code
stringlengths 1
1.05M
| repo_name
stringlengths 6
83
| path
stringlengths 3
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| language
stringclasses 222
values | license
stringclasses 20
values | size
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|---|---|---|---|---|---|
import buildURL from '../helpers/buildURL'
import buildFullPath from '../core/buildFullPath'
import settle from '../core/settle'
import {isUndefined} from "../utils"
/**
* 返回可选值存在的配置
* @param {Array} keys - 可选值数组
* @param {Object} config2 - 配置
* @return {{}} - 存在的配置项
*/
const mergeKeys = (keys, config2) => {
let config = {}
keys.forEach(prop => {
if (!isUndefined(config2[prop])) {
config[prop] = config2[prop]
}
})
return config
}
export default (config) => {
return new Promise((resolve, reject) => {
let fullPath = buildURL(buildFullPath(config.baseURL, config.url), config.params, config.paramsSerializer)
const _config = {
url: fullPath,
header: config.header,
complete: (response) => {
config.fullPath = fullPath
response.config = config
response.rawData = response.data
try {
let jsonParseHandle = false
const forcedJSONParsingType = typeof config.forcedJSONParsing
if (forcedJSONParsingType === 'boolean') {
jsonParseHandle = config.forcedJSONParsing
} else if (forcedJSONParsingType === 'object') {
const includesMethod = config.forcedJSONParsing.include || []
jsonParseHandle = includesMethod.includes(config.method)
}
// 对可能字符串不是json 的情况容错
if (jsonParseHandle && typeof response.data === 'string') {
response.data = JSON.parse(response.data)
}
// eslint-disable-next-line no-empty
} catch (e) {
}
settle(resolve, reject, response)
}
}
let requestTask
if (config.method === 'UPLOAD') {
delete _config.header['content-type']
delete _config.header['Content-Type']
let otherConfig = {
// #ifdef MP-ALIPAY
fileType: config.fileType,
// #endif
filePath: config.filePath,
name: config.name
}
const optionalKeys = [
// #ifdef APP-PLUS || H5
'files',
// #endif
// #ifdef H5
'file',
// #endif
// #ifdef H5 || APP-PLUS || MP-WEIXIN || MP-ALIPAY || MP-TOUTIAO || MP-KUAISHOU
'timeout',
// #endif
'formData'
]
requestTask = uni.uploadFile({..._config, ...otherConfig, ...mergeKeys(optionalKeys, config)})
} else if (config.method === 'DOWNLOAD') {
const optionalKeys = [
// #ifdef H5 || APP-PLUS || MP-WEIXIN || MP-ALIPAY || MP-TOUTIAO || MP-KUAISHOU
'timeout',
// #endif
// #ifdef MP
'filePath',
// #endif
]
requestTask = uni.downloadFile({..._config, ...mergeKeys(optionalKeys, config)})
} else {
const optionalKeys = [
'data',
'method',
// #ifdef H5 || APP-PLUS || MP-ALIPAY || MP-WEIXIN
'timeout',
// #endif
'dataType',
// #ifndef MP-ALIPAY
'responseType',
// #endif
// #ifdef APP-PLUS
'sslVerify',
// #endif
// #ifdef H5
'withCredentials',
// #endif
// #ifdef APP-PLUS
'firstIpv4',
// #endif
// #ifdef MP-WEIXIN
'enableHttp2',
'enableQuic',
// #endif
// #ifdef MP-TOUTIAO || MP-WEIXIN
'enableCache',
// #endif
// #ifdef MP-WEIXIN
'enableHttpDNS',
'httpDNSServiceId',
'enableChunked',
'forceCellularNetwork',
// #endif
// #ifdef MP-ALIPAY
'enableCookie',
// #endif
// #ifdef MP-BAIDU
'cloudCache',
'defer'
// #endif
]
requestTask = uni.request({..._config, ...mergeKeys(optionalKeys, config)})
}
if (config.getTask) {
config.getTask(requestTask, config)
}
})
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/luch-request/adapters/index.js
|
JavaScript
|
unknown
| 3,884
|
'use strict'
function InterceptorManager() {
this.handlers = []
}
/**
* Add a new interceptor to the stack
*
* @param {Function} fulfilled The function to handle `then` for a `Promise`
* @param {Function} rejected The function to handle `reject` for a `Promise`
*
* @return {Number} An ID used to remove interceptor later
*/
InterceptorManager.prototype.use = function use(fulfilled, rejected) {
this.handlers.push({
fulfilled: fulfilled,
rejected: rejected
})
return this.handlers.length - 1
}
/**
* Remove an interceptor from the stack
*
* @param {Number} id The ID that was returned by `use`
*/
InterceptorManager.prototype.eject = function eject(id) {
if (this.handlers[id]) {
this.handlers[id] = null
}
}
/**
* Iterate over all the registered interceptors
*
* This method is particularly useful for skipping over any
* interceptors that may have become `null` calling `eject`.
*
* @param {Function} fn The function to call for each interceptor
*/
InterceptorManager.prototype.forEach = function forEach(fn) {
this.handlers.forEach(h => {
if (h !== null) {
fn(h)
}
})
}
export default InterceptorManager
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/luch-request/core/InterceptorManager.js
|
JavaScript
|
unknown
| 1,174
|
/**
* @Class Request
* @description luch-request http请求插件
* @Author lu-ch
* @Email webwork.s@qq.com
* 文档: https://www.quanzhan.co/luch-request/
* github: https://github.com/lei-mu/luch-request
* DCloud: http://ext.dcloud.net.cn/plugin?id=392
*/
import dispatchRequest from './dispatchRequest'
import InterceptorManager from './InterceptorManager'
import mergeConfig from './mergeConfig'
import defaults from './defaults'
import { isPlainObject } from '../utils'
import clone from '../utils/clone'
export default class Request {
/**
* @param {Object} arg - 全局配置
* @param {String} arg.baseURL - 全局根路径
* @param {Object} arg.header - 全局header
* @param {String} arg.method = [GET|POST|PUT|DELETE|CONNECT|HEAD|OPTIONS|TRACE] - 全局默认请求方式
* @param {String} arg.dataType = [json] - 全局默认的dataType
* @param {String} arg.responseType = [text|arraybuffer] - 全局默认的responseType。支付宝小程序不支持
* @param {Object} arg.custom - 全局默认的自定义参数
* @param {Number} arg.timeout - 全局默认的超时时间，单位 ms。默认60000。H5(HBuilderX 2.9.9+)、APP(HBuilderX 2.9.9+)、微信小程序（2.10.0）、支付宝小程序
* @param {Boolean} arg.sslVerify - 全局默认的是否验证 ssl 证书。默认true.仅App安卓端支持（HBuilderX 2.3.3+）
* @param {Boolean} arg.withCredentials - 全局默认的跨域请求时是否携带凭证（cookies）。默认false。仅H5支持（HBuilderX 2.6.15+）
* @param {Boolean} arg.firstIpv4 - 全DNS解析时优先使用ipv4。默认false。仅 App-Android 支持 (HBuilderX 2.8.0+)
* @param {Function(statusCode):Boolean} arg.validateStatus - 全局默认的自定义验证器。默认statusCode >= 200 && statusCode < 300
*/
constructor(arg = {}) {
if (!isPlainObject(arg)) {
arg = {}
console.warn('设置全局参数必须接收一个Object')
}
this.config = clone({...defaults, ...arg})
this.interceptors = {
request: new InterceptorManager(),
response: new InterceptorManager()
}
}
/**
* @Function
* @param {Request~setConfigCallback} f - 设置全局默认配置
*/
setConfig(f) {
this.config = f(this.config)
}
middleware(config) {
config = mergeConfig(this.config, config)
let chain = [dispatchRequest, undefined]
let promise = Promise.resolve(config)
this.interceptors.request.forEach(function unshiftRequestInterceptors(interceptor) {
chain.unshift(interceptor.fulfilled, interceptor.rejected)
})
this.interceptors.response.forEach(function pushResponseInterceptors(interceptor) {
chain.push(interceptor.fulfilled, interceptor.rejected)
})
while (chain.length) {
promise = promise.then(chain.shift(), chain.shift())
}
return promise
}
/**
* @Function
* @param {Object} config - 请求配置项
* @prop {String} options.url - 请求路径
* @prop {Object} options.data - 请求参数
* @prop {Object} [options.responseType = config.responseType] [text|arraybuffer] - 响应的数据类型
* @prop {Object} [options.dataType = config.dataType] - 如果设为 json，会尝试对返回的数据做一次 JSON.parse
* @prop {Object} [options.header = config.header] - 请求header
* @prop {Object} [options.method = config.method] - 请求方法
* @returns {Promise<unknown>}
*/
request(config = {}) {
return this.middleware(config)
}
get(url, options = {}) {
return this.middleware({
url,
method: 'GET',
...options
})
}
post(url, data, options = {}) {
return this.middleware({
url,
data,
method: 'POST',
...options
})
}
// #ifndef MP-ALIPAY || MP-KUAISHOU || MP-JD
put(url, data, options = {}) {
return this.middleware({
url,
data,
method: 'PUT',
...options
})
}
// #endif
// #ifdef APP-PLUS || H5 || MP-WEIXIN || MP-BAIDU
delete(url, data, options = {}) {
return this.middleware({
url,
data,
method: 'DELETE',
...options
})
}
// #endif
// #ifdef H5 || MP-WEIXIN
connect(url, data, options = {}) {
return this.middleware({
url,
data,
method: 'CONNECT',
...options
})
}
// #endif
// #ifdef H5 || MP-WEIXIN || MP-BAIDU
head(url, data, options = {}) {
return this.middleware({
url,
data,
method: 'HEAD',
...options
})
}
// #endif
// #ifdef APP-PLUS || H5 || MP-WEIXIN || MP-BAIDU
options(url, data, options = {}) {
return this.middleware({
url,
data,
method: 'OPTIONS',
...options
})
}
// #endif
// #ifdef H5 || MP-WEIXIN
trace(url, data, options = {}) {
return this.middleware({
url,
data,
method: 'TRACE',
...options
})
}
// #endif
upload(url, config = {}) {
config.url = url
config.method = 'UPLOAD'
return this.middleware(config)
}
download(url, config = {}) {
config.url = url
config.method = 'DOWNLOAD'
return this.middleware(config)
}
get version () {
return '3.1.0'
}
}
/**
* setConfig回调
* @return {Object} - 返回操作后的config
* @callback Request~setConfigCallback
* @param {Object} config - 全局默认config
*/
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/luch-request/core/Request.js
|
JavaScript
|
unknown
| 5,371
|
'use strict'
import isAbsoluteURL from '../helpers/isAbsoluteURL'
import combineURLs from '../helpers/combineURLs'
/**
* Creates a new URL by combining the baseURL with the requestedURL,
* only when the requestedURL is not already an absolute URL.
* If the requestURL is absolute, this function returns the requestedURL untouched.
*
* @param {string} baseURL The base URL
* @param {string} requestedURL Absolute or relative URL to combine
* @returns {string} The combined full path
*/
export default function buildFullPath(baseURL, requestedURL) {
if (baseURL && !isAbsoluteURL(requestedURL)) {
return combineURLs(baseURL, requestedURL)
}
return requestedURL
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/luch-request/core/buildFullPath.js
|
JavaScript
|
unknown
| 681
|
/**
* 默认的全局配置
*/
export default {
baseURL: '',
header: {},
method: 'GET',
dataType: 'json',
paramsSerializer: null,
// #ifndef MP-ALIPAY
responseType: 'text',
// #endif
custom: {},
// #ifdef H5 || APP-PLUS || MP-WEIXIN || MP-ALIPAY || MP-TOUTIAO || MP-KUAISHOU
timeout: 60000,
// #endif
// #ifdef APP-PLUS
sslVerify: true,
// #endif
// #ifdef H5
withCredentials: false,
// #endif
// #ifdef APP-PLUS
firstIpv4: false,
// #endif
validateStatus: function validateStatus(status) {
return status >= 200 && status < 300
},
// 是否尝试将响应数据json化
forcedJSONParsing: true
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/luch-request/core/defaults.js
|
JavaScript
|
unknown
| 653
|
import adapter from '../adapters/index'
export default (config) => {
return adapter(config)
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/luch-request/core/dispatchRequest.js
|
JavaScript
|
unknown
| 98
|
import {deepMerge, isUndefined} from '../utils'
/**
* 合并局部配置优先的配置，如果局部有该配置项则用局部，如果全局有该配置项则用全局
* @param {Array} keys - 配置项
* @param {Object} globalsConfig - 当前的全局配置
* @param {Object} config2 - 局部配置
* @return {{}}
*/
const mergeKeys = (keys, globalsConfig, config2) => {
let config = {}
keys.forEach(prop => {
if (!isUndefined(config2[prop])) {
config[prop] = config2[prop]
} else if (!isUndefined(globalsConfig[prop])) {
config[prop] = globalsConfig[prop]
}
})
return config
}
/**
*
* @param globalsConfig - 当前实例的全局配置
* @param config2 - 当前的局部配置
* @return - 合并后的配置
*/
export default (globalsConfig, config2 = {}) => {
const method = config2.method || globalsConfig.method || 'GET'
let config = {
baseURL: config2.baseURL || globalsConfig.baseURL || '',
method: method,
url: config2.url || '',
params: config2.params || {},
custom: {...(globalsConfig.custom || {}), ...(config2.custom || {})},
header: deepMerge(globalsConfig.header || {}, config2.header || {})
}
const defaultToConfig2Keys = ['getTask', 'validateStatus', 'paramsSerializer', 'forcedJSONParsing']
config = {...config, ...mergeKeys(defaultToConfig2Keys, globalsConfig, config2)}
// eslint-disable-next-line no-empty
if (method === 'DOWNLOAD') {
const downloadKeys = [
// #ifdef H5 || APP-PLUS || MP-WEIXIN || MP-ALIPAY || MP-TOUTIAO || MP-KUAISHOU
'timeout',
// #endif
// #ifdef MP
'filePath',
// #endif
]
config = {...config, ...mergeKeys(downloadKeys, globalsConfig, config2)}
} else if (method === 'UPLOAD') {
delete config.header['content-type']
delete config.header['Content-Type']
const uploadKeys = [
// #ifdef APP-PLUS || H5
'files',
// #endif
// #ifdef MP-ALIPAY
'fileType',
// #endif
// #ifdef H5
'file',
// #endif
'filePath',
'name',
// #ifdef H5 || APP-PLUS || MP-WEIXIN || MP-ALIPAY || MP-TOUTIAO || MP-KUAISHOU
'timeout',
// #endif
'formData',
]
uploadKeys.forEach(prop => {
if (!isUndefined(config2[prop])) {
config[prop] = config2[prop]
}
})
// #ifdef H5 || APP-PLUS || MP-WEIXIN || MP-ALIPAY || MP-TOUTIAO || MP-KUAISHOU
if (isUndefined(config.timeout) && !isUndefined(globalsConfig.timeout)) {
config['timeout'] = globalsConfig['timeout']
}
// #endif
} else {
const defaultsKeys = [
'data',
// #ifdef H5 || APP-PLUS || MP-ALIPAY || MP-WEIXIN
'timeout',
// #endif
'dataType',
// #ifndef MP-ALIPAY
'responseType',
// #endif
// #ifdef APP-PLUS
'sslVerify',
// #endif
// #ifdef H5
'withCredentials',
// #endif
// #ifdef APP-PLUS
'firstIpv4',
// #endif
// #ifdef MP-WEIXIN
'enableHttp2',
'enableQuic',
// #endif
// #ifdef MP-TOUTIAO || MP-WEIXIN
'enableCache',
// #endif
// #ifdef MP-WEIXIN
'enableHttpDNS',
'httpDNSServiceId',
'enableChunked',
'forceCellularNetwork',
// #endif
// #ifdef MP-ALIPAY
'enableCookie',
// #endif
// #ifdef MP-BAIDU
'cloudCache',
'defer'
// #endif
]
config = {...config, ...mergeKeys(defaultsKeys, globalsConfig, config2)}
}
return config
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/luch-request/core/mergeConfig.js
|
JavaScript
|
unknown
| 3,526
|
/**
* Resolve or reject a Promise based on response status.
*
* @param {Function} resolve A function that resolves the promise.
* @param {Function} reject A function that rejects the promise.
* @param {object} response The response.
*/
export default function settle(resolve, reject, response) {
const validateStatus = response.config.validateStatus
const status = response.statusCode
if (status && (!validateStatus || validateStatus(status))) {
resolve(response)
} else {
reject(response)
}
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/luch-request/core/settle.js
|
JavaScript
|
unknown
| 518
|
'use strict'
import * as utils from './../utils'
function encode(val) {
return encodeURIComponent(val).replace(/%40/gi, '@').replace(/%3A/gi, ':').replace(/%24/g, '$').replace(/%2C/gi, ',').replace(/%20/g, '+').replace(/%5B/gi, '[').replace(/%5D/gi, ']')
}
/**
* Build a URL by appending params to the end
*
* @param {string} url The base of the url (e.g., http://www.google.com)
* @param {object} [params] The params to be appended
* @returns {string} The formatted url
*/
export default function buildURL(url, params, paramsSerializer) {
/*eslint no-param-reassign:0*/
if (!params) {
return url
}
var serializedParams
if (paramsSerializer) {
serializedParams = paramsSerializer(params)
} else if (utils.isURLSearchParams(params)) {
serializedParams = params.toString()
} else {
var parts = []
utils.forEach(params, function serialize(val, key) {
if (val === null || typeof val === 'undefined') {
return
}
if (utils.isArray(val)) {
key = key + '[]'
} else {
val = [val]
}
utils.forEach(val, function parseValue(v) {
if (utils.isDate(v)) {
v = v.toISOString()
} else if (utils.isObject(v)) {
v = JSON.stringify(v)
}
parts.push(encode(key) + '=' + encode(v))
})
})
serializedParams = parts.join('&')
}
if (serializedParams) {
var hashmarkIndex = url.indexOf('#')
if (hashmarkIndex !== -1) {
url = url.slice(0, hashmarkIndex)
}
url += (url.indexOf('?') === -1 ? '?' : '&') + serializedParams
}
return url
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/luch-request/helpers/buildURL.js
|
JavaScript
|
unknown
| 1,614
|
'use strict'
/**
* Creates a new URL by combining the specified URLs
*
* @param {string} baseURL The base URL
* @param {string} relativeURL The relative URL
* @returns {string} The combined URL
*/
export default function combineURLs(baseURL, relativeURL) {
return relativeURL
? baseURL.replace(/\/+$/, '') + '/' + relativeURL.replace(/^\/+/, '')
: baseURL
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/luch-request/helpers/combineURLs.js
|
JavaScript
|
unknown
| 375
|
'use strict'
/**
* Determines whether the specified URL is absolute
*
* @param {string} url The URL to test
* @returns {boolean} True if the specified URL is absolute, otherwise false
*/
export default function isAbsoluteURL(url) {
// A URL is considered absolute if it begins with "<scheme>://" or "//" (protocol-relative URL).
// RFC 3986 defines scheme name as a sequence of characters beginning with a letter and followed
// by any combination of letters, digits, plus, period, or hyphen.
return /^([a-z][a-z\d+\-.]*:)?\/\//i.test(url)
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/luch-request/helpers/isAbsoluteURL.js
|
JavaScript
|
unknown
| 556
|
import Request from './core/Request'
export default Request
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/luch-request/index.js
|
JavaScript
|
unknown
| 60
|
/* eslint-disable */
var clone = (function() {
'use strict';
function _instanceof(obj, type) {
return type != null && obj instanceof type;
}
var nativeMap;
try {
nativeMap = Map;
} catch(_) {
// maybe a reference error because no `Map`. Give it a dummy value that no
// value will ever be an instanceof.
nativeMap = function() {};
}
var nativeSet;
try {
nativeSet = Set;
} catch(_) {
nativeSet = function() {};
}
var nativePromise;
try {
nativePromise = Promise;
} catch(_) {
nativePromise = function() {};
}
/**
* Clones (copies) an Object using deep copying.
*
* This function supports circular references by default, but if you are certain
* there are no circular references in your object, you can save some CPU time
* by calling clone(obj, false).
*
* Caution: if `circular` is false and `parent` contains circular references,
* your program may enter an infinite loop and crash.
*
* @param `parent` - the object to be cloned
* @param `circular` - set to true if the object to be cloned may contain
* circular references. (optional - true by default)
* @param `depth` - set to a number if the object is only to be cloned to
* a particular depth. (optional - defaults to Infinity)
* @param `prototype` - sets the prototype to be used when cloning an object.
* (optional - defaults to parent prototype).
* @param `includeNonEnumerable` - set to true if the non-enumerable properties
* should be cloned as well. Non-enumerable properties on the prototype
* chain will be ignored. (optional - false by default)
*/
function clone(parent, circular, depth, prototype, includeNonEnumerable) {
if (typeof circular === 'object') {
depth = circular.depth;
prototype = circular.prototype;
includeNonEnumerable = circular.includeNonEnumerable;
circular = circular.circular;
}
// maintain two arrays for circular references, where corresponding parents
// and children have the same index
var allParents = [];
var allChildren = [];
var useBuffer = typeof Buffer != 'undefined';
if (typeof circular == 'undefined')
circular = true;
if (typeof depth == 'undefined')
depth = Infinity;
// recurse this function so we don't reset allParents and allChildren
function _clone(parent, depth) {
// cloning null always returns null
if (parent === null)
return null;
if (depth === 0)
return parent;
var child;
var proto;
if (typeof parent != 'object') {
return parent;
}
if (_instanceof(parent, nativeMap)) {
child = new nativeMap();
} else if (_instanceof(parent, nativeSet)) {
child = new nativeSet();
} else if (_instanceof(parent, nativePromise)) {
child = new nativePromise(function (resolve, reject) {
parent.then(function(value) {
resolve(_clone(value, depth - 1));
}, function(err) {
reject(_clone(err, depth - 1));
});
});
} else if (clone.__isArray(parent)) {
child = [];
} else if (clone.__isRegExp(parent)) {
child = new RegExp(parent.source, __getRegExpFlags(parent));
if (parent.lastIndex) child.lastIndex = parent.lastIndex;
} else if (clone.__isDate(parent)) {
child = new Date(parent.getTime());
} else if (useBuffer && Buffer.isBuffer(parent)) {
if (Buffer.from) {
// Node.js >= 5.10.0
child = Buffer.from(parent);
} else {
// Older Node.js versions
child = new Buffer(parent.length);
parent.copy(child);
}
return child;
} else if (_instanceof(parent, Error)) {
child = Object.create(parent);
} else {
if (typeof prototype == 'undefined') {
proto = Object.getPrototypeOf(parent);
child = Object.create(proto);
}
else {
child = Object.create(prototype);
proto = prototype;
}
}
if (circular) {
var index = allParents.indexOf(parent);
if (index != -1) {
return allChildren[index];
}
allParents.push(parent);
allChildren.push(child);
}
if (_instanceof(parent, nativeMap)) {
parent.forEach(function(value, key) {
var keyChild = _clone(key, depth - 1);
var valueChild = _clone(value, depth - 1);
child.set(keyChild, valueChild);
});
}
if (_instanceof(parent, nativeSet)) {
parent.forEach(function(value) {
var entryChild = _clone(value, depth - 1);
child.add(entryChild);
});
}
for (var i in parent) {
var attrs = Object.getOwnPropertyDescriptor(parent, i);
if (attrs) {
child[i] = _clone(parent[i], depth - 1);
}
try {
var objProperty = Object.getOwnPropertyDescriptor(parent, i);
if (objProperty.set === 'undefined') {
// no setter defined. Skip cloning this property
continue;
}
child[i] = _clone(parent[i], depth - 1);
} catch(e){
if (e instanceof TypeError) {
// when in strict mode, TypeError will be thrown if child[i] property only has a getter
// we can't do anything about this, other than inform the user that this property cannot be set.
continue
} else if (e instanceof ReferenceError) {
//this may happen in non strict mode
continue
}
}
}
if (Object.getOwnPropertySymbols) {
var symbols = Object.getOwnPropertySymbols(parent);
for (var i = 0; i < symbols.length; i++) {
// Don't need to worry about cloning a symbol because it is a primitive,
// like a number or string.
var symbol = symbols[i];
var descriptor = Object.getOwnPropertyDescriptor(parent, symbol);
if (descriptor && !descriptor.enumerable && !includeNonEnumerable) {
continue;
}
child[symbol] = _clone(parent[symbol], depth - 1);
Object.defineProperty(child, symbol, descriptor);
}
}
if (includeNonEnumerable) {
var allPropertyNames = Object.getOwnPropertyNames(parent);
for (var i = 0; i < allPropertyNames.length; i++) {
var propertyName = allPropertyNames[i];
var descriptor = Object.getOwnPropertyDescriptor(parent, propertyName);
if (descriptor && descriptor.enumerable) {
continue;
}
child[propertyName] = _clone(parent[propertyName], depth - 1);
Object.defineProperty(child, propertyName, descriptor);
}
}
return child;
}
return _clone(parent, depth);
}
/**
* Simple flat clone using prototype, accepts only objects, usefull for property
* override on FLAT configuration object (no nested props).
*
* USE WITH CAUTION! This may not behave as you wish if you do not know how this
* works.
*/
clone.clonePrototype = function clonePrototype(parent) {
if (parent === null)
return null;
var c = function () {};
c.prototype = parent;
return new c();
};
// private utility functions
function __objToStr(o) {
return Object.prototype.toString.call(o);
}
clone.__objToStr = __objToStr;
function __isDate(o) {
return typeof o === 'object' && __objToStr(o) === '[object Date]';
}
clone.__isDate = __isDate;
function __isArray(o) {
return typeof o === 'object' && __objToStr(o) === '[object Array]';
}
clone.__isArray = __isArray;
function __isRegExp(o) {
return typeof o === 'object' && __objToStr(o) === '[object RegExp]';
}
clone.__isRegExp = __isRegExp;
function __getRegExpFlags(re) {
var flags = '';
if (re.global) flags += 'g';
if (re.ignoreCase) flags += 'i';
if (re.multiline) flags += 'm';
return flags;
}
clone.__getRegExpFlags = __getRegExpFlags;
return clone;
})();
export default clone
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/luch-request/utils/clone.js
|
JavaScript
|
unknown
| 8,184
|
'use strict'
// utils is a library of generic helper functions non-specific to axios
var toString = Object.prototype.toString
/**
* Determine if a value is an Array
*
* @param {Object} val The value to test
* @returns {boolean} True if value is an Array, otherwise false
*/
export function isArray (val) {
return toString.call(val) === '[object Array]'
}
/**
* Determine if a value is an Object
*
* @param {Object} val The value to test
* @returns {boolean} True if value is an Object, otherwise false
*/
export function isObject (val) {
return val !== null && typeof val === 'object'
}
/**
* Determine if a value is a Date
*
* @param {Object} val The value to test
* @returns {boolean} True if value is a Date, otherwise false
*/
export function isDate (val) {
return toString.call(val) === '[object Date]'
}
/**
* Determine if a value is a URLSearchParams object
*
* @param {Object} val The value to test
* @returns {boolean} True if value is a URLSearchParams object, otherwise false
*/
export function isURLSearchParams (val) {
return typeof URLSearchParams !== 'undefined' && val instanceof URLSearchParams
}
/**
* Iterate over an Array or an Object invoking a function for each item.
*
* If `obj` is an Array callback will be called passing
* the value, index, and complete array for each item.
*
* If 'obj' is an Object callback will be called passing
* the value, key, and complete object for each property.
*
* @param {Object|Array} obj The object to iterate
* @param {Function} fn The callback to invoke for each item
*/
export function forEach (obj, fn) {
// Don't bother if no value provided
if (obj === null || typeof obj === 'undefined') {
return
}
// Force an array if not already something iterable
if (typeof obj !== 'object') {
/*eslint no-param-reassign:0*/
obj = [obj]
}
if (isArray(obj)) {
// Iterate over array values
for (var i = 0, l = obj.length; i < l; i++) {
fn.call(null, obj[i], i, obj)
}
} else {
// Iterate over object keys
for (var key in obj) {
if (Object.prototype.hasOwnProperty.call(obj, key)) {
fn.call(null, obj[key], key, obj)
}
}
}
}
/**
* 是否为boolean 值
* @param val
* @returns {boolean}
*/
export function isBoolean(val) {
return typeof val === 'boolean'
}
/**
* 是否为真正的对象{} new Object
* @param {any} obj - 检测的对象
* @returns {boolean}
*/
export function isPlainObject(obj) {
return Object.prototype.toString.call(obj) === '[object Object]'
}
/**
* Function equal to merge with the difference being that no reference
* to original objects is kept.
*
* @see merge
* @param {Object} obj1 Object to merge
* @returns {Object} Result of all merge properties
*/
export function deepMerge(/* obj1, obj2, obj3, ... */) {
let result = {}
function assignValue(val, key) {
if (typeof result[key] === 'object' && typeof val === 'object') {
result[key] = deepMerge(result[key], val)
} else if (typeof val === 'object') {
result[key] = deepMerge({}, val)
} else {
result[key] = val
}
}
for (let i = 0, l = arguments.length; i < l; i++) {
forEach(arguments[i], assignValue)
}
return result
}
export function isUndefined (val) {
return typeof val === 'undefined'
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/luch-request/utils.js
|
JavaScript
|
unknown
| 3,327
|
export default {
props: {
lang: String,
sessionFrom: String,
sendMessageTitle: String,
sendMessagePath: String,
sendMessageImg: String,
showMessageCard: Boolean,
appParameter: String,
formType: String,
openType: String
}
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/mixin/button.js
|
JavaScript
|
unknown
| 303
|
import * as index from '../function/index.js';
import * as test from '../function/test.js';
import route from '../util/route.js';
import debounce from '../function/debounce.js';
import throttle from '../function/throttle.js';
export default {
// 定义每个组件都可能需要用到的外部样式以及类名
props: {
// 每个组件都有的父组件传递的样式，可以为字符串或者对象形式
customStyle: {
type: [Object, String],
default: () => ({})
},
customClass: {
type: String,
default: ''
},
// 跳转的页面路径
url: {
type: String,
default: ''
},
// 页面跳转的类型
linkType: {
type: String,
default: 'navigateTo'
}
},
data() {
return {}
},
onLoad() {
// getRect挂载到$uv上，因为这方法需要使用in(this)，所以无法把它独立成一个单独的文件导出
this.$uv.getRect = this.$uvGetRect
},
created() {
// 组件当中，只有created声明周期，为了能在组件使用，故也在created中将方法挂载到$uv
this.$uv.getRect = this.$uvGetRect
},
computed: {
$uv() {
return {
...index,
test,
route,
debounce,
throttle,
unit: uni?.$uv?.config?.unit
}
},
/**
* 生成bem规则类名
* 由于微信小程序，H5，nvue之间绑定class的差异，无法通过:class="[bem()]"的形式进行同用
* 故采用如下折中做法，最后返回的是数组（一般平台）或字符串（支付宝和字节跳动平台），类似['a', 'b', 'c']或'a b c'的形式
* @param {String} name 组件名称
* @param {Array} fixed 一直会存在的类名
* @param {Array} change 会根据变量值为true或者false而出现或者隐藏的类名
* @returns {Array|string}
*/
bem() {
return function(name, fixed, change) {
// 类名前缀
const prefix = `uv-${name}--`
const classes = {}
if (fixed) {
fixed.map((item) => {
// 这里的类名，会一直存在
classes[prefix + this[item]] = true
})
}
if (change) {
change.map((item) => {
// 这里的类名，会根据this[item]的值为true或者false，而进行添加或者移除某一个类
this[item] ? (classes[prefix + item] = this[item]) : (delete classes[prefix + item])
})
}
return Object.keys(classes)
// 支付宝，头条小程序无法动态绑定一个数组类名，否则解析出来的结果会带有","，而导致失效
// #ifdef MP-ALIPAY || MP-TOUTIAO || MP-LARK || MP-BAIDU
.join(' ')
// #endif
}
}
},
methods: {
// 跳转某一个页面
openPage(urlKey = 'url') {
const url = this[urlKey]
if (url) {
// 执行类似uni.navigateTo的方法
uni[this.linkType]({
url
})
}
},
// 查询节点信息
// 目前此方法在支付宝小程序中无法获取组件跟接点的尺寸，为支付宝的bug(2020-07-21)
// 解决办法为在组件根部再套一个没有任何作用的view元素
$uvGetRect(selector, all) {
return new Promise((resolve) => {
uni.createSelectorQuery()
.in(this)[all ? 'selectAll' : 'select'](selector)
.boundingClientRect((rect) => {
if (all && Array.isArray(rect) && rect.length) {
resolve(rect)
}
if (!all && rect) {
resolve(rect)
}
})
.exec()
})
},
getParentData(parentName = '') {
// 避免在created中去定义parent变量
if (!this.parent) this.parent = {}
// 这里的本质原理是，通过获取父组件实例(也即类似uv-radio的父组件uv-radio-group的this)
// 将父组件this中对应的参数，赋值给本组件(uv-radio的this)的parentData对象中对应的属性
// 之所以需要这么做，是因为所有端中，头条小程序不支持通过this.parent.xxx去监听父组件参数的变化
// 此处并不会自动更新子组件的数据，而是依赖父组件uv-radio-group去监听data的变化，手动调用更新子组件的方法去重新获取
this.parent = this.$uv.$parent.call(this, parentName)
if (this.parent.children) {
// 如果父组件的children不存在本组件的实例，才将本实例添加到父组件的children中
this.parent.children.indexOf(this) === -1 && this.parent.children.push(this)
}
if (this.parent && this.parentData) {
// 历遍parentData中的属性，将parent中的同名属性赋值给parentData
Object.keys(this.parentData).map((key) => {
this.parentData[key] = this.parent[key]
})
}
},
// 阻止事件冒泡
preventEvent(e) {
e && typeof(e.stopPropagation) === 'function' && e.stopPropagation()
},
// 空操作
noop(e) {
this.preventEvent(e)
}
},
onReachBottom() {
uni.$emit('uvOnReachBottom')
},
beforeDestroy() {
// 判断当前页面是否存在parent和chldren，一般在checkbox和checkbox-group父子联动的场景会有此情况
// 组件销毁时，移除子组件在父组件children数组中的实例，释放资源，避免数据混乱
if (this.parent && test.array(this.parent.children)) {
// 组件销毁时，移除父组件中的children数组中对应的实例
const childrenList = this.parent.children
childrenList.map((child, index) => {
// 如果相等，则移除
if (child === this) {
childrenList.splice(index, 1)
}
})
}
},
// 兼容vue3
unmounted() {
if (this.parent && test.array(this.parent.children)) {
// 组件销毁时，移除父组件中的children数组中对应的实例
const childrenList = this.parent.children
childrenList.map((child, index) => {
// 如果相等，则移除
if (child === this) {
childrenList.splice(index, 1)
}
})
}
}
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/mixin/mixin.js
|
JavaScript
|
unknown
| 5,703
|
export default {
// #ifdef MP-WEIXIN
// 将自定义节点设置成虚拟的（去掉自定义组件包裹层），更加接近Vue组件的表现，能更好的使用flex属性
options: {
virtualHost: true
}
// #endif
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/mixin/mpMixin.js
|
JavaScript
|
unknown
| 247
|
export default {
onLoad() {
// 设置默认的转发参数
uni.$uv.mpShare = {
title: '', // 默认为小程序名称
path: '', // 默认为当前页面路径
imageUrl: '' // 默认为当前页面的截图
}
},
onShareAppMessage() {
return uni.$uv.mpShare
}
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/mixin/mpShare.js
|
JavaScript
|
unknown
| 310
|
export default {
props: {
openType: String
},
emits: ['getphonenumber','getuserinfo','error','opensetting','launchapp','contact','chooseavatar','addgroupapp','chooseaddress','subscribe','login','im'],
methods: {
onGetPhoneNumber(event) {
this.$emit('getphonenumber', event.detail)
},
onGetUserInfo(event) {
this.$emit('getuserinfo', event.detail)
},
onError(event) {
this.$emit('error', event.detail)
},
onOpenSetting(event) {
this.$emit('opensetting', event.detail)
},
onLaunchApp(event) {
this.$emit('launchapp', event.detail)
},
onContact(event) {
this.$emit('contact', event.detail)
},
onChooseavatar(event) {
this.$emit('chooseavatar', event.detail)
},
onAgreeprivacyauthorization(event) {
this.$emit('agreeprivacyauthorization', event.detail)
},
onAddgroupapp(event) {
this.$emit('addgroupapp', event.detail)
},
onChooseaddress(event) {
this.$emit('chooseaddress', event.detail)
},
onSubscribe(event) {
this.$emit('subscribe', event.detail)
},
onLogin(event) {
this.$emit('login', event.detail)
},
onIm(event) {
this.$emit('im', event.detail)
}
}
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/mixin/openType.js
|
JavaScript
|
unknown
| 1,445
|
const MIN_DISTANCE = 10
function getDirection(x, y) {
if (x > y && x > MIN_DISTANCE) {
return 'horizontal'
}
if (y > x && y > MIN_DISTANCE) {
return 'vertical'
}
return ''
}
export default {
methods: {
getTouchPoint(e) {
if (!e) {
return {
x: 0,
y: 0
}
} if (e.touches && e.touches[0]) {
return {
x: e.touches[0].pageX,
y: e.touches[0].pageY
}
} if (e.changedTouches && e.changedTouches[0]) {
return {
x: e.changedTouches[0].pageX,
y: e.changedTouches[0].pageY
}
}
return {
x: e.clientX || 0,
y: e.clientY || 0
}
},
resetTouchStatus() {
this.direction = ''
this.deltaX = 0
this.deltaY = 0
this.offsetX = 0
this.offsetY = 0
},
touchStart(event) {
this.resetTouchStatus()
const touch = this.getTouchPoint(event)
this.startX = touch.x
this.startY = touch.y
},
touchMove(event) {
const touch = this.getTouchPoint(event)
this.deltaX = touch.x - this.startX
this.deltaY = touch.y - this.startY
this.offsetX = Math.abs(this.deltaX)
this.offsetY = Math.abs(this.deltaY)
this.direction = this.direction || getDirection(this.offsetX, this.offsetY)
}
}
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/mixin/touch.js
|
JavaScript
|
unknown
| 1,657
|
var __getOwnPropNames = Object.getOwnPropertyNames;
var __commonJS = (cb, mod) => function __require() {
return mod || (0, cb[__getOwnPropNames(cb)[0]])((mod = { exports: {} }).exports, mod), mod.exports;
};
var require_dayjs_min = __commonJS({
"uvuidayjs"(exports, module) {
!function(t, e) {
"object" == typeof exports && "undefined" != typeof module ? module.exports = e() : "function" == typeof define && define.amd ? define(e) : (t = "undefined" != typeof globalThis ? globalThis : t || self).dayjs = e();
}(exports, function() {
"use strict";
var t = 1e3, e = 6e4, n = 36e5, r = "millisecond", i = "second", s = "minute", u = "hour", a = "day", o = "week", f = "month", h = "quarter", c = "year", d = "date", l = "Invalid Date", $ = /^(\d{4})[-/]?(\d{1,2})?[-/]?(\d{0,2})[Tt\s]*(\d{1,2})?:?(\d{1,2})?:?(\d{1,2})?[.:]?(\d+)?$/, y = /\[([^\]]+)]|Y{1,4}|M{1,4}|D{1,2}|d{1,4}|H{1,2}|h{1,2}|a|A|m{1,2}|s{1,2}|Z{1,2}|SSS/g, M = { name: "en", weekdays: "Sunday_Monday_Tuesday_Wednesday_Thursday_Friday_Saturday".split("_"), months: "January_February_March_April_May_June_July_August_September_October_November_December".split("_"), ordinal: function(t2) {
var e2 = ["th", "st", "nd", "rd"], n2 = t2 % 100;
return "[" + t2 + (e2[(n2 - 20) % 10] || e2[n2] || e2[0]) + "]";
} }, m = function(t2, e2, n2) {
var r2 = String(t2);
return !r2 || r2.length >= e2 ? t2 : "" + Array(e2 + 1 - r2.length).join(n2) + t2;
}, v = { s: m, z: function(t2) {
var e2 = -t2.utcOffset(), n2 = Math.abs(e2), r2 = Math.floor(n2 / 60), i2 = n2 % 60;
return (e2 <= 0 ? "+" : "-") + m(r2, 2, "0") + ":" + m(i2, 2, "0");
}, m: function t2(e2, n2) {
if (e2.date() < n2.date())
return -t2(n2, e2);
var r2 = 12 * (n2.year() - e2.year()) + (n2.month() - e2.month()), i2 = e2.clone().add(r2, f), s2 = n2 - i2 < 0, u2 = e2.clone().add(r2 + (s2 ? -1 : 1), f);
return +(-(r2 + (n2 - i2) / (s2 ? i2 - u2 : u2 - i2)) || 0);
}, a: function(t2) {
return t2 < 0 ? Math.ceil(t2) || 0 : Math.floor(t2);
}, p: function(t2) {
return { M: f, y: c, w: o, d: a, D: d, h: u, m: s, s: i, ms: r, Q: h }[t2] || String(t2 || "").toLowerCase().replace(/s$/, "");
}, u: function(t2) {
return void 0 === t2;
} }, g = "en", D = {};
D[g] = M;
var p = function(t2) {
return t2 instanceof _;
}, S = function t2(e2, n2, r2) {
var i2;
if (!e2)
return g;
if ("string" == typeof e2) {
var s2 = e2.toLowerCase();
D[s2] && (i2 = s2), n2 && (D[s2] = n2, i2 = s2);
var u2 = e2.split("-");
if (!i2 && u2.length > 1)
return t2(u2[0]);
} else {
var a2 = e2.name;
D[a2] = e2, i2 = a2;
}
return !r2 && i2 && (g = i2), i2 || !r2 && g;
}, w = function(t2, e2) {
if (p(t2))
return t2.clone();
var n2 = "object" == typeof e2 ? e2 : {};
return n2.date = t2, n2.args = arguments, new _(n2);
}, O = v;
O.l = S, O.i = p, O.w = function(t2, e2) {
return w(t2, { locale: e2.$L, utc: e2.$u, x: e2.$x, $offset: e2.$offset });
};
var _ = function() {
function M2(t2) {
this.$L = S(t2.locale, null, true), this.parse(t2);
}
var m2 = M2.prototype;
return m2.parse = function(t2) {
this.$d = function(t3) {
var e2 = t3.date, n2 = t3.utc;
if (null === e2)
return new Date(NaN);
if (O.u(e2))
return new Date();
if (e2 instanceof Date)
return new Date(e2);
if ("string" == typeof e2 && !/Z$/i.test(e2)) {
var r2 = e2.match($);
if (r2) {
var i2 = r2[2] - 1 || 0, s2 = (r2[7] || "0").substring(0, 3);
return n2 ? new Date(Date.UTC(r2[1], i2, r2[3] || 1, r2[4] || 0, r2[5] || 0, r2[6] || 0, s2)) : new Date(r2[1], i2, r2[3] || 1, r2[4] || 0, r2[5] || 0, r2[6] || 0, s2);
}
}
return new Date(e2);
}(t2), this.$x = t2.x || {}, this.init();
}, m2.init = function() {
var t2 = this.$d;
this.$y = t2.getFullYear(), this.$M = t2.getMonth(), this.$D = t2.getDate(), this.$W = t2.getDay(), this.$H = t2.getHours(), this.$m = t2.getMinutes(), this.$s = t2.getSeconds(), this.$ms = t2.getMilliseconds();
}, m2.$utils = function() {
return O;
}, m2.isValid = function() {
return !(this.$d.toString() === l);
}, m2.isSame = function(t2, e2) {
var n2 = w(t2);
return this.startOf(e2) <= n2 && n2 <= this.endOf(e2);
}, m2.isAfter = function(t2, e2) {
return w(t2) < this.startOf(e2);
}, m2.isBefore = function(t2, e2) {
return this.endOf(e2) < w(t2);
}, m2.$g = function(t2, e2, n2) {
return O.u(t2) ? this[e2] : this.set(n2, t2);
}, m2.unix = function() {
return Math.floor(this.valueOf() / 1e3);
}, m2.valueOf = function() {
return this.$d.getTime();
}, m2.startOf = function(t2, e2) {
var n2 = this, r2 = !!O.u(e2) || e2, h2 = O.p(t2), l2 = function(t3, e3) {
var i2 = O.w(n2.$u ? Date.UTC(n2.$y, e3, t3) : new Date(n2.$y, e3, t3), n2);
return r2 ? i2 : i2.endOf(a);
}, $2 = function(t3, e3) {
return O.w(n2.toDate()[t3].apply(n2.toDate("s"), (r2 ? [0, 0, 0, 0] : [23, 59, 59, 999]).slice(e3)), n2);
}, y2 = this.$W, M3 = this.$M, m3 = this.$D, v2 = "set" + (this.$u ? "UTC" : "");
switch (h2) {
case c:
return r2 ? l2(1, 0) : l2(31, 11);
case f:
return r2 ? l2(1, M3) : l2(0, M3 + 1);
case o:
var g2 = this.$locale().weekStart || 0, D2 = (y2 < g2 ? y2 + 7 : y2) - g2;
return l2(r2 ? m3 - D2 : m3 + (6 - D2), M3);
case a:
case d:
return $2(v2 + "Hours", 0);
case u:
return $2(v2 + "Minutes", 1);
case s:
return $2(v2 + "Seconds", 2);
case i:
return $2(v2 + "Milliseconds", 3);
default:
return this.clone();
}
}, m2.endOf = function(t2) {
return this.startOf(t2, false);
}, m2.$set = function(t2, e2) {
var n2, o2 = O.p(t2), h2 = "set" + (this.$u ? "UTC" : ""), l2 = (n2 = {}, n2[a] = h2 + "Date", n2[d] = h2 + "Date", n2[f] = h2 + "Month", n2[c] = h2 + "FullYear", n2[u] = h2 + "Hours", n2[s] = h2 + "Minutes", n2[i] = h2 + "Seconds", n2[r] = h2 + "Milliseconds", n2)[o2], $2 = o2 === a ? this.$D + (e2 - this.$W) : e2;
if (o2 === f || o2 === c) {
var y2 = this.clone().set(d, 1);
y2.$d[l2]($2), y2.init(), this.$d = y2.set(d, Math.min(this.$D, y2.daysInMonth())).$d;
} else
l2 && this.$d[l2]($2);
return this.init(), this;
}, m2.set = function(t2, e2) {
return this.clone().$set(t2, e2);
}, m2.get = function(t2) {
return this[O.p(t2)]();
}, m2.add = function(r2, h2) {
var d2, l2 = this;
r2 = Number(r2);
var $2 = O.p(h2), y2 = function(t2) {
var e2 = w(l2);
return O.w(e2.date(e2.date() + Math.round(t2 * r2)), l2);
};
if ($2 === f)
return this.set(f, this.$M + r2);
if ($2 === c)
return this.set(c, this.$y + r2);
if ($2 === a)
return y2(1);
if ($2 === o)
return y2(7);
var M3 = (d2 = {}, d2[s] = e, d2[u] = n, d2[i] = t, d2)[$2] || 1, m3 = this.$d.getTime() + r2 * M3;
return O.w(m3, this);
}, m2.subtract = function(t2, e2) {
return this.add(-1 * t2, e2);
}, m2.format = function(t2) {
var e2 = this, n2 = this.$locale();
if (!this.isValid())
return n2.invalidDate || l;
var r2 = t2 || "YYYY-MM-DDTHH:mm:ssZ", i2 = O.z(this), s2 = this.$H, u2 = this.$m, a2 = this.$M, o2 = n2.weekdays, f2 = n2.months, h2 = function(t3, n3, i3, s3) {
return t3 && (t3[n3] || t3(e2, r2)) || i3[n3].slice(0, s3);
}, c2 = function(t3) {
return O.s(s2 % 12 || 12, t3, "0");
}, d2 = n2.meridiem || function(t3, e3, n3) {
var r3 = t3 < 12 ? "AM" : "PM";
return n3 ? r3.toLowerCase() : r3;
}, $2 = { YY: String(this.$y).slice(-2), YYYY: this.$y, M: a2 + 1, MM: O.s(a2 + 1, 2, "0"), MMM: h2(n2.monthsShort, a2, f2, 3), MMMM: h2(f2, a2), D: this.$D, DD: O.s(this.$D, 2, "0"), d: String(this.$W), dd: h2(n2.weekdaysMin, this.$W, o2, 2), ddd: h2(n2.weekdaysShort, this.$W, o2, 3), dddd: o2[this.$W], H: String(s2), HH: O.s(s2, 2, "0"), h: c2(1), hh: c2(2), a: d2(s2, u2, true), A: d2(s2, u2, false), m: String(u2), mm: O.s(u2, 2, "0"), s: String(this.$s), ss: O.s(this.$s, 2, "0"), SSS: O.s(this.$ms, 3, "0"), Z: i2 };
return r2.replace(y, function(t3, e3) {
return e3 || $2[t3] || i2.replace(":", "");
});
}, m2.utcOffset = function() {
return 15 * -Math.round(this.$d.getTimezoneOffset() / 15);
}, m2.diff = function(r2, d2, l2) {
var $2, y2 = O.p(d2), M3 = w(r2), m3 = (M3.utcOffset() - this.utcOffset()) * e, v2 = this - M3, g2 = O.m(this, M3);
return g2 = ($2 = {}, $2[c] = g2 / 12, $2[f] = g2, $2[h] = g2 / 3, $2[o] = (v2 - m3) / 6048e5, $2[a] = (v2 - m3) / 864e5, $2[u] = v2 / n, $2[s] = v2 / e, $2[i] = v2 / t, $2)[y2] || v2, l2 ? g2 : O.a(g2);
}, m2.daysInMonth = function() {
return this.endOf(f).$D;
}, m2.$locale = function() {
return D[this.$L];
}, m2.locale = function(t2, e2) {
if (!t2)
return this.$L;
var n2 = this.clone(), r2 = S(t2, e2, true);
return r2 && (n2.$L = r2), n2;
}, m2.clone = function() {
return O.w(this.$d, this);
}, m2.toDate = function() {
return new Date(this.valueOf());
}, m2.toJSON = function() {
return this.isValid() ? this.toISOString() : null;
}, m2.toISOString = function() {
return this.$d.toISOString();
}, m2.toString = function() {
return this.$d.toUTCString();
}, M2;
}(), T = _.prototype;
return w.prototype = T, [["$ms", r], ["$s", i], ["$m", s], ["$H", u], ["$W", a], ["$M", f], ["$y", c], ["$D", d]].forEach(function(t2) {
T[t2[1]] = function(e2) {
return this.$g(e2, t2[0], t2[1]);
};
}), w.extend = function(t2, e2) {
return t2.$i || (t2(e2, _, w), t2.$i = true), w;
}, w.locale = S, w.isDayjs = p, w.unix = function(t2) {
return w(1e3 * t2);
}, w.en = D[g], w.Ls = D, w.p = {}, w;
});
}
});
export default require_dayjs_min();
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/util/dayjs.js
|
JavaScript
|
unknown
| 11,038
|
/**
* 路由跳转方法，该方法相对于直接使用uni.xxx的好处是使用更加简单快捷
* 并且带有路由拦截功能
*/
import { queryParams, deepMerge, page } from '@/uni_modules/uv-ui-tools/libs/function/index.js'
class Router {
constructor() {
// 原始属性定义
this.config = {
type: 'navigateTo',
url: '',
delta: 1, // navigateBack页面后退时,回退的层数
params: {}, // 传递的参数
animationType: 'pop-in', // 窗口动画,只在APP有效
animationDuration: 300, // 窗口动画持续时间,单位毫秒,只在APP有效
intercept: false ,// 是否需要拦截
events: {} // 页面间通信接口，用于监听被打开页面发送到当前页面的数据。hbuilderx 2.8.9+ 开始支持。
}
// 因为route方法是需要对外赋值给另外的对象使用，同时route内部有使用this，会导致route失去上下文
// 这里在构造函数中进行this绑定
this.route = this.route.bind(this)
}
// 判断url前面是否有"/"，如果没有则加上，否则无法跳转
addRootPath(url) {
return url[0] === '/' ? url : `/${url}`
}
// 整合路由参数
mixinParam(url, params) {
url = url && this.addRootPath(url)
// 使用正则匹配，主要依据是判断是否有"/","?","="等，如“/page/index/index?name=mary"
// 如果有url中有get参数，转换后无需带上"?"
let query = ''
if (/.*\/.*\?.*=.*/.test(url)) {
// object对象转为get类型的参数
query = queryParams(params, false)
// 因为已有get参数,所以后面拼接的参数需要带上"&"隔开
return url += `&${query}`
}
// 直接拼接参数，因为此处url中没有后面的query参数，也就没有"?/&"之类的符号
query = queryParams(params)
return url += query
}
// 对外的方法名称
async route(options = {}, params = {}) {
// 合并用户的配置和内部的默认配置
let mergeConfig = {}
if (typeof options === 'string') {
// 如果options为字符串，则为route(url, params)的形式
mergeConfig.url = this.mixinParam(options, params)
mergeConfig.type = 'navigateTo'
} else {
mergeConfig = deepMerge(this.config, options)
// 否则正常使用mergeConfig中的url和params进行拼接
mergeConfig.url = this.mixinParam(options.url, options.params)
}
// 如果本次跳转的路径和本页面路径一致，不执行跳转，防止用户快速点击跳转按钮，造成多次跳转同一个页面的问题
if (mergeConfig.url === page()) return
if (params.intercept) {
mergeConfig.intercept = params.intercept
}
// params参数也带给拦截器
mergeConfig.params = params
// 合并内外部参数
mergeConfig = deepMerge(this.config, mergeConfig)
// 判断用户是否定义了拦截器
if (typeof mergeConfig.intercept === 'function') {
// 定一个promise，根据用户执行resolve(true)或者resolve(false)来决定是否进行路由跳转
const isNext = await new Promise((resolve, reject) => {
mergeConfig.intercept(mergeConfig, resolve)
})
// 如果isNext为true，则执行路由跳转
isNext && this.openPage(mergeConfig)
} else {
this.openPage(mergeConfig)
}
}
// 执行路由跳转
openPage(config) {
// 解构参数
const {
url,
type,
delta,
animationType,
animationDuration,
events
} = config
if (config.type == 'navigateTo' || config.type == 'to') {
uni.navigateTo({
url,
animationType,
animationDuration,
events
})
}
if (config.type == 'redirectTo' || config.type == 'redirect') {
uni.redirectTo({
url
})
}
if (config.type == 'switchTab' || config.type == 'tab') {
uni.switchTab({
url
})
}
if (config.type == 'reLaunch' || config.type == 'launch') {
uni.reLaunch({
url
})
}
if (config.type == 'navigateBack' || config.type == 'back') {
uni.navigateBack({
delta
})
}
}
}
export default (new Router()).route
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/libs/util/route.js
|
JavaScript
|
unknown
| 3,957
|
// 此文件为uvUI的主题变量，这些变量目前只能通过uni.scss引入才有效，另外由于
// uni.scss中引入的样式会同时混入到全局样式文件和单独每一个页面的样式中，造成微信程序包太大，
// 故uni.scss只建议放scss变量名相关样式，其他的样式可以通过main.js或者App.vue引入
$uv-main-color: #303133;
$uv-content-color: #606266;
$uv-tips-color: #909193;
$uv-light-color: #c0c4cc;
$uv-border-color: #dadbde;
$uv-bg-color: #f3f4f6;
$uv-disabled-color: #c8c9cc;
$uv-primary: #3c9cff;
$uv-primary-dark: #398ade;
$uv-primary-disabled: #9acafc;
$uv-primary-light: #ecf5ff;
$uv-warning: #f9ae3d;
$uv-warning-dark: #f1a532;
$uv-warning-disabled: #f9d39b;
$uv-warning-light: #fdf6ec;
$uv-success: #5ac725;
$uv-success-dark: #53c21d;
$uv-success-disabled: #a9e08f;
$uv-success-light: #f5fff0;
$uv-error: #f56c6c;
$uv-error-dark: #e45656;
$uv-error-disabled: #f7b2b2;
$uv-error-light: #fef0f0;
$uv-info: #909399;
$uv-info-dark: #767a82;
$uv-info-disabled: #c4c6c9;
$uv-info-light: #f4f4f5;
@mixin flex($direction: row) {
/* #ifndef APP-NVUE */
display: flex;
/* #endif */
flex-direction: $direction;
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-ui-tools/theme.scss
|
SCSS
|
unknown
| 1,174
|
<template>
<uv-popup ref="popup" @change="change">
<view class="video-view" v-if="show">
<video class="video" :src="getSec" :autoplay="autoplay"></video>
</view>
</uv-popup>
</template>
<script>
export default {
props: {
src: {
type: String,
default: ''
},
autoplay: {
type: Boolean,
default: true
}
},
data() {
return {
videoSrc: '',
show: false
}
},
computed: {
getSec() {
return this.src || this.videoSrc;
}
},
methods: {
open(url) {
this.videoSrc = url;
this.$refs.popup.open();
},
close() {
this.$refs.popup.close();
},
change(e) {
this.show = e.show;
}
}
}
</script>
<style scoped lang="scss">
.video-view {
width: 750rpx;
.video {
width: 750rpx;
}
}
</style>
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-upload/components/uv-preview-video/uv-preview-video.vue
|
Vue
|
unknown
| 789
|
import { error } from '@/uni_modules/uv-ui-tools/libs/function/index.js'
export default {
watch: {
// 监听accept的变化，判断是否符合个平台要求
// 只有微信小程序才支持选择媒体，文件类型，所以这里做一个判断提示
accept: {
immediate: true,
handler(val) {
// #ifndef MP-WEIXIN
if (val === 'all' || val === 'media') {
error('只有微信小程序才支持把accept配置为all、media之一')
}
// #endif
// #ifndef H5 || MP-WEIXIN
if (val === 'file') {
error('只有微信小程序和H5(HX2.9.9)才支持把accept配置为file')
}
// #endif
}
}
}
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-upload/components/uv-upload/mixin.js
|
JavaScript
|
unknown
| 836
|
export default {
props: {
// 接受的文件类型, 可选值为all media image file video
accept: {
type: String,
default: 'image'
},
// 图片或视频拾取模式，当accept为image类型时设置capture可选额外camera可以直接调起摄像头
capture: {
type: [String, Array],
default: () => ['album', 'camera']
},
// 当accept为video时生效，是否压缩视频，默认为true
compressed: {
type: Boolean,
default: true
},
// 当accept为video时生效，可选值为back或front
camera: {
type: String,
default: 'back'
},
// 当accept为video时生效，拍摄视频最长拍摄时间，单位秒
maxDuration: {
type: Number,
default: 60
},
// 上传区域的图标，只能内置图标
uploadIcon: {
type: String,
default: 'camera-fill'
},
// 上传区域的图标的颜色，默认
uploadIconColor: {
type: String,
default: '#D3D4D6'
},
// 是否开启文件读取前事件
useBeforeRead: {
type: Boolean,
default: false
},
// 读取后的处理函数
afterRead: {
type: Function,
default: null
},
// 读取前的处理函数
beforeRead: {
type: Function,
default: null
},
// 是否开启图片预览功能
previewFullImage: {
type: Boolean,
default: true
},
// 是否开启视频预览功能
previewFullVideo: {
type: Boolean,
default: true
},
// 最大上传数量
maxCount: {
type: [String, Number],
default: 52
},
// 是否禁用
disabled: {
type: Boolean,
default: false
},
// 预览上传的图片时的裁剪模式，和image组件mode属性一致
imageMode: {
type: String,
default: 'aspectFill'
},
// 标识符，可以在回调函数的第二项参数中获取
name: {
type: String,
default: ''
},
// 所选的图片的尺寸, 可选值为original compressed
sizeType: {
type: Array,
default: () => ['original', 'compressed']
},
// 是否开启图片多选，部分安卓机型不支持
multiple: {
type: Boolean,
default: false
},
// 是否展示删除按钮
deletable: {
type: Boolean,
default: true
},
// 文件大小限制，单位为byte
maxSize: {
type: [String, Number],
default: Number.MAX_VALUE
},
// 显示已上传的文件列表
fileList: {
type: Array,
default: () => []
},
// 上传区域的提示文字
uploadText: {
type: String,
default: ''
},
// 内部预览图片区域和选择图片按钮的区域宽度
width: {
type: [String, Number],
default: 80
},
// 内部预览图片区域和选择图片按钮的区域高度
height: {
type: [String, Number],
default: 80
},
// 是否在上传完成后展示预览图
previewImage: {
type: Boolean,
default: true
},
...uni.$uv?.props?.upload
}
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-upload/components/uv-upload/props.js
|
JavaScript
|
unknown
| 2,839
|
function pickExclude(obj, keys) {
// 某些情况下，type可能会为
if (!['[object Object]', '[object File]'].includes(Object.prototype.toString.call(obj))) {
return {}
}
return Object.keys(obj).reduce((prev, key) => {
if (!keys.includes(key)) {
prev[key] = obj[key]
}
return prev
}, {})
}
function formatImage(res) {
return res.tempFiles.map((item) => ({
...pickExclude(item, ['path']),
type: 'image',
url: item.path,
thumb: item.path,
size: item.size,
// #ifdef H5
name: item.name
// #endif
}))
}
function formatVideo(res) {
return [
{
...pickExclude(res, ['tempFilePath', 'thumbTempFilePath', 'errMsg']),
type: 'video',
url: res.tempFilePath,
thumb: res.thumbTempFilePath,
size: res.size,
// #ifdef H5
name: res.name
// #endif
}
]
}
function formatMedia(res) {
return res.tempFiles.map((item) => ({
...pickExclude(item, ['fileType', 'thumbTempFilePath', 'tempFilePath']),
type: res.type,
url: item.tempFilePath,
thumb: res.type === 'video' ? item.thumbTempFilePath : item.tempFilePath,
size: item.size
}))
}
function formatFile(res) {
return res.tempFiles.map((item) => ({
...pickExclude(item, ['path']),
url: item.path,
size:item.size,
// #ifdef H5
name: item.name,
type: item.type
// #endif
}))
}
export function chooseFile({
accept,
multiple,
capture,
compressed,
maxDuration,
sizeType,
camera,
maxCount
}) {
return new Promise((resolve, reject) => {
switch (accept) {
case 'image':
uni.chooseImage({
count: multiple ? Math.min(maxCount, 9) : 1,
sourceType: capture,
sizeType,
success: (res) => resolve(formatImage(res)),
fail: reject
})
break
// #ifdef MP-WEIXIN
// 只有微信小程序才支持chooseMedia接口
case 'media':
wx.chooseMedia({
count: multiple ? Math.min(maxCount, 9) : 1,
sourceType: capture,
maxDuration,
sizeType,
camera,
success: (res) => resolve(formatMedia(res)),
fail: reject
})
break
// #endif
case 'video':
uni.chooseVideo({
sourceType: capture,
compressed,
maxDuration,
camera,
success: (res) => resolve(formatVideo(res)),
fail: reject
})
break
// #ifdef MP-WEIXIN || H5
// 只有微信小程序才支持chooseMessageFile接口
case 'file':
// #ifdef MP-WEIXIN
wx.chooseMessageFile({
count: multiple ? maxCount : 1,
type: accept,
success: (res) => resolve(formatFile(res)),
fail: reject
})
// #endif
// #ifdef H5
// 需要hx2.9.9以上才支持uni.chooseFile
uni.chooseFile({
count: multiple ? maxCount : 1,
type: accept,
success: (res) => resolve(formatFile(res)),
fail: reject
})
// #endif
break
// #endif
default:
// 此为保底选项，在accept不为上面任意一项的时候选取全部文件
// #ifdef MP-WEIXIN
wx.chooseMessageFile({
count: multiple ? maxCount : 1,
type: 'all',
success: (res) => resolve(formatFile(res)),
fail: reject
})
// #endif
// #ifdef H5
// 需要hx2.9.9以上才支持uni.chooseFile
uni.chooseFile({
count: multiple ? maxCount : 1,
type: 'all',
success: (res) => resolve(formatFile(res)),
fail: reject
})
// #endif
}
})
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-upload/components/uv-upload/utils.js
|
JavaScript
|
unknown
| 4,031
|
<template>
<view class="uv-upload" :style="[$uv.addStyle(customStyle)]">
<view class="uv-upload__wrap">
<template v-if="previewImage">
<view class="uv-upload__wrap__preview" v-for="(item, index) in lists" :key="index">
<image
v-if="item.isImage || (item.type && item.type === 'image')"
:src="item.thumb || item.url" :mode="imageMode"
class="uv-upload__wrap__preview__image"
@tap="onPreviewImage(item,index)"
:style="[{
width: $uv.addUnit(width),
height: $uv.addUnit(height)
}]"
/>
<view
v-else
class="uv-upload__wrap__preview__other"
@tap="onPreviewVideo(item,index)"
:style="[{
width: $uv.addUnit(width),
height: $uv.addUnit(height)
}]"
>
<uv-icon color="#80CBF9" size="26" :name="item.isVideo || (item.type && item.type === 'video') ? 'movie' : 'folder'"></uv-icon>
<text class="uv-upload__wrap__preview__other__text">{{item.isVideo || (item.type && item.type === 'video') ? '视频' : '文件'}}</text>
</view>
<view class="uv-upload__status" v-if="item.status === 'uploading' || item.status === 'failed'">
<view class="uv-upload__status__icon">
<uv-icon v-if="item.status === 'failed'" name="close-circle" color="#ffffff" size="25" />
<uv-loading-icon size="22" mode="circle" v-else />
</view>
<text v-if="item.message" class="uv-upload__status__message">{{ item.message }}</text>
</view>
<view class="uv-upload__deletable" v-if="item.status !== 'uploading' && (deletable || item.deletable)" @tap.stop="deleteItem(index)">
<view class="uv-upload__deletable__icon">
<uv-icon name="close" color="#ffffff" size="10"></uv-icon>
</view>
</view>
<view class="uv-upload__success" v-if="item.status === 'success'">
<!-- #ifdef APP-NVUE -->
<image :src="successIcon" class="uv-upload__success__icon"></image>
<!-- #endif -->
<!-- #ifndef APP-NVUE -->
<view class="uv-upload__success__icon">
<uv-icon name="checkmark" color="#ffffff" size="12"></uv-icon>
</view>
<!-- #endif -->
</view>
</view>
</template>
<template v-if="isInCount">
<view @tap="chooseFile">
<slot>
<view class="uv-upload__button" :hover-class="!disabled ? 'uv-upload__button--hover' : ''" hover-stay-time="150" @tap.stop="chooseFile" :class="[disabled && 'uv-upload__button--disabled']" :style="[{
width: $uv.addUnit(width),
height: $uv.addUnit(height)
}]">
<uv-icon :name="uploadIcon" size="26" :color="uploadIconColor"></uv-icon>
<text v-if="uploadText" class="uv-upload__button__text">{{ uploadText }}</text>
</view>
</slot>
</view>
</template>
</view>
<uv-preview-video ref="previewVideo"></uv-preview-video>
</view>
</template>
<script>
import { func, image, video, array, promise } from '@/uni_modules/uv-ui-tools/libs/function/test.js';
import mpMixin from '@/uni_modules/uv-ui-tools/libs/mixin/mpMixin.js'
import mixin from '@/uni_modules/uv-ui-tools/libs/mixin/mixin.js'
import { chooseFile } from './utils';
import mixin_accept from './mixin.js';
import props from './props.js';
/**
* upload 上传
* @description 该组件用于上传图片场景
* @tutorial https://www.uvui.cn/components/upload.html
* @property {String} accept 接受的文件类型, 可选值为all media image file video （默认 'image' ）
* @property {String | Array} capture 图片或视频拾取模式，当accept为image类型时设置capture可选额外camera可以直接调起摄像头（默认 ['album', 'camera'] ）
* @property {Boolean} compressed 当accept为video时生效，是否压缩视频，默认为true（默认 true ）
* @property {String} camera 当accept为video时生效，可选值为back或front（默认 'back' ）
* @property {Number} maxDuration 当accept为video时生效，拍摄视频最长拍摄时间，单位秒（默认 60 ）
* @property {String} uploadIcon 上传区域的图标，只能内置图标（默认 'camera-fill' ）
* @property {String} uploadIconColor 上传区域的图标的字体颜色，只能内置图标（默认 #D3D4D6 ）
* @property {Boolean} useBeforeRead 是否开启文件读取前事件（默认 false ）
* @property {Boolean} previewFullImage 是否开启图片预览功能（默认 true ）
* @property {Boolean} previewFullVideo 是否开启视频预览功能（默认 true ）
* @property {String | Number} maxCount 最大上传数量（默认 52 ）
* @property {Boolean} disabled 是否启用（默认 false ）
* @property {String} imageMode 预览上传的图片时的裁剪模式，和image组件mode属性一致（默认 'aspectFill' ）
* @property {String} name 标识符，可以在回调函数的第二项参数中获取
* @property {Array} sizeType 所选的图片的尺寸, 可选值为original compressed（默认 ['original', 'compressed'] ）
* @property {Boolean} multiple 是否开启图片多选，部分安卓机型不支持 （默认 false ）
* @property {Boolean} deletable 是否展示删除按钮（默认 true ）
* @property {String | Number} maxSize 文件大小限制，单位为byte （默认 Number.MAX_VALUE ）
* @property {Array} fileList 显示已上传的文件列表
* @property {String} uploadText 上传区域的提示文字
* @property {String | Number} width 内部预览图片区域和选择图片按钮的区域宽度（默认 80 ）
* @property {String | Number} height 内部预览图片区域和选择图片按钮的区域高度（默认 80 ）
* @property {Object} customStyle 组件的样式，对象形式
* @event {Function} afterRead 读取后的处理函数
* @event {Function} beforeRead 读取前的处理函数
* @event {Function} oversize 文件超出大小限制
* @event {Function} clickPreview 点击预览时触发
* @event {Function} delete 删除图片
* @example <uv-upload :action="action" :fileList="fileList" ></uv-upload>
*/
export default {
name: "uv-upload",
// #ifdef VUE3
emits: ['error', 'beforeRead', 'oversize', 'afterRead', 'delete', 'clickPreview'],
// #endif
mixins: [mpMixin, mixin, mixin_accept, props],
data() {
return {
// #ifdef APP-NVUE
successIcon: 'data:image/png;base64,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',
// #endif
lists: [],
isInCount: true,
}
},
watch: {
// 监听文件列表的变化，重新整理内部数据
fileList: {
deep: true,
immediate: true,
handler() {
this.formatFileList()
}
},
deletable(newVal) {
if(!newVal) {
this.lists.map(item=>{
item.deletable = this.deletable;
})
}
}
},
methods: {
formatFileList() {
const {
fileList = [], maxCount
} = this;
const lists = fileList.map((item) =>
Object.assign(Object.assign({}, item), {
// 如果item.url为本地选择的blob文件的话，无法判断其为video还是image，此处优先通过accept做判断处理
isImage: this.accept === 'image' || image(item.url || item.thumb),
isVideo: this.accept === 'video' || video(item.url || item.thumb),
deletable: typeof(item.deletable) === 'boolean' ? item.deletable : this.deletable,
})
);
this.lists = lists
this.isInCount = lists.length < maxCount
},
chooseFile() {
this.timer && clearTimeout(this.timer);
this.timer = setTimeout(() => {
const {
maxCount,
multiple,
lists,
disabled
} = this;
if (disabled) return;
// 如果用户传入的是字符串，需要格式化成数组
let capture;
try {
capture = array(this.capture) ? this.capture : this.capture.split(',');
} catch (e) {
capture = [];
}
chooseFile(
Object.assign({
accept: this.accept,
multiple: this.multiple,
capture: capture,
compressed: this.compressed,
maxDuration: this.maxDuration,
sizeType: this.sizeType,
camera: this.camera,
}, {
maxCount: maxCount - lists.length,
})
)
.then((res) => {
this.onBeforeRead(multiple ? res : res[0]);
})
.catch((error) => {
this.$emit('error', error);
});
}, 100)
},
// 文件读取之前
onBeforeRead(file) {
const {
beforeRead,
useBeforeRead,
} = this;
let res = true
// beforeRead是否为一个方法
if (func(beforeRead)) {
// 如果用户定义了此方法，则去执行此方法，并传入读取的文件回调
res = beforeRead(file, this.getDetail());
}
if (useBeforeRead) {
res = new Promise((resolve, reject) => {
this.$emit(
'beforeRead',
Object.assign(Object.assign({
file
}, this.getDetail()), {
callback: (ok) => {
ok ? resolve() : reject();
},
})
);
});
}
if (!res) {
return;
}
if (promise(res)) {
res.then((data) => this.onAfterRead(data || file));
} else {
this.onAfterRead(file);
}
},
getDetail(index) {
return {
name: this.name,
index: index == null ? this.fileList.length : index,
};
},
onAfterRead(file) {
const {
maxSize,
afterRead
} = this;
const oversize = Array.isArray(file) ?
file.some((item) => item.size > maxSize) :
file.size > maxSize;
if (oversize) {
this.$emit('oversize', Object.assign({
file
}, this.getDetail()));
return;
}
if (typeof afterRead === 'function') {
afterRead(file, this.getDetail());
}
this.$emit('afterRead', Object.assign({
file
}, this.getDetail()));
},
deleteItem(index) {
this.$emit(
'delete',
Object.assign(Object.assign({}, this.getDetail(index)), {
file: this.fileList[index],
})
);
},
// 预览图片
onPreviewImage(item, index) {
const lists = this.$uv.deepClone(this.lists);
lists.map((i,j)=>{
if(j == index) {
i.current = true;
}
});
const filters = lists.filter(i=>i.isImage);
const findIndex = filters.findIndex(i=>i.current);
this.onClickPreview(item, index);
if (!item.isImage || !this.previewFullImage) return
uni.previewImage({
// 先filter找出为图片的item，再返回filter结果中的图片url
urls: this.lists.filter((item) => this.accept === 'image' || image(item.url || item.thumb)).map((item) => item.url || item.thumb),
current: findIndex,
fail() {
this.$uv.toast('预览图片失败')
},
});
},
onPreviewVideo(item, index) {
this.onClickPreview(item, index);
if (!this.previewFullVideo || !item.isVideo) return;
this.$refs.previewVideo.open(item.url);
},
onClickPreview(item, index) {
this.$emit(
'clickPreview',
Object.assign(Object.assign({}, item), this.getDetail(index))
);
}
}
}
</script>
<style lang="scss" scoped>
@import '@/uni_modules/uv-ui-tools/libs/css/components.scss';
@import '@/uni_modules/uv-ui-tools/libs/css/color.scss';
$uv-upload-preview-border-radius: 2px !default;
$uv-upload-preview-margin: 0 8px 8px 0 !default;
$uv-upload-image-width: 80px !default;
$uv-upload-image-height: $uv-upload-image-width;
$uv-upload-other-bgColor: rgb(242, 242, 242) !default;
$uv-upload-other-flex: 1 !default;
$uv-upload-text-font-size: 11px !default;
$uv-upload-text-color: $uv-tips-color !default;
$uv-upload-text-margin-top: 2px !default;
$uv-upload-deletable-right: 0 !default;
$uv-upload-deletable-top: 0 !default;
$uv-upload-deletable-bgColor: rgb(55, 55, 55) !default;
$uv-upload-deletable-height: 14px !default;
$uv-upload-deletable-width: $uv-upload-deletable-height;
$uv-upload-deletable-boder-bottom-left-radius: 100px !default;
$uv-upload-deletable-zIndex: 3 !default;
$uv-upload-success-bottom: 0 !default;
$uv-upload-success-right: 0 !default;
$uv-upload-success-border-style: solid !default;
$uv-upload-success-border-top-color: transparent !default;
$uv-upload-success-border-left-color: transparent !default;
$uv-upload-success-border-bottom-color: $uv-success !default;
$uv-upload-success-border-right-color: $uv-upload-success-border-bottom-color;
$uv-upload-success-border-width: 9px !default;
$uv-upload-icon-top: 0px !default;
$uv-upload-icon-right: 0px !default;
$uv-upload-icon-h5-top: 1px !default;
$uv-upload-icon-h5-right: 0 !default;
$uv-upload-icon-width: 16px !default;
$uv-upload-icon-height: $uv-upload-icon-width;
$uv-upload-success-icon-bottom: -10px !default;
$uv-upload-success-icon-right: -10px !default;
$uv-upload-status-right: 0 !default;
$uv-upload-status-left: 0 !default;
$uv-upload-status-bottom: 0 !default;
$uv-upload-status-top: 0 !default;
$uv-upload-status-bgColor: rgba(0, 0, 0, 0.5) !default;
$uv-upload-status-icon-Zindex: 1 !default;
$uv-upload-message-font-size: 12px !default;
$uv-upload-message-color: #FFFFFF !default;
$uv-upload-message-margin-top: 5px !default;
$uv-upload-button-width: 80px !default;
$uv-upload-button-height: $uv-upload-button-width;
$uv-upload-button-bgColor: rgb(244, 245, 247) !default;
$uv-upload-button-border-radius: 2px !default;
$uv-upload-botton-margin: 0 8px 8px 0 !default;
$uv-upload-text-font-size: 11px !default;
$uv-upload-text-color: $uv-tips-color !default;
$uv-upload-text-margin-top: 2px !default;
$uv-upload-hover-bgColor: rgb(230, 231, 233) !default;
$uv-upload-disabled-opacity: .5 !default;
.uv-upload {
@include flex(column);
flex: 1;
&__wrap {
@include flex;
flex-wrap: wrap;
flex: 1;
&__preview {
border-radius: $uv-upload-preview-border-radius;
margin: $uv-upload-preview-margin;
position: relative;
overflow: hidden;
@include flex;
&__image {
width: $uv-upload-image-width;
height: $uv-upload-image-height;
}
&__other {
width: $uv-upload-image-width;
height: $uv-upload-image-height;
background-color: $uv-upload-other-bgColor;
flex: $uv-upload-other-flex;
@include flex(column);
justify-content: center;
align-items: center;
&__text {
font-size: $uv-upload-text-font-size;
color: $uv-upload-text-color;
margin-top: $uv-upload-text-margin-top;
}
}
}
}
&__deletable {
position: absolute;
top: $uv-upload-deletable-top;
right: $uv-upload-deletable-right;
background-color: $uv-upload-deletable-bgColor;
height: $uv-upload-deletable-height;
width: $uv-upload-deletable-width;
@include flex;
border-bottom-left-radius: $uv-upload-deletable-boder-bottom-left-radius;
align-items: center;
justify-content: center;
z-index: $uv-upload-deletable-zIndex;
&__icon {
position: absolute;
transform: scale(0.7);
top: $uv-upload-icon-top;
right: $uv-upload-icon-right;
/* #ifdef H5 */
top: $uv-upload-icon-h5-top;
right: $uv-upload-icon-h5-right;
/* #endif */
}
}
&__success {
position: absolute;
bottom: $uv-upload-success-bottom;
right: $uv-upload-success-right;
@include flex;
// 由于weex(nvue)为阿里巴巴的KPI(部门业绩考核)的laji产物，不支持css绘制三角形
// 所以在nvue下使用图片，非nvue下使用css实现
/* #ifndef APP-NVUE */
border-style: $uv-upload-success-border-style;
border-top-color: $uv-upload-success-border-top-color;
border-left-color: $uv-upload-success-border-left-color;
border-bottom-color: $uv-upload-success-border-bottom-color;
border-right-color: $uv-upload-success-border-right-color;
border-width: $uv-upload-success-border-width;
align-items: center;
justify-content: center;
/* #endif */
&__icon {
/* #ifndef APP-NVUE */
position: absolute;
transform: scale(0.7);
bottom: $uv-upload-success-icon-bottom;
right: $uv-upload-success-icon-right;
/* #endif */
/* #ifdef APP-NVUE */
width: $uv-upload-icon-width;
height: $uv-upload-icon-height;
/* #endif */
}
}
&__status {
position: absolute;
top: $uv-upload-status-top;
bottom: $uv-upload-status-bottom;
left: $uv-upload-status-left;
right: $uv-upload-status-right;
background-color: $uv-upload-status-bgColor;
@include flex(column);
align-items: center;
justify-content: center;
&__icon {
position: relative;
z-index: $uv-upload-status-icon-Zindex;
}
&__message {
font-size: $uv-upload-message-font-size;
color: $uv-upload-message-color;
margin-top: $uv-upload-message-margin-top;
}
}
&__button {
@include flex(column);
align-items: center;
justify-content: center;
width: $uv-upload-button-width;
height: $uv-upload-button-height;
background-color: $uv-upload-button-bgColor;
border-radius: $uv-upload-button-border-radius;
margin: $uv-upload-botton-margin;
/* #ifndef APP-NVUE */
box-sizing: border-box;
/* #endif */
&__text {
font-size: $uv-upload-text-font-size;
color: $uv-upload-text-color;
margin-top: $uv-upload-text-margin-top;
}
&--hover {
background-color: $uv-upload-hover-bgColor;
}
&--disabled {
opacity: $uv-upload-disabled-opacity;
}
}
}
</style>
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-upload/components/uv-upload/uv-upload.vue
|
Vue
|
unknown
| 18,306
|
export default {
props: {
// 列表数据
list: {
type: Array,
default: ()=>[]
},
// 从list元素对象中读取的键名，默认name
keyName: {
type: String,
default: 'name'
},
// 当前选中项
current: {
type: [Number, String],
default: 0
},
// 头部内容的高度
hdHeight: {
type: [Number, String],
default: 0
},
// 是否联动，默认开启联动
chain: {
type: Boolean,
default: true
},
// 整个列表的高度，默认auto屏幕高度
height: {
type: [Number, String],
default: 'auto'
},
// 左边列表的宽度，默认200rpx
barWidth: {
type: [Number, String],
default: '180rpx'
},
// 左边列表是否允许滚动
barScrollable: {
type: Boolean,
default: true
},
// 背景颜色 默认主题颜色 $bg-color
barBgColor: {
type: String,
default: ''
},
// 左边列表的自定义样式
barStyle: {
type: [Object, String],
default: ''
},
// 左边列表项的自定义样式
barItemStyle: {
type: [Object, String],
default: ''
},
// 左边选择项激活时的自定义样式
barItemActiveStyle: {
type: [Object, String],
default: ''
},
// 左边选择项激活时的左边线条自定义样式
barItemActiveLineStyle: {
type: [Object, String],
default: ''
},
// 菜单项中的徽标自定义样式，比如定位位置
barItemBadgeStyle: {
type: [Object, String],
default: ''
},
// 右边区域自定义样式
contentStyle: {
type: [Object, String],
default: ''
}
}
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-vtabs/components/uv-vtabs/props.js
|
JavaScript
|
unknown
| 1,570
|
<template>
<view
class="uv-vtabs"
:style="[vtabsStyle]"
>
<scroll-view
class="uv-vtabs__bar"
ref="uv-vtabs__bar"
:style="[getBarStyle]"
:scroll-y="barScrollable"
:scroll-x="scrollX"
:show-scrollbar="false"
:scroll-with-animation="true"
:scroll-top="barScrollTop"
:scroll-into-view="barScrollToView"
>
<view
:class="[
'uv-vtabs__bar-item',
`uv-vtabs__bar-item--${index}`,
index == activeIndex && 'uv-vtabs__bar-item-active'
]"
:ref="`uv-vtabs__bar-item--${index}`"
v-for="(item,index) in list"
:key="index"
:id="`bar_${index}`"
:style="[itemStyle(index)]"
@tap.stop="clickHandler(index)"
>
<view
class="uv-vtabs__bar-item--line"
v-if="index == activeIndex"
:style="[$uv.addStyle(barItemActiveLineStyle)]"
></view>
<text
:class="[
'uv-vtabs__bar-item--value',
index == activeIndex && 'uv-vtabs__bar-item-active--value'
]"
:style="[itemStyle(index),textStyle(index)]"
>{{item[keyName]}}</text>
<view
class="uv-vtabs__bar-item--badge"
:style="[$uv.addStyle(barItemBadgeStyle)]"
v-if="!!(item.badge && (item.badge.show || item.badge.isDot || item.badge.value))"
>
<uv-badge
:show="!!(item.badge && (item.badge.show || item.badge.isDot || item.badge.value))"
:isDot="item.badge && item.badge.isDot || propsBadge.isDot"
:value="item.badge && item.badge.value || propsBadge.value"
:max="item.badge && item.badge.max || propsBadge.max"
:type="item.badge && item.badge.type || propsBadge.type"
:showZero="item.badge && item.badge.showZero || propsBadge.showZero"
:bgColor="item.badge && item.badge.bgColor || propsBadge.bgColor"
:color="item.badge && item.badge.color || propsBadge.color"
:shape="item.badge && item.badge.shape || propsBadge.shape"
:numberType="item.badge && item.badge.numberType || propsBadge.numberType"
:inverted="item.badge && item.badge.inverted || propsBadge.inverted"
></uv-badge>
</view>
</view>
</scroll-view>
<scroll-view
class="uv-vtabs__content"
:style="[getContentStyle,$uv.addStyle(contentStyle)]"
:scroll-y="true"
:scroll-x="scrollX"
:show-scrollbar="false"
:scroll-top="contentScrollTop"
:scroll-into-view="contentScrollTo"
:scroll-with-animation="true"
@scroll="scrollHandler"
@scrolltolower="scrolltolower"
v-if="chain"
>
<slot />
</scroll-view>
<scroll-view
v-else
class="uv-vtabs__content"
:style="[getContentStyle,$uv.addStyle(contentStyle)]"
:scroll-y="true"
:scroll-x="scrollX"
:show-scrollbar="false"
:scroll-top="contentScrollTop2"
@scrolltolower="scrolltolower"
>
<slot />
</scroll-view>
</view>
</template>
<script>
import mpMixin from '@/uni_modules/uv-ui-tools/libs/mixin/mpMixin.js'
import mixin from '@/uni_modules/uv-ui-tools/libs/mixin/mixin.js'
import debounce from '@/uni_modules/uv-ui-tools/libs/function/debounce.js'
import throttle from '@/uni_modules/uv-ui-tools/libs/function/throttle.js'
import uvBadgeProps from '@/uni_modules/uv-badge/components/uv-badge/props.js'
import props from './props.js';
// #ifdef APP-NVUE
const dom = uni.requireNativePlugin('dom')
// #endif
/**
* 垂直选项卡
* @description 该组件兼容所有端，提供了分类展示和联动等功能
* @tutorial https://www.uvui.cn/components/vtabs.html
* @property {Array} list 选项数组，元素为对象，如[{name:'uv-ui'}]（默认 [] ）
* @property {String} keyName 从list元素对象中读取的键名（默认 name ）
* @property {Number} current 当前选中项，从0开始（默认 0 ）
* @property {Number | String} hdHeight 头部内容的高度，头部有内容必传，否则会有联动误差（默认 0 ）
* @property {Boolean} chain 是否开启联动，开启后右边区域可以滑动查看内容（默认 true ）
* @property {Number|String} height 整个列表的高度，默认auto或空则为屏幕高度（默认 auto屏幕高度 ）
* @property {Number|String} barWidth 左边选项区域的宽度（默认 180rpx ）
* @property {Boolean} barScrollable 左边选项区域是否允许滚动 （默认 true ）
* @property {String} barBgColor 左边选项区域的背景颜色（默认$uv-bg-color）
* @property {Object} barStyle 左边选项区域的自定义样式 （默认{}）
* @property {Object} barItemStyle 左边选项区域每个选项的自定义样式 （默认{}）
* @property {Object} barItemActiveStyle 左边选项区域选中选项的自定义样式 （默认{}）
* @property {Object} barItemActiveLineStyle 左边选项区域选中选项竖线条的自定义样式 （默认{}）
* @property {Object} barItemBadgeStyle 左边选项区域选中选项徽标的自定义样式，主要用于设置位置 （默认{}）
* @property {Object} contentStyle 右边区域自定义样式 （默认{}）
* @example <uv-vtabs :list="list"><uv-vtabs-item>...</uv-vtabs-item></uv-vtabs>
*/
export default {
name: 'uv-vtabs',
mixins: [mpMixin, mixin, props],
created() {
this.children = []
},
mounted() {
this.$nextTick(()=>{
this.init(this.current);
})
},
data() {
return {
activeIndex: 0,
// 微信小程序下，scroll-view的scroll-into-view属性无法对slot中的内容的id生效，只能通过设置scrollTop的形式去移动滚动条
contentScrollTop: 0,
contentScrollTop2: 0,//针对非联动
contentScrollTo: '',
scrolling: false,
barScrolling: false,
touching: false,
hasHeight: 0,
scrollViewHeight: 0,
barScrollTop: 0,
barScrollToView: '',
timer2: 0
}
},
computed: {
scrollX(){
// #ifdef APP-NVUE
return true;
// #endif
return false;
},
vtabsStyle() {
const style = {};
style.height = this.getHeight();
return this.$uv.deepMerge(style, this.$uv.addStyle(this.customStyle));
},
getBarStyle() {
const style = {};
style.width = this.$uv.getPx(this.barWidth, true);
style.background = this.barBgColor;
style.height = this.getHeight();
return this.$uv.deepMerge(style, this.$uv.addStyle(this.barStyle));
},
itemStyle(){
return index =>{
const style = {};
let barItemInitStyle = this.barItemStyle;
// 避免在nvue模式下，切换时候上一个选中颜色不变
if(this.barItemStyle && !this.barItemStyle?.background) {
barItemInitStyle.background = 'transparent';
}
// 是否激活的样式
const customeStyle = index === this.activeIndex ? this.$uv.addStyle(this.barItemActiveStyle) : this.$uv.addStyle(barItemInitStyle);
if (this.list[index].disabled) {
style.color = '#c8c9cc'
}
return this.$uv.deepMerge(style, customeStyle);
}
},
// nvue设置字体样式必须要text标签上进行
textStyle(){
return index=>{
const style = {};
style.width = this.$uv.getPx(this.barWidth, true);
return style;
}
},
getContentStyle() {
const style = {};
style.height = this.getHeight();
return style;
},
propsBadge() {
return uvBadgeProps
}
},
watch: {
current(newVal){
if(!this.touching)
this.$nextTick(()=>{
this.init(newVal?newVal:0);
})
},
list(newVal) {
if (newVal.length) {
this.$uv.sleep(30).then(res => {
this.resize();
})
}
},
activeIndex(newVal){
if(!this.chain) {// 解决：非联动，内容过多的情况，滚动一段距离，再切换未滚动到顶部的BUG
this.contentScrollTop2 = 0 - Math.random() * 4 - 4;
}
this.$emit('change',newVal);
}
},
methods: {
init(index){
let num = 0;
clearInterval(this.timer2);
this.timer2 = setInterval(async ()=>{
num++;
if(num>50) clearInterval(this.timer2);
if(this.children.length) {
clearInterval(this.timer2);
await this.$uv.sleep(300);
this.clickHandler(index);
}
},100)
},
// 内容滚动到底部触发
scrolltolower(){
this.$emit('scrolltolower',this.activeIndex);
},
async resize() {
// 如果list数组长度为0就不处理 || 选中目标未变则不处理
if (this.list.length == 0 || !this.barScrollable) return;
// 避免滑太快，修复位置
Promise.all([this.getTabsRect(), this.getAllItemRect()]).then(([tabsRect, itemRect = []]) => {
this.tabsRect = tabsRect;
this.scrollViewHeight = 0
itemRect.map((item, index) => {
this.scrollViewHeight += item.height;
this.list[index].rect = item;
})
this.setBarScrollTop();
})
},
// 设置左边菜单滚动条的位置，目标：将当前的选项移到中间位置
setBarScrollTop() {
const tabRect = this.list[this.activeIndex];
const offsetTop = this.list
.slice(0, this.activeIndex)
.reduce((total, item) => {
return total + item.rect.height;
}, 0);
const scrollViewHeight = this.$uv.getPx(this.getHeight());
let barScrollTop = tabRect.rect.height / 2 + offsetTop - scrollViewHeight / 2;
// 先给一点随机值，避免出现不能滚动的BUG
barScrollTop = Math.min(barScrollTop, this.scrollViewHeight - this.tabsRect.height);
this.barScrollTop = Math.max(0, barScrollTop);
// 已经不能滚动的时候，就使用scroll-into-view的方式进行定位，避免失效
if(barScrollTop>=(this.scrollViewHeight - this.tabsRect.height)) {
this.timer && clearTimeout(this.timer);
this.timer = setTimeout(()=>{
this.barScrollToView = `bar_${this.activeIndex}`;
},400)
}
},
// 左边菜单点击
async clickHandler(currentIndex) {
if (currentIndex == this.activeIndex) return;
this.touching = true;
this.activeIndex = currentIndex;
if(this.chain) {
// 给一点随机值，避免出现不能滚动的BUG。微信端必须用此方法
this.contentScrollTop = this.children[currentIndex].top - this.$uv.getPx(this.hdHeight) - Math.random() * 4 - 4;
// #ifndef MP-WEIXIN
this.contentScrollTo = `content_${currentIndex}`;
// #endif
}
this.timer && clearTimeout(this.timer);
throttle(()=>{
this.resize();
},300,false)
debounce(() => {
this.touching = false;
}, 900)
},
// 内容滚动
scrollHandler(e) {
if (this.touching || this.scrolling) return;
// 每过一定时间取样一次，减少资源损耗以及可能带来的卡顿
this.scrolling = true;
this.$uv.sleep(80).then(() => {
this.scrolling = false;
})
const scrollTop = e.detail.scrollTop;
let children = this.children;
const len = children.length;
let top = 0;
let activeIndex = 0;
children = this.children.map((item, index) => {
if (item.height > 0) this.hasHeight = item.height;
item.height = item.height > 0 ? item.height : this.hasHeight;
const child = {
height: item.height,
top
}
// 进行累加，给下一个item提供计算依据
top += item.height;
return child;
})
for (let i = 0; i < len; i++) {
const item = children[i];
const nextItem = children[i + 1];
// 如果滚动条高度小于第一个item的top值，此时无需设置任意字母为高亮
if (scrollTop <= children[0].top) {
activeIndex = 0;
break
} else if (!nextItem) {
// 当不存在下一个item时，意味着历遍到了最后一个
activeIndex = len - 1;
break
} else if (scrollTop > item.top && scrollTop < nextItem.top) {
activeIndex = i;
break
}
}
this.activeIndex = activeIndex;
// 当前选中项索引必然来源于前后两个索引，满足才执行，避免闪烁的bug
this.timer4 && clearTimeout(this.timer4);
this.timer4 = setTimeout(()=>{
this.resize();
},100)
},
// 设置高度
getHeight() {
let height = 0;
const isEmpty = this.$uv.test.empty(this.height);
if (isEmpty || this.height=='auto') height = this.$uv.addUnit(this.$uv.sys().windowHeight);
else height = this.$uv.getPx(this.height, true);
return height;
},
// 获取导航菜单的尺寸
getTabsRect() {
return new Promise(resolve => {
this.queryRect('uv-vtabs__bar').then(size => resolve(size))
})
},
// 获取所有标签的尺寸
getAllItemRect() {
return new Promise(resolve => {
const promiseAllArr = this.list.map((item, index) => this.queryRect(
`uv-vtabs__bar-item--${index}`, true))
Promise.all(promiseAllArr).then(sizes => resolve(sizes))
})
},
// 获取各个标签的尺寸
queryRect(el, item) {
// #ifndef APP-NVUE
// $uvGetRect为uv-ui自带的节点查询简化方法，详见文档介绍：https://www.uvui.cn/js/getRect.html
// 组件内部一般用this.$uvGetRect，对外的为getRect，二者功能一致，名称不同
return new Promise(resolve => {
this.$uvGetRect(`.${el}`).then(size => {
resolve(size)
})
})
// #endif
// #ifdef APP-NVUE
// nvue下，使用dom模块查询元素高度
// 返回一个promise，让调用此方法的主体能使用then回调
return new Promise(resolve => {
dom.getComponentRect(item ? this.$refs[el][0] : this.$refs[el], res => {
resolve(res.size)
})
})
// #endif
}
}
}
</script>
<style scoped lang="scss">
@import '@/uni_modules/uv-ui-tools/libs/css/components.scss';
@import '@/uni_modules/uv-ui-tools/libs/css/color.scss';
.uv-vtabs {
@include flex;
&__bar {
background: $uv-bg-color;
&-item {
position: relative;
@include flex;
align-items: center;
justify-content: center;
padding: 35rpx 12rpx 35rpx 20rpx;
&--value {
/* #ifdef APP-NVUE */
padding: 0 12rpx;
/* #endif */
font-size: 14px;
color: $uv-content-color;
}
&-active {
background: #fff;
&--value {
color: $uv-primary;
}
}
&--line {
position: absolute;
width: 2px;
left: 0;
top: 0;
bottom: 0;
z-index: 1;
background-color: $uv-primary;
}
&--badge {
position: absolute;
top: 4px;
right: 10px;
z-index: 1;
}
}
}
&__content {
flex: 1;
background: #fff;
}
}
</style>
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-vtabs/components/uv-vtabs/uv-vtabs.vue
|
Vue
|
unknown
| 14,374
|
<template>
<view
class="uv-vtabs-item"
:id="`content_${index}`"
ref="uv-vtabs-item"
>
<slot />
</view>
</template>
<script>
import mpMixin from '@/uni_modules/uv-ui-tools/libs/mixin/mpMixin.js';
import mixin from '@/uni_modules/uv-ui-tools/libs/mixin/mixin.js';
// #ifdef APP-NVUE
const dom = uni.requireNativePlugin('dom');
// #endif
export default {
name: 'uv-vtabs-item',
mixins: [mpMixin, mixin],
props: {
index: {
type: [Number,String],
default: 0
}
},
data(){
return {
// 记录item的离顶部的距离
top: 0,
// 记录item的高度
height: 0
// 是否为联动
}
},
mounted() {
this.init();
},
methods: {
async init(){
this.getParentData('uv-vtabs');
if (!this.parent) {
return this.$uv.error('uv-vtabs必须要搭配uv-vtabs-item组件使用')
}
if(!this.parent.chain) return;
await this.$uv.sleep();
this.getItemRect().then(size=>{
// 由于对象的引用特性，此处会同时生效到父组件的children数组的本实例的top属性中，供父组件判断读取
this.top = size.top;
this.height = size.height;
});
},
getItemRect(){
return new Promise(resolve => {
// #ifndef APP-NVUE
this.$uvGetRect('.uv-vtabs-item').then(size => {
resolve(size)
})
// #endif
// #ifdef APP-NVUE
const ref = this.$refs['uv-vtabs-item']
dom.getComponentRect(ref, res => {
resolve(res.size)
})
// #endif
})
}
}
}
</script>
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-vtabs/components/uv-vtabs-item/uv-vtabs-item.vue
|
Vue
|
unknown
| 1,537
|
export default {
props: {
// 瀑布流数据
// #ifdef VUE2
value: {
type: Array,
default: () => []
},
// #endif
// #ifdef VUE3
modelValue: {
type: Array,
default: () => []
},
// #endif
// 数据的id值，根据id值对数据执行删除操作
// 如数据为：{id: 1, name: 'uv-ui'}，那么该值设置为id
idKey: {
type: String,
default: 'id'
},
// 每次插入数据的事件间隔，间隔越长能保证两列高度相近，但是用户体验不好，单位ms
addTime: {
type: Number,
default: 200
},
// 瀑布流的列数，默认2，最高为5
columnCount: {
type: [Number, String],
default: 2
},
// 列与列的间隙，默认20
columnGap: {
type: [Number, String],
default: 20
},
// 左边和列表的间隙
leftGap: {
type: [Number, String],
default: 0
},
// 右边和列表的间隙
rightGap: {
type: [Number, String],
default: 0
},
// 是否显示滚动条，仅nvue生效
showScrollbar: {
type: [Boolean],
default: false
},
// 列宽，nvue生效
columnWidth: {
type: [Number, String],
default: 'auto'
},
// 瀑布流的宽度，nvue生效
width: {
type: [Number, String],
default: ''
},
// 瀑布流的高度，nvue生效
height: {
type: [Number, String],
default: ''
},
...uni.$uv?.props?.waterfall
}
}
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-waterfall/components/uv-waterfall/props.js
|
JavaScript
|
unknown
| 1,377
|
<template>
<view class="uv-waterfall">
<!-- #ifndef APP-NVUE -->
<view class="uv-waterfall__gap_left" :style="[gapLeftStyle]"></view>
<template v-if="columnNum>=1">
<view id="uv-waterfall-1" class="uv-waterfall__column">
<slot name="list1"></slot>
</view>
</template>
<template v-if="columnNum>=2">
<view class="uv-waterfall__gap_center" :style="[gapCenterStyle]"></view>
<view id="uv-waterfall-2" class="uv-waterfall__column">
<slot name="list2"></slot>
</view>
</template>
<template v-if="columnNum>=3">
<view class="uv-waterfall__gap_center" :style="[gapCenterStyle]"></view>
<view id="uv-waterfall-3" class="uv-waterfall__column">
<slot name="list3"></slot>
</view>
</template>
<template v-if="columnNum>=4">
<view class="uv-waterfall__gap_center" :style="[gapCenterStyle]">
</view>
<view id="uv-waterfall-4" class="uv-waterfall__column">
<slot name="list4"></slot>
</view>
</template>
<template v-if="columnNum>=5">
<view class="uv-waterfall__gap_center" :style="[gapCenterStyle]">
</view>
<view id="uv-waterfall-5" class="uv-waterfall__column">
<slot name="list5"></slot>
</view>
</template>
<view class="uv-waterfall__gap_right" :style="[gapRightStyle]">
</view>
<!-- #endif -->
<!-- #ifdef APP-NVUE -->
<view class="waterfall-warapper">
<waterfall :column-count="columnNum" :show-scrollbar="false" column-width="auto" :column-gap="columnGap" :left-gap="leftGap" :right-gap="rightGap" :always-scrollable-vertical="true" :style="[nvueWaterfallStyle]"
@loadmore="scrolltolower">
<slot></slot>
</waterfall>
</view>
<!-- #endif -->
</view>
</template>
<script>
import mpMixin from '@/uni_modules/uv-ui-tools/libs/mixin/mpMixin.js'
import mixin from '@/uni_modules/uv-ui-tools/libs/mixin/mixin.js'
import props from './props.js';
/**
* 瀑布流
* @description 该组件兼容所有端，nvue参考https://uniapp.dcloud.net.cn/component/waterfall.html
* @tutorial https://www.uvui.cn/components/list.html
* @property {Array} value/modelValue 瀑布流数组数据，非nvue生效 （默认 [] ）
* @property {String} idKey 数据的id值，根据id值对数据执行删除操作，如数据为：{id: 1, name: 'uv-ui'}，那么该值设置为id，非nvue有效 （默认 '' ）
* @property {String ｜ Number} addTime 每次插入数据的事件间隔，间隔越长能保证两列高度相近，但是用户体验不好，单位ms，非nvue生效（默认 200 ）
* @property {String ｜ Number} columnCount 瀑布流的列数（默认 2 ）
* @property {String ｜ Number} columnGap 列与列的间隙（默认 0 ）
* @property {String ｜ Number} leftGap 左边和列表的间隙（默认 0 ）
* @property {String ｜ Number} rightGap 右边和列表的间隙（默认 0 ）
* @property {Boolean} showScrollbar 控制是否出现滚动条，仅nvue有效 （默认 false ）
* @property {String ｜ Number} columnWidth 描述瀑布流每一列的列宽，nvue生效 （默认 auto）
* @property {String ｜ Number} width 瀑布流的宽度，nvue生效 （默认 屏幕宽 ）
* @property {String ｜ Number} height 瀑布流的高度，nvue生效 （默认 屏幕高 ）
* @property {Object} customStyle 定义需要用到的外部样式
*
* @example <uv-waterfall v-model="list"></uv-waterfall>
*/
export default {
name: 'uv-waterfall',
mixins: [mpMixin, mixin, props],
data() {
return {
list1: [],
list2: [],
list3: [],
list4: [],
list5: [],
// 临时列表
tempList: []
}
},
computed: {
// 破坏value变量引用，否则数据会保持不变
copyValue() {
// #ifdef VUE2
return this.$uv.deepClone(this.value)
// #endif
// #ifdef VUE3
return this.$uv.deepClone(this.modelValue)
// #endif
},
columnNum() {
return this.columnCount <= 0 ? 0 : this.columnCount >= 5 ? 5 : this.columnCount;
},
gapLeftStyle() {
const style = {}
style.width = this.$uv.addUnit(this.leftGap)
return style;
},
gapRightStyle() {
const style = {}
style.width = this.$uv.addUnit(this.rightGap)
return style;
},
gapCenterStyle() {
const style = {}
style.width = this.$uv.addUnit(this.columnGap)
return style;
},
nvueWaterfallStyle() {
const style = {};
if (this.width != 0) style.width = this.$uv.addUnit(this.width)
if (this.height != 0) style.height = this.$uv.addUnit(this.height)
// 如果没有定义列表高度，则默认使用屏幕高度
if (!style.width) style.width = this.$uv.addUnit(this.$uv.sys().windowWidth, 'px')
if (!style.height) style.height = this.$uv.addUnit(this.$uv.sys().windowHeight, 'px')
return this.$uv.deepMerge(style, this.$uv.addStyle(this.customStyle))
}
},
watch: {
copyValue(nVal, oVal) {
// #ifndef APP-NVUE
if (nVal.length != 0) {
// 取出数组发生变化的部分
let startIndex = Array.isArray(oVal) && oVal.length > 0 ? oVal.length : 0
// 拼接原有数据
this.tempList = this.tempList.concat(this.$uv.deepClone(nVal.slice(startIndex)))
this.splitData()
}
// #endif
}
},
mounted() {
// #ifndef APP-NVUE
this.tempList = this.$uv.deepClone(this.copyValue)
this.splitData()
// #endif
},
methods: {
// 滚动到底部触发事件
scrolltolower(e) {
this.$uv.sleep(30).then(() => {
this.$emit('scrolltolower')
})
},
// 拆分数据
async splitData() {
let rectArr = [];
let emitList = {};
if (!this.tempList.length) return
for (let i = 1; i <= this.columnNum; i++) {
const rect = await this.$uvGetRect(`#uv-waterfall-${i}`);
rectArr.push({ ...rect, name: i });
}
let item = this.tempList[0]
// 因为经过上面两个await节点查询和定时器，数组有可能会变成空[]，导致item的值为undefined
// 解决多次快速滚动会导致数据乱的问题
if (!item) return
const minCol = this.getMin(rectArr);
// 列宽可能使用的到
item.width = minCol.width;
this[`list${minCol.name}`].push(item);
emitList.name = `list${minCol.name}`;
emitList.value = item;
this.$emit('changeList', emitList);
// 移除临时数组中已处理的数据
this.tempList.splice(0, 1)
// 如果还有数据则继续执行
if (this.tempList.length) {
let _timeout = this.addTime;
// 部分平台在延时较短的情况会出现BUG
// #ifdef MP-BAIDU
_timeout = _timeout < 200 ? 200 : _timeout;
// #endif
await this.$uv.sleep(_timeout);
this.splitData()
} else {
this.$emit('finish')
}
},
getMin(arr) {
let result = null;
const filter = arr.filter(item => item.height == 0);
if (!filter.length) {
const min = Math.min.apply(Math, arr.map(item => {
return item.height;
}))
const [item] = arr.filter(item => item.height == min);
result = item;
} else {
let newArr = [];
arr.map((item, index) => {
newArr.push({ len: this[`list${index+1}`].length, item: item });
});
const minLen = Math.min.apply(Math, newArr.map(item => {
return item.len;
}))
try {
const { item } = newArr.find(item => item.len == minLen && item.item.height == 0);
result = item;
} catch (e) {
const { item } = newArr.find(item => item.item.height == 0);
result = item;
}
}
return result;
},
// 清空数据列表
async clear() {
// 清除数据
for (let i = 0; i < this.columnCount; i++) {
this[`list${i+1}`] = [];
}
// #ifdef VUE2
this.$emit('input', [])
// #endif
// #ifdef VUE3
this.$emit('update:modelValue', [])
// #endif
this.tempList = []
await this.$uv.sleep(300);
this.$emit('clear');
},
// 清除指定的某一条数据，根据id来实现
remove(id) {
let index = -1
// 删除组件数据
for (let i = 1; i <= this.columnCount; i++) {
index = this[`list${i}`].findIndex(item => item[this.idKey] == id)
if (index != -1) {
this[`list${i}`].splice(index, 1)
}
}
// 同时删除父组件对应的数据
// #ifdef VUE2
index = this.value.findIndex(item => item[this.idKey] == id)
if (index != -1) this.$emit('input', this.value.splice(index, 1))
// #endif
// #ifdef VUE3
index = this.modelValue.findIndex(item => item[this.idKey] == id)
if (index != -1) this.$emit('update:modelValue', this.modelValue.splice(index, 1))
// #endif
this.$emit('remove', id);
}
}
}
</script>
<style lang="scss" scoped>
@import '@/uni_modules/uv-ui-tools/libs/css/components.scss';
.uv-waterfall {
@include flex(row);
align-items: flex-start;
&__column {
@include flex(column);
flex: 1;
// #ifndef APP-NVUE
height: auto;
// #endif
}
}
</style>
|
2301_77169380/AppruanjianApk
|
uni_modules/uv-waterfall/components/uv-waterfall/uv-waterfall.vue
|
Vue
|
unknown
| 8,927
|
# 8. (a)
college = read.csv("C:/Users/djsta/Desktop/College.csv")
# 8. (b)
fix(college)
rownames(college) = college[,1]
college = college[,-1]
fix(college)
# 8. (c)
# i.
summary(college)
# ii.
pairs(college[,2:11])
# iii.
library(car)
scatterplot(college$Outstate,as.factor(college$Private))
# iv.
Elite = rep("No", nrow(college))
Elite[college$Top10perc>50] = "Yes"
Elite = as.factor(Elite)
college = data.frame(college, Elite)
college
summary(college$Elite)
scatterplot( college$Outstate,college$Elite)
# v.
par(mfrow=c(2,2))
hist(college$Apps)
hist(college$perc.alumni, col=2)
hist(college$S.F.Ratio, col=3, breaks=10)
hist(college$Expend, breaks=100)
# vi.
par(mfrow=c(1,1))
plot(college$Outstate, college$Grad.Rate)
# High tuition correlates to high graduation rate.
plot(college$Accept / college$Apps, college$S.F.Ratio)
# Colleges with low acceptance rate tend to have low S:F ratio.
plot(college$Top10perc, college$Grad.Rate)
# Colleges with the most students from top 10% perc don't necessarily have
# the highest graduation rate. Also, rate > 100 is erroneous!
# 9.
Auto = read.csv("C:/Users/djsta/Desktop/Auto.csv", header=T, na.strings="?")
fix(Auto)
Auto = na.omit(Auto)
dim(Auto)
summary(Auto)
# (a)
# quantitative: mpg, cylinders, displacement, horsepower, weight,
# acceleration, year
# qualitative: name, origin
# (b)
# apply the range function to the first seven columns of Auto
sapply(Auto[, 1:7], range)
# mpg cylinders displacement horsepower weight acceleration year
# [1,] 9.0 3 68 46 1613 8.0 70
# [2,] 46.6 8 455 230 5140 24.8 82
# (c)
sapply(Auto[, 1:7], mean)
# mpg cylinders displacement horsepower weight acceleration
# 23.445918 5.471939 194.411990 104.469388 2977.584184 15.541327
# year
# 75.979592
sapply(Auto[, 1:7], var)
# mpg cylinders displacement horsepower weight acceleration
# 7.805007 1.705783 104.644004 38.491160 849.402560 2.758864
# year
# 3.683737
# (d)
newAuto = auto[-c(10,84),]
sapply(newAuto[, 1:7], range)
# mpg cylinders displacement horsepower weight acceleration year
# [1,] 11.0 3 68 46 1649 8.5 70
# [2,] 46.6 8 455 230 4997 24.8 82
sapply(newAuto[, 1:7], mean)
# mpg cylinders displacement horsepower weight acceleration
# 24.404430 5.373418 187.240506 100.721519 2935.971519 15.726899
# year
# 77.145570
sapply(newAuto[, 1:7], sd)
# mpg cylinders displacement horsepower weight acceleration
# 7.867283 1.654179 99.678367 35.708853 811.300208 2.693721
# year
# 3.106217
# (e)
pairs(Auto[1:7])
plot(Auto$mpg, Auto$weight)
# Heavier weight correlates with lower mpg.
plot(Auto$mpg, Auto$cylinders)
# More cylinders, less mpg.
plot(Auto$mpg, Auto$year)
# Cars become more efficient over time.
# (f)
pairs(Auto)
# See descriptions of plots in (e).
# All of the predictors show some correlation with mpg. The name predictor has
# too little observations per name though, so using this as a predictor is
# likely to result in overfitting the data and will not generalize well.
# 10.
# (a)
library(MASS)
?Boston
dim(Boston)
# 506 rows, 14 columns
# 14 features, 506 housing values in Boston suburbs
fix(Boston)
# (b)
pairs(Boston)
# X correlates with: a, b, c
# crim: age, dis, rad, tax, ptratio
# zn: indus, nox, age, lstat
# indus: age, dis
# nox: age, dis
# dis: lstat
# lstat: medv
# (c)
plot(Boston$age, Boston$crim)
# Older homes, more crime
plot(Boston$dis, Boston$crim)
# Closer to work-area, more crime
plot(Boston$rad, Boston$crim)
# Higher index of accessibility to radial highways, more crime
plot(Boston$tax, Boston$crim)
# Higher tax rate, more crime
plot(Boston$ptratio, Boston$crim)
# Higher pupil:teacher ratio, more crime
# (d)
par(mfrow=c(1,3))
hist(Boston$crim[Boston$crim>1], breaks=25)
# most cities have low crime rates, but there is a long tail: 18 suburbs appear
# to have a crime rate > 20, reaching to above 80
hist(Boston$tax, breaks=25)
# there is a large divide between suburbs with low tax rates and a peak at 660-680
hist(Boston$ptratio, breaks=25)
# a skew towards high ratios, but no particularly high ratios
# (e)
dim(subset(Boston, chas == 1))
# 35 suburbs
# (f)
median(Boston$ptratio)
# 19.05
# (g)
t(subset(Boston, medv == min(Boston$medv)))
# 399 406
# crim 38.3518 67.9208 above 3rd quartile
# zn 0.0000 0.0000 at min
# indus 18.1000 18.1000 at 3rd quartile
# chas 0.0000 0.0000 not bounded by river
# nox 0.6930 0.6930 above 3rd quartile
# rm 5.4530 5.6830 below 1st quartile
# age 100.0000 100.0000 at max
# dis 1.4896 1.4254 below 1st quartile
# rad 24.0000 24.0000 at max
# tax 666.0000 666.0000 at 3rd quartile
# ptratio 20.2000 20.2000 at 3rd quartile
# black 396.9000 384.9700 at max; above 1st quartile
# lstat 30.5900 22.9800 above 3rd quartile
# medv 5.0000 5.0000 at min
summary(Boston)
# Not the best place to live, but certainly not the worst.
# (h)
dim(subset(Boston, rm > 7))
# 64
dim(subset(Boston, rm > 8))
# 13
summary(subset(Boston, rm > 8))
summary(Boston)
# relatively lower crime (comparing range), lower lstat (comparing range)
|
2301_76574743/tjjm-R
|
实验1/applied.R
|
R
|
unknown
| 5,416
|
# 10.
# (a)
library(MASS)
?Boston
dim(Boston)
# 506 rows, 14 columns
# 14 features, 506 housing values in Boston suburbs
fix(Boston)
# (b)
pairs(Boston)
# X correlates with: a, b, c
# crim: age, dis, rad, tax, ptratio
# zn: indus, nox, age, lstat
# indus: age, dis
# nox: age, dis
# dis: lstat
# lstat: medv
# (c)
plot(Boston$age, Boston$crim)
# Older homes, more crime
plot(Boston$dis, Boston$crim)
# Closer to work-area, more crime
plot(Boston$rad, Boston$crim)
# Higher index of accessibility to radial highways, more crime
plot(Boston$tax, Boston$crim)
# Higher tax rate, more crime
plot(Boston$ptratio, Boston$crim)
# Higher pupil:teacher ratio, more crime
# (d)
par(mfrow=c(1,3))
hist(Boston$crim[Boston$crim>1], breaks=25)
# most cities have low crime rates, but there is a long tail: 18 suburbs appear
# to have a crime rate > 20, reaching to above 80
hist(Boston$tax, breaks=25)
# there is a large divide between suburbs with low tax rates and a peak at 660-680
hist(Boston$ptratio, breaks=25)
# a skew towards high ratios, but no particularly high ratios
# (e)
dim(subset(Boston, chas == 1))
# 35 suburbs
# (f)
median(Boston$ptratio)
# 19.05
# (g)
t(subset(Boston, medv == min(Boston$medv)))
# 399 406
# crim 38.3518 67.9208 above 3rd quartile
# zn 0.0000 0.0000 at min
# indus 18.1000 18.1000 at 3rd quartile
# chas 0.0000 0.0000 not bounded by river
# nox 0.6930 0.6930 above 3rd quartile
# rm 5.4530 5.6830 below 1st quartile
# age 100.0000 100.0000 at max
# dis 1.4896 1.4254 below 1st quartile
# rad 24.0000 24.0000 at max
# tax 666.0000 666.0000 at 3rd quartile
# ptratio 20.2000 20.2000 at 3rd quartile
# black 396.9000 384.9700 at max; above 1st quartile
# lstat 30.5900 22.9800 above 3rd quartile
# medv 5.0000 5.0000 at min
summary(Boston)
# Not the best place to live, but certainly not the worst.
# (h)
dim(subset(Boston, rm > 7))
# 64
dim(subset(Boston, rm > 8))
# 13
summary(subset(Boston, rm > 8))
summary(Boston)
# relatively lower crime (comparing range), lower lstat (comparing range)
|
2301_76574743/tjjm-R
|
实验1/p40-10.R
|
R
|
unknown
| 2,118
|
library(ISLR)
summary(Carseats)
attach(Carseats)
lm.fit = lm(Sales~Price+Urban+US)
summary(lm.fit)
#10c.
#Sales = 13.04 + -0.05 Price + -0.02 UrbanYes + 1.20 USYes
#10d.
#10e.
lm.fit2 = lm(Sales ~ Price + US)
summary(lm.fit2)
#10f.
#10g.
confint(lm.fit2)
#10h.
plot(predict(lm.fit2), rstudent(lm.fit2))
par(mfrow=c(2,2))
plot(lm.fit2)
|
2301_76574743/tjjm-R
|
实验2/实验2-85-10.R
|
R
|
unknown
| 336
|
\relax
\gdef \@abspage@last{1}
|
2301_76574743/tjjm-R
|
实验3/实验2-P86-11/实验2-1.aux
|
TeX
|
unknown
| 32
|
\documentclass[11pt]{ctexart}
\usepackage[top=2cm, bottom=2cm, left=2cm, right=2cm]{geometry}
\usepackage{algorithm}
\usepackage{algorithmicx}
\usepackage{algpseudocode}
\usepackage{amsmath}
\floatname{algorithm}{算法}
\renewcommand{\algorithmicrequire}{\textbf{输入:}}
\renewcommand{\algorithmicensure}{\textbf{输出:}}
\begin{document}
$$
\begin{array}{cc}
t = \hat{\beta} / SE(\hat{\beta})
\\
\\ \hat{\beta} = \frac {\sum{x_i y_i}} {\sum{x_i^2}}
\\
\\ SE(\hat{\beta}) = \sqrt{\frac {\sum{(y_i - x_i \hat{\beta})^2}} {(n-1) \sum{x_i^2}}}
\\
\\
t = {\frac {\sum{x_i y_i}} {\sum{x_i^2}}}
{\sqrt{\frac {(n-1) \sum{x_i^2}} {\sum{(y_i - x_i \hat{\beta})^2}}}}
\\
\\
= \frac {\sqrt{n-1} \sum{x_i y_i}}
{\sqrt{\sum{x_i^2} \sum{(y_i - x_i \hat{\beta})^2}}}
\\
\\
= \frac {\sqrt{n-1} \sum{x_i y_i}}
{\sqrt{\sum{x_i^2} \sum{(y_i^2 - 2 \hat{\beta} x_i y_i + x_i^2 \hat{\beta}^2)}}}
\\
\\
= \frac {\sqrt{n-1} \sum{x_i y_i}}
{\sqrt{\sum{x_i^2} \sum{y_i^2} -
\sum{x_i^2} \hat{\beta} (2 \sum{x_i y_i} - \hat{\beta} \sum{x_i^2})}}
\\
\\
= \frac {\sqrt{n-1} \sum{x_i y_i}}
{\sqrt{\sum{x_i^2} \sum{y_i^2} -
\sum{x_i y_i} (2 \sum{x_i y_i} - \sum{x_i y_i})}}
\\
\\
t = \frac {\sqrt{n-1} \sum{x_i y_i}}
{\sqrt{\sum{x_i^2} \sum{y_i^2} - (\sum{x_i y_i})^2 }}
\end{array}
$$
\end{document}
|
2301_76574743/tjjm-R
|
实验3/实验2-P86-11/实验2-1.tex
|
TeX
|
unknown
| 1,394
|
<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>实验2</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<style type="text/css">
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
.highlight .hll { background-color: var(--jp-cell-editor-active-background) }
.highlight { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
.highlight .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
.highlight .err { color: var(--jp-mirror-editor-error-color) } /* Error */
.highlight .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
.highlight .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
.highlight .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
.highlight .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
.highlight .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
.highlight .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
.highlight .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
.highlight .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
.highlight .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
.highlight .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
.highlight .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
.highlight .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
.highlight .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
.highlight .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
.highlight .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
.highlight .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
.highlight .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
.highlight .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
.highlight .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
.highlight .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
.highlight .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
.highlight .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
.highlight .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
.highlight .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
.highlight .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
.highlight .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
.highlight .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
.highlight .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
.highlight .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
.highlight .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
.highlight .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
.highlight .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
.highlight .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
</style>
<style type="text/css">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*
* Mozilla scrollbar styling
*/
/* use standard opaque scrollbars for most nodes */
[data-jp-theme-scrollbars='true'] {
scrollbar-color: rgb(var(--jp-scrollbar-thumb-color))
var(--jp-scrollbar-background-color);
}
/* for code nodes, use a transparent style of scrollbar. These selectors
* will match lower in the tree, and so will override the above */
[data-jp-theme-scrollbars='true'] .CodeMirror-hscrollbar,
[data-jp-theme-scrollbars='true'] .CodeMirror-vscrollbar {
scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent;
}
/* tiny scrollbar */
.jp-scrollbar-tiny {
scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent;
scrollbar-width: thin;
}
/*
* Webkit scrollbar styling
*/
/* use standard opaque scrollbars for most nodes */
[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar,
[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar-corner {
background: var(--jp-scrollbar-background-color);
}
[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar-thumb {
background: rgb(var(--jp-scrollbar-thumb-color));
border: var(--jp-scrollbar-thumb-margin) solid transparent;
background-clip: content-box;
border-radius: var(--jp-scrollbar-thumb-radius);
}
[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar-track:horizontal {
border-left: var(--jp-scrollbar-endpad) solid
var(--jp-scrollbar-background-color);
border-right: var(--jp-scrollbar-endpad) solid
var(--jp-scrollbar-background-color);
}
[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar-track:vertical {
border-top: var(--jp-scrollbar-endpad) solid
var(--jp-scrollbar-background-color);
border-bottom: var(--jp-scrollbar-endpad) solid
var(--jp-scrollbar-background-color);
}
/* for code nodes, use a transparent style of scrollbar */
[data-jp-theme-scrollbars='true'] .CodeMirror-hscrollbar::-webkit-scrollbar,
[data-jp-theme-scrollbars='true'] .CodeMirror-vscrollbar::-webkit-scrollbar,
[data-jp-theme-scrollbars='true']
.CodeMirror-hscrollbar::-webkit-scrollbar-corner,
[data-jp-theme-scrollbars='true']
.CodeMirror-vscrollbar::-webkit-scrollbar-corner {
background-color: transparent;
}
[data-jp-theme-scrollbars='true']
.CodeMirror-hscrollbar::-webkit-scrollbar-thumb,
[data-jp-theme-scrollbars='true']
.CodeMirror-vscrollbar::-webkit-scrollbar-thumb {
background: rgba(var(--jp-scrollbar-thumb-color), 0.5);
border: var(--jp-scrollbar-thumb-margin) solid transparent;
background-clip: content-box;
border-radius: var(--jp-scrollbar-thumb-radius);
}
[data-jp-theme-scrollbars='true']
.CodeMirror-hscrollbar::-webkit-scrollbar-track:horizontal {
border-left: var(--jp-scrollbar-endpad) solid transparent;
border-right: var(--jp-scrollbar-endpad) solid transparent;
}
[data-jp-theme-scrollbars='true']
.CodeMirror-vscrollbar::-webkit-scrollbar-track:vertical {
border-top: var(--jp-scrollbar-endpad) solid transparent;
border-bottom: var(--jp-scrollbar-endpad) solid transparent;
}
/* tiny scrollbar */
.jp-scrollbar-tiny::-webkit-scrollbar,
.jp-scrollbar-tiny::-webkit-scrollbar-corner {
background-color: transparent;
height: 4px;
width: 4px;
}
.jp-scrollbar-tiny::-webkit-scrollbar-thumb {
background: rgba(var(--jp-scrollbar-thumb-color), 0.5);
}
.jp-scrollbar-tiny::-webkit-scrollbar-track:horizontal {
border-left: 0px solid transparent;
border-right: 0px solid transparent;
}
.jp-scrollbar-tiny::-webkit-scrollbar-track:vertical {
border-top: 0px solid transparent;
border-bottom: 0px solid transparent;
}
/*
* Phosphor
*/
.lm-ScrollBar[data-orientation='horizontal'] {
min-height: 16px;
max-height: 16px;
min-width: 45px;
border-top: 1px solid #a0a0a0;
}
.lm-ScrollBar[data-orientation='vertical'] {
min-width: 16px;
max-width: 16px;
min-height: 45px;
border-left: 1px solid #a0a0a0;
}
.lm-ScrollBar-button {
background-color: #f0f0f0;
background-position: center center;
min-height: 15px;
max-height: 15px;
min-width: 15px;
max-width: 15px;
}
.lm-ScrollBar-button:hover {
background-color: #dadada;
}
.lm-ScrollBar-button.lm-mod-active {
background-color: #cdcdcd;
}
.lm-ScrollBar-track {
background: #f0f0f0;
}
.lm-ScrollBar-thumb {
background: #cdcdcd;
}
.lm-ScrollBar-thumb:hover {
background: #bababa;
}
.lm-ScrollBar-thumb.lm-mod-active {
background: #a0a0a0;
}
.lm-ScrollBar[data-orientation='horizontal'] .lm-ScrollBar-thumb {
height: 100%;
min-width: 15px;
border-left: 1px solid #a0a0a0;
border-right: 1px solid #a0a0a0;
}
.lm-ScrollBar[data-orientation='vertical'] .lm-ScrollBar-thumb {
width: 100%;
min-height: 15px;
border-top: 1px solid #a0a0a0;
border-bottom: 1px solid #a0a0a0;
}
.lm-ScrollBar[data-orientation='horizontal']
.lm-ScrollBar-button[data-action='decrement'] {
background-image: var(--jp-icon-caret-left);
background-size: 17px;
}
.lm-ScrollBar[data-orientation='horizontal']
.lm-ScrollBar-button[data-action='increment'] {
background-image: var(--jp-icon-caret-right);
background-size: 17px;
}
.lm-ScrollBar[data-orientation='vertical']
.lm-ScrollBar-button[data-action='decrement'] {
background-image: var(--jp-icon-caret-up);
background-size: 17px;
}
.lm-ScrollBar[data-orientation='vertical']
.lm-ScrollBar-button[data-action='increment'] {
background-image: var(--jp-icon-caret-down);
background-size: 17px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-Widget, /* </DEPRECATED> */
.lm-Widget {
box-sizing: border-box;
position: relative;
overflow: hidden;
cursor: default;
}
/* <DEPRECATED> */ .p-Widget.p-mod-hidden, /* </DEPRECATED> */
.lm-Widget.lm-mod-hidden {
display: none !important;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-CommandPalette, /* </DEPRECATED> */
.lm-CommandPalette {
display: flex;
flex-direction: column;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/* <DEPRECATED> */ .p-CommandPalette-search, /* </DEPRECATED> */
.lm-CommandPalette-search {
flex: 0 0 auto;
}
/* <DEPRECATED> */ .p-CommandPalette-content, /* </DEPRECATED> */
.lm-CommandPalette-content {
flex: 1 1 auto;
margin: 0;
padding: 0;
min-height: 0;
overflow: auto;
list-style-type: none;
}
/* <DEPRECATED> */ .p-CommandPalette-header, /* </DEPRECATED> */
.lm-CommandPalette-header {
overflow: hidden;
white-space: nowrap;
text-overflow: ellipsis;
}
/* <DEPRECATED> */ .p-CommandPalette-item, /* </DEPRECATED> */
.lm-CommandPalette-item {
display: flex;
flex-direction: row;
}
/* <DEPRECATED> */ .p-CommandPalette-itemIcon, /* </DEPRECATED> */
.lm-CommandPalette-itemIcon {
flex: 0 0 auto;
}
/* <DEPRECATED> */ .p-CommandPalette-itemContent, /* </DEPRECATED> */
.lm-CommandPalette-itemContent {
flex: 1 1 auto;
overflow: hidden;
}
/* <DEPRECATED> */ .p-CommandPalette-itemShortcut, /* </DEPRECATED> */
.lm-CommandPalette-itemShortcut {
flex: 0 0 auto;
}
/* <DEPRECATED> */ .p-CommandPalette-itemLabel, /* </DEPRECATED> */
.lm-CommandPalette-itemLabel {
overflow: hidden;
white-space: nowrap;
text-overflow: ellipsis;
}
.lm-close-icon {
border:1px solid transparent;
background-color: transparent;
position: absolute;
z-index:1;
right:3%;
top: 0;
bottom: 0;
margin: auto;
padding: 7px 0;
display: none;
vertical-align: middle;
outline: 0;
cursor: pointer;
}
.lm-close-icon:after {
content: "X";
display: block;
width: 15px;
height: 15px;
text-align: center;
color:#000;
font-weight: normal;
font-size: 12px;
cursor: pointer;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-DockPanel, /* </DEPRECATED> */
.lm-DockPanel {
z-index: 0;
}
/* <DEPRECATED> */ .p-DockPanel-widget, /* </DEPRECATED> */
.lm-DockPanel-widget {
z-index: 0;
}
/* <DEPRECATED> */ .p-DockPanel-tabBar, /* </DEPRECATED> */
.lm-DockPanel-tabBar {
z-index: 1;
}
/* <DEPRECATED> */ .p-DockPanel-handle, /* </DEPRECATED> */
.lm-DockPanel-handle {
z-index: 2;
}
/* <DEPRECATED> */ .p-DockPanel-handle.p-mod-hidden, /* </DEPRECATED> */
.lm-DockPanel-handle.lm-mod-hidden {
display: none !important;
}
/* <DEPRECATED> */ .p-DockPanel-handle:after, /* </DEPRECATED> */
.lm-DockPanel-handle:after {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
content: '';
}
/* <DEPRECATED> */
.p-DockPanel-handle[data-orientation='horizontal'],
/* </DEPRECATED> */
.lm-DockPanel-handle[data-orientation='horizontal'] {
cursor: ew-resize;
}
/* <DEPRECATED> */
.p-DockPanel-handle[data-orientation='vertical'],
/* </DEPRECATED> */
.lm-DockPanel-handle[data-orientation='vertical'] {
cursor: ns-resize;
}
/* <DEPRECATED> */
.p-DockPanel-handle[data-orientation='horizontal']:after,
/* </DEPRECATED> */
.lm-DockPanel-handle[data-orientation='horizontal']:after {
left: 50%;
min-width: 8px;
transform: translateX(-50%);
}
/* <DEPRECATED> */
.p-DockPanel-handle[data-orientation='vertical']:after,
/* </DEPRECATED> */
.lm-DockPanel-handle[data-orientation='vertical']:after {
top: 50%;
min-height: 8px;
transform: translateY(-50%);
}
/* <DEPRECATED> */ .p-DockPanel-overlay, /* </DEPRECATED> */
.lm-DockPanel-overlay {
z-index: 3;
box-sizing: border-box;
pointer-events: none;
}
/* <DEPRECATED> */ .p-DockPanel-overlay.p-mod-hidden, /* </DEPRECATED> */
.lm-DockPanel-overlay.lm-mod-hidden {
display: none !important;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-Menu, /* </DEPRECATED> */
.lm-Menu {
z-index: 10000;
position: absolute;
white-space: nowrap;
overflow-x: hidden;
overflow-y: auto;
outline: none;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/* <DEPRECATED> */ .p-Menu-content, /* </DEPRECATED> */
.lm-Menu-content {
margin: 0;
padding: 0;
display: table;
list-style-type: none;
}
/* <DEPRECATED> */ .p-Menu-item, /* </DEPRECATED> */
.lm-Menu-item {
display: table-row;
}
/* <DEPRECATED> */
.p-Menu-item.p-mod-hidden,
.p-Menu-item.p-mod-collapsed,
/* </DEPRECATED> */
.lm-Menu-item.lm-mod-hidden,
.lm-Menu-item.lm-mod-collapsed {
display: none !important;
}
/* <DEPRECATED> */
.p-Menu-itemIcon,
.p-Menu-itemSubmenuIcon,
/* </DEPRECATED> */
.lm-Menu-itemIcon,
.lm-Menu-itemSubmenuIcon {
display: table-cell;
text-align: center;
}
/* <DEPRECATED> */ .p-Menu-itemLabel, /* </DEPRECATED> */
.lm-Menu-itemLabel {
display: table-cell;
text-align: left;
}
/* <DEPRECATED> */ .p-Menu-itemShortcut, /* </DEPRECATED> */
.lm-Menu-itemShortcut {
display: table-cell;
text-align: right;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-MenuBar, /* </DEPRECATED> */
.lm-MenuBar {
outline: none;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/* <DEPRECATED> */ .p-MenuBar-content, /* </DEPRECATED> */
.lm-MenuBar-content {
margin: 0;
padding: 0;
display: flex;
flex-direction: row;
list-style-type: none;
}
/* <DEPRECATED> */ .p--MenuBar-item, /* </DEPRECATED> */
.lm-MenuBar-item {
box-sizing: border-box;
}
/* <DEPRECATED> */
.p-MenuBar-itemIcon,
.p-MenuBar-itemLabel,
/* </DEPRECATED> */
.lm-MenuBar-itemIcon,
.lm-MenuBar-itemLabel {
display: inline-block;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-ScrollBar, /* </DEPRECATED> */
.lm-ScrollBar {
display: flex;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/* <DEPRECATED> */
.p-ScrollBar[data-orientation='horizontal'],
/* </DEPRECATED> */
.lm-ScrollBar[data-orientation='horizontal'] {
flex-direction: row;
}
/* <DEPRECATED> */
.p-ScrollBar[data-orientation='vertical'],
/* </DEPRECATED> */
.lm-ScrollBar[data-orientation='vertical'] {
flex-direction: column;
}
/* <DEPRECATED> */ .p-ScrollBar-button, /* </DEPRECATED> */
.lm-ScrollBar-button {
box-sizing: border-box;
flex: 0 0 auto;
}
/* <DEPRECATED> */ .p-ScrollBar-track, /* </DEPRECATED> */
.lm-ScrollBar-track {
box-sizing: border-box;
position: relative;
overflow: hidden;
flex: 1 1 auto;
}
/* <DEPRECATED> */ .p-ScrollBar-thumb, /* </DEPRECATED> */
.lm-ScrollBar-thumb {
box-sizing: border-box;
position: absolute;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-SplitPanel-child, /* </DEPRECATED> */
.lm-SplitPanel-child {
z-index: 0;
}
/* <DEPRECATED> */ .p-SplitPanel-handle, /* </DEPRECATED> */
.lm-SplitPanel-handle {
z-index: 1;
}
/* <DEPRECATED> */ .p-SplitPanel-handle.p-mod-hidden, /* </DEPRECATED> */
.lm-SplitPanel-handle.lm-mod-hidden {
display: none !important;
}
/* <DEPRECATED> */ .p-SplitPanel-handle:after, /* </DEPRECATED> */
.lm-SplitPanel-handle:after {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
content: '';
}
/* <DEPRECATED> */
.p-SplitPanel[data-orientation='horizontal'] > .p-SplitPanel-handle,
/* </DEPRECATED> */
.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle {
cursor: ew-resize;
}
/* <DEPRECATED> */
.p-SplitPanel[data-orientation='vertical'] > .p-SplitPanel-handle,
/* </DEPRECATED> */
.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle {
cursor: ns-resize;
}
/* <DEPRECATED> */
.p-SplitPanel[data-orientation='horizontal'] > .p-SplitPanel-handle:after,
/* </DEPRECATED> */
.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle:after {
left: 50%;
min-width: 8px;
transform: translateX(-50%);
}
/* <DEPRECATED> */
.p-SplitPanel[data-orientation='vertical'] > .p-SplitPanel-handle:after,
/* </DEPRECATED> */
.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle:after {
top: 50%;
min-height: 8px;
transform: translateY(-50%);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-TabBar, /* </DEPRECATED> */
.lm-TabBar {
display: flex;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/* <DEPRECATED> */ .p-TabBar[data-orientation='horizontal'], /* </DEPRECATED> */
.lm-TabBar[data-orientation='horizontal'] {
flex-direction: row;
align-items: flex-end;
}
/* <DEPRECATED> */ .p-TabBar[data-orientation='vertical'], /* </DEPRECATED> */
.lm-TabBar[data-orientation='vertical'] {
flex-direction: column;
align-items: flex-end;
}
/* <DEPRECATED> */ .p-TabBar-content, /* </DEPRECATED> */
.lm-TabBar-content {
margin: 0;
padding: 0;
display: flex;
flex: 1 1 auto;
list-style-type: none;
}
/* <DEPRECATED> */
.p-TabBar[data-orientation='horizontal'] > .p-TabBar-content,
/* </DEPRECATED> */
.lm-TabBar[data-orientation='horizontal'] > .lm-TabBar-content {
flex-direction: row;
}
/* <DEPRECATED> */
.p-TabBar[data-orientation='vertical'] > .p-TabBar-content,
/* </DEPRECATED> */
.lm-TabBar[data-orientation='vertical'] > .lm-TabBar-content {
flex-direction: column;
}
/* <DEPRECATED> */ .p-TabBar-tab, /* </DEPRECATED> */
.lm-TabBar-tab {
display: flex;
flex-direction: row;
box-sizing: border-box;
overflow: hidden;
}
/* <DEPRECATED> */
.p-TabBar-tabIcon,
.p-TabBar-tabCloseIcon,
/* </DEPRECATED> */
.lm-TabBar-tabIcon,
.lm-TabBar-tabCloseIcon {
flex: 0 0 auto;
}
/* <DEPRECATED> */ .p-TabBar-tabLabel, /* </DEPRECATED> */
.lm-TabBar-tabLabel {
flex: 1 1 auto;
overflow: hidden;
white-space: nowrap;
}
.lm-TabBar-tabInput {
user-select: all;
width: 100%;
box-sizing : border-box;
}
/* <DEPRECATED> */ .p-TabBar-tab.p-mod-hidden, /* </DEPRECATED> */
.lm-TabBar-tab.lm-mod-hidden {
display: none !important;
}
.lm-TabBar-addButton.lm-mod-hidden {
display: none !important;
}
/* <DEPRECATED> */ .p-TabBar.p-mod-dragging .p-TabBar-tab, /* </DEPRECATED> */
.lm-TabBar.lm-mod-dragging .lm-TabBar-tab {
position: relative;
}
/* <DEPRECATED> */
.p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab,
/* </DEPRECATED> */
.lm-TabBar.lm-mod-dragging[data-orientation='horizontal'] .lm-TabBar-tab {
left: 0;
transition: left 150ms ease;
}
/* <DEPRECATED> */
.p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab,
/* </DEPRECATED> */
.lm-TabBar.lm-mod-dragging[data-orientation='vertical'] .lm-TabBar-tab {
top: 0;
transition: top 150ms ease;
}
/* <DEPRECATED> */
.p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging,
/* </DEPRECATED> */
.lm-TabBar.lm-mod-dragging .lm-TabBar-tab.lm-mod-dragging {
transition: none;
}
.lm-TabBar-tabLabel .lm-TabBar-tabInput {
user-select: all;
width: 100%;
box-sizing : border-box;
background: inherit;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-TabPanel-tabBar, /* </DEPRECATED> */
.lm-TabPanel-tabBar {
z-index: 1;
}
/* <DEPRECATED> */ .p-TabPanel-stackedPanel, /* </DEPRECATED> */
.lm-TabPanel-stackedPanel {
z-index: 0;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
@charset "UTF-8";
html{
-webkit-box-sizing:border-box;
box-sizing:border-box; }
*,
*::before,
*::after{
-webkit-box-sizing:inherit;
box-sizing:inherit; }
body{
font-size:14px;
font-weight:400;
letter-spacing:0;
line-height:1.28581;
text-transform:none;
color:#182026;
font-family:-apple-system, "BlinkMacSystemFont", "Segoe UI", "Roboto", "Oxygen", "Ubuntu", "Cantarell", "Open Sans", "Helvetica Neue", "Icons16", sans-serif; }
p{
margin-bottom:10px;
margin-top:0; }
small{
font-size:12px; }
strong{
font-weight:600; }
::-moz-selection{
background:rgba(125, 188, 255, 0.6); }
::selection{
background:rgba(125, 188, 255, 0.6); }
.bp3-heading{
color:#182026;
font-weight:600;
margin:0 0 10px;
padding:0; }
.bp3-dark .bp3-heading{
color:#f5f8fa; }
h1.bp3-heading, .bp3-running-text h1{
font-size:36px;
line-height:40px; }
h2.bp3-heading, .bp3-running-text h2{
font-size:28px;
line-height:32px; }
h3.bp3-heading, .bp3-running-text h3{
font-size:22px;
line-height:25px; }
h4.bp3-heading, .bp3-running-text h4{
font-size:18px;
line-height:21px; }
h5.bp3-heading, .bp3-running-text h5{
font-size:16px;
line-height:19px; }
h6.bp3-heading, .bp3-running-text h6{
font-size:14px;
line-height:16px; }
.bp3-ui-text{
font-size:14px;
font-weight:400;
letter-spacing:0;
line-height:1.28581;
text-transform:none; }
.bp3-monospace-text{
font-family:monospace;
text-transform:none; }
.bp3-text-muted{
color:#5c7080; }
.bp3-dark .bp3-text-muted{
color:#a7b6c2; }
.bp3-text-disabled{
color:rgba(92, 112, 128, 0.6); }
.bp3-dark .bp3-text-disabled{
color:rgba(167, 182, 194, 0.6); }
.bp3-text-overflow-ellipsis{
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal; }
.bp3-running-text{
font-size:14px;
line-height:1.5; }
.bp3-running-text h1{
color:#182026;
font-weight:600;
margin-bottom:20px;
margin-top:40px; }
.bp3-dark .bp3-running-text h1{
color:#f5f8fa; }
.bp3-running-text h2{
color:#182026;
font-weight:600;
margin-bottom:20px;
margin-top:40px; }
.bp3-dark .bp3-running-text h2{
color:#f5f8fa; }
.bp3-running-text h3{
color:#182026;
font-weight:600;
margin-bottom:20px;
margin-top:40px; }
.bp3-dark .bp3-running-text h3{
color:#f5f8fa; }
.bp3-running-text h4{
color:#182026;
font-weight:600;
margin-bottom:20px;
margin-top:40px; }
.bp3-dark .bp3-running-text h4{
color:#f5f8fa; }
.bp3-running-text h5{
color:#182026;
font-weight:600;
margin-bottom:20px;
margin-top:40px; }
.bp3-dark .bp3-running-text h5{
color:#f5f8fa; }
.bp3-running-text h6{
color:#182026;
font-weight:600;
margin-bottom:20px;
margin-top:40px; }
.bp3-dark .bp3-running-text h6{
color:#f5f8fa; }
.bp3-running-text hr{
border:none;
border-bottom:1px solid rgba(16, 22, 26, 0.15);
margin:20px 0; }
.bp3-dark .bp3-running-text hr{
border-color:rgba(255, 255, 255, 0.15); }
.bp3-running-text p{
margin:0 0 10px;
padding:0; }
.bp3-text-large{
font-size:16px; }
.bp3-text-small{
font-size:12px; }
a{
color:#106ba3;
text-decoration:none; }
a:hover{
color:#106ba3;
cursor:pointer;
text-decoration:underline; }
a .bp3-icon, a .bp3-icon-standard, a .bp3-icon-large{
color:inherit; }
a code,
.bp3-dark a code{
color:inherit; }
.bp3-dark a,
.bp3-dark a:hover{
color:#48aff0; }
.bp3-dark a .bp3-icon, .bp3-dark a .bp3-icon-standard, .bp3-dark a .bp3-icon-large,
.bp3-dark a:hover .bp3-icon,
.bp3-dark a:hover .bp3-icon-standard,
.bp3-dark a:hover .bp3-icon-large{
color:inherit; }
.bp3-running-text code, .bp3-code{
font-family:monospace;
text-transform:none;
background:rgba(255, 255, 255, 0.7);
border-radius:3px;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2);
color:#5c7080;
font-size:smaller;
padding:2px 5px; }
.bp3-dark .bp3-running-text code, .bp3-running-text .bp3-dark code, .bp3-dark .bp3-code{
background:rgba(16, 22, 26, 0.3);
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
color:#a7b6c2; }
.bp3-running-text a > code, a > .bp3-code{
color:#137cbd; }
.bp3-dark .bp3-running-text a > code, .bp3-running-text .bp3-dark a > code, .bp3-dark a > .bp3-code{
color:inherit; }
.bp3-running-text pre, .bp3-code-block{
font-family:monospace;
text-transform:none;
background:rgba(255, 255, 255, 0.7);
border-radius:3px;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
color:#182026;
display:block;
font-size:13px;
line-height:1.4;
margin:10px 0;
padding:13px 15px 12px;
word-break:break-all;
word-wrap:break-word; }
.bp3-dark .bp3-running-text pre, .bp3-running-text .bp3-dark pre, .bp3-dark .bp3-code-block{
background:rgba(16, 22, 26, 0.3);
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
color:#f5f8fa; }
.bp3-running-text pre > code, .bp3-code-block > code{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:inherit;
font-size:inherit;
padding:0; }
.bp3-running-text kbd, .bp3-key{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
background:#ffffff;
border-radius:3px;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
color:#5c7080;
display:-webkit-inline-box;
display:-ms-inline-flexbox;
display:inline-flex;
font-family:inherit;
font-size:12px;
height:24px;
-webkit-box-pack:center;
-ms-flex-pack:center;
justify-content:center;
line-height:24px;
min-width:24px;
padding:3px 6px;
vertical-align:middle; }
.bp3-running-text kbd .bp3-icon, .bp3-key .bp3-icon, .bp3-running-text kbd .bp3-icon-standard, .bp3-key .bp3-icon-standard, .bp3-running-text kbd .bp3-icon-large, .bp3-key .bp3-icon-large{
margin-right:5px; }
.bp3-dark .bp3-running-text kbd, .bp3-running-text .bp3-dark kbd, .bp3-dark .bp3-key{
background:#394b59;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
color:#a7b6c2; }
.bp3-running-text blockquote, .bp3-blockquote{
border-left:solid 4px rgba(167, 182, 194, 0.5);
margin:0 0 10px;
padding:0 20px; }
.bp3-dark .bp3-running-text blockquote, .bp3-running-text .bp3-dark blockquote, .bp3-dark .bp3-blockquote{
border-color:rgba(115, 134, 148, 0.5); }
.bp3-running-text ul,
.bp3-running-text ol, .bp3-list{
margin:10px 0;
padding-left:30px; }
.bp3-running-text ul li:not(:last-child), .bp3-running-text ol li:not(:last-child), .bp3-list li:not(:last-child){
margin-bottom:5px; }
.bp3-running-text ul ol, .bp3-running-text ol ol, .bp3-list ol,
.bp3-running-text ul ul,
.bp3-running-text ol ul,
.bp3-list ul{
margin-top:5px; }
.bp3-list-unstyled{
list-style:none;
margin:0;
padding:0; }
.bp3-list-unstyled li{
padding:0; }
.bp3-rtl{
text-align:right; }
.bp3-dark{
color:#f5f8fa; }
:focus{
outline:rgba(19, 124, 189, 0.6) auto 2px;
outline-offset:2px;
-moz-outline-radius:6px; }
.bp3-focus-disabled :focus{
outline:none !important; }
.bp3-focus-disabled :focus ~ .bp3-control-indicator{
outline:none !important; }
.bp3-alert{
max-width:400px;
padding:20px; }
.bp3-alert-body{
display:-webkit-box;
display:-ms-flexbox;
display:flex; }
.bp3-alert-body .bp3-icon{
font-size:40px;
margin-right:20px;
margin-top:0; }
.bp3-alert-contents{
word-break:break-word; }
.bp3-alert-footer{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:horizontal;
-webkit-box-direction:reverse;
-ms-flex-direction:row-reverse;
flex-direction:row-reverse;
margin-top:10px; }
.bp3-alert-footer .bp3-button{
margin-left:10px; }
.bp3-breadcrumbs{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
cursor:default;
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-ms-flex-wrap:wrap;
flex-wrap:wrap;
height:30px;
list-style:none;
margin:0;
padding:0; }
.bp3-breadcrumbs > li{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
display:-webkit-box;
display:-ms-flexbox;
display:flex; }
.bp3-breadcrumbs > li::after{
background:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M10.71 7.29l-4-4a1.003 1.003 0 00-1.42 1.42L8.59 8 5.3 11.29c-.19.18-.3.43-.3.71a1.003 1.003 0 001.71.71l4-4c.18-.18.29-.43.29-.71 0-.28-.11-.53-.29-.71z' fill='%235C7080'/%3e%3c/svg%3e");
content:"";
display:block;
height:16px;
margin:0 5px;
width:16px; }
.bp3-breadcrumbs > li:last-of-type::after{
display:none; }
.bp3-breadcrumb,
.bp3-breadcrumb-current,
.bp3-breadcrumbs-collapsed{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
display:-webkit-inline-box;
display:-ms-inline-flexbox;
display:inline-flex;
font-size:16px; }
.bp3-breadcrumb,
.bp3-breadcrumbs-collapsed{
color:#5c7080; }
.bp3-breadcrumb:hover{
text-decoration:none; }
.bp3-breadcrumb.bp3-disabled{
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed; }
.bp3-breadcrumb .bp3-icon{
margin-right:5px; }
.bp3-breadcrumb-current{
color:inherit;
font-weight:600; }
.bp3-breadcrumb-current .bp3-input{
font-size:inherit;
font-weight:inherit;
vertical-align:baseline; }
.bp3-breadcrumbs-collapsed{
background:#ced9e0;
border:none;
border-radius:3px;
cursor:pointer;
margin-right:2px;
padding:1px 5px;
vertical-align:text-bottom; }
.bp3-breadcrumbs-collapsed::before{
background:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cg fill='%235C7080'%3e%3ccircle cx='2' cy='8.03' r='2'/%3e%3ccircle cx='14' cy='8.03' r='2'/%3e%3ccircle cx='8' cy='8.03' r='2'/%3e%3c/g%3e%3c/svg%3e") center no-repeat;
content:"";
display:block;
height:16px;
width:16px; }
.bp3-breadcrumbs-collapsed:hover{
background:#bfccd6;
color:#182026;
text-decoration:none; }
.bp3-dark .bp3-breadcrumb,
.bp3-dark .bp3-breadcrumbs-collapsed{
color:#a7b6c2; }
.bp3-dark .bp3-breadcrumbs > li::after{
color:#a7b6c2; }
.bp3-dark .bp3-breadcrumb.bp3-disabled{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-breadcrumb-current{
color:#f5f8fa; }
.bp3-dark .bp3-breadcrumbs-collapsed{
background:rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-breadcrumbs-collapsed:hover{
background:rgba(16, 22, 26, 0.6);
color:#f5f8fa; }
.bp3-button{
display:-webkit-inline-box;
display:-ms-inline-flexbox;
display:inline-flex;
-webkit-box-orient:horizontal;
-webkit-box-direction:normal;
-ms-flex-direction:row;
flex-direction:row;
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
border:none;
border-radius:3px;
cursor:pointer;
font-size:14px;
-webkit-box-pack:center;
-ms-flex-pack:center;
justify-content:center;
padding:5px 10px;
text-align:left;
vertical-align:middle;
min-height:30px;
min-width:30px; }
.bp3-button > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-button > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-button::before,
.bp3-button > *{
margin-right:7px; }
.bp3-button:empty::before,
.bp3-button > :last-child{
margin-right:0; }
.bp3-button:empty{
padding:0 !important; }
.bp3-button:disabled, .bp3-button.bp3-disabled{
cursor:not-allowed; }
.bp3-button.bp3-fill{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
width:100%; }
.bp3-button.bp3-align-right,
.bp3-align-right .bp3-button{
text-align:right; }
.bp3-button.bp3-align-left,
.bp3-align-left .bp3-button{
text-align:left; }
.bp3-button:not([class*="bp3-intent-"]){
background-color:#f5f8fa;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
color:#182026; }
.bp3-button:not([class*="bp3-intent-"]):hover{
background-clip:padding-box;
background-color:#ebf1f5;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
.bp3-button:not([class*="bp3-intent-"]):active, .bp3-button:not([class*="bp3-intent-"]).bp3-active{
background-color:#d8e1e8;
background-image:none;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-button:not([class*="bp3-intent-"]):disabled, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled{
background-color:rgba(206, 217, 224, 0.5);
background-image:none;
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed;
outline:none; }
.bp3-button:not([class*="bp3-intent-"]):disabled.bp3-active, .bp3-button:not([class*="bp3-intent-"]):disabled.bp3-active:hover, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled.bp3-active, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled.bp3-active:hover{
background:rgba(206, 217, 224, 0.7); }
.bp3-button.bp3-intent-primary{
background-color:#137cbd;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
color:#ffffff; }
.bp3-button.bp3-intent-primary:hover, .bp3-button.bp3-intent-primary:active, .bp3-button.bp3-intent-primary.bp3-active{
color:#ffffff; }
.bp3-button.bp3-intent-primary:hover{
background-color:#106ba3;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
.bp3-button.bp3-intent-primary:active, .bp3-button.bp3-intent-primary.bp3-active{
background-color:#0e5a8a;
background-image:none;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-button.bp3-intent-primary:disabled, .bp3-button.bp3-intent-primary.bp3-disabled{
background-color:rgba(19, 124, 189, 0.5);
background-image:none;
border-color:transparent;
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(255, 255, 255, 0.6); }
.bp3-button.bp3-intent-success{
background-color:#0f9960;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
color:#ffffff; }
.bp3-button.bp3-intent-success:hover, .bp3-button.bp3-intent-success:active, .bp3-button.bp3-intent-success.bp3-active{
color:#ffffff; }
.bp3-button.bp3-intent-success:hover{
background-color:#0d8050;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
.bp3-button.bp3-intent-success:active, .bp3-button.bp3-intent-success.bp3-active{
background-color:#0a6640;
background-image:none;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-button.bp3-intent-success:disabled, .bp3-button.bp3-intent-success.bp3-disabled{
background-color:rgba(15, 153, 96, 0.5);
background-image:none;
border-color:transparent;
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(255, 255, 255, 0.6); }
.bp3-button.bp3-intent-warning{
background-color:#d9822b;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
color:#ffffff; }
.bp3-button.bp3-intent-warning:hover, .bp3-button.bp3-intent-warning:active, .bp3-button.bp3-intent-warning.bp3-active{
color:#ffffff; }
.bp3-button.bp3-intent-warning:hover{
background-color:#bf7326;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
.bp3-button.bp3-intent-warning:active, .bp3-button.bp3-intent-warning.bp3-active{
background-color:#a66321;
background-image:none;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-button.bp3-intent-warning:disabled, .bp3-button.bp3-intent-warning.bp3-disabled{
background-color:rgba(217, 130, 43, 0.5);
background-image:none;
border-color:transparent;
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(255, 255, 255, 0.6); }
.bp3-button.bp3-intent-danger{
background-color:#db3737;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
color:#ffffff; }
.bp3-button.bp3-intent-danger:hover, .bp3-button.bp3-intent-danger:active, .bp3-button.bp3-intent-danger.bp3-active{
color:#ffffff; }
.bp3-button.bp3-intent-danger:hover{
background-color:#c23030;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
.bp3-button.bp3-intent-danger:active, .bp3-button.bp3-intent-danger.bp3-active{
background-color:#a82a2a;
background-image:none;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-button.bp3-intent-danger:disabled, .bp3-button.bp3-intent-danger.bp3-disabled{
background-color:rgba(219, 55, 55, 0.5);
background-image:none;
border-color:transparent;
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(255, 255, 255, 0.6); }
.bp3-button[class*="bp3-intent-"] .bp3-button-spinner .bp3-spinner-head{
stroke:#ffffff; }
.bp3-button.bp3-large,
.bp3-large .bp3-button{
min-height:40px;
min-width:40px;
font-size:16px;
padding:5px 15px; }
.bp3-button.bp3-large::before,
.bp3-button.bp3-large > *,
.bp3-large .bp3-button::before,
.bp3-large .bp3-button > *{
margin-right:10px; }
.bp3-button.bp3-large:empty::before,
.bp3-button.bp3-large > :last-child,
.bp3-large .bp3-button:empty::before,
.bp3-large .bp3-button > :last-child{
margin-right:0; }
.bp3-button.bp3-small,
.bp3-small .bp3-button{
min-height:24px;
min-width:24px;
padding:0 7px; }
.bp3-button.bp3-loading{
position:relative; }
.bp3-button.bp3-loading[class*="bp3-icon-"]::before{
visibility:hidden; }
.bp3-button.bp3-loading .bp3-button-spinner{
margin:0;
position:absolute; }
.bp3-button.bp3-loading > :not(.bp3-button-spinner){
visibility:hidden; }
.bp3-button[class*="bp3-icon-"]::before{
font-family:"Icons16", sans-serif;
font-size:16px;
font-style:normal;
font-weight:400;
line-height:1;
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased;
color:#5c7080; }
.bp3-button .bp3-icon, .bp3-button .bp3-icon-standard, .bp3-button .bp3-icon-large{
color:#5c7080; }
.bp3-button .bp3-icon.bp3-align-right, .bp3-button .bp3-icon-standard.bp3-align-right, .bp3-button .bp3-icon-large.bp3-align-right{
margin-left:7px; }
.bp3-button .bp3-icon:first-child:last-child,
.bp3-button .bp3-spinner + .bp3-icon:last-child{
margin:0 -7px; }
.bp3-dark .bp3-button:not([class*="bp3-intent-"]){
background-color:#394b59;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
color:#f5f8fa; }
.bp3-dark .bp3-button:not([class*="bp3-intent-"]):hover, .bp3-dark .bp3-button:not([class*="bp3-intent-"]):active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-active{
color:#f5f8fa; }
.bp3-dark .bp3-button:not([class*="bp3-intent-"]):hover{
background-color:#30404d;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-button:not([class*="bp3-intent-"]):active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-active{
background-color:#202b33;
background-image:none;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-dark .bp3-button:not([class*="bp3-intent-"]):disabled, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-disabled{
background-color:rgba(57, 75, 89, 0.5);
background-image:none;
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-button:not([class*="bp3-intent-"]):disabled.bp3-active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-disabled.bp3-active{
background:rgba(57, 75, 89, 0.7); }
.bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-button-spinner .bp3-spinner-head{
background:rgba(16, 22, 26, 0.5);
stroke:#8a9ba8; }
.bp3-dark .bp3-button:not([class*="bp3-intent-"])[class*="bp3-icon-"]::before{
color:#a7b6c2; }
.bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-icon, .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-icon-standard, .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-icon-large{
color:#a7b6c2; }
.bp3-dark .bp3-button[class*="bp3-intent-"]{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-button[class*="bp3-intent-"]:hover{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-button[class*="bp3-intent-"]:active, .bp3-dark .bp3-button[class*="bp3-intent-"].bp3-active{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-dark .bp3-button[class*="bp3-intent-"]:disabled, .bp3-dark .bp3-button[class*="bp3-intent-"].bp3-disabled{
background-image:none;
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(255, 255, 255, 0.3); }
.bp3-dark .bp3-button[class*="bp3-intent-"] .bp3-button-spinner .bp3-spinner-head{
stroke:#8a9ba8; }
.bp3-button:disabled::before,
.bp3-button:disabled .bp3-icon, .bp3-button:disabled .bp3-icon-standard, .bp3-button:disabled .bp3-icon-large, .bp3-button.bp3-disabled::before,
.bp3-button.bp3-disabled .bp3-icon, .bp3-button.bp3-disabled .bp3-icon-standard, .bp3-button.bp3-disabled .bp3-icon-large, .bp3-button[class*="bp3-intent-"]::before,
.bp3-button[class*="bp3-intent-"] .bp3-icon, .bp3-button[class*="bp3-intent-"] .bp3-icon-standard, .bp3-button[class*="bp3-intent-"] .bp3-icon-large{
color:inherit !important; }
.bp3-button.bp3-minimal{
background:none;
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-button.bp3-minimal:hover{
background:rgba(167, 182, 194, 0.3);
-webkit-box-shadow:none;
box-shadow:none;
color:#182026;
text-decoration:none; }
.bp3-button.bp3-minimal:active, .bp3-button.bp3-minimal.bp3-active{
background:rgba(115, 134, 148, 0.3);
-webkit-box-shadow:none;
box-shadow:none;
color:#182026; }
.bp3-button.bp3-minimal:disabled, .bp3-button.bp3-minimal:disabled:hover, .bp3-button.bp3-minimal.bp3-disabled, .bp3-button.bp3-minimal.bp3-disabled:hover{
background:none;
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed; }
.bp3-button.bp3-minimal:disabled.bp3-active, .bp3-button.bp3-minimal:disabled:hover.bp3-active, .bp3-button.bp3-minimal.bp3-disabled.bp3-active, .bp3-button.bp3-minimal.bp3-disabled:hover.bp3-active{
background:rgba(115, 134, 148, 0.3); }
.bp3-dark .bp3-button.bp3-minimal{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:inherit; }
.bp3-dark .bp3-button.bp3-minimal:hover, .bp3-dark .bp3-button.bp3-minimal:active, .bp3-dark .bp3-button.bp3-minimal.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-button.bp3-minimal:hover{
background:rgba(138, 155, 168, 0.15); }
.bp3-dark .bp3-button.bp3-minimal:active, .bp3-dark .bp3-button.bp3-minimal.bp3-active{
background:rgba(138, 155, 168, 0.3);
color:#f5f8fa; }
.bp3-dark .bp3-button.bp3-minimal:disabled, .bp3-dark .bp3-button.bp3-minimal:disabled:hover, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled:hover{
background:none;
color:rgba(167, 182, 194, 0.6);
cursor:not-allowed; }
.bp3-dark .bp3-button.bp3-minimal:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal:disabled:hover.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled:hover.bp3-active{
background:rgba(138, 155, 168, 0.3); }
.bp3-button.bp3-minimal.bp3-intent-primary{
color:#106ba3; }
.bp3-button.bp3-minimal.bp3-intent-primary:hover, .bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#106ba3; }
.bp3-button.bp3-minimal.bp3-intent-primary:hover{
background:rgba(19, 124, 189, 0.15);
color:#106ba3; }
.bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{
background:rgba(19, 124, 189, 0.3);
color:#106ba3; }
.bp3-button.bp3-minimal.bp3-intent-primary:disabled, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled{
background:none;
color:rgba(16, 107, 163, 0.5); }
.bp3-button.bp3-minimal.bp3-intent-primary:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled.bp3-active{
background:rgba(19, 124, 189, 0.3); }
.bp3-button.bp3-minimal.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
stroke:#106ba3; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary{
color:#48aff0; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:hover{
background:rgba(19, 124, 189, 0.2);
color:#48aff0; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{
background:rgba(19, 124, 189, 0.3);
color:#48aff0; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled{
background:none;
color:rgba(72, 175, 240, 0.5); }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled.bp3-active{
background:rgba(19, 124, 189, 0.3); }
.bp3-button.bp3-minimal.bp3-intent-success{
color:#0d8050; }
.bp3-button.bp3-minimal.bp3-intent-success:hover, .bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#0d8050; }
.bp3-button.bp3-minimal.bp3-intent-success:hover{
background:rgba(15, 153, 96, 0.15);
color:#0d8050; }
.bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{
background:rgba(15, 153, 96, 0.3);
color:#0d8050; }
.bp3-button.bp3-minimal.bp3-intent-success:disabled, .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled{
background:none;
color:rgba(13, 128, 80, 0.5); }
.bp3-button.bp3-minimal.bp3-intent-success:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled.bp3-active{
background:rgba(15, 153, 96, 0.3); }
.bp3-button.bp3-minimal.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
stroke:#0d8050; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-success{
color:#3dcc91; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:hover{
background:rgba(15, 153, 96, 0.2);
color:#3dcc91; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{
background:rgba(15, 153, 96, 0.3);
color:#3dcc91; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled{
background:none;
color:rgba(61, 204, 145, 0.5); }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled.bp3-active{
background:rgba(15, 153, 96, 0.3); }
.bp3-button.bp3-minimal.bp3-intent-warning{
color:#bf7326; }
.bp3-button.bp3-minimal.bp3-intent-warning:hover, .bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#bf7326; }
.bp3-button.bp3-minimal.bp3-intent-warning:hover{
background:rgba(217, 130, 43, 0.15);
color:#bf7326; }
.bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{
background:rgba(217, 130, 43, 0.3);
color:#bf7326; }
.bp3-button.bp3-minimal.bp3-intent-warning:disabled, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled{
background:none;
color:rgba(191, 115, 38, 0.5); }
.bp3-button.bp3-minimal.bp3-intent-warning:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled.bp3-active{
background:rgba(217, 130, 43, 0.3); }
.bp3-button.bp3-minimal.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
stroke:#bf7326; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning{
color:#ffb366; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:hover{
background:rgba(217, 130, 43, 0.2);
color:#ffb366; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{
background:rgba(217, 130, 43, 0.3);
color:#ffb366; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled{
background:none;
color:rgba(255, 179, 102, 0.5); }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled.bp3-active{
background:rgba(217, 130, 43, 0.3); }
.bp3-button.bp3-minimal.bp3-intent-danger{
color:#c23030; }
.bp3-button.bp3-minimal.bp3-intent-danger:hover, .bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#c23030; }
.bp3-button.bp3-minimal.bp3-intent-danger:hover{
background:rgba(219, 55, 55, 0.15);
color:#c23030; }
.bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{
background:rgba(219, 55, 55, 0.3);
color:#c23030; }
.bp3-button.bp3-minimal.bp3-intent-danger:disabled, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled{
background:none;
color:rgba(194, 48, 48, 0.5); }
.bp3-button.bp3-minimal.bp3-intent-danger:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled.bp3-active{
background:rgba(219, 55, 55, 0.3); }
.bp3-button.bp3-minimal.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
stroke:#c23030; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger{
color:#ff7373; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:hover{
background:rgba(219, 55, 55, 0.2);
color:#ff7373; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{
background:rgba(219, 55, 55, 0.3);
color:#ff7373; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled{
background:none;
color:rgba(255, 115, 115, 0.5); }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled.bp3-active{
background:rgba(219, 55, 55, 0.3); }
.bp3-button.bp3-outlined{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
border:1px solid rgba(24, 32, 38, 0.2);
-webkit-box-sizing:border-box;
box-sizing:border-box; }
.bp3-button.bp3-outlined:hover{
background:rgba(167, 182, 194, 0.3);
-webkit-box-shadow:none;
box-shadow:none;
color:#182026;
text-decoration:none; }
.bp3-button.bp3-outlined:active, .bp3-button.bp3-outlined.bp3-active{
background:rgba(115, 134, 148, 0.3);
-webkit-box-shadow:none;
box-shadow:none;
color:#182026; }
.bp3-button.bp3-outlined:disabled, .bp3-button.bp3-outlined:disabled:hover, .bp3-button.bp3-outlined.bp3-disabled, .bp3-button.bp3-outlined.bp3-disabled:hover{
background:none;
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed; }
.bp3-button.bp3-outlined:disabled.bp3-active, .bp3-button.bp3-outlined:disabled:hover.bp3-active, .bp3-button.bp3-outlined.bp3-disabled.bp3-active, .bp3-button.bp3-outlined.bp3-disabled:hover.bp3-active{
background:rgba(115, 134, 148, 0.3); }
.bp3-dark .bp3-button.bp3-outlined{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:inherit; }
.bp3-dark .bp3-button.bp3-outlined:hover, .bp3-dark .bp3-button.bp3-outlined:active, .bp3-dark .bp3-button.bp3-outlined.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-button.bp3-outlined:hover{
background:rgba(138, 155, 168, 0.15); }
.bp3-dark .bp3-button.bp3-outlined:active, .bp3-dark .bp3-button.bp3-outlined.bp3-active{
background:rgba(138, 155, 168, 0.3);
color:#f5f8fa; }
.bp3-dark .bp3-button.bp3-outlined:disabled, .bp3-dark .bp3-button.bp3-outlined:disabled:hover, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled:hover{
background:none;
color:rgba(167, 182, 194, 0.6);
cursor:not-allowed; }
.bp3-dark .bp3-button.bp3-outlined:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined:disabled:hover.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled:hover.bp3-active{
background:rgba(138, 155, 168, 0.3); }
.bp3-button.bp3-outlined.bp3-intent-primary{
color:#106ba3; }
.bp3-button.bp3-outlined.bp3-intent-primary:hover, .bp3-button.bp3-outlined.bp3-intent-primary:active, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#106ba3; }
.bp3-button.bp3-outlined.bp3-intent-primary:hover{
background:rgba(19, 124, 189, 0.15);
color:#106ba3; }
.bp3-button.bp3-outlined.bp3-intent-primary:active, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-active{
background:rgba(19, 124, 189, 0.3);
color:#106ba3; }
.bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{
background:none;
color:rgba(16, 107, 163, 0.5); }
.bp3-button.bp3-outlined.bp3-intent-primary:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled.bp3-active{
background:rgba(19, 124, 189, 0.3); }
.bp3-button.bp3-outlined.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
stroke:#106ba3; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary{
color:#48aff0; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:hover{
background:rgba(19, 124, 189, 0.2);
color:#48aff0; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-active{
background:rgba(19, 124, 189, 0.3);
color:#48aff0; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{
background:none;
color:rgba(72, 175, 240, 0.5); }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled.bp3-active{
background:rgba(19, 124, 189, 0.3); }
.bp3-button.bp3-outlined.bp3-intent-success{
color:#0d8050; }
.bp3-button.bp3-outlined.bp3-intent-success:hover, .bp3-button.bp3-outlined.bp3-intent-success:active, .bp3-button.bp3-outlined.bp3-intent-success.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#0d8050; }
.bp3-button.bp3-outlined.bp3-intent-success:hover{
background:rgba(15, 153, 96, 0.15);
color:#0d8050; }
.bp3-button.bp3-outlined.bp3-intent-success:active, .bp3-button.bp3-outlined.bp3-intent-success.bp3-active{
background:rgba(15, 153, 96, 0.3);
color:#0d8050; }
.bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{
background:none;
color:rgba(13, 128, 80, 0.5); }
.bp3-button.bp3-outlined.bp3-intent-success:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled.bp3-active{
background:rgba(15, 153, 96, 0.3); }
.bp3-button.bp3-outlined.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
stroke:#0d8050; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-success{
color:#3dcc91; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:hover{
background:rgba(15, 153, 96, 0.2);
color:#3dcc91; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-active{
background:rgba(15, 153, 96, 0.3);
color:#3dcc91; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{
background:none;
color:rgba(61, 204, 145, 0.5); }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled.bp3-active{
background:rgba(15, 153, 96, 0.3); }
.bp3-button.bp3-outlined.bp3-intent-warning{
color:#bf7326; }
.bp3-button.bp3-outlined.bp3-intent-warning:hover, .bp3-button.bp3-outlined.bp3-intent-warning:active, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#bf7326; }
.bp3-button.bp3-outlined.bp3-intent-warning:hover{
background:rgba(217, 130, 43, 0.15);
color:#bf7326; }
.bp3-button.bp3-outlined.bp3-intent-warning:active, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-active{
background:rgba(217, 130, 43, 0.3);
color:#bf7326; }
.bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{
background:none;
color:rgba(191, 115, 38, 0.5); }
.bp3-button.bp3-outlined.bp3-intent-warning:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled.bp3-active{
background:rgba(217, 130, 43, 0.3); }
.bp3-button.bp3-outlined.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
stroke:#bf7326; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning{
color:#ffb366; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:hover{
background:rgba(217, 130, 43, 0.2);
color:#ffb366; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-active{
background:rgba(217, 130, 43, 0.3);
color:#ffb366; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{
background:none;
color:rgba(255, 179, 102, 0.5); }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled.bp3-active{
background:rgba(217, 130, 43, 0.3); }
.bp3-button.bp3-outlined.bp3-intent-danger{
color:#c23030; }
.bp3-button.bp3-outlined.bp3-intent-danger:hover, .bp3-button.bp3-outlined.bp3-intent-danger:active, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#c23030; }
.bp3-button.bp3-outlined.bp3-intent-danger:hover{
background:rgba(219, 55, 55, 0.15);
color:#c23030; }
.bp3-button.bp3-outlined.bp3-intent-danger:active, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-active{
background:rgba(219, 55, 55, 0.3);
color:#c23030; }
.bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{
background:none;
color:rgba(194, 48, 48, 0.5); }
.bp3-button.bp3-outlined.bp3-intent-danger:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled.bp3-active{
background:rgba(219, 55, 55, 0.3); }
.bp3-button.bp3-outlined.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
stroke:#c23030; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger{
color:#ff7373; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:hover{
background:rgba(219, 55, 55, 0.2);
color:#ff7373; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-active{
background:rgba(219, 55, 55, 0.3);
color:#ff7373; }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{
background:none;
color:rgba(255, 115, 115, 0.5); }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled.bp3-active{
background:rgba(219, 55, 55, 0.3); }
.bp3-button.bp3-outlined:disabled, .bp3-button.bp3-outlined.bp3-disabled, .bp3-button.bp3-outlined:disabled:hover, .bp3-button.bp3-outlined.bp3-disabled:hover{
border-color:rgba(92, 112, 128, 0.1); }
.bp3-dark .bp3-button.bp3-outlined{
border-color:rgba(255, 255, 255, 0.4); }
.bp3-dark .bp3-button.bp3-outlined:disabled, .bp3-dark .bp3-button.bp3-outlined:disabled:hover, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled:hover{
border-color:rgba(255, 255, 255, 0.2); }
.bp3-button.bp3-outlined.bp3-intent-primary{
border-color:rgba(16, 107, 163, 0.6); }
.bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{
border-color:rgba(16, 107, 163, 0.2); }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary{
border-color:rgba(72, 175, 240, 0.6); }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{
border-color:rgba(72, 175, 240, 0.2); }
.bp3-button.bp3-outlined.bp3-intent-success{
border-color:rgba(13, 128, 80, 0.6); }
.bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{
border-color:rgba(13, 128, 80, 0.2); }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-success{
border-color:rgba(61, 204, 145, 0.6); }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{
border-color:rgba(61, 204, 145, 0.2); }
.bp3-button.bp3-outlined.bp3-intent-warning{
border-color:rgba(191, 115, 38, 0.6); }
.bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{
border-color:rgba(191, 115, 38, 0.2); }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning{
border-color:rgba(255, 179, 102, 0.6); }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{
border-color:rgba(255, 179, 102, 0.2); }
.bp3-button.bp3-outlined.bp3-intent-danger{
border-color:rgba(194, 48, 48, 0.6); }
.bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{
border-color:rgba(194, 48, 48, 0.2); }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger{
border-color:rgba(255, 115, 115, 0.6); }
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{
border-color:rgba(255, 115, 115, 0.2); }
a.bp3-button{
text-align:center;
text-decoration:none;
-webkit-transition:none;
transition:none; }
a.bp3-button, a.bp3-button:hover, a.bp3-button:active{
color:#182026; }
a.bp3-button.bp3-disabled{
color:rgba(92, 112, 128, 0.6); }
.bp3-button-text{
-webkit-box-flex:0;
-ms-flex:0 1 auto;
flex:0 1 auto; }
.bp3-button.bp3-align-left .bp3-button-text, .bp3-button.bp3-align-right .bp3-button-text,
.bp3-button-group.bp3-align-left .bp3-button-text,
.bp3-button-group.bp3-align-right .bp3-button-text{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto; }
.bp3-button-group{
display:-webkit-inline-box;
display:-ms-inline-flexbox;
display:inline-flex; }
.bp3-button-group .bp3-button{
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
position:relative;
z-index:4; }
.bp3-button-group .bp3-button:focus{
z-index:5; }
.bp3-button-group .bp3-button:hover{
z-index:6; }
.bp3-button-group .bp3-button:active, .bp3-button-group .bp3-button.bp3-active{
z-index:7; }
.bp3-button-group .bp3-button:disabled, .bp3-button-group .bp3-button.bp3-disabled{
z-index:3; }
.bp3-button-group .bp3-button[class*="bp3-intent-"]{
z-index:9; }
.bp3-button-group .bp3-button[class*="bp3-intent-"]:focus{
z-index:10; }
.bp3-button-group .bp3-button[class*="bp3-intent-"]:hover{
z-index:11; }
.bp3-button-group .bp3-button[class*="bp3-intent-"]:active, .bp3-button-group .bp3-button[class*="bp3-intent-"].bp3-active{
z-index:12; }
.bp3-button-group .bp3-button[class*="bp3-intent-"]:disabled, .bp3-button-group .bp3-button[class*="bp3-intent-"].bp3-disabled{
z-index:8; }
.bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:first-child) .bp3-button,
.bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:first-child){
border-bottom-left-radius:0;
border-top-left-radius:0; }
.bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button,
.bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:last-child){
border-bottom-right-radius:0;
border-top-right-radius:0;
margin-right:-1px; }
.bp3-button-group.bp3-minimal .bp3-button{
background:none;
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-button-group.bp3-minimal .bp3-button:hover{
background:rgba(167, 182, 194, 0.3);
-webkit-box-shadow:none;
box-shadow:none;
color:#182026;
text-decoration:none; }
.bp3-button-group.bp3-minimal .bp3-button:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-active{
background:rgba(115, 134, 148, 0.3);
-webkit-box-shadow:none;
box-shadow:none;
color:#182026; }
.bp3-button-group.bp3-minimal .bp3-button:disabled, .bp3-button-group.bp3-minimal .bp3-button:disabled:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover{
background:none;
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed; }
.bp3-button-group.bp3-minimal .bp3-button:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button:disabled:hover.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover.bp3-active{
background:rgba(115, 134, 148, 0.3); }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:inherit; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button:hover, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button:hover{
background:rgba(138, 155, 168, 0.15); }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-active{
background:rgba(138, 155, 168, 0.3);
color:#f5f8fa; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled:hover, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover{
background:none;
color:rgba(167, 182, 194, 0.6);
cursor:not-allowed; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled:hover.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover.bp3-active{
background:rgba(138, 155, 168, 0.3); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary{
color:#106ba3; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#106ba3; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover{
background:rgba(19, 124, 189, 0.15);
color:#106ba3; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{
background:rgba(19, 124, 189, 0.3);
color:#106ba3; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled{
background:none;
color:rgba(16, 107, 163, 0.5); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled.bp3-active{
background:rgba(19, 124, 189, 0.3); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
stroke:#106ba3; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary{
color:#48aff0; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover{
background:rgba(19, 124, 189, 0.2);
color:#48aff0; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{
background:rgba(19, 124, 189, 0.3);
color:#48aff0; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled{
background:none;
color:rgba(72, 175, 240, 0.5); }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled.bp3-active{
background:rgba(19, 124, 189, 0.3); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success{
color:#0d8050; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#0d8050; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover{
background:rgba(15, 153, 96, 0.15);
color:#0d8050; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{
background:rgba(15, 153, 96, 0.3);
color:#0d8050; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled{
background:none;
color:rgba(13, 128, 80, 0.5); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled.bp3-active{
background:rgba(15, 153, 96, 0.3); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
stroke:#0d8050; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success{
color:#3dcc91; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover{
background:rgba(15, 153, 96, 0.2);
color:#3dcc91; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{
background:rgba(15, 153, 96, 0.3);
color:#3dcc91; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled{
background:none;
color:rgba(61, 204, 145, 0.5); }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled.bp3-active{
background:rgba(15, 153, 96, 0.3); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning{
color:#bf7326; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#bf7326; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover{
background:rgba(217, 130, 43, 0.15);
color:#bf7326; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{
background:rgba(217, 130, 43, 0.3);
color:#bf7326; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled{
background:none;
color:rgba(191, 115, 38, 0.5); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled.bp3-active{
background:rgba(217, 130, 43, 0.3); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
stroke:#bf7326; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning{
color:#ffb366; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover{
background:rgba(217, 130, 43, 0.2);
color:#ffb366; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{
background:rgba(217, 130, 43, 0.3);
color:#ffb366; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled{
background:none;
color:rgba(255, 179, 102, 0.5); }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled.bp3-active{
background:rgba(217, 130, 43, 0.3); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger{
color:#c23030; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#c23030; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover{
background:rgba(219, 55, 55, 0.15);
color:#c23030; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{
background:rgba(219, 55, 55, 0.3);
color:#c23030; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled{
background:none;
color:rgba(194, 48, 48, 0.5); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled.bp3-active{
background:rgba(219, 55, 55, 0.3); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
stroke:#c23030; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger{
color:#ff7373; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover{
background:rgba(219, 55, 55, 0.2);
color:#ff7373; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{
background:rgba(219, 55, 55, 0.3);
color:#ff7373; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled{
background:none;
color:rgba(255, 115, 115, 0.5); }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled.bp3-active{
background:rgba(219, 55, 55, 0.3); }
.bp3-button-group .bp3-popover-wrapper,
.bp3-button-group .bp3-popover-target{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto; }
.bp3-button-group.bp3-fill{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
width:100%; }
.bp3-button-group .bp3-button.bp3-fill,
.bp3-button-group.bp3-fill .bp3-button:not(.bp3-fixed){
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto; }
.bp3-button-group.bp3-vertical{
-webkit-box-align:stretch;
-ms-flex-align:stretch;
align-items:stretch;
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-ms-flex-direction:column;
flex-direction:column;
vertical-align:top; }
.bp3-button-group.bp3-vertical.bp3-fill{
height:100%;
width:unset; }
.bp3-button-group.bp3-vertical .bp3-button{
margin-right:0 !important;
width:100%; }
.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:first-child .bp3-button,
.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:first-child{
border-radius:3px 3px 0 0; }
.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:last-child .bp3-button,
.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:last-child{
border-radius:0 0 3px 3px; }
.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button,
.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:not(:last-child){
margin-bottom:-1px; }
.bp3-button-group.bp3-align-left .bp3-button{
text-align:left; }
.bp3-dark .bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button,
.bp3-dark .bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:last-child){
margin-right:1px; }
.bp3-dark .bp3-button-group.bp3-vertical > .bp3-popover-wrapper:not(:last-child) .bp3-button,
.bp3-dark .bp3-button-group.bp3-vertical > .bp3-button:not(:last-child){
margin-bottom:1px; }
.bp3-callout{
font-size:14px;
line-height:1.5;
background-color:rgba(138, 155, 168, 0.15);
border-radius:3px;
padding:10px 12px 9px;
position:relative;
width:100%; }
.bp3-callout[class*="bp3-icon-"]{
padding-left:40px; }
.bp3-callout[class*="bp3-icon-"]::before{
font-family:"Icons20", sans-serif;
font-size:20px;
font-style:normal;
font-weight:400;
line-height:1;
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased;
color:#5c7080;
left:10px;
position:absolute;
top:10px; }
.bp3-callout.bp3-callout-icon{
padding-left:40px; }
.bp3-callout.bp3-callout-icon > .bp3-icon:first-child{
color:#5c7080;
left:10px;
position:absolute;
top:10px; }
.bp3-callout .bp3-heading{
line-height:20px;
margin-bottom:5px;
margin-top:0; }
.bp3-callout .bp3-heading:last-child{
margin-bottom:0; }
.bp3-dark .bp3-callout{
background-color:rgba(138, 155, 168, 0.2); }
.bp3-dark .bp3-callout[class*="bp3-icon-"]::before{
color:#a7b6c2; }
.bp3-callout.bp3-intent-primary{
background-color:rgba(19, 124, 189, 0.15); }
.bp3-callout.bp3-intent-primary[class*="bp3-icon-"]::before,
.bp3-callout.bp3-intent-primary > .bp3-icon:first-child,
.bp3-callout.bp3-intent-primary .bp3-heading{
color:#106ba3; }
.bp3-dark .bp3-callout.bp3-intent-primary{
background-color:rgba(19, 124, 189, 0.25); }
.bp3-dark .bp3-callout.bp3-intent-primary[class*="bp3-icon-"]::before,
.bp3-dark .bp3-callout.bp3-intent-primary > .bp3-icon:first-child,
.bp3-dark .bp3-callout.bp3-intent-primary .bp3-heading{
color:#48aff0; }
.bp3-callout.bp3-intent-success{
background-color:rgba(15, 153, 96, 0.15); }
.bp3-callout.bp3-intent-success[class*="bp3-icon-"]::before,
.bp3-callout.bp3-intent-success > .bp3-icon:first-child,
.bp3-callout.bp3-intent-success .bp3-heading{
color:#0d8050; }
.bp3-dark .bp3-callout.bp3-intent-success{
background-color:rgba(15, 153, 96, 0.25); }
.bp3-dark .bp3-callout.bp3-intent-success[class*="bp3-icon-"]::before,
.bp3-dark .bp3-callout.bp3-intent-success > .bp3-icon:first-child,
.bp3-dark .bp3-callout.bp3-intent-success .bp3-heading{
color:#3dcc91; }
.bp3-callout.bp3-intent-warning{
background-color:rgba(217, 130, 43, 0.15); }
.bp3-callout.bp3-intent-warning[class*="bp3-icon-"]::before,
.bp3-callout.bp3-intent-warning > .bp3-icon:first-child,
.bp3-callout.bp3-intent-warning .bp3-heading{
color:#bf7326; }
.bp3-dark .bp3-callout.bp3-intent-warning{
background-color:rgba(217, 130, 43, 0.25); }
.bp3-dark .bp3-callout.bp3-intent-warning[class*="bp3-icon-"]::before,
.bp3-dark .bp3-callout.bp3-intent-warning > .bp3-icon:first-child,
.bp3-dark .bp3-callout.bp3-intent-warning .bp3-heading{
color:#ffb366; }
.bp3-callout.bp3-intent-danger{
background-color:rgba(219, 55, 55, 0.15); }
.bp3-callout.bp3-intent-danger[class*="bp3-icon-"]::before,
.bp3-callout.bp3-intent-danger > .bp3-icon:first-child,
.bp3-callout.bp3-intent-danger .bp3-heading{
color:#c23030; }
.bp3-dark .bp3-callout.bp3-intent-danger{
background-color:rgba(219, 55, 55, 0.25); }
.bp3-dark .bp3-callout.bp3-intent-danger[class*="bp3-icon-"]::before,
.bp3-dark .bp3-callout.bp3-intent-danger > .bp3-icon:first-child,
.bp3-dark .bp3-callout.bp3-intent-danger .bp3-heading{
color:#ff7373; }
.bp3-running-text .bp3-callout{
margin:20px 0; }
.bp3-card{
background-color:#ffffff;
border-radius:3px;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
padding:20px;
-webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-card.bp3-dark,
.bp3-dark .bp3-card{
background-color:#30404d;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); }
.bp3-elevation-0{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); }
.bp3-elevation-0.bp3-dark,
.bp3-dark .bp3-elevation-0{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); }
.bp3-elevation-1{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-elevation-1.bp3-dark,
.bp3-dark .bp3-elevation-1{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-elevation-2{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 1px 1px rgba(16, 22, 26, 0.2), 0 2px 6px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 1px 1px rgba(16, 22, 26, 0.2), 0 2px 6px rgba(16, 22, 26, 0.2); }
.bp3-elevation-2.bp3-dark,
.bp3-dark .bp3-elevation-2{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.4), 0 2px 6px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.4), 0 2px 6px rgba(16, 22, 26, 0.4); }
.bp3-elevation-3{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); }
.bp3-elevation-3.bp3-dark,
.bp3-dark .bp3-elevation-3{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
.bp3-elevation-4{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2); }
.bp3-elevation-4.bp3-dark,
.bp3-dark .bp3-elevation-4{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); }
.bp3-card.bp3-interactive:hover{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
cursor:pointer; }
.bp3-card.bp3-interactive:hover.bp3-dark,
.bp3-dark .bp3-card.bp3-interactive:hover{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
.bp3-card.bp3-interactive:active{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
opacity:0.9;
-webkit-transition-duration:0;
transition-duration:0; }
.bp3-card.bp3-interactive:active.bp3-dark,
.bp3-dark .bp3-card.bp3-interactive:active{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-collapse{
height:0;
overflow-y:hidden;
-webkit-transition:height 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:height 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-collapse .bp3-collapse-body{
-webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-collapse .bp3-collapse-body[aria-hidden="true"]{
display:none; }
.bp3-context-menu .bp3-popover-target{
display:block; }
.bp3-context-menu-popover-target{
position:fixed; }
.bp3-divider{
border-bottom:1px solid rgba(16, 22, 26, 0.15);
border-right:1px solid rgba(16, 22, 26, 0.15);
margin:5px; }
.bp3-dark .bp3-divider{
border-color:rgba(16, 22, 26, 0.4); }
.bp3-dialog-container{
opacity:1;
-webkit-transform:scale(1);
transform:scale(1);
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-pack:center;
-ms-flex-pack:center;
justify-content:center;
min-height:100%;
pointer-events:none;
-webkit-user-select:none;
-moz-user-select:none;
-ms-user-select:none;
user-select:none;
width:100%; }
.bp3-dialog-container.bp3-overlay-enter > .bp3-dialog, .bp3-dialog-container.bp3-overlay-appear > .bp3-dialog{
opacity:0;
-webkit-transform:scale(0.5);
transform:scale(0.5); }
.bp3-dialog-container.bp3-overlay-enter-active > .bp3-dialog, .bp3-dialog-container.bp3-overlay-appear-active > .bp3-dialog{
opacity:1;
-webkit-transform:scale(1);
transform:scale(1);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:300ms;
transition-duration:300ms;
-webkit-transition-property:opacity, -webkit-transform;
transition-property:opacity, -webkit-transform;
transition-property:opacity, transform;
transition-property:opacity, transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
.bp3-dialog-container.bp3-overlay-exit > .bp3-dialog{
opacity:1;
-webkit-transform:scale(1);
transform:scale(1); }
.bp3-dialog-container.bp3-overlay-exit-active > .bp3-dialog{
opacity:0;
-webkit-transform:scale(0.5);
transform:scale(0.5);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:300ms;
transition-duration:300ms;
-webkit-transition-property:opacity, -webkit-transform;
transition-property:opacity, -webkit-transform;
transition-property:opacity, transform;
transition-property:opacity, transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
.bp3-dialog{
background:#ebf1f5;
border-radius:6px;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-ms-flex-direction:column;
flex-direction:column;
margin:30px 0;
padding-bottom:20px;
pointer-events:all;
-webkit-user-select:text;
-moz-user-select:text;
-ms-user-select:text;
user-select:text;
width:500px; }
.bp3-dialog:focus{
outline:0; }
.bp3-dialog.bp3-dark,
.bp3-dark .bp3-dialog{
background:#293742;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
color:#f5f8fa; }
.bp3-dialog-header{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
background:#ffffff;
border-radius:6px 6px 0 0;
-webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
min-height:40px;
padding-left:20px;
padding-right:5px;
z-index:30; }
.bp3-dialog-header .bp3-icon-large,
.bp3-dialog-header .bp3-icon{
color:#5c7080;
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
margin-right:10px; }
.bp3-dialog-header .bp3-heading{
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal;
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
line-height:inherit;
margin:0; }
.bp3-dialog-header .bp3-heading:last-child{
margin-right:20px; }
.bp3-dark .bp3-dialog-header{
background:#30404d;
-webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.4);
box-shadow:0 1px 0 rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-dialog-header .bp3-icon-large,
.bp3-dark .bp3-dialog-header .bp3-icon{
color:#a7b6c2; }
.bp3-dialog-body{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
line-height:18px;
margin:20px; }
.bp3-dialog-footer{
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
margin:0 20px; }
.bp3-dialog-footer-actions{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-pack:end;
-ms-flex-pack:end;
justify-content:flex-end; }
.bp3-dialog-footer-actions .bp3-button{
margin-left:10px; }
.bp3-multistep-dialog-panels{
display:-webkit-box;
display:-ms-flexbox;
display:flex; }
.bp3-multistep-dialog-left-panel{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-flex:1;
-ms-flex:1;
flex:1;
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-ms-flex-direction:column;
flex-direction:column; }
.bp3-dark .bp3-multistep-dialog-left-panel{
background:#202b33; }
.bp3-multistep-dialog-right-panel{
background-color:#f5f8fa;
border-left:1px solid rgba(16, 22, 26, 0.15);
border-radius:0 0 6px 0;
-webkit-box-flex:3;
-ms-flex:3;
flex:3;
min-width:0; }
.bp3-dark .bp3-multistep-dialog-right-panel{
background-color:#293742;
border-left:1px solid rgba(16, 22, 26, 0.4); }
.bp3-multistep-dialog-footer{
background-color:#ffffff;
border-radius:0 0 6px 0;
border-top:1px solid rgba(16, 22, 26, 0.15);
padding:10px; }
.bp3-dark .bp3-multistep-dialog-footer{
background:#30404d;
border-top:1px solid rgba(16, 22, 26, 0.4); }
.bp3-dialog-step-container{
background-color:#f5f8fa;
border-bottom:1px solid rgba(16, 22, 26, 0.15); }
.bp3-dark .bp3-dialog-step-container{
background:#293742;
border-bottom:1px solid rgba(16, 22, 26, 0.4); }
.bp3-dialog-step-container.bp3-dialog-step-viewed{
background-color:#ffffff; }
.bp3-dark .bp3-dialog-step-container.bp3-dialog-step-viewed{
background:#30404d; }
.bp3-dialog-step{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
background-color:#f5f8fa;
border-radius:6px;
cursor:not-allowed;
display:-webkit-box;
display:-ms-flexbox;
display:flex;
margin:4px;
padding:6px 14px; }
.bp3-dark .bp3-dialog-step{
background:#293742; }
.bp3-dialog-step-viewed .bp3-dialog-step{
background-color:#ffffff;
cursor:pointer; }
.bp3-dark .bp3-dialog-step-viewed .bp3-dialog-step{
background:#30404d; }
.bp3-dialog-step:hover{
background-color:#f5f8fa; }
.bp3-dark .bp3-dialog-step:hover{
background:#293742; }
.bp3-dialog-step-icon{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
background-color:rgba(92, 112, 128, 0.6);
border-radius:50%;
color:#ffffff;
display:-webkit-box;
display:-ms-flexbox;
display:flex;
height:25px;
-webkit-box-pack:center;
-ms-flex-pack:center;
justify-content:center;
width:25px; }
.bp3-dark .bp3-dialog-step-icon{
background-color:rgba(167, 182, 194, 0.6); }
.bp3-active.bp3-dialog-step-viewed .bp3-dialog-step-icon{
background-color:#2b95d6; }
.bp3-dialog-step-viewed .bp3-dialog-step-icon{
background-color:#8a9ba8; }
.bp3-dialog-step-title{
color:rgba(92, 112, 128, 0.6);
-webkit-box-flex:1;
-ms-flex:1;
flex:1;
padding-left:10px; }
.bp3-dark .bp3-dialog-step-title{
color:rgba(167, 182, 194, 0.6); }
.bp3-active.bp3-dialog-step-viewed .bp3-dialog-step-title{
color:#2b95d6; }
.bp3-dialog-step-viewed:not(.bp3-active) .bp3-dialog-step-title{
color:#182026; }
.bp3-dark .bp3-dialog-step-viewed:not(.bp3-active) .bp3-dialog-step-title{
color:#f5f8fa; }
.bp3-drawer{
background:#ffffff;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-ms-flex-direction:column;
flex-direction:column;
margin:0;
padding:0; }
.bp3-drawer:focus{
outline:0; }
.bp3-drawer.bp3-position-top{
height:50%;
left:0;
right:0;
top:0; }
.bp3-drawer.bp3-position-top.bp3-overlay-enter, .bp3-drawer.bp3-position-top.bp3-overlay-appear{
-webkit-transform:translateY(-100%);
transform:translateY(-100%); }
.bp3-drawer.bp3-position-top.bp3-overlay-enter-active, .bp3-drawer.bp3-position-top.bp3-overlay-appear-active{
-webkit-transform:translateY(0);
transform:translateY(0);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:200ms;
transition-duration:200ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-drawer.bp3-position-top.bp3-overlay-exit{
-webkit-transform:translateY(0);
transform:translateY(0); }
.bp3-drawer.bp3-position-top.bp3-overlay-exit-active{
-webkit-transform:translateY(-100%);
transform:translateY(-100%);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:100ms;
transition-duration:100ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-drawer.bp3-position-bottom{
bottom:0;
height:50%;
left:0;
right:0; }
.bp3-drawer.bp3-position-bottom.bp3-overlay-enter, .bp3-drawer.bp3-position-bottom.bp3-overlay-appear{
-webkit-transform:translateY(100%);
transform:translateY(100%); }
.bp3-drawer.bp3-position-bottom.bp3-overlay-enter-active, .bp3-drawer.bp3-position-bottom.bp3-overlay-appear-active{
-webkit-transform:translateY(0);
transform:translateY(0);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:200ms;
transition-duration:200ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-drawer.bp3-position-bottom.bp3-overlay-exit{
-webkit-transform:translateY(0);
transform:translateY(0); }
.bp3-drawer.bp3-position-bottom.bp3-overlay-exit-active{
-webkit-transform:translateY(100%);
transform:translateY(100%);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:100ms;
transition-duration:100ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-drawer.bp3-position-left{
bottom:0;
left:0;
top:0;
width:50%; }
.bp3-drawer.bp3-position-left.bp3-overlay-enter, .bp3-drawer.bp3-position-left.bp3-overlay-appear{
-webkit-transform:translateX(-100%);
transform:translateX(-100%); }
.bp3-drawer.bp3-position-left.bp3-overlay-enter-active, .bp3-drawer.bp3-position-left.bp3-overlay-appear-active{
-webkit-transform:translateX(0);
transform:translateX(0);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:200ms;
transition-duration:200ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-drawer.bp3-position-left.bp3-overlay-exit{
-webkit-transform:translateX(0);
transform:translateX(0); }
.bp3-drawer.bp3-position-left.bp3-overlay-exit-active{
-webkit-transform:translateX(-100%);
transform:translateX(-100%);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:100ms;
transition-duration:100ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-drawer.bp3-position-right{
bottom:0;
right:0;
top:0;
width:50%; }
.bp3-drawer.bp3-position-right.bp3-overlay-enter, .bp3-drawer.bp3-position-right.bp3-overlay-appear{
-webkit-transform:translateX(100%);
transform:translateX(100%); }
.bp3-drawer.bp3-position-right.bp3-overlay-enter-active, .bp3-drawer.bp3-position-right.bp3-overlay-appear-active{
-webkit-transform:translateX(0);
transform:translateX(0);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:200ms;
transition-duration:200ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-drawer.bp3-position-right.bp3-overlay-exit{
-webkit-transform:translateX(0);
transform:translateX(0); }
.bp3-drawer.bp3-position-right.bp3-overlay-exit-active{
-webkit-transform:translateX(100%);
transform:translateX(100%);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:100ms;
transition-duration:100ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right):not(.bp3-vertical){
bottom:0;
right:0;
top:0;
width:50%; }
.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right):not(.bp3-vertical).bp3-overlay-enter, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right):not(.bp3-vertical).bp3-overlay-appear{
-webkit-transform:translateX(100%);
transform:translateX(100%); }
.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right):not(.bp3-vertical).bp3-overlay-enter-active, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right):not(.bp3-vertical).bp3-overlay-appear-active{
-webkit-transform:translateX(0);
transform:translateX(0);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:200ms;
transition-duration:200ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right):not(.bp3-vertical).bp3-overlay-exit{
-webkit-transform:translateX(0);
transform:translateX(0); }
.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right):not(.bp3-vertical).bp3-overlay-exit-active{
-webkit-transform:translateX(100%);
transform:translateX(100%);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:100ms;
transition-duration:100ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right).bp3-vertical{
bottom:0;
height:50%;
left:0;
right:0; }
.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right).bp3-vertical.bp3-overlay-enter, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right).bp3-vertical.bp3-overlay-appear{
-webkit-transform:translateY(100%);
transform:translateY(100%); }
.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right).bp3-vertical.bp3-overlay-enter-active, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right).bp3-vertical.bp3-overlay-appear-active{
-webkit-transform:translateY(0);
transform:translateY(0);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:200ms;
transition-duration:200ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right).bp3-vertical.bp3-overlay-exit{
-webkit-transform:translateY(0);
transform:translateY(0); }
.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right).bp3-vertical.bp3-overlay-exit-active{
-webkit-transform:translateY(100%);
transform:translateY(100%);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:100ms;
transition-duration:100ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-drawer.bp3-dark,
.bp3-dark .bp3-drawer{
background:#30404d;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
color:#f5f8fa; }
.bp3-drawer-header{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
border-radius:0;
-webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
min-height:40px;
padding:5px;
padding-left:20px;
position:relative; }
.bp3-drawer-header .bp3-icon-large,
.bp3-drawer-header .bp3-icon{
color:#5c7080;
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
margin-right:10px; }
.bp3-drawer-header .bp3-heading{
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal;
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
line-height:inherit;
margin:0; }
.bp3-drawer-header .bp3-heading:last-child{
margin-right:20px; }
.bp3-dark .bp3-drawer-header{
-webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.4);
box-shadow:0 1px 0 rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-drawer-header .bp3-icon-large,
.bp3-dark .bp3-drawer-header .bp3-icon{
color:#a7b6c2; }
.bp3-drawer-body{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
line-height:18px;
overflow:auto; }
.bp3-drawer-footer{
-webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
padding:10px 20px;
position:relative; }
.bp3-dark .bp3-drawer-footer{
-webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.4);
box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.4); }
.bp3-editable-text{
cursor:text;
display:inline-block;
max-width:100%;
position:relative;
vertical-align:top;
white-space:nowrap; }
.bp3-editable-text::before{
bottom:-3px;
left:-3px;
position:absolute;
right:-3px;
top:-3px;
border-radius:3px;
content:"";
-webkit-transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-editable-text:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15);
box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15); }
.bp3-editable-text.bp3-editable-text-editing::before{
background-color:#ffffff;
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-editable-text.bp3-disabled::before{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-editable-text.bp3-intent-primary .bp3-editable-text-input,
.bp3-editable-text.bp3-intent-primary .bp3-editable-text-content{
color:#137cbd; }
.bp3-editable-text.bp3-intent-primary:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(19, 124, 189, 0.4);
box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(19, 124, 189, 0.4); }
.bp3-editable-text.bp3-intent-primary.bp3-editable-text-editing::before{
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-editable-text.bp3-intent-success .bp3-editable-text-input,
.bp3-editable-text.bp3-intent-success .bp3-editable-text-content{
color:#0f9960; }
.bp3-editable-text.bp3-intent-success:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px rgba(15, 153, 96, 0.4);
box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px rgba(15, 153, 96, 0.4); }
.bp3-editable-text.bp3-intent-success.bp3-editable-text-editing::before{
-webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-editable-text.bp3-intent-warning .bp3-editable-text-input,
.bp3-editable-text.bp3-intent-warning .bp3-editable-text-content{
color:#d9822b; }
.bp3-editable-text.bp3-intent-warning:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px rgba(217, 130, 43, 0.4);
box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px rgba(217, 130, 43, 0.4); }
.bp3-editable-text.bp3-intent-warning.bp3-editable-text-editing::before{
-webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-editable-text.bp3-intent-danger .bp3-editable-text-input,
.bp3-editable-text.bp3-intent-danger .bp3-editable-text-content{
color:#db3737; }
.bp3-editable-text.bp3-intent-danger:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px rgba(219, 55, 55, 0.4);
box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px rgba(219, 55, 55, 0.4); }
.bp3-editable-text.bp3-intent-danger.bp3-editable-text-editing::before{
-webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-dark .bp3-editable-text:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(255, 255, 255, 0.15);
box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(255, 255, 255, 0.15); }
.bp3-dark .bp3-editable-text.bp3-editable-text-editing::before{
background-color:rgba(16, 22, 26, 0.3);
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-editable-text.bp3-disabled::before{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-editable-text.bp3-intent-primary .bp3-editable-text-content{
color:#48aff0; }
.bp3-dark .bp3-editable-text.bp3-intent-primary:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(72, 175, 240, 0), 0 0 0 0 rgba(72, 175, 240, 0), inset 0 0 0 1px rgba(72, 175, 240, 0.4);
box-shadow:0 0 0 0 rgba(72, 175, 240, 0), 0 0 0 0 rgba(72, 175, 240, 0), inset 0 0 0 1px rgba(72, 175, 240, 0.4); }
.bp3-dark .bp3-editable-text.bp3-intent-primary.bp3-editable-text-editing::before{
-webkit-box-shadow:0 0 0 1px #48aff0, 0 0 0 3px rgba(72, 175, 240, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #48aff0, 0 0 0 3px rgba(72, 175, 240, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-editable-text.bp3-intent-success .bp3-editable-text-content{
color:#3dcc91; }
.bp3-dark .bp3-editable-text.bp3-intent-success:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(61, 204, 145, 0), 0 0 0 0 rgba(61, 204, 145, 0), inset 0 0 0 1px rgba(61, 204, 145, 0.4);
box-shadow:0 0 0 0 rgba(61, 204, 145, 0), 0 0 0 0 rgba(61, 204, 145, 0), inset 0 0 0 1px rgba(61, 204, 145, 0.4); }
.bp3-dark .bp3-editable-text.bp3-intent-success.bp3-editable-text-editing::before{
-webkit-box-shadow:0 0 0 1px #3dcc91, 0 0 0 3px rgba(61, 204, 145, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #3dcc91, 0 0 0 3px rgba(61, 204, 145, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-editable-text.bp3-intent-warning .bp3-editable-text-content{
color:#ffb366; }
.bp3-dark .bp3-editable-text.bp3-intent-warning:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(255, 179, 102, 0), 0 0 0 0 rgba(255, 179, 102, 0), inset 0 0 0 1px rgba(255, 179, 102, 0.4);
box-shadow:0 0 0 0 rgba(255, 179, 102, 0), 0 0 0 0 rgba(255, 179, 102, 0), inset 0 0 0 1px rgba(255, 179, 102, 0.4); }
.bp3-dark .bp3-editable-text.bp3-intent-warning.bp3-editable-text-editing::before{
-webkit-box-shadow:0 0 0 1px #ffb366, 0 0 0 3px rgba(255, 179, 102, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #ffb366, 0 0 0 3px rgba(255, 179, 102, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-editable-text.bp3-intent-danger .bp3-editable-text-content{
color:#ff7373; }
.bp3-dark .bp3-editable-text.bp3-intent-danger:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(255, 115, 115, 0), 0 0 0 0 rgba(255, 115, 115, 0), inset 0 0 0 1px rgba(255, 115, 115, 0.4);
box-shadow:0 0 0 0 rgba(255, 115, 115, 0), 0 0 0 0 rgba(255, 115, 115, 0), inset 0 0 0 1px rgba(255, 115, 115, 0.4); }
.bp3-dark .bp3-editable-text.bp3-intent-danger.bp3-editable-text-editing::before{
-webkit-box-shadow:0 0 0 1px #ff7373, 0 0 0 3px rgba(255, 115, 115, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #ff7373, 0 0 0 3px rgba(255, 115, 115, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-editable-text-input,
.bp3-editable-text-content{
color:inherit;
display:inherit;
font:inherit;
letter-spacing:inherit;
max-width:inherit;
min-width:inherit;
position:relative;
resize:none;
text-transform:inherit;
vertical-align:top; }
.bp3-editable-text-input{
background:none;
border:none;
-webkit-box-shadow:none;
box-shadow:none;
padding:0;
white-space:pre-wrap;
width:100%; }
.bp3-editable-text-input::-webkit-input-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-editable-text-input::-moz-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-editable-text-input:-ms-input-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-editable-text-input::-ms-input-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-editable-text-input::placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-editable-text-input:focus{
outline:none; }
.bp3-editable-text-input::-ms-clear{
display:none; }
.bp3-editable-text-content{
overflow:hidden;
padding-right:2px;
text-overflow:ellipsis;
white-space:pre; }
.bp3-editable-text-editing > .bp3-editable-text-content{
left:0;
position:absolute;
visibility:hidden; }
.bp3-editable-text-placeholder > .bp3-editable-text-content{
color:rgba(92, 112, 128, 0.6); }
.bp3-dark .bp3-editable-text-placeholder > .bp3-editable-text-content{
color:rgba(167, 182, 194, 0.6); }
.bp3-editable-text.bp3-multiline{
display:block; }
.bp3-editable-text.bp3-multiline .bp3-editable-text-content{
overflow:auto;
white-space:pre-wrap;
word-wrap:break-word; }
.bp3-divider{
border-bottom:1px solid rgba(16, 22, 26, 0.15);
border-right:1px solid rgba(16, 22, 26, 0.15);
margin:5px; }
.bp3-dark .bp3-divider{
border-color:rgba(16, 22, 26, 0.4); }
.bp3-control-group{
-webkit-transform:translateZ(0);
transform:translateZ(0);
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:horizontal;
-webkit-box-direction:normal;
-ms-flex-direction:row;
flex-direction:row;
-webkit-box-align:stretch;
-ms-flex-align:stretch;
align-items:stretch; }
.bp3-control-group > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-control-group > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-control-group .bp3-button,
.bp3-control-group .bp3-html-select,
.bp3-control-group .bp3-input,
.bp3-control-group .bp3-select{
position:relative; }
.bp3-control-group .bp3-input{
border-radius:inherit;
z-index:2; }
.bp3-control-group .bp3-input:focus{
border-radius:3px;
z-index:14; }
.bp3-control-group .bp3-input[class*="bp3-intent"]{
z-index:13; }
.bp3-control-group .bp3-input[class*="bp3-intent"]:focus{
z-index:15; }
.bp3-control-group .bp3-input[readonly], .bp3-control-group .bp3-input:disabled, .bp3-control-group .bp3-input.bp3-disabled{
z-index:1; }
.bp3-control-group .bp3-input-group[class*="bp3-intent"] .bp3-input{
z-index:13; }
.bp3-control-group .bp3-input-group[class*="bp3-intent"] .bp3-input:focus{
z-index:15; }
.bp3-control-group .bp3-button,
.bp3-control-group .bp3-html-select select,
.bp3-control-group .bp3-select select{
-webkit-transform:translateZ(0);
transform:translateZ(0);
border-radius:inherit;
z-index:4; }
.bp3-control-group .bp3-button:focus,
.bp3-control-group .bp3-html-select select:focus,
.bp3-control-group .bp3-select select:focus{
z-index:5; }
.bp3-control-group .bp3-button:hover,
.bp3-control-group .bp3-html-select select:hover,
.bp3-control-group .bp3-select select:hover{
z-index:6; }
.bp3-control-group .bp3-button:active,
.bp3-control-group .bp3-html-select select:active,
.bp3-control-group .bp3-select select:active{
z-index:7; }
.bp3-control-group .bp3-button[readonly], .bp3-control-group .bp3-button:disabled, .bp3-control-group .bp3-button.bp3-disabled,
.bp3-control-group .bp3-html-select select[readonly],
.bp3-control-group .bp3-html-select select:disabled,
.bp3-control-group .bp3-html-select select.bp3-disabled,
.bp3-control-group .bp3-select select[readonly],
.bp3-control-group .bp3-select select:disabled,
.bp3-control-group .bp3-select select.bp3-disabled{
z-index:3; }
.bp3-control-group .bp3-button[class*="bp3-intent"],
.bp3-control-group .bp3-html-select select[class*="bp3-intent"],
.bp3-control-group .bp3-select select[class*="bp3-intent"]{
z-index:9; }
.bp3-control-group .bp3-button[class*="bp3-intent"]:focus,
.bp3-control-group .bp3-html-select select[class*="bp3-intent"]:focus,
.bp3-control-group .bp3-select select[class*="bp3-intent"]:focus{
z-index:10; }
.bp3-control-group .bp3-button[class*="bp3-intent"]:hover,
.bp3-control-group .bp3-html-select select[class*="bp3-intent"]:hover,
.bp3-control-group .bp3-select select[class*="bp3-intent"]:hover{
z-index:11; }
.bp3-control-group .bp3-button[class*="bp3-intent"]:active,
.bp3-control-group .bp3-html-select select[class*="bp3-intent"]:active,
.bp3-control-group .bp3-select select[class*="bp3-intent"]:active{
z-index:12; }
.bp3-control-group .bp3-button[class*="bp3-intent"][readonly], .bp3-control-group .bp3-button[class*="bp3-intent"]:disabled, .bp3-control-group .bp3-button[class*="bp3-intent"].bp3-disabled,
.bp3-control-group .bp3-html-select select[class*="bp3-intent"][readonly],
.bp3-control-group .bp3-html-select select[class*="bp3-intent"]:disabled,
.bp3-control-group .bp3-html-select select[class*="bp3-intent"].bp3-disabled,
.bp3-control-group .bp3-select select[class*="bp3-intent"][readonly],
.bp3-control-group .bp3-select select[class*="bp3-intent"]:disabled,
.bp3-control-group .bp3-select select[class*="bp3-intent"].bp3-disabled{
z-index:8; }
.bp3-control-group .bp3-input-group > .bp3-icon,
.bp3-control-group .bp3-input-group > .bp3-button,
.bp3-control-group .bp3-input-group > .bp3-input-left-container,
.bp3-control-group .bp3-input-group > .bp3-input-action{
z-index:16; }
.bp3-control-group .bp3-select::after,
.bp3-control-group .bp3-html-select::after,
.bp3-control-group .bp3-select > .bp3-icon,
.bp3-control-group .bp3-html-select > .bp3-icon{
z-index:17; }
.bp3-control-group .bp3-select:focus-within{
z-index:5; }
.bp3-control-group:not(.bp3-vertical) > *:not(.bp3-divider){
margin-right:-1px; }
.bp3-control-group:not(.bp3-vertical) > .bp3-divider:not(:first-child){
margin-left:6px; }
.bp3-dark .bp3-control-group:not(.bp3-vertical) > *:not(.bp3-divider){
margin-right:0; }
.bp3-dark .bp3-control-group:not(.bp3-vertical) > .bp3-button + .bp3-button{
margin-left:1px; }
.bp3-control-group .bp3-popover-wrapper,
.bp3-control-group .bp3-popover-target{
border-radius:inherit; }
.bp3-control-group > :first-child{
border-radius:3px 0 0 3px; }
.bp3-control-group > :last-child{
border-radius:0 3px 3px 0;
margin-right:0; }
.bp3-control-group > :only-child{
border-radius:3px;
margin-right:0; }
.bp3-control-group .bp3-input-group .bp3-button{
border-radius:3px; }
.bp3-control-group .bp3-numeric-input:not(:first-child) .bp3-input-group{
border-bottom-left-radius:0;
border-top-left-radius:0; }
.bp3-control-group.bp3-fill{
width:100%; }
.bp3-control-group > .bp3-fill{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto; }
.bp3-control-group.bp3-fill > *:not(.bp3-fixed){
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto; }
.bp3-control-group.bp3-vertical{
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-ms-flex-direction:column;
flex-direction:column; }
.bp3-control-group.bp3-vertical > *{
margin-top:-1px; }
.bp3-control-group.bp3-vertical > :first-child{
border-radius:3px 3px 0 0;
margin-top:0; }
.bp3-control-group.bp3-vertical > :last-child{
border-radius:0 0 3px 3px; }
.bp3-control{
cursor:pointer;
display:block;
margin-bottom:10px;
position:relative;
text-transform:none; }
.bp3-control input:checked ~ .bp3-control-indicator{
background-color:#137cbd;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
color:#ffffff; }
.bp3-control:hover input:checked ~ .bp3-control-indicator{
background-color:#106ba3;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
.bp3-control input:not(:disabled):active:checked ~ .bp3-control-indicator{
background:#0e5a8a;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-control input:disabled:checked ~ .bp3-control-indicator{
background:rgba(19, 124, 189, 0.5);
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-control input:checked ~ .bp3-control-indicator{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-control:hover input:checked ~ .bp3-control-indicator{
background-color:#106ba3;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-control input:not(:disabled):active:checked ~ .bp3-control-indicator{
background-color:#0e5a8a;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-dark .bp3-control input:disabled:checked ~ .bp3-control-indicator{
background:rgba(14, 90, 138, 0.5);
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-control:not(.bp3-align-right){
padding-left:26px; }
.bp3-control:not(.bp3-align-right) .bp3-control-indicator{
margin-left:-26px; }
.bp3-control.bp3-align-right{
padding-right:26px; }
.bp3-control.bp3-align-right .bp3-control-indicator{
margin-right:-26px; }
.bp3-control.bp3-disabled{
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed; }
.bp3-control.bp3-inline{
display:inline-block;
margin-right:20px; }
.bp3-control input{
left:0;
opacity:0;
position:absolute;
top:0;
z-index:-1; }
.bp3-control .bp3-control-indicator{
background-clip:padding-box;
background-color:#f5f8fa;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
border:none;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
cursor:pointer;
display:inline-block;
font-size:16px;
height:1em;
margin-right:10px;
margin-top:-3px;
position:relative;
-webkit-user-select:none;
-moz-user-select:none;
-ms-user-select:none;
user-select:none;
vertical-align:middle;
width:1em; }
.bp3-control .bp3-control-indicator::before{
content:"";
display:block;
height:1em;
width:1em; }
.bp3-control:hover .bp3-control-indicator{
background-color:#ebf1f5; }
.bp3-control input:not(:disabled):active ~ .bp3-control-indicator{
background:#d8e1e8;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-control input:disabled ~ .bp3-control-indicator{
background:rgba(206, 217, 224, 0.5);
-webkit-box-shadow:none;
box-shadow:none;
cursor:not-allowed; }
.bp3-control input:focus ~ .bp3-control-indicator{
outline:rgba(19, 124, 189, 0.6) auto 2px;
outline-offset:2px;
-moz-outline-radius:6px; }
.bp3-control.bp3-align-right .bp3-control-indicator{
float:right;
margin-left:10px;
margin-top:1px; }
.bp3-control.bp3-large{
font-size:16px; }
.bp3-control.bp3-large:not(.bp3-align-right){
padding-left:30px; }
.bp3-control.bp3-large:not(.bp3-align-right) .bp3-control-indicator{
margin-left:-30px; }
.bp3-control.bp3-large.bp3-align-right{
padding-right:30px; }
.bp3-control.bp3-large.bp3-align-right .bp3-control-indicator{
margin-right:-30px; }
.bp3-control.bp3-large .bp3-control-indicator{
font-size:20px; }
.bp3-control.bp3-large.bp3-align-right .bp3-control-indicator{
margin-top:0; }
.bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator{
background-color:#137cbd;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
color:#ffffff; }
.bp3-control.bp3-checkbox:hover input:indeterminate ~ .bp3-control-indicator{
background-color:#106ba3;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
.bp3-control.bp3-checkbox input:not(:disabled):active:indeterminate ~ .bp3-control-indicator{
background:#0e5a8a;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{
background:rgba(19, 124, 189, 0.5);
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-control.bp3-checkbox:hover input:indeterminate ~ .bp3-control-indicator{
background-color:#106ba3;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-control.bp3-checkbox input:not(:disabled):active:indeterminate ~ .bp3-control-indicator{
background-color:#0e5a8a;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-dark .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{
background:rgba(14, 90, 138, 0.5);
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-control.bp3-checkbox .bp3-control-indicator{
border-radius:3px; }
.bp3-control.bp3-checkbox input:checked ~ .bp3-control-indicator::before{
background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M12 5c-.28 0-.53.11-.71.29L7 9.59l-2.29-2.3a1.003 1.003 0 00-1.42 1.42l3 3c.18.18.43.29.71.29s.53-.11.71-.29l5-5A1.003 1.003 0 0012 5z' fill='white'/%3e%3c/svg%3e"); }
.bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator::before{
background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 7H5c-.55 0-1 .45-1 1s.45 1 1 1h6c.55 0 1-.45 1-1s-.45-1-1-1z' fill='white'/%3e%3c/svg%3e"); }
.bp3-control.bp3-radio .bp3-control-indicator{
border-radius:50%; }
.bp3-control.bp3-radio input:checked ~ .bp3-control-indicator::before{
background-image:radial-gradient(#ffffff, #ffffff 28%, transparent 32%); }
.bp3-control.bp3-radio input:checked:disabled ~ .bp3-control-indicator::before{
opacity:0.5; }
.bp3-control.bp3-radio input:focus ~ .bp3-control-indicator{
-moz-outline-radius:16px; }
.bp3-control.bp3-switch input ~ .bp3-control-indicator{
background:rgba(167, 182, 194, 0.5); }
.bp3-control.bp3-switch:hover input ~ .bp3-control-indicator{
background:rgba(115, 134, 148, 0.5); }
.bp3-control.bp3-switch input:not(:disabled):active ~ .bp3-control-indicator{
background:rgba(92, 112, 128, 0.5); }
.bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator{
background:rgba(206, 217, 224, 0.5); }
.bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator::before{
background:rgba(255, 255, 255, 0.8); }
.bp3-control.bp3-switch input:checked ~ .bp3-control-indicator{
background:#137cbd; }
.bp3-control.bp3-switch:hover input:checked ~ .bp3-control-indicator{
background:#106ba3; }
.bp3-control.bp3-switch input:checked:not(:disabled):active ~ .bp3-control-indicator{
background:#0e5a8a; }
.bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator{
background:rgba(19, 124, 189, 0.5); }
.bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator::before{
background:rgba(255, 255, 255, 0.8); }
.bp3-control.bp3-switch:not(.bp3-align-right){
padding-left:38px; }
.bp3-control.bp3-switch:not(.bp3-align-right) .bp3-control-indicator{
margin-left:-38px; }
.bp3-control.bp3-switch.bp3-align-right{
padding-right:38px; }
.bp3-control.bp3-switch.bp3-align-right .bp3-control-indicator{
margin-right:-38px; }
.bp3-control.bp3-switch .bp3-control-indicator{
border:none;
border-radius:1.75em;
-webkit-box-shadow:none !important;
box-shadow:none !important;
min-width:1.75em;
-webkit-transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
width:auto; }
.bp3-control.bp3-switch .bp3-control-indicator::before{
background:#ffffff;
border-radius:50%;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
height:calc(1em - 4px);
left:0;
margin:2px;
position:absolute;
-webkit-transition:left 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:left 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
width:calc(1em - 4px); }
.bp3-control.bp3-switch input:checked ~ .bp3-control-indicator::before{
left:calc(100% - 1em); }
.bp3-control.bp3-switch.bp3-large:not(.bp3-align-right){
padding-left:45px; }
.bp3-control.bp3-switch.bp3-large:not(.bp3-align-right) .bp3-control-indicator{
margin-left:-45px; }
.bp3-control.bp3-switch.bp3-large.bp3-align-right{
padding-right:45px; }
.bp3-control.bp3-switch.bp3-large.bp3-align-right .bp3-control-indicator{
margin-right:-45px; }
.bp3-dark .bp3-control.bp3-switch input ~ .bp3-control-indicator{
background:rgba(16, 22, 26, 0.5); }
.bp3-dark .bp3-control.bp3-switch:hover input ~ .bp3-control-indicator{
background:rgba(16, 22, 26, 0.7); }
.bp3-dark .bp3-control.bp3-switch input:not(:disabled):active ~ .bp3-control-indicator{
background:rgba(16, 22, 26, 0.9); }
.bp3-dark .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator{
background:rgba(57, 75, 89, 0.5); }
.bp3-dark .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator::before{
background:rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator{
background:#137cbd; }
.bp3-dark .bp3-control.bp3-switch:hover input:checked ~ .bp3-control-indicator{
background:#106ba3; }
.bp3-dark .bp3-control.bp3-switch input:checked:not(:disabled):active ~ .bp3-control-indicator{
background:#0e5a8a; }
.bp3-dark .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator{
background:rgba(14, 90, 138, 0.5); }
.bp3-dark .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator::before{
background:rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-control.bp3-switch .bp3-control-indicator::before{
background:#394b59;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator::before{
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-control.bp3-switch .bp3-switch-inner-text{
font-size:0.7em;
text-align:center; }
.bp3-control.bp3-switch .bp3-control-indicator-child:first-child{
line-height:0;
margin-left:0.5em;
margin-right:1.2em;
visibility:hidden; }
.bp3-control.bp3-switch .bp3-control-indicator-child:last-child{
line-height:1em;
margin-left:1.2em;
margin-right:0.5em;
visibility:visible; }
.bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:first-child{
line-height:1em;
visibility:visible; }
.bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:last-child{
line-height:0;
visibility:hidden; }
.bp3-dark .bp3-control{
color:#f5f8fa; }
.bp3-dark .bp3-control.bp3-disabled{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-control .bp3-control-indicator{
background-color:#394b59;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-control:hover .bp3-control-indicator{
background-color:#30404d; }
.bp3-dark .bp3-control input:not(:disabled):active ~ .bp3-control-indicator{
background:#202b33;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-dark .bp3-control input:disabled ~ .bp3-control-indicator{
background:rgba(57, 75, 89, 0.5);
-webkit-box-shadow:none;
box-shadow:none;
cursor:not-allowed; }
.bp3-dark .bp3-control.bp3-checkbox input:disabled:checked ~ .bp3-control-indicator, .bp3-dark .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{
color:rgba(167, 182, 194, 0.6); }
.bp3-file-input{
cursor:pointer;
display:inline-block;
height:30px;
position:relative; }
.bp3-file-input input{
margin:0;
min-width:200px;
opacity:0; }
.bp3-file-input input:disabled + .bp3-file-upload-input,
.bp3-file-input input.bp3-disabled + .bp3-file-upload-input{
background:rgba(206, 217, 224, 0.5);
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed;
resize:none; }
.bp3-file-input input:disabled + .bp3-file-upload-input::after,
.bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after{
background-color:rgba(206, 217, 224, 0.5);
background-image:none;
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed;
outline:none; }
.bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active, .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active:hover,
.bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active,
.bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active:hover{
background:rgba(206, 217, 224, 0.7); }
.bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input, .bp3-dark
.bp3-file-input input.bp3-disabled + .bp3-file-upload-input{
background:rgba(57, 75, 89, 0.5);
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input::after, .bp3-dark
.bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after{
background-color:rgba(57, 75, 89, 0.5);
background-image:none;
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active, .bp3-dark
.bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active{
background:rgba(57, 75, 89, 0.7); }
.bp3-file-input.bp3-file-input-has-selection .bp3-file-upload-input{
color:#182026; }
.bp3-dark .bp3-file-input.bp3-file-input-has-selection .bp3-file-upload-input{
color:#f5f8fa; }
.bp3-file-input.bp3-fill{
width:100%; }
.bp3-file-input.bp3-large,
.bp3-large .bp3-file-input{
height:40px; }
.bp3-file-input .bp3-file-upload-input-custom-text::after{
content:attr(bp3-button-text); }
.bp3-file-upload-input{
-webkit-appearance:none;
-moz-appearance:none;
appearance:none;
background:#ffffff;
border:none;
border-radius:3px;
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
color:#182026;
font-size:14px;
font-weight:400;
height:30px;
line-height:30px;
outline:none;
padding:0 10px;
-webkit-transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
vertical-align:middle;
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal;
color:rgba(92, 112, 128, 0.6);
left:0;
padding-right:80px;
position:absolute;
right:0;
top:0;
-webkit-user-select:none;
-moz-user-select:none;
-ms-user-select:none;
user-select:none; }
.bp3-file-upload-input::-webkit-input-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-file-upload-input::-moz-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-file-upload-input:-ms-input-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-file-upload-input::-ms-input-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-file-upload-input::placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-file-upload-input:focus, .bp3-file-upload-input.bp3-active{
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-file-upload-input[type="search"], .bp3-file-upload-input.bp3-round{
border-radius:30px;
-webkit-box-sizing:border-box;
box-sizing:border-box;
padding-left:10px; }
.bp3-file-upload-input[readonly]{
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); }
.bp3-file-upload-input:disabled, .bp3-file-upload-input.bp3-disabled{
background:rgba(206, 217, 224, 0.5);
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed;
resize:none; }
.bp3-file-upload-input::after{
background-color:#f5f8fa;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
color:#182026;
min-height:24px;
min-width:24px;
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal;
border-radius:3px;
content:"Browse";
line-height:24px;
margin:3px;
position:absolute;
right:0;
text-align:center;
top:0;
width:70px; }
.bp3-file-upload-input::after:hover{
background-clip:padding-box;
background-color:#ebf1f5;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
.bp3-file-upload-input::after:active, .bp3-file-upload-input::after.bp3-active{
background-color:#d8e1e8;
background-image:none;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-file-upload-input::after:disabled, .bp3-file-upload-input::after.bp3-disabled{
background-color:rgba(206, 217, 224, 0.5);
background-image:none;
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed;
outline:none; }
.bp3-file-upload-input::after:disabled.bp3-active, .bp3-file-upload-input::after:disabled.bp3-active:hover, .bp3-file-upload-input::after.bp3-disabled.bp3-active, .bp3-file-upload-input::after.bp3-disabled.bp3-active:hover{
background:rgba(206, 217, 224, 0.7); }
.bp3-file-upload-input:hover::after{
background-clip:padding-box;
background-color:#ebf1f5;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
.bp3-file-upload-input:active::after{
background-color:#d8e1e8;
background-image:none;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-large .bp3-file-upload-input{
font-size:16px;
height:40px;
line-height:40px;
padding-right:95px; }
.bp3-large .bp3-file-upload-input[type="search"], .bp3-large .bp3-file-upload-input.bp3-round{
padding:0 15px; }
.bp3-large .bp3-file-upload-input::after{
min-height:30px;
min-width:30px;
line-height:30px;
margin:5px;
width:85px; }
.bp3-dark .bp3-file-upload-input{
background:rgba(16, 22, 26, 0.3);
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
color:#f5f8fa;
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-file-upload-input::-webkit-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-file-upload-input::-moz-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-file-upload-input:-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-file-upload-input::-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-file-upload-input::placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-file-upload-input:focus{
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-file-upload-input[readonly]{
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-file-upload-input:disabled, .bp3-dark .bp3-file-upload-input.bp3-disabled{
background:rgba(57, 75, 89, 0.5);
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-file-upload-input::after{
background-color:#394b59;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
color:#f5f8fa; }
.bp3-dark .bp3-file-upload-input::after:hover, .bp3-dark .bp3-file-upload-input::after:active, .bp3-dark .bp3-file-upload-input::after.bp3-active{
color:#f5f8fa; }
.bp3-dark .bp3-file-upload-input::after:hover{
background-color:#30404d;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-file-upload-input::after:active, .bp3-dark .bp3-file-upload-input::after.bp3-active{
background-color:#202b33;
background-image:none;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-dark .bp3-file-upload-input::after:disabled, .bp3-dark .bp3-file-upload-input::after.bp3-disabled{
background-color:rgba(57, 75, 89, 0.5);
background-image:none;
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-file-upload-input::after:disabled.bp3-active, .bp3-dark .bp3-file-upload-input::after.bp3-disabled.bp3-active{
background:rgba(57, 75, 89, 0.7); }
.bp3-dark .bp3-file-upload-input::after .bp3-button-spinner .bp3-spinner-head{
background:rgba(16, 22, 26, 0.5);
stroke:#8a9ba8; }
.bp3-dark .bp3-file-upload-input:hover::after{
background-color:#30404d;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-file-upload-input:active::after{
background-color:#202b33;
background-image:none;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-file-upload-input::after{
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
.bp3-form-group{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-ms-flex-direction:column;
flex-direction:column;
margin:0 0 15px; }
.bp3-form-group label.bp3-label{
margin-bottom:5px; }
.bp3-form-group .bp3-control{
margin-top:7px; }
.bp3-form-group .bp3-form-helper-text{
color:#5c7080;
font-size:12px;
margin-top:5px; }
.bp3-form-group.bp3-intent-primary .bp3-form-helper-text{
color:#106ba3; }
.bp3-form-group.bp3-intent-success .bp3-form-helper-text{
color:#0d8050; }
.bp3-form-group.bp3-intent-warning .bp3-form-helper-text{
color:#bf7326; }
.bp3-form-group.bp3-intent-danger .bp3-form-helper-text{
color:#c23030; }
.bp3-form-group.bp3-inline{
-webkit-box-align:start;
-ms-flex-align:start;
align-items:flex-start;
-webkit-box-orient:horizontal;
-webkit-box-direction:normal;
-ms-flex-direction:row;
flex-direction:row; }
.bp3-form-group.bp3-inline.bp3-large label.bp3-label{
line-height:40px;
margin:0 10px 0 0; }
.bp3-form-group.bp3-inline label.bp3-label{
line-height:30px;
margin:0 10px 0 0; }
.bp3-form-group.bp3-disabled .bp3-label,
.bp3-form-group.bp3-disabled .bp3-text-muted,
.bp3-form-group.bp3-disabled .bp3-form-helper-text{
color:rgba(92, 112, 128, 0.6) !important; }
.bp3-dark .bp3-form-group.bp3-intent-primary .bp3-form-helper-text{
color:#48aff0; }
.bp3-dark .bp3-form-group.bp3-intent-success .bp3-form-helper-text{
color:#3dcc91; }
.bp3-dark .bp3-form-group.bp3-intent-warning .bp3-form-helper-text{
color:#ffb366; }
.bp3-dark .bp3-form-group.bp3-intent-danger .bp3-form-helper-text{
color:#ff7373; }
.bp3-dark .bp3-form-group .bp3-form-helper-text{
color:#a7b6c2; }
.bp3-dark .bp3-form-group.bp3-disabled .bp3-label,
.bp3-dark .bp3-form-group.bp3-disabled .bp3-text-muted,
.bp3-dark .bp3-form-group.bp3-disabled .bp3-form-helper-text{
color:rgba(167, 182, 194, 0.6) !important; }
.bp3-input-group{
display:block;
position:relative; }
.bp3-input-group .bp3-input{
position:relative;
width:100%; }
.bp3-input-group .bp3-input:not(:first-child){
padding-left:30px; }
.bp3-input-group .bp3-input:not(:last-child){
padding-right:30px; }
.bp3-input-group .bp3-input-action,
.bp3-input-group > .bp3-input-left-container,
.bp3-input-group > .bp3-button,
.bp3-input-group > .bp3-icon{
position:absolute;
top:0; }
.bp3-input-group .bp3-input-action:first-child,
.bp3-input-group > .bp3-input-left-container:first-child,
.bp3-input-group > .bp3-button:first-child,
.bp3-input-group > .bp3-icon:first-child{
left:0; }
.bp3-input-group .bp3-input-action:last-child,
.bp3-input-group > .bp3-input-left-container:last-child,
.bp3-input-group > .bp3-button:last-child,
.bp3-input-group > .bp3-icon:last-child{
right:0; }
.bp3-input-group .bp3-button{
min-height:24px;
min-width:24px;
margin:3px;
padding:0 7px; }
.bp3-input-group .bp3-button:empty{
padding:0; }
.bp3-input-group > .bp3-input-left-container,
.bp3-input-group > .bp3-icon{
z-index:1; }
.bp3-input-group > .bp3-input-left-container > .bp3-icon,
.bp3-input-group > .bp3-icon{
color:#5c7080; }
.bp3-input-group > .bp3-input-left-container > .bp3-icon:empty,
.bp3-input-group > .bp3-icon:empty{
font-family:"Icons16", sans-serif;
font-size:16px;
font-style:normal;
font-weight:400;
line-height:1;
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased; }
.bp3-input-group > .bp3-input-left-container > .bp3-icon,
.bp3-input-group > .bp3-icon,
.bp3-input-group .bp3-input-action > .bp3-spinner{
margin:7px; }
.bp3-input-group .bp3-tag{
margin:5px; }
.bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus),
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus){
color:#5c7080; }
.bp3-dark .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus), .bp3-dark
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus){
color:#a7b6c2; }
.bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-standard, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-large,
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon,
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-standard,
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-large{
color:#5c7080; }
.bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled,
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled{
color:rgba(92, 112, 128, 0.6) !important; }
.bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon-standard, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon-large,
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon,
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon-standard,
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon-large{
color:rgba(92, 112, 128, 0.6) !important; }
.bp3-input-group.bp3-disabled{
cursor:not-allowed; }
.bp3-input-group.bp3-disabled .bp3-icon{
color:rgba(92, 112, 128, 0.6); }
.bp3-input-group.bp3-large .bp3-button{
min-height:30px;
min-width:30px;
margin:5px; }
.bp3-input-group.bp3-large > .bp3-input-left-container > .bp3-icon,
.bp3-input-group.bp3-large > .bp3-icon,
.bp3-input-group.bp3-large .bp3-input-action > .bp3-spinner{
margin:12px; }
.bp3-input-group.bp3-large .bp3-input{
font-size:16px;
height:40px;
line-height:40px; }
.bp3-input-group.bp3-large .bp3-input[type="search"], .bp3-input-group.bp3-large .bp3-input.bp3-round{
padding:0 15px; }
.bp3-input-group.bp3-large .bp3-input:not(:first-child){
padding-left:40px; }
.bp3-input-group.bp3-large .bp3-input:not(:last-child){
padding-right:40px; }
.bp3-input-group.bp3-small .bp3-button{
min-height:20px;
min-width:20px;
margin:2px; }
.bp3-input-group.bp3-small .bp3-tag{
min-height:20px;
min-width:20px;
margin:2px; }
.bp3-input-group.bp3-small > .bp3-input-left-container > .bp3-icon,
.bp3-input-group.bp3-small > .bp3-icon,
.bp3-input-group.bp3-small .bp3-input-action > .bp3-spinner{
margin:4px; }
.bp3-input-group.bp3-small .bp3-input{
font-size:12px;
height:24px;
line-height:24px;
padding-left:8px;
padding-right:8px; }
.bp3-input-group.bp3-small .bp3-input[type="search"], .bp3-input-group.bp3-small .bp3-input.bp3-round{
padding:0 12px; }
.bp3-input-group.bp3-small .bp3-input:not(:first-child){
padding-left:24px; }
.bp3-input-group.bp3-small .bp3-input:not(:last-child){
padding-right:24px; }
.bp3-input-group.bp3-fill{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
width:100%; }
.bp3-input-group.bp3-round .bp3-button,
.bp3-input-group.bp3-round .bp3-input,
.bp3-input-group.bp3-round .bp3-tag{
border-radius:30px; }
.bp3-dark .bp3-input-group .bp3-icon{
color:#a7b6c2; }
.bp3-dark .bp3-input-group.bp3-disabled .bp3-icon{
color:rgba(167, 182, 194, 0.6); }
.bp3-input-group.bp3-intent-primary .bp3-input{
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input-group.bp3-intent-primary .bp3-input:focus{
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input-group.bp3-intent-primary .bp3-input[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #137cbd;
box-shadow:inset 0 0 0 1px #137cbd; }
.bp3-input-group.bp3-intent-primary .bp3-input:disabled, .bp3-input-group.bp3-intent-primary .bp3-input.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input-group.bp3-intent-primary > .bp3-icon{
color:#106ba3; }
.bp3-dark .bp3-input-group.bp3-intent-primary > .bp3-icon{
color:#48aff0; }
.bp3-input-group.bp3-intent-success .bp3-input{
-webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input-group.bp3-intent-success .bp3-input:focus{
-webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input-group.bp3-intent-success .bp3-input[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #0f9960;
box-shadow:inset 0 0 0 1px #0f9960; }
.bp3-input-group.bp3-intent-success .bp3-input:disabled, .bp3-input-group.bp3-intent-success .bp3-input.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input-group.bp3-intent-success > .bp3-icon{
color:#0d8050; }
.bp3-dark .bp3-input-group.bp3-intent-success > .bp3-icon{
color:#3dcc91; }
.bp3-input-group.bp3-intent-warning .bp3-input{
-webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input-group.bp3-intent-warning .bp3-input:focus{
-webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input-group.bp3-intent-warning .bp3-input[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #d9822b;
box-shadow:inset 0 0 0 1px #d9822b; }
.bp3-input-group.bp3-intent-warning .bp3-input:disabled, .bp3-input-group.bp3-intent-warning .bp3-input.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input-group.bp3-intent-warning > .bp3-icon{
color:#bf7326; }
.bp3-dark .bp3-input-group.bp3-intent-warning > .bp3-icon{
color:#ffb366; }
.bp3-input-group.bp3-intent-danger .bp3-input{
-webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input-group.bp3-intent-danger .bp3-input:focus{
-webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input-group.bp3-intent-danger .bp3-input[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #db3737;
box-shadow:inset 0 0 0 1px #db3737; }
.bp3-input-group.bp3-intent-danger .bp3-input:disabled, .bp3-input-group.bp3-intent-danger .bp3-input.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input-group.bp3-intent-danger > .bp3-icon{
color:#c23030; }
.bp3-dark .bp3-input-group.bp3-intent-danger > .bp3-icon{
color:#ff7373; }
.bp3-input{
-webkit-appearance:none;
-moz-appearance:none;
appearance:none;
background:#ffffff;
border:none;
border-radius:3px;
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
color:#182026;
font-size:14px;
font-weight:400;
height:30px;
line-height:30px;
outline:none;
padding:0 10px;
-webkit-transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
vertical-align:middle; }
.bp3-input::-webkit-input-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-input::-moz-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-input:-ms-input-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-input::-ms-input-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-input::placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-input:focus, .bp3-input.bp3-active{
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input[type="search"], .bp3-input.bp3-round{
border-radius:30px;
-webkit-box-sizing:border-box;
box-sizing:border-box;
padding-left:10px; }
.bp3-input[readonly]{
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); }
.bp3-input:disabled, .bp3-input.bp3-disabled{
background:rgba(206, 217, 224, 0.5);
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed;
resize:none; }
.bp3-input.bp3-large{
font-size:16px;
height:40px;
line-height:40px; }
.bp3-input.bp3-large[type="search"], .bp3-input.bp3-large.bp3-round{
padding:0 15px; }
.bp3-input.bp3-small{
font-size:12px;
height:24px;
line-height:24px;
padding-left:8px;
padding-right:8px; }
.bp3-input.bp3-small[type="search"], .bp3-input.bp3-small.bp3-round{
padding:0 12px; }
.bp3-input.bp3-fill{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
width:100%; }
.bp3-dark .bp3-input{
background:rgba(16, 22, 26, 0.3);
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
color:#f5f8fa; }
.bp3-dark .bp3-input::-webkit-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-input::-moz-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-input:-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-input::-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-input::placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-input:focus{
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-input[readonly]{
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-input:disabled, .bp3-dark .bp3-input.bp3-disabled{
background:rgba(57, 75, 89, 0.5);
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(167, 182, 194, 0.6); }
.bp3-input.bp3-intent-primary{
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input.bp3-intent-primary:focus{
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input.bp3-intent-primary[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #137cbd;
box-shadow:inset 0 0 0 1px #137cbd; }
.bp3-input.bp3-intent-primary:disabled, .bp3-input.bp3-intent-primary.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-input.bp3-intent-primary{
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-input.bp3-intent-primary:focus{
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-input.bp3-intent-primary[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #137cbd;
box-shadow:inset 0 0 0 1px #137cbd; }
.bp3-dark .bp3-input.bp3-intent-primary:disabled, .bp3-dark .bp3-input.bp3-intent-primary.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input.bp3-intent-success{
-webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input.bp3-intent-success:focus{
-webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input.bp3-intent-success[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #0f9960;
box-shadow:inset 0 0 0 1px #0f9960; }
.bp3-input.bp3-intent-success:disabled, .bp3-input.bp3-intent-success.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-input.bp3-intent-success{
-webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-input.bp3-intent-success:focus{
-webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #0f9960, 0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-input.bp3-intent-success[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #0f9960;
box-shadow:inset 0 0 0 1px #0f9960; }
.bp3-dark .bp3-input.bp3-intent-success:disabled, .bp3-dark .bp3-input.bp3-intent-success.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input.bp3-intent-warning{
-webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input.bp3-intent-warning:focus{
-webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input.bp3-intent-warning[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #d9822b;
box-shadow:inset 0 0 0 1px #d9822b; }
.bp3-input.bp3-intent-warning:disabled, .bp3-input.bp3-intent-warning.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-input.bp3-intent-warning{
-webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-input.bp3-intent-warning:focus{
-webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #d9822b, 0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-input.bp3-intent-warning[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #d9822b;
box-shadow:inset 0 0 0 1px #d9822b; }
.bp3-dark .bp3-input.bp3-intent-warning:disabled, .bp3-dark .bp3-input.bp3-intent-warning.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input.bp3-intent-danger{
-webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input.bp3-intent-danger:focus{
-webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input.bp3-intent-danger[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #db3737;
box-shadow:inset 0 0 0 1px #db3737; }
.bp3-input.bp3-intent-danger:disabled, .bp3-input.bp3-intent-danger.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-input.bp3-intent-danger{
-webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-input.bp3-intent-danger:focus{
-webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #db3737, 0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-input.bp3-intent-danger[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #db3737;
box-shadow:inset 0 0 0 1px #db3737; }
.bp3-dark .bp3-input.bp3-intent-danger:disabled, .bp3-dark .bp3-input.bp3-intent-danger.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input::-ms-clear{
display:none; }
textarea.bp3-input{
max-width:100%;
padding:10px; }
textarea.bp3-input, textarea.bp3-input.bp3-large, textarea.bp3-input.bp3-small{
height:auto;
line-height:inherit; }
textarea.bp3-input.bp3-small{
padding:8px; }
.bp3-dark textarea.bp3-input{
background:rgba(16, 22, 26, 0.3);
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
color:#f5f8fa; }
.bp3-dark textarea.bp3-input::-webkit-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark textarea.bp3-input::-moz-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark textarea.bp3-input:-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark textarea.bp3-input::-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark textarea.bp3-input::placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark textarea.bp3-input:focus{
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark textarea.bp3-input[readonly]{
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark textarea.bp3-input:disabled, .bp3-dark textarea.bp3-input.bp3-disabled{
background:rgba(57, 75, 89, 0.5);
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(167, 182, 194, 0.6); }
label.bp3-label{
display:block;
margin-bottom:15px;
margin-top:0; }
label.bp3-label .bp3-html-select,
label.bp3-label .bp3-input,
label.bp3-label .bp3-select,
label.bp3-label .bp3-slider,
label.bp3-label .bp3-popover-wrapper{
display:block;
margin-top:5px;
text-transform:none; }
label.bp3-label .bp3-button-group{
margin-top:5px; }
label.bp3-label .bp3-select select,
label.bp3-label .bp3-html-select select{
font-weight:400;
vertical-align:top;
width:100%; }
label.bp3-label.bp3-disabled,
label.bp3-label.bp3-disabled .bp3-text-muted{
color:rgba(92, 112, 128, 0.6); }
label.bp3-label.bp3-inline{
line-height:30px; }
label.bp3-label.bp3-inline .bp3-html-select,
label.bp3-label.bp3-inline .bp3-input,
label.bp3-label.bp3-inline .bp3-input-group,
label.bp3-label.bp3-inline .bp3-select,
label.bp3-label.bp3-inline .bp3-popover-wrapper{
display:inline-block;
margin:0 0 0 5px;
vertical-align:top; }
label.bp3-label.bp3-inline .bp3-button-group{
margin:0 0 0 5px; }
label.bp3-label.bp3-inline .bp3-input-group .bp3-input{
margin-left:0; }
label.bp3-label.bp3-inline.bp3-large{
line-height:40px; }
label.bp3-label:not(.bp3-inline) .bp3-popover-target{
display:block; }
.bp3-dark label.bp3-label{
color:#f5f8fa; }
.bp3-dark label.bp3-label.bp3-disabled,
.bp3-dark label.bp3-label.bp3-disabled .bp3-text-muted{
color:rgba(167, 182, 194, 0.6); }
.bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button{
-webkit-box-flex:1;
-ms-flex:1 1 14px;
flex:1 1 14px;
min-height:0;
padding:0;
width:30px; }
.bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button:first-child{
border-radius:0 3px 0 0; }
.bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button:last-child{
border-radius:0 0 3px 0; }
.bp3-numeric-input .bp3-button-group.bp3-vertical:first-child > .bp3-button:first-child{
border-radius:3px 0 0 0; }
.bp3-numeric-input .bp3-button-group.bp3-vertical:first-child > .bp3-button:last-child{
border-radius:0 0 0 3px; }
.bp3-numeric-input.bp3-large .bp3-button-group.bp3-vertical > .bp3-button{
width:40px; }
form{
display:block; }
.bp3-html-select select,
.bp3-select select{
display:-webkit-inline-box;
display:-ms-inline-flexbox;
display:inline-flex;
-webkit-box-orient:horizontal;
-webkit-box-direction:normal;
-ms-flex-direction:row;
flex-direction:row;
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
border:none;
border-radius:3px;
cursor:pointer;
font-size:14px;
-webkit-box-pack:center;
-ms-flex-pack:center;
justify-content:center;
padding:5px 10px;
text-align:left;
vertical-align:middle;
background-color:#f5f8fa;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
color:#182026;
-moz-appearance:none;
-webkit-appearance:none;
border-radius:3px;
height:30px;
padding:0 25px 0 10px;
width:100%; }
.bp3-html-select select > *, .bp3-select select > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-html-select select > .bp3-fill, .bp3-select select > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-html-select select::before,
.bp3-select select::before, .bp3-html-select select > *, .bp3-select select > *{
margin-right:7px; }
.bp3-html-select select:empty::before,
.bp3-select select:empty::before,
.bp3-html-select select > :last-child,
.bp3-select select > :last-child{
margin-right:0; }
.bp3-html-select select:hover,
.bp3-select select:hover{
background-clip:padding-box;
background-color:#ebf1f5;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
.bp3-html-select select:active,
.bp3-select select:active, .bp3-html-select select.bp3-active,
.bp3-select select.bp3-active{
background-color:#d8e1e8;
background-image:none;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-html-select select:disabled,
.bp3-select select:disabled, .bp3-html-select select.bp3-disabled,
.bp3-select select.bp3-disabled{
background-color:rgba(206, 217, 224, 0.5);
background-image:none;
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed;
outline:none; }
.bp3-html-select select:disabled.bp3-active,
.bp3-select select:disabled.bp3-active, .bp3-html-select select:disabled.bp3-active:hover,
.bp3-select select:disabled.bp3-active:hover, .bp3-html-select select.bp3-disabled.bp3-active,
.bp3-select select.bp3-disabled.bp3-active, .bp3-html-select select.bp3-disabled.bp3-active:hover,
.bp3-select select.bp3-disabled.bp3-active:hover{
background:rgba(206, 217, 224, 0.7); }
.bp3-html-select.bp3-minimal select,
.bp3-select.bp3-minimal select{
background:none;
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-html-select.bp3-minimal select:hover,
.bp3-select.bp3-minimal select:hover{
background:rgba(167, 182, 194, 0.3);
-webkit-box-shadow:none;
box-shadow:none;
color:#182026;
text-decoration:none; }
.bp3-html-select.bp3-minimal select:active,
.bp3-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal select.bp3-active,
.bp3-select.bp3-minimal select.bp3-active{
background:rgba(115, 134, 148, 0.3);
-webkit-box-shadow:none;
box-shadow:none;
color:#182026; }
.bp3-html-select.bp3-minimal select:disabled,
.bp3-select.bp3-minimal select:disabled, .bp3-html-select.bp3-minimal select:disabled:hover,
.bp3-select.bp3-minimal select:disabled:hover, .bp3-html-select.bp3-minimal select.bp3-disabled,
.bp3-select.bp3-minimal select.bp3-disabled, .bp3-html-select.bp3-minimal select.bp3-disabled:hover,
.bp3-select.bp3-minimal select.bp3-disabled:hover{
background:none;
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed; }
.bp3-html-select.bp3-minimal select:disabled.bp3-active,
.bp3-select.bp3-minimal select:disabled.bp3-active, .bp3-html-select.bp3-minimal select:disabled:hover.bp3-active,
.bp3-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-html-select.bp3-minimal select.bp3-disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-disabled:hover.bp3-active,
.bp3-select.bp3-minimal select.bp3-disabled:hover.bp3-active{
background:rgba(115, 134, 148, 0.3); }
.bp3-dark .bp3-html-select.bp3-minimal select, .bp3-html-select.bp3-minimal .bp3-dark select,
.bp3-dark .bp3-select.bp3-minimal select, .bp3-select.bp3-minimal .bp3-dark select{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:inherit; }
.bp3-dark .bp3-html-select.bp3-minimal select:hover, .bp3-html-select.bp3-minimal .bp3-dark select:hover,
.bp3-dark .bp3-select.bp3-minimal select:hover, .bp3-select.bp3-minimal .bp3-dark select:hover, .bp3-dark .bp3-html-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal .bp3-dark select:active,
.bp3-dark .bp3-select.bp3-minimal select:active, .bp3-select.bp3-minimal .bp3-dark select:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-html-select.bp3-minimal select:hover, .bp3-html-select.bp3-minimal .bp3-dark select:hover,
.bp3-dark .bp3-select.bp3-minimal select:hover, .bp3-select.bp3-minimal .bp3-dark select:hover{
background:rgba(138, 155, 168, 0.15); }
.bp3-dark .bp3-html-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal .bp3-dark select:active,
.bp3-dark .bp3-select.bp3-minimal select:active, .bp3-select.bp3-minimal .bp3-dark select:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-active{
background:rgba(138, 155, 168, 0.3);
color:#f5f8fa; }
.bp3-dark .bp3-html-select.bp3-minimal select:disabled, .bp3-html-select.bp3-minimal .bp3-dark select:disabled,
.bp3-dark .bp3-select.bp3-minimal select:disabled, .bp3-select.bp3-minimal .bp3-dark select:disabled, .bp3-dark .bp3-html-select.bp3-minimal select:disabled:hover, .bp3-html-select.bp3-minimal .bp3-dark select:disabled:hover,
.bp3-dark .bp3-select.bp3-minimal select:disabled:hover, .bp3-select.bp3-minimal .bp3-dark select:disabled:hover, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled:hover,
.bp3-dark .bp3-select.bp3-minimal select.bp3-disabled:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled:hover{
background:none;
color:rgba(167, 182, 194, 0.6);
cursor:not-allowed; }
.bp3-dark .bp3-html-select.bp3-minimal select:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select:disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select:disabled:hover.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-select.bp3-minimal .bp3-dark select:disabled:hover.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled:hover.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled:hover.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-disabled:hover.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled:hover.bp3-active{
background:rgba(138, 155, 168, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-primary,
.bp3-select.bp3-minimal select.bp3-intent-primary{
color:#106ba3; }
.bp3-html-select.bp3-minimal select.bp3-intent-primary:hover,
.bp3-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-html-select.bp3-minimal select.bp3-intent-primary:active,
.bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#106ba3; }
.bp3-html-select.bp3-minimal select.bp3-intent-primary:hover,
.bp3-select.bp3-minimal select.bp3-intent-primary:hover{
background:rgba(19, 124, 189, 0.15);
color:#106ba3; }
.bp3-html-select.bp3-minimal select.bp3-intent-primary:active,
.bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active{
background:rgba(19, 124, 189, 0.3);
color:#106ba3; }
.bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled,
.bp3-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled,
.bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled{
background:none;
color:rgba(16, 107, 163, 0.5); }
.bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active{
background:rgba(19, 124, 189, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
stroke:#106ba3; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary{
color:#48aff0; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:hover,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:hover{
background:rgba(19, 124, 189, 0.2);
color:#48aff0; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-active{
background:rgba(19, 124, 189, 0.3);
color:#48aff0; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled{
background:none;
color:rgba(72, 175, 240, 0.5); }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled.bp3-active{
background:rgba(19, 124, 189, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-success,
.bp3-select.bp3-minimal select.bp3-intent-success{
color:#0d8050; }
.bp3-html-select.bp3-minimal select.bp3-intent-success:hover,
.bp3-select.bp3-minimal select.bp3-intent-success:hover, .bp3-html-select.bp3-minimal select.bp3-intent-success:active,
.bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-success.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#0d8050; }
.bp3-html-select.bp3-minimal select.bp3-intent-success:hover,
.bp3-select.bp3-minimal select.bp3-intent-success:hover{
background:rgba(15, 153, 96, 0.15);
color:#0d8050; }
.bp3-html-select.bp3-minimal select.bp3-intent-success:active,
.bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-success.bp3-active{
background:rgba(15, 153, 96, 0.3);
color:#0d8050; }
.bp3-html-select.bp3-minimal select.bp3-intent-success:disabled,
.bp3-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled,
.bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled{
background:none;
color:rgba(13, 128, 80, 0.5); }
.bp3-html-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active{
background:rgba(15, 153, 96, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-success .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
stroke:#0d8050; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success{
color:#3dcc91; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:hover,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:hover{
background:rgba(15, 153, 96, 0.2);
color:#3dcc91; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-active{
background:rgba(15, 153, 96, 0.3);
color:#3dcc91; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled{
background:none;
color:rgba(61, 204, 145, 0.5); }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled.bp3-active{
background:rgba(15, 153, 96, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-warning,
.bp3-select.bp3-minimal select.bp3-intent-warning{
color:#bf7326; }
.bp3-html-select.bp3-minimal select.bp3-intent-warning:hover,
.bp3-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-html-select.bp3-minimal select.bp3-intent-warning:active,
.bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#bf7326; }
.bp3-html-select.bp3-minimal select.bp3-intent-warning:hover,
.bp3-select.bp3-minimal select.bp3-intent-warning:hover{
background:rgba(217, 130, 43, 0.15);
color:#bf7326; }
.bp3-html-select.bp3-minimal select.bp3-intent-warning:active,
.bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active{
background:rgba(217, 130, 43, 0.3);
color:#bf7326; }
.bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled,
.bp3-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled,
.bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled{
background:none;
color:rgba(191, 115, 38, 0.5); }
.bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active{
background:rgba(217, 130, 43, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
stroke:#bf7326; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning{
color:#ffb366; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:hover,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:hover{
background:rgba(217, 130, 43, 0.2);
color:#ffb366; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-active{
background:rgba(217, 130, 43, 0.3);
color:#ffb366; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled{
background:none;
color:rgba(255, 179, 102, 0.5); }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled.bp3-active{
background:rgba(217, 130, 43, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-danger,
.bp3-select.bp3-minimal select.bp3-intent-danger{
color:#c23030; }
.bp3-html-select.bp3-minimal select.bp3-intent-danger:hover,
.bp3-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-html-select.bp3-minimal select.bp3-intent-danger:active,
.bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active{
background:none;
-webkit-box-shadow:none;
box-shadow:none;
color:#c23030; }
.bp3-html-select.bp3-minimal select.bp3-intent-danger:hover,
.bp3-select.bp3-minimal select.bp3-intent-danger:hover{
background:rgba(219, 55, 55, 0.15);
color:#c23030; }
.bp3-html-select.bp3-minimal select.bp3-intent-danger:active,
.bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active{
background:rgba(219, 55, 55, 0.3);
color:#c23030; }
.bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled,
.bp3-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled,
.bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled{
background:none;
color:rgba(194, 48, 48, 0.5); }
.bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active{
background:rgba(219, 55, 55, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
stroke:#c23030; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger{
color:#ff7373; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:hover,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:hover{
background:rgba(219, 55, 55, 0.2);
color:#ff7373; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-active{
background:rgba(219, 55, 55, 0.3);
color:#ff7373; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled{
background:none;
color:rgba(255, 115, 115, 0.5); }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled.bp3-active{
background:rgba(219, 55, 55, 0.3); }
.bp3-html-select.bp3-large select,
.bp3-select.bp3-large select{
font-size:16px;
height:40px;
padding-right:35px; }
.bp3-dark .bp3-html-select select, .bp3-dark .bp3-select select{
background-color:#394b59;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
color:#f5f8fa; }
.bp3-dark .bp3-html-select select:hover, .bp3-dark .bp3-select select:hover, .bp3-dark .bp3-html-select select:active, .bp3-dark .bp3-select select:active, .bp3-dark .bp3-html-select select.bp3-active, .bp3-dark .bp3-select select.bp3-active{
color:#f5f8fa; }
.bp3-dark .bp3-html-select select:hover, .bp3-dark .bp3-select select:hover{
background-color:#30404d;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-html-select select:active, .bp3-dark .bp3-select select:active, .bp3-dark .bp3-html-select select.bp3-active, .bp3-dark .bp3-select select.bp3-active{
background-color:#202b33;
background-image:none;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-dark .bp3-html-select select:disabled, .bp3-dark .bp3-select select:disabled, .bp3-dark .bp3-html-select select.bp3-disabled, .bp3-dark .bp3-select select.bp3-disabled{
background-color:rgba(57, 75, 89, 0.5);
background-image:none;
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-html-select select:disabled.bp3-active, .bp3-dark .bp3-select select:disabled.bp3-active, .bp3-dark .bp3-html-select select.bp3-disabled.bp3-active, .bp3-dark .bp3-select select.bp3-disabled.bp3-active{
background:rgba(57, 75, 89, 0.7); }
.bp3-dark .bp3-html-select select .bp3-button-spinner .bp3-spinner-head, .bp3-dark .bp3-select select .bp3-button-spinner .bp3-spinner-head{
background:rgba(16, 22, 26, 0.5);
stroke:#8a9ba8; }
.bp3-html-select select:disabled,
.bp3-select select:disabled{
background-color:rgba(206, 217, 224, 0.5);
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed; }
.bp3-html-select .bp3-icon,
.bp3-select .bp3-icon, .bp3-select::after{
color:#5c7080;
pointer-events:none;
position:absolute;
right:7px;
top:7px; }
.bp3-html-select .bp3-disabled.bp3-icon,
.bp3-select .bp3-disabled.bp3-icon, .bp3-disabled.bp3-select::after{
color:rgba(92, 112, 128, 0.6); }
.bp3-html-select,
.bp3-select{
display:inline-block;
letter-spacing:normal;
position:relative;
vertical-align:middle; }
.bp3-html-select select::-ms-expand,
.bp3-select select::-ms-expand{
display:none; }
.bp3-html-select .bp3-icon,
.bp3-select .bp3-icon{
color:#5c7080; }
.bp3-html-select .bp3-icon:hover,
.bp3-select .bp3-icon:hover{
color:#182026; }
.bp3-dark .bp3-html-select .bp3-icon, .bp3-dark
.bp3-select .bp3-icon{
color:#a7b6c2; }
.bp3-dark .bp3-html-select .bp3-icon:hover, .bp3-dark
.bp3-select .bp3-icon:hover{
color:#f5f8fa; }
.bp3-html-select.bp3-large::after,
.bp3-html-select.bp3-large .bp3-icon,
.bp3-select.bp3-large::after,
.bp3-select.bp3-large .bp3-icon{
right:12px;
top:12px; }
.bp3-html-select.bp3-fill,
.bp3-html-select.bp3-fill select,
.bp3-select.bp3-fill,
.bp3-select.bp3-fill select{
width:100%; }
.bp3-dark .bp3-html-select option, .bp3-dark
.bp3-select option{
background-color:#30404d;
color:#f5f8fa; }
.bp3-dark .bp3-html-select option:disabled, .bp3-dark
.bp3-select option:disabled{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-html-select::after, .bp3-dark
.bp3-select::after{
color:#a7b6c2; }
.bp3-select::after{
font-family:"Icons16", sans-serif;
font-size:16px;
font-style:normal;
font-weight:400;
line-height:1;
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased;
content:""; }
.bp3-running-text table, table.bp3-html-table{
border-spacing:0;
font-size:14px; }
.bp3-running-text table th, table.bp3-html-table th,
.bp3-running-text table td,
table.bp3-html-table td{
padding:11px;
text-align:left;
vertical-align:top; }
.bp3-running-text table th, table.bp3-html-table th{
color:#182026;
font-weight:600; }
.bp3-running-text table td,
table.bp3-html-table td{
color:#182026; }
.bp3-running-text table tbody tr:first-child th, table.bp3-html-table tbody tr:first-child th,
.bp3-running-text table tbody tr:first-child td,
table.bp3-html-table tbody tr:first-child td,
.bp3-running-text table tfoot tr:first-child th,
table.bp3-html-table tfoot tr:first-child th,
.bp3-running-text table tfoot tr:first-child td,
table.bp3-html-table tfoot tr:first-child td{
-webkit-box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15);
box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15); }
.bp3-dark .bp3-running-text table th, .bp3-running-text .bp3-dark table th, .bp3-dark table.bp3-html-table th{
color:#f5f8fa; }
.bp3-dark .bp3-running-text table td, .bp3-running-text .bp3-dark table td, .bp3-dark table.bp3-html-table td{
color:#f5f8fa; }
.bp3-dark .bp3-running-text table tbody tr:first-child th, .bp3-running-text .bp3-dark table tbody tr:first-child th, .bp3-dark table.bp3-html-table tbody tr:first-child th,
.bp3-dark .bp3-running-text table tbody tr:first-child td,
.bp3-running-text .bp3-dark table tbody tr:first-child td,
.bp3-dark table.bp3-html-table tbody tr:first-child td,
.bp3-dark .bp3-running-text table tfoot tr:first-child th,
.bp3-running-text .bp3-dark table tfoot tr:first-child th,
.bp3-dark table.bp3-html-table tfoot tr:first-child th,
.bp3-dark .bp3-running-text table tfoot tr:first-child td,
.bp3-running-text .bp3-dark table tfoot tr:first-child td,
.bp3-dark table.bp3-html-table tfoot tr:first-child td{
-webkit-box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15);
box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15); }
table.bp3-html-table.bp3-html-table-condensed th,
table.bp3-html-table.bp3-html-table-condensed td, table.bp3-html-table.bp3-small th,
table.bp3-html-table.bp3-small td{
padding-bottom:6px;
padding-top:6px; }
table.bp3-html-table.bp3-html-table-striped tbody tr:nth-child(odd) td{
background:rgba(191, 204, 214, 0.15); }
table.bp3-html-table.bp3-html-table-bordered th:not(:first-child){
-webkit-box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15);
box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15); }
table.bp3-html-table.bp3-html-table-bordered tbody tr td,
table.bp3-html-table.bp3-html-table-bordered tfoot tr td{
-webkit-box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15);
box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15); }
table.bp3-html-table.bp3-html-table-bordered tbody tr td:not(:first-child),
table.bp3-html-table.bp3-html-table-bordered tfoot tr td:not(:first-child){
-webkit-box-shadow:inset 1px 1px 0 0 rgba(16, 22, 26, 0.15);
box-shadow:inset 1px 1px 0 0 rgba(16, 22, 26, 0.15); }
table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td{
-webkit-box-shadow:none;
box-shadow:none; }
table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td:not(:first-child){
-webkit-box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15);
box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15); }
table.bp3-html-table.bp3-interactive tbody tr:hover td{
background-color:rgba(191, 204, 214, 0.3);
cursor:pointer; }
table.bp3-html-table.bp3-interactive tbody tr:active td{
background-color:rgba(191, 204, 214, 0.4); }
.bp3-dark table.bp3-html-table{ }
.bp3-dark table.bp3-html-table.bp3-html-table-striped tbody tr:nth-child(odd) td{
background:rgba(92, 112, 128, 0.15); }
.bp3-dark table.bp3-html-table.bp3-html-table-bordered th:not(:first-child){
-webkit-box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15);
box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); }
.bp3-dark table.bp3-html-table.bp3-html-table-bordered tbody tr td,
.bp3-dark table.bp3-html-table.bp3-html-table-bordered tfoot tr td{
-webkit-box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15);
box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15); }
.bp3-dark table.bp3-html-table.bp3-html-table-bordered tbody tr td:not(:first-child),
.bp3-dark table.bp3-html-table.bp3-html-table-bordered tfoot tr td:not(:first-child){
-webkit-box-shadow:inset 1px 1px 0 0 rgba(255, 255, 255, 0.15);
box-shadow:inset 1px 1px 0 0 rgba(255, 255, 255, 0.15); }
.bp3-dark table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td{
-webkit-box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15);
box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); }
.bp3-dark table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td:first-child{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark table.bp3-html-table.bp3-interactive tbody tr:hover td{
background-color:rgba(92, 112, 128, 0.3);
cursor:pointer; }
.bp3-dark table.bp3-html-table.bp3-interactive tbody tr:active td{
background-color:rgba(92, 112, 128, 0.4); }
.bp3-key-combo{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:horizontal;
-webkit-box-direction:normal;
-ms-flex-direction:row;
flex-direction:row;
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center; }
.bp3-key-combo > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-key-combo > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-key-combo::before,
.bp3-key-combo > *{
margin-right:5px; }
.bp3-key-combo:empty::before,
.bp3-key-combo > :last-child{
margin-right:0; }
.bp3-hotkey-dialog{
padding-bottom:0;
top:40px; }
.bp3-hotkey-dialog .bp3-dialog-body{
margin:0;
padding:0; }
.bp3-hotkey-dialog .bp3-hotkey-label{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1; }
.bp3-hotkey-column{
margin:auto;
max-height:80vh;
overflow-y:auto;
padding:30px; }
.bp3-hotkey-column .bp3-heading{
margin-bottom:20px; }
.bp3-hotkey-column .bp3-heading:not(:first-child){
margin-top:40px; }
.bp3-hotkey{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-pack:justify;
-ms-flex-pack:justify;
justify-content:space-between;
margin-left:0;
margin-right:0; }
.bp3-hotkey:not(:last-child){
margin-bottom:10px; }
.bp3-icon{
display:inline-block;
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
vertical-align:text-bottom; }
.bp3-icon:not(:empty)::before{
content:"" !important;
content:unset !important; }
.bp3-icon > svg{
display:block; }
.bp3-icon > svg:not([fill]){
fill:currentColor; }
.bp3-icon.bp3-intent-primary, .bp3-icon-standard.bp3-intent-primary, .bp3-icon-large.bp3-intent-primary{
color:#106ba3; }
.bp3-dark .bp3-icon.bp3-intent-primary, .bp3-dark .bp3-icon-standard.bp3-intent-primary, .bp3-dark .bp3-icon-large.bp3-intent-primary{
color:#48aff0; }
.bp3-icon.bp3-intent-success, .bp3-icon-standard.bp3-intent-success, .bp3-icon-large.bp3-intent-success{
color:#0d8050; }
.bp3-dark .bp3-icon.bp3-intent-success, .bp3-dark .bp3-icon-standard.bp3-intent-success, .bp3-dark .bp3-icon-large.bp3-intent-success{
color:#3dcc91; }
.bp3-icon.bp3-intent-warning, .bp3-icon-standard.bp3-intent-warning, .bp3-icon-large.bp3-intent-warning{
color:#bf7326; }
.bp3-dark .bp3-icon.bp3-intent-warning, .bp3-dark .bp3-icon-standard.bp3-intent-warning, .bp3-dark .bp3-icon-large.bp3-intent-warning{
color:#ffb366; }
.bp3-icon.bp3-intent-danger, .bp3-icon-standard.bp3-intent-danger, .bp3-icon-large.bp3-intent-danger{
color:#c23030; }
.bp3-dark .bp3-icon.bp3-intent-danger, .bp3-dark .bp3-icon-standard.bp3-intent-danger, .bp3-dark .bp3-icon-large.bp3-intent-danger{
color:#ff7373; }
span.bp3-icon-standard{
font-family:"Icons16", sans-serif;
font-size:16px;
font-style:normal;
font-weight:400;
line-height:1;
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased;
display:inline-block; }
span.bp3-icon-large{
font-family:"Icons20", sans-serif;
font-size:20px;
font-style:normal;
font-weight:400;
line-height:1;
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased;
display:inline-block; }
span.bp3-icon:empty{
font-family:"Icons20";
font-size:inherit;
font-style:normal;
font-weight:400;
line-height:1; }
span.bp3-icon:empty::before{
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased; }
.bp3-icon-add::before{
content:""; }
.bp3-icon-add-column-left::before{
content:""; }
.bp3-icon-add-column-right::before{
content:""; }
.bp3-icon-add-row-bottom::before{
content:""; }
.bp3-icon-add-row-top::before{
content:""; }
.bp3-icon-add-to-artifact::before{
content:""; }
.bp3-icon-add-to-folder::before{
content:""; }
.bp3-icon-airplane::before{
content:""; }
.bp3-icon-align-center::before{
content:""; }
.bp3-icon-align-justify::before{
content:""; }
.bp3-icon-align-left::before{
content:""; }
.bp3-icon-align-right::before{
content:""; }
.bp3-icon-alignment-bottom::before{
content:""; }
.bp3-icon-alignment-horizontal-center::before{
content:""; }
.bp3-icon-alignment-left::before{
content:""; }
.bp3-icon-alignment-right::before{
content:""; }
.bp3-icon-alignment-top::before{
content:""; }
.bp3-icon-alignment-vertical-center::before{
content:""; }
.bp3-icon-annotation::before{
content:""; }
.bp3-icon-application::before{
content:""; }
.bp3-icon-applications::before{
content:""; }
.bp3-icon-archive::before{
content:""; }
.bp3-icon-arrow-bottom-left::before{
content:"↙"; }
.bp3-icon-arrow-bottom-right::before{
content:"↘"; }
.bp3-icon-arrow-down::before{
content:"↓"; }
.bp3-icon-arrow-left::before{
content:"←"; }
.bp3-icon-arrow-right::before{
content:"→"; }
.bp3-icon-arrow-top-left::before{
content:"↖"; }
.bp3-icon-arrow-top-right::before{
content:"↗"; }
.bp3-icon-arrow-up::before{
content:"↑"; }
.bp3-icon-arrows-horizontal::before{
content:"↔"; }
.bp3-icon-arrows-vertical::before{
content:"↕"; }
.bp3-icon-asterisk::before{
content:"*"; }
.bp3-icon-automatic-updates::before{
content:""; }
.bp3-icon-badge::before{
content:""; }
.bp3-icon-ban-circle::before{
content:""; }
.bp3-icon-bank-account::before{
content:""; }
.bp3-icon-barcode::before{
content:""; }
.bp3-icon-blank::before{
content:""; }
.bp3-icon-blocked-person::before{
content:""; }
.bp3-icon-bold::before{
content:""; }
.bp3-icon-book::before{
content:""; }
.bp3-icon-bookmark::before{
content:""; }
.bp3-icon-box::before{
content:""; }
.bp3-icon-briefcase::before{
content:""; }
.bp3-icon-bring-data::before{
content:""; }
.bp3-icon-build::before{
content:""; }
.bp3-icon-calculator::before{
content:""; }
.bp3-icon-calendar::before{
content:""; }
.bp3-icon-camera::before{
content:""; }
.bp3-icon-caret-down::before{
content:"⌄"; }
.bp3-icon-caret-left::before{
content:"〈"; }
.bp3-icon-caret-right::before{
content:"〉"; }
.bp3-icon-caret-up::before{
content:"⌃"; }
.bp3-icon-cell-tower::before{
content:""; }
.bp3-icon-changes::before{
content:""; }
.bp3-icon-chart::before{
content:""; }
.bp3-icon-chat::before{
content:""; }
.bp3-icon-chevron-backward::before{
content:""; }
.bp3-icon-chevron-down::before{
content:""; }
.bp3-icon-chevron-forward::before{
content:""; }
.bp3-icon-chevron-left::before{
content:""; }
.bp3-icon-chevron-right::before{
content:""; }
.bp3-icon-chevron-up::before{
content:""; }
.bp3-icon-circle::before{
content:""; }
.bp3-icon-circle-arrow-down::before{
content:""; }
.bp3-icon-circle-arrow-left::before{
content:""; }
.bp3-icon-circle-arrow-right::before{
content:""; }
.bp3-icon-circle-arrow-up::before{
content:""; }
.bp3-icon-citation::before{
content:""; }
.bp3-icon-clean::before{
content:""; }
.bp3-icon-clipboard::before{
content:""; }
.bp3-icon-cloud::before{
content:"☁"; }
.bp3-icon-cloud-download::before{
content:""; }
.bp3-icon-cloud-upload::before{
content:""; }
.bp3-icon-code::before{
content:""; }
.bp3-icon-code-block::before{
content:""; }
.bp3-icon-cog::before{
content:""; }
.bp3-icon-collapse-all::before{
content:""; }
.bp3-icon-column-layout::before{
content:""; }
.bp3-icon-comment::before{
content:""; }
.bp3-icon-comparison::before{
content:""; }
.bp3-icon-compass::before{
content:""; }
.bp3-icon-compressed::before{
content:""; }
.bp3-icon-confirm::before{
content:""; }
.bp3-icon-console::before{
content:""; }
.bp3-icon-contrast::before{
content:""; }
.bp3-icon-control::before{
content:""; }
.bp3-icon-credit-card::before{
content:""; }
.bp3-icon-cross::before{
content:"✗"; }
.bp3-icon-crown::before{
content:""; }
.bp3-icon-cube::before{
content:""; }
.bp3-icon-cube-add::before{
content:""; }
.bp3-icon-cube-remove::before{
content:""; }
.bp3-icon-curved-range-chart::before{
content:""; }
.bp3-icon-cut::before{
content:""; }
.bp3-icon-dashboard::before{
content:""; }
.bp3-icon-data-lineage::before{
content:""; }
.bp3-icon-database::before{
content:""; }
.bp3-icon-delete::before{
content:""; }
.bp3-icon-delta::before{
content:"Δ"; }
.bp3-icon-derive-column::before{
content:""; }
.bp3-icon-desktop::before{
content:""; }
.bp3-icon-diagnosis::before{
content:""; }
.bp3-icon-diagram-tree::before{
content:""; }
.bp3-icon-direction-left::before{
content:""; }
.bp3-icon-direction-right::before{
content:""; }
.bp3-icon-disable::before{
content:""; }
.bp3-icon-document::before{
content:""; }
.bp3-icon-document-open::before{
content:""; }
.bp3-icon-document-share::before{
content:""; }
.bp3-icon-dollar::before{
content:"$"; }
.bp3-icon-dot::before{
content:"•"; }
.bp3-icon-double-caret-horizontal::before{
content:""; }
.bp3-icon-double-caret-vertical::before{
content:""; }
.bp3-icon-double-chevron-down::before{
content:""; }
.bp3-icon-double-chevron-left::before{
content:""; }
.bp3-icon-double-chevron-right::before{
content:""; }
.bp3-icon-double-chevron-up::before{
content:""; }
.bp3-icon-doughnut-chart::before{
content:""; }
.bp3-icon-download::before{
content:""; }
.bp3-icon-drag-handle-horizontal::before{
content:""; }
.bp3-icon-drag-handle-vertical::before{
content:""; }
.bp3-icon-draw::before{
content:""; }
.bp3-icon-drive-time::before{
content:""; }
.bp3-icon-duplicate::before{
content:""; }
.bp3-icon-edit::before{
content:"✎"; }
.bp3-icon-eject::before{
content:"⏏"; }
.bp3-icon-endorsed::before{
content:""; }
.bp3-icon-envelope::before{
content:"✉"; }
.bp3-icon-equals::before{
content:""; }
.bp3-icon-eraser::before{
content:""; }
.bp3-icon-error::before{
content:""; }
.bp3-icon-euro::before{
content:"€"; }
.bp3-icon-exchange::before{
content:""; }
.bp3-icon-exclude-row::before{
content:""; }
.bp3-icon-expand-all::before{
content:""; }
.bp3-icon-export::before{
content:""; }
.bp3-icon-eye-off::before{
content:""; }
.bp3-icon-eye-on::before{
content:""; }
.bp3-icon-eye-open::before{
content:""; }
.bp3-icon-fast-backward::before{
content:""; }
.bp3-icon-fast-forward::before{
content:""; }
.bp3-icon-feed::before{
content:""; }
.bp3-icon-feed-subscribed::before{
content:""; }
.bp3-icon-film::before{
content:""; }
.bp3-icon-filter::before{
content:""; }
.bp3-icon-filter-keep::before{
content:""; }
.bp3-icon-filter-list::before{
content:""; }
.bp3-icon-filter-open::before{
content:""; }
.bp3-icon-filter-remove::before{
content:""; }
.bp3-icon-flag::before{
content:"⚑"; }
.bp3-icon-flame::before{
content:""; }
.bp3-icon-flash::before{
content:""; }
.bp3-icon-floppy-disk::before{
content:""; }
.bp3-icon-flow-branch::before{
content:""; }
.bp3-icon-flow-end::before{
content:""; }
.bp3-icon-flow-linear::before{
content:""; }
.bp3-icon-flow-review::before{
content:""; }
.bp3-icon-flow-review-branch::before{
content:""; }
.bp3-icon-flows::before{
content:""; }
.bp3-icon-folder-close::before{
content:""; }
.bp3-icon-folder-new::before{
content:""; }
.bp3-icon-folder-open::before{
content:""; }
.bp3-icon-folder-shared::before{
content:""; }
.bp3-icon-folder-shared-open::before{
content:""; }
.bp3-icon-follower::before{
content:""; }
.bp3-icon-following::before{
content:""; }
.bp3-icon-font::before{
content:""; }
.bp3-icon-fork::before{
content:""; }
.bp3-icon-form::before{
content:""; }
.bp3-icon-full-circle::before{
content:""; }
.bp3-icon-full-stacked-chart::before{
content:""; }
.bp3-icon-fullscreen::before{
content:""; }
.bp3-icon-function::before{
content:""; }
.bp3-icon-gantt-chart::before{
content:""; }
.bp3-icon-geolocation::before{
content:""; }
.bp3-icon-geosearch::before{
content:""; }
.bp3-icon-git-branch::before{
content:""; }
.bp3-icon-git-commit::before{
content:""; }
.bp3-icon-git-merge::before{
content:""; }
.bp3-icon-git-new-branch::before{
content:""; }
.bp3-icon-git-pull::before{
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:"−"; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:"⎙"; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
.bp3-icon-remove-column-right::before{
content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
.bp3-icon-rig::before{
content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:"⦿"; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:"★"; }
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content:"☆"; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:"✓"; }
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content:""; }
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content:"⏲"; }
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content:""; }
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content:""; }
.bp3-icon-timeline-events::before{
content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:"⎁"; }
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content:"⎌"; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
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content:""; }
.bp3-icon-vertical-bar-chart-desc::before{
content:""; }
.bp3-icon-vertical-distribution::before{
content:""; }
.bp3-icon-video::before{
content:""; }
.bp3-icon-volume-down::before{
content:""; }
.bp3-icon-volume-off::before{
content:""; }
.bp3-icon-volume-up::before{
content:""; }
.bp3-icon-walk::before{
content:""; }
.bp3-icon-warning-sign::before{
content:""; }
.bp3-icon-waterfall-chart::before{
content:""; }
.bp3-icon-widget::before{
content:""; }
.bp3-icon-widget-button::before{
content:""; }
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content:""; }
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content:""; }
.bp3-icon-wrench::before{
content:""; }
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content:""; }
.bp3-icon-zoom-out::before{
content:""; }
.bp3-icon-zoom-to-fit::before{
content:""; }
.bp3-submenu > .bp3-popover-wrapper{
display:block; }
.bp3-submenu .bp3-popover-target{
display:block; }
.bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{ }
.bp3-submenu.bp3-popover{
-webkit-box-shadow:none;
box-shadow:none;
padding:0 5px; }
.bp3-submenu.bp3-popover > .bp3-popover-content{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); }
.bp3-dark .bp3-submenu.bp3-popover, .bp3-submenu.bp3-popover.bp3-dark{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-submenu.bp3-popover > .bp3-popover-content, .bp3-submenu.bp3-popover.bp3-dark > .bp3-popover-content{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
.bp3-menu{
background:#ffffff;
border-radius:3px;
color:#182026;
list-style:none;
margin:0;
min-width:180px;
padding:5px;
text-align:left; }
.bp3-menu-divider{
border-top:1px solid rgba(16, 22, 26, 0.15);
display:block;
margin:5px; }
.bp3-dark .bp3-menu-divider{
border-top-color:rgba(255, 255, 255, 0.15); }
.bp3-menu-item{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:horizontal;
-webkit-box-direction:normal;
-ms-flex-direction:row;
flex-direction:row;
-webkit-box-align:start;
-ms-flex-align:start;
align-items:flex-start;
border-radius:2px;
color:inherit;
line-height:20px;
padding:5px 7px;
text-decoration:none;
-webkit-user-select:none;
-moz-user-select:none;
-ms-user-select:none;
user-select:none; }
.bp3-menu-item > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-menu-item > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-menu-item::before,
.bp3-menu-item > *{
margin-right:7px; }
.bp3-menu-item:empty::before,
.bp3-menu-item > :last-child{
margin-right:0; }
.bp3-menu-item > .bp3-fill{
word-break:break-word; }
.bp3-menu-item:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{
background-color:rgba(167, 182, 194, 0.3);
cursor:pointer;
text-decoration:none; }
.bp3-menu-item.bp3-disabled{
background-color:inherit;
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed; }
.bp3-dark .bp3-menu-item{
color:inherit; }
.bp3-dark .bp3-menu-item:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-menu-item{
background-color:rgba(138, 155, 168, 0.15);
color:inherit; }
.bp3-dark .bp3-menu-item.bp3-disabled{
background-color:inherit;
color:rgba(167, 182, 194, 0.6); }
.bp3-menu-item.bp3-intent-primary{
color:#106ba3; }
.bp3-menu-item.bp3-intent-primary .bp3-icon{
color:inherit; }
.bp3-menu-item.bp3-intent-primary::before, .bp3-menu-item.bp3-intent-primary::after,
.bp3-menu-item.bp3-intent-primary .bp3-menu-item-label{
color:#106ba3; }
.bp3-menu-item.bp3-intent-primary:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-menu-item.bp3-intent-primary.bp3-active{
background-color:#137cbd; }
.bp3-menu-item.bp3-intent-primary:active{
background-color:#106ba3; }
.bp3-menu-item.bp3-intent-primary:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-menu-item.bp3-intent-primary:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-menu-item.bp3-intent-primary:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after,
.bp3-menu-item.bp3-intent-primary:hover .bp3-menu-item-label,
.bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-primary:active, .bp3-menu-item.bp3-intent-primary:active::before, .bp3-menu-item.bp3-intent-primary:active::after,
.bp3-menu-item.bp3-intent-primary:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-primary.bp3-active, .bp3-menu-item.bp3-intent-primary.bp3-active::before, .bp3-menu-item.bp3-intent-primary.bp3-active::after,
.bp3-menu-item.bp3-intent-primary.bp3-active .bp3-menu-item-label{
color:#ffffff; }
.bp3-menu-item.bp3-intent-success{
color:#0d8050; }
.bp3-menu-item.bp3-intent-success .bp3-icon{
color:inherit; }
.bp3-menu-item.bp3-intent-success::before, .bp3-menu-item.bp3-intent-success::after,
.bp3-menu-item.bp3-intent-success .bp3-menu-item-label{
color:#0d8050; }
.bp3-menu-item.bp3-intent-success:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-menu-item.bp3-intent-success.bp3-active{
background-color:#0f9960; }
.bp3-menu-item.bp3-intent-success:active{
background-color:#0d8050; }
.bp3-menu-item.bp3-intent-success:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-menu-item.bp3-intent-success:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-menu-item.bp3-intent-success:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after,
.bp3-menu-item.bp3-intent-success:hover .bp3-menu-item-label,
.bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-success:active, .bp3-menu-item.bp3-intent-success:active::before, .bp3-menu-item.bp3-intent-success:active::after,
.bp3-menu-item.bp3-intent-success:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-success.bp3-active, .bp3-menu-item.bp3-intent-success.bp3-active::before, .bp3-menu-item.bp3-intent-success.bp3-active::after,
.bp3-menu-item.bp3-intent-success.bp3-active .bp3-menu-item-label{
color:#ffffff; }
.bp3-menu-item.bp3-intent-warning{
color:#bf7326; }
.bp3-menu-item.bp3-intent-warning .bp3-icon{
color:inherit; }
.bp3-menu-item.bp3-intent-warning::before, .bp3-menu-item.bp3-intent-warning::after,
.bp3-menu-item.bp3-intent-warning .bp3-menu-item-label{
color:#bf7326; }
.bp3-menu-item.bp3-intent-warning:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-menu-item.bp3-intent-warning.bp3-active{
background-color:#d9822b; }
.bp3-menu-item.bp3-intent-warning:active{
background-color:#bf7326; }
.bp3-menu-item.bp3-intent-warning:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-menu-item.bp3-intent-warning:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-menu-item.bp3-intent-warning:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after,
.bp3-menu-item.bp3-intent-warning:hover .bp3-menu-item-label,
.bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-warning:active, .bp3-menu-item.bp3-intent-warning:active::before, .bp3-menu-item.bp3-intent-warning:active::after,
.bp3-menu-item.bp3-intent-warning:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-warning.bp3-active, .bp3-menu-item.bp3-intent-warning.bp3-active::before, .bp3-menu-item.bp3-intent-warning.bp3-active::after,
.bp3-menu-item.bp3-intent-warning.bp3-active .bp3-menu-item-label{
color:#ffffff; }
.bp3-menu-item.bp3-intent-danger{
color:#c23030; }
.bp3-menu-item.bp3-intent-danger .bp3-icon{
color:inherit; }
.bp3-menu-item.bp3-intent-danger::before, .bp3-menu-item.bp3-intent-danger::after,
.bp3-menu-item.bp3-intent-danger .bp3-menu-item-label{
color:#c23030; }
.bp3-menu-item.bp3-intent-danger:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-menu-item.bp3-intent-danger.bp3-active{
background-color:#db3737; }
.bp3-menu-item.bp3-intent-danger:active{
background-color:#c23030; }
.bp3-menu-item.bp3-intent-danger:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-menu-item.bp3-intent-danger:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-menu-item.bp3-intent-danger:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after,
.bp3-menu-item.bp3-intent-danger:hover .bp3-menu-item-label,
.bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-danger:active, .bp3-menu-item.bp3-intent-danger:active::before, .bp3-menu-item.bp3-intent-danger:active::after,
.bp3-menu-item.bp3-intent-danger:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-danger.bp3-active, .bp3-menu-item.bp3-intent-danger.bp3-active::before, .bp3-menu-item.bp3-intent-danger.bp3-active::after,
.bp3-menu-item.bp3-intent-danger.bp3-active .bp3-menu-item-label{
color:#ffffff; }
.bp3-menu-item::before{
font-family:"Icons16", sans-serif;
font-size:16px;
font-style:normal;
font-weight:400;
line-height:1;
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased;
margin-right:7px; }
.bp3-menu-item::before,
.bp3-menu-item > .bp3-icon{
color:#5c7080;
margin-top:2px; }
.bp3-menu-item .bp3-menu-item-label{
color:#5c7080; }
.bp3-menu-item:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{
color:inherit; }
.bp3-menu-item.bp3-active, .bp3-menu-item:active{
background-color:rgba(115, 134, 148, 0.3); }
.bp3-menu-item.bp3-disabled{
background-color:inherit !important;
color:rgba(92, 112, 128, 0.6) !important;
cursor:not-allowed !important;
outline:none !important; }
.bp3-menu-item.bp3-disabled::before,
.bp3-menu-item.bp3-disabled > .bp3-icon,
.bp3-menu-item.bp3-disabled .bp3-menu-item-label{
color:rgba(92, 112, 128, 0.6) !important; }
.bp3-large .bp3-menu-item{
font-size:16px;
line-height:22px;
padding:9px 7px; }
.bp3-large .bp3-menu-item .bp3-icon{
margin-top:3px; }
.bp3-large .bp3-menu-item::before{
font-family:"Icons20", sans-serif;
font-size:20px;
font-style:normal;
font-weight:400;
line-height:1;
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased;
margin-right:10px;
margin-top:1px; }
button.bp3-menu-item{
background:none;
border:none;
text-align:left;
width:100%; }
.bp3-menu-header{
border-top:1px solid rgba(16, 22, 26, 0.15);
display:block;
margin:5px;
cursor:default;
padding-left:2px; }
.bp3-dark .bp3-menu-header{
border-top-color:rgba(255, 255, 255, 0.15); }
.bp3-menu-header:first-of-type{
border-top:none; }
.bp3-menu-header > h6{
color:#182026;
font-weight:600;
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal;
line-height:17px;
margin:0;
padding:10px 7px 0 1px; }
.bp3-dark .bp3-menu-header > h6{
color:#f5f8fa; }
.bp3-menu-header:first-of-type > h6{
padding-top:0; }
.bp3-large .bp3-menu-header > h6{
font-size:18px;
padding-bottom:5px;
padding-top:15px; }
.bp3-large .bp3-menu-header:first-of-type > h6{
padding-top:0; }
.bp3-dark .bp3-menu{
background:#30404d;
color:#f5f8fa; }
.bp3-dark .bp3-menu-item{ }
.bp3-dark .bp3-menu-item.bp3-intent-primary{
color:#48aff0; }
.bp3-dark .bp3-menu-item.bp3-intent-primary .bp3-icon{
color:inherit; }
.bp3-dark .bp3-menu-item.bp3-intent-primary::before, .bp3-dark .bp3-menu-item.bp3-intent-primary::after,
.bp3-dark .bp3-menu-item.bp3-intent-primary .bp3-menu-item-label{
color:#48aff0; }
.bp3-dark .bp3-menu-item.bp3-intent-primary:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active{
background-color:#137cbd; }
.bp3-dark .bp3-menu-item.bp3-intent-primary:active{
background-color:#106ba3; }
.bp3-dark .bp3-menu-item.bp3-intent-primary:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-primary:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-primary:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after,
.bp3-dark .bp3-menu-item.bp3-intent-primary:hover .bp3-menu-item-label,
.bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label,
.bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-primary:active, .bp3-dark .bp3-menu-item.bp3-intent-primary:active::before, .bp3-dark .bp3-menu-item.bp3-intent-primary:active::after,
.bp3-dark .bp3-menu-item.bp3-intent-primary:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active::after,
.bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active .bp3-menu-item-label{
color:#ffffff; }
.bp3-dark .bp3-menu-item.bp3-intent-success{
color:#3dcc91; }
.bp3-dark .bp3-menu-item.bp3-intent-success .bp3-icon{
color:inherit; }
.bp3-dark .bp3-menu-item.bp3-intent-success::before, .bp3-dark .bp3-menu-item.bp3-intent-success::after,
.bp3-dark .bp3-menu-item.bp3-intent-success .bp3-menu-item-label{
color:#3dcc91; }
.bp3-dark .bp3-menu-item.bp3-intent-success:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active{
background-color:#0f9960; }
.bp3-dark .bp3-menu-item.bp3-intent-success:active{
background-color:#0d8050; }
.bp3-dark .bp3-menu-item.bp3-intent-success:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-success:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-success:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after,
.bp3-dark .bp3-menu-item.bp3-intent-success:hover .bp3-menu-item-label,
.bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label,
.bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-success:active, .bp3-dark .bp3-menu-item.bp3-intent-success:active::before, .bp3-dark .bp3-menu-item.bp3-intent-success:active::after,
.bp3-dark .bp3-menu-item.bp3-intent-success:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active::after,
.bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active .bp3-menu-item-label{
color:#ffffff; }
.bp3-dark .bp3-menu-item.bp3-intent-warning{
color:#ffb366; }
.bp3-dark .bp3-menu-item.bp3-intent-warning .bp3-icon{
color:inherit; }
.bp3-dark .bp3-menu-item.bp3-intent-warning::before, .bp3-dark .bp3-menu-item.bp3-intent-warning::after,
.bp3-dark .bp3-menu-item.bp3-intent-warning .bp3-menu-item-label{
color:#ffb366; }
.bp3-dark .bp3-menu-item.bp3-intent-warning:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active{
background-color:#d9822b; }
.bp3-dark .bp3-menu-item.bp3-intent-warning:active{
background-color:#bf7326; }
.bp3-dark .bp3-menu-item.bp3-intent-warning:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-warning:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-warning:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after,
.bp3-dark .bp3-menu-item.bp3-intent-warning:hover .bp3-menu-item-label,
.bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label,
.bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-warning:active, .bp3-dark .bp3-menu-item.bp3-intent-warning:active::before, .bp3-dark .bp3-menu-item.bp3-intent-warning:active::after,
.bp3-dark .bp3-menu-item.bp3-intent-warning:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active::after,
.bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active .bp3-menu-item-label{
color:#ffffff; }
.bp3-dark .bp3-menu-item.bp3-intent-danger{
color:#ff7373; }
.bp3-dark .bp3-menu-item.bp3-intent-danger .bp3-icon{
color:inherit; }
.bp3-dark .bp3-menu-item.bp3-intent-danger::before, .bp3-dark .bp3-menu-item.bp3-intent-danger::after,
.bp3-dark .bp3-menu-item.bp3-intent-danger .bp3-menu-item-label{
color:#ff7373; }
.bp3-dark .bp3-menu-item.bp3-intent-danger:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active{
background-color:#db3737; }
.bp3-dark .bp3-menu-item.bp3-intent-danger:active{
background-color:#c23030; }
.bp3-dark .bp3-menu-item.bp3-intent-danger:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-danger:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-danger:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after,
.bp3-dark .bp3-menu-item.bp3-intent-danger:hover .bp3-menu-item-label,
.bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label,
.bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-danger:active, .bp3-dark .bp3-menu-item.bp3-intent-danger:active::before, .bp3-dark .bp3-menu-item.bp3-intent-danger:active::after,
.bp3-dark .bp3-menu-item.bp3-intent-danger:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active::after,
.bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active .bp3-menu-item-label{
color:#ffffff; }
.bp3-dark .bp3-menu-item::before,
.bp3-dark .bp3-menu-item > .bp3-icon{
color:#a7b6c2; }
.bp3-dark .bp3-menu-item .bp3-menu-item-label{
color:#a7b6c2; }
.bp3-dark .bp3-menu-item.bp3-active, .bp3-dark .bp3-menu-item:active{
background-color:rgba(138, 155, 168, 0.3); }
.bp3-dark .bp3-menu-item.bp3-disabled{
color:rgba(167, 182, 194, 0.6) !important; }
.bp3-dark .bp3-menu-item.bp3-disabled::before,
.bp3-dark .bp3-menu-item.bp3-disabled > .bp3-icon,
.bp3-dark .bp3-menu-item.bp3-disabled .bp3-menu-item-label{
color:rgba(167, 182, 194, 0.6) !important; }
.bp3-dark .bp3-menu-divider,
.bp3-dark .bp3-menu-header{
border-color:rgba(255, 255, 255, 0.15); }
.bp3-dark .bp3-menu-header > h6{
color:#f5f8fa; }
.bp3-label .bp3-menu{
margin-top:5px; }
.bp3-navbar{
background-color:#ffffff;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
height:50px;
padding:0 15px;
position:relative;
width:100%;
z-index:10; }
.bp3-navbar.bp3-dark,
.bp3-dark .bp3-navbar{
background-color:#394b59; }
.bp3-navbar.bp3-dark{
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-navbar{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-navbar.bp3-fixed-top{
left:0;
position:fixed;
right:0;
top:0; }
.bp3-navbar-heading{
font-size:16px;
margin-right:15px; }
.bp3-navbar-group{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
display:-webkit-box;
display:-ms-flexbox;
display:flex;
height:50px; }
.bp3-navbar-group.bp3-align-left{
float:left; }
.bp3-navbar-group.bp3-align-right{
float:right; }
.bp3-navbar-divider{
border-left:1px solid rgba(16, 22, 26, 0.15);
height:20px;
margin:0 10px; }
.bp3-dark .bp3-navbar-divider{
border-left-color:rgba(255, 255, 255, 0.15); }
.bp3-non-ideal-state{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-ms-flex-direction:column;
flex-direction:column;
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
height:100%;
-webkit-box-pack:center;
-ms-flex-pack:center;
justify-content:center;
text-align:center;
width:100%; }
.bp3-non-ideal-state > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-non-ideal-state > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-non-ideal-state::before,
.bp3-non-ideal-state > *{
margin-bottom:20px; }
.bp3-non-ideal-state:empty::before,
.bp3-non-ideal-state > :last-child{
margin-bottom:0; }
.bp3-non-ideal-state > *{
max-width:400px; }
.bp3-non-ideal-state-visual{
color:rgba(92, 112, 128, 0.6);
font-size:60px; }
.bp3-dark .bp3-non-ideal-state-visual{
color:rgba(167, 182, 194, 0.6); }
.bp3-overflow-list{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-ms-flex-wrap:nowrap;
flex-wrap:nowrap;
min-width:0; }
.bp3-overflow-list-spacer{
-ms-flex-negative:1;
flex-shrink:1;
width:1px; }
body.bp3-overlay-open{
overflow:hidden; }
.bp3-overlay{
bottom:0;
left:0;
position:static;
right:0;
top:0;
z-index:20; }
.bp3-overlay:not(.bp3-overlay-open){
pointer-events:none; }
.bp3-overlay.bp3-overlay-container{
overflow:hidden;
position:fixed; }
.bp3-overlay.bp3-overlay-container.bp3-overlay-inline{
position:absolute; }
.bp3-overlay.bp3-overlay-scroll-container{
overflow:auto;
position:fixed; }
.bp3-overlay.bp3-overlay-scroll-container.bp3-overlay-inline{
position:absolute; }
.bp3-overlay.bp3-overlay-inline{
display:inline;
overflow:visible; }
.bp3-overlay-content{
position:fixed;
z-index:20; }
.bp3-overlay-inline .bp3-overlay-content,
.bp3-overlay-scroll-container .bp3-overlay-content{
position:absolute; }
.bp3-overlay-backdrop{
bottom:0;
left:0;
position:fixed;
right:0;
top:0;
opacity:1;
background-color:rgba(16, 22, 26, 0.7);
overflow:auto;
-webkit-user-select:none;
-moz-user-select:none;
-ms-user-select:none;
user-select:none;
z-index:20; }
.bp3-overlay-backdrop.bp3-overlay-enter, .bp3-overlay-backdrop.bp3-overlay-appear{
opacity:0; }
.bp3-overlay-backdrop.bp3-overlay-enter-active, .bp3-overlay-backdrop.bp3-overlay-appear-active{
opacity:1;
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:200ms;
transition-duration:200ms;
-webkit-transition-property:opacity;
transition-property:opacity;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-overlay-backdrop.bp3-overlay-exit{
opacity:1; }
.bp3-overlay-backdrop.bp3-overlay-exit-active{
opacity:0;
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:200ms;
transition-duration:200ms;
-webkit-transition-property:opacity;
transition-property:opacity;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-overlay-backdrop:focus{
outline:none; }
.bp3-overlay-inline .bp3-overlay-backdrop{
position:absolute; }
.bp3-panel-stack{
overflow:hidden;
position:relative; }
.bp3-panel-stack-header{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
-webkit-box-shadow:0 1px rgba(16, 22, 26, 0.15);
box-shadow:0 1px rgba(16, 22, 26, 0.15);
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-ms-flex-negative:0;
flex-shrink:0;
height:30px;
z-index:1; }
.bp3-dark .bp3-panel-stack-header{
-webkit-box-shadow:0 1px rgba(255, 255, 255, 0.15);
box-shadow:0 1px rgba(255, 255, 255, 0.15); }
.bp3-panel-stack-header > span{
-webkit-box-align:stretch;
-ms-flex-align:stretch;
align-items:stretch;
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-flex:1;
-ms-flex:1;
flex:1; }
.bp3-panel-stack-header .bp3-heading{
margin:0 5px; }
.bp3-button.bp3-panel-stack-header-back{
margin-left:5px;
padding-left:0;
white-space:nowrap; }
.bp3-button.bp3-panel-stack-header-back .bp3-icon{
margin:0 2px; }
.bp3-panel-stack-view{
bottom:0;
left:0;
position:absolute;
right:0;
top:0;
background-color:#ffffff;
border-right:1px solid rgba(16, 22, 26, 0.15);
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-ms-flex-direction:column;
flex-direction:column;
margin-right:-1px;
overflow-y:auto;
z-index:1; }
.bp3-dark .bp3-panel-stack-view{
background-color:#30404d; }
.bp3-panel-stack-view:nth-last-child(n + 4){
display:none; }
.bp3-panel-stack-push .bp3-panel-stack-enter, .bp3-panel-stack-push .bp3-panel-stack-appear{
-webkit-transform:translateX(100%);
transform:translateX(100%);
opacity:0; }
.bp3-panel-stack-push .bp3-panel-stack-enter-active, .bp3-panel-stack-push .bp3-panel-stack-appear-active{
-webkit-transform:translate(0%);
transform:translate(0%);
opacity:1;
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:400ms;
transition-duration:400ms;
-webkit-transition-property:opacity, -webkit-transform;
transition-property:opacity, -webkit-transform;
transition-property:transform, opacity;
transition-property:transform, opacity, -webkit-transform;
-webkit-transition-timing-function:ease;
transition-timing-function:ease; }
.bp3-panel-stack-push .bp3-panel-stack-exit{
-webkit-transform:translate(0%);
transform:translate(0%);
opacity:1; }
.bp3-panel-stack-push .bp3-panel-stack-exit-active{
-webkit-transform:translateX(-50%);
transform:translateX(-50%);
opacity:0;
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:400ms;
transition-duration:400ms;
-webkit-transition-property:opacity, -webkit-transform;
transition-property:opacity, -webkit-transform;
transition-property:transform, opacity;
transition-property:transform, opacity, -webkit-transform;
-webkit-transition-timing-function:ease;
transition-timing-function:ease; }
.bp3-panel-stack-pop .bp3-panel-stack-enter, .bp3-panel-stack-pop .bp3-panel-stack-appear{
-webkit-transform:translateX(-50%);
transform:translateX(-50%);
opacity:0; }
.bp3-panel-stack-pop .bp3-panel-stack-enter-active, .bp3-panel-stack-pop .bp3-panel-stack-appear-active{
-webkit-transform:translate(0%);
transform:translate(0%);
opacity:1;
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:400ms;
transition-duration:400ms;
-webkit-transition-property:opacity, -webkit-transform;
transition-property:opacity, -webkit-transform;
transition-property:transform, opacity;
transition-property:transform, opacity, -webkit-transform;
-webkit-transition-timing-function:ease;
transition-timing-function:ease; }
.bp3-panel-stack-pop .bp3-panel-stack-exit{
-webkit-transform:translate(0%);
transform:translate(0%);
opacity:1; }
.bp3-panel-stack-pop .bp3-panel-stack-exit-active{
-webkit-transform:translateX(100%);
transform:translateX(100%);
opacity:0;
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:400ms;
transition-duration:400ms;
-webkit-transition-property:opacity, -webkit-transform;
transition-property:opacity, -webkit-transform;
transition-property:transform, opacity;
transition-property:transform, opacity, -webkit-transform;
-webkit-transition-timing-function:ease;
transition-timing-function:ease; }
.bp3-panel-stack2{
overflow:hidden;
position:relative; }
.bp3-panel-stack2-header{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
-webkit-box-shadow:0 1px rgba(16, 22, 26, 0.15);
box-shadow:0 1px rgba(16, 22, 26, 0.15);
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-ms-flex-negative:0;
flex-shrink:0;
height:30px;
z-index:1; }
.bp3-dark .bp3-panel-stack2-header{
-webkit-box-shadow:0 1px rgba(255, 255, 255, 0.15);
box-shadow:0 1px rgba(255, 255, 255, 0.15); }
.bp3-panel-stack2-header > span{
-webkit-box-align:stretch;
-ms-flex-align:stretch;
align-items:stretch;
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-flex:1;
-ms-flex:1;
flex:1; }
.bp3-panel-stack2-header .bp3-heading{
margin:0 5px; }
.bp3-button.bp3-panel-stack2-header-back{
margin-left:5px;
padding-left:0;
white-space:nowrap; }
.bp3-button.bp3-panel-stack2-header-back .bp3-icon{
margin:0 2px; }
.bp3-panel-stack2-view{
bottom:0;
left:0;
position:absolute;
right:0;
top:0;
background-color:#ffffff;
border-right:1px solid rgba(16, 22, 26, 0.15);
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-ms-flex-direction:column;
flex-direction:column;
margin-right:-1px;
overflow-y:auto;
z-index:1; }
.bp3-dark .bp3-panel-stack2-view{
background-color:#30404d; }
.bp3-panel-stack2-view:nth-last-child(n + 4){
display:none; }
.bp3-panel-stack2-push .bp3-panel-stack2-enter, .bp3-panel-stack2-push .bp3-panel-stack2-appear{
-webkit-transform:translateX(100%);
transform:translateX(100%);
opacity:0; }
.bp3-panel-stack2-push .bp3-panel-stack2-enter-active, .bp3-panel-stack2-push .bp3-panel-stack2-appear-active{
-webkit-transform:translate(0%);
transform:translate(0%);
opacity:1;
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:400ms;
transition-duration:400ms;
-webkit-transition-property:opacity, -webkit-transform;
transition-property:opacity, -webkit-transform;
transition-property:transform, opacity;
transition-property:transform, opacity, -webkit-transform;
-webkit-transition-timing-function:ease;
transition-timing-function:ease; }
.bp3-panel-stack2-push .bp3-panel-stack2-exit{
-webkit-transform:translate(0%);
transform:translate(0%);
opacity:1; }
.bp3-panel-stack2-push .bp3-panel-stack2-exit-active{
-webkit-transform:translateX(-50%);
transform:translateX(-50%);
opacity:0;
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:400ms;
transition-duration:400ms;
-webkit-transition-property:opacity, -webkit-transform;
transition-property:opacity, -webkit-transform;
transition-property:transform, opacity;
transition-property:transform, opacity, -webkit-transform;
-webkit-transition-timing-function:ease;
transition-timing-function:ease; }
.bp3-panel-stack2-pop .bp3-panel-stack2-enter, .bp3-panel-stack2-pop .bp3-panel-stack2-appear{
-webkit-transform:translateX(-50%);
transform:translateX(-50%);
opacity:0; }
.bp3-panel-stack2-pop .bp3-panel-stack2-enter-active, .bp3-panel-stack2-pop .bp3-panel-stack2-appear-active{
-webkit-transform:translate(0%);
transform:translate(0%);
opacity:1;
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:400ms;
transition-duration:400ms;
-webkit-transition-property:opacity, -webkit-transform;
transition-property:opacity, -webkit-transform;
transition-property:transform, opacity;
transition-property:transform, opacity, -webkit-transform;
-webkit-transition-timing-function:ease;
transition-timing-function:ease; }
.bp3-panel-stack2-pop .bp3-panel-stack2-exit{
-webkit-transform:translate(0%);
transform:translate(0%);
opacity:1; }
.bp3-panel-stack2-pop .bp3-panel-stack2-exit-active{
-webkit-transform:translateX(100%);
transform:translateX(100%);
opacity:0;
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:400ms;
transition-duration:400ms;
-webkit-transition-property:opacity, -webkit-transform;
transition-property:opacity, -webkit-transform;
transition-property:transform, opacity;
transition-property:transform, opacity, -webkit-transform;
-webkit-transition-timing-function:ease;
transition-timing-function:ease; }
.bp3-popover{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
-webkit-transform:scale(1);
transform:scale(1);
border-radius:3px;
display:inline-block;
z-index:20; }
.bp3-popover .bp3-popover-arrow{
height:30px;
position:absolute;
width:30px; }
.bp3-popover .bp3-popover-arrow::before{
height:20px;
margin:5px;
width:20px; }
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover{
margin-bottom:17px;
margin-top:-17px; }
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow{
bottom:-11px; }
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow svg{
-webkit-transform:rotate(-90deg);
transform:rotate(-90deg); }
.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover{
margin-left:17px; }
.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow{
left:-11px; }
.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow svg{
-webkit-transform:rotate(0);
transform:rotate(0); }
.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover{
margin-top:17px; }
.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow{
top:-11px; }
.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow svg{
-webkit-transform:rotate(90deg);
transform:rotate(90deg); }
.bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover{
margin-left:-17px;
margin-right:17px; }
.bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow{
right:-11px; }
.bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow svg{
-webkit-transform:rotate(180deg);
transform:rotate(180deg); }
.bp3-tether-element-attached-middle > .bp3-popover > .bp3-popover-arrow{
top:50%;
-webkit-transform:translateY(-50%);
transform:translateY(-50%); }
.bp3-tether-element-attached-center > .bp3-popover > .bp3-popover-arrow{
right:50%;
-webkit-transform:translateX(50%);
transform:translateX(50%); }
.bp3-tether-element-attached-top.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow{
top:-0.3934px; }
.bp3-tether-element-attached-right.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow{
right:-0.3934px; }
.bp3-tether-element-attached-left.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow{
left:-0.3934px; }
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow{
bottom:-0.3934px; }
.bp3-tether-element-attached-top.bp3-tether-element-attached-left > .bp3-popover{
-webkit-transform-origin:top left;
transform-origin:top left; }
.bp3-tether-element-attached-top.bp3-tether-element-attached-center > .bp3-popover{
-webkit-transform-origin:top center;
transform-origin:top center; }
.bp3-tether-element-attached-top.bp3-tether-element-attached-right > .bp3-popover{
-webkit-transform-origin:top right;
transform-origin:top right; }
.bp3-tether-element-attached-middle.bp3-tether-element-attached-left > .bp3-popover{
-webkit-transform-origin:center left;
transform-origin:center left; }
.bp3-tether-element-attached-middle.bp3-tether-element-attached-center > .bp3-popover{
-webkit-transform-origin:center center;
transform-origin:center center; }
.bp3-tether-element-attached-middle.bp3-tether-element-attached-right > .bp3-popover{
-webkit-transform-origin:center right;
transform-origin:center right; }
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-left > .bp3-popover{
-webkit-transform-origin:bottom left;
transform-origin:bottom left; }
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-center > .bp3-popover{
-webkit-transform-origin:bottom center;
transform-origin:bottom center; }
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-right > .bp3-popover{
-webkit-transform-origin:bottom right;
transform-origin:bottom right; }
.bp3-popover .bp3-popover-content{
background:#ffffff;
color:inherit; }
.bp3-popover .bp3-popover-arrow::before{
-webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2);
box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2); }
.bp3-popover .bp3-popover-arrow-border{
fill:#10161a;
fill-opacity:0.1; }
.bp3-popover .bp3-popover-arrow-fill{
fill:#ffffff; }
.bp3-popover-enter > .bp3-popover, .bp3-popover-appear > .bp3-popover{
-webkit-transform:scale(0.3);
transform:scale(0.3); }
.bp3-popover-enter-active > .bp3-popover, .bp3-popover-appear-active > .bp3-popover{
-webkit-transform:scale(1);
transform:scale(1);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:300ms;
transition-duration:300ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
.bp3-popover-exit > .bp3-popover{
-webkit-transform:scale(1);
transform:scale(1); }
.bp3-popover-exit-active > .bp3-popover{
-webkit-transform:scale(0.3);
transform:scale(0.3);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:300ms;
transition-duration:300ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
.bp3-popover .bp3-popover-content{
border-radius:3px;
position:relative; }
.bp3-popover.bp3-popover-content-sizing .bp3-popover-content{
max-width:350px;
padding:20px; }
.bp3-popover-target + .bp3-overlay .bp3-popover.bp3-popover-content-sizing{
width:350px; }
.bp3-popover.bp3-minimal{
margin:0 !important; }
.bp3-popover.bp3-minimal .bp3-popover-arrow{
display:none; }
.bp3-popover.bp3-minimal.bp3-popover{
-webkit-transform:scale(1);
transform:scale(1); }
.bp3-popover-enter > .bp3-popover.bp3-minimal.bp3-popover, .bp3-popover-appear > .bp3-popover.bp3-minimal.bp3-popover{
-webkit-transform:scale(1);
transform:scale(1); }
.bp3-popover-enter-active > .bp3-popover.bp3-minimal.bp3-popover, .bp3-popover-appear-active > .bp3-popover.bp3-minimal.bp3-popover{
-webkit-transform:scale(1);
transform:scale(1);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:100ms;
transition-duration:100ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-popover-exit > .bp3-popover.bp3-minimal.bp3-popover{
-webkit-transform:scale(1);
transform:scale(1); }
.bp3-popover-exit-active > .bp3-popover.bp3-minimal.bp3-popover{
-webkit-transform:scale(1);
transform:scale(1);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:100ms;
transition-duration:100ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-popover.bp3-dark,
.bp3-dark .bp3-popover{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
.bp3-popover.bp3-dark .bp3-popover-content,
.bp3-dark .bp3-popover .bp3-popover-content{
background:#30404d;
color:inherit; }
.bp3-popover.bp3-dark .bp3-popover-arrow::before,
.bp3-dark .bp3-popover .bp3-popover-arrow::before{
-webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4);
box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4); }
.bp3-popover.bp3-dark .bp3-popover-arrow-border,
.bp3-dark .bp3-popover .bp3-popover-arrow-border{
fill:#10161a;
fill-opacity:0.2; }
.bp3-popover.bp3-dark .bp3-popover-arrow-fill,
.bp3-dark .bp3-popover .bp3-popover-arrow-fill{
fill:#30404d; }
.bp3-popover-arrow::before{
border-radius:2px;
content:"";
display:block;
position:absolute;
-webkit-transform:rotate(45deg);
transform:rotate(45deg); }
.bp3-tether-pinned .bp3-popover-arrow{
display:none; }
.bp3-popover-backdrop{
background:rgba(255, 255, 255, 0); }
.bp3-transition-container{
opacity:1;
display:-webkit-box;
display:-ms-flexbox;
display:flex;
z-index:20; }
.bp3-transition-container.bp3-popover-enter, .bp3-transition-container.bp3-popover-appear{
opacity:0; }
.bp3-transition-container.bp3-popover-enter-active, .bp3-transition-container.bp3-popover-appear-active{
opacity:1;
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:100ms;
transition-duration:100ms;
-webkit-transition-property:opacity;
transition-property:opacity;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-transition-container.bp3-popover-exit{
opacity:1; }
.bp3-transition-container.bp3-popover-exit-active{
opacity:0;
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:100ms;
transition-duration:100ms;
-webkit-transition-property:opacity;
transition-property:opacity;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-transition-container:focus{
outline:none; }
.bp3-transition-container.bp3-popover-leave .bp3-popover-content{
pointer-events:none; }
.bp3-transition-container[data-x-out-of-boundaries]{
display:none; }
span.bp3-popover-target{
display:inline-block; }
.bp3-popover-wrapper.bp3-fill{
width:100%; }
.bp3-portal{
left:0;
position:absolute;
right:0;
top:0; }
@-webkit-keyframes linear-progress-bar-stripes{
from{
background-position:0 0; }
to{
background-position:30px 0; } }
@keyframes linear-progress-bar-stripes{
from{
background-position:0 0; }
to{
background-position:30px 0; } }
.bp3-progress-bar{
background:rgba(92, 112, 128, 0.2);
border-radius:40px;
display:block;
height:8px;
overflow:hidden;
position:relative;
width:100%; }
.bp3-progress-bar .bp3-progress-meter{
background:linear-gradient(-45deg, rgba(255, 255, 255, 0.2) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.2) 50%, rgba(255, 255, 255, 0.2) 75%, transparent 75%);
background-color:rgba(92, 112, 128, 0.8);
background-size:30px 30px;
border-radius:40px;
height:100%;
position:absolute;
-webkit-transition:width 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:width 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
width:100%; }
.bp3-progress-bar:not(.bp3-no-animation):not(.bp3-no-stripes) .bp3-progress-meter{
animation:linear-progress-bar-stripes 300ms linear infinite reverse; }
.bp3-progress-bar.bp3-no-stripes .bp3-progress-meter{
background-image:none; }
.bp3-dark .bp3-progress-bar{
background:rgba(16, 22, 26, 0.5); }
.bp3-dark .bp3-progress-bar .bp3-progress-meter{
background-color:#8a9ba8; }
.bp3-progress-bar.bp3-intent-primary .bp3-progress-meter{
background-color:#137cbd; }
.bp3-progress-bar.bp3-intent-success .bp3-progress-meter{
background-color:#0f9960; }
.bp3-progress-bar.bp3-intent-warning .bp3-progress-meter{
background-color:#d9822b; }
.bp3-progress-bar.bp3-intent-danger .bp3-progress-meter{
background-color:#db3737; }
@-webkit-keyframes skeleton-glow{
from{
background:rgba(206, 217, 224, 0.2);
border-color:rgba(206, 217, 224, 0.2); }
to{
background:rgba(92, 112, 128, 0.2);
border-color:rgba(92, 112, 128, 0.2); } }
@keyframes skeleton-glow{
from{
background:rgba(206, 217, 224, 0.2);
border-color:rgba(206, 217, 224, 0.2); }
to{
background:rgba(92, 112, 128, 0.2);
border-color:rgba(92, 112, 128, 0.2); } }
.bp3-skeleton{
-webkit-animation:1000ms linear infinite alternate skeleton-glow;
animation:1000ms linear infinite alternate skeleton-glow;
background:rgba(206, 217, 224, 0.2);
background-clip:padding-box !important;
border-color:rgba(206, 217, 224, 0.2) !important;
border-radius:2px;
-webkit-box-shadow:none !important;
box-shadow:none !important;
color:transparent !important;
cursor:default;
pointer-events:none;
-webkit-user-select:none;
-moz-user-select:none;
-ms-user-select:none;
user-select:none; }
.bp3-skeleton::before, .bp3-skeleton::after,
.bp3-skeleton *{
visibility:hidden !important; }
.bp3-slider{
height:40px;
min-width:150px;
width:100%;
cursor:default;
outline:none;
position:relative;
-webkit-user-select:none;
-moz-user-select:none;
-ms-user-select:none;
user-select:none; }
.bp3-slider:hover{
cursor:pointer; }
.bp3-slider:active{
cursor:-webkit-grabbing;
cursor:grabbing; }
.bp3-slider.bp3-disabled{
cursor:not-allowed;
opacity:0.5; }
.bp3-slider.bp3-slider-unlabeled{
height:16px; }
.bp3-slider-track,
.bp3-slider-progress{
height:6px;
left:0;
right:0;
top:5px;
position:absolute; }
.bp3-slider-track{
border-radius:3px;
overflow:hidden; }
.bp3-slider-progress{
background:rgba(92, 112, 128, 0.2); }
.bp3-dark .bp3-slider-progress{
background:rgba(16, 22, 26, 0.5); }
.bp3-slider-progress.bp3-intent-primary{
background-color:#137cbd; }
.bp3-slider-progress.bp3-intent-success{
background-color:#0f9960; }
.bp3-slider-progress.bp3-intent-warning{
background-color:#d9822b; }
.bp3-slider-progress.bp3-intent-danger{
background-color:#db3737; }
.bp3-slider-handle{
background-color:#f5f8fa;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
color:#182026;
border-radius:3px;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
cursor:pointer;
height:16px;
left:0;
position:absolute;
top:0;
width:16px; }
.bp3-slider-handle:hover{
background-clip:padding-box;
background-color:#ebf1f5;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
.bp3-slider-handle:active, .bp3-slider-handle.bp3-active{
background-color:#d8e1e8;
background-image:none;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-slider-handle:disabled, .bp3-slider-handle.bp3-disabled{
background-color:rgba(206, 217, 224, 0.5);
background-image:none;
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed;
outline:none; }
.bp3-slider-handle:disabled.bp3-active, .bp3-slider-handle:disabled.bp3-active:hover, .bp3-slider-handle.bp3-disabled.bp3-active, .bp3-slider-handle.bp3-disabled.bp3-active:hover{
background:rgba(206, 217, 224, 0.7); }
.bp3-slider-handle:focus{
z-index:1; }
.bp3-slider-handle:hover{
background-clip:padding-box;
background-color:#ebf1f5;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
cursor:-webkit-grab;
cursor:grab;
z-index:2; }
.bp3-slider-handle.bp3-active{
background-color:#d8e1e8;
background-image:none;
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 1px rgba(16, 22, 26, 0.1);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 1px rgba(16, 22, 26, 0.1);
cursor:-webkit-grabbing;
cursor:grabbing; }
.bp3-disabled .bp3-slider-handle{
background:#bfccd6;
-webkit-box-shadow:none;
box-shadow:none;
pointer-events:none; }
.bp3-dark .bp3-slider-handle{
background-color:#394b59;
background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
color:#f5f8fa; }
.bp3-dark .bp3-slider-handle:hover, .bp3-dark .bp3-slider-handle:active, .bp3-dark .bp3-slider-handle.bp3-active{
color:#f5f8fa; }
.bp3-dark .bp3-slider-handle:hover{
background-color:#30404d;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-slider-handle:active, .bp3-dark .bp3-slider-handle.bp3-active{
background-color:#202b33;
background-image:none;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
.bp3-dark .bp3-slider-handle:disabled, .bp3-dark .bp3-slider-handle.bp3-disabled{
background-color:rgba(57, 75, 89, 0.5);
background-image:none;
-webkit-box-shadow:none;
box-shadow:none;
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-slider-handle:disabled.bp3-active, .bp3-dark .bp3-slider-handle.bp3-disabled.bp3-active{
background:rgba(57, 75, 89, 0.7); }
.bp3-dark .bp3-slider-handle .bp3-button-spinner .bp3-spinner-head{
background:rgba(16, 22, 26, 0.5);
stroke:#8a9ba8; }
.bp3-dark .bp3-slider-handle, .bp3-dark .bp3-slider-handle:hover{
background-color:#394b59; }
.bp3-dark .bp3-slider-handle.bp3-active{
background-color:#293742; }
.bp3-dark .bp3-disabled .bp3-slider-handle{
background:#5c7080;
border-color:#5c7080;
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-slider-handle .bp3-slider-label{
background:#394b59;
border-radius:3px;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
color:#f5f8fa;
margin-left:8px; }
.bp3-dark .bp3-slider-handle .bp3-slider-label{
background:#e1e8ed;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
color:#394b59; }
.bp3-disabled .bp3-slider-handle .bp3-slider-label{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-slider-handle.bp3-start, .bp3-slider-handle.bp3-end{
width:8px; }
.bp3-slider-handle.bp3-start{
border-bottom-right-radius:0;
border-top-right-radius:0; }
.bp3-slider-handle.bp3-end{
border-bottom-left-radius:0;
border-top-left-radius:0;
margin-left:8px; }
.bp3-slider-handle.bp3-end .bp3-slider-label{
margin-left:0; }
.bp3-slider-label{
-webkit-transform:translate(-50%, 20px);
transform:translate(-50%, 20px);
display:inline-block;
font-size:12px;
line-height:1;
padding:2px 5px;
position:absolute;
vertical-align:top; }
.bp3-slider.bp3-vertical{
height:150px;
min-width:40px;
width:40px; }
.bp3-slider.bp3-vertical .bp3-slider-track,
.bp3-slider.bp3-vertical .bp3-slider-progress{
bottom:0;
height:auto;
left:5px;
top:0;
width:6px; }
.bp3-slider.bp3-vertical .bp3-slider-progress{
top:auto; }
.bp3-slider.bp3-vertical .bp3-slider-label{
-webkit-transform:translate(20px, 50%);
transform:translate(20px, 50%); }
.bp3-slider.bp3-vertical .bp3-slider-handle{
top:auto; }
.bp3-slider.bp3-vertical .bp3-slider-handle .bp3-slider-label{
margin-left:0;
margin-top:-8px; }
.bp3-slider.bp3-vertical .bp3-slider-handle.bp3-end, .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start{
height:8px;
margin-left:0;
width:16px; }
.bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start{
border-bottom-right-radius:3px;
border-top-left-radius:0; }
.bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start .bp3-slider-label{
-webkit-transform:translate(20px);
transform:translate(20px); }
.bp3-slider.bp3-vertical .bp3-slider-handle.bp3-end{
border-bottom-left-radius:0;
border-bottom-right-radius:0;
border-top-left-radius:3px;
margin-bottom:8px; }
@-webkit-keyframes pt-spinner-animation{
from{
-webkit-transform:rotate(0deg);
transform:rotate(0deg); }
to{
-webkit-transform:rotate(360deg);
transform:rotate(360deg); } }
@keyframes pt-spinner-animation{
from{
-webkit-transform:rotate(0deg);
transform:rotate(0deg); }
to{
-webkit-transform:rotate(360deg);
transform:rotate(360deg); } }
.bp3-spinner{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-pack:center;
-ms-flex-pack:center;
justify-content:center;
overflow:visible;
vertical-align:middle; }
.bp3-spinner svg{
display:block; }
.bp3-spinner path{
fill-opacity:0; }
.bp3-spinner .bp3-spinner-head{
stroke:rgba(92, 112, 128, 0.8);
stroke-linecap:round;
-webkit-transform-origin:center;
transform-origin:center;
-webkit-transition:stroke-dashoffset 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:stroke-dashoffset 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-spinner .bp3-spinner-track{
stroke:rgba(92, 112, 128, 0.2); }
.bp3-spinner-animation{
-webkit-animation:pt-spinner-animation 500ms linear infinite;
animation:pt-spinner-animation 500ms linear infinite; }
.bp3-no-spin > .bp3-spinner-animation{
-webkit-animation:none;
animation:none; }
.bp3-dark .bp3-spinner .bp3-spinner-head{
stroke:#8a9ba8; }
.bp3-dark .bp3-spinner .bp3-spinner-track{
stroke:rgba(16, 22, 26, 0.5); }
.bp3-spinner.bp3-intent-primary .bp3-spinner-head{
stroke:#137cbd; }
.bp3-spinner.bp3-intent-success .bp3-spinner-head{
stroke:#0f9960; }
.bp3-spinner.bp3-intent-warning .bp3-spinner-head{
stroke:#d9822b; }
.bp3-spinner.bp3-intent-danger .bp3-spinner-head{
stroke:#db3737; }
.bp3-tabs.bp3-vertical{
display:-webkit-box;
display:-ms-flexbox;
display:flex; }
.bp3-tabs.bp3-vertical > .bp3-tab-list{
-webkit-box-align:start;
-ms-flex-align:start;
align-items:flex-start;
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-ms-flex-direction:column;
flex-direction:column; }
.bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab{
border-radius:3px;
padding:0 10px;
width:100%; }
.bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab[aria-selected="true"]{
background-color:rgba(19, 124, 189, 0.2);
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab-indicator-wrapper .bp3-tab-indicator{
background-color:rgba(19, 124, 189, 0.2);
border-radius:3px;
bottom:0;
height:auto;
left:0;
right:0;
top:0; }
.bp3-tabs.bp3-vertical > .bp3-tab-panel{
margin-top:0;
padding-left:20px; }
.bp3-tab-list{
-webkit-box-align:end;
-ms-flex-align:end;
align-items:flex-end;
border:none;
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
list-style:none;
margin:0;
padding:0;
position:relative; }
.bp3-tab-list > *:not(:last-child){
margin-right:20px; }
.bp3-tab{
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal;
color:#182026;
cursor:pointer;
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
font-size:14px;
line-height:30px;
max-width:100%;
position:relative;
vertical-align:top; }
.bp3-tab a{
color:inherit;
display:block;
text-decoration:none; }
.bp3-tab-indicator-wrapper ~ .bp3-tab{
background-color:transparent !important;
-webkit-box-shadow:none !important;
box-shadow:none !important; }
.bp3-tab[aria-disabled="true"]{
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed; }
.bp3-tab[aria-selected="true"]{
border-radius:0;
-webkit-box-shadow:inset 0 -3px 0 #106ba3;
box-shadow:inset 0 -3px 0 #106ba3; }
.bp3-tab[aria-selected="true"], .bp3-tab:not([aria-disabled="true"]):hover{
color:#106ba3; }
.bp3-tab:focus{
-moz-outline-radius:0; }
.bp3-large > .bp3-tab{
font-size:16px;
line-height:40px; }
.bp3-tab-panel{
margin-top:20px; }
.bp3-tab-panel[aria-hidden="true"]{
display:none; }
.bp3-tab-indicator-wrapper{
left:0;
pointer-events:none;
position:absolute;
top:0;
-webkit-transform:translateX(0), translateY(0);
transform:translateX(0), translateY(0);
-webkit-transition:height, width, -webkit-transform;
transition:height, width, -webkit-transform;
transition:height, transform, width;
transition:height, transform, width, -webkit-transform;
-webkit-transition-duration:200ms;
transition-duration:200ms;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-tab-indicator-wrapper .bp3-tab-indicator{
background-color:#106ba3;
bottom:0;
height:3px;
left:0;
position:absolute;
right:0; }
.bp3-tab-indicator-wrapper.bp3-no-animation{
-webkit-transition:none;
transition:none; }
.bp3-dark .bp3-tab{
color:#f5f8fa; }
.bp3-dark .bp3-tab[aria-disabled="true"]{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-tab[aria-selected="true"]{
-webkit-box-shadow:inset 0 -3px 0 #48aff0;
box-shadow:inset 0 -3px 0 #48aff0; }
.bp3-dark .bp3-tab[aria-selected="true"], .bp3-dark .bp3-tab:not([aria-disabled="true"]):hover{
color:#48aff0; }
.bp3-dark .bp3-tab-indicator{
background-color:#48aff0; }
.bp3-flex-expander{
-webkit-box-flex:1;
-ms-flex:1 1;
flex:1 1; }
.bp3-tag{
display:-webkit-inline-box;
display:-ms-inline-flexbox;
display:inline-flex;
-webkit-box-orient:horizontal;
-webkit-box-direction:normal;
-ms-flex-direction:row;
flex-direction:row;
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
background-color:#5c7080;
border:none;
border-radius:3px;
-webkit-box-shadow:none;
box-shadow:none;
color:#f5f8fa;
font-size:12px;
line-height:16px;
max-width:100%;
min-height:20px;
min-width:20px;
padding:2px 6px;
position:relative; }
.bp3-tag.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-interactive:hover{
background-color:rgba(92, 112, 128, 0.85); }
.bp3-tag.bp3-interactive.bp3-active, .bp3-tag.bp3-interactive:active{
background-color:rgba(92, 112, 128, 0.7); }
.bp3-tag > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-tag > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-tag::before,
.bp3-tag > *{
margin-right:4px; }
.bp3-tag:empty::before,
.bp3-tag > :last-child{
margin-right:0; }
.bp3-tag:focus{
outline:rgba(19, 124, 189, 0.6) auto 2px;
outline-offset:0;
-moz-outline-radius:6px; }
.bp3-tag.bp3-round{
border-radius:30px;
padding-left:8px;
padding-right:8px; }
.bp3-dark .bp3-tag{
background-color:#bfccd6;
color:#182026; }
.bp3-dark .bp3-tag.bp3-interactive{
cursor:pointer; }
.bp3-dark .bp3-tag.bp3-interactive:hover{
background-color:rgba(191, 204, 214, 0.85); }
.bp3-dark .bp3-tag.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-interactive:active{
background-color:rgba(191, 204, 214, 0.7); }
.bp3-dark .bp3-tag > .bp3-icon, .bp3-dark .bp3-tag .bp3-icon-standard, .bp3-dark .bp3-tag .bp3-icon-large{
fill:currentColor; }
.bp3-tag > .bp3-icon, .bp3-tag .bp3-icon-standard, .bp3-tag .bp3-icon-large{
fill:#ffffff; }
.bp3-tag.bp3-large,
.bp3-large .bp3-tag{
font-size:14px;
line-height:20px;
min-height:30px;
min-width:30px;
padding:5px 10px; }
.bp3-tag.bp3-large::before,
.bp3-tag.bp3-large > *,
.bp3-large .bp3-tag::before,
.bp3-large .bp3-tag > *{
margin-right:7px; }
.bp3-tag.bp3-large:empty::before,
.bp3-tag.bp3-large > :last-child,
.bp3-large .bp3-tag:empty::before,
.bp3-large .bp3-tag > :last-child{
margin-right:0; }
.bp3-tag.bp3-large.bp3-round,
.bp3-large .bp3-tag.bp3-round{
padding-left:12px;
padding-right:12px; }
.bp3-tag.bp3-intent-primary{
background:#137cbd;
color:#ffffff; }
.bp3-tag.bp3-intent-primary.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-intent-primary.bp3-interactive:hover{
background-color:rgba(19, 124, 189, 0.85); }
.bp3-tag.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-primary.bp3-interactive:active{
background-color:rgba(19, 124, 189, 0.7); }
.bp3-tag.bp3-intent-success{
background:#0f9960;
color:#ffffff; }
.bp3-tag.bp3-intent-success.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-intent-success.bp3-interactive:hover{
background-color:rgba(15, 153, 96, 0.85); }
.bp3-tag.bp3-intent-success.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-success.bp3-interactive:active{
background-color:rgba(15, 153, 96, 0.7); }
.bp3-tag.bp3-intent-warning{
background:#d9822b;
color:#ffffff; }
.bp3-tag.bp3-intent-warning.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-intent-warning.bp3-interactive:hover{
background-color:rgba(217, 130, 43, 0.85); }
.bp3-tag.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-warning.bp3-interactive:active{
background-color:rgba(217, 130, 43, 0.7); }
.bp3-tag.bp3-intent-danger{
background:#db3737;
color:#ffffff; }
.bp3-tag.bp3-intent-danger.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-intent-danger.bp3-interactive:hover{
background-color:rgba(219, 55, 55, 0.85); }
.bp3-tag.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-danger.bp3-interactive:active{
background-color:rgba(219, 55, 55, 0.7); }
.bp3-tag.bp3-fill{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
width:100%; }
.bp3-tag.bp3-minimal > .bp3-icon, .bp3-tag.bp3-minimal .bp3-icon-standard, .bp3-tag.bp3-minimal .bp3-icon-large{
fill:#5c7080; }
.bp3-tag.bp3-minimal:not([class*="bp3-intent-"]){
background-color:rgba(138, 155, 168, 0.2);
color:#182026; }
.bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:hover{
background-color:rgba(92, 112, 128, 0.3); }
.bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive.bp3-active, .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:active{
background-color:rgba(92, 112, 128, 0.4); }
.bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]){
color:#f5f8fa; }
.bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive{
cursor:pointer; }
.bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:hover{
background-color:rgba(191, 204, 214, 0.3); }
.bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:active{
background-color:rgba(191, 204, 214, 0.4); }
.bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]) > .bp3-icon, .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]) .bp3-icon-standard, .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]) .bp3-icon-large{
fill:#a7b6c2; }
.bp3-tag.bp3-minimal.bp3-intent-primary{
background-color:rgba(19, 124, 189, 0.15);
color:#106ba3; }
.bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:hover{
background-color:rgba(19, 124, 189, 0.25); }
.bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:active{
background-color:rgba(19, 124, 189, 0.35); }
.bp3-tag.bp3-minimal.bp3-intent-primary > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-primary .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-primary .bp3-icon-large{
fill:#137cbd; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary{
background-color:rgba(19, 124, 189, 0.25);
color:#48aff0; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive{
cursor:pointer; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:hover{
background-color:rgba(19, 124, 189, 0.35); }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:active{
background-color:rgba(19, 124, 189, 0.45); }
.bp3-tag.bp3-minimal.bp3-intent-success{
background-color:rgba(15, 153, 96, 0.15);
color:#0d8050; }
.bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:hover{
background-color:rgba(15, 153, 96, 0.25); }
.bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:active{
background-color:rgba(15, 153, 96, 0.35); }
.bp3-tag.bp3-minimal.bp3-intent-success > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-success .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-success .bp3-icon-large{
fill:#0f9960; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success{
background-color:rgba(15, 153, 96, 0.25);
color:#3dcc91; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive{
cursor:pointer; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:hover{
background-color:rgba(15, 153, 96, 0.35); }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:active{
background-color:rgba(15, 153, 96, 0.45); }
.bp3-tag.bp3-minimal.bp3-intent-warning{
background-color:rgba(217, 130, 43, 0.15);
color:#bf7326; }
.bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:hover{
background-color:rgba(217, 130, 43, 0.25); }
.bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:active{
background-color:rgba(217, 130, 43, 0.35); }
.bp3-tag.bp3-minimal.bp3-intent-warning > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-warning .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-warning .bp3-icon-large{
fill:#d9822b; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning{
background-color:rgba(217, 130, 43, 0.25);
color:#ffb366; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive{
cursor:pointer; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:hover{
background-color:rgba(217, 130, 43, 0.35); }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:active{
background-color:rgba(217, 130, 43, 0.45); }
.bp3-tag.bp3-minimal.bp3-intent-danger{
background-color:rgba(219, 55, 55, 0.15);
color:#c23030; }
.bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:hover{
background-color:rgba(219, 55, 55, 0.25); }
.bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:active{
background-color:rgba(219, 55, 55, 0.35); }
.bp3-tag.bp3-minimal.bp3-intent-danger > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-danger .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-danger .bp3-icon-large{
fill:#db3737; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger{
background-color:rgba(219, 55, 55, 0.25);
color:#ff7373; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive{
cursor:pointer; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:hover{
background-color:rgba(219, 55, 55, 0.35); }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:active{
background-color:rgba(219, 55, 55, 0.45); }
.bp3-tag-remove{
background:none;
border:none;
color:inherit;
cursor:pointer;
display:-webkit-box;
display:-ms-flexbox;
display:flex;
margin-bottom:-2px;
margin-right:-6px !important;
margin-top:-2px;
opacity:0.5;
padding:2px;
padding-left:0; }
.bp3-tag-remove:hover{
background:none;
opacity:0.8;
text-decoration:none; }
.bp3-tag-remove:active{
opacity:1; }
.bp3-tag-remove:empty::before{
font-family:"Icons16", sans-serif;
font-size:16px;
font-style:normal;
font-weight:400;
line-height:1;
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased;
content:""; }
.bp3-large .bp3-tag-remove{
margin-right:-10px !important;
padding:0 5px 0 0; }
.bp3-large .bp3-tag-remove:empty::before{
font-family:"Icons20", sans-serif;
font-size:20px;
font-style:normal;
font-weight:400;
line-height:1; }
.bp3-tag-input{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:horizontal;
-webkit-box-direction:normal;
-ms-flex-direction:row;
flex-direction:row;
-webkit-box-align:start;
-ms-flex-align:start;
align-items:flex-start;
cursor:text;
height:auto;
line-height:inherit;
min-height:30px;
padding-left:5px;
padding-right:0; }
.bp3-tag-input > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-tag-input > .bp3-tag-input-values{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-tag-input .bp3-tag-input-icon{
color:#5c7080;
margin-left:2px;
margin-right:7px;
margin-top:7px; }
.bp3-tag-input .bp3-tag-input-values{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:horizontal;
-webkit-box-direction:normal;
-ms-flex-direction:row;
flex-direction:row;
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
-ms-flex-item-align:stretch;
align-self:stretch;
-ms-flex-wrap:wrap;
flex-wrap:wrap;
margin-right:7px;
margin-top:5px;
min-width:0; }
.bp3-tag-input .bp3-tag-input-values > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-tag-input .bp3-tag-input-values > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-tag-input .bp3-tag-input-values::before,
.bp3-tag-input .bp3-tag-input-values > *{
margin-right:5px; }
.bp3-tag-input .bp3-tag-input-values:empty::before,
.bp3-tag-input .bp3-tag-input-values > :last-child{
margin-right:0; }
.bp3-tag-input .bp3-tag-input-values:first-child .bp3-input-ghost:first-child{
padding-left:5px; }
.bp3-tag-input .bp3-tag-input-values > *{
margin-bottom:5px; }
.bp3-tag-input .bp3-tag{
overflow-wrap:break-word; }
.bp3-tag-input .bp3-tag.bp3-active{
outline:rgba(19, 124, 189, 0.6) auto 2px;
outline-offset:0;
-moz-outline-radius:6px; }
.bp3-tag-input .bp3-input-ghost{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
line-height:20px;
width:80px; }
.bp3-tag-input .bp3-input-ghost:disabled, .bp3-tag-input .bp3-input-ghost.bp3-disabled{
cursor:not-allowed; }
.bp3-tag-input .bp3-button,
.bp3-tag-input .bp3-spinner{
margin:3px;
margin-left:0; }
.bp3-tag-input .bp3-button{
min-height:24px;
min-width:24px;
padding:0 7px; }
.bp3-tag-input.bp3-large{
height:auto;
min-height:40px; }
.bp3-tag-input.bp3-large::before,
.bp3-tag-input.bp3-large > *{
margin-right:10px; }
.bp3-tag-input.bp3-large:empty::before,
.bp3-tag-input.bp3-large > :last-child{
margin-right:0; }
.bp3-tag-input.bp3-large .bp3-tag-input-icon{
margin-left:5px;
margin-top:10px; }
.bp3-tag-input.bp3-large .bp3-input-ghost{
line-height:30px; }
.bp3-tag-input.bp3-large .bp3-button{
min-height:30px;
min-width:30px;
padding:5px 10px;
margin:5px;
margin-left:0; }
.bp3-tag-input.bp3-large .bp3-spinner{
margin:8px;
margin-left:0; }
.bp3-tag-input.bp3-active{
background-color:#ffffff;
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-tag-input.bp3-active.bp3-intent-primary{
-webkit-box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-tag-input.bp3-active.bp3-intent-success{
-webkit-box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-tag-input.bp3-active.bp3-intent-warning{
-webkit-box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-tag-input.bp3-active.bp3-intent-danger{
-webkit-box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-dark .bp3-tag-input .bp3-tag-input-icon, .bp3-tag-input.bp3-dark .bp3-tag-input-icon{
color:#a7b6c2; }
.bp3-dark .bp3-tag-input .bp3-input-ghost, .bp3-tag-input.bp3-dark .bp3-input-ghost{
color:#f5f8fa; }
.bp3-dark .bp3-tag-input .bp3-input-ghost::-webkit-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-webkit-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-tag-input .bp3-input-ghost::-moz-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-moz-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-tag-input .bp3-input-ghost:-ms-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost:-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-tag-input .bp3-input-ghost::-ms-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-tag-input .bp3-input-ghost::placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-tag-input.bp3-active, .bp3-tag-input.bp3-dark.bp3-active{
background-color:rgba(16, 22, 26, 0.3);
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-tag-input.bp3-active.bp3-intent-primary, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-primary{
-webkit-box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-tag-input.bp3-active.bp3-intent-success, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-success{
-webkit-box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-tag-input.bp3-active.bp3-intent-warning, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-warning{
-webkit-box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-tag-input.bp3-active.bp3-intent-danger, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-danger{
-webkit-box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-input-ghost{
background:none;
border:none;
-webkit-box-shadow:none;
box-shadow:none;
padding:0; }
.bp3-input-ghost::-webkit-input-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-input-ghost::-moz-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-input-ghost:-ms-input-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-input-ghost::-ms-input-placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-input-ghost::placeholder{
color:rgba(92, 112, 128, 0.6);
opacity:1; }
.bp3-input-ghost:focus{
outline:none !important; }
.bp3-toast{
-webkit-box-align:start;
-ms-flex-align:start;
align-items:flex-start;
background-color:#ffffff;
border-radius:3px;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
display:-webkit-box;
display:-ms-flexbox;
display:flex;
margin:20px 0 0;
max-width:500px;
min-width:300px;
pointer-events:all;
position:relative !important; }
.bp3-toast.bp3-toast-enter, .bp3-toast.bp3-toast-appear{
-webkit-transform:translateY(-40px);
transform:translateY(-40px); }
.bp3-toast.bp3-toast-enter-active, .bp3-toast.bp3-toast-appear-active{
-webkit-transform:translateY(0);
transform:translateY(0);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:300ms;
transition-duration:300ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
.bp3-toast.bp3-toast-enter ~ .bp3-toast, .bp3-toast.bp3-toast-appear ~ .bp3-toast{
-webkit-transform:translateY(-40px);
transform:translateY(-40px); }
.bp3-toast.bp3-toast-enter-active ~ .bp3-toast, .bp3-toast.bp3-toast-appear-active ~ .bp3-toast{
-webkit-transform:translateY(0);
transform:translateY(0);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:300ms;
transition-duration:300ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
.bp3-toast.bp3-toast-exit{
opacity:1;
-webkit-filter:blur(0);
filter:blur(0); }
.bp3-toast.bp3-toast-exit-active{
opacity:0;
-webkit-filter:blur(10px);
filter:blur(10px);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:300ms;
transition-duration:300ms;
-webkit-transition-property:opacity, -webkit-filter;
transition-property:opacity, -webkit-filter;
transition-property:opacity, filter;
transition-property:opacity, filter, -webkit-filter;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-toast.bp3-toast-exit ~ .bp3-toast{
-webkit-transform:translateY(0);
transform:translateY(0); }
.bp3-toast.bp3-toast-exit-active ~ .bp3-toast{
-webkit-transform:translateY(-40px);
transform:translateY(-40px);
-webkit-transition-delay:50ms;
transition-delay:50ms;
-webkit-transition-duration:100ms;
transition-duration:100ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-toast .bp3-button-group{
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
padding:5px;
padding-left:0; }
.bp3-toast > .bp3-icon{
color:#5c7080;
margin:12px;
margin-right:0; }
.bp3-toast.bp3-dark,
.bp3-dark .bp3-toast{
background-color:#394b59;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
.bp3-toast.bp3-dark > .bp3-icon,
.bp3-dark .bp3-toast > .bp3-icon{
color:#a7b6c2; }
.bp3-toast[class*="bp3-intent-"] a{
color:rgba(255, 255, 255, 0.7); }
.bp3-toast[class*="bp3-intent-"] a:hover{
color:#ffffff; }
.bp3-toast[class*="bp3-intent-"] > .bp3-icon{
color:#ffffff; }
.bp3-toast[class*="bp3-intent-"] .bp3-button, .bp3-toast[class*="bp3-intent-"] .bp3-button::before,
.bp3-toast[class*="bp3-intent-"] .bp3-button .bp3-icon, .bp3-toast[class*="bp3-intent-"] .bp3-button:active{
color:rgba(255, 255, 255, 0.7) !important; }
.bp3-toast[class*="bp3-intent-"] .bp3-button:focus{
outline-color:rgba(255, 255, 255, 0.5); }
.bp3-toast[class*="bp3-intent-"] .bp3-button:hover{
background-color:rgba(255, 255, 255, 0.15) !important;
color:#ffffff !important; }
.bp3-toast[class*="bp3-intent-"] .bp3-button:active{
background-color:rgba(255, 255, 255, 0.3) !important;
color:#ffffff !important; }
.bp3-toast[class*="bp3-intent-"] .bp3-button::after{
background:rgba(255, 255, 255, 0.3) !important; }
.bp3-toast.bp3-intent-primary{
background-color:#137cbd;
color:#ffffff; }
.bp3-toast.bp3-intent-success{
background-color:#0f9960;
color:#ffffff; }
.bp3-toast.bp3-intent-warning{
background-color:#d9822b;
color:#ffffff; }
.bp3-toast.bp3-intent-danger{
background-color:#db3737;
color:#ffffff; }
.bp3-toast-message{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
padding:11px;
word-break:break-word; }
.bp3-toast-container{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
display:-webkit-box !important;
display:-ms-flexbox !important;
display:flex !important;
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-ms-flex-direction:column;
flex-direction:column;
left:0;
overflow:hidden;
padding:0 20px 20px;
pointer-events:none;
right:0;
z-index:40; }
.bp3-toast-container.bp3-toast-container-in-portal{
position:fixed; }
.bp3-toast-container.bp3-toast-container-inline{
position:absolute; }
.bp3-toast-container.bp3-toast-container-top{
top:0; }
.bp3-toast-container.bp3-toast-container-bottom{
bottom:0;
-webkit-box-orient:vertical;
-webkit-box-direction:reverse;
-ms-flex-direction:column-reverse;
flex-direction:column-reverse;
top:auto; }
.bp3-toast-container.bp3-toast-container-left{
-webkit-box-align:start;
-ms-flex-align:start;
align-items:flex-start; }
.bp3-toast-container.bp3-toast-container-right{
-webkit-box-align:end;
-ms-flex-align:end;
align-items:flex-end; }
.bp3-toast-container-bottom .bp3-toast.bp3-toast-enter:not(.bp3-toast-enter-active),
.bp3-toast-container-bottom .bp3-toast.bp3-toast-enter:not(.bp3-toast-enter-active) ~ .bp3-toast, .bp3-toast-container-bottom .bp3-toast.bp3-toast-appear:not(.bp3-toast-appear-active),
.bp3-toast-container-bottom .bp3-toast.bp3-toast-appear:not(.bp3-toast-appear-active) ~ .bp3-toast,
.bp3-toast-container-bottom .bp3-toast.bp3-toast-exit-active ~ .bp3-toast,
.bp3-toast-container-bottom .bp3-toast.bp3-toast-leave-active ~ .bp3-toast{
-webkit-transform:translateY(60px);
transform:translateY(60px); }
.bp3-tooltip{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
-webkit-transform:scale(1);
transform:scale(1); }
.bp3-tooltip .bp3-popover-arrow{
height:22px;
position:absolute;
width:22px; }
.bp3-tooltip .bp3-popover-arrow::before{
height:14px;
margin:4px;
width:14px; }
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip{
margin-bottom:11px;
margin-top:-11px; }
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow{
bottom:-8px; }
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow svg{
-webkit-transform:rotate(-90deg);
transform:rotate(-90deg); }
.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip{
margin-left:11px; }
.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow{
left:-8px; }
.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow svg{
-webkit-transform:rotate(0);
transform:rotate(0); }
.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip{
margin-top:11px; }
.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow{
top:-8px; }
.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow svg{
-webkit-transform:rotate(90deg);
transform:rotate(90deg); }
.bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip{
margin-left:-11px;
margin-right:11px; }
.bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow{
right:-8px; }
.bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow svg{
-webkit-transform:rotate(180deg);
transform:rotate(180deg); }
.bp3-tether-element-attached-middle > .bp3-tooltip > .bp3-popover-arrow{
top:50%;
-webkit-transform:translateY(-50%);
transform:translateY(-50%); }
.bp3-tether-element-attached-center > .bp3-tooltip > .bp3-popover-arrow{
right:50%;
-webkit-transform:translateX(50%);
transform:translateX(50%); }
.bp3-tether-element-attached-top.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow{
top:-0.22183px; }
.bp3-tether-element-attached-right.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow{
right:-0.22183px; }
.bp3-tether-element-attached-left.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow{
left:-0.22183px; }
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow{
bottom:-0.22183px; }
.bp3-tether-element-attached-top.bp3-tether-element-attached-left > .bp3-tooltip{
-webkit-transform-origin:top left;
transform-origin:top left; }
.bp3-tether-element-attached-top.bp3-tether-element-attached-center > .bp3-tooltip{
-webkit-transform-origin:top center;
transform-origin:top center; }
.bp3-tether-element-attached-top.bp3-tether-element-attached-right > .bp3-tooltip{
-webkit-transform-origin:top right;
transform-origin:top right; }
.bp3-tether-element-attached-middle.bp3-tether-element-attached-left > .bp3-tooltip{
-webkit-transform-origin:center left;
transform-origin:center left; }
.bp3-tether-element-attached-middle.bp3-tether-element-attached-center > .bp3-tooltip{
-webkit-transform-origin:center center;
transform-origin:center center; }
.bp3-tether-element-attached-middle.bp3-tether-element-attached-right > .bp3-tooltip{
-webkit-transform-origin:center right;
transform-origin:center right; }
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-left > .bp3-tooltip{
-webkit-transform-origin:bottom left;
transform-origin:bottom left; }
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-center > .bp3-tooltip{
-webkit-transform-origin:bottom center;
transform-origin:bottom center; }
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-right > .bp3-tooltip{
-webkit-transform-origin:bottom right;
transform-origin:bottom right; }
.bp3-tooltip .bp3-popover-content{
background:#394b59;
color:#f5f8fa; }
.bp3-tooltip .bp3-popover-arrow::before{
-webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2);
box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2); }
.bp3-tooltip .bp3-popover-arrow-border{
fill:#10161a;
fill-opacity:0.1; }
.bp3-tooltip .bp3-popover-arrow-fill{
fill:#394b59; }
.bp3-popover-enter > .bp3-tooltip, .bp3-popover-appear > .bp3-tooltip{
-webkit-transform:scale(0.8);
transform:scale(0.8); }
.bp3-popover-enter-active > .bp3-tooltip, .bp3-popover-appear-active > .bp3-tooltip{
-webkit-transform:scale(1);
transform:scale(1);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:100ms;
transition-duration:100ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-popover-exit > .bp3-tooltip{
-webkit-transform:scale(1);
transform:scale(1); }
.bp3-popover-exit-active > .bp3-tooltip{
-webkit-transform:scale(0.8);
transform:scale(0.8);
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:100ms;
transition-duration:100ms;
-webkit-transition-property:-webkit-transform;
transition-property:-webkit-transform;
transition-property:transform;
transition-property:transform, -webkit-transform;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-tooltip .bp3-popover-content{
padding:10px 12px; }
.bp3-tooltip.bp3-dark,
.bp3-dark .bp3-tooltip{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
.bp3-tooltip.bp3-dark .bp3-popover-content,
.bp3-dark .bp3-tooltip .bp3-popover-content{
background:#e1e8ed;
color:#394b59; }
.bp3-tooltip.bp3-dark .bp3-popover-arrow::before,
.bp3-dark .bp3-tooltip .bp3-popover-arrow::before{
-webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4);
box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4); }
.bp3-tooltip.bp3-dark .bp3-popover-arrow-border,
.bp3-dark .bp3-tooltip .bp3-popover-arrow-border{
fill:#10161a;
fill-opacity:0.2; }
.bp3-tooltip.bp3-dark .bp3-popover-arrow-fill,
.bp3-dark .bp3-tooltip .bp3-popover-arrow-fill{
fill:#e1e8ed; }
.bp3-tooltip.bp3-intent-primary .bp3-popover-content{
background:#137cbd;
color:#ffffff; }
.bp3-tooltip.bp3-intent-primary .bp3-popover-arrow-fill{
fill:#137cbd; }
.bp3-tooltip.bp3-intent-success .bp3-popover-content{
background:#0f9960;
color:#ffffff; }
.bp3-tooltip.bp3-intent-success .bp3-popover-arrow-fill{
fill:#0f9960; }
.bp3-tooltip.bp3-intent-warning .bp3-popover-content{
background:#d9822b;
color:#ffffff; }
.bp3-tooltip.bp3-intent-warning .bp3-popover-arrow-fill{
fill:#d9822b; }
.bp3-tooltip.bp3-intent-danger .bp3-popover-content{
background:#db3737;
color:#ffffff; }
.bp3-tooltip.bp3-intent-danger .bp3-popover-arrow-fill{
fill:#db3737; }
.bp3-tooltip-indicator{
border-bottom:dotted 1px;
cursor:help; }
.bp3-tree .bp3-icon, .bp3-tree .bp3-icon-standard, .bp3-tree .bp3-icon-large{
color:#5c7080; }
.bp3-tree .bp3-icon.bp3-intent-primary, .bp3-tree .bp3-icon-standard.bp3-intent-primary, .bp3-tree .bp3-icon-large.bp3-intent-primary{
color:#137cbd; }
.bp3-tree .bp3-icon.bp3-intent-success, .bp3-tree .bp3-icon-standard.bp3-intent-success, .bp3-tree .bp3-icon-large.bp3-intent-success{
color:#0f9960; }
.bp3-tree .bp3-icon.bp3-intent-warning, .bp3-tree .bp3-icon-standard.bp3-intent-warning, .bp3-tree .bp3-icon-large.bp3-intent-warning{
color:#d9822b; }
.bp3-tree .bp3-icon.bp3-intent-danger, .bp3-tree .bp3-icon-standard.bp3-intent-danger, .bp3-tree .bp3-icon-large.bp3-intent-danger{
color:#db3737; }
.bp3-tree-node-list{
list-style:none;
margin:0;
padding-left:0; }
.bp3-tree-root{
background-color:transparent;
cursor:default;
padding-left:0;
position:relative; }
.bp3-tree-node-content-0{
padding-left:0px; }
.bp3-tree-node-content-1{
padding-left:23px; }
.bp3-tree-node-content-2{
padding-left:46px; }
.bp3-tree-node-content-3{
padding-left:69px; }
.bp3-tree-node-content-4{
padding-left:92px; }
.bp3-tree-node-content-5{
padding-left:115px; }
.bp3-tree-node-content-6{
padding-left:138px; }
.bp3-tree-node-content-7{
padding-left:161px; }
.bp3-tree-node-content-8{
padding-left:184px; }
.bp3-tree-node-content-9{
padding-left:207px; }
.bp3-tree-node-content-10{
padding-left:230px; }
.bp3-tree-node-content-11{
padding-left:253px; }
.bp3-tree-node-content-12{
padding-left:276px; }
.bp3-tree-node-content-13{
padding-left:299px; }
.bp3-tree-node-content-14{
padding-left:322px; }
.bp3-tree-node-content-15{
padding-left:345px; }
.bp3-tree-node-content-16{
padding-left:368px; }
.bp3-tree-node-content-17{
padding-left:391px; }
.bp3-tree-node-content-18{
padding-left:414px; }
.bp3-tree-node-content-19{
padding-left:437px; }
.bp3-tree-node-content-20{
padding-left:460px; }
.bp3-tree-node-content{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
display:-webkit-box;
display:-ms-flexbox;
display:flex;
height:30px;
padding-right:5px;
width:100%; }
.bp3-tree-node-content:hover{
background-color:rgba(191, 204, 214, 0.4); }
.bp3-tree-node-caret,
.bp3-tree-node-caret-none{
min-width:30px; }
.bp3-tree-node-caret{
color:#5c7080;
cursor:pointer;
padding:7px;
-webkit-transform:rotate(0deg);
transform:rotate(0deg);
-webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-tree-node-caret:hover{
color:#182026; }
.bp3-dark .bp3-tree-node-caret{
color:#a7b6c2; }
.bp3-dark .bp3-tree-node-caret:hover{
color:#f5f8fa; }
.bp3-tree-node-caret.bp3-tree-node-caret-open{
-webkit-transform:rotate(90deg);
transform:rotate(90deg); }
.bp3-tree-node-caret.bp3-icon-standard::before{
content:""; }
.bp3-tree-node-icon{
margin-right:7px;
position:relative; }
.bp3-tree-node-label{
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal;
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
position:relative;
-webkit-user-select:none;
-moz-user-select:none;
-ms-user-select:none;
user-select:none; }
.bp3-tree-node-label span{
display:inline; }
.bp3-tree-node-secondary-label{
padding:0 5px;
-webkit-user-select:none;
-moz-user-select:none;
-ms-user-select:none;
user-select:none; }
.bp3-tree-node-secondary-label .bp3-popover-wrapper,
.bp3-tree-node-secondary-label .bp3-popover-target{
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
display:-webkit-box;
display:-ms-flexbox;
display:flex; }
.bp3-tree-node.bp3-disabled .bp3-tree-node-content{
background-color:inherit;
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed; }
.bp3-tree-node.bp3-disabled .bp3-tree-node-caret,
.bp3-tree-node.bp3-disabled .bp3-tree-node-icon{
color:rgba(92, 112, 128, 0.6);
cursor:not-allowed; }
.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content{
background-color:#137cbd; }
.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content,
.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon, .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon-standard, .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon-large{
color:#ffffff; }
.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-tree-node-caret::before{
color:rgba(255, 255, 255, 0.7); }
.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-tree-node-caret:hover::before{
color:#ffffff; }
.bp3-dark .bp3-tree-node-content:hover{
background-color:rgba(92, 112, 128, 0.3); }
.bp3-dark .bp3-tree .bp3-icon, .bp3-dark .bp3-tree .bp3-icon-standard, .bp3-dark .bp3-tree .bp3-icon-large{
color:#a7b6c2; }
.bp3-dark .bp3-tree .bp3-icon.bp3-intent-primary, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-primary, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-primary{
color:#137cbd; }
.bp3-dark .bp3-tree .bp3-icon.bp3-intent-success, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-success, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-success{
color:#0f9960; }
.bp3-dark .bp3-tree .bp3-icon.bp3-intent-warning, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-warning, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-warning{
color:#d9822b; }
.bp3-dark .bp3-tree .bp3-icon.bp3-intent-danger, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-danger, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-danger{
color:#db3737; }
.bp3-dark .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content{
background-color:#137cbd; }
.bp3-omnibar{
-webkit-filter:blur(0);
filter:blur(0);
opacity:1;
background-color:#ffffff;
border-radius:3px;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
left:calc(50% - 250px);
top:20vh;
width:500px;
z-index:21; }
.bp3-omnibar.bp3-overlay-enter, .bp3-omnibar.bp3-overlay-appear{
-webkit-filter:blur(20px);
filter:blur(20px);
opacity:0.2; }
.bp3-omnibar.bp3-overlay-enter-active, .bp3-omnibar.bp3-overlay-appear-active{
-webkit-filter:blur(0);
filter:blur(0);
opacity:1;
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:200ms;
transition-duration:200ms;
-webkit-transition-property:opacity, -webkit-filter;
transition-property:opacity, -webkit-filter;
transition-property:filter, opacity;
transition-property:filter, opacity, -webkit-filter;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-omnibar.bp3-overlay-exit{
-webkit-filter:blur(0);
filter:blur(0);
opacity:1; }
.bp3-omnibar.bp3-overlay-exit-active{
-webkit-filter:blur(20px);
filter:blur(20px);
opacity:0.2;
-webkit-transition-delay:0;
transition-delay:0;
-webkit-transition-duration:200ms;
transition-duration:200ms;
-webkit-transition-property:opacity, -webkit-filter;
transition-property:opacity, -webkit-filter;
transition-property:filter, opacity;
transition-property:filter, opacity, -webkit-filter;
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-omnibar .bp3-input{
background-color:transparent;
border-radius:0; }
.bp3-omnibar .bp3-input, .bp3-omnibar .bp3-input:focus{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-omnibar .bp3-menu{
background-color:transparent;
border-radius:0;
-webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
max-height:calc(60vh - 40px);
overflow:auto; }
.bp3-omnibar .bp3-menu:empty{
display:none; }
.bp3-dark .bp3-omnibar, .bp3-omnibar.bp3-dark{
background-color:#30404d;
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); }
.bp3-omnibar-overlay .bp3-overlay-backdrop{
background-color:rgba(16, 22, 26, 0.2); }
.bp3-select-popover .bp3-popover-content{
padding:5px; }
.bp3-select-popover .bp3-input-group{
margin-bottom:0; }
.bp3-select-popover .bp3-menu{
max-height:300px;
max-width:400px;
overflow:auto;
padding:0; }
.bp3-select-popover .bp3-menu:not(:first-child){
padding-top:5px; }
.bp3-multi-select{
min-width:150px; }
.bp3-multi-select-popover .bp3-menu{
max-height:300px;
max-width:400px;
overflow:auto; }
.bp3-select-popover .bp3-popover-content{
padding:5px; }
.bp3-select-popover .bp3-input-group{
margin-bottom:0; }
.bp3-select-popover .bp3-menu{
max-height:300px;
max-width:400px;
overflow:auto;
padding:0; }
.bp3-select-popover .bp3-menu:not(:first-child){
padding-top:5px; }
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */
/**
* (DEPRECATED) Support for consuming icons as CSS background images
*/
/* Icons urls */
:root {
--jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-bug: url(data:image/svg+xml;base64,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);
--jp-icon-build: url(data:image/svg+xml;base64,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);
--jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-caret-right: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik03LjIsNS4yTDEwLjksOWwtMy44LDMuOFY1LjJINy4yeiIvPgogIDwvZz4KPC9zdmc+Cg==);
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--jp-icon-tab: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIxIDNIM2MtMS4xIDAtMiAuOS0yIDJ2MTRjMCAxLjEuOSAyIDIgMmgxOGMxLjEgMCAyLS45IDItMlY1YzAtMS4xLS45LTItMi0yem0wIDE2SDNWNWgxMHY0aDh2MTB6Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-table-rows: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMSw4SDNWNGgxOFY4eiBNMjEsMTBIM3Y0aDE4VjEweiBNMjEsMTZIM3Y0aDE4VjE2eiIvPgogICAgPC9nPgo8L3N2Zz4=);
--jp-icon-tag: url(data:image/svg+xml;base64,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);
--jp-icon-terminal: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0IiA+CiAgICA8cmVjdCBjbGFzcz0ianAtaWNvbjIganAtaWNvbi1zZWxlY3RhYmxlIiB3aWR0aD0iMjAiIGhlaWdodD0iMjAiIHRyYW5zZm9ybT0idHJhbnNsYXRlKDIgMikiIGZpbGw9IiMzMzMzMzMiLz4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uLWFjY2VudDIganAtaWNvbi1zZWxlY3RhYmxlLWludmVyc2UiIGQ9Ik01LjA1NjY0IDguNzYxNzJDNS4wNTY2NCA4LjU5NzY2IDUuMDMxMjUgOC40NTMxMiA0Ljk4MDQ3IDguMzI4MTJDNC45MzM1OSA4LjE5OTIyIDQuODU1NDcgOC4wODIwMyA0Ljc0NjA5IDcuOTc2NTZDNC42NDA2MiA3Ljg3MTA5IDQuNSA3Ljc3NTM5IDQuMzI0MjIgNy42ODk0NUM0LjE1MjM0IDcuNTk5NjEgMy45NDMzNiA3LjUxMTcyIDMuNjk3MjcgNy40MjU3OEMzLjMwMjczIDcuMjg1MTYgMi45NDMzNiA3LjEzNjcyIDIuNjE5MTQgNi45ODA0N0MyLjI5NDkyIDYuODI0MjIgMi4wMTc1OCA2LjY0MjU4IDEuNzg3MTEgNi40MzU1NUMxLjU2MDU1IDYuMjI4NTIgMS4zODQ3NyA1Ljk4ODI4IDEuMjU5NzcgNS43MTQ4NEMxLjEzNDc3IDUuNDM3NSAxLjA3MjI3IDUuMTA5MzggMS4wNzIyNyA0LjczMDQ3QzEuMDcyMjcgNC4zOTg0NCAxLjEyODkxIDQuMDk1NyAxLjI0MjE5IDMuODIyMjdDMS4zNTU0NyAzLjU0NDkyIDEuNTE1NjIgMy4zMDQ2OSAxLjcyMjY2IDMuMTAxNTZDMS45Mjk2OSAyLjg5ODQ0IDIuMTc5NjkgMi43MzQzNyAyLjQ3MjY2IDIuNjA5MzhDMi43NjU2MiAyLjQ4NDM4IDMuMDkxOCAyLjQwNDMgMy40NTExNyAyLjM2OTE0VjEuMTA5MzhINC4zODg2N1YyLjM4MDg2QzQuNzQwMjMgMi40Mjc3MyA1LjA1NjY0IDIuNTIzNDQgNS4zMzc4OSAyLjY2Nzk3QzUuNjE5MTQgMi44MTI1IDUuODU3NDIgMy4wMDE5NSA2LjA1MjczIDMuMjM2MzNDNi4yNTE5NSAzLjQ2NjggNi40MDQzIDMuNzQwMjMgNi41MDk3NyA0LjA1NjY0QzYuNjE5MTQgNC4zNjkxNCA2LjY3MzgzIDQuNzIwNyA2LjY3MzgzIDUuMTExMzNINS4wNDQ5MkM1LjA0NDkyIDQuNjM4NjcgNC45Mzc1IDQuMjgxMjUgNC43MjI2NiA0LjAzOTA2QzQuNTA3ODEgMy43OTI5NyA0LjIxNjggMy42Njk5MiAzLjg0OTYxIDMuNjY5OTJDMy42NTAzOSAzLjY2OTkyIDMuNDc2NTYgMy42OTcyNyAzLjMyODEyIDMuNzUxOTVDMy4xODM1OSAzLjgwMjczIDMuMDY0NDUgMy44NzY5NSAyLjk3MDcgMy45NzQ2MUMyLjg3Njk1IDQuMDY4MzYgMi44MDY2NCA0LjE3OTY5IDIuNzU5NzcgNC4zMDg1OUMyLjcxNjggNC40Mzc1IDIuNjk1MzEgNC41NzgxMiAyLjY5NTMxIDQuNzMwNDdDMi42OTUzMSA0Ljg4MjgxIDIuNzE2OCA1LjAxOTUzIDIuNzU5NzcgNS4xNDA2MkMyLjgwNjY0IDUuMjU3ODEgMi44ODI4MSA1LjM2NzE5IDIuOTg4MjggNS40Njg3NUMzLjA5NzY2IDUuNTcwMzEgMy4yNDAyMyA1LjY2Nzk3IDMuNDE2MDIgNS43NjE3MkMzLjU5MTggNS44NTE1NiAzLjgxMDU1IDUuOTQzMzYgNC4wNzIyNyA2LjAzNzExQzQuNDY2OCA2LjE4NTU1IDQuODI0MjIgNi4zMzk4NCA1LjE0NDUzIDYuNUM1LjQ2NDg0IDYuNjU2MjUgNS43MzgyOCA2LjgzOTg0IDUuOTY0ODQgNy4wNTA3OEM2LjE5NTMxIDcuMjU3ODEgNi4zNzEwOSA3LjUgNi40OTIxOSA3Ljc3NzM0QzYuNjE3MTkgOC4wNTA3OCA2LjY3OTY5IDguMzc1IDYuNjc5NjkgOC43NUM2LjY3OTY5IDkuMDkzNzUgNi42MjMwNSA5LjQwNDMgNi41MDk3NyA5LjY4MTY0QzYuMzk2NDggOS45NTUwOCA2LjIzNDM4IDEwLjE5MTQgNi4wMjM0NCAxMC4zOTA2QzUuODEyNSAxMC41ODk4IDUuNTU4NTkgMTAuNzUgNS4yNjE3MiAxMC44NzExQzQuOTY0ODQgMTAuOTg4MyA0LjYzMjgxIDExLjA2NDUgNC4yNjU2MiAxMS4wOTk2VjEyLjI0OEgzLjMzMzk4VjExLjA5OTZDMy4wMDE5NSAxMS4wNjg0IDIuNjc5NjkgMTAuOTk2MSAyLjM2NzE5IDEwLjg4MjhDMi4wNTQ2OSAxMC43NjU2IDEuNzc3MzQgMTAuNTk3NyAxLjUzNTE2IDEwLjM3ODlDMS4yOTY4OCAxMC4xNjAyIDEuMTA1NDcgOS44ODQ3NyAwLjk2MDkzOCA5LjU1MjczQzAuODE2NDA2IDkuMjE2OCAwLjc0NDE0MSA4LjgxNDQ1IDAuNzQ0MTQxIDguMzQ1N0gyLjM3ODkxQzIuMzc4OTEgOC42MjY5NSAyLjQxOTkyIDguODYzMjggMi41MDE5NSA5LjA1NDY5QzIuNTgzOTggOS4yNDIxOSAyLjY4OTQ1IDkuMzkyNTggMi44MTgzNiA5LjUwNTg2QzIuOTUxMTcgOS42MTUyMyAzLjEwMTU2IDkuNjkzMzYgMy4yNjk1MyA5Ljc0MDIzQzMuNDM3NSA5Ljc4NzExIDMuNjA5MzggOS44MTA1NSAzLjc4NTE2IDkuODEwNTVDNC4yMDMxMiA5LjgxMDU1IDQuNTE5NTMgOS43MTI4OSA0LjczNDM4IDkuNTE3NThDNC45NDkyMiA5LjMyMjI3IDUuMDU2NjQgOS4wNzAzMSA1LjA1NjY0IDguNzYxNzJaTTEzLjQxOCAxMi4yNzE1SDguMDc0MjJWMTFIMTMuNDE4VjEyLjI3MTVaIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSgzLjk1MjY0IDYpIiBmaWxsPSJ3aGl0ZSIvPgo8L3N2Zz4K);
--jp-icon-text-editor: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTUgMTVIM3YyaDEydi0yem0wLThIM3YyaDEyVjd6TTMgMTNoMTh2LTJIM3Yyem0wIDhoMTh2LTJIM3Yyek0zIDN2MmgxOFYzSDN6Ii8+Cjwvc3ZnPgo=);
--jp-icon-toc: url(data:image/svg+xml;base64,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);
--jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4=);
--jp-icon-trusted: url(data:image/svg+xml;base64,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);
--jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-yaml: url(data:image/svg+xml;base64,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);
}
/* Icon CSS class declarations */
.jp-AddIcon {
background-image: var(--jp-icon-add);
}
.jp-BugIcon {
background-image: var(--jp-icon-bug);
}
.jp-BuildIcon {
background-image: var(--jp-icon-build);
}
.jp-CaretDownEmptyIcon {
background-image: var(--jp-icon-caret-down-empty);
}
.jp-CaretDownEmptyThinIcon {
background-image: var(--jp-icon-caret-down-empty-thin);
}
.jp-CaretDownIcon {
background-image: var(--jp-icon-caret-down);
}
.jp-CaretLeftIcon {
background-image: var(--jp-icon-caret-left);
}
.jp-CaretRightIcon {
background-image: var(--jp-icon-caret-right);
}
.jp-CaretUpEmptyThinIcon {
background-image: var(--jp-icon-caret-up-empty-thin);
}
.jp-CaretUpIcon {
background-image: var(--jp-icon-caret-up);
}
.jp-CaseSensitiveIcon {
background-image: var(--jp-icon-case-sensitive);
}
.jp-CheckIcon {
background-image: var(--jp-icon-check);
}
.jp-CircleEmptyIcon {
background-image: var(--jp-icon-circle-empty);
}
.jp-CircleIcon {
background-image: var(--jp-icon-circle);
}
.jp-ClearIcon {
background-image: var(--jp-icon-clear);
}
.jp-CloseIcon {
background-image: var(--jp-icon-close);
}
.jp-CodeIcon {
background-image: var(--jp-icon-code);
}
.jp-ConsoleIcon {
background-image: var(--jp-icon-console);
}
.jp-CopyIcon {
background-image: var(--jp-icon-copy);
}
.jp-CopyrightIcon {
background-image: var(--jp-icon-copyright);
}
.jp-CutIcon {
background-image: var(--jp-icon-cut);
}
.jp-DownloadIcon {
background-image: var(--jp-icon-download);
}
.jp-EditIcon {
background-image: var(--jp-icon-edit);
}
.jp-EllipsesIcon {
background-image: var(--jp-icon-ellipses);
}
.jp-ExtensionIcon {
background-image: var(--jp-icon-extension);
}
.jp-FastForwardIcon {
background-image: var(--jp-icon-fast-forward);
}
.jp-FileIcon {
background-image: var(--jp-icon-file);
}
.jp-FileUploadIcon {
background-image: var(--jp-icon-file-upload);
}
.jp-FilterListIcon {
background-image: var(--jp-icon-filter-list);
}
.jp-FolderIcon {
background-image: var(--jp-icon-folder);
}
.jp-Html5Icon {
background-image: var(--jp-icon-html5);
}
.jp-ImageIcon {
background-image: var(--jp-icon-image);
}
.jp-InspectorIcon {
background-image: var(--jp-icon-inspector);
}
.jp-JsonIcon {
background-image: var(--jp-icon-json);
}
.jp-JuliaIcon {
background-image: var(--jp-icon-julia);
}
.jp-JupyterFaviconIcon {
background-image: var(--jp-icon-jupyter-favicon);
}
.jp-JupyterIcon {
background-image: var(--jp-icon-jupyter);
}
.jp-JupyterlabWordmarkIcon {
background-image: var(--jp-icon-jupyterlab-wordmark);
}
.jp-KernelIcon {
background-image: var(--jp-icon-kernel);
}
.jp-KeyboardIcon {
background-image: var(--jp-icon-keyboard);
}
.jp-LauncherIcon {
background-image: var(--jp-icon-launcher);
}
.jp-LineFormIcon {
background-image: var(--jp-icon-line-form);
}
.jp-LinkIcon {
background-image: var(--jp-icon-link);
}
.jp-ListIcon {
background-image: var(--jp-icon-list);
}
.jp-ListingsInfoIcon {
background-image: var(--jp-icon-listings-info);
}
.jp-MarkdownIcon {
background-image: var(--jp-icon-markdown);
}
.jp-NewFolderIcon {
background-image: var(--jp-icon-new-folder);
}
.jp-NotTrustedIcon {
background-image: var(--jp-icon-not-trusted);
}
.jp-NotebookIcon {
background-image: var(--jp-icon-notebook);
}
.jp-NumberingIcon {
background-image: var(--jp-icon-numbering);
}
.jp-OfflineBoltIcon {
background-image: var(--jp-icon-offline-bolt);
}
.jp-PaletteIcon {
background-image: var(--jp-icon-palette);
}
.jp-PasteIcon {
background-image: var(--jp-icon-paste);
}
.jp-PdfIcon {
background-image: var(--jp-icon-pdf);
}
.jp-PythonIcon {
background-image: var(--jp-icon-python);
}
.jp-RKernelIcon {
background-image: var(--jp-icon-r-kernel);
}
.jp-ReactIcon {
background-image: var(--jp-icon-react);
}
.jp-RedoIcon {
background-image: var(--jp-icon-redo);
}
.jp-RefreshIcon {
background-image: var(--jp-icon-refresh);
}
.jp-RegexIcon {
background-image: var(--jp-icon-regex);
}
.jp-RunIcon {
background-image: var(--jp-icon-run);
}
.jp-RunningIcon {
background-image: var(--jp-icon-running);
}
.jp-SaveIcon {
background-image: var(--jp-icon-save);
}
.jp-SearchIcon {
background-image: var(--jp-icon-search);
}
.jp-SettingsIcon {
background-image: var(--jp-icon-settings);
}
.jp-SpreadsheetIcon {
background-image: var(--jp-icon-spreadsheet);
}
.jp-StopIcon {
background-image: var(--jp-icon-stop);
}
.jp-TabIcon {
background-image: var(--jp-icon-tab);
}
.jp-TableRowsIcon {
background-image: var(--jp-icon-table-rows);
}
.jp-TagIcon {
background-image: var(--jp-icon-tag);
}
.jp-TerminalIcon {
background-image: var(--jp-icon-terminal);
}
.jp-TextEditorIcon {
background-image: var(--jp-icon-text-editor);
}
.jp-TocIcon {
background-image: var(--jp-icon-toc);
}
.jp-TreeViewIcon {
background-image: var(--jp-icon-tree-view);
}
.jp-TrustedIcon {
background-image: var(--jp-icon-trusted);
}
.jp-UndoIcon {
background-image: var(--jp-icon-undo);
}
.jp-VegaIcon {
background-image: var(--jp-icon-vega);
}
.jp-YamlIcon {
background-image: var(--jp-icon-yaml);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* (DEPRECATED) Support for consuming icons as CSS background images
*/
.jp-Icon,
.jp-MaterialIcon {
background-position: center;
background-repeat: no-repeat;
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-cover {
background-position: center;
background-repeat: no-repeat;
background-size: cover;
}
/**
* (DEPRECATED) Support for specific CSS icon sizes
*/
.jp-Icon-16 {
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-18 {
background-size: 18px;
min-width: 18px;
min-height: 18px;
}
.jp-Icon-20 {
background-size: 20px;
min-width: 20px;
min-height: 20px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for icons as inline SVG HTMLElements
*/
/* recolor the primary elements of an icon */
.jp-icon0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-accent0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-accent1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-accent2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-accent3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-accent4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-accent0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-accent1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-accent2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-accent3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-accent4[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-none[fill] {
fill: none;
}
.jp-icon-none[stroke] {
stroke: none;
}
/* brand icon colors. Same for light and dark */
.jp-icon-brand0[fill] {
fill: var(--jp-brand-color0);
}
.jp-icon-brand1[fill] {
fill: var(--jp-brand-color1);
}
.jp-icon-brand2[fill] {
fill: var(--jp-brand-color2);
}
.jp-icon-brand3[fill] {
fill: var(--jp-brand-color3);
}
.jp-icon-brand4[fill] {
fill: var(--jp-brand-color4);
}
.jp-icon-brand0[stroke] {
stroke: var(--jp-brand-color0);
}
.jp-icon-brand1[stroke] {
stroke: var(--jp-brand-color1);
}
.jp-icon-brand2[stroke] {
stroke: var(--jp-brand-color2);
}
.jp-icon-brand3[stroke] {
stroke: var(--jp-brand-color3);
}
.jp-icon-brand4[stroke] {
stroke: var(--jp-brand-color4);
}
/* warn icon colors. Same for light and dark */
.jp-icon-warn0[fill] {
fill: var(--jp-warn-color0);
}
.jp-icon-warn1[fill] {
fill: var(--jp-warn-color1);
}
.jp-icon-warn2[fill] {
fill: var(--jp-warn-color2);
}
.jp-icon-warn3[fill] {
fill: var(--jp-warn-color3);
}
.jp-icon-warn0[stroke] {
stroke: var(--jp-warn-color0);
}
.jp-icon-warn1[stroke] {
stroke: var(--jp-warn-color1);
}
.jp-icon-warn2[stroke] {
stroke: var(--jp-warn-color2);
}
.jp-icon-warn3[stroke] {
stroke: var(--jp-warn-color3);
}
/* icon colors that contrast well with each other and most backgrounds */
.jp-icon-contrast0[fill] {
fill: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[fill] {
fill: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[fill] {
fill: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[fill] {
fill: var(--jp-icon-contrast-color3);
}
.jp-icon-contrast0[stroke] {
stroke: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[stroke] {
stroke: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[stroke] {
stroke: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[stroke] {
stroke: var(--jp-icon-contrast-color3);
}
/* CSS for icons in selected items in the settings editor */
#setting-editor .jp-PluginList .jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
#setting-editor
.jp-PluginList
.jp-mod-selected
.jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* CSS for icons in selected filebrowser listing items */
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* CSS for icons in selected tabs in the sidebar tab manager */
#tab-manager .lm-TabBar-tab.jp-mod-active .jp-icon-selectable[fill] {
fill: #fff;
}
#tab-manager .lm-TabBar-tab.jp-mod-active .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
#tab-manager
.lm-TabBar-tab.jp-mod-active
.jp-icon-hover
:hover
.jp-icon-selectable[fill] {
fill: var(--jp-brand-color1);
}
#tab-manager
.lm-TabBar-tab.jp-mod-active
.jp-icon-hover
:hover
.jp-icon-selectable-inverse[fill] {
fill: #fff;
}
/**
* TODO: come up with non css-hack solution for showing the busy icon on top
* of the close icon
* CSS for complex behavior of close icon of tabs in the sidebar tab manager
*/
#tab-manager
.lm-TabBar-tab.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon3[fill] {
fill: none;
}
#tab-manager
.lm-TabBar-tab.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon-busy[fill] {
fill: var(--jp-inverse-layout-color3);
}
#tab-manager
.lm-TabBar-tab.jp-mod-dirty.jp-mod-active
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon-busy[fill] {
fill: #fff;
}
/**
* TODO: come up with non css-hack solution for showing the busy icon on top
* of the close icon
* CSS for complex behavior of close icon of tabs in the main area tabbar
*/
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon3[fill] {
fill: none;
}
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon-busy[fill] {
fill: var(--jp-inverse-layout-color3);
}
/* CSS for icons in status bar */
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* special handling for splash icon CSS. While the theme CSS reloads during
splash, the splash icon can loose theming. To prevent that, we set a
default for its color variable */
:root {
--jp-warn-color0: var(--md-orange-700);
}
/* not sure what to do with this one, used in filebrowser listing */
.jp-DragIcon {
margin-right: 4px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for alt colors for icons as inline SVG HTMLElements
*/
/* alt recolor the primary elements of an icon */
.jp-icon-alt .jp-icon0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-alt .jp-icon0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[stroke] {
stroke: var(--jp-layout-color4);
}
/* alt recolor the accent elements of an icon */
.jp-icon-alt .jp-icon-accent0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-alt .jp-icon-accent0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-icon-hoverShow:not(:hover) svg {
display: none !important;
}
/**
* Support for hover colors for icons as inline SVG HTMLElements
*/
/**
* regular colors
*/
/* recolor the primary elements of an icon */
.jp-icon-hover :hover .jp-icon0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-hover :hover .jp-icon-none-hover[fill] {
fill: none;
}
.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
stroke: none;
}
/**
* inverse colors
*/
/* inverse recolor the primary elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* inverse recolor the accent elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-switch {
display: flex;
align-items: center;
padding-left: 4px;
padding-right: 4px;
font-size: var(--jp-ui-font-size1);
background-color: transparent;
color: var(--jp-ui-font-color1);
border: none;
height: 20px;
}
.jp-switch:hover {
background-color: var(--jp-layout-color2);
}
.jp-switch-label {
margin-right: 5px;
}
.jp-switch-track {
cursor: pointer;
background-color: var(--jp-border-color1);
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 34px;
height: 16px;
width: 35px;
position: relative;
}
.jp-switch-track::before {
content: '';
position: absolute;
height: 10px;
width: 10px;
margin: 3px;
left: 0px;
background-color: var(--jp-ui-inverse-font-color1);
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 50%;
}
.jp-switch[aria-checked='true'] .jp-switch-track {
background-color: var(--jp-warn-color0);
}
.jp-switch[aria-checked='true'] .jp-switch-track::before {
/* track width (35) - margins (3 + 3) - thumb width (10) */
left: 19px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/* Sibling imports */
/* Override Blueprint's _reset.scss styles */
html {
box-sizing: unset;
}
*,
*::before,
*::after {
box-sizing: unset;
}
body {
color: unset;
font-family: var(--jp-ui-font-family);
}
p {
margin-top: unset;
margin-bottom: unset;
}
small {
font-size: unset;
}
strong {
font-weight: unset;
}
/* Override Blueprint's _typography.scss styles */
a {
text-decoration: unset;
color: unset;
}
a:hover {
text-decoration: unset;
color: unset;
}
/* Override Blueprint's _accessibility.scss styles */
:focus {
outline: unset;
outline-offset: unset;
-moz-outline-radius: unset;
}
/* Styles for ui-components */
.jp-Button {
border-radius: var(--jp-border-radius);
padding: 0px 12px;
font-size: var(--jp-ui-font-size1);
}
/* Use our own theme for hover styles */
button.jp-Button.bp3-button.bp3-minimal:hover {
background-color: var(--jp-layout-color2);
}
.jp-Button.minimal {
color: unset !important;
}
.jp-Button.jp-ToolbarButtonComponent {
text-transform: none;
}
.jp-InputGroup input {
box-sizing: border-box;
border-radius: 0;
background-color: transparent;
color: var(--jp-ui-font-color0);
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
}
.jp-InputGroup input:focus {
box-shadow: inset 0 0 0 var(--jp-border-width)
var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-InputGroup input::placeholder,
input::placeholder {
color: var(--jp-ui-font-color3);
}
.jp-BPIcon {
display: inline-block;
vertical-align: middle;
margin: auto;
}
/* Stop blueprint futzing with our icon fills */
.bp3-icon.jp-BPIcon > svg:not([fill]) {
fill: var(--jp-inverse-layout-color3);
}
.jp-InputGroupAction {
padding: 6px;
}
.jp-HTMLSelect.jp-DefaultStyle select {
background-color: initial;
border: none;
border-radius: 0;
box-shadow: none;
color: var(--jp-ui-font-color0);
display: block;
font-size: var(--jp-ui-font-size1);
height: 24px;
line-height: 14px;
padding: 0 25px 0 10px;
text-align: left;
-moz-appearance: none;
-webkit-appearance: none;
}
/* Use our own theme for hover and option styles */
.jp-HTMLSelect.jp-DefaultStyle select:hover,
.jp-HTMLSelect.jp-DefaultStyle select > option {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color0);
}
select {
box-sizing: border-box;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Collapse {
display: flex;
flex-direction: column;
align-items: stretch;
border-top: 1px solid var(--jp-border-color2);
border-bottom: 1px solid var(--jp-border-color2);
}
.jp-Collapse-header {
padding: 1px 12px;
color: var(--jp-ui-font-color1);
background-color: var(--jp-layout-color1);
font-size: var(--jp-ui-font-size2);
}
.jp-Collapse-header:hover {
background-color: var(--jp-layout-color2);
}
.jp-Collapse-contents {
padding: 0px 12px 0px 12px;
background-color: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
overflow: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-commandpalette-search-height: 28px;
}
/*-----------------------------------------------------------------------------
| Overall styles
|----------------------------------------------------------------------------*/
.lm-CommandPalette {
padding-bottom: 0px;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Modal variant
|----------------------------------------------------------------------------*/
.jp-ModalCommandPalette {
position: absolute;
z-index: 10000;
top: 38px;
left: 30%;
margin: 0;
padding: 4px;
width: 40%;
box-shadow: var(--jp-elevation-z4);
border-radius: 4px;
background: var(--jp-layout-color0);
}
.jp-ModalCommandPalette .lm-CommandPalette {
max-height: 40vh;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
margin-left: 4px;
margin-right: 4px;
}
.jp-ModalCommandPalette
.lm-CommandPalette
.lm-CommandPalette-item.lm-mod-disabled {
display: none;
}
/*-----------------------------------------------------------------------------
| Search
|----------------------------------------------------------------------------*/
.lm-CommandPalette-search {
padding: 4px;
background-color: var(--jp-layout-color1);
z-index: 2;
}
.lm-CommandPalette-wrapper {
overflow: overlay;
padding: 0px 9px;
background-color: var(--jp-input-active-background);
height: 30px;
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
}
.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-SearchIconGroup {
color: white;
background-color: var(--jp-brand-color1);
position: absolute;
top: 4px;
right: 4px;
padding: 5px 5px 1px 5px;
}
.jp-SearchIconGroup svg {
height: 20px;
width: 20px;
}
.jp-SearchIconGroup .jp-icon3[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-input {
background: transparent;
width: calc(100% - 18px);
float: left;
border: none;
outline: none;
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
line-height: var(--jp-private-commandpalette-search-height);
}
.lm-CommandPalette-input::-webkit-input-placeholder,
.lm-CommandPalette-input::-moz-placeholder,
.lm-CommandPalette-input:-ms-input-placeholder {
color: var(--jp-ui-font-color2);
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Results
|----------------------------------------------------------------------------*/
.lm-CommandPalette-header:first-child {
margin-top: 0px;
}
.lm-CommandPalette-header {
border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
color: var(--jp-ui-font-color1);
cursor: pointer;
display: flex;
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin-top: 8px;
padding: 8px 0 8px 12px;
text-transform: uppercase;
}
.lm-CommandPalette-header.lm-mod-active {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-header > mark {
background-color: transparent;
font-weight: bold;
color: var(--jp-ui-font-color1);
}
.lm-CommandPalette-item {
padding: 4px 12px 4px 4px;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
font-weight: 400;
display: flex;
}
.lm-CommandPalette-item.lm-mod-disabled {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item.lm-mod-active {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-inverse-font-color0);
}
.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-inverse-font-color0);
}
.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-itemContent {
overflow: hidden;
}
.lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.lm-CommandPalette-item.lm-mod-disabled mark {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
margin: 0 4px 0 0;
position: relative;
width: 16px;
top: 2px;
flex: 0 0 auto;
}
.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
opacity: 0.6;
}
.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
flex: 0 0 auto;
}
.lm-CommandPalette-itemCaption {
display: none;
}
.lm-CommandPalette-content {
background-color: var(--jp-layout-color1);
}
.lm-CommandPalette-content:empty:after {
content: 'No results';
margin: auto;
margin-top: 20px;
width: 100px;
display: block;
font-size: var(--jp-ui-font-size2);
font-family: var(--jp-ui-font-family);
font-weight: lighter;
}
.lm-CommandPalette-emptyMessage {
text-align: center;
margin-top: 24px;
line-height: 1.32;
padding: 0px 8px;
color: var(--jp-content-font-color3);
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Dialog {
position: absolute;
z-index: 10000;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
top: 0px;
left: 0px;
margin: 0;
padding: 0;
width: 100%;
height: 100%;
background: var(--jp-dialog-background);
}
.jp-Dialog-content {
display: flex;
flex-direction: column;
margin-left: auto;
margin-right: auto;
background: var(--jp-layout-color1);
padding: 24px;
padding-bottom: 12px;
min-width: 300px;
min-height: 150px;
max-width: 1000px;
max-height: 500px;
box-sizing: border-box;
box-shadow: var(--jp-elevation-z20);
word-wrap: break-word;
border-radius: var(--jp-border-radius);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color1);
resize: both;
}
.jp-Dialog-button {
overflow: visible;
}
button.jp-Dialog-button:focus {
outline: 1px solid var(--jp-brand-color1);
outline-offset: 4px;
-moz-outline-radius: 0px;
}
button.jp-Dialog-button:focus::-moz-focus-inner {
border: 0;
}
button.jp-Dialog-close-button {
padding: 0;
height: 100%;
min-width: unset;
min-height: unset;
}
.jp-Dialog-header {
display: flex;
justify-content: space-between;
flex: 0 0 auto;
padding-bottom: 12px;
font-size: var(--jp-ui-font-size3);
font-weight: 400;
color: var(--jp-ui-font-color0);
}
.jp-Dialog-body {
display: flex;
flex-direction: column;
flex: 1 1 auto;
font-size: var(--jp-ui-font-size1);
background: var(--jp-layout-color1);
overflow: auto;
}
.jp-Dialog-footer {
display: flex;
flex-direction: row;
justify-content: flex-end;
flex: 0 0 auto;
margin-left: -12px;
margin-right: -12px;
padding: 12px;
}
.jp-Dialog-title {
overflow: hidden;
white-space: nowrap;
text-overflow: ellipsis;
}
.jp-Dialog-body > .jp-select-wrapper {
width: 100%;
}
.jp-Dialog-body > button {
padding: 0px 16px;
}
.jp-Dialog-body > label {
line-height: 1.4;
color: var(--jp-ui-font-color0);
}
.jp-Dialog-button.jp-mod-styled:not(:last-child) {
margin-right: 12px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-HoverBox {
position: fixed;
}
.jp-HoverBox.jp-mod-outofview {
display: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-IFrame {
width: 100%;
height: 100%;
}
.jp-IFrame > iframe {
border: none;
}
/*
When drag events occur, `p-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-IFrame {
position: relative;
}
body.lm-mod-override-cursor .jp-IFrame:before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
.jp-Input-Boolean-Dialog {
flex-direction: row-reverse;
align-items: end;
width: 100%;
}
.jp-Input-Boolean-Dialog > label {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MainAreaWidget > :focus {
outline: none;
}
/**
* google-material-color v1.2.6
* https://github.com/danlevan/google-material-color
*/
:root {
--md-red-50: #ffebee;
--md-red-100: #ffcdd2;
--md-red-200: #ef9a9a;
--md-red-300: #e57373;
--md-red-400: #ef5350;
--md-red-500: #f44336;
--md-red-600: #e53935;
--md-red-700: #d32f2f;
--md-red-800: #c62828;
--md-red-900: #b71c1c;
--md-red-A100: #ff8a80;
--md-red-A200: #ff5252;
--md-red-A400: #ff1744;
--md-red-A700: #d50000;
--md-pink-50: #fce4ec;
--md-pink-100: #f8bbd0;
--md-pink-200: #f48fb1;
--md-pink-300: #f06292;
--md-pink-400: #ec407a;
--md-pink-500: #e91e63;
--md-pink-600: #d81b60;
--md-pink-700: #c2185b;
--md-pink-800: #ad1457;
--md-pink-900: #880e4f;
--md-pink-A100: #ff80ab;
--md-pink-A200: #ff4081;
--md-pink-A400: #f50057;
--md-pink-A700: #c51162;
--md-purple-50: #f3e5f5;
--md-purple-100: #e1bee7;
--md-purple-200: #ce93d8;
--md-purple-300: #ba68c8;
--md-purple-400: #ab47bc;
--md-purple-500: #9c27b0;
--md-purple-600: #8e24aa;
--md-purple-700: #7b1fa2;
--md-purple-800: #6a1b9a;
--md-purple-900: #4a148c;
--md-purple-A100: #ea80fc;
--md-purple-A200: #e040fb;
--md-purple-A400: #d500f9;
--md-purple-A700: #aa00ff;
--md-deep-purple-50: #ede7f6;
--md-deep-purple-100: #d1c4e9;
--md-deep-purple-200: #b39ddb;
--md-deep-purple-300: #9575cd;
--md-deep-purple-400: #7e57c2;
--md-deep-purple-500: #673ab7;
--md-deep-purple-600: #5e35b1;
--md-deep-purple-700: #512da8;
--md-deep-purple-800: #4527a0;
--md-deep-purple-900: #311b92;
--md-deep-purple-A100: #b388ff;
--md-deep-purple-A200: #7c4dff;
--md-deep-purple-A400: #651fff;
--md-deep-purple-A700: #6200ea;
--md-indigo-50: #e8eaf6;
--md-indigo-100: #c5cae9;
--md-indigo-200: #9fa8da;
--md-indigo-300: #7986cb;
--md-indigo-400: #5c6bc0;
--md-indigo-500: #3f51b5;
--md-indigo-600: #3949ab;
--md-indigo-700: #303f9f;
--md-indigo-800: #283593;
--md-indigo-900: #1a237e;
--md-indigo-A100: #8c9eff;
--md-indigo-A200: #536dfe;
--md-indigo-A400: #3d5afe;
--md-indigo-A700: #304ffe;
--md-blue-50: #e3f2fd;
--md-blue-100: #bbdefb;
--md-blue-200: #90caf9;
--md-blue-300: #64b5f6;
--md-blue-400: #42a5f5;
--md-blue-500: #2196f3;
--md-blue-600: #1e88e5;
--md-blue-700: #1976d2;
--md-blue-800: #1565c0;
--md-blue-900: #0d47a1;
--md-blue-A100: #82b1ff;
--md-blue-A200: #448aff;
--md-blue-A400: #2979ff;
--md-blue-A700: #2962ff;
--md-light-blue-50: #e1f5fe;
--md-light-blue-100: #b3e5fc;
--md-light-blue-200: #81d4fa;
--md-light-blue-300: #4fc3f7;
--md-light-blue-400: #29b6f6;
--md-light-blue-500: #03a9f4;
--md-light-blue-600: #039be5;
--md-light-blue-700: #0288d1;
--md-light-blue-800: #0277bd;
--md-light-blue-900: #01579b;
--md-light-blue-A100: #80d8ff;
--md-light-blue-A200: #40c4ff;
--md-light-blue-A400: #00b0ff;
--md-light-blue-A700: #0091ea;
--md-cyan-50: #e0f7fa;
--md-cyan-100: #b2ebf2;
--md-cyan-200: #80deea;
--md-cyan-300: #4dd0e1;
--md-cyan-400: #26c6da;
--md-cyan-500: #00bcd4;
--md-cyan-600: #00acc1;
--md-cyan-700: #0097a7;
--md-cyan-800: #00838f;
--md-cyan-900: #006064;
--md-cyan-A100: #84ffff;
--md-cyan-A200: #18ffff;
--md-cyan-A400: #00e5ff;
--md-cyan-A700: #00b8d4;
--md-teal-50: #e0f2f1;
--md-teal-100: #b2dfdb;
--md-teal-200: #80cbc4;
--md-teal-300: #4db6ac;
--md-teal-400: #26a69a;
--md-teal-500: #009688;
--md-teal-600: #00897b;
--md-teal-700: #00796b;
--md-teal-800: #00695c;
--md-teal-900: #004d40;
--md-teal-A100: #a7ffeb;
--md-teal-A200: #64ffda;
--md-teal-A400: #1de9b6;
--md-teal-A700: #00bfa5;
--md-green-50: #e8f5e9;
--md-green-100: #c8e6c9;
--md-green-200: #a5d6a7;
--md-green-300: #81c784;
--md-green-400: #66bb6a;
--md-green-500: #4caf50;
--md-green-600: #43a047;
--md-green-700: #388e3c;
--md-green-800: #2e7d32;
--md-green-900: #1b5e20;
--md-green-A100: #b9f6ca;
--md-green-A200: #69f0ae;
--md-green-A400: #00e676;
--md-green-A700: #00c853;
--md-light-green-50: #f1f8e9;
--md-light-green-100: #dcedc8;
--md-light-green-200: #c5e1a5;
--md-light-green-300: #aed581;
--md-light-green-400: #9ccc65;
--md-light-green-500: #8bc34a;
--md-light-green-600: #7cb342;
--md-light-green-700: #689f38;
--md-light-green-800: #558b2f;
--md-light-green-900: #33691e;
--md-light-green-A100: #ccff90;
--md-light-green-A200: #b2ff59;
--md-light-green-A400: #76ff03;
--md-light-green-A700: #64dd17;
--md-lime-50: #f9fbe7;
--md-lime-100: #f0f4c3;
--md-lime-200: #e6ee9c;
--md-lime-300: #dce775;
--md-lime-400: #d4e157;
--md-lime-500: #cddc39;
--md-lime-600: #c0ca33;
--md-lime-700: #afb42b;
--md-lime-800: #9e9d24;
--md-lime-900: #827717;
--md-lime-A100: #f4ff81;
--md-lime-A200: #eeff41;
--md-lime-A400: #c6ff00;
--md-lime-A700: #aeea00;
--md-yellow-50: #fffde7;
--md-yellow-100: #fff9c4;
--md-yellow-200: #fff59d;
--md-yellow-300: #fff176;
--md-yellow-400: #ffee58;
--md-yellow-500: #ffeb3b;
--md-yellow-600: #fdd835;
--md-yellow-700: #fbc02d;
--md-yellow-800: #f9a825;
--md-yellow-900: #f57f17;
--md-yellow-A100: #ffff8d;
--md-yellow-A200: #ffff00;
--md-yellow-A400: #ffea00;
--md-yellow-A700: #ffd600;
--md-amber-50: #fff8e1;
--md-amber-100: #ffecb3;
--md-amber-200: #ffe082;
--md-amber-300: #ffd54f;
--md-amber-400: #ffca28;
--md-amber-500: #ffc107;
--md-amber-600: #ffb300;
--md-amber-700: #ffa000;
--md-amber-800: #ff8f00;
--md-amber-900: #ff6f00;
--md-amber-A100: #ffe57f;
--md-amber-A200: #ffd740;
--md-amber-A400: #ffc400;
--md-amber-A700: #ffab00;
--md-orange-50: #fff3e0;
--md-orange-100: #ffe0b2;
--md-orange-200: #ffcc80;
--md-orange-300: #ffb74d;
--md-orange-400: #ffa726;
--md-orange-500: #ff9800;
--md-orange-600: #fb8c00;
--md-orange-700: #f57c00;
--md-orange-800: #ef6c00;
--md-orange-900: #e65100;
--md-orange-A100: #ffd180;
--md-orange-A200: #ffab40;
--md-orange-A400: #ff9100;
--md-orange-A700: #ff6d00;
--md-deep-orange-50: #fbe9e7;
--md-deep-orange-100: #ffccbc;
--md-deep-orange-200: #ffab91;
--md-deep-orange-300: #ff8a65;
--md-deep-orange-400: #ff7043;
--md-deep-orange-500: #ff5722;
--md-deep-orange-600: #f4511e;
--md-deep-orange-700: #e64a19;
--md-deep-orange-800: #d84315;
--md-deep-orange-900: #bf360c;
--md-deep-orange-A100: #ff9e80;
--md-deep-orange-A200: #ff6e40;
--md-deep-orange-A400: #ff3d00;
--md-deep-orange-A700: #dd2c00;
--md-brown-50: #efebe9;
--md-brown-100: #d7ccc8;
--md-brown-200: #bcaaa4;
--md-brown-300: #a1887f;
--md-brown-400: #8d6e63;
--md-brown-500: #795548;
--md-brown-600: #6d4c41;
--md-brown-700: #5d4037;
--md-brown-800: #4e342e;
--md-brown-900: #3e2723;
--md-grey-50: #fafafa;
--md-grey-100: #f5f5f5;
--md-grey-200: #eeeeee;
--md-grey-300: #e0e0e0;
--md-grey-400: #bdbdbd;
--md-grey-500: #9e9e9e;
--md-grey-600: #757575;
--md-grey-700: #616161;
--md-grey-800: #424242;
--md-grey-900: #212121;
--md-blue-grey-50: #eceff1;
--md-blue-grey-100: #cfd8dc;
--md-blue-grey-200: #b0bec5;
--md-blue-grey-300: #90a4ae;
--md-blue-grey-400: #78909c;
--md-blue-grey-500: #607d8b;
--md-blue-grey-600: #546e7a;
--md-blue-grey-700: #455a64;
--md-blue-grey-800: #37474f;
--md-blue-grey-900: #263238;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Spinner {
position: absolute;
display: flex;
justify-content: center;
align-items: center;
z-index: 10;
left: 0;
top: 0;
width: 100%;
height: 100%;
background: var(--jp-layout-color0);
outline: none;
}
.jp-SpinnerContent {
font-size: 10px;
margin: 50px auto;
text-indent: -9999em;
width: 3em;
height: 3em;
border-radius: 50%;
background: var(--jp-brand-color3);
background: linear-gradient(
to right,
#f37626 10%,
rgba(255, 255, 255, 0) 42%
);
position: relative;
animation: load3 1s infinite linear, fadeIn 1s;
}
.jp-SpinnerContent:before {
width: 50%;
height: 50%;
background: #f37626;
border-radius: 100% 0 0 0;
position: absolute;
top: 0;
left: 0;
content: '';
}
.jp-SpinnerContent:after {
background: var(--jp-layout-color0);
width: 75%;
height: 75%;
border-radius: 50%;
content: '';
margin: auto;
position: absolute;
top: 0;
left: 0;
bottom: 0;
right: 0;
}
@keyframes fadeIn {
0% {
opacity: 0;
}
100% {
opacity: 1;
}
}
@keyframes load3 {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
button.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: none;
box-sizing: border-box;
text-align: center;
line-height: 32px;
height: 32px;
padding: 0px 12px;
letter-spacing: 0.8px;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input.jp-mod-styled {
background: var(--jp-input-background);
height: 28px;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color1);
padding-left: 7px;
padding-right: 7px;
font-size: var(--jp-ui-font-size2);
color: var(--jp-ui-font-color0);
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input[type='checkbox'].jp-mod-styled {
appearance: checkbox;
-webkit-appearance: checkbox;
-moz-appearance: checkbox;
height: auto;
}
input.jp-mod-styled:focus {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-FileDialog-Checkbox {
margin-top: 35px;
display: flex;
flex-direction: row;
align-items: end;
width: 100%;
}
.jp-FileDialog-Checkbox > label {
flex: 1 1 auto;
}
.jp-select-wrapper {
display: flex;
position: relative;
flex-direction: column;
padding: 1px;
background-color: var(--jp-layout-color1);
height: 28px;
box-sizing: border-box;
margin-bottom: 12px;
}
.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-input-active-background);
}
select.jp-mod-styled:hover {
background-color: var(--jp-layout-color1);
cursor: pointer;
color: var(--jp-ui-font-color0);
background-color: var(--jp-input-hover-background);
box-shadow: inset 0 0px 1px rgba(0, 0, 0, 0.5);
}
select.jp-mod-styled {
flex: 1 1 auto;
height: 32px;
width: 100%;
font-size: var(--jp-ui-font-size2);
background: var(--jp-input-background);
color: var(--jp-ui-font-color0);
padding: 0 25px 0 8px;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0px;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
:root {
--jp-private-toolbar-height: calc(
28px + var(--jp-border-width)
); /* leave 28px for content */
}
.jp-Toolbar {
color: var(--jp-ui-font-color1);
flex: 0 0 auto;
display: flex;
flex-direction: row;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: 2px;
z-index: 1;
overflow-x: auto;
}
/* Toolbar items */
.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
flex-grow: 1;
flex-shrink: 1;
}
.jp-Toolbar-item.jp-Toolbar-kernelStatus {
display: inline-block;
width: 32px;
background-repeat: no-repeat;
background-position: center;
background-size: 16px;
}
.jp-Toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
display: flex;
padding-left: 1px;
padding-right: 1px;
font-size: var(--jp-ui-font-size1);
line-height: var(--jp-private-toolbar-height);
height: 100%;
}
/* Toolbar buttons */
/* This is the div we use to wrap the react component into a Widget */
div.jp-ToolbarButton {
color: transparent;
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0px;
margin: 0px;
}
button.jp-ToolbarButtonComponent {
background: var(--jp-layout-color1);
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0px 6px;
margin: 0px;
height: 24px;
border-radius: var(--jp-border-radius);
display: flex;
align-items: center;
text-align: center;
font-size: 14px;
min-width: unset;
min-height: unset;
}
button.jp-ToolbarButtonComponent:disabled {
opacity: 0.4;
}
button.jp-ToolbarButtonComponent span {
padding: 0px;
flex: 0 0 auto;
}
button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
font-size: var(--jp-ui-font-size1);
line-height: 100%;
padding-left: 2px;
color: var(--jp-ui-font-color1);
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar.jp-Toolbar-micro {
padding: 0;
min-height: 0;
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar {
border: none;
box-shadow: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ body.p-mod-override-cursor *, /* </DEPRECATED> */
body.lm-mod-override-cursor * {
cursor: inherit !important;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-JSONEditor {
display: flex;
flex-direction: column;
width: 100%;
}
.jp-JSONEditor-host {
flex: 1 1 auto;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0px;
background: var(--jp-layout-color0);
min-height: 50px;
padding: 1px;
}
.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
border-color: red;
outline-color: red;
}
.jp-JSONEditor-header {
display: flex;
flex: 1 0 auto;
padding: 0 0 0 12px;
}
.jp-JSONEditor-header label {
flex: 0 0 auto;
}
.jp-JSONEditor-commitButton {
height: 16px;
width: 16px;
background-size: 18px;
background-repeat: no-repeat;
background-position: center;
}
.jp-JSONEditor-host.jp-mod-focused {
background-color: var(--jp-input-active-background);
border: 1px solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
.jp-Editor.jp-mod-dropTarget {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
/* BASICS */
.CodeMirror {
/* Set height, width, borders, and global font properties here */
font-family: monospace;
height: 300px;
color: black;
direction: ltr;
}
/* PADDING */
.CodeMirror-lines {
padding: 4px 0; /* Vertical padding around content */
}
.CodeMirror pre.CodeMirror-line,
.CodeMirror pre.CodeMirror-line-like {
padding: 0 4px; /* Horizontal padding of content */
}
.CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {
background-color: white; /* The little square between H and V scrollbars */
}
/* GUTTER */
.CodeMirror-gutters {
border-right: 1px solid #ddd;
background-color: #f7f7f7;
white-space: nowrap;
}
.CodeMirror-linenumbers {}
.CodeMirror-linenumber {
padding: 0 3px 0 5px;
min-width: 20px;
text-align: right;
color: #999;
white-space: nowrap;
}
.CodeMirror-guttermarker { color: black; }
.CodeMirror-guttermarker-subtle { color: #999; }
/* CURSOR */
.CodeMirror-cursor {
border-left: 1px solid black;
border-right: none;
width: 0;
}
/* Shown when moving in bi-directional text */
.CodeMirror div.CodeMirror-secondarycursor {
border-left: 1px solid silver;
}
.cm-fat-cursor .CodeMirror-cursor {
width: auto;
border: 0 !important;
background: #7e7;
}
.cm-fat-cursor div.CodeMirror-cursors {
z-index: 1;
}
.cm-fat-cursor-mark {
background-color: rgba(20, 255, 20, 0.5);
-webkit-animation: blink 1.06s steps(1) infinite;
-moz-animation: blink 1.06s steps(1) infinite;
animation: blink 1.06s steps(1) infinite;
}
.cm-animate-fat-cursor {
width: auto;
border: 0;
-webkit-animation: blink 1.06s steps(1) infinite;
-moz-animation: blink 1.06s steps(1) infinite;
animation: blink 1.06s steps(1) infinite;
background-color: #7e7;
}
@-moz-keyframes blink {
0% {}
50% { background-color: transparent; }
100% {}
}
@-webkit-keyframes blink {
0% {}
50% { background-color: transparent; }
100% {}
}
@keyframes blink {
0% {}
50% { background-color: transparent; }
100% {}
}
/* Can style cursor different in overwrite (non-insert) mode */
.CodeMirror-overwrite .CodeMirror-cursor {}
.cm-tab { display: inline-block; text-decoration: inherit; }
.CodeMirror-rulers {
position: absolute;
left: 0; right: 0; top: -50px; bottom: 0;
overflow: hidden;
}
.CodeMirror-ruler {
border-left: 1px solid #ccc;
top: 0; bottom: 0;
position: absolute;
}
/* DEFAULT THEME */
.cm-s-default .cm-header {color: blue;}
.cm-s-default .cm-quote {color: #090;}
.cm-negative {color: #d44;}
.cm-positive {color: #292;}
.cm-header, .cm-strong {font-weight: bold;}
.cm-em {font-style: italic;}
.cm-link {text-decoration: underline;}
.cm-strikethrough {text-decoration: line-through;}
.cm-s-default .cm-keyword {color: #708;}
.cm-s-default .cm-atom {color: #219;}
.cm-s-default .cm-number {color: #164;}
.cm-s-default .cm-def {color: #00f;}
.cm-s-default .cm-variable,
.cm-s-default .cm-punctuation,
.cm-s-default .cm-property,
.cm-s-default .cm-operator {}
.cm-s-default .cm-variable-2 {color: #05a;}
.cm-s-default .cm-variable-3, .cm-s-default .cm-type {color: #085;}
.cm-s-default .cm-comment {color: #a50;}
.cm-s-default .cm-string {color: #a11;}
.cm-s-default .cm-string-2 {color: #f50;}
.cm-s-default .cm-meta {color: #555;}
.cm-s-default .cm-qualifier {color: #555;}
.cm-s-default .cm-builtin {color: #30a;}
.cm-s-default .cm-bracket {color: #997;}
.cm-s-default .cm-tag {color: #170;}
.cm-s-default .cm-attribute {color: #00c;}
.cm-s-default .cm-hr {color: #999;}
.cm-s-default .cm-link {color: #00c;}
.cm-s-default .cm-error {color: #f00;}
.cm-invalidchar {color: #f00;}
.CodeMirror-composing { border-bottom: 2px solid; }
/* Default styles for common addons */
div.CodeMirror span.CodeMirror-matchingbracket {color: #0b0;}
div.CodeMirror span.CodeMirror-nonmatchingbracket {color: #a22;}
.CodeMirror-matchingtag { background: rgba(255, 150, 0, .3); }
.CodeMirror-activeline-background {background: #e8f2ff;}
/* STOP */
/* The rest of this file contains styles related to the mechanics of
the editor. You probably shouldn't touch them. */
.CodeMirror {
position: relative;
overflow: hidden;
background: white;
}
.CodeMirror-scroll {
overflow: scroll !important; /* Things will break if this is overridden */
/* 50px is the magic margin used to hide the element's real scrollbars */
/* See overflow: hidden in .CodeMirror */
margin-bottom: -50px; margin-right: -50px;
padding-bottom: 50px;
height: 100%;
outline: none; /* Prevent dragging from highlighting the element */
position: relative;
}
.CodeMirror-sizer {
position: relative;
border-right: 50px solid transparent;
}
/* The fake, visible scrollbars. Used to force redraw during scrolling
before actual scrolling happens, thus preventing shaking and
flickering artifacts. */
.CodeMirror-vscrollbar, .CodeMirror-hscrollbar, .CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {
position: absolute;
z-index: 6;
display: none;
outline: none;
}
.CodeMirror-vscrollbar {
right: 0; top: 0;
overflow-x: hidden;
overflow-y: scroll;
}
.CodeMirror-hscrollbar {
bottom: 0; left: 0;
overflow-y: hidden;
overflow-x: scroll;
}
.CodeMirror-scrollbar-filler {
right: 0; bottom: 0;
}
.CodeMirror-gutter-filler {
left: 0; bottom: 0;
}
.CodeMirror-gutters {
position: absolute; left: 0; top: 0;
min-height: 100%;
z-index: 3;
}
.CodeMirror-gutter {
white-space: normal;
height: 100%;
display: inline-block;
vertical-align: top;
margin-bottom: -50px;
}
.CodeMirror-gutter-wrapper {
position: absolute;
z-index: 4;
background: none !important;
border: none !important;
}
.CodeMirror-gutter-background {
position: absolute;
top: 0; bottom: 0;
z-index: 4;
}
.CodeMirror-gutter-elt {
position: absolute;
cursor: default;
z-index: 4;
}
.CodeMirror-gutter-wrapper ::selection { background-color: transparent }
.CodeMirror-gutter-wrapper ::-moz-selection { background-color: transparent }
.CodeMirror-lines {
cursor: text;
min-height: 1px; /* prevents collapsing before first draw */
}
.CodeMirror pre.CodeMirror-line,
.CodeMirror pre.CodeMirror-line-like {
/* Reset some styles that the rest of the page might have set */
-moz-border-radius: 0; -webkit-border-radius: 0; border-radius: 0;
border-width: 0;
background: transparent;
font-family: inherit;
font-size: inherit;
margin: 0;
white-space: pre;
word-wrap: normal;
line-height: inherit;
color: inherit;
z-index: 2;
position: relative;
overflow: visible;
-webkit-tap-highlight-color: transparent;
-webkit-font-variant-ligatures: contextual;
font-variant-ligatures: contextual;
}
.CodeMirror-wrap pre.CodeMirror-line,
.CodeMirror-wrap pre.CodeMirror-line-like {
word-wrap: break-word;
white-space: pre-wrap;
word-break: normal;
}
.CodeMirror-linebackground {
position: absolute;
left: 0; right: 0; top: 0; bottom: 0;
z-index: 0;
}
.CodeMirror-linewidget {
position: relative;
z-index: 2;
padding: 0.1px; /* Force widget margins to stay inside of the container */
}
.CodeMirror-widget {}
.CodeMirror-rtl pre { direction: rtl; }
.CodeMirror-code {
outline: none;
}
/* Force content-box sizing for the elements where we expect it */
.CodeMirror-scroll,
.CodeMirror-sizer,
.CodeMirror-gutter,
.CodeMirror-gutters,
.CodeMirror-linenumber {
-moz-box-sizing: content-box;
box-sizing: content-box;
}
.CodeMirror-measure {
position: absolute;
width: 100%;
height: 0;
overflow: hidden;
visibility: hidden;
}
.CodeMirror-cursor {
position: absolute;
pointer-events: none;
}
.CodeMirror-measure pre { position: static; }
div.CodeMirror-cursors {
visibility: hidden;
position: relative;
z-index: 3;
}
div.CodeMirror-dragcursors {
visibility: visible;
}
.CodeMirror-focused div.CodeMirror-cursors {
visibility: visible;
}
.CodeMirror-selected { background: #d9d9d9; }
.CodeMirror-focused .CodeMirror-selected { background: #d7d4f0; }
.CodeMirror-crosshair { cursor: crosshair; }
.CodeMirror-line::selection, .CodeMirror-line > span::selection, .CodeMirror-line > span > span::selection { background: #d7d4f0; }
.CodeMirror-line::-moz-selection, .CodeMirror-line > span::-moz-selection, .CodeMirror-line > span > span::-moz-selection { background: #d7d4f0; }
.cm-searching {
background-color: #ffa;
background-color: rgba(255, 255, 0, .4);
}
/* Used to force a border model for a node */
.cm-force-border { padding-right: .1px; }
@media print {
/* Hide the cursor when printing */
.CodeMirror div.CodeMirror-cursors {
visibility: hidden;
}
}
/* See issue #2901 */
.cm-tab-wrap-hack:after { content: ''; }
/* Help users use markselection to safely style text background */
span.CodeMirror-selectedtext { background: none; }
.CodeMirror-dialog {
position: absolute;
left: 0; right: 0;
background: inherit;
z-index: 15;
padding: .1em .8em;
overflow: hidden;
color: inherit;
}
.CodeMirror-dialog-top {
border-bottom: 1px solid #eee;
top: 0;
}
.CodeMirror-dialog-bottom {
border-top: 1px solid #eee;
bottom: 0;
}
.CodeMirror-dialog input {
border: none;
outline: none;
background: transparent;
width: 20em;
color: inherit;
font-family: monospace;
}
.CodeMirror-dialog button {
font-size: 70%;
}
.CodeMirror-foldmarker {
color: blue;
text-shadow: #b9f 1px 1px 2px, #b9f -1px -1px 2px, #b9f 1px -1px 2px, #b9f -1px 1px 2px;
font-family: arial;
line-height: .3;
cursor: pointer;
}
.CodeMirror-foldgutter {
width: .7em;
}
.CodeMirror-foldgutter-open,
.CodeMirror-foldgutter-folded {
cursor: pointer;
}
.CodeMirror-foldgutter-open:after {
content: "\25BE";
}
.CodeMirror-foldgutter-folded:after {
content: "\25B8";
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.CodeMirror {
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
border: 0;
border-radius: 0;
height: auto;
/* Changed to auto to autogrow */
}
.CodeMirror pre {
padding: 0 var(--jp-code-padding);
}
.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-dialog {
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
}
/* This causes https://github.com/jupyter/jupyterlab/issues/522 */
/* May not cause it not because we changed it! */
.CodeMirror-lines {
padding: var(--jp-code-padding) 0;
}
.CodeMirror-linenumber {
padding: 0 8px;
}
.jp-CodeMirrorEditor {
cursor: text;
}
.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
border-left: var(--jp-code-cursor-width0) solid var(--jp-editor-cursor-color);
}
/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
@media screen and (min-width: 2138px) and (max-width: 4319px) {
.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
border-left: var(--jp-code-cursor-width1) solid
var(--jp-editor-cursor-color);
}
}
/* When zoomed out less than 33% */
@media screen and (min-width: 4320px) {
.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
border-left: var(--jp-code-cursor-width2) solid
var(--jp-editor-cursor-color);
}
}
.CodeMirror.jp-mod-readOnly .CodeMirror-cursor {
display: none;
}
.CodeMirror-gutters {
border-right: 1px solid var(--jp-border-color2);
background-color: var(--jp-layout-color0);
}
.jp-CollaboratorCursor {
border-left: 5px solid transparent;
border-right: 5px solid transparent;
border-top: none;
border-bottom: 3px solid;
background-clip: content-box;
margin-left: -5px;
margin-right: -5px;
}
.CodeMirror-selectedtext.cm-searching {
background-color: var(--jp-search-selected-match-background-color) !important;
color: var(--jp-search-selected-match-color) !important;
}
.cm-searching {
background-color: var(
--jp-search-unselected-match-background-color
) !important;
color: var(--jp-search-unselected-match-color) !important;
}
.CodeMirror-focused .CodeMirror-selected {
background-color: var(--jp-editor-selected-focused-background);
}
.CodeMirror-selected {
background-color: var(--jp-editor-selected-background);
}
.jp-CollaboratorCursor-hover {
position: absolute;
z-index: 1;
transform: translateX(-50%);
color: white;
border-radius: 3px;
padding-left: 4px;
padding-right: 4px;
padding-top: 1px;
padding-bottom: 1px;
text-align: center;
font-size: var(--jp-ui-font-size1);
white-space: nowrap;
}
.jp-CodeMirror-ruler {
border-left: 1px dashed var(--jp-border-color2);
}
/**
* Here is our jupyter theme for CodeMirror syntax highlighting
* This is used in our marked.js syntax highlighting and CodeMirror itself
* The string "jupyter" is set in ../codemirror/widget.DEFAULT_CODEMIRROR_THEME
* This came from the classic notebook, which came form highlight.js/GitHub
*/
/**
* CodeMirror themes are handling the background/color in this way. This works
* fine for CodeMirror editors outside the notebook, but the notebook styles
* these things differently.
*/
.CodeMirror.cm-s-jupyter {
background: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
}
/* In the notebook, we want this styling to be handled by its container */
.jp-CodeConsole .CodeMirror.cm-s-jupyter,
.jp-Notebook .CodeMirror.cm-s-jupyter {
background: transparent;
}
.cm-s-jupyter .CodeMirror-cursor {
border-left: var(--jp-code-cursor-width0) solid var(--jp-editor-cursor-color);
}
.cm-s-jupyter span.cm-keyword {
color: var(--jp-mirror-editor-keyword-color);
font-weight: bold;
}
.cm-s-jupyter span.cm-atom {
color: var(--jp-mirror-editor-atom-color);
}
.cm-s-jupyter span.cm-number {
color: var(--jp-mirror-editor-number-color);
}
.cm-s-jupyter span.cm-def {
color: var(--jp-mirror-editor-def-color);
}
.cm-s-jupyter span.cm-variable {
color: var(--jp-mirror-editor-variable-color);
}
.cm-s-jupyter span.cm-variable-2 {
color: var(--jp-mirror-editor-variable-2-color);
}
.cm-s-jupyter span.cm-variable-3 {
color: var(--jp-mirror-editor-variable-3-color);
}
.cm-s-jupyter span.cm-punctuation {
color: var(--jp-mirror-editor-punctuation-color);
}
.cm-s-jupyter span.cm-property {
color: var(--jp-mirror-editor-property-color);
}
.cm-s-jupyter span.cm-operator {
color: var(--jp-mirror-editor-operator-color);
font-weight: bold;
}
.cm-s-jupyter span.cm-comment {
color: var(--jp-mirror-editor-comment-color);
font-style: italic;
}
.cm-s-jupyter span.cm-string {
color: var(--jp-mirror-editor-string-color);
}
.cm-s-jupyter span.cm-string-2 {
color: var(--jp-mirror-editor-string-2-color);
}
.cm-s-jupyter span.cm-meta {
color: var(--jp-mirror-editor-meta-color);
}
.cm-s-jupyter span.cm-qualifier {
color: var(--jp-mirror-editor-qualifier-color);
}
.cm-s-jupyter span.cm-builtin {
color: var(--jp-mirror-editor-builtin-color);
}
.cm-s-jupyter span.cm-bracket {
color: var(--jp-mirror-editor-bracket-color);
}
.cm-s-jupyter span.cm-tag {
color: var(--jp-mirror-editor-tag-color);
}
.cm-s-jupyter span.cm-attribute {
color: var(--jp-mirror-editor-attribute-color);
}
.cm-s-jupyter span.cm-header {
color: var(--jp-mirror-editor-header-color);
}
.cm-s-jupyter span.cm-quote {
color: var(--jp-mirror-editor-quote-color);
}
.cm-s-jupyter span.cm-link {
color: var(--jp-mirror-editor-link-color);
}
.cm-s-jupyter span.cm-error {
color: var(--jp-mirror-editor-error-color);
}
.cm-s-jupyter span.cm-hr {
color: #999;
}
.cm-s-jupyter span.cm-tab {
background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
background-position: right;
background-repeat: no-repeat;
}
.cm-s-jupyter .CodeMirror-activeline-background,
.cm-s-jupyter .CodeMirror-gutter {
background-color: var(--jp-layout-color2);
}
/* Styles for shared cursors (remote cursor locations and selected ranges) */
.jp-CodeMirrorEditor .remote-caret {
position: relative;
border-left: 2px solid black;
margin-left: -1px;
margin-right: -1px;
box-sizing: border-box;
}
.jp-CodeMirrorEditor .remote-caret > div {
white-space: nowrap;
position: absolute;
top: -1.15em;
padding-bottom: 0.05em;
left: -2px;
font-size: 0.95em;
background-color: rgb(250, 129, 0);
font-family: var(--jp-ui-font-family);
font-weight: bold;
line-height: normal;
user-select: none;
color: white;
padding-left: 2px;
padding-right: 2px;
z-index: 3;
transition: opacity 0.3s ease-in-out;
}
.jp-CodeMirrorEditor .remote-caret.hide-name > div {
transition-delay: 0.7s;
opacity: 0;
}
.jp-CodeMirrorEditor .remote-caret:hover > div {
opacity: 1;
transition-delay: 0s;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| RenderedText
|----------------------------------------------------------------------------*/
:root {
/* This is the padding value to fill the gaps between lines containing spans with background color. */
--jp-private-code-span-padding: calc(
(var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
);
}
.jp-RenderedText {
text-align: left;
padding-left: var(--jp-code-padding);
line-height: var(--jp-code-line-height);
font-family: var(--jp-code-font-family);
}
.jp-RenderedText pre,
.jp-RenderedJavaScript pre,
.jp-RenderedHTMLCommon pre {
color: var(--jp-content-font-color1);
font-size: var(--jp-code-font-size);
border: none;
margin: 0px;
padding: 0px;
}
.jp-RenderedText pre a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* console foregrounds and backgrounds */
.jp-RenderedText pre .ansi-black-fg {
color: #3e424d;
}
.jp-RenderedText pre .ansi-red-fg {
color: #e75c58;
}
.jp-RenderedText pre .ansi-green-fg {
color: #00a250;
}
.jp-RenderedText pre .ansi-yellow-fg {
color: #ddb62b;
}
.jp-RenderedText pre .ansi-blue-fg {
color: #208ffb;
}
.jp-RenderedText pre .ansi-magenta-fg {
color: #d160c4;
}
.jp-RenderedText pre .ansi-cyan-fg {
color: #60c6c8;
}
.jp-RenderedText pre .ansi-white-fg {
color: #c5c1b4;
}
.jp-RenderedText pre .ansi-black-bg {
background-color: #3e424d;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-bg {
background-color: #e75c58;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-bg {
background-color: #00a250;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-bg {
background-color: #ddb62b;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-bg {
background-color: #208ffb;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-bg {
background-color: #d160c4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-bg {
background-color: #60c6c8;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-bg {
background-color: #c5c1b4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-black-intense-fg {
color: #282c36;
}
.jp-RenderedText pre .ansi-red-intense-fg {
color: #b22b31;
}
.jp-RenderedText pre .ansi-green-intense-fg {
color: #007427;
}
.jp-RenderedText pre .ansi-yellow-intense-fg {
color: #b27d12;
}
.jp-RenderedText pre .ansi-blue-intense-fg {
color: #0065ca;
}
.jp-RenderedText pre .ansi-magenta-intense-fg {
color: #a03196;
}
.jp-RenderedText pre .ansi-cyan-intense-fg {
color: #258f8f;
}
.jp-RenderedText pre .ansi-white-intense-fg {
color: #a1a6b2;
}
.jp-RenderedText pre .ansi-black-intense-bg {
background-color: #282c36;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-intense-bg {
background-color: #b22b31;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-intense-bg {
background-color: #007427;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-intense-bg {
background-color: #b27d12;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-intense-bg {
background-color: #0065ca;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-intense-bg {
background-color: #a03196;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-intense-bg {
background-color: #258f8f;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-intense-bg {
background-color: #a1a6b2;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-default-inverse-fg {
color: var(--jp-ui-inverse-font-color0);
}
.jp-RenderedText pre .ansi-default-inverse-bg {
background-color: var(--jp-inverse-layout-color0);
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-bold {
font-weight: bold;
}
.jp-RenderedText pre .ansi-underline {
text-decoration: underline;
}
.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
background: var(--jp-rendermime-error-background);
padding-top: var(--jp-code-padding);
}
/*-----------------------------------------------------------------------------
| RenderedLatex
|----------------------------------------------------------------------------*/
.jp-RenderedLatex {
color: var(--jp-content-font-color1);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
}
/* Left-justify outputs.*/
.jp-OutputArea-output.jp-RenderedLatex {
padding: var(--jp-code-padding);
text-align: left;
}
/*-----------------------------------------------------------------------------
| RenderedHTML
|----------------------------------------------------------------------------*/
.jp-RenderedHTMLCommon {
color: var(--jp-content-font-color1);
font-family: var(--jp-content-font-family);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
/* Give a bit more R padding on Markdown text to keep line lengths reasonable */
padding-right: 20px;
}
.jp-RenderedHTMLCommon em {
font-style: italic;
}
.jp-RenderedHTMLCommon strong {
font-weight: bold;
}
.jp-RenderedHTMLCommon u {
text-decoration: underline;
}
.jp-RenderedHTMLCommon a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* Headings */
.jp-RenderedHTMLCommon h1,
.jp-RenderedHTMLCommon h2,
.jp-RenderedHTMLCommon h3,
.jp-RenderedHTMLCommon h4,
.jp-RenderedHTMLCommon h5,
.jp-RenderedHTMLCommon h6 {
line-height: var(--jp-content-heading-line-height);
font-weight: var(--jp-content-heading-font-weight);
font-style: normal;
margin: var(--jp-content-heading-margin-top) 0
var(--jp-content-heading-margin-bottom) 0;
}
.jp-RenderedHTMLCommon h1:first-child,
.jp-RenderedHTMLCommon h2:first-child,
.jp-RenderedHTMLCommon h3:first-child,
.jp-RenderedHTMLCommon h4:first-child,
.jp-RenderedHTMLCommon h5:first-child,
.jp-RenderedHTMLCommon h6:first-child {
margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
}
.jp-RenderedHTMLCommon h1:last-child,
.jp-RenderedHTMLCommon h2:last-child,
.jp-RenderedHTMLCommon h3:last-child,
.jp-RenderedHTMLCommon h4:last-child,
.jp-RenderedHTMLCommon h5:last-child,
.jp-RenderedHTMLCommon h6:last-child {
margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
}
.jp-RenderedHTMLCommon h1 {
font-size: var(--jp-content-font-size5);
}
.jp-RenderedHTMLCommon h2 {
font-size: var(--jp-content-font-size4);
}
.jp-RenderedHTMLCommon h3 {
font-size: var(--jp-content-font-size3);
}
.jp-RenderedHTMLCommon h4 {
font-size: var(--jp-content-font-size2);
}
.jp-RenderedHTMLCommon h5 {
font-size: var(--jp-content-font-size1);
}
.jp-RenderedHTMLCommon h6 {
font-size: var(--jp-content-font-size0);
}
/* Lists */
.jp-RenderedHTMLCommon ul:not(.list-inline),
.jp-RenderedHTMLCommon ol:not(.list-inline) {
padding-left: 2em;
}
.jp-RenderedHTMLCommon ul {
list-style: disc;
}
.jp-RenderedHTMLCommon ul ul {
list-style: square;
}
.jp-RenderedHTMLCommon ul ul ul {
list-style: circle;
}
.jp-RenderedHTMLCommon ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol ol {
list-style: upper-alpha;
}
.jp-RenderedHTMLCommon ol ol ol {
list-style: lower-alpha;
}
.jp-RenderedHTMLCommon ol ol ol ol {
list-style: lower-roman;
}
.jp-RenderedHTMLCommon ol ol ol ol ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol,
.jp-RenderedHTMLCommon ul {
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon ul ul,
.jp-RenderedHTMLCommon ul ol,
.jp-RenderedHTMLCommon ol ul,
.jp-RenderedHTMLCommon ol ol {
margin-bottom: 0em;
}
.jp-RenderedHTMLCommon hr {
color: var(--jp-border-color2);
background-color: var(--jp-border-color1);
margin-top: 1em;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon > pre {
margin: 1.5em 2em;
}
.jp-RenderedHTMLCommon pre,
.jp-RenderedHTMLCommon code {
border: 0;
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
font-family: var(--jp-code-font-family);
font-size: inherit;
line-height: var(--jp-code-line-height);
padding: 0;
white-space: pre-wrap;
}
.jp-RenderedHTMLCommon :not(pre) > code {
background-color: var(--jp-layout-color2);
padding: 1px 5px;
}
/* Tables */
.jp-RenderedHTMLCommon table {
border-collapse: collapse;
border-spacing: 0;
border: none;
color: var(--jp-ui-font-color1);
font-size: 12px;
table-layout: fixed;
margin-left: auto;
margin-right: auto;
}
.jp-RenderedHTMLCommon thead {
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
vertical-align: bottom;
}
.jp-RenderedHTMLCommon td,
.jp-RenderedHTMLCommon th,
.jp-RenderedHTMLCommon tr {
vertical-align: middle;
padding: 0.5em 0.5em;
line-height: normal;
white-space: normal;
max-width: none;
border: none;
}
.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
max-width: none;
}
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
text-align: right;
}
.jp-RenderedHTMLCommon th {
font-weight: bold;
}
.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
background: var(--jp-layout-color0);
}
.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
background: var(--jp-rendermime-table-row-background);
}
.jp-RenderedHTMLCommon tbody tr:hover {
background: var(--jp-rendermime-table-row-hover-background);
}
.jp-RenderedHTMLCommon table {
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon p {
text-align: left;
margin: 0px;
}
.jp-RenderedHTMLCommon p {
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon img {
-moz-force-broken-image-icon: 1;
}
/* Restrict to direct children as other images could be nested in other content. */
.jp-RenderedHTMLCommon > img {
display: block;
margin-left: 0;
margin-right: 0;
margin-bottom: 1em;
}
/* Change color behind transparent images if they need it... */
[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
background-color: var(--jp-inverse-layout-color1);
}
[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
background-color: var(--jp-inverse-layout-color1);
}
/* ...or leave it untouched if they don't */
[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-dark-background {
}
[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-light-background {
}
.jp-RenderedHTMLCommon img,
.jp-RenderedImage img,
.jp-RenderedHTMLCommon svg,
.jp-RenderedSVG svg {
max-width: 100%;
height: auto;
}
.jp-RenderedHTMLCommon img.jp-mod-unconfined,
.jp-RenderedImage img.jp-mod-unconfined,
.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
.jp-RenderedSVG svg.jp-mod-unconfined {
max-width: none;
}
.jp-RenderedHTMLCommon .alert {
padding: var(--jp-notebook-padding);
border: var(--jp-border-width) solid transparent;
border-radius: var(--jp-border-radius);
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon .alert-info {
color: var(--jp-info-color0);
background-color: var(--jp-info-color3);
border-color: var(--jp-info-color2);
}
.jp-RenderedHTMLCommon .alert-info hr {
border-color: var(--jp-info-color3);
}
.jp-RenderedHTMLCommon .alert-info > p:last-child,
.jp-RenderedHTMLCommon .alert-info > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-warning {
color: var(--jp-warn-color0);
background-color: var(--jp-warn-color3);
border-color: var(--jp-warn-color2);
}
.jp-RenderedHTMLCommon .alert-warning hr {
border-color: var(--jp-warn-color3);
}
.jp-RenderedHTMLCommon .alert-warning > p:last-child,
.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-success {
color: var(--jp-success-color0);
background-color: var(--jp-success-color3);
border-color: var(--jp-success-color2);
}
.jp-RenderedHTMLCommon .alert-success hr {
border-color: var(--jp-success-color3);
}
.jp-RenderedHTMLCommon .alert-success > p:last-child,
.jp-RenderedHTMLCommon .alert-success > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-danger {
color: var(--jp-error-color0);
background-color: var(--jp-error-color3);
border-color: var(--jp-error-color2);
}
.jp-RenderedHTMLCommon .alert-danger hr {
border-color: var(--jp-error-color3);
}
.jp-RenderedHTMLCommon .alert-danger > p:last-child,
.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon blockquote {
margin: 1em 2em;
padding: 0 1em;
border-left: 5px solid var(--jp-border-color2);
}
a.jp-InternalAnchorLink {
visibility: hidden;
margin-left: 8px;
color: var(--md-blue-800);
}
h1:hover .jp-InternalAnchorLink,
h2:hover .jp-InternalAnchorLink,
h3:hover .jp-InternalAnchorLink,
h4:hover .jp-InternalAnchorLink,
h5:hover .jp-InternalAnchorLink,
h6:hover .jp-InternalAnchorLink {
visibility: visible;
}
.jp-RenderedHTMLCommon kbd {
background-color: var(--jp-rendermime-table-row-background);
border: 1px solid var(--jp-border-color0);
border-bottom-color: var(--jp-border-color2);
border-radius: 3px;
box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
display: inline-block;
font-size: 0.8em;
line-height: 1em;
padding: 0.2em 0.5em;
}
/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
* At the bottom of cells this is a bit too much as there is also spacing
* between cells. Going all the way to 0 gets too tight between markdown and
* code cells.
*/
.jp-RenderedHTMLCommon > *:last-child {
margin-bottom: 0.5em;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MimeDocument {
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-filebrowser-button-height: 28px;
--jp-private-filebrowser-button-width: 48px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FileBrowser {
display: flex;
flex-direction: column;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
}
.jp-FileBrowser-toolbar.jp-Toolbar {
border-bottom: none;
height: auto;
margin: var(--jp-toolbar-header-margin);
box-shadow: none;
}
.jp-BreadCrumbs {
flex: 0 0 auto;
margin: 8px 12px 8px 12px;
}
.jp-BreadCrumbs-item {
margin: 0px 2px;
padding: 0px 2px;
border-radius: var(--jp-border-radius);
cursor: pointer;
}
.jp-BreadCrumbs-item:hover {
background-color: var(--jp-layout-color2);
}
.jp-BreadCrumbs-item:first-child {
margin-left: 0px;
}
.jp-BreadCrumbs-item.jp-mod-dropTarget {
background-color: var(--jp-brand-color2);
opacity: 0.7;
}
/*-----------------------------------------------------------------------------
| Buttons
|----------------------------------------------------------------------------*/
.jp-FileBrowser-toolbar.jp-Toolbar {
padding: 0px;
margin: 8px 12px 0px 12px;
}
.jp-FileBrowser-toolbar.jp-Toolbar {
justify-content: flex-start;
}
.jp-FileBrowser-toolbar.jp-Toolbar .jp-Toolbar-item {
flex: 0 0 auto;
padding-left: 0px;
padding-right: 2px;
}
.jp-FileBrowser-toolbar.jp-Toolbar .jp-ToolbarButtonComponent {
width: 40px;
}
.jp-FileBrowser-toolbar.jp-Toolbar
.jp-Toolbar-item:first-child
.jp-ToolbarButtonComponent {
width: 72px;
background: var(--jp-brand-color1);
}
.jp-FileBrowser-toolbar.jp-Toolbar
.jp-Toolbar-item:first-child
.jp-ToolbarButtonComponent:focus-visible {
background-color: var(--jp-brand-color0);
}
.jp-FileBrowser-toolbar.jp-Toolbar
.jp-Toolbar-item:first-child
.jp-ToolbarButtonComponent
.jp-icon3 {
fill: white;
}
/*-----------------------------------------------------------------------------
| Other styles
|----------------------------------------------------------------------------*/
.jp-FileDialog.jp-mod-conflict input {
color: var(--jp-error-color1);
}
.jp-FileDialog .jp-new-name-title {
margin-top: 12px;
}
.jp-LastModified-hidden {
display: none;
}
.jp-FileBrowser-filterBox {
padding: 0px;
flex: 0 0 auto;
margin: 8px 12px 0px 12px;
}
/*-----------------------------------------------------------------------------
| DirListing
|----------------------------------------------------------------------------*/
.jp-DirListing {
flex: 1 1 auto;
display: flex;
flex-direction: column;
outline: 0;
}
.jp-DirListing:focus-visible {
border: 1px solid var(--jp-brand-color1);
}
.jp-DirListing-header {
flex: 0 0 auto;
display: flex;
flex-direction: row;
overflow: hidden;
border-top: var(--jp-border-width) solid var(--jp-border-color2);
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
}
.jp-DirListing-headerItem {
padding: 4px 12px 2px 12px;
font-weight: 500;
}
.jp-DirListing-headerItem:hover {
background: var(--jp-layout-color2);
}
.jp-DirListing-headerItem.jp-id-name {
flex: 1 0 84px;
}
.jp-DirListing-headerItem.jp-id-modified {
flex: 0 0 112px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-id-narrow {
display: none;
flex: 0 0 5px;
padding: 4px 4px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
color: var(--jp-border-color2);
}
.jp-DirListing-narrow .jp-id-narrow {
display: block;
}
.jp-DirListing-narrow .jp-id-modified,
.jp-DirListing-narrow .jp-DirListing-itemModified {
display: none;
}
.jp-DirListing-headerItem.jp-mod-selected {
font-weight: 600;
}
/* increase specificity to override bundled default */
.jp-DirListing-content {
flex: 1 1 auto;
margin: 0;
padding: 0;
list-style-type: none;
overflow: auto;
background-color: var(--jp-layout-color1);
}
.jp-DirListing-content mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
color: var(--jp-ui-inverse-font-color0);
}
/* Style the directory listing content when a user drops a file to upload */
.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
outline: 5px dashed rgba(128, 128, 128, 0.5);
outline-offset: -10px;
cursor: copy;
}
.jp-DirListing-item {
display: flex;
flex-direction: row;
padding: 4px 12px;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-DirListing-item[data-is-dot] {
opacity: 75%;
}
.jp-DirListing-item.jp-mod-selected {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.jp-DirListing-item.jp-mod-dropTarget {
background: var(--jp-brand-color3);
}
.jp-DirListing-item:hover:not(.jp-mod-selected) {
background: var(--jp-layout-color2);
}
.jp-DirListing-itemIcon {
flex: 0 0 20px;
margin-right: 4px;
}
.jp-DirListing-itemText {
flex: 1 0 64px;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
user-select: none;
}
.jp-DirListing-itemModified {
flex: 0 0 125px;
text-align: right;
}
.jp-DirListing-editor {
flex: 1 0 64px;
outline: none;
border: none;
}
.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon:before {
color: var(--jp-success-color1);
content: '\25CF';
font-size: 8px;
position: absolute;
left: -8px;
}
.jp-DirListing-item.jp-mod-running.jp-mod-selected
.jp-DirListing-itemIcon:before {
color: var(--jp-ui-inverse-font-color1);
}
.jp-DirListing-item.lm-mod-drag-image,
.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
font-size: var(--jp-ui-font-size1);
padding-left: 4px;
margin-left: 4px;
width: 160px;
background-color: var(--jp-ui-inverse-font-color2);
box-shadow: var(--jp-elevation-z2);
border-radius: 0px;
color: var(--jp-ui-font-color1);
transform: translateX(-40%) translateY(-58%);
}
.jp-DirListing-deadSpace {
flex: 1 1 auto;
margin: 0;
padding: 0;
list-style-type: none;
overflow: auto;
background-color: var(--jp-layout-color1);
}
.jp-Document {
min-width: 120px;
min-height: 120px;
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/
:root {
}
/*-----------------------------------------------------------------------------
| Main OutputArea
| OutputArea has a list of Outputs
|----------------------------------------------------------------------------*/
.jp-OutputArea {
overflow-y: auto;
}
.jp-OutputArea-child {
display: flex;
flex-direction: row;
}
body[data-format='mobile'] .jp-OutputArea-child {
flex-direction: column;
}
.jp-OutputPrompt {
flex: 0 0 var(--jp-cell-prompt-width);
color: var(--jp-cell-outprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
opacity: var(--jp-cell-prompt-opacity);
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
body[data-format='mobile'] .jp-OutputPrompt {
flex: 0 0 auto;
text-align: left;
}
.jp-OutputArea-output {
height: auto;
overflow: auto;
user-select: text;
-moz-user-select: text;
-webkit-user-select: text;
-ms-user-select: text;
}
.jp-OutputArea-child .jp-OutputArea-output {
flex-grow: 1;
flex-shrink: 1;
}
body[data-format='mobile'] .jp-OutputArea-child .jp-OutputArea-output {
margin-left: var(--jp-notebook-padding);
}
/**
* Isolated output.
*/
.jp-OutputArea-output.jp-mod-isolated {
width: 100%;
display: block;
}
/*
When drag events occur, `p-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
position: relative;
}
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/* pre */
.jp-OutputArea-output pre {
border: none;
margin: 0px;
padding: 0px;
overflow-x: auto;
overflow-y: auto;
word-break: break-all;
word-wrap: break-word;
white-space: pre-wrap;
}
/* tables */
.jp-OutputArea-output.jp-RenderedHTMLCommon table {
margin-left: 0;
margin-right: 0;
}
/* description lists */
.jp-OutputArea-output dl,
.jp-OutputArea-output dt,
.jp-OutputArea-output dd {
display: block;
}
.jp-OutputArea-output dl {
width: 100%;
overflow: hidden;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dt {
font-weight: bold;
float: left;
width: 20%;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dd {
float: left;
width: 80%;
padding: 0;
margin: 0;
}
/* Hide the gutter in case of
* - nested output areas (e.g. in the case of output widgets)
* - mirrored output areas
*/
.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
display: none;
}
/*-----------------------------------------------------------------------------
| executeResult is added to any Output-result for the display of the object
| returned by a cell
|----------------------------------------------------------------------------*/
.jp-OutputArea-output.jp-OutputArea-executeResult {
margin-left: 0px;
flex: 1 1 auto;
}
/* Text output with the Out[] prompt needs a top padding to match the
* alignment of the Out[] prompt itself.
*/
.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
padding-top: var(--jp-code-padding);
border-top: var(--jp-border-width) solid transparent;
}
/*-----------------------------------------------------------------------------
| The Stdin output
|----------------------------------------------------------------------------*/
.jp-OutputArea-stdin {
line-height: var(--jp-code-line-height);
padding-top: var(--jp-code-padding);
display: flex;
}
.jp-Stdin-prompt {
color: var(--jp-content-font-color0);
padding-right: var(--jp-code-padding);
vertical-align: baseline;
flex: 0 0 auto;
}
.jp-Stdin-input {
font-family: var(--jp-code-font-family);
font-size: inherit;
color: inherit;
background-color: inherit;
width: 42%;
min-width: 200px;
/* make sure input baseline aligns with prompt */
vertical-align: baseline;
/* padding + margin = 0.5em between prompt and cursor */
padding: 0em 0.25em;
margin: 0em 0.25em;
flex: 0 0 70%;
}
.jp-Stdin-input:focus {
box-shadow: none;
}
/*-----------------------------------------------------------------------------
| Output Area View
|----------------------------------------------------------------------------*/
.jp-LinkedOutputView .jp-OutputArea {
height: 100%;
display: block;
}
.jp-LinkedOutputView .jp-OutputArea-output:only-child {
height: 100%;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Collapser {
flex: 0 0 var(--jp-cell-collapser-width);
padding: 0px;
margin: 0px;
border: none;
outline: none;
background: transparent;
border-radius: var(--jp-border-radius);
opacity: 1;
}
.jp-Collapser-child {
display: block;
width: 100%;
box-sizing: border-box;
/* height: 100% doesn't work because the height of its parent is computed from content */
position: absolute;
top: 0px;
bottom: 0px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Header/Footer
|----------------------------------------------------------------------------*/
/* Hidden by zero height by default */
.jp-CellHeader,
.jp-CellFooter {
height: 0px;
width: 100%;
padding: 0px;
margin: 0px;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Input
|----------------------------------------------------------------------------*/
/* All input areas */
.jp-InputArea {
display: flex;
flex-direction: row;
overflow: hidden;
}
body[data-format='mobile'] .jp-InputArea {
flex-direction: column;
}
.jp-InputArea-editor {
flex: 1 1 auto;
overflow: hidden;
}
.jp-InputArea-editor {
/* This is the non-active, default styling */
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0px;
background: var(--jp-cell-editor-background);
}
body[data-format='mobile'] .jp-InputArea-editor {
margin-left: var(--jp-notebook-padding);
}
.jp-InputPrompt {
flex: 0 0 var(--jp-cell-prompt-width);
color: var(--jp-cell-inprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
opacity: var(--jp-cell-prompt-opacity);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
opacity: var(--jp-cell-prompt-opacity);
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
body[data-format='mobile'] .jp-InputPrompt {
flex: 0 0 auto;
text-align: left;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Placeholder {
display: flex;
flex-direction: row;
flex: 1 1 auto;
}
.jp-Placeholder-prompt {
box-sizing: border-box;
}
.jp-Placeholder-content {
flex: 1 1 auto;
border: none;
background: transparent;
height: 20px;
box-sizing: border-box;
}
.jp-Placeholder-content .jp-MoreHorizIcon {
width: 32px;
height: 16px;
border: 1px solid transparent;
border-radius: var(--jp-border-radius);
}
.jp-Placeholder-content .jp-MoreHorizIcon:hover {
border: 1px solid var(--jp-border-color1);
box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.25);
background-color: var(--jp-layout-color0);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-cell-scrolling-output-offset: 5px;
}
/*-----------------------------------------------------------------------------
| Cell
|----------------------------------------------------------------------------*/
.jp-Cell {
padding: var(--jp-cell-padding);
margin: 0px;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Common input/output
|----------------------------------------------------------------------------*/
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: flex;
flex-direction: row;
padding: 0px;
margin: 0px;
/* Added to reveal the box-shadow on the input and output collapsers. */
overflow: visible;
}
/* Only input/output areas inside cells */
.jp-Cell-inputArea,
.jp-Cell-outputArea {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Collapser
|----------------------------------------------------------------------------*/
/* Make the output collapser disappear when there is not output, but do so
* in a manner that leaves it in the layout and preserves its width.
*/
.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
border: none !important;
background: transparent !important;
}
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
min-height: var(--jp-cell-collapser-min-height);
}
/*-----------------------------------------------------------------------------
| Output
|----------------------------------------------------------------------------*/
/* Put a space between input and output when there IS output */
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
margin-top: 5px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
overflow-y: auto;
max-height: 200px;
box-shadow: inset 0 0 6px 2px rgba(0, 0, 0, 0.3);
margin-left: var(--jp-private-cell-scrolling-output-offset);
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
flex: 0 0
calc(
var(--jp-cell-prompt-width) -
var(--jp-private-cell-scrolling-output-offset)
);
}
/*-----------------------------------------------------------------------------
| CodeCell
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| MarkdownCell
|----------------------------------------------------------------------------*/
.jp-MarkdownOutput {
flex: 1 1 auto;
margin-top: 0;
margin-bottom: 0;
padding-left: var(--jp-code-padding);
}
.jp-MarkdownOutput.jp-RenderedHTMLCommon {
overflow: auto;
}
.jp-showHiddenCellsButton {
margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
margin-top: var(--jp-code-padding);
border: 1px solid var(--jp-border-color2);
background-color: var(--jp-border-color3) !important;
color: var(--jp-content-font-color0) !important;
}
.jp-showHiddenCellsButton:hover {
background-color: var(--jp-border-color2) !important;
}
.jp-collapseHeadingButton {
display: none;
}
.jp-MarkdownCell:hover .jp-collapseHeadingButton {
display: flex;
min-height: var(--jp-cell-collapser-min-height);
position: absolute;
right: 0;
top: 0;
bottom: 0;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-NotebookPanel-toolbar {
padding: 2px;
}
.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
border: none;
box-shadow: none;
}
.jp-Notebook-toolbarCellTypeDropdown select {
height: 24px;
font-size: var(--jp-ui-font-size1);
line-height: 14px;
border-radius: 0;
display: block;
}
.jp-Notebook-toolbarCellTypeDropdown span {
top: 5px !important;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-notebook-dragImage-width: 304px;
--jp-private-notebook-dragImage-height: 36px;
--jp-private-notebook-selected-color: var(--md-blue-400);
--jp-private-notebook-active-color: var(--md-green-400);
}
/*-----------------------------------------------------------------------------
| Imports
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Notebook
|----------------------------------------------------------------------------*/
.jp-NotebookPanel {
display: block;
height: 100%;
}
.jp-NotebookPanel.jp-Document {
min-width: 240px;
min-height: 120px;
}
.jp-Notebook {
padding: var(--jp-notebook-padding);
outline: none;
overflow: auto;
background: var(--jp-layout-color0);
}
.jp-Notebook.jp-mod-scrollPastEnd::after {
display: block;
content: '';
min-height: var(--jp-notebook-scroll-padding);
}
.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
contain: strict;
}
.jp-Notebook-render * {
contain: none !important;
}
.jp-Notebook .jp-Cell {
overflow: visible;
}
.jp-Notebook .jp-Cell .jp-InputPrompt {
cursor: move;
float: left;
}
/*-----------------------------------------------------------------------------
| Notebook state related styling
|
| The notebook and cells each have states, here are the possibilities:
|
| - Notebook
| - Command
| - Edit
| - Cell
| - None
| - Active (only one can be active)
| - Selected (the cells actions are applied to)
| - Multiselected (when multiple selected, the cursor)
| - No outputs
|----------------------------------------------------------------------------*/
/* Command or edit modes */
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
/* cell is active */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
background: var(--jp-brand-color1);
}
/* cell is dirty */
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
color: var(--jp-warn-color1);
}
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt:before {
color: var(--jp-warn-color1);
content: '•';
}
.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
background: var(--jp-warn-color1);
}
/* collapser is hovered */
.jp-Notebook .jp-Cell .jp-Collapser:hover {
box-shadow: var(--jp-elevation-z2);
background: var(--jp-brand-color1);
opacity: var(--jp-cell-collapser-not-active-hover-opacity);
}
/* cell is active and collapser is hovered */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
background: var(--jp-brand-color0);
opacity: 1;
}
/* Command mode */
.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
background: var(--jp-notebook-multiselected-color);
}
.jp-Notebook.jp-mod-commandMode
.jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
background: transparent;
}
/* Edit mode */
.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-cell-editor-active-background);
}
/*-----------------------------------------------------------------------------
| Notebook drag and drop
|----------------------------------------------------------------------------*/
.jp-Notebook-cell.jp-mod-dropSource {
opacity: 0.5;
}
.jp-Notebook-cell.jp-mod-dropTarget,
.jp-Notebook.jp-mod-commandMode
.jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
border-top-color: var(--jp-private-notebook-selected-color);
border-top-style: solid;
border-top-width: 2px;
}
.jp-dragImage {
display: block;
flex-direction: row;
width: var(--jp-private-notebook-dragImage-width);
height: var(--jp-private-notebook-dragImage-height);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
overflow: visible;
}
.jp-dragImage-singlePrompt {
box-shadow: 2px 2px 4px 0px rgba(0, 0, 0, 0.12);
}
.jp-dragImage .jp-dragImage-content {
flex: 1 1 auto;
z-index: 2;
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
line-height: var(--jp-code-line-height);
padding: var(--jp-code-padding);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background-color);
color: var(--jp-content-font-color3);
text-align: left;
margin: 4px 4px 4px 0px;
}
.jp-dragImage .jp-dragImage-prompt {
flex: 0 0 auto;
min-width: 36px;
color: var(--jp-cell-inprompt-font-color);
padding: var(--jp-code-padding);
padding-left: 12px;
font-family: var(--jp-cell-prompt-font-family);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: 1.9;
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
}
.jp-dragImage-multipleBack {
z-index: -1;
position: absolute;
height: 32px;
width: 300px;
top: 8px;
left: 8px;
background: var(--jp-layout-color2);
border: var(--jp-border-width) solid var(--jp-input-border-color);
box-shadow: 2px 2px 4px 0px rgba(0, 0, 0, 0.12);
}
/*-----------------------------------------------------------------------------
| Cell toolbar
|----------------------------------------------------------------------------*/
.jp-NotebookTools {
display: block;
min-width: var(--jp-sidebar-min-width);
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
overflow: auto;
}
.jp-NotebookTools-tool {
padding: 0px 12px 0 12px;
}
.jp-ActiveCellTool {
padding: 12px;
background-color: var(--jp-layout-color1);
border-top: none !important;
}
.jp-ActiveCellTool .jp-InputArea-prompt {
flex: 0 0 auto;
padding-left: 0px;
}
.jp-ActiveCellTool .jp-InputArea-editor {
flex: 1 1 auto;
background: var(--jp-cell-editor-background);
border-color: var(--jp-cell-editor-border-color);
}
.jp-ActiveCellTool .jp-InputArea-editor .CodeMirror {
background: transparent;
}
.jp-MetadataEditorTool {
flex-direction: column;
padding: 12px 0px 12px 0px;
}
.jp-RankedPanel > :not(:first-child) {
margin-top: 12px;
}
.jp-KeySelector select.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: var(--jp-border-width) solid var(--jp-border-color1);
}
.jp-KeySelector label,
.jp-MetadataEditorTool label {
line-height: 1.4;
}
.jp-NotebookTools .jp-select-wrapper {
margin-top: 4px;
margin-bottom: 0px;
}
.jp-NotebookTools .jp-Collapse {
margin-top: 16px;
}
/*-----------------------------------------------------------------------------
| Presentation Mode (.jp-mod-presentationMode)
|----------------------------------------------------------------------------*/
.jp-mod-presentationMode .jp-Notebook {
--jp-content-font-size1: var(--jp-content-presentation-font-size1);
--jp-code-font-size: var(--jp-code-presentation-font-size);
}
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
flex: 0 0 110px;
}
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Cell-Placeholder {
padding-left: 55px;
}
.jp-Cell-Placeholder-wrapper {
background: #fff;
border: 1px solid;
border-color: #e5e6e9 #dfe0e4 #d0d1d5;
border-radius: 4px;
-webkit-border-radius: 4px;
margin: 10px 15px;
}
.jp-Cell-Placeholder-wrapper-inner {
padding: 15px;
position: relative;
}
.jp-Cell-Placeholder-wrapper-body {
background-repeat: repeat;
background-size: 50% auto;
}
.jp-Cell-Placeholder-wrapper-body div {
background: #f6f7f8;
background-image: -webkit-linear-gradient(
left,
#f6f7f8 0%,
#edeef1 20%,
#f6f7f8 40%,
#f6f7f8 100%
);
background-repeat: no-repeat;
background-size: 800px 104px;
height: 104px;
position: relative;
}
.jp-Cell-Placeholder-wrapper-body div {
position: absolute;
right: 15px;
left: 15px;
top: 15px;
}
div.jp-Cell-Placeholder-h1 {
top: 20px;
height: 20px;
left: 15px;
width: 150px;
}
div.jp-Cell-Placeholder-h2 {
left: 15px;
top: 50px;
height: 10px;
width: 100px;
}
div.jp-Cell-Placeholder-content-1,
div.jp-Cell-Placeholder-content-2,
div.jp-Cell-Placeholder-content-3 {
left: 15px;
right: 15px;
height: 10px;
}
div.jp-Cell-Placeholder-content-1 {
top: 100px;
}
div.jp-Cell-Placeholder-content-2 {
top: 120px;
}
div.jp-Cell-Placeholder-content-3 {
top: 140px;
}
</style>
<style type="text/css">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.
Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:
* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations
Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/
:root {
/* Elevation
*
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
*
* https://github.com/material-components/material-components-web
* https://material-components-web.appspot.com/elevation.html
*/
--jp-shadow-base-lightness: 0;
--jp-shadow-umbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.2
);
--jp-shadow-penumbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.14
);
--jp-shadow-ambient-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.12
);
--jp-elevation-z0: none;
--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
0px 1px 1px 0px var(--jp-shadow-penumbra-color),
0px 1px 3px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
0px 2px 2px 0px var(--jp-shadow-penumbra-color),
0px 1px 5px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
0px 4px 5px 0px var(--jp-shadow-penumbra-color),
0px 1px 10px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
0px 6px 10px 0px var(--jp-shadow-penumbra-color),
0px 1px 18px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
0px 8px 10px 1px var(--jp-shadow-penumbra-color),
0px 3px 14px 2px var(--jp-shadow-ambient-color);
--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
0px 12px 17px 2px var(--jp-shadow-penumbra-color),
0px 5px 22px 4px var(--jp-shadow-ambient-color);
--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
0px 16px 24px 2px var(--jp-shadow-penumbra-color),
0px 6px 30px 5px var(--jp-shadow-ambient-color);
--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
0px 20px 31px 3px var(--jp-shadow-penumbra-color),
0px 8px 38px 7px var(--jp-shadow-ambient-color);
--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
0px 24px 38px 3px var(--jp-shadow-penumbra-color),
0px 9px 46px 8px var(--jp-shadow-ambient-color);
/* Borders
*
* The following variables, specify the visual styling of borders in JupyterLab.
*/
--jp-border-width: 1px;
--jp-border-color0: var(--md-grey-400);
--jp-border-color1: var(--md-grey-400);
--jp-border-color2: var(--md-grey-300);
--jp-border-color3: var(--md-grey-200);
--jp-border-radius: 2px;
/* UI Fonts
*
* The UI font CSS variables are used for the typography all of the JupyterLab
* user interface elements that are not directly user generated content.
*
* The font sizing here is done assuming that the body font size of --jp-ui-font-size1
* is applied to a parent element. When children elements, such as headings, are sized
* in em all things will be computed relative to that body size.
*/
--jp-ui-font-scale-factor: 1.2;
--jp-ui-font-size0: 0.83333em;
--jp-ui-font-size1: 13px; /* Base font size */
--jp-ui-font-size2: 1.2em;
--jp-ui-font-size3: 1.44em;
--jp-ui-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica,
Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
/*
* Use these font colors against the corresponding main layout colors.
* In a light theme, these go from dark to light.
*/
/* Defaults use Material Design specification */
--jp-ui-font-color0: rgba(0, 0, 0, 1);
--jp-ui-font-color1: rgba(0, 0, 0, 0.87);
--jp-ui-font-color2: rgba(0, 0, 0, 0.54);
--jp-ui-font-color3: rgba(0, 0, 0, 0.38);
/*
* Use these against the brand/accent/warn/error colors.
* These will typically go from light to darker, in both a dark and light theme.
*/
--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
--jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
--jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
/* Content Fonts
*
* Content font variables are used for typography of user generated content.
*
* The font sizing here is done assuming that the body font size of --jp-content-font-size1
* is applied to a parent element. When children elements, such as headings, are sized
* in em all things will be computed relative to that body size.
*/
--jp-content-line-height: 1.6;
--jp-content-font-scale-factor: 1.2;
--jp-content-font-size0: 0.83333em;
--jp-content-font-size1: 14px; /* Base font size */
--jp-content-font-size2: 1.2em;
--jp-content-font-size3: 1.44em;
--jp-content-font-size4: 1.728em;
--jp-content-font-size5: 2.0736em;
/* This gives a magnification of about 125% in presentation mode over normal. */
--jp-content-presentation-font-size1: 17px;
--jp-content-heading-line-height: 1;
--jp-content-heading-margin-top: 1.2em;
--jp-content-heading-margin-bottom: 0.8em;
--jp-content-heading-font-weight: 500;
/* Defaults use Material Design specification */
--jp-content-font-color0: rgba(0, 0, 0, 1);
--jp-content-font-color1: rgba(0, 0, 0, 0.87);
--jp-content-font-color2: rgba(0, 0, 0, 0.54);
--jp-content-font-color3: rgba(0, 0, 0, 0.38);
--jp-content-link-color: var(--md-blue-700);
--jp-content-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI',
Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
'Segoe UI Symbol';
/*
* Code Fonts
*
* Code font variables are used for typography of code and other monospaces content.
*/
--jp-code-font-size: 13px;
--jp-code-line-height: 1.3077; /* 17px for 13px base */
--jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
--jp-code-font-family-default: Menlo, Consolas, 'DejaVu Sans Mono', monospace;
--jp-code-font-family: var(--jp-code-font-family-default);
/* This gives a magnification of about 125% in presentation mode over normal. */
--jp-code-presentation-font-size: 16px;
/* may need to tweak cursor width if you change font size */
--jp-code-cursor-width0: 1.4px;
--jp-code-cursor-width1: 2px;
--jp-code-cursor-width2: 4px;
/* Layout
*
* The following are the main layout colors use in JupyterLab. In a light
* theme these would go from light to dark.
*/
--jp-layout-color0: white;
--jp-layout-color1: white;
--jp-layout-color2: var(--md-grey-200);
--jp-layout-color3: var(--md-grey-400);
--jp-layout-color4: var(--md-grey-600);
/* Inverse Layout
*
* The following are the inverse layout colors use in JupyterLab. In a light
* theme these would go from dark to light.
*/
--jp-inverse-layout-color0: #111111;
--jp-inverse-layout-color1: var(--md-grey-900);
--jp-inverse-layout-color2: var(--md-grey-800);
--jp-inverse-layout-color3: var(--md-grey-700);
--jp-inverse-layout-color4: var(--md-grey-600);
/* Brand/accent */
--jp-brand-color0: var(--md-blue-900);
--jp-brand-color1: var(--md-blue-700);
--jp-brand-color2: var(--md-blue-300);
--jp-brand-color3: var(--md-blue-100);
--jp-brand-color4: var(--md-blue-50);
--jp-accent-color0: var(--md-green-900);
--jp-accent-color1: var(--md-green-700);
--jp-accent-color2: var(--md-green-300);
--jp-accent-color3: var(--md-green-100);
/* State colors (warn, error, success, info) */
--jp-warn-color0: var(--md-orange-900);
--jp-warn-color1: var(--md-orange-700);
--jp-warn-color2: var(--md-orange-300);
--jp-warn-color3: var(--md-orange-100);
--jp-error-color0: var(--md-red-900);
--jp-error-color1: var(--md-red-700);
--jp-error-color2: var(--md-red-300);
--jp-error-color3: var(--md-red-100);
--jp-success-color0: var(--md-green-900);
--jp-success-color1: var(--md-green-700);
--jp-success-color2: var(--md-green-300);
--jp-success-color3: var(--md-green-100);
--jp-info-color0: var(--md-cyan-900);
--jp-info-color1: var(--md-cyan-700);
--jp-info-color2: var(--md-cyan-300);
--jp-info-color3: var(--md-cyan-100);
/* Cell specific styles */
--jp-cell-padding: 5px;
--jp-cell-collapser-width: 8px;
--jp-cell-collapser-min-height: 20px;
--jp-cell-collapser-not-active-hover-opacity: 0.6;
--jp-cell-editor-background: var(--md-grey-100);
--jp-cell-editor-border-color: var(--md-grey-300);
--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
--jp-cell-editor-active-background: var(--jp-layout-color0);
--jp-cell-editor-active-border-color: var(--jp-brand-color1);
--jp-cell-prompt-width: 64px;
--jp-cell-prompt-font-family: var(--jp-code-font-family-default);
--jp-cell-prompt-letter-spacing: 0px;
--jp-cell-prompt-opacity: 1;
--jp-cell-prompt-not-active-opacity: 0.5;
--jp-cell-prompt-not-active-font-color: var(--md-grey-700);
/* A custom blend of MD grey and blue 600
* See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
--jp-cell-inprompt-font-color: #307fc1;
/* A custom blend of MD grey and orange 600
* https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
--jp-cell-outprompt-font-color: #bf5b3d;
/* Notebook specific styles */
--jp-notebook-padding: 10px;
--jp-notebook-select-background: var(--jp-layout-color1);
--jp-notebook-multiselected-color: var(--md-blue-50);
/* The scroll padding is calculated to fill enough space at the bottom of the
notebook to show one single-line cell (with appropriate padding) at the top
when the notebook is scrolled all the way to the bottom. We also subtract one
pixel so that no scrollbar appears if we have just one single-line cell in the
notebook. This padding is to enable a 'scroll past end' feature in a notebook.
*/
--jp-notebook-scroll-padding: calc(
100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
var(--jp-code-padding) - var(--jp-cell-padding) - 1px
);
/* Rendermime styles */
--jp-rendermime-error-background: #fdd;
--jp-rendermime-table-row-background: var(--md-grey-100);
--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
/* Dialog specific styles */
--jp-dialog-background: rgba(0, 0, 0, 0.25);
/* Console specific styles */
--jp-console-padding: 10px;
/* Toolbar specific styles */
--jp-toolbar-border-color: var(--jp-border-color1);
--jp-toolbar-micro-height: 8px;
--jp-toolbar-background: var(--jp-layout-color1);
--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
--jp-toolbar-header-margin: 4px 4px 0px 4px;
--jp-toolbar-active-background: var(--md-grey-300);
/* Statusbar specific styles */
--jp-statusbar-height: 24px;
/* Input field styles */
--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
--jp-input-active-background: var(--jp-layout-color1);
--jp-input-hover-background: var(--jp-layout-color1);
--jp-input-background: var(--md-grey-100);
--jp-input-border-color: var(--jp-border-color1);
--jp-input-active-border-color: var(--jp-brand-color1);
--jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
/* General editor styles */
--jp-editor-selected-background: #d9d9d9;
--jp-editor-selected-focused-background: #d7d4f0;
--jp-editor-cursor-color: var(--jp-ui-font-color0);
/* Code mirror specific styles */
--jp-mirror-editor-keyword-color: #008000;
--jp-mirror-editor-atom-color: #88f;
--jp-mirror-editor-number-color: #080;
--jp-mirror-editor-def-color: #00f;
--jp-mirror-editor-variable-color: var(--md-grey-900);
--jp-mirror-editor-variable-2-color: #05a;
--jp-mirror-editor-variable-3-color: #085;
--jp-mirror-editor-punctuation-color: #05a;
--jp-mirror-editor-property-color: #05a;
--jp-mirror-editor-operator-color: #aa22ff;
--jp-mirror-editor-comment-color: #408080;
--jp-mirror-editor-string-color: #ba2121;
--jp-mirror-editor-string-2-color: #708;
--jp-mirror-editor-meta-color: #aa22ff;
--jp-mirror-editor-qualifier-color: #555;
--jp-mirror-editor-builtin-color: #008000;
--jp-mirror-editor-bracket-color: #997;
--jp-mirror-editor-tag-color: #170;
--jp-mirror-editor-attribute-color: #00c;
--jp-mirror-editor-header-color: blue;
--jp-mirror-editor-quote-color: #090;
--jp-mirror-editor-link-color: #00c;
--jp-mirror-editor-error-color: #f00;
--jp-mirror-editor-hr-color: #999;
/* Vega extension styles */
--jp-vega-background: white;
/* Sidebar-related styles */
--jp-sidebar-min-width: 250px;
/* Search-related styles */
--jp-search-toggle-off-opacity: 0.5;
--jp-search-toggle-hover-opacity: 0.8;
--jp-search-toggle-on-opacity: 1;
--jp-search-selected-match-background-color: rgb(245, 200, 0);
--jp-search-selected-match-color: black;
--jp-search-unselected-match-background-color: var(
--jp-inverse-layout-color0
);
--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
/* Icon colors that work well with light or dark backgrounds */
--jp-icon-contrast-color0: var(--md-purple-600);
--jp-icon-contrast-color1: var(--md-green-600);
--jp-icon-contrast-color2: var(--md-pink-600);
--jp-icon-contrast-color3: var(--md-blue-600);
}
</style>
<style type="text/css">
/* Force rendering true colors when outputing to pdf */
* {
-webkit-print-color-adjust: exact;
}
/* Misc */
a.anchor-link {
display: none;
}
.highlight {
margin: 0.4em;
}
/* Input area styling */
.jp-InputArea {
overflow: hidden;
}
.jp-InputArea-editor {
overflow: hidden;
}
.CodeMirror pre {
margin: 0;
padding: 0;
}
/* Using table instead of flexbox so that we can use break-inside property */
/* CSS rules under this comment should not be required anymore after we move to the JupyterLab 4.0 CSS */
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
min-width: calc(
var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
);
}
.jp-OutputArea-child {
display: table;
width: 100%;
}
.jp-OutputPrompt {
display: table-cell;
vertical-align: top;
min-width: var(--jp-cell-prompt-width);
}
body[data-format='mobile'] .jp-OutputPrompt {
display: table-row;
}
.jp-OutputArea-output {
display: table-cell;
width: 100%;
}
body[data-format='mobile'] .jp-OutputArea-child .jp-OutputArea-output {
display: table-row;
}
.jp-OutputArea-output.jp-OutputArea-executeResult {
width: 100%;
}
/* Hiding the collapser by default */
.jp-Collapser {
display: none;
}
@media print {
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: block;
}
.jp-OutputArea-child {
break-inside: avoid-page;
}
}
</style>
<!-- Load mathjax -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML-full,Safe"> </script>
<!-- MathJax configuration -->
<script type="text/x-mathjax-config">
init_mathjax = function() {
if (window.MathJax) {
// MathJax loaded
MathJax.Hub.Config({
TeX: {
equationNumbers: {
autoNumber: "AMS",
useLabelIds: true
}
},
tex2jax: {
inlineMath: [ ['$','$'], ["\\(","\\)"] ],
displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
processEscapes: true,
processEnvironments: true
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displayAlign: 'center',
CommonHTML: {
linebreaks: {
automatic: true
}
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init_mathjax();
</script>
<!-- End of mathjax configuration --></head>
<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
<div class="jp-Cell jp-CodeCell jp-Notebook-cell ">
<div class="jp-Cell-inputWrapper">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="CodeMirror cm-s-jupyter">
<div class=" highlight hl-r"><pre><span></span><span class="c1"># 11.</span>
<span class="nf">set.seed</span><span class="p">(</span><span class="m">1</span><span class="p">)</span>
<span class="n">x</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">rnorm</span><span class="p">(</span><span class="m">100</span><span class="p">)</span>
<span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">2</span><span class="o">*</span><span class="n">x</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="nf">rnorm</span><span class="p">(</span><span class="m">100</span><span class="p">)</span>
<span class="c1"># 11a.</span>
<span class="n">lm.fit</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">lm</span><span class="p">(</span><span class="n">y</span><span class="o">~</span><span class="n">x</span><span class="m">+0</span><span class="p">)</span>
<span class="nf">summary</span><span class="p">(</span><span class="n">lm.fit</span><span class="p">)</span>
<span class="c1"># The p-value of the t-statistic is near zero so the null hypothesis is rejected.</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="jp-Cell-outputWrapper">
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
</div>
<div class="jp-OutputArea jp-Cell-outputArea">
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output " data-mime-type="text/plain">
<pre>
Call:
lm(formula = y ~ x + 0)
Residuals:
Min 1Q Median 3Q Max
-1.9154 -0.6472 -0.1771 0.5056 2.3109
Coefficients:
Estimate Std. Error t value Pr(>|t|)
x 1.9939 0.1065 18.73 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9586 on 99 degrees of freedom
Multiple R-squared: 0.7798, Adjusted R-squared: 0.7776
F-statistic: 350.7 on 1 and 99 DF, p-value: < 2.2e-16
</pre>
</div>
</div>
</div>
</div>
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell ">
<div class="jp-Cell-inputWrapper">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="CodeMirror cm-s-jupyter">
<div class=" highlight hl-r"><pre><span></span><span class="c1"># 11b.</span>
<span class="n">lm.fit</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">lm</span><span class="p">(</span><span class="n">x</span><span class="o">~</span><span class="n">y</span><span class="m">+0</span><span class="p">)</span>
<span class="nf">summary</span><span class="p">(</span><span class="n">lm.fit</span><span class="p">)</span>
<span class="c1"># The p-value of the t-statistic is near zero so the null hypothesis is rejected.</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="jp-Cell-outputWrapper">
<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
</div>
<div class="jp-OutputArea jp-Cell-outputArea">
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output " data-mime-type="text/plain">
<pre>
Call:
lm(formula = x ~ y + 0)
Residuals:
Min 1Q Median 3Q Max
-0.8699 -0.2368 0.1030 0.2858 0.8938
Coefficients:
Estimate Std. Error t value Pr(>|t|)
y 0.39111 0.02089 18.73 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4246 on 99 degrees of freedom
Multiple R-squared: 0.7798, Adjusted R-squared: 0.7776
F-statistic: 350.7 on 1 and 99 DF, p-value: < 2.2e-16
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<div class=" highlight hl-r"><pre><span></span><span class="c1"># 11c.</span>
<span class="c1"># Both results in (a) and (b) reflect the same line created in 11a. In other</span>
<span class="c1"># words, $y = 2x + \epsilon$ could also be written $x = 0.5 * (y - \epsilon)$.</span>
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</div><p># 11d.
$$
\begin{array}{cc}
t = \beta / SE(\beta) &
\beta = \frac {\sum{x_i y_i}} {\sum{x_i^2}} &
SE(\beta) = \sqrt{\frac {\sum{(y_i - x_i \beta)^2}} {(n-1) \sum{x_i^2}}}
\end{array}
\\
t = {\frac {\sum{x_i y_i}} {\sum{x_i^2}}}
{\sqrt{\frac {(n-1) \sum{x_i^2}} {\sum{(y_i - x_i \beta)^2}}}}
\\
\frac {\sqrt{n-1} \sum{x_i y_i}}
{\sqrt{\sum{x_i^2} \sum{(y_i - x_i \beta)^2}}}
\\
\frac {\sqrt{n-1} \sum{x_i y_i}}
{\sqrt{\sum{x_i^2} \sum{(y_i^2 - 2 \beta x_i y_i + x_i^2 \beta^2)}}}
\\
\frac {\sqrt{n-1} \sum{x_i y_i}}
{\sqrt{\sum{x_i^2} \sum{y_i^2} -
\sum{x_i^2} \beta (2 \sum{x_i y_i} - \beta \sum{x_i^2})}}
\\
\frac {\sqrt{n-1} \sum{x_i y_i}}
{\sqrt{\sum{x_i^2} \sum{y_i^2} -
\sum{x_i y_i} (2 \sum{x_i y_i} - \sum{x_i y_i})}}
\\
t = \frac {\sqrt{n-1} \sum{x_i y_i}}
{\sqrt{\sum{x_i^2} \sum{y_i^2} - (\sum{x_i y_i})^2 }}
$$
# ```{r}
# (sqrt(length(x)-1) * sum(x*y)) / (sqrt(sum(x*x) * sum(y*y) - (sum(x*y))^2))
# ```
# This is same as the t-statistic shown above.
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<div class=" highlight hl-r"><pre><span></span><span class="c1"># 11e.</span>
<span class="c1"># If you swap t(x,y) as t(y,x), then you will find t(x,y) = t(y,x).</span>
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<div class=" highlight hl-r"><pre><span></span><span class="c1"># 11f.</span>
<span class="n">lm.fit</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">lm</span><span class="p">(</span><span class="n">y</span><span class="o">~</span><span class="n">x</span><span class="p">)</span>
<span class="n">lm.fit2</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">lm</span><span class="p">(</span><span class="n">x</span><span class="o">~</span><span class="n">y</span><span class="p">)</span>
<span class="nf">summary</span><span class="p">(</span><span class="n">lm.fit</span><span class="p">)</span>
<span class="nf">summary</span><span class="p">(</span><span class="n">lm.fit2</span><span class="p">)</span>
<span class="c1"># You can see the t-statistic is the same for the two linear regressions.</span>
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<pre>
Call:
lm(formula = y ~ x)
Residuals:
Min 1Q Median 3Q Max
-1.8768 -0.6138 -0.1395 0.5394 2.3462
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.03769 0.09699 -0.389 0.698
x 1.99894 0.10773 18.556 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9628 on 98 degrees of freedom
Multiple R-squared: 0.7784, Adjusted R-squared: 0.7762
F-statistic: 344.3 on 1 and 98 DF, p-value: < 2.2e-16
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Call:
lm(formula = x ~ y)
Residuals:
Min 1Q Median 3Q Max
-0.90848 -0.28101 0.06274 0.24570 0.85736
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.03880 0.04266 0.91 0.365
y 0.38942 0.02099 18.56 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4249 on 98 degrees of freedom
Multiple R-squared: 0.7784, Adjusted R-squared: 0.7762
F-statistic: 344.3 on 1 and 98 DF, p-value: < 2.2e-16
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2301_76574743/tjjm-R
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实验3/实验2.html
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# Chapter 3 Lab: Linear Regression
library(MASS)
library(ISLR)
# Simple Linear Regression
fix(Boston)
names(Boston)
lm.fit=lm(medv~lstat)
lm.fit=lm(medv~lstat,data=Boston)
attach(Boston)
lm.fit=lm(medv~lstat)
lm.fit
summary(lm.fit)
names(lm.fit)
coef(lm.fit)
confint(lm.fit)
predict(lm.fit,data.frame(lstat=(c(5,10,15))), interval="confidence",level=0.99)
predict(lm.fit,data.frame(lstat=(c(5,10,15))), interval="prediction",level=0.99)
plot(lstat,medv)
abline(lm.fit)
abline(lm.fit,lwd=3)
abline(lm.fit,lwd=3,col="red")
plot(lstat,medv,col="red")
plot(lstat,medv,pch=20)
plot(lstat,medv,pch="+")
plot(1:20,1:20,pch=1:20)
par(mfrow=c(2,2))
plot(lm.fit)
plot(predict(lm.fit), residuals(lm.fit))
plot(predict(lm.fit), rstudent(lm.fit))
plot(hatvalues(lm.fit))
which.max(hatvalues(lm.fit))
# Multiple Linear Regression
lm.fit=lm(medv~lstat+age,data=Boston)
summary(lm.fit)
lm.fit=lm(medv~.,data=Boston)
summary(lm.fit)
library(car)
vif(lm.fit)
lm.fit1=lm(medv~.-age,data=Boston)
summary(lm.fit1)
lm.fit1=update(lm.fit, ~.-age)
# Interaction Terms
summary(lm(medv~lstat*age,data=Boston))
# Non-linear Transformations of the Predictors
lm.fit2=lm(medv~lstat+I(lstat^2))
summary(lm.fit2)
lm.fit=lm(medv~lstat)
anova(lm.fit,lm.fit2)
par(mfrow=c(2,2))
plot(lm.fit2)
lm.fit5=lm(medv~poly(lstat,5))
summary(lm.fit5)
summary(lm(medv~log(rm),data=Boston))
# Qualitative Predictors
fix(Carseats)
names(Carseats)
lm.fit=lm(Sales~.+Income:Advertising+Price:Age,data=Carseats)
summary(lm.fit)
attach(Carseats)
contrasts(ShelveLoc)
# Writing Functions
LoadLibraries
LoadLibraries()
LoadLibraries=function(){
library(ISLR)
library(MASS)
print("The libraries have been loaded.")
}
LoadLibraries
LoadLibraries()
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2301_76574743/tjjm-R
|
线性回归.R
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R
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unknown
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# Chapter 2 Lab: Introduction to R
# Basic Commands
x <- c(1,3,2,5)
x
x = c(1,6,2)
x
y = c(1,4,3)
length(x)
length(y)
x+y
ls()
rm(x,y)
ls()
rm(list=ls())
?matrix
x=matrix(data=c(1,2,3,4), nrow=2, ncol=2)
x
x=matrix(c(1,2,3,4),2,2)
matrix(c(1,2,3,4),2,2,byrow=TRUE)
sqrt(x)
x^2
x=rnorm(50)
y=x+rnorm(50,mean=50,sd=.1)
cor(x,y)
set.seed(1303)
rnorm(50)
set.seed(3)
y=rnorm(100)
mean(y)
var(y)
sqrt(var(y))
sd(y)
# Graphics
x=rnorm(100)
y=rnorm(100)
plot(x,y)
plot(x,y,xlab="this is the x-axis",ylab="this is the y-axis",main="Plot of X vs Y")
pdf("Figure.pdf")
plot(x,y,col="green")
dev.off()
x=seq(1,10)
x
x=1:10
x
x=seq(-pi,pi,length=50)
y=x
f=outer(x,y,function(x,y)cos(y)/(1+x^2))
contour(x,y,f)
contour(x,y,f,nlevels=45,add=T)
fa=(f-t(f))/2
contour(x,y,fa,nlevels=15)
image(x,y,fa)
persp(x,y,fa)
persp(x,y,fa,theta=30)
persp(x,y,fa,theta=30,phi=20)
persp(x,y,fa,theta=30,phi=70)
persp(x,y,fa,theta=30,phi=40)
# Indexing Data
A=matrix(1:16,4,4)
A
A[2,3]
A[c(1,3),c(2,4)]
A[1:3,2:4]
A[1:2,]
A[,1:2]
A[1,]
A[-c(1,3),]
A[-c(1,3),-c(1,3,4)]
dim(A)
# Loading Data
Auto=read.table("Auto.data")
fix(Auto)
Auto=read.table("Auto.data",header=T,na.strings="?")
fix(Auto)
Auto=read.csv("data/Auto.csv",header=T,na.strings="?")
fix(Auto)
dim(Auto)
Auto[1:4,]
Auto=na.omit(Auto)
dim(Auto)
names(Auto)
# Additional Graphical and Numerical Summaries
plot(cylinders, mpg)
plot(Auto$cylinders, Auto$mpg)
attach(Auto)
plot(cylinders, mpg)
cylinders=as.factor(cylinders)
plot(cylinders, mpg)
plot(cylinders, mpg, col="red")
plot(cylinders, mpg, col="red", varwidth=T)
plot(cylinders, mpg, col="red", varwidth=T,horizontal=T)
plot(cylinders, mpg, col="red", varwidth=T, xlab="cylinders", ylab="MPG")
hist(mpg)
hist(mpg,col=2)
hist(mpg,col=2,breaks=15)
pairs(Auto)
pairs(~ mpg + displacement + horsepower + weight + acceleration, Auto)
plot(horsepower,mpg)
identify(horsepower,mpg,name)
summary(Auto)
summary(mpg)
# Chapter 3 Lab: Linear Regression
library(MASS)
library(ISLR)
# Simple Linear Regression
fix(Boston)
names(Boston)
lm.fit=lm(medv~lstat)
lm.fit=lm(medv~lstat,data=Boston)
attach(Boston)
lm.fit=lm(medv~lstat)
lm.fit
summary(lm.fit)
names(lm.fit)
coef(lm.fit)
confint(lm.fit)
predict(lm.fit,data.frame(lstat=(c(5,10,15))), interval="confidence")
predict(lm.fit,data.frame(lstat=(c(5,10,15))), interval="prediction")
plot(lstat,medv)
abline(lm.fit)
abline(lm.fit,lwd=3)
abline(lm.fit,lwd=3,col="red")
plot(lstat,medv,col="red")
plot(lstat,medv,pch=20)
plot(lstat,medv,pch="+")
plot(1:20,1:20,pch=1:20)
par(mfrow=c(2,2))
plot(lm.fit)
plot(predict(lm.fit), residuals(lm.fit))
plot(predict(lm.fit), rstudent(lm.fit))
plot(hatvalues(lm.fit))
which.max(hatvalues(lm.fit))
# Multiple Linear Regression
lm.fit=lm(medv~lstat+age,data=Boston)
summary(lm.fit)
lm.fit=lm(medv~.,data=Boston)
summary(lm.fit)
library(car)
vif(lm.fit)
lm.fit1=lm(medv~.-age,data=Boston)
summary(lm.fit1)
lm.fit1=update(lm.fit, ~.-age)
# Interaction Terms
summary(lm(medv~lstat*age,data=Boston))
# Non-linear Transformations of the Predictors
lm.fit2=lm(medv~lstat+I(lstat^2))
summary(lm.fit2)
lm.fit=lm(medv~lstat)
anova(lm.fit,lm.fit2)
par(mfrow=c(2,2))
plot(lm.fit2)
lm.fit5=lm(medv~poly(lstat,5))
summary(lm.fit5)
summary(lm(medv~log(rm),data=Boston))
# Qualitative Predictors
fix(Carseats)
names(Carseats)
lm.fit=lm(Sales~.+Income:Advertising+Price:Age,data=Carseats)
summary(lm.fit)
attach(Carseats)
contrasts(ShelveLoc)
# Writing Functions
LoadLibraries
LoadLibraries()
LoadLibraries=function(){
library(ISLR)
library(MASS)
print("The libraries have been loaded.")
}
LoadLibraries
LoadLibraries()
# Chapter 4 Lab: Logistic Regression, LDA, QDA, and KNN
# The Stock Market Data
library(ISLR)
names(Smarket)
dim(Smarket)
summary(Smarket)
pairs(Smarket)
cor(Smarket)
cor(Smarket[,-9])
attach(Smarket)
plot(Volume)
# Logistic Regression
glm.fit=glm(Direction~Lag1+Lag2+Lag3+Lag4+Lag5+Volume,data=Smarket,family=binomial)
summary(glm.fit)
coef(glm.fit)
summary(glm.fit)$coef
summary(glm.fit)$coef[,4]
glm.probs=predict(glm.fit,type="response")
glm.probs[1:10]
contrasts(Direction)
glm.pred=rep("Down",1250)
glm.pred[glm.probs>.5]="Up"
table(glm.pred,Direction)
(507+145)/1250
mean(glm.pred==Direction)
train=(Year<2005)
Smarket.2005=Smarket[!train,]
dim(Smarket.2005)
Direction.2005=Direction[!train]
glm.fit=glm(Direction~Lag1+Lag2+Lag3+Lag4+Lag5+Volume,data=Smarket,family=binomial,subset=train)
glm.probs=predict(glm.fit,Smarket.2005,type="response")
glm.pred=rep("Down",252)
glm.pred[glm.probs>.5]="Up"
table(glm.pred,Direction.2005)
mean(glm.pred==Direction.2005)
mean(glm.pred!=Direction.2005)
glm.fit=glm(Direction~Lag1+Lag2,data=Smarket,family=binomial,subset=train)
glm.probs=predict(glm.fit,Smarket.2005,type="response")
glm.pred=rep("Down",252)
glm.pred[glm.probs>.5]="Up"
table(glm.pred,Direction.2005)
mean(glm.pred==Direction.2005)
106/(106+76)
predict(glm.fit,newdata=data.frame(Lag1=c(1.2,1.5),Lag2=c(1.1,-0.8)),type="response")
# Linear Discriminant Analysis
library(MASS)
lda.fit=lda(Direction~Lag1+Lag2,data=Smarket,subset=train)
lda.fit
plot(lda.fit)
lda.pred=predict(lda.fit, Smarket.2005)
names(lda.pred)
lda.class=lda.pred$class
table(lda.class,Direction.2005)
mean(lda.class==Direction.2005)
sum(lda.pred$posterior[,1]>=.5)
sum(lda.pred$posterior[,1]<.5)
lda.pred$posterior[1:20,1]
lda.class[1:20]
sum(lda.pred$posterior[,1]>.9)
# Quadratic Discriminant Analysis
qda.fit=qda(Direction~Lag1+Lag2,data=Smarket,subset=train)
qda.fit
qda.class=predict(qda.fit,Smarket.2005)$class
table(qda.class,Direction.2005)
mean(qda.class==Direction.2005)
# K-Nearest Neighbors
library(class)
train.X=cbind(Lag1,Lag2)[train,]
test.X=cbind(Lag1,Lag2)[!train,]
train.Direction=Direction[train]
set.seed(1)
knn.pred=knn(train.X,test.X,train.Direction,k=1)
table(knn.pred,Direction.2005)
(83+43)/252
knn.pred=knn(train.X,test.X,train.Direction,k=3)
table(knn.pred,Direction.2005)
mean(knn.pred==Direction.2005)
# An Application to Caravan Insurance Data
dim(Caravan)
attach(Caravan)
summary(Purchase)
348/5822
standardized.X=scale(Caravan[,-86])
var(Caravan[,1])
var(Caravan[,2])
var(standardized.X[,1])
var(standardized.X[,2])
test=1:1000
train.X=standardized.X[-test,]
test.X=standardized.X[test,]
train.Y=Purchase[-test]
test.Y=Purchase[test]
set.seed(1)
knn.pred=knn(train.X,test.X,train.Y,k=1)
mean(test.Y!=knn.pred)
mean(test.Y!="No")
table(knn.pred,test.Y)
9/(68+9)
knn.pred=knn(train.X,test.X,train.Y,k=3)
table(knn.pred,test.Y)
5/26
knn.pred=knn(train.X,test.X,train.Y,k=5)
table(knn.pred,test.Y)
4/15
glm.fit=glm(Purchase~.,data=Caravan,family=binomial,subset=-test)
glm.probs=predict(glm.fit,Caravan[test,],type="response")
glm.pred=rep("No",1000)
glm.pred[glm.probs>.5]="Yes"
table(glm.pred,test.Y)
glm.pred=rep("No",1000)
glm.pred[glm.probs>.25]="Yes"
table(glm.pred,test.Y)
11/(22+11)
# Chaper 5 Lab: Cross-Validation and the Bootstrap
# The Validation Set Approach
library(ISLR)
set.seed(1)
train=sample(392,196)
lm.fit=lm(mpg~horsepower,data=Auto,subset=train)
attach(Auto)
mean((mpg-predict(lm.fit,Auto))[-train]^2)
lm.fit2=lm(mpg~poly(horsepower,2),data=Auto,subset=train)
mean((mpg-predict(lm.fit2,Auto))[-train]^2)
lm.fit3=lm(mpg~poly(horsepower,3),data=Auto,subset=train)
mean((mpg-predict(lm.fit3,Auto))[-train]^2)
set.seed(2)
train=sample(392,196)
lm.fit=lm(mpg~horsepower,subset=train)
mean((mpg-predict(lm.fit,Auto))[-train]^2)
lm.fit2=lm(mpg~poly(horsepower,2),data=Auto,subset=train)
mean((mpg-predict(lm.fit2,Auto))[-train]^2)
lm.fit3=lm(mpg~poly(horsepower,3),data=Auto,subset=train)
mean((mpg-predict(lm.fit3,Auto))[-train]^2)
# Leave-One-Out Cross-Validation
glm.fit=glm(mpg~horsepower,data=Auto)
coef(glm.fit)
lm.fit=lm(mpg~horsepower,data=Auto)
coef(lm.fit)
library(boot)
glm.fit=glm(mpg~horsepower,data=Auto)
cv.err=cv.glm(Auto,glm.fit)
cv.err$delta
cv.error=rep(0,5)
for (i in 1:5){
glm.fit=glm(mpg~poly(horsepower,i),data=Auto)
cv.error[i]=cv.glm(Auto,glm.fit)$delta[1]
}
cv.error
# k-Fold Cross-Validation
set.seed(17)
cv.error.10=rep(0,10)
for (i in 1:10){
glm.fit=glm(mpg~poly(horsepower,i),data=Auto)
cv.error.10[i]=cv.glm(Auto,glm.fit,K=10)$delta[1]
}
cv.error.10
# The Bootstrap
alpha.fn=function(data,index){
X=data$X[index]
Y=data$Y[index]
return((var(Y)-cov(X,Y))/(var(X)+var(Y)-2*cov(X,Y)))
}
alpha.fn(Portfolio,1:100)
set.seed(1)
alpha.fn(Portfolio,sample(100,100,replace=T))
boot(Portfolio,alpha.fn,R=1000)
# Estimating the Accuracy of a Linear Regression Model
boot.fn=function(data,index)
return(coef(lm(mpg~horsepower,data=data,subset=index)))
boot.fn(Auto,1:392)
set.seed(1)
boot.fn(Auto,sample(392,392,replace=T))
boot.fn(Auto,sample(392,392,replace=T))
boot(Auto,boot.fn,1000)
summary(lm(mpg~horsepower,data=Auto))$coef
boot.fn=function(data,index)
coefficients(lm(mpg~horsepower+I(horsepower^2),data=data,subset=index))
set.seed(1)
boot(Auto,boot.fn,1000)
summary(lm(mpg~horsepower+I(horsepower^2),data=Auto))$coef
# Chapter 6 Lab 1: Subset Selection Methods
# Best Subset Selection
library(ISLR)
fix(Hitters)
names(Hitters)
dim(Hitters)
sum(is.na(Hitters$Salary))
Hitters=na.omit(Hitters)
dim(Hitters)
sum(is.na(Hitters))
library(leaps)
regfit.full=regsubsets(Salary~.,Hitters)
summary(regfit.full)
regfit.full=regsubsets(Salary~.,data=Hitters,nvmax=19)
reg.summary=summary(regfit.full)
names(reg.summary)
reg.summary$rsq
par(mfrow=c(2,2))
plot(reg.summary$rss,xlab="Number of Variables",ylab="RSS",type="l")
plot(reg.summary$adjr2,xlab="Number of Variables",ylab="Adjusted RSq",type="l")
which.max(reg.summary$adjr2)
points(11,reg.summary$adjr2[11], col="red",cex=2,pch=20)
plot(reg.summary$cp,xlab="Number of Variables",ylab="Cp",type='l')
which.min(reg.summary$cp)
points(10,reg.summary$cp[10],col="red",cex=2,pch=20)
which.min(reg.summary$bic)
plot(reg.summary$bic,xlab="Number of Variables",ylab="BIC",type='l')
points(6,reg.summary$bic[6],col="red",cex=2,pch=20)
plot(regfit.full,scale="r2")
plot(regfit.full,scale="adjr2")
plot(regfit.full,scale="Cp")
plot(regfit.full,scale="bic")
coef(regfit.full,6)
# Forward and Backward Stepwise Selection
regfit.fwd=regsubsets(Salary~.,data=Hitters,nvmax=19,method="forward")
summary(regfit.fwd)
regfit.bwd=regsubsets(Salary~.,data=Hitters,nvmax=19,method="backward")
summary(regfit.bwd)
coef(regfit.full,7)
coef(regfit.fwd,7)
coef(regfit.bwd,7)
# Choosing Among Models
set.seed(1)
train=sample(c(TRUE,FALSE), nrow(Hitters),rep=TRUE)
test=(!train)
regfit.best=regsubsets(Salary~.,data=Hitters[train,],nvmax=19)
test.mat=model.matrix(Salary~.,data=Hitters[test,])
val.errors=rep(NA,19)
for(i in 1:19){
coefi=coef(regfit.best,id=i)
pred=test.mat[,names(coefi)]%*%coefi
val.errors[i]=mean((Hitters$Salary[test]-pred)^2)
}
val.errors
which.min(val.errors)
coef(regfit.best,10)
predict.regsubsets=function(object,newdata,id,...){
form=as.formula(object$call[[2]])
mat=model.matrix(form,newdata)
coefi=coef(object,id=id)
xvars=names(coefi)
mat[,xvars]%*%coefi
}
regfit.best=regsubsets(Salary~.,data=Hitters,nvmax=19)
coef(regfit.best,10)
k=10
set.seed(1)
folds=sample(1:k,nrow(Hitters),replace=TRUE)
cv.errors=matrix(NA,k,19, dimnames=list(NULL, paste(1:19)))
for(j in 1:k){
best.fit=regsubsets(Salary~.,data=Hitters[folds!=j,],nvmax=19)
for(i in 1:19){
pred=predict(best.fit,Hitters[folds==j,],id=i)
cv.errors[j,i]=mean( (Hitters$Salary[folds==j]-pred)^2)
}
}
mean.cv.errors=apply(cv.errors,2,mean)
mean.cv.errors
par(mfrow=c(1,1))
plot(mean.cv.errors,type='b')
reg.best=regsubsets(Salary~.,data=Hitters, nvmax=19)
coef(reg.best,11)
# Chapter 6 Lab 2: Ridge Regression and the Lasso
x=model.matrix(Salary~.,Hitters)[,-1]
y=Hitters$Salary
# Ridge Regression
library(glmnet)
grid=10^seq(10,-2,length=100)
ridge.mod=glmnet(x,y,alpha=0,lambda=grid)
dim(coef(ridge.mod))
ridge.mod$lambda[50]
coef(ridge.mod)[,50]
sqrt(sum(coef(ridge.mod)[-1,50]^2))
ridge.mod$lambda[60]
coef(ridge.mod)[,60]
sqrt(sum(coef(ridge.mod)[-1,60]^2))
predict(ridge.mod,s=50,type="coefficients")[1:20,]
set.seed(1)
train=sample(1:nrow(x), nrow(x)/2)
test=(-train)
y.test=y[test]
ridge.mod=glmnet(x[train,],y[train],alpha=0,lambda=grid, thresh=1e-12)
ridge.pred=predict(ridge.mod,s=4,newx=x[test,])
mean((ridge.pred-y.test)^2)
mean((mean(y[train])-y.test)^2)
ridge.pred=predict(ridge.mod,s=1e10,newx=x[test,])
mean((ridge.pred-y.test)^2)
ridge.pred=predict(ridge.mod,s=0,newx=x[test,],exact=T,x=x[train,],y=y[train])
mean((ridge.pred-y.test)^2)
lm(y~x, subset=train)
predict(ridge.mod,s=0,exact=T,type="coefficients",x=x[train,],y=y[train])[1:20,]
set.seed(1)
cv.out=cv.glmnet(x[train,],y[train],alpha=0)
plot(cv.out)
bestlam=cv.out$lambda.min
bestlam
ridge.pred=predict(ridge.mod,s=bestlam,newx=x[test,])
mean((ridge.pred-y.test)^2)
out=glmnet(x,y,alpha=0)
predict(out,type="coefficients",s=bestlam)[1:20,]
# The Lasso
lasso.mod=glmnet(x[train,],y[train],alpha=1,lambda=grid)
plot(lasso.mod)
set.seed(1)
cv.out=cv.glmnet(x[train,],y[train],alpha=1)
plot(cv.out)
bestlam=cv.out$lambda.min
lasso.pred=predict(lasso.mod,s=bestlam,newx=x[test,])
mean((lasso.pred-y.test)^2)
out=glmnet(x,y,alpha=1,lambda=grid)
lasso.coef=predict(out,type="coefficients",s=bestlam)[1:20,]
lasso.coef
lasso.coef[lasso.coef!=0]
# Chapter 6 Lab 3: PCR and PLS Regression
# Principal Components Regression
library(pls)
set.seed(2)
pcr.fit=pcr(Salary~., data=Hitters,scale=TRUE,validation="CV")
summary(pcr.fit)
validationplot(pcr.fit,val.type="MSEP")
set.seed(1)
pcr.fit=pcr(Salary~., data=Hitters,subset=train,scale=TRUE, validation="CV")
validationplot(pcr.fit,val.type="MSEP")
pcr.pred=predict(pcr.fit,x[test,],ncomp=7)
mean((pcr.pred-y.test)^2)
pcr.fit=pcr(y~x,scale=TRUE,ncomp=7)
summary(pcr.fit)
# Partial Least Squares
set.seed(1)
pls.fit=plsr(Salary~., data=Hitters,subset=train,scale=TRUE, validation="CV")
summary(pls.fit)
validationplot(pls.fit,val.type="MSEP")
pls.pred=predict(pls.fit,x[test,],ncomp=2)
mean((pls.pred-y.test)^2)
pls.fit=plsr(Salary~., data=Hitters,scale=TRUE,ncomp=2)
summary(pls.fit)
# Chapter 7 Lab: Non-linear Modeling
library(ISLR)
attach(Wage)
# Polynomial Regression and Step Functions
fit=lm(wage~poly(age,4),data=Wage)
coef(summary(fit))
fit2=lm(wage~poly(age,4,raw=T),data=Wage)
coef(summary(fit2))
fit2a=lm(wage~age+I(age^2)+I(age^3)+I(age^4),data=Wage)
coef(fit2a)
fit2b=lm(wage~cbind(age,age^2,age^3,age^4),data=Wage)
agelims=range(age)
age.grid=seq(from=agelims[1],to=agelims[2])
preds=predict(fit,newdata=list(age=age.grid),se=TRUE)
se.bands=cbind(preds$fit+2*preds$se.fit,preds$fit-2*preds$se.fit)
par(mfrow=c(1,2),mar=c(4.5,4.5,1,1),oma=c(0,0,4,0))
plot(age,wage,xlim=agelims,cex=.5,col="darkgrey")
title("Degree-4 Polynomial",outer=T)
lines(age.grid,preds$fit,lwd=2,col="blue")
matlines(age.grid,se.bands,lwd=1,col="blue",lty=3)
preds2=predict(fit2,newdata=list(age=age.grid),se=TRUE)
max(abs(preds$fit-preds2$fit))
fit.1=lm(wage~age,data=Wage)
fit.2=lm(wage~poly(age,2),data=Wage)
fit.3=lm(wage~poly(age,3),data=Wage)
fit.4=lm(wage~poly(age,4),data=Wage)
fit.5=lm(wage~poly(age,5),data=Wage)
anova(fit.1,fit.2,fit.3,fit.4,fit.5)
coef(summary(fit.5))
(-11.983)^2
fit.1=lm(wage~education+age,data=Wage)
fit.2=lm(wage~education+poly(age,2),data=Wage)
fit.3=lm(wage~education+poly(age,3),data=Wage)
anova(fit.1,fit.2,fit.3)
fit=glm(I(wage>250)~poly(age,4),data=Wage,family=binomial)
preds=predict(fit,newdata=list(age=age.grid),se=T)
pfit=exp(preds$fit)/(1+exp(preds$fit))
se.bands.logit = cbind(preds$fit+2*preds$se.fit, preds$fit-2*preds$se.fit)
se.bands = exp(se.bands.logit)/(1+exp(se.bands.logit))
preds=predict(fit,newdata=list(age=age.grid),type="response",se=T)
plot(age,I(wage>250),xlim=agelims,type="n",ylim=c(0,.2))
points(jitter(age), I((wage>250)/5),cex=.5,pch="|",col="darkgrey")
lines(age.grid,pfit,lwd=2, col="blue")
matlines(age.grid,se.bands,lwd=1,col="blue",lty=3)
table(cut(age,4))
fit=lm(wage~cut(age,4),data=Wage)
coef(summary(fit))
# Splines
library(splines)
fit=lm(wage~bs(age,knots=c(25,40,60)),data=Wage)
pred=predict(fit,newdata=list(age=age.grid),se=T)
plot(age,wage,col="gray")
lines(age.grid,pred$fit,lwd=2)
lines(age.grid,pred$fit+2*pred$se,lty="dashed")
lines(age.grid,pred$fit-2*pred$se,lty="dashed")
dim(bs(age,knots=c(25,40,60)))
dim(bs(age,df=6))
attr(bs(age,df=6),"knots")
fit2=lm(wage~ns(age,df=4),data=Wage)
pred2=predict(fit2,newdata=list(age=age.grid),se=T)
lines(age.grid, pred2$fit,col="red",lwd=2)
plot(age,wage,xlim=agelims,cex=.5,col="darkgrey")
title("Smoothing Spline")
fit=smooth.spline(age,wage,df=16)
fit2=smooth.spline(age,wage,cv=TRUE)
fit2$df
lines(fit,col="red",lwd=2)
lines(fit2,col="blue",lwd=2)
legend("topright",legend=c("16 DF","6.8 DF"),col=c("red","blue"),lty=1,lwd=2,cex=.8)
plot(age,wage,xlim=agelims,cex=.5,col="darkgrey")
title("Local Regression")
fit=loess(wage~age,span=.2,data=Wage)
fit2=loess(wage~age,span=.5,data=Wage)
lines(age.grid,predict(fit,data.frame(age=age.grid)),col="red",lwd=2)
lines(age.grid,predict(fit2,data.frame(age=age.grid)),col="blue",lwd=2)
legend("topright",legend=c("Span=0.2","Span=0.5"),col=c("red","blue"),lty=1,lwd=2,cex=.8)
# GAMs
gam1=lm(wage~ns(year,4)+ns(age,5)+education,data=Wage)
library(gam)
gam.m3=gam(wage~s(year,4)+s(age,5)+education,data=Wage)
par(mfrow=c(1,3))
plot(gam.m3, se=TRUE,col="blue")
plot.Gam(gam1, se=TRUE, col="red")
gam.m1=gam(wage~s(age,5)+education,data=Wage)
gam.m2=gam(wage~year+s(age,5)+education,data=Wage)
anova(gam.m1,gam.m2,gam.m3,test="F")
summary(gam.m3)
preds=predict(gam.m2,newdata=Wage)
gam.lo=gam(wage~s(year,df=4)+lo(age,span=0.7)+education,data=Wage)
plot.Gam(gam.lo, se=TRUE, col="green")
gam.lo.i=gam(wage~lo(year,age,span=0.5)+education,data=Wage)
library(akima)
plot(gam.lo.i)
gam.lr=gam(I(wage>250)~year+s(age,df=5)+education,family=binomial,data=Wage)
par(mfrow=c(1,3))
plot(gam.lr,se=T,col="green")
table(education,I(wage>250))
gam.lr.s=gam(I(wage>250)~year+s(age,df=5)+education,family=binomial,data=Wage,subset=(education!="1. < HS Grad"))
plot(gam.lr.s,se=T,col="green")
# Chapter 8 Lab: Decision Trees
# Fitting Classification Trees
library(tree)
library(ISLR)
attach(Carseats)
High=factor(ifelse(Sales<=8,"No","Yes"))
Carseats=data.frame(Carseats,High)
tree.carseats=tree(High~.-Sales,Carseats)
summary(tree.carseats)
plot(tree.carseats)
text(tree.carseats,pretty=0)
tree.carseats
set.seed(2)
train=sample(1:nrow(Carseats), 200)
Carseats.test=Carseats[-train,]
High.test=High[-train]
tree.carseats=tree(High~.-Sales,Carseats,subset=train)
tree.pred=predict(tree.carseats,Carseats.test,type="class")
table(tree.pred,High.test)
(86+57)/200
set.seed(3)
cv.carseats=cv.tree(tree.carseats,FUN=prune.misclass)
names(cv.carseats)
cv.carseats
par(mfrow=c(1,2))
plot(cv.carseats$size,cv.carseats$dev,type="b")
plot(cv.carseats$k,cv.carseats$dev,type="b")
prune.carseats=prune.misclass(tree.carseats,best=9)
plot(prune.carseats)
text(prune.carseats,pretty=0)
tree.pred=predict(prune.carseats,Carseats.test,type="class")
table(tree.pred,High.test)
(94+60)/200
prune.carseats=prune.misclass(tree.carseats,best=15)
plot(prune.carseats)
text(prune.carseats,pretty=0)
tree.pred=predict(prune.carseats,Carseats.test,type="class")
table(tree.pred,High.test)
(86+62)/200
# Fitting Regression Trees
library(MASS)
set.seed(1)
train = sample(1:nrow(Boston), nrow(Boston)/2)
tree.boston=tree(medv~.,Boston,subset=train)
summary(tree.boston)
plot(tree.boston)
text(tree.boston,pretty=0)
cv.boston=cv.tree(tree.boston)
plot(cv.boston$size,cv.boston$dev,type='b')
prune.boston=prune.tree(tree.boston,best=5)
plot(prune.boston)
text(prune.boston,pretty=0)
yhat=predict(tree.boston,newdata=Boston[-train,])
boston.test=Boston[-train,"medv"]
plot(yhat,boston.test)
abline(0,1)
mean((yhat-boston.test)^2)
# Bagging and Random Forests
library(randomForest)
set.seed(1)
bag.boston=randomForest(medv~.,data=Boston,subset=train,mtry=13,importance=TRUE)
bag.boston
yhat.bag = predict(bag.boston,newdata=Boston[-train,])
plot(yhat.bag, boston.test)
abline(0,1)
mean((yhat.bag-boston.test)^2)
bag.boston=randomForest(medv~.,data=Boston,subset=train,mtry=13,ntree=25)
yhat.bag = predict(bag.boston,newdata=Boston[-train,])
mean((yhat.bag-boston.test)^2)
set.seed(1)
rf.boston=randomForest(medv~.,data=Boston,subset=train,mtry=6,importance=TRUE)
yhat.rf = predict(rf.boston,newdata=Boston[-train,])
mean((yhat.rf-boston.test)^2)
importance(rf.boston)
varImpPlot(rf.boston)
# Boosting
library(gbm)
set.seed(1)
boost.boston=gbm(medv~.,data=Boston[train,],distribution="gaussian",n.trees=5000,interaction.depth=4)
summary(boost.boston)
par(mfrow=c(1,2))
plot(boost.boston,i="rm")
plot(boost.boston,i="lstat")
yhat.boost=predict(boost.boston,newdata=Boston[-train,],n.trees=5000)
mean((yhat.boost-boston.test)^2)
boost.boston=gbm(medv~.,data=Boston[train,],distribution="gaussian",n.trees=5000,interaction.depth=4,shrinkage=0.2,verbose=F)
yhat.boost=predict(boost.boston,newdata=Boston[-train,],n.trees=5000)
mean((yhat.boost-boston.test)^2)
# Chapter 9 Lab: Support Vector Machines
# Support Vector Classifier
set.seed(1)
x=matrix(rnorm(20*2), ncol=2)
y=c(rep(-1,10), rep(1,10))
x[y==1,]=x[y==1,] + 1
plot(x, col=(3-y))
dat=data.frame(x=x, y=as.factor(y))
library(e1071)
svmfit=svm(y~., data=dat, kernel="linear", cost=10,scale=FALSE)
plot(svmfit, dat)
svmfit$index
summary(svmfit)
svmfit=svm(y~., data=dat, kernel="linear", cost=0.1,scale=FALSE)
plot(svmfit, dat)
svmfit$index
set.seed(1)
tune.out=tune(svm,y~.,data=dat,kernel="linear",ranges=list(cost=c(0.001, 0.01, 0.1, 1,5,10,100)))
summary(tune.out)
bestmod=tune.out$best.model
summary(bestmod)
xtest=matrix(rnorm(20*2), ncol=2)
ytest=sample(c(-1,1), 20, rep=TRUE)
xtest[ytest==1,]=xtest[ytest==1,] + 1
testdat=data.frame(x=xtest, y=as.factor(ytest))
ypred=predict(bestmod,testdat)
table(predict=ypred, truth=testdat$y)
svmfit=svm(y~., data=dat, kernel="linear", cost=.01,scale=FALSE)
ypred=predict(svmfit,testdat)
table(predict=ypred, truth=testdat$y)
x[y==1,]=x[y==1,]+0.5
plot(x, col=(y+5)/2, pch=19)
dat=data.frame(x=x,y=as.factor(y))
svmfit=svm(y~., data=dat, kernel="linear", cost=1e5)
summary(svmfit)
plot(svmfit, dat)
svmfit=svm(y~., data=dat, kernel="linear", cost=1)
summary(svmfit)
plot(svmfit,dat)
# Support Vector Machine
set.seed(1)
x=matrix(rnorm(200*2), ncol=2)
x[1:100,]=x[1:100,]+2
x[101:150,]=x[101:150,]-2
y=c(rep(1,150),rep(2,50))
dat=data.frame(x=x,y=as.factor(y))
plot(x, col=y)
train=sample(200,100)
svmfit=svm(y~., data=dat[train,], kernel="radial", gamma=1, cost=1)
plot(svmfit, dat[train,])
summary(svmfit)
svmfit=svm(y~., data=dat[train,], kernel="radial",gamma=1,cost=1e5)
plot(svmfit,dat[train,])
set.seed(1)
tune.out=tune(svm, y~., data=dat[train,], kernel="radial", ranges=list(cost=c(0.1,1,10,100,1000),gamma=c(0.5,1,2,3,4)))
summary(tune.out)
table(true=dat[-train,"y"], pred=predict(tune.out$best.model,newx=dat[-train,]))
# ROC Curves
library(ROCR)
rocplot=function(pred, truth, ...){
predob = prediction(pred, truth)
perf = performance(predob, "tpr", "fpr")
plot(perf,...)}
svmfit.opt=svm(y~., data=dat[train,], kernel="radial",gamma=2, cost=1,decision.values=T)
fitted=attributes(predict(svmfit.opt,dat[train,],decision.values=TRUE))$decision.values
par(mfrow=c(1,2))
rocplot(fitted,dat[train,"y"],main="Training Data")
svmfit.flex=svm(y~., data=dat[train,], kernel="radial",gamma=50, cost=1, decision.values=T)
fitted=attributes(predict(svmfit.flex,dat[train,],decision.values=T))$decision.values
rocplot(fitted,dat[train,"y"],add=T,col="red")
fitted=attributes(predict(svmfit.opt,dat[-train,],decision.values=T))$decision.values
rocplot(fitted,dat[-train,"y"],main="Test Data")
fitted=attributes(predict(svmfit.flex,dat[-train,],decision.values=T))$decision.values
rocplot(fitted,dat[-train,"y"],add=T,col="red")
# SVM with Multiple Classes
set.seed(1)
x=rbind(x, matrix(rnorm(50*2), ncol=2))
y=c(y, rep(0,50))
x[y==0,2]=x[y==0,2]+2
dat=data.frame(x=x, y=as.factor(y))
par(mfrow=c(1,1))
plot(x,col=(y+1))
svmfit=svm(y~., data=dat, kernel="radial", cost=10, gamma=1)
plot(svmfit, dat)
# Application to Gene Expression Data
library(ISLR)
names(Khan)
dim(Khan$xtrain)
dim(Khan$xtest)
length(Khan$ytrain)
length(Khan$ytest)
table(Khan$ytrain)
table(Khan$ytest)
dat=data.frame(x=Khan$xtrain, y=as.factor(Khan$ytrain))
out=svm(y~., data=dat, kernel="linear",cost=10)
summary(out)
table(out$fitted, dat$y)
dat.te=data.frame(x=Khan$xtest, y=as.factor(Khan$ytest))
pred.te=predict(out, newdata=dat.te)
table(pred.te, dat.te$y)
# Chapter 10 Lab 1: Principal Components Analysis
states=row.names(USArrests)
states
names(USArrests)
apply(USArrests, 2, mean)
apply(USArrests, 2, var)
pr.out=prcomp(USArrests, scale=TRUE)
names(pr.out)
pr.out$center
pr.out$scale
pr.out$rotation
dim(pr.out$x)
biplot(pr.out, scale=0)
pr.out$rotation=-pr.out$rotation
pr.out$x=-pr.out$x
biplot(pr.out, scale=0)
pr.out$sdev
pr.var=pr.out$sdev^2
pr.var
pve=pr.var/sum(pr.var)
pve
plot(pve, xlab="Principal Component", ylab="Proportion of Variance Explained", ylim=c(0,1),type='b')
plot(cumsum(pve), xlab="Principal Component", ylab="Cumulative Proportion of Variance Explained", ylim=c(0,1),type='b')
a=c(1,2,8,-3)
cumsum(a)
# Chapter 10 Lab 2: Clustering
# K-Means Clustering
set.seed(2)
x=matrix(rnorm(50*2), ncol=2)
x[1:25,1]=x[1:25,1]+3
x[1:25,2]=x[1:25,2]-4
km.out=kmeans(x,2,nstart=20)
km.out$cluster
plot(x, col=(km.out$cluster+1), main="K-Means Clustering Results with K=2", xlab="", ylab="", pch=20, cex=2)
set.seed(4)
km.out=kmeans(x,3,nstart=20)
km.out
plot(x, col=(km.out$cluster+1), main="K-Means Clustering Results with K=3", xlab="", ylab="", pch=20, cex=2)
set.seed(3)
km.out=kmeans(x,3,nstart=1)
km.out$tot.withinss
km.out=kmeans(x,3,nstart=20)
km.out$tot.withinss
# Hierarchical Clustering
hc.complete=hclust(dist(x), method="complete")
hc.average=hclust(dist(x), method="average")
hc.single=hclust(dist(x), method="single")
par(mfrow=c(1,3))
plot(hc.complete,main="Complete Linkage", xlab="", sub="", cex=.9)
plot(hc.average, main="Average Linkage", xlab="", sub="", cex=.9)
plot(hc.single, main="Single Linkage", xlab="", sub="", cex=.9)
cutree(hc.complete, 2)
cutree(hc.average, 2)
cutree(hc.single, 2)
cutree(hc.single, 4)
xsc=scale(x)
plot(hclust(dist(xsc), method="complete"), main="Hierarchical Clustering with Scaled Features")
x=matrix(rnorm(30*3), ncol=3)
dd=as.dist(1-cor(t(x)))
plot(hclust(dd, method="complete"), main="Complete Linkage with Correlation-Based Distance", xlab="", sub="")
# Chapter 10 Lab 3: NCI60 Data Example
# The NCI60 data
library(ISLR)
nci.labs=NCI60$labs
nci.data=NCI60$data
dim(nci.data)
nci.labs[1:4]
table(nci.labs)
# PCA on the NCI60 Data
pr.out=prcomp(nci.data, scale=TRUE)
Cols=function(vec){
cols=rainbow(length(unique(vec)))
return(cols[as.numeric(as.factor(vec))])
}
par(mfrow=c(1,2))
plot(pr.out$x[,1:2], col=Cols(nci.labs), pch=19,xlab="Z1",ylab="Z2")
plot(pr.out$x[,c(1,3)], col=Cols(nci.labs), pch=19,xlab="Z1",ylab="Z3")
summary(pr.out)
plot(pr.out)
pve=100*pr.out$sdev^2/sum(pr.out$sdev^2)
par(mfrow=c(1,2))
plot(pve, type="o", ylab="PVE", xlab="Principal Component", col="blue")
plot(cumsum(pve), type="o", ylab="Cumulative PVE", xlab="Principal Component", col="brown3")
# Clustering the Observations of the NCI60 Data
sd.data=scale(nci.data)
par(mfrow=c(1,3))
data.dist=dist(sd.data)
plot(hclust(data.dist), labels=nci.labs, main="Complete Linkage", xlab="", sub="",ylab="")
plot(hclust(data.dist, method="average"), labels=nci.labs, main="Average Linkage", xlab="", sub="",ylab="")
plot(hclust(data.dist, method="single"), labels=nci.labs, main="Single Linkage", xlab="", sub="",ylab="")
hc.out=hclust(dist(sd.data))
hc.clusters=cutree(hc.out,4)
table(hc.clusters,nci.labs)
par(mfrow=c(1,1))
plot(hc.out, labels=nci.labs)
abline(h=139, col="red")
hc.out
set.seed(2)
km.out=kmeans(sd.data, 4, nstart=20)
km.clusters=km.out$cluster
table(km.clusters,hc.clusters)
hc.out=hclust(dist(pr.out$x[,1:5]))
plot(hc.out, labels=nci.labs, main="Hier. Clust. on First Five Score Vectors")
table(cutree(hc.out,4), nci.labs)
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2301_76574743/tjjm-R
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统计学习导论.R
|
R
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<!DOCTYPE html>
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<title>Chapter 10: Exercise 1</title>
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<h1 class="title">Chapter 10: Exercise 1</h1>
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<div id="a" class="section level3">
<h3>a</h3>
<p>Proof of Equation (10.12) <span class="math">\[
\frac{1}{|C_k|} \sum\limits_{i,i^{\prime} \in C_k} \sum\limits_{j=1}^p (x_{ij} - x_{i^\prime j})^2 =
2 \sum\limits_{i \in C_k} \sum\limits_{j=1}^{p} (x_{ij} - \bar{x}_{kj})^2
\\
= \frac{1}{|C_k|} \sum\limits_{i,i^{\prime} \in C_k} \sum\limits_{j=1}^p ((x_{ij} - \bar{x}_{kj}) - (x_{i^\prime j} - \bar{x}_{kj}))^2
\\
= \frac{1}{|C_k|} \sum\limits_{i,i^{\prime} \in C_k} \sum\limits_{j=1}^p ((x_{ij} - \bar{x}_{kj})^2 - 2 (x_{ij} - \bar{x}_{kj})(x_{i^\prime j} - \bar{x}_{kj}) + (x_{i^\prime j} - \bar{x}_{kj})^2)
\\
= \frac{|C_k|}{|C_k|} \sum\limits_{i \in C_k} \sum\limits_{j=1}^p (x_{ij} - \bar{x}_{kj})^2 +
\frac{|C_k|}{|C_k|} \sum\limits_{i^{\prime} \in C_k} \sum\limits_{j=1}^p (x_{i^\prime j} - \bar{x}_{kj})^2 -
\frac{2}{|C_k|} \sum\limits_{i,i^{\prime} \in C_k} \sum\limits_{j=1}^p (x_{ij} - \bar{x}_{kj})(x_{i^\prime j} - \bar{x}_{kj})
\\
= 2 \sum\limits_{i \in C_k} \sum\limits_{j=1}^p (x_{ij} - \bar{x}_{kj})^2 + 0
\]</span></p>
</div>
<div id="b" class="section level3">
<h3>b</h3>
<p>Equation (10.12) shows that minimizing the sum of the squared Euclidean distance for each cluster is the same as minimizing the within-cluster variance for each cluster.</p>
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统计学习导论_基于R应用习题答案/ch10/1.html
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<title>Chapter 10: Exercise 10</title>
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<h1 class="title">Chapter 10: Exercise 10</h1>
</div>
<div id="a" class="section level3">
<h3>a</h3>
<pre class="r"><code>set.seed(2)
x = matrix(rnorm(20*3*50, mean=0, sd=0.001), ncol=50)
x[1:20, 2] = 1
x[21:40, 1] = 2
x[21:40, 2] = 2
x[41:60, 1] = 1</code></pre>
<p>The concept here is to separate the three classes amongst two dimensions.</p>
</div>
<div id="b" class="section level3">
<h3>b</h3>
<pre class="r"><code>pca.out = prcomp(x)
summary(pca.out)</code></pre>
<pre><code>## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.01 0.582 0.00173 0.00167 0.00165 0.00158 0.00154
## Proportion of Variance 0.75 0.250 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion 0.75 1.000 0.99997 0.99997 0.99997 0.99997 0.99998
## PC8 PC9 PC10 PC11 PC12 PC13
## Standard deviation 0.0015 0.00147 0.00141 0.00139 0.00134 0.0013
## Proportion of Variance 0.0000 0.00000 0.00000 0.00000 0.00000 0.0000
## Cumulative Proportion 1.0000 0.99998 0.99998 0.99998 0.99998 1.0000
## PC14 PC15 PC16 PC17 PC18 PC19
## Standard deviation 0.00126 0.00124 0.00123 0.00116 0.00112 0.00109
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion 0.99999 0.99999 0.99999 0.99999 0.99999 0.99999
## PC20 PC21 PC22 PC23 PC24 PC25
## Standard deviation 0.00102 0.00101 0.000985 0.000938 0.000932 0.000908
## Proportion of Variance 0.00000 0.00000 0.000000 0.000000 0.000000 0.000000
## Cumulative Proportion 0.99999 0.99999 0.999990 0.999990 0.999990 0.999990
## PC26 PC27 PC28 PC29 PC30
## Standard deviation 0.000867 0.000823 0.000801 0.000749 0.000712
## Proportion of Variance 0.000000 0.000000 0.000000 0.000000 0.000000
## Cumulative Proportion 1.000000 1.000000 1.000000 1.000000 1.000000
## PC31 PC32 PC33 PC34 PC35
## Standard deviation 0.000697 0.000673 0.000632 0.000591 0.000565
## Proportion of Variance 0.000000 0.000000 0.000000 0.000000 0.000000
## Cumulative Proportion 1.000000 1.000000 1.000000 1.000000 1.000000
## PC36 PC37 PC38 PC39 PC40
## Standard deviation 0.000538 0.000532 0.000476 0.000448 0.000426
## Proportion of Variance 0.000000 0.000000 0.000000 0.000000 0.000000
## Cumulative Proportion 1.000000 1.000000 1.000000 1.000000 1.000000
## PC41 PC42 PC43 PC44 PC45
## Standard deviation 0.000391 0.000377 0.000314 0.000296 0.000273
## Proportion of Variance 0.000000 0.000000 0.000000 0.000000 0.000000
## Cumulative Proportion 1.000000 1.000000 1.000000 1.000000 1.000000
## PC46 PC47 PC48 PC49 PC50
## Standard deviation 0.00025 0.000191 0.000147 0.000129 7.79e-05
## Proportion of Variance 0.00000 0.000000 0.000000 0.000000 0.00e+00
## Cumulative Proportion 1.00000 1.000000 1.000000 1.000000 1.00e+00</code></pre>
<pre class="r"><code>pca.out$x[,1:2]</code></pre>
<pre><code>## PC1 PC2
## [1,] -0.7079 -7.077e-01
## [2,] -0.7072 -7.069e-01
## [3,] -0.7062 -7.059e-01
## [4,] -0.7081 -7.078e-01
## [5,] -0.7073 -7.071e-01
## [6,] -0.7072 -7.069e-01
## [7,] -0.7068 -7.065e-01
## [8,] -0.7075 -7.072e-01
## [9,] -0.7059 -7.056e-01
## [10,] -0.7074 -7.071e-01
## [11,] -0.7070 -7.067e-01
## [12,] -0.7066 -7.063e-01
## [13,] -0.7076 -7.073e-01
## [14,] -0.7080 -7.078e-01
## [15,] -0.7060 -7.058e-01
## [16,] -0.7089 -7.087e-01
## [17,] -0.7067 -7.064e-01
## [18,] -0.7073 -7.070e-01
## [19,] -0.7066 -7.063e-01
## [20,] -0.7070 -7.067e-01
## [21,] 1.4141 -7.089e-05
## [22,] 1.4141 -6.894e-05
## [23,] 1.4141 -7.309e-05
## [24,] 1.4141 -6.935e-05
## [25,] 1.4141 -7.045e-05
## [26,] 1.4141 -6.983e-05
## [27,] 1.4141 -7.448e-05
## [28,] 1.4141 -6.910e-05
## [29,] 1.4141 -7.290e-05
## [30,] 1.4141 -7.055e-05
## [31,] 1.4141 -6.990e-05
## [32,] 1.4141 -7.065e-05
## [33,] 1.4141 -7.014e-05
## [34,] 1.4141 -7.006e-05
## [35,] 1.4141 -7.296e-05
## [36,] 1.4141 -6.608e-05
## [37,] 1.4141 -7.286e-05
## [38,] 1.4141 -6.920e-05
## [39,] 1.4141 -6.744e-05
## [40,] 1.4141 -6.950e-05
## [41,] -0.7064 7.064e-01
## [42,] -0.7070 7.070e-01
## [43,] -0.7074 7.074e-01
## [44,] -0.7077 7.077e-01
## [45,] -0.7078 7.078e-01
## [46,] -0.7057 7.057e-01
## [47,] -0.7065 7.065e-01
## [48,] -0.7058 7.058e-01
## [49,] -0.7075 7.075e-01
## [50,] -0.7074 7.074e-01
## [51,] -0.7079 7.079e-01
## [52,] -0.7074 7.074e-01
## [53,] -0.7068 7.069e-01
## [54,] -0.7062 7.062e-01
## [55,] -0.7068 7.068e-01
## [56,] -0.7069 7.069e-01
## [57,] -0.7063 7.063e-01
## [58,] -0.7064 7.064e-01
## [59,] -0.7071 7.071e-01
## [60,] -0.7077 7.078e-01</code></pre>
<pre class="r"><code>plot(pca.out$x[,1:2], col=2:4, xlab="Z1", ylab="Z2", pch=19) </code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk 10b" alt="plot of chunk 10b" width="672" /></p>
</div>
<div id="c" class="section level3">
<h3>c</h3>
<pre class="r"><code>km.out = kmeans(x, 3, nstart=20)
table(km.out$cluster, c(rep(1,20), rep(2,20), rep(3,20)))</code></pre>
<pre><code>##
## 1 2 3
## 1 20 0 0
## 2 0 20 0
## 3 0 0 20</code></pre>
<p>Perfect match.</p>
</div>
<div id="d" class="section level3">
<h3>d</h3>
<pre class="r"><code>km.out = kmeans(x, 2, nstart=20)
km.out$cluster</code></pre>
<pre><code>## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [36] 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1</code></pre>
<p>All of one previous class absorbed into a single class.</p>
</div>
<div id="e" class="section level3">
<h3>e</h3>
<pre class="r"><code>km.out = kmeans(x, 4, nstart=20)
km.out$cluster</code></pre>
<pre><code>## [1] 4 4 4 4 4 1 4 1 4 4 1 1 1 1 4 1 4 1 1 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [36] 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3</code></pre>
<p>All of one previous cluster split into two clusters.</p>
</div>
<div id="f" class="section level3">
<h3>f</h3>
<pre class="r"><code>km.out = kmeans(pca.out$x[,1:2], 3, nstart=20)
table(km.out$cluster, c(rep(1,20), rep(2,20), rep(3,20)))</code></pre>
<pre><code>##
## 1 2 3
## 1 0 0 20
## 2 0 20 0
## 3 20 0 0</code></pre>
<p>Perfect match, once again.</p>
</div>
<div id="g" class="section level3">
<h3>g</h3>
<pre class="r"><code>km.out = kmeans(scale(x), 3, nstart=20)
km.out$cluster</code></pre>
<pre><code>## [1] 1 1 1 1 1 3 3 3 2 3 1 3 3 3 1 3 2 3 3 1 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2
## [36] 2 2 2 2 2 1 1 1 3 1 3 1 1 1 3 2 3 3 1 1 3 1 3 3 1</code></pre>
<p>Poorer results than (b): the scaling of the observations effects the distance between them.</p>
</div>
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch10/10.html
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<title>Chapter 10: Exercise 11</title>
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<h1 class="title">Chapter 10: Exercise 11</h1>
</div>
<div id="a" class="section level3">
<h3>a</h3>
<pre class="r"><code>data = read.csv("./Ch10Ex11.csv", header=F)
dim(data)</code></pre>
<pre><code>## [1] 1000 40</code></pre>
</div>
<div id="b" class="section level3">
<h3>b</h3>
<pre class="r"><code>dd = as.dist(1 - cor(data))
plot(hclust(dd, method="complete"))</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk 2b" alt="plot of chunk 2b" width="672" /></p>
<pre class="r"><code>plot(hclust(dd, method="single"))</code></pre>
<p><img src="data:image/png;base64,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CCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAsUC12jVs8rTxV1artmosQ/vZ/+WvemAAAIIIIAAAggg0CcCM/bJqAyKAAIIIIAAAggggEDfCsyr4RdU3u3iMIY19uFdzN7FftgUAQQQQAABBBBAoEKB6SvcN7tGAAEEEEAAAQQQQAABBBBAAAEEEEAAAQT6VIACaJ/yMzgCCCCAAAIIIIAAAggggAACCCCAAAIIVClAAbRKXfaNAAIIIIAAAggggAACCCCAAAIIIIAAAn0qQAG0T/kZHAEEEEAAAQQQQAABBBBAAAEEEEAAAQSqFKAAWqUu+0YAAQQQQAABBBDoK4HlNPB45S7lRWWCcqzi5WWb/8jRHsq1ymTlCeU8ZTdlBoWGAAIIIIAAAggggAACCCCAAAIIIIAAAgjULuBC5/vK/+bkVS3bVLmzsW6apnltGS18XMnbh5ddqoxJ1h+heRoCCCCAAAIIIIAAAggggAACCCCAAAIIIFCpwPHaexQt39P875QDlHGKrwL1uneV1xrzeQXQxbTuhcZ6939U+aHyDeUM5SXFy//RmHqeAqgQaAgggAACCCCAAAIIIIAAAggggAACCCBQncAK2rWLm1Gc3DhnqIO0zIVR93HcP9su0IJYf5HmZ890WFy371aij6dHKjQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKAygfO15yhKHthklLOTftkC6LLJuuc0P6uS15bSwii2UgDNE2IZAggggAACCCCAAAIIIIAAAggggAACCPRMYEbtaariYqQ/oj6HUtRGaUVcBZotgLpwGkXUw4t20Fj+y6QvV4C2wGI1AggggAACCCDQVwL8Ffi+kmdcBBBAAAEEEEAAgV4KrKidzdXY4e2a+js+i9rDWjGpYOXoZPmNyXze7GV5C1mGAAIIIIAAAggg0L8EKID2r/uDo0EAAQQQQAABBBDoTGBEstnTyXzRbFGfdvZTtI+iMVmOAAIIIIAAAggg0AcCFED7AJ0hEUAAAQQQQAABBHouMH+yx78l80Wzfy1Yke5nSkGfWFy0j1jPFAEEEEAAAQQQQKAfCFAA7Qd3AoeAAAIIIIAAAggg0LXAq8ke4qPwyaKPzfo7Q/Naup/heR2SZUX7SLowiwACCCCAAAIIINDXAhRA+/oeYHwEEEAAAQQQQACBXgg8m+xkZDJfNLtYwYp29lO0j4JdsxgBBBBAAAEEEECgLwQogPaFOmMigAACCCCAAAII9Frgfu0w/qL7v2h+uiYDeN2SBevvTpYvn8znzS6Vt5BlCCCAAAIIIIAAAggggAACCCCAAAIIIIBAFQJXaaf/28hWTQbYOukXRdPo/sVk3T2aLyqkevm9Sd8jNU9DAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQqE9hce44C6EOaXyBnpHm07MGkX7YA6sLmHcn6/TSf1/5dC2MsTymA5imxDAEEEEAAAQQQQAABBBBAAAEEEEAAAQR6KvB77S0Kk5M176Ko/5jRJ5VNlScUr3+/Mc0WQLX4E6soXh77OUXzyyn++qhllROUdB+epwAqBBoCCCCAAAIIIIAAAggggAACCCCAAAIIVCvgQuc1ShQvPX2vkVh2hW7/ptFnmqZ5zR+hf1OJbTx137j9hub3SW4foXkaAggggAACCCCAAAIIIIAAAggggAACCCBQucDMGuFw5TklCpaevqacpXi9p15WVADVqg+uBL280c99I49ofl1lsWQZBVBh0BBAAAEEEEAAgf4oUPSl7v3xWDkmBBBAAAEEEEAAAQTaERimzv7o+hLKk4r/wnveR961uGlbRGv90fdZlVuU5xUaAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg0D8FZuifh8VRIYAAAggggAACCCDwocAsmttIWUx5/MOlH53ZRDdHKX9X3vjoKm4NUYGldN5fVPye58UhasBpI4AAAggggAACCCCAAAIIIIAAAi0F7lCPZ5X7W/astsNjjeOYWO0wPdn74j3Zyz938j3N/q/yg38u+tjcy40+X/jYmt4uGKbd+fEQcdHV7SYllm31wZLe/LNdst89erPLyvayT3KsvTSIA27Xfklt+LYyRZk7dsIUAQQQQAABBIaewPRD75Q5YwQQQAABBBBAoC2B+dV7QWWBtrbqfWeP7+OYr/e77tkeZ9eeXKz8c8/2+IlPrKJ9HaS8pBzTZL++8tPNRciqm++HyIyNwXy/xLJZengA3lfsd7Ye7reKXfn44lh7aZAea+zf01b2/qXBKcpCyo8UGgIIIIAAAggMUQEKoEP0jue0EUAAAQQQQACBCgQmaJ+HKjP3aN8ucP1c8UeYj1J8lWe0XTRzlrJOY0EUPp/TbReLD1O8nja0BY7W6U9VdlY2VGgIIIAAAgggMAQFKIAOwTudU0YAAQQQQAABBCoSmKexX39cvRfNV36uqDyjnJbZ4dd1e2flRuU2ZV7FH3c+V/mrcpyyg+IreAdyu0EHv3Ujlw/kE+mjY3fR/ITG2H4MVXVlah+dHsMigAACCCCAAAIIIIAAAggggAAC3Qu4mOaC3gvd76qrPbzaOI5Hu9pLtRs/0TjGXlj549T+wzW2P1jJNn8f5nmK/yiS+6Txx+VdLDxC8cefe918lWmMF98teVWybN1eDzhA9ndgYjCmomPuxH64jsV/GMv32Z4VHRe7RQABBBBAAAEEEEAAAQQQQAABBAasAAXQ8nfdE+rqIlMvCqD/3tjX+5qOVJo1X+HncR3/0aqqvyvzmsZY/uM60X6omTgGX406FFsdBdBO7S/SHeL75yFluqF453DOCCCAAAIIDGWB+OLwoWzAuSOAAAIIIIAAAu0IzPn/2jsTWDvKMgxHoICsmrJvLUvDJmsRRLEogpUIoqKAVmxpUCKILEVRUQoGo4IQEAlL1DaioQJK2QSFCCqECiKyyG4KSNmRHQQK+r51Pvwc5sw55945t/fcPl/y3vnnX7755zlzk5v3/v8cdbY5t630TulZyV/6Y/1S8krNdrGdOuwvjZds7nnMvdIfpZMl5+wmPMZbex+Tjq4Z6O3k/qZuxyWFFpykH2NV/qK0jbSBZLPo9kLX6niuVA5/wYxNx9FFw3I6nlGU79ax2y+g8WuaDi/G/0nHB4py1cEr+g6VzM/m517S+dJHpPlSL+IWJf2AdEdKfmtR9rtIvQK1HGNV0S1X59hemuyCwux/t6D0vx/e6u9VqOY0Q1pJ2kd6t+SxZmAufj5Pl16R6mITNZqnP38/m3Ol66RfSVdLO0lm7JgueUVmtzFKA6ZK/v3ZUhoj3SX9VbpSmi21ioGwd67zpI9JG0q7SxdJBAQgAAEIQAACEIAABCAAAQhAAAIQgIAI5BWg79C5jZp/t9A1ql9WahU2T22Sthrveptne0jlsEnq9qot8HVtOY/zxrWn54aibIPOhln0qTperPZViv5xeKpmzFXRqYujzcu4dhihVcNtxr1a9P24jt7u/kxxbrOvVzFFiT2/09IFxhd1V6S6KA6Uq8dPloLFIZEwHeP5PFt1YyUbztG/fLy+6KNDZXxbta9L5XE+f006UrI5Gu02E3NM00m0tdoCv7H62JCNflXHWWqPVwuo+H8xRWce0yn7GOzfvX8VY8smcvThCAEIQAACEIAABCAAAQhAAAIQgAAEFkkCYTB55dwLks2XpyUbmcdK50huCyPH5krVLpslVX9T6vesyjOkgyUbW7+RIseLKm8r5agzOevaco49dBLXmJ4bVN5dssnldt/fqdKBks27C6W4htt9HzlO0okNR9+T218qzl13mNRtnKkBzmN5FWKr2E0NT0q/Th38blCbeKdIi6f6JovLK5lXSK6eknpVo+vGpjoXB8PV4ydLwcLPSTni+ZyjhnmS+9oE/ZH0PelaKUxit3k1Z1W4b1zHz4FXfB4hfUOyqRttD6VytwbojhrrZzty3aDycdL+0snSnVK03afyilI5umFfHuvn2PnNw3kICEAAAhCAAAQgAAEIQAACEIAABCAAAREIgymMmbNUVzbWNlfd81L0maByOY5RRbTfrPK4cgede7Vj9Lmx1B4G5D2lep/WteXudQZoGLA2P9fNg4qyDSNv7475bVXRZ27RPth3gPoefZ0nKq5RrlpTFWuUKsvmcal5SE8Hy3WyZhvM6wzQ6PM19S8b8BNVZ9Mv+oxXOceWOgnz+ymVd82NRXmKjja2I4ePG0k5pukk2iflBpU9p9tSu83VxaQcS+tkhhQ5bKw3GUcrWeT+cJOJyQUBCEAAAhCAAAQgAAEIQAACEIAABPqZQDZAL9GNlM3PuLfvqBDmytejsjja2InVozaaysZR7n61TiJP7ldncta15dytDFAbUZHjqjygVPaKUG8jvkPar9Tm07mS5z4YA3TtIofz3CT1czTBdbIAmIXVzgC9qAbWCSnPUaV+F6S2L5Ta8qlXPMdcfMzPp/vVGaBeSRxjf+zONeH3lbrvK1L5GjXD2jZNUY+Yw4lte9MBAhCAAAQgAIERQ6D8X9cRc2PcCAQgAAEIQAACEOgBAW/XtYFZFXNS5Uqp7OLO0jJF3SwdvdW3Vdio8vsNvyR5xd1QhLeMP1Jc6F06tlpB6S3tvo+NpRlSL2KLlNTmcz/HUHM9vgbWNaktP59vVb1XiDoekLx1vlXYNPxnq8Y29Qeldm+rr4tY+enXCny6rmOXbfl5ys9Zl2noDgEIQAACEIBAvxHAAO23T4z5QgACEIAABCCwMAmUt6XnuTyYTlZMZRd3TOdXp3JV8VJVerXcqdL9VR16VOet2o6lJb830kbtvtIqUoRXz9nU62WsnJLbkOv3GEqu99bAysal30cbsb0KNkEd10neKt8q/I7XG1o11tR71fR6RftcHR+u6eum61N71asiUnNXxfw85eesqyR0hgAEIAABCECg/wgs0X9TZsYQgAAEIAABCEBgoRB4XletM4fmp1mV/8m8WmrLJkyqXujF4zQDm2FbS/4bce9CNj3/Il0mzZbqTGA1DzqyMZVX7A068UJKMFRcX9b9PVpzj95OHpGfzzWjUsf7UrlVsZM+5bFjVBGm6xoqP1juUHPepAGan6f8nNVcniYIQAACEIAABEYCgfzHz0i4H+4BAhCAAAQgAAEI9IqA33050Mhmy7yBJunxOG+BnyDNlGymRbxFhfGSty3/WbpN8pfm9CpGp8TDlVWaYtviUHF9TjOxWd1tZN6PdzC43erNqhQbpMqlVLbp2k4xZP0oNHD07/CTRZ583w2kJgUEIAABCEAAAsOZgP+7T0AAAhCAAAQgAAEI9JZAmC6+Snl7fJNXtllZF+3+9ntBg/eTDpN2lyZKu0h5G/ymOveX1EySLpCaDht5ETbLRkIMB66tOD6UGlZP5VbFVVs11NT7/iO8vf3sOOng+GoHfbrpEs9Ufs66GU9fCEAAAhCAAAT6kEC7P4L78JaYMgQgAAEIQAACEBh2BPK7GddqMzubmItLeUt9myFvNI96o1Rd6NR8fVrDbVJZno+3xe8mfU7yyj2/M/JoqRcG6MPKG5G/rCfq+vm4MLm24nZPalg7lVsV12nVUFN/d6nth6XzoTq1+blccbH8nA3V9bkOBCAAAQhAAAILiQBb4BcSeC4LAQhAAAIQgMAiRSAboOPa3Pl2avdW3fukI6ROIlbJxZfZtBqzbmqwsZnDxqxXe5YNLm+r9ns/j5W2kOZJDpfzu00XVDbwIxtTI8EAHS5cW300NkBfLxq31bFugYQNRJvh3Ya31tv8dWwmjVpQav1jGTVNlDaU2j3TrbO8uSU/T/k5e3NPaiAAAQhAAAIQGFEEMEBH1MfJzUAAAhCAAAQgMEwJ+N2Z8X7GA1SOL4Spmu6uqvQKUH9xzN+rOlTU+du5HTZ4Wr3b0MaWV3FWxSRV/kP6rXRgVYeizlv541vAbaDa3MvxWnHi+Q80sjHVyZbsgV5nKMY1xbWXc31eyWcVFxir475FuergZ3egn8mcIqENzYOrkqc6X+dy6U5pptRU5Lnnrf9N5ScPBCAAAQhAAAIQgAAEIAABCEAAAhDoSwI2Bm1etvuCGK+IdD9rhlQO10X7UeXG4nw9Hf1uQvezMeWVcBFR7xV75Zitisg9s9xYnLs++vh4jBSxggpedep632d5FaiqFsTb9dNf6uN+L0rLSzlu1onbXpWWzg1dlL068BnJearutYtUlV3NeJtCZQO3csAgKpvgOlnXNwvrkIq5dPp8emVx5Dm9lGeczv2Zuf1RaYJUjomqeEqKHD5uVOo0LbVPKrVtrvP5Rbuf5U1L7XG6oQpPSHGd90RDA8eDUt7PN5CPFBCAAAQgAAEIQAACEIAABCAAAQhAYEQQ6NRgameAevVZmJg2d2ZKW0k2/FaU9pbiWm7/rJQjxlaZgl41GoaRjza4bFh5y/t+0sWS6+cVR5ePkXJ4TOTwytNPSaOLDn5v4i7SjVL0OaloywevII32K1U+TJqaO3RY/oX6RZ5VOxzTabdzUu4TOh00iH6D5To5zbdXBqhv77vpOjZDfy55peah0rmSt8n7M3FbfDY2K3NM00m0TcoNRfkHqf0llY+QxkleTfw2yc+qV2ZGjvNUbjLis/e9rNFkYnJBAAIQgAAEIAABCEAAAhCAAAQgAIF+JhCm5ONtbqKdAerhO0vZhLTR84oUhk8cq8zFOgPUBtJFFXkin493SV79GHXTVc7hbfnXSNEeRxtVUY7j8apbTCpHXmEXfR8rd+rg/DPqE+P37KB/N13CBHP+oTBAB8t1cmLRSwPUDG14ZoMzPgMfvXrThuVlUtSvpXKOdgbosup8phTj4/hyRd0VqltKajLid/mGJpOSCwIQgAAEIACB4U+g6g/X4T9rZggBCEAAAhCAAAT6k4BXRW4meXWdt7g7Rv33sODn7fr5QenwVNdJ0UbSHtKXJW8fzuH3g14o7SDdnxtKZRuxce2HUltsZXf7TZJX9n1F8iq6cpymim9L3kYdsbIKo+Okw6NNtnif6MQOxwzXbk1wHap7O1UX2lGyAf8Hya8i+Jv0E+n90vel/Ly6vZt4QZ0PkPyc3SrFZ7ykyhFefTxV8qpmG6NNxSZKFIat/1lAQAACEIAABCAAAQhAAAIQgAAEIAABCPSYgFdtri99VNpJWl1qKsYokQ3RLaWB/MN7CY1bV7IZtpe0tZRNKp22DZtNG0te9TeQ+JkG2di1gTvQHK2uu2mRu2pFZasxTdQ3wbWJeQwmh1dP+nOZL/kZHkzYXPcz+knpfdI60kCeVw1rGyeqh+ftd92u0bY3HSAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0GMCXrHnVaY2raZITcZUJXPe7ZpM2se5vqq5HyhNaHMPK6j9acnsvFq5X8Lm/eOS531av0yaeUIAAhCAAAQgAAEIQAACEIAABCAAAQiMfALn6xZtWvndpE3Fakp0s/Si1O2q1qbmMNzyXKoJmbO1Tc3kzkr9TqnpN9yaPlHM268jGDPcJsd8IAABCEAAAhCAAAQgAAEIQAACEIAABBZdApvr1r3V2sbcexvC8FPl8WrAHRrKNxLSfEs3EQbo71X2e2pzeKv7vlL0sZG4Ve4wjMuee2zbP2MYz5OpQQACEIAABCAAAQhAAAIQgAAEIAABCCyiBPylSjbermvo/scoz3oN5RopaZbVjdwihcHp40PSLGm25C8mym3+tvd+iX00Uc/d3wC/Yr9MmnlCAAIQgAAEIAABCEAAAhCAAAQgAAEILDoEvE09zLk9F53bHvI7HaMrXixlo7NcfkTt/fQZ+NkJ8/ZDKhMQgAAEIAABCCyiBAb7zY2LKDZuGwIQgAAEIAABCEBgCAl4u/Xx0j2Sv6yH6B0Bv3ZgV8mG6NrSc9I8yVvjL5f8SoJ+iZ010SOlOdI3+2XSzBMCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAv1G4D+VUbAkKNl3qQAAAABJRU5ErkJggg==" title="plot of chunk 2b" alt="plot of chunk 2b" width="672" /></p>
<pre class="r"><code>plot(hclust(dd, method="average"))</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk 2b" alt="plot of chunk 2b" width="672" /></p>
<p>Two or three groups depending on the linkage method.</p>
</div>
<div id="c" class="section level3">
<h3>c</h3>
<p>To look at which genes differ the most across the healthy patients and diseased patients, we could look at the loading vectors outputted from PCA to see which genes are used to describe the variance the most.</p>
<pre class="r"><code>pr.out = prcomp(t(data))
summary(pr.out)</code></pre>
<pre><code>## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 11.941 6.0682 5.9348 5.8312 5.7521 5.7003 5.6345
## Proportion of Variance 0.127 0.0327 0.0313 0.0302 0.0294 0.0289 0.0282
## Cumulative Proportion 0.127 0.1594 0.1907 0.2209 0.2503 0.2792 0.3074
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 5.5773 5.5494 5.5062 5.4885 5.4602 5.4023 5.3344
## Proportion of Variance 0.0276 0.0274 0.0269 0.0268 0.0265 0.0259 0.0253
## Cumulative Proportion 0.3350 0.3624 0.3893 0.4160 0.4425 0.4685 0.4938
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Standard deviation 5.2776 5.2159 5.200 5.1514 5.1160 5.0559 5.0384
## Proportion of Variance 0.0248 0.0242 0.024 0.0236 0.0232 0.0227 0.0226
## Cumulative Proportion 0.5185 0.5427 0.567 0.5903 0.6135 0.6362 0.6588
## PC22 PC23 PC24 PC25 PC26 PC27 PC28
## Standard deviation 5.0187 4.9597 4.9139 4.864 4.8180 4.8081 4.7348
## Proportion of Variance 0.0224 0.0219 0.0215 0.021 0.0206 0.0205 0.0199
## Cumulative Proportion 0.6812 0.7030 0.7245 0.745 0.7661 0.7866 0.8066
## PC29 PC30 PC31 PC32 PC33 PC34 PC35
## Standard deviation 4.7010 4.6556 4.6162 4.5673 4.5303 4.495 4.3650
## Proportion of Variance 0.0196 0.0193 0.0189 0.0185 0.0182 0.018 0.0169
## Cumulative Proportion 0.8262 0.8455 0.8644 0.8829 0.9012 0.919 0.9360
## PC36 PC37 PC38 PC39 PC40
## Standard deviation 4.3586 4.2670 4.2028 4.1392 5.25e-15
## Proportion of Variance 0.0169 0.0162 0.0157 0.0152 0.00e+00
## Cumulative Proportion 0.9529 0.9691 0.9848 1.0000 1.00e+00</code></pre>
<pre class="r"><code>total_load = apply(pr.out$rotation, 1, sum)
indices = order(abs(total_load), decreasing=T)
indices[1:10]</code></pre>
<pre><code>## [1] 865 68 911 428 624 11 524 803 980 822</code></pre>
<pre class="r"><code>total_load[indices[1:10]]</code></pre>
<pre><code>## [1] 0.7765 0.7138 -0.7100 -0.6364 -0.6196 0.5885 0.5583 0.5535
## [9] -0.5217 0.4982</code></pre>
<p>This shows one representation of the top 1% of differing genes.</p>
<p>(*) I’m not sure this is the correct way to aggregate the loading vector.</p>
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<title>Chapter 10: Exercise 2</title>
<script 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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<h1 class="title">Chapter 10: Exercise 2</h1>
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<div id="a" class="section level3">
<h3>a</h3>
<pre class="r"><code>d = as.dist(matrix(c(0, 0.3, 0.4, 0.7,
0.3, 0, 0.5, 0.8,
0.4, 0.5, 0.0, 0.45,
0.7, 0.8, 0.45, 0.0), nrow=4))
plot(hclust(d, method="complete"))</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk 2a" alt="plot of chunk 2a" width="672" /></p>
</div>
<div id="b" class="section level3">
<h3>b</h3>
<pre class="r"><code>plot(hclust(d, method="single"))</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk 2b" alt="plot of chunk 2b" width="672" /></p>
</div>
<div id="c" class="section level3">
<h3>c</h3>
<p>(1,2), (3,4)</p>
</div>
<div id="d" class="section level3">
<h3>d</h3>
<p>(1, 2, 3), (4)</p>
</div>
<div id="e" class="section level3">
<h3>e</h3>
<pre class="r"><code>plot(hclust(d, method="complete"), labels=c(2,1,4,3))</code></pre>
<p><img src="data:image/png;base64,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统计学习导论_基于R应用习题答案/ch10/2.html
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<title>Chapter 10: Exercise 3</title>
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<h1 class="title">Chapter 10: Exercise 3</h1>
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<pre class="r"><code>set.seed(1)
x = cbind(c(1, 1, 0, 5, 6, 4), c(4, 3, 4, 1, 2, 0))
x</code></pre>
<pre><code>## [,1] [,2]
## [1,] 1 4
## [2,] 1 3
## [3,] 0 4
## [4,] 5 1
## [5,] 6 2
## [6,] 4 0</code></pre>
<div id="a" class="section level3">
<h3>a</h3>
<pre class="r"><code>plot(x[,1], x[,2])</code></pre>
<p><img 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QIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYRmCXZXayD4F1CNwi+z47tfs6xrArAQIECBAgQIAAAQIECBAgQGC7CPw8B/qK1Pnb5YBbH+durQc0HoE1BCr8PGyNPjYTIECAAAECBAgQIECAAAECBAj8SuDkPH3Xr156toiAAHQRLX1bCEyv/Hx/BvtsiwGNQYAAAQIECBAgQIAAAQIECBAYqcDBOa57paZ5ykgPc2MPSwC6sb5GX1mgws9Xr7zZFgIECBAgQIAAAQIECBAgQIDAtheo8LNKW4fAVdexr10JECBAgAABAgQIECBAgAABAgQIECDQtYAAtOvlMTkCBAgQIECAAAECBAgQIECAAAECBNYjIABdj559CRAgQIAAAQIECBAgQIAAAQIECBDoWkAA2vXymBwBAgQIECBAgAABAgQIECBAgAABAusREICuR8++BAgQIECAAAECBAgQIECAAAECBAh0LSAA7Xp5TI4AAQIECBAgQIAAAQIECBAgQIAAgfUICEDXo2dfAgQIECBAgAABAgQIECBAgAABAgS6FhCAdr08JkeAAAECBAgQIECAAAECBAgQIECAwHoEBKDr0bMvAQIECBAgQIAAAQIECBAgQIAAAQJdCwhAu14ekyNAgAABAgQIECBAgAABAgQIECBAYD0CAtD16NmXAAECBAgQIECAAAECBAgQIECAAIGuBXbrenYmR2DzBa6Tj7xtau/U6al/TF2U0gj0JnCzTOiWqV1Tp6W+m9IIECBAgAABAgQIECBAgACBHQQEoDuAdPSyQo37T+bzvTye2tHcxjiVB+egnp96UGr2z8V5ef3h1BGpb6U0AlspsEc+/JmpP07dcYeJ1N8Tb0m9MnXuDtu8JECAAAECBAgQIECAAAECBAh0J7BXZnTFpI7sbnbLT+gvJsf0X5cfoumeV8tob57MqbwvTp2Q+nTqm6nLU/X+pannpTQCWyVwu3xwhfDTvxfOzPPjU59N/Wjm/Xp+n5RGgAABAgQIECBAgAABAsMXOCqHUP8dePDwD8URELiygAD0yiat36nfwK2rO+svkrpirgLOcp9t9TXjv0xdlqp+dSWoRmCzBW6dDzwjVedgBfOPTNVV4tO2S54ckKpAtPr8IiUEDYJGgAABAgQIECBAgACBgQsIQBss4OxXfRsMZ4idCNw27y3jfO2ZsfbJ8zvMvJ4+/fb0icelBOor7xUkVbD0kNRJqR3baXnjOaljUu9NHZ76QurjKY3AZghUUF/n3vVTdd49JnVBarZV6Hl86oGpN6Semjo6tV+qfsZBI0CAAAECBAgQIECAAAECBAhsmMCPM3KFExtRGzbpDRy4l6/AV5hUV33WutTvf87TDkun6r+zoHSe/fUhsIxAfc2hzrtTU7P/MLLSWHVl6BdTtc9LVurkfQIECBAgQIAAAQIECBAYhIArQBssU11ZpBHYjgJ/kIO+VqquqDt2ToBXp98PU3dJ7T/nProRWK/AkycDvDyPFdqv1ernGl4w6TTdd619bCdAgAABAgQIECBAgAABAqMVWOar2aPF2KADOyLj/nnq6pPxK8D4aKquzlqt7ZGN/27SoW58cuJqnTd4W/2+YF0xWY/rbddY7wCN9n/QZJz6avG87ZJ0/GDqGana/6spjcBGCtQ/Uj0gVTfjet8CH/S59K2fdrhN6hap76c0AgQIECBAgAABAgQIECBAgMCGCdwpI/99qkLPqrrq8Cap1VrdjGfa/8jVOm7CttfPzGU6p/U+fngT5r3aR1RAVMdw/9U67WTbIZP9XrWTbd4i0Fpg3wxY5+lPlhh4+jX4+y6xr10IECBAgAABAgQIECBAoA8BX4FvsA6uAG2AOMcQdQVn3ZG5gsxDU7+Tqt+RrCsJ353qvZ2aCVYAc9UGE907Y+ye2rPBWOsZoq7mrFZzWaRN/8xcushO+hJYUmDZ87Q+bnpuO1eXxLcbAQIECBAgQIAAAQIECBAgsJzAgdmtvo46vYLybXleV3vu2Hq6AnTHua3n9QnZuY697lC9le1N+fCax58sOIm3TvZ71oL76U5gWYH62Yw6V9e6anx2/Ao/fz7Z74azGzwnQIAAAQIECBAgQIAAgUEJuAK0wXK1uKKvwTS21RCfydHeLfWOyVE/IY91NeiDJ689bI7AJyYfU/7ztgqlf2/Sebr/vPvqR2BZgU9Odqy7wc/bHp2O10l9LXX6vDvpR4AAAQIECBAgQIAAAQIECBBoLVDh289SdXVX3eSkfldyerMkV4AGYwPbNTP2j1Jl/x/n/JzXTfofM2d/3Qi0EHhoBqnz9KzUzecYsILP76Rqn6fP0V8XAgQIECBAgAABAgQIEOhXwBWg/a6NmS0gUHdo/myqwoqqb6TumRKABmGDW11RV+a/SNUVc6u1F2Zj9b0wdY/VOtpGYAMEPpAxp38/1N8ZK7X6jd3jUtX371PT36zNU40AAQIECBAgQIAAAQIEBiggAB3gopnyzgXqpwien7o4VcFFPf6PyfN6XTdPGkvr5TdAp5511W0Z1xW49ZdK3axql1S1PVIPSx2Xqj6XpZ6Y0ghstkD9g0j9VEadh2enXpCavRr0BnldN1U7LVV96urmW6Y0AgQIECBAgAABAgQIEBi2gAB02Otn9jsR2D/vnZyqAGO2BKA7wWr41nMzVl0FOjW/KM/rdxPr7tnT987I80emNAJbJVAh6PtT03OyHs9J1VfjZ9/7Ql7fNKURIECAAAECBAgQIECAwPAFBKDDX0NHsBOBa+S96W9NTkMNAehOoBq/VVfT/XnqlFRdDVr2FYB+NVVX29XvKmoEehB4SCbxrlRdCTr9O+K8PP9Q6jEpjQABAgQIECBAgAABAgTGIyAAHc9aOpKdCDw8771+Uo/ayfahvtXbV+B35lhffa+vFO+6s43eI9CRwLUzl/rdT40AAQIECBAgQIAAAQIExikgAG2wrrs1GMMQGyPwsQxbpW2+QP0Ga33lXSPQu8C5vU/Q/AgQIECAAAECBAgQIECAwFYL1M13NAIECBAgQIAAAQIECBAgQIAAAQIECIxSQAA6ymV1UAQIECBAgAABAgQIECBAgAABAgQIlIAA1HlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgIAA1DlAgAABAgQIECBAgAABAgQIECBAgMBoBQSgo11aB0aAAAECBAgQIECAAAECBAgQIECAgADUOUCAAAECBAgQIECAAAECBAgQIECAwGgFBKCjXVoHRoAAAQIECBAgQIAAAQIECBAgQICAANQ5QIAAAQIECBAgQIAAAQIECBAgQIDAaAUEoKNdWgdGgAABAgQIECBAgAABAgQIECBAgMBuCAgQIEBgUALXzWwfm/qd1C1Su6Z+mDo29e7Uj1MaAQIECBAgQIAAAQIECBAgQKB7gRtnhvdPPSx1o+5nO/8ET0jXK1JHz7+LngQITASelcezUvVnaGd1Qd5/eapCUY0AAQIECBAgQIAAAQIEhi9wVA6h/vvv4OEfiiPYTgJXy8Hul7reCgd917z/4dSO4UZd4fXc1C6pITcB6JBXz9y3SqD+3L8pNf174Zg8f0rq3ql7pQ5K1T8qXJ6qPp9IXT2lESBAgAABAgQIECBAgMCwBQSgw16/bTf7vXPEb0jVFVoVUFya+nzqGalpq/Dz7NQ05NjZ46eyfchXhApAp6vtkcD8Ai9L1/r74JzUH6RWavfLhvoKfPV960qdvE+AAAECBAgQIECAAAECgxEQgA5mqUz0GiE4PrWzQLPem4agJ830OT/Pv5T6QOqU1PTKrupfIehQmwB0qCtn3lslcLt88CWp+keTh8wxiTulTwWl9XfFPP3nGFIXAgQIECBAgAABAgQIENgiAQHoFsH72MUEdk33D6am4Wdd4Xlc6u9SF0/er3DjRZPn1e/tqeulZtt98uLk1HScJ85uHNBzAeiAFstUuxD4q8yi/ty/foHZPG+yT30VXiNAgAABAgQIECBAgACB4QoIQIe7dttq5hVUTkPLt+T5tWaOft88nwaC0z4fmdm+49P6Gn39Fmj1PW3HjQN5PT1eN0EayIKZ5pYLTP/M322BmdSd4usfVuofWWb/zllgCF0JECBAgAABAgQIECBAoAMBAWiDRditwRiGWF3gCZPNX8/jU1P1NdZp+2me1F3e/zl1zVS1Q3/5sNP/ratHD0m9K3XT1A1Tp6c2uv12PqCC3Ks2+KBbTcZwg5YGmIYYvUCFlzdJnZc6cYGj/Vn61hXjd03dJrXIvumuESBAgAABAgQIECBAgACB8QgIQDd+Le88+Yh35HE2/Jx+8hl58rbUH6fOTf1DarV23MzGGnszAtBn5nMePfO5LZ7eoMUgxiAwcoG9Jsf38yWOs0LQatMxfvnK/xIgQIAAAQIECBAgQIAAgW0mIADd+AW//uQjTlvlo+r3QCsArRsf1dfbV2sVmF6Yqiso6yrQzWh1k6a66rTFFaBHZJxbp1bzyGaNAIEI1J/3+juhrvbePVVfa5+3Tf9+qCvNNQIECBAgQIAAAQIECBAgQIDAhglMb1z00lU+4QHZ9qNU3el9rau1bpE+FYhU3Tc1tOY3QIe2Yua71QJfywTqz3v9FMW87bbpWPuclfIPXfOq6UeAAAECBAgQIECAAIH+BPwGaIM1aXFFX4NpjHqIb0+Orn7/c6WbkXwu2+p3/up3QOu3/lZrz5nZ+K2Z554SIDBOgXdPDuuFCxzeiyZ962ZjO/vpjQWG0pUAAQIECBAgQIAAAQIECBAgsLrA72fz9IrNY/J8+rXU1ffa+dYKP+srsDXep3bepft3XQHa/RKZYGcC18l86rd+68/9S+eY25MmfS/KY/3chEaAAAECBAgQIECAAAECwxVwBehw127bzfy4HHGFF1X1O58fSt0rNU/bN53qzvB/m5qOcXGe3zE1xCYAHeKqmfNWCzwsE6grOevvgDem9knt2OoK8iNT9VMa1e/pKY0AAQIECBAgQIAAAQIEhi0gAB32+m2r2VeIeXxqGmDW40PnFPjDHfarK0CfNee+PXYTgPa4KuY0BIHHZZIXpOrvj/qpjPemXpZ6car+D/FfUrXtslT9o4lGgAABAgQIECBAgAABAsMXEIAOfw231RHUHZyPSH0/VSFFhaLztLpStPpXnZa6f2rITQA65NUz960WuHMm8OHU9CrP6d8N08cvZNv9tnqSPp8AAQIECBAgQIAAAQIEmgkIQBtQ7tZgDEPMJ1BXbtaVWi9J3Tv109Q87ZR0ennqk6kvp9zQJAgagW0q8M0c9++mbpqqu8LfIrVr6oepY1PfSWkECBAgQIAAAQIECBAgQIAAAQJbKOAK0C3E99EECBAgQIAAAQIECBAgQIDAoARcAdpgua7aYAxDECBAgAABAgQIECBAgAABAgQIECBAoEsBAWiXy2JSBAgQIECAAAECBAgQIECAAAECBAi0EBCAtlA0BgECBAgQIECAAAECBAgQIECAAAECXQoIQLtcFpMiQIAAAQIECBAgQIAAAQIECBAgQKCFgAC0haIxCBAgQIAAAQIECBAgQIAAAQIECBDoUkAA2uWymBQBAgQIECBAgAABAgQIECBAgAABAi0EBKAtFI1BgAABAgQIECBAgAABAgQIECBAgECXAgLQLpfFpAgQIECAAAECBAgQIECAAAECBAgQaCEgAG2haAwCBAgQIECAAAECBAgQIECAAAECBLoUEIB2uSwmRYAAAQIECBAgQIAAAQIECBAgQIBACwEBaAtFYxAgQIAAAQIECBAgQIAAAQIECBAg0KWAALTLZTEpAgQIECBAgAABAgQIECBAgAABAgRaCAhAWygagwABAgQIECBAgAABAgQIECBAgACBLgUEoF0ui0kRIECAAAECBAgQIECAAAECBAgQINBCQADaQtEYBAgQIECAAAECBAgQIECAAAECBAh0KSAA7XJZTIoAAQIECBAgQIAAAQIECBAgQIAAgRYCAtAWisYgQIAAAQIECBAgQIAAAQIECBAgQKBLAQFol8tiUgQIECBAgAABAgQIECBAgAABAgQItBAQgLZQNAYBAgQIECBAgAABAgQIECBAgAABAl0KCEC7XBaTIkCAAAECBAgQIECAAAECBAgQIECghYAAtIWiMQgQIECAAAECBAgQIECAAAECBAgQ6FJAANrlspgUAQIECBAgQIAAAQIECBAgQIAAAQItBASgLRSNQYAAAQIECBAgQIAAAQIECBAgQIBAlwIC0C6XxaQIECBAgAABAgQIECBAgAABAgQIEGghIABtoWgMAgQIECBAgAABAgQIECBAgAABAgS6FBCAdrksJkWAAAECBAgQIECAAAECBAgQIECAQAsBAWgLRWMQIECAAAECBAgQIECAAAECBAgQINClgAC0y2UxKQIECBAgQIAAAQIECBAgQIAAAQIEWggIQFsoGoMAAQIECBAgQIAAAQIECBAgQIAAgS4FBKBdLotJESBAgAABAgQIECBAgAABAgQIECDQQkAA2kLRGAQIECBAgAABAgQIECBAgAABAgQIdCkgAO1yWUyKAAECBAgQIECAAAECBAgQIECAAIEWAgLQForGIECAAAECBAgQIECAAAECBAgQIECgSwEBaJfLYlIECBAgQIAAAQIECBAgQIAAAQIECLQQEIC2UDQGAQIECBAgQIAAAQIECBAgQIAAAQJdCghAu1wWkyJAgAABAgQIECBAgAABAgQIECBAoIWAALSFojEIECBAgAABAgQIECBAgAABAgQIEOhSQADa5bKYFAECBAgQIECAAAECBAgQIECAAAECLQQEoC0UjUGAAAECBAgQIECAAAECBAgQIECAQJcCAtAul8WkCBAgQIAAAQIECBAgQIAAAQIECBBoISAAbaFoDAIECBAgQIAAAQIECBAgQIAAAQIEuhQQgHa5LCZFgAABAgQIECBAgAABAgQIECBAgEALAQFoC0VjECBAgAABAgQIECBAgAABAgQIECDQpYAAtMtlMSkCBAgQIECAAAECBAgQIECAAAECBFoICEBbKBqDAAECBAgQIECAAAECBAgQIECAAIEuBQSgXS6LSREgQIAAAQIECBAgQIAAAQIECBAg0EJAANpC0RgECBAgQIAAAQIECBAgQIAAAQIECHQpIADtcllMigABAgQIECBAgAABAgQIECBAgACBFgIC0BaKxiBAgAABAgQIECBAgAABAgQIECBAoEsBAWiXy2JSBAgQIECAAAECBAgQIECAAAECBAi0EBCAtlA0BgECBAgQIECAAAECBAgQIECAAAECXQoIQLtcFpMiQIAAAQIECBAgQIAAAQIECBAgQKCFgAC0haIxCBAgQIAAAQIECBAgQIAAAQIECBDoUkAA2uWymBQBAgQIECBAgAABAgQIECBAgAABAi0EBKAtFI1BgAABAgQIECBAgAABAgQIECBAgECXAgLQLpfFpAgQIECAAAECBAgQIECAAAECBAgQaCEgAG2haAwCBAgQIECAAAECBAgQIECAAAECBLoUEIB2uSwmRYAAAQIECBAgQIAAAQIECBAgQIBACwEBaAtFYxAgQIAAAQIECBAgQIAAAQIECBAg0KWAALTLZTEpAgQIECBAgAABAgQIECBAgAABAgRaCAhAWygagwABAgQIECBAgAABAgQIECBAgACBLgUEoF0ui0kRIECAAAECBAgQIECAAAECBAgQINBCQADaQtEYBAgQIECAAAECBAgQIECAAAECBAh0KSAA7XJZTIoAAQIECBAgQIAAAQIECBAgQIAAgRYCAtAWisYgQIAAAQIECBAgQIAAAQIECBAgQKBLAQFol8tiUgQIECBAgAABAgQIECBAgAABAgQItBAQgLZQNAYBAgQIECBAgAABAgQIECBAgAABAl0KCEC7XBaTIkCAAAECBAgQIECAAAECBAgQIECghYAAtIWiMQgQIECAAAECBAgQIECAAAECBAgQ6FJAANrlspgUAQIECBAgQIAAAQIECBAgQIAAAQItBASgLRSNQYAAAQIECBAgQIAAAQIECBAgQIBAlwIC0C6XxaQIECBAgAABAgQIECBAgAABAgQIEGghIABtoWgMAgQIECBAgAABAgQIECBAgAABAgS6FBCAdrksJkWAAAECBAgQIECAAAECBAgQIECAQAsBAWgLRWMQIECAAAECBAgQIECAAAECBAgQINClgAC0y2UxKQIECBAgQIAAAQIECBAgQIAAAQIEWggIQFsoGoMAAQIECBAgQIAAAQIECBAgQIAAgS4FBKBdLotJESBAgAABAgQIECBAgAABAgQIECDQQkAA2kLRGAQIECBAgAABAgQIECBAgAABAgQIdCkgAO1yWUyKAAECBAgQIECAAAECBAgQIECAAIEWAgLQForGIECAAAECBAgQIECAAAECBAgQIECgSwEBaJfLYlIECBAgQIAAAQIECBAgQIAAAQIECLQQEIC2UDQGAQIECBAgQIAAAQIECBAgQIAAAQJdCghAu1wWkyJAgAABAgQIECBAgAABAgQIECBAoIWAALSFojEIECBAgAABAgQIECBAgAABAgQIEOhSQADa5bKYFAECBAgQIECAAAECBAgQIECAAAECLQQEoC0UjUGAAAECBAgQIECAAAECBAgQIECAQJcCAtAul8WkCBAgQIAAAQIECBAgQIAAAQIECBBoISAAbaFoDAIECBAgQIAAAQIECBAgQIAAAQIEuhQQgHa5LCZFgAABAgQIECBAgAABAgQIECBAgEALAQFoC0VjECBAgAABAgQIECBAgAABAgQIECDQpYAAtMtlMSkCBAgQIECAAAECBAgQIECAAAECBFoICEBbKBqDAAECBAgQIECAAAECBAgQIECAAIEuBQSgXS6LSREgQIAAAQIECBAgQIAAAQIECBAg0EJAANpC0RgECBAgQIAAAQIECBAgQIAAAQIECHQpIADtcllMigABAgQIECBAgAABAgQIECBAgACBFgIC0BaKxiBAgAABAgQIECBAgAABAgQIECBAoEsBAWiXy2JSBAgQIECAAAECBAgQIECAAAECBAi0EBCAtlA0BgECBAgQIECAAAECBAgQIECAAAECXQrstuCsHpv+hy+4z3q7fz8DPGq9g9ifAAECBAgQIECAAAECBAgQIECAAIHtJ7BoALpPiO6+yUx7bvLn+TgCBAgQIECAAAECBAgQIECAAAECBEYi4CvwI1lIh0GAAAECBAgQIECAAAECBAgQIECAwJUFlg1A/yVD7bLB9fwrT9c7BAgQIECAAAECBAgQIECAAAECBAgQmF9g2QB0/k/QkwABAgQIECBAgAABAgQIECBAgAABAlskIADdIngfS4AAAQIECBAgQIAAAQIECBAgQIDAxgssehOkz2ZKT0tdtPFTu8rH8hlnps7ZhM/yEQQIECBAgAABAgQIECBAgAABAgQIjFBg0QD02zGo2rHtkTcuSV2x44Y1Xu+e7ftO+lTQORt2npjXVRoBAgQIECBAgAABAgQIECBAgAABAgSWEljPV+Cvn0/8X6mvpc5LnZ36dOrw1DVS87S7pdMPJnXIPDvoQ4AAAQIECBAgQIAAAQIECBAgQIAAgXkFlg1AH5oPOCn1zNTdU3Ul516ph6SOSH09db+URoAAAQIECBAgQIAAAQIECBAgQIAAgS0TWCYAvXlm+77UjWZm/b08P3fm9e3y/HOpuhpUI0CAAAECBAgQIECAAAECBAgQIECAwJYILBOA/kVmuudktm/P4w1Tt0rVFaD3Th2bqlZj19WgL68XGgECBAgQIECAAAECBAgQIECAAAECBDZbYNEA9MaZ4GMmk3xvHg9OnTF5XTdA+kqqvgb/X1KXp6q9OPW8f33mfwgQIECAAAECBAgQIECAAAECBAgQILCJAosGoHecmdtqoebr0u/xqYsn/f80jwdNnnsgQIAAAQIECBAgQIAAAQIECBAgQIDApggsGoDuN5nV2Xk8dY0ZvifbK/SsK0F3Sf116sCURoAAAQIECBAgQIAAAQIECBAgQIAAgU0RWDQA3WMyqwvmnN3R6ffcSd/at15PQ9TJ2x4IECBAgAABAgQIECBAgAABAgQIECCwMQKLBqDfmUzjpnmsmx7N016TTnXjpGp7pz6aukG90AgQIECAAAECBAgQIECAAAECBAgQILCRAssGoDWnutHRvO2/peP/m3S+TR4/npo3QJ3s5oEAAQIECBAgQIAAAQIECBAgQIAAAQKLCSwagJ6S4f928hGH5/Fxc35c/Q5o3TH+byb99588n72p0mSTBwIECBAgQIAAAQIECBAgQIAAAQIECLQRWDQAvSIfe+jko6+Wx3emPp/6n6k7p1ZrF2bjo1InTjpV+PmWyXMPBAgQIECAAAECBAgQIECAAAECBAgQaC6waABaEzg+9YLUZfUi7YDUYal5ruY8M/0elJpeRTr7+XWneI0AAQIECBAgQIAAAQIECBAgQIAAAQLNBGYDyEUG/dN0PjD1zZmdfjTzfLWnZ2VjhabPS52/WkfbCBAgQIAAAQIECBAgQIAAAQIECBAgsB6BZQPQ+sy6EvQuqZukDkp9OzVvuyQdX5G6Q+qtqX9KTa8ozVONAAECBAgQIECAAAECBAgQIECAAAEC6xfYbf1DXOXHGaN+C3SZdlp2evIyO9qHAAECBAgQIECAAAECBAgQIECAwJICt89+9XOO102dnvpaqjIubYQCLQLQEbJsyiHVTaQuWuKT9sk+15jsVwGyRoAAAQIECBAgQIAAAQIECBAgsLZAfRP64FTd26a+lTzb6sbfX0i9LHVMSiNAYEmBx2a/j6V+mro8dXKqfgLgfql524fSsf5QVg2xnZBJ19yPHuLkzZkAAQIECBAgQIAAAQIECBAYpEBd6fmJ1DRTqas+P5D661S9f15quu0v87yXiwaPmsyrgluNQNcCe2Z2b0lN/yDt+Fi/f/qq1PTKzjxdsQlAV6SxgQABAgQIECBAgAABAgQIECBwJYH6Fu7fpCqPqa+5H5Ta8b44lcnUDbt/kap+b0z10ASgPayCOcwl8Jr0mg09618Vvpuqq0Bn3/+HvL51arUmAF1NxzYCBAgQIECAAAECBAgQIECAwK8LvDIvK3+pLOZmv77pSq/um3fOT1X/x19p6+a/IQDdfHOfuITAPbLPpan6g/OT1KNS039l2CvPD0v9LDUNQr+X56uFoALQAGkECBAgQIAAAQIECBAgQIAAgTkEbpo+F6bq27f3nKN/dXlKahqYTjOcOXdt3k0A2pzUgBsh8IYMWn9oLkndfYUPuHHer7uNTUPQ+heJfVfoKwBdAcbbBAgQIECAAAECBAgQIECAAIEdBJ6T15W3vGeH91d7WaHnKanab5H7tqw25rLbBKDLys3s18sPus5MaXRP7zg5ov+bx6+vcHT1+xMPSH0w9cBUXQH6kcnzuux6q9vVM4E6jhb/6nHNrT4Yn0+AAAECBAgQIECAAAECBAhsG4EDJkdaNzyat9VPFn449Sep2r/uDq8NWEAAuvGLt9/kI+ru56u1c7Lx4aljUvV7E/dKvTtVX5mvy7S3sr0pH35Q4wncpPF4hiNAgAABAgQIECBAgAABAgQI7CgwzR9O3XHDGq+n/etbu9rABQSgG7+Ae0w+4oI5PqruNFaBZ92Z7LapR6Rem3pmaivbp/Pht0vt0mASd8gYe6bObTCWIQgQIECAAAECBAgQIECAAAECqwlM85hFv5Fad4WvVlmNNnABAejGL+B38hH/NnWnOT/qzPR7WKpC0BuknpGq3514VWqrWl0BWtWi1ZWw+6cEoC00jUGAAAECBAgQIECAAAECBAisJjC9krOyiE+t1nGHbdW/2nd/+eB/hyzQ4jcdh3z8mzH3CkCrPSF1vX99tvb//FO61JWg039leGWeP2nt3fQgQIAAAQIECBAgQIAAAQIECBCYEfjo5PmT87jrzPurPa0bUz9y0uFjq3W0bRgCWx2A1s11rjWpqw2DbOFZ1s2Pqt0wVc/rD9E87UvpVKHp5an66vmbUy9NbfWaZQoaAQIECBAgQIAAAQIECBAgQGAQAhWA1jdr6+bOh84549ekX31l/ujUaXPuoxuBFQU+lC1XTKpu9HNS6o2pusPWmNqncjDT4zwrzyvMfPacB/j09KsQdLp/OU2fzzlEV93qK/A1//pLRCNAgAABAgQIECBAgAABAgQIbLTAI/IBla1UpvLMVT6sfiryf6cqt/h5qu7PstXtqEyg5nPwVk/E5y8vMBuAfjXDfDF1Yerc1N1SY2m3yYGcmJoGl/X4jQUO7j+k7yWp2f3r+RCbAHSIq2bOBAgQIECAAAECBAgQIEBg2AKHZvrTXOUzef6YVH1Lt25efcvU01LfTlWfX6Tq/iw9NAFoD6uwzjnMBqAvnYxVv5N5cuq4dY7d2+71df/Xpc5L1R+mT6YWaXdJ58+lat9pLbJ/L30FoL2shHkQIECAAAECBAgQIECAAIHtJfDvc7inp6a5ys4eKwStm1n30gSgvazEOuZxs+y736T2mRnnxnlev7UwxlaXU/9m6reXPLi6GVLdIb4uxR5iE4AOcdXMmQABAgQIECBAgAABAgQIjEPg2jmMQ1KfSf0kdVHqB6kPpJ6YqtympyYA7Wk1zIXAnAIC0DmhdCNAgAABAgQIECBAgAABAgS2vYAAtMEp4I7iDRANQYAAAQIECBAgQIAAAQIECBAgQIBAnwKLXtZ7+xzGb6XqRkXv3OBDumvGv1fqnJQ7hm8wtuEJECBAgAABAgQIECBAgAABAgQIjFFg0StAHxSEN6fqZj4b3R6RD6jP+rON/iDjEyBAgAABAgQIECBAgAABAgQIECAwToFFA9BxKjgqAgQIECBAgNJNklcAAEAASURBVAABAgQIECBAgAABAgRGKSAAHeWyOigCBAgQIECAAAECBAgQIECAAAECBEpg0d8AnartkyeXTl9s0OMuGzSuYQkQIECAAAECBAgQIECAAAECBAgQ2CYCywagxbPrNjFymAQIECBAgAABAgQIECBAgAABAgQIDFRg0QD07BznNzb5WH+wyZ/n4wgQIECAAAECBAgQIECAAAECBAgQGInAogHou3PcVRoBAgQIECBAgAABAgQIECBAgAABAgS6F3ATpO6XyAQJECBAgAABAgQIECBAgAABAgQIEFhWoFUAukcmsOeyk8h+t0w9ch3725UAAQIECBAgQIAAAQIECBAgQIAAAQJXEmgVgO6dkU9I3eNKn7D2Gweny9dTv7F2Vz0IECBAgAABAgQIECBAgAABAgQIECAwv0CrALQ+cb/Ul1LPrhdztOumzztTR6X2mqO/LgQIECBAgAABAgQIECBAgAABAgQIEFhIoGUAWh98tdRrU+9PXS+1UntwNpyUetxMh7NmnntKgAABAgQIECBAgAABAgQIECBAgACBdQu0CkBPz0wOS108mdGj8/i11AGT19OHCkhfmfp06maTN8/M4x+lKjjVCBAgQIAAAQIECBAgQIAAAQIECBAg0K3APTOzk1NXTOrSPB6eqqD1Lqn6rc/ptnp8T+qGKW37CNRvxdbaH719DtmREiBAgAABAgQIECBAgAABAgSWEqifjqwc5eCl9rbThglcMyO/PjUbdFbodeHMez/N88emtO0nIADdfmvuiAkQIECAAAECBAgQIECAAIHlBASgy7n92l6tvgI/O+gFefGfU7+b+slkw/55rK+/V3t76k6p99YLjQABAgQIECBAgAABAgQIECBAgAABAhslsBEB6HSuFX5OA9Dpe/V4+aRm3/OcAAECBAgQIECAAAECBAgQIECAAAECzQU2IgCtr8C/KvXl1D0mM64g9KLJ8yfm8ZupR01eeyBAgAABAgQIECBAgAABAgQIECBAgMCGCLQOQA/MLE9MHZLaNVWtvup+59RvpGpbtRunPpCq3zHYO6URIECAAAECBAgQIECAAAECBAgQIECgW4HdM7O/StXX26c3Pzonz5+cmm31O6CvTM32+3Feuxp0Vmncz90Eadzr6+gIECBAgAABAgQIECBAgACBdgJugtTOct0j7ZsRpsFnPR6fuvUqoz44236Qmt3n6av0t2k8AgLQ8aylIyFAgAABAgQIECBAgAABAgQ2VkAA2sC39VfgL82cXpx6YOrUVeZ3bLbdNfWumT43mnnuKQECBAgQIECAAAECBAgQIECAAAECBNYt0DIA/cfM5rdSR6Yum2NmP0ufx6fqpkg/n6O/LgQIECBAgAABAgQIECBAgAABAgQIEFhIYLeFeq/c+exs2j91/spdVtzytmz5fOouK/awgQABAgQIECBAgAABAgQIECBAgAABAksItApAL85nVy3bvpcdqzQCBAgQIECAAAECBAgQIECAAAECBAg0E2j5FfhmkzIQAQIECBAgQIAAAQIECBAgQIAAAQIEWggIQFsoGoMAAQIECBAgQIAAAQIECBAgQIAAgS4FBKBdLotJESBAgAABAgQIECBAgAABAgQIECDQQkAA2kLRGAQIECBAgAABAgQIECBAgAABAgQIdCkgAO1yWUyKAAECBAgQIECAAAECBAgQIECAAIEWAgLQForGIECAAAECBAgQIECAAAECBAgQIECgSwEBaJfLYlIECBAgQIAAAQIECBAgQIAAAQIECLQQEIC2UDQGAQIECBAgQIAAAQIECBAgQIAAAQJdCghAu1wWkyJAgAABAgQIECBAgAABAgQIECBAoIWAALSFojEIECBAgAABAgQIECBAgAABAgQIEOhSQADa5bKYFAECBAgQIECAAAECBAgQIECAAAECLQQEoC0UjUGAAAECBAgQIECAAAECBAgQIECAQJcCAtAul8WkCBAgQIAAAQIECBAgQIAAAQIECBBoISAAbaFoDAIECBAgQIAAAQIECBAgQIAAAQIEuhQQgHa5LCZFgAABAgQIECBAgAABAgQIECBAgEALAQFoC0VjECBAgAABAgQIECBAgAABAgQIECDQpYAAtMtlMSkCBAgQIECAAAECBAgQIECAAAECBFoICEBbKBqDAAECBAgQIECAAAECBAgQIECAAIEuBQSgXS6LSREgQIAAAQIECBAgQIAAAQIECBAg0EJAANpC0RgECBAgQIAAAQIECBAgQIAAAQIECHQpIADtcllMigABAgQIECBAgAABAgQIECBAgACBFgIC0BaKxiBAgAABAgQIECBAgAABAgQIECBAoEsBAWiXy2JSBAgQIECAAAECBAgQIECAAAECBAi0EBCAtlA0BgECBAgQIECAAAECBAgQIECAAAECXQoIQLtcFpMiQIAAAQIECBAgQIAAAQIECBAgQKCFgAC0haIxCBAgQIAAAQIECBAgQIAAAQIECBDoUkAA2uWymBQBAgQIECBAgAABAgQIECBAgAABAi0EBKAtFI1BgAABAgQIECBAgAABAgQIECBAgECXAgLQLpfFpAgQIECAAAECBAgQIECAAAECBAgQaCEgAG2haAwCBAgQIECAAAECBAgQIECAAAECBLoUEIB2uSwmRYAAAQIECBAgQIAAAQIECBAgQIBACwEBaAtFYxAgQIAAAQIECBAgQIAAAQIECBAg0KWAALTLZTEpAgQIECBAgAABAgQIECBAgAABAgRaCAhAWygagwABAgQIECBAgAABAgQIECBAgACBLgUEoF0ui0kRIECAAAECBAgQIECAAAECBAgQINBCQADaQtEYBAgQIECAAAECBAgQIECAAAECBAh0KSAA7XJZTIoAAQIECBAgQIAAAQIECBAgQIAAgRYCAtAWisYgQIAAAQIECBAgQIAAAQIECBAgQKBLAQFol8tiUgQIECBAgAABAgQIECBAgAABAgQItBAQgLZQNAYBAgQIECBAgAABAgQIECBAgAABAl0KCEC7XBaTIkCAAAECBAgQIECAAAECBAgQIECghYAAtIWiMQgQIECAAAECBAgQIECAAAECBAgQ6FJAANrlspgUAQIECBAgQIAAAQIECBAgQIAAAQItBASgLRSNQYAAAQIECBAgQIAAAQIECBAgQIBAlwIC0C6XxaQIECBAgAABAgQIECBAgAABAgQIEGghIABtoWgMAgQIECBAgAABAgQIECBAgAABAgS6FBCAdrksJkWAAAECBAgQIECAAAECBAgQIECAQAsBAWgLRWMQIECAAAECBAgQIECAAAECBAgQINClgAC0y2UxKQIECBAgQIAAAQIECBAgQIAAAQIEWggIQFsoGoMAAQIECBAgQIAAAQIECBAgQIAAgS4FBKBdLotJESBAgAABAgQIECBAgAABAgQIECDQQkAA2kLRGAQIECBAgAABAgQIECBAgAABAgQIdCkgAO1yWUyKAAECBAgQIECAAAECBAgQIECAAIEWAgLQForGIECAAAECBAgQIECAAAECBAgQIECgSwEBaJfLYlIECBAgQIAAAQIECBAgQIAAAQIECLQQEIC2UDQGAQIECBAgQIAAAQIECBAgQIAAAQJdCghAu1wWkyJAgAABAgQIECBAgAABAgQIECBAoIWAALSFojEIECBAgAABAgQIECBAgAABAgQIEOhSQADa5bKYFAECBAgQIECAAAECBAgQIECAAAECLQQEoC0UjUGAAAECBAgQIECAAAECBAgQIECAQJcCAtAul8WkCBAgQIAAAQIECBAgQIAAAQIECBBoISAAbaFoDAIECBAgQIAAAQIECBAgQIAAAQIEuhQQgHa5LCZFgAABAgQIECBAgAABAgQIECBAgEALAQFoC0VjECBAgAABAgQIECBAgAABAgQIECDQpYAAtMtlMSkCBAgQIECAAAECBAgQIECAAAECBFoICEBbKBqDAAECBAgQIECAAAECBAgQIECAAIEuBQSgXS6LSREgQIAAAQIECBAgQIAAAQIECBAg0EJAANpC0RgECBAgQIAAAQIECBAgQIAAAQIECHQpIADtcllMigABAgQIECBAgAABAgQIECBAgACBFgIC0BaKxiBAgAABAgQIECBAgAABAgQIECBAoEsBAWiXy2JSBAgQIECAAAECBAgQIECAAAECBAi0EBCAtlA0BgECBAgQIECAAAECBAgQIECAAAECXQoIQLtcFpMiQIAAAQIECBAgQIAAAQIECBAgQKCFgAC0haIxCBAgQIAAAQIECBAgQIAAAQIECBDoUkAA2uWymBQBAgQIECBAgAABAgQIECBAgAABAi0EBKAtFI1BgAABAgQIECBAgAABAgQIECBAgECXAgLQLpfFpAgQIECAAAECBAgQIECAAAECBAgQaCEgAG2haAwCBAgQIECAAAECBAgQIECAAAECBLoUEIB2uSwmRYAAAQIECBAgQIAAAQIECBAgQIBACwEBaAtFYxAgQIAAAQIECBAgQIAAAQIECBAg0KWAALTLZTEpAgQIECBAgAABAgQIECBAgAABAgRaCAhAWygagwABAgQIECBAgAABAgQIECBAgACBLgUEoF0ui0kRIECAAAECBAgQIECAAAECBAgQINBCQADaQtEYBAgQIECAAAECBAgQIECAAAECBAh0KSAA7XJZTIoAAQIECBAgQIAAAQIECBAgQIAAgRYCAtAWisYgQIAAAQIECBAgQIAAAQIECBAgQKBLAQFol8tiUgQIECBAgAABAgQIECBAgAABAgQItBAQgLZQNAYBAgQIECBAgAABAgQIECBAgAABAl0KCEC7XBaTIkCAAAECBAgQIECAAAECBAgQIECghYAAtIWiMQgQIECAAAECBAgQIECAAAECBAgQ6FJAANrlspgUAQIECBAgQIAAAQIECBAgQIAAAQItBASgLRSNQYAAAQIECBAgQIAAAQIECBAgQIBAlwIC0C6XxaQIECBAgAABAgQIECBAgAABAgQIEGghIABtoWgMAgQIECBAgAABAgQIECBAgAABAgS6FBCAdrksJkWAAAECBAgQIECAAAECBAgQIECAQAsBAWgLRWMQIECAAAECBAgQIECAAAECBAgQINClgAC0y2UxKQIECBAgQIAAAQIECBAgMDiBh2TGb0x9I3Vm6p9Tn04dkto7pREgQIAAgW0hcEKO8orU0dviaB0kAQIECBAgQIAAAQIExi9w8xzisan6b72V6qxse1pKI0BgMYGj0r3+XB282G56zwq4AnRWw3MCBAgQIECAAAECBAgQIEBgEYE7pfNXUg9K1VWfL07tn7p+6lapP0x9MlVXgNbVoX+W0ggQIECAwKgFXAE66uV1cAQIECBAgAABAgQIbCOB6+RYT0nV1WmfSK32NfcnZPuFk75PyaNGgMB8Aq4Anc9p1V6uAF2Vx0YCBAgQIECAAAECBAgQIEBgBYH/nvf/TervUo9OnZ1aqb09G6ZfgX9Fnu+1UkfvEyBAoLWAALS1qPEIECBAgAABAgQIECBAgMD4BXbPIT5rcpjPyGNd3blWe1s6HJO6XupJa3W2nQABAq0Edms1kHFWFDgjW3ZZcev6NtRvqmgECBAgQIAAAQIECBAgQGCzBQ7IB9ZVnF9P1RWg87b/k451t/hHpl477076ESBAYD0CAtD16M23b/3L1tCvtP2jHMN/anQct5uwXWvy6IEAAQIECBAgQIAAAQIEhidQX32v9pVfPsz9v9OwdLr/3DvqSIAAgWUFBKDLys2/3/3T9c2p28/s8pM8v2zmde9Pfz8TPLDxJP3eS2NQwxEgQIAAAQIECBAgQGATBfacfNb5C37mtP90/wV3150AAQKLCwhAFzdbdI8vZoffTH08de/Jzm/K44smz4fw8JRMsr6a0OKr/G/MOBUG/zClESBAgAABAgQIECBAgMAwBX48mfYtF5z+tH9dGKQRIECAwMgErpPj+VbqilRd/fng1HZsJ+Sgy+Do7XjwjpkAAQIECBAgQIAAAQIjEbh5jqP+2+6c1DUXOKYjJ/u9boF9dCWwnQWOysHXn7WDtzPCeo996L9Nud7j38z96/8Unpq6PFXudQJXKKoRIECAAAECBAgQIECAAIGhCfwgE/5C6tqpQ+ec/A3S79mTvu+Ycx/dCBAgQGCAAq/OnCu5r3rhAOe/3im7AnS9gvYnQIAAAQIECBAgQIBAHwIPyDTqv20vST18jSnVb35+LlX9P7JGX5sJEPiVgCtAf2Xh2YAE6i/976bqL/0zU9vtbugC0Cy6RoAAAQIECBAgQIAAgZEIHJHjmIagL83znd3c6IC8f+Kk3/fzuG9KI0BgPgEB6HxOq/ZyE6RVeTZkY93x7qDU701Gv3UeT5o890CAAAECBAgQIECAAAECBIYk8JJMtgLQF6deljos9ZlUBZ0VhtbNgO+QqnZyqv5b+Kf1QiNAgAABAmMVcAXoWFfWcREgQIAAAQIECBAgsJ0F6irPz6TqvhcViM7WGXl9eGqRmyWlu0aAQARcAdrgNHAFaANEQxAgQIAAAQIECBAgQIAAgW0ucHyO/8DUTVL3Sd0oVd+APCX15dRlKY0AAQJbIiAA3RJ2H0qAAAECBAgQIECAAAECBEYp8KMc1ftGeWQOigCBwQpcdbAzN3ECBAgQIECAAAECBAgQIECAAAECBAisISAAXQPIZgIECBAgQIAAAQIECBAgQIAAAQIEhisgAB3u2pk5AQIECBAgQIAAAQIECBAgQIAAAQJrCAhA1wCymQABAgQIECBAgAABAgQIECBAgACB4QoIQIe7dmZOgAABAgQIECBAgAABAgQIECBAgMAaAgLQNYBsJkCAAAECBAgQIECAAAECBAgQIEBguAIC0OGunZkTIECAAAECBAgQIECAAAECBAgQILCGgAB0DSCbCRAgQIAAAQIECBAgQIAAAQIECBAYroAAdLhrZ+YECBAgQIAAAQIECBAgQIAAAQIECKwhIABdA8hmAgQIECBAgAABAgQIECBAgAABAgSGKyAAHe7amTkBAgQIECBAgAABAgQIECBAgAABAmsICEDXALKZAAECBAgQIECAAAECBAgQIECAAIHhCghAh7t2Zk6AAAECBAgQIECAAAECBAgQIECAwBoCAtA1gGwmQIAAAQIECBAgQIAAAQIECBAgQGC4AgLQ4a6dmRMgQIAAAQIECBAgQIAAAQIECBAgsIaAAHQNIJsJECBAgAABAgQIECBAgAABAgQIEBiugAB0uGtn5gQIECBAgAABAgQIECBAgAABAgQIrCEgAF0DyGYCBAgQIECAAAECBAgQIECAAAECBIYrIAAd7tqZOQECBAgQIECAAAECBAgQIECAAAECawgIQNcAspkAAQIECBAgQIAAAQIECBAgQIAAgeEKCECHu3b/v717gbanqu8DXvjzUAGjIqBoREDBF8aktkY0wUfVGsGSmkWMUIOmRpPUxuWKWiFpjC61xqaaRKNVG6kv1JrUpohWbeor2uCDEJRKNAF8KxIeEnlDv794dhgu994593XOzDmfvdbvzpyZPbP3fGbWnHN+d2aOnhMgQIAAAQIECBAgQIAAAQIECBAg0CMgAdoDZDYBAgQIECBAgAABAgQIECBAgAABAuMVkAAd777TcwIECBAgQIAAAQIECBAgQIAAAQIEegQkQHuAzCZAgAABAgQIECBAgAABAgQIECBAYLwCEqDj3Xd6ToAAAQIECBAgQIAAAQIECBAgQIBAj4AEaA+Q2QQIECBAgAABAgQIECBAgAABAgQIjFdAAnS8+07PCRAgQIAAAQIECBAgQIAAAQIECBDoEZAA7QEymwABAgQIECBAgAABAgQIECBAgACB8QpIgI533+k5AQIECBAgQIAAAQIECBAgQIAAAQI9AhKgPUBmEyBAgAABAgQIECBAgAABAgQIECAwXgEJ0PHuOz0nQIAAAQIECBAgQIAAAQIECBAgQKBHQAK0B8hsAgQIECBAgAABAgQIECBAgAABAgTGKyABOt59p+cECBAgQIAAAQIECBAgQIAAAQIECPQISID2AJlNgAABAgQIECBAgAABAgQIECBAgMB4BSRAx7vv9JwAAQIECBAgQIAAAQIECBAgQIAAgR4BCdAeILMJECBAgAABAgQIECBAgAABAgQIEBivgAToePednhMgQIAAAQIECBAgQIAAAQIECBAg0CMgAdoDZDYBAgQIECBAgAABAgQIECBAgAABAuMVkAAd777TcwIECBAgQIAAAQIECBAgQIAAAQIEegQkQHuAzCZAgAABAgQIECBAgAABAgQIECBAYLwCEqDj3Xd6ToAAAQIECBAgQIAAAQIECBAgQIBAj4AEaA+Q2QQIECBAgAABAgQIECBAgAABAgQIjFdAAnS8+07PCRAgQIAAAQIECBAgQIAAAQIECBDoEZAA7QEymwABAgQIECBAgAABAgQIECBAgACB8QpIgI533+k5AQIECBAgQIAAAQIECBAgQIAAAQI9AhKgPUBmEyBAgAABAgQIECBAgAABAgQIECAwXgEJ0PHuOz0nQIAAAQIECBAgQIAAAQIECBAgQKBHQAK0B8hsAgQIECBAgAABAgQIECBAgAABAgTGKyABOt59p+cECBAgQIAAAQIECBAgQIAAAQIECPQISID2AJlNgAABAgQIECBAgAABAgQIECBAgMB4BSRAx7vv9JwAAQIECBAgQIAAAQIECBAgQIAAgR4BCdAeILMJECBAgAABAgQIECBAgAABAgQIEBivgAToePednhMgQIAAAQIECBAgQIAAAQIECBAg0CMgAdoDZDYBAgQIECBAgAABAgQIECBAgAABAuMVkAAd777TcwIECBAgQIAAAQIECBAgQIAAAQIEegQkQHuAzCZAgAABAgQIECBAgAABAgQIECBAYLwCEqDj3Xd6ToAAAQIECBAgQIAAAQIECBAgQIBAj4AEaA+Q2QQIECBAgAABAgQIECBAgAABAgQIjFdAAnS8+07PCRAgQIAAAQIECBAgQIAAAQIECBDoEZAA7QEymwABAgQIECBAgAABAgQIECBAgACB8QpIgI533+k5AQIECBAgQIAAAQIECBAgQIAAAQI9AhKgPUBmEyBAgAABAgQIECBAgAABAgQIECAwXgEJ0PHuOz0nQIAAAQIECBAgQIAAAQIECBAgQKBHQAK0B8hsAgQIECBAgAABAgQIECBAgAABAgTGKyABOt59p+cECBAgQIAAAQIECBAgQIAAAQIECPQISID2AJlNgAABAgQIECBAgAABAgQIECBAgMB4BSRAx7vv9JwAAQIECBAgQIAAAQIECBAgQIAAgR4BCdAeILMJECBAgAABAgQIECBAgAABAgQIEBivgAToePednhMgQIAAAQIECBAgQIAAAQIECBAg0CMgAdoDZDYBAgQIECBAgAABAgQIECBAgAABAuMVkAAd777TcwIECBAgQIAAAQIECBAgQIAAAQIEegQkQHuAzCZAgAABAgQIECBAgAABAgQIECBAYLwCEqDj3Xd6ToAAAQIECBAgQIAAAQIECBAgQIBAj4AEaA+Q2QQIECBAgAABAgQIECBAgAABAgQIjFdAAnS8+07PCRAgQIAAAQIECBAgQIAAAQIECBDoEZAA7QEymwABAgQIECBAgAABAgQIECBAgACB8QpIgI533+k5AQIECBAgQIAAAQIECBAgQIAAAQI9AhKgPUBmEyBAgAABAgQIECBAgAABAgQIECAwXgEJ0PHuOz0nQIAAAQIECBAgQIAAAQIECBAgQKBHQAK0B8hsAgQIECBAgAABAgQIECBAgAABAgTGKyABOt59p+cECBAgQIAAAQIECBAgQIAAAQIECPQISID2AJlNgAABAgQIECBAgAABAgQIECBAgMB4BSRAx7vv9JwAAQIECBAgQIAAAQIECBAgQIAAgR4BCdAeILMJECBAgAABAgQIECBAgAABAgQIEBivgAToePednhMgQIAAAQIECBAgQIAAAQIECBAg0CMgAdoDZDYBAgQIECBAgAABAgQIECBAgAABAuMVkAAd777TcwIECBAgQIAAAQIECBAgQIAAAQIEegQkQHuAzCZAgAABAgQIECBAgAABAgQIECBAYLwCEqDj3Xd6ToAAAQIECBAgQIAAAQIECBAgQIBAj4AEaA+Q2QQIECBAgAABAgQIECBAgAABAgQIjFdAAnS8+07PCRAgQIAAAQIECBAgQIAAAQIECBDoEZAA7QEymwABAgQIECBAgAABAgQIECBAgACB8QpIgI533+k5AQIECBAgQIAAAQIECBAgQIAAAQI9AhKgPUBmEyBAgAABAgQIECBAgAABAgQIECAwXgEJ0PHuOz0nQIAAAQIECBAgQIAAAQIECBAgQKBHQAK0B8hsAgQIECBAgAABAgQIECBAgAABAgTGKyABOt59p+cECBAgQIAAAQIECBAgQIAAAQIECPQISID2AJlNgAABAgQIECBAgAABAgQIECBAgMB4BSRAx7vv9JwAAQIECBAgQIAAAQIECBAgQIAAgR4BCdAeILMJECBAgAABAgQIECBAgAABAgQIEBivgAToePednhMgQIAAAQIECBAgQIAAAQIECBAg0CMgAdoDZDYBAgQIECBAgAABAgQIECBAgAABAuMVkAAd777TcwIECBAgQIAAAQIECBAgQIAAAQIEegQkQHuAzCZAgAABAgQIECBAgAABAgQIECBAYLwCEqDj3Xd6ToAAAQIECBAgQIAAAQIECBAgQIBAj4AEaA+Q2QQIECBAgAABAgQIECBAgAABAgQIjFdAAnS8+07PCRAgQIAAAQIECBAgQIAAAQIECBDoEZAA7QEymwABAgQIECBAgAABAgQIECBAgACB8QpIgI533+k5AQIECBAgQIAAAQIECBAgQIAAAQI9AhKgPUBmEyBAgAABAgQIECBAgAABAgQIECAwXgEJ0PHuOz0nQIAAAQIECBAgQIAAAQIECBAgQKBHQAK0B8hsAgQIECBAgAABAgQIECBAgAABAgTGKyABOt59p+cECBAgQIAAAQIECBAgQIAAAQIECPQISID2AJlNgAABAgQIECBAgAABAgQIECBAgMB4BSRAx7vv9JwAAQIECBAgQIAAAQIECBAgQIAAgR4BCdAeILMJECBAgAABAgQIECBAgAABAgQIEBivgAToePednhMgQIAAAQIECBAgQIAAAQIECBAg0CMgAdoDZDYBAgQIECBAgAABAgQIECBAgAABAuMVkAAd777TcwIECBAgQIAAAQIECBAgQIAAAQIEegQkQHuAzCZAgAABAgQIECBAgAABAgQIECBAYLwCEqDj3Xd6ToAAAQIECBAgQIAAAQIECBAgQIBAj4AEaA+Q2QQIECBAgAABAgQIECBAgAABAgQIjFdgj/F2Xc8JECBAgAABAgQIECCwNAL7Z0sflTgksSvxtcRHEl9PKAQIECBAgMA6AhKg6+DMaNbBaedOidtN4uoML09ckbgkUa8VAgQIECBAgAABAgSWU+DwbPbLEk9KVOKzW27Kiw8nXpA4uzvDOAECBAgQIHCzgATozRazGtsvDT01cWLiAYl6vVa5PjPOTfx54ozEmYn6kKMQIECAAAECBAgQILD4Ak/MJr49sW/imsSHEp9P3JA4IvGYSTwyw2cnXp9QCBAgQIAAAQJzEzgoLb82UVd2VhJzM1HJ0Cckxlw+m87Xtv/xmDdC3wkQIECAAAECBAjssMAjsv7rEvXZ+R2JuyZWlh/KhFcl2neLutBCIUCAAIHFEnhrNqfO8yct1mbZmkUUuGM26i8T7YNJDW9M1PN6PpX4k8S7Em9J1IebSg7WrSznJ+oW+O5y9d/e5yTGWiRAx7rn9JsAAQIECBAgQGBWAvukoW8m6nvAK6Zo9FmTuldl+MNT1FeFAAECBMYjIAE6nn211D2tDy+fTLQk5lkZf3LigMQ0Zc9UeljiPyeuTbT1/FTGx1gkQMe41/SZAAECBAgQIEBglgLPT2P1ub++R+w2ZcN1IUUt4zb4KcFUI0CAwEgEJEBHsqOWvZtPC0BLWp6e8d23AFJJz5YErStKt7KuLXRjS4tKgG6Jz8IECBAgQIAAAQJLIPCZbGN9h9jIRQ/3mSxzcYYrfywpkxQCBAgQGKmABOg27Dg/grQNiD2rOHoyvxKW9UyeuvV9s6V+BOnXEr+bOCpxaOKvEztd9k8DdRXqtP99Xq8/9ZyiKtuxrh+syV8CBAgQIECAAAECiyNQd4D9WKKe/1mPxZq2fDEVL0zcM3FY4ksJhQABAgQIEIiABOjOHwaVOKzyPxP1IWar5Y+ygkqAVjkiMYsE6BvTzk9Xg9tY7raN67IqAgQIECBAgAABAosiUI/KqosF6krOuvtrI+VrqXzPRP0A65cSCgECBAgQIBABCdCdPwzuPmniq9vU1CVZTyVS6z/Dt92mdfat5rRJhe245b7+m10PZv/EZJ0GBAgQIECAAAECBAjcLPC9yeh+N0+aeqwt09Yx9YIqEiBAgAABAgS2InB2Fq7n95y2lZV0ln1UxtszRQ/tTB/L6Ksm/R/zL9mPxVo/CRAgQIAAAQIExinQfgH+fhvo/u1T95pEXSxR4woBAgQILIaAZ4Buw37cjiv6tqEbC72Kz0627mczPGaLW3qHLP87k3X8bYYXbHF9FidAgAABAgQIECBAYHgCZ0y69KwNdO3pqbtX4mOJKzawnKoECBAgQIAAgS0LHJ41tF9uvyzjz0zUB5ONlgdlgbMS7erPl250BQOp7wrQgewI3SBAgAABAgQIEBisQF35eX2ivkc8fIpe3jt1Lk3Ud4XHT1FfFQIECBAYj4ArQLdhX3kG6DYg9qyifqTolMQrEz+UeP1k/KMZ/kWiruL8duKqxNWJ2ie3SdRtK/WszHslfjLxgEQrH8zIb7QXhgQIECBAgAABAgQILJTAedmauvPr+Yk/STw5Ud8BViv1jP3/nqi7xd6TeH9CIUCAAAECBAjMReBpafX7iXYF52aH9YHmTnPZgu1p1BWg2+NoLQQIECBAgAABAostUI8re2eifW+oROgJifsnjkwcl3hLoq4UrTp16/s+CYUAAQIEFkvAFaCLtT+XYmsOyFbWrevtoebtw0zfsK4MfW/i2MTYiwTo2Peg/hMgQIAAAQIECMxKYLc09IJE/ar7Wt8Z6jb5ulp0M4/ZymIKAQIECAxcQAJ0G3aQW+C3AXEDq7g4dU+dxD0z/PFEPa+nbnev2+P3S9SvNl6ZuCJRt8/X7S/nJGqaQoAAAQIECBAgQIDA8ghU0vMViTcnnpJ4bOKQxK7E1xJ/mnhH4sKEQoAAAQIECKwhIAG6BswMJl+YNioUAgQIECBAgAABAgQIrCfwncx89STWq2ceAQIECBAgsIpAPVdGIUCAAAECBAgQIECAAAECBAgQIECAwEIKSIAu5G61UQQIECBAgAABAgQIECBAgAABAgQIlIAEqOOAAAECBAgQIECAAAECBAgQIECAAIGFFZAAXdhda8MIECBAgAABAgQIECBAgAABAgQIEJAAdQwQIECAAAECBAgQIECAAAECBAgQILCwAhKgC7trbRgBAgQIECBAgAABAgQIECBAgAABAhKgjgECBAgQIECAAAECBAgQIECAAAECBBZWQAJ0YXetDSNAgAABAgQIECBAgAABAgQIECBAQALUMUCAAAECBAgQIECAAAECBAgQIECAwMIKSIAu7K61YQQIECBAgAABAgQIECBAgAABAgQISIA6BggQIECAAAECBAgQIECAAAECBAgQWFgBCdCF3bU2jAABAgQIECBAgAABAgQIECBAgACBPRAQmJPAC9PuM+fUdl+z+6dCxfWJm/oqm09gjgLtHF7HqkJgqAK7pWN1rN6YuGGondQvAhGoCwN2Jbz/OxyGLuD9f+h7SP9KwPu/42AsAu39/2vp8PcH2umDB9qvUXWrvXmOqtM6O2qBCye9PzDDiiGXvYbcOX0j0BGoL+wKgaEL1IdLnzuGvpf0rwS8/zsOxiLg/X8se2q5++n9f7n3/5i2/h4D72xdnHXRwPs46O7Vf2UUArMWODwN7jnrRjfQ3m+l7gmJ30+8fwPLqUpg1gLvSoP7Jep4vXLWjWuPwJQCD0q9lyXOTpw65TKqEZiHwLPT6OMT3v/noa/NjQh4/9+IlrrzEvD+Py957W5UoL3//2YWfPdGF55h/fq+V1epKpsUcCXGJuEstiWBv97S0ju/8GWTJj6foQTozntrYfMC7db3D2cVl25+NZYksKMC103WfkmGzqk7Sm3lWxQ4frK89/8tQlp8xwW8/+84sQa2QcD7/zYgWsVMBNr7/7fS2hdn0qJG5iJQl6MrBAgQIECAAAECBAgQIECAAAECBAgQWEgBCdCF3K02igABAgQIECBAgAABAgQIECBAgACBEpAAdRwQIECAAAECBAgQIECAAAECBAgQILCwAhKgC7trbRgBAgQIECBAgAABAgQIECBAgAABAhKgjgECBAgQIECAAAECBAgQIECAAAECBBZWQAJ0YXetDSNAgAABAgQIECBAgAABAgQIECBAQALUMUCAAAECBAgQIECAAAECBAgQIECAwMIKSIAu7K61YQQIECBAgAABAgQIECBAgAABAgQISIA6BggQIECAAAECBAgQIECAAAECBAgQWFgBCdCF3bU2jAABAgQIECBAgAABAgQIECBAgAABCVDHAAECBAgQIECAAAECBAgQIECAAAECCysgAbqwu9aGESBAgAABAgQIECBAgAABAgQIECAgAeoYIECAAAECBAgQIECAAAECBAgQIEBgYQUkQBd219owAgQIECBAgAABAgQIECBAgAABAgQkQB0DBAgQIECAAAECBAgQIECAAAECBAgsrIAE6MLuWhtGgAABAgQIECBAgAABAgQIECBAgIAEqGOAwK0FrphMuvzWs0whMCiBOlavTVw1qF7pDIFbCrRzaTu33nKuVwSGI9CO0TYcTs/0hMAtBbz/39LDq2EKeP8f5n7Rq1sLtPf9Nrx1DVMIECCwoAL7ZbuOTexa0O2zWYsjcJ9syoMXZ3NsyQILPCbbdtACb59NWwwB7/+LsR+XYSu8/y/DXl6MbfT+vxj7cdG3wvv/ou9h20eAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECSyKw25Jsp80k0CewKxVOTvxc4t6J2yfOSvxZ4szEZxIKgSEKHJ1OfTxxaeLOQ+ygPi2lwP7Z6ucm/nGizql3TVyQOD9Rx+trE9cmFALzFjgwHXhB4p8m7pPYO/GVxAcSb0zUMasQGKrAL6dj/37SuYdn+OWhdlS/lkLguGzlE6bc0u+nXn1OUAjMU6A+p/6LxIMTNV7li4lPJH47cXlCIUCAwEIJ3C1bc27ipjXiukw/KaEQGJrAHdOh+nJex+53h9Y5/VlagX+bLa+E/Frn1JpeHy4fkVAIzFPgGWm8vtysdaxemXmPm2cHtU1gHYH7Zd5ViXb8VgJfITBPgXem8XY89g0vm2dHtU0gAs9O1D/j1zpWv5N50yb0U1UhQIDA8AXqSs9zEu3Ed3bGX5x4ZuLtifrvZM27MfErCYXAUATaVcrt2JUAHcqeWe5+nJjNb8dkDc9MvDDx84mXJL6QaPPr/Fpf4BUC8xCoLzX13t6Ox/ri/kuJpydel2hfimr4lIRCYEgCe6Uz9Zm1Hb81lAAd0h5azr60f8p3j8u1xiVAl/MYGcJW113Qb0m0Y/OajL8v8azE8xKfTLR538v4kQmFAAECCyHwH7MV7QT3rozXB8puqduJ6g266tSVoAcnFALzFnhoOnBeoh27NZQAnfde0f6hIbgiUcdjJY2OT6wse2bC7yXasVtf4GuaQmCWAndKY+29/YaMH7tK4z+aaX+XqGO13v/vkVAIDEWgbs1s59E2lAAdyt5Zzn7sk82u82kdjx9K3KUnDsx8hcA8BOofne28WUn7u63oxO553T3HfmzFfC8JECAwSoH6AlT/1akT4EWJlcnPTPr7Ul+M2knyRT+Y5C+BuQjUh8tXJ9oHzHZc1lACdC67RKMdgRdmvB2TL+1MXzm6KxPO6tR9yMoKXhPYYYF/nfW3Y7XOqWuVX8+MVu831qpkOoEZCzwi7bXPAe2fTnWcSoDOeEdo7hYC9c/5dr6sOz4UAkMUqOd8X5yoY7X+ybnWPzfrs2olR6tenW8PSCgECBAYtUDd5t7eqOuL+3qlnldXdb+RcLXSelLm7ZRAJYn+JtGO2XozriTTNyfTJEADocxV4ANpvR2fh/T05AWdunXbsUJglgLvT2PtWG0/erBa+8d06p2+WgXTCMxY4A5p7yuJOn7rnPvOyXi9PjKhEJiXQL2Xt/Pq8fPqhHYJ9Aj8dOa347QvUV/5gb9K1C3xD0soBAgQGLXAael9OwGu9wWoNvI1nbqPrQkKgRkL1Jt0O16/nvFHTtr/6mR6/TdTITBPgdPS+KcS9Q+jer7SeuWpmdmO5/WuFl1vHeYR2KxA3fHxwEQ923O9Y7V+0bgdp/8p4wqBeQtUIr6OyUsS9Vim9rqmSYAGQZmbwBvScjtf/vDceqFhAusLvCOz23F6+PpVzSVAgMBiCbSrOutKur6rOp+ROu1k+dzFYrA1IxGoBGhd5XlKon4AqRUJ0CZhOCaB30ln2zn1Z8fUcX1dKoHf6xynj16qLbexQxQ4sXM8njDpoAToEPfUcvapPdrm253Nr38wHZLYvzPNKIF5CrTb2i+aZye0TYAAgXkIXJ9G6wt43UrUVx6XCu3Lev2HUyEwa4Gj0uC+qzQqAboKikmDFtgvvftWop1TDx10b3VuWQV+IRt+Y6KO068l+v5RmioKgR0TOCRrvixRx+PbOq1IgHYwjM5NYFdavipRx+eZiaMT9YiGKxM1raK+b/23xOEJhcA8BG6bRuvCpzoe/8ekA7tnWD96XBeY1DH7Z4n65+eTEzVPIUCAwEII1I/JtDfkT0+xRT/Wqf+eKeqrQmBWAhKgs5LWznYJdH9Zsz5sKgSGIHCXdOJnEv8u8blE+4xQVzUdnFAIzEugvoR/NNGSSPUc0FYkQJuE4TwF7pfG2znzis54m9YdVlL0l+fZWW0vrUD9UFw7Fv8w4/WPze45tM1rw/+d+XdPKAQIEBi9QH2ZaSe3j02xNfft1K//bCoEhiIgATqUPaEf0wicnErt3Ht5xj0nbBo1dWYh8ItppB2bbfiVTLvrLBrXBoF1BNqPxtUVyY9aUa/75f3IFfO8JDArge7jGer8WUnO9yWel/hXiVckvpxo59Ya/lxCITBLgX+Sxtox+AcZbz+IeF3Gz0nURU41rNetXv3GQv2DVCFAgMCoBepDYjuxfXCKLTmsU/8jU9RXhcCsBCRAZyWtna0KHJcVdD9UPn2rK7Q8gW0UeFnWdXWizqnt1vf6nPD9xMsT9Sw7hcCsBeoOpGsTdSy+apXGJUBXQTFp5gKvTIvte1U9MqSuCF1ZbpcJb0i0epVYOmBlJa8J7KBA/QOpHX/t82jd8bHyeH1App3bqfvOjCsECBAYtUA3ofmnU2zJEanTTpgfmKK+KgRmJSABOitp7WxF4OQs3L7E17n0xVtZmWUJ7IBA3Va8a7Leet7yLyQuTbT3/jdN5hkQmJVAPa/u/yXqGPxC4jaJlUUCdKWI1/MQuFcafVLi1xL3TKxV6hzbfcRI/eNJITArgX+ehtp7eg0rWV/PpV+t3DkTu58BfnK1SqYRIEBgLAIHpqPtBPjJKTr9wE79d01RXxUCsxKQAJ2VtHY2K/CbWbCdb2v4os2uyHIEZixQV4Fck2jHb90+pxCYlcBr0lAde/XPox9do1EJ0DVgTB6swBPTs3ZO9Vixwe6mhexY/dhRO/ZqeGLPVj6/U//Xe+qaTYAAgUEL1H/V2wnw81P0tHvCfOMU9VUhMCsBCdBZSWtnowJ7ZIE/TLRz7fUZf+ZGV6I+gTkLvDztt2P49XPui+aXR+DxneOujsG6Qnm1+KNOvUrQtzp1/lUIDFHgkHSqnVPrM6xCYFYCP5KG2rFXw77n0D+sU/8ds+qkdggQILBTAl/PiuvkVz/E0VfqQd3thPmSvsrmE5ihgAToDLE1NbXA7VPzg4l23rwy48dOvbSKBIYj0L1lrn4RViEwC4HXpZF2/tzM8JhZdFIbBDYhsCvLtOcs1zOWFQKzEtg/DbXzaV1Z3/ds74M69c+aVSe1s7MCu+/s6q2dwKAFzpv0rr6o9z2E+96dLfl0Z9woAQIECNxSoJ6b9LHEYyaTv5lhfRk/Y/LagMA8Beqz72GJf5Z47BQd+Xanzj6dcaMECBAg8AOB+i51r8RRU4DUVXct8fSlKeqrQmC7BC7JiuozaZU9E4f//djaf+7YmVXPA1UWQMDtEQuwE23CpgX+PEvWF6Aq9WDjuo1orfITnRm1nEKAAAECtxao2y/rys+6zahKPWLkpxJ1pbJCYAgCe6cT9Q/QGtYXmgMT9XiGtcr9OzP+ojNulMBOCrw/K//uFA38y9Rpv2Bcj2hoy1w0xbKqENgOgf2ykkosVV7hqsSdElcn1irteK35X1yrkukEdkigvscfP1n3QzP88jrtdC+AumCdemYRIEBgFAIPSi/bZfDvXafH98i8uky+6rr6cx0os+Yi4Bb4ubBrdA2B0zO9nVfrdqH6IqQQGJrAh9Khdpw+oadzZ3bqPqOnrtkEZi3QPeceOevGtUdgIvCJDNs5tSWX1sL5SKfuiWtVMp3ADgmckPW2Y/V9GW9XI6/W3B936j5+tQqmESBAYGwCn0mH6yRYz6I5aZXO3zbTus+x+5lV6phEYJ4CEqDz1Nd2V+DRedE+VH4j43fpzjROYEACz0pf2rF6Qcbr9s3VSrdeXVF3h9UqmUZgjgISoHPE1/Q/CDwvY+2cWufK7q3D/1ApI8/u1PtcxtdLPnWXM05guwRukxVdmGjH68vXWHE9t77VqWN61xr1TCZAgMCoBH48vW0P4r4h46cmDk3Uc0Eenvhoop38PpVxz80NgjIoAQnQQe2Ope3MXtnyupWtnS/Pz3g983OaqC9ECoFZCtSX7rrFuB2vX8j44xK3S9RtnEcl3pxo8+vzwTEJhcDQBE5Ph9pxeuTQOqc/SyNQ583uVaD1bM/jEvsmal7ddXdaoh2rdat8PX5MITAPgXr+dz36ph2Pf5DxRyTqWK1n1J6S6M6f5nnhWUQhQIDAOATqUvgrE+0kWMN2y3ubVm/kd04oBIYmIAE6tD2ynP2p5FE7X250+KblJLPVcxao9/RzEt3jtRKd16yYdnlePy2hEBiigAToEPfKcvbpbtnsv0p0z6l1kcnKc+pXMu3BCYXAPAUemca/legeryuP1fpM8Jx5dlLbBAgQ2CmBB2TF9XzP7n976oRYJ8JXJfZPKASGKCABOsS9snx9em42ufshciPjEqDLd7wMZYvrao/6cnNZYuUxW/8IfU+ivtQrBIYqIAE61D2znP2qu0HqdvjVzqnfyfR3Jw5MKASGIHDXdOK9iSsSKz8DfDzTHp1QFkygbgFSCBC4WaBuf3tQ4h6Jv0mcn6irPxQCBAgQIEBgMQXq8TaHJe6bqGd/1y3xdSXTdQmFAAECBDYmUOfUQxL1WIY6p9bzPi9KKASGKFA5sSMSP5KoZGi9/1ceQCFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRIlLc5AAAK/0lEQVQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBaQWOTcVvrRKfnnYF21Tv6KznhsR311nfUzJvtb5+eJ1lzCJAgAABAgQIENikwB6bXM5iBAgQIECAAAECBIYksHc6c9AqHbpqlWk7NemOWfGbE7v3NHDbzF+tr9/sWc5sAgQIECBAgACBTQhIgG4CzSIECBAgQIAAAQKDFjgjvfvApIffm1FPb592/lfiiCna+1Tq/JtOvdd0xo0SIECAAAECBAgQIECAAAECBAgQIEDgVgJPypSbJnHqrebu7ISHZvXnTdpufVjvFviVvWnLnL1yhtcECBAgQIAAAQJbF+i7PWfrLVgDAQIECBAgQIAAgcUU2Ceb9erEJxL3XcxNtFUECBAgQIAAgfELSICOfx/aAgIECBAgQIAAgdkLPCRNnpv41UR9pr4x8bJE/biRQoAAAQIECBAgMCABzwAd0M7QFQIECBAgQIDAkgqcmO3+icm2/22GdQt73Ra+WnlOJt5nMqOuvHzbapVmMK1+df7QSTvfyPCkxP9JPHUyba3+T2YbECBAgAABAgQIECBAgAABAgQIECCwLAJ3z4ZW4rM9C/NX1tjw4zt1Ls74wZ16s34G6EvSdj3n85RE/QBSK1/NSG1H9W/a0rbbM0CnFVOPAAECBAgQIECAAAECBAgQIECAwMgETkh/WyKwfrn9kBX9r2RnJRyrTt1u/oREt8w6AXpUGt+324HJuAToKigmESBAgAABAgTmKeAZoPPU1zYBAgQIECBAgEATeHdG3jp5UYnFN7QZGe6W+K+J/SfT6oeH3jcZn9fg3DR85bwa1y4BAgQIECBAgAABAgQIECBAgAABAuMTqFvJL0i0K0F/frIJz+1M+0zG95pM7w5mfQVot+3uuCtAuxrGCRAgQIAAAQIECBAgQIAAAQIECBC4hUD9GNINiUqC1nM0j0lcPXl9RYb3SqxWJEBXUzGNAAECBAgQIEDgH7kF3kFAgAABAgQIECAwJIGPpzP/YdKhO2f44cTek9e/lOGXJ+MGBAgQIECAAAECBKYSkACdikklAgQIECBAgACBGQq8KG19dtLeHpPhaRm+fTJuQIAAAQIECBAgQGBqAQnQqalUJECAAAECBAgQmJHAdWnndSvaOmPFay8JECBAgAABAgQITCUgAToVk0oECBAgQIAAAQIzFDggbb10RXuvzeuarhAgQIAAAQIECBDYkIAE6Ia4VCZAgAABAgQIEJiBwH9JGwdN2vncZFiv3zQZNyBAgAABAgQIECAwtYAE6NRUKhIgQIAAAQIECMxA4BfTxnGTds7K8OhES4I+MeM1XyFAgAABAgQIECBAgAABAgQIECBAgMDoBO6dHl+ZuClxdeJ+iSoPTFybqOk1v+qtLE/KhJpfcerKmTN8/dVJHy7eQJut32dvYBlVCRAgQIAAAQIEphRwBeiUUKoRIECAAAECBAjsqED92vvbEvtMWnlRhudNxv8yw/ZM0Jpf9dqvw0+qTD04LDUfPIm7T72UigQIECBAgAABAgQIECBAgAABAgQIENiCwIuzbLsSsm5937ViXXvm9TmdOlW/W6a9AvT0LNTaeWV3Bds07grQbYK0GgIECBAgQIDAdgm4AnS7JK2HAAECBAgQIEBgswIPzYKnTBa+JsOTEzdMXrfBdRl5WuL6yYSqX8spBAgQIECAAAECBNYVkABdl8dMAgQIECBAgACBHRbYN+uvW9rbFZ+/lfF26/vKpuvHkF4xmVj135qo5RUCBAgQIECAAAECBAgQIECAAAECBAgstMC0t8AXwv0TdRv8r9aLAZR2S74fQRrAztAFAgQIECBAYPEEXAG6ePvUFhEgQIAAAQIECKwv8JDJ7P+7fjVzCRAgQIAAAQIEFkFAAnQR9qJtIECAAAECBAgQmFbgLqlYV35elXDF5bRq6hEgQIAAAQIERiwgATrinafrBAgQIECAAAECGxb47SxxcOKxiWs3vLQFCBAgQIAAAQIECBAgQIAAAQIECBAgMAeB7jNA6xfjr57E+Sv6ckheH7Zi2qxfnpwGW/9q6Bmgs94D2iNAgAABAgSWSmCPpdpaG0uAAAECBAgQILAMAvUZt33O3WvFBl+04vU8XtYv2O89j4a1SYAAAQIECBBYRoH2wXAZt902EyBAgAABAgQILI7Ap7MpJ62yOX+3yrR5T/pIOrBaXy+dd8e0T4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgekE/j9oVVPbu6uaEQAAAABJRU5ErkJggg==" title="plot of chunk unnamed-chunk-2" alt="plot of chunk unnamed-chunk-2" width="672" /></p>
</div>
<div id="b" class="section level3">
<h3>b</h3>
<pre class="r"><code>labels = sample(2, nrow(x), replace=T)
labels</code></pre>
<pre><code>## [1] 1 1 2 2 1 2</code></pre>
</div>
<div id="c" class="section level3">
<h3>c</h3>
<pre class="r"><code>centroid1 = c(mean(x[labels==1, 1]), mean(x[labels==1, 2]))
centroid2 = c(mean(x[labels==2, 1]), mean(x[labels==2, 2]))
centroid1</code></pre>
<pre><code>## [1] 2.667 3.000</code></pre>
<pre class="r"><code>centroid2</code></pre>
<pre><code>## [1] 3.000 1.667</code></pre>
<pre class="r"><code>plot(x[,1], x[,2], col=(labels+1), pch=20, cex=2)
points(centroid1[1], centroid1[2], col=2, pch=4)
points(centroid2[1], centroid2[2], col=3, pch=4)</code></pre>
<p><img 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CBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEATgV6TjWxDYB0CD45tXxS1bR37sCkBAgQIECBAgAABAgQIECBAYFIEbosTfXPUnZNywm2f50zbO7Q/AhUCOfx8RcUYqwkQIECAAAECBAgQIECAAAECBO4VuDLefuDeH70bRUAAOoqWsW0IDK/8/Ejs7NNt7NA+CBAgQIAAAQIECBAgQIAAAQJbVOD0OK/HRw3zlC16muM9LQHoeH3tfXWBHH6+ffXV1hAgQIAAAQIECBAgQIAAAQIEJl4gh5+5LOsQmFrHtjYlQIAAAQIECBAgQIAAAQIECBAgQIBApwUEoJ2eHs0RIECAAAECBAgQIECAAAECBAgQILAeAQHoevRsS4AAAQIECBAgQIAAAQIECBAgQIBApwUEoJ2eHs0RIECAAAECBAgQIECAAAECBAgQILAeAQHoevRsS4AAAQIECBAgQIAAAQIECBAgQIBApwUEoJ2eHs0RIECAAAECBAgQIECAAAECBAgQILAeAQHoevRsS4AAAQIECBAgQIAAAQIECBAgQIBApwUEoJ2eHs0RIECAAAECBAgQIECAAAECBAgQILAeAQHoevRsS4AAAQIECBAgQIAAAQIECBAgQIBApwVmOt2d5ghslMCZadvRj5o5OU2nk/r9/kPisPv3Uro9pf5XFvuLF9zwxHTJRrXiOATWEohfysPm0uwpvdR/fEq9o+P3tNdPvZtSWrxsd9r1yQNSumat7a0jQIAAAQIECBAgQIAAAQKTJiAAnbQZd757C5ydZrc/eOoFqd97TeqlI/LKXi8ipXuWXprqTaXtF6erImY64/oTFz50zypvCGygwF0pPXAmzb4hfjufE7+l26LuOfrSu6m0Lc32d6X00cWUXjOX5q+8Z4A3BAgQIECAAAECBAgQIEBgggXcAj/Bkz/pp374Z9NDtz9o5ksRcb49Qs894edqJpGJPryXeh/cfvH0uQdfkA5ebZzPCYxDYGea/bkIN/85fgefF/uv+Ier3o/HH/bLd6W5V4yjF/skQIAAAQIECBAgQIAAAQKlCQhAuztj09HaDw7qmO62WWZn2z+XHr+tN31JBJuPqHsG/dTvR1B6ygH7z3zuyL9fulq07rbGEWgqsCvN/sZU6p0VV3zuv7SPvS5RXrHbe9blv+2/N5/m/mjFAD8SIECAAAECBAgQIECAAIGJExCAdnfK7xetXTCoX+xum+V1dtiFaXuvP/3RuNf9kFG6j6vvhvccHzc1NfOhFLfPj7K9sQRGFYgrP38qtvndeBZtf7Rt8+9qBPYpPT+uBH35aNsaTYAAAQIECBAgQIAAAQIEtpaAAHRrzaezqSGwbXb6nZFlfnuNofscsnQlaHrS0Q+eeuk+B/iQQAsC30rpkPgD/cf5qbRLNepO9wT2EZz2f3dHmjtu1K2NJ0CAAAECBAgQIECAAAECW0Wg4llyW+U0N/U8HhpHb+J8/2VdHxrvH77s5+Hbq4ZvvNYTOOqz6XvjSs7T9oSY917RWW/jwah7rwTtveqgi9KZtz053TrSDgwmUENg/zT76gg+R7pKeR+7zeHpzHTq/3asO20f631EgAABAgQIECBAgAABAgS2vECTYG7Lo7R8ghfF/o5a5z5/JbbPtXKJcMMyisBUb/qFefy9IeYoW68c2zvowOmpZ9+WFv9g5Ro/E1iPwBUpzcbVn/91Pfu4d9s9t8//xJ0pHX1gStfd+7l3BAgQIECAAAECBAgQIEBgMgTcAj8Z8+wsBwJxP/CPtIkR34r0jDb3Z18EssDD0syJEdMf3I7Gnlvop7albU9rZ3/2QoAAAQIECBAgQIAAAQIEyhJwBej45+uNcYj/GbXf4FB3xOsnoqq+1CR/wc5PDrb5p3j98uD9ZrzkK00Pi2rjitPBN1lv/GkcfkE6Kr7F/Yg2jxwgx7e5P/sikAX6qfeYNv6HbYXmY1b87EcCBAgQIECAAAECBAgQIDARAgLQ8U/z/4pD/F3UWVGPjcrP9sxXdj0vaq3bUQ+K9cMA9MPx/oyozVr+MA78/JYP/sOxv7e3vM81dzc1lw5fc0CTlb3UaqDapAXbbD2BCECPaD8AbTf833rqzogAAQIECBAgQIAAAQIEtqqAAHRjZjZfwfmEqN+KelnU06Muj3pB1NlRXV+ujgZviGrjkQn5S122RcXjCDd26S2mxVbOYHnb/dinhUD7Agvt7zKNY59jaNMuCRAgQIAAAQIECBAgQIBAuwIC0HY919rbfKx8ZdQnov406kFRH4g6NepXo26L6uryP6KxXG0sl8ZOToja8G9Ov7ufrms7de331ryKtw0v+5hAgV7qx9Xh7V4DGs/cWOuK8wlUdsoECBAgQIAAAQIECBAgMCkCbVzRNylWbZ3n38WO8nMj/3yww2fHa74a9CmDn72MSeC2J6db+6mfr2ZtbYmI6gut7cyOCNwrkP+hoO1lHPtsu0f7I0CAAAECBAgQIECAAAECrQsIQFsnrbXDb8Won4s6PSpf+ZmvBj0v6q1Rwy9LireW1gUWex9rc59xVV2r+2uzN/sqV+B30q5/jO6vbecM+vFr2t9xZ5r/m3b2Zy8ECBAgQIAAAQIECBAgQKAsAQHo5s5X/mKkfDXohVH5fteXROUrCh8XZRmDwEJ/9+9HGLQrrgSNUGi9S/+a6+9e+OB692J7AisFXp/2PFv2bSs/b/ZzrxdfqvTuB3T7MRvNTs1WBAgQIECAAAECBAgQIECghoAAtAbSmId8I/Z/ctSro3ZFPTLqH6JeFWVpWeCmk9LX+v3eH/ZSr/EDFofh6eJiOiNmbkfLLdodgT0CX0873xUp/dfXz9G/Y2fa+Yb178ceCBAgQIAAAQIECBAgQIBAmQIC0G7MW/4m8TdFnRh1VVT+lnQBaCCMY+ndvPvVcf3nF5vsO4efOTzt9/tn3fDEhT9tsg/bEKgjcGxKO+NC0Gfl29frjL/vmD23vvfjj8tz75/STfdd7xMCBAgQIECAAAECBAgQIDAZAgLQbs1zDuXyN6S/q1ttba1urvuxdNfOHbufGfHQlfnMhld0Vp3lsvDzk9fvWPilqvHWE1ivwGza9fkIMON5wY1C0PgV7//6XJr/yHr7sD0BAgQIECBAgAABAgQIEChZQADavdm7O1p6UdQzos4c1CXxamlR4JaT07U7b919YgREHxneDr9aEJq/QWbPoft7nsv45uuvWfhRt763OBl2taZABJgfjt/N749fwqvXHLj3ym8upN6PRYAaz7y1ECBAgAABAgQIECBAgACByRaYmezT7/TZnxvd5bKMSeCWZ6TbU1r4iSP/fuYpU9PpdXGYJ+/rUL3U3x2fx1V0C6+77sSlq0b3Nc5nBMYlkK8EvSKlhx+b5l4Yx3hxPMD2O1Y51k2LqX/m7Wn+zYendMcqY3xMgAABAgQIECBAgAABAgQmSkAAOlHT7WT3JXDjSbvPj8/PP+zCtH1mevqk1Osfk3rpgAiZbu/3el/ZecfC39/61HTbvrb1GYGNEohvR5uPx4K+PY73jvm07fh4PSGuXj46XqfiCtEb4/Wyj6RdX4iHhi7EewsBAgQIECBAgAABAgQIECAwEBCA+lUgMBC4+fvT9ZEdfRAIgY4L9OOK0Muix1wWAgQIECBAgAABAgQIECBAoELAM0ArgKwmQIAAAQIECBAgQIAAAQIECBAgQKBcAQFouXOncwIECBAgQIAAAQIECBAgQIAAAQIEKgQEoBVAVhMgQIAAAQIECBAgQIAAAQIECBAgUK6AALTcudM5AQIECBAgQIAAAQIECBAgQIAAAQIVAgLQCiCrCRAgQIAAAQIECBAgQIAAAQIECBAoV0AAWu7c6ZwAAQIECBAgQIAAAQIECBAgQIAAgQoBAWgFkNUECBAgQIAAAQIECBAgQIAAAQIECJQrIAAtd+50ToAAAQIECBAgQIAAAQIECBAgQIBAhYAAtALIagIECBAgQIAAAQIECBAgQIAAAQIEyhUQgJY7dzonQIAAAQIECBAgQIAAAQIECBAgQKBCQABaAWQ1AQIECBAgQIAAAQIECBAgQIAAAQLlCghAy507nRMgQIAAAQIECBAgQIAAAQIECBAgUCEgAK0AspoAAQIECBAgQIAAAQIECBAgQIAAgXIFBKDlzp3OCRAgQIAAAQIECBAgQIAAAQIECBCoEBCAVgBZTYAAAQIECBAgQIAAAQIECBAgQIBAuQIC0HLnTucECBAgQIAAAQIECBAgQIAAAQIECFQICEArgKwmQIAAAQIECBAgQIAAAQIECBAgQKBcAQFouXOncwIECBAgQIAAAQIECBAgQIAAAQIEKgQEoBVAVhMgQIAAAQIECBAgQIAAAQIECBAgUK6AALTcudM5AQIECBAgQIAAAQIECBAgQIAAAQIVAgLQCiCrCRAgQIAAAQIECBAgQIAAAQIECBAoV0AAWu7c6ZwAAQIECBAgQIAAAQIECBAgQIAAgQoBAWgFkNUECBAgQIAAAQIECBAgQIAAAQIECJQrIAAtd+50ToAAAQIECBAgQIAAAQIECBAgQIBAhYAAtALIagIECBAgQIAAAQIECBAgQIAAAQIEyhUQgJY7dzonQIAAAQIECBAgQIAAAQIECBAgQKBCQABaAWQ1AQIECBAgQIAAAQIECBAgQIAAAQLlCghAy507nRMgQIAAAQIECBAgQIAAAQIECBAgUCEgAK0AspoAAQIECBAgQIAAAQIECBAgQIAAgXIFBKDlzp3OCRAgQIAAAQIECBAgQIAAAQIECBCoEBCAVgBZTYAAAQIECBAgQIAAAQIECBAgQIBAuQIC0HLnTucECBAgQIAAAQIECBAgQIAAAQIECFQICEArgKwmQIAAAQIECBAgQIAAAQIECBAgQKBcAQFouXOncwIECBAgQIAAAQIECBAgQIAAAQIEKgQEoBVAVhMgQIAAAQIECBAgQIAAAQIECBAgUK6AALTcudM5AQIECBAgQIAAAQIECBAgQIAAAQIVAgLQCiCrCRAgQIAAAQIECBAgQIAAAQIECBAoV0AAWu7c6ZwAAQIECBAgQIAAAQIECBAgQIAAgQoBAWgFkNUECBAgQIAAAQIECBAgQIAAAQIECJQrIAAtd+50ToAAAQIECBAgQIAAAQIECBAgQIBAhYAAtALIagIECBAgQIAAAQIECBAgQIAAAQIEyhUQgJY7dzonQIAAAQIECBAgQIAAAQIECBAgQKBCQABaAWQ1AQIECBAgQIAAAQIECBAgQIAAAQLlCghAy507nRMgQIAAAQIECBAgQIAAAQIECBAgUCEgAK0AspoAAQIECBAgQIAAAQIECBAgQIAAgXIFBKDlzp3OCRAgQIAAAQIECBAgQIAAAQIECBCoEBCAVgBZTYAAAQIECBAgQIAAAQIECBAgQIBAuQIC0HLnTucECBAgQIAAAQIECBAgQIAAAQIECFQICEArgKwmQIAAAQIECBAgQIAAAQIECBAgQKBcAQFouXOncwIECBAgQIAAAQIECBAgQIAAAQIEKgQEoBVAVhMgQIAAAQIECBAgQIAAAQIECBAgUK6AALTcudM5AQIECBAgQIAAAQIECBAgQIAAAQIVAgLQCiCrCRAgQIAAAQIECBAgQIAAAQIECBAoV0AAWu7c6ZwAAQIECBAgQIAAAQIECBAgQIAAgQoBAWgFkNUECBAgQIAAAQIECBAgQIAAAQIECJQrIAAtd+50ToAAAQIECBAgQIAAAQIECBAgQIBAhYAAtALIagIECBAgQIAAAQIECBAgQIAAAQIEyhUQgJY7dzonQIAAAQIECBAgQIAAAQIECBAgQKBCQABaAWQ1AQIECBAgQIAAAQIECBAgQIAAAQLlCghAy507nRMgQIAAAQIECBAgQIAAAQIECBAgUCEgAK0AspoAAQIECBAgQIAAAQIECBAgQIAAgXIFBKDlzp3OCRAgQIAAAQIECBAgQIAAAQIECBCoEBCAVgBZTYAAAQIECBAgQIAAAQIECBAgQIBAuQIC0HLnTucECBAgQIAAAQIECBAgQIAAAQIECFQICEArgKwmQIAAAQIECBAgQIAAAQIECBAgQKBcAQFouXOncwIECBAgQIAAAQIECBAgQIAAAQIEKgQEoBVAVhMgQIAAAQIECBAgQIAAAQIECBAgUK6AALTcudM5AQIECBAgQIAAAQIECBAgQIAAAQIVAgLQCiCrCRAgQIAAAQIECBAgQIAAAQIECBAoV0AAWu7c6ZwAAQIECBAgQIAAAQIECBAgQIAAgQoBAWgFkNUECBAgQIAAAQIECBAgQIAAAQIECJQrIAAtd+50ToAAAQIECBAgQIAAAQIECBAgQIBAhYAAtALIagIECBAgQIAAAQIECBAgQIAAAQIEyhUQgJY7dzonQIAAAQIECBAgQIAAAQIECBAgQKBCQABaAWQ1AQIECBAgQIAAAQIECBAgQIAAAQLlCghAy507nRMgQIAAAQIECBAgQIAAAQIECBAgUCEgAK0AspoAAQIECBAgQIAAAQIECBAgQIAAgXIFBKDlzp3OCRAgQIAAAQIECBAgQIAAAQIECBCoEBCAVgBZTYAAAQIECBAgQIAAAQIECBAgQIBAuQIC0HLnTucECBAgQIAAAQIECBAgQIAAAQIECFQICEArgKwmQIAAAQIECBAgQIAAAQIECBAgQKBcAQFouXOncwIECBAgQIAAAQIECBAgQIAAAQIEKgQEoBVAVhMgQIAAAQIECBAgQIAAAQIECBAgUK6AALTcudM5AQIECBAgQIAAAQIECBAgQIAAAQIVAgLQCiCrCRAgQIAAAQIECBAgQIAAAQIECBAoV0AAWu7c6ZwAAQIECBAgQIAAAQIECBAgQIAAgQoBAWgFkNUECBAgQIAAAQIECBAgQIAAAQIECJQrIAAtd+50ToAAAQIECBAgQIAAAQIECBAgQIBAhYAAtALIagIECBAgQIAAAQIECBAgQIAAAQIEyhUQgJY7dzonQIAAAQIECBAgQIAAAQIECBAgQKBCQABaAWQ1AQIECBAgQIAAAQIECBAgQIAAAQLlCghAy507nRMgQIAAAQIECBAgQIAAAQIECBAgUCEgAK0AspoAAQIECBAgQIAAAQIECBAgQIAAgXIFBKDlzp3OCRAgQIAAAQIECBAgQIAAAQIECBCoEBCAVgBZTYAAAQIECBAgQIAAAQIECBAgQIBAuQIC0HLnTucECBAgQIAAAQIECBAgQIAAAQIECFQICEArgKwmQIAAAQIECBAgQIAAAQIECBAgQKBcAQFouXOncwIECBAgQIAAAQIECBAgQIAAAQIEKgQEoBVAVhMgQIAAAQIECBAgQIAAAQIECBAgUK6AALTcudM5AQIECBAgQIAAAQIECBAgQIAAAQIVAgLQCiCrCRAgQIAAAQIECBAgQIAAAQIECBAoV0AAWu7c6ZwAAQIECBAgQIAAAQIECBAgQIAAgQoBAWgFkNUECBAgQIAAAQIECBAgQIAAAQIECJQrIAAtd+50ToAAAQIECBAgQIAAAQIECBAgQIBAhYAAtALIagIECBAgQIAAAQIECBAgQIAAAQIEyhUQgJY7dzonQIAAAQIECBAgQIAAAQIECBAgQKBCQABaAWQ1AQIECBAgQIAAAQIECBAgQIAAAQLlCghAy507nRMgQIAAAQIECBAgQIAAAQIECBAgUCEgAK0AspoAAQIECBAgQIAAAQIECBAgQIAAgXIFBKDlzp3OCRAgQIAAAQIECBAgQIAAAQIECBCoEBCAVgBZTYAAAQIECBAgQIAAAQIECBAgQIBAuQIC0HLnTucECBAgQIAAAQIECBAgQIAAAQIECFQICEArgKwmQIAAAQIECBAgQIAAAQIECBAgQKBcAQFouXOncwIECBAgQIAAAQIECBAgQIAAAQIEKgQEoBVAVhMgQIAAAQIECBAgQIAAAQIECBAgUK6AALTcudM5AQIECBAgQIAAAQIECBAgQIAAAQIVAgLQCiCrCRAgQIAAAQIECBAgQIAAAQIECBAoV0AAWu7c6ZwAgckV6N2V0oPm07YnRJ24I6WHvD4lf88n9/fBmRMgQIAAAQIECBAgQIDAGgL+H+Y1cKwiQIBAlwR2ptnj59Ps/96V5m7clua+0UtTn4u6eDrNfe030+wtse4Du9LMk7vUs14IECBAgAABAgQIECBAgMBmCwhAN3sGHJ8AAQIVArekdFCEm++JP9j/2Eu958Xww++7Se+gWPeslKYv3JVm/zKuEH3gfcf4hAABAgQIECBAgAABAgQITJ6AAHTy5twZEyBQkEDc3v7Q+6XZf4hw8+ej7V516/1+DPvRuEL08/kW+erxRhAgQGBJIK4yf/Z8mvtMvD5iFJMdae5hsd2F8TfnF0bZzlgCBAgQIECAAAECGyUgAN0o6dGPsz02ybeynhJ11Oib24IAgdIF7ogrPafS3N9G+Hnc0rn0agSg94w5Krb7ZAQTg21L19A/AQLjFuil/vfEH5mTplLvgrohaA4/p1O6ILaL/5tl6nvG3aP9EyBAgAABAgQIECBQhsBctJkDiQes0u6j4/O/ioqruPaqf4ufXxpVIwCJUd1dLo3W8rmd090WdUagGwLxrM/zovrrqbh1/sorUprtxhnpggCBLgtcndJ+8ffmrwd/c26oCkFz+Blj/20w/lPXpLR/l89PbwQIECBAgACBQgXeF33nHOX0QvvX9oQJHBLne2ZUPJpvzy/u7ni9KOoFUcMlh5+3Rq0MP5f//LexvuQrQgWgw9n2SmANgQgWTl0KFWYXB+FC4yA0bk198RqHsooAAQL3CNQNQYWf95B5Q4AAAQIECBAYt4AAdNzC9t+aQL4i4jNRy4PM5e+HIejly8bcGe8/F/XRqK9GLUYNt8khaKmLALTUmdP3hgrElZufX2/wuWz7Gy5IaWZDT8DBCBAoVuDqiitBhZ/FTq3GCRAgQIAAgTIFBKBlztvEdT0dZ/yxqGF4ma/wjCwifSFqfvD5rnj9zcH7PO6sqJW3yOcvM7ly2ZjnxPsSFwFoibOm5w0VuDulY9q6+nMYgt6dZn5oQ0/CwQgQKFpgtRB0Zfh5XUoHFH2imidAgAABAgQIdF9AANr9OdJhCOSgchh+vjfe32+ZypHxfhgIDsd8fNn6lW/zbfT5WaB57LUrVxby8/B8PQO0kAnT5sYL5G9iHgaX7b3Ovnbjz8QRCRAoWWBlCLorzZwSf5Pueean8LPk2dU7AQIECBAgUJCAALSFyXJLZAuIFbt49mD9ZfH6i1H52Z/D5cZ4k7/l/etRwysoXhbvV1vy1aMvifpA1LdHHRF1U9S4l6fFAXKQO9XCgb5zsI/9WtiXXRDYkgLxP2jHtH1i8a8mre+z7R7tjwCBbgnEH40dV6edz3xgmsuP44n/W2Aqf0njdNT530w7f+zopeead6tp3RAgQIAAAQIECBDYh4AAdB8oLX/0yMH+/jxel4efw8N8M978WdQvR90R9ZWotZYLlq3M+96IAPSFcZxnLjtuG28Pb2Mn9kFgKwrEA3/3b+NfG1bY5GcRWwgQIDCSQA5Bd6b+y+Jv0udT6sU/Xvbn51P/5cLPkRgNJkCAAAECBAgQ2GQBAej4J+CwwSGuXeNQ+XmgOQDNX3yUb29fa8mB6Y6ofAVlvgp0I5b8JU35qtM2Mpk3xn7ylWhrecRqC4HJFZhKvW+1f/bj2Gf7XdojAQLdEsjP/Iz/5f/X0dV+vdT/937qHTqbeufGozpOnkvz+dnkFgIECBAgQIAAAQKdFxCAjn+Kvh6HeHjUQ9c4VL7q8/qoo6IOirotarXlwbFiePv411Yb1PLnubd8BWsby0tjJzkArQp62ziWfRAoUmAhpavyPaYtL1e1vD+7I0Bgiwvk8DP+FuU7T+KCz3T+f6T50w5Mc++P90+Lf6i5QAi6xX8BnB4BAgQIECBAYAsJtHFF3xbiGMupDEOHX4y9L/8CpOUHuzB+yP/PRX4O6H8sX7GP97+27LN/WvbeWwIEtojAjrTzM/FvBLuiWviHgqV99FP//C3C4zQIENgAgZXhZ37m58Ep3XptPBM0Dv83UUcOQtBHbEA7DkGAAAECBAgQIEBgXQIC0HXx1dr4vYNRD4rX/CUCa922nm9tj4u/Vl1y+Pnrg7XnxetaV4quuhMrCBDotkAOGaLD+HvR662/014vws8vxq2ql69/X/ZAgMAkCOwr/Bw+8zNu4dghBJ2E3wLnSIAAAQIECBAgQGB0gXz7WL6SK1d+zudfRj0+qs5yZAx6WdQlUcN9zMf7Uq+4uHRwHufEq4UAgVUE4tbSR+xKs7ujFnelubgctEktbbsjzTx9lcP4mAABAnsJ5PAz/t782+BvzqeuW7o7Za8x+Yer43E8MeavB+NuyH+z7jPIBwQIECBAgAABAm0IvC92kvOg09vYmX0QGKdADjHjltZ7Asz8i/vUmgf8Lyu2i9ti06/W3LaLwwSgXZwVPXVSIAKFNy2FC+sKQc/u5MlpigCBzglE+Hls/M2pDD+Hje8jBM3PPLcQIECAAAECBAi0KyAAbdfT3sYssC32/8aob0TlADSHonWWfKVoHp/r2qgnR5W8CEBLnj29b6jA61OaiitA/2r0EHQpMJ1Ps19c7eqtDT0RByNAoAiB+Jvx3sEVnate+bnyRJaHoLF9W1+YuPIwfiZAgAABAgQITLKAAHSSZ7/gc8/P9HvCCP3nb4V/Q9RJUTMjbNfVoQLQrs6MvjopcEVKsxEqvHsQSoxyK/y5t6SU/35YCBAgUEsgrjp/dPyjy2tG/YeTpRB09lXzadtjax3IIAIECBAgQIAAgVEEBKCjaBlLoCMCAtCOTIQ2yhKIYOJZ82nuqzWC0BsihHhB3Pc+XdYZ6pYAAQIECBAgQIAAAQIE9iEgAN0HyqgfbYUrCkc9Z+MJECBQnEB8i/vZX0jpw49Os6fGZeSnxgmcEK/xxcwpvuU93RSvl8WTMj5xS9r1waOWvmytuHPUMAECBAgQIECAAAECBAgQGIeAAHQcqvZJgACBMQh8d0rxJWjzH4pd57IQIECAAAECBAgQIECAAAECNQSmaowxhAABAgQIECBAgAABAgQIECBAgAABAkUKCECLnDZNEyBAgAABAgQIECBAgAABAgQIECBQR0AAWkfJGAIECBAgQIAAAQIECBAgQIAAAQIEihQQgBY5bZomQIAAAQIECBAgQIAAAQIECBAgQKCOgAC0jpIxBAgQIECAAAECBAgQIECAAAECBAgUKSAALXLaNE2AAAECBAgQIECAAAECBAgQIECAQB0BAWgdJWMIECBAgAABAgQIECBAgAABAgQIEChSQABa5LRpmgABAgQIECBAgAABAgQIECBAgACBOgIC0DpKxhAgQIAAAQIECBAgQIAAAQIECBAgUKSAALTIadM0AQIECBAgQIAAAQIECBAgQIAAAQJ1BASgdZSMIUCAAAECBAgQIECAAAECBAgQIECgSAEBaJHTpmkCBAgQIECAAAECBAgQIECAAAECBOoICEDrKBlDgAABAgQIECBAgAABAgQIECBAgECRAgLQIqdN0wQIECBAgAABAgQIECBAgAABAgQI1BEQgNZRMoYAAQIECBAgQIAAAQIECBAgQIAAgSIFBKBFTpumCRAgQIAAAQIECBAgQIAAAQIECBCoIyAAraNkDAECBAgQIECAAAECBAgQIECAAAECRQoIQIucNk0TIECAAAECBAgQIECAAAECBAgQIFBHQABaR8kYAgQIECBAgAABAgQIECBAgAABAgSKFBCAFjltmiZAgAABAgQIECBAgAABAgQIECBAoI6AALSOkjEECBAgQIAAAQIECBAgQIAAAQIECBQpIAAtcto0TYAAAQIECBAgQIAAAQIECBAgQIBAHQEBaB0lYwgQIECAAAECBAgQIECAAAECBAgQKFJAAFrktGmaAAECBAgQIECAAAECBAgQIECAAIE6AgLQOkrGECBAgAABAgQIECBAgAABAgQIECBQpIAAtMhp0zQBAgQIECBAgAABAgQIECBAgAABAnUEBKB1lIwhQIAAAQIECBAgQIAAAQIECBAgQKBIAQFokdOmaQIECBAgQIAAAQIECBAgQIAAAQIE6ggIQOsoGUOAAAECBAgQIECAAAECBAgQIECAQJECAtAip03TBAgQIECAAAECBAgQIECAAAECBAjUERCA1lEyhgABAgQIECBAgAABAgQIECBAgACBIgUEoEVOm6YJECBAgAABAgQIECBAgAABAgQIEKgjIACto2QMAQIECBAgQIAAAQIECBAgQIAAAQJFCghAi5w2TRMgQIAAAQIECBAgQIAAAQIECBAgUEdAAFpHyRgCBAgQIECAAAECBAgQIECAAAECBIoUEIAWOW2aJkCAAAECBAgQIECAAAECBAgQIECgjoAAtI6SMQQIECBAgAABAgQIECBAgAABAgQIFCkgAC1y2jRNgAABAgQIECBAgAABAgQIECBAgEAdAQFoHSVjCBAgQIAAAQIECBAgQIAAAQIECBAoUkAAWuS0aZoAAQIECBAgQIAAAQIECBAgQIAAgToCAtA6SsYQIECAAAECBAgQIECAAAECBAgQIFCkgAC0yGnTNAECBAgQIECAAAECBAgQIECAAAECdQQEoHWUjCFAgAABAgQIECBAgAABAgQIECBAoEgBAWiR06ZpAgQIECBAgAABAgQIECBAgAABAgTqCAhA6ygZQ4AAAQIECBAgQIAAAQIECBAgQIBAkQIC0CKnTdMECBAgQIAAAQIECBAgQIAAAQIECNQREIDWUTKGAAECBAgQIECAAAECBAgQIECAAIEiBQSgRU6bpgkQIECAAAECBAgQIECAAAECBAgQqCMgAK2jZAwBAgQIECBAgAABAgQIECBAgAABAkUKCECLnDZNEyBAgAABAgQIECBAgAABAgQIECBQR0AAWkfJGAIECBAgQIAAAQIECBAgQIAAAQIEihQQgBY5bZomQIAAAQIECBAgQIAAAQIECBAgQKCOgAC0jpIxBAgQIECAAAECBAgQIECAAAECBAgUKSAALXLaNE2AAAECBAgQIECAAAECBAgQIECAQB0BAWgdJWMIECBAgAABAgQIECBAgAABAgQIEChSQABa5LRpmgABAgQIECBAgAABAgQIECBAgACBOgIC0DpKxhAgQIAAAQIECBAgQIAAAQIECBAgUKSAALTIadM0AQIECBAgQIAAAQIECBAgQIAAAQJ1BASgdZSMIUCAAAECBAgQIECAAAECBAgQIECgSAEBaJHTpmkCBAgQIECAAAECBAgQIECAAAECBOoICEDrKBlDgAABAgQIECBAgAABAgQIECBAgECRAgLQIqdN0wQIECBAgAABAgQIECBAgAABAgQI1BEQgNZRMoYAAQIECBAgQIAAAQIECBAgQIAAgSIFBKBFTpumCRAgQIAAAQIECBAgQIAAAQIECBCoIyAAraNkDAECBAgQIECAAAECBAgQIECAAAECRQoIQIucNk0TIECAAAECBAgQIECAAAECBAgQIFBHQABaR8kYAgQIECBAgAABAgQIECBAgAABAgSKFBCAFjltmiZAgAABAgQIECBAgAABAgQIECBAoI6AALSOkjEECBAgQIAAAQIECBAgQIAAAQIECBQpIAAtcto0TYAAAQIECBAgQIAAAQIECBAgQIBAHQEBaB0lYwgQIECAAAECBAgQIECAAAECBAgQKFJAAFrktGmaAAECBAgQIECAAAECBAgQIECAAIE6AgLQOkrGECBAgAABAgQIECBAgAABAgQIECBQpIAAtMhp0zQBAgQIECBAgAABAgQIECBAgAABAnUEBKB1lIwhQIAAAQIECBAgQIAAAQIECBAgQKBIAQFokdOmaQIECBAgQIAAAQIECBAgQIAAAQIE6ggIQOsoGUOAAAECBAgQIECAAAECBAgQIECAQJECAtAip03TBAgQIECAAAECBAgQIECAAAECBAjUERCA1lEyhgABAgQIECBAgAABAgQIECBAgACBIgUEoEVOm6YJECBAgAABAgQIECBAgAABAgQIEKgjIACto2QMAQIECBAgQIAAAQIECBAgQIAAAQJFCghAi5w2TRMgQIAAAQIECBAgQIAAAQIECBAgUEdAAFpHyRgCBAgQIECAAAECBAgQIECAAAECBIoUEIAWOW2aJkCAAAECBAgQIECAAAECBAgQIECgjoAAtI6SMQQIECBAgAABAgQIECBAgAABAgQIFCkgAC1y2jRNgAABAgQIECBAgAABAgQIECBAgEAdAQFoHSVjCBAgQIAAAQIECBAgQIAAAQIECBAoUkAAWuS0aZoAAQIECBAgQIAAAQIECBAgQIAAgToCAtA6SsYQIECAAAECBAgQIECAAAECBAgQIFCkgAC0yGnTNAECBAgQIECAAAECBAgQIECAAAECdQQEoHWUjCFAgAABAgQIECBAgAABAgQIECBAoEgBAWiR06ZpAgQIECBAgAABAgQIECBAgAABAgTqCAhA6ygZQ4AAAQIECBAgQIAAAQIECBAgQIBAkQIC0CKnTdMECBAgQIAAAQIECBAgQIAAAQIECNQREIDWUTKGAAECBAgQIECAAAECBAgQIECAAIEiBQSgRU6bpgkQIECAAAECBAgQIECAAAECBAgQqCMgAK2jZAwBAgQIECBAgAABAgQIECBAgAABAkUKzIzY9Wkx/owRt1nv8G/EDk5d705sT4AAAQIECBAgQIAAAQIECBAgQIDA5AmMGoAeGkSP2WCmAzf4eA5HgAABAgQIECBAgAABAgQIECBAgMAWEXAL/BaZSKdBgAABAgQIECBAgAABAgQIECBAgMB9BZoGoP8eu+qNuV5133Z9QoAAAQIECBAgQIAAAQIECBAgQIAAgfoCTQPQ+kcwkgABAgQIECBAgAABAgQIECBAgAABApskIADdJHiHJUCAAAECBAgQIECAAAECBAgQIEBg/AKjfgnSp6OlX4raOf7W0rlxjJujbt+AYzkEAQIECBAgQIAAAQIECBAgQIAAAQJbUGDUAPSqMMi1cpmND3ZF9VeuqPh5W6w/cjAmB53Lw84vx8+5LAQIECBAgAABAgQIECBAgAABAgQIEGgksJ5b4A+LI74r6ktR/xF1a9R5UWdE7R9VZzk+Bl0zqJfU2cAYAgQIECBAgAABAgQIECBAgAABAgQI1BVoGoA+NQ5wedQLox4Tla/kPCjqh6LeGHVZ1ElRFgIECBAgQIAAAQIECBAgQIAAAQIECGyaQJMA9EHR7YejjlrW9b/G+zuW/XxsvL8wKl8NaiFAgAABAgQIECBAgAABAgQIECBAgMCmCDQJQN8WnR446PaseD0i6juj8hWg3xt1flRe8r7z1aD/Pf9gIUCAAAECBAgQIECAAAECBAgQIECAwEYLjBqAbo8Gf2rQ5Afj9fSobw5+zl+A9PmofBv8f4tajMrLa6Neueed/xAgQIAAAQIECBAgQIAAAQIECBAgQGADBUYNQB+xrLe1Qs0/iHE/EzU/GP+meP3ZwXsvBAgQIECAAAECBAgQIECAAAECBAgQ2BCBUQPQ4wZd3RqvV1d0+BexPoee+UrQXtR7on4wykKAAAECBAgQIECAAAECBAgQIECAAIENERg1AJ0ddHVXze7OiXEvHYzN2+afhyHq4GMvBAgQIECAAAECBAgQIECAAAECBAgQGI/AqAHovwza+PZ4zV96VGd5RwzKX5yUl0OiPhF1eP7BQoAAAQIECBAgQIAAAQIECBAgQIAAgXEKNA1Ac0/5i47qLi+PgR8aDH5IvH4yqm6AOtjMCwECBAgQIECAAAECBAgQIECAAAECBEYTGDUA/Wrs/pLBIc6I15+uebj8HND8jfEXD8afMHi//EuVBqu8ECBAgAABAgQIECBAgAABAgQIECBAoB2BUQPQfhz2ZYNDz8Xr+6Muivq9qEdGrbXsiJWnRn15MCiHn+8dvPdCgAABAgQIECBAgAABAgQIECBAgACB1gVGDUBzA5+JenXUQv4hlidFvSKqztWcN8e4k6OGV5EuP37+pngLAQIECBAgQIAAAQIECBAgQIAAAQIEWhOYabinN8V2OQj9o6jhlZ/X1dzXLTEuh6Yvjnpd1IFRFgIECBAgQIAAAQIECBAgQIAAAQJjFbgtpQfsn7b9eEq9J/VS75h+6h8Qr7fHLc9fifcXXJfmP35MSvkuZguBvQS2x08/E/WAvT6t98MDY1i+Df6rUa+tt4lRhQtcGv3nRymcU/h5aJ8AAQIECBAgQIAAAQIECBAoROBbKR2yM82+eVea3bErzfVXr9mb59Pci69IabYjp/a+6CPnKKd3pB9tECBQQ0AAWgPJEAIECBAgQIAAAQIECBAgQKAdgfm07QkReF6/FHrOLq4eft4bjM6n2S/eldKD2ulgXXsRgK6Lb2nj5c/gbGF3djGCQP4SqSbLobFRvnI2l4UAAQIECBAgQIAAAQIECBAgQGAVgR1p5qlxi/unY/VRS0N6tb6DJrZ53LY0d0ncC/+QVXbt44IEBKAbO1mnxeHOjbox6u6oK6P+NOqkqLrLe2LgNYOqu41xBAgQIECAAAECBAgQIECAAIGJEtiR5o6dTlNnx/M+m16EdtRUmv3YTSndb6LgtuDJCkA3ZlLzFz3lZ53+RdQpUUdE5X9xeHjUc6IujHpr1P5RFgIECBAgQIAAAQIECBAgQIAAgXUKTKf0rohfDl7PbuJK0EcelGZ/cz37sO3mCwhAN2YOficO89xlh7oz3l8dlR9im5c8Dy+J+lLUMVEWAgQIECBAgAABAgQIECBAgACBhgJ3p5kfik2fGtHLMHtpuKfUj9Dm1yPIyfOAHngAAEAASURBVF8CbilUQAA6/ol7bBziVweHybe+PzPq26IeEnVI1CujbovKy8Oi/i5KCBoIFgIECBAgQIAAAQIECBAgQIBAE4G49f2Xl7ar98zPNY4Rd/D29p9Ns/kOXkuhAgLQ8U/cC+IQcdV12h319KiPRS1G5SUHn2+OekTUZVF5eXDUp6KOzD9YCBAgQIAAAQIECBAgQIAAAQIERhLIqWdc/dnm0ntam3uzr40VmNnYw03k0XK4mZf/EzUMOfd8sOw/18f774/K4egPROUrQD8+eJ9vl9/sZb9oIJ9HG4H5AZt9Mo5PgAABAgQIECBAgAABAgQIbF2Bu1I6Oq7aPKTlM3xUy/uzuw0UEICOH/u4wSEurTjU7bH+R6Ly1Z/fF/X4qPimsnRq1ELUZi7vjoP/bMsNxB8jCwECBAgQIECAAAECBAgQIECgXYHpNHtou3vMe+uPYZ/td2mP+xYQgO7bpc1PZwc7i3+AqFzujhE58Lw46qFRz4j6/agXRm3mcl4c/NiouIJ83cvDYw8HRt2x7j3ZAQECBAgQIECAAAECBAgQIEDgvgL5MYRtL7va3qH9bZyAAHT81v8Sh/ieqO+qeaibY9wpUTkEPTzqBVFfjXpr1GYt+QrQXG0s+UrYE6IEoG1o2gcBAgQIECBAgAABAgQIECCwl8Dtaf6ag9NcfJa/AX7dX4K0Z9+xo2v2OogfihJo45mORZ3wJjSbA9C8PDvqAXveVf/nazEkXwmarwjNy1uinrvnnf8QIECAAAECBAgQIECAAAECBAisKhBXk93RT/0r2wo/Bwe6ZNUDWtF5gc0OQPOX69xvUDma34pL/vKjvBwRld/X/Xb3z8XYHJouRuVbz/8k6nVRmz1n0YKFAAECBAgQIECAAAECBAgQINBdgbhi88Ntdtf2/trszb66L/CX0WL8Du2p/EU/l0f9cdSTorbS8rdxMsPzvCXe/0nUi2qe4PNjXA5Bh9tnp+H7mrvo1LB8C3zu/5xOdaUZAgQIECBAgAABAgQIECBAYMsI3JnS9l1p9s5daa6/3ppPs1eendL0JuG8L46bc5TTN+n4W+KwXbqa8LIQzc+FfE7UuVHHR22VJYeYOdzNyyFRvxD1K1F1ljNj0POihg/w7dKc1enfGAIECBAgQIAAAQIECBAgQIDAhgrEty9fH1eTvXnpoPlZoE2Wpe0WU++Vz0opX5BmKVSgS2HaR8PwiVFHR10b9Y6orbL8vziR7416V1T8I8Se5brBa52X98Sgx0VdVGewMQQIECBAgAABAgQIECBAgACBSRf4bJr/rTD41NKzQEcNQZe+QKmfFt+6X9qZ72C2EGgs8MDY8rhBHbpsL9vj/QHLft5Kb2fiZE6MelrDk3pubHdx1G0Nt9/szdwCv9kz4PgECBAgQIAAAQIECBAgQGBCBL4Vd+LOp7mLlm6Dn10c5Xb4uPX93a/f/O9icQv8hPyuOs2tJSAA3Vrz6WwIECBAgAABAgQIECBAgECnBa5IaTZC0HfGM0F31wtAZ++I8b/WkZMSgLYwEV26Bb6F07ELAgQIECBAgAABAgQIECBAgAABAvcKPDKl+dm089fimaCP7qf+WfGdQnfdu3avd9+MW97ftiPN/6cY/8691vihaIF8O/Yoy8NicH5O546o94+yYYOxj45tHh91e5RvDG8AaBMCBAgQIECAAAECBAgQIECAAIElgbk0f2W8Oz1fEXps2va4fuo9pJf6B8RrfszgV343zV/x+pQiJ7VMukD+NvP8zVk3bwDEbwyO9S8bcCyH2DgBt8BvnLUjESBAgAABAgQIECBAgAABAmULuAW+hflzC3wLiHZBgAABAgQIECBAgAABAgQIECBAgEA3BQSg3ZwXXREgQIAAAQIECBAgQIAAAQIECBAg0ILAqM8AHR7y0Hize/jDmF57Y9qv3RIgQIAAAQIECBAgQIAAAQIECBAgMCECTQPQzDM9IUZOkwABAgQIECBAgAABAgQIECBAgACBQgVGDUBvjfP8vxt8rtds8PEcjgABAgQIECBAgAABAgQIECBAgACBLSIwagB6dpx3LgsBAgQIECBAgAABAgQIECBAgAABAgQ6L+BLkDo/RRokQIAAAQIECBAgQIAAAQIECBAgQKCpQFsB6Gw0cGDTJmK774j6z+vY3qYECBAgQIAAAQIECBAgQIAAAQIECBC4j0BbAeghsedLox57nyNUf3B6DLks6rurhxpBgAABAgQIECBAgAABAgQIECBAgACB+gJtBaD5iMdFfS7qRfmHGsvBMeb9Ue+LOqjGeEMIECBAgAABAgQIECBAgAABAgQIECAwkkCbAWg+8FzU70d9JOoBUastT4kVl0f99LIBtyx77y0BAgQIECBAgAABAgQIECBAgAABAgTWLdBWAHpTdPKKqPlBR8+M1y9FPWnw8/AlB6RviTov6oGDD2+O15+LysGphQABAgQIECBAgAABAgQIECBAgAABAp0VeFx0dmVUf1C74/WMqBy0PioqP+tzuC6//kXUEVGWyRHIz4rNc3/O5JyyMyVAgAABAgQIECBAgAABAgQINBLIj47MOcrpjba20dgEDog9/1HU8qAzh147ln12Y7w/LcoyeQIC0Mmbc2dMgAABAgQIECBAgAABAgQINBMQgDZz22urtm6BX77Tu+KHX4n60agbBitOiNd8+3tezor6rqgP5h8sBAgQIECAAAECBAgQIECAAAECBAgQGJfAOALQYa85/BwGoMPP8uvioJZ/5j0BAgQIECBAgAABAgQIECBAgAABAgRaFxhHAJpvgX9r1D9EPXbQcQ5Cdw7ePyder4g6dfCzFwIECBAgQIAAAQIECBAgQIAAAQIECIxFoO0A9Aejyy9HvSRqOiov+Vb3R0Z9d1Rel5ftUR+Nys8xOCTKQoAAAQIECBAgQIAAAQIECBAgQIAAgc4KbIvO/jAq394+/PKj2+P9z0ctX/JzQN8StXzc9fGzq0GXK23t974EaWvPr7MjQIAAAQIECBAgQIAAAQIE2hPwJUjtWa57T0fGHobBZ379TNQxa+z1KbHumqjl2zx/jfFWbR0BAejWmUtnQoAAAQIECBAgQIAAAQIECIxXQADagm/bt8Dvjp5eG/UDUVev0d/5se7RUR9YNuaoZe+9JUCAAAECBAgQIECAAAECBAgQIECAwLoF2gxA/zm6eWLUb0Ut1OjsWzHmZ6LylyLdVmO8IQQIECBAgAABAgQIECBAgAABAgQIEBhJYGak0asPvjVWnRB15+pDVl3zZ7HmoqhHrTrCCgIECBAgQIAAAQIECBAgQIAAAQIECDQQaCsAnY9j52q6/GtsmMtCgAABAgQIECBAgAABAgQIECBAgACB1gTavAW+tabsiAABAgQIECBAgAABAgQIECBAgAABAm0ICEDbULQPAgQIECBAgAABAgQIECBAgAABAgQ6KSAA7eS0aIoAAQIECBAgQKBtgaM+M3Py9s9OvTqdmbaNsu/7n5cO3X7xzG8fdlF62CjbGUuAAAECBAgQINANAQFoN+ZBFwQIECBAgAABAmMWmJrpn9Gbmvqd7cdPv79uCHr0Bemw+x84c36vl14zu23ml8bcot0TIECAAAECBAiMQUAAOgZUuyRAgAABAgQIEOigwOLCy1O/f2uv1/vJOiFoDj/TfjOfSr10fL+frlxY2P2WDp6VlggQIECAAAECBCoEBKAVQFYTIECAAAECBAhsDYHrnpj+MaWFH64Tgt4n/OztPvnGk9JNW0PCWRAgQIAAAQIEJktAADpZ8+1sCRAgQIAAAQITLXDd96UvVoWg+wo/bzox3TjRcE6eAAECBAgQIFCwgAC04MnTOgECBAgQIECAwOgCa4Wgws/RPW1BgAABAgQIEOi6gAC06zOkPwIECBAgQIAAgdYF9hWCHn5BOmqvZ37Gbe+u/Gyd3g4JECBAgAABAhsuIADdcHIHJECAAAECBAgQ6ILAyhB02/7TV93zhUfCzy5MkR4IECBAgAABAq0ICEBbYbQTAgQIECBAgACBEgVyCLqwuHBaP/V3p9Q7KF5vn+/vfqorP0ucTT0TIECAAAECBPYtIADdt4tPCRAgQIAAAQIEJkAgP/Nzemrmbb3Um+n3+wvx+m2zvel3pjPTtgk4fadIgAABAgQIEJgIAQHoREyzkyRAgAABAgQIEFgpsPILjxbTwimp37+11+v95Pbjp98vBF0p5mcCBAgQIECAQJkCAtAy503XBAgQIECAAAEC6xBYGX4uxDM/b/y+dF5KCz8sBF0HrE0JECBAgAABAh0UEIB2cFK0RIAAAQIECBAgMD6BfYWfw2d+rvxiJFeCjm8e7JkAAQIECBAgsFECAtCNknYcAgQIECBAgACBTRdYK/wcNicEHUp4JUCAAAECBAhsDQEB6NaYR2dBgAABAgQIECBQIVAn/BzuQgg6lPBKgAABAgQIEChfQABa/hw6AwIECBAgQIAAgRoC/f2n/zj10vH9froyP/NzeNv7apveNwSd+o3VxvqcAAECBAgQIECguwIC0O7Ojc4IECBAgAABAgRaFOgt9j+VUv/jdcLP4WGHIWiEpp/uLU5dMvzcKwECBAgQIECAAAECBFYTuDRW9KPOWW2AzwkQIECAAAECBAgQIECAAAECBPYIvC/+m3OU03k0F3AFaHM7WxIgQIAAAQIECBAgQIAAAQIECBAg0HEBAWjHJ0h7BAgQIECAAAECBAgQIECAAAECBAg0FxCANrezJQECBAgQIECAAAECBAgQIECAAAECHRcQgHZ8grRHgAABAgQIECBAgAABAgQIECBAgEBzAQFocztbEiBAgAABAgQIECBAgAABAgQIECDQcQEBaMcnSHsECBAgQIAAAQIECBAgQIAAAQIECDQXEIA2t7MlAQIECBAgQIAAAQIECBAgQIAAAQIdFxCAdnyCtEeAAAECBAgQIECAAAECBAgQIECAQHMBAWhzO1sSIECAAAECBAgQIECAAAECBAgQINBxAQFoxydIewQIECBAgAABAgQIECBAgAABAgQINBcQgDa3syUBAgQIECBAgAABAgQIECBAgAABAh0XEIB2fIK0R4AAAQIECBAgQIAAAQIECBAgQIBAcwEBaHM7WxIgQIAAAQIECBAgQIAAAQIECBAg0HEBAWjHJ0h7BAgQIECAAAECBAgQIECAAAECBAg0FxCANrezJQECBAgQIECAAAECBAgQIECAAAECHRcQgHZ8grRHgAABAgQIECBAgAABAgQIECBAgEBzAQFocztbEiBAgAABAgQIECBAgAABAgQIECDQcQEBaMcnSHsECBAgQIAAAQIECBAgQIAAAQIECDQXEIA2t7MlAQIECBAgQIAAAQIECBAgQIAAAQIdFxCAdnyCtEeAAAECBAgQIECAAAECBAgQIECAQHMBAWhzO1sSIECAAAECBAgQIECAAAECBAgQINBxAQFoxydIewQIECBAgAABAgQIECBAgAABAgQINBcQgDa3syUBAgQIECBAgAABAgQIECBAgAABAh0XEIB2fIK0R4AAAQIECBAgQIAAAQIECBAgQIBAcwEBaHM7WxIgQIAAAQIECBAgQIAAAQIECBAg0HEBAWjHJ0h7BAgQIECAAAECBAgQIECAAAECBAg0FxCANrezJQECBAgQIECAAAECBAgQIECAAAECHRcQgHZ8grRHgAABAgQIECBAgAABAgQIECBAgEBzAQFocztbEiBAgAABAgQIECBAgAABAgQIECDQcQEBaMcnSHsECBAgQIAAAQIECBAgQIAAAQIECDQXEIA2t7MlAQIECBAgQIAAAQIECBAgQIAAAQIdFxCAdnyCtEeAAAECBAgQIECAAAECBAgQIECAQHMBAWhzO1sSIECAAAECBAgQIECAAAECBAgQINBxAQFoxydIewQIECBAgAABAgQIECBAgAABAgQINBcQgDa3syUBAgQIECBAgAABAgQIECBAgAABAh0XEIB2fIK0R4AAAQIECBAgQIAAAQIECBAgQIBAcwEBaHM7WxIgQIAAAQIECBAgQIAAAQIECBAg0HEBAWjHJ0h7BAgQIECAAAECBAgQIECAAAECBAg0FxCANrezJQECBAgQIECAAAECBAgQIECAAAECHRcQgHZ8grRHgAABAgQIECBAgAABAgQIECBAgEBzAQFocztbEiBAgAABAgQIECBAgAABAgQIECDQcQEBaMcnSHsECBAgQIAAAQIECBAgQIAAAQIECDQXEIA2t7MlAQIECBAgQIAAAQIECBAgQIAAAQIdFxCAdnyCtEeAAAECBAgQIECAAAECBAgQIECAQHMBAWhzO1sSIECAAAECBAgQIECAAAECBAgQINBxAQFoxydIewQIECBAgAABAgQIECBAgAABAgQINBcQgDa3syUBAgQIECBAgAABAgQIECBAgAABAh0XEIB2fIK0R4AAAQIECBAgQIAAAQIECBAgQIBAcwEBaHM7WxIgQIAAAQIECBAgQIAAAQIECBAg0HEBAWjHJ0h7BAgQIECAAAECBAgQIECAAAECBAg0FxCANrezJQECBAgQIECAAAECBAgQIECAAAECHRcQgHZ8grRHgAABAgQIECBAgAABAgQIECBAgEBzAQFocztbEiBAgAABAgQIECBAgAABAgQIECDQcQEBaMcnSHsECBAgQIAAAQIECBAgQIAAAQIECDQXEIA2t7MlAQIECBAgQIAAAQIECBAgQIAAAQIdFxCAdnyCtEeAAAECBAgQIECAAAECBAgQIECAQHMBAWhzO1sSIECAAAECBAgQIECAAAECBAgQINBxAQFoxydIewQIECBAgAABAgQIECBAgAABAgQINBcQgDa3syUBAgQIECBAgAABAgQIECBAgAABAh0XEIB2fIK0R4AAAQIECBAgQIAAAQIECBAgQIBAcwEBaHM7WxIgQIAAAQIECBAgQIAAAQIECBAg0HEBAWjHJ0h7BAgQIECAAAECBAgQIECAAAECBAg0FxCANrezJQECBAgQIECAAAECBAgQIECAAAECHRcQgHZ8grRHgAABAgQIECBAgAABAgQIECBAgEBzAQFocztbEiBAgAABAgQIECBAgAABAgQIECDQcQEBaMcnSHsECBAgQIAAAQIECBAgQIAAAQIECDQXEIA2t7MlAQIECBAgQIAAAQIECBAgQIAAAQIdFxCAdnyCtEeAAAECBAgQIECAAAECBAgQIECAQHMBAWhzO1sSIECAAAECBAgQIECAAAECBAgQINBxAQFoxydIewQIECBAgAABAgQIECBAgAABAgQINBcQgDa3syUBAgQIECBAgAABAgQIECBAgAABAh0XEIB2fIK0R4AAAQIECBAgQIAAAQIECBAgQIBAcwEBaHM7WxIgQIAAAQIECBAgQIAAAQIECBAg0HGBmY73pz0CBAgQIECAAAECBAgQIEDg/7d3J3CyVOWhwKtnLpsIKiDciwhBRYW4x6eAKxr1KZAQ9eFTfC4YxWh8+vPpM0pMjMYlaqIxblGj5mmiEpNfVEDiEhTZXCIikbhFBAPcK6KCKOC9M/2+79KFxTBLz0x19+nu/4HvVnXV6XNO/at+1TPfVFWPicCeZ1e322lmwyFz3e4es93q+qozd8llX68uqE6oto7JJhgmAQITKCABOoE71SYRIECAAAECBAgQIECAAIGhCZxe7bxxl5nndrozz+h0qoOz39mYqeL/qtpQbbpn9+fVudXH56q51/3w0OrruVQhQIDAMAXcAj9MbX0RIECAAAECBAgQIECAAIEJEth41uyR++4y+52ZauaNmfzsVt3uws3rVJ1dI544W82et++5s3+z7yeqWyys4zUBAgQGKSABOkhdbRMgQIAAAQIECBAgQIAAgQkV2Hj2zIs6s9XH41LP/epNjETn9us+69cLpnlZ6PHVbTectdeZ1b4L1nlJgACBgQlIgA6MVsMECBAgQIAAAQIECBAgQGAyBTaeO/v0mZmZN8TWLZfwvMnGN5Kj99pxdsPJrgS9CY8XBAgMUEACdIC4miZAgAABAgQIECBAgAABApMmsPGM6pBIJrwzt6uR1FzdZnaqe3f3mv3L1b1JbQIECKxNQAJ0bW7eRYAAAQIECBAgQIAAAQIEplJgZofZ10Xqc8f1bPz2Z4V2quP3Oau623ra8V4CBAj0IyAB2o+SOgQIECBAgAABAgQIECBAgEC1PWHZ6Ry92JcdrYYnrxyNiLvoZ1+ymvepS4AAgbUISICuRc17CBAgQIAAAQIECBAgQIDAFAp0OjOPzc1e863vNzf77er0asPNF1tCgACB9gScZNqzXKqlK2JF3w+FXqqRJZbvtcRyiwkQIECAAAECBAgQIECAQOsCcd3moW02Gu3tts9O1cFbquqCNtvVFgECBJoCEqBNjcHM7xHNjvuVtk+KbXhmS9txUI/5lr2pCQECBAgQIECAAAECBAiMi0Cns3/bQ+10Z6PNOQnQtmG1R4DAjQISoDdSDGzmQdHy+yLu3Ohhc8zPNV6XPntMDPChLQ/yVi23pzkCBAgQIECAAAECBAgQGLBA3N64Q9tdxMNA1/WFSm2PR3sECEyegATo4Pfp2dFF3iJwWsT9et29N6Yn9ubHYXJ8DPKvItq4lf/d0U4mgy+NUAgQIECAAAECBAgQIEBgvAR+3PZwu9Vc6222PUbtESAw3gISoMPZfz+Jbh4RcW7EwRF/EPHZiH+NGIdyTQzyCy0NNNtSCBAgQIAAAQIECBAgQGAsBboXxrUxrTwHNL9JfvuXKW2rok2FAAECgxMY92dTDk6m/ZavjiafETEfke4fiNg9QiFAgAABAgQIECBAgAABAmMhEM9y+3RbA+19k/zXNj+4yi8PVggQIDAwAQnQgdEu2vA5sTRvJc+yb8Tvb5/zDwECBAgQIECAAAECBAgQGAOBLfNzH+t2uz9sa6jz1Xw+Jk0hQIDAQAUkQAfKu2jj+ezPi3prXhhT34a+KJOFBAgQIECAAAECBAgQIFCcwOHVtZEAfWWOK29hX8/44v3f23y+BOh6DL2XAIH+BCRA+3Nqs9bPo7EnRrw64p0RB0YoBAgQIECAAAECBAgQIEBgLAQ2XzD/rsh8fqF3C/uqxxzvjcxpd9v8fOdZ1QnV1lU34A0ECBBYpYAvQVolWEvVvxjtZCgECBAgQIAAAQIECBAgQGC8BCJp2T1j2+M6O8yeU3U6d7zxy4z63IpOfIvSfNV9/pbD5/LLgRUCBAgMXMAVoAMn1gEBAgQIECBAgAABAgQIEJgsgfziovmtc4f1eyXor26X7147V3WfuPnQ+bdPloitIUCgZAEJ0JL3jrERIECAAAECBAgQIECAAIFCBTIJevm12x42351/TiQ4tyw3zO23y3e7J22dn7vHlkPnPrxcXesIECDQtoBb4NsW1R4BAgQIECBAgAABAgQIEJgWgSOqbZur+XdUp1fv27jj7FGd2eo34w73Q2Lz94hvSbq+6nQv6XS7527tzP/TFYdV35kWFttJgEBZAhKgZe0PoyFAgAABAgQIECBAgAABAuMncER13eZq7qMx8AyFAAECRQm4Bb6o3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpAArSo3WEwBAgQIECAAAECBAgQIECAAAECBAi0KSAB2qamtggQIECAAAECBAgQIECAAAECBAgQKEpgQ1GjMRgCBAgQIECAAAECBAgQ+JXAK6qZTY/c8MhOp3t0VXV+o1t1N3U61Uy32/lhLPva/Fz1yc1XzX2iekx1/a/eZI4AAQIECBBoCkiANjXMEyBAgAABAgQIECBAoBCBjWfNHtmZ6bwuEp53i+Tn9lF16mmn2i+W3Wdmtjp+021mL+meXb188+FzH4hK3UKGbxgECBAgQKAYAbfAF7MrDIQAAQIECBAgQIAAAQIhcFI1u/Gc2bfMzHZOviH5ubxKp9PZf2am87ebzpn96D7/Uu26fG1rCRAgQIDA9Am4AnT0+3zfGMIeEbfoxXUxvSri6ogrI/K1QoAAAQIECBAgQIDAdAh0Nu0/++G40vPxcbt7N6Y3XPq5wrZvr9vpPHb2Vhs2Vadve1h1hN8jViCzmgABAgSmSEACdPg7e7fo8ikRx0XErSxVvl6qbIsVF0R8MeLkiFMj3NISCAoBAgQIECBAgACBSRTYeM7MH602+ZkOjUTpYZt2mf3ry6u5p06ij20iQIAAAQJrEXAL/FrU1vaefeJtb4u4NOKtEYdFLJf8jNVVJqjvHfHsiEyAfj3iyAiFAAECBAgQIECAAIEJE7jt2dWdZjqdE1dz5edCgt57n7LxzA1HLFznNQECBAgQmFYBV4AOZ8/fJrr5dMTdG93llZyXR1wScUXEtRH5zY25T3aO2D3i9hEHROwUkSWvGP14xP+JeHOEQoAAAQIECBAgQIDAhAhsmJl9ZVzLuUNf97wvsc31laDx5UivjSqHLlHNYgIECBAgMFUCEqCD3935EPJTIurk55dj/i8iPhuRic+Vyg5R4X4Redv80yPy9Zsivh2Rt8QrBAgQIECAAAECBAiMucAep1a7R+LzcXGVRDz3s/dV72vcpt7zQO+/8YzqkM0Pri5cYzPeRoAAAQIEJkbALfCD35XHRhd5u3uWD0fkX2Fz2k/yM6pVWyPOijgh4pje65hUr4uw/1JCIUCAAAECBAgQIDDmAjveavZBkffccb3Jz2S48SrQHWYePuYshk+AAAECBFoRcAVoK4zLNnJ4b20+vzOv4pxftvbyK/OKzxdF/GVEXlF6YMR/Rgy67BkdPCAifh5bd7lVr4U22lr3YDRAgAABAgQIECBAoAiBTvfgdn7cbm5NJ9pUCBAgQIAAAQnQwR8DmTjM8omIvJpzveUfo4FMgGa5c8QwEqDvjn5+JztssdyuxbY0RYAAAQIECBAgQGCsBTqdmVu3vQFxL33rbbY9Ru0RIECAAIFhCEiADl55v14XP2ipqyujnUyk5rNAd2mpzZWaeX+vQhu33N8n2rp9xJm9Nk0IECBAgAABAgQITL1AtzN/faflJ1x1utu/ZHXqbQEQIECAAAEJ0MEfA3mF5r0i8jmgf91Cd3lLfSY/s5x3w2Tg/+Y3z2e0UfILnF4QcUkbjWmDAAECBAgQIECAwCQIdOY6369m292S+ar7/XZb1BoBAgQIEBhPgTau6BvPLR/eqP+t19UTYvqQdXabt7D8ea+NH8f0onW25+0ECBAgQIAAAQIECBQgsK0zd04OI7/Bva3hzMzPbG+zrfa0Q4AAAQIExlVAAnTwe+610UXesr5zxMci8tvcd4xYbcmrSD8VkdMs77xh4l8CBAgQIECAAAECBMZd4IrDq+/GNpxff4P7+rene+Vll2773Prb0QIBAgQIEBh/AbfAD34f5i3wL4t4Q0R+A3omLnP+8xFfi8irOLdEXBtxXUTuk0yW7h6Rz8q8U8SDI+4WUZdMhL68fmFKgAABAgQIECBAgMD4C8zPdf90ZrbzD3kV6LoTod3u66tjq1+Ov4otIECAAAEC6xeQAF2/YT8tvDEq5ZcXvS0iv7hot4ijehGTVZXTovZxEfOrepfKBAgQIECAAAECBAgULbD5AXMf3XTOhjM7nc4D1zPQSKB+7/Lr5t+ynja8lwABAgQITJKAW+CHtzffF10dEPGaiM2r7Pb6qJ+3zx8d8eiIfP6nQoAAAQIECBAgQIDAhAls3brt2LgA9NK1bla3271mfn7umOqI7XeXrbUZ7yNAgAABAhMl4ArQ4e7OK6K7E3vxazE9NOKgiLzdPW+PzytD83mh10RcHZG3z18YcX5ELlMIECBAgAABAgQIEJhggR89uLp845lzj+psmP1E3AZ/YD+3w/+qTvfKbnfucVsOry6YYCKbRoAAAQIEVi0gAbpqstbe8P1oKUMhQIAAAQIECBAgQIDAjQKbH1h9Y9/T5+7X3WX2HZEEffyNKxbMxNfFx7NCq84Nzwvtnj63de53tzyo+t6Cal4SIECAAIGpF3AL/NQfAgAIECBAgAABAgQIEChN4LIjqh9dfujc/5jbtu2wuCX+pLi1/WcLx9jpduNRWd1T5rvdx1x26NzDJD8XCnlNgAABAgRuEHAFqCOBAAECBAgQIECAAAEChQpseWB1blXNPaE6vdqwaefqoPn5DZuqmfivu23Llp9W364eU+X3BSgECBAgQIDAMgISoMvgWEWAAAECBAgQIECAAIEiBI6otl1eVf9RVdsiFAIECBAgQGA1Am6BX42WugQIECBAgAABAgQIECBAgAABAgQIjJWABOhY7S6DJUCAAAECBAgQIECAAAECBAgQIEBgNQISoKvRUpcAAQIECBAgQIAAAQIECBAgQIAAgbESkAAdq91lsAQIECBAgAABAgQIECBAgAABAgQIrEZAAnQ1WuoSIECAAAECBAgQIECAAAECBAgQIDBWAhKgY7W7DJYAAQIECBAgQIAAAQIECBAgQIAAgdUISICuRktdAgQIECBAgAABAgQIECBAgAABAgTGSkACdKx2l8ESIECAAAECBAgQIECAAAECBAgQILAaAQnQ1WipS4AAAQIECBAgQIAAAQIECBAgQIDAWAlsGKvRGuwkCbw0NuaEQjdozxhXxraIbqFjNCwCKVCfw/NYVQiUKtCJgeWxOh8xV+ogjYtACOSFAbMRPv8dDqUL+PwvfQ8ZXwr4/HccjItA/fn/XzHgXxQ66H0LHddYDav+8ByrQRvsWAt8vzf6vWOaUXLZseTBGRuBhkD+wq4QKF0gf7j0c0fpe8n4UsDnv+NgXAR8/o/Lnprucfr8n+79P05bv3/hg82Lsy4ufIxFDy//KqMQGLbAHaPDHYbd6Sr6+5Ooe2zEX0V8chXvU5XAsAU+Eh3uFpHH6zXD7lx/BPoUuFfUe03EeREn9vke1QiMQuB50emjI3z+j0Jfn6sR8Pm/Gi11RyXg839U8vpdrUD9+f/H8caTVvvmIdbP3/fyKlVljQKuxFgjnLetS+A/1/Xuwb/5p70u/j2mEqCD99bD2gXqW98/E038ZO3NeCeBgQps7bV+ZUydUwdKrfF1ChzTe7/P/3VCevvABXz+D5xYBy0I+PxvAVETQxGoP/83R2/fHEqPOhmJQF6OrhAgQIAAAQIECBAgQIAAAQIECBAgQGAiBSRAJ3K32igCBAgQIECAAAECBAgQIECAAAECBFJAAtRxQIAAAQIECBAgQIAAAQIECBAgQIDAxApIgE7srrVhBAgQIECAAAECBAgQIECAAAECBAhIgDoGCBAgQIAAAQIECBAgQIAAAQIECBCYWAEJ0IndtTaMAAECBAgQIECAAAECBAgQIECAAAEJUMcAAQIECBAgQIAAAQIECBAgQIAAAQITKyABOrG71oYRIECAAAECBAgQIECAAAECBAgQICAB6hggQIAAAQIECBAgQIAAAQIECBAgQGBiBSRAJ3bX2jACBAgQIECAAAECBAgQIECAAAECBCRAHQMECBAgQIAAAQIECBAgQIAAAQIECEysgAToxO5aG0aAAAECBAgQIECAAAECBAgQIECAgASoY4AAAQIECBAgQIAAAQIECBAgQIAAgYkVkACd2F1rwwgQIECAAAECBAgQIECAAAECBAgQkAB1DBAgQIAAAQIECBAgQIAAAQIECBAgMLECEqATu2ttGAECBAgQIECAAAECBAgQIECAAAECEqCOAQI3F7i6t+iqm6+yhEBRAnms/jLi2qJGZTAEbipQn0vrc+tN13pFoByB+hitp+WMzEgI3FTA5/9NPbwqU8Dnf5n7xahuLlB/7tfTm9ewhAABAhMqsFts11ERsxO6fTZrcgTuGpty38nZHFsywQKPiG3bZ4K3z6ZNhoDP/8nYj9OwFT7/p2EvT8Y2+vyfjP046Vvh83/S97DtI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEpEehMyXbaTAIrCcxGhadFPDHioIjdI74UcVbEqRFfiVAIlChweAzqCxE/idirxAEa01QK7Blb/cKI34jIc+qmiIsivhWRx+vbIn4ZoRAYtcDeMYCXRNwv4q4RO0VcEnFaxLsj8phVCJQq8JwY2B/1BvfAmH631IEa11QIHB1beWSfW/qLqJc/JygERimQP6f+dsR9I3I+yzcjzox4fcRVEQoBAgQmSuB2sTUXRHSXiK2x/MkRCoHSBG4TA8pfzvPY/VFpgzOeqRX437HlmZBf6pyay/OHy4dGKARGKfDM6Dx/uVnqWL0m1j1qlAPUN4FlBA6JdddG1MdvJvAVAqMU+HB0Xh+PK01/OsqB6ptACDwvIv8Yv9Sx+sNY129CP6oqBAgQKF8gr/Q8P6I+8Z0X86+MOCHi7yLyr5O5bj7iuREKgVIE6quU62NXArSUPTPd4zguNr8+JnN6asRLI54a8aqIb0TU6/P8mr/AKwRGIZC/1ORne3085i/uvxdxfMQ7IupfinL6pAiFQEkCO8Zg8mfW+vjNqQRoSXtoOsdS/1G+eVwuNS8BOp3HSAlbnXdB/7+I+ti8PuZPiXh2xIsjzo6o1/0s5u8SoRAgQGAiBN4YW1Gf4D4S8/kDZbPk7UT5AZ118krQfSMUAqMWOCwGcGFEfezmVAJ01HtF/wcGwdUReTxm0uiYiIVlh1jwloj62M1f4HOZQmCYAntEZ/Vn+1zMH7VI5/eOZT+PyGM1P//3j1AIlCKQt2bW59F6KgFayt6ZznHsGpud59M8Hj8dsXGF2DvWKwRGIZB/6KzPm5m0v92CQczE6+Y59owF670kQIDAWArkL0D5V508AV4csTD5GYu2l/zFqD5JvuKGRf4lMBKB/OHyzRH1D5j1cZlTCdCR7BKdNgReGvP1MfnqxvKFs7Ox4EuNuvdfWMFrAgMW+N1ovz5W85y6VPnDWFHXe/lSlSwnMGSBh0Z/9c8B9R+d8jiVAB3yjtDdTQTyj/P1+TLv+FAIlCiQz/m+IiKP1fwj51J/3MyfVTM5mvXyfHvbCIUAAQJjLZC3udcf1PmL+3Iln1eXdS+LcLXSclLWDUogk0Tfi6iP2fwwziTT5b1lEqABoYxU4LTovT4+D1hhJC9p1M3bjhUCwxT4ZHRWH6v1lx4s1v9DGvU+tFgFywgMWeDW0d8lEXn85jn3w735fH2XCIXAqATys7w+rx4zqkHol8AKAr8T6+vjdKVEfeYHvh2Rt8Q/IEIhQIDAWAu8P0ZfnwCX+wUoN/KtjbqPzAUKgSEL5Id0fbxeGvNH9Pr/QW95/jVTITBKgfdH5+dE5B+M8vlKy5WnxMr6eF7uatHl2rCOwFoF8o6Pe0Tksz2XO1bzG43r4/QvYl4hMGqBTMTnMXllRD6WqX6dyyRAA0EZmcC7ouf6fHn7kY1CxwSWF/j7WF0fp3dcvqq1BAgQmCyB+qrOvJJupas6nxl16pPlCyeLwdaMiUAmQPMqz5dF5Bcg1UUCtJYwHSeBP4/B1ufUJ4zTwI11qgTe0jhOHz5VW25jSxQ4rnE8HtsboARoiXtqOsdUP9pmS2Pz8w9MB0Ts2VhmlsAoBerb2i8e5SD0TYAAgVEIbItO8xfwvJVopfKoqFD/sp5/4VQIDFvg7tHhLRfpVAJ0ERSLihbYLUa3OaI+px5Y9GgNbloFnhEbPh+Rx+l/Raz0h9KoohAYmMAB0fJPI/J4/GCjFwnQBobZkQnMRs/XRuTxeWrE4RH5iIZrInJZRv6+9Q8Rd4xQCIxCYJfoNC98yuPxY70BzMQ0v/Q4LzDJY/asiPzj5/+MyHUKAQIEJkIgv0ym/kD+ch9bdJ9G/Y/2UV8VAsMSkAAdlrR+2hJofrNm/rCpEChBYGMM4vERfxDx1Yj6Z4S8qmnfCIXAqATyl/DPR9RJpHwOaF0kQGsJ01EKHBKd1+fMqxvz9bLmNJOizxnlYPU9tQL5RXH1sfjemM8/bDbPofW6evrZWL9fhEKAAIGxF8hfZuqT2xl9bM3Bjfr5l02FQCkCEqCl7Anj6EfgaVGpPvdeFfOeE9aPmjrDEHhWdFIfm/X0kli2aRid64PAMgL1l8blFckPW1Cv+cv7XRas85LAsASaj2fI82cmOU+JeHHE/4r4s4jvRtTn1pw+MUIhMEyB/xad1cfg22O+/kLErTF/fkRe5JTTfF3Xy+9YyD+QKgQIEBhrgfwhsT6xfaqPLblDo/7n+qivCoFhCUiADktaP+sVODoaaP5Qefx6G/R+Ai0KvCbaui4iz6n1re/5c8IvIl4bkc+yUwgMWyDvQPplRB6Lb1qkcwnQRVAsGrrAG6LH+veqfGRIXhG6sNwiFrwroq6XiaXbLqzkNYEBCuQfkOrjr/55NO/4WHi83i2WXdCo++GYVwgQIDDWAs2E5r/2sSV3jjr1CfO0PuqrQmBYAhKgw5LWz3oEnhZvrn+Jz3PpK9fTmPcSGIBA3lY822s3n7f8jIifRNSf/e/prTMhMCyBfF7df0TkMfiNiJ0jFhYJ0IUiXo9C4E7R6eMiXhTxaxFLlTzHNh8xkn94UggMS+C/R0f1Z3pOM1mfz6VfrOwVC5s/Azx4sUqWESBAYFwE9o6B1ifAs/sY9D0a9T/SR31VCAxLQAJ0WNL6WavAH8cb6/NtTl+x1oa8j8CQBfIqkOsj6uM3b59TCAxL4K3RUR57+cejey/RqQToEjAWFyvwWzGy+pzqsWLF7qaJHFh+2VF97OX0uBW28v826v/hCnWtJkCAQNEC+Vf1+gT4732MtHku/b8sAAAUWklEQVTCfHcf9VUhMCwBCdBhSetntQIb4g3vjajPtdti/oTVNqI+gRELvDb6r4/hd454LLqfHoFHN467PAbzCuXF4h8b9TJBX9fJ869CoESBA2JQ9Tk1f4ZVCAxL4J7RUX3s5XSl59A/oFH/74c1SP0QIEBgUAKXRsN58ssv4lip5IO66xPmq1aqbD2BIQpIgA4RW1d9C+weNT8VUZ83r4n5o/p+t4oEyhFo3jKX3wirEBiGwDuik/r8uZbpQ4YxSH0QWIPAbLynfs5yPmNZITAsgT2jo/p8mlfWr/Rs730a9b80rEHqZ7ACM4NtXusEiha4sDe6/EV9pYdwH9TYki835s0SIECAwE0F8rlJZ0Q8orf48pjmL+Mn916bEBilQP7se4eI34x4ZB8D2dKos2tj3iwBAgQI3CCQv0vdKeLufYDkVXd14uk7fdRXhUBbAldGQ/kzaZYdIu64fW7pf27TWJXPA1UmQMDtEROwE23CmgW+GO/MX4Cy5ION8zaipcqDGivyfQoBAgQI3Fwgb7/MKz/zNqMs+YiRx0TklcoKgRIEdopB5B9Ac5q/0OwdkY9nWKr8emPF1xrzZgkMUuCT0fiP+ujgsVGn/gbjfERD/Z6L+3ivKgTaENgtGsnEUuYVro3YI+K6iKVKfbzm+m8uVclyAgMSyN/jj+m1fVhMv7tMP80LoC5app5VBAgQGAuBe8Uo68vg/3mZEe8f6/Iy+azr6s9loKwaiYBb4EfCrtMlBD4Uy+vzat4ulL8IKQRKE/h0DKg+To9cYXCnNuo+c4W6VhMYtkDznHuXYXeuPwI9gTNjWp9T6+TSUjifa9Q9bqlKlhMYkMCx0W59rJ4S8/XVyIt190+Nuo9erIJlBAgQGDeBr8SA8ySYz6J58iKD3yWWNZ9j9/hF6lhEYJQCEqCj1Nd3U+Dh8aL+ofKymN/YXGmeQEECz46x1MfqRTGft28uVpr18oq6Wy9WyTICIxSQAB0hvq5vFHhxzNXn1DxXNm8dvrFSzDyvUe+rMb9c8qn5PvME2hLYORr6fkR9vL52iYbzufV1nTymZ5eoZzEBAgTGSuDQGG39IO65mD8x4sCIfC7IAyM+H1Gf/M6Jec/NDQSlKAEJ0KJ2x9QOZsfY8ryVrT5ffivm85mf/UT+QqQQGKZA/tKdtxjXx+s3Yv5REbeIyNs47x7xvoh6ff588JAIhUBpAh+KAdXH6V1KG5zxTI1AnjebV4Hmsz2PjrhlRK7Lu+7eH1Efq3mrfD5+TCEwCoF8/nc++qY+Ht8e8w+NyGM1n1H7sojm+n6eFx5vUQgQIDAeAnkp/DUR9Ukwp/Ut7/Wy/CDfK0IhUJqABGhpe2Q6x5PJo/p8udrpe6aTzFaPWCA/08+PaB6vmei8fsGyq+L10yMUAiUKSICWuFemc0y3i83+dkTznJoXmSw8p14Sy+4boRAYpcAR0fnmiObxuvBYzZ8JXjDKQeqbAAECgxK4WzScz/ds/rUnT4h5InxTxJ4RCoESBSRAS9wr0zemF8YmN3+IXM28BOj0HS+lbHFe7ZG/3Pw0YuExm38I/WhE/lKvEChVQAK01D0znePKu0HydvjFzqk/jOUnRewdoRAoQWBTDOKfI66OWPgzwBdi2cMjlAkTyFuAFAIEfiWQt7/dK2L/iO9FfCsir/5QCBAgQIAAgckUyMfb3CHi4Ih89nfeEp9XMm2NUAgQIEBgdQJ5Tj0gIh/LkOfUfN7nxREKgRIFMid254h7RmQyND//Mw+gECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEC/AkdFxc2LxJf7baCleodHO3MRP1qmvSfFusXG+pll3mMVAQIECBAgQIDAGgU2rPF93kaAAAECBAgQIECgJIGdYjD7LDKgaxdZNqhFt4mG3xcxs0IHu8T6xcZ6+Qrvs5oAAQIECBAgQGANAhKga0DzFgIECBAgQIAAgaIFTo7RndYb4c+GNNLdo59/ibhzH/2dE3V+v1HvrY15swQIECBAgAABAgQIECBAgAABAgQIELiZwONiSbcXJ95s7WAXHBbNX9jrux7DcrfALxxN/Z7zFq7wmgABAgQIECBAYP0CK92es/4etECAAAECBAgQIEBgMgV2jc16c8SZEQdP5ibaKgIECBAgQIDA+AtIgI7/PrQFBAgQIECAAAECwxe4f3R5QcTzI/Jn6vmI10TklxspBAgQIECAAAECBQl4BmhBO8NQCBAgQIAAAQJTKnBcbPeDetv+45jmLex5W/hi5QWx8K69FXnl5QcXqzSEZfmt8wf2+rkspk+OOD3iKb1lS42/t9qEAAECBAgQIECAAAECBAgQIECAAIFpEdgvNjQTn/WzMJ+7xIYf06hzRczv26g37GeAvir6zud8viwivwCpLj+ImdyOHF+/pd5uzwDtV0w9AgQIECBAgAABAgQIECBAgAABAmMmcGyMt04E5je3H7Bg/JnszIRj1snbzY+MaJZhJ0DvHp3fsjmA3rwE6CIoFhEgQIAAAQIERingGaCj1Nc3AQIECBAgQIBALXBSzHyg9yITi++qV8S0E/G3EXv2luUXD53Smx/V5ILo+JpRda5fAgQIECBAgAABAgQIECBAgAABAgTGTyBvJb8oor4S9Km9TXhhY9lXYn7H3vLmZNhXgDb7bs67ArSpYZ4AAQIECBAgQIAAAQIECBAgQIAAgZsI5JchzUVkEjSfo/mQiOt6r6+O6Z0iFisSoIupWEaAAAECBAgQIFC5Bd5BQIAAAQIECBAgUJLAF2Iwr+sNaK+YfiZip97r34vpd3vzJgQIECBAgAABAgT6EpAA7YtJJQIECBAgQIAAgSEKvCL6+rdefxt60/fH9O968yYECBAgQIAAAQIE+haQAO2bSkUCBAgQIECAAIEhCWyNft6xoK+TF7z2kgABAgQIECBAgEBfAhKgfTGpRIAAAQIECBAgMESB20Zfr17Q39vidS5XCBAgQIAAAQIECKxKQAJ0VVwqEyBAgAABAgQIDEHgb6KPfXr9fLU3zdfv6c2bECBAgAABAgQIEOhbQAK0byoVCRAgQIAAAQIEhiDwrOjj6F4/X4rp4RF1EvS3Yj7XKwQIECBAgAABAgQIECBAgAABAgQIEBg7gYNixNdEdCOuizgkIss9In4ZkctzfdZbWB4XC3J9xokLVw7x9Q96Y7hiFX3W4z5vFe9RlQABAgQIECBAoE8BV4D2CaUaAQIECBAgQIDAQAXy294/GLFrr5dXxPTC3vzXY1o/EzTXZ7362+F7Vfqe3CFq3rcX+/X9LhUJECBAgAABAgQIECBAgAABAgQIECCwDoFXxnvrKyHz1vfZBW3tEK/Pb9TJ+s3S7xWgH4o31f28odlAS/OuAG0JUjMECBAgQIAAgbYEXAHalqR2CBAgQIAAAQIE1ipwWLzxZb03Xx/Tp0XM9V7Xk60x8/SIbb0FWT/fpxAgQIAAAQIECBBYVkACdFkeKwkQIECAAAECBAYscMtoP29pr6/4/JOYr299X9h1fhnSn/UWZv0PROT7FQIECBAgQIAAAQIECBAgQIAAAQIECEy0QL+3wCfCr0fkbfDPzxcFlPqWfF+CVMDOMAQCBAgQIEBg8gRcATp5+9QWESBAgAABAgQILC9w/97qc5evZi0BAgQIECBAgMAkCEiATsJetA0ECBAgQIAAAQL9CmyMinnl57URrrjsV009AgQIECBAgMAYC0iAjvHOM3QCBAgQIECAAIFVC7w+3rFvxCMjfrnqd3sDAQIECBAgQIAAAQIECBAgQIAAAQIERiDQfAZofmP8db341oKxHBCv77Bg2bBfPi06rMeXU88AHfYe0B8BAgQIECAwVQIbpmprbSwBAgQIECBAgMA0COTPuPXPuTsu2OCLF7wexcv8BvudRtGxPgkQIECAAAEC0yhQ/2A4jdtumwkQIECAAAECBCZH4MuxKU9eZHN+vsiyUS/6XAxgsbH+ZNQD0z8BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT6E/j/sDZ+kZ6qCOIAAAAASUVORK5CYII=" title="plot of chunk unnamed-chunk-4" alt="plot of chunk unnamed-chunk-4" width="672" /></p>
</div>
<div id="d" class="section level3">
<h3>d</h3>
<pre class="r"><code>euclid = function(a, b) {
return(sqrt((a[1] - b[1])^2 + (a[2]-b[2])^2))
}
assign_labels = function(x, centroid1, centroid2) {
labels = rep(NA, nrow(x))
for (i in 1:nrow(x)) {
if (euclid(x[i,], centroid1) < euclid(x[i,], centroid2)) {
labels[i] = 1
} else {
labels[i] = 2
}
}
return(labels)
}
labels = assign_labels(x, centroid1, centroid2)
labels</code></pre>
<pre><code>## [1] 1 1 1 2 2 2</code></pre>
</div>
<div id="e" class="section level3">
<h3>e</h3>
<pre class="r"><code>last_labels = rep(-1, 6)
while (!all(last_labels == labels)) {
last_labels = labels
centroid1 = c(mean(x[labels==1, 1]), mean(x[labels==1, 2]))
centroid2 = c(mean(x[labels==2, 1]), mean(x[labels==2, 2]))
print(centroid1)
print(centroid2)
labels = assign_labels(x, centroid1, centroid2)
}</code></pre>
<pre><code>## [1] 0.6667 3.6667
## [1] 5 1</code></pre>
<pre class="r"><code>labels</code></pre>
<pre><code>## [1] 1 1 1 2 2 2</code></pre>
</div>
<div id="f" class="section level3">
<h3>f</h3>
<pre class="r"><code>plot(x[,1], x[,2], col=(labels+1), pch=20, cex=2)
points(centroid1[1], centroid1[2], col=2, pch=4)
points(centroid2[1], centroid2[2], col=3, pch=4)</code></pre>
<p><img 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统计学习导论_基于R应用习题答案/ch10/3.html
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<!DOCTYPE html>
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<title>Chapter 10: Exercise 4</title>
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<h1 class="title">Chapter 10: Exercise 4</h1>
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<h3>a</h3>
<p>Not enough information to tell. The maximal intercluster dissimilarity could be equal or not equal to the minimial intercluster dissimilarity. If the dissimilarities were equal, they would fuse at the same height. If they were not equal, they single linkage dendogram would fuse at a lower height.</p>
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<h3>b</h3>
<p>They would fuse at the same height because linkage does not affect leaf-to-leaf fusion.</p>
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统计学习导论_基于R应用习题答案/ch10/4.html
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<title>Chapter 10: Exercise 5</title>
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<h1 class="title">Chapter 10: Exercise 5</h1>
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<p>Clusters selected based on two-dimensional Euclidean distance.</p>
<div id="a" class="section level3">
<h3>a</h3>
<p>Least socks and computers (3, 4, 6, 8) versus more socks and computers (1, 2, 7, 8).</p>
</div>
<div id="b" class="section level3">
<h3>b</h3>
<p>Purchased computer (5, 6, 7, 8) versus no computer purchase (1, 2, 3, 4). The distance on the computer dimension is greater than the distance on the socks dimension.</p>
</div>
<div id="c" class="section level3">
<h3>c</h3>
<p>Purchased computer (5, 6, 7, 8) versus no computer purchase (1, 2, 3, 4).</p>
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统计学习导论_基于R应用习题答案/ch10/5.html
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<title>Chapter 10: Exercise 6</title>
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<h1 class="title">Chapter 10: Exercise 6</h1>
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<div id="a" class="section level3">
<h3>a</h3>
<p>The first principal component “explains 10% of the variation” means 90% of the information in the gene data set is lost by projecting the tissue sample observations onto the first principal component. Another way of explaining it is 90% of the variance in the data is not contained in the first principal component.</p>
</div>
<div id="b" class="section level3">
<h3>b</h3>
<p>Given the flaw shown in pre-analysis of a time-wise linear trend amongst the tissue samples’ first principal component, I would advise the researcher to include the machine used (A vs B) as a feature of the data set. This should enhance the PVE of the first principal component before applying the two-sample t-test.</p>
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<div id="c" class="section level3">
<h3>c</h3>
<pre class="r"><code>set.seed(1)
Control = matrix(rnorm(50*1000), ncol=50)
Treatment = matrix(rnorm(50*1000), ncol=50)
X = cbind(Control, Treatment)
X[1,] = seq(-18, 18 - .36, .36) # linear trend in one dimension</code></pre>
<pre class="r"><code>pr.out = prcomp(scale(X))
summary(pr.out)$importance[,1]</code></pre>
<pre><code>## Standard deviation Proportion of Variance Cumulative Proportion
## 3.14815 0.09911 0.09911</code></pre>
<p>9.911% variance explained by the first principal component.</p>
<p>Now, adding in A vs B via 10 vs 0 encoding.</p>
<pre class="r"><code>X = rbind(X, c(rep(10, 50), rep(0, 50)))
pr.out = prcomp(scale(X))
summary(pr.out)$importance[,1]</code></pre>
<pre><code>## Standard deviation Proportion of Variance Cumulative Proportion
## 3.3978 0.1154 0.1154</code></pre>
<p>11.54% variance explained by the first principal component. That’s an improvement of 1.629%.</p>
<p>(*) I’m sure a better simulation could be derived from someone more versed in PCA.</p>
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<title>Chapter 10: Exercise 7</title>
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<h1 class="title">Chapter 10: Exercise 7</h1>
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<pre class="r"><code>library(ISLR)
set.seed(1)</code></pre>
<pre class="r"><code>dsc = scale(USArrests)
a = dist(dsc)^2
b = as.dist(1 - cor(t(dsc)))
summary(b/a)</code></pre>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.069 0.134 0.234 0.263 4.890</code></pre>
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统计学习导论_基于R应用习题答案/ch10/7.html
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<title>Chapter 10: Exercise 8</title>
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<h1 class="title">Chapter 10: Exercise 8</h1>
</div>
<pre class="r"><code>library(ISLR)
set.seed(1)</code></pre>
<div id="a" class="section level3">
<h3>a</h3>
<pre class="r"><code>pr.out = prcomp(USArrests, center=T, scale=T)
pr.var = pr.out$sdev^2
pve = pr.var / sum(pr.var)
pve</code></pre>
<pre><code>## [1] 0.62006 0.24744 0.08914 0.04336</code></pre>
</div>
<div id="b" class="section level3">
<h3>b</h3>
<pre class="r"><code>loadings = pr.out$rotation
pve2 = rep(NA, 4)
dmean = apply(USArrests, 2, mean)
dsdev = sqrt(apply(USArrests, 2, var))
dsc = sweep(USArrests, MARGIN=2, dmean, "-")
dsc = sweep(dsc, MARGIN=2, dsdev, "/")
for (i in 1:4) {
proto_x = sweep(dsc, MARGIN=2, loadings[,i], "*")
pc_x = apply(proto_x, 1, sum)
pve2[i] = sum(pc_x^2)
}
pve2 = pve2/sum(dsc^2)
pve2</code></pre>
<pre><code>## [1] 0.62006 0.24744 0.08914 0.04336</code></pre>
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统计学习导论_基于R应用习题答案/ch10/8.html
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<title>Chapter 10: Exercise 9</title>
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<h1 class="title">Chapter 10: Exercise 9</h1>
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<pre class="r"><code>library(ISLR)
set.seed(2)</code></pre>
<div id="a" class="section level3">
<h3>a</h3>
<pre class="r"><code>hc.complete = hclust(dist(USArrests), method="complete")
plot(hc.complete)</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-2" alt="plot of chunk unnamed-chunk-2" width="672" /></p>
</div>
<div id="b" class="section level3">
<h3>b</h3>
<pre class="r"><code>cutree(hc.complete, 3)</code></pre>
<pre><code>## Alabama Alaska Arizona Arkansas California
## 1 1 1 2 1
## Colorado Connecticut Delaware Florida Georgia
## 2 3 1 1 2
## Hawaii Idaho Illinois Indiana Iowa
## 3 3 1 3 3
## Kansas Kentucky Louisiana Maine Maryland
## 3 3 1 3 1
## Massachusetts Michigan Minnesota Mississippi Missouri
## 2 1 3 1 2
## Montana Nebraska Nevada New Hampshire New Jersey
## 3 3 1 3 2
## New Mexico New York North Carolina North Dakota Ohio
## 1 1 1 3 3
## Oklahoma Oregon Pennsylvania Rhode Island South Carolina
## 2 2 3 2 1
## South Dakota Tennessee Texas Utah Vermont
## 3 2 2 3 3
## Virginia Washington West Virginia Wisconsin Wyoming
## 2 2 3 3 2</code></pre>
<pre class="r"><code>table(cutree(hc.complete, 3))</code></pre>
<pre><code>##
## 1 2 3
## 16 14 20</code></pre>
</div>
<div id="c" class="section level3">
<h3>c</h3>
<pre class="r"><code>dsc = scale(USArrests)
hc.s.complete = hclust(dist(dsc), method="complete")
plot(hc.s.complete)</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-4" alt="plot of chunk unnamed-chunk-4" width="672" /></p>
</div>
<div id="d" class="section level3">
<h3>d</h3>
<pre class="r"><code>cutree(hc.s.complete, 3)</code></pre>
<pre><code>## Alabama Alaska Arizona Arkansas California
## 1 1 2 3 2
## Colorado Connecticut Delaware Florida Georgia
## 2 3 3 2 1
## Hawaii Idaho Illinois Indiana Iowa
## 3 3 2 3 3
## Kansas Kentucky Louisiana Maine Maryland
## 3 3 1 3 2
## Massachusetts Michigan Minnesota Mississippi Missouri
## 3 2 3 1 3
## Montana Nebraska Nevada New Hampshire New Jersey
## 3 3 2 3 3
## New Mexico New York North Carolina North Dakota Ohio
## 2 2 1 3 3
## Oklahoma Oregon Pennsylvania Rhode Island South Carolina
## 3 3 3 3 1
## South Dakota Tennessee Texas Utah Vermont
## 3 1 2 3 3
## Virginia Washington West Virginia Wisconsin Wyoming
## 3 3 3 3 3</code></pre>
<pre class="r"><code>table(cutree(hc.s.complete, 3))</code></pre>
<pre><code>##
## 1 2 3
## 8 11 31</code></pre>
<pre class="r"><code>table(cutree(hc.s.complete, 3), cutree(hc.complete, 3))</code></pre>
<pre><code>##
## 1 2 3
## 1 6 2 0
## 2 9 2 0
## 3 1 10 20</code></pre>
<p>Scaling the variables effects the max height of the dendogram obtained from hierarchical clustering. From a cursory glance, it doesn’t effect the bushiness of the tree obtained. However, it does affect the clusters obtained from cutting the dendogram into 3 clusters. In my opinion, for this data set the data should be standardized because the data measured has different units (<span class="math">\(UrbanPop\)</span> compared to other three columns).</p>
</div>
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch10/9.html
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HTML
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unknown
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# Chapter 10 Lab 1: Principal Components Analysis
states=row.names(USArrests)
states
names(USArrests)
apply(USArrests, 2, mean)
apply(USArrests, 2, var)
pr.out=prcomp(USArrests, scale=TRUE)
names(pr.out)
pr.out$center
pr.out$scale
pr.out$rotation
dim(pr.out$x)
biplot(pr.out, scale=0)
pr.out$rotation=-pr.out$rotation
pr.out$x=-pr.out$x
biplot(pr.out, scale=0)
pr.out$sdev
pr.var=pr.out$sdev^2
pr.var
pve=pr.var/sum(pr.var)
pve
plot(pve, xlab="Principal Component", ylab="Proportion of Variance Explained", ylim=c(0,1),type='b')
plot(cumsum(pve), xlab="Principal Component", ylab="Cumulative Proportion of Variance Explained", ylim=c(0,1),type='b')
a=c(1,2,8,-3)
cumsum(a)
# Chapter 10 Lab 2: Clustering
# K-Means Clustering
set.seed(2)
x=matrix(rnorm(50*2), ncol=2)
x[1:25,1]=x[1:25,1]+3
x[1:25,2]=x[1:25,2]-4
km.out=kmeans(x,2,nstart=20)
km.out$cluster
plot(x, col=(km.out$cluster+1), main="K-Means Clustering Results with K=2", xlab="", ylab="", pch=20, cex=2)
set.seed(4)
km.out=kmeans(x,3,nstart=20)
km.out
plot(x, col=(km.out$cluster+1), main="K-Means Clustering Results with K=3", xlab="", ylab="", pch=20, cex=2)
set.seed(3)
km.out=kmeans(x,3,nstart=1)
km.out$tot.withinss
km.out=kmeans(x,3,nstart=20)
km.out$tot.withinss
# Hierarchical Clustering
hc.complete=hclust(dist(x), method="complete")
hc.average=hclust(dist(x), method="average")
hc.single=hclust(dist(x), method="single")
par(mfrow=c(1,3))
plot(hc.complete,main="Complete Linkage", xlab="", sub="", cex=.9)
plot(hc.average, main="Average Linkage", xlab="", sub="", cex=.9)
plot(hc.single, main="Single Linkage", xlab="", sub="", cex=.9)
cutree(hc.complete, 2)
cutree(hc.average, 2)
cutree(hc.single, 2)
cutree(hc.single, 4)
xsc=scale(x)
plot(hclust(dist(xsc), method="complete"), main="Hierarchical Clustering with Scaled Features")
x=matrix(rnorm(30*3), ncol=3)
dd=as.dist(1-cor(t(x)))
plot(hclust(dd, method="complete"), main="Complete Linkage with Correlation-Based Distance", xlab="", sub="")
# Chapter 10 Lab 3: NCI60 Data Example
# The NCI60 data
library(ISLR)
nci.labs=NCI60$labs
nci.data=NCI60$data
dim(nci.data)
nci.labs[1:4]
table(nci.labs)
# PCA on the NCI60 Data
pr.out=prcomp(nci.data, scale=TRUE)
Cols=function(vec){
cols=rainbow(length(unique(vec)))
return(cols[as.numeric(as.factor(vec))])
}
par(mfrow=c(1,2))
plot(pr.out$x[,1:2], col=Cols(nci.labs), pch=19,xlab="Z1",ylab="Z2")
plot(pr.out$x[,c(1,3)], col=Cols(nci.labs), pch=19,xlab="Z1",ylab="Z3")
summary(pr.out)
plot(pr.out)
pve=100*pr.out$sdev^2/sum(pr.out$sdev^2)
par(mfrow=c(1,2))
plot(pve, type="o", ylab="PVE", xlab="Principal Component", col="blue")
plot(cumsum(pve), type="o", ylab="Cumulative PVE", xlab="Principal Component", col="brown3")
# Clustering the Observations of the NCI60 Data
sd.data=scale(nci.data)
par(mfrow=c(1,3))
data.dist=dist(sd.data)
plot(hclust(data.dist), labels=nci.labs, main="Complete Linkage", xlab="", sub="",ylab="")
plot(hclust(data.dist, method="average"), labels=nci.labs, main="Average Linkage", xlab="", sub="",ylab="")
plot(hclust(data.dist, method="single"), labels=nci.labs, main="Single Linkage", xlab="", sub="",ylab="")
hc.out=hclust(dist(sd.data))
hc.clusters=cutree(hc.out,4)
table(hc.clusters,nci.labs)
par(mfrow=c(1,1))
plot(hc.out, labels=nci.labs)
abline(h=139, col="red")
hc.out
set.seed(2)
km.out=kmeans(sd.data, 4, nstart=20)
km.clusters=km.out$cluster
table(km.clusters,hc.clusters)
hc.out=hclust(dist(pr.out$x[,1:5]))
plot(hc.out, labels=nci.labs, main="Hier. Clust. on First Five Score Vectors")
table(cutree(hc.out,4), nci.labs)
|
2301_76574743/tjjm-R
|
统计学习导论_基于R应用习题答案/ch10/lab.R
|
R
|
unknown
| 3,558
|
# 8. (a)
college = read.csv("C:/Users/djsta/Desktop/College.csv")
# 8. (b)
fix(college)
rownames(college) = college[,1]
college = college[,-1]
fix(college)
# 8. (c)
# i.
summary(college)
# ii.
pairs(college[,2:11])
# iii.
library(car)
scatterplot(college$Outstate,as.factor(college$Private))
# iv.
Elite = rep("No", nrow(college))
Elite[college$Top10perc>50] = "Yes"
Elite = as.factor(Elite)
college = data.frame(college, Elite)
college
summary(college$Elite)
scatterplot( college$Outstate,college$Elite)
# v.
par(mfrow=c(2,2))
hist(college$Apps)
hist(college$perc.alumni, col=2)
hist(college$S.F.Ratio, col=3, breaks=10)
hist(college$Expend, breaks=100)
# vi.
par(mfrow=c(1,1))
plot(college$Outstate, college$Grad.Rate)
# High tuition correlates to high graduation rate.
plot(college$Accept / college$Apps, college$S.F.Ratio)
# Colleges with low acceptance rate tend to have low S:F ratio.
plot(college$Top10perc, college$Grad.Rate)
# Colleges with the most students from top 10% perc don't necessarily have
# the highest graduation rate. Also, rate > 100 is erroneous!
# 9.
Auto = read.csv("C:/Users/djsta/Desktop/Auto.csv", header=T, na.strings="?")
fix(Auto)
Auto = na.omit(Auto)
dim(Auto)
summary(Auto)
# (a)
# quantitative: mpg, cylinders, displacement, horsepower, weight,
# acceleration, year
# qualitative: name, origin
# (b)
# apply the range function to the first seven columns of Auto
sapply(Auto[, 1:7], range)
# mpg cylinders displacement horsepower weight acceleration year
# [1,] 9.0 3 68 46 1613 8.0 70
# [2,] 46.6 8 455 230 5140 24.8 82
# (c)
sapply(Auto[, 1:7], mean)
# mpg cylinders displacement horsepower weight acceleration
# 23.445918 5.471939 194.411990 104.469388 2977.584184 15.541327
# year
# 75.979592
sapply(Auto[, 1:7], var)
# mpg cylinders displacement horsepower weight acceleration
# 7.805007 1.705783 104.644004 38.491160 849.402560 2.758864
# year
# 3.683737
# (d)
newAuto = auto[-c(10,84),]
sapply(newAuto[, 1:7], range)
# mpg cylinders displacement horsepower weight acceleration year
# [1,] 11.0 3 68 46 1649 8.5 70
# [2,] 46.6 8 455 230 4997 24.8 82
sapply(newAuto[, 1:7], mean)
# mpg cylinders displacement horsepower weight acceleration
# 24.404430 5.373418 187.240506 100.721519 2935.971519 15.726899
# year
# 77.145570
sapply(newAuto[, 1:7], sd)
# mpg cylinders displacement horsepower weight acceleration
# 7.867283 1.654179 99.678367 35.708853 811.300208 2.693721
# year
# 3.106217
# (e)
pairs(Auto[1:7])
plot(Auto$mpg, Auto$weight)
# Heavier weight correlates with lower mpg.
plot(Auto$mpg, Auto$cylinders)
# More cylinders, less mpg.
plot(Auto$mpg, Auto$year)
# Cars become more efficient over time.
# (f)
pairs(Auto)
# See descriptions of plots in (e).
# All of the predictors show some correlation with mpg. The name predictor has
# too little observations per name though, so using this as a predictor is
# likely to result in overfitting the data and will not generalize well.
# 10.
# (a)
library(MASS)
?Boston
dim(Boston)
# 506 rows, 14 columns
# 14 features, 506 housing values in Boston suburbs
fix(Boston)
# (b)
pairs(Boston)
# X correlates with: a, b, c
# crim: age, dis, rad, tax, ptratio
# zn: indus, nox, age, lstat
# indus: age, dis
# nox: age, dis
# dis: lstat
# lstat: medv
# (c)
plot(Boston$age, Boston$crim)
# Older homes, more crime
plot(Boston$dis, Boston$crim)
# Closer to work-area, more crime
plot(Boston$rad, Boston$crim)
# Higher index of accessibility to radial highways, more crime
plot(Boston$tax, Boston$crim)
# Higher tax rate, more crime
plot(Boston$ptratio, Boston$crim)
# Higher pupil:teacher ratio, more crime
# (d)
par(mfrow=c(1,3))
hist(Boston$crim[Boston$crim>1], breaks=25)
# most cities have low crime rates, but there is a long tail: 18 suburbs appear
# to have a crime rate > 20, reaching to above 80
hist(Boston$tax, breaks=25)
# there is a large divide between suburbs with low tax rates and a peak at 660-680
hist(Boston$ptratio, breaks=25)
# a skew towards high ratios, but no particularly high ratios
# (e)
dim(subset(Boston, chas == 1))
# 35 suburbs
# (f)
median(Boston$ptratio)
# 19.05
# (g)
t(subset(Boston, medv == min(Boston$medv)))
# 399 406
# crim 38.3518 67.9208 above 3rd quartile
# zn 0.0000 0.0000 at min
# indus 18.1000 18.1000 at 3rd quartile
# chas 0.0000 0.0000 not bounded by river
# nox 0.6930 0.6930 above 3rd quartile
# rm 5.4530 5.6830 below 1st quartile
# age 100.0000 100.0000 at max
# dis 1.4896 1.4254 below 1st quartile
# rad 24.0000 24.0000 at max
# tax 666.0000 666.0000 at 3rd quartile
# ptratio 20.2000 20.2000 at 3rd quartile
# black 396.9000 384.9700 at max; above 1st quartile
# lstat 30.5900 22.9800 above 3rd quartile
# medv 5.0000 5.0000 at min
summary(Boston)
# Not the best place to live, but certainly not the worst.
# (h)
dim(subset(Boston, rm > 7))
# 64
dim(subset(Boston, rm > 8))
# 13
summary(subset(Boston, rm > 8))
summary(Boston)
# relatively lower crime (comparing range), lower lstat (comparing range)
|
2301_76574743/tjjm-R
|
统计学习导论_基于R应用习题答案/ch2/applied.R
|
R
|
unknown
| 5,416
|
# Chapter 2 Lab: Introduction to R
# Basic Commands
x <- c(1,3,2,5)
x
x = c(1,6,2)
x
y = c(1,4,3)
length(x)
length(y)
x+y
ls()
rm(x,y)
ls()
rm(list=ls())
?matrix
x=matrix(data=c(1,2,3,4), nrow=2, ncol=2)
x
x=matrix(c(1,2,3,4),2,2)
matrix(c(1,2,3,4),2,2,byrow=TRUE)
sqrt(x)
x^2
x=rnorm(50)
y=x+rnorm(50,mean=50,sd=.1)
cor(x,y)
set.seed(1303)
rnorm(50)
set.seed(3)
y=rnorm(100)
mean(y)
var(y)
sqrt(var(y))
sd(y)
# Graphics
x=rnorm(100)
y=rnorm(100)
plot(x,y)
plot(x,y,xlab="this is the x-axis",ylab="this is the y-axis",main="Plot of X vs Y")
pdf("Figure.pdf")
plot(x,y,col="green")
dev.off()
x=seq(1,10)
x
x=1:10
x
x=seq(-pi,pi,length=50)
y=x
f=outer(x,y,function(x,y)cos(y)/(1+x^2))
contour(x,y,f)
contour(x,y,f,nlevels=45,add=T)
fa=(f-t(f))/2
contour(x,y,fa,nlevels=15)
image(x,y,fa)
persp(x,y,fa)
persp(x,y,fa,theta=30)
persp(x,y,fa,theta=30,phi=20)
persp(x,y,fa,theta=30,phi=70)
persp(x,y,fa,theta=30,phi=40)
# Indexing Data
A=matrix(1:16,4,4)
A
A[2,3]
A[c(1,3),c(2,4)]
A[1:3,2:4]
A[1:2,]
A[,1:2]
A[1,]
A[-c(1,3),]
A[-c(1,3),-c(1,3,4)]
dim(A)
# Loading Data
Auto=read.table("Auto.data")
fix(Auto)
Auto=read.table("Auto.data",header=T,na.strings="?")
fix(Auto)
Auto=read.csv("Auto.csv",header=T,na.strings="?")
fix(Auto)
dim(Auto)
Auto[1:4,]
Auto=na.omit(Auto)
dim(Auto)
names(Auto)
# Additional Graphical and Numerical Summaries
plot(cylinders, mpg)
plot(Auto$cylinders, Auto$mpg)
attach(Auto)
plot(cylinders, mpg)
cylinders=as.factor(cylinders)
plot(cylinders, mpg)
plot(cylinders, mpg, col="red")
plot(cylinders, mpg, col="red", varwidth=T)
plot(cylinders, mpg, col="red", varwidth=T,horizontal=T)
plot(cylinders, mpg, col="red", varwidth=T, xlab="cylinders", ylab="MPG")
hist(mpg)
hist(mpg,col=2)
hist(mpg,col=2,breaks=15)
pairs(Auto)
pairs(~ mpg + displacement + horsepower + weight + acceleration, Auto)
plot(horsepower,mpg)
identify(horsepower,mpg,name)
summary(Auto)
summary(mpg)
|
2301_76574743/tjjm-R
|
统计学习导论_基于R应用习题答案/ch2/lab.R
|
R
|
unknown
| 1,900
|
set.seed(11)
## create a data frame of 3 columns and 10,000 rows filled with
## random uniform data
cube <- data.frame(matrix(runif(3*10000, min = 0, 1), ncol=3))
## get the max and min for each row
cube[, "max"] <- apply(cube, MARGIN = 1, max)
cube[, "min"] <- apply(cube, MARGIN = 1, min)
## add a column to mark if the row is considered in the boundary
## initially this column will be filled with 0 (not boundary)
cube$boundary <-0
## set boundary to 1 if min is less than .05
cube$boundary[cube$min < .05] <-1
## or max greater than .95
cube$boundary[cube$max > .95] <-1
sum(cube$boundary)
## Expected answer
10000 * (1-.9^3)
##--------------------------------------------------------
set.seed(11)
## create a data frame of 50 columns and 10,000 rows filled with
## random uniform data
hypercube <- data.frame(matrix(runif(50*10000, min = 0, 1), ncol=50))
## get the max and min for each row
hypercube[, "max"] <- apply(hypercube, MARGIN = 1, max)
hypercube[, "min"] <- apply(hypercube, MARGIN = 1, min)
## add a column to mark if the row is considered in the boundary
## initially this column will be filled with 0 (not boundary)
hypercube$boundary <-0
## set boundary to 1 if min is less than .05
hypercube$boundary[hypercube$min < .05] <-1
## or max greater than .95
hypercube$boundary[hypercube$max > .95] <-1
sum(hypercube$boundary)
## Expected answer
10000 * (1-.9^50)
|
2301_76574743/tjjm-R
|
统计学习导论_基于R应用习题答案/ch2/notes/Hypercube-boundary-experiment.R
|
R
|
unknown
| 1,407
|
# This script creates plots illustrating the curse of dimensionality
# Choose points, randomly distributed
n <- 3000 # number of points
p <- 2 # number of dimensions
x <- matrix(runif(n*p), ncol=p)
Fringe <- function(z){
# Determine if z lies within 0.05 distance from the boundary
# z is a vector of length p
any(abs(z - 0.5) > 0.45)
}
result <- apply(x, 1, Fringe)
png('edges.png')
plot(x[,1:2])
points(x[,1][result], x[,2][result], col='red')
dev.off()
ratio <- sum(result) / n
# plot p versus ratio
p <- 1:50
GetRatio <- function(dim){
1 - (0.9)^dim
}
ratio <- sapply(p, GetRatio)
png('curse_dimensionality.png')
plot(p, ratio)
dev.off()
|
2301_76574743/tjjm-R
|
统计学习导论_基于R应用习题答案/ch2/notes/dimension.R
|
R
|
unknown
| 661
|
# Graph the technique described by Trevor Hastie in Lecture 2.1
set.seed(37)
# Generate sample data
n <- 20
x0 <- sort(runif(n, min=0, max=10))
y0 <- 0.5 * x0 + rnorm(n, sd=0.2)
Estimate <- function(x, neighborhood=2){
# Estimates x as the average value of y within neighborhood
# Note this implementation is not k nearest neighbors,
# since it uses a neighborhood of fixed size.
mean(y0[abs(x - x0) < neighborhood])
}
x <- seq(0, 10, length.out=2000)
yhat <- sapply(x, Estimate)
png('neighbor.png')
plot(x, yhat, col='green')
points(x0, y0)
dev.off()
|
2301_76574743/tjjm-R
|
统计学习导论_基于R应用习题答案/ch2/notes/neighbor.R
|
R
|
unknown
| 573
|
<!DOCTYPE html>
<!-- saved from url=(0014)about:internet -->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<meta http-equiv="x-ua-compatible" content="IE=9" >
<title>Chapter 3, Problem 7</title>
<style type="text/css">
body, td {
font-family: sans-serif;
background-color: white;
font-size: 12px;
margin: 8px;
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tt, code, pre {
font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace;
}
h1 {
font-size:2.2em;
}
h2 {
font-size:1.8em;
}
h3 {
font-size:1.4em;
}
h4 {
font-size:1.0em;
}
h5 {
font-size:0.9em;
}
h6 {
font-size:0.8em;
}
a:visited {
color: rgb(50%, 0%, 50%);
}
pre {
margin-top: 0;
max-width: 95%;
border: 1px solid #ccc;
white-space: pre-wrap;
}
pre code {
display: block; padding: 0.5em;
}
code.r, code.cpp {
background-color: #F8F8F8;
}
table, td, th {
border: none;
}
blockquote {
color:#666666;
margin:0;
padding-left: 1em;
border-left: 0.5em #EEE solid;
}
hr {
height: 0px;
border-bottom: none;
border-top-width: thin;
border-top-style: dotted;
border-top-color: #999999;
}
@media print {
* {
background: transparent !important;
color: black !important;
filter:none !important;
-ms-filter: none !important;
}
body {
font-size:12pt;
max-width:100%;
}
a, a:visited {
text-decoration: underline;
}
hr {
visibility: hidden;
page-break-before: always;
}
pre, blockquote {
padding-right: 1em;
page-break-inside: avoid;
}
tr, img {
page-break-inside: avoid;
}
img {
max-width: 100% !important;
}
@page :left {
margin: 15mm 20mm 15mm 10mm;
}
@page :right {
margin: 15mm 10mm 15mm 20mm;
}
p, h2, h3 {
orphans: 3; widows: 3;
}
h2, h3 {
page-break-after: avoid;
}
}
</style>
<!-- MathJax scripts -->
<script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>
</head>
<body>
<h1>Chapter 3, Problem 7</h1>
<h2>Generic Solution</h2>
<p><strong>Proposition</strong>: Prove that in case of simple linear regression:</p>
<p>\[ y = \beta_0 + \beta_1 x + \varepsilon \]</p>
<p>the \( R^2 \) is equal to correlation between X and Y squared, e.g.:</p>
<p>\[ R^2 = corr^2(x, y) \]</p>
<p>We'll be using the following definitions to prove the above proposition.</p>
<p><strong>Def</strong>:
\[ R^2 = \frac{TSS - RSS}{TSS} \]</p>
<p><strong>Def</strong>:
\[ TSS = \sum (y_i - \bar{y})^2 \label{TSS} \]</p>
<p><strong>Def</strong>:
\[ RSS = \sum (y_i - \hat{y}_i)^2 \label{RSS} \] </p>
<p><strong>Def</strong>:
\[
\begin{align}
corr(x, y) &= \frac{\sum (x_i - \bar{x}) (y_i - \bar{y})}
{\sigma_x \sigma_y} \\
\sigma_x^2 &= \sum (x_i - \bar{x})^2 \\
\sigma_y^2 &= \sum (y_i - \bar{y})^2
\end{align}
\]</p>
<p><strong>Proof</strong>:</p>
<p>Substitute defintions of TSS and RSS into \( R^2 \):</p>
<p>\[
R^2 = \frac{\sum (y_i - \bar{y})^2 - \sum (y_i - \hat{y}_i)^2}
{\sum (y_i - \bar{y})^2}
\]</p>
<p>Let's work on the numerator:</p>
<p>\[
\begin{align}
A &= \sum (y_i - \bar{y})^2 - \sum (y_i - \hat{y}_i)^2 \\
&= \sum \left[ (y_i - \bar{y}) - (y_i - \hat{y}_i) \right]
\left[ (y_i - \bar{y}) + (y_i - \hat{y}_i) \right] \\
&= \sum (\hat{y}_i - \bar{y})
(2y_i - \bar{y} - \hat{y}_i)
\end{align}
\]</p>
<p>Recall that:</p>
<p>\[
\begin{align}
\hat{\beta}_0 &= \bar{y} - \hat{\beta}_1 \bar{x} \label{beta0} \\
\hat{\beta}_1 &= \frac{\sum (x_i - \bar{x})(y_i - \bar{y})}
{\sum (x_j - \bar{x})^2}
\end{align}
\]</p>
<p>Substitute the expression for \( \hat{\beta}_0 \) into \( \hat{y}_i \):</p>
<p>\[
\begin{align}
\hat{y}_i &= \hat{\beta}_0 + \hat{\beta}_1 x_i \\
&= \bar{y} - \hat{\beta}_1 \bar{x} + \hat{\beta}_1 x_i \\
&= \bar{y} + \hat{\beta}_1 (x_i - \bar{x})
\end{align}
\]</p>
<p>Let's analyze two terms from \( A \):</p>
<p>\[
\begin{align}
\hat{y}_i - \bar{y} &= \hat{\beta}_1 (x_i - \bar{x}) \\
2y_i - \bar{y} - \hat{y}_i &= 2y_i - \bar{y} - \bar{y} -
\hat{\beta}_1 (x_i - \bar{x}) \\
&= 2(y_i - \bar{y}) -
\hat{\beta}_1 (x_i - \bar{x})
\end{align}
\]</p>
<p>and substitute these expressions back into \( A \):</p>
<p>\[
\begin{align}
A &= \sum \hat{\beta}_1 (x_i - \hat{x})
\left[ 2(y_i - \bar{y}) - \hat{\beta}_1 (x_i - \bar{x}) \right] \\
&= \hat{\beta}_1 \sum (x_i - \bar{x})
\left[ 2(y_i - \bar{y}) -
\hat{\beta}_1 (x_i - \bar{x}) \right] \\
&= \hat{\beta}_1
\left[ 2 \sum (x_i - \bar{x})(y_i - \bar{y}) -
\hat{\beta}_1 \sum (x_i - \bar{x})^2 \label{A4} \right]
\end{align}
\]</p>
<p>Using formula for \( \hat{\beta}_1 \) it is easy to see that the last term is
nothing but:</p>
<p>\[ \sum (x_i - \bar{x}) (y_i - \bar{y}) \]</p>
<p>Thus, we get:</p>
<p>\[
\begin{align}
A &= \hat{\beta}_1 \sum (x_i - \bar{x}) (y_i - \bar{y}) \\
&= \frac{\left[ \sum (x_i - \bar{x}) (y_i - \bar{y}) \right]^2}
{\sum (x_j - \bar{x})^2}
\end{align}
\]</p>
<p>Plug the final expression for \( A \) back into \( R^2 \):</p>
<p>\[
R^2 = \frac{\left[ \sum (x_i - \bar{x}) (y_i - \bar{y}) \right]^2}
{\sum (x_j - \bar{x})^2 \sum (y_k - \bar{y})^2}
\]</p>
<p>Compare this to the definition of correlation and get:</p>
<p>\[ R^2 = corr^2(x, y) \]</p>
</body>
</html>
|
2301_76574743/tjjm-R
|
统计学习导论_基于R应用习题答案/ch3/7.html
|
HTML
|
unknown
| 5,770
|
# Chapter 3 Lab: Linear Regression
library(MASS)
library(ISLR)
# Simple Linear Regression
fix(Boston)
names(Boston)
lm.fit=lm(medv~lstat)
lm.fit=lm(medv~lstat,data=Boston)
attach(Boston)
lm.fit=lm(medv~lstat)
lm.fit
summary(lm.fit)
names(lm.fit)
coef(lm.fit)
confint(lm.fit)
predict(lm.fit,data.frame(lstat=(c(5,10,15))), interval="confidence")
predict(lm.fit,data.frame(lstat=(c(5,10,15))), interval="prediction")
plot(lstat,medv)
abline(lm.fit)
abline(lm.fit,lwd=3)
abline(lm.fit,lwd=3,col="red")
plot(lstat,medv,col="red")
plot(lstat,medv,pch=20)
plot(lstat,medv,pch="+")
plot(1:20,1:20,pch=1:20)
par(mfrow=c(2,2))
plot(lm.fit)
plot(predict(lm.fit), residuals(lm.fit))
plot(predict(lm.fit), rstudent(lm.fit))
plot(hatvalues(lm.fit))
which.max(hatvalues(lm.fit))
# Multiple Linear Regression
lm.fit=lm(medv~lstat+age,data=Boston)
summary(lm.fit)
lm.fit=lm(medv~.,data=Boston)
summary(lm.fit)
library(car)
vif(lm.fit)
lm.fit1=lm(medv~.-age,data=Boston)
summary(lm.fit1)
lm.fit1=update(lm.fit, ~.-age)
# Interaction Terms
summary(lm(medv~lstat*age,data=Boston))
# Non-linear Transformations of the Predictors
lm.fit2=lm(medv~lstat+I(lstat^2))
summary(lm.fit2)
lm.fit=lm(medv~lstat)
anova(lm.fit,lm.fit2)
par(mfrow=c(2,2))
plot(lm.fit2)
lm.fit5=lm(medv~poly(lstat,5))
summary(lm.fit5)
summary(lm(medv~log(rm),data=Boston))
# Qualitative Predictors
fix(Carseats)
names(Carseats)
lm.fit=lm(Sales~.+Income:Advertising+Price:Age,data=Carseats)
summary(lm.fit)
attach(Carseats)
contrasts(ShelveLoc)
# Writing Functions
LoadLibraries
LoadLibraries()
LoadLibraries=function(){
library(ISLR)
library(MASS)
print("The libraries have been loaded.")
}
LoadLibraries
LoadLibraries()
|
2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch3/lab.R
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R
|
unknown
| 1,688
|
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<h1>1</h1>
<p>\[
(4.2) p(X) = \frac {e^{\beta_0 + \beta_1 X}} {1 + e^{\beta_0 + \beta_1 X}}
\]</p>
<p>So, \( \frac {p(X)} {1 - p(X)} \)</p>
<p>\[
= \frac {\frac {e^{\beta_0 + \beta_1 X}} {1 + e^{\beta_0 + \beta_1 X}}}
{1 - \frac {e^{\beta_0 + \beta_1 X}} {1 + e^{\beta_0 + \beta_1 X}}}
\\
= \frac {\frac {e^{\beta_0 + \beta_1 X}} {1 + e^{\beta_0 + \beta_1 X}}}
{
\frac {1 + e^{\beta_0 + \beta_1 X}} {1 + e^{\beta_0 + \beta_1 X}}
- \frac {e^{\beta_0 + \beta_1 X}} {1 + e^{\beta_0 + \beta_1 X}}
}
\\
= \frac {\frac {e^{\beta_0 + \beta_1 X}} {1 + e^{\beta_0 + \beta_1 X}}}
{\frac {1} {1 + e^{\beta_0 + \beta_1 X}}}
\\
(4.3) \frac {p(X)} {1 - p(X)} =e^{\beta_0 + \beta_1 X}
\]</p>
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2301_76574743/tjjm-R
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<h1>10</h1>
<h3>a</h3>
<pre><code class="r">library(ISLR)
summary(Weekly)
</code></pre>
<pre><code>## Year Lag1 Lag2 Lag3
## Min. :1990 Min. :-18.195 Min. :-18.195 Min. :-18.195
## 1st Qu.:1995 1st Qu.: -1.154 1st Qu.: -1.154 1st Qu.: -1.158
## Median :2000 Median : 0.241 Median : 0.241 Median : 0.241
## Mean :2000 Mean : 0.151 Mean : 0.151 Mean : 0.147
## 3rd Qu.:2005 3rd Qu.: 1.405 3rd Qu.: 1.409 3rd Qu.: 1.409
## Max. :2010 Max. : 12.026 Max. : 12.026 Max. : 12.026
## Lag4 Lag5 Volume Today
## Min. :-18.195 Min. :-18.195 Min. :0.087 Min. :-18.195
## 1st Qu.: -1.158 1st Qu.: -1.166 1st Qu.:0.332 1st Qu.: -1.154
## Median : 0.238 Median : 0.234 Median :1.003 Median : 0.241
## Mean : 0.146 Mean : 0.140 Mean :1.575 Mean : 0.150
## 3rd Qu.: 1.409 3rd Qu.: 1.405 3rd Qu.:2.054 3rd Qu.: 1.405
## Max. : 12.026 Max. : 12.026 Max. :9.328 Max. : 12.026
## Direction
## Down:484
## Up :605
##
##
##
##
</code></pre>
<pre><code class="r">pairs(Weekly)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk 10a"/> </p>
<pre><code class="r">cor(Weekly[, -9])
</code></pre>
<pre><code>## Year Lag1 Lag2 Lag3 Lag4 Lag5 Volume
## Year 1.00000 -0.032289 -0.03339 -0.03001 -0.031128 -0.030519 0.84194
## Lag1 -0.03229 1.000000 -0.07485 0.05864 -0.071274 -0.008183 -0.06495
## Lag2 -0.03339 -0.074853 1.00000 -0.07572 0.058382 -0.072499 -0.08551
## Lag3 -0.03001 0.058636 -0.07572 1.00000 -0.075396 0.060657 -0.06929
## Lag4 -0.03113 -0.071274 0.05838 -0.07540 1.000000 -0.075675 -0.06107
## Lag5 -0.03052 -0.008183 -0.07250 0.06066 -0.075675 1.000000 -0.05852
## Volume 0.84194 -0.064951 -0.08551 -0.06929 -0.061075 -0.058517 1.00000
## Today -0.03246 -0.075032 0.05917 -0.07124 -0.007826 0.011013 -0.03308
## Today
## Year -0.032460
## Lag1 -0.075032
## Lag2 0.059167
## Lag3 -0.071244
## Lag4 -0.007826
## Lag5 0.011013
## Volume -0.033078
## Today 1.000000
</code></pre>
<p>Year and Volume appear to have a relationship. No other patterns are
discernible.</p>
<h3>b</h3>
<pre><code class="r">attach(Weekly)
glm.fit = glm(Direction ~ Lag1 + Lag2 + Lag3 + Lag4 + Lag5 + Volume, data = Weekly,
family = binomial)
summary(glm.fit)
</code></pre>
<pre><code>##
## Call:
## glm(formula = Direction ~ Lag1 + Lag2 + Lag3 + Lag4 + Lag5 +
## Volume, family = binomial, data = Weekly)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.695 -1.256 0.991 1.085 1.458
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2669 0.0859 3.11 0.0019 **
## Lag1 -0.0413 0.0264 -1.56 0.1181
## Lag2 0.0584 0.0269 2.18 0.0296 *
## Lag3 -0.0161 0.0267 -0.60 0.5469
## Lag4 -0.0278 0.0265 -1.05 0.2937
## Lag5 -0.0145 0.0264 -0.55 0.5833
## Volume -0.0227 0.0369 -0.62 0.5377
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1496.2 on 1088 degrees of freedom
## Residual deviance: 1486.4 on 1082 degrees of freedom
## AIC: 1500
##
## Number of Fisher Scoring iterations: 4
</code></pre>
<p>Lag 2 appears to have some statistical significance with a Pr(>|z|) = 3%.</p>
<h3>c</h3>
<pre><code class="r">glm.probs = predict(glm.fit, type = "response")
glm.pred = rep("Down", length(glm.probs))
glm.pred[glm.probs > 0.5] = "Up"
table(glm.pred, Direction)
</code></pre>
<pre><code>## Direction
## glm.pred Down Up
## Down 54 48
## Up 430 557
</code></pre>
<p>Percentage of currect predictions: (54+557)/(54+557+48+430) = 56.1%. Weeks the
market goes up the logistic regression is right most of the time, 557/(557+48)
= 92.1%. Weeks the market goes up the logistic regression is wrong most of the
time 54/(430+54) = 11.2%.</p>
<h3>d</h3>
<pre><code class="r">train = (Year < 2009)
Weekly.0910 = Weekly[!train, ]
glm.fit = glm(Direction ~ Lag2, data = Weekly, family = binomial, subset = train)
glm.probs = predict(glm.fit, Weekly.0910, type = "response")
glm.pred = rep("Down", length(glm.probs))
glm.pred[glm.probs > 0.5] = "Up"
Direction.0910 = Direction[!train]
table(glm.pred, Direction.0910)
</code></pre>
<pre><code>## Direction.0910
## glm.pred Down Up
## Down 9 5
## Up 34 56
</code></pre>
<pre><code class="r">mean(glm.pred == Direction.0910)
</code></pre>
<pre><code>## [1] 0.625
</code></pre>
<h3>e</h3>
<pre><code class="r">library(MASS)
lda.fit = lda(Direction ~ Lag2, data = Weekly, subset = train)
lda.pred = predict(lda.fit, Weekly.0910)
table(lda.pred$class, Direction.0910)
</code></pre>
<pre><code>## Direction.0910
## Down Up
## Down 9 5
## Up 34 56
</code></pre>
<pre><code class="r">mean(lda.pred$class == Direction.0910)
</code></pre>
<pre><code>## [1] 0.625
</code></pre>
<h2>f</h2>
<pre><code class="r">qda.fit = qda(Direction ~ Lag2, data = Weekly, subset = train)
qda.class = predict(qda.fit, Weekly.0910)$class
table(qda.class, Direction.0910)
</code></pre>
<pre><code>## Direction.0910
## qda.class Down Up
## Down 0 0
## Up 43 61
</code></pre>
<pre><code class="r">mean(qda.class == Direction.0910)
</code></pre>
<pre><code>## [1] 0.5865
</code></pre>
<p>A correctness of 58.7% even though it picked Up the whole time!</p>
<h3>g</h3>
<pre><code class="r">library(class)
train.X = as.matrix(Lag2[train])
test.X = as.matrix(Lag2[!train])
train.Direction = Direction[train]
set.seed(1)
knn.pred = knn(train.X, test.X, train.Direction, k = 1)
table(knn.pred, Direction.0910)
</code></pre>
<pre><code>## Direction.0910
## knn.pred Down Up
## Down 21 30
## Up 22 31
</code></pre>
<pre><code class="r">mean(knn.pred == Direction.0910)
</code></pre>
<pre><code>## [1] 0.5
</code></pre>
<h3>h</h3>
<p>Logistic regression and LDA methods provide similar test error rates.</p>
<h3>i</h3>
<pre><code class="r"># Logistic regression with Lag2:Lag1
glm.fit = glm(Direction ~ Lag2:Lag1, data = Weekly, family = binomial, subset = train)
glm.probs = predict(glm.fit, Weekly.0910, type = "response")
glm.pred = rep("Down", length(glm.probs))
glm.pred[glm.probs > 0.5] = "Up"
Direction.0910 = Direction[!train]
table(glm.pred, Direction.0910)
</code></pre>
<pre><code>## Direction.0910
## glm.pred Down Up
## Down 1 1
## Up 42 60
</code></pre>
<pre><code class="r">mean(glm.pred == Direction.0910)
</code></pre>
<pre><code>## [1] 0.5865
</code></pre>
<pre><code class="r">
# LDA with Lag2 interaction with Lag1
lda.fit = lda(Direction ~ Lag2:Lag1, data = Weekly, subset = train)
lda.pred = predict(lda.fit, Weekly.0910)
mean(lda.pred$class == Direction.0910)
</code></pre>
<pre><code>## [1] 0.5769
</code></pre>
<pre><code class="r">
# QDA with sqrt(abs(Lag2))
qda.fit = qda(Direction ~ Lag2 + sqrt(abs(Lag2)), data = Weekly, subset = train)
qda.class = predict(qda.fit, Weekly.0910)$class
table(qda.class, Direction.0910)
</code></pre>
<pre><code>## Direction.0910
## qda.class Down Up
## Down 12 13
## Up 31 48
</code></pre>
<pre><code class="r">mean(qda.class == Direction.0910)
</code></pre>
<pre><code>## [1] 0.5769
</code></pre>
<pre><code class="r">
# KNN k =10
knn.pred = knn(train.X, test.X, train.Direction, k = 10)
table(knn.pred, Direction.0910)
</code></pre>
<pre><code>## Direction.0910
## knn.pred Down Up
## Down 17 18
## Up 26 43
</code></pre>
<pre><code class="r">mean(knn.pred == Direction.0910)
</code></pre>
<pre><code>## [1] 0.5769
</code></pre>
<pre><code class="r">
# KNN k = 100
knn.pred = knn(train.X, test.X, train.Direction, k = 100)
table(knn.pred, Direction.0910)
</code></pre>
<pre><code>## Direction.0910
## knn.pred Down Up
## Down 9 12
## Up 34 49
</code></pre>
<pre><code class="r">mean(knn.pred == Direction.0910)
</code></pre>
<pre><code>## [1] 0.5577
</code></pre>
<p>Out of these permutations, the original LDA and logistic regression have better
performance in terms of test error rate.</p>
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch4/10.html
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<h1>11</h1>
<h3>a</h3>
<pre><code class="r">library(ISLR)
summary(Auto)
</code></pre>
<pre><code>## mpg cylinders displacement horsepower
## Min. : 9.0 Min. :3.00 Min. : 68 Min. : 46.0
## 1st Qu.:17.0 1st Qu.:4.00 1st Qu.:105 1st Qu.: 75.0
## Median :22.8 Median :4.00 Median :151 Median : 93.5
## Mean :23.4 Mean :5.47 Mean :194 Mean :104.5
## 3rd Qu.:29.0 3rd Qu.:8.00 3rd Qu.:276 3rd Qu.:126.0
## Max. :46.6 Max. :8.00 Max. :455 Max. :230.0
##
## weight acceleration year origin
## Min. :1613 Min. : 8.0 Min. :70 Min. :1.00
## 1st Qu.:2225 1st Qu.:13.8 1st Qu.:73 1st Qu.:1.00
## Median :2804 Median :15.5 Median :76 Median :1.00
## Mean :2978 Mean :15.5 Mean :76 Mean :1.58
## 3rd Qu.:3615 3rd Qu.:17.0 3rd Qu.:79 3rd Qu.:2.00
## Max. :5140 Max. :24.8 Max. :82 Max. :3.00
##
## name
## amc matador : 5
## ford pinto : 5
## toyota corolla : 5
## amc gremlin : 4
## amc hornet : 4
## chevrolet chevette: 4
## (Other) :365
</code></pre>
<pre><code class="r">attach(Auto)
mpg01 = rep(0, length(mpg))
mpg01[mpg > median(mpg)] = 1
Auto = data.frame(Auto, mpg01)
</code></pre>
<h3>b</h3>
<pre><code class="r">cor(Auto[, -9])
</code></pre>
<pre><code>## mpg cylinders displacement horsepower weight
## mpg 1.0000 -0.7776 -0.8051 -0.7784 -0.8322
## cylinders -0.7776 1.0000 0.9508 0.8430 0.8975
## displacement -0.8051 0.9508 1.0000 0.8973 0.9330
## horsepower -0.7784 0.8430 0.8973 1.0000 0.8645
## weight -0.8322 0.8975 0.9330 0.8645 1.0000
## acceleration 0.4233 -0.5047 -0.5438 -0.6892 -0.4168
## year 0.5805 -0.3456 -0.3699 -0.4164 -0.3091
## origin 0.5652 -0.5689 -0.6145 -0.4552 -0.5850
## mpg01 0.8369 -0.7592 -0.7535 -0.6671 -0.7578
## acceleration year origin mpg01
## mpg 0.4233 0.5805 0.5652 0.8369
## cylinders -0.5047 -0.3456 -0.5689 -0.7592
## displacement -0.5438 -0.3699 -0.6145 -0.7535
## horsepower -0.6892 -0.4164 -0.4552 -0.6671
## weight -0.4168 -0.3091 -0.5850 -0.7578
## acceleration 1.0000 0.2903 0.2127 0.3468
## year 0.2903 1.0000 0.1815 0.4299
## origin 0.2127 0.1815 1.0000 0.5137
## mpg01 0.3468 0.4299 0.5137 1.0000
</code></pre>
<pre><code class="r">pairs(Auto) # doesn't work well since mpg01 is 0 or 1
</code></pre>
<p><img 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" alt="plot of chunk 11b"/> </p>
<p>Anti-correlated with cylinders, weight, displacement, horsepower.
(mpg, of course)</p>
<h3>c</h3>
<pre><code class="r">train = (year%%2 == 0) # if the year is even
test = !train
Auto.train = Auto[train, ]
Auto.test = Auto[test, ]
mpg01.test = mpg01[test]
</code></pre>
<h3>d</h3>
<pre><code class="r"># LDA
library(MASS)
lda.fit = lda(mpg01 ~ cylinders + weight + displacement + horsepower, data = Auto,
subset = train)
lda.pred = predict(lda.fit, Auto.test)
mean(lda.pred$class != mpg01.test)
</code></pre>
<pre><code>## [1] 0.1264
</code></pre>
<p>12.6% test error rate.</p>
<h3>e</h3>
<pre><code class="r"># QDA
qda.fit = qda(mpg01 ~ cylinders + weight + displacement + horsepower, data = Auto,
subset = train)
qda.pred = predict(qda.fit, Auto.test)
mean(qda.pred$class != mpg01.test)
</code></pre>
<pre><code>## [1] 0.1319
</code></pre>
<p>13.2% test error rate.</p>
<h3>f</h3>
<pre><code class="r"># Logistic regression
glm.fit = glm(mpg01 ~ cylinders + weight + displacement + horsepower, data = Auto,
family = binomial, subset = train)
glm.probs = predict(glm.fit, Auto.test, type = "response")
glm.pred = rep(0, length(glm.probs))
glm.pred[glm.probs > 0.5] = 1
mean(glm.pred != mpg01.test)
</code></pre>
<pre><code>## [1] 0.1209
</code></pre>
<p>12.1% test error rate.</p>
<h3>g</h3>
<pre><code class="r">library(class)
train.X = cbind(cylinders, weight, displacement, horsepower)[train, ]
test.X = cbind(cylinders, weight, displacement, horsepower)[test, ]
train.mpg01 = mpg01[train]
set.seed(1)
# KNN(k=1)
knn.pred = knn(train.X, test.X, train.mpg01, k = 1)
mean(knn.pred != mpg01.test)
</code></pre>
<pre><code>## [1] 0.1538
</code></pre>
<pre><code class="r"># KNN(k=10)
knn.pred = knn(train.X, test.X, train.mpg01, k = 10)
mean(knn.pred != mpg01.test)
</code></pre>
<pre><code>## [1] 0.1648
</code></pre>
<pre><code class="r"># KNN(k=100)
knn.pred = knn(train.X, test.X, train.mpg01, k = 100)
mean(knn.pred != mpg01.test)
</code></pre>
<pre><code>## [1] 0.1429
</code></pre>
<p>k=1, 15.4% test error rate.
k=10, 16.5% test error rate.
k=100, 14.3% test error rate.
K of 100 seems to perform the best. 100 nearest neighbors.</p>
</body>
</html>
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch4/11.html
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HTML
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unknown
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<body>
<h1>12</h1>
<h3>a</h3>
<pre><code class="r">Power = function() {
2^3
}
print(Power())
</code></pre>
<pre><code>## [1] 8
</code></pre>
<h3>b</h3>
<pre><code class="r">Power2 = function(x, a) {
x^a
}
Power2(3, 8)
</code></pre>
<pre><code>## [1] 6561
</code></pre>
<h3>c</h3>
<pre><code class="r">Power2(10, 3)
</code></pre>
<pre><code>## [1] 1000
</code></pre>
<pre><code class="r">Power2(8, 17)
</code></pre>
<pre><code>## [1] 2.252e+15
</code></pre>
<pre><code class="r">Power2(131, 3)
</code></pre>
<pre><code>## [1] 2248091
</code></pre>
<h3>d</h3>
<pre><code class="r">Power3 = function(x, a) {
result = x^a
return(result)
}
</code></pre>
<h3>e</h3>
<pre><code class="r">x = 1:10
plot(x, Power3(x, 2), log = "xy", ylab = "Log of y = x^2", xlab = "Log of x",
main = "Log of x^2 versus Log of x")
</code></pre>
<p><img 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" alt="plot of chunk 12e"/> </p>
<h3>f</h3>
<pre><code class="r">PlotPower = function(x, a) {
plot(x, Power3(x, a))
}
PlotPower(1:10, 3)
</code></pre>
<p><img 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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch4/12.html
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<h1>13</h1>
<pre><code class="r">library(MASS)
summary(Boston)
</code></pre>
<pre><code>## crim zn indus chas
## Min. : 0.01 Min. : 0.0 Min. : 0.46 Min. :0.0000
## 1st Qu.: 0.08 1st Qu.: 0.0 1st Qu.: 5.19 1st Qu.:0.0000
## Median : 0.26 Median : 0.0 Median : 9.69 Median :0.0000
## Mean : 3.61 Mean : 11.4 Mean :11.14 Mean :0.0692
## 3rd Qu.: 3.68 3rd Qu.: 12.5 3rd Qu.:18.10 3rd Qu.:0.0000
## Max. :88.98 Max. :100.0 Max. :27.74 Max. :1.0000
## nox rm age dis
## Min. :0.385 Min. :3.56 Min. : 2.9 Min. : 1.13
## 1st Qu.:0.449 1st Qu.:5.89 1st Qu.: 45.0 1st Qu.: 2.10
## Median :0.538 Median :6.21 Median : 77.5 Median : 3.21
## Mean :0.555 Mean :6.29 Mean : 68.6 Mean : 3.79
## 3rd Qu.:0.624 3rd Qu.:6.62 3rd Qu.: 94.1 3rd Qu.: 5.19
## Max. :0.871 Max. :8.78 Max. :100.0 Max. :12.13
## rad tax ptratio black
## Min. : 1.00 Min. :187 Min. :12.6 Min. : 0.3
## 1st Qu.: 4.00 1st Qu.:279 1st Qu.:17.4 1st Qu.:375.4
## Median : 5.00 Median :330 Median :19.1 Median :391.4
## Mean : 9.55 Mean :408 Mean :18.5 Mean :356.7
## 3rd Qu.:24.00 3rd Qu.:666 3rd Qu.:20.2 3rd Qu.:396.2
## Max. :24.00 Max. :711 Max. :22.0 Max. :396.9
## lstat medv
## Min. : 1.73 Min. : 5.0
## 1st Qu.: 6.95 1st Qu.:17.0
## Median :11.36 Median :21.2
## Mean :12.65 Mean :22.5
## 3rd Qu.:16.95 3rd Qu.:25.0
## Max. :37.97 Max. :50.0
</code></pre>
<pre><code class="r">attach(Boston)
crime01 = rep(0, length(crim))
crime01[crim > median(crim)] = 1
Boston = data.frame(Boston, crime01)
train = 1:(dim(Boston)[1]/2)
test = (dim(Boston)[1]/2 + 1):dim(Boston)[1]
Boston.train = Boston[train, ]
Boston.test = Boston[test, ]
crime01.test = crime01[test]
</code></pre>
<pre><code class="r"># logistic regression
glm.fit = glm(crime01 ~ . - crime01 - crim, data = Boston, family = binomial,
subset = train)
</code></pre>
<pre><code>## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
</code></pre>
<pre><code class="r">glm.probs = predict(glm.fit, Boston.test, type = "response")
glm.pred = rep(0, length(glm.probs))
glm.pred[glm.probs > 0.5] = 1
mean(glm.pred != crime01.test)
</code></pre>
<pre><code>## [1] 0.1818
</code></pre>
<p>18.2% test error rate.</p>
<pre><code class="r">glm.fit = glm(crime01 ~ . - crime01 - crim - chas - tax, data = Boston, family = binomial,
subset = train)
</code></pre>
<pre><code>## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
</code></pre>
<pre><code class="r">glm.probs = predict(glm.fit, Boston.test, type = "response")
glm.pred = rep(0, length(glm.probs))
glm.pred[glm.probs > 0.5] = 1
mean(glm.pred != crime01.test)
</code></pre>
<pre><code>## [1] 0.1858
</code></pre>
<p>18.6% test error rate.</p>
<pre><code class="r"># LDA
lda.fit = lda(crime01 ~ . - crime01 - crim, data = Boston, subset = train)
lda.pred = predict(lda.fit, Boston.test)
mean(lda.pred$class != crime01.test)
</code></pre>
<pre><code>## [1] 0.1344
</code></pre>
<p>13.4% test error rate.</p>
<pre><code class="r">lda.fit = lda(crime01 ~ . - crime01 - crim - chas - tax, data = Boston, subset = train)
lda.pred = predict(lda.fit, Boston.test)
mean(lda.pred$class != crime01.test)
</code></pre>
<pre><code>## [1] 0.1225
</code></pre>
<p>12.3% test error rate.</p>
<pre><code class="r">lda.fit = lda(crime01 ~ . - crime01 - crim - chas - tax - lstat - indus - age,
data = Boston, subset = train)
lda.pred = predict(lda.fit, Boston.test)
mean(lda.pred$class != crime01.test)
</code></pre>
<pre><code>## [1] 0.1186
</code></pre>
<p>11.9% test error rate.</p>
<pre><code class="r"># KNN
library(class)
train.X = cbind(zn, indus, chas, nox, rm, age, dis, rad, tax, ptratio, black,
lstat, medv)[train, ]
test.X = cbind(zn, indus, chas, nox, rm, age, dis, rad, tax, ptratio, black,
lstat, medv)[test, ]
train.crime01 = crime01[train]
set.seed(1)
# KNN(k=1)
knn.pred = knn(train.X, test.X, train.crime01, k = 1)
mean(knn.pred != crime01.test)
</code></pre>
<pre><code>## [1] 0.4585
</code></pre>
<p>45.8% test error rate.</p>
<pre><code class="r"># KNN(k=10)
knn.pred = knn(train.X, test.X, train.crime01, k = 10)
mean(knn.pred != crime01.test)
</code></pre>
<pre><code>## [1] 0.1186
</code></pre>
<p>11.1% test error rate.</p>
<pre><code class="r"># KNN(k=100)
knn.pred = knn(train.X, test.X, train.crime01, k = 100)
mean(knn.pred != crime01.test)
</code></pre>
<pre><code>## [1] 0.4901
</code></pre>
<p>49.0% test error rate.</p>
<pre><code class="r"># KNN(k=10) with subset of variables
train.X = cbind(zn, nox, rm, dis, rad, ptratio, black, medv)[train, ]
test.X = cbind(zn, nox, rm, dis, rad, ptratio, black, medv)[test, ]
knn.pred = knn(train.X, test.X, train.crime01, k = 10)
mean(knn.pred != crime01.test)
</code></pre>
<pre><code>## [1] 0.2727
</code></pre>
<p>28.5% test error rate.</p>
</body>
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统计学习导论_基于R应用习题答案/ch4/13.html
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}
@page :left {
margin: 15mm 20mm 15mm 10mm;
}
@page :right {
margin: 15mm 10mm 15mm 20mm;
}
p, h2, h3 {
orphans: 3; widows: 3;
}
h2, h3 {
page-break-after: avoid;
}
}
</style>
<!-- MathJax scripts -->
<script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>
</head>
<body>
<h1>2</h1>
<p>Assuming that \( f_k(x) \) is normal, the probability that an observation \( x \) is in class \( k \) is given by
\[
p_k(x) = \frac {\pi_k
\frac {1} {\sqrt{2 \pi} \sigma}
\exp(- \frac {1} {2 \sigma^2} (x - \mu_k)^2)
}
{\sum {
\pi_l
\frac {1} {\sqrt{2 \pi} \sigma}
\exp(- \frac {1} {2 \sigma^2} (x - \mu_l)^2)
}}
\]
while the discriminant function is given by
\[
\delta_k(x) = x \frac {\mu_k} {\sigma^2} - \frac {\mu_k^2} {2 \sigma^2}
+ \log(\pi_k)
\] </p>
<p><em>Claim: Maximizing \( p_k(x) \) is equivalent to maximizing \( \delta_k(x) \).</em></p>
<p><em>Proof.</em> Let \( x \) remain fixed and observe that we are maximizing over the parameter \( k \). Suppose that \( \delta_k(x) \geq \delta_i(x) \). We will show that \( f_k(x) \geq f_i(x) \). From our assumption we have
\[
x \frac {\mu_k} {\sigma^2} - \frac {\mu_k^2} {2 \sigma^2} + \log(\pi_k)
\geq
x \frac {\mu_i} {\sigma^2} - \frac {\mu_i^2} {2 \sigma^2} + \log(\pi_i).
\]
Exponentiation is a monotonically increasing function, so the following inequality holds
\[
\pi_k \exp (x \frac {\mu_k} {\sigma^2} - \frac {\mu_k^2} {2 \sigma^2})
\geq
\pi_i \exp (x \frac {\mu_i} {\sigma^2} - \frac {\mu_i^2} {2 \sigma^2})
\]
Multipy this inequality by the positive constant
\[
c = \frac {
\frac {1} {\sqrt{2 \pi} \sigma}
\exp(- \frac {1} {2 \sigma^2} x^2)
}
{\sum {
\pi_l
\frac {1} {\sqrt{2 \pi} \sigma}
\exp(- \frac {1} {2 \sigma^2} (x - \mu_l)^2)
}}
\]
and we have that
\[
\frac {\pi_k
\frac {1} {\sqrt{2 \pi} \sigma}
\exp(- \frac {1} {2 \sigma^2} (x - \mu_k)^2)
}
{\sum {
\pi_l
\frac {1} {\sqrt{2 \pi} \sigma}
\exp(- \frac {1} {2 \sigma^2} (x - \mu_l)^2)
}}
\geq
\frac {\pi_i
\frac {1} {\sqrt{2 \pi} \sigma}
\exp(- \frac {1} {2 \sigma^2} (x - \mu_i)^2)
}
{\sum {
\pi_l
\frac {1} {\sqrt{2 \pi} \sigma}
\exp(- \frac {1} {2 \sigma^2} (x - \mu_l)^2)
}}
\]
or equivalently, \( f_k(x) \geq f_i(x) \). Reversing these steps also holds, so we have that maximizing \( \delta_k \) is equivalent to maximizing \( p_k \).</p>
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| 4,703
|
<!DOCTYPE html>
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<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<title>3</title>
<style type="text/css">
body, td {
font-family: sans-serif;
background-color: white;
font-size: 12px;
margin: 8px;
}
tt, code, pre {
font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace;
}
h1 {
font-size:2.2em;
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font-size:1.8em;
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font-size:1.4em;
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font-size:1.0em;
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font-size:0.9em;
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font-size:0.8em;
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a:visited {
color: rgb(50%, 0%, 50%);
}
pre {
margin-top: 0;
max-width: 95%;
border: 1px solid #ccc;
white-space: pre-wrap;
}
pre code {
display: block; padding: 0.5em;
}
code.r, code.cpp {
background-color: #F8F8F8;
}
table, td, th {
border: none;
}
blockquote {
color:#666666;
margin:0;
padding-left: 1em;
border-left: 0.5em #EEE solid;
}
hr {
height: 0px;
border-bottom: none;
border-top-width: thin;
border-top-style: dotted;
border-top-color: #999999;
}
@media print {
* {
background: transparent !important;
color: black !important;
filter:none !important;
-ms-filter: none !important;
}
body {
font-size:12pt;
max-width:100%;
}
a, a:visited {
text-decoration: underline;
}
hr {
visibility: hidden;
page-break-before: always;
}
pre, blockquote {
padding-right: 1em;
page-break-inside: avoid;
}
tr, img {
page-break-inside: avoid;
}
img {
max-width: 100% !important;
}
@page :left {
margin: 15mm 20mm 15mm 10mm;
}
@page :right {
margin: 15mm 10mm 15mm 20mm;
}
p, h2, h3 {
orphans: 3; widows: 3;
}
h2, h3 {
page-break-after: avoid;
}
}
</style>
<!-- MathJax scripts -->
<script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>
</head>
<body>
<h1>3</h1>
<p>\[
p_k(x) = \frac {\pi_k
\frac {1} {\sqrt{2 \pi} \sigma_k}
\exp(- \frac {1} {2 \sigma_k^2} (x - \mu_k)^2)
}
{\sum {
\pi_l
\frac {1} {\sqrt{2 \pi} \sigma_l}
\exp(- \frac {1} {2 \sigma_l^2} (x - \mu_l)^2)
}}
\\
\log(p_k(x)) = \frac {\log(\pi_k) +
\log(\frac {1} {\sqrt{2 \pi} \sigma_k}) +
- \frac {1} {2 \sigma_k^2} (x - \mu_k)^2
}
{\log(\sum {
\pi_l
\frac {1} {\sqrt{2 \pi} \sigma_l}
\exp(- \frac {1} {2 \sigma_l^2} (x - \mu_l)^2)
})}
\\
\log(p_k(x))
\log(\sum {
\pi_l
\frac {1} {\sqrt{2 \pi} \sigma_l}
\exp(- \frac {1} {2 \sigma_l^2} (x - \mu_l)^2)
})
= \log(\pi_k) +
\log(\frac {1} {\sqrt{2 \pi} \sigma_k}) +
- \frac {1} {2 \sigma_k^2} (x - \mu_k)^2
\\
\delta(x)
= \log(\pi_k) +
\log(\frac {1} {\sqrt{2 \pi} \sigma_k}) +
- \frac {1} {2 \sigma_k^2} (x - \mu_k)^2
\]</p>
<p>As you can see, \( \delta(x) \) is a quadratic function of \( x \).</p>
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统计学习导论_基于R应用习题答案/ch4/3.html
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|
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| 3,260
|
<!DOCTYPE html>
<!-- saved from url=(0014)about:internet -->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<title>4</title>
<style type="text/css">
body, td {
font-family: sans-serif;
background-color: white;
font-size: 12px;
margin: 8px;
}
tt, code, pre {
font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace;
}
h1 {
font-size:2.2em;
}
h2 {
font-size:1.8em;
}
h3 {
font-size:1.4em;
}
h4 {
font-size:1.0em;
}
h5 {
font-size:0.9em;
}
h6 {
font-size:0.8em;
}
a:visited {
color: rgb(50%, 0%, 50%);
}
pre {
margin-top: 0;
max-width: 95%;
border: 1px solid #ccc;
white-space: pre-wrap;
}
pre code {
display: block; padding: 0.5em;
}
code.r, code.cpp {
background-color: #F8F8F8;
}
table, td, th {
border: none;
}
blockquote {
color:#666666;
margin:0;
padding-left: 1em;
border-left: 0.5em #EEE solid;
}
hr {
height: 0px;
border-bottom: none;
border-top-width: thin;
border-top-style: dotted;
border-top-color: #999999;
}
@media print {
* {
background: transparent !important;
color: black !important;
filter:none !important;
-ms-filter: none !important;
}
body {
font-size:12pt;
max-width:100%;
}
a, a:visited {
text-decoration: underline;
}
hr {
visibility: hidden;
page-break-before: always;
}
pre, blockquote {
padding-right: 1em;
page-break-inside: avoid;
}
tr, img {
page-break-inside: avoid;
}
img {
max-width: 100% !important;
}
@page :left {
margin: 15mm 20mm 15mm 10mm;
}
@page :right {
margin: 15mm 10mm 15mm 20mm;
}
p, h2, h3 {
orphans: 3; widows: 3;
}
h2, h3 {
page-break-after: avoid;
}
}
</style>
<!-- MathJax scripts -->
<script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>
</head>
<body>
<h1>4</h1>
<h3>a.</h3>
<p>On average, 10%. For simplicity, ignoring cases when X < 0.05 and X > 0.95.</p>
<h3>b.</h3>
<p>On average, 1%</p>
<h3>c.</h3>
<p>On average, \( 0.10^{100} * 100 = 10^{-98} \)%.</p>
<h3>d.</h3>
<p>As \( p \) increases linear, observations that are geometrically near decrease
exponentially.</p>
<h3>e.</h3>
<p>\[
p = 1, l = 0.10
\\
p = 2, l = \sqrt{0.10} ~ 0.32
\\
p = 3, l = 0.10^{1/3} ~ 0.46
\\
...
\\
p = N, l = 0.10^{1/N}
\]</p>
</body>
</html>
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch4/4.html
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|
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| 2,588
|
<!DOCTYPE html>
<!-- saved from url=(0014)about:internet -->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<meta http-equiv="x-ua-compatible" content="IE=9" >
<title>5</title>
<style type="text/css">
body, td {
font-family: sans-serif;
background-color: white;
font-size: 12px;
margin: 8px;
}
tt, code, pre {
font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace;
}
h1 {
font-size:2.2em;
}
h2 {
font-size:1.8em;
}
h3 {
font-size:1.4em;
}
h4 {
font-size:1.0em;
}
h5 {
font-size:0.9em;
}
h6 {
font-size:0.8em;
}
a:visited {
color: rgb(50%, 0%, 50%);
}
pre {
margin-top: 0;
max-width: 95%;
border: 1px solid #ccc;
white-space: pre-wrap;
}
pre code {
display: block; padding: 0.5em;
}
code.r, code.cpp {
background-color: #F8F8F8;
}
table, td, th {
border: none;
}
blockquote {
color:#666666;
margin:0;
padding-left: 1em;
border-left: 0.5em #EEE solid;
}
hr {
height: 0px;
border-bottom: none;
border-top-width: thin;
border-top-style: dotted;
border-top-color: #999999;
}
@media print {
* {
background: transparent !important;
color: black !important;
filter:none !important;
-ms-filter: none !important;
}
body {
font-size:12pt;
max-width:100%;
}
a, a:visited {
text-decoration: underline;
}
hr {
visibility: hidden;
page-break-before: always;
}
pre, blockquote {
padding-right: 1em;
page-break-inside: avoid;
}
tr, img {
page-break-inside: avoid;
}
img {
max-width: 100% !important;
}
@page :left {
margin: 15mm 20mm 15mm 10mm;
}
@page :right {
margin: 15mm 10mm 15mm 20mm;
}
p, h2, h3 {
orphans: 3; widows: 3;
}
h2, h3 {
page-break-after: avoid;
}
}
</style>
<!-- MathJax scripts -->
<script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>
</head>
<body>
<h1>5</h1>
<h3>a.</h3>
<p>If the Bayes decision boundary is linear, we expect QDA to perform better on the
training set because it's higher flexiblity will yield a closer fit. On the test
set, we expect LDA to perform better than QDA because QDA could overfit the
linearity of the Bayes decision boundary.</p>
<h3>b.</h3>
<p>If the Bayes decision bounary is non-linear, we expect QDA to perform better
both on the training and test sets.</p>
<h3>c.</h3>
<p>We expect the test prediction accuracy of QDA relative to LDA to improve, in
general, as the the sample size \( n \) increases because a more flexibile method
will yield a better fit as more samples can be fit and variance is offset by
the larger sample sizes.</p>
<h3>d.</h3>
<p>False. With fewer sample points, the variance from using a more flexible method,
such as QDA, would lead to overfit, yielding a higher test rate than LDA.</p>
</body>
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统计学习导论_基于R应用习题答案/ch4/5.html
|
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|
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| 3,071
|
<!DOCTYPE html>
<!-- saved from url=(0014)about:internet -->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<meta http-equiv="x-ua-compatible" content="IE=9" >
<title>6</title>
<style type="text/css">
body, td {
font-family: sans-serif;
background-color: white;
font-size: 12px;
margin: 8px;
}
tt, code, pre {
font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace;
}
h1 {
font-size:2.2em;
}
h2 {
font-size:1.8em;
}
h3 {
font-size:1.4em;
}
h4 {
font-size:1.0em;
}
h5 {
font-size:0.9em;
}
h6 {
font-size:0.8em;
}
a:visited {
color: rgb(50%, 0%, 50%);
}
pre {
margin-top: 0;
max-width: 95%;
border: 1px solid #ccc;
white-space: pre-wrap;
}
pre code {
display: block; padding: 0.5em;
}
code.r, code.cpp {
background-color: #F8F8F8;
}
table, td, th {
border: none;
}
blockquote {
color:#666666;
margin:0;
padding-left: 1em;
border-left: 0.5em #EEE solid;
}
hr {
height: 0px;
border-bottom: none;
border-top-width: thin;
border-top-style: dotted;
border-top-color: #999999;
}
@media print {
* {
background: transparent !important;
color: black !important;
filter:none !important;
-ms-filter: none !important;
}
body {
font-size:12pt;
max-width:100%;
}
a, a:visited {
text-decoration: underline;
}
hr {
visibility: hidden;
page-break-before: always;
}
pre, blockquote {
padding-right: 1em;
page-break-inside: avoid;
}
tr, img {
page-break-inside: avoid;
}
img {
max-width: 100% !important;
}
@page :left {
margin: 15mm 20mm 15mm 10mm;
}
@page :right {
margin: 15mm 10mm 15mm 20mm;
}
p, h2, h3 {
orphans: 3; widows: 3;
}
h2, h3 {
page-break-after: avoid;
}
}
</style>
<!-- MathJax scripts -->
<script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>
</head>
<body>
<h1>6</h1>
<p>\[
p(X) = \frac {\exp(\beta_0 + \beta_1 X_1 + \beta_2 X_2)}
{1 + \exp(\beta_0 + \beta_1 X_1 + \beta_2 X_2)}
\\
X_1 = hours studied, X_2 = undergrad GPA
\\
\beta_0 = -6, \beta_1 = 0.05, \beta_2 = 1
\]</p>
<h3>a.</h3>
<p>\[
X = [40 hours, 3.5 GPA]
\\
p(X) = \frac {\exp(-6 + 0.05 X_1 + X_2)} {1 + \exp(-6 + 0.05 X_1 + X_2)}
\\
= \frac {\exp(-6 + 0.05 40 + 3.5)} {1 + \exp(-6 + 0.05 40 + 3.5)}
\\
= \frac {\exp(-0.5)} {1 + \exp(-0.5)}
\\
= 37.75\%
\]</p>
<h3>b.</h3>
<p>\[
X = [X_1 hours, 3.5 GPA]
\\
p(X) = \frac {\exp(-6 + 0.05 X_1 + X_2)} {1 + \exp(-6 + 0.05 X_1 + X_2)}
\\
0.50 = \frac {\exp(-6 + 0.05 X_1 + 3.5)} {1 + \exp(-6 + 0.05 X_1 + 3.5)}
\\
0.50 (1 + \exp(-2.5 + 0.05 X_1)) = \exp(-2.5 + 0.05 X_1)
\\
0.50 + 0.50 \exp(-2.5 + 0.05 X_1)) = \exp(-2.5 + 0.05 X_1)
\\
0.50 = 0.50 \exp(-2.5 + 0.05 X_1)
\\
\log(1) = -2.5 + 0.05 X_1
\\
X_1 = 2.5 / 0.05 = 50 hours
\]</p>
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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<title></title>
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oPEio8DsNzZQqgUMoq0%2BNMALrygrOlErJohXvIJ%2F0IqziZok6OuApG2eoGAEFsTc6yWf9eRcklYZRO3PkA%2BW8aN5ME8aLNC3Kdwtm64WyqytOEW1WQIg6bOFHAs1ovo3OfCvvZASxBcxg2SRCRTTJDBKajFH0LL1kSBroe6L9xvVxMCIFS7Zo6y4ZOEIkAI9QtGVIKKBlCvUUEq2S8FiR7VkJGbvBFaS6UzQV%2BayV6NKw62tbJbEjkJ7AMC17DK399DZr%2FtE5sUtaAPwaHNj9VCoVB4VAG0iNI9IjDo0mr97FWjYeDRSr3pstZTVZ6nj5Vp3IbssG3g0r9E2eSmHY97LYobIHPnEGkKDJP15WPWtwMgS8HeLd6NEnP4mIFlRJB4zLeDeoBElpUNRIQMzLbRQsGLj3DZhQytDQPLAUE9JWHiLKAJxVTOJNZsObEiutLqwox%2BM51t7vJQhBeQm2i5bTa4D8qIUjqI1sL9G6Vha6U6IPUabeCUMIufG%2F%2F6USyxymXNlfnVinkNvKZGUjz0EWKIgZEC4WkUEr%2BgP%2BFpkIPUVM%2BQEJPxFxKDlpBEeiUSaTgCm4sEfRnBxu0eF%2FLgLp0IFHMEt8aZrsQzTF3wRw8TdpzHNGMZrHHa6nU8KCQppWBVpJ8b2Xjq278mtoTfrgfAMSAm3cYEcDw%2BzcSsXTaUyd2PH6DCU0DdBiauxpsyoKlAh9IUAzS32X%2F0d3x7h4gL36Q5sC4YzkbttmSwhenP5pxxrBEEcoAohUGB1jHGtB2%2BI0FEcCM2y8EY1e2NPXJS7wp7APp46lC7gSit7FEFQCIDnNX%2F196lJs%2BgXTyJrXAze%2FQ%2B9GEFC7p5W7W5FEooh5QlO01B1OcYDH4VTmRSO8fHPjRo4FkT9GrnUZw9pXnDplu0YFyOaAsLOkxdsu2gfYJ%2BDzBNihSUutbDbDy7VKS3KwwUTChYQkWCmldyS%2Fy05EvDGbLbpkDfbbHNGEhTSiaAEDmoKTERX3pEuKYJT%2FlNS4IawkdDACC5Jk4hgNa2EGV8yblsXoSHDrqHDJs4JDXLLA67oCyia7LABDzBMInY5gnZCpFuyyuaWXjKHRgy409j2wNaI1bGlosUnhaB5iqHHZf2%2BbDPpsIwoahsWRL69BhhyPHQBUNCyBR%2BUJzMOBRMxTVhNsS0%2B1n5kboqmNZcC%2Bt6HYVscRaBeox%2Fvv3%2FxwdoUoRvs6CamykRGihd8HTNaL%2BxRuoo3QVZvFT7xS9opWLiNbQRti9PXbNg0J5WDsxaNesdmkYOcwpiqsURuor%2BS8hhgWniGGll%2BtD55enFSsyATy9MonNXUJV5Q7eAouzR%2FP4HlA5YWBYYjm6NDAEk%2Bx9On0MGm65%2FEm1GKndnRp4cv%2FjFAdM7hnXsaXnGKAeK4aE%2F6BhOq1%2FAWmgCMQ4zC8G0wBzhuMSeeVhgAl0jpwicoR64K%2BBYlYbWz0gTGAGtI3puhx3F5%2F5QKXradYFuhJPYWq3ITCUUNLpugdVBegedngCO%2FDN5mJBWzGEL932jL1YntvCgzzLeR0WczKl0J4g8Gcbo6p5LOslpYBw4kE3ZDMiHPjBGyAUjkrWXqYTrl7gRUwex0zKVekIhoI0dsFRDtQxsLctjOlRs5Y2gYpQeBORQFR4C3hXh8EnZUzuloqFLFATXzpzv6k5XoHB3fZLNUiPV5DvAfroLwH8tYhkcOxWnHqePfQulM0DCPTadXa%2FTpoHYkh9BeeAwpmi9Kbo%2Fm88ob17pFzKP2lJpfWxbyD3PccS041A0MQWUqNDHOPOFt57Q6TiHQEKJQsI4v1JqcD7Q3orOdsLvExRQwS0nlUtV%2FpQbx0wSlrRSneWAQ%2FJhPaT6rMvIcid93ahrnKe34YxGA3D30BI2lEiLGh5iAUkBLBoXRshvpjGJGLzGcvonIqzVubgEJv4mbEl7JggQUWb6AYaQMmGUXEwIjylByVLbgcEbUQL5RFiRkX%2FbOhzb9XavzmFpR0cOdx3etgoaCPbiL5gR8o0wJNdPMCYcmyP0SDmmsW8o0O%2Fy5HNRxqz3IhmaxvIT%2FxpN8Pj%2Bx4i%2FGawfTOod%2BpvHN9BMNQUKG555l7yAKsma4r8JwVm6gxqBECXHA3e%2BdCsONR41WrcokynYzYqB0xlCdg9Nu%2FVL4rgwOiTVy8K70TghEjAOHeFBYvGwmkrDIzlwQsyBsr3K%2FEBFKO8UQ6J1hU%2FYGFA8fGgTnBoNnAmZc%2B9S%2BXnpbpJ4QKgdip6TZnI6woSQaYgxGpdaxxSuQkxu0avCBOSwchx0SS0x5HgR6iz9AkbeoDFTnGInFfbnMUP%2B8p3uSQu2CVkHxeVeEMICXKlVDsj89UY7AfyMvxTyws7ilT3y4zYSLVToLb1IwDJHJ804mZKVSKgSOa0mHCQrjWQLaz9LrCiISicIHspx5moJ%2F3Ng4rjU8aMqTDl5gjJAFgAMupkEoWEalYEtHh8CvvMqUP0Ra4v6BRr%2BdgNXFRLo%2BjKWRhaP4x63qqfsnw%2B%2FlDAo2doOxQP7GwahE%2BiZ2OX2l5DodFT8bGiL0YglcRmgDoYIEaRBLqdbj7HFD8xfZz45H2Tg6upN9XjuGCxtdxuVuRjssowtewrBo9gBM3T7d77hNjF9rd8QTMjwGLx3oWax6aa17t0I2gcG6kcQgw%2FphwfsB4gsCGgMGZU8P%2BORAg4iQDmTP9XqByg%2BjtRqWtpaHjiHSL8CNah084UP6Rfjx8GU2eaRw4mH%2BA9Ad42DummqhAj0%2FeMNS3V8lEHUSc1ntW779KspgJUBtJQEpS59ODiBRghAyioIrf%2FKoPh0eAJWqxh%2BSurG%2F5xVdzZBLgKEiYy4izBuKhCRIhfoADEt%2FB%2BirTWSOZVkcfzWBbA%2FAO817phMFhWQnvTwzjirEna6JgrjgHyk%2B8nWk0k06KkkSOOryl2OrOta3U1Vai9SLWxcE3cY161P64wA5VUgBExRGc8qtWCkEHIJfWtybVbTm%2BhrXnbVSsP4ucJTC9onJ8N4r9lRaV%2Fc4a2SZKmPNCgEUW7LDBXhvFxUvZYtnIuGD61WQtIrDXUc13hksUbImFeHrIBgvnG4ogmKBk5CClzY5DWqCCMzdrHBGSSOTyO%2BE8inLMGSApQqKJB3XKm%2F%2FMFoO17KuwfwqcaqjAyaJ5JOWW1Myr%2Fs7ZQiw%2FPqVAMH%2BlK6DYqjGUMcAXnVxy6z6CDl6j5mDa5EG4j8C6pr0hKyRNnoiR1vl9DWOfC2%2BM4v44bO3onXro%2BNlz00ZlGV2ICJ5CcCazVCJHIETBqVFhGT7KSBdNDv5ILpSacSTL0S6fX%2FP1CEIT5NZgcwpI0rkV7LqiHRoLUAPjXEqCZ5mLntX5JkkUEelCsU2tXCMZ6cZivQI6hYQNbXl4nc1sSNtBUTkQ8Y442KxRe7Uy5yOzB%2BUlA3MhvFmXweJJj%2F%2BG%2BqwJFWFLNYp0JhzD%2B4sl4JYn6a%2FvShCCo8hchMfrswjZgUqiToKfoOGaHdIAnKE%2BiEedBkyLwMSoNVX83TJERFy8PLMpBjsXtBITuS9lpPCTVl%2Fvs2Aqzlzwnj7lpl6VX%2FQ74tWqUSzaKziwue1phr9BpcZjNc%2FGXuJUq15TMDnCsJS0X2wAtW5JQA5MaTyUH4cDRif3MDxZx3Se89P4WSIvEEeLjToJbXf%2FrRhBMtRevJxuf%2Fs8EK4woZOFli3yTq3xqjBjrlierqgmUpFA15HFkeUOuLDjuhzLDjAGKK6WThTynzd%2FtCQWU3Nzd8%2Fa0q%2FxwZYn%2FUPEHU6lsdG%2Fn8gN%2BYACURCU%2BW%2FldrXpdUK7WxhgWmNd%2BLDoQFCU0sFyHn3KqFz%2BIg9e2C5oe9EOi7KdTBxPE1k4k5tkX%2FoZzSih8DJDFd%2BrITKNSsAAGtscSW9VDasqkEamMzeUOhRqRRAj9lc%2B7An2YOdL46GdcOLAsgYaFsR9hs0ToUzZ9rFRLG1%2F7eYLz1l5ijumbe1druwaHNFzZjtwt%2Bn7paZaMZu2EIx%2BlxX%2F2SBrWJQQM0wmE19eWrGPIVMeAzVWWRp76m1skeqzaBBZ6So4eD8Gpv31bn%2BnO4WG%2FOg%2FB7xQu9kpA3eZL3w9cwzrZfMmbUSk5OiF3Qx7bQYhL0VoUQQP3Hmps1bBJDQU0qAJpWwnb4iFOMBthzwP81ilVnh1bIuk0C9Xe%2B1kSK%2FK%2FMYOh7vIa7ghqGEKzCiHrehs3cA1pzgec5NpLDtCqMPp1GVECA4QZKiZX0hQ%2FGqjOhhObmpH0vwx0WHctTrmGC84Ub6BOQVdXIqoxqMVNhQjd%2B0CYMRk1zp6%2FPnIOsW7k%2BhQEruSB7%2BDEGv1lziYEEwur9gsigQBG8EJ3zeigdA74WvD0h2IbdNzPw3okAFsdmi3qKH41lxijDPDtvZcEzXwUnRNn4WpCP3vPNHhO8czUiYjRn6GRIQ4SymW9MhjUu%2FjbWOhOqNeh9OSRNh1EaFZcalxYnJMLCpTlEDttzYxhNCcZ86PUpbEkFmkU5HRiAF9AahZ8jQ769NUzJGkq9YLj9HWZLEK%2BIryXyPLZHAAHsBSe5moWq2RRQT655EACRYKQ1AR2mNQU6hNdrqLyfpSeJ5PrccWf3dXVxmIidWyyIkCm1v1oFYgyGcXwrCRPXPMLYHRFvGVuYyAUhzgYntJmDhE4VCNKoSxWlSFiFuMGumZHJnCJyAdT%2BHcgUhIEJZgWOjmy%2FZWisKRYo4JjuPntQGSxlJg3XJ5DBsFM3kGGQjqECyjqYFW6ccOkCDy3EhUt6taBIaHclyblt3Xxs353KjciddJC7gI4cZaPj4zBxOVNfei9MW9cLUHspngSFFni3AL7sLsbqo3totPWMf0yD2bdWKWuK2a4eF5jKcYCZyvORgcHeETtqeqhAYtIyiFGAYJQMOX4VJzPjyDKiavWJRtjWCnaTUdPR6FRkTfFAARc%2B3Ln3oFDETvxxiaDT3roIpaWwEEjRlhjvs9ZKiU2o1HJOMuVRFAA5fWzMgAqSKDaSLNQcQZbaFzsgKxXWIvjaOqsoiS0kA1bx2TN4NrJBVMJcmRWNIqrjepyobiDa9yXYKmJvgQWZhEBvF8wEx0pMQqCzcf9JemmcVMROHpvzARvqDrI5WOXmah0Zkh%2B6PIozOYm78ieRAJObTPxSCLuwQiRlWaLSxJ5hgIIrjsEUKPWMqRFuMgh1FsNDFw5jPGKPTWzyiSEIS6IPweoS55G9SZZ6wcpW%2BHYMSkoSETssJ3QQM9bQRo1JdCQdS1np6F5WR9wujL48y6xB4M6Y7FMnX7f22J6F5gyYVK0QlgEQxVXFUuC964aXqqhT5cX8XaZv5hVzeTU7bSoy40QuTOUjLVShZVi5iWJMAbFhku4I5QyO1x0Pm%2BcTesk5XsjnzwoAIsmXCFbWyIZQuawuVuXuR8DK92lWJDrNrbFtNbCj5sTTCx7e2Wb0UydtbZ%2F0CSIT7OnVIY0fRntwN3jNOdh7nGpmmq9CbBm1uLafMptryjPHHp7tKZEuAL7OjXHySRjUkXydXE%2BSrNxA2Um5Al8Dh3EZC5V4SFynxsMTKCAIMqEqNKzuJCQM3tvVBjRfT7xHSuCO72a6pSo2wn8%2FaXBmKI9sW2BccnPF8aML8%2Bohhb7ciWy%2Fxch7McUey2CPGCyVKaKDX8%2BSBNgqZqMLbCgZ4NPkIF6NfeH7gtElIrOqbudNqU8yyyewVZneKXuzObCUxU5b2tecTa7Jn7SE6UlSYMTkMfCs3Q7fVCaqK5k6pf%2F2wW%2F%2FosIJEgW3skUNgQHyb%2BsAi4ECMNcyjicIITuXGyXkkRIXyN50Jg3r1FpMmaQSUl9pGxnNMp8jf24vS5eX%2FyJYlMUISy9e6JooaPn7fiuG4QEscaDgCZpra85ygZUcJInLV6C314YPJdG2UL6qljb1QV1ZhrNcFecs0j2qb5gvkKHOFxHphwdBgEJc3y8qihwdZCQpJ62h1voTUccffiDJEzkU8jbXRvLFdPbcmVBUhArEuTKQFe570IglefLJ30dWmVAtx23vIGmF7N6auVNpZUQVHNmAlGgjNet51laJgLQSpfZyxBEqLwoCEGlF6ISp0NUmhUHT5a12%2FZElPE5UGO9AVnKpae3D5llVvXAHK0FIH3uE8AXFPF%2FSwx3OdCN6aViMIxEVcqv1UsLJo03R0grVOHRogBCiYVrK%2FqFBPmGrDyXLgr6DxBMSKnf1uSBgiRjCiwL1QoM981XHZnusKIVJ405xXwx6aBUuM2YXS0jaCPwaC2Zdp07J8gxM1KnpWMqNkRCfPaYTelLbBmd97YeNAOFaFOlV29YQn0qIInmIeMFxikX4U1pEusNYtOX6tKZKkgVONLhIF9ZPpxNWGA5fslgv82Lx49LF6FltT3Qp7q4Rcu4hKczn2m1GTA4unFBAkDCqY%2BD5s2dyVIfDeGeBgwDnPbGYXQqwM0E%2FBCl69QtVYbUwkQZAtSB618VkAzCPAs93Ip9qWnBMX%2BRUX0PFxCACZQwpfvNvAMGrD69RE6JSyNNOvRD8v3gzw46n8oB8mFQiYiwgidAyJgrfY1SaNrL5QDEjIIgYLECALRSK49wPmSO8jZLnesJ8oFZjWJyTrfBrNRCeOMoaVxOojhX6WVAhhJ6dvBPZ4AbMXC37YPHBFKUgA7t8mCk8U07mFgWg3wbOZDgsEZBOC%2FApoLJL8CzpXhZuoEh0ntzsvrUPhE40%2BUUkCAm26DxGMecbxuJ%2BpPJct9BZL6lVcWLmHps6SSRwqmRJeypwrd3vNnIawZkqBn%2BumMrwB%2Fc%2F%2BIAx3X3Ehjr%2FzJIVUK8Luxlv5IeNoBQPnIhvJDZfGo8KIpLh8O4iRusiuQOtytJ61yCvQUGFmCxEEliRlQmkN5wrVLkx3CUrlDG3ABe14lLsoysj9hXY%2FTJpUb3tCvpw8Ez7Wb20lfIScq%2F6wGFIrBkgzjtNLBoYqAgJoKvDHcdkTRNbmMKP7Fha6v0z1qPmfqQzKW%2Bi9g0kSWn1vQuqaJyaTAX7hrJwJgfaPjJvb6wXroHZRAD5NH2E2KaWtBw9TGd9mIea9%2BPrxlBpyjnQhg6rLogT7414uB8V19PPcBZUhnNx%2Fhr1%2BtX%2F1jKG4nnHv2EtR3VOjo9oWEibOHX8508Tra1pFg4lnAwoQ2u2JcsDi7diOc%2Fuhvd8qexCvYgoI6m9VYAg7Vl0PRXiuCdgHdKDIIHG0j73u07ULaOJJ60Y178Lsgtd7zYdOUGlMCYhR2Uhyglt3KUjPCpM8MKjVS6OgTHgqD1SSQ%2BqhzInvSk8VzFP1cI8vLxtdDvH35uPzMi4mQtJCrVUqhBWbQRke9Sjt9laOS7Y9YqUkXldpaAoofKmUNsx0D7eYLc%2BspsKbO71qdmmaGglAJjo4kj6XlnIWS1hQZORC%2Fh4i%2FCdcbaFFuxb0AYMJ7EdQExeIHEdPRtCxVECvtxHzbEyldDvie9cGKwImUuI0xEL9W%2B7FhefRJ05ePV427NJDYmhOCwBDNBP5LrIiblD6aw7pSeWFA%2FlDoWYMm%2BZEcF16VDdA3ALq7hDBlLUzNTL9AoVyczpCwb7xHucfK6T64W0AnDzYLg0HYWadOMPdoP%2FlliqiAlAWP4rio6stY02elA%2FLOPBnEr6rsAqtMRsS2g6HJtNANGwpxTdZEP5Xt5mE2gPs7dE0gyQv%2BFdd8QTBEqmAeMHtNnjBtKvg7KmIZCj9303GT9mWQXZxolBupTHIhJT%2B0WN7uu1FbRp6NnSRDIj1aXRJD25s%2FAD5%2FIc2OQ0iDp7%2F3Dbv3Oa8McFBhXrPpAKzhrFANFpcnLmEVTXjq0MO5sJAPVxFbMh93VfD%2FDppG4mBzXSgVfXJeWNNxoKF8QTwcQFXwQPB7QLE2PENdF%2F4vDL9zn0ZgQ1i6Nyr35KAwetsESlUtEENQbCFOeUFb9lg4Q%2FW5lUNo2fsscGbWY4T6pynpP0Krl%2BJFEfY%2BGGHI3aj8aBO0K%2FndAEPa5FX51Niq7CEOhoB2hSpGAgBT5%2BuzxAMzSuVEeQmZIbYew4tM4UsHYdZtT%2F9ibeR1u6rlX6lBQlSBT6LQ9YUEAgmAxLkAKQL9PSm%2F3NQF4w7ZvdqRqMJ5DYZh3j3I1rDmyXyti0llyZjkLnoPtHHwZd%2BfwWVz9ztMTMCFjpKBVWAdcYhRV6wY0kRv47nqddzEhQvXsqNstE6GNiLTd8iliAfI5vfm2KjfTQwbxz2uFzaUL0A9VKWzB7VrWS0QvlqVw9VCXOB6RDSgbNtW05PB7pEz%2FHZkv%2BXQTUD8mSQ3mJk%2FEUe3%2F2ixiuDVAOBfyIscXixo%2Frh7BHtl5KSBigokAOClzsk5pj5X5S9GLKgu5AbQNotZBWU5zoIzScHQKjeFNr0MiX0Mp%2BlHmHKuaDcfU2H%2F%2FhJmeXl1nPE8E%2Fk8ULZ%2FCLYtwJWG4CHI8h5ySfOAFbkRVFJyi0KYkQ%2FfhDAjciw08ETminGEMzG%2FwOERVW0AEslZEl4sfr5AYRKGVSMzkDSQ%2FnhkipQ8y8EF9ZRGKCOxRzfpNMubhc%2Bs9lZBHqNegMaRLozjP%2Bg8CUsaVzp5Wd0ZI5SaeSdNAD1HIc0vTPw7SLi4Jpa1gGgtEY865KzsFCn6I1RvKT3r6BVdYGsBPWdVzeLfbxKi8BVQVzd9f83goEpezGYiAXT8S%2FpW37TxnZ6f%2Bog248Vvj9otMMAmzAfvCpPYmXIx6ZAKAwBH737j2KJpWyuPMnFIBfqRrEBYiTiWVjvdx5T7k0dwFjgjLrLVm28m%2FXLNfjkSOzkoh3hangxcOjvxzVDIGSxYBxI9ak%2BDsrVTPAlMWZD7OThbuxdkWjCtGwtH4QzQdpmaBsHV8Yrg1iAK64TC2WMbQJAFFciwJFPSRQ86OtUrnPSODgRE%2FhD4qWAHDlWBDy1KPUCEbpodyyU4TnaIFzU%2BtJJAes10UhzVTNE6fGDGUN33wCgAZYoIz44wHk6fDO7wGbAEwmcVQg1HGGByFjYGdwJD1joA5AM8olCJ1LlTUGB%2FkhlrmbJbCbD4Uj0GZTy06zCTjOeVkJKZzqk0nE4%2B3RmlgDHpJwCwwZKOl8SEEH%2B8TQK7R6TiIPAy%2F5M08zWqjpXg4KWF%2FMNQpvipZJ4KZfGsivxCXyinAY16NjMD%2Bkgtfc8pGpAgSyN27l%2BuW3OjjOCnldBAJLpzjAYdBoh3b0yuYkLj7c6iwxR9NHW9WBeGVYjvaTEHVq2t96HA16vtKz3ZgtNuJgpjnNZRGEjbmQuiQEBJ2BdgHxFDqf0F1dQJcS4sIvDHlCASh65anDjgr07Qa98i8Jb1rAbSdOOz0cM9wlKXm7ugzOMTmi8EbeTBIoHrudZ9RSwEtADDsgyFwnxyfzXecDzLGFMmkRrGWCdnWVnSMMcc4cl7NaF%2Bt%2BgTHgy9s1SV05LLaw7YYgNxNKt2kpiEgpahsRp%2F0tarSWdfS3AdWYIAnrT68WTwX1vtPgL7EMcIzmOpOxL7oZh%2BcoizveUkJVmjedZZlkwkno1E7%2BjVJIoz8APlgweTgHUL%2FGciPnYgtfrq4TtxE4adiNQ2ZgyJHWYWmrMlW6ebFkPaAUwzKsAhoGXxXGRGKFObHKQOqFOnGgkyLajFNwhUa4fqva2yI2DlREZ6p0CNOGwR4XCrS4K3SH8pDQGvZIaDcMPtJUoLRM0owJ0iT9QcdRK5IRIIpAOzsUotf3TbAVvbQH8n5voA6kUzw6LqVnwPkmnYxgJLYiQ7hQYlEsYL4k5GCZvd58ypvZfwO5t1fXMTj11hE8GuAZhX78JMPsK%2FJ0yQqDPk4Z%2BJbbXS1tia0EBwCjgWUJsUI18clQ%2FL%2FH39pOY3jRcZFFfW7bC5v98f%2FwQjY4osg1CYvJfYmKVWbiPLEpDvlTEGLjUbCmh74cUqKtsVVr9hspTEdTKrKSRjqUHkhwgyUQeNizUsUp3cLmGnOoxPIuzYDGHFJxwclXasrQR5Pp4%2FX9adXL0zyaNRcp5govE%2FKIiLMyYKgb3rKpuxbm7t8U7GiR8qcIvzarYOuIr0z8D1QgHRWmwQj1jMXJlqpuDBiVUQD80Fnr7quJElyEAs47dXRoms8yEtA2VxNaYbJmUQ%2BlsKhyYLlk2q0QMXQSsMctgTGOka0GYae3AxbsfNKwpf8YnyAYZzDx20P8xvD5aQ4OkVtbj%2FxeO9gPGmBGT3BiXsWroQWXXm7Mw8XnUAttSlNyNEsqWLOLy5MJLmMa9m7Abm0SuOJF9RWA8VDp5xMP%2FI6LaAKUmkqt6%2Fxs1XyWrgcDo5hjmvshCSHhWpwCludJPk5i4bpUhqFw%2BRmK4VAI0O11JGtbQ64l%2By0LUdW7GarSUUwP47kW2CYDEdvsA%2FTXw0I4OPf2SvJ0FKjn7aZxxwVp2xjhpWMTDxaZMkiOxaFFLydXq16fefH2qVJybgXVpioGcKFhS4AjNTEa5T37zLo22Qv1apFD5qUjB9w8yA0vSc2RMiA7xIsRIGBTWOjHH9SZlK6QJGru7w%2Bq2GoC22fzSVxkr%2BWlT9rAABqi3RiLzDxns68PSEUIxqrqFw8oMUhIeL%2BSdTHAvw8M5C0b29DIIfScMQS0TKwvPEq3kAQvxMTC0D%2B7tnB7QNRliGiWl0NuVnIUY7QMwRkP%2FgVT2WDBX08%2BWLDc%2BiW0mz2CZ3IAvWqesMkIe3lWqMvUDL5Q33McSklm9U0P9SHgPGGVlX40diyUBp%2BpfWi%2Fh9sKTOvCRCj%2BeFIWvUXobOyAxrHlhKFGhOiw1vyKUiNMgN3k9No1jfVEpQEU35laY1OWX0u%2BF%2F3AcQq9opTw0RwEyxVosGLDgIkSdn7hs%2BIHrv2jR%2BJUh2kADlTtyX8HxtYxLUhMLj3%2FFpJAhfxzoRspqTIQicEOQtI1iv4n0xi9GiED3BLS%2BriXyEyU1v6KfcGAWCaq%2FgQ5kImR6wna1UoP6KtpT9XLI4EebxCRQriHsHkCOgDlZM8hJEHcCfGIERpbdeZFFHtE3lBjH2cIQ5p7TNVfRTwBkflxGhmddY2YfZbMF0bFv76YdpbH3dxSKmVVsblZW2xdvOxqSIQqbw536IFhJKhELagVlMG3LQqHU43e%2BRZjx4BJExsLcLX1oShyJFvhFevBTFOUF3Exw2yNBWwMCW5QA%2B7UmPhwM7AMOXRddPg6OxuaNAmiDYRKlcHo4FwUeta%2FLSOUyYddHQi3OFkb4z35KYG%2BxJAOZ8LTMyJ%2FCgARNXgZ%2FC%2BfKy8W7VwKPzhRpDnIMS0ClN6sPMpa421GfuLwApsG0C4FpdCsaLM0NtsctgKVSkeql94%2BXUmITqdGBajY4m4NmDFEk2HiFjYtNLmZSxNY1QM4KakEvIQIFts%2BOJ0QCRKvhCmQcfRAjN4VmYgb58CBUMiSUpcyuEfRFZS0SThDQPO4aFcJiNJUvlFdO0s8RaeSdpOSHscV%2BJAIchHCniOYTsEqhdLZtWVSiAw6hEDO7LI4or%2BkXTE6zB0CA9o4NUeZIpS0Fn7Q8lU4X9sZNCO0uPfjoWqOaqDVcbxBJSAs8RV9IcKmOPY%2FLDUnXDLSYfFT1xyNTWRLKRW0kFxctTIuvuU6R%2F%2F9ApGo0EHrqGH7dEmsI%2BgA0UXpfNwWFxUHz9CYK9toXP6IlTFRhuGh7z3bhwL5qzzoRAF8dkwboKslbtuftJDrshFMoBNaIvpSXyAFRCCKFOKEkzCkB3NDQB2qtV%2FANwHNea%2BAUDr6WWScJazRx4glReLp8vQ8sFtz%2BhqehlJO6MM5NMIc8%2FkWnwbE768NqCDKBi%2BpY288WynJ1CT2Kq9%2Bo6rhlUpRatsVIsacJhA%2Bq8M9X%2FUQMRZhshDAvsXwlZfxDn%2BWCfyJyLFA2s54on9a54PSC3WgchQWZLZgEl8xGrFUqCofhkvqYtXjA9XyewEKlnAtD26XlSvqtdhXOT%2Bi5BrX6S6hHVcqbZ8qbh%2BEzffUpCfTG5HULg9DQQaQ6ENkkq3DvUSAQwcxh24qkmtdRoIMDxOVDdy0T2w3afIBABsQGxnqJaAOY1gWawTUuhslaSivzrXaPcRyGhUkH0R541UbNvLKiY070WOGYY4R%2Fg1rfKhHcjoV4p45o21tldJXmpG0XsOekNlYucZlkAWkVUHtjkSeUbdfBMKrGATodKL9arS5JFVovaKRlOWbNJIS6GHxuLt8CX%2BBYQUYDfxlQrQQhL7z6vChINNDAVVN0rXxaKjegwIPOJf7rXfHBBHGpBNpaeLGFXCBrWAdpIwF6mcE7pE2z4HdIxcWkU0QA3TKjZQ%2FPNc4VwWA3OqmXuoleHBmMRLsU3ENaVgIpVnIWvBLVDYNLzVRdIefms6mo9VoRQZ6cCkQSOdK6KDkWEMZkQyIEM6QUltTfQ1xFpxUL%2FlFho06RSy0MhFQCIEh5FrTToDEgpZU4QcT%2FbYWc9XTPTTM6ZiDUk6d8MHSyFOgi3cE0Oh5I2Tv%2FThHNjxg7A0zwweFEVnLpE0Ai8Q9OWYcdB1zNKwLEBtxjc45oHjshf4AMMWhwwRhRdBnjEU3iL3TIBscW%2BvDMwGJnW4omSkqIJxsnzyWwZBg9lQCsSZ2CK25fZnAEtrlT3WZ1xUoqsQBEiEMEmCZAjQU8EhBFQSUEVhPwf6Ekh%2FQtYOKDqg%2BIfAJ8CuQ0oaANSG8CrQwYJAhV0I0D4CXAOIBYAcYMUAO4A0AuQNYAwAWgJAFzA9A0AgoIOA4ARIJoBoDTBVATUAVA%2FfmHjl6j%2B5Pa79V8K%2Fh%2FQfNvjz3P7ler%2FHHh7%2FPw68DnObsHvPy3cPPA5wScuOB7qBzX7b8SuTXQrw88fvOjUnszbV9wOhztB3%2FrTR53FdNbVFq3a82jxmuwOcluBDDjLjOjALxD3%2FQiP39Y7a5Rt0oNm7G8V06nAGXPxV3oiByNCcCfXAkpuOrai62YwdiJaYXnQsst7b8q9lbH1AhQ4fOtIhMRO4kqFfJyARyYkd8VFgh1WJGVCPlA1ZMLjSDAUNwDDwkxBcqWcEmILYipcSYqtCIqlSAqaSAqUCAqkjRU7GCpiLFSkWKlAgVIxAqRiBUbDiowGFRIMKhYMKgwKK%2FYQV6wYrughXTBCtmAFaz8VpvxWa%2FFYZ4KujsVY%2FYqr%2BxVOdCp85FSnwKkjcVGeoqJdRUiaio4xFRNgKhy0VBtoqArRXysFeyurkV1bquqMqqonq%2Bh2r6EqfoBp%2B%2BNH3jn%2B5M325m%2BzqX6%2F4%2FrnjqtKKqtoqqPhqmWGqVIapJgqjWCqxYKqxeqp52qenapVdqkp2qOnaopdqh52qD3Krsbqs1uquGqqZaqmdqqVmapLZqjdgLkVYtPvgiXswWobLWxLWyK0qjACE6uL48VDS74vVFa9UPr1QitVAa1XzWq9S1XKUq4ilUQUqiClULTqhKdUDSqgSNX2hV9IVfB9XkdViTasMbVeTarwZVcjKrSZVYS6q6XVVS6qiXVCBdUEF1fgqrzlVcsqrdlVbQqrWk%2FZ8n7Vkfagj7JEfYwf7Cj%2FXof68j%2FW8f62DVWIarWDVaUarJi1YsWrEC1XsWq9C1XQWq1iVWMSqMBKoqEqhgSqBxKvuJV7g6ucHVwg6tqHVrg6vuFV6QquaFVwQatsDVrwatGDVnAascDVhwaoyAqiv9UO%2FqhL9UBfq%2Bn6vB%2F7kf%2B2%2Fx%2BtVskpnZEuCUxHC%2B%2FYuIK6NNKiB%2FEzIk5DuCcPq3Ew4GQBYmlqw4gUEVMiRKFQwiMCQcTgAAAAEDAWAAF5ItM%2B6bFophhd3N1nA4zIKh4NARWfqCooqII1UQ4kBYJ2AQsGyO2lAp%2B6Xiqm%2FCkfz5urtugisAuGzxeNHQh1A73Vh4dG0%2FNDkMQKtSO%2FBTm3DojsrbF3jq6kKdaxNxwpKvTKteAnM7mCQ85oHLN5AxXtO3FxElkoEAFo%2FY7SdVN5daSO3q5Y%2BGBpQAYrusw%2BEY9%2FXMmtYT6y1gbXXfGgzHr8ic6AN9hcvHwEYELAoOjr69A1yaVwaL10euR3YUZUZvQl5xZXoT8YeJLqVKFrjMLT83%2BN7REmekLvRn0TLh%2BZMzVhvjjOcuh7%2FDGoSIzhcgAEyu2q%2BgmErl0SoAyvHf0gOOHkuRFZQSSkFEDSG4HCQ838BiFt5mdNnEIYrPlCai6RoN3%2FEdkyVeA7o%2BHQvKrwD5CYAAo2RJZIeP1xRy%2BlJaWuskPYYJ6GKPdNLzxcio1M6LXHJDQbx0X1i8B9YnLsQYerRiZjBe%2B2alDqaPIASEoxCeRAUMVuivCDeCNo43Zi%2BmFz4viBZJrJMbwxhXGCv8CUG%2BGpgMJzaVH06hQdq5woKyZyKYDByVHQl05OpUqPrSuURMAxrqnfhkQENbMcnlzk3MLnnmnljBLSdHj488rWP0ithH7Mq2sETRkNmgYc3AZSu6gL%2BCk4IDhPtHR057XmxZs5enMzU7IR5rdjAD5bhqPdP0mGax4%2FBC5rzvd%2FNmFSTuivxBTftCPQY%2BTO9CX4l81r5FFfrkZ1yrTcaX%2FE35vu8hUNdTlgS2iD0q93mIVdiG%2Bzz6ZoWvKaG4axR1VGYKVxYs%2FhBQR7uUeb1mU%2F3VlT93WVa55WteFObdmuaGauUaHua6G9Lh9q5rpXj%2Fwgi3iPylvEGKzMA9lXBUjbXHUnsEBSjM4dcmrgF7f0G9lwAQt4RLmuXHbpvBr9TrIO6tAdGiB79MRHozMKD8uVjof8zfR8GjzHzhs033A2Py%2BejgTAGqXxpL%2Fw2WVk1lFTJqz%2B0kBeRuJCR5GRySPRQSmUbztwmWP9kLIWUhlXSVdCicr0AP0QVDW1JhE6NsgqFunp2ST1zYgQv6Qnhu0JlL3GiFmbc6pMG8mzBJsoRi1z3Z5NTM0f1ndDFjciaUQqsNg6uq3fi8dFUAWOQGs1VX8XhLFmvpemPhnFsJ4EJp5fMYZskQQsqfYfD9NJDx6cnQ0sTSuR2L40hG49L%2Bmrs3RoC4ghACgxYOijJAtdpXHsZI0rFXEhZYgy3algwWFOreuLxQmZTNtXroCxgYhVHmAFChJHArQSkYK5FLPlzPCQ38AzsClDlC4ke6RHiVatgqkBMcH5a6mGXLmcLkgxPCTi3IYssNhBMcXoBJkK7iAw%2Fq1cJO27YZZ1dgwK6TjpcOKZhOClzaJK94ByvnLhalwYwwHNGbpKKrMoiCUVvMU4ZDGftbGmdYDSWM4M18njvCm2QeRAFzCJCDrWq4fFsh0jgTZAy3lAyEhSb5p3l%2B7zqhPPDOxcQ5kAj9XEi83gCXQDB330FUKUN8JTMzokg%2BMHLm0D3oGAQxNjGVUC4qv3mNqY80W8Wma%2FG%2FRJg0Pokw2Jil%2BUKRbOEH5QZhYg2DwSICOBEYPtpPgwWBiCfyJ0YCZEGKrTfFYsGyw21JEW52PoQdw%2BSszylGkFpTMhd5kWLnyTIQai8z1ij55qLRN75GxKwkR9E1MhMUCbxmmw63GyiBjM6OORqQNMpJtSUqMssFsiIRB3bOWC1CBqx4cDrtymvUnfKZrNpclIQX0iC%2B0SWyKIyxEbrSIkB4ly9nnkYJu6fFFI5UkewzCvGzpCmkfDtvWO%2FvOQBSKG%2Bw4WeOGpxQqahRKjqs7pmglalwleZKZnV2WNi1CBepIZxSes8CtZlRI0GWmUOVcYlpvK0hUKRhWaEyXRViM53DzUU8eSDYlUJQqDMmUYc2RuqTtfeLBUIWJzpxmeJiOCoAXmCaSqYM441iWisAz0U0EQHSkfuH3o9uyZk0g2C%2B1Huy1lYiCySpvTSdjMKwTiKdQVCax08qRNA1Vs2wiL2IyamfCpg%2BlZwXvOo2sB1QB9xIdX6Bj6VzRgd4UoHI9PenyASnBA8k3EjsE1S2ipMclWB3LPJeWzSHOikaNhJsFX5zoP45XxaGhDK%2FpP%2FDIYztiS6vqMPACWcgP2kpZWjOfJBHYRQNI4tp3WUQGWpE8ZqoZEFvYJaFCTkUJPdTqeYpTEWodgDyHYOTQomy%2BoKxLC8BtejPZfK3UMphCswZ56qASoLK60kVHNgZzT8%2FUWcqkls5xsQ57uoPAv%2BCrnsSCROispXTpjPKe4ND10sCf%2FElOsPgR%2B1K2fX5hXhJfNGzBWCpiE4EjGUBgUzovLqcdL1yqDbTutsHLe0YqwBcpKRqcP65%2Fvot41Eg4cbCrFOObHzAyIO3Np2zmgIymMqrmdZ5wgWiOKMA%2BV8kKigbnyOAnZzQK1uMozbw7SBWI%2BtpCLJTN4yFsnUOPsR6JqbOmnG2mYUYJV0I%2FFDRgDb2YBKeBAzYRv4d82IRIjKNJ6kthxBIf4f6tg5Rewqogb%2BBL89Xa%2B8Y7mPwJBnr%2BqyHL3Me2KqwE3nnFG4NiIrI%2BvziW0DG1jcQIHeE7iAwtWHXwFYTmEfOmdkgt4Pr1n2vJwnSQymN0R0NLRTgAHjDVSIfITmkvlvfz7MU64jgUChw5hCXsYgG5gXyQ1osKsxCRhvPgsghFG5zI1CXCBIAwKkjyKLeqBc%2BfEUFiCvfGOta3gR%2FU26my2RNT0gywIO1yxoMTM8Mlfo35lpDQ4jfU18jrE6QiUumMhcIndvm8iDoRH8amzyNFXNUKcSoCnh5AQYr61lElsUE1avUS%2FnDanpv%2FZFa3VH2JIq5OrC2wDxYOhlkZf5BIr5I2JAQY6MewSa45Ob%2Fz%2BEjpgzBQTlG3AdVyHpu2GkjfOxGCSQ%2F4KBsYV%2B4WC4tHrRIEC3vi8DWBGimk2R3cQNWDhpiR%2F9KQZE618tdreVPJR485XjWwoxbnLTn6IZ4EjKrhRraDtVPs4zmDldmpWjWM5L49vWKkkh21cJ0GELX5Hje5CUKJRNkIrvqKWk7bw8b7gYD5fEhk7VgXTHJI0UsIqSimKH3B62CsLrQWXjB%2BoWwKWamDL6RlAYnwhMdNFIyfJuJIyo2hI%2BRLmmREVRHaJdaBYDQo4bDl91nK3Zy7hcqlXpT%2F%2Fg9jl5m4DVYTnaJOAoq7zjeA2QzPBWRnDjFKG0AJybS5gxwd7oVkVIAgAqWCrwA2TuCFHn%2BlQSxm2EjeZemixAGSIByWTtmzQII9iyI6QxlnEfiedIUpRSDRKzAojtJx830mEfqcSqHRLRGSUmpIkhYAfBXhL3h5wRAsiPnzp5CmVoG70nYHSlNBsQEOYNSYjVHmDMCMyyhMYHx%2BhksBoo78rYc0vB6ClzXpCsSQL0xuJj0lRG0q3NgS%2FgqHn0%2FdBeijOqfmwAAwNmamjdRSr62goEAhLCK0y0mqVtUz8B3F3pE3rUMN9wQoCtpvRAK3sFTK3tcPZnt6Z27rdUEcCb1qxd8b7ARVibBsC%2BRyonUCCcPwOsBKtzEuPpnx3V%2BQo3FTRLkQsTzT%2Ft5GIpAhUffp2BuiRh%2FhQEK3waIhUoJczWFQtyb9PP5ODdKmoRpQHa5EoivjH117VzUEyb9h3I8XFhYGY2GgobkujLXrm%2BbJ7HN7wo3WOTojHaQLy5ZQH60wuIar4h1s2FLyTWktIBtEtP6PSDSPebTewSizvHczvHVzmh8LyM8TZbIzBlYhsTDRW2MtA4tMbKoUyaOwZNB0EcTEoAHayrWzlDMhazQx8GCH8%2BXZI4EskKhE1hnN1De89ZjtBMDWCxLN%2B4bn5AnyOhxpTflbO1kA%2F0EGhUliSyMtWT0UMb%2BqEPinhEFYyHEbAUyp8tYuqYDRipLscjyGJA3zbmKdMTMI8V72v2ZQ8R1C6WY4qjndJBCREkDNEWaBovAOsU5JvqCCECn5%2Fq30Wfs27Q1NX3PB1CxWQYK4rW6gl2DEhCJLvzF%2BBEKd6LLjbQMVuJRpueMSQJN40n5WAdyXvuYQeXUQsoQDysgsemARNTywlJKFiZeuo5AiRfoTmTq6hkwijNVgqADIQ0SrFqBN9hK5RC2ZzmikwVm0CCZzso1HPlzzN0IP%2BQCIQZ%2FzA%2FJ0cIiWLfbbDGvgdRmzRiOWxhWyl0iXpQfpiIVEQuNo2d7rj0OZeiVOeay6x5oKlhwZCcg8KUij44xrtH4IX6UM37YQMPXh6QvlpkcPG94IVYA3iwMJ0L8LuuaogTlCTJsenAjL0PQ7L1Pi8hmGJdOqOJeVo7ECLPOdRHN4hjBEITrTPlmX622a1Nkllc%2BXvRIMYsWFbQasKlkKf0TuoImAklJXaN12zLmMOiREi6fUAhVGrkOVhqjWR2eenOmgUY7GLcmbnAqWpftrThQTmBEC5zJQBDcPyN4AoXnInBM2UJew3uo3Osd%2FVotG4JFRB1dhhN21R2ig74o0Jw8ZN7VnA0lWB0lDFZ5PDtyXLlhvcGuF0S6Wr3U2CqpnkDjwB2mBQdPGRuL5x1IFo03LEOsHEh4MKRvE9edrMh0Snaf0cShzOIJdiyBYWbPeMzTRGYsBHAfX1laNIl1ZVxzggRI5m51PG59wVXQ7HQGRYRRnGy89qI5okgmTpsdUtZgAxPvJ%2BWcqIbSWx6tf5yN%2BuJkKOjROUqEcP4y6WDOTFIXxQkr7hHBS7hmbyT8AljD77OJLW4CQGYAb2Pei8bNTgMttEECV0uBXZksyIJd39JlDyojumSt0T2TM5i3S2cviYGBEzQtmTKzAFB8QEcXkZq4YmJ%2FTDWdYLS0Kazft%2Ba%2BC7HwNZAJaVEY3kfqPHY2SBxchsqiGqJLn5a%2FmjEa8QgmABHka5AWJ%2BG18I9w3WjonkFGEwKhtgp0j8YPUeYEncSOLNhhwVJnEiplRLfp6lN7z5sAMYVx%2BA71pVUbhIzIaaNzUbAarADBLMSSoXqKXumbEiOijClhzIbSA5gGB2qtutd0MjQyWBaAzJmfqki7MMaEQAOCXbwaXixbEGNFnHCqmWg8qjxIZ0oeAxEPKD1A7yRyuS4AIRwOrQJ6DxVHVPpAc7aCkBHpHC9GMxBqe9Qt62zjQGJy9sjB5efNIj3%2Fyv2r6z%2BSM%2FzuFE0Za9wOJ37u2rcQy7P6SvWZqCuEJKtvjAUTarXAcyMQRrVOlrD0HTsN2KDiUivtwmgj5U9inAK5%2BC4IqL6iNHhU7OH98NmQILOWxNwJiPwwlnf1QAEMLagJuGLoYUdlqBRDn8QcRAg9AuLhe4myJn%2FEoxUfepXHhZ8Dk0BKaH6znSNmNJJ2IRdEvxoTfFkKRNn0cEvSio6ek7OG7AZljFvqNgRejEgmOwp7toPkAA5CIjKCsWmYPIlxUdVDDj%2F6UQEQZwMPCocey9jKiCj3ryLWjtGlYDdewTAsT%2FytgOZKoG6jDf%2FUzE%2BDBwpk%2FPx891KJ7wcBDtmnc4Zr8PjCwfnWIE7Tz5Wh4XZOloMKHqBYibF9JCb589kpcmC6MqgAH60Nwcj%2BnBhrvYWI7IfIiYD0IThyXLu%2BBjtuDKdFNmVqTy8ELHUIcco0LUngtn%2BupQtGnWSsHRGOZxzUOEULCoCKWugvY7ZqEN0MDmNHqeKtmwQSOPoR%2FZ0KGKGB2wdDYj1CrZsodEQx2TukU2%2FMDvJyPuRgDSC80jH6IO5DbRYrS0rIXfANDM017avY1V29khrSTX8G%2FWm5HQ3j02j1MGoCeHm%2B25tIebrUOsJUhfKhD0EIEeXj3%2B%2BQ43UQYWPGKRd5tI6RgOxzkuNv3Ji8YcsIU0cpJNn1rQt1eBb5t%2Fe6vSIMsxyrbVRxAHaSs8%2Fijk5AW1W3ajQf6DP51FkKbjEEaQp7ThQOL8pUajXNZgjP%2BAGWp2PntYtYtaMRzL3lOTlAMAU2jKG8l3wMD%2BbxPb8RDFLiiPnwnwm42DzJDXbmbGIZ0jLkON0Ub4bgHg3f5pBR%2FoVAu0YDFztE%2Bzps%2BP3bAAWSgSxlY2S3LazAF0KZAmzACvKJ%2FMz0beXxl%2BeIEFgZCLRMTD0eI7mFnojAK95ZgCTCfQOybhXCo6576fGouBIpcPFCU9ALkeU0pQaG70p3LmSrRNvUUN3vI9R8lCXjgQBKD9twFivCVAT47aBVWQPv7zSwNj8Un%2FSFG3FqLOS5c%2BWZHKZKVGCPSmIgHEwoN5C6HgtxiaB5%2BUvBrgQmTbO5rMJ%2BCuldZzfzxgdXp9XgD508hFWCeR8zw0tDDW2xoicZACrgqgnQ48NRFvjGjGQLa0bZ%2FYIPRHYC9plKdMERvlMGhAQeqDBMhf6LZGmyElMCwEIYOo1BZ6PH7%2BWHUp1YWZa7wqK1XoOCvECZSwoJH1Mkaogtg1cnvzmDI1q5S6K8zNTnhUkglWVoNqdHdJkfg0Fxp37eM%2FnNEjkRlbYvQhIWSCHT%2FqEBqUlbxYgGY%2BMSFSRxt4bg1PVnRhKNZIXooJOn0HVNY%2FcfiwOh4kjntxHmybS7MJkM71QCMhZGbYSMrJ68qAj1YN82a3v5utE6PJoWbuOva8RkRMPNZOJG3h4NcQ9LTUNP7WRG34U%2B6pZFDjhFpbJyEq7ddJp5LrxHhOcWeKH5m0I1Q2anhxKm%2BO1yIfm7BGh7B%2FjqYiPKEBJ6cFrT2ZB6TmSyLIpnTVkU%2FYqVla2WfZcp7SSoTzv6vLYtG8AgtXlxfWZxN6Gzmta1EwxIU6yE9s0x%2FZlSkSYE8X5a7%2FVPKA%2Flig684F00nmacRk4WooPYSFBnHTa1ebr6zgD%2BMcOV4BeWlJJLrL0qnYZSAVtBA5UxpOASkxyTpYRBUMiUqxjxU%2Bce5iSPwywJQRsno2JDcLSzoQnpUALmNJE%2BWSFPdsteXHMocr%2FC0IqI26humQ1wKQoHcGmUNYBVwIFZqI1azUTzdFmIOugxHeiO7FGrlsIVmnE3L1JrCuYeQCJCpQc%2F0JCGgQIe79NvrOoYeDQJ2REZcyhK7ihxb2NeHkVj0aI0LIicFJjQk21YaBO4G64TBu2IKBTzYheH2CASyWnlbZNFDbFlJqA1Q4kIljGpwnmhmXbOSQEgWsMmsHkBZYKRUWBv9HZZ1N1JSN64WPlCBOt7zqAJMKpczyNBpzQk5o%2F%2FxDjQg86ozQyN4waltVoDaLJOfWjYqewlULt9QO8N4AqlP%2Fo1ZIqJMaZ%2FORyILlDFoGoeY2%2FswI7MBMVXSMJXCUeRsJjHIU6kNEsAqZoBDoHo8Y8pZtM%2BGL8OLfcPdBbsbvRZuDQcyyrZ42OoTsMJ2GH21od%2FRCfgHrkeCyZfMOkWTSYgXtqYxGIGLcI5gKg%2FZR2xnb1upteR3rcNGITv8oKHpUWdLK9kIZlFfEtPUNAgnOY66o0dWYJq2a0UT3usCxxDZm0UIViE7Q13vLoyXH6VgLZoJt476o2dZ1DSsnihertfmTnTdREBRHm5ASIGxMbMSITKCmV5fMqgFiewxFtxqh5yfCPkmlKNcrJfmvyKlbaqps0Oa%2FBSPXk4FJjtWF2wOHfpFY63qA49US3T180HXmbqEjYsSpRFkkOVMCBH1C9JAJTRgjCWNAjl2MSA%2BvZjADldAJGTIoWBOcOEUI4qwvBSI4hMj8m7WHoFosA0M4Q4yS8XtN%2B5emVGJDGqX8D5SVbE%2F0bWETJRyRFYKQ8umFTkY2ZnV9Lz%2BVW9hgHq%2BHYhsrghzcANy%2B02yEIiJuDFngZnEHSFnTnqDAicaKOgtLZkRXBqGS3bgcqBK6B%2BmV5G4%2BxzA1IlYTTtL30wAPCkFZuhlgsViFEnA5Rg4UXmBbXBAW7JuNvZlXTVsYDMV305DdUPX6QyUFMwndSexwLkpBDsIQ4lUnD9QoEF8QmpcQk8FJckIIkVwvETqBSrIvHRim18B7iqFwNCcFoUKmSj9cSfX37WL%2BCT4mAKH%2BS76AYlpBE3eYErTSL68qrzEKiOZo1mbN1ujQapDrmTWb1s3iBTeQMCYcYfVeS5ks0rTlCPWr9AjV%2FbgZ8To6s7qOr3%2BkRUCKT5GAAUzFlHOkmV3fJEImmhCZ4S5ivYOcC3SUx70Is%2BgfKTQfQBsES9NEX4x0WTcQkWV5JKEJNl0UV8pDmBNnC%2F4qRYIkjLTUknCCTbfLBLJzy4rnSGTntJHjdpYIWlqzxK%2BG%2BRBy22IJ8gMQIibkjgh8Y9Q2H5aenHLwEJg2iDmhQybR8G3gfKxBAZwppQkDi9rFMfLSe8Fgalu2z9mKNwe%2BF24HWYNjNI3zoQfXsIck6YiAwCMhAsEDMvxfp9PxAAxF94h42eFp8V8BZxS3g%2FoQhPG05yIyOc5GxA27VgPq%2FN9uxGcdZCP9PAWYlmmiIlMHE%2FRj3IzE8YKgGEh8OmOkN4KuO6GYyNnRhoWNSgmY9%2BVfMfhBm7xIwZPp7ggg%3D%3D%29%3Bsrc%3Aurl%28data%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cb08AABFOAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAjPL%2FpQAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAAxADsAUABTACAAMAAwADEALgAwADAAMQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAHa4ADV2ADV8AC1cEs3pishg2FfJaEtxSn94IlU6ciwvljRcm9wVDgxsadLb0%2FGIypoPBp1Fx0xGTbQdxoAU51YoZ9RXGB0bXNPyK3JLMApRwa%2FIMy1PPoJDx39kimekZX1c%2BDSW41tEZBuFiwdwx1dRoPVA2vWPSlsSDqhNkqYfhrqlVUGD0J3HEAgZavmtLnDC5WBriSpD8Uk02KsUkJ9vCFz2CZXAd5viwGZ2xcVYRPa1bEIai51nMYlbL2ERuB2TCzLAbPWWRZ3%2FsZ%2FKBjLk8%2FgAZzK1OaxNw4oGBbsNhx6Reg5HRFVCrwa15kGmJEy5kX1YypVm%2FHo7TjKP3l%2B%2FnuCTOyiOa6S8QEJbuiGYNlCnM7tHChCHRRQHXQh7yPXASlcvc5KrNKol6orb35kbo%2BiEwl8d230cfWPwTy00bFDRYURYGchbwsoDcaR2AJsFGCrbWCzBdQ0qCobWwLfueqAzSkzaHX3yCjDlGYPV0VZqVXlqbr4poRaG5NNPMDv5MCHkLSFyABMRBhMiGSZiDgABYmwsLWDjUZCUnHwvXt0VAUy4%2FqjwVgjfUqp1evzUqutJB7DW99Cq5aEhGePsa4omKUp7LnSQUz%2BZq51pU72ApMxkaVZE9jF3IGKVnF0GlK1E6iqLJXFzy1BEv6Kr0ngCzIgVANRwCkVHYJj86Sv2dzqgVrjYKlg8W94FbPGQghEbTakZ4AoS9h0u39DOoRJTA2iirs9xa5m9Ir0KwkogoqFNVDbTIGO0%2FWuJYfp61Kq0bhgbcbnvbTDj4HBESsXMdvrvpp7Owf%2FS2EbndoEoglO%2FjigGAuLUTUmG07ZGlRCZ4QZLu81tPEgTtAvF99BxfnxNslgVYH5Jc4AfHwsiktDBO4wjgjizFMsIeBTGmIYrhtrK3tvtBu5tYB0PDMXILUkFYPM%2B5yLLGOU9DtoDLYWLQig%2B8wNRZbJZZWeK5JyKNwumLgP7X6pqgiZMFUvfCTl6Urx1gUOjzhcICumUUsiNESZCmTANkgHoh%2BKU0t1fgQYOBkNfp4MRiJysezS1Vz%2BahE7ohM0iJBn4I%2BT9RJN%2FuQk6SjQGwC%2Bom%2FSD7Zq2RxvcSjtdxHfo7NtktA46mEg5gBkTtMKu7oFb1KgqwqBLzQqZI%2Fs4QSRhYF3ani1URTcMD38kDDqlc3DChmYJTCR9WpaWrYFiXeNi7jYa1Oe%2BSP30gxVyeC10DYOakGpWaU1qkQ9OXlujEbM4A6A%2BQ243TA2QnI8mKzNXNo1HPN1CFawST6uC9dkyQTqq5bOKkkuGjz0nkj35Sr%2BQ%2FkLGg2iQMrTKImSGshnX7%2F0Jyh0JziDbTzxp3IrbosXgpchLcBJpLLUBrFbt%2FU6qXqAaNFJoeBItBoqgQlPZkpTq31s5VZsgIkFupAno5G9oKwCGRqv5294xFqOrEpewcjx%2Be06mxDkN85c0ckmWmMzbkFX9YHAf83p0EsnBywjZbcPxZw91kqg7voxasp3VfDhEDAufUx0MTddLO5kCyKkUBwOm%2FMurQH4bJQgK%2Bp14dkBBhgxpBUkTlSbTE8DDu9qKiDSCtvmPY7FRMn0wuS6XR7J%2BnpjjeaDNwiOgIKUWCP9aLb%2BXpc4toQCryp6gjCVq1Cl1Di%2BtEx4WnBp2hmGZpEksZwTSggXuqAWD4B7WAMcir2wmA%2BI43ECxUSeTDT%2BxgR3O4XFPOGxHh09lxpScampLahUbICao0db7%2F%2F%2B4CBMcCokQ7aOQ02MOzh0NEaJGGOvPR1t4sY1I55lNfQ3IkF04onnAomXxiNg2EbBbPTMBBlv%2BggPSmkb4GNFW6Ehd6nDzJlnIXFfzr1bVWQlfylDodFBbhCDf%2FVB13j441dc2rx3h4hfAWIJuZqbH%2Fwbuafg%2FnTQJ9bnMfR3U6TwYgDQJIOPOtmZW4ZHsR2VifZGaC6Pzv4ZzC2nSAaqSe09F9MxuI4UmUVA2RAUqBUIETEIAhB%2BtBhsxhVMXUmv3RPe1grhYWLM%2BPHwwO5W069qIgDXtETXZQ3741DSk%2BvO6y8k%2F7Mg6GFC5fSK02yXZ1XV0NZe8iknoIbSFnDm%2BbMP%2BBCQhoLKOeU3FVNQ%2BgPEntwkHT950cQYEYQc%2FXuL4nxwcRW2bIN%2ByskPaD5Rz6lN8LW5IDLYfuvhxlLcjWXSADeTshWsJk%2FHCuRbwjqLcx66fc6UlF33HipliQ1cvfKlgRpVcsmIsiYnJjzJIovUyqi%2F2OTQcIwt%2B3WW44BAQh%2F2GymOIscSpIt0YUePiwOCJN%2BQ60lqkSRbWi10lotzgWZsqzEI7OGFGeIoFyqli41%2FNn7LfLQ0qtxkaAwbsKZQ0D9s%2FwhBqoxhAlxCVNVF%2FJ6rsUBQ49MLtR3ICtKPLKZ%2FQPEeIRgWUNLbNR3v3tTaL8uiXO%2BOj3tSJc56KJHol9nGPuclbrQpCn4keT05ETSOQ%2FDwwKjCRLgIzBi7x%2BU7ByTZGUWcGLPY0VtXWC7kQ38C5thIGJx%2FxLM3KsoGyRwC5AFUNWPzU0QBCaVXo6z1NCclLuPVA8%2FwWuNhiAG9CPPSSlIOCuh2AQM1d6MyIEIQArIa9YenQ5ug37h9gPNDHVLImcjE91ewI7pz7HgOEVqTD%2FjREYRtEoDIfgxzDePkVvHWJAtcpCJ%2Bg2o4akZpphRUQ0U92lJcKZGJI9mnpl8UVzpMi8SBGXUAPpgcS2cDAusSE6q0hNibhCgZHLReLsA%2BSv8wF6LiDL3TogB1DsO%2FHeMg0ETucRO2hIOvbp1NVkFMclluNmBQbTy%2Bosr2bJ7oWdUtSHi1s%2B2vWxq8njY1eW4BCVr2EqSyIUuFKS9iwVF5FBGeg0hRcHwCKMtqYLBfvVSYXHdGZK3Ug95C2d%2B3t9ZWEYqq6F4TocgcDS4tI8cvEEXK1%2ByB9RofdKpHoA3eP9%2FB%2FEBG%2BIgvmwZ4Iir1SOYFEtUG%2FoVkECQqoPTdHoNpQKpA9K9k7TisAUBsKPtWgBriyeOxs1o%2B4KCIFumN744dH3Yf7X8F426LGtEhFLULhlKFgXF8Wq2D6kFSBkViLad9D9U%2FP6A%2Fr8SE7jsr9s%2Bsb5ODWI5uS6BkZJI5RQ5QDVZ5%2FKOG6Dzc1RLQKAdGkgwLiZLQdEYQ9yhQLtD5kWr4wGHTv4GqloWePyHYyuy3Ek3iQVQkdkAz0MxcsHzccdMLO8KFh7ywo4jXywraIhIicAI4dWdD5wEXBhsMklzbWc8zi8opC3IlPsLXHCWOqOgq4iPU6xyDS2tdSLnlhSFjsCCWvMRt5TAI30EHR4f16NYyzoG%2FoKgCUsW4182Wvj5gpb0bGGLe8YRIYRGVgF2hwIlEfm7eUwwLZWW%2BmmBZPggPnIKI0b6G%2BlHCncx0wkaVPRQpZKWIIrzh4yC4Ar2vTKzhVKSutNi1dSzmM73AGUXSBJrQH5J4Dfb%2BRU25tYrIVdLr4ogiwTE2i2hl39ffJWHNTSWq3PvVJplNqDTJgJcvCHYoQAuHeOew0gZGfmX3p4D0aSr1BhXbXRA1qipfS6LiggtiT1eDg4FTCWwCdAFC9FCOZ52EdrFetMBxUdC0dfnqpr0mELJE2m5FVMvwplyf0XIKclHa6%2FQrw%2FFOVkVKK71Ab5iAVWm4GA3HnkCJhYkW06XJ3TDGB9YhVW9RuC39AFUcFWwU5eTNzuck7IQty7ZC9IjAGNHuZhQ8yDWZhGUoNyhDcKYQiUDqAC782makzJ1o3XJFIEDXgObv%2BTlS%2FdAr5rn2C%2FCDmkI0WO3cSTJwSLrS5dBdkyfpxAu4XghB3jgGShG8UCorOibUp7NciZgEDLmT6eOxDegpLRk7HomganEaTmRZAAMrhobApDjFWtOxht%2BNQngtKVgGL2yy6Qnsmju6DAg4d%2FRvxHa5mBaEMcBekCMtwaPPGeKFPMsgAJTidusMLUbTAUBJMOmXRwhONkoLPqBhpTuLSYN%2Bf8aeX1NKSxF2OUST9ezObkHETTsr04iWClpoFDE8OdIiDPQJqkHhnUPTcE43OsKBGS%2Fzk6F57sqVmhZKjPxle2S%2BiIE%2BMXOiTOoRKF0Q7mbNRYGPZDp5eIdPOZHboa4CTvwnlZb0zCOW6kZJh6klDDvYghmQ2FCDKB5DD%2B0nImiNcfSzaGT8aSPzEZjMQ2GPpA1Ws4Xeysyv0zSSMVVsiOoiowo2SfYFdapUBsWIoRXMAKYdguyVcwqX%2BzVWYLiNddi8p%2BxMV4dQIoUs6SQ4C1%2FBjtBw0ocOzLmMkEv5D8N4%2FZu4gYA4ryDb5Y3gkIxdb37qMCWYmDGgsyEE00y3BZ4FgyT%2FdJLHxMQwx0gViQjWL0QyBfcufCgRYYGiCHrxez874MrcqldLCEQLkCCK6pWCnULCh%2BVK6FW%2B1nm%2BN668e16QTKWprvRw3mkwitjPefHt5QKrVumkXsvsVrHdAJrodfWckxhhzknXZ8cMVIvKphJVfhvWbhoZbbgo3fygOr8PlKcVEYLTmUbjp8rTFlPRtPrdA8tljZ0REfnhwApCZ65Igtn1gBROJdPCCb9ZIqlQUq3W89ZjSgSnUz8nEBW03JCcQ%2FSxouQtYBE7ichYHbASPB5J1%2FzVfPINn38CqTMws1KKo%2FyvNXpDgBvCI08SBLSwX5Szp6WlpzoPEio8DsNzZQqgUMoq0%2BNMALrygrOlErJohXvIJ%2F0IqziZok6OuApG2eoGAEFsTc6yWf9eRcklYZRO3PkA%2BW8aN5ME8aLNC3Kdwtm64WyqytOEW1WQIg6bOFHAs1ovo3OfCvvZASxBcxg2SRCRTTJDBKajFH0LL1kSBroe6L9xvVxMCIFS7Zo6y4ZOEIkAI9QtGVIKKBlCvUUEq2S8FiR7VkJGbvBFaS6UzQV%2BayV6NKw62tbJbEjkJ7AMC17DK399DZr%2FtE5sUtaAPwaHNj9VCoVB4VAG0iNI9IjDo0mr97FWjYeDRSr3pstZTVZ6nj5Vp3IbssG3g0r9E2eSmHY97LYobIHPnEGkKDJP15WPWtwMgS8HeLd6NEnP4mIFlRJB4zLeDeoBElpUNRIQMzLbRQsGLj3DZhQytDQPLAUE9JWHiLKAJxVTOJNZsObEiutLqwox%2BM51t7vJQhBeQm2i5bTa4D8qIUjqI1sL9G6Vha6U6IPUabeCUMIufG%2F%2F6USyxymXNlfnVinkNvKZGUjz0EWKIgZEC4WkUEr%2BgP%2BFpkIPUVM%2BQEJPxFxKDlpBEeiUSaTgCm4sEfRnBxu0eF%2FLgLp0IFHMEt8aZrsQzTF3wRw8TdpzHNGMZrHHa6nU8KCQppWBVpJ8b2Xjq278mtoTfrgfAMSAm3cYEcDw%2BzcSsXTaUyd2PH6DCU0DdBiauxpsyoKlAh9IUAzS32X%2F0d3x7h4gL36Q5sC4YzkbttmSwhenP5pxxrBEEcoAohUGB1jHGtB2%2BI0FEcCM2y8EY1e2NPXJS7wp7APp46lC7gSit7FEFQCIDnNX%2F196lJs%2BgXTyJrXAze%2FQ%2B9GEFC7p5W7W5FEooh5QlO01B1OcYDH4VTmRSO8fHPjRo4FkT9GrnUZw9pXnDplu0YFyOaAsLOkxdsu2gfYJ%2BDzBNihSUutbDbDy7VKS3KwwUTChYQkWCmldyS%2Fy05EvDGbLbpkDfbbHNGEhTSiaAEDmoKTERX3pEuKYJT%2FlNS4IawkdDACC5Jk4hgNa2EGV8yblsXoSHDrqHDJs4JDXLLA67oCyia7LABDzBMInY5gnZCpFuyyuaWXjKHRgy409j2wNaI1bGlosUnhaB5iqHHZf2%2BbDPpsIwoahsWRL69BhhyPHQBUNCyBR%2BUJzMOBRMxTVhNsS0%2B1n5kboqmNZcC%2Bt6HYVscRaBeox%2Fvv3%2FxwdoUoRvs6CamykRGihd8HTNaL%2BxRuoo3QVZvFT7xS9opWLiNbQRti9PXbNg0J5WDsxaNesdmkYOcwpiqsURuor%2BS8hhgWniGGll%2BtD55enFSsyATy9MonNXUJV5Q7eAouzR%2FP4HlA5YWBYYjm6NDAEk%2Bx9On0MGm65%2FEm1GKndnRp4cv%2FjFAdM7hnXsaXnGKAeK4aE%2F6BhOq1%2FAWmgCMQ4zC8G0wBzhuMSeeVhgAl0jpwicoR64K%2BBYlYbWz0gTGAGtI3puhx3F5%2F5QKXradYFuhJPYWq3ITCUUNLpugdVBegedngCO%2FDN5mJBWzGEL932jL1YntvCgzzLeR0WczKl0J4g8Gcbo6p5LOslpYBw4kE3ZDMiHPjBGyAUjkrWXqYTrl7gRUwex0zKVekIhoI0dsFRDtQxsLctjOlRs5Y2gYpQeBORQFR4C3hXh8EnZUzuloqFLFATXzpzv6k5XoHB3fZLNUiPV5DvAfroLwH8tYhkcOxWnHqePfQulM0DCPTadXa%2FTpoHYkh9BeeAwpmi9Kbo%2Fm88ob17pFzKP2lJpfWxbyD3PccS041A0MQWUqNDHOPOFt57Q6TiHQEKJQsI4v1JqcD7Q3orOdsLvExRQwS0nlUtV%2FpQbx0wSlrRSneWAQ%2FJhPaT6rMvIcid93ahrnKe34YxGA3D30BI2lEiLGh5iAUkBLBoXRshvpjGJGLzGcvonIqzVubgEJv4mbEl7JggQUWb6AYaQMmGUXEwIjylByVLbgcEbUQL5RFiRkX%2FbOhzb9XavzmFpR0cOdx3etgoaCPbiL5gR8o0wJNdPMCYcmyP0SDmmsW8o0O%2Fy5HNRxqz3IhmaxvIT%2FxpN8Pj%2Bx4i%2FGawfTOod%2BpvHN9BMNQUKG555l7yAKsma4r8JwVm6gxqBECXHA3e%2BdCsONR41WrcokynYzYqB0xlCdg9Nu%2FVL4rgwOiTVy8K70TghEjAOHeFBYvGwmkrDIzlwQsyBsr3K%2FEBFKO8UQ6J1hU%2FYGFA8fGgTnBoNnAmZc%2B9S%2BXnpbpJ4QKgdip6TZnI6woSQaYgxGpdaxxSuQkxu0avCBOSwchx0SS0x5HgR6iz9AkbeoDFTnGInFfbnMUP%2B8p3uSQu2CVkHxeVeEMICXKlVDsj89UY7AfyMvxTyws7ilT3y4zYSLVToLb1IwDJHJ804mZKVSKgSOa0mHCQrjWQLaz9LrCiISicIHspx5moJ%2F3Ng4rjU8aMqTDl5gjJAFgAMupkEoWEalYEtHh8CvvMqUP0Ra4v6BRr%2BdgNXFRLo%2BjKWRhaP4x63qqfsnw%2B%2FlDAo2doOxQP7GwahE%2BiZ2OX2l5DodFT8bGiL0YglcRmgDoYIEaRBLqdbj7HFD8xfZz45H2Tg6upN9XjuGCxtdxuVuRjssowtewrBo9gBM3T7d77hNjF9rd8QTMjwGLx3oWax6aa17t0I2gcG6kcQgw%2FphwfsB4gsCGgMGZU8P%2BORAg4iQDmTP9XqByg%2BjtRqWtpaHjiHSL8CNah084UP6Rfjx8GU2eaRw4mH%2BA9Ad42DummqhAj0%2FeMNS3V8lEHUSc1ntW779KspgJUBtJQEpS59ODiBRghAyioIrf%2FKoPh0eAJWqxh%2BSurG%2F5xVdzZBLgKEiYy4izBuKhCRIhfoADEt%2FB%2BirTWSOZVkcfzWBbA%2FAO817phMFhWQnvTwzjirEna6JgrjgHyk%2B8nWk0k06KkkSOOryl2OrOta3U1Vai9SLWxcE3cY161P64wA5VUgBExRGc8qtWCkEHIJfWtybVbTm%2BhrXnbVSsP4ucJTC9onJ8N4r9lRaV%2Fc4a2SZKmPNCgEUW7LDBXhvFxUvZYtnIuGD61WQtIrDXUc13hksUbImFeHrIBgvnG4ogmKBk5CClzY5DWqCCMzdrHBGSSOTyO%2BE8inLMGSApQqKJB3XKm%2F%2FMFoO17KuwfwqcaqjAyaJ5JOWW1Myr%2Fs7ZQiw%2FPqVAMH%2BlK6DYqjGUMcAXnVxy6z6CDl6j5mDa5EG4j8C6pr0hKyRNnoiR1vl9DWOfC2%2BM4v44bO3onXro%2BNlz00ZlGV2ICJ5CcCazVCJHIETBqVFhGT7KSBdNDv5ILpSacSTL0S6fX%2FP1CEIT5NZgcwpI0rkV7LqiHRoLUAPjXEqCZ5mLntX5JkkUEelCsU2tXCMZ6cZivQI6hYQNbXl4nc1sSNtBUTkQ8Y442KxRe7Uy5yOzB%2BUlA3MhvFmXweJJj%2F%2BG%2BqwJFWFLNYp0JhzD%2B4sl4JYn6a%2FvShCCo8hchMfrswjZgUqiToKfoOGaHdIAnKE%2BiEedBkyLwMSoNVX83TJERFy8PLMpBjsXtBITuS9lpPCTVl%2Fvs2Aqzlzwnj7lpl6VX%2FQ74tWqUSzaKziwue1phr9BpcZjNc%2FGXuJUq15TMDnCsJS0X2wAtW5JQA5MaTyUH4cDRif3MDxZx3Se89P4WSIvEEeLjToJbXf%2FrRhBMtRevJxuf%2Fs8EK4woZOFli3yTq3xqjBjrlierqgmUpFA15HFkeUOuLDjuhzLDjAGKK6WThTynzd%2FtCQWU3Nzd8%2Fa0q%2FxwZYn%2FUPEHU6lsdG%2Fn8gN%2BYACURCU%2BW%2FldrXpdUK7WxhgWmNd%2BLDoQFCU0sFyHn3KqFz%2BIg9e2C5oe9EOi7KdTBxPE1k4k5tkX%2FoZzSih8DJDFd%2BrITKNSsAAGtscSW9VDasqkEamMzeUOhRqRRAj9lc%2B7An2YOdL46GdcOLAsgYaFsR9hs0ToUzZ9rFRLG1%2F7eYLz1l5ijumbe1druwaHNFzZjtwt%2Bn7paZaMZu2EIx%2BlxX%2F2SBrWJQQM0wmE19eWrGPIVMeAzVWWRp76m1skeqzaBBZ6So4eD8Gpv31bn%2BnO4WG%2FOg%2FB7xQu9kpA3eZL3w9cwzrZfMmbUSk5OiF3Qx7bQYhL0VoUQQP3Hmps1bBJDQU0qAJpWwnb4iFOMBthzwP81ilVnh1bIuk0C9Xe%2B1kSK%2FK%2FMYOh7vIa7ghqGEKzCiHrehs3cA1pzgec5NpLDtCqMPp1GVECA4QZKiZX0hQ%2FGqjOhhObmpH0vwx0WHctTrmGC84Ub6BOQVdXIqoxqMVNhQjd%2B0CYMRk1zp6%2FPnIOsW7k%2BhQEruSB7%2BDEGv1lziYEEwur9gsigQBG8EJ3zeigdA74WvD0h2IbdNzPw3okAFsdmi3qKH41lxijDPDtvZcEzXwUnRNn4WpCP3vPNHhO8czUiYjRn6GRIQ4SymW9MhjUu%2FjbWOhOqNeh9OSRNh1EaFZcalxYnJMLCpTlEDttzYxhNCcZ86PUpbEkFmkU5HRiAF9AahZ8jQ769NUzJGkq9YLj9HWZLEK%2BIryXyPLZHAAHsBSe5moWq2RRQT655EACRYKQ1AR2mNQU6hNdrqLyfpSeJ5PrccWf3dXVxmIidWyyIkCm1v1oFYgyGcXwrCRPXPMLYHRFvGVuYyAUhzgYntJmDhE4VCNKoSxWlSFiFuMGumZHJnCJyAdT%2BHcgUhIEJZgWOjmy%2FZWisKRYo4JjuPntQGSxlJg3XJ5DBsFM3kGGQjqECyjqYFW6ccOkCDy3EhUt6taBIaHclyblt3Xxs353KjciddJC7gI4cZaPj4zBxOVNfei9MW9cLUHspngSFFni3AL7sLsbqo3totPWMf0yD2bdWKWuK2a4eF5jKcYCZyvORgcHeETtqeqhAYtIyiFGAYJQMOX4VJzPjyDKiavWJRtjWCnaTUdPR6FRkTfFAARc%2B3Ln3oFDETvxxiaDT3roIpaWwEEjRlhjvs9ZKiU2o1HJOMuVRFAA5fWzMgAqSKDaSLNQcQZbaFzsgKxXWIvjaOqsoiS0kA1bx2TN4NrJBVMJcmRWNIqrjepyobiDa9yXYKmJvgQWZhEBvF8wEx0pMQqCzcf9JemmcVMROHpvzARvqDrI5WOXmah0Zkh%2B6PIozOYm78ieRAJObTPxSCLuwQiRlWaLSxJ5hgIIrjsEUKPWMqRFuMgh1FsNDFw5jPGKPTWzyiSEIS6IPweoS55G9SZZ6wcpW%2BHYMSkoSETssJ3QQM9bQRo1JdCQdS1np6F5WR9wujL48y6xB4M6Y7FMnX7f22J6F5gyYVK0QlgEQxVXFUuC964aXqqhT5cX8XaZv5hVzeTU7bSoy40QuTOUjLVShZVi5iWJMAbFhku4I5QyO1x0Pm%2BcTesk5XsjnzwoAIsmXCFbWyIZQuawuVuXuR8DK92lWJDrNrbFtNbCj5sTTCx7e2Wb0UydtbZ%2F0CSIT7OnVIY0fRntwN3jNOdh7nGpmmq9CbBm1uLafMptryjPHHp7tKZEuAL7OjXHySRjUkXydXE%2BSrNxA2Um5Al8Dh3EZC5V4SFynxsMTKCAIMqEqNKzuJCQM3tvVBjRfT7xHSuCO72a6pSo2wn8%2FaXBmKI9sW2BccnPF8aML8%2Bohhb7ciWy%2Fxch7McUey2CPGCyVKaKDX8%2BSBNgqZqMLbCgZ4NPkIF6NfeH7gtElIrOqbudNqU8yyyewVZneKXuzObCUxU5b2tecTa7Jn7SE6UlSYMTkMfCs3Q7fVCaqK5k6pf%2F2wW%2F%2FosIJEgW3skUNgQHyb%2BsAi4ECMNcyjicIITuXGyXkkRIXyN50Jg3r1FpMmaQSUl9pGxnNMp8jf24vS5eX%2FyJYlMUISy9e6JooaPn7fiuG4QEscaDgCZpra85ygZUcJInLV6C314YPJdG2UL6qljb1QV1ZhrNcFecs0j2qb5gvkKHOFxHphwdBgEJc3y8qihwdZCQpJ62h1voTUccffiDJEzkU8jbXRvLFdPbcmVBUhArEuTKQFe570IglefLJ30dWmVAtx23vIGmF7N6auVNpZUQVHNmAlGgjNet51laJgLQSpfZyxBEqLwoCEGlF6ISp0NUmhUHT5a12%2FZElPE5UGO9AVnKpae3D5llVvXAHK0FIH3uE8AXFPF%2FSwx3OdCN6aViMIxEVcqv1UsLJo03R0grVOHRogBCiYVrK%2FqFBPmGrDyXLgr6DxBMSKnf1uSBgiRjCiwL1QoM981XHZnusKIVJ405xXwx6aBUuM2YXS0jaCPwaC2Zdp07J8gxM1KnpWMqNkRCfPaYTelLbBmd97YeNAOFaFOlV29YQn0qIInmIeMFxikX4U1pEusNYtOX6tKZKkgVONLhIF9ZPpxNWGA5fslgv82Lx49LF6FltT3Qp7q4Rcu4hKczn2m1GTA4unFBAkDCqY%2BD5s2dyVIfDeGeBgwDnPbGYXQqwM0E%2FBCl69QtVYbUwkQZAtSB618VkAzCPAs93Ip9qWnBMX%2BRUX0PFxCACZQwpfvNvAMGrD69RE6JSyNNOvRD8v3gzw46n8oB8mFQiYiwgidAyJgrfY1SaNrL5QDEjIIgYLECALRSK49wPmSO8jZLnesJ8oFZjWJyTrfBrNRCeOMoaVxOojhX6WVAhhJ6dvBPZ4AbMXC37YPHBFKUgA7t8mCk8U07mFgWg3wbOZDgsEZBOC%2FApoLJL8CzpXhZuoEh0ntzsvrUPhE40%2BUUkCAm26DxGMecbxuJ%2BpPJct9BZL6lVcWLmHps6SSRwqmRJeypwrd3vNnIawZkqBn%2BumMrwB%2Fc%2F%2BIAx3X3Ehjr%2FzJIVUK8Luxlv5IeNoBQPnIhvJDZfGo8KIpLh8O4iRusiuQOtytJ61yCvQUGFmCxEEliRlQmkN5wrVLkx3CUrlDG3ABe14lLsoysj9hXY%2FTJpUb3tCvpw8Ez7Wb20lfIScq%2F6wGFIrBkgzjtNLBoYqAgJoKvDHcdkTRNbmMKP7Fha6v0z1qPmfqQzKW%2Bi9g0kSWn1vQuqaJyaTAX7hrJwJgfaPjJvb6wXroHZRAD5NH2E2KaWtBw9TGd9mIea9%2BPrxlBpyjnQhg6rLogT7414uB8V19PPcBZUhnNx%2Fhr1%2BtX%2F1jKG4nnHv2EtR3VOjo9oWEibOHX8508Tra1pFg4lnAwoQ2u2JcsDi7diOc%2Fuhvd8qexCvYgoI6m9VYAg7Vl0PRXiuCdgHdKDIIHG0j73u07ULaOJJ60Y178Lsgtd7zYdOUGlMCYhR2Uhyglt3KUjPCpM8MKjVS6OgTHgqD1SSQ%2BqhzInvSk8VzFP1cI8vLxtdDvH35uPzMi4mQtJCrVUqhBWbQRke9Sjt9laOS7Y9YqUkXldpaAoofKmUNsx0D7eYLc%2BspsKbO71qdmmaGglAJjo4kj6XlnIWS1hQZORC%2Fh4i%2FCdcbaFFuxb0AYMJ7EdQExeIHEdPRtCxVECvtxHzbEyldDvie9cGKwImUuI0xEL9W%2B7FhefRJ05ePV427NJDYmhOCwBDNBP5LrIiblD6aw7pSeWFA%2FlDoWYMm%2BZEcF16VDdA3ALq7hDBlLUzNTL9AoVyczpCwb7xHucfK6T64W0AnDzYLg0HYWadOMPdoP%2FlliqiAlAWP4rio6stY02elA%2FLOPBnEr6rsAqtMRsS2g6HJtNANGwpxTdZEP5Xt5mE2gPs7dE0gyQv%2BFdd8QTBEqmAeMHtNnjBtKvg7KmIZCj9303GT9mWQXZxolBupTHIhJT%2B0WN7uu1FbRp6NnSRDIj1aXRJD25s%2FAD5%2FIc2OQ0iDp7%2F3Dbv3Oa8McFBhXrPpAKzhrFANFpcnLmEVTXjq0MO5sJAPVxFbMh93VfD%2FDppG4mBzXSgVfXJeWNNxoKF8QTwcQFXwQPB7QLE2PENdF%2F4vDL9zn0ZgQ1i6Nyr35KAwetsESlUtEENQbCFOeUFb9lg4Q%2FW5lUNo2fsscGbWY4T6pynpP0Krl%2BJFEfY%2BGGHI3aj8aBO0K%2FndAEPa5FX51Niq7CEOhoB2hSpGAgBT5%2BuzxAMzSuVEeQmZIbYew4tM4UsHYdZtT%2F9ibeR1u6rlX6lBQlSBT6LQ9YUEAgmAxLkAKQL9PSm%2F3NQF4w7ZvdqRqMJ5DYZh3j3I1rDmyXyti0llyZjkLnoPtHHwZd%2BfwWVz9ztMTMCFjpKBVWAdcYhRV6wY0kRv47nqddzEhQvXsqNstE6GNiLTd8iliAfI5vfm2KjfTQwbxz2uFzaUL0A9VKWzB7VrWS0QvlqVw9VCXOB6RDSgbNtW05PB7pEz%2FHZkv%2BXQTUD8mSQ3mJk%2FEUe3%2F2ixiuDVAOBfyIscXixo%2Frh7BHtl5KSBigokAOClzsk5pj5X5S9GLKgu5AbQNotZBWU5zoIzScHQKjeFNr0MiX0Mp%2BlHmHKuaDcfU2H%2F%2FhJmeXl1nPE8E%2Fk8ULZ%2FCLYtwJWG4CHI8h5ySfOAFbkRVFJyi0KYkQ%2FfhDAjciw08ETminGEMzG%2FwOERVW0AEslZEl4sfr5AYRKGVSMzkDSQ%2FnhkipQ8y8EF9ZRGKCOxRzfpNMubhc%2Bs9lZBHqNegMaRLozjP%2Bg8CUsaVzp5Wd0ZI5SaeSdNAD1HIc0vTPw7SLi4Jpa1gGgtEY865KzsFCn6I1RvKT3r6BVdYGsBPWdVzeLfbxKi8BVQVzd9f83goEpezGYiAXT8S%2FpW37TxnZ6f%2Bog248Vvj9otMMAmzAfvCpPYmXIx6ZAKAwBH737j2KJpWyuPMnFIBfqRrEBYiTiWVjvdx5T7k0dwFjgjLrLVm28m%2FXLNfjkSOzkoh3hangxcOjvxzVDIGSxYBxI9ak%2BDsrVTPAlMWZD7OThbuxdkWjCtGwtH4QzQdpmaBsHV8Yrg1iAK64TC2WMbQJAFFciwJFPSRQ86OtUrnPSODgRE%2FhD4qWAHDlWBDy1KPUCEbpodyyU4TnaIFzU%2BtJJAes10UhzVTNE6fGDGUN33wCgAZYoIz44wHk6fDO7wGbAEwmcVQg1HGGByFjYGdwJD1joA5AM8olCJ1LlTUGB%2FkhlrmbJbCbD4Uj0GZTy06zCTjOeVkJKZzqk0nE4%2B3RmlgDHpJwCwwZKOl8SEEH%2B8TQK7R6TiIPAy%2F5M08zWqjpXg4KWF%2FMNQpvipZJ4KZfGsivxCXyinAY16NjMD%2Bkgtfc8pGpAgSyN27l%2BuW3OjjOCnldBAJLpzjAYdBoh3b0yuYkLj7c6iwxR9NHW9WBeGVYjvaTEHVq2t96HA16vtKz3ZgtNuJgpjnNZRGEjbmQuiQEBJ2BdgHxFDqf0F1dQJcS4sIvDHlCASh65anDjgr07Qa98i8Jb1rAbSdOOz0cM9wlKXm7ugzOMTmi8EbeTBIoHrudZ9RSwEtADDsgyFwnxyfzXecDzLGFMmkRrGWCdnWVnSMMcc4cl7NaF%2Bt%2BgTHgy9s1SV05LLaw7YYgNxNKt2kpiEgpahsRp%2F0tarSWdfS3AdWYIAnrT68WTwX1vtPgL7EMcIzmOpOxL7oZh%2BcoizveUkJVmjedZZlkwkno1E7%2BjVJIoz8APlgweTgHUL%2FGciPnYgtfrq4TtxE4adiNQ2ZgyJHWYWmrMlW6ebFkPaAUwzKsAhoGXxXGRGKFObHKQOqFOnGgkyLajFNwhUa4fqva2yI2DlREZ6p0CNOGwR4XCrS4K3SH8pDQGvZIaDcMPtJUoLRM0owJ0iT9QcdRK5IRIIpAOzsUotf3TbAVvbQH8n5voA6kUzw6LqVnwPkmnYxgJLYiQ7hQYlEsYL4k5GCZvd58ypvZfwO5t1fXMTj11hE8GuAZhX78JMPsK%2FJ0yQqDPk4Z%2BJbbXS1tia0EBwCjgWUJsUI18clQ%2FL%2FH39pOY3jRcZFFfW7bC5v98f%2FwQjY4osg1CYvJfYmKVWbiPLEpDvlTEGLjUbCmh74cUqKtsVVr9hspTEdTKrKSRjqUHkhwgyUQeNizUsUp3cLmGnOoxPIuzYDGHFJxwclXasrQR5Pp4%2FX9adXL0zyaNRcp5govE%2FKIiLMyYKgb3rKpuxbm7t8U7GiR8qcIvzarYOuIr0z8D1QgHRWmwQj1jMXJlqpuDBiVUQD80Fnr7quJElyEAs47dXRoms8yEtA2VxNaYbJmUQ%2BlsKhyYLlk2q0QMXQSsMctgTGOka0GYae3AxbsfNKwpf8YnyAYZzDx20P8xvD5aQ4OkVtbj%2FxeO9gPGmBGT3BiXsWroQWXXm7Mw8XnUAttSlNyNEsqWLOLy5MJLmMa9m7Abm0SuOJF9RWA8VDp5xMP%2FI6LaAKUmkqt6%2Fxs1XyWrgcDo5hjmvshCSHhWpwCludJPk5i4bpUhqFw%2BRmK4VAI0O11JGtbQ64l%2By0LUdW7GarSUUwP47kW2CYDEdvsA%2FTXw0I4OPf2SvJ0FKjn7aZxxwVp2xjhpWMTDxaZMkiOxaFFLydXq16fefH2qVJybgXVpioGcKFhS4AjNTEa5T37zLo22Qv1apFD5qUjB9w8yA0vSc2RMiA7xIsRIGBTWOjHH9SZlK6QJGru7w%2Bq2GoC22fzSVxkr%2BWlT9rAABqi3RiLzDxns68PSEUIxqrqFw8oMUhIeL%2BSdTHAvw8M5C0b29DIIfScMQS0TKwvPEq3kAQvxMTC0D%2B7tnB7QNRliGiWl0NuVnIUY7QMwRkP%2FgVT2WDBX08%2BWLDc%2BiW0mz2CZ3IAvWqesMkIe3lWqMvUDL5Q33McSklm9U0P9SHgPGGVlX40diyUBp%2BpfWi%2Fh9sKTOvCRCj%2BeFIWvUXobOyAxrHlhKFGhOiw1vyKUiNMgN3k9No1jfVEpQEU35laY1OWX0u%2BF%2F3AcQq9opTw0RwEyxVosGLDgIkSdn7hs%2BIHrv2jR%2BJUh2kADlTtyX8HxtYxLUhMLj3%2FFpJAhfxzoRspqTIQicEOQtI1iv4n0xi9GiED3BLS%2BriXyEyU1v6KfcGAWCaq%2FgQ5kImR6wna1UoP6KtpT9XLI4EebxCRQriHsHkCOgDlZM8hJEHcCfGIERpbdeZFFHtE3lBjH2cIQ5p7TNVfRTwBkflxGhmddY2YfZbMF0bFv76YdpbH3dxSKmVVsblZW2xdvOxqSIQqbw536IFhJKhELagVlMG3LQqHU43e%2BRZjx4BJExsLcLX1oShyJFvhFevBTFOUF3Exw2yNBWwMCW5QA%2B7UmPhwM7AMOXRddPg6OxuaNAmiDYRKlcHo4FwUeta%2FLSOUyYddHQi3OFkb4z35KYG%2BxJAOZ8LTMyJ%2FCgARNXgZ%2FC%2BfKy8W7VwKPzhRpDnIMS0ClN6sPMpa421GfuLwApsG0C4FpdCsaLM0NtsctgKVSkeql94%2BXUmITqdGBajY4m4NmDFEk2HiFjYtNLmZSxNY1QM4KakEvIQIFts%2BOJ0QCRKvhCmQcfRAjN4VmYgb58CBUMiSUpcyuEfRFZS0SThDQPO4aFcJiNJUvlFdO0s8RaeSdpOSHscV%2BJAIchHCniOYTsEqhdLZtWVSiAw6hEDO7LI4or%2BkXTE6zB0CA9o4NUeZIpS0Fn7Q8lU4X9sZNCO0uPfjoWqOaqDVcbxBJSAs8RV9IcKmOPY%2FLDUnXDLSYfFT1xyNTWRLKRW0kFxctTIuvuU6R%2F%2F9ApGo0EHrqGH7dEmsI%2BgA0UXpfNwWFxUHz9CYK9toXP6IlTFRhuGh7z3bhwL5qzzoRAF8dkwboKslbtuftJDrshFMoBNaIvpSXyAFRCCKFOKEkzCkB3NDQB2qtV%2FANwHNea%2BAUDr6WWScJazRx4glReLp8vQ8sFtz%2BhqehlJO6MM5NMIc8%2FkWnwbE768NqCDKBi%2BpY288WynJ1CT2Kq9%2Bo6rhlUpRatsVIsacJhA%2Bq8M9X%2FUQMRZhshDAvsXwlZfxDn%2BWCfyJyLFA2s54on9a54PSC3WgchQWZLZgEl8xGrFUqCofhkvqYtXjA9XyewEKlnAtD26XlSvqtdhXOT%2Bi5BrX6S6hHVcqbZ8qbh%2BEzffUpCfTG5HULg9DQQaQ6ENkkq3DvUSAQwcxh24qkmtdRoIMDxOVDdy0T2w3afIBABsQGxnqJaAOY1gWawTUuhslaSivzrXaPcRyGhUkH0R541UbNvLKiY070WOGYY4R%2Fg1rfKhHcjoV4p45o21tldJXmpG0XsOekNlYucZlkAWkVUHtjkSeUbdfBMKrGATodKL9arS5JFVovaKRlOWbNJIS6GHxuLt8CX%2BBYQUYDfxlQrQQhL7z6vChINNDAVVN0rXxaKjegwIPOJf7rXfHBBHGpBNpaeLGFXCBrWAdpIwF6mcE7pE2z4HdIxcWkU0QA3TKjZQ%2FPNc4VwWA3OqmXuoleHBmMRLsU3ENaVgIpVnIWvBLVDYNLzVRdIefms6mo9VoRQZ6cCkQSOdK6KDkWEMZkQyIEM6QUltTfQ1xFpxUL%2FlFho06RSy0MhFQCIEh5FrTToDEgpZU4QcT%2FbYWc9XTPTTM6ZiDUk6d8MHSyFOgi3cE0Oh5I2Tv%2FThHNjxg7A0zwweFEVnLpE0Ai8Q9OWYcdB1zNKwLEBtxjc45oHjshf4AMMWhwwRhRdBnjEU3iL3TIBscW%2BvDMwGJnW4omSkqIJxsnzyWwZBg9lQCsSZ2CK25fZnAEtrlT3WZ1xUoqsQBEiEMEmCZAjQU8EhBFQSUEVhPwf6Ekh%2FQtYOKDqg%2BIfAJ8CuQ0oaANSG8CrQwYJAhV0I0D4CXAOIBYAcYMUAO4A0AuQNYAwAWgJAFzA9A0AgoIOA4ARIJoBoDTBVATUAVA%2FfmHjl6j%2B5Pa79V8K%2Fh%2FQfNvjz3P7ler%2FHHh7%2FPw68DnObsHvPy3cPPA5wScuOB7qBzX7b8SuTXQrw88fvOjUnszbV9wOhztB3%2FrTR53FdNbVFq3a82jxmuwOcluBDDjLjOjALxD3%2FQiP39Y7a5Rt0oNm7G8V06nAGXPxV3oiByNCcCfXAkpuOrai62YwdiJaYXnQsst7b8q9lbH1AhQ4fOtIhMRO4kqFfJyARyYkd8VFgh1WJGVCPlA1ZMLjSDAUNwDDwkxBcqWcEmILYipcSYqtCIqlSAqaSAqUCAqkjRU7GCpiLFSkWKlAgVIxAqRiBUbDiowGFRIMKhYMKgwKK%2FYQV6wYrughXTBCtmAFaz8VpvxWa%2FFYZ4KujsVY%2FYqr%2BxVOdCp85FSnwKkjcVGeoqJdRUiaio4xFRNgKhy0VBtoqArRXysFeyurkV1bquqMqqonq%2Bh2r6EqfoBp%2B%2BNH3jn%2B5M325m%2BzqX6%2F4%2FrnjqtKKqtoqqPhqmWGqVIapJgqjWCqxYKqxeqp52qenapVdqkp2qOnaopdqh52qD3Krsbqs1uquGqqZaqmdqqVmapLZqjdgLkVYtPvgiXswWobLWxLWyK0qjACE6uL48VDS74vVFa9UPr1QitVAa1XzWq9S1XKUq4ilUQUqiClULTqhKdUDSqgSNX2hV9IVfB9XkdViTasMbVeTarwZVcjKrSZVYS6q6XVVS6qiXVCBdUEF1fgqrzlVcsqrdlVbQqrWk%2FZ8n7Vkfagj7JEfYwf7Cj%2FXof68j%2FW8f62DVWIarWDVaUarJi1YsWrEC1XsWq9C1XQWq1iVWMSqMBKoqEqhgSqBxKvuJV7g6ucHVwg6tqHVrg6vuFV6QquaFVwQatsDVrwatGDVnAascDVhwaoyAqiv9UO%2FqhL9UBfq%2Bn6vB%2F7kf%2B2%2Fx%2BtVskpnZEuCUxHC%2B%2FYuIK6NNKiB%2FEzIk5DuCcPq3Ew4GQBYmlqw4gUEVMiRKFQwiMCQcTgAAAAEDAWAAF5ItM%2B6bFophhd3N1nA4zIKh4NARWfqCooqII1UQ4kBYJ2AQsGyO2lAp%2B6Xiqm%2FCkfz5urtugisAuGzxeNHQh1A73Vh4dG0%2FNDkMQKtSO%2FBTm3DojsrbF3jq6kKdaxNxwpKvTKteAnM7mCQ85oHLN5AxXtO3FxElkoEAFo%2FY7SdVN5daSO3q5Y%2BGBpQAYrusw%2BEY9%2FXMmtYT6y1gbXXfGgzHr8ic6AN9hcvHwEYELAoOjr69A1yaVwaL10euR3YUZUZvQl5xZXoT8YeJLqVKFrjMLT83%2BN7REmekLvRn0TLh%2BZMzVhvjjOcuh7%2FDGoSIzhcgAEyu2q%2BgmErl0SoAyvHf0gOOHkuRFZQSSkFEDSG4HCQ838BiFt5mdNnEIYrPlCai6RoN3%2FEdkyVeA7o%2BHQvKrwD5CYAAo2RJZIeP1xRy%2BlJaWuskPYYJ6GKPdNLzxcio1M6LXHJDQbx0X1i8B9YnLsQYerRiZjBe%2B2alDqaPIASEoxCeRAUMVuivCDeCNo43Zi%2BmFz4viBZJrJMbwxhXGCv8CUG%2BGpgMJzaVH06hQdq5woKyZyKYDByVHQl05OpUqPrSuURMAxrqnfhkQENbMcnlzk3MLnnmnljBLSdHj488rWP0ithH7Mq2sETRkNmgYc3AZSu6gL%2BCk4IDhPtHR057XmxZs5enMzU7IR5rdjAD5bhqPdP0mGax4%2FBC5rzvd%2FNmFSTuivxBTftCPQY%2BTO9CX4l81r5FFfrkZ1yrTcaX%2FE35vu8hUNdTlgS2iD0q93mIVdiG%2Bzz6ZoWvKaG4axR1VGYKVxYs%2FhBQR7uUeb1mU%2F3VlT93WVa55WteFObdmuaGauUaHua6G9Lh9q5rpXj%2Fwgi3iPylvEGKzMA9lXBUjbXHUnsEBSjM4dcmrgF7f0G9lwAQt4RLmuXHbpvBr9TrIO6tAdGiB79MRHozMKD8uVjof8zfR8GjzHzhs033A2Py%2BejgTAGqXxpL%2Fw2WVk1lFTJqz%2B0kBeRuJCR5GRySPRQSmUbztwmWP9kLIWUhlXSVdCicr0AP0QVDW1JhE6NsgqFunp2ST1zYgQv6Qnhu0JlL3GiFmbc6pMG8mzBJsoRi1z3Z5NTM0f1ndDFjciaUQqsNg6uq3fi8dFUAWOQGs1VX8XhLFmvpemPhnFsJ4EJp5fMYZskQQsqfYfD9NJDx6cnQ0sTSuR2L40hG49L%2Bmrs3RoC4ghACgxYOijJAtdpXHsZI0rFXEhZYgy3algwWFOreuLxQmZTNtXroCxgYhVHmAFChJHArQSkYK5FLPlzPCQ38AzsClDlC4ke6RHiVatgqkBMcH5a6mGXLmcLkgxPCTi3IYssNhBMcXoBJkK7iAw%2Fq1cJO27YZZ1dgwK6TjpcOKZhOClzaJK94ByvnLhalwYwwHNGbpKKrMoiCUVvMU4ZDGftbGmdYDSWM4M18njvCm2QeRAFzCJCDrWq4fFsh0jgTZAy3lAyEhSb5p3l%2B7zqhPPDOxcQ5kAj9XEi83gCXQDB330FUKUN8JTMzokg%2BMHLm0D3oGAQxNjGVUC4qv3mNqY80W8Wma%2FG%2FRJg0Pokw2Jil%2BUKRbOEH5QZhYg2DwSICOBEYPtpPgwWBiCfyJ0YCZEGKrTfFYsGyw21JEW52PoQdw%2BSszylGkFpTMhd5kWLnyTIQai8z1ij55qLRN75GxKwkR9E1MhMUCbxmmw63GyiBjM6OORqQNMpJtSUqMssFsiIRB3bOWC1CBqx4cDrtymvUnfKZrNpclIQX0iC%2B0SWyKIyxEbrSIkB4ly9nnkYJu6fFFI5UkewzCvGzpCmkfDtvWO%2FvOQBSKG%2Bw4WeOGpxQqahRKjqs7pmglalwleZKZnV2WNi1CBepIZxSes8CtZlRI0GWmUOVcYlpvK0hUKRhWaEyXRViM53DzUU8eSDYlUJQqDMmUYc2RuqTtfeLBUIWJzpxmeJiOCoAXmCaSqYM441iWisAz0U0EQHSkfuH3o9uyZk0g2C%2B1Huy1lYiCySpvTSdjMKwTiKdQVCax08qRNA1Vs2wiL2IyamfCpg%2BlZwXvOo2sB1QB9xIdX6Bj6VzRgd4UoHI9PenyASnBA8k3EjsE1S2ipMclWB3LPJeWzSHOikaNhJsFX5zoP45XxaGhDK%2FpP%2FDIYztiS6vqMPACWcgP2kpZWjOfJBHYRQNI4tp3WUQGWpE8ZqoZEFvYJaFCTkUJPdTqeYpTEWodgDyHYOTQomy%2BoKxLC8BtejPZfK3UMphCswZ56qASoLK60kVHNgZzT8%2FUWcqkls5xsQ57uoPAv%2BCrnsSCROispXTpjPKe4ND10sCf%2FElOsPgR%2B1K2fX5hXhJfNGzBWCpiE4EjGUBgUzovLqcdL1yqDbTutsHLe0YqwBcpKRqcP65%2Fvot41Eg4cbCrFOObHzAyIO3Np2zmgIymMqrmdZ5wgWiOKMA%2BV8kKigbnyOAnZzQK1uMozbw7SBWI%2BtpCLJTN4yFsnUOPsR6JqbOmnG2mYUYJV0I%2FFDRgDb2YBKeBAzYRv4d82IRIjKNJ6kthxBIf4f6tg5Rewqogb%2BBL89Xa%2B8Y7mPwJBnr%2BqyHL3Me2KqwE3nnFG4NiIrI%2BvziW0DG1jcQIHeE7iAwtWHXwFYTmEfOmdkgt4Pr1n2vJwnSQymN0R0NLRTgAHjDVSIfITmkvlvfz7MU64jgUChw5hCXsYgG5gXyQ1osKsxCRhvPgsghFG5zI1CXCBIAwKkjyKLeqBc%2BfEUFiCvfGOta3gR%2FU26my2RNT0gywIO1yxoMTM8Mlfo35lpDQ4jfU18jrE6QiUumMhcIndvm8iDoRH8amzyNFXNUKcSoCnh5AQYr61lElsUE1avUS%2FnDanpv%2FZFa3VH2JIq5OrC2wDxYOhlkZf5BIr5I2JAQY6MewSa45Ob%2Fz%2BEjpgzBQTlG3AdVyHpu2GkjfOxGCSQ%2F4KBsYV%2B4WC4tHrRIEC3vi8DWBGimk2R3cQNWDhpiR%2F9KQZE618tdreVPJR485XjWwoxbnLTn6IZ4EjKrhRraDtVPs4zmDldmpWjWM5L49vWKkkh21cJ0GELX5Hje5CUKJRNkIrvqKWk7bw8b7gYD5fEhk7VgXTHJI0UsIqSimKH3B62CsLrQWXjB%2BoWwKWamDL6RlAYnwhMdNFIyfJuJIyo2hI%2BRLmmREVRHaJdaBYDQo4bDl91nK3Zy7hcqlXpT%2F%2Fg9jl5m4DVYTnaJOAoq7zjeA2QzPBWRnDjFKG0AJybS5gxwd7oVkVIAgAqWCrwA2TuCFHn%2BlQSxm2EjeZemixAGSIByWTtmzQII9iyI6QxlnEfiedIUpRSDRKzAojtJx830mEfqcSqHRLRGSUmpIkhYAfBXhL3h5wRAsiPnzp5CmVoG70nYHSlNBsQEOYNSYjVHmDMCMyyhMYHx%2BhksBoo78rYc0vB6ClzXpCsSQL0xuJj0lRG0q3NgS%2FgqHn0%2FdBeijOqfmwAAwNmamjdRSr62goEAhLCK0y0mqVtUz8B3F3pE3rUMN9wQoCtpvRAK3sFTK3tcPZnt6Z27rdUEcCb1qxd8b7ARVibBsC%2BRyonUCCcPwOsBKtzEuPpnx3V%2BQo3FTRLkQsTzT%2Ft5GIpAhUffp2BuiRh%2FhQEK3waIhUoJczWFQtyb9PP5ODdKmoRpQHa5EoivjH117VzUEyb9h3I8XFhYGY2GgobkujLXrm%2BbJ7HN7wo3WOTojHaQLy5ZQH60wuIar4h1s2FLyTWktIBtEtP6PSDSPebTewSizvHczvHVzmh8LyM8TZbIzBlYhsTDRW2MtA4tMbKoUyaOwZNB0EcTEoAHayrWzlDMhazQx8GCH8%2BXZI4EskKhE1hnN1De89ZjtBMDWCxLN%2B4bn5AnyOhxpTflbO1kA%2F0EGhUliSyMtWT0UMb%2BqEPinhEFYyHEbAUyp8tYuqYDRipLscjyGJA3zbmKdMTMI8V72v2ZQ8R1C6WY4qjndJBCREkDNEWaBovAOsU5JvqCCECn5%2Fq30Wfs27Q1NX3PB1CxWQYK4rW6gl2DEhCJLvzF%2BBEKd6LLjbQMVuJRpueMSQJN40n5WAdyXvuYQeXUQsoQDysgsemARNTywlJKFiZeuo5AiRfoTmTq6hkwijNVgqADIQ0SrFqBN9hK5RC2ZzmikwVm0CCZzso1HPlzzN0IP%2BQCIQZ%2FzA%2FJ0cIiWLfbbDGvgdRmzRiOWxhWyl0iXpQfpiIVEQuNo2d7rj0OZeiVOeay6x5oKlhwZCcg8KUij44xrtH4IX6UM37YQMPXh6QvlpkcPG94IVYA3iwMJ0L8LuuaogTlCTJsenAjL0PQ7L1Pi8hmGJdOqOJeVo7ECLPOdRHN4hjBEITrTPlmX622a1Nkllc%2BXvRIMYsWFbQasKlkKf0TuoImAklJXaN12zLmMOiREi6fUAhVGrkOVhqjWR2eenOmgUY7GLcmbnAqWpftrThQTmBEC5zJQBDcPyN4AoXnInBM2UJew3uo3Osd%2FVotG4JFRB1dhhN21R2ig74o0Jw8ZN7VnA0lWB0lDFZ5PDtyXLlhvcGuF0S6Wr3U2CqpnkDjwB2mBQdPGRuL5x1IFo03LEOsHEh4MKRvE9edrMh0Snaf0cShzOIJdiyBYWbPeMzTRGYsBHAfX1laNIl1ZVxzggRI5m51PG59wVXQ7HQGRYRRnGy89qI5okgmTpsdUtZgAxPvJ%2BWcqIbSWx6tf5yN%2BuJkKOjROUqEcP4y6WDOTFIXxQkr7hHBS7hmbyT8AljD77OJLW4CQGYAb2Pei8bNTgMttEECV0uBXZksyIJd39JlDyojumSt0T2TM5i3S2cviYGBEzQtmTKzAFB8QEcXkZq4YmJ%2FTDWdYLS0Kazft%2Ba%2BC7HwNZAJaVEY3kfqPHY2SBxchsqiGqJLn5a%2FmjEa8QgmABHka5AWJ%2BG18I9w3WjonkFGEwKhtgp0j8YPUeYEncSOLNhhwVJnEiplRLfp6lN7z5sAMYVx%2BA71pVUbhIzIaaNzUbAarADBLMSSoXqKXumbEiOijClhzIbSA5gGB2qtutd0MjQyWBaAzJmfqki7MMaEQAOCXbwaXixbEGNFnHCqmWg8qjxIZ0oeAxEPKD1A7yRyuS4AIRwOrQJ6DxVHVPpAc7aCkBHpHC9GMxBqe9Qt62zjQGJy9sjB5efNIj3%2Fyv2r6z%2BSM%2FzuFE0Za9wOJ37u2rcQy7P6SvWZqCuEJKtvjAUTarXAcyMQRrVOlrD0HTsN2KDiUivtwmgj5U9inAK5%2BC4IqL6iNHhU7OH98NmQILOWxNwJiPwwlnf1QAEMLagJuGLoYUdlqBRDn8QcRAg9AuLhe4myJn%2FEoxUfepXHhZ8Dk0BKaH6znSNmNJJ2IRdEvxoTfFkKRNn0cEvSio6ek7OG7AZljFvqNgRejEgmOwp7toPkAA5CIjKCsWmYPIlxUdVDDj%2F6UQEQZwMPCocey9jKiCj3ryLWjtGlYDdewTAsT%2FytgOZKoG6jDf%2FUzE%2BDBwpk%2FPx891KJ7wcBDtmnc4Zr8PjCwfnWIE7Tz5Wh4XZOloMKHqBYibF9JCb589kpcmC6MqgAH60Nwcj%2BnBhrvYWI7IfIiYD0IThyXLu%2BBjtuDKdFNmVqTy8ELHUIcco0LUngtn%2BupQtGnWSsHRGOZxzUOEULCoCKWugvY7ZqEN0MDmNHqeKtmwQSOPoR%2FZ0KGKGB2wdDYj1CrZsodEQx2TukU2%2FMDvJyPuRgDSC80jH6IO5DbRYrS0rIXfANDM017avY1V29khrSTX8G%2FWm5HQ3j02j1MGoCeHm%2B25tIebrUOsJUhfKhD0EIEeXj3%2B%2BQ43UQYWPGKRd5tI6RgOxzkuNv3Ji8YcsIU0cpJNn1rQt1eBb5t%2Fe6vSIMsxyrbVRxAHaSs8%2Fijk5AW1W3ajQf6DP51FkKbjEEaQp7ThQOL8pUajXNZgjP%2BAGWp2PntYtYtaMRzL3lOTlAMAU2jKG8l3wMD%2BbxPb8RDFLiiPnwnwm42DzJDXbmbGIZ0jLkON0Ub4bgHg3f5pBR%2FoVAu0YDFztE%2Bzps%2BP3bAAWSgSxlY2S3LazAF0KZAmzACvKJ%2FMz0beXxl%2BeIEFgZCLRMTD0eI7mFnojAK95ZgCTCfQOybhXCo6576fGouBIpcPFCU9ALkeU0pQaG70p3LmSrRNvUUN3vI9R8lCXjgQBKD9twFivCVAT47aBVWQPv7zSwNj8Un%2FSFG3FqLOS5c%2BWZHKZKVGCPSmIgHEwoN5C6HgtxiaB5%2BUvBrgQmTbO5rMJ%2BCuldZzfzxgdXp9XgD508hFWCeR8zw0tDDW2xoicZACrgqgnQ48NRFvjGjGQLa0bZ%2FYIPRHYC9plKdMERvlMGhAQeqDBMhf6LZGmyElMCwEIYOo1BZ6PH7%2BWHUp1YWZa7wqK1XoOCvECZSwoJH1Mkaogtg1cnvzmDI1q5S6K8zNTnhUkglWVoNqdHdJkfg0Fxp37eM%2FnNEjkRlbYvQhIWSCHT%2FqEBqUlbxYgGY%2BMSFSRxt4bg1PVnRhKNZIXooJOn0HVNY%2FcfiwOh4kjntxHmybS7MJkM71QCMhZGbYSMrJ68qAj1YN82a3v5utE6PJoWbuOva8RkRMPNZOJG3h4NcQ9LTUNP7WRG34U%2B6pZFDjhFpbJyEq7ddJp5LrxHhOcWeKH5m0I1Q2anhxKm%2BO1yIfm7BGh7B%2FjqYiPKEBJ6cFrT2ZB6TmSyLIpnTVkU%2FYqVla2WfZcp7SSoTzv6vLYtG8AgtXlxfWZxN6Gzmta1EwxIU6yE9s0x%2FZlSkSYE8X5a7%2FVPKA%2Flig684F00nmacRk4WooPYSFBnHTa1ebr6zgD%2BMcOV4BeWlJJLrL0qnYZSAVtBA5UxpOASkxyTpYRBUMiUqxjxU%2Bce5iSPwywJQRsno2JDcLSzoQnpUALmNJE%2BWSFPdsteXHMocr%2FC0IqI26humQ1wKQoHcGmUNYBVwIFZqI1azUTzdFmIOugxHeiO7FGrlsIVmnE3L1JrCuYeQCJCpQc%2F0JCGgQIe79NvrOoYeDQJ2REZcyhK7ihxb2NeHkVj0aI0LIicFJjQk21YaBO4G64TBu2IKBTzYheH2CASyWnlbZNFDbFlJqA1Q4kIljGpwnmhmXbOSQEgWsMmsHkBZYKRUWBv9HZZ1N1JSN64WPlCBOt7zqAJMKpczyNBpzQk5o%2F%2FxDjQg86ozQyN4waltVoDaLJOfWjYqewlULt9QO8N4AqlP%2Fo1ZIqJMaZ%2FORyILlDFoGoeY2%2FswI7MBMVXSMJXCUeRsJjHIU6kNEsAqZoBDoHo8Y8pZtM%2BGL8OLfcPdBbsbvRZuDQcyyrZ42OoTsMJ2GH21od%2FRCfgHrkeCyZfMOkWTSYgXtqYxGIGLcI5gKg%2FZR2xnb1upteR3rcNGITv8oKHpUWdLK9kIZlFfEtPUNAgnOY66o0dWYJq2a0UT3usCxxDZm0UIViE7Q13vLoyXH6VgLZoJt476o2dZ1DSsnihertfmTnTdREBRHm5ASIGxMbMSITKCmV5fMqgFiewxFtxqh5yfCPkmlKNcrJfmvyKlbaqps0Oa%2FBSPXk4FJjtWF2wOHfpFY63qA49US3T180HXmbqEjYsSpRFkkOVMCBH1C9JAJTRgjCWNAjl2MSA%2BvZjADldAJGTIoWBOcOEUI4qwvBSI4hMj8m7WHoFosA0M4Q4yS8XtN%2B5emVGJDGqX8D5SVbE%2F0bWETJRyRFYKQ8umFTkY2ZnV9Lz%2BVW9hgHq%2BHYhsrghzcANy%2B02yEIiJuDFngZnEHSFnTnqDAicaKOgtLZkRXBqGS3bgcqBK6B%2BmV5G4%2BxzA1IlYTTtL30wAPCkFZuhlgsViFEnA5Rg4UXmBbXBAW7JuNvZlXTVsYDMV305DdUPX6QyUFMwndSexwLkpBDsIQ4lUnD9QoEF8QmpcQk8FJckIIkVwvETqBSrIvHRim18B7iqFwNCcFoUKmSj9cSfX37WL%2BCT4mAKH%2BS76AYlpBE3eYErTSL68qrzEKiOZo1mbN1ujQapDrmTWb1s3iBTeQMCYcYfVeS5ks0rTlCPWr9AjV%2FbgZ8To6s7qOr3%2BkRUCKT5GAAUzFlHOkmV3fJEImmhCZ4S5ivYOcC3SUx70Is%2BgfKTQfQBsES9NEX4x0WTcQkWV5JKEJNl0UV8pDmBNnC%2F4qRYIkjLTUknCCTbfLBLJzy4rnSGTntJHjdpYIWlqzxK%2BG%2BRBy22IJ8gMQIibkjgh8Y9Q2H5aenHLwEJg2iDmhQybR8G3gfKxBAZwppQkDi9rFMfLSe8Fgalu2z9mKNwe%2BF24HWYNjNI3zoQfXsIck6YiAwCMhAsEDMvxfp9PxAAxF94h42eFp8V8BZxS3g%2FoQhPG05yIyOc5GxA27VgPq%2FN9uxGcdZCP9PAWYlmmiIlMHE%2FRj3IzE8YKgGEh8OmOkN4KuO6GYyNnRhoWNSgmY9%2BVfMfhBm7xIwZPp7ggg%3D%3D%29%20format%28%27embedded%2Dopentype%27%29%2Curl%28data%3Aapplication%2Fx%2Dfont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFsYABEAAAAAoUAAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABgAAAABwAAAAcalXC8EdERUYAAAGcAAAAHgAAACABCAAET1MvMgAAAbwAAABDAAAAYGenS4RjbWFwAAACAAAAARsAAAJySvAJmmN2dCAAAAMcAAAACAAAAAgAKAOHZnBnbQAAAyQAAAGxAAACZVO0L6dnYXNwAAAE2AAAAAgAAAAIAAAAEGdseWYAAATgAABODAAAiTweHjMhaGVhZAAAUuwAAAA0AAAANgJiWP5oaGVhAABTIAAAABwAAAAkCjIED2htdHgAAFM8AAABFAAAAvTBwRGObG9jYQAAVFAAAAGrAAABuDSPVk5tYXhwAABV%2FAAAACAAAAAgAgQBoG5hbWUAAFYcAAABggAAA3zUr5ntcG9zdAAAV6AAAANAAAAIhLlGpmlwcmVwAABa4AAAAC4AAAAusPIrFHdlYmYAAFsQAAAABgAAAAZYr1LmAAAAAQAAAADMPaLPAAAAAM8MFvIAAAAAzwwJLnjaY2BkYGDgA2IJBhBgYmAEwltAzALmMQAADagBDQAAeNpjYGZpZJzAwMrAwszDdIGBgSEKQjMuYTBi2gHkA6Wwg1DvcD8GBwbeRwzMB%2F4LANVJMNQAhRmRlCgwMAIAC2EJ1gB42s2RP0vDYBDG723aSIrSUESsiHcIWqqDXbvFRe0gBJw6tTgUCx2Kk926dusixc0P4OiXaQZzjx2cnNRFhPiagENdHBx84P693P0O7iUihzLbJGM9mb6tTFrnTWhjSAEVyLfZCgnt060U5UDacrdd3vnYNVWvWlJHPa1oTRva1JZ2tKdDHesUHiqooYEjNNFCD0OMcY2bR0qSr10pcc8S6QfRaEF9Fa1roKElnutARzqBgQ9BHQFOEKKDAUaYYJoSTfKWzJMo6epSPI%2Fv44sHJ9qI1malWVEqsi5lWRZXiN%2F5lV%2F4mZ8YfMWX3Ocud7jNLT7jUz7mQw62ouwafyvj0jfW5KzLLTZkX5EpX6B%2FLXfxYfU3U5%2BPg2iWAAAAAI8AKAL4eNpdUbtOW0EQ3Q0PA4HE2CA52hSzmZDGe6EFCcTVjWJkO4XlCGk3cpGLcQEfQIFEDdqvGaChpEibBiEXSHxCPiESM2uIojQ7O7NzzpkzS8qRqnfpa89T5ySQwt0GzTb9Tki1swD3pOvrjYy0gwdabGb0ynX7%2FgsGm9GUO2oA5T1vKQ8ZTTuBWrSn%2FtH8Cob7%2FB%2FzOxi0NNP01DoJ6SEE5ptxS4PvGc26yw%2F6gtXhYjAwpJim4i4%2FplL%2BtzTnasuwtZHRvIMzEfnJNEBTa20Emv7UIdXzcRRLkMumsTaYmLL%2BJBPBhcl0VVO1zPjawV2ys%2BhggyrNgQfYw1Z5DB4ODyYU0rckyiwNEfZiq8QIEZMcCjnl3Mn%2BpED5SBLGvElKO%2BOGtQbGkdfAoDZPs%2F88m01tbx3C%2BFkcwXe%2FGUs6%2BMiG2hgRYjtiKYAJREJGVfmGGs%2B9LAbkUvvPQJSA5fGPf50ItO7YRDyXtXUOMVYIen7b3PLLirtWuc6LQndvqmqo0inN%2B17OvscDnh4Lw0FjwZvP%2B%2F5Kgfo8LK40aA4EQ3o3ev%2BiteqIq7wXPrIn07%2BxWgAAAAABAAH%2F%2FwAPeNq9vQlgG%2BWVADzfzEijWxpJo5FkS7IkS%2FIpxZJlxfGR4Nx3yM2dgIBCOMIVrhAghXK0CYZCKA0tKSyQco6UUNpu2G3pwoq26hnSQrcsvVhajm0hu90m8fC%2F981Ilu0Eyu7%2F%2F3EkzSXNe%2B973%2FvePQzLtDEM%2BQzXwnCMwKRLhMkMlgWefS9bMhr%2BbbDMsbDJlDg8bMDDZcHIHRssEzyeE6NiIifG28gs9e0%2F%2F5lrOfZmG%2FsThjBFpsgv4ZcwMtPKKExGsecUUlWsWaL4M4rnkGLIKu6qImRLAdLJTOvx5KOpgizmxIIsRKWoLKTEuCikCkXCPb%2Fj%2BQq8CKeO1TYPTzqgjtHLAA0G%2FtH7WplFTNnCMJ14c4He3JAtE8bSuW8W4cydRLFlFMshhc0q5qrCZ8tmC54yC%2BbOssWMmxbG3FmyU%2BgCJCrW%2Fsgo6SKj6mb14PiWupmMUpwN%2FBf4Z5gCs4VRshmlrVpuy%2BJPtaXNFI4ohSOSVQwZpTmnGKtKMKtIGcVaLUtWvFByIWTTM0qBQharlkKRLHy6St2kU3FllXRVcWZL%2FaSzFCuIbkXoV7rFsrUl29%2Ffj1Qs5HoL8UKur9CXy8o%2BOd6bZuMxBytEhahRgrcwn8sOs%2FmcUTDGY6k0SRWLlxleL96WWHzV%2Fg82Dxqfyy1ZFPb3z5npIdcX1YNGsgvezdP6Z%2Bek0KIluWciGzc9Xr26dcRGDhcz%2BeLjp%2B544fwLC%2BunBbxdpw4X033FRVeOdDqD09bln7rwqq%2Bmn7yGwTGpkFF%2BCXsA%2BMtNeYGrEoXPlAzayANRK9wtx7YiORlGH8Pavx7Y%2F%2BgD0sW%2FyJ%2FESEyEUbiM4qjCmBHFlynJ8BMlMye6S1axv39aD%2Bf15aLZvt5kPCakSTxmlLyygzguW86%2Bf80TT1yT7up67pIv%2FZIdWUPeX3H50x885dh49S8eCNgcm5Bt4FXkFeAdHuaBhbEzTJ7IBZIQzQYY9QpgcXQF6VIPsuvZ9TD6XUU8qG6u0L3DY4%2Byp6k20jW2F36H%2B%2BjDjz7kX%2BBfYFjGyLgYRkgAexN4Ffp6MyQZE%2Bxk6b%2Bfeog979DaQ%2BfY7Q84W532zf9%2BsnbgdHvK8YDdXqMFwKQwZibHlI3Iz0JV4WAaWTKK6RCQssyZkHM4AzCuicNNkxEY10qJK0aJCNMpHxV5BYE79iaQUhl7fez1YpFN4u87GBPMl2eZFoZJhklhmOTFhJgUHETW93qTgsFBJDhm5FtvX34SID9r0fJ1bvfty%2BetcljGXBYH7H%2F%2BkvagtKGDPXP32F9dcvDyvkJ7QD65wF1msXLf5TyWseWiP8AANeSP3uX%2FhX%2BA8TBNzClM2YEYuTOKXFWaNMZozijkkCJVFcmFskExwBwAdg9Ionsfx7rcrXK%2FYhCB85mS2wFTwNyvyKLi6lea3PsIIxjg%2FLQetwsQkrwC8XmdxBhLEWB31uVr6XMlW2TSDKzWvIx0CcLFJq9JPXj1zspnXyTu735XfZ%2B8h%2BfU31d2Xq0ehJMXCwLpWsZepP75xe%2FCFRp%2FFpn1%2FLP8HCYAHEmUYEZhDiFPOqqlJo2le4fZMJHhjZW8Dk5I88Xpp1599bVd066%2F5qr1fbOvvWXv8PDjt1w7m3PN2rKqm184Z%2B4CvnvVlln91950Y3nNmvKNN10LtProm8x8%2Fj4YewtjA5aKenKeKPGYiYcrLCD%2Ffg97D%2Fmluv1u9SZ1%2Bz33slwLFUV%2FUmcSj%2Foe%2BWf4pHzY%2BBs%2Bxs8odhC%2FVAzxwERypuSnIE%2F4dY8sFORUIZ4Spt5n9r8s%2FM73Fn33naXLjnNH9sqdv%2F3Cjj984ec%2Fn8C7HpjHeZKPJYdIb1%2FWFyJeY1wiEhlNzFT%2BR5mZIKN3EVK8L1fZqihbK7n7iupHdzE4%2F%2Bj3l9B1yco44Xdk4JoIE2dSTCeTgRmhMGUGOYivlvwtICiFjBLLKaaq0ppVrBklmVNsVaUtqzgzSkdOcVWVrqziySjpnOKtKtOyiH84m0NyEKWXjiNf3WeyubytclbhXSii9pntooS7%2FqrSDO8aV4aySkt1X2tb1zQ81eIqReHKRHt3D%2B5y1VIeBZPNCoIp2NTfrzjFUnMIBVQPXS%2Fz8fykF0pBmKVRcpxzvHJ0f7H%2BD8TN2F76OjB%2BkF%2FSeAnKUhBOx7bWjzC6fNPWRifQMMPMZEaYsh2p15UDtJFkoYwySEnTC7wxi9LDRddHP7y7Si2AOBCtvapMz5ZOonwD8qEPJK5PEmFMo7FkgEzcJ59wPgRcIIVCkroZ38e32fUnOpNtOMxe3LAz9sKJzjAgPSfjvpw5i7mUuZG5C9ZqSoXS4EU5pEOpt5hFSpSWbM0hLUpzroT97kzp9C%2FAfmu1tPqWLFBnFKlTciHTmYH52rKUQtNAUs1aAdv91dKC9fjpKm2AY5tvgu0LqqVrbs9mS3dTyuFcGCDabOgksaSY%2F%2Fh9kv%2F%2F9vqQVEGCneCNjP7fzmfre%2BTR420e%2B6%2F%2F6wWwztbkReMYr9V5fJDyeC%2Bd%2FktySnNVmYOjqpyOY6qshhHdMGFE5ROMaGnj8UfP64PdPjichF2jNPk8jgbyPZV9UY36H7f%2FydRmD%2BDO2Bx8P%2F52I83Hv%2F0pKIoyPMTE%2BA%2F4DsbAMLA4pEgqRB7hsgfHvvYj8rJ6OtcLWz%2FG6y5kLuTn8fNATuN1BTORzUQwkwtJUH3rIAmS4EH1LfoGH8MT9w%2FiNbBWTdANUszzE7QDUAviOSVUVaLZcnMI1ZzmJGg8oWbcDEVAv9b1h7YG%2FSEJAiucVRJVpSVbTiTx0kQcvpVM4GayGb6VqGsZ7TDSSdAyyqwPRHa%2FkhAVd78SAK1DdgUbtA7ZDVqHq78UAu1jH2OQ%2FHiuSSx7A6S%2F%2FxN0Dw7keU7KSXEpnv9YPWResQKi%2FhO0EfUUvAile412X6a0izBnHE%2BzapmqWUV1zeo51KyaQx%2BjWz2HulVz%2BBO1Kw6WsY%2FHDLUG8r6O2I5r1VcFH1xlqiH2InG9CIhRXZGR%2BQdAf2iC1X4ug4ajBdDoouuSUC0L1JgTwJhTBFfJBhh5qmWbBw%2FawI5Ck6lkE2CQZH9zHACnmpm7IIE26wVg873JVN7nBg2NjaVZok1g1HfpBJZ%2F9vj5W4%2BQs49sPf%2Fxn522%2B9V3X919Gvl1SCri7CjiAvU8aRt%2BrFC5pnTkSOmaSuGxYfWXz2%2BBq%2BBiYhtfs2DdLTIE5FKprsdMY8o8rjgmWy5HFFdG4REfRMIESFjBTNbQcVZLIpU2BbCwCRjHtb8iGCNdFRj6CkGDhBxWbWh5g6KwAukWZJr4XfwuZj5zMnM2g0ywoqoszih9QL2VlHoLqsoCV2kp3AMWtFVApwUM2lBDwPZLxX1OKdePrNDqLgUyYFiWVgDLlwRQYJTF4j4umpqLZ%2Fvcpc6TkOE93gjrixDBJxdkH2zmsjPZvplE7iukCn2wme%2FNsMkMKSRTQioJm%2FGYkzU6ScooyIIRNg2oFEpeYywZNN7POvzciu7BWw3dPYZkW6wpkzSmM4bPTZ%2B2gvM7yZcMhi8Rl8yt6Br8nKF7mlG7wpDtMtw6kF7BBezs%2FUZy%2FrbyNvjPnhJtTxp7ugyfG0ifzAXhlMFwP2sPcid3DX3O0NWDX27uThpzHYbPzciczAUc2s87AtzJmRmfM2TSxuS0wPpt29afsm0byEKBKX70Ea8YvKD3j2ugBeYOphzFudYK1v603iwsFalMuSPblwN1oakKKiYqFY4uOJ7JULsVzPvgIVx1Wl2lBM7DrNKGmmk50UYlFAMM3eZCeaRMAy3VVeqBre6s0ltV7Nlybw9e1OuBi3pdqGeCUESHAHWi4CJSX0kalhMPqJ3aFrKSB16obnr07aLTQjwD7aSrfYB4LE6H9ehbVkeRu2WgfWxO%2B0CRyhjkM03a8CRrcRbbBwba4XtZq8Nx7BJkvvbBwXb2wNgc9gBYxkf3a5uavQR%2FBon%2FNdggXUzZTPV14HvQz82HUFc3UT0bqQRMXzKYQPAQOlnNJF5zurCj7C0V9SD%2Bsaewt4xtHduLcoRdj%2FyOAvBtkBMORmRiTBmMpU6CXA%2FSzlgtGwkSzGgCieDRZhPVSesUISIpOqzkV2TUYT32jNXBriddAX6b1aHaxs4A9Dg34o4ylmd4%2Fin%2BKZjDHuCB7Qza4M6q4kUhW5a9eBtZgnHx4JBTwxAgsMOouUpeQHD8ogBcJLtwcsO8R6Ox5LWDnLJwDg%2FMrJIow46Zd6Kty5S8TtgTGLsLT3nwlJGINl0Qu1tbeLeL5Vta3boA9qCEkPg9xE3mEPeePer76gH1fd%2BHZO2HH6pPzAeR8Y3GE3v2sGepT3yIp8dUIOlBaiuwHz3IMAYv0BTlVVq3soQcYgwWlTNDHQ9O6nggZmRhlGO6tIqTHAd%2FJMrFOU%2BOixfJsz%2BVHvb%2BhDw79lb7B209bzY9wSvoQDm6gq4FhzW%2FD6vr6No9FzFlK95TuxuYICQ7LirLvIC35hlN9NuBjhYqK0sCD5KKNYOksosgX6lXDL0g6KcEiKjtRf6kvoHyU30Dtn7z7LPUjwciFP14lSKMM%2FAr2PfPAhQyE2bO0TVID53LBhjYCB1YsaqI2kIKymSzqyTBFiiOLbikiqJ7v513y1Q3aBaVcL8iuffbDB5fiI6p7IE1l5jMTFBfUXuH2WyYpUsRqY0kR4U9R87a88bhN%2FacpX2c%2FgFZ%2FcEH6lMrdlV2HSENJ%2BCDZdWnPsDzKp2uwLQTeTbIbKzxrM6iTQ0sipiAjeuv82UzYgJ8uR%2F50ouY%2BEVF6lfgCDKnj2LicSImRoGhetDxWZJrgokcF0%2FEk8uPHNl1Yq5Uz9ZwoUzC1n0KiE%2B7Lk2AR1xUsWcBJW8GRwKMbzOocjxxUJ0sL3qiTTVZmJOjhRwXrXAt%2F0pA1m23OiqVLOnKVjaOPRwgv0LJpSZg4pO3YJk9PIGGEuNn5jXMe6RhgNIQYLDVKRcEyok2IAxvtnA%2Bf30aU0L55CmE8uj6SAvMbMOJyPQhmUHaJ5OJ7IcRf%2BpJOH8nubsCnFvTmZqZVQy6mWFsxYwSADhDFE6A2%2BkqWTReDQOcFoTLwHu8cgCH2CyWJB%2Bu%2BSKOPGHMXgkPB0SFAu0Ns%2BhAjnuNLUkXOpmFqCjAiu4gsq4k7TqifhU0otsvuYeM3v2Vl0EjYn%2F0rqYWbQFVCTSqk%2FHMdaftZjQdj6Hwupgocx1TFnE8YX75qmVfBOe4Lwhz3I7zrmw34AE7gy7ymOacgIHXGDdYLcUBGReqMaK7HwF%2BXjBYLaCbRBH%2BoLtkNiFavggsL1EqHsx4nQGd6JrLDrApiNFCEt3GnBgVQJHpy%2BdgTsZjKcRuy8tfuVvd%2FMXNt5WOLCSj9HOXdphdj4rfdXh2xZESLk34SY8xsOoVdZ49nh9rBaOYMoo%2FhyIumMWgRHMOBy2URWnXkkMxE80ir7XmUHdPZKmJYzpUtrm8qGxYq2WH2wdbqDCAtMOVDZf7BBgaqYaXBxb8IRKVEnntpfmMuFuOvVlzEqEMHH%2Bh1wnd8tryr%2F0f21us7zbIa9SHQF7LOHYtmo7TCiN2CEalbAjioBlQUgdduNQpDgC4CQ86QIdBBYgpteCqxjsMQToz0Kc0MG4w5xq8SaDcaOo5KB9bVpKulVuoInJsK2guIMWp3Vpk17cPVFZu2bKyAgrMXjjP%2FmEX1cR1XYH6gTiYy7K2rmEkAbmJq9J4ApAQ5K5nmITZYVIQHSTNGQVAe%2FC2a7ZefmGxve36W0Zvu%2FJUL9KPjA5Os0ebDMtPJodPnmtpa7PMPRnJwuly6n7g6h7mJOZ8ppxB6gznlGRV6aPjHAIyjei2DK5iMaBNZ1XpdJWysDVQVQZcJRdVG0qzgbWznUAjmzXUTKVJMgOzU3B5vBxytw3WOifKObFuxQyTljCRxvfTbMzBSh5Rc0sgQdEtkZi077CCQrfj98T4%2Bx1085xH3nj7jUfOqdhMe0w2%2BsauH98mHpCSVtJ1408vv%2FynN6oHtb0r4QvwvSvHXiU%2FxAvVXnxv2NbleIVfz73NGEBnY8SoQTRESQHjIzJYBSkwczWpzyo7dsyv%2FSejILMr6pMNh2q%2FxW2mv%2BVk3AxVExyHUL6JehAEfz1RABtDSIGYrf%2F2gbmXXjh7rfar%2Ffnbnv36rX2X3HlPbfy%2BzZ%2FN%2FZX%2BZogZpHEr9BKH9d9G35BHEzwRGB0PrDb7XD5ZW94Jgwqcw6Pxc4BEE2KC1O8PVpIEhrNcQMuIaNCQeygsZwrXXy2cbNw5aiR3A1zH3qyQVwCwJ24tXHLn3VXT%2Fd99YZdpxFT%2B3VtlU90%2FroBUEYGbAwBnlNokM5nZYPstZpYTonF4adocMEbc1XJ7z1yUGcFMOdW5RDNQyonupXgslimxRjgSB4U8C2iWHAtgL1ot9S5Ef%2BcKNFpKhjD6RqslDqOSIc07ZkfTJldV8vDuKqWtnUoHdbnMypbT1M%2BSjpk7y8TqxLskXKUCfGfOAtgeqJaSi%2FDTVVoClBzJKsur5f6heSjLToaLpoVgZfJK%2FkDvTFR9CwlY0Nvap2OosxRrAgp7vNOpnRoXy%2FyMAfTZhN2zzAZfoK8wODRrNiV%2BT1SM59HdkstH0ZmuCUcCLw6EIofqYB5UQ%2BqRwYu0oyAAorirXc2BuCQoQuFFDqPdTUaLRdVGtSw0xFF4dmkWueajh%2B3K2F78wEuobNVlbBcIDYwhHsB3lKfsAfwlelGxAltF7m1kzgqK4814EQpo7hYQvnM0P765Pu5T15PNmvVT5r3NOLr2akkKZbP0GNVcW2nQUPMwoGyxZVGtRX0QTBQryhq0S%2FywBZZrk%2Bbxj1dRSJdcGMwQjP39pSaQ1jScgf5%2FNCGBPCmgmIwrDXxK8JnXP3Gfol2pryKVCvf20RWwxb19zF857bTjfk7Q9QRN1xN0jxZPtTxUZmCKU13PLQBrMEYr1fVED64cZsJ4ZV%2B20NebSsYEAhbu6%2BwBsOh%2BGQqvCofwDYbgVxYn6Ho27nerQ%2BFwCN%2Fo3Cdg%2B%2Fxav28A5Ld2Zzmn3VzxZmu2nVmL%2BNH774f7%2BwKU55rIVAgSOfSeElA4dUB%2Bod0Q3lhrEY9kyYsIktUxCSQF9%2BFkLfao0cQCs1y3yjS910qXEktVsUwwpy0MejQFE501Au4Q3kDplBMLoLuBnhMVi89yXymOuYvs%2Bzx59pgfWLBIhwBj93S9LMH9GMBA7E3COmyUKuRF8mJIOvamFCIH1Ln8Nm847G1Y91A%2Fl0G7OYUpJxBGsJAs1Ga0cLXUCkXIljnLBAuyjaIA3MlmkUFNmm9E0FyzJiuuf85UTPPkAYOhGg%2BTOyp5YItLE4RM4ArDBPWGCPFRlaEC6gCw3LE3823siuEzWZdFHba42JEk67GQwxYPm2StlrG9FhwDmLNzKhX2ta1byam4lh29%2BUsWh8OCbxPyA0JMgukGvriUZiOAqGvPKN2gpmVgMhFM%2FAAsItVyhPonIxnALuIqpejyXsoBKhEYBiXQX%2BpMAUrR1kQLXTS622EvEoujRahExVK6BwSax73fGghmpmmKkZbdoPko0zws2A7WSbMcGHSheY0x1uvD7IcYKn8D7dzbqBzt4p25wqqOSvvKGWmrbRdoSsXRyuhoxTi8fnh4PQHq4FWoNJma5uY7YUfyDrSDKQzXjJIWvGj4TKrv5enYPgs2cQxeGvfZc0q4WmZtKN2JEs%2BUWnUtNA%2BW7EwSlWEYpGg%2BmUoTWOviYAA5iJMQj%2BzJk4vbh1s9p5PbVro68%2BRrsTZ32GhUbzxDvczfbOtwOsmmcvrKEV9f15%2Fe6F43MkI6PGmHnXvrmHtaky0oCORfyStfUL8HPIf%2Bn%2B8Bz7WAlOhhzmXKLQhZtKp9Ejo%2BHVWlJ6NZusBhdJT4Q3ANLlZRWLYwvQa065ZDIjArHaVEGkbJBwPTHoWN5n6lQ4RdpQed%2F4BgniZHpLlU3oDeSxyJMA%2BfJJ5CuwGHKK9txEFHZc2%2B1gj5xTXflqMxqw0I3du55bHlla%2BdctMNZz70%2BSUX7d19ipBr4waa5ZDdKcwnyhcKZxTaTAJny520Zd7qu5dWNq4%2B9ebi9UtXbqzPS%2B5Sqnu36CNBaJgWxIADo94oovR0BAS14OnDIUjlo7KDcxLO6JOlytIrTE9b2uYbzQbydTY6IxYwGG6yTJvfL8zOcCdPb%2FUQjvT3m%2BKphM127F97B439DPvRc7pfyAxzYBtTbtLstTJr8OGCYwOut1GudwHXW4AjklRQahouWp0wW0AHCFFfQ9lPAzn%2BIAZy%2FDSQ0wRf82trk7OKc6ZkAdlaMlDbLdKkxSRsosLDEKRg9hfkuJgTPGIuCjswGp1ElIHdCiKYpJy0ePHiG26A15Fd7IFdWZeUjsYqRXVzsRKLZrwiqH2PP37szce5c3CZDaUkM3vsmWyxmOVWsmYpFaJrUPSjb%2FB3gfxDfO9gQO0CfNkqNVEb0bUA3E4NXcuhT49kCHSbfazJTFDRcfnRGgJRRsUCawL8GRDZSkQE2R2ksnsmASRlAWQfdwLUo49r2BU11L2ZcdTTkit3g0YZ7sd11Me2IOrsnTXU9XyBxngqowsgjHLSZeDT7ockfglaYkf34zvXgu%2B4hsD223TbTyOY62kEcy%2BoLris0IhL5WO3%2Fh5YxUn7nk84PxHW8e3KCcOtdXDI4fqmuvl4R48Db1L3MoJ%2B0Z3B4EAog4kYQxmlt4q5GHq%2BxRCZGCf25D%2FdfiMmxUayVxrHozJhELSQFwDPXlTfVN8%2B3tGPx0vDSMPuk%2FASP3W8%2FPijdaLtcaTIrE%2FYRJRMx80twPyRlTp%2BS2h%2BwRyaQ3M6zaFZTfMLLqL5BcUsYrw1hzGcK0FM3IQ6QtnVMi1Lsw3K%2FrbpaPXc%2FL%2FKDDle5sf%2FjZa1IOfHv6G%2FZcIc%2FsTtT0H1Y6OfZoSMH%2B3Wx8cImmqQ6WBmMItgZUSrPUmz3dJA9cUZNDQ185yuIBiOBylK42CfXrRNSPg495XHHkPzBmcOznOcOTDnu3BbPTg%2B%2F1VbDWoUBWAWohxWbfySo2%2F9vcIO%2FhmpjSI18ONswFdmyrOoP2txRpl1SJldpehO6yn8H3HD0LNHpulo5NPhx7UUo49XKn%2BvHD%2F6FjrV2OQNi4uLdR%2BRwr1NvTrdNHMRphhafx6qYoP15YW108zQfAymxNt15y%2B6XCammYHl0hVh3490dUXG3JEu7hYw1z3snXR%2FC7yTopa4yzXMcxkoe4o%2Bu2G5r4VyaR7FBN9zqIquED38gxkVoQBAJPYrkljyuKniApCVXR4JXRRNouLtn5o4N5MYUsQjRt26Jh908%2Bs9gYDn6F53sPhX0rGIJN495r9o2eZlyzZ3cYfdwaD7mA3e3%2FvGZx8kw%2Bqb5LD6Izy3DOh270cf8s38C4wXtPQhHYOQBnuEwq7lgKAz1qQFrJokIJ2dR1hDCKvA%2BKme4ekFayobJl6qtaf5VBJ5iZoYxnuv%2Fel11%2F%2Fk2oUL%2F7G%2F3xI754wrOme%2BeP%2BFm%2B6%2F%2F9Au9o9bf3Hztlf%2F%2B77L%2F3tkxBzbdOmexZ%2FfRc%2FcjzrVeHxNYhaOR9e8WnTN1xBd82hEldHDJuogYojCgJG9ktcDihJG0Y4bQ8uJsYmhs6VHFWVyxMysfvUItxZguorhuX83OBkf6HkMoM0VwkbU6oU0WwgTuZBmU8m%2BmcRBrppz8RVXhKWFy9ctnZlYtu3Lyy%2F%2F3k3bHKed5hJ8aYuTNZtPL5BdZ37za195acP8u7ZcfcVVn5t7ygPFAd545je3rbwoeLbRt6ht4Y7e3CW76vbyS%2FxnmGYmCmvlOqYcRGo4qxgVwAFL0QHToktIDV%2B11IbUsGEcOBRG01HxiaVIDAliwlCxFNJCHry4ryUWT%2BguSOA2XhYmKDoFSWO9RIGkzIBihrzzKLkk8m31FTQIyeH2gco7j6oHH32H7P8aefBydRP522WXSaejo%2F3Rd4ymb4ORaMMrb6j85tF33rkwSx68DK75n8suW9M8Hs%2B9D%2BZSM5NmFuiYwbSJZZRUVTFrs9mlGAHLDMXSXkWHIlOKuYAFpVArsGDJHIS5lOgv8Ub4TNantmwU4uO45GuokGEuQiQzkWIOAyBjYG9dc%2FdkZB4Pn3%2F%2BWeEQ%2BYz6gOBfMHvt7H4NoxUt62sYPZAHhEg02esxE%2FJTMocM%2F5J1yLnZF4%2FjtZf6AeK4%2FjfpeJm1EWvVcRkP%2B1P%2FGY3wC%2BEI%2Bi8wqyoK42UGRPf5wObXxqvUEm0QXicYLTObIinArWtopKw%2BMhm5taSJLdyhrrxHw0ku1VCaW%2Fk64rRD%2FT2eP%2FmLWn62lpckM3OZshux8FHrEewbs1ao4z2kcFnEzUQLdUouL0xCI5VpPswJssswH1mxxNio541IsEaYSRw%2BzCQPH2bMZ5bQGwoCN%2FU96g2tfE99Dd4r7Gmk7WXtyMvqL9WDL1eoM%2FXlcfv2FqCxB6isRQWtOer202gL8Ej1ZA4tgkHgT1OAKpp6cli10Y2spuWqi8l%2FHN1P3lYX1XIba7IeJX1ZQj%2B53YkZWX56F7GGNVMLSdXuMn4n%2FUY0e2DKzdSAfkN%2F%2FaaEub2up3j1eg7MhREy6O3CnBft129ne%2FE32SSbHP%2BVBn%2FcEuRCoC4tnMGrKF9WmCJ3C10vIwxTmJTQNjnBrXL85N1dx9fe9HsX%2BSX095nCuCtKcwg2fmkXLugN30M6t9Vwpr4KQyPOwDciLNDkMNs79kN%2BCSKMFSrk7SljRGMZINlzNebksyg0AvWfIZPiWXk8BsMzrrSiqoV3gNdz4yYn%2BQYOEtwPswy4t2t8R2F1aHznphVZ1qrOd9Hj3aur8U70PhPuocUfQafh26hO49Z1GlRoUFQgO0%2FWzaNkgpnGLxl7%2FUhdCydOthdo9C1G4P7Gf53m6GLqbepb7Io%2Fqw%2BRjX9m140pfyYbYQtxc330M34lfyVaMwkzKRBZkOAg2ag%2B9Bd2UCJjyov00rGXfISMlTRZxzEc%2FzT%2FNPXnjtDMB0cVS9YQav%2FUzIeAnvmwDzMfqP9CwjRZzHuYmvWQAzmBSmVcinN7iAfkrGfPHvU99YD63h0V%2Bo%2BmO9QP7tlDTqaHJ8DlBB1iPC8J4XI1wGXX4MLcIPsEuGwWjGcwsMTwsGqWiLF%2FMnjRfHQKWFh4cRygptIqS2HS9VV%2F3QmmUUggcEebC8Uoj2DYpSk3lwtyIVVICSlBngzEqld37Hh15054P7hjCixttTPwPhEmcSJM7gaYPHWYHOMwOafABJwlpCZDs4l4ye5n3z1vCiS3qO%2BQ3c%2B8e54WTx2HYzozyFxEIemvKoNUF4hS9bpXG70hOtfCVSWsuWUBvmGAL4zrTbQX1pu0uN%2FMt80YoMOYGtSHUYmKSgdmKpdtzjQq2r1iyUim4JDLood82DCD5hV4jYKDl3PDfL43zWGlpAf4cTKGr9zFOwUja2DNvIP3cB6fSTY4UyE%2FqRjc3bHmeM%2BcnqbsyXOTt0%2FlVzbAGm0WwcCxxOvy2VyElyNDSc7Snl2fzS1p8xv9%2BQ1qujiBPhjJHahxtEQXZDdN4vFVMdeIH3ftltxSjYd9qB1PRbcQxYgnJpwfD7H3yKgWeJwK%2BB1gEBUrcL4xDucHS%2FMCpuzV43ARWhDFZMqOQDRHi6jKtng3jQNMQ%2BlcNjS3o3%2FDUy0L4RT6N3pQcwUVYl8sns7Q4XPDniL1lxhMywn1K0Qsh9vatThePjdMCvmcFGZlQMBBBCmeT5NUDtRxONpXiOdzsAsH4VRO%2Bl7kts9X%2BrZscv%2FgB4HnT6l84fbwBv%2Bliyv39b5SCZy7qLL4Ev9PyeHKDfcFKxXvis%2FOrZy3N%2FrSS03337DwYv%2BPfjTtq5VFF%2Fp%2F%2BAP5ogUVjV8b8%2BYi4zlfnroJWM%2Bb0yUfGn6YM0VzvrzBiclxNC%2FgBMlxDVkPJ0r9WnjbbRfX%2Fp84ffNXDVcxU%2FNVJ%2BMQnIpD0wQc%2FJNwCHwMDoABORH0m3as%2B%2BFO9bETZ%2FjFd6yr7lQfnwJzkw6zj7I%2FXy8O1SR7QIOZFofWcu2cXgqz6NNhdjQdJ9dOJpji5aHpXb2pKVAPs9OLVz75YXXj5ic%2FHJiab3dFkS0Mfzj2wcCHT27eWKtXPof6KYxMkObcYThW125KrAEYm6HKMc1kxuLhsb1snLxEs5l%2FPuX7ep5J7ft1XxXWoIOWpH2fpqx2qQPqLNKtwfBDmKOLYI4aUU8yUC%2BQQHOw%2BCr%2BUIk3NKRVIyA%2FJC%2BSA%2BohrZx5bC%2FWFOLvfAYG4a3a7xhrv0PGfweWCcXYrxdug3ryGdIGsHSoM2u%2FU6PJv1K9DauowZhG0B2ccd4d%2F%2FEgXnWW8g%2FbK7e8%2BvT5JuLUsGBb51zVKGuM8E2xlp%2FlyJScuo4nE%2FjzwEuMyjeT5E719Zvf2UmSN6ubbyaj%2BgHOjEdgF34vxsT5nfxO%2BnsMGLpREWxd7VdiJIUXqa%2BRvTeT1E71tZsVOKJu3q6%2Bxubxx1I71Ne2Y20Nzal8gOrVcWY51dpaqkqc%2BrNDmVpmBWl0Cpk1qy%2FgwuxKm52PxrUs0DjIaTMuVyER6Nk%2F2W9BPGGCajrG61BNR7Fdy6zUPsjaGXzc2%2BeN8zN4v%2FfHkn8aSO%2Ffjp%2BHD3ae%2BjNvKOQlaXgfewlkuKFO1yTTwUxjemGF6WXKKZTkPTkMjqLUhsW4h3Zm6KWdGYYA%2Ft4e0b3favPkZ2hhdzEnRWlyDMDV15vMEKyyd8JUg02cVU7iIF6JlrN6YlgEQrNtCmHMAh%2FF9eRSwrEx61CqJfSfPrdHej8aSg1Z4ix7aRFzwYtczON%2B9Vewedf3DY4WN8u1aBZikVg9pqdS08RRu89vG3WnkyWT10Jo14DDG6Zl%2Fhm31M3cLS%2FZ2tf1Ix999Cf%2BWVo7lB3P88uBtoEpsKVeyk1SvpDGHL9hQ8HLOvg0Gxf6fIW%2BxDAmEaSSMS1WzQu%2FNJs4aXo6c8G5X77j7c90LfrMZZ%2B95vrTVznPcadmF8j0eRs3ndbu402CNyrGvzI0pN51iu%2Bu7%2BYHbzp7%2B8Dgmlx3uD%2F8gvrDn%2BxekzMbPc7CjfZVI3uTM8%2B6aXXWazaEArkrWlpe%2BiHOmUsZGz%2BH%2Fw9mAXAZURZSjppbVea6SoMwFIvgNTgX2GdGH029cXtlWAl9GrBhArCCLhMmiA9f6GNTPtmXSqbSbAE0nDDn4AUjbpNLDeZ58yIjM6a3OVsc3uVrRUPEZzE7Oc7kCLT6%2B%2Bdsmj3LmXz0H3xSar7TvXyVIWfvOH9R2snaeBMhVlfQnZwhC47emeRbI%2BHpN3ZLlsTwUHjkvdSKvcXowi6Pt8Upm6yEN3qbh2ZfMvspsurirjUPGVgh%2BrnX7jNFzrv48VW2gabepjY54OIt6aVrW9KXYJ3695gb%2BPf4HBNlZjLzmdkMLk%2FtVWWGlk66IKMMHlIK1dJCIEIBVL5ZFnMgaGpvyw%2FTPC%2BmZJ4hup9nxNZ0fnhkPhX6QBdjNmyIAFmMsbQhVQgbskCZNJ%2FSio3yvQWj7JMLmJgwFDQ2z1t%2BxZYvjn5xyxXL5zUbg5MP7BZmd5%2B2%2Brqbrl55StcCKymEZvSHe5T%2FUdLn3dpzwQWJJR4vu77N0rZu9Yb5mcz8DavXtWE26MR9y4ahM6d3tuVOH%2FiMgyxMzJ0RWHQKlryvWbjuiq2zL%2FBvaG3FaQq0YIAWCuOjGQ0nMV9jys6aJTqSwWxQpS%2F47aH%2F%2FM9fM1KnRXGmHYrtO4aSg%2FzNodi%2Fozhd%2B6xOm6dzn4u%2BB%2Bh7kL630vcEvpfhbMudLXfGjWCg9SuBfiXYr7T2K4l%2BxdrPPG%2B12V2BYGsirf8jsyxwyOGccDCdVmYFCaPRGtOncySOBC64kaw6gWcSmvoBVxSSMAgsDghfJ%2FiCocHEverL9yZWPnb5BSnL7p89n9h0FyVqRg7PcDpIW9LXnYlN82TIxs7U7JvT9mBQSJx67hfHyS3uPLriLunc86%2Bd7mxT3%2Brb4X78Eo2imXXNK5uCY4%2Ff7UxmkzP9s9gbp93Td9csb1ubrXfT2efR%2FghfBeHopvqGG3PRk9StkQKQBbSkQX2WxCaSj1%2FalDA4Ek3S2DbvdO%2FYtv8iL5AX1Mcx4DNnzvwWviVmaDl6qMj%2BfKy7iIIMu4PU8rUkKnfbYVZj8ihyc0dGSVA568OOOGUfzZL0%2Bc2d5YSvVqNa6gRO9yVguofCaJOZw7DpS1CnfL63r4DeB%2BqPChP0FaQSIvUZZEhcMGKfhYJoMCaLPifZtH7rerLJ6fOL6gPFoOfMN870BIvqA6KfGNu9PQ4yl6waXr9%2BWH1a%2FUdHj7fda7f9Tf3bab5ZXtG8bJlZ9M7ynUaEv9lwNa7le5sndWEowGpSzzllVjHr6tH3PJaZabkwmAUEhj5G3huz0j%2FltuYLObqfXxKSdmHi5C4pVPzYfzT2xL5fuzAkHVtUS2GH9ebv2tL89JzhGv4ZxsK4QJc%2BieppfhpZ9NCkGxNtL8Flx40zQmsRfLDUW8V9gt1N6yhcYKU5cBCHCPZ6QC8pcBmY%2BlFSkLV9WCDX%2FwUXvYfG9qpPEu66Hc%2BpT7KP7bjuITz4l0qFXf8yelBfQpXtup37x%2B7beb12oNFf5gZdUqsBdoEhmZvoZZoYHiNiPJasZYuNPaX%2BpIjZ9JgQpuXPIzcPMFqeFR3%2FBHBzN7OBKUfw983VspmWapgxj0%2FKYg5MRw7N%2Bk6gRpr6UpPYFAMbcXCukpvQDGEZSx5LGeByDlVckDxusRSOALlkTI7TekJJXhAgoKgPG1O9WLzgdRhAlNfaMYGNahTXpPKJgfaIaAUbG%2Fb9qaXnfuU7Xzl3acpvxPZMpItdXzlyWcvpAavV2xxv60r7RdORSsfKbRdetiKXW3HZhdtWkrVaIi9W%2BWG%2Fod8y3%2BQ%2Fw72BWhMno6Ms%2FDuygWz87dg%2Fkd2%2FQ3fZ79jZSOt5zOv8D3mmVhtvJvPYOezIb9WH1D1caOwFdvbv0Amn1dPNYIqGLaDjm2AetTGoU%2BtOGIGq1uiB4QVNRdc2HLiE0QpIGTQoeKFjXXyZdJKuH5Cbf3HsTXLvL8gNuN9ZYQ%2BQJvUPtBUR1rVtVv9AmlCdBxjdMGZPwJhlAdpyDMcrkFO6gPYuJallB2Pwha8im6BsAhWpFbMXMWWxWYtegLJUkpsBpG46KskC8A7oHOj7RM8n52A7CV1R6QjRzzgur3hZNJZMuQ1OR4C7KWloEs%2FEydj3CmsKuyOhjpB6EN6yNI1GnjZ2W0u3gXvS7%2FY8LEpw%2FNhM3u6jszervYe9UqjS4WMacoOxjsPZmAHHozau2LMUl3pFInZdIfmczvRRCQulK3rB62EsXuVu0Qpatd%2FW8n%2Fxt10wVvXfdtL8QYP222IGGbnBPsuRKBgW4%2FeIEmy1pB5suNMxP9aW1aYWOhoISQOPbacyNapVs9HOTtQzhz2cNLccb6YaH8lpTJCDr738MukgHS%2B%2FrB5CDteSyXX%2Bhf8ZsCe3w2820%2BoitEctGdoSajydjIZroihxotL4T8An%2FVH1Fy%2B91JADpgCVtfqChVqGuRKsYmeFEC1RMlDu8VZpWVZTzXcXoVZQOUKbqUWwmRpWaJkjoj69J6f8NBEtc1f2YNAIhGG8JpOO%2BdsHIk8ZWr3sHk%2FC8JR6V1FLtKdDBm9kDfs7X6pbHmul3iy6ru%2FWc3WtIKsKYPXQsmsGC4IVo6vUAbBladl0qcOIfs8gJTAHTDuTSGlezg9yw4YBkhATcY%2FPAfyMHv4US9vZ6HIzljw3GwjwjljQ9wphWd4g8BX2kbEzkhnWYnMauFXeEF%2Fg3g5JyLzYMCDrz9rtR1hB8kXXHAF92sILnIElR449U1HftVlJs0ROVn%2FLraS%2B%2FmdoXgvLLAL6nwf074R1Nq9XEeVoRmskhzLVny2305zq9gTWxfVmtNZD7TnAqqtHi%2BH3DZCCB%2ByCVJIGLASjFEYbuu4ABVTQkDOE0fA0xhY1ed1XzXRet8ptdHvP9ML7quucM7eI3qag2xQuXvjQ8hv%2BMmOGO0get02fN9067SJyphR6msxfus3b4glKnlbiuXGp%2Bq2nAfM2gbOGJHe71yu0%2F1vP0FCPEflTAp3oQf5B1Ldoqyn8S6G3uyCgwzslC7L0x2Vvdu%2Fe3f3msj%2Ft3%2F%2Bn2vYf95EX6Mc%2BevrBrt8s%2B%2BP%2B%2FX9c9puuB7U1uqjXRcSYNKx%2FNK7cdghj5lio4msD1vPWqhaId4BgRAwsbS3xpZ7v4mRBRxFnkpwkRCUunsLgZXFoq%2FNIOvF%2BaCilfrN5KJVtmubYcLe3uKsIhvOLRw7djm2ZPlxc7CfrI53FrpO3CLu6Tm5rfvlfggn1DnL1qwef%2Fc0N6h3FxtqwJTqcm5lyM45qgnp7mRw2A4BVU8CSZ2x2YanHx2G4Yf3kaU2GEtfam2gVu9grQNSwTMZFd9nU1oUucK1et92tdMKqkkYR3kE9STmxIPYOEOQG2aNPwHr6WIZNFiIwBVPRPBcX4jDTPgn3Ioq0sb03kFEggLp3MgEOvgrIk6vpvIT1r2iQ%2BDUU95OYaxl0pbbSFiXtNEPbi2iXe2idfA8t9Rw5Lu7dWnJtN82o7R4E6YJFct2A%2FH6b2OSdSY3CplZAOQ4UEBV%2Ff8nbA5SRB0%2Bi3uWCSIkA0z0BMzpCaElWtLaiEdoqQY566DJHk7ijHSzmv%2BTB8k0ZTJVis8NBLnRYRdvFDusakl6z6eLVm7YLTU71SeExsAESnzU2OX%2FgcrtdaoFYeTMn8DxrsHxxrfo07eC1mLWLCYv5LaNts8Prdly7ZHSZ%2BrQv9XDTKWSV1BaWvCFCWM7A2UwO%2B%2FOr3tVl2rnAN2uZMI2hXEs7omiZorBeT8f6CmVGtjydioPp%2FUi7IbqUYC0CbYIQ6dJqEZBxgNBeF0pAZVALsCQjWFJoa%2BbbKe1CrTThQomICtNf4qZj5Uiqi9KOBSFo0EiBJcCGMFvLYkpx8VgqhzI8WhATAha6CQ5DB0ElMyeeexESyuq42O6yOsjFgjf6lEl90tVk3L5pteUNZ5PxsySxSf36urstBmIAgpl5K1FbuT9W2NmjS8i1Lpdkv8xmfIu3p8SxD%2Bxfa5PIqmK7V336FPLf7636ptVpNXMYaSGqDSmszbWNqLMaHGCx9DEzwEIpF3CuTYc5NZBRpENKAfuZldslKkJnAHlycMCFein280O%2FSzsGWdx9%2Ff0lR4bmdTFY4RNma%2FHvVCKP6VCSmEoancSITVAKw4ZBrA7ow0nlkw2iEMUaso0UdNZuf7hNUp9eNrrkWmfCsdl32qrnfQEN8mwWkTpFNZvY9eyP1fuMnshjAlmLpLlw9SWb1qg%2FW20j9kt81rX3uO11GpEKutK%2Bj4RUXwe7paLHMFjGwr1n8MO63QscM5dR8loPmH6apJ7PKp3VcictwemcDphHs1h8mtKy0mcA5p0YB7SBSl5ypgBzO1X%2FjBEwOWeCrJCw1mSIJLQmkZgF4CScJyqLBgc1SZN5cRBWlVTSSVCTZ12%2Bzfak81qydHQZWeVtf9g%2B9iHMAMuX3yV%2FPUV9ytuWzXKC0e%2F75j2A%2BsKI7xI7sa7JVdOrL7pk9YU4mcha4bFmh1PdDqOuvr7d2Oz8vs%2Ft9pGKVXCZOLv7HvYyijiL%2BjjMkycZB6w0M3R9xUX1Nz2%2FjKbvac1DXTZ0yLq1Hn2Mh1aEl0ze%2FnpNuB42YEEFF8xEMpy159fXPVwPlb27hyRYUJ%2FIPeol7Jrrfr3nrD3qu3oA1EMsr7xIkqTjJV1ffRJgcsGqN123zGAK%2BjSYtGwGi5bKUPJi7yU35nWVLYzYr1Wpo0tLpBBhV0kKErqzC9E8QHRt450r5B4EST2oAUQB1gB6VH39xVdemkKjYZ1GYp06Wvadezz7TkRj1ubsp1aaQcIsPHfJ6tW6zk6gU4J2rSBT6QTraKUGUwORHkXLi4xOhmmpDpO3cdC0vMUGsKQJYLl0sESaHGjDdZBx9U8dyoSZ9qM4Doi0irNyHBg3oyWp6dd8HU4XczvzJebLzFZ9PDuqyvKM8oUMUXZTcDURu17rTvIg5oAx2sCuF2c5HAZPNN46uGjNNbfcese96BuwuGdZ7LHktIFTz9%2FywJepEF7eIbqf903rG5y3YM06vOYL4iyzhfGuv%2BjWHTvvpW5Oz0Se8Bh9gtcnZNE1LIfZgg9dcF4HQaHEFmA6wn%2FsCGwEZRCjqX1pgtuFvkKYlcMkAko5nCkMc4VkAUPlhTSX6kvhjIcrU8aUAyYptocV4D4OnPwFXxbevD4pzRVylKTXUuJpJP2bqckYIzaf3%2BRf7RvuD5nSXEe3kRg%2Bc1U4Fuccabtjnmgeakm7si6eGNt51hQIyh6P1egytjUZbW0Oh4fnE7zBIvh9Rpcp4pbNlvb4TJvVHOmzWYXMSqfH7ewMDpucQ05pmOM8hOshHBfkLKLVLcTMXc2tD5Mp84CcEr262TwvYPXwtrApE%2Bbdi7zzzUaPxcZdEWodjpiJILishLVa4zKbYe0mzpd0hwKh5rDLSIhg8STMJm6hJHdaHB3egNnt4cxWOSW1CCmDnTPwrXGfjeNsbqOFALWElNMqC7HLLrO1ChabyPvXENbI286qxclY%2Fln%2BVsbLMIOkQID0fbJBLoCSniLGWIY42fXRZRv%2BYac6dvdR%2Bz9cv33sKWen89K7Ol3sunNf6Tv93Dvf3vbs6fMyY0%2B5XJcwtAfiBuDNp%2FS8cM03t4LBfgO9VaVArePmDPaVAkMfphPol9OruBQmqjT%2Bkz9U6ukf72lZzlPrLY%2FWG3ZGxJjQ%2F6Jag2CzKdpwaqJRd6JtsEIPqzatdrpS%2BZdxQ%2B9bx9tUH0BHIHugON7fAW0ArNjS6rX0kKrWMVmzq5kSY9INd1ycDKCfCGKRe%2FvYIu4W9eEKv6SIBd7qQWwAwzTaFminJ5gN1INDaFVrKIeagj%2BG1e1aBWgyozgPKTHaddAZQwKCagJg0Ebeca0nIRaGxWlhGHYhZLO47jIlB6y4SkRzD0qNHVPrJR7Ua0fd0rQzbI12pGugvdI%2BgH4GIBz9Rw1jMto%2BoBEKSzppHTozwa7HuKgXtKNaZp47h1F2jKpr4VbMTCA0Fwk9lNgfhStgEFW4ubKzglFX7YN9Td%2BrYEi1gi84wLXQj2MPaCfZt7RrGL1X515%2BL813iYCNPsIsYX6md2x3VtFTviinBKrK3CyWBffTvgS92XIbpVnbSebOWvudpQ0pAbAk6IXrUWsndtpIaqb%2BcFUZdtG4yYLqvsKC2aZOpYdaGwXaB0%2FPfliGSjD2FbA4aFuN0jBI31L3IOg%2FBTAdnjM7PXxGyzoKaz25bKLWG2dfR%2FfgMG62aUk4SlwsZQZw4fGT43X58ekd7%2BKxPupPwC0ZbBGWes20%2FZSghakSWkEA2Ogn6gt0cSTd0iL7lsKrpcUnr2yPXEfM2o7DCXbOghwJt6QjcKqFlE%2FQPug27fzvu6It6ZZvt%2FQYthGHw4c7lrt9odZcbqWvBW9D%2FaO%2FZAb4bwPvLAftCvMdUlW04kCa9MGoVbFXBPZvYEopzDmY2a%2B0iPutzmBTD1JIcpeyOWpEGGG5yWVxVeorEIy3pjCALBDgN71%2FvYCNJeGwfgk26AnDwgNqZoTgN1C5jMdAwpKm4LT4lnkzp0dntafaRHJn3O9sOtVrTp8UV68X5pJrvR65Kel0tY79undh4VSzwTmtNen3ktN7hzb1BeSztpiE046NCXNZw%2Bzpouu85XNXbTq1bb7KkEP%2FtKx%2FVrOrp6OzC3%2F17Mw6kY3G1SuF2eQqn0tuTeEvjsyMD89qbZXw9wp5Xtx99rmn%2Fe0jRhgkH83ef8a6z7VGC374Mc23uILp47%2FHr4cZl2eGGJTC3dSPI2QJkg%2B0Bq2bWIemNWCzjI5mbH0uOLV8HC4HRDVofh0gCHVHYZ0CdlRMsylYhb24jPtkMG2HWbRcY05KJ%2BOKl2656%2Bzz77r5xfi62fNf2iC6Oi%2BfNzJ%2F9rr4E0PDs3xnbFx5mW3W7OEN%2FYtmbLk8t2DwHM712Zduvvmlz6ZPvWj%2BvH%2F%2BrCzPuHneSfPmX3RqumntScO%2BUy89%2FVLb8Lz1zfM3rnrymRXnaPj1f%2FQBfzX%2FT4wf1h7G4wVAYIwJbXqfErADPoyzYKQnQKgQkC7wCSZEL80fgBHvH9kx57QzRkjTyMgOk%2FWCg%2Br1fz3LnQ4VRg6O7BBsFxwkt8J%2BJhyKF0Z2jJxx1iz1DyNk41mzdo7gV749ssNswu%2F8z1nu7umFEcKfAd%2By0G9tEHtyofiFnXBkZET9wyxNrlf03oHYQSWEefA00hqgVrWbemSMWtcXGBnYd2tVGLSDBhU1Gdr%2BpckNyqbR4NMew1AQtZSOmlVciIpRt16ZXxk878EnvlycoYVWimio%2FsvQ2qGhtUXuW1I2HQ6ns9Kx%2BSCx%2F8IeOPYanhia0EvdP54VE8hgozEU0wTjR6lkb6EvKxOfV0DFgYCMJpkf%2FwxUWmvc6XR0OEgT%2FWhV%2F3jwJ2TrTw4SuRV2nU71LSd%2BxNX%2FUg%2F%2B7McMR1KMG%2BzK3zA9wKGzmfsZrGPsoPWNw9lyN089LuhJGKBOSHe17B7AY24Puhzm0AY5sNZlq7hAZl2lLtJZ5t0zMBUQDE5vZJhuuUqzgJAgOpqx5r6pWpqLTY4wu4jtV7pExYyBkG7Ybe9XBsRvMI6m1Awa61fcmDGI3nDQccU0iaf5fK%2BeKcjLcRAKooyV%2BEbMF8Sid483zAGXiQ7ioU1fSerzhnWLjB42PbIsE12%2FfXkxOXflYCf3kKlv0azY4IpCW3l38Utr2gJ7XWKHt1nghxb%2B6Yn1K0lp6TlOspwYHYFM%2F%2FrC6bfNFpYt5z1dMy8YWbDYrlYdgqdr8Nzhzz9hXbpMXNe2iQ2HuvySUQCz3m0aGOt03zprfkCPCZ%2FH3cs%2FDbTdweAqBHYDqChhGk1spo3srNWyhz4QxOPQaSocUvppD%2FrWbNlPux%2F6w%2BbOskD9XwKjVZQ7Na8FUtKPjW%2FZ6SOUYk7xOWukpaMnhzsOt9IFtO3owcUNjUtGLHUNaoHlHNILaEmlSryXPkJDAEsfKJ2T4j7YFoZJrg%2FntIx90ZI0QyY1bMAclOJjfq%2FFzGW6Nz528%2FcfmLOiNbHa2x6Vw395ySRJ7TPiZ0vRL0SXDGbbl6c65J9lU2tlf8EoWUSbaJ5ma2XXF3sKwYG1Z7atL1%2Fdu7g5JHX1N6%2FqyBWz20MDfVYHiYW%2FGJC5EZ4P2qx38w6j3SpaCnfNB5kD1jX%2FjK7LRGr9Px0ZxYbNRJAoJSsYg4qp3lQ0LppZfGdpDAXsZf2TRgFpSQUNhxzbyt2C0T1WlxFG%2BgwF7H7xmN4TxpgDIa1tmmhipbdaYnhgbzvNk6eeuXKIRkdDMrZAwz5LOIosbZah5dXhgwYwtc4uwxct%2BiNfLLSisNSE7chdoLIGstl9bpdkopVyHpRN2Gmjlo6nmERYE8DoNdFaSMUuKiIOqGaLR2nPaQz0aI2QOFFrilQAFZJ9uYIxpNEirb45p3gOOruLBFv2ICFo01c8q7U4OowNiiqVCTRx0DrDGLNHyxcsCRbsPER9koCrifpdQHK6qJO3mYpU31QCCICMUwJkgX9FJIDDReNfoRaNKB4gzr6gxWrSNLwMNt5qJICgESAWwWoD3oDVBqVmF5zzYx2mEmigho49N5kqDdRAGhTrJDlnnBoaBdgD41Q5thVLcigPLtF5xA1csqnWvxRNAnRfACEoU5TtDtrGUMSJLWcUEX1R%2Bzwu0UQf7SLQRyV5aB4hdmSyUuzxIR20A85%2BhuWMtEuEA6tdDTydt7o%2FgyLGYXBDREtL527kZRgzbUxrDA4aOiDCvQ1GDj4oosJ8HA4Chd6NmJTdtL%2B2G9t7YLFYleJgRd%2FVPg8dHTvFU6A4WDQcRMrCFAfPRBx8k3FIUOg1Bk014kBbEBfPacShSIHHP8qVuERSvtTtGgtwJmKxktERsNZqRFz6VKWwOw%2BVBasHlyRgUmw7Sbva%2BixmfAfpq8FtNaE45YxaFUuN4LQvWO2Vk2rihNaD0da2mLrYQHTatrv%2B%2Bt%2FBawV4nRReEDAgfCx0nbC4EV6L7xPgzUlcA8x1eCsakHoz3hODW%2B%2F5qNWdd4zX7jZPLY2W6wXRzNSC7Mn1Y1jQ%2BPD77z2CRYwVraRRq1zU6hi1E%2B%2BcVCt3HGivxdFqsMTQizkJmvhUaFBkyCAWaKklU2r24GbseCXjkyH0dJP3H0ZYsNLykfcmAFkhS9V9COkj72l1mI2QqrYjR8bjfv9reGPj8Eb%2FHnjlJiznrUM0Ad7ikSMatO8%2FPBnaIiCy9P932kY5hLU2zhNhJYePaEyAiACPzGwg7WaAlfKkwUvrC4PMIFNfjqnbBysH3FqnY1AHZNrVGESpQI2oWvaGIooKp%2BXYY0KEJ18HLl%2BDWnvUW6UGFxlFeBCuivqw%2BjA%2BBmxCIS7V814DQXeI%2FyoTxYwfh%2BYbgiWxmUakMRnC16wlQ5Q86PF34kwFwoAtF0HLuI%2FJ9YHhZuhzF%2FoyoImBrsUIxtdMMknvNhjMZrPDZmWdxGE2yerPyZdZVjCbnRb%2BgPrhYhcreNRFXslqctrZCzyiSHjyrM1pM7uMP1d%2FutZTn8uUbn4Y45lMmdPpZsKabCw1CtK8DhCL3vrUtmoTuhTk9MxExSUqzVrnURjSQj6KhZDUY4VZn7R5BKUpjm8mr9Uik8OOyhlnPPoOrkxU6AAxnd%2FS6PctXmDZd2hQQKNsQ01AiGllVutwOumiGgTAEg22mVXz5CRR5QOT7Dl%2FIBxpaUWtF%2FSDOFYtYzWLyRyOxenRoEi9bfUi65Sge920gt9CCvMVE7RqQJ9Ny66rVVQ%2FuGVR8dF3yJ%2FJxvvI4fvUJzSWOHvh9ffWGPS6Ze88aiZr71Nt96kPTaltObVWj9NEH9Lhaaxw0apysKUEWKKBuoeq9iC053mzxeF0e7RKRrkJjhCjYLO7ROaENTqs3ljiRFU68uV7Lr98z4mLdNx4%2BvIGOYa%2BUBfofmm9C7eod3CgD3mUaBGfCfQF%2BnQ6NzqQfbpPsSYLzCxt7Q%2BrvMPK%2B62uY37yAnkRMxmxWyY2l3Va1Pe4FuxGjGwytiXLHOf%2BnZPu%2F%2FfdXEshNE%2B4excWqauzyZnjd2cP0PDU4aNvTbj3jXBv7LtZw11boQ1aY0rQ6GxZvL1Iby%2Fi7aUpuIMZOuHmPezqL6vzdrG3Ntz8u%2BqP2dW71Xn3j83I1p%2FfhXijz9Zfx1yqYgDRoPUi91HMffTWPrx1cDLmciHFJTxYWdoIwMaDGzez3%2BKO3HfwPvKjBvL%2F29mvbjz2JmH%2F8T%2FhjGo%2FHhzpE8Dhyf4doHhkQS7I1gZQth48%2B1UO7%2FY3ck4DMYoHNx4k7wB8977HfuvoH7OaDJPxmRz8s6BJmbDOg6e2n5lWPhmr2HFOK6rHmh8zkfkH1IT6%2FeXsnrEN5CHOdfQV4lKvJnuq3BNj02sysUhxwyrMpQyuHkYYSCMdSHzmhiNbNor0gRI87Jmytef%2FgfwxUhHkpO4hWkrnNIrustkna14hTaTgcwhkWjGOOm4siXiHpCPA8ZWwd9fYnF2YKVYs7uLe9oapEDz2DBboq5tD3PkVyn%2FDtP%2Beh%2BbjzWJQPwQInRRCJxgVZaNzAnjh2kJtpJ2KmZIRRGDZKvv1xvh1oCSQfhSkRAHLo7hxwNR3r9mmfg4hm3v9NWTj9WriN40Qvq6%2Bd82NAGAJTm64Xm3lnqjU5orR4KCwykycPiuxtQGYRENnBY0qQorUaeJpKIvUwTj%2FCfWQ%2BrxOoOITT9xY%2Bz8OivohvYgS6%2Bir9ORNX8f3Cb1J3ZR2F9E1GWjnoLRzoEljdKHKHcRy5BJvytbJ5wQKM%2BOBHLpAOl3Iz9j9x5alTsAgtijxYGk5rOYlJLB2yDKRzvhATkpmTSnnaFa7RufNqo2ip4WhKw00BjsCsHqSCiPaPrhSYY6D02nHwel42EyAvxF4JSRinkcJi0LKVlsA%2FUOwSE5GQR8oLdZvJuMoVID6l1AcaLRfPdiIwyUVOjY6Fvj01BoOFZoPZ6acMrl2QNMQwVzR%2BrcdXcErev86MnqE5g4ni7RrxZib%2FiDGls6D33tGt6m0ztg3Y6Si5AvmsL1JSW6mD4ssWZ05fFxkyebSqSPUWuhaqSsNvRA%2BugpTIlktaFUZNOPVh40NeM7p0mNAbq8kah0btZJPUDlKxAv0MyFhtXRb%2BKNuB%2FqiehHuDmDWv6Zf0h7Ro3rTaLoJBtrL7LdpRA%2Fzcg%2B9TON79V72tbrsGVpdNi29ZmpOFdmKS9M%2Bs0s2Yf9njNWYqcZusVJHYY8mHkX9ycvam1DvZ5HwYrXOeA957UmwxWxW%2F08bhtBGgVYL3W4Nqu8GEuwB2h4ba2Bq75t%2FoJ5Ee2D8czGUj%2FqNBvUK2o9vZcQjXinOjTT2Nl7C5DBDhkYjbTklVy2zVgfau22Zsr85gZXmKXxWwniyaxsmuxpbaFPjaB4hp8%2F2kbAHLZenSX7APjRwiksPboc51OqdBCi8OEAOBxaTUZrJmw0E1M2LA6otsFjdXDsy3ZYkd6pbkrbp04nBLDm4a3wBwk6Hw%2BoWcmf98LHb8bAe29RsJz%2FTzLSAnZxmskzZr2sKvgz1mdNSH7cX3UCZjNb4yO2iLBVoplqcOMVKKuQS2K%2B5bsNHZSkB6KSiJJ%2BQtCxz0E4PotE0WrkAdJl1RfWOYq24AQswUxfQY6hQaVdrBs3YnMrZ6qFi3dwfrZx9dqU4Nu%2BRWx%2BBcXExLv4h%2FiF8RrA7zMlhLjeT9GHBJxAwTVKFYYLpw07iIpHhe847%2F6f3Xx%2BJPCIKnu94el1bdh47d4todHze0e1%2BmPt95x13HTzv%2FHuGouRhd9pxu4MXt5x7bOcWscf7oscoPjwxtx%2FXt4zeXwmf45qheXsnZUoj9AEvTWhfZmCCtYn7ku3ZWZRomJpQIPneYS6LzCvp9VE0K8Hrm0mGyRDpa5ElL3aIBo0%2FzcdjDt5JJAe5mPUlV25emfQRQi52SChkvOGQ9%2Fve0A4yvINuGIPFra9vO%2F2xq4ozu8zm7EFvzkEyWd7eEpC8Tc0WS1b9qSPnxXZ47JNSgI%2FyMekev%2F8eKQabAaliCpzU0y5F29qiVkt9%2FmIecKAefycTO%2FdGpUQ0hT1dJa2LPX2JMn16x0fcN26gj%2BPQus5Xdh2hT6TGf%2Brmou5zqN3DTDvatOoRdm%2BOSjvLIepN1vpUaQuB9gSReuO%2FWoubXH2riKkGtEKIfhT1Hc0cr6VwsAc0rqImJj5LSu%2Bph70UsGpsge5PcNXtnq4Gf4Ko%2BRPw%2BX2iiz4DoCXe1qF1I6D7wZZkW7tW5DmpjR6G2jzoPMxpXf0NOXQogjqBfeiVrewBrLccm7NVoZ2TKMNjLyqgm%2F6UM3IYr1Bt8I7VGns1IUa930XNoff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AxNRE0NjMhMhYVERQGIyEiJhkBNDYzITIWFREUBiMhIiYBETQ2MyEyFhURFAYjISImGQE0NjMhMhYVERQGIyEiJh0VAZAVHR0V%2FnAVHR0VAZAVHR0V%2FnAVHQJYHRUBkBUdHRX%2BcBUdHRUBkBUdHRX%2BcBUdMgGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHf29AZAVHR0V%2FnAVHR0CbQGQFR0dFf5wFR0dAAAJAAAAAARMBEwADwAfAC8APwBPAF8AbwB%2FAI8AdgCyDQAAK7E8bDMzsATNsTRkMjKwHS%2BxTHwzM7AUzbFEdDIysC0vsVyMMzOwJM2xVIQyMgGwkC%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%2BwTDOwHM2wRDKwLS%2BwXDOwJM2wVDIBsGAvsADWsRAgMjKwCc2xFygyMrAJELEwASuxQFAyMrA5zbFIWDIysWEBKwAwMT0BNDY7ATIWHQEUBisBIiYRFBY7ATI2PQE0JisBIgYVPQE0NjsBMhYdARQGKwEiJgE1NDYzITIWHQEUBiMhIiYRNTQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJh0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHQGQHRUCvBUdHRX9RBUdHRUCvBUdHRX9RBUdHRUCvBUdHRX9RBUdMsgVHR0VyBUdHQGlFR0dFcgVHR0VyMgVHR0VyBUdHfz1yBUdHRXIFR0dAaXIFR0dFcgVHR0BpcgVHR0VyBUdHQAAAAEAHQAiBPIEKgAFAAATCQEnAScdAaMDMtT9oc4Bxv5cAzTU%2FaHPAAABAGoAagRGBEYACwAAEwkBNwkBFwkBBwkBagEa%2FubUARoBGtT%2B5gEa1P7m%2FuYBPgEaARrU%2FuYBGtT%2B5v7m1AEa%2FuYAAAMAF%2F%2FsBMQEmQATABsAJwC3ALIOAAArsBIvsBfNsBwvsCMzsB3NsCEyshwdCiuzQBwmCSuyHRwKK7NAHR8JK7AbL7ADzQGwKC%2BwAdawFc2wFRCxJgErsB4ysCXNsCAysiUmCiuzQCUjCSuyJiUKK7NAJhwJK7AlELEZASuwBc2xKQErsSYVERKyAhYbOTk5sCURsBI5sBkSswMXEBokFzmwBRGwBzkAsRcSERKwEDmxHQ4RErMABRUYJBc5sBsRshQZATk5OTAxEhAAIAAVFAcBFhQPAQYiJwEGIyICEBYgNhAmIAM1MzUzFTMVIxUjNRcBHAGQARxOASwHB20IFAj%2B1HeOyIPCARLBwf7uRmTIZGTIAe0BkAEc%2FuTIjnf%2B1AgUCG0HBwEsTgJs%2FvDCwQESwf5ZyGRkyGRkAAADABf%2F7ATEBJoAEwAbAB8AXQCyDgAAK7ASL7AXzbAbL7ADzQGwIC%2BwAdawFc2wFRCxGQErsAXNsSEBK7EZFREStQMCEBIcHSQXObAFEbAHOQCxEg4RErAJObAXEbAQObAbErUBAAcFHB4kFzkwMRIQACAAFRQHARYUDwEGIicBBiMiAhAWIDYQJiADITUhFwEcAZABHE4BLAcHbQgUCP7Ud47Ig8IBEsHB%2Fu5GAZD%2BcAHuAZABHP7kyI15%2FtUHFgdtCAgBLE4CbP7wwsIBEML%2BWcgAAAIAFwAXBJkEsAAbACsARQCwGC%2BwCs0BsCwvsADWsAfNsgcACiuzQAcDCSuwBxCxDAErsBPNsgwTCiuzQAwQCSuxLQErsQwHERKzFxgcIyQXOQAwMRM0EjcVDgEVFBYgNjU0Jic1FhIVFA4CIi4CARQWOwEyNjURNCYrASIGFRfSp2d8%2BgFi%2Bnxnp9Jbm9Xs1ZtbAd0dFWQVHR0VZBUdAli3ASg%2BpjfIeLH6%2BrF4yDemPv7Yt3bVm1tbm9UBDBUdHRUBkBUdHRUABABkAAEEsASxAAMABwALAA8AMACyBAAAK7EIDDMzAbAQL7AE1rAHzbAHELEIASuwC82wCxCxDAErsA%2FNsREBKwAwMTczESMBETMRMxEzETMRMxFkyMgBLMhkyGTIAQEs%2FtQB9P4MAyD84ASw%2B1AAAAIAGgAbBJYElgBHAFEAYgCwEi%2BwUM2wSy%2BwNs0BsFIvsADWsEjNsEgQsU0BK7AkzbFTASuxSAARErELPTk5sE0Rsw8VMzkkFzmwJBKxGS85OQCxUBIRErEHHTk5sEsRswMhJ0UkFzmwNhKxK0E5OTAxExQfAhYfAQcWFzcXFh8CFjMyPwI2PwEXNjcnNzY%2FAjY1NC8CJi8BNyYnBycmLwImIyIPAgYPAScGBxcHBg8CBgU0NjIWFRQGIiYaBpcCDhgDUC08hQUtMQUmKCIbLyYGLi8FhjgwUAMYDwGYBQWYARAXA1AsPIYFLTAGJigiGy8mBTIsBYU7LlADGQ0ClwYBZ36yfn6yfgJZISkmBjEsBYY7LlEDGg0ClwUFlwINGgNRLD2GBSwxBiYoIhwtJgYzKgWGOi9RAxkOAZgFBZgBDhkDUS86hgUvLgYmMBlYfn5YWX5%2BAAAABwBk%2F%2F8EsAUUABkAIwAnACsALwAzADcAiQCyIQAAK7AkzbIoMDQyMjKwJy%2ByKjI2MzMzsBvNsBcvsATNsQ4sMjKwLy%2BwCc0BsDgvsBrWsCTNsCQQsSUBK7AFMrAozbAsMrIlKAors0AlAAkrsCgQsSkBK7AwzbAwELExASuwLTKwNM2wDTKyNDEKK7NANBMJK7A0ELE1ASuwHc2xOQErADAxEzU0NjMhNTQ2MyEyFh0BITIWHQEUBiMhIiYTESERFAYjISImNzMRIxMzESMRITUhEzMRIxMzESNkDwoBEzspASwpOwETCg8OC%2FvmCw5kA4Q7Kf1EKTtkZGTIZGQBLP7UyGRkyGRkBAEyCg9kKTs7KWQPCjILDg78bgMg%2FOApPDwpArz9RAK8ASxk%2B7QCvP1EArwAAAAAAQABAAEFFQTdAAoALACyCQAAK7AEMwGwCy%2BwCdawCM2wCBCxBQErsATNsQwBK7EFCBESsAE5ADAxEwkBIxEhESERIREBApAChMj%2B1P7U%2FtQCWQKE%2FXz9qAGQ%2FnACWAAAAgBkAAAD6ASwAA4AEQAiALIMAAArAbASL7AB1rAGzbIGAQors0AGCAkrsRMBKwAwMTcRNDYzIREhERQGIyEiJgERAWQOCwHbAZAOC%2FyuCw4CWAEsGQR%2BCw7%2BDP1dCw4OAxIBLP7UAAADAAQABASsBKwACwATABkAggCwCi%2BwD82wFC%2BwF82yFxQKK7NAFxUJK7ATL7AEzQGwGi%2BwAdawDc2wDRCxFAErsBfNshcUCiuzQBcZCSuwFxCxEQErsAfNsRsBK7EUDRESswoDDhMkFzmxERcRErMJBA8SJBc5ALEUDxESswcADRAkFzmxExcRErMGAREMJBc5MDESEBIkIAQSEAIEICQSEBYgNhAmIBMRMxEzFQSgARIBRAESoKD%2B7v68%2Fu4W8wFW8%2FP%2BqkdkyAG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP9%2FgGQ%2FtRkAAAAAAL%2FnAAABRQEsAALAA8ALgCyAAAAK7AHM7AKL7AMzbAPL7ADzbIDDwors0ADAQkrsAUyAbAQL7ERASsAMDEjATMDMwMzASEDIwMTMwMjZAGv0RWiFNABr%2F3mKfIoMeAbqgSw%2FtQBLPtQAZD%2BcAH0ASwAAAAAAgAAAAAETASwAAsADwBKALILAAArsAzNsA8vsAnNsAEyAbAQL7AE1rAHzbIEBwors0AEAAkrsAcQsQ0BK7AKzbERASuxBwQRErECCTk5sA0RsQgMOTkAMDExESEBMxEhETMBIRElMzUjAer%2B3sgBLMj%2B3gHq%2FuGvrwGQASwB9P4M%2FtT%2BcMhkAAMAAQABBK8ErwAPABcAHgBjALINAAArsBPNsBcvsAXNAbAfL7AB1rARzbARELEZASuwHM2wHBCxFQErsAnNsSABK7EZEREStA0EEhcYJBc5sBwRsB45sBUStAwFExYdJBc5ALEXExEStQEICQAaHiQXOTAxEjQ%2BAjIeAhQOAiIuARIQFiA2ECYgAzMRMxEzAwFfoN703qBfX6De9N6gXPIBVPLy%2FqxQlsiW%2BgHe9N6gX1%2Bg3vTeoF9foAIC%2Fqzy8gFU8v5kASz%2B1P7UAAMABAAEBKwErAALABMAGgBhALAKL7APzbATL7AEzQGwGy%2BwAdawDc2wDRCxGQErsBjNsBgQsREBK7AHzbEcASuxGQ0RErQKAw4TFCQXObAYEbAVObARErQJBA8SFiQXOQCxEw8RErUBBgcAFRgkFzkwMRIQEiQgBBIQAgQgJBIQFiA2ECYgAxsBIxEjEQSgARIBRAESoKD%2B7v68%2Fu4W8wFW8%2FP%2Bqk%2F6%2BpbIAbYBRAESoKD%2B7v68%2Fu6goAJf%2Fqrz8wFW8%2F5iASz%2B1P7UASwAAAACAAAAAASwBLAACwATACkAsgkAACuwDM2wEDKwEy%2BwAs0BsBQvsRUBKwCxDAkRErIEAQ45OTkwMTUREyEbARQGIyEiJhMzFyE3MwMhyAMgxwEOC%2FuCCw7IyDIBLDLIYf2iGQHbArz9RP4lCw4OAebIyAH0AAMABAAEBKwErAALABUAGABGALAKL7APzbAUL7AEzQGwGS%2BwAdawDM2wDBCxEQErsAfNsRoBK7ERDBEStQQJCgMWGCQXOQCxFA8RErUBBgcAFhckFzkwMRIQEiQgBBIQAgQgJBMUFiA2NTQmIAYBEQUEoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqrzAToBKQG2AUQBEqCg%2Fu7%2BvP7uoKABtKzy8qyr8%2FP%2BigGRyAABABcAFwSZBLAAHABTALAFL7ANzbARL7AZzQGwHS%2BwAdawD82wDxCxCgErsAnNsR4BK7EKDxEStQUEExQXGSQXObAJEbEVFjk5ALERDREStAEACRQVJBc5sBkRsBc5MDESFB4CMj4CNSMUBiAmEDYzMhcHIREHJiMiDgEXW5vV7NWbW5b6%2Fp76%2BrGIbpIBkJGdxnbVmwLO7NWbW1ub1Xax%2BvoBYvpRkgGQkXpbmwAAAAIAFwAABJkEsAAQACEAegCyEQAAK7AfL7AWzbIWHwors0AWGgkrsA0vsAXNsg0FCiuzQA0ACSsBsCIvsADWsBDNsBAQsRkBK7AazbEjASuxEAARErEREjk5sBkRtwcFCwoTFB8hJBc5sBoSsQkIOTkAsRYfERKwITmwDRGxCRI5ObAFErAHOTAxEzQ%2BAjMyFzcRITcmIyIGFQMRIQcWMzI2NTMUDgIjIicXW5vVdsadkf5wk3CHsfpJAZCTcIex%2BpZbm9V2xp0CWHbVm1t6kf5wk1D6sf2oAZCTUPqxdtWbW3oAAAoAZAAABLAEsAADAAcACwAPABMAFwAbAB8AIwAnAFAAsAgvsBgzsAnNsBoysAwvsBwzsA3NsB0ysBAvsCAzsBHNsCEysBQvsCQzsBXNsCUyAbAoL7AI1rIMEBQyMjKwC82yDhIWMjIysSkBKwAwMTMhESETESERJTUzFSc1MxUnNTMVJzUzFRMhNSE9ASEVJTUhFSU1IRVkBEz7tGQDhPzgZGRkZGRkZGQB9P4MAfT%2BDAH0%2FgwB9ASw%2B7QDhPx8ZGRkyGRkyGRkyGRk%2FahkZGRkyGRkyGRkAAIAAAAABEwEsAAZACMASgCyFwAAK7AgL7AJzQGwJC%2BwBdawGs2yBRoKK7NABQAJK7AaELEbASuwDs2yDhsKK7NADhMJK7ElASsAsSAXERKzBA4FGiQXOTAxNRE0NjsBNTQ2MyEyFh0BMzIWFREUBiMhIiYBITU0JisBIgYVOylkdlIBLFJ2ZCk7Oyn8fCk7AZABLB0VyBUdZAJYKTvIUnZ2Usg7Kf2oKTs7AuWWFR0dFQAAAAIAZAAABEwETAADABUAFwCyAAAAKwGwFi%2BwANawA82xFwErADAxMxEzERM%2BAR4CPgE3EQ4BLgMGB2RkZDyHeHxyamQpKHuEkId0WhQETPu0AZA8MA0hGwVPUQH0UUUKKCgKRVEAAAAAAwAAAAAEsASXACEAMQBBAGcAsi8AACuwPjOwJs2wNjKwDC%2BwHc0BsEIvsADWsAfNsAcQsSIBK7ArzbArELEyASuwO82wOxCxEAErsBfNsUMBK7EyKxESswwLHRwkFzkAsSYvERK0BxATFAMkFzmwDBGxCA85OTAxERQWOwEyNjURND4BIB4BFREUFjsBMjY1ETQuAiIOAhUTETQ2OwEyFhURFAYrASImJRE0NjsBMhYVERQGKwEiJg4LMgsOjeQBBuSNDgsyCw5jo97o3qNjyAwIoAgMDAigCAwCWAwIoAgMDAigCAwBEwsODgsBLH%2FRcnLRf%2F7UCw4OCwEsdN6jY2Oj3nT91QHMCAwMCP40CAwMCAHMCAwMCP40CAwMAAAAAgAAAMgEWAPoAAUAEQAAESEFEQUhATcnNxc3FwcXBycHASwBLP7U%2FtQCsI2NR42NR42NR42NAZDIAyDI%2FquNjUeNjUeNjUeNjQAAAAIAAADIA3AD6AAFAA8AEgABsBAvsA7WsAnNsREBKwAwMREhBREFISU3FhUUByc2NTQBLAEs%2FtT%2B1AK8RW9qQ1YBkMgDIMg5NYevqYU2boqSAAAAAAMAAAC6BGID9wAFAA8AHQA8ALAAL7ABzQGwHi%2BwDtawCc2wCRCxEwErsBrNsR8BK7ETCRESsxAWFx0kFzkAsQEAERKzCQ4TGiQXOTAxGQEhJRElATcWFRQHJzY1NDcXFhUUDwEXNzY1NC8BASwBLP7UAZJFb2pDVl4He3cHUQaOkAYBkQGQyPzgyAHJNYevqoU3boqRzQiXwb2WCEIIsuPmsggADQAAAAAEsASwAAcAEQAVABkAHQAhAC8AMwA%2FAEMARwBLAE8BAQCyAAAAK7EwRDMzsBLNsikxRTIyMrAaL7InK0wzMzOwG82yJS1NMjIysCIvsQIGMzOwI82wCDKwHi%2BxDkgzM7AhzbE0STIyAbBQL7Aa1rEFHjIysB3NsQMfMjKwHRCxMAErsQ0sMjKwM82wNTKwMxCxLgsrsCoysCXNsEAysi4lCiuzQC4iCSuyAQsPMjIysCUQsTcBK7FESDIysDvNsSZKMjKwOxCxTAErsEIysE%2FNsjk9RjIyMrFRASuxMB0RErUUFRgZND8kFzmxNyURErIoKTg5OTkAsSIbERKzExQ4OSQXObAjEbIEOjs5OTmwHhJACQUWGTY3PD1AQyQXOTAxMSERIzUjFSM1MzUhNTM1IxEhExEhEQERIREDNTMVAzM1IwE1IREzFSMVIzUjNTM1AzUzFQMzETMRITUjNTMRIRMRIREDNSEVATUzFRM1MxUB9MhkyGQBkGRk%2FgxkASz%2B1AEsyGRkZGQBLAEsyGTIZGRkZGRkyAEsyMj9qMgBLMgBLP7UZGRkAfRkZGRkZGQBLPu0ASz%2B1AK8ASz%2B1P2oZGQCvGT%2BDGT%2B1GRkZGTI%2FgxkZAPo%2FtT%2B1MhkAfT%2BcAEs%2FtT84GRkA4RkZP1EZGQAAAAACQAAAAAEsASwAAMABwALAA8AEwAXABsAHwAjAHAAsgwAACuyBBQcMzMzsA3NsRUdMjKyDAAAK7AFzQGwJC%2BwCNawC82wCxCxEAErsAwysBPNsA%2FNsBMQsRQLK7AXzbAXELEYCyuwG82wGxCxIAErsCPNsSUBK7EQCxESsQcGOTmxGxcRErEcHTk5ADAxNTMRIxM1IRUnETMRFzUzFScRMxEVNTMVNREzERU1MxUnETMRZGRkASzIZMhkZMhkZMhkyMgD6PtQZGTIA%2Bj8GMhbW8gD6PwYyFtbyAPo%2FBjIW1vIA%2Bj8GAAAAAIAAAAABLAEsAAHABMAKQCyBwAAK7ASL7AEzQGwFC%2BwAdawCc2xFQErALESBxESsgAGCzk5OTAxERM0NjMhCQEAFBcWMjc2NCcmIgcBDwoB2gK8%2Fgz92B0eUx4dHR5THgK8AdsKD%2F1E%2FgwD41QdHh4dVB0eHgAAAAMAAQAABd0EsAAHABMAGQAxALIHAAArsBczsBIvsATNsBQyAbAaL7AB1rAJzbEbASsAsRIHERK0AAYLFhkkFzkwMRsBNDYzIQkBABQXFjI3NjQnJiIHJTMJAScBAQEOCwHaArz%2BDP3XHh1UHR4eHVQdAgtkArz%2BDDIBwgK8AdsLDv1E%2FgwD41QdHh4dVB0eHrD9RP4MMgHCAAAAAAEAZAAABLAEsAAKAD8AsgAAACuwBy%2BwAs0BsAsvsADWsArNsAoQsQUBK7AEzbEMASuxCgARErICBwg5OTkAsQcAERKyAQQFOTk5MDEzETchEQcRIQchEWSvA51k%2FRJkAu4EAa%2F8GGQD6GT8GAAAAAABAMgAAARMBLEACgAAMwkBETQmIyEiBhXIAcIBwh0V%2FOAVHQG8%2FkUEfhQeHhQAAAADAAAAAASwBLAACwAXACcAWQCyJQAAK7AczbAKL7ADzbIKAwors0AKAAkrsAcysgMKCiuzQAMBCSuwBTIBsCgvsADWsAvNsAIysAsQsQgBK7AFMrAHzbEpASuxCAsRErMMDyciJBc5ADAxNREzFyE3MxEjNSEVExchNwMuASMhIgYHAzc%2BATMhMhYfARYGIyEiJshkAlhkyMj84DUoAlA%2BXgIQCv4%2BChACQiYCEwoB9AoTAiYCCwr9qAoLZAK8yMj9RMjIAtl8fAFaCw4OC%2FuBmAoODgqYCg4OAAAABAAAAGQEsARMAB0AJQAtADEAbwCwAy%2BwJc2wKS%2BwLc2wIS%2BwE80BsDIvsADWsB%2FNsh8ACiuzQB8vCSuwHxCxJwErsCvNsTMBK7EfABESsBk5sSsnERKzISQlICQXOQCxLSkRErMfIiMeJBc5sCERsS4xOTmwExK0CxkaLzAkFzkwMTUUFjMhMjY1ETQmKwEuBCsBIg4CDwEjIgYVADQ2MhYUBiICFBYyNjQmIiU1MxU7KQPoKTs7KZYEDzM3UyrIKVI6LgsMlik7AWSQyJCQyAY%2BWD4%2BWAFYZMgpOzspAlgpOwgbRTUrKTs7FRQ7Kf5wyJCQyJABIFg%2BPlg%2BXmRkAAIANQAABLAErwAeACIAHgCyAAAAK7ANM7AezbICDA8yMjIBsCMvsSQBKwAwMTMhNSIuAT8BIRcWBiMVITUmJy4BLwEBIwEGBw4BDwEBExcTNQFtKT4kE1wBh1IQKzUBoSIoEh4GBv5%2FXf5xGBwMKg8PAWuyLnRCFjYs6t4tV0JCASoTLg4NA%2Bb8EjAbDBoHBwHHAcmM%2FsMAAwBkAAADwwSwACAAKQAxAGUAsiAAACuwIc2yIAAAK7ABzbApL7AqzbAxL7ANzbANELALzQGwMi%2BwBNawIc2wKjKwIRCxLgErsBDNsCUg1hGwHM2xMwErsSUuERKwFjkAsSkhERKwHDmwKhGwFjmwMRKwEDkwMTM1PgE1ETQuAyc1BTIWFRQOAg8BHgQVFA4BIyczMjY1NCYrATUzMjY1NCYjZCk7AgkWJB8B13i6FyEiCwwIG0U0K3amT8ihWYB9Xp%2BLTGyom1kHMygDOxwXHQ0PB0cBsIw3XTcoCAcDDDNBdkZUkU3IYVRagWR7TVJhAAAAAAEAyAAAA28EsAAZACAAsgAAACuwAc2wGDKwCy%2BwDjOwDM0BsBovsRsBKwAwMTM1PgE3EzYmJy4BJzUhFw4DDwEDBhYXFchNcwitCihHBgkFAakCIToiGQUFgAowRzkHQy8DUTgkEwEDATk5CCMnJQwM%2FMc0PAY5AAL%2FtQAABRQEsAAJACUAfgCyGwAAK7AfL7ICBRYzMzOwDM2yHwwKK7NAHxAJK7AKMgGwJi%2BwAdawB82wBxCxCgErsCXNsCUQsR0BK7AYzbIYHQors0AYGgkrsh0YCiuzQB0bCSuwGBCxEAErsA%2FNsScBK7EKBxESsQUIOTkAsR8bERKwCTmwDBGwBDkwMSczESM3FyMRMwcTETMhMxEjNC4DKwERFxUhNTcRIyIOAxVLS0t9fUtLffqWAryWMhAVLiEiyGT%2BcGTIIiEvFBHIAyCnp%2FzgpwNjASz%2B1B0nFQkC%2FK4yZGQyA1ICCRUnHQAAAAIAIf%2B2BI8EsQAJACUAiQCyCAAAK7ACzbAfL7AWM7AMzbIfDAors0AfCgkrsA8yAbAmL7AK1rAlzbAlELEdASuwGM2yGB0KK7NAGBoJK7IdGAors0AdGwkrsBgQsRABK7APzbEnASuxHSURErMCCAkBJBc5sRAYERKzBAYHAyQXOQCxAggRErEABTk5sB8RsgEEGjk5OTAxPwEVITUXBzUhFQMRMyEzESM0LgMrAREXFSE1NxEjIg4DFSGnAyCnp%2FzgZJYCvJYyEBQvISLIZP5wZMgiIS4VEDN9S0t9fUtLA88BLP7UHScVCQL9djJkZDICigIJFScdAAAAAAQAAAAABLAETAAPAB8ALwA%2FAAA1FBYzITI2PQE0JiMhIgYVNRQWMyEyNj0BNCYjISIGFTUUFjMhMjY9ATQmIyEiBhU1FBYzITI2PQE0JiMhIgYVHRUETBUdHRX7tBUdHRUDIBUdHRX84BUdHRUD6BUdHRX8GBUdHRUCWBUdHRX9qBUdMhQeHhRkFR0dFcgUHh4UZBUdHRXIFB4eFGQVHR0VyBQeHhRkFR0dFQAEAAAAAASwBEwADwAfAC8APwAANRQWMyEyNj0BNCYjISIGFREUFjMhMjY9ATQmIyEiBhUTFBYzITI2PQE0JiMhIgYVERQWMyEyNj0BNCYjISIGFR0VBEwVHR0V%2B7QVHR0VBEwVHR0V%2B7QVHcgdFQK8FR0dFf1EFR0dFQK8FR0dFf1EFR0yFB4eFGQVHR0VAfQUHh4UZBUdHRX%2BcBQeHhRkFR0dFQH0FB4eFGQVHR0VAAQAAAAABLAETAAPAB8ALwA%2FACYAsg0AACuwBM2wLS%2BwJM2wHS%2BwFM2wPS%2BwNM0BsEAvsUEBKwAwMT0BNDYzITIWHQEUBiMhIiYTNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJhM1NDYzITIWHQEUBiMhIiYdFQRMFR0dFfu0FR1kHRUD6BUdHRX8GBUdyB0VAyAVHR0V%2FOAVHcgdFQJYFR0dFf2oFR0yZBUdHRVkFB4eAmxkFR0dFWQUHh7%2B6GQVHR0VZBQeHgJsZBUdHRVkFB4eAAQAAAAABLAETAAPAB8ALwA%2FACYAsg0AACuwBM2wHS%2BwFM2wLS%2BwJM2wPS%2BwNM0BsEAvsUEBKwAwMT0BNDYzITIWHQEUBiMhIiYRNTQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJhE1NDYzITIWHQEUBiMhIiYdFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0yZBUdHRVkFB4eAUBkFR0dFWQUHh4BQGQVHR0VZBQeHgFAZBUdHRVkFB4eAAAAAAgAAAAABLAETAAPAB8ALwA%2FAE8AXwBvAH8AUgCyDQAAK7BMM7AEzbBEMrAdL7BcM7AUzbBUMrAtL7BsM7AkzbBkMrA9L7B8M7A0zbB0MgGwgC%2BwANayECAwMjIysAnNshgoODIyMrGBASsAMDE9ATQ2OwEyFh0BFAYrASImETU0NjsBMhYdARQGKwEiJhE1NDY7ATIWHQEUBisBIiYRNTQ2OwEyFh0BFAYrASImATU0NjMhMhYdARQGIyEiJhE1NDYzITIWHQEUBiMhIiYRNTQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJh0VZBUdHRVkFR0dFWQVHR0VZBUdHRVkFR0dFWQVHR0VZBUdHRVkFR0BLB0VAyAVHR0V%2FOAVHR0VAyAVHR0V%2FOAVHR0VAyAVHR0V%2FOAVHR0VAyAVHR0V%2FOAVHTJkFR0dFWQUHh4BQGQVHR0VZBQeHgFAZBUdHRVkFB4eAUBkFR0dFWQUHh78kGQVHR0VZBQeHgFAZBUdHRVkFB4eAUBkFR0dFWQUHh4BQGQVHR0VZBQeHgAABv%2BbAAAEsARMAAYACgAaACoAOgBKACAAsAAvsCYzsAHNsC4yAbBLL7FMASsAsQEAERKwBDkwMQM1MzUXBzUTMxEjExQWMyEyNj0BNCYjISIGFTUUFjMhMjY9ATQmIyEiBhU1FBYzITI2PQE0JiMhIgYVNRQWOwEyNj0BNCYrASIGFWXJpqbIZGTIHRUCWBQeHhT9qBUdHRUBLBQeHhT%2B1BUdHRUB9BQeHhT%2BDBUdHRVkFB4eFGQVHQH0ZEt9fUv%2BDARM%2B%2BYUHh4UZBUdHRXIFB4eFGQVHR0VyBQeHhRkFR0dFcgUHh4UZBUdHRUAAAAABgABAAAFFQRMAA8AHwAvAD8AQwBKABcAskAAACsBsEsvsEDWsEPNsUwBKwAwMTcUFjMhMjY9ATQmIyEiBhU1FBYzITI2PQE0JiMhIgYVNRQWMyEyNj0BNCYjISIGFTUUFjsBMjY9ATQmKwEiBhUBETMRExc1MzUjNQEdFQJYFB4eFP2oFR0dFQEsFB4eFP7UFR0dFQH0FB4eFP4MFR0dFWQUHh4UZBUdAyBkIafIyDIUHh4UZBUdHRXIFB4eFGQVHR0VyBQeHhRkFR0dFcgUHh4UZBUdHRX75gRM%2B7QCJn1LZEsAAgAAAMgEsAPoAA8AEgAtALANL7AEzbAEzQGwEy%2BwANawCc2xFAErsQkAERKwEDkAsQQNERKxERI5OTAxGQE0NjMhMhYVERQGIyEiJgkBESwfAu4fLCwf%2FRIfLAOEASwBEwKKHywsH%2F12HywsAWQBLP2oAAADAAAAAASwBEwADwAXAB8AWQCyDQAAK7AfL7AbzbAXL7AEzQGwIC%2BwANawEM2wEBCxGQErsB3NsB0QsRUBK7AJzbEhASuxHRkRErARObAVEbETEjk5ALEfDRESshATFTk5ObAbEbAUOTAxNRE0NjMhMhYVERQGIyEiJj8BBScBExEhEjQ2MhYUBiIaEgRYExkZE%2FuoEhpk9wEqSgEl7PwYbE5wTk5wLAP0EhoaEvwMEhoa7baDnAE%2B%2FuAB9P7OcE5OcE4AAgCU%2F%2FMEHAS9ABQAHgA9ALINAAArsB0vsATNAbAfL7AA1rAVzbAVELEbASuwCM2xIAErsRsVERKxDQQ5OQCxHQ0RErIHABg5OTkwMRM0PgEzMh4BFAcOAQ8BLgQnJjcUFjMyNjQmIgaUedF6e9B5SUm7OTkKImNdcys%2Fwpdqa5eX1pYC6XzXgX7V9pVy9kJCCSJrb6BLi5Zrl5fWlpcAAAIAAQABBK8ErwAPABUASQCyDQAAK7ATzbAUL7AFzQGwFi%2BwAdawEc2wERCxEwErsAnNsRcBK7ETERESsQ0EOTmwCRGxBQw5OQCxFBMRErMBCAkAJBc5MDESND4CMh4CFA4CIi4BEhAWMxEiAV%2Bg3vTeoF9foN703qBN%2B7CwAd703qBfX6De9N6gX1%2BgAgn%2BnvoDVgACAHUABAPfBQ8AFgAlAAATND4DNx4GFRQOAgcuAjceARc3LgInJjY%2FAQ4BdURtc3MeFUlPV00%2FJU5%2Bmk9yw4B%2BDltbEAcWLgoPAgkJXDcBll64oZ3FYEePdndzdYZFWZlkOwQGXrh%2BUmwaYgYWSihJjTQzbpYAAAADAAAAAATFBGgAHAAhACYAVwCyGgAAK7APzbAIL7AEzQGwJy%2BwANawDM2wDBCxEwErsBbNsSgBK7ETDBESswYdHiAkFzmwFhGxHyI5OQCxCA8RErMVHR8hJBc5sAQRsyAiIyUkFzkwMRkBNDYzBBcHISIGFREUFjMhMjY9ATcVFAYjISImJTcBJwkBFzcvAeulAW4fuv7JKTs7KQH0KTvI66X%2B1KXrAbShAZxy%2FmsB%2BXFxFVwBkAEspesGCLo7Kf4MKTs7KX3I4aXr62oyAZxx%2FmsB%2BHFxVRwAAAAAAgAAAAAElQRMABwALgBIALIaAAArsBDNsCIvsCfNsAkvsATNsAQQsAbNAbAvL7AA1rANzbEwASsAsSIQERKyFR0kOTk5sQkaERKwJTmxBAYRErAmOTAxGQE0NjMhFwYHIyIGFREUFjMhMjY1NxUUBiMhIiYBPgMfARUJARUiDgXrpQEFAoVVkSk7OykB9Ck7yOul%2FtSl6wGnHmdnXx4dAWj%2BmQcYSENWQzkBkAEspetQIFg7Kf4MKTs7KZk1pevrASEmNBMJAQHRAUQBPtgCDhczQ20AAAAAAgAAAAAEqARMAB0AIwBSALIbAAArsBDNsAkvsATNAbAkL7AA1rANzbANELEUASuwF82xJQErsRQNERKzBx4fIiQXObAXEbAhOQCxCRARErMWHyIjJBc5sAQRsSAhOTkwMRkBNDYzITIXByEiBhURFBYzITI2PQE3FRQGIyEiJgkCJwEn66UBLD1Csv6jKTs7KQH0KTvI66X%2B1KXrAVYBGwI3if5SkgGQASyl6xexOyn%2BDCk7OylFyKml6%2BsBjf7kAjeJ%2FlGTAAABAAAAAQSwBLEAFwBFALISAAArsBYvsA4zsALNsAkyAbAYL7AU1rADMrAQzbAIMrEZASuxEBQRErEGEjk5ALEWEhESsQ0XOTmwAhGxAAw5OTAxEQEVMzUjCQEjFTM1CQE1IxUzCQEzNSMVASzIyAEsASfDyAEs%2FtTIw%2F7Z%2FtTIyAJbASjGyAEs%2FtTIxv7Y%2FtTGyP7UASzIxgAAAAABAMgAAAOEBEwAEwAdALIRAAArsAszAbAUL7AA1rANzbAIMrEVASsAMDE3ETQ2OwEyFhURAREBERQGKwEiJsgdFWQVHQH0%2FgwdFWQVHTID6BUdHRX%2BSwHn%2B7QB6P5KFR0dAAAAAQAAAAAEsARMABcAHwCyFQAAK7ENDzMzAbAYL7AA1rARzbAIMrEZASsAMDE1ETQ2OwEyFhURAREBEQERAREUBisBIiYdFWQVHQH0AfT%2BDP4MHRVkFR0yA%2BgVHR0V%2FksB5%2F4ZAef7tAHo%2FhgB6P5KFR0dAAABAIgAAASwBEwABgAUALIGAAArsAQzAbAHL7EIASsAMDETAREBEQERiAI0AfT%2BDAImAib%2BGQHn%2B7QB6P4YAAAAAQDIAAAETARMAAIAADMJAcgDhPx8AiYCJgAAAAIAyABkA4QD6AAPAB8AADcUFjsBMjY1ETQmKwEiBhUBFBY7ATI2NRE0JisBIgYVyB0VyBUdHRXIFR0BkB0VyBUdHRXIFR2WFR0dFQMgFR0dFfzgFR0dFQMgFR0dFQAAAAEAyABkBEwD6AAPAAA3FBYzITI2NRE0JiMhIgYVyB0VAyAVHR0V%2FOAVHZYUHh4UAyAVHR0VAAAAAQAAAAAEKARMAAYAFACyAAAAK7AFMwGwBy%2BxCAErADAxMREBEQkBEQH0AjT9zARM%2FhkB5%2F3a%2FdoB6AAAAQAAAAAEsARMABcAHwCyAAAAK7EQFjMzAbAYL7AU1rAEMrANzbEZASsAMDExEQERARE0NjsBMhYVERQGKwEiJjURAREB9AH0HRVkFR0dFWQVHf4MBEz%2BGQHn%2FhkBtRUdHRX8GBUdHRUBtv4YAegAAAEBLAAAA%2BgETAATAB0AsgAAACuwDjMBsBQvsBLWsAIysAvNsRUBKwAwMSERARE0NjsBMhYVERQGKwEiJjURASwB9B0VZBUdHRVkFR0ETP4ZAbUVHR0V%2FBgVHR0VAbYAAAIAZADIBLAEKAAPABIAEgCwDS%2BwBM0BsBMvsRQBKwAwMTc1NDYzITIWHQEUBiMhIiYRIQFkHRUD6BUdHRX8GBUdBEz92vpkFR0dFWQVHR0BDwI0AAEAuQAHA%2FkEqQAFAAATATcJASe5AlDw%2Fp8BYfACV%2F2w8AFhAWHwAAABARD%2F0gRSBHQACAAAJQkBNwEXBxUBARABYf6f8QI8FQH9sMIBYQFh8P3FFgEB%2FbEAAAAAAgADAAMErQStAAsAFwBCALAKL7AOzbAVL7AEzQGwGC%2BwAdawDM2wDBCxEQErsAfNsRkBK7ERDBESswQJCgMkFzkAsRUOERKzAQYHACQXOTAxEhASJCAEEhACBCAkEzMVMzUzNSM1IxUjA6ABEwFEAROgoP7t%2Frz%2B7YnIyMjIyMgBtgFEAROgoP7t%2Frz%2B7aCgAVHIyMjIyAACAAMAAwStBK0ACwAPAEkAsAovsAzNsA8vsATNAbAQL7AB1rAMzbAMELENASuwB82xEQErsQ0MERKzBAkKAyQXOQCxDAoRErEHADk5sQQPERKxAQY5OTAxEhASJCAEEhACBCAkEyE1IQOgARMBRAEToKD%2B7f68%2Fu2JAlj9qAG2AUQBE6Cg%2Fu3%2BvP7toKABUcgAAAAAAgADAAMErQStAAsAFwAyALAKL7AEzbAEzQGwGC%2BwAdawB82wB82xGQErsQcBERKxDBA5OQCxBAoRErENFTk5MDESEBIkIAQSEAIEICQTFzcXNyc3JwcnBxcDoAETAUQBE6Cg%2Fu3%2BvP7tU9WNjdWOjtWNjdSNAbYBRAEToKD%2B7f68%2Fu2goAEo1Y6O1Y2N1I2O1Y0AAAIAAwADBK0ErQALABEAMgCwCi%2BwBM2wBM0BsBIvsAHWsAfNsAfNsRMBK7EHARESsQwOOTkAsQQKERKxDQ85OTAxEhASJCAEEhACBCAkEwkBJwcnA6ABEwFEAROgoP7t%2Frz%2B7WsBFAGbr%2BxmAbYBRAEToKD%2B7f68%2Fu2goAGE%2FusBm67sZgAAAAADAAMAAwStBK0ACwA4ADwAbACwCi%2BwOc2wPC%2BwJ82wIS%2BwG82wNC%2BwBM0BsD0vsBLWsB7NsB4QsS4BK7AHzbE%2BASuxHhIRErEhNDk5sC4RtAkEKDo7JBc5ALEnPBESsQcAOTmwIRGwKjmwGxKyDA8uOTk5sDQRsQYBOTkwMRIQEiQgBBIQAgQgJBMzMhYyNjQ%2BBToBMzIWFRQGBw4EFzM%2BBDU0LgMjIg4CEzM1IwOgARMBRAEToKD%2B7f68%2Fu3JjwQPBwYCBQIJBA4EEwMTFggXBQ8nHRgByAUSLSIcIzFEMRsyUUUmiMjIAbYBRAEToKD%2B7f68%2Fu2goAIZAgYMCgcFAwIBFBAWDBABBBcfPSYDCikyWDIzTCgYBhg1YP4uZAADAAMAAwStBK0ACwAVABkAOwCwCi%2BwDM2wFS%2BwDjOwEs2wES%2BwFs2wGS%2BwBM0BsBovsRsBKwCxEhURErEHADk5sRYRERKxBgE5OTAxEhASJCAEEhACBCAkNyE1IxEhFTMVIxMzNSMDoAETAUQBE6Cg%2Fu3%2BvP7t7QGQZP7UZGRkyMgBtgFEAROgoP7t%2Frz%2B7aCgiWQBLGTIAZBkAAACAAAAAASwBLAAGAAvAGkAshQAACuwEi%2BwH82wHDKwAC%2ByDhkhMzMzsAHNsgwjLTIyMgGwMC%2BwFNayBRwpMjIysBPNsgceJzIyMrExASuxExQRErMiIy4vJBc5ALESFBESsBU5sQAfERKwHTmwARGyHigpOTk5MDERNTM%2BATc1MxUeAhczFSMOAQcVIzUuASczHgEXNTMVNjcjNTMuAScVIzUOAQczFcMfh4vINnZrEsvLGbdZyIyIHmAYb0vIlTTJyBllSshLbhjRAfTIfZUayMgUUINFyGaoIcXFG5d9SW0Yzs4wnshKahjMyxdsSMgAAAAAAwAEAAQErASsAAsAEwAfAEYAsAovsA%2FNsBMvsATNAbAgL7AB1rANzbANELERASuwB82xIQErsRENERK1BAkKAxQaJBc5ALETDxEStQEGBwAXHSQXOTAxEhASJCAEEhACBCAkEhAWIDYQJiADNyc3FzcXBxcHJwcEoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqpJh4dth4dth4dth4cBtgFEARKgoP7u%2Frz%2B7qCgAl%2F%2BqvPzAVbz%2FduHh22Hh22Hh22HhwAAAAMABAAEBKwErAALABMAGQBGALAKL7APzbATL7AEzQGwGi%2BwAdawDc2wDRCxEQErsAfNsRsBK7ERDREStQQJCgMUGCQXOQCxEw8RErUBBgcAFxkkFzkwMRIQEiQgBBIQAgQgJBIQFiA2ECYgAzcXNxcBBKABEgFEARKgoP7u%2Frz%2B7hbzAVbz8%2F6qa41XzI7%2BpgG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP%2BI41XzY7%2BpwAAAAMABAAEBKwErAALABMAGwBGALAKL7AWzbARL7AEzQGwHC%2BwAdawDM2wDBCxGQErsAfNsR0BK7EZDBEStQQJCgMPFCQXOQCxERYRErUBBgcADhskFzkwMRIQEiQgBBIQAgQgJBMUFwEmIyIGExYzMjY1NCcEoAESAUQBEqCg%2Fu7%2BvP7uFj4COGR0q%2FPNYXCr8zsBtgFEARKgoP7u%2Frz%2B7qCgAbRzZAI3PvP98jvzq3BhAAAAAAEAAABjBLAD6AAGABoAsAUvsALNAbAHL7EIASsAsQIFERKwADkwMREBESERIRECWAJY%2FagCIwHF%2FtT%2B1P7TAAABAAAAYwSwA%2BgABgAaALAAL7ABzQGwBy%2BxCAErALEBABESsAQ5MDEZASERCQERAlgCWP2oAZABLAEs%2Fjv%2BQAEtAAAAAAEAzAAABEoEsAAGAB8AsgUAACsBsAcvsAXWsATNsQgBK7EEBRESsAE5ADAxEwkBIREhEcwBwgG8%2Ftb%2B1AJYAlj9qP2oAlgAAAEAaAAAA%2BYEsAAGAB8AsgYAACsBsAcvsAHWsATNsQgBK7EEARESsAY5ADAxEyERIREhAWgBKAEsASr%2BPwJYAlj9qP2oAAAAAAEAAADHBLAETAANAAA1PgM3EQkBEQ4DBkaJ55wCWP2oX7CkgsiE1a1nCAEP%2Fjv%2BQAEtAiREdQAAAgAAAAAEsASwAAYADQARALIAAAArAbAOL7EPASsAMDExERcBFwEXExcBFxEhF4EBJo7%2B2oHrjgEmgf5wgQGQgQEmjv7agQMJjgEmgQGQgQACACIAIwSOBI4ABgANAAA3ASchEScJAREXARcBFyIBJ4EBkIH%2B2QGogQEnjv7ZgbABJ4H%2BcIL%2B2QI1AZCBASeN%2FtmCAAMAFwAXBJkEmQAPAB8AIwBPALANL7AgzbAjL7AUzbAdL7AFzQGwJC%2BwAdawEM2wEBCxGQErsAnNsSUBK7EZEBEStQUMDQQhIyQXOQCxFCMRErEJADk5sB0RsQgBOTkwMRI0PgIyHgIUDgIiLgEBEx4BOwEyNjcTNiYrASIGEzM1Ixdbm9Xs1ZtbW5vV7NWbAVY6BCMUNhQjBDoEGBTPFRgwyMgB4uzVm1tbm9Xs1ZtbW5sCRv7SFB0dFAEuFB0d%2FcVkAAAFAAAAAASwBLAAJgAqADAANAA7ADQAsicAACuwMTOwKs2wMjIBsDwvsDHWsAUysDTNsAcysT0BK7E0MREStAsMEzU6JBc5ADAxETMVIREzESE1MzUjNjQmLwEuASMiDwEGByYvASYjIgYPAQ4BFBUjEyERIRMiNj8BFxMRIREBNx4DI2QBkMgBkGRvAQMCIgs9JyAd7xYSExXuIR0nPQojAgJvZAGQ%2FnBkAyITEtXbAZD%2Bj8oFDiASAgMgyAEs%2FtTIZAELEwisJzARkA0WGAyQEi4msQgUCgH8fAGQAfRgMC%2B%2F%2FHwBkP5wA4TFDClXOQACAAD%2F6gSvBLAAGwAyABcAsgAAACsBsDMvsCfWsA%2FNsTQBKwAwMRU1Ny4CPgE3PgU3FAIOBC4CIwc2Fjc2JT4DNz4BJyYiBgcOAQ8BBAfYCQgDFTguL2llmonoaCxKaHGDeHtcUw9jEidDNwE4RmFrWykWBAgHFCERI509Pf6PWRaPwTU8gGKCOzxVMy0eOR69%2FszQm1UzCQYTDzd%2FDVNCqCY%2FX4BUMhQJBR0ZM3MgIMXMAAABAG8ADAREBOcASAAjAAGwSS%2BwAdawRc2wRRCxPAErsUoBK7E8RRESsTo2OTkAMDESFBceARcWPgM3PgEnHgEHDgEHDgQeAT4BNz4ENzYCJxYXFicmJy4CNw4EFx4DDgQHBi4CNw4BbwUJRkYfQjo4KA8gDhRPVhEFHxYKCQ8DAwgOGSQYOURrQ0APJqWkFhUnRw8ST1MFMw0qZ0ouDwIMBAgBAQsQGhImOhcHDjQ%2FAblCHjh%2FLRUKJT49HkLtJ1CoZCFJLBMUIA8XCAsBBAYUHD1DbkOsAVNtLFWfBQIHIYbZlQgfZm2nUww7GzQbKBcZEAQKLk1WIC5uAAAD%2F8MAfQTtBDMAIQA%2FAEcAQwCwGi%2BwKc2wOi%2BwCc0BsEgvsDzWsDfNsUkBK7E3PBESQAoJGRoIKSg1PkBDJBc5ALEJOhEStwARJC41PkJHJBc5MDEDNz4GMh4FHwEHDgYiLgUnNx4FMj4ENy4EJxYVFAYiJjU0NwYXFhc3LgEvAT0aBhxGT3N2k5CTdnNPRhwGGhoGHEZPc3aTkJN2c09GHAabB0MtW1R6gHdSWSxICwE3HTo5HjGw%2BLAuZoUxaWklTBMUAlgoCihXVGBHLy9HYFRXKAooKAooV1RgRy8vR2BUVygKKApgPV44KygzXDtoDgFJJUU6GUpZfLCwfFVJV3N8Q2kYYCQkAAAABP%2FDAAAE7QSwABYAIAApAEEAoQCyDwAAK7AOMwGwQi%2BxQwErsDYauj3v790AFSsKsA8uDrAMwAWxDgH5DrANwLAPELMLDwwTK7MQDwwTK7MZDwwTK7MaDwwTK7MkDwwTK7MlDwwTK7IQDwwgiiCKIwYOERI5sBk5sBo5sCQ5sCU5sAs5ALcLDA0QGRokJS4uLi4uLi4uAUAKCwwNDg8QGRokJS4uLi4uLi4uLi6wQBoBADAxAzc%2BBjMyFzczASM3LgQnNxIXNy4BNTQ3BhcWFz8BLgEvAQE3PgY3Jic3HgIfAQcOBD0aBhxGT3N2k0g9PCWU%2FsaUJVKmcmknCpvStyVrjy5mhTFpLxceOg8OASgmFi0vIjATLwFhKydDgS4NGhoHJVplkwJYKAooV1RgRy8RjvtQjxVlZ3k4Dyj%2B5jaNEqduVUlXc3xDL1ccUhsa%2FaeRDyYyJj8YQAJ%2FMJI2j0AUKCgMNGtiZgAAAAP%2FngAABRIErAALABIAFwAAJhYzITI2JwEuAQcBNwkBITUjFREbATUjbxslBQ4lGxX9fhQ4FP1%2B9QG9Ab3%2Bp8hkZMhEREcgBCAiBSD71mQC0%2F0tZGQBkP7UASxkAAAAAAEAZAAVBLAEsAApAEgAsB4vsAnNAbAqL7Al1rAFMrAWzbALMrIWJQors0AWGAkrsiUWCiuzQCUjCSuxKwErsRYlERKxHR45OQCxCR4RErEWJTk5MDETNTQ2NwERNDYyFhURAR4BHQEUBiclERYdARQGLwEjBwYmPQE0NxEFBiZkFg8Ba1h8WAFrDxYYEf6ZZBoTXt5eExpk%2FpkRGAEGKRQxDgFFAVM%2BWFg%2B%2Fq3%2Buw4xFCkUDQz5%2FvlbFkAUEQlOTgkRFEAWWwEH%2BQwNABEAAAAABEwEsAAJABsAHwAjACcAKwAvADMANwA7AD8AQwBHAEsATwBTAFcAADUUFjMhMjY1ESE1ITU0JisBNSMVITUjFSMiBhUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUdFQPoFR37tARMHRWWZP4MZJYVHWRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZDIUHh4UAu5klhUdZGRkZB0V%2FEpkZMhkZMhkZP5wZGTIZGTIZGT%2BcGRkyGRkyGRk%2FnBkZMhkZMhkZP5wZGTIZGTIZGQAAAMAAAADBXgErgAKABAAGQBBALAAL7AYM7ABzbATMrALL7AIM7AQzbADMgGwGi%2BxGwErALEBABESsREWOTmwCxGzBw0SFSQXObAQErEGDjk5MDE9ASEBMzUJATUjCQEhFzcnIQE3FzM1CQE1IwEDAljxASz%2B1J%2F9qP6rAQN6jbX%2BqwKmjXqfASz%2B1PHIyAJYxv7Z%2FtTF%2FagCWHqOtP2VjnvG%2Ftn%2B1MUAAAEAAAAABLAETAASABoAsg4AACuwEC%2BwDDOwBM0BsBMvsRQBKwAwMRkBNDYzITIWFREUBiMhAREjIiY7KQPoKTs7Kf2s%2FtBkKTsBkAJYKTs7Kf2oKTv%2B1AEsOwAAAwBkAAAETASwACUAKQAtAGAAsh8AACuwCc2yCR8KK7NACQEJK7AVMrAmL7AqM7AnzbArMgGwLi%2BwANawJjKwA82wKDKwAxCxEgErsCoysBfNsCwysS8BK7EDABESsCQ5sBIRsR4fOTmwFxKwGTkAMDETNSEVFBcWFxYzMj4GJzQ9ASEVFA4FIi4FGQEhESERIRFkASwGEVUnNSU7KR8RCwMCAQEsBhgnTWWdwJ1lTScYBgEsAZABLAJYyPpxIFwZCwsUHCMoLC4YEQj6yCpSfmpxUDMzUHFqflIBVgEs%2FtQBLP7UAAAAAAH%2F4gC4BGgD3gAFAAADFwkBNwEe4wFgAWHi%2Fb4Bm%2BMBYf6f4wJDAAABAEYA2gTMBAAABQAAEwkBJwkBRgJEAkLi%2Fp%2F%2BoAMd%2Fb0CQ%2BP%2BnwFhAAAAAAL%2FOgBkBXYD6AAIABEAKACwBy%2BwBM0BsBIvsAfWsATNsRMBK7EEBxESsAE5ALEEBxESsA45MDEDCQEjESEXIREBFyERIwkBIxHGASsBLMsBgdf84AGU1wF9xgErASvIArwBG%2F7l%2FnDIAlgBLMj%2BcP7lARsCWAAAAAEAEgAABKoEsAAyAEYAsiIAACuwGTOwLM2wLBCwJs2xFR0yMrAvL7AEzbAQL7AJzQGwMy%2BwJNawH82wHxCxHAErsBfNsTQBK7EXHBESsC05ADAxEyY3NjMhNz4BOwEyFhQGKwEDDgIrARUUBiImPQEhFRQGIiY9ASMiJjU0NjMhNyEiJicSBQ8OGQOAJgUbEV4UHh4UNskCCB4SHx0qHf7UHSodMhUdHRUCFzD9hyAtBQOrGBITohEVHSod%2FD8EDRYyFB4eFDIyFB4eFDIeFBUdyCoWAAAAAAIAAAAABLAETAADAA8AIACyAAAAK7ABzbAEL7AFzbANMrAJzQGwEC%2BxEQErADAxMREhEQE1MzQ2MyEyFhUhFQSw%2B1DIOykBLCk7AfQDIPzgA4RkKTs7KWQAAAAAAgABAAAF3QRMAAMAEAAoALIAAAArsAHNsA8vsA3NsAUysAnNAbARL7ESASsAsQEAERKwBDkwMTMBIQkBETM0NjMhMhYVIRUhAQEsBLD%2B1PtQyDspASwpOwH0%2FBgCvP1EAZACWCk7OynIAAAAAQEuAAADggSwAAkAIQCyCQAAKwGwCi%2BwAdawB82xCwErsQcBERKxBAk5OQAwMQEzESMJASMRMwEBLsbGASoBKsbG%2FtYBLAJYASz%2B1P2o%2FtQAAAAAAQAAAS8EsAOCAAkAHACwCC%2BwAs0BsAovsQsBKwCxAggRErEABTk5MDERARUhNQkBNSEVASwCWAEs%2FtT9qAJYASrGxv7W%2FtfFxQAAAAQAAAAABLAEsAAPABkAHQAhAEkAsgwAACuwGs2wHjKwHS%2BwIDOwBc2wEC%2BwFM0BsCIvsBvWsB7NsB4QsR8BK7AJzbIfCQors0AfAAkrsSMBK7EJHxESsBk5ADAxPQE0NjMhMhYdARQGIyEiJhsBPgEzITIWFxMBMzUjFzM1IzspA%2BgpOzsp%2FBgpOx%2BsBSQUAqATJQWs%2Fo9kZMhkZGRkKTs7KWQpOzsBVQLjFictF%2F0k%2FtRkZGQAAAAD%2F5sAZASwBEwACwApADcAJgABsDgvsADWsAbNsAYQsSoBK7AyzbE5ASuxKgYRErEMGjk5ADAxAzU0Nj8BFS4EFzU8Az4FOwElESUjExYOASMiKwEiJicCARE0NjMyFhURFAYjIiZlMhkZBA4iGhbJAQICBAUHBMgCo%2F1dJi8CCgwPBQNTFB0ENwPoHRUUHh4UFR0CWDIYMg0N%2BgIHFRYhVfoCDAQKBAcDBQIB%2BvyuyP7sDAsBHBUBUf7iA1IVHR0V%2FK4UHh4AAAIASgAABGYEsAArADMANQCyLwAAK7AzzbApL7AfM7ADzbAYMrIpAwors0ApJQkrAbA0L7E1ASsAsSkzERKxLDE5OTAxEzQ2OwE3Ez4BNycmNjsBMhYHBhUeARcTFzMyFhUUBgcOBCMiJi8BLgEFHgEyNjcGIkobFBJ1Pw96UxIGEhReFBIGElN6Dz92ERQbGhIIHmRqn0998To6EhoBpww4RjgLMGwBXhUdrQFHTX4UIBMaFRMkARN%2FTf65rR0VFCgHAwsdFRIpFBQHKdwxPT0xBgAAAQAVABUEnAScABcAABMXBzcXNxc3Fyc3JzcnNwcnBycHJxcHFxXpTuAtm5st4E7qtLTqTuAtm5st4E7pswG9LeBO6bOz6U7gLZucLOFO6bS06U7hLJwAAAMAAABkBLAEsAADACIALgAaAAGwLy%2BwKNawFs2xMAErsRYoERKwFDkAMDE1MxEjARQ7ARY7ATI3EzY9ATQmIyE2PQE0JisBIgYPAgYVExE%2FATMVByEVAyMnyMgBLGQ9fA%2F6LiXuHT0n%2FrgcPScyGzAOYJEUZJZkMjIBwvrWiMgCWP3zS2Q5AVgfK2QsUXYHlixRKBzGxBol%2FokBd9XUr%2BF9%2FolkAAAAAAMAAAAABLAETAADACIALgBwALIcAAArsCXNsBUvsAAzsCjNsC4vsAfNsAEysCwvsArNAbAvL7AA1rADzbADELEEASuwI82wIxCxJgErsBjNsBgQsSkBK7ARzbEwASuxJiMRErIIKCw5OTmwGBGwFTmwKRKwKzkAsS4cERKwKjkwMRkBMxE3ETQ7ATY7ATIXExYdARQGIyEWHQEUBisBIiYvAiY3HwEzNSchNQMjByPIZGQ9fA%2F6LiXuHT0n%2FrgcPScyGzAOYJEUZJZkMjIBwvrWiGQBkAJY%2Fah9AZBLZDn%2BqB8rZCxRdgeWLFEoHMbEGiXU1a%2FhfQF3ZAAAAAMACABkBRUEVQADACIAQQB5ALAgL7AkzbAbL7ApzbAxL7AUzbABMrIxFAors0AxAAkrAbBCL7AA1rADzbADELEEASuwI82wIxCxLQErsBjNsi0YCiuzQC08CSuxQwErsS0jERK0DBEbFD8kFzkAsRskERKwIzmwKRGwGDmxFDERErIXPEE5OTkwMTcRMxE3ETQ2PwElNjMyHwEWFRQPASEyFhQGKwEDDgEjISImNxchEz4BOwEyNjU0JiMhKgIuBCcmNTQ%2FAScFCMhkHA4OAWoOCxEMbQ4LVQEuVWttVGuCBxsP%2FqsHpmRkASWDBhsPyxASEhD%2BNwELBAkDBwQEAgUKk1b%2BrcgCWP2oSwINESUKCeYGDHAOFBIOeUyQTv6tFieiG1kBUxUoHhUUHQEBAgMFAwwIDg23U%2BwAAAAD%2F5sAZQSwBFYAHgA4ADwAeQCwGC%2BwJM2wHS%2BwH82wOC%2BwA82wOjKyOAMKK7NAODkJKwGwPS%2BwAdawH82yHwEKK7NAHywJK7AfELEmASuwFM2wFBCxOQErsDzNsT4BK7EmHxEStAcMHAQpJBc5ALEdJBESsCY5sB8RsAA5sQM4ERKyAScsOTk5MDECNDYzBScmNTQ%2FATYzMhcFHgIVERQGIyEiJicDIyInMzIWFxMhNxElBxcWFRQHDgUqASMhAREzEWVsVQEuVQsObQ0QCw4BbQcUIawI%2FqsQGwaCa1QK3g8bBoMBJWv%2BqVeRCgUBBQMHBAkECwH%2BJAPpyAJDkEwBeRAQFQ1xDAbmBA0nEf3yDaEoFQFTZCkU%2Fq1ZAfbtU7gLDwsJAwUDAgEB%2FgwCWP2oAAAAAAMAYQAABEwFDgAbADYAOgBHALI3AAArsDjNAbA7L7AV1rA3MrApzbIpFQors0ApOgkrsDMysCkQsS8BK7AOzbE8ASuxKRURErESNjk5sQ4vERKwETkAMDEbAR4CMyEyNjURNCYnJTU0JiIGFREnJgYPAQYXNxcWNz4FPAE1ETQ2Fh0BFBYXBREHIQM1IRVh5gQNJxECDQ2iKBX%2BrU6QTHkPJQ5wFltTtxYZAwUDAgEBMjIoFQFTWf4JCAJYAs%2F%2BlQYTH6YHAVYPGwaDalRua1X%2B0lQMAQ1uFgtWkhINAQUDBwQJBAsBAcgWEhMVyhAbBoL%2B2mT%2BcMjIAAAAAAMAAgAKA%2B0FGAAdADQAOABFALA1L7A2zQGwOS%2BwCtawNTKwK82yKwoKK7NAKzgJK7AhMrArELEnASuwD82xOgErsSsKERKxDB85ObEPJxESsA05ADAxEwYfAR4BPwEUBhUUFjI2PQElPgE1ETQmIyEiBg8BAxMhFxEFDgEdARQGJjURPAEuAScmDwETNSEVAhAWcA0mD3kBTZBOAVMUKaIN%2FfMRJQoKmuwB91n%2BrBQoMjIDBwYYFriSAlgCSR8Wbg0BC1UzzS5UbG5UaoMGGw8BVgemHA4P%2FoIBU2T%2B2oIGHA%2FKFhISFgHICwcQCAMNEpICccjIAAAAAgAFAAAEsASrAA4AFQA6ALIMAAArsBDNsA8vsAXNAbAWL7AA1rAQzbEXASsAsRAMERKxCRI5ObAPEbEAEzk5sAUSsQgUOTkwMRM0PgIzMgQSEAIEICQCARchBwkBFQVfoN16ogEToKD%2B7f68%2FuygASUCASwCAZL%2BbgJVet2gX6D%2B7P68%2Fu2goAETAQrJwgEmASrFAAIAAAAABKsEqwAQABcAOACyDgAAK7AUzbAWL7AFzQGwGC%2BwFNawCs2xGQErALEUDhESsBI5sBYRsgoAETk5ObAFErAXOTAxETQ%2BAjMyHgIVFAIEICQCNwEnITchNV%2Bg3Xl63aBfoP7s%2Frz%2B7aDIAZICASwC%2FtQCVXrdoF9foN16ov7toKABE6X%2B2sLJxQAAAAIABQAABLAEqwAOABUAPgCyDAAAK7ARzQGwFi%2BwANawEc2wERCxEgErsAnNsRcBK7ERABESsQwPOTmwEhGxBRU5ObAJErELFDk5ADAxEzQ%2BAjMyBBIQAgQgJAIlMxEzETMBBV%2Bg3XqiAROgoP7t%2Frz%2B7KABJ8jIyP7UAlV63aBfoP7s%2Frz%2B7aCgAROl%2FtQBLAGQAAACAAUAAASwBKsADgAVAE0AsgwAACuwFC%2BwBc0BsBYvsADWsBXNsBUQsRIBK7AJzbEXASuxFQARErEMDzk5sBIRsQUQOTmwCRKxCxE5OQCxFAwRErIIABA5OTkwMRM0PgIzMgQSEAIEICQCJQkBIxEjEQVfoN16ogEToKD%2B7f68%2FuygAScBLAEsyMgCVXrdoF%2Bg%2Fuz%2BvP7toKABE6X%2BcAGQASz%2B1AAAAAAEAAUAAASwBKsAEACIAJgAmgB8ALIOAAArsCrNsE8vsIwvAbCbL7AA1rAUzbAUELFYASuwCs2xnAErsRQAERKwEjmwWBFADg4FEyEjJDxKVXiEhYmUJBc5sAoSQAoNIiYwO1pndpmaJBc5ALFPKhEStxYwNjxGSFVXJBc5sIwRQAkKABRYbIWNjpQkFzkwMRM0PgIzMh4CFRQCBCAkAhMGFgcUFgcyHgEXFhceAjcWBhcWFxQOARcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQcGJyY3NjceAhceAR8BNDYnJjY3PgM3JjcyFjI2Ny4DJzYnHgE%2FATYuAScGJw4DBwYmBw4BBwYWBw4BJT4BNxYyPgE3FBYVLgM3MwVfoN16ed2gX6D%2B7f68%2Fuyg%2BQgbBiIBDBYYCBhUFj45HQguAyotBgEFaHUeIiMDDi4NDkYRCT0gLhAyEAQBBikEAggZGhcTEwsGEAYoGwYMKA4OEwQEJQQFCgcYFgYQCB8SFwkKKSM%2FDAsJHzYMCwcvUg8TEg8rGj4IDz0PFT4DAxMBAzEBAwMaAwoRCxIHIgksHCSiAQJVet2gX1%2Bg3Xqi%2Fu2goAETAVkhdxwJRhkLEwQMHggvHgQSShRHCQYTCgwDcx0kPh8JAQcHEAsBAgsLIxcCLwINCAMWJhIdGR0cHhAGAQEHChMlCQgDSRUXKwoOKhQZCRITAwkLFycVIAcnBQ0DBQQkIxYMAwMMEgYKAQMHBgcnDwsXByJxcQwkBwoMEQQYVQECBgQMXwAAAAABAAAAAgSvBIUAFAAAPAE3ASY2NzYXBRc3FgcGJwEGIi8BDwJYIU5gpI7%2B%2FZH7DaR7gv2sDysPb48rEAJXZck2XGWK6H6vXEYv%2FawQEG4AAAYAAABgBLAErAAPAB8ALwAzADcAOwBQALAML7A0zbA3L7AFzbAcL7AwzbAzL7AVzbAsL7A4zbA7L7AlzQGwPC%2BwNdaxMTkyMrAJzbEYKDIysjUJCiuzQDUACSuxECAyMrE9ASsAMDE9ATQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJhE1NDYzITIWHQEUBiMhIiYBITUhEyE1IRMzNSM7KQPoKTs7KfwYKTs7KQPoKTs7KfwYKTs7KQPoKTs7KfwYKTsCWAH0%2FgzIASz%2B1GTIyMRkKTs7KWQpOzsBuWQpOzspZCk7OwG5ZCk7OylkKTs7%2Fplk%2FgxkArxkAAACAGQAAARMBLAAAwAJACUAsggAACuwAC%2BwAc0BsAovsAjWsAfNsQsBKwCxAAgRErAEOTAxEzUhFQUhAREHEWQD6PxKA4T%2BosgETGRkZP4M%2FtTIAfQAAAAAAwAAAGQEsASwAAkAIQAlAGAAsAcvsAHNsAovsB0zsA7NsRgiMjKwJS%2BwE80BsCYvsA%2FWsCLNsCAysg8iCiuzQA8LCSuwADKwIhCxIwErsB4ysBjNshgjCiuzQBgcCSuwAjKxJwErALEOChESsB85MDE9ASEVFAYjISImGQE0NjMhNTQ2OwEyFh0BITIWFREhNSMVETM1IwSwOyn8GCk7OykBLDspyCk7ASwpO%2F4MyMjIyMjIKTs7AVUBkCk7ZCk7OylkOyn%2BcGRkAfRkAAAAAAQAAAAABLAEsAAGAA0AFAAbABQAsgAAACuwEjMBsBwvsR0BKwAwMTERFzcXBxcBNxc3JzchATcXNxEhNwM3JyERJweByI7Igf5wgciOyIH%2BcALZjsiB%2FnCByMiBAZCByAGQgciOyIEDIIHIjsiB%2FJmOyIH%2BcIEC5siB%2FnCByAAABgAAAAAEqASoAAsAFQAfACkAQgBMANIAsgoAACuwD82wHi%2BwSjOwGc2wRTKwKC%2BwOTOwI82wNDKyKCMKK7NAKEEJK7AUL7AEzQGwTS%2BwAdawDM2wDBCxFwErsBvNsBsQsSELK7AmzbAmELEqASuwPs2wPhCxQwErsEjNszdIQwgrsDHNsDEvsDfNsEgQsREBK7AHzbFOASuxJhsRErMKDhQDJBc5sT4qERKxLTw5ObE3MREStQkPEwQvOyQXOQCxHg8RErMHAAwRJBc5sBkRsyotPD4kFzmwKBKxBgE5ObAjEbEvOzk5MDEYARIkIAQSEAIEICQTFBYgNjU0JiAGFjQ2MhYVFAYjIjY0NjMyFhQGIyIXNDY%2FAiY1NDYzMhYUBiMiJwcWFRQGIiYlNDYyFhQGIyImoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqrzbR8uICAXFk0gFxYgIBYXUikfegEJIBcWICAWDg83ETNIMwEeIC4fIBYXIAGyAUQBEqCg%2Fu7%2BvP7uoKABtKzy8qyr8%2FOHLh8gFhcg5CwhIC4guiAxBX4BDg4WISAuIAqRFh0kMzNSFiAfLiAgAAAAAf%2FYADsEugSwAE8AOgCwBS%2BwJ82wIC%2BwFc2wNi%2BwSs0BsFAvsVEBKwCxJwURErA%2FObAgEbQLDxobMSQXObAVErEyMzk5MDECBhceATMyNz4CNzY3AT4BJyYnJiMiBgcBBxcBNjc2MzIXFgcBBiMiJicmPgI3NjcBPgIzMhceAQcGDwEDHwEBPgEnLgEnJiMiBwYHARsaMCN2Rj84IUApJygRAYojGA8bWhQJLkEj%2FnsHRQF5FBMXGyYPECT93TRJN1oJBQ8wJCYYFAFcND1rNhkXX3YIB1v8%2FQdFAgVDOBEQZk9FU2taKEf%2BAAHWvk45QBwQMSorLBEBiiNiL1cRAiEj%2FnQHQwF1FhAXJCck%2Fd00Qj8jPkAkJBUUAVw0NzUEEZtiZVv5%2FwAHPAH%2FQ7RdV4YkITcYR%2F4AAAAAAAIAUAA2BMMEWAAbADUAPQCwMy%2BwLTOwA82wBzIBsDYvsADWsBzNsBwQsSoBK7AKzbE3ASuxKhwRErMDBw8YJBc5ALEDMxESsAU5MDETNDYzMhc2MzIWFRQOAgcGDwEnLgInLgM3FB4BHwEWFzY%2FAT4CNTQmIyIPAScmIyIGUMWEj2Jnj4HCI1dDR8VgERArckZCR0NXI6o9PkAWXWFScQxAQz5gOUo6dnIzSDxjAxCDxYGBxYMuWmxHRr%2BDFxc6gUZBRkdsWi4bVkE%2BFlpvXG8MPkZYHEdhU6uuUGMAAAAAAgA5%2F%2FIEdwS%2BABgAMwAAExQfARYzMjcBNjQvASYnBxcBJzcnJicHBhMUHwEWFzcnARcHFxYXNz4BNTQvASYjIgcBBjlCjURbXUIBG0JCjQgLadT%2Be%2FdfEi4dN0LUQo0HDGnUAYX3XxIvHh0jN0KNQl1fQP7lQgFhX0COQkIBG0K6Qo0JCGnU%2Fnv4XxItODdCAQRdQo0HCmnUAYX3YBExMx0jaitdQo1CQv7lQAAAAAADAMgAAAPoBLAAEQAVAB0ARQCyDwAAK7AZzbAdL7ASzbAVL7AGzQGwHi%2BwANawEs2wEhCxEwErsAvNsAsQsBvNsBsvsR8BK7EbEhESsgYFFjk5OQAwMTcRND4CMh4CFREUBiMhIiY3IREhEhQWMjY0JiLIPGacqppkOjsp%2FagpO2QCWP2oxD1WPT1WZAO5FTIuHh4uMhX8Ryk7O%2FECvPzZVj09Vj0AAAABAAAAAASwBLAAGAARALIAAAArAbAZL7EaASsAMDExATcnIQEnJjQ3NjIXARYUBwYiLwEBEScHAS%2FP0gEsAQsjDw8OKg4BGw8PDioOJP7p1NABfNDUARckDioODw%2F%2B5g8qDg8PI%2F71%2FtTSzwADAScAEgQJBOEAMQA9AEMAlwCwLS%2BwKjOwBM2wPjKyLQQKK7NALSwJK7A7L7AfM7ASzbISOwors0ASEwkrAbBEL7AO1rAAMrAyzbABzbAyELEsASuyBBI6MjIysCvNshQfPjIyMrArELFAASuwJ82wHCDWEbAbzbFFASuxMgERErACObAsEbAJOQCxBC0RErApObA7EbYADhsnOkBDJBc5sBISsBU5MDEBMx4BFxEuASMuBDU0PgE3NTMVHgQXIy4BJxEXHgQVFAYHFSM1JicuARMUHgMXFhcRDgETNjU0JicBJ4sFV0oGEwIuQk4vIViCT2QmRVI8KwOfCDZKQCI8UDcosptkmFUoGagQESoUHAcEPUnqqlhSAbFNYw8BTwEGDhkvOVg3XIdDB05PBBMsP2lCSEsN%2Fs0OBxMsPGU%2Bi6oLTU4RVyhrAh4dLBgVBgcCAQESCDv9KxKFQEcZAAAAAQBkAGYDlAStAEMAjQCwMS%2BwKs2wAC%2BwHjOwAc2wHDKwEy%2BwC82yEwsKK7NAEw4JKwGwRC%2BwB9awOTKwGM2wJDKyGAcKK7NAGB4JK7IHGAors0AHAAkrsBgQsQ8BK7AOzbFFASuxGAcRErMCOEJDJBc5sA8RtQsfICoxMyQXObAOErAsOQCxKjERErEtOTk5sAARsSw8OTkwMRM1MyYnLgE%2BATc2MzIWFSM0LgEjIgYHBhUUHgEXMxUjFgYHBgc%2BATM2FjMyNxcOAiMiJgcOAQ8BJz4FNz4BJ2SmGBQKCQMvLWGmgcqZRFAkJVQUKSEXHvHFCBUVKTojYhUhjCFMPDIpTycqF9IyJ1YXGDcGFQoRDBEJMAwkAlhkMTcaO1ZeKFiydzRLHB0VLDkcUyozZDKCHTs2Cw4BIh6TGRcDQgQEGgwLkQQOBg0LEQo3j0cAAgACAAAErgSwAAYADQAfALIMAAArAbAOL7AM1rALzbEPASuxCwwRErAIOQAwMRMJASMRIxEJAiMRIxECASoBKsbIAZIBKgEqxsgBLP7UASwDhPx8AlgBLP7U%2FHwDhAAABQACAAAD6ASwAAYADAAWAB4AIgCmALIHAAArsAYzsArNsgcAACuwCM2wEy%2BwFM2xAAQyMrANL7AOzbAdL7AfzbIdHwors0AdFwkrsBoysCIvsBjNsAIyAbAjL7AB1rAEzbAEELEIASuxDRcyMrAKzbEdHzIysAoQsRUBK7EbIDIysBDNsQsZMjKzEhAVCCuwE82wEy%2BwEs2xJAErsQQBERKwBjmwCBGwBTkAsRQIERKwEDmwDRGwETkwMRMzETMRMwEhNTMVMxUBNSEVIxUjNTM1AxEhESM1IxU3MzUjAsbIxv7WAZBkyP7UASxjZGPIASxkZAFkZAEsA4T8fP7UyGRkAZBkyGRkZAEsAfT%2BDGRkyMgABQACAAAD6ASwAAYADgAUAB4AIgCgALIGAAArsQcKMzOwDS%2BwH82wIi%2BwCM2wDy%2BwEs2wEM2wGy%2BwHM2wFS%2BwFs2wAjIBsCMvsAHWsATNsAQQsQcBK7EPFTIysA7NsREfMjKwDhCxCwErsR0gMjKwCs2xExcyMrMaCgsIK7AbzbAbL7AazbEkASuxBAERErAGObAHEbAFOQCxIh8RErMBBAUAJBc5sRwQERKwGDmwFRGwGTkwMRMzETMRMwEhESERIzUjFQM1MxUzFQE1IRUjFSM1MzUDMzUjAsbIxv7WAZABLGRkZGTI%2FtQBLGNkY2NkZAEsA4T8fP7UAfT%2BDGRkArzIZGQBkGTIZGRk%2FHzIAAAEAAIAAARMBLAABgAMABIAFgBrALILAAArsAwvsBPNsBYvsAjNsA0vsA7Nsg0OCiuzQA0RCSsBsBcvsBHWsBDNsxMQEQgrsAfNsAcvsA0zsBPNsBAQsRQLK7ALMrAKzbEYASsAsQgLERK0AAIDBgEkFzmxDg0RErEFBDk5MDETCQEjESMRBREhESM1AzUzESMREzM1IwIBKgEqxsgCWAEsZMjIZAFkZAEs%2FtQBLAOE%2FHzIAZD%2BDGQD6GT%2BDAGQ%2FHzIAAAABAACAAAETASwAAYADAASABYAawCyCwAAK7AHL7AIzbASL7ATzbISEwors0ASEAkrsBYvsA7NAbAXL7AL1rAKzbMTCgsIK7ANzbANL7AHM7ATzbAKELEUCyuwETKwEM2xGAErALETCxEStAACAwYBJBc5sQ4WERKxBQQ5OTAxEwkBIxEjESU1MxEjEQMRIREjNSczNSMCASoBKsbIAljIZGQBLGRjZGQBLP7UASwDhPx8ZGT%2BDAGQAZABkP4MZGTIAAAAAAUAAgAABLAEsAAGAAoADgASABYAUgCwBy%2BwCM2wCy%2BwDM2wDy%2BwEM2wEy%2BwFM0BsBcvsA%2FWsgcLEzIyMrASzbAWzbIWDwors0AWCgkrs0AWDgkrsRgBKwCxCwgRErMCAwYAJBc5MDETCQEjESMRBTUhFQE1IRUBNSEVATUzFQIBKgEqxsgB9AH0%2FgwBkP5wASz%2B1MgBLP7UASwDhPx8yMjIASzIyAEsyMgBLMjIAAAABQACAAAEsASwAAYACgAOABIAFgBSALAHL7AIzbALL7AMzbAPL7AQzbATL7AUzQGwFy%2BwC9ayBw8TMjIysA7NsArNsgoLCiuzQAoSCSuzQAoWCSuxGAErALELCBESswIDBgAkFzkwMRMJASMRIxEFNTMVAzUhFQE1IRUBNSEVAgEqASrGyAH0yMgBLP7UAZD%2BcAH0ASz%2B1AEsA4T8fMjIyAEsyMgBLMjIASzIyAAAAAACAAAAAARMBEwADwAfACoAsg0AACuwE82wHC%2BwBM0BsCAvsADWsBDNsBAQsRcBK7AJzbEhASsAMDEZATQ2MyEyFhURFAYjISImNxQWMyEyNjURNCYjISIGFeulASyi7u2j%2FtSl68g7KQH0KTs7Kf4MKTsBkAEspevto%2F7UpevrQSk7OykB9Ck7OykAAAMAAAAABEwETAAPAB8AIgA%2BALINAAArsBPNsBwvsATNAbAjL7AA1rAQzbAQELEXASuwCc2xJAErsRcQERKxICE5OQCxHBMRErEgIjk5MDEZATQ2MyEyFhURFAYjISImNxQWMyEyNjURNCYjISIGFRMtAe6iASyl6%2Bul%2FtSj7cg7KQH0KTs7Kf4MKTvIAU3%2BswGQASyj7eul%2FtSl6%2BtBKTs7KQH0KTs7Kf4M%2BvoAAAAAAwAAAAAETARMAA8AHwAiAD4Asg0AACuwE82wHC%2BwBM0BsCMvsADWsBDNsBAQsRcBK7AJzbEkASuxFxARErEgIjk5ALEcExESsSAhOTkwMRkBNDYzITIWFREUBiMhIiY3FBYzITI2NRE0JiMhIgYVFxsB66UBLKPt66X%2B1KXryDspAfQpOzsp%2FgwpO2T6%2BgGQASyj7e6i%2FtSl6%2BtBKTs7KQH0KTs7KWT%2BswFNAAMAAAAABEwETAAPAB8AIgA%2BALINAAArsBPNsBwvsATNAbAjL7AA1rAQzbAQELEXASuwCc2xJAErsRcQERKxICE5OQCxHBMRErEgIjk5MDEZATQ2MyEyFhURFAYjISImNxQWMyEyNjURNCYjISIGFRMhA%2BulASyl6%2B2j%2FtSl68g7KQH0KTs7Kf4MKTtkAfT6AZABLKXr66X%2B1KLu7T8pOzspAfQpOzsp%2FnABTQACAAAAAAUUBEwABgAaADwAsgcAACuwCM2wAC%2BwAc2wES%2BwEs0BsBsvsAzWsBfNsRwBKwCxCAcRErAFObEBABESsAQ5sBERsAM5MDEZASE1CQE1EzUhMjY1ETQmIyE1ITIWFREUBiMBLAGQ%2FnDIAfQpOzsp%2FgwBkKXr66UBkAEsyP6i%2FqLI%2FnDIOykB9Ck7yOul%2FtSl6wAAAAEA2QACA9YEngAhACgAsA4vsBPNAbAiL7Ac1rAWzbEjASuxFhwRErAZOQCxEw4RErAPOTAxExYzIQIHBh8BMzI3NgAzNicmIwU2Ejc2LwEjIgcOAQAHBtkIFwEumwUFCQkJDgwLAg8BDgkIF%2F7TAZoCAgcJCQ8KBLz%2B80sQAgcT%2FkoUFQsIDw0CaRMREQEEAa8MDwwJEAbT%2FtFWEwAAAAACAAAAAAUUBEwAGAAfAD8AsgMAACuwCM2wCBCwBs2wGS%2BwGs2wEC%2BwEs2wFM0BsCAvsADWsAvNsSEBKwCxGgMRErEdHjk5sBARsBw5MDERFBYzITI3NSEiJjURNDYzITUuAS8BIgYVAREhNQkBNeulASwvNf4MKTs7KQH0DshdXaXrAlgBLAGQ%2FnABkKXrD7k7KQH0KTu5BAcCAuul%2FtQBLMj%2Bov6iyAACAAAAAASwBLAAHQAkAFQAsgMAACuwD82wFi%2BwGs0BsCUvsADWsBLNsBIQsQsBK7AHzbEmASuxCxIRErUYGR4fICQkFzmwBxGwIzkAsRYPERK1CAkeIiMkJBc5sBoRsB85MDERFBYzITI2PQEnBxUUBiMhIiY1ETQ2OwE3JyMiBhUlASchEScB66UBLKPtTno7Kf4MKTs7KZx2SmSl6wHwAWGVAfSV%2FqoBkKXr66ViSXuUKTs7KQH0KTt6TuulCQFWlf4Mlf6fAAMABAAEBKwErAALABMAGwBaALAKL7APzbAbL7AXzbATL7AEzQGwHC%2BwAdawDc2wDRCxFQErsBnNsBkQsREBK7AHzbEdASuxGRURErcECQoODxITAyQXOQCxFxsRErcBBgcMDRARACQXOTAxEhASJCAEEhACBCAkEhAWIDYQJiACNDYyFhQGIgSgARIBRAESoKD%2B7v68%2Fu4W8wFW8%2FP%2BqhdyoHJyoAG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP%2BEqBycqByAAAAAwAAAAAETASwAAkAEAAUAC4AsgkAACuwEc2wFC%2BwBc2wCzIBsBUvsBLWsAjNshIICiuzQBIACSuxFgErADAxMRE0NjMhMhYVEQkCIREhEQEzNSMOCwQYCxD8GAG9AcL%2B2f7UAfRkZAETCw4PCv7tAyD%2BDAH0AZD%2BcP12MgAAAAADAAAAAARMBLAACQAQABQAKwCyCQAAK7ARzbAUL7AFzQGwFS%2BwEtawCM2yEggKK7NAEgAJK7EWASsAMDExETQ2MyEyFhURASERIREhCQEzNSMOCwQYCxD8GAEsASwBJ%2F5DAV5kZAETCw4PCv7tArz%2B1AEsAfT75jIAAAAAAwAAAAAETAR%2FAAkADwATAC4AsgkAACuwEM2wEy%2BwBc0BsBQvsBHWsAwysAjNshEICiuzQBEACSuxFQErADAxMRE0NjMhMhYVEQkCJwEnATM1Iw4LBBgLEPwYATECVJr%2BRpYChWRkARMLDg8K%2Fu0Cwf7PAlSb%2FkaX%2FToyAAQAAAAABEwEsAAJAA0AFAAYACsAsgkAACuwFc2wGC%2BwBc0BsBkvsBbWsAjNshYICiuzQBYACSuxGgErADAxMRE0NjMhMhYVEQEXNycDJRMHJwcXATM1Iw4LBBgLEPwYYdRhcAK5A%2FqV1JUBzmRkARMLDg8K%2Fu0D3GLVYfzgAQK775XUlf4NMgAEAAAAAARMBLAACQANABQAGAAuALIJAAArsBXNsBgvsAXNAbAZL7AW1rATMrAIzbIWCAors0AWAAkrsRoBKwAwMTERNDYzITIWFREBFzcnExcHFzcXCwEzNSMOCwQYCxD8fNRi1QPvldSV%2BQFjZGQBEwsODwr%2B7QJk1GHUAev6ldSU7QK5%2B%2BkyAAAAAAIAF%2F%2F%2FBLAErwAFAAgAFwCyBAAAKwGwCS%2BwBdawBs2xCgErADAxEwERCQERFwkBFwSZ%2FiX%2Byk8CoP1gAZ8DEPvJARD%2BdwGgzQOq%2FTgAAAAAAgAAAGQETASwABUAGQBNALARL7AGzbIRBgors0AREwkrsA4ysgYRCiuzQAYECSuwCDIBsBovsADWsBLNsAbNsBIQsQ8BK7ALzbEbASuxDwYRErIJFhc5OTkAMDE1ETQ2OwERIREzFxEUBisBESERIyImATM1Ix0V%2BgH0ZMgeFJb9RJYVHQJYZGSWA%2BgUHv7UASzI%2FK4VHQGQ%2FnAdA2fIAAMAAAA%2BBRQEsAATABkAHQBAALAPL7AGzbIPBgors0APEQkrsgYPCiuzQAYECSuwCDIBsB4vsADWsBDNsAbNsR8BKwCxBg8RErILFxg5OTkwMTURNDY7AREhETMXFQEnByERIyImJTcXARcBAzM1Ix0V%2BgH0ZMj%2B7Hh%2B%2FoaWFR0CRXt4AWF7%2FiXhZGSWA%2BgUHv7UASzI2v7teH%2F%2BcB2xe3gBYHv%2BJAOqyAAAAAADAAAABgUOBLAAEwAXACMAFQABsCQvsADWsBDNsAbNsSUBKwAwMTURNDY7AREhETMXEQcnASERIyImATM1IxM3JzcXNxcHFwcnBx0V%2BgH0ZMhnqv7W%2FreWFR0CWGRkZKqqf6qqf6qqf6qqlgPoFB7%2B1AEsyP7zZ6r%2B1v5wHQNnyPvVqqp%2FqqqAqap%2FqqoAAAADAAAAAASwBLAAEgAZAB0AbACwDi%2BwBs2yDgYKK7NADhAJK7AGELAMzbAaL7AbzbEECDIyAbAeL7AA1rAPzbIPAAors0APCwkrsAAQsAbNsA8QsRoBK7AdzbAMMrEfASuxGgYRErATOQCxDA4RErEXGDk5sRoGERKwCjkwMTURNDY7AREhETMXESEVIREjIiYlCQEjESMRAzUzFR0V%2BgH0ZMj%2BcP4MlhUdAlgBLAEsyMjIZJYD6BQe%2FtQBLMj%2B1Mj%2BcB2r%2FtQBLAEs%2FtQCvMjIAAAAAAMAAAAABLAEsAASABkAHQBbALAOL7AGzbIOBgors0AOEAkrsBovsBvNsQQIMjIBsB4vsADWsA%2FNsAbNsA8QsRoBK7AdzbEfASuxGgYRErATObAdEbANOQCxBg4RErILDBk5OTmwGhGwCjkwMTURNDY7AREhETMXEScBIREjIiYlMxEzETMJATUzFR0V%2BgH0ZMjI%2Ftb%2BbpYVHQJYyMjI%2FtT%2B1GSWA%2BgUHv7UASzI%2Fm7I%2Ftb%2BcB2r%2FtQBLAEsAZDIyAAAAAADAAAAyASwBEwACQATABcAADUUFjMhMjY1ESE1ITU0JiMhIgYVEzUhFR0VBEwVHftQBLAdFfu0FR1kAZD6FR0dFQImZJYVHR0V%2FRLIyAAAAAYAAABmBLAErgAGAAoADgAVABkAHQCBALAFL7EWGjMzsALNsRccMjKwBy%2BxCw8zM7AIzbEMEDIyAbAeL7AH1rAKzbAKELELASuwDs2wDhCxFgErsBnNsR8BK7ELChESswIFBgEkFzmxFg4RErMEAw8QJBc5sBkRsxIUFREkFzkAsQIFERKwADmwBxGxARQ5ObAIErATOTAxEQEVIRUhFQM1MxUzNTMVMzUhNQkBNQM1MxU7ATUjASwBkP5wyGRkZGQBkAEs%2FtRkZGRkZAGQASrGyMYCusjIyMjIxv7W%2FtbG%2FgzIyMgAAAACAGQAAASwBLAAGAAvADoAshQAACsBsDAvsADWsATNsAQQsRcLK7AQzbMJEBcIK7AFzbAFL7AJzbAQELEKCyuwDs2xMQErADAxExE3FxEzETcXETMRNxcRBxEUBisBIiY1ESUUHgIfAREUFjsBMjY1ETQmBwUOARVkMjJkMjJkMjJkHRXIFR0CWBUdHQsKHRXIFR0kGv7sGSUCvAGQZGT%2B1AEsZGT%2B1AEsZGT%2BcMv%2BQRUdHRUBv2QdNSEYBgX%2BcxUdHRUEUh8TEXQRRR8AAAAAAQBkAAAEsARMADMAOACyAAAAK7AMM7AzzbICCw4yMjKwKC%2ByGBwlMzMzsCfNsBoyAbA0L7E1ASsAsSgzERKxBiA5OTAxMyE1IiY1ESERFAYjFSE1Ii4DNRE0Nj8BNSEVMhYVESERNDYzNSEVMh4DFREUBg8BZAGQSxkB9BlLAZAEDiIaFjIZGf5wSxn%2BDBlL%2FnAEDiIaFjIZGTgMJgGK%2FnYmDDg4AQUJFQ4DeBYZAQI4OAwm%2FnYBiiYMODgBBQkVDvyIFhkBAgAGAAAAAARMBEwADwAYABwAIAAqAC4AMgCyGAAAK7AQzbAWL7ASzbISFgors0ASFAkrAbAvL7EwASsAsRIQERKzBAMZHCQXOTAxERQWMyEyNjURNCYjISIGFRMhNzMlESEHIQM1IRUBNSEVASEXFSUzNSEnIQE1JRU7KQEsKTs7Kf7UKTtkAZDIaQEn%2Fldk%2FolkASz%2B1AEs%2FtQBkMgBJ2n%2BV2T%2BiQH0AZABLCk7OykB9Ck7Oyn9RMhi%2FtZkASzIyAEsyMgBkMhiYshk%2FUajhaMAAQAQABAEnwSfACAAABIeAxceAzM%2FATYmLwEmBg8BLgEnNz4BLwEuAQ8BEAEfPpJmZ9GXex8foxEHE8ATNBB2jvxldhEGDosOLRKiA%2BQriY%2FUZmeSPSEBohIuDogOBBF2ZfyOdhExFMITBhGiAAAAAAIAAAAABLAETAAdAEAALwCyGwAAK7AMzbAoL7A4zQGwQS%2BxQgErALEMGxESsSAvOTmwKBGzJikyQCQXOTAxPQE0NjcBNTQ%2BAzIeAh8BFQEeAR0BFAYjISImERQWPwE%2BAT0BNiAXFRQWHwEWNj0BLgQjIg4EDwEVDgFtAhYmUnBSJhYBAQFtDhUdFfu0FB4dFMoUHY0BPo0dFMoUHQYaZHzaflymdWQ%2FLAkJMtQUMw4BLzIEDSAZFRQbHAoKMv7RDjMU1BUdHQKrFRkEIQQiFZIYGJIVIgQhBBkVyAgZQTEpFSEoKCELCgACAGQAAASwBEwAAwAZABQAsgAAACuwAc0BsBovsRsBKwAwMTM1IRUlISc1NxEjFSM1IxUjNSMVIzUjERcVZARM%2B%2F8Dtn1kZGTIZMhkZGRkZMiW%2BmQBkMjIyMjIyP5wZPoAAAAAAwBkAAAEsARMAAkAEwAdACQAsgoAACuwFDMBsB4vsArWsBPNsBMQsRQBK7AdzbEfASsAMDEzIRE0JisBIgYVARE0NjsBMhYVETMRNDY7ATIWFRFkASw7KWQpOwGQOylkKTtkOylkKTsBkCk7Oyn%2BcAPoKTs7KfwYArwpOzsp%2FUQAAAAABf%2BcAAAEsARMAA8AEwAfACcAKwBIALINAAArsBDNsBMvsATNAbAsL7AA1rAQzbAQELERASuwCc2xLQErsREQERK1FBUgIygqJBc5ALETEBEStRQaICYoKSQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETIREjNTM1IREzFSMFMzUzESM1IxMRMxFksHwCvHywsHz9RHywyAOE%2FHxkASzIyP7UyMgBkMhkZMhkZAEsAfR8sLB8%2Fgx8sLAYArz9qAEsZGT%2B1GRkZAEsZP5wASz%2B1AAAAAAF%2F5wAAASwBEwADwATAB8AJwArAEgAsg0AACuwEM2wEy%2BwBM0BsCwvsADWsBDNsBAQsREBK7AJzbEtASuxERARErUUGSAjKCokFzkAsRMQERK1FBogJigpJBc5MDEDETQ2MyEyFhURFAYjISImNyERIRMzNTMVMxEjFSM1IwEzNTMRIzUjExEzEWSwfAK8fLCwfP1EfLDIA4T8fGRkZGRkZGQBkMhkZMhkZAEsAfR8sLB8%2Fgx8sLAYArz9qMjIAfTIyP4MZAEsZP5wASz%2B1AAABP%2BcAAAEsARMAA8AEwAbACMARACyDQAAK7AQzbATL7AEzQGwJC%2BwANawEM2wEBCxEQErsAnNsSUBK7EREBESsxQVHB0kFzkAsRMQERKzFBocIiQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETITUjETM1IQEhNSMRMzUhZLB8Arx8sLB8%2FUR8sMgDhPx8ZAEsyMj%2B1AGQASzIyP7UASwB9HywsHz%2BDHywsBgCvP2oZAEsZP4MZAEsZAAAAAAE%2F5wAAASwBEwADwATABYAGQBEALINAAArsBDNsBMvsATNAbAaL7AA1rAQzbAQELERASuwCc2xGwErsREQERKzFBUXGCQXOQCxExARErMVFhcZJBc5MDEDETQ2MyEyFhURFAYjISImNyERIRMFERMtAWSwfAK8fLCwfP1EfLDIA4T8fGQBLGQBLP7UASwB9HywsHz%2BDHywsBgCvP6ilgEs%2FtSWlgAAAAAF%2F5wAAASwBEwADwATABcAHwAnAFoAsg0AACuwEM2wFC%2BwGM2wIzKwHy%2BwJTOwFc2wEy%2BwBM0BsCgvsADWsBDNsBAQsRQBK7AYzbAYELEcASuwIc2wIRCxJAErsBfNsBcQsREBK7AJzbEpASsAMDEDETQ2MyEyFhURFAYjISImNyERIRMRIRElMzI2NCYrAQQUFjsBESMiZLB8Arx8sLB8%2FUR8sMgDhPx8ZAK8%2FaiCKTY5JoIBEzYpgoImASwB9HywsHz%2BDHywsBgCvP2oAfT%2BDGRUglZWglQBLAAABf%2BcAAAEsARMAA8AEwAfACMAKQBIALINAAArsBDNsBMvsATNAbAqL7AA1rAQzbAQELERASuwCc2xKwErsREQERK1FBUgISQnJBc5ALETEBEStRQaICImKCQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETIREjNTM1IREzFSMFMzUjEzMRMxEjZLB8Arx8sLB8%2FUR8sMgDhPx8ZAEsyMj%2B1MjIAZFkZGNkZMgBLAH0fLCwfP4MfLCwGAK8%2FagBLGRk%2FtRkZGQBLP5wAfQABv%2BcAAAEsARMAA8AEwAZAB0AIQAnAEwAsg0AACuwEM2wEy%2BwBM0BsCgvsADWsBDNsBAQsREBK7AJzbEpASuxERARErcUFRocHh8iJSQXOQCxExARErcUGBobHiAkJiQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETIREjNSMTNTMVFzM1IxMzETMRI2SwfAK8fLCwfP1EfLDIA4T8fGQBLMhkZWTIZGRjZGTIASwB9HywsHz%2BDHywsBgCvP2oAZBk%2FnDIyGRkASz%2BcAH0AAAAAAb%2FnAAABLAETAAPABMAHQAhACUAKwCbALINAAArsBDNsB4vsSIpMzOwH82wIzKwGi%2BwG82wFC%2BwJjOwFc2wJzKwEy%2BwBM0BsCwvsADWsBDNsBAQsR4BK7AUMrAhzbAhELEcASuwF82zGRccCCuwGs2wGi%2BwGc2wFxCxIgErsCXNsCUQsSoBK7ApzbApELAmzbAmL7ApELERASuwCc2xLQErALEbHxESsBc5sBQRsBg5MDEDETQ2MyEyFhURFAYjISImNyERIRc1IREjFSM1MzUDNTMVITUzFQM1MxEjEWSwfAK8fLCwfP1EfLDIA4T8fGQBLGNkY8dkASxkAchkASwB9HywsHz%2BDHywsBgCvMhk%2FtRkZMj%2BcGRkZGQBkGT%2BDAGQAAADAAQABASsBKwACwATAB0AeQCwCi%2BwD82wHS%2BwGs2wGS%2BwFs2wEy%2BwBM0BsB4vsAHWsA3NsA0QsRQBK7AazbAaELERASuwB82xHwErsRoUERK1Cg4TAxYdJBc5sBERtQkPBBIXGyQXOQCxGh0RErQHDRAAFCQXObAZEbAVObAWErMGDBEBJBc5MDESEBIkIAQSEAIEICQSEBYgNhAmIAM1NyEVIRUhFSEEoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqodZAEs%2FtQBLP7UAbYBRAESoKD%2B7v68%2Fu6goAJf%2Fqrz8wFW8%2F3%2ByGRkyGQAAAQAAAAEBKgErAALABMAIAAkAKAAsAovsA%2FNsCEvsBQzsCLNsBsvsBXNsBMvsATNAbAlL7AB1rANzbANELEUASuwIM2wGzKyIBQKK7NAIB4JK7AgELEhASuwGTKwJM2wFzKwJBCxEQErsAfNsSYBK7EgFBESswoOEwMkFzmwIRGwFjmwJBKzCQ8SBCQXOQCxIiERErQHDRAAHiQXObAbEbIXGB85OTmwFRKzBgwRASQXOTAxGAESJCAEEhACBCAkEhAWIDYQJiADESEXFSM1IxUzFSMVMzUzFaABEgFEARKgoP7u%2Frz%2B7hbzAVbz8%2F6qGQEsZGTIyMjIZAG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP9mgGQZGRkZGRkZGQA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%2F4WwAY0AS7AIUFixAQGOWbFGBitYIbAQWUuwFFJYIbCAWR2wBitcWFmwFCsAAAABUuZYrgAA%29%20format%28%27truetype%27%29%2Curl%28data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPD94bWwgdmVyc2lvbj0iMS4wIiBzdGFuZGFsb25lPSJubyI%2FPgo8IURPQ1RZUEUgc3ZnIFBVQkxJQyAiLS8vVzNDLy9EVEQgU1ZHIDEuMS8vRU4iICJodHRwOi8vd3d3LnczLm9yZy9HcmFwaGljcy9TVkcvMS4xL0RURC9zdmcxMS5kdGQiID4KPHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPgo8bWV0YWRhdGE%2BPC9tZXRhZGF0YT4KPGRlZnM%2BCjxmb250IGlkPSJnbHlwaGljb25zX2hhbGZsaW5nc3JlZ3VsYXIiIGhvcml6LWFkdi14PSIxMjAwIiA%2BCjxmb250LWZhY2UgdW5pdHMtcGVyLWVtPSIxMjAwIiBhc2NlbnQ9Ijk2MCIgZGVzY2VudD0iLTI0MCIgLz4KPG1pc3NpbmctZ2x5cGggaG9yaXotYWR2LXg9IjUwMCIgLz4KPGdseXBoIC8%2BCjxnbHlwaCAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZDsiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDAzOyIgaG9yaXotYWR2LXg9IjEzMDQiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA0OyIgaG9yaXotYWR2LXg9IjQzNCIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDU7IiBob3Jpei1hZHYteD0iMzI2IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwNjsiIGhvcml6LWFkdi14PSIyMTciIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA3OyIgaG9yaXotYWR2LXg9IjIxNyIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDg7IiBob3Jpei1hZHYteD0iMTYzIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwOTsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDBhOyIgaG9yaXotYWR2LXg9IjcyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAyZjsiIGhvcml6LWFkdi14PSIyNjAiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMjEyOyIgZD0iTTIwMCA0MDBoOTAwdjMwMGgtOTAwdi0zMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjVmYzsiIGhvcml6LWFkdi14PSI1MDAiIGQ9Ik0wIDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjYwMTsiIGQ9Ik0tMTQgNDk0cTAgLTgwIDU2LjUgLTEzN3QxMzUuNSAtNTdoNzUwcTEyMCAwIDIwNSA4Ni41dDg1IDIwNy41dC04NSAyMDd0LTIwNSA4NnEtNDYgMCAtOTAgLTE0cS00NCA5NyAtMTM0LjUgMTU2LjV0LTIwMC41IDU5LjVxLTE1MiAwIC0yNjAgLTEwNy41dC0xMDggLTI2MC41cTAgLTI1IDIgLTM3cS02NiAtMTQgLTEwOC41IC02Ny41dC00Mi41IC0xMjIuNXoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDEzOyIgZD0iTTI5IDQ1NGw0MTkgLTQyMGw4MTggODIwbC0yMTIgMjEybC02MDcgLTYwN2wtMjA2IDIwN3oiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDE4OyIgZD0iTTEwMCAxaDIwMHYzMDBoLTIwMHYtMzAwek00MDAgMXY1MDBoMjAwdi01MDBoLTIwMHpNNzAwIDF2ODAwaDIwMHYtODAwaC0yMDB6TTEwMDAgMXYxMjAwaDIwMHYtMTIwMGgtMjAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMTk7IiBkPSJNMjYgNjAxcTAgLTMzIDYgLTc0bDE1MSAtMzhsMiAtNnExNCAtNDkgMzggLTkzbDMgLTVsLTgwIC0xMzRxNDUgLTU5IDEwNSAtMTA1bDEzMyA4MWw1IC0zcTQ1IC0yNiA5NCAtMzlsNSAtMmwzOCAtMTUxcTQwIC01IDc0IC01cTI3IDAgNzQgNWwzOCAxNTFsNiAycTQ2IDEzIDkzIDM5bDUgM2wxMzQgLTgxcTU2IDQ0IDEwNCAxMDVsLTgwIDEzNGwzIDVxMjQgNDQgMzkgOTNsMSA2bDE1MiAzOHE1IDQwIDUgNzRxMCAyOCAtNSA3M2wtMTUyIDM4IGwtMSA2cS0xNiA1MSAtMzkgOTNsLTMgNWw4MCAxMzRxLTQ0IDU4IC0xMDQgMTA1bC0xMzQgLTgxbC01IDNxLTQ1IDI1IC05MyAzOWwtNiAxbC0zOCAxNTJxLTQwIDUgLTc0IDVxLTI3IDAgLTc0IC01bC0zOCAtMTUybC01IC0xcS01MCAtMTQgLTk0IC0zOWwtNSAtM2wtMTMzIDgxcS01OSAtNDcgLTEwNSAtMTA1bDgwIC0xMzRsLTMgLTVxLTI1IC00NyAtMzggLTkzbC0yIC02bC0xNTEgLTM4cS02IC00OCAtNiAtNzN6TTM4NSA2MDEgcTAgODggNjMgMTUxdDE1MiA2M3QxNTIgLTYzdDYzIC0xNTFxMCAtODkgLTYzIC0xNTJ0LTE1MiAtNjN0LTE1MiA2M3QtNjMgMTUyeiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMjA7IiBkPSJNMTAwIDEwMjV2NTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWgyNzV2MTAwcTAgNDEgMjkuNSA3MC41dDcwLjUgMjkuNWgzMDBxNDEgMCA3MC41IC0yOS41dDI5LjUgLTcwLjV2LTEwMGgyNzVxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di01MHEwIC0xMSAtNyAtMTh0LTE4IC03aC0xMDUwcS0xMSAwIC0xOCA3dC03IDE4ek0yMDAgMTAwdjgwMGg5MDB2LTgwMHEwIC00MSAtMjkuNSAtNzF0LTcwLjUgLTMwaC03MDBxLTQxIDAgLTcwLjUgMzAgdC0yOS41IDcxek0zMDAgMTAwaDEwMHY3MDBoLTEwMHYtNzAwek01MDAgMTAwaDEwMHY3MDBoLTEwMHYtNzAwek01MDAgMTEwMGgzMDB2MTAwaC0zMDB2LTEwMHpNNzAwIDEwMGgxMDB2NzAwaC0xMDB2LTcwMHpNOTAwIDEwMGgxMDB2NzAwaC0xMDB2LTcwMHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI3OyIgZD0iTTQgNjAwcTAgMTYyIDgwIDI5OXQyMTcgMjE3dDI5OSA4MHQyOTkgLTgwdDIxNyAtMjE3dDgwIC0yOTl0LTgwIC0yOTl0LTIxNyAtMjE3dC0yOTkgLTgwdC0yOTkgODB0LTIxNyAyMTd0LTgwIDI5OXpNMTg2IDYwMHEwIC0xNzEgMTIxLjUgLTI5Mi41dDI5Mi41IC0xMjEuNXQyOTIuNSAxMjEuNXQxMjEuNSAyOTIuNXQtMTIxLjUgMjkyLjV0LTI5Mi41IDEyMS41dC0yOTIuNSAtMTIxLjV0LTEyMS41IC0yOTIuNXpNMzUwIDYwMGwyNTAgMzAwIGwyNTAgLTMwMGgtMTUwdi0zMDBoLTIwMHYzMDBoLTE1MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI4OyIgZD0iTTAgMjV2NDc1bDIwMCA3MDBoODAwbDE5OSAtNzAwbDEgLTQ3NXEwIC0xMSAtNyAtMTh0LTE4IC03aC0xMTUwcS0xMSAwIC0xOCA3dC03IDE4ek0yMDAgNTAwaDIwMGw1MCAtMjAwaDMwMGw1MCAyMDBoMjAwbC05NyA1MDBoLTYwNnoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI5OyIgZD0iTTQgNjAwcTAgMTYyIDgwIDI5OXQyMTcgMjE3dDI5OSA4MHQyOTkgLTgwdDIxNyAtMjE3dDgwIC0yOTl0LTgwIC0yOTl0LTIxNyAtMjE3dC0yOTkgLTgwdC0yOTkgODB0LTIxNyAyMTd0LTgwIDI5OXpNMTg2IDYwMHEwIC0xNzIgMTIxLjUgLTI5M3QyOTIuNSAtMTIxdDI5Mi41IDEyMXQxMjEuNSAyOTNxMCAxNzEgLTEyMS41IDI5Mi41dC0yOTIuNSAxMjEuNXQtMjkyLjUgLTEyMS41dC0xMjEuNSAtMjkyLjV6TTUwMCAzOTd2NDAxIGwyOTcgLTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDMwOyIgZD0iTTIzIDYwMHEwIC0xMTggNDUuNSAtMjI0LjV0MTIzIC0xODR0MTg0IC0xMjN0MjI0LjUgLTQ1LjV0MjI0LjUgNDUuNXQxODQgMTIzdDEyMyAxODR0NDUuNSAyMjQuNWgtMTUwcTAgLTE3NyAtMTI1IC0zMDJ0LTMwMiAtMTI1dC0zMDIgMTI1dC0xMjUgMzAydDEyNSAzMDJ0MzAyIDEyNXExMzYgMCAyNDYgLTgxbC0xNDYgLTE0Nmg0MDB2NDAwbC0xNDUgLTE0NXEtMTU3IDEyMiAtMzU1IDEyMnEtMTE4IDAgLTIyNC41IC00NS41dC0xODQgLTEyMyB0LTEyMyAtMTg0dC00NS41IC0yMjQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDMxOyIgZD0iTTIzIDYwMHEwIDExOCA0NS41IDIyNC41dDEyMyAxODR0MTg0IDEyM3QyMjQuNSA0NS41cTE5OCAwIDM1NSAtMTIybDE0NSAxNDV2LTQwMGgtNDAwbDE0NyAxNDdxLTExMiA4MCAtMjQ3IDgwcS0xNzcgMCAtMzAyIC0xMjV0LTEyNSAtMzAyaC0xNTB6TTEwMCAwdjQwMGg0MDBsLTE0NyAtMTQ3cTExMiAtODAgMjQ3IC04MHExNzcgMCAzMDIgMTI1dDEyNSAzMDJoMTUwcTAgLTExOCAtNDUuNSAtMjI0LjV0LTEyMyAtMTg0dC0xODQgLTEyMyB0LTIyNC41IC00NS41cS0xOTggMCAtMzU1IDEyMnoiIC8%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDU5OyIgZD0iTTAgMjc1djY1MHEwIDMxIDIyIDUzdDUzIDIyaDc1MHEzMSAwIDUzIC0yMnQyMiAtNTN2LTY1MHEwIC0zMSAtMjIgLTUzdC01MyAtMjJoLTc1MHEtMzEgMCAtNTMgMjJ0LTIyIDUzek05MDAgNjAwbDMwMCAzMDB2LTYwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDYwOyIgZD0iTTAgNDR2MTAxMnEwIDE4IDEzIDMxdDMxIDEzaDExMTJxMTkgMCAzMS41IC0xM3QxMi41IC0zMXYtMTAxMnEwIC0xOCAtMTIuNSAtMzF0LTMxLjUgLTEzaC0xMTEycS0xOCAwIC0zMSAxM3QtMTMgMzF6TTEwMCAyNjNsMjQ3IDE4MmwyOTggLTEzMWwtNzQgMTU2bDI5MyAzMThsMjM2IC0yODh2NTAwaC0xMDAwdi03Mzd6TTIwOCA3NTBxMCA1NiAzOSA5NXQ5NSAzOXQ5NSAtMzl0MzkgLTk1dC0zOSAtOTV0LTk1IC0zOXQtOTUgMzl0LTM5IDk1eiAiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDY3OyIgZD0iTTAgNDAwdjMwMHEwIDE2NSAxMTcuNSAyODIuNXQyODIuNSAxMTcuNWgzMDBxNjEgMCAxMjcgLTIzbC0xNzggLTE3N2gtMzQ5cS00MSAwIC03MC41IC0yOS41dC0yOS41IC03MC41di01MDBxMCAtNDEgMjkuNSAtNzAuNXQ3MC41IC0yOS41aDUwMHE0MSAwIDcwLjUgMjkuNXQyOS41IDcwLjV2NjlsMjAwIDIwMHYtMTY5cTAgLTE2NSAtMTE3LjUgLTI4Mi41dC0yODIuNSAtMTE3LjVoLTMwMHEtMTY1IDAgLTI4Mi41IDExNy41IHQtMTE3LjUgMjgyLjV6TTM0MiA2MzJsMjgzIC0yODRsNTY3IDU2N2wtMTM3IDEzN2wtNDMwIC00MzFsLTE0NiAxNDd6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA2ODsiIGQ9Ik0wIDYwM2wzMDAgMjk2di0xOThoMjAwdjIwMGgtMjAwbDMwMCAzMDBsMjk1IC0zMDBoLTE5NXYtMjAwaDIwMHYxOThsMzAwIC0yOTZsLTMwMCAtMzAwdjE5OGgtMjAwdi0yMDBoMTk1bC0yOTUgLTMwMGwtMzAwIDMwMGgyMDB2MjAwaC0yMDB2LTE5OHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcxOyIgZD0iTTEzNiA1NTBsNTY0IDU1MHYtNDg3bDUwMCA0ODd2LTExMDBsLTUwMCA0ODh2LTQ4OHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDc4OyIgZD0iTTEwMCAyNTB2MTAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNWgxMDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0xMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTEwMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41ek0xMDAgNTAwaDExMDBsLTU1MCA1NjR6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3OTsiIGQ9Ik0xODUgNTk5bDU5MiAtNTkybDI0MCAyNDBsLTM1MyAzNTNsMzUzIDM1M2wtMjQwIDI0MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDgwOyIgZD0iTTI3MiAxOTRsMzUzIDM1M2wtMzUzIDM1M2wyNDEgMjQwbDU3MiAtNTcxbDIxIC0yMmwtMSAtMXYtMWwtNTkyIC01OTF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA4MTsiIGQ9Ik0zIDYwMHEwIDE2MiA4MCAyOTkuNXQyMTcuNSAyMTcuNXQyOTkuNSA4MHQyOTkuNSAtODB0MjE3LjUgLTIxNy41dDgwIC0yOTkuNXQtODAgLTI5OS41dC0yMTcuNSAtMjE3LjV0LTI5OS41IC04MHQtMjk5LjUgODB0LTIxNy41IDIxNy41dC04MCAyOTkuNXpNMzAwIDUwMGgyMDB2LTIwMGgyMDB2MjAwaDIwMHYyMDBoLTIwMHYyMDBoLTIwMHYtMjAwaC0yMDB2LTIwMHoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDkxOyIgZD0iTTAgNTQ3bDYwMCA0NTN2LTMwMGg2MDB2LTMwMGgtNjAwdi0zMDF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5MjsiIGQ9Ik0wIDQwMHYzMDBoNjAwdjMwMGw2MDAgLTQ1M2wtNjAwIC00NDh2MzAxaC02MDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5MzsiIGQ9Ik0yMDQgNjAwbDQ1MCA2MDBsNDQ0IC02MDBoLTI5OHYtNjAwaC0zMDB2NjAwaC0yOTZ6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5NDsiIGQ9Ik0xMDQgNjAwaDI5NnY2MDBoMzAwdi02MDBoMjk4bC00NDkgLTYwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk1OyIgZD0iTTAgMjAwcTYgMTMyIDQxIDIzOC41dDEwMy41IDE5M3QxODQgMTM4dDI3MS41IDU5LjV2MjcxbDYwMCAtNDUzbC02MDAgLTQ0OHYzMDFxLTk1IC0yIC0xODMgLTIwdC0xNzAgLTUydC0xNDcgLTkyLjV0LTEwMCAtMTM1LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5NjsiIGQ9Ik0wIDB2NDAwbDEyOSAtMTI5bDI5NCAyOTRsMTQyIC0xNDJsLTI5NCAtMjk0bDEyOSAtMTI5aC00MDB6TTYzNSA3NzdsMTQyIC0xNDJsMjk0IDI5NGwxMjkgLTEyOXY0MDBoLTQwMGwxMjkgLTEyOXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk3OyIgZD0iTTM0IDE3NmwyOTUgMjk1bC0xMjkgMTI5aDQwMHYtNDAwbC0xMjkgMTMwbC0yOTUgLTI5NXpNNjAwIDYwMHY0MDBsMTI5IC0xMjlsMjk1IDI5NWwxNDIgLTE0MWwtMjk1IC0yOTVsMTI5IC0xMzBoLTQwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTAxOyIgZD0iTTIzIDYwMHEwIDExOCA0NS41IDIyNC41dDEyMyAxODR0MTg0IDEyM3QyMjQuNSA0NS41dDIyNC41IC00NS41dDE4NCAtMTIzdDEyMyAtMTg0dDQ1LjUgLTIyNC41dC00NS41IC0yMjQuNXQtMTIzIC0xODR0LTE4NCAtMTIzdC0yMjQuNSAtNDUuNXQtMjI0LjUgNDUuNXQtMTg0IDEyM3QtMTIzIDE4NHQtNDUuNSAyMjQuNXpNNDU2IDg1MWw1OCAtMzAycTQgLTIwIDIxLjUgLTM0LjV0MzcuNSAtMTQuNWg1NHEyMCAwIDM3LjUgMTQuNSB0MjEuNSAzNC41bDU4IDMwMnE0IDIwIC04IDM0LjV0LTMyIDE0LjVoLTIwN3EtMjEgMCAtMzMgLTE0LjV0LTggLTM0LjV6TTUwMCAzMDBoMjAwdjEwMGgtMjAwdi0xMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEwMjsiIGQ9Ik0wIDgwMGgxMDB2LTIwMGg0MDB2MzAwaDIwMHYtMzAwaDQwMHYyMDBoMTAwdjEwMGgtMTExcTEgMSAxIDYuNXQtMS41IDE1dC0zLjUgMTcuNWwtMzQgMTcycS0xMSAzOSAtNDEuNSA2M3QtNjkuNSAyNHEtMzIgMCAtNjEgLTE3bC0yMzkgLTE0NHEtMjIgLTEzIC00MCAtMzVxLTE5IDI0IC00MCAzNmwtMjM4IDE0NHEtMzMgMTggLTYyIDE4cS0zOSAwIC02OS41IC0yM3QtNDAuNSAtNjFsLTM1IC0xNzdxLTIgLTggLTMgLTE4dC0xIC0xNXYtNiBoLTExMXYtMTAwek0xMDAgMGg0MDB2NDAwaC00MDB2LTQwMHpNMjAwIDkwMHEtMyAwIDE0IDQ4dDM2IDk2bDE4IDQ3bDIxMyAtMTkxaC0yODF6TTcwMCAwdjQwMGg0MDB2LTQwMGgtNDAwek03MzEgOTAwbDIwMiAxOTdxNSAtMTIgMTIgLTMyLjV0MjMgLTY0dDI1IC03MnQ3IC0yOC41aC0yNjl6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEwMzsiIGQ9Ik0wIC0yMnYxNDNsMjE2IDE5M3EtOSA1MyAtMTMgODN0LTUuNSA5NHQ5IDExM3QzOC41IDExNHQ3NCAxMjRxNDcgNjAgOTkuNSAxMDIuNXQxMDMgNjh0MTI3LjUgNDh0MTQ1LjUgMzcuNXQxODQuNSA0My41dDIyMCA1OC41cTAgLTE4OSAtMjIgLTM0M3QtNTkgLTI1OHQtODkgLTE4MS41dC0xMDguNSAtMTIwdC0xMjIgLTY4dC0xMjUuNSAtMzB0LTEyMS41IC0xLjV0LTEwNy41IDEyLjV0LTg3LjUgMTd0LTU2LjUgNy41bC05OSAtNTV6IE0yMzguNSAzMDAuNXExOS41IC02LjUgODYuNSA3Ni41cTU1IDY2IDM2NyAyMzRxNzAgMzggMTE4LjUgNjkuNXQxMDIgNzl0OTkgMTExLjV0ODYuNSAxNDhxMjIgNTAgMjQgNjB0LTYgMTlxLTcgNSAtMTcgNXQtMjYuNSAtMTQuNXQtMzMuNSAtMzkuNXEtMzUgLTUxIC0xMTMuNSAtMTA4LjV0LTEzOS41IC04OS41bC02MSAtMzJxLTM2OSAtMTk3IC00NTggLTQwMXEtNDggLTExMSAtMjguNSAtMTE3LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEwNDsiIGQ9Ik0xMTEgNDA4cTAgLTMzIDUgLTYzcTkgLTU2IDQ0IC0xMTkuNXQxMDUgLTEwOC41cTMxIC0yMSA2NCAtMTZ0NjIgMjMuNXQ1NyA0OS41dDQ4IDYxLjV0MzUgNjAuNXEzMiA2NiAzOSAxODQuNXQtMTMgMTU3LjVxNzkgLTgwIDEyMiAtMTY0dDI2IC0xODRxLTUgLTMzIC0yMC41IC02OS41dC0zNy41IC04MC41cS0xMCAtMTkgLTE0LjUgLTI5dC0xMiAtMjZ0LTkgLTIzLjV0LTMgLTE5dDIuNSAtMTUuNXQxMSAtOS41dDE5LjUgLTV0MzAuNSAyLjUgdDQyIDhxNTcgMjAgOTEgMzR0ODcuNSA0NC41dDg3IDY0dDY1LjUgODguNXQ0NyAxMjJxMzggMTcyIC00NC41IDM0MS41dC0yNDYuNSAyNzguNXEyMiAtNDQgNDMgLTEyOXEzOSAtMTU5IC0zMiAtMTU0cS0xNSAyIC0zMyA5cS03OSAzMyAtMTIwLjUgMTAwdC00NCAxNzUuNXQ0OC41IDI1Ny41cS0xMyAtOCAtMzQgLTIzLjV0LTcyLjUgLTY2LjV0LTg4LjUgLTEwNS41dC02MCAtMTM4dC04IC0xNjYuNXEyIC0xMiA4IC00MS41dDggLTQzdDYgLTM5LjUgdDMuNSAtMzkuNXQtMSAtMzMuNXQtNiAtMzEuNXQtMTMuNSAtMjR0LTIxIC0yMC41dC0zMSAtMTJxLTM4IC0xMCAtNjcgMTN0LTQwLjUgNjEuNXQtMTUgODEuNXQxMC41IDc1cS01MiAtNDYgLTgzLjUgLTEwMXQtMzkgLTEwN3QtNy41IC04NXoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTI2OyIgZD0iTTAgNDAwdjYwMGgyMDB2LTYwMGgtMjAwek0zMDAgNTI1djQwMHEwIDc1IDEwMCA3NWg2MXExMjQgMTAwIDEzOSAxMDBoMjUwcTQ2IDAgODMgLTU3bDIzOCAtMzQ0cTI5IC0zMSAyOSAtNzR2LTEwMHEwIC00NCAtMzAuNSAtODQuNXQtNjkuNSAtNDAuNWgtMzI4cTI4IC0xMTggMjggLTEyNXYtMTUwcTAgLTQ0IC0zMC41IC04NC41dC02OS41IC00MC41aC01MHEtMjcgMCAtNTEgMjB0LTM4IDQ4bC05NiAxOThsLTE0NSAxOTYgcS0yMCAyNiAtMjAgNjN6TTQwMCA1MjVsMTUwIC0yMTJsMTAwIC0yMTNoNTB2MTc1bC01MCAyMjVoNDUwdjEyNWwtMjUwIDM3NWgtMjE0bC0xMzYgLTEwMGgtMTAwdi0zNzV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEyNzsiIGQ9Ik04IDIwMHY2MDBoMjAwdi02MDBoLTIwMHpNMzA4IDI3NXY1MjVxMCAxNyAxNCAzNS41dDI4IDI4LjVsMTQgOWwzNjIgMjMwcTE0IDYgMjUgNnExNyAwIDI5IC0xMmwxMDkgLTExMnExNCAtMTQgMTQgLTM0cTAgLTE4IC0xMSAtMzJsLTg1IC0xMjFoMzAycTg1IDAgMTM4LjUgLTM4dDUzLjUgLTExMHQtNTQuNSAtMTExdC0xMzguNSAtMzloLTEwN2wtMTMwIC0zMzlxLTcgLTIyIC0yMC41IC00MS41dC0yOC41IC0xOS41aC0zNDEgcS03IDAgLTkwIDgxdC04MyA5NHpNNDA4IDI4OWwxMDAgLTg5aDI5M2wxMzEgMzM5cTYgMjEgMTkuNSA0MXQyOC41IDIwaDIwM3ExNiAwIDI1IDE1dDkgMzZxMCAyMCAtOSAzNC41dC0yNSAxNC41aC00NTdoLTYuNWgtNy41dC02LjUgMC41dC02IDF0LTUgMS41dC01LjUgMi41dC00IDR0LTQgNS41cS01IDEyIC01IDIwcTAgMTQgMTAgMjdsMTQ3IDE4M2wtODYgODNsLTMzOSAtMjM2di01MDN6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEyODsiIGQ9Ik0tMTAxIDY1MXEwIDcyIDU0IDExMHQxMzkgMzhsMzAyIC0xbC04NSAxMjFxLTExIDE2IC0xMSAzMnEwIDIxIDE0IDM0bDEwOSAxMTNxMTMgMTIgMjkgMTJxMTEgMCAyNSAtNmwzNjUgLTIzMHE3IC00IDE3IC0xMC41dDI2LjUgLTI2dDE2LjUgLTM2LjV2LTUyNnEwIC0xMyAtODYgLTkzLjV0LTk0IC04MC41aC0zNDFxLTE2IDAgLTI5LjUgMjB0LTE5LjUgNDFsLTEzMCAzMzloLTEwN3EtODQgMCAtMTM5IDM5dC01NSAxMTF6TS0xIDYwMWgyMjIgcTE1IDAgMjguNSAtMjAuNXQxOS41IC00MC41bDEzMSAtMzM5aDI5M2wxMDcgODl2NTAybC0zNDMgMjM3bC04NyAtODNsMTQ1IC0xODRxMTAgLTExIDEwIC0yNnEwIC0xMSAtNSAtMjBxLTEgLTMgLTMuNSAtNS41bC00IC00dC01IC0yLjV0LTUuNSAtMS41dC02LjUgLTF0LTYuNSAtMC41aC03LjVoLTYuNWgtNDc2di0xMDB6TTEwMDAgMjAxdjYwMGgyMDB2LTYwMGgtMjAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxMjk7IiBkPSJNOTcgNzE5bDIzMCAtMzYzcTQgLTYgMTAuNSAtMTUuNXQyNiAtMjV0MzYuNSAtMTUuNWg1MjVxMTMgMCA5NCA4M3Q4MSA5MHYzNDJxMCAxNSAtMjAgMjguNXQtNDEgMTkuNWwtMzM5IDEzMXYxMDZxMCA4NCAtMzkgMTM5dC0xMTEgNTV0LTExMCAtNTMuNXQtMzggLTEzOC41di0zMDJsLTEyMSA4NHEtMTUgMTIgLTMzLjUgMTEuNXQtMzIuNSAtMTMuNWwtMTEyIC0xMTBxLTIyIC0yMiAtNiAtNTN6TTE3MiA3MzlsODMgODZsMTgzIC0xNDYgcTIyIC0xOCA0NyAtNXEzIDEgNS41IDMuNWw0IDR0Mi41IDV0MS41IDUuNXQxIDYuNXQwLjUgNi41djcuNXY2LjV2NDU2cTAgMjIgMjUgMzF0NTAgLTAuNXQyNSAtMzAuNXYtMjAycTAgLTE2IDIwIC0yOS41dDQxIC0xOS41bDMzOSAtMTMwdi0yOTRsLTg5IC0xMDBoLTUwM3pNNDAwIDB2MjAwaDYwMHYtMjAwaC02MDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEzMDsiIGQ9Ik0yIDU4NXEtMTYgLTMxIDYgLTUzbDExMiAtMTEwcTEzIC0xMyAzMiAtMTMuNXQzNCAxMC41bDEyMSA4NXEwIC01MSAtMC41IC0xNTMuNXQtMC41IC0xNDguNXEwIC04NCAzOC41IC0xMzh0MTEwLjUgLTU0dDExMSA1NXQzOSAxMzl2MTA2bDMzOSAxMzFxMjAgNiA0MC41IDE5LjV0MjAuNSAyOC41djM0MnEwIDcgLTgxIDkwdC05NCA4M2gtNTI1cS0xNyAwIC0zNS41IC0xNHQtMjguNSAtMjhsLTEwIC0xNXpNNzcgNTY1bDIzNiAzMzloNTAzIGw4OSAtMTAwdi0yOTRsLTM0MCAtMTMwcS0yMCAtNiAtNDAgLTIwdC0yMCAtMjl2LTIwMnEwIC0yMiAtMjUgLTMxdC01MCAwdC0yNSAzMXY0NTZ2MTQuNXQtMS41IDExLjV0LTUgMTJ0LTkuNSA3cS0yNCAxMyAtNDYgLTVsLTE4NCAtMTQ2ek0zMDUgMTEwNHYyMDBoNjAwdi0yMDBoLTYwMHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTM5OyIgZD0iTTAgMjAwdjIwMGgxMjAwdi0yMDBxMCAtNDEgLTI5LjUgLTcwLjV0LTcwLjUgLTI5LjVoLTEwMDBxLTQxIDAgLTcwLjUgMjkuNXQtMjkuNSA3MC41ek0wIDUwMHY0MDBxMCA0MSAyOS41IDcwLjV0NzAuNSAyOS41aDMwMHYxMDBxMCA0MSAyOS41IDcwLjV0NzAuNSAyOS41aDIwMHE0MSAwIDcwLjUgLTI5LjV0MjkuNSAtNzAuNXYtMTAwaDMwMHE0MSAwIDcwLjUgLTI5LjV0MjkuNSAtNzAuNXYtNDAwaC01MDB2MTAwaC0yMDB2LTEwMGgtNTAweiBNNTAwIDEwMDBoMjAwdjEwMGgtMjAwdi0xMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE0MDsiIGQ9Ik0wIDB2NDAwbDEyOSAtMTI5bDIwMCAyMDBsMTQyIC0xNDJsLTIwMCAtMjAwbDEyOSAtMTI5aC00MDB6TTAgODAwbDEyOSAxMjlsMjAwIC0yMDBsMTQyIDE0MmwtMjAwIDIwMGwxMjkgMTI5aC00MDB2LTQwMHpNNzI5IDMyOWwxNDIgMTQybDIwMCAtMjAwbDEyOSAxMjl2LTQwMGgtNDAwbDEyOSAxMjl6TTcyOSA4NzFsMjAwIDIwMGwtMTI5IDEyOWg0MDB2LTQwMGwtMTI5IDEyOWwtMjAwIC0yMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE0MTsiIGQ9Ik0wIDU5NnEwIDE2MiA4MCAyOTl0MjE3IDIxN3QyOTkgODB0Mjk5IC04MHQyMTcgLTIxN3Q4MCAtMjk5dC04MCAtMjk5dC0yMTcgLTIxN3QtMjk5IC04MHQtMjk5IDgwdC0yMTcgMjE3dC04MCAyOTl6TTE4MiA1OTZxMCAtMTcyIDEyMS41IC0yOTN0MjkyLjUgLTEyMXQyOTIuNSAxMjF0MTIxLjUgMjkzcTAgMTcxIC0xMjEuNSAyOTIuNXQtMjkyLjUgMTIxLjV0LTI5Mi41IC0xMjEuNXQtMTIxLjUgLTI5Mi41ek0yOTEgNjU1IHEwIDIzIDE1LjUgMzguNXQzOC41IDE1LjV0MzkgLTE2dDE2IC0zOHEwIC0yMyAtMTYgLTM5dC0zOSAtMTZxLTIyIDAgLTM4IDE2dC0xNiAzOXpNNDAwIDg1MHEwIDIyIDE2IDM4LjV0MzkgMTYuNXEyMiAwIDM4IC0xNnQxNiAtMzl0LTE2IC0zOXQtMzggLTE2cS0yMyAwIC0zOSAxNi41dC0xNiAzOC41ek01MTQgNjA5cTAgMzIgMjAuNSA1Ni41dDUxLjUgMjkuNWwxMjIgMTI2bDEgMXEtOSAxNCAtOSAyOHEwIDIyIDE2IDM4LjV0MzkgMTYuNSBxMjIgMCAzOCAtMTZ0MTYgLTM5dC0xNiAtMzl0LTM4IC0xNnEtMTQgMCAtMjkgMTBsLTU1IC0xNDVxMTcgLTIyIDE3IC01MXEwIC0zNiAtMjUuNSAtNjEuNXQtNjEuNSAtMjUuNXQtNjEuNSAyNS41dC0yNS41IDYxLjV6TTgwMCA2NTVxMCAyMiAxNiAzOHQzOSAxNnQzOC41IC0xNS41dDE1LjUgLTM4LjV0LTE2IC0zOXQtMzggLTE2cS0yMyAwIC0zOSAxNnQtMTYgMzl6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE0MjsiIGQ9Ik0tNDAgMzc1cS0xMyAtOTUgMzUgLTE3M3EzNSAtNTcgOTQgLTg5dDEyOSAtMzJxNjMgMCAxMTkgMjhxMzMgMTYgNjUgNDAuNXQ1Mi41IDQ1LjV0NTkuNSA2NHE0MCA0NCA1NyA2MWwzOTQgMzk0cTM1IDM1IDQ3IDg0dC0zIDk2cS0yNyA4NyAtMTE3IDEwNHEtMjAgMiAtMjkgMnEtNDYgMCAtNzguNSAtMTYuNXQtNjcuNSAtNTEuNWwtMzg5IC0zOTZsLTcgLTdsNjkgLTY3bDM3NyAzNzNxMjAgMjIgMzkgMzhxMjMgMjMgNTAgMjMgcTM4IDAgNTMgLTM2cTE2IC0zOSAtMjAgLTc1bC01NDcgLTU0N3EtNTIgLTUyIC0xMjUgLTUycS01NSAwIC0xMDAgMzN0LTU0IDk2cS01IDM1IDIuNSA2NnQzMS41IDYzdDQyIDUwdDU2IDU0cTI0IDIxIDQ0IDQxbDM0OCAzNDhxNTIgNTIgODIuNSA3OS41dDg0IDU0dDEwNy41IDI2LjVxMjUgMCA0OCAtNHE5NSAtMTcgMTU0IC05NC41dDUxIC0xNzUuNXEtNyAtMTAxIC05OCAtMTkybC0yNTIgLTI0OWwtMjUzIC0yNTZsNyAtN2w2OSAtNjAgbDUxNyA1MTFxNjcgNjcgOTUgMTU3dDExIDE4M3EtMTYgODcgLTY3IDE1NHQtMTMwIDEwM3EtNjkgMzMgLTE1MiAzM3EtMTA3IDAgLTE5NyAtNTVxLTQwIC0yNCAtMTExIC05NWwtNTEyIC01MTJxLTY4IC02OCAtODEgLTE2M3oiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTQzOyIgZD0iTTgwIDc4NHEwIDEzMSA5OC41IDIyOS41dDIzMC41IDk4LjVxMTQzIDAgMjQxIC0xMjlxMTAzIDEyOSAyNDYgMTI5cTEyOSAwIDIyNiAtOTguNXQ5NyAtMjI5LjVxMCAtNDYgLTE3LjUgLTkxdC02MSAtOTl0LTc3IC04OS41dC0xMDQuNSAtMTA1LjVxLTE5NyAtMTkxIC0yOTMgLTMyMmwtMTcgLTIzbC0xNiAyM3EtNDMgNTggLTEwMCAxMjIuNXQtOTIgOTkuNXQtMTAxIDEwMHEtNzEgNzAgLTEwNC41IDEwNS41dC03NyA4OS41dC02MSA5OSB0LTE3LjUgOTF6TTI1MCA3ODRxMCAtMjcgMzAuNSAtNzB0NjEuNSAtNzUuNXQ5NSAtOTQuNWwyMiAtMjJxOTMgLTkwIDE5MCAtMjAxcTgyIDkyIDE5NSAyMDNsMTIgMTJxNjQgNjIgOTcuNSA5N3Q2NC41IDc5dDMxIDcycTAgNzEgLTQ4IDExOS41dC0xMDUgNDguNXEtNzQgMCAtMTMyIC04M2wtMTE4IC0xNzFsLTExNCAxNzRxLTUxIDgwIC0xMjMgODBxLTYwIDAgLTEwOS41IC00OS41dC00OS41IC0xMTguNXoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTQ2OyIgZD0iTTAgMGwzMDMgMzgwbDIwNyAyMDhsLTIxMCAyMTJoMzAwbDI2NyAyNzlsLTM1IDM2cS0xNSAxNCAtMTUgMzV0MTUgMzVxMTQgMTUgMzUgMTV0MzUgLTE1bDI4MyAtMjgycTE1IC0xNSAxNSAtMzZ0LTE1IC0zNXEtMTQgLTE1IC0zNSAtMTV0LTM1IDE1bC0zNiAzNWwtMjc5IC0yNjd2LTMwMGwtMjEyIDIxMGwtMjA4IC0yMDd6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE0ODsiIGQ9Ik0yOTUgNDMzaDEzOXE1IC03NyA0OC41IC0xMjYuNXQxMTcuNSAtNjQuNXYzMzVxLTYgMSAtMTUuNSA0dC0xMS41IDNxLTQ2IDE0IC03OSAyNi41dC03MiAzNnQtNjIuNSA1MnQtNDAgNzIuNXQtMTYuNSA5OXEwIDkyIDQ0IDE1OS41dDEwOSAxMDF0MTQ0IDQwLjV2NzhoMTAwdi03OXEzOCAtNCA3Mi41IC0xMy41dDc1LjUgLTMxLjV0NzEgLTUzLjV0NTEuNSAtODR0MjQuNSAtMTE4LjVoLTE1OXEtOCA3MiAtMzUgMTA5LjV0LTEwMSA1MC41IHYtMzA3bDY0IC0xNHEzNCAtNyA2NCAtMTYuNXQ3MCAtMzEuNXQ2Ny41IC01MnQ0Ny41IC04MC41dDIwIC0xMTIuNXEwIC0xMzkgLTg5IC0yMjR0LTI0NCAtOTZ2LTc3aC0xMDB2NzhxLTE1MiAxNyAtMjM3IDEwNHEtNDAgNDAgLTUyLjUgOTMuNXQtMTUuNSAxMzkuNXpNNDY2IDg4OXEwIC0yOSA4IC01MXQxNi41IC0zNHQyOS41IC0yMi41dDMxIC0xMy41dDM4IC0xMHE3IC0yIDExIC0zdjI3NHEtNjEgLTggLTk3LjUgLTM3LjV0LTM2LjUgLTEwMi41IHpNNzAwIDIzN3ExNzAgMTggMTcwIDE1MXEwIDY0IC00NCA5OS41dC0xMjYgNjAuNXYtMzExeiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNDk7IiBkPSJNMTAwIDYwMHYxMDBoMTY2cS0yNCA0OSAtNDQgMTA0cS0xMCAyNiAtMTQuNSA1NS41dC0zIDcyLjV0MjUgOTB0NjguNSA4N3E5NyA4OCAyNjMgODhxMTI5IDAgMjMwIC04OXQxMDEgLTIwOGgtMTUzcTAgNTIgLTM0IDg5LjV0LTc0IDUxLjV0LTc2IDE0cS0zNyAwIC03OSAtMTQuNXQtNjIgLTM1LjVxLTQxIC00NCAtNDEgLTEwMXEwIC0yOCAxNi41IC02OS41dDI4IC02Mi41dDQxLjUgLTcyaDI0MXYtMTAwaC0xOTdxOCAtNTAgLTIuNSAtMTE1IHQtMzEuNSAtOTRxLTQxIC01OSAtOTkgLTExM3EzNSAxMSA4NCAxOHQ3MCA3cTMzIDEgMTAzIC0xNnQxMDMgLTE3cTc2IDAgMTM2IDMwbDUwIC0xNDdxLTQxIC0yNSAtODAuNSAtMzYuNXQtNTkgLTEzdC02MS41IC0xLjVxLTIzIDAgLTEyOCAzM3QtMTU1IDI5cS0zOSAtNCAtODIgLTE3dC02NiAtMjVsLTI0IC0xMWwtNTUgMTQ1bDE2LjUgMTF0MTUuNSAxMHQxMy41IDkuNXQxNC41IDEydDE0LjUgMTR0MTcuNSAxOC41cTQ4IDU1IDU0IDEyNi41IHQtMzAgMTQyLjVoLTIyMXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTUwOyIgZD0iTTIgMzAwbDI5OCAtMzAwbDI5OCAzMDBoLTE5OHY5MDBoLTIwMHYtOTAwaC0xOTh6TTYwMiA5MDBsMjk4IDMwMGwyOTggLTMwMGgtMTk4di05MDBoLTIwMHY5MDBoLTE5OHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTUxOyIgZD0iTTIgMzAwaDE5OHY5MDBoMjAwdi05MDBoMTk4bC0yOTggLTMwMHpNNzAwIDB2MjAwaDEwMHYtMTAwaDIwMHYtMTAwaC0zMDB6TTcwMCA0MDB2MTAwaDMwMHYtMjAwaC05OXYtMTAwaC0xMDB2MTAwaDk5djEwMGgtMjAwek03MDAgNzAwdjUwMGgzMDB2LTUwMGgtMTAwdjEwMGgtMTAwdi0xMDBoLTEwMHpNODAxIDkwMGgxMDB2MjAwaC0xMDB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTUyOyIgZD0iTTIgMzAwaDE5OHY5MDBoMjAwdi05MDBoMTk4bC0yOTggLTMwMHpNNzAwIDB2NTAwaDMwMHYtNTAwaC0xMDB2MTAwaC0xMDB2LTEwMGgtMTAwek03MDAgNzAwdjIwMGgxMDB2LTEwMGgyMDB2LTEwMGgtMzAwek03MDAgMTEwMHYxMDBoMzAwdi0yMDBoLTk5di0xMDBoLTEwMHYxMDBoOTl2MTAwaC0yMDB6TTgwMSAyMDBoMTAwdjIwMGgtMTAwdi0yMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE1MzsiIGQ9Ik0yIDMwMGwyOTggLTMwMGwyOTggMzAwaC0xOTh2OTAwaC0yMDB2LTkwMGgtMTk4ek04MDAgMTAwdjQwMGgzMDB2LTUwMGgtMTAwdjEwMGgtMjAwek04MDAgMTEwMHYxMDBoMjAwdi01MDBoLTEwMHY0MDBoLTEwMHpNOTAxIDIwMGgxMDB2MjAwaC0xMDB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTU0OyIgZD0iTTIgMzAwbDI5OCAtMzAwbDI5OCAzMDBoLTE5OHY5MDBoLTIwMHYtOTAwaC0xOTh6TTgwMCA0MDB2MTAwaDIwMHYtNTAwaC0xMDB2NDAwaC0xMDB6TTgwMCA4MDB2NDAwaDMwMHYtNTAwaC0xMDB2MTAwaC0yMDB6TTkwMSA5MDBoMTAwdjIwMGgtMTAwdi0yMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE1NTsiIGQ9Ik0yIDMwMGwyOTggLTMwMGwyOTggMzAwaC0xOTh2OTAwaC0yMDB2LTkwMGgtMTk4ek03MDAgMTAwdjIwMGg1MDB2LTIwMGgtNTAwek03MDAgNDAwdjIwMGg0MDB2LTIwMGgtNDAwek03MDAgNzAwdjIwMGgzMDB2LTIwMGgtMzAwek03MDAgMTAwMHYyMDBoMjAwdi0yMDBoLTIwMHoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY3OyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0xMDAgNzAwaDMwMHYtMzAwaDMwMHYzMDBoMjk1bC00NDUgNTAwek05MDAgMTUwaDEwMHY1MGgtMTAwdi01MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY4OyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0xMDAgNzA1bDMwNSAtMzA1bDU5NiA1OTZsLTE1NCAxNTVsLTQ0MiAtNDQybC0xNTAgMTUxek05MDAgMTUwaDEwMHY1MGgtMTAwdi01MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY5OyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0xMDAgOTg4bDk3IC05OGwyMTIgMjEzbC05NyA5N3pNMjAwIDQwMGw2OTcgMWwzIDY5OWwtMjUwIC0yMzlsLTE0OSAxNDlsLTIxMiAtMjEybDE0OSAtMTQ5ek05MDAgMTUwaDEwMHY1MGgtMTAwdi01MHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTczOyIgZD0iTTAgMTUwdjEwMDBxMCAyMCAxNC41IDM1dDM1LjUgMTVoMjUwdi0zMDBoNTAwdjMwMGgxMDBsMjAwIC0yMDB2LTIxOGwtMjc2IC0yNzVsLTEyMCAxMjBsLTEyNiAtMTI3aC0zNzh2LTQwMGgtMTUwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXpNNTgxIDMwNmwxMjMgMTIzbDEyMCAtMTIwbDM1MyAzNTJsMTIzIC0xMjNsLTQ3NSAtNDc2ek02MDAgMTAwMGgxMDB2MjAwaC0xMDB2LTIwMHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTg0OyIgZD0iTTEwMCAwdjEwMGgxMTAwdi0xMDBoLTExMDB6TTE3NSAyMDBoOTUwbC0xMjUgMTUwdjI1MGwxMDAgMTAwdjQwMGgtMTAwdi0yMDBoLTEwMHYyMDBoLTIwMHYtMjAwaC0xMDB2MjAwaC0yMDB2LTIwMGgtMTAwdjIwMGgtMTAwdi00MDBsMTAwIC0xMDB2LTI1MHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTg4OyIgZD0iTS0xMDAgMzAwdjUwMHEwIDEyNCA4OCAyMTJ0MjEyIDg4aDcwMHExMjQgMCAyMTIgLTg4dDg4IC0yMTJ2LTUwMHEwIC0xMjQgLTg4IC0yMTJ0LTIxMiAtODhoLTcwMHEtMTI0IDAgLTIxMiA4OHQtODggMjEyek0xMDAgMjAwaDkwMHY3MDBoLTkwMHYtNzAwek0yMDAgMzAwaDMwMHYxMDBoLTIwMHYzMDBoMjAwdjEwMGgtMzAwdi01MDB6TTYwMCAzMDBoMzAwdjEwMGgtMjAwdjMwMGgyMDB2MTAwaC0zMDB2LTUwMHoiIC8%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%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window.setTimeout(function() {
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</script>
</head>
<body>
<style type="text/css">
.main-container {
max-width: 940px;
margin-left: auto;
margin-right: auto;
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code {
color: inherit;
background-color: rgba(0, 0, 0, 0.04);
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img {
max-width:100%;
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<div class="container-fluid main-container">
<div id="section" class="section level1">
<h1>7</h1>
<p><span class="math">\[
p_k(x) = \frac {\pi_k
\frac {1} {\sqrt{2 \pi} \sigma}
\exp(- \frac {1} {2 \sigma^2} (x - \mu_k)^2)
}
{\sum {
\pi_l
\frac {1} {\sqrt{2 \pi} \sigma}
\exp(- \frac {1} {2 \sigma^2} (x - \mu_l)^2)
}}
\\
p_{yes}(x)= \frac {\pi_{yes}
\exp(- \frac {1} {2 \sigma^2} (x - \mu_{yes})^2)
}
{\sum {
\pi_l
\exp(- \frac {1} {2 \sigma^2} (x - \mu_l)^2)
}}
\\
= \frac {\pi_{yes} \exp(- \frac {1} {2 \sigma^2} (x - \mu_{yes})^2)}
{
\pi_{yes} \exp(- \frac {1} {2 \sigma^2} (x - \mu_{yes})^2) +
\pi_{no} \exp(- \frac {1} {2 \sigma^2} (x - \mu_{no})^2)
}
\\
= \frac {0.80 \exp(- \frac {1} {2 * 36} (x - 10)^2)}
{
0.80 \exp(- \frac {1} {2 * 36} (x - 10)^2) +
0.20 \exp(- \frac {1} {2 * 36} x^2)
}
\\
p_{yes}(4) = \frac {0.80 \exp(- \frac {1} {2 * 36} (4 - 10)^2)}
{
0.80 \exp(- \frac {1} {2 * 36} (4 - 10)^2) +
0.20 \exp(- \frac {1} {2 * 36} 4^2)
}
= 75.2\%
\]</span></p>
</div>
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统计学习导论_基于R应用习题答案/ch4/7.html
|
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|
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| 646,505
|
<!DOCTYPE html>
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<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<meta http-equiv="x-ua-compatible" content="IE=9" >
<title>8</title>
<style type="text/css">
body, td {
font-family: sans-serif;
background-color: white;
font-size: 12px;
margin: 8px;
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background: transparent !important;
color: black !important;
filter:none !important;
-ms-filter: none !important;
}
body {
font-size:12pt;
max-width:100%;
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p, h2, h3 {
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h2, h3 {
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</style>
</head>
<body>
<h1>8</h1>
<p>Given:</p>
<p>Logistic regression: 20% training error rate, 30% test error rate
KNN(K=1): average error rate of 18%</p>
<p>For KNN with K=1, the training error rate is 0% because for any training
observation, its nearest neighbor will be the response itself. So, KNN has a
test error rate of 36%. I would choose logistic regression because of its lower
test error rate of 30%.</p>
</body>
</html>
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统计学习导论_基于R应用习题答案/ch4/8.html
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<!DOCTYPE html>
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<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<title>9</title>
<style type="text/css">
body, td {
font-family: sans-serif;
background-color: white;
font-size: 12px;
margin: 8px;
}
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* {
background: transparent !important;
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</head>
<body>
<h1>9</h1>
<h3>a.</h3>
<p>\[
\frac {p(X)} {1 - p(X)} = 0.37
\\
p(X) = 0.37 (1 - p(X))
\\
1.37 p(X) = 0.37
\\
p(X) = \frac {0.37} {1.37} = 27\%
\]</p>
<h3>b.</h3>
<p>\[
odds = \frac {p(X)} {1 - p(X)} = .16 / .84 = 0.19
\]</p>
</body>
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch4/9.html
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# Chapter 4 Lab: Logistic Regression, LDA, QDA, and KNN
# The Stock Market Data
library(ISLR)
names(Smarket)
dim(Smarket)
summary(Smarket)
pairs(Smarket)
cor(Smarket)
cor(Smarket[,-9])
attach(Smarket)
plot(Volume)
# Logistic Regression
glm.fit=glm(Direction~Lag1+Lag2+Lag3+Lag4+Lag5+Volume,data=Smarket,family=binomial)
summary(glm.fit)
coef(glm.fit)
summary(glm.fit)$coef
summary(glm.fit)$coef[,4]
glm.probs=predict(glm.fit,type="response")
glm.probs[1:10]
contrasts(Direction)
glm.pred=rep("Down",1250)
glm.pred[glm.probs>.5]="Up"
table(glm.pred,Direction)
(507+145)/1250
mean(glm.pred==Direction)
train=(Year<2005)
Smarket.2005=Smarket[!train,]
dim(Smarket.2005)
Direction.2005=Direction[!train]
glm.fit=glm(Direction~Lag1+Lag2+Lag3+Lag4+Lag5+Volume,data=Smarket,family=binomial,subset=train)
glm.probs=predict(glm.fit,Smarket.2005,type="response")
glm.pred=rep("Down",252)
glm.pred[glm.probs>.5]="Up"
table(glm.pred,Direction.2005)
mean(glm.pred==Direction.2005)
mean(glm.pred!=Direction.2005)
glm.fit=glm(Direction~Lag1+Lag2,data=Smarket,family=binomial,subset=train)
glm.probs=predict(glm.fit,Smarket.2005,type="response")
glm.pred=rep("Down",252)
glm.pred[glm.probs>.5]="Up"
table(glm.pred,Direction.2005)
mean(glm.pred==Direction.2005)
106/(106+76)
predict(glm.fit,newdata=data.frame(Lag1=c(1.2,1.5),Lag2=c(1.1,-0.8)),type="response")
# Linear Discriminant Analysis
library(MASS)
lda.fit=lda(Direction~Lag1+Lag2,data=Smarket,subset=train)
lda.fit
plot(lda.fit)
lda.pred=predict(lda.fit, Smarket.2005)
names(lda.pred)
lda.class=lda.pred$class
table(lda.class,Direction.2005)
mean(lda.class==Direction.2005)
sum(lda.pred$posterior[,1]>=.5)
sum(lda.pred$posterior[,1]<.5)
lda.pred$posterior[1:20,1]
lda.class[1:20]
sum(lda.pred$posterior[,1]>.9)
# Quadratic Discriminant Analysis
qda.fit=qda(Direction~Lag1+Lag2,data=Smarket,subset=train)
qda.fit
qda.class=predict(qda.fit,Smarket.2005)$class
table(qda.class,Direction.2005)
mean(qda.class==Direction.2005)
# K-Nearest Neighbors
library(class)
train.X=cbind(Lag1,Lag2)[train,]
test.X=cbind(Lag1,Lag2)[!train,]
train.Direction=Direction[train]
set.seed(1)
knn.pred=knn(train.X,test.X,train.Direction,k=1)
table(knn.pred,Direction.2005)
(83+43)/252
knn.pred=knn(train.X,test.X,train.Direction,k=3)
table(knn.pred,Direction.2005)
mean(knn.pred==Direction.2005)
# An Application to Caravan Insurance Data
dim(Caravan)
attach(Caravan)
summary(Purchase)
348/5822
standardized.X=scale(Caravan[,-86])
var(Caravan[,1])
var(Caravan[,2])
var(standardized.X[,1])
var(standardized.X[,2])
test=1:1000
train.X=standardized.X[-test,]
test.X=standardized.X[test,]
train.Y=Purchase[-test]
test.Y=Purchase[test]
set.seed(1)
knn.pred=knn(train.X,test.X,train.Y,k=1)
mean(test.Y!=knn.pred)
mean(test.Y!="No")
table(knn.pred,test.Y)
9/(68+9)
knn.pred=knn(train.X,test.X,train.Y,k=3)
table(knn.pred,test.Y)
5/26
knn.pred=knn(train.X,test.X,train.Y,k=5)
table(knn.pred,test.Y)
4/15
glm.fit=glm(Purchase~.,data=Caravan,family=binomial,subset=-test)
glm.probs=predict(glm.fit,Caravan[test,],type="response")
glm.pred=rep("No",1000)
glm.pred[glm.probs>.5]="Yes"
table(glm.pred,test.Y)
glm.pred=rep("No",1000)
glm.pred[glm.probs>.25]="Yes"
table(glm.pred,test.Y)
11/(22+11)
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch4/lab.R
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R
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unknown
| 3,213
|
<!DOCTYPE html>
<!-- saved from url=(0014)about:internet -->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<title>Chapter 5: Exercise 1</title>
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font-size: 12px;
margin: 8px;
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height: 0px;
border-bottom: none;
border-top-width: thin;
border-top-style: dotted;
border-top-color: #999999;
}
@media print {
* {
background: transparent !important;
color: black !important;
filter:none !important;
-ms-filter: none !important;
}
body {
font-size:12pt;
max-width:100%;
}
a, a:visited {
text-decoration: underline;
}
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visibility: hidden;
page-break-before: always;
}
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padding-right: 1em;
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page-break-inside: avoid;
}
img {
max-width: 100% !important;
}
@page :left {
margin: 15mm 20mm 15mm 10mm;
}
@page :right {
margin: 15mm 10mm 15mm 20mm;
}
p, h2, h3 {
orphans: 3; widows: 3;
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h2, h3 {
page-break-after: avoid;
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}
</style>
<!-- MathJax scripts -->
<script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>
</head>
<body>
<h1>Chapter 5: Exercise 1</h1>
<p>Using the following rules:</p>
<p>\[
Var(X+Y) = Var(X) + Var(Y) + 2 Cov(X,Y)
\\
Var(cX) = c^2 Var(X)
\\
Cov(cX,Y) = Cov(X,cY) = c Cov(X,Y)
\]</p>
<p>Minimizing two-asset financial portfolio:
\[
Var(\alpha X + (1 - \alpha)Y)
\\
= Var(\alpha X) + Var((1 - \alpha) Y) + 2 Cov(\alpha X, (1 - \alpha) Y)
\\
= \alpha^2 Var(X) + (1 - \alpha)^2 Var(Y) + 2 \alpha (1 - \alpha) Cov(X, Y)
\\
= \sigma_X^2 \alpha^2 + \sigma_Y^2 (1 - \alpha)^2 + 2 \sigma_{XY} (-\alpha^2 +
\alpha)
\]</p>
<p>Take the first derivative to find critical points:
\[
0 = \frac {d} {d\alpha} f(\alpha)
\\
0 = 2 \sigma_X^2 \alpha + 2 \sigma_Y^2 (1 - \alpha) (-1) + 2 \sigma_{XY}
(-2 \alpha + 1)
\\
0 = \sigma_X^2 \alpha + \sigma_Y^2 (\alpha - 1) + \sigma_{XY} (-2 \alpha + 1)
\\
0 = (\sigma_X^2 + \sigma_Y^2 - 2 \sigma_{XY}) \alpha - \sigma_Y^2 + \sigma_{XY}
\\
\alpha = \frac {\sigma_Y^2 - \sigma_{XY}}
{\sigma_X^2 + \sigma_Y^2 - 2 \sigma_{XY}}
\]</p>
</body>
</html>
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch5/1.html
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unknown
| 3,106
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<!DOCTYPE html>
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<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<title>Chapter 5: Exercise 2</title>
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<h1>Chapter 5: Exercise 2</h1>
<h3>a</h3>
<p>\( 1 - 1/n \)</p>
<h3>b</h3>
<p>\( 1 - 1/n \)</p>
<h3>c</h3>
<p>In bootstrap, we sample with replacement so each observation in the bootstrap
sample has the same 1/n (independent) chance of equaling the jth observation.
Applying the product rule for a total of n observations gives us \( (1 - 1/n)^n \).</p>
<h3>d</h3>
<p>\( Pr(in) = 1 - Pr(out) = 1 - (1 - 1/5)^5 = 1 - (4/5)^5 = 67.2\% \)</p>
<h3>e</h3>
<p>\( Pr(in) = 1 - Pr(out) = 1 - (1 - 1/100)^{10} = 1 - (99/100)^{100} = 63.4\% \)</p>
<h3>f</h3>
<p>\( 1 - (1 - 1/10000)^{10000} = 63.2\% \)</p>
<h3>g</h3>
<pre><code class="r">pr = function(n) return(1 - (1 - 1/n)^n)
x = 1:1e+05
plot(x, pr(x))
</code></pre>
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alt="plot of chunk 2g"/> </p>
<p>The plot quickly reaches an asymptote of about 63.2%.</p>
<h3>h</h3>
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<h1>Chapter 5: Exercise 3</h1>
<h3>a</h3>
<p>k-fold cross-validation is implemented by taking the set of n observations and
randomly splitting into k non-overlapping groups. Each of these groups acts as
a validation set and the remainder as a training set. The test error is
estimated by averaging the k resulting MSE estimates.</p>
<h3>b</h3>
<p>i. The validation set approach is conceptually simple and easily implemented as
you are simply partitioning the existing training data into two sets. However,
there are two drawbacks: (1.) the estimate of the test error rate can be highly
variable depending on which observations are included in the training and
validation sets. (2.) the validation set error rate may tend to overestimate
the test error rate for the model fit on the entire data set.</p>
<p>ii. LOOCV is a special case of k-fold cross-validation with k = n. Thus, LOOCV
is the most computationally intense method since the model must be fit n times.
Also, LOOCV has higher variance, but lower bias, than k-fold CV.</p>
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<h1>Chapter 5: Exercise 4</h1>
<p>If we suppose using some statistical learning method to make a prediction for
the response \( Y \) for a particular value of the predictor \( X \) we might estimate
the standard deviation of our prediction by using the bootstrap approach. The
bootstrap approach works by repeatedly sampling observations (with replacement)
from the original data set \( B \) times, for some large value of \( B \), each time
fitting a new model and subsequently obtaining the RMSE of the estimates for all
\( B \) models.</p>
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch5/4.html
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<h1>Chapter 5: Exercise 5</h1>
<pre><code class="r">library(ISLR)
summary(Default)
</code></pre>
<pre><code>## default student balance income
## No :9667 No :7056 Min. : 0 Min. : 772
## Yes: 333 Yes:2944 1st Qu.: 482 1st Qu.:21340
## Median : 824 Median :34553
## Mean : 835 Mean :33517
## 3rd Qu.:1166 3rd Qu.:43808
## Max. :2654 Max. :73554
</code></pre>
<pre><code class="r">attach(Default)
</code></pre>
<h3>a</h3>
<pre><code class="r">set.seed(1)
glm.fit = glm(default ~ income + balance, data = Default, family = binomial)
</code></pre>
<h3>b</h3>
<pre><code class="r">FiveB = function() {
# i.
train = sample(dim(Default)[1], dim(Default)[1]/2)
# ii.
glm.fit = glm(default ~ income + balance, data = Default, family = binomial,
subset = train)
# iii.
glm.pred = rep("No", dim(Default)[1]/2)
glm.probs = predict(glm.fit, Default[-train, ], type = "response")
glm.pred[glm.probs > 0.5] = "Yes"
# iv.
return(mean(glm.pred != Default[-train, ]$default))
}
FiveB()
</code></pre>
<pre><code>## [1] 0.0286
</code></pre>
<p>2.86% test error rate from validation set approach.</p>
<h3>c</h3>
<pre><code class="r">FiveB()
</code></pre>
<pre><code>## [1] 0.0236
</code></pre>
<pre><code class="r">FiveB()
</code></pre>
<pre><code>## [1] 0.028
</code></pre>
<pre><code class="r">FiveB()
</code></pre>
<pre><code>## [1] 0.0268
</code></pre>
<p>It seems to average around 2.6% test error rate.</p>
<h3>d</h3>
<pre><code class="r">train = sample(dim(Default)[1], dim(Default)[1]/2)
glm.fit = glm(default ~ income + balance + student, data = Default, family = binomial,
subset = train)
glm.pred = rep("No", dim(Default)[1]/2)
glm.probs = predict(glm.fit, Default[-train, ], type = "response")
glm.pred[glm.probs > 0.5] = "Yes"
mean(glm.pred != Default[-train, ]$default)
</code></pre>
<pre><code>## [1] 0.0264
</code></pre>
<p>2.64% test error rate, with student dummy variable. Using the validation set
approach, it doesn't appear adding the student dummy variable leads to a
reduction in the test error rate.</p>
</body>
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<body>
<h1>Chapter 5: Exercise 6</h1>
<pre><code class="r">library(ISLR)
summary(Default)
</code></pre>
<pre><code>## default student balance income
## No :9667 No :7056 Min. : 0 Min. : 772
## Yes: 333 Yes:2944 1st Qu.: 482 1st Qu.:21340
## Median : 824 Median :34553
## Mean : 835 Mean :33517
## 3rd Qu.:1166 3rd Qu.:43808
## Max. :2654 Max. :73554
</code></pre>
<pre><code class="r">attach(Default)
</code></pre>
<h3>a</h3>
<pre><code class="r">set.seed(1)
glm.fit = glm(default ~ income + balance, data = Default, family = binomial)
summary(glm.fit)
</code></pre>
<pre><code>##
## Call:
## glm(formula = default ~ income + balance, family = binomial,
## data = Default)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.473 -0.144 -0.057 -0.021 3.724
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.15e+01 4.35e-01 -26.54 <2e-16 ***
## income 2.08e-05 4.99e-06 4.17 3e-05 ***
## balance 5.65e-03 2.27e-04 24.84 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2920.6 on 9999 degrees of freedom
## Residual deviance: 1579.0 on 9997 degrees of freedom
## AIC: 1585
##
## Number of Fisher Scoring iterations: 8
</code></pre>
<h3>b</h3>
<pre><code class="r">boot.fn = function(data, index) return(coef(glm(default ~ income + balance,
data = data, family = binomial, subset = index)))
</code></pre>
<h3>c</h3>
<pre><code class="r">library(boot)
boot(Default, boot.fn, 50)
</code></pre>
<pre><code>##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot(data = Default, statistic = boot.fn, R = 50)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* -1.154e+01 1.181e-01 4.202e-01
## t2* 2.081e-05 -5.467e-08 4.542e-06
## t3* 5.647e-03 -6.975e-05 2.283e-04
</code></pre>
<h3>d</h3>
<p>Similar answers to the second and third significant digits.</p>
</body>
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统计学习导论_基于R应用习题答案/ch5/6.html
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<h1>Chapter 5: Exercise 7</h1>
<pre><code class="r">library(ISLR)
summary(Weekly)
</code></pre>
<pre><code>## Year Lag1 Lag2 Lag3
## Min. :1990 Min. :-18.195 Min. :-18.195 Min. :-18.195
## 1st Qu.:1995 1st Qu.: -1.154 1st Qu.: -1.154 1st Qu.: -1.158
## Median :2000 Median : 0.241 Median : 0.241 Median : 0.241
## Mean :2000 Mean : 0.151 Mean : 0.151 Mean : 0.147
## 3rd Qu.:2005 3rd Qu.: 1.405 3rd Qu.: 1.409 3rd Qu.: 1.409
## Max. :2010 Max. : 12.026 Max. : 12.026 Max. : 12.026
## Lag4 Lag5 Volume Today
## Min. :-18.195 Min. :-18.195 Min. :0.087 Min. :-18.195
## 1st Qu.: -1.158 1st Qu.: -1.166 1st Qu.:0.332 1st Qu.: -1.154
## Median : 0.238 Median : 0.234 Median :1.003 Median : 0.241
## Mean : 0.146 Mean : 0.140 Mean :1.575 Mean : 0.150
## 3rd Qu.: 1.409 3rd Qu.: 1.405 3rd Qu.:2.054 3rd Qu.: 1.405
## Max. : 12.026 Max. : 12.026 Max. :9.328 Max. : 12.026
## Direction
## Down:484
## Up :605
##
##
##
##
</code></pre>
<pre><code class="r">set.seed(1)
attach(Weekly)
</code></pre>
<h3>a</h3>
<pre><code class="r">glm.fit = glm(Direction ~ Lag1 + Lag2, data = Weekly, family = binomial)
summary(glm.fit)
</code></pre>
<pre><code>##
## Call:
## glm(formula = Direction ~ Lag1 + Lag2, family = binomial, data = Weekly)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.62 -1.26 1.00 1.08 1.51
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2212 0.0615 3.60 0.00032 ***
## Lag1 -0.0387 0.0262 -1.48 0.13967
## Lag2 0.0602 0.0265 2.27 0.02323 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1496.2 on 1088 degrees of freedom
## Residual deviance: 1488.2 on 1086 degrees of freedom
## AIC: 1494
##
## Number of Fisher Scoring iterations: 4
</code></pre>
<h3>b</h3>
<pre><code class="r">glm.fit = glm(Direction ~ Lag1 + Lag2, data = Weekly[-1, ], family = binomial)
summary(glm.fit)
</code></pre>
<pre><code>##
## Call:
## glm(formula = Direction ~ Lag1 + Lag2, family = binomial, data = Weekly[-1,
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.63 -1.26 1.00 1.08 1.51
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2232 0.0615 3.63 0.00028 ***
## Lag1 -0.0384 0.0262 -1.47 0.14268
## Lag2 0.0608 0.0266 2.29 0.02197 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1494.6 on 1087 degrees of freedom
## Residual deviance: 1486.5 on 1085 degrees of freedom
## AIC: 1493
##
## Number of Fisher Scoring iterations: 4
</code></pre>
<h3>c</h3>
<pre><code class="r">predict.glm(glm.fit, Weekly[1, ], type = "response") > 0.5
</code></pre>
<pre><code>## 1
## TRUE
</code></pre>
<p>Prediction was UP, true Direction was DOWN.</p>
<h3>d</h3>
<pre><code class="r">count = rep(0, dim(Weekly)[1])
for (i in 1:(dim(Weekly)[1])) {
glm.fit = glm(Direction ~ Lag1 + Lag2, data = Weekly[-i, ], family = binomial)
is_up = predict.glm(glm.fit, Weekly[i, ], type = "response") > 0.5
is_true_up = Weekly[i, ]$Direction == "Up"
if (is_up != is_true_up)
count[i] = 1
}
sum(count)
</code></pre>
<pre><code>## [1] 490
</code></pre>
<p>490 errors.</p>
<h3>e</h3>
<pre><code class="r">mean(count)
</code></pre>
<pre><code>## [1] 0.45
</code></pre>
<p>LOOCV estimates a test error rate of 45%. </p>
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<h1>Chapter 5: Exercise 8</h1>
<h3>a</h3>
<pre><code class="r">set.seed(1)
y = rnorm(100)
x = rnorm(100)
y = x - 2 * x^2 + rnorm(100)
</code></pre>
<p>n = 100, p = 2.</p>
<p>\( Y = X - 2 X^2 + \epsilon \).</p>
<h3>b</h3>
<pre><code class="r">plot(x, y)
</code></pre>
<p><img 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alt="plot of chunk 8b"/> </p>
<p>Quadratic plot. \( X \) from about -2 to 2. \( Y \) from about -8 to 2.</p>
<h3>c</h3>
<pre><code class="r">library(boot)
Data = data.frame(x, y)
set.seed(1)
# i.
glm.fit = glm(y ~ x)
cv.glm(Data, glm.fit)$delta
</code></pre>
<pre><code>## [1] 5.891 5.889
</code></pre>
<pre><code class="r"># ii.
glm.fit = glm(y ~ poly(x, 2))
cv.glm(Data, glm.fit)$delta
</code></pre>
<pre><code>## [1] 1.087 1.086
</code></pre>
<pre><code class="r"># iii.
glm.fit = glm(y ~ poly(x, 3))
cv.glm(Data, glm.fit)$delta
</code></pre>
<pre><code>## [1] 1.103 1.102
</code></pre>
<pre><code class="r"># iv.
glm.fit = glm(y ~ poly(x, 4))
cv.glm(Data, glm.fit)$delta
</code></pre>
<pre><code>## [1] 1.115 1.114
</code></pre>
<h3>d</h3>
<pre><code class="r">set.seed(10)
# i.
glm.fit = glm(y ~ x)
cv.glm(Data, glm.fit)$delta
</code></pre>
<pre><code>## [1] 5.891 5.889
</code></pre>
<pre><code class="r"># ii.
glm.fit = glm(y ~ poly(x, 2))
cv.glm(Data, glm.fit)$delta
</code></pre>
<pre><code>## [1] 1.087 1.086
</code></pre>
<pre><code class="r"># iii.
glm.fit = glm(y ~ poly(x, 3))
cv.glm(Data, glm.fit)$delta
</code></pre>
<pre><code>## [1] 1.103 1.102
</code></pre>
<pre><code class="r"># iv.
glm.fit = glm(y ~ poly(x, 4))
cv.glm(Data, glm.fit)$delta
</code></pre>
<pre><code>## [1] 1.115 1.114
</code></pre>
<p>Exact same, because LOOCV will be the same since it evaluates n folds of a
single observation.</p>
<h3>e</h3>
<p>The quadratic polynomial had the lowest LOOCV test error rate. This was
expected because it matches the true form of \( Y \).</p>
<h3>f</h3>
<pre><code class="r">summary(glm.fit)
</code></pre>
<pre><code>##
## Call:
## glm(formula = y ~ poly(x, 4))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8913 -0.5244 0.0749 0.5932 2.7796
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.828 0.104 -17.55 <2e-16 ***
## poly(x, 4)1 2.316 1.041 2.22 0.029 *
## poly(x, 4)2 -21.059 1.041 -20.22 <2e-16 ***
## poly(x, 4)3 -0.305 1.041 -0.29 0.770
## poly(x, 4)4 -0.493 1.041 -0.47 0.637
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.085)
##
## Null deviance: 552.21 on 99 degrees of freedom
## Residual deviance: 103.04 on 95 degrees of freedom
## AIC: 298.8
##
## Number of Fisher Scoring iterations: 2
</code></pre>
<p>p-values show statistical significance of linear and quadratic terms, which
agrees with the CV results.</p>
</body>
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统计学习导论_基于R应用习题答案/ch5/8.html
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<h1>Chapter 5: Exercise 9</h1>
<pre><code class="r">library(MASS)
summary(Boston)
</code></pre>
<pre><code>## crim zn indus chas
## Min. : 0.01 Min. : 0.0 Min. : 0.46 Min. :0.0000
## 1st Qu.: 0.08 1st Qu.: 0.0 1st Qu.: 5.19 1st Qu.:0.0000
## Median : 0.26 Median : 0.0 Median : 9.69 Median :0.0000
## Mean : 3.61 Mean : 11.4 Mean :11.14 Mean :0.0692
## 3rd Qu.: 3.68 3rd Qu.: 12.5 3rd Qu.:18.10 3rd Qu.:0.0000
## Max. :88.98 Max. :100.0 Max. :27.74 Max. :1.0000
## nox rm age dis
## Min. :0.385 Min. :3.56 Min. : 2.9 Min. : 1.13
## 1st Qu.:0.449 1st Qu.:5.89 1st Qu.: 45.0 1st Qu.: 2.10
## Median :0.538 Median :6.21 Median : 77.5 Median : 3.21
## Mean :0.555 Mean :6.29 Mean : 68.6 Mean : 3.79
## 3rd Qu.:0.624 3rd Qu.:6.62 3rd Qu.: 94.1 3rd Qu.: 5.19
## Max. :0.871 Max. :8.78 Max. :100.0 Max. :12.13
## rad tax ptratio black
## Min. : 1.00 Min. :187 Min. :12.6 Min. : 0.3
## 1st Qu.: 4.00 1st Qu.:279 1st Qu.:17.4 1st Qu.:375.4
## Median : 5.00 Median :330 Median :19.1 Median :391.4
## Mean : 9.55 Mean :408 Mean :18.5 Mean :356.7
## 3rd Qu.:24.00 3rd Qu.:666 3rd Qu.:20.2 3rd Qu.:396.2
## Max. :24.00 Max. :711 Max. :22.0 Max. :396.9
## lstat medv
## Min. : 1.73 Min. : 5.0
## 1st Qu.: 6.95 1st Qu.:17.0
## Median :11.36 Median :21.2
## Mean :12.65 Mean :22.5
## 3rd Qu.:16.95 3rd Qu.:25.0
## Max. :37.97 Max. :50.0
</code></pre>
<pre><code class="r">set.seed(1)
attach(Boston)
</code></pre>
<h3>a</h3>
<pre><code class="r">medv.mean = mean(medv)
medv.mean
</code></pre>
<pre><code>## [1] 22.53
</code></pre>
<h3>b</h3>
<pre><code class="r">medv.err = sd(medv)/sqrt(length(medv))
medv.err
</code></pre>
<pre><code>## [1] 0.4089
</code></pre>
<h3>c</h3>
<pre><code class="r">boot.fn = function(data, index) return(mean(data[index]))
library(boot)
bstrap = boot(medv, boot.fn, 1000)
bstrap
</code></pre>
<pre><code>##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot(data = medv, statistic = boot.fn, R = 1000)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 22.53 0.008518 0.4119
</code></pre>
<p>Similar to answer from (b) up to two significant digits. (0.4119 vs 0.4089)</p>
<h3>d</h3>
<pre><code class="r">t.test(medv)
</code></pre>
<pre><code>##
## One Sample t-test
##
## data: medv
## t = 55.11, df = 505, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 21.73 23.34
## sample estimates:
## mean of x
## 22.53
</code></pre>
<pre><code class="r">c(bstrap$t0 - 2 * 0.4119, bstrap$t0 + 2 * 0.4119)
</code></pre>
<pre><code>## [1] 21.71 23.36
</code></pre>
<p>Bootstrap estimate only 0.02 away for t.test estimate.</p>
<h3>e</h3>
<pre><code class="r">medv.med = median(medv)
medv.med
</code></pre>
<pre><code>## [1] 21.2
</code></pre>
<h3>f</h3>
<pre><code class="r">boot.fn = function(data, index) return(median(data[index]))
boot(medv, boot.fn, 1000)
</code></pre>
<pre><code>##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot(data = medv, statistic = boot.fn, R = 1000)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 21.2 -0.0098 0.3874
</code></pre>
<p>Median of 21.2 with SE of 0.380. Small standard error relative to median value.</p>
<h3>g</h3>
<pre><code class="r">medv.tenth = quantile(medv, c(0.1))
medv.tenth
</code></pre>
<pre><code>## 10%
## 12.75
</code></pre>
<h3>h</h3>
<pre><code class="r">boot.fn = function(data, index) return(quantile(data[index], c(0.1)))
boot(medv, boot.fn, 1000)
</code></pre>
<pre><code>##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot(data = medv, statistic = boot.fn, R = 1000)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 12.75 0.00515 0.5113
</code></pre>
<p>Tenth-percentile of 12.75 with SE of 0.511. Small standard error relative to
tenth-percentile value.</p>
</body>
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2301_76574743/tjjm-R
|
统计学习导论_基于R应用习题答案/ch5/9.html
|
HTML
|
unknown
| 17,257
|
# Chaper 5 Lab: Cross-Validation and the Bootstrap
# The Validation Set Approach
library(ISLR)
set.seed(1)
train=sample(392,196)
lm.fit=lm(mpg~horsepower,data=Auto,subset=train)
attach(Auto)
mean((mpg-predict(lm.fit,Auto))[-train]^2)
lm.fit2=lm(mpg~poly(horsepower,2),data=Auto,subset=train)
mean((mpg-predict(lm.fit2,Auto))[-train]^2)
lm.fit3=lm(mpg~poly(horsepower,3),data=Auto,subset=train)
mean((mpg-predict(lm.fit3,Auto))[-train]^2)
set.seed(2)
train=sample(392,196)
lm.fit=lm(mpg~horsepower,subset=train)
mean((mpg-predict(lm.fit,Auto))[-train]^2)
lm.fit2=lm(mpg~poly(horsepower,2),data=Auto,subset=train)
mean((mpg-predict(lm.fit2,Auto))[-train]^2)
lm.fit3=lm(mpg~poly(horsepower,3),data=Auto,subset=train)
mean((mpg-predict(lm.fit3,Auto))[-train]^2)
# Leave-One-Out Cross-Validation
glm.fit=glm(mpg~horsepower,data=Auto)
coef(glm.fit)
lm.fit=lm(mpg~horsepower,data=Auto)
coef(lm.fit)
library(boot)
glm.fit=glm(mpg~horsepower,data=Auto)
cv.err=cv.glm(Auto,glm.fit)
cv.err$delta
cv.error=rep(0,5)
for (i in 1:5){
glm.fit=glm(mpg~poly(horsepower,i),data=Auto)
cv.error[i]=cv.glm(Auto,glm.fit)$delta[1]
}
cv.error
# k-Fold Cross-Validation
set.seed(17)
cv.error.10=rep(0,10)
for (i in 1:10){
glm.fit=glm(mpg~poly(horsepower,i),data=Auto)
cv.error.10[i]=cv.glm(Auto,glm.fit,K=10)$delta[1]
}
cv.error.10
# The Bootstrap
alpha.fn=function(data,index){
X=data$X[index]
Y=data$Y[index]
return((var(Y)-cov(X,Y))/(var(X)+var(Y)-2*cov(X,Y)))
}
alpha.fn(Portfolio,1:100)
set.seed(1)
alpha.fn(Portfolio,sample(100,100,replace=T))
boot(Portfolio,alpha.fn,R=1000)
# Estimating the Accuracy of a Linear Regression Model
boot.fn=function(data,index)
return(coef(lm(mpg~horsepower,data=data,subset=index)))
boot.fn(Auto,1:392)
set.seed(1)
boot.fn(Auto,sample(392,392,replace=T))
boot.fn(Auto,sample(392,392,replace=T))
boot(Auto,boot.fn,1000)
summary(lm(mpg~horsepower,data=Auto))$coef
boot.fn=function(data,index)
coefficients(lm(mpg~horsepower+I(horsepower^2),data=data,subset=index))
set.seed(1)
boot(Auto,boot.fn,1000)
summary(lm(mpg~horsepower+I(horsepower^2),data=Auto))$coef
|
2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch5/lab.R
|
R
|
unknown
| 2,094
|
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<h1>Chapter 6: Exercise 1</h1>
<h3>a</h3>
<p>Best subset selection has the smallest training RSS because the other two
methods determine models with a path dependency on which predictors they pick
first as they iterate to the k'th model.</p>
<h3>b (*)</h3>
<p>Best subset selection may have the smallest test RSS because it considers more
models then the other methods. However, the other models might have better luck
picking a model that fits the test data better.</p>
<h3>c</h3>
<p>i. True.
ii. True.
iii. False.
iv. False.
v. False.</p>
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统计学习导论_基于R应用习题答案/ch6/1.html
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<h1>Chapter 6: Exercise 10 </h1>
<h2>a</h2>
<pre><code class="r">set.seed(1)
p = 20
n = 1000
x = matrix(rnorm(n * p), n, p)
B = rnorm(p)
B[3] = 0
B[4] = 0
B[9] = 0
B[19] = 0
B[10] = 0
eps = rnorm(p)
y = x %*% B + eps
</code></pre>
<h2>b</h2>
<pre><code class="r">train = sample(seq(1000), 100, replace = FALSE)
y.train = y[train, ]
y.test = y[-train, ]
x.train = x[train, ]
x.test = x[-train, ]
</code></pre>
<h2>c</h2>
<pre><code class="r">library(leaps)
regfit.full = regsubsets(y ~ ., data = data.frame(x = x.train, y = y.train),
nvmax = p)
val.errors = rep(NA, p)
x_cols = colnames(x, do.NULL = FALSE, prefix = "x.")
for (i in 1:p) {
coefi = coef(regfit.full, id = i)
pred = as.matrix(x.train[, x_cols %in% names(coefi)]) %*% coefi[names(coefi) %in%
x_cols]
val.errors[i] = mean((y.train - pred)^2)
}
plot(val.errors, ylab = "Training MSE", pch = 19, type = "b")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-3"/> </p>
<h2>d</h2>
<pre><code class="r">val.errors = rep(NA, p)
for (i in 1:p) {
coefi = coef(regfit.full, id = i)
pred = as.matrix(x.test[, x_cols %in% names(coefi)]) %*% coefi[names(coefi) %in%
x_cols]
val.errors[i] = mean((y.test - pred)^2)
}
plot(val.errors, ylab = "Test MSE", pch = 19, type = "b")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-4"/> </p>
<h2>e</h2>
<pre><code class="r">which.min(val.errors)
</code></pre>
<pre><code>## [1] 16
</code></pre>
<p>16 parameter model has the smallest test MSE.</p>
<h2>f</h2>
<pre><code class="r">coef(regfit.full, id = 16)
</code></pre>
<pre><code>## (Intercept) x.1 x.2 x.5 x.6 x.7
## 0.09613 0.28257 0.19386 0.99995 -0.28598 -1.50482
## x.8 x.11 x.12 x.13 x.14 x.15
## 0.77817 0.90816 0.48478 -0.19747 -0.71979 -0.74282
## x.16 x.17 x.18 x.19 x.20
## -0.33901 0.12235 1.74270 -0.12435 -1.03003
</code></pre>
<p>Caught all but one zeroed out coefficient at x.19.</p>
<h2>g</h2>
<pre><code class="r">val.errors = rep(NA, p)
a = rep(NA, p)
b = rep(NA, p)
for (i in 1:p) {
coefi = coef(regfit.full, id = i)
a[i] = length(coefi) - 1
b[i] = sqrt(sum((B[x_cols %in% names(coefi)] - coefi[names(coefi) %in% x_cols])^2) +
sum(B[!(x_cols %in% names(coefi))])^2)
}
plot(x = a, y = b, xlab = "number of coefficients", ylab = "error between estimated and true coefficients")
</code></pre>
<p><img src="data:image/png;base64,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alt="plot of chunk unnamed-chunk-7"/> </p>
<pre><code class="r">which.min(b)
</code></pre>
<pre><code>## [1] 16
</code></pre>
<p>Model with 9 coefficients (10 with intercept) minimizes the error between the
estimated and true coefficients. Test error is minimized with 16 parameter model.
A better fit of true coefficients as measured here doesn't mean the model will have
a lower test MSE.</p>
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统计学习导论_基于R应用习题答案/ch6/10.html
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<body>
<h1>Chapter 6: Exercise 11</h1>
<h2>a</h2>
<pre><code class="r">set.seed(1)
library(MASS)
library(leaps)
library(glmnet)
</code></pre>
<pre><code>## Loading required package: Matrix Loading required package: lattice Loaded
## glmnet 1.9-5
</code></pre>
<h3>Best subset selection</h3>
<pre><code class="r">predict.regsubsets = function(object, newdata, id, ...) {
form = as.formula(object$call[[2]])
mat = model.matrix(form, newdata)
coefi = coef(object, id = id)
mat[, names(coefi)] %*% coefi
}
k = 10
p = ncol(Boston) - 1
folds = sample(rep(1:k, length = nrow(Boston)))
cv.errors = matrix(NA, k, p)
for (i in 1:k) {
best.fit = regsubsets(crim ~ ., data = Boston[folds != i, ], nvmax = p)
for (j in 1:p) {
pred = predict(best.fit, Boston[folds == i, ], id = j)
cv.errors[i, j] = mean((Boston$crim[folds == i] - pred)^2)
}
}
rmse.cv = sqrt(apply(cv.errors, 2, mean))
plot(rmse.cv, pch = 19, type = "b")
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-2"/> </p>
<pre><code class="r">which.min(rmse.cv)
</code></pre>
<pre><code>## [1] 9
</code></pre>
<pre><code class="r">rmse.cv[which.min(rmse.cv)]
</code></pre>
<pre><code>## [1] 6.593
</code></pre>
<h3>Lasso</h3>
<pre><code class="r">x = model.matrix(crim ~ . - 1, data = Boston)
y = Boston$crim
cv.lasso = cv.glmnet(x, y, type.measure = "mse")
plot(cv.lasso)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-3"/> </p>
<pre><code class="r">coef(cv.lasso)
</code></pre>
<pre><code>## 14 x 1 sparse Matrix of class "dgCMatrix"
## 1
## (Intercept) 1.0894
## zn .
## indus .
## chas .
## nox .
## rm .
## age .
## dis .
## rad 0.2643
## tax .
## ptratio .
## black .
## lstat .
## medv .
</code></pre>
<pre><code class="r">sqrt(cv.lasso$cvm[cv.lasso$lambda == cv.lasso$lambda.1se])
</code></pre>
<pre><code>## [1] 7.405
</code></pre>
<h3>Ridge regression</h3>
<pre><code class="r">x = model.matrix(crim ~ . - 1, data = Boston)
y = Boston$crim
cv.ridge = cv.glmnet(x, y, type.measure = "mse", alpha = 0)
plot(cv.ridge)
</code></pre>
<p><img 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alt="plot of chunk unnamed-chunk-4"/> </p>
<pre><code class="r">coef(cv.ridge)
</code></pre>
<pre><code>## 14 x 1 sparse Matrix of class "dgCMatrix"
## 1
## (Intercept) 1.017517
## zn -0.002806
## indus 0.034406
## chas -0.225251
## nox 2.249887
## rm -0.162546
## age 0.007343
## dis -0.114929
## rad 0.059814
## tax 0.002659
## ptratio 0.086423
## black -0.003342
## lstat 0.044495
## medv -0.029125
</code></pre>
<pre><code class="r">sqrt(cv.ridge$cvm[cv.ridge$lambda == cv.ridge$lambda.1se])
</code></pre>
<pre><code>## [1] 7.457
</code></pre>
<h3>PCR</h3>
<pre><code class="r">library(pls)
</code></pre>
<pre><code>## Attaching package: 'pls'
##
## The following object is masked from 'package:stats':
##
## loadings
</code></pre>
<pre><code class="r">pcr.fit = pcr(crim ~ ., data = Boston, scale = TRUE, validation = "CV")
summary(pcr.fit)
</code></pre>
<pre><code>## Data: X dimension: 506 13
## Y dimension: 506 1
## Fit method: svdpc
## Number of components considered: 13
##
## VALIDATION: RMSEP
## Cross-validated using 10 random segments.
## (Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
## CV 8.61 7.170 7.163 6.733 6.728 6.740 6.765
## adjCV 8.61 7.169 7.162 6.730 6.723 6.737 6.760
## 7 comps 8 comps 9 comps 10 comps 11 comps 12 comps 13 comps
## CV 6.760 6.634 6.652 6.642 6.652 6.607 6.546
## adjCV 6.754 6.628 6.644 6.635 6.643 6.598 6.536
##
## TRAINING: % variance explained
## 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps 7 comps
## X 47.70 60.36 69.67 76.45 82.99 88.00 91.14
## crim 30.69 30.87 39.27 39.61 39.61 39.86 40.14
## 8 comps 9 comps 10 comps 11 comps 12 comps 13 comps
## X 93.45 95.40 97.04 98.46 99.52 100.0
## crim 42.47 42.55 42.78 43.04 44.13 45.4
</code></pre>
<p>13 component pcr fit has lowest CV/adjCV RMSEP.</p>
<h2>b</h2>
<p>See above answers for cross-validate mean squared errors of selected models. </p>
<h2>c</h2>
<p>I would choose the 9 parameter best subset model because it had the best
cross-validated RMSE, next to PCR, but it was simpler model than the 13
component PCR model.</p>
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<h1>Chapter 6: Exercise 2</h1>
<h3>a (Lasso)</h3>
<p>iii. Less flexible and better predictions because of less variance, more bias</p>
<h3>b (Ridge regression)</h3>
<p>Same as lasso. iii.</p>
<h3>c (Non-linear methods)</h3>
<p>ii. More flexible, less bias, more variance </p>
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<h1>Chapter 6: Exercise 3</h1>
<h3>a</h3>
<p>(iv) Steadily decreases: As we increase \( s \) from \( 0 \), all \( \beta \) 's increase from \( 0 \) to their least square estimate values. Training error for \( 0 \) \( \beta \) s is the maximum and it steadily decreases to the Ordinary Least Square RSS</p>
<h3>b</h3>
<p>(ii) Decrease initially, and then eventually start increasing in a U shape: When \( s = 0 \), all \( \beta \) s are \( 0 \), the model is extremely simple and has a high test RSS. As we increase \( s \), \( beta \) s assume non-zero values and model starts fitting well on test data and so test RSS decreases. Eventually, as \( beta \) s approach their full blown OLS values, they start overfitting to the training data, increasing test RSS. </p>
<h3>c</h3>
<p>(iii) Steadily increase: When \( s = 0 \), the model effectively predicts a constant and has almost no variance. As we increase \( s \), the models includes more \( \beta \) s and their values start increasing. At this point, the values of \( \beta \) s become highly dependent on training data, thus increasing the variance. </p>
<h3>d</h3>
<p>(iv) Steadily decrease: When \( s = 0 \), the model effectively predicts a constant and hence the prediction is far from actual value. Thus bias is high. As \( s \) increases, more \( \beta \) s become non-zero and thus the model continues to fit training data better. And thus, bias decreases.</p>
<h4>e</h4>
<p>(v) Remains constant: By definition, irreducible error is model independent and hence irrespective of the choice of \( s \), remains constant.</p>
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<h1>Chapter 6: Exercise 4</h1>
<h3>a</h3>
<p>(iii) Steadily increase: As we increase \( \lambda \) from \( 0 \), all \( \beta \) 's decrease from their least square estimate values to \( 0 \). Training error for full-blown-OLS \( \beta \) s is the minimum and it steadily increases as \( \beta \) s are reduced to \( 0 \).</p>
<h3>b</h3>
<p>(ii) Decrease initially, and then eventually start increasing in a U shape: When \( \lambda = 0 \), all \( \beta \) s have their least square estimate values. In this case, the model tries to fit hard to training data and hence test RSS is high. As we increase \( \lambda \), \( beta \) s start reducing to zero and some of the overfitting is reduced. Thus, test RSS initially decreases. Eventually, as \( beta \) s approach \( 0 \), the model becomes too simple and test RSS increases.</p>
<h3>c</h3>
<p>(iv) Steadily decreases: When \( \lambda = 0 \), the \( \beta \) s have their least square estimate values. The actual estimates heavily depend on the training data and hence variance is high. As we increase \( \lambda \), \( \beta \) s start decreasing and model becomes simpler. In the limiting case of \( \lambda \) approaching infinity, all \( beta \) s reduce to zero and model predicts a constant and has no variance. </p>
<h3>d</h3>
<p>(iii) Steadily increases: When \( \lambda = 0 \), \( \beta \) s have their least-square estimate values and hence have the least bias. As \( \lambda \) increases, \( \beta \) s start reducing towards zero, the model fits less accurately to training data and hence bias increases. In the limiting case of \( \lambda \) approaching infinity, the model predicts a constant and hence bias is maximum.</p>
<h4>e</h4>
<p>(v) Remains constant: By definition, irreducible error is model independent and hence irrespective of the choice of \( \lambda \), remains constant.</p>
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<h1>Chapter 6: Exercise 5</h1>
<h3>a</h3>
<p>A general form of Ridge regression optimization looks like</p>
<p>Minimize: \( \sum\limits_{i=1}^n {(y_i - \hat{\beta}_0 - \sum\limits_{j=1}^p {\hat{\beta}_jx_j} )^2} + \lambda \sum\limits_{i=1}^p \hat{\beta}_i^2 \)</p>
<p>In this case, \( \hat{\beta}_0 = 0 \) and \( n = p = 2 \). So, the optimization looks like:</p>
<p>Minimize: \( (y_1 - \hat{\beta}_1x_{11} - \hat{\beta}_2x_{12})^2 + (y_2 - \hat{\beta}_1x_{21} - \hat{\beta}_2x_{22})^2 + \lambda (\hat{\beta}_1^2 + \hat{\beta}_2^2) \)</p>
<h3>b</h3>
<p>Now we are given that, \( x_{11} = x_{12} = x_1 \) and \( x_{21} = x_{22} = x_2 \). We take derivatives of above expression with respect to both \( \hat{\beta_1} \) and \( \hat{\beta_2} \) and setting them equal to zero find that,
\( \hat{\beta^*}_1 = \frac{x_1y_1 + x_2y_2 - \hat{\beta^*}_2(x_1^2 + x_2^2)}{\lambda + x_1^2 + x_2^2} \) and
\( \hat{\beta^*}_2 = \frac{x_1y_1 + x_2y_2 - \hat{\beta^*}_1(x_1^2 + x_2^2)}{\lambda + x_1^2 + x_2^2} \)</p>
<p>Symmetry in these expressions suggests that \( \hat{\beta^*}_1 = \hat{\beta^*}_2 \)</p>
<h3>c</h3>
<p>Like Ridge regression, </p>
<p>Minimize: \( (y_1 - \hat{\beta}_1x_{11} - \hat{\beta}_2x_{12})^2 + (y_2 - \hat{\beta}_1x_{21} - \hat{\beta}_2x_{22})^2 + \lambda (| \hat{\beta}_1 | + | \hat{\beta}_2 |) \)</p>
<h3>d</h3>
<p>Here is a geometric interpretation of the solutions for the equation in <em>c</em> above. We use the alternate form of Lasso constraints \( | \hat{\beta}_1 | + | \hat{\beta}_2 | < s \).</p>
<p>The Lasso constraint take the form \( | \hat{\beta}_1 | + | \hat{\beta}_2 | < s \), which when plotted take the familiar shape of a diamond centered at origin \( (0, 0) \). Next consider the squared optimization constraint \( (y_1 - \hat{\beta}_1x_{11} - \hat{\beta}_2x_{12})^2 + (y_2 - \hat{\beta}_1x_{21} - \hat{\beta}_2x_{22})^2 \). We use the facts \( x_{11} = x_{12} \), \( x_{21} = x_{22} \), \( x_{11} + x_{21} = 0 \), \( x_{12} + x_{22} = 0 \) and \( y_1 + y_2 = 0 \) to simplify it to </p>
<p>Minimize: \( 2.(y_1 - (\hat{\beta}_1 + \hat{\beta}_2)x_{11})^2 \).</p>
<p>This optimization problem has a simple solution: \( \hat{\beta}_1 + \hat{\beta}_2 = \frac{y_1}{x_{11}} \). This is a line parallel to the edge of Lasso-diamond \( \hat{\beta}_1 + \hat{\beta}_2 = s \). Now solutions to the original Lasso optimization problem are contours of the function \( (y_1 - (\hat{\beta}_1 + \hat{\beta}_2)x_{11})^2 \) that touch the Lasso-diamond \( \hat{\beta}_1 + \hat{\beta}_2 = s \). Finally, as \( \hat{\beta}_1 \) and \( \hat{\beta}_2 \) very along the line \( \hat{\beta}_1 + \hat{\beta}_2 = \frac{y_1}{x_{11}} \), these contours touch the Lasso-diamond edge \( \hat{\beta}_1 + \hat{\beta}_2 = s \) at different points. As a result, the entire edge \( \hat{\beta}_1 + \hat{\beta}_2 = s \) is a potential solution to the Lasso optimization problem!</p>
<p>Similar argument can be made for the opposite Lasso-diamond edge: \( \hat{\beta}_1 + \hat{\beta}_2 = -s \). </p>
<p>Thus, the Lasso problem does not have a unique solution. The general form of solution is given by two line segments:</p>
<p>\( \hat{\beta}_1 + \hat{\beta}_2 = s; \hat{\beta}_1 \geq 0; \hat{\beta}_2 \geq 0 \)
and
\( \hat{\beta}_1 + \hat{\beta}_2 = -s; \hat{\beta}_1 \leq 0; \hat{\beta}_2 \leq 0 \)</p>
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<h1>Chapter 6: Exercise 6</h1>
<h3>a</h3>
<p>For \( p=1 \), (6.12) takes the form \( (y - \beta)^2 + \lambda\beta^2 \). We plot this function for \( y = 2, \lambda = 2 \).</p>
<pre><code class="r">y = 2
lambda = 2
betas = seq(-10, 10, 0.1)
func = (y - betas)^2 + lambda * betas^2
plot(betas, func, pch = 20, xlab = "beta", ylab = "Ridge optimization")
est.beta = y/(1 + lambda)
est.func = (y - est.beta)^2 + lambda * est.beta^2
points(est.beta, est.func, col = "red", pch = 4, lwd = 5, cex = est.beta)
</code></pre>
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" alt="plot of chunk 6a"/> </p>
<p>The red cross shows that function is indeed minimized at \( \beta = y / (1 + \lambda) \).</p>
<h3>b</h3>
<p>For \( p=1 \), (6.13) takes the form \( (y - \beta)^2 + \lambda | \beta | \). We plot this function for \( y = 2, \lambda = 2 \).</p>
<pre><code class="r">y = 2
lambda = 2
betas = seq(-3, 3, 0.01)
func = (y - betas)^2 + lambda * abs(betas)
plot(betas, func, pch = 20, xlab = "beta", ylab = "Lasso optimization")
est.beta = y - lambda/2
est.func = (y - est.beta)^2 + lambda * abs(est.beta)
points(est.beta, est.func, col = "red", pch = 4, lwd = 5, cex = est.beta)
</code></pre>
<p><img 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" alt="plot of chunk 6b"/> </p>
<p>The red cross shows that function is indeed minimized at \( \beta = y - \lambda / 2 \).</p>
</body>
</html>
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统计学习导论_基于R应用习题答案/ch6/6.html
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HTML
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<title>Chapter 6: Exercise 7</title>
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<h1>Chapter 6: Exercise 7</h1>
<h3>a</h3>
<p>The likelihood for the data is:</p>
<p>\[
\begin{aligned}
L(\theta \mid \beta)
&= p(\beta \mid \theta)
\\
&= p(\beta_1 \mid \theta)
\times \cdots
\times p(\beta_n \mid \theta)
\\
&= \prod_{i = 1}^{n}
p(\beta_i \mid \theta)
\\
&= \prod_{i = 1}^{n}
\frac{
1
}{
\sigma \sqrt{2\pi}
}
\exp
\left(-
\frac{
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
}{
2\sigma^2
}
\right)
\\
&=
\left(
\frac{
1
}{
\sigma \sqrt{2\pi}
}
\right)^n
\exp
\left(
- \frac{
1
}{
2\sigma^2
}
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
\right)
\end{aligned}
\]</p>
<h3>b</h3>
<p>The posterior with double exponential (Laplace Distribution) with mean 0 and
common scale parameter \( b \), i.e. \( p(\beta) = \frac{1}{2b}\exp(- \lvert \beta
\rvert / b) \) is:</p>
<p>\[
f(\beta \mid X, Y)
\propto f(Y \mid X, \beta) p(\beta \mid X)
= f(Y \mid X, \beta) p(\beta)
\]</p>
<p>Substituting our values from (a) and our density function gives us:</p>
<p>\[
\begin{aligned}
f(Y \mid X, \beta)p(\beta)
&=
\left(
\frac{
1
}{
\sigma \sqrt{2\pi}
}
\right)^n
\exp
\left(
- \frac{
1
}{
2\sigma^2
}
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
\right)
\left(
\frac{
1
}{
2b
}
\exp(- \lvert \beta \rvert / b)
\right)
\\
&=
\left(
\frac{
1
}{
\sigma \sqrt{2\pi}
}
\right)^n
\left(
\frac{
1
}{
2b
}
\right)
\exp
\left(
- \frac{
1
}{
2\sigma^2
}
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
-
\frac{
\lvert \beta \rvert
}{
b
}
\right)
\end{aligned}
\]</p>
<h3>c</h3>
<p>Showing that the Lasso estimate for \( \beta \) is the mode under this posterior
distribution is the same thing as showing that the most likely value for
\( \beta \) is given by the lasso solution with a certain \( \lambda \).</p>
<p>We can do this by taking our likelihood and posterior and showing that it can
be reduced to the canonical Lasso Equation 6.7 from the book.</p>
<p>Let's start by simplifying it by taking the logarithm of both sides:</p>
<p>\[
\begin{aligned}
\log
f(Y \mid X, \beta)p(\beta)
&=
\log
\left[
\left(
\frac{
1
}{
\sigma \sqrt{2\pi}
}
\right)^n
\left(
\frac{
1
}{
2b
}
\right)
\exp
\left(
- \frac{
1
}{
2\sigma^2
}
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
-
\frac{
\lvert \beta \rvert
}{
b
}
\right)
\right]
\\
&=
\log
\left[
\left(
\frac{
1
}{
\sigma \sqrt{2\pi}
}
\right)^n
\left(
\frac{
1
}{
2b
}
\right)
\right]
-
\left(
\frac{
1
}{
2\sigma^2
}
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
+
\frac{
\lvert \beta \rvert
}{
b
}
\right)
\end{aligned}
\]</p>
<p>We want to maximize the posterior, this means:
\[
\begin{aligned}
\arg\max_\beta \, f(\beta \mid X, Y)
&=
\arg\max_\beta
\,
\log
\left[
\left(
\frac{
1
}{
\sigma \sqrt{2\pi}
}
\right)^n
\left(
\frac{
1
}{
2b
}
\right)
\right]
-
\left(
\frac{
1
}{
2\sigma^2
}
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
+
\frac{
\lvert \beta \rvert
}{
b
}
\right)
\\
\end{aligned}
\]</p>
<p>Since we are taking the difference of two values, the maximum of this value is
the equivalent to taking the difference of the second value in terms of
\( \beta \). This results in:</p>
<p>\[
\begin{aligned}
&=
\arg\min_\beta
\,
\frac{
1
}{
2\sigma^2
}
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
+
\frac{
\lvert \beta \rvert
}{
b
}
\\
&=
\arg\min_\beta
\,
\frac{
1
}{
2\sigma^2
}
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
+
\frac{
1
}{
b
}
\sum_{j = 1}^{p} \lvert \beta_j \rvert
\\
&=
\arg\min_\beta
\,
\frac{
1
}{
2\sigma^2
}
\left(
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
+
\frac{
2\sigma^2
}{
b
}
\sum_{j = 1}^{p} \lvert \beta_j \rvert
\right)
\end{aligned}
\]</p>
<p>By letting \( \lambda = 2\sigma^2/b \), we can see that we end up with:</p>
<p>\[
\begin{aligned}
&=
\arg\min_\beta
\,
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
+
\lambda
\sum_{j = 1}^{p} \lvert \beta_j \rvert
\\
&=
\arg\min_\beta
\,
\text{RSS}
+
\lambda
\sum_{j = 1}^{p} \lvert \beta_j \rvert
\end{aligned}
\]</p>
<p>which we know is the Lasso from Equation 6.7 in the book. Thus we know that
when the posterior comes from a Laplace distribution with mean zero and common
scale parameter \( b \), the mode for \( \beta \) is given by the Lasso solution when
\( \lambda = 2\sigma^2 / b \).</p>
<h3>d</h3>
<p>The posterior distributed according to Normal distribution with mean 0 and
variance \( c \) is:</p>
<p>\[
\begin{aligned}
f(\beta \mid X, Y)
\propto f(Y \mid X, \beta) p(\beta \mid X)
= f(Y \mid X, \beta) p(\beta)
\end{aligned}
\]</p>
<p>Our probability distribution function then becomes:
\[
p(\beta)
= \prod_{i = 1}^{p} p(\beta_i)
= \prod_{i = 1}^{p}
\frac{
1
}{
\sqrt{
2c\pi
}
}
\exp \left(
- \frac{
\beta_i^2
}{
2c
}
\right)
= \left(
\frac{
1
}{
\sqrt{
2c\pi
}
}
\right)^p
\exp \left(
- \frac{
1
}{
2c
}
\sum_{i = 1}^{p} \beta_i^2
\right)
\]</p>
<p>Substituting our values from (a) and our density function gives us:</p>
<p>\[
\begin{aligned}
f(Y \mid X, \beta)p(\beta)
&=
\left(
\frac{
1
}{
\sigma \sqrt{2\pi}
}
\right)^n
\exp
\left(
- \frac{
1
}{
2\sigma^2
}
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
\right)
\left(
\frac{
1
}{
\sqrt{
2c\pi
}
}
\right)^p
\exp \left(
- \frac{
1
}{
2c
}
\sum_{i = 1}^{p} \beta_i^2
\right)
\\
&=
\left(
\frac{
1
}{
\sigma \sqrt{2\pi}
}
\right)^n
\left(
\frac{
1
}{
\sqrt{
2c\pi
}
}
\right)^p
\exp
\left(
- \frac{
1
}{
2\sigma^2
}
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
- \frac{
1
}{
2c
}
\sum_{i = 1}^{p} \beta_i^2
\right)
\end{aligned}
\]</p>
<h3>e</h3>
<p>Like from part c, showing that the Ridge Regression estimate for \( \beta \) is
the mode and mean under this posterior distribution is the same thing as
showing that the most likely value for \( \beta \) is given by the lasso solution
with a certain \( \lambda \).</p>
<p>We can do this by taking our likelihood and posterior and showing that it can
be reduced to the canonical Ridge Regression Equation 6.5 from the book.</p>
<p>Let's start by simplifying it by taking the logarithm of both sides:</p>
<p>Once again, we can take the logarithm of both sides to simplify it:</p>
<p>\[
\begin{aligned}
\log f(Y \mid X, \beta)p(\beta)
&=
\left(
\frac{
1
}{
\sigma \sqrt{2\pi}
}
\right)^n
\left(
\frac{
1
}{
\sqrt{
2c\pi
}
}
\right)^p
\exp
\left(
- \frac{
1
}{
2\sigma^2
}
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
- \frac{
1
}{
2c
}
\sum_{i = 1}^{p} \beta_i^2
\right)
\\
&=
\log
\left[
\left(
\frac{
1
}{
\sigma \sqrt{2\pi}
}
\right)^n
\left(
\frac{
1
}{
\sqrt{
2c\pi
}
}
\right)^p
\right]
-
\left(
\frac{
1
}{
2\sigma^2
}
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
+
\frac{
1
}{
2c
}
\sum_{i = 1}^{p} \beta_i^2
\right)
\end{aligned}
\]</p>
<p>We want to maximize the posterior, this means:
\[
\begin{aligned}
\arg\max_\beta \, f(\beta \mid X, Y)
&=
\arg\max_\beta
\,
\log
\left[
\left(
\frac{
1
}{
\sigma \sqrt{2\pi}
}
\right)^n
\left(
\frac{
1
}{
\sqrt{
2c\pi
}
}
\right)^p
\right]
-
\left(
\frac{
1
}{
2\sigma^2
}
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
+
\frac{
1
}{
2c
}
\sum_{i = 1}^{p} \beta_i^2
\right)
\end{aligned}
\]</p>
<p>Since we are taking the difference of two values, the maximum of this value is
the equivalent to taking the difference of the second value in terms of
\( \beta \). This results in:</p>
<p>\[
\begin{aligned}
&=
\arg\min_\beta
\,
\left(
\frac{
1
}{
2\sigma^2
}
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
+
\frac{
1
}{
2c
}
\sum_{i = 1}^{p} \beta_i^2
\right)
\\
&=
\arg\min_\beta
\,
\left(
\frac{
1
}{
2\sigma^2
}
\right)
\left(
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
+
\frac{
\sigma^2
}{
c
}
\sum_{i = 1}^{p} \beta_i^2
\right)
\end{aligned}
\]</p>
<p>By letting \( \lambda = \sigma^2/ c \), we end up with:</p>
<p>\[
\begin{aligned}
&=
\arg\min_\beta
\,
\left(
\frac{
1
}{
2\sigma^2
}
\right)
\left(
\sum_{i = 1}^{n}
\left[
Y_i - (\beta_0 + \sum_{j = 1}^{p} \beta_j X_{ij})
\right]^2
+
\lambda
\sum_{i = 1}^{p} \beta_i^2
\right)
\\
&=
\arg\min_\beta
\,
\text{RSS}
+
\lambda
\sum_{i = 1}^{p} \beta_i^2
\end{aligned}
\]</p>
<p>which we know is the Ridge Regression from Equation 6.5 in the book. Thus we
know that when the posterior comes from a normal distribution with mean zero
and variance \( c \), the mode for \( \beta \) is given by the Ridge Regression
solution when \( \lambda = \sigma^2 / c \). Since the posterior is Gaussian, we
also know that it is the posterior mean.</p>
</body>
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch6/7.html
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<body>
<h1>Chater 6: Exercise 8</h1>
<h3>a</h3>
<p>Create 100 \( X \) and \( \epsilon \) variables</p>
<pre><code class="r">set.seed(1)
X = rnorm(100)
eps = rnorm(100)
</code></pre>
<h3>b</h3>
<p>We are selecting \( \beta_0 = 3 \), \( \beta_1 = 2 \), \( \beta_2 = -3 \) and \( \beta_3 = 0.3 \).</p>
<pre><code class="r">beta0 = 3
beta1 = 2
beta2 = -3
beta3 = 0.3
Y = beta0 + beta1 * X + beta2 * X^2 + beta3 * X^3 + eps
</code></pre>
<h3>c</h3>
<p>Use \( regsubsets \) to select best model having polynomial of \( X \) of degree 10</p>
<pre><code class="r">library(leaps)
data.full = data.frame(y = Y, x = X)
mod.full = regsubsets(y ~ poly(x, 10, raw = T), data = data.full, nvmax = 10)
mod.summary = summary(mod.full)
# Find the model size for best cp, BIC and adjr2
which.min(mod.summary$cp)
</code></pre>
<pre><code>## [1] 3
</code></pre>
<pre><code class="r">which.min(mod.summary$bic)
</code></pre>
<pre><code>## [1] 3
</code></pre>
<pre><code class="r">which.max(mod.summary$adjr2)
</code></pre>
<pre><code>## [1] 3
</code></pre>
<pre><code class="r"># Plot cp, BIC and adjr2
plot(mod.summary$cp, xlab = "Subset Size", ylab = "Cp", pch = 20, type = "l")
points(3, mod.summary$cp[3], pch = 4, col = "red", lwd = 7)
</code></pre>
<p><img 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" alt="plot of chunk 8c"/> </p>
<pre><code class="r">plot(mod.summary$bic, xlab = "Subset Size", ylab = "BIC", pch = 20, type = "l")
points(3, mod.summary$bic[3], pch = 4, col = "red", lwd = 7)
</code></pre>
<p><img 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" alt="plot of chunk 8c"/> </p>
<pre><code class="r">plot(mod.summary$adjr2, xlab = "Subset Size", ylab = "Adjusted R2", pch = 20,
type = "l")
points(3, mod.summary$adjr2[3], pch = 4, col = "red", lwd = 7)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk 8c"/> </p>
<p>We find that with Cp, BIC and Adjusted R2 criteria, \( 3 \), \( 3 \), and \( 3 \) variable models are respectively picked. </p>
<pre><code class="r">coefficients(mod.full, id = 3)
</code></pre>
<pre><code>## (Intercept) poly(x, 10, raw = T)1 poly(x, 10, raw = T)2
## 3.07627 2.35624 -3.16515
## poly(x, 10, raw = T)7
## 0.01047
</code></pre>
<p>All statistics pick \( X^7 \) over \( X^3 \). The remaining coefficients are quite close to \( \beta \) s.</p>
<h3>d</h3>
<p>We fit forward and backward stepwise models to the data.</p>
<pre><code class="r">mod.fwd = regsubsets(y ~ poly(x, 10, raw = T), data = data.full, nvmax = 10,
method = "forward")
mod.bwd = regsubsets(y ~ poly(x, 10, raw = T), data = data.full, nvmax = 10,
method = "backward")
fwd.summary = summary(mod.fwd)
bwd.summary = summary(mod.bwd)
which.min(fwd.summary$cp)
</code></pre>
<pre><code>## [1] 3
</code></pre>
<pre><code class="r">which.min(bwd.summary$cp)
</code></pre>
<pre><code>## [1] 3
</code></pre>
<pre><code class="r">which.min(fwd.summary$bic)
</code></pre>
<pre><code>## [1] 3
</code></pre>
<pre><code class="r">which.min(bwd.summary$bic)
</code></pre>
<pre><code>## [1] 3
</code></pre>
<pre><code class="r">which.max(fwd.summary$adjr2)
</code></pre>
<pre><code>## [1] 3
</code></pre>
<pre><code class="r">which.max(bwd.summary$adjr2)
</code></pre>
<pre><code>## [1] 4
</code></pre>
<pre><code class="r"># Plot the statistics
par(mfrow = c(3, 2))
plot(fwd.summary$cp, xlab = "Subset Size", ylab = "Forward Cp", pch = 20, type = "l")
points(3, fwd.summary$cp[3], pch = 4, col = "red", lwd = 7)
plot(bwd.summary$cp, xlab = "Subset Size", ylab = "Backward Cp", pch = 20, type = "l")
points(3, bwd.summary$cp[3], pch = 4, col = "red", lwd = 7)
plot(fwd.summary$bic, xlab = "Subset Size", ylab = "Forward BIC", pch = 20,
type = "l")
points(3, fwd.summary$bic[3], pch = 4, col = "red", lwd = 7)
plot(bwd.summary$bic, xlab = "Subset Size", ylab = "Backward BIC", pch = 20,
type = "l")
points(3, bwd.summary$bic[3], pch = 4, col = "red", lwd = 7)
plot(fwd.summary$adjr2, xlab = "Subset Size", ylab = "Forward Adjusted R2",
pch = 20, type = "l")
points(3, fwd.summary$adjr2[3], pch = 4, col = "red", lwd = 7)
plot(bwd.summary$adjr2, xlab = "Subset Size", ylab = "Backward Adjusted R2",
pch = 20, type = "l")
points(4, bwd.summary$adjr2[4], pch = 4, col = "red", lwd = 7)
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAYAAACmKP9/AAAACXBIWXMAAAsSAAALEgHS3X78AAAgAElEQVR4nO3deZhkVZmg8Tdrg6IK2YoSlK3YZRNtWRVkERRtSxQERbpnbFSGQcUGHsVucJmx1Wlb7RbtEXVwXAFFkFVlkU0QcENkkUVkX0uqZK0CqnL++CImI6PiZkbGXc6NiPf3PPfJyMiIc7+qzC/OueeeBSRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkrSykdQBTGABcEjqIKTEfgjclTqICpjvUsH5PqOogkrwBmA94JrUgUiJ7AocAHwldSAVMN817ArP9zpX8ADXEi0aaVjNTx1Ahcx3DbtC831akYV1sBC4DVgK3N74vghrANsVVJakYpjvUo2UXcEfQ9xXmwccBBxZULlrACcVVJakYpjvUo2UXcGPdjiKcC+wAfW/xSANkzLz/aWY79KUlF3BnwycCTwOnAWcUmDZ1wE7F1iepHzKzPfrgZ0KLE8aeFVewUOx0/J+Bry+wPIk5WO+SzVSdpdX857cn4h5rp8Fzu3wulWBo9vi2Qe4bIKyrwA+UkyYkgrQbb6vAryf8fm+N5Pn+4eLCVMaDmVX8N3ek3se+H3bc68HNpqg7KWNY22iS1BSWt3m+wt0zvdNJih7KbAM813qWtkVfPOe3MbAPcBxGa9bDlzS9twRwHOTlH8JsC/OnZXqIG++L5uk/Esx36WulX0PfjVioM0jRMt7tYLL976cVB9V5Pv+BZcpDayyr+APIe6l/x1wA/Ad4IwCy78Z2KbA8iT1rux8vwnYtsDypIFW9hX8WsCNwExiRG0Zm9vcipW8VAdV5PsfgZeVUK40cMqu4D8PbAh8H7gQ+HQJ57CbXqoH812qkbIr+POIkbGrEF1rPy3hHBcTA28kpVVFvl8CvK6EcqWBU3YFX4XFwBxgdupAJJXuL8DqmO/SpAahgge4EtgjdRCSKnEl8OrUQUh1NygVvPflpN4dDjwMPNp4XHfmu9SFQangr8ONKKReHQPsCewOfDBxLN24FtgldRBS3dVl+8W5xO5TrdNqtmPl1a6yLCeuPjYE7is2NGng/ZbIIYhpaGWbQ6xGN7Plue2Ai7p8/wvAIuAlwIPFhiYNvjp02Z0G/PsUXv9e4B9KikVK4e3EJkxl67SG/L9VcN5WU833/wb813JCkZIoPN+zuuj7rcsO4Cd4X06aquaCNKsxfoGa41MG1YWf4rK10oSyKvhml90o1XTZFeF+oot+eupApD5xMHB24/HmxHatB6YLZ0ruJubcD8o4IqlwWcnxPuDOxvF3pOmy68X1wKtSByH1ieOBgxqP/0AsGHVUunCm7DfAK1MHIdVVp0F2zS662cQ+7S9UGlE+FxHd9NelDkTqA/czNrgOYnvmZxLF0ovmdLlfpw5EqqP2K/iiu+wWArcBS4HbG9+X6XLgtSWfQxoUy4jBamsSG8V8gGjU96rqfL8M2Kvkc0h9q72CL7rL7hhiC8l5jXKPzFFWN54hehzWLvk80iB4P7F+xO2N42+ICr9XVef708TtwzVKPo/Ul9or+KK77DpNvynbJcDeFZxH6neLgSOA+cC6xLSzx3OUlyLff45X8VJH7RV80V12JwNnEh8aZwGn5CirWz/D6TNSCqny3emxUgftFXzRXXbtLfmRCV5blD8A21dwHqnfdbriznPVnSLfbwB2rOA8Ut9pH0Xf7LIrSvOe3J+ABcBngXM7vG46MSCnNZ4NG/FM1SjRONmKGPAjqbNmBfxt4H8AK4ATcpTXbb6PEINhW/P9xfR2e2C0cb7NiWm9khrKXou+26uDWcQ9wPbnZvV43ma3nRW8NLmtG1+nke9quNt8n83Km8WsDaza43mb02Ot4KUJFN1l9xbianpZ4+ubp/Deqa5N3WoecF6P75Xqoqq16P8OeIz8e0+kyvcXAz/u8b1SXZS+Fn1zkZvvAFsAmwFfz1H+OcCWwCrEaPyqKt1FwOqN80qa2B1ED9p84Ls5ykmV748QPQC99vhJAylrqdqiuuyeYawX4OVUM22m6RfAayo8n9SvDifGvOSVMt+vAXar8HxS7WVV8CcDvySS5ks5yt8XOImxe/1VjKptcvqM1J2jgXvJf0vOfJdqJGuQXbPLLq9fEiNjv1hAWb2c+18SnFfqN6cD72g8vjFHOSnz/WrgEwnOK9VW1hV8UV12ECPZP00MIKjSC8S9+JdUfF6p30wndpDcjFgJMo9U+f4c8FdiHIEksiv4orrsmh4mVriq2kXAfgnOK/WTecRc8r0opoJMle8XA69LcF6plrIq+NNbHufpskvtp3hfTprMdcRV/LHEFLd+5X14qUXWPfhml92lVDMPtyx3A5sQDZkVSSOR6utGYk/17VIHktPtxPTeEaodwS/VUtYVfNFddin9Bnhl6iCkGpsJfI5YsvbYxLHkdSOwQ+ogpDrIuoJv7bK7toI45gA/JD5omrYj7qHn1ey2+3UBZUmD6DFi9Pt6RI9X2eYQC2i1fv7sAlxeQNnNZWt/X0BZ0kB6J7FdbEp5lq5sNYcYfCP1m6qWqr0c+B5wEMVMj+3GWm3Hj8i35kbTi4CfFFCOVLXC8z3rCr7ZZTeL2I7xC0WetGJPE/ff1yCm0Ugab2/iNtaJwJeB9Ss4Z/tOkc9RzDiZJ4jPrbnAUwWUJ/WtrHvwzS47qKbLrmw/Jz7EJK3sPGKjmM8AL00cSxEuB/ZMHYSUWlYF/xEi0c8B/md14ZSmeV9O0sqOBT5G9NZ9NHEsRXC6nER2Bb830S1/OP09D77pBmLzC0krOwk4hLiSvyVxLEX4DfCq1EFIqWVV8EV12S0klq5cSsxRXZijrDxGiWl/WyQ6v1RnRwFHAscAZ+copy75vhy4H1iQ6PxSLWRV8J8iuuyuJ9/Al2OIK4N5xAjdI3OUlZfddtJ4zaWonwT2AW4l3wIxdct3l63VUCt7s5nRDkcqF+O69FKrkcZxEFHBv5l8jeA65fvPgP0Tnl9KruzNZk4mNp14HDgLOCVHWXk9AqwNrJIwBqmODgeuIuaPH5ajnDrl+wPEdL+sqcDSwMuq4P8eeJSYLnd4jvLbW/IjOcoqwi+A3RLHINXNusCmxD3rzXOUU7d8vw7YOXEMUjJZFfyRwO7ArsQAnF5N5Z7cpm3HXGK53CJ5H15a2eeJueNXE4NRezWVfF+d8SvZzZzgtb1yeqyG2kTdV0WsKtXtPbm5xNz7Vi9jbLGdolwDfLLgMqV+dyhjq9ftmKOcbvN9DvANxl/h70ysOlmkK1j5c0UaGlkV/FeJ7i3It7tU857cxsA9wHEZr3uKlVv7p7HycpZ5PQcsIT7MHiq4bKlfFbU9dLf5/jTRqGhVRr4vbRxrU/zFglR77V30pze+fpfo3prfeNyrBUT321HAlsAROcoqykXAvqmDkGqkqO2h65jvl2C+a0hl3YMHeFcB5R8IvAJ4A9XtUjUZ78NL47VuD70sRznmu1QjE1XwRXiEmK5yAtVse9mNO4lBfKlH+EqpHUF0i+9EbLO6DtG13qs65vvNwDapg5BSaK/gD2X84Ji88+DPIVbEuwu4ifrsTPd78g0mkgbBB4iZMt8j7p2/jXyrv9U132/FSl5DqH2QXdFXtd9veXxm46iDZrfd71IHIiV0J7F2/F3AqcC15Bt8Wtd8b06XG4SNdKSuld1FX1fuDy/BC42vzze+plxatkwOrNVQGtYK/kni37566kCkhDrdkmufvjYIFhNrbcxOHYhUpWGt4AEuI6YFScNqJOMYRFcCr0kdhFSlYa7gnT4jDQ+XrdXQqctOS3OAf2V8PK8iNocpy++AL5VYvqTOViWm0bXm+9YUv1Rtq2uBz5RYvlQ7dangnyFG8bbauPF8WVYAdwObkW+DDUlT8zwxVbXV64mlpMvyArFD5kuAB0s8j1QbdangR4HftD23mLHRvWW5CNgf+N8ln0fSmOXEErKtjqDcCh7G8v3/lnweqRaG+R48xH34/VMHIakSjrvRUBn2Cv5hYs3sMvaillQvdwMb4eeehoR/6LFH/G6pg5BUid8Af5M6CKkKVvB200vDxOlyGhpW8HAV8OrUQUiqxGXAa1MHIVWhLqPoU3oOeAqYT0yj6clorHX9QWAW8GNimt9rgNuBk0bybeIhqRhPE1Nk1wD+2msh5rv6gRV8OI/Y+ep64A4iSe8A7u/mzaPwcuACYJXGU29o+fEewO6jsO3I4G7mIfWTS4GvAlcTO+r9iRiA19W0XPNd/aLsCn4h8DmidXsvcDxwbsnn7MXXgNOBLRrHHsS83Jc2fv4oYxV/s/L/S8v738RYsnfyssbhdpX9ZQ5xhdZurS7eOwKs2eV5/kD5c8A15gvE/vCbAzsAbyP2rp8BLCMq/DtbjrsY//sx3zWZuXSenTUNWEKsBVG6siv4Y4BDiIRZAHyWelbwAE8QI2zbF9yBmEq3JbAV8BaiEbBO42f3/CvM+fDEZb8APJAjtpnEH0wnWZXQRBXMKsBqGT97ETC9w/PTGz/rZDWyP/DWpPMGJrOI2DvplBxTqTCL8jSdK94lTH51Ntp43WRWAH8GHp9aaLXULw36F4AbG0e7VYnVLTcn8v1NxGfXLOJv4c9fgDWOnbz8PPnei7rke6usz6Z2nfJ9dVaunzo91yn+rH/TDMbvIPpXIv9g7N/UTW534yk69wgtJ+rFnm8HT0XZFfxoh6OTucAP2+LZiri3VQePNY6r254fATY6D175j7BwZsYf8w3w1CviFkCvnif+YCbzHN2t572MlZcBXgo828Vzzzaeb/Vgh+eashJmon9TVnIszni96qHbBv0cIt9bP9S3Bs4pO8AuLAVubhztZgELfgCv+AAcVGK+d2rQN3O7tdLslO+dcrvTc01P0PlqcnnjZ510+gzo5OkO8XVyH53zfaJGcqfPpqZO/6bWynxolF3Bn0z8oW8M3AMcl/G6p4AD2p47ivpf1YwC91wFuzJBS3VHWG0U3jrSXSUt9atuG/RPA29se64f8v054LZrYUfMd/WBsiv4c6hHq7xsW0zy81nEPb6byg9FSqbbBn2/M9/VFzrdG62LtwIfpppW8IbA+sS9synbDWZcBS/qdNMH4D5YsQksydE/NJvokqrrqNzVKHfnvzymEV2ey1IHkmEWMXCz09XrHODTwPmVRpSG+T7GfO+d+d6izhV8ld5L/MF+r9cCRuFvgW0b3z5LJGnT2SPxS+3VD4j9sx/LUUaZLgP2Th1Ehp2Bg4CPpA4kw4nEXuXtu6upPOZ7PuZ77yrNd+fBF2QkWl3DcKUlDT3zXf3ApWolSRpAVvCSJA2grHEiw2YGsTf8w6kDyTAb+B09DgqqwCp0XiCoDkaJObZ57omWaRaxUppz/KtjvudjvvfOfJckSZIkSZIkSZIkSZIkSZIkSZIkSd06DDgDuAL4UOJYsuxGPTd3eDNwOXA28M60oXR0InA6sbb3iYljaTUN2J6xedgLibWpL208VnnM996Z770x3xP6OLHpzgbUc3OHrYg/1jruLHU3cBWwBDg2bSgdPURsPrFL43FdrE9ULs3f6e3ADsDLgdtSBTUkzPfe3Y353gvzPbFNiVbfEakDabMe8CVixcE6Jvwy4GDgLcT+33VzGPH/Ngq8I3EsnTR/p8uIrSLnEtuEqlzme2/M93zM9wQOBj4BzE8cRycHM/YHOwrckDacldwMbAxsBCxKHEsnDwO7El2edWrRNzUT/g5gO6Ibr65LbA4K87135ns+lee7+8FHEt3d8v2BieKYzCj1+30dAJzQePxV4LSEsXRyIpFE04DfA59KG85Kmr/ThcQe5dOAU4BzUwY14Mz33pnv+ZjvkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiQNg8OAS4n9rq8EDurwml72mt58knPUcf9qadCZ79IQeRzYrPF4S+DCDq/pJTlb39PpHKv2UKakfMx3ZZqROgAV7lTgDOBHwGXAmxi/t3Qzcb9J7J38ZWAb4MXATGARsZfynsCTwG+Bdbo4x4rGOb5M7HO8ENgAOKKtrFML/LdKw858l4bIDGBd4K3A+cC3GN8aH20cmxGt8UXAtcDHicR8JfBEy+sWtbyv23P8E7BX43GnsiQVw3yXhsitwI6Nx1sBjxLJNgvYhPEJvznwEPBGYAfgOOAG4A5gAdFCf1OjrNaEzjoHjde/u+W1ncqSVAzzXZlGJn+J+szewInAYmA+8B/AgcQ9szuBExqvOxXYDvgisDOwHrAceJAYUHM88ABwHnAWcAvRtZd1jjOJv6fbgIsbr3s/sH+HsiQVw3yXJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJElDbiR1ABNYABySOggpsR8Cd6UOogLmu1Rwvs8oqqASvAFYD7gmdSBSIrsCBwBfSR1IBcx3DbvC873OFTzAtUSLRhpW81MHUCHzXcOu0HyfVmRhFVoD2C51EJIqYb5LPejXCn4u8PHUQUiqxOqY79KU9WsF/wCwLrBq6kAkle5+ouvSfJemoF8reIDLgL1SByGpEpcBr00dhNRP+rmCPx94U+ogJFXiAsx3aUr6uYL/LfDK1EFIqsSvMd+lKennCn4UuAnYPnUgkko3CvwR2DZ1IFK/6OcKHqLb7m9TByGpEnbTS1NQlwp+LvBLohuueXwS2HuS910M7FNuaJIKNge4kMjf5vFR4DWTvO8i4HXlhiYNjrqsZPcUsFvbc6cBSyd537ON48XAIyXEJal4TwNvbHvuNGB5F+97HlgH+EsJcUkDpS5X8HlcSKxjLWnw/RR4feogpH4wCBW80+Wk4WG+S10ahAr+fqKLflbqQCSV7s/ARtTn9qJUW4NQwQNcAeyZOghJlbga2D11EFLdDUoF73Q5qTcjja+z6Z+rYqfLSV0YlAr+V8BOqYOQ+szBwNmNx5sT670fmC6crl3DyrNuJLXpVMH3Y4t+BbHK1ctSByL1keOBgxqP/wDsCxyVLpyuLQfuJRolkjK0V/D92qKHGF375tRBSH3kfsbPPX8OeCZRLFN1ASvPpZfUor2C79cWPbjKlTRVy4D/BqwJrAV8gFhIph84H16aRHsF388t+tZVriRN7v3E2JXbG8ffEBV+P1gMzARWTx2IVFftFXzRLfqFwG3EkrO3N74v009wVTupW48D/wCsC8wD/gv5loCtOt8vBvYr+RxS32qv4Itu0R8DHEJ8eBwEHJmjrG6ci9NnpG6NZBy9qjrfXdVOmkB7BV90i360w1Gme4lVrmaWfB5pEBxBdHVfQlTIDwEn5Siv6ny/FdiawZnuKxWqPTGKbtGfDJxJNBzOAk7JUVa3rgReXcF5pH73AWBX4HtEnr6NfANVU+T7r4BXVXAeqe+0V/BFt+hfAnyoUc6awPQcZXXrfFzVTurGncQ98+82vr+WyNVepch3V7WTMrRX8EW36F9PtOI/CexCJH/ZrgV2ruA8Ur97ofG1OZA2b5d6iny/AnhtBeeR+k77SnXNFv1dwKnkb9GvQoyovRB4pHF0Mh3Yu+25FxO9CVO1gthxaktioKCkzg5tHE3NCv4dPZbXbb4DbNr2/VxgUQ/nfK7xvg2Iab6SGtor+KJb9L8j5tHvR8yvvz7jdbOIEfut1iY+MHrR7Kb/Qo/vl4ZBnvE1nXSb73OBj7Q99zLi3n0vmqvafa3H90tDodMo2KJGwk71w+Q04N97PNeLiJWupH72duDo1EH0qMp8n8/YEttSvyo838seRf8wY42EFZQ/babpica51qzofFI/KrpBnyrfHyVyfbWKzif1hbLnj+4P/CPFNBam6me4VrU0kWZOfgfYAtgM+HqO8lLm+8+BvSo8n1R77RV80S36m4Azcrw/j3NxupzUja0bX6cBO+YoJ2W+u6qd1Cari76oFv0K8o3Cz+MuYAH9s6e9lMrJwC+Ba4Av5SgnZb7fALyCansNpFrLqvyKatE3jZIm8a4h5vX/IsG5pX5xB7E8dVFS5PsocCOwfeOrNPSy7sEX1aJvuq2AMnrhqnbS5A4HNiywvFT5fgHmu/T/ZVXwzRb9fMaWscxj68lfUoqrgd0TnVvqF0cTGzUVNS02Vb5fAuyT6NxS7WRV8EW36FNZDtwHbJ46EKnGTm953M/d2882jvmpA5HqIKuCL7pFn9L5xCpXkjqbDryPGFR7SeJY8voJ8IbUQUh1kFXBD0qLHmJdbOfDS9nmAX8i5pH3+9XveThdTgKyK/hBatH/lZgtsHrqQKSauo7I+WOBZYljyes+YtvaWakDkVLLquAHqUUPcBGxypakld0I/BrYDnhP4liKcCXwmtRBSKllVfCD1KIHp8tJE5kJfA74NpHz/e4C7KaXMhe6aW3RD4LbiJX5phMj6yWNeYzYqnU9YJO0oRTiWuCzqYOQUsuq4Jst+lnEEpCDsK/69cDOxAI+ksZ8BHgAOIvo3u53K4A/E436OxLHIiWT1UXfbNHDYLTowW56KcveRCP+cPp/1kyTq9pp6GVdwVfdop8LnNr23K7AFQWe4yrg4wWWJw2K84DfAp8hbs2VbRXg/Yz//NkaeKrAc/yU2NnuiwWWKfWVrAp+b+CVwInAl4H1S47jKeDItue+ASwu8BzPEztdbQLcXWC5Ur87FriduCX3UeBfSj7fCmIhrVZPN54vyhPEhjdrAksKLFfqG1ld9OcBbyFa9C+tKJbFbcdzFJvw4Kp2UicnAYcQeX9LBed7Hvhh23EfscxskS4C9iu4TKlvZFXwnwI+RgxMK7qSTelC4IDUQUg1cxTRg3YMcHbiWIp0Pk6X0xDL6qI/nLgHf1+FsVThcWA2sardk4ljkVJr32fi1sbXqvdyL8vtxEZTTo/VUBqGzWbaXQzsmzoIqQZGGsdBxDarb2bw9m24jpgeKw2drAr+74FHielyh1cXTiWcLieNdzgxy+QnwGGJYymaq9ppaGVV8EcCuxNT1Y6qLpxK3AxsxeB0Q0p5rQtsCiwgurQHyVW4Lr2GVFYFDzG4btC655t+C7wqdRBSTXweuBy4mthkapA8DzwCbJw6EKlqWRX8V4l7V9c1Hg8au+mlMYcSa13MB3ZMHEsZLsDpsRpC7RX86Y2v3yXmkM5vPB40VwCvTR2EVBPTgfcBmwGXJI6lDE6P1VCaqIv+XZVFUb3ngEXABqkDkWpgHtE1vxfRqB80i4A5wGqpA5GqNFEFX4SFxFatS4k5qQtLPt9UuAiGFK4jruKPBZblKKfO+X4pTo/VkGmv4A9l/Nz3vPPgjyGWwJxHzLVtX28+pQuw207D7QhiWeidgBcB6wD35Civzvlug15Dp72CH8k4ejXa4aiLx4gPNbvtNKw+QEyF/R5wJvA24HU5yqtzvt8IbI/TYzVEyu6iP5n44Hic2Hr2lJLPN1WXEit4ScPoTqJLvTmQ9lpix8Ve1T3fb2AwZwlIHZVdwb8E+BDxobEmcZ+vTs7D6XIaXi80vj7f+Jr3irvu+X4B5ruGSNkV/OuJVvwngV2I5K8Tu+00zDqNuTk0R3l1z/efA3unDkKqStkV/CrEiNoLgT8TK0plWR1Yq+WYWXJsTX8AdqjoXFKracTfeqoGZtFjbqaS7yksBZ4A1ksdiFSFrO1ii/I74BlgP2K7xuszXjcH+AbjP1y2A+4qNbrQXNXu9xWcS4NpbcY3TjsdzdfMZawiHSVGsf8X4OHKoy5et/k+F/gh4z9/tgV+Vmp04afA/sC3KziXhs8IsbfDusRskvmNo/n9usAHqajxW3YF/yjwXuLe1zeBs4l1r9s9zcpdg0cRg3XKdinwj8C/VHAu1d8qRBK+uHGsSyToeowl7kzGX+0uaRyLWx4vAR7s8NxTFf07Uug2359i5SmqVeX7BcC/YQWv7rVWzs3PhtYKfJ221z9KLK70WON4kLiAXNT4WRV/50D5FfyBwCuALxAD2uroWeBJ4hf1aOJYVI61yK6s1yemS0JU2EuJpHyUuKp+jLiNcxFjCfs86qQf8v0+4vc/E3+Pw2pVIvfXY+zqur1B3zpAtFlZN78+QnwmLGp5rpbKruAfAR4ATgCOLvlceZxGTO/5CnHV8VzacDSJWYyvoJuPWxN2FcYGj/2VGNnd3qJ+jKjE/1ph7IOsX/L9KmINgP8kGnTqb9OJq+nmMZ+VG/SrN147QlzUNSvqRxqPf8dYg/4xxmaY9LWyK/hzgI8Ro2pvIlr4dXQGcA3RvfgR4j7d14mBQqrGWoy/V9Xc3WwekaBrNV43Qiyn2kzEh4mr7VuAyxhLWBtp1euXfP834P3AL4i5//+H+IBXPazGWEXd/DxorbznEWNaWmd/NCvn5hX1/cTv9DGicf9kdeHXR52nhzXvyZ1R8XlnEGtovxdYAXyNGIi3vOI4+t0arHyv6sWMT9TWWRxLGKucFzFWcTdb2pXdt6qRtxP/T19JHUgFUuT7CDFt7h+ATYHvE6v6La4whkE2naiIJzqaA1DnMVYfPU3kfjP/FxGfAa33thdRr5USi1B4vpd9Bd80Sr0bE61eIFbhOgvYnKjoP05cnXyD6IIcRqszVmE3K+31GD8AZSZjSfcE41vVi4iV01pb2TaaBlO/5PsoMTf+58TCPO8iGvN3A6cSPUIrUgVXM3MYf586q8JelbEBqCuIxtJiovHWfLwIuKPl+8XE54H/1wWrqoK/raLzFO1Oosv+Y8Q63d8m7teeAlxMyx/kaOxU9UHi/vCPgY2B1ymrUmMAABhASURBVBC7ap00km8J0DLMYfxVdfN+VWu3WOt97CdZuVV9HeNb1QNx30q59WO+LyGunL5CLGf7D8C/Eg37bxKD8/6/Psz3dtMYa6x3Gni6LtFV3pzO2byqbjbaFxP/1vbKO89uhCpYnVvZqbroJ7MNsUvWa4AfAN8cjYS4jqgQO7kV2HakvC6l2USFvA5jlfM6LV+bj1u7xJ9m7B5265SO1m5y72OnZRd9WqsCbyXWKVhBVPTnjsLWpM33LC8iKupOU7qaFXnzM3+UscZ68z51+7iWZyqMXX3cRT9IbiG2xVyNmLt/5n/CGv89O9kBXtY4bumi/JmsPKhkXcYq6mZlPYOxD5BniQr5L4wfZHJD47nm83aBSd1bSsywOQ3YBHg38NFvwrPvLi7fJ9J6W6xZabffJpvF+NtizQZ6s5F+Tcv3doMPGSv43j1DtOi/uQ38MxMsdzsKy18dV/kLGKu4W5N0TuOlI8RV8yLGz7F8gJjW1VpZe/9aqs7dxFicT64OnwF2n+C1L3wwuqo3Je7tr952rNXhuTVZuUf1KeJK+hHGPgt+xfjbZM7lVyYr+ALsBV8FPspYRT3OrbDol7EtbbPSvoWxVY0WEd3lkupvxcFxb/5oMvL9RnjiZPgUcUW9hBi/0jwWE9Nvn+zwvFQoK/hivI6MZAfYBtYahc+MDPYypdKwmDDfd4C5o/Be812plb2b3LDYYpKfzyLu4Unqf+a7+kKdr+AfBj4MvKeCc21I3CPvaZrX7jDjKsYvXtzqPlixCVzRY2wQo+SXUt+FHVajviNupxEDF+s6fWcWMd2o00I+c4BPVxtOMub7GPO9d+Z7izpPk6vSe4k/2O/1WsBobDm7bePbZ4kkbTp7JH6pvfoBcc+vrpsaXEasCFZHOwMHEesZ1NGJxHKpl6QOZIiY7/mY772rNN/rfAXfV0ZiBazzU8chqXzmu/qB9+AlSRpAVvCSJA2grHEiw2YGMcjn4dSBZJhNbH1Y17XeVwF+kzqIDKPEYiB57omWaRZwF86DrpL5no/53jvzXZIkSZIkSZIkSZIkSZIkSZIkSZIkdesw4Axic4gPJY4ly27Uc3OHNwOXA2cD70wbSkcnAqcTa3ufmDiWVtOA7Rmbh72QWJv60sZjlcd875353hvzPaGPE5vubEA9N3fYivhjrePOUncDVwFLgGPThtLRQ8TmE7s0HtfF+kTl0vyd3g7sALwcuC1VUEPCfO/d3ZjvvTDfE9uUaPUdkTqQNusBXyJWHKxjwi8DDgbeAtyTOJZODiP+30aBdySOpZPm73QZsVXkXGKbUJXLfO+N+Z6P+Z7AwcAngPmJ4+jkYMb+YEeBG9KGs5KbgY2BjYBFiWPp5GFgV6LLs04t+qZmwt8BbEd049V1ic1BYb73znzPp/J8dz/4SKK7W74/MFEckxmlfr+vA4ATGo+/CpyWMJZOTiSSaBrwe+BTacNZSfN3upDYo3wacApwbsqgBpz53jvzPR/zXZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZKkYXAYcCmx3/WVwEEdXtPLXtObT3KOOu5fLQ06810aIo8DmzUebwlc2OE1vSRn63s6nWPVHsqUlI/5rkwzUgegwp0KnAH8CLgMeBPj95ZuJu43ib2TvwxsA7wYmAksIvZS3hN4EvgtsE4X51jROMeXiX2OFwIbAEe0lXVqgf9WadiZ79IQmQGsC7wVOB/4FuNb46ONYzOiNb4IuBb4OJGYrwSeaHndopb3dXuOfwL2ajzuVJakYpjv0hC5Fdix8Xgr4FEi2WYBmzA+4TcHHgLeCOwAHAfcANwBLCBa6G9qlNWa0FnnoPH6d7e8tlNZkophvivTyOQvUZ/ZGzgRWAzMB/4DOJC4Z3YncELjdacC2wFfBHYG1gOWAw8SA2qOBx4AzgPOAm4huvayznEm8fd0G3Bx43XvB/bvUJakYpjvkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRpyI2kDmACC4BDUgfRjVVh2ktgleb368GqS2H5EngeYAVwNzybKj71tR8Cd6UOogJ9k+9FWwNmrAMzm99vAKsugReeghcAnoMV98OydBGqQoXme50r+KOALYFrUgcymTNg30PgyAleMronHHkVLKksKA2CXYlk/0rqQCrQN/letKvhXbvDW7J+/jw8vQYc8WxcK2hwFZ7vM4oqqCTXEi2aWjsENp3kJSNXwpUjcFslAWmQzE8dQIX6It+LtjvsN9HPZ8KsZ+D8EXi6qpiUTKH5XvcKvl+cD/wLML3TD/8Kj64JryWOXjwPPNXjeztZBjzTxeuW0t2thWcbr53M08BzXbzuKRq3N1S6DwH/DHwEOBX4MXBg0oj6xyrAFsBWwDq9FvIJWPqJCX7+Z7hzU3hXhx89R3eVfrf53q1u871bRef7E8DyLl73Vwa8V8QKfupmElfsWxOJvdVrYfvLYVrW/Y4ZMG06LFkOoyv/iNW7OOdqFNuyW6VRZlFWo2UMwgTmALO6eN1cWu5JTmB1iv0b7jbh1yrwnMuJCvWhAsucigOBVwBfAM5LFEOdjQAbErcPtiRyfgtgNlFx3gn8EXik1xPMn6TRu2p8bizu8KNZdPe32G2+z47TVa7bfO/Wi8i42GqzBjCtoHOOAGt28bpK873Th6Mt+vilbwJsC2zDWIU+HXiQuE9yC3Ap8PUr4Ghgp6zC5sC8F+DmEbi55LilqXoEeAA4gfg7HkYjxCC/TRnL+QWN518Abidy93bgp8C9jecL8d/hgIl+vj5sNQo/GSm2F09DoFMFP4wt+pcQXWBbEkk+HVhEJPQfgcsaj7MGyd05SfnPA/cUEqlUrHOAjwGfBG5iOBrzLwEOI/J9AXH1uITI8duB7xLjZR6tKJ4/TfLzh6zc1YtOFfwwtuiPJZLsfwF/prv7N61+DFwCvK7x/YPEPblmt/UJJqhq6vstj89sHIPuOCLfP0fke2FX4z36KnAosD3x2fN74rbAukRs/5guNPWzTvcfmi36u4gW/SZVBpTIzsDXiCvxqVbujMSAkwOAVwN7A5sBGwF/C2w5Er0hUt18mmh4XkvMQX+EGIcw6HYGTgHuIH3lzgj8BdgF2AfYYgT+hqjgDwAWjAxHo0t96BjgSWKAyCXAy6fw3qOIVm3ZNgLOqOA80lS9nXJ70a4hrhJ3IQZyTTbdczL9ku8/qOA80lQVnu/tV/BFt+gPAN4A/KJR9mdzlFWW/YCfpQ5CSuBe4DHgusb3eVfQ6od83x/zXUOivYLfixh0cgxxVbsbMZWgV48TA8xeDfyBei63aMJrWB1KXLk3p2+2Pu6F+S7VSPsgu2aL/rHG93lb9H8ALiam3TXve9XJdGJE7QOpA5ESKHqp6rrn+wwi3+9PHYhUhfYK/lDG3wdrtuZ7/SD4TONougn4SYfXzQH+tS2enYALKPf++M7A9SWWLw2TbvN9LjGotTXftycG+JrvUkHaK/iyN5/ZLuP5p4HPtz33H3S3MlAer8fuOqksWfn+FCsPJvoGxa6u2In5rqFS1DJ9WRYSC0YsJRaQWDjBa+9qO56ihylrU7QHcGXJ55DqajTj6NVU8n1x2/Ec5a8LvgdwRcnnkGqj7Ar+GGI0/jzgICbeUrVqa1P8pglSPxlpHN8h1lffDPh6jvLqnO/rYL5ryLR30We13nvtui/y6qBo+xJzdaVht3Xj6zRgxxzlmO9SjbRfwRfdoj+ZWIXpceAs6jWq1vtxUjgZ+CWx8M2XcpZjvks1kbXVZlEt+nMaRx29DLg1dRBSDcwkGvb/le62/c1S13wfIfL9ltSBSFXKugdfVIu+rrbBZJeaXknco76Q2D9h0JjvGkpZV/BFtejryu46aUxztcpNgJcmjKMs5ruGUtYV/KC36B1wI425gtgk5iJWXo9iEJjvGkpZV/CD3KKf3TiWpA5ESmxzYsvUptWBfye6tAfFao1jcepApKplXcEPcot+T1zcRgK4k7gV92HGZtAMUuUOke8ubqOh1F7Bb07MXf060ZrfnGjRDxLvx0njbUTclvs99dziNQ/zXUOrvYIfhhb9q4BfpQ5CqpF5xAYw3yJ2WxskO+EGMxpSWV30g9qi3wh4iPLXuJf6yfPAPsRAtOmJYynSRsCDmO8aUlkV/KC26PfD7jqp3SNEV/ZLgYcTx1Kk/THfNcSyKviiWvQnEVfM7yBa0v+co6wimPDSyo4HdiWmxR6boxzzXaqRrAq+qBb99sCBwGnE7lIvz1FWXtOJ3ogHEsYg1VFzvM0C8t2Sq1O+zyDy/f6EMUhJZc2DP77x9cLG1+N6LL91f+fUu0s52Eaa2IPEzJleme9SjWRdwRfVor8Z+DHwrsbXP+QoKy+ny0idNSvjZcSe6b0y36U+Mgv4YYHl3TTBz1YH1mo5zqTYOfiXAKsWWJ5UtrcDR6cOIoeJ8n2ttuNHFLux1aWY7+ovhed7Vhd9a9fadwo833YZz88F/k/bczuT72qi1drE1cnSgsqTBsFNRE62d6WvAP4T+EDO8ifK9/a94ncCnsl5vibzXSK7gh8pqPyFwOeAjYF7iXv753Z43VPAIW3PnQb8paA4mjMCJI3Zls73ydcB/ncP5dUl391cRmLle/DNLrXRtmM5sUf8VB1DJPI8YlTtkb2FmZv346SVNcfafAfYAtiMWKZ6CfDOHsoz36Uaaa/gJ2rRz+uh/PaGQqpRtdsCtyQ6t1R3Wze+TgN2zFFOHfJ9hMj3mxOcW6qV9i76Ztf8t4H/QdyLO4HeW/QnE4PlNgbuoffpdnlsg8kuTeRk4JdEhXz8JK+drBzzXaqJrHvwRbXoz2kcKdldJ03se8CfiVG8RxIN/F6Y71KNZFXwRbXo62Bf4PDUQUg1tCfwVmJU+3uIUe8fShpRfua71JC10M33iMQ/jXQDZYowu3EsSR2IVENnAzcyluM3k3b1ubxWaxyLUwci1UF7Bb8n8EVijupVwNeA11QdVIH2AK5MHYRUU5sAzwFfaHw9jJhD3q/2AK5IHYRUF+0V/KC16L0fJ2V7kuit+xAwH5gJ/N+UAeVkvkst2iv4TRisFv2rgF+lDkLqA38FvkUsVtOv3GBGatFewQ9Si35DYtvb5akDkVS6jYitrc13qSFrkB30f4t+P+yukybSaWGafr0lZ75LbSaq4Pvd/pjw0kSylqrtR/sDP00dhFQn7fPgs1rvRW0+U5XpwAbA/akDkfpAUQtbpTID811aSfsV/KC06B1sI3WvubDVNRS7J3tVzHepg6wu+qJa9A8Su0pVzekyUvfuANYlBtZ+N0c55rtUI1kVfFEt+uuJleQ+T/QGVMUFL6TuHU7MOskrZb5fXuH5pL6QtRZ9s0VfhO8C1wIfJT5EXl9QuVnWBpYBS0s+jzQojm4cTXnG3JjvUk1kVfCHAw8A9xV0njuJzSzWLKi8iewDXFLBeaRBcTrwjsbjGwsor8p83xfzXeooq4IvqkV/YNv3vyB2rGo3G/jntnh2IK4Epur1xHr6krozHXgfcCnj836qzHepRrIq+KJb9E2dkh1iWdyz257bkd663bYBbunhfdKwmgf8CdiLGGhXlKx8X0qx+X5zD++TBl7WILtmi34z8nV/LQRuIxL3drJXxVsO/KbtWAw8P8XzvQy4tadIpeF1HZHzxxL3s3vVbb6PUky+b4P5LmXKquCLatEfAxzSKO8gyt9b3uky0tTdCPyauOJ+T45yzHepRrIq+KJa9FWvc+2AG2nqZgKfA75N5HyvUuT7xSWfQ+pbWffgW1v0eZwMnAlsDNwDHJezvInMBuYQXX2SuvcY8DiwHrFldK+qzPfVMN+lCWVV8M0W/SzgBmJ/+F6c0ziqsAdwZUXnkgbJR4hpsWeRL4fMd6lGsrromy16yNeir5L346Te7E004g+n2FkzZTLfpUlkVfAfAV5KtMb/Z3Xh5PIq3HBC6sV5wFuAzxB53w92IsYKScqQVcH3W4t+Q+ARYrqdpKk5FvgYcTvuo4lj6cZGwMOY79KEsir4fmvR74fddVKvTiKmt51HfywSZb5LXcgaZPcpels2MpX9geNTByH1qaOI23FHA39MHEs39qfcEfrSQKhqs5kyTQc2AO5PHYjUZ9rnqTdXhcuzm1zZzHepS2VvNlMFB9dJvWnm9duI+eRziH0h6mwnzHepK1n34P8eeJSYLnd4deH0xOkyUj6HA1cBPwEOSxzLZMx3qUtZFfyRwO7ArsT9uTrbA7gidRBSH1sX2BRYAGyeOJbJ7In5LnUlq4IHWEH+taTfSQyGuRdYBByas7x2axM7UPWyzaSk8HngcuBqYpOpXlWR788BzxZcrjSQsu7Bf5WxRSTybD5xCLAP8HfEHNvvAGfkKK/dPri5jJTXocD6jcc75iin7Hx3MylpCtqv4E9vfP0ucBGxVex3c5S/FrFQzkxiQE/Rg/W8HyflNx14H7AZ+SpQ812qkYm66N9VQPmfJ1aZ+z5wIfDpAspstQ1wc8FlSsNmHtE1vxfRqO9VFfl+U8FlSgNrogq+COcRm9WsAmwL/FuBZb+MsXm7knp3HXEVfyywLEc5Zeb7Npjv0pS0V/CHEgPrmoPrWh8XIWt/+bnAL4k96JvHPsSHTha766R8jiDmv+8EvAhYh9jHvSjmuzTAFgK3EaPcb298362jmHgU7nnEPT9pUL2d8QtOFe0GYCvg3URDfjfyTUErM9/Px3zXYCs838vuoj+GGFk7DziImF9fhNnEqluLCypPGkZ3EhVycyDttcBDOcoz36UaKbuCH+1wFGEj4IKCypKG1QuNr883vubNzzLz/fyCypKGRtY8+KKcDJwJbEzc2ytqB6jbGoek3h3K+G7xZoX8jh7LM9+lGim7gj+ncUiqn6LnqZvvUo2UXcHn8TDwYeA9FZxrQ2Ilrxcme2Eis4mBS0XOaCjSasAzqYPIMI1YeCXP9K8yzSIGpD3e4WdzKH4ueV2Z72PM996Z71rJeylmYZ+y/IDYEKSuLksdwAR2Bv5X6iAmcCLwutRBDBnzPR/zvXeV5nvZg+wkSVICVvCSJA0gK3hJkgbQREtDDpMZxCCfh1MHkmE28DvqOyhoFeA3qYPIMErM8749dSAZZgF34SIuVTLf8zHfe2e+S5IkSZIkSZIkSZIkSZIkSZIkSZKkbh0GnAFcAXwocSxZdqOemzu8GbgcOBt4Z9pQOjoROJ1Y2/vExLG0mgZsz9g87IXAJcCljccqj/neO/O9N+Z7Qh8nts3cAHgscSydbEX8sdZxZ6m7gauAJcCxaUPp6CFi84ldGo/rYn2icmn+Tm8HdgBejvuel818793dmO+9MN8T25Ro9R2ROpA26wFfIlYcrGPCLwMOBt4C3JM4lk4OI/7fRoF3JI6lk+bvdBmxVeRcYptQlct87435no/5nsDBwCeA+Ynj6ORgxv5gR4Eb0oazkpuBjYGNgEWJY+nkYWBXosuzTi36pmbC3wFsR3Tj1XWJzUFhvvfOfM+n8nwfKbPwPnED0fXUdGCiOCYzSv1+XwcAJzQefxU4LWEsnZxIJNE04PfAp9KGs5Lm73QhsUf5NOAU4NyUQQ0487135ns+5rskSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkScPgMOBSYr/rK4GDOryml72mN5/kHHXcv1oadOa7NEQeBzZrPN4SuLDDa3pJztb3dDrHqj2UKSkf812ZZqQOQIU7FTgD+BFwGfAmxu8t3UzcbxJ7J38Z2AZ4MTATWETspbwn8CTwW2CdLs6xonGOLxP7HC8ENgCOaCvr1AL/rdKwM9+lITIDWBd4K3A+8C3Gt8ZHG8dmRGt8EXAt8HEiMV8JPNHyukUt7+v2HP8E7NV43KksScUw36UhciuwY+PxVsCjRLLNAjZhfMJvDjwEvBHYATgOuAG4A1hAtNDf1CirNaGzzkHj9e9ueW2nsiQVw3xXppHJX6I+szdwIrAYmA/8B3Agcc/sTuCExutOBbYDvgjsDKwHLAceJAbUHA88AJwHnAXcQnTtZZ3jTOLv6Tbg4sbr3g/s36EsScUw3yVJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJGiL/DxTRiEvMgqGOAAAAAElFTkSuQmCC" alt="plot of chunk 8d"/> </p>
<p>We see that all statistics pick \( 3 \) variable models except backward stepwise with adjusted R2. Here are the coefficients</p>
<pre><code class="r">coefficients(mod.fwd, id = 3)
</code></pre>
<pre><code>## (Intercept) poly(x, 10, raw = T)1 poly(x, 10, raw = T)2
## 3.07627 2.35624 -3.16515
## poly(x, 10, raw = T)7
## 0.01047
</code></pre>
<pre><code class="r">coefficients(mod.bwd, id = 3)
</code></pre>
<pre><code>## (Intercept) poly(x, 10, raw = T)1 poly(x, 10, raw = T)2
## 3.07888 2.41982 -3.17724
## poly(x, 10, raw = T)9
## 0.00187
</code></pre>
<pre><code class="r">coefficients(mod.fwd, id = 4)
</code></pre>
<pre><code>## (Intercept) poly(x, 10, raw = T)1 poly(x, 10, raw = T)2
## 3.112359 2.369859 -3.275727
## poly(x, 10, raw = T)4 poly(x, 10, raw = T)7
## 0.027674 0.009997
</code></pre>
<p>Here forward stepwise picks \( X^7 \) over \( X^3 \). Backward stepwise with \( 3 \) variables picks \( X^9 \) while backward stepwise with \( 4 \) variables picks \( X^4 \) and \( X^7 \). All other coefficients are close to \( \beta \) s.</p>
<h3>e</h3>
<p>Training Lasso on the data</p>
<pre><code class="r">library(glmnet)
</code></pre>
<pre><code>## Loading required package: Matrix
## Loading required package: lattice
## Loaded glmnet 1.9-5
</code></pre>
<pre><code class="r">xmat = model.matrix(y ~ poly(x, 10, raw = T), data = data.full)[, -1]
mod.lasso = cv.glmnet(xmat, Y, alpha = 1)
best.lambda = mod.lasso$lambda.min
best.lambda
</code></pre>
<pre><code>## [1] 0.03991
</code></pre>
<pre><code class="r">plot(mod.lasso)
</code></pre>
<p><img 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" alt="plot of chunk 8e"/> </p>
<pre><code class="r"># Next fit the model on entire data using best lambda
best.model = glmnet(xmat, Y, alpha = 1)
predict(best.model, s = best.lambda, type = "coefficients")
</code></pre>
<pre><code>## 11 x 1 sparse Matrix of class "dgCMatrix"
## 1
## (Intercept) 3.0398151
## poly(x, 10, raw = T)1 2.2303371
## poly(x, 10, raw = T)2 -3.1033193
## poly(x, 10, raw = T)3 .
## poly(x, 10, raw = T)4 .
## poly(x, 10, raw = T)5 0.0498411
## poly(x, 10, raw = T)6 .
## poly(x, 10, raw = T)7 0.0008068
## poly(x, 10, raw = T)8 .
## poly(x, 10, raw = T)9 .
## poly(x, 10, raw = T)10 .
</code></pre>
<p>Lasso also picks \( X^5 \) over \( X^3 \). It also picks \( X^7 \) with negligible coefficient.</p>
<h3>f</h3>
<p>Create new Y with different \( \beta_7 = 7 \).</p>
<pre><code class="r">beta7 = 7
Y = beta0 + beta7 * X^7 + eps
# Predict using regsubsets
data.full = data.frame(y = Y, x = X)
mod.full = regsubsets(y ~ poly(x, 10, raw = T), data = data.full, nvmax = 10)
mod.summary = summary(mod.full)
# Find the model size for best cp, BIC and adjr2
which.min(mod.summary$cp)
</code></pre>
<pre><code>## [1] 2
</code></pre>
<pre><code class="r">which.min(mod.summary$bic)
</code></pre>
<pre><code>## [1] 1
</code></pre>
<pre><code class="r">which.max(mod.summary$adjr2)
</code></pre>
<pre><code>## [1] 4
</code></pre>
<pre><code class="r">coefficients(mod.full, id = 1)
</code></pre>
<pre><code>## (Intercept) poly(x, 10, raw = T)7
## 2.959 7.001
</code></pre>
<pre><code class="r">coefficients(mod.full, id = 2)
</code></pre>
<pre><code>## (Intercept) poly(x, 10, raw = T)2 poly(x, 10, raw = T)7
## 3.0705 -0.1417 7.0016
</code></pre>
<pre><code class="r">coefficients(mod.full, id = 4)
</code></pre>
<pre><code>## (Intercept) poly(x, 10, raw = T)1 poly(x, 10, raw = T)2
## 3.0763 0.2914 -0.1618
## poly(x, 10, raw = T)3 poly(x, 10, raw = T)7
## -0.2527 7.0091
</code></pre>
<p>We see that BIC picks the most accurate 1-variable model with matching coefficients. Other criteria pick additional variables.</p>
<pre><code class="r">xmat = model.matrix(y ~ poly(x, 10, raw = T), data = data.full)[, -1]
mod.lasso = cv.glmnet(xmat, Y, alpha = 1)
best.lambda = mod.lasso$lambda.min
best.lambda
</code></pre>
<pre><code>## [1] 12.37
</code></pre>
<pre><code class="r">best.model = glmnet(xmat, Y, alpha = 1)
predict(best.model, s = best.lambda, type = "coefficients")
</code></pre>
<pre><code>## 11 x 1 sparse Matrix of class "dgCMatrix"
## 1
## (Intercept) 3.820
## poly(x, 10, raw = T)1 .
## poly(x, 10, raw = T)2 .
## poly(x, 10, raw = T)3 .
## poly(x, 10, raw = T)4 .
## poly(x, 10, raw = T)5 .
## poly(x, 10, raw = T)6 .
## poly(x, 10, raw = T)7 6.797
## poly(x, 10, raw = T)8 .
## poly(x, 10, raw = T)9 .
## poly(x, 10, raw = T)10 .
</code></pre>
<p>Lasso also picks the best 1-variable model but intercet is quite off (\( 3.8 \) vs \( 3 \)).</p>
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统计学习导论_基于R应用习题答案/ch6/8.html
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<body>
<h1>Chapter 6: Exercise 9</h1>
<h3>a</h3>
<p>Load and split the College data</p>
<pre><code class="r">library(ISLR)
set.seed(11)
sum(is.na(College))
</code></pre>
<pre><code>## [1] 0
</code></pre>
<pre><code class="r">train.size = dim(College)[1] / 2
train = sample(1:dim(College)[1], train.size)
test = -train
College.train = College[train, ]
College.test = College[test, ]
</code></pre>
<h3>b</h3>
<p>NUmber of applications is the Apps variable.</p>
<pre><code class="r">lm.fit = lm(Apps~., data=College.train)
lm.pred = predict(lm.fit, College.test)
mean((College.test[, "Apps"] - lm.pred)^2)
</code></pre>
<pre><code>## [1] 1538442
</code></pre>
<p>Test RSS is \( 1538442 \)</p>
<h3>c</h3>
<p>Pick \( \lambda \) using College.train and report error on College.test</p>
<pre><code class="r">library(glmnet)
</code></pre>
<pre><code>## Warning: package 'glmnet' was built under R version 2.15.2
</code></pre>
<pre><code>## Loading required package: Matrix
</code></pre>
<pre><code>## Warning: package 'Matrix' was built under R version 2.15.3
</code></pre>
<pre><code>## Loading required package: lattice
</code></pre>
<pre><code>## Warning: package 'lattice' was built under R version 2.15.3
</code></pre>
<pre><code>## Loaded glmnet 1.9-3
</code></pre>
<pre><code class="r">train.mat = model.matrix(Apps~., data=College.train)
test.mat = model.matrix(Apps~., data=College.test)
grid = 10 ^ seq(4, -2, length=100)
mod.ridge = cv.glmnet(train.mat, College.train[, "Apps"], alpha=0, lambda=grid, thresh=1e-12)
lambda.best = mod.ridge$lambda.min
lambda.best
</code></pre>
<pre><code>## [1] 18.74
</code></pre>
<pre><code class="r">ridge.pred = predict(mod.ridge, newx=test.mat, s=lambda.best)
mean((College.test[, "Apps"] - ridge.pred)^2)
</code></pre>
<pre><code>## [1] 1608859
</code></pre>
<p>Test RSS is slightly higher that OLS, \( 1608859 \).</p>
<h3>d</h3>
<p>Pick \( \lambda \) using College.train and report error on College.test</p>
<pre><code class="r">mod.lasso = cv.glmnet(train.mat, College.train[, "Apps"], alpha=1, lambda=grid, thresh=1e-12)
lambda.best = mod.lasso$lambda.min
lambda.best
</code></pre>
<pre><code>## [1] 21.54
</code></pre>
<pre><code class="r">lasso.pred = predict(mod.lasso, newx=test.mat, s=lambda.best)
mean((College.test[, "Apps"] - lasso.pred)^2)
</code></pre>
<pre><code>## [1] 1635280
</code></pre>
<p>Again, Test RSS is slightly higher that OLS, \( 1635280 \).</p>
<p>The coefficients look like</p>
<pre><code class="r">mod.lasso = glmnet(model.matrix(Apps~., data=College), College[, "Apps"], alpha=1)
predict(mod.lasso, s=lambda.best, type="coefficients")
</code></pre>
<pre><code>## 19 x 1 sparse Matrix of class "dgCMatrix"
## 1
## (Intercept) -6.038e+02
## (Intercept) .
## PrivateYes -4.235e+02
## Accept 1.455e+00
## Enroll -2.004e-01
## Top10perc 3.368e+01
## Top25perc -2.403e+00
## F.Undergrad .
## P.Undergrad 2.086e-02
## Outstate -5.782e-02
## Room.Board 1.246e-01
## Books .
## Personal 1.833e-05
## PhD -5.601e+00
## Terminal -3.314e+00
## S.F.Ratio 4.479e+00
## perc.alumni -9.797e-01
## Expend 6.968e-02
## Grad.Rate 5.160e+00
</code></pre>
<h3>e</h3>
<p>Use validation to fit pcr</p>
<pre><code class="r">library(pls)
</code></pre>
<pre><code>##
## Attaching package: 'pls'
##
## The following object(s) are masked from 'package:stats':
##
## loadings
</code></pre>
<pre><code class="r">pcr.fit = pcr(Apps~., data=College.train, scale=T, validation="CV")
validationplot(pcr.fit, val.type="MSEP")
</code></pre>
<p><img 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" alt="plot of chunk 9e"/> </p>
<pre><code class="r">pcr.pred = predict(pcr.fit, College.test, ncomp=10)
mean((College.test[, "Apps"] - data.frame(pcr.pred))^2)
</code></pre>
<pre><code>## [1] 3014496
</code></pre>
<p>Test RSS for PCR is about \( 3014496 \).</p>
<h3>f</h3>
<p>Use validation to fit pls</p>
<pre><code class="r">pls.fit = plsr(Apps~., data=College.train, scale=T, validation="CV")
validationplot(pls.fit, val.type="MSEP")
</code></pre>
<p><img 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" alt="plot of chunk 9f"/> </p>
<pre><code class="r">pls.pred = predict(pls.fit, College.test, ncomp=10)
mean((College.test[, "Apps"] - data.frame(pls.pred))^2)
</code></pre>
<pre><code>## [1] 1508987
</code></pre>
<p>Test RSS for PLS is about \( 1508987 \).</p>
<h3>g</h3>
<p>Results for OLS, Lasso, Ridge are comparable. Lasso reduces the \( F.Undergrad \) and \( Books \) variables to zero and shrinks coefficients of other variables. Here are the test \( R^2 \) for all models.</p>
<pre><code class="r">test.avg = mean(College.test[, "Apps"])
lm.test.r2 = 1 - mean((College.test[, "Apps"] - lm.pred)^2) /mean((College.test[, "Apps"] - test.avg)^2)
ridge.test.r2 = 1 - mean((College.test[, "Apps"] - ridge.pred)^2) /mean((College.test[, "Apps"] - test.avg)^2)
lasso.test.r2 = 1 - mean((College.test[, "Apps"] - lasso.pred)^2) /mean((College.test[, "Apps"] - test.avg)^2)
pcr.test.r2 = 1 - mean((College.test[, "Apps"] - data.frame(pcr.pred))^2) /mean((College.test[, "Apps"] - test.avg)^2)
pls.test.r2 = 1 - mean((College.test[, "Apps"] - data.frame(pls.pred))^2) /mean((College.test[, "Apps"] - test.avg)^2)
barplot(c(lm.test.r2, ridge.test.r2, lasso.test.r2, pcr.test.r2, pls.test.r2), col="red", names.arg=c("OLS", "Ridge", "Lasso", "PCR", "PLS"), main="Test R-squared")
</code></pre>
<p><img 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alt="plot of chunk 9g"/> </p>
<p>The plot shows that test \( R^2 \) for all models except PCR are around 0.9, with PLS having slightly higher test \( R^2 \) than others. PCR has a smaller test \( R^2 \) of less than 0.8. All models except PCR predict college applications with high accuracy. </p>
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统计学习导论_基于R应用习题答案/ch6/9.html
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# Chapter 6 Lab 1: Subset Selection Methods
# Best Subset Selection
library(ISLR)
fix(Hitters)
names(Hitters)
dim(Hitters)
sum(is.na(Hitters$Salary))
Hitters=na.omit(Hitters)
dim(Hitters)
sum(is.na(Hitters))
library(leaps)
regfit.full=regsubsets(Salary~.,Hitters)
summary(regfit.full)
regfit.full=regsubsets(Salary~.,data=Hitters,nvmax=19)
reg.summary=summary(regfit.full)
names(reg.summary)
reg.summary$rsq
par(mfrow=c(2,2))
plot(reg.summary$rss,xlab="Number of Variables",ylab="RSS",type="l")
plot(reg.summary$adjr2,xlab="Number of Variables",ylab="Adjusted RSq",type="l")
which.max(reg.summary$adjr2)
points(11,reg.summary$adjr2[11], col="red",cex=2,pch=20)
plot(reg.summary$cp,xlab="Number of Variables",ylab="Cp",type='l')
which.min(reg.summary$cp)
points(10,reg.summary$cp[10],col="red",cex=2,pch=20)
which.min(reg.summary$bic)
plot(reg.summary$bic,xlab="Number of Variables",ylab="BIC",type='l')
points(6,reg.summary$bic[6],col="red",cex=2,pch=20)
plot(regfit.full,scale="r2")
plot(regfit.full,scale="adjr2")
plot(regfit.full,scale="Cp")
plot(regfit.full,scale="bic")
coef(regfit.full,6)
# Forward and Backward Stepwise Selection
regfit.fwd=regsubsets(Salary~.,data=Hitters,nvmax=19,method="forward")
summary(regfit.fwd)
regfit.bwd=regsubsets(Salary~.,data=Hitters,nvmax=19,method="backward")
summary(regfit.bwd)
coef(regfit.full,7)
coef(regfit.fwd,7)
coef(regfit.bwd,7)
# Choosing Among Models
set.seed(1)
train=sample(c(TRUE,FALSE), nrow(Hitters),rep=TRUE)
test=(!train)
regfit.best=regsubsets(Salary~.,data=Hitters[train,],nvmax=19)
test.mat=model.matrix(Salary~.,data=Hitters[test,])
val.errors=rep(NA,19)
for(i in 1:19){
coefi=coef(regfit.best,id=i)
pred=test.mat[,names(coefi)]%*%coefi
val.errors[i]=mean((Hitters$Salary[test]-pred)^2)
}
val.errors
which.min(val.errors)
coef(regfit.best,10)
predict.regsubsets=function(object,newdata,id,...){
form=as.formula(object$call[[2]])
mat=model.matrix(form,newdata)
coefi=coef(object,id=id)
xvars=names(coefi)
mat[,xvars]%*%coefi
}
regfit.best=regsubsets(Salary~.,data=Hitters,nvmax=19)
coef(regfit.best,10)
k=10
set.seed(1)
folds=sample(1:k,nrow(Hitters),replace=TRUE)
cv.errors=matrix(NA,k,19, dimnames=list(NULL, paste(1:19)))
for(j in 1:k){
best.fit=regsubsets(Salary~.,data=Hitters[folds!=j,],nvmax=19)
for(i in 1:19){
pred=predict(best.fit,Hitters[folds==j,],id=i)
cv.errors[j,i]=mean( (Hitters$Salary[folds==j]-pred)^2)
}
}
mean.cv.errors=apply(cv.errors,2,mean)
mean.cv.errors
par(mfrow=c(1,1))
plot(mean.cv.errors,type='b')
reg.best=regsubsets(Salary~.,data=Hitters, nvmax=19)
coef(reg.best,11)
# Chapter 6 Lab 2: Ridge Regression and the Lasso
x=model.matrix(Salary~.,Hitters)[,-1]
y=Hitters$Salary
# Ridge Regression
library(glmnet)
grid=10^seq(10,-2,length=100)
ridge.mod=glmnet(x,y,alpha=0,lambda=grid)
dim(coef(ridge.mod))
ridge.mod$lambda[50]
coef(ridge.mod)[,50]
sqrt(sum(coef(ridge.mod)[-1,50]^2))
ridge.mod$lambda[60]
coef(ridge.mod)[,60]
sqrt(sum(coef(ridge.mod)[-1,60]^2))
predict(ridge.mod,s=50,type="coefficients")[1:20,]
set.seed(1)
train=sample(1:nrow(x), nrow(x)/2)
test=(-train)
y.test=y[test]
ridge.mod=glmnet(x[train,],y[train],alpha=0,lambda=grid, thresh=1e-12)
ridge.pred=predict(ridge.mod,s=4,newx=x[test,])
mean((ridge.pred-y.test)^2)
mean((mean(y[train])-y.test)^2)
ridge.pred=predict(ridge.mod,s=1e10,newx=x[test,])
mean((ridge.pred-y.test)^2)
ridge.pred=predict(ridge.mod,s=0,newx=x[test,],exact=T)
mean((ridge.pred-y.test)^2)
lm(y~x, subset=train)
predict(ridge.mod,s=0,exact=T,type="coefficients")[1:20,]
set.seed(1)
cv.out=cv.glmnet(x[train,],y[train],alpha=0)
plot(cv.out)
bestlam=cv.out$lambda.min
bestlam
ridge.pred=predict(ridge.mod,s=bestlam,newx=x[test,])
mean((ridge.pred-y.test)^2)
out=glmnet(x,y,alpha=0)
predict(out,type="coefficients",s=bestlam)[1:20,]
# The Lasso
lasso.mod=glmnet(x[train,],y[train],alpha=1,lambda=grid)
plot(lasso.mod)
set.seed(1)
cv.out=cv.glmnet(x[train,],y[train],alpha=1)
plot(cv.out)
bestlam=cv.out$lambda.min
lasso.pred=predict(lasso.mod,s=bestlam,newx=x[test,])
mean((lasso.pred-y.test)^2)
out=glmnet(x,y,alpha=1,lambda=grid)
lasso.coef=predict(out,type="coefficients",s=bestlam)[1:20,]
lasso.coef
lasso.coef[lasso.coef!=0]
# Chapter 6 Lab 3: PCR and PLS Regression
# Principal Components Regression
library(pls)
set.seed(2)
pcr.fit=pcr(Salary~., data=Hitters,scale=TRUE,validation="CV")
summary(pcr.fit)
validationplot(pcr.fit,val.type="MSEP")
set.seed(1)
pcr.fit=pcr(Salary~., data=Hitters,subset=train,scale=TRUE, validation="CV")
validationplot(pcr.fit,val.type="MSEP")
pcr.pred=predict(pcr.fit,x[test,],ncomp=7)
mean((pcr.pred-y.test)^2)
pcr.fit=pcr(y~x,scale=TRUE,ncomp=7)
summary(pcr.fit)
# Partial Least Squares
set.seed(1)
pls.fit=plsr(Salary~., data=Hitters,subset=train,scale=TRUE, validation="CV")
summary(pls.fit)
validationplot(pls.fit,val.type="MSEP")
pls.pred=predict(pls.fit,x[test,],ncomp=2)
mean((pls.pred-y.test)^2)
pls.fit=plsr(Salary~., data=Hitters,scale=TRUE,ncomp=2)
summary(pls.fit)
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统计学习导论_基于R应用习题答案/ch6/lab.R
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<h1>Chapter 7: Exercise 1</h1>
<h2>a</h2>
<p>For \( x \le \xi \), \( f_1(x) \) has coefficients
\( a_1 = \beta_0, b_1 = \beta_1, c_1 = \beta_2, d_1 = \beta_3 \).</p>
<h2>b</h2>
<p>For \( x \gt \xi \), \( f(x) \) has the form of:
\[
\beta_0 + \beta_1 x + \beta_2 x^2 + \beta_3 x^3 + \beta_4 (x - \xi)^3
\\
= \beta_0 + \beta_1 x + \beta_2 x^2 + \beta_3 x^3 + \beta_4 (x^3 - 3 x^2 \xi + 3 x \xi^2 - \xi^3)
\\
= (\beta_0 - \beta_4 \xi^3) + (\beta_1 + 3 \beta_4 \xi^2) x + (\beta_2 - 3 \beta_4 \xi) x^2 + (\beta_3 + \beta_4) x^3
\]</p>
<p>Thus, \( a_2 = \beta_0 - \beta_4 \xi^3, b_2 = \beta_1 + 3 \beta_4 \xi^2, c_2 = \beta_2 - 3 \beta_4 \xi, d_2 = \beta_3 + \beta_4 \).</p>
<h2>c</h2>
<p>\[
f_1(\xi) = \beta_0 + \beta_1 \xi + \beta_2 \xi^2 + \beta_3 \xi^3
\\
f_2(\xi) = (\beta_0 - \beta_4 \xi^3) + (\beta_1 + 3 \beta_4 \xi^2) \xi + (\beta_2 - 3 \beta_4 \xi) \xi^2 + (\beta_3 + \beta_4) \xi^3
\\
= \beta_0 - \beta_4 \xi^3 + \beta_1 \xi + 3 \beta_4 \xi^3 + \beta_2 \xi^2 - 3 \beta_4 \xi^3 + \beta_3 \xi^3 + \beta_4 \xi^3
\\
= \beta_0 + \beta_1 \xi + \beta_2 \xi^2 + 3 \beta_4 \xi^3 - 3 \beta_4 \xi^3 + \beta_3 \xi^3 + \beta_4 \xi^3 - \beta_4 \xi^3
\\
= \beta_0 + \beta_1 \xi + \beta_2 \xi^2 + \beta_3 \xi^3
\]</p>
<h2>d</h2>
<p>\[
f'(x) = b_1 + 2 c_1 x + 3 d_1 x^2
\\
f_1'(\xi) = \beta_1 + 2 \beta_2 \xi + 3 \beta_3 \xi^2
\\
f_2'(\xi) = \beta_1 + 3 \beta_4 \xi^2 + 2 (\beta_2 - 3 \beta_4 \xi) \xi + 3 (\beta_3 + \beta_4) \xi^2
\\
= \beta_1 + 3 \beta_4 \xi^2 + 2 \beta_2 \xi - 6 \beta_4 \xi^2 + 3 \beta_3 \xi^2 + 3 \beta_4 \xi^2
\\
= \beta_1 + 2 \beta_2 \xi + 3 \beta_3 \xi^2 + 3 \beta_4 \xi^2 + 3 \beta_4 \xi^2 - 6 \beta_4 \xi^2
\\
= \beta_1 + 2 \beta_2 \xi + 3 \beta_3 \xi^2
\]</p>
<h2>e</h2>
<p>\[
f''(x) = 2 c_1 + 6 d_1 x
\\
f_1''(\xi) = 2 \beta_2 + 6 \beta_3 \xi
\\
f_2''(\xi) = 2 (\beta_2 - 3 \beta_4 \xi) + 6 (\beta_3 + \beta_4) \xi
\\
= 2 \beta_2 + 6 \beta_3 \xi
\]</p>
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch7/1.html
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<h1>Chapter 7: Exercise 10</h1>
<h3>a</h3>
<pre><code class="r">set.seed(1)
library(ISLR)
library(leaps)
attach(College)
train = sample(length(Outstate), length(Outstate)/2)
test = -train
College.train = College[train, ]
College.test = College[test, ]
reg.fit = regsubsets(Outstate ~ ., data = College.train, nvmax = 17, method = "forward")
reg.summary = summary(reg.fit)
par(mfrow = c(1, 3))
plot(reg.summary$cp, xlab = "Number of Variables", ylab = "Cp", type = "l")
min.cp = min(reg.summary$cp)
std.cp = sd(reg.summary$cp)
abline(h = min.cp + 0.2 * std.cp, col = "red", lty = 2)
abline(h = min.cp - 0.2 * std.cp, col = "red", lty = 2)
plot(reg.summary$bic, xlab = "Number of Variables", ylab = "BIC", type = "l")
min.bic = min(reg.summary$bic)
std.bic = sd(reg.summary$bic)
abline(h = min.bic + 0.2 * std.bic, col = "red", lty = 2)
abline(h = min.bic - 0.2 * std.bic, col = "red", lty = 2)
plot(reg.summary$adjr2, xlab = "Number of Variables", ylab = "Adjusted R2",
type = "l", ylim = c(0.4, 0.84))
max.adjr2 = max(reg.summary$adjr2)
std.adjr2 = sd(reg.summary$adjr2)
abline(h = max.adjr2 + 0.2 * std.adjr2, col = "red", lty = 2)
abline(h = max.adjr2 - 0.2 * std.adjr2, col = "red", lty = 2)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk 10a"/> </p>
<p>All cp, BIC and adjr2 scores show that size 6 is the minimum size for the subset for which the scores are withing 0.2 standard deviations of optimum. We pick 6 as the best subset size and find best 6 variables using entire data.</p>
<pre><code class="r">reg.fit = regsubsets(Outstate ~ ., data = College, method = "forward")
coefi = coef(reg.fit, id = 6)
names(coefi)
</code></pre>
<pre><code>## [1] "(Intercept)" "PrivateYes" "Room.Board" "PhD" "perc.alumni"
## [6] "Expend" "Grad.Rate"
</code></pre>
<h3>b</h3>
<pre><code class="r">library(gam)
</code></pre>
<pre><code>## Loading required package: splines
## Loaded gam 1.09
</code></pre>
<pre><code class="r">gam.fit = gam(Outstate ~ Private + s(Room.Board, df = 2) + s(PhD, df = 2) +
s(perc.alumni, df = 2) + s(Expend, df = 5) + s(Grad.Rate, df = 2), data = College.train)
par(mfrow = c(2, 3))
plot(gam.fit, se = T, col = "blue")
</code></pre>
<p><img 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alt="plot of chunk 10b"/> </p>
<h3>c</h3>
<pre><code class="r">gam.pred = predict(gam.fit, College.test)
gam.err = mean((College.test$Outstate - gam.pred)^2)
gam.err
</code></pre>
<pre><code>## [1] 3745460
</code></pre>
<pre><code class="r">gam.tss = mean((College.test$Outstate - mean(College.test$Outstate))^2)
test.rss = 1 - gam.err/gam.tss
test.rss
</code></pre>
<pre><code>## [1] 0.7697
</code></pre>
<p>We obtain a test R-squared of 0.77 using GAM with 6 predictors. This is a slight improvement over a test RSS of 0.74 obtained using OLS. </p>
<h3>d</h3>
<pre><code class="r">summary(gam.fit)
</code></pre>
<pre><code>##
## Call: gam(formula = Outstate ~ Private + s(Room.Board, df = 2) + s(PhD,
## df = 2) + s(perc.alumni, df = 2) + s(Expend, df = 5) + s(Grad.Rate,
## df = 2), data = College.train)
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -4977.7 -1184.5 58.3 1220.0 7688.3
##
## (Dispersion Parameter for gaussian family taken to be 3300711)
##
## Null Deviance: 6.222e+09 on 387 degrees of freedom
## Residual Deviance: 1.231e+09 on 373 degrees of freedom
## AIC: 6942
##
## Number of Local Scoring Iterations: 2
##
## Anova for Parametric Effects
## Df Sum Sq Mean Sq F value Pr(>F)
## Private 1 1.78e+09 1.78e+09 539.1 < 2e-16 ***
## s(Room.Board, df = 2) 1 1.22e+09 1.22e+09 370.2 < 2e-16 ***
## s(PhD, df = 2) 1 3.82e+08 3.82e+08 115.9 < 2e-16 ***
## s(perc.alumni, df = 2) 1 3.28e+08 3.28e+08 99.5 < 2e-16 ***
## s(Expend, df = 5) 1 4.17e+08 4.17e+08 126.2 < 2e-16 ***
## s(Grad.Rate, df = 2) 1 5.53e+07 5.53e+07 16.8 5.2e-05 ***
## Residuals 373 1.23e+09 3.30e+06
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Anova for Nonparametric Effects
## Npar Df Npar F Pr(F)
## (Intercept)
## Private
## s(Room.Board, df = 2) 1 3.56 0.060 .
## s(PhD, df = 2) 1 4.34 0.038 *
## s(perc.alumni, df = 2) 1 1.92 0.167
## s(Expend, df = 5) 4 16.86 1e-12 ***
## s(Grad.Rate, df = 2) 1 3.72 0.055 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
</code></pre>
<p>Non-parametric Anova test shows a strong evidence of non-linear relationship between response and Expend, and a moderately strong non-linear relationship (using p value of 0.05) between response and Grad.Rate or PhD. </p>
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统计学习导论_基于R应用习题答案/ch7/10.html
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<body>
<h1>Chapter 7: Exercise 11</h1>
<h3>a</h3>
<p>We create variables according to the equation \( Y = -2.1 + 1.3X_1 + 0.54X_2 \).</p>
<pre><code class="r">set.seed(1)
X1 = rnorm(100)
X2 = rnorm(100)
eps = rnorm(100, sd = 0.1)
Y = -2.1 + 1.3 * X1 + 0.54 * X2 + eps
</code></pre>
<h3>b</h3>
<p>Create a list of 1000 \( \hat{beta}_0 \), \( \hat{beta}_1 \) and \( \hat{beta}_2 \). Initialize first of the \( \hat{\beta}_1 \) to 10.</p>
<pre><code class="r">beta0 = rep(NA, 1000)
beta1 = rep(NA, 1000)
beta2 = rep(NA, 1000)
beta1[1] = 10
</code></pre>
<h3>c, d, e</h3>
<p>Accumulate results of 1000 iterations in the beta arrays.</p>
<pre><code class="r">for (i in 1:1000) {
a = Y - beta1[i] * X1
beta2[i] = lm(a ~ X2)$coef[2]
a = Y - beta2[i] * X2
lm.fit = lm(a ~ X1)
if (i < 1000) {
beta1[i + 1] = lm.fit$coef[2]
}
beta0[i] = lm.fit$coef[1]
}
plot(1:1000, beta0, type = "l", xlab = "iteration", ylab = "betas", ylim = c(-2.2,
1.6), col = "green")
lines(1:1000, beta1, col = "red")
lines(1:1000, beta2, col = "blue")
legend("center", c("beta0", "beta1", "beta2"), lty = 1, col = c("green", "red",
"blue"))
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk 11e"/> </p>
<p>The coefficients quickly attain their least square values.</p>
<h3>f</h3>
<pre><code class="r">lm.fit = lm(Y ~ X1 + X2)
plot(1:1000, beta0, type = "l", xlab = "iteration", ylab = "betas", ylim = c(-2.2,
1.6), col = "green")
lines(1:1000, beta1, col = "red")
lines(1:1000, beta2, col = "blue")
abline(h = lm.fit$coef[1], lty = "dashed", lwd = 3, col = rgb(0, 0, 0, alpha = 0.4))
abline(h = lm.fit$coef[2], lty = "dashed", lwd = 3, col = rgb(0, 0, 0, alpha = 0.4))
abline(h = lm.fit$coef[3], lty = "dashed", lwd = 3, col = rgb(0, 0, 0, alpha = 0.4))
legend("center", c("beta0", "beta1", "beta2", "multiple regression"), lty = c(1,
1, 1, 2), col = c("green", "red", "blue", "black"))
</code></pre>
<p><img 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alt="plot of chunk 11f"/> </p>
<p>Dotted lines show that the estimated multiple regression coefficients match exactly with the coefficients obtained using backfitting.</p>
<h3>g</h3>
<p>When the relationship between \( Y \) and \( X \)'s is linear, one iteration is sufficient to attain a good approximation of true regression coefficients.</p>
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统计学习导论_基于R应用习题答案/ch7/11.html
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<body>
<h1>Chapter 7: Exercise 12</h1>
<pre><code class="r">set.seed(1)
p = 100
n = 1000
x = matrix(ncol = p, nrow = n)
coefi = rep(NA, p)
for (i in 1:p) {
x[, i] = rnorm(n)
coefi[i] = rnorm(1) * 100
}
y = x %*% coefi + rnorm(n)
</code></pre>
<pre><code class="r">beta = rep(0, p)
max_iterations = 1000
errors = rep(NA, max_iterations + 1)
iter = 2
errors[1] = Inf
errors[2] = sum((y - x %*% beta)^2)
threshold = 1e-04
while (iter < max_iterations && errors[iter - 1] - errors[iter] > threshold) {
for (i in 1:p) {
a = y - x %*% beta + beta[i] * x[, i]
beta[i] = lm(a ~ x[, i])$coef[2]
}
iter = iter + 1
errors[iter] = sum((y - x %*% beta)^2)
print(c(iter - 2, errors[iter - 1], errors[iter]))
}
</code></pre>
<pre><code>## [1] 1.000e+00 1.016e+09 3.747e+07
## [1] 2 37472751 1669889
## [1] 3 1669889 77924
## [1] 4 77924 6157
## [1] 5 6157 1277
## [1] 6.0 1277.0 928.3
## [1] 7.0 928.3 904.8
## [1] 8.0 904.8 903.2
## [1] 9.0 903.2 903.1
## [1] 10.0 903.1 903.1
## [1] 11.0 903.1 903.1
</code></pre>
<p>10 iterations to get to a “good” approximation defined by the threshold on sum of squared errors between subsequent iterations. The error increases on the 11th
iteration.</p>
<pre><code class="r">plot(1:11, errors[3:13])
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-3"/> </p>
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