diff --git a/README.md b/README.md index 400c0ac..1812e8b 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,43 @@ # GeoSMART Curriculum Jupyter Book (ESS 469/569) -[![Deploy](https://github.com/geo-smart/mlgeo-book/actions/workflows/deploy.yaml/badge.svg)](https://github.com/geo-smart/mlgeo-book/actions/workflows/deploy.yaml) -[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://geo-smart.github.io/mlgeo-book) -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/geo-smart/mlgeo-book/HEAD?urlpath=lab) +[![Deploy](https://github.com/geo-smart/mlgeo-instructor/actions/workflows/deploy.yaml/badge.svg)](https://github.com/geo-smart/mlgeo-instructor/actions/workflows/deploy.yaml) +[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://geo-smart.github.io/mlgeo-instructor) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/geo-smart/mlgeo-instructor/HEAD?urlpath=lab) [![GeoSMART Library Badge](book/img/curricula_badge.svg)](https://geo-smart.github.io/curriculum) +[![Student Version](book/img/student_version_badge.svg)](https://geo-smart.github.io/mlgeo-book/) -## About +## Repository Overview -This repository stores configuration for GeoSMART curriculum content, specifically the student version of the book. This version of the book should never be directly edited, as the student version is automatically generated on push. \ No newline at end of file +This repository stores configuration for GeoSMART curriculum content, specifically the teacher version of the book. Only this version of the book should ever be edited, as the student version is automatically generated on push by github actions. + +## Making Changes + +Edit the book content by modifying the `_config.yml`, `_toc.yml` and `*.ipynb` files in the `book` directory. The book is hosted on Github Pages and will be automatically updated on push, and the student book will also be created automatically on push. + +Making changes requires that you set up a conda environment and build locally before making sure that it will build with github actions. We accepted rendered notebooks, but some oddities, such as kernels different than python, will make it crash. So we recommend that contributors first build the book with the added notebooks. + +```sh + conda env create -f ./conda/environment.yml + conda activate curriculum_book + +``` + +To modify the exact differences between this book and the student book, edit `.github/workflows/clean_book.py`. When you push, a github action will clone the repo and run this python file which modifies certain parts of `*.ipynb` file contents, then pushes to the student repo. To edit the student repo's README, edit `STUDENT_README.md`. The Github Actions workflow also automatically replaces `README.md` with `STUDENT_README.md` in the student repo. + +### `Student Response Sections` + +One modifications made by the `clean_book.py` workflow is to clear sections marked for student response. Code cells marked for student response may contain code in the teacher version of the book, but will have their code removed and replaced with a TODO comment in the student version. + +To mark a code cell to be cleared, insert a markdown cell directly preceding it with the following content: + +````markdown +```{admonition} Student response section +This section is left for the student to complete. +``` +```` + +## Serving Locally + +Activate the `curriculum_book` conda environment (or any conda environment that has the necessary jupyter book dependencies). Navigate to the root folder of the curriculum book repository in anaconda prompt, then run `python server.py`. + +On startup, the server will run `jb build book` to build all changes to the notebook and create the compiled HTML. The server code can take a `--no-build` flag (or `--nb` shorthand) if you don't want to build any changes you've made to the notebooks. In the case that you don't want to build changes made to the notebooks, you can just run `python serer.py --nb` from any terminal with python installed. diff --git a/STUDENT_README.md b/STUDENT_README.md new file mode 100644 index 0000000..400c0ac --- /dev/null +++ b/STUDENT_README.md @@ -0,0 +1,10 @@ +# GeoSMART Curriculum Jupyter Book (ESS 469/569) + +[![Deploy](https://github.com/geo-smart/mlgeo-book/actions/workflows/deploy.yaml/badge.svg)](https://github.com/geo-smart/mlgeo-book/actions/workflows/deploy.yaml) +[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://geo-smart.github.io/mlgeo-book) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/geo-smart/mlgeo-book/HEAD?urlpath=lab) +[![GeoSMART Library Badge](book/img/curricula_badge.svg)](https://geo-smart.github.io/curriculum) + +## About + +This repository stores configuration for GeoSMART curriculum content, specifically the student version of the book. This version of the book should never be directly edited, as the student version is automatically generated on push. \ No newline at end of file diff --git a/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb b/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb index f6117ca..908efd0 100644 --- a/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb +++ b/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb @@ -1,574804 +1,574809 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "50e82750", - "metadata": {}, - "source": [ - "# 2.3 Pandas\n", - "\n", - "This section aims to provide new skills in python to handle structured, tabular data. \n", - "\n", - "Learning outcome:\n", - "- Manipulation of data frames (describing, filtering, ...) \n", - "- Learn about Lambda functions\n", - "- Intro to datetime objects\n", - "- Plotting data from data frames (histograms and maps)\n", - "- Introduction to Plotly\n", - "- Introduction to CSV & Parquet\n", - "\n", - "This tutorial can be offered in a 2-hour course. Sections are labeled as Level 1, 2, 3 in Section 1, 2, 3, and instructors may choose to leave higher levels for asynchronous, self-guided learning.\n", - "\n", - "\n", - "We will work on several structured data sets: sensor metadata, seismic data product (earthquake catalog).\n", - "\n", - "First, we import all the modules we need:" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "id": "50e82750", + "metadata": {}, + "source": [ + "# 2.3 Pandas\n", + "\n", + "This section aims to provide new skills in python to handle structured, tabular data. \n", + "\n", + "Learning outcome:\n", + "- Manipulation of data frames (describing, filtering, ...) \n", + "- Learn about Lambda functions\n", + "- Intro to datetime objects\n", + "- Plotting data from data frames (histograms and maps)\n", + "- Introduction to Plotly\n", + "- Introduction to CSV & Parquet\n", + "\n", + "This tutorial can be offered in a 2-hour course. Sections are labeled as Level 1, 2, 3 in Section 1, 2, 3, and instructors may choose to leave higher levels for asynchronous, self-guided learning.\n", + "\n", + "\n", + "We will work on several structured data sets: sensor metadata, seismic data product (earthquake catalog).\n", + "\n", + "First, we import all the modules we need:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "28d926f9", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import io\n", + "import requests\n", + "import matplotlib.pyplot as plt\n", + "from datetime import datetime, timedelta\n" + ] + }, + { + "cell_type": "markdown", + "id": "2ebf112a", + "metadata": {}, + "source": [ + "This notebook uses ``plotly`` to make interactive plots" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "3bf66863", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 35, - "id": "28d926f9", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import io\n", - "import requests\n", - "import matplotlib.pyplot as plt\n", - "from datetime import datetime, timedelta\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: plotly in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (5.17.0)\n", + "Requirement already satisfied: tenacity>=6.2.0 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from plotly) (8.2.3)\n", + "Requirement already satisfied: packaging in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from plotly) (23.2)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install plotly\n", + "import plotly.express as px\n", + "import plotly.io as pio\n", + "# pio.renderers.default = 'vscode' # writes as standalone html, \n", + "# pio.renderers.default = 'iframe' # writes files as standalone html, \n", + "# pio.renderers.default = 'png' # writes files as standalone png, \n", + "# try notebook, jupyterlab, png, vscode, iframe\n" + ] + }, + { + "cell_type": "markdown", + "id": "3f1829a3", + "metadata": {}, + "source": [ + "# 1 Pandas Fundamentals\n", + "\n", + "## 1.1 Basics\n", + "\n", + "Pandas are composed of ``Series`` and ``DataFrame``. ``Series`` are columns with attributes or keys. The ``DataFrame`` is a multi-dimensional table made up of ``Series``.\n", + "\n", + "We can create a DataFrame composed of series from scratch using Python dictionary:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "3cdfc90e", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "2ebf112a", - "metadata": {}, - "source": [ - "This notebook uses ``plotly`` to make interactive plots" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " temperature precipitation\n", + "0 36 3\n", + "1 37 1\n", + "2 30 0\n", + "3 50 0\n" + ] + } + ], + "source": [ + "data = {\n", + " 'temperature' : [36,37,30,50],\n", + " 'precipitation':[3,1,0,0]\n", + "}\n", + "my_pd = pd.DataFrame(data)\n", + "print(my_pd)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "8e041ce5", + "metadata": {}, + "source": [ + "Each (key,value) item in the dataframe corresponds to a value in ``data``. To get the keys of the dataframe, type:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "fb8d9c69", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 36, - "id": "3bf66863", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: plotly in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (5.17.0)\n", - "Requirement already satisfied: tenacity>=6.2.0 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from plotly) (8.2.3)\n", - "Requirement already satisfied: packaging in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from plotly) (23.2)\n", - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" - ] - } - ], - "source": [ - "!pip install plotly\n", - "import plotly.express as px\n", - "import plotly.io as pio\n", - "# pio.renderers.default = 'vscode' # writes as standalone html, \n", - "# pio.renderers.default = 'iframe' # writes files as standalone html, \n", - "# pio.renderers.default = 'png' # writes files as standalone png, \n", - "# try notebook, jupyterlab, png, vscode, iframe\n" + "data": { + "text/plain": [ + "Index(['temperature', 'precipitation'], dtype='object')" ] - }, + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_pd.keys()" + ] + }, + { + "cell_type": "markdown", + "id": "ff7935a4", + "metadata": {}, + "source": [ + "get a specific ``Series`` (different from the array)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "c5d734fd", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "3f1829a3", - "metadata": {}, - "source": [ - "# 1 Pandas Fundamentals\n", - "\n", - "## 1.1 Basics\n", - "\n", - "Pandas are composed of ``Series`` and ``DataFrame``. ``Series`` are columns with attributes or keys. The ``DataFrame`` is a multi-dimensional table made up of ``Series``.\n", - "\n", - "We can create a DataFrame composed of series from scratch using Python dictionary:" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0 36\n", + "1 37\n", + "2 30\n", + "3 50\n", + "Name: temperature, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "print(my_pd.temperature[:])\n", + "print(type(my_pd.temperature[:]))" + ] + }, + { + "cell_type": "markdown", + "id": "1f5f2426", + "metadata": {}, + "source": [ + "to get the _value_ of a specific key (e.g., temperature), at a specific index (e.g., 2) type:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "2ab0ad9d", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 37, - "id": "3cdfc90e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " temperature precipitation\n", - "0 36 3\n", - "1 37 1\n", - "2 30 0\n", - "3 50 0\n" - ] - } - ], - "source": [ - "data = {\n", - " 'temperature' : [36,37,30,50],\n", - " 'precipitation':[3,1,0,0]\n", - "}\n", - "my_pd = pd.DataFrame(data)\n", - "print(my_pd)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "30\n", + "\n" + ] + } + ], + "source": [ + "print(my_pd.temperature[2])\n", + "print(type(my_pd.temperature[2]))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9fca0cf5", + "metadata": {}, + "source": [ + "## 1.2 Reading a DataFrame from a CSV file\n", + "\n", + "We can read a pandas directly from a standard file. We will download a catalog of earthquakes" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "f029ed84", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "8e041ce5", - "metadata": {}, - "source": [ - "Each (key,value) item in the dataframe corresponds to a value in ``data``. To get the keys of the dataframe, type:" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-10-07 05:13:23-- https://raw.githubusercontent.com/UW-MLGEO/MLGeo-dataset/refs/heads/main/data/Global_Quakes_IRIS.csv\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.110.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 129629 (127K) [text/plain]\n", + "Saving to: ‘Global_Quakes_IRIS.csv.1’\n", + "\n", + "Global_Quakes_IRIS. 100%[===================>] 126.59K --.-KB/s in 0.05s \n", + "\n", + "2024-10-07 05:13:23 (2.35 MB/s) - ‘Global_Quakes_IRIS.csv.1’ saved [129629/129629]\n", + "\n" + ] + } + ], + "source": [ + "fname = \"\"\n", + "gist_dir = \"\"\n", + "!wget \"https://raw.githubusercontent.com/UW-MLGEO/MLGeo-dataset/refs/heads/main/data/Global_Quakes_IRIS.csv\"\n", + "# !wget \"https://github.com/UW-MLGEO/MLGeo-dataset/blob/main/data/Global_Quakes_IRIS.csv\"\n", + "ff = \"Global_Quakes_IRIS.csv\"\n", + "quake = pd.read_csv(ff)" + ] + }, + { + "cell_type": "markdown", + "id": "62e31c2c", + "metadata": {}, + "source": [ + "Now you use the ``head`` function to display what is in the file" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "ed565400", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 38, - "id": "fb8d9c69", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['temperature', 'precipitation'], dtype='object')" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
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22010-07-10 11:43:33.00011.1664146.082316900.06.3SOUTH OF MARIANA ISLANDS
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" ], - "source": [ - "my_pd.keys()" + "text/plain": [ + " time latitude longitude depth magnitude \\\n", + "0 2010-07-02 06:04:03.570 -13.6098 166.6541 34400.0 6.3 \n", + "1 2010-07-04 21:55:52.370 39.6611 142.5792 30100.0 6.3 \n", + "2 2010-07-10 11:43:33.000 11.1664 146.0823 16900.0 6.3 \n", + "3 2010-07-12 00:11:20.060 -22.2789 -68.3159 109400.0 6.2 \n", + "4 2010-07-14 08:32:21.850 -38.0635 -73.4649 25700.0 6.6 \n", + "\n", + " description \n", + "0 VANUATU ISLANDS \n", + "1 NEAR EAST COAST OF HONSHU \n", + "2 SOUTH OF MARIANA ISLANDS \n", + "3 NORTHERN CHILE \n", + "4 NEAR COAST OF CENTRAL CHILE " ] - }, + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# enter answer here\n", + "quake.head()" + ] + }, + { + "cell_type": "markdown", + "id": "73bfcd15", + "metadata": {}, + "source": [ + "Display the depth using two ways to use the pandas object" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "9b01ce51", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "ff7935a4", - "metadata": {}, - "source": [ - "get a specific ``Series`` (different from the array)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0 34400.0\n", + "1 30100.0\n", + "2 16900.0\n", + "3 109400.0\n", + "4 25700.0\n", + " ... \n", + "1780 10000.0\n", + "1781 105000.0\n", + "1782 608510.0\n", + "1783 10000.0\n", + "1784 35440.0\n", + "Name: depth, Length: 1785, dtype: float64\n", + "0 34400.0\n", + "1 30100.0\n", + "2 16900.0\n", + "3 109400.0\n", + "4 25700.0\n", + " ... \n", + "1780 10000.0\n", + "1781 105000.0\n", + "1782 608510.0\n", + "1783 10000.0\n", + "1784 35440.0\n", + "Name: depth, Length: 1785, dtype: float64\n" + ] + } + ], + "source": [ + "print(quake.depth)\n", + "print(quake['depth'])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9c623644", + "metadata": {}, + "source": [ + "Calculate basic statistic of the data using the function ``describe``." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "2b9f083e", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 39, - "id": "c5d734fd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 36\n", - "1 37\n", - "2 30\n", - "3 50\n", - "Name: temperature, dtype: int64\n", - "\n" - ] - } + "data": { + "text/html": [ + "
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max74.391800179.998100685500.0000009.100000
\n", + "
" ], - "source": [ - "print(my_pd.temperature[:])\n", - "print(type(my_pd.temperature[:]))" - ] - }, - { - "cell_type": "markdown", - "id": "1f5f2426", - "metadata": {}, - "source": [ - "to get the _value_ of a specific key (e.g., temperature), at a specific index (e.g., 2) type:" + "text/plain": [ + " latitude longitude depth magnitude\n", + "count 1785.000000 1785.000000 1785.000000 1785.000000\n", + "mean 0.840700 40.608674 82773.187675 6.382403\n", + "std 30.579308 125.558363 146988.302031 0.429012\n", + "min -69.782500 -179.957200 0.000000 6.000000\n", + "25% -19.905100 -73.832200 12000.000000 6.100000\n", + "50% -4.478900 113.077800 24400.000000 6.300000\n", + "75% 27.161700 145.305700 58000.000000 6.600000\n", + "max 74.391800 179.998100 685500.000000 9.100000" ] - }, + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quake.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "8b54218c", + "metadata": {}, + "source": [ + "Calculate mean and median of specific ``Series``, for example depth." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "83f21ca3", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 40, - "id": "2ab0ad9d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "30\n", - "\n" - ] - } - ], - "source": [ - "print(my_pd.temperature[2])\n", - "print(type(my_pd.temperature[2]))" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "82773.18767507003\n", + "24400.0\n" + ] + } + ], + "source": [ + "# answer it here\n", + "print(quake.depth.mean())\n", + "print(quake.depth.median())" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "591f8d33", + "metadata": {}, + "source": [ + "## 1.3 Manipulating Pandas with Python\n", + "\n", + "### Classic functions\n", + "We will now practice how to modify the content of the DataFrame using functions. We will take the example where we want to change the depth values from meters to kilometers. First we can define this operation as a function" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "5b4f94b4", + "metadata": {}, + "outputs": [], + "source": [ + "# this function converts a value in meters to a value in kilometers\n", + "m2km = 1000 # this is defined as a global variable\n", + "def meters2kilometers(x):\n", + " return x/m2km\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "16cb70b9", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "9fca0cf5", - "metadata": {}, - "source": [ - "## 1.2 Reading a DataFrame from a CSV file\n", - "\n", - "We can read a pandas directly from a standard file. We will download a catalog of earthquakes" + "data": { + "text/plain": [ + "34.4" ] - }, + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now test it using the first element of the quake DataFrame\n", + "meters2kilometers(quake.depth[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "7a360cc5", + "metadata": {}, + "outputs": [], + "source": [ + "# this function converts a value in meters to a value in kilometers\n", + "def meters2kilometers2(x):\n", + " m2km = 1000 # this is defined as a global variable\n", + " return x/m2km" + ] + }, + { + "cell_type": "markdown", + "id": "aa52810b", + "metadata": {}, + "source": [ + "Let's define another function that uses a local instead of global variable" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "b01a33fe", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 41, - "id": "f029ed84", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2024-10-07 05:13:23-- https://raw.githubusercontent.com/UW-MLGEO/MLGeo-dataset/refs/heads/main/data/Global_Quakes_IRIS.csv\n", - "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.110.133, ...\n", - "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 129629 (127K) [text/plain]\n", - "Saving to: ‘Global_Quakes_IRIS.csv.1’\n", - "\n", - "Global_Quakes_IRIS. 100%[===================>] 126.59K --.-KB/s in 0.05s \n", - "\n", - "2024-10-07 05:13:23 (2.35 MB/s) - ‘Global_Quakes_IRIS.csv.1’ saved [129629/129629]\n", - "\n" - ] - } + "data": { + "text/html": [ + "
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timelatitudelongitudedepthmagnitudedescriptiondepth_km
02010-07-02 06:04:03.570-13.6098166.654134400.06.3VANUATU ISLANDS34.4
12010-07-04 21:55:52.37039.6611142.579230100.06.3NEAR EAST COAST OF HONSHU30.1
22010-07-10 11:43:33.00011.1664146.082316900.06.3SOUTH OF MARIANA ISLANDS16.9
32010-07-12 00:11:20.060-22.2789-68.3159109400.06.2NORTHERN CHILE109.4
42010-07-14 08:32:21.850-38.0635-73.464925700.06.6NEAR COAST OF CENTRAL CHILE25.7
\n", + "
" ], - "source": [ - "fname = \"\"\n", - "gist_dir = \"\"\n", - "!wget \"https://raw.githubusercontent.com/UW-MLGEO/MLGeo-dataset/refs/heads/main/data/Global_Quakes_IRIS.csv\"\n", - "# !wget \"https://github.com/UW-MLGEO/MLGeo-dataset/blob/main/data/Global_Quakes_IRIS.csv\"\n", - "ff = \"Global_Quakes_IRIS.csv\"\n", - "quake = pd.read_csv(ff)" + "text/plain": [ + " time latitude longitude depth magnitude \\\n", + "0 2010-07-02 06:04:03.570 -13.6098 166.6541 34400.0 6.3 \n", + "1 2010-07-04 21:55:52.370 39.6611 142.5792 30100.0 6.3 \n", + "2 2010-07-10 11:43:33.000 11.1664 146.0823 16900.0 6.3 \n", + "3 2010-07-12 00:11:20.060 -22.2789 -68.3159 109400.0 6.2 \n", + "4 2010-07-14 08:32:21.850 -38.0635 -73.4649 25700.0 6.6 \n", + "\n", + " description depth_km \n", + "0 VANUATU ISLANDS 34.4 \n", + "1 NEAR EAST COAST OF HONSHU 30.1 \n", + "2 SOUTH OF MARIANA ISLANDS 16.9 \n", + "3 NORTHERN CHILE 109.4 \n", + "4 NEAR COAST OF CENTRAL CHILE 25.7 " ] - }, + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Apply the meters2kilometers2 function to the 'depth' column and add it as a new column 'depth_km' to the quake DataFrame\n", + "quake['depth_km'] = quake['depth'].apply(meters2kilometers2)\n", + "\n", + "# Display the first few rows to verify the new column\n", + "quake.head()" + ] + }, + { + "cell_type": "markdown", + "id": "f6db26f1", + "metadata": {}, + "source": [ + "### Lambda functions\n", + "We now discuss the **lambda** functions. \n", + "* Lambda functions are small, anonymous functions in Python.\n", + "* They are used for quick, simple operations without needing to define a full function using def.\n", + "* In geoscience, where we deal with large datasets (e.g., climate data, seismic measurements), lambda functions allow us to process data efficiently.\n", + "* Lambda functions in Pandas allow you to quickly transform, filter, or process this data with minimal code.\n", + "* Please read additional tutorials from [RealPython](!https://realpython.com/python-lambda/).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "a2713d55", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "62e31c2c", - "metadata": {}, - "source": [ - "Now you use the ``head`` function to display what is in the file" + "data": { + "text/plain": [ + "(x)>" ] - }, + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lambda x: x * 1.2" + ] + }, + { + "cell_type": "markdown", + "id": "d3ad802b", + "metadata": {}, + "source": [ + "This lambda function multiplies any input ``x`` by 1.2, which could be useful for tasks like converting units (e.g., from km to m)." + ] + }, + { + "cell_type": "markdown", + "id": "8820efb8", + "metadata": {}, + "source": [ + "Below is an example on how to use lambda in a pandas dataframe. \n", + "**Example 1**:\n", + "The lambda function lambda ``x: (x - 32) * 5 / 9`` converts Fahrenheit to Celsius for each value in the Temperature_F column." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "cafff8c2", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 42, - "id": "ed565400", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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timelatitudelongitudedepthmagnitudedescription
02010-07-02 06:04:03.570-13.6098166.654134400.06.3VANUATU ISLANDS
12010-07-04 21:55:52.37039.6611142.579230100.06.3NEAR EAST COAST OF HONSHU
22010-07-10 11:43:33.00011.1664146.082316900.06.3SOUTH OF MARIANA ISLANDS
32010-07-12 00:11:20.060-22.2789-68.3159109400.06.2NORTHERN CHILE
42010-07-14 08:32:21.850-38.0635-73.464925700.06.6NEAR COAST OF CENTRAL CHILE
\n", - "
" - ], - "text/plain": [ - " time latitude longitude depth magnitude \\\n", - "0 2010-07-02 06:04:03.570 -13.6098 166.6541 34400.0 6.3 \n", - "1 2010-07-04 21:55:52.370 39.6611 142.5792 30100.0 6.3 \n", - "2 2010-07-10 11:43:33.000 11.1664 146.0823 16900.0 6.3 \n", - "3 2010-07-12 00:11:20.060 -22.2789 -68.3159 109400.0 6.2 \n", - "4 2010-07-14 08:32:21.850 -38.0635 -73.4649 25700.0 6.6 \n", - "\n", - " description \n", - "0 VANUATU ISLANDS \n", - "1 NEAR EAST COAST OF HONSHU \n", - "2 SOUTH OF MARIANA ISLANDS \n", - "3 NORTHERN CHILE \n", - "4 NEAR COAST OF CENTRAL CHILE " - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# enter answer here\n", - "quake.head()" + "name": "stdout", + "output_type": "stream", + "text": [ + " Temperature_F Temperature_C\n", + "0 32 0.000000\n", + "1 50 10.000000\n", + "2 77 25.000000\n", + "3 100 37.777778\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Sample DataFrame\n", + "data = {'Temperature_F': [32, 50, 77, 100]}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Convert Fahrenheit to Celsius\n", + "df['Temperature_C'] = df['Temperature_F'].apply(lambda x: (x - 32) * 5 / 9)\n", + "\n", + "print(df)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "cbb26a36", + "metadata": {}, + "outputs": [], + "source": [ + "# Now the equivalent in lambda is:\n", + "lambda_meters2kilometers = lambda x:x/1000\n", + "# x is the variable" + ] + }, + { + "cell_type": "markdown", + "id": "e604fca3", + "metadata": {}, + "source": [ + "Now apply the function to the entire series\n", + "```{admonition} Student response section\n", + "This section is left for the student to complete.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "7d94f6a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 34.40\n", + "1 30.10\n", + "2 16.90\n", + "3 109.40\n", + "4 25.70\n", + " ... \n", + "1780 10.00\n", + "1781 105.00\n", + "1782 608.51\n", + "1783 10.00\n", + "1784 35.44\n", + "Name: depth, Length: 1785, dtype: float64" ] - }, + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# apply it to the entire series\n", + "lambda_meters2kilometers(quake.depth)" + ] + }, + { + "cell_type": "markdown", + "id": "cef03d96", + "metadata": {}, + "source": [ + "Lambda functions can take several inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "caad68a3", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "73bfcd15", - "metadata": {}, - "source": [ - "Display the depth using two ways to use the pandas object" + "data": { + "text/plain": [ + "1" ] - }, + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you can add several variables into lambda functions\n", + "remove_anything = lambda x,y:x-y\n", + "remove_anything(3,2)" + ] + }, + { + "cell_type": "markdown", + "id": "a6da00e2", + "metadata": {}, + "source": [ + "This did not affect the values of the DataFrame, check it:" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "40367bfb", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 43, - "id": "9b01ce51", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 34400.0\n", - "1 30100.0\n", - "2 16900.0\n", - "3 109400.0\n", - "4 25700.0\n", - " ... \n", - "1780 10000.0\n", - "1781 105000.0\n", - "1782 608510.0\n", - "1783 10000.0\n", - "1784 35440.0\n", - "Name: depth, Length: 1785, dtype: float64\n", - "0 34400.0\n", - "1 30100.0\n", - "2 16900.0\n", - "3 109400.0\n", - "4 25700.0\n", - " ... \n", - "1780 10000.0\n", - "1781 105000.0\n", - "1782 608510.0\n", - "1783 10000.0\n", - "1784 35440.0\n", - "Name: depth, Length: 1785, dtype: float64\n" - ] - } - ], - "source": [ - "print(quake.depth)\n", - "print(quake['depth'])" + "data": { + "text/plain": [ + "0 34400.0\n", + "1 30100.0\n", + "2 16900.0\n", + "3 109400.0\n", + "4 25700.0\n", + " ... \n", + "1780 10000.0\n", + "1781 105000.0\n", + "1782 608510.0\n", + "1783 10000.0\n", + "1784 35440.0\n", + "Name: depth, Length: 1785, dtype: float64" ] - }, + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quake.depth" + ] + }, + { + "cell_type": "markdown", + "id": "6347a1ec", + "metadata": {}, + "source": [ + "Instead, you could overwrite ``quake.depth=X``. Try two approaches but just do it once! You can use python functions ``map`` (a function to apply functions that is generic to Python) and ``apply`` (a function to apply functions that is specific to Pandas)." + ] + }, + { + "cell_type": "markdown", + "id": "ade5723d", + "metadata": {}, + "source": [ + "```{admonition} Student response section\n", + "This section is left for the student to complete.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "0d45bc9b", + "metadata": {}, + "source": [ + "Try ``map`` to apply a ``lambda`` function that rescale depth from meters to kilometers." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "8a1da18f", + "metadata": {}, + "outputs": [], + "source": [ + "#type answer below\n", + "quake.depth=quake.depth.map(lambda x:x/1000)" + ] + }, + { + "cell_type": "markdown", + "id": "9b11b4cb", + "metadata": {}, + "source": [ + "Discuss in class: What happened to the depth field?\n", + "\n", + "What happened to the original data frame?" + ] + }, + { + "cell_type": "markdown", + "id": "a520ada6", + "metadata": {}, + "source": [ + "Try ``apply`` to apply a ``lambda`` function that rescale depth from meters to kilometers." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "ec3420af", + "metadata": {}, + "outputs": [], + "source": [ + "# or like this\n", + "quake.depth=quake.depth.apply(lambda x:x/1000) " + ] + }, + { + "cell_type": "markdown", + "id": "fa66b4e8", + "metadata": {}, + "source": [ + "Plot a histogram of the depth distributions using matplotlib function ``hist``." + ] + }, + { + "cell_type": "markdown", + "id": "ade5723d", + "metadata": {}, + "source": [ + "```{admonition} Student response section\n", + "This section is left for the student to complete.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "ae368fd4", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "9c623644", - "metadata": {}, - "source": [ - "Calculate basic statistic of the data using the function ``describe``." + "data": { + "image/png": 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" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# answer here\n", + "plt.hist(quake.depth,100)\n", + "plt.grid(True)\n", + "plt.xlabel('Quake depth (km)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8bb58e3c", + "metadata": {}, + "source": [ + "You can use the interactive plotting package Plotly. First we will show a histogram of the event depth using the function ``histogram``." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "5f07dc52", + "metadata": { + "scrolled": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 44, - "id": "2b9f083e", - "metadata": {}, - "outputs": [ + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ { - "data": { - "text/html": [ - "
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"showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "width": 600, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "depth" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "count" + } } - ], - "source": [ - "quake.describe()" - ] - }, - { - "cell_type": "markdown", - "id": "8b54218c", - "metadata": {}, - "source": [ - "Calculate mean and median of specific ``Series``, for example depth." - ] - }, + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.histogram(quake, #specify what dataframe to use\n", + " x=\"depth\", #specify the variable for the histogram \n", + " nbins=50, #number of bins for the histogram \n", + " height=400, #dimensions of the figure\n", + " width=600);\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "60a133d4", + "metadata": {}, + "source": [ + "### Example 2: Conditional Logic for Earthquake Magnitude \n", + "You can use lambda functions to add conditional logic, such as classifying earthquake magnitudes into categories.\n", + "\n", + "Scenario: You have a column Magnitude containing seismic event magnitudes, and you want to classify events as \"Minor\", \"Moderate\", or \"Severe\"." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "7f7520de", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 45, - "id": "83f21ca3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "82773.18767507003\n", - "24400.0\n" - ] - } + "data": { + "text/html": [ + "
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timelatitudelongitudedepthmagnitudedescriptiondepth_kmCategory
02010-07-02 06:04:03.570-13.6098166.65410.03446.3VANUATU ISLANDS34.4Moderate
12010-07-04 21:55:52.37039.6611142.57920.03016.3NEAR EAST COAST OF HONSHU30.1Moderate
22010-07-10 11:43:33.00011.1664146.08230.01696.3SOUTH OF MARIANA ISLANDS16.9Moderate
32010-07-12 00:11:20.060-22.2789-68.31590.10946.2NORTHERN CHILE109.4Moderate
42010-07-14 08:32:21.850-38.0635-73.46490.02576.6NEAR COAST OF CENTRAL CHILE25.7Severe
\n", + "
" ], - "source": [ - "# answer it here\n", - "print(quake.depth.mean())\n", - "print(quake.depth.median())" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "591f8d33", - "metadata": {}, - "source": [ - "## 1.3 Manipulating Pandas with Python\n", - "\n", - "### Classic functions\n", - "We will now practice how to modify the content of the DataFrame using functions. We will take the example where we want to change the depth values from meters to kilometers. First we can define this operation as a function" + "text/plain": [ + " time latitude longitude depth magnitude \\\n", + "0 2010-07-02 06:04:03.570 -13.6098 166.6541 0.0344 6.3 \n", + "1 2010-07-04 21:55:52.370 39.6611 142.5792 0.0301 6.3 \n", + "2 2010-07-10 11:43:33.000 11.1664 146.0823 0.0169 6.3 \n", + "3 2010-07-12 00:11:20.060 -22.2789 -68.3159 0.1094 6.2 \n", + "4 2010-07-14 08:32:21.850 -38.0635 -73.4649 0.0257 6.6 \n", + "\n", + " description depth_km Category \n", + "0 VANUATU ISLANDS 34.4 Moderate \n", + "1 NEAR EAST COAST OF HONSHU 30.1 Moderate \n", + "2 SOUTH OF MARIANA ISLANDS 16.9 Moderate \n", + "3 NORTHERN CHILE 109.4 Moderate \n", + "4 NEAR COAST OF CENTRAL CHILE 25.7 Severe " ] - }, + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Classify earthquake magnitudes\n", + "quake['Category'] = quake['magnitude'].apply(lambda x: 'Minor' if x < 4.0 else ('Moderate' if x < 6.5 else 'Severe'))\n", + "quake.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8be50d1c", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "b87c6c42", + "metadata": {}, + "source": [ + "We will now make a new plot of the location of the earthquakes. We will use Plotly tool. \n", + "\n", + "The markersize will be scaled with the earthquake magnitude. To do so, we add a ``marker_size`` series in the DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "d9cb41c3", + "metadata": {}, + "outputs": [], + "source": [ + "quake['marker_size'] = quake['magnitude'].apply(lambda x: np.fix(np.exp(x))) # add marker size as exp(mag)\n", + "quake['magnitude_bin'] = quake['magnitude'].apply(lambda x: 0.5*np.fix(2*x)) # add marker size as exp(mag)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "6c7befa4", + "metadata": {}, + "outputs": [], + "source": [ + "# another way to do it\n", + "quake['marker_size'] = np.fix(np.exp(quake['magnitude'])) # add marker size as exp(mag)\n", + "quake['magnitude_bin'] = 0.5*np.fix(2*quake['magnitude']) # add marker size as exp(mag)" + ] + }, + { + "cell_type": "markdown", + "id": "2fed2184", + "metadata": {}, + "source": [ + "### 1.4 Intermediate Manipulation with Pandas\n", + "\n", + "You can also apply lambda functions to work with multiple columns, which is common in geoscientific datasets, where you might have spatial or temporal data.\n", + "\n", + "**Example 3: Calculating an Index Based on Multiple Measurements**\n", + "Scenario: You have rainfall (``Rainfall_mm``) and evaporation (``Evaporation_mm``) data, and you want to calculate the ``Net Water Balance ``for each record." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "7931923f", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 46, - "id": "5b4f94b4", - "metadata": {}, - "outputs": [], - "source": [ - "# this function converts a value in meters to a value in kilometers\n", - "m2km = 1000 # this is defined as a global variable\n", - "def meters2kilometers(x):\n", - " return x/m2km\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " Rainfall_mm Evaporation_mm Net_Water_Balance\n", + "0 100 60 40\n", + "1 80 70 10\n", + "2 120 65 55\n" + ] + } + ], + "source": [ + "data = {'Rainfall_mm': [100, 80, 120], 'Evaporation_mm': [60, 70, 65]}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Calculate Net Water Balance\n", + "df['Net_Water_Balance'] = df.apply(lambda row: row['Rainfall_mm'] - row['Evaporation_mm'], axis=1)\n", + "\n", + "print(df)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b5b64a6b", + "metadata": {}, + "source": [ + "In this example, ``lambda row: row['Rainfall_mm'] - row['Evaporation_mm']`` calculates the net water balance by subtracting evaporation from rainfall for each record.\n", + "\n", + "### 1.5 Advanced: Time Series Data Manipulation with Lambda Functions\n", + "Geoscientists frequently work with time series data (e.g., climate data). Lambda functions can be used for efficient data transformations within time series.\n", + "\n", + "**Example 4: Applying a Rolling Window Calculation**\n", + "Scenario: Suppose you have daily temperature data, and you want to calculate a 7-day rolling average." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "6933e64a", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 47, - "id": "16cb70b9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "34.4" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
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" ], - "source": [ - "# now test it using the first element of the quake DataFrame\n", - "meters2kilometers(quake.depth[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "7a360cc5", - "metadata": {}, - "outputs": [], - "source": [ - "# this function converts a value in meters to a value in kilometers\n", - "def meters2kilometers2(x):\n", - " m2km = 1000 # this is defined as a global variable\n", - " return x/m2km" + "text/plain": [ + " Date Temperature_C 7_day_avg\n", + "0 2023-09-01 20 NaN\n", + "1 2023-09-02 22 NaN\n", + "2 2023-09-03 23 21.666667\n", + "3 2023-09-04 21 22.000000\n", + "4 2023-09-05 19 21.000000" ] - }, + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Sample daily temperature data\n", + "data = {'Date': pd.date_range(start='2023-09-01', periods=10, freq='D'),\n", + " 'Temperature_C': [20, 22, 23, 21, 19, 24, 25, 26, 22, 20]}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Calculate 7-day rolling average using lambda\n", + "df['7_day_avg'] = df['Temperature_C'].rolling(window=3).apply(lambda x: x.mean())\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "e3d3328a", + "metadata": {}, + "source": [ + "Here, ``lambda x: x.mean()`` calculates the rolling mean over a 3-day window for temperature data, which is essential in climate analysis for smoothing short-term fluctuations.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e01cf834", + "metadata": {}, + "source": [ + "### 1.6 Aggregate function\n", + "The ``agg`` is a powerful method in Pandas that allows you to perform multiple operations on DataFrames and Series. You can apply one or more aggregation functions such as ``sum``, ``mean``, ``min``, ``max``, etc., on different columns or groups of data." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "aea0ee4e", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "aa52810b", - "metadata": {}, - "source": [ - "Let's define another function that uses a local instead of global variable" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "sum 10.0\n", + "mean 2.5\n", + "Name: A, dtype: float64\n" + ] + } + ], + "source": [ + "# Sample DataFrame\n", + "data = {\n", + " 'A': [1, 2, 3, 4],\n", + " 'B': [5, 6, 7, 8]\n", + "}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Apply aggregation functions\n", + "result = df['A'].agg(['sum', 'mean'])\n", + "print(result)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f2664926", + "metadata": {}, + "source": [ + "#### Aggregating Multiple Columns with Multiple Functions\n", + "You can pass a dictionary to agg where the keys are column names and the values are the functions to be applied." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "9df9cde2", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 49, - "id": "b01a33fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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timelatitudelongitudedepthmagnitudedescriptiondepth_km
02010-07-02 06:04:03.570-13.6098166.654134400.06.3VANUATU ISLANDS34.4
12010-07-04 21:55:52.37039.6611142.579230100.06.3NEAR EAST COAST OF HONSHU30.1
22010-07-10 11:43:33.00011.1664146.082316900.06.3SOUTH OF MARIANA ISLANDS16.9
32010-07-12 00:11:20.060-22.2789-68.3159109400.06.2NORTHERN CHILE109.4
42010-07-14 08:32:21.850-38.0635-73.464925700.06.6NEAR COAST OF CENTRAL CHILE25.7
\n", - "
" - ], - "text/plain": [ - " time latitude longitude depth magnitude \\\n", - "0 2010-07-02 06:04:03.570 -13.6098 166.6541 34400.0 6.3 \n", - "1 2010-07-04 21:55:52.370 39.6611 142.5792 30100.0 6.3 \n", - "2 2010-07-10 11:43:33.000 11.1664 146.0823 16900.0 6.3 \n", - "3 2010-07-12 00:11:20.060 -22.2789 -68.3159 109400.0 6.2 \n", - "4 2010-07-14 08:32:21.850 -38.0635 -73.4649 25700.0 6.6 \n", - "\n", - " description depth_km \n", - "0 VANUATU ISLANDS 34.4 \n", - "1 NEAR EAST COAST OF HONSHU 30.1 \n", - "2 SOUTH OF MARIANA ISLANDS 16.9 \n", - "3 NORTHERN CHILE 109.4 \n", - "4 NEAR COAST OF CENTRAL CHILE 25.7 " - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Apply the meters2kilometers2 function to the 'depth' column and add it as a new column 'depth_km' to the quake DataFrame\n", - "quake['depth_km'] = quake['depth'].apply(meters2kilometers2)\n", - "\n", - "# Display the first few rows to verify the new column\n", - "quake.head()" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " A B\n", + "sum 10.0 NaN\n", + "mean 2.5 NaN\n", + "min NaN 5.0\n", + "max NaN 8.0\n" + ] + } + ], + "source": [ + "# Apply different functions to different columns\n", + "result = df.agg({'A': ['sum', 'mean'], 'B': ['min', 'max']})\n", + "print(result)\n" + ] + }, + { + "cell_type": "markdown", + "id": "5ccea12a", + "metadata": {}, + "source": [ + "In this example, column 'A' gets ``sum`` and ``mean``, while column 'B' gets ``min`` and ``max``." + ] + }, + { + "cell_type": "markdown", + "id": "6b9e44e0", + "metadata": {}, + "source": [ + "You may also use costum functions" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "79ff0c94", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "f6db26f1", - "metadata": {}, - "source": [ - "### Lambda functions\n", - "We now discuss the **lambda** functions. \n", - "* Lambda functions are small, anonymous functions in Python.\n", - "* They are used for quick, simple operations without needing to define a full function using def.\n", - "* In geoscience, where we deal with large datasets (e.g., climate data, seismic measurements), lambda functions allow us to process data efficiently.\n", - "* Lambda functions in Pandas allow you to quickly transform, filter, or process this data with minimal code.\n", - "* Please read additional tutorials from [RealPython](!https://realpython.com/python-lambda/).\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " A B\n", + "mean 2.5 NaN\n", + "data_range 3.0 3.0\n" + ] + } + ], + "source": [ + "# Custom function to calculate range (max - min)\n", + "def data_range(x):\n", + " return x.max() - x.min()\n", + "\n", + "# Apply custom function\n", + "result = df.agg({'A': ['mean', data_range], 'B': data_range})\n", + "print(result)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94abaccd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f004df73", + "metadata": {}, + "source": [ + "## 2.4 Mapping using Plotly\n", + "\n", + "Now we will plot the earthquakes locations on a map using the Plotly package. More tutorials on [Plotly](https://plotly.com/). The input of the function is self-explanatory and typical of Python's function. The code [documentation](https://plotly.com/python/scatter-plots-on-maps/) of Plotly scatter_geo lists the variables." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "ff53045a", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 50, - "id": "a2713d55", - "metadata": {}, - "outputs": [ + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ { - "data": { - "text/plain": [ - "(x)>" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" + "customdata": [ + [ + "VANUATU ISLANDS", + 6.3, + 0.0344 + ], + [ + "NEAR EAST COAST OF HONSHU", + 6.3, + 0.030100000000000002 + ], + [ + "SOUTH OF MARIANA ISLANDS", + 6.3, + 0.0169 + ], + [ + "NORTHERN CHILE", + 6.2, + 0.10940000000000001 + ], + [ + "NEAR COAST OF CENTRAL CHILE", + 6.6, + 0.0257 + ], + [ + "FOX ISLANDS", + 6.7, + 0.0215 + ], + [ + "NEW BRITAIN REGION", + 6.9, + 0.0458 + ], + [ + "NEW BRITAIN 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+ "NEAR EAST COAST OF HONSHU", + "SULAWESI", + "SOUTH OF PANAMA", + "OFF EAST COAST OF HONSHU", + "LOYALTY ISLANDS", + "COSTA RICA", + "NEAR EAST COAST OF HONSHU", + "HINDU KUSH REGION", + "CENTRAL MID-ATLANTIC RIDGE", + "SOLOMON ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "OFF EAST COAST OF HONSHU", + "WEST OF MACQUARIE ISLAND", + "SOUTH ISLAND", + "NORTHERN MOLUCCA SEA", + "NEW BRITAIN REGION", + "CHILE-BOLIVIA BORDER REGION", + "SANTA CRUZ ISLANDS", + "NEAR EAST COAST OF HONSHU", + "FOX ISLANDS", + "SANTA CRUZ ISLANDS", + "IRIAN JAYA REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "NEGROS", + "SOUTH SANDWICH ISLANDS REGION", + "OFF COAST OF CENTRAL CHILE", + "ALASKA PENINSULA", + "TAJIKISTAN", + "SOLOMON ISLANDS", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "NEW IRELAND REGION", + "GULF OF CALIFORNIA", + "SOUTH OF FIJI ISLANDS", + "NEAR EAST COAST OF HONSHU", + "VANUATU ISLANDS REGION", + "NEAR N COAST OF NEW GUINEA", + "NEAR S. COAST OF HONSHU", + "KURIL ISLANDS", + "ASCENSION ISLAND REGION", + "CERAM SEA", + "OFF EAST COAST OF HONSHU", + "FIJI ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "SUNDA STRAIT", + "PERU-BRAZIL BORDER REGION", + "VANUATU ISLANDS", + "BANDA SEA", + "SANTA CRUZ ISLANDS", + "FOX ISLANDS", + "SANTIAGO DEL ESTERO PROV.", + "SOUTH SANDWICH ISLANDS REGION", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "NORTHERN SUMATRA", + "AUCKLAND ISLANDS", + "VANCOUVER ISLAND", + "NEAR ISLANDS", + "EAST OF NORTH ISLAND", + "NEAR EAST COAST OF HONSHU", + "FIJI ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SIKKIM", + "TONGA ISLANDS", + "SOUTH OF KERMADEC ISLANDS", + "SOUTH OF BALI", + "EASTERN NEW GUINEA REG.", + "SOUTHEASTERN SIBERIA", + "NEW BRITAIN REGION", + "HOKKAIDO", + "KERMADEC ISLANDS REGION", + "TURKEY", + "TURKEY", + "FIJI ISLANDS REGION", + "NEAR COAST OF PERU", + "REVILLA GIGEDO ISLANDS REGION", + "PACIFIC-ANTARCTIC RIDGE", + "NICARAGUA", + "NORTHEAST OF TAIWAN", + "SOUTHERN EAST PACIFIC RISE", + "SOUTHERN MOLUCCA SEA", + "OFF COAST OF ECUADOR", + "CENTRAL BOLIVIA", + "NEAR EAST COAST OF HONSHU", + "HOKKAIDO", + "NEW IRELAND REGION", + "LUZON", + "NEAR COAST OF NORTHERN CHILE", + "GUERRERO", + "SOUTH SANDWICH ISLANDS REGION", + "MINAHASSA PENINSULA", + "EASTERN NEW GUINEA REG.", + "TONGA ISLANDS", + "SOUTHWESTERN SIBERIA", + "SOUTHEAST OF HONSHU", + "SANTA CRUZ ISLANDS", + "OFF W COAST OF NORTHERN SUMATRA", + "SOUTH SHETLAND ISLANDS", + "SOUTH SHETLAND ISLANDS", + "NEAR COAST OF CHIAPAS", + "SOUTH SANDWICH ISLANDS REGION", + "NEAR COAST OF CENTRAL CHILE", + "SOUTH OF FIJI ISLANDS", + "NEAR COAST OF PERU", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "NEGROS", + "NEGROS", + "FIJI ISLANDS REGION", + "SOLOMON ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOUTHWESTERN SIBERIA", + "SOUTHEAST OF LOYALTY ISLANDS", + "SANTIAGO DEL ESTERO PROV.", + "VANUATU ISLANDS", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "NEW BRITAIN REGION", + "IRIAN JAYA", + "NEAR COAST OF GUERRERO", + "EASTERN NEW GUINEA REG.", + "NEAR COAST OF CENTRAL CHILE", + "NORTHERN EAST PACIFIC RISE", + "NEAR EAST COAST OF HONSHU", + "NEAR COAST OF GUERRERO", + "NEW IRELAND REGION", + "OFF W COAST OF NORTHERN SUMATRA", + "NORTH INDIAN OCEAN", + "OFF W COAST OF NORTHERN SUMATRA", + "OFF COAST OF OREGON", + "MICHOACAN", + "BAJA CALIFORNIA", + "BAJA CALIFORNIA", + "DRAKE PASSAGE", + "VANUATU ISLANDS", + "OFF W COAST OF NORTHERN SUMATRA", + "NEAR COAST OF CENTRAL CHILE", + "EASTERN NEW GUINEA REG.", + "EAST OF SOUTH SANDWICH ISLANDS", + "IRIAN JAYA REGION", + "IRIAN JAYA REGION", + "KERMADEC ISLANDS REGION", + "TONGA ISLANDS", + "NEAR COAST OF CHIAPAS", + "PERU-BOLIVIA BORDER REGION", + "OFF COAST OF SOUTHERN CHILE", + "NORTHERN ITALY", + "OFF EAST COAST OF HONSHU", + "HOKKAIDO", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "GREENLAND SEA", + "BONIN ISLANDS", + "SANTIAGO DEL ESTERO PROV.", + "SALTA PROVINCE", + "SOUTH OF PANAMA", + "SOUTH OF PANAMA", + "OFF EAST COAST OF HONSHU", + "CHILE-ARGENTINA BORDER REGION", + "SOUTHERN PERU", + "DODECANESE ISLANDS", + "LUZON", + "NEAR EAST COAST OF HONSHU", + "NEAR ISLANDS", + "NORTHERN SUMATRA", + "NEAR EAST COAST OF KAMCHATKA", + "NORTHERN XINJIANG", + "COOK STRAIT", + "VANUATU ISLANDS", + "KURIL ISLANDS", + "KURIL ISLANDS", + "NORTHERN SUMATRA", + "SOLOMON ISLANDS", + "MAURITIUS - REUNION REGION", + "NEW IRELAND REGION", + "PERU-BRAZIL BORDER REGION", + "NEW IRELAND REGION", + "FOX ISLANDS", + "ARMENIA-AZERBAIJAN-IRAN BORD REG", + "ARMENIA-AZERBAIJAN-IRAN BORD REG", + "XIZANG", + "SEA OF OKHOTSK", + "SULAWESI", + "NEAR N COAST OF NEW GUINEA", + "NORTHERN MOLUCCA SEA", + "OFF COAST OF CENTRAL AMERICA", + "JAN MAYEN ISLAND REGION", + "PHILIPPINE ISLANDS REGION", + "SOUTH OF JAVA", + "SANTA CRUZ ISLANDS", + "COSTA RICA", + "IRIAN JAYA REGION", + "KURIL ISLANDS", + "SOUTHERN SUMATRA", + "BAJA CALIFORNIA", + "ANDREANOF ISLANDS", + "COLOMBIA", + "OFF EAST COAST OF HONSHU", + "GULF OF CALIFORNIA", + "BANDA SEA", + "WEST OF MACQUARIE ISLAND", + "IRIAN JAYA REGION", + "CELEBES SEA", + "VANUATU ISLANDS", + "COSTA RICA", + "QUEEN CHARLOTTE ISLANDS REGION", + "QUEEN CHARLOTTE ISLANDS REGION", + "QUEEN CHARLOTTE ISLANDS REGION", + "MINDANAO", + "NEAR COAST OF CHIAPAS", + "VANCOUVER ISLAND", + "CENTRAL PERU", + "MYANMAR", + "NEAR COAST OF CHIAPAS", + "GULF OF ALASKA", + "OFF COAST OF SOUTHERN CHILE", + "NEAR COAST OF CENTRAL CHILE", + "GUERRERO", + "KURIL ISLANDS", + "NEW BRITAIN REGION", + "NEAR N COAST OF NEW GUINEA", + "VANUATU ISLANDS", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NORTH ISLAND", + "BANDA SEA", + "NORTHERN MOLUCCA SEA", + "OFF W. COAST OF BAJA CALIFORNIA", + "NEAR ISLANDS", + "NEW IRELAND REGION", + "MINAHASSA PENINSULA", + "VANUATU ISLANDS", + "SOUTHEASTERN ALASKA", + "PACIFIC-ANTARCTIC RIDGE", + "NORTHERN SUMATRA", + "LAKE ISSYK-KUL REGION", + "CENTRAL CHILE", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "HOKKAIDO", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS REGION", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS REGION", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "COLOMBIA", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "EASTERN SIBERIA", + "MINDANAO", + "SANTIAGO DEL ESTERO PROV.", + "KURIL ISLANDS", + "KURIL ISLANDS", + "KURIL ISLANDS", + "NEW BRITAIN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "EAST OF KURIL ISLANDS", + "VANUATU ISLANDS REGION", + "GUATEMALA", + "TAIWAN", + "OFF EAST COAST OF HONSHU", + "E. RUSSIA-N.E. CHINA BORDER REG.", + "IRIAN JAYA", + "SOUTHERN IRAN", + "VANUATU ISLANDS", + "SOLOMON ISLANDS", + "SOUTHERN IRAN", + "NEAR N COAST OF NEW GUINEA", + "KURIL ISLANDS", + "EAST OF KURIL ISLANDS", + "SICHUAN", + "BANDA SEA", + "KURIL ISLANDS", + "SOUTHEAST OF HONSHU", + "MICHOACAN", + "NEW IRELAND REGION", + "KERMADEC ISLANDS REGION", + "SOUTHERN IRAN", + "TONGA ISLANDS", + "MARIANA ISLANDS", + "NEAR EAST COAST OF HONSHU", + "OFF COAST OF SOUTHERN CHILE", + "OFF EAST COAST OF KAMCHATKA", + "OFF EAST COAST OF KAMCHATKA", + "SOUTH OF FIJI ISLANDS", + "TONGA ISLANDS", + "SEA OF OKHOTSK", + "SEA OF OKHOTSK", + "TAIWAN", + "SANTA CRUZ ISLANDS", + "SOUTH OF JAVA", + "SOUTH OF KERMADEC ISLANDS", + "CRETE", + "NEAR COAST OF NICARAGUA", + "CRETE", + "NORTHERN MID-ATLANTIC RIDGE", + "NORTHERN SUMATRA", + "SOLOMON ISLANDS", + "SOUTHERN SUMATRA", + "NEW IRELAND REGION", + "NEW BRITAIN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTHERN PERU", + "COOK STRAIT", + "GANSU", + "PRINCE EDWARD ISLANDS REGION", + "SOUTH OF FIJI ISLANDS", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS", + "BANDA SEA", + "KERMADEC ISLANDS REGION", + "NEAR COAST OF NORTHERN PERU", + "SOUTH OF PANAMA", + "COOK STRAIT", + "SOUTHWEST INDIAN RIDGE", + "NEAR COAST OF GUERRERO", + "KERMADEC ISLANDS REGION", + "ANDREANOF ISLANDS", + "ANDREANOF ISLANDS", + "BANDA SEA", + "QUEEN CHARLOTTE ISLANDS REGION", + "SOUTHEAST OF HONSHU", + "QUEEN CHARLOTTE ISLANDS REGION", + "ANDREANOF ISLANDS", + "ANDREANOF ISLANDS", + "NORTHERN MID-ATLANTIC RIDGE", + "PHILIPPINE ISLANDS REGION", + "NEAR COAST OF CHIAPAS", + "CENTRAL EAST PACIFIC RISE", + "ANDREANOF ISLANDS", + "FLORES SEA", + "PAKISTAN", + "SOUTHERN EAST PACIFIC RISE", + "NEAR COAST OF PERU", + "PAKISTAN", + "KERMADEC ISLANDS", + "SEA OF OKHOTSK", + "MID-INDIAN RIDGE", + "SOUTH OF MARIANA ISLANDS", + "WEST CHILE RISE", + "SAKHALIN", + "KERMADEC ISLANDS", + "NEAR COAST OF VENEZUELA", + "CRETE", + "MINDANAO", + "SOLOMON ISLANDS", + "GULF OF CALIFORNIA", + "SOUTH OF FIJI ISLANDS", + "EAST OF SOUTH SANDWICH ISLANDS", + "OFF EAST COAST OF HONSHU", + "BALLENY ISLANDS REGION", + "OFF COAST OF CENTRAL CHILE", + "TAIWAN", + "NEAR COAST OF CENTRAL CHILE", + "EASTER ISLAND REGION", + "TONGA ISLANDS REGION", + "CELEBES SEA", + "NEAR EAST COAST OF KAMCHATKA", + "SCOTIA SEA", + "SCOTIA SEA", + "SCOTIA SEA", + "HALMAHERA", + "MARIANA ISLANDS", + "FIJI ISLANDS REGION", + "KURIL ISLANDS", + "SOUTH ATLANTIC OCEAN", + "BANDA SEA", + "NORTHERN SUMATRA", + "KURIL ISLANDS", + "MARIANA ISLANDS REGION", + "VANUATU ISLANDS", + "PUERTO RICO REGION", + "NORTH ISLAND", + "TONGA ISLANDS", + "JAVA", + "GREECE", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTH OF KERMADEC ISLANDS", + "GREECE", + "VANUATU ISLANDS", + "XIZANG", + "NORTH ATLANTIC OCEAN", + "FOX ISLANDS", + "NEAR COAST OF NICARAGUA", + "RYUKYU ISLANDS", + "NEAR COAST OF CHIAPAS", + "VANUATU ISLANDS", + "OFF COAST OF NORTHERN CALIFORNIA", + "EASTERN NEW GUINEA REG.", + "EAST OF SOUTH SANDWICH ISLANDS", + "BISMARCK SEA", + "KYUSHU", + "NEAR COAST OF PERU", + "NEAR COAST OF NORTHERN PERU", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "NICOBAR ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "SOUTH OF FIJI ISLANDS", + "SANTA CRUZ ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "OFF COAST OF NORTHERN CHILE", + "SOUTH OF PANAMA", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "SOLOMON ISLANDS", + "NICARAGUA", + "NEAR COAST OF NORTHERN CHILE", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "NEAR COAST OF NICARAGUA", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "BOUVET ISLAND REGION", + "BALLENY ISLANDS REGION", + "SANTA CRUZ ISLANDS REGION", + "GUERRERO", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "VANCOUVER ISLAND", + "TONGA ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOUTH OF FIJI ISLANDS", + "NEAR S. COAST OF HONSHU", + "THAILAND", + "WEST CHILE RISE", + "SOLOMON ISLANDS", + "GUERRERO", + "TONGA ISLANDS", + "GUERRERO", + "SOUTHERN EAST PACIFIC RISE", + "SOUTH OF PANAMA", + "E. CAROLINE ISLANDS", + "E. CAROLINE ISLANDS", + "NEGROS", + "OFF W COAST OF NORTHERN SUMATRA", + "BAY OF BENGAL", + "AEGEAN SEA", + "OFF COAST OF JALISCO", + "SOUTH INDIAN OCEAN", + "VANUATU ISLANDS", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS", + "RAT ISLANDS", + "RAT ISLANDS", + "RAT ISLANDS", + "RAT ISLANDS", + "RAT ISLANDS", + "VOLCANO ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS", + "TONGA ISLANDS", + "BONIN ISLANDS", + "BALLENY ISLANDS REGION", + "BALLENY ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "NEW BRITAIN REGION", + "OFF W COAST OF NORTHERN SUMATRA", + "NEAR COAST OF CHIAPAS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "MINDANAO", + "MINDANAO", + "SOUTHEASTERN ALASKA", + "TONGA ISLANDS", + "TONGA ISLANDS", + "OWEN FRACTURE ZONE REGION", + "KURIL ISLANDS", + "FIJI ISLANDS REGION", + "SOUTHEASTERN ALASKA", + "NORTHERN MID-ATLANTIC RIDGE", + "OAXACA", + "BISMARCK SEA", + "BISMARCK SEA", + "E. CAROLINE ISLANDS", + "XIZANG", + "YUNNAN", + "BANDA SEA", + "BANDA SEA", + "HOKKAIDO", + "HOKKAIDO", + "IRAN-IRAQ BORDER REGION", + "IRAN-IRAQ BORDER REGION", + "NEAR COAST OF CENTRAL CHILE", + "NORTHERN CALIFORNIA", + "CENTRAL PERU", + "CENTRAL PERU", + "EASTER ISLAND REGION", + "TONGA ISLANDS", + "EASTER ISLAND REGION", + "OFF COAST OF JALISCO", + "SOUTHERN MOLUCCA SEA", + "MARIANA ISLANDS", + "JUJUY PROVINCE", + "SOLOMON ISLANDS", + "SOUTHERN ALASKA", + "NEW BRITAIN REGION", + "YUNNAN", + "GULF OF CALIFORNIA", + "SOUTHERN EAST PACIFIC RISE", + "SOUTHERN EAST PACIFIC RISE", + "HOKKAIDO", + "OFF COAST OF CENTRAL AMERICA", + "TONGA ISLANDS", + "EASTER ISLAND REGION", + "FIJI ISLANDS REGION", + "BALLENY ISLANDS REGION", + "MID-INDIAN RIDGE", + "NEW BRITAIN REGION", + "PRIMOR'YE", + "FIJI ISLANDS REGION", + "NORTHERN MOLUCCA SEA", + "OFF E. COAST OF N. ISLAND", + "PRINCE EDWARD ISLANDS REGION", + "NORTHERN MOLUCCA SEA", + "SICHUAN", + "EASTERN HONSHU", + "NORTHERN MOLUCCA SEA", + "MINDANAO", + "SOUTH OF PANAMA", + "BANDA SEA", + "SOLOMON ISLANDS", + "NEAR COAST OF GUATEMALA", + "SOUTH OF PANAMA", + "TAIWAN REGION", + "PHILIPPINE ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "NORTHERN MOLUCCA SEA", + "SOUTH OF PANAMA", + "SOUTH OF PANAMA", + "MINDANAO", + "FIJI ISLANDS REGION", + "SOUTHEAST OF EASTER ISLAND", + "SOUTH OF PANAMA", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "ADMIRALTY ISLANDS REGION", + "MENDOZA PROVINCE", + "JUJUY PROVINCE", + "REYKJANES RIDGE", + "TAIWAN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "SANTA CRUZ ISLANDS REGION", + "VANUATU ISLANDS", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "OFF COAST OF JALISCO", + "FLORES SEA", + "SOUTHERN SUMATRA", + "MID-INDIAN RIDGE", + "NORTHERN COLOMBIA", + "MINAHASSA PENINSULA", + "NORTHERN MOLUCCA SEA", + "NEAR COAST OF CENTRAL CHILE", + "FIJI ISLANDS REGION", + "NORTHERN CHILE", + "NEW BRITAIN REGION", + "SAMOA ISLANDS REGION", + "SAMOA ISLANDS REGION", + "SAMOA ISLANDS REGION", + "NEW BRITAIN REGION", + "TONGA ISLANDS", + "CRETE", + "FIJI ISLANDS REGION", + "TAIWAN REGION", + "TAIWAN REGION", + "SANTA CRUZ ISLANDS", + "SOUTH ISLAND", + "QUEEN CHARLOTTE ISLANDS REGION", + "NEPAL", + "NEPAL", + "NEPAL", + "NEPAL", + "FIJI ISLANDS REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "MOROCCO", + "SOLOMON ISLANDS", + "NEPAL", + "NEPAL", + "NEAR EAST COAST OF HONSHU", + "SOUTHERN SUMATRA", + "PACIFIC-ANTARCTIC RIDGE", + "TONGA ISLANDS", + "SANTA CRUZ ISLANDS REGION", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOUTHERN MID-ATLANTIC RIDGE", + "TONGA ISLANDS", + "ALASKA PENINSULA", + "BONIN ISLANDS", + "TONGA ISLANDS", + "SOUTHEAST OF HONSHU", + "BORNEO", + "HOKKAIDO", + "NORTHERN CHILE", + "SAMOA ISLANDS REGION", + "FIJI ISLANDS REGION", + "SOUTHERN MID-ATLANTIC RIDGE", + "NEAR COAST OF CENTRAL CHILE", + "FIJI ISLANDS REGION", + "BONIN ISLANDS", + "NEW BRITAIN REGION", + "SOUTHERN XINJIANG", + "LEYTE", + "EAST OF KURIL ISLANDS", + "SOLOMON ISLANDS", + "NORTH ATLANTIC OCEAN", + "SANTA CRUZ ISLANDS", + "FOX ISLANDS", + "IRIAN JAYA", + "SOUTHERN ALASKA", + "SOUTH OF FIJI ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "MID-INDIAN RIDGE", + "SOLOMON ISLANDS", + "KERMADEC ISLANDS REGION", + "SOUTHEAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "SOUTH OF KERMADEC ISLANDS", + "FOX ISLANDS", + "GULF OF CALIFORNIA", + "NORTHERN MOLUCCA SEA", + "NORTHERN MOLUCCA SEA", + "NEW BRITAIN REGION", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "NORTHERN MID-ATLANTIC RIDGE", + "OFF COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "IRIAN JAYA REGION", + "IRIAN JAYA REGION", + "NEAR COAST OF CENTRAL CHILE", + "JUJUY PROVINCE", + "PACIFIC-ANTARCTIC RIDGE", + "EAST OF KURIL ISLANDS", + "EAST OF KURIL ISLANDS", + "TONGA ISLANDS", + "VANUATU ISLANDS", + "BOUVET ISLAND REGION", + "PRINCE EDWARD ISLANDS REGION", + "PRINCE EDWARD ISLANDS REGION", + "HINDU KUSH REGION", + "TIMOR REGION", + "OFF COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NICOBAR ISLANDS", + "ANDREANOF ISLANDS", + "OFF COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "NORTHWEST OF RYUKYU ISLANDS", + "GREECE", + "SOLOMON ISLANDS", + "BANDA SEA", + "MARIANA ISLANDS", + "PERU-BRAZIL BORDER REGION", + "PERU-BRAZIL BORDER REGION", + "PERU-BRAZIL BORDER REGION", + "NEAR COAST OF NORTHERN CHILE", + "SOUTHEAST INDIAN RIDGE", + "TAJIKISTAN", + "BANDA SEA", + "FIJI ISLANDS REGION", + "NEAR COAST OF CHIAPAS", + "VANUATU ISLANDS", + "BORNEO", + "SOUTHERN EAST PACIFIC RISE", + "AFGHANISTAN-TAJIKISTAN BORD REG.", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "MYANMAR-INDIA BORDER REGION", + "MYANMAR-INDIA BORDER REGION", + "PACIFIC-ANTARCTIC RIDGE", + "TALAUD ISLANDS", + "HOKKAIDO", + "SOUTHERN BOLIVIA", + "HOKKAIDO", + "OFF COAST OF JALISCO", + "SOUTHERN ALASKA", + "STRAIT OF GIBRALTAR", + "KAMCHATKA PENINSULA", + "BALLENY ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "TAIWAN", + "SOLOMON ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "SUMBA REGION", + "SOUTHERN EAST PACIFIC RISE", + "HALMAHERA", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "SOUTHWEST OF SUMATRA", + "ANDREANOF ISLANDS", + "ANDREANOF ISLANDS", + "LEEWARD ISLANDS", + "NEAR EAST COAST OF KAMCHATKA", + "NEAR N COAST OF NEW GUINEA", + "ALASKA PENINSULA", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "JAVA", + "VANUATU ISLANDS", + "AFGHANISTAN-TAJIKISTAN BORD REG.", + "MYANMAR-INDIA BORDER REGION", + "MINDANAO", + "KYUSHU", + "KYUSHU", + "VANUATU ISLANDS", + "OFF COAST OF CHIAPAS", + "KYUSHU", + "NEAR COAST OF ECUADOR", + "SOUTH SANDWICH ISLANDS REGION", + "NEAR COAST OF ECUADOR", + "NEAR COAST OF ECUADOR", + "NEAR COAST OF ECUADOR", + "NEAR COAST OF CHIAPAS", + "NEAR COAST OF CHIAPAS", + "VANUATU ISLANDS", + "NORTHERN EAST PACIFIC RISE", + "NEAR COAST OF ECUADOR", + "NEAR COAST OF ECUADOR", + "NORTHERN TERRITORY", + "FIJI ISLANDS REGION", + "FIJI ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "TAIWAN REGION", + "SOUTHERN SUMATRA", + "BANDA SEA", + "KERMADEC ISLANDS", + "OFF COAST OF JALISCO", + "NORTHERN MOLUCCA SEA", + "SOUTH OF SUMBAWA", + "NICARAGUA", + "SOLOMON ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "LOYALTY ISLANDS", + "NORTHERN MID-ATLANTIC RIDGE", + "NEW IRELAND REGION", + "TAJIKISTAN-XINJIANG BORDER REG.", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "NEAR COAST OF ECUADOR", + "KERMADEC ISLANDS REGION", + "VANUATU ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "ADMIRALTY ISLANDS REGION", + "MARIANA ISLANDS", + "JUJUY PROVINCE", + "VOLCANO ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOUTH GEORGIA ISLAND REGION", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SOUTH GEORGIA ISLAND REGION", + "FLORES SEA", + "CENTRAL ITALY", + "MYANMAR", + "NORTH OF ASCENSION ISLAND", + "NEW IRELAND REGION", + "OFF E. COAST OF N. ISLAND", + "OFF E. COAST OF N. ISLAND", + "KOMANDORSKIYE OSTROVA REGION", + "MACQUARIE ISLAND REGION", + "NORTHERN PERU", + "NORTHERN COLOMBIA", + "SOLOMON ISLANDS", + "NEAR NORTH COAST OF IRIAN JAYA", + "SOUTHEAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "MINDANAO", + "TONGA ISLANDS", + "FIJI ISLANDS REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "JAVA SEA", + "WESTERN HONSHU", + "TONGA ISLANDS", + "CENTRAL ITALY", + "CENTRAL ITALY", + "CHILE-ARGENTINA BORDER REGION", + "NEAR EAST COAST OF HONSHU", + "SOUTH ISLAND", + "SOUTH ISLAND", + "SOUTH ISLAND", + "SOUTH ISLAND", + "SOUTH ISLAND", + "SAN JUAN PROVINCE", + "NEAR EAST COAST OF HONSHU", + "OFF COAST OF CENTRAL AMERICA", + "SOUTHERN XINJIANG", + "SOUTHERN PERU", + "NEAR ISLANDS", + "BANDA SEA", + "NORTHERN SUMATRA", + "OFF COAST OF NORTHERN CALIFORNIA", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "MARIANA ISLANDS REGION", + "NEW IRELAND REGION", + "NEW IRELAND REGION", + "SOLOMON ISLANDS", + "W. CAROLINE ISLANDS", + "PERU-BRAZIL BORDER REGION", + "SOLOMON ISLANDS", + "BANDA SEA", + "NEW IRELAND REGION", + "SOUTHERN CHILE", + "SUMBAWA REGION", + "SOUTH OF FIJI ISLANDS", + "SOUTH OF FIJI ISLANDS", + "QUEEN ELIZABETH ISLANDS", + "CELEBES SEA", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOUTHWESTERN PAKISTAN", + "MINDANAO", + "JUJUY PROVINCE", + "SOUTHERN BOLIVIA", + "SOUTH OF FIJI ISLANDS", + "SOLOMON ISLANDS", + "NEW BRITAIN REGION", + "NICOBAR ISLANDS", + "SOLOMON ISLANDS", + "NEAR ISLANDS", + "NEAR EAST COAST OF KAMCHATKA", + "BOTSWANA", + "NORTHERN AND CENTRAL IRAN", + "CHILE-ARGENTINA BORDER REGION", + "FIJI ISLANDS REGION", + "OFF COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "MINDANAO", + "SOUTHEASTERN ALASKA", + "SOUTHEASTERN ALASKA", + "TAJIKISTAN", + "ANDREANOF ISLANDS", + "RYUKYU ISLANDS", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "OFF COAST OF CENTRAL AMERICA", + "NEW BRITAIN REGION", + "NEGROS", + "SULAWESI", + "NEAR ISLANDS", + "AEGEAN SEA", + "GUATEMALA", + "KERMADEC ISLANDS", + "SOUTH OF FIJI ISLANDS", + "NEAR COAST OF GUATEMALA", + "FIJI ISLANDS REGION", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS REGION", + "NEAR COAST OF ECUADOR", + "LEYTE", + "AUCKLAND ISLANDS", + "NEW IRELAND REGION", + "KOMANDORSKIYE OSTROVA REGION", + "KOMANDORSKIYE OSTROVA REGION", + "NEAR COAST OF PERU", + "MAURITIUS - REUNION REGION", + "DODECANESE ISLANDS", + "SOUTHEAST OF RYUKYU ISLANDS", + "NORTH ATLANTIC OCEAN", + "GANSU", + "NORTHERN XINJIANG", + "MINDORO", + "SOUTHERN SUMATRA", + "NORTH OF ASCENSION ISLAND", + "FIJI ISLANDS REGION", + "ADMIRALTY ISLANDS REGION", + "SOUTHERN SUMATRA", + "NORTH KOREA", + "SOUTH SANDWICH ISLANDS REGION", + "BONIN ISLANDS", + "NEAR COAST OF CHIAPAS", + "CENTRAL MEXICO", + "AUCKLAND ISLANDS", + "OFF EAST COAST OF HONSHU", + "VANUATU ISLANDS", + "OAXACA", + "SOUTH OF FIJI ISLANDS", + "OFF EAST COAST OF HONSHU", + "TONGA ISLANDS", + "BALLENY ISLANDS REGION", + "RAT ISLANDS", + "NORTHERN CHILE", + "BOUVET ISLAND REGION", + "TONGA ISLANDS", + "BANDA SEA", + "SOUTHEAST OF LOYALTY ISLANDS", + "SERAM", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "TONGA ISLANDS", + "NEW GUINEA", + "SOUTHEAST OF HONSHU", + "ASCENSION ISLAND REGION", + "IRAN-IRAQ BORDER REGION", + "COSTA RICA", + "EASTERN XIZANG-INDIA BORDER REG.", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "SAMOA ISLANDS REGION", + "NEW IRELAND REGION", + "CENTRAL MID-ATLANTIC RIDGE", + "NORTHERN AND CENTRAL IRAN", + "EASTERN NEW GUINEA REG.", + "NEAR COAST OF ECUADOR", + "W. CAROLINE ISLANDS", + "W. CAROLINE ISLANDS", + "W. CAROLINE ISLANDS", + "NORTHERN AND CENTRAL IRAN", + "NORTHERN AND CENTRAL IRAN", + "BOUVET ISLAND REGION", + "JAVA", + "NORTH OF HONDURAS", + "MYANMAR", + "NEAR COAST OF PERU", + "GULF OF CALIFORNIA", + "NORTHERN CHILE", + "JAVA", + "GULF OF ALASKA", + "HOKKAIDO", + "KOMANDORSKIYE OSTROVA REGION", + "NEAR N COAST OF NEW GUINEA", + "SOUTHWEST OF AFRICA", + "HINDU KUSH REGION", + "FIJI ISLANDS REGION", + "PACIFIC-ANTARCTIC RIDGE", + "TAIWAN", + "TAIWAN", + "SOUTH OF MARIANA ISLANDS", + "OAXACA", + "NEW GUINEA", + "NEW GUINEA", + "CERAM SEA", + "NEW GUINEA", + "WEST OF MACQUARIE ISLAND", + "NEW GUINEA", + "NEW GUINEA", + "NEW GUINEA", + "NEW IRELAND REGION", + "NEW BRITAIN REGION", + "SOUTHEAST INDIAN RIDGE", + "BANDA SEA", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "SOUTH OF FIJI ISLANDS", + "SOUTHERN BOLIVIA", + "MINDANAO", + "NEW GUINEA", + "NEAR COAST OF CENTRAL CHILE", + "NORTHERN MOLUCCA SEA", + "PRINCE EDWARD ISLANDS REGION", + "EASTER ISLAND REGION", + "HAWAII", + "LUZON", + "NEW BRITAIN REGION", + "AFGHANISTAN-TAJIKISTAN BORD REG.", + "SOUTH OF KERMADEC ISLANDS", + "VANUATU ISLANDS", + "NEAR EAST COAST OF KAMCHATKA", + "KERMADEC ISLANDS", + "VANUATU ISLANDS", + "EASTERN GULF OF ADEN", + "EASTERN GULF OF ADEN", + "SANTA CRUZ ISLANDS", + "NEW BRITAIN REGION", + "FLORES SEA", + "SUMBAWA REGION", + "SUMBAWA REGION", + "NORTHERN ALASKA", + "NORTHERN ALASKA", + "SOUTH SANDWICH ISLANDS REGION", + "ANDREANOF ISLANDS", + "VOLCANO ISLANDS", + "FLORES SEA", + "COSTA RICA", + "FIJI ISLANDS REGION", + "FIJI ISLANDS REGION", + "SUMBAWA REGION", + "FIJI ISLANDS REGION", + "SUMBAWA REGION", + "NEAR COAST OF VENEZUELA", + "VANUATU ISLANDS", + "OFF COAST OF OREGON", + "ANDREANOF ISLANDS", + "PERU-BRAZIL BORDER REGION", + "WESTERN IRAN", + "TIMOR REGION", + "MARIANA ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "HOKKAIDO", + "FIJI ISLANDS", + "ECUADOR", + "MINDANAO", + "SOLOMON ISLANDS", + "KERMADEC ISLANDS REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOUTHWEST INDIAN RIDGE", + "MINAHASSA PENINSULA", + "MINAHASSA PENINSULA", + "FIJI ISLANDS REGION", + "SUMBA REGION", + "KURIL ISLANDS", + "BALI SEA", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "KURIL ISLANDS", + "NORTHWEST OF KURIL ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "VANCOUVER ISLAND", + "VANCOUVER ISLAND", + "VANCOUVER ISLAND", + "IONIAN SEA", + "OFF COAST OF CENTRAL AMERICA", + "DRAKE PASSAGE", + "NORTH ISLAND", + "NORTHERN CHILE", + "MINDANAO", + "JAN MAYEN ISLAND REGION", + "TONGA ISLANDS", + "NORTH ATLANTIC OCEAN", + "NEAR EAST COAST OF KAMCHATKA", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTHERN EAST PACIFIC RISE", + "SOLOMON ISLANDS", + "FIJI ISLANDS REGION", + "CARIBBEAN SEA", + "IRAN-IRAQ BORDER REGION", + "SOUTHERN ALASKA", + "BANDA SEA", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "PACIFIC-ANTARCTIC RIDGE", + "IRIAN JAYA", + "SOUTHEAST OF EASTER ISLAND", + "KOMANDORSKIYE OSTROVA REGION", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "KOMANDORSKIYE OSTROVA REGION", + "MINDANAO", + "ALASKA PENINSULA", + "WESTERN BRAZIL", + "NORTHERN MOLUCCA SEA", + "KYUSHU", + "VANUATU ISLANDS", + "BISMARCK SEA", + "VANUATU ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "SUMBA REGION", + "SUMBA REGION", + "PRINCE EDWARD ISLANDS REGION", + "SOLOMON ISLANDS", + "FIJI ISLANDS REGION", + "NEAR COAST OF CHIAPAS", + "SOUTHERN SUMATRA", + "SOUTHERN SUMATRA", + "MARIANA ISLANDS", + "NORTHERN MID-ATLANTIC RIDGE", + "NEW IRELAND REGION", + "PERU-ECUADOR BORDER REGION", + "CENTRAL PERU", + "SOUTH OF KERMADEC ISLANDS", + "PHILIPPINE ISLANDS REGION", + "FIJI ISLANDS REGION", + "D'ENTRECASTEAUX ISLANDS REGION", + "CENTRAL BOLIVIA", + "VANUATU ISLANDS", + "COLOMBIA", + "NORTHERN MOLUCCA SEA", + "EAST OF KURIL ISLANDS", + "NEW BRITAIN REGION", + "NEAR COAST OF ECUADOR", + "MAURITIUS - REUNION REGION", + "RAT ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "BANDA SEA", + "SOUTH SANDWICH ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "SULAWESI", + "TAIWAN", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "LUZON", + "SOUTH SANDWICH ISLANDS REGION", + "SAMAR", + "SOUTH OF FIJI ISLANDS", + "CARLSBERG RIDGE", + "SOLOMON ISLANDS", + "EASTERN NEW GUINEA REG.", + "KYUSHU", + "PANAMA-COSTA RICA BORDER REGION", + "NEW BRITAIN REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "ANDREANOF ISLANDS", + "NORTHERN PERU", + "EL SALVADOR", + "FIJI ISLANDS REGION", + "MINDANAO", + "TONGA ISLANDS", + "SOUTHEAST OF HONSHU", + "OFF COAST OF CENTRAL CHILE", + "TONGA ISLANDS", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS", + "NEAR WEST COAST OF HONSHU", + "KERMADEC ISLANDS", + "IRIAN JAYA", + "KERMADEC ISLANDS", + "IRIAN JAYA", + "BANDA SEA", + "KOMANDORSKIYE OSTROVA REGION", + "KOMANDORSKIYE OSTROVA REGION", + "PANAMA-COSTA RICA BORDER REGION", + "KERMADEC ISLANDS REGION", + "MARIANA ISLANDS", + "VANUATU ISLANDS", + "QUEEN CHARLOTTE ISLANDS REGION", + "CENTRAL CALIFORNIA", + "CENTRAL CALIFORNIA", + "NORTHERN MOLUCCA SEA", + "SOLOMON ISLANDS", + "NORTHWEST OF RYUKYU ISLANDS", + "WESTERN AUSTRALIA", + "HALMAHERA", + "NEW BRITAIN REGION", + "BALLENY ISLANDS REGION", + "PHILIPPINE ISLANDS REGION", + "NEAR S. COAST OF HONSHU", + "VANUATU ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "SOUTHERN EAST PACIFIC RISE", + "SOUTHWEST OF SUMATRA", + "NEAR EAST COAST OF HONSHU", + "SANTA CRUZ ISLANDS", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "OFF COAST OF OREGON", + "FIJI ISLANDS REGION", + "JAVA", + "MONA PASSAGE", + "SOUTHWEST INDIAN RIDGE", + "SERAM", + "CENTRAL CHILE", + "KERMADEC ISLANDS", + "MINDANAO", + "OFF COAST OF CENTRAL CHILE", + "MINDANAO", + "VANUATU ISLANDS", + "NORTH OF ASCENSION ISLAND", + "MINDANAO", + "MINDANAO", + "SOUTH SANDWICH ISLANDS REGION", + "NEAR COAST OF CENTRAL CHILE", + "TONGA ISLANDS", + "EAST OF SOUTH SANDWICH ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "TONGA ISLANDS", + "NORTHERN MOLUCCA SEA", + "NORTHERN MOLUCCA SEA", + "OFF COAST OF CHIAPAS", + "SEA OF OKHOTSK", + "LAOS", + "IRIAN JAYA REGION", + "ANDREANOF ISLANDS", + "ALBANIA", + "CRETE", + "ANDREANOF ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "VANUATU ISLANDS", + "MINDANAO", + "HINDU KUSH REGION", + "VANCOUVER ISLAND", + "VANCOUVER ISLAND", + "SANTIAGO DEL ESTERO PROV.", + "COLOMBIA", + "VANCOUVER ISLAND", + "SOUTHERN EAST PACIFIC RISE", + "NORTHERN SUMATRA", + "PUERTO RICO REGION", + "NEW BRITAIN REGION", + "EASTERN SIBERIA", + "NEAR NORTH COAST OF IRIAN JAYA", + "SOUTHERN XINJIANG", + "MINAHASSA PENINSULA", + "SOUTH SANDWICH ISLANDS REGION", + "ANDREANOF ISLANDS", + "TURKEY", + "ANDREANOF ISLANDS", + "SOLOMON ISLANDS", + "CUBA REGION", + "NORTH OF HONDURAS", + "SOLOMON ISLANDS", + "JAVA", + "MINDANAO", + "SOUTH SANDWICH ISLANDS REGION", + "NEW BRITAIN REGION", + "KURIL ISLANDS", + "TURKEY-IRAN BORDER REGION", + "KERMADEC ISLANDS REGION", + "SAMOA ISLANDS REGION", + "VANUATU ISLANDS", + "SOUTH OF BALI", + "CENTRAL EAST PACIFIC RISE", + "EAST OF KURIL ISLANDS", + "MINDANAO", + "WESTERN IDAHO", + "NORTHERN MOLUCCA SEA", + "MID-INDIAN RIDGE", + "KERMADEC ISLANDS REGION", + "NORTH OF HONDURAS", + "BONIN ISLANDS", + "NEAR EAST COAST OF HONSHU", + "SOLOMON ISLANDS", + "CRETE", + "BANDA SEA", + "SOLOMON ISLANDS", + "SANTA CRUZ ISLANDS", + "NEVADA", + "OFF COAST OF CENTRAL MEXICO", + "VANUATU ISLANDS", + "SOUTHERN PERU", + "NORTHERN CHILE", + "SOUTH OF FIJI ISLANDS", + "HALMAHERA", + "SOUTHERN MID-ATLANTIC RIDGE", + "RYUKYU ISLANDS", + "MARIANA ISLANDS", + "SOUTH OF KERMADEC ISLANDS", + "ICELAND REGION", + "MINAHASSA PENINSULA", + "NEAR COAST OF OAXACA", + "XIZANG", + "W. CAROLINE ISLANDS", + "JAVA SEA", + "EASTERN NEW GUINEA REG.", + "ANDAMAN ISLANDS", + "SAMOA ISLANDS REGION", + "FIJI ISLANDS REGION", + "ALASKA PENINSULA", + "SOUTH OF ALASKA", + "XIZANG", + "SOUTH SANDWICH ISLANDS REGION", + "ALASKA PENINSULA", + "MINDANAO", + "BISMARCK SEA", + "VANUATU ISLANDS", + "PRINCE EDWARD ISLANDS REGION", + "TANZANIA", + "SAMAR", + "SOUTHERN SUMATRA", + "SOUTHERN SUMATRA", + "BANDA SEA", + "ASCENSION ISLAND REGION", + "COSTA RICA", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "CENTRAL MID-ATLANTIC RIDGE", + "CHAGOS ARCHIPELAGO REGION", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF CENTRAL CHILE", + "VANUATU ISLANDS", + "CENTRAL MID-ATLANTIC RIDGE", + "MINDANAO", + "VANUATU ISLANDS", + "NORTHERN CHILE", + "NEAR EAST COAST OF HONSHU", + "KAMCHATKA PENINSULA", + "FIJI ISLANDS", + "CENTRAL MID-ATLANTIC RIDGE", + "SOUTH OF AFRICA", + "TONGA ISLANDS", + "NEW BRITAIN REGION", + "FIJI ISLANDS REGION", + "EASTERN NEW GUINEA REG.", + "SOUTH OF ALASKA", + "SOUTH OF FIJI ISLANDS", + "DODECANESE ISLANDS", + "OFF COAST OF SOUTHERN CHILE", + "SOUTH SHETLAND ISLANDS", + "TONGA ISLANDS", + "FIJI ISLANDS REGION", + "MINDANAO", + "OFF COAST OF CENTRAL CHILE", + "PRIMOR'YE", + "CHILE-ARGENTINA BORDER REGION", + "FOX ISLANDS", + "BALLENY ISLANDS REGION", + "NORTHERN CHILE", + "TAIWAN REGION", + "CHILE-BOLIVIA BORDER REGION", + "MINDANAO", + "NEAR EAST COAST OF HONSHU", + "W. CAROLINE ISLANDS", + "MINDORO", + "OFF COAST OF CENTRAL CHILE", + "NORTHWESTERN BALKAN REGION", + "ANDREANOF ISLANDS", + "KERMADEC ISLANDS REGION", + "MINAHASSA PENINSULA", + "KERMADEC ISLANDS", + "VANUATU ISLANDS", + "SALTA PROVINCE", + "VANUATU ISLANDS", + "RUSSIA-MONGOLIA BORDER REGION", + "SULAWESI", + "SAN JUAN PROVINCE", + "PHILIPPINE ISLANDS REGION", + "SOUTH SHETLAND ISLANDS", + "WEST CHILE RISE", + "MINDANAO", + "BISMARCK SEA", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHWEST OF SUMATRA", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "NEAR EAST COAST OF HONSHU", + "VANUATU ISLANDS", + "TONGA ISLANDS REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "GREECE", + "OFF E. COAST OF N. ISLAND", + "VANUATU ISLANDS", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "OFF E. COAST OF N. ISLAND", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "OFF EAST COAST OF KAMCHATKA", + "NORTHERN ALGERIA", + "NORTHERN MID-ATLANTIC RIDGE", + "WEST OF MACQUARIE ISLAND", + "NEAR EAST COAST OF HONSHU", + "KERMADEC ISLANDS", + "FIJI ISLANDS REGION", + "EAST OF SOUTH SANDWICH ISLANDS", + "OFF E. COAST OF N. ISLAND", + "KERMADEC ISLANDS REGION", + "JAVA", + "CELEBES SEA", + "NEAR N COAST OF NEW GUINEA", + "OFF W COAST OF NORTHERN SUMATRA", + "FIJI ISLANDS REGION", + "FIJI ISLANDS REGION", + "NEAR N COAST OF NEW GUINEA", + "NORTHEASTERN INDIA", + "KERMADEC ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "WEST OF MACQUARIE ISLAND", + "FIJI ISLANDS REGION", + "NEAR COAST OF GUATEMALA", + "MAURITIUS - REUNION REGION", + "SOUTH OF PANAMA", + "NEAR EAST COAST OF HONSHU", + "OFF W COAST OF NORTHERN SUMATRA", + "SOUTHERN EAST PACIFIC RISE", + "YUNNAN", + "QINGHAI", + "FIJI ISLANDS REGION", + "CENTRAL ALASKA", + "NORTHERN MOLUCCA SEA", + "KERMADEC ISLANDS", + "FIJI ISLANDS REGION", + "NEAR COAST OF CENTRAL CHILE", + "CALIFORNIA-NEVADA BORDER REGION", + "SOUTH OF PANAMA", + "BISMARCK SEA", + "SOUTH OF PANAMA", + "MINDORO", + "KERMADEC ISLANDS REGION", + "MINAHASSA PENINSULA", + "ALASKA PENINSULA", + "PERU-ECUADOR BORDER REGION", + "NICOBAR ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTH 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"name": "stdout", - "output_type": "stream", - "text": [ - " Temperature_F Temperature_C\n", - "0 32 0.000000\n", - "1 50 10.000000\n", - "2 77 25.000000\n", - "3 100 37.777778\n" + ], + "layout": { + "coloraxis": { + "cmax": 9, + "cmin": 6, + "colorbar": { + "title": { + "text": "magnitude" + } + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# Sample DataFrame\n", - "data = {'Temperature_F': [32, 50, 77, 100]}\n", - "df = pd.DataFrame(data)\n", - "\n", - "# Convert Fahrenheit to Celsius\n", - "df['Temperature_C'] = df['Temperature_F'].apply(lambda x: (x - 32) * 5 / 9)\n", - "\n", - "print(df)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "cbb26a36", - "metadata": {}, - "outputs": [], - "source": [ - "# Now the equivalent in lambda is:\n", - "lambda_meters2kilometers = lambda x:x/1000\n", - "# x is the variable" - ] - }, - { - "cell_type": "markdown", - "id": "e604fca3", - "metadata": {}, - "source": [ - "Now apply the function to the entire series\n", - "```{admonition} Student response section\n", - "This section is left for the student to complete.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "7d94f6a8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 34.40\n", - "1 30.10\n", - "2 16.90\n", - "3 109.40\n", - "4 25.70\n", - " ... \n", - "1780 10.00\n", - "1781 105.00\n", - "1782 608.51\n", - "1783 10.00\n", - "1784 35.44\n", - "Name: depth, Length: 1785, dtype: float64" - ] + ] + }, + "geo": { + "center": {}, + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, 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"autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# you can add several variables into lambda functions\n", - "remove_anything = lambda x,y:x-y\n", - "remove_anything(3,2)" - ] - }, - { - "cell_type": "markdown", - "id": "a6da00e2", - "metadata": {}, - "source": [ - "This did not affect the values of the DataFrame, check it:" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "40367bfb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 34400.0\n", - "1 30100.0\n", - "2 16900.0\n", - "3 109400.0\n", - "4 25700.0\n", - " ... \n", - "1780 10000.0\n", - "1781 105000.0\n", - "1782 608510.0\n", - "1783 10000.0\n", - "1784 35440.0\n", - "Name: depth, Length: 1785, dtype: float64" + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ 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Try two approaches but just do it once! You can use python functions ``map`` (a function to apply functions that is generic to Python) and ``apply`` (a function to apply functions that is specific to Pandas)." - ] - }, - { - "cell_type": "markdown", - "id": "ade5723d", - "metadata": {}, - "source": [ - "```{admonition} Student response section\n", - "This section is left for the student to complete.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "0d45bc9b", - "metadata": {}, - "source": [ - "Try ``map`` to apply a ``lambda`` function that rescale depth from meters to kilometers." - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "8a1da18f", - "metadata": {}, - "outputs": [], - "source": [ - "# implement answer here" - ] - }, - { - "cell_type": "markdown", - "id": "9b11b4cb", - "metadata": {}, - "source": [ - "Discuss in class: What happened to the depth field?\n", - "\n", - "What happened to the original data frame?" - ] - }, - { - "cell_type": "markdown", - "id": "a520ada6", - "metadata": {}, - "source": [ - "Try ``apply`` to apply a ``lambda`` function that rescale depth from meters to kilometers." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "ec3420af", - "metadata": {}, - "outputs": [], - "source": [ - "# or like this\n", - "quake.depth=quake.depth.apply(lambda x:x/1000) " - ] - }, - { - "cell_type": "markdown", - "id": "fa66b4e8", - "metadata": {}, - "source": [ - "Plot a histogram of the depth distributions using matplotlib function ``hist``." - ] - }, - { - "cell_type": "markdown", - "id": "ade5723d", - "metadata": {}, - "source": [ - "```{admonition} Student response section\n", - "This section is left for the student to complete.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "ae368fd4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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0, - 1 - ], - "title": { - "text": "depth" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "count" - } - } - } - } + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = px.histogram(quake, #specify what dataframe to use\n", - " x=\"depth\", #specify the variable for the histogram \n", - " nbins=50, #number of bins for the histogram \n", - " height=400, #dimensions of the figure\n", - " width=600);\n", - "fig.show()" - ] - }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "width": 600 + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.scatter_geo(quake,\n", + " lat='latitude',lon='longitude', \n", + " range_color=(6,9),\n", + " height=600, width=600,\n", + " size='marker_size', color='magnitude',\n", + " hover_name=\"description\",\n", + " hover_data=['description','magnitude','depth']);\n", + "fig.update_geos(resolution=110, showcountries=True)\n", + "fig.update_geos(resolution=110, showcountries=True,projection_type=\"orthographic\")\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "7a9e55fb", + "metadata": {}, + "source": [ + "The lambda function ``lambda x: 'Minor' if x < 4.0 else ('Moderate' if x < 6.5 else 'Severe')`` classifies earthquake magnitudes based on their values." + ] + }, + { + "cell_type": "markdown", + "id": "69b7c97f", + "metadata": {}, + "source": [ + "The data was sorted by time. We now want to sort and show the data instead by magnitude. We use the pandas function ``sort`` to create a new DataFrame with sorted values." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "7738e4d0", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "60a133d4", - "metadata": {}, - "source": [ - "### Example 2: Conditional Logic for Earthquake Magnitude \n", - "You can use lambda functions to add conditional logic, such as classifying earthquake magnitudes into categories.\n", - "\n", - "Scenario: You have a column Magnitude containing seismic event magnitudes, and you want to classify events as \"Minor\", \"Moderate\", or \"Severe\"." + "data": { + "text/html": [ + "
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timelatitudelongitudedepthmagnitudedescriptiondepth_kmCategorymarker_sizemagnitude_bin
02010-07-02 06:04:03.570-13.6098166.65410.034406.3VANUATU ISLANDS34.40Moderate544.06.0
11122017-08-31 17:06:55.750-1.159099.68810.043146.3SOUTHERN SUMATRA43.14Moderate544.06.0
11112017-08-27 04:17:51.010-1.4513148.08030.008006.3ADMIRALTY ISLANDS REGION8.00Moderate544.06.0
11102017-08-19 02:00:52.550-17.9609-178.84060.544006.4FIJI ISLANDS REGION544.00Moderate601.06.0
11082017-08-13 03:08:10.560-3.7682101.62280.031006.4SOUTHERN SUMATRA31.00Moderate601.06.0
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" + ], + "text/plain": [ + " time latitude longitude depth magnitude \\\n", + "0 2010-07-02 06:04:03.570 -13.6098 166.6541 0.03440 6.3 \n", + "1112 2017-08-31 17:06:55.750 -1.1590 99.6881 0.04314 6.3 \n", + "1111 2017-08-27 04:17:51.010 -1.4513 148.0803 0.00800 6.3 \n", + "1110 2017-08-19 02:00:52.550 -17.9609 -178.8406 0.54400 6.4 \n", + "1108 2017-08-13 03:08:10.560 -3.7682 101.6228 0.03100 6.4 \n", + "\n", + " description depth_km Category marker_size magnitude_bin \n", + "0 VANUATU ISLANDS 34.40 Moderate 544.0 6.0 \n", + "1112 SOUTHERN SUMATRA 43.14 Moderate 544.0 6.0 \n", + "1111 ADMIRALTY ISLANDS REGION 8.00 Moderate 544.0 6.0 \n", + "1110 FIJI ISLANDS REGION 544.00 Moderate 601.0 6.0 \n", + "1108 SOUTHERN SUMATRA 31.00 Moderate 601.0 6.0 " ] - }, + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quakes2plot=quake.sort_values(by='magnitude_bin')\n", + "quakes2plot.head()" + ] + }, + { + "cell_type": "markdown", + "id": "dcf7472d", + "metadata": {}, + "source": [ + "Now we will plot again using Plotly" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "c21ce4ea", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 60, - "id": "7f7520de", - "metadata": {}, - "outputs": [ + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ { - "data": { - "text/html": [ - "
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timelatitudelongitudedepthmagnitudedescriptiondepth_kmCategory
02010-07-02 06:04:03.570-13.6098166.65410.03446.3VANUATU ISLANDS34.4Moderate
12010-07-04 21:55:52.37039.6611142.57920.03016.3NEAR EAST COAST OF HONSHU30.1Moderate
22010-07-10 11:43:33.00011.1664146.08230.01696.3SOUTH OF MARIANA ISLANDS16.9Moderate
32010-07-12 00:11:20.060-22.2789-68.31590.10946.2NORTHERN CHILE109.4Moderate
42010-07-14 08:32:21.850-38.0635-73.46490.02576.6NEAR COAST OF CENTRAL CHILE25.7Severe
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ISLAND", + "CENTRAL ITALY", + "FLORES SEA", + "SOUTH GEORGIA ISLAND REGION", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "VOLCANO ISLANDS", + "JUJUY PROVINCE", + "ADMIRALTY ISLANDS REGION", + "NEAR COAST OF NORTHERN CHILE", + "VANUATU ISLANDS", + "KERMADEC ISLANDS REGION", + "NEAR COAST OF ECUADOR", + "TONGA ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "OFF COAST OF CHIAPAS", + "VANUATU ISLANDS", + "KYUSHU", + "AFGHANISTAN-TAJIKISTAN BORD REG.", + "SOUTHERN EAST PACIFIC RISE", + "BORNEO", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "NEAR COAST OF NORTHERN CHILE", + "MARIANA ISLANDS", + "SOUTHEASTERN AFGHANISTAN", + "OFF COAST OF CENTRAL CHILE", + "PRINCE EDWARD ISLANDS REGION", + "PRINCE EDWARD ISLANDS REGION", + "BOUVET ISLAND REGION", + "TONGA ISLANDS", + "EAST OF KURIL ISLANDS", + "EAST OF KURIL ISLANDS", + "PACIFIC-ANTARCTIC RIDGE", + "JUJUY PROVINCE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "NEAR COAST OF CENTRAL CHILE", + "PACIFIC-ANTARCTIC RIDGE", + "SOUTHERN BOLIVIA", + "KYUSHU", + "MINDANAO", + "JAVA", + "ALASKA PENINSULA", + "NEAR N COAST OF NEW GUINEA", + "NEAR EAST COAST OF KAMCHATKA", + "LEEWARD ISLANDS", + "ANDREANOF ISLANDS", + "ANDREANOF ISLANDS", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "HALMAHERA", + "SOUTHERN EAST PACIFIC RISE", + "SUMBA REGION", + "NEAR COAST OF CENTRAL CHILE", + "SOLOMON ISLANDS", + "TAIWAN", + "KERMADEC ISLANDS REGION", + "BALLENY ISLANDS REGION", + "STRAIT OF GIBRALTAR", + "HOKKAIDO", + "FIJI ISLANDS REGION", + "SOUTHEAST OF EASTER ISLAND", + "TONGA ISLANDS", + "NEAR N COAST OF NEW GUINEA", + "CELEBES SEA", + "JAVA", + "KERMADEC ISLANDS REGION", + "OFF E. COAST OF N. ISLAND", + "FIJI ISLANDS REGION", + "WEST OF MACQUARIE ISLAND", + "NORTHERN MID-ATLANTIC RIDGE", + "OFF W COAST OF NORTHERN SUMATRA", + "NORTHERN ALGERIA", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "OFF E. COAST OF N. ISLAND", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "FIJI ISLANDS REGION", + "NEAR N COAST OF NEW GUINEA", + "NORTHEASTERN INDIA", + "SOUTH SANDWICH ISLANDS REGION", + "NICOBAR ISLANDS", + "PERU-ECUADOR BORDER REGION", + "MINAHASSA PENINSULA", + "KERMADEC ISLANDS REGION", + "BISMARCK SEA", + "SOUTH OF PANAMA", + "CALIFORNIA-NEVADA BORDER REGION", + "NEAR COAST OF CENTRAL CHILE", + "FIJI ISLANDS REGION", + "NORTHERN MOLUCCA SEA", + "CENTRAL ALASKA", + "YUNNAN", + "NEAR EAST COAST OF HONSHU", + "SOUTH OF PANAMA", + "NEAR COAST OF GUATEMALA", + "FIJI ISLANDS REGION", + "WEST OF MACQUARIE ISLAND", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "VANUATU ISLANDS", + "GREECE", + "W. CAROLINE ISLANDS", + "NEAR EAST COAST OF HONSHU", + "MINDANAO", + "CHILE-BOLIVIA BORDER REGION", + "TAIWAN REGION", + "NORTHERN CHILE", + "BALLENY ISLANDS REGION", + "FOX ISLANDS", + "CHILE-ARGENTINA BORDER REGION", + "PRIMOR'YE", + "OFF COAST OF CENTRAL CHILE", + "MINDANAO", + "FIJI ISLANDS REGION", + "TONGA ISLANDS", + "SOUTH SHETLAND ISLANDS", + "OFF COAST OF SOUTHERN CHILE", + "SOUTH OF FIJI ISLANDS", + "EASTERN NEW GUINEA REG.", + "FIJI ISLANDS REGION", + "MINDORO", + "SOUTH SANDWICH ISLANDS REGION", + "NORTHWESTERN BALKAN REGION", + "KERMADEC ISLANDS REGION", + "FIJI ISLANDS REGION", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHWEST OF SUMATRA", + "SOUTHEAST OF LOYALTY ISLANDS", + "BISMARCK SEA", + "MINDANAO", + "SAN JUAN PROVINCE", + "SULAWESI", + "VANUATU ISLANDS", + "SALTA PROVINCE", + "VANUATU ISLANDS", + "KERMADEC ISLANDS", + "MINAHASSA PENINSULA", + "ANDREANOF ISLANDS", + "NEW BRITAIN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "KURIL ISLANDS", + "LUZON", + "FIJI ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "NORTHERN SUMATRA", + "JUJUY PROVINCE", + "BALLENY ISLANDS REGION", + "GUATEMALA", + "SOUTH OF MARIANA ISLANDS", + "NEAR EAST COAST OF HONSHU", + "CENTRAL MID-ATLANTIC RIDGE", + "BANDA SEA", + "SOUTH OF PANAMA", + "FIJI ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "FOX ISLANDS", + "TALAUD ISLANDS", + "SHIKOKU", + "SOLOMON ISLANDS", + "SOUTHEAST INDIAN RIDGE", + "SOUTH OF FIJI ISLANDS", + "KERMADEC ISLANDS REGION", + "VANUATU ISLANDS", + "RAT ISLANDS", + "WEST OF MACQUARIE ISLAND", + "TONGA ISLANDS", + "TIMOR REGION", + "SOUTHERN EAST PACIFIC RISE", + "OFF EAST COAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "LUZON", + "BISMARCK SEA", + "TAIWAN REGION", + "PHILIPPINE ISLANDS REGION", + "BISMARCK SEA", + "MINDANAO", + "MINDANAO", + "VANUATU ISLANDS", + "NEW BRITAIN REGION", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "NORTHERN MID-ATLANTIC RIDGE", + "SOUTH OF KERMADEC ISLANDS", + "NEAR COAST OF NICARAGUA", + "BANDA SEA", + "SOUTHERN MOLUCCA SEA", + "OFF W COAST OF NORTHERN SUMATRA", + "SOUTH SANDWICH ISLANDS REGION", + "TAIWAN", + "SOUTH OF FIJI ISLANDS", + "VANUATU ISLANDS", + "SOLOMON ISLANDS", + "CRETE", + "HAWAII", + "SOUTH SANDWICH ISLANDS REGION", + "MID-INDIAN RIDGE", + "SEA OF JAPAN", + "CRETE", + "ANDREANOF ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "KURIL ISLANDS", + "CHILE-ARGENTINA BORDER REGION", + "FIJI ISLANDS REGION", + "KERMADEC ISLANDS", + "NEAR COAST OF NICARAGUA", + "SOUTH SANDWICH ISLANDS REGION", + "IRIAN JAYA REGION", + "SOUTHERN IRAN", + "TAIWAN REGION", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "OFF COAST OF JALISCO", + "NEAR COAST OF NORTHERN CALIF.", + "SOUTH SANDWICH ISLANDS REGION", + "FIJI ISLANDS", + "PRINCE EDWARD ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "RYUKYU ISLANDS", + "NORTH OF HALMAHERA", + "EASTER ISLAND REGION", + "NEW IRELAND REGION", + "NEW IRELAND REGION", + "SOUTHEAST OF HONSHU", + "MYANMAR-INDIA BORDER REGION", + "SANTA CRUZ ISLANDS", + "NEW IRELAND REGION", + "SOUTH INDIAN OCEAN", + "SOUTHERN IRAN", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "FIJI ISLANDS", + "CENTRAL CALIFORNIA", + "QUEEN CHARLOTTE ISLANDS REGION", + "VANUATU ISLANDS", + "MARIANA ISLANDS", + "KERMADEC ISLANDS REGION", + "PANAMA-COSTA RICA BORDER REGION", + "KOMANDORSKIYE OSTROVA REGION", + "KOMANDORSKIYE OSTROVA REGION", + "SOLOMON ISLANDS", + "IRIAN JAYA", + "IRIAN JAYA", + "KERMADEC ISLANDS", + "NEAR WEST COAST OF HONSHU", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS REGION", + "TONGA ISLANDS", + "OFF COAST OF CENTRAL CHILE", + "SOUTHEAST OF HONSHU", + "KERMADEC ISLANDS", + "NORTHWEST OF RYUKYU ISLANDS", + "NEW BRITAIN REGION", + "BALLENY ISLANDS REGION", + "NEAR COAST OF CENTRAL CHILE", + "SOUTH SANDWICH ISLANDS REGION", + "NORTH OF ASCENSION ISLAND", + "VANUATU ISLANDS", + "MINDANAO", + "MINDANAO", + "KERMADEC ISLANDS", + "CENTRAL CHILE", + "SOUTHWEST INDIAN RIDGE", + "MONA PASSAGE", + "JAVA", + "OFF COAST OF OREGON", + "VANUATU ISLANDS", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "SANTA CRUZ ISLANDS", + "NEAR EAST COAST OF HONSHU", + "SOUTHERN EAST PACIFIC RISE", + "NEAR S. COAST OF HONSHU", + "PHILIPPINE ISLANDS REGION", + "TONGA ISLANDS", + "MINDANAO", + "FIJI ISLANDS REGION", + "ANDREANOF ISLANDS", + "CENTRAL BOLIVIA", + "D'ENTRECASTEAUX ISLANDS REGION", + "FIJI ISLANDS REGION", + "PHILIPPINE ISLANDS REGION", + "SOUTH OF KERMADEC ISLANDS", + "NEW IRELAND REGION", + "NORTHERN MID-ATLANTIC RIDGE", + "MARIANA ISLANDS", + "SOUTHERN SUMATRA", + "SOUTHERN SUMATRA", + "FIJI ISLANDS REGION", + "SOLOMON ISLANDS", + "SUMBA REGION", + "SUMBA REGION", + "VANUATU ISLANDS", + "BISMARCK SEA", + "KYUSHU", + "ALASKA PENINSULA", + "KOMANDORSKIYE OSTROVA REGION", + "VANUATU ISLANDS", + "EAST OF SOUTH SANDWICH ISLANDS", + "COLOMBIA", + "EAST OF KURIL ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "PANAMA-COSTA RICA BORDER REGION", + "KYUSHU", + "SOLOMON ISLANDS", + "CARLSBERG RIDGE", + "SOUTH OF FIJI ISLANDS", + "SAMAR", + "SOUTH SANDWICH ISLANDS REGION", + "LUZON", + "TAIWAN", + "OFF EAST COAST OF HONSHU", + "BANDA SEA", + "SOUTH SANDWICH ISLANDS REGION", + "RAT ISLANDS", + "MAURITIUS - REUNION REGION", + "NEAR COAST OF ECUADOR", + "NEW BRITAIN REGION", + "NORTHERN MOLUCCA SEA", + "SOUTH OF AFRICA", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "SAMOA ISLANDS REGION", + "ANDAMAN ISLANDS", + "W. CAROLINE ISLANDS", + "XIZANG", + "MINAHASSA PENINSULA", + "ICELAND REGION", + "MARIANA ISLANDS", + "SOUTHERN MID-ATLANTIC RIDGE", + "FIJI ISLANDS REGION", + "HALMAHERA", + "SOUTHERN PERU", + "VANUATU ISLANDS", + "OFF COAST OF CENTRAL MEXICO", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "NEAR EAST COAST OF HONSHU", + "NORTH OF HONDURAS", + "KERMADEC ISLANDS REGION", + "SOUTH OF FIJI ISLANDS", + "SOUTH OF ALASKA", + "XIZANG", + "SOUTH SANDWICH ISLANDS REGION", + "KAMCHATKA PENINSULA", + "NEAR EAST COAST OF HONSHU", + "NORTHERN CHILE", + "VANUATU ISLANDS", + "MINDANAO", + "VANUATU ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "CHAGOS ARCHIPELAGO REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "COSTA RICA", + "ASCENSION ISLAND REGION", + "TANZANIA", + "PRINCE EDWARD ISLANDS REGION", + "VANUATU ISLANDS", + "BISMARCK SEA", + "MINDANAO", + "ALASKA PENINSULA", + "MID-INDIAN RIDGE", + "NORTHERN MOLUCCA SEA", + "MINDANAO", + "CENTRAL EAST PACIFIC RISE", + "VANCOUVER ISLAND", + "COLOMBIA", + "SANTIAGO DEL ESTERO PROV.", + "VANCOUVER ISLAND", + "VANCOUVER ISLAND", + "HINDU KUSH REGION", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "VANUATU ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "ANDREANOF ISLANDS", + "CRETE", + "ALBANIA", + "ANDREANOF ISLANDS", + "IRIAN JAYA REGION", + "LAOS", + "SEA OF OKHOTSK", + "OFF COAST OF CHIAPAS", + "NORTHERN MOLUCCA SEA", + "SOUTHERN EAST PACIFIC RISE", + "VANUATU ISLANDS", + "NORTHERN SUMATRA", + "NEW BRITAIN REGION", + "SOUTH OF BALI", + "VANUATU ISLANDS", + "SAMOA ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "TURKEY-IRAN BORDER REGION", + "NEW BRITAIN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "MINDANAO", + "JAVA", + "SOLOMON ISLANDS", + "NORTH OF HONDURAS", + "SOLOMON ISLANDS", + "ANDREANOF ISLANDS", + "ANDREANOF ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "MINAHASSA PENINSULA", + "SOUTHERN XINJIANG", + "NEAR NORTH COAST OF IRIAN JAYA", + "EASTERN SIBERIA", + "PUERTO RICO REGION", + "MID-INDIAN RIDGE", + "BANDA SEA", + "PHILIPPINE ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "EAST OF KURIL ISLANDS", + "VANUATU ISLANDS REGION", + "GUATEMALA", + "TAIWAN", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SOUTH OF PANAMA", + "E. RUSSIA-N.E. CHINA BORDER REG.", + "SULAWESI", + "SOUTHERN IRAN", + "VANUATU ISLANDS", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "EAST OF KURIL ISLANDS", + "BANDA SEA", + "KURIL ISLANDS", + "TONGA ISLANDS", + "FIJI ISLANDS REGION", + "OFF EAST COAST OF KAMCHATKA", + "OFF EAST COAST OF KAMCHATKA", + "OFF COAST OF SOUTHERN CHILE", + "NEAR EAST COAST OF HONSHU", + "COSTA RICA", + "KYUSHU", + "NEAR EAST COAST OF HONSHU", + "SOUTHERN IRAN", + "KERMADEC ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "MICHOACAN", + "SOUTHEAST OF HONSHU", + "TONGA ISLANDS", + "CRETE", + "NEAR EAST COAST OF HONSHU", + "CENTRAL MID-ATLANTIC RIDGE", + "MINAHASSA PENINSULA", + "TAJIKISTAN", + "PACIFIC-ANTARCTIC RIDGE", + "NORTHERN SUMATRA", + "LAKE ISSYK-KUL REGION", + "ALASKA PENINSULA", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "OFF COAST OF CENTRAL CHILE", + "SANTA CRUZ ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "SANTA CRUZ ISLANDS REGION", + "SANTA CRUZ ISLANDS", + "KURIL ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "OFF EAST COAST OF HONSHU", + "SANTIAGO DEL ESTERO PROV.", + "MINDANAO", + "WEST OF MACQUARIE ISLAND", + "HINDU KUSH REGION", + "SOUTH ISLAND", + "NORTHERN MOLUCCA SEA", + "NEW BRITAIN REGION", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "IRIAN JAYA REGION", + "SANTA CRUZ ISLANDS", + "NEAR EAST COAST OF HONSHU", + "FIJI ISLANDS REGION", + "TAIWAN", + "NEAR EAST COAST OF HONSHU", + "EASTERN HONSHU", + "NEAR EAST COAST OF HONSHU", + "NEGROS", + "SOUTHWEST INDIAN RIDGE", + "NEAR COAST OF GUERRERO", + "KERMADEC ISLANDS REGION", + "ANDREANOF ISLANDS", + "NEAR EAST COAST OF HONSHU", + "QUEEN CHARLOTTE ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "QUEEN CHARLOTTE ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "FLORES SEA", + "ANDREANOF ISLANDS", + "OFF EAST COAST OF HONSHU", + "NEAR COAST OF NORTHERN PERU", + "CENTRAL EAST PACIFIC RISE", + "NEAR EAST COAST OF HONSHU", + "PHILIPPINE ISLANDS REGION", + "NORTHERN MID-ATLANTIC RIDGE", + "NEAR EAST COAST OF HONSHU", + "ANDREANOF ISLANDS", + "NEAR EAST COAST OF HONSHU", + "NEAR COAST OF CHIAPAS", + "KERMADEC ISLANDS REGION", + "NEAR WEST COAST OF HONSHU", + "BANDA SEA", + "SOUTHERN SUMATRA", + "SOLOMON ISLANDS", + "NORTHERN SUMATRA", + "NEAR EAST COAST OF HONSHU", + "CRETE", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR COAST OF NICARAGUA", + "SOUTH OF KERMADEC ISLANDS", + "NEAR EAST COAST OF HONSHU", + "FIJI ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SANTA CRUZ ISLANDS", + "CRETE", + "SOLOMON ISLANDS", + "PHILIPPINE ISLANDS REGION", + "VANUATU ISLANDS", + "NEAR EAST COAST OF HONSHU", + "TONGA ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "VANUATU ISLANDS", + "NEAR EAST COAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "GANSU", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "EASTERN HONSHU", + "SOUTHERN PERU", + "OFF EAST COAST OF HONSHU", + "PRINCE EDWARD ISLANDS REGION", + "NEW IRELAND REGION", + "NEAR ISLANDS", + "OFF W. COAST OF BAJA CALIFORNIA", + "NORTHERN ITALY", + "NEAR COAST OF NORTHERN CHILE", + "LUZON", + "OFF EAST COAST OF HONSHU", + "HOKKAIDO", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "GREENLAND SEA", + "BONIN ISLANDS", + "NEW IRELAND REGION", + "SALTA PROVINCE", + "HOKKAIDO", + "SOUTH OF PANAMA", + "SOUTH OF PANAMA", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "CHILE-ARGENTINA BORDER REGION", + "SOUTHERN PERU", + "KURIL ISLANDS", + "NICARAGUA", + "VANUATU ISLANDS", + "COOK STRAIT", + "NORTHERN XINJIANG", + "NEAR EAST COAST OF KAMCHATKA", + "OFF COAST OF SOUTHERN CHILE", + "NORTHERN SUMATRA", + "NEAR ISLANDS", + "NEAR EAST COAST OF HONSHU", + "LUZON", + "SOUTHERN MOLUCCA SEA", + "DODECANESE ISLANDS", + "OFF COAST OF ECUADOR", + "SOUTHERN EAST PACIFIC RISE", + "PERU-BOLIVIA BORDER REGION", + "NEAR COAST OF CHIAPAS", + "KERMADEC ISLANDS REGION", + "IRIAN JAYA", + "SOUTH SANDWICH ISLANDS REGION", + "NEW BRITAIN REGION", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CHIAPAS", + "SOUTH OF FIJI ISLANDS", + "NEAR COAST OF PERU", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOLOMON ISLANDS", + "FIJI ISLANDS REGION", + "SANTIAGO DEL ESTERO PROV.", + "KURIL ISLANDS", + "SOUTH SHETLAND ISLANDS", + "NEAR EAST COAST OF HONSHU", + "IRIAN JAYA REGION", + "EAST OF SOUTH SANDWICH ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "OFF W COAST OF NORTHERN SUMATRA", + "VANUATU ISLANDS", + "DRAKE PASSAGE", + "NORTHERN EAST PACIFIC RISE", + "MINAHASSA PENINSULA", + "BAJA CALIFORNIA", + "OFF COAST OF OREGON", + "NORTH INDIAN OCEAN", + "NEW IRELAND REGION", + "NEAR COAST OF GUERRERO", + "SANTA CRUZ ISLANDS", + "TONGA ISLANDS", + "SOUTHERN EAST PACIFIC RISE", + "NORTHERN SUMATRA", + "REVILLA GIGEDO ISLANDS REGION", + "FIJI ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "CERAM SEA", + "CELEBES SEA", + "ASCENSION ISLAND REGION", + "VANUATU ISLANDS", + "COSTA RICA", + "KURIL ISLANDS", + "NEAR S. COAST OF HONSHU", + "QUEEN CHARLOTTE ISLANDS REGION", + "QUEEN CHARLOTTE ISLANDS REGION", + "MINDANAO", + "VANCOUVER ISLAND", + "CENTRAL PERU", + "VANUATU ISLANDS REGION", + "NEAR COAST OF CHIAPAS", + "GULF OF ALASKA", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "NORTHERN MOLUCCA SEA", + "NEW IRELAND REGION", + "GULF OF CALIFORNIA", + "NORTH ISLAND", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR N COAST OF NEW GUINEA", + "NEW BRITAIN REGION", + "NEAR EAST COAST OF HONSHU", + "GUERRERO", + "NEAR COAST OF CENTRAL CHILE", + "OFF COAST OF SOUTHERN CHILE", + "VANUATU ISLANDS", + "BANDA SEA", + "SUNDA STRAIT", + "VANUATU ISLANDS", + "BALLENY ISLANDS REGION", + "NEAR N COAST OF NEW GUINEA", + "SULAWESI", + "SOUTH OF BALI", + "SOUTHEASTERN SIBERIA", + "NEW BRITAIN REGION", + "SOUTH OF KERMADEC ISLANDS", + "HOKKAIDO", + "ARMENIA-AZERBAIJAN-IRAN BORD REG", + "TURKEY", + "FOX ISLANDS", + "FIJI ISLANDS REGION", + "NEW IRELAND REGION", + "PERU-BRAZIL BORDER REGION", + "XIZANG", + "PACIFIC-ANTARCTIC RIDGE", + "TONGA ISLANDS", + "SOUTH OF JAVA", + "GULF OF CALIFORNIA", + "OFF EAST COAST OF HONSHU", + "SANTA CRUZ ISLANDS", + "BAJA CALIFORNIA", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS", + "OFF EAST COAST OF HONSHU", + "SOUTHERN SUMATRA", + "AUCKLAND ISLANDS", + "IRIAN JAYA REGION", + "NEAR ISLANDS", + "SANTA CRUZ ISLANDS", + "EAST OF NORTH ISLAND", + "NEAR EAST COAST OF HONSHU", + "KURIL ISLANDS", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SOUTHERN EAST PACIFIC RISE", + "TONGA ISLANDS", + "IRIAN JAYA REGION", + "E. CAROLINE ISLANDS", + "E. CAROLINE ISLANDS", + "PACIFIC-ANTARCTIC RIDGE", + "NEGROS", + "OFF W COAST OF NORTHERN SUMATRA", + "BAY OF BENGAL", + "SOUTHERN SUMATRA", + "OFF COAST OF JALISCO", + "VANUATU ISLANDS", + "HALMAHERA", + "GUERRERO", + "ANDREANOF ISLANDS", + "SOUTHWESTERN RYUKYU ISL.", + "IRIAN JAYA REGION", + "ARU ISLANDS REGION", + "RAT ISLANDS", + "RAT ISLANDS", + "HINDU KUSH REGION", + "RAT ISLANDS", + "RAT ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "VOLCANO ISLANDS", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS", + "ANDREANOF ISLANDS", + "TONGA ISLANDS", + "PACIFIC-ANTARCTIC RIDGE", + "NEW BRITAIN REGION", + "JAN MAYEN ISLAND REGION", + "SOUTHERN IRAN", + "NORTHERN SUMATRA", + "TAJIKISTAN", + "SOLOMON ISLANDS", + "NORTHERN SUMATRA", + "SOUTHERN SUMATRA", + "LOYALTY ISLANDS", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "VANUATU ISLANDS", + "SOUTH OF FIJI ISLANDS", + "PACIFIC-ANTARCTIC RIDGE", + "BALLENY ISLANDS REGION", + "SANTA CRUZ ISLANDS REGION", + "NEAR ISLANDS", + "BONIN ISLANDS", + "SOLOMON ISLANDS", + "BANDA SEA", + "OFF EAST COAST OF HONSHU", + "SOLOMON ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS", + "FIJI ISLANDS REGION", + "SOUTH OF FIJI ISLANDS", + "NEAR S. COAST OF HONSHU", + "THAILAND", + "WEST CHILE RISE", + "SOLOMON ISLANDS", + "FIJI ISLANDS REGION", + "BONIN ISLANDS", + "BALLENY ISLANDS REGION", + "TONGA ISLANDS", + "HOKKAIDO", + "HOKKAIDO", + "IRAN-IRAQ BORDER REGION", + "IRAN-IRAQ BORDER REGION", + "NEAR COAST OF CENTRAL CHILE", + "ANDREANOF ISLANDS", + "NORTHERN CALIFORNIA", + "NORTHERN MOLUCCA SEA", + "OFF EAST COAST OF KAMCHATKA", + "EASTER ISLAND REGION", + "TONGA ISLANDS", + "EASTER ISLAND REGION", + "OFF COAST OF JALISCO", + "SOUTHERN MOLUCCA SEA", + "NORTH OF HALMAHERA", + "JUJUY PROVINCE", + "SOLOMON ISLANDS", + "SOUTHERN ALASKA", + "NEW BRITAIN REGION", + "YUNNAN", + "GULF OF CALIFORNIA", + "NEW BRITAIN REGION", + "FOX ISLANDS", + "HOKKAIDO", + "TONGA ISLANDS", + "EASTER ISLAND REGION", + "NORTHERN CHILE", + "SOUTH OF MARIANA ISLANDS", + "NEAR EAST COAST OF HONSHU", + "BANDA SEA", + "TONGA ISLANDS", + "BANDA SEA", + "XIZANG", + "BALLENY ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "OFF COAST OF JALISCO", + "KERMADEC ISLANDS REGION", + "NORTHERN SUMATRA", + "NEW BRITAIN REGION", + "SOLOMON ISLANDS", + "OFF W COAST OF NORTHERN SUMATRA", + "SOUTH OF MARIANA ISLANDS", + "FIJI ISLANDS REGION", + "MAURITIUS - REUNION REGION", + "VANUATU ISLANDS", + "NEW BRITAIN REGION", + "VANUATU ISLANDS", + "SOUTH OF MARIANA ISLANDS", + "SOUTH OF MARIANA ISLANDS", + "MINDANAO", + "MINDANAO", + "SOUTHEASTERN ALASKA", + "TONGA ISLANDS", + "KURIL ISLANDS", + "TONGA ISLANDS", + "OWEN FRACTURE ZONE REGION", + "KURIL ISLANDS", + "SOUTHEASTERN ALASKA", + "NORTHERN MID-ATLANTIC RIDGE", + "OAXACA", + "BISMARCK SEA", + "BISMARCK SEA", + "YUNNAN", + "MYANMAR-INDIA BORDER REGION", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS REGION", + "EASTER ISLAND REGION", + "OAXACA", + "VANUATU ISLANDS", + "SOUTH ISLAND", + "EASTER ISLAND REGION", + "NEAR EAST COAST OF HONSHU", + "TAIWAN", + "NEAR EAST COAST OF KAMCHATKA", + "EASTERN NEW GUINEA REG.", + "EAST OF SOUTH SANDWICH ISLANDS", + "BISMARCK SEA", + "KYUSHU", + "NEAR EAST COAST OF HONSHU", + "NEAR COAST OF PERU", + "PUERTO RICO REGION", + "OFF COAST OF CENTRAL CHILE", + "BALLENY ISLANDS REGION", + "NEAR COAST OF NORTHERN CHILE", + "NICOBAR ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "CELEBES SEA", + "NEAR EAST COAST OF HONSHU", + "SCOTIA SEA", + "OFF EAST COAST OF HONSHU", + "KURIL ISLANDS", + "NEW BRITAIN REGION", + "NORTH ISLAND", + "TONGA ISLANDS", + "JAVA", + "NEAR EAST COAST OF HONSHU", + "NORTHERN SUMATRA", + "NEAR EAST COAST OF HONSHU", + "SOLOMON ISLANDS", + "GREECE", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "GREECE", + "KURIL ISLANDS", + "NEAR EAST COAST OF HONSHU", + "MARIANA ISLANDS", + "HALMAHERA", + "OFF EAST COAST OF HONSHU", + "FOX ISLANDS", + "NEAR COAST OF NICARAGUA", + "NORTHERN CHILE", + "NEAR COAST OF CHIAPAS", + "SOUTH OF KERMADEC ISLANDS", + "SOUTH OF FIJI ISLANDS", + "NEAR COAST OF NORTHERN PERU", + "MARIANA ISLANDS REGION", + "KERMADEC ISLANDS", + "SANTA CRUZ ISLANDS", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "SOUTH OF PANAMA", + "NEAR COAST OF CENTRAL CHILE", + "OFF EAST COAST OF HONSHU", + "NEAR COAST OF CENTRAL CHILE", + "TONGA ISLANDS", + "SOUTH OF MARIANA ISLANDS", + "WEST CHILE RISE", + "NEAR COAST OF VENEZUELA", + "SOLOMON ISLANDS", + "NICARAGUA", + "NEAR COAST OF NORTHERN CHILE", + "SULAWESI", + "KERMADEC ISLANDS", + "OFF EAST COAST OF HONSHU", + "MID-INDIAN RIDGE", + "NEAR EAST COAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "OFF EAST COAST OF HONSHU", + "MACQUARIE ISLAND REGION", + "OFF COAST OF CENTRAL CHILE", + "NORTHERN MOLUCCA SEA", + "TONGA ISLANDS", + "PERU-BRAZIL BORDER REGION", + "KERMADEC ISLANDS", + "OFF EAST COAST OF HONSHU", + "NEW BRITAIN REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "FIJI ISLANDS REGION", + "MINDANAO", + "FOX ISLANDS", + "MINDANAO", + "JUJUY PROVINCE", + "NEAR COAST OF CENTRAL CHILE", + "NORTHEAST OF TAIWAN", + "SOUTH OF MARIANA ISLANDS", + "SOUTHERN SUMATRA", + "LUZON", + "OFF EAST COAST OF HONSHU", + "WESTERN AUSTRALIA", + "GUERRERO", + "KERMADEC ISLANDS", + "EASTERN NEW GUINEA REG.", + "SOUTH SANDWICH ISLANDS REGION", + "FIJI ISLANDS REGION", + "WEST CHILE RISE", + "SOUTH SHETLAND ISLANDS", + "MINDANAO", + "EAST OF SOUTH SANDWICH ISLANDS", + "NEAR EAST COAST OF HONSHU", + "RUSSIA-MONGOLIA BORDER REGION", + "MINDANAO", + "NEAR COAST OF NICARAGUA", + "SOUTHEAST OF LOYALTY ISLANDS", + "TONGA ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "SOUTHWESTERN SIBERIA", + "NEAR COAST OF CENTRAL CHILE", + "SERAM", + "VANUATU ISLANDS", + "OFF EAST COAST OF KAMCHATKA", + "SOUTHEAST OF HONSHU", + "NEW BRITAIN REGION", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "TAIWAN", + "NORTHERN MID-ATLANTIC RIDGE", + "OFF COAST OF CENTRAL CHILE", + "SOUTH SHETLAND ISLANDS", + "NEAR EAST COAST OF HONSHU", + "SOUTHWEST OF SUMATRA", + "CENTRAL BOLIVIA", + "SOUTH SANDWICH ISLANDS REGION", + "FIJI ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "NEVADA", + "ALASKA PENINSULA", + "NEAR COAST OF CENTRAL CHILE", + "SOUTHEAST OF RYUKYU ISLANDS", + "SOUTHEAST OF RYUKYU ISLANDS", + "SANTA CRUZ ISLANDS", + "BANDA SEA", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTHERN IRAN", + "CENTRAL MID-ATLANTIC RIDGE", + "OFF COAST OF CENTRAL CHILE", + "NEAR COAST OF NORTHERN CHILE", + "SOLOMON ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "SOUTHEAST OF HONSHU", + "NEW BRITAIN REGION", + "CRETE", + "LOYALTY ISLANDS", + "NEGROS", + "NORTHERN CHILE", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SAMAR", + "SOUTHERN SUMATRA", + "NEAR COAST OF NICARAGUA", + "CELEBES SEA", + "EAST OF KURIL ISLANDS", + "SOUTHERN SUMATRA", + "CHILE-BOLIVIA BORDER REGION", + "JAVA SEA", + "VANUATU ISLANDS", + "CELEBES SEA", + "NEAR EAST COAST OF HONSHU", + "BANDA SEA", + "VANUATU ISLANDS REGION", + "LOYALTY ISLANDS", + "RYUKYU ISLANDS", + "ALASKA PENINSULA", + "BONIN ISLANDS", + "SOUTH OF KERMADEC ISLANDS", + "WESTERN IDAHO", + "BONIN ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "NORTHERN SUMATRA", + "FIJI ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "VANCOUVER ISLAND", + "SOUTHERN EAST PACIFIC RISE", + "MYANMAR", + "OFF W COAST OF NORTHERN SUMATRA", + "MINDANAO", + "NEAR EAST COAST OF HONSHU", + "SIKKIM", + "KERMADEC ISLANDS REGION", + "MAURITIUS - REUNION REGION", + "ANDREANOF ISLANDS", + "EASTERN NEW GUINEA REG.", + "NORTHERN PERU", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "SANTIAGO DEL ESTERO PROV.", + "BALI SEA", + "FOX ISLANDS", + "BANDA SEA", + "CENTRAL MID-ATLANTIC RIDGE", + "WEST OF MACQUARIE ISLAND", + "SOUTHEAST INDIAN RIDGE", + "MINDORO", + "SOUTH OF PANAMA", + "NEAR EAST COAST OF HONSHU", + "NEAR N COAST OF NEW GUINEA", + "CHIAPAS", + "CENTRAL MID-ATLANTIC RIDGE", + "SOUTH OF JAVA", + "GULF OF CALIFORNIA", + "SOUTH OF FIJI ISLANDS", + "QINGHAI", + "VANUATU ISLANDS", + "KERMADEC ISLANDS", + "TURKEY", + "CYPRUS REGION", + "FOX ISLANDS", + "FOX ISLANDS", + "SUNDA STRAIT", + "BONIN ISLANDS", + "NEW BRITAIN REGION", + "NEGROS", + "FIJI ISLANDS REGION", + "SCOTIA SEA", + "NEAR EAST COAST OF KAMCHATKA", + "NEAR COAST OF CENTRAL CHILE", + "EAST OF SOUTH SANDWICH ISLANDS", + "GULF OF CALIFORNIA", + "SOLOMON ISLANDS", + "MYANMAR", + "NEW IRELAND REGION", + "CRETE", + "SAKHALIN", + "SEA OF OKHOTSK", + "PAKISTAN", + "FIJI ISLANDS REGION", + "NEW BRITAIN REGION", + "SOUTH ATLANTIC OCEAN", + "BANDA SEA", + "VANUATU ISLANDS", + "SOUTHERN SUMATRA", + "OFF COAST OF JALISCO", + "NEAR COAST OF NORTHERN CHILE", + "OFF COAST OF NORTHERN CALIFORNIA", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "AFGHANISTAN-TAJIKISTAN BORD REG.", + "JAVA SEA", + "MYANMAR-INDIA BORDER REGION", + "NORTH ATLANTIC OCEAN", + "XIZANG", + "VANUATU ISLANDS", + "NORTHERN EAST PACIFIC RISE", + "NEAR COAST OF ECUADOR", + "NEAR COAST OF ECUADOR", + "FIJI ISLANDS REGION", + "RYUKYU ISLANDS", + "CENTRAL ITALY", + "SOUTH ISLAND", + "SOUTH ISLAND", + "OFF COAST OF CENTRAL CHILE", + "MINDANAO", + "NORTHERN MID-ATLANTIC RIDGE", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "SULAWESI", + "NEAR ISLANDS", + "NEW BRITAIN REGION", + "SOUTH OF JAVA", + "NEAR COAST OF GUATEMALA", + "SEA OF OKHOTSK", + "LEYTE", + "AUCKLAND ISLANDS", + "DODECANESE ISLANDS", + "MARIANA ISLANDS", + "GANSU", + "GUATEMALA", + "HOKKAIDO", + "BOTSWANA", + "COOK STRAIT", + "NEAR EAST COAST OF HONSHU", + "ANDREANOF ISLANDS", + "SOUTHEAST OF HONSHU", + "NORTHERN SUMATRA", + "OFF COAST OF NORTHERN CALIFORNIA", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "NEAR EAST COAST OF KAMCHATKA", + "BANDA SEA", + "COOK STRAIT", + "SOUTH OF PANAMA", + "SOUTH OF FIJI ISLANDS", + "SOLOMON ISLANDS", + "MINDANAO", + "SOUTHERN BOLIVIA", + "SOUTH OF FIJI ISLANDS", + "BANDA SEA", + "NEW IRELAND REGION", + "NEAR COAST OF NORTHERN CHILE", + "MYANMAR-INDIA BORDER REGION", + "NEPAL", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "NEAR COAST OF CHIAPAS", + "NEAR EAST COAST OF HONSHU", + "PACIFIC-ANTARCTIC RIDGE", + "SANTA CRUZ ISLANDS REGION", + "SOLOMON ISLANDS", + "ALASKA PENINSULA", + "TONGA ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTHERN MID-ATLANTIC RIDGE", + "BONIN ISLANDS", + "KERMADEC ISLANDS", + "SOLOMON ISLANDS", + "NEPAL", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "FIJI ISLANDS REGION", + "OFF E. COAST OF N. ISLAND", + "NORTHERN MOLUCCA SEA", + "NORTHERN MOLUCCA SEA", + "MINDANAO", + "SOUTHERN EAST PACIFIC RISE", + "SOLOMON ISLANDS", + "SOUTH OF PANAMA", + "NORTH ATLANTIC OCEAN", + "MARIANA ISLANDS", + "VANUATU ISLANDS", + "CENTRAL PERU", + "JUJUY PROVINCE", + "CENTRAL PERU", + "OFF EAST COAST OF HONSHU", + "E. CAROLINE ISLANDS", + "FIJI ISLANDS REGION", + "SOUTH OF PANAMA", + "SANTA CRUZ ISLANDS", + "FOX ISLANDS", + "KERMADEC ISLANDS", + "TIMOR REGION", + "NEAR COAST OF CENTRAL CHILE", + "NICOBAR ISLANDS", + "ANDREANOF ISLANDS", + "OFF COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "NORTHWEST OF RYUKYU ISLANDS", + "SOLOMON ISLANDS", + "GREECE", + "NEAR COAST OF NICARAGUA", + "PERU-BRAZIL BORDER REGION", + "SOLOMON ISLANDS", + "BANDA SEA", + "NEAR COAST OF CHIAPAS", + "NEAR COAST OF NORTHERN CHILE", + "MYANMAR-INDIA BORDER REGION", + "SOLOMON ISLANDS", + "TALAUD ISLANDS", + "BOUVET ISLAND REGION", + "IRIAN JAYA REGION", + "KERMADEC ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOUTH INDIAN OCEAN", + "AEGEAN SEA", + "GULF OF CALIFORNIA", + "SOUTH OF PANAMA", + "SOLOMON ISLANDS", + "GUERRERO", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "SOUTH OF FIJI ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "VANCOUVER ISLAND", + "IRIAN JAYA REGION", + "NEAR COAST OF CENTRAL CHILE", + "NORTH OF ASCENSION ISLAND", + "SOUTHERN XINJIANG", + "NORTHWEST OF KURIL ISLANDS", + "HAWAII", + "SOUTHERN BOLIVIA", + "NEW BRITAIN REGION", + "MAURITIUS - REUNION REGION", + "SOLOMON ISLANDS", + "FIJI ISLANDS REGION", + "NEW BRITAIN REGION", + "NEW IRELAND REGION", + "NEW GUINEA", + "SOUTHEAST OF LOYALTY ISLANDS", + "NEW IRELAND REGION", + "VANUATU ISLANDS", + "NORTHERN MOLUCCA SEA", + "CENTRAL CHILE", + "VANUATU ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "PRINCE EDWARD ISLANDS REGION", + "HOKKAIDO", + "SANTIAGO DEL ESTERO PROV.", + "NEAR COAST OF CHIAPAS", + "TONGA ISLANDS", + "IRIAN JAYA REGION", + "WESTERN BRAZIL", + "JAN MAYEN ISLAND REGION", + "KURIL ISLANDS", + "MYANMAR", + "HOKKAIDO", + "SOLOMON ISLANDS", + "ANDREANOF ISLANDS", + "KERMADEC ISLANDS REGION", + "SICHUAN", + "SOUTH OF FIJI ISLANDS", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "JAN MAYEN ISLAND REGION", + "NORTHERN MOLUCCA SEA", + "SUMBAWA REGION", + "KURIL ISLANDS", + "FIJI ISLANDS REGION", + "VANCOUVER ISLAND", + "WEST OF MACQUARIE ISLAND", + "IRIAN JAYA REGION", + "VANCOUVER ISLAND", + "FLORES SEA", + "VANCOUVER ISLAND", + "IONIAN SEA", + "ANDREANOF ISLANDS", + "SUMBAWA REGION", + "ARMENIA-AZERBAIJAN-IRAN BORD REG", + "EASTERN NEW GUINEA REG.", + "NEAR COAST OF CENTRAL CHILE", + "SOUTHWEST OF AFRICA", + "EL SALVADOR", + "RAT ISLANDS", + "BOUVET ISLAND REGION", + "EASTERN NEW GUINEA REG.", + "SANTA CRUZ ISLANDS", + "BANDA SEA", + "EASTERN SIBERIA", + "CENTRAL MID-ATLANTIC RIDGE", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "KURIL ISLANDS", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "TONGA ISLANDS", + "KURIL ISLANDS", + "COSTA RICA", + "NEW GUINEA", + "SANTA CRUZ ISLANDS", + "NEW BRITAIN REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHWESTERN SIBERIA", + "NEAR N COAST OF NEW GUINEA", + "BOUVET ISLAND REGION", + "JAVA", + "SOLOMON ISLANDS", + "SANTA CRUZ ISLANDS", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "MICHOACAN", + "SULAWESI", + "SOUTH SANDWICH ISLANDS REGION", + "BONIN ISLANDS", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "SOUTHEAST INDIAN RIDGE", + "GUERRERO", + "NORTHERN MOLUCCA SEA", + "VANUATU ISLANDS", + "OFF COAST OF CENTRAL AMERICA", + "NEW BRITAIN REGION", + "VANUATU ISLANDS REGION", + "SOLOMON ISLANDS", + "SANTIAGO DEL ESTERO PROV.", + "NEAR COAST OF CENTRAL CHILE", + "VANUATU ISLANDS REGION", + "NEW BRITAIN REGION", + "OFF COAST OF CENTRAL AMERICA", + "CENTRAL PERU", + "FIJI ISLANDS REGION", + "SOUTHWESTERN PAKISTAN", + "KERMADEC ISLANDS", + "BAJA CALIFORNIA", + "OFF EAST COAST OF HONSHU", + "IRIAN JAYA", + "IRIAN JAYA REGION", + "MINDANAO", + "OFF W COAST OF NORTHERN SUMATRA", + "NEAR COAST OF GUERRERO", + "EASTERN NEW GUINEA REG.", + "KOMANDORSKIYE OSTROVA REGION", + "VANUATU ISLANDS", + "ECUADOR", + "SOUTH ISLAND", + "CENTRAL PERU", + "SOLOMON ISLANDS", + "NEPAL", + "SOLOMON ISLANDS", + "NEAR EAST COAST OF HONSHU", + "KERMADEC ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "TURKEY", + "NORTHERN MOLUCCA SEA", + "SOUTHERN EAST PACIFIC RISE", + "EASTERN NEW GUINEA REG.", + "CENTRAL CALIFORNIA", + "NEAR COAST OF PERU", + "FLORES SEA", + "FLORES SEA", + "MINDANAO", + "SOUTHERN ALASKA", + "BANDA SEA", + "NEAR COAST OF CENTRAL CHILE", + "REYKJANES RIDGE", + "HALMAHERA", + "NEAR COAST OF CENTRAL CHILE", + "TAJIKISTAN", + "HAITI REGION", + "SOLOMON ISLANDS", + "BANDA SEA", + "OFF COAST OF CENTRAL AMERICA", + "SANTA CRUZ ISLANDS", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SANTA CRUZ ISLANDS REGION", + "OFF E. COAST OF N. ISLAND", + "NEAR COAST OF PERU", + "KERMADEC ISLANDS", + "OFF E. COAST OF N. ISLAND", + "NORTH OF ASCENSION ISLAND", + "MINDANAO", + "NEAR EAST COAST OF HONSHU", + "SOUTH GEORGIA ISLAND REGION", + "OFF EAST COAST OF HONSHU", + "SOUTHEAST OF LOYALTY ISLANDS", + "OAXACA", + "BANDA SEA", + "SOUTH OF KERMADEC ISLANDS", + "PHILIPPINE ISLANDS REGION", + "CENTRAL MEXICO", + "KURIL ISLANDS", + "NEAR EAST COAST OF HONSHU", + "IRIAN JAYA", + "SOUTH OF FIJI ISLANDS", + "DODECANESE ISLANDS", + "IRAN-IRAQ BORDER REGION", + "FOX ISLANDS", + "NEW IRELAND REGION", + "LOYALTY ISLANDS", + "COLOMBIA", + "CELEBES SEA", + "SANTA CRUZ ISLANDS", + "EASTERN NEW GUINEA REG.", + "ANDREANOF ISLANDS", + "NEAR COAST OF OAXACA", + "SOUTH SANDWICH ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "NEAR COAST OF PERU", + "QINGHAI", + "KAMCHATKA PENINSULA", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "OFF COAST OF NORTHERN CHILE", + "VANUATU ISLANDS", + "KYUSHU", + "SOUTHERN ALASKA", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "KURIL ISLANDS", + "NEAR COAST OF VENEZUELA", + "VANUATU ISLANDS", + "COLOMBIA", + "NEAR COAST OF CHIAPAS", + "PERU-BRAZIL BORDER REGION", + "PERU-BRAZIL BORDER REGION", + "NEAR EAST COAST OF HONSHU", + "NORTH OF HONDURAS", + "SOLOMON ISLANDS", + "NEPAL", + "SOUTHERN IRAN", + "SOLOMON ISLANDS", + "COSTA RICA", + "KOMANDORSKIYE OSTROVA REGION", + "NEW IRELAND REGION", + "NEW BRITAIN REGION", + "ALASKA PENINSULA", + "MINDANAO", + "PERU-BRAZIL BORDER REGION", + "SOUTHERN CHILE", + "MINDANAO", + "PHILIPPINE ISLANDS REGION", + "NEW BRITAIN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "NEAR COAST OF NORTHERN CHILE", + "MINAHASSA PENINSULA", + "SOLOMON ISLANDS", + "NEAR COAST OF GUERRERO", + "SOUTHWEST OF SUMATRA", + "SOUTH OF ALASKA", + "SOUTHEAST OF LOYALTY ISLANDS", + "EAST OF KURIL ISLANDS", + "SCOTIA SEA", + "QUEEN CHARLOTTE ISLANDS REGION", + "PRIMOR'YE", + "MARIANA ISLANDS", + "SEA OF OKHOTSK", + "SOUTHERN SUMATRA", + "NORTHERN PERU", + "SOUTHEAST OF LOYALTY ISLANDS", + "NEAR EAST COAST OF 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lat='latitude',lon='longitude', \n", + " range_color=(6,9),\n", + " height=600, width=600,\n", + " size='marker_size', color='magnitude',\n", + " hover_name=\"description\",\n", + " hover_data=['description','magnitude','depth']);\n", + "fig.update_geos(resolution=110, showcountries=True)\n", + "# fig.update_geos(resolution=110, showcountries=True,projection_type=\"orthographic\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "3355fe19", + "metadata": {}, + "source": [ + "# 3 Create a Pandas from a generic text file.\n", + "\n", + "The python package pandas is very useful to read csv files, but also many text files that are more or less formatted as one observation per row and one column for each feature.\n", + "\n", + "As an example, we are going to look at the list of seismic stations from the Northern California seismic network, available [here](http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day):\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "ef9f302d", + "metadata": {}, + "outputs": [], + "source": [ + "url = 'http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day'" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "3a60ef42", + "metadata": {}, + "outputs": [], + "source": [ + "# this gets the file linked in the URL page and convert it to a string\n", + "s = requests.get(url).content " + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "9edd0bf5", + "metadata": {}, + "outputs": [], + "source": [ + "# this will convert the string, decode it , and make it a table\n", + "data = pd.read_csv(io.StringIO(s.decode('utf-8')), header=None, skiprows=2, sep='\\s+', usecols=list(range(0, 13)))\n", + "# because columns/keys were not assigned, assign them now\n", + "data.columns = ['station', 'network', 'channel', 'location', 'rate', 'start_time', 'end_time', 'latitude', 'longitude', 'elevation', 'depth', 'dip', 'azimuth']" + ] + }, + { + "cell_type": "markdown", + "id": "b2def912", + "metadata": {}, + "source": [ + "Let us look at the data. They are now stored into a pandas dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "448b69ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuth
0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.0
1AARNCEHZ--0.01977/12/01,22:37:001981/06/12,19:15:0039.27594-121.02696911.00.0-90.00.0
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3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.0
4AARNCEHZ--100.01985/11/03,22:00:001987/03/10,23:15:0039.27594-121.02696911.00.0-90.00.0
\n", + "
" ], - "source": [ - "# Classify earthquake magnitudes\n", - "quake['Category'] = quake['magnitude'].apply(lambda x: 'Minor' if x < 4.0 else ('Moderate' if x < 6.5 else 'Severe'))\n", - "quake.head()\n" + "text/plain": [ + " station network channel location rate start_time \\\n", + "0 AAR NC EHZ -- 0.0 1976/07/20,17:38:00 \n", + "1 AAR NC EHZ -- 0.0 1977/12/01,22:37:00 \n", + "2 AAR NC EHZ -- 0.0 1981/06/12,19:15:00 \n", + "3 AAR NC EHZ -- 100.0 1984/01/01,00:00:00 \n", + "4 AAR NC EHZ -- 100.0 1985/11/03,22:00:00 \n", + "\n", + " end_time latitude longitude elevation depth dip azimuth \n", + "0 1977/12/01,22:37:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "1 1981/06/12,19:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "2 1984/01/01,00:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "3 1985/11/03,22:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "4 1987/03/10,23:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 " ] - }, - { - "cell_type": "markdown", - "id": "8be50d1c", - "metadata": {}, - "source": [] - }, + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "d05055aa", + "metadata": {}, + "source": [ + "We can output the first element of the DataFrame:" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "51971cde", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "b87c6c42", - "metadata": {}, - "source": [ - "We will now make a new plot of the location of the earthquakes. We will use Plotly tool. \n", - "\n", - "The markersize will be scaled with the earthquake magnitude. To do so, we add a ``marker_size`` series in the DataFrame" + "data": { + "text/plain": [ + "station AAR\n", + "network NC\n", + "channel EHZ\n", + "location --\n", + "rate 0.0\n", + "start_time 1976/07/20,17:38:00\n", + "end_time 1977/12/01,22:37:00\n", + "latitude 39.27594\n", + "longitude -121.02696\n", + "elevation 911.0\n", + "depth 0.0\n", + "dip -90.0\n", + "azimuth 0.0\n", + "Name: 0, dtype: object" ] - }, + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "d9bb3b35", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 61, - "id": "d9cb41c3", - "metadata": {}, - "outputs": [], - "source": [ - "quake['marker_size'] = quake['magnitude'].apply(lambda x: np.fix(np.exp(x))) # add marker size as exp(mag)\n", - "quake['magnitude_bin'] = quake['magnitude'].apply(lambda x: 0.5*np.fix(2*x)) # add marker size as exp(mag)" + "data": { + "text/plain": [ + "station object\n", + "network object\n", + "channel object\n", + "location object\n", + "rate float64\n", + "start_time object\n", + "end_time object\n", + "latitude float64\n", + "longitude float64\n", + "elevation float64\n", + "depth float64\n", + "dip float64\n", + "azimuth float64\n", + "dtype: object" ] - }, + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# display the type of each column\n", + "data.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "b3cc09c6", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 62, - "id": "6c7befa4", - "metadata": {}, - "outputs": [], - "source": [ - "# another way to do it\n", - "quake['marker_size'] = np.fix(np.exp(quake['magnitude'])) # add marker size as exp(mag)\n", - "quake['magnitude_bin'] = 0.5*np.fix(2*quake['magnitude']) # add marker size as exp(mag)" + "data": { + "text/plain": [ + "0 AAR\n", + "1 AAR\n", + "2 AAR\n", + "3 AAR\n", + "4 AAR\n", + " ... \n", + "29434 WMP\n", + "29435 WMP\n", + "29436 WMP\n", + "29437 WSL\n", + "29438 WWVB\n", + "Name: station, Length: 29439, dtype: object" ] - }, + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.iloc[:, 0]" + ] + }, + { + "cell_type": "markdown", + "id": "9afc9c2f", + "metadata": {}, + "source": [ + "The ``start_time`` and ``end_time`` are stored as ``object`` or ``string``, convert these columns to datetime objects. With a too-simple prompt, CoPilot may fail at converting:" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "08156920", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "2fed2184", - "metadata": {}, - "source": [ - "### 1.4 Intermediate Manipulation with Pandas\n", - "\n", - "You can also apply lambda functions to work with multiple columns, which is common in geoscientific datasets, where you might have spatial or temporal data.\n", - "\n", - "**Example 3: Calculating an Index Based on Multiple Measurements**\n", - "Scenario: You have rainfall (``Rainfall_mm``) and evaporation (``Evaporation_mm``) data, and you want to calculate the ``Net Water Balance ``for each record." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/js/lzmy975n0l5bjbmr9db291m00000gn/T/ipykernel_26544/3238846712.py:2: UserWarning:\n", + "\n", + "Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", + "\n" + ] }, { - "cell_type": "code", - "execution_count": 63, - "id": "7931923f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Rainfall_mm Evaporation_mm Net_Water_Balance\n", - "0 100 60 40\n", - "1 80 70 10\n", - "2 120 65 55\n" - ] - } + "ename": "DateParseError", + "evalue": "Unknown datetime string format, unable to parse: 1976/07/20,17:38:00, at position 0", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mDateParseError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[78], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# convert start_time and end_time to datetime\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstart_time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstart_time\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mend_time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mend_time\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", + "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/tools/datetimes.py:1108\u001b[0m, in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m 1106\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mtz_localize(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutc\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, ABCSeries):\n\u001b[0;32m-> 1108\u001b[0m cache_array \u001b[38;5;241m=\u001b[39m \u001b[43m_maybe_cache\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert_listlike\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m cache_array\u001b[38;5;241m.\u001b[39mempty:\n\u001b[1;32m 1110\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mmap(cache_array)\n", + "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/tools/datetimes.py:254\u001b[0m, in \u001b[0;36m_maybe_cache\u001b[0;34m(arg, format, cache, convert_listlike)\u001b[0m\n\u001b[1;32m 252\u001b[0m unique_dates \u001b[38;5;241m=\u001b[39m unique(arg)\n\u001b[1;32m 253\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(unique_dates) \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(arg):\n\u001b[0;32m--> 254\u001b[0m cache_dates \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_listlike\u001b[49m\u001b[43m(\u001b[49m\u001b[43munique_dates\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[38;5;66;03m# GH#45319\u001b[39;00m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/tools/datetimes.py:490\u001b[0m, in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m 487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 488\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _array_strptime_with_fallback(arg, name, utc, \u001b[38;5;28mformat\u001b[39m, exact, errors)\n\u001b[0;32m--> 490\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m \u001b[43mobjects_to_datetime64ns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 491\u001b[0m \u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 492\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 493\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 495\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 496\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_object\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 497\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 502\u001b[0m dta \u001b[38;5;241m=\u001b[39m DatetimeArray(result, dtype\u001b[38;5;241m=\u001b[39mtz_to_dtype(tz_parsed))\n", + "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/arrays/datetimes.py:2346\u001b[0m, in \u001b[0;36mobjects_to_datetime64ns\u001b[0;34m(data, dayfirst, yearfirst, utc, errors, allow_object)\u001b[0m\n\u001b[1;32m 2343\u001b[0m \u001b[38;5;66;03m# if str-dtype, convert\u001b[39;00m\n\u001b[1;32m 2344\u001b[0m data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(data, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mobject_)\n\u001b[0;32m-> 2346\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m \u001b[43mtslib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray_to_datetime\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2347\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2348\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2349\u001b[0m \u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2350\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2351\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2352\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 2355\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 2356\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 2357\u001b[0m \u001b[38;5;66;03m# Return i8 values to denote unix timestamps\u001b[39;00m\n\u001b[1;32m 2358\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mi8\u001b[39m\u001b[38;5;124m\"\u001b[39m), tz_parsed\n", + "File \u001b[0;32mtslib.pyx:403\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mtslib.pyx:552\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mtslib.pyx:517\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mconversion.pyx:546\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.conversion.convert_str_to_tsobject\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mparsing.pyx:331\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.parsing.parse_datetime_string\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mparsing.pyx:660\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.parsing.dateutil_parse\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mDateParseError\u001b[0m: Unknown datetime string format, unable to parse: 1976/07/20,17:38:00, at position 0" + ] + } + ], + "source": [ + "# convert start_time and end_time to datetime\n", + "data['start_time'] = pd.to_datetime(data['start_time'])\n", + "data['end_time'] = pd.to_datetime(data['end_time'])" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "b8bcf253", + "metadata": {}, + "outputs": [], + "source": [ + "data['startdate'] = pd.to_datetime(data['start_time'], format='%Y/%m/%d,%H:%M:%S')" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "da911e32", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuthstartdate
0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.01976-07-20 17:38:00
1AARNCEHZ--0.01977/12/01,22:37:001981/06/12,19:15:0039.27594-121.02696911.00.0-90.00.01977-12-01 22:37:00
2AARNCEHZ--0.01981/06/12,19:15:001984/01/01,00:00:0039.27594-121.02696911.00.0-90.00.01981-06-12 19:15:00
3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.01984-01-01 00:00:00
4AARNCEHZ--100.01985/11/03,22:00:001987/03/10,23:15:0039.27594-121.02696911.00.0-90.00.01985-11-03 22:00:00
\n", + "
" ], - "source": [ - "data = {'Rainfall_mm': [100, 80, 120], 'Evaporation_mm': [60, 70, 65]}\n", - "df = pd.DataFrame(data)\n", - "\n", - "# Calculate Net Water Balance\n", - "df['Net_Water_Balance'] = df.apply(lambda row: row['Rainfall_mm'] - row['Evaporation_mm'], axis=1)\n", - "\n", - "print(df)\n" + "text/plain": [ + " station network channel location rate start_time \\\n", + "0 AAR NC EHZ -- 0.0 1976/07/20,17:38:00 \n", + "1 AAR NC EHZ -- 0.0 1977/12/01,22:37:00 \n", + "2 AAR NC EHZ -- 0.0 1981/06/12,19:15:00 \n", + "3 AAR NC EHZ -- 100.0 1984/01/01,00:00:00 \n", + "4 AAR NC EHZ -- 100.0 1985/11/03,22:00:00 \n", + "\n", + " end_time latitude longitude elevation depth dip azimuth \\\n", + "0 1977/12/01,22:37:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "1 1981/06/12,19:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "2 1984/01/01,00:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "3 1985/11/03,22:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "4 1987/03/10,23:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "\n", + " startdate \n", + "0 1976-07-20 17:38:00 \n", + "1 1977-12-01 22:37:00 \n", + "2 1981-06-12 19:15:00 \n", + "3 1984-01-01 00:00:00 \n", + "4 1985-11-03 22:00:00 " ] - }, + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "4a2f3dc4", + "metadata": {}, + "outputs": [], + "source": [ + "# do the same for end times\n", + "# Avoid 'OutOfBoundsDatetime' error with year 3000\n", + "enddate = data['end_time'].str.replace('3000', '2026')\n", + "enddate = pd.to_datetime(enddate, format='%Y/%m/%d,%H:%M:%S')\n", + "data['enddate'] = enddate" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "4c3af296", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "b5b64a6b", - "metadata": {}, - "source": [ - "In this example, ``lambda row: row['Rainfall_mm'] - row['Evaporation_mm']`` calculates the net water balance by subtracting evaporation from rainfall for each record.\n", - "\n", - "### 1.5 Advanced: Time Series Data Manipulation with Lambda Functions\n", - "Geoscientists frequently work with time series data (e.g., climate data). Lambda functions can be used for efficient data transformations within time series.\n", - "\n", - "**Example 4: Applying a Rolling Window Calculation**\n", - "Scenario: Suppose you have daily temperature data, and you want to calculate a 7-day rolling average." + "data": { + "text/html": [ + "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuthstartdateenddate
0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.01976-07-20 17:38:001977-12-01 22:37:00
1AARNCEHZ--0.01977/12/01,22:37:001981/06/12,19:15:0039.27594-121.02696911.00.0-90.00.01977-12-01 22:37:001981-06-12 19:15:00
2AARNCEHZ--0.01981/06/12,19:15:001984/01/01,00:00:0039.27594-121.02696911.00.0-90.00.01981-06-12 19:15:001984-01-01 00:00:00
3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.01984-01-01 00:00:001985-11-03 22:00:00
4AARNCEHZ--100.01985/11/03,22:00:001987/03/10,23:15:0039.27594-121.02696911.00.0-90.00.01985-11-03 22:00:001987-03-10 23:15:00
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average using lambda\n", - "df['7_day_avg'] = df['Temperature_C'].rolling(window=3).apply(lambda x: x.mean())\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "id": "e3d3328a", - "metadata": {}, - "source": [ - "Here, ``lambda x: x.mean()`` calculates the rolling mean over a 3-day window for temperature data, which is essential in climate analysis for smoothing short-term fluctuations.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "e01cf834", - "metadata": {}, - "source": [ - "### 1.6 Aggregate function\n", - "The ``agg`` is a powerful method in Pandas that allows you to perform multiple operations on DataFrames and Series. You can apply one or more aggregation functions such as ``sum``, ``mean``, ``min``, ``max``, etc., on different columns or groups of data." - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "aea0ee4e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sum 10.0\n", - "mean 2.5\n", - "Name: A, dtype: float64\n" + ], + "layout": { + "geo": { + "center": {}, + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 ] - } - ], - "source": [ - "# Sample DataFrame\n", - "data = {\n", - " 'A': [1, 2, 3, 4],\n", - " 'B': [5, 6, 7, 8]\n", - "}\n", - "df = pd.DataFrame(data)\n", - "\n", - "# Apply aggregation functions\n", - "result = df['A'].agg(['sum', 'mean'])\n", - "print(result)\n" - ] - }, - { - "cell_type": "markdown", - "id": "f2664926", - "metadata": {}, - "source": [ - "#### Aggregating Multiple Columns with Multiple Functions\n", - "You can pass a dictionary to agg where the keys are column names and the values are the functions to be applied." - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "9df9cde2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " A B\n", - "sum 10.0 NaN\n", - "mean 2.5 NaN\n", - "min NaN 5.0\n", - "max NaN 8.0\n" - ] - } - ], - "source": [ - "# Apply different functions to different columns\n", - "result = df.agg({'A': ['sum', 'mean'], 'B': ['min', 'max']})\n", - "print(result)\n" - ] - }, - { - "cell_type": "markdown", - "id": "5ccea12a", - "metadata": {}, - "source": [ - "In this example, column 'A' gets ``sum`` and ``mean``, while column 'B' gets ``min`` and ``max``." - ] - }, - { - "cell_type": "markdown", - "id": "6b9e44e0", - "metadata": {}, - "source": [ - "You may also use costum functions" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "79ff0c94", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " A B\n", - "mean 2.5 NaN\n", - "data_range 3.0 3.0\n" + }, + "resolution": 110, + "showcountries": true + }, + "height": 600, + "legend": { + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": 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"type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } ] - } - ], - "source": [ - "# Custom function to calculate range (max - min)\n", - "def data_range(x):\n", - " return x.max() - x.min()\n", - "\n", - "# Apply custom function\n", - "result = df.agg({'A': ['mean', data_range], 'B': data_range})\n", - "print(result)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "94abaccd", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f004df73", - "metadata": {}, - "source": [ - "## 2.4 Mapping using Plotly\n", - "\n", - "Now we will plot the earthquakes locations on a map using the Plotly package. More tutorials on [Plotly](https://plotly.com/). The input of the function is self-explanatory and typical of Python's function. The code [documentation](https://plotly.com/python/scatter-plots-on-maps/) of Plotly scatter_geo lists the variables." - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "ff53045a", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "customdata": [ - [ - "VANUATU ISLANDS", - 6.3, - 0.0344 - ], - [ - "NEAR EAST COAST OF HONSHU", - 6.3, - 0.030100000000000002 - ], - [ - "SOUTH OF MARIANA ISLANDS", - 6.3, - 0.0169 - ], - [ - "NORTHERN CHILE", - 6.2, - 0.10940000000000001 - ], - [ - "NEAR COAST OF CENTRAL CHILE", - 6.6, - 0.0257 - ], - [ - "FOX ISLANDS", - 6.7, - 0.0215 - ], - [ - "NEW BRITAIN REGION", - 6.9, - 0.0458 - ], - [ - "NEW BRITAIN REGION", - 7.3, - 0.0431 - ], - [ - "FOX ISLANDS", - 6, - 0.0356 - ], - [ - "NEW BRITAIN REGION", - 6.3, - 0.041100000000000005 - ], - [ - "NORTH OF HALMAHERA", - 6.1, - 0.1119 - ], - [ - "MINDANAO", - 7.3, - 0.6102000000000001 - 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magnitude=%{marker.color}
depth=%{customdata[2]}", - "hovertext": [ - "VANUATU ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF MARIANA ISLANDS", - "NORTHERN CHILE", - "NEAR COAST OF CENTRAL CHILE", - "FOX ISLANDS", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "FOX ISLANDS", - "NEW BRITAIN REGION", - "NORTH OF HALMAHERA", - "MINDANAO", - "MINDANAO", - "MINDANAO", - "MINDANAO", - "MINDANAO", - "OFF EAST COAST OF KAMCHATKA", - "NORTHERN MOLUCCA SEA", - "EASTERN NEW GUINEA REG.", - "ANDREANOF ISLANDS", - "NEW BRITAIN REGION", - "KURIL ISLANDS", - "VANUATU ISLANDS", - "ECUADOR", - "SOUTH OF MARIANA ISLANDS", - "SOUTH OF MARIANA ISLANDS", - "SOUTH OF MARIANA ISLANDS", - "NEW BRITAIN REGION", - "MAURITIUS - REUNION REGION", - "FIJI ISLANDS REGION", - "SOUTH OF MARIANA ISLANDS", - "SOLOMON ISLANDS", - "NORTHERN SUMATRA", - "OFF COAST OF JALISCO", - "ANDREANOF ISLANDS", - "SOUTH ISLAND", - "TONGA ISLANDS", - "FIJI ISLANDS REGION", - "VANUATU ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "HINDU KUSH REGION", - "ARU ISLANDS REGION", - "IRIAN JAYA REGION", - "IRIAN JAYA REGION", - "SOUTHWESTERN RYUKYU ISL.", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "HALMAHERA", - "GULF OF CALIFORNIA", - "SOUTHERN SUMATRA", - "SOUTHERN SUMATRA", - "PACIFIC-ANTARCTIC RIDGE", - "IRIAN JAYA REGION", - "TONGA ISLANDS", - "SOUTHEAST INDIAN RIDGE", - "PACIFIC-ANTARCTIC RIDGE", - "NEW BRITAIN REGION", - "BONIN ISLANDS", - "FIJI ISLANDS REGION", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOLOMON ISLANDS", - "BANDA SEA", - "SOUTHERN IRAN", - "BONIN ISLANDS", - "BONIN ISLANDS", - "NEAR ISLANDS", - "PACIFIC-ANTARCTIC RIDGE", - "VANUATU ISLANDS REGION", - "SOUTH OF FIJI ISLANDS", - "VANUATU ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "NEAR COAST OF CENTRAL CHILE", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "BONIN ISLANDS", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "SOUTHERN SUMATRA", - "NORTHERN SUMATRA", - "SOUTHWESTERN PAKISTAN", - "TAJIKISTAN", - "NORTHERN SUMATRA", - "SOUTHERN IRAN", - "JAN MAYEN ISLAND REGION", - "TONGA ISLANDS", - "MYANMAR-INDIA BORDER REGION", - "SOLOMON ISLANDS", - "CELEBES SEA", - "CELEBES SEA", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "TONGA ISLANDS", - "OFF COAST OF CENTRAL CHILE", - "SULAWESI", - "NEAR EAST COAST OF KAMCHATKA", - "SOUTH OF FIJI ISLANDS", - "SOUTH ISLAND", - "OAXACA", - "EASTER ISLAND REGION", - "NORTHERN CHILE", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEW BRITAIN REGION", - "BALI SEA", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "EASTERN HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR WEST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "EASTERN HONSHU", - "OFF EAST COAST OF HONSHU", - "VANUATU ISLANDS", - "NEAR EAST COAST OF HONSHU", - "PHILIPPINE ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "MYANMAR", - "NEAR EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "CRETE", - "FIJI ISLANDS REGION", - "SOUTH OF JAVA", - "CHIAPAS", - "NEAR EAST COAST OF HONSHU", - "KYUSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SOUTH OF KERMADEC ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SULAWESI", - "SOUTH OF PANAMA", - "OFF EAST COAST OF HONSHU", - "LOYALTY ISLANDS", - "COSTA RICA", - "NEAR EAST COAST OF HONSHU", - "HINDU KUSH REGION", - "CENTRAL MID-ATLANTIC RIDGE", - "SOLOMON ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "OFF EAST COAST OF HONSHU", - "WEST OF MACQUARIE ISLAND", - "SOUTH ISLAND", - "NORTHERN MOLUCCA SEA", - "NEW BRITAIN REGION", - "CHILE-BOLIVIA BORDER REGION", - "SANTA CRUZ ISLANDS", - "NEAR EAST COAST OF HONSHU", - "FOX ISLANDS", - "SANTA CRUZ ISLANDS", - "IRIAN JAYA REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "NEGROS", - "SOUTH SANDWICH ISLANDS REGION", - "OFF COAST OF CENTRAL CHILE", - "ALASKA PENINSULA", - "TAJIKISTAN", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEW IRELAND REGION", - "GULF OF CALIFORNIA", - "SOUTH OF FIJI ISLANDS", - "NEAR EAST COAST OF HONSHU", - "VANUATU ISLANDS REGION", - "NEAR N COAST OF NEW GUINEA", - "NEAR S. COAST OF HONSHU", - "KURIL ISLANDS", - "ASCENSION ISLAND REGION", - "CERAM SEA", - "OFF EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "SUNDA STRAIT", - "PERU-BRAZIL BORDER REGION", - "VANUATU ISLANDS", - "BANDA SEA", - "SANTA CRUZ ISLANDS", - "FOX ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "SOUTH SANDWICH ISLANDS REGION", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "NORTHERN SUMATRA", - "AUCKLAND ISLANDS", - "VANCOUVER ISLAND", - "NEAR ISLANDS", - "EAST OF NORTH ISLAND", - "NEAR EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SIKKIM", - "TONGA ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "SOUTH OF BALI", - "EASTERN NEW GUINEA REG.", - "SOUTHEASTERN SIBERIA", - "NEW BRITAIN REGION", - "HOKKAIDO", - "KERMADEC ISLANDS REGION", - "TURKEY", - "TURKEY", - "FIJI ISLANDS REGION", - "NEAR COAST OF PERU", - "REVILLA GIGEDO ISLANDS REGION", - "PACIFIC-ANTARCTIC RIDGE", - "NICARAGUA", - "NORTHEAST OF TAIWAN", - "SOUTHERN EAST PACIFIC RISE", - "SOUTHERN MOLUCCA SEA", - "OFF COAST OF ECUADOR", - "CENTRAL BOLIVIA", - "NEAR EAST COAST OF HONSHU", - "HOKKAIDO", - "NEW IRELAND REGION", - "LUZON", - "NEAR COAST OF NORTHERN CHILE", - "GUERRERO", - "SOUTH SANDWICH ISLANDS REGION", - "MINAHASSA PENINSULA", - "EASTERN NEW GUINEA REG.", - "TONGA ISLANDS", - "SOUTHWESTERN SIBERIA", - "SOUTHEAST OF HONSHU", - "SANTA CRUZ ISLANDS", - "OFF W COAST OF NORTHERN SUMATRA", - "SOUTH SHETLAND ISLANDS", - "SOUTH SHETLAND ISLANDS", - "NEAR COAST OF CHIAPAS", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "SOUTH OF FIJI ISLANDS", - "NEAR COAST OF PERU", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "NEGROS", - "NEGROS", - "FIJI ISLANDS REGION", - "SOLOMON ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTHWESTERN SIBERIA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "VANUATU ISLANDS", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEW BRITAIN REGION", - "IRIAN JAYA", - "NEAR COAST OF GUERRERO", - "EASTERN NEW GUINEA REG.", - "NEAR COAST OF CENTRAL CHILE", - "NORTHERN EAST PACIFIC RISE", - "NEAR EAST COAST OF HONSHU", - "NEAR COAST OF GUERRERO", - "NEW IRELAND REGION", - "OFF W COAST OF NORTHERN SUMATRA", - "NORTH INDIAN OCEAN", - "OFF W COAST OF NORTHERN SUMATRA", - "OFF COAST OF OREGON", - "MICHOACAN", - "BAJA CALIFORNIA", - "BAJA CALIFORNIA", - "DRAKE PASSAGE", - "VANUATU ISLANDS", - "OFF W COAST OF NORTHERN SUMATRA", - "NEAR COAST OF CENTRAL CHILE", - "EASTERN NEW GUINEA REG.", - "EAST OF SOUTH SANDWICH ISLANDS", - "IRIAN JAYA REGION", - "IRIAN JAYA REGION", - "KERMADEC ISLANDS REGION", - "TONGA ISLANDS", - "NEAR COAST OF CHIAPAS", - "PERU-BOLIVIA BORDER REGION", - "OFF COAST OF SOUTHERN CHILE", - "NORTHERN ITALY", - "OFF EAST COAST OF HONSHU", - "HOKKAIDO", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "GREENLAND SEA", - "BONIN ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "SALTA PROVINCE", - "SOUTH OF PANAMA", - "SOUTH OF PANAMA", - "OFF EAST COAST OF HONSHU", - "CHILE-ARGENTINA BORDER REGION", - "SOUTHERN PERU", - "DODECANESE ISLANDS", - "LUZON", - "NEAR EAST COAST OF HONSHU", - "NEAR ISLANDS", - "NORTHERN SUMATRA", - "NEAR EAST COAST OF KAMCHATKA", - "NORTHERN XINJIANG", - "COOK STRAIT", - "VANUATU ISLANDS", - "KURIL ISLANDS", - "KURIL ISLANDS", - "NORTHERN SUMATRA", - "SOLOMON ISLANDS", - "MAURITIUS - REUNION REGION", - "NEW IRELAND REGION", - "PERU-BRAZIL BORDER REGION", - "NEW IRELAND REGION", - "FOX ISLANDS", - "ARMENIA-AZERBAIJAN-IRAN BORD REG", - "ARMENIA-AZERBAIJAN-IRAN BORD REG", - "XIZANG", - "SEA OF OKHOTSK", - "SULAWESI", - "NEAR N COAST OF NEW GUINEA", - "NORTHERN MOLUCCA SEA", - "OFF COAST OF CENTRAL AMERICA", - "JAN MAYEN ISLAND REGION", - "PHILIPPINE ISLANDS REGION", - "SOUTH OF JAVA", - "SANTA CRUZ ISLANDS", - "COSTA RICA", - "IRIAN JAYA REGION", - "KURIL ISLANDS", - "SOUTHERN SUMATRA", - "BAJA CALIFORNIA", - "ANDREANOF ISLANDS", - "COLOMBIA", - "OFF EAST COAST OF HONSHU", - "GULF OF CALIFORNIA", - "BANDA SEA", - "WEST OF MACQUARIE ISLAND", - "IRIAN JAYA REGION", - "CELEBES SEA", - "VANUATU ISLANDS", - "COSTA RICA", - "QUEEN CHARLOTTE ISLANDS REGION", - "QUEEN CHARLOTTE ISLANDS REGION", - "QUEEN CHARLOTTE ISLANDS REGION", - "MINDANAO", - "NEAR COAST OF CHIAPAS", - "VANCOUVER ISLAND", - "CENTRAL PERU", - "MYANMAR", - "NEAR COAST OF CHIAPAS", - "GULF OF ALASKA", - "OFF COAST OF SOUTHERN CHILE", - "NEAR COAST OF CENTRAL CHILE", - "GUERRERO", - "KURIL ISLANDS", - "NEW BRITAIN REGION", - "NEAR N COAST OF NEW GUINEA", - "VANUATU ISLANDS", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NORTH ISLAND", - "BANDA SEA", - "NORTHERN MOLUCCA SEA", - "OFF W. COAST OF BAJA CALIFORNIA", - "NEAR ISLANDS", - "NEW IRELAND REGION", - "MINAHASSA PENINSULA", - "VANUATU ISLANDS", - "SOUTHEASTERN ALASKA", - "PACIFIC-ANTARCTIC RIDGE", - "NORTHERN SUMATRA", - "LAKE ISSYK-KUL REGION", - "CENTRAL CHILE", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "HOKKAIDO", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS REGION", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS REGION", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "COLOMBIA", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "EASTERN SIBERIA", - "MINDANAO", - "SANTIAGO DEL ESTERO PROV.", - "KURIL ISLANDS", - "KURIL ISLANDS", - "KURIL ISLANDS", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "EAST OF KURIL ISLANDS", - "VANUATU ISLANDS REGION", - "GUATEMALA", - "TAIWAN", - "OFF EAST COAST OF HONSHU", - "E. RUSSIA-N.E. CHINA BORDER REG.", - "IRIAN JAYA", - "SOUTHERN IRAN", - "VANUATU ISLANDS", - "SOLOMON ISLANDS", - "SOUTHERN IRAN", - "NEAR N COAST OF NEW GUINEA", - "KURIL ISLANDS", - "EAST OF KURIL ISLANDS", - "SICHUAN", - "BANDA SEA", - "KURIL ISLANDS", - "SOUTHEAST OF HONSHU", - "MICHOACAN", - "NEW IRELAND REGION", - "KERMADEC ISLANDS REGION", - "SOUTHERN IRAN", - "TONGA ISLANDS", - "MARIANA ISLANDS", - "NEAR EAST COAST OF HONSHU", - "OFF COAST OF SOUTHERN CHILE", - "OFF EAST COAST OF KAMCHATKA", - "OFF EAST COAST OF KAMCHATKA", - "SOUTH OF FIJI ISLANDS", - "TONGA ISLANDS", - "SEA OF OKHOTSK", - "SEA OF OKHOTSK", - "TAIWAN", - "SANTA CRUZ ISLANDS", - "SOUTH OF JAVA", - "SOUTH OF KERMADEC ISLANDS", - "CRETE", - "NEAR COAST OF NICARAGUA", - "CRETE", - "NORTHERN MID-ATLANTIC RIDGE", - "NORTHERN SUMATRA", - "SOLOMON ISLANDS", - "SOUTHERN SUMATRA", - "NEW IRELAND REGION", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTHERN PERU", - "COOK STRAIT", - "GANSU", - "PRINCE EDWARD ISLANDS REGION", - "SOUTH OF FIJI ISLANDS", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS", - "BANDA SEA", - "KERMADEC ISLANDS REGION", - "NEAR COAST OF NORTHERN PERU", - "SOUTH OF PANAMA", - "COOK STRAIT", - "SOUTHWEST INDIAN RIDGE", - "NEAR COAST OF GUERRERO", - "KERMADEC ISLANDS REGION", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "BANDA SEA", - "QUEEN CHARLOTTE ISLANDS REGION", - "SOUTHEAST OF HONSHU", - "QUEEN CHARLOTTE ISLANDS REGION", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "NORTHERN MID-ATLANTIC RIDGE", - "PHILIPPINE ISLANDS REGION", - "NEAR COAST OF CHIAPAS", - "CENTRAL EAST PACIFIC RISE", - "ANDREANOF ISLANDS", - "FLORES SEA", - "PAKISTAN", - "SOUTHERN EAST PACIFIC RISE", - "NEAR COAST OF PERU", - "PAKISTAN", - "KERMADEC ISLANDS", - "SEA OF OKHOTSK", - "MID-INDIAN RIDGE", - "SOUTH OF MARIANA ISLANDS", - "WEST CHILE RISE", - "SAKHALIN", - "KERMADEC ISLANDS", - "NEAR COAST OF VENEZUELA", - "CRETE", - "MINDANAO", - "SOLOMON ISLANDS", - "GULF OF CALIFORNIA", - "SOUTH OF FIJI ISLANDS", - "EAST OF SOUTH SANDWICH ISLANDS", - "OFF EAST COAST OF HONSHU", - "BALLENY ISLANDS REGION", - "OFF COAST OF CENTRAL CHILE", - "TAIWAN", - "NEAR COAST OF CENTRAL CHILE", - "EASTER ISLAND REGION", - "TONGA ISLANDS REGION", - "CELEBES SEA", - "NEAR EAST COAST OF KAMCHATKA", - "SCOTIA SEA", - "SCOTIA SEA", - "SCOTIA SEA", - "HALMAHERA", - "MARIANA ISLANDS", - "FIJI ISLANDS REGION", - "KURIL ISLANDS", - "SOUTH ATLANTIC OCEAN", - "BANDA SEA", - "NORTHERN SUMATRA", - "KURIL ISLANDS", - "MARIANA ISLANDS REGION", - "VANUATU ISLANDS", - "PUERTO RICO REGION", - "NORTH ISLAND", - "TONGA ISLANDS", - "JAVA", - "GREECE", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH OF KERMADEC ISLANDS", - "GREECE", - "VANUATU ISLANDS", - "XIZANG", - "NORTH ATLANTIC OCEAN", - "FOX ISLANDS", - "NEAR COAST OF NICARAGUA", - "RYUKYU ISLANDS", - "NEAR COAST OF CHIAPAS", - "VANUATU ISLANDS", - "OFF COAST OF NORTHERN CALIFORNIA", - "EASTERN NEW GUINEA REG.", - "EAST OF SOUTH SANDWICH ISLANDS", - "BISMARCK SEA", - "KYUSHU", - "NEAR COAST OF PERU", - "NEAR COAST OF NORTHERN PERU", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "NICOBAR ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "SOUTH OF FIJI ISLANDS", - "SANTA CRUZ ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "OFF COAST OF NORTHERN CHILE", - "SOUTH OF PANAMA", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "SOLOMON ISLANDS", - "NICARAGUA", - "NEAR COAST OF NORTHERN CHILE", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "NEAR COAST OF NICARAGUA", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "BOUVET ISLAND REGION", - "BALLENY ISLANDS REGION", - "SANTA CRUZ ISLANDS REGION", - "GUERRERO", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "VANCOUVER ISLAND", - "TONGA ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTH OF FIJI ISLANDS", - "NEAR S. COAST OF HONSHU", - "THAILAND", - "WEST CHILE RISE", - "SOLOMON ISLANDS", - "GUERRERO", - "TONGA ISLANDS", - "GUERRERO", - "SOUTHERN EAST PACIFIC RISE", - "SOUTH OF PANAMA", - "E. CAROLINE ISLANDS", - "E. CAROLINE ISLANDS", - "NEGROS", - "OFF W COAST OF NORTHERN SUMATRA", - "BAY OF BENGAL", - "AEGEAN SEA", - "OFF COAST OF JALISCO", - "SOUTH INDIAN OCEAN", - "VANUATU ISLANDS", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS", - "RAT ISLANDS", - "RAT ISLANDS", - "RAT ISLANDS", - "RAT ISLANDS", - "RAT ISLANDS", - "VOLCANO ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS", - "TONGA ISLANDS", - "BONIN ISLANDS", - "BALLENY ISLANDS REGION", - "BALLENY ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "NEW BRITAIN REGION", - "OFF W COAST OF NORTHERN SUMATRA", - "NEAR COAST OF CHIAPAS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "MINDANAO", - "MINDANAO", - "SOUTHEASTERN ALASKA", - "TONGA ISLANDS", - "TONGA ISLANDS", - "OWEN FRACTURE ZONE REGION", - "KURIL ISLANDS", - "FIJI ISLANDS REGION", - "SOUTHEASTERN ALASKA", - "NORTHERN MID-ATLANTIC RIDGE", - "OAXACA", - "BISMARCK SEA", - "BISMARCK SEA", - "E. CAROLINE ISLANDS", - "XIZANG", - "YUNNAN", - "BANDA SEA", - "BANDA SEA", - "HOKKAIDO", - "HOKKAIDO", - "IRAN-IRAQ BORDER REGION", - "IRAN-IRAQ BORDER REGION", - "NEAR COAST OF CENTRAL CHILE", - "NORTHERN CALIFORNIA", - "CENTRAL PERU", - "CENTRAL PERU", - "EASTER ISLAND REGION", - "TONGA ISLANDS", - "EASTER ISLAND REGION", - "OFF COAST OF JALISCO", - "SOUTHERN MOLUCCA SEA", - "MARIANA ISLANDS", - "JUJUY PROVINCE", - "SOLOMON ISLANDS", - "SOUTHERN ALASKA", - "NEW BRITAIN REGION", - "YUNNAN", - "GULF OF CALIFORNIA", - "SOUTHERN EAST PACIFIC RISE", - "SOUTHERN EAST PACIFIC RISE", - "HOKKAIDO", - "OFF COAST OF CENTRAL AMERICA", - "TONGA ISLANDS", - "EASTER ISLAND REGION", - "FIJI ISLANDS REGION", - "BALLENY ISLANDS REGION", - "MID-INDIAN RIDGE", - "NEW BRITAIN REGION", - "PRIMOR'YE", - "FIJI ISLANDS REGION", - "NORTHERN MOLUCCA SEA", - "OFF E. COAST OF N. ISLAND", - "PRINCE EDWARD ISLANDS REGION", - "NORTHERN MOLUCCA SEA", - "SICHUAN", - "EASTERN HONSHU", - "NORTHERN MOLUCCA SEA", - "MINDANAO", - "SOUTH OF PANAMA", - "BANDA SEA", - "SOLOMON ISLANDS", - "NEAR COAST OF GUATEMALA", - "SOUTH OF PANAMA", - "TAIWAN REGION", - "PHILIPPINE ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "NORTHERN MOLUCCA SEA", - "SOUTH OF PANAMA", - "SOUTH OF PANAMA", - "MINDANAO", - "FIJI ISLANDS REGION", - "SOUTHEAST OF EASTER ISLAND", - "SOUTH OF PANAMA", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "ADMIRALTY ISLANDS REGION", - "MENDOZA PROVINCE", - "JUJUY PROVINCE", - "REYKJANES RIDGE", - "TAIWAN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS REGION", - "VANUATU ISLANDS", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF COAST OF JALISCO", - "FLORES SEA", - "SOUTHERN SUMATRA", - "MID-INDIAN RIDGE", - "NORTHERN COLOMBIA", - "MINAHASSA PENINSULA", - "NORTHERN MOLUCCA SEA", - "NEAR COAST OF CENTRAL CHILE", - "FIJI ISLANDS REGION", - "NORTHERN CHILE", - "NEW BRITAIN REGION", - "SAMOA ISLANDS REGION", - "SAMOA ISLANDS REGION", - "SAMOA ISLANDS REGION", - "NEW BRITAIN REGION", - "TONGA ISLANDS", - "CRETE", - "FIJI ISLANDS REGION", - "TAIWAN REGION", - "TAIWAN REGION", - "SANTA CRUZ ISLANDS", - "SOUTH ISLAND", - "QUEEN CHARLOTTE ISLANDS REGION", - "NEPAL", - "NEPAL", - "NEPAL", - "NEPAL", - "FIJI ISLANDS REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "MOROCCO", - "SOLOMON ISLANDS", - "NEPAL", - "NEPAL", - "NEAR EAST COAST OF HONSHU", - "SOUTHERN SUMATRA", - "PACIFIC-ANTARCTIC RIDGE", - "TONGA ISLANDS", - "SANTA CRUZ ISLANDS REGION", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOUTHERN MID-ATLANTIC RIDGE", - "TONGA ISLANDS", - "ALASKA PENINSULA", - "BONIN ISLANDS", - "TONGA ISLANDS", - "SOUTHEAST OF HONSHU", - "BORNEO", - "HOKKAIDO", - "NORTHERN CHILE", - "SAMOA ISLANDS REGION", - "FIJI ISLANDS REGION", - "SOUTHERN MID-ATLANTIC RIDGE", - "NEAR COAST OF CENTRAL CHILE", - "FIJI ISLANDS REGION", - "BONIN ISLANDS", - "NEW BRITAIN REGION", - "SOUTHERN XINJIANG", - "LEYTE", - "EAST OF KURIL ISLANDS", - "SOLOMON ISLANDS", - "NORTH ATLANTIC OCEAN", - "SANTA CRUZ ISLANDS", - "FOX ISLANDS", - "IRIAN JAYA", - "SOUTHERN ALASKA", - "SOUTH OF FIJI ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "MID-INDIAN RIDGE", - "SOLOMON ISLANDS", - "KERMADEC ISLANDS REGION", - "SOUTHEAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "FOX ISLANDS", - "GULF OF CALIFORNIA", - "NORTHERN MOLUCCA SEA", - "NORTHERN MOLUCCA SEA", - "NEW BRITAIN REGION", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "NORTHERN MID-ATLANTIC RIDGE", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "IRIAN JAYA REGION", - "IRIAN JAYA REGION", - "NEAR COAST OF CENTRAL CHILE", - "JUJUY PROVINCE", - "PACIFIC-ANTARCTIC RIDGE", - "EAST OF KURIL ISLANDS", - "EAST OF KURIL ISLANDS", - "TONGA ISLANDS", - "VANUATU ISLANDS", - "BOUVET ISLAND REGION", - "PRINCE EDWARD ISLANDS REGION", - "PRINCE EDWARD ISLANDS REGION", - "HINDU KUSH REGION", - "TIMOR REGION", - "OFF COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NICOBAR ISLANDS", - "ANDREANOF ISLANDS", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "NORTHWEST OF RYUKYU ISLANDS", - "GREECE", - "SOLOMON ISLANDS", - "BANDA SEA", - "MARIANA ISLANDS", - "PERU-BRAZIL BORDER REGION", - "PERU-BRAZIL BORDER REGION", - "PERU-BRAZIL BORDER REGION", - "NEAR COAST OF NORTHERN CHILE", - "SOUTHEAST INDIAN RIDGE", - "TAJIKISTAN", - "BANDA SEA", - "FIJI ISLANDS REGION", - "NEAR COAST OF CHIAPAS", - "VANUATU ISLANDS", - "BORNEO", - "SOUTHERN EAST PACIFIC RISE", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "MYANMAR-INDIA BORDER REGION", - "MYANMAR-INDIA BORDER REGION", - "PACIFIC-ANTARCTIC RIDGE", - "TALAUD ISLANDS", - "HOKKAIDO", - "SOUTHERN BOLIVIA", - "HOKKAIDO", - "OFF COAST OF JALISCO", - "SOUTHERN ALASKA", - "STRAIT OF GIBRALTAR", - "KAMCHATKA PENINSULA", - "BALLENY ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "TAIWAN", - "SOLOMON ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "SUMBA REGION", - "SOUTHERN EAST PACIFIC RISE", - "HALMAHERA", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "SOUTHWEST OF SUMATRA", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "LEEWARD ISLANDS", - "NEAR EAST COAST OF KAMCHATKA", - "NEAR N COAST OF NEW GUINEA", - "ALASKA PENINSULA", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "JAVA", - "VANUATU ISLANDS", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "MYANMAR-INDIA BORDER REGION", - "MINDANAO", - "KYUSHU", - "KYUSHU", - "VANUATU ISLANDS", - "OFF COAST OF CHIAPAS", - "KYUSHU", - "NEAR COAST OF ECUADOR", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF CHIAPAS", - "NEAR COAST OF CHIAPAS", - "VANUATU ISLANDS", - "NORTHERN EAST PACIFIC RISE", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "NORTHERN TERRITORY", - "FIJI ISLANDS REGION", - "FIJI ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "TAIWAN REGION", - "SOUTHERN SUMATRA", - "BANDA SEA", - "KERMADEC ISLANDS", - "OFF COAST OF JALISCO", - "NORTHERN MOLUCCA SEA", - "SOUTH OF SUMBAWA", - "NICARAGUA", - "SOLOMON ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "LOYALTY ISLANDS", - "NORTHERN MID-ATLANTIC RIDGE", - "NEW IRELAND REGION", - "TAJIKISTAN-XINJIANG BORDER REG.", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "NEAR COAST OF ECUADOR", - "KERMADEC ISLANDS REGION", - "VANUATU ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "ADMIRALTY ISLANDS REGION", - "MARIANA ISLANDS", - "JUJUY PROVINCE", - "VOLCANO ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTH GEORGIA ISLAND REGION", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SOUTH GEORGIA ISLAND REGION", - "FLORES SEA", - "CENTRAL ITALY", - "MYANMAR", - "NORTH OF ASCENSION ISLAND", - "NEW IRELAND REGION", - "OFF E. COAST OF N. ISLAND", - "OFF E. COAST OF N. ISLAND", - "KOMANDORSKIYE OSTROVA REGION", - "MACQUARIE ISLAND REGION", - "NORTHERN PERU", - "NORTHERN COLOMBIA", - "SOLOMON ISLANDS", - "NEAR NORTH COAST OF IRIAN JAYA", - "SOUTHEAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "MINDANAO", - "TONGA ISLANDS", - "FIJI ISLANDS REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "JAVA SEA", - "WESTERN HONSHU", - "TONGA ISLANDS", - "CENTRAL ITALY", - "CENTRAL ITALY", - "CHILE-ARGENTINA BORDER REGION", - "NEAR EAST COAST OF HONSHU", - "SOUTH ISLAND", - "SOUTH ISLAND", - "SOUTH ISLAND", - "SOUTH ISLAND", - "SOUTH ISLAND", - "SAN JUAN PROVINCE", - "NEAR EAST COAST OF HONSHU", - "OFF COAST OF CENTRAL AMERICA", - "SOUTHERN XINJIANG", - "SOUTHERN PERU", - "NEAR ISLANDS", - "BANDA SEA", - "NORTHERN SUMATRA", - "OFF COAST OF NORTHERN CALIFORNIA", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "MARIANA ISLANDS REGION", - "NEW IRELAND REGION", - "NEW IRELAND REGION", - "SOLOMON ISLANDS", - "W. CAROLINE ISLANDS", - "PERU-BRAZIL BORDER REGION", - "SOLOMON ISLANDS", - "BANDA SEA", - "NEW IRELAND REGION", - "SOUTHERN CHILE", - "SUMBAWA REGION", - "SOUTH OF FIJI ISLANDS", - "SOUTH OF FIJI ISLANDS", - "QUEEN ELIZABETH ISLANDS", - "CELEBES SEA", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOUTHWESTERN PAKISTAN", - "MINDANAO", - "JUJUY PROVINCE", - "SOUTHERN BOLIVIA", - "SOUTH OF FIJI ISLANDS", - "SOLOMON ISLANDS", - "NEW BRITAIN REGION", - "NICOBAR ISLANDS", - "SOLOMON ISLANDS", - "NEAR ISLANDS", - "NEAR EAST COAST OF KAMCHATKA", - "BOTSWANA", - "NORTHERN AND CENTRAL IRAN", - "CHILE-ARGENTINA BORDER REGION", - "FIJI ISLANDS REGION", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "MINDANAO", - "SOUTHEASTERN ALASKA", - "SOUTHEASTERN ALASKA", - "TAJIKISTAN", - "ANDREANOF ISLANDS", - "RYUKYU ISLANDS", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "OFF COAST OF CENTRAL AMERICA", - "NEW BRITAIN REGION", - "NEGROS", - "SULAWESI", - "NEAR ISLANDS", - "AEGEAN SEA", - "GUATEMALA", - "KERMADEC ISLANDS", - "SOUTH OF FIJI ISLANDS", - "NEAR COAST OF GUATEMALA", - "FIJI ISLANDS REGION", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS REGION", - "NEAR COAST OF ECUADOR", - "LEYTE", - "AUCKLAND ISLANDS", - "NEW IRELAND REGION", - "KOMANDORSKIYE OSTROVA REGION", - "KOMANDORSKIYE OSTROVA REGION", - "NEAR COAST OF PERU", - "MAURITIUS - REUNION REGION", - "DODECANESE ISLANDS", - "SOUTHEAST OF RYUKYU ISLANDS", - "NORTH ATLANTIC OCEAN", - "GANSU", - "NORTHERN XINJIANG", - "MINDORO", - "SOUTHERN SUMATRA", - "NORTH OF ASCENSION ISLAND", - "FIJI ISLANDS REGION", - "ADMIRALTY ISLANDS REGION", - "SOUTHERN SUMATRA", - "NORTH KOREA", - "SOUTH SANDWICH ISLANDS REGION", - "BONIN ISLANDS", - "NEAR COAST OF CHIAPAS", - "CENTRAL MEXICO", - "AUCKLAND ISLANDS", - "OFF EAST COAST OF HONSHU", - "VANUATU ISLANDS", - "OAXACA", - "SOUTH OF FIJI ISLANDS", - "OFF EAST COAST OF HONSHU", - "TONGA ISLANDS", - "BALLENY ISLANDS REGION", - "RAT ISLANDS", - "NORTHERN CHILE", - "BOUVET ISLAND REGION", - "TONGA ISLANDS", - "BANDA SEA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SERAM", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "TONGA ISLANDS", - "NEW GUINEA", - "SOUTHEAST OF HONSHU", - "ASCENSION ISLAND REGION", - "IRAN-IRAQ BORDER REGION", - "COSTA RICA", - "EASTERN XIZANG-INDIA BORDER REG.", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "SAMOA ISLANDS REGION", - "NEW IRELAND REGION", - "CENTRAL MID-ATLANTIC RIDGE", - "NORTHERN AND CENTRAL IRAN", - "EASTERN NEW GUINEA REG.", - "NEAR COAST OF ECUADOR", - "W. CAROLINE ISLANDS", - "W. CAROLINE ISLANDS", - "W. CAROLINE ISLANDS", - "NORTHERN AND CENTRAL IRAN", - "NORTHERN AND CENTRAL IRAN", - "BOUVET ISLAND REGION", - "JAVA", - "NORTH OF HONDURAS", - "MYANMAR", - "NEAR COAST OF PERU", - "GULF OF CALIFORNIA", - "NORTHERN CHILE", - "JAVA", - "GULF OF ALASKA", - "HOKKAIDO", - "KOMANDORSKIYE OSTROVA REGION", - "NEAR N COAST OF NEW GUINEA", - "SOUTHWEST OF AFRICA", - "HINDU KUSH REGION", - "FIJI ISLANDS REGION", - "PACIFIC-ANTARCTIC RIDGE", - "TAIWAN", - "TAIWAN", - "SOUTH OF MARIANA ISLANDS", - "OAXACA", - "NEW GUINEA", - "NEW GUINEA", - "CERAM SEA", - "NEW GUINEA", - "WEST OF MACQUARIE ISLAND", - "NEW GUINEA", - "NEW GUINEA", - "NEW GUINEA", - "NEW IRELAND REGION", - "NEW BRITAIN REGION", - "SOUTHEAST INDIAN RIDGE", - "BANDA SEA", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "SOUTH OF FIJI ISLANDS", - "SOUTHERN BOLIVIA", - "MINDANAO", - "NEW GUINEA", - "NEAR COAST OF CENTRAL CHILE", - "NORTHERN MOLUCCA SEA", - "PRINCE EDWARD ISLANDS REGION", - "EASTER ISLAND REGION", - "HAWAII", - "LUZON", - "NEW BRITAIN REGION", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "SOUTH OF KERMADEC ISLANDS", - "VANUATU ISLANDS", - "NEAR EAST COAST OF KAMCHATKA", - "KERMADEC ISLANDS", - "VANUATU ISLANDS", - "EASTERN GULF OF ADEN", - "EASTERN GULF OF ADEN", - "SANTA CRUZ ISLANDS", - "NEW BRITAIN REGION", - "FLORES SEA", - "SUMBAWA REGION", - "SUMBAWA REGION", - "NORTHERN ALASKA", - "NORTHERN ALASKA", - "SOUTH SANDWICH ISLANDS REGION", - "ANDREANOF ISLANDS", - "VOLCANO ISLANDS", - "FLORES SEA", - "COSTA RICA", - "FIJI ISLANDS REGION", - "FIJI ISLANDS REGION", - "SUMBAWA REGION", - "FIJI ISLANDS REGION", - "SUMBAWA REGION", - "NEAR COAST OF VENEZUELA", - "VANUATU ISLANDS", - "OFF COAST OF OREGON", - "ANDREANOF ISLANDS", - "PERU-BRAZIL BORDER REGION", - "WESTERN IRAN", - "TIMOR REGION", - "MARIANA ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "HOKKAIDO", - "FIJI ISLANDS", - "ECUADOR", - "MINDANAO", - "SOLOMON ISLANDS", - "KERMADEC ISLANDS REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTHWEST INDIAN RIDGE", - "MINAHASSA PENINSULA", - "MINAHASSA PENINSULA", - "FIJI ISLANDS REGION", - "SUMBA REGION", - "KURIL ISLANDS", - "BALI SEA", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "KURIL ISLANDS", - "NORTHWEST OF KURIL ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANCOUVER ISLAND", - "VANCOUVER ISLAND", - "VANCOUVER ISLAND", - "IONIAN SEA", - "OFF COAST OF CENTRAL AMERICA", - "DRAKE PASSAGE", - "NORTH ISLAND", - "NORTHERN CHILE", - "MINDANAO", - "JAN MAYEN ISLAND REGION", - "TONGA ISLANDS", - "NORTH ATLANTIC OCEAN", - "NEAR EAST COAST OF KAMCHATKA", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTHERN EAST PACIFIC RISE", - "SOLOMON ISLANDS", - "FIJI ISLANDS REGION", - "CARIBBEAN SEA", - "IRAN-IRAQ BORDER REGION", - "SOUTHERN ALASKA", - "BANDA SEA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "PACIFIC-ANTARCTIC RIDGE", - "IRIAN JAYA", - "SOUTHEAST OF EASTER ISLAND", - "KOMANDORSKIYE OSTROVA REGION", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "KOMANDORSKIYE OSTROVA REGION", - "MINDANAO", - "ALASKA PENINSULA", - "WESTERN BRAZIL", - "NORTHERN MOLUCCA SEA", - "KYUSHU", - "VANUATU ISLANDS", - "BISMARCK SEA", - "VANUATU ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "SUMBA REGION", - "SUMBA REGION", - "PRINCE EDWARD ISLANDS REGION", - "SOLOMON ISLANDS", - "FIJI ISLANDS REGION", - "NEAR COAST OF CHIAPAS", - "SOUTHERN SUMATRA", - "SOUTHERN SUMATRA", - "MARIANA ISLANDS", - "NORTHERN MID-ATLANTIC RIDGE", - "NEW IRELAND REGION", - "PERU-ECUADOR BORDER REGION", - "CENTRAL PERU", - "SOUTH OF KERMADEC ISLANDS", - "PHILIPPINE ISLANDS REGION", - "FIJI ISLANDS REGION", - "D'ENTRECASTEAUX ISLANDS REGION", - "CENTRAL BOLIVIA", - "VANUATU ISLANDS", - "COLOMBIA", - "NORTHERN MOLUCCA SEA", - "EAST OF KURIL ISLANDS", - "NEW BRITAIN REGION", - "NEAR COAST OF ECUADOR", - "MAURITIUS - REUNION REGION", - "RAT ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "BANDA SEA", - "SOUTH SANDWICH ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "SULAWESI", - "TAIWAN", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "LUZON", - "SOUTH SANDWICH ISLANDS REGION", - "SAMAR", - "SOUTH OF FIJI ISLANDS", - "CARLSBERG RIDGE", - "SOLOMON ISLANDS", - "EASTERN NEW GUINEA REG.", - "KYUSHU", - "PANAMA-COSTA RICA BORDER REGION", - "NEW BRITAIN REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "ANDREANOF ISLANDS", - "NORTHERN PERU", - "EL SALVADOR", - "FIJI ISLANDS REGION", - "MINDANAO", - "TONGA ISLANDS", - "SOUTHEAST OF HONSHU", - "OFF COAST OF CENTRAL CHILE", - "TONGA ISLANDS", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS", - "NEAR WEST COAST OF HONSHU", - "KERMADEC ISLANDS", - "IRIAN JAYA", - "KERMADEC ISLANDS", - "IRIAN JAYA", - "BANDA SEA", - "KOMANDORSKIYE OSTROVA REGION", - "KOMANDORSKIYE OSTROVA REGION", - "PANAMA-COSTA RICA BORDER REGION", - "KERMADEC ISLANDS REGION", - "MARIANA ISLANDS", - "VANUATU ISLANDS", - "QUEEN CHARLOTTE ISLANDS REGION", - "CENTRAL CALIFORNIA", - "CENTRAL CALIFORNIA", - "NORTHERN MOLUCCA SEA", - "SOLOMON ISLANDS", - "NORTHWEST OF RYUKYU ISLANDS", - "WESTERN AUSTRALIA", - "HALMAHERA", - "NEW BRITAIN REGION", - "BALLENY ISLANDS REGION", - "PHILIPPINE ISLANDS REGION", - "NEAR S. COAST OF HONSHU", - "VANUATU ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "SOUTHERN EAST PACIFIC RISE", - "SOUTHWEST OF SUMATRA", - "NEAR EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "OFF COAST OF OREGON", - "FIJI ISLANDS REGION", - "JAVA", - "MONA PASSAGE", - "SOUTHWEST INDIAN RIDGE", - "SERAM", - "CENTRAL CHILE", - "KERMADEC ISLANDS", - "MINDANAO", - "OFF COAST OF CENTRAL CHILE", - "MINDANAO", - "VANUATU ISLANDS", - "NORTH OF ASCENSION ISLAND", - "MINDANAO", - "MINDANAO", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "TONGA ISLANDS", - "EAST OF SOUTH SANDWICH ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "TONGA ISLANDS", - "NORTHERN MOLUCCA SEA", - "NORTHERN MOLUCCA SEA", - "OFF COAST OF CHIAPAS", - "SEA OF OKHOTSK", - "LAOS", - "IRIAN JAYA REGION", - "ANDREANOF ISLANDS", - "ALBANIA", - "CRETE", - "ANDREANOF ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "VANUATU ISLANDS", - "MINDANAO", - "HINDU KUSH REGION", - "VANCOUVER ISLAND", - "VANCOUVER ISLAND", - "SANTIAGO DEL ESTERO PROV.", - "COLOMBIA", - "VANCOUVER ISLAND", - "SOUTHERN EAST PACIFIC RISE", - "NORTHERN SUMATRA", - "PUERTO RICO REGION", - "NEW BRITAIN REGION", - "EASTERN SIBERIA", - "NEAR NORTH COAST OF IRIAN JAYA", - "SOUTHERN XINJIANG", - "MINAHASSA PENINSULA", - "SOUTH SANDWICH ISLANDS REGION", - "ANDREANOF ISLANDS", - "TURKEY", - "ANDREANOF ISLANDS", - "SOLOMON ISLANDS", - "CUBA REGION", - "NORTH OF HONDURAS", - "SOLOMON ISLANDS", - "JAVA", - "MINDANAO", - "SOUTH SANDWICH ISLANDS REGION", - "NEW BRITAIN REGION", - "KURIL ISLANDS", - "TURKEY-IRAN BORDER REGION", - "KERMADEC ISLANDS REGION", - "SAMOA ISLANDS REGION", - "VANUATU ISLANDS", - "SOUTH OF BALI", - "CENTRAL EAST PACIFIC RISE", - "EAST OF KURIL ISLANDS", - "MINDANAO", - "WESTERN IDAHO", - "NORTHERN MOLUCCA SEA", - "MID-INDIAN RIDGE", - "KERMADEC ISLANDS REGION", - "NORTH OF HONDURAS", - "BONIN ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOLOMON ISLANDS", - "CRETE", - "BANDA SEA", - "SOLOMON ISLANDS", - "SANTA CRUZ ISLANDS", - "NEVADA", - "OFF COAST OF CENTRAL MEXICO", - "VANUATU ISLANDS", - "SOUTHERN PERU", - "NORTHERN CHILE", - "SOUTH OF FIJI ISLANDS", - "HALMAHERA", - "SOUTHERN MID-ATLANTIC RIDGE", - "RYUKYU ISLANDS", - "MARIANA ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "ICELAND REGION", - "MINAHASSA PENINSULA", - "NEAR COAST OF OAXACA", - "XIZANG", - "W. CAROLINE ISLANDS", - "JAVA SEA", - "EASTERN NEW GUINEA REG.", - "ANDAMAN ISLANDS", - "SAMOA ISLANDS REGION", - "FIJI ISLANDS REGION", - "ALASKA PENINSULA", - "SOUTH OF ALASKA", - "XIZANG", - "SOUTH SANDWICH ISLANDS REGION", - "ALASKA PENINSULA", - "MINDANAO", - "BISMARCK SEA", - "VANUATU ISLANDS", - "PRINCE EDWARD ISLANDS REGION", - "TANZANIA", - "SAMAR", - "SOUTHERN SUMATRA", - "SOUTHERN SUMATRA", - "BANDA SEA", - "ASCENSION ISLAND REGION", - "COSTA RICA", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "CENTRAL MID-ATLANTIC RIDGE", - "CHAGOS ARCHIPELAGO REGION", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF CENTRAL CHILE", - "VANUATU ISLANDS", - "CENTRAL MID-ATLANTIC RIDGE", - "MINDANAO", - "VANUATU ISLANDS", - "NORTHERN CHILE", - "NEAR EAST COAST OF HONSHU", - "KAMCHATKA PENINSULA", - "FIJI ISLANDS", - "CENTRAL MID-ATLANTIC RIDGE", - "SOUTH OF AFRICA", - "TONGA ISLANDS", - "NEW BRITAIN REGION", - "FIJI ISLANDS REGION", - "EASTERN NEW GUINEA REG.", - "SOUTH OF ALASKA", - "SOUTH OF FIJI ISLANDS", - "DODECANESE ISLANDS", - "OFF COAST OF SOUTHERN CHILE", - "SOUTH SHETLAND ISLANDS", - "TONGA ISLANDS", - "FIJI ISLANDS REGION", - "MINDANAO", - "OFF COAST OF CENTRAL CHILE", - "PRIMOR'YE", - "CHILE-ARGENTINA BORDER REGION", - "FOX ISLANDS", - "BALLENY ISLANDS REGION", - "NORTHERN CHILE", - "TAIWAN REGION", - "CHILE-BOLIVIA BORDER REGION", - "MINDANAO", - "NEAR EAST COAST OF HONSHU", - "W. CAROLINE ISLANDS", - "MINDORO", - "OFF COAST OF CENTRAL CHILE", - "NORTHWESTERN BALKAN REGION", - "ANDREANOF ISLANDS", - "KERMADEC ISLANDS REGION", - "MINAHASSA PENINSULA", - "KERMADEC ISLANDS", - "VANUATU ISLANDS", - "SALTA PROVINCE", - "VANUATU ISLANDS", - "RUSSIA-MONGOLIA BORDER REGION", - "SULAWESI", - "SAN JUAN PROVINCE", - "PHILIPPINE ISLANDS REGION", - "SOUTH SHETLAND ISLANDS", - "WEST CHILE RISE", - "MINDANAO", - "BISMARCK SEA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHWEST OF SUMATRA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "NEAR EAST COAST OF HONSHU", - "VANUATU ISLANDS", - "TONGA ISLANDS REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "GREECE", - "OFF E. COAST OF N. ISLAND", - "VANUATU ISLANDS", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "OFF E. COAST OF N. ISLAND", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "OFF EAST COAST OF KAMCHATKA", - "NORTHERN ALGERIA", - "NORTHERN MID-ATLANTIC RIDGE", - "WEST OF MACQUARIE ISLAND", - "NEAR EAST COAST OF HONSHU", - "KERMADEC ISLANDS", - "FIJI ISLANDS REGION", - "EAST OF SOUTH SANDWICH ISLANDS", - "OFF E. COAST OF N. ISLAND", - "KERMADEC ISLANDS REGION", - "JAVA", - "CELEBES SEA", - "NEAR N COAST OF NEW GUINEA", - "OFF W COAST OF NORTHERN SUMATRA", - "FIJI ISLANDS REGION", - "FIJI ISLANDS REGION", - "NEAR N COAST OF NEW GUINEA", - "NORTHEASTERN INDIA", - "KERMADEC ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "WEST OF MACQUARIE ISLAND", - "FIJI ISLANDS REGION", - "NEAR COAST OF GUATEMALA", - "MAURITIUS - REUNION REGION", - "SOUTH OF PANAMA", - "NEAR EAST COAST OF HONSHU", - "OFF W COAST OF NORTHERN SUMATRA", - "SOUTHERN EAST PACIFIC RISE", - "YUNNAN", - "QINGHAI", - "FIJI ISLANDS REGION", - "CENTRAL ALASKA", - "NORTHERN MOLUCCA SEA", - "KERMADEC ISLANDS", - "FIJI ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "CALIFORNIA-NEVADA BORDER REGION", - "SOUTH OF PANAMA", - "BISMARCK SEA", - "SOUTH OF PANAMA", - "MINDORO", - "KERMADEC ISLANDS REGION", - "MINAHASSA PENINSULA", - "ALASKA PENINSULA", - "PERU-ECUADOR BORDER REGION", - "NICOBAR ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "ALASKA PENINSULA", - "HAITI REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "KURIL ISLANDS", - "KERMADEC ISLANDS", - "FIJI ISLANDS REGION", - "NEAR COAST OF GUERRERO", - "CHILE-ARGENTINA BORDER REGION", - "KURIL ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF NICARAGUA", - "ANDREANOF ISLANDS", - "CRETE", - "EAST OF KURIL ISLANDS", - "SEA OF JAPAN", - "VANUATU ISLANDS REGION", - "MID-INDIAN RIDGE", - "SOUTH SANDWICH ISLANDS REGION", - "VANUATU ISLANDS REGION", - "HAWAII", - "ALASKA PENINSULA", - "CRETE", - "SOLOMON ISLANDS", - "VANUATU ISLANDS", - "SOUTH OF FIJI ISLANDS", - "TAIWAN", - "SOUTH SANDWICH ISLANDS REGION", - "OFF W COAST OF NORTHERN SUMATRA", - "SOUTHERN MOLUCCA SEA", - "NEAR COAST OF NICARAGUA", - "SOUTHEAST OF RYUKYU ISLANDS", - "SOUTHEAST OF RYUKYU ISLANDS", - "IRIAN JAYA REGION", - "SOUTHERN IRAN", - "SOUTHERN IRAN", - "SOUTH INDIAN OCEAN", - "NEW IRELAND REGION", - "SANTA CRUZ ISLANDS", - "MYANMAR-INDIA BORDER REGION", - "NORTHERN PERU", - "SOUTHEAST OF HONSHU", - "SOUTHEAST OF HONSHU", - "NEW IRELAND REGION", - "NEW IRELAND REGION", - "EASTER ISLAND REGION", - "NORTH OF HALMAHERA", - "RYUKYU ISLANDS", - "WEST OF MACQUARIE ISLAND", - "FLORES SEA", - "SOUTH SANDWICH ISLANDS REGION", - "PRINCE EDWARD ISLANDS REGION", - "FIJI ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR COAST OF NORTHERN CALIF.", - "OFF COAST OF JALISCO", - "FIJI ISLANDS REGION", - "BANDA SEA", - "VANUATU ISLANDS", - "TAIWAN REGION", - "BANDA SEA", - "NEAR COAST OF NICARAGUA", - "QINGHAI", - "SOUTH OF KERMADEC ISLANDS", - "NORTHERN MID-ATLANTIC RIDGE", - "CYPRUS REGION", - "FOX ISLANDS", - "FOX ISLANDS", - "SUNDA STRAIT", - "SOLOMON ISLANDS", - "SHIKOKU", - "TALAUD ISLANDS", - "FOX ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS REGION", - "SOUTH OF PANAMA", - "KERMADEC ISLANDS REGION", - "BANDA SEA", - "NORTHERN PERU", - "SOUTHEAST INDIAN RIDGE", - "CENTRAL MID-ATLANTIC RIDGE", - "SOUTH OF MARIANA ISLANDS", - "GUATEMALA", - "SOUTH OF FIJI ISLANDS", - "BALLENY ISLANDS REGION", - "JUJUY PROVINCE", - "NORTHERN SUMATRA", - "KERMADEC ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS REGION", - "LUZON", - "LUZON", - "SOUTHERN SUMATRA", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "KERMADEC ISLANDS REGION", - "NORTHERN MID-ATLANTIC RIDGE", - "TAIWAN", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "NEW BRITAIN REGION", - "VANUATU ISLANDS", - "MINDANAO", - "MINDANAO", - "NEAR COAST OF NICARAGUA", - "BISMARCK SEA", - "PHILIPPINE ISLANDS REGION", - "TAIWAN REGION", - "BISMARCK SEA", - "JUJUY PROVINCE", - "MACQUARIE ISLAND REGION", - "LUZON", - "SOUTH OF FIJI ISLANDS", - "OFF EAST COAST OF HONSHU", - "CENTRAL PERU", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHERN EAST PACIFIC RISE", - "TIMOR REGION", - "TONGA ISLANDS", - "WEST OF MACQUARIE ISLAND", - "RAT ISLANDS", - "PERU-BRAZIL BORDER REGION", - "SOUTHEASTERN AFGHANISTAN", - "PHILIPPINE ISLANDS REGION" - ], - "lat": [ - -13.6098, - 39.6611, - 11.1664, - -22.2789, - -38.0635, - 52.7536, - -5.9807, - -6.0444, - 52.8204, - -5.9808, - 3.0029, - 6.7113, - 6.4232, - 6.7403, - 6.1711, - 6.5583, - 52.4218, - 1.2978, - -5.5179, - 51.3848, - -5.8249, - 46.0031, - -17.5345, - -1.2779, - 12.4748, - 12.3454, - 12.2011, - -5.7825, - -17.8076, - -20.8416, - 12.247, - -6.5338, - 2.1984, - 18.5693, - 51.6273, - -43.3608, - -17.4526, - -15.8867, - -20.7545, - -37.0545, - 36.5352, - -5.2995, - -4.9188, - -4.9863, - 24.2175, - 51.3459, - 51.4225, - 2.7637, - 24.7878, - -3.5248, - -3.0065, - -57.116, - -4.6494, - -20.519, - -45.5992, - -54.8356, - -6.0155, - 28.3941, - -15.8273, - -6.1002, - -56.4318, - -6.5265, - -7.2664, - 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timelatitudelongitudedepthmagnitudedescriptiondepth_kmCategorymarker_sizemagnitude_bin
02010-07-02 06:04:03.570-13.6098166.65410.034406.3VANUATU ISLANDS34.40Moderate544.06.0
11122017-08-31 17:06:55.750-1.159099.68810.043146.3SOUTHERN SUMATRA43.14Moderate544.06.0
11112017-08-27 04:17:51.010-1.4513148.08030.008006.3ADMIRALTY ISLANDS REGION8.00Moderate544.06.0
11102017-08-19 02:00:52.550-17.9609-178.84060.544006.4FIJI ISLANDS REGION544.00Moderate601.06.0
11082017-08-13 03:08:10.560-3.7682101.62280.031006.4SOUTHERN SUMATRA31.00Moderate601.06.0
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" + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" ], - "text/plain": [ - " time latitude longitude depth magnitude \\\n", - "0 2010-07-02 06:04:03.570 -13.6098 166.6541 0.03440 6.3 \n", - "1112 2017-08-31 17:06:55.750 -1.1590 99.6881 0.04314 6.3 \n", - "1111 2017-08-27 04:17:51.010 -1.4513 148.0803 0.00800 6.3 \n", - "1110 2017-08-19 02:00:52.550 -17.9609 -178.8406 0.54400 6.4 \n", - "1108 2017-08-13 03:08:10.560 -3.7682 101.6228 0.03100 6.4 \n", - "\n", - " description depth_km Category marker_size magnitude_bin \n", - "0 VANUATU ISLANDS 34.40 Moderate 544.0 6.0 \n", - "1112 SOUTHERN SUMATRA 43.14 Moderate 544.0 6.0 \n", - "1111 ADMIRALTY ISLANDS REGION 8.00 Moderate 544.0 6.0 \n", - "1110 FIJI ISLANDS REGION 544.00 Moderate 601.0 6.0 \n", - "1108 SOUTHERN SUMATRA 31.00 Moderate 601.0 6.0 " + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "quakes2plot=quake.sort_values(by='magnitude_bin')\n", - "quakes2plot.head()" - ] - }, - { - "cell_type": "markdown", - "id": "dcf7472d", - "metadata": {}, - "source": [ - "Now we will plot again using Plotly" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "c21ce4ea", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "customdata": [ - [ - "VANUATU ISLANDS", - 6.3, - 0.0344 - ], - [ - "SOUTHERN SUMATRA", - 6.3, - 0.04314 - ], - [ - "ADMIRALTY ISLANDS REGION", - 6.3, - 0.008 - ], - [ - "FIJI ISLANDS REGION", - 6.4, - 0.544 - ], - [ - "SOUTHERN SUMATRA", - 6.4, - 0.031 - ], - [ - "MINDORO", - 6.2, - 0.172 - ], - [ - "NORTHERN XINJIANG", - 6.3, - 0.02 - ], - [ - "NORTH ATLANTIC OCEAN", - 6, - 0.01 - ], - [ - "SOUTHEAST OF RYUKYU ISLANDS", - 6, - 0.012 - ], - [ - "NORTH KOREA", - 6.3, - 0 - ], - [ - "MAURITIUS - REUNION REGION", - 6, - 0.01 - ], - [ - "KOMANDORSKIYE OSTROVA REGION", - 6.3, - 0.01 - ], - [ - "NEW IRELAND REGION", - 6.4, - 0.03478 - ], - [ - "NEAR COAST OF ECUADOR", - 6, - 0.013 - ], - [ - "KERMADEC ISLANDS REGION", - 6, - 0.41461000000000003 - ], - [ - "KERMADEC ISLANDS", - 6, - 0.019 - ], - [ - "FIJI ISLANDS REGION", - 6.2, - 0.038270000000000005 - ], - [ - "SOUTH OF FIJI ISLANDS", - 6.1, - 0.511 - ], - [ - "KERMADEC ISLANDS", - 6, - 0.034 - ], - [ - "NEAR COAST OF PERU", - 6.4, - 0.04492 - ], - [ - "SOUTH SANDWICH ISLANDS REGION", - 6, - 0.035 - ], - [ - "BONIN ISLANDS", - 6.1, - 0.451 - ], - [ - "AUCKLAND ISLANDS", - 6.1, - 0.01 - ], - [ - "NEW IRELAND REGION", - 6, - 0.052 - ], - [ - "SAMOA ISLANDS REGION", - 6, - 0.006 - ], - [ - "LOYALTY ISLANDS", - 6, - 0.01 - ], - [ - "LOYALTY ISLANDS", - 6.3, - 0.004 - ], - [ - "EASTERN XIZANG-INDIA BORDER REG.", - 6.4, - 0.008 - ], - [ - "ASCENSION ISLAND REGION", - 6.1, - 0.01 - ], - [ - "SOUTHEAST OF HONSHU", - 6, - 0.012 - ], - [ - "LOYALTY ISLANDS", - 6, - 0.01 - ], - [ - "LOYALTY ISLANDS", - 6.1, - 0.01 - ], - [ - "SERAM", - 6.1, - 0.01847 - ], - [ - "TONGA ISLANDS", - 6.1, - 0.01 - ], - [ - "NORTHERN CHILE", - 6.3, - 0.08243 - ], - [ - "BALLENY ISLANDS REGION", - 6.3, - 0.01 - ], - [ - "TONGA ISLANDS", - 6.1, - 0.01 - ], - [ - "OFF EAST COAST OF HONSHU", - 6.2, - 0.009 - ], - [ - "SOUTH OF FIJI ISLANDS", - 6.4, - 0.09806999999999999 - ], - [ - "OAXACA", - 6.1, - 0.00962 - ], - [ - "VANUATU ISLANDS", - 6.4, - 0.197 - ], - [ - "OFF EAST COAST OF HONSHU", - 6.1, - 0.011 - ], - [ - "AEGEAN SEA", - 6.3, - 0.012 - ], - [ - "NEGROS", - 6, - 0.533 - ], - [ - "NEW BRITAIN REGION", - 6.2, - 0.01 - ], - [ - "OFF COAST OF CENTRAL AMERICA", - 6.2, - 0.01 - ], - [ - "W. 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marker_size=%{marker.size}
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depth=%{customdata[2]}", - "hovertext": [ - "VANUATU ISLANDS", - "SOUTHERN SUMATRA", - "ADMIRALTY ISLANDS REGION", - "FIJI ISLANDS REGION", - "SOUTHERN SUMATRA", - "MINDORO", - "NORTHERN XINJIANG", - "NORTH ATLANTIC OCEAN", - "SOUTHEAST OF RYUKYU ISLANDS", - "NORTH KOREA", - "MAURITIUS - REUNION REGION", - "KOMANDORSKIYE OSTROVA REGION", - "NEW IRELAND REGION", - "NEAR COAST OF ECUADOR", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS", - "FIJI ISLANDS REGION", - "SOUTH OF FIJI ISLANDS", - "KERMADEC ISLANDS", - "NEAR COAST OF PERU", - "SOUTH SANDWICH ISLANDS REGION", - "BONIN ISLANDS", - "AUCKLAND ISLANDS", - "NEW IRELAND REGION", - "SAMOA ISLANDS REGION", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "EASTERN XIZANG-INDIA BORDER REG.", - "ASCENSION ISLAND REGION", - "SOUTHEAST OF HONSHU", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "SERAM", - "TONGA ISLANDS", - "NORTHERN CHILE", - "BALLENY ISLANDS REGION", - "TONGA ISLANDS", - "OFF EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "OAXACA", - "VANUATU ISLANDS", - "OFF EAST COAST OF HONSHU", - "AEGEAN SEA", - "NEGROS", - "NEW BRITAIN REGION", - "OFF COAST OF CENTRAL AMERICA", - "W. CAROLINE ISLANDS", - "SOLOMON ISLANDS", - "NEW IRELAND REGION", - "MARIANA ISLANDS REGION", - "SOLOMON ISLANDS", - "BANDA SEA", - "NEAR ISLANDS", - "SOUTHERN PERU", - "SAN JUAN PROVINCE", - "SOUTH ISLAND", - "SOUTH ISLAND", - "NEAR EAST COAST OF HONSHU", - "CHILE-ARGENTINA BORDER REGION", - "CENTRAL ITALY", - "TONGA ISLANDS", - "WESTERN HONSHU", - "NEW BRITAIN REGION", - "TONGA ISLANDS", - "MINDANAO", - "PERU-BRAZIL BORDER REGION", - "NORTHERN AND CENTRAL IRAN", - "SOLOMON ISLANDS", - "SUMBAWA REGION", - "RYUKYU ISLANDS", - "ANDREANOF ISLANDS", - "TAJIKISTAN", - "SOUTHEASTERN ALASKA", - "SOUTHEASTERN ALASKA", - "OFF COAST OF CENTRAL CHILE", - "FIJI ISLANDS REGION", - "CHILE-ARGENTINA BORDER REGION", - "NORTHERN AND CENTRAL IRAN", - "NEAR ISLANDS", - "SOLOMON ISLANDS", - "NICOBAR ISLANDS", - "NEW BRITAIN REGION", - "SOLOMON ISLANDS", - "JUJUY PROVINCE", - "SOUTHWESTERN PAKISTAN", - "SOLOMON ISLANDS", - "QUEEN ELIZABETH ISLANDS", - "SOUTH OF FIJI ISLANDS", - "NEW IRELAND REGION", - "OFF EAST COAST OF HONSHU", - "EASTERN NEW GUINEA REG.", - "W. CAROLINE ISLANDS", - "MINAHASSA PENINSULA", - "SOUTHWEST INDIAN RIDGE", - "SOUTHEAST OF LOYALTY ISLANDS", - "MINDANAO", - "ECUADOR", - "MARIANA ISLANDS", - "TIMOR REGION", - "WESTERN IRAN", - "SUMBA REGION", - "ANDREANOF ISLANDS", - "SUMBAWA REGION", - "FIJI ISLANDS REGION", - "COSTA RICA", - "VOLCANO ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "NORTHERN ALASKA", - "NORTHERN ALASKA", - "SUMBAWA REGION", - "OFF COAST OF OREGON", - "KURIL ISLANDS", - "BALI SEA", - "NEW BRITAIN REGION", - "PACIFIC-ANTARCTIC RIDGE", - "SOUTHEAST OF LOYALTY ISLANDS", - "BANDA SEA", - "IRAN-IRAQ BORDER REGION", - "CARIBBEAN SEA", - "SOLOMON ISLANDS", - "SOUTHERN EAST PACIFIC RISE", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR EAST COAST OF KAMCHATKA", - "NORTH ATLANTIC OCEAN", - "TONGA ISLANDS", - "MINDANAO", - "NORTHERN CHILE", - "NORTH ISLAND", - "DRAKE PASSAGE", - "OFF COAST OF CENTRAL AMERICA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "NEW BRITAIN REGION", - "FLORES SEA", - "NEW BRITAIN REGION", - "SANTA CRUZ ISLANDS", - "EASTERN GULF OF ADEN", - "CERAM SEA", - "NEW GUINEA", - "SOUTH OF MARIANA ISLANDS", - "TAIWAN", - "TAIWAN", - "PACIFIC-ANTARCTIC RIDGE", - "FIJI ISLANDS REGION", - "HINDU KUSH REGION", - "NEAR N COAST OF NEW GUINEA", - "KOMANDORSKIYE OSTROVA REGION", - "HOKKAIDO", - "JAVA", - "NORTHERN CHILE", - "GULF OF CALIFORNIA", - "MYANMAR", - "NORTHERN AND CENTRAL IRAN", - "NORTHERN AND CENTRAL IRAN", - "W. CAROLINE ISLANDS", - "W. CAROLINE ISLANDS", - "NEW GUINEA", - "NEAR COAST OF ECUADOR", - "WEST OF MACQUARIE ISLAND", - "NEW GUINEA", - "EASTERN GULF OF ADEN", - "VANUATU ISLANDS", - "KERMADEC ISLANDS", - "NEAR EAST COAST OF KAMCHATKA", - "VANUATU ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "NEW BRITAIN REGION", - "LUZON", - "EASTER ISLAND REGION", - "PRINCE EDWARD ISLANDS REGION", - "NORTHERN MOLUCCA SEA", - "NEAR COAST OF CENTRAL CHILE", - "NEW GUINEA", - "MINDANAO", - "SOUTH OF FIJI ISLANDS", - "BANDA SEA", - "SOUTHEAST INDIAN RIDGE", - "NEW BRITAIN REGION", - "NEW GUINEA", - "IRIAN JAYA", - "SOUTHEAST OF HONSHU", - "SOLOMON ISLANDS", - "NEW BRITAIN REGION", - "FIJI ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "FIJI ISLANDS REGION", - "SAMOA ISLANDS REGION", - "NORTHERN CHILE", - "HOKKAIDO", - "BORNEO", - "SOUTHERN XINJIANG", - "SOUTHEAST OF HONSHU", - "TONGA ISLANDS", - "SOUTHERN MID-ATLANTIC RIDGE", - "TONGA ISLANDS", - "SOUTHERN SUMATRA", - "NEPAL", - "MOROCCO", - "NEW BRITAIN REGION", - "FIJI ISLANDS REGION", - "TONGA ISLANDS", - "LEYTE", - "EAST OF KURIL ISLANDS", - "SOUTHERN ALASKA", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "NORTHERN MID-ATLANTIC RIDGE", - "OFF COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEW BRITAIN REGION", - "NORTHERN MOLUCCA SEA", - "NORTHERN MOLUCCA SEA", - "FOX ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTHEAST OF HONSHU", - "KERMADEC ISLANDS REGION", - "SOLOMON ISLANDS", - "MID-INDIAN RIDGE", - "SOUTH OF FIJI ISLANDS", - "NEPAL", - "QUEEN CHARLOTTE ISLANDS REGION", - "SOUTH ISLAND", - "SANTA CRUZ ISLANDS", - "MENDOZA PROVINCE", - "ADMIRALTY ISLANDS REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "FIJI ISLANDS REGION", - "SOUTHEAST OF EASTER ISLAND", - "FIJI ISLANDS REGION", - "MINDANAO", - "SOUTH OF PANAMA", - "SOUTH OF PANAMA", - "NORTHERN MOLUCCA SEA", - "NEAR EAST COAST OF HONSHU", - "PHILIPPINE ISLANDS REGION", - "TAIWAN REGION", - "NEAR COAST OF GUATEMALA", - "BANDA SEA", - "SOUTH OF PANAMA", - "EASTERN HONSHU", - "SICHUAN", - "PRINCE EDWARD ISLANDS REGION", - "TAIWAN REGION", - "OFF COAST OF CENTRAL CHILE", - "SOUTH SANDWICH ISLANDS REGION", - "VANUATU ISLANDS", - "TAIWAN REGION", - "TAIWAN REGION", - "CRETE", - "TONGA ISLANDS", - "NEW BRITAIN REGION", - "SAMOA ISLANDS REGION", - "SAMOA ISLANDS REGION", - "SAMOA ISLANDS REGION", - "NORTHERN CHILE", - "FIJI ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "NORTHERN MOLUCCA SEA", - "MINAHASSA PENINSULA", - "NORTHERN COLOMBIA", - "MID-INDIAN RIDGE", - "SOUTHERN SUMATRA", - "OFF COAST OF JALISCO", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS REGION", - "NEAR NORTH COAST OF IRIAN JAYA", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "LOYALTY ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "SOLOMON ISLANDS", - "NICARAGUA", - "SOUTH OF SUMBAWA", - "NORTHERN MOLUCCA SEA", - "OFF COAST OF JALISCO", - "NORTHERN MID-ATLANTIC RIDGE", - "KERMADEC ISLANDS", - "TAIWAN REGION", - "FIJI ISLANDS REGION", - "NORTHERN TERRITORY", - "NEAR COAST OF CHIAPAS", - "NEAR COAST OF CHIAPAS", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "BANDA SEA", - "NEW IRELAND REGION", - "TAJIKISTAN-XINJIANG BORDER REG.", - "VANUATU ISLANDS", - "NORTHERN COLOMBIA", - "NORTHERN PERU", - "MACQUARIE ISLAND REGION", - "KOMANDORSKIYE OSTROVA REGION", - "OFF E. COAST OF N. ISLAND", - "CENTRAL ITALY", - "FLORES SEA", - "SOUTH GEORGIA ISLAND REGION", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "VOLCANO ISLANDS", - "JUJUY PROVINCE", - "ADMIRALTY ISLANDS REGION", - "NEAR COAST OF NORTHERN CHILE", - "VANUATU ISLANDS", - "KERMADEC ISLANDS REGION", - "NEAR COAST OF ECUADOR", - "TONGA ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "OFF COAST OF CHIAPAS", - "VANUATU ISLANDS", - "KYUSHU", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "SOUTHERN EAST PACIFIC RISE", - "BORNEO", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "NEAR COAST OF NORTHERN CHILE", - "MARIANA ISLANDS", - "SOUTHEASTERN AFGHANISTAN", - "OFF COAST OF CENTRAL CHILE", - "PRINCE EDWARD ISLANDS REGION", - "PRINCE EDWARD ISLANDS REGION", - "BOUVET ISLAND REGION", - "TONGA ISLANDS", - "EAST OF KURIL ISLANDS", - "EAST OF KURIL ISLANDS", - "PACIFIC-ANTARCTIC RIDGE", - "JUJUY PROVINCE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "NEAR COAST OF CENTRAL CHILE", - "PACIFIC-ANTARCTIC RIDGE", - "SOUTHERN BOLIVIA", - "KYUSHU", - "MINDANAO", - "JAVA", - "ALASKA PENINSULA", - "NEAR N COAST OF NEW GUINEA", - "NEAR EAST COAST OF KAMCHATKA", - "LEEWARD ISLANDS", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "HALMAHERA", - "SOUTHERN EAST PACIFIC RISE", - "SUMBA REGION", - "NEAR COAST OF CENTRAL CHILE", - "SOLOMON ISLANDS", - "TAIWAN", - "KERMADEC ISLANDS REGION", - "BALLENY ISLANDS REGION", - "STRAIT OF GIBRALTAR", - "HOKKAIDO", - "FIJI ISLANDS REGION", - "SOUTHEAST OF EASTER ISLAND", - "TONGA ISLANDS", - "NEAR N COAST OF NEW GUINEA", - "CELEBES SEA", - "JAVA", - "KERMADEC ISLANDS REGION", - "OFF E. COAST OF N. ISLAND", - "FIJI ISLANDS REGION", - "WEST OF MACQUARIE ISLAND", - "NORTHERN MID-ATLANTIC RIDGE", - "OFF W COAST OF NORTHERN SUMATRA", - "NORTHERN ALGERIA", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "OFF E. COAST OF N. ISLAND", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS REGION", - "NEAR N COAST OF NEW GUINEA", - "NORTHEASTERN INDIA", - "SOUTH SANDWICH ISLANDS REGION", - "NICOBAR ISLANDS", - "PERU-ECUADOR BORDER REGION", - "MINAHASSA PENINSULA", - "KERMADEC ISLANDS REGION", - "BISMARCK SEA", - "SOUTH OF PANAMA", - "CALIFORNIA-NEVADA BORDER REGION", - "NEAR COAST OF CENTRAL CHILE", - "FIJI ISLANDS REGION", - "NORTHERN MOLUCCA SEA", - "CENTRAL ALASKA", - "YUNNAN", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF PANAMA", - "NEAR COAST OF GUATEMALA", - "FIJI ISLANDS REGION", - "WEST OF MACQUARIE ISLAND", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "VANUATU ISLANDS", - "GREECE", - "W. CAROLINE ISLANDS", - "NEAR EAST COAST OF HONSHU", - "MINDANAO", - "CHILE-BOLIVIA BORDER REGION", - "TAIWAN REGION", - "NORTHERN CHILE", - "BALLENY ISLANDS REGION", - "FOX ISLANDS", - "CHILE-ARGENTINA BORDER REGION", - "PRIMOR'YE", - "OFF COAST OF CENTRAL CHILE", - "MINDANAO", - "FIJI ISLANDS REGION", - "TONGA ISLANDS", - "SOUTH SHETLAND ISLANDS", - "OFF COAST OF SOUTHERN CHILE", - "SOUTH OF FIJI ISLANDS", - "EASTERN NEW GUINEA REG.", - "FIJI ISLANDS REGION", - "MINDORO", - "SOUTH SANDWICH ISLANDS REGION", - "NORTHWESTERN BALKAN REGION", - "KERMADEC ISLANDS REGION", - "FIJI ISLANDS REGION", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHWEST OF SUMATRA", - "SOUTHEAST OF LOYALTY ISLANDS", - "BISMARCK SEA", - "MINDANAO", - "SAN JUAN PROVINCE", - "SULAWESI", - "VANUATU ISLANDS", - "SALTA PROVINCE", - "VANUATU ISLANDS", - "KERMADEC ISLANDS", - "MINAHASSA PENINSULA", - "ANDREANOF ISLANDS", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "KURIL ISLANDS", - "LUZON", - "FIJI ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "NORTHERN SUMATRA", - "JUJUY PROVINCE", - "BALLENY ISLANDS REGION", - "GUATEMALA", - "SOUTH OF MARIANA ISLANDS", - "NEAR EAST COAST OF HONSHU", - "CENTRAL MID-ATLANTIC RIDGE", - "BANDA SEA", - "SOUTH OF PANAMA", - "FIJI ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "FOX ISLANDS", - "TALAUD ISLANDS", - "SHIKOKU", - "SOLOMON ISLANDS", - "SOUTHEAST INDIAN RIDGE", - "SOUTH OF FIJI ISLANDS", - "KERMADEC ISLANDS REGION", - "VANUATU ISLANDS", - "RAT ISLANDS", - "WEST OF MACQUARIE ISLAND", - "TONGA ISLANDS", - "TIMOR REGION", - "SOUTHERN EAST PACIFIC RISE", - "OFF EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "LUZON", - "BISMARCK SEA", - "TAIWAN REGION", - "PHILIPPINE ISLANDS REGION", - "BISMARCK SEA", - "MINDANAO", - "MINDANAO", - "VANUATU ISLANDS", - "NEW BRITAIN REGION", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "NORTHERN MID-ATLANTIC RIDGE", - "SOUTH OF KERMADEC ISLANDS", - "NEAR COAST OF NICARAGUA", - "BANDA SEA", - "SOUTHERN MOLUCCA SEA", - "OFF W COAST OF NORTHERN SUMATRA", - "SOUTH SANDWICH ISLANDS REGION", - "TAIWAN", - "SOUTH OF FIJI ISLANDS", - "VANUATU ISLANDS", - "SOLOMON ISLANDS", - "CRETE", - "HAWAII", - "SOUTH SANDWICH ISLANDS REGION", - "MID-INDIAN RIDGE", - "SEA OF JAPAN", - "CRETE", - "ANDREANOF ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "KURIL ISLANDS", - "CHILE-ARGENTINA BORDER REGION", - "FIJI ISLANDS REGION", - "KERMADEC ISLANDS", - "NEAR COAST OF NICARAGUA", - "SOUTH SANDWICH ISLANDS REGION", - "IRIAN JAYA REGION", - "SOUTHERN IRAN", - "TAIWAN REGION", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "OFF COAST OF JALISCO", - "NEAR COAST OF NORTHERN CALIF.", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS", - "PRINCE EDWARD ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "RYUKYU ISLANDS", - "NORTH OF HALMAHERA", - "EASTER ISLAND REGION", - "NEW IRELAND REGION", - "NEW IRELAND REGION", - "SOUTHEAST OF HONSHU", - "MYANMAR-INDIA BORDER REGION", - "SANTA CRUZ ISLANDS", - "NEW IRELAND REGION", - "SOUTH INDIAN OCEAN", - "SOUTHERN IRAN", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "FIJI ISLANDS", - "CENTRAL CALIFORNIA", - "QUEEN CHARLOTTE ISLANDS REGION", - "VANUATU ISLANDS", - "MARIANA ISLANDS", - "KERMADEC ISLANDS REGION", - "PANAMA-COSTA RICA BORDER REGION", - "KOMANDORSKIYE OSTROVA REGION", - "KOMANDORSKIYE OSTROVA REGION", - "SOLOMON ISLANDS", - "IRIAN JAYA", - "IRIAN JAYA", - "KERMADEC ISLANDS", - "NEAR WEST COAST OF HONSHU", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS REGION", - "TONGA ISLANDS", - "OFF COAST OF CENTRAL CHILE", - "SOUTHEAST OF HONSHU", - "KERMADEC ISLANDS", - "NORTHWEST OF RYUKYU ISLANDS", - "NEW BRITAIN REGION", - "BALLENY ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "SOUTH SANDWICH ISLANDS REGION", - "NORTH OF ASCENSION ISLAND", - "VANUATU ISLANDS", - "MINDANAO", - "MINDANAO", - "KERMADEC ISLANDS", - "CENTRAL CHILE", - "SOUTHWEST INDIAN RIDGE", - "MONA PASSAGE", - "JAVA", - "OFF COAST OF OREGON", - "VANUATU ISLANDS", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "SANTA CRUZ ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOUTHERN EAST PACIFIC RISE", - "NEAR S. COAST OF HONSHU", - "PHILIPPINE ISLANDS REGION", - "TONGA ISLANDS", - "MINDANAO", - "FIJI ISLANDS REGION", - "ANDREANOF ISLANDS", - "CENTRAL BOLIVIA", - "D'ENTRECASTEAUX ISLANDS REGION", - "FIJI ISLANDS REGION", - "PHILIPPINE ISLANDS REGION", - "SOUTH OF KERMADEC ISLANDS", - "NEW IRELAND REGION", - "NORTHERN MID-ATLANTIC RIDGE", - "MARIANA ISLANDS", - "SOUTHERN SUMATRA", - "SOUTHERN SUMATRA", - "FIJI ISLANDS REGION", - "SOLOMON ISLANDS", - "SUMBA REGION", - "SUMBA REGION", - "VANUATU ISLANDS", - "BISMARCK SEA", - "KYUSHU", - "ALASKA PENINSULA", - "KOMANDORSKIYE OSTROVA REGION", - "VANUATU ISLANDS", - "EAST OF SOUTH SANDWICH ISLANDS", - "COLOMBIA", - "EAST OF KURIL ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "PANAMA-COSTA RICA BORDER REGION", - "KYUSHU", - "SOLOMON ISLANDS", - "CARLSBERG RIDGE", - "SOUTH OF FIJI ISLANDS", - "SAMAR", - "SOUTH SANDWICH ISLANDS REGION", - "LUZON", - "TAIWAN", - "OFF EAST COAST OF HONSHU", - "BANDA SEA", - "SOUTH SANDWICH ISLANDS REGION", - "RAT ISLANDS", - "MAURITIUS - REUNION REGION", - "NEAR COAST OF ECUADOR", - "NEW BRITAIN REGION", - "NORTHERN MOLUCCA SEA", - "SOUTH OF AFRICA", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "SAMOA ISLANDS REGION", - "ANDAMAN ISLANDS", - "W. CAROLINE ISLANDS", - "XIZANG", - "MINAHASSA PENINSULA", - "ICELAND REGION", - "MARIANA ISLANDS", - "SOUTHERN MID-ATLANTIC RIDGE", - "FIJI ISLANDS REGION", - "HALMAHERA", - "SOUTHERN PERU", - "VANUATU ISLANDS", - "OFF COAST OF CENTRAL MEXICO", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NORTH OF HONDURAS", - "KERMADEC ISLANDS REGION", - "SOUTH OF FIJI ISLANDS", - "SOUTH OF ALASKA", - "XIZANG", - "SOUTH SANDWICH ISLANDS REGION", - "KAMCHATKA PENINSULA", - "NEAR EAST COAST OF HONSHU", - "NORTHERN CHILE", - "VANUATU ISLANDS", - "MINDANAO", - "VANUATU ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "CHAGOS ARCHIPELAGO REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "COSTA RICA", - "ASCENSION ISLAND REGION", - "TANZANIA", - "PRINCE EDWARD ISLANDS REGION", - "VANUATU ISLANDS", - "BISMARCK SEA", - "MINDANAO", - "ALASKA PENINSULA", - "MID-INDIAN RIDGE", - "NORTHERN MOLUCCA SEA", - "MINDANAO", - "CENTRAL EAST PACIFIC RISE", - "VANCOUVER ISLAND", - "COLOMBIA", - "SANTIAGO DEL ESTERO PROV.", - "VANCOUVER ISLAND", - "VANCOUVER ISLAND", - "HINDU KUSH REGION", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "VANUATU ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "ANDREANOF ISLANDS", - "CRETE", - "ALBANIA", - "ANDREANOF ISLANDS", - "IRIAN JAYA REGION", - "LAOS", - "SEA OF OKHOTSK", - "OFF COAST OF CHIAPAS", - "NORTHERN MOLUCCA SEA", - "SOUTHERN EAST PACIFIC RISE", - "VANUATU ISLANDS", - "NORTHERN SUMATRA", - "NEW BRITAIN REGION", - "SOUTH OF BALI", - "VANUATU ISLANDS", - "SAMOA ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "TURKEY-IRAN BORDER REGION", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "MINDANAO", - "JAVA", - "SOLOMON ISLANDS", - "NORTH OF HONDURAS", - "SOLOMON ISLANDS", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "MINAHASSA PENINSULA", - "SOUTHERN XINJIANG", - "NEAR NORTH COAST OF IRIAN JAYA", - "EASTERN SIBERIA", - "PUERTO RICO REGION", - "MID-INDIAN RIDGE", - "BANDA SEA", - "PHILIPPINE ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "EAST OF KURIL ISLANDS", - "VANUATU ISLANDS REGION", - "GUATEMALA", - "TAIWAN", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SOUTH OF PANAMA", - "E. RUSSIA-N.E. CHINA BORDER REG.", - "SULAWESI", - "SOUTHERN IRAN", - "VANUATU ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "EAST OF KURIL ISLANDS", - "BANDA SEA", - "KURIL ISLANDS", - "TONGA ISLANDS", - "FIJI ISLANDS REGION", - "OFF EAST COAST OF KAMCHATKA", - "OFF EAST COAST OF KAMCHATKA", - "OFF COAST OF SOUTHERN CHILE", - "NEAR EAST COAST OF HONSHU", - "COSTA RICA", - "KYUSHU", - "NEAR EAST COAST OF HONSHU", - "SOUTHERN IRAN", - "KERMADEC ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "MICHOACAN", - "SOUTHEAST OF HONSHU", - "TONGA ISLANDS", - "CRETE", - "NEAR EAST COAST OF HONSHU", - "CENTRAL MID-ATLANTIC RIDGE", - "MINAHASSA PENINSULA", - "TAJIKISTAN", - "PACIFIC-ANTARCTIC RIDGE", - "NORTHERN SUMATRA", - "LAKE ISSYK-KUL REGION", - "ALASKA PENINSULA", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "OFF COAST OF CENTRAL CHILE", - "SANTA CRUZ ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "SANTA CRUZ ISLANDS REGION", - "SANTA CRUZ ISLANDS", - "KURIL ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "OFF EAST COAST OF HONSHU", - "SANTIAGO DEL ESTERO PROV.", - "MINDANAO", - "WEST OF MACQUARIE ISLAND", - "HINDU KUSH REGION", - "SOUTH ISLAND", - "NORTHERN MOLUCCA SEA", - "NEW BRITAIN REGION", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "IRIAN JAYA REGION", - "SANTA CRUZ ISLANDS", - "NEAR EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "TAIWAN", - "NEAR EAST COAST OF HONSHU", - "EASTERN HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEGROS", - "SOUTHWEST INDIAN RIDGE", - "NEAR COAST OF GUERRERO", - "KERMADEC ISLANDS REGION", - "ANDREANOF ISLANDS", - "NEAR EAST COAST OF HONSHU", - "QUEEN CHARLOTTE ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "QUEEN CHARLOTTE ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "FLORES SEA", - "ANDREANOF ISLANDS", - "OFF EAST COAST OF HONSHU", - "NEAR COAST OF NORTHERN PERU", - "CENTRAL EAST PACIFIC RISE", - "NEAR EAST COAST OF HONSHU", - "PHILIPPINE ISLANDS REGION", - "NORTHERN MID-ATLANTIC RIDGE", - "NEAR EAST COAST OF HONSHU", - "ANDREANOF ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NEAR COAST OF CHIAPAS", - "KERMADEC ISLANDS REGION", - "NEAR WEST COAST OF HONSHU", - "BANDA SEA", - "SOUTHERN SUMATRA", - "SOLOMON ISLANDS", - "NORTHERN SUMATRA", - "NEAR EAST COAST OF HONSHU", - "CRETE", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR COAST OF NICARAGUA", - "SOUTH OF KERMADEC ISLANDS", - "NEAR EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS", - "CRETE", - "SOLOMON ISLANDS", - "PHILIPPINE ISLANDS REGION", - "VANUATU ISLANDS", - "NEAR EAST COAST OF HONSHU", - "TONGA ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "VANUATU ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "GANSU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "EASTERN HONSHU", - "SOUTHERN PERU", - "OFF EAST COAST OF HONSHU", - "PRINCE EDWARD ISLANDS REGION", - "NEW IRELAND REGION", - "NEAR ISLANDS", - "OFF W. COAST OF BAJA CALIFORNIA", - "NORTHERN ITALY", - "NEAR COAST OF NORTHERN CHILE", - "LUZON", - "OFF EAST COAST OF HONSHU", - "HOKKAIDO", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "GREENLAND SEA", - "BONIN ISLANDS", - "NEW IRELAND REGION", - "SALTA PROVINCE", - "HOKKAIDO", - "SOUTH OF PANAMA", - "SOUTH OF PANAMA", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "CHILE-ARGENTINA BORDER REGION", - "SOUTHERN PERU", - "KURIL ISLANDS", - "NICARAGUA", - "VANUATU ISLANDS", - "COOK STRAIT", - "NORTHERN XINJIANG", - "NEAR EAST COAST OF KAMCHATKA", - "OFF COAST OF SOUTHERN CHILE", - "NORTHERN SUMATRA", - "NEAR ISLANDS", - "NEAR EAST COAST OF HONSHU", - "LUZON", - "SOUTHERN MOLUCCA SEA", - "DODECANESE ISLANDS", - "OFF COAST OF ECUADOR", - "SOUTHERN EAST PACIFIC RISE", - "PERU-BOLIVIA BORDER REGION", - "NEAR COAST OF CHIAPAS", - "KERMADEC ISLANDS REGION", - "IRIAN JAYA", - "SOUTH SANDWICH ISLANDS REGION", - "NEW BRITAIN REGION", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CHIAPAS", - "SOUTH OF FIJI ISLANDS", - "NEAR COAST OF PERU", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOLOMON ISLANDS", - "FIJI ISLANDS REGION", - "SANTIAGO DEL ESTERO PROV.", - "KURIL ISLANDS", - "SOUTH SHETLAND ISLANDS", - "NEAR EAST COAST OF HONSHU", - "IRIAN JAYA REGION", - "EAST OF SOUTH SANDWICH ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "OFF W COAST OF NORTHERN SUMATRA", - "VANUATU ISLANDS", - "DRAKE PASSAGE", - "NORTHERN EAST PACIFIC RISE", - "MINAHASSA PENINSULA", - "BAJA CALIFORNIA", - "OFF COAST OF OREGON", - "NORTH INDIAN OCEAN", - "NEW IRELAND REGION", - "NEAR COAST OF GUERRERO", - "SANTA CRUZ ISLANDS", - "TONGA ISLANDS", - "SOUTHERN EAST PACIFIC RISE", - "NORTHERN SUMATRA", - "REVILLA GIGEDO ISLANDS REGION", - "FIJI ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "CERAM SEA", - "CELEBES SEA", - "ASCENSION ISLAND REGION", - "VANUATU ISLANDS", - "COSTA RICA", - "KURIL ISLANDS", - "NEAR S. COAST OF HONSHU", - "QUEEN CHARLOTTE ISLANDS REGION", - "QUEEN CHARLOTTE ISLANDS REGION", - "MINDANAO", - "VANCOUVER ISLAND", - "CENTRAL PERU", - "VANUATU ISLANDS REGION", - "NEAR COAST OF CHIAPAS", - "GULF OF ALASKA", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NORTHERN MOLUCCA SEA", - "NEW IRELAND REGION", - "GULF OF CALIFORNIA", - "NORTH ISLAND", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR N COAST OF NEW GUINEA", - "NEW BRITAIN REGION", - "NEAR EAST COAST OF HONSHU", - "GUERRERO", - "NEAR COAST OF CENTRAL CHILE", - "OFF COAST OF SOUTHERN CHILE", - "VANUATU ISLANDS", - "BANDA SEA", - "SUNDA STRAIT", - "VANUATU ISLANDS", - "BALLENY ISLANDS REGION", - "NEAR N COAST OF NEW GUINEA", - "SULAWESI", - "SOUTH OF BALI", - "SOUTHEASTERN SIBERIA", - "NEW BRITAIN REGION", - "SOUTH OF KERMADEC ISLANDS", - "HOKKAIDO", - "ARMENIA-AZERBAIJAN-IRAN BORD REG", - "TURKEY", - "FOX ISLANDS", - "FIJI ISLANDS REGION", - "NEW IRELAND REGION", - "PERU-BRAZIL BORDER REGION", - "XIZANG", - "PACIFIC-ANTARCTIC RIDGE", - "TONGA ISLANDS", - "SOUTH OF JAVA", - "GULF OF CALIFORNIA", - "OFF EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS", - "BAJA CALIFORNIA", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS", - "OFF EAST COAST OF HONSHU", - "SOUTHERN SUMATRA", - "AUCKLAND ISLANDS", - "IRIAN JAYA REGION", - "NEAR ISLANDS", - "SANTA CRUZ ISLANDS", - "EAST OF NORTH ISLAND", - "NEAR EAST COAST OF HONSHU", - "KURIL ISLANDS", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SOUTHERN EAST PACIFIC RISE", - "TONGA ISLANDS", - "IRIAN JAYA REGION", - "E. CAROLINE ISLANDS", - "E. CAROLINE ISLANDS", - "PACIFIC-ANTARCTIC RIDGE", - "NEGROS", - "OFF W COAST OF NORTHERN SUMATRA", - "BAY OF BENGAL", - "SOUTHERN SUMATRA", - "OFF COAST OF JALISCO", - "VANUATU ISLANDS", - "HALMAHERA", - "GUERRERO", - "ANDREANOF ISLANDS", - "SOUTHWESTERN RYUKYU ISL.", - "IRIAN JAYA REGION", - "ARU ISLANDS REGION", - "RAT ISLANDS", - "RAT ISLANDS", - "HINDU KUSH REGION", - "RAT ISLANDS", - "RAT ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "VOLCANO ISLANDS", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS", - "ANDREANOF ISLANDS", - "TONGA ISLANDS", - "PACIFIC-ANTARCTIC RIDGE", - "NEW BRITAIN REGION", - "JAN MAYEN ISLAND REGION", - "SOUTHERN IRAN", - "NORTHERN SUMATRA", - "TAJIKISTAN", - "SOLOMON ISLANDS", - "NORTHERN SUMATRA", - "SOUTHERN SUMATRA", - "LOYALTY ISLANDS", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANUATU ISLANDS", - "SOUTH OF FIJI ISLANDS", - "PACIFIC-ANTARCTIC RIDGE", - "BALLENY ISLANDS REGION", - "SANTA CRUZ ISLANDS REGION", - "NEAR ISLANDS", - "BONIN ISLANDS", - "SOLOMON ISLANDS", - "BANDA SEA", - "OFF EAST COAST OF HONSHU", - "SOLOMON ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS", - "FIJI ISLANDS REGION", - "SOUTH OF FIJI ISLANDS", - "NEAR S. COAST OF HONSHU", - "THAILAND", - "WEST CHILE RISE", - "SOLOMON ISLANDS", - "FIJI ISLANDS REGION", - "BONIN ISLANDS", - "BALLENY ISLANDS REGION", - "TONGA ISLANDS", - "HOKKAIDO", - "HOKKAIDO", - "IRAN-IRAQ BORDER REGION", - "IRAN-IRAQ BORDER REGION", - "NEAR COAST OF CENTRAL CHILE", - "ANDREANOF ISLANDS", - "NORTHERN CALIFORNIA", - "NORTHERN MOLUCCA SEA", - "OFF EAST COAST OF KAMCHATKA", - "EASTER ISLAND REGION", - "TONGA ISLANDS", - "EASTER ISLAND REGION", - "OFF COAST OF JALISCO", - "SOUTHERN MOLUCCA SEA", - "NORTH OF HALMAHERA", - "JUJUY PROVINCE", - "SOLOMON ISLANDS", - "SOUTHERN ALASKA", - "NEW BRITAIN REGION", - "YUNNAN", - "GULF OF CALIFORNIA", - "NEW BRITAIN REGION", - "FOX ISLANDS", - "HOKKAIDO", - "TONGA ISLANDS", - "EASTER ISLAND REGION", - "NORTHERN CHILE", - "SOUTH OF MARIANA ISLANDS", - "NEAR EAST COAST OF HONSHU", - "BANDA SEA", - "TONGA ISLANDS", - "BANDA SEA", - "XIZANG", - "BALLENY ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "OFF COAST OF JALISCO", - "KERMADEC ISLANDS REGION", - "NORTHERN SUMATRA", - "NEW BRITAIN REGION", - "SOLOMON ISLANDS", - "OFF W COAST OF NORTHERN SUMATRA", - "SOUTH OF MARIANA ISLANDS", - "FIJI ISLANDS REGION", - "MAURITIUS - REUNION REGION", - "VANUATU ISLANDS", - "NEW BRITAIN REGION", - "VANUATU ISLANDS", - "SOUTH OF MARIANA ISLANDS", - "SOUTH OF MARIANA ISLANDS", - "MINDANAO", - "MINDANAO", - "SOUTHEASTERN ALASKA", - "TONGA ISLANDS", - "KURIL ISLANDS", - "TONGA ISLANDS", - "OWEN FRACTURE ZONE REGION", - "KURIL ISLANDS", - "SOUTHEASTERN ALASKA", - "NORTHERN MID-ATLANTIC RIDGE", - "OAXACA", - "BISMARCK SEA", - "BISMARCK SEA", - "YUNNAN", - "MYANMAR-INDIA BORDER REGION", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS REGION", - "EASTER ISLAND REGION", - "OAXACA", - "VANUATU ISLANDS", - "SOUTH ISLAND", - "EASTER ISLAND REGION", - "NEAR EAST COAST OF HONSHU", - "TAIWAN", - "NEAR EAST COAST OF KAMCHATKA", - "EASTERN NEW GUINEA REG.", - "EAST OF SOUTH SANDWICH ISLANDS", - "BISMARCK SEA", - "KYUSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR COAST OF PERU", - "PUERTO RICO REGION", - "OFF COAST OF CENTRAL CHILE", - "BALLENY ISLANDS REGION", - "NEAR COAST OF NORTHERN CHILE", - "NICOBAR ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "CELEBES SEA", - "NEAR EAST COAST OF HONSHU", - "SCOTIA SEA", - "OFF EAST COAST OF HONSHU", - "KURIL ISLANDS", - "NEW BRITAIN REGION", - "NORTH ISLAND", - "TONGA ISLANDS", - "JAVA", - "NEAR EAST COAST OF HONSHU", - "NORTHERN SUMATRA", - "NEAR EAST COAST OF HONSHU", - "SOLOMON ISLANDS", - "GREECE", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "GREECE", - "KURIL ISLANDS", - "NEAR EAST COAST OF HONSHU", - "MARIANA ISLANDS", - "HALMAHERA", - "OFF EAST COAST OF HONSHU", - "FOX ISLANDS", - "NEAR COAST OF NICARAGUA", - "NORTHERN CHILE", - "NEAR COAST OF CHIAPAS", - "SOUTH OF KERMADEC ISLANDS", - "SOUTH OF FIJI ISLANDS", - "NEAR COAST OF NORTHERN PERU", - "MARIANA ISLANDS REGION", - "KERMADEC ISLANDS", - "SANTA CRUZ ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF PANAMA", - "NEAR COAST OF CENTRAL CHILE", - "OFF EAST COAST OF HONSHU", - "NEAR COAST OF CENTRAL CHILE", - "TONGA ISLANDS", - "SOUTH OF MARIANA ISLANDS", - "WEST CHILE RISE", - "NEAR COAST OF VENEZUELA", - "SOLOMON ISLANDS", - "NICARAGUA", - "NEAR COAST OF NORTHERN CHILE", - "SULAWESI", - "KERMADEC ISLANDS", - "OFF EAST COAST OF HONSHU", - "MID-INDIAN RIDGE", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "OFF EAST COAST OF HONSHU", - "MACQUARIE ISLAND REGION", - "OFF COAST OF CENTRAL CHILE", - "NORTHERN MOLUCCA SEA", - "TONGA ISLANDS", - "PERU-BRAZIL BORDER REGION", - "KERMADEC ISLANDS", - "OFF EAST COAST OF HONSHU", - "NEW BRITAIN REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "FIJI ISLANDS REGION", - "MINDANAO", - "FOX ISLANDS", - "MINDANAO", - "JUJUY PROVINCE", - "NEAR COAST OF CENTRAL CHILE", - "NORTHEAST OF TAIWAN", - "SOUTH OF MARIANA ISLANDS", - "SOUTHERN SUMATRA", - "LUZON", - "OFF EAST COAST OF HONSHU", - "WESTERN AUSTRALIA", - "GUERRERO", - "KERMADEC ISLANDS", - "EASTERN NEW GUINEA REG.", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS REGION", - "WEST CHILE RISE", - "SOUTH SHETLAND ISLANDS", - "MINDANAO", - "EAST OF SOUTH SANDWICH ISLANDS", - "NEAR EAST COAST OF HONSHU", - "RUSSIA-MONGOLIA BORDER REGION", - "MINDANAO", - "NEAR COAST OF NICARAGUA", - "SOUTHEAST OF LOYALTY ISLANDS", - "TONGA ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "SOUTHWESTERN SIBERIA", - "NEAR COAST OF CENTRAL CHILE", - "SERAM", - "VANUATU ISLANDS", - "OFF EAST COAST OF KAMCHATKA", - "SOUTHEAST OF HONSHU", - "NEW BRITAIN REGION", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "TAIWAN", - "NORTHERN MID-ATLANTIC RIDGE", - "OFF COAST OF CENTRAL CHILE", - "SOUTH SHETLAND ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOUTHWEST OF SUMATRA", - "CENTRAL BOLIVIA", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "NEVADA", - "ALASKA PENINSULA", - "NEAR COAST OF CENTRAL CHILE", - "SOUTHEAST OF RYUKYU ISLANDS", - "SOUTHEAST OF RYUKYU ISLANDS", - "SANTA CRUZ ISLANDS", - "BANDA SEA", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTHERN IRAN", - "CENTRAL MID-ATLANTIC RIDGE", - "OFF COAST OF CENTRAL CHILE", - "NEAR COAST OF NORTHERN CHILE", - "SOLOMON ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "SOUTHEAST OF HONSHU", - "NEW BRITAIN REGION", - "CRETE", - "LOYALTY ISLANDS", - "NEGROS", - "NORTHERN CHILE", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SAMAR", - "SOUTHERN SUMATRA", - "NEAR COAST OF NICARAGUA", - "CELEBES SEA", - "EAST OF KURIL ISLANDS", - "SOUTHERN SUMATRA", - "CHILE-BOLIVIA BORDER REGION", - "JAVA SEA", - "VANUATU ISLANDS", - "CELEBES SEA", - "NEAR EAST COAST OF HONSHU", - "BANDA SEA", - "VANUATU ISLANDS REGION", - "LOYALTY ISLANDS", - "RYUKYU ISLANDS", - "ALASKA PENINSULA", - "BONIN ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "WESTERN IDAHO", - "BONIN ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "NORTHERN SUMATRA", - "FIJI ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "VANCOUVER ISLAND", - "SOUTHERN EAST PACIFIC RISE", - "MYANMAR", - "OFF W COAST OF NORTHERN SUMATRA", - "MINDANAO", - "NEAR EAST COAST OF HONSHU", - "SIKKIM", - "KERMADEC ISLANDS REGION", - "MAURITIUS - REUNION REGION", - "ANDREANOF ISLANDS", - "EASTERN NEW GUINEA REG.", - "NORTHERN PERU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "BALI SEA", - "FOX ISLANDS", - "BANDA SEA", - "CENTRAL MID-ATLANTIC RIDGE", - "WEST OF MACQUARIE ISLAND", - "SOUTHEAST INDIAN RIDGE", - "MINDORO", - "SOUTH OF PANAMA", - "NEAR EAST COAST OF HONSHU", - "NEAR N COAST OF NEW GUINEA", - "CHIAPAS", - "CENTRAL MID-ATLANTIC RIDGE", - "SOUTH OF JAVA", - "GULF OF CALIFORNIA", - "SOUTH OF FIJI ISLANDS", - "QINGHAI", - "VANUATU ISLANDS", - "KERMADEC ISLANDS", - "TURKEY", - "CYPRUS REGION", - "FOX ISLANDS", - "FOX ISLANDS", - "SUNDA STRAIT", - "BONIN ISLANDS", - "NEW BRITAIN REGION", - "NEGROS", - "FIJI ISLANDS REGION", - "SCOTIA SEA", - "NEAR EAST COAST OF KAMCHATKA", - "NEAR COAST OF CENTRAL CHILE", - "EAST OF SOUTH SANDWICH ISLANDS", - "GULF OF CALIFORNIA", - "SOLOMON ISLANDS", - "MYANMAR", - "NEW IRELAND REGION", - "CRETE", - "SAKHALIN", - "SEA OF OKHOTSK", - "PAKISTAN", - "FIJI ISLANDS REGION", - "NEW BRITAIN REGION", - "SOUTH ATLANTIC OCEAN", - "BANDA SEA", - "VANUATU ISLANDS", - "SOUTHERN SUMATRA", - "OFF COAST OF JALISCO", - "NEAR COAST OF NORTHERN CHILE", - "OFF COAST OF NORTHERN CALIFORNIA", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "JAVA SEA", - "MYANMAR-INDIA BORDER REGION", - "NORTH ATLANTIC OCEAN", - "XIZANG", - "VANUATU ISLANDS", - "NORTHERN EAST PACIFIC RISE", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "FIJI ISLANDS REGION", - "RYUKYU ISLANDS", - "CENTRAL ITALY", - "SOUTH ISLAND", - "SOUTH ISLAND", - "OFF COAST OF CENTRAL CHILE", - "MINDANAO", - "NORTHERN MID-ATLANTIC RIDGE", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SULAWESI", - "NEAR ISLANDS", - "NEW BRITAIN REGION", - "SOUTH OF JAVA", - "NEAR COAST OF GUATEMALA", - "SEA OF OKHOTSK", - "LEYTE", - "AUCKLAND ISLANDS", - "DODECANESE ISLANDS", - "MARIANA ISLANDS", - "GANSU", - "GUATEMALA", - "HOKKAIDO", - "BOTSWANA", - "COOK STRAIT", - "NEAR EAST COAST OF HONSHU", - "ANDREANOF ISLANDS", - "SOUTHEAST OF HONSHU", - "NORTHERN SUMATRA", - "OFF COAST OF NORTHERN CALIFORNIA", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF KAMCHATKA", - "BANDA SEA", - "COOK STRAIT", - "SOUTH OF PANAMA", - "SOUTH OF FIJI ISLANDS", - "SOLOMON ISLANDS", - "MINDANAO", - "SOUTHERN BOLIVIA", - "SOUTH OF FIJI ISLANDS", - "BANDA SEA", - "NEW IRELAND REGION", - "NEAR COAST OF NORTHERN CHILE", - "MYANMAR-INDIA BORDER REGION", - "NEPAL", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "NEAR COAST OF CHIAPAS", - "NEAR EAST COAST OF HONSHU", - "PACIFIC-ANTARCTIC RIDGE", - "SANTA CRUZ ISLANDS REGION", - "SOLOMON ISLANDS", - "ALASKA PENINSULA", - "TONGA ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTHERN MID-ATLANTIC RIDGE", - "BONIN ISLANDS", - "KERMADEC ISLANDS", - "SOLOMON ISLANDS", - "NEPAL", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "OFF E. COAST OF N. ISLAND", - "NORTHERN MOLUCCA SEA", - "NORTHERN MOLUCCA SEA", - "MINDANAO", - "SOUTHERN EAST PACIFIC RISE", - "SOLOMON ISLANDS", - "SOUTH OF PANAMA", - "NORTH ATLANTIC OCEAN", - "MARIANA ISLANDS", - "VANUATU ISLANDS", - "CENTRAL PERU", - "JUJUY PROVINCE", - "CENTRAL PERU", - "OFF EAST COAST OF HONSHU", - "E. CAROLINE ISLANDS", - "FIJI ISLANDS REGION", - "SOUTH OF PANAMA", - "SANTA CRUZ ISLANDS", - "FOX ISLANDS", - "KERMADEC ISLANDS", - "TIMOR REGION", - "NEAR COAST OF CENTRAL CHILE", - "NICOBAR ISLANDS", - "ANDREANOF ISLANDS", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "NORTHWEST OF RYUKYU ISLANDS", - "SOLOMON ISLANDS", - "GREECE", - "NEAR COAST OF NICARAGUA", - "PERU-BRAZIL BORDER REGION", - "SOLOMON ISLANDS", - "BANDA SEA", - "NEAR COAST OF CHIAPAS", - "NEAR COAST OF NORTHERN CHILE", - "MYANMAR-INDIA BORDER REGION", - "SOLOMON ISLANDS", - "TALAUD ISLANDS", - "BOUVET ISLAND REGION", - "IRIAN JAYA REGION", - "KERMADEC ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOUTH INDIAN OCEAN", - "AEGEAN SEA", - "GULF OF CALIFORNIA", - "SOUTH OF PANAMA", - "SOLOMON ISLANDS", - "GUERRERO", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "SOUTH OF FIJI ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "VANCOUVER ISLAND", - "IRIAN JAYA REGION", - "NEAR COAST OF CENTRAL CHILE", - "NORTH OF ASCENSION ISLAND", - "SOUTHERN XINJIANG", - "NORTHWEST OF KURIL ISLANDS", - "HAWAII", - "SOUTHERN BOLIVIA", - "NEW BRITAIN REGION", - "MAURITIUS - REUNION REGION", - "SOLOMON ISLANDS", - "FIJI ISLANDS REGION", - "NEW BRITAIN REGION", - "NEW IRELAND REGION", - "NEW GUINEA", - "SOUTHEAST OF LOYALTY ISLANDS", - "NEW IRELAND REGION", - "VANUATU ISLANDS", - "NORTHERN MOLUCCA SEA", - "CENTRAL CHILE", - "VANUATU ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "PRINCE EDWARD ISLANDS REGION", - "HOKKAIDO", - "SANTIAGO DEL ESTERO PROV.", - "NEAR COAST OF CHIAPAS", - "TONGA ISLANDS", - "IRIAN JAYA REGION", - "WESTERN BRAZIL", - "JAN MAYEN ISLAND REGION", - "KURIL ISLANDS", - "MYANMAR", - "HOKKAIDO", - "SOLOMON ISLANDS", - "ANDREANOF ISLANDS", - "KERMADEC ISLANDS REGION", - "SICHUAN", - "SOUTH OF FIJI ISLANDS", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "JAN MAYEN ISLAND REGION", - "NORTHERN MOLUCCA SEA", - "SUMBAWA REGION", - "KURIL ISLANDS", - "FIJI ISLANDS REGION", - "VANCOUVER ISLAND", - "WEST OF MACQUARIE ISLAND", - "IRIAN JAYA REGION", - "VANCOUVER ISLAND", - "FLORES SEA", - "VANCOUVER ISLAND", - "IONIAN SEA", - "ANDREANOF ISLANDS", - "SUMBAWA REGION", - "ARMENIA-AZERBAIJAN-IRAN BORD REG", - "EASTERN NEW GUINEA REG.", - "NEAR COAST OF CENTRAL CHILE", - "SOUTHWEST OF AFRICA", - "EL SALVADOR", - "RAT ISLANDS", - "BOUVET ISLAND REGION", - "EASTERN NEW GUINEA REG.", - "SANTA CRUZ ISLANDS", - "BANDA SEA", - "EASTERN SIBERIA", - "CENTRAL MID-ATLANTIC RIDGE", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "KURIL ISLANDS", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "TONGA ISLANDS", - "KURIL ISLANDS", - "COSTA RICA", - "NEW GUINEA", - "SANTA CRUZ ISLANDS", - "NEW BRITAIN REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHWESTERN SIBERIA", - "NEAR N COAST OF NEW GUINEA", - "BOUVET ISLAND REGION", - "JAVA", - "SOLOMON ISLANDS", - "SANTA CRUZ ISLANDS", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "MICHOACAN", - "SULAWESI", - "SOUTH SANDWICH ISLANDS REGION", - "BONIN ISLANDS", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "SOUTHEAST INDIAN RIDGE", - "GUERRERO", - "NORTHERN MOLUCCA SEA", - "VANUATU ISLANDS", - "OFF COAST OF CENTRAL AMERICA", - "NEW BRITAIN REGION", - "VANUATU ISLANDS REGION", - "SOLOMON ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "NEAR COAST OF CENTRAL CHILE", - "VANUATU ISLANDS REGION", - "NEW BRITAIN REGION", - "OFF COAST OF CENTRAL AMERICA", - "CENTRAL PERU", - "FIJI ISLANDS REGION", - "SOUTHWESTERN PAKISTAN", - "KERMADEC ISLANDS", - "BAJA CALIFORNIA", - "OFF EAST COAST OF HONSHU", - "IRIAN JAYA", - "IRIAN JAYA REGION", - "MINDANAO", - "OFF W COAST OF NORTHERN SUMATRA", - "NEAR COAST OF GUERRERO", - "EASTERN NEW GUINEA REG.", - "KOMANDORSKIYE OSTROVA REGION", - "VANUATU ISLANDS", - "ECUADOR", - "SOUTH ISLAND", - "CENTRAL PERU", - "SOLOMON ISLANDS", - "NEPAL", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF HONSHU", - "KERMADEC ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "TURKEY", - "NORTHERN MOLUCCA SEA", - "SOUTHERN EAST PACIFIC RISE", - "EASTERN NEW GUINEA REG.", - "CENTRAL CALIFORNIA", - "NEAR COAST OF PERU", - "FLORES SEA", - "FLORES SEA", - "MINDANAO", - "SOUTHERN ALASKA", - "BANDA SEA", - "NEAR COAST OF CENTRAL CHILE", - "REYKJANES RIDGE", - "HALMAHERA", - "NEAR COAST OF CENTRAL CHILE", - "TAJIKISTAN", - "HAITI REGION", - "SOLOMON ISLANDS", - "BANDA SEA", - "OFF COAST OF CENTRAL AMERICA", - "SANTA CRUZ ISLANDS", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS REGION", - "OFF E. COAST OF N. ISLAND", - "NEAR COAST OF PERU", - "KERMADEC ISLANDS", - "OFF E. COAST OF N. ISLAND", - "NORTH OF ASCENSION ISLAND", - "MINDANAO", - "NEAR EAST COAST OF HONSHU", - "SOUTH GEORGIA ISLAND REGION", - "OFF EAST COAST OF HONSHU", - "SOUTHEAST OF LOYALTY ISLANDS", - "OAXACA", - "BANDA SEA", - "SOUTH OF KERMADEC ISLANDS", - "PHILIPPINE ISLANDS REGION", - "CENTRAL MEXICO", - "KURIL ISLANDS", - "NEAR EAST COAST OF HONSHU", - "IRIAN JAYA", - "SOUTH OF FIJI ISLANDS", - "DODECANESE ISLANDS", - "IRAN-IRAQ BORDER REGION", - "FOX ISLANDS", - "NEW IRELAND REGION", - "LOYALTY ISLANDS", - "COLOMBIA", - "CELEBES SEA", - "SANTA CRUZ ISLANDS", - "EASTERN NEW GUINEA REG.", - "ANDREANOF ISLANDS", - "NEAR COAST OF OAXACA", - "SOUTH SANDWICH ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "NEAR COAST OF PERU", - "QINGHAI", - "KAMCHATKA PENINSULA", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "OFF COAST OF NORTHERN CHILE", - "VANUATU ISLANDS", - "KYUSHU", - "SOUTHERN ALASKA", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "KURIL ISLANDS", - "NEAR COAST OF VENEZUELA", - "VANUATU ISLANDS", - "COLOMBIA", - "NEAR COAST OF CHIAPAS", - "PERU-BRAZIL BORDER REGION", - "PERU-BRAZIL BORDER REGION", - "NEAR EAST COAST OF HONSHU", - "NORTH OF HONDURAS", - "SOLOMON ISLANDS", - "NEPAL", - "SOUTHERN IRAN", - "SOLOMON ISLANDS", - "COSTA RICA", - "KOMANDORSKIYE OSTROVA REGION", - "NEW IRELAND REGION", - "NEW BRITAIN REGION", - "ALASKA PENINSULA", - "MINDANAO", - "PERU-BRAZIL BORDER REGION", - "SOUTHERN CHILE", - "MINDANAO", - "PHILIPPINE ISLANDS REGION", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR COAST OF NORTHERN CHILE", - "MINAHASSA PENINSULA", - "SOLOMON ISLANDS", - "NEAR COAST OF GUERRERO", - "SOUTHWEST OF SUMATRA", - "SOUTH OF ALASKA", - "SOUTHEAST OF LOYALTY ISLANDS", - "EAST OF KURIL ISLANDS", - "SCOTIA SEA", - "QUEEN CHARLOTTE ISLANDS REGION", - "PRIMOR'YE", - "MARIANA ISLANDS", - "SEA OF OKHOTSK", - "SOUTHERN SUMATRA", - "NORTHERN PERU", - "SOUTHEAST OF LOYALTY ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOLOMON ISLANDS", - "SOUTHEASTERN ALASKA", - "NEAR COAST OF ECUADOR", - "NEW GUINEA", - "OFF EAST COAST OF HONSHU", - "FIJI ISLANDS", - "RAT ISLANDS", - "SANTA CRUZ ISLANDS", - "PERU-ECUADOR BORDER REGION", - "BONIN ISLANDS", - "GULF OF ALASKA", - "PAKISTAN", - "SOUTH ISLAND", - "CUBA REGION", - "PERU-BRAZIL BORDER REGION", - "KERMADEC ISLANDS REGION", - "HINDU KUSH REGION", - "NEW BRITAIN REGION", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF CHIAPAS", - "NORTHERN PERU", - "SOUTH SANDWICH ISLANDS REGION", - "ALASKA PENINSULA", - "OFF W COAST OF NORTHERN SUMATRA", - "FIJI ISLANDS REGION", - "KERMADEC ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "SEA OF OKHOTSK", - "OFF W COAST OF NORTHERN SUMATRA", - "NEAR EAST COAST OF HONSHU" - ], - "lat": [ - -13.6098, - -1.159, - -1.4513, - -17.9609, - -3.7682, - 13.9987, - 44.3014, - 13.396, - 26.8975, - 41.3343, - -17.4154, - 54.5825, - -4.7722, - -0.2882, - -31.106, - -30.3103, - -19.6664, - -24.0934, - -30.5139, - -16.4206, - -57.7886, - 27.7829, - -50.371, - 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"# fig.update_geos(resolution=110, showcountries=True,projection_type=\"orthographic\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "3355fe19", - "metadata": {}, - "source": [ - "# 3 Create a Pandas from a generic text file.\n", - "\n", - "The python package pandas is very useful to read csv files, but also many text files that are more or less formatted as one observation per row and one column for each feature.\n", - "\n", - "As an example, we are going to look at the list of seismic stations from the Northern California seismic network, available [here](http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day):\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "ef9f302d", - "metadata": {}, - "outputs": [], - "source": [ - "url = 'http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day'" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "3a60ef42", - "metadata": {}, - "outputs": [], - "source": [ - "# this gets the file linked in the URL page and convert it to a string\n", - "s = requests.get(url).content " - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "9edd0bf5", - "metadata": {}, - "outputs": [], - "source": [ - "# this will convert the string, decode it , and make it a table\n", - "data = pd.read_csv(io.StringIO(s.decode('utf-8')), header=None, skiprows=2, sep='\\s+', usecols=list(range(0, 13)))\n", - "# because columns/keys were not assigned, assign them now\n", - "data.columns = ['station', 'network', 'channel', 'location', 'rate', 'start_time', 'end_time', 'latitude', 'longitude', 'elevation', 'depth', 'dip', 'azimuth']" - ] - }, - { - "cell_type": "markdown", - "id": "b2def912", - "metadata": {}, - "source": [ - "Let us look at the data. They are now stored into a pandas dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "448b69ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.0
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2AARNCEHZ--0.01981/06/12,19:15:001984/01/01,00:00:0039.27594-121.02696911.00.0-90.00.0
3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.0
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mDateParseError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[78], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# convert start_time and end_time to datetime\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstart_time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstart_time\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mend_time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mend_time\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", - "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/tools/datetimes.py:1108\u001b[0m, in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m 1106\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mtz_localize(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutc\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, ABCSeries):\n\u001b[0;32m-> 1108\u001b[0m cache_array \u001b[38;5;241m=\u001b[39m \u001b[43m_maybe_cache\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert_listlike\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m cache_array\u001b[38;5;241m.\u001b[39mempty:\n\u001b[1;32m 1110\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mmap(cache_array)\n", - "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/tools/datetimes.py:254\u001b[0m, in \u001b[0;36m_maybe_cache\u001b[0;34m(arg, format, cache, convert_listlike)\u001b[0m\n\u001b[1;32m 252\u001b[0m unique_dates \u001b[38;5;241m=\u001b[39m unique(arg)\n\u001b[1;32m 253\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(unique_dates) \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(arg):\n\u001b[0;32m--> 254\u001b[0m cache_dates \u001b[38;5;241m=\u001b[39m 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\u001b[38;5;28;01mreturn\u001b[39;00m _array_strptime_with_fallback(arg, name, utc, \u001b[38;5;28mformat\u001b[39m, exact, errors)\n\u001b[0;32m--> 490\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m \u001b[43mobjects_to_datetime64ns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 491\u001b[0m \u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 492\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 493\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 495\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 496\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_object\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 497\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 502\u001b[0m dta \u001b[38;5;241m=\u001b[39m DatetimeArray(result, dtype\u001b[38;5;241m=\u001b[39mtz_to_dtype(tz_parsed))\n", - "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/arrays/datetimes.py:2346\u001b[0m, in 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\u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2350\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2351\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2352\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 2355\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 2356\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 2357\u001b[0m \u001b[38;5;66;03m# Return i8 values to denote unix timestamps\u001b[39;00m\n\u001b[1;32m 2358\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mi8\u001b[39m\u001b[38;5;124m\"\u001b[39m), tz_parsed\n", - "File \u001b[0;32mtslib.pyx:403\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mtslib.pyx:552\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mtslib.pyx:517\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mconversion.pyx:546\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.conversion.convert_str_to_tsobject\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mparsing.pyx:331\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.parsing.parse_datetime_string\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mparsing.pyx:660\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.parsing.dateutil_parse\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mDateParseError\u001b[0m: Unknown datetime string format, unable to parse: 1976/07/20,17:38:00, at position 0" + "mapbox": { + "center": { + "lat": 38.04061885424097, + "lon": -121.71164549237406 + }, + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 ] - } - ], - "source": [ - "# convert start_time and end_time to datetime\n", - "data['start_time'] = pd.to_datetime(data['start_time'])\n", - "data['end_time'] = pd.to_datetime(data['end_time'])" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "b8bcf253", - "metadata": {}, - "outputs": [], - "source": [ - "data['startdate'] = pd.to_datetime(data['start_time'], format='%Y/%m/%d,%H:%M:%S')" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "da911e32", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuthstartdate
0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.01976-07-20 17:38:00
1AARNCEHZ--0.01977/12/01,22:37:001981/06/12,19:15:0039.27594-121.02696911.00.0-90.00.01977-12-01 22:37:00
2AARNCEHZ--0.01981/06/12,19:15:001984/01/01,00:00:0039.27594-121.02696911.00.0-90.00.01981-06-12 19:15:00
3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.01984-01-01 00:00:00
4AARNCEHZ--100.01985/11/03,22:00:001987/03/10,23:15:0039.27594-121.02696911.00.0-90.00.01985-11-03 22:00:00
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0.0 \n", - "1 1981/06/12,19:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", - "2 1984/01/01,00:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", - "3 1985/11/03,22:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", - "4 1987/03/10,23:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", - "\n", - " startdate \n", - "0 1976-07-20 17:38:00 \n", - "1 1977-12-01 22:37:00 \n", - "2 1981-06-12 19:15:00 \n", - "3 1984-01-01 00:00:00 \n", - "4 1985-11-03 22:00:00 " - ] + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 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0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.01976-07-20 17:38:001977-12-01 22:37:00
1AARNCEHZ--0.01977/12/01,22:37:001981/06/12,19:15:0039.27594-121.02696911.00.0-90.00.01977-12-01 22:37:001981-06-12 19:15:00
2AARNCEHZ--0.01981/06/12,19:15:001984/01/01,00:00:0039.27594-121.02696911.00.0-90.00.01981-06-12 19:15:001984-01-01 00:00:00
3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.01984-01-01 00:00:001985-11-03 22:00:00
4AARNCEHZ--100.01985/11/03,22:00:001987/03/10,23:15:0039.27594-121.02696911.00.0-90.00.01985-11-03 22:00:001987-03-10 23:15:00
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"ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Northern California Seismic Network" - }, - "width": 500 - } - } + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = px.scatter_mapbox(data,\n", - " lat='latitude',lon='longitude', \n", - " range_color=(6,9),mapbox_style=\"carto-positron\",\n", - " height=600, width=500,\n", - " hover_name=\"station\",\n", - " hover_data=['network','station','channel','rate']);\n", - "fig.update_layout(title=\"Northern California Seismic Network\")\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "id": "373751b7", - "metadata": {}, - "source": [ - "# 4: Exercise\n", - "We will now practice on manipulating pandas. This exercise pulls station metadata from a URL and students are expected to practice on specific tasks.\n", - "\n", - "Download data from the NCEDC URL" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "18059dae", - "metadata": {}, - "outputs": [], - "source": [ - "url = 'http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day'" - ] - }, - { - "cell_type": "markdown", - "id": "ceba5236", - "metadata": {}, - "source": [ - "The column names are shown at the top of the text file, make a list of strings of these names in order to rename the columns once the dataframe is made" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "db521722", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "59c9c285", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here\n", - "# request the data from the URL and use the IO package\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "f67ba2d0", - "metadata": {}, - "outputs": [], - "source": [ - "# assign the column names\n", - "data.columns = ['station', 'network', 'channel', 'location', 'rate', 'start_time', 'end_time', 'latitude', 'longitude', 'elevation', 'depth', 'dip', 'azimuth']" - ] - }, - { - "cell_type": "markdown", - "id": "2e81e4b9", - "metadata": {}, - "source": [ - "Find the row of channel KCPB" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d28d4e95", - "metadata": {}, - "outputs": [], - "source": [ - "# find the row of station KCPB" - ] - }, - { - "cell_type": "markdown", - "id": "40806ebd", - "metadata": {}, - "source": [ - "Now select two stations of your choice using ``|``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb5561d5", - "metadata": {}, - "outputs": [], - "source": [ - "# answer below" - ] - }, - { - "cell_type": "markdown", - "id": "479e019a", - "metadata": {}, - "source": [ - "Now select a given station and a specific channel code, example is ``KCPB`` and channel code ``HNZ``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ebba2b90", - "metadata": {}, - "outputs": [], - "source": [ - "# Select two stations, use the typical \"AND\" " - ] - }, - { - "cell_type": "markdown", - "id": "bc0abb79", - "metadata": {}, - "source": [ - "You may also choose the pandas function ``isin`` to select rows that have a given attribute that belongs to a list. For instance, use ``isin`` to select all rows that have the key ``station`` within a list ``['KCBP','KHBB' ]``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "561aecf9", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "markdown", - "id": "9133ae6b", - "metadata": {}, - "source": [ - "**Q** Use panda native functions to calculate how many unique sites (station names) there are in the network." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f235cec3", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f5d93eb9", - "metadata": {}, - "source": [ - "**Q** Use pandas native functions to select the unique set of channel codes that end with ``Z``: this will tell you of how many types digitized data the seismic network manages." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7a13877", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "markdown", - "id": "c801ed50", - "metadata": {}, - "source": [ - "**Q** What station names has the most number of channels? __hint__ you may use ``value_counts()``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "031731dd", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "markdown", - "id": "4edf820a", - "metadata": {}, - "source": [ - "**Q** What is the maximum difference in elevation between the stations using lambda functions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19f7184c", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "markdown", - "id": "141ac2c1", - "metadata": {}, - "source": [ - "Here, pandas does not recognize the start_time and end_time columns as a datetime format, so we cannot use datetime operations on them. We first need to convert these columns into a datetime format:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02663a77", - "metadata": {}, - "outputs": [], - "source": [ - "# answer here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "afaf1851", - "metadata": {}, - "outputs": [], - "source": [ - "# Transform column from string into datetime format" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "bae00773", - "metadata": {}, - "outputs": [], - "source": [ - "# do the same for end times" - ] - }, - { - "cell_type": "markdown", - "id": "f00ce758", - "metadata": {}, - "source": [ - "We can now look when each seismic station was installed using ``groupby`` and sorting by the earliest deployment (i.e., the minimum of the start_time)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0cc2a86", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "markdown", - "id": "5183304a", - "metadata": {}, - "source": [ - "Select the stations that were deployed first and recovered last using ``agg`` and lambda functions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e7b38a0", - "metadata": {}, - "outputs": [], - "source": [ - "# answer here" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c5c38796", - "metadata": {}, - "source": [ - "# 4. CSV vs Parquet\n", - "\n", - "Parquet is a compressed data format that stores and compresses the columns. It is fast for I/O and compact format.\n", - "\n", - "Save ``data`` into a CSV file:" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "f20f5de3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "195 ms ± 1.29 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", - "-rw-r--r--@ 1 marinedenolle staff 4.2M Oct 7 05:15 my_metadata.csv\n" - ] - } - ], - "source": [ - "%timeit data.to_csv(\"my_metadata.csv\")\n", - "!ls -lh my_metadata.csv" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "4b5dd078", - "metadata": {}, - "source": [ - "Try and save in Parquet and compare time and memory." - ] - }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Northern California Seismic Network" + }, + "width": 500 + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.scatter_mapbox(data,\n", + " lat='latitude',lon='longitude', \n", + " range_color=(6,9),mapbox_style=\"carto-positron\",\n", + " height=600, width=500,\n", + " hover_name=\"station\",\n", + " hover_data=['network','station','channel','rate']);\n", + "fig.update_layout(title=\"Northern California Seismic Network\")\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "373751b7", + "metadata": {}, + "source": [ + "# 4: Exercise\n", + "We will now practice on manipulating pandas. This exercise pulls station metadata from a URL and students are expected to practice on specific tasks.\n", + "\n", + "Download data from the NCEDC URL" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "18059dae", + "metadata": {}, + "outputs": [], + "source": [ + "url = 'http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day'" + ] + }, + { + "cell_type": "markdown", + "id": "ceba5236", + "metadata": {}, + "source": [ + "The column names are shown at the top of the text file, make a list of strings of these names in order to rename the columns once the dataframe is made" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "db521722", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59c9c285", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here\n", + "# request the data from the URL and use the IO package\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f67ba2d0", + "metadata": {}, + "outputs": [], + "source": [ + "# assign the column names\n", + "data.columns = ['station', 'network', 'channel', 'location', 'rate', 'start_time', 'end_time', 'latitude', 'longitude', 'elevation', 'depth', 'dip', 'azimuth']" + ] + }, + { + "cell_type": "markdown", + "id": "2e81e4b9", + "metadata": {}, + "source": [ + "Find the row of channel KCPB" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d28d4e95", + "metadata": {}, + "outputs": [], + "source": [ + "# find the row of station KCPB" + ] + }, + { + "cell_type": "markdown", + "id": "40806ebd", + "metadata": {}, + "source": [ + "Now select two stations of your choice using ``|``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb5561d5", + "metadata": {}, + "outputs": [], + "source": [ + "# answer below" + ] + }, + { + "cell_type": "markdown", + "id": "479e019a", + "metadata": {}, + "source": [ + "Now select a given station and a specific channel code, example is ``KCPB`` and channel code ``HNZ``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebba2b90", + "metadata": {}, + "outputs": [], + "source": [ + "# Select two stations, use the typical \"AND\" " + ] + }, + { + "cell_type": "markdown", + "id": "bc0abb79", + "metadata": {}, + "source": [ + "You may also choose the pandas function ``isin`` to select rows that have a given attribute that belongs to a list. For instance, use ``isin`` to select all rows that have the key ``station`` within a list ``['KCBP','KHBB' ]``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "561aecf9", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "markdown", + "id": "9133ae6b", + "metadata": {}, + "source": [ + "**Q** Use panda native functions to calculate how many unique sites (station names) there are in the network." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f235cec3", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f5d93eb9", + "metadata": {}, + "source": [ + "**Q** Use pandas native functions to select the unique set of channel codes that end with ``Z``: this will tell you of how many types digitized data the seismic network manages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7a13877", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "markdown", + "id": "c801ed50", + "metadata": {}, + "source": [ + "**Q** What station names has the most number of channels? __hint__ you may use ``value_counts()``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "031731dd", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "markdown", + "id": "4edf820a", + "metadata": {}, + "source": [ + "**Q** What is the maximum difference in elevation between the stations using lambda functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19f7184c", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "markdown", + "id": "141ac2c1", + "metadata": {}, + "source": [ + "Here, pandas does not recognize the start_time and end_time columns as a datetime format, so we cannot use datetime operations on them. We first need to convert these columns into a datetime format:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02663a77", + "metadata": {}, + "outputs": [], + "source": [ + "# answer here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afaf1851", + "metadata": {}, + "outputs": [], + "source": [ + "# Transform column from string into datetime format" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "bae00773", + "metadata": {}, + "outputs": [], + "source": [ + "# do the same for end times" + ] + }, + { + "cell_type": "markdown", + "id": "f00ce758", + "metadata": {}, + "source": [ + "We can now look when each seismic station was installed using ``groupby`` and sorting by the earliest deployment (i.e., the minimum of the start_time)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0cc2a86", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "markdown", + "id": "5183304a", + "metadata": {}, + "source": [ + "Select the stations that were deployed first and recovered last using ``agg`` and lambda functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e7b38a0", + "metadata": {}, + "outputs": [], + "source": [ + "# answer here" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c5c38796", + "metadata": {}, + "source": [ + "# 4. CSV vs Parquet\n", + "\n", + "Parquet is a compressed data format that stores and compresses the columns. It is fast for I/O and compact format.\n", + "\n", + "Save ``data`` into a CSV file:" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "f20f5de3", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 89, - "id": "0952eb9f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "25.2 ms ± 10.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", - "-rw-r--r--@ 1 marinedenolle staff 591K Oct 7 05:15 my_metadata.pq\n" - ] - } - ], - "source": [ - "%timeit data.to_parquet(\"my_metadata.pq\")\n", - "!ls -lh my_metadata.pq" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "195 ms ± 1.29 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", + "-rw-r--r--@ 1 marinedenolle staff 4.2M Oct 7 05:15 my_metadata.csv\n" + ] } - ], - "metadata": { - "kernelspec": { - "display_name": "mlgeo", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" + ], + "source": [ + "%timeit data.to_csv(\"my_metadata.csv\")\n", + "!ls -lh my_metadata.csv" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "4b5dd078", + "metadata": {}, + "source": [ + "Try and save in Parquet and compare time and memory." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "0952eb9f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25.2 ms ± 10.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", + "-rw-r--r--@ 1 marinedenolle staff 591K Oct 7 05:15 my_metadata.pq\n" + ] } + ], + "source": [ + "%timeit data.to_parquet(\"my_metadata.pq\")\n", + "!ls -lh my_metadata.pq" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlgeo", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/Chapter3-MachineLearning/3.3_clustering.ipynb b/book/Chapter3-MachineLearning/3.3_clustering.ipynb index 1712c76..2d08e53 100644 --- a/book/Chapter3-MachineLearning/3.3_clustering.ipynb +++ b/book/Chapter3-MachineLearning/3.3_clustering.ipynb @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -332,7 +332,7 @@ "[5 rows x 23 columns]" ] }, - "execution_count": 3, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -347,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -372,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -381,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -586,7 +586,7 @@ "[5 rows x 23 columns]" ] }, - "execution_count": 20, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -597,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -38471,7 +38471,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -38507,10 +38507,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, @@ -38537,7 +38537,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -38573,10 +38573,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 37, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" }, @@ -38624,7 +38624,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -38650,7 +38650,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -38669,16 +38669,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4" + "7" ] }, - "execution_count": 23, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -38689,7 +38689,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -38722,7 +38722,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -38755,7 +38755,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -38773,7 +38773,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -38802,7 +38802,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -38833,7 +38833,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -38874,7 +38874,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -38899,14 +38899,14 @@ }, "data": [ { - "hovertemplate": "clusterID=5.0
norm.scatter=%{x}
norm.red=%{y}
norm.orange=%{z}", - "legendgroup": "5.0", + "hovertemplate": "clusterID=0.0
norm.scatter=%{x}
norm.red=%{y}
norm.orange=%{z}", + "legendgroup": "0.0", "marker": { "color": "#636efa", "symbol": "circle" }, "mode": "markers", - "name": "5.0", + "name": "0.0", "scene": "scene", "showlegend": true, "type": "scatter3d", @@ -89885,14 +89885,14 @@ ] }, { - "hovertemplate": "clusterID=2.0
norm.scatter=%{x}
norm.red=%{y}
norm.orange=%{z}", - "legendgroup": "2.0", + "hovertemplate": "clusterID=3.0
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ax.set_ylabel('norm.orange')\n", - "# plt.title('Clusters for cytometer data')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### K-means with Scikit learn\n", - "\n", - "We will now use [scikit learn](!https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#) toolbox to run kmeans. Follow that tutorial and apply it to your problem.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ax.set_ylabel('norm.orange')\n", + "# plt.title('Clusters for cytometer data')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### K-means with Scikit learn\n", + "\n", + "We will now use [scikit learn](!https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#) toolbox to run kmeans. Follow that tutorial and apply it to your problem.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " norm.scatter norm.red norm.orange pop clusterID\n", + "4770 -2.095846 -1.703296 -2.813916 prochloro 0.0\n", + "4771 -2.130636 -1.860400 -2.813916 prochloro 0.0\n", + "4772 -2.095846 -1.850147 -2.813916 prochloro 0.0\n", + "4773 -2.053854 -1.826709 -2.813916 prochloro 0.0\n", + "4774 -1.872152 -2.234668 -2.813916 prochloro 0.0\n", + "... ... ... ... ... ...\n", + "23235 -1.812582 -1.821948 -2.813916 prochloro 0.0\n", + "23236 -1.895101 -1.754993 -2.813916 prochloro 0.0\n", + "23237 -2.146871 -1.946094 -2.813916 prochloro 0.0\n", + "23238 -2.034872 -1.918567 -2.813916 prochloro 0.0\n", + "23239 -1.948812 -2.087634 -2.813916 prochloro 0.0\n", + "\n", + "[18470 rows x 5 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning:\n", + "\n", + "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "array([0, 0, 0, ..., 0, 0, 0], dtype=int32)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "from sklearn.cluster import KMeans\n", + "\n", + "X = data\n", + "Y = np.asarray(df['pop'])\n", + "kmeans = KMeans(n_clusters=2, random_state=0).fit(data)\n", + "kmeans.labels_" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning:\n", + "\n", + "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + "\n", + "/var/folders/js/lzmy975n0l5bjbmr9db291m00000gn/T/ipykernel_13460/3068295825.py:16: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ { - "hovertemplate": "clusterID=0
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norm.scatter=%{x}
norm.red=%{y}
norm.orange=%{z}", - "legendgroup": "1", + "hovertemplate": "clusterID=7
norm.scatter=%{x}
norm.red=%{y}
norm.orange=%{z}", + "legendgroup": "7", "marker": { "color": "#19d3f3", "symbol": "circle" }, "mode": "markers", - "name": "1", + "name": "7", "scene": "scene", "showlegend": true, "type": "scatter3d", @@ -148070,14 +148082,14 @@ ] }, { - "hovertemplate": "clusterID=3
norm.scatter=%{x}
norm.red=%{y}
norm.orange=%{z}", - "legendgroup": "3", + "hovertemplate": "clusterID=1
norm.scatter=%{x}
norm.red=%{y}
norm.orange=%{z}", + "legendgroup": "1", "marker": { "color": "#FF6692", "symbol": "circle" }, "mode": "markers", - "name": "3", + "name": "1", "scene": "scene", "showlegend": true, "type": "scatter3d", @@ -150315,14 +150327,14 @@ ] }, { - "hovertemplate": "clusterID=5
norm.scatter=%{x}
norm.red=%{y}
norm.orange=%{z}", - "legendgroup": "5", + "hovertemplate": "clusterID=2
norm.scatter=%{x}
norm.red=%{y}
norm.orange=%{z}", + "legendgroup": "2", "marker": { "color": "#B6E880", "symbol": "circle" }, "mode": "markers", - "name": "5", + "name": "2", "scene": "scene", "showlegend": true, "type": "scatter3d", @@ -152021,7 +152033,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -152030,7 +152042,7 @@ "Text(0.5, 1.0, 'Old Faithful')" ] }, - "execution_count": 42, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" }, @@ -152100,7 +152112,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -152129,14 +152141,14 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "For n_clusters = 3 The average silhouette_score is : 0.6660137226595727\n" + "For n_clusters = 2 The average silhouette_score is : 0.560931744795642\n" ] }, { @@ -152152,16 +152164,16 @@ { "data": { "text/plain": [ - "Text(0.5, 0.98, 'Silhouette analysis for KMeans clustering on sample data with n_clusters = 3')" + "Text(0.5, 0.98, 'Silhouette analysis for KMeans clustering on sample data with n_clusters = 2')" ] }, - "execution_count": 49, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -152183,6 +152195,7 @@ "# plots of individual clusters, to demarcate them clearly.\n", "ax1.set_ylim([0, len(data_faithful) + (ncluster + 1) * 10])\n", "\n", + "ncluster = 2\n", "# Initialize the clusterer with n_clusters value and a random generator\n", "# seed of 10 for reproducibility.\n", "clusterer = KMeans(n_clusters=ncluster, random_state=10)\n", @@ -152292,7 +152305,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ @@ -152315,14 +152328,31 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 116, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "E = np.zeros(8)\n", "for k in range(1, 9):\n", " (clusters, objective, centers) = my_kmeans(data_faithful, k)\n", - " E[k - 1] = compute_elbow(data_faithful, clusters, centers, k)" + " E[k - 1] = compute_elbow(data_faithful, clusters, centers, k)\n", + "\n", + "plt.figure(1, figsize=(3, 3))\n", + "plt.plot(np.arange(1, 9), E)\n", + "plt.xlabel('Number of clusters')\n", + "plt.ylabel('Elbow criterion')\n", + "plt.grid(True)" ] }, { @@ -152334,24 +152364,14 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 97, "metadata": {}, "outputs": [ { "data": { + "image/png": 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gMFHqMSZL5iYoJjJELe092rm/znQcAAAAAAhIlHqMidPp0OolUyWxCz4AAAAAmEKpx5gNTsF//4MqdfX0GU4DAAAAAIGHUo8xy5gRq6TYcHV29yu/uMZ0HAAAAAAIOJR6jJllWVpz8mn95kKm4AMAAADARKPUY1zWLBtYV59fXKu2zl7DaQAAAAAgsFDqMS4zpkTrrJQo9fW79N7uStNxAAAAACCgUOoxLgNT8Ad3wa8wnAYAAAAAAgulHuM2uAv+7gN1amrpMpwGAAAAAAIHpR7jlhI/Selnxcrllt7exdN6AAAAAJgolHp4xJqsk1PwCyn1AAAAADBRKPXwiHOXTJXDkkqPNqm6od10HAAAAAAICJR6eERsdJgWz0mUJG0u4sx6AAAAAJgIlHp4zOAu+JsLK+R2uw2nAQAAAAD7o9TDY1YtTlWQ06FjNa0qr2oxHQcAAAAAbI9SD4+JDA9WTmaSJM6sBwAAAICJQKmHR+VlDZxZv6XoOFPwAQAAAMDLKPXwqNz5KQoPdaq2qVMl5U2m4wAAAACArVHq4VGhwU6tXDhFErvgAwAAAIC3UerhcWuWDUzBf2dXhfr7XYbTAAAAAIB9UerhcUvnJSp6Uoia23q0q6zedBwAAAAAsC1KPTwuyOnQOUtSJTEFHwAAAAC8iVIPr8g7OQX/vT1V6u7tN5wGAAAAAOyJUg+vyJwRp4TJ4ers7lN+cY3pOAAAAABgS5R6eIXDYSlv2VRJ0uZCpuADAAAAgDdQ6uE1g7vg5xfXqL2z13AaAAAAALAfSj28ZmZqtKYlR6q3z6X39lSZjgMAAAAAtkOph9dYljX0tH4Lu+ADAAAAgMdR6uFVa06uq99VVqem1i7DaQAAAADAXij18KrUhEjNnTZZLre0dVel6TgAAAAAYCuUenhdXtbAFHx2wQcAAAAAz6LUw+tWL0mVZUklR5pU3dBuOg4AAAAA2AalHl4XHxOuRbMTJElv76wwnAYAAAAA7INSjwnx4S74lHoAAAAA8BRKPSbEOYunKMhpqbyqRUeqWkzHAQAAAABboNRjQkRGhCg7I1mStJkz6wEAAADAIyj1mDB5H5mC73a7DacBAAAAAP9HqceEyV2QrLAQp2oaO1R6tMl0HAAAAADwe5R6TJiwkCCtXDhFEmfWAwAAAIAnUOoxodYsmypJemdXpfr7XYbTAAAAAIB/o9RjQi1LT1JURIhOtHZr94F603EAAAAAwK9R6jGhgpwOnbMkVRJn1gMAAADAeFHqMeHyTk7Bf3dPpXp6+w2nAQAAAAD/RanHhJs/M14JMWHq6OpTQUmN6TgAAAAA4Lco9ZhwDoelc0+eWb+5kCn4AAAAADBWlHoYMbgL/o591ero6jWcBgAAAAD8E6UeRsyeGqOpiZHq6XPp/Q+qTMcBAAAAAL9EqYcRlmUpL+vkFHx2wQcAAACAMaHUw5jBXfB37q9Tc1u34TQAAAAA4H8o9TAmNTFSc6ZNlsvl1ju7Kk3HAQAAAAC/Q6mHUYNP6zcXHjecBAAAAAD8D6UeRp27dKosSyoub1RtY4fpOAAAAADgVyj1MCo+JlwLZyVIkrbsZMM8AAAAABgNSj2My8samIK/pYgp+AAAAAAwGpR6GHf24lQFOS0drmzR0eoW03EAAAAAwG9Q6mFcVESIstKTJUlbOLMeAAAAAEaMUg+fsGZwF/yi43K73YbTAAAAAIB/oNTDJ6xYkKLQEKeqGzpUduyE6TgAAAAA4Bco9fAJYaFBWrEgRRJn1gMAAADASFHq4TPystIkSW/vrFC/iyn4AAAAAPBpKPXwGcvmJSkyPFhNrd364EC96TgAAAAA4PMo9fAZwUEOnbMkVdLAhnkAAAAAgDOj1MOn5C0bmIL/7u5K9fb1G04DAAAAAL6NUg+fMn9WvOJjwtTe1aeCklrTcQAAAADAp1Hq4VOcDkvnLj15Zj274AMAAADAGVHq4XMGp+Bv31ejjq5ew2kAAAAAwHdR6uFzZqfFKDVhknp6+7Vtb7XpOAAAAADgsyj18DmWZQ2dWb+lqMJwGgAAAADwXZR6+KQ1ywbW1ReV1qq5rdtwGgAAAADwTZR6+KS0pCjNTotRv8utd3dXmo4DAAAAAD6JUg+ftWbpwBT8zUzBBwAAAIBTotTDZ61ZNlWWJe091KC6pk7TcQAAAADA51Dq4bMSJodr/sx4SdLbOzmzHgAAAAA+jlIPnza4Cz5T8AEAAADgkyj18GnnLE6V02HpUEWzjtW0mo4DAAAAAD6FUg+fFj0pRMvSkyRxZj0AAAAAfBylHj4v7+SZ9ZuLjsvtdhtOAwAAAAC+g1IPn7di4RSFBDtVVd+uA8dPmI4DAAAAAD6DUg+fFx4apBULUiRJmwuZgg8AAAAAgyj18AuDU/Df3lmhfhdT8AEAAABAotTDT2RlJGlSeLAaW7q091C96TgAAAAA4BMo9fALwUFOnbM4VRK74AMAAADAIEo9/Maak1Pwt+6qVG+fy3AaAAAAADCPUg+/sXB2guKiQ9XW2aui0lrTcQAAAADAOEo9/IbTYWn10pNn1hceN5wGAAAAAMyj1MOv5C1LkyRt21etzu4+w2kAAAAAwCxKPfzK3GmTNSV+krp7+rVtb7XpOAAAAABgFKUefsWyLK3JGpiCv6WIKfgAAAAAApvxUn/ixAl961vf0po1a5SVlaXLLrtM+fn5p72/rKxMX/7yl7VixQqtWrVKX/nKV1RZWTnsnrVr1yo9PX3Y11133eXtj4IJMjgFv7CkVi3tPYbTAAAAAIA5QaYD3HHHHWpoaNBjjz2muLg4bdy4Udddd51+85vfaPbs2cPubWpq0rXXXqvc3Fy9/PLL6u7u1ne/+11df/31euWVVxQaGqq2tjZVVlbqueee04IFC4a+NywsbKI/GrxkWnKUZqXG6FBls97dXam/WzXDdCQAAAAAMMLok/ojR45o69atuvfee5WTk6NZs2Zp/fr1Sk5O1quvvvqJ+9966y11dnbqoYce0ty5c7Vw4UL993//tw4ePKjCwkJJ0v79++V2u5WVlaXExMShr6ioqIn+ePCiwTPrNzMFHwAAAEAAM1rqY2Nj9cMf/lALFy4ces2yLLndbjU3N3/i/lWrVunpp59WaGjoJ64N3l9aWqrExERFR0d7LziMO/dkqd97qEH1JzoNpwEAAAAAM4xOv4+OjlZeXt6w115//XUdPXpUq1ev/sT9aWlpSktLG/bac889p9DQUOXm5koaeFIfERGhW2+9VUVFRYqLi9Mll1yiq666Sg7H6f8OY926dae9VlVVpZSUFHV0dIzm4024zs7OYf+0s8hQKWP6ZJUcOaFNOw7rwnNmmI40IQJpjAMR42t/jLH9Mcb2xxjbG+Nrf/40xm63W5Zlfep9xtfUf1RBQYG++c1vat26dVq7du2n3v/SSy9p48aN+sY3vqH4+HhJAxvptba26oILLtAtt9yi/Px8PfLII2pubtZtt9025mw9PT0qLi4e8/dPpPLyctMRJsTsJKnkiLRpe7nmxPn+b0pPCpQxDlSMr/0xxvbHGNsfY2xvjK/9+cMY9/T0nHKW+sf5TKl/6623dNddd2nJkiV67LHHzniv2+3WE088oWeeeUY33HCDrrnmmqFrGzZsUHd3tyIjIyVJ6enpam9v1zPPPKNbb731tE/rN23adNqft27dOrndbmVmZo7+g02gzs5OlZeXa8aMGQoPDzcdx+umntWj1wu2qKqxVzGJZyk1YZLpSF4XaGMcaBhf+2OM7Y8xtj/G2N4YX/vzpzEOCQkZ0X0+Uepffvll3X///TrvvPP0yCOPnDF8b2+vvvGNb+jVV1/VPffco+uuu27Y9eDgYAUHBw97bd68eero6FBzc7NiY2PHlNGyLEVERIzpeydaeHi432Qdj4iICC2bl6iCklrtKG7QZecnmo40YQJljAMV42t/jLH9Mcb2xxjbG+Nrf/4wxiOZei/5wDn1Gzdu1Le//W198Ytf1OOPP/6pfxtxzz336E9/+pMeffTRTxR6l8ultWvX6plnnhn2+p49e5SQkDDmQg/ftebkmfWbi47L7XYbTgMAAAAAE8vok/rDhw/rgQce0HnnnacbbrhBDQ0NQ9fCwsIUERGhxsZGRUVFKSwsTL/5zW/02muv6Z577tHy5ctVV1c3dP/gPeeff76ef/55zZgxQwsWLNB7772n559/XuvXrzfxEeFlKxemKCTIoYq6dh2saNactMmmIwEAAADAhDFa6t944w319vbqzTff1Jtvvjns2sUXX6xbbrlF69at04MPPqhLLrlk6Oz6hx9+WA8//PCw+wfvufPOOxUdHa1HH31U1dXVSktL0/r16/WFL3xhwj4XJk5EWLByF6Ro665KbS48TqkHAAAAEFCMlvobb7xRN9544xnvKS0tHfrfP/nJTz71PYOCgnTTTTfppptuGnc++Ie8ZWnauqtSb++s0LX/uEAOx8jWngAAAACAvzO+ph4Yr5zMJE0KC1JDc5f2Hm749G8AAAAAAJug1MPvBQc5dfbiVEnSlqIKw2kAAAAAYOJQ6mELa5ZNlSRt3VWh3j6X4TQAAAAAMDEo9bCFRXMSNTkqVK0dvdq5v9Z0HAAAAACYEJR62ILTYencpQNP6zcXMgUfAAAAQGCg1MM28k5Owd+2t0pd3X2G0wAAAACA91HqYRvzzopVSnyEunr6tX1ftek4AAAAAOB1lHrYhmVZWrMsTRK74AMAAAAIDJR62MrgLvgFJTVq7egxnAYAAAAAvItSD1uZnhKtGVOi1dfv1ru7q0zHAQAAAACvotTDdgaf1m8pOm44CQAAAAB4F6UetjO4rn7PwXo1NHcaTgMAAAAA3kOph+0kx0Uoc0ac3G7p7Z2VpuMAAAAAgNdQ6mFLeUzBBwAAABAAKPWwpXOWTJXDYans2AlV1rWZjgMAAAAAXkGphy1NjgrV0rmJkqQtOzmzHgAAAIA9UephW4O74G8uPC632204DQAAAAB4HqUetrVq0RQFBzl0vLZNhytbTMcBAAAAAI+j1MO2IsKClTs/WdLA03oAAAAAsBtKPWwt7+SZ9Vt2VsjlYgo+AAAAAHuh1MPWcjKTFREWpPoTnSoubzQdBwAAAAA8ilIPWwsJdmrVoimSpM2cWQ8AAADAZij1sL01J6fgv7OzUn39LsNpAAAAAMBzKPWwvSVzEjQ5MlStHT3aub/OdBwAAAAA8BhKPWzP6XRo9ZJUSUzBBwAAAGAvlHoEhLysgSn42z6oUldPn+E0AAAAAOAZlHoEhPTpsUqKi1Bnd7927KsxHQcAAAAAPIJSj4BgWZbylk2VJG1hCj4AAAAAm6DUI2AM7oKfX1yrts5ew2kAAAAAYPwo9QgYM6ZEa3pKlPr6XXpvd6XpOAAAAAAwbpR6BJTBp/Xsgg8AAADADij1CChrTq6r33OgXo0tXYbTAAAAAMD4UOoRUFLiJyl9eqxcbumdnRWm4wAAAADAuFDqEXDyTk7B31JEqQcAAADg3yj1CDirl6bKYUmlR5tUVd9uOg4AAAAAjBmlHgEnNipMi+cmSpK27GTDPAAAAAD+i1KPgJR3csO8zYUVcrvdhtMAAAAAwNhQ6hGQVi1KVXCQQ8dqWlVe1WI6DgAAAACMCaUeAWlSeLByMpMlSZsLmYIPAAAAwD9R6hGwBnfBf3tnhVwupuADAAAA8D+UegSsnPnJCg8NUm1Tp0qONJqOAwAAAACjRqlHwAoNdmrVoimSOLMeAAAAgH+i1COgrTm5C/47uyrU3+8ynAYAAAAARodSj4C2ZG6iYiJD1NzWo11l9abjAAAAAMCoUOoR0IKcDp2zOFWStLmIXfABAAAA+BdKPQJeXtbALvjv7alSd2+/4TQAAAAAMHKUegS8jOlxSooNV2d3n/L31ZiOAwAAAAAjRqlHwHM4LJ27dGDDPKbgAwAAAPAnlHpAH07Bzy+uUXtnr+E0AAAAADAylHpA0owp0ZqWHKXePpfe21NlOg4AAAAAjAilHpBkWZbyspiCDwAAAMC/UOqBk9YsHZiCv7usTk2tXYbTAAAAAMCno9QDJ01JmKT0s2Llckvv7Kw0HQcAAAAAPhWlHviINcsGpuBvYQo+AAAAAD9AqQc+YvXSqXJYUsmRJlU3tJuOAwAAAABnRKkHPiIuOkyL5iRIkt7eWWE4DQAAAACcGaUe+Ji8ZQMb5m0uZAo+AAAAAN9GqQc+ZtXiVAU5HTpS3aryqhbTcQAAAADgtCj1wMdEhgcrJzNJEhvmAQAAAPBtlHrgFNYMTsEvqpDb7TacBgAAAABOjVIPnELu/GSFhzpV29ih0iNNpuMAAAAAwClR6oFTCAsJ0oqFUyRJm5mCDwAAAMBHUeqB0xjcBf+dnZXq73cZTgMAAAAAn0SpB05j6bxERUWE6ERbt3YfqDcdBwAAAAA+gVIPnEaQ06HVS1MlMQUfAAAAgG+i1ANnMDgF/709Verp7TecBgAAAACGo9QDZ5A5I04Jk8PV0dWn/OIa03EAAAAAYBhKPXAGDoelNUunSpK2FFUYTgMAAAAAw1HqgU+RlzUwBX/7vmp1dPUaTgMAAAAAH6LUA59iZmq00pIi1dvn0nt7qkzHAQAAAIAhlHrgU1iWNfS0nin4AAAAAHwJpR4YgTXLBtbV7yyr04nWbsNpAAAAAGAApR4YgdSESM2dNlkul1tbd/G0HgAAAIBvoNQDI7Tm5Jn1m5mCDwAAAMBHUOqBETp3aaosSyoub1RNY4fpOAAAAABAqQdGKj4mXItmJ0iSthQdN5wGAAAAACj1wKgMTsFnF3wAAAAAvoBSD4zCOYunKMhpqbyqRUeqW0zHAQAAABDgKPXAKERGhCg7I1kST+sBAAAAmEepB0Zp8Mz6LUXH5Xa7DacBAAAAEMgo9cAoLZ+forAQp6obOrT/aJPpOAAAAAACGKUeGKWw0CCtWDBFEmfWAwAAADCLUg+MQV7WwBT8t3dWqN/FFHwAAAAAZlDqgTFYOi9JURHBOtHarT0H6kzHAQAAABCgKPXAGAQHOXTOksEN85iCDwAAAMAMSj0wRoO74L+7u1K9ff2G0wAAAAAIRJR6YIwWzIxXfEyY2rv6lF9cazoOAAAAgABEqQfGyOGwdO7Sgaf1m4uOG04DAAAAIBBR6oFxyMtKkyTt2Futjq5ew2kAAAAABBpKPTAOs6fGaGriJPX0ufT+B9Wm4wAAAAAIMJR6YBwsy1LesoGn9VuYgg8AAABgglHqgXFac3IKftH+OjW3dRtOAwAAACCQUOqBcZqaGKk5aTFyudzaurvSdBwAAAAAAYRSD3jAmpNT8DcXMgUfAAAAwMSh1AMecO7SqbIsad/hRtU2dZiOAwAAACBAUOoBD0iYHK4Fs+IlSW8XVRhOAwAAACBQUOoBD/lwF3xKPQAAAICJQakHPOTsxalyOiwdqmzWsZpW03EAAAAABABKPeAh0ZNClJWRJEnazJn1AAAAACYApR7woMFd8LcUVsjtdhtOAwAAAMDuKPWAB61YkKLQEKeqGtpVduyE6TgAAAAAbI5SD3hQeGiQVsxPkcQUfAAAAADeR6kHPCwva2AK/js7K9TvYgo+AAAAAO+h1AMetiw9SZHhwWps6dYHB+tNxwEAAABgY5R6wMOCgxw6Z0mqJM6sBwAAAOBdlHrAC9YsmypJ2rq7Ur19/YbTAAAAALArSj3gBQtmJSguOkztnb0qLKk1HQcAAACATVHqAS9wOiydu3Tgaf1mpuADAAAA8BJKPeAleVkDpX7b3mp1dvcZTgMAAADAjij1gJfMSZusKQmT1NPbr20fVJmOAwAAAMCGKPWAl1iWpbxlA2fWMwUfAAAAgDcYL/UnTpzQt771La1Zs0ZZWVm67LLLlJ+ff9r7m5qadOeddyo3N1e5ubn6z//8T3V0dAy75/XXX9cFF1ygRYsW6cILL9SWLVu8/TGAUxrcBb+otFYt7T2G0wAAAACwG+Ol/o477tCuXbv02GOP6Ve/+pUWLFig6667TgcPHjzl/V/5yld07NgxvfDCC3ryySe1detW/b//9/+Grr///vu6++67dfnll+u3v/2tVq9erZtvvvm07wd407TkKM2aGqN+l1tbd1eajgMAAADAZoyW+iNHjmjr1q269957lZOTo1mzZmn9+vVKTk7Wq6+++on7i4qKtH37dj344INasGCBVq1apf/6r//S7373O9XU1EiSfvSjH+m8887TFVdcodmzZ+trX/uaFixYoBdffHGiPx4gSco7+bR+c+Fxw0kAAAAA2I3RUh8bG6sf/vCHWrhw4dBrlmXJ7Xarubn5E/fn5+crMTFRs2fPHnpt+fLlsixLBQUFcrlcKiws1MqVK4d934oVK844pR/wpnOXDqyr33e4QXVNnYbTAAAAALCTIJM/PDo6Wnl5ecNee/3113X06FGtXr36E/fX1NRoypQpw14LCQnR5MmTVVVVpZaWFnV0dCglJWXYPUlJSaqqOvPu4+vWrTvttaqqKqWkpHxi7b6v6ezsHPZP+IZJoVLG9MkqOXJCf9lxWBeunjHm92KM7Y3xtT/G2P4YY/tjjO2N8bU/fxpjt9sty7I+9T6jpf7jCgoK9M1vflPr1q3T2rVrP3G9s7NTISEhn3g9NDRU3d3d6urqkqRP3DN4fTx6enpUXFw8rveYKOXl5aYj4GNmJ0klR6RN28s1J378/wJhjO2N8bU/xtj+GGP7Y4ztjfG1P38Y456eHoWGhn7qfT5T6t966y3dddddWrJkiR577LFT3hMWFqaenk/uIN7d3a2IiIihD/zxe7q7uxUeHn7Gn79p06bTXlu3bp3cbrcyMzM/7WMY1dnZqfLycs2YMeNTPy8m1tSzevSngi2qaupVTMJZSk2cNKb3YYztjfG1P8bY/hhj+2OM7Y3xtT9/GuNTPdA+FZ8o9S+//LLuv/9+nXfeeXrkkUdOGz4lJUVvvfXWsNd6enp04sQJJScna/LkyYqIiFBtbe2we2praz8xJX+0LMtSRETEuN5jooSHh/tN1kARERGhZelJyi+u0faSBl0+PXFc78cY2xvja3+Msf0xxvbHGNsb42t//jDGI5l6L/nAkXYbN27Ut7/9bX3xi1/U448/fsa/jcjNzVV1dbWOHDky9Nq2bdskSVlZWbIsS1lZWdq+ffuw79u2bZuys7O98wGAEVrzkV3w3W634TQAAAAA7MBoqT98+LAeeOABnXfeebrhhhvU0NCguro61dXVqbW1Vf39/aqrqxtaK79kyRJlZWXpq1/9qnbv3q33339f9957ry666CIlJydLkq699lr98Y9/1IYNG3Tw4EE9/PDDKi4u1tVXX23yowJasSBFIcFOVda36+DxT57uAAAAAACjZbTUv/HGG+rt7dWbb76p1atXD/u6//77VVVVpdWrV+u1116TNDD94KmnnlJaWpquvvpq3X777VqzZo3uu+++ofdcvXq1HnjgAf3sZz/TxRdfrPfff1/PPvvssGPwABMiwoK1fP7AXz5tLuLMegAAAADjZ3RN/Y033qgbb7zxjPeUlpYO+3V8fLyefPLJM37PRRddpIsuumi88QCPy8tK0zu7KrWlqELX/OMCOR0jWycDAAAAAKdifE09EEiyM5I0KTxYjS1d2neowXQcAAAAAH6OUg9MoOAgp85eNEUSU/ABAAAAjB+lHphgecvSJElbd1Wqt89lOA0AAAAAf0apBybYwjkJio0KVVtnr4r215qOAwAAAMCPUeqBCeZ0WDp36Ydn1gMAAADAWFHqAQPysgam4G/bW62u7j7DaQAAAAD4K0o9YMDcaZM1JX6Sunv6tW1vtek4AAAAAPwUpR4wwLIsrVk2MAV/S1GF4TQAAAAA/BWlHjBksNQXltaotaPHcBoAAAAA/ohSDxhyVkq0ZqZGq6/frXd3V5qOAwAAAMAPUeoBg9acPLN+cyFT8AEAAACMHqUeMGjNyaPtPjhUr4bmTsNpAAAAAPgbSj1gUFJchDJnxMntlt7eydN6AAAAAKNDqQcMGzyzfjO74AMAAAAYJUo9YNjqJalyOCwdOHZClXVtpuMAAAAA8COUesCwmMhQLZ2XKImn9QAAAABGh1IP+IC8k2fWby48LrfbbTgNAAAAAH9BqQd8wMqFUxQS5FBFXZsOVTSbjgMAAADAT1DqAR8QERas3PkpkpiCDwAAAGDkKPWAj8jLGpiC/3bRcblcTMEHAAAA8Oko9YCPyM5IVkRYkOqbu7TvcIPpOAAAAAD8AKUe8BEhwU6dvShVkrSFKfgAAAAARoBSD/iQwSn47+yqVF+/y3AaAAAAAL6OUg/4kEVzEjU5KlStHT3aub/OdBwAAAAAPo5SD/gQp8PSuUs/PLMeAAAAAM6EUg/4mDXLBkr9+x9Uqaunz3AaAAAAAL6MUg/4mPSzYpUcF6Gunn7t2FtjOg4AAAAAH0apB3yMZVlDT+s3FzEFHwAAAMDpUeoBH5SXlSZJKiipUVtHj+E0AAAAAHwVpR7wQdNTojVjSrT6+t16d0+V6TgAAAAAfBSlHvBRQ1Pw2QUfAAAAwGlQ6gEftWbZwBT8PQfr1djSZTgNAAAAAF9EqQd8VHJchDJnxMntlt7eWWE6DgAAAAAfRKkHfNjgFPwt7IIPAAAA4BQo9YAPO2dJqhwOS/uPnlBlfZvpOAAAAAB8DKUe8GGxUWFaMidBkvR2EVPwAQAAAAxHqQd83OCZ9ZuLjsvtdhtOAwAAAMCXUOoBH7dq0RQFBzl0rKZNR6qZgg8AAADgQ5R6wMdFhAUrd36yJGnr7mrDaQAAAAD4Eko94AcGz6x/d0+1XEzBBwAAAHASpR7wAzmZyYoIC1J9c5eO1fWYjgMAAADAR1DqAT8QGuzUyoVTJEl7j3YYTgMAAADAV1DqAT9x9qKBUl9W2cUu+AAAAAAkUeoBv7F4bqKCnJaa2vpVVc/TegAAAACUesBvhIcGaf6MWElSUVm94TQAAAAAfAGlHvAjy9ITJUmFpZR6AAAAAJR6wK8smxcvSSo+0qSOrl7DaQAAAACYRqkH/MiU+EmKiwxSf79bu5iCDwAAAAQ8Sj3gZ+amhkmSCkpqDCcBAAAAYBqlHvAzg6U+v7iGo+0AAACAAEepB/zM9ORQhQQ71NDcpfKqFtNxAAAAABhEqQf8TLDT0sJZcZIGntYDAAAACFyUesAPLZuXIEkqKKk1nAQAAACASZR6wA8tnTtQ6ovLG9XW0WM4DQAAAABTKPWAH0qKDde05Ci5XG4V7a8zHQcAAACAIZR6wE/lZCZLYl09AAAAEMgo9YCfyslMkiQVltTK5eJoOwAAACAQUeoBPzV/ZrzCQ4N0oq1bBytOmI4DAAAAwABKPeCngpwOLUtPlCTl72MKPgAAABCIKPWAH8vJOLmuvoRSDwAAAAQiSj3gx7IyBtbVlx07oROt3YbTAAAAAJholHrAj8XHhGvW1Bi53VJhaa3pOAAAAAAmGKUe8HODR9sVcLQdAAAAEHAo9YCfG1xXX1haq/5+l+E0AAAAACZS0Fi+ye12q7i4WB0dHXK7P3k+dm5u7riDARiZedNjFRURrNaOXpUebdL8mfGmIwEAAACYIKMu9bt379Ztt92m6urqT1xzu92yLEvFxcUeCQfg0zkdlpalJ2lLUYXyi2so9QAAAEAAGXWpf+CBBxQUFKQHH3xQKSkpcjiYwQ+YlpOZrC1FFSoortVVF8w3HQcAAADABBl1qd+3b58ee+wxffazn/VGHgBjkJWeJMuSDlU2q6G5U/Ex4aYjAQAAAJgAo37MHh8fz9N5wMfERIZq3rRYSVJBCUfbAQAAAIFi1O388ssv1w9/+EN1dHR4Iw+AMco+ebRdPkfbAQAAAAFj1NPvjxw5ooMHD+qcc87R3LlzFRYWNuy6ZVl68cUXPRYQwMjkZiZr4xsl2rm/Tr19LgUHMaMGAAAAsLsxlfqMjIyhX3/8SLtTHXEHwPtmTY3R5KhQnWjt1r7DDVoyN9F0JAAAAABeNupS/9Of/tQbOQCMk8NhKTsjSZt2HFN+cQ2lHgAAAAgAoy71gw4ePKjt27ertbVVsbGxys7O1qxZszyZDcAo5WQma9OOYyooqdF1/7TQdBwAAAAAXjbqUu92u3Xvvffql7/85bCp9pZl6eKLL9b9998vy7I8GhLAyCydlySHw9KxmjZVN7QrJX6S6UgAAAAAvGjUO2k9//zz+vWvf62vfOUr2rRpk3bv3q233npLt9xyi37/+9/rhRde8EJMACMRGR6szBlxkjjaDgAAAAgEoy71v/rVr3T99dfrpptu0tSpUxUSEqK0tDTdfPPNuv766/XLX/7SGzkBjFAOR9sBAAAAAWPUpb6qqkorV6485bUVK1bo+PHj4w4FYOwGS/3uA/Xq7u03nAYAAACAN4261E+dOlUlJSWnvLZv3z7FxcWNOxSAsZueEqWEmDD19Pbrg4P1puMAAAAA8KJRl/p//Md/1Pe//3398Y9/lMvlkiS5XC69+uqrevrpp3XBBRd4PCSAkbMsS9lMwQcAAAACwqhL/b/9279pyZIluvPOO7V48WKtXr1aixcv1t13361Fixbptttu80ZOAKPw0XX1Hz2lAgAAAIC9jPpIu5CQEG3YsEGbN2/W9u3b1dLSopiYGOXm5iovL88bGQGM0pK5iQpyWqpu6FBlfbumJkaajgQAAADAC0Zd6gfl5eVR4gEfFR4apIWzErSzrE75xTWUegAAAMCmRlTqr7rqKt17772aPXu2rrrqqjPea1mWXnzxRY+EAzB2OfOTh0r9P6+ZbToOAAAAAC8Y0Zr6j67JdbvdZ/wa3DwPgFmD6+o/ONigzu4+w2kAAAAAeMOIntT/9Kc/Hfrf9913n2bP5qkf4OtSEyZpSvwkVTW0a1dZnVYunGI6EgAAAAAPG/Xu99ddd51++9vfeiEKAE8aONouSRJH2wEAAAB2NepS39fXp9jYWG9kAeBhg1PwCzjaDgAAALClUe9+f9ttt+k73/mO6uvrNXfuXCUkJHzintTUVI+EAzA+C2cnKCTYqfrmLh2pbtWMKdGmIwEAAADwoFGX+vvuu0/9/f1av369LMs65T3FxcXjDgZg/EKDnVo8J0H5xTXKL66h1AMAAAA2M+pS/53vfMcbOQB4SU5m8lCp/9zauabjAAAAAPCgUZf6iy++2Bs5AHhJdsbAZnnF5Y1q6+xVZHiw4UQAAAAAPGXUG+VJUk9PjzZu3KhbbrlFl156qQ4ePKif/exn2r17t6fzARinlPhJmpYcKZfLrZ37a03HAQAAAOBBoy71jY2N+pd/+Rfdf//9OnLkiHbv3q2uri5t3rxZV155pYqKiryRE8A4ZGcM7ILP0XYAAACAvYy61D/88MNqb2/Xa6+9pldeeWXomKwnnnhCixYt0pNPPunxkADGZ+hou5JauVwcbQcAAADYxahL/f/93//ptttu0/Tp04ftfh8aGqovfelL2rt3r0cDAhi/+TPjFR4apBOt3TpU0Ww6DgAAAAAPGXWp7+7u1uTJk095zel0qre3d7yZAHhYcJBDS+clSpLyS5iCDwAAANjFqEv9okWLtHHjxlNe+8Mf/qCFCxeOOxQAzxucgp+/j1IPAAAA2MWoj7S77bbbdM011+if//mflZeXJ8uy9Oqrr+r73/++3nnnHT3//PPeyAlgnAaPttt/rEnNbd2KiQw1nAgAAADAeI36SX1OTo42bNig8PBwPf/883K73XrhhRdUV1en5557TitXrvRGTgDjFB8TrlmpMXK7pcJSjrYDAAAA7GDUT+olKTc3Vz//+c/V1dWl5uZmRUZGatKkSZKkvr4+BQWN6W0BeFl2ZpIOVTYrv7hGn8meZjoOAAAAgHEa9ZP6devWqaSkRJIUFham5OTkoUK/e/dunXPOOZ5NCMBjBtfVF5bUqp+j7QAAAAC/N6JH6q+++qr6+vokSRUVFXrzzTeHiv1Hvffee+x+D/iw9LNiFRkerLbOXu0/0qTMmXGmIwEAAAAYhxGV+g8++EAvvPCCJMmyLD399NOnvffaa6/1SDAAnud0OpSVnqQtOyuUX1JDqQcAAAD83IhK/R133KErr7xSbrdbn/3sZ/XUU08pMzNz2D1Op1ORkZGKjIz0SlAAnpGdmTxQ6otrdOXfZ376NwAAAADwWSMq9SEhIZo6daokadOmTUpKSlJwcLBXgwHwjqz0JFmWdKiiWQ3NnYqPCTcdCQAAAMAYjajUP/XUU/r85z+v5ORkvfLKK2e817Is3XzzzR4JB8DzJkeFau60ydp/9IQKS2p13orppiMBAAAAGKMRl/o1a9YoOTlZTz311BnvpdQDvi8nI1n7j55QfkkNpR4AAADwYyMq9R/d6X7v3r1yOp1eCwTA+7Izk7Xxz6UqKq1TX79LQc5Rn24JAAAAwAeM+k/yn//857Vp0yZvZAEwQeakTdbkyFB1dvep+HCj6TgAAAAAxmjUpf7YsWPscA/4OYfDUlZGkiRpR3GN4TQAAAAAxmrUpf4f/uEf9Nxzz+nYsWMeD/ODH/xAV1555Wmvf//731d6evopv77xjW8M3bd27dpPXL/rrrs8nhfwZzmZyZKkfEo9AAAA4LdGtKb+o8rLy5Wfn6+//du/VVhYmOLi4oZdtyxLb7311qiDvPDCC3ryySeVm5t72nu+9KUv6V//9V+HvfarX/1Kzz77rK6++mpJUltbmyorK/Xcc89pwYIFQ/eFhYWNOhNgZ8vmJcrhsHSsplU1jR1KjoswHQkAAADAKI261E+ZMkUXXnihxwLU1NRo/fr1Kigo0MyZM89476RJkzRp0qShXx89elTPPfecvv71rysjI0OStH//frndbmVlZSk6OtpjOQG7iYwIUeaMOO091KCCkhpdcPaZf/8BAAAA8D2jLvUPPvjgJ17r6+uTw+GQwzH6HbT37t2rmJgY/f73v9fTTz+tioqKEX/vQw89pLlz5+rSSy8deq20tFSJiYkUemAEsjOStPdQg/KLKfUAAACAPxp1qZekZ555Rvn5+frxj38sSSooKNDtt9+uG264Qddcc82o3mvt2rVau3btqDPs2bNHmzZt0osvvjjsLxP279+viIgI3XrrrSoqKlJcXJwuueQSXXXVVWf8S4d169ad9lpVVZVSUlLU0dEx6pwTqbOzc9g/YT+eHuOFM2MkSbvK6nSiuVUhwRxXaRK/h+2PMbY/xtj+GGN7Y3ztz5/G2O12y7KsT71v1KX++eef11NPPaWrrrpq6LXp06frn//5n/Xoo48qPDx82JNzb3nhhRe0ZMkSrVy5ctjrZWVlam1t1QUXXKBbbrlF+fn5euSRR9Tc3KzbbrttzD+vp6dHxcXF4409IcrLy01HgJd5aozdbreiwp1q7ezXG1t2a04qe0/4An4P2x9jbH+Msf0xxvbG+NqfP4xxT0+PQkNDP/W+UZf6X/ziF/rqV7+q66+/fui1lJQUff3rX1dcXJxeeuklr5f6jo4Ovfnmm7r33ns/cW3Dhg3q7u4eOnYvPT1d7e3teuaZZ3Trrbee9mn9pk2bTvvz1q1bJ7fbrczMTM98AC/p7OxUeXm5ZsyYofDwcNNx4AXeGOPlZdKm/Ao1dIXrwswMj7wnxobfw/bHGNsfY2x/jLG9Mb72509jHBISMqL7Rl3qa2pqhu0q/1GLFi3S008/Pdq3HLW3335bLpdL55133ieuBQcHKzg4eNhr8+bNU0dHh5qbmxUbGzumn2lZliIi/GN38PDwcL/JirHx5BivWDhVm/IrtPNAI/9/4yP4PWx/jLH9Mcb2xxjbG+Nrf/4wxiOZei+N4Zz6adOm6d133z3ltW3btiklJWW0bzlqBQUFWrBgwSc2w3O5XFq7dq2eeeaZYa/v2bNHCQkJYy70gJ0tmZugIKelqvp2Vda1mY4DAAAAYBRG/aT+sssu0wMPPKC+vj599rOfVXx8vBobG/XWW2/ppZde0l133eWxcP39/WpsbFRUVNSwc+ZLSko0b968T9zvcDh0/vnn6/nnn9eMGTO0YMECvffee3r++ee1fv16j+UC7CQiLFgLZsVrV1m98otr9E+JkaYjAQAAABihUZf6L37xi6qurtaGDRv0wgsvDL3udDp19dVXj3r3+zOpqqrSunXr9OCDD+qSSy4Zer2+vl5Lliw55ffceeedio6O1qOPPqrq6mqlpaVp/fr1+sIXvuCxXIDd5GSmfFjq18w2HQcAAADACI3pSLs777xTX/7yl7Vz506dOHFC0dHRWrx48bintz/00EPDfp2WlqbS0tJP3Pfaa6+d9j2CgoJ000036aabbhpXFiCQ5GQm6ce/l/YcbFBXd5/CQsf0rwYAAAAAE2zMf3KPiorSueee68ksAAyZmhiplPgIVTd0aFdZnVYsnGI6EgAAAIARGPVGeQDsx7Is5WQkS5LyS2oNpwEAAAAwUpR6AJKk7MyTpb64Rm6323AaAAAAACNBqQcgSVo0J0EhQQ7Vn+jU0epW03EAAAAAjMCoS31FRYU3cgAwLDTYqcVzEyUNPK0HAAAA4PtGXerXrVunCy+8UN/73ve0a9cub2QCYEhORpIkKb+EUg8AAAD4g1GX+ueee04rVqzQn/70J1166aU6++yz9Y1vfENvvvmmOjo6vJERwAQZXFe/73Cj2jt7DacBAAAA8GlGfaRdXl6e8vLyJElHjhzRli1btGXLFt1zzz3q7+/X8uXL9fzzz3s8KADvS4mfpLSkSB2vbdPO/XU6Z0mq6UgAAAAAzmBcG+WlpKRozpw5WrBggWbNmqWenh69++67nsoGwICcj+yCDwAAAMC3jfpJ/datW7V9+3Zt375dH3zwgXp7ezVnzhytXLlSN954o5YvX+6NnAAmSE5Gsn67+aAKSmrkcrnlcFimIwEAAAA4jVGX+uuuu06WZWnBggV64IEHdM455yguLs4b2QAYMH9WvMJDnWpq7dahymbNSZtsOhIAAACA0xj19Pvbb79dK1euVFlZmb71rW/p7rvv1g9/+EPt2rVLLpfLGxkBTKDgIIeWzhvYBb+AKfgAAACATxt1qb/xxhu1YcMG7dixQz/4wQ+0cOFC/eUvf9EVV1yh3NxcffnLX/ZGTgATKDuDdfUAAACAPxj19PtBISEhWrVqlc466yylpaVp8uTJ+utf/6p33nnHk/kAGJCTOfCkvvRok5rbuhUTGWo4EQAAAIBTGXWpb25u1vvvv693331X7733no4dO6bIyEitWrVKDzzwwNBxdwD8V3xMuGamRutwZYuKSmv1N9nTTEcCAAAAcAqjLvWrVq2S2+3WjBkztG7dOuXl5SknJ0dBQWN+6A/AB+VkJutwZYvyiyn1AAAAgK8adRP/+te/rs985jOaNo0/5AN2lp2RrF9uKlNhaY36XW45OdoOAAAA8DmjLvVXXXWVenp69POf/1zbtm1TS0uLYmNjlZOTo4svvlihoay9BewgY3qsJoUHq7WjV2VHm5Qxg6MrAQAAAF8z6t3vW1pa9IUvfEH33Xefdu3apba2NhUWFuq+++7T5z73ObW2tnojJ4AJ5nQ6lJU+sGEeu+ADAAAAvmnUpf7RRx9VdXW1Xn75Zf3lL3/R//7v/+ovf/mLXn75ZTU0NOiJJ57wRk4ABgzugp9fQqkHAAAAfNGoS/2mTZt0++23KycnZ9jrOTk5+spXvqI///nPHgsHwKys9IHz6g8eb1ZjS5fhNAAAAAA+btSlvr29/bSb5E2bNk0nTpwYbyYAPmJyVKjmTpssSSrkaT0AAADgc0Zd6mfNmqX/+7//O+W1TZs2afr06eMOBcB35GQOPK3PL641nAQAAADAx4169/vrrrtOd9xxh3p6enThhRcqISFB9fX1+sMf/qBf/vKXuu+++7wQE4ApOZnJ+tmfS1W0v1Z9/S4FOUf9d4EAAAAAvGTUpf6CCy5QeXm5nn32Wf3yl7+UJLndboWEhOjmm2/WpZde6vGQAMyZkzZZMZEham7rUXF5oxbNTjAdCQAAAMBJoy71kvTv//7vuuKKK7Rz5041NzcrJiZGS5YsUUxMjKfzATDM4bCUnZGsv+QfU0FxDaUeAAAA8CFjnkfb0NCgiooKVVZWqqamRi0tLZ7MBcCH5GQMrKvfwXn1AAAAgE8Z9ZP6rq4u/ed//qf++Mc/yuVyDb3ucDj0uc99Tvfdd58cDtbcAnayLD1RDks6Wt2q2sYOJcVFmI4EAAAAQGN4Uv/II4/ozTff1Ne//nX99a9/1e7du/WXv/xFt99+u1555RU9/fTT3sgJwKDIiBBlzIiTJBVwtB0AAADgM0Zd6l977TV99atf1VVXXaWUlBSFhIQoNTVVX/7yl3XrrbfqF7/4hTdyAjCMo+0AAAAA3zPqUt/Z2alZs2ad8trixYvV1tY27lAAfM9gqd91oE49vf2G0wAAAACQxlDqP/vZz2rjxo2nvPbqq69qzZo14w4FwPfMmBKtuOgwdff064NDDabjAAAAANAIN8p76qmnhv53QkKCXn/9dV144YU6//zzlZCQoJaWFr399tv64IMPdNNNN3ktLABzLMtSTmay/rztiAqKa5SVnmQ6EgAAABDwRl3qB5WVlamsrOwTr3/ve9/Tl7/85fEnA+BzcjKT9OdtR5RfXKN/u2iR6TgAAABAwBtRqS8pKfF2DgB+YMncRAU5LVXWt6uyrk2piZGmIwEAAAABjQPlAYxYRFiw5s+MlyTlc7QdAAAAYNyIntRfddVVI35Dy7L04osvjjkQAN+Wk5ms3QfqVVBcq386d7bpOAAAAEBAG9GTerfbPeIvl8vl7cwADBo82m7PwXp1dfcZTgMAAAAEthE9qf/pT3/q7RwA/ERaUqSS4yJU09ih3QfrtXx+iulIAAAAQMDy6Jr6rq4u7d2715NvCcDHDB5tJ0n5+1hXDwAAAJg0olK/atUq7du3b9hrzz77rOrr64e9Vlpaqs997nOeSwfAJw2V+pIaud1uw2kAAACAwDWiUt/U1KS+vg/Xzvb39+uJJ55QTQ1P6YBAtHB2vEKCHKpr6tTRmlbTcQAAAICANebp9zydAwJXWEiQFs1JkCQVFPOXewAAAIApnFMPYEyGpuAX1xpOAgAAAAQuSj2AMRks9fsON6i9s9dwGgAAACAwUeoBjElK/CRNTYxUv8utnWV1puMAAAAAAWlcpd6yLE/lAOCHBp/Ws64eAAAAMCNopDfefPPNCgkJGfbajTfeqODg4KFf9/T0eC4ZAJ+Xk5mk3205qPzigaPt+Is+AAAAYGKNqNRffPHF3s4BwA8tmBWvsBCnmlq7daiiWbPTJpuOBAAAAASUEZX6Bx980Ns5APih4CCnlsxN1La91covqaHUAwAAABOMjfIAjEvu/MF19RxtBwAAAEw0Sj2AccnOGCj1pUca1dLOvhoAAADARKLUAxiXhMnhmjElWi63VFjK03oAAABgIlHqAYwbR9sBAAAAZlDqAYzbUKkvqVW/y204DQAAABA4KPUAxi1jeqwmhQWptaNHZceaTMcBAAAAAgalHsC4OZ0OLUtPkiTlMwUfAAAAmDCUegAewbp6AAAAYOJR6gF4RFbGwJP6A8eb1dTSZTgNAAAAEBgo9QA8IjYqTHOmTZY0sGEeAAAAAO+j1APwmJyMgSn4+SVMwQcAAAAmAqUegMfkZA5Mwd9ZWqu+fpfhNAAAAID9UeoBeMycabGKnhSi9q4+lZQ3mo4DAAAA2B6lHoDHOB2WsjM42g4AAACYKJR6AB41dLQdm+UBAAAAXkepB+BRy9KT5LCk8qoW1TZ1mI4DAAAA2BqlHoBHRUWEKH16nCSe1gMAAADeRqkH4HFDU/BZVw8AAAB4FaUegMcNlvqdZXXq7es3nAYAAACwL0o9AI+bmRqtuOhQdff064ODDabjAAAAALZFqQfgcZZlKTtj4Gl9fglT8AEAAABvodQD8ArW1QMAAADeR6kH4BVL5yXK6bBUUdeuyvo203EAAAAAW6LUA/CKiLBgLZgVL0kqKOZoOwAAAMAbKPUAvIZ19QAAAIB3UeoBeE3u/IFSv+dAvbp6+gynAQAAAOyHUg/Aa9KSIpUUF6HePpf2HKg3HQcAAACwHUo9AK+xLEs5GUmSpHx2wQcAAAA8jlIPwKsGj7bLL66R2+02nAYAAACwF0o9AK9aNCdBwUEO1TZ16lhNq+k4AAAAgK1Q6gF4VVhIkBbNSZAk5XO0HQAAAOBRlHoAXpdz8mi7Ao62AwAAADyKUg/A67IzBzbL23uoQR1dvYbTAAAAAPZBqQfgdakJkZqaOEn9Lrd27q8zHQcAAACwDUo9gAmR/ZFd8AEAAAB4BqUewIT46Lp6jrYDAAAAPINSD2BCLJwdr9AQpxpbunW4ssV0HAAAAMAWKPUAJkRwkFNL5yZKYgo+AAAA4CmUegATJod19QAAAIBHUeoBTJjsk+vqS480qrWjx3AaAAAAwP9R6gFMmMTYcM2YEi2XWyoqrTUdBwAAAPB7lHoAEyo7I0mStIMp+AAAAMC4UeoBTKjBdfWFJbXqd3G0HQAAADAelHoAEypjRpwmhQWppb1HB441mY4DAAAA+DVKPYAJFeR0aGn6wBT8/GLW1QMAAADjQakHMOFyTu6Cn1/CunoAAABgPCj1ACbc4GZ5B46dUFNrl+E0AAAAgP+i1AOYcLHRYZqTFiNpYMM8AAAAAGNDqQdgRPbJXfDzOdoOAAAAGDNKPQAjBo+2KyqtVX+/y3AaAAAAwD9R6gEYMXdarKInhai9q08lRzjaDgAAABgLSj0AI5wOS1kZg0fbMQUfAAAAGAtKPQBjho62o9QDAAAAY0KpB2DMsvQkOSypvKpF9Sc6TccBAAAA/A6lHoAx0ZNClD49ThJP6wEAAICxoNQDMCo7k3X1AAAAwFhR6gEYNbiufldZnXr7+g2nAQAAAPwLpR6AUbOmxiguOlRdPf3ae6jBdBwAAADAr1DqARhlWZayh3bBrzWcBgAAAPAvlHoAxmVncrQdAAAAMBaUegDGLZ2bKKfDUkVdm6rq203HAQAAAPyGT5X6H/zgB7ryyivPeM8rr7yi9PT0T3wdOXJk6J7XX39dF1xwgRYtWqQLL7xQW7Zs8XZ0AOMwKTxY82fGS5IKSnhaDwAAAIyUz5T6F154QU8++eSn3ldaWqrly5frnXfeGfaVlpYmSXr//fd199136/LLL9dvf/tbrV69WjfffLMOHjzo7Y8AYBxyONoOAAAAGDXjpb6mpkbXX3+9nnjiCc2cOfNT79+/f78yMjKUmJg47MvpdEqSfvSjH+m8887TFVdcodmzZ+trX/uaFixYoBdffNHbHwXAOAyuq99zoF5dPX2G0wAAAAD+wXip37t3r2JiYvT73/9eS5Ys+dT7S0tLNWfOnFNec7lcKiws1MqVK4e9vmLFCuXn53skLwDvOCs5Skmx4erpc+mDgxxtBwAAAIxEkOkAa9eu1dq1a0d0b2Njo+rr67Vjxw799Kc/1YkTJ7RkyRLdddddmjlzplpaWtTR0aGUlJRh35eUlKSqqqozvve6detOe62qqkopKSnq6OgYUU5TOjs7h/0T9mP3MV4yN15vbj+u9/dUaP70KNNxJpzdxxeMcSBgjO2PMbY3xtf+/GmM3W63LMv61PuMl/rR2L9/vyTJ6XTqu9/9rjo6OvSDH/xAl19+uf7whz+or29gym5ISMiw7wsNDVV3d/e4fnZPT4+Ki4vH9R4Tpby83HQEeJldxzghfOBfrts+qNTKWa4R/UvMjuw6vvgQY2x/jLH9Mcb2xvjanz+McU9Pj0JDQz/1Pr8q9StXrtT27dsVExMz9NrTTz+tz3zmM/rNb36jz3/+85IGPvxHdXd3Kzw8/IzvvWnTptNeW7dundxutzIzM8eR3vs6OztVXl6uGTNmfOrnhX+y+xjPnN2vX239q0609ys64SylJUWajjSh7D6+YIwDAWNsf4yxvTG+9udPY/zxh9Wn41elXtKwQi9JERERSktLU01NjSZPnqyIiAjV1tYOu6e2tvYTU/JHy7IsRUREjOs9Jkp4eLjfZMXY2HWMIyKkRbMTVFhaq73lLZo3I8l0JCPsOr74EGNsf4yx/THG9sb42p8/jPFIZ60a3yhvNDZu3KgVK1aoq6tr6LW2tjaVl5drzpw5sixLWVlZ2r59+7Dv27Ztm7Kzsyc6LoAxyOZoOwAAAGDEfLrU9/f3q66ubqjEf+Yzn5Hb7dY999yjsrIy7dmzR7feeqvi4uJ08cUXS5KuvfZa/fGPf9SGDRt08OBBPfzwwyouLtbVV19t8qMAGKGck0fb7TvcoI6uXsNpAAAAAN/m06W+qqpKq1ev1muvvSZJmjJlil588UW1t7frsssu0zXXXKOoqCi99NJLCgsLkyStXr1aDzzwgH72s5/p4osv1vvvv69nn31Ws2fPNvlRAIxQakKkUhMmqa/frV1ldabjAAAAAD7Np9bUP/TQQ8N+nZaWptLS0mGvZWZm6sc//vEZ3+eiiy7SRRdd5Ol4ACZITmayfv/2IeUX12rVolTTcQAAAACf5dNP6gEEpuyTU/Dzi2vkdrsNpwEAAAB8F6UegM9ZOCteoSFONbZ0qbyqxXQcAAAAwGdR6gH4nJBgp5bMSZTELvgAAADAmVDqAfikHI62AwAAAD4VpR6ATxpcV19S3qi2jh7DaQAAAADfRKkH4JOSYiM0PSVKLrdUVMrRdgAAAMCpUOoB+KycwV3wS5iCDwAAAJwKpR6Azxqcgl9QUiOXi6PtAAAAgI+j1APwWZkz4hQRFqTmth4dOH7CdBwAAADA51DqAfisIKdDy+axCz4AAABwOpR6AD6No+0AAACA06PUA/BpWRkD6+rLjp1QU2uX4TQAAACAb6HUA/BpcdFhmp0WI0kqKq01nAYAAADwLZR6AD4v5+TT+vxiSj0AAADwUZR6AD5v8Lz6wtJa9fe7DKcBAAAAfAelHoDPm3tWrKIigtXe2auSI02m4wAAAAA+g1IPwOc5HZay0gee1heUsAs+AAAAMIhSD8Av5MwfXFdPqQcAAAAGUeoB+IWs9CRZlnS4skUNzZ2m4wAAAAA+gVIPwC9ETwpR+lmxktgFHwAAABhEqQfgNwZ3wc8vrjacBAAAAPANlHoAfiP7ZKnfVVan3r5+w2kAAAAA8yj1APzGrNQYxUaFqrO7X/sONZqOAwAAABhHqQfgNxwOS9kZJ6fgc7QdAAAAQKkH4F8+XFdPqQcAAAAo9QD8ytJ5iXI4LB2vbVN1Q7vpOAAAAIBRlHoAfmVSeLDmz4yTJBXwtB4AAAABjlIPwO/kDK2r57x6AAAABDZKPQC/M7iufndZnbp7OdoOAAAAgYtSD8DvnJUSpcTYcPX0ubTnQL3pOAAAAIAxlHoAfseyrKEp+KyrBwAAQCCj1APwS0NH25XUyO12G04DAAAAmEGpB+CXFs9JUJDToeqGDlXUtZmOAwAAABhBqQfgl8JCg7RodrwkKZ8p+AAAAAhQlHoAfmtoCj6lHgAAAAGKUg/Abw2W+r2HGtTR1Ws4DQAAADDxKPUA/FZqYqSmJExSX79bu8o42g4AAACBh1IPwK8NPq0vKGEKPgAAAAIPpR6AXxs8rz6/mKPtAAAAEHgo9QD82sLZ8QoJdqqhuUvlVS2m4wAAAAATilIPwK+FBDu1ZG6CJHbBBwAAQOCh1APwex+uq681nAQAAACYWJR6AH5vcF19cXmj2jp6DKcBAAAAJg6lHoDfS4qL0FkpUXK53CraX2c6DgAAADBhKPUAbOGju+ADAAAAgYJSD8AWBtfVF5bUyuXiaDsAAAAEBko9AFvInBmn8NAgnWjr1oHjJ0zHAQAAACYEpR6ALQQ5HVqWnihJKmAKPgAAAAIEpR6AbQytqy+h1AMAACAwUOoB2EZWRpIkqezYCZ1o7TacBgAAAPA+Sj0A24iPCdesqTFyu6XC0lrTcQAAAACvo9QDsJXBXfBZVw8AAIBAQKkHYCuD6+oLS2vV3+8ynAYAAADwLko9AFuZNz1WURHBauvsVenRJtNxAAAAAK+i1AOwFafD0rL0gQ3z8pmCDwAAAJuj1AOwndyhdfVslgcAAAB7o9QDsJ1l6UmyLOlQZbMamjtNxwEAAAC8hlIPwHZiIkM176xYSVJBCU/rAQAAYF+UegC2NHi0HevqAQAAYGeUegC2NHi03c79dert42g7AAAA2BOlHoAtzZoao8lRoers7tO+ww2m4wAAAABeQakHYEsOh6XsDI62AwAAgL1R6gHY1uC6+oISSj0AAADsiVIPwLaWzkuSw2HpWE2bqhvaTccBAAAAPI5SD8C2IsODlTkjThJH2wEAAMCeKPUAbI2j7QAAAGBnlHoAtjZY6ncfqFd3b7/hNAAAAIBnUeoB2Nr0lCglxISpp7dfHxysNx0HAAAA8ChKPQBbsyxLOfNTJDEFHwAAAPZDqQdgezkfOa/e7XYbTgMAAAB4DqUegO0tnpuoIKdD1Q0dqqznaDsAAADYB6UegO2FhwZp4ex4SUzBBwAAgL1Q6gEEhKGj7fZR6gEAAGAflHoAAWGw1H9wqF6d3X2G0wAAAACeQakHEBBSEyZpSvwk9fW7tausznQcAAAAwCMo9QACgmVZys78cBd8AAAAwA4o9QACxuAU/AKOtgMAAIBNUOoBBIyFsxMUEuxUfXOXjlS3mo4DAAAAjBulHkDACA12avGcBElMwQcAAIA9UOoBBJSho+0o9QAAALABSj2AgDJY6ovLG9XW2Ws4DQAAADA+lHoAASU5LkLTkqPkcrm1c3+t6TgAAADAuFDqAQQcpuADAADALij1AAJOzsnz6gtKauVycbQdAAAA/BelHkDAyZwRr/DQIJ1o7dahimbTcQAAAIAxo9QDCDjBQQ4tnZcoSdrBFHwAAAD4MUo9gIA0uK6+gFIPAAAAP0apBxCQsjMG1tXvP9ak5rZuw2kAAACAsaHUAwhI8THhmpUaI7dbKizlaDsAAAD4J0o9gICVfXIXfI62AwAAgL+i1AMIWIPr6gtLatXP0XYAAADwQ5R6AAEr/axYRYYHq62zV/uPNJmOAwAAAIwapR5AwHI6HcpKPzkFv4Qp+AAAAPA/lHoAAS375BR81tUDAADAH1HqAQS07IwkWZZ0qKJZDc2dpuMAAAAAo0KpBxDQYiJDNW9arKSBDfMAAAAAf0KpBxDwhqbgs64eAAAAfoZSDyDg5Zw8r76otE59/S7DaQAAAICRo9QDCHizp07W5MhQdXb3ad/hBtNxAAAAgBGj1AMIeA6HpayMk0fbFbOuHgAAAP6DUg8AknI42g4AAAB+iFIPAJKWzUuUw2HpWE2raho7TMcBAAAARoRSDwCSIiNClDkjTpJUwC74AAAA8BOUegA4KXtoXT2lHgAAAP6BUg8AJw2uq99VVq+e3n7DaQAAAIBPR6kHgJNmTIlWQkyYenr79cFBjrYDAACA76PUA8BJlmUpe3AXfNbVAwAAwA9Q6gHgIzjaDgAAAP6EUg8AH7FkbqKCnJaq6ttVWddmOg4AAABwRpR6APiI8NAgLZyVIImn9QAAAPB9lHoA+JhspuADAADAT1DqAeBjcjIHzqvfc7BBnd19htMAAAAAp+dTpf4HP/iBrrzyyjPeU1ZWpi9/+ctasWKFVq1apa985SuqrKwcds/atWuVnp4+7Ouuu+7yZnQANjI1MVIp8RHq63dpd1md6TgAAADAaflMqX/hhRf05JNPnvGepqYmXXvttZo0aZJefvll/ehHP1JTU5Ouv/56dXd3S5La2tpUWVmp5557Tu+8887Q17333jsRHwOADViWpZyMwaPtag2nAQAAAE4vyHSAmpoarV+/XgUFBZo5c+YZ733rrbfU2dmphx56SKGhoZKk//7v/1ZeXp4KCwu1atUq7d+/X263W1lZWYqOjp6IjwDAhrIzk/Xq1sPKL66R2+2WZVmmIwEAAACfYLzU7927VzExMfr973+vp59+WhUVFae9d9WqVXr66aeHCv1HNTc3S5JKS0uVmJg46kK/bt26016rqqpSSkqKOjo6RvWeE62zs3PYP2E/jPHEmZMaoeAgh+pPdKq0vE5nJUd6/WcyvvbHGNsfY2x/jLG9Mb72509jPNIHS8ZL/dq1a7V27doR3ZuWlqa0tLRhrz333HMKDQ1Vbm6uJGn//v2KiIjQrbfeqqKiIsXFxemSSy7RVVddJYdj7KsNenp6VFxcPObvn0jl5eWmI8DLGOOJMSMpRGWVXXrjnX1aPT9qwn4u42t/jLH9Mcb2xxjbG+Nrf/4wxj09Pad8oP1xxkv9eLz00kvauHGjvvGNbyg+Pl7SwEZ6ra2tuuCCC3TLLbcoPz9fjzzyiJqbm3Xbbbed9r02bdp02mvr1q2T2+1WZmamxz+DJ3V2dqq8vFwzZsxQeHi46TjwAsZ4Yp3bckxllSWqPOGYkN//jK/9Mcb2xxjbH2Nsb4yv/fnTGIeEhIzoPr8s9W63W0888YSeeeYZ3XDDDbrmmmuGrm3YsEHd3d2KjByYKpuenq729nY988wzuvXWW8f8tN6yLEVERHgivteFh4f7TVaMDWM8MVYtTtNPXi1RydETclvBmhQePCE/l/G1P8bY/hhj+2OM7Y3xtT9/GOOR7unkM7vfj1Rvb6/uvvtuPfvss7rnnnt0xx13DLseHBw8VOgHzZs3Tx0dHUPr7gFgJFLiJ2lacqRcLrd27udoOwAAAPgevyv199xzj/70pz/p0Ucf1XXXXTfsmsvl0tq1a/XMM88Me33Pnj1KSEhQbGzsREYFYAPZg0fbFdcYTgIAAAB8kk9Pv+/v71djY6OioqIUFham3/zmN3rttdd0zz33aPny5aqr+/DJ2eA9559/vp5//nnNmDFDCxYs0Hvvvafnn39e69evN/hJAPirnMxk/XbzQRWU1Mjlcsvh4Gg7AAAA+A6fLvVVVVVat26dHnzwQV1yySV69dVXJUkPP/ywHn744WH3Dt5z5513Kjo6Wo8++qiqq6uVlpam9evX6wtf+IKJjwDAz82fGa/wUKeaWrt1qLJZc9Imm44EAAAADPGpUv/QQw8N+3VaWppKS0uHfv2Tn/zkU98jKChIN910k2666SaP5wMQeIKDHFo6L0nv7alSQXENpR4AAAA+xe/W1APARBtcV7+DdfUAAADwMZR6APgUOZlJkqT9R5vU3NZtOA0AAADwIUo9AHyK+JhwzUyNltstFZXWmo4DAAAADKHUA8AI5GQOHm1HqQcAAIDvoNQDwAgMrqsvLK1Rv8ttOA0AAAAwgFIPACOQMT1Wk8KD1drRq7KjTabjAAAAAJIo9QAwIk6nQ1npAxvm5bMLPgAAAHwEpR4ARmhwF/z8Eko9AAAAfAOlHgBGKCs9WZYlHTzerMaWLtNxAAAAAEo9AIzU5KhQzZ02WZJUyNN6AAAA+ABKPQCMQk4GR9sBAADAd1DqAWAUsk+eV1+0v1Z9/S7DaQAAABDoKPUAMApz0iYrJjJEHV19Ki5vNB0HAAAAAY5SDwCj4HBYyh6cgr+PdfUAAAAwi1IPAKM0tK6ezfIAAABgGKUeAEZpWXqiHJZ0tLpVtY0dpuMAAAAggFHqAWCUIiNClDEjTpJUwNN6AAAAGESpB4AxyMnkaDsAAACYR6kHgDEYLPW7DtSpp7ffcBoAAAAEKko9AIzBjCnRiosOU3dPvz441GA6DgAAAAIUpR4AxsCyrKGn9QXFrKsHAACAGZR6ABijD9fVU+oBAABgBqUeAMZoydwEBTktVda3q7KuzXQcAAAABCBKPQCMUURYsBbMipck5XO0HQAAAAyg1APAOHy4rp6j7QAAADDxKPUAMA7ZGQOlfs/BenV19xlOAwAAgEBDqQeAcUhLilRyXIR6+1zafaDedBwAAAAEGEo9AIzDR4+2Yxd8AAAATDRKPQCM01CpL6mR2+02nAYAAACBhFIPAOO0cHa8QoIcqmvq1NGaVtNxAAAAEEAo9QAwTmEhQVo0J0GSVMAUfAAAAEwgSj0AeMCH6+o52g4AAAATh1IPAB4wWOr3HW5Qe2ev4TQAAAAIFJR6APCAlPhJmpoYqX6XWzvL6kzHAQAAQICg1AOAh+TOH3haz7p6AAAATBRKPQB4SE7Gh+fVc7QdAAAAJgKlHgA8ZP6sOIWHOtXU2q1DFc2m4wAAACAAUOoBwEOCg5xaMjdRkpRfwhR8AAAAeB+lHgA8aHAX/AKOtgMAAMAEoNQDgAdln1xXX3qkUS3tPYbTAAAAwO4o9QDgQQmTwzVjSrRcbqmwlKf1AAAA8C5KPQB42IdT8FlXDwAAAO+i1AOAhw2V+pJa9bs42g4AAADeQ6kHAA/LmB6rSWFBau3oUdmxJtNxAAAAYGOUegDwMKfToWXpSZKkfKbgAwAAwIso9QDgBayrBwAAwESg1AOAFwwebXfgeLOaWroMpwEAAIBdUeoBwAsmR4Vq7rTJkgY2zAMAAAC8gVIPAF4yOAU/v4Qp+AAAAPAOSj0AeMlgqd9ZWqu+fpfhNAAAALAjSj0AeMmctMmKiQxRe1efSsobTccBAACADVHqAcBLHA5LWRxtBwAAAC+i1AOAFw2tq6fUBzS3262j1S36/Tvl2rqvleUYAADAY4JMBwAAO1uWniSHJR2pblVtU4eSYiNMR8IE6ent156D9crfV6PtxTWqbewYulZxolDfvHaFYiJDDSYEAAB2QKkHAC+KighR+vQ4FZc3qqCkVn+/aobpSPCihuZO5RfXaMe+Gu0sq1N3T//QteAghzJnxKqkvFH7ypv01cc36z+uXaFZU2MMJgYAAP6OUg8AXpaTmTxQ6otrKPU243K5VXasSTtOFvlDFc3DrsdFhyl3frKWz0/R4jkJcvX3aPP7u/Wb91pU3dipu7//tm6/dJnOXTbV0CcAAAD+jlIPAF6Wk5msn75erJ1ldert61dwkNN0JIxDR1evikrrtH1ftQpLanWirXvommVJ886KVW5msnLnp2hmarQsy/rwezt6lBQTrPtvXKGnf71PhaW1evjlfB2uatYX/y5TTod1qh8JAABwWpR6APCymanRiosOVWNLtz442KBlJ3fEh/+oqGvTjn012rGvWnsPNajf5R66FhEWpGXpSVo+P1nZGckjWicfGR6sb12/Ui/9cZ9+89cD+uWmMh2ubNFdX8zWpPBgb34UAABgM5R6APAyy7KUnZGsN7cfVX5JDaXeD/T2ubTvUIO2F1crf1+NKuvbh12fmhip3PnJyp2frPkz4xXkHP1hMk6HpWsvXKCZqdH6/i92Kr+4Rnc+sUX/8aXlSkuK8tRHAQAANkepB4AJkDt/oNQXFNfo3/55kek4OIWm1i4VFNdoR3GNikrr1NndN3QtyGlp4awE5c5PVs78ZKUmRHrs5/5N9jSlJUXp/g3bVFHXpjuf2KK7r8gZOg4RAADgTCj1ADABlsxNVJDTUkVduyrr2zxaCjE2brdbByuatWNfjfKLq7X/6Ilh1ydHhSo3M1k5mclaOi9REWHemxY/Z9pkPfbVPD34wg4Vlzfqv378vq78+0x9bu3cYWvyAQAAPo5SDwATICIsWPNnxmv3gXoVFNcq9VxKvQmd3X3aub9O+cUDRb6xpXvY9TlpMcqdn6KczGTNSZssxwRuXBcbFab7bzpHz72yW2+8f0QvvVasw5Ut+sqlSxUWwn+uAQDAqfGnBACYIDmZydp9oF75JTW68NxZpuMEjOqG9qFN7vYcbFBfv2voWliIU8vSk5Rz8ol8XHSYwaQDZ9nf8vmlmj01Rs+9skdv76xQRW2b1l+7XElxEUazAQAA30SpB4AJkpOZrJ/8Ya/2HKhXV08fT1+9pK/fpeLyRuXvq9GO4modq2kbdj0lPkK581OUm5mshbPjffKIwb8/e6amJUfpoZd26FBls776+GZ9/epcLZqdYDoaAADwMfyJEgAmSFpSpJLiIlTb2KE9B+qVOz/FdCTbaG7rVmFprXbsq1Fhaa3aO3uHrjkclhbMjFdO5sBu9WlJkX6xTn3h7AQ9dnue7t+wXYcqmvWfz76rL1+8SH+/aoZf5AcAABODUg8AE8SyLOVkJOm1d8u1o7iGUj8Obrdb5VUtyi+u0Y59NSo90qiPHB2v6Ekhys5IUu78FC1LT1Kkn579nhQboe/eslrf/9+d2rKzQs/8ercOVTTrhosXKzho9MfoAQAA+6HUA8AEyslM1mvvlquguEZut5snrqPQ3duv3WV1A+vji2tUf6Jz2PWZqdFD0+rnnhUr5wRucudNYSFBuuuKbM2aGqMXX9unN94/oqPVrfrG1bmKNbwHAAAAMI9SDwATaNGcBAUHOVTb1KljNa06KyXadCSfVtvUMfQ0fndZnXr6PtzkLiTYqSVzEwZ2q89IVmJsuMGk3mVZlv5l7VxNnxKtR17OV3F5o776+Gatv3a55k6LNR0PAAAYRKkHgAkUFhKkRXMSVFhSq/ziWkr9x/S73Np/pEk7iqu1Y1+Nyqtahl1PjA1XbmaycuenaNGcBIUG+94md96Uk5msR2/P03d+sk3Ha9v09afe0S1fWKrPZE8zHQ0AABhCqQeACZaTkazCkloVlNToks/MMR3HuLaOnoFN7oprVFBcq9aOnqFrDktKnx6n3PkDRX56SlTAL1mYmhipR76yRo/8T4Hyi2v02MZCHa5s0dX/MN82Sw4AAMDIUeoBYIJlZyZJv5X2HmpQR1evIsL8cxO3sXK73Tpe26Yd+6q1fV+Nissb5frILneTwoMHNrnLTFZWRrKiJ4UYTOubJoUH6z++tEL/86di/XJTmV756wGVVzbrnitzFBnB/70AAAgklHoAmGCpCZGamjhJFXXt2rm/TmcvTjUdyet6evv1wcGGoWn1NY0dw65PS47S8pNP4zOmx8rpZGf3T+N0WLrqgvmaNTVGj/+8SEX763TH41u0/kvLNZ1lHQAABAxKPQAYkJOZooq6g8ovrrFtqW9o7lR+ca127KvWrrI6dfX0D10LDnJo0ZwE5WYmKyczWSnxkwwm9W+rl0zV1MRIfecn21TV0K67n9yiOy7P1sqFU0xHAwAAE4BSDwAG5GQm6XdbDqqgxD5H27lcbh04fuLkkXPVOni8edj1uOiwgbXxmclaMjdRYaH8J8hTZqbG6LHb8/Tdl/K152C97t+wXZefn6FLPztPDtbZAwBga/yJCgAMWDArXmEhTjW2dOtwZYtmTY0xHWlMOrp6VbS/Tjv2VauguFYn2rqHrlmWNG9arHJOFvlZU2Ns8ZcXviomMlT/dcMq/fh3H+jVrYe18Y0SHa5s1lcvy1I4f4ECAIBt8V95ADAgOMipJXMTtW1vtfKLa/yq1FfWtWn7vhrlF1dr76EG9fV/uMldRFiQls1LUu78ZGVnJGtyVKjBpIEnyOnQDZcs1sypMXrm17v03p4qVdW/rfXXLmeJAwAANkWpBwBDcjKTh0r9Fz47z3Sc0+rtc2nf4QbtOFnkK+rah12fmjhJufNTlJOZrPkz4xUcxCZ3pv3tiuk6KzlKD7ywXeVVLbrj8c362pW5WjIv0XQ0AADgYZR6ADAkOyNZklR6pFGtHT1yGs7zUSdau5VfXKP84hoVltaqs7tv6FqQ09KCWfHKnZ+i3MxkpSZGGkyK08mYEafvfTVP92/YrrJjJ/StH72n6y5coAvPncUyCAAAbIRSDwCGJMaGa8aUaJVXtaiwpFa5GXHGsrjdbh2qaNaO4hrt2FetsmMn5P5wVr0mR4YqJzNZOfOTtWxeoiLCgo1lxcjFx4TroZtX6+lf7dJf8o/pR7/7QAcrmnXz55YoJNiX/hoJAACMFaUeAAzKzkhSeVWL8ktqJrzUd3X3aWdZnfKLa7RjX40aW7qGXZ+dFqPczBTlzk/WnLTJ7KLup0KCnbr9X5dp1tQY/eT3H+gv+cd0vLZV37xmueJjwk3HAwAA40SpBwCDcjKT9ev/O6DCklq5XBle/3nVDe1DJX7PwXr19rmGroWFOLV0XqJyMlOUk5lE4bMRy7L0z2tm66zkKD3803ztP3pCdzy+Wd+4ZrkyppubIQIAAMaPUg8ABmXMiNOksCC1tPfoYEWLx9+/v9+l4vJG5RfXaPu+Gh2raR12PTkuYuDs+PkpWjQ7XsFBTMm2s2XpSXrs9jx9Z8M2Ha1u1Tee3qqbP7dYn10+3XQ0AAAwRpR6ADAoyOnQ0vQkbd1VqaL99VqUOv73bGnvUWHJwNP4gtJatXf2Dl1zOCzNnxmn3MyBIp+WFMmmaQFmSsIk/fet5+rxnxfpvT1VeuJ/d+pQZYu+dOECBTk5uQAAAH9DqQcAw3IykgdKfVm9FqVGj/r73W63jlS3ase+au3YV6PSI41yfWSTu6iIEGVnJml5ZoqWpScqMiLEg+nhjyLCgvX1q3L1v2+WauOfS/WHtw/pSFWL7rkyRzGRoabjAQCAUaDUA4Bh2RlJkqRDFS1q65w0ou/p7u3XngP12r5v4Jz7uqbOYddnTIkemFafmaJ502PlZJM7fIzDYemy8zM0IzVG3/tZgXYfqNcdT2zRf1y7XDNTY0zHAwAAI0SpBwDDYqPDNGfaZB04dkIHqrqUe5r76po6lV9crR3FNdpVVq+e3v6hayFBDi2em6jl85OVnZmspNiIiQkPv7dq0RSlJqzRdzZsU3VDh+7+/tv66r9m6ZwlHlgLAgAAvI5SDwA+ICcjWQeOnVBZ5YfHyvW73Co72qTtJ6fVl1cN30gvYXL4yafxyVo0J0FhIfwrHWMzfUq0Hrs9Tw//NF8799fpoZd26NLPztPl52dwlCEAwDZaO3r0600HdOhoo2bO7leETZ6B8CdAAPABOZlJ+vmbpTpQ1aV391Rr98EmFZTUqqW9Z+gehyWlT48b2q1+ekoUm9zBY6IiQnTf9Sv1wh/36bebD+p/39qvw5UtuvOLWYoICzYdDwCAMWvr7NXvNh/U77YcVGd3nySpqbVLcZOjDCfzDEo9APiAOdNiFRURrNaOXj3xiz1Dr08KD1Z2epJy5icrKz2JTczgVU6nQ9f900LNTI3WU7/cpe37qnXXk1v0H9euUGpipOl4AACMSkdXr/7w9iG9svng0GlA01MidU56qKbEj2wfI39AqQcAH+B0WPqbZan6w9YjSkucpOULpih3frIyZ8TJyTFjmGBrc85SWlKU7t+wXcdq2nTHE1t0zxU5yjq5qSMAAL6ss7tPf9x6WL/5vzK1dgyU+bNSonT5+RlaMitGpaUlhhN6FqUeAHzEF8+fqwWpvVq2eIEi7LLIC35r3lmx+t5X8/TgC9tVcqRJ/+/593T1P8zXxX8zh2UfAACf1NXTpz+9V65f/aVMzW0DSxinJkbq8vPTdc6SqXI6LHV0dBhO6XmUegDwEZZlKSyYp/LwHXHRYXrg38/RM7/erTe3H9WGV/fpUEWLbr10qUKDnabjAQAgSerp7dcb7x/RLzftV1NrtyRpSvwk/evfpitv2VTbz3qk1AMAgNMKDnLq1i8s1eypMfrh7z7Q5qLjOl7XqvXXrFBibLjpeACAANbb59Jb24/oF2/tV33zwAlCSXER+tfPztNncqYpyOZlfhClHgAAnJFlWfqH1bN0Vkq0Hnpphw4eb9Ydj2/W16/O1YJZ8abjAQACTF+/S5t2HNP/vlWquqZOSVJCTJi+cF66Ppt7loKDAqPMD6LUAwCAEVk0J0GP3Z6n+zds0+HKFv3Hs1t1w8WL9XerZpiOBgAIAP39Lv218Lh+/mapqhsG1sbHRYfq8+vm6fyV0xUcFJhLwyj1AABgxJLjIvTwLefq8f8t0tZdlXr6V7t0qKJZ/3bRooB7MgIAmBj9Lrfe3lmhn/+5RBV17ZKkyZGh+pe1c/X3Z88I+H1eKPUAAGBUwkKD9LUrc/SrqWX66evFev29ch2tadXXr8rV5KhQ0/EAADbhcrn17p5KbXyjVMdqWiVJUREh+pfPzNE/nDNTYaHUWYlSDwAAxsCyLH1+3TxNnxKtR14u0N5DDfrq45u1/trlmpM22XQ8AIAfc7vdev+Dam18o0TlVS2SpMjwYF38N3P0j6tnKiIs2HBC30KpBwAAY7Z8fooevW2N7t+wTRV17fraU+/oK19YqrysNNPRAAB+xu12K7+4Rv/zRokOHm+WJEWEBemiNbP1T2tma1I4Zf5UKPUAAGBcpiVH6ZHb8vTIy/kqKKnVI/9ToMOVzbrygvlyOizT8QAAPs7tdqtof502/qlEpUebJEnhoU5deO5sXZQ3W1ERIYYT+jZKPQAAGLfI8GD953Ur9fLrxfrVX8r06/87oPKqFt11RY4iebICADiNXWV1+p8/lai4vFGSFBri1D+eM1MX/80cxUSyT8tIUOoBAIBHOB2Wrv6H+ZoxJVpP/mKnCkpqdefjm/UfX1qhaclRpuMBAHzI3kMN+p8/lWjPwXpJUkiQQ39/9kz9y9o5io0KM5zOv1DqAQCAR+VlpWlqUqTu37BdlfXtuvOJLbrrimwtn59iOhoAwLCSI436nz+VaOf+OklSkNOhv1s5XZ9bN1fxMeGG0/knSj0AAPC4OWmT9b3b8/TQSzu091CDvvOTbbri7zL1+XVzZVmssweAQFN2rEkb3yhVfnGNpIHZXeetmK4vrJunxFjK/HhQ6gEAgFdMjgrVt284Wz/63R69/m65fvp6sQ5VNuv2S5dxtjAABIhDFc3a+EaJtu2tliQ5HJbW5UzTpeelKzkuwnA6e+C/qAAAwGuCgxz6939ZolmpMXruld3auqtSlXVtWn/tCv4wBwA2dqSqRRv/XKJ3d1dJkhyW9DfZ03TpefOUmhBpON3/b+++w6Oq8j+Ofya99wqhlxAkhNBFiJoo8FNklRVXUYyugAKKNJemgOuCuiIdQUBFBRVcFFwWdIFddUVaKBakE5ASSO89md8fgUggIGXCzQzv1/PkyeTOvXe+NyeB+eSce45tIdQDAIAa1/PWhqoX7KnX3t+uxFPZGjHjG42L76DIpgFGlwYAsKDjZ3L0yb/3638/nJTZLJlMUrc2dfXw3eFMmlpDCPUAAOCGuKWxv6YPv11Tl2zVoRNZevHt7zXwD610722NuM8eAKzcqdRcffLv/fpm5wmVmyu23da6jh7pHq4GoV7GFmfjCPUAAOCGCfR11WvPdtPcFbv19c4Tevvzn3TkZJYG/7G1HB3sjS4PAHCVTqflacWGA9qYcFzlZ9N8p1tC1K9HCzWu621wdTcHQj0AALihnB3tNbJfWzWq4633/7VH67f9quNncjTuiY7y82JtYgCwBikZBVqx8YDWbz2msrNhvn1EsPr1CFezer4GV3dzIdQDAIAbzmQyqc+dTdUw1Et/X5qgfccyNGLGN5rwZEc1r8+bQQCordKyCvSPjQf15ZZjKi0rlyS1aR6oR3u2UIsGfgZXd3OyM7qA87311lvq37//ZffJyMjQqFGj1KFDB3Xo0EEvvfSS8vPzq+yzbt063XPPPYqMjNR9992nb7/9tibLBgAA16htiyBNfz5G9YI9lJ5dqLHzvtN/En41uiwAwAUycgq1aPVPGjR1g9ZsSlRpWbkimwTotaFd9crTXQj0Bqo1oX7JkiWaPXv27+43bNgwHT9+vHL/TZs26eWXX658fsuWLXrhhRfUr18/rVq1Sl27dtXQoUN1+PDhmiwfAABcozqBHpo2LEadbglRSWm5Zny8S4tX/6yysz1AAADjZOUW6b1/7tGAKRv0xbdHVFxaroiGfpoyuIumDrlNtzT2N7rEm57hw+/PnDmjCRMmaMeOHWrUqNFl9921a5e2bdumtWvXqkmTJpKkv/71rxowYIBGjhyp4OBgLVq0SHfffbcee+wxSdKYMWO0a9cuvf/++/rrX/9a49cDAACunpuLo8Y/0VEf/Xuflq8/oNXfHtaxpGz95fH28nRzMro8ALjp5OQX6/OvD2nNd0dUUFQmSQqv76t+PVsounkgq5bUIob31O/Zs0fe3t764osvFBUVddl9ExISFBgYWBnoJaljx44ymUzasWOHysvLtXPnTnXu3LnKcZ06dVJCQkKN1A8AACzDzs6kx3pGaGx8B7k42Wv3wRSNnPmNjiVlG10aANw08gpK9NFX+zRgynp9uvGgCorK1CTMWxOf6qQ3hnVT2/AgAn0tY3hPfWxsrGJjY69o3zNnzig0NLTKNicnJ/n4+CgpKUnZ2dnKz89XSEhIlX2CgoKUlJR02XPHxcVd8rmkpCSFhIRcdO9+bVNQUFDlM2wPbWzbaF/bRxtfmeimPvrrwA56Y9lunU7L16jZ3+rZP7ZSx5ZBRpf2u2hj20cb27abuX0Likq1bvOvWrPpmPIKSyVJ9YM99FBsE7WPqOiZt4XvizW1sdlsvqI/oBge6q9GQUGBnJwuHoLn7OysoqIiFRYWStJF+5x7/noUFxdr796913WOG+Xo0aNGl4AaRhvbNtrX9tHGV+aJWF/9Y1O6Es8U6c2Pf9DtrTx1e6SX7Kygh4g2tn20sW27mdq3uLRc2w7kadPeHBUUVcxlEujtoDsivRRRz1V2pjTt25dmcJWWZw1tXFxcLGdn59/dz6pCvYuLi4qLiy/aXlRUJDc3t8oLvnCfoqIiubq6XvbcGzduvORzcXFxMpvNioiIuIaqb5yCggIdPXpUDRs2/N3rhXWijW0b7Wv7aOOrF926XB9+dVDrNv+qb37OUX6Zi4b+sZVcnWvnWxja2PbRxrbtZmrf4pIyrd9+Qqu/PaqsvIr8FOrvpgfvbKwukSGys6v9f0C9FtbUxtV1aFendv6PeAkhISHasGFDlW3FxcXKzMxUcHCwfHx85ObmpuTk5Cr7JCcnXzQk/2qZTCa5ubld1zluFFdXV6upFdeGNrZttK/to42vzpAHo9W8vp/m/eNHbd+boomLE/Tik50UGuBudGmXRBvbPtrYttly+5aUlumrLcf06cYDSs+uGM0c4u+mR7qH6/boMNnbGz7t2g1hDW18pXMXWFWLdejQQadPn9axY8cqt23dulWS1LZtW5lMJrVt21bbtm2rctzWrVvVrl27G1orAACwnLs6NtCrQ2+Tn5ezfj2do5Ezv9Gu/cm/fyAAQJJUUlqudZuPatDUDXr785+Unl2kIF9XPfdQG80fE6fY9vVvmkBva2p1T31ZWZnS09Pl6ekpFxcXRUVFqW3bthoxYoQmT56s/Px8TZo0Sffff7+Cg4MlSU8++aQGDRqkli1bKiYmRitXrtTevXs1ZcoUg68GAABcjxYN/DR9+O16dcl27f81Q5MXbdaT97XSH2IaMxMzAFxCaVm5/pNwXMvX71dyRsXkcP7eLvrTXc11V8cGcnQgyFu7Wt2CSUlJ6tq1q9auXSupYvjB3LlzFRYWpvj4eA0fPlwxMTGaPHly5TFdu3bV1KlT9fHHH+uBBx7Qli1btGDBgirL4AEAAOvk7+2qqUNuU1yHeio3S+988bNmfLxTRSVlRpcGALVKWblZ/0n4VUNe/4/mrNit5IwC+Xo6a9D9kVo47i79X5dGBHobUat66l977bUqX4eFhWn//v1Vtvn7+2v27NmXPc/999+v+++/39LlAQCAWsDJ0V7P/ylajet6650v9ui/O07oRHKuJjzZUf7etXvSIwCoaWXlZn23+6Q+/vd+nUzJlSR5ezjpwdhm+r8ujeTsaG9whbC0WhXqAQAAroTJZFLvbk3UINhLr3+4XQePZ2rEjG80Lr6jIhr5GV0eANxw5eVmbf4pSR/9e59+PZ0jSfJ0c1SfO5up122N5FJLVw3B9aNlAQCA1YpqHqjpw2/XlPe26WhStsbP/07P9IlSj84NjC4NAG4Is9msrXtO66Ov9inxVLYkyd3VUQ/c3kT3dWssNxdHgytETSPUAwAAqxbi766/P9dNMz/Zqe9/TNLcT3cr8VSWBvyhlRyYyRmAjTKbzdqxL1nLvtyrQyeyJEluLg76Q0wT9Y5pIg9XwvzNglAPAACsnquzg8b076AVGw9o2Zf79K9Nifr1dI7GPN5e3h7ORpcHABZjNpu1+0CKln21T/uPZUiSXJzsdV+3xnrgjqbydHMyuELcaIR6AABgE+zsTHr47nA1DPXS9I926KfDqRo58xtNeLKTGtf1Nro8ALhuPx5K0bIv9+mXxHRJFROH9rqtkfrc2ZQ/YN7ECPUAAMCmdG4VqjeGxWjKe9uUlJqnv8z9n57/U7S6talrdGkAcE1+SUzTsi/36cdDqZIkRwc7/V+Xhnrwzmby9XIxuDoYjVAPAABsToMQL01/PkZ//zBBuw6k6O8fJijxVJYe7RkhezuT0eUBwBXZfyxdy77cp10HUiRJDvYm9ejcUH3jmrGEJyoR6gEAgE3ycHPSpIG36oN//aLPvj6kTzceVOKpbI1+tJ3cmUAKQC126Himln21Twl7z0iS7O1MuqtjfT10V3MF+boZXB1qG0I9AACwWfZ2Jj153y1qVMdLc1bsVsLeMxo161u9+OeOCgvyNLo8AKgi8VSWPvpqn7b8fFpSxVwhse3q6U93N1eIv7vB1aG2ItQDAACbd0e7eqob5KGp723TyZRcjZ71rUY/1l7tI4KNLg0AdOx0tj7+ar82/XhKkmRnkmLahumRu8NVJ9DD4OpQ2xHqAQDATaFZPV9NH367Xn1/u/YeTddf39mix+9pqT/e2VQmE/fZA7jxTiTn6ON/79f/dp+U2SyZTFLXqLp6pHu46gUzmghXhlAPAABuGr5eLpoy+Da9/fmP+mrLMb3/r1+UeDJLz/2pjVyceFsE4MZISs3TJ+v36+sdx1VurtjWpXWoHuneQg1DvYwtDlaH/70AAMBNxdHBTs/2baMmdb319uc/6dvdJ3UiJVcTnuzIBFQAatSZ9HwtX79fGxOOq/xsmu90S4ge6R6uJmE+xhYHq0WoBwAAN6X/69JI9YI99doH23XkZJZGzvxGYx/voFZNAowuDYCNScko0KcbD2j9tmMqLasI8+1aBKlfjxZqXt/X4Opg7Qj1AADgptWqSYCmD79dU97bpiMns/Tigu816IFI3dOlkdGlAbAB6dmF+nTjAX25+ZhKy8olSW2aBapfjxaKaORncHWwFYR6AABwUwvyddPrz3bVnOW79e3uk5q/8kcdOZmlpx9oLUcHO6PLA2CFMnOKtPK/B7V2U6KKSyvCfKsm/nq0RwtGA8HiCPUAAOCm5+LkoNGPtVOjut76YO0v+mrLMf16OkfjnuggX08Xo8sDYCWycov0+deHtGZTooqKyyRJEQ399GjPFmrdNICVNlAjCPUAAACSTCaTHoxtpoahXpq2NEF7j6Zr5IxvNP7JjmpWj3teAVxabn6xVn1zWF/877AKiirCfLN6PnqsZ4SiwwMJ86hRhHoAAIDztI8I1rTnY/S3d7fpZEquxs79Ts891EZ3tKtndGkAapm8ghJ98e1hrfr2sPILSyVJjet669GeLdQhIpgwjxuCUA8AAHCBsCBPvfl8jKYt26GEvWf05kc7deRUtuLvbSl7O96kAze7/MISrfkuUZ9/fUi5BSWSpIahXurXI1ydW4US5nFDEeoBAACq4e7qqBf/3EnLvtyrTzce1OdfH9KxpGy98Fg7ebg5GV0eAAMUFpdq7aZErfzvIWXnFUuS6gV76JHuLXRb6zqy449+MAChHgAA4BLs7Ux6/J6WalTHW7OW79LO/ckaOetbvfhkR9UP8TK6PAA3SFFJmb7cfFT/+M9BZeYUSZLqBLjrke7h6hYdxggeGIpQDwAA8Du6tamruoEemvLeViWl5mn07G81ql87dWoVanRpAGpQSWmZ/r3lmFZsPKj07EJJUrCfmx6+O1x3tguTvT3LXsJ4hHoAAIAr0Liut6YPv12vfbBdPx9O09/e26ZHe7bQQ3HNGXIL2JjS0nJ9ufmolm84oNTMAklSoK+r/nRXuOI61JMDYR61CKEeAADgCnl7OOuVp7vondU/a82mRC37cp8ST2Vp+MNt5erM2yrAmpnNZqVnF2rn4TzNW7tJKZkVPfP+3i566K7murtjfTk62BtcJXAx/vcBAAC4Cg72dnq6T2s1rOOtBZ/9oO9/TNKplP9pwpMd5eVKjz1Qm5nNZmXmFulUSp6SUnN1KjVPp1LydCo1V0mpeSosLqvc18fTWX3jmqln54ZyciTMo/Yi1AMAAFyDHp0bqH6wp6a+v01Hk7I1cuY3ev6hSN5cAQYzm83KziuuCO5puWdDe0VwP5WSp4Ki0kseazJJvh4Ouue2RvrD7c3l4sRvNGo/fkoBAACuUUQjP80YfrumLNmmQ8czNfWDXYpu7KYjGccUFuytQF83Bfu5ycPVkXWrAQvLyS/WqZSqve2nUvOUlJKrvMLLB/dAH1fVCfBQaKC76gR4qE6gu+oEuMvLxaSDB/crIqIhgR5Wg59UAACA6xDg46rXhnbVvE936787TmjHoTztOHSgyj6uzvYK8nWrDPlBvq4K8nM7u81VPh7OhH6gGnkFJZU97Od625POBvic/JLLHhvg7aI6gR4KDaga3EP83S85nD4/P78mLgOoUYR6AACA6+TsaK8Rj7RVdDM/bfnhiMz27krLLlJyRoEyc4pUUFSmY6dzdOx0TrXHOznaK9DHVcF+FSG/4rObgn3dFOTnKl9PF2bYh83KLyw528Oep1Nnh8snnQ3wWbnFlz3Wz8vlbFj3UJ0A94oAH+ihEH83etpx0+AnHQAAwAJMJpM63RIsL7t0RUREyM3NTZJUVFKmlIx8JacXKDkjv+LjvMfp2YUqLinTyZRcnUzJrfbcDvZ2CvRxrRr4/Vwrg7+/twvrZaNWKywqVVLaecPkzxsun5lTdNljfTydVadKb3vF51B/d7mw6gRAqAcAAKhJzo72CgvyVFiQZ7XPl5SWKzXzbMhPz1dyxnnhP6NAqZkFKi0rV1JanpLS8qo9h52dSQHeLgr0rTq0P9jXTYF+rgr0cWUpLtS4opIynU7Nu2i4/KmUPKVnF172WG8PJ4X6V/SynwvwoWeHy7u5ON6gKwCsE6EeAADAQI4Odgo9O2y4OmVl5UrLLqwa+NN/C/0pGRWhv+K5Au2p5hwmk+Tr6VIl8Aedu7f/7H39DFXGlSgpLTs7NL7qUnCnUnKVmnX54O7p5lh1croA94oe9wAPebgS3IFrxb/eAAAAtZi9vV1FCPd1q/b58nKzMnIKlZJRoDPnhf3ze/6LS8qUnl2o9OxC7TuWUe15fDycFXg25AddMJlfkK8rvaU3kZLScp1JvyC4n/2cklkgs/nSx7q7OCj0vN72c5PT1Qn0kKeb0427COAmQqgHAACwYnZ2Jvl7u8rf21UtGvpd9LzZbFZWbrGSM/Irg39KRr7OnPd1QVGpMnOLlJlbpIPHM6t9HQ9Xxyq9+xc+Ztk+61JaVq7k9PwLZpSveJycnq/yywR3V2eHKpPTVdzfXhHgvdyd+DkAbjBCPQAAgA0zmUzy8XSWj6ezmtf3veh5s9msvIKSs738BRcF/pSMfOXklyi3oES5J7N05GRWta/j6uxwUe/+b4/d5O1B2LvRysrNSsnIrzIp3bl13ZPT81V2meTu4mR/0VJwoWcfswQjULsQ6gEAAG5iJpNJHm5O8nBzUpMwn2r3yS8sqQj5GflKORv+K4J/xUz+mblFKigq/d1l+y7s5WfZvutXXm5WambBeaH9t8npzqTnqbTs0sHd6ex8Ducmpws9L8D7ebkQ3AErQagHAADAZbm5OKpBqKMahHpV+3xhcalSzk7ad37Yv3DZvhPJuTqRfPll+4L8qg/+N/OyfeXlZqVlFVbpbT83Wd3ptDyVlJZf8lhHBzuF+P92X/u54fJ1Ajzk58UfUgBbQKgHAADAdXFxclC9YE/VC/6dZfvOTuR3bnj/ucn8UrMKr2rZvmC/ihn7g88O7beFZfvMZrPSswsre9uTzg/wafkqLim75LEO9iYF+7lXWcP93ER1/j6usie4AzaNUA8AAIAadUXL9mUVVvbsJ2cUXLBsX75Ky8y/Ldt3JO2ic5xbtq8y8Pu5KdC36mR+zo7Ghn6z2azM3KILQvtvy8IVFl86uNvbmRTs53beOu6/DZcP9HG9aUcxACDUAwAAwGD29nYVw+39Lr9s3/lD+i8M/ucv27f3aPWvU7ls39kJ/IJ9XRVo4WX7zGazsvOKL5qcLimtIsAXFJVe8lg7kxTk51Y5q3zoeT3vQb5uciC4A6gGoR4AAAC12vnL9kU0uvyyfckX3M+ffHZiv6tetu+8mfuDzvb8u7v+Fvpz80t0PCW96uR0qXlKSslVXuGlg7vJJAX6uKpOgEeV0F4nwF3Bfu5ydCC4A7g6hHoAAABYtatdtu/CwJ+cnl+xZN8VLNvn6+msjOwCFRSfuGxNAT6uVSenO/s4xN/Nqu/9B1D7EOoBAABg065l2b4z503kl5Lx27J95w+f9/Ny+W1yuvNmlQ8JcDf8/n0ANw9CPQAAAG56V7ps38nkTKWdOaFb27eSn0/1s/0DwI1EqAcAAAB+x7ll+/w97bW3OFkuTvTEA6gdmIkDAAAAAAArRagHAAAAAMBKEeoBAAAAALBShHoAAAAAAKwUoR4AAAAAACtFqAcAAAAAwEoR6gEAAAAAsFKEegAAAAAArBShHgAAAAAAK0WoBwAAAADAShHqAQAAAACwUoR6AAAAAACsFKEeAAAAAAArRagHAAAAAMBKEeoBAAAAALBShHoAAAAAAKwUoR4AAAAAACtFqAcAAAAAwEoR6gEAAAAAsFKEegAAAAAArBShHgAAAAAAK0WoBwAAAADAShHqAQAAAACwUoR6AAAAAACsFKEeAAAAAAArRagHAAAAAMBKEeoBAAAAALBShHoAAAAAAKwUoR4AAAAAACtFqAcAAAAAwEoR6gEAAAAAsFKEegAAAAAArBShHgAAAAAAK0WoBwAAAADAShHqAQAAAACwUoR6AAAAAACsFKEeAAAAAAArRagHAAAAAMBKORhdgDVITk5WWVmZ4uLijC7lssxms4qLi+Xk5CSTyWR0OagBtLFto31tH21s+2hj20cb2zba1/ZZUxsnJSXJ3t7+d/ejp/4KODs7y8Gh9v/94/Tp00pPT6/1P5y4drSxbaN9bR9tbPtoY9tHG9s22tf2WVMbOzg4yNnZ+Xf3M5nNZvMNqAc3wLmRBBs3bjS4EtQU2ti20b62jza2fbSx7aONbRvta/tssY3pqQcAAAAAwEoR6gEAAAAAsFKEegAAAAAArBShHgAAAAAAK0WoBwAAAADAShHqAQAAAACwUixpBwAAAACAlaKnHgAAAAAAK0WoBwAAAADAShHqAQAAAACwUoR6AAAAAACsFKEeAAAAAAArRai3MW+99Zb69+9vdBmwsMzMTE2cOFExMTFq27atHnnkESUkJBhdFiwoLS1NL7zwgjp37qzo6GgNGjRIhw4dMros1IDExERFR0frs88+M7oUWNDJkycVHh5+0cenn35qdGmwoFWrVumee+5RZGSk7r33Xq1bt87okmAhW7durfZ3ODw8XHFxcUaXBwspKSnRjBkzdMcddyg6Olr9+vXTzp07jS7rujkYXQAsZ8mSJZo9e7Y6dOhgdCmwsJEjRyotLU3Tp0+Xn5+fPvroIz311FP67LPP1KRJE6PLgwUMHjxYdnZ2WrRokdzc3DRr1iw98cQTWr9+vVxdXY0uDxZSUlKi0aNHKz8/3+hSYGH79++Xs7OzNmzYIJPJVLnd09PTwKpgSatXr9b48eM1ZswY3XHHHVqzZo1GjhypkJAQRUdHG10erlN0dLS+++67KtsOHDigQYMG6ZlnnjGoKlja/PnztXLlSr322muqV6+eFi1apIEDB2rt2rUKDg42urxrRk+9DThz5owGDBigWbNmqVGjRkaXAws7duyYNm3apEmTJql9+/Zq3LixJkyYoODgYK1Zs8bo8mABGRkZCgsL0yuvvKLIyEg1adJEQ4YMUUpKig4ePGh0ebCgOXPmyN3d3egyUAMOHDigRo0aKSgoSIGBgZUfLi4uRpcGCzCbzZo1a5bi4+MVHx+vBg0aaOjQoerSpYu2bdtmdHmwACcnpyq/uz4+Pnr11VfVvXt39e3b1+jyYCEbN25Ur1691LVrVzVo0EBjx45Vbm6udu/ebXRp14VQbwP27Nkjb29vffHFF4qKijK6HFiYr6+vFi5cqFatWlVuM5lMMpvNysrKMrAyWIqvr6+mT5+uZs2aSZJSU1P1zjvvKCQkRE2bNjW4OljK9u3btXz5cr3++utGl4IasH//fn5fbdiRI0d08uRJ3XfffVW2v/POO3r66acNqgo1admyZUpKStK4ceOMLgUW5OPjo//+9786ceKEysrKtHz5cjk5OSkiIsLo0q4Lw+9tQGxsrGJjY40uAzXEy8tLt99+e5Vt69at06+//qquXbsaVBVqyksvvaQVK1bIyclJ8+fPl5ubm9ElwQKys7P1l7/8RS+++KJCQ0ONLgc14MCBAwoMDFS/fv109OhRNWjQQEOGDFG3bt2MLg0WcPToUUlSfn6+nnrqKf3yyy8KCwvT4MGDeQ9mg4qKirRgwQLFx8crKCjI6HJgQRMmTNCIESMUFxcne3t72dnZadasWapfv77RpV0XeuoBK7Njxw6NHz9ecXFxvJGwQfHx8Vq5cqV69+6toUOHas+ePUaXBAuYPHmy2rRpc1EvH2xDcXGxjh49qtzcXA0fPlwLFy5UZGSkBg4cqM2bNxtdHiwgNzdXkjRmzBj16tVL7777rm677TYNGTKENrZBq1evVlFREZNP26DDhw/Ly8tL8+bN0/Lly9WnTx+NGTNG+/btM7q060JPPWBFNmzYoNGjRysqKkrTp083uhzUgHPDd1955RXt3r1bS5cu1auvvmpwVbgeq1atUkJCgv75z38aXQpqiJOTk7Zv3y4HBwc5OTlJklq1aqXDhw/rnXfe0a233mpwhbhejo6OkqSnnnpKDzzwgCQpIiJCv/zyi9577z3a2MasWrVK3bt3l6+vr9GlwIJOnjypF154QUuWLFH79u0lSZGRkTp06JDmzJmjefPmGVzhtaOnHrASS5cu1XPPPaeYmBgtWrSIyZdsSFpamtasWaOysrLKbXZ2dmrSpImSk5MNrAyWsHLlSqWlpVUun3NuluxJkybp3nvvNbg6WIqbm1tloD+nefPmOnPmjEEVwZJCQkIkVbTp+Zo2baoTJ04YURJqSHp6unbt2qV77rnH6FJgYT/++KNKSkoUGRlZZXtUVFTlLTbWilAPWIGPPvpIr7zyih599FHNnDnzojeOsG7JyckaNWpUlRmUS0pK9Msvv7BkoQ2YNm2a1q5dq1WrVlV+SNKwYcO0cOFCY4uDRezbt0/R0dFKSEiosv3nn39m8jwb0bJlS7m7u+uHH36osv3AgQNWfy8uqtq5c6dMJpM6duxodCmwsHNz2uzfv7/K9gMHDqhBgwZGlGQxDL8HarnExERNnTpVd999t55++mmlpaVVPufi4sIayDagRYsW6tq1q15++WX97W9/k5eXlxYsWKDs7Gw98cQTRpeH63SpdW/9/f1Vt27dG1wNakLz5s3VrFkzvfzyy5o0aZJ8fX21YsUK7d69W//4xz+MLg8W4OLiogEDBmjevHkKDg5W69at9a9//UubNm3SkiVLjC4PFrRv3z7Vq1dPrq6uRpcCC2vdurXat2+vMWPGaNKkSQoJCdGqVau0efNmffTRR0aXd10I9UAt99VXX6mkpETr16/X+vXrqzz3wAMP6LXXXjOoMliKyWTSzJkz9eabb2r48OHKyclR+/bttWzZMtWpU8fo8gD8Djs7Oy1YsEDTpk3T8OHDlZ2drZYtW+q9995TeHi40eXBQoYMGSJXV1fNmDFDZ86cUZMmTTRnzhx16tTJ6NJgQampqfLx8TG6DNQAOzs7vfXWW5o5c6bGjRunrKwsNW/eXEuWLFGbNm2MLu+6mMxms9noIgAAAAAAwNXjnnoAAAAAAKwUoR4AAAAAACtFqAcAAAAAwEoR6gEAAAAAsFKEegAAAAAArBShHgAAAAAAK0WoBwAAAADAShHqAQAAzjKbzUaXAADAVSHUAwBgIf3791fLli31008/Vft8bGysxo4de0NqmTNnjsLDw2/Ia12tadOmqVOnTmrTpo1WrVp1TecIDw/XnDlzLFrXoUOH9Mgjj1j0nAAA1DRCPQAAFlRWVqZx48apuLjY6FJqpQMHDmjRokXq3r27Fi9erJiYGKNLqrRu3Trt2rXL6DIAALgqhHoAACzI09NTBw8e1Lx584wupVbKzMyUJN17771q3769/Pz8jC0IAAArR6gHAMCCIiIidP/992vx4sX6+eefL7tvdcPxP/vsM4WHh+vEiROSKobR9+zZUxs2bFCvXr0UGRmpP/zhD9q1a5d2796tvn37qnXr1urVq5c2b9580Wts2LBBPXr0UGRkpPr27XvRPpmZmZo4caK6dOmiyMhIPfTQQxftEx4errlz5+qPf/yj2rVrp7feeuuS17R27Vr16dNH0dHRuu222zRx4kRlZWVVXkv//v0lSfHx8YqNjb3kedLS0jR+/Hh16dJF0dHRevTRR7Vjx45q9926davCw8O1devWKtv79+9f+XqStGfPHsXHx6tdu3aKjo7WE088oR9++KGytrlz51Ze77mh/eXl5Vq4cKHuvvtutWrVSj169NCHH3540euMHj1aw4YNU9u2bTVo0KDK70Xv3r3VunVrde7cWaNHj1ZycvIlrxkAgGtBqAcAwMImTJggPz8/iw3DP336tF599VU988wzmjlzprKysjRs2DCNHDlSDz30kKZPn67y8nKNGDFChYWFVY4dP368Hn/8cc2ZM0fu7u4aOHCgDh06JEkqKipSfHy8Nm7cqBEjRmju3LkKCQnRgAEDLgr28+fPV48ePTR9+nTFxcVVW+dbb72lESNGKCoqSrNnz9bQoUP11VdfqX///iosLFTfvn01ceJESdLEiRMrQ/SF8vPz9fDDD+v777/XqFGjNHfuXLm7u2vAgAE6fPjwNX0Pc3NzNWDAAPn6+mr27NmaMWOGCgoK9NRTTyknJ0d9+/bVgw8+KElavny5+vbtK0maPHmyZs+erd69e2vBggXq2bOnpk6detFIjHXr1snR0VHz5s3T448/rh07dmj06NHq3r27Fi1apHHjxmnLli0aNWrUNdUPAMClOBhdAAAAtsbLy0svv/yyBg8erHnz5mnEiBHXdb6CggJNmjSp8v7zw4cP680339SUKVMqg2hZWZmGDRumxMRERUREVB47adIk3XvvvZKkW2+9VXFxcZo/f77efPNNrV69Wvv27dOKFSsUFRUlSYqJiVH//v01bdo0rVy5svI8rVu3ruyBrk5WVpbmz5+vvn37atKkSZXbmzdvrkcffVSfffaZ+vXrp6ZNm0qSmjZtqpYtW1Z7rs8//1zHjx/XqlWr1KJFC0lS+/btdf/992v79u1q0qTJVX8PDx06pPT0dPXv31/t2rWTJDVu3FiffPKJcnNzFRoaqpCQEElSmzZtJEmJiYlasWKFRo4cWXntXbt2lclk0ttvv61+/frJ19dXkmRnZ6dXXnlFbm5ukqSFCxfK2dlZAwcOlLOzsyTJx8dHP/30k8xms0wm01VfAwAA1aGnHgCAGhAbG6vevXtr8eLF2rNnz3Wfr23btpWPAwICJP0WPqWKwChJ2dnZldvs7e3VvXv3yq+dnZ0VExOj77//XpK0efNmBQYG6pZbblFpaalKS0tVVlamO++8Uz///HPlsHmpIpxfzu7du1VcXKz77ruvyvb27durbt26Fw2Nv5yEhASFhYVVBvpzta9bt04PP/zwFZ/nfM2aNZOfn58GDx6sSZMm6T//+Y8CAwP1l7/8RaGhodUes2XLFpnNZsXGxlZ+f0pLSxUbG6uioqIqtwOEhYVVBnpJ6tChgwoLC3XfffdpxowZ2rFjh7p27apnn32WQA8AsCh66gEAqCEvvviiNm/erLFjx1bp9b4WHh4eF21zcXG57DE+Pj5ydHSsss3f378y+GdmZiolJUW33HJLtcenpKTI29tb0m9/SLiUc38AqG6/gIAA5eTkXPb482VmZsrf3/+K978S7u7uWrZsmebPn6+1a9fqk08+kaurq3r37q0JEyZU9qZfWIekypEOFzpz5kzl4wuvOzo6WgsXLtSSJUv0zjvvaMGCBQoMDNTAgQMVHx9vuQsDANz0CPUAANQQb29vTZ48WUOHDtX8+fOr3aesrKzK1/n5+RZ7/ZycnIuGeqemplbOOO/p6amGDRtq2rRp1R4fFhZ2xa91LvynpqZeNDw+JSVF9erVu+JzeXp6Vk4UeL5du3bJw8NDzZo1q7L93PWVl5dX2Z6Xlyd3d/fKrxs3bqw33nhDZWVl+vHHH7V69Wp9/PHHCgsLq/bWAi8vL0nS+++/X+U859SpU+ey19GtWzd169ZNBQUF2rJliz744ANNnTpVbdq0qbzdAQCA68XwewAAatBdd92lXr16aeHChUpPT6/ynIeHh06fPl1l286dOy322sXFxdqyZUvl13l5efr666/VqVMnSVLHjh2VlJQkf39/RUZGVn5s3rxZixcvlr29/RW/VlRUlJycnPTPf/6zyvaEhASdOnWqyu0Dv6d9+/Y6fvy49u/fX+VannvuOa1YseKi/c+NYkhKSqrclpWVVWVSvS+//FKdO3dWSkqK7O3tFR0drcmTJ8vLy6uyDezsqr4t6tChgyQpIyOjyvcnMzNTM2fOrOzJr87rr7+uBx98UGazWa6urrrzzjs1ZsyYi+oEAOB60VMPAEANe+mll7RlyxalpqZW2X7nnXfq7bff1oIFC9SmTRt9/fXX1S5Ld60cHR01fvx4jRw5Uh4eHlq4cKEKCws1ZMgQSVKfPn20dOlSPfnkk3rmmWcUGhqq77//XosWLdJjjz120dD9y/Hx8dGgQYM0d+5cOTo6Ki4uTidOnNCsWbPUtGlT9enT54rP1adPH3344YcaPHiwnn/+efn5+WnZsmUqLCysskTdOeHh4QoNDdXcuXPl6ekpOzs7LVy4UK6urpX7tG3bVuXl5Ro6dKgGDRokd3d3rVu3Tjk5OZXzDpzrmV+zZo2ioqLUvHlz9e7dWy+99JJOnjypVq1aKTExUTNmzFBYWJgaNmx4yWu49dZb9d5772ns2LHq3bu3SkpKtHjxYvn4+Khz585X/L0AAOD3EOoBAKhhPj4+mjx5sp599tkq259++mmlp6fr3XffVUlJie644w5NmTJFgwcPtsjrent764UXXtC0adOUkpKiqKgoLV26VI0bN5Ykubm5admyZXrzzTf1xhtvKCcnR3Xr1tWoUaP05z//+apf77nnnlNAQICWLl2qTz/9VD4+PurZs6eGDx9eJWD/Hg8PDy1dulR///vfNWXKFJWWlioqKkoffvih6tevf9H+9vb2mj17tqZOnaqRI0cqICBA8fHxOnLkiBITEyVJQUFBWrx4sWbNmqUJEyaooKBAzZo105w5cypDdvfu3bV69WqNHTtWDz74oCZPnqxXX31Vb7/9tj755BOdPn1a/v7+uueeezR8+PDLjmSIiYnRtGnT9O6771ZOjteuXTt98MEHlZMaAgBgCSaz2Ww2uggAAAAAAHD1uKceAAAAAAArRagHAAAAAMBKEeoBAAAAALBShHoAAAAAAKwUoR4AAAAAACtFqAcAAAAAwEoR6gEAAAAAsFKEegAAAAAArBShHgAAAAAAK0WoBwAAAADAShHqAQAAAACwUv8PqpYaUsCcfMsAAAAASUVORK5CYII=", "text/plain": [ - "[]" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -152359,7 +152379,10 @@ } ], "source": [ - "plt.plot(np.arange(1, 9), E)" + "plt.plot(np.arange(1, 9), E)\n", + "plt.xlabel('Number of clusters')\n", + "plt.ylabel('Elbow criterion')\n", + "plt.grid(True)" ] }, { @@ -152371,22 +152394,22 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 53, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -152419,22 +152442,22 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 54, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -152467,7 +152490,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -152499,7 +152522,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -152541,16 +152564,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 56, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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XIHFBEITOMGdXNBoNpFIpSJKEtbU1zM7O2n5ut9qP3LqucRMIBBCPxxGPx/W6HV6zUSwWkc/nAUAXGdFoFOFw2JbYcGvdTCexIcsyWq1WR7HhBaFFEMTZhsQFQRAAoAc+6XQazWYTFy5ccGx2xdHREfb29hAKhXDdddchFAo5sGJikhAEAcFgEMFgELOzs2CMQZIkXWwUCgXk8/m2WRxcbLhRPNilk9ioVqvY29vD6uoqQqEQfD6fbqMisUEQxCRC4oIgiLbZFaqqQlVVRwI6RVGws7ODUqmEZDKJ5eXlgYIlpzIETgerXgh+R40gCAiFQgiFQkgkEvowO16zcXR0hFwup4sNXiAeCoU88X4bxYaxWQLPbADW08NJbBAE4XZIXBDEGcZqdoUoio4E8LVaDalUCoqiYGNjA/F43IEVO4fTASrZogZDEASEw2GEw2FdbDQaDV1s5HI5ZLNZiKLYJja88r5zoWHObLRaLUiSpIsREhsEQbgdEhcEcUbpNLti0OwAYwzZbBaZTAaRSASbm5sIBoOOrJlqG84ORntUMpnUxQYvEM9ms/q5oCgKCoUCIpEIgsHgRGY2zGu2slHxDCPPbJjFBu9GRRAEMU5IXBDEGaTb7IpBAnhZlpFKpVCtVjE/P4+lpSVHgx0SF2cXo9iYm5vTh9nt7e2BMYaDgwMA2gwKY9vbQCDg6oC71/OZF38b/46LDavMhrEbFUEQxCghcUEQZ4heZ1f0E8BXKhWk02kAwIULF/SpzmcBEj2jRxRFRKNR+P1+hEIhLC4u6haqer2OcrkMQJvFYRYbXqAXsSGK4okCcRIbBEEMGxIXBHFG6GSDMmM3UGaMIZPJIJvNYmpqCuvr6/D7h3NpoSCesIIH0rFYDLFYDIBmlarX67rgKJVKALT2uMaajWGdq73Cz+dBg/5exYa5ZoPEBkEQTkPigiDOADxbYWWDMmMn2JAkCalUCvV6HUtLS5ifnx96sELiwnm8+J76fD5MTU3pGTRFUfSshlFsBIPBNrHR61BHt2MUG/zz5XZI4/RwEhsEQTgNiQuC8DB8doUsywB6m7Tda3agWCxiZ2cHPp8PFy9eRDQadWTNp63NjUxyRsWt76nT+Hw+TE9PY3p6GoBWH8SFRq1WQ7FYBKCJDS40IpGIJ8QG/4xJbBAEMQpIXBCER+GWCFVVAVzrPnMapwXKqqpif38f+XweMzMzWFtbG1kANslBPOEu/H5/m9hotVq62KhUKigUCgCAUCjUJjacbv3qlC3KDt3ERrPZhCRJAKznbJDYIAjiNEhcEITHMHqte7FBmekWwDebTaRSKTSbTayuriKRSFCwQXiCQCCAQCCAmZkZAJrY4FmNUqmEo6MjAEA4HNaFxjDExjgwig2fz6fP2OCDDY2ZDV4c7vf7bV9bCII4G5C4IAgP0WvRdi/PY/7vQqGA3d1dBINBbG1tIRwOO7JmOziVuZBlGfl8XrfADJp5EQRBzxAR3iAQCCAejyMej+vfK6OFKp/P64P/eM1GOBy2LTbGkbk4DWOW0yw2Go2G/hguNnhmg8QGQRAAiQuC8Aw8W6EoykA3ef53jDEIggBFUbC7u4tisYjZ2Vmsrq6ObbfWCXFRrVaRSqX0OhRg+NYXYniMwiYnCAKCwSCCwSBmZ2fBGIMkSbrYKBQKutjgGQ0uNrwQbJ8mNlRVRalUQjweRyQSIbFBEGccEhcEMeHw2RWyLPdlgzJjFBeNRkMPxNfX1zE7O+vQqkcPYwyHh4c4ODhANBrFuXPnoKpqW/cgbn0xdg/qJUCkAGq8jPr9FwQBoVAIoVAIiURCtw/xblRHR0fI5XK62ODnUigU6rjWSTqHzGJDkiQcHR21fVd4typjzQaJDYI4G5C4IIgJxikblBH+97lcDgcHBwiFQtja2kIoFBp4vYPSb+ZClmWk02lUKhUsLCxgYWEBsiyDMYZgMKhbX4y70XYDRCo0P7twexS3CnJhzoVrLpdDNpuFKIpt51IwGPTUecMH9vGsBi8QbzQaEEXxRIE4iQ2C8CYkLghiQrEzu8IOvHYgk8lgbm4OS0tLrrIJ2Q3GuA2KMaZPDrd6jm670eYAkQeHXpr4TDiH0R6VTCZ1scHPpWw2C8aYvrMPaDNjumU23Iy5bsTcmY6LDUVRoChKx9a3JDYIwhuQuCCICcM4u4Ix5ugNuVqtIpPJAIArbVB2jtNog4rFYlhfX7clBIy70clkEqqq6gFivV7HwcEBAOhBEaB1GCKxQZgxio25ubm2c6lcLgMAtre34fP52mp/AoGAJ4JtLjb498QoNmRZ1n9vtlH12j6bIAh3QeKCICYIVVUhy7KjNiigPRAPhUJQFAWxWGzg53WaXm1RsiwjlUqhWq1iYWEBi4uLA79PxowFgLZ6jWKxCFVVceXKFQQCgbYAke9MEwTHeC4FAgFkMhmsrq7q5xMXHH6//4TYcCN2O151EhuyLKPVapHYIIgJh+56BDEBGGdX8C5OTt1kW60WUqkUarUaFhcXEQ6Hsb29PbFe8EqlgnQ6DQC6DWoYiKKIWCyGWCymWz2SyeTETXymwYTuIBaL6eeqoii60KjX6yiVSgCgC1det+EW4TpoO107YoPP2TBmDAmCcBfuuDIRBNERc9G2k8KiXC4jnU5DEARsbm4iFouhUqnor+s2ugXCg9qgBl0XgJ4mPvMhbP3ORSDa4WJ7UrEKzH0+H6amptrEBhet9Xq9TbgaC8TdJlz75TSxAVhPD6fvEkG4AxIXBOFinJpdYfW8mUwGuVwO09PTWFtb03dB3R6oWYkLow1qcXERCwsLYz8O48Tn04aw2Wl7S5w9fD5fm3CVZVkXGm7Ikg17EGAnsdFqtSBJEgASGwThJkhcEIQLcXp2hZFms4l0Oo1Go4Hl5WXMzc21PbdxzoXbsMpcjMoGNQinDWHL5/PI5XKWrUpJbHgfu5+x3+/HzMwMZmZmAHTOko1qOOSop4xbiQ2+EcMzG4IgkNggiDFB4oIgXMYwZldwCoUCdnd34ff7cfHiRUQikROPmRRx4YQNyklLjZ33y6rtbadWpUaPvVe6BxHXcOJ7ZpUlsxoO6VVLHq/H4PQiNvx+P32XCGJIkLggCBehqiokSRrK7Ird3V0UCgXE43Gsrq52tEy4WVwA18RXOp12jQ3KicGFnVqVduoe5KaCXsI9GLNkfDjkaZY8fu71ex6POnNxGt3EhiRJetbDXCDulvUTxKRDdyaCcAHcBsW7QTkpLBqNBlKpFCRJwtraGmZnZ3t6bjeKC0EQoCgKLl++DMC9NqhBMbe9NXYP4rvRgHcLes8Swy5IP82SZzWJPhKJ9FX/49bgvFexYbZRufV4CMLtkLggiDEzzNkVR0dH2NvbQzAYxNbWFsLh8Kl/59bMBWMM9XodzWYTsVgMGxsbrtm5H3YQYu4eJMtym9jgBb2j8tgTk0u3SfT1eh25XE7f4OAZjWg02nV6uNuuFadhFBt87aqq4vDwEKVSCRsbGyQ2CGIA3HFnJogzyDBnVyiKgp2dHZRKJSSTSSwvL/ccaLpRXHAbVLPZhN/vx4ULF1x3ox/l++X3+0+0vTVmNbjH3rgTPYjtxc1MeitaYLw7/sZJ9AD0+h8uXnO5HLLZbNdmA26zRdmBr5mLDVVV4fP5dItqs9mkzAZB2ITEBUGMAd6zXZZlAM7OrqjVakilUlAUBRsbG4jH47b+3m03zEqlglQqBUEQMD09rQ/VIq4RCAQQj8d1jz23vdTrdUvbi3Enmt7L8eImEQ+01/8kk0m9/oeLDWOzAX4+qao67mU7gnGTxyqz0Ww2u7a+pe8SQWiQuCCIEaOqKorFIkRRRDAYdMy6whhDNptFJpNBJBLB5uYmgsGg7edxS+aCMYaDgwMcHh7qNqiDgwP95u4m3BRUdLO9mHeio9EoFEWBqqqeyAAQzmOs/7FqNnBwcKA/NpPJ6I8d1QBLJ7H6DhgzGz6fT5+xwb9XRrHBi8P9fr+jdXMEMWmQuCCIEWGcXXH16lXMzc1hcXHRkeeWZRnpdBqVSgXz8/NYWlrq+8bmBnHRqRuUUzfrYdz0xy3GOmG0vRh3onlmo16vAwCefvrptk5UkxgcTiKTJurMzQZ4rUKxWIQkSZadzSKRyMScT6d9FsbrkFlsNBoN/TFcbPDMBokN4ixB4oIgRoDV7AqnglE+RI4x5mj3pHEFy0Yb1ObmJmKxmP47qyF6/XJWb/Tm4PDg4ADlchkzMzNtbW8DgUBbcOiW4nnCXYiiiFAoBAA4f/58x85m/HziVio3nk/9XFtIbBDESdz37SYIj8GLthVF0W8oTgTJjDFkMhlks9m+h8hZMa7MhdEGNTU1hfX1dVcGIFY4KXpGDQ98FhYWAGjNAHhgWK/X9U5UwWCwTWxQ21vnmPQg05h9MXc263Y+ua2NshNZpF7FBrdPkdggvMhk3LkJYgLpNrti0GBUkiSk02nUajUsLS1hfn7eccvQKIPlVquFVCqFWq3WdSjeJAfxk4LP52vrRCXLsh4cVioVFAoFAO6Z9jzp58Okrx/oHpR3O5/MbZSN3c3GITaGYVHrJDZ4gXij0YAoiicKxElsEJMMiQuCGAJWNijjjWKQILlYLGJnZwc+nw8XL17U7S1OMeobWrlcRjqdtrRBWeGFYGyS8Pv9mJmZwczMTE/TnrnYGOV5REHY5GA8n4BrbZTr9XqbeB3HzJZR1L+Ya8e42FAUBYqidGx9S2KDmCRIXBCEw/BshaqqHW8I/YgLVVWxv7+PfD6PmZkZrK2tDW13bxQZgn5sUG69uZ6VjEqv0567zUQg2pm0gm4rBjkGcxvlVqul12wYZ7aMIlM2js+Ciw1+PJ3EBrdR8X9TG2nCzZC4IAiHMM+u6LbTZDcYbTabSKVSaDabWFlZQTKZHOqNZdjBstEGZcfWRQXd7sKq7a2xTanVTATeiYref+/gVFBuFK9GsTGqTJkbhF4nsSHLsj7jx6pmg8QG4SZIXBCEA/CibT5M6rQLfa9BMmMMhUIBu7u7CAaD2Nra0ifpDpNhigu7NqhhcRYyDaPGOICt20wEY5tSt3YOIsZPr5ky44DISCTSt9jgtXFuwo7Y4HM2uI2KIMYFXdEJYgB4Yd5pNigzvQTviqJgd3cXxWIRs7OzWF1dHekNw+ng29jdqt9uUG61H7l1XePG3Pa2U5tSN3YOGhVu2C0flFEdQy8DIrlA4CLXOI3eLccxCL2IDX4tCofDuuAgsUGMEhIXBNEnpxVtd+O0YLReryOVSkGWZayvr2N2dtaJJfeM08FyvzaoYa+LGK09zNymVJblNrFh7Bw06mJeYjDGEZSbB0QabXn1er1tGn2vNUBuFxdmrMRGvV5HOp3G2toagsHgieJwEhvEsCFxQRB9YDW7wg6CIOiixAhjDLlcDplMBqFQCFtbW/qAqlHi5A3WLTYoojPjEmx+v7+tTanRX28s5jXuQkcikRPn56QFhEYmYbf8NNwi+I22PAAnptEfHh4CQMcaILccxyAYzyU+tI9vhEmSpIsREhvEMCFxQRA24F08ZFm2ZYMyY3Ujk2UZOzs7KJfLmJubw9LS0tgu+E4P+XNqKJ5bA4BJDw7dgrlzkCRJemajUCjoxbzGwJDXORHjw60CyWzLU1UV9XpdP6d4DZDP50M0GoUsy44MIh03/BrJhYQ5s8E3x1qtlv44o9jg3agIol9IXBBEjwxigzJjDpKr1SpSqRQYYzh37pzeA35cuHnIH+DOYMaNomeSMfrreTGv2V+fzWYBAKVSCYIgUNtboiuiKCIWi+nZU1VV9axGrVaDLMsol8uo1+tttrxJExxGcWGGF38bH8vFBs9siKJo2Y2KIHqFxAVB9EAvsyvsIIqiXoh3eHiIg4MDRKNRbGxsuOJGNoi44DYoURQdH/Ln5A2ObpaThZW/vl6vY2dnB6qqtllejJ2o3PB96oYbhbJdJvUYRFFsqwF6+umnEQqFEAgE2hoOBAKBNrHh9u5m3cSFGTtiw9iNahI/b2J0uPsbQhBjxji7gnchcaqfu6qqeOaZZ1CtVrGwsIDFxUVXXbDtigujDWp6ehpra2uO34T5++O2YMat6/IyPFMhCAJmZ2cxOzvbVhxeLpcBTF5gOIl46bwPhUKYn58H0N5woF6v6w0HgsGgfj65sbsZv3b3Y6s1ig3+PFZiw1yz4ZXPn3AGusoSRAdUVYUsy47YoMzwC7Wqqrhw4YK+c+YW7GYujDao5eVlzM3N0c2GGClmy4uiKLrQsAoMeXDohsCQvivuwCySzA0HZFnWz6lqtYpCoQDgWnczXkw+7nPKqTok/l5YiQ1JkvTp4SQ2CDMkLgjChDEtzG82Tl0oVVVFJpPRPeLXXXedK3dS7YiLUqmEnZ2dodigrNYFOFPfQDc/b+Pz+ToGhpVKRQ8MzZOeR91EwQu1Ol7JXJx2HH6/HzMzM3pNHO9uVq/XUS6X9e5m4XC4rbvZOM6pYUzsPk1sSJIEACQ2CBIXBGHEXLTt5AW62WwinU6j0WhgamoK9XrdlcIC6C3wHoUNqttruwmyRbkfY2DIv+fGrAbvRGUWG/R5no5Xznu7x2HubmY8p4ytlEctYEc51BBoFxv8n2az2SY2eL2G3+931AVAuBN3RjYEMQYGnV3RjUKhgN3dXfj9fly8eBGVSgX1et2x53ea0zIXkiQhlUqhXq+P1AZFNyTCTD+BlCAICAaDCAaDeicqSZL0zMbR0RFyuZyt4WujXD8xHAb5LDqdU1atlIctYMd1Thk343w+X5vYaDQa+mO42OCZDRIb3oPEBXHmcWp2hRWqqmJvbw9HR0eIx+NYXV2Fz+dDtVp13e67kW7iYpQ2KKt1Ac5kLprNJprNpl4UfFZx6+yQUWJse5tIJNomPddqNWSzWTDGOg5fO+vwZheTjJ0OS71g1UrZSsAa57ZEIhFHxIZbBCuJjbMLiQviTOPk7AozjUYDqVQKkiRhbW0Ns7Oz+nNPQkBnXt84bVCnrc3u3+ZyOezv7wOAI7vTTooeYvwYJz3Pzc21TXo2Dl/z+/1tbW/7/T5MeiDllmDWCYZ1HFYC1jy3hYs0o9gIhUK21+RWsder2DDP2CCxMXmQuCDOLLwIzelsBWMMR0dH2NvbQzAYxNbWFsLhcNtjuLhw602Zt8rljMsGZbWuQVAUBTs7OyiVSkgmk4hEInrQaJ6TEIvFJnKAFuE85knPiqK0tb3l8xCCwWCbUO2la5BbrwFnjVFvDFjNbbHKlvFzj59XvWx+qKo6EedUJ7HBxTxwrWvj9PS0XrdBYsP9kLggzhzcBsW7QTl5oTIHr8vLy5Y7SG6/MBozK+O0QXWin0CgXq8jlUpBlmWcO3cO0WgUrVYLsVhM3522mpPgxtalxHjx+Xxtw9eM8xBqtZre9pa3KB1X16BR4QWB5LQtyi7dsmX1ev3E5gcXG1bWvEn9PKzERrFYxMHBATY3N9FsNiGK4oluVCQ23AeJC+JMMUwbVK1WQyqVgqIo2NjYQDwe7/hY/pqqqroyWOXiYm9vD7lcDtPT01hfXx/7Wvv5rIyZpFAohK2tLYRCIciy3PY485wEY8Bo7Glv1fmFbFFnG/M8BN41iGc1eNcgHjwaz51JDQSNeOG8H7e4MGPOlvHND35N4psffr//RB2QF84poP2z4MfFNwcVRWmbs2HsRjWMNryEPUhcEGeCYc6uYIwhm80ik8kgEolgc3MTwWCw69+4PRjlu2aNRsNVQ/Hsvm+qqmJ3dxeFQgGJRAIrKys97x6bA0ZejGluXWqc+uzWz5MYLZ1alJq7BkUiEbRaLV3Mu+E71i+TvHbAfeLCjNWQSCuxwYNwURTRarUm3tbJbcvAtcwG/2+j2JBlWf+9uWaDxMboIXFBeB7GGGRZ1neqnbzQyLKMdDqNSqWC+fl5LC0t9fTcxouj2yiVSvqNanNz0xU2KDO9vG/NZhPb29uQJAnr6+uYnZ0d6DXNbSaNxZj8/bp69WpbgS91Exo+bg/KrVqUGs8dfm26fPny0NveDgu3fwa94HZxYcZszTPWARWLRciyjCtXriAQCLRZ89w6W6kT3c6tTmJDlmVdtHOxwTMb3EZFDJfJOssIwiY8W8GLk528qFQqFaTTaTDGcP78eX2HuxfcmLng08NzuZweFLtNWPR64+dzRQKBgGVBvRPrMBZjVioV7O7uYmZmBo1Gw/FuQoR3MJ87V69e1VvcWjUWMApVYnhMmrgwYxQbfHhdPB4/UQfEa8i4kB231fU07AhXEhvuge50hCcx2qCG0Q3q4OAAh4eHiMViWF9ft33jd5u44N2gGo0GVlZW0Gw2UalUxr2sE5z2vqmqiv39feTz+ba5It2ey8l1JRIJBAKBjt2EzkqBL9E7giDA7/djbm6ua2MBvgPNg0I3CVXKXLgLxhgCgUCbrVOWZf2cMtaQ8WsSrwdym9gw2qLscprYAHCiOJzEhjO45+pEEA4xzKJtSZKQTqdRq9WwtLSE+fn5vp7bTeKiVCohnU7D5/PpNqj9/X1XrM0ORoG0urqKRCIxtkDBqpuQMVg0FvgOc1JvL3ghmJpkzIG5lbee1/pY7UC7oYsZiQt3YdWK1u/3Y2ZmBjMzMwDamw4Yr0nhcLhtzsa4A20nz61OYqPVakGSJP33JDYGh8QF4SlUVUWxWMTu7i7Onz/vqJXAGIQP2pLVDeLCaIOamZnB2tqaHqC4dchfp/fN/NlEIpGen2uY6+IYb+zGAt9qtapP6nVimF+/uPGzJjR8Pl/HHehKpdK1ixlhHy+Ii14C8k5NB+r1+qkdzkbJIJmL07ASG9zxwDMbZrHBu1ER3SFxQXgC4+wKWZbRaDQcC5iMVhtzEN4v4xYXZhtUMplsu2C6/eLJ3zfz1HA3tMs9DasCX6vhWeS5J6zoJFTNXczMYmOY32kvZS68gN3Pw+qaZOyOZ+xwNsrzChjtucXrMYyvzcWGVWbD2I2KaIfEBTHxmG1Q/OJgnDDdL81mE6lUCs1m0zII75dxiotedvndnrkAtLR+KpVCrVbru12uG24KVsOzunnu7Ux/JtzPIMFTt6CwVquNLCvmJXEx6ccBDP55CIKAUCiEUCiERCKhdzjj1yV+XvFrFz+vQqGQ4+/fOKeN9yI2RFE8USDuhXNoUEhcEBMNz1YYi7adavPKB6/5/f6erTa9Mg5x0c0GZbU+N4sLPrBQEARsbm7q/vRxr8uJ96yT595q+nMsFjvTNhgvBYROYBUUNhoNPSg0ZsXMg9fO+nvopXPJabFn7HBmFBv8mpTL5ZDNZttEbCQScURs8PPVDfQqNsw1G144p+xC4oKYSMyzK4xF24MGeoqiYG9vD4VCAbOzs1hdXXU8cBu1uJAkCdvb27YyMG4UF3xNBwcHiMVi2NjYcFXXnGFg9twbCzHNw/yGuYNIDIdh7vobs2LJZFIfjsnPH6uWyZFIxLYFjzIX7oIP0RsW5nbKnaydxinjkUikr4yZm88to9jg54+qqpAkqW16+FkUG96+KxOexDy7gvsgOfz/92OLqtfrSKVSkGXZkcFrnRiluCgWi9jZ2bFd7Ow2cSHLMnZ2dgBo/dvX19fPxEXajLkQ02iDMe4g0jA/wozxvADaB6/xQl6gPwvepJ9fXhMXozwOK2snFxv1el0XsbyOjG+E9HJdGmZBt5Pw4zhNbEiShFAopHd486rYIHFBTAy9zq7oxxbFGEM+n8f+/j5CoRC2trYQCoUcW7uZUYiLQQrRjetzw4WvVqthe3tbf79mZmZcsS7OuGpoOtlgqtVqx51pt81IIMaHVctkY72P0YLXbT6L2zYi+sEr4oK3Vx3ncZhFrLGOrF6v63Vkfr//hD3PzLiPpV+sxAZjDLu7u4jH40gkEgCs52xM4vGaoTsMMRHYmV1hN9DjO+Llchlzc3NYWloa+k7JINmVXhi0EN0tFzfGGHK5HPb39xGNRrG6uoqnnnpq3MtyLcYdREA7v4z1Gnxn2jgjYVjnINEbbgqe/H5/RwuesT2psWMQ34RxyzH0ixcEEuBOkWRVR1av13XBYWxaYRQbfr9/YjIXp8EdFowxXUhwwdFsNttsVIFAAH6/H+VyGYIgIJlMjnv5tiFxQbgenq1QFKWngXh2AvdqtYpUKgXGGM6dO6cPGBo2w9zp5jaoQQrR3ZC5UBQFOzs7KJVKmJ+fx9LSUlsLWjfhphu5EVEUOw7zM85IAIBsNotYLDa2YX6E++g0C6FWq7W1JwW0aynPok3i+cOvdZO4diNuFBdmzBkz86BIoz1PURRIkgRZlic+48rFBI9jjNkN/juefQaAt771rQCAT3/602Nbc79M9idFeBo+u0KW5a42KDO92KIYYzg8PMTBwQGi0Sg2NjZGOkfAuIvhFE7O4xj3HA5j7YtR9LlVXHDcui6O1YyEw8NDVKvVtmDRaKEa5TA/u0xCIHUabspcdMOq7W2z2US1WkUul0O1WkWlUhnrMMhBmZR1dmMSvxNWgyKNE+nL5TLK5bKrptL3A/9srDIxVmIjk8kgHo+PdI1OQeKCcCV2bFBmTguMW60W0uk0qtUqFhYWsLi4OJYLsZPiwmiDWl1dRSKRGLjPOTD6YJkxprcADoVCuO666xAMBk+sixgcHixGo1HU63VsbW21tZekYX5EN3jHoEAggFwuh6WlJQQCAf38OTw8BICJOX/cvjHQK5MoLsxwe97U1BSKxSLm5+fh9/tPZFx5LRAXs263Txmb0JyGIAio1+tYXl4e9rKGAokLwnVYza6wiyAIlraocrmMdDoNQRBw4cIFPS07DpwSF07YoDox6jkcu7u7KBQKSCQSWFlZ6Xiz8Eog4Bb47rmxvaSx40u1WqVhfkRXrDoGdRoGafbVu4FJySCdRrfd8UmDH4sx4wqcXgvEz0O3vQc8JullXfz7w4viJw13fKsJAu2zK4y+xH4QRbEtADUOkJuamsL6+vrYb2qDigujDSoej2N1ddWxQG/UmYtms4nt7W1IktS1BbBbb/7jtpENA2PHl/n5+a7D/Lp1EiK6M+lBbaed8k5FvN2aC4zT6jLpnwPHzu642+kUjHerBeKzfwDoIsMtg0btCL9mswnGmKObhaOExAXhClRVhSzLfdmgrDAG7pIkIZVKodFoYHl5GXNzc6648A4iLpy2QVmtbVQUCgXs7u4iEAhga2sL4XC46+PdOIPjLNBtmB/fPeTZDx4sUnG49+nVhmPV9taqucC4dp+9Ii68YIvi9HIsVrVAfPZPvV5vqyUzi41Rv0d2hF+9XgcAXZxPGiQuiLFinF3hZLcObosapmVoUPoNkkdxTKOew2E380LiYvx0G+Z3dHSEXC5Hw/x65Cy+J2ariyRJbUW8PCAclVglceE+7NiIOFazf5rNpn5u8WsTFxvGlsrDfs/sHE+tVgMAskURhF3MRdtOtgEUBAHlchnZbNZxy5BT2BUXw7RBWa0NGF4Qb8wm2c28uPGm6UVblB06DfPjYoOG+XVm0s8Zp4JZvvvcTawaO5lFIhFHA0ISF+7DiWMx1pIZr01cbORyOWSz2ZF0OSNxQRBDxu7sCjs0Gg09E7K2tobZ2VlXXmjtiIth26Cs1gYMJ/AplUpIp9Pw+Xx9ZV7IFuV+Tivuddpv78bv91lhGMFsJ7HKzyFjJzPzhOd+10Hiwn0M69zi16ZkMnliI4SfW8asayQScURs2Dkebotyk9vCDiQuiJHS7+yKXp+btzEFgOnpaSQSCUeeexj0GiTzmoRxWLucDOJ53+5sNovp6Wmsr6/3HUySuJgszMW9p/nt+Q39tGuDF84DrwS1w8QcEBo7mXXKjEUiEVttb73yOXipW1Q/tii7WG2EWJ1bvKUyF7P9CNl+ai4oc0EQpzDI7IrTME5zTiaTaDQarr+4niYuVFXF3t4ejo6ORm7tcjpz0Wq1kEqlUKvVBi6qd2MAcNZtUXaxGuZnNfmZ38hjsdhEDWM7S4xjp9y4qwxo18pOE557bZvsNXFBx9IfVueWVUtlLmSNYuM07Gyoki2KIHpAVVVIkuR4tgLQvoSpVAqKomBjYwPxeBzPPPOM6wO9TrM4gNHboKzWBjgTLFcqFaRSKQiCgM3NTUe6X7j9syV6p9PkZ34z557oSRnGdtZwQzArimJbJ6p+2iZ7RVx0uqdMIqPIXJxGt5bK9Xq9TcieNr/FzjlG4oIgusBtULwGwmkbVDabRSaTQSQSwebmpj7NeRJ8+Z3WaLRB9dKadVhrGxTGGA4PD3FwcODobBE3BgBuXNOk0m2YX61WQyaTAXDtZg5MbkDlhsDci/TSNhlot+F5RVw42XVx3Ljx+2FuqcyFrDlrFgwG28SGz+fTN1d7gVrREkQHhmmDkmUZ6XQalUoF8/PzWFpaantuURQhy7IjrzUszOJinDYoq7UB/WcIjJ/P4uIiFhYWHO0E5lbh6NZ1dcNNN24rzDYF485htVoFAGQyGRQKBRrmNwbcGACa6TZ0jdvwAC1wzOfzI2tNOgy8IpKAyTi3zEKW15MZ2yoD0Dc++Ybrafd2qrkgCBPDml3BqVQqSKfTYIzh/Pnz+pfaiJsDUI5RAI3bBmVmEHFRq9Wwvb0NxhguXLig7/A4ids/20ljkt5P486hoii4fPky4vE4VFWduGF+kxA8eY1ONrzd3V0wxkbamnQYeElc2Nnpdwvm+S2tVksXGuVyGYwxXL58uatFD7hmixo0c1EoFHDnnXfiq1/9KiqVCp797GfjXe96F172spdZPv7Tn/40fvu3f/vEz7/0pS/h/PnzPb8uiQvCURhjkGVZD5qdFBaMMRwcHODw8BCxWAzr6+sdfdeTIC74Gu1OqB4ldt5DfmPe399HNBrFxsbGUHzxTrcldNPzEP0Ri8UwNTXVdZjfJAaKk8CkCyQuRP1+P4LBIJaWlixbk05KzY+XxIUXjiUQCCAQCGBmZgaqqkKWZcTj8RMWvUAggE9/+tNIJBJ4xSteoWdlB+0O+c53vhO5XA533nknkskk7rrrLvzyL/8yPvWpT2Fra+vE4y9duoSbb74Zd955Z9vPk8mkrdclcUE4Bs9WDKMIS5IkpNNp1Go1LC0tYX5+vutFRxTFifBhN5tNpNPpsdugzNjNXBi7dVnZ1Jxem9Mtcp0UwMToML/fpw3zOzw8BHCtrWQsFrPdsnQYTHIANenigmPMsneb0cK7BfVSwDsOeG2jF5jEzEU3VFWF3++3tOjt7u7if/yP/4Fms4mPfOQj+jXpK1/5CsLhMLa2tmx/x65evYqvf/3r+OQnP4kbb7wRAPDe974X9913Hz7/+c/jN37jN078zRNPPIHnPOc5WFhYGOhY3fFtICYaow1qGN2gSqUSdnZ2IIoiLl682JMH0e2Zi2aziXK5DEVRXDnoz464qNfr2N7ehqIoOHfunJ4OngS8sDNGdKbXQNGpYX52cfM16qzR6VrQrVvQMAZCDoqqqp65pnnt+mw+HrNF72/+5m/wwAMP4KGHHsLf//3fo9Vq4aMf/Sg++tGPYn5+HjfffDNuueUWvPzlL8f58+dPfW8SiQT+7M/+DC94wQvaXpMxpteCmLl06RJe97rXDXysJC6IgRhm0baqqtjf30c+n8fMzAxWV1d73h1ys7jgNihBEBAIBFw56K+Xz9A4tDAUCrV16xr22tz62RLuxmqYn7E43GqYXzgc9tTuqdN4LXNxGuZuQZ0GQp7mqR8WXgrIvSSUgNMzMfPz83jDG96AN7zhDXjXu96Fe++9F7/3e7+Hb3/723jwwQdx99134+677wYAnD9/Hp/4xCewuLjY8flmZmbw6le/uu1nX/jCF7C9vY1XvepVJx6fz+eRzWbx8MMP4+Mf/zgKhQJuuOEGvPvd78bm5qatYyVxQfQNz1YoiuJ4tsJY4LyysoJkMmnr+bvNkBgXxm5Qs7Oz8Pl8+s6p2zgtc6GqKnZ3d1EoFJBMJrG8vDzSAMwJcdFqtVAoFBAOh3uaBt0NGqI3mfj9/o4tS4vF4olhfsPqIjTJAdRZExdmrAp4zW1vR9lgwEviwksWL8CezYt3i3rzm9+Mn//5nwdjDNvb23jwwQfx4IMPYnd31/Z788gjj+A973kPbr/9dtx2220nfv/EE08A0AT0hz70IdRqNXz0ox/FL/zCL+Bzn/sc5ufne34tEheEbYY5uwKAvhvu9/tx8eLFvgqaRFF0VaDXaDSQSqUgSRLW1taQSCSQyWRctUYznTIE/FharRbW19cxOzs78nUNCh/sZ8y4RSIRxGIxvVjTKzdoonfMLUuthvkZW+MOeq64+ft/1nAqKDefQ1YNBgRBaJvu7KRg9Zq48MqxAPYyMfV6HaFQSLfXCYKA8+fP4/z583jzm99s+7W//OUv493vfjduuOGGE8XanFtuuQUPPfQQ4vG4/rOPfOQjeM1rXoNPfepT+JVf+ZWeX4/EBWGLYdqgFEXB3t4eCoUCZmdnsbKy0rdv1U3WmaOjI+zu7iIYDLZ1g3LTGq2wWp+xs9XFixfHNuCv3/fNONgvFothYWGhzRpzcHAAQNuN5AW/fAAScbYwD/NjjLV57c3nitsKe0eFVzIXgPPH0KnBAD+PhiVYvfBZAN4r6LaTianVagN3iuJ84hOfwPvf/3689rWvxR133NHVvmwUFoA2Z2N9fV0fXtorZ+sqSAyEoig4ODiAIAiOFyDX63WkUinIsuzIbjgPQMd5oTVah2ZnZ7G6utp2YXG7uACuBQ7G+pd4PI61tbWJu+grioJ0Oo1yuYyFhQUsLCxAkiQEAgFEo1G94JcHj8ZizdP802SLGg+jfL/5brPVMD8754oRLwTmXjnnR3EcxgYDVtPnrQSr3W5mvG2uF/CSUOKNb+zYopwQF3fddRf+4A/+AG95y1vwnve8p+vr33XXXfiTP/kT3HvvvfrGYaVSwTPPPIOf+7mfs/W6JC6IUzHOrigUCo4WITPGkM/nsb+/j1AohK2tLYRCoYGfl3+BxnVxsrJBmXG7uODrkyQJ29vbrhrwZ/d94x2tVFXVBy9anRuiKFoWa1ar1Tb/9LA9+KNkktfOGccxdCvsLZfLev9647ni1mF+TjDpxzWOe4V5+jzf3OCilQtWvgHC/+kmHrwUkHspc8HvWXbEhd3ZEmauXLmCP/qjP8JrX/ta/Oqv/ipyuZz+O14DlM/nMT09jXA4jNe85jX44z/+Y/zmb/4mfu3Xfg2NRkOfj/HGN77R1muTuCC6YjW7wqmAWJZl7OzsoFwuY25uDktLS45dSMa5k9zJBmVmEsRFs9nEU089BZ/P13f9yzDWZed9y+fzfXe0MhZrGv3T1WpVtzTwmQkAdLvgJOKloGQcmM8VY2Fvp2F+/P2e5PfdK+eNG47DvLmhKEpbJpW3D+XZMX4uGe+bXuqw5IbPxCnsZinr9XpPbfe78cUvfhGtVgv33HMP7rnnnrbfvfGNb8Tb3/523H777fjABz6AN73pTVhZWcFf/uVf4o477tCLyF/5ylfir/7qr2xboElcEJZ0ml3h1HC6arWKVCoFxthQZiPwL7CqqiNLEZ9mg7Ja47itW53gn3+xWMTMzAzW1tYmLtVu7M6VSCSwsrIykHg1+6eNlgY+TTWXy6FUKum1GuPsd0+MD3P/eqPXvlqttk19BrTrIZ/kO4m47fplF7fa03w+34luZjyrYcyOGTtRkbhwJ3aGCyuKgkajMbC4eNvb3oa3ve1tXR9z6dKltv9+7nOfi7/4i78Y6HUBEheEBd2Ktgdt8WosqI1Go9jY2BjKDdVoixoFvdigzBizK266gLZaLaRSKaiqilgsho2NDVetr5dz0Gjl6vXzsIvR0jA/P48nnngCMzMzEATBcmZCLBbztC2G6IyV175er6NcLut2u6OjI1v2F7fg5uxrr7hVXJjhAtQqO1YoFJDP5wFoPnm/349IJDLR1xwv2aLsiItGowEArnAK9AuJC6KN02ZXDGKLarVaSKfTqFarWFhYwOLi4tAueqO0RfVqgzIzagHUC7xFKx/w59YbU7f3rFwuI5VKjdzKJYqintUA0NaCkt/4jbaYWCxGLW/PKHyYn9/vR6lUwurqKhhjlvYXngVz6zA/t22O9MOkiAsjVtmxZrOJdDoNQRCQy+X07kRGK14wGJyY4/TCucWxc47xGReDZi7GCYkLAsC12RWyLLfZoMz0m7kol8v6Re/ChQu6p3RYGG1Rw8Jog+rHduOmDkPGjNLU1BTW19dx5coVV6zNTKeLM2MMBwcHODw8xPT0NNbX10e+82t8v6xu/NVqFbVaDYeHhzg8PDzzbUwJDZ/Ph0gkMvZhfmeVSRQXZnjrZEEQEI/HkUgk2jpRGa145ra3buWsZi5IXBCewM7sCruZC1VVcXBwgGw2qwetowighp0V6McGZcYt4kKWZaTTaVQqFSwuLmJhYQGCILi24NxqXbIsI5VKoVqtYmlpCfPz864KFIwzE3jLW+6/77eNKeGNoBA4uf5ug9iGMRthELywu+yV8wi49nkYrXjGa46xoxmgnWtG0eqWDQ4vfSaAPXHBa/hIXBATC5+03S1bYcRO5kKSJKRSKTQaDSwvL2Nubm5kF4phBe6MMX2QnF0blBk3iAtjYb05o+RWcWGmVqvpNSKjyIp1ws65zW0xsVgMQHsbU6+2vCVO0sv3q9Mgtmq1inq9PvZhfl4QFxwvHEengm7zNafTnJZgMNi2wTGuuh+7rVvdjh2xxGsuSFwQE4dxdgX3ZfZy0veauSgWi9jZ2YHP58Pm5ubIvyTDsEUZJ4g71X0IGI+4YIwhl8thf3+/Y2G9W2+0xi5bfEZKOBzGuXPnxp7i7/ez7NTy1mqnmnvwx32sxHgw7kgD12YjdAsSze1KiXa8sktu5zi6zWkxNqQYVzaV37sn/TPh2Mlc1Go1ACQuiAlDVVXIstyTDcrMaa1oje0/4/E4VldXx7Lz4bQtitugWq2WIxPEgfGJC0VRsLOzg1KphPn5eSwtLXWsr3Fr5kJVVaTTaRSLRSSTSSwvL3smeLLaqTbuMGYyGQD2Bmud9nrEeHAiqO00+LFWq6FSqQy9a5kXMhduvc7ZZZDzybjBAbTX/Rizqca2t8Ns+OEVwcfhGSUq6CY8h3F2hdGXaYdusxmMdQjjnuTsVODOGMPR0RH29vZ0G5QTE8SB8YgLPqlaUZRT54u4VVyoqgpJkiBJkmNCzwmG9X4JgtA2xbfTYC3jTT8Sidj+7nkhSCS6D/PjXcuM59SgHYS8cN54JZB18ji61f3woZDDtG7a2emfBOx8T6jmgpgYzEXb/QgLoD0jYAyOzQF4v3UITuGELUpRFOzu7qJYLDpigzIzSnFh/Ix6nVTtRnFRKpV024cT55kbj/E0rAZr8cLwYQSPxPAZZktuq65lk9xBaBiQuOiOVTbVeB4No8mAVz4Tjp3OV7zmgtfHTCIkLs4Ap82usIMxaBdFsc1iM4wAvF8GDdwbjQa2t7chy/LQdsdHJS5UVcXOzk5fFiK3BN6MMWQyGWSzWV0UjVvAuoVAIIDZ2dkTwaN5EjSv1XBTR5izzqi/X8auZXyYn3HKvLGDUK+WO8pcuIdRFUF3O49qtdqJJgM8u2FHtHotc2FHXFDNBeFqep1dYQdj5oJ36VEUBRsbG4jH404s2xH6baVq3uF30gZltUb+msNikFqRfmeaOA2fGF6r1bC8vIxWq6UHQW7CDYGJ1U2/U0cYLjbcIiD7wStB4bgwT5nvZLnrVtRL4sI9jKsI2nge8XVYXXfsilZg8j8Tjp3vCRcXlLkgXIed2RV24M/B06CRSKQni804sCsuhm2DslofMLxBf7xlbiAQwMWLF23v9Lvhos5b5QLA5uYmYrEY9vf3XRsQu21d3VrelstlHB0d6Y/N5/OYmpqilrcjxG0BlJXl7rSiXred8/3gts+hX9xyHFZtb7uJVp7ZMItWYPzH4hT92KJ4R7hJhMSFB+EFr05lK4zwL3w2m+3aacgNnNbZysgobFBmhpW5UFUV+/v7yOfzmJ2dxerqal8iaZz1CN1a5br1fJsErFre5vN5XWjk8/m2XchYLHbm/PfENXop6gU0UVIoFMY6zG8QvBLIuvU4rEQrz2wYNzmMotW4MeoF+rFFUeaCcAXcBsW7QTktLCqVCtLpNABgeXkZ8/Pzjj33MOglOB6lDcrMMKaIS5KE7e1tNJvNgTt2jUtcnNYq161F2G67oZ8GL9KMxWIol8u4ePEiJEnSi8O5b9qplrfEZNNpmN/+/j4URRn7ML9BcGtQbpdJOY5AIIBAINC1oxnn6Oho6G1vRwGvfesFakVLuIZh2aD4cx8cHODw8BCRSAT1en0iimlPC0KNNqhxzEpwOnNRKpWQTqfh9/tx8eLFgVOq4wjijTUi3ep43CguAPeuqxfMw9n4BF8uNpxqeUtcY1KCQSv4+cItsSsrK66e+NwLk/g5GJnE86lTR7NcLodqtYp8Po9cLgdRFNva3k5aBzxVVXsW2jxzQbYoYmw4MbuiG5IkIZ1Oo1arYXFxEbOzs3jiiScmIojqZouq1+tIpVKQZXnsxehOzOLY399HLpfDzMwM1tbWHLuBj/JzNtaIdMsgTdINZZIxT/A17i4Wi0W95W0kEtGLwyfthk8MDs+SW9X3cHFqNczPTeJ0Eu5nvTCqblHDhNf08I3Mra2ttk5UVu2TjSLXrdixRfHMBYkLYiwwxiDLMmRZBtD/7IpOlEol7OzsQBRFvZiWv5YbugidhtXO+zhtUFbrGzQ7YO6kNDc359g5MKrMhaqqyGQyyOVyiMfjWFtb63oRdrMtyo3rcgqz/77bvAQuNibBEjNOJnGnuVf8fr/us+82zI/vRsdisbGJU698Dl45DqB9ojXPqM7NzbV1oqrX68hkMgDcb8fjIrwXuDvEzVm+03DXu0/0DM9WDKMXtLEgeHp6Gmtra/oXdRh1AsPCHOyN2wZlxSABaaVSQSqVgiAIuHjxouP+zFEEy1wc1et1rKysIJlMjvzG6IVWmoPQz7Gf1vKWtwo2WmLM3WCcwEvB1KTSy/fntGF+vPvguIb5eeUaMK5WtMOgUzBu1YlqEux4XCz1Qr1en+h6C4DExcRhtEENoxtUs9lEKpVCs9m0DPaG3T7VSYxzGtxkgzLS7yyOw8NDHBwcYGpqCuvr60PZpRn2DcoojjY3N3u+mHo9QzAuBnlPu7W8NVpijJ7pSS/QdJJJfh/6CcxPG8LGd6NH1UzAK+LCS9fFXj8Ts33TeO2pVqv6tafbrJZRYLdb1CRbogASFxPFMIu2Aeh2oW4FwU5YeUYFr7ngLU3HbYOywu57KcsyUqkUqtUqFhcXsbCwMLSb4rA+Z8YYstksMpkMYrEYNjY2bIsjN55/k/K9GAXmlrfcElOtVvUWpsaWt5PawnRQ6HzRMA9hM+9G9zLMbxC8JC6ctkePCzvBuBHjtQfobVbLsDc6GGO2bFGNRgMLCwtDW88oIHExIfBshaIojosKRVGwt7eHQqGA2dlZrKysdN0hsjM/Ytw0Gg1UKhXX2KDM2AlI+UA5xhguXLig79QME6eDH0VRkE6nUS6XsbCwgMXFxb52PYnJwcoSY9ylNrYw5bUa1PJ2chhGYG7VTICLjWEEiF4TF17AqWPpZVaLsfYnGo06PkjUrn2zVquRLYoYLsOeXWG0C/U6PM5oN3IrvEuJqqquskGZ6XUWR6eBcuNemx2MgwrPnTun7yz1sy7AWzfSs4S5QPO0XepYLIZwOOy6jQEnmeTzeBTfQ/NcBKsAcZBWpV65lnjlOID+MxfdsJrVYlX743RW1U5trKIokCSJxAUxPIY9uyKfz/dlFxJF0bXpfONxiaKIUCjkWmEB9DaLg+/0j3oiupPi4ujoCLu7uwiFQrjuuuscaRvothsp2aL6o1vLW+MutRu6CjkNnS/26TTMr1vnskgk0nVDxm3Xkn6xY71xO6M4ltNqf4xZVaNwtbu5Z0dceKENLUDiwrXwbMUwirZlWcbOzg7K5TLm5uawtLRk60vs1syFcbJzMpkEY0z/orqVbgFpvV7H9vY2FEUZaKd/kLUNiqqq2Nvbw9HREWZnZ7G6ujrwDcMLQQDRGSsbAx/kZ+4qxG/ydE6Mj3EH5t1alXbqXGbuHjTuY3AKOx2J3M44PhNz7U+nc8luowE7tigvTOcGSFy4DvPsCqeFRbVaRTqdhqqqfQesbsxcGANxboPa29tzpQgyYiXUzLM4Njc3xzIgiAuffi/ykiQhlUqh0WhgdXUVyWTSsXUBg+/6euUm7GWMu9TGnUUuNvjNfnd3t61eY1J2b73SStdN67dqVWrVPchYr+G2+1m/eEUkAcOxRdml27lkZeHk2Q3zuu1kLvh0bhIXhGOYZ1c42fXB2L40Go1ifX2974DVTQXdRhtUOBxuC8TdKILMuHkWxyDnXrlcRjqdhiiKHTuPuQEnb8Rkixo+5p3FSqWi2+2o5e14cPs57/P59GF+gPWkeUA7t/L5/FAKekeFl8SFG4/F6lzimY1yuYyjoyMAONFooB9x4dZ7Zq+QuHABw55d0Wq1kE6nUa1W++7QY8QtQZTRBmVl73LLOrthXGOj0UAqlUKr1eq5uH7YawPsXeRHMYPDqcwFMflwO8Lc3ByCwWBb4Ggu9OWZDbe1vHXTWvrBjUFgN6xsd/v7+2i1WkMr6B0Vk/ZZdMMNmYvTMDca6DSFnts3m80m/H5/18+IbFGEIzDGUC6XUalUkEgkHBcWfAdZEATH2pe6IXNhZYMy49baECM8u1IoFLCzs4NgMOiaWRx2g3hZlpFOp1GpVIY+g8POuojueCUYsdPylhf6jrvlrVfO4Uk9h7jtLhAIwOfzYW1trc1jbz5n+D/DGFrqBF4SF5N2LFbXH97VjE8N393dPbWrGc9ccCvWIBQKBdx555346le/ikqlgmc/+9l417vehZe97GWWjz86OsIf/uEf4r777gMA/MRP/AR+53d+py+h485vyBmBZyvK5TL29/dPTMMe9LkPDg6QzWYd30EeZ9DezQZlZhJsUYAmlMrlsmMFz07Ty3vIxZ6qqjh//ryeNh4Gbr3hTEKm7CxhVehr9EvzG/64J/dOOl44543D57oN8+PnTDAY1MWpm86ZSQvIuzHpna+M9WKCIODg4ADr6+v6+WTsava3f/u3ODo6wstf/nJHMxfvfOc7kcvlcOeddyKZTOKuu+7CL//yL+NTn/oUtra2Tjz+13/919FsNvGxj30MpVIJ733ve/G+970PH/rQh2y/NomLMcBnV8iyDFVV9Z0zpy4MxkLa5eVlzM3NOXrBGVfQfpoNysygBcnDptls6rM4VldXkUgkXLXOXtZiLD4Ph8PY2NgYevE52aKGg9ffT1EU21reyrKsF/latbwdhfferdcmO3jlGKzuJeY2yfycMXvs3VLjw4NVL+DFzlf8HOGbHTyz+sADD+CHP/whPvOZz+h/c++99+LcuXN46Utf2pfQuHr1Kr7+9a/jk5/8JG688UYAwHvf+17cd999+PznP4/f+I3faHv8t771LTz00EO4++67deHx+7//+3jrW9+Kd77znVhaWrL1+iQuRozV7Ap+MXDCY1gsFrGzswOfz4fNzc2h+PbGkbmo1WpIpVK22rK6edhaqVRCOp0GAL3Htts4LYhXVRW7u7soFApjLz7vBz7xnF/0eT98r9yczxL9fL/9fj9mZmZODGarVqsnWt7229+emAx6vUeYzxmrGh9jEGl3mN+gqKrqWsuWHbzSRY1jFdsZ63o+9rGP4bvf/S4eeOABfOELX8Du7i7uvfde3HvvvQgEArjhhhvwile8Arfccgte9KIX9bSBl0gk8Gd/9md4wQteoP+Mb7jyLldGvvnNb2JhYaEto3HzzTdDEAQ88sgjeP3rX2/rmCf/LJwgOs2u4P8eJGA3zhOIx+NYXV0dWpA0yswFn06dyWROtUGZ4V9mN+3IMsawv7+PXC6HmZkZiKKIZrM57mVZ0k1cNJtNpFIpNJtNrK2tIZFIuGJdvWCceB6JRBAMBtvaCobDYd3yYGcXchJqfLyGU99t82A2465itVpt629vtMM4cY2d9ADKjZs3dunnGKw89nzac7VaPTHMbxQC1QufBWCvdeskcNrGsd/vx4tf/GK8+MUvRjgcxkc+8hG8613vQqVSwTe+8Q08+uij+OY3v4l/9+/+HWKxGP78z/9cz0Z0YmZmBq9+9avbfvaFL3wB29vbeNWrXnXi8ZlMBisrK20/4+f23t6ejaM9Pibbf0HYxji7gqdfjReAQYNg3mVIkqSR2GtGVdBtnE7d77A/wD3iotVqIZVKoVar6Xa1vb0916zPTKf3j2dd/H4/tra2EA6HXbGuXlBVFTs7OygWi5ibm9PT06Io6jvX5k5DPCiIxWK0c31GMH7u8/Pzp85KiMVifdlh3Prd7xWv7DA7EZRbTXt2YgCbHbwiLrxyXnHsfC68oPulL30pXvrSlwLQGvM8/PDDun2qn3vuI488gve85z24/fbbcdttt534fb1et9y4DYVCfW2AkrgYMqqqQpblNhuU+STjAbPdgN3od+ddhkYR6I2icLUfG5QZJzJCTlGpVHQLzsWLF3W7mpuLgM1BPGMMmUwG2WwW09PTWF9fnygLUbPZxPb2Nlqtlt5hrNVq6b/v1GmoWq3qXWN4YEAWqrOFub+9UYjylpPGLjBciHolODoLOP1Z2RnAZsyUDrJb7xVxcdYyF0Z4QbexW9T09DRuu+02S1HQC1/+8pfx7ne/GzfccAPuvPNOy8eEw2FIknTi581mk7pFuQnj7ApjJwor+hEXxmFriUQCKysrI/siDjNzMYgNyowbbFGMMRwcHODw8NCya5ebxQWHZ95SqRSq1SqWlpYwPz8/tptYP5kLY7bl4sWLp4pwc6ch3jWGT4bmgYGxkNPumojJxcoOw8+Nw8NDHB4e9tS+dNKDQa/sMI/ic+hlmN+gDQUm/XzieOW84gwqLgbhE5/4BN7//vfjta99Le64446O8dTy8jK+/OUvt/1MkiQUCgXbxdwAiYuhYC7aPm3Stl1xYdzV7zTjYZgMqwvToDYoq3UC4wv4jAF5p7kPbhYXfK31eh1Xr14FY8yxWSmjwijuBsm2mLvGWFmoAO27XCgUyEJ1hjDaYbjNzihEvdzy1itB4DiuwVbD/Pg1pd9hfpPevpXjlfOK048tyolmPHfddRf+4A/+AG95y1vwnve8p+u5cdNNN+GOO+7A1atXcf78eQDAgw8+CACn1ndYQeLCYXi2QlGUngfi9SouzIWog+zqD4IxI+DUl58LJlVV+7ZBmRmnuOCdiE4LyCehCHh3dxfRaBQbGxuuCJh7/VwVRUEqlRrKUD8rC1Umk4Esy20WKm53iEajnrjpE6djtsMY25eaW94aLYdeCaQmkXG//+aGAqcNgOyUDfNK+1Yv2qJ67eLFxUUkEhnoNa9cuYI/+qM/wmtf+1r86q/+qr4BBlyrFcvn85ienkY4HMYNN9yAG2+8Ee94xzvwe7/3e6jVavjd3/1d/MzP/AxlLsaJeXaFnUnbvdQGGKcfz8/PY2lpaey2FCda55oFk5NzEvqtZRkE4/H0EpC7NXOhqioymQwArevExsaG625a3d63RqOBq1evnjrUz4lj4oEi96yura3pQUGlUtGLf41+/GHPTyDcQ6eWt/wfALh8+XLb1HA3iPhe8MoO87jFhZleB0AGg8E2seG24+gXr5xXHDuxUqPR0LOhg/DFL34RrVYL99xzD+655562373xjW/E29/+dtx+++34wAc+gDe96U0QBAF/+qd/ive97334xV/8RYRCIX1Cdz+QuHAAq9kVdr4Up+3EVioVpNNpMMaGPv24F5yqZZBlGTs7O47ZoMyMOnNhtHX1KgDdKC544TMv7nJycrwTnLaWQqGAnZ0dhEKhsWT3jN5qYy/8arV6oguVG4LJcdsHB2HSghDzDvXBwQHK5TLi8ThqtZou6IfZUchJJu3974Tbg/JOAyDNGxiAljUPhUJjHeY3KF7LXNixq9XrdUdsk29729vwtre9retjLl261Pbfc3Nz+PCHPzzQ63JIXAyIqqqQJMl2tsKIIAiWRdJGv3gsFsP6+rordrScyAgMwwZlZpRBU71ex/b2tu3uVuOadt4JPoQxEAjg/PnzeOaZZ1y1PqDz52qcIRKPx7G2tjb2m5NVL/xGo6H78Xkwad6BHPe6idHh8/kwPz8PoHNHIW5j4PUakxo0uhW3iwszVsP8qtUqDg8P9Y2McQ7zGxSviFaOHbtarVYbyvDjUUPiok+4DYp3g+pXWHDM4kKSJKTTadRqNcf94oMySNA+TBuUmVHYooztgPvpbuWWKeLm4X5ra2ttfnA3YlyXcYbIyspKz9kWJ9/zXrJQRrsD0B5Mmi1UPKtBFqqzg1VHIS5EectbNwWNXgkCx339HQS+geHz+XB4eIilpSV9MGitVhvLML9B4ffsSf1MzNi1RQ1ab+EGSFz0waA2KCuM4qJUKmFnZweiKGJzc9OxlmRO0W/QbrRBjaJuZNiZC2M74GQyieXlZds7zm4QF1bD/QRBgCzL+trchPl9qtVq2N7eBgBXfl+6YWWh4sGksWOMsTDczUEB4SyBQACzs7OnToA2nh+9Fo46ySQHgV4SSIB2fzYP8zMWh0+C9e609v2Thh1bFL8PTzokLmxgZ3aFXURR1INVXsG/trY2lhvFafQznM5ogxpV3cgwxQWfim4cyNYP4/a7865WwMnA3K0XduP5Z86CTXLgbbRQ8Y4xfMJvtVrVJ/xyCxUf5EcWqsnFzqbCaROgjUW+XGwM+/xw28ZDP3hRXBgxtrMFug/zc0ur5EnOJJmx27a/Xq+TLeoswQeJ8d1cp1U1YwylUgmqqtqydYwDOwXd5u5J6+vrIyuw5Z+R07YoXjDMp6KHQqG+n2tc4sL4ucRiMWxsbJwQsuMWPqeRz+dRrVb7zhpxnDo+p4vzjfaX+fl5PSioVqu6hYoHnGShOnt0a3lbLpf1lre8XmMYXcq8EpgDk38MvVqJug3zM7ZKNtb5jLo43IlOlG7BTnG6LMtotVokLs4KPFsxrA4GR0dHaDabEEURFy9edL3frldb1KhtUFY4GfCpqoq9vT0cHR1hdnYWq6urA58L4wjgFUXBzs4OSqVS18/FreKi1WoB0LJh6+vrmJ2d7fu53HZs3ejFQjVJvmonmKTPzwond2i7tbzN5/NtXcq42HDq/JjkwHzSzyFOv0Kv2zA/Y3c7Yx3YacP8BsVLmQs7cSOfzu32GLAXSFx0wWiDGqQbVCcURcHe3h4KhQL8fj/C4fBEnFS9BJ3jsEFZ4ZS4aDabSKVSaDabWF1dRSKRcGxGAjC6G1yj0cD29jZkWe65q5Wbbr7lchnpdBoAsLCwMJCwmGTIQtWOVwIRp+g0lI2LUeOgx0F89266NvSLV7IvThyHU8P8BsVLmQs7nwufezNJdYOdIHHRgWEUbRup1+tIpVKQZRlra2uoVCq65crtdKu5YIwhm80ik8mM3AZlhVWLX7uUSiWk02n4/X7HM0ujFBd27VzcVuaGAMJ4XsViMVSr1bGeV1aM873q1ULFB/mRhepsYdWlrF6v62Kj35a3XgjMvXAMwHCOw2qYX6c6H+N5M2hx+FnNXDg1ndsNkLiwgGcrFEVxXFQwxpDP57G/v49QKKQHebVabaSTpAehU9DppininEECPqv2rE531BiFuFBVFfv7+8jn833ZucYtLozDCRcWFrCwsIDvf//7Y1+XmzFbqIxWB7OFyo1NI84K4wqifD5f21A2o+++WCzqLW+NVphJmpNgBxIXvWNV58NFqrGVtrlew24W4qyLC6q58BhOz64w020itRM77KPEvF7edcgtU8Q5/YqLTu1Zh7E+YHgBvCRJSKVSaDQafdm5xp25sLJxuX3+htswWx14a0q+a80tVOl0GlNTU67oFkOMFrPvnre8tZqTwMWG3+/3RGDuhWMAOneLGiZ+v7+tONy4iWGcy2I3Y3pWbVG85oLEhYcYtg2qWq0inU53nEg9jK5Gw4Sv12yDcls70H5EG/f18wL7YX7RhykuKpUKUqmUPi+ln+MYp7gwTgs32rjcGgSMW4j1irk1Zblcxt7eHkKhUFu3GB4QxGIxz+5auwG3va+ntbw11vPwa/0knPed8Iq4cMPgOV4HZp7LYpUx5Vkxq3jBzlwIt9NPQTeJC4/AsxXDKNpmjOHw8BAHBwddaxAmMXOhKAquXr3qKhuUGTsBH2MMBwcHODw8xNTUFNbX14duGelnZshpGM+5Tm1m7a5vlDDGkMlkkM1mO9rRJiWQnwT4ezs/P49AIKDvPlar1RMBgXHXmhicSTiHu7W8rVarAIBnnnmmbXd61K1LB8Er4sJtx2ElUo3F4VykWjUVUFXVM9eYfmxRVNA94ZhnVzgtLFqtFtLpNKrVKhYWFrC4uNjx+UVRnIgbjZGjoyOIougqG5SZXoNQWZaRSqVQrVaxuLiIhYWFkVyknc5cmOsTup1zva5vlOel8XMYph2N45YbsVvo1ULlloFbbguozgLGlrelUgn7+/uYn59HvV5va11qbons1s/IK+eQ24/DapifMSNmHOYny7K+4TrpGYx+bFFU0D3BmGdXOD0Uj1trBEHAhQsX9MK5TvAvktsLmbgNSpIk+P1+bG1tucoGZaaXjJCxXqSXz8pJ7AwkPI16vY7t7W3H2/+OSlwY13/a5+DGzIUb1zQo5oDAuGtNFqrBcfv1/jT42mdnZ5FMJru2LuVZr35a3o6CSf4cgMk7l6yaCnCxwe1Uly9fHuswPyewI5C4uKDMxQQy7NkVRkuHHWuNMch065fH2A0qEAggEom4WlgA3WtZ3FAv4lTm4ujoCLu7uwiFQtjc3HSsTeuoAma+/nA4jI2NjZ7W78S63PpdcyudBrWZLVTGQNIr9gbiJObvoLl1abfdaS5G++km5CRe2RBwc+zQC4FAAIFAADMzM6hWq5iamkIwGLQc5sevLZOwkWFHXFC3qAll2EXbvDNPvV63bekwTr12YxrQ3A0ql8uNe0k90Sk4NtqHxlkvMqi4ME4NTyQSWFlZcfT8Gba46Hf9bryhuHFNw8TKQmXVA99NgaQbmeTz5jTLR7eWt27JfLndTtQrky4uzPj9fiQSCcthflYdzNy62WnncyFxMYEMc3YFcK2zjc/n66vD0DAKe53AXJDOd/ePjo5ct1YrrDIXRvtNr1Oqh8Ug4kKSJGxvb6PZbGJtbQ2JRMLp5Q31ZtVqtbC9va23yU0mk7bW5ZTocTpzeVbpVPhbrVZPBJJen53QK14LCE/D3PJWkiS9nsfcPMBYrzFMvCQuvCLczZuspw3zM3Ywc3KYnxP0Y4sicTEB8NkVsiwPxQZl3HkdZNCaMXPhFow2KHNx8KS0zjUWyhsHGNqx34wCu0GpuV3usArAhpW5cKJNLuFurCxUPJA07jyShWpyGSQwN2a+jN2EzM0DjAFjNBp1PID2irhQVXXijwHQPo/TRLd5I0NRlLYOZk4N83MCqrnwIMO2QTUaDaRSKUiS1NeAMiNOFvY6wWlD8SaluxUPjhVFwe7uLorFIpLJJJaXl12xy9Np2nknjO1yp6ensb6+PtTdGafFBWMMuVwO+/v7A7XJ9WLx9LgYVVc0cyDJdx55ZgM4exaqSc9cOLl+q25C/PwwTn92uuWtV8TFpJ9LnH6GAfp8vrZhft0mztsZ5ucEdkQfZS4mgGHPrjg6OsLe3h6CwSC2trYQDocHek63ZC462aDMTMpcDkEQoCgKnn76abRaLWxsbCAej497WW3YaZfLM0nd2uUyRcXRo0+h8K2nAQDxGzaRfNn1EHz2AzUng3ijwHOizsVt4oIET+8Ydx4XFhY6WqiMO9ZWFiqvBIbESYwBI98o5AGjucCXZ7/6aXnrlXPIa+JikGOxst+ZG0+Mql2yHbsa1Vy4GOPsCv6hOnnCGAMkJwto3SAujDMGTpuRMCmBVLPZhCzL8Pl8bVOe3UQv72WtVkMqlTq1TStTVDz54c8i86VvQZFaEADsfPp+LNx2A571f/40xID9r7wTn3Oz2cT29rZjAs8LN1G3Mc7vs9lCZZzsyy1Ufr+/bZCfG/zUTjDJ5/KoAlpBEE5Mf+7U8tbuOeKloNwrxwE4970wN55gjLXVa5jPHactmnYGAtbrdYii6Mo4xS6eExfDtEHxAE9RFMd3wMctLiqVCtLpdM+zHtyeueC1MOVyGYIgYGtry7UWi27iwpgl66VOJHv/D7D/xUcRmI0hMqPtfrTKNRx85dtIvux6LL7mRY6trVdKpRLS6TT8fj8uXrw4cJbPqXUR7sRqsm+9Xte9+EYLFb9pT+q5MKnr5owroLUq8OXBolWnsm7DHr0UlHvhOOxMtO4HY0YUODnMj587wWBQF6mDDAq1a4uKRqOe+Bw9Jy4A5wfiGX3ikUjE0TkCnHF1izLaoGKxGNbX13vqzuHm4K7ZbCKVSqHZbGJmZgblctm1wgLo/F6qqord3V0UCoWe60Ry3/ghmKwgMHMtrRqYjkI6LCH3jR/2JS76ZdT1IePGzd+JScZcvGm0x1QqFQDA008/rQcMsVjM1ROhCecRRbGt5W23TmVWnnsvnCteERejtqmZ2yUbB4WWy2UcHR0B6L/Wx64tyonNNzfgOXHhdKGx0ec+Pz+PxcXFoQSq4xAXdmxQZtyaueAtgfkueb1e13ci3IpV5y1uI5IkCevr65idne3puVhLBqw+Q1GAKsmOrK0Xeq0P6RcK5M8uRj91sVhEJpNBMpnULVSHh4cTY6Ga9IDQrevvddhjNBp15X2sH+zYb9zMsDMXp2E+d6xqfXqpB+PY6RbVaDQ8UW8BeFBcOHmhM1qFrDomOYkgCCPtwMSPDUBPNigzPLhzy82FMYb9/X3kcrm2lsCNRkP/vRvWaYU5UDbaiOw2C5i94SIO/v4xKM0WfCEtA6U2W4DKMPviiwOvrReMc0SG+b0hcUHw73QikWjrf29loTJaHNx6LZg03Hxd5VgNe+T1GtVqFc1mEwBw5cqVtnPErYK0E5PwWfSCmwrsrWp9rOrBus1msduK1s68JzfjOXHhBEY7hx2r0KCMIhvQrw3KjLF17rgvAsbJ6CsrK0gmkyfS3aqquvZmYRRqmUwG2Wy275kpCz/2QmT+7js4evhJMAEQA34IDIjfcAGLt93Q99p6pVAoYGdnB6FQaCj2QeO63IZxIKIb13cW6GahKhQKektKslCdXYxdgubn55HJZFCpVBCNRi1nJPC2yG4/R7xy3eExkBuPpVM9mHmYXyAQ0M+xXj8XxhhqtRplLryKJElIp9Oo1WpDsXN0Y9jiwmiDGvTYjAXo46xnMA+TM38x3TY/xApRFKEoCp555hlUq1UsLy9jbm6ur89GlWSIwQDUloxWqQbR58PsSy7i2f/XzyEw3d+gvV7eO1VVsb+/j3w+j9nZWayurg71vCBbFGGk03fF3JKS7zpWq1UcHh7qFirjIL9RbkJMekA46esHtHPH5/NhaWkJAHQLlVGQ8pa3bhakXvgsgP7mXIyLbsP8+IwNADg6OoIsy12H+fFmRE4PxP3oRz+KBx54AB//+Mc7PubTn/40fvu3f/vEz7/0pS/h/Pnzfb2u58TFIF+uUqmEnZ0dCIKAzc3NkU9JHKa4GNQGZca4SzsOjNmlqakprK+vW/pNx73OXlBVFeVyGT6fb6DzjjGGJ/7tZ5D7+vcQXkogtrkMudJA9UoGqf96L571jp+x/Zy9BPGtVgupVAq1Wu1E5miYDPqZcitdpVJp65Xf703NCzf2ScPOOWC162gVCEzajvU48UJAaz4GKxsMt9kZBekw2pYOghc+C8Bdtii7mIf5NRoNbG9vw+/3Ww7zCwQCaDabmJ+fH8oAvY997GP48Ic/jJtuuqnr4y5duoSbb74Zd955Z9vPB7Fojf8b4QJUVUUmk0Eul8P09DTW1tbGcrHot3i2G07ZoMyMs3WunQzMuLpw9QLvQtZoNPQ5HIN8NrVnMjh6+EkEkzN6t6jgbAxQVRz8/WM4/5bbEJqfsfWcp4kLPskdgGXmaFgMeuORZRnb29uo1WqYmppqCy6NQqOfKa5eucl7HXOHIW6h4tYY4471IEPaToPOlfHS7ftqFKSn1fSc1vJ22HjluuNmW5RdeBY0mUwiGo22ZcVyuRw+9rGP4Ytf/CJWVlbwvOc9DwAciT0zmQze+9734pFHHsHm5uapj3/iiSfwnOc8BwsLCwO/NufMiwtj29JR7rpa4XTmotVqIZ1OO2KDMjOujIAxmO11HgfgvsyFcRhjIBBAKBQaWPQ1DopQGhKCyfb3xBcNQTqqQMqV+hIXVjDGkM/nsbe313WS+zDp9zM1F5wHAgGIoti1owwPLt2wQ+k0XriJO4GVhYoHkXzQltFL7YSFym3XJbt4IaC1cwxmG4yxbWkvLW+Hybgtyk7BP49JP6+A9s5XVsP8fvZnfxaVSgXf/va38ZWvfAUA8IUvfAHb29t4xStegVtvvRUvfelLbben/d73vod4PI7Pfvaz+MhHPoKdnZ2uj7906RJe97rX9XeQHfDendIGhUIBu7u7ettSp71udnGyW5TTNigzo85cMMaQzWaRyWRsBbNutEU1Gg2kUil9WnWxWHTkfYysJOGLBCFXGgjEr1mr5EoD/lgIocVZ289plbkwzt+Ym5vD8vLyyG8E/b7e0dERdnd3EQ6Hce7cOfh8PkiSBOCkHcK4Q8kL9fgOZSwWO9F1aNJvhm76jowbqx1rHkRWq1WyUBmY9GMeRCB1annLd6az2axeQG7Mfg0DLwg9wDsiCejeVlcQBNx444248cYboSgKPv/5z+MP/uAPsLq6iieffBLf+9738Od//ucIBoN4yUtegltvvRVvfvObe7Iq3Xbbbbjtttt6WmM+n0c2m8XDDz+Mj3/84ygUCrjhhhvw7ne/u6esRyc8Jy56+XIpioK9vT0UCgXMzs5iZWXFFZ2ERFGELNufRWDE3OlqY2NjKLuto7QbKYqCdDqNcrmM+fl5LC0t9XwRdZu44HM4AoEAtra2EAqFUCqVHFlf9NwC5l/1POz/7aNgigpfLAy5XIdSa2Dlp34EwYR9gWkWF5IkYXt7G81m09b8DaexW9BtbFVsLDjvdP6ap7h2GsplDBqMr0V4CysLFReeRguVuff9aUx6QOiFc92pz8BqZ9rYSSiTyQBwPvvFmfRzieOV4wB6rx/x+XxYXV0FAPzsz/4s3vrWt+LRRx/FAw88gPvvvx8PPfQQHnzwQeTzebznPe9xdI1PPPGEvoYPfehDqNVq+OhHP4pf+IVfwOc+9znMz8/39byeExenUa/XkUqlIMsy1tbWkEgkxr0knUFtUcO0QZkZld1o0JkJ46wNMWIMbuPxOFZXV/WbipOdj67/9X8CMRjA4b2PQ8qX4Y+GsPJPX4XN//Uf9fV8xrUZO3PZnb8xDHp9z4z1Ff1aH807lFaWGf55VqtVTE1NeWb3bRIYdTASCAQwOzurZ7n43IRhWqjciBcCQTsTlO1g3qDo1EmIZ78GmcEyyUXQZrxwTnHsDASs1WoAoHeUuvXWW3HrrbfiXe96FwqFAr7zne/gBS94geNrvOWWW/DQQw8hHo/rP/vIRz6C17zmNfjUpz6FX/mVX+nrec+MuOAe8f39fYRCIX3X2E0MIi4qlQpSqdTIOl0NO2g3fl7hcBgbGxt9zUxwQ+bitG5KTooL/1QEz373m3D+X9wOKVdCeDnRV8bCiKqqODg4wMHBAaamprCxsTH2QKnX98woTp36XpgtM4qioF6vo1AooFarYX9/H8DgheHE6bhh55x77CORSNv5wMWnMYjk5wO3UHkpkJpkRvEZmDsJWWW/7Ex+NuIlcXFWbFFmjOLCzOzsLF796lc7uzgDRmHB17C+vq5n2/rBc+LC6sslyzJ2dnZQLpeRTCaxvLzsypO3n25Ro7JBmRlm0G4sdh708xq3uDCKvk7dlIYxsyG8GEd4MX76A0+BMaaLi4WFBSwuLrriBtbLe2asr+hXnPaCz+fT7TK1Wg0bGxt6ZuMsFYYTGvx8sLJQHR0dIZfL6RYqVVWhKMqYV9w/XhBH47o3mLNfxhksxsnPxhksna4bXhIXXjinOHY+l2G0ou2Fu+66C3/yJ3+Ce++9V3cjVCoVPPPMM/i5n/u5vp/X83e4arWKdDoNVVVx7tw5zMzY65YzSuxmLow74qMe+DesmgveF1qWZWxsbJxQ1HYZly3KWIB+muhz60C4RqOBfD4PAK787nR6z9rqK+JxrAgShMe+BlX0QTj3LAjzq0NdVyAQQCQS6bswnPAWnSxU1WoVjDEUCgVUq9W282HcmcFeGZalaJS4IZg9bfIzb3kbDAZ1sWFseeslceG1zEWvna+4uBi260RRFOTzeUxPTyMcDuM1r3kN/viP/xi/+Zu/iV/7tV9Do9HAnXfeiWQyiTe+8Y19v44nxQXPAPD5DjzFM6ydS6ew0y2K+9/HNfCPf2GcDNr5TnMwGHTMtjaOzIXdAvRhzDcZFF54zi/ybhMWnd5P4wyUlaVFxB+/D+qlRwFZ6wiFb4YhvuQ1EF9220huxP0Uhrv9OkX0j9lC9eSTT+r1OeZZK/y8OatdqEaFG8SFmW4tb8vlsn7d4PUa/JrhhaDcjZ9Hv9gRSt1sUU6yt7eH22+/HR/4wAfwpje9CSsrK/jLv/xL3HHHHfj5n/95MMbwyle+En/1V381UF2lJ8UF39GvVquusnKcBs9cdPty9TqZehQ41TpXVVXs7e3h6OiorZOPE4xaXBgzL73u9rspc8EYQyaTQTabRTweRyQS0WsI3Ib5PTPXV0S2fwDlBw8D4Rgwc9y+r1qE+sjfQVzbBFYvAnBut6+Xc82qMJyLDXMh8KATwwn3wzsM8faSxlamZgsVPydGPU+mG14IBCfhGLq1vM3n8/o1J5fLQZIk150ndvBCNoxj59wali3qgx/8YNt/r6+v49KlS20/e+5zn4u/+Iu/cPR1PScuVFXF5cuXwRgbynyHYWJMcVqdkOO0QVnhxI67cYjhsLp3OT2csBM882K3YYBbxIVx1395eRlzc3MoFAoA3HcDNq/Fqr5Cvvw4wFQIkWtZPRaLA7k9qFd+AN+xuADGc3xWVgguNJycGN7LOojxYXz/zbNWGo2G5SA/ow9/3IHYpJ8/bru2nYZVy9tisYiDgwMoijLx3cpUVfVMTZqdzMW4ai6GhTc+QQOiKGJtbQ3hcHjiTlBjfYD5hBy3DcqKQTMX3Hoz7CGGww7eVVXF/v4+8vl8X5kXJ4cn9kutVsP29jYYY5bnl9tuwPwz7TS/AgDQrAGi7+TfAUCrqf/MyT73g2CepXBWJ4afJbp9740WKqC9lWmlUtGFP7dQxWKxkXclG/d1ywncdm2zCxcbALCysgK/39+xW9mgLW9HwaR/Hkb6sUW5IbZzAk/emWZmZlznYe8Fc5G0oih49P5v4+H7H4WstPDyV78Mz3vBcyHAHSnDfjMCqqoik8kgl8thZmYGa2trQ91VGaa4kCQJqVQKjUYDq6urSCQSti+M485c5PN57O3t6VOrjen0cXfb6oaqqnjmmWe0+gqrFr9rW2A7T4OpCoRjkcHkFiCIEJY2hrYup96rQSeGj2vdo8RLgchpGFuZMsbQarV08Wm2UA17GjTHC++/V44B0K7XVt3KjLM1eMtbY0a015a3o8BLBd1usEWNC0+Ki0nFmLmo1+p4/7v/Nb71wHegqCqYyvDZj38BwXAQkWgYL775RfgXb/95nN86N7b19hMUG4Pxfgea2WVYBdPGoXKDZF7GJS6MtS6dWv66VVwoigJJkqAoSsdMnvi8m8EuPw6W2wMLRQFVBVpNCGtbELZeOIZV9w8VhnuTfgNbQRC6WqiM06DdZKFyI14TF2YCgQDi8Tji8XhbnVetVmtreeuWjKgXPg8O2aI8xqSemMaai0/+x7/GQ/d9E6FQELHpKWT3c2i1ZEiSBKaquO9LX8cPH7uEOz/+QSytLp54rmajib10BvHZaSTmO9cxMMbQbDQRCAZsZw/sZi6Mwfjm5ubIvkRO244YY3onMieK6o0B/KjOXaPI61br4sbvUqFQQLFYhCAI2Nra6hhECzNJ+H7yl6A+9nWwK98DfH4I170I4oteBSHo/HTxUb5XVBhOGBmXhcorgeCkH0Ovw9pOa3nLM6LBYLCtXmOU1w4vZS7s1I/U63X4fL6JLcQ340lxManwL9Q37nsIn/jof0NLaqFRa6JwVAKOY2OmMlRKVQDA9tNp/N9vfz/+t3f9S2xefx5zi9rF4lN/9Vl8+uOfxVG2gFarhenZGbzqx2/BG//5G7D5rAv66331C1/DX/+nT2Pn6i5i01H8xM++Fm/+5Z9FKNTbjmevGYFxd7hyMjMgyzLS6TQqlYpjRfWjFhd8sF8vGRc3ZS6M9RWhUAiKopy6Oy/MzsP3oz8N/OhPW/9+CO/3qN8rtxSGE+6hm4Uqn8/rFipjVqOfoMYL4sIrxwDYv551a3lrJUpH0RrZC58Hh8+56IVarYZoNOqZYydx4SIURUEhV8S//u0/QUtqATg9UPn+t36Ad/zz3wIARGMRPOuF1+PpH15BqyWjWW9q6fK9Q3z2rrvxD/c8gJ/552/AfjqDyz98Gpd/eAWiKCISC6OQL+Gu/+e/Y+fqHn77Q+/sab29ZC6MHYjG1eHKKVuUsdXp+fPnMT097cDqRhfAM8aQy+Wwv7/f8zR3t4iLtvkVKytotVp60Ey0Y7cwnIrCx8OoBp9ZWaiMu9XcQjXO3epxwZtCTDpOnUvmjGi3uh6jKHXqHOafh1cCbDttdev1+tCa2owDT95VJvHE5Jahv//cfSgXKn09R61ax7e/8VjbzwRRgE/0QVEUHGay+I93/Oe234s+EcFwENPxKdRrDXz9Kw/gh48/gQvXnUM40t06clrQXq1WkUqlAGCsbYGdsEUZi555q1OnGEUArygKdnZ2UCqVehrsZ2acN2Dz/IpYLIZMJuPYmpyec+E2eikMBzS7GWPM1Z1kiMEx1+9wC1W1WtV3q3k27LRMl1cCwUk/hmEI1U51PVyUcvul3+9vy4AN0pyFH4dXhK3dmotJGp1wGp4UF5OEcWjZ1NQU7v/Kw84+v8qgQDn+j5O/VxUV5UIZgUAArZaESrGKf/VP34FINIIbb30xfvHt/wxbz9m0fO5OQTtjDNlsFplMBtFoFBsbG2P1EQ5ii1JVFbu7uygUCkgkElhZWXH8wjdscdFsNrG9vY1Wq4WNjQ3E43HXrO00CoUCdnZ2Tog6NwcDbt4JtSoMLxaLyOVyqFarKJfLbY+JxWJUGD4kRpW5OA0rCxUXn+ZMl9lCNeniwi2fwaCM4nMwT5dXFKUtA8YzycYOduFw2Nb90iufB8euuFhcPFk/O6mQuBgjkiQhnU6jVqthaWkJD9/7KCp9Zi0GQVVVZDPZa+tqtiA1W/jqF76Gxx7+Lv70rn+NqUoL1e1DlH+QQmP/CNGNBQReeR3URPugOFmWsbOzg3K57Jrp6P3aoiRJwvb29lAH/AHDDeBLpRLS6TQCgQAuXryIcNheIfO4xEXX+RUYf/ter+D3+zE1NYVcLqe/x3wX+/DwEIeHh64uDJ/04NZtGHer+YA2HkBy8Qlcs1BNuq3IK8HsOL4H3VreGjvYGZsInNbyttfC9Emh18+Ff8+80ikK8Ki4mIQLhbmNqSqr+OBv3TnuZbXBVIZopoKv//QfIK76oNQaAABBFCH4fRA/HsLSb70BuP56AFpBUiqVcrwmYVBEUYQsy7b+hgflwx7wB5ycb+IExiL6QWaJjOO7ZK6vGEW7YicQmIpA5QhCaQoIrQATsGbg2mdspzB8XEPbvIib3z9jFmt+fv6EhUpVVeTzeTQaDf1xk3ROkLhwDnPLW0mSes6AcbzyeQDX6kd6EUrNZhOqqlLNBdE/RhvU9PQ01tbW4Pf78cHf+jdgqrt2gOLMh59iCQTKTSii71rHKkUFU1SozRb2/vAzeP4bfhSlehX7+/tDqUkYFDu73MagfHp6Guvr60Md8MfXx1/bCYwdrZaWljA/P9/3xXrUmQur+opO63LVjun2E/A/8ndYzqQReCwMLJ8HbnotMLc87pX1TbfCcN5xyE398ScNV52/PWK2UD355JP6d9QqgHR7wwCvBLNua9/Kp4aHQiF9o8I4h6VTy1uvfB6AvSxMo6Ft3FLmgugLPlugXq9jeXkZc3NzumXnf/73L457eSd4PqIIQYQCBnQQPmqpjn/4p+/H9D+7FVPRKIpf/j72rh4gtrWC8//sx5C86VkAAKXRApgKXyRk+TzDpFdblHHHfNCg3O76AGeCDWNw7kQR/SjFRaf6ik7rcnxNjAE7l4Fnvg8068DiOrB1AxA1vYeVIvDUd4DsDhCOAbPzwLfug9ioQQ2EwPxBIPUkUCkAr/+lk3/vUk57Pzt1HDLaZZycGE64G0EQ9MxGIpHQA0grCxUXn5FIxFVBsFeCWTdkLrph7DAFWDcRAKBf81ut1tBb3g4bO+dWrVYDQOLC9bjxhCyVStjZ2dFtUMaT6P/5//2nMa6sMwnmQxACBAhgADq9q7XvplH7//7/kVG0AF7w+1B+cheHX30c1//GP0HhO1eQve+7AAOSNz8L1/+fP42Z526M7Dh66RZVq9Wwvb0NxtjIO1s5FcAfHR1hd3cXoVAIm5ubjmSPRiEuTquvGBnfvhf49n1ASwJEEbj8OPDkd4DX/jwwfVxvc3QIfOW/AvkM4PNpU7/rVYAxsLUtqLUaEI4CkRhwlAGe+R7wvJeffC3GgEIWUFrA7ALgn6zBSWa7TLeJ4VxsONmy0ktM8ntiDGqNAaTxnBjEgz8q3LCGQXC7uDDTaQ5LqVQCAOzv7+Pg4KBtNs+kXT/sZC6qVW12GdmiiJ7pZIMy8sk/++sxra4LDFhGwCAouskLAMq1zABTFO0fleEH7/9vEAN+iAEfmMpweO93UXz8Gbz8rv8LsQtLpy6jVaqheVhEaHEWgen+vnjddrkZY8jn87ql69y5cyPvbGWczN4Pqqpif38f+Xx+aMH5sMRFv/UVjmcujg6Ax+8HfAEgPg/ILaBRAfauaILjR46H8D32NSC/DyRXNAECAM/8QHu83Lr2fNxGWDo6+Vr5DPDwPcDhDsBUTbi86FXAxRc4dzwjptvEcKvC8EgkMnS7oduZRFuUkdN2Zs3nxGnzVsZhq6PMxfgxNhHw+/3Y3d3F6uqqfg0xtrw1nituv36QLYoYGp1sUEYeffA7rrzJxJmIOZiKrcAgdBMY1x6o/aulAKoKVRSg1CVttxZAY7+FK//pHrzg9/95x6dQGi1c/vefx+5nH4JSb8IXDWH1n9yCrf/99fCF7AX/nWxRqqpiZ2cHxWIRc3NzWFpaGsuO+SDZgVarpZ9jq6urSCQSjvc673dtp9FrfUU3HLup7l0BmjUguQwUDoFiTssqKCrwza8AF1+o2aTSTwGRqWvCAtCyFMUs0KgCYpAvDAADoqamBo0acN+nNYEyndSyH6U88MDdQCgCrG0NfiyjRm4BuT3tmJPLEIIhKgwn2jB68I0WKisP/igtVCQu3AX/PCKRCKampjA3N6dfP4xZMOCaBdONdjugP1tUP/dAt0LiYkh0s0EZ+ZPf/ciIV9Ybr8ZMLzKiO8cBPWu2d2piiortT96L5/3fPw/Rb7378MS/+RTS/+PrEEMB+CJBKHUJV//qK1CbLTznt37O1jJEUYQqK0j99T9g97PfgJQtY+q56/D92PXwX7eI9fV1zM7O9nWITtBvAG8cUri5uTmUXY9h3bB4fUW/Fi7H18Xf+0pRy2IIAhAMaxapVhP4h78BfuIt1n87NQuU8hBqZSA6qwXb1az28wvPbX/s9iXt+ZMrmrAAgMQikN3VLFgrFwBF0WxS/Ryj1ACufB/Yf0bLnqxfB5x/jvb/h8HOZeCRvwdKWe09nE4AN/wIsPl8/SHDLAyf5KBq0gPbQdZv9uCPy0Llxo29fpjk74ERqyF65utHJwsmP1fc0rHMTuaiXq8DoMyF6xnnSWX0j3eyQRlJX9kZ4ep65wKs5yH0nL04BSbJ+Oprfgc3/JtfxtzNzwYAtIpVHH3raSj1Bnbv/iZ8kSACM9qXTQwF0CrUsPv5h3Dhl16L8KK9QXD5j92H3XsvaTGkX0Tly9+C74Hv44Uf/CXMPm924OMZBLviglu59vb2EI1Gce7cuaHZCZzOXDhdX+HYTXVlEwhFNcsSY0AwpP1bVbWaiHIB2L0CrF8PXPrmcfbiOGBXFe0xkSkEykUIcgNILgI33gZUi0C1BMyvAoGgVuTNoAkLxrTC8VpZq9v4wUNakbgiA3MrwPNv0cRGrzTrwL2f0gJ+QdSe/+nvAtfdALziHzsvMApZ4IH/qa09PgdAAMpHwINfBGJxLdNjgd3CcLuDuIjR4sT3r1cLlXEStBPXvEkXeBy3dYvql14ar9ix23VqeTsK7IgLKuieIMbRqrIXG5QZFc7NNnAMBuxDwmYHgeEU9e1DfOOffhBTz15D8qXXYf+eb0Eu1qDKCqAy+Gdj8MfCkGtNSNkSlKbmaf/B+/8bXvCHb+m5BqPxzCEq9/4QwXAQiAShyDICsRDYURWp//wVLP/oC8d6c7ETwJutXMvLy0Ndu5Piwsn5FU5bv1hiEXjBK4C/++9a5qDZ0ILzUARILADFPFCvaLvyhzvA0T4g+LSaiUAIuPUnIW8+HwePP4qFxSVEWAt49O81MQEAM3PAja8BYjOa3WrvKlDOXXsddnwdqFeApXNA6gntdX7sZ3sXGJcfA9KXtfX6jzNBjRpw+Tta9mL9OsfeMwDA1R8ClRKwsHYtyzK7oAmkK9/rKC6MUGH45Aa2w7q/WlmojJOgzbaYQQSoV8SFlzIXvAtZL3Sy25k3KwKBQJvdbhT1GnbOLcpcEB3p1QZlZnVjFduXU0NenX2ykHEBXUu4HaNyaQeVSyczOHKhikqlrs3/4PXkjGH/bx9Bq1jFTf/pNyD00onh+ykwSYYyEwFkGX5/AH6/D/KUispTu5DyZYTmZpw/sB7pNYBvNptIpVJoNpsjs3I5JS6cqK8YdF1MboFdvQQUDoDIFIQLz9UyENoTAi/5MWD7Ca0VbSgMhKeA6Vntd4IAzCS1trP/+F9ogfzhLhCJAheeD6xcgKAoaMytgfkB3P8FzR41Mw+AAaWctst/w49qO/7NOvTiJP2gRK1uo5jTMiS5XS2b0au4SD0J+P3XhAWgda4q54H9q9fERb2i1YiEopoY6JdaSas9Md48BUF7/bJFIXsP2C0Md3Lw5KiZdEvOqAJzURQRi8X0a0Y3WwwPIHu1UJG4cBeDZmDMHct4y1t+vvCWt+FwuE2YDuO949cmakVL9IWqqshkMroNyu7Qtdf+k9fgL/7tXw1xhfaJMiB4PN/CPxJ50RkmHwcPogAwzR7lCweRf+gJ5L5xCfO3Prf7EwBQfceBqKIiFA5d686kqhB8PtsF4k7TS6BcLpeRSqXg9/uxtbWFcHi4WSUzgwRCg9ZXOLEuVilA+dInwfav6sXWwswcxNf8HLB43BZZEICb/5EWGEsNbT6F1NSsTYsbwDnNvofYjNbdqQO+7UtaxmB+9VrgnVjSMhH3fVrLXAjCtToPjnicCSnlgEJcEyCXH9fsUUvnenxDOvxcgFY78uCXgO/er1mxBBFY3IDwyjcAsLih5/aB7R9qYmR2Uct+xAwifCap2caYqj0XoB1TSxpMtPAl9zgxHNAGuFFh+NnAyhbDz4lsNgvGWM8WKhIX7sLp4zC2vAWu1XvVajUUCgXk83mIothW2+NUZpQLJTuZCyrongBGYYviNqhGo9GzDcrM7FzvtQOjYgVBXN/FEuVU3cXJ523H/Api0A//VASqokJpSHjkV/4dQgtxrPzUzbj4v71Or8/Qn48x5HI5SJuz8M1EIFSaEI6DcrUlQ6k2sfKTN8E/Nf7e0p3OV8YYDg8PcXBwMLKJ4eZ18XXYZZjzK+x+z9SH7gHbeRpILEAIBMFUFSyfgXrfZ4Cf/tVrcyZWN4Ef/Rmt/WwxqwX8Wy8CXna7VuBtRmpqNQ5SQ2thyxiEahHw+TUBIctAtaAF3NWiVp8QCAFBQQvw9bP82BolCFrNxUFKG1zp8wF/99fAS28DnvWS7gd57tnA7tOaiBBFLeCXGtqxLZ0HHv6y1gJXkbX6D1UFdp6C/4ufQPClrwdgsDE9/T3gm/cAtYr2XEzVBgf+yM9otitAExtPfEsrRp9OamsvH2kCZAhtda0Kww8PDyd2YvikB7ZuWL/VJGhjDc9pFio3HIMTeEVcDLt2xFzv1Ww2dWHKM6O85e2gtT12PhMuLmjOBYFSqYR0Og2fzzdQp55EctbZhZk4NSgUcEIoZCFBBYPfajdzSFit0jhZIzgbg+D3QZVVyMUqoDKokoz6Xh5P/9nfInv/93HLXb+pZyEURcHOzg5KpRIS60to/uKrUP34A2geXtvtnH72Gq57+08N/dh6wUpcKIqCdDqNcrmMxcVFLCwsjOUG0o9Qd7K+otOagN5ED6tXoT7zAyA2DSGgZU0EUQSbnQcrHAJXfwBceN41gXH+OcDG9VoRtz/QvltvZO8Z4B8+q9mYmApfIIi5mRWoC0uA8rSWvdi/CkjajUOrr1ABMQT4Q9DPbp7F4AXkgDZvQ1SB+TXtb759n1Z0LoramkIWN6GtFwI//KZ2PHJLe/5IDHjhKzWR8+jfa9mQcEQTOKKgiadyDtPb3wee92LteRo14Ntf1QQRr6dQVU1EPP51TXwBWjesH/lp4Fv3ar8DAxbWgRf/CJA8fYbNoASDQYTDYTQaDVy8eJEKw0eMGwNzo4VqYWHhVAuVHeuKm/GKuBjlcRgzo7zlbb1e18XGoC1v7QglakVLtNmgZmZmsLa2NtBOcnRmOCdTz8Eg0zIRRpFRFIACayEGcSgZCosldP2dAICpWrZErja0HV1ok8CVhgQoKgrffAoPveUO3Pjv/xVYLIjt7W3IsoyNjQ34fD6UbrkOz77tZuS/+l20ClVMXb+KxdtfDH80NPTj6wXzLI5Go6Efw/nz5/W07rjWZgdjfcWwp533dJ7LktbRKWD4rBnTujjlM8A9n4SQXAauvwF44a3HgbfvuANSB5p14Gt/o1mY4vPa4xtVTO0+AZZIaN2S0k9oQX4woq0hHNH+rtUCfEEtu6G0DPao49kYvKvTzJyWJRAErVPV//xPWqbE59ME0A0/qtWEcPIZoF4DQjEgBADHdqXMttY1qlwAmKKJh5YERGeAQABoNREsZq69lwdprSVvcumarUsUgam4JpbqVU20AJr168f/F21WB1O1NYsGi1S9qomY8PCuc7wAdFILw92wBq/Si4UKADKZjH5euH04mxUkLganU21Pp/bIp7W8tSMuyBZ1xjHaoJzajd1xuBVt31awY5EhCAIgAI+wGtYH6BbFTvy/wWRKq1SDGPSDScdTkAUBSrXR9pj8w0/iwX/5b5F8/5sQioSxtbWFUCiEarUKAAivzWHzf/1HA6xieBizA8YaBX4MblnbaQyzvsK8pp6JxSHMLoAdpMBCEU2sZlLX2s5WClrQXzjQbD0/+sbTZ0ykn9KExezCNTEQjkH1FRHYf0ZrQ7v3jJZ9UBVtmF5yCTjY0Qqs1ZaWgVAVLSj3+bXi8pakzYqYX9EKrhVFK5zOHwDFQy2jAWiZguwe8Pp/qbXNBbSshVQHNq67tv56TctkTM9qoqnV0NaryNrQwEAIEAA5Mo1rn9S176zpXdd+Zz4XBOGkEDtMA999QFunqgLL54AX/1h3weYgvRSG+/1+PZiIhMPwtWpAs6q9x7GEJsx4fYzP+bosKugeLVYWqsPDQxSLRTQajbadamNnoUk4Pi+1onXLcXRqeVur1fSWt7yAnJ8vxpa3jDHbmQuyRU0ATl8QnLJBmbnns3/vyPMAztys+M7BDhoooIVZ2L+pMsP/6gHJoJUaKoPaaBkXavmY8uNXkfhWGltveZ3+xeb/dnNnGZ652NvbQy6XQzwex9ramisutL2Ii2HWV3R93VYT6t4VQGpAWFiFYFFILIgixJfeBuUr/x3I7YOpsiYswKAGw1DEAASpqQXBTz0O9uyXwb98SgF1s6adg8bZEYIA5vNrdQ6JeW04XiCoBf88a7K4rmUxYtOaVcm/oFmP1q/TRMXeFa3eoVYB0k9qVqqWBO07FAR484GWpLV7vfoD4PoXa2vJ7mrdoYzXPlnSsieKqn0VlWMxI/i051YUIDqF6tqzr4mL2QVtvcVDrRAd0P6mWtQyJpFTdteODoF/+BxQONDqvBUZ+P4usHcZ7HVvARIr3f/eYToVhuuZjUIeido+ptQqAgKDzx+AEIoCwSgESbvps6k5sPkLWlaIADB54sKMKIp6V6kLFy6g1Wrp50WxWEQ+n3dttssMZS6Gi7nlrXE+T61WQyaTAQC9k100GoWiKD0fS6OhbZSSuDhDOG2DOolDw8kceh5A+4KXIcAPoaMk6E0qCIZ/H1s+LP6G/7bbM/TEcava2le+D/EX//G1H/foz68+k0H+4Sch+ETMveI5iKwk7bz6QAiCgGKxCFmWh1KjMAiniQun6iuYImsBbygC4ZTvmCAICB3tQ/jOF6AUs1qQHI5CfM5NEG/9xxB87Zc2cfN5wD/6BajffQDsuw9omS9/CIo/CNHng+DXgm25XMDhDx+H3GTddy8Ti1rmodnQ2tYCAGPwSXWoS+tAchmIJ7VAO3psaWs1tfqMuRXg9jdrO+PxuWu/BzTxcflxYPeyltXQnlj7l6oc10xEteLyekVrnXv9izVBEZkCjjLt65QkLbivHGnZEd9x1gLHxeOxOagvey1q8Q0kVEXLNjz5LS0rU8pr8zim4ppASixptjHje8GYZrviRe3JZeDqJWDnKQiioP1dbFqrETk6gPCdvwN75c+1W9RGjLkwXNm9BKFaRVMMoKIwiA0ZsdI2BFGAPL2EQCAAMZ8GGhWwcy92bO2THpx7AWMwGwgEEI/HEY/HT2S7stlsW7Evvza4xULl1qDcLqqquroJA8coOgG0tbw1drITRRHZbPbULFitVkMgEHA00//Rj34UDzzwAD7+8Y93fMzR0RH+8A//EPfddx8A4Cd+4ifwO7/zO45snrv/Uxwjw7BBmXnla1+Bxx7+3uBPNIQM+z+ggNdhvo+/tBYR/DccwfT/T+sW1dPLAsj+w/dwz8t+A1PXrWLtp2/B/E++FKyldAyQGWO4/O/vxvZ/+SqUurZL7J+KYOt/fz3O/fyr7a7CNrVaDZIkQRAER2ZADINO750T9RVMUcAe+weo33sQrF6FEJuB+MJXQHj+LR3nmAhSA/PfvResVYeQWNAC51oZ6mNfgzCTgHDDyVax4rlnQVi5gFYmDWXvChgD/MadSFFEIODH7NIKSsGgvntpmfpeOq8VfT/9PUCKaEKjVoHqD2ptW7/5Fa3WQpG1oXLNOlAta8F4PAl852vALa9vFxaAdhyhqDYrQpG1521Uj4u9FS2Al1taXYPKAFW+9rfXvQj4xt9qtSTRae3xpdzxsfm0TIlwXMStKlpdyJv+D6hzq8CVK/Bf+ibw/W9oQq1RO37+pmYVm1/V5oDMmbIO339Ia23bagIQgG99FSgXIIBptRY1n1YnklgARD9QzEEoZcDmemyt6xSMWVvdVAWByiEQicIfmkIUgFrOQ5BFqAxo1BuoSCp8og/RwiHUaAr+pYuuyCiOGy+Io05BuVW2q1Ox77gtVF74HDiTKpLMLW9brRbS6TQYY3rLW6Mg4V2ouDit1+uOzrj42Mc+hg9/+MO46aabuj7u13/919FsNvGxj30MpVIJ733ve/G+970PH/rQhwZeg2fFxaAnaLFYxM7ODnw+Hy5evDi0dNWFrfMDP8ewvLuLCEIFg892mG+SCm3LO1mNAUHLgTh2SVEYpMMS8ocl5L9xCeLv3QXmE3B03Qqe9fY3YPkf3dj28IO/ewxX//LLEHw+hBbjAANahQqe/Hefw/Rz1pF4yZZTK2uDMYajoyPs7e1BFEVMT0+7Ulh0ylw4VV+hPnQP1Ef/TvO1B8NgxSyUr30WYqsJ342vsV5T6gn4q0VgcRUC7/IUmwFrNaF88ytQD9JAvQJh6RzE57wUwrHXv9aUUBeDiPgCCCgtCEwFg6gF24oMYXYBsetfgJg/2NaqsFqt6qnvYDCoefVf9jpEZuYgXH4ckFsQljYgFYuYuvzt9rkPzboWoPsDmrCIz2uF0fd/DviJf6GJhfy+XhQOqaHt+AvHoqXVPO7+hGt2QFkBwNoLpa+7Qcs2PPWYZpESxONC7FmgXr4mLgDttQQATzwK3LIModWE7/LjmqipHGqvM5PQXpd3sXrq25otij9HIQt8/0FNEM0uaLap7C4EpmivHQgCEIBa9Vp3q0DwWvesYcMYUDqAcLQDQaqBhWJgiVVgevHaMaiyVv8iardCAYAPMgS/Hz4A01NRtHwhtFottKoyyvtpFCvMUavMJAZTgDeC2l6DWXOxr9FCZRU8jtJC5YXPgaOqqieOIxAIQBRFhMNhLC4unsiCPf744/jQhz6ECxcu4GUvexny+bwjs6symQze+9734pFHHsHm5mbXx37rW9/CQw89hLvvvhtbW1qM8/u///t461vfine+851YWhqs459nxUW/qKqK/f195PP5Idmg2jnYOxzacw8C00IudMtC9PAkPT7u2NQlDKE3FWNQGxIQ9KF6aQffecd/hPqhX8LqT92sP2Tv7oehygrCyeOWowIQSEyheVDA/t8+MhRxoaoqdnd3USgUkEwmUa/XXXtRNa/LWF8xaG0Iq5agfv9BIBiBMHU88yUSAyvlwR67H+z5t0CwaLsq1LUi/baaB0Dbcc/tgzXrgN8PlnoS7PJj8L3un+FICGF3dxfJrRch3ixrAX+rqWUIVEULwn/8f9EnXJtbFfLUt96BSFEgJDYRfeWzMRUKIJZNI3jf30BJLsIXO85I5Pe0mRWiTwtoS0dakfXiujbh+6EvaZaickELhqPT0Ls8qQqAgLYe2VBvpLer9V/LTPD/vum1wHUv1uxR/qAW/Kee0MQFhPYdfLkFPPpV+DJpzAamINTLWtZEFzeCNvG72dBETHZXEy+8KPsgpb3fiUXtb8oFCLJ03AVL1upCfH6tXqOYB5KLQCJ5uq2oU5ahA52+N0I+DSHzhPZ8/iCESg5C9QhsRQZLrB2/Z0Gw4BSEWgEIHN/cRb/2Hvv8EAMhhPwhhAJBABICiWX4I3OWheE8sOz1fjHpBd1eoN+d8m4WqkHPC7t4SVzYKYJ2O8YheuYsmM/nw0te8hI89thjuHz5MgDt8/ulX/ol3HrrrXjlK1+J5zznObbfi+9973uIx+P47Gc/i4985CPY2encMOib3/wmFhYWdGEBADfffDMEQcAjjzyC17/+9f0d+DEkLgxIkoTt7W00m82R+d5nZqch+kSoSn/FxkO7QQnAPmvhhTb/jAHtxdbdCipO/DEDM7TEdRRJAWJ+qLKKJz/8WSz/xI0Qj72dUrZ0wn4jCALAAOmo4vxSDOfZ2toaEokErly54tpgw5i5MNZX9Ds40gjLH2gBqrmLUGQKrFrSdseXNk7+4ey8FoC2JMB3LD5aTa3zUygKYWFVe35VBTvcRfFrd2P3upcjmUxi+XnPA5IJKI99HSgcAooCNr8K4TU/q1maOmBMfettLQtHaB7uIierUJ58FBFBQAsCBLkFvyhCKB63aBWDWq0Ez2TwTlUPf1n7PTvuKnWYvlZwLUD7mVFYaAcFTSiowOP3A6kntQniL341cPH5mgWJD7qrlbRBd/Lx4DxB1AJ/pQU06kBY0FroHu5CUCRDUM8FiHy8NhkQ/NrncZjWAvBKQet8VcwBUCE0atpa/f7jmSFMa7urKtp/n78AIbEEdcZiR4wxCIU9CPkUINXAIjNgyQ1geqGr0Oj4nVFaEPLb2vsYObbqBaNAowwhdxVsZkkfdMjmNiA0SkCteCx8tPeWiX5ADGjH36gA/hD8yVUkIzOWheHcZ22c9tt1YrgsISjXtKLxQMCWoHIDXghqnbDhdGsYYPTfh8PhtpkrTr1vvFGJF4LySbVFWdGp85Uoirh48SL+w3/4D2g2m3j00Ufxjne8A36/Hw888ADuv/9+3HHHHUgkEnjFK16BW2+9FT/+4z+ORCJx6mvedtttuO2223paXyaTwcpKu82VDxjc29vr7SC74FlxYfcEHZUNyswLX/r8voXFsHkCTdwIGYuwtruYi7pPCAv9hzZgABOGMwFcrTYBn4jKpR3cc+NvILo+j9DiLFrlOlSppc3SEI+Lv4+768w81yKwHYBKpYJUKqVfYPh5NoqJ8v3C1zaM+RVC+Nj6I7e0YI8jS1qQGu7gQ12/Ds3EMoKlQzBlRvtbXsw8b7xgCqiLAWDnKaze+pNILi5rP37ezRCuuwGsmIUs+MBicesicsa04LleAWaS+kA9AUDoyuMIPX4/UC2B+XxQGzVIigJFUVGr1uCT6og26hAEEQJTARzvyPsDWtZEVQGo2ndEPLYwKbL2c+H4d1Lj5JpEn1YPoRzXW+Qz2hqv/hB49RsBbiVrVLXnkptagCw1TMXY0N63xBKaYgih/I42NVw87iTFjgvsRZ9m4wpGgK99+njODNOOoVoCAiHt+KTG8eu1gNgUMB3XhJTUBDY2ICytQ914kfY85vPg8MpxlgGALwChdAChkgdbfz7Y7Kr1OdAJpQVknwGqeSA03Z4JCUQAqab9EznOVM4sQQUg5LYhNKtAKAoWS2iPqRe1DzsQgbq4de1vcLIwvNVq6Ra6o6OjtonhPKj0+7VMjnD4DELZq1golxDYPoIwswC2dP1YC93tQuLCGqvzwmyhEkVRH+TH67j6XYcXPgeOm1rRDkov51YoFMItt9wCRVHwkpe8BB/+8IfxjW98A1//+tdx//334+6778bdd9+Nz3zmM/gv/+W/OLq+er1uaWUOhUJoNpsDP79nxUWvjNoGZWZmdnzD0U5DAVCDDHQQF0YshUW/DFFg4FjIycUaSsVt4PvbEIMBqC0FTMkiMDsFMAal1kBkY6HNPjUIjDFks1lkMhlMTU1hfX29rSuG28VFs9nE008/7fz8ivlVCMvnwbYvgYk+CIEgmNQEqiUI19+g10qcWJM/gMMbbkfs8EmI6Se1rMVMUtuZn9ICQEXWbEyiqiISjSGYaO/+JQRDWqFyq3XNamSkVgEeuBvYeUoTO8Gw1pnppbcD25e04mlBAGIzEBQZvmIWwXoN4twSAtFpyGWt7SsTfFAhALUa4A9AbNUhKC2txkT74ug1H3rGgCnta+E3KX2K9/G54jve8ea1DA/8T+A5N2k77V/77LElqnbtecxZxei0Jmp8frDYjPY8RweajUpVNPHh9wMQNbFSyAGbz9Oe/3AHEEUIrbqW4TC+h43GNVtVIgmsnQebXtDmRxhhDKjkIez9QBNA0cSx1S0K1EsQDp/Wsgxm+1snagWIO98DagWgXobQ1GotMH08j4Qpx0KuvYUw4stgM0tgiqRlZUSfdk7VC9obFZ3V7XKdCAQCmJ2dxezsbFurSuPE8GAwiKRaxnRlF/AF0PKFwHx+iIVdQFXANl40cRmMSWYUO+VWFipeGH5wcAAAA1movCQuvJK5YIz1LJSazSYYY4hEIkgmk3j961+P17/+9WCM4erVq3jggQdw8eJFx9cYDochSZLleqhb1IA0m02kUqmR2qDMuLXmAgAkMBxAxnn0UHXhdGDMcGyRGjIMUGUZgk8EO96RFXwCFl/7Elz3f/wkQgvxgV9CURTs7OygVCphYWEBi4uLJ84z84Rut8DtP7IsD2X2hiAI8P3Ym6Dc81+1IXfHAa2wcT18r3pD179VQlEor/pphH0C0NIu0Oqn/wNY6Qit8DRqjTp8ACKQ4dt6PoSADUHEGPD1zwJXvq/ZalQVyO4Dh/9T262vV7XgO3mcCQmEgMVzYFcvwZ/bgyjFEVQUwCcCwRCUmTltp19qAq0WVF8ASmQagUre9N2x6pl2XPfE7VDGxwiClingbWuLeeDLn9REwmH6uONUUBued+IYj/9dKcBfKgCtmmZ/isS0OR7ycaHz1Ky2xnpFywrUK8e/80FQ1GsZEL9wTfj4j+sW1jYgLC4BoQhYy1TIzVQI+09COHgKQiWvvVarARZNaPMkAhFtHVINEHwQivva/w+EweLLgD8If+0I0VoBKMeA0AzEvR9qwmdqTnu/mtVrg/Gis0CzqomVoMXNUxAAvyFzEAgBgf6KGjtNDK9VysDeDqqKBNkvggkimgoQCsbgq+bB6iUgOvg1ZxR4Jagd5frNdVydrHV2LFRe+RwYY56pueCfSS/HwgfomQN6Pn/lwoULjq8PAJaXl/HlL3+57WeSJKFQKAxczA14WFyc9kXjNii/3z9SG5QZNwaUHCYAdaaiBQVBdN5JcXLGRvsT2yvs7BuFgSkKVADX/aufwuo/eTn8scE7NwCagN3e3kar1cK5c+cwMzNj+Tg3Zi54fYUsywiFQlhfXx/KDUyIz8H3xl8FS1/WOg5NJyGsXezYhhZo/34LkRgQiWlh900/Dum+z0IpphDx+xAIBCEsn4f4kvaWwqxaAnviW1D3r2pB5PnnAueefe18y+0BO09rtqxMSssKsGM70INf0rojzZjmoATDUCLTYBvPgi82pe1+X3gu8MSj8FUKWpF0MARWE6AklqAwhkA5h+4cnxNMbf9vjiwZfn+89u1LmqCITmlZCLlTipsBBymIhzuIMVX760AQSK4cW7V8WjtZqQFA1H4mS5rVKRTR3itVOfaIHQ/va9a0dUxPA7ICIRLR3tLqEdCsQqgWoJ6/AZheglDYh3B4WWt9Kwi6LUwoZ7WZJ7xtbqMCMfOkJhIE8ViUPAEwFYlaEVBk+MpPg0VmNHE0vaA9LpbUdkKbNaB6BCYIYLGkZj8acSCmT/uNBCGWIpARQ11WITWbqNfrqDOGiFpH7WAPgTnRVTMUTmOSg9px75R3slBVq9WOFipz1thL4gKY/OMA7B0LFxej7hR500034Y477sDVq1dx/rzWtfTBBx8EANx4443d/rQnPCsuOjFuG5SZ5bXBFeIwkaAF3V0ZYkw8QK8q+6/VUvD9992F4nev4vnv+2cQ/YOdF3yqeyAQwNbWFkKhzn5qt4kLY31FOBzWJ9kOC8Hnh3D+2b0/3mIwoqIoSE8to/nC27HcOELQB4gLqxC2XqQJkGNYMQflC38FdrgL+EQIigLxiW8DL3k18NLjYrhK8dgWU7kW1B4X+UORtenVPp8W1FdL2r+Pu1q1Ljwf4RfcpE3Qfvh4Z4ipWrZjdh7C2hYChUMEtBZp6PQFYnqtRvsjOn4KTNWC8UBQK7yulTQhcBpcWABagXxmu92G1WwcB/WK9uq7T2t2skBQyxKIPi1LwVvmiiJQLAI+H5iiaB2kwCC0GsDRDnylA6jnbgBqRQi1ovYastQmgoRWQ0vWBCIQU49prx1NHguYKoTCHqC2ILDjNUp1CHIDEHxgkbhW1+ELADOLWjZAaUFdfT4wu9y7xWoY+AJg/hD8cgOhYAhSs4nZ2VkoUgNqU0VNUlA7LqbsuTB8TLjpetUvbjsGs4Wq0WjomQ1uoTJOgY5Go54Jyr1yHIC9InsuLoa9wa0oCvL5PKanpxEOh3HDDTfgxhtvxDve8Q783u/9Hmq1Gn73d38XP/MzP0OZC7sYbVCrq6tIJBJjP5GDIecmMg4DCaI2EKsDQ8taGF5hdPICYKKInf/xdUxfv4oL//LH+3sOxpDJZJDNZnsWsG6yRZnnV+zt7bnuJszh62o2m7h69SpkWcbGDTfpw4ysUL91H9jhDpBc0oRBo6C1gv3657S6ivPP0aZS+wOayGAAuMTm74OqAsWs1m1K9GmPKWQhhGNaS9fP/AfgmR9owXZ8Hli/DoCgZRJ4Afvhbufj4q9xfP4bJYjKtG+EIJr/4Hinv3TcYrfVb1Eeu1YLov/IMC1cagIHaSAc0QbxSXVNOIFpx+YPaI9hCpA7AJs6b7jOCoAiQdz+9vH7xgvTzY0gFK3+vVkFpDpYeAYI1LT3pF7Ufq+qECBAFY5rKPjMikoWSG7AeO1gM4tAYnX89QyiD0iuAXuXILRkCEyBIDcRVBpg8+tYX3seWrJ8ojCcz1loKwwfM14IBsedueiGIAiIRCKIRCJ6K2zjID9uoeKZjGazObLZGsPAS12v7BxLva5tADk5RM+Kvb093H777fjABz6AN73pTRAEAX/6p3+K973vffjFX/xFhEIhfUK3E4z/CjUkzF8wt9igzAiCgEgsgnp1RIOlbJJCHRKmMLYeJqPVFhB9IpiqIvXfv9aXuDC2al1aWsL8/HxPF3s3ZC46za9wk/DhGN/TcrmMVCrVU4aIqSrYM9/TbDxHh9rcBu0XWk3E3R8DFta01q7hWFuAf/zK2r/U4yF2/uBxxkDUrFlSE5EH/1bLaijHLVhLeW1nfnVLsxk99W3td0w53kVnwHEhO2Oq5gYET5Rck/YCd0gd368UFfAJmn2R+fzwRae0IvRmTVsPYwPUQnX4u2NbElqSlg360TcA2W3g0fu19yrg1/42FASCQaBcBppNIBw+fs7j51VamqjgGRH+vO0vdrwURWsVK9f1ugz+/jPRpz1OPH4joGU2cHgFwvGxs/CU1tbWJUEXS6xpxduZZxBQqhBUFSy5DrZ4HSAIPReG86Fu4XDYEwHZOJgkj7/P57PsTsanhfNhrOZBfpOCF8Qqx86xDEtcfPCDH2z77/X1dVy6dKntZ3Nzc/jwhz/s6OtyPCsuOEYbVDwex+rqquu8rK+47eX4u899ta+/HXZQWgLQgjrqGH9syJU6fOEgmodF2387SKvWcYuLbvMrxr02K/jaisUiisUipqensb6+3vt3W5a0OQ3Hsw5QP55noqpadiG7d7JtqwAtaAeOOykFtJqKZl0TCf4AfE9+G0KjrNly5CaAoPa7ehU42tcKrnmnqOi0ZkNiKhSVQWUq/MevIxheVgUg4tpyfNA0gygc51MY0JBa8KtlhNhxx6lQ7LiguTaAwLCC8VyAltXZf0rrzhWLHXedMgVq9TogScfiwvxMTJvmDVgIi+PX0P+vAqg+IBTS3j9VvvYQAW3HKCjHszmYAObzaXUclbxWyO2GwEUQweYvoO6fQXY3hfCFLfgi1teKjoXhtZo2yPHoiCZDD4CbMxenwUWoKIrY39/H+vq6ntkwW6hisRgikYjrYh8jZzVz0amge9LxtLgwDitziw3Kire87f/Tt7gYJoKg7ZoeMQWJUx89PEYqbFQGtSVj5vnnbf3Z0dERdnd3EQ6HsbGxYbtV6zgD+F5EkdvEBb94F4vFjh24rBBEEcKF54M9/OVrE6SVlpZJ8B1P0W7UtI5J8nFRMS/kZrhmDzoWE/D5NZHAVGD3CgRecyBL2nMqxxlJQQDyh9rfT8WvPXd0GkrpCLLUQADXYl9Djyj4gRN1T8bHCQBiIsDUFiAI2gZ+IKjt3EsNnGhrOwjG80BVgO2ngWddry1CUbTMBX9Mq6W9px2+C+1mrx5QJM2CJfCjZhCYCsaOn4dnagQfEIlrv1da2vEXdrWMgZ1OTK2GNuW7dKC9rzOLYIl1x2ZRMNGPlj8CIdR7UKEXhs/MXBvkeGyhymazI58M7cb7qR28cgzAtRqdbhYq/phoNOroID8n8IJY5ZC48LC4UBQFly9fHvlQvL5wYELoMCd177AmNhEZztwJF8JaCuZufW5PjzVmxhKJBFZWVvraeRFFcSwBvLm+wkoUuS1zwTcNACCZTHYtPmPc0y/69O5Twg2vBL75FU1cyJIhYBaOHytqASRjAAsYahd4SkHQAuZQVBs6F53W/l0rH9tzVEBWzAvRip8jU8DCulbjUTiEoqhAqwE/2gWD8d+AlrnQn8ri90wAGBOgqoBPZGCl07pQ9ceJK4DIIMoS1GQSyGSAJrQ2tLKs/ZNIQLDIWvSNImsF25FZCOVDMF6MLviOMyFM++zU46nm4rF4lOoQGmWwXsWF3ISQegxCNX/cnpZByDyldZ06d4OWeep5za3joZCh9kGRAyIIAkKhEEKhUNeJ4eFwWLdQubEwfJx4SVwYj8NsoZIkST83jHU8brJQ2Wnf6nbcYIsaN54VFz6fD+vr6xPR0u9Ln/nKuJdwAuOX4grquAVxBM+IuACAy3/6Oaz+1E2IrFoPcQM0z+v29jYajQZWV1eRTCY7PvY0Rh3AG4vOT5tf4SZxUa1Wsb29ra+126YB230ayne/oRVOB0MQr38xhOe//FoxciOk7b4zpv1blbXInQ9LU9VjK9RxV6hgWKsNUBQtUNx6oTZgr1bSah2UFhR/CH6pS/1UMAxMJ7QWt60mlMM9+JmhORO6Z+mshMXxLO82L5WT8VLXp4rHtcLqhQXtHDk60mosfD5gbg7CykqXP7Z7ThkOMBgFiy+hKTOwVh2RSFQrpG81j7NG/E8EMIhajYONwF4oZiBUj46H+l2zwgnVHFA60LIgp6HKELLbQGkfgiKD+YPA7CpYcr2tGt+p4LaXieFOFoZ7JTA/C8cQDAYRDAb1Oh7ehaqThSoajY48yOe7/ZP+eQD9FXSPuhXtsPGsuACAeDzuukJUK3audu4a0ytOBoDmL3ceAmpQEIA4FnkxjtdsVep47Lf+M5S6BDHgx/I/fhk23vwj8IW13R0e5AqCgM3NzYF3HUYZwHerr+i0NjeQz+ext7eHSCSCjY2NE8VpRtju01C+8tdAowoWmQaqZSgPfQni0QFYowb4AhDWLmrtaKWmJh5UVQv6/MfZCp8PUAVtOJxyLDx8PmB2XhMjWy8Env1STWBc/SGw8/+y9+dxsmVlnS/8XXuKKTNynjPPWFUUVQUUBVUUQzGICA2O4IQC3k+/6LUnfdu22u6+dIPKBRVEW9HGfsWLtlfxervtVqBRQBQBuygKSgpqnk7OY0TGPO291/vHih25IzIiMyIzxjz5+3xOnTox7Fh7Wvv5ref5Pb+nEYU6ZnV+FLIqaxGJkh6awNlYJayrifik5X8CDlxoWqw0ambbjd8U6phpGkLXEfPzyKkppbEwTUTD0kCNKnE3olx+dtxcrcqeRCGD1A3k9A3sM4yd3GN+cRGx9i20nafVdoShtisdhFNAmkFEPo0MpCDYuJNYBdl42efDFxx44vFcApogF2L7GcTeMtIMII2gyoZsPan2ZOJCx+/3VoTh4XCYUCjUckDZL3PDaTDo+9AqQarXhcrLavSyhOqslUUJIVryuTjPXJyj7Vi4ON+W7bQjQK13MxSRPEyGlzF6qm0PFBzJ7t89jBcAxb7yGBufvJ+7/uBniKcSbG5uEolEWFpaaktbyG6Ri5OIznuduZBSsrGxQSwWY3x8nDnfarh/XFJK2FnD3V3H/caXVZvZmQsHnhj5DPLZR5S5nKbB0AgiEEKmE6qkKRlXGyqWTeLGZmHrWplomOUOTC7kskrEPDym/B7yGXjiQbCLaE7x6J2xbYhtwvrTaLkseVfi6geh9nGh3XGPqsr73RAqlTMVcmLioJTLNOHY8gqf4Z/aUPOEyLXBDCCnroDrMrLzEI5jI2KobWq6aqcl3YOMVPl7YvspRHwVOX2Dyh4cBc0sdwqrgZTN+WQUc5DcQlohJe4HMExkPg3xdRhtz5zfLGqF4Y7jVOrxTyoM75ds5mlwFjIXruueKsug6zrDw8OV9t1eCVVtxsuf1ehECdVZEnS3cl2dl0Wdo2OIjjSxktYk1AUtW28QIzyBZf33DKnhINHrfabNq6XV2+7hxO8PXG2X2P96lH/48H/Dev2tTExMMDs727YHkxfAd/Jh14y+4qix9QK2bbO8vEwulzuy9EzaJdwv/A/kU99AFvLKg8IwYWhUCagBAmFkKo6IjMDuhjJ4My0YmUSGhxGGBTe9EHbWVHYhly47UGuqnMnTEmT2YXIWJuYUgfna59Xr4SGVBTnqZnAdlU2REquUI2gciLJbRW2JVOVXm7ofm7tpj+Qouq66QeVyypG7FVSE8v5fOgblzIEMDkEuqcqWACFBbD2hPDGQSmehGUrILYQihsFhiIwpA77tp5CRMdVRqxZ2EQoZpBVWGp1iDsyyZqSUA91ADk0dP9ZSDmEXD2s8zACimEPahZ6u1Oq6XlcYns1mmxaGn4XA/JwgHUa9Eirv2tja2gJUVsx/bbSDEJy1zEWzx+S8LGoAMSgXaTKRbvMWRblHfhMrgkeRCt9ndqR97KpqJ9DzM1jVjlSy82df4e53voGRkRa6zjT1M53b01b0FY3G1ouHcG2WpXby9Y9LfvPvcR+5HyJRRShyGVWCtPks8tItCMMsl90IxKXnqgB/awUZCILjIkoFWLoRXv6dKhjdXoFH7ofHvqZISiquBOCaBqFh5YEhBGw8o9yw82ko5HENC93nNF2NculPIIxMxtF9kbs/iK8r2K7zGqgcQMuKMs9Ru0k0JBiGUXbmLrW+CHBsCVS970ildSkWEPubEBnDLdpYpR2EnStvU5a7VllII6AE3tJVIm8hFKHIxhGZGNJPLqRUnaFiy8pHgzL5KebVHyFBt5CTlxVJ8fYhtYNI7ihSEh5Bjsyq39AtpfFwSmVBeBlOCambZUF4qfVj0AG0Kgz3ymS87w4yzgpB6tQ++EuogKoSqnQ6zf7+PtAeN/nTZmD6Ca3sy3lZ1Dk6hotXj0nRnxDC3zD/lJBIMjgMdfOS6Ys5v9JIX/0rnm07sYCDh3S7HxSt6isaodvkIpFIsLq6emyWxcv2uI8+AIapjN0AGR2FvQIU8pDeR0bHIbaFGBlHu+H5cPFm3Ie/gnz2EaTQkBdvRtxy10Gb0dmLsLUMVkCVPo1PKy2BYSjiIqUiHA98HvY2KuU3mtCQiMOu9pqhSnqEancrCuqB4l1dDgJNSY8rbWcF9cukaoP9IgKj7D9xbDWUaSl3cITKPHhaE99vHoJedtL2PucZ9IHyr2hnN6ijICU4JUSp7CviFAg4Er2ULXdhKpMmIZTJn+sgNQMRiBxkKbw2trXkJrWN2H5SZamC5dbCuRTSCsL4ImgGMjyq3vP8X3afVZoKUN3IsvuQ2kUu3KI+NzSB2N9ABgHdUpqLUh45eancNCDbl4FtI2F4bachXddxXRfbtvvCMfwkOCcXrcFfQiWlpFQqVUhoLBY71DSglRKqs3AuPHiai2ZwXhZ1jo7h27/7NfzGL36EbDrb66E0RIHS0ZmLDpRG9UXrW1n5DwBmtDMTgJ9ctAunMfWrHVu3yIWUku3tbXZ2dprqYlX+kgr4jYOHmIhOIIsFlVXY31FZh+go4urzkE/+A4SjaHe8GvGS78C2baRtV1rVVjA5r4LWQlmDYVgqeC3uqfKpL31CaTLcAzogGq7Il4+fbkAhWyEf3hXuEYuaT+NQvw2tp1ooFwrVbV/beBjlRQcvED9UolQDTVNlX6CIBqhWvLoO4bD6U4UmJ4NGHzlqJzwNRTkLpdtZNDwXdVSWQNNUCVM50yGHpw50EnYBNB0ZjFb/5P6G+nzFzE6H8Cgil8C1wjAyWz2OsnZCGoGKpkJKqUq14mvIuZuV47aUqp1tIYvUTeTYInLiYuXzg4BaYbhXJpNIJHAch6effvrUwvBe4SwEtL3aByHEoRIqr2mAv4TKsqwK0TiqhOosnAsPrTi/e+Sir+0SToBzctEPEHDbi2/mK3/ztV6PpCGWcTHRjl4ZbSfB6Mgkc1TBCXXeq4EmmLv7CoX7voZ1+22IQGtGeUeh3eTipPqKo8bWaTiOw+rqKqlUipmZGSYnJ4/tYiWlVKRgZgmeeRgZiarvaBqMTAKgveAeGJtGPvEg8qt/rYSDQiAm59C/7Qcg2qDd8NxluHwLPPWQajerGaoGf2wGxmfh63+rhOCVa6jm3PnJj6arYFzTIJ87dB9pdf5fogpnzPJrXgjtaTQcqkuijj1L/oyDR4JkE7dtMAiZjNofTVOC7VBIEY5iseocueUlgYYGeVVkvd6IvVLOelnXsuzdCKgskF04IHNOucRISjxXdGkEYXQW8ilFPF0l8pbjixAerdqyKHrZDx88XxS7eHhP8imEU6ourRJCZToycUWCzABy4VZkIa3KpsxgfZ3HAMFfJuO6LplMhomJiUPC8FAoVPHW6JZj+ElwFgLaftkHf0MAOLqEyiOi/hKq67UsKpfLEQgEBjb71whna29q0A833HFIJBKsra0xOTPZ66EciSKwR5FZGpdACARStND5peGGjspZtCP4PmobjciGZCjgMPr1z7L/jc+ijY0SvfefE3jJi9owHqom2NPgtPqKRmPr9CproVBgeXmZUqnExYsXK51LjoN0XdzVJ1WdfS4Nq08hxyZVkJnPIS7fivby78T53P+D3HwWRqcRpoW0S8jtVZy/+3PkG3+s/sY1DV72nTCtiAvFPMxfgefcAY9/DZJ7KmDVTdWqtvbakVIJwXVdBeJzl2F3TY1V6DRyzvbCcg2wgDwaFm6ZYKich41ALxdQNdNlCgBXgmUoYiGPpRSKTExMQiRS3hdLHRMve1EqQaDWrVptV/qyKZVBHkkqOPyeLFMpUfO+0NTxc+06++gARbWP4THcC7errEQmps5TdBo5Mndo8UIGhpQTt26osjFNU58XAmnWmfP8RK1qQ2UiWXFEFA1b3/ZLUHhSeKuzpxWG9wpnRUDcSglON1GvhMq7Nvb29tjd3UXX9cp14ThOX+7HSeC6btNkIZvNnrmsBZxxctHP8Ds7j4yM8II7nsen/uSvej2sxhDwpMwzQ/CY0OBkBEMcjkT6AgIXS3eZjWaYGHHQw2oScGP7JH7hg0z8/34NfX72mK008TttyFy0S19Rb2ydJBepVIqVlRUMw+Dq1asEDgWsjcdlPXIf7uP3I0sFtTqdS8POBmJ6AXHHq9HueLXKOqw+CZER1R0KEIaJjE7A9gpid121na0Hw4SbX6z++BFTKX80TQXtjVDMq4yHYaiuVZkEBMK4yRgiFW/YKcr/WgkdB0EYB+VDDaavjOpYYiH0A7FzMcexuQrLUn+EgLFRxPg4MpNWmhNPX1FU4nYxNlb1Va1csOXP5Yiqa6eV69GX+alcx1KVPAkNELhCR5O2b6uykjVQPiUOcuYGtf+lgvpeHbIto7OInWcQ6b1KmZgUAjm2CJE6HcrCo0grrLIing6jVEAU0qocSpyNFdijUEuOTioM71VAeVbIRSslOL2Cv4RqbGysoe+KEILt7W0ikchAldfVopWFg3w+f04uztEeFItFVlZWyOfzzM3NMT4+zpNDz/Z6WEdDwLoslVckj75pPCG5162q/+fu+sVeuiYJ6jY3TO5jaBJhg0yUEOEQIhxC5nLk/urzDP1vbz31CLxJ9KRBfLv0FfXQKXIhpWRvb4/NzU2GhoZYWlpqaVXTSMexHrkPaZqISeUbIG1bCayv3Ib2vJcqwXYqjizmYXi0ZgOmakdbbNTd6Qi4rgpej2s9C2qFvWir8iqnBBJ0TcMR4Hi+fdS/CgUQ8emdat93aYJcGIYKrCuoLd9SvhCVbZdKBwH97i7MzCAWF5Hr65DPH2xzelo5dB8a8cE4XRd0UTvyY2Xnh7cpayYSqfZcw63+XHnb0gyBYyNiKyorkd6FQkbta2QcOXX5wH9CSkQ+VS6lCvh0M7LyPoktRTyQEB5DRqeQ01cQW08iUruqM5lTUCVbqV0wghCdOnB7b7Rn/T85nhjNCsPb5RjeKs4SuRi0fajnu7K6uopt25USKiFEFRE9aReqXqDVblGNWqwPMs40uejHCzGVSrG6uoqmaVXOzlbARNM1XKd/HcVvIEAKmxGquz9UrRrWvt7jUyBqCjQErm/NVwUiQ1aJi6NJ9nMBtjIRHFdDCBcNyaXxJIZWvV8ym6uIf93t3faM8xSZi2Y7K50W7XyIua7L2toaiUSCyclJZmZmWt52cG8dUczB2EFJoTAMZCiC/ObfYz/1kDK4k6jOToWsKmvykE0hghEYm25t8FJCeh/yOVrKshVzB+VBzoFPtfd3I3h0S/o+JzggFnWlCVWpjyNangoNGR5Sfh2V78uD9rLpNDKbRYtGVXlUJqMYQzhcx4G7LA5HgHTUvolGlMk/2NrX6g60DicptxY+9FGhOjdpuuou5djKnyI6rd7bX4dSDrlwGyK5BfE19ZpmwvAUslLWJBCFFKz+AyKbpDKAxCakdpALtyAXn49Y/praXmgShIbYX0fElpGRcRhbRE5ePPDK8O/5gAi6G6HV+aCRMLydjuEnQT/GCa1gEMlFLXRdR9d1DMNgfn7+2BKqbhPRVtGq5uKsdYqCM04u+gn+Wvjh4WEWFhaqbo5bX/hcwpEQ6WSmh6M8Gp8lze1U1w/3+5SmCclCNMFyYgQQaBpcHYtxbX+EyUiWrVRElZMbLrPRHGPhAsv7UTJFk2iwgKXXr4uX2RxYJvrSQlvGeRJy0Ql9xXFja8dDrFQqsby8TD6fZ3FxkdHR0VONqwIplcA6tq1Ixdg0TM6p1Wq7BKk4cvMaDI+rQN91EXe8BiJRZPEYZ+34Nmyvqu2sPwVPf4uWvRqkrJTzgERIiSag5IKhHZ+B8H7N20LzOZ4jxild1a630XhdVxGKoSGErkM0Wv+z6gsVHYKLKoc6vuNbjfbhyMyoVFoR39vVLX99mQak2jdNA0eqLEZ6T3mMIBGJbbC/hiikFSFwXXByijREp1XmQUolGk9uQ3j8IAvh2ojUDiTKDtyuo3Qcrq0+K12VsS1mIbaqiMfi85pz9h4gnGY+8AvDj3IM76QwfNDJnYezQC5A7Yeu64dKqFzXJZ/PHyqh6ucOZc2eE2/fzsuiznEilEolVldXyWQyDbvgjI6P8J0//EY+/p//tEejPB6agD+R27yVmR78eu2ypdeM0yWkOxQdDaccbo0ECpQcDYEkYwcwDVgaSWE7gv18EAeNW2Zi5IqCsekCsWwQ29WwdJeA4XLT1D4FW1fZC28xthauixYdJvgdr0a6yitAnGIlpVVy0Sl9RTvGdhSy2SzLy8sIIbhy5cqpJtXCxByuGVSr7pER5O46JGOqdSxCZRekREwvwuwF5JoNpqUC0tFptFvuRNxyF85RInrXha9+Dh69X7W7TeyqEqPTHAvdKAt/DSjl0aSsn4GowUHB0RG/3WrFEahORod+rFyKJCUUWi8bEyc8PIeJhX+HBM3toOfHoSsNjqaB6yCKWWQxhxAaws4jsnFkcBh0E6lpCNtRZWu5pMo02AV1/jWjurxJM9QY4quIoUlFJnQDkY2rEjgzVC5/k8jwCCITR6ZjqkwK1DZdG9mnQtxeoB2O4a3ivCyqv9BoPzRNqyqhsm27ouVJpVJVRNTLaliW1dNj0mzmIl8uMz3PXAwY+uGGS6fTrK6uAnD58uUjLd7/2b/9cVaeXuFLn/1f3RpeS3CAVzJKFocwehuyFl6gIDkgC/6CDxtvfXY0kGO/ECKiFyg4GjYGAa1EQHcImJJosMhWKkLALAKCyxNJdAFFW8OWEA245G2dqaEcedvAcQUhS9WZz0WVv4j/cgkY9TMWFZgG0X//r0h/7I8pfPrzyEIBMTZC+Pu/m8iPfv9hz4Rj0EoAn8/nuXbtWkf0FUeN7bSIxWJsbGwQCoW4cOHCqdPazvAExRvvIPD015EbzygyIYQKBDVN/Z1JINNDiOg4hCKIy7eq9rO6UTlHMpNEPPwVWH9WtVm9fCtcuU2RgKcfgoe+pHwu8tnmNBZ+VHlIlJlqWXeBU9ZSlDXhmjg6bC5fIb7+ZTWfrzcsTVfZEqcFN2jP+wLAMOqUPzWxiROxnPpbOoA8+KuZTTsloFRVFInrgF727RAlRM498M4AvE510gyBW1Ita4u56jFkE5DZQ+STyFxSaTkCQ4qMiHLAKx0wQur4S4ko5ZDSRcTXIbEBdolIyaUowof1JAOCTgW13RKGn5OL/kKzAblhGIeIqHdt1JZQeddHt7U8zYrsz6o7N5xxctFLSCnZ3d1la2uLSCTC0tJSUxf4+37nPfyrH/t3fPWLfeh5IeBbMst3MHZM8OCvDvd/zv+6hk6JgF6g6JjYmAREASF0olaOfElH1wXZosZQyCZiFpiLZolnBaOawXDAxnElEUsiBKQKBjdO7ZMt6YRNp9LF0jJcLNSzO2w5CMA0bG93Tozgj3w/6Y/8PvbDj1WCR7kbI/ORj1F69AlG3/vvWprwmyUX3dJXnGRsjSClZGNjg1gsxtjYGHNzc21JYQtNI3/1dqKWjnzw7xShGJ9TgV185yBgy6aQkRFVGjM2XekYBYpY8MmPoW0tlwNBV7WdXX0SXvVmJcKWUnWNyqbKLUjd5jMX/s9pojqQ9V6Gg86wDS6ZY5UJ9T6gG2Vvh2OIcqPtBgLKz6IDjvQng3eQmicutUVXopRX3aYoO6k7dtkc0am0oZV2AWlYMHYJGQijrTwEnobDLiCyCRVARMZU+VQuiYivqQ5VsghOUVVlBYcOzr9uInavIXafReom6Ba6nWCkmEAkZpGj8208Tt1Bt7oUtSoMb9YV+qyQi7PiD3ESkuQnov4SKu/66EUJVSvX1Vl154ZzctER2LbN6uoq6XSaqakppqenm75pNE3jV3//fXz4l/4T/+/v/nnf1YU+S448UcqetFTbe0m8vjdBCuQJENaLaMImb2voSAwdDN1lPJgnW9QJBSRBM0+moKNrLoYuMXUImVBy8Ix4McsLgrNRiSaKhxb6RsKqHeVw4OhAqln56JEQAueJp7AffeIgePAZphW/8PeU/uFbWLff1sImjw7gu6WvOMnYjoK/fGt+fr6tXTHM/W2G/+GvcXNJpbFwHNXqdWIe0knVBla6KuOw/BgIDfeR+yGbRnvhKxEjE7gPfRm2lpEjEwjP4TufhScfhBteoMpqDFNt37t6NL3sa1EPAin8Tt1CZT3Cw6pkq4H+QXj/OeYC9Z8BUfti7XcajrHOb1f9iFSdsCIRmJ5G9FU98DFlYcfi4GAJ6ZR1EEL97TpIMwBWGDl3EwxNQGZf6WyyMfU1p6RyR8OTEIyCpiFH5xHJTXXcyuVQRMZVeVR2HxmIIAMRxO4zyjPDUoGEbUaQpRLE1yA6c+Y0GZ1Cu4ThZ4VcXG+Zi6PgL6ECqkqoarU8nSqh8ryqmtmXTEZpbM/JxYChFzdcNptlZWUF13VbMgPzQ9M0vv17X8NLX/MSfv6f/RKJePL4L3UJF3QIkUV3LEYCBYqOQUgvUZQGYatEyTYQQhLUc5hGBtsVRMrlR7kSDAfLlSEujA05lXKQSMA5FKdbRgtBhEQFdY3fruDUV4WUlL58v2+DNVt0XQp/f3/byEU39RWtju0o+NvjHlcS2Cqk6xJ96AvoyT2YWYBgCLZWlC5ifwdmL0JsE5JxKBUhEISpRdV+9vGv4cS20N/wNuQzD4NpVbszB8NKx7H+tDLQ21krl1r5SYU/G+e9JHB1SxELp1yrP3tBvTe1qLIhyT21Su441A2Sy1VUnlWMp1/2CgW9NXvh/+nTXgo+YgwoYjE9DQsLaE16jlRBtmNQ1ah1Iz85ykexrMNQGy+WNTAahKKqjKyQRWQTiLVvKa2GGVTnM5dWm9DNA7+MQBhCo7gTS4hSUZVMOSVEPoUMRZXPhusop+9QdRbI0Uwo5dWfAXPv7oegtlVheDgcrgST5+Siv9CJ/Wi2hMqf9TptCVUr5OJcc3GOYyGlJBaLsbm5STAYZGlp6VQlK5qmcfHqBf7igT/lvr/9Kp/7xOf5/Ce/QCF/TFebDmIR1d4+EskwWSpgma7qciNA1wrqb71IyQZDB5AIISslxaFynCIElWa2Vbef4HRC2bLxVT1JqP8nOg5BOXhs4SsNAvhu6yuOQivkouPlWzurmMkdnPAwpqZDOKpKn9L7kI4rwmAFFcmwSzB/EVE2NpPhIdhdRz7zrcZOy6Au1JtfDNceVUJuj1y4TnVw6kGCZhfV/wgNxqZgcl5lQlYeV9uLRBVxkbJuiZT3s1AmEVI1O5ICSlK9polyoN3Oi1nX1TU7HEXccBNYOsI0+0YLoONTaJ1AouBXd6ltuEo87q1uIJFCV+cKF+3a18HOIxwHaQVVBswpKZLqOlDMlq8D1e4WTVdtbMOjUMoj82UheWhEvVf20cArw/L2yxP268eX8fQj+i2oPU4YLqXEMIyqILLf9qFVnJOL5nBcCVUyqRZxA4FAhWicpISqFdJ6rrkYYHTaXRjAcRzW1tZIJpNMTEwwMzPTlvSeW+4mcver7+TuV9/J81/8PP7vj/wJ8b19sulsm0bfHCzgpWHVMnPHkdw47JTNscrwBfVWzXOymflCwumIRWVD1QTDv7jbsaugdtwSVaveAuqRi17oK5odWyNIKdne3mZnZ6ej5VuyVERIF9crJdE0xPQi0gooT4uZC+g334Fz7THYWa0QCwCh6cp9Ob6NuHIb9tozFLMZjEBQtULMZxQ5WbwBxmfgtT8E3/ii0l8kY+qC1jTVlUozVKYjm1LBplcOFR5SWQ9Q7yOViDg6rla/ba+BwTHHVCgTOglIM4DmOmhuc+VOzR9MqYiFpycJWgij9yU6tcoK7/9PEn+o7I9AInCEji6Vj03FSVu3EI6NdFMQHgOnqPQYHpHUrHKXsLLexi6qP0IgChnk8LTKeoDqNFXraxEYQobHEMltlb3QDYRTRHcKMHzlWLO9fkS/lezW4jhheKns/7KxscHQ0FDPHcNPgrOUfem203grJVS1Wa+j0Erm4lxzcY6GyOfzLC8vY9s2S0tLjLRJ/OiRCz++64f/EbfcfjNf/OyX+dhv/CF2qbXV8dPgJQEoAd8V1rBq+0yecmJrG7GobFARDA9dn3aFIPvx/4Z5+QLBb7unya+oUbqu21N9xVFjOw6ey2oqlWrYcrltY5qYww1E0PIZGC4HdZoGQkMs3oD+vT+BMC1kfFt1kvJBSgmuxA2E2BhdIjQ6Szi+gZt0Vc8yw6R004vQxuYIeK1sv+0H4Ybb4ZH7VJmU66oMycxFSOwpfYctwXFVUDw6pQiK+sFywCkVOQkPl70lmr/mBWBKu6wNaMMBrIXnwaF7q+unJRfNi64bob1XjkAEwhCZQAgDEuuUpEVJGOg46K6rFks0C2kG0Z2S0knkEohiXomzdUORDSsAUqoyJ81Ajs4jp64cEJW6Py+Q01dVy+rcPrgOmmOTCY4SmrjQ1j3tFgZtxbxWGL6/v8/29ja6rlcJw/1dhpoRhvcSZ4lcQG/3o14JVW3Wq5kSqlbIxXnm4hx1EY/HWV9fJxAIcPXqVQInqU9ugHrkAuDqzZdZvLzAp//rZ9lY3cCxO+voXZY7Mq3Ddw5rFCWE2h3odmIFrFw70bWpSqACv4AFlgW5PJmPfZzAq1/edFtaIQSO43Dt2jXS6XRP9BWNxgVHr1QWCgWWl5cplUon1hq1NKZQhOwNdzD08JeRe5uqBKqYA8NCe+GrKx2hxOXbkE9/C5nYg+iYui4Se8hgmDUtQq7kMPJd/xh99XHYeJaihPT4AvvRGdyVlcrDZGT9MYJf/zwiGVMaDi9LIXQl9jYtCIZxsym0UlGZ7o1MqHKXxJ4aWy6rXML9pMIIqGC+iY5OqtSuMwsKUtNBE4jIMDIyirBzx3+pETqaJvR+oPnPykAYGZ0FBAIXXdMRkTFEcBjdClMqFrHTe8hcQtl6ZHOEXAfQMHQT4ZRUlqLceUuGRpFjcwjdRFoRGJk+mlh4sELIpdtU+1q7SCqRJuNojA9g1uIswAsMve51/hKZra0toL+N2qA/gvJ2oJWAvBuol/XK5XKVrFdtCVUkEiEYDKJp2om6RbVTj9gvOPPkohNlUa7rsr6+zv7+fltba/ohhKhLLgACAYu7X30nn/2Lz1PIFchlGzjsnhIv0FWXphdYcGNAYABDvlqoStB5it/o78R6kxACzHL9rvcf08DZ3MLdi6NPTTS9qb29PYCe6yv8OI5cpFIpVlZWMAyj7ST7KORueCGEh5jYeUZpImYvoN16N+LyLQdjX7yK9uJvw/2HLyJ3NgBwQkNszd9MaXiCqxcvYpomhaEo+q0vIQgEgQkpyeVyZDIZcntbyPs/h5PYQcNVbUUNA5FLQ3xLCXoDZplsCFwriFYqwMazEIyowNTTWYSjqkbfC1YFaiW8VFQEQ9TRcnQaQomcha4jZuYQmicZ78e7s8mMiG6prkyOrc5LdAo5dQVpF1QWaftpxP46BMJYwQBCDoGdRuoWTmgUJx1DFHMUJUjNRLMdDNeGoTEwTLT9TcBFCB2Z2kHOPae50iahKe8MwMlJyHdm7u4GBi1zUQt/EOgXhoPKwvoDyZOWyHQa/RaUnxT9TpK8VseRSISpqalKCVW968MzdzzXXJyjJXgrtMVikYWFBcbGxjryO34GXA8/9P95C08+/BRPP/5sR8iFBrgCvjMCm67GFatDWYBO1u12y5xKSiiq+l1ZLEG2vOorBJk/+n8Zeufb0SJHTx6JRKLysL5y5UrP9BX10IhcSCnZ29tjc3OToaEhlpaWTu2a29K4NI384nMwXv4djT8jBOK2lyIu3YLcWiGVTrPm6ITGJrlS9p6pR+KFEAf1uMlNZFG1onUNq2JjJ8wgejGHLObVMRICaYUojc0SzMRh4SpMLcDXPq8IhRVSBm5mCBxLZTG8zkqaAC2g2tamEwfaDSGqCYco6yLKrVPbcyAB00ReugFtZl5tv3iKzEVlo6cvjWq46eMgHeVnoZsI14XkNnL+FnUOSgUwA+p+S2wirfCBKNuwCIYjyuNjfwNKBUpGgAI6cXMMveQSKiYQ4RHMQBAdF5HaVtubvaml3TgLwfmgj78RdF1neHiY4eHhY4XhHtno5tznod+D8mbh7cegkKSjSqi8dsjXrl2rPEMikUjd66Nd5MJ1XT784Q/zp3/6pySTSV70ohfx7ne/m4sXL9b9/J/92Z/xb/7Nvzn0+l/91V81/E6rOCcXLSCRSLC2tlZZoQ0Gg8d/6YTQNA3bbizcnF2c4b0feTd//cm/5WO/8Yfs7yUaZjpOgglUh5oJQ3BJQNKRjNTJWpwG3VgX7VB40xw0jdyf/yX209cY+9VfrCuS9esrhBCMjo72FbHww/8w9mfvJicnmZmZ6dkDTrquaj8LMDpVvwwtEmUzNE4sB+NT48zNzVWN97ixi2Ie7CK6YytvA8NCarr6g0YhPIKwgtiBkApmg2GYvwxPfkN5bzhuWZdRUu+V9SFYAYiMKBKR2FWv63pZ8I26gDVNdVKwQuq78a1y61Tl/qwIevncmEGIjirxeHKvirzXNjkAEGYAosO4M3No8xfKXaz8vZVOiCa/3tr92cqny9kXu6BIWHoX7dHPq5ayxTzCK2tzSkqQPb6InL6KSO0g8il1ngJDyPFFjJFZ9FAUs1iC5QcpyBDFXAFyeTRNI6CBGdtAjC2hB/rJE+QcR6HZwLxRiUxtl6HTOoZ3ch/6HV7sMoj7UXt9xGIxdnd3GR4ePlRCFYlEuHbtGmNjY1y+fLltrWh/+7d/m49//OO8//3vZ2Zmhg984AP8+I//OJ/4xCfqxhOPPfYYd911Fx/60IeqXm+nD9WZJxftuFhd12Vzc5NYLMbIyAjz8/MdX6VopLnwY2Qsyve97bu48xV38Os//1s88KUHsUsn7yQzAiRQZSE7wP85Jsi4EDYOshaDePP3CiI6BK6L/a1HKd7/NQIvvbPqfb/Z4uzsbKUkqt9Qm7kolUosLy+Tz+dZXFxkdHS0Z+PSd1ZxHvgUcnddiWzHZ9DufgPa0o2Vz53ayC+XKessXNBU1ydRKqrjYgURgRCmrlPSDUQug17MkRyfx3zym1j5LHogDE7qIANRzKuWptJVuoxASHWQCoRU16naNsYCpd0YKpMQTVdjQR5kODwEwzB3BbauVYL72jtWgCIso1Nw12uhtI90igi7AHauXN4FSM8g86Q4OntxFFXwd3mrek0IxLGsRRz8ZduARBSzkHQQtspQyOiM2k/XhWIKsb8B82PIiy+E9UcQyW1AKKLh2DBzFdMw0UwDKzyKBOxSiWKxSKmQwy6k2H76KayhkUqAGQgEzvR8eVYyF63ug79EBho7hndDGH5WyMVZ2Q+gIvyempqqlFB518fm5iY/+ZM/iZSSiYmJqjK8k6JYLPJ7v/d73HvvvbzqVa8C4Nd+7de45557+MxnPsOb3vSmQ995/PHHufnmm5mamjrx7x6HM08uTotiscjKygr5fJ65uTnGx8e7cgM0Qy48LF5a4Fc++l4+/N7f4b/+/v8AAa7TelDwyjCkXNix4dag4Ct5eH3E18KzwX73a3W2Qu9yF0LT1Cp3sUTp4ceqyEU9/4p4PN6X7R395z2bzbK8vAzAlStXKpNjL6Cl94ne/z+Rdh6GRlVZ0tYKzmc/jviudyIm5yrd3BzHOZmRn2PDo1+FyLAy5PPfk1Kqfz//5ei5DPreBnkgu3gTYu4y4qt/RSY0jBGMEsqlVWtTIaBUUivjQjv4GwGhiPJZCITUH7ukiI3rHGQ2zCCMz0KxoMp7QBkIaroSjg+NqJKmdAIlYK7T2c0wYGERbnwezC0i1hJoJVvpSRCqK5LQkJqOKOUPE5hmIVDpz/I96DXfrSUM9f7tf80FhFR+FEKIcjbG03tVitQ4mIXKpMvzHAEww+oYlQpqTKlthGYgDUuJ6gsZxNaTkEsg0jEl1LbK13Y+hdh6Ejl/i/p8KYewwpiWhWlZoLu4xgjO+DyZbK6uOVe9soizEJyfj7+xY3g3hOFnJSg/K9oROOw0bhgGIyMjjIyMMDs7y/ve9z4+85nP8NWvfrWyoPgDP/AD3HrrrbziFa/g5S9/ObfffnvTFQyPPvoomUyGu+++u/JaNBrllltu4f77769LLh577DFe//rXn3JPj8Y5uTgCqVSK1dVVNE3reiDVCrnwPv+j/+SH+NJn/57tjR1o8fsAKyX41xM6CcdFCo2p3re6Pz16xS10HTedUeUwjkPuk59BBIOEvu9NpErFuv4V3fBkOQm8B1cqlSIejxMKhbhw4cKpnUxPCpnPIr/594x96X9iJHZgbBqh66ru3QrC7jruow+QfcGrWFlZwTRNrl692txkXcjB5jUVwM4sqWA/vQ+j06rbU6mACl5RgboVUMH/698GhRzxnR0czWBRK0IwgDU8jB2OUEJiJLbVyjkuaCYyEkWMTqnjm9qHXEqRpJkLZRJSUOZ7paLKMngoFcE04dLNioSAIiGOo8a9cQ1KhQNi4XfgDgTg5lvQRkaQbhFWvoEoFdArGQpZbrdqwdAUJDaQRkBlDEp5pU2okI2yHqRup6tysF8mGF4g578dy3zh0Lc8OFL90ZDoejl7Kn3jBCTKs0QTVDQo0gwiNF2VRDllb3OnCG6ZxLkuwnWQoeCBM7ssk6ntpwCJ0AzIahAcQgaGEbk4FDIwuoDYfQbp2CqbZBcQQkNMXmR0ZJzRMRXweWUz/rKI2rKZc/QWnSBH3RaGnxVycVb2A6j4k9WDEILXve51vO51r8N1XX7iJ36CBx98kDvvvJOvf/3rfPOb3+QjH/kI4XCYl7zkJbzxjW/ku7/7u4/8vc3NTUB1PfNjenqajY2NQ5/3yrbuv/9+/st/+S/s7+/zghe8gJ/92Z/l8uXLJ9zrwzjz5OIkF6u/Dn54eJjFxcWui7WO6hbVCBNT4/zcL/8MH/4//zNba1u4jksoHOJ5d93KX//F3zb8no5aHbzJggcLkhcFNQ6tH5yBm76rcJyq8hY3vk/69/6I1Bf/F7s/8aOMTE4e8q/oV3LhIRaLdaw7WrOQpSLOX/0xcuUJ9FxKRajJPWQ+g5i9qLwtdJPc+rNcG73U2v379Lfgvk8rMiGl8qO45S4VQOczSgsRHAPXVkGrdJXJ3vaqIgKhCJhJdd4n52FoFC2dwBqdhLFJcG1kKoarG7hGgOzYPJQcDN3AGBohsLumTPa8ey2XPliMT8UPRNzFvPpMOqHGODyqfnNqQZVFLT9e1nNYZV2Bc9DcwDAQwxFFCBxlRCjNEE6pgC6dg/lSaGBaSDOAcErIYFRlUmTRfzYOiIUnNPe/52UZBID6f4lQ/8thYgFqHvKurFzJwUYnGvA3k6jOk2o4uOhgBJB2URGqyITyqNh7Rn1eoHQksnQggtfEgWbFKUEgorJQ+bTq+mTpUMqrjEYgpbpPuTZy8oJ6P7GhMiPhMdyxeRg+IH/+ZgBTU1OUSqVKgOkvm/H+2LbdM6J+GpxnLo5Hp4XhZ2XF/6zsB9C0GaCmaZX7/mMf+xjFYpH777+fL33pS3zpS1/i85//PF/84hd5wxvecOTCmNfOtvYzgUCARCJx6POPP/44oK7NX/7lXyabzfLbv/3b/MiP/Ah/8Rd/weTkZNP7ehQGb0brMEqlEisrK2Sz2Y4bgR2F47pFNcIdL72dD//Jr/IPX3mIfDbPDbdcIVvI8tW/+xrJ/VTd75hAWMCNlsaMAc8UXS6YGqZvtwf2EVJz7npYJIU0deSjTzD91ApTt99+6LrqR3Jh23alDGp0dJT5+fmeBhTymYeRa0/C2CSuXVItX80AFPPIVBxGpygVciQxWhOaxzbhi/9DBe4jE+XgfR/+4e9gbEZlEMqtZtENsPOqzawZKDs4HwQEUkrlu/H8V8D9n1HfTe+D4yAsC12CXswRdYuUwmOUbJt8sYThStxsBjufwzAMDMdGuLYqzbIC6reyvns4PKzGm4ypcqlivnIshF2CYBCEAYbnKl3WYxWLEAggPDKgG8iyNqGSiSjlIbULCCQ6IrVXJhLCRyR8ygjNALdUJSCvEpPrBq5rK74tXTQhcBGqGRY1XxES14WQpaMd0lgcvj+ELGdbdANphdQxyCfLrX7LRVZujZeIK1SWStfV90JRpbPQ9IPyKadMRkp5MIPIYl6Vi43Nw+hc+XrQjl14MU2zUhbhL5vZ39/Htm2efvrpitjTK5sZlKB9UMZZD90mR50Qhp+VFf+zsh9wuCzqKORyOYLBILquEwqFeOUrX8krX/lKALa2tsjlcsdm3L0saLFYrMqIFgqFutU2d999N1/5yleqDJ9/67d+i9e85jX8t//23/iJn/iJpsZ+HM7JhQ/pdJrV1VWAk9VntxFeWdRJJsBwJMRLX3NXpW2u7ZT4zh9+A//jjz5FIVs41IWqAPzoEIzpMG0INM4F3KdGpTZcQRaV8ZomBIEnnq57XPuNXPh1IaCMfnp9PcidVZAuwrRwwyNo6X21giwEZJIUikUc3WToha8gOjvb9HbFMw+rTEEwAnsbapXeCqqAfP6Kaiv71EMquNcNVY40Oa86Qd1y14Ebtx9Xn6cyCZ/6mKrrnxhX/95Zg3wGsf401swi1vgsMhRS2gnXxS0WyJVKGEWbUKmcKTCDiGKurL8oP7gcG8amVBkXAmGYoOtIw1RahEJekRLK2hBdV+VUlXOoNEECcD3dh7TLq/kuIhPzHaDybwqhyoE0A8rtedX2bRA6GGY5w2FTUU8I9T1NNzF0gXCKlYopP1mQZRrhCoHUDXQh1fZKRY4SlguUvkJYIZzLd8LwtCpvsouIYkaRgxpIJKKURRojyMikOpZOCRkZR+TTqgTKmwWlC66DSO0gC0sqyyGE2t8W4S+bKRaLOI7DyMgImUyGRCJBLBariIG91ex+dYnup7nqpOjlfNYOYfhZCcrPyn7A0WVRtcjlcg3L7WdmZprahlcOtb29zYULFyqvb29vc/PNN9f9jp9YgOpWtbi4WNEItQNnnlw0c5KllOzs7LC9vU0kEmGp3P++l/CY70lXV5LJJKurq5W2uTf89A1IF/7ur75MfDdOMZtjQkiGDI1XBmFSE0zrEh3KHVk6j/4WgrcPlfVdW62cOmuH6yChv8iF13bZsiwuXbrEE0880R9jMy1VjSMl0gpQHJ0hlN1H5tI4EuyhCQIvfyOhG29rbbuZpFrJ9szuNA0KeRWUx3fg+/8ZfOWz8M0vHxCPXApmL8LzXl7ZzKF7NRhWnaFGp9Q2N55Vv4NQAe3uBuSyiEgUJubRBUQSu0gErtCQugGOg1MsoBcLCMelolzIpiCbQpSKivBoJjg2olSofA/HKXtrlEnF0JA6hrqJDEbLHaKKimBoBnqj9tfeKr2mqwyFP8sBSN1SpUHBIUhsIXJJdYNrhvpMWYStSeV8jS7KJUoHFEOgoZsmWBG0Uk5pJjQTqdmqvW+9YVW2AM7kJRhfUv8IRZWfSL5+tlZJQYQqexK7yOCQ0pZAef84IGFCB6EjsnHI7iMDdRadpIRCWh0niSJmpQIEwsjhqQNxeNVXVPmEv19+oVCoaDW2t7eB/nWJPi+Lai9OIgw/K0F5K6v9/Q6vW1QzyOVyp25De/PNNzM0NMR9991XIRfJZJKHH36Yt73tbYc+/0d/9Ef8x//4H/nbv/3bSqYjnU7z7LPP8v3f//2nGosfZ55cHAd/O9CpqSmmp6f74kb1brRWbzq/XiQajbKwsFC50P/pv/1xvv9/+16eefwaf/Er/5lvPbHMZVPjZSFIuuCahur/3k3UrPC3ffMd2/IxkLLaT8D3//ZTz+Jmc2jh6oCjH8iFlJLt7W12dnaIRqMsLi5WEd2ejm1vExnbViVGuRRaZIxSIIQtbaRtk716OyNvegdm5ATO5pFhJd42TEUGAHSpAvhcOVvxsjfCc++EZx6CbBqmFuHyLYpo+MfpP06GVQ7IbchkFLEIRdT2Cjl1YaT3ITyktBOpuDJ+c0roQ1G46YXw8P9COOXgHrvSktUxLPSCMmESnrGeBtgS4ZRF2YapAnmhQSgMl58D4QjusHKs1ta+CbkEmlNAc/3Eog71l+5BaZGXLSorJIRbgswu0rUhHMUdmUYkt9R29IDKNpVyKmPgZUEkqrysTG7QNUUKwiPI1K76QCCs/mT3FdmogURT+g3dQASHDvpFDY0jRFlP0QACZZonXVuJ1QEyewi7iNQMlaFxbXUOjYDKgNjFwxvKpxA7zyhCVcwqc0TDRFphdV72N5Dzz1X7dgSEEASDQYLBIBMTE1ViYK+Zgl/PEYlEeuaJ0+u5oB3o531oVhjuZTKKxWJfOIafFP1G9E6DVsuiTtsO1rIs3va2t/HBD36Q8fFxFhYW+MAHPsDs7Cyve93rcByHWCzG8PAwwWCQ17zmNfz6r/86//pf/2v+xb/4F+TzeT70oQ8xPj7O933f951qLH5c1+Qim82ysrKC67pcvHiR4eHhXg+pAj+5aBalUonV1VUymQyzs7NMTEwcumGn56aYnpviluVn+NrH/pSshFAxw1DAwBBAscvkopPopTbAG0K99zIZ7Ecex3rRC6peF0Kcqt/1aeE4Dqurq6RSKaanp5mamqpcP72e+OX2Ks5nP45MxiA0BMk4RmYZo7x0La0gI/ubaA98Fvny70TorU1tcnhCdRhynXJ7WKHKcayACtpT+6qW/+H74NmHVdZhe1V97sbbG19r0XHV/enaoyoTIspqZlfC8DhMzKoWs6GIMgIcnYTJOTWG2JYKVG98IWLtKVWKVTbQk4ZZFiSr+UG6LrJUQmhljYJ01W8vXVWkKRSE6QWEaYEVVMHu0ASuriN2r5HbWSfkZjHsvC/bUEswxIG/BuW/NL2st7DLztcF5MgsIruPkIBdQDolCEZxrVGEqzYrsnG1PStUyWrI0JDKvmQTgECgQTGLDEXVNvfXfWShLMh2JQIXiUDsb6rvl4Uc0gwdvbggHbUnTkkRidF55cpt7ykRu1cCZoUqLYgr2Q0PdhGx+Tgil1JkohRX2xNCiczNICK7D7vXkIu3tTQnNRIDZzIZdnZ22NnZwTTNykp2OBzu+upvr+eF02CQAtpG14In2L127VpfOIafFGcpc9HKvmSz2VNnLgB+6qd+Ctu2ede73kU+n+fOO+/kox/9KJZlsbq6ymtf+1re//738+Y3v5m5uTl+//d/nw9+8IO89a1vRUrJy1/+cv7gD/6grV3srktyIaVkb2+Pra0tgsEgS0tLfeeK7E16zZKLTCbDysoK0JxeJPii53PL//gUUgjkbkEFO273V3Iq5QkdWEXq1WPjKGIBgOMi67TK6WXmwtPnlEolLly4QDRavcray7FJKXH+4YvIZByml9RYMknk099CSAdnYgFzYgbyGdyH/h4xMoF4wT2t/chQVAm3C+WVZ69b1FBU9UNdfxqefAg2nlbkxgopX4kvf0JpGa4+H6gTbAkBd36HynQ88y1FWKRU2ZHpRVU2lU5AIg7j0wetZQ1T6Sn2d+Ce71GaD8/tG4nQDQwkQtNVBybXQdhFJTgul3U5i5fRp6cRY/Nq1byQVeU/o3MHq+gjs8jwGNs5izktjbH75MG9WHtfVoTcZS2Fbqisgqapi76YU8F2fBUCEWVUl9lD5FKQS5QzAQ6MziN1Q630uw7SDCCnriBnbkDsLSP2liE4jDQCiHwKkdpDBkLKXZuy14VHBN3SQWlUNo5I76pMQzCqsgyaUSYGh0m7lK4qI9P0cjZIg5FpZCkPpZz6txEol5cV1DajNZ1UMnFEPoUMjyhy5drqmNhFRCGtnNwlsL8Ow5MwMlPRrzTbVca7rmrFwN5KdjqdZn9/v60tTo/DWSjHGSRy4Yf/WgDY29tjfn6+LxzDT4pBPRf10Oy+OI5DsVhsi8WBruvce++93HvvvYfeW1xc5LHHHqt67bnPfS4f/ehHT/27R+HMk4vak+w4DmtraySTSSYmJpiZmelLxtxsKYpHlDY3NwmHwywtLTUlADSeexPB73g1+U//NZgmMpevNggbdPRoojqWWAAIgXH5Yp2XexPAp9NpVlZW0HWdK1eu1F296GnJVqmgRMtDUdWi2XHIJxNYQkPqAjMYVIaF4WFkMY/78P2I57+itYfV9JLKGMS3Ye4iIFT72bWnVFD8uT9ReozRKQhH1fUVCKnswrfugyu3VQWNVRidhH/0Y/CVv1Ldp0Jh5ZuhG6okyrRUa1Sj5r41rHKHJwHPexncdjd85o9h4xlFcIQO+xuI2I5asQ8GFHF1HcT4OFo0gsylcPNPkJm6CTF/M+HI0MF857qInacQsVVm0nGMQACp64hKdsBPLMpdlDQTaViKyOgmFe1IudWtcEpI11Ki51xKidBNUy1cmEFENo5MbsHYojKqK2YVwRhfAiOASO0qjw67AEIofYPrqDawMzdCLqk0HT5S4WoGIlAuQTIMkC7SVKVMMr2HqEMsgHKmylYbMUwOnNDHIFlSHbg8EhMcRi7cqlrS+rdhF8r7rtVuHAo5RGlDlVNJF7H2LWR6Dzn3nPKxOzk0TWNoaIihoSGklBUxcCaTqWpx6s9qDNJKdjdwFgJa13UrZo394Bh+UrQigu53NJu58FrItiNz0Y848+TCj1wux8rKCrZts7S0dEgx309opiyqlijNzs42fYMKIRh659uxnn8rhb+/H2dtg+ITz8DuXlvG3wranr3okiDdj0b6inrQJsbRRg7XX5+0/fBJ4SemQ0NDLC0tNQxAekouvPagTgnHtkmn05iOXa4wEtWBnRlA5suGcq00ZTBMuOt18OVPqoyE46j2tEKDyQW1kp3aV21fQ0PK7A5UOVMyphyzA0esQFkBpdkwTXj8QZWRAPWd218Jq09Cck9lMjxkU+rfIxPq30KDW+9W48tlIBBUpT/BkCI7mgDLQgwPIxaX0ANBpOuCbROIL7NXKLERmiYUChEOh4lmt7D2ngHDwtZNTKGrjEMhq9zEyx2apGYidFMZc05fRQ5Poj31lbJ7N+pzXoAtXVUWVMgiCimV1dAtcFWbXBkZR2T3kaltRZ5QpEpbfwTsPCK5U94X5UEhyCODw+q7o/Mw/1yIrcDeKqR3cCQUA1FCAUuJt8sO2hSy4JTKHuVa2R+86qIqL6a46u9CBjIxCI9CYAhCeWRgWGWljACMLajfr4E0A+p+d131Od1UGRMpFenSdbU/wSgyOIxIbIIVRk5faVtwK4TAsiwsy2JsbKyqxam/dMaf1QgEAqf67fPMRX+g3j40Kwz3yEY/NAloJYvX7zgnFwrXDbmIx+Osr68TCAS4evVqJaXYrziOXOTzeVZWViiVSicmSkLTCNz9YgJ3vxiA3Cf/iuSv/w5kcycf+AlQCVnbQTB6SCya/d3gG7+9561oXddlfX2d/f39pv0gekUuhGEiLj0X+8EvkCnY6IEgoegoMr6JqxuIoK8EMJdBLN2ogrpWMXcZ3vAO5Uvx5DdUcD+9qDIM0qmsipOMHZCLYkGVTtVrRVsLTYe7Xg+Xb4WtZRWMz1+BsWkYHoOv/CXsbSrCUiyojM1z71LaCQ8LV+FVb4anvqFa5s5cQC5cRjg5xOrDSMNEmAai7L+hnKiHCFoh5vQi8fFxMrkc8Z0tAoknKCERIoAUOq4RgIAFgSHckVlVZlT2znAj48iJCxAZU5qPvRXE9pNlfUOZVJQF4aKUh9SO+rcZPPCb0E2VAZBSeUUEhhCxFbWyb4WVT4eXNdH0SvZRZPeRkTGleTGDyJkbkTM3Ila/SXHjaexAFDSfcFuiypKc0sF14HpkSSjCU9GOCFVu5Tqq/KqYAyuEnLyMnL1JZS08UX49RMaRoRFETnWRksEoIrVV1u0AUlOi8fCo+tsMQnIbJg9nLtuFRi1OM5kMe3t77O7uVla7vQCz1azGObnoDxy3D0cJw3tRTtcIZ+FcQGv3RTarmnGck4sBhZSS1dVV9vf3e+4u3AqOIhdem1DTNNtKlAKvfBnG//xr7Acf6mgHJz8OBeaibJnV6u8L9Z9+JxYAuf/+Kczn3kjwlS+rer1b5KJUKrG8vEw+n2dxcZHR0dFjv9NrzcXO3E1oTzxMJL2L5aoVczcYVoFqPqMIQDYFVhDt+S8/+YMqEoWbX6wIxNpTarugAn4rqLQTxby6PnNptUp90wsrweexx0kIVYI1vVT9+pXb1G899oDqGBWJKh3Hc+44vI3pRfXHf4wAxqYRaw8rLYN0VYCrGUpfIUBzbMaGw4xNTOBm9hG5ZyiiUyq3n83nc9i6TtAt4BghrIgA9tW4gsOqzay3D4EI0oqo7nLS9mkxZFkUr3xdkCjvDSusiIZTUlmIiYuI9J7qABUeU4sCbk05ligL1t2yQDpUvYAihydh8xllNGgpPUali5WmIR2pSKEVABuVSVAbVn9permF7gQgkPmkKr+auoKcuHhw7o+Cbqoyp51nIJdAaAI5tqB8QtI7EIqqDIhX8qZp5ePilg9l52es2pVsf1bDX5/vEY1+r89vF87CanmrQXmnHcNPirMi6G7FadzLXLRDc9GPOPPkwmPqCwsLjI2N9Xo4TaMeuXBdl62tLfb29hgZGWF+fr6tN742PMToe/416d/9L+T/8vPKybeDaBSYV8qk4HiS4a1utnNgTeIkxAJAFouk/9PHCLz0TjCMqo5MnQ7gs9lsxXH7ypUrTU9svQo2HMdhZWWFdLbA3Bt+lGBqG+I7iECQpDFE8ZH7mUptI+0SYnoR7YWvQrv03NP/sFeK5NgqyBQaTMxB4Rl1wmMbSrh7612qVOm0EAIuPRcuPkdlLcwjVssbQE7foFbklx9EoCGDYVXiY1iQT6vg3lAZFs0KoVkBQkDICrO/H8c0lZaiWCrirDyCIyTCCmPoAiP/hBIoLyptibCLEBlDajoisUFVYaBXElQ215NWUJUblfKq+9PIrPr37jOK/HjXlivL/1/OYHqO4GjKs6L2GhyeIh8aJ5iPQ0kihYawCyo74JTJlW4pkmIEwc3h+VBIIcqaClO5egciEB5BFDKKtLTSbSwQUXqMUk6VoVkhJW6/9nW1HZ+WRhTz5e2bPSHr/la2U1NT2LZdIRr7+/sVEz9/VqOe59N55qI/cJp9aMUxvNPE8yycC2iNXJxnLgYcpmly00039XoYLaO2W1SpVGJlZYVcLsfc3Bzj4+MduRn1yXFG/s1PM/wz/5TMH/4/ZP/Ln6o3TBNMHfaTp/6NZvQJhzIZR32mSYia3z4pWtFX1IWu42xssftD70Tm85g33UD4R96CuLTY0YDDKw0MhUJNC/899CJz4e9gdenSJYaGhmD2oO7d3dlhXwsye+WSKiEKR5Wwux24dAtMfFmZ3EWGVaCfTcLUPNzxGohOqHImj4SU0fI9KV3Y31XB9Oik+p162o1CRomZNU2t9Bt1yrCEUKU8+SQisaVKjTRdfde1VVmTR1jKGgax8zRee1wTF8uQSGsY17HJGxGKJZts0UV3XaztZYp6lODkPFYoipaNK9LlelkL1N+6Xi6F0pDBqOqQZOdB6MiJi8jpq+o3y+VIFehea11BxYBPaCoQt8oPYNeBTLndayBCaniRUmiMYFhpNFxQepFSHtJ7ZVPBnCJBmqYE74aFcIrqPg6EFHEB5dCtm0o70SqEUMfbQ3AIRmcReytIW5kcilIeaQSUeL1PYBgGIyMjjIyMVNXne94aAIFAoMq4zT8XDHpAOOjjb2dQXq+cziuh8hPPTgjDz0rm4iRlUcd19hxUnHlyAT0Wo54Cmqbhum6lm48QgsuXL3eF6WqWifX8W8lH/ydibFS1tlxZO/V2T7La30/T/0mzFVUoFMB2cPcTCMOk+NDDlP7DE/DTP4G8tNCGUVZDSsnm5iZ7e3snLg3s9j3k72DVqPTPG5MIhI4WU58EgRB8+w+rDk8bz6hV8MkFeOGrVYbhCDR9nLaW4Wt/U/GuYHRKibsXrvo3hth+SmkT7AIgIBDGnbtZOWLXQgjk4vNUB6XUjvqOYSGnLiNr6vzlzI1Ka5DYxHJyCBlCji0gcgl0EyJlJ2rHcSiWSpCJkdnbZDNdJIxk2gErt4cuJZ5+QWkwdM9bT+k1Ltyu3teNqsBdRmcQ+xsqq6IZ6hgf7Ag4RZVZCA0rgpJLIdYfRuSTirzoJsNugOzoBeTsQcBeOfrJbcT2E5AoqsBeMxQpExoyoOZQGRhW3yjmEMWcKocy29PrXU5fVYRjfxOcInJkTmlNwqPq/T5brfXX509OTmLbdiW4TCQSVcFlv2sWm8EgxgS16GRQbppmXeLZCWF4v90LJ8VJyqLOMxfn6DqEEKTTaba2tohEIiwtLdVNUXcKxpWLiHAYmc4gQiEolcA0wHZOpMloS2DeQ7Rt/LYDmkAbKtewSwuZzcKf/jnyX/3vp9169U/ZNisrK2QymVNlvLpFLqSUxGIxNjY2ju1g1XGMTsF3/KhqF2uXVLaiXQ/yZAy++OeqA1VUaQ7YXYcvfUKRmvEZ9bnEpsouGJbKWCCVO/naw7hX7lIlOLUwg8gLtyMLGRWgByL1V+N1A7l4G3LqMjtPPUZ0YoaRqTl49quIfPrgY7pOSNPACTI7v8iwNaqM3KTLROExgtJF46C0TxnwucqZ2wqVSYUv0yJd1dkJkFNXELFlRHJbZSXMsqmepinhuHSRoRFFRFYfUoZ0oajKaNhFQpkdpBkA6mQDotPIyBgyu6+ICaiMTXAYDAuxtwLJLUQ+jdRN5MQF5NTl+ufLdVQGSGjqeDZzDwlN6S/GFiqtegcJhmEQjUaJRqNIKSkUCpWsRjqtro/NzU2Gh4crWY1BWn0+CwFtt3QjnRaGn4VzAefkwo/rglwMYubCcRxc1yWTyTA1NcX09HTXbz59fIzgG7+d3H/9C9z9/bLJXgODrWNwTixq4JtQhBDKa2R1HTLZdv0C+Xyea9eu4bruQVnRKdDpe8h1XTY2NojH4021Vu7afe11hmoCTd+jjz0AWytKuF3MQ2REuXXvrMHT36yQC6VnwFdyI1RwnY0jUjuq1Kn+QA4E2MchECFvDjNklQ3xRudg/VFVamZYKEKThEAEMTzFkBlQ/grT0zg7wzjL/4BbyCBcW8XQEjQEUjdgaLKaWOSSiK3HEdmy6NwMIkcXlM4kKCE0rI5HIUNFHD62CMWsyliEolWlXa5mEsjF1OfqaVR0U2V4hqcOlUTK2RthfBFpFxT5qkfUQBGQ3WvKs0NoyjF8+ooSuTeLAQ+chBAEg0GCwSATExNks1lWV1cJBAKkUini8XiVniMSifSdMW0tzkJA26t9qCcM98jGSYThZ6UsyiMX592irhNyMWjI5/MsLy8jpWRoaIiZmZmejSXyo9+PPj5K9pOfwc4+qVbdgzrkCoc+W3HKrXw5hMzmy/3qBxOn1lc03HBNqOO6YOi4ht6WB0YymWR1dRXLsrh8+fKpH/SdDuRt22Z5eZlcLtdS84V+XDQ4dkwrj8NXP6eyFvkMICA8BHOXVCC+v1v5qCgVVMmQH9614ZToBOToPOTTypOhWCa7gTDu7HNUO9jKMATGxCIiF1dlW/lypsR1kAjsUonkziY5OUR4eJRI0CK49rDKWgTCyq08m1CtZs2Q2i3dhJCpSIZ0IZtQn3VKFR2HH67Q0Msme60K4AFFKBqRCoBMDLH5OLiu0n1It9zlqoi88IL62pcmMcjBrRcITkxMEAgEKl2HMpkMOzs77OzsYJpmlYlfvwWPg3z8PfTDPviF4bU+K80Kw/thP9oBb+4/z1yck4u+g9+PIxgMdrUMqh6EphF603cQfOPrKH3zEZK/8mGctQ3fB4Qy73J8LXMtC8ZGcFMZhBCIPgwAm0FHsy25PLJsZCVtB+m4aK94iTp2p4CUkp2dHba3t4lGoywsLLSlrKiT5KI2w9KswK2dD6OuPdgKObjvL8tk0lAmea6r3L/3yqZ90QNiJcOjiFyiuqzGVV2UZKBDQkBNR87drPQX+RRS0yEyXj+Q1g3k4vNgeBKx9i1Ix1TGJDiCrhuM5NLoyTW28yUyxX0ms5sQjBBI7GB4LttOSQmwAxH1txFQ+5tX2RI5NKFe9wzqfARHd0s41vAh5+x2QSS2ELaNjIxWXpO6gcgmkOk9leW5DuEXrtbrOtSoZMaf1eh1MHkWAtp+3IeTCMPPWubivFvUdUIu+u3mqwfXddnc3CQWizE6Osr8/Hwl4OoHCCGwnncLo7/078n80X8l/xd/qVyMdV1F3x650HXE5Dgl00CzbYR0oWSrz2pCBVIDwDU6XsYlJTKbRWoawpUYVy+ivf0HIZPCdd0TEQK/Y/v09DRTU1Ntu/Y7dQ+dJsPijaldD9h2bOdYErbxrPKxmFqAzWchn1M+DJquhN1zV5XnhTemsQWlR8jGVWmU60IpjxyegOHJU431mB1RHg2hw07yh6AbShOx8wyMh8EKIwAd0HWTMafE8NIsxT2Jnt/EzSZw7QI5zUDTdQyho5WKEIyqDEWxbOIZGFLCdcNSHaNGphGx1YMsRamARCM3NE24U3N8IaPKu/woZ0+EXTj1VDYIz6ajUG/8mqYxNDSkSuekrGviZxhGVVajF7qqfgzMW8Ug7EMzwnBQTTxM0xw47Y4frutWCPdx8DIX592iztExFItFVlZWyOfzzM/PMzY2hhCi0i2qn2AszDFy7z/HWJgj/Z//AGz7oMRHgAwFKRk6uq5hDg3hahraSBTn6Wersxt9jK7oQzQNMRQh9IZvx7z5BgIvfwnJXBYyqRNlCIrFIteuXaNUKnHhwgWi0SaCwhbQ7syFlJLd3V22trZOnWFpxwO2aw9pu6jul0BIGentbapVecdWfgh3vhYmD9rtEoriLj0fsfssIptQNf9Tl5CTlw6XS/USdkHtQ22JkWFCKYvm2ASHR9FiJsgi0gihSYHjOqoblTBwcnnyUzcSCgUIBMOI4QmVrYBym92bwQypDlOujQyPkgwEcIId9C8KRBC5VDWJKBvgyZO0rPVvZkAzutD82IUQWJaFZVl1S2YSiQRAlRA4UM7odhqDEJgfh0Hbh3rC8Ewmw+bmJvl8nrW1tVMJw3uNVs7HObk4R0eRSqVYXV1F07RDpmaapmHb9hHf7h0iP/IWzOffSua//Amlx55ExhO4oQDu6AimaWIYBm46gT46wuiHf4n4P/s5nKeePVGXqW6hY/qKenAc5G6M4n1fJfT61yAsE5E/WIlvBf62rVeuXCEYbE8rTT+EEG0juq7rsra2RiKROFWzgkF54FRhcl4Ri2xKiblDQ5DPwv62anF7wwvU56SE+LbKbkiJnF5Czt+iVuxbMXjrFjyDPrt4QAhAicJ1S70fiiJDw2iZOMIMoOkaBiAtC4KjUCqQLtrslHREKkUobavyinAIyy0o3cP4kiJWrg26RWF1FaOD14EcmYH0rhK0e5qLQkZ1nBqaOH4DZxQn9bmoVzJTm9XQdb3KxK9TWY1BC8zrYdDLiXRdr5QFzczMYFnWqYThvUYr5+O8LOoMoB8nEH9t/NDQEIuLi4f0Ff2YufDDuu1mrF9+t5oI/v37sR55HMt20HQXN50AxyH0xm+HdBaZycLEOMRi5a5T/YVedbOyn11m/9+9l/Hf+VWE1hq58Ldt7XSr4nZlLkqlEsvLy+TzeZaWlhgZGTnVmKC/VoCPnWtGp+CmO+Bbf6/0F6alyMXIJDz/noMubA99CR76eyiUBdVWEJ7zInjxt3V+J04C3VSmfNtPqX8blipxKuWVaZznK7H0AmQ6jihmFGnSTQiPYEqJGRxl8fJzKDqysrK9v7EMuS1st4hpaGiBCNrMFcT4Ync6MEXGkbM3lbtFZVXmaGhCdYs6hZgbzkZwe1qYpsno6Cijo6NIKStZjUwm0xWH6EE//mfhGvKLoE8rDO81WiEXXubCv6B8lnBdkIt+g23brK6ukk6nj6yN73dyAVSCW/1H34z2F5/BfOQJZC6HiIQJvfF1hN7yXZS+8TAUS2ihIG7bfLLbh562yQ0EcHdj5D/3BcR3vFqNp4lg2XVd1tfX2d/fb6ptaztw2iA+l8tx7do1gENZurOEY4/Ti75NuXE/+Q2VwbjwHEUcphfV+5vX4BtfVBkKr0Qql4aH71NajWNM/HoFOXkJALG/rnQTulE27/N5RwSHkZdeBOvfUjosM6jKqYRATlxEGBYBQ7lCj0eHIL+CW3IpaGGyJRs9nUCkv052P4kxPt+d+TE6gxyaVL4hrfhcnGF0wqHb38p2amoK27YrRMMvBPZnNU66mCKlPDOB+aDvQyMRdC8dw0+KVsuivBbPZxHn5KLLyOVyLC8v47ouFy9eZHi4ca/0fiYXfk8Cr0NI4e3fz8L4BO5+Em16Ei2iViu1yXEwDaTj9N1Dudf+G0LTkKUSpW8+ivkGtSp9XHDqX/1vpW3rqcZ5yszF/v4+a2trBINBLly40JaHQLsyF11/OGsa3Hi7+lMPK48r34cpn2A7PKyIyPJjHSEXbTkGmoacvoIcX6w4g9cz75Ojc0jdROyvKQO78Ihyrh6erv5geg+tkEGLTmAIjbAEx3Vwk3vY2T22HHUNlUoltre3T+0SfPS+6cpjo80Y1MCwE+SiFoZh1BUCZzIZUillwhgIBKocolsdz6AefzhbBAmOPxfddAw/KVrNXAyyeP04XBfkoh9uPikl8XicjY0NgsEgS0tLx3bGaWedezvhF6B7we3Gxoa6scZG0cZGqz5vLC1g3X4bhfu+BsEAZLN9kbzoNbEAkEn1kMx//ovYdgm++zuODJaz2SzLy8sAXL58uWv1mie9h6SUbG9vs7Ozw8jICAsLC22fTAeqLKoZlIr1Sbimq1KqfodhHV0yJAREp5DRKXXuGhwzYZe9dLzuTAIMXYdwBNOwGL58QyUT5m956l/J7Gcjt366bvsdfiHw5OQktm1XVrGTySTxeLyyiu2d/6MWMM7Sse+H+OY0aKV9q4dWHMO72f64Vc3FWc3ew3VCLnoNfwnL+Pg4s7OzTV2A/Zi58MTDtQL044jQ8L/4caTzOxQffEjpL3qIfiAVhyBd7C/fT8SxkbfUX5n2PFDaufrfLE6SuXAch9XVVVKpFDMzM0xOTvZtvXQ7t3XqwGVyHh7/Gtgl1W0JVAmRY8NsA0fuNqAnAdcRx12aIXWP1rpv20WIjKNpGrquY1kWMzMzVUZu29vbABUjt16tZJ5VdCNzcRQMwyAajRKNRpFSUigUKqvY3rm3LKtCMmvPfa/H3w6chX2A9uzHUY7h/vbHnRaGSymbnmPy+fyZFXPDObnoOAqFAisrKxQKhZZLWDRN65vUp5SSvb09Njc364qHvbE2gjY6wsi77yX3558m9VsfhUJRBUzInmQx+m46dhywAgS/9hBuIgm+SUdKyebmJnt7exUPlG4HSa2Si2KxyPLyMsVisSOtcb0xwdlahQRU2dNT31CeGMEQIJToe2oBrj6/16PrHoYmkOERRGZf6Rw0TQncDVOVUflQa+TmrWRms9m6WY3jVra7gX6Y10+Lfhi/V7ceDAaZmJioWsVOpVLE4/EqPUckEqnMn/0w/pOiFTfofka7SVI7HMNPCtd1m9YBZbNZZmdnT/2b/Yrrglz0agLxDMIMwziRgNWbNHr9EPKbs01OTjIzM3NoPM1kWYQQiGAQYVkwPITc3QPb6eTQq9CXGQshqGjcNQElGze2D3Nq0rFtm5WVFTKZDHNzc4yPj/fkWmiFXGQyGZaXlyvZrX4XrEkpcRwHKSW6rp/qYd2WcxMIwau/Hx65H649oozzbnwhPPdOpb24XqAbyPlbYecpRCYOjosMDiEnLyrH8KO+Wmclszar0ev67EFGPxP6o879zs4OOzs7lQDQWz0exHPvPW8HmSDBycqiWkErwvBmSuqOQquai/PMDCTaNgABAABJREFUxTlagr/OfHh4mMXFxROl4LyLtJe9rAuFAsvLy5RKpSNbhzYbfBqXlhCmCYaBGB/HjcWVEV+H0VePQo9MCHHQehRU8BSwYFIFTvl8nuXlZRzH4dKlSwwNDfVsyNBcQOF1DwuHwx1tjQvtyVxIKatIsecr45lYen+3us1TIzwEL3oN3PFqygM6/Tb7BYUMIhMDx1Z+EVYEkdiA1I56f3gKObYAZgACYeTCbchSXpVHWWGVwWgBjbIatfXZvchqDGpgOCglObXn3nVdstksqVSKVCpFLBYjHo93vTa/HRiUc3Acur0f9YTh3nxw2oWHZheCbdumVCqdk4tzNA//SvNp68z95KIX8DIvpmly9epVAoHGjrTNlnAZN13FuuP5FO57ABGwIBKGRLL8pq48MNq4v7XZip6TjEgYSrbaR7+7uWmAdMm//E6GQsFDx77XwtTjyKO/dGt8fJy5ubmuPSxOEsx716rruriui2VZVf92XRfHOciqaZpW+dNVDHjgcAj7G2hbTypX8nLSDqcEQgeznNndfgqyceTi85QwXIjDzt+nQLNZjUb1+u1CP6/+N4NBDGo1TWNoaAjTNEmlUszOzlZcortZm98OnBVy4bquqmroUUbeE4bXltSdRBje7ELwWfe4gHNy0VZ4nXyklG1Zae4VufBnXqLRKAsLC8dOsP5V5KMmCSEEwz/zT9D+7/+Xwt98CZHNIXUdMTmOPhLF3tqGRKo9++H9Zlu21iYUSxhXLmKvbUIuq8iUrqMNDxF447ez87IXsb+/TzKZbPrYdwNHnVPHcVhZWSGdTjM3N8fERHeci0/TwcojEEAlQ+HPUriuW1UuJaVsKqvR7H1wXaKYQ9t+EqQDkTFFGjJxyOzD8BQEy/OlG1JlUKkdlcE4Aqc9zkdlNbwuRP2m1egHDDox8mBZFsFg8MjafH9gGQgE+ua+Pivkop/mytMKw5slF547t1eqdRZxXZCLbpiLxWIxNjc3O9LHv5vkwm/w10rmpZUSLi0SZvgn3sHQ23+Q0mNPknjvrx6s4LepjKbnxELXy4L1GpRKOJvbjP/WL+PuJxChEELX0OdnccMhePRRksnkkeaKvUCjzEWhUODatWs9Kd06SVmUnzBA4zpf73XvweFlMjxi0hdZjQGDyMShmD8gFgCujRAaspRTc4AQqjOU0CCbgGPIRbvRSlbjpN4KHvopqGoVgzx2qB+Y16vN9859LBZjb28PXderTPx6ufBzVsiFl7noNzQShvsXH+BAGB4Oh5u+LzxycZ65OAM4rQlYI7iuy9raGolEgomJCWZmZtoWZPgF3d2A3+Cv1UDxJERIhIJYt99G+Ae+h+yf/BnOzh6USi2PuxY9JRbBgPqTyqja8DrHQyaS5P/yrxn+Z/+fymvFYpFrzzwDwNjYGNPT04e+10vUu39SqRQrKyuYpsmVK1eOLJvrB3jEwiPArTzQ/OTBn9Xwl1F573l/90PGqa8gncM3pdBUeZSU1SWL0gWtt4+n86zG2UUzgblpmoyOjjI6OoqUsmsdh5rFWeoWNQj74CefU1NTdYXhAIlEAuDI+cArizqt5sJ1XT784Q/zp3/6pySTSV70ohfx7ne/m4sXL9b9fDwe573vfS9f+MIXAHjDG97Av/23/7Yj2o/rhlx0An6x8+LiIqOjo23dfjfLojwPhUAgwOXLl1uu8T8NEQr/4Pdg3nwjhS/+L9xUmuKD38RdXT/IZtSDXwhdRs+zFaBa7BbLBOmI85b79AG58HuHAH3ZXclPLvxtiYeGhlhaWupJIN1K5sKfsWiVWNSiUVbDtm3S6TRCCGzbriIx/f7w7EZQJIPDCM1QPhVmmYiaQaTrHvhYSAmlHGg6crg75XXNojarUSgU6gpB25HV6HeclcxFs/C3sgWV4feyGv6OQ/6sRiebWcDZ6RY1qNdSrTA8k8mwvr6O67rHCsO9zMVpg/rf/u3f5uMf/zjvf//7mZmZ4QMf+AA//uM/zic+8Ym6MdxP/dRPUSgU+NjHPkYymeT/+D/+D37+53+eX/7lXz7VOOrhnFycEIlEgrW1tcqqbScCwm6QC9d12dzcJBaLncpD4TRjFUJgveBWrBfcqrYR3yf1679D4b4HkIUiIhjAfN4tlB55TLWn3IuplU0fWiEWssnPtQxNO+gEdcxxkKk0bj5PPJNhY2Oj4h3y+OOP9209s1cStLGxQTweb9iWuBfjOuo9v8bitMSiHjRNw3EcNjY2KJVKzM/PYxjGeflULUIjyNE5RGxVCbo1HewCDE2o/8/G1ed0Ezl56dh2s72E31vhJFmNQS9pGdSA0MNpj79hGIc6DnlkI5VSmsFAIFAVWLb7eA36NeShl90w2wUvywkwPT1NMBg8JAz/3d/9XR588EHuuOMOpqamgNORi2KxyO/93u9x77338qpXvQqAX/u1X+Oee+7hM5/5DG9605uqPv/1r3+dr3zlK3zqU5/i6tWrAPzCL/wC73znO/mZn/kZZmZmTjyWerhuyEW7yqL8XXE6LbjtNLkolUqsrKyQy+WYn59nbGzsxBNVOw3NtLFRRn7+53B2Y8hkEn1uluK3HiX587+C6ziHAve+yFgIfJ19mjgGpsHGzg7xcjnd7OxsRVTcj+TCO7/PPvssuVyuZUPITo6pERoJt9uNXC5XyTxdunSpqjzMIxheKVU7Wt0OLIRAztwEwSgkt1SXqLF55MgcIBHZfQBkeBQCQwPVKateVqORY7RXQnOO3qGdgbm/49Dk5CS2bR8imu3yUfDjrJCLQSeqHvzPmXrardtuu40HH3yQz372s5Xv/OEf/iGpVIpXvOIV3H777S1dF48++iiZTIa777678lo0GuWWW27h/vvvP0QuvvrVrzI1NVUhFgB33XUXQggeeOAB3vjGN5501+viuiEX7YAXjHvOihMTEx29KbxgsxPkIpPJsLKyAsDly5dPnZ7rBBHSJ8crng/m1cuIUAi2d6o+U0Us/KVSDfQOHYM/W9EEObAXZtlPJg8F6f1KLryguFAotOV6aQeOIrTNCrdPi2Qyyfr6OsFgkMXFxUOlELVajb5rdetDV647TVPu2jUO26DKps4CjnKM9mc14MBIa9C0GoMeEHYyMDcMg2g0SjQaPZJontbA8ayQi7OQuYDGZoBeVuMd73gHb3/723nyySf5nd/5Hf7mb/6GnZ0dPvKRj/CRj3yESCTC3XffzSte8Qpe+cpXsri4eOTvbW5uAjA3N1f1+vT0NBsbG4c+v7W1deizlmUxOjpa9/OnxTm5aBK1wXi3Woi1m1x4na08o7MLFy60pTa0nZmLetDGRgi84dvI/l9/rH7H+13vPxLVacp1VXCvH0Mu6mg2msJR3xM0tU0JOKMjXByfJBKN1my+/8hFMplkZ0eRun4Ubtcer06XQXm/sbe3V2nXPDc3d3yXtDqicG+c131W44QYhCC3XlYjnU4Ti8VIJpMkk8mB02oMwnE/Ct0KzI8imrUGjn4fhWZwVsjFoAi6j0Mz50MIwY033siLXvQi/uZv/oZf+ZVfYWhoiC9+8Yt88Ytf5HOf+xyf+9zn0DSNP/mTP+H5z39+w215ovDa6yUQCFRE5bWfr3dtBQIBCoVCU/vYCs7JxTHwi1c91+FurjJpmtY2clHb2corxWkHuqEPibz1LeT+7JO4yRS4EqFr4LgHJMIrmWpmn04awB/1Pbc5YgEQeOAbZH74neQmxgm+7tVE3vYDaMNDfUUupJTs7Oywvb1NKBQil8t1XKTYCupdu6fpCNUsPJ1SIpFgcnLyREaZ561ur094waZlWcRisUp3wXolNJ44eNCyGoOAXgXmjVodZ7NZdnZ22NnZwTTNqvPf6L4fdILn4azsR6PMRT14gu7R0VFe/vKXVzQTa2trfOlLX+LJJ5/kwoULR27DK60sFotVZZaFQqFui9tgMEixWDz0eqFQOO8WdRqc5OJ1HIe1tTWSyWTPxKue8/VpUSwWWV5eplAodKSzVTc8OdKlItm7XkjgS/ejjQwjkmlkOgOaAN2AoTDEE4oA2DUeE5qmXu9R4F75VSFUtkVKsF3c+D65P/sk9uNPMfqB9/QNufAT0enpaQKBACsrK30xNg+12TJ/yVGniIXnA5PP55mfn2dkZKQt223U6rYZw7/rEo4NqR2iqVXMYgBCQgnDxWAcE3+5Xj2tRiaTYWtrC+jPDlSDHhD2w6p/batj13UrWY1sNksikajoOeq5Q5+VFf+zXhZVD41a0S4sLPCDP/iDTf2eV+K0vb1dRUS2t7e5+eabD31+dna2Su8BKi7c399vu5gbriNy0Sry+TzLy8vYts2FCxeI1pSvdAvtyFx4fgSGYXD16tWOiAk7WRZV5Rj+lu8kkMpgP/K4IhZI0A1EOIhMpv1fqt2IKpvyfDQMA8plKJ1GpYRLCDBNNQbvoea4EA1Sevgxil/5GmJ6vOcBfKlU4tq1axQKBZaWlhgZGal0QOn12OqhdrW/U8SiUCiwsrKC67pcvHixYwZIx2U1/C0or8vyKcdGrD+MSO0QKmTRbR1tNYUcXUTO3jhQQnA/6pXQ+H0VarMa3Wh32ghngVz02/g1TWNoaIihoSGklFUmfvXcoW3b7rt9OAn68VycBK20Bm5HK9qbb76ZoaEh7rvvvgq5SCaTPPzww7ztbW879Pk777yTD37wg1y7dq3ig3HfffcBcMcdd5x4HI1wTi7qYH9/n7W1NSzL4urVqz2tMT8NufCXtQwPD7O4uNixzlZegNPuzIXjOKysrFQ5hvO+d1H40ldI/tJ/VAZcQ0O4W2Whdz3S4GkhdA2MEOTzqnyqC6LvKm1IJAzZXPkFUcmkCNNE5guUHn0SMfOSngbw2WyW5eVlhBBcuXLlUADdT+TCm8Qdx6kiFp1AOp2utJ6+ePFi10sjGxn41ZZPDVxWI59GpHZUa1orjIxOg3U0aRPJLURyGxmKUiwJTNNEWgZifw05PAlD/dvCthZHBSK6rtcVBvuzGp1ud3qW0c/HSgiBZVlYllXlDl1r4ieEYG9vj0gkQiAQ6Ot9aoSzkrnwSFIz58DLXJxGu2tZFm9729v44Ac/yPj4OAsLC3zgAx9gdnaW173udTiOQywWY3h4mGAwyAte8ALuuOMO/uW//Je85z3vIZvN8u53v5vv/d7vPc9cnAbNnHC/58PIyAgLCws9v+hPKuh2HIfV1VVSqRTT09NMTU11RbzWzuDTnz26ePEiw8PlbjKGQfBVLyP/11+geP+DyFy+7OirHyYLXrbAdZXB3WgUKVD6BsPE3djsGME41CLXK+HyD88jrlKiDYV7WhblkepgMMjFixerVkU7Ldg/CbyxJBKJioFVJxCPx9nc3CQSiXS09XQzaJTV8AvEYUCyGuldxMZjiGIWhK7u4cQ6cv5WCB2RKU7tIjVdlUJ6MAKQzyByCeQAkIuTmLjVy2o00mp0Oqsx6KvNgzZ+vzs0qOzy5uYm+XyeWCzG3t4euq5Xnf9ezlOtYNDORSO0QpLa5dD9Uz/1U9i2zbve9S7y+Tx33nknH/3oR7Esi9XVVV772tfy/ve/nze/+c0IIfjwhz/Mz//8z/NjP/ZjBAKBikN3J3DdkIvjUCqVWF5eJp/PMzc3x/j4eF9c8CfJBjQMyjuMdmYukskkq6urmKbZMHsU+dEfwH7yWZzNrbKY2ik75Pk7OpX/FkL9KdpgmYpoGIYK9r0hn7SDVB0c6mYVDEIufyD6dhyVOYmEkak0Ihgk8MqXInLZrjiyV41VSra2ttjd3W1opNgP94Ifnjne+Pg4qVSK1dXVStcVr7SgVZf5er+xvb1NLBZjbGysLwwDa9FKVqOvROGug9h+Wpnohccr957IxmHnaeTSC44ob6oh6L11uOkJms1q+H012n3t9tu90Ar6aZHkJDBNE8uycF2XCxcuVGU1vBLWYDBYIRudOP/twvWoHWkXudB1nXvvvZd777330HuLi4s89thjVa9NTEzwG7/xG6f6zWZxTi5QJQ+eCVa/9PD3oGkaJU8n0AS81edAIND1kq52tM2t0lccY1Jo3nSV0fe/i/Tv/iGFL3wZDAMRHUbuxlTwDgcCT68MKmBReNNrGf5fX8PZ2lGah4Mfr/6BYFCVULUy/vLfVdO4aaKNjyEzWWQiqcqzhIYwDcjlEKEgw//ix9FnZxDPPtvVB58/w3WUd0s/ZS787Vunp6eZnp6mWCySTqdJp9NsbW2xtbWFZVkVohEOh1t6uDqOw/r6eqUcb3y8/1fD/VkN7z5s1sCv64FHPoUoZKrN8oSAQASRSyKLOQg0mIeHJxDpHaRjH9xwpQJoOjLUHoF9p9FuE7dGWY1EIkEsFmt7VqMf5oHT4CyslvvLcLxWtqCaTnjnf39/v3L+ve5TvdTq1IPrugN/LqC1ayqbzaJpWt+1dW8n+ucK6zAata30NAmRSISlpaW+uumg+W5RfufwXpV0nbazlT/Q9fQVx92sxoVFRv7DvSR+8YMUH/gHhO0gAwEoC6YQQmkwhFAZi9kpiq9+GWNv+2Fi//vP4LguICCdrt5wwEKbHFdlU06dUiuVIqkiJA2dwh01eYqhCK7rol9YYPgnfozSo48jwmECL38J+vRkedPdK4sqFotcu3aNUql0bIarH8iFl62oJ9z2uq74g6t0Ol0VXEUikQrZOOo+98wyS6USS0tLDA0NdWX/2gnv3m/WwA+6fG6lpHIPVUH43mvw1egMpPcQqR2sUhZD5hEiiBxbgkhvXeP7AbVZjXw+X+lC1K6sxqAH54M+fmi84m8YBiMjI4yMjFTOv5fV6DetTj907WoXWs1ctLrgNWjor0i6i/AHslNTU0xPT/fliW6m1Mi2bZaXl8lmsz0t6TpN5uI0pVzC0Bn5Nz9N7hN/Sf7zX0Jks8hcDndzR9VxmwYYBtpQBPd7/hEuYD/2OG4iiT42ijAN3LEoMplGZrJg22hDEdAEIhRCZjKqhGpqEnb3oFju9hQMqFInjiAWAK6LLJZwMxnI5XA2trGvrRD+/u9BBKpLdzrlyF6LTCbD8vIyuq5z5cqVYzuI9Zpc1DpuHyWcqxdceVkNz4k0GAxWiIY/uMrlcpUs5qVLl87MytJRBn7FYrGS4SiVSt3RagSHkVYEUUhDMHpQkljMICPjYB2RPdYN5MItyNQu2eUnCQRDmLMXyq1o+28OPwrd0MGFQiFCoRATExPYtl0hGv5V7X7oQNVNnAVy0cyKv//8A1Xn36/VCYVClfPfzWYVrbRv7Xe0Qi6y2WxHunb2E87+LFIHuVyO5eVlHMfpqibhJDiOXHjdfaC7zuH1cNLMRTP6iuMgQkHCP/A9hH/gewCQjkP+U58l9+nP4cbiGFcuEX7zm0hcWEDu7uJmc6pUqlxypZkmTIzhRsK4sTjSlbCfRBoGYmREfTadQVqWKnMKBsE0cZ9/C4VsFvPJZ9FyDUqopMTd3asY/MlcjvR//gNKDz1M9F3/CuEr++pG5iIWi7G+vn6ibF0vyMVpHLf9D9epqSls264QjVgsVmnv6AkgY7EYoVCIxcXFMxtk+cunstks6+vrldVOoDtaDd1ATl1BbDwK2Xi5GYONtCLIqcvHkwTNgJFZkkM5RRKHp9o7vg6jVyTdMIxDxPskWo1Br5M/C+TiJOeg9vx7Wp1sNsv29jaguhD5sxqdPM9nKXPRyjWVz+f7qvy+EzibT8868E56PB5nfX2dQCDA5cuXTy367DQarWRLKYnH42xsbBAMBrlw4ULPnVxbFXS3oq9oFULXCX3X6wl91+urX9/dRUqJeeMVRDCAzGYRQz5ClsthXFxi5D/ci7O2jjY6gv6cG7C/+Qilx55Ci4SxXnYnjETZXF8nXnY7D/7GRyl+4cuNB+S6YJlo0aj63UKBwn0PUHrwIawX3V75WLtME+tBSsnGxgaxWIzx8XHm5uaangx7lblot+O2YRiMjo4yOjqKlLIigEwmk5WgWghBMplsiyi8n5FIJNjY2KiQKU+r4T/mHTXwi07hmgFEclu1og1EVCvaQO8WSK4n+In35ORkw6yGRzTC4XCFcA96cD7o44fT70M9rY53/tPpNPv7+1V6Di+r0c7jdtYyF80uSOVyuYHQ8Z0G1w258ByH4/E4Y2NjzM3NDcQFXS9gd12X9fV19vf3GR8fZ3Z2ti/2pZWSnpPoK9oBb4z67AzB73gNuU/8JW58HywLmS8gTIPwD30vxqUljEtLle9Ztz8P6/bnAeUytGvXyOVyLCwsMDY2RnJqQpVfOY7qCFVpE4XKjkRCaENDlX0UgQAyX6D4D9+qIhedylzYts3KygqZTIb5+fmWJ7ZePIg77bjtBVf7+/s4jsPY2BimaZLJZNje3j61KLxfIaVkb2+PnZ0dRkZGqkhm11vdhqLIo9rOnlH044rtUVkNrwORl9VwHGdgWp3Wwzm5OAxd16vc4ovFYiWrsbOzw87ODqZpVohGOBw+ddzRj/fBSdFqWdR55uKMIJ/Pk0gkThRY9RLeSrY3kRSLRZaXlykUCpXAtl+gaVqlI81R6FWrXKgWrw698+3os9PkPvkZ3Pg+5k1XCb35TQRf+bKG38/lcly7dg2gqrNY4OV3kfvs3ypBuV06KLnSdbRgAGk7VROoLItWhXVYc9FucuEdb8dxuHTp0okEyt3MXBwl3G4nbNtmdXWVfD7PwsIC0agKcicmJnBdt7KCl0wmWxaF9yu87FUikWBycvJYUt9qq1vv/7uJsxCY9BuOy2p4+pyNjY1DWY1BwaBfN50kSEKISpOM8fFxXNetnP9sNksikahcIx7ZsCzrRI0BvN8bdDR7PkqlEo7jHDKoPWsYrNngFIhEIjznOc8ZuNUWfzCcyWQqYtN67sm9RjOZi3boK04Df5CsGTrh730joe/5R+C6VdqHevCbzNWWoVl3vpDwm15H7n9+FuEZ5WkagZe+GG1qktyffRJp2wjDUMFzJoswTayXvvjQ+NoZwKdSKVZWVirH+6RlPt0iF97quOM4lRKcTjx4CoUCKysruK7LxYsXD91LmqZVreIVCoWmReH9Ci9bmM1mmZ+fr2gsmsVxWY2jWt12CoPeErXfrxkPtVmNZ599Fk3TKBaLh7Ia/e6rAOeZi1ahaVplrpNSUiqVKlmtvb29inbNn9VoJtY6a2VRzexHttzJ8jxzcYYwaMQCDm46Ly05NDTUt2LTo/QCndRXtAJvMvZPBEKIirC7HpoymdM0hv7pPyZwz90U7/sa0nGwXvh8rBe/AJnOUPrmo9hPPn2wUmOahN/6Fsyrlw+Nrx0Bk1f6srm5yfDwcKWm/qToBrnwr4p3MjBNp9Osra1hmiYXL148Vqvkr032VnG9rIYnCtd1vfLw7Ud3XH973QsXLrSl8UNtVuOoVrd9ZeDXBxhkUuTdm8FgkJmZmZa0Gv2CQT7+HnolqhdCYFkWlmUxNjaG67pVJn7JZBKgKqsRCAQaNgbwtjnoaJZctMtAr9/RX3d8h9FND4F2Y2dnp69b5kLjzEWv9BX10GpPf8dxWFlZIZ1OH2kyB+VJ9/m3Yj3/1urXR6KMffA95P/27yk98hhaKETgnrsxb3lO3W2c9hr1a3ImJyfb6izdSbF5O4XbjRCPx9nc3CQSiZyY4Nb2kc/lcpWsRiKRADjkFN7Le7Yb7XWPanXbq6zGOToH/6p5K1qNfslqDHq3K+gf8zk/kQSqshqxWIy9vT10Xa9qd+zPfnrbGHQ0m0nKZDIAfVd50m5cV+Ri0FAoFColGLOzs0xOTvZ4REejXuail/qKemhlBb5QKHDt2rVTaRUqvxsMEnr9awi9/jXHju80AbzneZLL5VhcXGR0dPTE26odF3SGXHSDWHjZp3g8zvj4eNtIur+byvT0NKVSqUI0PINO0zSrROHdfJCmUinW1tYIBAJdMwmtVz51ntWoxllYsa039npajUZu0bWBZjdxXhbVOZimWdWRz5/V8MhmMBisWrnvx/1oBZ5OsJXMRS9tA7qBc3LRp/C0Cd7EOwgptNrOVr3WV9SDf3X1KPjHfuXKla6N/TTkwvNvkVJWic3bNS5oL7nolnDbcRzW1tbIZDLMzs52tAmCaZqMjY1VygWy2WyFbMTjcYQQVaLwTraPjsVibG1tMTw8XLeUr1toJAr3zv95VmPw0Ow80Mgt2p/VCAaDlfKpbmU1+jUwbwWDsA/+xRegQjaz2WylMQBQySYPqoljKxmY87KoM4hBKIvy1/dHo1GmpqZ46qmnuuLafFp4x7df9BX1cFyQLKVkd3e3EpSdVqtwkvGd5BpNJBKsrq4SCASa0hGcZFzthL9sxtt+Jx6Ufq3B0tLSqbJPraJWBFksFkmn06RSKTY3NwFVLuJ9JhQKteUYdCpL0w40EoX7hfz+z571rEa/nJdWcZLA9qisRjweP7J8ph/G30/wd5AcJNSSzZ2dHfb397Ftu8rE0W/iNwj72Eom8lzQfY6uw+9F4NX3e6t6/U6K4KAs6tq1a6TTaaanp5mamuqryeEocuF5oSQSiZ7pW/wErZnf9ibo7e1tRkZGWFhY6Fgw1k6xuRdQQufqbbuhNWgW/taOnmGVl9HY39+vBFb+rMZJAivvGvY0Qv3Uqroejmp124yB3yDMi/UwqOP247RzYz3tkicMr81qHCUKPgkGMTCvh0HeByEEhmGgaRoXLlzAcZwK2Uwmk8TjcTRNIxQKVa6BXhsFN0IrmYtzcnGOrsIraXFdt6q+v9kynn6AR4Sy2Wxf6Cvqwd8tyg+/f8jS0lLLbTrbBT/5Oe7B4bouq6urJJPJrhC5dpCLbgm3PffpYDDYl93VdF1vKAr3d1vxiEYzgZW3OOFdw93M0rQDJzHw8/59ju6i3eTIXz7TjazGoJOLTi/MdAv+Dku6rlc1BigUCpUSqu3tbQAsy6rKavTL/p+XRR1Gfz1xO4x+nUzi8Tjr6+sEg0GWlpaqvAgGhVwkk8nKBHDp0qW+vXHqdYvKZDIsLy+jaRpXr14lGAz2anhNaxs8MlQsFrlw4ULFAK7TYztNUNEt4fbu7m6lrHBubq5vHkCNcJQofHd3l52dHQzDqGp1W7tPhUKB5eVlQN1/vbyG24XjDPy8DJ/fxX3Q0K/PpOPQ6eC8Xlajnij4NFmNQT32QBXRHmQ0uo787b+9TK+X1fKyvV6ZnT+r0avj0UpZ1Lmg+xwdh+u6bG5uEovFGBsbqxsIeaUA/Uou/PqKcDhMNpvtu1ViP2qD91gsxvr6OpFIpGvddI5CM61ys9ksy8vLCCG4cuVKVwPJk5CLekFgJx4CruuysbFBMplkamrqyLbB/YyjROHeQ9Xf6rZUKlWaDywtLfVt6cBpUJvVsG2b9fV1XNclEAgMnCj8vCyqtd/xi4JLpVIl0PRnNTxReDNZjbOSuRjkfYDmvSF0Xa8yNS0WixWy6S3AmKZZZeLXzfv/JGVR561oz9ERlEollpeXyefzzM/PMz4+3vCz/Uou/P4V09PThMNhnn322b5+cPrLotbW1iqi17m5ub6YqI/LXHhZrlAoxIULF7pKhk6SuagVbneKWNi2zerqKvl8noWFha5kcrqBRqLwdDrN1tZWRQRpmiZTU1M9J8fdQG35VzgcPm9122X0co43TbNuVsOr1Yfjsxrn5KI/cNLGAJ5+bXx8vLIA45GNRCJRyWp4ZKPTXkMnKYs6z1yco+3wynCEEE21DK1t8doP8Dwg/P4V3k3Tb2P1w7v5t7e3KZVKxxK7bqMRufB3EWuU5erW2JpFt4TbhUKBlZUVXNfl4sWLZ3ZFqPah6nWEsiyrQvQ9DwGPkJw1suGVA9ae60Ez8Bv04LBfgnN/VmNqaurIrIa3oq3rel8vgDWDQb9+PLTjOqpdgPFM/LLZLHt7e+zu7mIYRlVWo91dyE5SFtWvpePtwtl68hyDXt+IUkr29vYqPZ2bLcOpZ07XSzTyr+ik0Vq74N3Ytm233QuiHah3DFtxCe/02Jo9t90SbqfTadbW1jBNsyMtePsR/vKv6enpCjnO5/OVrIZnvhkMBisP3n5wRj4NvO5fuq5z6dKlKm2ah/NWt9c3ms1quK6Lbdt9Q5JaxVkhF+3WSgkhsCwLy7IqZaV+vY6/WYZHNtrRhawVDYxXFnWeuThHW+Bvczo5OcnMzEzTF3S/ZC6O86/od/H5/v4+a2trAExOTvYdsYDD5MIT6pZKpZ534GqWXPgzFp0kFp5J3NDQEPPz833jpdJJHFX+5XkITE1NYdt2hWjEYrHK6p2X1eiVM/JJ4TmNt9r96ygDv1qflW5mNQY5OByUsdfLanhBJqjnQSqVOpTVGAQMyjk4Ds26Wp8Ufjd4oHINZDIZYrFY27qQtbKI5i1wntUMu4dzctEF+APEk7Q57QdyUauvqNf2tFGb117DX1I0MjJCKpXq20nZTy7S6XRlpbYfHM6bIRf+2vdOdoTqV5O4TqJYLLKysoLjOFy4cOFIcmwYBqOjo4yOjiKlrBKFezXJflF4vSxAvyAej7O5uXlqp/Fmsxp+P43zrMZhDGpga5omo6OjjIyM8MQTT1Q8YPxZjXavaHcKrdT49zNc1+1q6aZ3DXjzYqMuZN410Gy213Xdpq+Vc83FGUQvJopUKsXKygqGYZy4s0+vBd319BX10Eyno26jXknRY4891ldj9MO7Rj1jtUgkwoULF/pmRe0oZ/NuCLcdx2Ftba1iNNnvJnHtQjabZXV19ciSoEYQQlRW5WZmZqpE4dvb22xtbWFZVoVohMPhvgiqPIPIvb09xsbGWsr2NoPTGvidYzDhzWGBQIBoNFqV1ahd0e7XrMagErxa9LIsrbYLmeetks1m2d/fJxaLoWlaVVajERFqJQOTy+XQdf3Ml/BeV+Sim/A7Jw8PD7O4uHjiyamXmYtG+op66LfMRSNS1C6n6U5ib2+PiYkJZmdn++YB0ui4dUu4XSwWWV1dpVQqceHChTO/8uMhmUxW+eCcNsixLIvx8fFKpxWvd3wymaw8UHstCpdSsrGxQSKRqOhKOnkfnMTA7zTX+SAHh4M8dqg//kYr2rVZDY9o9DqrMejnwEM/aV5qvVXy+XyFbHhd+QKBQJWJnz/maXY+yGazfbOA00mck4sOoJkSolagaRqlUqmNIzwex+kr6qGfBN1exqgeKeqHMrN6sG27ogmZmJhgbm6uxyOqRj1y0Q3/CjhYudc0jUuXLvW8RKwb8BpAePdgJzqEaZpW1T++UCj0XBTuZaey2Szz8/Mtl5G2A8cZ+NV+7nrMaAxqcHRcYN5Iq5HJZCrdh3qd1Tgr5KJfzS+9VraeLsJxnCpReDweR9O0CuG0bbvp/cjn82debwHXGbnoxo2Yz+dZXl7GcZy2CXC73S3qpOTIW83rZeDuOTRvbW01zBj1Y+Yin89z7dq1yrHrx1X5er3iu9ERKpFIsLGx0bKYd5AhpWRzc5P9/X0mJyeZnJzs+Pzld8WdnJyslAn4ReG6rlc5hbc7qCqVSqysrFT0af1wH/izGt796c9qnKbV7SAGh/02d7aKVgNzf1bDdd3Kina9rEY3PBXg7JCLfspcHAVd14lGo0Sj0coijEc2tre3AXUutre3K1mNRnNANptlaGioreMrFAr80i/9Ep/+9KfJ5/Pcc889vPvd72ZiYqLhdz784Q/zm7/5m4de/9a3vtWWZ+zZf0p3EV43okAg0HJd9FHoZsDerL6iEXqpD/F35Jqammoo9O03cuEvPVtaWuLpp5/uq/F58B+3bgm3d3d3K0L82dnZvlzlajf8upK5uTlGR0d7Mo7aMoFcLlclCgcOicJPcx14jS+Avs1OedefP6txEgO/fry/m8VZCWxPMn6vBv+orEanPRXg7JyDfs1cHAX/IszExERF1+m6Lul0mv39/Urmw6/V8PYzl8sxPT3d1jG95z3v4YEHHuA3f/M3sSyLd7/73fz0T/80f/iHf9jwO4899hjf8z3fw7333lv1ersW787JRRvgrTLu7e0xOjp6qo4m9dAtctGKvqIReuXJ4Zlrea69R5VS9Dq74qFelsX/Xr/BIxf+IKpTxMLv5TA1NdUzb49uw79y30+6En+pyPT0NKVSqUI0PG2ZaZpVovBW5sBMJlNFsAdF7FivfKrfDfzahUG9H9sZmNdmNXK5XMXEr5NZjU63cO0GpJQDk7k4Crquo2kawWCw0jDDy2rs7u7yyU9+kg9/+MPcdNNN3H333WSz2RM19mmEra0t/vt//+/8zu/8Di9+8YsB+NCHPsQb3vAGHnzwQW6//fa633v88cd561vfytTUVNvG4sd1RS46cRF7wUA2m2Vubq4jwsNOZwNOoq9ohF5kLjzHc03TuHLlyrH1jP2QuXBdl/X1dfb396uyLN6x6/X4GqGWWHQCR3k5nGXkcjlWV1cRQvTtyr0H0zQZGxurGFX5W93G4/FKhyqPbBxFFjzBejgcPtXc02u0YuDnb3s7aOjXualZdGr8fk+FTmc1Wml92q/odAOQbsIjSUIIAoEAgUCg0jCjWCzynOc8h0ceeYSHH34YgIceeoh7772Xe+65h1e84hUVI9ST4IEHHgDgJS95SeW1y5cvMzMzw/3331+XXORyOZaXl7nhhhtO/LvH4boiF+1GNputpPEvX77csVXGTq60d0J83s2HTywWY2Njg1AoxIULF5pK6fWaXJRKJZaXl8nn8ywuLlaVvfSTKN4Pb6UsnU6zurrK8PDwsUHjSZDP51ldXcV1XS5evHhdCN/gwCQuEAiwtLQ0ULoSTdMqJEJKWWl1m0ql2NzcBFSXFe8zXpcVKSWxWIzt7W1GRkaYm5sb+IDJj0aicMdxyGazCCEoFosD1+p20EtyujX+elmNek7RJ8lqnIUV/0G/jvxoVN6laRq33norv//7v08qleIzn/kM73vf+9B1nT//8z/nz//8zxFCcNttt3HPPfdwzz33cPvtt7c0D2xtbTE2NnZoMWp6errSlKMWTzzxBK7r8ulPf5pf+IVfoFgsctddd/GzP/uzbSvZGpwnWJvQjsDSeyhubm4SCoU6nsb3AvZ2Tyin1VfUQ7cyF16rylgsxvj4eEuBiaZplXKFbiOXy3Ht2jVAEdJaIzQvyOgncuEFRRMTEwQCAdLp9KGgcXh4+NSdhNLpNGtra5imycWLFwemNOa08DuNLywsDESA2Qj+lTuvHtnLaHjeLV6nHa8Dy8TExKkXNfod3jkVQrC5uUk2m2VmZqYyt/fKKfx6RC+C2qOcov1ZDS+jEYlEjjz/5+Siv9CMdmR4eJi7774bgDe84Q28853v5O/+7u/4whe+wFe/+lUeeughfvu3f5t/+S//JT/5kz9Z+d7q6iqvfe1rG273p3/6p+vqewOBAIVCoe53nnjiicqYfuM3foPd3V0+9KEP8Y53vIM/+7M/a8ui3nVHLk4LfznL+Ph4V0SmfnO6dt2I7dBX1EM3Mhe2bbO8vEwul2N+fr7llGKvgvdEIsHq6irBYJALFy40DJ77iVz4xaqWZTExMVEJGjOZDKlUqipoPGknobMUYDcLrxzRI8hn0Wlc1/VDonDvmvEC6mw2y97eHkNDQz33D+gkbNtmZWWFYrHI0tJSpWNMM61uvf/vBwx6UNgP42+U1chkMpVmCUdlNc4CuTgrLuPQ/PnIZDKAaoRx4403cuONN/KP//E/JpPJ8JWvfIWvfvWr3HPPPVXfmZmZ4VOf+lTDbf7t3/4txWLx0OuFQqEhSXjLW97Ct3/7t1dpU2+88UZe9apX8fnPf543vvGNx+7LcTgnFy3ALxquLWfpJPyp9dPeiH5zv9PqK+qh02Jpr1bQdV0uXbp0olK0bgfvfk3LyMjIscFzP5ALL1PWSLhd25rP6ySUSqUqD0d/zX2jzmlSSra2tojH42c2wK4Hb5EilUoxMzNzqprbQYEQAsuyyOVySCmZm5tDSkk6nWZ3d5ednR0Mw6giqGch8ICDUkjHcbhw4ULVQ/84A79+E4X3Q3B+GvTb+GuzGsVisSIKr81qeJmNs0Au+u08nBRe5rGZezKfzwMcqliIRCK85jWv4TWvec2h73gLwI3w2GOPsb+/T7FYrHrObm9vMzs72/B7tU1vZmZmGB0drVQlnBbXHbk4aeCWSqUqJl7NiIbbCT+5OA3ara+oByFE1cpbO+Gt/AcCAS5fvnziVr/d7BbltRVNJpPMzMw05VfQa3JR67jtlWo1Qm0nIa/mPp1Os7W1xdbWFpZlVXQaXs29v+Xq7OwsY2Nj3drFnsJbwfYWKdpRjjgIKBaLrKysVDyAvDm0nijca+dY2+p2EJHP51lZWakI9Y/bj1qtxkla3Z6jMfo9qLUsC8uyjsxq6LqOEIJCodAVX41O4KxkLloRpmezWaC9PlYvetGLcF2XBx54gJe+9KUAPP3002xtbVW6R9XiV3/1V/nc5z7HJz/5ycq1s7q6Sjweb5vI+7ojF63C3y50aGioJyZe7SAXndBX1EMn9Aytrvwfh24F716mq1gscuHChaa7HvWSXLTDGM+yLMbHxxkfH6+UT9XW3IdCIfL5fGUlt19arnYahUKh0hP9ehKs53I5VlZWKg7rtQF2I1F4LUH1t7odhIAqm82ysrKCaZpNN5zwox9b3fZ7cH4cBmn89bIamUyGWCyGbdtcu3btUFZjUIL1QToPR6GV/cjlcsDhzMVpMDMzw5ve9Cbe9a538b73vY9QKMS73/1u7rrrrkqnqGKxSCKRYGRkBMuyeMMb3sDHPvYxfvEXf5G3v/3t7O7u8r73vY877rjjUFnWSXFOLo6Af6X/KFO2TsP7zZOSi07pK+qh3YJu/zloduX/OHQjeK9tj9tKX+tekYtOOG7Xlk/l83lisVilWwrA7u4u+Xy+UnN/VuH3cmhWsL6+vsn+fpKlpXmGh9vr6toteB3Gmu2EVU8U7hHURCJBLBarBF0e2ejH7lpei91QKMTi4uKpy0/rlU/1Mqsx6EHhII7fy2pks1mklIyNjVVlNTzzNk8U3s9ZDX8Tg0FGKxkYL3PRTnIB8Iu/+Iu8733v45//838OwCtf+Ure9a53Vd7/+te/zjve8Q7+4A/+gJe85CXceuut/O7v/i6//uu/zpvf/GYsy+K1r30tP/dzP9e289F/M3KH0eyBy+fzLC8vY9t2S6vOncBJMxed1lfUQzsF3Z5jb6lUamu2pdMdreLxeCWoOMlqZS/IRTcct73Wm6lUilAoxMzMDPl8nlQqVblOB3F1uhns7++zsbFBJBJp6j7c2dnjVz7wW3zpy/dTLJYYG43y1rd+H//bj/3QwKxMwsF+n0aoX4+gelkNr9ViMBisXDen7VrWDsTjcTY3NxkeHm67qaqHRq1uPb1Up7IavdaDnRZnYcXcaw1eL6txlFajn+aOs+Jz0Qq56ETmwtvee9/7Xt773vfWff8lL3kJjz322KHX/viP/7it4/DjuiMXzSCRSFRaYnZ6pb8Z+LtFNYtu6CvqoV2BeyqVYmVlBcMw2n4OOtXRyu/UPjY2xtzc3Ikmzm6Si+OE2+38nd3dXXZ3dxkZGal0WQuFQpWae291OplMVlan/eLeflydPg7+/R4dHWV2dvbY4+s4Dv/m3/2f3Hff1xgfGyUyFiaxn+Q3f/OjBINBfuSt39el0Z8cJ9nvZuCtzIZCIaamprBtu0I0YrFYVUB1kq5lp4V/v8fGxpiZmenKvNuKgd9psxqDHpwPOjmC+t2JvKyGN5/WajW8e8cjGr3Oagz6deShlQxMpzIX/YjBe1p3EF7nGi8Amp+f7wu32FYzF51a8W8Gpw3c/RqX4eHhtpQT1KITwbvjOKysrJBOp0/t1N5NrxAvW+H9bicmetd12djYIJlMMjU1xcTExKHf0TSN4eFhhoeHD61Or6+vA6o1o+ep0esHYzNwXZfNzU0SiUTD/a6Hrz7wDR588JvMzkwTDitNRmg2yNr6Jh//k//OD/7Ad/U10fI8aFrd75PAMIxKS08pZZUo3AuouiUK9xYX9vf3O77fx6FRVsOfofQ+d1IDv36//xrhLAS1x3WLOiqrsbu7i5Sy51mNs1IW1UoGplOZi35E/z6hOoRGF7LXwcXrXNPLB0MtWiEX3dRX1MNpOjG5rsva2hqJRILJycmOrfp55KJd7fz8YvlLly5V+tefdnydhD/Y6KQI1LZtVldXyefzLCwsNFVeWLs6XSqVKgGj17LUNM2q8ql+S617mUPPi6W27d9RWF1dp1SyK8TCw/BQhN3dGPv7CSYnJ9o95LbAdV1WV1fJZDIt7/dpIYSoBEszMzNVovDt7e2OisL9rYXn5ua61qa8GRzX6rZVA79BX/k/K21cW9mH2qxGNputtLutzWpEIhFM0+z4MfL2YdDPRT+URfUjrjtyUQ+13gmnDQ7bDe8GPCpo9+srOrXi3wxOGhiXSiWuXbtGoVBgaWmpo0FJO00J0+k0y8vLbSVznSYXnRBu14PXghM4VWck0zQZGxs71LI0lUoRj8f7Ttzrb7l64cKFlh8kM9OT6LpOPl8gGDy4njLZHJMTY30r7PabxPVDBzB/17JGZXftuG68rGU+n29va2Hpqj8IEBq06T49KqvRjIHfoK/8nxVycdIFldrObH63cP/ijScK79TizWn2oZ9wkrKoXs+N3cB1Ty488e1xrsm9xlHkolf6inrwyqJamcAzmUylD3w3PES8cZ22fCsWi1XEqktLS20jc510Oe8WsUin0xXd0tLSUtvuK/+DcWZmhkKhUFfc63lqdNvx+biWq83gJS+5g5ufc5VvfvNRpqenCAQCJBJJisUib3nLm3quAasHr8WulJKLFy+21B2tG6gtu2t03bQqCi+VSqysrFAqlU5EJOtCSoSTRzgFhJQgQGomrhFWJKONOImBn4dBDdDPArnwMs6nhWdsWZvVyGQyZLPZjmY12rUPvcZJyqLOycUZhlcHHo/HTyW+7RYalRv59RW97moF1W1zmwm2vQD9pJ2VToLTkgv/tTMxMdE2sap/fO3WXHiEr9MdoaSUxOPxii/MaT1JjoIQgmAwSDAYZHJyskrcu7e3V+X4PDw83PHyKa/1aDAYPJUfjmma/NL738V7fv4DfPNbj1HcizE0FOFHf+TNvOPtP9DmUZ8e2WyW1dVVDMNoK5HsFOpdN15WwxOF67pe1Uyg3lzmJ1SXLl1qG+kTbhHNziERSKEBEuEU0aTENYfalsGoh2YM/EqlUuX9ftAktoqzQC46tQ/dzGqchfMABySplczF9eBvdN2RCyEEpVKJ5eVl8vk88/PzjI+P93pYx6Leanav9RX10GxnK0/0GYvFGB8fr3QP6gZOY0po2zbLy8vkcjkWFhY64ird7rKoWuF2J4nF1tYW8Xic8fHxhr4wUkqefmaZYqHI1asX2yayrSfuTaVSVY7P/jKYdgXBXhbLa/ncjoWKixcX+b2P/jrf+tZj7O8nuHLlIvPzs20ZbzvRbi+HXsAwDEZGRhgZGUFKSS6XqxKFA4dE4V7Jn67rTXuWNAUpEXYBiQDNO5YCiUC4NkgbRHfIW73yqUKhQCKRwDAMHMfBcZyuGvi1A2chqO3GPhyV1Wik1WhlLvcWuQYdrWRg8vk8cE4uziTS6TRPP/10xdxsUE6yP3PRL/qKemjG8M8vnu8FuTtp5iKfz3Pt2jVc1+Xy5csdE2W1k1z4e99D53qKO47D2tpapSFCI9L18MOP8wvv/RDf/OajSFcyNz/D//enfpzv/M7XtXU8fnGv3/E5lUqxubkJtMcbwd8haGJioq0liUIIbrvt5rZsqxOIxWJsbW21jVD1A7zuUuFwmOnp6apmAt6cq+s6juNgWVYHSmklAhdZew0JoYiHdOmFnFrTNPL5PGtrawghKs+cTrS67QbOyUXraDar4WU0jstqnAWSB61pR7LZLKZpdrRrXb/guiMXXvu1+fn5ngs/W4FHLvpJX1EPx2Uu/OL5y5cv96T28CTkwssSWZbF5cuXOzo5tItcdEtf4QmYPcPJRud0d3ePf/JPf47VtQ3GRkfQNI3la6v8u3e9n7GxEV7+8rs6Mj4hDjs+1/NG8JfBNPOw8BOqfusQ1ElIKdne3q5kHRtlqM4CapsJbG9vE4/HK4aQTz31VJuzYQKJBtIB/yGVUr3XZs1Fs/DmbdM0D5WvdtPArx0Y9KC2nZ0OT4qjshr+TPFRWY2zlLlodj9yudzALGifFoMTXbcJXm3/oMEr53r66af7Rl9RD0eVHCUSCVZXVwkEAh0P0I9CK2VRtS7ni4uLHZ8Q20Eu/A/7ThILr97eEzAfVZr3F5/4DOvrW8zNTlcybcFggPWNLf7wj/5bx8hFLXRdryqD8XsjeA9FrwxmeHi4bsDoF/IuLS31XYe5TsHvWTIzMzMQJaXtgKclisfjjI6OMjMzU8lq+LNhgUCgQjRCoVDr950QSCOAVsogpQOUNRfSRWomiO4/stPpNKurqwSDwbqNK7pp4NcODHorXQ/9RJBqsxrFYrFCNnZ2dqqyGpFIhFAo1HOC1C60UhaVy+Wuiza0cB2Si0GF57gZCAT6Rl9RD/WyAt5K587ODiMjIx0V+TaDZjMXft+Nqamprq3QnoZcdEu4DYosemL8hYWFYzOBy8trSGRVcCKEIBiwePzxpzoyxuNQzxvB02lsbW2xtbV1KGD0hLxAX3ZG6hT83h3NepacBfgzNf7St0bZsP39ffb29tB1vSqr0WzpqtQsXMMtd4tyVbco3Sp3i+puMJZMJllbW2upOUM3DPxOg0FvgdqKr0Iv4L83jspqCCEwDINisTjQZUKtXE+5XK6r3j+9xDm56HN4K+e5XA5d17ly5Urf6CvqoTYr4C/jmpmZYXJysuerFc3oQvyi/077btTipOSim8Lt3d3dipP93NxcU78zPT0JHE4jF4pFlpYW2j7Ok8CyLCYmJioBo/+BuLe3VylP9Frs9ivJbze8++Gk3h2DCikl6+vrx2ZqarNhflF4MpkEDhzmj22RLATSCCH1QEd8LppFPB5nc3OTaDTK/Pz8ieaSdhv4tQODnrkYNJ+RelmNTCZDLBajWCzy7LPPHspq9CtxqodWy6JmZ/uvMUcncN2Ri0G5IaE6MPdWR/uZWEB14O5vk3vx4sX2mUudEsfpQrLZLMvLywA9Ef2fhFx0S7jtdyKemppqycn+u77zO/jY7/8Jm1s7TIyPoesa+/tJTNPkh37wuzsy3tNA13Wi0SjRaLRq9VrTtEqJon9lepBX346C1xlJCMHFixevG0LlaWqy2WxLmZqjROGe8LUpjY/Q2u5r0QyklJVWzmNjY8zMzLTtudmqgV+nzNsGKQ6oxaCRCz/8WY1UKlXJCtdmNbz7p9UOVL2A67pN6Xe9RYdzzcU5eopa/wrv5ut3eA+DXC5X6X3fb2VcR5VF7e/vs7a21lNTxVZ9LrpVBuV1+SoUCicqi1lcnONDH3wP//7dv8L6+iauKxkZGeYn//d38IbXv6YjY24H/MTCC7b8AeP29jZbW1tYllXRaZyo3r4PkclkKo0MlpaWBqoJxmngdxtfWlo6VeOJRg7z9TQ+vSap/mt9cnKyo5nmkxj4tYNsnJOL/oDnk1Ivq3GUVqPfshrNXk+FQgEp5XWT9b0+nhQ1aLePQLtRz78il8u13VitE/Busp2dnbY7V7cL9cqiPI+G3d1dRkdHmZ+f79kk1sr12a2OUN7qNSidwUlXX17+8rv4n5/8v3nggW+QLxS4/fbbGB8bbeNI2wt/psZfFmNZFuPj44yPj1eVTyUSiUp2w3totlJv309IJBKsr68TiUS60sigX1AsFisd7dqtqalXIuIRDU/j45HUoaEhwuFw14JIz3sokUj0RKzfjIGf/3OnMW8b5Gv5rJCL2qDcn9UYHx9vqNXwMhqeW3iv0WxZlGegd04uztF1HOVf0QnX5nbDC8QAhoaGuHjxYl9OgLWZC3/52ezsbEulPp0aXzPkwv/w7SSxSKVSrK+vV3QGp53QA4EAL3vZnW0aXedg2zarq6vk83kWFxcblvXVlk/l8/lKFyGv3r52ZToWi/PFL36FQrHIi1/8Aq5cvnjotx944Busb2wxPTXBnXfe3rUVbX9ZTCuamrOAfD7P8vJypftZp1tO14rC65FUf+ldpzJHXvOKdDrN/Px8z0Wn9cqnvLnutFmN88xFf+C4oLyRViOTybC9vQ3QF1mNVslFL9rv9wLn5KJPcJx/hd9Erx/hF0ALIRgaGurbyc/rVOFNWNeuXesrXchx5MLrCOWt5nVSuB2Px9na2mqpW8xZgNcRylu9bjZT4/V2D4VCTE1N1TVh+8LffYXf/d0/JplMgYBIOMwP//D38rP/6p+gaRpbWzv8h/d8gG9+81FKpRK6rvPcm2/kPe/+WS5c6Kzw3W8K2OmymH5Dr0vAGpHUdDrNxsYG0B7jx1r4u4AdRaJ7hXa3uj0r5GLQ5+JWzkNtVsNxnL7JajS7H7lcDrg+3LnhOiUX/VYWVauvqFfLrmlaX5jn1EOtAPrZZ5/tayIE6hooFAoVx90rV670TUtRf2al9lx3S7jtDzLPulFaLbwg0zCMU69e19bbf+Ohh/lP/+kPyOZyjI2NomuCdCbL//Wxj3P16iXe8uY38Wu//p954IFvsLgwRzgcIp8v8I2HHuGXfuXD/NZvvq9j58G/en09mQKCKkVdX18nHA73RQlYLUm1bbuu8aOX1YhEIicqvfNrSwalC9hpW9324zO0Ffg7bA0qThvL6LrO8PAww8PDDbMalmVVyEYnsxrnZVH1cV2Si35CPX1FPfg7HPXTpBKPx1lfX6+YExqGUSFC/Y5YLEYkEuk7oWojctEt4fb16jwNBzoDL8j8/7N33mFy1WX7/5wyfWd7b9kk9E6oUiOINFFARbFgpbz+FLuvr4iKHUSxIF2KSFGqCNJLQu8tgfRkey/Ty2m/P86ck5nd2T6zO5vNfV3oldkp58yc8r2f57nvO5daCVEUeXb1y8RicRob683fU9Mo9vvp6e3jttvupqG+ihdefJWKijK8XpPsut0u6mqrWbt2HRs2bGb33XeZ2QbEwxAZAl0DdxH4ykE09y9drL+YQgHBvA709vbOynI135BlmdLSUkpLS8cEPwYCgRmJwtPthRdqXstMrG6tfy9U7AhjUbksjk3U1QiFQnntalgkaSr7YXUudpKLncgrJtJXZEN6pWa+q2qwXfxnOejU1dXZ21XI+hBru3Vdx+1209LSUnAX6WxWuXMl3E4mk7S3t6OqKs3NzYtmPnSm2R3TxfBIAAMyKqqyw2yVh8NRYrE4sVgct8tJJBJBkmVkScbldpIcVAgEQzP84C7o2wxKwvy3KIK/Cur3IKkZGQLmxdK2t67Bg4ODC6o7ly34cTznsvFE4Va3XBCEvGtL5hKTWd2md34LPYxuPOxI5CIf+zDVroZ1Drnd7lmZA8DU9mNn52IRYL5Pysn0FdkwOpxuPmFVOSORCPX19WNcRQq1c6FpGm1tbeaiLWWBN9/HQjaMFpzPlXA7Go3S0dFhi1kLyT44n0h3yZludsd0seuuyxAEAUVRcThk+/MTiSQHHbQfhxxyEHW1NQwPj1DkL0JVVZSkwuDQMG63i4ryUlRVnV6nLRGF/i1gAEUV5mOaCoFeEpKH1rBpCbkjLTInQ/pvXl1dTUVFxXxv0oyR7lym67o9hx4MBrOKwhVFob29HVmWc2LQUKgY3dXQNI2enh5UVcXlcuXN6jbf2BHIxVwRu4m6GsFgkOHh4Vl1NaazHzs7FzuRV0xFX5ENhUIu4vE4ra2t6LrO0qVLs1a2C1F8nkgkaG1tRdM0Wlpa6OnpKbhttJBulZsuWMwnsQgEAnR3d+PxeGhoaCioMbF8Il3MOhcuOSefdBy33XYPGzZuwe83w9MCgRBlZSV89jMfp6jIx6c+9VH+etXN9PYOUOwvIhgKk0yqnHrK8SSTcTZu3GgLe/1+/8RpzwDRYbNj4S3b/pgkkzQg1LkJZ9UeBWkZnS8UmjNSLiGKYkbVNpFIjBGFgzlmVV1dvWjOc0tDFgwGqauro7i4OG9Wt/nGjkAu5msfptvVmCyvaDrkYmfnYifyhlAoRHt7+6T6imzIls0w1wgEAnR2duJ0Olm6dOm4Vc5CE8ynf+/Lli2zF2OFtI3psH5rVVUzZoTz5QhljYYsNtvR0SNgc3HRLy0t5uqrLuVPf76eVateRNd0Dj9sBV//+pfZZ589ADj702fg9Xq5++4H6esfoLGxjtM/djKf/MRHALIKeydMezYMwIC03zUWjxGPRHG5PJQ3NxfsQirX0DSN9vZ24vH4Dq8tEQQBt9uN2+2msrKSkZERuru7kSQJXddpb2/PCDGbqSi80JFOJtPDP0cX7OYiwC8X2EkucoNcdDWmsx9W52KxjBovSnIx1wf0dPUV2TCfnQsrtbW/v5/i4uJJ3VQKpXNh+fX39PRkzQ0pVHJhIRQKUVpamldveysgrrq6mvLy8nHPjUAgxLPPvkQ8keCwQ1fQ1FSfl22aK8RiMdrb2+dlBKypqZ7Lf/dTgsEQiqJQXl42xnb6jNNP5qOnfZhwOIrP58k4BrIJe9OFi+kjMA6HA9x+kBygxDEcLiKRCLFIhCJZwF3XglAgi6Z8Y7SAebFoSwCbWPj9fhoaTDvjWCyWIQqHsXksC3kBC9gkKhaLjUsmrXvZXAT45QKForucDQpR7zK6q5FIJIhEIkSj0XG7Gjs7F+NjUZKLuYTlvBMMBqesr8iGbCLfuUC6PqSmpmZKvveCIGRckOcD1sLZ8uuvqakZkwZaCARoNAzDQJZlXC4X/f399Pf34/V68fv9U3aBmQrS3YHSq3nZ8OCDj/Pji3/L4NAIYODxePjKl8/m+9/7WkHdHKaKUChEZ2cnbrebxsbGeRsNKS6eOE9AkiRKSsZ/Trqwt7q6OkPY29PTA5iBhf6iIkq8lThCPcQCQ6iKSrFLxlVSA+X5zc0oFKQLmJcsWbJo9EQAg4OD9PX1UVpaSm1trX0d9Hq9eL1eqqurs+axOByODFH4QjvXrS6V5YA21YrxeKJwyxlovrsaheYYORMUQudiIqR3/ayAy2xdDes6MpW1xE7NxU7kDDPVV2TDfHQuZqMPsS7A8wFVVWlrayMWi9HQ0EBZWdmY5xSi6DxdtL106VJUVSUUCmW4wLhcLnvWfqYhWvF4nPb2doBJK7ibNm/jez/4OdFojLKyEgRBIByOcPU1t7B8eQuf+PhHZry/c430UEC/3099fX3WRYGu67zwwqu88+77+It8nHDCsdTWVs/DFk8d2dKe7fGp4WEGNAGP6sODgc/vx1HVAGX14Nzxq/fRaNQei9yRBcyjkT7yWFFRMWFha3QeS7rVrbWQGtMRK2BY9wDr3jXTLlWuA/xygR2BXBRi52IijNfVCAaDALS3t0+q1cj3WNRFF12Epmn89re/nfB5HR0d/OIXv+DVV1/F7XZzxhln8J3vfCfnI5GLklzMxYlpzfnLsjxtfUU2zDW5CIfD9kzudAPm5rMrEIvFaGtrwzAMli5dOm6VoJA6F+MlbjscDtsFRtO0jDTSwcFBe17a7/dnn7XPglAoRFdXF06nk8bGxkkXCfff/zDRaDRjdKe42M/g4BB33HnfgiEXhmHQ29vL8PDwhAutSCTKN7/1Y5597hU0VcUArvjjdfzi5//Lqad+aO43fIYwux4llJSUkEwmTbItlKB4KxhSVYRhBW+i3z5+Cn2xOFNYXSqPx5Pz3JJCRnoI5nTdsERRtEmEJXrN1hGznjOZ6HWukc/8jtkG+OUCOwK5KPTOxURI72o4HA56enqoqakhFovZXQ1RFPF4PLzyyitUVFRw6KGH5m0sStM0Lr/8cu6++27OOOOMCZ+rKApf+cpXWLp0KXfeeSdtbW1cdNFFuFwuLrzwwpxu16IkF/lELvQV2WBdqPK9IE7XKRQVFc3IRWa+ugKBQICOjg5cLhdLliyZcMFUKOQiPRgPxhduS5JEcXExxcXFGIZhz0uHQiE7RGuiymJ61b6oqIiGhoYp3fT6+gYwjLE3AVmW6erqzXhs7dr1vPTy63g8Hk740DFUVZkLGk3TSCSSeDwz67TMFumCztra2qydLAvXXX8rTz/zAiXFfrxec6a2f2CIi39yKStW7EtdXc0cbvnskd6lsswMrMViKBSit7c3oyNWiIvFmSJdZzBel2pHRLqWarYhmNk6YtmKHOnXnvkkcBaRNgwj79bKMwnwy8UxuCORi4V+Tlq/cXFxMSUlJRldjZ6eHn79619jGIadFg7mmGJNTW7uI5s3b+b//u//aG9vp75+ch3ko48+SldXF3fddRfFxcXstttuDA4Octlll3HBBRfk9HzZSS5yiFzpK8ZDvhfEk+kUpoq5FnSnE7qpCM6hMMai0qte07nxWA4W1rx0Mpm0x6esymK6VanT6aS3t5eRkZFJxyNGY/fdlyNgHtvWTdQwDBRFZb999wTMEYT/u+jX3HPPQyiKAgb8wu/jR//3TbZubePuex4kFo2x5567cuGF5/Kh44+e/pc1Q1ie/oqiTOoOZBgG99z7XxyyjNdrjlGIokhVZTm9fQM89vgqvnDOWXO16bNGJBKho6NjzDhQei7CRB2xheogZBVI+vv7x+gMdnTouk5HRwfRaJTGxkb8/ol1PdPFeEUOK1cDzEBI6/iZ1CY5h7DGeEVRnLS4lA9MFuCX7XkzWVzvCOSiEAp7ucDo7KnRWo2bbrqJxx57jBdffJGtW7cCcMYZZ9DS0sLRRx/NMcccw6GHHjrj7torr7zCnnvuydVXX823vvWtSZ//2muvsffee2eMuB9++OGEw2HWrVvHfvvtN6PtyIad5CJHyKW+Yjzkc9FutZLj8TiNjY2zrnbN1cLduplOl9DNt1tULhO3nU4nFRUVGbP2oVDItiq19rW8vHxKgvx0nH76ydzwt9vp6OjC4/EgiiKRSASv18OXv/wZAP5x2z3885//xul0UlZmuhiNjAT4znd/isPhwOl0IEkSr7/xLudf8H2uveZ3c0IwRmtLJruA67pOOBSxw+0sWL9PKBjO27bmGlZuidfrpaGhYVyCMNFiMd1BKNeGAvlC+vhbZWXltI/3hYyZCphnitFFjnRR+MDAAP39/ZPbJOcIlvubLMs0NzfPe37HZF2NKYvCDR0z/VIw/0sVGBd6xd8iSAv93JyM6O2zzz7ss88+AHz2s59l8+bNHH/88Tz//PPceuut3Hrrrbjdbg477DA+85nPsHLlyml9/tlnnz2t5/f09FBbW5vxWHW1qSfs6uraSS5mi1wf0LnWV4yHfFXbo9EobW1tgDk6MVuLxrnqXCiKQmtrq30znU4Y1nyOReWSWIxG+qy9RXhVVUWSJIaGhhgZGcHn89k6jcluwhXlZfz9lr9wySWX8/Irb6KqKnvusSs/+MHXOezQAwG485//xjDA5zPbvlb1JhAI4XQ6bFckr9fD0NAIf/rT9Rx/3FF5vbGEw2E6OzunJeKVJIkDDtib1c++RHGx396+WCyOKIrss+8eedveXMEwDIaGhujr65t2bkm2jpi1WLQMBZxOp90Rs8anNFUjHori9LpxuOZPu5E+DjTZ+NuOBqtDZ2W2zIfN7kSicMsmebTVbS5gCfadTifNzc0F2Wkb3dWY1OpWEBB0BdHYboyiCzKG6NhhOhcLnSDB9PZDVVWKior485//jKIovPXWW6xevZrVq1ezatUqBgcHM8hFR0cHxx9//Ljv99xzz1FVVTWt7Y3H42MK39Z6NZFITOu9JsOiJBe5Qr70FeMhH4v24eFhurrMqnSunFTmoitgESJBEGZEiOZjLGo84XY+EI1G6ejoQJIkli9fjsPhsNN6LVE3mCMMVlV6PFK86y5L+cetf6W3t594PEFTU+b8+sDAILKcedwrigKAIGx/nkk6XKxbt5FYLG6PHoG5gL/v/od57LFn2LK1jXg8QW1tFaee8iE+99mP28RlKhgeHqanpwefzzdh1T4b/ueCL/DmW2vo7unD5/WiqirJpMKRRx3K0UcdNuX3mQ9MVbQ+VaSPT+m6TiQSsTU+Q0NDCILAwLsdbHt+HfGRKO4iD3t8cH/2/8gH5pxkpCet52McqJCRrjMoFJvdiUThls7HIqqW1e1MjtVIJEJ7e/uCEuxnG5+yiIbV1ZAMBQkdTZBSFX4QDRVd3zHGonaEfYDpkYt4PG6vUxwOB4cccgiHHHII3/3ud+nr6xtDtmtqavjvf/877vuVl5dPe3vdbjfJZDLjMYtU5FpovpNczBD51ldkQy7JheUmMjg4SFlZGXV1dTmrJFgL93xdQNIJ0Uxb4HM9FjVV4XYuYAlZvV5vxg03Pa03m699tqp0OmpqsldJDjxgXx5+5KmM39v6bkf/Npqm4fUX4XJtv5BGIlE+/4Vv8NJLrxONxuzvaOPGLbz00uvcfMs/+eed19LcNHEmQ7r15kxn7Q87bAXXXn0ZV119C2+/s5YyfwlnnHEK55/3+YJeuOS7ai+KYoYVYzwe582HXuT1O1ahGwZOn4t4f4zn//EEwYERPnjeaXO2eBhtO7pYfOTBXLC0tbUhSdK86AymgolE4RZRFUUxQxQ+lWu65QRmXecWYiU82/iUrmmImoKup+5RtrMSiIKKgAHCwtvXdOwonYvprHFM18XshMAaTUqHw+Fg+fLls9q+0aitrWXDhg0Zj1kBgbkSmVtYlORitje9udBXZEOuyIUVoBaJRKirq5swmXkmSK/K5HJBZlVmBwYGKC0tnZUDzFyORaWHMEH+HDLSF9eTjcSMHmEYfbOfjqj3/PM/z7PPvsTg4LDtsKQoKk6Hg3g8QSIeR1FUBNGcK/7SFz+d8X63/uNuXn75DXsfLOKnaRqaprF27XqOPOo0Lv3txXzm7OxWe7qu093dbZP92RzThx9+EIcffhDJZBJZlgv+JqiqKh0dHbZeKt9Ve0EQcEgy2557H7fHQ0ldOaqqoqoq4aEga558g/L9Gqhb1jirqvRUMLpqn0vb0UJH+jhQU1PTvOsMporROp94PG4XOrq7u4HthhRFRUVZ83wCgQBdXV124viOUAWHVFdDAAEJURQwSHU3MDB0A81Q0TQNUTKDauc6wC9XWIydi1gsNu+Fj0MOOYT777+fcDhsm5u8+OKL+Hw+9tgjt2O/C+NqlAfMtHI9V/qKbMjFgjgej9Pa2oqu67S0tEzonjNTjK5e5wLpSeG1tbVUVFTM6uI0V2NR+dRXpCO9cj3dxXV6Vbq2tpZ4PG67T1miXquqmC0T4eCD9ueG63/P7y6/mrXvrUeWJM4442RKiv1cdfUtKTcsswDncMg4HDKapvHCC6/RPzDIXXf9B9g+SpUNkUiMi378G/bbd0/22SfzIpi+uJ4sbXw6KHTxMpiL6/b2dtvTf65m7UP9ASJDIbylRYiiiNPpxOl04na56dvahRpIEAwGZ1yVngosEW8hV+3zhR0lv0MQBDweDx6Ph6qqKpOgWuGPKUMKWZbt48fn8xEMBunp6Zm2pmjhwBRvC4KBIEjbi3WaSigUIZ5IUFHhn5cAv1xhsZELXdczxqLmCslkkkAgQElJCU6nkw996EP88Y9/5Fvf+hbf+9736Ojo4IorruDLX/5yzu93i5ZcTBdzra/IBlEUJ1yATYZgMEhHRwdOp5OlS5fmbfGU68C/9E7RkiVLclKZtchlPi9yc0UsFEWho6ODRCIx68p1+s1+tKh3dCZCekr4UUcdxpFHHsrwSACnw4HH4+aED38Kr9eDy+W0vb6TySQ3/O12HnroCTo6ulE1lXjcnPm0jpt00md9Zw6HTDKR5N77/ptBLqzKta7rc7q4LgRYi2tRFPPu6T8a7iIPssuBEk/i8m3vFqgJBZfHTf2SRup2aSKRSNhE1apK58KqNBwO25k2C6lqnwtYVfvp5NUsFMiyTGlpKaWlputcuijcKnSAeQzNtsBUsBAEDFFG1JIYaICAgUE0EmYoEKKqqpqysrJ5CfDLFRbbWFQ8Hgdyr2uYDG+++SbnnHMOf//73znssMNwuVzccMMNXHLJJZx11lmUlJTwmc98hq997Ws5/+zFc0WeBdL1FVVVVVRXV8/LRW2m1faZ5EDMBrnsXKQnheeyU5S+jbn+LS3SYl3w80ks0u1WW1pacj4Wki0TIRQKMTw8zODg4BirybLSEl588TXuuPN+3l3zPhgm+TG/5xiyLBKNxolEopSVleJwyAwMDBGJRHE6x688e70e4vEEAwND9mPpovW5XlzPN6zFtdvtprGxcc4X154SH8sP25N3/vsKssuBy+dGTSiMdA/SsHcLtbs1Zni+j65KW1alDocjQ9Q7leuStbj2+XwLdtZ+phgaGqK3t3cHrtpvhxUM6vP5qK6uts0KHA4H8XicLVu25EQUXpAQHeiAYKgYukEgGGRoJERJeQXl5dvT1uc6wC9XWGydi3ylc6fj1ltvHfPYYYcdxvr16zMeW7JkCTfeeGPetsPCoiUXUx2Lmi99RTbMRHMxX8JzmH3nYnBwkO7ubnw+X84tBrNVyXOB0cLtfBILazTC5XLR2NiY97GQ0bPSVlUxFAoxMjICwN9vvYc7//kAsVicRCI55j0stzuzk2Fub3l5KfF4gmRSQZKkjHa/9fsEgyFkWcbvL+K66/+BLIvstdcuOBxOnE4XyaSyaMiF5YY135XrQz+1kshImPa3NhPqG0GSJer2aGbl+R9BlMZuU3pVerRV6fDwsL2YtNzLshGmwcHBGdnsLnQYhsHAwAADAwOUl5fPW4FrPmAYBn19fQwPD1NVVUVlZWWGTizb+J3P51vYY3KCAJITXZfp6eshEAhQO0Ha+nQD/OabaOi6vkN0G6dKLmKxGMCi6qwv/F83j5hPfUU2TJdcJJNJWltb55wYzbZzYRgG3d3dDA0NUVFRkZeE3XzoQuZSuG1lGfj9/lkJ22eK0VXFZDLJ8y+8wh13/tvayglfryjmOJQsy8TjcTu4rr6ullg8zsDAoPk55oehKCqqqnHzLf/MsG10OByIoojX6+Gznz2TH/3wwh3ippUN6QvMQkiedvu9nPTdT9K7sZNA9xDeUh/1ey1Bckz+/WezKh09PpUu6nW5XPT399vXhLkokhQK0i2Gq6qqdtxxoCywXA1HRkaoqamx3XZGu5dZNtvTEYUXOgzDoCtlUlFfXz/lHKecBfjlETtK52Kq+2F1LvIdbFlI2DHvwrNEIegrsmE65CJ9nGjZsmVz6qIym85FupNVfX39jLycpwLrgpBLa9+50Fek32wLZZFlWU2+8spb6LpOWWkJgUBo3O6gZUzQ3z+IrmuW0yKCIDASCCCKIm63G8MwXafAvFGqqpoKtDOD2zRdJx5P4HQ6CAaDXH751dx773+59urLOOywFXP5FeQdFuEOBAIFtcAUBIHa3Rqp3a1xVu9hWZVWVlaiqqo9fjc6Zb60tHRRpW4bhkFXVxfBYHDRBQOm73vdBFX79PG79OMnXRQ+Hfe7QoBhGHR2dhIKhWZtUjHtAL85IBqmycfCP4en27mYb7eoucROcjEKhaKvyIapuEVZVe3u7m6KiopoamqaF+E5TH/hbnm2a5rG0qVL88ryczkWld6xyCexsByzotHohDfb+UJSUQABQRRNT3ZRQNO2f7+SJKLrhinOTioZNzYwj2+/v4ieHjNgq6a6Ck3XEYDunl4Au+qW8blJxbRtNAy2bm3jc+d8nQfuv4U999w1n7s7Z7CuSRbhnk4S/UKELMt2yrymabS1tRGPx5EkiZGREQKBQIb71IIef5kAuq7T0dFBJBLJqQvarGDopu2bACCa4zt5gK7rdHZ2Eg6Hp73v6cePYRjEYrExovDRSeGFco+HzH3PtbX0VAL8IP9dDeteudBRSJqLQsOiJRfZLiaFpK/IhsnC6Sw70pGRESorK6mpqZmXi+ZMRo6sETQrOCbf8/O5GotKrwLlk1ikW442NzcXZHv1iA8cwo033kEikcDr9RAKReyK8/aqmIDD4SCZzHQ9s4hzIpFAFCWSySSCICBLEtFYDE3TM5479ncTACM1OjTIxT+5lLv+dX1BLRpmAkVRaG9vt69Jhfi75wtWFzOZTNr7nj7+0tPTA4DL5bJ1Ggtx/CUbNE2jvb2deDxOU1NTXizDpwXDQNCSYKgIhoEhAIKMITlzHuim6zrt7e3EYrFZ77sgCHi9XrxeL9XV1VnDQ2diKpAvWIQyGo3m/Xcfb3zKIhz57GrsCGNR1j1oKvuxU3OxiJGur5jrMaKpIr3aPvqAVhTFrvA1NjbOa1V7Op0LwzAYHBykp6dnTkfQcqELmSvh9kJxRfrgyiM47rijeOKJZwGz4mXdoMzujk5xsZ9oNIrH40aWJUKhiP3dWeTC6XQQj+uMBIIImKNS6cj2m6Ufa5qm8+RTz3LV1Tfz/772pbzu81QwnZtQOhKJBO3t7TMKiDMMg3fffZ916zdRVVXBUUceuqAq/KPJtHVTHp30PDoTIX38paioaEFWR9MTxwvGXllXEHQFQxAxBBEwEHQFMDAkd846GBapSiQSeUlbHx0eOp6pwHx0xUaTqrkuJIwnCh/vXjebrsaOYEWb/n1Mhp2ai0WIQtVXZEP6iZ9+QEejUdra2gBYunTpvLfeprpwn89Oy2x0IXMl3AYYGRmhu7sbr9db0McmmBWwq678Lf+47W7+/e9HGRkJUF5eitfrQVFUivxFLF+2hMefWM3WrW14PG4iEbOiY/3sgiAioHHQin1oa+uir39wWttgHT+CIPDHP13Ppz/1MSoqZqbbUVWVBx96nEceeQZVVfngyiM488xT8XimtsgPBkM8+NDjPLPqRRKJJIcctD+nnfZhliyZXJ9gpS87HA6ampqmtcgJhcJ881sX88yqF0gmkyYpXdLE1Vddyl577Tbl95kvWPbKgiBMSKYlSco6/hIKhQgEAnbVOn38pdCRnjje0tIy7yYigNm1MFQMQUjrUggYCGBo5qiUMPvrkkWqVFXNIJT5QjZTgWxdMes5Ho8nryOv6Z2q+V6ETtbVSLe6tUjGdO6DO0LnYjrkwupczPfvOpdYtORCEISC1ldkQ7YF8fDwMF1dXbjdbpqbmwumOjmZ+Hy+Oy0z7VzMVX6FRXoHBwcLwhloqnC7XXz1K5/lq1/57LjP2XXXZfzv//4CRVHwej1EIlFUVbfHnZYuW8Llv/sJq1a/yK9/cyVut4tQKJwxGjUZioqKiESivPzym5xyyvHT3g9VVbngf37Agw89YR8jDz74OHfceT933nENRUUT3yQSiQS/u/xqXn75dfz+IiRJ4t8PPMrb76zlpz/5Ho2NdeO+NhgM0tXVNeP05d9ediWPP7Ear9eN31+Eqqps2dLKBV/7AU889q85W2QrCYXhjn4AyhqrcLgmvzZFIhE6OjpwOBw0NzdP2flr9PjLROGP+V4ozhTppGrJkiUFRIYMU2fBqO9LEBB0MCZxhpsKrPuB1ama68mBdFMBqytmicJHRkYYHBxEkqSMrkauCj357tbkAhNZ3U63q2GNdi/0zsV0OtI7NReLCIlEgi1bthSsviIbRp/cPT099uJzPuxIJ8JE4vNYLEZrayswf52WmZCLuXKEsjo6oVCI6upqysvLC24hNBuc9cnT2LBhM3+/9S4EgVRKM+yyvIXTTz+Zk09aiSQJVFVW4nA48PnMcKyRkeCUfq+iIi8ulxNNU3FMEMw3ER586HEefOgJnE6HvchTVZXXXn+bm2/+J1//+pcnfP2rr77F62+8zdKlzXano7a2ivff38Sjjz3NV778mayvs0LSiouLqaurm/Y5HQqFue++h3E4HHbl1+FwUFziZ9u2DlY/+zIfOv7oab3nTND5XitrH3uNYN8wAMXVZex9wkE07N0y7mtmS6rSkS38cfRCsZDcg2KxGG1tbdMmVXODVMdC1zP5haGP6mbMDKO7NYVAqkZn+qSLwoPBIJCbpHnLsMDSFRXECNwkmMzqdrIAv5mOiRYaZtK52EkuFgECgQCGYRSsviIbrINYURS6u7uJRCLU1dUV5OJzvDTxQCBgJwvPZ6dlumNRcyXcVhSFjo4OEolEzp1CCgWiKPKTi7/D5z//CV555U3cLhfHHnsExcVFtktKTU0NJ59Sxy9+9SfC4SglxX5kSaI/LaEbxoq7fT4vxcV+AoEQPp+XoaFhNm9pZfmyJdPaxrvvfghFUZEkCV3TESURWZYxjDj/efCxScnF5i2tqKqWMUJlevP7ePvt98Y83woKm22Ow/BwIKVbyby0y7KMruv09Q1M+z2nvQ2dA7xx33Mko3HKGqoAGOke5I37n8dbWkRZQ2WW7R62dVe5LpSMXijG43E7U2O0e5Df75/zxW162vp8uPtNCkHAEGVEPYGha6kZRgPB0DHE2Qm6LRMVURRZsmRJwXTe0zGRKNxKmpdlOYOsTuX4TR8Dm66mqpAw3QC/neRicWDRkovq6mpKSkoK70I+AayTsbOz067yzLuLyDgYPRZlLZ76+/spKSmZ12RhmHrnwmrhWhfJfBILaywCoKWlZcHebKaKpS3NLG1pBswbbWtr6xhS9X8//AY/u+RyhkfMRWC24yodkUgURVFIJhXC4Qjnnf89HA4HJ590HFdfdemk40yGYfD7P1zDw488iaKo5kJCEPD5vHi95iiNabk7MUxSMdbZLZlUKC7OJIzpnar0oLCZoLa2ioryMnr7+jOOn0QigcPhYLfdls34vaeKzvdaiQyHqNu9yX6sckkN3evb6XyvNYNcpAcDlpWV5V13JQgCHo8Hj8djLxQtomFp75xOZ4Z7UD63JxgM0tnZOe9p65NCkNFlEHTV1FggYIhO0y1qhojFYraJSuF1a8bHRKLwkZGRKWl90sfAlixZUhjamhwgvathXadHB/il24kvZGF3eodmMuwUdC8iCIKwoIgFmPPIYG77smXLCqJ9PB7SK8qWvV4wGKSmpqYgQrCmQi7mUrgdCoXo7OzE5XLR2NhYkBW8fCHdFWk0qfrCOWexy/IWbrvjXjZt2sbw8Ajr12/O+j5OpwNJkojHE/ZvZhjmgv4/Dz5GWXkpv/z5//LPfz3AM6tewOV0cuqpH+JjHz3RXtg88ujT/OGKa5EkEUXZLjSPRKJIqd//xA+vnHSfDj54f+655yHaO7pobKhLjXUF0HWdY4453H5eupAzF50qp9PJued+jl/+6o8MDwfweNwm2UokOeaYwzloxX4Zzx8ZCaIoCpWVuet+RodDyM6xtxbZKRMdDtn/Tg+EnK9gQIfDYY9PJWIJwsEQCS1JIBBgaGgoQ/Sbyzl72N6tKS4upr6+ft6viRNCEEBwYAgykNJfzGJ7LcMCp9NJc3PzgrsXW5hIFG5pfUaTVatjoev6DkUsRsO6X6Z3NVRVpbe31x6VsojGjK1uDR1BVxEwMDA7bLm2Rh73o6exLthpRbsTBYl0Ryswuy6FTCxge4XZmqe1ZkoLRdtiic7GG4uaS+H20NCQ7VZWaNqZfCNdwDueK9KRRx5K/8AgTz75HIODw+O+l6ZpVFVV0NnZgygKKYJr/k3Xdf71rwd444132Ly51e4oPPrYMzz22DP89crfIMsyd955P7quU1xcjKYFUBTTbhMgEo2yx+678tWvZNdLpGPZ0iV88Yuf4u9/v8smQ16vh1NOOZ4PrjwCGCtizVXL/CtfPhvDMLju+n8wPDSCw+ngjNNP5scXfds+jtvbu/jzlX/juWdfRtN19thjOV+74IscfvhBs/78kppy1KSKoRsIYorE6wZqUqWkxuzKpHdr5jsQMjIU5I1/v8i2V9ehazo1uzVywGmHU9JSaS8Uu7q6AHNxYGVqzDR8zbLf7u/vn5NuTU4hCIwRdk8T1hhYLrQ1hYSJROHpZNW69jQ3N++wxGI89PT0EI/HaWhowOv1zi7Az9AQ9SSCNWYFGLqGLjpz4l42GXaORU0MwchFRPEChFVlKHSMdrTq7++noaGBsrKy+d60CbF161bAHPWx5mkLbcznvffeo7q6msrKzBnwuRJup1duZzNnv1Bh2ez6fD4aGhrGXWT09PSx8oNnEo5EicfiE44lVVaWMzCOLkMURZxOUyAuSRKiKJBMKiiKyg3XXc7BBx/A6Wd8kY2btlJS7Ec3zJtCIpFAVTWWLVvC7bddRVtbJ8X+Ig455IBJRzm6unp48801KKrC7rvtwh577IIgCHYavSiKNDU15WWREY8n6OruoayslLLS7anewWCIc754IevXbaKkxI8kSQyPBCgrLeHqqy5lv/32mtXnhgeDPHfTIwR7RyiuLTU/s2eE4ppSjvrSSXhKfXa3pqGhYV51RclYgv9eeieda7bhLStClCQiQ0H8VaWc/P1PUdlSA5AxZx+JRDAMww5f8/v9Ux6fStfWVFZWFkQXdy5hdWgta+3FUkgxDINQKER3d7c9agvgdrvtrsaOEgCZDaPDAUePB422uk1flmbtahgGop4wdT9pSfGCoWEIIrroylt6vIXh4WEGBgbYddddJ33uBRdcwGuvvcZ77723w5DpybCzc1HASCaTtLa2ZjhaDQwMzCibYa6haRrxeByv11uw87TZkp7nSritaZp9sZ3vyu1cY7o2u488+jThSJTSEj+9icS4z5MkEUkau1jZPiJl/r/VHTEMHVmWCYcjnP8/PyAUitjamkQiSbG/CJ/Pi8fjJhyO4Pf7OO74T6AoCrIss6S5kb/+9TccfND+vPPOe6x+9mVcLicnfnglzc0NANTX11JfX5uxPeFwmM7OTpxOJ01NTXk7N9xuF8uWjhWyP/rYM2zcuIWGhlr7u/D7i2hr7+SOO++bNbkoqijm0E+t5P2n3mSg1ey2NuyzhD2POxBXsYfW1lY7y2C+K3mtr2+ke107FS01yClnMW9ZEX2bunj/qTc4+ssnA9nn7EOhEKFQiOHhYURRzLApzfabGoZBd3c3gUBg1tqahYhAIEBXVxd+v5+GhoYddiGdDclkkt7eXltfIgjCmABIWZbtY6gQHMxyhcmIBczA6hYDaRSxADAQEQwde3Qvj5hOVkcsFsPtdu8wv+lUUHgrvp0AzAVIe3s7kiRlOFpNlh8x37Cq8fF4HFmWaWlpKdjqVPpY1FwKt9PTh5csWTLvC6y5hK7rdHd3EwwGp2yzGwqGzZFvQcDjcZNIZO84appOb+/4bkimU1CCvr4BiotN4hCNxkkmTQH46OcGgiEUVbVvbm+9tdb+u6KobNnayufP+TorVx7BAw88Zh87v/jlH7j4x9/h3K+OzfpI79ZMtXL7zjvvcc+9D7FlSxvLljXz8TNPnRUB2Lx5m115tyAIAh63m3feeX/G75uO8qZqjjjnw0SGTI2Fr9xPMplk27ZtAAUzaz7U0Y+hGzaxgNR4S5Gbng0dWV8zes4+kUjY4X3d3d3AWJtSwzBsJ7T6+npKSkqyvveOCktfUlJSQl1d3aIiFlaXcrRwvbS0lNLSUgzDyBCFWw5m6WS10Megx8NUiMVoTMXqVjB0RPSUS3L+7tUTYTpi9Fgstqj0FrCTXBQcrBl8awEyurI5kU5gvmGJU8PhsH0iFSqxgO12uaOF25YeIx+IRCJ0dnaaqckF4uk+V1BVlY6ODnscZqr6mwMP3AdREAmFI4iiNGuCres6gUDIzs6YCIlEkqamelpbzYWmKJo6Dmussrd3gLvu+g9utxufzySJ0WiMS35+OQcdtB8rDtwXyJyzn04o4mOPr+L/fvQrgsEwsizx0suv8+CDj/PrX/9oSsLybCgrK7W3KX0bEskk1dVjbWJnCkEQKKowf+N0Z6DpJo7nEy6fWbQZ/V2ocQVv6eROfIIg4Ha7cbvdVFZWoqqqvUi0fm/r+q2q6g5rLz0RBgcH6evrW3j6khxgPGKRDkEQ8Pl8+Hw+ampqMkThfX19WUXhC+E7nAmxyIasXQ1VRdc0RENF00RAQBBAFEDH/He+MV1ysZiKiLCIyUUhnpyWyNGawc+2ABkvP2K+YfmVK4pCS0sLwWDQtl8rVFhEbS70FbC9am3NGy+mFml6t2a64zC77roMh0Omf2CQ0Yd+OtHIRjokScrwWrdgGMakoXyiKFBS4kdPI57W062ROvO9Rdzu7VV4K3X8nnseYsWB+87YFSmRSHDZZVcSDkdpaKi1P7O7u4/fXX4VK4/9wIyq/x/+8LHc8vd/0dXVS01NFaIoMDwSQBRFTv/YSdN+v8lQyDkOS1bsytsPvsRwRz+l9ZUIokBkKIQgCux65D7Tfj9Zlu2KtK7rhEIhent77WOws7MzoyJdKCQrH0i3GV6MmjIrGHG6jljpAZC6rtui8GAwaIvC08enCvEY0nWd9vZ2YrHYrIjFaGR0NQwZlDiirqEbOhigGQIaAqjq1EThs8B0x6Kqqqrysh2FikVLLiD7zP18wXKOsaq64wm2C3EsKn2Ea/ny5bhcLkKhUMF8t+NBEARUVUXTNCRJyqtwezoagx0N0WiUjo6OGXdrfnzxb4nFExQV+YjFEnb1ShAEHA7ZHpOyHks/7iY6VyZzDBMEkabGelrbOsc970yhuDTqdeZ7Dg+PoOu6PQ4zXW3NmrXr6e7po6Ki1D5eBEGgoqKU7q5e1qxdP8ZadiL09Jj6h6Utzfz0J9/lN7/9C13dPRiGQVFREV/8wlmcdtqHp/x+U4FFqOcrx0HXdNrf3kzHmq1gQP1eS2g+cBck2fzNyhoqOeqLJ/LCP55gcFsPBuAu8rD/qYexyxF7z+qzNU1jYMAc07PGQ61MjZ6eHmDHFfSmC9erq6upqKiY702aU1hWuy6Xa1aE2gze9OP3+zNG8MLhsD2CV2jHUL6IxRgIEjg8CIaGrOvogGEIiMb2USoLM7a6nQDT6VxEo9GdnYudmHtEo1Ha2toAWLp06YQHYSGRi/QRrqKiooyLaCFtZzbouinmDQaDxGIx214y1y3ndMvNqWoMdiQEg0G6urpwu900NjZOW7z8+utv8+BDT9gi6pISP7Is098/kFoU+zLIxWhM3JkQkWUzFyMbHA6ZL37xU9x994O88upbY1xMwMzWEAQBVVERJTFFLEySs+++e9La2koymaSpqWnagZfWjWv0Lhh2zMDUjqPXXn+bH/7fr3jrrTUA7LffXvzm1xdx37038tKLr5FIJjlg/31oaWka89pA7zDtb23GMAya9ltGaf3UFonpFsvzRah1TWf13x5m/TNvmda4wLuPvMouR+7NyvM+YmdxLP/AXtTt2Uzn2m1oikr1Lg2UN86uymh1cgVByCDULpfLHp+yKtLpgt7ppjwXItI7dbW1tQXvbJhrRCIR2tvbc261m20Eb/QxJElSxjE0113CdGIxJ4YNggiCiCGag1DW3SVdnzFjq9tJMFVyoWkayWRyp+ZiJ+YWIyMjdHZ24na7aW5unrTFWSiLdkuYOzw8nHWEq5C6QulI11jU1dURi8UyfMjTL85FRUWzuvgoikJHR8eY1OnFgPTFZXFxMXV1ddP+Ljs6uvnmt39CNBpLaRwUotEYkijaugc51XEa71gTRXOxnw2SJFFeVsrA4NAYQbcsS5x37uf44hc+RV1tDV859zuoisLodzr/vHO4/Y57iUSiyLJsV8wa6ms55OB9SCaTM7Zh3nuv3WhqrGfL1jbq62vs/RwcHGbZ0mb22Xv3Sd9j0+ZtnHHml4lGowipcKm3317LJz7xFZ566h5OPvn4cV/75r+f58XbniQeNMcbXUUeDv7kMRx61soJiUKh2K1ue30D655+C2+pD0+xWT2Nh2NsfG4NzfsvZ7ej97Wf6y0tmtEYVDZMRV9iEuUSSkpKxgh601OeraJHIY6+ZINhGHR1dREMBhelcN0aAZwLq93Rx5B1L0sXhY9OCs/neTjnxGICZBOFp/+Xi66GZW8+GRZjxgXAwiyN5AjzWUG2qjsdHR2UlJSwdOnSKd1ACoFcqKrKtm3bGBkZoaGhIav7RyFs52hYlQzrwiJJEn6/n7q6OnbddVdaWlooLS0lFovR2dnJhg0baG9vZ3h42K56TBWxWIxt27ahqiotLS2Ljlj09PTQ19dHZWXljIMB/37rXbS1dWYI7QG0VCVKFEVEaWIN0njEwtxOneuuu5ympoYxf3M6nXi9HiRJ4pRTjueA/ffOsDx0OBy4XC7uuusBrrvucg5asR+JRIJYLIamaiSTCs89/yq6rtPT08Pg4CCJCWx0s8HpdPLDH36DkhI/nZ09dHX10NnZQ0mJnx/+8BtTGi+74YbbiMViyLKMLEup/2Ri8TjXXPP3cb+7jne38tzNj6EmVcoaKylrqsLQdV66/Sm2vbZh3M+zFpdDQ0PU1NTM65x921ub0RTVJhZgjjwZusG218ffh9kgEonYc/ZLliyZ0jXdEvTW1NSwfPlyli1bRlVVlX0ebdq0iS1bttDX10csFivIog1sF/AGg0EaGhoWHbEIhUJzRixGwyKj1dXVLFu2jF122YXa2lpEUaS/v58tW7awefNmenp6CIfDOb83W8QiHo/PO7HIBrNLLeN0Ou3/zKwj0e5qJJNJFEWxNZiTYaqdi8VKLnZ2LuYB6a5KdXV10xqVmW+3qHg8TmtrK7qu09LSMu485Xxv52ik+2Zna4eaNqcePB4P1dXVJJNJ28e+p6eHnp4e217S7/dPKKS1gqJcLheNjY0LpuqYC1ihj5FIZNb5HatXv0gykbSJaraRJGsEaSYLLk3V+J+v/S/d3X1j/haNxrjij9fxkVNPoKWliffXbaSoyIfL5UIUBXubgqEwPd19hEJhJEnG5XIhCDAwOMTvfncNdbW1HHHEQfT399PX12e7vvj9fjweT8Z5H4lEeeA/j/HCC68iyzLHffBITjxxJbfd+lfuu/9htm1rp6WliTNOP5nddls+pX1848130TQ9YxzNMMyu2o033c5tt9/Dh44/mh9f9C322GN7GNSGZ98lGY1TsaTGfqyosoTBtj7WrXqbpYeM7ZpYv300Gp2WG1i+oGt6VtMYQQBdHSvyny1yFRCXLeU5FAoxMjLC4ODgvI++ZMPoOfvpjgAudASDQTo7O+dNWzQa2XJZrK7G8PCwTWhzYSyQTiyampoKfhE9ntXt6OKj9dzxuhpTJReWsc3OsaidyCssazpN02hpaZnRHPZ8Va6CwSAdHR04nU6WLl06YeV0+7z41B0V8oWZJG47nU4qKiqoqKiw7SVDoRADAwP09/dnXSSmjwL5/f4ZV+wXKhRFob293Q59nK2QT5IlFFWxSYSiKGO6GLNxJNN0nY6O7nHPp0QiyZe/+m3uuftvqQW6Wfm3YG3Diy+9zqZN2/B4XLZI3O02g/duvOlOPvGJ03j66ed5/IlV6LrOoYfuz9577ZYxY6/rBl+/8CJeffUte3seefQpnnr6eS679Mf87w++PqN9rKutzggWtEShhoH9nT78yNM899wrPPHEXeyyvAWAaCCMkCJQfeEAkiBSWVSMJEtEh0NjPkdVVdrb2219Sd5EnNNA/V7NrHv6TZR4EofbvFapCQVd12ncb1lOP8sSruc6IE6SJIqLiykuLh539GW+8xCsYlkikSjIqnW+UejhgKNzWdKtbi1jAZfLZT9ndNFjIui6TltbG4lEYkEQi2yYdoBfqjg51bXNzs7FIsRcXwSsxbnD4WD58uUzuhHMx7iRZSnY29tLcXExDQ0Nk1bL0k/W+ayszYRYjMZoe0mrkjhap6EoCtFodFHaLsbjcdrb2wHTGScXAWknn3QcL730Orpu4HQ6kGXJrjAVFxfxwZVHsW79JjZv3koioWS1nJ0MkxH1LVtaefjhp1i2rJn16zdlzC0nEklkOeUyJmwXClrHvizLvPf+Br7y1e/w6KNP23qN226/j8+cfQbf/94FRCIRAoEA9973CC+++BqVleV4PKbjSyQS5ZFHnuLEE1dy0okfnPa+AZxzziczBPGKomYklUupsbJgKMynzz6f0pJi6utr2b9+KesGOnjinUcZjoYBqCsu56TGfTl0j5UZn5FMJmlra0PX9RnrS/KB5YftyeYX36P1jY3mbyIIaIpK037L2PXI2TlBpcPKcci3cN0afbHGX9IXib29vXYegqXTmM4icaZQVZW2tjY7cX2xVWctUrlQwgEFQcjaGbO0PlZnLJ2wjnf/TieVC5VYjMZUAvxg+2TGVH5vqwC2I3w/08GiJhdzBcuK1BK3TmVxPh7mmlxYVpqBQICqqiqqq6undEJZz5mvLosl3LYqD7nKsBhtDRiLxQgGg4yMjNj7mkgkCAQCFBUVTdsdaSHCEjFatou52udzPv9J/nHbPaxbtyltnE3A7XLR1NjA7y//GaWlxWzb1s5Xz/0Or772ds6PN1XVuOKP1/LbX1/Ehd+6mFAojCzLJpEx4LTTTmBJc13qnMwct9M0DbfbyyOPPo3DYc77mp2DJLfdfi8f/vBKPnzCsSSTSd58cy2iICAKAol4AkEUcDmdDKkqq599acbk4vjjjuaiH32T3176FxRFtbVDsizbHQ1dN2eOt2xpZenSZrq7+3jxxdfo7x/MqM51B4a4LfoCX9nzW/b7W53YQgyFdLidnHDhmaxf/Q6tr2/AMAyWrNiV3Y7ZD5dv9ovgdIvp+SgopOchTLRI9Pv9eRmfsuzTreyaQiGVcwUrdXwh24uP7ozF43HbLjkYNANGR6fNC4Iwplu1o5LK8boa4XAYTdMQBIFkMjnh+JRFLgqhmzuX2PFXPvOMdJFbdXX1rG9Ac0ku0rM3mpqapiXQSz8h5xrppMLalnxc+AVBQJZlIpEIoihSU1ODoigZHuQej8cmI4W08MoVhoaG6O3tzdmscWdnNzff8i+effYlvF4PZ3/6dB559Gneffd9VNVc0Lvdbk499UN4PGZ3pKWliUcevoOrr72FSy75/Rjnp9mit3eA5198jVtu/hO/vfRK3kzpGEqK/Xh9bvbdZw/8RUWEIxG8Xi+iKNj2uEU+L+FwxP7tzZEpF+FwhH8/8AgfPuFYnE4nDoeMJMu43W40XUPXdFTNzGAZHhq27Z5nYlH6nW+fz1mf/CiPP7GKf9x2D2++uSZjvMsiHKZNqmk1++6776MbBpIobpctCAKaoXPvw4+yz8F7E4lE7DHJXJLKXMLpdbHvSYew70mH5PR90+1WCyHHYSqLxHTnoNl2Fq1uFVBwpHIuYF33dqTU8dG6Q+teFg6H7XFgWZbx+XzEYjEURWHJkiU7LLEYDeu6G4/H6evrw+fzUVpaahcyx7O6tcaiFsv3ZKHw7gY7EKwLcDKZpLm5OScCx7kiF7FYjNbWVgCWLVs27RNjvjoXVmXB+tx8ah6sxZUsyxk3WMuD3Lq5p4t5LaJRCGFHs0G63Wh5efmUO1o9PX1s2LCFiooy9tprt4zXtLd3cfZnLqCtvQtZFtF1gzfefJeVK49g+bIW7rv/YTRNI5FM8NerbuL119/hbzf8gaIiH7Is843/9xXOPP0UDvvAKQSD4ZzspyAIOJ0O7r33Ic765Gn09PQhSRIul5N4IsEdd/ybNWs28Je//Irvfu9nDA2NAGZn4ONnnsq7a96np7c/6/cXDkXsf3/wg0fx+hvvklSStpOJElVxOp0cfdRhRCIR26LUqkZPpzPW2FjHl774aZqaGjjrU+fZwZHprf7q6kr7+YpiEjRRyqzGKYrCmjXrbAGrz+ebc2ec+YCu63Su2Uawd5jShgp0vzSjYMS5wHiLxFAoNMZYYCbZPlaGhyiKU7JP39FgjcFN57q3EJFNFG6NA1v31/7+/nnV+8w1rAyT0aYNE1ndhkKmRm3nWNQiQj4vClZqtSiKLFu2LGctY0s4nE+h9HSzN7JhPjoXudBXTBXWrK3P58s65ibLcsaFOd3DfnBw0Bbz+v3+VLV74SzO0oMBa2pqKC8vn/Q1yWSSX//mz9x193+IxeI4ZJn9D9ibyy/7Kc3NphXsddffSlt7FxUVpfb3GY/HeeKJZ1MLJjd+fxGCYHYGXnzpdW686Q4u/MZX7c9paDAX0X/68w052VdzDM4M6rvqqpvp7x/E6XSQTCqIgoDH7Wbt2vUMD43w6suP8MSTqwkFwxx88AHsueeu/OSnl7Fh/ZYMZxGrnX7EEdur6Wd98qM8+eSzvPnWGgTBdHQSBYEPHX80n/jEaTgcDnvGPhQKZXTGRo8sTITjjzuK/7ngC1xz7S0Z56bfX5RBLpxOR6r7sv39DMNAkmQqK8vp7OykuLiY+vr6HXZxZSHQO8x9F99E13utaKqGIApU7tHAmT//YsERi2wYvUi0xqeCwSBDQ0MZgl+fzzchYU3P8Ghubi7IblU+YVXwF5uuThRFPB6PnTjf0NBgk9Z0vc9MCetCgJW6ns0NLtv4lK7rJJNJ7rvvPgDbgGGxQDAK1TR7DqBp2rTzCyZDemq1z+fL+bjAyMgIHR0d7LnnnjmfobWq0f39/ZSWls7K7Sgej7Np0yaWLl06J7OG6R0Ly9EhX59jzVnPZNbW0mlYNreKoiAIgk00JhLQFQIsV6BEIkFDQ8OU8zuu+OO1/Pkvf8PlcuHzeUgmFULBMHvvswf/vu8mZFnmqGM+Rnd3L2VlmeN3VtZFY2PmQnZoaJhly5bw1BN3Zzz/+Rde5SOnfX5GAu/R8PuLcDplDAPKykrZtq3N1leQ+l/ZIXP2p8/gL3/+1ZjXt7d3ccqpn6Wvf8B2GDEMg112WcpD//kHpaXbu5nBYIj77n+YZ599GYdD5rjjjuKjp3046wiL5WBm/WcYBg6HI4OwTnRcvvHmuzz44OMkk0nWrF3P++9voKGhHofDDALctHErg0PDSJJkazM0TUMURK6//jKOPOLQHbpqa8EwDG792p9ofXMTLp8bQ0w5TiVU9vrQQXz8V1+e702cMazxKesYisfjABmW2+kGBtbiyul00tzcXNDXqVzDMjUZGBiY12DI+YKmaRlTGOmTDOl6H0uLIIpihih8oZPQWCxGW1sbbrebpqamKSdzX3zxxTz88MMsXbqUe+65Z1HpLhb2L15gmCy1OhdIt3jNJTRNo6Ojw65Gz/bima/tHI18CbezwRK3h8NhampqKCsrm/ZnjXZ8SSQSdjW6q6sLICOZt5BazYlEgvb2dtsVaKqjcvF4gtvvuC+VKGuSEVNQLLFu3Uaef/5Vjj32AzidDgwjs9Nl/b4wljCKokgsGh/zeQ31tZSU+AmHoySTyZntbAqJRAJd1znl5ONZ/exLaJqeQV7N6pSCNk6Hrqmpnn//+2b++KfreeyxVUiSyMc+ehLf+ua5GcQCoLjYzxfOOYsvnHPWpNs12sHMGlkIhUIMDw9nVKOzEdYVB+7LigPNhOqt29r4xoUXsWWLOQZpGNDYVMcppxzPv+56AEUxCzBut4vvfec8jj3miHnXGMwVeta307lmG06vC0QBMPD4vCTFBBufX8NI9xCldZN37goR6eNTVVVVWWfsnU4HPp+5OOzv77ertouNWFgFpaqqKiorKyd/0Q6EiYgFZNf7WMeR1WF1u932tWihjQRbxMIyLJkKsdB1nZ///Oc8/PDDrFixguuvv35REQtY5OQilwe4ZckXi8VoaGigrKwsZ++djnyMGyWTSVpbW+18glxpQyC/Y1FzJdyGzAyHxsbGnCRuW3kIbrebysrKjNnovr4+ent7bf/x+dZpWPoSh8Mx5eRhC0NDw4RCYdzuzAq8y+VkZESjo9O8AZ16yof4819uIJk0sy0AwuEIbrcLXTddllwuk2zpuo6iqKxc+YExn1dc4qe6upKhoU0z3V0byaSCrhu89PLrjIxktrWtLplhGPi84xOtZUuX8Oc//nLW2zIeRvvYJxIJW++TTljTq9HpWNrSzG23XsVjjz/Dtm3tVFVVcOKHV1JXV8OPL/oWz6x6gWAwyAH778Uee+y2qJKXwwNBNFVDdIoYmN0hQRCQnTLJSILwQGDBkovRSB+fMtQkWjyEoSmoikIoHkVKhUeGQqEdoho9FRiGQW9vL8PDw1MeAd2RMBmxGI3RhDW9wzo0NMTAwIAtCi+kEMjxYLnhTYdYGIbBr3/9ax544AH23XdfrrvuukUXKgmLnFzkCuni56VLl+ZVuJPrRXskErHFebnWhkD+OhdzKdyOxWJ0dHQA5NXHP/3mnp7MOzw8PEan4fP55oxoTKYvmQwVFWWUlBQzNDSMN20RHo8nkGWZJSnNxVe+/Bmefe5l3npzDVrKetbhkPnSFz/Nhg1beOHF14hEzAWOoqjU1VZz7rmfs98vFovztxtv59LL/kowODbobaYwR8G67H9v76ZsR1t7Jw8++DhNTfXst99eGb+NYRg8/czz3HPPQ7S1dbBkSRPnnfs59ttvr5xto4V0wppejR4t5h2dhVBS4ueTnzhtzPtVVVXwgcMPJB6P09jYuOhukr6aEhAF1ISGp3i7pkWJJ3F4nJQ3Vc3zFuYBuoqkRZBlAwURRTGo8LspKfLSMxLPqEZbx9FU9D4LDemOYLW1tXkrGBYqpksssiG9w2oYRkZSeKGEQI4Hi1hYbnhTue8ZhsGll17KPffcw1577cXf/va3nBQiFyIWtebCrH7OzrYyF+Ln6cDSMixbtmzWJMbShni93pxrQwzDYO3atXnp4lgdC2u2M583tVAoRGdnJy6Xi8bGxnlxRrEuylY1WlGUScdecvW51pzxbEOirrzyRv5wxbU4nDI+n5dkUiEcirD//ntz7z1/s7c/Go3xnwcf49FHn6arq5eGhjpOPHElToeDG2+6kw0bt+B2uzjpxA9y3nmfY9nSJYDZffvSl7/NE0+uJhYbOyo1FzBdnHwccsgBXPH7n9HW3sWbb65h9eoXefyJ1cTjiYwu21lnfZS//OmXtqZi85ZWHn30aTRVY+XKI9h33z1zun3pxgLWbLQVAGn9N9oZysoxaGpqWnRWipbG4OVrH6PthfVITgeyS0aNK2iqxqGf+iAf/taZ872ZOYeoRBC0OAlFJRqN43Q68Xo8CGjojiIUQ7KPoUgkgq7rC9qgIhsMw6C7u5tAIFCQjmD5Ri6IxWRID4GMRqMYhlEwovBEIkFraysOh2PK+iLDMPjDH/7AP/7xD3bffXduueWWRUdI07GoyYVhGDOeybbapQMDA7MWP08HiUSCjRs30tLSMuMqolWRGRwcpLy8PG/JomvWrKG+vj6nreS5coSyhPl9fX34/f45+32nsl3pYy+WCNOq/vj9/pwQoPSba1VVFRUVFbP6rlVV5fLfX83td9xHJBLF4ZA59NAV/PbXP6K+vjbjubffcR+/+OUVxGKxFLGKoes6Pq8XQRRxOGTO+fwnuPjH37G36d//foRvfPPHxONxEomknaA6lzCdpbyEwzEMQzcdn0QxQ1ie3tGTZYmv/c+XaGlp5C9X3khraweiKCLLMrIs8bnPfpyvfPls/nXXf3h3zfvU19XwsY+dxMpjj5j1cW8ZC1hdjWQyaeuBrNGprq4uBEGgqakpJ4nrCwlWUcHj8VBdXsUz1/yHNY+8hpJUcHpcHPDRD7DyvI8gO3e85r+YGEFNxglHE7hcLrO7BWCo6LIHQ95e1MpW+LDskq1F4kKzqjUMg66uLoLBIPX19YtqDBC2EwtrTHrKnXrDAEMDDBBEEKZe8Ep3MQuHw6iqmiEK9/l8c3YcWcRiOo5ohmHwl7/8hZtuuonly5dz6623Lhpd2njYSS5mQC6sdMpwOExtbe2sF17TgaIorF+/fsbaCOvCEYlEqKury+sJ8N5771FdXZ0zAVy6j3S+iYW1sC50y0FFUewbeyRi5ia4XC47T2Mm4wqWuD8Wi1FXV5fTm+vg0DCbNm2joqKMXZa3jPl7e3sXJ51yNvF4gvLyUgKBEH19AxiGQWlpMZWV5YTDEVRV4283/IGVxx4BwPe+/3Pu/Of9aJopbp4TcmEYWBdP6xu2/y2kP7od6UYHpsOTjK4bGQREFM25ZVVVqaqsQFVVZIcDTdNwOh1c+I2vcu5XPzvLTTdYu3Y9z6x6EYDDDjuQZUub7CqiuQ8CZWVlFBcXLzgR5mwQCATo6uoaEwwZHQkTHghQXFuOu2jH7OIYhkEi0IugKwiS0/zdzT+AoaE7vBhy9n237qcWYbXCwxaSmNcwDDo7OwmFQjQ0NOREf7iQMGNioWsIWhwhRS4MRBAdGJLLuhhOGVYBzSIac3kcWfpTSZKmZbV8zTXXcO2119LS0sI//vEPqqp2wHHJaWLHK7vkGdYcnqZps+oezBSzcWGyGPlcbXuuFnjWQsxagOWTWKQvrBdC1crhcFBeXk55eTmapo0Rz03HnhTMi2t7ezuaptHc3Jxz/VBFeRkVh47fKn7iydVEIjEqK00nrnDYDMMTRZFwOEJVZQV+fxF9fYM8+ujTNrkwE6cNBMHIqonIJQzDwPQNSntszHNAwCQfo/UX6f9vOTGlQ9cNdM0UrPf1D7DPPnsiiuZ7DAwMce11t3LaR06gtrZ6xtt/6WVXcvMt/yKRSAACLqeDs88+g29986tEo1GcTicul4uRkRGGhoZ2uLGX8WAlL2cbA/SWFuEt3XE1J1Y3XomFaKgsNivF1nlkaCCIGOL4M/GCIOByuXC5XFRUVGS9HlnH0UzT5vMJyw0wEonkzLRjIUHTNFpbW1FVddodC4tYGHa3wkDQk+YxI01PRzHa6ERVVburYR1H6eOcuRKFW8TCCoecKrG44YYbuPbaa2lqauKWW27ZSSxSWNTkYroL1GAwaDvmLFu2bF5GBWYq6A6FQrS3t8/ptlu+/rPBXAq3E4kEHR0deVtY5xuSJFFSUkJJSQmGYdgX5NH2pJYgfPQF2QrIEkUxI3F8LmHqJbYHROq6jiBgB8upmpbqBxiEI1H7dcccczjX3/CPrIv13MGwWcRUjmrrOaarFIAw5fMhlhp3U1UIBIKUlhYjCALl5aV0dfXyyitv8tGPnjjdHQDgqaef42833oEsy3ZwXjgc4Za//4uamnI+cuoJ9hjg6LEXKyU8XaexI7gGpeuLdvTk5WxI79bW1tYguV2gJQCzoGOIojkOJU59ETf6epQu5rWOo3QXs/kcn9J1nY6ODqLR6KI0LrDcLqdNLAAMdTuxsM+Z1LVOV0B0TLt7kQ7Twnz7cWSNc6aLwq3jyBKFT/fcnSmx+Pvf/85f//pX6uvrueWWW6itrZ38RYsEC/+uMAewfK6t+fv59Pm2PPanSi4Mw2BwcJCenh6Kioqm7HqQC4iiOKvOxVzlV8B2q1VZludtYZ1LpC8Aa2pqbO/xUChEMBjMmK8vKioiFovR1dWF2+2msbFx3haMBx+8Pw6Hg0AgRCwWJx5P2seQJEm0tXVgGAaiKFJdZS6M33p7Lddc+3dUVbNJyFRg2chOBbPuhBiAYOZ1TAe6rrN1aytlZaW0tDTZ9rezOYcfeugJkokkRW4PWlJFdjrweNwEg2FefPENzj/vi/a5Zs3P+3w+e+zFIhrpKeHpuSwLbVGebje6GHMM0keBrG6tAWiSKzXmAoboMOfoZ4j046impiZjfMpKeJ4v221d12lvbycWi9HU1DQ2j8DSEhh6apEsmHqCBXacj4dZEQtSlzWDVA5M+h+mcTGe6meNyolKz2ax1mhWt94ShU9WkLSMKwRBmJYpzx133MEVV1xBbW0tf//732loaMjFLu4wWPTkYrIFhtUqtYSthVDRmuqiPT3Ur7Kykpqamjnd9uks3kZjroTbMHur1ULHaO/x9Bt7T0+P/TyXy0VNTc287v8hBx/AEUcczL///ajdtbCgadvJg6bpXPnXG3n99bd57vlXx2in3G43iURiwuNvysdmDm6Q27nF9AiG1fEYHg5QWlpCIpGkrKyEwz9w0Iy2IzoSZs2zb5EIxwjGTbtfyeXAXepFkkQSCWXccy197MUaVwiHw3RubKP1jY14K/yUNVTaRGM+3V6mCku8G4uEaKirprhkcbm7pI8CjdEYiDJGnpYITqczY5zT6rKOjIwwODiYl7GXbLD0k4lEInu3WtcQ1aipQdFVUmcyhuhAd3gwpPzYks8VZkssAAxBMK9Thp5BQAVDxxDlvJKwdPt2K0zUIhvDw8OTmgsoimLHCEwnv+nuu+/msssuo6qqiltuuYWmpqac79tCx6InFxMhmUzadmxNTU0FM38/lc7FXIX6TYSZdi7mUrjd19fH0NAQpaWleUlUL0RYN/aysjLbFcXpdKIoCtu2bcPhcMzbAlEQBGprqnC7nTaJAGzykL7Oj8XiPPX08/br0smC5aI1GSRJpLa2iq6u3lwX2cZgutzC6h7puoauG3R19VBfX8v3v/c1Kspndj4/eeX9+CMiCAKCJIABSiyBgYEgCRx22IFTfi81luSZK/7NxufXoCYVRFmi6aBd2P+zRzHkGpp0DG++oes63Z0dyCi01FXgdMqgRDBmKERdaEjXl83nKNDohOdsYy/5yEKY1G7VMBDVCIKmbHdBAkAHQ0FM6ujO6WsKCgW5IBYACBKG6EDQFAzBMK8tho6BMKFGJ9cYHSaabnVrFdGs7pg1ztne3o5hGNMiFv/+97/51a9+RUVFBbfccgstLS153KuFi53kYhzkK1wuF5hs0W7F1RuGkfdQv4kw3c7FXAq3rYpdOBympqaGsrKyRUEsLGiaZlcsrYAoq/ITCoUIBAIMDQ1NmIOQL7z8ypsUFRVRXl4KQFtbJzN0jJ4Uum5wwgkruf32e0kmx2be5FoYbphvOqVjzeN24XQ5SSSSxGJxdtttGVdd+dsZ51+MdA+x+aX3WdG0C+siPQxEgsgpAWYkHGbZLi18+lOnT/n9HvnD3bz3xBs4vS48JUWo8STbXlyH1+flxP89K2MMD8gYw5vvsUOrYl3kMCgvKUF2ujBZn46gJWYkRC0YGOY+mJV2AUNypsaath9zoxfWhaIvGz32kr5A7Ovro7e3N2sI5HRhBmO2T5jjYHUrDEFANFJVAUEwv18EDHQELbYgj5N0YrFkyZLZaTAFIdXBEUBXTWmaIJvfizg/S8xs5gKju2PW86qqqqZc+HjooYe45JJLKCsrs21ndyI7Fj25yLYAHhwctMdkch0ulwtMJJS2ROdOp5Pm5uZ5vYlPp3MxWrhtaUvyAUVRaG9vR1GURekKkr7/TU1NdsUyvfJTW1tLPB4nFArZZCO9xez3+/N2XhT7/WhaR9r2Ti7Sns343TNPP4+q5lMInompHNWiKOJymzbCsizjdDr45oXnzipYLzIURE2qlJb4+dQ+R/J86/tsHOrBMAx299Xw5VPOpPuljQh7JanZrXHC82+4c4CNz76L0+vClbJllVL/v/G5NRx77qlUNVTZY3hvPvQiz/7nRUY6B/HXlLL7iSvY+0Mr5sXm1lpYSehUlJUhyeldCskUqOrKmAX5goChISbDCIZqTfAg6AkMyY0ue0EQxlSsCzkcMX18Kj0EMn18Kr2rMZVF4uiF9fiFQ/PeJVgdizSxsmkHJyLoWvaXFjBySiwsCAKG7E61ltOIWIEgvTtmjUKpqorD4aCvr4++vj48Ho99HGWzcH/sscf4yU9+gt/v56abbmL33Xefp71ZGCisVfM8I12jkM9wudki26I9XXReXFxMY2PjvNv8CYKQ4d8/HuZSuB2LxejoMBeuE99YdkzE43Ha29uBifd/tE4jkUgQiURsnUZPT499MbbyNHKFM844mXfXvE80GsPjcSNJIsrYpkJOIIoCHZ096PpYcpIvO9updC90XWd4OIDT6UBA4IgPHMxHT/vwrD63tK4ch8dJJBCipNjLGfseAYLASOcAsZEwnU+vpXv1OhwuJ3t+6EA+9PXTkRzZbxGBniHUpIqnJFP8KrudxAIRAj1DlDWkBPf3v8Cqax5E0zQcTgfDW/t4+frHiA6HWH78vnZ3zBqfyud1yxp1NQyDpqZ6JEEduwgSRHN+fJbQNZ31q97m/affIjIUona3RvY9+VBqd2uc9XuPB1GNmx0LQdousE11MpBcKKpBW1sbuq7nbmE5RxBFMWN8arRJBWS6BmXbt/TU+cn3P2X7jrA960NImVALImCkWa8uDOSFWKTDErwXKKyOpa7rLF26FJfLlSEKHxgYoL+/n3/9619s3LiRD3zgAxx33HF0dXXxox/9CK/Xy4033siee868yLNYsJNcpJCuUch1qnSuMZpcpIvOq6urCyb0TRTFSSvCcyncDgaDdHV14XK5CrIjlW+Ew2E6OztxOBw0NTVNOmM6ODjEtdf9g4cffpKkonDMMYdz3lc/x6677mrf1K2LsdPptBeIMx1VsPCZs8/g1Vff4rHHVxEORzOci3K94K+rq6W/fyDr30ZnWcw1NE3D5fRRVV2J2+PhxhvvoLauhsbGOg5asd+0v2PR46DuoKVsevId5KiMhkQsFCEyFMLpcVHWaF434qEY7z78KrW7NrL/Rw7P+l6l9ZXIThklnrQ7FgBKPInslCmtM8M54+EYL932JADF1aUAePARHgyy5Yk1HHnW8SQN1Z6vz1e6s7UY7ejoQBAEc8ZaFkFR0xaN1pNTQtRZ4sXbnuCN+57H0HVkl4PB1l62vbaBk75/Fk37LZv1+2eFrmB7N9swR3m0ZIzWjl7ALCzM92jabDC6+JHNNci6JlnaMVVVaW1ttWfsJ1tYG6IDQ5QRtCRWso1g5dSYW4EhLxxylndiUeCwRgFH7382UbjL5WLdunWsXbuWG264ATC7HxdccAHLluXp3N3BsKgTugHb8cSqZhXS/Ol4sCpPLS0tdosvkUjQ2NhYMKJzgK6uLqLRKLvsskvWv8+lcHtwcJD+/n78fr/t4b+YMDw8bNsRp6cOj4dwOMIXv/wt3n57LW63C1EUiUSi1NfXcvNNf2TZ0iWA+RtaHY1wOIymaTmpROu6zksvvc5LL79BLJbgnnsfpLe3j1gsYY/PiYKAPsvLl8/nJRqNZSUt+bw0miZQMzvenU4HBx64LzfecAVNTfVTek04HDYzeiSZ1iff473HXicZS5CMJUlG49Ts2ogkb6/CjnQPUb9nM5/50/8b9z0f+MWtrHn0NRweFw63EyWeRIkl2PuEg/jYT88BoPXNTdz5navxlHgzuiBqUiUejHL2H79G8/7m3HJ6Kq+VEu52u+35+pmkzQd6h3nln0+z7pm3TWOOg3fh+K9+lIrGKrN7lHICwqpApzoWusNr+vPPEMOdA/zze9ciiOArNx2YDMNgsLWXJQfuyumXfCEv1zsxMYJg6Nv3x/xgdF2hezBEJGlMy25zIcK6JlnHkqqq9iixKIo0NTVNfRTM0BCVKIKeTGlYAAQMQUKXPWZaeQEU8iaDRaym1rHZ8TCT5PFgMMhNN93ELbfcknEvcDgcHHbYYRx77LGsXLmS5ubmfG76gsWiJxf9/f20t7fjdrsXzEXXmpevra3N8GcutNnZ7u5uwuEwu+66a8bjo4Xb+dRXpIdDVVZWUllZWRBdnbmCNS43ODhIWVnZlO2I777nQS768W+orCy3K5y6rtPV1csXzjmLH1/0rayfZTm9hEIhksmkXYm2Fogz7RZt3tLKn/98PU8++RyRaIzS0mLCoQi9ff0zer90jKcNKlRyAeY2H3DA3jz95D2T/p6BQICurq4MYhkLRgn1j/DWf17krQdforwhM9shNBDAX1nCV2/533HfNx6O8dgV97B+9dtoCRXJJbP7Mftx9JdPYu1jr7Ph2XdJRBMMbO3BV+HH5dt+Q1diSZKxJOdcdSG1u4+1cbRSeUOhEJFIBF3XkWXZPo58Pt+k+x0ZDnHnd6+hb1MngiyaY0KKTuXSOj79+wsoqig2R4bUeJpGwRRyz1ak+/5Tb/LYFfdQviTTujwWiKCrGp+/6pt5SfsWlAiiGgNhuwWopiVJxBP0hRTqGgq0Y2sYYKQW74I0q0yNzLc1CIfDdHV12fcdIGOkc9JsFsMwSaehAykxtyjnbBvzDYtY6LpOc3PzoiQW6XbDU10nvfLKK1x44YUAXH311RQVFfHMM8+watUq1q5daz9v77335pZbbll02s3JUIBXmblFLBajuLh4StXcQoEoiiiKwtatW3G73SxZsqQgbxjjaUPmSritqiqdnZ32qFshdXXmAiYZ6CIUCk3bEeudd95D142M0QlRFHE6Hbz08htZXzPa6SWRSIwbuGbd1KeK5cuW8Kc//tIes3vvvQ2c+fEvI0kShqGnFg5TfrsMWHkaC6nMous6b7zxLqd99PP8+lc/Yr/99sr6vMHBQfr6+igpKcnQkHmKvXiKvTTs3cLbD76EmlSRneY1xDAMlHiShn2WTrgN7iIPH734c4x0n0KgZ4iS2nJcPjf//N41dK9rQ5QlDN1AiScY6UxQ2VKH5JTQNY1YIELdns1U79pgf6aaVJEcEqIo2qm8xcXFJCIxEqpCLB7LSJu3SKvP58t6/Vvz6Gv0be7CWerF4XDgdDqJByO0v7OZG75wKXt9aAWHnrWS0voKDEO3Rbq5qETLToc5jaTpCGkdIU3VECVxXC3LbGHIHoyUIB1dQDc0EvEkkaRBfUMTUgHeJwQtaXaQrMA+QTL3Q5r9IjiZTNLT04MsyzQ3NyMIwpiRzklD14RUaB7m77iALhOLnlhYAYnTJRZvvPEG3/zmNzEMg6uuuoojjzwSgP33359vfvOb9PX1sWrVKlatWkVfX9+swoJ3VBTelWaOUVdXlwrnWhjVbKs6rCgKpaWlBT3iMzqPI11fIQhCXrc7kUjYwq2FMOqWa6iqSkdHB/F4fEaOWD6f+X1ZidAWNE2jtGRq7zU6cM0iGukz0RbRmKpjkLWIfPW1t1A1jYqKMgYGhmbVZRAEk/REo7EZv8f0P9T67NnpSF57/R2+8KULuetfN7DL8hb78fQMl4qKinF1WLseuQ91ezbTtbYVp8+FKEnEQ1GKyotZcfqRU9qG0rpySutMjdor/3yGnvXtFFUU2wtop8fJcOcAI92DOD1mfklpfQUnfe8sRFFk80vv89o9z9K1dqvpZlVfwf6nfQB/ZQkbn1vDSNcALp+bXY/al31OOgQd3V4gdnV1AdmFvFteW4emmyJyp9NJuH+E4a5BMKB/azfP3fQIr/5rFV+64bvU77UkpzrUxv2W4a8qJdAzTGl9BYIooCZVYoEI+550aEYXJ6cQRHSHH0FLkoxHGAmE0ZCprm1ALLCcEQB0FVGJYDozmdsn6BqCEkETxFmNpsXjcdra2mxiYV07SktLKS0tzQhds0irIAgZ1tuFWLSbKizx+k5ikZjWKNzbb7/NhRdeiKqqXHnllRx99NFjnlNdXc0nP/lJPvnJT+Z6s3cYLNwzJ0eYadDbfMAKPYrH44iiSENDQ0GTonTL3LkUbkciETo6OpBlmZaWlgUtXJwJ0onVkiVLZjQu9+EPr+T2O+5jaGiY8lRgWzhszsF/5CPTdy2SZTlDNDfaUlKWZXtMIWv1cBSsmdmSEj+yLNHT05fV8WkqMAyyEot8djMsDfFsR6+Kirz09vbzt7/dxm9+fVHqvQ07HLGmpmZCcwqn18XpP/sCL93+JBtWv4umauxyxF4cdvbx1KS6CtZ7bn7xPd576k3iwSiN+y5l/1MPs/UEFra9th4gozLv9nvxlPioaK5l92P2paSunD2O3R9PiY+Nz63h0SvuZqRrEDVpiqvDg0F6N3YiOWVK6ysoqigmMhLmlX8+Q7B3mGPOPYXKykqcukR3Zzs4JQSfQH9/P92d3fS8tY1wb4Ch7gEEBJxOJ5qiMtI1ZJedRVFEkETi4RgP/eYOzr31h7P6HUbDU+zl2K+ewlNXP8BgW589Ble/ZzOHfnplTj9rDASRQDRJZ2fflDVW8wVBV1LJzlKmHTAaopZEnyG5iMVitLe3jyEW6Ui33q6pqcnQ/FidVrfbneGIV8j323SkE4uFLt6fCXRdtwMip1NcXLt2LV//+tdJJBJcccUVfPCDH8zzlu64WPSaC03T5tTjfqZIJpO0traiKArFxcWEQqGCt0Oz8kL23nvvOSMWlnDZ5/PR0NBQcKnA+UY0GrVvqk1NTbO6qVx/w21cddVNRFILb6fTwSknH8+vfvnDnLr4xGIxO09DURS7emjN12f7Dfv6Bjj+hE8SDkeoqChjaGiEwcHhGS3WJUlC17UxRCLfl0aHw5Gza4/T6eSPf7iEz3zmTDo6OohEIjQ0NFBcXDz5i1NQkyq6puH0jK1wPn3tg7x8+5NoimZKEwQoa6ziM1d8jdL6Cvt59/z4RjasfpeS2swE8UDPMHsedwCn/+wL9mPJRJK7vn8dne+1kgjHkJ0ORElETaokwjFESaBhn6VULKkhNBCgf3M3iXCU5gN2MbVU69pRE2YyeN0eTRx05tHc/7NbiAej9mcIokBRdSmiKBDoGko9CLJDRpBEdFUDBH7w5O8oqsz92ORI1yBbXn6fWDBKeXM1yw/bE6c3vxXkkZERuru7KS4upr6+vqAXxGIybNrkjnbn0lUMyYHunPrxa8EKkbWynmZyD7CMXsLhcIbmxyIj+bZMng12EguTWESjUZqamvD5fJO/CFi/fj3nnXce4XCY3//+95xyyil53tIdG4u+c7EQEA6HaW9vR5Ikli1bRiQSIRAIzPdmTQrrpmYtGPPtCGWNgUxHuLwjIRAI0N3djcfjobGxcdbE6tyvfpaVK4/gmWdeIJlMcthhK1hx4L45valm02lkG3kZnexcXV3Jz376PS7+yaUM9A9hYMx4xMjUbGTZNvI3Xy0IApIkZZALy0J/Jp+paRo//L9fIUkCK1bsQ3Nz85RvqhZMzcXYW0Lvxk5eufNpBFGkqNIUIeuazlBbH8/d/Cgf+dFn7OfufvR+bHpuDclowl5EJ6JxRElkt6P2BUwnqudveZQNq99hpGsQ0SERUhJsCQ0RVRPUukuo07y4HS7Cg0Ecbifd69rRVY1kLMmG59agqxqCKOD2exFliba3N7PphfcwrC609V3qBpGBAGJaJ0WURARRwGZJBmha7rrXalKl7a1NRAaDlDVWccDHjpizhejQ0BC9vb1jNDaFCkOUEHQy7YCtILYZZEhYxRXLbnym10BZlu3xKcMwMsanRkZG8maZPFssdmJhGAadnZ3TJhYbN27kggsuIBQKcdlll+0kFjnAou9c6LqOkq+ErhxgaGiIrq6ujLRw67G99967YG8ehmEQCATstPDpztZPB1bORzgcnrZweUdAutXuQllUTAWWd30oFCIajWIYBi6Xy+5quN1uNm3exkMPPU5f3wCrVr3Au2vWp0wCmPGYVAYMIy8EQ5Zl6utr6OjoTn3M7D5FkiQcDpkPHL6C22+7etrEIhs0ReWN+59n9Q0PM9zZj9PrwltahMNtkoZYMILD7eTb//21vXjWFJWHfnsH7z/1FnrKDU6SJfY8bgWn/PDTJMIxbrvwSga29eJwOwn1DbMh1MujQ+vQDcP83QyDCoePTzSsoL6pDi3VyUAQiAyHzI1L+7oEQUjrQqSKGnZ+nIEgChxw+pG89e8X7EWsoet2kHBZYxXfefg3OSEAg629/PeyfzKwtRtd05GcMk37LePk739qfHcoQ0/ZnAoY4gydkgyd0MggoWAAt7eIsopqhAKtrGfA0BCToZSY29peHRDRnP6xHY0JEIlEaG9vx+Px0NTUlBdCZxgGyWQyq2WyRTTmOnHewk5iYdjrgMbGRoqKpubGtmXLFs477zwGBwf59a9/zcc//vE8b+niwM7ORYHCslAdGhoakxZuXTR1XS/IsR9LX+H1eqmvrycSiTA8PGzP1ltEw+v1zvoirCiKbc3b1NQ05QvKjoId2Wo3PdxI0zTbmjT9WCoqKuLsT3+MoSFT1L1m7QZE0eySSZI53lBo5RMz40ygu7svZ6NXmqahaRrPPf8qp33si3z2M2fwxS98Kuv1IRyO8MB/HmPT5q1UVVZy5BEH09LSRHFxplD/kd/fzTsPvUQylsAwIBFJoMQViiqLwTAD89KzMcDUWnzkR59lr+NXsOWVdQiCwNJDdmfZYXsiSiJrH3+dwdZeiqtKEGWJQCTEo63r0Aw9xQdEBAyGlCirBjby+eXNxAIRBFkiOhxOdacgnV0YhoGRIhbmF2wVv1N6L91gnxMOwldSxHM3PwLppNOARDzBiw+uYtkhe8xq5EXXdB75w130buykpKYMySmjxBJsfXU9q//2MCd9d6z4U9AS5lhQKl9DEEQMyT09K1wtiRoewo1KUbkpQjbUMLqjqPDtUgUJ3VGEqEbtHAlDlNFl77SIhZXj4vV6aWxszFunSBAE26iioqICTdNsojE0NMTAwMCcJs5bsPKurIDAxUosQqHQtIhFa2sr559/PoODg/zsZz/bSSxyiJ3kogChqirt7e1EIpGsaeHWxaoQm06jhdslJSWUlJTYrWVrtn54eHjWF+FYLJaRuDuVYJwdCZbAfzFY7UqSRHFxMcXFxRnHUiAQsA0ZVh57OH+/9W6i0VjamIuQNm2x/XwxzQayj0NlIC+qbiFvOi9RlGhv7+TXv/kzGzdu5Wc//R5u9/YZ/zfefJezz76AgcEhdN2wbXhLSoo5/WMn8ZOLv0NFRTm9GztZ+9hryG4HriI3w51mirmh6QR7h7HmjgxN56XbnqS4uozQQIDKlhqWHboHuxyxN7scsfeY7etZ32GGmckSyWiCNb1taMb238owDAQEdGBjtB/dIaLEFZtLyG4nSiyZ/lWOmSMzsnSs1jz2GrsdvQ/ekiI0VUVXdZxeF/7qUiJDIbatfp+avZoyRl6mm83S9V4r/Zu78VeVILvMMRmn142nWGXzi+8RGQ7hK0sjcLqKoMZT+7E9xE9QYxiCOKXFtaHrqOEhBF1FkGRkp9t8Dy2BKIgmwSh0iDK6w28TrOnaAYdCITo7O22d3VxqISRJyrjHWfqx9MR5y8nM7/fnZXxqJ7EwbMv16TgjdnR0cP755zMwMMBFF13E2WefnectXVxY9OSi0Kq8ln2epmm0tLRkZeDpnYtCwkSOUNYN2+fzUVNTQzwet4mGdRG22sp+v3/SjkwwGKSrq8uerV3IloEzQTKZpL29HVVVpzVbuiPAumFbxKKsrAxJknC5XKw89nCeWfUium7Y50lRkZdLf/sT1q/fyO//cA3xeGJa546QIhi5oBj5vt7IsgQYjAwHuPa6W3n8idV84ZxP8j8XfIGenn5O++jnCYUiGa8xDAgEQtx113/YvKWV+++9iY53t6AmFIqqTEGtu9hLbCSyPZ9GBEk2RdEP/+5fOD1OM9fCgKqltZz9h/+huKZs9ObhLvZCyqJ6pGeImJI0Q8lGfbsCYAjwoW+fybt3PUf7u1uJjYQxdANRFNC11POtl01AAp1eF1teWoeuaDg8Tqrq6jL+riYUAm0DLF++3B55CYVCGdksUwlciwUjaIqKw5W5gJRdDhLhGPFgNINcCJpi7nk6iRAkkyjoSubjWWAYBkP9PRQ7dQTZgdPpSvsuBAQtCbJe+N0LSMuSmB6CwSCdnZ34/f55d09M14/V1NRkHEu9vb309vbaY51FRUV4PJ6cdO5bW1sBFi2x6O7uJhgM0tDQMGVi0dXVxfnnn09vby8/+MEPOOecc/K8pYsPi2tFVuAIhUK0t7fjcDhYvnz5uBeKQiMXVvKprutTcoQSBAGPx4PH48kattbd3W2LeEdXe9L1BcXFxdTV1RWsa0e+YNksiqJIS0vLovQvtxyR6urqKC0tBaCqqoprrv4dV119E/fc8xAjI0Eqq8qpqCjj7rsfIBiMkEzOUF81zuI1M2nbmFCQPRcLn0gkSiQSRRTN8aHBwSH+9OcbCIejvP/+hjHEwoJhGDgcDt56ay3PrHqR6pRjlKEbiJKIv7IENa6QjCZAAG9ZER6/j5HuQXRNw9AN1ISCEldoe2sTV378Z5x12Xljuhd7rNyfdx56iWDvCGpCocFbihFIERZrW1JUo9zlY9ney2he3sTjf76PDc++Q3gohJ7ld3D53GiqhpZUMjoXTp+b6l3qCfYME+gZSom89Qw9gppQqVhSYo+8OGQHUgLKfCUYDoGtb2zg5eeeJBGMUb6kmr2OP5CaJfVjxjorltTg9LqJhaJ4S7YXhWLBKEUVxVnIlp71WDGnviamstYYCGqcCp8fSXaar9Q1hFSSNIaAoEQxHL6cBAMWGqzk+UJ1xXI6nZSXl1NeXm6Pdabbb1ude2sUb7ojzunEorm5edESi0AgMC1nvN7eXs4//3y6urr49re/zVe+8pU8b+nixKIXdFsCrfnehsHBQXp6evD7/ZM6/cTjcTZt2sSyZcvmPRwunVTA7BO300W8kYi5EHK73fbo1NDQEMFgcIfTF0wV1giA2+2msbFx0XVsrJHBZDJJQ0PDuLO1hmHwi1/+gZtv+ReKopJMJonH47OecDLHdgwMJjjO07ocQub/zDncbheGYeDzeQkGQ6jp2oRRqKgoQ1EUfvD9/8dXP/9prv3sr82FcbkfQRQYau8nGUvgKfFRUlNGIhIn0D1kjlZt/1bs7obD4+Ssy85jr+NXZHzOK/98hqevfoDQQAADuL//HToTgYzuhYDA6S0r+N6l32OfEw8mGU/w/M2P8fIdTxEZDqGr5vVGckoUVZQgiCJaUqG0roL+rd24iry4/W6cXlNcO9I9SPP+yxls6yM6EsZfVYooicSCEZRYkhMuPJMDPvoB1q96h2eu/Q/9W3tQkypOrwtBFGxtia7r+KqLOfS8D1PaWJERuCZJEo//6V7e+e/LONxOHG4n8bBp47zyvFM58GOZoYSClkBQY5kZD4YBhjZhQnWG1WZDHcWO1IidoaeIxfZZMQMR3VmEIU8/66aQYdntLkQDC2t8ytJqJBIJILsr3njYSSwMenp6GBkZmdZIcH9/P+eeey6tra38v//3/7jwwgvzvKWLF4trZZIF831R0nWdrq4uRkZGqKysnJKFqvX3+e5cWGNQ1mIiFx2E0SJei2gMDAzQ398PYFd6Fhssm0m/31/Qyez5ghUOaM0WT6SxeevttfzjtntxOp1UVpbT0dFtimWN8c8ZWZYQRQlJFFFUNasuwjz3BISJWEqqy5HZ1ZgfJBIJBEFgaGhkwucJgoAsyyQTSSorK/CWFnHSdz/Jfy/7J+HBIGCe76Ik2qM9hq6b576BTQzSCYISS3LPRTdSu1sj5U3V9uOHfmoldXs2c9s3rkTTVD4q7seLI1tYG+4hqWtUOLwcXbcHu/lrCfWPYBgGz/7tEV667UmiI+GM7RYlCTWhILscHHj6kVQ21/DEX+/HV+5PWeuCqqgIwG5H70tpfSWP/+kegn2BVAdDoHJJDaH+YV7+59Osvv6/hPqt70ogktr34tpyKpqrMXSd4c4BOldvYNnXTyUcDmdYJu//qaNwl3hZ9+RbJCIxyhoqOfBjR7DfKYeN+c4N0YEgJBEMjdTRgmAYGKKEMU54XLrOqrGxEZ/Ph6GEEbR4ynEpjViIMhgganE0yT3vx2KuYGUZlZaWUltbm797uGGAoZq/iSCOCvqbOUbbbyuKYnfu+/r66O3txel02qN4o8enRo9CFYoN7lzBMAx6e3sZGRmhrq5uysRiaGiICy64gNbWVs477zy+8Y1v5HlLFzcWfecCsCsHcw3LOi4ej9PQ0GCPd0zldevXr6e5uXlaIVm5xFwmbicSCdtiz+v1EovF0DQNSZLs0SmfzzfvRDFfsC6mw8PDVFRUUFVVtcPu63iIRqN0dHQgSRLNzc2T3lD/+Kfr+OOfrqeqqgJBEGhtbSeZVGyht5Ueb40DuVxOzj/vc3zi4x+hvr6Ww4/4CD09ffb7CSmdwGwgSWJO8xSm+pmCIEzYsQBTVyBJIhXlZaxefT9lpeYNe7hzgPefepPoSAS3381Ltz1JPBTDXexFUzRCfSMmoch2F0m5YlUtq6N6eT27HLkPB370CHvRv/pvD/PS7U8SHgiY4XyGgY6BLMt2n+fE730Sf2UJD/z8VuKhsSnqCHDgR49AEAWiIxEwoGPNVhLhGA6P2T0wdIOGvZfwid98FbffSyISY/NL63jln0/Tv7Ubs08C0UAENZEEQcDhdpq5GqkxMNkhU7t7E06vy/wcAc695X9x+71ZLZMlQcQpOqisrcLnL5qgy6UhqAkEI+WUJDgwZGdW/YGmabS1tZFMJmlqatretTYMRCWEqFrfj4ghphbDhg4YaM5SEAvPWXC6sAosec8y0jVENZJKEMe0Lxad6HkeMdN13XbFC4fDaJqWkSTudDrNcTgWL7Gw8qxqa2spKxur68qGkZERzj//fDZs2MAXv/hFfvjDHy66e+hcY9F3LoAZh2/NBlaKqGEYLF26dFrjTfPtFpXescg3sYhEInR0dCDLsi1YG53qPDIyYl+ALaJRiBa9M0F6hsd0LqY7Eizx/nTCAXXdyDgu3W43iYRin+vpREEURaqqKjjl5A8iigZbt21lYGAw4/wafa7N5Jox18QCrKyPqWynQWVFOVdf9VubWACUNVRy+GeP56XbnuTlO54iHo6jJBTUwRCeYi/lTVUEeoZQk5ldHkEU7c5G36Yu+jZ3sfax13nmmv9w5i++RMvBu3HUl06kpLaMp69+gP4tPQiCgNPhAMFAUzUEBJ698RHUhDKWWFgFegPee/JNnB4X8XAMJZaw/67Ek3hKfXzwgtNYcfpRuP3mNdbl85CMxhls7aWoohiX141hGIRSJMfhdqbGu1K/c+p7tIIBLfmN9fOnd1t1XbfHXcLhMO2dHRmz9UVFRZkdR0HCcHi3H0vjXEtVVaWtrQ1VVWlubsbjSRtzEgR02WMKuBFGuS0ZgLgwRN2TYHBwkL6+PsrLy6murs5rx8ImFkipiUYDQU8gqIKpYckTRFG0C2aGYRCPx+1jyeqQCYJAeXk5eur8WiyL5HRiYeVZTQXBYJCvfe1rbNiwgc9+9rM7icUcYSe5mAdY4XJut3tKVdjRmE9Bt6WvmIuOhdX+tiwGrUVltlRni2gEg0H779ZFeqHqEhRFoaOjw65ULrYMD5j5KNiRRx7K1dfcQiQSpajIR2lpMaFQJOuok2EY/OD7/49DDjkITdP4xS//gKZNXOmfCbmQJDF1zgqp10/BCneWmMo2Ohxyyq3NSywaJ9g3jK/Mj5RKtX71X6t4+ur/gAAunwvZKaPEklQtq+Vzf72QJ/9yP8/d/Ji92hZSXaF0CKKIoemMdA1yy//8kab9lnHkF05k/1MPJxlN8PDv/oWmqOiahq6a6XYGEO4PmGnaE0BNJCmqLEkbZ8LmU7FAFF3R8BRnFm82PPsuCAIurzlaJ6S6FWpCSYX/OZBkESXVlREAQTTD96KBKLscseeY9wRIRuIMrOsyzRb2XoJqbB/tTLcmtWbr7Wv/BNfR0eFoWQ0cBNkcs9IVtrsKmAzI7ITM32LKdhibxTb09/czMDAwN51bQ00jFtbnpEYd9SSG4Z2T7zPd+KSkpMS2m3W5XAwNDTE4OIjD4bCPpVzkRhUqDMOgv7/fJhaj7fnHQygU4utf/zrvv/8+Z511FhdffPEO+x0VGhbmqmuBwjpB+vr6KCkpmbEntyWanktyMVq4nU9ikV6hmKz9LQgCbrcbt9tNVVVVhv1fT08PPT09eDwem2gsFOFbPB6nvb0dYFFmeKQfAzOpVB56yAF8/MxT+dddDxCNxsyquNOBqqr2+zgcMqWlJSQSSR5/fBWf/cyZaJrGvx94zNQfjOMsJYrijM49q3MhyyI+nxdFUfF43AwODk/7vXIFAfC5PcguBxs3bOXzZ3+Nzyw9jObaOg77zHEcdOZRvHzHU4BhOyDJTgeSLNG9roOuNa2c/P1P4Skp4ok/35ciTPrY7Im0ro2uaAxs7eGRy/+Fy+fG4TLHl8obqwj2DxMbiSBIIlqqG5KVHqU96PS5CQ8Esu+gYfDaPas5+isnZzycjCXGXHv9lSXEglF0TUfTVATMMEZd0zEE8zWJSJySmjKO+PyHx3zU2w++xPO3Pk50OIQgCPirSznufz7KLkfsTXV1Nclk0h536enpAbYnO/v9flwu15hjPJlM0traamf5jHv9EoRUGF3YDKMzTAtaQ3KbgXRzBF3X2bx5Mxs3bmTbtm20traSSJjfdU1NDcuWLaOlpYV99tkns/syDqx75uDgIFVVVVRWVuZ9HwTr+B1zubG0Vjowd53x9GOgpaUFh8Nhj0+Fw2GCwSBDQ0OIoojP57M7ZAu1qJYNAwMDDA4OUl1dPWViEYlEuPDCC3n33Xc544wzuOSSS/K2ZtF1nSuvvJK77rqLYDDIQQcdxE9/+lOWLFmS9fnDw8P88pe/ZPXq1QCcdNJJ/N///d+8G/TkEjvO0TcLzMVYlOXwEQwGqa6unnX1ZaYLnJkgH8Lt8ZA+BjSdCoWFdPs/VVVtomGROpfLZRONbDfzQkA4HKazsxOHw0FTU9Oim6vVdd32Lp/JMQDmOf2rX/6Qww9fwX/+8xiBQAjDMHhm1QuoqoYggKpqDA2NUFTk5ZVX3yKRSBCJxohEoggTjJGUlRazpKWRd95ZZ5IVwCE7UFR1TF5DNpjjDkkqyksRRRFZlibVROQCsiDicjhRNJWkZi7cfbILQQctriBpBjFd4Y6tL1Pd5efFTWv5ysAIkeEwDnfmolZyyuiROAOtPbQcvBvHnnsKyViCl+94Cl3RSMbG17EZGHhKvCTCcV6/91lO+t4ncfs9RIZDJCLxMVVhczRp/O/VW1rESNfguH+PDIXGPLb04N3pXteOrmqIKScoV5Ebl8+FEkuSjFjjVQKeEh/NK3bB5XFTt0cT+5x0COWNVRnvt+31DTx9zX/QNQ1/VSmGbhDsHeaRy+/i03+opLKlBqfTSUVFxbjJzlbivN/vx+v1kkwmaWtrm7LOCFFCdxSbOg5DxxCkOdNZGIbBG2+8wZNPPsm6deuIRqM4HA58Ph+yLKPrOuvWreOtt95CkiQaGho45phj+OAHPzjugiq9wFBdXU1FRcXc7Ist3B7FMAwdQ5SAuRsxG00urWNg9PhUIpEYN5+lqKioYO91U8HAwAADAwNUVVVN+RiIRqN861vf4q233uIjH/kIv/rVr/K6brnqqqu48847+c1vfkNNTQ2/+93vOPfcc3nwwQezFgQuvPBCEokEN998M8FgkIsuuohLLrmESy+9NG/bONfYSS7mANZNIplM5kyEPVedi+nkV8wWiqLQ3t6Ooig5GQOSZZnS0lJKS0vtWehQKGTfzB0OR8bNvBAuvpbF4uhRsMWCdDec6XiXZ4MkSZz+sZM5/WNm1foLX7oQVTUFkpIkpj5PJxgM4/eblb6hoWESieSEJg/xRJK7/nk9Dz70OD+66LeUFJtz9L19AyjK5MnbgiCgJJO24NkkMvknF6qh45MkPA4nQ9EQhgEaBmoijq4bKLqGgUFETdChqXTERuj+89V8pOVAlFgig2Doqm5W5ytL7X064cIz2HPl/qz+28O898Qb426HgIDD40LXDPo2d1FcXcYHLziNJ668Hy2pmkWMtK/R6nrILhk1qZnjVwIUV5fhKy8mMhREFMVxv8GiyrFuMvudchgbn19D/9YeHC6HmXSdVPFVlJAIR1ETSsodS8LpdbHvhw8eYyWbjjWPvoYST1LWsL2yXlJbznDnAOuefpOjvnRSxvNHJztbifNWDoJV8JJlmYaGhqkXGATBHJEyDHO0R1XMDoboyNsoz/DwMPfffz+rVq1C0zTq6+spKhpfxJ5MJunp6eHWW2/l9ddf56yzzmKPPfbIeE66icVMCwwzhiBhiE4EPZEa9UsRDQGMOXTdsoiFKIoTksv07n1lZaVdVAuHw3YmlHWvs8anForToLX9lvX8VJBIJPjOd77Da6+9xoknnsill16a1/toMpnkxhtv5Pvf/z7HHnssAFdccQVHH300jz/+OKeeemrG8998801eeeUV/vvf/7J8+XIAfv7zn/PVr36V73znO9TU1ORtW+cSC+MIW8CIRqNs2bIFTdNYtmxZztyd5qJzYXUsLMeKfC6+Y7EYW7duteeKc60vEEWR4uJiGhoa2G233WzyEgqFaGtrY+PGjXR1dREKheZFy2JV6bq7uyktLaWpqWnREQvLYjEej+fFCW3z5tbUMWykdeFM7cOS5kaCoTBf/sq3JyQW1nnw5ltr+ehpJ1JaWkI8kUSS5SmP3MmyjAGEQmEMw8iqA8kXArEII9GwnUsRU5PEVYWkvr3rIiPiEmUkBN4b7EBfWoymaiSiCXN7kyqxUJTyxiqWf2BP+70FQaBp/+UkonGYQCchux2IokgyGqeoylz4H3DaBzjnrxdS2VJrBtwJAqIsZhSONUXDU+Kl5eDduOCOH/P9J37HRy/+LL5y/7gaGUEQ2PO4A0hE4iQi20Xh/qoSzvj5F2jYawnBvmFGuodweFzEgxES4RhqQkFNqmhJlUQ4zqt3rx4jWk/HSPcgkiPzfBUEAQGBYN/IuK+znufz+aitrWX58uXUpRLETZcvla1bt7Jt2zYGBwdJJBKTd9l1DTEZNP9TQub/J0YQlAiCGgc9d0S2p6eHv/zlLzzyyCNUVlay++674/f7J7xXOJ1Ompub2W233Vi/fj1//vOfeemll+y/WxkGw8PD1NbWzi2xgNSImdckEiliYQgiuuwbN3sk15gqscgGq6jW2NjIrrvuat/rwuEw7e3tbNiwgfb2dkZGRub02jNdWAL+yspKqqqqJn8B5vf23e9+l5dffpnjjjuO3//+93kfD1u3bh2RSITDDz/cfqy4uJi99tqLV199dczzX3vtNaqqqmxiAXDooYciCAKvv/56Xrd1LrGzc0H+si6Gh4dtl5vm5uacHuRiFtFkLjGXwm3LDWiuguFM8apZxampqSEej9uCcEt0aXU0rHCsfCJ9DMiaKS2ELspcwtKYWHPF+Ugdj8XiFBX5iEajGc5NkiRzwAH78MADj9La2kl1dQVtbV1ZSaYkSTgcsmnbWlHOd759Pr/5zZ8ZHg4gTiI8tgoCoijgcDhIJhR6+/rn3PVNMwyKHC5CSjzr3x2pURoJAQ2DSKXMihMP4f2n3yIyFEaURaqW1XHmz7+I7Mxc9OiazsDW3gkTpgVRpOv9NgzdoG9jJy/d/iT7fexw3ty8hoEGBW2LhgCoqtnlSP9W6/Zo5rQffQZRllEVBYfbyW5H78tgay9JZdSiWYDS+nKGuwa55uxfAtB8wC4c+YUPU728nif/+gBrH3/dziXp39KVKfBIfbCmagxu6yU8GKS0LvtCt2ppLR3vbGGosx81oeJwOfCUmK5C5U1TWxiBOSdu6cSampoyOq7WaKeVgZBVxGsYiGoYUU+a4z0ICLqKYCRAT2IggiiiSx4zWG8W15nh4WGuu+461q5dyx577DHt8U2n08kee+zBtm3buPnmm3G5XBxwwAF26nJdXd2U7dlzDkE0bWdlT6pTJhZcx2IqSLextQKDrQ6ZNT41me5nPjA0NERfXx8VFRVT7lgoisIPfvADnn/+eY455hj+9Kc/zclIsaWfsooCFqqrq+3vOB29vb1jnut0OiktLc36/IWKneQiD7AqL4ODg5SVlVFXV5fzNmS+OheW979VCcy3cNtqexYXF+fle5oM6Y4co52nLOs/SyTn9/tzfrFSVZWOjg4762S+ckvmE5bdcL41JocdeiAP/fcJGhvrCYcj6Lpu6x2O+MDBPP/Cq2AYKIqadcEvCgKqavre33LLv3jiiWc588xTuO66y7n9jntpb+/C5/Xw5ltrGB7OFBhbYy6CALFYAkmScLudxGLZF/j5gL/IhySIRKMx4pqCx+UiqShoE1xHBFGgqMzP6d//Akd96UR6N3biK/PTfOAuiNLYc1WUxEndnZLRBIIo4Pa7URJJHrz8Ti679DJe6n8XwzD4VMlKXIIDAREdHcVQSRgKPjxsW7uJaz/3G7OebBjIbifxUBQ1rtifawX7YUAiEmfbq+txF3sRgA2r36FjzVb2P/VwXr/nWVIvSP3/qA01TPE5gkA8EsfpHZ/wCoJIZCiErukpN1iRYN8I5U1V7DkqoXw8BINBOjs7KSoqss0+RFHMGO3MJuLNsOAWDARNTYW+Camci7TfVxABA1GNoosODGlm55phGNx7770zJhb25qSKCZs2beL2229HlmVkWd6eupwKshO1pKl5ECSzezBXmR1CZvcs38glsRgNQRBwuVy4XC57fMrK1Bit+7GCaudjfGp4eJje3l7Ky8unrE1VVZUf/ehHrFq1iiOOOIIrr7xyzsxbYjGzIzr681wuF4HAWKOJWCyWddtcLte8Za7lAzvJRY6haRrt7e2Ew2Hq6uryVoXOB7mYa+F2T08PgUDAnqcshIpJ+sU3PTm1t7eX3t5e3G53hiB8NrC0ONYo2FTcU3Y0BAIBurq65kRjcu65n+PZ514mEAji8XjQNA0lqXDgin350IeOYf2GzSCYx+b28antr9dT/4jFEzz9zAsIAtx9z4P8+KJvceMNV9jPSyaTPPvcy9xww208s+pFIpFohh2nIAhUVJTjcjmIRKJz4hYlyzLFxX4MDJKaiqZplJaVIkoCI33DxBUFLbUItXZZNXScTicnnGDOEVe21FLZUjvpZ5U3VjHSNYAoiRi6kUHUREnEU+LDU+IjEovS29uLpAnUGsWUFBXj9LgY1CLU6CUYqMhIOAUHTmREQ0QNJlCFJKK14ovGEQyLVIDslDF0A01VwcAMvBMFNE2npKYMTYvQt7GTJ6+8f+pfnmEgyWJWMgWwftXbvHTHUyZ5FE0Saeg6oizh8Djxlk4+4mlprYqLi6mvr896LRwvA8Gy4AYoKy6itsSBIMqIUsr5CLBXyAIgSAiGiqAlMAS2L9xFGUPKHuA3Gq+//jqrV6/OSTFAEASWLl3KG2+8wf3338+FF15opy4LWgJRjWLaAAiIJDH0JLqjCMQda/mST2KRDbIsj9H9WMeTpftJd5+aiy7AyMgIPT09lJWVTdkhUNM0Lr74Yp544gkOPfRQrrrqqrx0vseD5eSYTCYzXB0TiUTWe7rb7SaZTI55PJFI7HSL2tGQq0WtlSStKApLlizB7/fn5H2zIdfkYi6F2+nVertCVYBwOBy285Tl7hIKhRgYGKC/vx+n02nf7N1u97S+s/TE6ZaWlgVjkZsrpHetSkpKqKuryzu5PPCAfbjxb1dw5ZU38trrb+P1evjoaR/mwgu/itvt4mMfO4m/3XgH4XA4tY3Z36emphKHw4FhGIyMBLjssr9y8knHEQpH+NvfbueFF16luNjPoYcewNvvvGcn2RuG2br3+bz4i7zouo7b7ZrUrc7hcKAo2W1xx4MsiKjG9uuDQ5IwMEfDzFA3M+m+uNiPU3aAZhDVkxiAYmiohoaIyOc+dSYH7L/3pJ+nJBR0VcPlc7PHyv1pf2cLWspJS0AwMzMEMHQDb5mfgYF+hoaHzS9ZlHCLLsrlEiJCgm1iHzV6CS4ctg5EwKwgW+F2BqnwMM3AEKzvzkCzuk7pX6duEB0KEQ9GTHInCtMunGhJjVU3/Jc9V+5P0/7LEQSByHAIJa7wzPX/RYkncPrc5riqbm6HIEI8GKV7XRvN+y8f972tLJfS0lJqa2undB6kd1yrqqrsQkg0EiaRVBAEBd0Q8DhlTE6UltdAygdJV5CSyVR3QzDJhpaYdOGu6zqPP/44hmHkZGzJMAwikQgVFRVs3ryZkZGRVNdCR9BiKU21kLKINRAEHVGJojv9gIGgJRAMDRDQJdeCJB3W2kEURZYsWTLnNrIWkfD5fNTU1NjuU+m2yZbTYlFR0bTvd1OBRbBLS0unnL6u6zqXXHIJjzzyCCtWrOCaa66Z8yKdNeLU19dHc3Oz/XhfX98YowKA2tpannjiiYzHkskkIyMjO4yYG3aSi5zBEktJksTy5cvzzpxz6RZldSzmglgkEgna29vRdZ3m5uYFw9TT3V2s8QSrwjM4OJhhI+nz+Sb8DgOBAN3d3dNKnN6RYI0NjoyMzHnX6pCDD+CWm/9MIpFAluWM736X5S1c9tsfc/FPLiUY3G5fOrqLEYvFcTgcCIJASUkxQ0PD3PnP+7np5n/S329aouq6znPPv4IA1DfUIooiiUSS7u5eotEYScVMCzePKz8jI8Ex2+pwyOy5527UVFfS3tFFW1snyaQyrggznaSM5iqxRIJYl7lI8PuLOPigA3ju+VcYHgmgJpNohoaMSL2zBJck4xQkdvFWcdoBh098LPcM8cy1D7Lh2XcxdJ3GfZdx0CeOoaK5mkD3ILLLgSAKqAkVySmjJVX6e/sYDgybC31JQkTCwEARzFFMRdDQ0FHRbMWFlDabYrmE2unE27lFdpKWer6u6qZInJRYfBrQVI23//Mi7z/xBvudehiB7iG2vrqO8GDKPtcwUOOmBsQcDXOgxJNok7iHWYWK2aZOpxdCSIQQVFNXpGoaIgIGApoBGBqSmPo2Dc0c+xFTFWnDQDBURDWWWrhnx8aNG9mwYQP19fUz2tZ0GIZBMBhEURQaGxvZsGEDr776KkuWLDG1IrqW2q60e50BghZH111ISgTBUO1jQEht+1wJr3OB+SYW2WB18MezTZ4wdX4GsO6J0yHYhmHwq1/9iv/85z/st99+XHfddfh8+UtPHw977LEHRUVFvPzyyza5CAaDvPfee3zuc58b8/xDDjmEyy+/nNbWVjsH4+WXXwZgxYqpjVAuBMz/UbwDYHBwkO7uboqKiubM5SdXnYu5FG6n5zc0Nzcv2Gr96PGEWCxm6zRGRkYy5qDTL7yFoDGZb6TnmMynYDOd/Kuqyosvvk7/wCC77rKUp566hxUHnWDrITRNw+GQiUbjWd2dDAMeefRpenr6UBQVTVPR08aBrHa50+lAkkQ0TSMUDENKf6GqCm63C4/HQyAQMPWjgkBDQy1fOOfjHH3UobjdHt54Yw2vvPommzZt49nnXs7QawgpUbIFjezXBkEQUBSVZ597maOPOoye7j7WrVlPqdPLPp5a9vLUIIhialzfYNV1D7Hs0D2o33NsGFQ8FOWOb19N/9ZuHC4Hgiiy+eX36VrXxinf/xRvPfgiHe9uBQPq92rmmPNO5e6f30R88xCSIGKIgulMhUyEBA16OT3iCD7DhYhAhAQIIBsSPkYtFkcTjClCV83vJRlNIEpmQN5kECQRl89NWWMVof4RVt/wX5weF8loHE3R7O9dU1QMXcfl86AbOoau4ysvpm6P5jHvmR4Ol3OC7SwCScahxk0CYWgYuoGiahhGHFkUMBBTo17O7cZeggCGZFrYpkL4smHDhg3EYjFqa6ooLS4iGkswlIUcTwbDMAgEAqiqSnFxsS1qfe211zjzzDNTMXUGgnUsW99PKshOSoYQ0DGQTHey1ONiMozmdoy7/YWEQiQWozHaNjkWi2VNnbeIxnTv65ahS0lJybSIxaWXXsq9997LXnvtxQ033JDXSZGJ4HQ6+dznPsfll19OeXk5DQ0N/O53v6O2tpYTTjgBTdMYGhqyJxz2339/VqxYwbe//W1+9rOfEY1G+elPf8rpp5++Q3UuBGOurUoKELquT3vsAMwDvLu7m6GhISoqKqZ8YuQC3d3dhMNhdt111xm9frRw25oFzxeGh4fp6enZofMbrDAji2gkEomMuVWr21FIGpO5hKqqtLe3k0gkaGxszLnd8EywceMWvn7hRWzatBVVVXG7XBx77Afo7unjnXfeo6rKDG0yDIOOjm7i8TiVlRX4/UWIokAgEEKWJXTdoL9/0MxGSK3WLEeqoiKf/T7DwyNZuxSj4XQ6cLmcOBwOfvbT73LsMYcRj5tkwuPx8OyzL3Pht36Kpmn2cTTVS7koCni9HrxeL8+vfoB//s9f6N/UhaEbIAoZadpgLq6dbiee0iIa913KB8//CDW7NfL4n+/j+ZsfxeE2iYVl1WpoOgd+7Eg+9tPPE+wfQVc0imvLGBwc5OMnfoxdwzWUS0VIgpSqqhsk0QCDJCrbpH521epQUNEFAwEBv+HJrqtNxRAI2f+aFZJDQnLIdraF5JBp2n85A9t6CGWxjRUkkYqmKhBMW9lkJI7D60RTNBwuJ8lYIuM7E2UJDAOHx8UnLz2XPT94QOYmp2U45DUczkip2nUNUYsj6El03SCm6ISjcYpdIglVSzmgOXDIDsSUfllzlY67OP/rlVdSXerhiENWpFLvNTp7+3n2lbeJRGNZXzMauq4TDAZRVZWSkhJ7nn9kZIRwOMzPf/5zqqsqkeJDZmciNRZndihS+4WQCrxL207DQEBDcxRjyO4xnzsr6BqCnjQ/O5WFMS0HKcNA0JMIWtI0jtChtbMXQyhcYjEZksmk3dWIRCKAWbixiIbH45nwPmeZGEykNRoNwzD4/e9/z2233cbuu+/OLbfcQllZWc72aSbQNI0//OEP3HvvvcTjcQ455BB+8pOf0NjYSEdHB8cffzy/+c1vOPPMMwGzIH3JJZfw7LPP4nK57ITuudSK5Bs7yQUzIxfWQikajdrC7blEb28vIyMj7L777tN+7Wjhdj6JRXrKallZ2ZRnKXcEWLZ/oVAow1GitLQUv9+/YDs3M0G6eL2pqakgxOuKonDqaZ9nw4bNlJWZC5xoNEY4HOEDhx/Mq6+9haZp+P0+NE1naGjE7haYJELE6/Xwg+9/jUsv+yvhcCTr55jpuo2oqkp3d2+GDW42WDbTpaXFqKpGU1M9z666H4CtW1v5+oUXsWbNeuLxhN1FkWU5JRCf2uJOEAQcssxn9z6K8rBEMjpFlxIBJKeDypYaBrf1oiayXzclp8y3H/oNZQ0VGIbBcEc/199wA3+6/koOLt6DXcR6XMh23oaWIhYyEmFi6IJBseEhjoIh/H/2zjxOjrLc/t+q6r1nevYlmS0LYQsECJBFIAnMoIioVxEBNSqIgMoiCCICIsIPUQHxggqCCAjuC1wDZAIBggkQIBCWQEIIMPu+9d7Vtfz+qK5K9yzJTDJLz3Sfz+UK3dPdb9fUVL3neZ5zjo5Hd2JnhIKEPgbdnCBgd9oQRNEgF3EVT56XM352Pk/8/C/YXXZ6G7uIR2UU2fhuok3E6XEjh2NoqtEFAMNJy+FxGlqacMzSlCCAKEkUVpew7MyTWHLWKkNzwu6C1MDAAOXl5ZO7KTJv9QkXKSHah6aqxOIKStzIOXE7bMiahObw4vUODcLTdZ3H/v4wyxYfTlzRiEZj2O02fDleGlraefyZF/a6DE3TGBgYQFXVFGIBxn31/fff5wc/+AGHHXYYojyAqJjn9G7dSIJaTBq5ENQYYjyU6OgYo3i6ZDf0KaPpkOg6ghJGVI2/M1XTiEWjKJqOK68UyT79N5WqqlpuZsFgEFVVU8anvF5vSlExEAjQ3Nw8ZmJx55138oc//IH58+fz8MMPT34OShajwvSjymmAaDRKQ0MDmqYxZ86cKZnz29exqMnUV6iqSmtrK8FgcPJTVtMADocDn89ntY4LCgqQZdnyqzcFcunkLz4RiEQilh4pncTrL7zwKrt2fURBQZ61Jq/Xg6IovL1tO1d+79vc9/tH6O3tQ5Qk8vN9CILRkTAE2kaXorenz+oqDAdd12lpMfzLRyIWyXoJ899DoQhul4v33/+Qc79xGV84/TT++rfHeOONdwBDk2FUjhXKy0s5/9yzuP7GX+6VvJhrkuNx/rvzbb4w91ji0Tj6aK4nOqixOB07mvf4Y6qs8OAFt/Ppa7/Cc/esoXNXK93dXXwqZxm5ohs9IdXW0BAxxqMkHOjo5ONlJ61IgohHd5jhyGaTYoRl6XvtXgiigDvPSzQQQdcUBFHEmePG4Xbi7+hFlRXyZxXiLcglForS09BBuD+IrhkjYKItldzomm5kWrjsCAknKV1TE80CIxvjiV/8lTfXvszqX1+CO89LS0sLgUBgaowskq8vggh2LzYhjM0mgcuBqmnIikbXQJBwtA9RFPF6vbvzfkQRLRbmY8ccgYBAIBpB03VicpyBQIhZZcWUFRfS0d074hJMYqFp2hBiAcYIjqqqVrFPs3kSlX5zT29YBKCr6IKEoGvGY9Z309ER0cdT1K1rhmOVZo6KCcb/qTKiEDHyMPb6Hmoie0RE1XQCgZAR7JrjBhS0wWN/0xCSJOHz+fD5fJabmem2aFqymuNToijS0dFBbm7uqIkFwD333MMf/vAH5s6dy4MPPphxe4rphGzngrF1Lvx+P83NzVbK6FRtlHp6emhvb2fhwr07uZiYTGIRj8dpamoiHo9TUVGRFiMwkw1zUy2KIlVVVVbL06zwmBdeTdOw2+0W0dhbK3k6IRAI0NLSMmkBiWPB3//xH6648gbKylK91CORKLGYTP2Tf6a0tJgd7+2iq7Obiy75YUJPs3sz0dPTh6ZphMMRYrGh9oJg3HRFUcRmk4hG956wLEkSmpWibBANr9eDrmtEo3KSVa4BURRwSHZOLD2IJ5rfHNMxEIAVsw9mWf5cwv3BMb12r+8tiXjyvGiKiioaeiO36EQURELE8GBcO8VhSIGGxttiE7KoUKL5KNfyEwRkmO6FVZDfC7kQBES7aHUeTBRUlPClO77NP6+5H5vDZoXf9TZ30vVB+9D8iyHfU0BXh/8hQTQsbJevrmXhF5cTDoepqKiYsvnwIdDiRjVd160MCV0QLbegQCBANBrFJglUFufitktoqpwIZFPo6O4llujwFBfmUb9hMx80tg7/UYOIxXDXgsK8XFx2gU+d+knKZ1eii3ZEJYqgRhM9rgTRFO1okhsxbuoujN+9AGiSG80xfvcbQYkiyoFEv8T4FF0ULacz1ZG/1/EoQYkiKiEUTSAQCFi6PUEwvpHmyJu0kL6pQDwet84nc3zKLLgNGwY5DO677z5+/etfU11dzR//+EfKy/dui53F1CF97vRTiNG247q7uy22PdUuP2Z1c7SCxskUbidvqicqbTndYW6qnU4nVVVVKTfSwRUek2iYwVhmK9l0npquom9TZ2NWp9Lteyw4YB5Op5NIJIrHs3tMKxyOUFxcSHl5KS6Xk6OOPIx/P/okkUiM0tLU+Xiv10N7u9GFUhRl2K5BQYGPWFROhNXpCVH3yF0C4+/a+i/j70eHcDi1O2KOT6mqRkSNUd/y9piPgQ5sbHuPErePWYJrZP/dfYCuaoT6AuSU5BGJhFDRUAUNCREbIjo6EsOfEwICB2kVbBF34dTtiCMRC+OH90oAIFFckVVESUS0ScaIk6qjyHEKq0s54GMLeefpLcRjcUCnp6FzdO87ArEA0DUNDZ236l+hsu4Q5i2YPyWd7hEh2tHE1O6BgOHF73K5rLwfPdKHHZVITEZXZURBwG63UVKUT3NbF06HHTmu4A+Gh/0YVVUTZgX6iMRiXvVslh55KHabRJHPgxgPoYs2NJsXQXIgJEaKjPA/BwgiqigiKhEETTH+W3KNu1OUoMZ2i8oB0BE0I9Bv1H8vgoCm6QQCwd3EQhRBUyc1pG+qYLfbKSgowOFwEAqFcDqduFwuBgYGUsIgk92nkvcoDz74IL/+9a+pqKjgwQcfzBKLaYAsuRgFTIebgYEBSkpK9ssycLxgbtQ0TdsjyZnMxG3Y7fyQjpXqyYLpWz+aTbUgCNYFNbmVnOzEkew8NR2E8MlOOOmsszniiEM5/rglPPPsRuLxOA6Hg3A4gq7rfOPcL+Fy7d6k5Of7EmneijXKoaoagUDI+m4lJcW0t3emfIYhzHahqjq5bifhcAQQRsy2GGwxbUuM4oQjqVoKM5cm+T1Ufd/c43R0Xmp/n7NmH23Yqo5ysz7KNyfSF0SRjOuP6WJlkovhoCUelxCoVosp0nNG1luMFQJoqma5RAmiIdJed8e/OPnSz5Fbms+7T79G285my1lqb++3t2OlazqKrFBeUpZexGKUsNtEJIcESHgdLnq7O3E77WiajtNuJ8fjxumwsfOjZjq7e4dc75KJRX5+/rDXMKfDzrFHHIIoCnR291E9Zx4gIGhxRDWCZs81CMVgiHY0xwSGu+laQlCuY4nKIWGPqxrEbBTXtqisooYjOOwSbrfXIBa64YSlie4Z3bUwEQ6HaWpqwuv1UllZaRVHzDDIYDBIa2srv/zlL3n33XdZunQpq1atoqOjg7vuuovy8nIefPDBcbFAzmLikXk7vzEiHo/T2NhINBqlsrJyyqwzByPZ3nQkJAfjwcQLtzPdZjVZvL4vvvWDg7GSBeGtrcaoQfIM9GQkpo4VyYLV0tLSCUuoHw8IgsAvb/8JP73lf3n8iaeJxWIUFxdyztfP4hvnnp3ysx9bfgxlZSU0NDRTUJCPosTp7u5FVQ1yHw5HUFUVt9uVYhEbj8dpb+9kwYK5nHD8En5//1+GkIJkiKLIQQfNp6enj4EBPzk5XrpHmGEfr4lWGyLd0QCKpiLajREuVVGHuEbtExKbecuuFS1hHiqNWLA1HaR0dEr1vJE7Fgno6GhoSNj2WAQWbRKSJCY6E4ZQWxBENFXl7SdfpuaoA/jYV+o4aOUifn36j0f19USbhKaoeyUY+eWFFM0qGdV7pg0S1q4ksiYQDItiRBtd3b2UFBci2e3Y7RJb33mP9f99mUg0ht1ux+FwWCPD5rz9SMQCoKy4kFyvhw8bmpk3txobuy1+UaJg84Cwh+2KroOuIugquiAaPzsO1x0zZ0NHMroXSR1FK7RvL4hGozQ2NZPncVBe5EMUNEO/gYAuOsbf1SoNEQ6HaWxstLKdzL3BcGGQxx9/PA0NDaxfv57169cDRufj05/+9IgZP1mkH7KaiwRisaFuKZFIhIaGBgBqamrSwuHGRDAY5KOPPuLAAw8cVveRrK8QhLEn0o4FmqbR1taG3+/PWJvV5PyGiRCvmwm8gUCAcNgYPXC5XCmC8KmGqqq0tLRYDmrpmrw+HPr6B+jt7adidnlKxwIgFApz3Y9+xqOP1dPX12dlWAiCgMvlpLS0mL4+w0lK17FugObfgCAIHHvskVx80bl87euXWl3EZCR3Mu7+zc/o7unlp7fcSSQSQdMm7hJtFyRAxys5OafmYyz9Wi15JQV0vNMEuk75QdXEwhHeevIVuna17s6F2EvFXrCJeApyiPaH0FQNBY1gPIRLcmJDGlZnkYxksffejGY1dIJqGJ+UM+LPuXwedFUjFonBoOMpiAKe/ByqjpzPl+74Dg9/5395b+Nbo+veCCSUxvrIPy8KnHrlFznua58Y9ul4VGbnxrdpfvtDbE4785YcTM3iBVN6DRXUmOHSpJvEydgMIxrdu3ffeZfcHA9Op5N/1z9PV08fqqoiyzKxWGzIJtB0x0v+ToZjmYQcV6ipKKfu+GMIhcMcMHewLauOJrlG1iXoGmI8mMjnMJTfumhLODntZ8dLU5DkAXYHq2iJ/xWMMSxnPogjf0Y0GqWxsRGbzUZ1dTU2EWOd+2pnOw0RiURobGzE5XJRVVU1qr2Iruv87ne/4+6778Zms6W4W86ZM4dVq1axatUqjjnmmLQssmWRJRcWZFlOqQQODAzQ3NyMy+Wiuro67U7gcDjMBx98wAEHHIDLlVr5mEzhtqIoNDc3E41Gp92GcryQnN8wGWJNMzHVFITruo7D4bCIhsvlmvSNSbKAv7KyclqOf4yEa6/7GQ8/8g/cbhcul5OWlnai0RhOp4Pq6gqLGLS3dRKTZTweDy6XAyWRISCKIvF4nMLCApqbW5FlGUUZSjBMrFyxjGOOOZJf3vG7cQnKHAkiAnbJyOhYml/DkqJ5SDaJo885kXkrF+LxeFK6ZE/87C+8+Mh64zqp60OE0RYEcOXnUFxVQrg/SF9zN1qiiyqOJfAODRERFW1EbQaAgkpElckrzkcK6+hqwsnLJqHE4og2kdL5FcjhKL3NXUNIgCAZouui6lKO+9onePynfxq1Na+RMp4YUR3hd2pz2vnk989k6VknGiGGsTidu1oRRYG8WUU8fvOf+PDV9xKhfAqSXeLQuqP5nxu+NiXdX0GNIcpBLIsmMDIe0BNdAZGB/j76+wd4a/sutrz93pD3iMfjVsfCel9BwOFw4Ha7OeqwgzjkgDm4XQ4GAiHe+6CRA2pmc9ABc/F43AjW79vMsxBRnXm708STIMoBQ49hOjlhdDF0yYlmz93HLIqo4UQl2EBTEBPuVLuJpIouOfb4/snEoqamZlqMtI43TGLhdDqprq4e9fn8+OOPc91115Gfn89DDz1EcXExzz//PM8++ywbN24kGDTMJwoLC/n73/9OZWXlRH6NLPYBWXKRgEkuzNGWrq4u8vLyqKioSMvxnmg0yvvvv8+8efPweDzW45NJLGKxGE1NTWmVXTDZMI+BrutUVlZO+jHQNC3FeUpVVWw2m0U0RuPCsb8wU2YBqqur06KLMl7o7etn5arPIctx8vIM0tjc3EYoFEYURSoqjE6Hruu0tnYgx2RmzS7HkTQHrqoqXV09ABQU5KEoKm1tHSN+psfjRlEUQ0g76OpsWlAb3UhhVLazJqSE342laRBEbILIoQUVfLLmCERBIBqI4PS6+PL936XjgxY+eOld0KH66AP476/WEPVHcHqcaKqGqqiEev2gg8PrQlPUlNwLIREmKIgiGjp6fN/Eq3uymdUS+RiyKnPs12ppfnwbkYHQblehRB6JZLehqaqRqJ2AaJOQbCIIAkosTvmBleg6dH3YhirvffxCkIy+imS3oSoKusYQO1+724E714PN5eB/fvw14tEYGx+oJ9A5AIKAJIkEe/0osoIqx42qu6qBAEvOOpHTrv7S2A/Y/kDXEWW/UV1PrshrmtHFEG2ATlzR+Mdjj/P4umeHBLkqisLAwACiKJKXl5dIhY8jyzKyLLNq+dEce+RCw3JWUfG4XcjxOO/t+pC6FR9DkkySAJhBeehojtyhYm1dRYr1Gz+fkndhdBhUR15izaODEA8hqpEEpzFTv0EXRARL05TUGRmha2ESC7vdTnV1dUYSC9Oy3zQ1Ge0xWLduHVdffTU5OTk89NBDHHLIISnPy7LMli1bePbZZ/nwww/56U9/SnFx8UR8hSz2A1nNRRLMsQ6/309ZWVlaj/eY6zIrmyYxmixHqGAwSEtLC3a7nZqamrTr7EwGQqEQzc3N2O12qqqqpuQYmM4jubm56LpOOBy2xqf6+vosFw6zAj3eRDkdjsF4QtM03ntvF6qqceCB8+ho7yIajeHx7O4OOhx2wmFDeK0oRqiaHJeNMSm3E78/gN1uS+QEeAiHI9hsNhRFwWazEY/veeMaHpRynJxpY9aCDjhgLn19/fT29o/Y3UieXDrQXUqFw0dIk8l3eHDYHUSjUcocuVTPmoWUOC+cOS4i/jDP3fYoH215D00xxhHe+b9XjKq6w05cUYxU8qhibPoFo2qvyPGUDzU7G64cJxF/wkVoT2EVI2AkYqGiIRNH1EXa9D4OPvJQWp5810jf1vVEUJ6OKsfR1KE2waag3NRM9LZ0Ew/Hdo9+7QF2txNXjotgTwAAyWZL/B4E1LiKaJPILcrDW+TD5rDR39rD5r88Q/dHHcSjMt5CH7qm0fFeE3FZQZJEbK7E2JAOciTKW0+8zNKzTqRk7qy9HyRNsVyTdEFEtznRRec+jdwIujL0daIImo5mc6NLLkQnHHTYUTy94QWampqoqammclYpXreLjs5uIpEwXu/u642pwcjN8XDU4QcTlWUCgRC6rtPb309eTg4HL5gPkpTY14uJ/IrE9UpPCKoHw2Lfg79nIihQ1wY3qkZGIsncHP9KfIBBqgQJTXInErqNNHlBi6MLg0gNWWIBu4+Bw+EYE7F45pln+OEPf4jH4+H+++8fQizAOJeWL1/O8uXLx3vZWYwjsuQiAVmWaWhoQJZlqqur8fl8U72kPSLZLWqwcHuiiYVpMer1eqmoqMjIi2d/fz9tbW1pdQwEQcDr9eL1eikrKyMWi1lEw+/3W8+bRGN/nbwGBgZoa2uzRHrpcAz2B6+8upUf33Aru97/CB2diopZXPSdcxMi7ZjVkcnL8zEw4De8/mMymqoSjkQoLMxHFEUaGoyAOUGA7m5DsPg/nz2FdU9tIBQa3qozGWZXwtR2mNV3TdNwuRzYbHZu/cWP+Pa3f0BOjpdYNEZMHm7znHg/4DDfLCok45rm8nmwOWwEu/0AyKEotvycxM8KqLLC9ue2YqRZ23F4nKiyghxTUGMKcjCSyIyQLLKjKrvd6DRNtzZ9gijsHp8aTxcqDHIhItGr+PEsKmHHf14jHonhLcjFmeMGdDo/aNt9XJPXAuiKRlzVrHA8m92G6BV2E6ERINltFFYWW/vaxZ87nlmH19A30Mfb/9pM3wedFNWUWZ0bMGx5d734DmpcxVOYi65pONxOJLvdCDAUkyzRBePflbhCw2s7904utDiS7Lc20oIOoiyj2dTRBbwNgZjYUCc9ZKV7ixbxOOKII/jSl77Efx57lJOWHcW8OVXWz/UOBFi/6VX6BgIp71yUn4fb5aS7dwCHw0EsFkPXNMpKS8jLNTJRdFFA1XV0UcQmCglHJcfwwXjWKJQGKcJ/LaG9GP01SdCVhGYj6TWGgt14TjMcooxzWEXQg+ha6mhUlljs7mSbOpPRHoPnn3+eq666CpfLxe9//3sOP/zwCV5pFhOJLLnAqAZ++OGH6LrOvHnzhmgY0hHJ5CJZ7DSRI1y6rtPR0UFfX19aW4xOJMy8k+7ubvLz8ykvL0/LY2CIjQ2v+sHOU21txoYreaZ+LGGQuq7T29tLZ2cnPp9vTAmr6Yqmpla+/e0f0N3TS54vF0EQ+eijJn50/c9ZuWI5Tzy5noGBAHabjY7OblTVGE3q7es3sjCOOozqqtnUr9tAfn4ewWDIIv5Op4Prf3QFOTle/vjwP1LcpAbDvBHrupYYe9JTtGCiKPGlsz/HyhXLOe20k3nkT//Cl5tDdCCMrMaJ6bvHfhLbIiRRpMSeY2lyNUVDdEtIdgk1rqIqqjWWEwtFk2xaDbGxEounisr1xPhlLCFcFwVjHEgHTd9NLESbZOgfEoxCsGLQxgcBonygt9MYbePc2OfZuektNFUjHosj9fqxOe0p4u3BGhFz86+rGoJNJH9WEXIkNugYCKZGGF3XsTntOD1uwgMhRElk4cePYdnX62htb6PmgBJsYYH//v7JlFGuYI+fUF/AMDMVBEI9fiIDIYprynDleYgGwil5CcZYlIDNYdt7jIKuI8bDidyF3Rt/XdcQlAhIrj0KjodAENBsLsR4yBgt0k1GqCUEyKmdyZUrV3LwnHIkPU5TSxs2m80obhQXsHLZUTxW/3zKbzwmyyiqhiSK9Cestg89aAG+XIMEqbqIpqmIgnFfi8oyKiJx0Y7HNoztuiCiS07ju6KyW3Oho0uuMQq693AN03UEPQ6CHUzSqOsIahxBktElp6UvMAN2M5VYNDQ0jJlYvPDCC1xxxRXY7XZ+97vfceSRR07sQrOYcGTJBcZGrLKyEofDMW1yGUwSoSjKpIxBmSNjoVBoQtyQpgOSXbFKSkooKiqaNptqh8NBUVERRUVFKIpiEY3Ozk46OjpwOp0pzlMjfa9kgllUVERJScm0OQZ7wj/++R+6e3opLSm2/raczmI6Oruw2Wx87atf5B//XENbWweqouLxuMnP9xGLyYZYuyCftfXPoak6BQV52CQJOR5PhOvFeeHFV/jOt8/hb3/7P6LR6JCcC/MYFhbmI8syfv/utGybTUJVNdxOJ6s/cSpfOHYl4b4gl192AZs3v8b7uz5C1hU0XbdcmEx9hSSKHFFcg9flJh6OgQ6iZPyMw+si0h9CjSsEe/xDAsEEs4CxB/G55LDhzHERHQgnLGdBdNjQZAVNNaq88VjSuFTy/yagJoTbg8+iwRNU+qBn3NhpCLRwWukJBD7qSQjNjc25io4SHHndiW9oESNPnhdREnHluCmoLDZE6AlhuN3lwO3zcNCqI6k56gAat76PZJOYv3whs46ooaWtlZycHCoqKij4RB5vPv4S/W29uH1e1LhCX0s3AuDO9RALR5EcNlRZob+th6LqMgZae9A0nXhUts4Dh9uJJy+HmqMO2Mt3SB7PST5aRsVf0OJjqt4D6JILXVMT2gPNOt6aIBljV0l5E4KuMaukEH8gSE5OLuFwiP6+fpR4nKJ8H0WFeXT3Dlh5Bm+/+x6Hzq+iunIWqpZDZWUFvpyETk2QsEk2wG6MKIkaQVmnPxAmGh0A2oYtiGg2DwJCYqQpIQC3udBsHsYCXbQZRExTgWTxtjkmJ6Ye48QIm6DGCctaxhMLWZZpbGxEkiTDGWuUe6nNmzdz+eWXI0kSd999N8ccc8wErzSLycD02ElPAnJycibUmWUiIIoi3d3dxGIxfD4fbrd7QjZ6yU5AVVVV5OTkjPtnpDtUVaW5uZlIJEJFRUXaj83tCTabjYKCAgoKClBV1RKE9/b20t3djd1ut4hG8jmlaRqtra0EAgHKy8spKCiY4m8yfvjww0Z0PbXzJwgCNpuNXbs+Ys1//shhhx/CZZf9iJwcD06nA5tkw2F30NjUwprHn7bIwoDfnyD7JBJ5BT78sIG+vn4EUeCAA+aiKCqdnd1EIhFUVcNmk7Db7bjdLjweN6qqWSNUiqIiCsa40kOP/R/vbXqbE+YcymnXfJn/e+wh/vHP//DSplfpea+NsrAdVVVpCPegySoLCsqZU1iGKscNcgGIkoSqqKiygs1lNxKmDekESpKQWVPUvV5PREnkzF9cyEev7uDFPz6Nqmo4PA5CfUGra5CcA2F1LxLMQUEliowTO/Yht6NUemF0A4x3iaMgaSJLfIfikW3G5tImoSb0LHtKzU68BXnlBdicdgKd/RaREAQBT14OdreTvqYuiufNovLwuRyw7FAOWrkIyW7jqM98DNg9GpncvfOV5PP5G8/hud89zocvbyfYG0h0RiTksDECpMTiSDYJRY7j7+ilZG45cjROoHvAqIZLIjaHjUWfWkrJvFHoLcYbgoBmc1qJ2Eb1X0DQNYR4CFUQLZF0OBzCHpdxOl0cfOACZDlGOBymp7cP0Glva2PH+8ZUgGmdvaOhnUMOOZjKqqqEiiKR+ZCcYyEIiEgUFOZTUCwRj8cth7zkgogZQOp2u428CLPNJOxDB18Q0WxexHgwkZlhnIHGOJZgpIAPeY1OXInT2NSS8cSioaEBURSpqRlsJTwytmzZwne/+110XefXv/41y5Ytm+CVZjFZyJKLBKZL9TVZuF1RUZEi3pUkydoUer3ecflOkUiEpqYmRFFkzpw5M8oJaLSQZZmmpiZUVaW6ujrFnWu6Q5IkfD4fPp8PTdMsQfjAwAC9vb3WOeXxeOjt7SUWi1FZWTnhdruTjYqKWdboi2CNlugoikpNjWFzGBgIIIqCQSxshmC7rb1j2KKEpmk4HA40TUVVNV7a/BpLjj3KmKVXVJqbW1OE3aqqUTG7mN6+PmQ5TjyuWGvxulxIcUOgHNMUNvd+SLWnkDU3/4lv/eVazvvGlznvG18GwN/ZR2QgRN6sIp67Zw1b/rWRqD+MIArkFPsQBMGwWY1DTnEeDo+TYNcAOcV5+Dv6UORgyvfYm5lgPCLz1+/dzYErFvGpH36Jrf95kfb3mhPaC6NzMHgcShAEFE0lpitookYUBRcOtAR5ECwKISQe2/3foBNHQdFUXIKDObZydMUYaxIlAUGwocSVFG3HYK2F8cWMcSWH20n+7GIUOc5ARx8en9cwRvCHKJk/i9V3XUJO8VB77d7eXjo6OoYdjSw9oIITv/VpWt5pwK1pRAZC2Bx2NFVFV3RjZAlw57kpn1dK7fm1gMDfbvgH/o5+o6ul6ex66V0O+8Qxe9ZcCCK65EBUY8bvynI4Msakhk21HgxNTYjBDTKhi07jAAkYY0Dmd9MFY9OtyuiizQr3nF/uw20XAQWXQ8LlyKUwPxdF1fnCF88kEpURRdE6Vl6v11ijJqOpCpIaRk9OvwbM0D6TJNjtdqsgommalejc399PT08PkiRZxhVerxdxpFtf8vlsdSaURPaEgI4x+iVoMnoi5E6zeRD0OIIcNLoYlshcQ9N0Wjt6MppYxONxi1iMpWOxdetWLrnkEhRF4a677uKEE06Y4JVmMZnIkotphMHCba/XS05OjtVy9vv9BAIB+vv7x8UlyO/309raisvlorKyctqMjI0nwuEwzc3NSJLEnDlzxqRNmG4wz5mcnBzKy8uJRCIWee3v7wcMnYap85lJN9LP/c8neeRP/6Kzq5uC/HwEUaC/fwCX08HZZ/0PqqridBrnv67pSJJEJBJBTgipjakUMYVoKIqCIIDL6eTtt7Zz1hc/C0BbWwfxuOEQZNh0Gv+uaRo+Xy5tbZ2J9zM2P/FYHFG0ga7jECRimkKD0k/pgI8dz79pVdIBfKUF+EqNjtIp3zuDJWeuovmtD3G4HcxbegiCKNL81gcAVB4+j3tX/9RImdY0YqFUl6rRIhII887TW2h8fSdfv+97xCMx7j/3VmusKhoMo8QUzB2/5LCBItAZ6+f14E5W5R5JXFDRUbAnIvbMPsUOmimjgDzcyCiouopDt+MWHYiCaJEI09VKEARsdpshMBcEJLsEmp7SkQFAFFDkOLqmcepVZ+Erzef53z9Jb1MngiAwZ/ECTvr2Z4clFt3d3XR1dVFYWEhpaemwRZz3N21DicQoqiqlI9aCEpWN7oimI0oCDrcDSZJYcvoyqg6bw1+ueYR4OErZAbMta9vexg7qb/8HX/rVd1Kv37qKoBqWv7p5DHQNATWhkTByITSbd+8VfF1FkgeMcSeT3GlK4t9EkIYZA9I1Bvx+2lpbqCrJw2WXSGFzCdgkicMPPWR4giMIhq2s6EDTFcOFSYfd4mzQbK5h3a5EUbQKIrquE4lErK7GQELHMTijxdBHRBHUGIKuo4sSuug0Mi00OUE69IQoXkh0azQELYagiug2t6Ht0GKG2B3QNJ3OviCqYKN6DI5IMwkmsRAEYUx5YNu2beOiiy5ClmXuuOMOTjzxxAleaRaTjczbLU5TmPkVwwm3BcFwpHG73ZSWlg7rEpRMNPZ2EUwWLft8PmbNmpWWWR8TjUwmV+YNWhAE+vv7sdls+Hw+wuEwra2tACnOU9PdgvbAA+dz260/5oYbbqW9owtd1ykoyOey757P0qWLaWho4IgjDuHggw5gx45daLpOLCZbhVBxmLl2QYD8/DycTiearrNs2dEcdOB8Xnjx1YTmggRJE/H5cmnv6EQSJSoqyrHZbDQ1tiCoBqEwRdRg/K+sqQgCRAN7JgSFlSUUVpakPDb3mIPoa+mmr6mTysPnsXXNCwS6BwwhccoXYK/uTmZXwOa0E+oN8Orfn2fOsQcZomhNM9J1ZQVBFKyKvDc/F13TWSDk0qPEoBvimoIkiGiChiYYid5O7MylDCd2RF3AhmQE64kigijiyfOiazqRgZBxXFTNlB6DAKXzZxP1h9E1jWBvANB3j0tpu12uGre+z0nf+SyLP388fU2dFM8p55DaxUi21N+prut0dXXR09NDcXHxHq3KI4GwoXmxSeSVF9K1q9XqnhijV25iYZkNDz5HwexCWne04S3wYrMBuoJNgpwCD13vN9O5s5nyg6qN1yoRxHgIAc3aEBtkQkAwLYBFO6ojd1T5DqISA10BtCTLXz3Ra9JBtyV1LozPC0djtLR0UlWSR67HnqRJSDpWgg3B1IPsqXsiCGiO3KSEbaMzoNuc6NLeM4PM65TH46G0tNQyrggGg7S3twPgcrkoy3eT4zTHuQQELY6gysbxEyRDa5H4bwESGhYJdMOeVpWcaHYvguZA0OLI8TitHd1ogm1MVqszCSaxAMZkR799+3a+/e1vE41Gue222zj55JMncplZTBEyZ7e0F6TzWNRY8itGcgkyN8qwe1OYm5s7ZMM8nUXL44XBbkiZSq6CwSDNzc1WCJJ5rsTjcYu8mjdwt9ttEY3pOjp3ct0Kjj9uCVu2vEFcUTh68SKcTgcNDQ3ous4BBxzA7+65lauuvonXX387qWshIEmSVQAwUVJchC8vl67OHo5dchSzZpVx40++z6c+vRpZjgM6DocdTdPp6+03nJaI09PTR0lJEUXOHHrCgcS4i+E0pGjGprJM9CJIErMPqR7Td+xt6qT+9n/QsHUXaDpOr4tYYHj3KlE0shfkcNRwlpKHCqQNvqOjKYbD0fbntvL6f15AU1Q0VUM2Q+uMgjqCIOJwOYhFZdRwnKu/eyVP3f0Y4W4/grlH1cGBMQYlYU88ZHQlpITdqLcgh5yiPILdA8OvXRL57PWr+ejV93jlbxswZR5DvwC88vcNbHvqNexuu6Xd2LHhTT519dm4cj2J77nbyKC0tJSioqI9HufSebMtO1m7045kk9BUzfhH0+hr7QdRQI600/BmA6qi4rI5jAUlOjeSTUJTFJT+btBmGRaz8WBi2aLRqUj8l4CELprZDqZr0iigyQh6srZlEMHQlCS3KQ1Z0Whq7yU/P488rzNBQJNNj01xjTlyNAp3MEFEc/gSQmrDlWqfNBOkGleoqkok6MemRnDbNDRFI67KaIINu13CISQyMBIOWELS/989/mTY8gpaHN3mQpcchGWVxqaOMYfDzSQoikJjYyO6ro+JWOzcuZNvfetbBAIBfv7zn/PJT35ygleaxVQhm9CdgKqqKMreU1knG+OZuJ28KQyHDbGo2+3G5/ORm5uLIAg0NzcTjUaZPXv2tBYt7yt0Xae9vZ3+/v4Z5YY0VphiVdMFZyRypSiKNZIQChmhWA6HwyKvLpdr2h4/U29kup+YN1Bd13nvvV309Q1w+fd+zLZ3diAIRmXeJBeCAEVFhaiqSm5uDud940t0dvagqipPPf08XV09+LxeOjq7iCeuO+YWTxBFcnI8fMxdw6bu94lqiuWlpKMzy+HjM2WLOPzkY/jiL84fNfGNhaLcffZNdH/UToKvGGNce7gDCKKAzWnHVZhDsK1vqHYhAbvLgSAKiJIEAjg9TgLd/oSDlJl5IeLJ9yJH5ESSt07erCJEUaS3uStlI5r8KYIkWN9RVwzhtTvfS15ZIb1NncSjsuUUJdkN9yqAFed+khO+8Un6Wrp55JK7aNveOPJ3FaD8oCo8eV7kcIxgj59jv7iSVeefhq7rtLW1MTAwMGojAzkc469X3k3b9iYkm0R/W6/lumVz2BElwUo5P/rTx9D+fjvRQJjcYh/mJj3QE8DpdnDeb76BOy8nQQJGMh0RDVcoXUdAQ7Xnotv2XvkXY/27g+NSCEliNEl0JnzHICorNHf24/LmUl5agk0esMiFoCskExOzc6E6cg1L2KmApiDJfkNToWtGs0oHRVUJRhVy3XYkQSCOhM1mQ9J3j+7pgmR0OXQddBXNnoNuc1l2s5lOLBoaGtA0jZqamlGPCn/wwQd885vfpK+vj5tvvpnPf/7zE7zSLKYSWXKRQDqSC7NbMRFWs8NtCs33nzVrFnl5Q2eNZzqS7XZnzZpFfn7+VC9p0rE/OR6aplnOU8FgEFVVsdlsFtEwx6ymA8yuzd5G4rZt28EZX/wmHZ1dVvaF0+mgpqYKh8POkUccSldXLy+/8npirBE0VTVGqhQV2RI9g4SImtjUiZLEGeVHocoKW4PNdCoh7Igs8JSwyDObmkPncv4fr8bhGX2X6Ll7/sPTdz5qvL8ooun60FGoYeDO93LWL7/FU3f8i+Y3PuA43bkAAJPySURBVBjx5wRBwOZ24MnzYnfaUeMqsVCEiD+Mrum487yoskJcNvQCbp+HvLJCOj5oQYvvdpQaPI5lc9othylVUUHTESWRwjml9DV2W4TOlesmf5bRaQ109nPMGSs4+RJjA/Pmky/z1+/dvce1F1aXkFduWGwHe/w4PC7Oe/D7dHR1EggEmD179piui8HuAV54+Gne++9bdH3QhhKLG8TCbugkTGer0rllLP3csfz3kf+iaToOt4N4RAZB4IQvH8/HvjjYQWe4eTXBcDWyyIXPcE/aC4R4GCnuT3rf3dAR0Zw+dEGif2CAtvbO3flGgBTrN/QHgphwUtp9LumCZIihHb5hdROTAVEOIqpRwxJgEPlRkIxUc3QCkTg64HbYcNgMYbku2hEQMZ2sVGce4UiMpqamjCcWjY2NKIpCTU3NqLvUDQ0NnHfeeXR3d3PDDTdw1llnTfBKs5hqZMeiEkinTY/pCGXeNCciw8Jms5Gfn09+fr41MmV677e2ttLd3T0jqs+jRbLdbnV1teFokmFIrtDuy0icKIrWOaPruuU8ZbqZmc+bovF0HTUbGBigtbV1r10bgIULD+KpdX/lb3//P95+eztlZSX8z/98kiXHHgXAP//1OFdceQNerwe329jsRcJROls78UpO+rQwEiIOUcKGSESPE9cNwXxDpIfDnbOoKz+UktJiBAQUWSYyEMbmtPPvH/2B+csXsujUJTjce7/Jv/P06+i6jmSzGQnbuo66F3KRMysfLaKwff3rFFYU0/L2hyPavAqSgBZXrA6EZJfw5OdgdzsIdA6gxBWUmIwgijg8TgqrSrHZDRtZdEPobQbbqXE1hfiYjlOCICA6JHSgt6HTGMdKQE8IplXVEHPPPqTGeq50/mwrNHD3mwq7uyVC6j1AsttQYjIf7foQVdT3ySEtpziPj3/3dE781md45tePsvGBejRNRZcT4mFRxFvgRY7KHHziEXiLcnltzRb62/opnVvK4k8t5vC6hcO8c3JgyO4jtFuDIaJLoxtT0SUXuhJKjFLtHo8ytNUiuminu6d3WAG7ZnMhxUPoeiLAT0+MUgkSmt2bCLGbuvuGoJv5HyK6JqSMf0kCIBnbH5/XhqJqaLqaCL4ERYsiSRKCKIEzJ0ssMIpv+0IsmpubOf/88+nu7ubaa6/NEosMQZZcpBn2JNyeCJiWijk5OZZXu1l97uvro6enZ8Tcg5mCaDRKU1MTYAjTpkNC+3hD0zSam5sJhUJjrtAOB0EQ8Hq9RlpvWRnRaNTqaJiOLsmC8HQRy/f09NDZ2UleXh6zZs0a1bleUTGLy757wbDP1dc/i6ZqFrEAsCe+a4HDQ1SNIyDgSMy1e3QHES2OIAosqDuKwgYVtT9CZMDoLsYjhs6jc1crHe+38Pa6LTz24weR7DZ8Zfkcf84pHHP6CkRp6HUjPBAyNvKJryQIwp5F2wK4PR6iapgdz79FsHtgKLFIbNAlhw23z0NkIEw0GMXmsCfSrXXksExBVQkLTz6al//6LJ6EoDvSH8Sd58WWCJWz1oRx3TOJjxKLI4iiYeEqCLhy3eQW5dH1UTuSQ7Q6ALFQlJ7GDuwuBxWHzWHB8YdZy/SV5OPO8xLs9u9ee3KIoSTi8u3WV0QGQhTOL0MRNKqr9q/YYHfaOaR2MVv/8xKqohgCeLsNd34OsWAEb2EenlmVHFZeyKLaw1HjMqKUXFAa7pc09L8F3dC9qHYPo06mFkU0uw8xHki4IAmJc0RAtXvp6u6hu7t7WAG7LrlQERCVCIZWwo4mOY3wujS4R+iCiKCpCSs3GzrGeFTiWXTJhSa5ENUYNlEGwYEq2InGZFQtRiQcJRCOIau9aJqG3W6noqIi44lFdXX1qIlFa2srF1xwAZ2dnVx11VWsXr16gleaRbogPe7oWQBjE26Px2eZAsXBFanB1We/3z8k92A8szSmEsFgkJYWIwCpsrJy2rse7QsURaGpqQlZliekazPYzcw0GQgEArS1tQFY1pG5ublT8jvQdZ3Ozk56e3vHVWsTi8m7NaJqonCgGTkHkiBR5Szgo1ivYW8riCia4dpzdG4V3/jUZ1hw/OG8+o/neW/j24T7g/S3duPJy0GQRAbae63qvqbK9DR0sub/PULHzhY+fc2Xh6yldP4s+lt7jNeIg3IFhj0ooMhx4pEYod6A1VVIgWBsR925bkRRxJnjwulxEeoPYgaaOdxOPnH5FyisLOXlvzxLX0v37pe39mB32kDA0mckNOIp+RR6wuJXB5xeN+07m62fEQQzaRu0uEpN7cEcfsZyGpobycnJIdzmZ/0v/2XkXwyHxN69p7ELb0GOQWbsEvPrFlFTUzMuuTaVh8+l+qgDaNjyHk6fG5vDTiwUQdd1Fn/+BCS7DU3PBUQkKxU7Mfuf6AgMWXDi3zWbGzMXQpeco8u2SIJuc6KKkqG90FV0JDTJQWePkSFRUlJCcXHx0BcKRhK2KjkxxoeSEqw11RpF0kX7lJANXXQalr26MdpkEAyDQKmOHBCdhlvVoC6PK3H4PLqO1N9Pe3t7wjI6zvvvv29ZwCenhM9kmMRCluUxFd86Ojq44IILaG1t5bLLLuPcc8+d4JVmkU7Iai4S0HXdcn+Zqs8fL+H23pCsLRitQNH0Ezc3hfF4PGUMxuv1pu2Yy0jo6+ujvb19VOMvMxWxmNHu13WdqqqqSe/aKIpinVOhkGEp6nK5LOtkp9M54QTWHAX0+/2UlZVRWFg4bu993+8f4cab7sApSChhQ3isoRNRZD5WMI8FjiJe8jewK9KNqms4BIlDPGUsKZyDJ9fLaT/8EotOXQrAP66+j3ef3UpOkQ9/Zz9yKMnlKeHEpOsarhw33/zj1ZQfWJmylm1Pb+Hf1z1ALBTdvVlPCKHtLkci9yH1diBKxoZREAXcPg+xUJR4VCZ5r+vwOMkp9hHuCzLn6AM57Zov88aal+j6sJ288gKO+NRSyhZU8uo/n+f/fvIwqqomBPBY6eCFVSX4O/vRFM0IwxNF63NMt6zdoz8jQ3LY+NTVZ3PYaUuMfJbefup/9GcCbX04c9zoikJ4IEw8EkOySQiShN3lAE1DjsQQJJHyw2pY+D9LWHLq8eP69xDqC7D+rsfY9dI7qHEFd56XY05fwbFfXJl67VFlJNlvRAjqGLazw0IwhNv28Q31TCbao3HGGvRiwypXiyW6BAK6ICU285NcNNB1RCVsJI2bHQtBQrN7jIyNvSAcDtPY2Ijb7aaqqgpVVa3uq3mtMlPCZ+r4sKqqNDU1EYvFqK6uxu3eu0kAQFdXF9/85jdpaGjgoosu4uKLL57gle5GLBbjlltuYe3atUSjUU444QSuv/76PZ7Hd911F3feeeeQx7dt25Y2XfXphuxRSwNMpHB7MGRZprm5mXg8TlVVFTk5OaN63WA/8eQsDXPMZSxZGlOJ5JunJVCcYTeF0SAcDtPU1ITNZhuTneB4wmazWcm7qqpaybu9vb10d3dP+Ehe8jhYRUXFuDuknfGFz/D7Ox9iV2OTtaHWdJ0SRw4L82YjyhorfPM4NqeKiCqTY3PhsjvwleYTC0Z58eGnOeyUYxFFMaV2HY/GhnyWYDiREo/FaXht5xBycWjtYnobu3jxkfVE/cbmyFuYiyvXQ19rN/YcJ5HeYArB0DQdQdARTZ2GKA5N3JYE/O196ECwN8B7/32L4772cWPTnoQ31mzG5rTjEB3IEYNoiXYRQRQ4aOURxKMyH768A1VRiAWjqHEFVVERbZJx3FRtr86mWlxlwz2PU3ZABXOOPpDgh70EW/uQw1GifsMhT7JLuAtykAMRcovzcOW6iQyEkJx2YqEodredpZ86Ydwtlb0FuXzmuq8Q6BogMhAkb1YRTu8w5EVyoDrzjdRsNWoKIBJPJv1uROeoHKHGguSO9r4QbUGNIqoRdET0hG2woCtIcgDVmb/PFrP7BEFAs3tBchqCc8Hsoux9DYOJhSiKiKJIYWEhhYWGC1woFBqSEm7e/6ZjsW0wNE3bJ2LR29vLhRdeSENDA+effz4XXXTRBK80FT/+8Y/ZsmULd955Jw6Hg+uvv55LL72Uhx9+eMTX7Nixg89+9rNceeWVKY9nicW+I3vkphCTIdxOhmmtKYoic+bM2eeb5+AsjWSiMZosjamEpmm0trYSCATGvUo9nWCK+N1uN5WVlWlBBiVJIi8vj7y8PDRNswThw43keTye/b55T/Q4GIAU1/hk/sG8JjvZFTTGgQ4qnMXhvtk4sLHwfxaz7anXoHsAr92JzWW4LdkcdjSXRn9rD3IoiivXw4LlC9nx3BsocnzoBj/puiEANsfQvzlBEDjh3FM44rSlNG7dhc1hY+6xB9H8QROP3/gnune2Ght+yQips7sdODxOQj0BNEUjFooiR4aSGjksI9lteHLd+Dv7ePbu//DB5nc54rSlONwuqo88wHifvgCiJOLKdePMdRtp1aJIuD+IGlf4nx9/jYH2XsL9QZ761b/Y9dK7CII2pokaZ46LeFTmxYefZs7RB9L0xi4i/hCYWSToqHEVtT+MrmvIcoyB93t3Eypdp+mV9+lr7KJ8QeWeP2wfkVuSR27JXjRNog3NkYsUMzQapoZCMEXbum7kTwx3cJIZ2BgOXrIN92g72oM/V1SiiYRrMelhCUFXETR5amxpRZvhpDVKDEcsBkOSpCEp4WZXo7+/39KUmQW3dLoHjgbJxKKqqmrUxKKvr48LL7yQDz74gHPOOYfLL798Ugt3HR0dPProo9xzzz0cc8wxANx+++2ccsopbN26lSOPPHLY17333nucffbZlJSUDPt8FmPH9DrjZxAGC7dNn/yJwsDAAG1tbROSNu10OnE6nRQXFw8JWGtvb5/yeXoT5mYyFovtk/vLTIEp4k/ngEBRFK255vLy8pSRvP7+/pTn96VTJssyTU1NqKo6pqrcWNHb0o1NgRXVh3KiffffnKZqhAeCLKw7mrnHHMRjP/kjLp8nJRXaHJ2xJ5ygFn7iGLY9vYUPX9mBIImQ7KYkCEZmhQDOHDcLTjh8xDX5Sgs47OPHWInTsk3ljDsu4F+X3kvPR+1omo4Sk4kFIsSCEev95XAqsRAEAdEmoioqBRVFOD3GxrG/rYdtT23h/RffwelxkluSz8mXfp6qRfN4a+0rlu21kMh6QBCYdbARBphXXkheeSGzD63hg5e3Jwowpm5cH/L5yY8JokBuST5yJEbLto/QVM3Itkg8J4iGWFkQBIOgCQLR/rCxKU4IqFVVRZUV/vvAWr5w0zeGvSYrssLW/7zIO+tfI+oPU3XkfI45/QRK5s7aw5mwjxjUqdEFI49it7tT8pM6ghpBVKJGC0uU0Gx7GAHSNQQ1iqCp6IJIZ88A/f0D+2nDndA3JCORRj9ckne6IRQK0dTUtEdiMRjJXf2ysjJisZhl827eA10uV0rIaDp3ys1ubiQSobq6etSaI7/fz7e//W127tzJV77yFa666qpJ/55btmwBYOnSpdZjc+fOpaysjFdeeWVYcmFmlxxwwAGTtcyMQJZcJDCZfwSTqa9Izi2YjM2k3W63WsfJ8/QdHR10dHRYF1lznn6yYGoLzOCfidpMpjOSx8EGi/jTGcON5Jk3733plEWjURobG60O3kSKMnOL8wxbU1lBSiIXihxHstvILc2n4vC55Jbk4+/sw53nRZRE4hEZTdU48tPLLcJhd9r54s/P580nNrPt6ddpeO09ooEw6AkxNIb+4ZNXfhFfSf4e15VcpTbn6quPnE/79sbEht90kxIMi9fhLKUEAS1R8VdlBTww0NFHsMcPuuHeJEoi/e29PP6zv3DK985g58a3iPSHsLnshm1sXKGouozDPnFsylsf9ZmPsfU/L9Hf2o2W0GikfrQwyEpWIKfIh2iT0BQVt8+LIAqEegNIDjtaXEHDcA4yuhRG4J+mqAg2yXLOEnQQHTZ2vfQu2958m4LiQktTJkkSmqbxxM//wrvrX0+QK4k3/vMiu158hzNu+SZlCyrGcHbsHbrkQIzHjeNvHoOEBkUXU89bMR5MjCQlAvE0BVH2o9lzh2ZeJALmBF0FXUdRVQqcGjkVpXi9bkQrw0JCs7mNzxrNtUKQQI9jpIybX0JPSGamvju6J+wLsRgOZrGtqKgIRVEs98Wenh66urqw2+1WRyPdsn9MYhEOh6mqqho1sQgEAnznO99h+/btnHnmmVx77bVT8r06OjooKCgYsrcoLS21zEMGY+fOnWiaxtq1a/nJT36CLMssWbKEK664gtLS0slY9oxEllwkYXAlbCIwmcRC0zTa2trw+/37lFuwvxhunj4QCNDd3U1XVxdOpzOFaEzU2kKhEM3Nzdjt9inTFkw1Zso4WPJI3kidMrfbbZ1Xg4mDeS44HA6qqqomfFyhoKKY+csPZfuzWxFEwRBOx+LEQlHmLT2EknmG3e3p/+9c/v2jB/B39KJpOjaHnUPrjub4c05JeT+7y8HRnz+Boz9/Ap27WvnL5b+l68N2dF3H7rRz9OkncMSnl+9xTcnnglmlDvb42fXiOxZJsYTUe4jvNkXhCCDaJGKhKMGugd0v0SHqD2N3OwgDPQ0dnPXLb/PcPWtoeetDRJvEIScdxUnf+gxuX+ompqimjDNvvYDHb/kzrdsa0BQ1Ma4lIUrGdTo5s8JbmIM7ka6txBUWnboEQRAoqimjY2cLotdJLBg1XLnsEjaHncLqEjreazaSsxMbSclhx53nQZQk8n15xGIx/H6/RXADDT3s2PAGrlyPpZfQNZ2B9h5e+vMzfPZH42u1qdncCGoMQVNSMwZFR2pHQlMQ1Bg6YqqmQFcRlbDh6JRETsR4EEFX0BGJynFUVcXtdOCSNIgNJH5WAD2OKMcTCdV7KcgIAppoR1Jl0GOAYATYJbQOg8lQOsEkFh6Ph8rKynErwNlstiGjnuZ90Mz+SbbknsrxVF3XaWlpsYjFaMdEQ6EQl1xyCW+//Taf//zn+fGPfzxh9/Lm5mZqa2tHfP7SSy8dtljkdDqJxYaOdIJBLsBwyfzf//1furu7uf322/nqV7/Kv//974wsRI4HsuRiEjGZwm1FUWhubiYajU6IUHWsGDxPb1ZzJlq4awaieb3ejPYoN9vc6XAujCcGd8rMG3dXVxednZ0pbi6yLNPW1obH45nUc+HU759JPBKj4bX3iYX82Bw25ixewGeu/bJ1nlcePpcL/3ItH2x+l6g/TPlBVXusgiuywr+u/QMD7b3kFOYi2iTkSIyt//ciJXNnsfSsE4d9XXJlMnk08Pn7nqCvrWefvp8giog2kUDnwO7ijGAIpwHiUSOnItQboPqI+Xz1N5cSC0UQTaemEVC1aB4XPPJD+lq76f6wA4fbQUFlCe88/Rrbn3vD6O5oKp07W4gGIkQGwmiqitPjItA9QLDHz+LPHsfOjW+jyAp5ZQVomkYsGMWZ4+LQM5YycFcfuqIiShI2px1XrptQT4CqRfOomFNljIPJsmEy0NnDi39aT7A3gCtPRZDA5nAgigIOj4uGLTuta/u4QRB3i7s1GRBQJQe65E7pJAi6YrhLMficFowOhK7t7hzoWoKsCEamg6ricjmNe1KCcKR0GVIIyh6+mxZPZF4kPpdEqB42VHtuWmRfDIeJIhaDkTzKaY5PmToNswPr8Xis69Vk2tyaxCIUClFZWTlqYhEOh7n00kvZunUrn/70p7npppsmdDKirKyMJ554YsTnN2zYMKzrZywWG5EknH766dTV1aVkOy1YsICVK1fy7LPPcuqpp+7/wjMQWXIxCZhs4XY0GqW5uTltR4AGJzmbRGOwcNfn8+1z2zh5HGwsgWgzDfF4nMbGRktbMB6e/emK5NR5TdMs5ykzDBLA4XBQWFg4qToTT34OZ93+Ldp3NNHX0kPerEJmH1I95Hy0O+0ctGLRqN7z/RfepvvDNlw5bmvcyp3rIdwf5OW/PsexZ6wcEqSXLGBPrkzqus7b617da+zFSCidN4uIP0w0ELEeM7+bIAqg6uiqRmFVCZ27Wnn+90+yY8MbxKNxvIU5LP7scSz70kl4C4eSXkEQKKwoobBit9By+ZdrOfrzx/OPq+/jw5d3oGPoU9DB5rJj9zh54z8v0b6jmdW/voRPfv9Mnrv7PwR7AwgC5M0uZOEXl1FUUcqC5YcawnHRGIsK9QZw53s54ZxTrO/gcDjQA3Ge/vFf6Xi/BSUqE4zGCXX5cRd48RTkEpdlHJ4JGvMURMP1iD1t+EQMImFmOiQ6D8YbDNrYGx2pWKJj4XK5DKJtaSIGnwmiYSurq3skF2I8bJCJZPG09Z4aDCE+U4/JIhaDMdgUJR6PW9crszDicDisjsZEhteaxCIQCFBZWTlqB8lYLMbll1/Oli1b+MQnPsEtt9wy4QUbu93O/PnzR3x+x44d9Pf3I8tyCjnr7OykvLx8xNcNDo0tKysjPz+f9vb2/V90hiJLLpIwEWNRky3cNkPhpssIkGlhuyfh7lizNHRdp62tjYGBgSkZB0sXmMnjgiBQU1MzqRqXqYYoivh8PkuD0dPTg9PptHzbJUmyKoSTYRspJETLpnB5f9Hb1AUIKToOAJvTTrDHTywUTRk1GkwykwsO5ohR8pjRaGF3Ozj1qrPoa+nmP//vEVQ5kZ+h6YZoWhTRNR1voY+yBRU8/J3/pa+l27C4Bfpbe9lw7+N88OoOvnzHd/Dkj25j89q/N/Lhyztwel3oAcNiVpREtLiK3WFH8rro2NnMu8+8zpGfXs7Bq46g7d1GwtEwEWTeW/M67/79ZeRIzCAFgoAnz0vVovkc84UTUn5Puq6z9ta/09PYgc3lSAjbdXRVJ9wdIB6OIdntzF1Vzfvvv59iNDApm1VdBy0OumrQAh3QBXREBEFHk1wppEDTBWJRGbsILpczaUM40r1P363j2MMaBF1BF8RBREY01qUphg1sGmGqiMVwsNvt1ghxcmc/2ebWPKdM/c94wMz4MYnFaE1OZFnme9/7Hps3b6a2tpbbbrstLRyxjj76aDRNY8uWLSxfboyHfvDBB3R0dFjuUYNx2223sX79eh5//HFrn9Dc3ExfX19W5L0fmPqzYQYjWV9hesRPJEwXoJycHGbPnj3tRoCGE+76/f4xZWkkjwDNnj17SEUiU5CcPD4Z2oJ0xGDRsqkziUajQzJakgXh0+HvJm9WIaCjKmqKw5QiK+QW56XkJyQHJQ5HMkVRZN7Sg3nzic1jWoNok5h9SA01ixewdc1L2Bx2S4dhpG2Drmo4vS5O/3/nsPU/LxlBebpuBNglHKA0VaPt3UbefGIzy7408jx1Mt5Z/zpgkKl4txHoKYgimqoSC0fJLc4DXaft3UaO/PRyHG4nuXOKCLXHef/Pr9C29SNyin14i33EglFCvX4OOO4wPn7p54d8Vn9rD83bPsTmdBDq9ackhwPEIzJ5pQXUffMzKKI25LyaaDtSQYsjKmGDTGDqZHQEVHTBkeh6GLDINQrVpT4kAXRdRdBBF0AXbEb3YbdF1+5uxF4F2QIME/gnJP3/dEEwGKS5uTktiMVgDO7sm9erYDBonVfJ41P7WkA0i3B+v5+KiopRE4t4PM73v/99Nm3axMqVK7njjjvSpohZVlbGpz71Ka699lpuvvlm3G43119/PUuWLLGcomRZZmBggLy8PBwOB6eccgoPPPAAN954I6tXr6a7u5ubb76ZxYsXc8IJJ0ztF5rGyLwdxyRhsh2hzOCj6eQCtCckt40Hh/a1trambAhzcnKw2WxD7EVn8gjQntDf309bWxterzftbpyTBU3TaGlpIRgMDrHWdLvduN3uIc5TbW1tliYjHayT94QDjz+c/Mpiepu6cHlcluYCXeeYL6ywRqJMZyxJkvbYyVx53qlsW7fFGC8aBqJNtAiDIArYHHZK589Gctj42YnfIxaM4PC68OR5iQQiiElF8BXnncqcYw5izc1/ToTyCVZxW0iM72iKxgcvbx81uYhHZGOUCWM9Vog3RtfE6BQLRiq3rltOPTZZoHtHK94iH65c4/rg9nnQNY0PXnoX/9l9+EpT8x1ioSi6qqGqWkIQbkuQIhU0cHid2Bx28kuNcbvS0lJkWR7WaCA5eX68IKhRgxyItt0ifF0zSIFgt0iBqqo0NjZauS6604amRBE0BU0U0W0udESkeCDhIpU4noK0d82EIKCLDgQ1Yny+6eSla+iCiC6lz99ROhOLwRAEIeV6Zep/kh0YTWOUnJycUaeEJ3f3Z8+ePWodnqIoXH311WzYsIGPfexjVlBdOuHGG2/k5ptvtsL7VqxYwbXXXms9//rrr/PVr36Vhx56iKVLl7Jw4ULuu+8+7rjjDj7/+c/jcDiora2dEivdmYQsuZgATCaxUFXVEmLtU/DRNMFIWRqmvZzL5SIWi1mbqEwaATKRvInKZJ2JWZ2NRqN7bfUn20aac88jWSc7HI60OZ52l4Mzf34Bj/74QTp3taJFYjhcDo45fQXLvmxs0M0E9tF0r8oPquKEb3yS5+75z7DTMZqi4fJ5OO5rH6f6iPkEewM8ccufiX8gI9qM0adYIILucVIyp5x4VAZBQA5FKT3AEKYb3ZTUN08eQ7U5R78Bnbf0YLo+aENTNZxeN0osYOR8JI5NZCCE3eXg4BOPpKuri56eHoqLi4m0+lFiypDxK4fbSbDXT6g3MIRcFM8pw1voo7ep03rMIEgigk3AletBkZWUr+ZwOCgqKrLsSAc75TkcDoto7O88vaBr6Ja8IjG+JIjoump462JsChsbG1EUJWUsTnMMPeaqmI+gygi6ii5I6JJjVKnWmt2DqKuIWhz0hHmxIKLZc9LGhnY6EYvhYGrGzJRwU6dhGqPYbLYUm9vhvp/Z0TWJxWi7+6qqct1117F+/XqWLl3Kb37zm7S8z3o8Hm666SZuuummYZ9funQpO3bsGPLYn//858lYXsYgSy6SsL8bh8kWbsuyTHNzM/F4fMIShtMRgx2Curq66O/vB4ybaGtrqzVvn25VlYlC8ghQcXExxcXFabMRnkzE43GamppQFGXMZgbJc8/J1skmYRvPDeF4oGTeLL7xwPdpfaeByECIsgMrrXyLQCBAS0vLmBLYl3xxJRv/sBZFVhJeP0aXQXLY0FWNE849hVXnnwbAny//LfGojMvnBgSUWBwlriBHYihyHG9hLqG+IC6fh4rD5gBw2CnH0vpuI5oSs0iFkT4Ndpedg1ceMervfuwXV7Fjw5v0t3Yj2iQku4QiK0ZGSEzG6Xax8vxPIRY46OnpseyX/boNh8dJNBjBW7CbdEaDEZweF3nlQy2abQ47x3/94zzx878iR2R0xbi+C4C7IAdFjjN/2aFDBPTW6wcZDQxnYLE/+h9dkBCRU0aZjHEmDV2wDSEWLtdeUrIFs4sxRggimsOHrsmGxkIQDftZMb2IhekcON2IxWAkOzDqup5ic5ucEp7c3TenHPr7+5k1a9aoiYWmadxwww2sXbuWxYsX89vf/jbtjGKySC9kycU4Qdd1y2YWJl64HQ6HaW5utoLA0rGCMNHQdZ2BgQH6+/vJzc2lrKyMcDg8xIp0MrI0phJ7GgHKJMRiMRobjUTm/e1eDbZONs+rwY5me6oQTgZEUaTysLkpj5n2y7m5ucyePXvUaxMkEYfHid3tQBREEAVsDpuRVxEIkzeriOa3P6T5rQ95/4VtCNLu4onD60Lzhw2Xrh4/clRGFEWO/WIteWVGJ+CY00+g4bX3ePeZrUal30hBwOF1cWjdYg6tO3rU3zt/ViFfuetiXv7bc+zc9DY2u53ZC2vwleTj8nk44GMLidkU+vr6Ujq6vtICDlyxiDcffwld03F4nMRCUeRwlGM+f8KIgvKjPnscjhw3a/7fIwQ6BxAlAafXhaZq5BTmsvwrdaNa9+B5+kgkYm0IB+t/zA3h3mBlYegKui4gJOkeNCVGU1sbqqpNTkdXENAl58ip4FOEmUYsBsM8b7xeb8pYXjAYtLr7JhmIRCKWG9JooGkaN910E//5z3844ogj+N3vfpcxhcws9h2CPtGpcdMIiqJYXYexYLAj1ERfuAYGBmhra8PlclFZWZmxYl1TZ1JUVERJSUkKcTCtSM0LrKZpVpaGz+cb9WxqusO0F43FYmOyEZxpMMm2zWajqqpqwrQS5obQHMuLx+OWf31ykvNUwTR12JexOF3X+cN5t9H0xi7ceV7rtdFgBJvdxpxjD+KDl941RNMhI5DOk5+D3WUc63gsTjQQIbckj6pF81h06lIOqT0q5XqoqRq7XnqHN9a8xEBHH4WVJRz28WOYt+yQFGH6/iA5JHC4sY94VOalP63n3We2Eo/IOHJcHFp7FEvPOhHbMGNCKa+Nxdnyr/+ybd0WIv4Q1UcewJIzV1F+YOV+rzs59yASMWx99xQImQxBjSPKfgQ9bj6ChoCqKIRiCg5fGY4MLEDBzCcWe4OiKFamlJkBYd4Lc3Jy9mj3rus6t9xyC3/7299YuHAhDzzwwIzKScpi4pAlF0lQVRVFGV7QOBImW7idnN1QXl6ecRdKSK3Uj0ZnkpylEQgEUFUVm82WUnmejkRDlmUaGxvRNI2qqqqMbVObI0Aul4uqqqpJ29zrup5iNBCLxSwnF/Pcmizin3xt2B9Th+a3PuTPl/2GyEDIeky0Scw55kA+fHk7Nqcdm9NO1My1ECCnyIcoicSCUSS7jfMfuZqSubPG8+uNGskhgXtzwIkMhAj1BchJEnenC8wNYTAYJBQKoeu6lXuQm5s7tDii60ix3kQWhYSu6URjUUQBnA4HqqsA0swKdjJgXhtMc4vpeJ3fX+i6bumOSktLcTqd1rmlKIpVHJFlmVAoxCGHHIIkSei6zm233cYjjzzCwQcfzAMPPDBjNZ1ZjD+y5CIJYyUXk0ksNE2zbOMyObvBnKmPx+NUVFSMuVKfXHn2+/0oijLpmQfjgUgkYuU1VFVVZYy2ZDBMZ6ycnJwpr0omO7mEw0b2wmgrz/uD5C5eSUkJxcXF+/V+PY2dbPnnf2l9twFfaT5Hfno5j//0z/S391rZGbquE+oJEI/K2Bw2RJuRcP2Jy8/gmNOnxr4xWcifHBI43ZEcCBkMBq3iSIpwV9CRon3Gz+sQi0UBAZfTgSjoqPZcdFtmFR+yxMJAV1cX3d3dlJaWUlRUZD1u2tya16xbbrmFV155hby8PJYtW0Y0GmXDhg0ccMAB/PGPf7SsvLPIYjTIkoskjJZcmMJtU2Mx0cRCURSam5uJRqNjso2baTBD4QCqqqr2LkzcC5I9xAOBALIsp9WIy0hIrtRn8licKbTOz8+nvLw8rTYPyQ5BZuV5Xywj9wYzBMvv90+oW9zPTrwcRVZS8jN0XSfUG6Dy8HkcWnsUh5x0FAUV+0ds9hWD08dnqg31cGN5giCQm5NDRb4dUYRoNA6CgMvlRMDQYKgOX9rpICYSgUCA5uZmcnNzqaioSKtrw2TCdCcbTdFhx44dPPLII2zatIne3l7A0HIsW7aMU045hRNPPJGysrLJWHYWMwBZcpEETdOIx+N7/JnJFm5Ho1Gam5szfvQlOXl8ombqk0dcotHoPokrJxp9fX20t7ePWaw7k5BcqZ8Ozlgj6X+SK8/7sv7kEaCJLjr88aL/5cOXt6doMdS4ihyK8qkffomCimJcuW7KD6qa9N9FskPYqNyQZgh0XU/J08h16BTlOFE0HclmwyZJiIkAPNVZsOecihmELLEw0NPTQ2dnJ8XFxZSUlIz6dffeey+/+c1v8Pl8lJWVsXPnTuu5hQsXctJJJ1FXV8fBBx88EcvOYoYgSy6SsDdyMdnC7cnYUE8HmBvqyRx9Sb5pm+LKqQxXS56bLSgooKysLCNvmsnjgdMx18W0jDTPrX0dyxuvESBFjiOHY7h8e3a8+uDl7fz1e3cTj8rYXQ40TUORFTw+D0pcJR6NIYgi5Qsq+Z8bvkbJvMnRXMTjcRoaGtB1nerq6ox0zQNjTLKxsYFZ+R5y3GbKNsRVCKk23Dkzx8RiT8gSCwOmscNwZid7woMPPsgdd9xBRUUFDz/8MLNnz6atrY1nnnmGZ599lpdeesnaI91xxx188pOfnMivkcU0RpZcJGFP5GIy9RWw++KQDrPkU4V02VCb4kpzxAUmZ5beRPLoS2lpKYWFhRl501RVlebmZiKRyIwYDxxuLE8QBIto5OTkDDuWl1yp39duphyOseHex3nziZeJR2PkzSriY6vrWHTq0hHPrZ0b32bDvY/TuasNySZSWFVK67uNiJKI3WVHUzXiUZmCimIu/Mu1ONwTu9E3rYcFQaC6ujpjdUdmWKLT6TQMDQQdXZGJRGP0BcJWtyxZp+H1emfcNSRLLAzsK7H405/+xC9+8QtmzZrFH//4R6qqqob8TDAYZNOmTWzdupUvf/nLVFbuv0taFjMTWXKRBLPNPNzjZsdiosegkkc+9sf1Zboj2U4ynTbUZria3+8fMks/EVkaM21Dva8wg8Di8TiVlZUzRqybjMFjeTC0W2Y6hO1PpV7Xdf72/d/x3n/fQrJJiDYJJSoj2iROveosjvz08j2+NjIQQnLYuOfs/0d/a0+K05KmqsTCMT73k6+z6JNLxn4QRoloNEpjYyOSJFFdXZ2xXd1QKERTU9MendKSu2XBYDDFPtn8Jx21ZWOB3++npaUl44mF2eUf697h73//OzfffDMlJSU88sgj1NTUTPBKs5jpmPoh8jSHqa+YjI6Fqqq0tLQQCoWm5cjHeCFZwF5RUZFWG+rB4WrmLH1vby/d3d3Y7XYrHXx/xxCSnbFmkvvNWJG8oa6pqZmxM/VOpxOn00lxcTHxeNw6tzo6Oujo6MDhcBCPx7HZbPs1AtT6TgO7XnwHh9uJ3WVU+x1uJ6G+AJseWseiU5eOmDYtCAKe/BzkcIxA9wCSPfUWIkoSgiDQ19y1T2sbDYwRoEYcDgdVVVVpoYWaCpjEwu12U1VVNWJ3OzlgLdk+ORgM0traCoDX67W6GtONqGWJhYH+/n7a29spKCgYE7H497//zc0330xRUREPPvhgllhkMS7IzKvyKDBYuD3RxEKW5RRRYqZuJGOxGE1NTWiakSibzgJ2URTx+Xz4fL6UFOf+/n56enr2K0sj2RkrUxPYIdVyN5NGX+x2OwUFBRQUFKCqqkVewSCd5vjHvpDY9h1NqIqKMyf1b8vhduLv6CPQPWClao8Em8tOTlEeA209FkEBIyQPXZ8w16jRVOpnJHTNyrBAEK1gOI/HQ2Vl5ehT2AUBl8uFy+WipKSEeDxuEQ2TxE5kJ3a8YRILn8/H7Nmz03qtEwkzWDc/P39M48Nr1qzhxhtvpKCggAceeID58+dP8EqzyBRkyUUSzD/IyRZum+nCoihm9EbSPA6SJDFnzpxptZFMHjMoLy9PEe329fWNSbQbCoVobm7OeCG/eRwyvUIdiUTo6enB4/Ewe/ZsS6eRTGLHMkvv9nkRAF3VEJJSsVVFxeaw4crZe2dIFEWWnn0i6375T2KhqCHyVlXiUZn8WUUcvOrI/fzWQ2FaMI91Qz2toeuI8SCiGgV0QCCqCjQ3d+H17r8ez263U1hYSGFhoTXyGQwGUzqx++tqNlHIEgsDAwMDtLa2WsG6oz0O9fX1XH/99fh8Pv7whz9w4IEHTvBKs8gkZObdeg+YzPwK2F1xyOTMAth9HNxuN5WVldO6Ipk8hlBWVpYi2h0YGEjJ0sjJyUnZHJg3Cq/XS0VFxbQ+DvuD5OOQMRvJYWBuoJKNHex2O7m5uUMyD/r7+0eV03LAcQvJLc3H39FvWMuKAqqsoMoKiz65BKd3dN3CJWeuItQb4OW/PoccjiJIIuUHVfG5G76GwzO+BZLk0ZdMsmAW4wFEJYouAAjomoZNU6kqycNTOGtc70/JI5+6rhMKhazRvL6+vrTKAMoSCwN+v98iFrNmjf58WL9+Pddccw0ej4f777+fQw45ZIJXmkWmISvoTsKTTz7J+vXrOe644zjhhBMmNIhJ13W6u7vp7u62Kg6ZcsNMRnIY2lgvkNMNpmGA3+8nEAgQi8VSsjTi8bh1Pszk47A3mG4nmX4cTHHmaDZQybP0g88tc0OYXLhofGMX/7r2foJdAwAIkkjVEfM546fn4c4b20hmuD9I2/Ym3D4Psw6pHvffl5nCnnEbSU3FFutFRwdBRFVU4vE4dpuEZLOhugqNMakJxuAkZ/PcMs0GcnJyJrW7miUWBkx3rLEehw0bNnDFFVfgdDq5//77OfLIIyd2oVlkJLLkIgm3334799xzD2CIKz/2sY9RV1fHihUryMnJGbfPSfbqLykpoaioKCMvkLqu09bWxsDAwLQIQxtvDJelYY4pTEdh5f4i2Xo4k53Skgn3vlowm+dWMBgkHA4DQ+2TY6EI7/33bUJ9AcoWVFCzeMG4FDjUuMKr//wvb6x5kXB/iJqjF/Cxr9RRtmDstpUm0UzHFPaJhqDKSHI/OgKqatikS5KE3WZDEHRURz66NPmjo7IsW0TDPLdcLpdFNCZSp5ElFgb21XZ306ZNXHbZZdhsNn7/+99z9NFHT/BKs8hUZMlFEnRd57333mPt2rXU19eza9cuwNjwLVu2jLq6OlauXEleXt4+f4aiKDQ1NRGLxTLaWtS0WDXThffnmE5naJpGS0sLwWAQn8+HqqpTkqUx1UgmmqWlpRQVFU31kqYEuq7T2dlJb2/vuBFuRVGszeBE2yfrus4/r7mfd57eglFwF9E0DVeum6/cdQkVh47OiSaZYGUs0dQUbLE+VFVFjhthi0bBQUNAQHEWgDi1Y7SmTsM8t8z0eZNojKdOI1lbkMkdTVPMb45KjvY4bN68mUsvvRRBELjnnntYtmzZBK80i0xGllzsAbt27WLt2rWsW7eO7du3A8Zc6pIlS6irq2PVqlUUFhaO+v2SHYAqKyvT2glpIpFssTpTMwtGg8GWu7m5uYBxw04O7UveDPp8PhwOx4y6sSYTrEwmmskEq6ysbEzXltEi2T7ZDFcbz81gw+vv89C37kCySdidBiHWdZ2IP8SC4w/jS3d8Z6/vkdzByuTOLkDc34EdFQTR0DgIIOgamuRCc6bX30myY14gEEBVVcvIwvxnXztjWWJhwHRLM7Vooz0OW7Zs4aKLLkLTNH77299y/PHHT/BKs8h0ZMnFKNHQ0EB9fT3r1q3jrbfeAgyisXjxYmpraznppJMoKSkZ8fUDAwO0t7dnvAOQaS0qiiJVVVUZ64xlWg+rqrrHlOXhNoMOh2OfbUjTDckEq7KyclzHD6cTpoJgmaJd89xSFGVMrmbD4fn7nuC53z2OK8edcl7K4RiCJHDVs7cNycYYvKb29nb6+/snjGBNF3R3d9Pb083c8nzcDjFhFiWgi3ZUhw+E9NXojZQ+P5IGaE/IEgsDJrEYq1va1q1b+c53vkM8Hueuu+5i1apVE7vQLLIgSy72CS0tLaxbt476+npef/11wHAIOvLII6mtraW2tpby8nLA2Exfd911vPjii9x7770cfPDBGSncht1WkpnujJVMsMaS3aBpWspmUFXV/crSmGrE43EaGxv3SrBmOpJT2KeKYI20GUx2NRuNO9Cmh9ax/s5HceWmnouGXa2dK9ffOmJAX3LnZtasWeTn54/X15tWSDb7KC4uprioCAEVQdfQBSmRdTF9/s5heH2Z2+22zq+RikxZYmEgHA7T2Ng4ZmKxbds2LrjgAmKxGL/61a+oq6ub4JVmkYWBLLnYT7S3t/PUU09RX1/Pli1brNC9ww8/nGXLlvHMM8+wa9cujjjiCO67776M3VCbwsxMs5IcDHNe1ul07ld2g67rKSMIZtU5mWik8zE2RwQFQciocLzBUBSFxsZGK4V9Ih3qxoJk56loNAqMLsW5p6GDu7/0/9AUDafX6Kppiko0HGXxZz/Gp6/9yrCv0zSN1tZWAoEAFRUVGatFGzwSVlw8MWGEU4nhNEAOh8M6t9xuo+uVJRYGwuFwSnDkaK/r27dv5/zzzyccDnPbbbfxyU9+coJXmkUWu5ElF+OI7u5ui2hs3rzZIho+n48vfelLnHLKKdTUjE7QOFOg6zodHR309fVlrjAzAdNaNDmzYDyQXHX2+/3E4/E9ZmlMNcybpd1up7q6OmMJd3Lnprq6Gpdr7+F1U4HkFGfTbMB0Bxqu6vziw0/zzG8eQ42ridg3KJk3i6/cdQm5JUPHvTRNs8wdkrVHmYZkMX+mmBoM142VJAmHw0EkEsl4V6hIJEJjY+OYicXOnTv55je/SSAQ4Gc/+xmf+cxnJnilWWSRiiy5mABs2LCB7373u4TDYebMmWPN1gMsWLCAk046iZNPPpn58+dP8UonFslz5OXl5RQUFEz1kqYEyWMOE22puae8A5/PN+rxlomCORo3E8IS9wexWIzGxsZp17lJdgcKBoNW1XmwBqjlnQa2rXuVaCBMxcI5HPaJY3F6h5InVVVpamoiGo1SVVWVseYOWa0JVihkd3e3RWKTc4BycnIyqhBhEguzyz3aa+UHH3zAN7/5Tfr6+rj55pv5/Oc/P8ErzSKLociSi3GEruv88Y9/5Kc//SkOh4Nf/OIXfPzjH2dgYIBnn32W+vp6Nm7ciCzLAMyZM4fa2lpOPvlkDjzwwBlVnYnH4zQ3NyPLMhUVFRkr1E2eI58K55vhZp3Nm/VYRJXjAbNzk+mjcabmxmazTWtzh5E0QMmC8D2d66YttyzLVFdXZ6zmJvkakclFGNgdmJifn09BQQHBYJBgMJii08gEe+5oNEpDQwMOh4Pq6upRE4uGhgbOO+88uru7ueGGGzjrrLMmeKXD45prrkFVVW655ZY9/lxzczM33ngjr7zyCi6Xi8997nNcfvnlGVt0mknIkotxgq7r3HTTTTz88MOUlpby29/+lsMOO2zIzwWDQZ577jnWrVvHhg0brHnmyspKamtrqaurY+HChdOaaCRb7lZVVaXtuMdEQ1VVWlpaCIVCaWGxao63JIdfTcbNOrlzs6+hcDMFpuOLOeYwU26ipgbI7GrsbTRvuoyETTR0Xae1tRW/358W14ipRDKxGNzdVRQlZTRvpI7ZTEA0GqWxsdEaGx3tNaK5uZlvfOMbdHZ2cu2117J69eoJXulQqKrKrbfeyv3338/nPve5PZKLeDzOaaedxty5c7n88stpbGzkmmuu4ctf/jKXXHLJJK46i4lAllyME3p7e1m+fDmHHnood999N2VlZXt9TSQS4fnnn6e+vp7nnnvOagXPmjWLk046ibq6OhYtWjStKryhUIjm5uaMt9xN9yyP4USVe5qj31ckj3tkemaB3++ntbV1zI4v0w17Gs0zz62WlhZ0Xae6ujpj7ah1XaelpSXjReywZ2IxGKY9t/nP4I5ZuptZ7AmxWIyGhgZsNhs1NTWjJhatra2cd955tLW1cdVVV3HuuedO8EqHYteuXVx99dVW8WTp0qV7JBdr1qzh6quvZtOmTda5/9e//pWf//znvPjiizO6M5UJyJKLccSHH35IRUXFPv1RxGIxNm7cSH19Pc888wyBQACAkpISi2gcddRRaV3pNG8QXq+XioqKtF7rRCIWi9HU1ISu69Oic2Omgvv9/j3O0Y8VyQ5A2XEP428jEwWqw43mCYJAYWEh+fn5GbmJSNajZTqxMMcl90WPZuo0zPMrHo/vk4VyOiCZWIzF6KKjo4PzzjuP5uZmLr/8ci644IIJXunw+POf/8z27du55JJL+O53v0tFRcUeycWPf/xjtm/fzl/+8hfrsYaGBj7+8Y/z97//nUWLFk3GsrOYIGTJRRpClmVeeukl1q5dyzPPPENfXx8AhYWFnHjiidTV1XHMMcekjbgt2T5xogXL6Y5kJ6Tp2LkZaY7e5/Ol2ETuDcnZDZnsAATQ09NDZ2dnxv9tmHPkoihabkDJ6fNmV2OmH5+sO9Zu7A+xGAxd11OIrDly7PF4LKKRrkRWlmXrb6OmpmbU9/auri7OO+88Ghsbufjii7nooosmeKWjw+rVq/dKLi688EJcLhd33HGH9VgkEuHII4/kV7/6FaeccsokrDSLiUJ67E6zSIHD4WDFihWsWLECRVF45ZVXePLJJ1m/fj3//Oc/+ec//0leXh6rVq2irq6OpUuXTtkmVtM02tra8Pv9lJaWUlhYOOM3ByPBHHuZzk5IoihaG73kLA2/309vb29KlsZIgt3kkbDq6uq0yW6YbCST7qKiIkpKSjL2b8N0vnE4HFa+i9kxCwQC9Pb20t3djd1ut86v0RLZ6QRN02hqaprSwMR0gUksxkuHJQgCTqcTp9NJcXEx8XjcGv3s7Oyko6MDp9NpdTXSRaexr8Sit7eXCy+8kMbGRi644IIJJRbNzc3U1taO+PzGjRspKSkZ03tGo9EhHTtzRDIWi419kVmkFbLkIs1hs9lYvnw5y5cv5/rrr+e1115j7dq1PPXUUzz22GM89thj5OTksGLFCk4++WSWL18+aTPMiqLQ3NxMNBrN+Na+GRLo8/mYNWvWtJ35TYY5J+/1eikrK0sZP+jv7x9WsJs8EjZnzpyMnqfPdGtREyOJ2CVJwufz4fP50HXdIhrJRDbZeWq6/00ld/My2XYXxp9YDAe73U5BQQEFBQUpRLavr4+enp4xOZtNFExjA9OSerTEoq+vjwsvvJAPPviAc845h8suu2xC11lWVsYTTzwx4vP7cn1zuVyWc6YJk1RkakFqJiFLLqYRJEni2GOP5dhjj+Waa67hjTfeoL6+nvr6ep544gmeeOIJPB4Pxx9/PCeffDLHHXfchNk7yrJs5XfU1NRktI2kGXw1k6vTgiDg8XjweDyUlpamCHb9fj+CIOB2u4lEIthsNubMmTPtRsLGC8lak1mzZpGfnz/VS5oymLkmexOxm3PyOTk5KaGQgUCAgYGBaTtHb8LM84jFYhndzYPJIRaDMZjImh3ZYDCYUigx/5mM8ysej9PQ0ABATU3NqK+Xfr+fb3/72+zcuZOvfOUrXHXVVRN+DO12+7jncpWXl/Pee++lPNbZ2QkwKkOcLNIbWXIxTSGKIkcddRRHHXUUV111Fdu2bWPt2rXU19ezbt061q1bh9Pp5LjjjqOuro4VK1aMW6UsHA7T3NyMJEnMmTMnbedYJxrJm8hMqk4LgoDL5cLlclFSUkIsFqOnp4eBgQHAuGm2tbVNSZbGVCN5nr6ysjKj5+n9fj8tLS1jzjUxiarb7aakpCRljr61tRUgJVgt3Umsqqo0NjZmfJ4H7O7wTqUldXJHNtnZLBgMppxfJpmdiPPL7FiYHd7RfkYgEODb3/4227dv58wzz+Taa6+dtsWsY489lkcffZRgMGiNB7744ot4vV4OPvjgKV5dFvuLrKB7hkHXdXbs2MHatWtZt24du3btAgwdx7Jly6irq2PlypX7PMI0E3QF44HkZOHZs2dn9EjYwMAAra2t5OTkUFpaao0fmFkapqByom7U6YLkULh0tB+eTJjuWHl5ecyaNWvcNkDDZbVMhIXyeEFRFBobG1EUJaPzPGA3sSgsLKS0tDQtN8Xm+WXmaQDjbjigKAoNDQ1omkZNTc2oi3OhUIjvfOc7vPHGG5x++uncdNNNaTsqOJygW5ZlBgYGyMvLw+FwEIvFOO2006ipqeGKK66gubmZa665htWrV6eNMD2LfUeWXMxwvP/++1Y3Y/v27YCh41iyZIlFNEZTcdd1nZ6eHrq6umaUrmBfkBwAVllZmdEjDqYT0nCbSDNLw+/3Ew6HJyxLIx2QfE5UVVVlq9MdHRPujqWqqiXYHU8L5fFCNihwN6YDsRgM8/wy/9E0DbvdnpKnMdbvsa/EIhwOc8kll7BlyxY+85nPcMstt6R1YW84crF582a++tWv8tBDD7F06VLAsJ694YYbePXVV8nLy+MLX/gCF198ccbuLWYSsuQig/DRRx9ZROPtt98GjFnUo48+mtraWk466SSKi4uHvK63t5dNmzYxf/58SkpKKC4unhY3h4mAmZ4qiiJVVVUzaoM8FoxVa7KnjaDP55vWFqSxWIzGxkaAjA+FM5PYJ1t/NJKFskk09mUjuD8wiYWmaRl9TsD0JBaDYRoOmNcwRVFSDC28Xu9eN/vJXayamppRnxOxWIxLL72UzZs3c8opp3Dbbbdl1KhpFtMTWXKRoWhubmbdunXU19ezdetWwJhFPeqoo6itraW2tpaysjLee+89Lr74Yjo7O/nnP//JvHnzpnbhU4hgMEhLS0uKnWYmQtd1Wltb8fv9+6Q1Sd4IBgIBqyI4HS1ITbIpSRLV1dUzeuxrT0gmm1OdxJ4s2B1uI2g6m00UZFm25unHUp2eiZgJxGIwTMMBk2iYCfTJeRqDrwOqqtLQ0DBmYiHLMpdffjmbNm2itraWX/3qVxl7jclieiFLLrKgvb3dIhqvvfYamqYBMHfuXJqbm4nH43zrW9/i/PPPn+KVTh2S08f35Hoz05EsWB4PrYm5EfT7/QQCAVRVHVWWRjrADEzMks30td1NFuwmbwSTBeHj+XszMwsEQRiTA9BMxEwkFsNBlmWLaAzWAZnnV3Luz2jH4+LxOFdeeSUbNmxg5cqV3HXXXRlNVLOYXsiSiyxS0NXVxVNPPcXDDz9sicEBDj30UE466STq6uqoqamZwhVOLpK1JpmesDzRgmVd11OyNOLxeEqoXzplHZgWq5lubJDcxZoOtrvJzlORSAQYP8MBczxOFMWM7mLBbi3WTLbnHg7J45+hUAhN0xAEAV3XKS8vJz8/f1THQlEUfvCDH7B+/XqOO+447r777iyxyGJaIUsuskiBruv86le/4re//S15eXmceeaZvPvuu7z44osoigLAgQceaBGN8fa+TickV2SLi4szWmuSnGsyGeJUs+JsdjRkWU6brAOzizVWi9WZBk3TaGlpIRgMTssQTUVRUpyndF3fZ2eg5PG4saQsz0RkKrEYDFO8LcsykiRZXdnkPI3hrh2qqnLNNddQX1/P0qVLueeeezLaICKL6YksucjCgizLXH311axZs4Y5c+bwu9/9zupSDAwM8Mwzz7Bu3To2btxoJWvOnTuX2tpa6urqOPDAA2fMjcQc/wmFQtOiIjuRSBaxV1dXT0kFLXm0JRqNAruzDiYzS8Mc9Rhvi9XphuTxuIqKimmf55Gc4JzsDDQaHZD592Gz2caUsjwTkSUWBjRNs6zKzWKMGQwZDAat8bz29nZeeeUVPvaxj7FkyRIkSeL6669nzZo1LF68mPvuuy+jLa2zmL7IkossAKNSfM455/Diiy9yzDHHcNddd1FQUDDszwaDQZ599lnWrVvH888/b232qqqqLKJx6KGHTtsbS/L4T0VFhRXwk4kIhUI0Nzenla5guKyDic7SSHZCmukz5HtDctr0TMzz0DQtRRA+WAfk8XisinMkEqGxsRGHw0F1dXXGjsdBlliYMIlFJBIZMY3dHM+7//77+dOf/gQY17C8vDza2to47LDDeOCBB6Y9ac8ic5ElF1kAxsXu05/+NIsXL+aGG24YdXU6EomwYcMG6uvree6556zN3qxZsyzXqUWLFk2b0ZFYLEZTUxOapmV8XoEZmOjxeKioqEjLjVPyaIsZejXeWRq6rtPR0UFfXx+lpaUUFRXt93tOVyQT70xImx5JB+T1enE6nfT09OByuaiqqkrLv4/JQpZYGEju6I1ELAb//CuvvMLTTz/NE088Yd0/bTYby5Yt46STTqK2tpby8vLJWH4WWYwbsuQii3FDLBZj48aNrF27lmeffZZAIABAaWmpdZE86qij0vYmHA6HaW5uRpIkqqqqMlpAZ47/+Hw+Zs+ePS02C8Nlaexvum6yYLm8vHzEbl4mINND4XRdtyrOAwMD1miox+PB5/MNa0GaCeju7qarqyvjdWnJxKKqqmrUHT1d17ntttt45JFHmDNnDqeccgqbNm3irbfesn5m4cKF1NbWcuaZZw6bRZVFFumGLLnYC9ra2vjFL37B5s2bkWWZRYsW8YMf/IAFCxaM+Jq77rqLO++8c8jj27ZtS4uxksmALMu8+OKL1NfXs379evr7+wEoLCzkxBNPpK6ujmOOOSZtjofp/pPpVUhd1+nq6qKnp2daj/+MR5bGeNvuTmckZzdkeihcMBikubnZ6pAFg0Gr4ux2u61zLBOKE1liYUDXdUujV1lZOepRWtNA5cEHH2TBggU89NBDlpVzR0cH69evZ/369WzevJl4PM5pp53GbbfdNpFfJYssxgVZcrEHyLLM6aefTmFhIVdeeSVOp5Nf//rXvPzyy6xZs2ZEP/eLL74Yt9vNlVdemfJ4SUnJZCw77aAoCi+//DJr167l6aefpqenB4C8vDxWrVpFXV0dS5cunbKqn1mlz3T3n2R3rNLSUgoLC2fEZsFM102eod9berOpK4hGo2OqQs5EmBar2eyG3UUIj8eTknejKEqKBWlyAn1ubi4ul2tG/C0lI5lYZOq9DYzri+maNhZiAfCb3/yGe++9l7lz5/Lwww+P2JUIBoO89NJLHHTQQVRVVY3X0rPIYsKQJRd7wAsvvMA555zD888/T1lZGWAQjiVLlnDttdfyhS98YdjXfeITn+Dss8/m61//+iSudnpAVVW2bNlCfX0969ato7OzE4Dc3FxWrFhBXV0dy5cvn5TK6Eyp0o8Hkm1FZ7I71nAz9KY9pJmlYRILRVEyXneT7IRUVVWV0cTC7/fT0tJCTk4OFRUVIxYhkrtmwWBwVGR2uiFLLAwkj01WVlaOSYB933338etf/5rq6moefvhha4+RRRYzAVlysQd0dXXxzjvvsHLlSusxWZY59thjueSSS/jGN74x5DWRSITFixdz7733cvzxx0/mcqcdNE1j69atFtFobW0FjBnmE044gbq6Oo477rgJ2dxpmkZbWxt+vz/jRbrJVfqxVt6mM3Rdt+whk7M0AARByAjB8p6QTSDfDZNY5ObmUlFRMWpyYCbQm+eYoihWMOSesg7SGV1dXXR3d2eJRRKxGGvOywMPPMCvfvUrKioqeOSRR5g1a9YErjSLLCYfWXIxRtx3333ceuutPProoxx88MFDnn/zzTc544wzOOOMM3j55ZetTscVV1xBaWnpFKx4ekDXdd5++23Wrl3LunXraGxsBAznn+OOO466ujpOOOGEcRlPUVWV5uZmIpFIxs/SJ4t0M71Kb7pjgXE+CoJgZWnk5ORk1OY6FArR1NSU8Rok2B2auL/mBslkNjnrYDqdYyaxKCkpyWhhsa7rtLW1MTAwMGZi8cgjj3Drrbcya9Ys/vjHP2bHnLKYkchoctHc3Extbe2Iz2/cuDGlMrNu3Touu+wyvvSlL3HNNdcM+5p//vOf/PCHP+Tcc8/ls5/9LN3d3dx+++2Ew2H+/e9/Z/TmbbTQdZ0dO3bw5JNP8tRTT7Fr1y4AHA4Hy5cvp7a2lpUrV+4TKYjH4zQ1NRGPx6mqqtqrVeBMhjlLD2S8SNes0tvtdqqrq9E0jWAwiN/vJxKJABOfpZEuGElXkIno6+ujvb19QkITTeepQCAwLc6xLLEwkKxNmz17Nnl5eaN+7d///nduvvlmSktLefjhh62Q2iyymGnIaHJhVm1Hwpw5c6yK3Z///GduvPFGTj31VH7+85/v8YY7MDCQcsHp6Ohg5cqV3H777Zx66qnj9wUyBO+//z719fXU19ezY8cOwPABX7JkCXV1daxatWpUFqHRaJSmpiYgu5k2bXezs/S73X/cbjeVlZVDqvQjZWn4fL4Z5wo0MDBAa2trxpsbwG6jh4KCAsrKyiZUJ7GnvJacnJx9slEeT2SJhYFkYjFWbdq///1vfvKTn1BcXMxDDz3E/PnzJ26hWWQxxchocjFa3Hrrrdx7772sXr2aa665Zp8u8suWLeP888/n3HPPnYAVZg4++ugji2hs27YNAEmSOProo6mrq+PEE08ccvPTdZ3HHnsMgEWLFmX8ZtqsTI+0mc4kmJvpvYl0TUxElka6YCKr9NMNZijcVBg9mOeY+c++2CiPF5KT6bPEYneY5liJxZo1a/jRj35Efn4+f/zjH/doZZ9FFjMBWXKxF/ziF7/gvvvu4/vf//6wAu7BuO2221i/fj2PP/64dQMwx6/uvfdeVqxYMdFLzhg0NTWxbt066uvreeONNwAQRZEjjzySuro6TjrpJIqLi7npppt49NFHWb58OXfeeWdGb6bN+fFsZXr/N9Pm6JRJNJI3gT6fb1rZj5qb6cmo0qc7TCekdEib1jQtRRCuqiqSJKU4T03U33Cym16WWOh0dnbS29s75jDNtWvXcs0115Cbm8tDDz00rFYziyxmGrLkYg/YvHkzX/3qV1m9ejUXXHBBynMejwev14ssy9YYlMPhYNu2bZx11lmcccYZrF69mu7ubm6++WZcLhd/+tOfMvqmPZFoa2uziMZrr72GeVrn5uYSCASorq7m3nvvzVhRva7r9PT00NXVRX5+PuXl5Rl7LiYfi/HaTO9LlkY6ILkynQ6b6alE8rFIx1C44WyURVFMsVEer8JJMrHIdDe95GNRVlY2Yr7VcFi/fj1XXXUVHo+HBx54gMMOO2wCV5pFFumDLLnYA6677jr+9re/DfvcRRddxMUXX2wRkIceeoilS5cCBim544472L59Ow6Hg9raWq666qoxCb+y2Hd0dnby2GOPcffddxMMBq3HDz30UGpra6mtrc0oIV1yOz8dN02TieQK5EQdC3MT6Pf7LfvRwVka6dAxSj4W2cr09KrS67pOLBazumbRaBRBEFIE4fvqPJUlFqkw9SZjPRYbNmzgiiuuwOl08oc//IEjjjhiAleZRRbphSy5yGLGoampifPOO4+PPvqIT33qUxx77LE8/fTTvPTSSyiKAsCBBx5IbW0tdXV1zJs3b4pXPHHQNI3W1lYCgcCY2/kzDcn2kWOtQO7PZw7O0pioavNY12UKUyfrWKQrksn3dD0W8XjcOsfC4TAAbrfbIhqjNR3IEotU7Cux2LRpE5dddhk2m43f//73HH300RO4yiyySD9kyUUWMwpvvvkmF154IT09PXznO9/h4osvtirTAwMDPPPMM9TX17Np0yZkWQZg3rx5FtFYsGDBjKnqZ/M8diM5gXys9pHjBV3XLftRv98/ZTkHyeFfMzmNfTRIJlkzhXwrimJpgUKhkGU6YBLakbRAWWKRClN7M9ZO1ksvvcR3v/tdBEHgnnvuYdmyZRO4yiyySE9kyUUWMwbbt2/nrLPOQpZlfvzjH/PFL35xxJ8NBoM8++yz1NfX89///pdoNAoYFrXm6NShhx46bYmGoig0NjZm8zxIJVnplEA+FTkHySRrrOFfMw3JnayZSrJM0wGTbGiaNqwWKEssUmEaHIw1hXzLli1cdNFFaJrGb3/7W44//vgJXGUWWaQvsuQiixmD119/neuuu44rrriCVatWjfp14XCY559/nrVr17JhwwZrrGD27NkW0Tj88MPTYk5+NJBlmcbGRnRdp6qqCpfLNdVLmjJMF5IVj8dTqs2wb2Mte4KmaTQ3NxMOh9OKZE0Fkrs3U9XJmmzoup7iPGVqgbxer0VCputY2HjCzDcZq8HB1q1b+c53vkM8Hueuu+4a0z0oiyxmGrLkIosskhCLxfjvf//L2rVrefbZZy1BeGlpKSeddBJ1dXUceeSRaWtnG4lEaGpqQpIkqqurMzrPwwzJVFWV6urqaUOyVFVNCVQbjywNVVVpamoiFotRWVmJ1+udoNWnP3Rdp6WlhUAgkLHdm2QtUH9/P6qqAljjebm5uWl7jZtImMRirPkmb7/9NhdeeCGxWIxf/epX1NXVTfBKs8givZElFxmAtrY2fvGLX7B582ZkWWbRokX84Ac/2GOQT3NzMzfeeCOvvPIKLpeLz33uc1x++eUZdcORZZkXXniB+vp61q9fz8DAAABFRUWceOKJ1NXVcfTRR0/KnPxoEAwGaWlpweFwUFVVlTbrmgrEYjEaGxsRBIHq6uppm6I9XJaGw+GwiMZosjSSuzfV1dW43e5JWn36IXksrLKyktzc3Kle0pQh2S2sqKgISZImdUQv3WDm3ozVnvrdd9/lggsuIBwOc/vtt3PKKadM8EpHxjXXXIOqqtxyyy17/Lm77rqLO++8c8jj27Zty+j7Rhbjhyy5mOGQZZnTTz+dwsJCrrzySpxOJ7/+9a95+eWXWbNmzbAt8Hg8zmmnncbcuXO5/PLLaWxs5JprruHLX/4yl1xyyRR8i6lHPB7n5Zdfpr6+nqeeeore3l4A8vPzWbVqFXV1dSxZsmTKbsJm0rTX66WysnLajHBNBMzujc1mm1Fp7Gagmt/vJxgMjipLY7p2byYC2bGw3UgmFoNHoYYb0XO5XCkjetNVizYSzHDRsRKL9957j/PPP59AIMDPf/5zPv3pT0/wSoeHqqrceuut3H///Xzuc5/bK7m4+OKLcbvdXHnllSmPj0VfkkUWe0KWXMxwvPDCC5xzzjk8//zzlJWVAQbhWLJkCddeey1f+MIXhrxmzZo1XH311WzatMkaGfjrX//Kz3/+c1588cVpWwUeL6iqypYtW1i7di3r1q2jq6sLMEYKVq5cSV1dHcuXL5+042S28vc1aXomIRQK0dTUhMvloqqqasZ22kaan8/JycHn8+HxeKyOha7rVFdX43Q6p3rZUwZN02hqaiISiVBVVZXxY2Gjtd5VVTWFaCR3znJycnC73dP+emMWZsYaLrpr1y7OP/98+vr6uPnmm/n85z8/wSsdeR1XX321dd1bunTpXsnFJz7xCc4++2y+/vWvT84is8g4ZPtfMxwLFizgd7/7nUUsTOi6bo35DMarr77KwoULU2aRly1bRjAYZPv27SxatGhC15zukCSJJUuWWARt69atrF27lqeeeoo1a9awZs0avF4vJ5xwArW1tRx33HETMoqS7PCS6enKAH6/n9bWVjwez4zv3pgWtl6vl7KyMmt+3u/3MzAwYJ0HoihSU1OT0cQiWW9SXV2dtqL+yUAysRiN9a4kSeTl5ZGXl4emaYRCIYLBIP39/fT09CBJktXR8Hq90+76YxKLvLy8MRGLhoYGLrzwQnp7e/nJT34yZcQC4OWXX+aQQw7ht7/9Ld/97nf3+vORSITGxkYOOOCAiV9cFhmLLLmY4SgpKWHlypUpjz300EPEYjGOO+64YV/T3t5OeXl5ymOlpaUAtLa2Zjy5SIYoiixevJjFixdz9dVX89Zbb1kdjbVr17J27VpcLhfHH388tbW1rFixYlw2N1MRCJfOMOelfT4fs2fPnnabnP2BIAi43W7cbjclJSUEAgFaW1sBY2P94YcfWlkamSbUVVWVxsZGZFlOa7ewycBYicVgiKJonUPl5eVEIhGrc9bf358W4ZBjgVmMGGvHt6mpifPPP5/u7m6uu+46zjzzzAle6Z5x9tlnj+nnd+7ciaZprF27lp/85CfWJMMVV1xh3eezyGJ/kSUX0xzNzc3U1taO+PzGjRtT5ijXrVvHL3/5S1avXs3BBx887Gui0egQBxWz8hmLxcZh1TMTgiCwaNEiFi1axJVXXsn27dt58sknWbduHU8//TRPP/00TqeTZcuWUVdXx4oVK/bJqcacHQ+FQhljo7knmJ70Yx1rmImIRCK0tbXhdDqprq5OcZ5qa2ujra0tJbRvpuhRhoOiKDQ1NSHLcsYL2feXWAyGIAh4PB48Hg+lpaXEYjHrPPP7/dbzJhlJN5FwIBCgpaUFn883JmLR0tLCBRdcQGdnJz/4wQ/4yle+MqHrHOv9fTTYuXMnYIzx/u///i/d3d3cfvvtfPWrX+Xf//53Rv+dZDF+SK+/+CzGjLKyMp544okRn0+uaP/5z3/mxhtv5NRTT+Xqq68e8TUul8tKrzZhkopMrvyNBYIgcMghh3DIIYdw+eWXs3PnTurr66mvr2fDhg1s2LABm83G0qVLqaurY9WqVaMK8TI3TLFYjKqqqowXpWbHwnbD1Ju43W4qKyuRJAlJkigqKqKoqIh4PG5tANvb24Hxz9JIF5h6E0VRqKmpyWgh+3gTi8EQBAGXy4XL5aKkpARZli2dRnt7O+3t7Wl1ngUCAZqbm8nNzR1Tl7Ojo4MLLriAtrY2vve973HOOedM8ErHdn8fLU4//XTq6upSilILFixg5cqVPPvss5x66qn7tNYsskhGllxMc9jtdubPn7/Xn7v11lu59957Wb16Nddcc80eL6jl5eW89957KY91dnYCDNFuZDE6LFiwgAULFnDRRRfx4YcfWkRj06ZNbNq0iZtuuoljjjmGuro6TjzxxGETcrdu3cqOHTs46qijqKmpyegKk67rtLe309/fnx0LY3cldk96E7vdTmFhIYWFhSiKYm0Au7q66OzsxOl04vP5pr0jULJDVqbrTZL/TiaCWAwHh8Ox1/NsfzJb9gemXXdOTg4VFRWj/uyuri7OP/98WlpauPjiizn//PMneKUGRnt/HysGd7vLysrIz8+3ig5ZZLG/yJKLDMAvfvEL7rvvPr7//e/zjW98Y68/f+yxx/Loo48SDAatyviLL76I1+sdcZQqi9Fj7ty5XHjhhVx44YU0NTVZRGPz5s1s3ryZn/70pxx11FHU1dVx0kknUVpayv/93//xk5/8hMLCQtasWTPl1b+phKZptLa2EggEmDVr1qg6PjMZpig1Nzd31Bsmm81Gfn4++fn5qKpKKBQiEAjQ09NDV1fXmLM00gXxeJyGhgZ0Xc8SiyRiMVV/J8nnWXJmS29vL93d3djtdkunMZyV8ngiFArR3Nxs2XWP9rN6e3u58MILaWxs5MILL+Siiy6asDVOBm677TbWr1/P448/bh2D5uZm+vr6siLvLMYNWSvaGY7Nmzfz1a9+ldWrV3PBBRekPOfxePB6vciyzMDAAHl5eTgcDmKxGKeddho1NTVcccUVNDc3c80117B69eppf2FNZ7S1tVFfX8+6det47bXX0HUdQRAoLy+nra0Nj8fDr371K4455pipXuqUQVVVmpubiUQiVFRUZHQIGuwWso+XDbHpCGSG9iVnafh8vrS2HpVlmcbGRoBpHZw4HkgHYrEn7MlK2RSEj6fbmzkyOFYnub6+Pi644AJ27tzJueeey/e///20Pf8BVq9eTUVFRYoV7eD7+7Zt2zjrrLM444wzWL16Nd3d3dx88824XC7+9Kc/pfX3y2L6IEsuZjiuu+46/va3vw373EUXXcTFF19sEZCHHnqIpUuXAobV3g033MCrr75KXl4eX/jCF7j44otntL1nOqGzs5N169Zx33330dbWZj2+cOFCamtrqa2tpbq6egpXOPlIFuhWVlZmdFYB7BayjzX4a7QYaQOYjtajsizT0NCAIAjU1NTMaKH63pDuxGIwdF23rJQDgQCyLCMIgkU0cnJy9st5KhwO09jYOGZi4ff7ueCCC9i+fTtf+cpXuPbaa9PmfB8Jw5GL4e7vmzdv5o477mD79u04HA5qa2u56qqrMt4cJIvxQ5ZcZJFFGiIej3Pttdfy6KOPMmfOHL7whS/w4osvsnnzZhRFAeCggw6itraWuro65s6dO8Urnlgkz9FXVVVlvN6ku7ub7u7uSROymxtAv99PIBAgHo+nWI/m5ORMWeEhFovR2NiIKIpUV1dnicU0IhbDIdl5KhqNAqQ4T43l9xsOh1NCNUd7jgYCAb71rW+xbds2zjzzTG644Ya0JxZZZJFOyJKLLLJIM4TDYS699FKef/55jjrqKO6++25rk9Df388zzzxjicHj8TgA8+bNo66ujrq6Og444IAZdSM0N49AxidN67pOZ2cnvb29lJSUUFxcPCVrSN4AxmKxca00jwXRaJTGxkZsNhvV1dVpZ3k6mUjOvpmuxGIw4vF4SkI4GG6GyYLwkWCGxZm2zKMlFqFQiG9/+9u8+eabnH766dx0003Zjn0WWYwRWXKRRRZpBEVR+PKXv8zWrVs58cQT+eUvfzlilT4YDPLss8+ydu1aNm7caFX5qqurqauro7a2lkMOOWRaE41IJEJTUxOSJGWr0mnqkCXLskU0IpEIQEpo30Rt+M1zI0ssZiaxGAxVVS2iEQwG0XV9ROOBZGJRVVU1arIbDoe5+OKLee211/jMZz7Dz372syyxyCKLfUCWXGSRRRphYGCAT3ziE3z84x/nRz/60ag3TOFwmA0bNrB27Vqef/55wuEwALNnz7aIxuGHHz6tiIbp7uJwOKiqqsr4zWNrayt+vz+tN4/JWRrmOeh2uy2L2/Eih+bm0eFwUF1dnfZp0BOJZGKRKaGaIxkP5OTk4HQ6LcezsZwb0WiUSy+9lJdffplPfvKT3HbbbRl9XmWRxf4gSy6yyCLNoGnaflXLotEo//3vf6mvr+fZZ58lGAwChpf5SSedRF1dHUceeWRaV+TM3IbkQLhMhaZptLS0EAwGqaio2KdU96lAcsZBKBRC1/VRj7TsCeYc/Vir0jMRmUgsBkPXdSKRiJUObmrSTIez0eiBZFnmsssu44UXXqCuro477rgjo7ukWWSxv8iSiyymFa655hpUVU1xwxgOd911F3feeeeQx7dt25ZRFXBZltm0aRP19fU888wzDAwMAFBcXMyJJ55IXV0dixcvTqtj0t/fT1tbm5Wgm84kaKKhaRpNTU1EIhEqKyunbSL7WEZa9oTkFPKxCHRnIrLEIhWxWIyPPvrIcjQLhUKWHsgc08vJyRlyrYvH41xxxRU8//zzrFq1ijvvvDOjbYyzyGI8kCUXWUwLqKrKrbfeyv3338/nPve5vZKLiy++GLfbzZVXXpnyeElJyUQuM60Rj8d5+eWXWbt2LU899RR9fX0AFBQUsGrVKurq6jj22GOntGLX29tLR0fHuOU2TGeoqkpTUxOxWGxGWe8mj7QEAgE0TcNut1tEY6QsjWAwSHNz85gtRWcissQiFbFYjIaGhiH6m8F6oJ07d/K73/2OhQsXUldXx/Lly7n++ut55plnOP744/ntb3+bJRZZZDEOyJKLLNIeu3bt4uqrr7YsBZcuXbpXcvGJT3yCs88+m69//euTs8hpBlVVefXVVy2i0dXVBYDP52PlypXU1dWxbNmySbvR6rpOV1cXPT09k2avms5QFIXGxkbi8TjV1dUz1np3tFka5pic1+uloqIi44mFqb/JEovdGSeiKFJTUzNiF1ZRFN58802uvvpqOjs7ARBFEU3TmD9/Pvfffz/l5eWTufQsspixyJKLLNIef/7zn9m+fTuXXHIJ3/3ud4eEBA1GJBJh8eLF3HvvvRx//PGTuNLpCU3TeP311y2iYYb2eb1eVqxYQW1tLccddxwul2tCPj/ZBam0tJSioqIJ+ZzpguRMj+rq6gk77umG5Nn55CwNp9NJJBIhJyeHysrKjCadycRiOulvJgrJxGK0bnK6rvP222/z4x//mA8++MB6XJIkjj32WE4++WRqa2uZNWvWRC49iyxmNLLkIotpheESSAfjzTff5IwzzuCMM87g5ZdfRpZllixZwhVXXEFpaekkrnb6Qdd13nrrLZ588kmeeuopmpqaAMPx57jjjqOuro4TTjgBj8czbp9nbpbKy8spKCgYl/edrpBlmcbGRnRdp6amJmNHNMwsja6uLsuQYKqyNNIFWWKRing8TkNDA8CYUtk1TePGG2/k0Ucf5YgjjuD666/npZde4qmnnmLr1q2YW6KFCxdy9tlnc8YZZ0zYd8gii5mKLLnIYkrR3NxMbW3tiM9v3LgxRScxGnLxz3/+kx/+8Iece+65fPazn6W7u5vbb7+dcDjMv//97xk7YjLe0HWdd999l7Vr17Ju3To+/PBDAJxOJ8uXL7eIxr5ucjRNo7m5mXA4zOzZszN+s5RNmk6FKezPy8ujqKhoSGrzZGRppAuyxCIV+0osdF3npz/9KX//+9857LDD+MMf/pByLLu6unjmmWd4+umnefHFF8nPz2fjxo0T8h2yyGImI0susphSmCMgI2HOnDkpFcrRkAsw8iKSZ5E7OjpYuXIlt99+O6eeeur+LzwDsXPnTtauXUt9fT07d+4EwG63s3TpUurq6li5cuWo8xdMsXI0GqWqqmrGiJX3FdlAuFT09fXR3t5Ofn4+5eXlKaNQw2VpeDwei2jMNFKWJRapMO8ZmqaNqbun6zq33norf/rTnzj44IN58MEH93i9CgaDKIqStpkyWWSRzsjsO1gWUw673c78+fPH/X0HixzLysrIz8+nvb193D8rU7BgwQIWLFjAxRdfzAcffEB9fT319fVs3LiRjRs3WjPLdXV1nHjiiSMmSLe2ttLb24vD4aCmpibjO0lmbkM2EM6A6RhWUFBAWVnZEI2F/f+3d+9hUZZr28BPRBj2KLERhUEjlaUlJiqYCjozVstab8tV9motrDdFSsXMvVGpWViiuMnN66YyNdp4lJVpMwOoIKHghnKFS3T5FcMeUWDYzsDMfH/QzAsByhADA3P+jqM/1sPzyGWx9Dnnvu/rsrGBm5sb3NzcDLM0lEoliouLUVxc3CmzNMwFg0Vz+kYHHQkW27dvR3x8PIYOHYqPP/74nqGhp7Z9JjIHXLmgHqU9KxdbtmxBUlISTpw4YXgx0W+/2r9/P0JDQ7uqXIuQm5tr2Dp15coVAI1dWMaMGQOxWAyRSGQ463L69GmsXr0aY8aMwbZt23r8y9+fpW+vymGBjW7fvo2SkhK4ubnB09PTqMPbd5ul4eLiAoFA0KMOg+t0OuTn56OyspLBAv8XLDQajdHnkXbv3o39+/fj/vvvx+HDh+Hu7m7CSomI4YJ6lNbChVqtNmyDsrW1RVZWFmbNmoWZM2ciPDwcpaWliImJgZ2dHeLj43vUC0ZPU1BQALlcDplMhszMTOh0OlhZWSEwMBCenp5ITEyEjY0Ntm7digkTJnR3ud1K316VcxsalZaW4tatW53SirijszTMBYNFc/pg0dDQAD8/P6M+lNi/fz92794NoVCII0eOwMvLy4SVEhHAcEE9TGvhIj09HXPmzMGhQ4cQHBxsuLZt2zZcu3YNtra2EIvFWLVqlcX3hO9KxcXFSEhIgFwuR0ZGhqELy+DBg/HUU09BLBbD19e3m6vsHhUVFSgoKICzszMGDRpk1i+6ptZ0xom7u3unD7rU6XTNgoZGo2l1loa5aBosfHx84Ozs3N0ldSuNRmOY+WJssDh48CC2b98OHx8fHDlyhO1liboIwwURmYxOp8OuXbvwwQcfwNnZGffffz+ysrLQ0NAAABg+fDgkEgkkEgkGDx7cvcV2Ef1hZU4hb/z5KCkpwZ07d7pkxklbszSaBo3uXEFisGiuabAwdubLkSNHsGXLFnh7e+PIkSPw8fExYaVE1BTDBRGZhFarxbvvvosjR47Az88PH374IXx9fVFeXo5Tp05BKpUiLS0N9fX1AAB/f39D0PD39++VL936MwVtHVa2JDqdDsXFxSgrK4OXl1ebDQBM+f1VKhWUSiUqKyuhVqu7dZYGg0Vz+o5yKpXK6Cn1R48eRUxMDDw9PfHpp59CKBSasFIi+iOGCyLqdBqNBqtWrcLx48cREBCADz/8sNVDlJWVlTh9+jRkMhnOnj0LlUoFoLF3vUQigVgsRkBAQI9/CdfpdCgtLUVpaWmnnCno6ZpOZTeX4YkqlarZLA0rKys4ODjAxcUFTk5OJm0PrNPpkJeXh+rqagwaNMjig4VWq4VCoehQsDh27BjefvttuLu74/Dhw7j//vtNWCkRtYbhgog6XWZmJmbNmoWgoCD87//+b7sOpFZXVyM5ORkymQzJycmora0FAAwaNAhisRgSiQQPPvhgj3spb7r1x8PDw+I71eh0OhQWFqKiogLe3t5mOUegK2dpMFg0p9VqDTNwjA0Wx48fx9q1a9GvXz8cPnwYQ4cONWGlRNQWhgsi6nQajQYZGRkYM2ZMh9rN1tXVISUlBTKZDGfOnEFVVRUAYMCAARCJRJBIJAgMDDT7DktNP6Hvjq0/5qbp3IaBAwf2iAYLDQ0NhqBRXV0NAIZZGi4uLka1RP2jpsHCx8fH4mcr6INFbW0thEIhHBwc2v2sVCpFdHQ0nJ2dcejQIQQEBJiwUiK6G4YLIjJrarUaP/74I6RSKU6dOgWlUgkAcHd3NwSNMWPGmN2MiKYv0ub6CX1X6g3tVVubpSEQCJoN7WvvyppWq0V+fj6Dxe+0Wi3y8vJQU1MDX19fODo6tvvZxMRErF69Gg4ODjh48CAefPBBE1ZKRPfCcEHUxaKjo6HRaO46CBBoHPy3YcMGXLhwAXZ2dpgxYwaWLl1qdi/RXam+vh7p6emQSqVITExEWVkZAKB///6YOnUqJBIJxo4d26nbVjpC/+JYVVXVY1+kO1PTF+nesvVHq9U2CxrGzNJgsGiu6QqOscHizJkzWLFiBQQCAT7++GMEBgaasFIiag+GC6IuotFosHnzZnz00UeYMWPGXcNFfX09nnzySQwZMgRLly6FQqFAdHQ0nn/+eSxevLgLqzZfGo0GFy5cMASNW7duAQBcXV0RFhYGiUSC4ODgP7VtpSOabu3gi2PzT6R767+P1mZp9O3b1xA0HBwcDEGDwaK5P7M1LDU1FUuXLkXfvn3x4YcfIigoyISVElF7MVwQdYGbN29izZo1yM3NhZ2dHYKDg+8aLr7//nusWbMGP/74o+FT7y+++AKbNm3CuXPnuvyF2dxptVpkZmZCKpVCLpejqKgIAODo6IjQ0FBIJBI88sgjRvXJ74im7TN9fX2N2jPeGzUNWsZ+It1TNZ2loVQq0dDQAGtrazg5OcHJyQllZWUMnr9rulXO19fXqH8f58+fx5IlS2BlZYV9+/YZBqgSUfczXW89IjLIyMjAX/7yF+zZswdLliy55/0XL17EyJEjm22nCQkJQVVVFa5du4ZRo0aZsNqep0+fPggKCkJQUBBef/11XLlyxRA0fvjhB/zwww+wt7fHpEmTIJFIMHnyZKO60LRHQ0MDFAoFGhoajO5y0xv9cU6BpQQtfQtbBwcHeHp6oq6uzrCiUVFRAQCwt7eHVqs1TAu3RPozSfq5HsYEi4sXLxr+HN29ezeDBZGZYbgg6gKzZ8826v6ioiIMGDCg2TVPT08AQEFBAcPFXVhZWSEwMBCBgYFYuXIlrl69aggaCQkJSEhIgEAgwCOPPGIIGn/2DEB9fT0UCgU0Go3Rk4R7I/1kZbVabdFBy8rKCvb29hAIBKirq4NarYarqytUKhXy8/NhZWUFR0dHw9A+U87SMCdNmx0Yewbnp59+wuLFi6HVarFr1y5MnDjRhJW2VFhYiNjYWKSnp0OtVmPUqFFYvXr1Xdve8vwcWRrL+JOMyITy8vIgFovb/Hpqaio8PDyM+jXr6upaHALWt3TVD5qje7OyssLIkSMxcuRILF26FDdu3IBUKoVMJsPp06dx+vRp2NjYICQkBBKJBGFhYUa3R1Wr1VAoFNDpdBg8eLDFb1njCk5zTc+cCIVCw9YwtVptWNEoLCwEYLpZGuZEP+dEHyyMaXbwyy+/YNGiRaivr8eOHTsQFhZmwkpbUqvVmD9/Ptzc3LB3714IBALs2rULL7zwAr7//vtWW03X19dj7ty5GDJkCD7//HPD+TmBQMDzc9RrMVwQ/UleXl44efJkm1/vyGwDOzs7qNXqZtf0ocJStpd0NisrKwwbNgzDhg3D4sWLcfPmTchkMsjlcpw9exZnz56FtbU1xo8fD4lEgilTptzzv51KpYJCoUCfPn3g5+fXa18I2+uPwcLSV3Du1l7V1tYW9913H+67775mszSKi4tRXFwMe3t7Q9DoLYFVP/eloqICAwcONCpY/Pvf/8aCBQtQV1eHuLi4u36gYyoXL17E9evXkZKSAi8vLwDApk2bMH78eJw6dQrPPPNMi2dkMhkKCgpw9OhRuLi4YNiwYbh9+zY2bdqEl19+udf8tyVqiuGC6E+ysbGBv79/p/6aAwYMwPXr15tdKykpAQDDX2r05/j7+2PBggVYsGABFAoFpFIpEhIScO7cOZw7dw7vvvsugoKCIBKJIBKJDNvS9I4dO4YffvgBy5Ytw5AhQyxmS0tbmm4N8/Pz69DwxN7EmLkNffv2Rf/+/dG/f3/DLA2lUolbt26hpKSkw7M0zIlOp0NxcTHKy8vh7e1t1Arh9evX8corr6C6uhqbNm3C448/bsJK2zZ06FDs27evxZ/BOp3OcJ7mj3h+jiyRZf9tSGSmxo0bh2+++QZVVVWGg47nzp2Do6MjJ8+agFAoxPz58zF//nwUFBRALpdDJpPh4sWLuHDhAjZt2oTRo0dDJBJBLBZDJpNh+/btcHV1xcCBAxks6uuRk5PDrWG/+zMD4aytreHq6gpXV9dmszTu3LmD0tJS2NjYwMXFBc7OzrCzs+sRQUMfLMrKyjBgwACjBkrevHkTL7/8MpRKJTZu3Ii//e1vpiv0Hjw8PFpsxTp06BBUKlWbZz94fo4skWX/jUhkJtRqNSoqKuDq6gpbW1tIJBJs27YNS5YswfLly5GXl4etW7fipZdesvgXN1MbOHAgXnzxRbz44osoLi5GQkKCIWhkZmZiy5YtABq3p7377ru9YiDcn6E/cwIAfn5+Fv/z2Zntd/v06QMXFxe4uLhAq9WipqYGlZWVKC8vx+3bt9ucpWFOdDodSkpKDMGif//+7X72t99+Q2RkJMrKyrBhwwbMmDHDhJUaf35OLpdj69atCA8Pb/NDH56fI0vEcEFkBjIzMzFnzhwcOnQIwcHBEAgEOHDgANavX49nn30Wrq6ueO6557BgwYLuLtWieHl54Z///Cf++c9/4tatW1i1ahV+/PFHAEBNTQ0WLVqEgIAAiMViSCQSDB48uHsL7mL6MydWVlY8cwLTzvXo06ePYVbGgAEDDEGjsrISZWVlhlkazs7OcHR0RJ8+fTrte3eUTqfDrVu3cOfOHXh5eRkVLHJzcxEZGYnbt2/jrbfewrPPPmvCShsZc37us88+w4YNGzB9+nSsWbOmzWd4fo4sEYfoERHdg1arxdtvv43PPvsM/v7+2Lp1K3755RdIpVKcO3cO9fX1AIAHHngAYrEY06ZN6/RzOOZGpVIhJycH1tbWEAqFDBZNgkVXzvXQ6XTNZmmo1WpDENG3uO2uoHHr1i2UlpbC09MT9913X7ufy8/PR0REBAoLC7FmzRq8+OKLpiuyAzZv3oz9+/cjPDwc0dHRd10xWrduHa5fv474+HjDtZycHDz66KM4evQot0VRr8RwQUR0Fw0NDXj99dfx7bffYuTIkThw4ECzTzCVSiVOnz4NmUyG1NRUw6eSgwcPNqxoDB8+3Cy3rHRUXV0dFAoF+vbtC6FQaPFnTrorWPyRTqeDWq2GUqlEZWUlVCpVs1kazs7OXTZbobS0FLdu3YKHhwfc3d3b/VxRURHmzZuH/Px8LFu2DPPnzzdhlcaLjY3FgQMHsHLlSsydO/ee9584cQLR0dFITU01nJ/7/PPPsWnTJpw/f97itxFS78RwQUR0F+vWrcNnn32GMWPGYN++fXc9Y1FdXY0zZ85AJpMhJSUFtbW1AAAfHx9D0Bg5cmSPDhq1tbXIzc1lsPidPljU1dXB19fXrLa6NJ2lof9ZbDq0z1SrTR0NFiUlJYiIiIBCocDixYuxcOFCk9TXUenp6ZgzZw7Cw8MRGRnZ7GsODg5wdHRscX5OpVLhySefhJ+fn+H8XHR0NMLDw7Fo0aJu+p0QmRbDBRHRXWzZsgUFBQXYsGGDUS+OdXV1SElJgVQqxZkzZ1BdXQ2gsc2wPmiMGjXKLPbGt1dtbS0UCgVsbW0hFAotfsKwOQeLP6qvrzd0ntL/LJpilsbt27dRUlICd3d3o4aH3r59GxEREfj111/xyiuvYMmSJZ1ST2d688038eWXX7b6tUWLFiEqKsoQQPTn54DGbVDr16/HxYsX4erqimeeeQZRUVE96v/7RMZguCAiMjG1Wo3U1FTIZDKcOnUKSqUSQGNrS5FIBIlEgocfftisX9ZramqQm5sLgUAAX19fs661K2i1WigUCqhUKrMPFn+k0WgMKxrV1dXQ6XSGWRouLi6wtbXt0OranTt3UFxcjPvuuw8eHh7t/jXKysowf/58/Oc//8HcuXOxYsWKHr26R2TpGC6IqN1UKhXee+89SKVS1NXVYfLkyVi7du1dD2vu3LkTH3zwQYvrWVlZFrmlpr6+HufPn4dUKkVSUhLKysoANHaimTp1KsRiMcaOHWtWB6Srq6uRm5sLe3t7+Pr6WvwnrhqNBrm5uT0yWPxR01kaVVVV0Gq1sLW1NaxotHeWRllZGYqKiuDm5gZPT892h4OKigpERkYiOzu7XQekicj8MVwQUbutWbMGly5dQkxMDGxtbbF27Vo4OjriyJEjbT4TFRUFe3t7rFixotl1Y7ZM9FYNDQ24cOECpFIpEhMTUVpaCgBwdXXFlClTIBaLERIS0q1Bo6qqCnl5eXBwcICPjw+DRZNgIRQKYW9v390ldRr9LA2lUomqqipoNJp2zdLQB4v+/fvDy8ur3eGgsrISr7zyCrKysjBr1iysW7eOwYKoF2C4IKJ2KS4uxpQpU7B3716EhoYCAH799Vc8/vjj+OKLLzB69OhWn3vssccwe/Zss2snaW60Wi0uX74MqVQKuVyO4uJiAICTkxNCQ0MhkUgwYcIE2NnZdVlNlZWVyM/Ph6OjIwYNGsRg0YuDxR/pdLpmszQaGhoMszRcXFzg4OCAPn36oLy8HIWFhUYHi+rqaixYsABXrlzB008/jXfeecfif76IeguGCyJql5MnT2LZsmX46aefDBNmASA0NBTh4eGIiIho8UxtbS3GjBmD/fv3Y9KkSV1Zbo+m1Wpx5coVQ9DIz88H0NiRZuLEiZg2bRomTZpk0pdbpVKJ/Px8ODs7Y9CgQRb/ibIlBYs/ajpLQ6lUor6+Hn369IFAIEBtbS1cXV3h7e3d7p+RmpoaREVF4fLly/iv//ovvP/++wwWRL2I5W14JqIOKS4uRv/+/ZsFCwDw9PREYWFhq8/cuHEDWq0WUqkUb7/9NtRqNcaPH4/ly5fD09OzK8rukfr06YPRo0dj9OjRWLVqFa5evQqpVAqZTIaEhAQkJCRAIBDgkUcewbRp0zB58mRDD/3OUFFRgYKCAri4uGDgwIEMFhYcLADAysoK9vb2sLe3h4eHB1QqFUpLS1FZWQmgMYhqNBq4uLjAycnprof9a2tr8dprr+Hy5cv461//ivfee4/BgqiXYbggIgBAXl4exGJxm19/9dVXW21XKRAIDIPj/ujGjRsAAGdnZ+zYsQOlpaWIi4vDnDlzcOzYMYt7SesIKysrjBw5EiNHjsTSpUtx/fp1w4rG6dOncfr0adjY2CAkJATTpk1DaGgoXF1dO/z99NtcjP00urey9GDxR1ZWVob5GS4uLnB3dzccCC8oKADwf7M0amtr4eHhYTgzpFarsXz5cmRkZGDatGmIjY21+K5jRL0Rt0UREYDGLkYKhaLNrycnJ+PAgQNIS0trdv2ZZ57B6NGj8cYbb7T6nH6glF5xcTHCwsIQFxeH6dOnd07xFurmzZuGoHHt2jUAgLW1NYKDgyGRSBAWFtZsmvi96A/m9uvXDwMGDGCw0GigUCigVqsZLH5XWVmJvLy8Vle16uvrDWc0bty4gZUrV8LR0RGPPPIIRCIRvvvuO6SlpWHKlCn44IMPOJ2aqJfiygURAQBsbGzg7+/f5tezs7NRXl4OtVrd7KWgpKQEAwYMaPO5P36K7uXlhX79+qGoqOjPF23h/P39sXDhQixcuBA5OTmQyWSQy+VIS0tDWloarK2tERQUBJFIBJFI1GaHLp1Oh9zcXFRXVxt9MLe3YrBoSd85zNnZudXtcjY2NnBzc4Obmxs8PDwwa9YsJCUlQS6XQy6XAwDc3d0xffr0Fn+OEFHvwZULImoX/YrDxx9/jAkTJgAA/t//+3/461//2ma3qC1btiApKQknTpwwvIjot1/t37/f0HWKOld+fj7kcjlkMhkyMzMBNG5nefjhhyESiSAWiw2BUK1WY9myZTh37hyOHDmC4cOHM1gwWLSgDxaOjo7w8fFp989IQ0MDoqKicP78edjZ2aGurg5AYxDRNycQiURGrbARkXljuCCidtN3i4qJiYG9vT3Wrl0LJycnHD58GEDji6p+G5Stra2hf/3MmTMRHh6O0tJSxMTEwM7ODvHx8Rb/EtsVioqKkJCQAJlMhkuXLkGr1QIAHnroIYSFheHs2bP4+eefERQUhL1791r8HngGi5b0QxSNbUms1Wrx1ltv4cSJExg7diz279+PwsJCyOVyJCQkICsrC0BjA4NHHnkEmzdvRv/+/U35WyGiLsBwQUTtVlNTg5iYGMhkMgCNbWjfeOMNwwtBeno65syZg0OHDiE4ONhwbdu2bbh27RpsbW0hFouxatWqP3XomDqmtLTUEDTS09MNQcPR0RHh4eF4/PHH4efn181Vdh99sKivr4dQKOzSmSLmSh8sjB2iqNVqsWHDBnzzzTcYPXo0PvzwwxYdzfLy8pCYmAi5XI6bN2/iyy+/tOifP6LeguGCiMjCVFdXY+7cucjMzMR9992H8vJyaDQaAMDQoUMhFoshkUjuegant2GwaKmmpgYKhQL29vbw9fVtd7DQ6XTYuHEjjh49igcffBAff/wxXFxcTFwtEZkLhgsiIgtSWVmJiIgIZGZm4oknnsD777+P2tpanD59GlKpFD/++KOhtfCQIUMMQWPYsGG9dhsbg0VLtbW1UCgUsLOzMzpYbN68GfHx8QgICMAnn3yCfv36mbZYIjIrDBdERBaivLwcc+fOxS+//IIZM2bg3XffbXHGorq6GmfOnIFMJkNycrLhAK6vry9EIhGmTZuGESNG9JqgodFokJOTg4aGBgaL3+mDhUAggFAoNCpYbN++HZ988gmGDh2KQ4cO8aA2kQViuCAishBr1qzB119/jWeffRbr16+/50tjbW0tUlJSIJPJcObMGVRXVwMAvL29IRKJIJFIMGrUqB47YbmhoQEKhYLBoom6ujrk5ORAIBDA19fXqAP+u3btwoEDB+Dv749Dhw7B3d3dhJUSkbliuCAishDp6em4ceMGnn/+eaNXHlQqFVJTUyGTyXDq1ClUVlYCADw9PTF16lRIJBI8/PDDPabbFINFS3V1dVAoFLCxsYFQKDTqv+W+ffuwZ88e+Pn54fDhw/Dy8jJhpURkzhguiIjIKGq1GufPn4dMJkNSUhLKysoAAG5uboagMXbsWPTta55zWhksWlKpVMjJyelQsDh48CC2b98OHx8ffPrpp3cdqklEvR/DBRFZBK1Wi507d+Lo0aNQKpUICgrC2rVr22x9WVZWhnfeeQcpKSkAgMcffxxr1qyBg4NDV5Zt9hoaGnDhwgVIpVIkJiaitLQUQONk9ilTpkAikSA4OBg2NjbdXGmjpsHCz88PAoGgu0vqdvpg0bdvXwiFQqNC4ZEjR7BlyxZ4e3vjyJEj8PHxMWGlRNQTMFwQkUXYuXMn4uPjsXHjRnh5eSE2Nha5ubn4/vvvYWtr2+L+8PBwqFQqrF27FkqlEtHR0Rg3bhzef//9bqi+Z9BoNMjMzMQPP/yAhIQEFBcXAwCcnJwQFhYGiUSCCRMmdNsLPYNFS2q1Gjk5ObC2tjY6WHz55ZfYuHEjPD098emnn0IoFJqwUiLqKRguiKjXU6vVCAkJwYoVKzB79mwAgFKpxOTJkxETE4Mnnnii2f2ZmZmYNWsWTp48aZj1kJqainnz5iE5OZn7ydtBq9XiypUrkEqlkMlkKCgoAAA4ODhg0qRJmDZtGiZOnNhlE7AZLFrSB4s+ffrAz8/PqGBx7NgxvP3223B3d8eRI0cwZMgQE1ZKRD1Jz2zxQURkhGvXrqG6uhohISGGay4uLhgxYgQuXLjQ4v6LFy/Cw8Oj2RC58ePHw8rKCpcuXeqSmnu6Pn36YPTo0Vi9ejVOnTqFr776ChEREfDw8IBcLseKFSsgEomwbNkySKVSQycqU2CwaKlpsDB2xeL48ePYsGED+vfvj08++YTBgoiaMc/TdkREnaioqAhAYwvVpjw9PVFYWNji/uLi4hb32traol+/fq3eT3dnZWWFBx98EA8++CCWLVuG7OxsSKVSyOVynDp1CqdOnYKtrS1CQkIgkUgQFhbWaROdGxoakJOTA41Gw2Dxu/r6eigUClhZWUEoFBp1HkYqlWLdunVwdXXFwYMH8cADD5iwUiLqiRguiKjXq62tBYAWZysEAgEqKipavb+1cxgCgcAwvZo6xsrKCgEBAQgICMCSJUtw8+ZNQ9BISUlBSkoK+vbti/HjxxuCRkcHsemDhVarZbD4XX19PXJycgAAfn5+RgWLxMREvPHGG3B0dMRHH32EgIAAU5VJRD0YwwUR9Xr6VqNqtbpZ21GVStXqnn87Ozuo1eoW11UqFbtFdTJ/f38sXLgQCxcuRE5ODmQyGWQyGdLS0pCWlgZra2sEBQVBLBZDJBK1ezBb02AhFAoZLPB/KxY6nc7oYHHmzBmsXr0a9vb2+OijjzBy5EgTVkpEPRnPXBBRr6ff4lRSUtLseklJSas9+QcMGNDiXrVajfLych7mNiE/Pz/Mnz8fX331FZKSkrBq1So89NBDyMjIwMaNG/Hoo49i7ty5iI+PN3Siak1ubi4uX77MYNGE/tyJfhWntZW5tqSmpmLlypUQCATYt28fRo0aZcJKiainY7ggol4vICAATk5OSE9PN1xTKpW4evUqxo4d2+L+cePGoaioyLB9BIDh2TFjxpi+YIKPjw9eeuklfPHFF0hOTkZ0dDSCgoLw008/ITY2Fo8//jheeOEFHDp0CPn5+YbnLly4gGeffRbbt2/nVqjf6YOFRqOBUCg0KlicP38ey5Ytg7W1Nfbu3YugoCATVkpEvQFb0RKRRdi6dSs+//xzxMTEYNCgQYiNjUVeXh6OHz8Oa2tr3LlzB87OzrCzs4NOp8Nzzz0HlUqFdevWoaamBq+//jqCg4OxcePG7v6tWLRbt24hISEBcrkcGRkZ0Gg0AIARI0Zg+PDhOHHiBDQaDTZt2gSRSNTN1XY/jUaDnJycDnXKunjxIhYtWgQA2LNnDyZOnGiqMttUWFiI2NhYpKenQ61WY9SoUVi9ejWGDh3a5jM7d+7EBx980OJ6VlaW2U6NJ+pNGC6IyCJoNBrExcXh66+/Rl1dHcaNG4e33noLPj4+yMvLg1gsxsaNG/GPf/wDAHD79m2sX78eZ8+ehUAgMEzo5ifh5uPOnTtISkoynNHQBw1vb2/8/e9/h1gsbtZO2NJoNBooFArU19dDKBQ2O290Lz/99BMWLFiAhoYG7N69G6GhoSastHVqtRpPP/003NzcsGLFCggEAuzatQsZGRn4/vvv2zzoHxUVBXt7e6xYsaLZdQ8Pj64om8jiMVwQEVGPlpGRgfnz50OtVmPEiBHIzs42HMi///77IRKJIJFIMGzYMFhZWXVztV3jzwSLf/3rX3jllVegUqmwY8cOiMViE1batrS0NPzP//wPUlJSDGed1Go1xo8fjzfeeAPPPPNMq8899thjmD17Nl588cUurJaI9Lg+SEREPVZ6ejoiIyPR0NCAPXv2ICwsDFVVVThz5gxkMhlSUlJw4MABHDhwAEKh0BA0RowY0WuDhkajQW5uLtRqtdHB4urVq1i4cCHq6uoQFxfXbcECAIYOHYp9+/a1aKKg0+labSENNLaRVigUnL9B1I24ckFERD2SPlhoNBrs3LkTYWFhLe6pra1FcnIyZDIZkpOTDZPABw4caAgaDz30EPr06R39TbRaLRQKBVQqFYRCYautltuSnZ2NyMhIVFZWIjY2Fk8++aQJK+2YAwcOYPPmzfjmm29anbNx5coVzJw5EzNnzkRGRoZhpWP58uXw9PTshoqJLA/DBRER9Tg6nQ6TJ09GRUUFdu3a1a4zASqVCqmpqZDJZDh16hQqKysBNE5q1weN0aNHw9ra2tTlm4RWq0Vubi7q6urg6+tr1EyWmzdvIiIiAuXl5di4cSNmzJhhwkob6c86tSU1NbXZOQm5XI7XXnsNzz33HKKjo1t95quvvsLrr7+Ol156CU899RRKS0sRFxeHmpoaHDt2zKiwRUQdw3BBREQ90okTJ+Dj44PAwECjn1Wr1Th37hxkMhmSkpJQXl4OAHBzc4NIJIJYLMbYsWN7THchrVaLvLw81NTUQCgUGhUsfvvtN8ybNw+3b9/GO++8g5kzZ5qw0v+jH+rXlsGDBxuC3meffYYNGzZg+vTp2LRp011XmioqKuDq6mr438XFxQgLC0NcXBymT5/eeb8BImoVwwURUQ+j1Wqxc+dOHD16FEqlEkFBQVi7di38/Pxavf/YsWNYvXp1i+tyubzNZyxJQ0MDMjIyIJVKkZiYiNu3bwMAXF1dMXXqVIjFYgQHBxs10borNQ0Wvr6+cHR0bPezCoUCERERKCkpwVtvvYXnn3/ehJV2zObNm7F//36Eh4cjOjq6Q2dlQkJCMH/+fLz00ksmqJCImmK4ICLqYXbu3In4+Hhs3LgRXl5eiI2NRW5uLr7//vtWB6S99957yMrKQlxcXLPrbm5uPXYLkKloNBpcvnwZUqkUcrncMKnd2dkZoaGhkEgkmDBhgtm0JNbpdMjLy0N1dbXRwSI/Px/z5s1DUVER1qxZY5bdlWJjY3HgwAGsXLkSc+fOvef9W7ZsQVJSEk6cOGEIIfrtV/v37++WlrpElobhgoioB1Gr1QgJCcGKFSswe/ZsAI3TxidPnoyYmBg88cQTLZ556aWX4O/v3+Y+dWqdVqvFzz//bAgaBQUFAABHR0dMmjQJEokEEydO7LZ9/DqdDvn5+aiqqoKPjw+cnJza/WxRURHmzZuH/Px8LF++HBERESastGPS09MxZ84chIeHIzIystnXHBwc4OjoCLVabdgGZWtri6ysLMyaNQszZ85EeHg4SktLERMTAzs7O8THx/faDmFE5oThgoioB9F3w5FKpRgyZIjh+uzZszF8+HCsW7euxTMTJ07E4sWL8d///d9dWGnvotPp8MsvvxiChv6sgJ2dHSZOnAiJRILJkycbtXLwZ+vJz89HZWUlfHx84Ozs3O5nS0pKEBERAYVCgcWLF2PhwoUmrLTj3nzzTXz55Zetfm3RokWIiooyBJBDhw4hODgYQGMo2bZtG65duwZbW1uIxWKsWrWq2TkMIjIdhgsioh5ELpcjKioKP//8c7P5Ba+++irq6uqwd+/eZvffuXMHEyZMwN/+9jdcu3YN5eXlCAwMxPLly5uFE2o/nU6H7OxsQ9C4efMmAMDW1hYTJkyARCJBaGgoXFxcTPb9CwoKoFQqjQ4Wt2/fRkREBH799Ve88sorWLJkiUlqJCLL1TPaYBAREYDGuQ0AWpytEAgErQ4Wu379OgDA2toa77//PmpqarB7924899xzOH78ONzd3U1fdC9jZWWFgIAABAQEYMmSJfjPf/4DmUxmmKWRnJyMvn37Ijg4GGKxGFOmTEH//v075XvrdDoUFhZCqVRi0KBBRgWLsrIyvPzyy/j1118xb948vPrqq51SExFRU1y5ICLqQWQyGRYvXtzqyoVarcaePXtaPPPH1pw1NTWYOnUq5s6di/nz53dJ3Zbit99+MwSNrKwsAI3BbuzYsRCLxZg6dWqHA50+WFRUVGDgwIFGbfOpqKhAZGQksrOz/1TXJSKie+HKBRFRD+Lt7Q2gcd+8UCg0XC8pKWl1YjGAFi+hDg4O8PHxQXFxsekKtVCDBw9GZGQkIiMjkZubC7lcDplMhvT0dKSnp+O9997D6NGjIZFIIBaL2z01WqfToaioqEPBQqlUYsGCBcjOzsasWbMYLIjIpNqeQkNERGYnICAATk5OSE9PN1xTKpW4evUqxo4d2+L++Ph4BAcHo66uznCtqqoKv/32Gx544IEuqdlS+fr6Yu7cufjyyy+RnJyM119/HWPGjEFmZiY2bdqExx57DC+88AIOHz5s6ETVGrVajStXrqC8vBze3t5GBYuqqipERUXh6tWrePrpp7F27VoGCyIyKW6LIiLqYbZu3YrPP/8cMTExGDRoEGJjY5GXl4fjx4/D2toad+7cgbOzM+zs7FBYWIinnnoKISEhiIqKQl1dHeLi4gz3N91aRV2jpKQECQkJkMvluHDhAjQaDQBgxIgRhhUN/apUTU0NIiMjkZWVhW+//Ra+vr7t/j41NTWIiorC5cuX8dRTT+G9996762RrIqLOwHBBRNTDaDQaxMXF4euvv0ZdXR3GjRuHt956Cz4+PoaBYRs3bsQ//vEPAMC///1vbN68GT///DN0Oh0mTpyINWvWGLZYUfe5c+cOkpKSIJVKcf78eTQ0NAAAhg0bhtDQUJw9exbZ2dkQi8WIjY1t96pDbW0tlixZgoyMDEyfPh2bN2/mwEQi6hIMF0RERGagoqICp06dgkwmQ2pqKurr6wE0npGZPXs2Hn30UQwdOvSeAUOtVuO1115DWloapk2bhm3btqFvXx6xJKKuwXBBRERkRlQqFV5++WWkpaXBw8MDSqUSKpUKACAUCiEWiyGRSPCXv/ylRdCor6/HsmXLcPbsWUydOhUffPABbGxsuuO3QUQWiuGCiIjITKjVakRFReHMmTOYOnUqduzYgYaGBqSkpEAqlSI5ORk1NTUAgIEDBxqCxkMPPYSGhgasWrUKp0+fxuTJk7F79+4W81CIiEyN4YKIiMgM1NfXY8mSJUhMTERYWBh27tzZIhyoVCqcPXsWMpkMp0+fRmVlJQDAw8MD9vb2UCgUCAkJwd69e3lYn4i6BcMFERGRGdizZw+2bduGSZMmYffu3RAIBHe9X61WIy0tDTKZDImJiVAqlRAKhfj222/h4ODQRVUTETXHcEFERGQGLl++jMTERCxevNjoVYeGhgYkJSVh4sSJcHJyMlGFRET3xnBBRERERESdgtN0iIjIbOzevRvh4eF3vaesrAzLli3DuHHjMG7cOLz55puGQ85ERNS9GC6IiMgsHDx4EDt27LjnfYsXL0Zubq7h/h9//BHr16/vggqJiOheOFWHiIi6VXFxMaKjo3Hp0iUMGTLkrvdmZmYiIyMDJ0+ehL+/PwDg7bffxrx587B06VJ4eXl1RclERNQGrlwQEVG3ysrKgqurK7777jsEBgbe9d6LFy/Cw8PDECwAYPz48bCyssKlS5dMXSoREd0DVy6IiKhbiUQiiESidt1bXFwMb2/vZtdsbW3Rr18/FBYWmqI8IiIyAlcuiIiox6itrW116rRAIIBKpeqGioiIqCmGCyIi6jHs7OygVqtbXFepVBwcR0RkBhguiIioxxgwYABKSkqaXVOr1SgvL+dhbiIiM8BwQUREPca4ceNQVFSEnJwcw7X09HQAwJgxY7qrLCIi+h3DBRERmS2NRoNbt26hrq4OABAYGIgxY8bgtddew5UrV3D+/HmsXbsWf//737lyQURkBhguiIjIbBUWFmLSpEk4efIkAMDKygo7d+6Ej48PXnjhBSxZsgShoaFYt25d9xZKREQAACudTqfr7iKIiIiIiKjn48oFERERERF1CoYLIiIiIiLqFAwXRERERETUKRguiIiIiIioUzBcEBERERFRp2C4ICIiMpHdu3cjPDz8rvccO3YMw4cPb/FP00GBREQ9Rd/uLoCIiKg3OnjwIHbs2IFx48bd9b7s7GyMHz8ecXFxza67ubmZsjwiIpNguCAiIupExcXFiI6OxqVLlzBkyJB73n/9+nUEBATAw8OjC6ojIjItbosiIiLqRFlZWXB1dcV3332HwMDAe96fnZ2NBx54oAsqIyIyPa5cEBERdSKRSASRSNSue+/cuYPS0lJcuHABhw8fRnl5OQIDA7F8+fJ2rXoQEZkbrlwQERF1k+vXrwMArK2t8f7772Pr1q2oqanBc889h9LS0m6ujojIeFy5ICIi6iYhISHIyMiAq6ur4dquXbswdepUfP3115g/f343VkdEZDyuXBAREXWjpsECABwcHODj44Pi4uJuqoiIqOMYLoiIiLpJfHw8goODUVdXZ7hWVVWF3377jYe8iahHYrggIiLqIhqNBrdu3TKEialTp0Kn02HlypW4ceMG/vWvfyEqKgpubm6YMWNGN1dLRGQ8hgsiIqIuUlhYiEmTJuHkyZMAAG9vb3zyySeorq7G7Nmz8eKLL8LZ2RmHDh2CnZ1dN1dLRGQ8K51Op+vuIoiIiIiIqOfjygUREREREXUKhgsiIiIiIuoUDBdERERERNQpGC6IiIiIiKhTMFwQEREREVGnYLggIiIiIqJOwXBBRERERESdguGCiIiIiIg6BcMFERERERF1CoYLIiIiIiLqFAwXRERERETUKRguiIiIiIioUzBcEBERERFRp2C4ICIiIiKiTsFwQUREREREnYLhgoiIiIiIOgXDBRERERERdQqGCyIiIiIi6hQMF0RERERE1CkYLoiIiIiIqFMwXBARERERUadguCAiIiIiok7BcEFERERERJ2C4YKIiIiIiDoFwwUREREREXUKhgsiIiIiIuoUDBdERERERNQp/j92bex+A2OOFwAAAABJRU5ErkJggg==", + "image/png": 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NEhsEQXgNEhcEQej0c3ZFrVZDIpGAJElYWVnB1NSU7dd2q/3IresaNoFAALFYDLFYTK/b4TUb+Xwe2WwWAHSRMTY2hnA4bEtsuLVuppXYkGUZjUajpdjwgtAiCOJkQ+KCIAgA0AOfZDKJer2OM2fOODa74uDgADs7OwiFQrjiiisQCoUcWDExSgiCgGAwiGAwiKmpKTDGIEmSLjZyuRyy2WzTLA4uNtwoHuzSSmyUy2Xs7OxgeXkZoVAIPp9Pt1GR2CAIYhQhcUEQRNPsClVVoaqqIwGdoijY2tpCoVDA9PQ0FhcXewqWnMoQOB2seiH4HTSCICAUCiEUCiEej+vD7HjNxsHBATKZjC42eIF4KBTyxPttFBvGZgk8swFYTw8nsUEQhNshcUEQJxir2RWiKDoSwFcqFSQSCSiKgrW1NcRiMQdW7BxOB6hki+oNQRAQDocRDod1sVGr1XSxkclkkE6nIYpik9jwyvvOhYY5s9FoNCBJki5GSGwQBOF2SFwQxAml1eyKXrMDjDGk02mkUilEIhGsr68jGAw6smaqbTg5GO1R09PTutjgBeLpdFrfC4qiIJfLIRKJIBgMjmRmw7xmKxsVzzDyzIZZbPBuVARBEMOExAVBnEDaza7oJYCXZRmJRALlchmzs7NYWFhwNNghcXFyMYqNmZkZfZjdzs4OGGPY29sDoM2gMLa9DQQCrg64O93PvPjb+O+42LDKbBi7UREEQQwSEhcEcYLodHZFNwF8qVRCMpkEAJw5c0af6nwSINEzeERRxNjYGPx+P0KhEObn53ULVbVaRbFYBKDN4jCLDS/QidgQRfFIgTiJDYIg+g2JC4I4IbSyQZmxGygzxpBKpZBOpzE+Po7V1VX4/f25tVAQT1jBA+loNIpoNApAs0pVq1VdcBQKBQBae1xjzUa/9mqn8P3ca9Dfqdgw12yQ2CAIwmlIXBDECYBnK6xsUGbsBBuSJCGRSKBarWJhYQGzs7N9D1ZIXDiPF99Tn8+H8fFxPYOmKIqe1TCKjWAw2CQ2Oh3q6HaMYoN/vtwOaZweTmKDIAinIXFBEB6Gz66QZRlAZ5O2O80O5PN5bG1twefz4ezZsxgbG3NkzcetzY2MckbFre+p0/h8PkxMTGBiYgKAVh/EhUalUkE+nwegiQ0uNCKRiCfEBv+MSWwQBDEISFwQhEfhlghVVQFc7j5zHMcFyqqqYnd3F9lsFpOTk1hZWRlYADbKQTzhLvx+f5PYaDQautgolUrI5XIAgFAo1CQ2nG796pQtyg7txEa9XockSQCs52yQ2CAI4jhIXBCExzB6rTuxQZlpF8DX63UkEgnU63UsLy8jHo9TsEF4gkAggEAggMnJSQCa2OBZjUKhgIODAwBAOBzWhUY/xMYwMIoNn8+nz9jggw2NmQ1eHO73+23fWwiCOBmQuCAID9Fp0XYnr2P+71wuh+3tbQSDQWxsbCAcDjuyZjs4lbmQZRnZbFa3wPSaeREEQc8QEd4gEAggFoshFovp3yujhSqbzeqD/3jNRjgcti02hpG5OA5jltMsNmq1mv4zXGzwzAaJDYIgABIXBOEZeLZCUZSeHvL83zHGIAgCFEXB9vY28vk8pqamsLy8PLTTWifERblcRiKR0OtQgP5bX4j+MQibnCAICAaDCAaDmJqaAmMMkiTpYiOXy+lig2c0uNjwQrB9nNhQVRWFQgGxWAyRSITEBkGccEhcEMSIw2dXyLLclQ3KjFFc1Go1PRBfXV3F1NSUQ6sePIwx7O/vY29vD2NjYzh16hRUVW3qHsStL8buQZ0EiBRADZdBv/+CICAUCiEUCiEej+v2Id6N6uDgAJlMRhcbfC+FQqGWax2lPWQWG5Ik4eDgoOm7wrtVGWs2SGwQxMmAxAVBjDBO2aCM8H+fyWSwt7eHUCiEjY0NhEKhntfbK91mLmRZRjKZRKlUwtzcHObm5iDLMhhjCAaDuvXFeBptN0CkQvOTC7dHcasgF+ZcuGYyGaTTaYii2LSXgsGgp/YNH9jHsxq8QLxWq0EUxSMF4iQ2CMKbkLggiBHFzuwKO/DagVQqhZmZGSwsLLjKJmQ3GOM2KMaYPjnc6jXanUabA0QeHHpp4jPhHEZ71PT0tC42+F5Kp9NgjOkn+4A2M6ZdZsPNmOtGzJ3puNhQFAWKorRsfUtigyC8AYkLghgxjLMrGGOOPpDL5TJSqRQAuNIGZec6jTaoaDSK1dVVW0LAeBo9PT0NVVX1ALFarWJvbw8A9KAI0DoMkdggzBjFxszMTNNeKhaLAIDNzU34fL6m2p9AIOCJYJuLDf49MYoNWZb1vzfbqDptn00QhLsgcUEQI4SqqpBl2VEbFNAciIdCISiKgmg02vPrOk2ntihZlpFIJFAulzE3N4f5+fme3ydjxgJAU71GPp+Hqqq4cOECAoFAU4DIT6YJgmPcS4FAAKlUCsvLy/p+4oLD7/cfERtuxG7Hq1ZiQ5ZlNBoNEhsEMeLQU48gRgDj7Arexcmph2yj0UAikUClUsH8/DzC4TA2NzdH1gteKpWQTCYBQLdB9QNRFBGNRhGNRnWrx/T09MhNfKbBhO4gGo3qe1VRFF1oVKtVFAoFANCFK6/bcItw7bWdrh2xwedsGDOGBEG4C3fcmQiCaIm5aNtJYVEsFpFMJiEIAtbX1xGNRlEqlfTf6zbaBcK92qB6XReAjiY+8yFs3c5FIJrhYntUsQrMfT4fxsfHm8QGF63VarVJuBoLxN0mXLvlOLEBWE8Pp+8SQbgDEhcE4WKcml1h9bqpVAqZTAYTExNYWVnRT0HdHqhZiQujDWp+fh5zc3NDvw7jxOfjhrDZaXtLnDx8Pl+TcJVlWRcabsiS9XsQYCux0Wg0IEkSABIbBOEmSFwQhAtxenaFkXq9jmQyiVqthsXFRczMzDS9tnHOhduwylwMygbVC8cNYctms8hkMpatSklseB+7n7Hf78fk5CQmJycBtM6SDWo45KCnjFuJDX4QwzMbgiCQ2CCIIUHigiBcRj9mV3ByuRy2t7fh9/tx9uxZRCKRIz8zKuLCCRuUk5YaO++XVdvbVq1KjR57r3QPIi7jxPfMKktmNRzSq5Y8Xo/B6URs+P1++i4RRJ8gcUEQLkJVVUiS1JfZFdvb28jlcojFYlheXm5pmXCzuAAui69kMukaG5QTgwtbtSpt1T3ITQW9hHswZsn4cMjjLHl873W7jweduTiOdmJDkiQ962EuEHfL+gli1KEnE0G4AG6D4t2gnBQWtVoNiUQCkiRhZWUFU1NTHb22G8WFIAhQFAXnz58H4F4bVK+Y294auwfx02jAuwW9J4l+F6QfZ8mzmkQfiUS6qv9xa3Deqdgw26jcej0E4XZIXBDEkOnn7IqDgwPs7OwgGAxiY2MD4XD42H/n1swFYwzVahX1eh3RaBRra2uuObnvdxBi7h4ky3KT2OAFvYPy2BOjS7tJ9NVqFZlMRj/g4BmNsbGxttPD3XavOA6j2OBrV1UV+/v7KBQKWFtbI7FBED3gjiczQZxA+jm7QlEUbG1toVAoYHp6GouLix0Hmm4UF9wGVa/X4ff7cebMGdc96Af5fvn9/iNtb41ZDe6xN55E92J7cTOj3ooWGO6Jv3ESPQC9/oeL10wmg3Q63bbZgNtsUXbga+ZiQ1VV+Hw+3aJar9cps0EQNiFxQRBDgPdsl2UZgLOzKyqVChKJBBRFwdraGmKxmK1/77YHZqlUQiKRgCAImJiY0IdqEZcJBAKIxWK6x57bXqrVqqXtxXgSTe/lcHGTiAea63+mp6f1+h8uNozNBvh+UlV12Mt2BOMhj1Vmo16vt219S98lgtAgcUEQA0ZVVeTzeYiiiGAw6Jh1hTGGdDqNVCqFSCSC9fV1BINB26/jlswFYwx7e3vY39/XbVB7e3v6w91NuCmoaGd7MZ9Ej42NQVEUqKrqiQwA4TzG+h+rZgN7e3v6z6ZSKf1nBzXA0kmsvgPGzIbP59NnbPDvlVFs8OJwv9/vaN0cQYwaJC4IYkAYZ1dcunQJMzMzmJ+fd+S1ZVlGMplEqVTC7OwsFhYWun6wuUFctOoG5dTDuh8P/WGLsVYYbS/Gk2ie2ahWqwCAl156qakT1SgGh6PIqIk6c7MBXquQz+chSZJlZ7NIJDIy++m4z8J4HzKLjVqtpv8MFxs8s0FigzhJkLggiAFgNbvCqWCUD5FjjDnaPWlYwbLRBrW+vo5oNKr/ndUQvW45qQ96c3C4t7eHYrGIycnJpra3gUCgKTh0S/E84S5EUUQoFAIAnD59umVnM76fuJXKjfupm3sLiQ2COIr7vt0E4TF40baiKPoDxYkgmTGGVCqFdDrd9RA5K4aVuTDaoMbHx7G6uurKAMQKJ0XPoOGBz9zcHACtGQAPDKvVqt6JKhgMNokNanvrHKMeZBqzL+bOZu32k9vaKDuRRepUbHD7FIkNwouMxpObIEaQdrMreg1GJUlCMplEpVLBwsICZmdnHbcMDTJYbjQaSCQSqFQqbYfijXIQPyr4fL6mTlSyLOvBYalUQi6XA+Ceac+jvh9Gff1A+6C83X4yt1E2djcbhtjoh0WtldjgBeK1Wg2iKB4pECexQYwyJC4Iog9Y2aCMD4peguR8Po+trS34fD6cPXtWt7c4xaAfaMViEclk0tIGZYUXgrFRwu/3Y3JyEpOTkx1Ne+ZiY5D7iIKw0cG4n4DLbZSr1WqTeB3GzJZB1L+Ya8e42FAUBYqitGx9S2KDGCVIXBCEw/BshaqqLR8I3YgLVVWxu7uLbDaLyclJrKys9O10bxAZgm5sUG59uJ6UjEqn057bzUQgmhm1gm4rerkGcxvlRqOh12wYZ7YMIlM2jM+Ciw1+Pa3EBrdR8f+lNtKEmyFxQRAOYZ5d0e6kyW4wWq/XkUgkUK/XsbS0hOnp6b4+WPodLBttUHZsXVTQ7S6s2t4a25RazUTgnajo/fcOTgXlRvFqFBuDypS5Qei1EhuyLOszfqxqNkhsEG6CxAVBOAAv2ubDpI670XcaJDPGkMvlsL29jWAwiI2NDX2Sbj/pp7iwa4PqFych0zBojAPY2s1EMLYpdWvnIGL4dJopMw6IjEQiXYsNXhvnJuyIDT5ng9uoCGJY0B2dIHqAF+YdZ4My00nwrigKtre3kc/nMTU1heXl5YE+MJwOvo3drbrtBuVW+5Fb1zVszG1vW7UpdWPnoEHhhtPyXhnUNXQyIJILBC5yjdPo3XIdvdCJ2OD3onA4rAsOEhvEICFxQRBdclzRdjuOC0ar1SoSiQRkWcbq6iqmpqacWHLHOB0sd2uD6ve6iMHaw8xtSmVZbhIbxs5Bgy7mJXpjGEG5eUCk0ZZXrVabptF3WgPkdnFhxkpsVKtVJJNJrKysIBgMHikOJ7FB9BsSFwTRBVazK+wgCIIuSowwxpDJZJBKpRAKhbCxsaEPqBokTj5g3WKDIlozLMHm9/ub2pQa/fXGYl7jKXQkEjmyP0ctIDQyCqflx+EWwW+05QE4Mo1+f38fAFrWALnlOnrBuJf40D5+ECZJki5GSGwQ/YTEBUHYgHfxkGXZlg3KjNWDTJZlbG1toVgsYmZmBgsLC0O74Ts95M+poXhuDQBGPTh0C+bOQZIk6ZmNXC6nF/MaA0Ne50QMD7cKJLMtT1VVVKtVfU/xGiCfz4exsTHIsuzIINJhw++RXEiYMxv8cKzRaOg/ZxQbvBsVQXQLiQuC6JBebFBmzEFyuVxGIpEAYwynTp3Se8APCzcP+QPcGcy4UfSMMkZ/PS/mNfvr0+k0AKBQKEAQBGp7S7RFFEVEo1E9e6qqqp7VqFQqkGUZxWIR1Wq1yZY3aoLDKC7M8OJv489yscEzG6IoWnajIohOIXFBEB3QyewKO4iiqBfi7e/vY29vD2NjY1hbW3PFg6wXccFtUKIoOj7kz8kHHD0sRwsrf321WsXW1hZUVW2yvBg7Ubnh+9QONwplu4zqNYii2FQD9NJLLyEUCiEQCDQ1HAgEAk1iw+3dzdqJCzN2xIaxG9Uoft7E4HD3N4QghoxxdgXvQuJUP3dVVXHx4kWUy2XMzc1hfn7eVTdsu+LCaIOamJjAysqK4w9h/v64LZhx67q8DM9UCIKAqakpTE1NNRWHF4tFAKMXGI4iXtr3oVAIs7OzAJobDlSrVb3hQDAY1PeTG7ub8Xt3N7Zao9jgr2MlNsw1G175/AlnoLssQbRAVVXIsuyIDcoMv1GrqoozZ87oJ2duwW7mwmiDWlxcxMzMDD1siIFitrwoiqILDavAkAeHbggM6bviDswiydxwQJZlfU+Vy2XkcjkAl7ub8WLyYe8pp+qQ+HthJTYkSdKnh5PYIMyQuCAIE8a0MH/YOHWjVFUVqVRK94hfccUVrjxJtSMuCoUCtra2+mKDsloX4Ex9Az38vI3P52sZGJZKJT0wNE96HnQTBS/U6nglc3Hcdfj9fkxOTuo1cby7WbVaRbFY1LubhcPhpu5mw9hT/ZjYfZzYkCQJAEhsECQuCMKIuWjbyRt0vV5HMplErVbD+Pg4qtWqK4UF0FngPQgbVLvf7SbIFuV+jIEh/54bsxq8E5VZbNDneTxe2fd2r8Pc3cy4p4ytlActYAc51BBoFhv8/+r1epPY4PUafr/fURcA4U7cGdkQxBDodXZFO3K5HLa3t+H3+3H27FmUSiVUq1XHXt9pjstcSJKERCKBarU6UBsUPZAIM90EUoIgIBgMIhgM6p2oJEnSMxsHBwfIZDK2hq8Ncv1Ef+jls2i1p6xaKfdbwA5rTxkP43w+X5PYqNVq+s9wscEzGyQ2vAeJC+LE49TsCitUVcXOzg4ODg4Qi8WwvLwMn8+HcrnsutN3I+3ExSBtUFbrApzJXNTrddTrdb0o+KTi1tkhg8TY9jYejzdNeq5UKkin02CMtRy+dtLhzS5GGTsdljrBqpWylYA1zm2JRCKOiA23CFYSGycXEhfEicbJ2RVmarUaEokEJEnCysoKpqam9NcehYDOvL5h2qCOW5vdf5vJZLC7uwsAjpxOOyl6iOFjnPQ8MzPTNOnZOHzN7/c3tb3t9vsw6oGUW4JZJ+jXdVgJWPPcFi7SjGIjFArZXpNbxV6nYsM8Y4PExuhB4oI4sfAiNKezFYwxHBwcYGdnB8FgEBsbGwiHw00/w8WFWx/KvFUuZ1g2KKt19YKiKNja2kKhUMD09DQikYgeNJrnJESj0ZEcoEU4j3nSs6IoTW1v+TyEYDDYJFQ76Rrk1nvASWPQBwNWc1ussmV87/F91cnhh6qqI7GnWokNLuaBy10bJyYm9LoNEhvuh8QFceLgNijeDcrJG5U5eF1cXLQ8QXL7jdGYWRmmDaoV3QQC1WoViUQCsizj1KlTGBsbQ6PRQDQa1U+nreYkuLF1KTFcfD5f0/A14zyESqWit73lLUqH1TVoUHhBIDlti7JLu2xZtVo9cvjBxYaVNW9UPw8rsZHP57G3t4f19XXU63WIonikGxWJDfdB4oI4UfTTBlWpVJBIJKAoCtbW1hCLxVr+LP+dqqq6Mljl4mJnZweZTAYTExNYXV0d+lq7+ayMmaRQKISNjQ2EQiHIstz0c+Y5CcaA0djT3qrzC9miTjbmeQi8axDPavCuQTx4NO6dUQ0EjXhh3w9bXJgxZ8v44Qe/J/HDD7/ff6QOyAt7Cmj+LPh18cNBRVGa5mwYu1H1ow0vYQ8SF8SJoJ+zKxhjSKfTSKVSiEQiWF9fRzAYbPtv3B6M8lOzWq3mqqF4dt83VVWxvb2NXC6HeDyOpaWljk+PzQEjL8Y0ty41Tn126+dJDJZWLUrNXYMikQgajYYu5t3wHeuWUV474D5xYcZqSKSV2OBBuCiKaDQaI2/r5LZl4HJmg/+3UWzIsqz/vblmg8TG4CFxQXgexhhkWdZPqp280ciyjGQyiVKphNnZWSwsLHT02sabo9soFAr6g2p9fd0VNigznbxv9Xodm5ubkCQJq6urmJqa6ul3mttMGosx+ft16dKlpgJf6ibUf9welFu1KDXuHX5vOn/+fN/b3vYLt38GneB2cWHGbM0z1gHl83nIsowLFy4gEAg0WfPcOlupFe32ViuxIcuyLtq52OCZDW6jIvrLaO0ygrAJz1bw4mQnbyqlUgnJZBKMMZw+fVo/4e4EN2Yu+PTwTCajB8VuExadPvj5XJFAIGBZUO/EOozFmKVSCdvb25icnEStVnO8mxDhHcx759KlS3qLW6vGAkahSvSPURMXZoxigw+vi8ViR+qAeA0ZF7LDtroehx3hSmLDPdCTjvAkRhtUP7pB7e3tYX9/H9FoFKurq7Yf/G4TF7wbVK1Ww9LSEur1Okql0rCXdYTj3jdVVbG7u4tsNts0V6Tdazm5rng8jkAg0LKb0Ekp8CU6RxAE+P1+zMzMtG0swE+geVDoJqFKmQt3wRhDIBBosnXKsqzvKWMNGb8n8Xogt4kNoy3KLseJDQBHisNJbDiDe+5OBOEQ/SzaliQJyWQSlUoFCwsLmJ2d7eq13SQuCoUCkskkfD6fboPa3d11xdrsYBRIy8vLiMfjQwsUrLoJGYNFY4FvPyf1doIXgqlRxhyYW3nrea2P1Qm0G7qYkbhwF1ataP1+PyYnJzE5OQmguemA8Z4UDoeb5mwMO9B2cm+1EhuNRgOSJOl/T2Kjd0hcEJ5CVVXk83lsb2/j9OnTjloJjEF4ry1Z3SAujDaoyclJrKys6AGKW4f8tXrfzJ9NJBLp+LX6uS6O8cFuLPAtl8v6pF4nhvl1ixs/a0LD5/O1PIEulUptu5gR9vGCuOgkIG/VdKBarR7b4WyQ9JK5OA4rscEdDzyzYRYbvBsV0R4SF4QnMM6ukGUZtVrNsYDJaLUxB+HdMmxxYbZBTU9PN90w3X7z5O+beWq4G9rlHodVga/V8Czy3BNWtBKq5i5mZrHRz++0lzIXXsDu52F1TzJ2xzN2OBvkvgIGu7d4PYbxd3OxYZXZMHajIpohcUGMPGYbFL85GCdMd0u9XkcikUC9XrcMwrtlmOKik1N+t2cuAC2tn0gkUKlUum6X64aHgtXwrHaeezvTnwn300vw1C4orFQqA8uKeUlcjPp1AL1/HoIgIBQKIRQKIR6P6x3O+H2J7yt+7+L7KhQKOf7+DXPaeCdiQxTFIwXiXthDvULighhpeLbCWLTtVJtXPnjN7/d3bLXplGGIi3Y2KKv1uVlc8IGFgiBgfX1d96cPe11OvGetPPdW05+j0eiJtsF4KSB0AqugsFar6UGhMStmHrx20t9DL+0lp8WescOZUWzwe1Imk0E6nW4SsZFIxBGxwferG+hUbJhrNrywp+xC4oIYScyzK4xF270GeoqiYGdnB7lcDlNTU1heXnY8cBu0uJAkCZubm7YyMG4UF3xNe3t7iEajWFtbc1XXnH5g9twbCzHNw/z6eYJI9Id+nvobs2LT09P6cEy+f6xaJkciEdsWPMpcuAs+RK9fmNspt7J2GqeMRyKRrjJmbt5bRrHB94+qqpAkqWl6+EkUG95+KhOexDy7gvsgOfz/340tqlqtIpFIQJZlRwavtWKQ4iKfz2Nra8t2sbPbxIUsy9ja2gKg9W9fXV09ETdpM+ZCTKMNxniCSMP8CDPGfQE0D17jhbxAdxa8Ud9fXhMXg7wOK2snFxvValUXsbyOjB+EdHJf6mdBt5Pw6zhObEiShFAopHd486rYIHFBjAydzq7oxhbFGEM2m8Xu7i5CoRA2NjYQCoUcW7uZQYiLXgrRjetzw42vUqlgc3NTf78mJyddsS7OsGpoWtlgyuVyy5Npt81IIIaHVctkY72P0YLXbj6L2w4iusEr4oK3Vx3mdZhFrLGOrFqt6nVkfr//iD3PzLCvpVusxAZjDNvb24jFYojH4wCs52yM4vWaoScMMRLYmV1hN9DjJ+LFYhEzMzNYWFjo+0lJL9mVTui1EN0tNzfGGDKZDHZ3dzE2Nobl5WW8+OKLw16WazGeIALa/jLWa/CTaeOMhH7tQaIz3BQ8+f3+lhY8Y3tSY8cgfgjjlmvoFi8IJMCdIsmqjqxareqCw9i0wig2/H7/yGQujoM7LBhjupDggqNerzfZqAKBAPx+P4rFIgRBwPT09LCXbxsSF4Tr4dkKRVE6GohnJ3Avl8tIJBJgjOHUqVP6gKF+08+Tbm6D6qUQ3Q2ZC0VRsLW1hUKhgNnZWSwsLDS1oHUTbnqQGxFFseUwP+OMBABIp9OIRqNDG+ZHuI9WsxAqlUpTe1JAu5fyLNoo7h9+rxvFtRtxo7gwY86YmQdFGu15iqJAkiTIsjzyGVcuJngcY8xu8L/j2WcAeNe73gUA+MxnPjO0NXfLaH9ShKfhsytkWW5rgzLTiS2KMYb9/X3s7e1hbGwMa2trA50jYDzFcAon53EMew6HsfbFKPrcKi44bl0Xx2pGwv7+PsrlclOwaLRQDXKYn11GIZA6DjdlLtph1fa2Xq+jXC4jk8mgXC6jVCoNdRhkr4zKOtsxit8Jq0GRxon0xWIRxWLRVVPpu4F/NlaZGCuxkUqlEIvFBrpGpyBxQbgSOzYoM8cFxo1GA8lkEuVyGXNzc5ifnx/KjdhJcWG0QS0vLyMej/fc5xwYfLDMGNNbAIdCIVxxxRUIBoNH1kX0Dg8Wx8bGUK1WsbGx0dRekob5Ee3gHYMCgQAymQwWFhYQCAT0/bO/vw8AI7N/3H4w0CmjKC7McHve+Pg48vk8Zmdn4ff7j2RceS0QF7Nut08Zm9AchyAIqFarWFxc7Pey+gKJC8J1WM2usIsgCJa2qGKxiGQyCUEQcObMGT0tOwycEhdO2KBaMeg5HNvb28jlcojH41haWmr5sPBKIOAW+Om5sb2kseNLuVymYX5EW6w6BrUaBmn21buBUckgHUe70/FRg1+LMeMKHF8LxPeh294DHpN0si7+/eFF8aOGO77VBIHm2RVGX2I3iKLYFIAaB8iNj49jdXV16A+1XsWF0QYVi8WwvLzsWKA36MxFvV7H5uYmJElq2wLYrQ//YdvI+oGx48vs7GzbYX7tOgkR7Rn1oLbVSXmrIt52zQWGaXUZ9c+BY+d03O20Csbb1QLx2T8AdJHhlkGjdoRfvV4HY8zRw8JBQuKCcAWqqkKW5a5sUFYYA3dJkpBIJFCr1bC4uIiZmRlX3Hh7ERdO26Cs1jYocrkctre3EQgEsLGxgXA43Pbn3TiD4yTQbpgfPz3k2Q8eLFJxuPfp1IZj1fbWqrnAsE6fvSIuvGCL4nRyLVa1QHz2T7VabaolM4uNQb9HdoRftVoFAF2cjxokLoihYpxd4WS3Dm6L6qdlqFe6DZIHcU2DnsNhN/NC4mL4tBvmd3BwgEwmQ8P8OuQkvidmq4skSU1FvDwgHJRYJXHhPuzYiDhWs3/q9bq+t/i9iYsNY0vlfr9ndq6nUqkAANmiCMIu5qJtJ9sACoKAYrGIdDrtuGXIKeyKi37aoKzWBvQviDdmk+xmXtz40PSiLcoOrYb5cbFBw/xaM+p7xqlglp8+txOrxk5mkUjE0YCQxIX7cOJajLVkxnsTFxuZTAbpdHogXc5IXBBEn7E7u8IOtVpNz4SsrKxgamrKlTdaO+Ki3zYoq7UB/Ql8CoUCkskkfD5fV5kXskW5n+OKe53227vx+31S6Ecw20qs8j1k7GRmnvDc7TpIXLiPfu0tfm+anp4+chDC95Yx6xqJRBwRG3auh9ui3OS2sAOJC2KgdDu7otPX5m1MAWBiYgLxeNyR1+4HnQbJvCZhGNYuJ4N43rc7nU5jYmICq6urXQeTJC5GC3Nx73F+e/5AP+7e4IV94JWgtp+YA0JjJ7NWmbFIJGKr7a1XPgcvdYvqxhZlF6uDEKu9xVsqczHbjZDtpuaCMhcEcQy9zK44DuM05+npadRqNdffXI8TF6qqYmdnBwcHBwO3djmduWg0GkgkEqhUKj0X1bsxADjptii7WA3zs5r8zB/k0Wh0pIaxnSSGcVJuPFUGtHtlqwnPnbZN9pq4oGvpDqu9ZdVSmQtZo9g4DjsHqmSLIogOUFUVkiQ5nq0AtC9hIpGAoihYW1tDLBbDxYsXXR/otZrFAQzeBmW1NsCZYLlUKiGRSEAQBKyvrzvS/cLtny3ROa0mP/OHOfdEj8owtpOGG4JZURSbOlF10zbZK+Ki1TNlFBlE5uI42rVUrlarTUL2uPktdvYYiQuCaAO3QfEaCKdtUOl0GqlUCpFIBOvr6/o051Hw5bdao9EG1Ulr1n6trVcYY9jf38fe3p6js0XcGAC4cU2jSrthfpVKBalUCsDlhzkwugGVGwJzL9JJ22Sg2YbnFXHhZNfFYePG74e5pTIXsuasWTAYbBIbPp9PP1ztBGpFSxAt6KcNSpZlJJNJlEolzM7OYmFhoem1RVGELMuO/K5+YRYXw7RBWa0N6D5DYPx85ufnMTc352gnMLcKR7euqx1uenBbYbYpGE8Oy+UyACCVSiGXy9EwvyHgxgDQTLuha9yGB2iBYzabHVhr0n7gFZEEjMbeMgtZXk9mbKsMQD/45Aeuxz3bqeaCIEz0a3YFp1QqIZlMgjGG06dP619qI24OQDlGATRsG5SZXsRFpVLB5uYmGGM4c+aMfsLjJG7/bEeNUXo/jSeHiqLg/PnziMViUFV15Ib5jULw5DVa2fC2t7fBGBtoa9J+4CVxYeek3y2Y57c0Gg1daBSLRTDGcP78+bYWPeCyLarXzEUul8Pdd9+Nr3/96yiVSrjqqqvwvve9D6997Wstf/4zn/kM/uN//I9H/vwrX/kKTp8+3fHvJXFBOApjDLIs60Gzk8KCMYa9vT3s7+8jGo1idXW1pe96FMQFX6PdCdWDxM57yB/Mu7u7GBsbw9raWl988U63JXTT6xDdEY1GMT4+3naY3ygGiqPAqAskLkT9fj+CwSAWFhYsW5OOSs2Pl8SFF64lEAggEAhgcnISqqpClmXEYrEjFr1AIIDPfOYziMfjeN3rXqdnZXvtDvne974XmUwGd999N6anp3HPPffgl3/5l/HpT38aGxsbR37+3LlzuOmmm3D33Xc3/fn09LSt30vignAMnq3oRxGWJElIJpOoVCpYWFjA7Oxs25uOKIoj4cOu1+tIJpNDt0GZsZu5MHbrsrKpOb02p1vkOimAicFhfr+PG+a3v78P4HJbyWg0artlaT8Y5QBq1MUFx5hlbzejhXcL6qSAdxjw2kYvMIqZi3aoqgq/329p0dve3sY//MM/oF6v42Mf+5h+T/rqV7+KcDiMjY0N29+xS5cu4Vvf+hY+9alP4frrrwcAfPCDH8SDDz6IL3zhC/jN3/zNI//m+eefx8tf/nLMzc31dK3u+DYQI43RBtWPblCFQgFbW1sQRRFnz57tyIPo9sxFvV5HsViEoiiuHPRnR1xUq1Vsbm5CURScOnVKTwePAl44GSNa02mg6NQwP7u4+R510mh1L2jXLagfAyF7RVVVz9zTvHZ/Nl+P2aL3j//4j3jooYfwyCOP4Gtf+xoajQY+/vGP4+Mf/zhmZ2dx00034ZZbbsHNN9+M06dPH/vexONx/OVf/iVe+cpXNv1OxpheC2Lm3LlzeMtb3tLztZK4IHqin0Xbqqpid3cX2WwWk5OTWF5e7vh0yM3igtugBEFAIBBw5aC/Tj5D49DCUCjU1K2r32tz62dLuBurYX7G4nCrYX7hcNhTp6dO47XMxXGYuwW1Ggh5nKe+X3gpIPeSUAKOz8TMzs7ibW97G972trfhfe97Hx544AH87u/+Lr773e/i4Ycfxr333ot7770XAHD69Gn83d/9Hebn51u+3uTkJN70pjc1/dkXv/hFbG5u4g1veMORn89ms0in03j00UfxyU9+ErlcDtdeey3e//73Y3193da1krgguoZnKxRFcTxbYSxwXlpawvT0tK3XbzdDYlgYu0FNTU3B5/PpJ6du47jMhaqq2N7eRi6Xw/T0NBYXFwcagDkhLhqNBnK5HMLhcEfToNtBQ/RGE7/f37JlaT6fPzLMr19dhEY5gDpp4sKMVQGvue3tIBsMeElceMniBdizefFuUT/3cz+Hn//5nwdjDJubm3j44Yfx8MMPY3t72/Z78/jjj+MDH/gA7rjjDtx+++1H/v75558HoAnoj3zkI6hUKvj4xz+OX/iFX8DnP/95zM7Odvy7SFwQtunn7AoA+mm43+/H2bNnuypoEkXRVYFerVZDIpGAJElYWVlBPB5HKpVy1RrNtMoQ8GtpNBpYXV3F1NTUwNfVK3ywnzHjFolEEI1G9WJNrzygic4xtyy1GuZnbI3b615x8/f/pOFUUG7eQ1YNBgRBaJru7KRg9Zq48Mq1APYyMdVqFaFQSLfXCYKA06dP4/Tp0/i5n/s527/7/vvvx/vf/35ce+21R4q1ObfccgseeeQRxGIx/c8+9rGP4bbbbsOnP/1p/Mqv/ErHv4/EBWGLftqgFEXBzs4OcrkcpqamsLS01LVv1U3WmYODA2xvbyMYDDZ1g3LTGq2wWp+xs9XZs2eHNuCv2/fNONgvGo1ibm6uyRqzt7cHQDuN5AW/fAAScbIwD/NjjDV57c17xW2FvYPCK5kLwPlraNVggO+jfglWL3wWgPcKuu1kYiqVSs+dojh/93d/hz/8wz/EnXfeibvuuqutfdkoLABtzsbq6qo+vLRTTtZdkOgJRVGwt7cHQRAcL0CuVqtIJBKQZdmR03AegA7zRmu0Dk1NTWF5ebnpxuJ2cQFcDhyM9S+xWAwrKysjd9NXFAXJZBLFYhFzc3OYm5uDJEkIBAIYGxvTC3558Ggs1jzOP022qOEwyPebnzZbDfOzs1eMeCEw98qeH8R1GBsMWE2ftxKsdruZ8ba5XsBLQok3vrFji3JCXNxzzz34/d//fbzzne/EBz7wgba//5577sGf/umf4oEHHtAPDkulEi5evIif+ZmfsfV7SVwQx2KcXZHL5RwtQmaMIZvNYnd3F6FQCBsbGwiFQj2/Lv8CDevmZGWDMuN2ccHXJ0kSNjc3XTXgz+77xjtaqaqqD1602huiKFoWa5bL5Sb/dL89+INklNfOGcY1tCvsLRaLev96415x6zA/Jxj16xrGs8I8fZ4fbnDRygUrPwDh/9dOPHgpIPdS5oI/s+yIC7uzJcxcuHABf/RHf4Q777wTv/qrv4pMJqP/Ha8BymazmJiYQDgcxm233YY/+ZM/wW/91m/h13/911Gr1fT5GG9/+9tt/W4SF0RbrGZXOBUQy7KMra0tFItFzMzMYGFhwbEbyTBPklvZoMyMgrio1+t48cUX4fP5uq5/6ce67Lxv2Wy2645WxmJNo3+6XC7rlgY+MwGAbhccRbwUlAwD814xFva2GubH3+9Rft+9sm/ccB3mww1FUZoyqbx9KM+O8b1kfG56qcOSGz4Tp7CbpaxWqx213W/Hl7/8ZTQaDdx333247777mv7u7W9/O37t134Nd9xxBz784Q/jHe94B5aWlvA3f/M3uOuuu/Qi8te//vX427/9W9sWaBIXhCWtZlc4NZyuXC4jkUiAMdaX2Qj8C6yq6sBSxMfZoKzWOGzrViv455/P5zE5OYmVlZWRS7Ubu3PF43EsLS31JF7N/mmjpYFPU81kMigUCnqtxjD73RPDw9y/3ui1L5fLTVOfAe1+yCf5jiJuu3/Zxa32NJ/Pd6SbGc9qGLNjxk5UJC7ciZ3hwoqioFar9Swu3v3ud+Pd73532585d+5c039fffXV+Ou//uuefi9A4oKwoF3Rdq8tXo0FtWNjY1hbW+vLA9VoixoEndigzBizK266gTYaDSQSCaiqimg0irW1NVetr5M9aLRydfp52MVoaZidncXzzz+PyclJCIJgOTMhGo162hZDtMbKa1+tVlEsFnW73cHBgS37i1twc/a1U9wqLsxwAWqVHcvlcshmswA0n7zf70ckEhnpe46XbFF2xEWtVgMAVzgFuoXEBdHEcbMrerFFNRoNJJNJlMtlzM3NYX5+vm83vUHaojq1QZkZtADqBN6ilQ/4c+uDqd17ViwWkUgkBm7lEkVRz2oAaGpByR/8RltMNBqllrcnFD7Mz+/3o1AoYHl5GYwxS/sLz4K5dZif2w5HumFUxIURq+xYvV5HMpmEIAjIZDJ6dyKjFS8YDI7MdXphb3Hs7DE+46LXzMUwIXFBALg8u0KW5SYblJluMxfFYlG/6Z05c0b3lPYLoy2qXxhtUN3YbtzUYciYURofH8fq6iouXLjgirWZaXVzZoxhb28P+/v7mJiYwOrq6sBPfo3vl9WDv1wuo1KpYH9/H/v7+ye+jSmh4fP5EIlEhj7M76QyiuLCDG+dLAgCYrEY4vF4UycqoxXP3PbWrZzUzAWJC8IT2JldYTdzoaoq9vb2kE6n9aB1EAFUv7MC3digzLhFXMiyjGQyiVKphPn5eczNzUEQBNcWnFutS5ZlJBIJlMtlLCwsYHZ21lWBgnFmAm95y/333bYxJbwRFAJH199uEFs/ZiP0ghdOl72yj4DLn4fRime85xg7mgHaXjOKVrcccHjpMwHsiQtew0fighhZ+KTtdtkKI3YyF5IkIZFIoFarYXFxETMzMwO7UfQrcGeM6YPk7NqgzLhBXBgL680ZJbeKCzOVSkWvERlEVqwVdvY2t8VEo1EAzW1MvdryljhKJ9+vVoPYyuUyqtXq0If5eUFccLxwHa0Kus33nFZzWoLBYNMBx7Dqfuy2bnU7dsQSr7kgcUGMHMbZFdyX2cmm7zRzkc/nsbW1BZ/Ph/X19YF/SfphizJOEHeq+xAwHHHBGEMmk8Hu7m7Lwnq3PmiNXbb4jJRwOIxTp04NPcXf7WfZquWt1Uk19+AP+1qJ4WA8kQYuz0ZoFySa25USzXjllNzOdbSb02JsSDGsbCp/do/6Z8Kxk7moVCoASFwQI4aqqpBluSMblJnjWtEa23/GYjEsLy8P5eTDaVsUt0E1Gg1HJogDwxMXiqJga2sLhUIBs7OzWFhYaFlf49bMhaqqSCaTyOfzmJ6exuLiomeCJ6uTauMJYyqVAmBvsNZxv48YDk4Eta0GP1YqFZRKpb53LfNC5sKt9zm79LKfjAccQHPdjzGbamx728+GH14RfByeUaKCbsJzGGdXGH2Zdmg3m8FYhzDsSc5OBe6MMRwcHGBnZ0e3QTkxQRwYjrjgk6oVRTl2vohbxYWqqpAkCZIkOSb0nKBf75cgCE1TfFsN1jI+9CORiO3vnheCRKL9MD/etcy4p3rtIOSFfeOVQNbJ62hX98OHQvbTumnnpH8UsPM9oZoLYmQwF213IyyA5oyAMTg2B+Dd1iE4hRO2KEVRsL29jXw+74gNyswgxYXxM+p0UrUbxUWhUNBtH07sMzde43FYDdbiheH9CB6J/tPPltxWXctGuYNQPyBx0R6rbKpxH/WjyYBXPhOOnc5XvOaC18eMIiQuTgDHza6wgzFoF0WxyWLTjwC8W3oN3Gu1GjY3NyHLct9OxwclLlRVxdbWVlcWIrcE3owxpFIppNNpXRQNW8C6hUAggKmpqSPBo3kSNK/VcFNHmJPOoL9fxq5lfJifccq8sYNQp5Y7yly4h0EVQbfbR5VK5UiTAZ7dsCNavZa5sCMuqOaCcDWdzq6wgzFzwbv0KIqCtbU1xGIxJ5btCN22UjWf8Dtpg7JaI/+d/aKXWpFuZ5o4DZ8YXqlUsLi4iEajoQdBbsINgYnVQ79VRxguNtwiILvBK0HhsDBPmW9luWtX1Eviwj0MqwjauI/4OqzuO3ZFKzD6nwnHzveEiwvKXBCuw87sCjvw1+Bp0Egk0pHFZhjYFRf9tkFZrQ/o36A/3jI3EAjg7Nmztk/63XBT561yAWB9fR3RaBS7u7uuDYjdtq52LW+LxSIODg70n81msxgfH6eWtwPEbQGUleXuuKJet+35bnDb59AtbrkOq7a37UQrz2yYRSsw/Gtxim5sUbwj3ChC4sKD8IJXp7IVRvgXPp1Ot+005AaO62xlZBA2KDP9ylyoqord3V1ks1lMTU1heXm5K5E0zHqEdq1y3brfRgGrlrfZbFYXGtlstukUMhqNnjj/PXGZTop6AU2U5HK5oQ7z6wWvBLJuvQ4r0cozG8ZDDqNoNR6MeoFubFGUuSBcAbdB8W5QTguLUqmEZDIJAFhcXMTs7Kxjr90POgmOB2mDMtOPKeKSJGFzcxP1er3njl3DEhfHtcp1axG22x7ox8GLNKPRKIrFIs6ePQtJkvTicO6bdqrlLTHatBrmt7u7C0VRhj7MrxfcGpTbZVSuIxAIIBAItO1oxjk4OOh729tBwGvfOoFa0RKuoV82KP7ae3t72N/fRyQSQbVaHYli2uOCUKMNahizEpzOXBQKBSSTSfj9fpw9e7bnlOowgnhjjUi7Oh43igvAvevqBPNwNj7Bl4sNp1reEpcZlWDQCr5fuCV2aWnJ1ROfO2EUPwcjo7ifWnU0y2QyKJfLyGazyGQyEEWxqe3tqHXAU1W1Y6HNMxdkiyKGhhOzK9ohSRKSySQqlQrm5+cxNTWF559/fiSCqHa2qGq1ikQiAVmWh16M7sQsjt3dXWQyGUxOTmJlZcWxB/ggP2djjUi7DNIoPVBGGfMEX+PpYj6f11veRiIRvTh81B74RO/wLLlVfQ8Xp1bD/NwkTkfhedYJg+oW1U94TQ8/yNzY2GjqRGXVPtkoct2KHVsUz1yQuCCGAmMMsixDlmUA3c+uaEWhUMDW1hZEUdSLafnvckMXoeOwOnkfpg3Kan29ZgfMnZRmZmYc2wODylyoqopUKoVMJoNYLIaVlZW2N2E326LcuC6nMPvv281L4GJjFCwxw2QUT5o7xe/36z77dsP8+Gl0NBodmjj1yufglesAmida84zqzMxMUyeqarWKVCoFwP12PC7CO4G7Q9yc5TsOd737RMfwbEU/ekEbC4InJiawsrKif1H7USfQL8zB3rBtUFb0EpCWSiUkEgkIgoCzZ8867s8cRLDMxVG1WsXS0hKmp6cH/mD0QivNXujm2o9rectbBRstMeZuME7gpWBqVOnk+3PcMD/efXBYw/y8cg8YVivaftAqGLfqRDUKdjwuljqhWq2OdL0FQOJi5DDaoPrRDaperyORSKBer1sGe/1un+okxjkNbrJBGel2Fsf+/j729vYwPj6O1dXVvpzS9PsBZRRH6+vrHd9MvZ4hGBa9vKftWt4aLTFGz/SoF2g6ySi/D90E5scNYeOn0YNqJuAVceGl+2Knn4nZvmm895TLZf3e025WyyCw2y1qlC1RAImLkaKfRdsAdLtQu4JgJ6w8g4LXXPCWpsO2QVlh972UZRmJRALlchnz8/OYm5vr20OxX58zYwzpdBqpVArRaBRra2u2xZEb99+ofC8GgbnlLbfElMtlvYWpseXtqLYw7RXaLxrmIWzm0+hOhvn1gpfEhdP26GFhJxg3Yrz3AJ3Naun3QQdjzJYtqlarYW5urm/rGQQkLkYEnq1QFMVxUaEoCnZ2dpDL5TA1NYWlpaW2J0R25kcMm1qthlKp5BoblBk7ASkfKMcYw5kzZ/STmn7idPCjKAqSySSKxSLm5uYwPz/f1aknMTpYWWKMp9TGFqa8VoNa3o4O/QjMrZoJcLHRjwDRa+LCCzh1LZ3MajHW/oyNjTk+SNSufbNSqZAtiugv/Z5dYbQLdTo8zmg3ciu8S4mqqq6yQZnpdBZHq4Fyw16bHYyDCk+dOqWfLHWzLsBbD9KThLlA87hT6mg0inA47LqDAScZ5X08iO+heS6CVYDYS6tSr9xLvHIdQPeZi3ZYzWqxqv1xOqtqpzZWURRIkkTigugf/Z5dkc1mu7ILiaLo2nS+8bpEUUQoFHKtsAA6m8XBT/oHPRHdSXFxcHCA7e1thEIhXHHFFY60DXTbg5RsUd3RruWt8ZTaDV2FnIb2i31aDfNr17ksEom0PZBx272kW+xYb9zOIK7luNofY1bVKFztHu7ZERdeaEMLkLhwLTxb0Y+ibVmWsbW1hWKxiJmZGSwsLNj6Ers1c2Gc7Dw9PQ3GmP5FdSvtAtJqtYrNzU0oitLTSX8va+sVVVWxs7ODg4MDTE1NYXl5uecHhheCAKI1VjYGPsjP3FWIP+RpTwyPYQfm7VqVtupcZu4eNOxrcAo7HYnczjA+E3PtT6u9ZLfRgB1blBemcwMkLlyHeXaF08KiXC4jmUxCVdWuA1Y3Zi6MgTi3Qe3s7LhSBBmxEmrmWRzr6+tDGRDEhU+3N3lJkpBIJFCr1bC8vIzp6WnH1gX0furrlYewlzGeUhtPFrnY4A/77e3tpnqNUTm99UorXTet36pVqVX3IGO9htueZ93iFZEE9McWZZd2e8nKwsmzG+Z128lc8OncJC4IxzDPrnCy64OxfenY2BhWV1e7DljdVNBttEGFw+GmQNyNIsiMm2dx9LL3isUikskkRFFs2XnMDTj5ICZbVP8xnyyWSiXdbkctb4eD2/e8z+fTh/kB1pPmAW1vZbPZvhT0DgoviQs3XovVXuKZjWKxiIODAwA40migG3Hh1mdmp5C4cAH9nl3RaDSQTCZRLpe77tBjxC1BlNEGZWXvcss622FcY61WQyKRQKPR6Li4vt9rA+zd5Acxg8OpzAUx+nA7wszMDILBYFPgaC705ZkNt7W8ddNausGNQWA7rGx3u7u7aDQafSvoHRSj9lm0ww2Zi+MwNxpoNYWe2zfr9Tr8fn/bz4hsUYQjMMZQLBZRKpUQj8cdFxb8BFkQBMfal7ohc2FlgzLj1toQIzy7ksvlsLW1hWAw6JpZHHaDeFmWkUwmUSqV+j6Dw866iPZ4JRix0/KWF/oOu+WtV/bwqO4hbrsLBALw+XxYWVlp8tib9wz/v34MLXUCL4mLUbsWq/sP72rGp4Zvb28f29WMZy64FasXcrkc7r77bnz9619HqVTCVVddhfe973147Wtfa/nzBwcH+IM/+AM8+OCDAIAf/dEfxW//9m93JXTc+Q05IfBsRbFYxO7u7pFp2L2+9t7eHtLptOMnyMMM2tvZoMyMgi0K0IRSsVh0rODZaTp5D7nYU1UVp0+f1tPG/cCtD5xRyJSdJKwKfY1+af7AH/bk3lHHC3veOHyu3TA/vmeCwaAuTt20Z0YtIG/HqHe+MtaLCYKAvb09rK6u6vvJ2NXsS1/6Eg4ODnDzzTc7mrl473vfi0wmg7vvvhvT09O455578Mu//Mv49Kc/jY2NjSM//xu/8Ruo1+v4xCc+gUKhgA9+8IP40Ic+hI985CO2fzeJiyHAZ1fIsgxVVfWTM6duDMZC2sXFRczMzDh6wxlW0H6cDcpMrwXJ/aZer+uzOJaXlxGPx121zk7WYiw+D4fDWFtb63vxOdmi+oPX309RFJta3sqyrBf5WrW8HYT33q33Jjt45RqsniXmNsl8z5g99m6p8eHBqhfwYucrvkf4YQfPrD700EN47rnn8NnPflb/Nw888ABOnTqFG264oSuhcenSJXzrW9/Cpz71KVx//fUAgA9+8IN48MEH8YUvfAG/+Zu/2fTzTz75JB555BHce++9uvD4vd/7PbzrXe/Ce9/7XiwsLNj6/SQuBozV7Ap+M3DCY5jP57G1tQWfz4f19fW++PaGkbmoVCpIJBK22rK6edhaoVBAMpkEAL3Htts4LohXVRXb29vI5XJDLz7vBj7xnN/0eT98rzycTxLdfL/9fj8mJyePDGYrl8tHWt5229+eGA06fUaY94xVjY8xiLQ7zK9XVFV1rWXLDl7posaxiu2MdT2f+MQn8PTTT+Ohhx7CF7/4RWxvb+OBBx7AAw88gEAggGuvvRave93rcMstt+DVr351Rwd48Xgcf/mXf4lXvvKV+p/xA1fe5crIY489hrm5uaaMxk033QRBEPD444/jrW99q61rHv1dOEK0ml3B/7eXgN04TyAWi2F5eblvQdIgMxd8OnUqlTrWBmWGf5nddCLLGMPu7i4ymQwmJychiiLq9fqwl2VJO3FRr9eRSCRQr9exsrKCeDzuinV1gnHieSQSQTAYbGorGA6HdcuDnVPIUajx8RpOfbfNg9mMp4rlcrmpv73RDuPEPXbUAyg3Ht7YpZtrsPLY82nP5XL5yDC/QQhUL3wWgL3WraPAcQfHfr8fr3nNa/Ca17wG4XAYH/vYx/C+970PpVIJ3/nOd/DEE0/gsccew5/92Z8hGo3ir/7qr/RsRCsmJyfxpje9qenPvvjFL2JzcxNveMMbjvx8KpXC0tJS05/xvb2zs2Pjag+vyfa/IGxjnF3B06/GG0CvQTDvMiRJ0kDsNYMq6DZOp+522B/gHnHRaDSQSCRQqVR0u9rOzo5r1mem1fvHsy5+vx8bGxsIh8OuWFcnqKqKra0t5PN5zMzM6OlpURT1k2tzpyEeFESjUTq5PiEYP/fZ2dljZyVEo9Gu7DBu/e53ildOmJ0Iyq2mPTsxgM0OXhEXXtlXHDufCy/ovuGGG3DDDTcA0BrzPProo7p9qptn7uOPP44PfOADuOOOO3D77bcf+ftqtWp5cBsKhbo6ACVx0WdUVYUsy002KPMm4wGz3YDd6HfnXYYGEegNonC1GxuUGScyQk5RKpV0C87Zs2d1u5qbi4DNQTxjDKlUCul0GhMTE1hdXR0pC1G9Xsfm5iYajYbeYazRaOh/36rTULlc1rvG8MCALFQnC3N/e6MQ5S0njV1guBD1SnB0EnD6s7IzgM2YKe3ltN4r4uKkZS6M8IJuY7eoiYkJ3H777ZaioBPuv/9+vP/978e1116Lu+++2/JnwuEwJEk68uf1ep26RbkJ4+wKYycKK7oRF8Zha/F4HEtLSwP7IvYzc9GLDcqMG2xRjDHs7e1hf3/fsmuXm8UFh2feEokEyuUyFhYWMDs7O7SHWDeZC2O25ezZs8eKcHOnId41hk+G5oGBsZDT7pqI0cXKDsP3xv7+Pvb39ztqXzrqwaBXTpgH8Tl0Msyv14YCo76fOF7ZV5xexUUv/N3f/R3+8A//EHfeeSfuuuuulvHU4uIi7r///qY/kyQJuVzOdjE3QOKiL5iLto+btG1XXBhP9VvNeOgn/erC1KsNymqdwPACPmNA3mrug5vFBV9rtVrFpUuXwBhzbFbKoDCKu16yLeauMVYWKkD7LudyObJQnSCMdhhuszMKUS+3vPVKEDiMe7DVMD9+T+l2mN+ot2/leGVfcbqxRTnRjOeee+7B7//+7+Od73wnPvCBD7TdGzfeeCPuuusuXLp0CadPnwYAPPzwwwBwbH2HFSQuHIZnKxRF6XggXqfiwlyI2supfi8YMwJOffm5YFJVtWsblJlhigveiei4gHwUioC3t7cxNjaGtbU1VwTMnX6uiqIgkUj0ZaiflYUqlUpBluUmCxW3O4yNjXnioU8cj9kOY2xfam55a7QceiWQGkWG/f6bGwocNwCyVTbMK+1bvWiL6rSLFxcXkUikp9954cIF/NEf/RHuvPNO/Oqv/qp+AAZcrhXLZrOYmJhAOBzGtddei+uvvx7vec978Lu/+7uoVCr4nd/5HfzUT/0UZS6GiXl2hZ1J253UBhinH8/OzmJhYWHothQnWueaBZOTcxK6rWXpBeP1dBKQuzVzoaoqUqkUAK3rxNramuseWu3et1qthkuXLh071M+Ja+KBIvesrqys6EFBqVTSi3+Nfvx+z08g3EOrlrf8/wDg/PnzTVPD3SDiO8ErJ8zDFhdmOh0AGQwGm8SG266jW7yyrzh2YqVaraZnQ3vhy1/+MhqNBu677z7cd999TX/39re/Hb/2a7+GO+64Ax/+8Ifxjne8A4Ig4M///M/xoQ99CL/4i7+IUCikT+juBhIXDmA1u8LOl+K4k9hSqYRkMgnGWN+nH3eCU7UMsixja2vLMRuUmUFnLoy2rk4FoBvFBS985sVdTk6Od4Lj1pLL5bC1tYVQKDSU7J7RW23shV8ul490oXJDMDls+2AvjFoQYj6h3tvbQ7FYRCwWQ6VS0QV9PzsKOcmovf+tcHtQ3moApPkAA9Cy5qFQaKjD/HrFa5kLO3a1arXqiG3y3e9+N9797ne3/Zlz5841/ffMzAw++tGP9vR7OSQuekRVVUiSZDtbYUQQBMsiaaNfPBqNYnV11RUnWk5kBPphgzIzyKCpWq1ic3PTdnerYU07bwUfwhgIBHD69GlcvHjRVesDWn+uxhkisVgMKysrQ384WfXCr9Vquh+fB5PmE8hhr5sYHD6fD7OzswBadxTiNgZerzGqQaNbcbu4MGM1zK9cLmN/f18/yBjmML9e8Ypo5dixq1Uqlb4MPx40JC66hNugeDeoboUFxywuJElCMplEpVJx3C/eK70E7f20QZkZhC3K2A64m+5Wbpkibh7ut7Ky0uQHdyPGdRlniCwtLXWcbXHyPe8kC2W0OwDNwaTZQsWzGmShOjlYdRTiQpS3vHVT0OiVIHDY999e4AcYPp8P+/v7WFhY0AeDViqVoQzz6xX+zB7Vz8SMXVtUr/UWboDERRf0aoOywiguCoUCtra2IIoi1tfXHWtJ5hTdBu1GG9Qg6kb6nbkwtgOenp7G4uKi7RNnN4gLq+F+giBAlmV9bW7C/D5VKhVsbm4CgCu/L+2wslDxYNLYMcZYGO7moIBwlkAggKmpqWMnQBv3R6eFo04yykGglwQSoD2fzcP8jMXho2C9O659/6hhxxbFn8OjDokLG9iZXWEXURT1YJVX8K+srAzlQXEc3QynM9qgBlU30k9xwaeiGweydcOw/e68qxVwNDB3643duP/MWbBRDryNFireMYZP+C2Xy/qEX26h4oP8yEI1utg5VDhuArSxyJeLjX7vD7cdPHSDF8WFEWM7W6D9MD+3tEoe5UySGbtt+6vVKtmiThJ8kBg/zXVaVTPGUCgUoKqqLVvHMLBT0G3unrS6ujqwAlv+GTlti+IFw3wqeigU6vq1hiUujJ9LNBrF2traESE7bOFzHNlsFuVyueusEcep63O6ON9of5mdndWDgnK5rFuoeMBJFqqTR7uWt8ViUW95y+s1+tGlzCuBOTD619CplajdMD9jq2Rjnc+gi8Od6ETpFuwUp8uyjEajQeLipMCzFf3qYHBwcIB6vQ5RFHH27FnX++06tUUN2gZlhZMBn6qq2NnZwcHBAaamprC8vNzzXhhGAK8oCra2tlAoFNp+Lm4VF41GA4CWDVtdXcXU1FTXr+W2a2tHJxaqUfJVO8EofX5WOHlC267lbTabbepSxsWGU/tjlAPzUd9DnG6FXrthfsbudsY6sOOG+fWKlzIXduJGPp3b7TFgJ5C4aIPRBtVLN6hWKIqCnZ0d5HI5+P1+hMPhkdhUnQSdw7BBWeGUuKjX60gkEqjX61heXkY8HndsRgIwuAdcrVbD5uYmZFnuuKuVmx6+xWIRyWQSADA3N9eTsBhlyELVjFcCEadoNZSNi1HjoMdefPduujd0i1eyL05ch1PD/HrFS5kLO58Ln3szSnWDrSBx0YJ+FG0bqVarSCQSkGUZKysrKJVKuuXK7bSruWCMIZ1OI5VKDdwGZYVVi1+7FAoFJJNJ+P1+xzNLgxQXdu1c3FbmhgDCuK+i0SjK5fJQ95UVw3yvOrVQ8UF+ZKE6WVh1KatWq7rY6LblrRcCcy9cA9Cf67Aa5teqzse4b3otDj+pmQunpnO7ARIXFvBshaIojosKxhiy2Sx2d3cRCoX0IK9SqQx0knQvtAo63TRFnNNLwGfVntXpjhqDEBeqqmJ3dxfZbLYrO9ewxYVxOOHc3Bzm5ubw7LPPDn1dbsZsoTJaHcwWKjc2jTgpDCuI8vl8TUPZjL77fD6vt7w1WmFGaU6CHUhcdI5VnQ8XqcZW2uZ6DbtZiJMuLqjmwmM4PbvCTLuJ1E6csA8S83p51yG3TBHndCsuWrVn7cf6gP4F8JIkIZFIoFardWXnGnbmwsrG5fb5G27DbHXgrSn5qTW3UCWTSYyPj7uiWwwxWMy+e97y1mpOAhcbfr/fE4G5F64BaN0tqp/4/f6m4nDjIYZxLovdjOlJtUXxmgsSFx6i3zaocrmMZDLZciJ1P7oa9RO+XrMNym3tQLsRbdzXzwvs+/lF76e4KJVKSCQS+ryUbq5jmOLCOC3caONyaxAwbCHWKebWlMViETs7OwiFQk3dYnhAEI1GPXtq7Qbc9r4e1/LWWM/D7/WjsO9b4RVx4YbBc7wOzDyXxSpjyrNiVvGCnbkQbqebgm4SFx6BZyv6UbTNGMP+/j729vba1iCMYuZCURRcunTJVTYoM3YCPsYY9vb2sL+/j/HxcayurvbdMtLNzJDjMO65Vm1m7a5vkDDGkEqlkE6nW9rRRiWQHwX4ezs7O4tAIKCfPpbL5SMBgfHUmuidUdjD7VrelstlAMDFixebTqcH3bq0F7wiLtx2HVYi1VgczkWqVVMBVVU9c4/pxhZFBd0jjnl2hdPCotFoIJlMolwuY25uDvPz8y1fXxTFkXjQGDk4OIAoiq6yQZnpNAiVZRmJRALlchnz8/OYm5sbyE3a6cyFuT6h3Z7rdH2D3JfGz6GfdjSOWx7EbqFTC5VbBm65LaA6CRhb3hYKBezu7mJ2dhbVarWpdam5JbJbPyOv7CG3X4fVMD9jRsw4zE+WZf3AddQzGN3Yoqige4Qxz65weiget9YIgoAzZ87ohXOt4F8ktxcycRuUJEnw+/3Y2NhwlQ3KTCcZIWO9SCeflZPYGUh4HNVqFZubm463/x2UuDCu/7jPwY2ZCzeuqVfMAYHx1JosVL3j9vv9cfC1T01NYXp6um3rUp716qbl7SAY5c8BGL29ZNVUgIsNbqc6f/78UIf5OYEdgcTFBWUuRpB+z64wWjrsWGuMQaZbvzzGblCBQACRSMTVwgJoX8vihnoRpzIXBwcH2N7eRigUwvr6umNtWgcVMPP1h8NhrK2tdbR+J9bl1u+aW2k1qM1soTIGkl6xNxBHMX8Hza1L251OczHaTTchJ/HKgYCbY4dOCAQCCAQCmJycRLlcxvj4OILBoOUwP35vGYWDDDvigrpFjSj9LtrmnXmq1aptS4dx6rUb04DmblCZTGbYS+qIVsGx0T40zHqRXsWFcWp4PB7H0tKSo/un3+Ki2/W78YHixjX1EysLlVUPfDcFkm5klPfNcZaPdi1v3ZL5crudqFNGXVyY8fv9iMfjlsP8rDqYufWw087nQuJiBOnn7Argcmcbn8/XVYehfhT2OoG5IJ2f7h8cHLhurVZYZS6M9ptOp1T3i17EhSRJ2NzcRL1ex8rKCuLxuNPL6+vDqtFoYHNzU2+TOz09bWtdTokepzOXJ5VWhb/lcvlIIOn12Qmd4rWA8DjMLW8lSdLreczNA4z1Gv3ES+LCK8LdfMh63DA/YwczJ4f5OUE3tigSFyMAn10hy3JfbFDGk9deBq0ZMxduwWiDMhcHj0rrXGOhvHGAoR37zSCwG5Sa2+X2qwCsX5kLJ9rkEu7GykLFA0njySNZqEaXXgJzY+bL2E3I3DzAGDCOjY05HkB7RVyoqjry1wBon8dxott8kKEoSlMHM6eG+TkB1Vx4kH7boGq1GhKJBCRJ6mpAmREnC3ud4LiheKPS3YoHx4qiYHt7G/l8HtPT01hcXHTFKU+raeetMLbLnZiYwOrqal9PZ5wWF4wxZDIZ7O7u9tQm14vF08NiUF3RzIEkP3nkmQ3g5FmoRj1z4eT6rboJ8f1hnP7sdMtbr4iLUd9LnG6GAfp8vqZhfu0mztsZ5ucEdkQfZS5GgH7Prjg4OMDOzg6CwSA2NjYQDod7ek23ZC5a2aDMjMpcDkEQoCgKXnrpJTQaDaytrSEWiw17WU3YaZfLM0nt2uUyRcXBEy8i9+RLAIDYteuYfu2VEHz2AzUng3ijwHOizsVt4oIET+cYTx7n5uZaWqiMJ9ZWFiqvBIbEUYwBIz8o5AGjucCXZ7+6aXnrlT3kNXHRy7VY2e/MjScG1S7Zjl2Nai5cjHF2Bf9QndwwxgDJyQJaN4gL44yB42YkjEogVa/XIcsyfD5f05RnN9HJe1mpVJBIJI5t08oUFS989HNIfeVJKFIDAoCtz3wbc7dfi5f9u5+EGLD/lXfic67X69jc3HRM4HnhIeo2hvl9NluojJN9uYXK7/c3DfJzg5/aCUZ5Lw8qoBUE4cj051Ytb+3uES8F5V65DsC574W58QRjrKlew7x3nLZo2hkIWK1WIYqiK+MUu3hOXPTTBsUDPEVRHD8BH7a4KJVKSCaTHc96cHvmgtfCFItFCIKAjY0N11os2okLY5askzqR9Ld/gN0vP4HAVBSRSe30o1GsYO+r38X0a6/E/G2vdmxtnVIoFJBMJuH3+3H27Nmes3xOrYtwJ1aTfavVqu7FN1qo+EN7VPfCqK6bM6yA1qrAlweLVp3K2g179FJQ7oXrsDPRuhuMGVHg6DA/vneCwaAuUnsZFGrXFjU2NuaJz9Fz4gJwfiCe0SceiUQcnSPAGVa3KKMNKhqNYnV1taPuHG4O7ur1OhKJBOr1OiYnJ1EsFl0rLIDW76Wqqtje3kYul+u4TiTznefAZAWByctp1cDEGKT9AjLfea4rcdEtg64PGTZu/k6MMubiTaM9plQqAQBeeuklPWCIRqOunghNOI8oik0tb9t1KrPy3Hthr3hFXAzapmZul2wcFFosFnFwcACg+1ofu7YoJw7f3IDnxIXThcZGn/vs7Czm5+f7EqgOQ1zYsUGZcWvmgrcE5qfk1WpVP4lwK1adt7iNSJIkrK6uYmpqqqPXYg0ZsPoMRQGqJDuytk7otD6kWyiQP7kY/dT5fB6pVArT09O6hWp/f39kLFSjHhC6df2dDnscGxtz5XOsG+zYb9xMvzMXx2HeO1a1Pp3Ug3HsdIuq1WqeqLcAPCgunLzRGa1CVh2TnEQQhIF2YOLXBqAjG5QZHty55eHCGMPu7i4ymUxTS+Barab/vRvWaYU5UDbaiOw2C5i69iz2vvZ9KPUGfCEtA6XWG4DKMPWasz2vrROMc0T6+b0hcUHw73Q8Hm/qf29loTJaHNx6Lxg13Hxf5VgNe+T1GuVyGfV6HQBw4cKFpj3iVkHailH4LDrBTQX2VrU+VvVg7Waz2G1Fa2fek5vxnLhwAqOdw45VqFcGkQ3o1gZlxtg6d9g3AeNk9KWlJUxPTx9Jd6uq6tqHhVGopVIppNPprmemzL35VUj98/dw8OgLYAIgBvwQGBC79gzmb7+267V1Si6Xw9bWFkKhUF/sg8Z1uQ3jQEQ3ru8k0M5Clcvl9JaUZKE6uRi7BM3OziKVSqFUKmFsbMxyRgJvi+z2PeKV+w6Pgdx4La3qwczD/AKBgL7HOv1cGGOoVCqUufAqkiQhmUyiUqn0xc7Rjn6LC6MNqtdrMxagD7OewTxMzvzFdNv8ECtEUYSiKLh48SLK5TIWFxcxMzPT1WejSjLEYABqQ0ajUIHo82HqurO46t//DAIT3Q3a6+S9U1UVu7u7yGazmJqawvLycl/3BdmiCCOtvivmlpT81LFcLmN/f1+3UBkH+Q3yEGLUA8JRXz+g7R2fz4eFhQUA0C1URkHKW966WZB64bMAuptzMSzaDfPjMzYA4ODgALIstx3mx5sROT0Q9+Mf/zgeeughfPKTn2z5M5/5zGfwH//jfzzy51/5yldw+vTprn6v58RFL1+uQqGAra0tCIKA9fX1gU9J7Ke46NUGZcZ4SjsMjNml8fFxrK6uWvpNh73OTlBVFcViET6fr6d9xxjD8//vZ5H51jMIL8QRXV+EXKqhfCGFxH97AC97z0/Zfs1OgvhGo4FEIoFKpXIkc9RPev1MuZWuVCo19crv9qHmhQf7qGFnD1idOloFAqN2Yj1MvBDQmq/BygbDbXZGQdqPtqW94IXPAnCXLcou5mF+tVoNm5ub8Pv9lsP8AoEA6vU6Zmdn+zJA7xOf+AQ++tGP4sYbb2z7c+fOncNNN92Eu+++u+nPe7FoDf8b4QJUVUUqlUImk8HExARWVlaGcrPotni2HU7ZoMwMs3WunQzMsLpwdQLvQlar1fQ5HL18NpWLKRw8+gKC05N6t6jgVBRQVex97fs4/c7bEZqdtPWax4kLPskdgGXmqF/0+uCRZRmbm5uoVCoYHx9vCi6NQqObKa5eech7HXOHIW6h4tYY44l1L0PajoP2ynBp9301CtLjanqOa3nbb7xy33GzLcouPAs6PT2NsbGxpqxYJpPBJz7xCXz5y1/G0tISrrnmGgBwJPZMpVL44Ac/iMcffxzr6+vH/vzzzz+Pl7/85Zibm+v5d3NOvLgwti0d5KmrFU5nLhqNBpLJpCM2KDPDyggYg9lO53EA7stcGIcxBgIBhEKhnkVfbS8PpSYhON38nvjGQpAOSpAyha7EhRWMMWSzWezs7LSd5N5Puv1MzQXngUAAoii27SjDg0s3nFA6jRce4k5gZaHiQSQftGX0UjthoXLbfckuXgho7VyD2QZjbFvaScvbfjJsi7JT8M9j1PcV0Nz5ymqY30//9E+jVCrhu9/9Lr761a8CAL74xS9ic3MTr3vd63DrrbfihhtusN2e9plnnkEsFsPnPvc5fOxjH8PW1lbbnz937hze8pa3dHeRLfDek9IGuVwO29vbettSp71udnGyW5TTNigzg85cMMaQTqeRSqVsBbNutEXVajUkEgl9WnU+n3fkfYwsTcMXCUIu1RCIXbZWyaUa/NEQQvNTtl/TKnNhnL8xMzODxcXFgT8Iuv19BwcH2N7eRjgcxqlTp+Dz+SBJEoCjdgjjCSUv1OMnlNFo9EjXoVF/GLrpOzJsrE6seRBZLpfJQmVg1K+5F4HUquUtP5lOp9N6Abkx+9UPvCD0AO+IJKB9W11BEHD99dfj+uuvh6Io+MIXvoDf//3fx/LyMl544QU888wz+Ku/+isEg0Fcd911uPXWW/FzP/dzHVmVbr/9dtx+++0drTGbzSKdTuPRRx/FJz/5SeRyOVx77bV4//vf31HWoxWeExedfLkURcHOzg5yuRympqawtLTkik5CoihClu3PIjBi7nS1trbWl9PWQdqNFEVBMplEsVjE7OwsFhYWOr6Juk1c8DkcgUAAGxsbCIVCKBQKjqxv7NQcZt9wDXa/9ASYosIXDUMuVqFUalj6iR9CMG5fYJrFhSRJ2NzcRL1etzV/w2nsFnQbWxUbC85b7V/zFNdWQ7mMQYPxdxHewspCxYWn0UJl7n1/HKMeEHphrzv1GVidTBs7CaVSKQDOZ784o76XOF65DqDz+hGfz4fl5WUAwE//9E/jXe96F5544gk89NBD+Pa3v41HHnkEDz/8MLLZLD7wgQ84usbnn39eX8NHPvIRVCoVfPzjH8cv/MIv4POf/zxmZ2e7el3PiYvjqFarSCQSkGUZKysriMfjw16STq+2qH7aoMwMym7U68yEYdaGGDEGt7FYDMvLy/pDxcnOR1f+xr+AGAxg/4GnIGWL8I+FsPSzb8D6//EjXb2ecW3Gzlx252/0g07fM2N9RbfWR/MJpZVlhn+e5XIZ4+Pjnjl9GwUGHYwEAgFMTU3pWS4+N6GfFio34oVA0M4EZTuYDyhadRLi2a9eZrCMchG0GS/sKY6dgYCVSgUA9I5St956K2699Va8733vQy6Xw/e+9z288pWvdHyNt9xyCx555BHEYjH9zz72sY/htttuw6c//Wn8yq/8Sleve2LEBfeI7+7uIhQK6afGbqIXcVEqlZBIJAbW6arfQbvx8wqHw1hbW+tqZoIbMhfHdVNyUlz4xyO46v3vwOl/fQekTAHhxXhXGQsjqqpib28Pe3t7GB8fx9ra2tADpU7fM6M4dep7YbbMKIqCarWKXC6HSqWC3d1dAL0XhhPH44aTc+6xj0QiTfuBi09jEMn3A7dQeSmQGmUG8RmYOwlZZb/sTH424iVxcVJsUWaM4sLM1NQU3vSmNzm7OANGYcHXsLq6qmfbusFz4sLqyyXLMra2tlAsFjE9PY3FxUVXbt5uukUNygZlpp9Bu7HYudfPa9jiwij6WnVT6sfMhvB8DOH52PE/eAyMMV1czM3NYX5+3hUPsE7eM2N9RbfitBN8Pp9ul6lUKlhbW9MzGyepMJzQ4PvBykJ1cHCATCajW6hUVYWiKENecfd4QRwN69lgzn4ZZ7AYJz8bZ7C0um94SVx4YU9x7Hwu/WhF2wn33HMP/vRP/xQPPPCA7kYolUq4ePEifuZnfqbr1/X8E65cLiOZTEJVVZw6dQqTk/a65QwSu5kL44n4oAf+9avmgveFlmUZa2trRxS1XYZlizIWoB8n+tw6EK5WqyGbzQKAK787rd6zpvqKWAxLggTh+9+AKvognHoZhNnlvq4rEAggEol0XRhOeItWFqpyuQzGGHK5HMrlctN+GHZmsFP6ZSkaJG4IZo+b/Mxb3gaDQV1sGFveeklceC1z0WnnKy4u+u06URQF2WwWExMTCIfDuO222/Anf/In+K3f+i38+q//Omq1Gu6++25MT0/j7W9/e9e/x5PigmcA+HwHnuLp18mlU9jpFsX978Ma+Me/ME4G7fykORgMOmZbG0bmwm4Bej/mm/QKLzznN3m3CYtW76dxBsrSwjxiTz0I9dwTgKx1hMJjYYjX3QbxtbcP5EHcTWG42+9TRPeYLVQvvPCCXp9jnrXC981J7UI1KNwgLsy0a3lbLBb1+wav1+D3DC8E5W78PLrFjlBqZ4tykp2dHdxxxx348Ic/jHe84x1YWlrC3/zN3+Cuu+7Cz//8z4Mxhte//vX427/9257qKj0pLviJfrlcdpWV4zh45qLdl6vTydSDwKnWuaqqYmdnBwcHB02dfJxg0OLCmHnp9LTfTZkLxhhSqRTS6TRisRgikYheQ+A2zO+Zub4isvkDKD94FAhHgcnD9n3lPNTH/xniyjqwfBaAc6d9new1q8JwLjbMhcC9Tgwn3A/vMMTbSxpbmZotVHxPDHqeTDu8EAiOwjW0a3mbzWb1e04mk4EkSa7bJ3bwQjaMY2dv9csW9cd//MdN/726uopz5841/dnVV1+Nv/7rv3b093pOXKiqivPnz4Mx1pf5Dv3EmOK02pDDtEFZ4cSJu3GIYb+6dzk9nLAVPPNit2GAW8SF8dR/cXERMzMzyOVyANz3ADavxaq+Qj7/FMBUCJHLWT0WjQGZHagXfgDfobgAhnN9VlYILjScnBjeyTqI4WF8/82zVmq1muUgP6MPf9iB2KjvH7fd247DquVtPp/H3t4eFEUZ+W5lqqp6pibNTuZiWDUX/cIbn6ABURSxsrKCcDg8chvUWB9g3pDDtkFZ0Wvmgltv+j3EsN/Bu6qq2N3dRTab7Srz4uTwxG6pVCrY3NwEY8xyf7ntAcw/01bzKwAA9Qog+o7+OwBo1PU/c7LPfS+YZymc1InhJ4l233ujhQpobmVaKpV04c8tVNFodOBdyYZ933ICt93b7MLFBgAsLS3B7/e37FbWa8vbQTDqn4eRbmxRbojtnMCTT6bJyUnXedg7wVwkzZgKlt1CefslFCs1TMRXsXjqLHwuCSi6zQioqopUKoVMJoPJyUmsrKz09VSln+JCkiQkEgnUajUsLy8jHo/bvjEOO3ORzWaxs7OjT602ptOH3W2rHaqq4uLFi1p9hVWL35UNsK2XwFQFwqHIYHIDEEQIC2t9W5dT71WvE8OHte5B4qVA5DiMrUwZY2g0Grr4NFuo+j0NmuOF998r1wBo92urbmXG2Rq85a0xI9ppy9tB4KWCbjfYooaFO6JUAkBz5oIpDShPfRXsYAshVUVEECCWN4Hth8F8AbD4EoT1GyBEhzcEsJug2BiMdzvQzC79Kpg2DpXrJfMyLHFhrHVp1fLXreJCURRIkgRFUVpm8sRrbgI7/xRYZgcsNAaoKtCoQ1jZgLDxqiGsunuoMNybdBvYCoLQ1kJlnAbtJguVG/GauDATCAQQi8UQi8Wa6rwqlUpTy1u3ZES98HlwyBblMUZ1YxprLuovPg5fZhOq4IMvGIYoVQCmAqoMgAF7F8AK+8D1b4MQPlpXwhQZqBWBQAhCsPVmZYxpryn6IAj2Hjx2MxfGYHx9fX1gXyKnbUeMMb0TmRNF9cYAflB71yjy2tW6uPG7lMvlkM/nIQgCNjY2WgbRwuQ0fD/+S1C//y2wC88APj+EK14N8dVvgBB0frr4IN8rKgwnjAzLQuWVQHDUr6HTYW3HtbzlGdFgMNhUrzHIe4eXMhd26keq1Sp8Pt/IFuKb8aS4GFX4FyqfeAGzie9BgAqRKRDqUvMP8raalRzY978MtnEzEI1DDEe1IDrxNFjyaaBeBZgMFggDc+sQVl8BYXxafxmWOg+W+D5QKQD+ILB0FXDq1RB8nW2LTjMCw+5w5WRmQJZlJJNJlEolx4rqBy0u+GC/TjIubspcGOsrQqEQFEU59nRemJqF740/CbzxJ63/vg/v96DfK7cUhhPuoZ2FKpvN6hYqY1ajm6DGC+LCK9cA2L+ftWt5ayVKB9Ea2QufB4fPueiESqWCsbExz1w7iQsXoSgK/Eod8eSTEKCioy1W2AOe/DwAQPUFgIk5oJTRshzKYZajXga2ngHbewls7ZVaRqOYAUpZQBAAnx+QqmCXngSqeQjX3NbRejvJXBg7EA2rw5VTtihjq9PTp09jYmLCgdUNLoBnjCGTyWB3d7fjae5uERdN8yuWltBoNPSgmWjGbmE4FYUPh0ENPrOyUBlPq7mFapin1cOCN4UYdZzaS+aMaLu6HqModWoP88/DKwG2nba61Wq1b01thoEnnyqjuDG5ZWi6vA2/2uhMWJhRGkBu++ifCwLAGCBVgPOPmP9S66gTCGn/fv8i1MIehGgcgq/9SdZxQXu5XEYikQCAobYFdsIWZSx65q1OnWIQAbyiKNja2kKhUOhosJ+ZYT6AzfMrotEoUqmUY2tyes6F2+ikMBzQ7GaMMVd3kiF6x1y/wy1U5XJZP63m2bDjMl1eCQRH/Rr6IVRb1fVwUcrtl36/vykD1ktzFn4dXhG2dmsuRml0wnF4UlyMEsahZePj45ja7sPAsrZBGNPacgo+QFU0y9WjnwXzB8DiKxDO3gBhfMbyX7YK2hljSKfTSKVSGBsbw9ra2lB9hL3YolRVxfb2NnK5HOLxOJaWlhy/8fVbXNTrdWxubqLRaGBtbQ2xWMw1azuOXC6Hra2tI6LOzcGAm09CrQrD8/k8MpkMyuUyisVi089Eo1EqDO8Tg8pcHIeVhYqLT3Omy2yhGnVx4ZbPoFcG8TmYp8sritKUAeOZZGMHu3A4bOt56ZXPg2NXXMzPz/d5RYODxMUQkSQJyWQSlUoFCwsLmJHSYEwewkoYIJUN/6kADQXYOw+W24H6mreheCGH8uY+ij9IoLZ7gLG1OQRefwXUePOgOFmWsbW1hWKx6Jrp6N3aoiRJwubmZl8H/AH9DeALhQKSySQCgQDOnj2LcNheIfOwxEXb+RUYfvter+D3+zE+Po5MJqO/x/wUe39/H/v7+64uDB/14NZtGE+r+YA2HkBy8QlctlCNuq3IK8HsML4H7VreGjvYGZsIHNfyttPC9FGh08+Ff8+80ikK8Ki4GIUbhbmNaTjgAx74X8Ne1hEyT+3iud/5fZRTNSiVGgBAEEUIfh/ET4aw8B/eBlx5JQCtICmRSDhek9AroihClu2JNh6U93vAH3B0vokTGIvoe5klMozvkrm+YhDtip1AYCoCpQMIhXEgtKTZEUcA/t7aKQwf1tA2L+Lm98+YxZqdnT1ioVJVFdlsFrVaTf+5UdoTJC6cw9zyVpKkjjNgHK98HsDl+pFOhFK9XoeqqlRzQXSP0QY1MTGBlZUV+P1+qM98bdhLO0Jlv47v/9VLaJRlKA0GHB5QMUUFU1So9QZ2/uCzeMXb3ohCtYzd3d2+1CT0ip1TbmNQPjExgdXV1b4O+OPr47/bCYwdrRYWFjA7O9v1zXrQmQur+opW63LVienm8/A//s9YTCUR+H4YWDwN3HgnMLM47JV1TbvCcN5xyE398UcNV+3fDjFbqF544QX9O2oVQLq9YYBXglm3tW/lU8NDoZB+UGGcw9Kq5a1XPg/AXhamVtMObilzQXQFny1QrVaxuLiImZmZy0HSznPDXt4Rtr+ThlxVABGAav0gVAtVfPNn/xAT/+pWjI+NIX//s9i5tIfoxhJO/6s3Y/rGlwEAlFoDYCp8kZDl6/STTm1RxhPzXoNyu+sDnAk2jMG5E0X0gxQXreorWq3L8TUxBmydBy4+q7Vxnl8FNq4FxkzvYSkPvPg9IL0FhKPA1Czw5IMQaxWogRCYPwgkXgBKOeCtv3T037uU497PVh2HjHYZJyeGE+5GEAQ9sxGPx/UA0spCxcVnJBJxVRDslWDWDZmLdhg7TAHWTQQA6Pf8RqPR95a3/cbO3qpUKgBIXLgeN27IQqGAra0t3QZl3ETshe8McWWtKe1WIdcUPWPRisrTSVT+f/8LKUUL4AW/D8UXtrH/9adw5W/+C+S+dwHpB58GGDB908tw5b/7SUxevTaAK9DopFtUpVLB5uYmGGMD72zlVAB/cHCA7e1thEIhrK+vO5I9GoS4OK6+YmB89wHguw8CDQkQReD8U8AL3wPu/Hlg4rDe5mAf+Op/A7IpwOfTpn5XywBjYCsbUCsVIDwGRKLAQQq4+Axwzc1HfxdjQC6tdWibmgP8ozU4yWyXaTcxnIsNJ1tWeolRfk+MQa0xgDTuiV48+IPCDWvoBbeLCzOt5rAUCgUAwO7uLvb29ppm84za/cNO5qJc1mpeyRZFdEwrG1QTm98dytrawRhD/mL5WGGho1zODDBF0f5PZfjBH/53iAE/xIAPTGXYf+Bp5J+6iJvv+feInlk49mUbhQrq+3mE5qcQmOjui9fulJsxhmw2q1u6Tp06NfDOVsbJ7N2gqip2d3eRzWb7Fpz3S1x0W1/heObiYA946tuALwDEZgG5AdRKwM4FTXD80OEQvu9/A8juAtNLmgABgIs/0H5eblx+PdGnfXcKB0d/VzYFPHofsL+lzaOZiAOvfgNw9pXOXc+AaTcx3KowPBKJ9N1u6HZG0RZl5LiTWfOeOG7eyjBsdZS5GD7GJgJ+vx/b29tYXl7W7yHGlrfGveL2+wfZooi+0coGZUTNbg1pde2p7NdQ2a139495bUZDAVQVqihAqUp6S9zabgMX/ut9eOXv/e8tX0KpNXD+P38B2597BEq1Dt9YCMv/4hZs/Nu3wheyF/y3skWpqoqtrS3k83nMzMxgYWFhKCfmvWQHGo2GvseWl5cRj8cd73Xe7dqOo9P6inY49lDduQDUK8D0IpDbB/IZLaugqMBjXwXOvkqzSSVfBCLjl4UFoGUp8mmgVgbEIF8YAAaMmZoa1CrAg5/RBMrEtJb9KGSBh+4FQhFgZaP3axk0cgPI7GjXPL0IIRiiwnCiCaMH32ihsvLgD9JCReLCXfDPIxKJYHx8HDMzM/r9w5gFAy5bMN1otwO6s0V18wx0KyQu+kQ7G1QT57452IV1yLl/SPb+IocBPas3d2piiorNTz2Aa/6fn4fotz59eP4/fRrJf/gWxFAAvkgQSlXCpb/9KtR6Ay//Dz9jaxmiKEKVFST+5zex/bnvQEoXMX71KnxvvhL+K+axurqKqampri7RCboN4I1DCtfX1/ty6tGvBxavr+jWwuX4uvh7X8prWQxBAIJhzSLVqAPf/EfgR99p/W/Hp4BCFkKlCIxNacF2Oa39+Zmrm39285z2+tNLmrAAgPg8kN7WLFhLZwBF0WxS3VyjVAMuPAvsXtSyJ6tXAKdfrv3/+8HWeeDxrwGFtPYeTsSBa38IWH+F/iP9LAwf5aBq1APbXtZv9uAPy0I16tkjzih/D4xYDdEz3z9aWTD5XnFLxzI7mYtqtQqAMheuZ5ibyugfb2mDMlLJDWxtdsg8m+/r6zNJxtdv+21c+59+GTM3XQUAaOTLOHjyJSjVGrbvfQy+SBCBSe3LJoYCaOQq2P7CIzjzS3ciPG9vEFz2Ew9i+4FzWgzpF1G6/0n4HnoWr/rjX8LUNVN9uMLOsSsuuJVrZ2cHY2NjOHXqVN/sBE5nLpyur3Dsobq0DoTGNMsSY0AwpP2vqmo1EcUcsH0BWL0SOPfYYfbiMGBXFe1nIuMIFPMQ5BowPQ9cfztQzgPlAjC7DASCWpE3gyYsGNMKxytFrW7jB49oReKKDMwsAa+4RRMbnVKvAg98Wgv4BVF7/ZeeBq64FnjdjzkvMHJp4KF/0tYemwEgAMUD4OEvA9GYlumxwG5huN1BXMRgceL716mFyjgJ2ol73qgLPI7bukV1SyeNV+zY7Vq1vB0EdsQFFXSPEMNoVdmJDeoIzLnZBk7BGMPk6SiyPyj29fdUN/fxnZ/9Y4xftYLpG67A7n1PQs5XoMoKoDL4p6LwR8OQK3VI6QKUuuZp/8Ef/ne88g/e2XENRu3iPkoPPIdgOAhEglBkGYFoCOygjMT/91UsvvFVQ3242AngzVauxcXFvq7dSXHh5PwKp61fLD4PvPJ1wD//Dy1zUK9pwXkoAsTngHwWqJa0U/n9LeBgV5tqz1QgEAJu/XHI66/A3lNPYG5+ARHWAJ74miYmAGByBrj+NiA6qdmtdi4Bxczl38PvA9USsHAKSDyv/Z43/3TnAuP894HkeW29/sNMUK0CnP+elr1YvcKx9wwAcOk5oFQA5lYuZ1mm5jSBdOGZluLCCBWGj25g26/nq5WFyjgJ2myL6UWAekVceClzwbuQdUIru535sCIQCDTZ7QZRr2Fnb1HmgmhJxzYoM+EYUOtvlqAbokuRvosLTuncFkrnjtaeyLkySqUqmHo4Y0MAwBh2v/Q4Gvkybvyvvwmhk04MzybAJBnKZASQZfj9Afj9PsjjKkovbkPKFhGamXT+wjqk0wC+Xq8jkUigXq8PzMrllLhwor6i13UxuQF26RyQ2wMi4xDOXK1lILQXBK57M7D5vNaKNhQGwuPAxJT2d4IATE5rbWd/7F9rgfz+NhAZA868Alg6A0FRUJtZAfMD+PYXNXvU5CwABhQy2in/tW/UTvzrVRzpliCIWt1GPqNlSDLbWjajU3GReAHw+y8LC0DrXFXMAruXLouLakmrEQmNaWKgWyoFrfbE+PAUBO33Fy0K2TvAbmG4k4MnB82oW3IGFZiLoohoNKrfM9rZYngA2amFisSFu+g1A2PuWMZb3vL9wlvehsPhJmHaj/eO35uoFS3RFaqqIpVK6TYo20PXFq8ELj7WvwV2QfWgDlVSD+dbDHctTD5cgCgATLNH+cJBZB95HpnvnMPsrVe3fwEAqu8wEFVUhMKhy92ZVBWCz2e7QNxpOgmUi8UiEokE/H4/NjY2EA6HB7U8AL0FQr3WVzixLlbKQfnKp8B2L+nF1sLkDMTbfgaYP2yLLAjATT+iBcZSTZtPIdU1a9P8GnBKs+8hOql1d2qBb/OcljGYXb4ceMcXtEzEg5/RMheCcLnOgyMeZkIKGSAX0wTI+ac0e9TCqQ7fkBZ/LkCrHXn4K8DT39asWIIIzK9BeP3boH3ZTWR2gc3nNDEyNa9lP6IGET45rdnGmKq9FqBdU0PqTbTwJXc4MRzQBrhRYfjJwMoWw/dEOp0GY6xjCxWJC3fh9HUYW94Cl+u9KpUKcrkcstksRFFsqu1xKjPKhZKdzAUVdI8Ag7BFcRtUrVbr3AZlJtTfvsas+f8xIGgBB/T/0clfKGP3iezRvxgyYtAP/3gEqqJCqUl4/Ff+DKG5GJZ+4iac/T/fotdncBhjyGQykNan4JuMQCjVIRwG5WpDhlKuY+nHb4R/fPi9pVvtV8YY9vf3sbe3N7CJ4eZ18XXYpZ/zK+x+z9RH7gPbegmIz0EIBMFUFSybgvrgZ4Gf/NXLcyaW14E3/pTWfjaf1gL+jVcDr71DK/A2I9W1GgepprWwZQxCOQ/4/JqAkGWgnNMC7nJeq08IhICgoAX4+pfs0BolCFrNxV5CG1zp8wH//D+BG24HXnZd+4s8dRWw/ZImIkRRC/ilmnZtC6eBR+/XWuAqslb/oarA1ovwf/nvELzhrQAMNqaXngEeuw+olLTXYqo2OPCHfkqzXQGa2Hj+Sa0YfWJaW3vxQBMgfWira1UYvr+/P7ITw0c9sHXD+q0mQRtreI6zULnhGpzAK+Ki37Uj5nqver2uC1OeGeUtb3ut7bHzmXBxQXMuCBQKBSSTSfh8vt469QT6M7GaWQqKpp+43DLWIDQAIDwXBIMKKH1ZWlcEp6IQ/D6osgo5XwZUBlWSUd3J4qW//BLS334Wt9zzW3oWQlEUbG1toVAoIL66gPovvgHlTz6E+v7l086Jq1Zwxa/9xLAuqQkrcaEoCpLJJIrFIubn5zE3NzeUB0g3Qt3J+opWawI6Ez2sWoZ68QdAdAJCQMuaCKIINjULltsHLv0AOHPNZYFx+uXA2pVaEbc/0Hxab2TnIvDNz2k2JqbCFwhiZnIJ6twCoLykZS92LwGS9uDQ6itUQAwB/hAuq/vDLAYvIAe0eRuiCsyuaP/muw9qReeiqK3J6lBi41XAc49p1yM3tNePRIFXvV4TOU98TcuGhCPafUcUNPFUzGBi81ngmtdor1OrAN/9uiaIeD2Fqmoi4qlvaeIL0Lph/dBPAk8+oP0dGDC3Crzmh4Dp42fY9EowGEQ4HEatVsPZs2epMHzAuDEwN1qo5ubmjrVQ2bGuuBmviItBXocxM8pb3larVV1s9Nry1o5Qola0RJMNanJyEisrK72dJAectbdomsLuKTMXGprIiK2OIzITQrnS5ZyLPsBUBgEC5HJNO9GFNglcqUmAoiL32It45J134fr//H+DRYPY3NyELMtYW1uDz+dD4ZYrcNXtNyH79afRyJUxfuUy5u94Dfxj/RF3djHP4qjVavo1nD59Wk/rDmttdjDWV/R72nlHokeWtI5ORiHPmNbFKZsC7vsUhOlF4MprgVfdehh4+w47ILWgXgW+8Y+ahSk2q/18rYzx7efB4nGtW1LyeS3ID0a0NYQj2r9rNABfUMtuKA3D9/VwNgbv6jQ5o2UJBEHrVPVP/1XLlPh8mgC69o1aTQgnmwKqFSAUBUIAcGhXSm1qXaOKOYApmnhoSMDYJBAIAI06gvnU5fdyL6m15J1euGzrEkVgPKaJpWpZEy2AZv364X+pzepgqrZm0WCRqpY1ERPuz0PTWAA6qoXhbliDV+nEQgUAqVRK3xduH85mBYmL3mlV29OqPfJxLW/tiAuyRZ1wjDYox05jeTcZB+hOWJhegQGiKODM7fN45m8SDq2sdxqFCsSgH0w6nIIsCFDKtaafyT76Ah7+N/8vpv/wHQhFwtjY2EAoFEK5XAYAhFdmsP5//Migl94RxuyAsUaBX4Nb1nYc/ayvMK+pY6IxCFNzYHsJsFBE6wuQSlxuO1vKaUF/bk+z9bzx7cfPmEi+qAmLqbnLYiAcherLI7B7UWtDu3NRyz6oijZMb3oB2NvSCqzVhpaBUBUtKPf5teLyhqTNiphd0gquFUUrnM7uAfl9LaMBaJmC9A7w1n+jtc0FtKyFVAXWrri8/mpFy2RMTGmiqVHT1qvI2tDAQAgQADkygcufFP+sze+BoP2deS8IwlEhtp8Enn4I6t4WqrU6GnOrmLz1LRCnZtu/rw7RSWG43+/Xg4lwOIxEYhvbOymMR8dw9dVXIhAIoFQqQxAEjI87/9Cngu7BYmWh2t/fRz6fR61WazqpNnYWGoXr81IrWrdcR6uWt5VKRW95ywvI+X4xtrxljNnOXJAtagRw+obgmA3KzN6Lln/MmyN1Su/CwvBajGFqYxL+SRFywSXdWFQGtda4/N9W16oyFJ+6hPiTSWy88y36F5v/r5s7y/DMxc7ODjKZDGKxGFZWVlxxo+1EXPSzvqLt723Uoe5cAKQahLllCBaFxIIoQrzhdihf/R9AZhdMlTVhAQY1GIYiBiBIdc368+JTYFe9Fv7FYwqo6xVtDxpnRwgCmM+v1TnEZ7XheIGgFvzzrMn8qpbFiE5oViX/nGY9Wr1CExU7F7R6h0oJSL6gWakaErRveBDgzQcaktbu9dIPgCtfo60lva11hzLe+2RJy54oqnZDUQ7FjODTXltRgLFxlFeuuiwupua09eb3tUJ0QPs35byWMYkcE2gf7APf/DzyWwm8mDpAvVpDPPAcXnjsMcz+7K9g/erjmzA4SavCcC42Uqk9fPYfv4xnnnketbqEUDCExcU5zM/PYnc3BUEQ8KpXXYOf+PE7sbzcf7vXqDBq4sKMKIp6V6kzZ86g0Wjo+yKfzyObzbo222WGMhf9xdzy1jifp1KpIJVKAYDeyW5sbAyKonR8LbWadlBK4uIE4bgNqg84KSw4Y/MhqA33BuOWHLaqrXz1WYi/+GOX/7hDf375YgrZR1+A4BMx87qXI7I03c/VNiEIAvL5PGRZ7kuNQi8cJy6cqq9giqwFvKEIhGO+Y4IgIHSwC+F7X4SST2tBcngM4stvhHjrj0HwNd/axPVrgB/5BahPPwT29ENa5ssfguIPQvT5IPi1YFsu5rD/3FOQ66z96WV8Xss81Gta21oAYAw+qQp1YRWYXgRi01qgPXZoaWvUtfqMmSXgjp/TLEuxmct/D2ji4/xTwPZ5LauhvbD2P6pyWDMxphWXV0ta69wrX6MJisg4cJBqXqckaVmK0oGWHfEdZi1wWDwenYH62jtRia0hrirA0w8BLzypZWUKWW0ex3hME0jxBc02ZnwvGNNsV7yofXoRuHQO0sVz2NnPoSExyMEx7KsM8VIWX/7r/4L/7YP/D+LxqeO2Q98wF4b/9//xj3j8iacxOxPH/PwM8oUS7rvvAfj8Ptz6uhsRDAbw1a9+A5c2k/j37/u3mJrqfIBnO0Y9OPcCxmA2EAggFoshFosdyXal0+mmYl9+b3BLLODWoNwuqqq6ugkDxyg6ATS1vDV2shNFEel0+tgsWKVSQSAQcDTT//GPfxwPPfQQPvnJT7b8mYODA/zBH/wBHnzwQQDAj/7oj+K3f/u3HTk8d/+nOET6YoMyM3sayO/29hp9SK8rDRUbb13GC/+w7ejrGlfq+K3w8MXT33wG9732NzF+xTJWfvIWzP74DWANpWWAzBjD+f98Lzb//utQqtopsX88go1/+1ac+vk3Ob3KI1QqFUiSBEEQHJkB0Q9avXdO1FcwRQH7/jehPvMwWLUMIToJ8VWvg/CKW1rOMRGkGmaffgCsUYUQn9MC50oR6ve/AWEyDuHao61ixVMvg7B0Bo1UEsrOBTAG+I0nkaKIQMCPqYUlFIJB/fTSMvW9cFor+n7pGUCKaEKjUoLqD2ptWx/7qlZrocjaULl6FSgXte9qbBr43jeAW97aLCwA7TpCY9qsCEXWXrdWPiz2VrQAXm5odQ0qA1T58r+94tXAd76k1ZKMTWg/X8gcXptPy5QIh0XcqqLVhbzj/4I6swxcuAD/uceAZ7+jCbVa5fD165pVbHZZmwMys9S83mcf0VrbNuoABODJrwPFHFhDxoKfYTnsQ15VkFRDGBMYlK00vvPwE/ixH73d9j7phVZ2C0mS8O1vP4bZ2WmsrCwBjGFvLwuf3wdBEFCrVRGNhrG8PI8fPPs8vvmtR/DWH7vDFRnFYeMFcdQqKLfKdrUq9h22hcoLnwNnVEWSueVto9FAMpkEY0xveWsUJLwLFRen1WrV0RkXn/jEJ/DRj34UN954Y9uf+43f+A3U63V84hOfQKFQwAc/+EF86EMfwkc+8pGe1+BZcdHrBs3n89ja2oLP58PZs2f7l64ab1M02gH9cu2KooDSTrUPr2zsUnWIIDgrNBQGab+A7H4B2e+cg/i794D5BBxcsYSX/drbsPgj1zf9+N4/fx+X/uZ+CD4fQvMxgAGNXAkv/NnnMfHyVcSv23BydTqMMRwcHGBnZweiKGJiYsKVwqJV5sKp+gr1kfugPvHPWj1BMAyWT0P5xucgNurwXX+b9ZoSz8NfzgPzyxB4l6foJFijDuWxr0LdSwLVEoSFUxBffgOEw5qASl1CVQwi4gsgoDQgMBUMohZsKzKEqTlEr3wlov5gU6tCzT6jZQWCwaDm1X/tWxCZnIFw/ilAbkBYWIOUz2P8/Heb5z7Uq1qA7g9owiI2qxVGf/vzwI/+a00sZHf1onBINS1TIByKlkb9sPsTLh8kyAoA1lwofcW1Wrbhxe9rFilBPCzEngKqxcviAtB+lwDg+SeAWxYhNOrwnX9KEzWlfe33TMa138u7WL34Xc0WxV8jlwaefVgTRFNzQDkPtr8FVVZQkRrIS8CY6ENMlFFiIgQBqCgC9vczXe0TuzDG8PAjT+LrX/8Wdnb3sLK8iDe/+Vbc+NrX6M+HarWGarV6+f4uCCiVygiHQlAUFaFQGNFoFLIsQ1VVPP30s7jqZWcctcqMYjAFeCOo7TSYNRf7Gi1UVsHjIC1UXvgcOKqqeuI6AoEARFFEOBzG/Pz8kSzYU089hY985CM4c+YMXvva1yKbzToyuyqVSuGDH/wgHn/8cayvr7f92SeffBKPPPII7r33XmxsaDHO7/3e7+Fd73oX3vve92JhoTcLqGfFRbeoqord3V1ks9mB2KCEeqk3gdCnokBf0IeG1IdetFbLZeywWZXDIuPwtdWaBAR9KJ/bwvfe81+gfuSXsPwTN+k/snPvo1BlBeHpw5ajAhCIj6O+l8Pulx7vi7hQVRXb29vI5XKYnp5GtVp17U3VvC5jfUWvtSGsXID67MNAMAJh/NBuEomCFbJg3/822CtugWDRdlWoakX6TTUPgHbintkFq1cBvx8s8QLY+e/D95Z/hQMhhO3tbUxvvBqxelEL+Bt1LUOgKloQ/sP/Up9wbW5VyFPfegciRYEQX8fY66/CeCiAaDqJ4IP/CGV6Hr7oYUYiu6PNrBB9WlBeONCKrOdXtQnfj3xFsxQVc9p3eWwCepcnVQEQ0NYjG+qN9Ha1/suZCf7fN94JXPEazR7lD2rBf+J5TVzgsOUt/zzlBvDE1+FLJTEVGIdQLWpZE13cCNrE73pNEzHpbU288OLtvYT2fsfnAamGRi6DWrEMWZYBVYVfVVEsyfBHg1jwSUjIYfygBFwzHW+/J2yeXrb62fu/+g38/T2fBlNVTEyM4+mnn8MPnnsBlXdW8eY33woAmJgYx/LyIp5/4SVMT08BAMLhEOpSHZHIGCYnxxEKhfS2t+tnzmBmZsayMJwHlp0+L0a9oNsLdHtS3s5C1eu+sIuXxIWdImi3YxyiZ86C+Xw+XHfddfj+97+P8+fPA9A+v1/6pV/Crbfeite//vV4+ctfbvu9eOaZZxCLxfC5z30OH/vYx7C1tdXyZx977DHMzc3pwgIAbrrpJgiCgMcffxxvfetbu7vwQ0hcGJAkCZubm6jX6wPzvTN/952A+vloEgQBU6fHkX6sYOvf6fUfAmCUCszw/7b+xwzM9O8cQ1KAqB+qrOKFj34Oiz96PcRDb6eULhyx3wiCADBAOig5vxTDPltZWUE8HseFCxdcG2wYMxfG+oquB0caYNk9LUA1dxuKjIOVC9rp+MLa0X84NasFvw0J8B2Kj0Zd6/wUGoMwt6y9vqqC7W8j/417sX3FzZiensbiNdcA03Eo3/8WkNsHFAVsdhnCbT+tWZpaYEx9620tcweo728jI6tQXngCEUFAAwIEuQG/KELIH7ZoFYNarQTPZPBOVY/er/09O+wqtZ+8XHAtQPszo7DQLgqaUFCBp74NJF7QJoi/5k3A2Vdo7Wv5oLtKQRt0Jx8OzhNETUwpDaBWBcKC1kJ3fxuCIhnqKbgAkQ/XJgOCX/s89pOA6Nfe62JWqyOBikY+DygyGgyoN1SojCHqFyAqMgqSik8lFITmFnHzzc2ZQ0B7EH/zm4/g/q8+iJ3dPayfOYUfufNNuOGGV7fdX62+M+VyBV/60j8jEPDj1NoKAGBhYQ6XLiVx7xe/iptvvh6RiDb34i1vuQ0XLibw4osXMD0dhyiKaEgyZmbCGB+PQpYVJJPbiMdjuOmm6zA9PW1ZGM591sZpv+1aVebzRVy8mEQoFMGpU6sjFxx6Iah1wobTrmGA0X8fDoebZq449b7xRiVeCMpH1RZlRSsrpiiKOHv2LP7iL/4C9XodTzzxBN7znvfA7/fjoYcewre//W3cddddiMfjeN3rXodbb70VP/zDP4x4vP2hDADcfvvtuP32ziynqVQKS0vNNlc+YHBnZ6ezi2yDZ8WF3Q06MBuUmaml439mSCzeGMelr++gkeks6G0qLGcAE5jpLzt7ESawvggMtVwHfCJK57Zw3/W/ibHVWYTmp9AoVqFKDW2WhnhY/H3YXWfyaovAtgdKpRISiYR+g+H7bBAT5buFr60f8yuE8KH1R25oJ+8cWdJOzcMtfKirV6AeX0SwsA+mTGr/lhczzxq/UwKqYgDYehHLt/44pucXtT++5iYIV1wLlk9DFnxg0Zh1ETljWvBcLQGT0/pAPQFA6MJTCD31baBcAPP5oNYqkBQFiqKiUq7AJ1UxVqtCEEQITAVwmDXwB7SsiaoCULXvhnhoYVK0U38Ih38n1Y6uSfRp9RDKYb1FNqWt8dJzwJveDnArWa2svZZc1wSCVDMVY0N73+ILqIshhLJb2tRw8bCTFDsssBd9mo0rGAG+8ZnDOTNMu4ZyAYovgGq9AV+jjqhPQFBgSDIBz+VljAsK5iM+/NWzWWRWX4n/8Bvvwuzs0UYJ//i5L+Pv//4fwACMR8fwyCNP4umnn8O/ffe/xg/90C3We6AFpVIZ//RP9+OZZ87h1KnVpof8/PwMdnb3sLu7h/V1rSPYLYdi50tf+hq2d3axtLyAV7ziKqRSabzwwgUIAjA3P4uf/emf0P8NcLQwvNFo6Ba6g4ODponhPKj0+/2QZRmf/8JXcP/930AqtYeZmWm85jWvxM//y59yrFh8EJC4sMZqX5gtVKIo6oP8eB1Xt+vwwufAcVMr2l7pZG+FQiHccsstUBQF1113HT760Y/iO9/5Dr71rW/h29/+Nu69917ce++9+OxnP4u///u/d3R91WrV0socCoVQr/c+48yz4qJTBm2DOoJpQvdhJ/nO6HMw6vf5EBwPoZGxCHDMS7FaT7fL66PAgKKd8sj5Cgr5TeDZTYjBANSGAqakEZgaBxiDUqkhsjbXZJ/qBcYY0uk0UqkUxsfHsbq62tQVw+3iol6v46WXXnJ+fsXsMoTF02Cb58BEH4RAEEyqA+UChCuv1WsljqzJH8D+tXcguv8CxOQLWtZiclo7mR/XBIAiazYmUVURGYsiGG8OaoVgSCtUbjQuW42MVErAQ/cCWy9qYicY1joz3XAHsHlOK54WBCA6CUGR4cunEaxWIM4sIDA2AbmotX1lgg8qBKBSAfwBiI0qBKWh1ZhoXxy95kPPGDCTJZE/pPQp3od7xRfQ/i4Q1GZcPPRPwMtvBGol4BufO7REVS6/jnGPCdBsWKII+Pxg0UntdQ72NBuVqmjiw+8HIGpiJZcB1q/RXn9/CwoTUCkVEYJmbWSMwS8C82EfxGkRZZnhiVQZ36+N4V9c/2pc/fIrmy6LMYZnnnkOf/3X90CWFVx11QbC4RAWF+fx0kuX8OnPfhE333x9x/vt+edfwl/+l0/i3PPncelSUq+1uO66V2l2p7qEYCDQNDtGEAS87pYbcPNN16FYLCEcDiMUCuLgIIcXXrwAAQJedtUGYpPtB1kGAgFMTU1hamqqqVWlcWJ4MBjEww9/F5/93JcRm5zA0tI8AsEgHnjwO6jXJfzGr/+yZ4KrUWAQJ+VWFipeGL63twcAPVmovCQuvJK5YIx1LJTq9ToYY4hEIpiensZb3/pWvPWtbwVjDJcuXcJDDz2Es2fPOr7GcDgMSZIs10PdonqkXq8jkUgM1AZ1hJrzthun8I/5ET8TRfnS8eLCcaHDcGiR6jMMUGUZgk8EOzyRFXwC5u+8Dlf8Xz+O0FzvJ4mKomBrawuFQgFzc3OYn58/ss/ME7rdArf/yLLcl9kbgiDA9+Z3QLnvv2lD7g4DWmHtSvje8La2/1YJjUF5w08i7BOAhnaDVj/zF2CFAzTCE6jUqvABiECGb+MVEAI2BBFjwLc+B1x4VmvxqqpAehfY/yetG1O1rAXf04eZkEAImD8Fdukc/JkdiFIMQUUBfCIQDEGZnNFO+qU60GhA9QWgRCYQKGVN3x3z94gfNwiX7VDGnxEELevD29bms8D9n9JEwn7ysONUUBued+QaD/+3lIO/kAMaFc3+FIlqczzkhmZ/Gp/S1lgtaXaqaunw73yoSFUtOeLzoa4oaDRkrXBbVuET/fj0hQru36ogMDGFdDrb/PkpCu751Gfw3//HP+KZZ84hHAkjncni6pdfgeXlRW3OxM4edlP7CAYCeOg7j2N3dw9zszO4+ZbrMRWbxNNPP4dLl5I4yJVw+tQq/r9P/Ddsbm7hVa98OWRZwdbWDpJbOxgfj+JlLzuLra1d3HLLDVhaOmp/E0URsdik/t/x+BRuuvG643aKJa0mhudyOTzw4EMAY4hGI1pQEQ5jbXUJTz/9HF566RKuuKJ9IaZb8EpQO8j1m+u4Wlnr7FiovPI5MMY8U3PBP5NOroUP0DMH9Hz+ypkzZxxfHwAsLi7i/vvvb/ozSZKQy+V6LuYGPCwujvuicRuU3+8frA3qCO48rQYAf8iHwHjg2J9j/boGY/FpP1EYmKJABXDF//0TWP4XN8Mf7b1zA6AJ2M3NTTQaDZw6dQqTk5OWP+fGzAWvr5BlGaFQCKur/fGFC7EZ+N7+q2DJ89qgtolpCCtnW7ahBZq/30IkCkSiWth94w9DevBzUPIJRPw+BAJBCIunIV7X3FKYlQtgzz8JdfeSJgxOXw2cuuryfsvsAFsvabasVELLCrBDO9DDX9G6I02a7D3BMJTIBNjay+CLjmt2ojNXA88/AV8ppxVJB0NgFQFKfAEKYwgUj+ucxG2GavN/c2TJ8PeHa988pwmKsXEtCyG3SnEzYC8BcX8LUaZq/zoQBKaXDq1aPkAUDq1ZovZnsqTVjIQiUAEwRYFfFFCCDw1BhA8y6pKCx/cqWJwI4+FEFts5CbXtNLa3dvGD517E+977q7jpxuvwrW89iv/1D19ArVqHP+BHOBRErVbDk999BrVaHYIoIBQMIZnYxj2f+gySWzsIBPyQGzL+9u/+JxRFwcULCVSqVUSjY9g4exqVag3XX/8q+AMBXH31lWhIDezspvDsD56H6PPhmmtehn/5v/3kwAMxPu230VDAIGB5eQHBYBD1eh3VavUws5nB+ZcuYGFh1lUzFI5jlIPaYZ+Ut7JQlcvllhYqcxbPS+ICGP3rAOxdCxcXg+4UeeONN+Kuu+7CpUuXcPr0aQDAww8/DAC4/vqjdXF28ay4aMXQbVBmwu1T7cNG6GSH9DEmPjyzHQisoeDZD92D/NOX8IoP/SuI/t72BZ/qHggEsLGx0WTFMOM2cWGsrwiHw/ok234h+PwQTl/V+c9bDEZUFAXJ8UXUX3UHFmsHCPoAcW4ZwsarNQFyCMtnoHzxb8H2twGfCEFRID7/XeC6NwE3HBbDlfKa1apaAuplzW51WOQPRdamV/t8WlBfLmj/e9jVqnHmFQi/8kZtgvajhydDTNWyHVOzEFY2EMjtI6C1SEOrLxDTazWaf6Llp8BUTRAEglrhdaWgCYHj4MIC0ArkU5vNNqx6Tbt+pmi/ffslYHYZqi+AgAA0AIhgmPKpCEYCqPhFvG55HFWZQZVlFApFqKoKSZLwta99E4899l2859/9Cl548SJeeOElMJUhlyvomQ1RFJDNHoAxYHFxDn/25/8V1WoN11xzJURRxPZOCg888BCq1RpE0QfGVFSrWWSzOQSDQVxxxTrm5kIYH4/i5puvx0svXUKhWMKv/J//O15/640IhZwbVGWXiYko4lMxZDJZLC6Oo16vY2pqCrlcHhOT4wiHQ3oxZaeF4cPCTferbnHbNZgtVLVaTc9scAuVcQr02NiYZ4Jyr1wHYK/InouLfh9wK4qCbDaLiYkJhMNhXHvttbj++uvxnve8B7/7u7+LSqWC3/md38FP/dRPUebCLkYb1PLyMuLx+NA3siD6uo/NBaHvdRfBaPvMRf9vzYOUFwATRWz9w7cwceUyzvybH+7uNRhDKpVCOp3uWMC6yRZlnl+xs7Pjuocwh6+rXq/j0qVLkGUZa9feqA8zskJ98kGw/S1gekETBrWc1gr2W5/X6ipOv1ybSu0PaCKDAYDKf+Hhi6hAPq11mxJ92s/k0hDCUa2l62f/Arj4A80+FJsFVq8AIGiZBF7Avt96QCXjv+Nw/xsliKo1Y9NHaVz+B4K2lsJhi91Gt0V57HItiP5HhmnhUh3YS8IfjiDjC4PVapgJanYoiYkoMyASECBAxhsXQ/jOJa3wXBR98PlE5PNF3PWf/gJjY2HU6xKq1TpU9XKNiaqyQ+EgYnt7F6nUPs6un0YqlUZdknD+/EUoimbXCxzWT4TDIZTLWo3D9773DH74h98IpqoQRQEMDDffdB3e/KbXDd1yEQwGcdttr8fffvJ/Ynt7F8GgHwcHOezu7uH1t96EN/7QrZBl+UhhOJ+zYCwMHzZeCAaHnblohyAIiEQiiEQieits4yA/bqHimYx6vT6w2Rr9wEtdr+xcS7WqHQA5OUTPip2dHdxxxx348Ic/jHe84x0QBAF//ud/jg996EP4xV/8RYRCIX1CtxMM/w7VJ8xfMPfYoJoRBAFM8B0t4HQJ0bUQEALQIk6xVYDeDYPVFhB9IpiqIvE/vtGVuDC2al1YWMDs7GxHN3s3ZC5aza9wk/DhGN/TYrGIRCLRUYaIqSrYxWe0eQ4H+9rcBu0vtJqIez8BzK1orV3D0aYA//A3a/+jHg6x8wcPMwaiZs2S6og8/CUtq6EompAoZDU70fKGZjN68bva3zHlcE4HAw4L2RlTeSfnw0SJoH+/ePM1dvi8UlTAJ2i1Scznh29sXCtCr1e09TDWw+FDi38niFqRekNCRQjgscXX4qWnvo9fmCxAgIL9slbbUWDApXwVNy5N4NRkEJsFCY3G5Za6xWIJlUoFfr8f9bqEQMCPRkNu+g74RBGMaT7gly5cwt5+GuFwCNlsDvW6BFVlCIWC8PlE+Hy+w9dg2Eru4HOf+woajQZ8PhGrq0u48843uSZouf2216Ner+PLX/k6dndTmJudxR13vBE/+7NvgyAIHReG86Fu4XDYNdc2aoySx9/n81l2J+PTwvkwVvMgv1HBC2KVY+da+iUu/viP/7jpv1dXV3Hu3LmmP5uZmcFHP/pRR38vx7PigmO0QcViMSwvL7vPyzp3Btg7b+ufDCoMnVoZh+AHWO+dyUYCuVSFLxxEfT9v+9/20qp12OKi3fyKYa/NCr62fD6PfD6PiYkJrK6udv7dliVtToPPr0Xx1cPGCqqqZRfSO0fbtgrQgnbgsJNSQKupqFc1keAPwPfCdyHUilrbVrkOIKj9XbUMHOxqBde8U9TYhGZDYioUlUFlKvyHv0cw/FoVgIjLy/FB0wyicJhPYUBNasCvFhFihx2nQlFN9NQrDmc32eHvZBArJTz+zDexXRPwIqtjO1dGVVYABjRUBp8AnJ0KYzYSwGbhaFcSrQuZBMbYEWEBALIiHwpbLZOhzRGIo16XUKlUm06dGa+HAVCp1iCl9iGKWmFspVLF008/h1tuvt4VgYvP58NP/PiduOH6V+EHPziHa665GouL1vNVWhWGVyoVbZDjwQFNhu4BN2cujoOLUFEUsbu7i9XVVT2zYbZQRaNRRCIR98U+Bk5q5qJVQfeo42lxYRxW5hYblBXC+vVgNsRF8yO4v7mDQCSAieUxFF6oHP/DfWKgyQuVQW3ImHzFaVv/7ODgANvb2wiHw1hbW7PdqnWYAXwnosht4oLfvPP5fMsOXFYIogjhzCvAHr3/cACfXwv2FUXLNgiCNtQvEtUGyOnWw0OrEM8wHooJ+PyaSGAqsH0BgtzQBIgsaa+pHNY8CAKQ3df+/Xjs8muPTUApHECWagjgsoAw9IiCH7op6/J1GH5OABAVAaY2AEEAEwAEghAY04qxncyKMqZlUBgQ8gE/Ogf8yhfO4cdvnkU0IKLUUPXPZjLkR7mhYq/SsHgZ1rTnrfaXlni5/OeFQhH7+1oBfCDghyQ1UKvV4Pf7oaoMTGWQZRmRSAQvu/IsFFVFIV+AJDXwjW98B7e9+VZbnZgymQN89Z+/gUcf/R5EUcBNN12HO25/g2OzKKLRMayuLmF+frbjf8MLwycnJy8Pcjy0UKXT6YFPhnbj89QOXrkG4HKNTjsLFf+ZsbExRwf5OYEXxCqHxIWHxYWiKDh//vzgh+J1RedfpiOP4H5qC0GAIACT6+GhiotBwxoKZm69uqOfNWbG4vE4lpaW/v/s/XeYJOd13g3/KnTOk/PszEbsIi5yWASCIEGAYgBF0pRA0pJISrZkyZIFf/b36r0o6fMrWiZN2RLFl5REmQq2SFEiKQYwAQwgAoHFAgsssDlNzp1zpe+P6urtnumZ6Z7p6ekZzH1dC+zOVFc/VfVU1bmfc+5zr2nlRRTFTQngF+srKpGiZstcWIsGAC0tLSuKzwzDMAXYolTsPiVcdye8+KRJLtR8ycq+UNhWNDtIGQYYthLtgpVSEMBuN0ur0gmTXKQTBQO6QstYVVs8ELPttMsL7X2mxiM6h6bpoGSRKScMpf8HM3NR3FWF3xsCGIaAroMkGhjx1bpQrREGZqFWgXO9dHGaWDzOE5dlfuFQO4ZuEMkZ+OwS7W4b37sYYTxROWtR65yy2nbuHt7F/v27OXr0OLpuoKoaDocDXdfQDZlQKEg8YWai3B4XqVSamZk5RscmqiYXsVicP/2zv+L1k2cJ+P0YGPzDl77OyZNn+e1//zG83uo7uySTKRKJJMFgAJerPl3owDyHDocDh8OxomO40+ksllA1ozB8M7GdyEXpcSwuocrn88W5UarjaaYSqlratzY7mqEsarOxbcmFJEn09fVtiZZ+xtSZ1bdZ9O/ywGIjhN1XLOzaDwUZ/354xa23Gy585pv0vP1mXD2VTdzArHkdHR0lm83S09NDS8tS5+Fq0egAvlR0vpp/RTORi1QqxejoaHGsKy0aGJMX0V77mSmctjsQ916PcOhWmB0HpwuyDrO8yTDM/+uqeaPJBYKl64VSqEJXKLvT1BxoGsgO2H2NabCXjptaB01Bkx3I+RU6NNmd4AuZLW6VHNrcFHJJx+XVsnSViEXBy7uslqru8dIigbduGGRUnWfG48gi/OOpeUQB3rwrSI/XTlrR+eb5MF98dXbJrgRBQBAE1MUEbBVIkggIGIZOV2c7t99+E26ng5m5eTxuD+Pjk4QjUaKRaJFIWnqMlpCK01l9YP/cz45x8uRZ9u/bXQy6crk8r712mqNHj3PffXeuuo9sNsd3vvskzz13jFQqTSDg5957buf++4+UvZPqFdxW4xheT2H4dgnM3wjHYLfbsdvtRR2P1YVquRIqt9vd8CDfWu3f6tcD1ibobnQr2o3GtiUXAIFAoOmEqBWRia/465WIhfVvo84pjNL7OzDgQ/aLqPHK53Kjg8/NeNQoyQyv/n/+F1omj2iT6XrbTfS/7wiS0ww0rCBXEASGhobWverQyAB+JX3FcmNrBoTDYaampnC5XPT39y8Rp5XCmLyI9uRXIJvCcPkglUB74fuIkVmMbBokG0LvsNmONp8zyYOum+RBLmQrJAl0AeRCRsPA/FmwzSQju6+B/TeaBGPkNExcRMitYjiZS5tZC4+fpLcVbWoct2Q+iNda/idg6jCK/6j3NFq0P9Psyvy5WxbJqQZ5zeCLJ+b4+tkwnR474azCXFpdOlZBwGG3oahawcFWKAS4Aqqqrvi8NgwDTdOYnJrF7fHw3p9/O0fuuplz5y4yMDjA5z//d3zt64+j6QYuh4wkSeRyOZLJNJn2HBMT04yOjjMw0LfqIZ85fQG7w1G2mutw2BEEkQsXR6oiF1/7+uN897s/oqUlSGtriEgkxpe+/C8AvOUt9274/V6LMNztduNyuWoOKJvl2bAebPVjqJUgVepCZWU1NrOEaruVRVmLKKthpyxqBxsHZ2VjNVidWBR/XiiLqEtkseiGcHhsdN3SwvgT8+vf91aBZjD/05NY5zP8whmmvn2UW/72d4gkYkxPT+PxeOjv769LW8hGkYu1iM43O3NhGAZTU1OEw2FaWlro7u4u+13Z3+cm0Ocn0V991mwz2zlwRfSbTWFcPmWay4kieAMIDhdGMmaWNMUj5o7ypkkcoS6YGSkQDVuhA5MOmTR4/WYGoq0Hsik4dxzUPKK2tASoDKoK4WmYvIiYSZPVDfRCJ1uD8vKnSljtVVX8fb2ESpUuuyAgYOCURe7fFeS7l6JFTUg0pxFdwVvDMAxyeaW4cq8XdBKmX8XKc8wwzHaboVCA9zzyEPl8nv/y//xP8orCLTffgCSaZUKSpJLPK2hajnw+Xwiss3zlK9/giSee4v3vfwdvuu+uFb/L7XGhqku1Irqu4XIu343MwuzsPD/72Ut0drbT1mZmNL1eM7vy4588y1133brqPuqJxcJwTdOK9fhrFYY3SzZzPdgOmQuTpK89yyBJEj6fr9i+2yqhWpzxKs1qbEQJ1XYSdNcyr3bKonawcbBXTteXPrqrmaZW68q1l0gJlcspDDCUTXqRbOaDvzRwVXXCPzvNK5/5Kva3HqK1tZWurq66vZisAH4jX3bV6CtWGttmQFVVRkdHyWQyK5aeGaqC/tS/YFx4FSOXNT0oZBt4g6aAGsDhxkhEEDwBmJ/C0DQEmx0CbRhuH4Jsh303wNyEmV3IJM0MhSCa96gsm+QgFYW2LmjtNgnMSz8yf+72mlmQlQi+rpnZFMPArmRwyldE2bVicYlU8VurWmOodSHCMDtXWXXRCMTyKrtDTvaEXLw0k6p6T6IoIEkiaiGxoesGur40y7EYTqepFxgc7OPixRFef/0sXp8bXdP5hy99nampGTRNx+Nx43a5yOZyyLKI1+uhv6+Hgwf3MT4+xT/+4zc5sH8vPT1LtTqJRJLxiSk6O9uwyTbm5hdoazXn3OzsPG63i+uuv3rVsc7NL5BIJOnqKtd4hEJB5ubCRCIxPB7zub8Zwa0kSRWF4el0umph+HYIzHcI0lJUKqGy5sbMzAxgZsVK50Y9CMF2y1xUe052yqK2ILbMJFVX7vNay1EIWBoMqCl4EIRlv0eURTzd9RMi1oJNv4Jl7UgN5r72Ard95EECgfp0jLnyNRt3pLXoK5Yb22a8hBdnWRY/fMu6Db32HPqpo+Dxm4QikzJLkKYvY+w6iCDbzKwDAsKuq8wAf2YMw+EETUdQctC/F+58u5mlmB2DU0fhzEsmSUlETAG4KILLZ3pgCAJMXTLdsLNJyGXRZTvSsvdzQeztcGPEI0gl2YXSRENFwXaFn4GptahZUSZKBf+OamCg6QaKbiCLAqJgmtJphsFsKo9TFml1VX6NSJJYUVuhaTraahmeCtB1jdaWFhbmIzz99AscPLgfm03i2edeZGJimlwuB4apNfD5fLS0hFBVBVXVCIUCCIJAX183p06f47XXT5eRC8MweOLJn/Ld7/2IhfkwkmwWqi3Mh5mbmwcD/H4f73rngxw6uK9wHBrHXnqVF198hVgswb59w9x+20309HTi9/twu10kEimCwSuZ6UQihcvtxOfzVkWoGoFaheFWmYz12a2M7UKQNuoYSkuogLISqmQySTQaBerjJr/eDEwzoZZj2SmL2sHGwR0s/rXaMqiVIJT85wrHqFjfUNZTfyWIgrCimd6GoCme+VbYVwhiI+m6Ewu48pKu94uiVn3Fcmg0uYjFYoyPj6+aZbGyPfrpYyDbEFwmATH8QVjIQS4LySiGvwXCMwiBFsQ918LgAfSTL2BcPoUhiBiDBxAO3mJ2iQLoGoSZUbA7zNKnlg7I583sRSZlZrUSETj2I1iYKpjqgSiIGAgl1ncFiLIpGBfMdrdCznyhWLNLQ0DEQORK21mBymVSiyue8gjIJaqrFa+uzW66gyOY+hFLa1LpSwqIZlVcdlNMreg6imYSDYcscn4+zXi8/KEgiiK6rpudsOoEy+8imUozOzePw2EnGosjiiJTUzPYbTZkWUZVFERJJBaLk81mcbmc9PV209PbVdyPqe8oJzxHXzzOl7/8L9hsNnbtGiCvKIyOjNHe0cab7rsTl8vJgf17GBzsK94/3/zm9/nOd3+IYZiB99mzF3j55RN87GMfZKC/l2uuPsAzzx5FEMDn8xKJxFhYCPNzP/cW/H4v0Wi0KQPb5YThizsNSZKEruuoqtoUjuFrwQ65qA2lJVSmP41SJKHhcHhJ04BaSqi2w7WwYGkuqsFOWdQONg5de+HM0xiLVrLqcZsJ5f9Z+37sBpIgoC3r3EvdhaTL51IaiEXEzObfmAdAuRlYfbAeU7/FY2sUuTAMg9nZWebm5qrqYlX4kBnwy1deYoK/FSOfM7MK0Tkz6+APIuy+BuP8K+D2Ix6+F+HWt6CqKoaqFjsMFdHWY/pY5AoaDNluBuL5BbN86plvmZoM/QodEIzlAurC+ZNkyKWL5MOa4RaxWLQ1GpXb0OpcEXILBWJRur9lYRS2EigI2AV0BMSiCZ15I5t/E0yHbB28COgGZBQdHZhK5kmrOmcWMpwKXxGxm0GnOeJ6NtMQBKGwaprBMEBVNWRJYnJqhmw2h8NhR9c0gsEAdoed2dl5NE2jpSXE4cPX4Cy4tkejcVxOB8NDA2X7f/rpF1BVleHhQQDsdht79+7mwoVLdHa2c8ftN5VtPz4xxY9/8hzBYJD2drOjnGEYvH7yDD/64TN8+MPv4/3vfyeGASdeO8XU1Axer5c3v/luHn7ozcXttwIWC8OtMplYLIamaVy8eHHdwvDNwnYIaDfrGARBWFJCZTUNKC2hstvtRaKxUgnVdrgWFmpxfrfIRXPbJdSOHXLRBFBUjYyzBV96advGZkH3dW2c+puJZX8vIGAIRv0IRjM+ZESB/vfeDmoWJEddx1hvcrFWfcVKY9toaJrG+Pg4iUSCzs5O2traVu1iZRiGSQo6++HSSQyP3/yMKELANCcTrzsCoQ6Mc8cxXvyhGfQKAkJbN9Kb3gv+ZdoNdw/B0EG4cMJsNyvKptg71AktXfDyT0wheHHSL7p2peRHlMxMgShCNrMkwyBW+LsBKICt8DOdK+G/me0oL4la9SqJ4hUdkUWCDJNjiIK5Z4vMGIbpZaEaBiOxLF67G0EwyOsG8xmFswsZgk6ZTk/5vLLZZETRzDLUk5SaK4Hm8EOhAOl0loVwBEVR0HWdeDxR3M7n8+FyOWhvb+OO22/i8uUxfD4viqKgaRr333+EffuGy/Y/PT2L11dOvm02GcMwiMWWdvMbGRknnkhyVe+V5gKCINDe3sqp0+fI5/MEgwF+9Vc/yNj4JLFYgrbWEN3dy3uybAWUlsnouk4qlaK1tXWJMNzlchW9NRrlGL4WbIeAtlmOobQhAKxcQmUR0dISqjdqWVQmk8HhcGzZ7N9y2F5HswjNcMOthlgsxsTEBB2yyVpLEwD1avhSD8geGTEEemT5bepGMFbQf2waRIHQ9YMMf+AwQiYMgojhDIJcHy1K6QN2PVivvmK5sW30Kmsul2N0dBRFURgcHCx2LlkNhq6jj583vScySRi/gBFqM6PQbAZh6BDinW9He/IfMaYvQ7ADwWbHUBWM2XG0n34D46EPV965KMIdb4cOk7iQz0LPMOw/DGdfgviCGZlLNrNV7dKeraYQXJJMwXf3EMxPmGMVJJZzzraeASJgB7KI2NELBMPMeagISIUCqmq6TAGgG2CXTWJR0llOLslslu4nkdP45oUFLkTyOGSRiUSevKaTUszv7fCIzKTKOypJkkg+bwb89Z8zAqIoIksydptMOn0lY6JpZlvbbDaPoSdQVIWrDuzld//Dv+Gpn/6M1147jdfr4Zabb+DOO29eck/09/fwwtHjuJwOksk0druMz+dFEISioLsUNpv56tR1vUzorCoqDs+VlXtBEBjo74X+pUfTLEHhWmGtzq5XGL5Z2C4C4lpKcBqJSiVU1txYWFhgfn4eSZKK80LTtKY8jrVA1/WqyUI6nd52WQvY5uSimVHq7BwIBAj5diHERzZ7WMtCFEV6b2xn7Im5FbdbK8FoyoeKCJLdhrPTz+5fuZveh65FchZWag0NIRvBcLebK9rrRD0yF/XSV1Qa20aSi0QiwdjYGLIss3v3bhyO1Vt9WuOyn3oe/exRDCVnlkVlkjA3hdDRi3D4XsTD95pZh/Hz4AmY3aEAQbZh+FthdgxhftJsO1sJsg0O3GT+KUXYTPkjimbQvhzyWXN+yLLZtSoVA4cbPR5GSESW7RRV+jMFCQ0BNxpSoWTJVlJGtSqxEAraCgwz87KohrG4mGGAomuMJ/LMp03S8NRogp+Mxbm+w02rS2YkbhZGdnlsqLrBj0ZjZV+VyymYiSMRTavNJG81GIbZtnZufh5JkpFEAafTWRCIa+i6gWGoZHMGoVAAh8NBOp3hfe99B+955GEWFiLYbHLFGvBbb72Rr3/9u7xy/HVkm1QI/EXuf9NdXH31gSXb79+3m472VkZHJ9i1qx9BEIhGY0xNz/K+9/7ctluFrITF5GitwvDNevZvF3JRSwnOZqG0hCoUCi3ruyIIArOzs3g8ni1VXrcYtSwcmNqwHXKxgzogn88zNjZGNpulu7ublpYWjNmLmz2sFSGIAsE9nlXJBRS0EkJp5XaTQ8QsYdFKgkRRwOZ34ekLccv/+yHsQUtrUVKYYhigpMGxvE9J1UMoPETXGsTXS19RCRtFLgzDYGFhgenpabxeL/39/TWtasrJCPZTz2PYbAhtPeY+VdUUWA9fjXjN7aZgOxHByGfBF1y0A5vZjja/hi4Fum5mJFZrPQumkDuvmuVVmgIGSKKIJoBm+fZROVMpAB6UIoGo1C1q1devLJteHUWUj1czTN2GbhjYRIFujw2v3TTHe8/+Vr5+LsyfHpviY9d3sivgQBQEIlmFv3ttjqfHE+XjFYRi9kIQBERRqKuwG8zWtaAiijYymSyGoRczCZqmIUkinZ3tpNJpfvCDpxgY6OX4K68xMTGNLMtcfWg/73jHW+noMMvmDMNgbGwCu8NOS2uQfN4kVkIhU6LrOs+/8BInTpxGVVWuOrCXm266jp9/z9v58le+wfFXXmNycpp4LEkw6OeVV04SDAa4+abr8PlWvg+3emC7EqoVhtfLMbxWbCdysdWOoZLvyvj4OKqqFkuoBEEoI6Jr7UK1Gai1W9RyLda3MrY1uWjGiZhIJBgfH0cUxXJnZ1Gqu7FuvTFzPILgAmN5j6wyNAOtEN029PSV0g3JbUdLm20wBYeEkdNoPbyLa//gXUz/8CQXvvgMaiKL6LAhOiSu+/89gj1QuEbF+XRF+CoYy0rca8J6MhfVdlZaL+r5EtN1nYmJCWKxGG1tbXR2dta8b+fCJEI+A6G24s8EWcZweTBeew71wgnT4M7A7OyUS5tlTRbSCQSnB0IdtQ3eMCAZhWyGmlJ0+cyVBgFaQdvAlf8vB4tuGSXbCVwhFkvO2mKWoiw1gytCEMnbnTjyKWyi+SGXTcIpS+Q0HZsoMBRwcGw6xb9/4jKH2tzYJYGz4QzzmfIGFDabjCRKSLKEqmoFR+36EgsLum6Qz+WRbXKBbJgvc8Mwg9qzZy8UMhdZEokkbrfLNNqTRH781HPMzi3wa7/6IZ5/4SV+8pPneOqp53C7XRw+fA0Oux1JkpAkkZGRCf7H//xLLly4bAYMksizz77ISy+f4KMf+UV+8zd+hT/+b3/G3Ow81113EJvdzo9+9AyPf+dJrj60n/vfdBcPPXQ/LS2hJcewVQTdy6HW58FywvB6OoavBc0YJ9SCrUguFsO83yRkWaanp2fVEqpGE9FaUavmYrt1ioJtTi6aCaW18D6fj97e3vKbI9AJkh3W0P+9Ubj+l/bw/V89ttnDqAmSLHH1HzzMqx//Oggg2iRu+Ztf4dWP/wsDj9zI+f/1NLqi42j1svvDR+h+4GpO/JdvEjk+Sscde3H3hZZ5eJsRnFGHkihYG7nYCH3FamOrx0tMURRGR0fJZrP09fURDAbXNa4iDMMUWIdnTVIR6oC2bjM1oCqQiGBMj4CvxQz0dR3h8H3g8WPkV7nvIrMwO27uZ/ICXHz9iii6WhiGOZYiMTUQBVB0kMXVMxDWt1l7qD7Hs8I4DR03qinBoEBgTC03DknEKYtc2+7hTDhLRtV5cTq57K5UVUMXdPKKsiEZiyVDBxTFJDi6rhdrz63OUoqimhkNA3LZHCdeO013VzsCAi8ee4VPffqzXL48jt1mI68opObTHHvxVW6+5Xr8frNOPBKJcfToyxw8uA+/39QBZbM5Xnr5BM+/8BId7W3kcgpH7rqNdDrDC0dfQtXUgqv8LN9/4ilmZub4jd/4FRyOjSH9m4X1PA9KheErOYZvpDB8q5M7C9uBXIB5HJIkLSmh0nWdbDa7pISqmTuUVXtNrGPbKYvawZqgKArj4+OkUqllu+AIdhdG30EYOb45g6wCek7n6l/bxWufu7zZQymWjYsuG749HaSnoqixLIau03lkH/lEFgOD6LFRHC1eDv2nh1DTOaafPIUSzXD3l/8N8YtzHHngEBOPv4ISzyB7HLh7Q9z6uQ+TGgsjOWQkp235rlCCCLL7SgeedTzgayUXG6WvqMfYVkI6nWZ0dBRBEBgeHl7XQzXX2o1uc5ou2Z4AxvwkxMNm61gEM7tgGAgdfdA1gDGhgs1uSqKDHYgHb0Y4eAvaSiJ6XYcXn4TTR812t7F5s8RoPedCkgsdpGRQsoiGUTkDsQhC8b8rfPdaukCo+UKW8YpWKl8QSMuiQI+3+j71WuG81LELbU0wa7mziKKIw2E3y51sNuKJLFPTs8zNzSPLEuFwlJMnz7JrVx9erwebTSafV0mm0ly6OEprS4hINIaqqTgcziKxANMpXBJFfvjDZ7juukNomobL5eTU6XNkMlna29tIJlNousbePUOcPHWO1147zY03XguY74N0JouqqtsiKKwH6uEYXit2yqKaC8sdhyiKZSVUqqoWtTyJRKKMiFpZDbvdvqnnpNrMRTZrNqXYyVxsMTTDDZdMJhkfHwdgaGhoRYt3ce/t6MkILDSnsFt2y4x+Z9ZcMq2vVtOEFTc5JchqyH0+1PEEyND1wNVMf+912o7sITkWJjsRxrenE8/uTpwBN/v+zX1c+Oun8e3rwNAFrv9/7sfmd5GdT6DGM7TdPkzq8gKD77uF5MU5lGQW/54OBElk78fuLXz/lfni6V+9BtJwhhBycVCt8hgRw+4Bu69molFLAJ/NZhkZGdkQfcVKY1svwuEwU1NTuFwuBgYG1p3W1nyt5PcexnHxZYypSyaZEATTj0IUzf+nYhhJL4K/BVwehKFDZvtZSS76WhipOMLJF2DyMthsMHQIhq82ScDFE3DiGdPnIpuuTmNRCqt/qvmPgrbH1F2gFbQUBU24KKzMCwozpMzJu2z7SsMSJZMEayuURhWgG+YeFV1H0Q10zSCWU5lJV+8kvVlu7ouh6zrJZJJkMlXMaEiSSDabw+Nxk0ql0XUdRVEK+pBCxqbwl/b2VpKpNHv3DDM/v1Dcr2EYXLhwmROvnebS5VEuXhplYmKK3t4uIpFYsRlBPp+npTWE0+lA13Vm5+ZRVZWnfvoznn76edOp2+XkuuuuYmhoqCneVbVio4LaRgnDd8hFc6HagFyW5SVE1Jobi0uorPnRaC1PtSL77erODducXGwmDMNgfn6emZkZPB4P/f39VU1w4fq3wUvfxIgs7ymxWRAEga5bWoiPpNe3I58dEnkcgyE8/W1kpyOkJ6IErulFTyl0PnAViXNziAKkeuKEdnfgP9jN/l97E5M/PEHHkf2Eru7DMHR8w51IksDCSyPc+v9+iMS5afyHetELugpnmw/azGDfv78LBIGWG6wbeZVobgUYkhshFwO9NGjTEfIJ0BQMV0tNBKNactEofcVaxrYczBKRKcLhMKFQiO7u7rqksAVRJLv7evx2CeP4T01C0dJttniNzF05/+kEhicAmooQ6ih2jAKTWPDtLyLOjJqBuKGbbWfHz8M9j5gibMMwu0alEwWvCL36zEXpdqJQdPIuhUhJZ9hlpkyp1qLqDSTZTCEs0/K2dIyWnsMwDFTNFHfPZ1QuRLI8tagj1Mq72nxiYcHUYpjjMTtNaczNLTA7O4/dbpbY5HJ5vF4P+Xwep9NJMpkknkgSCPp5x8+9le6eTv7sM39d1HDE4gnOn7uEpmns37+HllCIixcu88MfPUMoFCSfz5NIJjEMg4GBvmLHLJ/Xw+OPP8m3vv0D3G43Pp+HqakZLl8epaO9nSNHbtvEM7U2NKpLUa3C8GpdobcLudgu/hBrIUmlRLS0hMqaH5tRQlXLvNqu7tywQy42BKqqMj4+TjKZpL29nY6OjqpvGkEQ4PDPMfX89+lKNkcHqdK4pe0aP2e/CqLXgZ7KgbWo6XWAbEA8DzoE7xgi/vokgQPdSAE32YkIss+B7LYjux0MvP8mYufncLf48O1qI3piDMnrxNnux+Z14PulNnKRFLpmINgkHD4nBgZ7Png3giQiyuUPh/bb94AgELzWdN4VfcuU29ShhAlAMNRFxKIEWtbUzsjVtVQ1h7NyAN8ofcVaxrYSSsu3enp66toVwxadxffKD9EzcVNjoWlmq9fWHkjGzTawhm5mHEbPgCCinzoK6STiDXcjBFrRTzwLM6MYgVYEy+E7m4bzx2HPdWZrW9lm7t+6E0Sp4GtRCQKGUOrULZhZD7fPLNlaRv8gWP9ZhUWUXgFh8Q8Xf2bZMZZD1c1siCgK6BjYJIFUTueV2RRfObXAZGr1rMfWgVGs7VYUBVmWkGUJw5BRlDwtLSE6Otp49BffwzVXX8X5C5dIJpKcOn0OTdNJJlNIosj111/N0K5BbDaZI0du4/nnj6FpGolECk3XuebqA3S0t3L+3CV6ejrp6+vmG9/8PqFQkM7OdsB0Ab90eZQf/+RZbrnl8LbTZGwU6iUM3y7k4o2WuVgJpSVUQFkJ1WItz0aVUFleVdUcSyqVAnbIxZbDZtxw6XSasbExdF2vyQysFIIgEPP34+odJnjhp6CuoVVmnbB4MdXd103P2yD82hTtD19HeiKCf38X6YkowX3dZOcTSHYJz54O7B+8EzWZI3CwG1ESSV4O03K4D1GUyccytF83hJ5XEW0S/uGOJQ97Z2uFc7fSjDXrGpY/EAv1mBf6ygJgQcti1IlcNFJfUevYVkJpe9zVSgJrhaHr+E88hRRfgM5ecLpgZszURUTnoGsQwtMQj4CSB4cT2vvM9rNnX0ILzyA9+CjGpZNgs5ur/BacblPHMXnRNNCbmyiUWpWSitLiJOtHArpkN4mFppuaii6T7NLeZ2ZD4gtm9kLTyj9b3EdhGhfkD4ZQ3hmqpBFyFemM6qAZoOkGTkFA0yGp6PzjqXk+//I00+ntQyxMHYbDJBUF4aggiESjcXTdbGd79dUDyLLE1OQsly6N8pk//1/MzMwRDAbwelyMjU9hGAZuj7vY/rarq4N9e4d569veRCQc5cSJU6RSaUZHJxgY7Of973sHmWyOWCzOnj1DZWMKBQPMz0cIhyNbzr27GYLaWoXhbre7GEzukIvmwkYcR7UlVKVZr/WWUNVCLnY0FztYFYZhEA6HmZ6exul00t/fv66SFVEUUR0BhHt+CWNhFKbOw+yF1Usc6oglVRp2DzaXk4P/7h7CZ6LYfS7s7R5Em4wj6EEQRWSHjXw6i2y3IUgCgigWHxre3lZzP4KAXDCjK5rSLf6uNQ+6AsEwygPAhmCNJoKLA/hG6ytWQi3kYsPLt+bGscXn0Nw+bKIEbr9Z+pSMQjJiEga70yQZqgI9gwhCQWPh9sL8JMal1wtlTssclyCY5nkjp00ht0UudK1gnrfoXjRAVPPmXwQRQu3Q1mNmQsbOmvvz+E3iYhgVS6Ssr4UCiTDM4N8QQCl0cRKFQqeoOk1lwzBIqxqSKHAmmuf3n5/m1bF5FjKNe9ZsNERRKNRCmy9+VVMBAV3TkWQJXdex2+3MzsyTV/L8t0/9OeGFCLl8nra2VhRFIRZP0tISJJvNMzMzRy6Xx+Gwk0qlsTsdXHfNQfbv3004HGFsbBLZJrNn9xAOh53R0XGcTtPUr9T7IpPJ4vP5cHu2ZnDRbEHtasJwwzCQZbksiGy2Y6gVO+SiOqxWQhWPxwFwOBxForGWEqpaSOuO5mILoxECQ03TmJiYIB6P09raSmdnZ13Se8XWim2D0DaIMdGNcells1+/Ub3IslZUPlsCtPWCIGCXNXruvWppoF74t93rWvTjKh4Y9bxGpQSjVEzbQCcRow6C7s3QV1Q7tuVgGAazs7PMzc1taPmWoeQRDB1dLHSMEUWEjj4Mu8P0tOgcQDpwGG3kDMyNF4kFgCBKGIKAEZlFGL4adeIS+XQK2eE0WyFmUyY56dsDLZ1w//vh1adN/UU8bM4tUTS7UomymelIJ0yyYJVDub1m1gPM32OAqoK/xSy1UnWqmpMCSEIhi2FzIOoaol7fe98hiWZbV83UkpyYjG8rYmGKtMFuN925A4EAqVQaQVAQJXNe+P0+Mpksly6PcujQfuLxBJlsjmw2i80m4/N5iUSixfa68VicRCJJMikwPTXN4RuvY8+eXQC0tISW+Fr09/eyf/8ejh17laGhAdxuF4lEkoWFCPfccwcBf+0Z7s1GM+lrKmE1YbhS8H+ZmprC6/VuumP4WrCdsi+NdhqvpYRqcdZrJdSSudjRXOxgWWSzWUZHR1FVlf7+fgKBQF32a5GLUgi9ByHQiTF7CS4ercv3LEbFmm6AUDdoGmLPXgxJYm09L5f70g14SVXKVjTwXSjkExiiDWzVtVq1Hli6rm+qvmKlsa0Gy2U1kUgs23K5bmNq7UZ3eBCzKfAVHNJFEQQRoW8P0rs+hmCzY0RmzU5SJTAMA3QD3eFiKtiPK9iFOzKFHtfRAUG2oey7ETHUjcNqZfum98Ge6+HU82aZlK6bGZLOQYgtmPoO1QBNN++MYLtJUMwvBJsTbIZJTtw+yGWpZUIKgM1QzexJvdu8CmATBVJ5jXhOxS5sH2IhiiKmU7hIR3s711x7FQG/jx/9+Fna2kL4vF7SmQzZbJZ8Po/P66GtLUQsFqevt4tz5y8xN7dAf38vbreLbDZHW1sIVdWIxWK4XC7uvud23v2ut63YGlUQBN778z+HoqicPXuBXD6PIMDhw9fw0Nvub+AZqR+22or5YmF4NBpldnYWSZLKhOGlXYaqEYZvJrYTuYDNPY5KJVSLs17VlFDVQi52Mhc7qIhIJMLk5CQOh4Pdu3cX2xDWA5XIBYDgbQVXAGPqLGSq7+JSDZYPdQRwuBG7d2M64Tb3A7cMS0qkqBsnWvWr83EM2Vl1KZZl/jUyMkIymdwUfcVy44KVVypzuRyjo6MoirJmrVFNY3J5SO85jPfksxgL02YJVD4Dsh3xhnuLHaGEoasxLr6OEVsAf8gM9GMLGE43E6KHjKIR+LlfRho/C1OXyRuQbOkl6u9EHxsrvkwCk2dwvvwjhHjY1HBYWQpBMsXeNjs43ejpBKKSN033Aq0g2QrkIwOZtJl1LL3TZIdZalVNuaOmUfce0IZ5SnI6CJLEvCZhD7UhJKeafmV6OVjPTlEUsckSHZ3t3H7rYSRZRlFURFFk794hhnYN0NXVQT6f58SJk5w/fxlBFMhl84WuWRper4d0KkM0GkNVVPKKwlUH9nDXnbfi8brp6enilpuvr8pzob29ld/49V/iwoUR4vEEhqHR3t5SVia1g8bBCgyt7nWlJTIzMzNAcxu1QXME5fVALQF5I1Ap65XJZIpZr8UlVB6PB6fTiVgoA7f2sRqszEU99YjNgm1PLjaiLErXdSYnJ4lGo3VtrVkKQRAqkgsAQZIx2gZg+rzZv74OZRIVMxbeFlPo6ms3fQIEAUEuKc1ZUnq0li/emgHM8hAoNs0vDUCrxMKC2VN/s/UVpViNXCQSCcbGxpBlue4keyVk9twAbi+tc5dMTUTXAOKh2xCGDl4Ze99uxJvehP7K0xhzUwBoLi8zPQdQfK3sHhzEZrOR8/qRDt2KE3ACrYZBJpMhlUqRWZjBOPokWmwOER0kG4IsI2SSEJkBhxsctsK1FtDtTkQlB1OXwekBNX9FZ+H2m/esmjezHwJgd5iExdBMrcYyWoyNgCFAXjMZRiqv8c1LCRRNxybL5JWtKeYWRQG/309nZzvZTA5FVbj55ht45zsfJBqN0dIS4mtfe5wfP/UcXV3tOJ0OQi0hdOMSQZ+f4eEBEskUc7PzCIJAR0croiCQzmQ4eHAfbrebnz79fEGnYePYsVf54Ad/vqrSJkmS2LdvGICZmZmioHMrYqtlLhajNAgsFYaDmYUtDSTXWiKz0Wi2oHytaHaSZLU69ng8tLe3F0uoKs0Pa6FhR3Oxg5pgrdDm83l6e3sJhUKrf2gNKGXAlSAMXIeRmIdUeF3kYvE3lN0OogBduxFzaQRvcIW9NFbPUBMamKkofmHJdRPyCQyH3wwaV0AsFiu+rIeHhzdNX1EJy5ELwzBYWFhgenoar9dLf3//ul1zaxqXKJLt249851uW30YQEK6+HWHXQYyZMRLJJBOahCvUxnDBe6ZihlAQrtTjxqcx8mYrWl22F23sBJsTKZ/ByGfNcyQIGHYXSqgLZyoCvbuhvRde+pFJKOwukESzVE6zF7RTAIJ5r4kOs21tMnZFuyEI5YRDKPhsiFLdSIiqG8TzOv8wqnBWceLzyczOzi+bPW12aJpOLpdnfj6M2+1CVTV+9vxL/Mqv/ALt7a1EozGCLQEMXefZ516kq7OdRDKFw27H6/UQCPi5+abreOqnzxONxvD5PIRCAe6++xZyuTxjY1Ps2zeMx+Mhm83x4ovHaWkJ8gsfeHdN49wOwflWH/9ykCQJn8+Hz+dbVRhukY1GPvssNHtQXi2s49gqJGmlEiqrHfLIyEjxHeLxeCrOj3qRC13X+cxnPsNXvvIV4vE4N954Ix//+McZHBysuP3XvvY1/tN/+k9Lfv79739/2c/Uih1yUQNisRgTExPFFVqn07lh3yWKIqq6PGkQXD649kGMmfNw6UWz5rvGAH9FYiHbQQfB7gSPH0PJIdhKVqTr8TBrSNai4eyiHEoaQVcxXK0Vz1mpvkIQBILBYFMRi1KUvoxLs3dtbW10dnZu2gvO0HWz/SxAsL3ovF0Gj59pVwvhDLS0t9Dd3V023tXGLuSzoOaRNBVEEUO2Y4iS+QeRnDuAYHeiOlwIum6KuHuG4PyrpveGphd0GYr5u4I+BLsDPAFzbsTmzZ9LUkHwjTl9RRFk0SQnTreZLTGMgumfRWYL18bmBH/QFI/HF1a9x/JI/Gw6ybdGUzyfkJEkCU3TCgJ4HUkSiyLmZobNZpY8wRWdhSAIxGIJstkcr756ko989D/Q2trC3Nw8iqKgKCqpZJoJdYYHHrib9/78O3j55RNcujRGIpmir7eb++67kyN33sLw8CCKovDfP/05+vt7UBSFaDSKKIr4fB5eeOEl3vbgfYRCwc09ETuoGtUG5suVyCzuMrRex/CNPIZmh7WIsRWPY/H8CIfDzM/P4/P5lpRQeTweRkZGCIVCDA0N1a0V7Wc/+1m+9KUv8YlPfILOzk4++clP8tGPfpRvfetbFeOJM2fOcMstt/DpT3+67Of19KHa9uSiHpNV13Wmp6cJh8MEAgF6eno2fJWimlVDwe5E6L8ao7Uf4/RPITJedcBekVhIdtP8TRBBzSPsuQFUpXIp1A6qQOFcaXnQciCXk9FSs8Wurq5iSVSzYXHmQlEURkdHyWaz9PX1EQwGN21c0tw42rHHMeYnwTAQWjoRb3sQsX9vcbt1G/llUgWdhQ6i2fVJUPLmebE7ERwubJKEIskImRRSPkO8pQfb+dewZ9NIDjdoiSsZiHzW9N0wdFOX4XCZHaQcLrPrlLYoIyFgaje8BRIiSuZYMK5kOCw43dA9DDMjxZvcvGxX7ngDyGug+Fo41X0Df/bTb3B2PEUulyefV4pBus0mk8+v7OfSaNjtpt5L13VU9cp5Kv27mUQyRdyZTBZFUZianiWby5FIpHA67dxyy40Iikoun2dmdp6nnnqOj330g/zHx/4tX/jC/+GFo8cRRYGx0Ql+kHmK97735woBgEB7ezuCYM6rfD6PKIpEojFOnz5DX19PMcB0OBxbMliqFtslc1HrMZSWyMDyjuGNEIZvF3KxXY4DKAq/29vbiyVU1vyYnp7m137t1zAMg9bW1rIyvLUin8/z13/91zz22GPcc889APzJn/wJR44c4Qc/+AEPP/zwks+cPXuWAwcO0N7evubvXQ3bnlysF/l8nrGxMbLZLN3d3bS0tDTkBqilJEFwB+CGhzHOPgtjJ1gpg7FitqKt1xSXZlMI/jaITiN2lJg+bYMbv6Eo0aQIWt4UdxdQyb8iEok0pYi2dL6n02lGR0cBGB4eLj4cNwNiMor/6Hcw1Cx4g2ZZ0swY2hNfQvi5jyC0dRe7uWmatjYjP02F0y+Cx2ca8pXek4Zh/vvaO5EyKaSFKbJAum8fQvcQwovfJ+XyITv9uDJJBE0154SimBkMQbzyfwRweUxPDIfL/KMqJrHRtSuZDZsTWrognwOlYK7pdJmEI7ZgEpB8BpIxDMzhabqOKIBuGBgGRHMaXzwZ5mklxzt//mbGFhIkEilyuRyGAR6PG7vdht1uY34+gqLkm0IaZU5Ds+mBKAo4HHZUVUXXjYKPhdVxzUDTdKLRGIqiIIoi/f09YEA4HEWSRI69eByny0kg4CMUCjA1PcuXvvx1zl+4xInXz9DT00l7u+nLMzIyzpe//C989CO/SDDoZ25ugc7Odmw2OzabnZmZBfbs3sWePbvJ53MVzbkqlUVsh+B8Z/zLO4Y3Qhi+XYLy7aIdgaVO47IsEwgECAQCdHV18Ud/9Ef84Ac/4MUXXywuKL73ve/l0KFD3HXXXdx5551cf/31VVcwnD59mlQqxW233Vb8md/v5+DBgxw9erQiuThz5gxvfetb13mkK2OHXKyARCLB+Pg4oig2PJCqtd5ZEATYdQPG3CXIpcpLJQpYkVgAZBJI+29Fz2URBcEMcHawdpSuKispMzqyeYjFExX9KxrhybIWWC+uRCJBJBLB5XIxMDCwbifTtcLIpjFee47QM99Bjs1BqANBksDmwLA7YX4S/fQx0tfdw9jYGDabjd27d1f3sM5lYHrEvH86+81gPxmFYIfZ7UnJYWYMMK+n3WEG/299FHIZInNzaKJMn5gHpwO7z4fq9qBgIMdmEVQF0EG0YXj8CMF28/wmopBJmCSpc6BAQnKm+Z6Sh2DJCpOSB5sNdh24co9mUmbGwzBgagSUXNE0TjMMdAMkUeBiNMt/fHqKo1NxWltzfPpP/oJYLE42m8MwdARBJJPJ4Pd7uenGa3nqp8/T2tKDLNuYnVsgmUyiKiqariNLEja7rfDZK/NWFEXsdlPLks9fKe00MwpiSSAhoOvLz3dRFLHZJFRVR9M0DIMCmdDRNPPzbpfLvG8wDekEQaCjvQWHw8HcfBhdN5Akkbm5Bew2O5Ikoigq2WyO4ZYQLpezWD/vdDr553/+Nqqm4XQ6sNvtDAz0MtDfw+nTFxifmOaee27nG9/4Pul0Bq/XQyQSxW638Za33Et7e5s5P0uaAZSWRSwum9nB5mIjyFGjheHbhVxsl+MAiv5klSAIAg888AAPPPAAuq7zsY99jOPHj3PzzTfz8ssv89prr/G5z30Ot9vNrbfeykMPPcQ73vGOFb9venoaMLuelaKjo4Opqakl21tlW0ePHuXv/u7viEajXHfddfzu7/4uQ0NDS7ZfK7Y9uVjLZC2tg/f5fPT19TVcrLVSt6hlP+Nww8H7zAxGLgG6AbIM/i6MuYvl21bagTeEEZ1FDHUut8UO1gpDR8glyKXijE9E8Ffwr2hWcmEhHA5vWHe0amEoebTv/wPG2DmkTMIMpuMLGNkUQteg6W0h2chMXmYkuKu2+/fi6/D8d00yYRimH8XBW8zMQjZlaiGcIbOBgqab5LGlE2bHTSLg8oAtbgb5bT3gDSImY9iDbRBqA13FSITRJRlddpAO9YCiIUsysjeAY37CNNmznlmZ5JUVgUTkiog7nzW3ScbMMfqC5ne295plUaNnUQ1I5EHTNRyiYDp7iwJpReOZkTCSJJJIJMlksnR1dRQM4VJmqZkkFQWLra0hEokkuwYHiBaaDmiF55Kma+g5M0Cz2+0oilL0aVEUbcmz1/S2LNXtrDzXnQ47Hq+LSCRe/JlUML3TdR1dN0hnsrjdzqK/kGEYHDy4n4GBHr7+L98jnc4gSRK6bpBIJMjnFRBMwa7DacMwDFKpNL093aiaysjoBH6/B5fTSTgcYXx8kpGeLvwBH9lMlofedj8Bv59nnn2BaDTOwYN7uefu27nhhmuKYyxtBtDe3o6iKMUAs7RsxvqjquqmEfX1YCdzsTo2Whi+XVb8t8txAFWbAYqiWLzvv/jFL5LP5zl69CjPPPMMzzzzDD/60Y94+umnefDBB1dcGLPa2S7exuFwEIsttSs4e/YsYM7NP/7jPyadTvPZz36WX/iFX+Cb3/wmbW1tVR/rSth6T7QNhqIojI2NkU6nN9wIbCWs1i1qOQgtvXDTuyA6CZqK4Wlhcj5C+/wYsqFUpgyCBLKM6G0xe/WnYojuRR2OtvBLpFmg6To2EQZ62vGGOpbMq2YkF6qqFsuggsEgPT09mxpQGJdOYkych1AbuqqYLV9tDshnMRIRCLaj5DLEkWsTmoen4el/MQP3QEF8n4zCKz+FUKeZQSi0mkWSQc2abWZtDlNwLV4JCAzDMH03rr0Ljv7A/GwyCpqGYLcjGSDlM/j1PIo7hKKqZPMKsm6gp1Oo2QyyLCNrKoKumqVZdof5XenElTG7feZ442GzXCqfLZ4LUVPJ6RDPaqAbyCL47RIg0OGSmEmrRc8Hr8eDoijkcnmzLW8ux8JChJdfPgEI2O02Xj1xknxeKRiRucnlFBRFwTB07HY7Xq+bRCKJohhFvxZJkhBFARDwuF2kM1kkSShmMyplLkp/llcU9IRRpqewnHyv/NvUXuRyOZxOB91dHbS0hDh/foR8XsHhsCGKMul0mnxeKQYxNpvE3FwYu92Gx+1m9+5BXnjhOE6nKcyMRmNEY3Hy+TyRSJyWlgALC2EkSeLuu2/jrrtuQVFU7HbbqvPLcgUPBAJlZTPRaBRVVbl48WJR7GmVzWyVoH2rjLMSGk2ONkIYvl1W/LfLccDSsqiVkMlkcDqdSJJkGnLefTd33303YLaqzmQyq2bcrSxoPp8vy4jmcrmK1Ta33XYbL7zwQpnh85//+Z9z33338dWvfpWPfexjVY19NeyQixIkk0nGx8cB1lafXUdYZVFreQAKsg3aBsva5rZ07cc2e9Zsibm4QMrQoPtAQZzqKdY2mzvb+jd7U8AwEAtCU7/DjlHhvDYbuSjVhYBp9LPZD39jbtzMAtns6O4AYjJqekYIAqTi5PJ5NMmG94a78Hd1Vb1f4dJJM1Pg9MDClKl3sDvNrks9w2Zb2QsnzOBeks1ypLYesxPUwVuuuHGXYvc1Zibh8S+aZnmtJnlnbsLUNU1exN7Zh72lC8PlMrUTuo6ez5FRFOS8ikspCKptToR8pqC/KLy4NBVC7TA9gqqDYpjEx+6wIwIBm0EmL5hyDlkkqWiEsyqqbhQEzxJ2u71YBpRKZYoC6Fwuz2uvnwHMgN8SpIqiWdLR1tbC5ORMQQCukEymsNlshEJBEokk6XQGURQQBNns3CSKeDwuZNlGNBpF142KmQtzNd/UfeRy+aLBXC6XL4ioy4WPhgHZbA5V1ejsaOP//r3f4aabruWf/vlbxOIJZmfnmJ6eW3JfqarO9PQsw8ODXH/9IVIps4XkoUP7GR0ZZ2x8sng/appOPu/ipZdP8Ja33Et3dyeiKOJw1N7VrbRsJp/Po2kagUCAVCpFLBYjHA4XxcDWanazukQ307NqrdjM51k9hOHbJSjfLscBK5dFLUYmk1m23L6zs7OqfVjlULOzswwMDBR/Pjs7y4EDByp+ppRYgNmtqq+vr6gRqge2Pbmo5iIbhsHc3Byzs7N4PB76C/3vNxMW813r6ko8Hmd8fPyKsZltP4ZdhtlLkE+bQYrNCbIdob0Pwe4yzcCERpKKJvbHqBdKOvUUr6NWucVwM5ELq+2y3W5n165dnDt3rjnGZrMXrEQMDLuDfLATVzqKkUmiGaB6W3Hc+RCuvVfXtt9U3NRbWGZ3ogi5rKmIjszBz/86vPAEvPbsFeKRSUDXIFxzZ3E3S+5Vp9vsDBVsN/c5ddn8HgRzHsxPQSaN4PFDaw+SAJ7YPAYCuiBiSDJoGlo+h5TPIWg6gtVeOZ3ASMVBzZPXIKaAKORwZ9O4ZQlZNHDKApouMJdW0AyD4zNJInmdgN/H0PAAC/NhEomEuYLvcZFIJItDt663phnoer6gS3CQSqXRdd0sn5J0DAz8fh+Hb7iagYE+nn3uKBcvjiLLcrFcysxkmHoOWTbF4plMttglRZJEJEnG43HT29tFOBwmm83h9XrIZrPk88uLyk2iJPD2tz/AAw+Yq367d++is7ONkZGxivNWFAVEQWR6epbXXjtDb28XoZYgkiiSLmg3ZNnM9NhsBg6Hg1Onz3PmzAW6u5e+9HVdZ3xiCiWvYACnTp1lYSFCd3cnNx6+lra2pR3KrPKJ0n75uVyuqNWYnZ0Fmtcleqcsqr5YizB8uwTltaz2NzusblHVIJPJrLsN7YEDB/B6vTz//PNFchGPxzl58iSPPvroku3/z//5P/zP//k/+clPflLMdCSTSS5fvszP//zPr2sspdj25GI1lLYDbW9vp6NjabnKZsC60Wq96Ur1In6/n97e3iuOkXtvx+i/BiMVhvHXIDaL4AkgtvZe6TyzlmNfK0doBLdYcjwN9L0obQFaOg5dKZTYlF/XZiAXhmEwOzvL3Nwcfr+fvr6+MqK7qWNbmMYIz5olRpkEoieE4nChGiqGqpLefT2Bhz+EzbMGZ3OPzxRvyzaTDABIhpmpyBSyFXc8BFfdDJdOQDoJ7X0wdNAkGqXjLD1Psr2glVAhlTKJhctj7i+XMadiMgpur6mdSERMB3BNQfL6Yd8NcPJnCJo1X9SC756AJtsxMklkA2QBZLvN1Bbk8hiajsNhI62rpHIqqiAxllJ5Pu/j0KEA1193kHe/+yH+/LNf5MKFyySTSRKJFGBlD65kTq0/um6a04FANBrHZpOx2WxoukYikeKVV0+RzebZs3uYO267ieeefwlZEgkEA2SzOebnwxiGjizbCuJxgfBClGgsjmEY2O029u4dYnhokFdefQ1dM+jq6qCnu5MzZy8QDkfLzq1VZgIGLpeTvr6e4u8OHdyPrumkUpnC888MJK3Mh64beLxufF4vsXgcm93UX7x8/DXi8QQulxO/31cUbre0BFlYiBCLl5SlFTA6NsHXvvYdLl4cYXZunsmJaXx+H91dHSiKwtNPP88v/9IHGBoaWPLZUgiCgNPpxOl00traWiYGtpoplOo5PB7PpnnibPazoB5o5mOoVhhuZTLy+XxTOIavFc1G9NaDWsui1tsO1m638+ijj/KpT32KlpYWent7+eQnP0lXVxcPPPAAmqYRDofx+Xw4nU7uu+8+/sf/+B/8x//4H/l3/+7fkc1m+fSnP01LSwvvfve71zWWUryhyUU6nWZsbAxd1xkcHMTn8232kIooJRfVQlEUxsfHSaVSdHV10draurSu3+lFcHoxvEGEhREMVQVRBqfNXFndQX1QfHEJFbiMYZanyY6yn1q16psFTdMYHx8nkUjQ0dFR6OcvFMe2mTBmx9Ge+BJGPAwuL8QjyKlR5AJXNOxOAtFpxGNPYNz5dgSptkeb4WtFsATTqlLo1pQ3tQ6iZHZzsjng5PNw+aSZdZgdN7fbe/3ypNzfYnZ/GjltZkIEweSbugG+FmjtMlvMujymEWCwDdq6zTGEZ0wX7703IExcMEuxCgZ6hmzDECXQDXRABFAV0lkVw9DxyxLHZjO8bviR81kmkiojQgjdr3CgvZVf/qUPcO21B3E4HHznO0/y8vETXLgwwvT0HB6Pm2w2iySJZWVIomh2WrJIh6bp2G0yHq+PbDZHeCFCNBbntlsPc+bcRQxdZy4SJR5PMjQ8SFtryGxUYRicOnUOQRBoa29BLbSWHRzsw+f1cPHSZUTBNMKzSpduv/0mnnrqZyQSieLKoNPpRBQFstkcoijys+eP4fW6EUVT69HR0QoYJQTpyrPUEnL7fF6SyRQ+r5e7776d5557kePHXyOZTCFJEl6vh67ODpSCoWkoVF5OkEgk+bu/+ycuj4wVSNB5kqk0oigSDAZob2vh1OlzfOvbP+A3fv2Xa7qPlhMDp1Ip5ubmmJubw2azFVey3W53w1d/N/u5sB5spYB2ublgCXZHRkaawjF8rdhOmYtajiWdTq87cwHwm7/5m6iqyu/93u+RzWa5+eab+cIXvoDdbmd8fJz777+fT3ziEzzyyCN0d3fzN3/zN3zqU5/iAx/4AIZhcOedd/K3f/u3de1i94YkF4ZhsLCwwMzMDE6nk/7+/qZzRbYeetWSi1QqxdjYGFCdXkSQnQj+dgTDADbRgVcQNs6pe7NeHEaFbMXSjZb8ZDMzF5Y+R1EUBgYG8Pv9TTM2wzDQXnkaIx6Bjn5zLKk4xsXXEQwNrbUXW2snZFPoJ55DCLQiXHekti/x+k3hdi5t/rG6RXn9oBkweRHOn4Cpiya5sbtMX4lnv2V2kdp9LVAh2BIEuPktZqbj0usmYTEMMzvS0WeWTSVjEItAS8eV1rKyzdRTROfgyDtNzYfl9o2BIMnIhk7OgJwBThHshoZXBpskkVF1vjcHP5ma5c67bmH3rYMIU7Mc6e/hyF23FlfRb7/tRq66ai/PPPMcTz99lC/89ZfRCmV7pku3VjwuWZYKLWEFZFnG43HR2dGO3W7DAGZn54lFY/zwR0/T09PFLbcc5uTJM1y4MMK5cxfxeX1kc1nuu/cOnE4Hly+PkclkaQkFefe7H+Rfvf9dfOe7P+K73/0hu3b1Ewz5uXx5nFeOv0ZnVwcd7W1mOZMo4nI6yeZyJJMp05EbOHXyLC+9dIJQMMDgYB/JVBqXy0k+r1Qsq9I0jWQiidtjvtxtssxNN17LwnyYqek5ZFkmGAiQzmSIxxMM7RrgxsPXlu3j9ZNnGRkZY9/eYSKRGKlUhp6eTqLROOPjEwQDPkRB4Kmf/owbrr+G22+/sRj0VdtVxjr/i8XA1kp2MpkkGo3WtcXpatgO5ThbiVyUonQuACwsLNDT09MUjuFrxVa9FpVQ7bFomkY+n6+LxYEkSTz22GM89thjS37X19fHmTNnyn521VVX8YUvfGHd37sStj25WHyRNU1jYmKCeDxOa2srnZ2dTcmYqy1FsYjS9PQ0breb/v7+6gSAog1kF6gZMDZb+7DZ318nLFcGVQni0mu0WQF8MplkbGwMSZIYHh6uuHqxqSVbSs70nvD6zZVvTSMbj2EXRAxJwOZ0IogiuH0Y+Sz6yaMI195V28uqo9/MGERmoXsQEMz2sxMXTHfsJ79s6jGC7eD2m9fX4TKzC68/D8NXF8vclpynYBu87cPwwvfN7lMut+mbIclmSZTNDqJgEopSyHZTUI4A19wBV98GP/gHmLoELi+CIDF39gzBfIKUDqNJBa9dRtJ0nhyJ86VXIiiKyvQ/f4tf/7f/ml//tx/G7/cXny2KovDPX/02TzzxU6ampwtlGE6SyVRhAFcWHRwOO3a7DYfDSyDgIx5P4Ha5ECWRVDptZhNEkXgiRSDgp7u7k8sjY0xNzxII+lAUldbWAKfPzPPcz45x/5uOMNDfy9SUKQp/61vuIxgM8NJLrxKJxAhHokiSRF9vN7lcnkw2y/vf/w4uXrzMs8++SDqTNV/igMfrprOzg1wuR0tLiHw+RzAUoLUtxCuvvF7ItpRfE0Ew3b4z2RwIArsG+xAEAY/Hw4EDe0gkU7S1tWK328grCgP9PXzsY4/i8ZSvMsZicQxYpNEzO2zNzC4QDj/PQjiCqqj8xV/+Ha+eOMmHPvhevN71NQsxu3Z58Xq9hda/SjGrUdritDSrsZVWshuB7RDQWtqnZnEMXytqEUE3O6rNXFgtZOuRuWhGbHtyUYpMJsPY2BiqqtLf379EMd9MqKYsajFR6urqqv4GFQQMhx9kB4KaNXUAlTpJNQIbwS0a/aCqhVggLtFbwNrbD68VpcTU6/XS39+/bACyqeRClMw/moKmqiSTSWyaWqgwEsrPpc2BkS0YytXSlEG2wS0PwLPfNjMSmma2pxVE07leyZilUVZZljdofs7lMX+Wz61sOml3mJoNmw3OHjczEmB+5vq7Yfw8xBfMTIaFdML8d8B0ikYQ4dBt5vgyKXA4aW1rJTaZJpbKk8prRHIGL07F+eLJCB5fAFVVyWZyfO1r30GWJd58/124XC7cbjePP/5D/vEr3yQQ8NHaEkRVdXp7u5iZmSORSKKqWqGjjRuPx4XD4eCRdz/EjYev5ff+7//KwkIEYyGCpmlomoYsm8Z5iUSK6elZxsYmkWUZn8/HwoLpkXL1oQOcPnOOF48dx+/zIUpmK9y/+sL/ZiEc4diLryLJEg67HU3XmJ8PMzjYTyDo5847b+GX/vX7+f4PfsITT/yUl4+/hiRJXHXVHmTZxsjIGH6fh7n5HNNTMyRTaWRZKmZcSuFwONBUnWwuRz6vMDU9y8mTZ9i3bzc9Pd0M7YrQP9CDy+kkFApw7z13cM89ty+5rKFQAAEzoAsG/fh8XuLxOKqikclmcDrMNpP9fT0MDvbx3M+O0dPdybve9ba6BbeWz4jdbicUCpW1OC0tnSnNajgcjnV9907mojlQ6RiqFYZbZKMZmgTUksVrduyQCxNvGHIRiUSYnJzE4XCY3ZMcjtU/tIlYjVxks1nGxsZQFGXtREkQQHZiyIWVaiWFkI2uccTrQL2D1mVfGBuUIVlRX1Fhc5u74hgbGcDrus7k5CTRaLRqP4jNIheCbEPYdRXq8adI5VQkhxOXP4gRmUaXZARnySpwJoXQv9csVaoV3UPw4IdMX4rzr5rBfUefmWEwCmTF0E0yYZGLfM4snarUinYxRAlueSsMHYKZUZMs9AxDqAN8IXjhe7AwbRKWfM7M2Fx1i6nbsNC7G+55BC68CgtTuDsHiF13Pz98+jg/+fZ3iOV1LswnCAZD2Gw2otEsPb2dtLa1cPz4SR5598MoSp6R0TG++a3vIcsigYDP7JHucuF0Ount6ebIkVs4efIs8/NhgsEAhw7t520PvokDB/ZgGAaPf/eH/NM/fRNREJCLotIcmmYwvxDmpZdPkMnkaGkJoCgKkijh8bjZtasPRVG499476O3r5okfPMXCQoSurnYW5iMkU2kAnE4HoiBgYHD27HmuumofoWCAlpYQ/+r97+Jfvf9dfP7zf8v3vv8T9u0bYnRssuxUx+IJEvGkWbJluJAksejTYbPZyOfz6LqBLEu0tYXIZLK88upJ5uYWaG9v5Z3vfCuP/uJ7yGZzBAK+ZctmDx3cz/DwLs6evUhvbzfDQwO8cPRlkkkzm2OTZQKBAPv378bn89LSEuT5F17moYfur3l6VovlWpymUikWFhaYn58vrnZbAWatWY0dctEcWO0YVhKGb0Y53XLYDtcCarsv0mnzWbdDLrYoDMNgfHycaDS66e7CtWAlcmG1CbXZbPUlSrILxJSZxVgL1tKIabE+oTQDsBY0PGNRbbbiCgQlhSHZwVa+0t0ocqEoCqOjo2SzWfr6+ggGg6t+ZrM1F3Pd+xDPncSTnMeuZ8EA3ek2hbrZlEkA0gmwOxGvvXPtLyqPHw7cZBKIiQvmfsEM+O1OUzuRz5rXPZM0PTb23VA00Vv1PAmCWYLV0V/+8+Grze86c8zsGOXxmzqO/YeX7qOjz/xTQDfwb2+7m5b+AT7/F3+PokTI5xWSSVNzMDQ0iCSKJBIpJEkulBCpqKpOS0sItSBWzmYyyLJEJpuht7cbURA5c+4CDoeDoaEB+vt7isfY19tNd3cnuVyOXC5PNpsFRATBNLRLxBPk8qbJntPhoKu7s+D2nSIUCvDQ2+7nxGuniESiXHXVPkRRIJlKIYkCiqoBBg6ns+ChkUYUBfbuHSo7Ddded4gnnnyKVCpNa0uIC6JIPJE0CYQsY2CQy+UJBPwkEkk0VS8+nwwDHHY7oVCAa665CkGAyyPjpDNZ3v3uh3jobffjcjmLPhvLThePmw9/6L38yze+x7lzF5EkkXvvuYNcTuGll19l395henq68PnMQN9ht5MrZEusc7nRWLySXZrVKK3Pt4hGs9fn1wvbYbW81qB8ox3D14rtIuiuxWncylzUQ3PRjNj25MJi6r29vYRCoc0eTtWoRC50XWdmZoaFhQUCgQA9PT31vfEFEcPVipCLg5qu334rYbkyIqHwn1oD2aYug1r6YSEXv5IxKunItNEBfDqdLjpuDw8PV/1g26xgQ9M0xsbGSKZzdD/4izgTsxCZQ3A4icte8qeO0p6YxVAVhI4+xBvuQdx11fq/2CpF0lQz6BdEaO2G3CVzjoanTF+YQ7eYpUrrhSDArqtgcL+ZtbDZy1y/q8H73vsOlLzKf/+TzyFJIt1dHfT19xDw+xkdm6CtNYTfbwbLra0hfD4viqrR0hIiGo0WMh0xcrkcX/rS18jnFdrbW0kl0/zDl77G6OgEv/5v/zWSJBGNxTl41T7sdhvPPHu0oGnQ0TQdQTBbwBoYZLMCHR1t7Ns7zPx8mNnZee6440b27RvmX77xXVxuqyTDdOIWRAmbTUDTdHK5HIIgYrfbCmLu8hf2jYev4ZZbbuDFF19BlCRkWSYcjtLW1oKmme1xfT4vTqcDmyyjKEpZUB9qCeL3eenoaKWnp4v9+/cwNj7J9dcdwuWqvmtKd3cnv/qxDzI7O09eUejsaGN2boE//uPP4HY7i8QCYG5+gRuuvwav10M4vFDT9a0HSlvZtre3o6pqkWhEo9GiiV9pVqOS59NO5qI5sJ5jqMUxfKOJ53a4FlAbudjJXGxx2Gw29u3bt9nDqBmLu0UpisLY2BiZTIbu7m5aWlo25mYUJQxXCPQA5BMISnL1z9SKasqISo9t2YC7ujKkumNdxMLah4qQmjHLbCQ7ht274eTCKg10uVzVC/8L2IzMRWkHq127duH1eqHripeBPjdHVHTSNbzLLCFy+01hdz2w6yC0Pmua3Hl8ZqCfjkN7Dxy+D/ytZjmTRUIKqPmeNHSIzptzPNhmfk8F7cbk5AyXLo1gs9u46sDeiivqoijyi7/4CBcvjRRr+50OB1NTM2SzWR588L5ieU8wGODuu2/jq199HEkUEQTI5dJEY0laQiE0TeOqq/aiKAq6rmOzSTz9zM+46aZruenG69izexenT53D7ggUA3ZJkpAkycykFubKrl393HbbTczPL+Bw2Hn44ft55JGHEUWR1tYWctmcdeZwOh0oSh5BEAqGeh4kyWxJ29XVAZj9/E+dOkcylaanu5P3PPIQ11xzgPn5KLquYRiQSqYJR6KcOHHKNKObW8DpdCDLMoZuGv4lkkkEQaC9o5X29jbAbCvr9XgIBmsvMRUEgc7OK/3q+3q7uevOW/ju935EPJ7E5XIRDkcJhfxFo79mgFwo2woEAmX1+Za3Bpj6lFLjttJnwVYPCLf6+OsZlFcqp7NKqEqJ50YIw7dL5mItZVGrdfbcqtj25AKaw5xsLbCMrKxuPoIgMDQ01BimK4qmD4OaptBFH4zKztI1oUZ9grlpE70A1jL+ZfelmfvRcgiZPA5J2pB5ahgG09PTLCwsrLk0sNH3UGkHq+VK/6wxCQ7XymLqtcDhgjf/K7PD09Ql03eirRduuNfMMKyAqs/TzCi89OOidwXBdlPc3bu7uImu6/zzV7/N9773Y6LRGKIo0t3dyYc/9D4OH75myS5FUeQ3fv2XcblcvPzyCSLRGAG/j1/8hUd4+KE3l237r97/LnI5hWeffYGZ2XkCAR9vuu9OLlwcAcMovvQ0TcPtdjM7u8Dp02dpaw2wd+8gfr+XEydOFX0vdN3AZpNxOBxFn4xQS4j/8Du/iqbpeDwuAoErLY5vveUGnn7mecbHJ3E6HaTTBedyAwxDJxqN4fN5Gejv4dZbDzMyMs5ffeF/c+HCCIqq4vW4ObB/N+9979t58K3lJVMAR48e5yv/9A0SLxxnYSGMy+Ui4PfjcNjp6DAJxUB/L4ahMz8fZmZmnre97T5aW+uT4X7Pex6ms7OdZ549SiyW4I47buKeu29n/37z+jbbam1pfX5bWxuqqhaDy1gsVhZcNrtmsRpsxZhgMTYyKLfZbBWJ50YIw5vtXlgr1lIWtZO52EHDIQgCyWSSmZkZPB4P/f39FVPUGwbRhkks6uRqvQZ9QlOh3uO3uhwVduuRtbq/8FRVZWxsjFQqta6MV6PIhWEYhMNhpqamVu1gteEItsNbftFsF6sqZraiXi/yeBie/obZgcofMufU/CQ88y2T1LR0AvDccy/y1a8+js/v48BVe9E0rRhk/37f7xaD5FK0toZ47Hf/DROT08TjCXp7usqCegtut4t/82sf4p3veCvHjr3E0NAgBw8e4A/+8L8zViKQts6/0+lkeHiInp4eAoEAjz76bj71qc8zN7dQ8J8QkCQJJZ9H0zS8Xg/t7a24XK5iORaYZOXy5TEEUeBd73wbP/jBT3jx2CukU5liSZPNJmMY5vzds3eY2249zGf+/K85c+YCQ0ODOBx2otE4P33mBdraWxgeXkoubr75eg4e3MfZsxcYGTENDwN+n+mhEQzwne8+yQsvHOfyyARej5sH33ov73rn2yperlwuz9TUDLJNprenuq58sixz7713cO+9d2zJlVlZlvH7/fj9fgzDIJfLFbMayaSZ0Z6ensbn8xWzGlvpGLdDQNso3chGC8O3w7WAHXJRijcEudiKmQtN09B1nVQqRXt7Ox0dHY2/+UQJw+ZCUFJm+cZaUY8yos3ERo9fMDUmsmAg1nH32WyWkZERdF2/Ula0Dmz0PaTrOlNTU0QikapaKzfsvrY6Q1WBqu/RM8dgZswUbuez4AmYbt1zE3DxtSK5ePqZFzAw6Ow0SYQsywwNDXD69HleevkED771vmXH0dfbDb3dqw6lp6eTVGoPbW2mruGuu27lf/2vLxGJRAkGA2iazuXLo3R3d3LdtQeL/goPvLkDQZD49Kc/x9T0HMlkyvQh0XXsAvj9Xm48fE0Zsbh8eYy//9//zIULl1FUlbbWFu6953bC4SiDA70MDQ0wO7fA1OQsmq6Rzyvc/6a7mJ6e5cKFEYaGBnE6zVXzUCiAf97L88+/zAcffW/Fjk4ej5sbbriGG25YmuX5hQ88wlseuJdIJEooFKStrWXJNgAvHH2Zxx9/kpnZOWRJYvfuIR555CEG+ntXPbcWtlLQXQmCIOB0OnE6nbS2tpJOpxkfH8fhcJBIJIhEImV6Do/H03TGtIuxHQLazTqGSsJwi2ysRRi+Fcl3JVjkYqdb1BuEXGw1ZLNZRkdHMQwDr9dLZ2fn5g3G7sMQJYT8GrtIGUYh77FJ+oj1osHESCuUmKz3hRGPxxkfH8dutzM0NLTuF/1GB/KqqjI6Okomk6mp+UIzLhqsOqaxs/Dik2bWIpsCBHB7oXuXaZwXnS9uGg5Hl4iLTUdqocTsrr645+7bGB+f5JlnjjI1PYtY0Dx86IPvJRQKFrcTBIG77ryVl196jSd++FPy+TzxeJJ8XkGSJLLZHJcvj3Dq1Gm6ujoxDPj8X/4dly+N0t3TRSwa5/z5i5w/f4n2jlYM3Syx2DXoZtdgP6qqce7cRbo620mm0iiqisNRPo+dTrMEK5PJrmmOt7W1LEsqAE6eOsvf//0/o6oqPV1dKKrKK6+8Tiye4Ld/62NlxKlWbOXg1goEW1tbcTgcxa5DqVSKubk55ubmsNlsZSZ+zRY8buXzb6EZjqFUGL7YZ6VaYXgzHEc9YD37dzIXO+Si6VDqx+F0OhtbBlUJggA2D4bsBi2PkIuCvrr2QgcMTUcShS1MLOqor1hu/8KVzlh5Q1q35YdhGMzNzTE7O4vf76e3t7cuZUUbSS4WZ1iqFbjV82XUsBdbLgPPfw903fTNcLrNv6fipseFIJplUgXs2zfM+fOXMHqvvHxzuRyiKNDT07UhQ7Tb7fzrD7+fe+6+ndHRCRxOBwcP7iPg9y3Z1uVy8hu/8ctcf8PV/MVf/j2vvXaa1pYgu3fvwuv1cO7cZb7+9e/xnve8jZdeeo2TJ8/Q09PF8eOvEYslwDBIpdLMzy/Q1d3JwkKElpYgqqpx6dIIvb1dXHPtQcILEbweN9FonFDoiuA6Go0xPLxr1Zaxa8XPnjtGMpXm4IG95vEC+/ft5uy5i5x47RR33nHzhnxvs6NUuFqp69ByJTOlE6ZOxgABAABJREFUWY3NDia3Q0DbjMewFmH4dstc7HSLeoOQi2a7+SpB13Wmp6cJh8MEg0F6enqKAVdTQBBAdmCIrZBLIFRsVWvmKHRDIK8oSKKItAXOfUU0Sh9ifY8ok9HMzhu6rq+JEJQ6tnd0dNDe3l63ub9R99B6MizWmOr1gq3HflYlYVOXTR+L9l6YvgzZjOneLUqmsLt7t+l5UcCb7ruLY8de5fSp87R3tKKqKgvzEa6//hCHK5T61AuCIDA8PMjw8OCq27rdLm699TDf/Ob36ehoo6uka5LD4eDS5TFcLg82ux1REDl39iKzBQG5zWZDlESSyTROp5NUOs3M7DyiKNLX282HP/w+An4ffp+X2269kSeefIp0Oo3L5WIhHMFms3HfvbdvWGAyOTWD31tOdq0Fn2gktu79b4V300qoNH5RFIulc4ZhVDTxk2W5LKuxGbqqZgzMa8VWOIZqhOFgNvGw2WxbTrtTCl3Xi4R7NViZi51uUTvYMOTzecbGxshms/T09BAKhRAEodgtqqkgyuAKYeRkhHx8ya91HXKKWRZhs9lMrYYoVpXtaAo0tAxKAJvbNNSTnVBIH68lQ5DP5xkZGUFRFAYGBvD7lwp41zXSOmcuDMNgfn6emZmZdWdY6vGCbdhLWs0XHNxcppHewjQoWdNPQ7bBzfdD25V2u0NDA/zmv/sI3/jm9zl//hI2m413vOMtvOPn3lLUHjQDYrE4qXSa9rby1rw+n4fZuXli8SR9fT2IBaO7UCiIzSaj6zq5XJ6WlgC6rvLudz2I3W4n4Pdz6NB+vIXAXhAEPvSh99Le0cpTT/2MdCbDvr1DHL7hEDfccHWlIdUFPT1dXLw0WvYzy3QwWJJBWQuasaSvWlQ7dkEQsNvt2O32iiUzsZhJ0EqFwA6HoyH34lYIzFfDVjuGSsLwVCrF9PQ02WyWiYmJdQnDNxu1XI8dcrGDDUUikWB8fBxRFJeYmomiWHyRNR0cPgzZjpBLgKZgoKFpBoqmYbPZzNU9QwNRxHC2ImQW6tPKdj0QWNn8u+HCcx20LNjcULLaUWvQUdq2dXh4GKezegOwamEJdesBXdeZmJggFoutq1nBVnnhlKGtxyQW6YQp5nZ5IZuG6KzZ4nbPdeZ2hgGRWZi+zAHDYP+/ficJux+b3VaTwVuj0BIK4vf5iMXieDxX0vzRaByf10Nba4jdw4MMDvbx2utnaG2RkCWJdDqD2+Viz54hUqk0LpeD/r5uBEEgGo2gKHmcTieTkzPkFYUH3nw3b3/4zWQyWXw+bzEY2Sjcdtthjh9/jYuXRuju6jT9hiam2LWrn2uuroNZ4xbFWn0uKpXMLM5qSJJUZuK3UVmNrRaYV8JWLyeSJKlYFtTZ2Yndbl+XMHyzUcv12CmL2gZoxgdIaW281+ulr69vib6iKTMXpZAcGG4H6VQKJT6D12XD4TBLH6zuUobNU9ArWO1sm3S1bqP1FctBVxEyCxie9prJRWnb1o1uVVyvzIWiKIyOjpLNZunv7ycQWPvq71rJ2EZi1WdNsB32HYbXnzP1Fza7SS4CbXDtkSsanBPPwInnIGe+gAS7E//+G+GmNzXgKGqH1+vh3ntu58tf+QZgmvQlEknm58M8+OB9RRO8f/9bH+XkybNMTM6g6QY+r5c9e4fQVI3e3h5uuflG7HZbcWX75Zdf5fHHf8jE5DQg0NHRzjt+7i286U13NeS5fvCqfTz66Ht4/PEnmZyeRpYkrrv2II888tC6xNywPYLb9cJmsxEMBgkGgxiGUcxqpFKphjhEb/Xzvx3mUKkIer3C8M1GLeTCylyULihvJ7whyEWzQVVVxsfHSSaTK9bGNz25gGJwK0siXYJE0FsgEIKAIXvA5gEtb/4MEdA2d8CVsNn+G4YOSqamYFnXdSYnJ4lGo1W1ba3LMNcZxGcyGUZGRgCWZOm2E1Y9Tze+yXTjPv+qmcEY2A/7b4SOPvP30yPw6tMgyVdKpDJJOPm8qdVYxcRvs/Dww6ZJ349/8hyzs/O43S7e9a4Hy7wjBgb6+P/+59/i83/xd2SyWdrbWkgl08iyxMMP318UZjscDmTZzhNPPsvE1CxdXR0YhsHs3Dx/8Zd/h6LkufXWww15Pt5y8w1cd+2hmn0utjM2wqG7tJVte3s7qqoWiUapELg0q7HWxRTDMLZNYL7Vj2E5EfRmOoavFbWWRVktnrcjdshFg5HJZBgdHUXXdQYHB/H5lnZgsdDM5KLUk8DqEDKfyhHo6CvoLKQrJnGiRFNmLZrCf8M8L4KWRxTNh8xqwWnp6n8tbVvXNcp1Zi6i0SgTExM4nU4GBgbq8hKoV+ai8f4xIuy93vxTCWNnIZ+D9hKDPLfPJCKjZzaEXNTjHNhsNt71rrdx//1HiESiBAL+iuZ9R47cisfj5sdPPcf42CR79gxxzz23c/NN15dtd+LEScbGJrjqwD5k2XSvb2lp4dTps7xw9GWGh/sB836YnZ1dt0vwSnA47Oza1V/3/W7VwHAjyMViyLJcUQicSqVIJBKASUJLHaJrHc9WPf+wvQgSrH4tGukYvlbUmrnYyuL11fCGIBfNcPMZhkEkEmFqagqn00l/f/+qnXHqWedeT5QK0K3gdmpqyhyrKAGLaiJFGSQ7aLnm4RdNQSxKxqBmcAgaorBysJxOpxkdNQWmQ0NDDavXXOs9ZBgGs7OzzM3NEQgE6O3trfvDdEuVRVUDJV95ToqSWUrV5PD5vCu2hhUEgRtvvJYbb7x2xeAoGo0DArIsFT8nyzItoRDZrMKePXuKmbDSlqelK5nNbOTWTPO22VEqBG5ra0NV1eIqdjweJxKJFFexreu/0gLGdjr3zRDfrAe1tG+1UItjeCPbH9equdiu2Xt4g5CLzUZpCUtLSwtdXV1VTcBmzFxY4uHFAvTViJDhCCDkYqDmMF0wNhGbpa9YBbKRpyvoWvbFZ3mg1HP1v1qsJXOhaRrj4+MkEgk6Oztpa2tr2nrpeu5r3YFLWw+cfQlUxewgBaBpZkeproH1D3AZbEbAtdJ5b21rQRAgn1ew26/M9UQiydVXH0AURSRJwm6309nZWWbkNjs7C1A0ctuslcztikZkLlaCLMv4/X78fj+GYZDL5Yqr2Na1t9vtRZK5+Npv9vjrge1wDFCf41jJMby0/fFGC8MNw6j6GZPNZretmBt2yMWGI5fLMTY2Ri6Xq7mERRTFpkl9GobBwsIC09PTFcXD1liXhShhOEOgpEySsdlowgeygUDAYyerl+tSDMNgenqahYWFogdKo4OkWslFPp9ndHSUfD6/Ia1xrTHB9lqFBMyypwuvmp4YThcgmKLv9l7Yfe1mj65huPaaq9izZ4jTp8/T29uFzWZjemYOj9fD3XffVrbtYiM3ayUznU5XzGqstrLdCDTDc329aIbxW3XrTqeT1tbWslXsRCJBJBIp03N4PJ7i87MZxr9W1OIG3cyoN0mqh2P4WqHretU6oHQ6TVfXxhihNgPeEORisx4glkGYLMtrErBaD43NfgmVmrO1tbXR2dm5ZDxVZVkEwdRhCGKhGqiBGYzSMqhmSleUQMBAAATjynlRVZWxsTFSqRTd3d20tLRsylyohVykUilGR0eL2a1mF6wZhoGmaRiGgSRJ63pZ1+XaOFxw78/DqaMwcso0j9l7A1x1s6m9eIPA7XbxsY8+yj/907c4efocqpKgv6+Hhx++n0MH96/42UormYuzGptdn72V0cyEfqVrPzc3x9zcXDEAtFaPt+K1t963W5kgwdrKompBLcLwakrqVkKtmoudzMUOakJpnbnP56Ovr29NKThrkm5mL+tcLsfo6CiKoqzYOrTq4FOUKYq7Bcn0wtholBGLZoaAYYBWID/ZbJbR0VE0TWPXrl14vetrfbleVHN9re5hbrd7Q1vjQn0yF4ZhlJFiy1fGMrG0/l/rPtcNtxduvA8O30thQOvfZ5NgcnKG114/TTaTZWCgl97ebp555gVefOlVAG46fC333nsHwWCArq4Ofv3Xf4m5uQXyikJnR1vNL/7lshqL67M3I6uxVQPDrVKSs/ja67pOOp0mkUiQSCQIh8NEIpGG1+bXA1vlGqyGRh9HJWG49TxY78JDtQvBqqqiKMoOudhB9ShdaV5vnXkpudgMWJkXm83G7t27cTiWdwSuuoRLtJnibjW3ASOugMX6iiZecQMIJ3M4AkvP/WYLU1cjj6WlWy0tLXR3dzfsZbGWYN6aq7quo+s6dru97N+6rqNpV4ivKIrFPw3FFg8cFuOZZ4/ypS99nYWFiHlLCgLpVBq73UFraxCAr/zTtzh1+jy/8eu/hM/nRRAEOjraVt5xDag2q7FcvX690Myr/9VgKwa1oiji9Xqx2WwkEgm6urqKLtGNrM2vB7YLudB1HaHERLaRKBWGLy6pW4swvNqF4O3ucQE75KKusDr5GIZRl5XmzSIXpZkXv99Pb2/vqg/Y0lXkFR8SgoDhDJrO3mqWKy1qRbMbjq7U7Tg23b9iOYi2pccpiOiSi5lYGL8RJR6PV33uG4GVrqmmaYyNjZFMJunu7qa1tXXTx7QSSkkEUMxQlGYpdF0vK5cyDKOqrEbV98EbEHNzC/zjP/4LuVyegwf3IQgCp0+f59Spc9x88/X095ueHp2dCq+fPMOLL77CfffdueI+13ueV8pqWF2Imk2r0QzY6sTIgt1ux+l0rlibXxpYOhyOprmvtwu5aKZn5XqF4dWSC8ud2yrV2o54Q5CLRpiLhcNhpqenN6SPfyPJRanBXy2Zl5pKuAQRwxkAwweagpCNlP6SdZcwNU2b2WWgqxju9oKLuWCOUZTRNPM6x+PxFc0VNwPLZS5yuRwjIyObUrq1lrKoUsIAy9f5Wj+3XhxWJsMiJk2R1dhiOHnyLPPzEQ4c2FO8dplMBpvdxszMHFddtRdBELDbbdhkifMXLq9KLuqNWrIaa/VWsNBMQVWt2Mpjh8qBeaXafOvah8NhFhYWkCSpzMRvMxd+tgu5sDIXzYblhOGliw9wRRjudrurvi8scrGTudgGWK8J2HLQdZ2JiQlisRitra10dnbWLcgoFXQ3AqUGf7UGimsiQoIIsgPD7kXIJ+ujv2gKYrEaQTJAyYDzin4ln88Xe/aHQiE6Ojo2dog1otL9k0gkGBsbw2azMTw8vGLZXDPAIhYWAa7lhVZKHkqzGqVlVNbvrP83Q8apmZBXlGIplAXZZr6CVLX83ldVHZdrcxsB7GQ1ti+qCcxtNhvBYJBgMIhhGA3rOFQttlO3qK1wDKXks729vaIwHCAWM7thrvQ8sMqi1qu50HWdz3zmM3zlK18hHo9z44038vGPf5zBwcGK20ciEf7Lf/kvPPXUUwA8+OCD/Of//J83RPvxhiEXG4FSsXNfXx/BYLCu+29kWZTloeBwOBgaGqq5xn9dRMjmwRBtCGoWdA20PLAGotE0/hWrnwNBSZvZG8q9Q4Cm7K5USi5K2xJ7vV76+/s3JZCuJXNRmrGolVgsxnJZDVVVSSaTCIKAqqplJKbZX56NCIp2DfbhdrmIRuOEQubcb28365wtImEYBnNzCzidDq677tCGj6kWLM5q5HK5ikLQemQ1mh3bJXNRLUpb2YKZ4beyGqUdh0qzGhvZzAK2T7eorTqXFgvDU6kUk5OT6Lq+qjDcylysN6j/7Gc/y5e+9CU+8YlP0NnZySc/+Uk++tGP8q1vfatiDPebv/mb5HI5vvjFLxKPx/m//q//iz/4gz/gj//4j9c1jkrYIRdrRCwWY2JiorhquxEBYSPIha7rTE9PEw6H1+WhsK6xCoKZwZALK9+6hpCNmo7eGIBgkg9dwWQNFb6jWfUVy0LH0HXCBdd2yzvk7NmzTVvPbJUETU1NEYlElm1LvBnjWul3pRqL9RKLShBFEU3TmJqaQlEUenp6kGV5p3xqEXbv3sVdd93CE0/8lIWFMA6Hg1gszqFDB3C7nJw6dQ4wHb7f/vYHuPrQyu1mNxOl3gpryWps9ZKWrRoQWljv+ZdleUnHIYtsJBIJABwOR1lgWe/ztdXnkIXN7IZZL1hZToCOjg6cTucSYfhf/dVfcfz4cQ4fPkx7ezuwPnKRz+f567/+ax577DHuueceAP7kT/6EI0eO8IMf/ICHH364bPuXX36ZF154gccff5zdu3cD8Id/+Id85CMf4Xd+53fo7Oxc81gq4Q1DLupVFlXaFWejBbcbTS4URWFsbIxMJkNPTw+hUGjND6q6GpqJEoa71cxiGLop9NbykI0s7fbUFGVQa4HAZCFIb21tpaurqygqbkZyYV3fy5cvk8lkajaE3MgxLYflhNv1RiaTKWaedu3aVVYeZhEMq5SqHq1utypEUeQXPvBuhnYN8LPnj5FIprj33js4cuRWNFXlzNmLAOzfN0x/f++WCpoqZTWWc4y2Smh2sHmoZ2Be2nGora0NVVWXEM16+SiUYruQi61OVC2Uvmcqabeuvvpqjh8/zhNPPFH8zN///d+TSCS46667uP7662uaF6dPnyaVSnHbbVcMRf1+PwcPHuTo0aNLyMWLL75Ie3t7kVgA3HLLLQiCwLFjx3jooYfWeugV8YYhF/WAFYxbzoqtra0belNYweZGkItUKsXY2BgAQ0ND607PbQgREiWgQNxEG0uyFluWWEAmrxKNJpYE6c1KLqygOJfL1WW+1AMrEdpqhdvrRTweZ3JyEqfTSV9f35JSiMVajaZrdVuCRsw7m83G3XfftsRhG2BgoG/Dv78RWMkxujSrAVeMtLaaVmOrB4QbGZjLsozf78fv969INNdr4LhdyMV2yFzA8maAVlbjQx/6EB/84Ac5f/48n//85/nxj3/M3Nwcn/vc5/jc5z6Hx+Phtttu46677uLuu++mr2/l5+H09DQA3d3dZT/v6OhgampqyfYzMzNLtrXb7QSDwYrbrxc75KJKLA7GG9VCrN7kwupsZRmdDQwM1KU2tK6Zi0oQJQzZjaCYKef16SvMUqtNg2GQVzSGBgdxL5pHzUgu4vE4c3NzAE0p3F58vja6DMr6joWFhWK75u7u7lVfkJVE4dY43+hZjbViKwS5lbIayWSScDhMPB4nHo9vOa3GVjjvK6FRgflKRHOxgWOpj0I12C7kYqsIuldDNddDEAT27t3LjTfeyI9//GP+23/7b3i9Xp5++mmefvppnnzySZ588klEUeTLX/4y11577bL7skThi+eLWW4aq7h9pbnlcDjI5ervO7ZDLlZBqXjVch1u5CqTKIp1IxeLO1tZpTj1QEPE53YvhpIEXV/SdWZLoCTTEvDYwYhBKgmyC8PhA0FsKnJhimvnmJ2dxeVykclkNlykWAsqXf/1dISqFpZOKRaL0dbWtiajzJ1Wt29MWMGm3W4nHA4XuwtWKqGxxMFbLauxFbBZgflyrY7T6TRzc3PMzc1hs9nKrv9y9/1WJ3gWtstxLJe5qARL0B0MBrnzzjuLmomJiQmeeeYZzp8/z8DAwIr7sEor8/l8WZllLper2OLW6XSSz+eX/DyXy+10i1oP1jJ5NU1jYmKCeDy+aeJVy/l6vcjn84yOjpLL5Taks1UjPDniySRKIkPIY0cULZ1L6bmpg0fGRmG5Ei5DAyWFoCsYrtamIRelRLSjowOHw8HY2FhTjM3C4mxZacnRRhELywcmm83S09NDIBBY/UNVYLlWt9UY/r0RkclkeemlV3nm2Rdwu13ccfstXHfdwS3T/re0XK+SViOVSjEzMwM0ZweqrR4QNsOq/+JWx7quF7Ma6XSaWCxW1HNUcofeLiv+270sqhKWa0Xb29vL+973vqq+zypxmp2dLSMis7OzHDhwYMn2XV1dZXoPMOPCaDRadzE3vIHIRa3IZrOMjo6iqioDAwP4/f5NGUc9MheWH4Esy+zevXtDxIQbWRZV6hgeDPhptbkQ9Dxbh1isVsIlmIJ1LdcU5EJRFEZGRsjlcvT39xMIBIodUDZ7bJWweLV/o4hFLpdjbGwMXdcZHBzcMAOk1bIapS0o34jlU+l0hr/8q//NSy+fIJvJIIgiL7/8Ovfddye/8IF3b9lzUamEptRXYXFWoxHtTpfDdiAXzTZ+URTxer14vV4Mwygz8avkDq2qatMdw1rQjNdiLailNXA9WtEeOHAAr9fL888/XyQX8XickydP8uijjy7Z/uabb+ZTn/oUIyMjRR+M559/HoDDhw+veRzLYYdcVEA0GmViYgK73c7u3bs3tcZ8PeSitKzF5/PR19e3YSt7VoBT78yFpmmMjY2VOYYDGGrGbFcLphlflQZ8ugFiI59j1bTIFQRzOy2/6eQinU4zOjqKIAgMDw8vCaCbiVxYD3FN08qIxUYgmUwWW08PDg42vDRyOQO/xeVTWy2rMT4xxUsvvcrCQoSuzg5uuuk62ttbV/zM88+/xLFjr7B79yDZbBabzYaiavzkJ89x/fVXN3UL28VYKRCRJKmiMLg0q7HR7U63M5r5XJlO9XbsdnuZO/RiEz9BEFhYWMDj8eBwOJr6mJbDdslcWCSpmmtgZS7Wo9212+08+uijfOpTn6KlpYXe3l4++clP0tXVxQMPPICmaYTDYXw+H06nk+uuu47Dhw/z27/92/z+7/8+6XSaj3/847zrXe/ayVysB9Vc8FLPh0AgQG9v76ZP+rUKujVNY3x8nEQiQUdHB+3t7Q0Rr9Uz+CzNHg0ODuLz+a780uYGNQtqbml72hVgAJoBkiAWgvo6uIIv90VVdbISSgjI5mouLFLtdDoZHBwsWxXdcMH+GmCNJRaLFQ2sNgKRSITp6Wk8Hs+Gtp6uBstlNUoF4rA1shqvvnqSL/7tPzI7O4/NJqPkVZ766XN89COPMjxc2WEW4JVXT+JwOnA6nWSzWQCCAT/jY1OcP39pS5CLtZi4VcpqLKfV2OisxlZfbd5q4y91hwYzuzw9PU02myUcDrOwsIAkSWXXfyuVCG6la7EcaiFJ9XLo/s3f/E1UVeX3fu/3yGaz3HzzzXzhC1/AbrczPj7O/fffzyc+8QkeeeQRBEHgM5/5DH/wB3/Ahz/8YRwOR9GheyPwhiEXq0FRFEZHR8lms3R3d9PS0tIUE34t2YAVg/INRD0zF/F4nPHxcWw227LZI8PuNY319OoJgoCAYehsaMeomlvkGgUjQeeGtR5e8dsNg5mZGebn55c1UmyGe6EUljleS0sLiUSC8fHxYtcVq7SgVpf5St8xOztLOBwmFAo1hWHgYtSS1WgmUXgul+efvvptYtE4hw7uK87706fP8/V/+Q7//rc+tuxYl16D5romjUC1WY1SX416z91muxdqQTMtkqwFNpsNu92OrusMDAyUZTWsElan01kkGxtx/euFN6J2pF7kQpIkHnvsMR577LElv+vr6+PMmTNlP2ttbeVP//RP1/Wd1WKHXGCWPFgmWM3Sw9+CKIooilL19tbqs8PhaHhJVz0C41J9xaomhZIdw9WCkI2Dli38UKSig7e1f2A+qdIZcoCurmuslb9gOX2FWPj2xToRQBAwHAEQ5YZnLkozXCt5tzRT5qK0fWtHRwcdHR3k83mSySTJZJKZmRlmZmaw2+1FouF2u2t6uWqaxuTkZLEcr6WlZQOPqD4ozWpY92G1Bn6NDjwuXx5jYmKK/v7u4neLokhvbxcXLowwOztPV1dHxc9ee81VHHvpVTKZbPF2i0ZjOJ0O9uwZatQhrAv1NnFbLqsRi8UIh8N1z2o0w3NgPdgOq+WlZThWK1swm05Y1z8ajRavv9V9ajO1OpWg6/qWvxZQ25xKp9OIoth0bd3rieaZYRuM5dpWWpoEj8dDf39/U910UH23qFLn8M0q6VpvZ6vSQNfSV6x6s4q2AsGIgFbo1WwsJ+4W0AyDaFqho6cNIT0HxkoEY2WisgQrCbcFwfy5oYMgYzgDpohbEEB2FQwDG+tzkc/nGRkZQVGUVTNczUAurGxFJeG21XWlNLhKJpNlwZXH4ymSjZXuc8ssU1EU+vv78Xq9DTm+esK696s18IPGXlvDMAoJu/IbxZr/K43l1lsP89rrp3nx2Kuk02lkScLj8fCmN93Jwav2bvTQmx6LsxrZbLbYhaheWY2tHpxv9fHD8iv+siwTCAQIBALF629lNZpNq9MMXbvqhVozF7UueG01NFck3UCUBrLt7e10dHQ05YWuptRIVVVGR0dJp9ObWtK1nszFukq5BAHDGQQljaBmTB8MQ6ecGAggiKTUQl26rpi/FyQzwNetUikrqLE0GaVEZYWOVNUIty19h6GZ32/3Ltm+UWVRqVSK0dFRJElieHh41Q5im00uFjturyScqxRcWVkNy4nU6XQWiUZpcJXJZIpZzF27dm2blaWVDPzy+Xwxw6EoSkO0GoODfXR3dzI+PsXw8GCRVExMznDo4H46O9uX/azb7eKjH3mUG288wbPPPm862956I9dee3DLlVc0QgfncrlwuVy0traiqmqRaJSuajdDB6pGYjuQi2pW/EuvP1B2/Uu1Oi6Xq3j9G9msopb2rc2OWshFOp3ekK6dzYTt/xSpgEwmw+joKJqmNVSTsBasRi6s7j7QWOfwSlhr5qIafcWqEETTZM9eWGU2jALZSJuaDMmGYfOiZOKFVVPd3MZ6OFsPBb2ElBjWea/wABdEQCCn6uRyOTwOGUla4cFSJhzXEXJx0PIYzlAZwWhE5iIcDjM5ObmmbN1mkIv1OG6Xvlzb29tRVbVINMLhcLG9oyWADIfDuFwu+vr6tm2QVVo+lU6nmZycLK52Ag3RarhcTt79rrfxN3/7j5w8eQ6n004mm6O7q4N3vuOtq36f0+ngjttvorurFa/XS3v78mSkGbFZJF2W5SXEey1aja1eJ78dyMVarsHi629pddLpNLOzs4DZhag0q7GR13k7ZS5qmVPZbLapyu83Atvz7VkB1kWPRCJMTk7icDgYGhpat+hzo7HcSrZhGEQiEaampnA6nQwMDGy6k2utgu6a9BW1QhDA7sGwexb9uBC8S7YCQVgk7BYAwWYG/YZayGzIZqZBV8z9Sk50A6amp4lEIrS2tuKz266UZa0IKyNimJ2utDzIV8hUvUwTK8EwDKampgiHw7S0tNDd3V31w3CzMhf1dtyWZZlgMEgwGMQwjKIAMh6PF4NqQRCIx+N1EYU3M2KxGFNTU0UyZWk1Ss/5Rhr43XjjtbS0BHnx2CvMzi3Q29PFzTddT29v17r3vYPVUUq829rals1qWETD7XYXCfdWD863+vhh/cdQSatjXf9kMkk0Gi3Tc1hZjXqet+2Wuah2QSqTyWwJHd968IYhF5bjcCQSIRQK0d3dvSUmdKWAXdd1JicniUajtLS00NXV1RTHUktJz5r0FXVAcYyibGod1PSVDEUhk2HYvSb5oISsiQ7AJAFWGVomk6G3t5dQKGR6bqxKLsQrWQqhoL/QcmXkYqMyF6qqMjY2RiqVoqenp+YH22a8iDfacdsKrqLRKJqmEQqFsNlspFIpZmdn1y0Kb1YYhsHCwgJzc3MEAoEyktnoVrdDQwMMDQ2svuE2QzOu2K6U1bA6EFlZDU3Ttkyr00rYIRdLIUlSmVt8Pp8vZjXm5uaYm5vDZrMViYbb7V533NGM98FaUWtZ1E7mYpsgm80Si8XWFFhtJqyVbOtBks/nGR0dJZfLFQPbZoEoisWONCths1rlwiLxqsNvdmhSUmagL9kwbB6wLe+8nMlkGBkZASjrLGbITgQlXdhqETkQxPISLHMA1i/LN90AcmGdb03T2LVr15oEyo3MXKwk3K4nVFVlfHycbDZLb28vfr8fMNv16bpeXMGLx+M1i8KbFVb2KhaL0dbWtiqpr7XVrfX3RmI7BCbNhtWyGpY+Z2pqaklWY6tgq8+bjSRIgiAUm2S0tLSg63rx+qfTaWKxWHGOWGTDbrevqTGA9X1bHdVeD0VR0DRtiUHtdsPWehqsAx6Ph/3792+51ZbSYDiVShXFppXckzcb1WQu6qKvWAdKg2RBFM3SKZvb+uWKny01mVtShiY5wOYpZEKK32ZmJQQJlNQVgmEUhOMFb4vF46tnAJ9IJBgbGyue77WW+TSKXFir45qmFUtwNuLFk8vlGBsbQ9d1BgcHl9xLoiiWreLlcrmqReHNCitbmE6n6enpKWosqsVqWY2VWt1uFLZ6S9RmnzMWFmc1Ll++jCiK5PP5JVmNZvdVgJ3MRa0QRbH4rDMMA0VRilmthYWFonatNKtRTay13cqiqjmOdNpchNzJXGwjbDViAVduOist6fV6m1ZsupJeYEP1FTXAehiXPQhWeUBXYzKHIGA4/KYRnpYDDAzJYZIOdAQ9D5pS1lXqSvlV+fjqETBZpS/T09P4fL5iTf1a0QhyUboqvpGBaTKZZGJiApvNxuDg4KpapdLaZGsV18pqWKJwSZKKL99mdMctba87MDBQl8YPi7MaK7W6bSYDv2bAViZF1r3pdDrp7OysSavRLNjK59/CZonqBUHAbrdjt9sJhULoul5m4hePxwHKshoOh2PZxgDWPrc6qiUX9TLQa3Y01x2/wWi0QVk9MTc319Qtc2H5zMVm6Ssqodae/pqmMTY2RjKZXNFkDihkIhwY8uJsjIThagU1i1DwtjBkF0hLswj1mKOlmpy2tra6OktvpNi8nsLt5RCJRJiensbj8ayZ4C7uI5/JZIpZjVgsBrDEKXwz79lGtNddqdXtZmU1drBxKF01r0Wr0SxZja3e7Qqax3yulEgCZVmNcDjMwsICkiSVtTsuzX5a+9jqqDaTlEqlAJqu8qTeeEORi62GXC5XLMHo6uqira1tk0e0MiplLjZTX1EJtazA53I5RkZG1qVVuPLFItjcV0qwVhjfegL4UrF5X18fwWBwzftaPC7YGHLRCGJhZZ8ikQgtLS11I+ml3VQ6OjpQFKVINCyDTpvNViYKb+SLNJFIMDExgcPhaJhJaKXyqZ2sRjm2w4ptpbFX0mos5xa9ONBsJHbKojYONputrCNfaVbDIptOp7Ns5b4Zj6MWWDrBWjIXm2kb0AjskIsmhaVNsB68WyGFtriz1WbrKyqhdHV1JZSOfXh4uGFjXw+5sPxbDMMoE5vXa1xQX3LRKOG2pmlMTEyQSqXo6ura0CYINpuNUChULBdIp9NFshGJRBAEoUwUvpHto8PhMDMzM/h8vsqlfA3CcqJw6/rvZDW2Hqp9DiznFl2a1XA6ncXyqUZlNZo1MK8FW+EYShdfgCLZTKfTxcYAQDGbvFVNHGvJwOyURW1DbIWyqNL6fr/fT3t7OxcuXGiIa/N6YZ3fZtFXVMJqQbJhGMzPzxeDsvVqFdYyvrXM0Vgsxvj4OA6HoyodwVrGVU+Uls1Y+9+IF2Wp1qC/v3992acasVgEmc/nSSaTJBIJpqenAbNcxNrG5XLV5RxsVJamHlhOFF4q5C/ddrtnNZrlutSKtQS2K2U1IpHIiuUzzTD+ZkJpB8mthMVkc25ujmg0iqqqZSaOpSZ+W+EYa8lE7gi6d9BwlHoRWPX91qpes5MiuFIWNTIyQjKZpKOjg/b29qZ6OKxELiwvlFgstmn6llKCVs13Ww/o2dlZAoEAvb29GxaM1VNsbgWUsHH1to3QGlSL0taOlmGVldGIRqPFwKo0q7GWwMqaw5ZGqJlaVVfCSq1uqzHw2wrPxUrYquMuRT0MLRdrlyxh+OKsxkqi4LVgKwbmlbCVj0EQBGRZRhRFBgYG0DStSDbj8TiRSARRFHG5XMU5sNlGwcuhlszFDrnYQUNhlbToul5W319tGU8zwCJC6XS6KfQVlVDaLaoUpf4h/f39NbfprBfKWuWu8uLQdZ3x8XHi8XhDiFw9yEWjhNuW+7TT6WzK7mqSJC0rCi/ttmIRjWoCK2txwprDjczS1ANrMfCz/r2DxqLe5Ki0fKYRWY2tTi42emGmUSjtsCRJUlljgFwuVyyhmp2dBcBut5dlNZrl+HfKopaiud64G4xmfZhEIhEmJydxOp309/eXeRFsFXIRj8eLD4Bdu3Y17Y1TqVtUKpVidHQUURTZvXs3TqdzuY9vOKrVNlhkKJ/PMzAwUDSA2+ixrSeoaJRwe35+vlhW2N3d3TQvoOWwkih8fn6eubk5ZFkua3W7+JhyuRyjo6OAef9t5hyuF1Yz8LMyfKUu7lsNzfpOWg0bHZxXympUEgWvJ6uxVc89UEa0tzKWm0el7b+tTK+V1bKyvVaZXWlWY7PORy1lUTuC7h1sOHRdZ3p6mnA4TCgUqhgIWaUAzUouSvUVbrebdDrddKvEpVgcvIfDYSYnJ/F4PA3rprMSqmmVm06nGR0dRRAEhoeHGxpIroVcVAoCN+IloOs6U1NTxONx2tvbV24b3MRYSRRuvVRLW90qilJsPtDf39+0pQPrweKshqqqTE5Oous6Dodjy4nCd8qiavueUlGwoijFQLM0q2GJwqvJamyXzMVWPgao3htCkqQyU9N8Pl8km9YCjM1mKzPxa+T9v5ayqJ1WtDvYECiKwujoKNlslp6eHlpaWpbdtlnJRal/RUdHB263m8uXLzf1i7O0LGpiYqIoeu3u7m6KB/VqmQsry+VyuRgYGGgoGVpL5mKxcHujiIWqqoyPj5PNZunt7W1IJqcRWE4UnkwmmZmZKYogbTYb7e3tm06OG4HF5V9ut3un1W2DsZnPeJvNVjGrYdXqw+pZjR1y0RxYa2MAS7/W0tJSXICxyEYsFitmNSyysdFeQ2spi9rJXOyg7rDKcARBqKpl6OIWr80AywOi1L/CummabaylsG7+2dlZFEVZldg1GsuRi9IuYstluRo1tmrRKOF2LpdjbGwMXdcZHBzctitCi1+qVkcou91eJPqWh4BFSLYb2bDKARdf661m4LfVg8NmCc5Lsxrt7e0rZjWsFW1Jkpp6AawabPX5Y6Ee82jxAoxl4pdOp1lYWGB+fh5ZlsuyGvXuQraWsqhmLR2vF7bXm2cVbPaNaBgGCwsLxZ7O1ZbhVDKn20ws51+xkUZr9YJ1Y6uqWncviHqg0jmsySV8g8dW7bVtlHA7mUwyMTGBzWbbkBa8zYjS8q+Ojo4iOc5ms8WshmW+6XQ6iy/eZnBGXg+s7l+SJLFr164ybZqFnVa3b2xUm9XQdR1VVZuGJNWK7UIu6q2VEgQBu92O3W4vlpWW6nVKm2VYZKMeXchq0cBYZVE7mYsd1AWlbU7b2tro7OysekI3S+ZiNf+KZhefR6NRJiYmAGhra2s6YgFLyYUl1FUUZdM7cFVLLkozFhtJLCyTOK/XS09PT9N4qWwkVir/sjwE2tvbUVW1SDTC4XBx9c7KamyWM/JaYTmN19r9ayUDv8U+K43Mamzl4HCrjL1SVsMKMsF8HyQSiSVZja2ArXINVkO1rtZrRakbPFCcA6lUinA4XLcuZLUsolkLnNs1w25hh1w0AKUB4lranDYDuVisr6jU9nS5Nq+bjdKSokAgQCKRaNqHcim5SCaTxZXaZnA4r4ZclNa+b2RHqGY1idtI5PN5xsbG0DSNgYGBFcmxLMsEg0GCwSCGYZSJwq2a5FJReKUsQLMgEokwPT29bqfxarMapX4aO1mNpdiqga3NZiMYDBIIBDh37lzRA6Y0q1HvFe2NQi01/s0MXdcbWrppzQHrubhcFzJrDlSb7dV1veq5sqO52IbYjAdFIpFgbGwMWZbX3NlnswXdlfQVlVBNp6NGo1JJ0ZkzZ5pqjKWw5qhlrObxeBgYGGiaFbWVnM0bIdzWNI2JiYmi0WSzm8TVC+l0mvHx8RVLgpaDIAjFVbnOzs4yUfjs7CwzMzPY7fYi0XC73U0RVFkGkQsLC4RCoZqyvdVgvQZ+O9iasJ5hDocDv99fltVYvKLdrFmNrUrwFmMzy9IWdyGzvFXS6TTRaJRwOIwoimVZjeWIUC0ZmEwmgyRJ276E9w1FLhqJUudkn89HX1/fmh9Om5m5WE5fUQnNlrlYjhTVy2l6I7GwsEBraytdXV1N8wJZ7rw1Sridz+cZHx9HURQGBga2/cqPhXg8XuaDs94gx26309LSUuy0YvWOj8fjxRfqZovCDcNgamqKWCxW1JVs5H2wFgO/9czzrRwcbuWxQ+XxL7eivTirYRGNzc5qbPVrYKGZNC+LvVWy2WyRbFhd+RwOR5mJX2nMU+3zIJ1ON80CzkZih1xsAKopIaoFoiiiKEodR7g6VtNXVEIzCbqtjFElUtQMZWaVoKpqURPS2tpKd3f3Jo+oHJXIRSP8K+DKyr0oiuzatWvTS8QaAasBhHUPbkSHMFEUy/rH53K5TReFW9mpdDpNT09PzWWk9cBqBn6Lt3sjZjS2anC0WmC+nFYjlUoVuw9tdlZju5CLZjW/tFrZWroITdPKROGRSARRFIuEU1XVqo8jm81ue70FvMHIRSNuxGw2y+joKJqm1U2A2+huUWslR9Zq3mYG7pZD88zMzLIZo2bMXGSzWUZGRornrhlX5Sv1im9ER6hYLMbU1FTNYt6tDMMwmJ6eJhqN0tbWRltb24Y/v0pdcdva2oplAqWicEmSypzC6x1UKYrC2NhYUZ/WDPdBaVbDuj9LsxrraXW7FYPDZnt21opaA/PSrIau68UV7UpZjUZ4KsD2IRfNlLlYCZIk4ff78fv9xUUYi2zMzs4C5rWYnZ0tZjWWewak02m8Xm9dx5fL5fiv//W/8t3vfpdsNsuRI0f4+Mc/Tmtr67Kf+cxnPsOf/dmfLfn566+/Xpd37PZ/SzcQVjcih8NRc130SmhkwF6tvmI5bKY+pLQjV3t7+7JC32YjF6WlZ/39/Vy8eLGpxmeh9Lw1Srg9Pz9fFOJ3dXU15SpXvVGqK+nu7iYYDG7KOBaXCWQymTJROLBEFL6eeWA1vgCaNjtlzb/SrMZaDPya8f6uFtslsF3L+K0a/JWyGhvtqQDb5xo0a+ZiJZQuwrS2thZ1nbquk0wmiUajxcxHqVbDOs5MJkNHR0ddx/T7v//7HDt2jD/7sz/Dbrfz8Y9/nN/6rd/i7//+75f9zJkzZ3jnO9/JY489Vvbzei3e7ZCLOsBaZVxYWCAYDK6ro0klNIpc1KKvWA6b5clhmWtZrr0rlVJsdnbFQqUsS+nvmg0WuSgNojaKWJR6ObS3t2+at0ejUbpy30y6ktJSkY6ODhRFKRINS1tms9nKROG1PANTqVQZwd4qYsdK5VPNbuBXL2zV+7GegfnirEYmkyma+G1kVmOjW7g2AoZhbJnMxUqQJAlRFHE6ncWGGVZWY35+nm9/+9t85jOfYd++fdx2222k0+k1NfZZDjMzM3z961/n85//PDfddBMAn/70p3nwwQc5fvw4119/fcXPnT17lg984AO0t7fXbSyleEORi42YxFYwkE6n6e7u3hDh4UZnA9air1gOm5G5sBzPRVFkeHh41XrGZshc6LrO5OQk0Wi0LMtinbvNHt9yWEwsNgIreTlsZ2QyGcbHxxEEoWlX7i3YbDZCoVDRqKq01W0kEil2qLLIxkpkwRKsu93udT17Nhu1GPiVtr3damjWZ1O12Kjxl3oqbHRWo5bWp82KjW4A0khYJEkQBBwOBw6Ho9gwI5/Ps3//fk6dOsXJkycBOHHiBI899hhHjhzhrrvuKhqhrgXHjh0D4NZbby3+bGhoiM7OTo4ePVqRXGQyGUZHR9mzZ8+av3c1vKHIRb2RTqeLafyhoaENW2XcyJX2jRCfN/LlEw6HmZqawuVyMTAwUFVKb7PJhaIojI6Oks1m6evrKyt7aSZRfCmslbJkMsn4+Dg+n2/VoHEtyGazjI+Po+s6g4ODbwjhG1wxiXM4HPT3928pXYkoikUSYRhGsdVtIpFgenoaMLusWNtYXVYMwyAcDjM7O0sgEKC7u3vLB0ylWE4Urmka6XQaQRDI5/NbrtXtVi/JadT4K2U1KjlFryWrsR1W/Lf6PCrFcuVdoihy6NAh/uZv/oZEIsEPfvAD/uiP/ghJkvjGN77BN77xDQRB4Oqrr+bIkSMcOXKE66+/vqbnwMzMDKFQaMliVEdHR7Epx2KcO3cOXdf57ne/yx/+4R+Sz+e55ZZb+N3f/d26lWxtnTdYnVCPwNJ6KU5PT+NyuTY8jW8F7PV+oKxXX1EJjcpcWK0qw+EwLS0tNQUmoigWyxUajUwmw8jICGAS0sVGaFaQ0UzkwgqKWltbcTgcJJPJJUGjz+dbdyehZDLJxMQENpuNwcHBLVMas16UOo339vZuiQBzOZSu3Fn1yFZGw/JusTrtWB1YWltb172o0eywrqkgCExPT5NOp+ns7Cw+2zfLKfyNiM0Ialdyii7NalgZDY/Hs+L13yEXzYVqtCM+n4/bbrsNgAcffJCPfOQj/PSnP+Wpp57ixRdf5MSJE3z2s5/lt3/7t/m1X/u14ufGx8e5//77l93vb/3Wb1XU9zocDnK5XMXPnDt3rjimP/3TP2V+fp5Pf/rTfOhDH+JrX/taXRb13nDkYr0oLWdpaWlpiMi01JyuXjdiPfQVldCIzIWqqoyOjpLJZOjp6ak5pbhZwXssFmN8fByn08nAwMCywXMzkYtSsardbqe1tbUYNKZSKRKJRFnQuNZOQtspwK4WVjmiRZC3o9O4JElLROHWnLEC6nQ6zcLCAl6vd9P9AzYSqqoyNjZGPp+nv7+/2DGmmla31t+bAVs9KGyG8S+X1UilUsVmCStlNbYDudguLuNQ/fVIpVKA2Qhj79697N27l1/+5V8mlUrxwgsv8OKLL3LkyJGyz3R2dvL4448vu8+f/OQn5PP5JT/P5XLLkoT3vOc9vPnNby7Tpu7du5d77rmHH/3oRzz00EOrHstq2CEXNaBUNLy4nGUjUZpaX++NWGrut159RSVstFjaqhXUdZ1du3atqRSt0cF7qaYlEAisGjw3A7mwMmXLCbcXt+azOgklEoniy7G05n65zmmGYTAzM0MkEtm2AXYlWIsUiUSCzs7OddXcbhUIgoDdbieTyWAYBt3d3RiGQTKZZH5+nrm5OWRZLiOo2yHwgCulkJqmMTAwUPbSX83Ar9lE4c0QnK8HzTb+xVmNfD5fFIUvzmpYmY3tQC6a7TqsFVbmsZp7MpvNAiypWPB4PNx3333cd999Sz5jLQAvhzNnzhCNRsnn82Xv2dnZWbq6upb93OKmN52dnQSDwWJVwnrxhiMXaw3cEolE0cSrGtFwPVFKLtaDeusrKkEQhLKVt3rCWvl3OBwMDQ2tudVvI7tFWW1F4/E4nZ2dVfkVbDa5WOy4bZVqLYfFnYSsmvtkMsnMzAwzMzPY7faiTsOquS9tudrV1UUoFGrUIW4qrBVsa5GiHuWIWwH5fJ6xsbGiB5D1DK0kCrfaOS5udbsVkc1mGRsbKwr1VzuOxVqNtbS63cHyaPag1m63Y7fbV8xqSJL0/2fvvOMkqev0/66qzmFyzrvLsuySc5CMKIIJPfPp3RnRM59nvjPjz5z1jAh4wgmiggISxF1Ack6bdyfn7ukcKv3+6Kna6p6eme6Z7p6emX1eL0Rmeqqruit8n8/n8zwPgiCQSqUqkqtRDqyVzkUxwvR4PA6UNsfq5JNPRtM0HnvsMc4880wA9u/fz/j4uOkelYtvfetb3H333fzlL38xz52hoSGCwWDJRN7rjlwUC6tdqM/nW5EQr1KQi3LoK/KhHHqGYiv/i6FSi3ej05VOp+np6SnY9WglyUUpgvEcDgcNDQ00NDSY41O5M/dut5tkMmlWcqvFcrXcSKVSpif6ehKsJxIJBgcHzYT13AX2fKLwXIJqtbpdDQuqeDzO4OAgdru9YMMJK6rR6rbaF+eLYTXtf76uRiwWIxAIoCgK/f39c7oaq2Wxvpq+h4VQzHEkEglgbudiOWhtbeWyyy7js5/9LFdeeSVut5vPfe5znHbaaaZTVDqdJhQKUVtbi8Ph4JJLLuHXv/41X/rSl3jrW9/K1NQUV155JSeddNKcsayl4jC5WADWSv9CoWzlhvGeSyUX5dJX5EOpBd3W76DQyv9iqMTiPdcetxhf65UiF+VI3M4dn0omkwQCAdMtBWBqaopkMmnO3K9VWLMcChWsq5PT6JEoYlszYgkfSJWE4TBWqBNWPlG4QVBDoRCBQMBcdBlkoxrdtQyLXbfbTVdX17LHT/ONT61kV2O1LwpX4/4bXY14PI6u69TX12d1NYzwNkMUXs1dDauJwWpGMR0Yo3NRSnIB8KUvfYkrr7yS97///QCce+65fPaznzV//8QTT/C2t72Na665htNPP52jjz6aX/ziF3z3u9/lNa95DQ6Hg4suuohPfOITJfs+qu+OXGYU+sElk0kGBgZQFKWoqnM5sNTORbn1FflQSkG3kdgry3JJuy3ldrQKBoPmomIp1cqVIBeVSNw2rDcjkQhut5vW1laSySSRSMQ8T1djdboQzMzMMDo6itfrLeg61IIzxK++Hvmp50GREfw+nC+9ENcrXoqwSiqTcOi4lyPUz0dQja6GYbXocrnM82a5rmWlQDAYZGxsDL/fX/JQVQPzWd0aeqlydTVWWg+2XKyFirlhDZ6vq7GQVqOauhprJeeiGHJRjs6Fsb0vf/nLfPnLX877+9NPP51du3bN+dl1111X0v2wYt2Ri0IQCoVMS8xyV/oLgdUtqlBUQl+RD6VauEciEQYHB7HZbCX/DsrlaGVNaq+vr6e9vX1JN85KkovFhNulfJ+pqSmmpqaora01Xdbcbrc5c29Up8PhsFmdtop7q7E6vRisx11XV0dbW9uin6+uqkR/8AuUZ19AqPEjuP3o0SiJ//sjgtOJ65ILK7T3S8dSjrsQGJVZt9tNc3MziqKYRCMQCGQtqJbiWrZcWI+7vr6e1tbWitx3iwnwW25XY7Uvzlc7OYL87kRGV8O4n+ZqNYxrxyAaK93VWO3nkYFiOjDl6lxUI1bf07qMMJxrjAVQR0dHVaTFFtu5KFfFvxAsd+Fu1bj4/f6SjBPkohyLd1VVGRwcJBqNLjupvZJZIUa3wnjfctzoNU1jdHSUcDhMc3MzjY2Nc95HFEX8fj9+v39OdXpkZATIWDMamRor/WAsBJqmMTY2RigUmve480F5YQ/K7r2IjfUIs+N0grMBdXKK1F//hvPF5yHYVv6+NB+MDJpij3spsNlspqWnrutZonBjQVUpUbhRXJiZmSn7cS+G+boa1g6l8bqlBvhV+/U3H9bConYxt6iFuhpTU1Pour7iXY21MhZVTAemXJ2LasS6IxfznciGg4vhXLOSD4ZcFEMuKqmvyIflODFpmsbw8DChUIimpqayVf0MclEqOz+rWL6vr8/0r1/u/pUT1sVGOUWgiqIwNDREMpmks7OzoPHC3Oq0LMvmgtGwLLXb7VnjU9XWWjc6h0YWS67t30LQxidBVk1iYUDwuNFmwujRKEJd4durJDRNY2hoiFgsVvRxLxeCIJiLpdbW1ixR+MTERFlF4VZr4fb29orZlBeCxaxuiw3wW+2V/7Vi41rMMeR2NeLxuGl3m9vV8Hq92O32sn9GxjGs9u+iGsaiqhHrjlzkQ252wnIXh6WGcQEutGi36ivKVfEvBEtdGMuyTH9/P6lUiu7u7rIuSkoZShiNRhkYGCgpmSs3uSiHcDsfDAtOYFnOSHa7nfr6+jmWpZFIhGAwWHXiXqvlak9PT9EPErGxDiQRPZ1GsFbaEymEuhqEKn0wWUPiqsEBzOpaNt/YXSnOG6NrmUwmS2otnIolCE/M4PC4qGmpK9l1ulBXo5AAv9Ve+V8r5GKpBZVcZzZrWri1eGOIwstVvFnOMVQTljIWtdL3xkpg3ZMLQ3y7WGrySmMhcrFS+op8MMaiirmBx2Ix0we+Ehkixn4td3wrEAiYYtXu7u6SkblyppxXilhEo1FTt9Td3V2y68r6YGxtbSWVSuUV9xqZGpVOfF7McrUQ2I7ZitTXjbr3AGJDPTjs6NEYuizjuuhcBEf13aMMi11d1+nt7S3KHa0SyB27m++8KVYULssyg4ODyLK8JCKZD5qm8cLdT7Dz70+RCEWxOex0bOvl5NecjbehtMYiSwnwM7BaF+hrgVwYHeflQpgNtsztasRiMeLxeFm7GqU6hpXGUsaiDpOLNQxjDjwYDC5LfFspzDduZNVXrLSrFWTb5hay2DYW6Et1VloKlksurOdOY2NjycSq1v0rtebCIHzldoTSdZ1gMGjmwiw3k2QhCIKAy+XC5XLR1NSUJe6dnp7OSnz2+/1lH58yrEddLtey8nAEmw3fB95F7KdXo+47iB6SEdxunC+7CNdlF5d4r5ePeDzO0NAQNputpESyXMh33hhdDUMULklSlplAvnuZlVD19fWVbAR17/3P8dgf7sPuclDb1kg6mWLfgy+QiiW56P2vRpTKdw4XEuAny7L5+2rQJBaLtUAuynUMlexqrIXvAQ6RpGI6F+sh32jdkQtBEJBlmYGBAZLJJB0dHTQ0NKz0bi2KfNXsldZX5EOhzlaG6DMQCNDQ0GC6B1UCywklVBSFgYEBEokEnZ2dZUmVLvVYVK5wu5zEYnx8nGAwSENDw7y5MLquow2PossyUlcHQokWo/nEvZFIJCvx2ToGU6pFsNHFMiyfS1GokNpb8X/uP1H3H8zkXHR2IDU3lmR/S4lSZzmsBGw2G7W1tdTW1qLrOolEIksUDswRhRsjf5IkFZxZUgg0VWP3vc8g2STq2jPft81px2a3M75niIm9w7Rt6S7Jey2GfONTqVSKUCiEzWZDVVVUVa1ogF8psBYWtZU4hoW6GvNpNYrp1BpFrtWOYjowyWQSOEwu1iSi0Sj79+83w81Wy5ds7VxUi74iHwoJ/LOK51eC3C21c5FMJunv70fTNDZs2FA2UVYpyYXV+x7K5ymuqirDw8OmIcJ8pEvZ30/859eg7DsIuo7Y1Ij7za/Bec6ZJd0fq7jXmvgciUQYGxsDSpONYHUIamxsLOlIoiAI2DZtKMm2yoFAIMD4+HjJCFU1wHCX8ng8tLS0ZJkJGPdcSZJQVRWHw1HyUdp0IkVsJorLn31vcXicKGmVWDBasvcqBqIokkwmGR4eRhAE85lTDqvbSuAwuSgehXY1jI7GYl2NtUDyoDjtSDwex263l9W1rlqw7siFYb/W0dGx4sLPYmCQi2rSV+TDYp0Lq3h+w4YNKzJ7uBRyYXSJHA4HGzZsKOvNoVTkolL6CkPAbAROzvedajMhIl/9LtrEFKLfB6KAOjZO7Ie/RPT7sZ9wTFn2TxDmJj7ny0awjsEU8rCwEqpqcwgqJ3RdZ2Jiwuw6ztehWgvINROYmJggGAyagZD79u0raTfM4XbirfMRGgvgqTtkLJJOpLA5JLz1K2M2Yty37Xb7nPHVSgb4lQKrfVFbSqfDpWKhroa1U7xQV2MtdS4KPY5EIrFqCtrLxepZXZcIxmz/aoMxzrV///6q0Vfkw0IjR6FQiKGhIZxOZ9kX6AuhmLGo3JTzrq6ust8QS0EurA/7chILY97eEDAvNJqX3vEA2uQ0YlODmTItOhxoU9Mkb7urbOQiF5IkZY3BWLMRjIeiMQbj9/vzLhitQt7u7u6qc5grF6yZJa2tratipLQUMLREwWCQuro6Wltbza6GtRvmdDpNouF2u4u+7kRJ5MhzjuXB395NaCyAt96PnEwTGgvQdewGmjd1lOPwFkQ0GmVoaAiXy5XXuKKSAX6lwGq30jVQTQQpt6uRTqdNsjE5OZnV1fB6vbjd7hUnSKVCMWNRiURiXdjQwjokF6sVRuKm0+msGn1FPuTrChiVzsnJSWpra8sq8i0EhXYurLkbzc3NFavQLodcVEq4DRmyaIjxOzs7F+0EqmMTgG4SC5i1WbY7UAeGyrKPiyFfNoKh0xgfH2d8fHzOgtEQ8gJV6YxULlizOwrNLFkLsHZqrKNv83XDZmZmmJ6eRpKkrK5GoaOrR5x1NOlEil3bnyI0No3NYWfj6UdxymvPQapweGI4HGZ4eLgoc4ZKBPgtB6vdArWYXIWVgPXaWKirIQgCNpuNdDq9qseEijmfEolERbN/VhKHyUWVw6icJxIJJEli48aNVaOvyIfcroB1jKu1tZWmpqYVr1YUoguxiv7LnbuRi6WSi0oKt6empswk+/b29oLeR2yoy/y9piOIh16vyzJia0vJ93MpcDgcNDY2mgtG6wNxenraHE80LHarleSXGsb1sNTsjtUKXdcZGRlZtFOT2w2zisLD4TBwKGF+MYtkURI55iWncMRZRxOZmMHhcVLTWl/x+2YwGGRsbIyamho6OjqW9P6lDvArBVZ752K15Yzk62rEYjECgQDpdJqDBw/O6WpUK3HKh2LHotra2sq8R9WBdUcuVssFCdkLc6M6Ws3EArIX7lab3N7e3pKFSy0Xi+lC4vE4AwMDACsi+l8KuaiUcNuaRNzc3FxUkr3jnDNJ3vJXtOkAYl0NiCJ6JIpgs+F6yfll2d/lQJIkampqqKmpyapei6JojihaK9Orufq2EAxnJEEQ6O3tXTeEytDUxOPxojo1C4nCDeFrIRofl8+Ny1f5+Wxd100r5/r6elpbW1cswK9c4W2raR2Qi9VGLqywdjUikYjZFc7tahjXT7EOVCsBTdMK0u8aRYfDmovDWFHk5lcYF1+1w3gYJBIJ0/u+2sa4FhqLmpmZYXh4eEVDFYvNuajUGJTh8pVKpZY0FiO1NuP7yHuJ/c+v0San0DUd0efF9U+vwH7mqWXZ51LASiyMxZZ1wTgxMcH4+DgOh8PUaSxl3r4aEYvFTCOD7u7uVWWCsRxY08a7u7uXZTwxX8J8Po3PSpNU67ne1NRU1k7zUgL8SkE2DpOL6oCRk5Kvq7GQVqPauhqFnk+pVApd19dN13d9PClyUOocgVIjX35FIpEoebBaOWBcZJOTkyVPri4V8o1FGRkNU1NT1NXV0dHRsWI3sWLOz0o5QhnVa8joDJZafbGfcAy13/8qygu70dNpbFuOQKypjo5WPlg7NdaxGIfDQUNDAw0NDVnjU6FQyOxuGA/NYubtqwmhUIiRkRG8Xm9FjAyqBel02nS0K7WmJt+IiEE0DI2PQVJ9Ph8ej6dii0gjeygUCq2IWL+QAD/r65YT3raaz+W1Qi5yF+XWrkZDQ8O8Wg2jo2Gkha80Ch2LMgL0DpOLw6g4FsqvKEdqc6lhLMQAfD4fvb29VXkDzO1cWMfP2traihr1Kdf+FUIurA/fchKLSCTCyMiIqTNY7g1dcNixH390ifaufFAUhaGhIZLJJF1dXfOO9eWOTyWTSdNFyJi3z61M66Ew8pPPossy9m1bkDrbs7apKyrKC7vRpqYR6+uwHb2lZGGDi8E6FlOMpmYtIJlMMjAwYLqfldtyOlcUno+kWkfvytU5MswrotEoHR0dKy46zTc+ZdzrltvVONy5qA4stiifT6sRi8WYmJgAqIquRrHkYiXs91cCh8lFlWCx/ApriF41wiqAFgQBn89XtTc/w6nCuGH19/dXlS5kMXJhOEIZ1bxyCreDwSDj4+NFucWsBRiOUEb1utBOjeHt7na7aW5uzhvC5nn6BTy33IEQSyAIILhcOF96AZ63vh5BFNGmg8R++muUvQdAUUCSkPp68L7nX5Dayit8t4YClnssptqw0iNg85HUaDTK6OgoUJrgx1xYXcAWItErhVJb3a4VcrHa78XFfA+5XQ1VVaumq1HocSQSCWB9pHPDOiUX1TYWlauvyDfLLopiVYTn5EOuAPrgwYNVTYQgcw6kUikzcXfjxo1VYylq7azkfteVEm5bF5lrPSgtF8Yi02azLbt6nTtvH9m9l/QfbkNLJtH8PhBFpEQS7ebbEbo68Fx0LvHf3oj8/G6k1mYElxM9nUbdu5/4r6/D94kPlu17sFav11MoIGRGUUdGRvB4PFUxApZLUhVFyRv8aHQ1vF7vkkbvrNqS1eICtlyr22p8hhYDq8PWasVy1zKSJOH3+/H7/fN2NRwOh0k2ytnVODwWlR/rklxUE/LpK/LB6nBUTTeVYDDIyMiIGU5os9lMIlTtCAQCeL3eqhOqzkcuKiXcXq/J03BIZ2AsMkuplRBFEfuTz6Gk09haW9DRUVUNTRRRp4ME/vJXJuv8eJ96DrHWD7P3AsHhQGxsQN13ELV/CFtf95Lef3BwhOdf2E06laaru4Ojtx1pEierWH89hQJC5j4wPj6+LMvVcsNms1FXV0ddXd2c4MdQKLQkUbjVXni15rUsxerW+O/VirUwFlXK4thCXY1IJFLWroZBkgo5DqNzcZhcHEZZsZC+Ih+slZqVrqrBIfGf4aDT3t5u7lc160OM/dY0DZfLRV9fX9XdpPNZ5VZKuJ1OpxkcHERRFHp6etbNfOhSszuKfp9IBHQQRAGB2Yqq3YbqdiIl0yTTaeRkEs0mIcRi2GxShrA77OghBT0WW9L73nvvQ/z+D39hJhhCEECSbJx88nG87a2vw263ZQmY10vb3rgHT09Pr6ruXL7gx/mcy+YThRvdckEQyq4tqSQWs7q1dn6rPYxuPqwlclGOYyi0q2FcQy6Xa1nmAFDYcRzuXKwDrPRFuZi+Ih9yw+lWEkaVMxaL0dHRMcdVpFo7F6qqMjAwQCwWMy3wVvpcyIdcwXmlhNvxeJyhoSFTzFpN9sHlhNUlp9jsjmIh9XSBIKArCsJst0zXdUgruLZuofH44wg1N6GGI6heD4qiIMsyYiiC6HAQ9XnwK0pRnbbx8Un++Kfb0HWdY445CoB4PMFDDz1OV1c7W47sQ5KkNbXIXAzW77ylpYXGxsaV3qUlw+pcpmmaOYceDofzisJlWWZwcBCbzVYSg4ZqRW5XQ1VVxsbGUBQFp9NZNqvbcmMtkItKEbuFuhrhcJhgMLisrkYxx3G4c3EYZUUh+op8qBZykUwm6e/vR9M0NmzYkLeyXY3i81QqRX9/P6qq0tfXx9jYWNXtowGrVa5VsFhOYhEKhRgdHcXtdtPZ2VlVY2LlhFXMWgmXHMdZp5G67W6U/kEErydDNKJxxBofrksvQvC4cb7kfJK/uxkxGMLu9aDF4uiqSurU4wnEY4zt2WMKe/1+/4JpzwA7d+4lEJhh27YjzZ95PG6cTgd3372DY485sioto8uFanNGKiVEUcyq2qZSqTmicMiMWbW0tKyb69zQkIXDYdrb26mpqSmb1W25sRbIxUodQ7FdjcXyioohF4c7F4dRNkQiEQYHBxfVV+RDvmyGSiMUCjE8PIzD4WDDhg3zVjmrTTBv/dw3btxoLsaqaR+tML5rRVGyZoTL5QhljIasN9vR3BGwStz0Rb8P36c+ROK3NyE//hS6pmM/divuN7wa26YNALheeiGiy0Xq7h1ogRnsrc04zn8RjRedRyt6XmHvQmnPinqoQmsgkUiQSiZxuzNhkdW6kCo1VFVlcHCQZDK55rUlgiDgcrlwuVw0NTUxMzPD6OgokiShaRqDg4NZIWZLFYVXO6xk0hr+mVuwq0SAXylwmFyUBqXoahRzHEbnYr2MGq9LclHpE7pYfUU+rGTnwkhtnZycpKamZlE3lWrpXBh+/WNjY3lzQ6qVXBiIRCLU1dWV1dveCIhraWmhoaFh3mtDi8aQn3ga0jK2Y45Cai2vJWq5kUgkGBwcXJERMKm1Bd9HrkCLxUGWEWprssOkRBHnBWfjOPdM9EQSweVCsGXOWxvkFfZahYvWERi73U5fXw8ej5tgcIa6utrM2EwkSiKZ4rIXnVY1i6ZyI1fAvF60JYBJLPx+P52dnUDmGrCKwmFuHstqXsACJolKJBLzkknj/K9EgF8pUC26y+WgGvUuuV2NVCpFLBYjHo/P29U43LmYH+uSXFQShvNOOBwuWF+RD/lEvpWAVR/S2tpakO+9IAhZN+SVgLFwNvz6W1tb56SBVgMByoWu69hsNpxOJ5OTk0xOTuLxePD7/QW7wBQCqzuQtZqXD6l7HyD246vQQ5lAOJxOXK+6BM/b3oBQRQ+HQhGJRBgeHsblctHV1bVioyGid+GHjCBJCL75q1xWYW9LS0uWsHdsbAwAp9OJ3+fj9NNPZMeOB+nvHwR0FFVn27ajOO/cM0t5SFULq4C5t7d33eiJAKanp5mYmKCuro62tjbzPujxePB4PLS0tOTNY7Hb7Vmi8GpaCBYCo0tlOKAVWjGeTxRuOAOtdFej2hwjl4Jq6FwsBGvXzwi4zNfVMO4jhawlDmsuDqNkWKq+Ih9WonOxHH2IcQNeCSiKwsDAAIlEgs7OTurr6+e8phpF51bR9oYNG1AUhUgkkuUC43Q6zVn7pYZoJZNJBgcHARat4KqDI8S++1P0ZApmcxmIJ0jeeAu27k6cF5275OOtNKyhgH6/n46OjryLAl3TUJ5+DmXPAQSPG8fpJyM2NeTZYvUgX9qzsVgMBoOceMI2HHaJ53fuRUDg5JOO58wzT6G5efUKmQtFPB43xyLXsoA5F9aRx8bGxgULW7l5LFarW2MhldsRq2YYzwDj2bXULlWpA/xKgbVALqqxc7EQ5utqhMOZgtvg4OCiWo1yj0V95jOfQVVV/t//+38Lvm5oaIgvfelLPPLII7hcLi6//HI++tGPlnwkcl2Si0pcmMacv81mK1pfkQ+VJhfRaNScyS02YG4luwKJRIKBgQF0XWfDhg3zVgmqqXMxX+K23W43XWBUVc1KI52enjbnpf1+f95Z+3yIRCKMjIzgcDjo6upadJGQ+vt96Mkk1FhGd7we9Jkwydv/tmrIha7rjI+PEwwGF1xo6Ykk0W/8EPnJZ9DVzPkR/98b8b7333Cec0ald3vJkCSJ2tpaamtrSafTDAwMcPzx2zj55ONMLU8iESMQEPD7/VW/WFwqjC6V2+0ueW5JNcMaglmsG5YoiiaJMESv+TpixmsWE71WGuXM71hugF8psBbIRbV3LhaCtatht9sZGxujtbWVRCJhdjVEUcTtdvPwww/T2NjIaaedVraxKFVV+eY3v8mNN97I5ZdfvuBrZVnmHe94Bxs2bOD6669nYGCAz3zmMzidTj74wQ+WdL/WJbkoJ0qhr8gH40ZV7gWxVafg8/mW5CKzUl2BUCjE0NAQTqeT3t7eBRdM1UIurMF4ML9wW5IkampqqKmpQdd1c146EomYIVoLVRatVXufz0dnZ2dBDz0tMJPJZcjdJ5uINjWd9SNl30HkZ19AcDpxnH4SYn1d5r1VFWQZFnE1Khesgs62tra8nSwDiZv+TPqxJxF8PkSXE13T0IMhYj+5CtvWzUhNq6vSb+1SGWYGxmIxEokwPj6e1RGrxsXiUmHVGczXpVqLsGqplhuCma8jlq/IYb33rCSBM4i0rutlt1ZeSoBfKc7BtUQuVvs1aXzHNTU11NbWZnU1xsbGuPLKK9F13UwLh8yYYmtra0nef9++fXzqU59icHCQjo6ORV//17/+lZGREW644QZqamo48sgjmZ6e5utf/zpXXHFFSa+Xw+SihCiVvmI+lHtBvJhOoVBUWtBtJXSFCM6hOsairFWvYh48hoOFMS+dTqfN8Smjsmi1KnU4HIyPjzMzM7PoeEQupNk0aF3VECSL7kdRsR2xMfPfikrsh78g9bd7QVFAh7jHjecdb0YdHiN19w70ZArbhh7cb3oNjtNOKvajWjIMT39Zlhd1B9J1PXMMNhuCazYdWxShvhY9MIP84KNIL39ppXZ92YjFYgwNDc0ZB7LmIizUEVutDkJGgWRycnKOzmCtQ9M0hoaGiMfjdHV14ff7S7r9+YocRq4GgNvtNs+fxWySSwljjFcUxUWLS+XAYgF++V63lMX1WiAX1VDYKwVys6dytRpXXXUVd9xxBw888AAHDhwA4PLLL6evr49zzjmHc889l9NOO23J3bWHH36YrVu38pOf/IQPf/jDi77+0Ucf5eijj84acT/jjDOIRqPs3LmT4447bkn7kQ+HyUWJUEp9xXwo56LdaCUnk0m6urqWXe2q1MLdeJgWS+hW2i2qlInbDoeDxsbGrFn7SCRiWpUax9rQ0FCQIN8K5/kvIvnHW9HGJ9GdThAFSCTB5cT1qpcBkLrtLlJ3/h3sdqjxg66jRaJEv/WTzELdbgNJQnlhD5GvfBv/Zz5aEYKRqy1Z9AauaejxBELOYloQRXRAjyXKtKelh5Fb4vF46OzsnJcgLLRYtDoIldpQoFywjr81NTUVfb6vZixVwLxU5BY5rKLwqakpJicnF7VJLhUM9zebzUZPT8+K53cs1tUoVBQeD8VIx5PYXQ48dT6zwLjaK/4GQVrt1+ZiRO+YY47hmGOOAeAtb3kL+/bt46KLLuL+++/n2muv5dprr8XlcnH66afz5je/mfPPP7+o93/Tm95U1OvHxsZoa2vL+llLS8b5cWRk5DC5WC5KfUKXWl8xH8pVbY/H4wwMDACZ0YnlWjRWqnMhyzL9/f3mw7SYMKyVHIsqJbHIhXXW3iC8iqIgSRKBQICZmRm8Xq+p01jsISzW1lDzxU8S+9nVyM/uBEVD6uvB869vxD6b9py84x50XUd0zy7eBQHd6YBoDOw20/FIdzkhFCHx299jP/XEsj5YotEow8PDRYl4BUnCtuUI5MefBp/3UFJ6KgWiiHTEhrLtb6mg6zqBQICJiYmic0vydcSMxaJhKOBwOMyOmDE+pSgq0XAUtzcTzLdSsI4DLTb+ttZgdOiMzJaVsNldSBRu2CTnWt2WAoZg3+Fw0NPTU5WdttyuxmJWt6qsMvDkXqb7x5ETaexOOw29LfSeuHnNdC5WO0GC4o5DURR8Ph/f//73kWWZJ598kh07drBjxw62b9/O9PR0FrkYGhrioosumnd79913H83NzUXtbzKZnFP4NtarqVSqqG0thnVJLkqFcukr5kM5Fu3BYJCRkRHcbnfJnFQq0RUwCJEgCEsiRCsxFjWfcLsciMfjDA0NIUkSmzZtwm63m2m9hqgbMiMMRlV6PlIsdXdS86VPo00H0dNpxNbmLAtaPRiaW+2XFXSyibwwSzqUg4OQSoGlk6CnUqTuuZ/0A4+gjoxBKo3YWI/j7DNwXfpiBHfhbeNgMMjY2Bher3fBqn0+uF/7CpSde9CnpsHtRlcyehH7CcdgP+GYgrezEihUtF4orONTmqYRi8VMjU8gEEAQBHY9tZfH7n2SyEwEj8/DWReewYtfdUHFSYY1ab0c40DVDKvOoFpsdhcShRs6H4OoGla3SzlXY7EYg4ODq0qwn298yiAaRldj4Mm9DD99gJrWenyNNcjJNGM7hxBFEWdXzaonF2uBIEFx5CKZTJrrFLvdzqmnnsqpp57Kf/zHfzAxMTGHbLe2tnLrrbfOu72GhuIdDF0uF+l0OutnBqkotdD8MLlYIsqtr8iHUpILw01kenqa+vp62tvbS1ZJMBbu5bqBWAnRUlvglR6LKlS4XQoYQlaPx5P1wLWm9ebztc9XlbZCbMxfCbZtOYLUPx4Gy/dtfLK6zUbWVlQVwevJjFAZr0kkCf/XV5GffSEzcjX7vagDQ8jPvEDyltup+X//jdS2cHCf1XpzqbP29mO34v/MR0jccDPKnv2INW6cF5yN67WvmEOgqgnlrtqLophlxZhMJrnrlnu4+Te3ousaHp+bqYkEv7/mT0xPBnjr+95YscVDru3oevGRh8yCZWBgAEmSVkRnUAgWEoUbRFUUxSxReCH3dMMJzLjPrcZKeL7xqUQ0TnBgEm+DH7vHiabrSE477novU/3jNNXZcflX9zm+VjoXxaxx4vH4vITAGE2ywm63s2nTpmXtXy7a2trYvXt31s+MgMBSicwNrEtysdyHXiX0FflQKnJhBKjFYjHa29sXTGZeCqxVmVJWkozK7NTUFHV1dctygKnkWJQ1hAnK55BhXVwvNhKTO8KQ+7AvRtTreu0rkJ94Bn0mjO52gqZnhN02G6RSaKkUKCoIAogizle+NGuhnrz1TpTnds7dsKZBOo26v5+Zt38Q7wffjeuSC/Pug6ZpjI6OmmR/Oee0/dht2I/dhi7LIElVHxSoKApDQ0OmXqrcVXtBEJBEiUd3PIHb7aa1sxlZVlAUhZnpGe6/+x9sOXETfZt6l1WVLgS5VftS2o5WO6zjQN3d3SuuMygUuTqfZDJpFjpGR0eBQ4YUPp8vb55PKBRiZGTETBxfC1VwmH02qDq6quOp9SLZbGZHw+5yEJ+JkowncfrcqKpa8QC/UmE9di4SicSKFz5OPfVU/vjHPxKNRk1zkwceeACv18tRRx1V0vdaHXejMmCpletK6SvyoRQL4mQySX9/P5qm0dfXt6B7zlJhVq9L2BmwJoW3tbXR2Ni4rJtTpcaiyqmvsMJauS52cW2tSre1tZFMJk33KUPUa1QV82Ui2Lcdif+//4P4Nb9D2X8QQRJxXHA2gtdD8sZbMiTBgE1CsNnQVRX56efRgzOk7tye+S5kJdO1EASze2FATySJ/eiX2DZvwLYpW/tgXVwvljZeDIQqrALnIp1OMzg4aHr6V2rWPjAVJBiYoabOjyCIOBwOHA4HLpeT/n2DRGfihMPhJVelC4Eh4q3mqn25sFbyOwRBwO1243a7aW5uRlEUk2gYhhQ2m808f7xeL+FwmLGxsaI1RasFDo8Lp9dFMprE3+Q0F6/JUBwVDaTMOOtKBPiVCuuNXGialjUWVSmk02lCoRC1tbU4HA5e/OIX893vfpcPf/jDfOxjH2NoaIjvfOc7vP3tby+5Wce6JRfFotL6inwQRRFZlpf89+FwmKGhIRwOBxs2bCib80upA/+snaLe3t6SVGYNclnOm1yliIUsywwNDZFKpZZdubY+7HNFvbmZCNaUcPsJx1Jz/DHokSiCzQZOB6H3fRxcTrDbEXQdHHZ0WSbxh9tI3/cQ2vgkqCp6anYGVBJBYA6xQBDAZkOXZVJ/uzeLXBiVa03TKrq4rgYYi2tRFMvu6Z8Lr8+D0+kglUzh8R2qxqWSMm63i56+bo44YiOpVMokqkZVuhRWpdFo1My0WU1V+1LAqNoXk1ezWmCz2airq6Ourg5d17NE4UahAzLn0HILTNUKu9NO25Fd7H/oBcKajtPnIh1NMj0+ia+vkY6eLurr61ckwK9UWG9jUclkEii9rmExPPHEE7ztbW/jmmuu4fTTT8fpdPKLX/yCL3zhC7z+9a+ntraWN7/5zbzvfe8r+XuvnzvyMmDVVzQ3N9PS0rIiN7WlVtuXkgOxHJSyc2FNCi9lp8i6j6X+Lg3SYtzwy0ksrHarfX19JR8LyZeJEIlECAaDTE9Pz7GaFPw+lKefJ/nXv6Hszfh6IyuZcyGZBFGEVBo1mUCoqQGbBDOhjNZCFJhlFzl7oSO4nOjpNNpM2PypVbRe6cX1SsNYXLtcLrq6uiq+uPbX+jnpzBO4+8/bcTgdeHwe0qk0EyMTHHnMZjZu6cvyfM+tShtWpXa7PUvUW8h9yVhce73eVTtrv1QEAgHGx8fXbNXeCiMY1Ov10tLSYpoV2O12kskk+/fvL4kovBrRdlQ3gigwvmeYVDRBPJXAv6mZo047JittvdIBfqXCeutclCud24prr712zs9OP/10du3alfWz3t5efvWrX5VtPwysW3JR6FjUSukr8mEpmouVEp7D8jsX09PTjI6O4vV6S24xaOxjqUejcoXb5SQWxmiE0+mkq6ur7GMhubPSRlUxEolkrCaB2ju2Y7/rXkilM6nc80EHwS4BAtTWoKfTmbEoUZzbudBBj8YyI1UeN4mb/kJSgInuNlwOB+0eL3ZFhXXCLQw3rJWuXL/iTZcyEwjx/BM7mZ4IINltbNq6kX9+3xvzXqvWqnSuVWkwGDQXk4Z7WT7CND09vSSb3dUOXdeZmppiamqKhoaGFStwrQR0XWdiYoJgMEhzczNNTU1ZOrF843der3dVj8mJokj7UT00b2xnqH+QaCJOV8/8+VPFBvitNNHQNG1NdBsLJReJRCYjaT111lf/t1tGrKS+Ih+KJRfpdJr+/v6KE6Pldi50XWd0dJRAIEBjY2NZEnbLoQuppHDbyDLw+/3LErYvFblVxXQ6TeSJp9Hu3IGq6wi6htGHyAtFQU/JCDYpQ0QkKUM4mhszY1IzIcubAaoKmkryljtAz5C3Bl1HsNmIiGIm1O9lF+H+tzcjroGHVj5YF5jVkDzt83u54hPv4MDug0yMTlJTV8ORRx+Bzb7455/PqjR3fMoq6nU6nUxOTpr3hEoUSaoFVovh5ubmNTsOlA+Gq+HMzAytra2m206ue5lhs12MKLzaoes645MTJJQU3b2F5ziVKsCvnFgrnYtCj8PoXJQ72LKasDafwstENegr8qEYcmEdJ9q4cWNFXVSW07mwOll1dHQsycu5EBg3hFJa+1ZCX2F92FbLIsuwmlR37iWh6+h+L4zFM6nWua/N/AHoOnpwBt36+QsCeiSa6V44HYdcpyCjx1BV9FQSBAFB0xE0DdIyus0G0RiJa28g9bf78H/6I9iP3VqZg68QDMIdCoWqaoEpCAIbt2xg45alBwxarUqbmppQFMUcv8tNma+rq1tXqdu6rjMyMkI4HF53wYDWY29vb5+3am8dv7OeP1ZReDHud9UAXdcZHh4mEoks26Si2AC/ShANTdPWxDVcbOdipd2iKonD5CIH1aKvyIdC3KKMqvbo6Cg+n4/u7u4VEZ5D8Qt3w7NdVVU2bNhQVpZfyrEoa8einMTCcMyKx+MLPmxXCrpsVMREdAEEUcgQBGZJhiiiaxq6JCEoSkbkbYUgIHg96FOBTLJ3Q13GaUoQ0KcCmW2o2txuiKKYhEUbHiX8X1+l9jtfwraht6zHWykY9ySDcBeTRL8aYbPZzJR5VVUZGBggmUwiSRIzMzOEQqEs96nVPP6yEDRNY2hoiFgsVlIXtOUgEY6TjMSxO+14G8sX5qZpGsPDw0Sj0aKP3Xr+6LpOIpGYIwrPTQqvlmc8ZB97qa2lCwnwg/J3NYxn5WpHNWkuqg3rllzku5lUk74iHxYLpzPsSGdmZmhqaqK1tXVFbppLGTkyRtCM4Jhyi3NLNRZlrQKVk1hYLUd7enqqsr1qP/5okn+6DdJyJn07njAX/YIgZATboi0j4laUrK6GIAizuRZypkshK5mfSRJaKmV2OKydj/mgz4SI/eQqar72uapaNCwFsiwzODho3pOq8XsvF4wuZjqdNo/dOv4yNjYGgNPpNHUaq3H8JR9UVWVwcJBkMkl3d3dZLMOL2h9FZeDJvYztGiIVS2Jz2GjobmbjaUeVPNBN0zQGBwdJJBLLPnZBEPB4PHg8HlpaWvKGhy7FVKBcMAhlPB4v+/c+3/iUQTjK2dVYC2NRxtqhkOM4rLlYx7DqKyo9RlQorNX23BNalmWzwtfVNb/wqxIopnOh6zrT09OMjY1VdAStFLqQSgm3V4srkv3k47GfdiLyQ49nfmAQBgOahuD1ZpyhnA6QbBCPowsCuvF9pNNgsyFoafRoDF0APRLLbM7YTr7vzPozTUN++AmSN9yM+/WvKv2BFoliHkJWpFIpBgcHlxQQp+s66t4DKAcHEOvrsJ9wTMYieJUgl0wbD+XcpOfcTATr+IvP51uV1VFr4ni12CuP7hyg/7E9eOp9NPa0ICdTjO0eQtd0tl50Ysk+Z4NUpVKpsqSt54aHzmcqsBJdsVxSVelCwnyi8PmedcvpaqwFK1rr57EYDmsu1iGqVV+RD9YL33pCx+NxBgYGANiwYcOKt94KXbivZKdlObqQSgm3AWZmZhgdHcXj8VT1uQkgSBL+T3yI5G13kf77/WiRKEJtDYLTCYqC4PUgdrUjP/Q46vAogtOJnkhkvnNDhzE7Wy8fuQlpfBJxVthd8FkhzFrZChD/7e9xvuR8xLqljRHpikr6vgdJ/+NhdFXFccoJOC88J3M8BUCLxUnveAD58afQ0zK2bUfiPPcspPbWRf/WSF+22+10d3cXtcjR4nGi3/gR8mNPZRy7JBGxvQ3/pz6MbWP1j4oZ9sqCICxIpiVJyjv+EolECIVCZtXaOv5S7bAmjvf19a24iQiAKiuM7RrC4XXhqctU0h0eF3UdjQQGJ4lOhahpWb4WxCBViqJkEcpyIZ+pQL6umPEat9td1pFXa6dqpRehi3U1rFa3Bsko5jm4FjoXxZALo3Ox0t9rJbFuyYUgCFWtr8iHfAviYDDIyMgILpeLnp6eqpk/Xkx8vtKdlqV2LiqVX2GQ3unp6apwBioUgtOB+9WX4n71pfO+JtnTRex7P0NXlcz4VDJpdjgEHWydHWgffBfRx57Ef82N6E47YjzzmsI+AQE8HvREEvnZnTjPPr3o49AVlciV3yF934NmVyS940GSf72H2is/i+BZeOGjp2Xi1/wfyrMvZF4rSqT//g+U3fvxvvttSK3N8/5tOBxmZGRkyenL8auuQ37oMXA6weMGVUUdGiFy5Xeo+8k3KpY8nkqlGRvMLNLauttwOhdf3MdiMYaGhrDb7fT09BRsV5k7/rJQ+GO5F4pLhZVU9fb2Vg0ZUtIKciKNw5NNdBxuJ6F0ADm59GBXA8bzwOhUVXpywGoqYHTFDFH4zMwM09PTSJKU1dUoVaGn3N2aUmAhq9tiuxrGaPdq71wU05E+rLlYR0ilUuzfv79q9RX5kHtxj42NmYvPlbAjXQgLic8TiQT9/f3AynValkIuKuUIZXR0IpEILS0tNDQ0VN1CaDlwvvg81P4hkn+5A10AHHZAQOpux3H+2UROO5GgrtLQ2IBkt6N7PCCImTGpnO8r76fidmUE4aqKUIAtaj6k73swQyzsNnMxrisqyvO7SN7yV9xvePWCf688vxPlhd1I7W0IrsyiTG+sRz04SPqBR+YlX0ZIWk1NDe3t7UVf01o8Tvqe+zOjZbPvi80GPi/a6DjyE8/gOO2kora5FOx+di/b/3ofU+PTADS1NHLuJS9iyzGb5/2b5ZIqK/KFP+YuFKvJPSiRSDAwMFA0qaoE7C4H7loP0ekwLt8hUp2MxHF4nLh8yyMCud2aaiBVuZk+VlF4OJwJ8ixF0rxhWGDoiqphBG4xLGZ1u1iA31LHRKsNS+lcHCYX6wChUAhd16tWX5EPxkksyzKjo6PEYjHa29urcvE5X5p4KBQyk4VXstNS7FhUpYTbsiwzNDREKpUquVNItUAQRbzveiuuyy5GfnYngsOO/eTjwesxXVJaW1up6+xi5qrrM8JwrwdECWbdXowzS7cEegsAblfGcSoaB7cLLRRGHRpB6uooah9Td20HRUWQpExXRRQRbBJ6ClI7HliUXKhDIxlnK9ehaq8giggeN8qe/XNebwSFLTfHQQ9H0eWMbiULkgSajhaYKXqbxWJseJzbf38H8XiK9q42AMZHJvjr7++ktr6Gts65Y2FGMGA5cltyF4rJZNLM1Mh1D/L7/RVf3FrT1lfC3W8xiFIm0G3XjqcIjQZw13pIJ9IkZqJ0HbcRb8PSC3OGiYooivT29lZN592KhUThRtK8zWbLIquFnL/WMbBiNVXVhGID/A6Ti/WBdUsuWlpaqK2trbob+UIwLsbh4WGzyrPSLiLzIXcsylg8TU5OUltbu6LJwlB458Jo4Ro3yXISC2MsAqCvr2/VPmwKhdTRhtSRWXwqisJgf/8cUuX5tzcR++k1mYRuXc9kYFjHo3QL0QBIJNFlBUGW0RMJol/+NthtOM46Df+nPrz4OJOuk/jNDaTvfziTqzFrcyu4XeByZhiMkb2xEJzOjIYkd7ZYzmhPrLB2qqxBYUuB2FiPWFuDNh3MiOYNpGWwSUg9XUvedqHY/exeZoJhNh210fxZV18n+3buZ/eze7PIhTUYsL6+vuy6K0EQcLvduN1uc6FoEA1De+dwOLLcg8q5P+FwmOHh4RVPW18MLUd0ADojz/cTD8WxOWxsOO0ouo5der5JIpEwTVSqrVuzEBYShc/MzBSk9bGOgfX29laFtqYUsHY1jOd/boCfYrl/rmZht7VDsxgOC7rXEQRBWFXEAjLzyDAbXLVxY1W0j+eDEXoFh+z1wuEwra2tVRGCVQi5qKRwOxKJMDw8jNPppKurqyoreOWC1RUpl1S5Xv5SpK5OkrffjTowjBaJoPUPZf29eSbZbZksjbScbXMrK6R3PECkxo/vfW8ndcc9GbGz3Y7z7DNwnHdWJikckB94hPj/3miG9gEZkpCYtdUFHGeesugx2bdtIXX3vWjjk4iz+go9EkXXNRwnHWe+zirkLEWnSrDbcV1+GfFf/m8mkNDpyJAhWcF+0nHYtmaPJWmRaEZsX1dbsmsyFAxhd8w9f+0OO6HgoeR1ayDkSgUD2u12c3wqmUgSDkdQVJlQKEQgEMgS/ZZyzh4OdWtqamro6OhY8XviQhAEgdbNXTT1tZFOpLE57didS79HGYYFDoeDnp6eVfcsNrCQKNzQ+uSSVaNjoWnamiIWuTCel9auhqIojI+Pm6NSBtFYqtVtfCbK9MAEiXAMd42Xhu5mvPWV6fYXsy44bEV7GFUJq6MVZLou1Uws4FDnwpinNWZKq0XbYojO5huLqqRwOxAImG5l1aadKTesAt75XJHsJxyDNhMi9vDjaKHI/BtTNYS6OpicyhABQUBHz+ixdZ3kHX8n9cJuhOHRTBdEEJAfeATHg4/i+8QHEWwSydv/lhmD8npBix7qUuhAMonU14NrAbG6AamzHfcrX0ryz3eg9me6UYLTiePs07GfcgIwV8Raqpa561UvAx2Sf/gzWiiCYLfhOP9sPO/8Z/M8VscnSFz/B+QnngFNR+rrxv26V2I/dtuy37+5rQk5LWdVJTVNQ07LNLc1mf9tdGtWOhByZnqGv/7xbzz50NOoqsqmLX1c/KoL6e7rNheKIyMjQGZxYGRqLDV8zbDfnpycrEi3ppSQ7DbcS9QxGTDGwEqhrakmLCQKt5JVo5vZ09OzZonFfBgbGyOZTNLZ2YnH41lWgF9oLMC+B54nPhPF5nSgpIaZ2DvMpjO3UdfeWPZjOTwWtTDWLblYLTfzXEerycnJld6lgiAIArIss2/fPkRRrEpti7W7YkWlhNvWyu1y5uxXKwybXa/XS2dn57yLDG0qQOz7P0dLphYMz0PTMtarkHmdrh8K3YPM7waGUF2ujD2rIIKikLr3ARznvwj7tiMzWgkt8/DX/T5IpkBOg6pl7Fz/+2MoL+xB8LqxbTvK7Hjkg+Os05A2b0LZuQdUFam3C6mvB0EQzDR6Y9a8lIsMQRRxv+YyXJddjDY1jVDjR/QfGp/UYnGiX/8h6sFB8HkQJBHl+V1Ev/M/GbvazRsX2Pri2HLskTz50DP07x2gqTVDJqbGp2hpb2bLsUeWvFuzHCQTSX75nWvY9exeaur8SDaJxx94moF9Q7z3U++kq6+T5ubmBcPX/H5/weNTVm1NU1NTVXRxKwmjQ2tYa6/lQkqu1icSiTA6OgpkFqYHDx7E5XKZXY21EgCZD7nhgMZ40FID/DRVY+iZAySjCRr7DjkpBocmGXpqPzUt9YhSec8tTdPMIuViMMaiDncuDqMqkE6n6e/vz3K0mpqaWlI2Q6WhqirJZBKPx1O187T5yEWlhNuqqpo325Wu3FYaxdrsph94JJOH4fOhp1Lzb1gUMyngc98QAEHXAQGb3YY2ux+aJCLEZUJf+TZiPGFa4upBOaONcLvQXU6IJxA8bmau+FimmyHZkNpa8H3yQ9i3HYmyZz/yE0+D3Y7jzFOR2loAkJobkZqzq2jRaJTh4WEcDgfd3d1luzYEpwOps33Oz+UHHkEdGEJoaTwUrOf1oI1NkLz9b/iWSS7qG+t45Zsu5b67HmD4YKbif+QxR3D2i8/EV+Olv7/fzDJY6Ure0488x76dB+jsbccxq1Gpra/h4J4B7rvzAd74rn8C8s/ZRyIRIpEIwWAQURSzbErzfae6rjM6OkooFFq2tmY1IhQKMTIygt/vp7Ozc80upPMhnU4zPj5u6ksEQZgTAGmz2cxzqBoczEqF+YiFFcVa3SZCMaJTYfxNdVnnka+5luh0hEQohrehvEWLYrI6EokELpdrzXynhaD6VnyHAWQWIIODg0iSlFX1Xyw/YqVhVOOTySQ2m42+vr6qrU5Zx6IqKdy2pg/39vau+AKrktA0jdHRUcLhcME2u1osDggZcYXLCfI8gmpNQ1/IDUnXIZ1GDyqIXg+Cy4WWSoKioEeVLJ0Gug7RGLjUDOHQNNTd+w79XlFQh0cJ/9dXcZxyPOnt/0CfPZfiv/gNnnf+M+7LL5uzC9ZuTaGVW2XPflJ370AdHkXqbMd50bnL6i6oQyOZnEHLAlgQBASnE2XvXCerpaCjp53X/dvlzAQyGou6hlrS6TQHDx4EqJpZ89GhMTRNM4kFZD4Lr8/N/l0H8v5N7px9KpUyw/uMynSuTamu66YTWkdHB7W1Swt2XK0w9CW1tbW0t7evK2JhdClzhet1dXXU1dWh63qWKNxwMLOS1Wofg54PhRCLXBRidasoCpqmoqoqNk1DMO6jWuWmUooRoycSiXXVtYDD5KLqYMzgGwuQ3MrmQjqBlYYx7hCNRs0LqVqJBRyyy80Vbhfa6lwKYrEYw8PDSJJUNZ7ulYKiKAwNDZkzt4Xqb+xHHUFCFNDjiUx3YjbNe8nQtExmBkAkCmTnZWS5T6XT6K3NiGMZvZM5YqXrIMvogWDGttbhyORrAHoyRfxn12DbeiT2ozbPvvzQnH0xoYjpBx8l9oNfoMXjCKKE/OwLpO99EO/731mQsDwfBL8fyOdkJSPW1y1pm3nfRxCob8xsz+oMVGzieDnh8bozjmM5n0UqJVNTv/j5KQgCLpcLl8tFU1MTiqKYi0Tj+zbu34qirPgY2EpgenqaiYmJVacvKQXmIxZWCIKA1+vF6/XS2tqaJQqfmJjIKwpfDZ/hUohFPuTraoj1fnzNdQSHJqnvakYQM2ui0ESQuo4G3HXld2UqllyspyIirGNyUY0XpyFyNGbw8y1A5suPWGkYfuWyLNPX10c4HDbnDKsVBlGrhL4CDlWtjXnj9dQitXZrih2HEbs7M7kNwZm5v7QSjXykY9a6dg6MzkQeZBENrwddU7OuOVPHoeugqYCEYKl86y4neiJJ+u4d2I/avGRXJD0tE7/6erREErG5yRzj06YCxK/5P+wnH4+Qx5VpMTjOPIXkn/+KPjkNjfUZh61wBEQR5/kvKnp7i6GacxyOPeVo7rr5HkYHx2jtbEEURWYCIURB4NSzTy56ezabzaxIa5pGJBJhfHzc7IgODw9nVaSrhWSVA1ab4fWoKTOCEYt1xLIGQGqaZorCw+GwKQq3jk9V4zmkaRqDg4MkEollEYtcWLsam045it0pheDgJIIkoskKnsYa2rf1mMXCxUThy0GxY1HNzc1l2Y9qxbolFzC/oHclYDjHGFXd+vr6vK+rxrEo6wjXpk2bcDqdRCKRqvls54MgCCiKgqqqSJJUVuF2MRqDtYZ4PM7Q0NCSuzXxH1+V0Vp43JDOiKszfQUBbNKhMal859ti5+B85GP2d1JbC+LYBPrs63TIGZ8CIUfnYZKASBRN08xxmGK1Ncq+A2hTAcRav3m+CIKAWOtHm5pG2XcA+9YjC96eNhUAMvki3nf/C7Grfos+GcgI3z1unC9/CY5zzyx4e4XAINQrleOgqirPP7mTnU/vBh02H72JY04+GtusEL+ts5XXv+M13HT1zQwdGAbA6/Ny0SvO55SzT1z2e09NTQGY46FGpsbY2BjAmhX0WoXrLS0tNDaW372nmmBY7TqdzmURalEU8fv9+P3+rBG8aDRqjuBV2zlULmKRC39zLUe/+CRmhqdIROI4PC5q2+txeF0FicKXi2I6F/F4/HDn4jAqj3g8zsDAAAAbNmxY8CSsJnJhHeHy+XxZN9Fq2s980DQNm81GOBwmkUiY9pKlbjlbLTcL1RisJYTDYUZGRnC5XHR1dRUtXpZf2E3qvgczBMImZSxibRIEZjLEweOBUHj+DSxELkQxs61UOv/vbTbcr7yE1F3bUZ7blQnvs2xPB7Db0AUBTVYyCdyiAFqG+IibNtDf3086naa7u7vowEthsQdXgeeR/PxuYj/4OcruvZnD2rwR7/vfRe23voj89PMgy9iO3GQGGloxOTbFc0+8ADpsPWELrR0tBb2n1WJ5pQi1qqpc/7Mb+cffHiKdlgGde27dwannnMw/v/cNZhbHyWedyOZtR7Drmd3IssKGzb20d8/9LIqB0ckVBCGLUDudTnN8yqhIWwW9xaY8VyOsnbq2trZ5C2VrFbFYjMHBwZJb7eYbwcs9hyRJyjqHKt0ltBKLShg2uHxu2rZ0590Pq538UqxuF0Oh5EJVVdLp9GHNxWFUFjMzMwwPD+Nyuejp6Vm0xVkti3ZDmBsMBvOOcFVTV8gKq8aivb2dRCKR5UNuvTn7fL5l3XxkWWZoaGhO6vR6gHVxWVNTQ3t7e9GfpTo+SfSbP4REMvMDRclYw1q2I0gSukBOO8GChfQZoojo96Opobmp25KE8zWX4Xr5SxAbG4h86ZtzXiMAzte8nNTtd2eSwSUJXdcQVB29uYGRI3rQ0ml6e3uXZMMsbexFbG1GHRlDbGo81BEJhZE62rBt6lt0G+rgCOGP/Td6MmmSEWX3PsIf/zx1P/0WzhedNu/f/vUPd3HTNTcTDWfGx7x+D5e9/hJe+aZLFyQK1WK3+vQjz3H/3Q9SU1+DvyZD7GLROA/veJRtJxzF6ecd0qzU1Pk59Zzix6DyoRB9ic1mo7a2ltra2jmCXmvKs1H0qMbRl3zQdZ2RkRHC4fC6FK4bI4CVsNrNPYeMZ5lVFJ6bFF7O67DSxGIh5BOFW/8pRVdD1/XDGRcLYF2Ti5VcAOu6zvj4OFNTU9TV1RUcnlYN5MJIGE0kEvOOcFXDfubCWsmAzI3HaDm3tbWRTCZNe8lQKGQK7Qwv+2Kq7olEgqGhTJJ0bur0Woe1crmcxWXyL3eijuXJdTHOK1HMrPAXuoQXvL51vJ/9KLFv/ghtaCT7VzYbosuJIEk4zz6dxJGbUJ5+/tD2bDYQBdJ3bcf/2f8g8ZsbUJ7bCYqKLmbmf6Wnn0c57UTGxsbMRWIxDkmC3Y7n395M7Ls/RZuYytjsajqi34fn396MUMCCM/HHv2RGyiQpS4yup1LEb7wF34ffk/e72fn0Ln73y5vQdWjryohwg9Mz/PE3t9C7qZsTTj9uzt9kNn1ocbnSdqvPP/kCiqKYxALA6/MQmAzwzKPPZZGLUsEIhSxmHCZX0GsdfTHGp5xOp3kfqobRl3ywjgAWY9iwVrCSGR4GGfV4PLS0tCyYy2J06Es9ImRk16w0sciH+axujWLjUroaxvTDYjhMLg6jYrC6KrW3txc1KrPSblHJZJL+/n40TaOvr2/eecqV3s9cWH2z8904BEHA7XbjdrtpaWkhnU6bRGNsbIyxsTHTXtLv9y+4SDQeMk6nk66urlVTdSwFjNDHWCy27PwO+fGnIC3P330wAuyW6h6lqsS++l20qem5v0ulSPz29zjOOQOpox31wAC43RkBtSAgiCK6pqHH4mjTgYyTlSSB3Y4ugBQKU3PdHxFbW4gdvcV8wBuuL36/H7fbnXXd64kkqR3/QH7qOQRJwn7qiTjOPAX/lz9F6p770EbHEdtbcV5wNrbeuaMA+aDs3JMhY7mLXFkh9afbSN12F47TTsLzzn/G1tdj/vqh7Y+SiCfp7O0wf9bQVM/IwCgP/P2RvOTC+O7j8XhVLC5VRUUg331VMBcTpUSpFpf5Up4jkQgzMzNMT0+v+OhLPuTO2Rc7ArjaEQ6HGR4eXjFtUS7y5bIYZCMYDGYVzpbbGbMSi+7u7qpfRM9ndVtogJ+BQsei1mOAHhwmFxWHYU2nqip9fX1F34RX0i0qHA4zNDSEw+Fgw4YNCwpzjYuuGEeFcmEpidsOh4PGxkYaGxtNe8lIJMLU1BSTk5N5F4nWUSC/319wN2qtQJZlBgcHzdDHZQv5JCkzimS3ZciDouaQCAE9kWTh1sUC0HS08QUS79MykS98k9pvfj6jt5BEBOtCzhgzevp51MFhcDjQBBAEEcEhQCKJeMuddF18AelHnyT+4KOkVZXYUZsI9HUjWWbsPYJI/Os/QH5uF+gZUp7+xyPIjzyB90Pvxvuvb1rSIYpNjSBYzsHZrA/z/8tK5n2efJa6n3wTqTtDJsKhCIIoIOg6DXIKVRCYsTmQJInQbHaFFYqiMDg4aOpLyiXiLAabjz6Cf9z9EMlECpc7UwxIp9JomsrW47eU9L0M4XqpA+JyU57zjb6sdB6CUSxLpVJVWbUuN6o9HDA3l8VqdZvbGfP5fHOKHgtB0zQGBgZIpVKrgljkQ7EBfkZxstC1zeHOxTpEpW8CxuLcbrezadOmJT0IVmLcyLAUHB8fp6amhs7OzkWrZdaLdSUra0shFrnItZc0Kom5Og1ZlonH4+vSdjGZTDI4OAhkxsBKEZDmeNFpKM/MjiLZ7RmyoWqZ//Z5cJxyIsrBAbTBEZDl+V2flgFteJTUfQ8jdrajHhwEu/3Q9yrLs2RDyLhIzRILUZol1jYJZf9BIl/8JukHHgV0JKDmr/dgu+RC0m9+DdFYjFAohHvHg3ieeg6xrgZpduxFTyRJ/+MRHGediuPMU5e0/67LLiZ974OzqeLSLEGb/aVNyoyW6Tp6LE7oU19C9PsQmxs52eUnqiT4p4EXqFFkACYdLv5XdbF526as90in0wwMDKBp2pL1JeXASWcez2P/eJJnHn0WUZIQyJCgrcdt4dSzTyrZ+xg5DuUWrueOvlgXiePj42YegjGCV8wicakwRmSNxPX1Vp01SOVqCQcUBCFvZ8zQ+hidMSthne/5bSWVq5VY5KKQAD84NJlRyPdtdC7WwudTDNY1uagUDCtSQ9xayOJ8PlSaXBhztKFQiObmZlpaWgq6oIzXrKSmxaqxKFWGRa41YCKRIBwOMzMzYx5rKpUiFArh8/mKdkdajTBEjMaceamO2XXZS0jdeldmUa+lM/oKQQSnA6m1Bd9HrkD0+1BHxoh86ZsZR6dSQ1VJ/PZGfB94J9Fv/Ag9HgfJhq5lFumOc88k2tKApGmIgoAgiVl/K7hcpB94BGw2BLs9c46kZZTb/0bdmafScsYppNNpwr+6Hk0EGZCTyUw1zS4hKArpx59eMrlwnHYSnre/mfivr88QC6PlL0mHhPG6DqqKNjwKHW2oU9McmZY5Qouga4e4SHM6yb8LaRwnHLK/NTqx1RgK6XQ5ecdH3sZDf3+Epx99Fk3TOPaUoznjvFPx+Jb/oLdaTK9EQcGah7DQItHv95dlfMqwTzeya6qFVFYKRur4arYXz+2MGbpDI1cD5qbNC4Iwp1u1VknlfF2NaDSKqqoIgkA6nV5wfMogF9XQza0k1v7KZ4VhpFSGw2FaWlqW/QCqJLmwZm90d3cX5fxhvSArjVzhdrnC8QRBwGazEYvFEEWR1tZWU0hneJC73W6TjFTTwqtUCAQCjI+Pl2zWWJ2YIvnnvyI//jSCy4XzpReS+sfDqHsPmAtjweHAefYZCLPdEamjjdoffI3EjTcT//k1h3IvSgR9Ooj81PP4v/AJ4r++DmXXXlA1BJ+XqMNGdEM3DR5PxjHK7cyMS6Uz1X7B40ZPJE3xtSAI4HSgxxOktv8Dxxmn4HA4cNjtKJINu+uQR7uiKIiqykwwSGTW7nkpFqWef34dzovPR37oMZK33YWycy/kkCAAHHbE+sw1ruzZn9HKC0JWRqFTFPA8/Agcv9UULzscjpKSylLC7XFx/qXncP6l55R0u1bTgmrIcShkkWh1DlpuZ9HoVgFVRyorAeO+t5ZSx3N1h1ZRuDEObLPZ8Hq9JBIJZFmmt7d3zRKLXBj33WQyycTEBF6vl7q6ukVF4cZY1Hr5nAxU39NgDcG4AafTaXp6ekoicKwUuUgkEvT39wOwcePGoi+MlepcWF0ggLJqHozFlc1my3rAGh7kxsPdKuY1iEa1Or4UCqvdaENDQ8EdLW0qgDIwhFhbg7SxN+tv1PEJwp/+CtrYhDmuo+zcg+2U45G6OpDvuQ9d09FlmcTv/oj8wm5q/vtjCB43gk3C88bLcVx4NjP/8n6IlTAd3m4j9bd7cb74PLTpQGbf7HbUVArbndtpOjCA9+MfIPad/0ELRwAdwWbDceE5KHsPwHQw3weYEYEbb3HKicgv7EZQFCS7HUmS0JMpVIcdTjiWSCxmWpQa1ehiOmNSazPSKy9BbGsh/IkvZkbIRDHTltBmr5X6ukN/oKoIgCSJSIZmQwBkBWXvQVPA6vV6K+6MsxLQNI1dz+xhcmyKts4W3LWuJQUjVgLzLRIjkcgcY4GlZPsYGR6iKBZkn77WYIzBFXPfW43IJwo3xoGN5+vk5OSK6n0qDSPDJNe0YSGr20gkAhwei1pXKOdNwUitFkWRjRs3lqxlbAiHyymULjZ7Ix9WonNRCn1FoTBmbb1eb94xN5vNlnVjtnrYT09Pm4FZfr+/5LaA5YY1GLBQu1Fdlon/6rek7tqOnkoj2CSkIzfh+/AVSG2ZYLbETX9BG5tAqK0xA+T0VBr5oScy2mmXC9EzK55PyyjPPE/iT7fhedNrzPextTTjesUlJK+/qTQHK4mZoD5ZJnHDn9CCIbDZ0BUlI+5zOND296NHItRf+2PSDz+OHotj23Yktg29xH7yaxIDQ+iaduiYNA0EAftxR5tv47z4PNIPP57pisza6wqigPO0k/C99EJabDZzxj4SiWR1xnJHFhaC/dQTcb3ulSRvvCUzDmVwf68HodFiKW2zzXZfBEzDJV0HSUKu9TM5PExNTQ0dHR1rdnFlYHJsiq9/6rvseX4fiiwjShKbtvbxsa98qOqIRT7kLhKN8alwOEwgEMgS/Hq93gUJqzXDo6enpyq7VeWEUcFfb7o6URRxu91m4nxnZ6dJWq16n6US1tUAI3U9nxtcvvEpTdNIp9P84Q9/ADANGNYLBL0ak84qBGP0oJSwplZ7vd6SjwvMzMwwNDTE1q1bSz5Da1SjJycni8reyIdkMsnevXvZsGFDRWYNrR0Lw9GhXO9jzFkvZdbW0GkYNreyLCMIgkk0FhLQVQMMV6BUKkVnZ2fBwYDx/72RxPV/AIcdwe0CWckswjf1UfOtLyHYJILv+BDa5DRiTfY21bFxAMSW7Ie5FgojdXVQ9z/fzHp9+qlnCX/4Mxnx93LhcSPMXr9CjR9tZCxDDphdcwsC2CScL70Q/8c/MOfP1fEJQh/8NFogeKhTgI7U1UHt965E9B9yi9NicdL33Ev6iWcynY9TT8Rx7lkZ+9scGA5mxj+6rps+9gZhXei8lHfuIX3vA5lOxL6DyAf6kVqaEWwSuqahDo5kks9F8dAIlaqhCwIzn/h3ak48bk1XbQ3ous6n3/15nnnseTw+N4IokE6lkVMyZ198Fp/82kdXeheXDGN8yjiHkslMWKXVctsavGYsrhwOBz09PVV9nyo1DFOTqampFQ2GXCmoqpo1hWGdZLDqfQwtgiiKWaLw1U5CE4kEAwMDuFwuuru7C07m/q//+i9uu+02NmzYwO9///t1pbtY3d94lWGx1OpSwGrxWkqoqsrQ0JBZjV7uzbNc+5mLcgm388EaEtXa2kp9fX3R75Xr+GIEZkUiEUZGMkFu1mTeamo1p1IpBgcHTVegQkfl9FSa1O1/A5uE6Ju9uc4KipWDg8hPPYvj5OMRDMtZ698a1XUhT6dRFNGTqTnvJzU3gdcLicTy9Reygq7rOF50OunHnzK7Dpl90TMjRbIyr1OV1NpC7be/RPy3v0d+8FEQJRznnYXnza/JIhYAoteD6+UvxfXyly66W7kOZsbIQiQSIRgMZlWj8xFW+1GbsR+1GQB1ZIzo13+AOjSKPtvKEFubsJ99Oqk7/g6qAgjoDjuRN76KhlNPWnGNQaWwb+cBdj6zG4/XlbGfRMfr9ZIUkzxy72NMjEzS0tG80ru5JFjHp5qbm+fM2E9MTCBoUFNTg9PjYnJy0qzarjdiYRSUmpubaWpqWuldqigWIhaQX+9jnEdGh9Xlcpn3otU2EmwQC8OwpBBioWkaX/ziF7nttts46aST+PnPf76uiAWsc3JRyhO8kNTqUqAc40bpdJr+/n4zn6BU2hAo71hUpYTbkJ3h0NXVVXDFfiEIgoDL5cLlctHU1JQ1Gz0xMcH4+HjVJPMa+hK73U5vb29Ro3JaOIIejyPkECXBYUePRjPp04Dj7DNIXP8HdFnJEA3IEASnHUHT0dOyWcXXNQ0UBfvJc8PcBJ8XqaEO9WBkiUdrwazFbfqZ59EisYxhlcF2Zv+FDsICY49SZzv+/3z/8vdlHuT62KdSKVPvYyWs1mp01v51tOH/8qeRH3wUdWQMsb4O+5mnIDU14nnHW0g/+hTBcIjwxh7aNx9RlLHDakdgMogiK9gdNiDTHRIEAbvDTiKWIDAVWLXkIhfW8amxPUM89uf7GH1hAFXXaN7WxYZztuLz+YhEImuiGl0IdF1nfHycYDC44onzK4HFiEUucgmrtcMaCASYmpoyReHVFAI5Hww3vGKIha7rXHnlldx8880ce+yx/OxnP1t3oZKwzslFqWAVP2/YsKGswp1SL9pjsZgpziu1NgTK17mopHA7kUgwNDQEUFYff+vD3ZrMGwwG5+g0vF5vxYjGYvqSxSDW1iD4vGihMILrkEuNnk6DKCG1ZzQXrle9DPmJZ1B27TU7BNhsuF7xMtSBQZSnn0dPCJlRHUVBbGrA/ZqXH9peKkXiT7eR+PX16NFYaQ4e0FUVxidniQUZUqFnX3/K+ASpex9Aam1B2rwxO3lb15EffZLU3TtQxyaQ2ltxveYy7Juz8yJKASthtVajc8W8uVkIos+L88XnzT32ulrGj9pIMpmkq6tr3T0km9oaECWRdErGX+MzgxOTyRROl5P27vYV3sPSY3pggrt/+CciEzM4a1ykYmkG7t+FEkzgf+v5RKNRIFONNs6jQvQ+qw1WR7C2trayFQyrFcUSi3ywdlh1Xc9KCq+WEMj5YBALww2vkOeerut87Wtf4/e//z3btm3jl7/8ZUkKkasRh8nFMlEK8XMxKCW5MLQhHo+n5NqQcnYuKjUGBRmnh+HhYZxOJ11dXRVzRsltNRtjL4YofLGxl1LAOme8nJAowWHHdenFxP/3BrRI9JDmIp7AduQmbMduA0D0+6j5yqdJ7XiQ9AOPoE1OIbY0I23qw3bUEejJFNrAEDgcOM46FfdrXo7UmVnc6bJM5EvfIv3Q45CaOyq15M/AOIZFXqfc/zCRx59G8HiwH70Fz0ffiz42ibJrD/LjT2f2K50GTUMBUrfdhePF5+P/+AfMbow6NEL6gUfRVRXHKSdgO2LDsvc/V8ybayxgBEAa/1hJujXHYD1ZThqIx+PIepoTzjiWx+9/ilg0jt3pIJ1Mocgql/3TS6itX36Xt9qw8+9PEpkIUtvdRDKZpM5Xj91mJ3hwEgJpNp+2xTyPpqenTYvS1WpQkQ+6rjM6OkooFKpKR7ByoxTEIheG053X66W1tTUrBNLo1FeLKNxwRLPb7QXri3Rd59vf/jb/93//x5YtW/jVr361rrq8uVjX5GI5J67RLp2amlq2+LkYGPu8nEW7UZGZnp6moaGhLMmi5epcVMoRyhDmT0xM4Pf7K/b95oP1pjzf2ItR/fH7/SUhQNaHa3NzM42Njcv6rF2vfQVaIkHq9r+hR+MINgn7Scfh/cC7ZlOuZ4/V5coswJ95Hj2ZQjkwQHr7P0DXMr8TxYxNqtOJ2NFm/l36/oeRn3gmo9kw9nOZ514hxML6DmoyhSyA876HMqnYkNkX67UqCJn90nTSd20n1lCH1NFG4vo/ZCx4ZwXiiV9fh/NlL8b5qktI37UdZe8BxKZGnOe/CPvJxy/puxBFMYuwJhIJs6sRCoVMPZAxOjUyMoIgCPT29pYkcX01wSgquN1u/vMrH+GaH/6W7bfdRzqRwuVx8ZI3XcQ/v/cNK72bZcH47iEEh0QymcThcJjdLV3XCQxNcsRZR8+pRlsLH8b9ylgkrjarWl3XGRkZIRwO09HRse4WiAaxMHIsCu3UK2mFmZEplLSCr8GPr2nhz80aAjmfi5l1fKpS51EqlaK/vx+bzVZUx+IHP/gBv/nNb9i0aRNXXXXVuut05WJdu0Xpuk46nS7674x0ymg0Sltb27IXXsVAlmV27dq1ZG2EceOIxWK0t7eXVZj5/PPP09LSUjIBnNVHutzEwlhYV7vloCzL5oM9FsuMAjmdTjNPYynjCoa4P5FI0N7eXtKHqxYKow6OZHIuujvmvvf4BKH3fxI9nUao8aNHY+jBmYyuwe9DqK2BRAJd1fD/98dwnHw8ANHv/pTUX+/JJGcbIu9l3NoWIhbmVvNsX0HHZvzV7OeetQ1h1trVEKpL0izZyCEgTidoKkJdbYZM2WyZsDu7HfcbL8d9+WVLPrbMruuo+w8iP/ZU5gdHH0W8rZloNGomygqCQH19PTU1NatOhLkchEIhRkZG5gRDhmciBCYDNLc14fWvTXGmruv84UtXM/DUPlo3duCc/d51XWdi7zBn/vOLOf6yM+b9W6tdshEetprEvLquMzw8TCQSobOzsyT6w9UEK7EoJnV9ZjTA83c/TmhkGk3TcHpddB27kc1nH4NkK66rbhTQjK5GJc8jQ38qSVJRVsv/8z//w09/+lP6+vr4zW9+Q3Pz2tBhLQfrunOxFBhzeKqq0tfXV/EZ5OW4MBmMvFL7LghCScaiDEcoI5imnMTCurBeDVUru91uVn9UVZ0jnivGnhQyN9fBwUFUVaWnp6fk+iGxtgaxdv4HtvzQ45lE67razPljBM2JAno8jlhXCx4PenCG9D8eMckFkmg6HZWrY6Eb/7vA5iXLX5nXqCBkb8uaL2EJW8r+vQaKih6cQTrikIZDmwmRvOnPOM85E7FpaeJSXdeJX309qVv+im4kidvtuC65APsbXk08HsfhcOB0OpmZmSEQCKy5sZf5YCQv5xsDrKnzU1O3duenjW583ZGtjO8cQonLOF0uNFUjODyFt76G3hM3z/v3giDgdDpxOp00NjbmvR8Z59FS0+bLCcMNMBaLlcy0YzVBVVX6+/tRFKUoYiEn0zx352PMjE7T0N2CZJeIz0TZ9+DzeOp99BxfnLYs1+hEURSzq2GcR9ZxzlKJwg1iYYRDFkosfvGLX/DTn/6U7u5urr766sPEYhbrmlwUu0ANh8OmY87GjRtXZFRgqVqGSCTC4OBgRfddFMVlj0VVUridSqUYGhoq28K63JAkidraWmpra9F13bwh59qTGoLw3BuyEZAlimJW4ngloadmO4nW4DZDSq3PiqtnK/96wpJwfeJxJP/wF1DyLNaLeX+y3z7r50Wey+a29FnaIwgIhW7D+BzQ0SNR8Psy+S01fvTJaeTnduI876yi9seA/MgTJP90G4gSgpHKHU+Q+PMdhPxe/OecYY4Bzjf2YtVprAXXIKu+aK0nL+eDtVt7wsWn4RVc7Pzbk0zuGwFBwNdUwxlvuoi6jsI73bn3I6uY1ziPrC5mKzk+pWkaQ0NDxOPxdWlcYLhdFkssAAKDE4RGp2nsbTW7FN56P+l4iqFnDtB93MZlXUs2my3rPDLGOa2icOM8MkThxb7fUonFNddcw49+9CM6Ojq4+uqraWtrW/yP1glW/1OhAjB8ro35+5X0+TYC4golF7quMz09zdjYGD6fr+AZwlJAFMVla0MqJdw2rFZtNtuKLaxLCesCsLW11fQej0QihMPhrPl6n89HIpFgZGQEl8tFV1fXii0YbduOzCRgx2LoyXQmJdo4hyQxo0vQdRAFxIY6AORde4n//paqIBa5f5vbsdBzuxiLQdPRhkcRavwZjYnBtZZBslP3PYiaSiN7vNhkGYfDgepyoMVi+J/fTfMbX2tea7l6n3Q6bRINa0q4NZdltS3KrXaj6zHHwDoKZHRrT3/DBRx59rFM7B1Bskt0bOvFU7f0Bfd8Yt5IJGImPK+U7bamaQwODpJIJOju7p6TR6DrOsHhKdLxFKIkIkoSdR2N2BxrY/m0HGIBICdldF2fM/5kdzlIx5JoiopkL81nlZsTZc1mMdZoRrfeEIUvVpA0jCsEQSjKlOe6667jO9/5Dm1tbVxzzTV0dnaW4hDXDNbG1bEMGPOk88FolRrC1mqoaBW6aLeG+jU1NdHa2lrRfV/ss10IlRJuw/KtVqsdud7j1gf72NiY+Tqn00lra+uKHr9t2xbsxx+dEXHnnuPWxG0NEv/3R+QX9qA8+Wwmj8IKhyPjzlQArGdoKToWBvI+0qyC80IhCOjhCLrfB7KMUOPHfty2Je1TeCbCC/c9Tms0TiiSQBAFHA4HNfV+7JKEQ1HnvdasYy/GuEI0GmX/ngM88+hz1DfV0dbVahKNlXR7KRTGwnrswDANtfX4XGtTSzEfrKNAuRqD+s4m6jvLQ7SsYl5rwnM+F7NyZiEY+slUKpW3Wx0aD/Lw9fcw9PQBJg+OISdTuPweWjd3cso/nctR5x1flv2qFJZLLAA89T5sDjupWBKn99DfJ2aitG/tKRmxyIdcNzxrdywYDC5qLiDLshkjUEx+04033sjXv/51mpubufrqq+nu7i75sa12rHtysRDS6bRpx9bd3V018/eFdC4qFeq3EJbauaikcHtiYoJAIEBdXV1ZEtWrEcaDvb6+3nRFcTgcyLLMwYMHsdvtK7ZAFAQBoaEO7HbMBGz0/EnbqTTKI0/M/iHZLKFQYiEIKI312KcC87yg+FGo+T4tS+ZeYd0LY0GlaaDrGWve5iY8b33DgrqVhXDV967FPhHmUknALkmomk4ykUQUoNFpx37M1oK3lUyk+OU3r+Hhex9DTsnY7BLHnno0r3zbpTicgUXH8FYamqax+5mdvHD3E2ihNEOI7Kl7mo2nb2XTGVurSg9QDlj1ZSs5CpRru51v7KUcWQiL2a0qaZn7fnU7Iy/0MzMyTSqWQBRFEuE447uH+ftPbsHpdbHhlC0l2Z9KoxTEAqCuo5G2Ld0MPrUPd50Xm8NOLBDB6XMXrbdYDnLDRK1Wt0YRzeiOGeOcg4OD6LpeFLH405/+xFe+8hUaGxu5+uqr6evrK+NRrV4cJhfzoFzhcqXAYot2I65e1/Wyh/othGI7F5UUbhsVu2g0SmtrK/X19euCWBhQVdWsWBoBUUblx7AmDQQCC+YglAvKc7sQvG7EmoygUh2byE8urFhCc8EQaDvOOBn99ntAUfL8fmnbzXcmmT8rcDxKcDrAbs+IrlMppJ5u/J/60JLzLyZGJnn8H0/i99VylhykXpFRENBFAVsyidrahPOlFxS8vZ9+/VfsuOMfuL1u/PU1pJIpHrvvSTxeL+/79DuzxvCArDG8lR47VFWVg/sP8tztjyJPJ2jpbcPmtBMNRHjur4/i9DjpOeGIFd3HpSIRirHnH88xtnMQh8dJ38lH0nPSEVnXbu7Culr0ZbljL/NlIeSGQBYLRVEYHBxcMMdh5IUBJvaPZMZ74ilcfncmUDGeAgHSiRTP3PbIqiQXVmKxXKtpURTZdtGJeOp9jD7fj5KWaTmig76TNtPY21rCvS4c+cwFcrtjxuuam5sLLnz85S9/4Qtf+AL19fVcddVVbNpUOfK02rDuyUW+BfD09LQ5JlPqcLlSYCGhtCE6dzgc9PT0rOhDvJjORa5w29CWlAOyLDM4OIgsy+vSFcR6/N3d3WbF0lr5aWtrI5lMEolEsnIQrHka5bouRK8HzToCtUwtRT4cEluD8NjTGaH4nBcVTy9KdsYKQma0azb3As2G582vWVawXjAwQzotI9f5+Y1d4kXJEFuVjG3vE4qAdtRx1Gx/nCOPPoKNW/oWvP7Ghsd5eMejuL1uvL7MwtQ2+++HdzzKW654PW2dreYY3l233MOdf/wbY8PjNLU1cd7LXsQ5LzlrRWxujYXV1MEx9HCa9iO6sDkzVcva1nqm+8fpf3wP3cdvWnUFh+h0mDu/dxNju4eQbCKaqrP3/uc45mWncvobL0AQhDkV62oOR8zNQsgXAmntahSySMxdWM9XOIwHI+iajqap6OiIUoaciZKEkpbx1vuYOjiW92+rGaUkFgbsLgebzzqaDadsQZUVHJ7qSmy3dseMUShFUbDb7UxMTDAxMYHb7TbPo3wW7nfccQf//d//jd/v56qrrmLLltVHKiuJ6lo1rzCsGoVyhcuVAvkW7VbReU1NDV1dXSve1hcEwexCLIRKCrcTiQRDQ0MARQUErRUkk0kGBweBhY9/jk4jlSIai5k6jbGxMfNmbORplArOC85G2XsAPZnM5D1IIizSuCgGWcJtQUCbmJpDJMoe/rNY90LX0cMRsNtAELAffwyOc89c1lu2tjfjcjsJz4RRa7zcVd/O3wSB8eExgoEItbfdh3THAzidTs6++Eze/uG3YptnXnpidBI5LePPsWZ1uhxEZiJMjE7S1pmpWv71pru49kfXoygKTqeDof3DXPeTGwkFQ5x18elmd8wYnyrnfcsYddV1nYaaeoYF0SQW5jH43MRnYqiyuizRrqqqPHjPI9x/94PMBGbYuGUDF152Lhu3LD95fT48d+djjO0apKmv1Zx1jwUiPH/X42w8dQv1vS0MDAygadqqC0fMDYHMNamAbNegfMeWmzq/0PF76/2ZZ62RyanpCKKApqg4fS7kZJrmjXOzeqoZ5SAWVtgctqoWuhsaG03T2LBhA06nM0sUPjU1xeTkJL/73e/Ys2cPZ555JhdeeCEjIyN8+tOfxuPx8Ktf/YqtWwsfH12vqN6zoMKwahQ6OjpoaFiah3wlkEsurKLzlpaWqgl9E0URRVl4VVhJ4XY4HGZkZASn01mVHalyIxqNMjw8jN1up7u7e9EZU20mROKmv5C+/yFQFJwnHUft5ZfB5s3mQ924GTscDnOBuNRRBQPOSy5Cfm4X6YcehURyydvJhzmOUM2NEJyB0jdHlg9NA7sdsaEO3eEg/sfbkJobkVqasW3dXPRn7PQ4OebUrdx/50PY4jZEQSQSijI9FcLtcdHeldEcxSIx7rl1Bxu39PHiV+Yfk2rraMXhsJNKpLH5D11HqUQah8NOa0cLALFIjJuuuQWAptaMONgPBKdnuP+vD/GqN74cVVfN+fpypTsbi9GhoSEzdXxGnEK0ScjJNHbXoQ5vIhyjeWM7kn15GpGbrrmZ239/J7qu43A6GD44wtOPPMt7P/lOth5fnqrnwcd24/S5s0S0nnofE/tGGXz2AGEy9s29vb0rPpq2HOQWP/K5Bhn3JEM7pigK/f395oz9Ygvr9q29tB7ZycBT+7E77CTDccRZRyTRJiGIIke/5ORKHG5JUG5iUe0wRgFzjz+fKNzpdLJz506ee+45fvGLXwCZ7scVV1zBxo0bV/IwVg3W1+oqDwRBqBqNQqGwkgujxZdKpapKdA6Lj0VVUrg9PT3N5OQkfr/f9PBfTwgGg6YdsTV1eD7o8QSRK7+LsntfZvZfEEjdcx/yszup+fzHqetsp66uDk3TiM12NHJ1GkutRAsOO75PfADlmReQn3kePZ0mdfe9aNOBTP6D0Q0ThKJGl/JazUaii+s5ikTBgu1CEI2hRWNoA8Mo9z2Y+Zndhm3LZvyf+xhSa0thm4lGGRoa4pVvuZTmlmZ23H4/sUgcOZ3G5XLSd0Sv+T35anzEYwl23HH/vOSipaOZ088/le233QfoOF1OUskUyXiScy852yQXB/b0EwlF5nQ4fH4vkVCU8eFJjj5xK62trVmpvIYA0+VymfP1S0mbnxyb4ubr/sIDf3uYtJzmuFOP5i3veSMOh4PG3lZaj+hk+LmD1LTWY3c6iAbCmZyXk49c1v1odGice27dgdvjoq6xDsjch4YODPPn/7udo45b3vYXRJ5rQtc0JqemqBPai7LbXC3IXSAa8/XhcJhAIGCOEouiSHd3d0ELa5vDxtn/egmP3PB3Djyyi4n9o6hpBafPja+hhhNeeSZbzj2uAke3fBjEqpCOzVpEbvL4fMdvjAZ/8pOf5H3vex9XXXUVV199tVkE/eY3v8n3vvc9Tj/9dM477zzOP/98enp6Knw0qwOCvtyUs1WOyclJBgcHcblcq+ama8zLt7W1ZfkzV9vs7OjoKNFolM2bs1Ndc4Xb5dRXWMOhmpqaaGpqqoquTqVgjMtNT09TX19fsB1x6u4dxH70y0xS9uw1oWsa2uQ0rle8BO87/jnvexlOL5FIhHQ6bVaijQXiUrtF6tAI8etvIv3w45BMgc+LHk9CIFjQ38+XYQFkMiPyjBmWHcVmXuT8rbTlCOp++q1Fv89QKMTIyEgWsYyEowQmAtz5p79x55/uoa0rW3gZmArS2NzAd//3a/NuNxaJ8fNv/ZoH73mEdDqNw+Hg9PNP4U3veh3bb7+Ph3Y8SiKeYHD/EPWNdbi9h4o2yUSSVCLFlT/7PJuOmjsmZKTyRiIRYrEYmqZhs9nM88jr9S563DOBEF/80Fc5uHcAySZlxjQVle4NXXzu+5+ivrGORCjGC/c8ydiuQVRZwVvvZ9NZR9N74vLE3Pff9SA/++ZVdPV1ZO1neCaCqqpc+dPPU1u/NMevhfDIDdt59MYdWWNRoYkgsUiMs977Mo4768Sq7NjKKZnJfSPouk7zhnYcntIsfnVdJxqNMjIyYj53gKyRzsWyWXRdJzweJBlNkAjFAGje2I63fnVo9QxioWnaggvrtYpcu+FC10kPP/wwH/zgBwH4yU9+gs/n4+9//zvbt2/nueeeM1939NFHc/XVV6877eZiqL67TIWRSCSoqakpqJpbLRBFEVmWOXDgAC6Xi97e3qp8YMynDamUcFtRFIaHh81Rt2rq6lQCmqYxMjJCJBIp2hFL2b0fXdMRLWRbEEUEux3lmRfy/k2u00sqlZo3cM14qBcKqasD/8fejz4r7lYP9DPzsc9l9BiaPm8HY6EMCxPLCHpcMeg66s49hD78GXzvfye2zflb9dPT00xMTFBbW5ulIfPX+PDX+DjymM3cdfM9yGkZu2OWROo6qUSKLcduzrtNA16/lw9//t+ZePckE2OTtLQ14/a5+eKH/h/7XtiPZJMy40iJFKND43Rv6MTucKCqKpFQhCO2bWLDkb3me6bTMna7DVEUzVTempoa4tE4siKTTCWz0uYN0ur1evPe/7bfdi/9+wbx1/mwO+w4HA6ioRgvPLWTD73545z7krN4xZsu5aRXv4j4TBQ5lRHp2hzLLzDZHfaMZbiqZYWLqaqKJInmZ11qHH3xyYw838/ozoFZ8baKisaRFx5XtcRi4Mm9PHT9PYTGAuiajr+5jlNeew5HnHX0sredTqcZGxvDZrPR09ODIAhzRjoXC10TBIHatgZW49NjvRMLIyCxWGLx+OOP86EPfQhd1/nxj3/Mi170IgCOP/54PvShDzExMcH27dvZvn07ExMTywoLXquovjtNhdHe3o6qzh8cVW0wqsOyLFNXV1fVIz65eRxWfYUgCGXd71QqZQq3qslqsVJQFIWhoSGSyeSSHLEEjwuEzHdmvTZ0TUXwFRY0lhu4ZhAN60y0QTQKdQwSZhdq8vO7EFQVamvQg6G8r12wW5ELlzPTETHfCMqv6qbo0a5cKC/sJvy5r1H7tc8hdR8Sl1ozXBobG+fVYZ16zskcsW0Tu5/dg9vjRpQkYpEY9U11XPKaiwvah5aOZlo6mgG4+bpb2bdzP/VNdaYY3Ol2MTY0xsTIJE63C9Bp7WjhvZ94B6Io8vgDT3LrDXew65ndpNMyrR0tXPzqC2lsqufhex9jbHgCj9fNaeeewgWXnoOObi4QR0ZGgPxC3qcefRZVVXE4HDgcDqYnAoyPTKDrOgP7B7n+F7/n5utu41tXf4XNR5fWdnbrCVtobG4whe2CKCCnZSIzUc6/9Bw83vJ0mT11Pl76H69j34PPM/jcAeKpBN3Hb+SkF59RdTkjAMHhKXb88jYSoRh1HY0gCITHg9x/zR34Gmto27L0cLJkMsnAwIBJLAxiVVdXZ450GqFrBmkVBCHLersayVihMMTrh4lFZmS8UGLx1FNP8cEPfhBFUfjhD3/IOeecM+c1LS0tvO51r+N1r3tdqXd7zWD1XjklwlKD3lYCRuhRMplEFEU6OzurmhRZLXMrKdyOxWIMDQ1hs9no6+tb1cLFpcBKrHp7e5c0Luc44xSSt92NHgqDEdiWSCDAklyLbDZb1kx0rqWkzWYzxxTyVQ9zIcx+p4LXC5KEPhXIWqQXRSwgm1iYf1kBdrHc8Su3G206SPyPf8H/gXfNblI3wxFbW1sXNKdwe1z8x5c/yB+uvZmH/v4oiqJwytkn8uq3vMLsKhjbfOwfT3LfnQ8QDUfZevwWLnr5eaaewMDTjzyLgJDlMuXze/DX+ujq6+SM806lpb2ZMy88DX+tn4d3PMbPv3kV4yMTpNMy6Doz0zMc2NOP3W6ntbOZhqZ6QsEwN1/3FybHJnnLFW+gqakJVIGJwWnsThter5fJyUlGhkd4/vFdTI1NMz4yAQhmQKRBLAAkUUKSROLRGD/48k/5/nXfWN73kAN/jY83X/E6rv7BbxnqH0EgU2zZvG0jr3rzZSV9r1y4fG66T9uM2OkpWGO1UjjwyC6iU2FaNh8aH6vvamJy7yh7H3h+yeQikUgwODg4h1hYYbXeztX8GJ1Wl8uV5YhXzc9bK6zEYrWL95cCTdPMgMhiiovPPfcc73//+0mlUnznO9/hggsKz/05jGyse3KxWpBOp+nv7zc7FpFIpOpvdEbnopLEwhAue71eOjs7q7JaV07E43HzobocYmXbcgTuN7yaxA03o01OgwCCzYbj3LNwXji3klMMci0lE4mEmacxMzNjVg+N+fp836Hj9JOI//o69HAEobYm08EIhTMWrrOvKfgsy6O5KDd0QJBEUJf5vsEZAFJ/vA37pg04LrmQoaEhYrEYnZ2d1NQsPtdf31jH2z/8Nt76vjehqiou91x74t/85P/4w7U3o8yK3x+59zHu/NPf+MIPP2OKtyEzDqTnIWWiKLJhcy///L43mj9LJVPccv2thGcyeQIerxtJkpDTMrFIjLSUwm7voLa+FlUJMjI1yg2/+gPPP7UTXdPZ98IB0qk0kk3iiK0bednrXsq3PvM9ouFY1vs6HDYES6FDEECURHMk84UndxKYCtLQVF/YZ14gTj7rRLr6Onn8gaeIhqJ09rZz4pkn4PaU1/56ZmaG0dFRampq6OjoqOrnRHQ6jChlj8YKgoDNZSM8UZieKheGQYuR9VTIM0AQBFwuFy6Xy+y0GkQjEAgwNTVlFkAMzU+1ErbDxCJDLOLxON3d3QUTi127dvG+972PeDzOt771LV7ykpeUeU/XNg6Ti1WAaDTK4OAgkiSxceNGYrEYoVD+UZBqgvHAkGXZHIMqp3DbGAMpRri8lhAKhRgdHcXtdtPV1bVsYuW+/DIcp5xA+tEnQVawHbMV21FHIJTwoZpPp5Fv5CU32VlsqMf77n8h9pNfoc+EMgtHy4RRUd/8CnhaKOg4RGn55MKAqhL9wc+JawqJzRvo6enB6y1sfM2A3WHHzlwtwIHd/dz8278giiINzQ2zb6cyMjDK7355Ex/4ryvM155+3ik8cu9jJOJJcxGdiCUQRZHTzjsFyCSF33DVTTz490cYH5lAstmoRed8CXxCmgM2gR2ahmi3MTM9g9PlZN8L+1FVlVQyxSPbH0dRFARRwFfjw2Gz8dwTO3n0vifMLrQRjqppGtOTQewW731Rkma7qpgNKrWEQY1yWubZx19gZnqG9u5WXnr5RRVbiAYCAcbHx+dobKoVde0NaJpuFp4gcy+XEzIN3YU5oVlhFFcMu/Gl3gNtNps5PqXretb4lFEAKYdl8nKx3omFrusMDw+bxKLQe+CePXu44ooriEQifP3rX+fSSy8t856ufax7clHtN99AIMDIyEhWWng8HjedL6p1/w3bP4CDBw8WPVtfDIycj2g0WrRweS3AarVb6kWF1N2Ju7uzJNtaDLnVQ8O7PhKJMDExwfj4OE6n0+xquC48G9uRG0nd+yCp6QDJx57Etn/g0LEXShryvE4A9GXqIeZ9OzILXKGhDn1ikixWtJztyjLSndvpvuAcPEUSi3xQZIXbf38n1/38BqYnArg8LuwOO06XE0mSsDsdPPj3h/n3z7zbvNbPfvGZPPHgU/zj7oeIhKIA2GwS57zkLM684HTCMxG+8rFvMHhgCKfLCTocpyX5YJ2IDQUNkCR4WYPA1+KZ0LLh/hFUTUMQBdIp2eyM6JpOZCZCNBRFkqRDxELMfP+CIMwmLGtccOm5/PUPd4M+Kx5PpU1C2tHdRmNLaXKNhg6O8OMrf8bggSFUVcNut7H1hKN436feRU1dft1TfCbKdP84gijS2NOCu7b47y4VS/LsjseZHJmgY3M3rZvLaHVbQmw49She+NuTTO0bxd9ajyAKRMZn8DXVsLlIQXcsFmNwcBC32013d3fJCJ1BJLxeLy0tLaTT6byWyQbRqHTivIHDxCJDLGKxGF1dXQUTi/379/Pe976XmZkZrrzySl75yleWeU/XB9a9Fa2maciyvNK7MQeGhWogEJiTFj4zM8PQ0BBbt26tyrEf6xhUNBo1LSWtdpLGbP1yb8KyLJvWvJ2dnfh8vhIdxerAerHaVVXVPI+i0ah5Lvl8PkRRJBAIUHvHDhw3/SUz92J8BgUkxM8HHUpOLlR0RAREYwZ8GftnhTkKZpOwHXkEzpddhOvlL0HIc3/Q4wlSOx5AHRxGqK/Dcfw2xI52RG/2+MCPvvIz7rrlHpKJJMl4RpMiSRL1zXWgZ+xkPV4P1/39qqyFnKqqPPHAUzz50NMgCJxw2rGceObxSJLEn6+/jau+/xsamxuQbBLxkXG+IkWwC6DN9puMs/eBNNza1svUeABRFAgFQrNjlgtbBQuikCERmf8B4Ks//wJPPvQ0v/vlTXP+tqG5nnd98l844bTjljXyoqoqX/jAV9nzwj6aWxszwWuJJIGpIOe/7Bze8/F3zPmbfQ88z86/P0l81uLUW+9n24tPou/kIwt+39GdA9z27d8RGJ5CkiScbhe9Jx7BRf/+Klz+6jeymNw/ysO/287k/hF0oLGrmZNfew6dR/cVvA0jx8Xj8dDV1VWxTpGqqibRMO5LlUycN2DkXRkBgeuVWEQikaIMTPr7+3nnO9/J1NQUn//853nTm95U5j1dP1j3nYtqhKIoDA4OEovF8qaFW9vH1YZcfUVtbS21tbVma9mYrQ8Gg8u+CScSiazEXZervLPM1QZD4L8erHYlScrSaRjnUigUMivW+onHwO13QyI1SwqMGak8nYwCdBbFdi8KCc8TmZ0vLxGpMN4X471FEXV8gtiv/hdlYAjfu/8lE4I4C3nnHsKf/jL6TChzXJpOHBB8Xhznn433Xf+MWFfLgd39bL/9PpwuJ16fh9GhcSBzzk2NTc++r46qavzh2ptpbGkkMBmke0MnJ55xPKecfRKnnH3SnH3dt+sAuq4j2SQSsQRHp2PYPaBZjkRHQBTgDKfAdoeNkWTKPEiHy0kqsbCrl67N/b62334fp593Mv46P4qsoCoqbq+bppYGZgIhHr7ncTYfvSlr5KXYbJY9z+2lf98Ajc0NOGY/c7fHjc8v89j9TzITCFHXcOgandg3wjN3PIpkk2jZ1ImOTmg0wNO3PkxNaz0NXc2Lvmc6keK279zA9NAkjT2teDxuUrEk+x54ntrWes7+t0sK2veVRPPGdi79xBsIjwfRVI3a9oaingWRSITh4WFTZ1dJLYQkSVnPOEM/Zk2cN5zM/H5/WcanDhML3bRcL4ZYDA0N8Z73vIepqSk+85nPHCYWJca6JxfVVuU17PNUVaWvry9vJd64eVaby9VCwm1ra7m1tZVkMmkSDeMmbLSV/X7/oh2ZcDjMyMiIOVu7mi0Dl4J0Os3g4CCKohQ1W7oWYDywDWJRX1+PJElEnU6044/G+cRzoGuIogToCB433g++G7V/kMS1N0A6XbCAO0MwWNQ4SgdSuoZLWMTlajnBefO8L1hIjSRluFA4Sur3f0Z+6HFcl12M+3WvRJsOEPrwZyCemLudaIzUXX9HGx6h5ptfZOfTu0il0jQ2Z0TO/hpfRng9S7REUcButyNJIj/+6i9wuV3YbJn37tnUxee+/2maWxvnvI+vJnM/U1WVidFJTpJ09HmcuWyiwIc+8Xau/c2t7Hx6F+FgBE3P3FuMAM5D/FGYt9ji9rh44oGnUGQFl9tJ66ZsB6JUKs1I/yibNm0yR14ikUhWNkshgWuRUBRZVnA4sxeQTqeDWCxONBzLIhcjz/cjx1M0HNmVOQYE6jubGNs1yNiuwUXJha7rPL39UYJDkzR1t+D2ZFzhJIcNm9POrnuf4bQ3XFCyQLpywsiSKBbhcJjh4WH8fv+Kuyda9WOtra1Z59L4+HjWWKfP58Ptdpekc9/f3w+wbonF6Ogo4XCYzs7OgonFyMgI73nPexgfH+fjH/84b3vb28q8p+sP62tFVuWIRCIMDg5it9vZtGnTvDeKaiMXhv5D07SCHKEEQcDtduN2u/OGrY2Ojpoi3txqj1VfUFNTQ3t7e9W6dpQLhs2iKIr09fWtS/9ywxGpvb2duro6AJqbm0n/18eI/N8fUe65DzUSRaurgRo/2p33ICaSoChFv5+AgC7oixIMxyLEYlmJ3HmQ1xUrkcz8I2QW7PpMiMT1f0BPJFEPHMxLLExINpRde5Effwqn25lZ8ms6oiTS0FxPKpkiEU+CALX1tfhrfIyPTqCpGpqmkk6ppFJpnnv8Bd71yn/ns9/6+JzuxVkXns7dt/ydqfFp0qk0+1wiQlbfwugywbQg0b1lE+//7Hv45bev4aEdjzIzPZOXRHi8bhRFJZ1OZ3UuPF43fZt7mRybYnJ0CnSyxMOQSYdu6MssTJ1OJzabDSWl4vf6kewSzzz2HA/veJRIKErXhk7OvvhMunu75ox1dvV14va6iYZjWfqKSDhKfXM9zW3ZZCsZTSA58likShKpaHL+74lDYyDBySCSZMPlcaMqKtP9E0QDYdSUjCCJPP6n+znt9ecjSmvvHmkkz1erK5bD4aChoYGGhgZzrNNqv2107o1RvGJHnK3EoqenZ90Si1AoVLAzHsD4+Djvec97GBkZ4SMf+QjveMfcccXDWD4Ok4sqgLFgHhsbw+/3L+r0U03kwkoqgCU5QlnD1qwiXqPa43K5zNGpQCBAOBxe0/qChWCMALhcLrq6utZdx8YYGUyn03R3d8/p7Dm8Xhrf/hb0f3szkZ9fS/rPd6BPTKHLCloqbb6u2LMmQzBAnj3P7TnnnbDYNktILApKHTfyZZKZRWripj9DLDbfq0HXM9a4ioay7yAnX/Jiaur8zARD1DfUIYhCZuxLEPDX+mhoriceS6DICjq6qcnIbEpnZjrEZ9/7JT7z7f/knIvPMn+39fgtvPFd/8Q1P/wtmqbxbAKec8I2R2ZkzBwtE+CmtJ2z7nuc8192Dh/4r/fQ9qsW/vibPzMTDBlzVNgddhqa6mfF3mk2dm5gYN8gXr8Hn9+L23OoOlzfVEc8FmdqbIqGlgYkUSISjgI6F1x6LgAP/v1hrv3x9QzsG0KWZdxuF4KYye0QgGcffYFHdjzOm9/3T7R1tWYFrrV3t3HWBadx95+3I6dlnG4n8WgcgJe99uKMgN2Chq5mBh7fg6Zq5uJfU1Q0TaW2ff4qvtVq88iTtrL/9qdJhOOExgJEp8ImYRElkcduuhdPrZfjLj19/u9+FcKw210trli5Y52JRMLUaRjOj/lc8ebDYWKhMzY2RigUoqOjo2BiMTk5yXve8x6Ghob493//d6644orF/+gwloT1tTLJg5W+KWmaxsjICDMzMzQ1NRVkoWr8fqXJhTEGdWhUYvnVMbvdboatGWK5SCTC1NQUk5OTAGalZ73BsJn0+/1VncxeLhjhgMZs8UIaG3X3PpTb7ka02xDqalAnpsAyOpN3gS6JGS2GKIKiztFFCIBdFIHFuxiH/ihbpLxcFJ3hkZYz+5COLPw6gcxIlSwj1tdRW1/Dez7+Dn781Z8RmAqa2gZREs3RHk3VTGlLvmyLZCLJ1z/xHTZt2UBHT7v581e+6VI2b9vEZ9/7RVRF5duRGP/kgfNd4BJ0BhW4FTdPaCJHTQbQdZ3rf34jN117C+FgOOs9JEkklUrjcNi55LUX09XbyVXfu5b6hjrsjkzHU07LIGRscls7W/jFt65meiKANutA1dXXyfTENH+67i/89n9+x/REYPYjEQjGZ0CAlrZmOnrbUVWNseFxHvnbE/zLh99MNBrNskx++ZsvwV/n4747HyQejdPW1cpLL38xF778vDmfT9exGxh4ch/je4bwNdWCrhOdDtOysYOObb1zXg/ZOivDEeeIs47m6VsfIjwRxGa3ocoKol2idXMXalrh2b8+yrYXn4TNUR12qcuFkWVUV1dHW1tb2Z7hmqoxsXeYRDhObVs99V35U+6LRa79tizLZufecMVzOBzmKF7u+FTuKFS12OBWCrquMz4+zszMDO3t7QVrDQOBAFdccQX9/f28+93v5gMf+ECZ93R9Y927RUFm0bISMKzjkskknZ2d5nhHIX+3a9cuenp6CmbspUYlg/FSqZRpsefxeEgkEqiqiiRJ5uiU1+tdcaJYLhg302AwSGNjI83NpXnIrSbE43GGhoaQJImenp5FH6jx3/6exHW/R6irRRAE1NFxkJVDWgtRRNe1zMLYJoHdjvLql+G66Fy8rS1E/u0DMG0J8RIAy7iNnv0/ORDM1X/Wt7TMsL6iiYXxnoUIyJ1OEEXEWj91v/weoj/TERobHuf+ux4kHAzj8Xv4w7W3EIvE8NV4UdIKUxOBQ8Qi56MQhMzn0Lupm74jejnlnJN46asvMhf91/38Bv5w7Z8JTAVQ0kqG3wCaJGL0ed7zibfT2FzPdz73o6xwvENvAi99dSZHIjQTAXR2Pr2beDSO0+3E5XKhaSpHHrOZT33jY/j8XuLROI8/+BQ3//YvDOwbMj/QyEyEVCo9a4nsRFYUkrNjYA6Hg41b+nB73IRnIiDA9377dXx+b1a31bAJFxCwiTZa21vx+X3zXq/hyRn23vccY7sHEQSB9q09HHHW0fga597XVVVlYGDA7NoZ4WBySubO7/2eR2+8F5vDhtPror6jCV9TLcloglQ8yRu+8W5qWkobErgSMAos5c4ymhkNcO8vb2V87whKSsbpc7HxtKM4660XY3eVr0ugaVqWK56qqllJ4g6Hg+HhYWD9Egsjz6qtrY36+sLO6ZmZGd7znvewe/du/vVf/5VPfvKT6+4ZWmms+84FLCwGLBeMFFFd19mwYUPBKZKw8m5R1o5FuYlFLBZjaGgIm81mCtbypTobN2CDaFSjRe9SYM3wKOZmupZgiPeLCgfUNLKW4Q5HhlxYfm/8VhBEqK9DOfNkxlUZDhygcSaEaL2+5uEQ8y318/600sQCmPVtnbMdA4Ll/wh1Nfg/9WGTWAC0dbZy+VtfwU3X3Myf/vfPxCIxUsk0sqzgr/HR0dPG5OgU6XS2nbcoiWhqhrwd3DNA/95Btt9+H9f+6Do+/tWPcPypx/CGd7yWlrZmrv7hbxnYN5jZN7sNSQBFUREEuP7nN5JOyXOIhWnipcO9dz6A2+MiFo1nyMDs8SQTKfQ6nbf++5u45LUX4/Nnup0en4dELMHQwRHqm+pwe93ouk5gMogiK5nxJYMczt7bVFWdDQZ0z763bt5/rd1Ww37b+GdoeChrtt6wTjZQ01zHSZe/CDklIwjM211QFIWBgQEURaGnpwe3223+zu60c8LLz6T/ib14a704fYe0IOlYEqfXtSosaRfD9PQ0ExMTNDQ00NLSUrbnjqqo3PvLWxl+9iB1nU3Y3Q4SoRgv/O0J3LVeTnv9+WV5X8g8242Cma7rJJNJ81wyOmSCINDQ0DBry1y9WVelhpVYGHlWhSAcDvO+972P3bt385a3vOUwsagQDpOLFUAoFGJoaAiXy1VQFTYXK6m5MPQVlehYGO1vw2LQWFTmS3U2iEY4HDZ/b9ykV6suQZZlhoaG5tUXrAcsdRTMfsKxJG68JSNu9rgR/V60BcTM3n95A00nHI+qqoR+fg2qqs2/EAdzxKroLoKm5bfGXQBLJhY572Ex5800YgCRTDYGgOhxk0okCI1PU9dQi82euW5uue5WrvnhdQhCZmHucDpIJpJ0b+ziyp9+jqu+95us/AhREufYwQqigKZqjA9P8Ml3/jdbj9/C69/+Gi56xfkk4gl+8v9+gTxrEasqmrnrgclg3u/cuvV0Kk1jc4M5zmR9QSQURVVU/DXZ185D2x/NGEt4M4t0QRBwupykU+lZJyq7eb/J+FkJiKKApmlEQlFOftEJc7YJEI8l2Pf8QSRJ5MhjjkDTNbOrYbUmNWbrjXu/3Tn/MyA3HC2fgUPblm46juph8Kl91AoidreTRChGMpbg2MtOw+FeOdMH47xYzrNicnKSqampinRux3cPMb5nmLquJvNz89T5UFIye+5/jhNecWZFPk+r8Ultba1pN+t0OgkEAkxPT2O3281zqRS5UdUKXdeZnJw0iUWuPf98iEQivP/97+eFF17g9a9/Pf/1X/+1Zj+jasPqXHWtUhgXyMTEBLW1tUv25BaEjFd+JclFKYTbxbyXUaFYrP1tTXVubm7Osv8bGxtjbGwMt9ttEo3VInxLJpMMDg4CrMsMD+s5sJRKpe3oLTgvOofUndvRk6nMgt5my4wHGWVvmw38PpBl5Acfw/2yFyNqGux4MBM+N+ssZSxkzQWtICAspWtoXK+CAB43yDK4XTATnvdPconFvA2URZDv72bVIyh2B3aHg9TBQQKf+BLfSLlI1Ndz+T+/gktf9xL++L9/BnRq6jKjOg6HHZtNYt/OA+x+di/v/eQ78df5+dW3r8lU9GeD7rIOXT10r1JkhcEDQ/zP136Bx+fG4XTidDlp62ojMBEgPBNGlKSMVoL8eg7rjzxeN4GpwNzXkDmP/vK723nju/4p6+fJRHKOg1JDcz3RcGYURVVUQDhkeytAIp4kHkvQ1NbIP/3rq+e8110338ONv/4joUAYQYCm1kbe9v43c8rZJ5nJzsa4S26ys9/vx+l0zjnH0+k0/f39ZpbPvA6Cksj5V7yCHT/7C6M7BwhPzeDyujn2ktM46dVn5/2bckDTNPbt28eePXs4ePAg/f39pFIpRFGktbWVjRs30tfXxzHHHJPVfZkPxjNzenqa5uZmmpqayn4MiXAcJS3PGX9yeJyk4ylS0WRFyZr1HOjr68Nut5vjU9FolHA4TCAQQBRFvF6v2SFbrUW1fJiammJ6epqWlpaCiUUsFuODH/wgzzzzDJdffjlf+MIXyqfP0TR++MMfcsMNNxAOhzn55JP53Oc+R29vfu1UMBjky1/+Mjt27ADgkksu4VOf+lRREyzVjrVz9i0DlRiLMhw+wuEwLS0ty66+iKJYMXJRDuH2fLCOARVToTBgtf9TFMUkGgapczqdJtHI9zCvBkSjUYaHh7Hb7XR3d6+7uVpN00zv8qWcAzCbq/K+t2M/dhvpHf9Ai8ZAB/nRJw8RDE2DcAQ8bpTnd6GnZfRkEj2RBNGS0WLZrg5ofi9KWwv2fQcRVA0NUAQRh67Nef28SKcRamsyHRBJBHXutVwIsTB+Xsh7pgFZkHCgIek6IhBGwKZqpBJJZFnFL8B/OBIMhFM88oNfEZ4OEgqGceUspuwOO7FogsEDwxx36jG85T2vJxlP8sff3IKqZEaIFoK/1k88GufWG+7gik+8A6/fSzgYIh6LZ8bULBAFAXUBBX1tfQ1jwxPz/n4mEJrzs+NPPZa9L+xHVVSk2c6N1+/B7XWTSqSIxzJdLkEQqKnzc/RJ23B7XByxdSMXXHou7d1tWdt7+tFnufZH16EoKg3N9ZlF8dg0//O1X/L5rla6+jpxOBw0NjbS2NiYlewcCASYmpoyE+f9fj8ej4d0Os3AwEDBOqPa1npe/tm3MLlvhHgoRl1HE3ULuE6VErqu8/jjj3P33Xezc+dO4vE4drsdr9eLzWZD0zR27tzJk08+iSRJdHZ2cu6553LBBRfMu6CyFhhaWlpobJybm1IO1LY14PC6SEbiuGsOGYfEQzFqW+tx11bOTCSXXBrnQO74VCqVmjefxefzVe2zrhBMTU0xNTVFc3NzwedAPB7nwx/+ME8++SQvf/nL+cpXvlLWdcuPf/xjrr/+er761a/S2trKN77xDd71rnfx5z//OW9B4IMf/CCpVIpf//rXhMNhPvOZz/CFL3yBr33ta2Xbx0rjMLmoAIyHRDqdLpkIu1Kdi2LyK5YLWZYZHBxEluWSjAHZbDbq6uqoq6szZ6EjkYj5MLfb7VkP82q4+RoWi7mjYOsFVjecYrzL80GQJJznvwjn+S8CIPy5r2UIhShmnKF0Mv8djWU6CZKIFgpnOgo5GgJzm4AkK9R9/fNEtv8D/X9+DR43NlGEQBAhD0nIC1nJ2L+KIro4l1wsaxRqHtiApCgQxka9KqMBdkCSZTRNo0bIdDP8wJGSxmZJ48mb/oTL6SOeSGZZqaqKiigKNM6KhAVB4B0feRtnXXg61/3sBu694x8L7ovL7URTNfr3DmQq/P/+Jq763rXIaSXT/VAOkQl19rNxOO3m7xGgqbWJ+sY6ZqZnFsxxaGieO5t94SvO45F7H2PgwBAOpwNN01DSSsauNhonlUyDriPaJFweF+e/7GxeevmL532Pv992H8lkirbOVvNnze1NpiD+De98bdbrc5OdjcR5IwfBKHjZbDY6OzsLLjAIgkDLEZ2oisrwcwcZfu4A/uY6Oo/uM0lUqREMBvnjH//I9u3bUVWVjo4OfL75RezpdJqxsTGuvfZaHnvsMV7/+tdz1FFHZb3GamKx1ALDUtHY20LfyUeya/vTKEl5dsQsiq5qHPOSU7DlySYpBwxiIYriguTS2r1vamoyi2rRaNTMhDKedcb41GpxGjT237CeLwSpVIqPfvSjPProo7z0pS/la1/7Wlmfo+l0ml/96lf853/+J+edl3GF+853vsM555zDnXfeyWWXXZb1+ieeeIKHH36YW2+9lU2bNgHwxS9+kXe+85189KMfpbW1dc57rEYcJhdlRjweZ2BgAEEQ2LhxY8nGWyrRuaikI5Q1GK4cY0CiKGb5jBst5UgkQjAYNEWXhiC80jdfa/u/3BaL1Qoruezp6Sl5i1gdGjn0H7qe6V6IAigaYnsrejxB5AvfQE+n59+IYS27ex8NF5zNzP/eiJhMoXvc6A5HputhfXm+bcyOXOnxBPi82UJz8hOLxfqqhXQvRMCvKqhk9BYS4EafHfE69A5pHWI6OASB47UUkZOO4/p7nyERT+Byu1BkhWgkRmdPByedecKhYxUEtp1wFPFYIpOLoeXfa6fLgShKJGIJOvs6ALj4VReyedsmPvf+rzB4cBi0zKiPqqrmrimyir/OR98Rvbz3E+/gyGM3s/Op3Xz38z8iGJjJ+16CIHDWhWdkOhG6jseXOacamxv42JUf4odf/imP3f8EsqzQ3t3GTCBEIpYw762SqhHX4c//dzsXXnae6XSVi4nhiTljKMb1OzWRf2TL+jqv14vX60XXdUKhEGNjYwiCgKIoHDhwwBztNByDFro3hCeC3PHd3zO2awhVVpHsEvVdzWw+axvuWh9dx26gvrM040VjY2P87Gc/47nnnqO7u7sgx0OHw0FPTw/pdJpdu3bx/e9/n7e97W2cccYZwKEMg5mZmRUxsRAEgRe97SV46nzsvf85UtEENS31HP2SUzjqghMqsg+FEot8yC2qxeNxk2wEg0HzfDPOp2odnzIE/E1NTTQ3L5xWbyCdTvMf//EfPPTQQ1x44YV861vfKvvx7dy5k1gsZp6/ADU1NWzbto1HHnlkDrl49NFHaW5uNokFwGmnnYYgCDz22GNceumlZd3fSqE6z6oKo1yLuGAwaLrc9PT0lPQkF0WxrKNclRRuG25AlQqGEwTBrOK0traSTCZNQbghujSIhs/nK3v3wDoGZMyUrjdiYWhMjLnicqSO66l0pkORTM3ays5eP5KEbcsRpLffjzY2AXW1MD6ZX3AtSQh2e8a2ta4W91v+ifhVv0WIxDIdEeO9cv4Ns/cZXc90TbCBrKAHgtatz9uxEFicYCwE69+LQASRWksytjWNIzlrsZvWdOySwMUb2xj11fGPvz3ITCCEJEn0burhP6/8EA5ndstfVVWGDgwvuLOiJLL3+b1oms6BXQf5w7W38JLXXMiu/Tup6XGj7dPMbeXiiK2b+OB/X4HNZjOD6k4/71SGDg6jpLNJmiAItHW2MD4ywfv+6SMAHHPSVl73b5fTe0QPv/7+/7Ljr/ebXYKBfYNZ91RhNvgwGVcZOjhMcOr/s3fe8XGU5/b/zszubF/13twwLmCMMRjTsU0NJZCeQNolIYWSEBJCID9CQggp5EJCQk0CCZBLKoQSbKrBBgwYDMbG3Vbv0vYy9ffHaNYrWbIlW5IlS+fz8b1B2vLuaHbmPc/znHNCFJf3v8mpnl7Jh+9tpqWxFSWlILtkArkBBATK+4xQ7Q3xeDyjE6uqqurVcbVHO+0MhP5EvKZpsvK+p2l4fyc5ZfnIHhftO5vZ8sr77HxzE4GiHDw5Po656CQWXHTSAV1nuru7M8Ri1qxZQx7flGWZWbNmsWvXLh588EFcLhfz58/PpC6XlZUN2p59uOHyuTn+M0s4+oLFpOMpvLmBMdexGAyybWxN0+yl+7HHp/al+zkY6Orqoq2tjYKCgkF3LFRV5Xvf+x6rV6/mlFNO4c477xyVkWJbP1VWVtbr58XFxZljnI3W1tY9HivLMrm5uf0+frxiklyMAOzKS2dnJ3l5eZSVlQ17JXykOhe2xaJ9Yx9p4bbd9gwGgyNynPaFbEeOvs5TtvWfLZILBALDfrHSNI2GhoZM1snByi05mLDthkdaY+KcOwtl1RtQXGi5SBkGSCKmbuCcNwf1vQ3WKIyu0+83SxAskbdpknxyOcqb7+BacjKBG79D6tkXMFraMd0ujC3bLS1HD/YgGuk0giiB7IS03vt37N8o1GCeI3g9KIaJkE7jNU1UhwOnYWTZ9pp7dEBEUcCfm8O1V3+V+p0Xs3NLLbn5OcxdMLtf0i1JEoK4dyqUjKesTU/ARzqtcPcv7ufnP/85dV07ME2TaZ45SIIj8xIGOoap4xRkNq3fzBWfvDazWtklE48mSKcs0bCJZb1rOfCaxONJ3ntzPf6e2fk3Xn6LTe9vYen5p/HM35cDu92M+hZrTNNEVTUEQSARS+L2DtxNFUSR7q4Qhm5YpEQU6GjrpKK6jJPOWLyPv4yFSCRCY2Mjfr8/Y/YhimKvKnR/It7sjmuosZOGD3YRKMpB9rhIdMeItoeRnBImJsHSPJRYijf/tpKSwyqpPHLqoNbWF6Zp8q9//Wu/iYUNu5iwbds2Hn30URwOBw6Hg/Ly8szIWNOHdex8cxOxzgiFU0qYsXguueWjo79w+Ty4fPsWng8XhpNY9IUgCLhcLlwuV2Z8ys7U6Kv7sYNqD8b4VHd3N62treTn5w9am6ppGj/4wQ9YuXIlJ5xwAnfdddeombckk5Y+q+/7uVyuTPp638f3tzaXy3XQMtdGApPkYpih6zr19fXEYjHKyspGrAo9EuRitIXbLS0thMPhzDzlWKiYZF98s5NTW1tbaW1txe129xKEHwhsLY5tMTkY95RDDeFwmKamplHRmHgu/gjquvWYsTiC24VpGKBqOGfNQD7uGPTaBktgbZj9twrsTaiioK19D00QSD//Cr7LPkfw/31398NUFeXd9aQefwZ17XsI9qhUzyiWCWg5fnA6EZNphB4iMtxnfy/CIkmIPi9uO01bNxByApiCQLS9G9k0kLOD/0xwiSDJMvLxxwBQNbWSqqmV+3zf8qpSWhvbkCQx48VvQ5JEArkBAjkBkskEra1tmJqBgJOAL4Db68ZIq0haj+00AhIOJEECQyAeiVtuXT1HS5SSmQ9qmiZO2dlDClQwIdIdQRAFdE2nqLSQiB5l55ZaHvzNw4M/jqaJ5JCQBtB1vPHSmzz+8JNWgrkoYpgGGCaSw4HL4yKYt++Cga21CgaDlJeX93stHCgDwbbgBkg0hVGSKdxBa/wr1hXB1A0cHhktrWJoBoHiXDp2trDt9Y043U52vLmJeFesZ+M+x0oL3wfWrl3LK6+8MizFAEEQmDp1Ku+88w6PP/44V111VSZ1edPL7/HmYy+jJFI4XE7q1m1j+xsfcvrXzqd4evkBve9Yw0gSi/7gcDj20P3Y55Ot+8l2nxqNLkAoFKKlpYW8vLxBOwTqus4Pf/hDnn/+eY477jh+//vfj0jneyDYI9yKovQa506n0/3e091uN0o/o7fpdHrSLepQw3Btau0kaVVVqampIRAIDMvr9ofhJhejKdzOrtbbFaqxCKfTmXGest1dotEoHR0dtLe3I8ty5mbvdruHdMyyE6enTJkybixyhwvZXaucnBzKyspGnFw6Dp9B4Kbvknzs32gbtyC43bhOWYznMxcjuGRcp55I6on/YsQTe30dIS8X0WltYs1ojMRDjyGfcBxmIkny8f+ivvcBot+HY+4stK07MHUDTMMiF5qO4HEje30YhoEum3u4Qe1xFByS9bw+j+v3sT2/V00QMbG36JogIAMoactm1+OGVBrR78OQRGKaTp5kvZ5bAFmwdBnGslORZk7v5116I51W0DUdr8/D4iWL2LhuM5qmISKCAA6nAwEBw9DJzc+hvb2D7u4uTBNEUcKBA6/bjynopJ0JnJobgezNvLA7tLCHIAmClZ2RfRA0VcUwzV4HyTRMQl1hopFYpqsgDLFwoqoqj973d044fRFzjp6FIAiEusIoqTQP3/s3Usk0Hp8bUZQscbiqIYgCsXCMbRu3M/fo2QO+tp3lMhStVXbHtaioKFMI6TAEBFki1NaJryCIklJAAEO1XLFkT891RhRo2rCTnW9uIhVNIDoktqxaz+aV77HsqospqC4e8L0Nw+C5557DNM1hGVuyNXAFBQVs376dUChETk4OiVCMdf95HUwT2esi3NyFrunEOiK89feVnHvdp1ESKTa/sp6OXS14c3zMPPlI8qsGXvtYhb13sDWHo62DyNb9lJSUZNynsm2TbadFv98/5PvdYGAT7Nzc3EGnrxuGwc0338yzzz7LggULuOeee0a9SGePOLW1tVFdXZ35eVtb2x5GBQClpaU8//zzvX6mKAqhUOiQEXPDJLkYNsRiMerr65EkienTp484cx5Ot6jRFG6n02nq6+sxDGNERLsjhWx3F3s8wa7wdHZ29rKR9Pl8ez2G4XCY5ubmoSVOH0LIFmyOdtfKOedwnDd/H1NRQRKtjXYPpKpyfFd9hfjdf+pNMDJx0D1IK+B0Wmv2+zAjEVIrXiL1n+UY3SEEQDdM1HUfWHviokJrM6soGB2dkFYwNc0avZJEhIAPovE9x6ccDsSpVTjy8zBaO9BbWkHVEPrRIljr3P1kwwSnIGR+JqkaenuntQ/3eXHOORx13QeYkSiSruMULa3FprRJ3ISkCW8qcFJRBZ/Yy9+mrbmdR+5+jDdWvoWhG8w+6nA+8smzqZhSTltTG07ZiSiJKGkVp9OJqqq0trQRCluVUUkSEOxZLKGnMyQYPQNaRqZD0YtB9HymTDpxVueiXxlaz+OzbWcdQ3RO0lWd5x5/kVdXvMbS80+jramddWvep7szRDKetOxAU1blUpIkRFFESaVR+4j1+8IuVBxo6nR2IaTjoyfz5mMvkeiMggBqWkV0GOSWF2D2fBZD1elq6MAd8FI8owKwskjadzTzzr9XccbVFw/4Xlu3bmXLli2Ulx9458A0TSKRCKqqUllZyZYtW3jrrbeoqamhbXsTsc4w6XiKUFNHxoNB1wzW//dNZi2Zz6o/PEvbjqbMOfD2P17ljKsv5vBT5x3w2kYLB5tY9Ae7gz+QbfLeUuf3B/Y9cSgE2zRNfvrTn/Lkk08yb9487rvvPny+0bMJtjFr1iz8fj9r1qzJkItIJMLGjRu55JJL9nj8sccey69+9Stqa2szORhr1qwBYMGCBaO38BHGwT+LDwF0dnbS3NyM3++nqqpqVDaLw9W5GE3hdnZ+Q3V19bit1vcdT0gmkxmdRigU6jUHnX3hHQsak4ON7ByTgynYFLJcf0xNR12/EbM7hFRdSc49t9P9ua+BPf9qGCA5IJ2ySEavzb21uVVeewujs8tygdJ7uhR2MrGqgstlhfb12M7q8TggICkKaLpFVjwuiMYtzYAoYBTmEznzVNSj5uL3ePBt3Ylj41aMhibUd9+3SE427IJ9z0hT9hJ7tUVUFe3d9TiPnofW0Ym5aTsdpsizMZ3n41aCuBUDYrLz7seYf/xRHDZnz+5FLBrnR1fdSv32BmS3jCAKvPP6e2zduJ1vXP8Vnv/PS3z4/mYwTWbOmc7nvv4pfv2j37Brq+WeJwoCmCIiIjoasuZBdaQQDckqnmCF1wmmgGj2uVXZBKMnPXuvf2uETBCfFY5nBeJJktSvaLwvREnE6/NSVlVKZ1snf733b7i9bpLxFJqqZca+NFUjaSTx+DxWwcYwyCvIZUY/xy7bHW64Cfbxn16CN+Bj/bNvEm0Po6dVQEB0inQ1daAkUnhz/BiqTrAkt9fn9BcGadpYSzIcHzDPYcuWLSSTSXySG6MjBbKEEHAMef22M5amaQSDwYyo9e233+biiy9GEEXS8TShpk4kpzMjqNbSKulYiufu+BeR1m5ySgssTYlpEmnt4sXfP0H10TPwBMd+4WosEou+6GubnEwm+02dt4nGUO/rtqFLTk7OkIjFz3/+c/71r38xZ84cHnjggRGdFNkbZFnmkksu4Ve/+hX5+flUVFTwy1/+ktLSUs444wx0Xaerqysz4XDUUUexYMECvv3tb/OjH/2IRCLBTTfdxEc/+tHJzsWhhv29qJumSXNzM11dXRQUFIyqfeiBukWNpnAbLJFWS0vLIZffYF9YvV7vHoLwSCTSa27V7naMJY3JaELTNOrr60mn08OSYzIsa6prIPaL36LXN4GuIcgyzgVH4ZgxFX3rdsQe8mOaJkZbu9V1kERLryEKmIk4uF3odY2WCxVY5dXsLkI8iehyWcTC68WMxhCi8d5bYl238jV6IEgSUiRK7l/+ielyEz1qDi0VJVBRgtfjwbf+Q/j1Pf2G7w0I+3KhapiShPbhZgIP3Mn1X/4h27fUYugmgq2T6HnZSHeEr110FS6Pm2BugNnzDueSb3yKaYdP5bH7/8G2jTtwuWVUVbXcmgTo6ujm7VXvcPNdN9DZ1oWmaRSVFtLZ2cmm1g8QDRmX5Ok19iSaEi7Vh1PzkHbGe6Vym0LvMafdB6nnM2VaHwN87D7XSYfTgVN2YuiWHsThlJh79GzqdzTS0dbZ72vkFuTQ1dFNZ3s3qaSV+K7rOm6Pi1QqnSEtuq6TSiQxTSvL40tXX4Krj6NWdobDSITDiZLI/AsWc+Q5x5KOp0iE46x78nVq396CiUnZyUfgLvTz/j9fIxqN4HA6cTqdOBxOBuNJtmPbdgq63CgtbZiqgSAJCAUunHNyETyD21IYhkEkEkHTNHJycjLz/IFAgFAoREdHB6UzKywPhbSKy2fNspumia7qOD0ybVsbya0oRHL2DP8JAoGiXMIt3exau4XZw2wbG27povadraTjKfKriqk+egZO1+B1CIZusOvtzex8ewupWILCaWW4qgIEinLHLLHoi773O0VRMl0NW5focrkyRMPj8ezdNrnHxMAutg2WWNx+++089thjHH744fzxj3886KPVV111FZqmceONN5JKpTj22GP5wx/+gCzLNDQ0sHTpUn72s59ZpFkQuOuuu7j55pv5whe+gMvlyiR0H0oY+2fzGIW9UUokEpSXl49qyA8cWOeir3BbEIQRdYSyU1bz8vIGPUs5HpEdZlRUVJSx/YtGo5m5VVmWEUURVVXHbedmfzAWxeumphH7+W/QaxsQgn6rs5BKo7zxNo55c0ByYIQjCF6PtYkXRetfKIIRilgkwu3C84VPkXzwsd2jU31Jf8rqeBiqihmPA/sQb4uC1c3wuDE1Denx/zLlvLPQTJNoQyPqz+/C2FHbq2IvSBI4ndZ7DYSMaMOEVBpdUfnVJddS3xTKbI5Nfc/Npa4bJGIJErEErY2trH7hDaqmVlC/sxElraD07aAAz/xjBZ+5/BOUVpRYRZj6Fh544AEaW+sp9VYh4+lzDARM00A0RWTFgyHoSIYj070wMfpoMHZ/JntcZjCwRrGk3V0lQcDr8/LZr32K3//0PgpLC2msayKdTKMqKiYmoigS6Y6QiCczxZhUImVleQCySyalp6y1GCaaruOQJAqK82luaEVTNRxOR8+hNzNWqyOd4SA5HXhz/Xhz/Sy74qNoioYgWD9PhGI0vL6VdDSJXGgRJNNIEGnspua4mWiisXvsLAumadK4ehv+bgl8IPgcoJkYLUlU3cR57L6LJoZhEA6H0XW9F7EA8Pv9tLS00NbWRvERxdQsOIyOXS2kYlZ2CibIXhmHy0ksre4RniiIIpgmurL3cbShYvvrG1n10AriXZEe3Y5Ixdwall7xUby5+y6SmKbJ2/94hQ3PvwOmgeCQ2L52EznlBXzkO58aF8SiP8iy3EuXaLuZ2ePC2eNTPp+vV1ExGo1miMVAJgZ9YZomv/3tb3nkkUeYPn06Dz744KjnoPQHSZL47ne/y3e/+909fldZWcnmzZt7/aygoIDf/OY3o7W8g4LxeUYfZKRSKWprazEMgylTphyUOb/9JRejqa/QdZ2mpiZisdiop6yOBciyTDAYzLSO8/LyUBQl41dvC+TGkr/4SMAOSBxr4nX1vQ3oDU0IQb+VXQHWhl7X0bbvwvv5T5J6/BmMcBREEcHvwxR6Nv722JMoYIajoOzFQrCn62GagGHuY4iH3RqPZApcLvT6RiI3/xL30lOQnnsZY9tO63EOCdPpAE1HK8gjfM5S8h56DMGej+pPAZ5Zkomg6cxPtfOW10c6lR7U9cQ0QUkrbN+0c6+PUxWV7335h3zrR9/g4d//H7u21dHR0UGVawYyLiy5uIi9UKHHFwrAYTpJSFFceBBNyXroMHhpiaLlVBWPxFANE0EU8Po9eLxu2lvaUVWN4oogOflBErEEDbuaiIQimIZBLBLfI+HaNEzUtIrslpEky+pVN6zQP13XadjVxO9/dj8vPf0Kt973IwI5fhobG4lGowfFyCI7p8Gb6+fYj53CG399kVhzCEmSUBSF3IoCqk88nIaGBkRR7BW2ZmgGm199D7G2x6FLFq0NvyyA4MDoVjBDCkLewHpDm1gYhrEHsQAyo2pqTxfv6AtPYNvrG9EVFYfsxOGWkb0ykbYQBTUlxLtjuPy7q+PJUAyXz0353JphO26JUIzXHn6eVDRBwdRSRFFES6vUvbeD9556g8WXDJzcbqOztpUtr76PN9eHO+glGo2QHygm2Rply8r1FF4y/sdhJEnqFVRr50fFYrGMJas9PiWKIq2trQQCgUETC4B7772XP/3pT0ydOpWHHnpowu0pxhMmycUQEYlEaGhoQJZlpk6detA2SvtDLkaTWGSnLY+VEZjRRnbq+NSpUzMif7vCk+0v7nQ6M0RjX63k8QS7OjVaAYlDgdkdsohC32RlpwMUDXnxsbg/cgZ6bQN6VzexX/wW0eu1Ohk9MMIRUk8ut1K3BxpREkUMQHBICIbe72Z/z8WZVnJ3jyBYXfMO6htvg6L2KtULgCkI6B1d/OcP/+aLYo+Nbl8r3Z7Tqe9bL3RCKzqP5/gJd0cGsbDBo3FXEz/+1m1WV0QATdd6xqEEdOzKcn/nuYBXC5CQw5iiiUNz4dTcPb/p5/EC/VbZ+8LEJBGzhPNWFgck4kly8oIcfsRheH0eYpEYgZwAvoCPQI6fUGco0+E1lD3/vqqqYhg6ep+/vcU9DURBZNP6LTx672Oc+5kzSSQSVFZWHrT58GzMWbaA3IpCtr/xIYlQlMKaEg476QgCRbkZtyA77yfRFeP9R1fRub0FMdlzPDrSiHkyglMEhwBJEzM9sIalL7Ho71qghxWCnQ4+/PdbiI1pph0/mwUfPZH3nnoDLa2gJFKoyTTVR03nqI8cz4r//SfdDR04PTJ6WkWQRBZ+/GTyKweX6DwY1L+3g/YdTRi6SXdjB7LHRW55Ae6Ah+1rPuTYT562z4C99h3NpONp/MW5RKMRBEEkEAwgqtC4fmfmnnyooG9+lKqqmfOpra0t8xin00kikdgjDLI/PPDAA9x7771UV1fz4IMPDjq1exIHB2PnTn8QMdh2XEdHR4ZtH2yXHztVdjA3VRhd4Xb2pnqk0pbHOuxNtcvloqqqqteNtG+FxyYadjCW3Uq2nafG603H1tnY1amx9jmkqgqQZUu4neVPbqbSiLk5iAX5CC4Zx+Ez0F9aZQmo83J7vYbgcWN2dFmBeJrWk/y9GyZgBHyIqoZgGNYPRLEnuG4A9HkNbPF53/EjUbAeaxjIJlwi6rvf1CYY9n8PQGgE4GxSNHv8PBcSDkjH1Re6rhPuipBfnEcimejRUViLExH3IcYW8KhB4u5uRFPM9DYOBKZhoioakiQiORxWoUU3UBSVippyFp60gFdXrEZJK1Z4W13ToI5HX2KRDUO3/uYv//dVFp91LDMOm3FQOt0DoXx2NeWzq/f4uT3eaef9PPmzR+nY3ownz4/Z0o6pGogaGCEFocCFoJkIDmFAzYWu64TDYUzTHJhYtCRQPugiGHfS/kE9qze38uFL77L0iouoPmo6O97chJpWqZhTw2EnHYHL58ZfmMO6J1+j+cN6AkU5zD3jGGYvPXpYj9GWVeuJtIVxumUkh0QyEicVTRAsycMhOy391T4gSpZFcTQaQRQlAoGAVSDUDURpZO/HYwFOp5O8vDxkWSYej+NyuXC73YTD4V5hkNnuU9nH5KGHHuJ3v/sdFRUVPPTQQ5SWDj71fhIHB5PkYhCwHW7C4TBFRUUHZBk4XLA3aoZh7JXkjLZw23Z+GIuV6tGC7Vs/mE21IAiZC2p2KznbiSPbeWo8COGznXDGss5Gmjkd5/wjUN96F1PTEZxOzB59hPvCcxCyhLhCwN/Tndjd6TANAzORsDb5gmARj87uzHNMAKcDSXZZmmOXjJkt+u5v49r35/ZMed/kVtMEo0es3EMmen3T9q5x7vUAAVhKilVeN4l4sv8Awf2EaZpEuiMg2TazZo/jbEaNPfDaTAFZ9eLQ5QMmFjYEQUDXDXTdImqCKNDR2sn9tz/IZdd8gYLifFY99xrbN+9C0/btJGUXefYGwzBQFZXiwuIxRSwGi2QoTvumRnJLCvAEvXTmtpPssIIfTUVHC6eQEBBK3Jj+Pa9P2cQiNze332uYqRpoWyLoioYQlCieWo6WUmnf2cLaf73KWdd8nCkLZ+7xvIHI0XAhEYrRsrkeySkiOSVrNMvlREmk6G7oYNrxs3G69z29kD+tGGSBREeM0mlW+rqaVklFE8xZtmBMXh+HG4lEgvr6enw+H5WVlRlTGjsMMhaL0dTUxP/+7//y4YcfsmjRIk477TRaW1u56667KC0t5aGHHhoWC+RJjDwm3s5viFBVlbq6OlKpFJWVlQfNOrMvsu1NB0J2MB6MvHB7otusZovX98e3vm8wVrYgvKmpCaDXDPRoJKYOFdmC1eLi4hFLqB8OCIKA/zvfIPGnR1FWrQFFQczNwX3+2bgvPKfXY53z5iLm56G3tFpEQ9cxQ2FrFMohWUJqXQeXbDlK2e+hatDZhVhdgePoeaQff2bvXQtBQJxSjRmJYEZj4PVAaIBxpT7ffcOkP8lzz+v2fa61uTdNAQUoQUM0JRxOB6IooGlWVf+AIFhjTLpmWfMKWWsWerVWBlqsiVNzDYpYWILvvRNvySEhOSQrWA4r60IQRHRd56WnX+HIBXP52Bcu5PjTj+MrF1wxKIIlOSR0Tes/XyMLRWVFlJSPr7l6QzeId0WJd4bRNQ3Za+nC8soLiUaiuAwBNBNJENCKnChVIvHubpxOJ7IsZ0aG7Xn7gYgFYOk1UjopVHKNALVrt6Irls3vByveZuHHTqZwysDVatM06djZQqS1G39hkOIZFcNy3emqb0dTNHLLC4m0dqOrOqIkoKsagigytR/C0xepVIruZISZy+ZTv2oz7dubLUM5UaTyyGnMPn14Oy1jEYlEgrq6uky2k7036C8M8qSTTqK2tpYXXniBF154AbA6H+effz6aNrxC/UmMHCbJBQOPRSWTSWprawGYNm3amHC4sZHduegP2foKQRBGdKNvGAbNzc1EIpEJa7Oand8wHOJ1QRAyQUb2aEJf5ym3291LEH6woes6jY2NGQe1g20POBiIfh/+K7+C8cXPYIYjiEWFvToWAGYyZQXrdYcs4pDq7NE9AC4ZIS/XIgLptJVR0fO83d8BE/x+nAvmkX7ivwN0LOiZoTLwfvoijFCYxIN/hXB0cB+kJ5G798+EXlqLPb+RAmkTBEwiBujApy//GAVFBWz9YBumCTNmTyMRS/DSM69Qu63e6oDaTrt72VBLDonc/CDh7qjlTGfYpGK3iHtf2DsB6f3hVVPFJQxMLgI5fnTdIJlIZn5mdSZ0y/XJMHnuiRc56cwTuPPm35OMJwd8rWxYepJM6Ea/EEWRJR85dcBrcDqV5s1X17L5/S3ILpn5x8/jyGPmHtRr6NbVH/DOv1cTau5Ecoik4yk0RaOgupic3BwcOS7UuIrsl3EtKkLMc6HrOoqikE6nicfjxHuc0YDMGFA2TMME3bT0GpiYuoGkCAiqji5rOGQHalol1hHhlT/8l4t+/MV+j0kykuCle56kft02lEQap0emfM4UlnzjAvwFwQM6DrJHRnJK+AuCeAJeou0hNEXD7ffiyfFSPnvvwvFUKkVdXR0Oh4MTP7aE8PFH0fDBTtSUQn5lEVVHTR9U52M8wx6T9ng8VFVV7XUv4nQ6+fznP8+ll17Kfffdxz333IPD4UDTNO6//37uv/9+pkyZwmmnncZpp53GwoULx2SRbRIgmMM5ZDuOoShKry5AOBymoaEBt9tNdXX1mDuBE4kEO3bsYMaMGbiz5sVhdIXbmqbR0NBAKpWirKxsXGwohxvZ+Q0VFRUjLta0E1NtJw7TNJFlOUM03G73qG9MsgX8lZWV43L8YyDEfv9H0s88j+B2YTodmO2dlrDa6UQsLc6MxhidXZiqiuFyIblkBN2wxpoEAVPTEYMBjLYOUJS9di+kY+bhnD2T1KP/3FN/0Qdm7//TB73zNvq6R+kCpE2QgH8l4BnVgdPp4OOXXcii0xfi9Xp7dcl+99N7+fdfnsToccoyBlqbADl5QSqqywh1RWhpaMHAxNCNIZ6XNgnZR44FBpqhkV+UjxJXrfwKw7S6FGkFySEx5bBqkvEUTfXNexwqURKRJImKmnI++eWL+O0t95CM78XSN/uj2ta2MGBVVXY5+cb1X+XCz30EQRBIpxVqt9YhSiLFZUX89pZ7eO/ND0jGk6iqitPp4OSzTuTaW646KN3f7W98yHO/+RdaSsWX50dTNLob21HTGoHCIO6Al7bmVkLd3bhnF+CevWchRVXVTMfChiAIVkfDKSM1KeiNcVAMBK8DscJL5P0WPEkJj8+Ly+sG0yQVT2W0FRf/5EuUzqzc472ev+txNj6/lmBRLi6/ByWeItzazfTFczj3uk8P6ZwzDIOtr37AppXriHVEKJpeTsfOZroa2imoKsYhO1HTKl31bcxYPIezvvOJAV8/m1jU1NSMi5HW4UYymaSurg6Xy0V1dfWgz+enn36aH/7wh+Tm5vLnP/+ZwsJCXnnlFV566SVWrVpFLBYDID8/n7///e9UVu55Xkzi4GKyc9EH9mhLe3s7OTk5VFRUjMnxnoE6F6NJLNLpNPX19WMqu2C0YR8D0zRH7RhkJ6YahpERhNve4g6HI0M0BuPCcaCwU2aBQ07Ab0SiKCtfA9mJ4LWyGXRJAkHbHXwnyz1NBwPBMJH8PqSsYoSp65iJEGaHgpATsBK8O/oPawPQN2xGf39j/8TC1mTYblGmaU847bn2nmq6iOXmavR0J+Se00E3rdyAtwSZ130+gkAsEufJh5/l1LNOZufmWt55fR2mCfOOnctrL67B6/fi8XkwdANN1Qh1WbP0Xp8HTdOtzAsTwl0RoiFrAyCKkpWR3U+Gxt4h9Pn//cMWip9+/om88sQbRHq6PabaY3ZhmjTsarK6DNlyFofUMxolZEjIPx96Ak0d3OiFnbEgOSU0VUOUxD1GydweF/6gn7//6V9UT68klUzztz/8i862TouYOCS6OkJoqoqqWEnaSSXJin+/gMfj5soffm1QaxkumKbJe0+/gZpMU1izewzJ6ZbpbGgjpywfJZmmZs40UrE66rQ2ZtKbXGiaRiQSyVynBEFAVVUURUFRFPTNERzNKoJDQHQ4EEIKSlcS3S3gUJ0Yqo6SsKyRnW6ZgpoSYp0Rws1de5CLaHuYnW9uwpcXwB2wErldfg8B06Rh/U666tooqBn8ONpbf1/JO/9ahWmaON0yW155H6fLiS8vSKipE9OwgibL59RwwufP2CexcDqdVFdXT0hiYR8D29RksPuoFStW8P/+3/8jEAjwpz/9iZkzrdGzj370o3z0ox9FURTWrl3LSy+9xM6dO/cork5ibGCSXGTBHuuIRCKUlJSM6fEee102ubCF26PlCBWLxWhsbMTpdFJTUzPmOjujgXg8TkNDA06nk6qqqoNyDERRzBAJ0zRJJBKZ8anu7u6MC4ddgR5uojwWjsFwwjQM9LoG0A2kmkqrG5FWENxZhMnpzIxAoesYpiXYFUURwSUjxBMYDsnKxnC7MdNpBMlhkRFJQtD0vY/zp/qIt21XKNitWagsh2gMMxzpZz5J6JVwDQKrkiYfKgZ5IrSaIoJTQkirbNMgkevCKVtDSL6Al1g4xv2/eIj331qP3rPW5//9MpqqIbtkNFXF4XSiJa1NuCBYGg1VUXsJnO1rk7fnNYcfJoYVgkFKSDBv/jxWPfkmTqfTsoGVJEzdtDa0yYFzSDRNxzShtaGVZCK5V/cnG26PC5/fR3dXCLBSvw3dQJJENFVHckjkF+aRV5CLU3bS2tjKE488Rf3ORtKpNLkFuZiGyfZNO1AUFYckIbvtrBuTZCLFi0+v5MLPnUf1tH1XZdt3NPPeM2to2VyPvyDIrNPnc/gp84Z8D9DSKl11bXiCvTuPLp8bp0tm/nmLOeKshYgOiffff5/f/e531NfXU1lRidGRQosqxLUkYoFMTk5O5npjazCMmIrSncRwOTCcoJk6mqmjxxXyA/nklPtJRZPIHhnZa3UsME2cshNfwZ4d4WQ4hqao+PN7jz/JHheJ7hiJcJzB5p+HW7v54Nm3cXpdBAqtDrxpmnTubKVwSg7Hfeo0dryxkXBLN5LTQe072zj8lHnI3t7FlElisfsYyLJMVVXVoI/Biy++yA9+8AO8Xi9//OMfmT179h6PkWWZxYsXs3jx4uFe9iSGEZPkogeKolBbW4uiKFRXVxMMHtis5kgju3PRV7g90sTCthj1+XxUVFRMyItnKBSiubl5TB0DQRDw+Xz4fD5KSkpIp9MZohGJRDK/t4nGgTp5hcNhmpubMyK9sXAMDgTqxs3E730Io74JExOpuBDPJy+yxqHSaYQeS1gx4MOIxcE0MFQVTdcRUwqOYABTFDGbWzOvafakeMunnoi65u29J2jbEITdHQrbDcomGC4ZHA783/4a8Z/dCV4PWiqNqGlZ3k/0+l8G8IoC7yWsTb8/6EF2OOkKWc5WnkQKZ89nExBQFZXXX1xjjbG4ZTxeN6qikohZadyxqHWNcTgcGSJhjwTZ+Tv2z209Q2ZBg5NbDAomVmcipSWYcfQUXnziFVLJFLn5OXj9XsCkbnt95vF9s4H0HtG6HY7ncDrw+PdNhJyyk7LqMutzCXDOx87g8HmHEQqHWf6P56nf3kjFlHJEYTeR1w2Tt1evQ9M0cvNyMHQDj9eNU3aSTimYUrZGR0AUBFRFY/3bG/ZJLho37OKpWx8l1hHG4ZZp29bErrVb6Kpr44RLzxjSMZVkhxXy1hbq9XNd1SzxbY4PqSdt/KijjuKzn/0sj/7xL3S9sBOvYhEslwCOXGC+DoE+Oou4BqqJFHTg6BkRU1NpAk4vRkeKqNtATaYRpAB51SWYmk6kPUTlkdP61TcES/Jw+70kI4le2oVkOI7L5yanbPDat/btTSQjcQqn7O50CIKArzBAZ10bm158l7btTThkK+G8aWMtDet3sOzKizLvPUksdneyHQ7HkI7BK6+8wnXXXYfb7eYPf/gDRx555AivdBIjibE373MQYJomO3fuRNd1pk2bNuaJBfQmF/YYlP3zkXSEamlpoaWlhby8vCFVJA4V2Darzc3N5ObmjtljIAgCbreboqIipk2bxvTp0ykqKkLXdZqbm9m6dSu1tbV0dXWhKMq+XzALtjNYU1MTgUDgkLiJ6q1txH52B/qOWpBlBLcbvamV+D1/wjH/CNB0jFgcI5XCaO/MaCbMcBQpkUQ+bBqOeXMxY/HdtrU2QXA68V3+eVynn4yZTGF2hwdeiCjuMfpk2c6amd+7z1qCvGAe8inHW7/zeekyBWKZ+age21dANSEtQJ3WQz0EAV3XESURR88mUVO1HvKQoLOt03KK6iEIqWSKcFeEWGS3OBfTchJSenI3bEtJ0zQxTCPTNXHYida9HKKGD7qoERW7aNMaccX9vPnqWpLJFB1tnTTWNdHR0tlLF9J3hFQQBQQBDN3qNJSUF5NfmNfrXBZEIfMPAVxuGV/AS6Q7QiKe4JSzTuQT/3MRRVUFzDt2Lqefc6pF1LIaR92dIcJdYasroup0d4bYta2OWCROIMffc4h2r83QLZG4LDv3aXNrmiZvPPoi0Y4w+dXF5JTkkV9VhMPl5N0nXqO7sWNIx1QUReYuW4CmqMR7xPixrghtO5rJKcunZsGMXo8/9dRTOS7/CFwJgXA6huo2kfwuzLCK9kH3HusXnCJIAoZqEI1GURSFfEcQpy7icDopPbwSb66PeFeUxg076e7opnBmOYu+uLRPN86CO+BlzrIFKMk04ZYu0vEUkbYQyUiCmScfSbAod9CfXZItp7S+o22GppOKJmjZUk9+VRH5VcUUVBeTV15A7Tvb2PnWZmC3AcxEJxa1tbVDJhavvfYa1157LU6nk/vuu4/58+eP7EInMeKY7Fxg3XArKyuRZXnc5DLY5ELTtFEZg7JHxuLx+LC4IY1HZLtiFRUVUVBQMGbH5vpClmUKCgooKChA07RMR6OtrY3W1lZcLlcv56mBPpdpmrS2ttLd3U1BQQFFRUXj5hjsDennX8EIRRDy83Z/njwnZlc3gkPCfd6ZpF94BaOjE3QD0+1C93uRVA1B0yEYQHn9LYsEBAMWSdB0cDpAN9De24D7kxeSfu5lTDsMr+/GURAsTYaqQTyx++eSBIaB4XCw5ch5RMprWNwdIfi5j6Ou/xBHQxNBSUA3TDQETEAz7VwJgTW4SDl0UC1rT/va4fV5iISiaKpKd0doj42gKAqYCJZWYQA4ZQc+v49oOIaOpWlwOp2oPR0dTEinlUzXIhPjkXXKWDayQ69ziYZEZ7KDmQVzadjZnOFV9uYwriT2+nwBAVGyrGgDOX5EScTn91JaWUJLQ4s1HmWC7JYJ5PhZfPpxHHnMEWx4ZyOSQ2LhiUcz++jDaWpuwu/3U1FRgf+cAC88+TKtTe0Ec/yoqkZzQwsCAoGgn0Q8gexyoqRVWpvaqKypoLWxHUM3UdLpzIHxeN0E8wIcecycvX6GZDhOy5Z6fLn+Xt9DX16Arvp2mjbWkldROKTjeuQ5iwi3dPPe02to2lSHoRk4XU4wTdq2NVF11PTMYyNtIZSWGEUVJTgTEeKJBNF4FFly4uwyEbvTOPLdu/MM1CiYCeRuAW+eh+LCYsI7OzBFgUBxLr4cP76jZtBV344oiZxy9fl4y3KIphNEt2zpZTJgW90u/NjJSLKDDSvetkaqvC7mnXscx3zslCF97oo5U8gpKyDU2EledRGiKFokqyuGy2dp6Rzy7rFPp1vGNAyaPqyjauGMzBjQRCUWiqJQV1eHJElUV1cPei+1Zs0arrnmGiRJ4p577mHhwoUjvNJJjAbGx056FOD3+we0dR2rEEWRjo4O0uk0wWAQj8czIhu9bCegqqoq/H7/sL/HWIeu6zQ0NJBMJqmoqBgX3a2B4HA4yMvLIy8vD13XM4Lwrq4uOjo6cDqdGaKRfU4ZhkFTUxPRaJTS0lLy8vIO8icZPuhNzT2b36yxIkHAlCSMhmYCd1yBNGMqsdt/j+l1ozscOCQHoqxhtLSjrXojQxbMeHz3aJP1QuiNzRiRqEUgKssxdR26w5BO7c7KcDjA5UZwC1bqb7JnhErX0QFFUSlf8ybvrnqXh+/5G1ff9HWO/d9bSL/wCry7nvUbd7KisQsMgzmiQUpR2STK1Ls9uDwqyYT1eg6HlNFIuNwyeo+LkyCAklYzn1/TLJvWvUFySPzwf7/Pe2+t558PPYGuaXh8HsJd4UzXQNe0rEp+lm2rYBEL6x+IQyAYJgamCWXeSoxUj2uTQ0RVrPGsfWZ0CFBcVoTskulo7bAcpkxrzCqYG8DjddNU30z1tCpmzTucY06cz+LTjsPhdHDmR5cAu0cjg8Eg5eXlCIJAQXE+3/vZt3j4nsdY98b7hDpDmdGrZCKJYZgoaQVRkkjFkzTuaqSkpAAdCHWFwTQRJQmn08HS806jenrVXj+G2JPVsYexh2F9FtEx9E2uQ3Ywe8nRbF29AQQIFObgCXqJtIV4+b6nOe8Hn80Qlu62TlLxJJ4ca9wy0hkmkUiQUJNoikbz9p2kmq1ja1tn1yydjadWx4gqpEMpdFXDX5RDQVVRZg3ePD9qIk31YVMIFuehqmrGIS+7IGIHkB5z0UnMO/s4EqEYnhxvhgwMBbLXxclfPpuX7nmKzl3WaKMgCJTNrsbt99Cwfme/zzMFc5JY9IyVi6JITU3NoInF2rVr+da3voVpmvzud7/j+OOPH+GVTmK0MEkuejBeqq/Zwu2Kiope4l1JkjKbQp/PNyyfyfaoFkXxkHMCGiwURaG+3vL4r66uxuv1HuwlDRskSSIYDBIMBjEMIyMID4fDdHV1Zc4pr9dLV1cX6XSaysrKEbfbHW1IxUXWZrdngwk9AZW6jlhmzWAb8QSmIFjEwuGwZvhbu/q3lTVNiywYOhgG6gcf4pw7yyIXuoHZ1mEJvO1yvmEg5uViRqJW50LTMlkaKYdMJK0giAIe4DxZY1M0wm9+fDe//8cd5Hz0XDwfPZfTgSNbO4mEo5SUF/Hw7x9j2z9WoIRiCKJIflEeAkKGZOQX5eH2eOjq6CK/KJ/25vZe5ALYrZcYAKlEmpu/9TOOP/VYrvzh13juiRfZsXknoihhGLbrkgDC7hwIQRAwTAPD1EAUMIDvSY4AAKSwSURBVAUdyRhoQ7anSMPEwDANJMGBS8pF16yxJlEUccpOtJ4OjQ1RFDFMo7ejlgndnd243G5KKkrQFJX2lk4COX5MwyAWiVEzvZqf3nsT+YV7kuiuri5aW1vJzc2ltLS017V2ymE1fP6bn2HLB1vRdZ1oOIbscvYaN0PRcJgCTtFgmjeI7HXzgWYQi8cRRBHDNHnn9fc49ZyT96q5cPs9TFk4k43Pv4M74EVySFYqels3vjw/NUfPGPC5NsKt3bzz+Gq2v74RQRCYefIRqCkVTVGoPGJK5rMVTimhbVsT29d8yMKLT7buPXoCSXbQua0ZXdMtTo2A25Dw5OVw8TUXIXllRFHMHCufz4eaUqh/bzuNG2pZ++9XCZbkI8m7tyPpWBJfbgBPz9iY0+nMFEQMw8gkOtsOeZIkZYwrnJ6B71GGYaCrOg7ZYZ2HhkHzh3W0bmlAkh3kVRYx5diZ7Hj9Q0SHyNRjD2fRZ5ZQu3YrjR/sJBVL4vZbxCURjiNIIo4iz4QmFqqqZojFUDoW69at46qrrkLTNO666y5OPvnkEV7pJEYTk+RiHKGvcNvn8+H3+zMt50gkkrEkHQ6XoEgkQlNTE263m8rKynEzMjacSCQSNDQ0IEkSU6ZMybTiD0XY54zf76e0tJRkMpkhr6FQCACv15vR+RxKN1L5tBNJ/fcFzO4QBAIW0YjFEWQZ91mno+s6XYKJjIkDi5QZqbSVd2EjM/PTA10DBJCd6Nt24jrzdACMzk6LPEgiIFqPEy1xtODzYnZ0WfvpHuclLa1YL2uYJAUBvwCL/A7+Hoqx5uW3OPOipZm3LCwpoLDE8se5/Htf5oLPnMuH72/G7XGzYPFRCKLIpvesGfFZRx3O1Z++FsnhsIjlIIPj+iIeifPqitV88M5GfvXgT0kl01xz6fczVq3xaNwiLT37b4fsQNd04mqCtngzFd4pVidCAMEUe7QZ1oR9yhHBqbuRTLnHG8pAMEUkwWEJpk27O6JbI1+CiEN2oPUIkG3nKCXdR28hCShpFV03+OZXvkphST5/ve/vNNW1IAgCRy6cyxeuvKRfYtHR0UF7ezv5+fkUFxf3W8R5e9U7pJNpKmrK2bmllnQqjWGHCQogmVZC85ypNVTlF/Ly+veJJWKUTSnHKTtR0wp12+u475d/5Me/u7HX9TvWGaFu3XYAgsW5iJKIllZo/GAXskfG4XLiyfFxymXn4s3de5c51hnhiR89RMuWRkxDR1N1WrbUI3tc5JTk7tHJEyWRWHuYSCTCzq07+PCfbxJvDZOKJKzP5XAgOiWLM5tQkVdG1bxpe7yv0y0zbdFsph43i+6Gdna+vRlDC+L0yKTCcbS0xhFnLbTGsfpAFMVMQcQ0TZLJZKarEQ6HEQRhj4wWNa2yYcXbbFm1nnQsReGUUmYvmc+21zeybfUGNEXtGYGKInvcBIpy0DWNjS+8iyfHz9EXnEDDB7vYtvoDwi3dgIkkOyg5uobKedMmPLEQBGFIeWAbNmzgiiuuQFEU7rjjDk4//fQRXukkRhsTb7c4TmHnV9gVueybjSAIeDwePB4PxcXF/boEZRONfV0ETdOko6ODjo4OgsEgZWVlYzLrY6QxkcmVfYMWBIFQKITD4SAYDJJIJGhqagLo5Tw13i1oHTVVlgPTvX/G6OwCTMRgAM9nPw5zZ1FbW4s6rQbvlGrM2garg5hNLHo20vTKchAQAn7LZco0cR45B6mmEu39jbudk0wdRAl8HszOLkxRRCgqRHBI6C1tJFWdeE/3wOgJyzMkARkTQYBYNEto3Q/Kqkopqyrt9bOjjjuSlsZWmuuamTXvcJ77z4t0t3db6dvZGCA/Ixu2A5Pskgl1hnjqsWc56rgjM5auDqcDVdF6xNNWpTg3z8pnyRNy8QoyShMYaAiCiCjYxMJEQMSl+xFN0TqWpoSAiCiICKJAMDeIoRtEe3ItDN0iH/RwuikzqolFYhiGSXdnyMooMawOhp25oWkaH7yzkS9e9TnO/fhZNNU2UTm9kpOWLd7j+26bOXR2dlJYWLhXq/JYJI6JFbBXXFZI7bb63eJy08SBBJLIhoZ6/G4PETWNU5AIN3YAFqnUMfjgjQ/YvG4zsxdYlpzrnnydVQ8uJxmJoysaqWgSh+xA9nkQJQ1N0ag++jDOuPoiiqaW7f2PB2x4bi0tm+tRUgq6omX+RqloEk3VKJiyuytjGlZwopzjprGxkV0vbqDl/VpEh4TDZYnPjZ5RuuIZ5ahJhdq1W/olFzYEQWDplR/l1T/8l9p3t5GOJXEHvRx1/mKOOn/fVqP2dcrr9VJcXIyiKJlw0ZaWFsDSm33wzzU0vr0Vd8CH0y1Tt24b21/fiJpWyK8qwh3w0rypHjWlWA5ReX5cPg/RjjDvP/UGMxbP4dSvnMv0RbNo3lSHpmkIeTLlc6dQXTOxiQUwJDv6TZs28Y1vfINUKsXtt9/OGWcMzdFsEuMDE2e3tA+M5bGooeRX2C5BtlOQfbG1N8qwe1MYCAT2uIGOZ9HycME0Tbq6umhra5vQ5CoWi9HQ0JAJQbLPFVVVM+TVvoF7PJ4M0Rivo3PyomNwzj8C7cMtmJqOY/ZMVIdEbW0tpmkyZcZ0HDd+h9hv7kffvM0K0LMhir27FoCQl4Pg82F2h3DMnYVYmI/v618ifPUNu5/rsIgHkdhuzUYkgpCXS6MhkI+5eyiop+JtmiabkjqS5OSwudMZCprqmrnvV3/ig3c+xDQMvD4v8Wj/wmdJlHC5ZZKJFA7ZgdpnZArIFDt0XUcQBF578Q1WPP4Cmqqj63omkE6w5mUQRRGX20UqmSaZSHLVtd/iz797lO72kFXVz7jWWuNQQg+xsF/DdpzKzc8hrzCP7vbu/tcuSXz7x1fw/lsf8ORf/wvQkyze9wPAU4/9l1XLX0P2yIg9wXZvvPQWV9x4Of6AL/M5bSOD4uJiCgr2np5QPb0KUQBVUZFdMpJDQtAtpy4TSKMjaAbhuE5HJNwzgmegGSay1zJUMBWVZDzJ4z99hPzbvoaaUnnpnifRVZ1gST6dta1oqoZhGuSU5pNblk+8K0L7jiac7sF9Bxs+2EkqlsI0DGSPK0Mok5E46WiCtu1N5JYVWKNWrSG8BX5cVTm4JZnW9+txB72kEykkpwOnR8bUrO6Hy+tGTSqDCiT05QU4+9pPEm7pIhmOk1OajyfHt8/n9Yds4wpd19n+zmZW/eVpdr2xCUES8RcrFFQXk1tVyPbVG6wRLr8XQzdIhuPIHpfVVeuK4vJ58BcEad/VQvOHdeRVFFKz4DCKZ1f2CoebiMRC0zTq6uoy4bGDJRZbt27l61//OtFolF/84hecc845I7zSSRwsTJKLMY4DTdzOvtj23RS2tLTg8XgIBoMEAgEEQaChoYFUKjXuRcv7C9tuNxQKHVJuSEOFLVa1XXCyyZXT6SQ/P5/8/Hw0TcuMJLS3t9PW1oYsyxny6na7x9XxE1wunPMtf/VkMkl9bS2SJO2+gZaVELz1BvTaBsxolNiv77bsa3Vjz/wG3cDsDiH4vDjnH0H8d3/ENAzE4kKM7hC6y4XZHULsGXPMpByk0pjhCE/FDC52WVEBqjVNgyjCZsXk9WiaE848hrlH7xkyNRAS8STXX34zDTutzouAQGd714CP13WdVDKNyyOTk59De1PHnsLhHnKhKhqiKNDe2okAFBTn0dnenRFK21qWQNBPR1snSspyzHrsvn/icXsIEe5z/LIyHySx59oHmmp1b635foFUMtVLU+F0OvAFfJimycZ3N3HJ1z/NsgtO58av/5htH+7o93OahkmoO8y0kqkEcwIkEynefeM9Hn/4SS75+qcxTZPm5mbC4fCgjQwWnXosK/79Ats+3GEF7PWMEoIlqDc06zhqhk5HLIpTFEmYCl6nRSzUlEJKVXEIApEtzTxy1V0YukGktRtBkkhG4qgpJfPaiXAcl9+DNzdAuKWLunXbyC07bp/rxDDRFBWX190rEF2UJBxuJ/7CHEtbIAjkTy+h8sTDKJ9WictwoCsqss+NN8dPKpLoSa+WMBWVdCKNKImUHr53QXo2ckrzySkdPhfCzp0tvHb/s3TWtiIIAg6Hg1hLCDWhUDizDMM00BWdVDKJ0x53tc0rbEMA0yL3Qk9nMplMThILTaO2thbDMKipqRn0qPCOHTv42te+Rjgc5tZbb+WCCy4Y4ZVO4mBiklyMYdjdiuGymh1oU2i7b9ivX1ZWNiGJRbbdbllZGbm5uQd7SaOO7JG4/sSqfeFwOMjNzSU3NxfDMDLOU7bQ0uFwZIiGPWY1HmB3bfobiRMEAccUa9Pkv/E7RK+7GaOr25pbkkRwOpHKSsDpxDFzOkZ3iOT//btHwA2moWMoKmosjtwz6mQCaRNcgpWuLSZTtOhwR8jgIz6RmS6RpGGyOmnw35hJ9awavvezbw+po/avh56gfnsd1oa9J0l7H2NP9sjTdbd9mwd+9RAb123q93HpVNrqmnpcBHIDuFwyxaWFxGMJYpE4hmHgD/pIpdJWPoYAgRw//hw/tdtqe71Wdso30HPszd0WtibEYwnyVLUnvM8iLv6gj5JySwPR0dpJPGZ1ZEorSvjs5Z/kx1ffNvAHFSCVSBHMsZyiUgk3q59/g0986WLaO9qIRqOUl5eTk5Ozz+MMlpXstbdezT8feoI3V76NJEnomo4sy5arlaFaI1qCQEc4QpnTzy7SpEwdKaWjqhoIAuWuALLDQaQtjJpKW5t+SURNKeiqDk7rkOiZDoGZOR6DQc0xh/H+f9+0yJpkPUdXrRwUb46fk790NoGiXCKRMElBJb8gn5KSEnRVw5cfJNLeTaAoh1hXBCWest7dMElFE0xfNJvpiwZPfocbHzy3llhXhNyKQtKxFLJHxumWURIpBMXE5fMQT0VJJVMoqooccBNrDSE6JFx+yz433BrCk+Oj4ogpJBIJ6uvrJzyxqKurQ9f1IRGL2tpaLr/8crq6urj55pu5+OKLR3ilkzjYmCQXPRhLmx670mdXukYiwyJ7U2iPTNk39aamJjo6OsZt9Xl/kG23W11djc+3f2358YzsCu3+jMSJopg5Z0zTzDhP2W5m9u9t0fhYHTULh8M0NTX127XpC+f0KeT8/pekn3sZfftOhPw8XKefZDlDAekXXiV2xz0IbjdCz6iKkU6RbumgS4dS0UQ1IWEKpEyTPNHEhWVFe5Qs8u+EySOKE0cgFxBQBAVdiSG7ZH75gztYeOLRLDnvVNwe9z4/16srXsM0TRxOKfNd19m7ZWtReSFKUmHVitcpqyxh0/otA9q8ipJobfZ7iIHD6SAnL4jb66aztQtFUUmn0oiiiMfrobyqrCcZ3FqLU3Yi9ljfaqpm5Uz0QOjRYgiCgKPHVahhV1OvDA5D7ynG6AaCKHDYnN0jY1NmVON0OqxNe9ZrZpOY7HPdITtJpxV27NiBILJfDmn5hXl85Ttf5PNXfJaH7nyEx/74T3RDx1QNEEEURGRRQjd0Fp4wH//7W2iMh4jGE3hFJ+XeHAoEq5Nh6DpCz+idIAo43TKGlsRQdQRJRPa4ME2TWFcUd8BHzTGHDWqN888/gdcffp6OXa2YPcdblERcfg8FNcWUzaommoyRimsU5BdkBOwO2cn8Cxbzyv3PEO0Ik1dRSLi1m1QkTm5FASd98SyOOOvYXqnZo43WzQ24fB58eQGcXhdKPIXs81hjX90xPH4vbp+HdFcCp9caXTMFi+qH2ruJdobxBH0c98lTcfjkCU8sdF2nrs7Sm9TU1Ax6/LWhoYGvfvWrdHR0cOONN/LpT396hFc6ibGASXIxxrA34fZIwLZU9Pv9Ga92u/rc3d1NZ2fngLkHhwpSqRT19fWAJUxzu/e9UTvUYBgGDQ0NxOPxIVVoB4IgCPh8Pnw+HyUlJaRSqYzQ0nZ0yRaEjxWxfGdnJ21tbeTk5FBWVjaoc10qLsT7uY/3+zsrWM/IEAsATZDQTZM2Q8DbEyGXNgEEug2THBEkQeCcj5zM2tWbaGgPQSiGaZqkk1Z1eNe2OnZtreWVZ1dz+w9/i8PhoKi0gE/+z8f4yCfP6nfzEwlH6bGhAnbrIAbqXgiCgM/rxdRN3nj5Lbp7Mhv6PsY0TZwuJ4Ggn2goSjyWwCnLVgifaZKMpyirLuXUs07kiUefJicviGGaRMJRAkE/suxEVdSeaBBrbaIoZsiFklZ6UsANBEHEH/SRX5BH3c4GRFm0rGcxScSTNNQ24fa4OPyIwzjulGMy6ywoLsCf46e7I5T5WTaxkCQpKy3bJBaOUjWjEgSTqqoDKza4XDInnnE8K554wRpx1Q2cspNgboBYJE5BUT5fvONKnr/zX3yw/G0iZghMi3yYponsc5OOJRElq8ikpVVEh4QoSeiahiiApqiEmjuRPS5O+uKZg06mdsgOLrzp8zx166NE2kJIDhHR6cRfEGDRp08nkojS0dHRr4D9yLOPRRRF1j35GrHOKEVTSpl9+nwWfvyUg0oqbHjz/IRauhAlkeLpZbRtayIdS6CmNbS0ypxlCzjy7OPYtHIddeu2k1uSz3EfPxVkkfbaFnCKFM2uwDklmEnerqiomPDEorq6etDEoqmpicsvv5y2tjauu+46Lr300hFe6STGCsbGHX0SwNCE28PxXrZAsa+lYt/qcyQS2SP3YDizNA4mYrEYjY2NyLJMZWXluHc92h9omkZ9fT2KooxI16avm5ltMhCNRmlubgbIWEcGAoGD8jcwTZO2tja6urqGVWtjqkrmfxuGgabpVkAeoCKwPg0L3CACqmkiC2Bg8lQCZsybz8++eRlP/+1Z3nxlLeFQhJbGVoK5ASRJor25PbMB1zWdhl1N/ObHd7Nz6y6u/n/f2GMtNdOraG1sG7SNsGmaKIpCKpGiuzOE1F+hoyfXwO/3IYoCXr8Xr89LpDvS4/gk4PK4uPy7X6K8qpwnHnma5obWzNNbRRFZtmZ7NE1HFM3Me9tOVPax61kVHp+HHVt2ZR4jCCCKEqZhoqs6C888mnM+dQb1DfX4/X7amzq5/5cPDiwu7vkzN9U1k5MXREkrOGQHJ511PDU1NcOSazP7qMM54pg5rH97A/4eQpWIWfatH/nU2TicDpZ880I8uT5e+8vzJLpjiLJIoCgPQYBkKIYJuANewETrEdf7C3JYcNGJJENxfAUBZp02f6/uTP2hev4MPvmLr7LppXV01rURLMnj8FPm4Sj00NHRQVFREYWFe6Z8C4LAEWctZPaS+SR7NB82qQg1d9G+ownZ66Zi7pRMt2k0MfPkI2naWEu8K4o3z0/F3BratjfjcDk565qPM33xHERRpPTw/nNETNMkFArR0mLZE6uqyrZt2zIW8Nkp4YcybGKhKMqQim+tra1cfvnlNDU18e1vf5svf/nLI7zSSYwlCGZ2+WYCw76RHsz3PxDh9lCQrS0YrEDR9hO3N4WqqvYag/H5fGN2zGUgdHd309LSMqjxl0MV6XSa+vp6TNOkqqpq1Ls2mqZlzql43LJVdbvdGetkl8s14gTWHgWMRCKUlJSQnz98otLk488Qf+BhorpBLJ7CNE0kAXymyT+SAi9FNT4VEDjWBU4gacKLCYNnFAlPwM+VP7ycpeedBsDPvvsrVr+whrzCXDrbOknEdudSWFV/S9js8/v47f/9kmmHT+21lldXrOYX199JMp7AMCy7V3rcp1xuF6qi7iHYlhyS1esQRQI5fhKxBOmU0qvq7/G6yS/KI9wdYd7CI7jqpq/z/BMvUbejgeKyQpacfxrTZk7h6b8t544f3WUlfws91rS6gSBAWXUZna2d6JqBKFkWrslkKrM+m8Ts63bllJ1ccePlLLvwNKv72tXNr3/wO9qa2vEGvOiqTiQUJZ1MIzkkHJJkjRQZBslECkkSmTnvMM782BLO+MiSYf0+hLrCPPibh1n72jo0VSOQ4+cjnziL8z9zbq9rT/OmWv79/x4i1hnB6XISaQuhJNLW38PpQBAFZI8L2edm6RUXsuDCE4dtjdCbaA/GGSsbuqaz6k/L2fj8WlLRJJJTIr+qiGVXXjQkcfdwwNAN3njkBTa++C7pWNLS5RTlcNIXz2LacbP2+fxEIkFdXR0ej4eqqiorELGn+2pfq+yU8EN1fFjXderr60mn01RXV+PxDC75vL29na985SvU1tZyxRVXcOWVV47wSncjnU5z22238eyzz5JKpTj55JO56aab9noe33XXXfz2t7/d4+cbNmwYM1318YZJctGDg0kuhlu4vTcoikJDQwOqqlJRUYHfv/eQpf5gmmavLI10Oj3kLI2DieybZ15eHiUlJYfcTWEwsAWKDodjSAFIIwVd1zPJu7FYDMMwRnwkL3scbCQc0oxYnE2XXoG3owMDwSIBJtTrcGfSQXdSQdd0/ALkStBpCKiiSGGJJYaunlbJbx/7FaIocut3f8VrPeSisbap94iSYFnH6rpubbBv+CoXfu68XmsxTZPH/vBP/v3nJ61sCEEgNz8Hf8BHS1MrksNBuDO0O48BMiTAKTvwB/wkkymSiWSv5G5fwJtJ9p4xZzpnX7SUsz92Bq4+dqjXXPp9Nq3fiiSJpBIW0RJEEVEUOP8z55JOplm3Zj2aqpKIJ4nHElb6tiSBsFtTsTcIgkBhaSE//N/vMW/hEax97V1uvuo2kolk5ng5nBIer4dYNE5RaSE+v49o2CqYJBNJ5i6cw09///9GzFK5s72LaChKcXkxXl//m7XOujbe/c9rbFj+Nl0N7bgDXrS0gqZoGUJ21HmL+ejNX0ByDN+1NrujvT9E+93/vMbK+5/G5fPgzfWjqRqRli7yKgr5zP9+HdcAn3ck0VnXRtv2JpwuJxVHTMUT3Hcnqi+x6Ft40nWdeDyeuVbZ3UD7/jcei219YRgGdXV1QyYWXV1dfOUrX2HHjh189atf5ZprrhnV++v111/P2rVrufXWW5FlmZtuugmfz8fDDz884HOuvPJKPB4P3/3ud3v9vKioaKSXe8hikpIdRIyGcDsbyWSS+vp6RFFkypQp+33z7JulkU00BpOlcTBhGAZNTU1Eo9Fhr1KPJ9gifo/HQ2Vl5Zggg5IkkZOTQ06OFbJmC8L7G8nzer0HfPMe6XEwgFBK4ZedGsebbuY7rM3th5KLl9MChtvkvAuX8eqzq+jq6CZhirg8LgpyAzhdTly6i9amNhLxJP6Aj4UnLuCNl95EUdQ9rhNiryRlkF17jmsIgsCnL/s4y84/nQ3vfohTdjL/uCPZuaOWO3/0e3ZtrsM0rb+DIAq4PW48XjfdXSF0TScRT5BMpvZw3E0mUjidDvw5ATpaO/jz7/7Ku2veZ+l5p+Hxupm7YA4er5tQVxjJIeEP+PD5fRimVUyJhCJoisZ3brmKtuZ2IqEoD/z6Id59bR2GkG3xu+86mNfvJZ1K86+HnmDewiPY8O4mYpEYgmB1Q0wMVFVDDUcxDRMlnaa9pQMzq5Ozfs0HNNW2MHVmzVD+1INGQVE+BUV7v+4UVBez7IqP0r69CSWRIqcsH8MwUeIpDMMkGY4RKMrpl1iYpomualaXYwj3k2wb7sF2tPs+/4Nn30KUJHz5lvjd6XKSW1FIqKmTnW9tZtZp84f0msOBgupiCqqLB/34fRELsL4jfVPC7a5GKBTKaMrsgttYugcOBoZhZDoWVVVVgyYW3d3dfO1rX2PHjh186UtfGnVi0drayuOPP869997LwoULAfj1r3/N2Wefzbp165g/f36/z9uyZQuf+cxnJsnEMGJ8nfGHEPoKt+0K4UghHA7T3Nw8ImnTLpcLl8tFYWFhv1kaB3ue3oa9mUyn0/vl/nKowBbxj+WAQFEUM3PNpaWlvUbyQqFQr9/vT6dMURTq6+vRdX1IVbmhorm+he60xqv+IK87d3/ndFFHD0U55czFzD/2CH79/+7Cn+Pr9b3UVJVATiDjBHXqOSfx6orVrFuzHkkU0dntlGQlK1vjRl6/j+NOWTjgmgpLCjj17JMyidOiE3501/Xc/M3bqN/ZiKEbKGmFeDSe0QUIgkAykQJ2b/Gt7AAJTdMpqyrF4/VYle+mdl5Zvpq1q9/F7XVTUJTPZdd8gTnzZ/HS06/0dCwEJKxOi4DAjNmWTqC4rIjisiJmzp3Oujfe322XK/SE4GU+8J5jUqIoUFCcTyqZYvMH29B1nW0btwNWh0QQBQQkBEFEURREQSDSI5S3xdK6rqMqGo/94R9cd1v/GyNVUXnuiRd59bnXiIXjzFkwm4984iyqp/U/u38g0DUdRFvkLuAOWOdpOpbE6JOorms67z6xmveffpNEOEZBdTHHXHwyM08+st/XjndF2PTye3Q3duDNC5A3qxR80n7bcOuqRiIUs8L4siA5JEwTEt2xIb/maCMej1NfX79XYtEX2SnhJSUlpNPpjM27fQ90u929QkbHcqfc7uYmk0mqq6sHrTmKRCJ84xvfYOvWrVxyySVcd911o/45165dC8CiRYsyP5s6dSolJSW89dZb/ZILO7tkxowZo7XMCYFJctGD0fwSjKa+Iju3YDQ2k32zNOwNYWtrK62trZmLrD1PP1qwtQV28M9IbSbHMrLHwfqK+Mcysm/excXFvW7e+9MpS6VS1NXVZTp4IynKLCjOx+l0oCgqjixyofb8d2FxAbOOnElBcT6dbZ34cyyilEqm0DWdMz+6BEdPddrlkrnx19/jxadW8uqK13h/7QbikXjP9cTqini8Lr7+/f+hoHjvlfHsKrU9Vz9n/my2fbjD2tAKPd2QnnGkPdoVWH8X3TAwMVHSKh6vh47WTkKd3Zg9WRSSKNHW3MHvbr2Py7/3P6xZ+TaR7giy27JOVRWVippyTj335F6vfeZHl/HcEy9lBOhWOncWsUHoZSUrCAK5BXk4HFaeRCBoWR2HusI4ZRlN0zKvYxgGAlYSt65pSA5HjwWuYAXxyQ7eeX0d699fT0FhQUZTJkkShmHwu1vvZ/Xzr4NgWXo//8SLvLP6XX7wq+8Oe7dj+qLZNH6wK9OJAFBTCqIkUj2/t93sy/c+xbtPrEaUJJxuJ40bdtG6tTHjjJSN9h3N/Ocnf6G7sQPTtMZ8nD6ZpVddRGu7ynPP/IPuhnbyKouYd+5xzDz5yH1eKySng/yqYho37MKbt3vc1lqvQG7FnqLwsYT9IRb9wS62FRQUoGlaxn2xs7OT9vZ2nE5npqMx1rJ/bGKRSCSoqqoaNLGIRqN885vfZNOmTXzqU5/ixhtvPCifq7W1lby8vD32FsXFxRnzkL7YunUrhmHw7LPP8uMf/xhFUTjuuOO49tprKS4efMdrEr0xSS6y0Nf3fCQwmsTCMAyam5uJRCL7lVtwoHA4HOTl5ZGXl5eZp49GLWvD9vZ2XC5XL6IxUmuLx+M0NDTgdDp3Jy1PMBwq42DZI3kDdco8Hk/mvOpLHOxzQZZlqqqqRnxcobSihGNOnM9rL6xBFAVcbhfplEIiluToxUdRPb0KQRD4/s+/zS9/cCftLR0YhonscnLyWSfyyf/5WK/Xc7ldnPPxMznn42eya1sdN1/1M+p31GOYJi6XzLmfOIszLlyy1zVlnwt2lbq7M8Q7r727OzfCtDsFZua/+3sd6OlgOB0kYgk627rIvoRGozFcbheCAI21Tfzotz/g4bv/j03vb0GSJE5cejxfuPKzBIK9tV+VU8r5f3d8n7tuuZfNH2xF13VESUISJSvszTR7ZVbkFuQQzLXStVVFZcl5pyEIApVTKti1tRaP5CERS2AaBk6nA8ElUlFTxvZNO9E1HVMUMKHHItbfM/aSQzqdJhKJZAhu485m3njpTXwBX0YvYRombc1tPP7Ik3z75iv2fVIMAUeddzxbVq2nZXMDokO0tC4mTFs0i5knH5F5XFdDOxtWrMXlc+PNtY6lN9dPqLmTNf/3EoefdlRmhMo0TV554Bm6GjrIKy9A0awwQqU7wUt3PI4gChiagex1Uf/edpo21pIMxZl/weK9rlUQBGoWHMbW1zYQWvMhTrfc424F1fOnUz1/7FaGbWLh9XqprKwctgKcw+HYY9TTvg/a2T/ZltwHczzVNE0aGxszxGKwY6LxeJyrrrqKDz74gIsvvpgf/ehHI3Yvb2hoYOnSpQP+/uqrr+63WORyuUin0/0+Z+vWrYDlkvmb3/yGjo4Ofv3rX/P5z3+ef//73xOyEDkcmCQXo4jRFG5rmkZDQwOpVGpEhKpDRd95erua09XVRUdHx4gJd+1ANJ/PN6E9yu0291g4F4YTA6XOt7e309bW1svNRVEUmpub8Xq9o3oufP37XyGVSLN+7QYS8SQOp4MjF87l6h99I3Oez5p3OL//5x2se+M9ouEY02dN22sVXFVUfvH9/6WtuZ3c/Fwkh9XtWPH4i1RPq9xDzG0juzKZPRr46L1/o7Wpbb8+nyBaeobOts7dxZmeqr4AKD3Bed2dIeYePZuf3XcziVgCySHtIfrOxuyjDueuv91OS2Mr9TsbcHvclFWW8uqK13j9pTWkkmkMw2Dn5lrikTiRUBRDN3B73XR1dNHdGeLsi5fx1qtrURSVotJC69oTjeML+Dj7U8v4y53/h6ZaYlyXW8YX8NHdGeKoow6nZko1giCgKAqxWIz2tnYef/gpQl0h/LofSRJxupxWKKDPw/q3N2Su7cMFb66fT9z2Fd57eg071nyI5JCYefKRHHH2sTjk3UWStm2NpONJ8ip7dwc8QR+R1m4ird3k9XQOIm0hmj6sw5frQ9FUdF3H43Hjcsg0bazFX5hD0dRS6wUKINzazVt/X8nspUfj8g3sntW0sZZ3n3wdURKRHBJKIo2SSFM2u5qzr/3kQbGjHQxGilj0RfYopz0+Zes07A6s1+vNXK9G0+bWJhbxeJzKyspBE4tEIsHVV1/NunXrOP/887nllltGdDKipKSEZ555ZsDfr1y5sl9jnnQ6PSBJ+NjHPsayZct6ZTsddthhnHrqqbz00kuce+65B77wCYix+W0/xDDawu1UKkVDQ8OYHQHqm+RsE42+wt1gMLjfbePscbChBKIdalBVlbq6uoy2YDg8+8cqslPnDcPIOLnYYZAAsiyTn58/qjqTnLwgN/3merZv2klLQyvFZUUcNnf6HuejyyWz6NRjB/Wab616h/qdDfgDvsy4ld/pJxKK8J+/PsN5nz5nD/KULWDPrkyapsnKZ1ft9+ermVFFLBwjFo1nfiYKYiYUz9TB0HUqqsuo3VbHo/f9nTdeepN0WiE3L4ezLl7KRZecT25B7h6vLQgCZZWllFWWZn528ecv4NxPnMnPvvsr1q1ZD1hdDMtSV8brc/Pc4y+yY9NOfnrvj/j69Zfxl9/9H6FOS2hbUlHMRz57JmWVpSw88WjWrl6XycsIdYYI5gT49GUfz/x9ZFkmEUlx5433sHPLLtKpNOmUQld7Nzn5QXLzclAUFbd3ZK6z3lw/iz+3lMWfG7hi6/JZBZlYRwTJ6cDl9yA5RHRVQ3RIvUiB0ZO1omgGkmRZP0uShBKPY+jGHgTCl+sn1hWhY1cLFXOnDLiGt//1KonuKOVzasC0gv3SiRS6ohLvihIoPLBgzpHAaBGLvuhriqKqauZ6ZRdGZFnOdDRGMrzWJhbRaJTKyspBO0im02muueYa1q5dy1lnncVtt9024gUbp9PJ9OnTB/z95s2bCYVCKIrSi5y1tbVRWlo64PP6hsaWlJSQm5tLS0vLgS96gmKSXGRhJMaiRlu4bYfCjZcRINvCdm/C3aFmaZimSXNzM+Fw+KCMg40V2MnjgiBQU1MzqhqXgw1RFAkGgxkNRmdnJy6XK+PbLklSpkI4GraRgmCJlm3h8oGiud666WXrOMByieruDJGIJ3uNGvUlmdkFB9M00VSt15jRYOH2uPnm9V+lpbGVO2++O2P3ahgG6bRi6RR0g9yCXKYcVsMPLr+ZlobWzEhVa7KNR+75G++tWc+Pf/9DgrmDM1n47z+e49033sfr92ZeS5JEVFXDKct4fT52bN7F6udf54wLl3DCkkVs3bidZDKJIei8+MQr/Pevz5NKpvB43QiiQCAnwJz5s/jIJ8/u9XcyTZN7fv4ADbsacXlcPcJ2E1M36W4PkYyncMpOFi1ZyLZt23oZDYzGZtU0TTp2tRAPxTJ6DIfTQaAoBzWtMveMYzKjUgCBklw8RQE6tjdTUF2S2RCmolYKuMPV+56ha9ZImtM18L1ETas0f1iHJ+jLJL873TIOl5OuulZaNtdTOnP4Be8HgoNFLPqD0+nMjBBnd/ZDoRCdnZ2Z65Vtcztcm3g748cmFoM1OVEUhe985zusWbOGpUuXcvvtt48JR6xjjjkGwzBYu3YtixdbY3w7duygtbU14x7VF7fffjsvvPACTz/9dGaf0NDQQHd396TI+wBw8M+GQxjZ+gpBEEb84mW7APn9fsrLy8fdCFB/wt1IJJLpagwmSyN7BKi8vHyPisREQXby+GhoC8Yi+oqWbZ1JKpXKEFj7vMoWhI+H701xmTXiomlar7+tqqjkFeb1yk/IDkrsj2SKosjRxx/Fi0+tHNIaHE4HM+ZM48iFc3n+Py8hu5w9Yml6CirWxtTr83Ddz7/N8/95iY7WTgzTsETqPcUcQzfY+uEOXnjyZS669PxBvfeq514DBGSXTFd7N6IoIEqWmDsZT+Ipspyrtm7czhkXLsHtcVMxtYyWlhae/MuzfPjuFvKL8sgvyiMeS9DdGeLYk4/hsmu+sMd7tTS2sXn9VlxuF92doV7J4QCpZIqikgIu/dqnQWSP82qk7Uh3rd3C6j8/hzfXTzIcR1dU1JRCd2MHU489nFMv2z3WYV8fDz93AamHXyXS3IXD5URTNByyk5LDKkmGYrh87h7Bu06sI0zFEVMpml4+4BpsQpPucRSzYeWhCJnk7rGCWCxGQ0PDmCAWfdG3s29fr2KxWOa8yh6f2t8Col2Ei0QiVFRUDJpYqKrK9773PVavXs2pp57KHXfcMWaKmCUlJXzkIx/hxhtv5NZbb8Xj8XDTTTdx3HHHZZyiFEUhHA6Tk5ODLMucffbZPPjgg/zkJz/h0ksvpaOjg1tvvZUFCxZw8skn7/0NJzEgJt6OY5Qw2o5QdvDReHIB2huy28Y20cjO0sjeEPr9fhwOxx72oofyCNDeEAqFaG5uxufzjbkb52jBMAwaGxuJxWJ7WGt6PB48Hs8ezlPNzc0ZTcZYsE7eG447ZSGllSU01bXg9Xl6NBdpDMPkvE+evbsa3eOMJUnSXjuZn738k6x8dhXaAN0LyWHZxmJaGgtZlpkyoxpZdvKJky4lHo3j8XkJ5gaIReKIgK3s/uzln+SoY4/ktz++x7KhtealgN0ufbqmsW7N+4MmF6lUusfhybJo1TINZzMzhgoCPr8X0zQzTj162tJo5BXm4g9YY2GBoB9DN3jntXV0fO48Ckt6J/laieaGRZgMA4fTsbsjbZh4fB6cskxhcSGiKFJcXIyiKP0aDWQnzw8XNr7wLlpKIb+yCKM0n2QkjqZqpKIJao45HH/POJKu69TV1aEoCseecyKHzZnJ+8+8Sdv2JnJK8ph7xjEEinN5+ra/0t3Qjm0Rll9VzJJvXLDXe4rkkJh5ypG8/Y9XcAe8ON0ypmESbunCm+enZsFhAz53tDGWiUVfCILQ63pl63+yHRhtYxS/3z/olPDs7n55efmgdXiapnH99dezcuVKTjjhBH7729+OqjZkMPjJT37CrbfeyhVXWOYKp5xyCjfeeGPm9++++y6f//zn+fOf/8yiRYuYO3cuDzzwAHfccQcXX3wxsiyzdOnSg2KleyhhklyMAEaTWOi6nhFi7U/w0XjBQFkatr2c2+0mnU5nNlETaQTIRvYmaiLrTOzRp1Qqtc9Wf7ZtpD33PJB1sizLY+Z4utwubvz1dfz6h79h19Y69ISB2+PiI584i4s+fwGwO4F9MN2r6bOm8tnLP8Gff/fXfp2hbHvXj3/pIuYePYtQV5i7fnovqR1pHJKEYZgWwfC6qZ5aSSqVBgGS8SRTDrOE6d6ejX42dgvABeS9jN30xYLjj6Juez2GbuD1e1GUcI+mzXLkioSiuDwyJyw9nvb2djo7OyksLKStoYN0SiEnt/dmyuN109URItQV3oNcVE6pIDc/h6a63fPX9nir6BDxB32oitLrs8myTEFBQcaOtK9TnizLGaJxoPP00bYQYo8TlOiQ8OVbn81QdZJhK1tC0zTq6urQNC0zFueZVU3ZrOo9Xu8zv/46217bSKStm2BxHjNOmJNxfdobFn7sFNq2N9GwfhemYWCaJr5cP6d+5SP4C8aGicR4Ihb9wdaM5efnZxwYY7FYxhjF4XD0srnt7/PZHV2bWAy2u6/rOj/84Q954YUXWLRoEb///e/H5H3W6/Vyyy23cMstt/T7+0WLFrF58+Y9fvbXv/51NJY3YTBJLrJwoBuH0RZuK4pCQ0MDqqqOWMLwWERfh6D29nZCoRBg3USbmpoy8/ZjraoyUsgeASosLKSwsHDMbIRHE6qqUl9fj6ZpQzYzyJ57zrZOtgnbcG4IhwM106v434d/zpYN24iGokw7fGom3yIajdLY2DikBPbzPnUOjz3wT5S0CoCJiSiIOGUnuq7zqa98jEu+/mkAfnTVraSTafwBa8Y+nVasankybQm183MId0cIBP0cfqRVtT7tnJPZunG7Zf3asxE3DRMEiywdf/pxg/7s53/6XF5/+S1aG1uRJAmHQ0JVNCSHRDqVxuP18Lmvf4pAvo/Ozs6M/bJgiHi8bmLROLn5uzdVsWgcr89DUemeWQyyS+aT/3Mxv7/1flLJFGhaT66fQDAviJpWOWbx/AGPcV+jgf4MLA5E/1M8o5z697ZnukKmYaCkVXTDoHBK6R7Ewu0e2PEJwB3wcsRZA4cwDgRPjo8Lb/o8u97eQvvOFlw+N9MWzSa3bGzYXtvEwnYOHG/Eoi+yHRhN0+xlc5udEp7d3benHEKhEGVlZYMmFoZhcPPNN/Pss8+yYMEC7r777jFnFDOJsYVJcjFMME0zYzMLIy/cTiQSNDQ0ZILAxmIFYaRhmibhcJhQKEQgEKCkpIREIrGHFeloZGkcTOxtBGgiIZ1OU1dXB3DA3au+1sn2edXX0WxvFcLRgCiKzDpyZq+f2fbLgUCA8vLyQa9NkiTcHreVSyGKiD3dBNOEWCRGcVkxm9ZvYdN7W3h71buZVGuwuhKxcAzd0Al1hkin0kiSyPmfviCzYf/IJ89i/doNrH7+DVRFzbyv1+/h5DMWc8qZJw76cxeXF/HTe/4f//nrM7z16lqcspOZcw+jsDgff9DHwpMWIDihu7u7V0e3sKSA4087lueffBnDMPD6PMRjCZKJJOd+4ixy8vqvsJ910TK8fi+/ufluOtu6ECURr8+DoevkFuRy8RcuHNS6+87TJ5PJzIawr/7H3hDuC/POOY7NL79HV0M7AgKJUBRNsUL3WrY2sGXDZpxeeVQ6ug7ZyYwT5jLjhLkj+j5DxaFGLPrCPm98Pl+vsbxYLJbp7ttkIJlMZtyQBgPDMLjlllt48sknOeqoo7jvvvsmTCFzEvsPwRzp1LhxBDvFdajo6wg10heucDhMc3MzbrebysrKCSvWtXUmBQUFFBUV9SIOthWpfYE1DCOTpREMBgc9mzrWYduLptPpIdkIHmqwybbD4aCqqmrEtBL2htAey1NVNeNfn53kfLBgmzrsz1icaZp894s3sGHdJgI5/sxz49E4TtnJUcceyTuvv4eu6yTiCUzDJJgXxOWyuoOKohKLxCgozmfOUbNYct6pnHTG4l7XQ13Xeee1dTz/n5dob+mgrKqMU88+iQUnzM8kkR8oskMC+xv7SKfS/Osv/+G1F6ysDK/Pw8lnnsAFnzkX2bX3Tmc6rfDfvy/nleWriYZjzF0whws+cw7TDp96wOvOzj1IJpMAew2EzEbD+p08desjNG2sBQRknwtP0Ec6maJs3hQ++6uvT8gCFBz6xGJf0DQtkyllZ0DY90K/379Xu3fTNLntttv429/+xty5c3nwwQcPqZykSYwcJslFFnRdR9OGZsc42sLt7OyG0tLSCXehhN6V+sHoTLKzNKLRKLqu43A4elWexyPRUBSFuro6DMOgqqpqwrap7REgt9tNVVXVqG3uTdPsZTSQTqczTi72uTVaxD/72nAgpg6b3t/MTVfeSjQcg57hH4dDYt7CI3h3zfu4XDKyWyYWiRGLxBEEgbyCXERJJBFL4HA6uOtvv6Z62sGxHc0OCdyXA040HCXUFSavMC8j7h4rsDeEsViMeDyOaZqZ3INAILBHcUTXdB786q/pqm8jWJSH4BBJp1KoKQVUk0/96vK9ZlQcqrCvDba5xXi8zh8oTNPM6I6Ki4txuVyZc0vTtExxRFEU4vE4s2fPRpIkTNPk9ttv55FHHmHWrFk8+OCDh6ymcxLDj0lykYWhkovRJBaGYWRs4yZydoM9U6+qKhUVFUOu1GdXniORCJqmjXrmwXAgmUxm8hqqqqomjLakL2xnLL/ff9CrktlOLolEAhh85flAkN3FKyoqorBwT93AUNBY28Qzf1/B1o3bKSjO58wLl/Dbn95LW1M7gRzr+2YaJt09409O2YnD6UCWnVz+vS9z7ifOGo6PNWRkC/mzQwLHO7IDIWOxWKY4ki3cTXRF+cOXf4VDduD0ukinUiAIuGSZcHMXZ3/nExx5zuA1LYcCJomFhfb2djo6OiguLqagYLdZgW1za1+zbrvtNt566y1ycnI4/vjjSaVSrFy5khkzZvCXv/wlY+U9iUkMBpPkIguDJRe2cNvWWIw0sdA0jYaGBlKp1JBs4w412KFwAFVVVfsUJu4L2R7i0WgURVHG1IjLQMiu1E/ksThbaJ2bm0tpaemY2jxkOwTZlef9sYzcF+wQrEgkMqJucR8/8RJUVeuVn2GaJqGuMLPnzeSkM07ghKXHU1ZZMiLvvy/0TR8/VG2o+xvLEwQBt9PFU9c9hJpM4/C5QBBwu1zomk68M8JFP/4i0xbNPtjLHzVEo1EaGhoIBAJUVFSMqWvDaMJ2JxtM0WHz5s088sgjrF69mq6uLsDSchx//PGcffbZnH766ZSUHJzv9yTGHybJRRYMw0BV1b0+ZrSF26lUioaGhgk/+pKdPD5SM/XZIy6pVGq/xJUjje7ublpaWoYs1j2UkF2pHw/OWAPpf7Irz/uz/uwRoJEuOtxw+c28u+Z9grmBzFo1VSMRT3LljZdTWlmCP+hn+qypo/63yHYIG4wb0qEC0zR75Wms/b+VbHl2HbLfhTfoBwNiHWGKp5dzyV1X4ZAP/vVrNDBJLCx0dnbS1tZGYWEhRUVFg37e/fffz+9//3uCwSAlJSVs3bo187u5c+eyZMkSli1bxqxZs0Zi2ZM4RDBJLrKwL3Ix2sLt0dhQjwfYG+rRHH3Jvmnb4sqDGa6WPTebl5dHSUnJhLxpZo8HjsdcF9sy0j639ncsb7hGgJS0QjKRIpDj3+v7vvvG+/z4Wz8jnVJwuWUM3UBVVAI5ftQeC1pRFJl2+BS+c8tV1Eyv2q/1DBWqqlJbW4tpmlRXV09Y0XIymWT7lm2899dVNK3bhZpMIzpEcisKOf2qC6meO+2QMbHYGyaJhQXb2KE/s5O94aGHHuKOO+6goqKChx9+mPLycpqbm3nxxRd56aWXeOONNzJ7pDvuuINzzjlnJD/GJMYxJslFFvZGLkZTXwG7Lw5jYZb8YGGsbKhtcaU94gKjM0tvI3v0pbi42PLrn4A3TV3XaWhoIJlMHhLjgf2N5QmCkCEafr+/37G87Er9/nYzk4kUj97zGC88tZJUMk1JeREf/+JHWXLeqQOeW2++spZH7/0bu7bV4XBIlFeXsXXjdiRJxOVyoRsG6VSa0opi7v7nHbg9I9tBsK2HBUGgurp6wuqO7LBEl8tFVVUVXXVttO1oRpBFAjX5JJJJDMPopdPw+XyH3DVkklhY2F9i8eijj/LLX/6SsrIy/vKXv1BVtWeBIBaLsXr1atatW8fnPvc5KisPjnHDJMY+JslFFuw2c38/tzsWIz0GlT3ycSCuL+Md2XaSY2lDbYerRSKRPWbpRyJL41DbUO8v7CAwVVWprKw8ZMS62eg7lgd7dstsh7ADqdSbpskt1/yCNSvfxuGQcDgdPbkUEt+84aucceGSvT43Go7hlB184+PfprWxDX9wt6mCrusk40muvfVqlnzk1KEfhEEilUpRV1eHJElUV1dP2K5uPB6nvr5+r05p2d2yWCzWyz7Z/jcWtWVDQSQSobGxccITC7vLP9S9w9///nduvfVWioqKeOSRR6ipqRnhlU7iUMfEGMI8ANj6itHoWOi6TmNjI/F4fFyOfAwXsgXsFRUVY2pD3TdczZ6l7+rqoqOjA6fTmUkHP9AxhGxnrEPJ/WaoyN5Q19TUHLIz9S6XC5fLRWFhIaqqZs6t1tZWWltbkWUZVVVxOBwHNAK0ZcM21q5+F7fHjdtjvYbb4ybcFebvf/o3S847dcDNpiAIBHMDJBMputq7ccq9N/WSJCGIAs31rfu1tsEgmUxSV1eHLMtUVVWNCS3UwYBNLDweD1VVVQN2t7MD1rLtk2OxGE1NTQD4fL5MV2O8EbVJYmEhFArR0tJCXl7ekIjFv//9b2699VYKCgp46KGHJonFJIYFE/OqPAj0FW6PNLFQFKWXKHGibiTT6TT19fUYhkFNTc2YFrCLokgwGCQYDPZKcQ6FQnR2dh5Qlka2M9ZETWCH3pa7E2n0xel0kpeXR15eHrquZ8grWKTTHv/YHxK7Y9NONE3H1yfbwe1109HSSVd7dyZVeyC43DL5RXm0Nrbhcu8+N3VdxzRMSiuLh/BpB4/BVOoPRSRCMSJtIYLFuXhz/ZlgOK/XS2Vl5aDHZgVBwO1243a7KSoqQlXVDNGwSexIdmKHGzaxCAaDlJeXj+m1jiTsYN3c3NwhjQ8/9dRT/OQnPyEvL48HH3yQ6dOnj/BKJzFRMEkusmB/IUdbuG2nC4uiOKE3kvZxkCSJKVOmjKuNZPaYQWlpaS/Rbnd395BEu/F4nIaGhgkv5LePw0SvUCeTSTo7O/F6vZSXl2d0Gtkkdiiz9P4cPwJg6AZSViq2pmo4ZSc+/75tXEVR5MLPnsf9v/oTiXgyI/JOpdIUlxVxwpLjD/Rj7wHbgnmoG+rxDCWZ5uV7n2LDc2tRUwpOt8xhp82j5ow55BbkHbAez+l0kp+fT35+fmbkMxaL9erEHqir2UhhklhYCIfDNDU1ZYJ1B3scli9fzk033UQwGORPf/oTM2fOHOGVTmIiYWLerfeC0cyvgN0Vh4mcWQC7j4PH46GysnJcVySzxxBKSkp6iXbD4XCvLA2/v7dLj32j8Pl8VFRUjOvjcCDIPg4TZSPZH+wNVLaxg9PpJBAI7JF5EAqFBpXTcuxJCygoyaejtZNATgBRFFEVFVVRWXLeqXgHQS4ALvjsuYS6wvznr0+TTKQQRZHph0/l2p9ehcc7vKNr2aMvE8mC+YW7nmDdk6/j9Mi4/R5SiRTv/ns1yVicj//oy8N6f8oe+TRNk3g8nhnN6+7uHlMZQJPEwkIkEskQi7KyskEfhxdeeIEbbrgBr9fLH//4R2bPnjgZKJMYHUwKurPw3//+lxdeeIETTzyRk08+eUSDmEzTpKOjg46OjkzFYaLcMLORHYY21AvkeINtGBCJRIhGo6TT6V5ZGqqqZs6HQ/k47Au228lEPw62OHMwG6jsWfq+55a9IcwuXGx490N+cf3/0tnWjQCIksico2fxg19eSyAnMKR1hrsjbN+0g0DQz4w504f972WnsE+0jWS4tZsHvvBzADxBL7quoygqWlLB6XRy2Z+vI1iUO+Lr6JvkbJ9bttmA3+8f1e7qJLGwYLtjDfU4rFy5kmuvvRaXy8Uf//hH5s+fP7ILncSExCS5yMKvf/1r7r33XsASV55wwgksW7aMU045Bb/fv49nDx7ZXv1FRUUUFBRMyAukaZo0NzcTDofHRRjacKO/LA17TGE8CisPFNnWwxPZKS2bcO+vBbN9bsViMRKJBLCnfXIiluDNV9YS6gozdWYNRy6cOywFDk3VePpvz/Lcf14iGopy5MK5XPyFC5k2c8qQX8smmmMxhX2kUfvOVv7vO/fgzbPuPYqq4pAkRAQS4Tif+d+vUz1/xqivS1GUDNGwzy23250hGiOp05gkFhb213Z39erVfPvb38bhcPCHP/yBY445ZoRXOomJiklykQXTNNmyZQvPPvssy5cvZ/v27YC14Tv++ONZtmwZp556Kjk5Ofv9HpqmUV9fTzqdntDWorbFqp0ufCDHdDzDMAwaGxuJxWIEg0F0XT8oWRoHG9lEs7i4mIKCgoO9pIMC0zRpa2ujq6tr2Ai3pmmZzeBI2yebpsnPr/s1r6x4DUwTUZLQdR1/wMdP77mJmUcMbjOcTbAmKtHsqm/jj/9zOwgmguzA4ZBwOpyk4klM3eBLf7iW/MrBJy+PBGydhn1u2enzNtEYTp1GtrZgInc0bTG/PSo52OOwZs0arr76agRB4N577+X444dfFzWJSdiYJBd7wfbt23n22WdZsWIFmzZtAqy51OOOO45ly5Zx2mmnkZ+fP+jXy3YAqqysHNNOSCOJbIvVQzWzYDDoa7kbCFjjKLqu9wrty94MBoNBZFk+pG6s2QRrIhPNbIJVUlIypGvLYJFtnxyLxYZ9M/jBOxv5/mU34XBIGRcpOxvjuFOO4ea7btjna2R3sCZyZxfg0WvvZvtrG/AEvbh9XtSUQiqS4PDTjuLin3zpYC+vF7Id86LRKLquZ4ws7H/72xmbJBYWbLc0W4s22OOwdu1arrjiCgzD4O677+akk04a4ZVOYqJjklwMErW1tSxfvpwVK1awfv16wCIaCxYsYOnSpSxZsoSiooGrSOFwmJaWlgnvAGRbi4qiSFVV1YR1xrKth3Vd32vKcn+bQVmW99uGdKwhm2BVVlYO6/jheMLBIFi2aNc+tzRNG5KrWX949N6/8cjdj+EL9HatSiaSiJLIP1Y9jMM5sGmFaZq0tLQQCoVGjGCNF3R0dFC/vZb1j66mdWMDuqLikJ3UHHMYH/n+Z/Dmjt3vykDp8wNpgPaGSWJhwSYWQ3VLW7duHd/85jdRVZW77rqL0047bWQXOolJMEku9guNjY2sWLGC5cuX8+677wKWQ9D8+fNZunQpS5cupbS0FLA20z/84Q95/fXXuf/++5k1a9aEFG7DbivJie6MlU2whpLdYBhGr82grusHlKVxsKGqKnV1dfskWIc6slPYDxbBGmgzmO1qNhh3oL//6d/86c6/4A/4e52LiXgCl9vFY688NODrZHduysrKyM3NHa6PN66QbfZRWFhIQUEB7dubCbd0kVuWT9H08ac16E9f5vF4MufXQEWmSWJhIZFIUFdXN2RisWHDBi6//HLS6TR33nkny5YtG+GVTmISFibJxQGipaWF5557juXLl7N27dpM6N6RRx7J8ccfz4svvsj27ds56qijeOCBBybshtoWZk40K8m+sOdlXS7XAWU3mKbZawTBrjpnE42xfIztEUFBECZUOF5faJpGXV1dJoV9JB3qhoJs56lUKgUMLsW5YVcT3/zEt9F1A6/PgyAI6JpOPJ7g7IuXcfVN3+j3eYZh0NTURDQapaKiYsJq0fqOhBUW7j3McDyiPw2QLMuZc8vjsc6bSWJhIZFI9AqOHOx1fdOmTXz1q18lkUhw++23c84554zwSicxid2YJBfDiI6OjgzRWLNmTYZoBINBPvvZz3L22WdTU1NzkFc5ujBNk9bWVrq7uyesMNOGbS2anVkwHMiuOkciEVRV3WuWxsGGfbN0Op1UV1dPWMKd3bmprq7G7R7ebIjhQnaKs202YLsD9Vd1/tefn+DB3zyCpllBpAJQPb2Kn957EwVFe445GYaRMXfI1h5NNGSL+SeKqUF/3VhJkpBlmWQyOeFdoZLJJHV1dUMmFlu3buUrX/kK0WiUn//851xwwQUjvNJJTKI3JsnFCGDlypV861vfIpFIMGXKlMxsPcBhhx3GkiVLOOOMM5g+ffpBXunIInuOvLS0lLy8vIO9pIOC7DGHkbbU3FveQTAYHPR4y0jBHo07FMISDwTpdJq6urpx17nJdgeKxWKZqnNfDdCWD7bxyvJVxCJxDj/yME4952S8vj3H3nRdp76+nlQqRVVV1YQ1d5jUmpAJhezo6MiQ2OwcIL/fP6EKETaxsLvcg71W7tixg6985St0d3dz6623cvHFF4/wSicxiT0xSS6GEaZp8pe//IWf/exnyLLML3/5S84880zC4TAvvfQSy5cvZ9WqVSiKAsCUKVNYunQpZ5xxBjNnzjykqjOqqtLQ0ICiKFRUVExYoW72HPnBcL7pb9bZvlkPRVQ5HLA7NxN9NM7W3DgcjnFt7jCQBihbEL63c9225VYUherq6gmrucm+RkzkIgzsDkzMzc0lLy+PWCxGLBbrpdOYCPbcqVSK2tpaZFmmurp60MSitraWyy67jI6ODm6++WY+/elPj/BK+8cNN9yAruvcdttte31cQ0MDP/nJT3jrrbdwu91cdNFFXHPNNRO26HQoYZJcDBNM0+SWW27h4Ycfpri4mLvvvpsjjjhij8fFYjFefvllVqxYwcqVKzPzzJWVlSxdupRly5Yxd+7ccU00si13q6qqxuy4x0hD13UaGxuJx+NjwmLVHm/JDr8ajZt1dudmf0PhDhXYji/2mMOhchO1NUB2V2Nfo3njZSRspGGaJk1NTUQikTFxjTiYyCYWfbu7mqb1Gs0bqGN2KCCVSlFXV5cZGx3sNaKhoYH/+Z//oa2tjRtvvJFLL710hFe6J3Rd51e/+hV//OMfueiii/ZKLlRV5bzzzmPq1Klcc8011NXVccMNN/C5z32Oq666ahRXPYmRwCS5GCZ0dXWxePFi5syZwz333ENJSck+n5NMJnnllVdYvnw5L7/8cqYVXFZWxpIlS1i2bBnz5s0bVxXeeDxOQ0PDhLfcHetZHv2JKvc2R7+/yB73mOiZBZFIhKampiE7vow37G00zz63GhsbMU2T6urqCWtHbZomjY2NE17EDnsnFn1h23Pb//p2zMa6mcXekE6nqa2txeFwUFNTM2hi0dTUxGWXXUZzczPXXXcdX/7yl0d4pXti+/btXH/99ZniyaJFi/ZKLp566imuv/56Vq9enTn3H3vsMX7xi1/w+uuvH9KdqYmASXIxjNi5cycVFRX79aVIp9OsWrWK5cuX8+KLLxKNRgEoKirKEI2jjz56TFc67RuEz+ejoqJiTK91JJFOp6mvr8c0zXHRubFTwSORyF7n6IeKbAegyXEP67sxEQWq/Y3mCYJAfn4+ubm5E3ITka1Hm+jEwh6X3B89mq3TsM8vVVX3y0J5LCCbWAzF6KK1tZXLLruMhoYGrrnmGi6//PIRXmn/+Otf/8qmTZu46qqr+Na3vkVFRcVeycWPfvQjNm3axP/93/9lflZbW8uZZ57J3//+d+bNmzcay57ECGGSXIxBKIrCG2+8wbPPPsuLL75Id3c3APn5+Zx++uksW7aMhQsXjhlxW7Z94kgLlsc6sp2QxmPnZqA5+mAw2Msmcl/Izm6YyA5AAJ2dnbS1tU3474Y9Ry6KYsYNKDt93u5qHOrHZ9IdazcOhFj0hWmavYisPXLs9XozRGOsEllFUTLfjZqamkHf29vb27nsssuoq6vjyiuv5IorrhjhlQ4Ol1566T7Jxde+9jXcbjd33HFH5mfJZJL58+dz5513cvbZZ4/CSicxUhgbu9NJ9IIsy5xyyimccsopaJrGW2+9xX//+19eeOEF/vnPf/LPf/6TnJwcTjvtNJYtW8aiRYsO2ibWMAyam5uJRCIUFxeTn59/yG8OBoI99jKenZBEUcxs9LKzNCKRCF1dXb2yNAYS7GaPhFVXV4+Z7IbRRjbpLigooKioaMJ+N2znG1mWM/kudscsGo3S1dVFR0cHTqczc34NlsiOJxiGQX19/UENTBwrsInFcOmwBEHA5XLhcrkoLCxEVdXM6GdbWxutra24XK5MV2Os6DT2l1h0dXXxta99jbq6Oi6//PIRJRYNDQ0sXbp0wN+vWrWKoqKiIb1mKpXao2Nnj0im0+mhL3ISYwqT5GKMw+FwsHjxYhYvXsxNN93EO++8w7PPPstzzz3HE088wRNPPIHf7+eUU07hjDPOYPHixaM2w6xpGg0NDaRSqQnf2rdDAoPBIGVlZeN25jcb9py8z+ejpKSk1/hBKBTqV7CbPRI2ZcqUCT1PP9GtRW0MJGKXJIlgMEgwGMQ0zQzRyCay2c5T4/07ld3Nm8i2uzD8xKI/OJ1O8vLyyMvL60Vku7u76ezsHJKz2UjBNjawLakHSyy6u7v52te+xo4dO/jSl77Et7/97RFdZ0lJCc8888yAv9+f65vb7c44Z9qwScVELUgdSpgkF+MIkiRx7LHHcuyxx3LDDTfw3nvvsXz5cpYvX84zzzzDM888g9fr5aSTTuKMM87gxBNPHDF7R0VRMvkdNTU1E9pG0g6+OpSr04Ig4PV68Xq9FBcX9xLsRiIRBEHA4/GQTCZxOBxMmTJl3I2EDReytSZlZWXk5uYe7CUdNNi5JvsSsdtz8n6/v1coZDQaJRwOj9s5eht2nkc6nZ7Q3TwYHWLRF32JrN2RjcVivQol9r/ROL9UVaW2thaAmpqaQV8vI5EI3/jGN9i6dSuXXHIJ11133YgfQ6fTOey5XKWlpWzZsqXXz9ra2gAGZYgzibGNSXIxTiGKIkcffTRHH3001113HRs2bODZZ59l+fLlrFixghUrVuByuTjxxBNZtmwZp5xyyrBVyhKJBA0NDUiSxJQpU8bsHOtII3sTOZGq04Ig4Ha7cbvdFBUVkU6n6ezsJBwOA9ZNs7m5+aBkaRxsZM/TV1ZWTuh5+kgkQmNj45BzTWyi6vF4KCoq6jVH39TUBNArWG2sk1hd16mrq5vweR6wu8N7MC2pszuy2c5msVis1/llk9mROL/sjoXd4R3se0SjUb7xjW+wadMmPvWpT3HjjTeO22LWsccey+OPP04sFsuMB77++uv4fD5mzZp1kFc3iQPFpKD7EINpmmzevJlnn32WFStWsH37dsDScRx//PEsW7aMU089db9HmA4FXcFwIDtZuLy8fEKPhIXDYZqamvD7/RQXF2fGD+wsDVtQOVI36rGC7FC4sWg/PJqw3bFycnIoKysbtg1Qf1ktI2GhPFzQNI26ujo0TZvQeR6wm1jk5+dTXFw8JjfF9vll52kAw244oGkatbW1GIZBTU3NoItz8Xicb37zm7z33nt87GMf45Zbbhmzo4L9CboVRSEcDpOTk4Msy6TTac477zxqamq49tpraWho4IYbbuDSSy8dM8L0Sew/JsnFIY5t27ZluhmbNm0CLB3HcccdlyEag6m4m6ZJZ2cn7e3th5SuYH+QHQBWWVk5oUccbCek/jaRdpZGJBIhkUiMWJbGWED2OVFVVTVZnW5tHXF3LF3XM4Ld4bRQHi5MBgXuxnggFn1hn1/2P8MwcDqdvfI0hvo59pdYJBIJrrrqKtauXcsFF1zAbbfdNqYLe/2RizVr1vD5z3+eP//5zyxatAiwrGdvvvlm3n77bXJycvj4xz/OlVdeOWH3FocSJsnFBMKuXbsyROODDz4ArFnUY445hqVLl7JkyRIKCwv3eF5XVxerV69m+vTpFBUVUVhYOC5uDiMBOz1VFEWqqqoOqQ3yUDBUrcneNoLBYHBcW5Cm02nq6uoAJnwonJ3EPtr6o4EslG2isT8bwQOBTSwMw5jQ5wSMT2LRF7bhgH0N0zStl6GFz+fb52Y/u4tVU1Mz6HMinU5z9dVXs2bNGs4++2xuv/32CTVqOonxiUlyMUHR0NDAihUrWL58OevWrQOsWdSjjz6apUuXsnTpUkpKStiyZQtXXnklbW1t/POf/2TatGkHd+EHEbFYjMbGxl52mhMRpmnS1NREJBLZL61J9kYwGo1mKoLj0YLUJpuSJFFdXX1Ij33tDdlk82AnsWcLdvvbCNrOZiMFRVEy8/RDqU4fijgUiEVf2IYDNtGwE+iz8zT6Xgd0Xae2tnbIxEJRFK655hpWr17N0qVLufPOOyfsNWYS4wuT5GIStLS0ZIjGO++8g2EYAEydOpWGhgZUVeXrX/86X/3qVw/ySg8estPH9+Z6c6gjW7A8HFoTeyMYiUSIRqPouj6oLI2xADswcZJsjl3b3WzBbvZGMFsQPpx/NzuzQBCEITkAHYo4FIlFf1AUJUM0+uqA7PMrO/dnsONxqqry3e9+l5UrV3Lqqady1113TWiiOonxhUlyMYleaG9v57nnnuPhhx/OiMEB5syZw5IlS1i2bBk1NTUHcYWji2ytyURPWB5pwbJpmr2yNFRV7RXqN5ayDmyL1YlubJDdxRoPtrvZzlPJZBIYPsMBezxOFMUJ3cWC3VqsQ9meuz9kj3/G43EMw0AQBEzTpLS0lNzc3EEdC03T+P73v88LL7zAiSeeyD333DNJLCYxrjBJLibRC6Zpcuedd3L33XeTk5PDpz71KT788ENef/11NE0DYObMmRmiMdze12MJ2RXZwsLCCa01yc41GQ1xql1xtjsaiqKMmawDu4s1VIvVQw2GYdDY2EgsFhuXIZqapvVynjJNc7+dgbLH44aSsnwoYqISi76wxduKoiBJUqYrm52n0d+1Q9d1brjhBpYvX86iRYu49957J7RBxCTGJybJxSQyUBSF66+/nqeeeoopU6Zw3333ZboU4XCYF198kRUrVrBq1apMsubUqVNZunQpy5YtY+bMmYfMjcQe/4nH4+OiIjuSyBaxV1dXH5QKWvZoSyqVAnZnHYxmloY96jHcFqvjDdnjcRUVFeM+zyM7wTnbGWgwOiD7++FwOIaUsnwoYpJYWDAMI2NVbhdj7GDIWCyWGc9raWnhrbfe4oQTTuC4445DkiRuuukmnnrqKRYsWMADDzwwoS2tJzF+MUkuJgFYleIvfelLvP766yxcuJC77rqLvLy8fh8bi8V46aWXWLFiBa+88kpms1dVVZUhGnPmzBm3N5bs8Z+KiopMwM9ERDwep6GhYUzpCvrLOhjpLI1sJ6RDfYZ8X8hOmz4U8zwMw+glCO+rA/J6vZmKczKZpK6uDlmWqa6unrDjcTBJLGzYxCKZTA6Yxm6P5/3xj3/k0UcfBaxrWE5ODs3NzRxxxBE8+OCD4560T2LiYpJcTAKwLnbnn38+CxYs4Oabbx50dTqZTLJy5UqWL1/Oyy+/nNnslZWVZVyn5s2bN25GR9LpNPX19RiGMeHzCuzARK/XS0VFxZjcOGWPttihV8OdpWGaJq2trXR3d1NcXExBQcEBv+Z4RTbxnghp0wPpgHw+Hy6Xi87OTtxuN1VVVWPy+zFamCQWFrI7egMRi76Pf+utt3j++ed55plnMvdPh8PB8ccfz5IlS1i6dCmlpaWjsfxJTGLY8P/bu/fwpuvz/+PPttDzAQptObQpiCiKAnIqyKHQhOmY+/pzigNdmRM5KBSRM1YFwYFyRk5T8ISIm14OnaBJOQmUQ8tRtI7DmJKeD9DzIUmT/P7oElPaQgNNmzb347r2x9KEvqmh+bw+7/d93xIuRIPR6XQkJiaiVqs5cOAAxcXFAISGhlp/ST7wwANO+yFcVlZGWloaHh4eREREuHQBneX4T2BgIJ06dWoWFwu1zdK43em6tgXLHTp0qHM3zxW4+lA4s9lsveNcWFhoPRrq6+tLYGBgrS1IXUFeXh65ubkuX5dmGywiIiLqvaNnNptZtWoVn3zyCV26dOHhhx/myJEj/PDDD9bn9OzZE6VSyR//+MdaZ1EJ4WwkXNxEZmYmK1asICkpCb1eT69evZg/fz7du3ev8zUbNmxg/fr1NR5PSUlximMljUGv13Ps2DE0Gg379u2joKAAgODgYEaOHIlKpaJ///5O8/OwdP9x9buQZrOZ3Nxcrl692qyP/zTELI2GbrvbnNnObnD1oXAlJSWkpaVZd8hKSkqsd5x9fHys7zFXuDkhwaKK2Wy21uiFh4fX+yitpYHKRx99RPfu3dm2bZu1lXN2djb79u1j3759JCUlYTAYeOSRR1i1apUj/ypCNAgJFzeg1+t5/PHHCQ4OZs6cOXh5ebFx40aSk5PZtWtXnf3c4+Li8PHxYc6cOdUeDwkJaYxlO53KykqSk5NRq9Xs3buXq1evAhAUFMSIESNQqVRERUU12V0/y116V+/+Y9sdKzQ0lODg4BZxsWCZrmt7hv5m05stdQUVFRV23YVsiSwtVmV2w683IXx9favNu6msrKzWgtR2An1AQADe3t4t4t+SLdtg4aqfbVD1+8XSNc2eYAGwadMmtmzZQteuXdm+fXuduxIlJSUcP36cu+++m4iIiIZauhAOI+HiBo4ePcpf/vIXDh06RFhYGFAVOAYOHMgrr7zCE088UevrHnroIcaNG8czzzzTiKttHoxGI6dOnUKj0ZCQkEBOTg4AAQEBDB8+HJVKxeDBgxvlzmhLuUvfEGzbirbk7li1naG3tIe0zNKwBIvKykqXr7ux7YQUERHh0sGiqKiI9PR0/P396dy5c503IWx3zUpKSuoVZpsbCRZVbI9NhoeH21WAvXXrVjZu3IhCoWD79u3WawwhWgIJFzeQm5vLTz/9RHR0tPUxvV7PgAEDmD59OhMmTKjxmvLycvr27cuWLVsYOnRoYy632TGZTJw9e9YaNDIyMoCqM8zDhg1DpVIxZMgQh1zcmUwmMjMzKSoqcvkiXdu79PbeeWvOzGaztT2k7SwNADc3N5coWL4RmUD+K0uwCAgIoHPnzvUOB5YJ9Jb3WGVlpXUw5I1mHTiz3Nxc8vLyJFjYBAt757x8+OGHrFu3js6dO/PJJ5/QsWNHB65UiMYn4cJOW7duZeXKlXz55Zf06NGjxtfPnTvHmDFjGDNmDMnJydadjtmzZxMaGtoEK24ezGYzP/74I2q1moSEBLRaLVDV+WfIkCGoVCqGDRvWIMdTjEYjaWlplJeXu/xZetsiXVe/S2/pjgVV70c3NzfrLA1/f3+XurguLS0lNTXV5WuQ4Nehibfb3MA2zNrOOmhO7zFLsAgJCXHpwmKz2UxmZiaFhYV2B4tPPvmElStX0rFjRz7++GM55iRaJJcOF2lpaSiVyjq/npiYWO3OTEJCAi+99BJPPfUU8fHxtb7miy++4OWXX+bZZ5/l0UcfJS8vj9WrV1NWVsbOnTtd+uKtvsxmMxcuXODbb79lz549XL58GQBPT08GDx6MUqkkOjr6lkKBwWAgNTUVg8FARETETVsFtmSWs/SAyxfpWu7St27dGoVCgclkoqSkhKKiIsrLywHHz9JwFnXVFbii/Px8srKyHDI00dJ5qri4uFm8xyRYVLGtTevUqRNBQUH1fu3nn3/O0qVLCQ0NZfv27dYhtUK0NC4dLix3bevSpUsX6x27Tz/9lCVLljB69GiWL19+ww/cwsLCar9wsrOziY6OZvXq1YwePbrh/gIu4j//+Q8ajQaNRsOFCxeAqj7gAwcORKVSMWLEiHq1CK2oqCA1NRWQi2lL2105S/9r9x8fHx/Cw8Nr3KWva5ZGYGBgi+sKVFhYSEZGhss3N4BfGz20bduWsLAwh9ZJ3Ghei7+//y21UW5IEiyq2AYLe2vTdu7cyeLFi2nfvj3btm2jW7dujluoEE3MpcNFfa1cuZItW7YQGxtLfHz8Lf2SHzRoEJMmTeLZZ591wApdxy+//GINGikpKQB4eHjQr18/VCoVI0eOrPHhZzab+eqrrwDo1auXy19MW+5M13Ux7UosF9M3K9K1cMQsDWfhyLv0zY1lKFxTNHqwvMcs/7uVNsoNxXYyvQSLX4dp2hssdu3axWuvvUabNm34+OOPb9jKXoiWQMLFTaxYsYKtW7cyd+7cWgu4r7dq1Sr27dvH7t27rR8AluNXW7ZsYfjw4Y5esstITU0lISEBjUbD999/D4C7uzt9+vRBpVIRExND+/bteeONN/jyyy8ZPHgw69evd+mLacv5cbkzffsX05ajU5agYXsRGBgY2Kzaj1ouphvjLr2zs3RCcoZp0yaTqVpBuNFoxMPDo1rnKUf9G7btpifBwkxOTg7Xrl2ze5imWq0mPj6egIAAtm3bVmutphAtjYSLG0hKSmL8+PHExsYyefLkal/z9fXFz88PvV5vPQbl6elJSkoKY8eOZcyYMcTGxpKXl8fSpUvx9vZmx44dLv2h7UiZmZnWoHH69Gksb+uAgACKi4tRKBRs2bLFZYvqzWYzV69eJTc3lzZt2tChQweXfS/a/iwa6mL6VmZpOAPbO9POcDHdlGx/Fs44FK62Nsru7u7V2ig31I0T22Dh6t30bH8WYWFhdc63qs2+ffuYN28evr6+fPjhh9x3330OXKkQzkPCxQ28+uqrfPbZZ7V+bdq0acTFxVkDyLZt24iKigKqQsnatWs5f/48np6eKJVK5s2bZ1fhl7h1OTk5fPXVV/ztb3+jpKTE+vi9996LUqlEqVS6VCGd7Xa+M140NSbbO5CO+llYLgKLioqs7Uevn6XhDDtGtj8LuTPdvO7Sm81mdDqdddesoqICNze3agXht9p5SoJFdZZ6E3t/FgcPHmT27Nl4eXnxwQcf0Lt3bweuUgjnIuFCtDipqak899xz/PLLL/zud79jwIAB7N27l+PHj1NZWQnAXXfdhVKpRKVScccddzTxih3HZDKRkZFBcXGx3dv5LY1t+0h770Dezve8fpaGo+4227suS2FqY/0snJVt+G6uPwuDwWB9j5WVlQHg4+NjDRr1bTogwaK6Ww0WR44c4aWXXqJVq1a899579OvXz4GrFML5SLgQLcq5c+eYMmUKV69eZerUqcTFxVnvTBcWFrJ//340Gg1HjhxBr9cDcMcdd1iDRvfu3VvMXX2Z5/Er2wnk9raPbChms9nafrSoqKjJ5hzYDv9qydPY68M2ZLWU8F1ZWWmtBSotLbU2HbAE2rpqgSRYVGepvbF3J+v48ePMmDEDNzc33nnnHQYNGuTAVQrhnCRciBbj/PnzjB07Fr1ez6JFi3jyySfrfG5JSQkHDhxAo9Fw+PBhKioqgKoWtZajU/fee2+zDRqVlZVotVqZ50H1kOVME8ibYs6Bbciyd/hXS2O7k9VSQ5al6YAlbJhMplprgSRYVGdpcGDvFPJTp04xbdo0TCYTmzdvZujQoQ5cpRDOS8KFaDHOnDnDq6++yuzZsxkxYkS9X1dWVsahQ4dQq9UcPHjQeqygU6dO1qBx//33O8U5+frQ6/VotVrMZjMRERF4e3s39ZKaTHMJWQaDodrdZri1Yy03YjKZSEtLo6yszKlCVlOw3b1pqp2sxmY2m6t1nrLUAvn5+VlDSHM9FtaQLPNN7G1wcPbsWaZOnYrBYGDDhg12fQYJ0dJIuBDChk6n4/Dhw6jVag4cOGAtCA8NDSUmJgaVSkWfPn2ctp1teXk5qampeHh4oFAoXHqeh2VIptFoRKFQNJuQZTQaqw1Ua4hZGkajkdTUVHQ6HeHh4fj5+Tlo9c7PbDaTnp5OcXGxy+7e2NYCFRQUYDQaAazH8wICApz2d5wjWYKFvfNNfvzxR6ZMmYJOp2PdunWoVCoHr1QI5ybhwgVkZmayYsUKkpKS0Ov19OrVi/nz599wkE9aWhpLlizhxIkTeHt789hjjzFz5kyX+sDR6/UcPXoUjUbDvn37KCwsBKBdu3aMHDkSlUpFv379GuWcfH2UlJSQnp6Op6cnERERTrOupqDT6dBqtbi5uaFQKJrtFO3aZml4enpag0Z9ZmnY7t4oFAp8fHwaafXOx/ZYWHh4OAEBAU29pCZj2y2sXbt2eHh4NOoRPWdjmXtjb3vqf//730yePJmysjJWr17Nww8/7OCV1i0+Ph6j0cibb755w+dt2LCB9evX13g8JSXFpT83RMORcNHC6fV6Hn/8cYKDg5kzZw5eXl5s3LiR5ORkdu3aVesWuMFg4JFHHqFr167MnDkTrVZLfHw8Tz/9NNOnT2+Cv0XTMxgMJCcno9Fo2LNnD9euXQOgTZs2jBgxApVKxcCBA5vsQ9gyadrPz4/w8PBmc4TLESy7N61atWpR09gtA9WKioooKSmp1yyN5rp74whyLOxXtsHi+qNQtR3R8/b2rnZEr7nWotXFMlzU3mBx8eJFJk2aRHFxMcuXL+f3v/+9g1daO6PRyMqVK3n//fd57LHHbhou4uLi8PHxYc6cOdUet6e+RIgbkXDRwh09epS//OUvHDp0iLCwMKAqcAwcOJBXXnmFJ554osZrdu3axYIFCzhy5Ij1yMA//vEPli9fzrFjx5rtXeCGYjQaOXXqFGq1moSEBHJzc4GqIwXR0dGoVCoGDx7caD8ny1b+rU6abklKS0tJTU3F29ubiIiIFrvTVtf5eX9/fwIDA/H19bXuWJjNZhQKBV5eXk297CZjMplITU2lvLyciIgIlz8WVt/Wu0ajsVrQsN058/f3x8fHp9n/vrHcmLF3uOjly5eZNGkS+fn5LF26lD/84Q8OXmnd61iwYIH1915UVNRNw8VDDz3EuHHjeOaZZxpnkcLlyP5XC9e9e3feffdda7CwMJvN1mM+1zt58iQ9e/asdhZ50KBBlJSUcP78eXr16uXQNTs7Dw8PBg4caA1oZ8+eRa1Ws2fPHnbt2sWuXbvw8/Nj2LBhKJVKhgwZ4pCjKLYdXlx9ujJAUVERGRkZ+Pr6tvjdG0sLWz8/P8LCwqzn54uKiigsLLS+D9zd3YmMjHTpYGFbb6JQKJy2qL8x2AaL+rTe9fDwICgoiKCgIEwmE6WlpZSUlFBQUMDVq1fx8PCw7mj4+fk1u98/lmARFBRkV7C4cuUKU6ZM4dq1ayxevLjJggVAcnIy99xzD5s3b2bGjBk3fX55eTlarZY777zT8YsTLkvCRQsXEhJCdHR0tce2bduGTqdjyJAhtb4mKyuLDh06VHssNDQUgIyMDJcPF7bc3d3p27cvffv2ZcGCBfzwww/WHQ21Wo1arcbb25uhQ4eiVCoZPnx4g1zcNMVAOGdmOS8dGBhIp06dmt1Fzu1wc3PDx8cHHx8fQkJCKC4uJiMjA6i6sP7555+tszRcrVDXaDSi1WrR6/VO3S2sMdgbLK7n7u5ufQ916NCB8vJy685ZQUGBUwyHtIflZoS9O76pqalMmjSJvLw8Xn31Vf74xz86eKU3Nm7cOLuef+nSJUwmE2q1msWLF1tPMsyePdv6OS/E7ZJw0cylpaWhVCrr/HpiYmK1c5QJCQmsWbOG2NhYevToUetrKioqanRQsdz51Ol0DbDqlsnNzY1evXrRq1cv5syZw/nz5/n2229JSEhg79697N27Fy8vLwYNGoRKpWL48OG31KnGcna8tLTUZdpo3oilJ729xxpaovLycjIzM/Hy8kKhUFTrPJWZmUlmZma1oX0tpR6lNpWVlaSmpqLX612+kP12g8X13Nzc8PX1xdfXl9DQUHQ6nfV9VlRUZP26JYw4W5FwcXEx6enpBAYG2hUs0tPTmTx5Mjk5OcyfP58//elPDl2nvZ/v9XHp0iWg6hjv22+/TV5eHqtXr2b8+PHs3LnTpf+diIbjXP/ihd3CwsL45ptv6vy67R3tTz/9lCVLljB69GgWLFhQ52u8vb2t06stLKHCle/82cPNzY177rmHe+65h5kzZ3Lp0iU0Gg0ajYaDBw9y8OBBWrVqRVRUFCqVihEjRtRriJflgkmn0xEREeHyRalyLOxXlnoTHx8fwsPD8fDwwMPDg3bt2tGuXTsMBoP1AjArKwto+FkazsJSb1JZWUlkZKRLF7I3dLC4npubG97e3nh7exMSEoJer7fWaWRlZZGVleVU77Pi4mLS0tIICAiwa5czOzubyZMnk5mZyaxZs/jLX/7i4JXa9/leX48//jgqlaraTanu3bsTHR3NgQMHGD169C2tVQhbEi6audatW9OtW7ebPm/lypVs2bKF2NhY4uPjb/gLtUOHDly8eLHaYzk5OQA1ajdE/XTv3p3u3bszbdo0fv75Z2vQOHLkCEeOHOGNN96gf//+qFQqRo4cWeuE3LNnz3LhwgUeeOABIiMjXfoOk9lsJisri4KCAjkWxq93Ym9Ub9K6dWuCg4MJDg6msrLSegGYm5tLTk4OXl5eBAYGNvuOQLYdsly93sT234kjgkVtPD09b/o+u52ZLbfD0q7b39+fzp071/t75+bmMmnSJNLT04mLi2PSpEkOXmmV+n6+2+v63e6wsDDatGljvekgxO2ScOECVqxYwdatW5k7dy4TJky46fMHDBjAl19+SUlJifXO+LFjx/Dz86vzKJWov65duzJlyhSmTJlCamqqNWgkJSWRlJTEsmXLeOCBB1CpVMTExBAaGsq//vUvFi9eTHBwMLt27Wryu39NyWQykZGRQXFxMR07dqzXjk9LZilKDQgIqPcFU6tWrWjTpg1t2rTBaDRSWlpKcXExV69eJTc31+5ZGs7CYDBw5coVzGazBAubYNFU/05s32e2M1uuXbtGXl4erVu3ttZp1NZKuSGVlpaSlpZmbddd3+917do1pkyZglarZcqUKUybNs1ha2wMq1atYt++fezevdv6M0hLSyM/P1+KvEWDkVa0LVxSUhLjx48nNjaWyZMnV/uar68vfn5+6PV6CgsLCQoKwtPTE51OxyOPPEJkZCSzZ88mLS2N+Ph4YmNjm/0vVmeWmZmJRqMhISGB06dPYzabcXNzo0OHDmRmZuLr68u6devo379/Uy+1yRiNRtLS0igvL6dz584uPQQNfi1kb6g2xJaOQJahfbazNAIDA5269aher0er1QI068GJDcEZgsWN3KiVsqUgvCG7vVmODNrbSS4/P5/Jkydz6dIlnn32WebOneu073+A2NhYOnfuXK0V7fWf7ykpKYwdO5YxY8YQGxtLXl4eS5cuxdvbmx07djj13080HxIuWrhXX32Vzz77rNavTZs2jbi4OGsA2bZtG1FRUUBVq73XX3+dkydPEhQUxBNPPEFcXFyLbu/pTHJyckhISGDr1q1kZmZaH+/ZsydKpRKlUolCoWjCFTY+2wLd8PBwl55VAL8Wsts7+Ku+6roAdMbWo3q9nitXruDm5kZkZGSLLlS/GWcPFtczm83WVsrFxcXo9Xrc3NysQcPf3/+2Ok+VlZWh1WrtDhZFRUVMnjyZ8+fP86c//YlXXnnFad7vdaktXNT2+Z6UlMTatWs5f/48np6eKJVK5s2b5/LNQUTDkXAhhBMyGAy88sorfPnll3Tp0oUnnniCY8eOkZSURGVlJQB33303SqUSlUpF165dm3jFjmV7jj4iIsLl603y8vLIy8trtEJ2ywVgUVERxcXFGAyGaq1H/f39m+zGg06nQ6vV4u7ujkKhkGDRjIJFbWw7T1VUVABU6zxlz3/fsrKyakM16/seLS4u5vnnnyclJYU//vGPvP76604fLIRwJhIuhHAyZWVlvPjiixw6dIgHHniAv/3tb9aLhIKCAvbv328tBjcYDADccccdqFQqVCoVd955Z4v6ILRcPAIuP2nabDaTk5PDtWvXCAkJoX379k2yBtsLQJ1O16B3mu1RUVGBVqulVatWKBQKp2t52phsZ98012BxPYPBUG1COFR1M7QtCK+LZVicpS1zfYNFaWkpL7zwAufOnePxxx/njTfekB17Iewk4UIIJ1JZWcnTTz/N2bNnGTlyJGvWrKnzLn1JSQkHDhxArVaTmJhovcunUChQqVQolUruueeeZh00ysvLSU1NxcPDQ+5KO2mHLL1ebw0a5eXlANWG9jnqgt/y3pBg0TKDxfWMRqM1aJSUlGA2m+tsPGAbLCIiIuoddsvKyoiLi+P06dP83//9H2+99ZYECyFugYQLIZxIYWEhDz30EL/5zW947bXX6n3BVFZWxsGDB1Gr1Rw6dIiysjIAOnXqZA0a999/f7MKGpbuLp6enkRERLj8xWNGRgZFRUVOffFoO0vD8h708fGxtrhtqHBouXj09PREoVA4/TRoR7INFq4yVLOuxgP+/v54eXlZO57Z896oqKjgxRdfJDk5md/+9resWrXKpd9XQtwOCRdCOBmTyXRbd8sqKio4fPgwGo2GAwcOUFJSAlT1Mo+JiUGlUtGnTx+nviNnmdtgOxDOVZlMJtLT0ykpKaFz5863NNW9KdjOOCgtLcVsNtf7SMuNWM7R23tXuiVyxWBxPbPZTHl5uXU6uKUmzdLhrD71QHq9npdeeomjR4+iUqlYu3atS++SCnG7JFyIZiU+Ph6j0VitG0ZtNmzYwPr162s8npKS4lJ3wPV6PUeOHEGj0bB//34KCwsBaN++PSNHjkSlUtG3b1+n+pkUFBSQmZlpnaDrzCHI0UwmE6mpqZSXlxMeHt5sJ7Lbc6TlRmynkNtToNsSSbCoTqfT8csvv1g7mpWWllrrgSzH9Pz9/Wv8rjMYDMyePZtDhw4xYsQI1q9f79JtjIVoCBIuRLNgNBpZuXIl77//Po899thNw0VcXBw+Pj7MmTOn2uMhISGOXKZTMxgMJCcno1ar2bNnD/n5+QC0bduWESNGoFKpGDBgQJPesbt27RrZ2dkNNrehOTMajaSmpqLT6VpU613bIy3FxcWYTCZat25tDRp1zdIoKSkhLS3N7paiLZEEi+p0Oh1XrlypUX9zfT3QpUuXePfdd+nZsycqlYrBgwezcOFC9u/fz9ChQ9m8ebMECyEagIQL4fQuX77MggULrC0Fo6KibhouHnroIcaNG8czzzzTOItsZoxGIydPnrQGjdzcXAACAwOJjo5GpVIxaNCgRvugNZvN5ObmcvXq1UZrr+rMKisr0Wq1GAwGFApFi229W99ZGpZjcn5+fnTu3Nnlg4Wl/kaCxa8zTtzd3YmMjKxzF7ayspJz586xYMECcnJyAHB3d8dkMtGtWzfef/99OnTo0JhLF6LFknAhnN6nn37K+fPnmT59OjNmzKgxJOh65eXl9O3bly1btjB06NBGXGnzZDKZOHPmjDVoWIb2+fn5MXz4cJRKJUOGDMHb29sh39+2C1JoaCjt2rVzyPdpLmxneigUCof93J2N7dl521kaXl5elJeX4+/vT3h4uEuHTttg0ZzqbxzFNljUt5uc2Wzmxx9/ZNGiRfz3v/+1Pu7h4cGAAQMYNWoUSqWSjh07OnLpQrRoEi5Es1LbBNLrnTt3jjFjxjBmzBiSk5PR6/UMHDiQ2bNnExoa2oirbX7MZjM//PAD3377LXv27CE1NRWo6vgzZMgQVCoVw4YNw9fXt8G+n+ViqUOHDrRt27ZB/tzmSq/Xo9VqMZvNREZGuuwRDcssjdzcXGtDgqaapeEsJFhUZzAYuHLlCoBdU9lNJhNLlizhyy+/pHfv3ixcuJDjx4+zZ88ezp49i+WSqGfPnowbN44xY8Y47O8gREsl4UI0qbS0NJRKZZ1fT0xMrFYnUZ9w8cUXX/Dyyy/z7LPP8uijj5KXl8fq1aspKytj586dLfaISUMzm838+9//Rq1Wk5CQwM8//wyAl5cXgwcPtgaNW73IMZlMpKWlUVZWRqdOnVz+YkkmTVdnKewPCgqiXbt2NaY2N8YsDWchwaK6Ww0WZrOZZcuW8fnnn3PffffxwQcfVPtZ5ubmsn//fvbu3cuxY8do06YNiYmJDvk7CNGSSbgQTcpyBKQuXbp0qXaHsj7hAqrmRdieRc7OziY6OprVq1czevTo21+4C7p06RJqtRqNRsOlS5cAaN26NVFRUahUKqKjo+s9f8FSrFxRUUFERESLKVa+VTIQrrr8/HyysrJo06YNHTp0qHYUqrZZGr6+vtag0dJCmQSL6iyfGSaTya7dPbPZzMqVK9mxYwc9evTgo48+uuHvq5KSEiorK512powQzsy1P8FEk2vdujXdunVr8D/3+iLHsLAw2rRpQ1ZWVoN/L1fRvXt3unfvTlxcHP/973/RaDRoNBoSExNJTEy0nllWqVSMHDmyzgnSGRkZXLt2DU9PTyIjI11+J8kyt0EGwlWxdAxr27YtYWFhNWosWrduTXBwMMHBwdZZGkVFRWRnZ5Odnd0gszSchQSL6iyNDm4lWKxbt44dO3bQvXt3Pvjgg5uGhuba9lkIZyA7F6JZqc/OxapVq9i3bx+7d++2XphYjl9t2bKF4cOHN9ZyXUJqaqr16NS5c+eAqi4sffv2RalUEhMTY611OXDgAPPnz6dv376sXbu22V/83S5Le1UZFljl6tWr5OTkEBwcTGhoqF3F2zeapREYGIiXl1ezKgY3m82kp6dTXFwswYJfg4XRaLS7HmnTpk1s2bKFO+64g48//pj27ds7cKVCCAkXolmpLVzo9XrrMShPT09SUlIYO3YsY8aMITY2lry8PJYuXYq3tzc7duxoVhcYzU1GRgYJCQloNBrOnDmD2WzGzc2N3r17Exoayt69e2ndujVr1qxh8ODBTb3cJmVprypzG6rk5eWRm5vbIK2Ib3WWhrOQYFGdJVhUVlYSGRlp102JLVu2sGnTJhQKBdu3bycsLMyBKxVCgIQL0czUFi6SkpIYP34827ZtIyoqyvrY2rVrOX/+PJ6eniiVSubNm+fyPeEbU3Z2Nnv27CEhIYHk5GRrF5YuXbrw6KOPolQqiYiIaOJVNo3CwkIyMjIICAigc+fOTn2h62i2M07at2/f4IMuzWZztaBhNBprnaXhLGyDRXh4OAEBAU29pCZlNBqtM1/sDRYffvgh69atIzw8nO3bt0t7WSEaiYQLIYTDmM1mNm7cyPr16wkICOCOO+4gJSWFyspKAO6++25UKhUqlYouXbo07WIbiaVYWaaQV70/cnJyuHbtWqPMOKlrloZt0GjKHSQJFtXZBgt7Z75s376dVatW0bFjR7Zv3054eLgDVyqEsCXhQgjhECaTib/+9a9s376dyMhI3nvvPSIiIigoKGD//v2o1WqOHj2KwWAAoFu3btag0a1btxZ50W2pKairWNmVmM1msrOzyc/PJywsrM4GAI78/jqdjqKiIoqLi9Hr9U06S0OCRXWWjnI6nc7uKfWff/45S5cuJTQ0lE8++QSFQuHAlQohrifhQgjR4IxGI/PmzePrr7+mR48evPfee7UWURYXF3PgwAE0Gg2HDx9Gp9MBVb3rVSoVSqWSHj16NPuLcLPZTF5eHnl5eQ1SU9Dc2U5ld5bhiTqdrtosDTc3N3x9fQkMDMTf39+h7YHNZjNpaWmUlpbSuXNnlw8WJpMJrVZ7S8Fi586dLF68mPbt2/Pxxx9zxx13OHClQojaSLgQQjS4M2fOMHbsWPr168ff/va3ehWklpaWcvDgQTQaDQcPHqS8vByAzp07o1QqUalU3Hfffc3uotz26E9ISIjLd6oxm81kZmZSWFhIx44dnXKOQGPO0pBgUZ3JZLLOwLE3WHz99dcsXLiQNm3a8PHHH9O9e3cHrlQIURcJF0KIBmc0GklOTqZv37631G62oqKCQ4cOodFo+O677ygpKQGgQ4cOxMTEoFKp6N27t9N3WLK9Q98UR3+cje3chk6dOjWLBguVlZXWoFFaWgpgnaURGBhoV0vU69kGi/DwcJefrWAJFuXl5SgUCnx9fev9WrVaTXx8PAEBAWzbto0ePXo4cKVCiBuRcCGEcGp6vZ4jR46gVqvZv38/RUVFALRv394aNPr27et0MyJsL6Sd9Q59Y2oJ7VVrm6Xh5eVVbWhffXfWTCYT6enpEiz+x2QykZaWRllZGREREfj5+dX7tXv37mX+/Pn4+vry4Ycfct999zlwpUKIm5FwIUQji4+Px2g03nAQIFQN/luyZAknTpzA29ubxx57jJkzZzrdRXRjMhgMJCUloVar2bt3L/n5+QC0bduWkSNHolKp6N+/f4MeW7kVlgvHkpKSZnsh3ZBsL6RbytEfk8lULWjYM0tDgkV1tjs49gaL7777jjlz5uDl5cUHH3xA7969HbhSIUR9SLgQopEYjUZWrlzJ+++/z2OPPXbDcGEwGHjkkUfo2rUrM2fORKvVEh8fz9NPP8306dMbcdXOy2g0cuLECWvQyM3NBSAoKIjo6GhUKhVRUVG3dWzlVtge7ZALx+p3pFvqz6O2WRqtWrWyBg1fX19r0JBgUd3tHA1LTExk5syZtGrVivfee49+/fo5cKVCiPqScCFEI7h8+TILFiwgNTUVb29voqKibhgudu3axYIFCzhy5Ij1rvc//vEPli9fzrFjxxr9gtnZmUwmzpw5g1qtJiEhgaysLAD8/PwYPnw4KpWKBx980K4++bfCtn1mRESEXWfGWyLboGXvHenmynaWRlFREZWVlXh4eODv74+/vz/5+fkSPP/H9qhcRESEXT+P48ePM2PGDNzc3Hj33XetA1SFEE3Pcb31hBBWycnJ3HPPPWzevJkZM2bc9PknT56kZ8+e1Y7TDBo0iJKSEs6fP0+vXr0cuNrmx93dnX79+tGvXz9efvllzp07Zw0a3377Ld9++y0+Pj4MHToUlUrFsGHD7OpCUx+VlZVotVoqKyvt7nLTEl0/p8BVgpalha2vry+hoaFUVFRYdzQKCwsB8PHxwWQyWaeFuyJLTZJlroc9weLkyZPW36ObNm2SYCGEk5FwIUQjGDdunF3Pz8rKokOHDtUeCw0NBSAjI0PCxQ24ubnRu3dvevfuzdy5c/npp5+sQWPPnj3s2bMHLy8vHnzwQWvQuN0aAIPBgFarxWg02j1JuCWyTFbW6/UuHbTc3Nzw8fHBy8uLiooK9Ho9QUFB6HQ60tPTcXNzw8/Pzzq0z5GzNJyJbbMDe2twzp49y/Tp0zGZTGzcuJEhQ4Y4cKU1ZWZmsmLFCpKSktDr9fTq1Yv58+ffsO2t1M8JV+Mav8mEcKC0tDSUSmWdX09MTCQkJMSuP7OioqJGEbClpatl0Jy4OTc3N3r27EnPnj2ZOXMmly5dQq1Wo9FoOHDgAAcOHKB169YMGjQIlUpFdHS03e1R9Xo9Wq0Ws9lMly5dXP7ImuzgVGdbc6JQKKxHw/R6vXVHIzMzE3DcLA1nYplzYgkW9jQ7+PHHH5k2bRoGg4G3336b6OhoB660Jr1ez6RJkwgODuadd97By8uLjRs38uc//5ldu3bV2mraYDAwYcIEunbtyt///ndr/ZyXl5fUz4kWS8KFELcpLCyMb775ps6v38psA29vb/R6fbXHLKHCVY6XNDQ3Nzfuuusu7rrrLqZPn87ly5fRaDQkJCRw+PBhDh8+jIeHBwMHDkSlUjFixIib/rfT6XRotVrc3d2JjIxssReE9XV9sHD1HZwbtVf19PSkXbt2tGvXrtosjezsbLKzs/Hx8bEGjZYSWC1zXwoLC+nUqZNdweLf//43L7zwAhUVFaxevfqGN3Qc5eTJk1y8eJFDhw4RFhYGwPLlyxk4cCD79+/niSeeqPEajUZDRkYGn3/+OYGBgdx1111cvXqV5cuXM2XKlBbz31YIWxIuhLhNrVu3plu3bg36Z3bo0IGLFy9WeywnJwfA+qEmbk+3bt144YUXeOGFF9BqtajVavbs2cOxY8c4duwYf/3rX+nXrx8xMTHExMRYj6VZ7Ny5k2+//ZZZs2bRtWtXlznSUhfbo2GRkZG3NDyxJbFnbkOrVq1o27Ytbdu2tc7SKCoqIjc3l5ycnFuepeFMzGYz2dnZFBQU0LFjR7t2CC9evMjzzz9PaWkpy5cv5+GHH3bgSuvWvXt33n333Rq/g81ms7We5npSPydckWt/GgrhpAYMGMCXX35JSUmJtdDx2LFj+Pn5yeRZB1AoFEyaNIlJkyaRkZFBQkICGo2GkydPcuLECZYvX06fPn2IiYlBqVSi0WhYt24dQUFBdOrUSYKFwcCVK1fkaNj/3M5AOA8PD4KCgggKCqo2S+PatWvk5eXRunVrAgMDCQgIwNvbu1kEDUuwyM/Pp0OHDnYNlLx8+TJTpkyhqKiIZcuW8fvf/95xC72JkJCQGkextm3bhk6nq7P2Q+rnhCty7U9EIZyEXq+nsLCQoKAgPD09UalUrF27lhkzZjB79mzS0tJYs2YNzz77rMtfuDlap06deOaZZ3jmmWfIzs5mz5491qBx5swZVq1aBVQdT/vrX//aIgbC3Q5LzQlAZGSky78/G7L9rru7O4GBgQQGBmIymSgrK6O4uJiCggKuXr1a5ywNZ2I2m8nJybEGi7Zt29b7tb/88guTJ08mPz+fJUuW8NhjjzlwpfbXzyUkJLBmzRpiY2PrvOkj9XPCFUm4EMIJnDlzhvHjx7Nt2zaioqLw8vJi69atvP766zz55JMEBQXx1FNP8cILLzT1Ul1KWFgYf/rTn/jTn/5Ebm4u8+bN48iRIwCUlZUxbdo0evTogVKpRKVS0aVLl6ZdcCOz1Jy4ublJzQmOnevh7u5unZXRoUMHa9AoLi4mPz/fOksjICAAPz8/3N3dG+x73yqz2Uxubi7Xrl0jLCzMrmCRmprK5MmTuXr1Kq+99hpPPvmkA1daxZ76uU8//ZQlS5YwevRoFixYUOdrpH5OuCIZoieEEDdhMplYvHgxn376Kd26dWPNmjX8+OOPqNVqjh07hsFgAODOO+9EqVQyatSoBq/DcTY6nY4rV67g4eGBQqGQYGETLBpzrofZbK42S0Ov11uDiKXFbVMFjdzcXPLy8ggNDaVdu3b1fl16ejoTJ04kMzOTBQsW8Mwzzzhukbdg5cqVbNmyhdjYWOLj42+4Y7Ro0SIuXrzIjh07rI9duXKF3/zmN3z++edyLEq0SBIuhBDiBiorK3n55Zf56quv6NmzJ1u3bq12B7OoqIgDBw6g0WhITEy03pXs0qWLdUfj7rvvdsojK7eqoqICrVZLq1atUCgULl9z0lTB4npmsxm9Xk9RURHFxcXodLpqszQCAgIabbZCXl4eubm5hISE0L59+3q/Lisri+eee4709HRmzZrFpEmTHLhK+61YsYKtW7cyd+5cJkyYcNPn7969m/j4eBITE631c3//+99Zvnw5x48fd/ljhKJlknAhhBA3sGjRIj799FP69u3Lu+++e8Mai9LSUr777js0Gg2HDh2ivLwcgPDwcGvQ6NmzZ7MOGuXl5aSmpkqw+B9LsKioqCAiIsKpjrrYztKwvBdth/Y5arfpVoNFTk4OEydORKvVMn36dKZOneqQ9d2qpKQkxo8fT2xsLJMnT672NV9fX/z8/GrUz+l0Oh555BEiIyOt9XPx8fHExsYybdq0JvqbCOFYEi6EEOIGVq1aRUZGBkuWLLHrwrGiooJDhw6hVqv57rvvKC0tBaraDFuCRq9evZzibHx9lZeXo9Vq8fT0RKFQuPyEYWcOFtczGAzWzlOW96IjZmlcvXqVnJwc2rdvb9fw0KtXrzJx4kR+/vlnnn/+eWbMmNEg62lIr776Kp999lmtX5s2bRpxcXHWAGKpn4OqY1Cvv/46J0+eJCgoiCeeeIK4uLhm9W9fCHtIuBBCCAfT6/UkJiai0WjYv38/RUVFQFVry5iYGFQqFQ888IBTX6yXlZWRmpqKl5cXERERTr3WxmAymdBqteh0OqcPFtczGo3WHY3S0lLMZrN1lkZgYCCenp63tLt27do1srOzadeuHSEhIfX+M/Lz85k0aRL/+c9/mDBhAnPmzGnWu3tCuDoJF0KIetPpdLz55puo1WoqKioYNmwYCxcuvGGx5oYNG1i/fn2Nx1NSUlzySI3BYOD48eOo1Wr27dtHfn4+UNWJZuTIkSiVSvr37+9UBdKlpaWkpqbi4+NDRESEy99xNRqNpKamNstgcT3bWRolJSWYTCY8PT2tOxr1naWRn59PVlYWwcHBhIaG1jscFBYWMnnyZC5cuFCvAmkhhPOTcCGEqLcFCxZw6tQpli5diqenJwsXLsTPz4/t27fX+Zq4uDh8fHyYM2dOtcftOTLRUlVWVnLixAnUajV79+4lLy8PgKCgIEaMGIFSqWTQoEFNGjRKSkpIS0vD19eX8PBwCRY2wUKhUODj49PUS2owllkaRUVFlJSUYDQa6zVLwxIs2rZtS1hYWL3DQXFxMc8//zwpKSmMHTuWRYsWSbAQogWQcCGEqJfs7GxGjBjBO++8w/DhwwH4+eefefjhh/nHP/5Bnz59an3dQw89xLhx45yunaSzMZlMnD59GrVaTUJCAtnZ2QD4+/szfPhwVCoVgwcPxtvbu9HWVFxcTHp6On5+fnTu3FmCRQsOFtczm83VZmlUVlZaZ2kEBgbi6+uLu7s7BQUFZGZm2h0sSktLeeGFFzh37hyPP/44b7zxhsu/v4RoKSRcCCHq5ZtvvmHWrFmcPXvWOmEWYPjw4cTGxjJx4sQarykvL6dv375s2bKFoUOHNuZymzWTycS5c+esQSM9PR2o6kgzZMgQRo0axdChQx16cVtUVER6ejoBAQF07tzZ5e8ou1KwuJ7tLI2ioiIMBgPu7u54eXlRXl5OUFAQHTt2rPd7pKysjLi4OE6fPs3//d//8dZbb0mwEKIFcb0Dz0KIW5KdnU3btm2rBQuA0NBQMjMza33NpUuXMJlMqNVqFi9ejF6vZ+DAgcyePZvQ0NDGWHaz5O7uTp8+fejTpw/z5s3jp59+Qq1Wo9Fo2LNnD3v27MHLy4sHH3yQUaNGMWzYMGsP/YZQWFhIRkYGgYGBdOrUSYKFCwcLADc3N3x8fPDx8SEkJASdTkdeXh7FxcVAVRA1Go0EBgbi7+9/w2L/8vJyXnrpJU6fPs1vf/tb3nzzTQkWQrQwEi6EEACkpaWhVCrr/PqLL75Ya7tKLy8v6+C46126dAmAgIAA3n77bfLy8li9ejXjx49n586dLneRdivc3Nzo2bMnPXv2ZObMmVy8eNG6o3HgwAEOHDhA69atGTRoEKNGjWL48OEEBQXd8vezHHOx9250S+XqweJ6bm5u1vkZgYGBtG/f3loQnpGRAfw6S6O8vJyQkBBrzZBer2f27NkkJyczatQoVqxY4fJdx4RoieRYlBACqOpipNVq6/z6wYMH2bp1K0ePHq32+BNPPEGfPn145ZVXan2dZaCURXZ2NtHR0axevZrRo0c3zOJd1OXLl61B4/z58wB4eHgQFRWFSqUiOjq62jTxm7EU5rZp04YOHTpIsDAa0Wq16PV6CRb/U1xcTFpaWq27WgaDwVqjcenSJebOnYufnx8PPvggMTEx/Otf/+Lo0aOMGDGC9evXy3RqIVoo2bkQQgDQunVrunXrVufXL1y4QEFBAXq9vtpFQU5ODh06dKjzddffRQ8LC6NNmzZkZWXd/qJdXLdu3Zg6dSpTp07lypUraDQaEhISOHr0KEePHsXDw4N+/foRExNDTExMnR26zGYzqamplJaW2l2Y21JJsKjJ0jksICCg1uNyrVu3Jjg4mODgYEJCQhg7diz79u0jISGBhIQEANq3b8/o0aNr/B4RQrQcsnMhhKgXy47DBx98wODBgwH473//y29/+9s6u0WtWrWKffv2sXv3buuFiOX41ZYtW6xdp0TDSk9PJyEhAY1Gw5kzZ4Cq4ywPPPAAMTExKJVKayDU6/XMmjWLY8eOsX37du6++24JFhIsarAECz8/P8LDw+v9HqmsrCQuLo7jx4/j7e1NRUUFUBVELM0JYmJi7NphE0I4NwkXQoh6s3SLWrp0KT4+PixcuBB/f38+/vhjoOpC1XIMytPT09q/fsyYMcTGxpKXl8fSpUvx9vZmx44dLn8R2xiysrLYs2cPGo2GU6dOYTKZALj//vuJjo7m8OHDfP/99/Tr14933nnH5c/AS7CoyTJE0d6WxCaTiddee43du3fTv39/tmzZQmZmJgkJCezZs4eUlBSgqoHBgw8+yMqVK2nbtq0j/ypCiEYg4UIIUW9lZWUsXboUjUYDVLWhfeWVV6wXBElJSYwfP55t27YRFRVlfWzt2rWcP38eT09PlEol8+bNu62iY3Fr8vLyrEEjKSnJGjT8/PyIjY3l4YcfJjIysolX2XQswcJgMKBQKBp1poizsgQLe4comkwmlixZwpdffkmfPn147733anQ0S0tLY+/evSQkJHD58mU+++wzl37/CdFSSLgQQggXU1payoQJEzhz5gzt2rWjoKAAo9EIQPfu3VEqlahUqhvW4LQ0EixqKisrQ6vV4uPjQ0RERL2DhdlsZtmyZXz++efcd999fPDBBwQGBjp4tUIIZyHhQgghXEhxcTETJ07kzJkz/O53v+Ott96ivLycAwcOoFarOXLkiLW1cNeuXa1B46677mqxx9gkWNRUXl6OVqvF29vb7mCxcuVKduzYQY8ePfjoo49o06aNYxcrhHAqEi6EEMJFFBQUMGHCBH788Ucee+wx/vrXv9aosSgtLeW7775Do9Fw8OBBawFuREQEMTExjBo1invvvbfFBA2j0ciVK1eorKyUYPE/lmDh5eWFQqGwK1isW7eOjz76iO7du7Nt2zYp1BbCBUm4EEIIF7FgwQL++c9/8uSTT/L666/f9KKxvLycQ4cOodFo+O677ygtLQWgY8eOxMTEoFKp6NWrV7OdsFxZWYlWq5VgYaOiooIrV67g5eVFRESEXQX+GzduZOvWrXTr1o1t27bRvn17B65UCOGsJFwIIYSLSEpK4tKlSzz99NN27zzodDoSExPRaDTs37+f4uJiAEJDQxk5ciQqlYoHHnig2XSbkmBRU0VFBVqtltatW6NQKOz6b/nuu++yefNmIiMj+fjjjwkLC3PgSoUQzkzChRBCCLvo9XqOHz+ORqNh37595OfnAxAcHGwNGv3796dVK+ec0yrBoiadTseVK1duKVh8+OGHrFu3jvDwcD755JMbDtUUQrR8Ei6EEC7BZDKxYcMGPv/8c4qKiujXrx8LFy6ss/Vlfn4+b7zxBocOHQLg4YcfZsGCBfj6+jbmsp1eZWUlJ06cQK1Ws3fvXvLy8oCqyewjRoxApVIRFRVF69atm3ilVWyDRWRkJF5eXk29pCZnCRatWrVCoVDYFQq3b9/OqlWr6NixI9u3byc8PNyBKxVCNAcSLoQQLmHDhg3s2LGDZcuWERYWxooVK0hNTWXXrl14enrWeH5sbCw6nY6FCxdSVFREfHw8AwYM4K233mqC1TcPRqORM2fO8O2337Jnzx6ys7MB8Pf3Jzo6GpVKxeDBg5vsgl6CRU16vZ4rV67g4eFhd7D47LPPWLZsGaGhoXzyyScoFAoHrlQI0VxIuBBCtHh6vZ5BgwYxZ84cxo0bB0BRURHDhg1j6dKl/O53v6v2/DNnzjB27Fi++eYb66yHxMREnnvuOQ4ePCjnyevBZDJx7tw51Go1Go2GjIwMAHx9fRk6dCijRo1iyJAhjTYBW4JFTZZg4e7uTmRkpF3BYufOnSxevJj27duzfft2unbt6sCVCiGak+bZ4kMIIexw/vx5SktLGTRokPWxwMBA7r33Xk6cOFHj+SdPniQkJKTaELmBAwfi5ubGqVOnGmXNzZ27uzt9+vRh/vz57N+/ny+++IKJEycSEhJCQkICc+bMISYmhlmzZqFWq62dqBxBgkVNtsHC3h2Lr7/+miVLltC2bVs++ugjCRZCiGqcs9pOCCEaUFZWFlDVQtVWaGgomZmZNZ6fnZ1d47menp60adOm1ueLG3Nzc+O+++7jvvvuY9asWVy4cAG1Wk1CQgL79+9n//79eHp6MmjQIFQqFdHR0Q020bmyspIrV65gNBolWPyPwWBAq9Xi5uaGQqGwqx5GrVazaNEigoKC+PDDD7nzzjsduFIhRHMk4UII0eKVl5cD1Kit8PLyorCwsNbn11aH4eXlZZ1eLW6Nm5sbPXr0oEePHsyYMYPLly9bg8ahQ4c4dOgQrVq1YuDAgdagcauD2CzBwmQySbD4H4PBwJUrVwCIjIy0K1js3buXV155BT8/P95//3169OjhqGUKIZoxCRdCiBbP0mpUr9dXazuq0+lqPfPv7e2NXq+v8bhOp5NuUQ2sW7duTJ06lalTp3LlyhU0Gg0ajYajR49y9OhRPDw86NevH0qlkpiYmHoPZrMNFgqFQoIFv+5YmM1mu4PFd999x/z58/Hx8eH999+nZ8+eDlypEKI5k5oLIUSLZznilJOTU+3xnJycWnvyd+jQocZz9Xo9BQUFUsztQJGRkUyaNIkvvviCffv2MW/ePO6//36Sk5NZtmwZv/nNb5gwYQI7duywdqKqTWpqKqdPn5ZgYcNSd2LZxaltZ64uiYmJzJ07Fy8vL95991169erlwJUKIZo7CRdCiBavR48e+Pv7k5SUZH2sqKiIn376if79+9d4/oABA8jKyrIeHwGsr+3bt6/jFywIDw/n2Wef5R//+AcHDx4kPj6efv36cfbsWVasWMHDDz/Mn//8Z7Zt20Z6err1dSdOnODJJ59k3bp1chTqfyzBwmg0olAo7AoWx48fZ9asWXh4ePDOO+/Qr18/B65UCNESSCtaIYRLWLNmDX//+99ZunQpnTt3ZsWKFaSlpfH111/j4eHBtWvXCAgIwNvbG7PZzFNPPYVOp2PRokWUlZXx8ssvExUVxbJly5r6r+LScnNz2bNnDwkJCSQnJ2M0GgG49957ufvuu9m9ezdGo5Hly5cTExPTxKttekajkStXrtxSp6yTJ08ybdo0ADZv3syQIUMctcw6ZWZmsmLFCpKSktDr9fTq1Yv58+fTvXv3Ol+zYcMG1q9fX+PxlJQUp50aL0RLIuFCCOESjEYjq1ev5p///CcVFRUMGDCA1157jfDwcNLS0lAqlSxbtow//OEPAFy9epXXX3+dw4cP4+XlZZ3QLXfCnce1a9fYt2+ftUbDEjQ6duzI//t//w+lUlmtnbCrMRqNaLVaDAYDCoWiWr3RzZw9e5YXXniByspKNm3axPDhwx240trp9Xoef/xxgoODmTNnDl5eXmzcuJHk5GR27dpVZ6F/XFwcPj4+zJkzp9rjISEhjbFsIVyehAshhBDNWnJyMpMmTUKv13Pvvfdy4cIFa0H+HXfcQUxMDCqVirvuugs3N7cmXm3juJ1g8cMPP/D888+j0+l4++23USqVDlxp3Y4ePcpf/vIXDh06ZK110uv1DBw4kFdeeYUnnnii1tc99NBDjBs3jmeeeaYRVyuEsJD9QSGEEM1WUlISkydPprKyks2bNxMdHU1JSQnfffcdGo2GQ4cOsXXrVrZu3YpCobAGjXvvvbfFBg2j0Uhqaip6vd7uYPHTTz8xdepUKioqWL16dZMFC4Du3bvz7rvv1miiYDaba20hDVVtpLVarczfEKIJyc6FEEKIZskSLIxGIxs2bCA6OrrGc8rLyzl48CAajYaDBw9aJ4F36tTJGjTuv/9+3N1bRn8Tk8mEVqtFp9OhUChqbbVclwsXLjB58mSKi4tZsWIFjzzyiANXemu2bt3KypUr+fLLL2uds3Hu3DnGjBnDmDFjSE5Otu50zJ49m9DQ0CZYsRCuR8KFEEKIZsdsNjNs2DAKCwvZuHFjvWoCdDodiYmJaDQa9u/fT3FxMVA1qd0SNPr06YOHh4ejl+8QJpOJ1NRUKioqiIiIsGsmy+XLl5k4cSIFBQUsW7aMxx57zIErrWKpdapLYmJitTqJhIQEXnrpJZ566ini4+Nrfc0XX3zByy+/zLPPPsujjz5KXl4eq1evpqysjJ07d9oVtoQQt0bChRBCiGZp9+7dhIeH07t3b7tfq9frOXbsGBqNhn379lFQUABAcHAwMTExKJVK+vfv32y6C5lMJtLS0igrK0OhUNgVLH755Reee+45rl69yhtvvMGYMWMcuNJfWYb61aVLly7WoPfpp5+yZMkSRo8ezfLly2+401RYWEhQUJD1/2dnZxMdHc3q1asZPXp0w/0FhBC1knAhhBDNjMlkYsOGDXz++ecUFRXRr18/Fi5cSGRkZK3P37lzJ/Pnz6/xeEJCQp2vcSWVlZUkJyejVqvZu3cvV69eBSAoKIiRI0eiVCqJioqya6J1Y7INFhEREfj5+dX7tVqtlokTJ5KTk8Nrr73G008/7cCV3pqVK1eyZcsWYmNjiY+Pv6VamUGDBjFp0iSeffZZB6xQCGFLwoUQQjQzGzZsYMeOHSxbtoywsDBWrFhBamoqu3btqnVA2ptvvklKSgqrV6+u9nhwcHCzPQLkKEajkdOnT6NWq0lISLBOag8ICGD48OGoVCoGDx7sNC2JzWYzaWlplJaW2h0s0tPTee6558jKymLBggVO2V1pxYoVbN26lblz5zJhwoSbPn/VqlXs27eP3bt3W0OI5fjVli1bmqSlrhCuRsKFEEI0I3q9nkGDBjFnzhzGjRsHVE0bHzZsGEuXLuV3v/tdjdc8++yzdOvWrc5z6qJ2JpOJ77//3ho0MjIyAPDz82Po0KGoVCqGDBnSZOf4zWYz6enplJSUEB4ejr+/f71fm5WVxXPPPUd6ejqzZ89m4sSJDlzprUlKSmL8+PHExsYyefLkal/z9fXFz88PvV5vPQbl6elJSkoKY8eOZcyYMcTGxpKXl8fSpUvx9vZmx44dLbZDmBDORMKFEEI0I5ZuOGq1mq5du1ofHzduHHfffTeLFi2q8ZohQ4Ywffp0/vjHPzbiSlsWs9nMjz/+aA0alloBb29vhgwZgkqlYtiwYXbtHNzuetLT0ykuLiY8PJyAgIB6vzYnJ4eJEyei1WqZPn06U6dOdeBKb92rr77KZ599VuvXpk2bRlxcnDWAbNu2jaioKKAqlKxdu5bz58/j6emJUqlk3rx51eowhBCOI+FCCCGakYSEBOLi4vj++++rzS948cUXqaio4J133qn2/GvXrjF48GB+//vfc/78eQoKCujduzezZ8+uFk5E/ZnNZi5cuGANGpcvXwbA09OTwYMHo1KpGD58OIGBgQ77/hkZGRQVFdkdLK5evcrEiRP5+eefef7555kxY4ZD1iiEcF3Now2GEEIIoGpuA1CjtsLLy6vWwWIXL14EwMPDg7feeouysjI2bdrEU089xddff0379u0dv+gWxs3NjR49etCjRw9mzJjBf/7zHzQajXWWxsGDB2nVqhVRUVEolUpGjBhB27ZtG+R7m81mMjMzKSoqonPnznYFi/z8fKZMmcLPP//Mc889x4svvtggaxJCCFuycyGEEM2IRqNh+vTpte5c6PV6Nm/eXOM117fmLCsrY+TIkUyYMIFJkyY1yrpdxS+//GINGikpKUBVsOvfvz9KpZKRI0fecqCzBIvCwkI6depk1zGfwsJCJk+ezIULF26r65IQQtyM7FwIIUQz0rFjR6Dq3LxCobA+npOTU+vEYqDGRaivry/h4eFkZ2c7bqEuqkuXLkyePJnJkyeTmppKQkICGo2GpKQkkpKSePPNN+nTpw8qlQqlUlnvqdFms5msrKxbChZFRUW88MILXLhwgbFjx0qwEEI4VN1TaIQQQjidHj164O/vT1JSkvWxoqIifvrpJ/r371/j+Tt27CAqKoqKigrrYyUlJfzyyy/ceeedjbJmVxUREcGECRP47LPPOHjwIC+//DJ9+/blzJkzLF++nIceeog///nPfPzxx9ZOVLXR6/WcO3eOgoICOnbsaFewKCkpIS4ujp9++onHH3+chQsXSrAQQjiUHIsSQohmZs2aNfz9739n6dKldO7cmRUrVpCWlsbXX3+Nh4cH165dIyAgAG9vbzIzM3n00UcZNGgQcXFxVFRUsHr1auvzbY9WicaRk5PDnj17SEhI4MSJExiNRgDuvfde646GZVeqrKyMyZMnk5KSwldffUVERES9v09ZWRlxcXGcPn2aRx99lDfffPOGk62FEKIhSLgQQohmxmg0snr1av75z39SUVHBgAEDeO211wgPD7cODFu2bBl/+MMfAPj3v//NypUr+f777zGbzQwZMoQFCxZYj1iJpnPt2jX27duHWq3m+PHjVFZWAnDXXXcxfPhwDh8+zIULF1AqlaxYsaLeuw7l5eXMmDGD5ORkRo8ezcqVK2VgohCiUUi4EEIIIZxAYWEh+/fvR6PRkJiYiMFgAKpqZMaNG8dvfvMbunfvftOAodfreemllzh69CijRo1i7dq1tGolJZZCiMYh4UIIIYRwIjqdjilTpnD06FFCQkIoKipCp9MBoFAoUCqVqFQq7rnnnhpBw2AwMGvWLA4fPszIkSNZv349rVu3boq/hhDCRUm4EEIIIZyEXq8nLi6O7777jpEjR/L2229TWVnJoUOHUKvVHDx4kLKyMgA6depkDRr3338/lZWVzJs3jwMHDjBs2DA2bdpUYx6KEEI4moQLIYQQwgkYDAZmzJjB3r17iY6OZsOGDTXCgU6n4/Dhw2g0Gg4cOEBxcTEAISEh+Pj4oNVqGTRoEO+8844U6wshmoSECyGEEMIJbN68mbVr1zJ06FA2bdqEl5fXDZ+v1+s5evQoGo2GvXv3UlRUhEKh4KuvvsLX17eRVi2EENVJuBBCCCGcwOnTp9m7dy/Tp0+3e9ehsrKSffv2MWTIEPz9/R20QiGEuDkJF0IIIYQQQogGIdN0hBBCOI1NmzYRGxt7w+fk5+cza9YsBgwYwIABA3j11VetRc5CCCGaloQLIYQQTuHDDz/k7bffvunzpk+fTmpqqvX5R44c4fXXX2+EFQohhLgZmaojhBCiSWVnZxMfH8+pU6fo2rXrDZ975swZkpOT+eabb+jWrRsAixcv5rnnnmPmzJmEhYU1xpKFEELUQXYuhBBCNKmUlBSCgoL417/+Re/evW/43JMnTxISEmINFgADBw7Ezc2NU6dOOXqpQgghbkJ2LoQQQjSpmJgYYmJi6vXc7OxsOnbsWO0xT09P2rRpQ2ZmpiOWJ4QQwg6ycyGEEKLZKC8vr3XqtJeXFzqdrglWJIQQwpaECyGEEM2Gt7c3er2+xuM6nU4GxwkhhBOQcCGEEKLZ6NChAzk5OdUe0+v1FBQUSDG3EEI4AQkXQgghmo0BAwaQlZXFlStXrI8lJSUB0Ldv36ZalhBCiP+RcCGEEMJpGY1GcnNzqaioAKB379707duXl156iXPnznH8+HEWLlzI//t//092LoQQwglIuBBCCOG0MjMzGTp0KN988w0Abm5ubNiwgfDwcP785z8zY8YMhg8fzqJFi5p2oUIIIQBwM5vN5qZehBBCCCGEEKL5k50LIYQQQgghRIOQcCGEEEIIIYRoEBIuhBBCCCGEEA1CwoUQQgghhBCiQUi4EEIIIYQQQjQICRdCCCGEg2zatInY2NgbPmfnzp3cfffdNf5nOyhQCCGai1ZNvQAhhBCiJfrwww95++23GTBgwA2fd+HCBQYOHMjq1aurPR4cHOzI5QkhhENIuBBCCCEaUHZ2NvHx8Zw6dYquXbve9PkXL16kR48ehISENMLqhBDCseRYlBBCCNGAUlJSCAoK4l//+he9e/e+6fMvXLjAnXfe2QgrE0IIx5OdCyGEEKIBxcTEEBMTU6/nXrt2jby8PE6cOMHHH39MQUEBvXv3Zvbs2fXa9RBCCGcjOxdCCCFEE7l48SIAHh4evPXWW6xZs4aysjKeeuop8vLymnh1QghhP9m5EEIIIZrIoEGDSE5OJigoyPrYxo0bGTlyJP/85z+ZNGlSE65OCCHsJzsXQgghRBOyDRYAvr6+hIeHk52d3UQrEkKIWyfhQgghhGgiO3bsICoqioqKCutjJSUl/PLLL1LkLYRoliRcCCGEEI3EaDSSm5trDRMjR47EbDYzd+5cLl26xA8//EBcXBzBwcE89thjTbxaIYSwn4QLIYQQopFkZmYydOhQvvnmGwA6duzIRx99RGlpKePGjeOZZ54hICCAbdu24e3t3cSrFUII+7mZzWZzUy9CCCGEEEII0fzJzoUQQgghhBCiQUi4EEIIIYQQQjQICRdCCCGEEEKIBiHhQgghhBBCCNEgJFwIIYQQQgghGoSECyGEEEIIIUSDkHAhhBBCCCGEaBASLoQQQgghhBANQsKFEEIIIYQQokFIuBBCCCGEEEI0CAkXQgghhBBCiAYh4UIIIYQQQgjRICRcCCGEEEIIIRqEhAshhBBCCCFEg5BwIYQQQgghhGgQEi6EEEIIIYQQDULChRBCCCGEEKJBSLgQQgghhBBCNAgJF0IIIYQQQogGIeFCCCGEEEII0SAkXAghhBBCCCEahIQLIYQQQgghRIOQcCGEEEIIIYRoEBIuhBBCCCGEEA1CwoUQQgghhBCiQUi4EEIIIYQQQjQICRdCCCGEEEKIBvH/AWddSzqq0oRsAAAAAElFTkSuQmCC", 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" ] @@ -152603,7 +152626,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -152627,7 +152650,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -152656,12 +152679,12 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 82, "metadata": {}, "outputs": [ { "data": { - "image/png": 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5Ew7UvZbW1ujZsjmO371z2jU9t242v3qC2Y4Y834DkEZnRooH8HLXQbU4CQdSIbN2TSy/bdukE9q27kwsv22budUTzHXEmPcbgLTp6miv6DqoFifhQGpk1q6JrlUDL3TvPns22hYujI7eFU5kJyh1xFjXqoGi7533G4A06e3JRKazvWhJeveC+dHbU/zKFVSbEA6kSktrq7FYM6jkiDHvdzrkczkPS4Cm1zqvJbZu7I8d+45Mu2bLhr5ondeS4K5gMiEcoMEYMdZcZnv3H6ARrVu5NLZvGpg0J7x7wfzYsqHPnHDqghAO0GCMGGsehbv/ExXu/ru/DzSjdSuXxuq+JTE0nI0zI+ejq6M9ensyTsCpG0I4QIMpjBgrVpJuxFj6VeruP0Ajap3XEv3Lumu9DZiS7uhU1GxmElMa7y2lKowYK8aIsfQr5+4/AFA/nIRTMe4lVo/3lnIVRoxN+rnpzkTPrX5uGoG7/wCQTkI4FeFeYvV4b5ktI8Yam7v/AJBOytGZs1LvJSqfLp/3lrkqjBhb9JoborO/TwBvIIW7/8W4+w8A9UcIZ87cS6we7y0wHXf/ASCdhHDmzL3E6vHeAsUU7v5PPBFv6864qgIAdcqdcObMvcTq8d4CM3H3HwDSRQhnzswkrh7vLVCKwt1/AKD+KUdnztxLrB7vLQAANBYhnIpwL7F6vLcAANA4lKNTMe4lVo/3FgAAGoMQTkW5l1g93lsAAEg/5egAAACQECEcAAAAEiKEAwAAQEKEcAAAAEiIEA4AAAAJEcIBAAAgIUI4AAAAJEQIBwAAgIQI4QAAAJAQIRwAAAASIoQDAABAQoRwAAAASIgQDgAAAAkRwgEAACAhQjgAAAAk5JJabwAAaD75XC5Ghh6J0bNno23hwujoXREtra213hYAVJ0QDgAkKnvocAzv3huj2ezF19oymejZsjkya9fUcGcAUH3K0QGAxGQPHY7jd+8cF8AjIkaz2Th+987IHjpco50BQDKEcAAgEflcLoZ37y26ZnjP3sjncgntCACSJ4QDAIkYGXpk0gn4RKOnszEy9EhCOwKA5AnhAEAiRs+ereg6AEgjjdkAgES0LVxY0XUAjSA3lo+h4WycGTkfXR3t0duTidZ5LbXeFlUkhAMAiejoXRFtmUzRkvS27kx09K5IcFdANQiWpTl49GTs2j8Y2XPnL76W6WyPrRv7Y93KpTXcGdUkhAMAiWhpbY2eLZvj+N07p13Tc+tm88Ih5QTL0hw8ejJ27Dsy6fXsufOxY9+R2L5pwPvVoNwJBwASk1m7Jpbfti3aMplxr7d1Z2L5bdvMCYeU+8r/+efYse/IuAAe8YNgefDoyRrtrL7kxvKxa/9g0TW77z8WubF8QjsiSU7CAYBEZdauia5VAy90Sz97NtoWLoyO3hVOwCHlvvL3/xw7//ihomt2338sVvctafrS9KHh7KQHFROdfva5GBrORv+y7oR2RVKEcAAgcS2trdHZ31frbQAVcvDoyXjvJ4oH8AjBsuDMSPEAXu460kU5OgAAMGullFa/mGAZ0dXRXtF1pIsQDgAAzFoppdUvJlhG9PZkItNZ/H3oXjA/ensyRdeQTkI4QBPK53JxbvBYPPPAl+Pc4LHI53K13hIAKVXOybZg+YLWeS2xdWN/0TVbNvQ1/d35RuVOOECTyR46HMO7946b1dyWyUTPls06UwNQtnJOtgXLH1i3cmls3zQwaZxb94L5sWVDn/FkDawqIXxsbCw++MEPxmc+85kYGRmJn/iJn4h3vetd8SM/8iNTrv+zP/uzuO222ya9/oUvfGHazwFgavlcbtqu09lDh6ec0Tyazcbxu3caEQVA2Qql1cVK0ue1RGy7+VWC5QTrVi6N1X1LYmg4G2dGzkdXR3v09mQ8qGhwVQnhH/7wh+PTn/507NixIxYvXhw7d+6MLVu2xOc+97loa2ubtP7RRx+NVatWxb333jvu9a6urmpsD6BhFTvl7lo1EMO79xb9/OE9e6Nr1YBRUQCUrFBavWPfkWnXbHvzq+L6a38owV2lR+u8lqbvFt9sKn4nfHR0NPbu3Rtvfetb48Ybb4zly5fHfffdF0899VR88YtfnPJzHnvssVi+fHksWrRo3H9a/UsgQMkKp9wvDuARPzjl/vZn/uekj000ejobI0OPVHObQIn0biBNCqXVE5uNdS+YH9s3DcT1PyaAQ0HFT8KPHz8e3/3ud2PNmh+UM3Z0dERvb28cOXIkfuZnfmbS5zz66KPx0z/905XeCkDTyOdyM55yn/rcX5T0tUbPnq3EloA50LuBNJqqtPqV/7orHv3HM/Glr59Qag3/V8VD+JNPPhkREUuWLBn3+hVXXBGnTp2atP7MmTNx+vTpOHLkSHziE5+IZ599Nq699tp4xzveES9/+cun/DPWr18/7Z9/6tSpSX82QKMbGXpkxlPu7//Ld0r6Wm0LF1ZiS8As6d1Amr24tPrg0ZOx9fe+OO6ueKazPbZu7Hc3nKZW8XL05557LiJi0t3vyy67LC5cuDBp/WOPPRYREa2trfHe97437rvvvvje974Xb3rTm+L06dOV3h5AQyr19Lr1ZS8r+vG27kx09K6oxJaAWSilqmV4z16l6dS9g0dPxo59RyY1a8ueOx879h2Jg0dP1mhnUHsVPwlvb3/hHsjo6OjF/x4RceHChZg/f/6k9WvWrImvfvWr0dnZefG1D33oQ/Fv/+2/jT/90z+NrVu3TvqcAwcOTPvnFzslh7Qp1uUaXqzU0+ul//5n4tuf+pNpP95z62Y/Y1BDpVS1FHo3dPb3JbQrKE9uLB+79g8WXbP7/mOxum+J0nSaUsVDeKEU/Omnn44f/uEfvvj6008/HcuXL5/yc14cwCMiXvKSl8RVV10VTz31VKW3B6nhPiDl6OhdEW2ZTNF/eW/rzsS/+g8/Hy/9kR+e/LPVnYmeW/1sJSWpB2we5KVPqVUtejdQz4aGs0XHlUVEnH72uRgazuoKTlOqeAhfvnx5vOxlL4sHH3zwYggfGRmJoaGhePOb3zxp/Sc/+cn47//9v8eXvvSliyfn3/nOd+If//Ef4xd+4RcqvT1IBfcBKVdLa2v0bNk85c9NQeGUO7N2TXStGhDOaiSpB2we5KVTqVUtejdQz86MFA/g5a6DRlPxO+FtbW3x5je/Oe655544cOBAHD9+PH7rt34rrrzyynjd614XuVwunnnmmTh//oVfun/7b/9t5PP5eOc73xmPP/54DA4Oxlvf+tbo6uqKn/u5n6v09qDuuQ/IbGXWronlt22Ltkxm3Ott3ZlJD25aWlujs78vFr3mhujs7xPAEzLTGLnsocOp+nOovEJVSzF6N1DvujraZ15UxjpoNBU/CY+IeNvb3hbf//7344477ojz58/HwMBAfPSjH422trY4ceJErF+/Pnbs2BFvfOMbY8mSJbFv376455574qabbop8Ph+vfvWr4+Mf//i4O+XQLNwHZC6cctevUh+wda0amNM/r6T+HKqjnKoWqFe9PZnIdLYXLUnvXjA/enuKP3CCRlWVEN7a2hrbtm2Lbdu2TfrYVVddFY8++ui411asWBEf/ehHq7EVSB33AZmrwik39SWpB2we5KVfoapF7wbSqnVeS2zd2B879h2Zds2WDX2astG0qhLCgdlzHxAaU1IP2DzIawyqWki7dSuXxvZNA7Fr/+C4E/HuBfNjy4Y+c8JpakI41JlSu1y7DwjpktQDNg/yGoeqFtJu3cqlsbpvSQwNZ+PMyPno6miP3p6ME3CaXsUbswFzU7gPWIz7gJA+STXc0tgLqCet81qif1l33HjdVdG/rFsAhxDCoS6V0+UaSIekHrB5kAcA9U05OtQp9wGh8STVcEtjLwCoX0I41DH3AaHxJPWAzYM8AKhPQjgAJCypB2we5AFA/XEnHAAAABIihAMAAEBClKMDAEAdy43lzdqGBiKEAwBAnTp49GTs2j8Y2XPnL76W6WyPrRv7Y93KpTXcGTBbytEBAKAOHTx6MnbsOzIugEdEZM+djx37jsTBoydrtDNgLoRwAACoM7mxfOzaP1h0ze77j0VuLJ/QjoBKEcKhTuVzuTg3eCyeeeDLcW7wWORzuVpvCQBIyNBwdtIJ+ESnn30uhoazCe0IqBR3wqEOZQ8djuHde2M0+4P/Y23LZKJny+bIrF1Tw50BAEk4M1I8gJe7DqgfTsKhzmQPHY7jd+8cF8AjIkaz2Th+987IHjpco50BAEnp6miv6DqgfgjhUEfyuVwM795bdM3wnr1VKU1X/g4A9aO3JxOZzuIBu3vB/OjtySS0I6BSlKNDHRkZemTSCfhEo6ezMTL0SHT291Xsz1X+DgD1pXVeS2zd2B879h2Zds2WDX3mhUMKOQmHOjJ69mxF15VC+TsA1Kd1K5fG9k0Dk07EuxfMj+2bBswJh5RyEg51pG3hwoqum0mp5e9dqwaipbW1In8mAFC6dSuXxuq+JTE0nI0zI+ejq6M9ensyTsCnkRvLe6+oe0I41JGO3hXRlskULUlv685ER++Kivx5tSp/h2rJ53Iv/FyfPRttCxdGR+8KD5CA1Gud1xL9y7prvY26d/Doydi1f3DcaLdMZ3ts3divaoC6IoRDHWlpbY2X3/LL8ej73j/tmp5bN1csVNSi/B2qJc29DTw8gMbmd7z6Dh49OeX9+ey587Fj3xHl+9QVIRzqSPbQ4fjmRz825cfaujPRc2tlw0TS5e9QLYXeBhMVehssv21b3QbxND88AGbmd7z6cmP52LV/sOia3fcfi9V9S5SmUxc0ZoM6MV2DtIKXb/7liv+fdaH8vZhKlr9DNdRytN9caYwIjc3veDKGhrPjStCncvrZ52JouPgVPEiKEA51oJQQ8c29H6t4iGhpbY2eLZuLrqlk+TtUQzm9DepJmh8eADPzO56cMyPFA3i566DahHCoA7UMEZm1a2L5bdsmnYi3dWfquoQXCtLa2yCtDw+A0vgdT05XR/vMi8pYB9XmTjjUgVqHiMzaNdG1akDTGFKp0r0NkmqgVOvfe6C6/I4np7cnE5nO9qIl6d0L5kdvT/EreJAUIRzqQD00SGtpbTWGjFSq5Gi/JBso1cPvPVA9fseT0zqvJbZu7J+yO3rBlg19mrJRN5SjQx3QIA1mr1K9DZJuoOT3Hhqb3/FkrVu5NLZvGohM5/iS8+4F840no+44CYc6UAgRU41YKtAgDaZX6G0w6RS7xNF+pTZQ6lo1ULHfw0b+vTcTGRr7d7xerVu5NFb3LYmh4WycGTkfXR3t0duTcQJO3RHCoU7MNURAs5tLb4NyGihV8tpGI/7em4kMP9CIv+P1rnVeS/Qv6671NqAoIRzqSLkhwmkTjDfb3ga1bKDUSI0RCyX9ExVK+k1coBk10u84UBlCONSZUkOE0yaonFo3UKpGY8SkH9LVoqQf0kLzU+DFhHBIIadN8IJKBc1KdlivB7V4SFerkn4aiwovoBkI4ZAyTpvgBZUMmo3UQKlWD+nMRGauVHgBzcKIMkiZck6boFFVY5xYoYHSxJFCbd2Z1FSXlPqQLp/LVfzPrnVJP+mW9IhAgFpyEg4p47SJZlfNapC0N1CqZUl4o5X0kxwVXkCzcRIOKeO0iWZX7WqQQgOlRa+5ITr7+1L1L/21fEhXKOkvJi0l/SRLhRfQbIRwSJnCaVMxTptoZKpBplfrh3SNUNJP8vxOA81GOTqkTCM1kILZqHXQrGf1UBKe9pJ+kud3Gmg2TsIhhZw20cxUg0yvXkrC01zST/L8TgPNxkk4pJTTJpqVapDiCg/pJo166s5Ez61GPVF//E4DzaYln8/na72JSlq/fn1ERBw4cKDGOwGgmqacKSxoXpTP5TykI1X8TgNpVk4OdRIOwKzUOuSpBimuUBIOaeF3GmgWQjgAZZvyxCqTiZ4tyZ5YCZrQWPxOA81AYzb4v/K5XJwbPBbPPPDlODd4LPK5XK23BHUpe+hwHL9756QO3KPZbBy/e2dkDx2u0c5IO38PA9AMnIRD1M+pHtS7fC4Xw7v3Fl0zvGdvdK0aUEJKWfw9DECzcBJO03OqB6UbGXqk6AzqiIjR09kYGXokoR3RCPw9DEAzEcJpaqWe6imJrC9KVmv3HoyePVvRdeDvYQCajXJ0mlo5p3oaxdQHJau1fQ/aFi6s6Drw9zAAzcZJOE3NqV66KFmt/XvQ0bsi2jKZomvaujPR0buiqvugcfh7GIBmI4TT1JzqpYeS1fp4D1paW6Nny+aia3pu3awpGyXz9zAAzUYIp6k51UsPDcHq5z3IrF0Ty2/bNul3p607E8tv29Y01wKoDH8PA9Bs3AmnqRVO9Y7fvXPaNU716oOS1fp6DzJr10TXqoEXHgycPRttCxdGR+8KvyuUzd/DkLzcWD6GhrNxZuR8dHW0R29PJlrntdR6W9A0hHCaXuFUb1Kjq+5M9NzaPM2+6p2S1fp7D1paWzXKoiL8PQzJOXj0ZOzaPxjZc+cvvpbpbI+tG/tj3cqlNdwZNA8hHMKpXhoUSlaLlWM3esmq94BG5u9hqKypTrsfPHYqduw7Mmlt9tz52LHvSGzfNCCIQwKEcPi/nOrVNyWr3gMan7+HoTKmO+2+8Hzxxp277z8Wq/uWKE2HKtOYDUgNDcG8BwAUd/Doydix78i4AB7xwmn3d773fNHPPf3sczE0XLwBKDB3TsKBVFGy6j0oJp/LeV+AppUby8eu/YNz+hpnRs7PvAiYEyEcSB0lq96DqWQPHZ7c2CuTiZ4tGnsBzWFoODvpBLxcXR3tFdoNMB3l6ACkXvbQ4Th+985JTetGs9k4fvfOyB46XKOdASRnrqfY3QvmR29PZuaFwJwI4QCkWj6Xi+Hde4uuGd6zN/K54g2JANJurqfYWzb0acoGCRDCAUi1kaFHio5ti4gYPZ2NkaFHEtoRQG309mQi01k8iF/+krbo6rhs3GvdC+YbTwYJciccoAo0CEvO6NmzFV0HkFat81pi68b+KWeBF7zlP1wbq/uWTJoh7gQckiOEA1SYBmHJalu4sKLrANJs3cqlsX3TwKQ54d0L5seWDX0XT7v7l3XXaovQ9IRwgAoqNAibqNAgzCzvyuvoXRFtmUzRkvS27kx09K5IcFcAtbNu5VKn3VDH3AmHlMnncnFu8Fg888CX49zgMc2m6ogGYbXR0toaPVs2F13Tc+tm1wGAptI6ryX6l3XHjdddFf3LugVwqCNOwiFFlDnXt3IahJnxXVmZtWti+W3bJv9+dGei51a/HwBA/RDCISUqWeasaVh1aBBWW5m1a6Jr1YCfbQCgrgnhkAKlljl3rRqYMXA4Ta8eDcJqr6W1VZUBAFDX3AmHFKjUHOTCafrEr1U4Tc8eOjznvTazQoOwYjQIAwBobkI4pEAlypw1Das+DcIAAJiJEA4pUIky50qdpk+lGTu2T/c9FxqETTwRb+vOGE8GAIA74ZAGlZiDXK2mYc14x3ym71mDsNJNbBJ4+StfGf/y6KPeNwCgYQnhkAKFMuepuqMXzFTmXI2mYZXs2J4WpX7PGoTNbKqHGTFvXsTY2MX/2egPdACaVW4sH0PD2Tgzcj66OtqjtydjljlNQwiHlJjrHORKnKa/WCU7tqdFM37P1TLdw4wXB/CIxn6gA9Q3IbF6Dh49Gbv2D0b23PmLr2U622Prxv5Yt3JpDXcGyRDCIUXmUuZcidP0FyvnjnmjnAg34/dcDaU8zJjIww0gSUJi9Rw8ejJ27Dsy6fXsufOxY9+R2L5pwHtMw9OYDVKmUOa86DU3RGd/X1mhpJJNw6p1x7yeNcP3nESTvVIeZkw026aBAOUqhMQXB/CIH4TEg0dP1mhn6Zcby8eu/YNF1+y+/1jkxvIJ7Qhqw0k4NJlKNQ2rxh3zetfo33NSTfZm+5AizQ83gHQoNSSu7luiNH0Whoazkx5uTHT62ediaDgb/cu6E9oVJM9JODShuZymFxTumBdTzh3zNGjk77lwR3viCXXhTnb20OGK/VmzfUiR1ocbs9GMY/+gHpQTEinfmZHi72256yCtnIQDs1LpO+almDjOKunxVbX4npOQdMO5UpoETpTWhxuz0Yxj/6BeCInV1dXRXtF1kFZOwoFZq+Qd85lkDx2Oh7b8Why7413x2Pt/P47d8a54aMuvVfSEthRJfs9JKafhXCUUHmaUI40PN2YjyYoEYLLZhMTcWD4GnzgdX/r6iRh84rT7zEX09mQi01n8Pe5eMD96e4pXnUHaOQkH5qRSd8yLqbd55El8z0mqRcO56UbuTZoTXuIIvkZgBB7UXiEkFitJf3FI1EW9PK3zWmLrxv4pu6MXbNnQ5749DU8IB+ascMd8rqYqN4+Iugwmlfqe60GtGs5N9TDj8le+Mv7l0Ucb4uFGuYzAg9orJyQatTU761Yuje2bBiY9vOheMD+2bOjzntEUhHCgLkx3D3bxT71WMKmyUu5oV+tO9lQPM5r1n2MzjMCDNCglJOqiPjfrVi6N1X1LYmg4G2dGzkdXR3v09mS8VzQNIRyouWLl5t/+1J+U9DUEk9lr1IZzadPoI/AgTWYKiUZtzV3rvBbvDU1LCAdqqpR7sKUQTOZmujvazXQnu9ZqWZEATFYsJOqiDsyFEA7UVCn3YGcimFRGozWcSxsVCZAeRm0BcyGEAzVViTJywaRyGqnhXBqpSIB0KLeLekFuLO8eNCCEA7VVahn5v7rpP8ZTX/hrwYSGpyIB6t9sRm0ZZwYUCOFATZV6D/Zf/Yefj3/1H35eMKEpqEiA+lfOqC3jzIAXE8KBmirlHuzi1702Tv/dQcEbgLpSyqgt48yAiYRwoOamuwd7yeUvi4gYN6asLZOJni1K0AGoDzON2jLODJhICAfqwsR7sM+dPDXljPDRbDaO370zlt+2TRAHoO4ZZwZMNK/WGwAoKNyD7X71unjqC39ddO3wnr2Rz+US2hmNJJ/LxbnBY/HMA1+Oc4PH/BwBVWWcGTCRk3Cg7pQyO3z0dDZGhh7RvIqyZA8dnjz+yxUH6lw+l9OUMsVmO84MaFxCOFB3Sp0dXokZ4zSP7KHDUzYAdMWBeubBUfrNZpwZ0NiUowN1p9TZ4aWug3wuF8O79xZd44oD9abw4GhiZVDhwVH20OEa7YxyFcaZZTrHl5x3L5hvPBk0ISfhQN0pdXZ4R++KBHdFmrniQNqU+uCoa9WA0vSUKGWcGdAcnIQDdacwO7yYnls3+xdPSuaKA2lTzoMj0qMwzuzG666K/mXdAjg0KSEcqEuF2eFtmfGNatq6M+7uUjZXHEgbD44AGldVytHHxsbigx/8YHzmM5+JkZGR+Imf+Il417veFT/yIz8y5fqzZ8/Ge97znnjggQciIuL1r399bN++PV7ykpdUY3tASkycHa4rMLPlikNjaYZu4R4cATSuqoTwD3/4w/HpT386duzYEYsXL46dO3fGli1b4nOf+1y0tbVNWv+2t70tLly4EB/72MdiZGQkbr/99rjzzjvjve99bzW2B6RIYXY4zEXhisNU3dELXHFIh2bpFu7BEUDjqng5+ujoaOzduzfe+ta3xo033hjLly+P++67L5566qn44he/OGn9ww8/HF/96ldjx44dcc0118TatWvj3e9+d9x///3x1FNPVXp7ADQpVxzSr5m6heuNAdC4Kn4Sfvz48fjud78ba9b84F9mOjo6ore3N44cORI/8zM/M279Qw89FIsWLYqrr7764murVq2KlpaW+NrXvhZveMMbKr1FAJqUKw4zq9dS72bsFl54cDTp5L87Ez23NtbJP0AzqXgIf/LJJyMiYsmSJeNev+KKK+LUqVOT1j/11FOT1ra1tcWCBQumXA8Ac+GKw/TqudS7WcfMeXAE0HgqHsKfe+65iIhJd78vu+yyOHfu3JTrp7onftlll8WFCxem/DPWr18/7Z9/6tSpSaEeABpFtU6qC6XeExVKvWtdst/M3cI9OAJoLBUP4e3t7RHxwt3wwn+PiLhw4ULMnz9/yvWjo6OTXr9w4YLu6ADwItU6qU5Dqbdu4QA0ioqH8MIp9NNPPx0//MM/fPH1p59+OpYvXz5p/ZVXXhl//dd/Pe610dHRePbZZ2Px4sVT/hkHDhyY9s8vdkoOAGlVzZPqNJR66xYOQKOoeHf05cuXx8te9rJ48MEHL742MjISQ0ND8apXvWrS+oGBgXjyySfjn/7pny6+Vvjc6667rtLbA4DUKfWkOp/Lzerrp6HUW7dwABpFxUN4W1tbvPnNb4577rknDhw4EMePH4/f+q3fiiuvvDJe97rXRS6Xi2eeeSbOnz8fERHXXnttXHfddfFbv/VbcfTo0Th8+HC8613vio0bN057Eg5QLflcLs4NHotnHvhynBs8NutQA5VUzkn1bKSl1NuYOQAaQcXL0SMi3va2t8X3v//9uOOOO+L8+fMxMDAQH/3oR6OtrS1OnDgR69evjx07dsQb3/jGaGlpiQ9+8INx5513xqZNm+Kyyy6L17/+9bF9+/ZqbA1gWvXcGZrmVu2T6jSVetdjt/B6HesGQH1qyefz+VpvopIKd8KL3RsHmGi6+7YFTtmopXODx+LYHe+acV3fe+6c9Z1tvwOz4+EdABHl5dCKl6MDpE2179tSvlpdC6jX6wiFk+pi5npSrdS7fIUHFxMrCArN8rKHDtdoZwDUs6qUowOkSRo6QzeTWp0s1vOJZqEpWbGT6ko0JavHUu96lYaxbgDUJyfhQNNLQ2foZlGrk8U0nGgmdVLd0toanf19seg1N0Rnf58AOY1qN8sDoHE5CQeaXlo6Qze6Wp0spulE00l1/fDwDoDZEsKBppemztCNrFbXAtJ2HaFwUk1teXgHwGwpRweaXuG+bTGVuG9LcbU6WbwwQwAvdx3NIYlmeQA0JiEcmKReO0RXk87QtVerk8Xnz41UdB3NoZSHd4tf99o4/XcHm+bvUQBKoxwdGKeeO0RXm/u2tVWrawGXdnZUdB3No/DwbuLfmZdc/rKIiPj2p/7k4mvN8vcoADMTwqFJ5XO5SWHzzFePTDkCqdAhuhlOhN23rZ2kxnBNdNkMJcXlrqO5THx499zJU+PCd0Ez/T1KaXJj+RgazsaZkfPR1dEevT2ZaJ3XUuttAQkQwqEJTXfaPXbhQtHPq5cO0TSu6U4W27oz0XNrdU4RNeZjrgoP7/K5XDy05deKrvX3KBERB4+ejF37ByN77vzF1zKd7bF1Y3+sW7m0hjsDkiCEQ5MpzEOeaKbu0BH11SGaxpX0tYBancDTeNLWaZ/aOHj0ZOzYd2TS69lz52PHviOxfdOAIA4NTmM2aCKlzEOeiZm3JKFwsrjoNTdEZ39f1QOwxnxUgtnhzCQ3lo9d+weLrtl9/7HIjeUT2hFQC07CoYmUckozEzNvaVQa89W3qfpY1Ns/G7PDmcnQcHZcCfpUTj/7XAwNZ6N/WXdCuwKSJoRDE5nr6Yt7sTS6NDfmS0NIna20TG3QX4CZnBkpHsDLXQekkxAOTWSupy/uxUJ9SktInY1ifSzqrdu4/gLMpKujvaLrgHRyJxyaSOGUpphLLr88Lu3qGveae7FQvwohdeLpayGkZg8drtHO5q6UPhbDe/ZGPpdLaEcz01+AYnp7MpHpLB6wuxfMj94eIxGhkTkJhyZSyinNst/4VfdiISVKDalpHYmV1m7j+gswndZ5LbF1Y/+U3dELtmzoMy8cGpwQDk2m1DnM9fQvtMDU0hpSS5XmbuNp7i9Ada1buTS2bxqYNCe8e8H82LKhz3gyaAJCODQhpzTQGNIcUkuh2ziNat3KpbG6b0kMDWfjzMj56Opoj96ejBNwaBJCODQppzSQfo0eUnUbp5G1zmtpiDFkubG8hwlQJiEcAFKq0UOqbuNQ3w4ePTmprD7T2R5bN/Yrq4cidEcHqIJ8LhfnBo/FMw98Oc4NHqur7s00jkJILSbtIVW3cahPB4+ejB37jowL4BER2XPnY8e+I3Hw6Mka7Qzqn5NwgApr5JnN1J9Smy2mmT4WlEJZdHJyY/nYtX+w6Jrd9x+L1X1L/DOAKQjhABVUmNk8UWFms5M7qqEZQqo+FhSjLDpZQ8PZSSfgE51+9rkYGs42xL13qDTl6AAVUurMZqXpVEMhpC56zQ3R2d/XUAEcilEWnbwzI8UDeLnroNkI4QAVUs7MZgDmrtSy6NxYflZfe/CJ0/Glr5+IwSdOz+prNKqujvaKroNmoxwdoEIafWYzQL2pVlm08vbiensykelsL/redy+YH709mWk/Ds3MSThAhTT6zGaAelONsmjl7TNrndcSWzf2F12zZUOfpmwwDSEcoEIKM5uLSfPMZoB6U+my6GqWtzeadSuXxvZNA5HpHP/edi+YH9s3DagYgCKUowNNJ5/LVaWLdGFm81Td0QvSPrOZyqrWzyI0i0qXRev6XZ51K5fG6r4lRsNBmYRwoKlUe4Z3M8xspjLMk4e5K5RF79h3ZNo15ZRF6/pdvtZ5LR5IQJmEcKBpJDXDuxlmNjM35slD5RTKoic2UuteMD+2bOgrqyxa128gCUI40BRKneHdtWqgYqXpnf19c/46NJ6kfxahGVSqLFrXbyAJGrMBTcEMb+qFn0WojkJZ9I3XXRX9y7pndS9Z128gCUI40BTM8KZe+FmE+qbrN1BtytGBpmCGd2l0664+P4tQ/9LQ9Ts3lq/r/QHTE8KBplCY4V2sDLjZZ3jr1p0MP4uQDvXc9fvg0ZOTGtFlOttj68Z+J/WQAsrRgaZQmOFdTDPP8C50654YDAvdurOHDtdoZ43HzyIwFwePnowd+45Mah6XPXc+duw7EgePnqzRzoBSCeFA0yjM8G7LjO9q29adaeqRUKV2687ncgntqPH5WQRmIzeWj137B4uu2X3/sciN5RPaETAbytGBpmKG92TldOs2dq1y/CwC5RoazhYdnxYRcfrZ52JoOFu3pfSAEA40ITO8x9Otu3b8LALlODNSPICXuw6oDSEcoMnp1g2QDl0d7TMvKmNdmukOT5oJ4QBNTrdugHTo7clEprO9aEl694L50duTmfbjjUB3eNJOYzaAJqdbN0A6tM5ria0b+4uu2bKhr6FPhHWHpxEI4QDo1g2QEutWLo3tmwYi0zm+5Lx7wfzYvmmgoU+CdYenUShHByAidOsGSIt1K5fG6r4lTXcnWnd4GoUQDsBFunUDpEPrvJamC5q6w9MohHAAAEiBZu8Irjs8jUIIBwCAOqcjuO7wNA6N2QAAEpbP5eLc4LF45oEvx7nBY5HP5Wq9JeqYjuAv0B2eRuEkHAAgQdlDh2N4994YzWYvvtaWyUTPls0mEcxBo5VqF76f0+eeiz33z9wRfHXfklR/v6UqdIefWBXQvWB+bNnQ1zRVAaSbEA4AkJDsocNx/O6dk14fzWbj+N07jQScpUYr1Z7q+ymm2TqCN2t3eBqHcnQAgATkc7kY3r236JrhPXuVppep0Uq1p/t+ZtJsHcEL3eFvvO6q6F/WLYCTKkI4lMDdPQDmamTokXEl6FMZPZ2NkaFHEtpR+uXG8rFr/8yl2rmxfEI7mptSvp/p6AgO6aEcHWbg7h4AlTB69mxF1xExNJyd8cQ4TaXapXw/U9ERHNLFSTgUUbi7N/HkonB3L3vocI12BkDatC1cWNF11ZC2yq9SS7DTUqo9233qCA7p4iQcplHq3b2uVQPR0tqa0K4ASKuO3hXRlskULUlv685ER++KBHf1A2ms/Cq1BDstpdrl7lNHcEgnIRymUc7dvc7+voR2BTSSfC73wt81Z89G28KF0dG7wkO9BtbS2ho9WzZP2R29oOfWzTX5GUhr1/benkxkOtuLlnBXs1S70mPRSvl+Ol/WFrf8+76L35cTcEgfIRym4e4eUE1pPHVk7jJr18Ty27ZN/mffnYmeW2vzz342lV/1MpO7dV5LbN3YHzv2HZl2TbVKtasxFq2U7+fXf/5aJ9+QckI4TCMNd/eAdErrqWMlOP1/IYh3rRqom/eh3MqvpGZyzxT0Cx9//vtj8aafXh5/dfgfx+2pmqXahTFiExXGom3fNDDrP3fdyqWxfdPApPdY6Tk0DiEcplHvd/eAdGrmfhNO/3+gpbW1bq4ylVP5Vc3w+WIzBf2pPt7VcVm86adfGUu7X1bV0/lSx6Kt7lsy6z9/3cqlsbpvSV1UGwCVpzs6TKNwd6+YWt3dgzRJW7flamvWWdGmTdSvUiu6LulckMhM7kLQn3gvuhD0/5//dWzKj58ZuRCf/KtH49JL5kX/su6qBdZyxqLNReu8luhf1h03XndVVb8fIHlOwqGIery7B2ni5HOyZuw30cin/41QXl9q5de3518R2XPDRb/WXGdyl3LKvP9L3yj68bmeQs+k0caiAckTwmEG9XZ3D9Kime89F9OM/SYaddpEozxkKrVr+7HvPF/S15tL+CzllHmmg/a5PgiYSaONRQOSpxwdSlC4u7foNTdEZ3+fAA4zKPXksxlL0wunjsU0Wr+JRjz9b7Ty+kLl18SfzbbuzMUHZrMNn+VcSanU6XE1T6ELY8SKqeZYNCD9nIQDUHGNevJZCfU8K7paGu30v1HL62eq/JrNTO5yqwUqdXpczVPoUsaI3fLvr3GHG5iWk3AAKq4RTz4rqZRTx0bSaKf/jdxcr1jlVyF8FvPimdyzqRYo5ZR5pmybxCl0YYzYdHvd8+fH4uDRk1XdA5BeQjgAFddoJ5/VkFm7Jl61+w+j7z13xo/+9m9G33vujFft+sOGC+ARjTdtopkfMk0XPrsXzB83nmy2V1JKCfobb7y66Mdf/CCgmtatXBq3bpi6kqfQyV0QB6aiHB2Aiiu123I9n3wm0fW6nmZFV1sjTZto9odMpcywnsuVlELQnzgHvHvB/NiyoS/WrVwar/yRrqIfT0JuLB977j9WdE21O7UD6SSEA1Bxab/33Chdr+tNo0ybaISHTHNVmGE9nblWC8wU9Et5EFBt5cwLr1andiCdhHAAqiKtJ59Gq1VXI5z+p/0hU6XlxvKTwnAlqgVmCvozfbzazAsHZksIB6Bq0nby2ahdr6m8tD5kqrSDR09OKgvPdLbHlp+9puGrBcwLB2ZLCAegqtJ08mm0WuOr5F3/Sj5kmuo0ud7vER88enLKMV3Zc+fj7k98Ld7yhjfFyz7xB9N+/kzVAvX+nsxmZBtAhBAOkHpJNBBrFs3c9boZVOOufyUeMk13mrx1Y39iTcbKlRvLx679g0XXfPqR0Xjff35H/OOe/6fsaoE0vCelzAtPqlM7kC5COECKaSBWWc3e9bqR1etd/2KnyTv2HRk39quelNqU7MkrrotX7f7Dsh4Upuk9KaWTO8BEQjhAStVrqEgzXa8bU73e9S/lNLleR1yV05SsnGqBNL4n9dCpHUiXebXeAADlKzVU5HO5hHbUGApdr4tppq7XjaKcu/5JKmfEVb2ZbVOy3Fg+Bp84HV/6+okYfOJ05Mby4z6e1vek0Kn9xuuuiv5l3QI4UJSTcIAU0kCsenS9bjz1etc/zSOuZtOUrJR73ml+TwBKJYQDpFC9hopGkbbRahRXr3f90zziqtymZKXe807zewJQKuXoAClUr6GikRTusS56zQ3R2d8ngKdY4a5/MbW46184TS6mnkdcFZqSTfweuhfMH9c8rdR73rmxfEnvyeUvuTRy+fykUnaAtHASDpBCGohB6Qp3/adqZFhQi7v+jTDiqpSmZOXc8+5f1j3je/Iv33s+fvePDtbdyLJKq/c56cDsCeEAKbX4p14b3/7Un0z7cQ3E4Adqddc/n8sVvdbQCCOuCk3JplPuPe/p3pOJ6nFkWaWkYU46MHtCOKTUTP9iR+Oaajb4i2kgBlNL+q5/9tDh+Mauj8bzZ85cfO3Srq64eust434/G33E1WzueRfek2NPnI67P3EkvvO956f9vHobWTZXaZqTDsyOEA4pNFUIa8tkomeL4NXoppsNXvCvbvqP8a/+w897IAPTKGdm9VxM97v6/JkzcfzunbH8tm3j/r6e6TQ5zWbTST3ihfdk3ryWogE8Ynwpe9qlcU46UD6N2SBlCv9iN/EUdDSbjeN374zsocM12ll65HO5ODd4LJ554MtxbvBYamZplzIb/Kkv/nVCuwGmk8/l4okP/mHRNU986A9T83fPXBXuvhcz3d33ZhtZltY56UB5nIRDipQSwob37I2uVQNOQqeR5ioCs8EhHc4d+4f4/ne+U3TN9//lO3Hu2D/EgmtXJrSr2prt3fdmG1nWbA8doFkJ4ZAiQtjcTFceWqgimFgeWm/MBod0OHfsWMnrmiWER8zu7vtsS9knSkun8WZ76ADNSgiHFBHCZq8RqgjMBoeUyJcY7kpd10DKvfteiTFuaeo0XqmHDkB9cyccUkQIm71yqgjqVWE2eDFmg9No0tjDobP/moqua3aFUvZM5/jT3+4F82fsFF7oND4x1BY6jR88erIqe56tudyfB9LDSTikSCGEFQuTQtjUGqGKoKW1NXq2bC7aHd1scBpJWns4dPZdE5dc/rL4/r9Mfy/8kssvj84+IbxUsyllT2un8UaYHQ8UJ4RDighhs9coVQSZtWti+W3bJgcTs8FpMGnu4dDS2hrLfuPXiv5dvew3ftXf1WUqt5S9nE7j9TberNFnx0OzE8IhZYSw2WmkKoLM2jXRtWrghRL7s2ejbeHC6Ohd4V/oaRiN0MNh2r+rU3CS3yjS3mm8kWfHQ7MTwiGFhLDyNVoVQUtrqw74NKxGmQTh7+ra0mkcqFdCOKSUEFY+VQSQDo3Qw6HA39W1k1Sn8bSMPwPqhxAONBUnU1D/GqWHA7VVifFmM0nT+DOgfhhRBjSdwsnUotfcEJ39fQI41Bnj+JpTbiwfg0+cji99/UQMPnE6cmP5OX/NuYw3m0naxp8B9cNJOFC38rmcE2toQo3Ww4GZTXWi/LL5l8bPvqYnfvG1r5zTafXqviXxkvmXxrEnTkc+IlZe3R19y7rn9DXTOv4MqA9COFCX0jofGKgMPRyaR+FEeaLvPPd8fPKvHo3/9eXheMt/+LFZnVpPFe4PHPnWnMvF0zz+DKg9IRyoO2meDwxUjh4Oja+UE+V/+d7zsWPfkbLLx6cL94Vy8bmUo6d9/BlQW+6EA3Wl1PnA+VwuoR0BtaSHQ2Mr5US5YPf9x0q+J15qufhs750bfwbMhRAO1JVy5gMDkG7lnBQXyrtLUU65+GwUxp8VU4nxZ0BjEsKButJI84EBKK7ck+JKl4HPtly8MP6smLmOPwMaV8VD+IULF+LOO++MtWvXxo//+I/H2972tsjOcKr1wQ9+MF75yldO+s/3v//9Sm8PqHPmAwM0j1JOlF+s0mXgcykXr+b4M6CxVbwx23/9r/81vva1r8Uf/MEfRFtbW7zrXe+K//Sf/lP88R//8bSf8+ijj8aGDRti27Zt4zd3ib5x0GwK84GLlaSbD0yBMXaQboUT5akaqE1UTnl3IdwXK0mvRLn4upVLY3XfkhgazsaZkfPR1dEevT0ZJ+BAURVNuU899VTs378/PvKRj8SrXvWqiIi499574/Wvf338/d//ffzYj/3YlJ/32GOPxU033RSLFi2q5HaAFDIfmFIZYweNoXCi/Aef+fv4zveen3ZdOeXdpYT7SpWLt85rMYYMKEtFy9G/9rWvRUTE6tWrL7728pe/PBYvXhxHjkz9l+Bzzz0X3/rWt2LZsmWV3AqQYoX5wG2Z8ScUbd0Z48mIiB+MsZtYMVEYY5c9dLhGOwNmY93KpfHHd/67eNNPL4+XveTScR+bbXm3cnGgXlX8JHzhwoVx2WWXjXv9iiuuiFOnTk35OY8//niMjY3FX/7lX8a73/3uGB0djVWrVsU73vGOuOKKK6b8nPXr10+7h1OnTsWSJUtm/00AdcF8YKZT6hi7rlUDfl4gRVrntcRNP/XK+MXX/mjFyruViwP1qKwQfuLEiaIB+D/9p/8UbW1tk16/7LLL4sKFC1N+zuOPPx4REZdffnl84AMfiNOnT8e9994bv/RLvxR/9md/FvPnzy9ni0ADKcwHhhcrZ4ydnx9In0qXdysXB+pNWSF88eLF8fnPf37aj3/pS1+K0dHRSa9fuHBh2jD98z//8/Ha1742Ojs7L772ile8Im688cb427/923jDG94w6XMOHDgw7R6KPSQAIP2MsQMA0qysEH7ppZfG1VdfPe3HH3300Xj22WdjdHR03In4008/HVdeeeW0n/fiAB7xQthfsGBBPPnkk+VsD4AmYIwdTJYbyyu5BkiJit4J/4mf+IkYGxuLr33ta7F27dqIiBgeHo6nnnrqYrf0id7//vfHgQMH4i/+4i+ipeWF/7M4ceJEnD17VrM2ACYxxg7GO3j0ZOzaPzhuHFemsz22buzXfAygDlW0O/rixYvjZ37mZ+KOO+6IBx98MI4ePRq//du/HatWrbo4nmx0dDSeeeaZi2Xrr3/96+Pb3/523HXXXfHNb34zjhw5Em9961vjuuuuixtuuKGS2wOgARTG2BVjjB3N4uDRk7Fj35FJ87Cz587Hjn1H4uDRkzXaGQDTqWgIj4i46667Yu3atfGWt7wlbrnllujp6YkPfOADFz/+8MMPx/XXXx8PP/xwRERcc801sWfPnnjkkUfijW98Y7zlLW+JFStWxB/90R9dPBkHgBczxg5eKEHftX+w6Jrd9x+L3Fg+oR01ntxYPgafOB1f+vqJGHzitPcSqIiWfD7fUH+bFBqzFWveBkBjyOdyxthRFWn42Rp84nT8zh/+3Yzrfu/XXq07+Cwo8wfKUU4OreidcABIkjF2VEP20OEY3r13XN+BtkwmerZsrqsqi9PPPlfRdfxAocx/okKZ//ZNA4I4MGsVL0cHAEir7KHDcfzunZMa/41ms3H87p2RPXS4RjubbOS7k8fCzrROefXMlPkD1eYkHAAgXihBH969t+ia4T17o2vVQF2Upne8rG3mRS9ap7y6NEPD2UmN7iY6/exzMTScVeYPzIqTcIAGkM/l4tzgsXjmgS/HucFjkc/lar0lSJ2RoUeKjr6LiBg9nY2RoUcS2lFx3Z3zS16ni/p4xSoCzowUD+AzrVNtAMzESThAyqXl/irUu9GzZyu6rtp6ezKR6WwvemrbvWB+vPJfd8XW3/ti0a+1+/5jsbpvSbTOa/zJNDNVBHR1tJf0daZap9oAKIWTcIAEVfrEOk33V6HetS1cWNF11dY6ryW2buwvumbLhr549B/PlFxe3ehKqQgoPNwopnvB/OjtGT8iUbUBUCohHCAh2UOH46EtvxbH7nhXPPb+349jd7wrHtrya7MOyqXeX1WaDqXp6F0xafb8RG3dmejoXZHQjma2buXS2L5pYFJo7F4w/2IH77mWVzeKUhuuRURJDzdeXDWgmRtQDuXoAAkonFhPVDixXn7btrJLx8u5v2qMF8yspbU1erZsnvJ3taDn1s110ZTtxdatXBqr+5bE0HA2zoycj66O9ujtyVwMiXMpr24k5TRcKzzcmFha3r1gfmzZ0DeptFwzN6AcQjhAlVWr43La7q9CGmTWronlt22b3GehOxM9t9Zvn4XWeS3ThrtS745PLK9uNOVWBMz0cGMuXxtobkI4QJVV68Q6bfdXIS0ya9dE16qBF353z56NtoULo6N3Rd2dgJeqcHd8x74j066ZWF7diGZTEVDs4cZcvzbQvNwJB6iyap1Yp/H+KqRFS2trdPb3xaLX3BCd/X2pDeAFpdwdb3SzbbhW668NNB4n4QBVVq0T67TeXwVqo5zy6kZUzYoA1QZAOZyEA1RZNU+sC/dXJ379tu7MrJq9AY2tUF5943VXRf+y7qYLhdWsCFBtAJTKSThAlVX7xLrR7q8CVFM1KwKavdoAKI0QDpCAandcLtxfBWBmpTZcq7evDTQGIRwgIU6sAQAQwgES5MQaAKC5acwGAAAACRHCAQAAICFCOAAAACRECAcAAICECOEAAACQECEcAAAAEiKEAwAAQEKEcAAAAEjIJbXeAAAAs5cby8fQcDbOjJyPro726O3JROu8lqbfC0C9EsIBAFLq4NGTsWv/YGTPnb/4WqazPbZu7I91K5c27V4A6plydACAFDp49GTs2HdkXOiNiMieOx879h2Jg0dPNuVeAOqdk3AAgJTJjeVj1/7Bomt23z8YL22/NJ79zoWqloaXtpdjsbpvSVOVpivNB6YjhAMApMzQcHbSqfNEp589H3d85ODF/12t0vDS9vJcDA1no39Zd0X/7HqlNB8oRjk6AEDKnBkpHnqnUq3S8FL3Mps9p5HSfGAmQjgAQMp0dbTP+nN3338scmP5xPcylz2nRaml+ZV8/4H0EcIBAFKmtycTmc7ZhdpCaXiSe+leMD96ezIV+zPrVTml+UDzEsIBAFKmdV5LbN3YP+vPr2RpeCl72bKhrymakinNB0ohhAMApNC6lUtj+6aBWZ2IV7o0fLq9dC+YH9s3DTRNMzKl+UApdEcHAEipdSuXxuq+JRdHYXVeflnc98mvxZmRC9N+TrVKwyfupRnHchVK84uVpDdLaT4wPSEcgBnlc7kYGXokRs+ejbaFC6Ojd0W0tLbWeltAvFAO/uLRX7/ycytjx74j066vZmn4xL00m0Jpfq3efyAdhHAAisoeOhzDu/fGaPYHjYTaMpno2bI5MmvX1HBnwFQKpeET51R3L5gfWzb0NU1peK14/4GZtOTz+YaakbB+/fqIiDhw4ECNdwKQftlDh+P43Tun/fjy27YJ4lCncmP5pi4NrzXvPzSXcnKok3AAppTP5WJ4996ia4b37I2uVQNK06eghJ9aa/bS8Frz/gPTEcIBmNLI0CPjStCnMno6GyNDj0Rnf19Cu0oHJfwAwHSMKANgSqNnz1Z0XbMolPBPfIAxms3G8bt3RvbQ4RrtDACoB0I4AFNqW7iwouuaQakl/PlcLqEdAQD1RggHqIF8LhfnBo/FMw98Oc4NHqvLUNbRuyLaMsVn2bZ1Z6Kjd0VCO6p/5ZTwAwDNyZ1wgIRV4r5wEk2/Wlpbo2fL5qLd0Xtu3azZ2Iso4QcAZiKEAyRoupFfhfvCpYz8SrLpV2btmlh+27bJf153Jnpu1WRsIiX8AMBMhHCAhFRi5FclQny5MmvXRNeqAeO2SlAo4S9Wkq6EH15gjjbQrIRwgITMdeRXLed2t7S2GkNWAiX8lKOZQ+jBoydj1/7ByJ47f/G1TGd7bN3YH+tWLq3hzgCqTwgHSMhc7wub250OSvgpRTOH0INHT8aOfUcmvZ49dz527DsS2zcNNPx7ADQ3IRwgIXO9L6zpV3oo4aeYZg6hubF87No/WHTN7vuPxeq+JU1TFQA0HyPKABIy15Ffmn6lS6GEf9FrbojO/j4BnIgoPYTmxvIJ7ShZQ8PZcaf/Uzn97HMxNFy86gcgzYRwgIQU7gsXU+y+sLndkH7NEEJzY/kYfOJ0fOnrJ2LwidPjHiicGSn+vZe7DiCNlKMDJGgu94U1/YL0a/QQOtNd966O9pK+TqnrANJICAdI2FzuC2v6BenWyCG0lLvuq/uWRKazvWg1QPeC+dHbU7zqByDNhHCAGpjLyC9NvyC9ensyDRlCy2m4duOP/1D86f/+xrTrXvNjSzVlAxqaO+EAKaTpF6RT67yW2Lqxv+iaLRv6UhdCS73rPviN0/Glh/+56LoH/v5kwzamA4gQwgEAErVu5dLYvmkgMp3jS867F8xP7XiyUu+wH3vidMM3pgOYiXJ0AICErVu5NFb3LYmh4WycGTkfXR3t0duTSd0JeEGpd9hLPd9Oa2M6gFII4QAANdA6ryX6l3VX9GvmxvI1Cfal3nVfeXV3/I+/fmzGr5fGxnQApRLCAQAawEzjwaqpcNd9qu7oBVs29EXfsu6GbEwHUA53wgEAUq4wHmxiuC2MBzt49GTV91DKXfdGbUwHUA4n4QAAKVbOeLBqh9tS7roXwvrEU/vuBfNjy4a+VDamAyiHEA4AkGKljgcbGs5W/A76VEq5695ojekAyiGEAwCkWKmdxOut43g1GtMBpIEQDgCQYqV2Eq9mx/FadWUHSCMhHAAgxUodD1atjuO17MoOkEa6owMApFgtO47XQ1d2gLQRwgEAEpAby8fgE6fjS18/EYNPnI7cWL5iX7uU8WCVVmpX9kp+nwCNQDk6AECVJVGynXTH8Xrryg6QFkI4AEAVFUq2JyqUbFfypDrJjuNp7coOUGvK0QEAqqSRS7broSs7QBoJ4QAAVVJOyXbaFLqyF1PNruwAaSWEAwBUSaml2IePnaryTiqvll3ZAdJMCAcAqJJSS7H//MvDqRznVauu7NXqMg+QBI3ZAACqpFCyPVNJesQLd8NX9y1J3clxkl3Zk+gyD1BtTsIBAKqklJLtgrTeDY/4QVf2G6+7KvqXdVctgO/Yd2TSA41Cl/k0VhIAzUkIBwCoonUrl8bP3tBT0lrjvKbWyF3mgeYjhAMAVNmaviUlrTPOa2qldpn/3JeHBXGg7gnhAJCQfC4X5waPxTMPfDnODR6LfC5X6y2REOO85qbUCoE9f34sbnnPF5SmA3VNYzYAUi2fy8XI0CMxevZstC1cGB29K6KltbXW25oke+hwDO/eG6PZH9z5bctkomfL5sisXVPDnZGEwt3wHfuOTLvGOK/plVMhULgjXq3u7ABzJYQDkFppCbbZQ4fj+N07J70+ms3G8bt3xvLbttXVfqmOwjivid29uxfMjy0b+mJ135IYfOJ01TuMp1E5XeYL0tptHmh8QjgAqZSWYJvP5WJ4996ia4b37I2uVQN1eYJPZU03zuvBY6filvd8weitaZRSSTBRodt8/7LuKu4MoHzuhAOQOqUG23q4cz0y9Mi4k/qpjJ7OxsjQIwntiFqbOM7rwWOnjN4qQaGSYKa79S+m2zxQj4RwAFInTcF29OzZiq6jsRi9VZ51K5fGR+/4qbj1Z/tKWq/bPFCPhHAAUidNwbZt4cKKrqOxlDp6a2i4+EOnZtI6ryX+fzf06DYPpJYQDkDqpCnYdvSuiLZM8SDQ1p2Jjt4VCe2IelJqubSy6vEKd8SL0W0eqFdCOACpk6Zg29LaGj1bNhdd03PrZk3ZmlSp5dLKqieb7o5494L5xpMBdU13dABSpxBsp+qOXlBPwTazdk0sv23b5HFq3ZnoubW+xqmRrFJGbymrnt503eadgAP1TAgHIJXSFmwza9dE16qBF5rKnT0bbQsXRkfvirp5UEBtlDJ6a7Zl1bmxfFOE00K3eYC0EMIBSK20BduW1tbo7C+tqzPNo1BWvWv/4LgT8e4F82PLhr5ZlVUfPHpy0tczdxygPgjhAKSaYEsjqGRZ9cGjJ6c8WS/MHXdfGqC2hHAAgDpQibLqUueOr+5b0pCl6QBpoDs6AECDMHccoP4J4QAADcLccYD6J4QDADQIc8cB6p8QDgDQIApzx4sxdxygtoRwAIAGUZg7Xsxs544DUBlCOABAAynMHZ94It69YL7xZAB1wIgyAIAGU8m54wBUlhAOANCAKjF3HIDKE8IBAJiV3FjeaTtAmYRwqi6fy8XI0CMxevZstC1cGB29K6KltbXW2wIA5uDg0ZOxa/9gZM/9YOZ4prM9tm7sd+8coIiqhvDbb789crlc3H333UXXnThxIu666644cuRItLe3x8/93M/F29/+9mgV1FIve+hwDO/eG6PZ7MXX2jKZ6NmyOTJr19RwZwDAbB08ejJ27Dsy6fXsufOxY98RDeAAiqhKd/RcLhfvfe9747Of/eyMa59//vm45ZZboqWlJT796U/Hu9/97vjsZz8bH/rQh6qxNRKUPXQ4jt+9c1wAj4gYzWbj+N07I3vocI12BgDMVm4sH7v2DxZds/v+Y5Ebyye0I4B0qfhJ+De+8Y3Yvn17fPvb346lS2d+AvpXf/VXcfLkyfjMZz4THR0d8aM/+qORzWbjfe97X/zqr/5qtLW1VXqLJCCfy8Xw7r1F1wzv2RtdqwaUpgNQFtecamtoODuuBH0qp599LoaGsxrDAUyh4iH8q1/9aqxYsSL+8A//MH7zN39zxvUPPfRQXHPNNdHR0XHxtTVr1sR3vvOdOH78eKxcubLSWyQBI0OPTDoBn2j0dDZGhh6Jzv6+hHYFQNq55lR7Z0aKB/By1wE0m4qH8Jtuuqms9U8++WRceeWV41674oorIiLi5MmTU4bw9evXT/v1Tp06FUuWLClrD1Te6NmzFV0HAIVrThMVrjktv21bVYK4DuDjdXW0V3RdJfhnBKRJWSH8xIkTRQPwV77ylVi0aFFZGzh//vy4U/CIiMsuuywiIi5cuFDW16J+tC1cWNF1ADSH6UrNa3XNSQfwyXp7MpHpbC9akt69YH709mQS2Y9/RkDalBXCFy9eHJ///Oen/XhXV1fZG2hvb4/R0dFxrxXC90te8pIpP+fAgQPTfr1iDwlITkfvimjLZIqWpLd1Z6Kjd0WCuwKgnhUrNb/kZS9L/JqTDuBTa53XEls39k/53hRs2dCXyEm0f0ZAGpXVHf3SSy+Nq6++etr/zGak2JVXXhlPP/30uNcK/3vx4sVlfz3qQ0tra/Rs2Vx0Tc+tmzXSASAiZp6ocear0we+cesrdM1JB/Di1q1cGts3DUSmc3zJefeC+YkFX/+MgLSq6pzwUgwMDMT+/fvjO9/5TrzsZS+LiIhDhw7FS1/60li+fHmNd8dcZNauieW3bZt8qtGdiZ5bNdCZDR2BgUZUSqn50//7gZK+VqWuOekAPrN1K5fG6r4lNbuL7Z8RkFaJh/DR0dE4d+5cdHZ2RltbW7z2ta+N3//934/f/M3fjHe84x1x4sSJuO+++2Lz5s3GkzWAzNo10bVqQHCsAB2BgUZVykSN74+MxCUdHfH9kZFp11TympMO4KVpnddSs4DrnxGQVmWVo1fCww8/HNdff308/PDDEfFCE7Y9e/bE2NhY/OIv/mLceeed8aY3vSl+/dd/PemtUSUtra3R2d8Xi15zQ3T29wngszBTmWb20OEa7Qxg7kotIV/0b15T9OOVvOZUjx3AGc8/IyCtqnoS/olPfGLSa6tXr45HH3103Gs/8iM/Env3Fi9Dg2ZVq47AAEkptYQ8s2ogOntXJHLNqVIdwI3Oqp5669IOUKqa3wkHiiulTLPSHYEBklTORI2W1tZErjlVogO40VnVVU9d2gHKkXg5OlCeUss0K9URGCBp5U7USOqa01w6gBdGZ008pS2Mzjp49GRV9lxrubF8DD5xOr709RMx+MTpqncmr4cu7QDlchIOda7UMs1KdQQGqIV6nagxmw7gpYzO+uBn/j5e2n5p9C3rbpiT2lqd/Ne6SztAuYRwqHPllGkCpFm9TtQotwN4KaOz/uV7z8cdHznYMOXphZP/iQon/9U+la5ll3aAcilHhzpXbpkmQJo1wkSNckZiNUJ5eikn/7vvP1b10nSAtBDCIQUKZZptmfEdXtu6M7H8tm3mhAPUkdmMxEpzSC3l5P/0s8/F0HDxJqMAzUI5OqREvZZpAjBeKaOzJiqE1DSWVJd68l9OhQBAI3MSDinSCGWaAI2uMDqrXGkNqaWe/M+mQgCgEQnhAAAVNt3orGLSGlILJ//FdC+YH709maJrAJqFEA4AUAXrVi6Nj97xU3HXr66Ll82/tOjaNIfUUk7+t2zoMzIM4P8SwgEAKiw3lo/BJ07HV/7+n6O1pSV+4xeuLbo+7SF1upP/7gXzqz6eDCBtNGYDAKigg0dPxq79g+Mas2U62+ON/+bq+NLD/zzu9e4F82PLhr6GCKnrVi6N1X1LYmg4G2dGzkdXR3v09mRS/XABoBqEcACACjl49GTs2Hdk0uvZc+fjT//3N+I//9KrovOllzVsSG2d15LKDu8ASRLCAQAqIDeWj137B4uu+eif/0Psuf11DRW8ASiPO+EAABUwNJydcTZ4YR44AM1LCAcAqIBS53yndR44AJUhhAMAVECpc77TOg8cgMoQwgEAKqC3JzNpRNdEaZ4HDkBlCOEAABXQOq8ltm7sL7qmmvPAC7PJv/T1EzH4xOnIjeWr8ucAMDe6owMAVMi6lUtj+6aBSXPCqz0PfLrZ5Fs39jfEDHKARiKEAwBU0LqVS2N135IYGs4mMg+82GzyHfuOxPZNA4I4QB0RwgEAKqx1Xkv0L+uu+p9Tymzy3fcfi9V9S8wmB6gT7oQDAKSU2eQA6SOEAwCklNnkAOkjhAMApJTZ5ADpI4QDAKSU2eQA6SOEAwCkVCmzydf1v9Cpfaq54WaLAyRPd3QAgBSbbjb5vJaIsXzEn395OP78y8OT5oabLQ5QGy35fL6hHnmuX78+IiIOHDhQ450AACQnN5aPoeFsHD52Kv78y8PTrtu+aSAiYsrZ4i9eI4gDlK6cHOokHIDUyOdyMTL0SIyePRttCxdGR++KaGltrfW2oC60zmuJ3p5MvP+TXyu6bqa54hFmiwNUkxAOQCpkDx2O4d17YzT7g3nHbZlM9GzZHJm1a2q4s+ZQOGU9M3I+ujrao7cnI6DVoVLmhs/08YgfzBbvX9Zdqa0B8H8J4QDUveyhw3H87p2TXh/NZuP43Ttj+W3bBPEqcnc4PSo5D9xscYDq0B0dgLqWz+ViePfeomuG9+yNfC6X0I6ay8GjJ2PHviOTTk+z587Hjn1H4uDRkzXaGVOp5Dxws8UBqkMIB6CujQw9Mq4EfSqjp7MxMvRIQjtqHrmx/Iz3h3fff8xYqzpSytzwTGe72eIANSSEA1DXRs+ereg6SlfK/eLC3WHqQylzw7du7J9xzZYNfTPe+TdjHGB23AkHoK61LVxY0XWUrtQ7we4O15fp5oZ3L5gfWzb0XbzHX8qa6egTADB7QjgAda2jd0W0ZTJFS9LbujPR0bsiwV01h1LvBLs7XH/WrVwaq/uWFO1oX8qaqRT6BExU6BNgxjhAcUI4AHWtpbU1erZsnrI7ekHPrZvNC6+Cwv3iYiXp7g5X32zHw7XOa5lxxFgpaybupZQ+AWaMA0xPCAeg7mXWronlt22bPCe8OxM9t5oTXi2F+8VTnXoWlHJ3mNmrt7LvcvoEmDEOMDUhHIBUyKxdE12rBl7oln72bLQtXBgdvSucgFdZqfeLqbx6LPvWJwBg7oRwAFKjpbU1Ovv7ar2NpjPbu8PMXr2WfesTADB3QjgAMKNy7w4zN/Va9q1PAMDcmRMOAFBn6rXsu5Q55PoEABQnhAMA1Jl6Lvsu9AnIdI7/s7sXzDeeDKAEytEBAOpMvZd96xMAMHtOwgEA6kwayr4LfQJuvO6q6F/WLYADlEgIBwCoQ8q+ARqTcnQAgDql7Bug8QjhAAA1lhvLTxu0jYcDaCxCOABADR08ejJ27R8c14Qt09keWzf2KzkHaEDuhAMA1MjBoydjx74jk7qgZ8+djx37jsTBoydrtDMAqkUIBwCogdxYPnbtHyy6Zvf9xyI3lk9oRwAkQQgHAKiBoeFs0TngERGnn30uhoazCe0IgCS4Ew4AUANnRooH8OnWFWviBkD9E8IBAGrg5OnvlLSuq+MHc8I1cQNIP+XoAAAJO3j0ZHzyrx6dcV33gvnR25O5+DmauAGknxAOAJCgUhqyFWzZ0Bet81o0cQNoIEI4AECCSmnIFhHxpp9efrHEXBM3gMYhhAMAJKjUhmxLu19a9ueUug6A2hHCAQAS9OJGa6Wum83nAFCfhHAAgAT19mQi01k8LL+4IdtsPweA+iSEAwAkqHVeS2zd2F90TaEh21w+B4D6JIQDACRs3cqlsX3TwKTT7e4F82P7poEpZ37P5nMAqD+X1HoDAADNaN3KpbG6b0kMDWfjzMj56Opoj96eTNHT7Nl8DgD1RQgHAKiR1nkt0b+su+qfA0D9UI4OAAAACRHCAQAAICFCOAAAACRECAcAAICECOEAAACQEN3RAQBKkBvLGw0GwJwJ4QAAMzh49GTs2j8Y2XPnL76W6WyPrRv7Y93KpTXcGQBpoxwdAKCIg0dPxo59R8YF8IiI7LnzsWPfkTh49GSNdgZAGgnhAADTyI3lY9f+waJrdt9/LHJj+YR2BEDaCeEAANMYGs5OOgGf6PSzz8XQcDahHQGQdkI4AMA0zowUD+DlrgMAIRwAYBpdHe0VXQcAuqMDQJNJ26itWu63tycTmc72oiXp3QvmR29PJpH9AJB+QjgANJG0jdqq9X5b57XE1o39sWPfkWnXbNnQV9cPMQCoL8rRAaBJpG3UVr3sd93KpbF900BkOseXnHcvmB/bNw3U5cMLAOqXk3AAaAKljtpa3bekLk51622/61YujdV9S1JVxg9AfRLCAaAJlDNqq39Zd0K7ml497rd1XktdvDcApJtydABoAmkbtZW2/QJAqZyEA0ATSNuorXL2m7Zu7wA0NyEcAJpA2kZtlbrfc9+9ELe85wup6fYOAMrRAaAJFEZtFVNPo7ZK2e9rfmxpvPfjD9W8ezoAlEMIB4AmkbZRW8X2+59vflV86eF/Lvr5u+8/FrmxfDW3CABlU44OAE0kbaO2pttvPXZPB4BSCOEA0GTSNmprqv3qng5AWilHBwBSJ23d3gGgQAgHAFKn0D29mHrq9g4ABUI4NIl8LhfnBo/FMw98Oc4NHot8LlfrLQHMWtq6vQNAgTvh0ASyhw7H8O69MZrNXnytLZOJni2bI7N2TQ13BjB7he7pu/YPjmvS1r1gfmzZ0Fd33d4BIEIIh4aXPXQ4jt+9c9Lro9lsHL97Zyy/bZsgDtSd3Fi+pA7uaev2DgBCODSwfC4Xw7v3Fl0zvGdvdK0aiJbW1oR2BaRdqQF5tg4ePTnpdDvT2R5bN/ZPebpdSrf3au+5WtK6bwCmJ4RDAxsZemRcCfpURk9nY2Tokejs70toV0CalRuQZ/P1d+w7Mun17LnzsWPfkdi+aaDsP6fae66WtO4bgOI0ZoMGNnr2bEXXAc2tEJBfHAojfhCQDx49OaevnxvLx679g0XX7L7/WOTG8iV/zWrvuVrSum8AZiaEQwNrW7iwouuA5lWNgDzR0HB2Uuic6PSzz8XQcPEKn4Ik9lwNad03AKURwqGBdfSuiLZM8Rm5bd2Z6OhdkdCOgLSqdECeypmR4l+/3HVJ7Lka0rpvAEojhEMDa2ltjZ4tm4uu6bl1s6ZswIwqHZCn0tXRXtF1Sey5GtK6bwBKI4RDg8usXRPLb9s26US8rTtjPBlQskoH5Kn09mQi01n887sXzI/enuIVPuXuZS57rqTcWD4Gnzgd337qX0paXy/7BqA8uqNDE8isXRNdqwZe6JZ+9my0LVwYHb0rnIADJSsE5GJl0uUE5Km0zmuJrRv7p+yOXrBlQ1/JI7qS2HOlTNUJvZh62TcA5avqSfjtt98et91224zrPvjBD8YrX/nKSf/5/ve/X83tQVNpaW2Nzv6+WPSaG6Kzv08AB8pSCMjFlBOQp7Nu5dLYvmlg0ol494L5ZY8nS2rPczVdJ/Ri6mHfAMxOVU7Cc7lc3HPPPfHZz342fu7nfm7G9Y8++mhs2LAhtm3bNn5zlzioB4B6UQjIE09suxfMjy0b+io2u3rdyqWxum9JDA1n48zI+ejqaI/ensysQmdSe56tUjqhv1i97BuA2at4yv3GN74R27dvj29/+9uxdGlp/wfx2GOPxU033RSLFi2q9HYAgAqqZEAupnVeS/Qv667I10pqz7NRSif0iIhffO2Pxo+9YlHd7BuA2at4OfpXv/rVWLFiRXzuc5+Lq666asb1zz33XHzrW9+KZcuWVXorAEAVFALyjdddFf3LulMRCgt7vv7HfigiIr7y9/8cg0+crvms7VI7nP/w4stT814DUFzFT8JvuummstY//vjjMTY2Fn/5l38Z7373u2N0dDRWrVoV73jHO+KKK66Y8nPWr18/7dc7depULFmypKw9AACNb6rmZ5nO9ti6sb9m5d1p6+AOwNyVFcJPnDhRNAB/5StfKbuk/PHHH4+IiMsvvzw+8IEPxOnTp+Pee++NX/qlX4o/+7M/i/nz55f19QCA2cuN5euybHuuCs3PJsqeOx879h0pu+lbpaSpgzsAlVFWCF+8eHF8/vOfn/bjXV1dZW/g53/+5+O1r31tdHZ2XnztFa94Rdx4443xt3/7t/GGN7xh0uccOHBg2q9X7CEBADC9ejwproRSmp/tvv9YrO5bkvgDh0qPZQOg/pUVwi+99NK4+uqrK76JFwfwiBfC/oIFC+LJJ5+s+J8FAExWryfFlVBK87PTzz4XQ8PZijWDK0e9d3AHoLJqPgPs/e9/fxw4cCD+4i/+IlpaXnjKe+LEiTh79qxmbQCQgHo+Ka6EUpuflbquGuq5gzsAlVXx7ugzGR0djWeeeSZGR0cjIuL1r399fPvb34677rorvvnNb8aRI0firW99a1x33XVxww03JL09AGg65ZwUp1Elm5/lxvIx+MTp+NLXT1S8u3oau84DUL7ET8Iffvjh+KVf+qX4+Mc/HqtXr45rrrkm9uzZE7//+78fb3zjG6OtrS3Wr18f//k//+eLJ+MAQPWk4aR4LirV/KxR78wDkKyqhvBPfOITk15bvXp1PProo5Ne+9SnPlXNrQAA02j0MVmVaH7WyHfmAUhW4uXoAEB9KZwUF5P2MVmF5mcTv8/uBfNnDNCl3pmvZGk6AI2r5o3ZAIDaapYxWbNtflbv3dUBSBchHABomjFZheZn5Wj0O/MAJEsIBwAiwpis6TT6nXkAkiWEAwAXzeakuNFVqrs6AERozAYAUFThznwxjXBnHoBkCOEAADOYS3d1AHgx5egAACVwZx6AShDCAQBK5M48AHOlHB0AAAASIoQDAABAQpSjA0ATyo3l3W0GgBoQwgGgyRw8ejJ27R8cN/c609keWzf26/INAFWmHB0AmsjBoydjx74j4wJ4RET23PnYse9IHDx6skY7A4DmIIQDQJPIjeVj1/7Bomt2338scmP5hHYEAM1HCAeAJjE0nJ10Aj7R6Wefi6HhbEI7AoDmI4QDQJM4M1I8gJe7DgAonxAOAE2iq6O9ousAgPIJ4QDQJHp7MpHpLB6wuxfMj96eTEI7AoDmI4QDQJNondcSWzf2F12zZUOfeeEAUEVCOAA0kXUrl8b2TQOTTsS7F8yP7ZsGzAkHgCq7pNYbAACStW7l0ljdtySGhrNxZuR8dHW0R29Pxgk4ACRACAeAJtQ6ryX6l3XXehsA0HSUowMAAEBChHAAAABIiHJ0AKBp5Mby7sIDUFNCOADQFA4ePRm79g9G9tz5i69lOttj68Z+XeEBSIxydACg4R08ejJ27DsyLoBHRGTPnY8d+47EwaMna7QzAJqNEA4ANLTcWD527R8sumb3/cciN5ZPaEcANDMhHABIXG4sH4NPnI4vff1EDD5xuqoBeGg4O+kEfKLTzz4XQ8PZqu0BAArcCQcAEpX03ewzI8UDeLnrAGAunIQDAImpxd3sro72iq4DgLkQwgGARNTqbnZvTyYyncUDdveC+dHbk6nonwsAUxHCAYBE1Opuduu8lti6sb/omi0b+swLByARQjgAkIha3s1et3JpbN80MOlEvHvB/Ni+acCccAASozEbAJCIWt/NXrdyaazuWxJDw9k4M3I+ujrao7cn4wQcgEQJ4QBAIgp3s4uVpFf7bnbrvJboX9Zdta8PADNRjg4AJMLdbAAQwgGABLmbDUCzU44OACTK3WwAmpkQDgAkzt1sAJqVcnQAAABIiBAOAAAACRHCAQAAICFCOAAAACRECAcAAICECOEAAACQECEcAAAAEiKEAwAAQEKEcAAAAEiIEA4AAAAJEcIBAAAgIUI4AAAAJEQIBwAAgIQI4QAAAJAQIRwAAAASIoQDAABAQi6p9QYAoFHlxvIxNJyNMyPno6ujPXp7MtE6r6XW2wIAakgIB4AqOHj0ZOzaPxjZc+cvvpbpbI+tG/tj3cqlNdwZAFBLytEBoMIOHj0ZO/YdGRfAIyKy587Hjn1H4uDRkzXaGQBQa0I4AFRQbiwfu/YPFl2z+/5jkRvLJ7QjAKCeCOEAUEFDw9lJJ+ATnX72uRgazia0IwCgngjhAFBBZ0aKB/By1wEAjUUIB4AK6upor+g6AKCxCOEAUEG9PZnIdBYP2N0L5kdvTyahHQEA9UQIB4AKap3XEls39hdds2VDn3nhANCkhHAAqLB1K5fG9k0Dk07EuxfMj+2bBswJB4AmdkmtNwAAjWjdyqWxum9JDA1n48zI+ejqaI/enowTcABockI4AFRJ67yW6F/WXettAAB1RDk6AAAAJEQIBwAAgIQI4QAAAJAQIRwAAAASIoQDAABAQoRwAAAASIgQDgAAAAkRwgEAACAhQjgAAAAkRAgHAACAhAjhAAAAkBAhHAAAABIihAMAAEBChHAAAABIiBAOAAAACRHCAQAAICGX1HoDAEBl5MbyMTScjTMj56Oroz16ezLROq+l1tsCAF5ECAeABnDw6MnYtX8wsufOX3wt09keWzf2x7qVS2u4MwDgxZSjA0DKHTx6MnbsOzIugEdEZM+djx37jsTBoydrtDMAYCIhHABSLDeWj137B4uu2X3/sciN5RPaEQBQjBAOACk2NJyddAI+0elnn4uh4WxCOwIAihHCASDFzowUD+DlrgMAqksIB4AU6+por+g6AKC6hHAASLHenkxkOosH7O4F86O3J5PQjgCAYoRwAEix1nktsXVjf9E1Wzb0mRcOAHVCCAeAlFu3cmls3zQw6US8e8H82L5pwJxwAKgjl9R6AwDA3K1buTRW9y2JoeFsnBk5H10d7dHbk3ECDgB1RggHgAbROq8l+pd113obAEARytEBAAAgIUI4AAAAJEQIBwAAgIQI4QAAAJCQiofwU6dOxdvf/vZ49atfHQMDA3HLLbfE448/XvRzTpw4Eb/yK78S1113Xaxbty527twZuVyu0lsDAACAmqpoCB8dHY2tW7dGNpuNj3zkI/HJT34yLr/88ti0aVOcOXNmys95/vnn45ZbbomWlpb49Kc/He9+97vjs5/9bHzoQx+q5NYAAACg5io6ouyhhx6Kxx57LB544IFYvHhxRES8733vi1WrVsXf/M3fxC/8wi9M+py/+qu/ipMnT8ZnPvOZ6OjoiB/90R+NbDYb73vf++JXf/VXo62trZJbBAAAgJqp6En4K17xiti1a9fFAF6Qz+fj3LlzU37OQw89FNdcc010dHRcfG3NmjXxne98J44fP17J7QEAAEBNVTSEL1q0KG688cZxr3384x+PCxcuxKtf/eopP+fJJ5+MK6+8ctxrV1xxRUREnDx5spLbAwAAgJoqqxz9xIkTsX79+mk//pWvfCUWLVp08X9/4QtfiPvuuy9uvvnmWL58+ZSfc/78+XGn4BERl112WUREXLhwYcrPKbaHU6dOxZIlS6b9OAAAANRKWSF88eLF8fnPf37aj3d1dV3875/61Kfirrvuije84Q2xffv2aT+nvb09RkdHx71WCN8veclLytkeAAAA1LWyQvill14aV1999Yzr7rnnnti9e3fcfPPNcfvtt0dLS8u0a6+88sp47LHHxr329NNPR0RMultecODAgWm/XrFTcgAAAKilis8J37lzZ+zevTve+c53xh133FE0gEdEDAwMxNDQUHznO9+5+NqhQ4fipS996bQl7AAAAJBGFQ3hDz74YOzZsyduvvnm+Nmf/dl45plnLv7nu9/9bkS8MEv8mWeeuViC/trXvjYWLVoUv/mbvxnHjx+Pv/7rv4777rsvNm/ebDwZAAAADaWiIfxzn/tcRER84hOfiOuvv37cf/bu3RsREQ8//HBcf/318fDDD0fEC03Y9uzZE2NjY/GLv/iLceedd8ab3vSm+PVf//VKbg0AAABqriWfz+drvYlKKtwJL3ZvHAAAACqlnBxa8TvhAAAAwNSEcAAAAEiIEA4AAAAJEcIBAAAgIUI4AAAAJEQIBwAAgIQI4QAAAJAQIRwAAAASIoQDAABAQoRwAAAASIgQDgAAAAkRwgEAACAhQjgAAAAkRAgHAACAhAjhAAAAkBAhHAAAABIihAMAAEBChHAAAABIiBAOAAAACRHCAQAAICFCOAAAACRECAcAAICECOEAAACQECEcAAAAEiKEAwAAQEIuqfUGAIDGlxvLx9BwNs6MnI+ujvbo7clE67yWWm8LABInhAMAVXXw6MnYtX8wsufOX3wt09keWzf2x7qVS2u4MwBInnJ0AKBqDh49GTv2HRkXwCMisufOx459R+Lg0ZM12hkA1IYQDgBURW4sH7v2DxZds/v+Y5Ebyye0IwCoPSEcAKiKoeHspBPwiU4/+1wMDWcT2hEA1J4QDgBUxZmR4gG83HUA0AiEcACgKro62iu6DgAagRAOAFRFb08mMp3FA3b3gvnR25NJaEcAUHtCOABQFa3zWmLrxv6ia7Zs6DMvHICmIoQDAFWzbuXS2L5pYNKJePeC+bF904A54QA0nUtqvQEAoLGtW7k0VvctiaHhbJwZOR9dHe3R25NxAg5AUxLCAYCqa53XEv3Lumu9DQCoOeXoAAAAkBAhHAAAABIihAMAAEBChHAAAABIiBAOAAAACRHCAQAAICFCOAAAACRECAcAAICECOEAAACQECEcAAAAEiKEAwAAQEKEcAAAAEiIEA4AAAAJEcIBAAAgIUI4AAAAJEQIBwAAgIQI4QAAAJAQIRwAAAASIoQDAABAQoRwAAAASIgQDgAAAAkRwgEAACAhQjgAAAAkRAgHAACAhAjhAAAAkBAhHAAAABIihAMAAEBChHAAAABIiBAOAAAACRHCAQAAICFCOAAAACRECAcAAICECOEAAACQECEcAAAAEiKEAwAAQEKEcAAAAEiIEA4AAAAJEcIBAAAgIUI4AAAAJEQIBwAAgIQI4QAAAJAQIRwAAAASIoQDAABAQoRwAAAASIgQDgAAAAkRwgEAACAhQjgAAAAkRAgHAACAhAjhAAAAkBAhHAAAABIihAMAAEBChHAAAABIiBAOAAAACRHCAQAAICFCOAAAACRECAcAAICECOEAAACQECEcAAAAEiKEAwAAQEKEcAAAAEiIEA4AAAAJEcIBAAAgIUI4AAAAJEQIBwAAgIQI4QAAAJCQSyr9BU+dOhU7d+6MBx98MEZHR2PlypVx2223xSte8YppP+eDH/xg/MEf/MGk1//hH/4hLrmk4lsEAACAmqhowh0dHY2tW7dGV1dXfOQjH4nLLrssPvShD8WmTZvic5/7XHR1dU35eY8++mhs2LAhtm3bNn5zAjgAAAANpKIp96GHHorHHnssHnjggVi8eHFERLzvfe+LVatWxd/8zd/EL/zCL0z5eY899ljcdNNNsWjRokpuBwAAAOpKRe+Ev+IVr4hdu3ZdDOAF+Xw+zp07N+XnPPfcc/Gtb30rli1bVsmtAAAAQN2p6En4okWL4sYbbxz32sc//vG4cOFCvPrVr57ycx5//PEYGxuLv/zLv4x3v/vdMTo6GqtWrYp3vOMdccUVV0z5OevXr592D6dOnYolS5bM/psAAACAKikrhJ84caJoAP7KV74yrqT8C1/4Qtx3331x8803x/Lly6f8nMcffzwiIi6//PL4wAc+EKdPn4577703fumXfin+7M/+LObPn1/OFgEAAKButeTz+Xypi59//vn41re+Ne3H//W//tfR2toaERGf+tSn4q677oo3vOEN8b73vS/mzZu+8v3cuXPR2dl58X8/9dRTceONN8a9994bb3jDG0rdXkT84JT8wIEDZX0eAAAAzEY5ObSsk/BLL700rr766hnX3XPPPbF79+64+eab4/bbb4+Wlpai618cwCMiFi9eHAsWLIgnn3yynO0BQEXlxvIxNJyNMyPno6ujPXp7MtE6r/j/pwEAFFPxGWA7d+6MPXv2xDvf+c645ZZbZlz//ve/Pw4cOBB/8Rd/cTGsnzhxIs6ePatZGwA1c/Doydi1fzCy585ffC3T2R5bN/bHupVLa7gzACDNKtod/cEHH4w9e/bEzTffHD/7sz8bzzzzzMX/fPe7342IF2aJP/PMMzE6OhoREa9//evj29/+dtx1113xzW9+M44cORJvfetb47rrrosbbrihktsDgJIcPHoyduw7Mi6AR0Rkz52PHfuOxMGjJ2u0MwAg7Soawj/3uc9FRMQnPvGJuP7668f9Z+/evRER8fDDD8f1118fDz/8cEREXHPNNbFnz5545JFH4o1vfGO85S1viRUrVsQf/dEfzVjGDgCVlhvLx679g0XX7L7/WOTGSm6pAgBwUVmN2dJAYzYA5mLwidPxO3/4dzOu+71fe3X0L+tOYEcAQL0rJ4dW9CQcANLuzMj5mReVsQ4A4MWEcAB4ka6O9oquAwB4MSEcAF6ktycTmc7iAbt7wfzo7ckktCMAoJEI4QDwIq3zWmLrxv6ia7Zs6DMvHACYFSEcACZYt3JpbN80MOlEvHvB/Ni+acCccABg1i6p9QYAoB6tW7k0VvctiaHhbJwZOR9dHe3R25NxAg4AzIkQDgDTaJ3XYgwZAFBRytEBAAAgIUI4AAAAJEQIBwAAgIQI4QAAAJAQIRwAAAASIoQDAABAQoRwAAAASIgQDgAAAAkRwgEAACAhQjgAAAAkRAgHAACAhAjhAAAAkBAhHAAAABIihAMAAEBChHAAAABIiBAOAAAACRHCAQAAICFCOAAAACRECAcAAICECOEAAACQECEcAAAAEiKEAwAAQEKEcAAAAEiIEA4AAAAJEcIBAAAgIUI4AAAAJEQIBwAAgIQI4QAAAJAQIRwAAAASIoQDAABAQoRwAAAASIgQDgAAAAkRwgEAACAhQjgAAAAk5JJab6DSnn766cjlcrF+/fpabwUAAIAmcOrUqWhtbS1pbcOdhF922WVxySUN92yhok6dOhWnTp2q9TaoA34WKPCzwIv5eaDAzwIFfhYo8LMwtUsuuSQuu+yykta25PP5fJX3Q50pVAkcOHCgxjuh1vwsUOBngRfz80CBnwUK/CxQ4Gdh7hruJBwAAADqlRAOAAAACRHCAQAAICFCOAAAACRECAcAAICECOEAAACQECPKAAAAICFOwgEAACAhQjgAAAAkRAgHAACAhAjhAAAAkBAhvImdOnUq3v72t8erX/3qGBgYiFtuuSUef/zxWm+LGrv99tvjtttuq/U2SNDY2Fh84AMfiBtuuCGuvfba2Lx5c/zTP/1TrbdFjX34wx+Om2++udbboEaeffbZ+C//5b/Ea17zmrjuuuvipptuioceeqjW26IGstlsbNu2LdasWRM//uM/Hlu3bo0nnnii1tuixr75zW/Gj//4j8ef/umf1norqSSEN6nR0dHYunVrZLPZ+MhHPhKf/OQn4/LLL49NmzbFmTNnar09aiCXy8V73/ve+OxnP1vrrZCwD3/4w/HpT3863vOe98Sf/MmfREtLS2zZsiVGR0drvTVq5GMf+1h84AMfqPU2qKG3v/3t8X/+z/+Je++9Nz772c/GNddcE7fcckt84xvfqPXWSNiv/dqvxbe//e3YvXt3fPazn4329vb45V/+5XjuuedqvTVq5Pnnn493vOMd8b3vfa/WW0ktIbxJPfTQQ/HYY4/F+973vujr64tXvOIV8b73vS++973vxd/8zd/Uensk7Bvf+EbcdNNNsX///li6dGmtt0OCRkdHY+/evfHWt741brzxxli+fHncd9998dRTT8UXv/jFWm+PhD311FNx6623xn//7/89Xv7yl9d6O9TIP/3TP8Xf/d3fxbve9a541ateFT09PXH77bfH4sWL43Of+1ytt0eCzp49G1dddVXcdddd0d/fH1dffXX8+q//ejzzzDOqJ5vYH/zBH8RLX/rSWm8j1YTwJvWKV7widu3aFYsXLx73ej6fj3PnztVoV9TKV7/61VixYkV87nOfi6uuuqrW2yFBx48fj+9+97uxZs2ai691dHREb29vHDlypIY7oxb+4R/+ITo7O+PP//zP49prr631dqiRhQsXxq5du6Kvr+/iay0tLf4doQktXLgw7r333njFK14RERGnT5+Oj370o3HllVfGsmXLarw7auHIkSPxJ3/yJ/He97631ltJtUtqvQFqY9GiRXHjjTeOe+3jH/94XLhwIV796lfXaFfUyk033VTrLVAjTz75ZERELFmyZNzrV1xxRZw6daoWW6KGfvInfzJ+8id/stbboMY6Ojom/TvC//f//X/xrW99K66//voa7Ypa+93f/d34H//jf0RbW1v84R/+YbzkJS+p9ZZI2MjISLzzne+MO+64Y9K/N1AeIbxBnThxItavXz/tx7/yla/EokWLLv7vL3zhC3HffffFzTffHMuXL09iiySk3J8FmkvhTl9bW9u41y+77DInXkBERHzta1+L3/md34n169d7SNPENm3aFP/xP/7H+NSnPhW/8Ru/EZ/85CfjmmuuqfW2SNB//a//NX7sx34s/v2///e13krqCeENavHixfH5z39+2o93dXVd/O+f+tSn4q677oo3vOENsX379iS2R4LK+Vmg+bS3t0fEC3fDC/89IuLChQsxf/78Wm0LqBN//dd/He94xzvi2muvjXvvvbfW26GGCuXnd911V/z93/99/PEf/3Hs2LGjxrsiKfv374+HHnoo/tf/+l+13kpDEMIb1KWXXhpXX331jOvuueee2L17d9x8881x++23R0tLSwK7I0ml/izQnArlZE8//XT88A//8MXXn376aVUx0OT++I//OP7bf/tv8brXvS7uueeeSRUzNL5sNhuHDh2Kf/fv/l20trZGRMS8efPi6quvjqeffrrGuyNJ//N//s/IZrPxb/7Nvxn3+rve9a746Ec/Gn/xF39Rm42llBDexHbu3Bl79uyJd77znXHLLbfUejtADSxfvjxe9rKXxYMPPngxhI+MjMTQ0FC8+c1vrvHugFr55Cc/GXfddVfcfPPN8Tu/8zsxb55evs3o6aefjt/+7d+OTCYTa9eujYgXxlMNDQ25mtBk7rnnnjh//vy4137qp34q3va2t8Ub3vCGGu0qvYTwJvXggw/Gnj174uabb46f/dmfjWeeeebix17ykpcYOwBNoq2tLd785jfHPffcE11dXfFDP/RDsXPnzrjyyivjda97Xa23B9TAN7/5zfi93/u9eN3rXhe/8iu/Etls9uLH2tvb4/LLL6/h7kjS8uXL4/rrr48777wz3vOe90RHR0f80R/9UYyMjMQv//Iv13p7JGjiRKWCTCYTP/RDP5TwbtJPCG9ShTmfn/jEJ+ITn/jEuI+95S1vibe+9a212BZQA29729vi+9//ftxxxx1x/vz5GBgYiI9+9KNKT6FJ/dVf/VU8//zz8cUvfjG++MUvjvvYz/3cz8Xdd99do52RtJaWlvj93//9eP/73x+/+Zu/Gf/yL/8Sr3rVq+L//X//31i6dGmttwep1ZLP5/O13gQAAAA0Axd8AAAAICFCOAAAACRECAcAAICECOEAAACQECEcAAAAEiKEAwAAQEKEcAAAAEiIEA4AAAAJEcIBAAAgIUI4AAAAJEQIBwAAgIQI4QAAAJCQ/z8FqlF+opfxRAAAAABJRU5ErkJggg==", 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7a2zf3BsbVi+v4c64HOXoAAAAdeTw8dOx68CxSQE8IiJ7fix2HTgWh4+frtHOKIUQDgAAUCdyE/nYe3Cg6Jp9952I3EQ+oR1RLiEcAACgTgwOZaedgE81/PiTMTiUTWhHlEsIBwAAqBPnRosH8HLXkTwhHAAAoE50tLVWdB3JE8IBAADqRHdXJjLtxQN255LF0d2VSWhHlEsIBwAAqBPNi5pi++beomv6N/WYF55iQjhAncvncnF+4ESc/fwX4vzAicjncrXeEgBwGbmJfAw8PByf+9ojMfDwcFndzDesXh47t/RNOxHvXLI4dm7pMyc85a6o9QYAmFk+l4vRwZMxPjISLUuXRlv3qmhqbp60JnvkaAzt2x/j2ac7oLZkMtHVvzUy69clvWUAoASHj5+OvQcHJnU5z7S3xvbNvSUH6A2rl8fanmUxOJSNc6Nj0dHWGt1dGSfgdUAIB0ihUsJ19sjROLV7z7T3Hc9m49TuPbHyjh2COACkzOHjp2PXgWPTXs+eH4tdB46VdZLdvKgpeld0VnqLVJlydICUKYTrSwN4xNPhOnvkaORzuRjat7/oxxm6d7/SdABIkdxEPvYeHCi6Zt99J8oqTaf+COEAKVJquD5/4uvTQvpU48PZGB08WcntAQDzMDiUnVSCPpPhx5+MwaHi/x9PfRPCAVJkdPBkSeH6/MDXS/p44yMjldgWAFAB50aLB/By11GfhHCAFCk5NDeVVqbWsnTpPHYDAFRSR1vx+d7lrqM+CeEAKVJqaG7v6YmWTKb4x+rMRFv3qkpsCwCogO6uzLSxYlN1Llkc3V3F/z+e+iaEA6RIW/eqksJ1e8/zo6t/a9F1Xdu2ThtpBgDUTvOipti+ubfomv5NPcaMNTghHCBFmpqbSw7XmfXrYuUdO6aF9pbOjPFkAJBSG1Yvj51b+qadiHcuWVzWeDLqlznhAClTCNfT5oR3ZqJr29ZJ4Tqzfl10rOm70NBtZCRali6Ntu5VTsABIMU2rF4ea3uWxeBQNs6NjkVHW2t0d2WcgC8QQjhACpUTrpuam6O9t6cGuwQA5qp5UVP0ruis9TaoASEcIKXqKVznczmn8QAAJRDCAZhVKeE6e+To9NL5TCa6+re6lw4AMIUQDsCMSgnX2SNH49TuPdPedzybjVO792gQBwAwhe7oAExTCNeXBvCIp8N19sjRyOdyMbRvf9GPM3Tv/sjnctXcKgBAXXESDsAkpYbr5quumhbSpxofzsbo4Mm6udsOQH3LTeR1HCf1hHAAJhkdPFlSuD4/8PWSPt74yEgltgUARR0+fjr2HhyI7Pmxi69l2ltj++Zes7dJFeXoAExScmhuype0rGXp0nnsBgAu7/Dx07HrwLFJATwiInt+LHYdOBaHj5+u0c5gOiEcgElKDc3tPT3RkskU/1idmWjrXlWJbQHAjHIT+dh7cKDomn33nYjcRGkPj6HahHAAJmnrXlVSuG7veX509W8tuq5r21bzwgFqJJ/LxfmBE3H281+I8wMnGrZR5uBQdtoJ+FTDjz8Zg0PFr1pBUtwJB2CSpubm6OrfOuPosYJCuM6sXxcr79gxfZRZZya6tpkTDlArpYyZbBTnRosH8HLXQbUJ4QBMU064zqxfFx1r+i40dBsZiZalS6Ote5UTcIAaKYyZnKowZnLlHTsaKoh3tLVWdB1UmxAOwIzKCddNzc3GkAGkQKljJjvW9DXMw9Lurkxk2luLlqR3Llkc3V3Fr1pBUtwJB2BWhXB9zYtuifbenob5BxtAoyp1zOTo4MmEdlR9zYuaYvvm3qJr+jf1mBdOagjhAADQIEodM1nyOMo6sWH18ti5pS8y7ZNLzjuXLI6dW/rMCSdVlKMDAECDKHXMZKnr6smG1ctjbc+yGBzKxrnRsehoa43urowTcFJHCAcAgAZRGDNZrCS9pTMTbd2rEtxVcpoXNUXvis5abwOKUo4OQGoslJm2ANVSGDNZTGHMJFAbTsIBSIWFNNMWoJrKGTMJJE8IB6DmFtpMWxpLPpcraZQfJKmcMZNAsoRwAGpqIc60pXGo4CDNCmMmgXRxJxyAmlqIM21pDIUKjqk/v4UKjuyRozXaWbL0cgAoj5NwAGpqoc60nY3S5vqgguMClQDUg9xE3tgyUkUIB6CmFvJM26kEmvpRTgVHo5YD6+VAPTh8/HTsPTgQ2fNjF1/LtLfG9s29sWH18hrujIVMOToANVWYaVtMI8+0LVDaXF8WegVHqZUAStOppcPHT8euA8cmBfCIiOz5sdh14FgcPn66RjtjoRPCAagpM20Fmnq00Cs49HIg7XIT+dh7cKDomn33nYjcRD6hHcHThHAAaq4w03bqiXhLZ2ZBlLQKNOVJQyOwhV7BsdArAUi/waHstBPwqYYffzIGh4r/3QvV4E44AKmwkGfaCjSlS8u9+UIFx0x3ogsauYJjoVcCkH7nRosH8HLXQSU5CQcgNQozba950S3R3tvTsAFmKoGmNGm7N7+QKzgWeiUA6dfR1lrRdVBJTsIBSJWFOKKrEGiKlaQv9ECT1pFgC7WCY6FXApB+3V2ZyLS3Fi1J71yyOLq7ij9MgmoQwgFIjbSUGidNoLm8NI8EK1RwLDSFSoBpv7Odmeja1ti/s6Rf86Km2L65N3YdODbrmv5NPeaFUxNCOACpsNBnDgs0xbk3n04LtRKA+rBh9fLYuaVv2pzwziWLo39Tjznh1IwQDkDNpbXUOGkCzezcm0+vhVoJQH3YsHp5rO1ZFoND2Tg3OhYdba3R3ZVxAk5NCeEA1FyaS42TJtDMzL15YK6aFzVF74rOWm8DLtIdHYCaU2rM5RTuzRez0O/NA1AfhHAAak6pMaVYyCPBgPnJTeRj4OHh+NzXHomBh4cjN5Gv9ZZYwJSjA1BzSo0plXvzQLkOHz89rTlbpr01tm/u1ZyNmnASDkDNKTWmHIV789e86JZo7+3xcwHM6vDx07HrwLFp88Kz58di14Fjcfj46RrtjIVMCAcgFZQaA1BJuYl87D04UHTNvvtOKE0ncVUpR5+YmIj3vOc98Zd/+ZcxOjoa//E//se466674t/9u3834/qPf/zjcccdd0x7/dOf/vSs7wNA41FqDEClDA5lp52ATzX8+JMxOJTVPZ1EVSWEv+9974uPfvSjsWvXrrj22mtjz5490d/fH5/85CejpaVl2voHH3ww1qxZE+9617smvd7R0VGN7QGQYkZ0ATAfuYl8DA5l40sllpqfGy0e1Ofyuc0kp5iKh/Dx8fHYv39/7NixI2699daIiHj3u98dt9xyS3zmM5+Jn/3Zn532Pg899FCsXLkyrrnmmkpvBwAAWCBmasJ2OR1trVX73BrAMZOK3wk/depUfO9734t1656+u9fW1hbd3d1x7NixGd/nwQcfjBUrVlR6KwAAwAIxWxO2YjqXLI7urszlF87xc2sAx0wqHsK/+93vRkTEsmXLJr3+7Gc/O86cOTNt/blz52J4eDiOHTsW/+W//Je4+eab4zd+4zfiW9/6VqW3BgAANKBSmrDNpH9Tz7zLxTWAo1wVL0d/8sknIyKm3f2+8sor4/z589PWP/TQQxER0dzcHO94xzvi+9//frzvfe+LV7ziFfE3f/M30dk5vUnCxo0bZ/38Z86cmfYAAAAAaFylNGG7VOeSxdG/qaciZeIawFGuiofw1tYLdyrGx8cv/veIiKeeeioWL148bf26deviK1/5SrS3t1987b3vfW/89E//dPz1X/91bN++vdJbBAAAGkipzdV+9oXPiReuXl7Rhmmlfu5KNoCjvlU8hBdOoR977LH48R//8YuvP/bYY7Fy5coZ3+fSAB4RcdVVV8X1118fjz766IzrDx06NOvnL3ZKDkD15HM5o8UAqIlSm6u9cPXyip9Gl/q5K9UAjvpX8RC+cuXKeNaznhVf/vKXL4bw0dHRGBwcjFe+8pXT1n/4wx+OP/7jP47Pfe5zF0/On3jiifjnf/7n+MVf/MVKbw+AKsgeORpD+/bHeDZ78bWWTCa6+rdGZv26Iu8JAPPX3ZWJTHtr0bLwSjVhS9Pnpj5VvDFbS0tLvPKVr4y77747Dh06FKdOnYrXv/71cd1118VLX/rSyOVycfbs2Rgbu/BD+tM//dORz+fjTW96U3zjG9+IgYGBeM1rXhMdHR3x8pe/vNLbA6DCskeOxqndeyYF8IiI8Ww2Tu3eE9kjR2u0MwAaQW4iHwMPD8fnvvZIDDw8PGODs+ZFTbF9c2/Rj1OJJmwzqeXnpj5V/CQ8IuK1r31t/PCHP4w777wzxsbGoq+vLz7wgQ9ES0tLPPLII7Fx48bYtWtX/PzP/3wsW7YsDhw4EHfffXfcdtttkc/n44UvfGF88IMfnHSnHID0yedyMbRvf9E1Q/fuj441fUrTAShbObO3N6xeHju39E1bX8kmbLOp5eem/jTl8/mG6pVfuBNe7N44AJVxfuBEnLjzrsuu63n7W6K9tyeBHQHQKAqzt2ezc0vfjOE2N5GPwaFsnBsdi4621oo2YbucWn5uaqucHFqVk3AAFobxkZGKrgOAiNJnb6/tWTYt5DYvaqrZKLBafm7qhxAO0ECS7lDesnRpRdfVA13gAarP7G0amRBeI/4RB1RaLTqUt3WvipZMZlpTtku1dGairXtVVT5/pZT6d7Iu8ADJMHubRiaE14B/xAGVVuhQPlWhQ/nKO3ZU9O+XS0Prtf/pJfGdj/zFrGu7tm1N9UPGUv9OTvprDLCQmb1NIxPCE+YfcUClJd2hfKbQesWznhXRFPHDf3vi4mstnZno2pbuh4ul/p2sCzxAsszeppFVfE44syv1H3H5XC6hHQGNYHTwZNFy8IiI8eFsjA6enPfnmm0m+A+feCJ++G9PRMeGdXH9//WL8fy33hU37X1/qgN4OX8nJ/k1BsDsbRqbEJ4g/4gDqiGpDuWlhNZzh4/GI3/5sfjGH78nzn1l9rEypX6+8wMn4uznvxDnB05U/AFlOX8n6wI/WbW/NwART8/ezrRPLjnvXLJ41vFkUA+UoyfIP+KAakiqQ3kpobVgvlds5ts7o5RGa+X8nbwQu8DPRl8TIEkbVi+PtT3LzN6moQjhCfKPOKAakupQPpcHhHO5Jz3f3hmlhsRy/k5ulC7w86WvCVALZm/TaJSjJ6jwj7hiFsI/4oDKampujq7+rUXXVKJD+VweEJZ7xWa+vTNmu7NeCInZI0cvvlbO38nlfo0bsVxbXxMAqAwhPEFJ/UMZWHgy69fFyjt2TAuVLZ2Zip1OlhJaZ1LOCfp8emeUGxLL/Tu51K9x9sjRuL//1+LEnXfFQ3/4R3Hizrvi/v5fm/QAoB7pawIAlaEcPWGFf8RNK5Wsg1E+QLpl1q+LjjV9l70LPVeF0DpTOXIx5Zygz6d3Rjkhsb23JyLK/zv5cl/jRi7XTrIBYLV+hgEgDYTwGqj2P5SBhaupufliwKyG2ULrbMq9YjOf3hlzDYnl/p0829e40WeJJ9HXRNM3ABYCIbxGqv0PZYBquTS0nvvKsTj9iU/OurbcKzbzaYA2n5BYib+T53ISX0+q3ZyukasIAOBS7oQDULZCaH3Oq//vit5Fn0/vjFo3v2z0MZTV7Gui6RsAC4mTcADmpdJXbObaO6OUO+vVbH65EMZQVquvSaNXEQDpkZvImzlOzQnhAMxbpa/YzDXY17L55UKZJV6NviaNXkUApMPh46dj78GByJ4fu/hapr01tm/ujQ2rl9dwZyw0QjgAqTTXYF+r5pe1PolPUqUfuiyEKgKgtg4fPx27Dhyb9nr2/FjsOnAsdm7pE8RJjDvhADScQki85kW3RHtvT2LBN4l57Y2o1vf5gcaWm8jH3oMDRdfsu+9E5CbyCe2Ihc5JOABUkDGU5VtIVQRA8gaHspNK0Gcy/PiTMTiUjd4VnQntioVMCAeACjOGsny1vM8PNLZzo8UDeLnrYL6EcIA6k8/lnLLSkFQRANXQ0dZa0XUwX0I4QB3JHjk6/aQwk4mufieFNAZVBECldXdlItPeWrQkvXPJ4ujuKt6bAipFYzZqKp/LxfmBE3H281+I8wMnIp/L1XpLkFrZI0fj1O4900ZgjWezcWr3nsgeOVqjnQFAejUvaortm3uLrunf1GNeOIlxEk7NONGD0uVzuRjat7/omqF790fHmj6luw3MVQSAudmwenns3NI3bU5455LF0b+px3gyEiWEUxOFE72pCid6RvnAZKODJ6edgE81PpyN0cGTSnkblAeXAPOzYfXyWNuzLAaHsnFudCw62lqjuyvjBJzEKUcncaWe6ClNh6eNj4xUdB31xVUEgMpoXtQUvSs649Ybr4/eFZ0CODUhhJO4ck70gAtali6t6DrqhweXANBYhHAS50QPytfWvSpaMsW7trZ0ZqKte1VCOyIpHlwCQGMRwkmcEz0oX1Nzc3T1by26pmvbVk26GpAHlwDQWIRwEudED+Yms35drLxjx7Tfn5bOjGaGUzTS+EMPLgGgseiOTuIKJ3ozdUcvcKIHM8usXxcda/oaZkxVNUZuNVoX8cKDy2Il6R5cAkD9EMKpicKJ3rR/KHdmomtbff5DGZLS1NzcEGPIqhGWG3H8oQeXANBYmvL5fL7Wm6ikjRs3RkTEoUOHarwTSlGNUzAg/WYLywVzCcv5XC7u7/+1y54Y37T3/XX598yMDy08uASAVCgnhzoJp6Ya5UQPKF2pI7c61vSVFZbL6SJej3/vNNpVBABYqIRwAKru0qqX8ZHHqxKWF0IXcQ8uAaD+CeEAVNVMZdSlKDcs6yKefq4gQbrlJvIxOJSNc6Nj0dHWGs/79x3x4D+fu/i/u7sy0byoqdbbhLonhANQNZe7+11MuWFZF/F0a7Su9dBoDh8/HXsPDkT2/NjF1xY1RUxc0j0q094a2zf3xobVy2uwQ2gc5oQDUBWl3P2ezVzCcqGLeDG6iNdG4WHM1Ackha712SNHa7QzIOJCAN914NikAB4xOYBHRGTPj8WuA8fi8PHTCe4OGo8QDkBVlNIobTZzDcuF8Yctmcyk11s6M3U5nqwRlNqIL5/LJbQj4FK5iXzsPThQ1vvsu+9E5KYmdKBkytEBqIq5NECrxMgtXcTTpdG71kO9GxzKTjsBv5zhx5+MwaFs9K7orNKuoLEJ4QBURal3uv/91ldFy9IlFQ3Luoinx0LoWg/17NxoeQF8vu8HCOEAVEmpjdKW/5eXOaVuYLrWQ7p1tLUm+n6AO+EAVIlGaUQ8/TCmGF3roXa6uzKRaS8vUHcuWRzdXcV/r8uVm8jHwMPD8bmvPRIDDw+7c05DcxIOQNUUGqVNG01Vgbvf1IfCw5hio+o8jIHaaV7UFNs398auA8dKfp/+TT0VnRc+03g049BoZE35fL6hHjNt3LgxIiIOHTpU450AUJDP5TRKW+BmnBPuYQykRilzwjuXLI7+TT0VDcaF8Wiz2bmlb06fLzeRj8GhbJwbHYuOttbo7spU9MEBTFVODnUSDkDVaZSGrvWQbhtWL4+1PcsmBdfn/fuOePCfz1UtyJYyHm3ffSdibc+ysj6vk3XSTggHABLhYQykW/Oipmljx6o5hqyU8WjljkOb7WQ9e34sdh04NueTdagkjdkAAIDElTrmrNR1pZ6sa/pGrQnhAABA4kodc1bqunJO1qGWlKMDAAAVVUpjtMJ4tGLBuZxxaJU+WYdqEcIBAICKKbUxWinj0coZh1bpk3WoFuXoAABARRQao0093S40Rjt8/PSk1zesXh47t/RFpn1yMO5csrjsJmqFk/ViyjlZh2pxEg4AwJzkczlj57horiPHZhqPNpdxaJU+WYdqEcIBAChb9sjRGNq3P8azTze5aslkoqt/a2TWr6vhzqiV+Ywcm2k82lwUTtanlsN3Llkc/Zt6jCcjFYRwAADKkj1yNE7t3jPt9fFsNk7t3hMr79ghiC9AaWmMVqmTdagWIRyAhqAsFpKRz+ViaN/+omuG7t0fHWv6/A4uMGlqjFapk3WoBiEcgLqnLBaSMzp4ctLv2kzGh7MxOngy2nt7EtoVaVDpkWPQqHRHB6CuFcpip4aCQlls9sjRGu0MGtP4yEhF19E4Co3RitEYDYRwgKrL53JxfuBEnP38F+L8wInI53K13lLDKLUs1td8Zn42mYuWpUsruo7GUsmRY9ColKMDVJEy6epSFjt3fjaZq7buVdGSyRT93WvpzERb96oEd0WaaIwGxTkJB6gSZdLVpyx2bvxsMh9Nzc3R1b+16JqubVs1ZVvgCo3Rbr3x+uhd0SmAwyWEcIAqUCadDGWx5fOzSSVk1q+LlXfsiJbM5AZbLZ0Z48kALkM5OkAVKJNOhrLY8vnZrF9pG8OXWb8uOtb0pWpPAPVACAeoAmXSySiUxZ7avWfWNcpiJ/OzWZ/Seoe/qbnZwxqAMilHB6gCZdLJURZbHj+b9ccdfupNbiIfAw8Px+e+9kgMPDwcuYl8rbcEqeIkHKAKlEknS1ls6fxs1pdS7/B3rOmb18972krdqV+Hj5+OvQcHInt+7OJrmfbW2L6513gy+BEhHKAKlEknT1lsafxs1pck7vCntdSd+nP4+OnYdeDYtNez58di14Fj5oTDjyhHB6gSZdKklZ/N+lHtO/xK3amU3EQ+9h4cKLpm330nlKZDOAkHqCpl0qSVn836UM07/EmVurMwDA5lJ5Wgz2T48SdjcCgbvSs6E9oVpJMQDlBlyqRJKz+b6VfNO/zG1VFJ50aLB/By10EjU44OAJBShTv8xcz1Dr9xdVRSR1trRddBIxPCAQBSrFp3+I2ro5K6uzKRaS8esDuXLI7urkzRNbAQKEcHAEi5atzhN64uXXIT+Rgcysa50bHoaGuN7q5MNC9qqvW2Sta8qCm2b+6dsTt6Qf+mnrr6M0G1COEAAHWg0nf4jatLj0aZrb1h9fLYuaVv2p+lc8ni6N/UU1d/Fqimpnw+31BzAjZu3BgREYcOHarxTgAA0m/GOeGdmejaZk54EmabrV1Qj7O16/1UH+ainBzqJBwAYAEzrq52Sp2tvbZnWV2F2OZFTcaQQRFCOADAAmdcXW2YrQ0LkxAOAAA1YLZ27SmdpxaEcIAGkc/llJMC1BGztWurURriUX+EcIAGMGNjpUwmuvo1VmJh83CKNCvM1i5Wkm62dnXM1hAve34sdh04VpcN8agfQjhACpUTHLJHjs44Ymg8m41Tu/fEyjt2COIsSB5OkXZma9dGozbEo34I4QApU05wyOdyMbRvf9GPN3Tv/uhY05eq0z+nk9Xl6+vhFPXDbO3kaYhHrQnhAClSbnAYHTw5KazPZHw4G6ODJ1PT+djpZHX5+tbvwykWrg2rl8fanmUahCVEQzxqbVGtNwDABaUGh3wud/F/j4+MlPSxS11XbYWHDFMfHBQeMmSPHK3RzhqDr+8F5TycgrQozNa+9cbro3dFpwBeRRriUWtCOEBKzCU4tCxdWtLHLnVdNc3lIQOl8/V9Wr09nAKSVWiIV4yGeFSTEA6QEnMJDm3dq6IlU/wfCS2dmWjrXjWvvVWC08nq8vV9Wj09nAKSV2iIV4yGeFSTEA6QEnMJDk3NzdHVv7Xo+q5tW1Nx79XpZHX5+j6tnh5OAbVRaIg39US8c8li48moOo3ZAFKiEByKnWbOFBwy69fFyjt2TG/G1ZmJrm3pacbldLK6fH2fVng4NVOTw4K0PJwCakdDPGpFCAdIifkEh8z6ddGxpi/VY6nm+pCB0lTj61vPo87q5eEUUFuFhniQJCEcIEXmExyamptTM4ZsJk4nq6vSX99GGHVWDw+nAFh4mvL5fL7Wm6ikjRs3RkTEoUOHarwTgLmr5xPIy5kx3DmdrJhKfH1nm1dfMHVePQAsdOXkUCfhACmU9lPt+XA6WV3z/fqWOuqsY02f7xkAzIEQDkDiGvkhQxrM5+tbzqgz30MAKJ8RZQDARUadAUB1CeEAwEVGnQFAdQnhAMBFhVFnxRglBwBzJ4QDABcVRp0VY5QcAMydEA4wD/lcLs4PnIizn/9CnB84EflcrtZbgnkrzKufeiLe0pkxngwA5kl3dIA5mnEecyYTXf3mXVP/jJIDgOoQwgHmIHvkaJzavWfa6+PZbJzavcdpIQ3BKDkAqDzl6ABlyudyMbRvf9E1Q/fuV5oOAMA0VQnhExMTcc8998Qtt9wSL3jBC2Lr1q3x7W9/e9b1IyMj8du//dvR19cXfX198Xu/93vx/e9/vxpbA5i30cGTk0rQZzI+nI3RwZMJ7QgAgHpRlRD+vve9Lz760Y/G29/+9viLv/iLaGpqiv7+/hgfH59x/Wtf+9r4zne+E3/2Z38W99xzT3zpS1+Kt7zlLdXYGsC8jY+MVHQdAAALR8VD+Pj4eOzfvz9e85rXxK233horV66Md7/73fHoo4/GZz7zmWnrH3jggfjKV74Su3btiuc///mxfv36eOtb3xr33XdfPProo5XeHsC8tSxdWtF1AAAsHBUP4adOnYrvfe97sW7d0w2J2traoru7O44dOzZt/f333x/XXHNN3HDDDRdfW7NmTTQ1NcVXv/rVSm8PYN7auldNG900VUtnJtq6VyW0IwAaUW4iHwMPD8fnvvZIDDw8HLmJfK23BFRAxbujf/e7342IiGXLlk16/dnPfnacOXNm2vpHH3102tqWlpZYsmTJjOsjIjZu3Djr5z9z5sy0jwdQSU3NzdHVv3XG7ugFXdu2GuUEwJwdPn469h4ciOz5sYuvZdpbY/vm3tiwenkNdwbMV8VPwp988smIuBCkL3XllVfGU089NeP6qWuLrQdIg8z6dbHyjh3TTsRbOjPGkwEwL4ePn45dB45NCuAREdnzY7HrwLE4fPx0jXYGVELFT8JbW1sj4sLd8MJ/j4h46qmnYvHixTOun6lh21NPPRVXXXXVjJ/j0KFDs37+YqfkAJWUWb8uOtb0XeiWPjISLUuXRlv3KifgpF4+l/NzCymVm8jH3oMDRdfsu+9ErO1ZFs2Lmir2OQeHsnFudCw62lqjuytTsY8NTFfxEF4oBX/sscfix3/8xy++/thjj8XKlSunrb/uuuvis5/97KTXxsfH4/HHH49rr7220tsDqKim5uZo7+2p9TaokIUQTrNHjsbQvv2Txuy1ZDLR1b9VBQekwMA3h6edgE81/PiTMTiUjd4VnfP+fMreIXkVL0dfuXJlPOtZz4ovf/nLF18bHR2NwcHBuOmmm6at7+vri+9+97uT5ogX3vfGG2+s9PYAYEbZI0fj/v5fixN33hUP/eEfxYk774r7+38tskeO1nprFZM9cjRO7d4zbc79eDYbp3bvaag/K9Sjw8dPxzsOTG9kPJNzo8WDeqmfT9k7JK/iIbylpSVe+cpXxt133x2HDh2KU6dOxetf//q47rrr4qUvfWnkcrk4e/ZsjI1d+GV/wQteEDfeeGO8/vWvj+PHj8fRo0fjrrvuis2bNzsJByARCyGc5nO5GNq3v+iaoXv3Rz6XS2hHwKUKgfiJJ39Q0vqOttbLLyqi1LJ3Hdmh8ioewiMiXvva18Yv/uIvxp133hm33XZbNDc3xwc+8IFoaWmJM2fOxM033xyf+tSnIiKiqakp3vOe98T1118fW7Zside97nXxohe9KH7/93+/GlsDgEkWSjgdHTw57SHDVOPD2RgdPJnQjoCCUgLxpTqXLI7uruKjMi9ncChbctk7UFkVvxMeEdHc3Bw7duyIHTt2THvb9ddfHw8++OCk1zKZTNxzzz3V2AoAFFVOOK3n+//jIyMVXQdUTimB+FL9m3rm3Tit1HL2SpS9A5NVJYQDQL1YKOG0ZenSiq4DKqfUoHv1Vc+I3/y/frIiDdNKLWefb9k7MF1VytEBoF4slHDa1r1q2lz7qVo6M9HWvSqhHQEFpQbd37m9r2Idy7u7MpFpL/55K1H2DkwnhAOwoC2UcNrU3Bxd/VuLrunatrXhRrJBPSg1EPdUYCRZQfOipti+ubfomkqUvQPTCeEALGgLKZxm1q+LlXfsmPbQoaUzEyvv2GFOONRIrQLxhtXLY+eWvmkPADqXLI6dWyafuucm8jHw8HB87muPxMDDw7qmwzw05fP5hvoN2rhxY0REHDp0qMY7AaCeZI8cjaF9+yc1aWvpzETXtq0NF07zudyFhnQjI9GydGm0da+a9JDhcm8HquPw8dOx9+DApCZtnUsWR/+mnoqVoc8kN5GPwaFsnBsdi4621ujuykwK/DPtK9PeGts391Z1X1BPysmhQjgA/Egaw2fSe5rxYUQmE139jfcwAtLocoE4aYX55bOZemIOC1U5OVR3dAD4kabm5lSNIUs6EGePHI1Tu/dMe308m41Tu/coWYcENC9qit4K3v2ej1Lml++770Ss7Vnm7jiUwZ1wgAUon8vF+YETcfbzX4jzAycin8vVektMUQjEU2eYFwJx9sjRy36Mcr7P+VwuhvbtL/rxhu7d72cFFpBS5pcPP/5kDA5li64BJnMSDrDAKDdOv1IDcceavllL08v9Po8OnpwW+KcaH87G6ODJVFULANVT6vzyUtcBFzgJB1hAKnG6SvWVE4hnMpfv8/jISEl7K3UdUP9KnV9e6jrgAiEcYIFQblw/5hOI5/p9blm6tKTPWeo6oP6VOr+8uytTdA0wmRAOsEDM93SV5MwnEM/1+9zWvWra/PBpn68zE23dq0raG1D/ajW/HBqdEA6wQCg3rh/zCcRz/T43NTdHV//Wou/TtW1rzUe2AcnasHp57NzSN+1EvHPJYuPJYI40ZgNYIJQb149CIJ5pXFjBbIF4Pt/nzPp1sfKOHdMbunVmomubxn2wUG1YvTzW9ixL1fxyqGdCOMACUThdLVaqrNw4PeYaiOf7fc6sXxcda/oulLWPjETL0qXR1r3KCTgscGmaXw71TggHWCDmc7pKbcwlEFfi+9zU3GwMGQBUiRAOsIAoN64/cwnE9fx9zudysz50KPY2oPZyE3kl61ACIRxggVFuvDDU4/c5e+To9AcHmczFhnGzvS3NDxVgoTh8/HTsPTgQ2fNjF1/LtLfG9s29mrfBFE35fD5f601U0saNGyMi4tChQzXeCQBOLilV9sjRoiX0xay8Y4cgDjV0+Pjp2HXg2Kxv10WdhaCcHOokHICqKHaqKTBxqXwuF0P79s/5/Yfu3R8da/o84IEayE3kY+/BgaJr9t13Itb2LFOaDj9iTjgAFVc41ZzaoXs8m41Tu/dE9sjRGu2MNBodPFm0m/vljA9nY3TwZAV3BJRqcCg7qQR9JsOPPxmDQ3P/HYdGI4QDUFGlnGoO3bs/8rlcQjsi7cZHRlLxMYDynRstHsDLXQcLgRAOQEWVcqrp5JJLtSxdmoqPAZSvo621outgIRDCAaioUk8knVxS0Na9KloymTm/f0tnJtq6V1VwR0CpursykWkvHrA7lyyO7q65/45DoxHCAaioUk8kn9HeXuWdUC+ampsvjiGbi65tWzVlgxppXtQU2zf3Fl3Tv6lHUza4hBAOQEWVeqr50B/9Sd00aMvncnF+4ESc/fwX4vzACffZqyCzfl2svGPHtJ+dls5MrLxjR9G36bYPtbVh9fLYuaVv2ol455LFxpPBDMwJB6Di/vnPPhj/+vH7Slqb9hBl1Fqyis2WN3ce0i03kY/BoWycGx2LjrbW6O7KOAFnwTAnHICayedycfbzXyx5fZpnPBdGrU1VGLWW9gcI9aipuTnae3vKfhtQe82LmqJ3RWettwGppxwdgIoqd+ZzWjulG7UGzCY3kY+Bh4fjc197JAYeHo7cREMVljb8nw9qzUk4ABU1l67naeyUXs6oNaezkA5JlEMfPn469h4ciOz5p+deZ9pbY/vm3oa4+9zofz5IAyEcgIqay7zmNM54NmoN6ksS4fHw8dOx68Cxaa9nz4/FrgPH6r4JWRJ/PvfGQQgHoMIK3dFLLUlP64znUh8MpPEBAiw0SYXHvQcHiq7Zd9+JWNuzrC5DZRJ/PqfscIE74QBUVLkzn9M647mUUWtpfYDA3BlHV39KDY/zvdc8OJSdFB5nMvz4kzE4VHpPjDSp9p+v8KBk6ucoPCg5fPz0nD4u1CMn4QBUXGHm89TRXpdq6cxE17b0jvkqPEyYqTt6QVofIDA3xtHVp3LC43w6d58bLf45yl2XNtX88zV6FQGUSwgHoCoy69dFx5q+i3Odn9HeHhERPzh/vm5mPM/2MCHtDxAon3F09SupcNzR1lrRdaVI8v50Nf98ST0ogXohhANQNZWc65zP5S4G+iRD/NSHCfXyAIHSlTqOLq3z7Be6pMJxd1cmMu2tRcNk55LF0d1V/BpLqeZzf3ouf19W88/X6FUEUC4hHIDUq3WZcCUfJpA+xtHVt6TCcfOipti+uXfGBnAF/Zt6KnJSPZ9Gc3P9+7Kaf75aVBFAmmnMBkCqFcqEp4akQplw9sjRGu2MRmEcXX0rhMdiKhWON6xeHju39EWmfXJY7FyyuGLjyebTaG6+f19W689XeFBSTCWrCCDtnIQDkFrKhEmCcXT1rxAep5Zvdy5ZHP2beio6/mrD6uWxtmdZ1e5qz/X+dKX+vqzGny/JKgKoB0I4AKmlTJgklDLb3ji65My1/0O1w/Glmhc1Va2B2FzvT1fy78tq/PmSfFACaSeEA5BayoRJgnF06THf/g/VDMdJmev96Xr4+zLJByWQZu6EA5BayoRJSmEcXUtm8p3Uls6M8WQJ0f/hgrnen66Xvy8LD0puvfH66F3RKYCzIDkJByC1lAlXR63GvaWdcXS1o//D0+Z6f9rfl1A/hHAAUkuZcOXVetxb2hlHVxv6P0w2l/vT/r6E+iGEA5BqhTLhacGxMxNd2wTHchTKfacqlPsqu6ZW6uE+c9Lmcn/a35dQH4RwoKEos21MyoTnT7kvaVYv95mTNpdGc/6+hPQTwoGGocy2sSkTnh/lvqSZ+8yV5e9LSDfd0YGGoKsuFKfclzQr3Gcuxn1moFEI4UDdK7XMNp/LJbQjSB/lvqSdMXHAQqEcHah7ymzh8pT7Ug/cZwYWAifhQN1TZguXp9yXelG4z3zNi26J9t4eP5NAwxHCgbqnzBZKo9wXAGpPOTpQ95TZUu+SHK2n3BcAaksIB+peocz21O49s65RZtvY6nk+fC1G6xlfBJWTm8jH4FA2zo2ORUdba3R3ZaJ5UVOttwWkmBAONIRCme20MNOZia5t5oQ3snqeD18YrTdVYbSeEnFIt8PHT8fegwORPT928bVMe2ts39wbG1Yvr+HOgDRryufz+VpvopI2btwYERGHDh2q8U6AWqjnE1HKN1uILUhziM3ncnF//69d9hrFTXvf72cYUujw8dOx68CxWd++c0tfVYO4E3hIl3JyqJNwoKEos104Sp0P37GmL5Uh1mg9qF+5iXzsPThQdM2++07E2p5lVQnGTuChvumODkBdKifEppHRelC/BoeykwLwTIYffzIGh4r/HTUXhRP4qZ8/e34sdh04FoePn6745wQqSwgHYFb5XC7OD5yIs5//QpwfOBH5XK7WW7qo3kOs0XpQv86NFg/g5a4rVakn8LmJhrptCg1HOToAM0p7w7N6D7FG60H96mhrrei6UpVzAt+7orOinxuoHCfhAExTaHg2NSAWunZnjxyt0c6eVgixxaQ5xBZG6xVjtB6kU3dXJjLtxQN255LF0d1V/O+ocs3nBD43kY+Bh4fjc197JAYeHnZaDjXkJByASeql4VkjzIc3Wo/ZmPSQbs2LmmL75t6i3dH7N/VUvCnbXE/gNXKDdBHCAZiknrp2F0Lsw+95f/zwiScmve2Kq59Vo12VJ7N+XXSs6RO4uCjtV0G4YMPq5bFzS9+0cNu5ZHH0b+qpSrgtnMAXK0mfegI/2yi1QiO3ao9SA6YTwgGYpB4bnk0N4BERP/y3J+LU7j2pnhVeYLQeBYWrIFMVroLUw8/zQrJh9fJY27MssXnd5Z7A13qUGjAzd8IBmCTphmfz6cBeaul8mrq6V9PUr+XE+A9S292e6fw816fmRU3Ru6Izbr3x+uhd0Vn1MFs4gZ96J71zyeJpp9q1HKUGzM5JOACTJNm1e75lt/VUOl9tM30tY9GiiImJi/9TSXO6+XmmVKWewA+ff7Kkj1fqOqAynIQDMElSXbsr0YG9Hkvnq2G2r+WlATwiXd3tmc7PM+Uo5QR+9Inxkj5WqeuAyhDCAZim0PBs6giwls5MRe6kVqrstt5nhVdCKV/LqZQ0p5Of58ZVq/Fgbc9sqeg6oDKUowMwo2p27a5U2W2SpfNpVcrXciolzenk57kx1XI8WOeSxRVdB1SGk3AAZlXo2n3Ni26J9t6eio3NqlTZbVKl82k219JkJc3p4+e58RTGg01tjlYYD3b4+Omqfv7CSLNipo40A6pPCAcgcZUsu6126XzazbU0WUlzOi30n+dGUup4sGqWphdGmhVz6UgzIBnK0QFIXKXLbqtZOp92pXwtp1LSnG4L+ee5kZQzHqx3RWfV9lEYaTa1JL5zyeLo39RT9ZJ4YDohHIDEFcpuT+3eM+uacstuC6XzC00pX8uplDSn30L9eW4k50aLB/By181HqSPNgGQI4QDURKHsdtqc8M5MdG0zy7ocs30tp80J97WFqslN5CeF3LarSus4vuRZV1Z5ZxcURpoBtSeEA1Azym4rZ6av5dXPe17824MP+tpClc3UAb3tmc8o6X3zDqNhwRHCAagpZbeVM9PX0tcWqqvQAX2q0e/9oKT3P/9vT1V6S0DKCeEA1L18Luc0HUhcKR3QL6ejrfgIMaDxCOEA1LXskaPT75VnMtHV7+4z6eAhUeMqpQN6MWZ0w8IkhANQt7JHjs7YFXw8m41Tu/eYq0zNeUjU2Obb2dyMbliYFtV6AwCkSz6Xi/MDJ+Ls578Q5wdORD6Xq/WWZpTP5WJo3/6ia4bu3Z/a/dP4Cg+Jps5wLzwkyh45WtXPXy+/y/Ws1FLytmdO7pTeuWRx7NzSZ0Y3LFBOwgG4qJ5O7UYHT04LN1OND2djdPCk5mQkrtSHRB1r+qpSml5Pv8v15tJRZO1XXxkdbVfGudHZm6t1Llkcf7rzJfHgP58zoxuICCEcgB+pt9Lu8ZGRiq6rNfeGG0stHxLV2+9yPZlpFNnVVxUfRda/qSdarlhUlRndU2eTC/dQH4RwAGp+ajcXLUuXVnTdfMw3QDu1bDy1ekhUj7/L9WK2UWT/9v0Lo8ieddUz4onvPz2WrHPJ4ujf1FO1kvOZHghk2ltj++ZeZe6QckI4AHVZ2t3WvSpaMpmi+27pzERb96qq7mO+AdqpZWOq1UOievxdrgeljCJrbWmOO27vi8efeKrqp9KzPRDInh+LXQeOuW8OKacxGwB1Wdrd1NwcXf1bi67p2ra1qqd98228pblc4yo8JCqmGg+J6vF3uR6UMops+PGxWLSoKW698froXdFZtQBeygOBffediNxEviqfH5g/IRyAVJV2lyOzfl2svGPHtLDT0pmp+glyJQJ0OaeW1JdaPSSq19/ltCt1FNl8R5aVorQHAk/G4FDxv1uA2lGODkBqSrvnIrN+XXSs6ataU7PZ7ntXouzXqWVjKzwkmnZdoTMTXduqc9+/nn+X06zUUWSXW1eJRmppeiAAzI0QDsDFU7uZ7iYXVLu0ez6ampurcr+12H3viR/8oMh7Pq1YgHZq2fiq/ZBoqnr/XU6r7q5MZNpbi55Ady5ZHN1ds19BqFQjtUo9EABqRzk6ABFR29LuNLrcfe8nT58p6eMUC9C1ujdMcflcLs4PnIizn/9CnB84Me87+YWHRNe86JZo7+2pegD2u1x5zYuaYvvm3qJr+jf1zHqqXWikNjXEFxqpHT5+uuS9FB4IFHO5BwJAbTkJB+CipE/t0qqU+96Pfvqz8YyOjvjBuXOzrrlcgHZqmT6NMi7O73LlbVi9PHZu6Zt2mn25UWSlNlJb27OspNL0wgOBmbqjFxR7IADUnhAOwCTVKu2uJyXd985m4/+47b/Fdz7yF7OuKSVA1+LeMDNrtHFxfpcrb8Pq5bG2Z1lZ97rLaaTWu6Kz5H3M5YEAkA5COABMUWojtMXLl1UkQDu1rL1Su913rOnzfWkA82mQ1ryoqeSwHFG9RmpzeSAApIMQDgBTlNMwrb23pyIB2qllbVWi2z31oVIN0kpVzUZq5T4QANJBYzYAmKLchmlJN96i8oyLWxgq2SCtVBqpAVMJ4QAwRaFhWjEapjUW4+IaX6kN0nIT+Yp+3vl2VgcajxAOADMw5mlhMS6u8ZXaIO2TXxiKz33tkRh4eLhigbzQSG3qiXjnksWxc0ufRmqwwLgTDgCz0DBt4TAurvGV2vjs3k+cuPjfK3lXXCM1oEAIX+DyuZx/XELC/N7VFw3TFg7j4hrbXBqfFe6KV+q0WiM1IEIIX9CyR45O/4dGJhNd/f6hAdXi9w7STfVD4yo0SLtcSfpM9t13Itb2LHNqDVSEO+ELVPbI0Ti1e8+0cSzj2Wyc2r0nskeO1mhn0Lj83kF90O2+MZXSIG02w48/GYNDxUfYVVpuIh8DDw9X/H46UHtOwhegfC4XQ/v2F10zdO/+6FjT5x8eUCF+7wBqr9Agbeqc8FKUeqe8EpKeZQ4kq+Ih/Kmnnordu3fH3/3d38XY2Fjccsstcdddd0WmSMfR97znPfEnf/In017/+te/Hldc4TlBpY0Onpx2EjfV+HA2RgdPugcJFeL3Lr3c0YeFZWqDtMf/7alJzdhmM5c75bmJfNmN2AqzzKeq9P10oHYqnnB///d/P7761a/Gn/zJn0RLS0vcdddd8Vu/9Vvx53/+57O+z4MPPhibNm2KHTt2TN6cAF4V4yMjFV0HXJ7fu3RyRx8WpksbpOUm8vHxzz1c9GS8c8ni6O4qPsJuqrmcZpc6y9z9dKhvFb0T/uijj8bBgwfjzjvvjJtuuilWr14d73rXu+LYsWPxj//4j7O+30MPPRTd3d1xzTXXTPoP1dGydGlF1wGX5/cufdzRByJKuyvev6mnrNBbOM2eGuwLp9mHj5+e8f1KnWWe9P10oLIqGsK/+tWvRkTE2rVrL772nOc8J6699to4dmx6WU1ExJNPPhn/8i//EitWrKjkViiirXtVtBS5HhBxYRxLW/eqhHYEjc/vXbqUekc/n8sltCOglgp3xTPtk0vOO5csLrv8u5TT7Pf91T/FP9z/nWkN10q9d57k/XSg8ipa7/3oo4/G0qVL48orr5z0+rOf/ew4c+bMjO/zjW98IyYmJuLv/u7v4q1vfWuMj4/HmjVr4o1vfGM8+9nPruT2+JGm5ubo6t8ap3bvmXVN17at7kRCBfm9Sxd39BuLe/1UwtS74qXe4Z6qlNPs80+Mx7s+8rWIuFCi/jPr/n0s73xmPP5vT5X0OeZyPx1Ij7JC+COPPBIbN26c9e2/9Vu/FS0tLdNev/LKK+Opp2b+S+Ub3/hGRERcffXVcc8998Tw8HC8613vil/+5V+Oj3/847F48eJp71NsD2fOnIlly5Zd7o+y4GXWr4uVd+yYfheyMxNd29yFhGrwe5ce7ug3Dvf6qaRL74rPVbmn1NnzY/Hhvz918X8vaoooNo1sLvfTgXQpK4Rfe+218alPfWrWt3/uc5+L8fHxaa8/9dRTM4bpiIhf+IVfiJe85CXR3t5+8bXnPve5ceutt8Y//MM/xMte9rJytkgZMuvXRceaPqcHkCC/d+ngjn5jKNzrn6pwr3/lHTsEcRI331Pqy40DL/d+OpA+ZYXwZzzjGXHDDTfM+vYHH3wwHn/88RgfH590Iv7YY4/FddddN+v7XRrAIy6E/SVLlsR3v/vdGdcfOnRo1o9V7JSc6Zqam5VaQsLS8Hu30Mt3C3f0i5Wku6OfbqXe6+9Y07egfrapve6uTGTaW8ueQz7V1BPxziWLo39Tj/Fk0AAqeif8P/7H/xgTExPx1a9+NdavXx8REUNDQ/Hoo4/GTTfdNOP7/OEf/mEcOnQo/vZv/zaami481XvkkUdiZGREszaAKlC+645+I3Cvn7QqdFufadZ3OSbyEdt+rieWXH3lnO+nA+lU0e7o1157bfzsz/5s3HnnnfHlL385jh8/Hr/9278da9asiZ/8yZ+MiIjx8fE4e/bsxbL1//yf/3N85zvfibe97W3xrW99K44dOxavec1r4sYbb4xbbrmlktsDWPCM5Xpa4Y7+1K71LZ0ZZcx1wL1+0my2buvlWnL1lXHrjddH74pOARwaSEVPwiMi3va2t8Uf/MEfxG/+5m9GRMSLXvSiuPPOOy++/YEHHohf/uVfjg9+8IOxdu3aeP7znx/33ntv/NEf/VH8/M//fLS0tMTGjRvjd37ndy6ejAMwf8p3p3NHv36510/aXdptffj8k3HvfQMx+r0flPUxdEGHxlTxEH7VVVfF29/+9nj7298+49vXrl0bDz744LTXPvKRj1R6KwBcQvnuzNJwR5/yuddPPbi02/qVz2guq0S97Zkt8bx/31GtrQE1VNFydADSS/kujaRwr78Y9/pJk3JL1Ee/Nx7b/+Azcfj46SrvDEhaxU/CASploXfwrjTluzSawr3+aY0GOzPRtW3hNBqkflxaon5udCxODz8Rf3fkn+Pc6FMzrs+eH4tdB47Fzi19uqJDAxHCgVTSwbvylO/SiNzrp95cWqIeEfELL/6J+L/f+vcx+r3xWd9n330nYm3PMs3ZoEEoRwdSRwfv6lC+S6Mq3Ou/5kW3RHtvj59h6sqD/3yuaACPiBh+/MkYHCre0wOoH0I4kCqldvDO53IJ7aixGMvVuPK5XJwfOBFnP/+FOD9wwu8I1Ilzo2MVXQekn3J0IFV08K4+5buNx/UNqF+ljiEzrgwah5NwIFV08E6G8t3G4foG1LfursxlO6Z3Llkc3V2ZomuA+iGEA6migzeUzvUNqH/Ni5pi++beomv6N/VoygYNRAgHUqXQwbsYHbzhgnKubwDpNdsM8c4li40ngwbkTjiQKoUO3qd275l1jQ7e1Kt8LlfRu/iub1CqSv/sUXlTZ4h3tLVGd1fGCTg0ICEcSJ1CB+9pjaY6M9G1TaMp6lM1mqe5vkEpNO6rH1NniAONSQgHUkkHbxpJoXnaVIXmaXMdD1e4vlGsJN31jYWtWj97lZCbyDv1BRYkIRxIrUIHb6hnpTZP61jTV/ZDJtc3KKaaP3vzdfj46dh7cCCy55+efZ1pb43tm3vdfwYansZsAFBF1W6eVri+MbWhYUtnpqannNReWhv3HT5+OnYdODYpgEdEZM+Pxa4Dx+Lw8dOJ7gcgaU7CAaCKkmie5voGM0lj477cRD72HhwoumbffSdibc8ypelAwxLCAaCKkmqe5voGU6Wxcd/gUHbaCfhUw48/GYNDWQ3KgIalHB0AZpHP5eL8wIk4+/kvxPmBE5HP5cr+GIXmacVonkY1pPFn79xo8QBe7jqAeuQkHABmUKmxTpqnUStp/NnraGut6DqAeuQkHACmKIx1mtrUqjDWKXvk6MXXSjkt1zyNWknbz153VyYy7cUDdueSxdHdVfwEP+1yE/kYeHg4Pve1R2Lg4eHITeRT+TGB2nASDgCXKGes07mvHCv5tFzzNGolTT97zYuaYvvm3th14Nisa/o39dR1U7ZqjF8z0g0aS1M+n2+ox2gbN26MiIhDhw7VeCcA1KPzAyfixJ13XXbd/3Hbf4vvfOQvZn27E26Y3UyhsnPJ4ujf1FPXobIwfm02O7f0lf3nq8bHBCqvnBzqJBwALlHquKYzf/O3Rd9eOC130g3TbVi9PNb2LIvBoWycGx2LjrbW6O7K1PUJeDXGrxnpBo3JnXAAuESp45p++MQTRd8+PpyN0cGTldgSNKTmRU3Ru6Izbr3x+uhd0Vn3IbKc8Wu1/JhA7QnhAHCJUsY6NV/9rJI+Vqmn6kD9q+T4tUITti8dP13Rzw2kgxAOAJcojHUqZvl/+dmSPlapp+pA/avU+LXDx0/Hq9/+6fjd938p/vZL36ro5wbSQQgHgCkuN9bp//i/fuGyp+UtnZlo615VzW0CKVKJ8WuFJmyXK0Ev52MC6aMxGwDM4HJjnbr6t8ap3Xtmff+ubVs1ZYMFZL7j10ppwlbuxwTSSQgHWEDyuVwqZgXXi6bm5mjv7ZnxbYXT8mlzwjsz0bVt+pxwYLLcRL6huqNHXOj6vnNL35zGr5XShO1S8x3p1ohff6gXQjjAApE9cnR6YMxkoqtfYJyry52WAzObaU54pr01tm/urfuZ13Mdv1Zqc7WffeFz4oWrl88rNDfy1x/qgTvhAAtA9sjROLV7z6QAHhExns3Gqd17InvkaI12Vv8Kp+XXvOiWaO/tEcAhnu7u/bmvPRIDDw9HbiJ/8W2z3XvOnh+LXQeOxeESO4Kn2VzGr5XaXO2Fq5fPa6TbQvj6Q9o5CQdocPlcLob27S+6Zuje/dGxpk+ABOat2Cnr2p5ll733vO++E7G2Z1ndlEZXqqy70NitWEn6fJuwlXLvvN6+/lCPhHCABjc6eHLaCfhU48PZGB08Oev9Z4BSFE5Zpyqcsr7iZ1Ze9t7z8ONPxuBQNnpXdFZrmxVTybLu+TZ2K0Up987r6esP9Uo5OkCDGx8Zqeg6gJmUcsr6N1/4Zkkfq9T70bVUjbLuQmO3qaPOOpcsjp1b+uZ9X7vUr2s9fP2hnjkJB2hwLUuXVnQdwExKOWX9t+//oKSPVer96FqpZln3XBu7laLUr2vav/5Q74RwgAbX1r0qWjKZoiXpLZ2ZaOteleCugEZT6unpsxY/I554cvYwPt97z0modll3obFbpSVx7xy4POXoAA2uqbk5uvq3Fl3TtW2rpmzAvJR6evpzL+oq+vb53ntOQr2WdRfunRdTD19/qHdCOMACkFm/LlbesSNaMpNPN1o6M7Hyjh3mhAPzVjhlLaZzyeL4pZc8ryr3nouNRau0ei7rrva9c+DylKMDLBCZ9euiY03fhW7pIyPRsnRptHWvcgJO3cvncn6uU6Cc7t6VvvdcyS7lpaj3su5q3jsHLq8pn89X7zFhDWzcuDEiIg4dOlTjnQAA1ZY9cjSG9u2f1POgJZOJrv6tDV/hUan51JU2UyDuXLI4+jf1VCUQzzYWraBap7u1+rxAOpWTQ52EAwB1KXvkaJzavWfa6+PZbJzavaehr1okffJbjiRPWavZpfxyCmXdST5wABqDEA4A1J18LhdD+/YXXTN07/7oWNPXcKXps53AFuZTp+EEtlrdvaeqdpfyy1HWDcyFEA4A1J3RwZNFx+5FRIwPZ2N08GS09/YktKvqq+XJbxqloUt5Ug8cgMahOzoAUHfGR0Yquq5elHPyuxCkqUt5kt3ZgfrmJBwAqDstS5dWdF29SMPJb5qkpUt5mu/oA+njJBwAqDtt3aumzb2fqqUzE23dqxLaUTLSdPKbBoWxaMUUxqJVS+GO/tQHAYU7+oePn67a5wbqkxAOANSdpubm6OrfWnRN17atDdeUrXDyW0ya51NXw9qeZfGKn3lePGvxMya93rlkcdWb1JV6R7/c0nSl7dDYlKMDAHUps35drLxjx/Q54Z2Z6NrWmHPCCye/xeZTV/vkN01mKgN/1lXPiJ+75Yb4pZf8RNW/DtXozq60HRqfEA4A1K3M+nXRsabvQrf0kZF4Rnt7RET84Pz5OD9wItq6VzXcabj51BfMNqrtie//ID7896fi3113ddW/FpW+o18P4+eA+RPCAYC61tTcHO29PZE9cjS+8cfvmXwqnslEV3/jnYov9PnUaRnVVs4d/dxEvuj3Ky1/JqD6hHAAoO5ljxyNU7v3THt9PJuNU7v3xMo7djRcEF/I86nnUwZ+uTBcjlK7s59/4ql49ds/XbTEvBql7UA6CeEAQF3L53IxtG9/0TVD9+6PjjV9DVeavlDNtQx8pvvWbc9siZ+68fpY17Os7EBeyh39F/3k8njHh+6f9vrUEnPj52Dh0B0dAKhro4MnJ5Wgz2R8OBujgycT2hHVNpdRbbONEhv93nh84gtD8bvv/1K8+u2fLnukWOGO/tSu9Z1LFsfv/PJN8bkH/rXo+xe6pxs/BwuHk3AAoK6Nj4xUdB3pV2oZeGFUWyn3rSPm3gBttjv65ZSYl/tnAuqXk3AAoK61LF1a0XWkX6EMvJhLR7WVEoYvNZfZ3oU7+rfeeH30ruiM5kVNZZWYl/tnAuqXEA4A1LW27lXRkil+OtjSmYm27lUJ7YgkFCsDn3qSXe496sLp9HyVW2Jezp8JqF/K0QGAutbU3Bxd/Vtn7I5e0LVtq6ZsDajUUW1zuUddiQZocykxX+jj52AhEMIBSLV8Lneh8dbISLQsXRpt3auEKabJrF8XK+/YEUP79k+eE96Zia5tjTcnnKeVMqqtlDA8VSUaoJXSPX2mEvOFPH4OFgIhHIDUyh45Oj1UZTLR1S9UMV1m/broWNPnoc0MKjkbux6VEoYvVckGaIUS86mj0TqXLI7+TT1KzGEBEsIBSKXskaMzlhePZ7NxaveeWHnHDkGcaZqam6O9t6fW20iVmWZjZ9pbY/vm3lkDYCOG9tnC8Ewq3QBNiTlwKSEcgNTJ53IxtG9/0TVD9+6PjjV9TjmhiMJs7KmKjeKaS2ivF5eG4aMnzsT/97XvxOj3fnDx7dU8nVZiDhQI4QCkzujgyUkl6DMZH87G6OBJp54wi1JmY++770Ss7Vl28UR2LqG93hTCcO+Kztj6cz2JnE43YmUBMHdCOACpMz4yUtF1sBCVMhu7MIqrd0XnnEJ7vUvidLqRKwuAuTEnHIDUaVm6tKLroJbyuVycHzgRZz//hTg/cCLyuVwin7fUEVuFdeWE9mJyE/kYeHg4Pve1R2Lg4eHITeRL23ADKlQWTP26FioLDh8/XaOdAbXkJByA1GnrXhUtmUzRkvSWzky0da9KcFdQvlp2+C91xFZhXbmhfSZOfZ+2ECsLgNI4CQcgdZqam6Orf2vRNV3btmrKRqoVOvxPfZhU6PCfPXK0qp+/MBu7mEtHcZUb2qdy6jtZpSoLgMYjhAOQSpn162LlHTuiJTN5Vm9LZ8Z4MlKv1A7/1SxNL8zGLubSUVzlhvZLlXrqu5BK0ytRWQA0JuXoAKRWZv266FjTd6Fb+shItCxdGm3dq5yAk3pp6fA/22zsmUZxFUL7TN3RC2abn11uE7hS1XNX8flWFgCNSwgHINWampuNIaPupKnD/6WzsS8XZssJ7Zeqxqlvvd8vL1QWFHs4MVtlAdDYhHAAgApLW4f/ckZxlRPaCyp96tsI88rnU1kANDZ3wgEAKqzQ4b+YNHf4L4T2W2+8PnpXdF42KM7nPvlUjXS/vFBZMPVr07lkcV08SACqw0k4AECFFTr8n9q9Z9Y1jdThv3lRU2zb1BPv+OD9s64p9dS3WvfLa2UulQVAYxPCAQCqoNDhf9qc8M5MdG2r/pzwqarZ5OyL//Sv8f6/Oj7j2y53n3yqRuwqXs51AKDxCeEAAFWSlg7/1Wxy9v/7mxPx1//fN2d9+6v/6/PL+hy6igONzp1wAIAqKnT4v+ZFt0R7b09NAviuA8emlXgXmpwdPn56zh/7i//4r0UDeETEB/7m62Xd367k/XKANBLCAQAaVDWbnOUm8vH+v/6ny64r3N8uVaGreDG6igP1TAgHAGhQ5TQ5m8vHHv3eD0paW+79bV3FgUbmTjgAQIOqZpOzct5nLve3dRUHGpUQDgDQoKrZ5KzU92l/Vsuc72/rKg40IuXoAAANqppNzkr52BERv/rzq51eA1xCCAcAaFDVbHJWysf++Z+6IW5+wY+V/bEBGpkQDgDQwKrZ5Gy2j932zJb4ndtviv/7v/bM+WMDNCp3wgEAGlw1m5xpoAZQHiEcAGABqGaTMw3UAEqnHB0AAAAS4iQcAKCB5HO5GB08GeMjI9GydGm0da+KpubmWm8LgB8RwgEAGkT2yNEY2rc/xrPZi6+1ZDLR1b81MuvX1XBnABQoRwcAaADZI0fj1O49kwJ4RMR4Nhundu+J7JGjNdoZAJcSwgEA6lw+l4uhffuLrhm6d3/kc7mEdgTAbIRwAIA6Nzp4ctoJ+FTjw9kYHTyZ0I4AmI0QDgBQ58ZHRiq6DoDqEcIBAOpcy9KlFV0HQPUI4QAAda6te1W0ZDJF17R0ZqKte1VCOwJgNkI4AECda2pujq7+rUXXdG3bal54EbmJfAw8PByf+9ojMfDwcOQm8rXeEtCgzAkHAGgAmfXrYuUdO6bPCe/MRNc2c8KLOXz8dOw9OBDZ82MXX8u0t8b2zb2xYfXyGu4MaERCOABAg8isXxcda/oudEsfGYmWpUujrXuVE/AiDh8/HbsOHJv2evb8WOw6cCx2bukTxIGKEsIBABpIU3NztPf21HobZclN5GNwKBvnRseio601ursy0byoKZHPu/fgQNE1++47EWt7liWyH2BhEMIBAKiZWpaCDw5lJ33emQw//mQMDmWjd0VnVfcCLBwaswEAUBOFUvCpQbhQCn74+Omqfv5zo8UDeLnrAEohhAMAkLhSS8Gr2aW8o621ousASiGEAwDUsXodrVVOKXi1dHdlItNePGB3Llkc3V3FZ7ADlMOdcACAOlXPo7XSUArevKgptm/unbE7ekH/ph5N2YCKchIOAFCHan2fer7SUgq+YfXy2Lmlb9qJeOeSxcaTAVVR1ZPwN7/5zZHL5WL37t1F1z3yyCPxtre9LY4dOxatra3x8pe/PN7whjdEs5mWAADTNMJorUIpeLGS9KRKwTesXh5re5bVZEwasPBU5SQ8l8vFO97xjvjYxz522bU/+MEP4tWvfnU0NTXFRz/60XjrW98aH/vYx+K9731vNbYGAFD30nCfer4KpeDFJFkK3ryoKXpXdMatN14fvSs6BXCgaip+Ev7Nb34zdu7cGd/5zndi+fLLl+/8/d//fZw+fTr+8i//Mtra2uInfuInIpvNxjvf+c741V/91Whpaan0FgEA6loa7lNXQqEUfOq99s4li6N/U49ScKAhVTyEf+UrX4lVq1bF+9///njd61532fX3339/PP/5z4+2traLr61bty6eeOKJOHXqVKxevbrSWwQAqGtpuU9dCUrBgYWm4iH8tttuK2v9d7/73bjuuusmvfbsZz87IiJOnz49YwjfuHHjrB/vzJkzsWzZsrL2AABQT9J0n7oSCqXgAAtBWSH8kUceKRqAv/jFL8Y111xT1gbGxsYmnYJHRFx55ZUREfHUU0+V9bEAABYCo7UA6ldZIfzaa6+NT33qU7O+vaOjo+wNtLa2xvj4+KTXCuH7qquumvF9Dh06NOvHK/aQAACgUbhPDVCfygrhz3jGM+KGG26o6Aauu+66eOihhya99thjj0XEhdAPAMDM3KcGqD9VnRNeir6+vjh48GA88cQT8axnPSsiIo4cORLPfOYzY+XKlTXeHQBAurlPDVBfqjInvJjx8fE4e/bsxRL0l7zkJXHNNdfE6173ujh16lR89rOfjXe/+92xdetW48kAAABoKImH8AceeCBuvvnmeOCBByLiQhO2e++9NyYmJuKXfumX4i1veUu84hWviF//9V9PemsAAABQVU35fD5f601UUqExW7HmbQAApE9uIu9+O1CXysmhNb8TDgAAh4+fntbpPdPeGts39+r0DjSUxMvRAQDgUoePn45dB45NCuAREdnzY7HrwLE4fPx0jXYGUHlOwgEAKEsly8ZzE/nYe3Cg6Jp9952ItT3LKlKaruQdqDUhHACAklW6bHxwKDvtBHyq4cefjMGh7LxHsSl5B9JAOToAACWpRtn4udHiAbzcdbNR8g6khRAOAMBllVo2npsob/BOR1trRdfNpFp7B5gLIRwAgMsqp2x8NrmJfAw8PByf+9ojMfDwcOQm8tHdlYlMe/GA3blkcXR3Zea074jK7B2gUtwJBwDgsuZbNl7sPvb2zb2x68CxWT9m/6aeeTVPS6rkHaAUTsIBALis+ZSNX+4+dkTEzi19007EO5csjp1b+ubdNC2JkneAUjkJBwDgsgpl48XKumcqGy/1Pva9b35prO1ZVpXxYXPdO0A1OAkHABrKTPeOmb/mRU2xfXNv0TUzlY2Xcx+7eVFT9K7ojFtvvD56V3RWbH73XPcOUA1OwgGAhmEOdHVtWL08dm7pm/Y17lyyOPo39cz4NU7Lfey57B2gGoRwAKAhFO4dT1W4d1yJu8VcCLPllI2n6T52uXsHqAYhHACoe6XeO17bs0zgqoBC2Xgp3McGmEwIB4AE5HO5GB08GeMjI9GydGm0da+KpubmWm+rYZRz77jU8EhlFO5jV3MEWalcVwDSQAgHgCrLHjkaQ/v2x3g2e/G1lkwmuvq3Rmb9uhrurHGk5d4xM0vDfWzXFYC0EMIBoIqyR47Gqd17pr0+ns3Gqd17YuUdOwTxCkjTveM0yk3ka34Pupb3sV1XANJECAeAKsnncjG0b3/RNUP37o+ONX1K0+fJvePZpakEu5y75JXkugKQJuaEA0CVjA6enFSCPpPx4WyMDp5MaEeNyxzomRVKsKcG0EIJ9uHjp2u0s2S5rgCkiRAOAFUyPjJS0XUUV7h3nGmfXHLeuWTxgrzvW2oJdm4in9COasd1BSBNlKMDQJW0LF1a0XVcnjnQT1OC/TTXFYA0cRIOAFXS1r0qWjLF/1Hf0pmJtu5VCe1oYSjcO771xuujd0XnggzgEUqwL+W6ApAmQjgAVElTc3N09W8tuqZr21ZN2agKJdiTua4ApIVydACoosz6dbHyjh3T54R3ZqJrmznhVE/aSrAX+pg0gAIhHACqLLN+XXSs6bvQLX1kJFqWLo227lVOwKmqQgn2rgPHZl2TVAm2MWkAT1OODgAJaGpujvbenrjmRbdEe2+PAE4i0lCCbUwawGROwgEAGlgtS7BLHZO2tmeZknBgwRDCAQAaXK1KsI1JA5hOOToAAFVhTBrAdEI4AABVYUwawHRCOAAAVVEYk1ZM55LWxMakAaSBEA4AsEDkJvIx8PBwfO5rj8TAw8ORm8hX9fMVxqQVMzaeiy+fOFPVfQCkicZsAAALQK1mdW9YvTx+5/ab4p7/9UA8+VRu2tuf+P4PYteBY4mNTAOoNSfhAAANrpazug8fPx33fuLEjAH8UvvuO1H1k3mANBDCAQBmkM/l4vzAiTj7+S/E+YETkc8VD5FpVeqs7moE4NnC/0wKo8oAGp1ydACAKbJHjsbQvv0xnn06FLZkMtHVvzUy69fVcGflq9Ws7lLC/1RGlQELgZNwAIBLZI8cjVO790wK4BER49lsnNq9J7JHjtZoZ3NTq1ndpYT/qYwqAxYCIRwA4EfyuVwM7dtfdM3QvfvrqjS9VrO6yw31nUsWG1UGLAhCOADAj4wOnpx2Aj7V+HA2RgdPJrSj+SttVnflA3C5ob5/U080L2qq6B4A0kgIBwD4kfGRkYquS9JsM8BLmdVdjQBcSviPuDAmzXgyYCHRmA0A4Edali6t6LqkXG4G+IbVy2Pnlr5pazqXLI7+TT1FA3BuIh+DQ9k4NzoWHW2t0d2VKSmwF8L/rgPHZl3zip9ZGb/0kp9wAg4sKEI4AMCPtHWvipZMpmhJektnJtq6VyW4q+IKY8CmKswAL5wyb1i9PNb2LCsrUF8u3F/OfMI/QKMSwgEAfqSpuTm6+rfGqd17Zl3TtW1rNDU3J7ir2ZU6A3xtz7JoXtQUzYuaSh5DVmq4v5y5hH+ARuZOOADAJTLr18XKO3ZES2Zyo7KWzkysvGNHquaElzMDvBylhvvCvfPLKYT/W2+8PnpXdArgwILmJBwAYIrM+nXRsabvQrf0kZFoWbo02rpXpeYEvKBaM8DLCfelnqwDcIEQDgAwg6bm5mjv7an1Noqq1gzwaoV7AJSjAwDUrWrNAK9WuAdACAcAqFvVmgFerXAPgBAOAFDXCmPApobmziWLS+5gPlW1wj0A7oQDANS9aowBM+MboDqEcACABlDODPBSmfENUHlCOAAAs6pGuAdYyNwJBwAAgIQI4QAAAJAQIRwAAAASIoQDAABAQoRwAAAASIgQDgAAAAkxogwAgIaQm8ibaQ6knhAOAEDdO3z8dOw9OBDZ82MXX8u0t8b2zb2xYfXyGu4MYDLl6AAA1LXDx0/HrgPHJgXwiIjs+bHYdeBYHD5+ukY7A5hOCAcAIHVyE/kYeHg4Pve1R2Lg4eHITeRnXbf34EDRj7XvvhOzvj9A0pSjAwCQKuWUlg8OZaedgE81/PiTMTiUjd4VnVXZL0A5nIQDAJAa5ZaWnxstHsDLXQdQbUI4AACpMJfS8o621pI+dqnrAKpNCAcAIBXKKS0v6O7KRKa9eMDuXLI4ursyFdkjwHwJ4QAApMJcSsubFzXF9s29Rdf3b+oxLxxIDSEcAIBUmGtp+YbVy2Pnlr5pJ+KdSxbHzi195oQDqaI7OgAAqVAoLS9Wkj5bafmG1ctjbc+yGBzKxrnRsehoa43urowTcCB1nIQDAJAK8y0tb17UFL0rOuPWG6+P3hWdAjiQSkI4AACpobQcaHTK0QEASBWl5UAjE8IBAEidQmk5QKNRjg4AAAAJEcIBAAAgIUI4AAAAJEQIBwAAgIQI4QAAAJAQIRwAAAASIoQDAABAQoRwAAAASIgQDgAAAAkRwgEAACAhV9R6AwAATJebyMfgUDbOjY5FR1trdHdlonlRU623BcA8CeEAAClz+Pjp2HtwILLnxy6+lmlvje2be2PD6uU13BkA86UcHQAgRQ4fPx27DhybFMAjIrLnx2LXgWNx+PjpGu0MgEoQwgEAUiI3kY+9BweKrtl334nITeQT2hEAlSaEAwCkxOBQdtoJ+FTDjz8Zg0PZiny+3EQ+Bh4ejs997ZEYeHhYuAdIgDvhAAApcW60eAAvd10x7p0D1IaTcACAlOhoa63outm4dw5QO0I4AEBKdHdlItNePGB3Llkc3V2ZOX8O984BaksIBwBIieZFTbF9c2/RNf2beuY1Lzzpe+cATCaEAwCkyIbVy2Pnlr5pJ+KdSxbHzi19876vneS9cwCm05gNACBlNqxeHmt7lsXgUDbOjY5FR1trdHdl5nUCXpDUvXMAZiaEAwCkUPOipuhd0Vnxj1u4d16sJH2+984BmJ1ydACABSSJe+cAzE4IBwBYYKp97xyA2SlHBwBYgKp577zS8rlcjA6ejPGRkWhZujTauldFU3NzrbcFMCdCOADAAlWte+eVlD1yNIb27Y/x7NMj01oymejq3xqZ9etquDOAuVGODgBAKmWPHI1Tu/dMCuAREePZbJzavSeyR47WaGcAcyeEAwCQOvlcLob27S+6Zuje/ZHP5RLaEUBlVLUc/c1vfnPkcrnYvXt30XXvec974k/+5E+mvf71r389rrhCxTwAwEIzOnhy2gn4VOPD2RgdPBntvT0J7Qpg/qqScHO5XNx9993xsY99LF7+8pdfdv2DDz4YmzZtih07dkzenAAOALAgjY+MVHQdQFpUPOV+85vfjJ07d8Z3vvOdWL68tPEWDz30UNx2221xzTXXVHo7AADUoZalSyu6DiAtKn4n/Ctf+UqsWrUqPvnJT8b1119/2fVPPvlk/Mu//EusWLGi0lsBAKBOtXWvipZMpuials5MtHWvSmhHAJVR8ZPw2267raz13/jGN2JiYiL+7u/+Lt761rfG+Ph4rFmzJt74xjfGs5/97EpvDwCgruQm8nUxy7vSmpqbo6t/a5zavWfWNV3btpoXDtSdskL4I488Ehs3bpz17V/84hfLLin/xje+ERERV199ddxzzz0xPDwc73rXu+KXf/mX4+Mf/3gsXrx42vsU28OZM2di2bJlZe0BACCNDh8/HXsPDkT2/NjF1zLtrbF9c29sWF3atb96llm/LlbesWP6nPDOTHRtMyccqE9lhfBrr702PvWpT8369o6OjrI38Au/8Avxkpe8JNrb2y++9tznPjduvfXW+Id/+Id42cteVvbHBACoR5eeep8efiI+/PcPTluTPT8Wuw4ci51b+hZMEO9Y03ehW/rISLQsXRpt3aucgAN1q6wQ/oxnPCNuuOGGim/i0gAecSHsL1myJL773e/OuP7QoUOzfqxip+QAAGk106l3MfvuOxFre5YtmNJ0Y8iARlHxxmzl+sM//MN42cteFvl8/uJrjzzySIyMjGjWBgAsCIePn45dB46VHMAjIoYffzIGh4rP0QYgfRIP4ePj43H27NkYHx+PiIj//J//c3znO9+Jt73tbfGtb30rjh07Fq95zWvixhtvjFtuuSXp7QEAJCo3kY+9Bwfm9L7nRksP7QCkQ+Ih/IEHHoibb745HnjggYiIeP7znx/33ntvnDx5Mn7+538+fvM3fzNWrVoV/+N//I9oamr88ioAYGEbHMqWdQJ+qe88+m8x8PBw5Cbyl18MQCo05S+tA28AhTvhxe6NAwCkxee+9kjc/T+/Oq+PsZA6pgOkUTk5tOZ3wgEAFrKOttZ5f4xCx/TDx09XYEcAVJMQDgBQQ91dmci0zz+IR1zomK40HSDdhHAAgBpqXtQU2zf3Fl3zip9ZGb/0kp+47MfSMR0g/YRwAIAa27B6eezc0jftRLxzyeLYuaUvbvtPz4sfv/bqkj6WjukA6XZFrTcAAMCFIL62Z1kMDmXj3OhYdLS1RndXJpoXXZgWU+rd8X/5Ucf0S98XgPQQwgEAUqJ5UVP0ruic8W2Fu+OXG2f2vz77UPyvzz6kYzpASilHBwCoA6XcHb+UjukA6SSEAwDUidnujhejYzpAuihHBwCoI5feHf+nb5yNv/jsQ0XXFzqmz1bmDkCyhHAAgDpTuDteaid0HdMB0kM5OgBAnSq1Y3qp6wCoPiEcAKBOFTqmF9O5ZHF0d2US2hEAlyOEAwDUqVI6pvdv6jEvHCBFhHAAgDo2W8f0ziWLY+eWPnPCAVJGYzYAgDp3acf0c6Nj0dHWGt1dGSfgACkkhAMANIBCx3QA0k05OgAAACTESTgAQB3ITeSVmwM0ACEcACDlDh8/HXsPDkT2/NjF1zLtrbF9c6/GawB1Rjk6AECKHT5+OnYdODYpgEdEZM+Pxa4Dx+Lw8dM12hkAcyGEAwCkVG4iH3sPDhRds+++E5GbyCe0IwDmSwgHAEipwaHstBPwqYYffzIGh7IJ7QiA+RLCAQBSavjxJ0tad260eFAHID2EcACAFDp8/HTc+4kTJa3taGut8m4AqBTd0QEAUqbQjK0UnUsWR3dXpso7AqBSnIQDAKRIKc3YLtW/qce8cIA64iQcACBFSmnGFhHR9syW+I1ffIE54QB1xkk4AECKlNpkbdumHgEcoA4J4QAAKVJqk7XO9sVV3gkA1SCEAwCkSHdXJjLtxYO4ZmwA9UsIBwBIkeZFTbF9c2/RNZqxAdQvIRwAIGU2rF4eO7f0TTsR71yyOHZu6XMXHKCO6Y4OAJBCG1Yvj7U9y2JwKBvnRseio601ursyTsAB6pwQDgCQUs2LmqJ3RWettwFABSlHBwAAgIQI4QAAAJAQIRwAAAASIoQDAABAQoRwAAAASIgQDgAAAAkRwgEAACAhQjgAAAAkRAgHAACAhAjhAAAAkJArar0BAIC0yk3kY3AoG+dGx6KjrTW6uzLRvKip1tsCoI4J4QAAMzh8/HTsPTgQ2fNjF1/LtLfG9s29sWH18hruDIB6phwdAGCKw8dPx64DxyYF8IiI7Pmx2HXgWBw+frpGOwOg3gnhAACXyE3kY+/BgaJr9t13InIT+YR2BEAjEcIBAC4xOJSddgI+1fDjT8bgUDahHQHQSIRwAIBLnBstHsDLXQcAlxLCAQAu0dHWWtF1AHApIRwA4BLdXZnItBcP2J1LFkd3VyahHQHQSIRwAIBLNC9qiu2be4uu6d/UY144AHMihAMATLFh9fLYuaVv2ol455LFsXNLnznhAMzZFbXeAABAGm1YvTzW9iyLwaFsnBsdi4621ujuyjgBB2BehHAAgFk0L2qK3hWdtd4GAA1EOToAAAAkRAgHAACAhAjhAAAAkBAhHAAAABIihAMAAEBChHAAAABIiBAOAAAACRHCAQAAICFCOAAAACRECAcAAICECOEAAACQECEcAAAAEiKEAwAAQEKEcAAAAEiIEA4AAAAJEcIBAAAgIUI4AAAAJEQIBwAAgIQI4QAAAJAQIRwAAAASIoQDAABAQoRwAAAASIgQDgAAAAkRwgEAACAhQjgAAAAkRAgHAACAhAjhAAAAkBAhHAAAABIihAMAAEBChHAAAABIiBAOAAAACRHCAQAAICFX1HoDAADF5CbyMTiUjXOjY9HR1hrdXZloXtRU620BwJwI4QBAah0+fjr2HhyI7Pmxi69l2ltj++be2LB6eQ13BgBzoxwdAEilw8dPx64DxyYF8IiI7Pmx2HXgWBw+frpGOwOAuRPCAYDUyU3kY+/BgaJr9t13InIT+YR2BACVIYQDAKkzOJSddgI+1fDjT8bgUDahHQFAZQjhAEDqnBstHsDLXQcAaSGEAwCp09HWWtF1AJAWQjgAkDrdXZnItBcP2J1LFkd3VyahHQFAZQjhAEDqNC9qiu2be4uu6d/UY144AHVHCAcAUmnD6uWxc0vftBPxziWLY+eWPnPCAahLV9R6AwAAs9mwenms7VkWg0PZODc6Fh1trdHdlXECDkDdEsIBgFRrXtQUvSs6a70NAKgI5egAAACQECEcAAAAElLxEH7mzJl4wxveEC984Qujr68vXv3qV8c3vvGNou/zyCOPxK/8yq/EjTfeGBs2bIg9e/ZELper9NYAAACgpioawsfHx2P79u2RzWbjT//0T+PDH/5wXH311bFly5Y4d+7cjO/zgx/8IF796ldHU1NTfPSjH423vvWt8bGPfSze+973VnJrAAAAUHMVbcx2//33x0MPPRSf//zn49prr42IiHe+852xZs2a+N//+3/HL/7iL057n7//+7+P06dPx1/+5V9GW1tb/MRP/ERks9l45zvfGb/6q78aLS0tldwiAAAA1ExFT8Kf+9znxt69ey8G8IJ8Ph/nz5+f8X3uv//+eP7znx9tbW0XX1u3bl088cQTcerUqUpuDwAAAGqqoifh11xzTdx6662TXvvgBz8YTz31VLzwhS+c8X2++93vxnXXXTfptWc/+9kREXH69OlYvXr1tPfZuHHjrHs4c+ZMLFu2rNytAwAAQNWVFcIfeeSRogH4i1/8YlxzzTUX//enP/3pePe73x233357rFy5csb3GRsbm3QKHhFx5ZVXRkTEU089Vc72AAAAINXKCuHXXnttfOpTn5r17R0dHRf/+0c+8pF429veFi972cti586ds75Pa2trjI+PT3qtEL6vuuqqGd/n0KFDs368Yg8JAAAAoJbKCuHPeMYz4oYbbrjsurvvvjv27dsXt99+e7z5zW+OpqamWdded9118dBDD0167bHHHouImHa3HAAAAOpZxeeE79mzJ/bt2xdvetOb4s477ywawCMi+vr6YnBwMJ544omLrx05ciSe+cxnzlrCDgAAAPWooiH8y1/+ctx7771x++23x8/93M/F2bNnL/7ne9/7XkRcmCV+9uzZiyXoL3nJS+Kaa66J173udXHq1Kn47Gc/G+9+97tj69atxpMBAADQUCoawj/5yU9GRMSHPvShuPnmmyf9Z//+/RER8cADD8TNN98cDzzwQERcaMJ27733xsTERPzSL/1SvOUtb4lXvOIV8eu//uuV3BoAAADUXFM+n8/XehOVVGjMVqx5GwAAAFRKOTm04nfCAQAAgJkJ4QAAAJAQIRwAAAASIoQDAABAQoRwAAAASIgQDgAAAAkRwgEAACAhQjgAAAAkRAgHAACAhAjhAAAAkBAhHAAAABIihAMAAEBChHAAAABIiBAOAAAACRHCAQAAICFCOAAAACRECAcAAICECOEAAACQECEcAAAAEiKEAwAAQEKEcAAAAEiIEA4AAAAJEcIBAAAgIUI4AAAAJEQIBwAAgIQI4QAAAJAQIRwAAAASIoQDAABAQoRwAAAASIgQDgAAAAkRwgEAACAhQjgAAAAkRAgHAACAhAjhAAAAkBAhHAAAABIihAMAAEBChHAAAABIiBAOAAAACRHCAQAAICFCOAAAACRECAcAAICECOEAAACQECEcAAAAEnJFrTcAAFBLuYl8DA5l49zoWHS0tUZ3VyaaFzXVelsANCghHAComrQH3MPHT8fegwORPT928bVMe2ts39wbG1Yvr+HOAGhUQjgAUBVpD7iHj5+OXQeOTXs9e34sdh04Fju39KVinwA0FnfCAYCKKwTcSwN4xNMB9/Dx0zXa2QW5iXzsPThQdM2++05EbiKf0I4AWCiEcACgouoh4A4OZac9IJhq+PEnY3Aom9COAFgohHAAoKLqIeCeGy2+v3LXAUCphHAAoKLqIeB2tLVWdB0AlEoIBwAqqh4CbndXJjLtxT9/55LF0d2VSWhHACwUQjgAUFH1EHCbFzXF9s29Rdf0b+pJ1Tg1ABqDEA4AVFS9BNwNq5fHzi190x4YdC5ZbDwZAFVjTjgAUHGFgDt1TnjnksXRv6knNQF3w+rlsbZnWQwOZePc6Fh0tLVGd1em5g8IAGhcQjgAUBX1EnCbFzVF74rOWm8DgAVCCAcAqkbABYDJ3AkHAACAhAjhAAAAkBAhHAAAABIihAMAAEBChHAAAABIiBAOAAAACRHCAQAAICFCOAAAACRECAcAAICECOEAAACQECEcAAAAEiKEAwAAQEKEcAAAAEiIEA4AAAAJEcIBAAAgIUI4AAAAJEQIBwAAgIQI4QAAAJAQIRwAAAASIoQDAABAQoRwAAAASIgQDgAAAAkRwgEAACAhQjgAAAAkRAgHAACAhAjhAAAAkBAhHAAAABIihAMAAEBChHAAAABIiBAOAAAACRHCAQAAICFCOAAAACRECAcAAICECOEAAACQkCtqvYFKe+yxxyKXy8XGjRtrvRUAAAAWgDNnzkRzc3NJaxvuJPzKK6+MK65ouGcLde3MmTNx5syZWm+DEvl+1Rffr/ri+1V/fM/qi+9XffH9qi++X8VdccUVceWVV5a0timfz+ervB8WuEJVwqFDh2q8E0rh+1VffL/qi+9X/fE9qy++X/XF96u++H5VTsOdhAMAAEBaCeEAAACQECEcAAAAEiKEAwAAQEKEcAAAAEiIEA4AAAAJMaIMAAAAEuIkHAAAABIihAMAAEBChHAAAABIiBAOAAAACRHCScy//Mu/xK/92q/FTTfdFDfddFO8/vWvj+9+97u13hazOHPmTLzhDW+IF77whdHX1xevfvWr4xvf+Eatt0UJ3vzmN8cdd9xR620wxcTERNxzzz1xyy23xAte8ILYunVrfPvb3671tijB+973vrj99ttrvQ2KePzxx+P/+X/+n3jRi14UN954Y9x2221x//3313pbzCKbzcaOHTti3bp18R/+w3+I7du3x8MPP1zrbVGCb33rW/Ef/sN/iL/+67+u9VbqmhBOIp566ql41ateFRERH/nIR+JDH/pQnD17Nn7lV34lNOhPn/Hx8di+fXtks9n40z/90/jwhz8cV199dWzZsiXOnTtX6+0xi1wuF+94xzviYx/7WK23wgze9773xUc/+tF4+9vfHn/xF38RTU1N0d/fH+Pj47XeGkX82Z/9Wdxzzz213gaX8YY3vCH+6Z/+Kd71rnfFxz72sXj+858fr371q+Ob3/xmrbfGDH7t134tvvOd78S+ffviYx/7WLS2tsarXvWqePLJJ2u9NYr4wQ9+EG984xvj+9//fq23UveEcBJx+vTp6O3tjf/+3/97PPe5z41Vq1bFq171qjh16lSMjIzUentMcf/998dDDz0U73znO6Onpyee+9znxjvf+c74/ve/H//7f//vWm+PGXzzm9+M2267LQ4ePBjLly+v9XaYYnx8PPbv3x+vec1r4tZbb42VK1fGu9/97nj00UfjM5/5TK23xwweffTR2LZtW/zxH/9xPOc5z6n1diji29/+dnzpS1+Ku+66K2666abo6uqKN7/5zXHttdfGJz/5yVpvjylGRkbi+uuvj7e97W3R29sbN9xwQ/z6r/96nD17VsVdyv3Jn/xJPPOZz6z1NhqCEE4invOc58Qf//EfR0dHR0REPPLII/HhD384nv/858fSpUtrvDumeu5znxt79+6Na6+9dtLr+Xw+zp8/X6NdUcxXvvKVWLVqVXzyk5+M66+/vtbbYYpTp07F9773vVi3bt3F19ra2qK7uzuOHTtWw50xm69//evR3t4en/jEJ+IFL3hBrbdDEUuXLo29e/dGT0/Pxdeampr8f1ZKLV26NN71rnfFc5/73IiIGB4ejg984ANx3XXXxYoVK2q8O2Zz7Nix+Iu/+It4xzveUeutNIQrar0BFp6tW7fGl770pWhvb48DBw5EU1NTrbfEFNdcc03ceuutk1774Ac/GE899VS88IUvrNGuKOa2226r9RYootD/YtmyZZNef/aznx1nzpypxZa4jBe/+MXx4he/uNbboARtbW3T/j/r//1//9/4l3/5l7j55ptrtCtK8Xu/93vxv/7X/4qWlpZ4//vfH1dddVWtt8QMRkdH401velPceeed0/5/jLkRwqmIRx55JDZu3Djr27/4xS/GNddcExERO3bsiN/6rd+K97///fGqV70qDh486Bc6YeV8vyIiPv3pT8e73/3uuP3222PlypVJbJFLlPv9In0K9xxbWlomvX7llVc6qYMK++pXvxq/+7u/Gxs3bvQgJeW2bNkS/+2//bf4yEc+Er/xG79xsUqSdPn93//9+Mmf/Mn4r//1v9Z6Kw1DCKcirr322vjUpz4169sLZegREatWrYqIiHe/+93xUz/1U/FXf/VX8Zu/+ZtV3yNPK+f79ZGPfCTe9ra3xcte9rLYuXNnEttjinK+X6RTa2trRFy4G1747xEXmlYuXry4VtuChvPZz3423vjGN8YLXvCCeNe73lXr7XAZhfLzt73tbfGP//iP8ed//uexa9euGu+KSx08eDDuv//++Ju/+Ztab6WhCOFUxDOe8Yy44YYbZn37v/7rv8aJEyfiZ37mZy6+tnjx4rj++uvjscceS2KLXOJy36+Cu+++O/bt2xe33357vPnNb3Z1oEZK/X6RXoVqn8ceeyx+/Md//OLrjz32mOoSqJA///M/j//+3/97vPSlL4277757WuUJ6ZDNZuPIkSPxf/6f/2c0NzdHRMSiRYvihhtu8G/CFPqrv/qryGaz8VM/9VOTXr/rrrviAx/4QPzt3/5tbTZW54RwEnHy5Ml47WtfG5/5zGcu/gN0dHQ0vvWtb8XP/dzP1Xh3zGTPnj1x7733xpve9KZ49atfXevtQF1buXJlPOtZz4ovf/nLk/4OHBwcjFe+8pU13h3Uvw9/+MPxtre9LW6//fb43d/93Vi0SO/htHrsscfit3/7tyOTycT69esj4sLoq8HBQdcHUujuu++OsbGxSa/9p//0n+K1r31tvOxlL6vRruqfEE4iXvSiF8Xznve8eNOb3hS/93u/F/l8Pvbs2RNLly6NX/iFX6j19pjiy1/+ctx7771x++23x8/93M/F2bNnL77tqquuMp4CytTS0hKvfOUr4+67746Ojo74sR/7sdizZ09cd9118dKXvrTW24O69q1vfSv+4A/+IF760pfGr/zKr0Q2m734ttbW1rj66qtruDumWrlyZdx8883xlre8Jd7+9rdHW1tb/I//8T9idHQ0XvWqV9V6e0wxdVJOQSaTiR/7sR9LeDeNQwgnES0tLXHvvffGO97xjnj1q18d4+PjcfPNN8fu3bvjWc96Vq23xxSFuaof+tCH4kMf+tCkt/3mb/5mvOY1r6nFtqCuvfa1r40f/vCHceedd8bY2Fj09fXFBz7wASWzME9///d/Hz/4wQ/iM5/5THzmM5+Z9LaXv/zlsXv37hrtjJk0NTXFH/3RH8Uf/uEfxute97r4t3/7t7jpppvif/7P/xnLly+v9fYgEU35fD5f600AAADAQuDCDAAAACR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" ] @@ -152686,7 +152709,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -152704,12 +152727,12 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 84, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -152734,12 +152757,12 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 85, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -153075,28 +153098,19 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 89, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "(-6.0, 3.0)" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "NameError", + "evalue": "name 'sqrt' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[89], line 7\u001b[0m\n\u001b[1;32m 5\u001b[0m X \u001b[38;5;241m=\u001b[39m centers[i, \u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m+\u001b[39m radius[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandn(\u001b[38;5;241m100\u001b[39m)\n\u001b[1;32m 6\u001b[0m Y \u001b[38;5;241m=\u001b[39m centers[i, \u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m radius[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandn(\u001b[38;5;241m100\u001b[39m)\n\u001b[0;32m----> 7\u001b[0m U \u001b[38;5;241m=\u001b[39m (X \u001b[38;5;241m+\u001b[39m Y) \u001b[38;5;241m*\u001b[39m \u001b[43msqrt\u001b[49m(\u001b[38;5;241m2\u001b[39m) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m 8\u001b[0m V \u001b[38;5;241m=\u001b[39m (X \u001b[38;5;241m-\u001b[39m Y) \u001b[38;5;241m*\u001b[39m sqrt(\u001b[38;5;241m2\u001b[39m) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m 9\u001b[0m synthetics \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate([synthetics, np\u001b[38;5;241m.\u001b[39mvstack((U, V))\u001b[38;5;241m.\u001b[39mT])\n", + "\u001b[0;31mNameError\u001b[0m: name 'sqrt' is not defined" + ] } ], "source": [ diff --git a/book/Chapter3-MachineLearning/3.8_robust_training.ipynb b/book/Chapter3-MachineLearning/3.8_robust_training.ipynb index af008d6..326a34c 100644 --- a/book/Chapter3-MachineLearning/3.8_robust_training.ipynb +++ b/book/Chapter3-MachineLearning/3.8_robust_training.ipynb @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -241,7 +241,7 @@ "4 0.00105 0.00328 -0.04815 0.00619 -0.33029 " ] }, - "execution_count": 5, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -289,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -301,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -311,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -324,10 +324,10 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 13, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, @@ -366,16 +366,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, @@ -410,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -429,7 +429,7 @@ "Text(0.5, 1.0, 'Random selection for data split')" ] }, - "execution_count": 16, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -500,7 +500,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -560,7 +560,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -645,7 +645,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -657,7 +657,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -666,7 +666,7 @@ "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR'])" ] }, - "execution_count": 3, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -680,7 +680,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -707,16 +707,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, @@ -737,7 +737,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -765,7 +765,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -796,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -812,7 +812,7 @@ " 'weights': 'uniform'}" ] }, - "execution_count": 11, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -845,7 +845,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -865,7 +865,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -874,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -915,7 +915,7 @@ "[CV 1/5] END algorithm=ball_tree, metric=euclidean, n_neighbors=4, weights=uniform;, score=0.931 total time= 0.0s\n", "[CV 2/5] END algorithm=ball_tree, metric=euclidean, n_neighbors=4, weights=uniform;, score=0.976 total time= 0.0s\n", "[CV 3/5] END algorithm=ball_tree, metric=euclidean, n_neighbors=4, weights=uniform;, score=0.951 total time= 0.0s\n", - "[CV 4/5] END algorithm=ball_tree, metric=euclidean, n_neighbors=4, weights=uniform;, score=0.955 total time= 0.0s\n", + "[CV 4/5] END algorithm=ball_tree, metric=euclidean, n_neighbors=4, weights=uniform;, score=0.955 total time= 0.1s\n", "[CV 5/5] END algorithm=ball_tree, metric=euclidean, n_neighbors=4, weights=uniform;, score=0.976 total time= 0.0s\n", "[CV 1/5] END algorithm=ball_tree, metric=euclidean, n_neighbors=4, weights=distance;, score=0.941 total time= 0.0s\n", "[CV 2/5] END algorithm=ball_tree, metric=euclidean, n_neighbors=4, weights=distance;, score=0.990 total time= 0.0s\n", @@ -1154,7 +1154,7 @@ "[CV 5/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=7, weights=distance;, score=0.972 total time= 0.0s\n", "[CV 1/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=8, weights=uniform;, score=0.917 total time= 0.0s\n", "[CV 2/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=8, weights=uniform;, score=0.944 total time= 0.0s\n", - "[CV 3/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=8, weights=uniform;, score=0.930 total time= 0.1s\n", + "[CV 3/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=8, weights=uniform;, score=0.930 total time= 0.0s\n", "[CV 4/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=8, weights=uniform;, score=0.951 total time= 0.0s\n", "[CV 5/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=8, weights=uniform;, score=0.962 total time= 0.0s\n", "[CV 1/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=8, weights=distance;, score=0.924 total time= 0.0s\n", @@ -1174,7 +1174,7 @@ "[CV 5/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=9, weights=distance;, score=0.965 total time= 0.0s\n", "[CV 1/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=10, weights=uniform;, score=0.920 total time= 0.0s\n", "[CV 2/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=10, weights=uniform;, score=0.951 total time= 0.0s\n", - "[CV 3/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=10, weights=uniform;, score=0.927 total time= 0.0s\n", + "[CV 3/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=10, weights=uniform;, score=0.927 total time= 0.1s\n", "[CV 4/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=10, weights=uniform;, score=0.944 total time= 0.0s\n", "[CV 5/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=10, weights=uniform;, score=0.955 total time= 0.0s\n", "[CV 1/5] END algorithm=ball_tree, metric=chebyshev, n_neighbors=10, weights=distance;, score=0.924 total time= 0.0s\n", @@ -1203,7 +1203,7 @@ "[CV 4/5] END algorithm=ball_tree, metric=minkowski, n_neighbors=2, weights=distance;, score=0.965 total time= 0.0s\n", "[CV 5/5] END algorithm=ball_tree, metric=minkowski, n_neighbors=2, weights=distance;, score=0.986 total time= 0.0s\n", "[CV 1/5] END algorithm=ball_tree, metric=minkowski, n_neighbors=3, weights=uniform;, score=0.944 total time= 0.0s\n", - "[CV 2/5] END algorithm=ball_tree, metric=minkowski, n_neighbors=3, weights=uniform;, score=0.990 total time= 0.0s\n", + "[CV 2/5] END algorithm=ball_tree, metric=minkowski, n_neighbors=3, weights=uniform;, score=0.990 total time= 0.1s\n", "[CV 3/5] END algorithm=ball_tree, metric=minkowski, n_neighbors=3, weights=uniform;, score=0.972 total time= 0.0s\n", "[CV 4/5] END algorithm=ball_tree, metric=minkowski, n_neighbors=3, weights=uniform;, score=0.948 total time= 0.0s\n", "[CV 5/5] END algorithm=ball_tree, metric=minkowski, n_neighbors=3, weights=uniform;, score=0.990 total time= 0.0s\n", @@ -1618,7 +1618,7 @@ "[CV 4/5] END algorithm=kd_tree, metric=minkowski, n_neighbors=4, weights=uniform;, score=0.955 total time= 0.0s\n", "[CV 5/5] END algorithm=kd_tree, metric=minkowski, n_neighbors=4, weights=uniform;, score=0.976 total time= 0.0s\n", "[CV 1/5] END algorithm=kd_tree, metric=minkowski, n_neighbors=4, weights=distance;, score=0.941 total time= 0.0s\n", - "[CV 2/5] END algorithm=kd_tree, metric=minkowski, n_neighbors=4, weights=distance;, score=0.990 total time= 0.0s\n", + "[CV 2/5] END algorithm=kd_tree, metric=minkowski, n_neighbors=4, weights=distance;, score=0.990 total time= 0.1s\n", "[CV 3/5] END algorithm=kd_tree, metric=minkowski, n_neighbors=4, weights=distance;, score=0.976 total time= 0.0s\n", "[CV 4/5] END algorithm=kd_tree, metric=minkowski, n_neighbors=4, weights=distance;, score=0.958 total time= 0.0s\n", "[CV 5/5] END algorithm=kd_tree, metric=minkowski, n_neighbors=4, weights=distance;, score=0.983 total time= 0.0s\n", @@ -1711,7 +1711,7 @@ " verbose=3)" ] }, - "execution_count": 14, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1722,7 +1722,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1751,7 +1751,7 @@ " 'verbose': 3}" ] }, - "execution_count": 15, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1762,156 +1762,156 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'mean_fit_time': array([0.00140777, 0.00114398, 0.00129838, 0.0011826 , 0.00129204,\n", - " 0.00114655, 0.00126467, 0.00125222, 0.00126867, 0.00128045,\n", - " 0.00125637, 0.00133281, 0.00118542, 0.0012094 , 0.00116515,\n", - " 0.00115423, 0.00118184, 0.00118876, 0.00122461, 0.00118771,\n", - " 0.00127654, 0.00118356, 0.00120368, 0.00111895, 0.0011518 ,\n", - " 0.00119176, 0.00112505, 0.00120587, 0.00110974, 0.00117784,\n", - " 0.0011219 , 0.00117283, 0.00113697, 0.00110488, 0.00116277,\n", - " 0.001121 , 0.00116334, 0.00110974, 0.00115099, 0.00111418,\n", - " 0.00137367, 0.00133452, 0.00137486, 0.00139213, 0.00138397,\n", - " 0.0013628 , 0.00139284, 0.00134025, 0.00137854, 0.00140023,\n", - " 0.00136886, 0.00139098, 0.00155859, 0.00141668, 0.00142055,\n", - " 0.00144706, 0.00151439, 0.00139289, 0.00139799, 0.00144835,\n", - " 0.00118885, 0.00134029, 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1.82379973e-03, 1.18121852e-04,\n", + " 3.63327082e-03, 1.85471843e-04, 2.97016986e-04, 9.42553289e-05,\n", + " 2.44834712e-04, 6.15933346e-05, 2.18382444e-04, 9.66214229e-05,\n", + " 4.52101981e-05, 1.41841735e-04, 1.00845997e-04, 1.13065179e-04,\n", + " 1.74605306e-04, 2.70219095e-04, 3.80271538e-04, 2.01323483e-04,\n", + " 2.68948526e-04, 2.60225403e-04, 1.66381228e-04, 1.43209894e-04,\n", + " 2.88827474e-04, 1.48091451e-04, 2.49118707e-04, 1.04051353e-04,\n", + " 2.38234048e-04, 5.42318369e-05, 5.06056383e-04, 1.26323370e-04,\n", + " 5.59945605e-04, 9.05619596e-05, 2.06687573e-04, 1.84920315e-04,\n", + " 1.20859643e-04, 6.70027644e-05, 1.89654114e-04, 1.91841053e-04,\n", + " 2.09914668e-04, 9.99266393e-05, 2.00604095e-04, 1.87628205e-04,\n", + " 3.40702389e-04, 3.27586087e-05, 3.24157758e-04, 1.63133999e-04,\n", + " 3.47303702e-04, 2.90929937e-04, 2.67093072e-04, 7.66858822e-05,\n", + " 3.75390942e-04, 1.46053392e-04, 1.48127635e-04, 1.25412705e-05,\n", + " 4.29569874e-04, 5.69374157e-04, 4.76405077e-04, 5.53443085e-04,\n", + " 7.04715499e-04, 5.51630154e-04, 5.25904551e-04, 4.97251052e-04,\n", + " 5.07950178e-04, 5.76934289e-04, 4.08719804e-03, 1.46238520e-03,\n", + " 4.32515259e-04, 4.69585374e-04, 5.72773115e-04, 5.38288317e-04,\n", + " 3.84504572e-04, 4.92084918e-04, 4.99882528e-04, 3.91507624e-04,\n", + " 2.74232428e-04, 4.37413982e-04, 2.69062878e-04, 2.30077333e-04,\n", + " 6.05872932e-03, 3.12848634e-03, 5.61351310e-03, 1.49289819e-02,\n", + " 5.44116923e-04, 1.53893981e-03, 1.14436706e-04, 3.79583332e-04,\n", + " 1.66641959e-04, 2.22551789e-04, 5.80779465e-04, 1.93461456e-04,\n", + " 2.60392446e-04, 1.11335973e-04, 4.32878410e-03, 1.45710123e-04]),\n", " 'param_algorithm': masked_array(data=['ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n", " 'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n", " 'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n", @@ -3004,7 +3004,7 @@ " 96, 87, 68, 82], dtype=int32)}" ] }, - "execution_count": 16, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -3015,7 +3015,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -3027,7 +3027,7 @@ " 'weights': 'uniform'}" ] }, - "execution_count": 17, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -3048,7 +3048,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -3059,14 +3059,14 @@ " param_distributions=[{'algorithm': ['ball_tree', 'kd_tree'],\n", " 'metric': ['euclidean', 'manhattan',\n", " 'chebyshev', 'minkowski'],\n", - " 'n_neighbors': array([5, 4, 2, 8, 2, 6, 1, 1, 3, 5]),\n", + " 'n_neighbors': array([2, 1, 4, 6, 9, 6, 3, 9, 8, 9]),\n", " 'weights': ['uniform', 'distance']}],\n", " random_state=0)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org." ], @@ -3076,12 +3076,12 @@ " param_distributions=[{'algorithm': ['ball_tree', 'kd_tree'],\n", " 'metric': ['euclidean', 'manhattan',\n", " 'chebyshev', 'minkowski'],\n", - " 'n_neighbors': array([5, 4, 2, 8, 2, 6, 1, 1, 3, 5]),\n", + " 'n_neighbors': array([2, 1, 4, 6, 9, 6, 3, 9, 8, 9]),\n", " 'weights': ['uniform', 'distance']}],\n", " random_state=0)" ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -3099,7 +3099,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -3107,7 +3107,7 @@ "output_type": "stream", "text": [ "{'algorithm': 'ball_tree', 'metric': 'euclidean', 'n_neighbors': 1, 'weights': 'uniform'}\n", - "{'weights': 'distance', 'n_neighbors': 2, 'metric': 'euclidean', 'algorithm': 'kd_tree'}\n" + "{'weights': 'distance', 'n_neighbors': 1, 'metric': 'minkowski', 'algorithm': 'kd_tree'}\n" ] } ], diff --git a/book/Chapter3-MachineLearning/Homework_CML.ipynb b/book/Chapter3-MachineLearning/Homework_CML.ipynb new file mode 100644 index 0000000..e419a77 --- /dev/null +++ b/book/Chapter3-MachineLearning/Homework_CML.ipynb @@ -0,0 +1,1053 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "fa3d2a9c-442b-44c2-bf4d-45bb544ec369", + "_uuid": "bcba1675eebc008a35c21f6b64174161ff7d4c48" + }, + "source": [ + "# Homework Classic Machine Learning (50 points)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "d4bef474-9b3a-4bfd-80ee-b79f7fa144dc", + "_uuid": "73e8f01266e59393a11b6e00300eb916ee50662f" + }, + "source": [ + "In this Homework, we will try to classify observations of space to be either stars, galaxies or quasars based on the RD14 from the Sloan Digital Sky Survey (SDSS). The Sloan Digital Sky Survey is a project which offers public data of space observations. Observations have been made since 1998 and have been made accessible to everyone who is interested. \n", + "\n", + "http://www.sdss.org/\n", + "![alt text](http://www.fingerprintdigitalmedia.com/wp-content/uploads/2014/08/sdss1.jpg)\n", + "\n", + "For this purpose a special 2.5 m diameter telescope was built at the Apache Point Observatory in New Mexico, USA. The telescope uses a camera of 30 CCD-Chips with 2048x2048 image points each. The chips are ordered in 5 rows with 6 chips in each row. Each row observes the space through different optical filters (u, g, r, i, z) at wavelengths of approximately 354, 476, 628, 769, 925 nm.\n", + "\n", + "\n", + "In this homework we will train several classifier to predict the class of a celestial object based on the observations (features). We will practice data prep, dimensionality reduction, model design and training, model comparison, and feature importance selection." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "b9d4b609-9f0f-4296-8462-98975b2ece09", + "_uuid": "16d228ddb3b0d71d6e13093552c04a21146b75e5" + }, + "source": [ + "### Importing Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "_cell_guid": "13914766-c2fb-4801-8846-6c78e6d1cb03", + "_uuid": "5bb212bdb34abc34f8bed1a0bc2d1a6287166221" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n", + "%matplotlib inline\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "2b272b59-8c37-4e8e-a78a-ac5f50919d2e", + "_uuid": "82c44461c45ae463a3d429f564f8bca17f8b0f4c" + }, + "source": [ + "## 1. Data Preparation (20 points)\n", + "\n", + "We follow the following steps:\n", + "* read (1 point)\n", + "* clean (3 points)\n", + "* correlate (4 points)\n", + "* explore, spread of values (3 points)\n", + "* dimensionality reduction (9 points)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!wget \"https://raw.githubusercontent.com/UW-MLGEO/MLGeo-dataset/refs/heads/main/data/Skyserver_SQL2_27_2018 6_51_39 PM.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 1.1 Data read\n", + "Read the pandas fata frame from the csv file \"Skyserver_SQL2_27_2018 6_51_39 PM.csv\" and skip the first row.\n", + "\n", + "**Task: read pandas data frame (1 point)**\n", + "\n", + "Save a copy of the data frame just in case." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "_cell_guid": "ffb06ef6-73f7-4f42-ab42-5d5b5f773ba7", + "_uuid": "04e88f8c9c12167a1c23e47b3e2046246510e983" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Description of the data fields**\n", + "\n", + "* objid = Object Identifier, self explanatory.\n", + "* ra = J2000 Right Ascension (r-band). Angular that is measured eastward along the celestial equator from the Sun at the March equinox to the hour circle of the point above the earth in question. https://en.wikipedia.org/wiki/Right_ascension\n", + "* dec = J2000 Declination (r-band). Angle that is measured north or south of the celestial equator, along the hour circle passing through the point in question. https://en.wikipedia.org/wiki/Declination\n", + "\n", + "\n", + "\n", + "The Gunn astronomic magnitude system. u, g, r, i, z represent the response of the 5 bands of the telescope.\n", + "\n", + "Further Information: http://astroweb.case.edu/ssm/ASTR620/mags.html \n", + "* u = better of DeV/Exp magnitude fit\n", + "* g = better of DeV/Exp magnitude fit\n", + "* r = better of DeV/Exp magnitude fit\n", + "* i = better of DeV/Exp magnitude fit\n", + "* z = better of DeV/Exp magnitude fit\n", + "\n", + "\n", + "\n", + "\n", + "Run, rerun, camcol and field are features which describe a field within an image taken by the SDSS. A field is basically a part of the entire image corresponding to 2048 by 1489 pixels. \n", + "* run = Run Number, which identifies the specific scan. \n", + "* rereun = Rerun Number, which specifies how the image was processed.\n", + "* camcol = Camera column, a number from 1 to 6, identifying the scanline within the run.\n", + "* field = Field number, which typically starts at 11 (after an initial rampup time), and can be as large as 800 for particularly long runs.\n", + "* specobjid = Object Identifier\n", + "* class = object class (galaxy, star or quasar object): The class identifies an object to be either a galaxy, star or quasar. This will be the response variable which we will be trying to predict.\n", + "\n", + "* redshift = Final Redshift: In physics, **redshift** happens when light or other electromagnetic radiation from an object is increased in wavelength, or shifted to the red end of the spectrum. \n", + "\n", + "* plate = plate number: Each spectroscopic exposure employs a large, thin, circular metal **plate** that positions optical fibers via holes drilled at the locations of the images in the telescope focal plane. These fibers then feed into the spectrographs. Each plate has a unique serial number, which is called plate in views such as SpecObj in the CAS.\n", + "\n", + "* mjd = MJD of observation, **Modified Julian Date**, used to indicate the date that a given piece of SDSS data (image or spectrum) was taken.\n", + "* fiberid = fiber ID. The SDSS spectrograph uses optical fibers to direct the light at the focal plane from individual objects to the slithead. Each object is assigned a corresponding **fiberID**. \n", + "\n", + "**Further information on SDSS images and their attributes:** \n", + "\n", + "http://www.sdss3.org/dr9/imaging/imaging_basics.php\n", + "\n", + "http://www.sdss3.org/dr8/glossary.php" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "ca3f2be5-d15c-47ef-bc29-9a254ce2199d", + "_uuid": "776947da30d7e7e2b88bd87dca65eaa8b04b158c" + }, + "source": [ + "### 1.2 Data Cleaning \n", + "\n", + "Basic stats about our dataset. \n", + "\n", + "**Task: Provide basic infor for the pandas dataframe head (0.5 point)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "26b460b4-d472-440b-b690-d8c12f267944", + "_uuid": "149d00bab55383b7014daf916df81410717b643a", + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "811c152a-331b-4b69-a7d3-ab52934ef15c", + "_uuid": "7b054433b30b6ea6b1f9024ef323270d86874e76" + }, + "source": [ + "**Task: Find the data types of the database (floats, string, etc etc) using the ``info()`` function (0.5 point).**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "bd29718c-d53e-4b52-a9ee-b8a9a252d864", + "_uuid": "926e1713524a5fcac0353e1d7c03d1f74042f348" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "f890c211-edfb-45d5-be7d-545eb358220b", + "_uuid": "529e41f022c84c223fc508224b63b527677a61fc" + }, + "source": [ + "Are there any obvious feature (or element of the dataframe) that should not impact our prediction?\n", + " \n", + "**objid** and **specobjid** are just identifiers for accessing the rows back when they were stored in the original databank. Therefore we will not need them for classification as they are not related to the outcome.\n", + "The features **run**, **rerun**, **camcol** and **field** are values which describe parts of the camera at the moment when making the observation, e.g. 'run' represents the corresponding scan which captured the oject.\n", + "\n", + "Source: http://www.sdss3.org/dr9/imaging/imaging_basics.php\n", + "\n", + "**Task: Drop these columns in the pandas dataframe. (1 point)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "d1246406-e5f7-4cb7-8365-50c8e44e19d9", + "_uuid": "4d9519ab0d8b9f843219ce89531c3fc13dafc5be" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "75a847f1-0dfc-4228-9cbc-49d6034463e5", + "_uuid": "9b01bc847e158cfa00d411ea687cb573a0037aef" + }, + "source": [ + "Find our how many examples there are, how many attributes or feature, and the type of class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "6723c745-8446-46f0-a866-8c22668607d3", + "_uuid": "77495f8526975b41e2ba43063b82d807e8ba1109", + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "47229348-1a06-4346-949b-1b2b135a20e9", + "_uuid": "d03d6a82a73b4889fc9bfee64e0773acd9b09302" + }, + "source": [ + "**Task: How many objects are in each class? (1 point)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "980baf01-358b-4f40-b1cd-886a8463befe", + "_uuid": "75724e4e2a11a567b6155503ce0671ae3f5e2e7d", + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The classes are \"GALAXY\", \"STAR\", and \"QSO\" (quasars). They are defined as strings, but we will convert them to integer in order to apply a loss function on the class labels during training. For this, we use the ``sklearn.preprocessing.LabelEncoder()`` function. We will do so and modify the classes in the dataframe. We should keep a copy of the original data frame to be safe." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 Data correlations\n", + "Now let's find the most basic correlations among features. This can be done using the ``corr()`` function to apply on the pandas dataframe. Evaluate this function and comment on what feature is correlated among others. It is convenient to use the matplotlib function ``matshow()`` for clarity. ``seaborn`` is a python module that makes really pretty statistical plots https://seaborn.pydata.org/index.html#. Install it with ``pip`` and import it." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Task: Plot the correlation matrix that can be called in the pandas dataframe. (2 points)**\n", + "\n", + "Hints:\n", + "\n", + "Use functions of ``heatmap``, add the labels in the axes. The colormap ``coolwarm`` is nice for divergent scales like correlations that vary between -1 and 1. The argument ``center=0`` ensures that the colormap is divergent from zero. Make sure to ignore the label column \"class\". Remember that dropping a column can be done in place ``sdss_df.drop('class', axis=1)``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Task: Reproduce the same plot for each of the three classes. (1 point)**\n", + "You can select the values from the pandas dataframe by selecting over the column 'class'. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Task: Can you comment on groups of observations that can be grouped together or that appear independent from each other given these correlations, and if there is any difference between the three celestial objects? (**1 point**)**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "6b86afe2-f926-416a-afd9-8b36edf9bbdd", + "_uuid": "bc4bc3db7af9a1e46c21c5fda26e023137e0e239" + }, + "source": [ + "### 1.5 Data exploration\n", + "Given the structure of the correlations, we will explore the values of the data.\n", + "\n", + "#### 1.5.a. Distributions of redshift\n", + "\"redshifting\" happens when the source of light is becoming more distant to the receiver: the object is moving away from Earth. \n", + "\n", + "**Task: plot histograms for the 'redshift' feature column for each class (1 point).**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "f7e55b21-caa1-4b91-8f5a-c465d56cc146", + "_uuid": "5a6a061a52d8e4bb3286be05e8b31c0c656f3dfd", + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "a44d0c69-fb05-4b09-99c3-d92dc1ec0b9a", + "_uuid": "f389689f8d958bdd4e281089036af6d26c1c4d10" + }, + "source": [ + "**Task : Describe briefly the difference between the three histograms. (0.5 point)**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "* **Star:** \n", + "\n", + "* **Galaxy:** \n", + "\n", + "* **QSO:** \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "0cbe1c99-0148-4406-a11d-17d1efa44025", + "_uuid": "7b2155d1a1261d2065a92008cb2a33d425f7b3dd" + }, + "source": [ + "#### 1.5.b. Right ascension (ra) and declination (dec) " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "e4dac296-e9a1-4a63-83fc-46c3ca2265a2", + "_uuid": "4e248ae503d23d0dd3f2473108007ad7b899b47a" + }, + "source": [ + "We will now plot the right ascension versus the declination depending on the class. You can use the ``lmplot`` function in ``seaborn`` (https://seaborn.pydata.org/generated/seaborn.lmplot.html) to represent the sky view of these objects." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "66068c6e-e97b-4e00-8baf-85d67ee6b155", + "_uuid": "6e0aa853466d5c45afec6ac1efd4f9db2a013642" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Task: do you see any obvious differences such that one could easily discriminate between the two coordinates? (0.5 point)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "f3174f32-1e96-47d5-ab74-2a14f1e7b1f7", + "_uuid": "32ac7f6965966d8f25013c5a2e1d2817d9e78cf3" + }, + "source": [ + "#### 1.5.c Filters - u,g,r,i,z " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "27c33528-10f3-400f-a59a-b0c28416e00a", + "_uuid": "4761d9058cb828d6e016fe43693019981ce22596" + }, + "source": [ + "Recall: u, g, r, i, z represent the different wavelengths which are used to capture the observations. According to the correlation matrix, they are correlated for all three classes.\n", + "\n", + "Therefore it is interesting to see that band 'u' is less correlated to the other bands. \n", + "\n", + "**Task Plot histograms and discuss why you expect these features to be correlated (1 points)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "aa0a41b4-c96c-4193-8b86-fee485af4361", + "_uuid": "4e3e3eccee7ba9f0f2bcb5b562bda8b4d3c5a424" + }, + "source": [ + "### 1.6 Data Dimensionality Reduction\n", + "At this point, we are left with 8 features: redshift, u, g, r, i, z, ra, and dec. Among these, the filters (u, g, r, i, z) are correlated to each other. There is therefore a potential for reducing the dimensions of the features using PCA on these 5 features.\n", + "\n", + "We will use the skilearn function ``sklearn.decomposition.PCA()`` to fit and transform the data into the PC coordinates. Lets' first explore how many PCs we need. Fit the PCA function over the total number of filters. You will fit the PCA function over an array with the columns selected from the dataframe. \n", + "\n", + "**Task: Perform the PCA over a max number of PCs, output the explained variance ratio values, decide on an appropriate maximum number of PC to use (6 points)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "fbb45762-272f-40b8-bde6-9d3dd9c1cd55", + "_uuid": "8a97dca248a7b0473c784af669ea00b59017fa8a" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Answer on how many PCs to use*\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now re-perform PCA with the number of PCs you found is most appropriate. Re-apply the fit-transform function. Update the dataframe by adding the PCA value(s) and dropping the columns of the 5 filter features.\n", + "\n", + "**Task: PCA again, fit and transform, update the dataframe with the new feature(s) (3 points)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Unsupervised Clustering with KMeans (20 points)\n", + "\n", + "In this section, we will explore if the data features will be sufficient for classification. As a first exploration, we will perform unsupervised classification with Kmeans clustering.\n", + "\n", + "## 2.1 Perform preliminary Kmeans (10 points)\n", + "\n", + "Implement Kmeans here for a given number of clusters and on the features of interest. Choose 3 features.\n", + "* Use ``sklearn`` to perform Kmeans.\n", + "* Repeat Kmeans and discuss (in a markdown cell) the stability of clustering (e.g., use visualization to qualitatively assess the stability).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.2 Find the optimal number of clusters (5 points)\n", + "\n", + "Use a method to establish the optimal number of clusters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.3 Discuss performance of clustering ( 5 points)\n", + "\n", + "1. Perform silhouette analysis (silhouette visualization and score)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Calculate (python cell) and discuss (net markdown cell) homogeneity with respect to the ground truth labels using 3 appropriate metrics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question:**\n", + "After performing KMeans clustering and calculating the completeness, homogeneity, and Fowlkes-Mallows scores, how can you determine if these scores are good? Compare the obtained scores to the ideal values and explain what each score indicates about the clustering quality. What do you find from your results?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "4e797850-cbb6-44f8-84d8-2649d9dcb461", + "_uuid": "73423e72a3e388e8d6b8f16672a7b77215359cfe" + }, + "source": [ + "## 3 Machine Learning Models (30 points)\n", + "\n", + "We will now train different models on this dataset. We have a total of 8 features, 3 classes, and 10,000 samples. We will use K-Nearest Neighbors, Naive Bayes, Random Forest, Support Vector Machine, Multi Layer Perceptron.\n", + "\n", + "We now follow a normal machine learning workflow:\n", + "* Feature scaling (3) points)\n", + "* Train/test set split (2 points)\n", + "* Model design, training, testing (15 points)\n", + "* Model comparisons, pick your winner, discuss feature importance using Random Forest. (10 points)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "0a8fc626-e05b-483e-bed1-17c5be108903", + "_uuid": "282a92a1d893a96b40b70b1c6d1fae063ba08ba3" + }, + "source": [ + "### 3.1 Feature Scaling\n", + "Scaling all values to be within the (0, 1) interval will reduce the distortion due to exceptionally high values and make some algorithms converge faster. You can scale the features only by dropping the \"class\" column without modifying the dataframe in place, using the pandas function ``drop()``.\n", + "\n", + "**Task: Scale just the features (3 points)**" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "_cell_guid": "23fc8398-331d-4164-8779-0516264ece29", + "_uuid": "c147b9e899cc2dd508d736274c856f88fb49321d" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "13e1fd6f-820c-4098-a486-0079b300e6c9", + "_uuid": "e76e964a4b93c27e1ab01c24a06be09d8b304970" + }, + "source": [ + "### 3.2 Test, train, validation data sets.\n", + "**Task: Split the data into a training and a test part. (2 points)**\n", + "\n", + "The models will be trained on the training data set and tested on the test data set" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "_cell_guid": "a94f2305-4f2a-4d11-b368-528820d28b0b", + "_uuid": "da9a96996b695f8ca2c268f43106e179ba6be1f5" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Computation time is important to account for when scaling up the data set and the model size. You can evaluate the relative computational time using the function ``time.perf_counter()`` to evaluate the absolute time. Then compare the computational time by making the difference between two time stamps:\n", + "\n", + "``t1=time.perf_counter()``\n", + "\n", + "``t2=time.perf_counter()``\n", + "\n", + "``tcomp = t2 - t1``\n", + "\n", + "We will also assess the model performance of these multi-class classifiers. We will evaluate the average of the scores over the 3 class labels." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following, we will be testing over several classifiers. Follow the steps:\n", + "1. model definition/design\n", + "2. training\n", + "3. prediction on test\n", + "4. evaluation: a) print the classification_report; b) save the precision, recall, fscore and accuracy in variables" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "d74dbe6b-ebf8-4b61-b568-c2dabc031e5c", + "_uuid": "3d19792d9ae5a949d2c524fb0c701a4084051816" + }, + "source": [ + "### 3.3.a K Nearest Neighbors (3 points)\n", + "Check out the function arguments and definition here: https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "a16b9525-b3c9-46cf-8714-12707caf2502", + "_uuid": "817a9fd86ca3d5195046fed7a72750538dee3051" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "cd50994d-fa25-4866-8a61-9d316a763a4a", + "_uuid": "e901dbdaed422f26f42d9396a3b7a668c876f401" + }, + "source": [ + "### 3.3.b Naive Bayes (3 points)\n", + "Check out the sklearn tutorial pages here: https://scikit-learn.org/stable/modules/naive_bayes.html#naive-bayes. We propose to use the Gaussian Naive Bayes." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_uuid": "23f16ebfd52f887cb78922a22ddfac00f3228bca" + }, + "source": [ + "Naive Bayes assumes the data to be normally distributed which can be achieved by scaling using the MaxAbsScaler. For this example then we will use the unscaled data, then rescale it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "2db753f8-9832-4b47-9ba8-35db1448602f", + "_uuid": "39aa691fa46edc353cf096543a19fa9d9bc1498f" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "75b6b173-0875-4716-b458-7b741295fcf2", + "_uuid": "d28d0e8f0d89945c4cb989b19d1829a50987ccdc" + }, + "source": [ + "### 3.3.c Random Forest Classifier (2 points)\n", + "Check out the tutorial page here: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "b5d4a318-08c2-4172-8755-a82aa4e0cafc", + "_uuid": "9dc11189c6289f7b3f0bb982bdf0c6b82da3e8d3" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "15b03625-4960-45a9-9751-b8e3ea82cfed", + "_uuid": "ce81bb5c1480e8cf041153ffd04efa02fbf1c6ef" + }, + "source": [ + "### 3.3.d Support Vector Machine Classifier (2 points)\n", + "Check out the sklearn information page here: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "c50ce8dd-8a29-4cd5-80aa-68e13f2f6cea", + "_uuid": "546fec58bfc86bd223f59efeac49abc5041a91c0", + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3.e Multi-Layer Perceptron (3 points)\n", + "\n", + "Check out the information page here: https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.4 Model performance and comparison \n", + "\n", + "### 3.4.a Confusion Matrix and interpretation\n", + "\n", + "**Task: Plot the confusion matrix (2 points)**\n", + "\n", + "Use sklearn ``ConfusionMatrixDisplay\" from the sklearn to visualize the confusion matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Task: Comment on what you see the best classifier is likely to be (1 point).** You can also comment on the misclassification and confusion rates." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "e1018b7a-65f5-46d3-9076-544fea2ea1ee", + "_uuid": "207493115d3d29e59fce048de6e25e82e7adc9fd" + }, + "source": [ + "### 3.4.a K Fold Cross Validation\n", + "We will now perform k fold cross valdiation for the classifiers. We use the function ``cross_val_score`` on each ewstimator, on the training set, with 10 folds, and use accuracy as a score metric.\n", + "\n", + "**Task: perform the cross validation over K folds, output the mean and standard deviation of the accuracy (3 points)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "1c330298-5c44-4c4f-81c6-633082a644d0", + "_uuid": "277ceb9eea218726a798af0f9d1dfce6d56fbb0b" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "80b3da72-6711-42fb-93e3-6670112cd96c", + "_uuid": "0941b7073b9d12b655476b6e44d453c065004478" + }, + "source": [ + "**Task: Which method won the Xval test (1 point) ?**\n", + "\n", + "see the cell below" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "1d7e36a9-414c-4212-a86c-763ca8422b86", + "_uuid": "937ada3280f7e4de9b7cb022c47c0af1908238e6" + }, + "source": [ + "### 3.4.c And the winner is ...\n", + "\n", + "Let's compare the results. \n", + "**Task: Create a pandas dataframe with all of the performance metrics, including the results from K-fold cross validation. (2 points)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "a79815ff-1d64-48ec-996d-32bcbba98250", + "_uuid": "c1fcf42a95967c7d25394c755e5014b027681d23" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "07452e0e-4f68-4f7c-a368-97ed43251eb4", + "_uuid": "d108af400cc3ddb2bae13b870688d7149300c0f6" + }, + "source": [ + "**Task: Comment on the accuracy and performance and choose a winner. (1 point)**\n", + "\n", + "see the cell below" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "9d6084f2-7121-4ef6-8f62-ce434f94f4ea", + "_uuid": "91e2f9ced455f5a8f02a80cb5ff035ba3228f7f4" + }, + "source": [ + "## 4 Summary (4 points)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "8d501e81-e7bf-4212-89b0-7e4dda3afd45", + "_uuid": "8538142325bbaf4c09449126856a8cf0725aeabe" + }, + "source": [ + "### 4.1 Feature Importance using Random Forest Classifier" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "58abedc0-f5ba-497e-8037-4fd6e03e283a", + "_uuid": "d201ab385d30b19c444b79d0d0b7697f9161f888" + }, + "source": [ + "Decision Trees have the unique property of being able to order features by their ability to split between the classes. If some features dominate over other in the predictive power of classes, one can further reduce the dimension of the features for additional analysis. The vector of feature importance is the module ``rfc.feature_importances_``, sorted with ascending importance. Store the vector of importance .\n", + "\n", + "**Task: plot a bar plot using the function ``matplotlib.pyplot.bar``. (2 points)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "1684a815-20b7-47cd-ba41-d326249961e2", + "_uuid": "12d49c95b6b762d0cf43eba7c28818237fa3ae97", + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "c20a7dc7-7226-4970-b480-44150db5ae8e", + "_uuid": "27931b2ee5325ff26b194ce646604116f315996b" + }, + "source": [ + "**Task: What are the top three features (1 point)?**\n", + "\n", + "enter in the cell below" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "93e6bbb6-c774-4ab9-b2a8-f71ee48196ed", + "_uuid": "d1f96b80f16c196f063143c7afc88254474dd4ea" + }, + "source": [ + "In this notebook, you have learned that redshift was the best predictor of what object you are observing. Now, did you actualy need to do this all to find this out? Probably not if you were an astrophysicist! But hey, we are not. So great job!\n", + "\n", + "**Task: Briefly comment on what you have learned (1 point)**\n", + "\n", + "see the cell below" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlgeo", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/book/_toc.yml b/book/_toc.yml index 05a1c00..0a4bca1 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -78,6 +78,7 @@ parts: - file: Chapter3-MachineLearning/3.9_ensemble_learning - file: Chapter3-MachineLearning/3.10_autoML - file: Chapter3-MachineLearning/3.20_final_project_cml + - file: Chapter3-MachineLearning/Homework_CML - caption: Chapter 4 - Deep Learning chapters: