisZjd6XNAoZBDKpWYOgyLoZ5iztRREBEZH5N4IiIi0it1csUW0uxSj4lnS7yu2BJPRLaK1VOIiIhIr9Td6dlEqoutWzdh6dI/AQBv375BqVKlTRuQBZFKpdi3bxeuX78KsViE339fiMqVq5g6LCIykb17d2P+/N9ztK9YLMLs2XNRrVoNPUdlGEziiYiISK9EInan19V//11EsWIe6No1AABQrpyXiSOyHP36DUSlSuqkffr0X/DgwT0m8UQ27Nq1KyhUqBD69OmX7X1nzZqGe/fuMoknIiIi28Sq4bqTyxXw9i4Pf/92pg7F4ri4uGjet2XL/oRczjnjiWyZXC6Hp2fZHN1PV65cDoVCYYCoDINj4omIiEivJBIm8bpSKhWQSFjMLrekUil7fxDZOIVCDokkZ23UUqnEoh4Emk0Sn5ycjCFDhqBVq1Zo3749+vbti4iIiHS3PXr0KFq3bo2WLVti2LBhiIuLAwBERkaiS5cu8Pf3x5IlSzTbh4eHY9CgQUa5DiIiIlunbolnQqULuVzOaeX0QCqVWtQXcCLSv9zcT2UymUXdQ8wmiQeAbt264Z9//sHu3bvRrFkzTJw4Mc028fHxmDBhAhYtWoSDBw/Czc0NixYtAgCsX78ePXv2xO7du7Fjxw7ExcVBEATMmDEDEyZMMPblEBER2SR2p9edQqGAVMrRjbkllUotqissEemfQqGETJbTlngZlErLuYeYTRJvb2+Pxo0bQyQSAQCqVauGqKioNNudOHECFSpUgKenJwCgR48e2LdvHwD1DTwxMREKhQKCIEAsFmPDhg3w8fFBiRIljHcxRERENkwsljCJ15E6iWdLfG5JpTIoFJbTikZE+qdQ5HyaTnVLvOUk8Wb76DckJAS+vr5plj9//hweHh6a1x4eHoiOjoZCoUDv3r0xbtw4bNq0Cf369UNsbCxCQ0OxcuXKLM/36tUrREdHay1zcHDQPCwgIiIi3YjFYmzatB6nTp1E585dUKyYR9Y72ZjDh0MRFhaGe/fuoE6duqYOx+JJpVIcO/YvkpKSs9y2UKFC6NmzjxGiIiJDEAQBq1b9hfj4eK3lt27dROnSn+XomBKJBCdPHtM8gG7SpCmqVKmWy0gNxyyT+CVLluDJkydYvXp1tvZzc3PDihUrNK8DAwMxduxYnD9/Hhs2bICdnR1Gjhyp9RAg1aZNmxAcHKy1rGLFitixY0eOroGIiMhWff11P9y/fxcHDuxDnjwOGDCAdWk+NXXqzyhYsBAqVqyE+vUbmjoci9e+fUccOLAXt25dz3S7pKRk7N+/B1991Q329vZGio6I9OnVq5cYO/ZHdOrUWdOLGwDKli2LL75okqNjtm3bAXv37sStW9cRFnYb4eH3MW/eIj1FrH9ml8SvWLECBw8exOrVq5EnT540693d3XH69GnN66ioKLi6uqYZTxYaGoqSJUuiQoUK8PPzw5YtW3Dz5k0sWLAAs2bNSnPcbt26pWn5d3Bw0NNVERER2Y4ffxwDAOjXrzfHKWdALpdjxIhRaNIkba9Dyr4OHb5Ehw5fZrldTEwM9u/fw+EeRBYstQDdkiUrtZL43Gjbtj3atm0PAPjjj9l4+DBcL8c1FLNK4letWoV9+/Zh1apVyJcvX7rbNGrUCEFBQQgPD4enpyfWr18Pf39/rW0+fPiAkJAQTat8UlISxGIxxGIxEhIS0j2um5sb3Nzc9HtBRERENkwmk1rUGENjYlV605BI1OWgmMQTWa7U+6e+EvhPWUKNDbNJ4l+8eIGZM2eiRIkS6NNHPU7Jzs4OW7Zswfz58+Hm5oaAgAA4OTlh2rRpGDp0KJRKJcqVK5emZX3OnDkYNmyYpiV98ODB6Ny5M2QyGaZPn270ayMiIrJFEonUoqr9GpNSqczxfMaUc2KxOokXBCbxRJZKqVQadFYP9WwX5j1Nqtn871G0aFHcvXs33XXDhw/Xet2sWTM0a9Ysw2MFBQVpve7atSu6du2a+yCJiIhIZ5Y2764xqVuSzOZrmM1ITeKVSvP+gk5EGZPLc16FXhfqXmTm/X+X2UwxR0RERNZF3Zph3l+ETEU9FRKTeGMTididnsjSqe+fEoMdXyIx//+7+L8HERERGYRUKkVMzBtERDzW63HFYjGKFy9hsPGQhvLy5QskJSUBAFJSDNuSROmTSNRf/FUqwcSREFFWoqOjkZAQn2b506dPDdwSL0NsbCwiIh5DJBKhePESml485oJJPBERERmEh0cJzJw5FevXh+j1uAqFAmvXbkKrVn56Pa4hPX/+DNWqlde0vtvZ2aNQoUImjsr2pH4RZ0s8kXmLi4tD1apeAJDuA9uqVQ03h7u7uzv+++8i6tevCYVCgUWLlqFLl+4GO19OMIknIiIigwgM/AGBgT/o/bht27ZMt3XGnMXGxsLRMS8eP35u6lBs2v+SeI6JJzJniYmJUCqVePr0Nezs7Ix67ubNWyEqKgYA0KtXV8TGxhr1/Lowr34BRERERFkQi8UWV5hMXYiJbSemxpZ4IsuQOibd1PdNc51lhUk8ERERWRSxWGxxSZhSqWA1ejPAJJ7IMigUCojFYpOPRVfPssIknoiIiChXJBKJxSVhhp4SiXRniT05iGyNufRekkolUCiYxBMRERHlikhkeS3xcrnCLL6QkmX25CCyNQqFwiwefEqlMrOcbo5JPBEREVkUsVhkcUkY54U3H0ziicyf+bTESyGXm18Sb/p3hoiIiCgbLKk7/Xff9cXDhw8RG/sB9vb2pg6HAMhkdujbtydmzPgNPj6NTB0Okc37668l2LRpg9ayxMQEo1elT4+9vT3Wrl2Nw4cPZrhNQEAv9Ov3rRGjYhJPREREFsaSxjQfPnwIY8aMh4dHCZQsWcrU4RCATZt2/F979x0W1Z3vD/w9zNCrlAFBoxIFYsPeG6iIETCWKDFqYkzWzc2ausmabJK9uXfz201ubsomKuoaTbIkG+taI2DQ2DBYsCDYUBGFKTRpM8OU8/uDO2dnpKgIM4y+X8/j88iUM9/PKd9zPudbDt5/fxnOn89jEk/UAfz661FERT2G+PhpVq937tzZTiX6t1deeQPjxsU0+/6OHf/C8ePZTOKJiIiIWuJI3aENBj1iYyejZ89e9i4K/Z9hw4YjJCTUYW4EET3o9Ho9hg4dhmnTEu1dlEZCQ8MQGhrW7PsFBZeQl5drwxI14Jh4IiIicihOTlIIgmMk8Xq9HlKp1N7FoNs40o0goged0WiAVOqYbcv2mmiVSTwRERE5FEfpTi8IAgwGA5yd7T/DMllrmFdBsHcxiAgNNzsdtZ60V13CJJ6IiIgcSkMrasdPwMw3Ghz14vRB5uQkcYgbQUQPA4PB6LD1pL3qEibxRERE5FCkUsfoCm1+LFFHeNYxWZNInBxmSAbRg86RH8Fpr6E5jrm2iIiI6KHl5OSEQ4d+afWF07BhIzB8+Ig2LtW/nThxDEeOHEZ9vQ4AIJNxTHxHwzHxRLal1+uxfv3fodXqGr1XVHTdYZN4qdQ+c7Q45toiIiKih9a0aUnYtWs7zp07c8/fLSgowLFjRzF8+D/boWQNUlKWo6DgMiIiIvDss4vh5eXdbr9FrSOVStmdnsiGrl69gj/96Y9ISnqi0XvDho3AgAGDbF+oNiCR2GeOFibxRERE5FASE59AYuITrfru+vVrkZ7+U9sW6DZ6vR7JyfPwm9/8R7v+DrUeW+KJbKu+vh5eXl5ISfna3kVpU/aqSzgmnoiIiB4azs7O4lj19tIwvpPj4DsyJvFEtmU0Gh7IerFhdnom8URERETtxhbdqA0Gg8OO73xYNCTx7E5PZCt6veNOXteShkeeMoknIiIiaje2aInX6/ls+I7OyYnPiSeyJYPhwawXnZzs86QLJvFERET00JDJZDAY2r87vVTKGek7MicnCbvTE9mQwWB4IOtFPmKOiIiIqJ3JZM6oq6tDYeG1dvuN2traB7LF6UHi5OSEysoKcT8ICekMV1dX+xaKyIEIgoCSkuK77tl08+aNB7JedHJygkJRApPJBCcn27WPM4knIiKih0ZwcDCuXCnAyJHt9zgjJycnBAeHtNvy6f517hyGv/71v/HDD/+A0WjE4sW/wf/7f/9j72IROYycnBOIj4+9p3HuY8aMa8cS2YdcHoyCgstITf0WCxY8a7PfZRJPRERED40hQ4ahqEht72KQnb388mt4+eXXAAAfffQhFIoSO5eIyLFUVVWhZ89eOHLkhL2LYlfjx8dg6tQEaDR1Nv1djoknIiIiooeWRCKBIHCSO6J70fDIOLYHA/apQ5jEExEREdFDSyLhJHdE90qvfzCf+94aDTPUM4knIiIiIrIJtsQT3TuDwQCZ7MGbbb41Gm4EMoknIiIiIrIJJvFE985g0LMl/v+wOz0RERERkQ3ZoysskaPT6/UcE/9/7JHEc80TERER0UOLLfFEzbt06SKWLl0Co9F63ojy8jL06PGonUrVsUgkYBJPRERERGQrTOKJmldQcBmlpWX44IMPG70XERFphxJ1PGyJJyIiIiKyoYYLcM5OT9QUvV6PgAB/TJuWaO+idFgSiRMAjoknIiIiIrIRtsQTNcdg0EMqZbtvSzixHRERERGRDXFiO6Lm6fV6ODtzFvqWNDxizra9eZjEExEREdFDq6EVzd6lIOqYjEYjHyV3B2yJJyIiIiKyIU5sR9S8hpZ4dqdvCSe2IyIiIiKyIXt0hSXqaEpKirF588ZGyejx49l8HvwdMIknIiIiIrIhezzjmaij2bNnN1avXoFRo0Zbve7u7oaJE+PsVCrHYI95NZjEExEREdFDi93piRpmoR84cDBSUr62d1EcDsfEExERERHZEGenJwL0egNnoW8lzk5PRERERGRDbIknAgwGA2Qyqb2L4ZAkEgkAtsQTEREREdmEPS7AiToag0HPR8m1ErvTExERERHZFGenJ9Lr9ZyFvtUksHVnHm4pIiIiInposTs9OYL6+nqUlBS32/LLy8vYEt9KnJ2eiIiIiMiGOLEdOYIPP/wAKSlfQSptv3Hrb775drst+0Fmj8dUMoknIiIioocWW+LJEVRXV+G1197EsmXv2rsodBuOiSciIiIisiEm8eQIGmaPZ/trR8RHzBERERER2ZA9LsCJ7pVer+dz3Duoh74l/tq1a0hOTsaUKVMwa9YsXLp0qcnPbdy4EXFxcZg0aRLeffdd6PV6AMDZs2cxffp0PP7449i6dav4+aysLLz//vs2iYGIiIiIHAdb4skRGI0GSKVsie+IHvok/v3338ecOXOQlpaGF154AcuWLWv0maKiInzxxRdITU1FRkYGSktLsWHDBgDAmjVr8O6772LTpk346quvAABarRZfffUVfv/739s0FiIiIiLq+BouwO1dCqKW6fUGODszie+IHuokvqysDLm5uUhKSgIATJkyBQqFAoWFhVafS0tLQ2xsLIKCgiCRSPDUU09h586dAACZTAaNRgOdTifO3Pjll19i4cKF8PHxsW1ARERERNThcXZ6cgQGA5/j3lE91I+YKykpQVBQkLhzSiQSdO7cGcXFxejWrZvV58LCwsS/w8LCUFJSAgB46aWX8P7770Oj0eCtt95Cfn4+ioqK8Oabb97x91UqFdRqtdVr7u7uCA8Ph1TaYe51EBEREVEbCg6Ww2jUY+nSJfYuClGzamtr0KVLF8hkzEs6mu7duyMjY0+jOmTo0GF47rkXWrXMO+WfHSaJbwuPPvooUlNTAQBGoxHPPfccPv74Y+zcuRNpaWnw8vLCsmXL4Ovr2+i7P/74o9gF3ywhIQH/+7//Cx8fd5uUn4iIiIhsa+7cWZg7d5a9i0FEDmrZsjexbNmdG43bUoe5ldO5c2eo1WoYDAYAgCAIKCkpQWhoaKPP3bx5U/z75s2b6Ny5c6PlffPNN4iPj4ePjw9WrFiBzz77DEOGDME333zT5O/PnTsXW7Zssfo3adIkvPHGG7hy5UobRmpfBQUFmDFjBgoKCuxdlDbDmBzHgxgXY3IMjMkxMCbH8SDGxZgcA2NyDIypfXWYJD4gIAB9+vTB9u3bATSMfQ8ODrbqSg80jJXPzMyEWq2GIAj44YcfMG3aNKvPFBUV4fDhw0hOTobBYIDRaIREIoGTkxPq6uqa/H25XI4+ffpY/XvkkUewc+dOaDSa9gnaDrRaLfLy8qDVau1dlDbDmBzHgxgXY3IMjMkxMCbH8SDGxZgcA2NyDIypfXWo7vQffPAB3n77baxatQqenp74y1/+AgD44x//iNjYWEycOBFdu3bFyy+/jKeeegoAMGzYMMydO9dqOR9++CHeeecdSCQSeHt7IyEhAYmJifDw8MBnn31m87iIiIiIiIiI2kKHSuLDw8Px448/Nnr9ww8/tPp7zpw5mDNnTrPLSUlJsfp76dKlWLp0adsUkoiIiIiIiMhOOkx3eiIiIiIiIiJqGZP4FgQFBeF3v/sdgoKC7F2UNsOYHMODGBPwYMbFmBwDY3IMjMlxPIhxMSbHwJgcA2NqXxLB1k+mJyIiIiIiIqJWYUs8ERERERERkYNgEk9ERERERETkIJjEExERERERETmIhyqJ//Of/4zY2FhERkYiPz9ffP3AgQOYOXMmEhMTMWfOHJw/f15878knn8T06dMxffp0JCQkIDIyUnxfo9Hg9ddfx+TJkzFlyhTs2bPH4WP69NNPER8fj6SkJMycORMHDx50+JjMCgoKEB0d3eiRhbbQHjGlpqZi6tSpSExMRFJSEnQ6nUPHdO3aNSxYsADTp09HfHw8/vrXv8JkMtk0ptbGdebMGcyZMwdPPPEEpk6dijVr1ojvOWo90VJMn332GRITE8VtuWvXLpvGA7R9TMuWLcPYsWPFmD766CObxgO0fUwvvfSSGM/06dMRFRWFn3/+2aFjunbtGhYtWoSkpCRMmzYNu3fvtmk8QOtjSk5ORlJSEqZPn46srCzxvU2bNiExMRG9e/fG+vXrbRmKqK1jMrPneRdon7gc8dzbUkwd4dyr0+nwH//xH5gyZQqSkpKwaNEiFBYWAgDKysqwePFixMXFISEhAceOHRO/19r3HDWmt99+W1xecnIyzpw54/AxLViwALGxseJ5ytZ1YHvEdDc5SZsQHiLZ2dlCSUmJEBMTI+Tl5QmCIAiVlZXCsGHDhIsXLwqCIAjHjh0Tpk2b1uT3f/rpJyEhIUH8+8svvxT+8Ic/CIIgCNevXxdGjBghlJeXt3MU1to6pv379wsajUYQBEHIz88XBg0aJNTW1rZzFNbaOiZBEIT6+nrhqaeeEl5//XXhz3/+c/sG0IS2jikjI0OYO3euUFVVJQiCIJSVlQkGg6Gdo7DW1jG9+OKLwjfffCMIgiBotVohISFB2L9/fztH0Vhr4kpKShL27t0rCIIgVFRUCCNGjBAuXbokCILj1hMtxXTr1i3xcwqFQhg4cKBQVlZmq3AEQWj7mP7whz8I69ats2kMt2vrmCydOXNGGDZsmKDT6WwQyb+1dUxz584VNmzYIAhCQ703YcIEQaFQ2DKke47JZDIJY8eOFQ4fPiwIgiBcuXJFGD9+vNW59vLly8Kbb75pt32wrWMSBPufdwWh7eNyxHPvnWLqCOderVYr7N+/XzCZTIIgCMJ3330nzJ8/XxAEQVi2bJnwt7/9TRAEQTh9+rQwduxYob6+/r7ec9SY9u7dK+j1ekEQBCEzM1OIiYmxWTztFdP8+fOFjIwMm8ZhqT1istRUTtJWHqqW+KFDhyIkJMTqtevXr8PPzw+9evUCAAwZMgTFxcU4d+5co+9v2rQJs2bNEv/+6aefkJycDADo2rUrhg0bhr1797ZjBI21dUzjx4+Hm5sbACAiIgKCIKC8vLwdI2isrWMCgOXLlyM+Ph7du3dvt3K3pK1jWrt2LX73u9/B29sbAODv7w+pVNqOETTW1jFJJBJUV1cDALRaLfR6vV0e4dGauCzLrtFo4OzsDF9fXwCOW0+0FJOPj4+4nNraWgiCAMHGDzpp65g6gvaMadOmTUhKSoKLi0s7R2GtrWO6cOECxo0bB6Ch3ouMjLR5a/y9xlRRUYHy8nKMGjUKANCjRw94e3vjwIEDAICoqCg8+uijcHKy3yVZW8cE2P+8C7R9XI547r1TTB3h3Ovq6orx48dDIpEAAKKjo3Hz5k0AwJ49e8RzaP/+/SGXy8VWz9a+56gxTZw4ETKZTFyeUqmEwWBw6Jjsrb1jaionaSsPVRLflO7du6OyshInT54EAPz888+ora0VN6BZSUkJjh07hqSkJPG14uJihIaGin+HhYWhuLjYNgVvwf3EZGnz5s3o2rUrwsLC2r3Md3I/MZ0+fRqnTp3CggULbFrmO7mfmAoKCpCbm4vk5GTMnDkT3377rU3L3pz7iemdd97Bnj17MGbMGIwdOxZPPPEEevfubdPyN+dOcf3lL3/BF198gQkTJmDKlCl4/fXXxYsgR60nWooJAL799ltMmTIFM2fOxH//938jICDALnFYaouYEhMTsWTJEqtuqvZ0vzEBDRfmu3btwuzZs21e/qbcT0x9+vTB9u3bAQBFRUXIyclpVL/YQ0sx+fv7Qy6Xizcbzpw5g6tXr3aIcrfkfmLqqOdd4P7icsRz751i6ojn3m+//RaxsbGoqKhodFPBfA5t7Xv2cr8xNbW8cePGiUm9PbRVTJ988gkSExPx6quvoqioyKYx3K4tt9Od8qz7Zb8t30F4e3vjiy++wKeffoq6ujoMGDAAPXv2bHRndcuWLZgwYQL8/f3tVNK71xYxZWVlYfny5fj666/Fu1P21NqYNBoNPvjgA3zxxRcdIg5L97OdDAYDbty4gdTUVNy6dQsLFixA165dERMTY+swrNxPTN9//z0SEhKwZMkSlJWVYeHChejXrx9Gjx5t6zAauVNcq1evxuuvv47ExEQUFRVh/vz56Nu3L3r27GnnkjfvfmNauHAhFi5ciPPnz+PNN9/E6NGj0alTJ3uGdF8xvfbaawgKCoKTkxMyMjLwwgsvIC0tDZ6eng4bk9mePXvQvXt3REZG2isMK/cT01//+ld89NFHmD59OkJDQzFy5Eibt4Q25U4xrVixAv/zP/+D1atXo2fPnhg8eHCHKHdLWhtTRz7vAve3rRz13NtSTB3t3JuSkoLr169j/fr10Gq1dilDW2vrmLZt24affvoJqampbVC61mmrmD7++GN07twZgiAgNTUVS5YssctcJ0Dbb6f2zh0f+iQeAEaMGIERI0YAAOrr6zF69GirCyBBELBlyxb853/+p9X3QkNDUVxcDLlcDgC4efMmxowZY7Nyt6S1MQFAdnY23n77baSkpCA8PNxWRb6j1sR0/fp1FBcX45lnngEAVFVVwWQyoaqqyi4TV93ufva9adOmQSqVwt/fH+PGjcOpU6fsfiEBtD6m77//Xpz0LSAgAOPGjUN2dnaHSOKB5uMqLy9HRkYGPvvsMwANXeajo6Nx8uRJ9OzZ0yHriTvFZCkqKgpyuRzZ2dmYMmWKzWO4XWtjCg4OFpcxefJkfPLJJ7h69Sr69u1rlzgs3e922rx5c4dphTdrbUxdunTBl19+KS5n8eLFHb6OABqOk7Vr14qfnTp1qtj1uSNrTUwd/bwLtH5bOeq5t6WYOtK5d+3atUhPT8f69evh7u4Od3d3yGQyqNVqsdXz5s2bCA0NRadOnVr1nqPGZLZ7924sX74c69evR2BgoM3jaeuYOnfuDKBhWMf8+fPx0UcfoaKiwuaNAm29nVrKs9rKQ9+dHgBUKpX4/+XLl2PEiBHo1q2b+NrRo0dhNBobVWjx8fH45z//CaChW192djYmTpxom0LfQWtjOnbsGN566y2sWLECUVFRNivv3WhNTJGRkTh69CgyMzORmZmJZ555BrNmzeowFxKt3U4JCQnikwO0Wi2ys7M7zPZqbUxdunQRY6qrq8Ovv/7aoS5ym4vL19cXHh4e4my/5eXlOHPmjFh2R6wn7hTT5cuXxe9dv34d+fn5HabXQWtjUigU4vdOnTqFyspKq/3WnlobEwAUFhYiNzcXCQkJNi93S1obU2lpqThz9sGDB1FQUIDExETbB9CEluo+y/c2bNgAd3d3MeHqyFoTU0c/7wKt31aOeu5tKaaOcu5dt24ddu3ahXXr1lnNu2J5Dj1z5gyUSiWGDh16X+/ZSlvHtHv3bnz++edYt26dXW5IAG0bk8FgQGlpqbiMtLQ0BAYG2jyBb+vtBDR/rduWJIKtZyOyo/fffx/79+9HaWkp/Pz84OnpiYyMDLz77rs4fvw4jEYjBgwYgPfee89qI77xxhvo1q0bXn75Zavl1dXV4Z133kFubi6kUileeeUVPP744w4dU1xcHGpqaqzGeXz88cc27YbZ1jFZ+vLLL1FVVYU//vGPtghF1NYx6XQ6vP/++zh79iwkEgni4uLwyiuvOHRMeXl5+K//+i/U1dVBr9cjNjYWv//9723eHbM1cR05cgSffPIJDAYDDAYDnnzySSxatAiA49YTLcW0ZMkS3LhxAzKZDFKpFC+88AKmTp3q0DE9++yzKCsrg5OTE9zc3PDaa6/ZPMlq65iAhseGKpVKuyVQbR3Txo0bsXr1ajg5OUEul+O9995DREREh4/pq6++wo4dOyAIAsLDw/GnP/1JbIHasmULPv/8c1RVVUEmk8HDwwMpKSk2HZfc1jFZstd5F2j7uBz13NtSTB3h3KtQKDB+/Hh07dpVHMLk4uKCjRs3orS0FG+99RZu3LgBZ2dnvPfee2Ld3Nr3HDWmPn36IDAwEH5+fuLvrF+/3mZJb1vHVFdXh/nz50Ov10MikaBTp054++23bXpjrD22E3B3Ocn9eqiSeCIiIiIiIiJHxu70RERERERERA6CSTwRERERERGRg2AST0REREREROQgmMQTEREREREROQgm8UREREREREQOgkk8ERERERERkYNgEk9ERERERETkIJjEExERERERETkIJvFEREREREREDoJJPBEREREREZGDYBJPRERERERE5CCYxBMRERERERE5CCbxRERERERERA6CSTwRERERERGRg2AST0REREREROQgZPYugCOorq5GWloaysvLodPp4OPjg2eeecbexbJy+fJlbNmyBT4+PgAAd3f3eyrj9u3bERUVhYiIiPsuS2ZmJvz8/DBo0CCr19euXQutVguJRAJnZ2c8+eST8PPzu+/fay9HjhxBbm4ujEYjTCYTEhIS0K1bt3tahkqlQlVVFXr27Nnm5du8eTOKi4vh7OwMQRAQHh6O2NhYODs7t/i9ixcvwt/fH4GBgff0e4IgYNWqVQgJCcETTzxxT981GAxYvnw5XnnllUbv7d27F71790ZoaOg9LfNe7d27F2fPnoW7uzv0ej3i4+PRq1evZj9//fp1ODk5oUuXLgCArVu3Ijo6GuHh4e1aznuxdetWTJo0Cd7e3s1+prXbu6PLy8tDXV0dhgwZYvW6yWTCF198gddee+2ul3XgwAG4ublh2LBhbVrG69ev4+jRo5gzZ06bLvdubd68GXl5eXjnnXcglUoBABs3bkR+fj7eeecdCIKATz75BC+//DK+++47AMCtW7fg6uoKNzc3SCQSPPvss3B1dRWXuXbtWsTHxyMsLEx8Ta/XY9OmTXjqqadsGyAREdFDikn8Xdi6dSsGDRqEvn37AgCUSmWbLNdkMsHJqW06Q6hUKgwZMgSxsbGt+r5CocCECRPu6rOCIAAAJBJJk+8rlUpERUU1+o5arcYbb7wBZ2dn7Nq1C9nZ2YiLi2tVee/kftftmTNncP36dSxevBhSqRQ1NTXNxtuSvLw8eHp6trocLVEqlZg3bx4CAgJgMBiwc+dO7N27F1OnTm3xe1lZWXj88cfv+fdyc3Ph6uoKlUrV7GcEQWhyPanVasjl8ia/M2nSpHsuS2uoVCokJiaiZ8+eyM/Px759+1pM4nNyctC7d2/xb4VCgfj4eFsU9Y7M+/eMGTPu+NnWbu+7+X17Ls9y21hSq9UICAi4p2UpFAqMGDHinr5zN0pKShASEnLXn2/r9apUKhEUFISKigoEBgaipKQEZWVlCAgIgEwmQ3FxMQIDA+Hp6Ynf/va3AIDvvvsOY8aMQY8ePRotz1yP334sOzs7M4EnIiKyISbxd2AymXDt2jXMnDlTfC04OBgAYDQakZ6ejmvXrkEQBEyaNAkRERG4fPkycnNzxdbK7du3IzIyEpGRkVizZg26dOmCoqIiDB06FD179sRPP/2EiooK6PV6zJgxA2FhYfj111+Rm5sLrVaLxx577I7JuVKpRGRkZKPXFQoFMjIyoNFo4OLigieffBKenp4oKyvDtm3boNPp0KdPH9TU1MDHxwcmkwn79+/H1atXodFoMHz4cAwdOhQqlQpbt25Fp06doFKpsGTJEuzevRsqlQparRZ9+vQRy9jURV5lZSXc3NzEVmI/Pz9otVrx/dzcXGRnZ0On0yE0NBRJSUkAgLS0NBQWFsJgMKBXr16Ii4tDRUUF0tLSUFVVBYlEghkzZiAwMBDp6emora1FRUUFgoODce7cObzxxhuQSqUwGAxYtWoVnn32WXG55eXlMBgMmDZtGrp27WpV3qtXr6JLly5i65WXlxcAIDs7G9XV1Zg4cSKAhgRJp9NhzJgx2L59O0pLS1FfX48hQ4bA29sbWVlZ8PHxwcmTJ7F48WIUFRXhl19+gVarha+vL2bPng1nZ2esWbMG3bp1Q2FhIXQ6HWbMmIFDhw5BoVAgOjq60Q0Wo9GIqqoq+Pv7AwBkMhnGjBmD1NRUTJ06FYIg4IcffkBNTQ10Oh1GjBiBoUOHIiMjA9evX8fmzZshl8sxY8aMJj93O6PRiIMHDyI5ORlr1qwRk/X6+nosX74c4eHhuHnzJubMmQOtVouMjAzodDoAwPPPPw+FQgFXV1d8//33UKlUGDx4MMaOHYv6+nqsWrUKS5cuxcqVK/HUU0+JvTPWrl2LxMREdOrUCenp6SgpKYFOp8PEiRMRFRWF8vJy7Ny5ExqNBnq9HklJSXjkkUdaPEbMrdHmff3ixYs4e/YsZs2aBQDIz8/HhQsXEBISgrNnz6KkpARHjhzB/PnzodPpcOjQIRQUFMDZ2RkLFy6Es7MzSktLsWvXLmg0Gnh4eGD27Nnw8PBAWloaBEGASqVCWVkZYmNjER0dbVUmg8HQaL8ZMWIESktLsWfPHtTU1MBkMuHpp5+GUqnE0aNHIZVKodFokJSUhD179mDhwoU4fvw48vPzodfrUV1dLe4zt2/v+Ph4bNu2DdXV1dDpdIiNjUWfPn2sypSdnY0zZ86gvr4efn5+mDNnDmQyGTZu3Ag3NzcolUpERERg8ODBdzyOLl++jP3790Mmk6G6uhrdunUTj+1PP/0UvXv3xrVr1zB58mS4uLggPT0der0enTp1wqxZs6BSqZCeni4etyqVCjt27MDixYuxZs0azJkzB76+vjh58iSOHDkCV1dXdOvWTayjm6srampqsG3bNty6dQs9evSAUqkUv2NWWlqKnTt3ir994MABuLq6Yvjw4fjxxx8RFBSEwsJCVFZWYvr06QgPD4fRaERaWhquXr0KX19fuLm5oV+/fgAa6pTmjn3Lc8LAgQPFMhQWFmLv3r0wGAwAgNmzZyMgIAC//vorFAoFampqoFarxePp9mO2pqYGgwcPRmlpKQIDA5GZmYnevXuLN6KVSmWjulqlUjVaF2aVlZXw9PRs1NsnOzsbGo0G48ePR3V19V2f07RaLVavXo2oqChcuXLF6rg6deoUsrOzYTQa4erqiueeew56vb7JuoCIiOhhwyT+DpycnBAeHo6UlBRERkYiOjpaTBT27dsHFxcXvPjii6iqqsLXX3+NiIiIRhdGKpUK48aNE1sxhgwZgqlTp8JkMmH16tWIjY1FREQE9Ho9TCYTTp8+jZqaGjz33HMAgK+//hqVlZXw9fXFunXrMG/ePLi5uVmVU6VSobi4GAcOHIBEIsHixYtRX1+P9PR0zJo1C56enjh27BiOHTuGMWPG4Mcff8T06dMRFhaGXbt2icnNwYMH4ePjg8WLF8NgMGDlypUYNGgQVCoVamtrkZycDF9fXwDA5MmT4eHhAZPJhOXLl2PMmDEwmUyQyWSQyax3LaVSKbaO1dXVobCwEAkJCQCAa9eu4fLly3jmmWcglUqxadMmFBYWQq/XQ6vVYsmSJQAArVYLo9GIHTt2ICEhAf7+/rh06RIOHz6M6dOnQ6VSwdvbG4sWLYJEIkFJSYnYAnXixAk89thj8PT0xA8//IDx48cjNDQUarUau3fvbjT0oEePHti+fTsKCwsRFRWF6OhoyGQyhIWFITMzU4wjJycHzz//PHJycuDv7y/e7NFqtXBzc8P+/fvx/PPPw9XVFaWlpTh69CjmzZsHFxcXZGRk4Ny5c4iOjoZarcbo0aMRFxeHHTt2IC0tDU8//TQEQcCKFSsaJfFqtRr+/v5Wrd7Ozs7ijRFBEDBz5ky4ubmhvr4eK1euxNChQzFy5EhcvXoVv/nNbwA03KRq6nO3y8nJQY8ePeDv7w9vb29UVFTA398fKpUKGo0GI0aMQHBwMMrLy7F161bMnz8fnTp1Qk1NDWQyGZRKJfR6PebOnYv6+nqsXr0aY8eOhUqlEo+VoKAglJaWws/PDxcuXECnTp0gl8vFoR7Tpk1DXV0d1q1bh6ioKGRmZmLcuHHo3r27OOShOVqtFhqNBr6+vjCZTDh27BgiIiIQFhaG9PR0AA1Jzy+//IJ58+bB29sbR48eFVsnFQoFdDodBg4ciMmTJ+Of//wnbt68iS5dumDDhg2YOXMmQkJCcOjQIWRlZWHixIlQqVQIDAzEggULoFAokJ6e3iiJb2q/0Wq1+P777zF79myEhoaKN+DOnj2Luro6PPPMM3B3d0dubq647pRKJUwmExYsWACTyYS//e1vGDlyZKPtnZGRgcceewwDBgyAIAior69vtK769u0rdivftm0brly5ItZrffr0QWJiIgDc1XGkVCpRX1+PefPmwdXVFSkpKVCr1XB3d0dNTQ169eqF+Ph41NbWYt26dViwYAF8fX2xc+dOnD59Gv3790dZWZm4vH379iEmJgaCIKC6uhq+vr64cuUKTp06hSVLlsBkMuHLL7/E5MmTW6wrNm7ciJEjRyIqKgqHDh2CIAhWXcbNZb+9Hjd33VepVOjatSsWLVqE/Px8nD17FuHh4fjll18glUrx0ksv4caNG1i7di0mT558x2PffE64nVwux3PPPQeJRIJTp07hxIkTiIuLg1KphFarRXJystXxZEmtViMwMBByuRylpaUoLCwUu8ibk/Tbb17U1dUBADw8PBqVpal1YrluevbsCZPJhNTU1Ls+p1VVVUGj0WDw4MGIi4sTj6vOnTvj8OHD+O1vfwupVCrWaz/99FOTdQEREdHDhkn8XXj66adRVFSECxcuIDU1FTNmzEBERATOnDmDl19+GUBDy57RaIQgCFAqleLFuiAIuHXrFvz8/FBWVoZOnTqJLS3nz59HYGCgOA7d3Lpx5MgRAMClS5cANFzYm0wmSCQS8SLIkslkwq1bt/Dmm29aJXW//vorysrKxLGORqMR/fv3x/nz5xEaGiqOaQwKCoJMJoPJZEJ2dja8vb1x/PhxAA0theaYRo4cKSbwVVVV+Pnnn8UWnVu3bkEqlaK4uLjJVhylUgmFQoGVK1eitLQUSUlJ4vj9I0eOoKKiAmvWrAEAMbHw8PDAlStXcPDgQfTv3x++vr44d+4cVCoVNmzYIMZuvqmiVqsxY8YMcR2Yk0JfX18cP34cixcvxs2bN3H9+nVs375dLJu5HJb69++Pbt264fz588jOzsbly5cxd+5chISEQK1WAwD279+PkSNHwsXFRWx1d3d3R9++feHp6SlewJqTg19//RVqtRpff/01gIZxpHK5HOXl5QgICLDqHjx8+HC4urpCq9U2Si7M6zMoKMjqtfLycrFlvqysDPv27UN5eTkEQRAT3Nsv2pv7nCW9Xo+srCwsWrRIXK8qlQr+/v5QKpUYOHCguMyjR49i5MiR6NSpE4B/92BQKpWYOnUqpFIpTCaTeBPKMikwb69HH30UBw4cwKxZs1BTU4O8vDwUFxeLN0/MvSO8vb2RmZmJ4cOHIzIyssW5AMz76erVq8Ubc+PGjYNMJoMgCNBqtTh58iQiIyPh4+MjHqtmCoUCAwcOFG92GY1GuLm54fz58+jatavYZTooKAgXLlwA0LA/zp49GxKJxCpmS03tN0eOHLGaI8Dd3R1AQ6I0fvx48W/LbalUKhEXFyeuAy8vL+h0OqjVaqvt7ePjg6ysLAiCgN69ezfat4xGI7KyslBQUCDWK/369YPBYBBbWgHc9XGkVCoxbtw4MSn09/eHRqNBVVUVevXqhUcffRRAw82M6OhosX4JCgpCbW0tnJ2dxZtTFRUV0Gq1CA8PR1lZmbivZ2VlISYmRozd19cXISEhOH/+fJN1xY0bNyCRSMTkTy6XN1lnKRQKq9fN69t8c9Hc/d58jBuNRuTk5GDp0qXict3d3eHr64tdu3Y1e+xbnhNud/HiRZw4cQL19fXQaDTieUWlUiEhIaHR8XT7upfL5ZDL5SgoKMDly5eRmJiI/fv3i70vVCqV1TwoljfVmtJSEj969Oh7PqepVCpER0eLN3jNx5WTkxMMBgPS0tIwYMAAhIaGtlgXEBERPWyYxN8FiUSCRx55BI888gg0Go14Mefl5SW2OFdXV8PT0xMSicTq4lqtVovJgFKptJoYTaFQiJNmmZlMJmi12nualMmccNw+FlmpVIrdPC1lZmaic+fO4t8lJSXo3r07KisrERgYKCZrlswJhNnWrVsxdOhQzJgxAxUVFfj+++8hlUqhVCqbHAOqUqkQFxeH6Oho5ObmYt++feIFqVKpxCuvvNLkWNAXXngBeXl5WLt2LebNmwelUonY2NhGk+bV1dXBzc3Navy5OSksKytDv379xK7AQ4cOvau5A3x9fTF8+HB07twZW7duBdBw0ejn54eLFy/i5s2bYutZVFQU5HI5zp07h5SUFCxZsgRVVVVWibZSqcT8+fPF5MMsLy/Paj9QKpViq1pzF9VNvX7y5EnxRsCPP/4odi+/evUqDh8+LC7bMjFp7nOWsrOzUVtbi7///e8AGi7Ag4ODERUVBaVSie7du4ufVSgUjbYN0NCt2bwuLBMBy+8HBQXhypUryMvLQ3BwMPz9/XHlyhVERkY2OfZ7ypQpKCoqwunTp3HkyBG88MILjT5jub769+8v9v6wFBYWhoKCAuTk5IjLuH09KZVKq67i5u7J+fn5Vp9TqVQICgpCXV0dXF1drRLu22+6AE3vNwqFosnx3kql0mpMvkqlwmOPPSbeZDMfdzqdDjqdDt7e3sjNzbUq3/Dhw9G9e3ecPXsWK1euxNKlS60SoUOHDsFgMOC5556DTCbD559/DrlcDpVKhS5duojH6N0eRwqFQqw3zD2RgoKCkJOTY1UXlpaWWsVsbtkF/n0cHzhwADExMeLvW+5D5hse9fX1Yu+bc+fONVlXnDx5slH911ydZR6ipNVqodfr4e7ujuLiYoSGhlqtC7lcjlu3bsHLywsuLi7ici1vsjR37Dc3Wea1a9eQk5OD5ORkeHh4YOvWrZDL5eKNYctlt1RHBAQE4MKFC4iKikJAQABUKpW43Zraz5vaTy2XefvwC8sb1Tk5Ofd0Tjt69KjVEBjzcSWTyfDiiy/i4sWL2LFjBwYNGoSAgIBm6wIiIqKHDR8xdweXL1+G0WgEANTW1qKoqAjh4eHw8PBAbW0t6uvrYTKZkJ6eLnZBNbeeCoKArKws8QLr9rGGXl5eVpOE1dbWwsnJCUajEQqFAkBDS7hld9KmNHcR5+XlhcuXL4t/m5fp7u4u/m5xcbF4oe/h4QG1Wo2qqioxjsrKSgCNx7mrVCpxDGhGRoYY1+2tV5ZlNF8o9+nTBzqdTozLspzm1hmg4eaEt7c3hg4dioCAAJhMJnh5eaGgoECcXE+pVIrjjm//XblcjuLiYpw6dUpsNfPy8sK1a9fEbVpRUSGO3bZcT7du3QLQcIGan58vthgCDUnfv/71L0yaNAkSiQR6vV4cnz5y5Eixy2plZaXVrOHe3t5W28PcOmxZdkEQUFVVJY4LVygUTW5by21uMpmQlZUFtVqNYcOGob6+HlqtFmFhYdDpdNi3b5+4fMsytfQ5M61Wi+PHj+OVV17Bq6++ildffRUJCQlib4SW9mmDwQCdTodbt27Bx8dHvMlkWXbL78vlcqjVahw6dEhM/Ly8vFBUVCRuo5qaGtTU1KC6uhr19fXo2rUrRo0aJY4Zbk5TY57NzENKRo0aJSZgt287y+9rNBo4OztDJpPBx8dHXBcVFRU4c+YMoqOjG93Maur3m9tvLNehyWSCRqOB0WhEfX29VTdn8zFZUVGB+vp68XjKzMzE4MGDG+2DGo0GdXV1CA4OxqhRoyCRSBrdOFOpVOjWrRtkMhmysrJgMBjE8ljuh3dzHJnrrtLSUgANCVuPHj3E+sdyfXh7e4vrsbi4GEVFRWJrrjnpFwRBTPgsv+/h4SGur19++QW+vr6QSqXN1hXu7u5Qq9UQBAEVFRU4duxYk/uGRqMRW7iPHj1qdROqqRZ6Nzc3VFVVQafTwWAwWB1Pd3Ps306lUiEkJAQeHh4oKirCuXPnEBwcjMrKSvj5+TV5PFkyl0smkyE2NhYxMTEwGo2orq6Gn5+fOFmn5Y3PlsrT3G/dunULvr6+jfZd4M7nNHOM5vVtPq7Kysrg4uKCvn37IiIiQtwPm6oLiIiIHkZsib+DvLw87N69Gy4uLpBKpYiJiRFb5MaNGyd2Ae/Xr5/Y4jNo0CB8++23CAoKgl6vF1uJLFuXAGDAgAHYvHkzVqxYAScnJ8TExCAyMhKJiYnYvHkzpFIppFIpJk2ahICAAOzcuRP9+vVr1HJjOWGXpVGjRmHr1q1Yvnw5pFIpHn30UYSEhCA6OhqpqalISUlBYGAg3NzcEBQUBKlUiokTJ+Kbb74Ru7EmJCRAp9NBKpVajXMfPXo0Vq1aBT8/P/j4+IjdIZVKpTjpm5nBYLBqlZZIJOjVqxcuXbqEgIAAxMfHY9euXfj555/h5OSEUaNGwdfXF1u2bIFer4dUKkXfvn0RGhqKoKAgXLt2DcuXL4dMJoNcLsfMmTObvLgMCgpCfn4+4uLixOSsV69euHjxIlasWAFnZ2d4e3sjOTnZ6nsVFRXYsmULgIY5Ebp164YpU6aI7wcGBqJr167i7M3V1dXYsGEDBEGAk5MTxowZA09PT4SFhWH//v3ihG0xMTHYtm0bTpw4AYlEgujoaAQHB1vtF+butZbbtqkZ1FUqFdLS0rBv3z4AQPfu3fHss8+K3Vf79OmD5cuXIyAgAB4eHuKFea9evbB7926cOHECCxcubPZzZkeOHEG/fv3EFmUAYmueeV1Zti5OmDAB27Ztw5EjR+Dk5IQ5c+Y0SgCVSqX4pAfL7/v7++PmzZsYPHiw2K1aLpejf//+WLVqFVxcXODu7o6ZM2fixo0byMzMhFQqhYuLizhZWnp6Onr06NFonZlb4psSGBgIHx8fDBgwQHytR48e2LRpE3Jzc7F48WKroQqWN6r69++PS5cuYeXKlZDJZEhKShKTytsTvdvnGmhuvxk5ciQ2b96Mc+fOwcnJCQkJCZDJZFbHuOUxqVQq0bt3b3FyvZ49e2L06NGNtndMTAx27NgBiUQCmUyGxx9/vFHvnSFDhmD79u04fPgwunbtatXaa/lIsbs5jtRqNbp164Zjx47h559/RmhoKKZNmyZuD8v1M2LECGzatAmnT5+Gh4cH5syZI/YQCAoKwr/+9S9xXL/5++ZjZsKECdi0aRO8vb3FrvQAMHDgwCbril69euHEiRNYsWIF/Pz84Orq2mRL/MCBA7F582aEhIRAIpFY3XiyXBfm/dvZ2RmDBg1CSkoKfHx8IJVKxeXezbF/u8ceewypqalYtWoVevToATc3NwQEBODSpUuN9i3z8WTJ8rgz73vmuUlu7zFm+Z2metIADfV4eXk5/vGPf4ivDRs2DAEBAeLv3Os5raKiQjx3WB5XBw8exI0bN+Ds7Ay5XI7ExETIZLIm6wIA+OabbzB79ux2exIIERFRRyMRzM0URHRHRqMR69evxxNPPHHPj7GijumHH37AyJEjrYYFOJJ9+/bB29u70fPS7S0nJwdlZWU2e4QgERER0cOCLfFEdyk3NxeZmZkYPXo0E/gHQGFhIbZv346+ffs6bAIPNN9bw96USmWj8dFEREREdP/YEk9ERERERETkIDixHREREREREZGDYBJPRERERERE5CCYxBMRERERERE5CCbxRERERERERA6CSTwRERERERGRg2AST0REREREROQg/j//u5KRaZPjHgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1200x440 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"time: 1.18 s (started: 2021-01-30 20:35:19 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xK3VJnlXfLuD"
},
"source": [
"### Effective FED Rate"
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "ZduClv1rfLuD",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 474
},
"outputId": "dd8cbfcd-28f3-4f2a-cf6e-7a69085f8445"
},
"source": [
"filename = fed_rates_dir + 'DFF.csv'\n",
"eff_fedrates_fred = pd.read_csv(filename, header=0)\n",
"eff_fedrates_fred = eff_fedrates_fred.set_index(pd.to_datetime(eff_fedrates_fred['DATE'], format=\"%Y-%m-%d\")).drop(columns=['DATE'])\n",
"eff_fedrates_fred['Point Difference'] = eff_fedrates_fred['DFF'].diff()\n",
"eff_fedrates_fred\n"
],
"execution_count": null,
"outputs": [
{
"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>DFF</th>\n",
" <th>Point Difference</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DATE</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1954-07-01</th>\n",
" <td>1.13</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1954-07-02</th>\n",
" <td>1.25</td>\n",
" <td>0.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1954-07-03</th>\n",
" <td>1.25</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1954-07-04</th>\n",
" <td>1.25</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1954-07-05</th>\n",
" <td>0.88</td>\n",
" <td>-0.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-05-30</th>\n",
" <td>0.05</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-05-31</th>\n",
" <td>0.05</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-01</th>\n",
" <td>0.05</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-02</th>\n",
" <td>0.06</td>\n",
" <td>0.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-06-03</th>\n",
" <td>0.06</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>24080 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" DFF Point Difference\n",
"DATE \n",
"1954-07-01 1.13 NaN\n",
"1954-07-02 1.25 0.12\n",
"1954-07-03 1.25 0.00\n",
"1954-07-04 1.25 0.00\n",
"1954-07-05 0.88 -0.37\n",
"... ... ...\n",
"2020-05-30 0.05 0.00\n",
"2020-05-31 0.05 0.00\n",
"2020-06-01 0.05 0.00\n",
"2020-06-02 0.06 0.01\n",
"2020-06-03 0.06 0.00\n",
"\n",
"[24080 rows x 2 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 180
},
{
"output_type": "stream",
"text": [
"time: 42.2 ms (started: 2021-01-30 20:35:41 +00:00)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "8w8KE60ZfLuF",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 633
},
"outputId": "cf44785c-b664-4c61-897c-cda88d2fdeb9"
},
"source": [
"# Computations: every nth points to trim the dataset\n",
"eff_fedrates_fred_redux = eff_fedrates_fred.iloc[::20, :]\n",
"print(eff_fedrates_fred_redux.shape)\n",
"\n",
"# Plot GDP\n",
"graph_tool(\n",
" 'plt_lineplot', \n",
" True, \n",
" 'percentage', \n",
" 20, \n",
" eff_fedrates_fred_redux.index.values, \n",
" eff_fedrates_fred_redux['DFF'].values, \n",
" 'black', \n",
" 0.5, \n",
" 'solid', \n",
" 'Effective Federal Funds Rate (FRED)', \n",
" 'Every 20th point was sampled to reduce noise in the plot', \n",
" ' ', \n",
" 'Rate (%(', \n",
" 'Effective Fed Rate', \n",
" 'eff_fedrates_fred')\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"(1204, 2)\n",
"Plot Dimensions: 15\" x 5.5'\n",
"Title: 'Effective Federal Funds Rate (FRED)'\n",
"Subtitle: 'Every 20th point was sampled to reduce noise in the plot'\n",
"X-Axis Label: ' '\n",
"Y-Axis Label: 'Rate (%('\n",
"Displaying a total of 20 ticks on the x-axis.\n",
"Figure saved as 'eff_fedrates_fred' in: /content/drive/My Drive/Colab Notebooks/proj2/src/data/graphs/eff_fedrates_fred.png\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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