From ac8b82c246ede22470ba16b001a0164ce0cfc617 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lu=C3=ADs=20Assun=C3=A7=C3=A3o?= Date: Tue, 23 Jan 2024 18:32:48 -0300 Subject: [PATCH] repost fantasy football --- _extensions/mps9506/quarto-cv/_extension.yml | 20 - .../pandoc-ext/multibib/_extension.yaml | 6 - .../pandoc-ext/multibib/multibib.lua | 141 -- _extensions/mps9506/quarto-cv/header.tex | 1 - .../mps9506/quarto-cv/partials/biblio.tex | 36 - .../mps9506/quarto-cv/partials/doc-class.tex | 120 -- _extensions/mps9506/quarto-cv/quarto-cv.tex | 212 -- _quarto.yml | 12 +- assets/custom.scss | 23 +- assets/custom.theme | 214 -- .../{non-monotonic => aging-curve}/.gitignore | 0 .../.python-version | 0 .../data/experiment-2.csv | 0 .../data/mind-in-eyes.csv | 0 .../index.ipynb | 0 .../index_files/execute-results/html.json | 0 .../libs/bootstrap/bootstrap-icons.css | 0 .../libs/bootstrap/bootstrap-icons.woff | Bin .../libs/bootstrap/bootstrap.min.css | 0 .../libs/bootstrap/bootstrap.min.js | 0 .../libs/clipboard/clipboard.min.js | 0 .../libs/quarto-html/anchor.min.js | 0 .../libs/quarto-html/popper.min.js | 0 .../quarto-syntax-highlighting.css | 0 .../index_files/libs/quarto-html/quarto.js | 0 .../index_files/libs/quarto-html/tippy.css | 0 .../libs/quarto-html/tippy.umd.min.js | 0 .../pyproject.toml | 25 +- .../requirements-dev.lock | 8 +- blog/aging-curve/requirements.lock | 150 ++ blog/fantasy-football/index.ipynb | 1788 +++++++++++++++++ blog/fantasy-football/index.tmp | 720 ------- blog/fantasy-football/pyproject.toml | 26 +- blog/fantasy-football/requirements-dev.lock | 169 +- blog/fantasy-football/requirements.lock | 139 +- .../src/fantasy_football/__init__.py | 2 + .../.ipynb_checkpoints/index-checkpoint.ipynb | 642 ------ blog/non-monotonic/requirements.lock | 54 - .../src/non_monotonic/__init__.py | 2 - custom.scss | 47 - docs/blog/aging-curve/index.html | 847 ++++++++ .../figure-html/bootstrap-curve-output-1.png | Bin 0 -> 92123 bytes .../bootstrap-distribution-output-1.png | Bin 0 -> 44544 bytes ...posable-models-additive-draws-output-1.png | Bin 0 -> 93130 bytes .../decomposable-models-peak-output-1.png | Bin 0 -> 67984 bytes .../figure-html/digit-span-plot-output-1.png | Bin 0 -> 166639 bytes .../figure-html/spline-plot-output-1.png | Bin 0 -> 78844 bytes docs/blog/fantasy-football/index.html | 457 ++++- ...diction-fixture-mixed-effects-output-1.png | Bin 0 -> 111031 bytes ...ore-prediction-player-average-output-1.png | Bin 0 -> 81616 bytes ...diction-player-random-effects-output-1.png | Bin 0 -> 96860 bytes ...eam-picking-backtest-champion-output-1.png | Bin 0 -> 110640 bytes .../team-picking-backtest-score-output-1.png | Bin 0 -> 55997 bytes ...cktest-score-unlimited-budget-output-1.png | Bin 0 -> 61830 bytes docs/cv.html | 5 +- docs/cv.pdf | Bin 26129 -> 26132 bytes docs/index.html | 11 +- docs/index.xml | 771 ++++++- docs/listings.json | 7 + docs/search.json | 65 +- docs/site_libs/bootstrap/bootstrap.min.css | 6 +- .../quarto-syntax-highlighting.css | 2 +- docs/sitemap.xml | 10 +- index.qmd | 8 +- src/blog/FiraCode-Regular.ttf | Bin 289624 -> 0 bytes 65 files changed, 4219 insertions(+), 2527 deletions(-) delete mode 100644 _extensions/mps9506/quarto-cv/_extension.yml delete mode 100644 _extensions/mps9506/quarto-cv/_extensions/pandoc-ext/multibib/_extension.yaml delete mode 100644 _extensions/mps9506/quarto-cv/_extensions/pandoc-ext/multibib/multibib.lua delete mode 100644 _extensions/mps9506/quarto-cv/header.tex delete mode 100644 _extensions/mps9506/quarto-cv/partials/biblio.tex delete mode 100644 _extensions/mps9506/quarto-cv/partials/doc-class.tex delete mode 100644 _extensions/mps9506/quarto-cv/quarto-cv.tex delete mode 100644 assets/custom.theme rename blog/{non-monotonic => aging-curve}/.gitignore (100%) rename blog/{non-monotonic => aging-curve}/.python-version (100%) rename blog/{non-monotonic => aging-curve}/data/experiment-2.csv (100%) rename blog/{non-monotonic => aging-curve}/data/mind-in-eyes.csv (100%) rename blog/{non-monotonic => aging-curve}/index.ipynb (100%) rename blog/{non-monotonic => aging-curve}/index_files/execute-results/html.json (100%) rename blog/{non-monotonic => aging-curve}/index_files/libs/bootstrap/bootstrap-icons.css (100%) rename blog/{non-monotonic => aging-curve}/index_files/libs/bootstrap/bootstrap-icons.woff (100%) rename blog/{non-monotonic => aging-curve}/index_files/libs/bootstrap/bootstrap.min.css (100%) rename blog/{non-monotonic => aging-curve}/index_files/libs/bootstrap/bootstrap.min.js (100%) rename blog/{non-monotonic => aging-curve}/index_files/libs/clipboard/clipboard.min.js (100%) rename blog/{non-monotonic => aging-curve}/index_files/libs/quarto-html/anchor.min.js (100%) rename blog/{non-monotonic => aging-curve}/index_files/libs/quarto-html/popper.min.js (100%) rename blog/{non-monotonic => aging-curve}/index_files/libs/quarto-html/quarto-syntax-highlighting.css (100%) rename blog/{non-monotonic => aging-curve}/index_files/libs/quarto-html/quarto.js (100%) rename blog/{non-monotonic => aging-curve}/index_files/libs/quarto-html/tippy.css (100%) rename blog/{non-monotonic => aging-curve}/index_files/libs/quarto-html/tippy.umd.min.js (100%) rename blog/{non-monotonic => aging-curve}/pyproject.toml (79%) rename blog/{non-monotonic => aging-curve}/requirements-dev.lock (95%) create mode 100644 blog/aging-curve/requirements.lock create mode 100644 blog/fantasy-football/index.ipynb delete mode 100644 blog/fantasy-football/index.tmp create mode 100644 blog/fantasy-football/src/fantasy_football/__init__.py delete mode 100644 blog/non-monotonic/.ipynb_checkpoints/index-checkpoint.ipynb delete mode 100644 blog/non-monotonic/requirements.lock delete mode 100644 blog/non-monotonic/src/non_monotonic/__init__.py delete mode 100644 custom.scss create mode 100644 docs/blog/aging-curve/index.html create mode 100644 docs/blog/aging-curve/index_files/figure-html/bootstrap-curve-output-1.png create mode 100644 docs/blog/aging-curve/index_files/figure-html/bootstrap-distribution-output-1.png create mode 100644 docs/blog/aging-curve/index_files/figure-html/decomposable-models-additive-draws-output-1.png create mode 100644 docs/blog/aging-curve/index_files/figure-html/decomposable-models-peak-output-1.png create mode 100644 docs/blog/aging-curve/index_files/figure-html/digit-span-plot-output-1.png create mode 100644 docs/blog/aging-curve/index_files/figure-html/spline-plot-output-1.png create mode 100644 docs/blog/fantasy-football/index_files/figure-html/score-prediction-fixture-mixed-effects-output-1.png create mode 100644 docs/blog/fantasy-football/index_files/figure-html/score-prediction-player-average-output-1.png create mode 100644 docs/blog/fantasy-football/index_files/figure-html/score-prediction-player-random-effects-output-1.png create mode 100644 docs/blog/fantasy-football/index_files/figure-html/team-picking-backtest-champion-output-1.png create mode 100644 docs/blog/fantasy-football/index_files/figure-html/team-picking-backtest-score-output-1.png create mode 100644 docs/blog/fantasy-football/index_files/figure-html/team-picking-backtest-score-unlimited-budget-output-1.png delete mode 100644 src/blog/FiraCode-Regular.ttf diff --git a/_extensions/mps9506/quarto-cv/_extension.yml b/_extensions/mps9506/quarto-cv/_extension.yml deleted file mode 100644 index 3d685a1..0000000 --- a/_extensions/mps9506/quarto-cv/_extension.yml +++ /dev/null @@ -1,20 +0,0 @@ -title: quarto-cv -author: Michael Schramm -version: 1.0.1 -quarto-required: ">=1.3.450" -contributes: - formats: - pdf: - filters: - - multibib - pdf-engine: lualatex - toc: false - linkcolor: blue - urlcolor: blue - geometry: margin=1in - biblio-config: false - include-in-header: header.tex - template: quarto-cv.tex - template-partials: - - "partials/doc-class.tex" - - "partials/biblio.tex" diff --git a/_extensions/mps9506/quarto-cv/_extensions/pandoc-ext/multibib/_extension.yaml b/_extensions/mps9506/quarto-cv/_extensions/pandoc-ext/multibib/_extension.yaml deleted file mode 100644 index c15d6d8..0000000 --- a/_extensions/mps9506/quarto-cv/_extensions/pandoc-ext/multibib/_extension.yaml +++ /dev/null @@ -1,6 +0,0 @@ -name: multibib -author: Albert Krewinkel -version: 1.0.0 -contributes: - filters: - - multibib.lua diff --git a/_extensions/mps9506/quarto-cv/_extensions/pandoc-ext/multibib/multibib.lua b/_extensions/mps9506/quarto-cv/_extensions/pandoc-ext/multibib/multibib.lua deleted file mode 100644 index 4e8c3b9..0000000 --- a/_extensions/mps9506/quarto-cv/_extensions/pandoc-ext/multibib/multibib.lua +++ /dev/null @@ -1,141 +0,0 @@ ---[[ -multibib – create multiple bibliographies - -Copyright © 2018-2022 Albert Krewinkel - -Permission to use, copy, modify, and/or distribute this software for any -purpose with or without fee is hereby granted, provided that the above -copyright notice and this permission notice appear in all copies. - -THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES -WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF -MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR -ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES -WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN -ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF -OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. -]] -PANDOC_VERSION:must_be_at_least '2.11' - -local List = require 'pandoc.List' -local utils = require 'pandoc.utils' -local stringify = utils.stringify -local run_json_filter = utils.run_json_filter - ---- get the type of meta object -local metatype = pandoc.utils.type or - function (v) - local metatag = type(v) == 'table' and v.t and v.t:gsub('^Meta', '') - return metatag and metatag ~= 'Map' and metatag or type(v) - end - ---- Collection of all cites in the document -local all_cites = {} ---- Document meta value -local doc_meta = pandoc.Meta{} - ---- Div used by citeproc to insert the bibliography. -local refs_div = pandoc.Div({}, pandoc.Attr('refs')) - --- Div filled by citeproc with properties set according to --- the output format and the attributes of cs:bibliography -local refs_div_with_properties - ---- Run citeproc on a pandoc document -local citeproc -if utils.citeproc then - -- Built-in Lua function - citeproc = utils.citeproc -else - -- Use pandoc as a citeproc processor - citeproc = function (doc) - local opts = {'--from=json', '--to=json', '--citeproc', '--quiet'} - return run_json_filter(doc, 'pandoc', opts) - end -end - ---- Resolve citations in the document by combining all bibliographies --- before running pandoc-citeproc on the full document. -local function resolve_doc_citations (doc) - -- combine all bibliographies - local meta = doc.meta - local bibconf = meta.bibliography - meta.bibliography = pandoc.MetaList{} - if metatype(bibconf) == 'table' then - for _, value in pairs(bibconf) do - table.insert(meta.bibliography, stringify(value)) - end - end - -- add refs div to catch the created bibliography - table.insert(doc.blocks, refs_div) - -- resolve all citations - doc = citeproc(doc) - -- remove catch-all bibliography and keep it for future use - refs_div_with_properties = table.remove(doc.blocks) - -- restore bibliography to original value - doc.meta.bibliography = orig_bib - return doc -end - ---- Explicitly create a new meta object with all fields relevant for ---- pandoc-citeproc. -local function meta_for_pandoc_citeproc (bibliography) - -- We could just indiscriminately copy all meta fields, but let's be - -- explicit about what's important. - local fields = { - 'bibliography', 'references', 'csl', 'citation-style', - 'link-citations', 'citation-abbreviations', 'lang', - 'suppress-bibliography', 'reference-section-title', - 'notes-after-punctuation', 'nocite' - } - local new_meta = pandoc.Meta{} - for _, field in ipairs(fields) do - new_meta[field] = doc_meta[field] - end - new_meta.bibliography = bibliography - return new_meta -end - -local function remove_duplicates(classes) - local seen = {} - return classes:filter(function(x) - if seen[x] then - return false - else - seen[x] = true - return true - end - end) -end - ---- Create a bibliography for a given topic. This acts on all divs whose --- ID starts with "refs", followed by nothing but underscores and --- alphanumeric characters. -local function create_topic_bibliography (div) - local name = div.identifier:match('^refs[-_]?([-_%w]*)$') - local bibfile = name and (doc_meta.bibliography or {})[name] - if not bibfile then - return nil - end - local tmp_blocks = {pandoc.Para(all_cites), refs_div} - local tmp_meta = meta_for_pandoc_citeproc(bibfile) - local tmp_doc = pandoc.Pandoc(tmp_blocks, tmp_meta) - local res = citeproc(tmp_doc) - -- First block of the result contains the dummy paragraph, second is - -- the refs Div filled by citeproc. - div.content = res.blocks[2].content - -- Set the classes and attributes as citeproc did it on refs_div - div.classes = remove_duplicates(refs_div_with_properties.classes) - div.attributes = refs_div_with_properties.attributes - return div -end - -return { - { - -- Collect all citations and the doc's Meta value for other filters. - Cite = function (c) all_cites[#all_cites + 1] = c end, - Meta = function (m) doc_meta = m end, - }, - { Pandoc = resolve_doc_citations }, - { Div = create_topic_bibliography }, -} diff --git a/_extensions/mps9506/quarto-cv/header.tex b/_extensions/mps9506/quarto-cv/header.tex deleted file mode 100644 index 090e0c7..0000000 --- a/_extensions/mps9506/quarto-cv/header.tex +++ /dev/null @@ -1 +0,0 @@ -% TODO: Add custom LaTeX header directives here diff --git a/_extensions/mps9506/quarto-cv/partials/biblio.tex b/_extensions/mps9506/quarto-cv/partials/biblio.tex deleted file mode 100644 index 21e26b8..0000000 --- a/_extensions/mps9506/quarto-cv/partials/biblio.tex +++ /dev/null @@ -1,36 +0,0 @@ -$-- Necessary content to creates the bibliography. -$-- Bibliography style is defined in the main template. Use `biblio-style` YAML key to customize the .bst file for natbib. -$-- If your CLS file already define a style file, then set `biblio-config: false` to deactivate the main template configuration, otherwise there could be a conflict. -$-- - -$if(natbib)$ -$if(bibliography)$ -$if(biblio-title)$ -$if(has-chapters)$ -\renewcommand\bibname{$biblio-title$} -$else$ -\renewcommand\refname{$biblio-title$} -$endif$ -$endif$ -$if(beamer)$ -\begin{frame}[allowframebreaks]{$biblio-title$} - \bibliographytrue -$endif$ - \bibliography{$for(bibliography)$$bibliography$$sep$,$endfor$} -$if(beamer)$ -\end{frame} -$endif$ - -$endif$ -$endif$ -$if(biblatex)$ -$if(beamer)$ -\begin{frame}[allowframebreaks]{$biblio-title$} - \bibliographytrue - \printbibliography[heading=none] -\end{frame} -$else$ -\printbibliography$if(biblio-title)$[title=$biblio-title$]$endif$ -$endif$ - -$endif$ diff --git a/_extensions/mps9506/quarto-cv/partials/doc-class.tex b/_extensions/mps9506/quarto-cv/partials/doc-class.tex deleted file mode 100644 index 4973a99..0000000 --- a/_extensions/mps9506/quarto-cv/partials/doc-class.tex +++ /dev/null @@ -1,120 +0,0 @@ -$-- Contains the document class declaration and options. -$-- By default we provide the identical document class that Pandoc provides, implementing many features. -$-- If provide this partial in your format, you will need to either implement support for the usaul document class options in addition to other one -$-- or be aware that pandoc supported options (e.g. font-size, paper-size, classoption, etc├ö├ç┬¬) will not be supported in your format. -$-- - -\documentclass[ -$if(fontsize)$ - $fontsize$, -$endif$ -$if(lang)$ - $babel-lang$, -$endif$ -$if(papersize)$ - $papersize$paper, -$endif$ -$for(classoption)$ - $classoption$$sep$,$endfor$ -] -{$documentclass$} - -$if(fontfamily)$ -\usepackage[$for(fontfamilyoptions)$$fontfamilyoptions$$sep$,$endfor$]{$fontfamily$} -$else$ -\usepackage{ebgaramond-maths} -%\usepackage[T1]{fontenc} -$endif$ -$if(linestretch)$ -\usepackage{setspace} -\setstretch{$linestretch$} -$endif$ -%\usepackage{amssymb,amsmath} -\usepackage{ifxetex,ifluatex} -\ifnum 0\ifxetex 1\fi\ifluatex 1\fi=0 % if pdftex - \usepackage[$if(fontenc)$$fontenc$$else$T1$endif$]{fontenc} - \usepackage[utf8]{inputenc} -$if(euro)$ - \usepackage{eurosym} -$endif$ -\else % if luatex or xelatex - \ifxetex - \usepackage{mathspec} - \else - \usepackage{fontspec} - \fi - \defaultfontfeatures{Ligatures=TeX,Scale=MatchLowercase} - -% this is for pandoc <3.1.8 -$if(csl-refs)$ -\newlength{\cslhangindent} -\setlength{\cslhangindent}{1.5em} -\newlength{\csllabelwidth} -\setlength{\csllabelwidth}{3em} -\newlength{\cslentryspacingunit} % times entry-spacing -\setlength{\cslentryspacingunit}{\parskip} -\newenvironment{CSLReferences}[2] % #1 hanging-ident, #2 entry spacing - {% don't indent paragraphs - \setlength{\parindent}{0pt} - % turn on hanging indent if param 1 is 1 - \ifodd #1 - \let\oldpar\par - \def\par{\hangindent=\cslhangindent\oldpar} - \fi - % set entry spacing - \setlength{\parskip}{#2\cslentryspacingunit} - }% - {} -\usepackage{calc} -\newcommand{\CSLBlock}[1]{#1\hfill\break} -\newcommand{\CSLLeftMargin}[1]{\parbox[t]{\csllabelwidth}{#1}} -\newcommand{\CSLRightInline}[1]{\parbox[t]{\linewidth - \csllabelwidth}{#1}\break} -\newcommand{\CSLIndent}[1]{\hspace{\cslhangindent}#1} -$endif$ - -$if(euro)$ - \newcommand{\euro}{€} -$endif$ -$if(mainfont)$ - \setmainfont[$for(mainfontoptions)$$mainfontoptions$$sep$,$endfor$]{$mainfont$} -$endif$ -$if(sansfont)$ - \setsansfont[$for(sansfontoptions)$$sansfontoptions$$sep$,$endfor$]{$sansfont$} -$endif$ -$if(monofont)$ - \setmonofont[Mapping=tex-ansi$if(monofontoptions)$,$for(monofontoptions)$$monofontoptions$$sep$,$endfor$$endif$]{$monofont$} -$endif$ -$if(mathfont)$ - \setmathfont(Digits,Latin,Greek)[$for(mathfontoptions)$$mathfontoptions$$sep$,$endfor$]{$mathfont$} -$endif$ -$if(CJKmainfont)$ - \usepackage{xeCJK} - \setCJKmainfont[$for(CJKoptions)$$CJKoptions$$sep$,$endfor$]{$CJKmainfont$} -$endif$ -\fi -% use upquote if available, for straight quotes in verbatim environments -\IfFileExists{upquote.sty}{\usepackage{upquote}}{} -% use microtype if available -\IfFileExists{microtype.sty}{% -\usepackage{microtype} -\UseMicrotypeSet[protrusion]{basicmath} % disable protrusion for tt fonts -}{} -$if(geometry)$ -\usepackage[$for(geometry)$$geometry$$sep$,$endfor$]{geometry} -$endif$ - - -$if(lang)$ -\ifnum 0\ifxetex 1\fi\ifluatex 1\fi=0 % if pdftex - \usepackage[shorthands=off,$for(babel-otherlangs)$$babel-otherlangs$,$endfor$main=$babel-lang$]{babel} -$if(babel-newcommands)$ - $babel-newcommands$ -$endif$ -\else - \usepackage{polyglossia} - \setmainlanguage[$polyglossia-lang.options$]{$polyglossia-lang.name$} -$for(polyglossia-otherlangs)$ - \setotherlanguage[$polyglossia-otherlangs.options$]{$polyglossia-otherlangs.name$} -$endfor$ -\fi -$endif$ diff --git a/_extensions/mps9506/quarto-cv/quarto-cv.tex b/_extensions/mps9506/quarto-cv/quarto-cv.tex deleted file mode 100644 index 9827158..0000000 --- a/_extensions/mps9506/quarto-cv/quarto-cv.tex +++ /dev/null @@ -1,212 +0,0 @@ -% CV ---- - -$doc-class.tex()$ - -$biblio.tex()$ - -$if(listings)$ -\usepackage{listings} -$endif$ -$if(lhs)$ -\lstnewenvironment{code}{\lstset{language=Haskell,basicstyle=\small\ttfamily}}{} -$endif$ -$if(highlighting-macros)$ -$highlighting-macros$ -$endif$ -$if(verbatim-in-note)$ -\usepackage{fancyvrb} -\VerbatimFootnotes % allows verbatim text in footnotes -$endif$ -$if(tables)$ -\usepackage{longtable,booktabs} -$endif$ -$if(graphics)$ -\usepackage{graphicx,grffile} -\makeatletter -\def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth\else\Gin@nat@width\fi} -\def\maxheight{\ifdim\Gin@nat@height>\textheight\textheight\else\Gin@nat@height\fi} -\makeatother -% Scale images if necessary, so that they will not overflow the page -% margins by default, and it is still possible to overwrite the defaults -% using explicit options in \includegraphics[width, height, ...]{} -\setkeys{Gin}{width=\maxwidth,height=\maxheight,keepaspectratio} -$endif$ -$if(links-as-notes)$ -% Make links footnotes instead of hotlinks: -\renewcommand{\href}[2]{#2\footnote{\url{#1}}} -$endif$ -$if(strikeout)$ -\usepackage[normalem]{ulem} -% avoid problems with \sout in headers with hyperref: -\pdfstringdefDisableCommands{\renewcommand{\sout}{}} -$endif$ -$if(indent)$ -$else$ - - -$endif$ -\setlength{\emergencystretch}{3em} % prevent overfull lines -\providecommand{\tightlist}{% - \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}} -$if(numbersections)$ -\setcounter{secnumdepth}{5} -$else$ -\setcounter{secnumdepth}{0} -$endif$ -$if(subparagraph)$ -$else$ -% Redefines (sub)paragraphs to behave more like sections -\ifx\paragraph\undefined\else -\let\oldparagraph\paragraph -\renewcommand{\paragraph}[1]{\oldparagraph{#1}\mbox{}} -\fi -\ifx\subparagraph\undefined\else -\let\oldsubparagraph\subparagraph -\renewcommand{\subparagraph}[1]{\oldsubparagraph{#1}\mbox{}} -\fi -$endif$ -$if(dir)$ -\ifxetex - % load bidi as late as possible as it modifies e.g. graphicx - $if(latex-dir-rtl)$ - \usepackage[RTLdocument]{bidi} - $else$ - \usepackage{bidi} - $endif$ -\fi -\ifnum 0\ifxetex 1\fi\ifluatex 1\fi=0 % if pdftex - \TeXXeTstate=1 - \newcommand{\RL}[1]{\beginR #1\endR} - \newcommand{\LR}[1]{\beginL #1\endL} - \newenvironment{RTL}{\beginR}{\endR} - \newenvironment{LTR}{\beginL}{\endL} -\fi -$endif$ -$for(header-includes)$ -$header-includes$ -$endfor$ - -% Now begins the stuff that I added. -% ---------------------------------- - -% Custom section fonts -\usepackage{sectsty} -\sectionfont{\rmfamily\mdseries\large\bf} -\subsectionfont{\rmfamily\mdseries\normalsize\scshape} - - -% Make lists without bullets -\renewenvironment{itemize}{ - \begin{list}{}{ - \setlength{\leftmargin}{1.5em} - } -}{ - \end{list} -} - - -% Make parskips rather than indent with lists. -\usepackage{parskip} -% \usepackage{titlesec} -% \titlespacing\section{0pt}{12pt plus 4pt minus 2pt}{12pt plus 2pt minus 2pt} -% \titlespacing\subsection{0pt}{12pt plus 4pt minus 2pt}{12pt plus 2pt minus 2pt} - -% Use fontawesome. Note: you'll need TeXLive 2015. Update. -$if(fontawesome)$\usepackage{fontawesome}$endif$ - -% Fancyhdr, as I tend to do with these personal documents. -\usepackage{fancyhdr,lastpage} -\pagestyle{fancy} -\renewcommand{\headrulewidth}{0.0pt} -\renewcommand{\footrulewidth}{0.0pt} -\lhead{} -\chead{} -\rhead{} -\lfoot{} -\cfoot{\scriptsize $author-meta$ - $title$$if(updated)$$else$ - $if(rdateformat)$$rdateformat$$else$\apstylekinda\today$endif$$endif$} -\rfoot{\scriptsize \thepage/{\hypersetup{linkcolor=black}\pageref{LastPage}}} - -% Always load hyperref last. -\usepackage{hyperref} -$if(colorlinks)$ -\PassOptionsToPackage{usenames,dvipsnames}{color} % color is loaded by hyperref -$endif$ - -\hypersetup{unicode=true, -$if(title-meta)$ - pdftitle={$if(author-meta)$$author-meta$: $endif$ $title-meta$ (Curriculum Vitae)}, -$endif$ -$if(author-meta)$ - pdfauthor={$author-meta$}, -$endif$ -$if(keywords)$ - pdfkeywords={$for(keywords)$$keywords$$sep$; $endfor$}, -$endif$ - colorlinks=true, - linkcolor=$if(linkcolor)$$linkcolor$$else$Maroon$endif$, - citecolor=$if(citecolor)$$citecolor$$else$blue$endif$, - urlcolor=$if(urlcolor)$$urlcolor$$else$blue$endif$, - breaklinks=true, bookmarks=true} -\urlstyle{same} % don't use monospace font for urls - -% Make AP style (kinda) dates for the updated/today field - -\usepackage{datetime} -\newdateformat{apstylekinda}{% - \shortmonthname[\THEMONTH]. \THEDAY, \THEYEAR} - -% \emph{Updated:} \apstylekinda\today -% ^ removed from that front bar. - -\usepackage{orcidlink} -%\usepackage{academicons} -%\definecolor{orcidlogocol}{HTML}{A6CE39} -% \newcommand{\orcid}[1]{\href{https://orcid.org/#1}{\textcolor[HTML]{A6CE39}{\aiOrcid}}} - -\begin{document} - - -\centerline{\huge \bf $author$} - -\vspace{2 mm} - -\hrule - -\vspace{2 mm} - -$if(jobtitle)$\moveleft.5\hoffset\centerline{$jobtitle$}$endif$ -$if(address)$\moveleft.5\hoffset\centerline{$address$}$endif$ -\moveleft.5\hoffset\centerline{ $if(email)$$if(fontawesome)$\faEnvelopeO \hspace{1 mm}$else$\emph{E-mail:}$endif$ \href{mailto:}{\tt $email$} \hspace{1 mm}$endif$ $if(phone)$$if(fontawesome)$ \faPhone \hspace{1 mm}$else$\emph{Phone:}$endif$ $phone$ \hspace{1 mm} $endif$ $if(github)$$if(fontawesome)$\faGithub \hspace{1 mm}$else$\emph{Github:}$endif$ \href{http://github.com/$github$}{\tt $github$} \hspace{1 mm} $endif$ $if(twitter)$$if(fontawesome)$\faTwitter \hspace{1 mm}$else$\emph{Twitter:}$endif$ \href{https:/twitter.com/$twitter$}{\tt $twitter$} \hspace{1 mm} $endif$ $if(osf)$$if(fontawesome)$\faUnlock \hspace{1 mm}$else$\emph{osf:}$endif$ \href{https:/osf.io/$osf$}{\tt osf.io/$osf$} \hspace{1 mm} $endif$ $if(orcid)$$if(fontawesome)$\orcidlink{$orcid$} \hspace{.5 mm}$else$\emph{ORCID:}$endif$ \href{https://orcid.org/$orcid$}{\tt $orcid$} \hspace{1 mm} $endif$ $if(web)$$if(fontawesome)$\faGlobe \hspace{1 mm}$else$\emph{Web:}$endif$ \href{http://$web$}{\tt $web$} $endif$ $if(updated)$ | \emph{Updated:} $if(rdateformat)$$rdateformat$$else$\apstylekinda\today$endif$$endif$} - - - -\vspace{2 mm} - -\hrule - - - -$body$ - -$if(natbib)$ -$if(bibliography)$ -$if(biblio-title)$ -$if(book-class)$ -\renewcommand\bibname{$biblio-title$} -$else$ -\renewcommand\refname{$biblio-title$} -$endif$ -$endif$ -\bibliography{$for(bibliography)$$bibliography$$sep$,$endfor$} - -$endif$ -$endif$ -$if(biblatex)$ -\printbibliography$if(biblio-title)$[title=$biblio-title$]$endif$ - -$endif$ -$for(include-after)$ -$include-after$ - -$endfor$ -\end{document} diff --git a/_quarto.yml b/_quarto.yml index 0737334..e002c91 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -3,22 +3,20 @@ project: output-dir: docs website: - title: "Luís Assunção" site-url: https://assuncaolfi.github.io/site + navbar: true repo-url: https://github.com/assuncaolfi/site - description: "My personal website" + # description: "My personal website" # favicon: assets/favicon.png - navbar: - background: light - foreground: dark search: false + title: "Luís Assunção" bibliography: references.bib execute: cache: true echo: false - freeze: auto + freeze: true format: html: @@ -27,8 +25,8 @@ format: code-copy: hover code-tools: source: repo - # TODO move includes to scss highlight-style: assets/custom.theme.json + html-table-processing: none reference-location: margin theme: - sandstone diff --git a/assets/custom.scss b/assets/custom.scss index 70dbd19..0ed6210 100644 --- a/assets/custom.scss +++ b/assets/custom.scss @@ -32,20 +32,25 @@ $danger: $red !default; $light: $gray-200 !default; $dark: $gray-800 !default; -$body-bg: $white !default; -$body-color: $dark !default; - +$btn-code-copy-color: $primary; +$btn-code-copy-color-active: $primary; +$code-bg: $light; +$code-color: $dark; $link-color: $primary; $link-hover-color: $primary; - -/*-- scss:rules --*/ +$navbar-bg: $light; +$navbar-fg: $dark; @import "https://fonts.googleapis.com/css2?family=Fira+Code&family=Fira+Sans&display=swap"; -body { - font-family: "Fira Sans"; -} +$font-family-sans-serif: "Fira Sans"; +$font-family-monospace: "Fira Code"; + +// $body-bg: $white !default; +// $body-color: $dark !default; + +/*-- scss:rules --*/ -code, h1, h2, h3, h4, h5, h6, header, navbar, #TOC, table, td, th, tr { +h1, h2, h3, h4, h5, h6, header, navbar, #TOC, table, td, th, tr { font-family: "Fira Code"; } diff --git a/assets/custom.theme b/assets/custom.theme deleted file mode 100644 index 6ea4143..0000000 --- a/assets/custom.theme +++ /dev/null @@ -1,214 +0,0 @@ -{ - "text-color": null, - "background-color": "#f1f3f5", - "line-number-color": "#aaaaaa", - "line-number-background-color": null, - "text-styles": { - "Normal": { - "text-color": "#3e3f3a" # ok - }, - "Other": { - "text-color": "#3e3f3a", # ok - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "Attribute": { - "text-color": "#657422", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "SpecialString": { - "text-color": "#20794D", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "Annotation": { - "text-color": "#5E5E5E", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "Function": { - "text-color": "#4758AB", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "String": { - "text-color": "#20794D", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "ControlFlow": { - "text-color": "#3e3f3a", # ok - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "Operator": { - "text-color": "#5E5E5E", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "Error": { - "text-color": "#AD0000", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "BaseN": { - "text-color": "#AD0000", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "Alert": { - "text-color": "#AD0000", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "Variable": { - "text-color": "#111111", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "BuiltIn": { - "text-color": null, - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "Extension": { - "text-color": null, - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "Preprocessor": { - "text-color": "#AD0000", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "Information": { - "text-color": "#5E5E5E", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "VerbatimString": { - "text-color": "#20794D", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "Warning": { - "text-color": "#5E5E5E", - "background-color": null, - "bold": false, - "italic": true, - "underline": false - }, - "Documentation": { - "text-color": "#5E5E5E", - "background-color": null, - "bold": false, - "italic": true, - "underline": false - }, - "Import": { - "text-color": "#00769E", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "Char": { - "text-color": "#20794D", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "DataType": { - "text-color": "#AD0000", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "Float": { - "text-color": "#AD0000", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "Comment": { - "text-color": "#5E5E5E", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "CommentVar": { - "text-color": "#5E5E5E", - "background-color": null, - "bold": false, - "italic": true, - "underline": false - }, - "Constant": { - "text-color": "#8f5902", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "SpecialChar": { - "text-color": "#5E5E5E", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "DecVal": { - "text-color": "#AD0000", - "background-color": null, - "bold": false, - "italic": false, - "underline": false - }, - "Keyword": { - "text-color": "#3e3f3a", # ok - "background-color": null, - "bold": false, - "italic": false, - "underline": false - } - } -} \ No newline at end of file diff --git a/blog/non-monotonic/.gitignore b/blog/aging-curve/.gitignore similarity index 100% rename from blog/non-monotonic/.gitignore rename to blog/aging-curve/.gitignore diff --git a/blog/non-monotonic/.python-version b/blog/aging-curve/.python-version similarity index 100% rename from blog/non-monotonic/.python-version rename to blog/aging-curve/.python-version diff --git a/blog/non-monotonic/data/experiment-2.csv b/blog/aging-curve/data/experiment-2.csv similarity index 100% rename from blog/non-monotonic/data/experiment-2.csv rename to blog/aging-curve/data/experiment-2.csv diff --git a/blog/non-monotonic/data/mind-in-eyes.csv b/blog/aging-curve/data/mind-in-eyes.csv similarity index 100% rename from blog/non-monotonic/data/mind-in-eyes.csv rename to blog/aging-curve/data/mind-in-eyes.csv diff --git a/blog/non-monotonic/index.ipynb b/blog/aging-curve/index.ipynb similarity index 100% rename from blog/non-monotonic/index.ipynb rename to blog/aging-curve/index.ipynb diff --git a/blog/non-monotonic/index_files/execute-results/html.json b/blog/aging-curve/index_files/execute-results/html.json similarity index 100% rename from blog/non-monotonic/index_files/execute-results/html.json rename to blog/aging-curve/index_files/execute-results/html.json diff --git a/blog/non-monotonic/index_files/libs/bootstrap/bootstrap-icons.css b/blog/aging-curve/index_files/libs/bootstrap/bootstrap-icons.css similarity index 100% rename from blog/non-monotonic/index_files/libs/bootstrap/bootstrap-icons.css rename to blog/aging-curve/index_files/libs/bootstrap/bootstrap-icons.css diff --git a/blog/non-monotonic/index_files/libs/bootstrap/bootstrap-icons.woff b/blog/aging-curve/index_files/libs/bootstrap/bootstrap-icons.woff similarity index 100% rename from blog/non-monotonic/index_files/libs/bootstrap/bootstrap-icons.woff rename to blog/aging-curve/index_files/libs/bootstrap/bootstrap-icons.woff diff --git a/blog/non-monotonic/index_files/libs/bootstrap/bootstrap.min.css b/blog/aging-curve/index_files/libs/bootstrap/bootstrap.min.css similarity index 100% rename from blog/non-monotonic/index_files/libs/bootstrap/bootstrap.min.css rename to blog/aging-curve/index_files/libs/bootstrap/bootstrap.min.css diff --git a/blog/non-monotonic/index_files/libs/bootstrap/bootstrap.min.js b/blog/aging-curve/index_files/libs/bootstrap/bootstrap.min.js similarity index 100% rename from blog/non-monotonic/index_files/libs/bootstrap/bootstrap.min.js rename to blog/aging-curve/index_files/libs/bootstrap/bootstrap.min.js diff --git a/blog/non-monotonic/index_files/libs/clipboard/clipboard.min.js b/blog/aging-curve/index_files/libs/clipboard/clipboard.min.js similarity index 100% rename from blog/non-monotonic/index_files/libs/clipboard/clipboard.min.js rename to blog/aging-curve/index_files/libs/clipboard/clipboard.min.js diff --git a/blog/non-monotonic/index_files/libs/quarto-html/anchor.min.js b/blog/aging-curve/index_files/libs/quarto-html/anchor.min.js similarity index 100% rename from blog/non-monotonic/index_files/libs/quarto-html/anchor.min.js rename to blog/aging-curve/index_files/libs/quarto-html/anchor.min.js diff --git a/blog/non-monotonic/index_files/libs/quarto-html/popper.min.js b/blog/aging-curve/index_files/libs/quarto-html/popper.min.js similarity index 100% rename from blog/non-monotonic/index_files/libs/quarto-html/popper.min.js rename to blog/aging-curve/index_files/libs/quarto-html/popper.min.js diff --git a/blog/non-monotonic/index_files/libs/quarto-html/quarto-syntax-highlighting.css b/blog/aging-curve/index_files/libs/quarto-html/quarto-syntax-highlighting.css similarity index 100% rename from blog/non-monotonic/index_files/libs/quarto-html/quarto-syntax-highlighting.css rename to blog/aging-curve/index_files/libs/quarto-html/quarto-syntax-highlighting.css diff --git a/blog/non-monotonic/index_files/libs/quarto-html/quarto.js b/blog/aging-curve/index_files/libs/quarto-html/quarto.js similarity index 100% rename from blog/non-monotonic/index_files/libs/quarto-html/quarto.js rename to blog/aging-curve/index_files/libs/quarto-html/quarto.js diff --git a/blog/non-monotonic/index_files/libs/quarto-html/tippy.css b/blog/aging-curve/index_files/libs/quarto-html/tippy.css similarity index 100% rename from blog/non-monotonic/index_files/libs/quarto-html/tippy.css rename to blog/aging-curve/index_files/libs/quarto-html/tippy.css diff --git a/blog/non-monotonic/index_files/libs/quarto-html/tippy.umd.min.js b/blog/aging-curve/index_files/libs/quarto-html/tippy.umd.min.js similarity index 100% rename from blog/non-monotonic/index_files/libs/quarto-html/tippy.umd.min.js rename to blog/aging-curve/index_files/libs/quarto-html/tippy.umd.min.js diff --git a/blog/non-monotonic/pyproject.toml b/blog/aging-curve/pyproject.toml similarity index 79% rename from blog/non-monotonic/pyproject.toml rename to blog/aging-curve/pyproject.toml index 02bf2e9..b8404fb 100644 --- a/blog/non-monotonic/pyproject.toml +++ b/blog/aging-curve/pyproject.toml @@ -1,19 +1,20 @@ [project] -name = "non-monotonic" +name = "aging-curve" version = "0.1.0" authors = [ { name = "Luís Assunção", email = "assuncaolfi@gmail.com" } ] dependencies = [ + "blog @ git+https://github.com/assuncaolfi/site/", + "jupyter-cache~=0.6.1", + "jupyter~=1.0.0", + "numpy~=1.24.4", + "patsy~=0.5.6", "polars~=0.19.17", - "rich~=13.7.0", - "seaborn~=0.13.0", "pyarrow~=14.0.1", "pymc~=5.6.1", - "numpy~=1.24.4", - "blog @ git+https://github.com/assuncaolfi/site/", - "patsy~=0.5.6", - "graphviz~=0.20.1", + "rich~=13.7.0", + "seaborn~=0.13.0", ] requires-python = ">= 3.8" @@ -23,15 +24,9 @@ build-backend = "hatchling.build" [tool.rye] managed = true -dev-dependencies = [ - "jupyter~=1.0.0", - "jupyter-cache~=0.6.1", - "black~=23.12.1", - "jupytext~=1.16.0", -] [tool.hatch.metadata] allow-direct-references = true -[tool.black] -line-length = 79 +[tool.hatch.build.targets.wheel] +bypass-selection = true diff --git a/blog/non-monotonic/requirements-dev.lock b/blog/aging-curve/requirements-dev.lock similarity index 95% rename from blog/non-monotonic/requirements-dev.lock rename to blog/aging-curve/requirements-dev.lock index 6351013..62750df 100644 --- a/blog/non-monotonic/requirements-dev.lock +++ b/blog/aging-curve/requirements-dev.lock @@ -5,6 +5,7 @@ # pre: false # features: [] # all-features: false +# with-sources: false -e file:. anyio==4.1.0 @@ -18,7 +19,6 @@ async-lru==2.0.4 attrs==23.1.0 babel==2.13.1 beautifulsoup4==4.12.2 -black==23.12.1 bleach==6.1.0 blog @ git+https://github.com/assuncaolfi/site/ cachetools==5.3.2 @@ -41,7 +41,6 @@ fastprogress==1.0.3 filelock==3.13.1 fonttools==4.45.1 fqdn==1.5.1 -graphviz==0.20.1 h5netcdf==1.3.0 h5py==3.10.0 idna==3.6 @@ -69,19 +68,16 @@ jupyterlab==4.0.9 jupyterlab-pygments==0.3.0 jupyterlab-server==2.25.2 jupyterlab-widgets==3.0.9 -jupytext==1.16.0 kiwisolver==1.4.5 logical-unification==0.4.6 markdown-it-py==3.0.0 markupsafe==2.1.3 matplotlib==3.8.2 matplotlib-inline==0.1.6 -mdit-py-plugins==0.4.0 mdurl==0.1.2 minikanren==1.0.3 mistune==3.0.2 multipledispatch==1.0.0 -mypy-extensions==1.0.0 nbclient==0.7.4 nbconvert==7.11.0 nbformat==5.9.2 @@ -94,7 +90,6 @@ packaging==23.2 pandas==2.1.3 pandocfilters==1.5.0 parso==0.8.3 -pathspec==0.12.1 patsy==0.5.6 pexpect==4.9.0 pillow==10.1.0 @@ -135,7 +130,6 @@ stack-data==0.6.3 tabulate==0.9.0 terminado==0.18.0 tinycss2==1.2.1 -toml==0.10.2 toolz==0.12.0 tornado==6.3.3 traitlets==5.14.0 diff --git a/blog/aging-curve/requirements.lock b/blog/aging-curve/requirements.lock new file mode 100644 index 0000000..d9eec18 --- /dev/null +++ b/blog/aging-curve/requirements.lock @@ -0,0 +1,150 @@ +# generated by rye +# use `rye lock` or `rye sync` to update this lockfile +# +# last locked with the following flags: +# pre: false +# features: [] +# all-features: false +# with-sources: false + +-e file:. +anyio==4.2.0 +appnope==0.1.3 +argon2-cffi==23.1.0 +argon2-cffi-bindings==21.2.0 +arrow==1.3.0 +arviz==0.16.1 +asttokens==2.4.1 +async-lru==2.0.4 +attrs==23.2.0 +babel==2.14.0 +beautifulsoup4==4.12.3 +bleach==6.1.0 +blog @ git+https://github.com/assuncaolfi/site/ +cachetools==5.3.2 +certifi==2023.11.17 +cffi==1.16.0 +charset-normalizer==3.3.2 +click==8.1.7 +cloudpickle==3.0.0 +comm==0.2.1 +cons==0.4.6 +contourpy==1.2.0 +cycler==0.12.1 +debugpy==1.8.0 +decorator==5.1.1 +defusedxml==0.7.1 +etuples==0.3.9 +executing==2.0.1 +fastjsonschema==2.19.1 +fastprogress==1.0.3 +filelock==3.13.1 +fonttools==4.45.1 +fqdn==1.5.1 +h5netcdf==1.3.0 +h5py==3.10.0 +idna==3.6 +importlib-metadata==7.0.1 +ipykernel==6.29.0 +ipython==8.20.0 +ipywidgets==8.1.1 +isoduration==20.11.0 +jedi==0.19.1 +jinja2==3.1.3 +json5==0.9.14 +jsonpointer==2.4 +jsonschema==4.21.1 +jsonschema-specifications==2023.12.1 +jupyter==1.0.0 +jupyter-cache==0.6.1 +jupyter-client==8.6.0 +jupyter-console==6.6.3 +jupyter-core==5.7.1 +jupyter-events==0.9.0 +jupyter-lsp==2.2.2 +jupyter-server==2.12.5 +jupyter-server-terminals==0.5.2 +jupyterlab==4.0.11 +jupyterlab-pygments==0.3.0 +jupyterlab-server==2.25.2 +jupyterlab-widgets==3.0.9 +kiwisolver==1.4.5 +logical-unification==0.4.6 +markdown-it-py==3.0.0 +markupsafe==2.1.4 +matplotlib==3.8.2 +matplotlib-inline==0.1.6 +mdurl==0.1.2 +minikanren==1.0.3 +mistune==3.0.2 +multipledispatch==1.0.0 +nbclient==0.7.4 +nbconvert==7.14.2 +nbformat==5.9.2 +nest-asyncio==1.6.0 +notebook==7.0.7 +notebook-shim==0.2.3 +numpy==1.24.4 +overrides==7.6.0 +packaging==23.2 +pandas==2.1.3 +pandocfilters==1.5.1 +parso==0.8.3 +patsy==0.5.6 +pexpect==4.9.0 +pillow==10.1.0 +platformdirs==4.1.0 +polars==0.19.17 +prometheus-client==0.19.0 +prompt-toolkit==3.0.43 +psutil==5.9.8 +ptyprocess==0.7.0 +pure-eval==0.2.2 +pyarrow==14.0.1 +pycparser==2.21 +pygments==2.17.2 +pymc==5.6.1 +pyparsing==3.1.1 +pytensor==2.12.3 +python-dateutil==2.8.2 +python-json-logger==2.0.7 +pytz==2023.3.post1 +pyyaml==6.0.1 +pyzmq==25.1.2 +qtconsole==5.5.1 +qtpy==2.4.1 +referencing==0.32.1 +requests==2.31.0 +rfc3339-validator==0.1.4 +rfc3986-validator==0.1.1 +rich==13.7.0 +rpds-py==0.17.1 +scipy==1.11.4 +seaborn==0.13.0 +send2trash==1.8.2 +six==1.16.0 +sniffio==1.3.0 +soupsieve==2.5 +sqlalchemy==2.0.25 +stack-data==0.6.3 +tabulate==0.9.0 +terminado==0.18.0 +tinycss2==1.2.1 +toolz==0.12.0 +tornado==6.4 +traitlets==5.14.1 +types-python-dateutil==2.8.19.20240106 +typing-extensions==4.8.0 +tzdata==2023.3 +uri-template==1.3.0 +urllib3==2.1.0 +wcwidth==0.2.13 +webcolors==1.13 +webencodings==0.5.1 +websocket-client==1.7.0 +widgetsnbextension==4.0.9 +xarray==2023.11.0 +xarray-einstats==0.6.0 +zipp==3.17.0 +# The following packages are considered to be unsafe in a requirements file: +setuptools==69.0.2 diff --git a/blog/fantasy-football/index.ipynb b/blog/fantasy-football/index.ipynb new file mode 100644 index 0000000..63590a8 --- /dev/null +++ b/blog/fantasy-football/index.ipynb @@ -0,0 +1,1788 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "fe604617-26e8-44c2-a15e-a7335540be73", + "metadata": {}, + "source": [ + "---\n", + "title: Drafting a fantasy football team\n", + "date: 2023-09-21\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "4f20452f", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "[Cartola](http://cartola.globo.com) is a fantasy football league following the\n", + "Brazilian Championship A Series.\n", + "\n", + "Cartola offers a public API to access data for the current round. A couple\n", + "of years ago, I created a script to automate data retrieval to a\n", + "[repository](https://github.com/assuncaolfi/tophat/tree/main), which now hosts\n", + "comprehensive historical data since 2022.\n", + "\n", + "In this post, I will delve into the data for the 2022 season, formulate a mixed\n", + "integer linear program to draft the optimal team, and present initial concepts\n", + "for forecasting player scores using mixed effects linear models.\n", + "\n", + "## The game\n", + "\n", + "We begin the season with a budget of C$ 100, the game’s paper currency.\n", + "\n", + "Each round is preceded by a market session, where players are assigned a value.\n", + "We are tasked with forming a team of 11 players plus a coach, all within our\n", + "budget and adhering to a valid formation. A captain must be chosen from among\n", + "the players, excluding the coach.\n", + "\n", + "The market is available until the round starts. Players then earn scores based\n", + "on their real-life match performances. Our team's score is the aggregate of\n", + "our players' scores, with our captain’s score doubled in the 2022 season.\n", + "\n", + "Following the conclusion of the round, player values are recalibrated based\n", + "on performance -— with increases for scores above their average and decreases for\n", + "below-average performances. Our budget for the next round is our previous\n", + "budget, plus the sum of our players' value variations.\n", + "\n", + "## Data wrangling\n", + "\n", + "Let's talk about data structures: each round has a market, and each market is a\n", + "list of players. A player is a structure like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c057edb8", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Player(round=0, player=42234, team=264, position=1, games=0, average=0.0, value=10.0, score=0.0, appreciation=0.0, minimum=4.53)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# | label: data-wrangling-players\n", + "from pydantic import BaseModel, Field, field_validator\n", + "from typing import Dict, List\n", + "from urllib import request\n", + "\n", + "\n", + "class Player(BaseModel):\n", + " round: int = Field(alias=\"rodada_id\")\n", + " player: int = Field(alias=\"atleta_id\")\n", + " team: int = Field(alias=\"clube_id\")\n", + " position: int = Field(alias=\"posicao_id\")\n", + " games: int = Field(alias=\"jogos_num\")\n", + " average: float = Field(alias=\"media_num\")\n", + " value: float = Field(alias=\"preco_num\")\n", + " score: float = Field(alias=\"pontos_num\")\n", + " appreciation: float = Field(alias=\"variacao_num\")\n", + " minimum: float | Dict | None = Field(alias=\"minimo_para_valorizar\")\n", + "\n", + " @field_validator(\"minimum\")\n", + " @classmethod\n", + " def dict_is_zero(cls, v: float | Dict | None):\n", + " if v == {} or v is None:\n", + " v = 0.0\n", + " return v\n", + "\n", + "\n", + "class Market(BaseModel):\n", + " players: List[Player] = Field(alias=\"atletas\")\n", + "\n", + "\n", + "base_url = \"https://raw.githubusercontent.com/assuncaolfi/tophat/main/2022/\"\n", + "markets = []\n", + "for round in range(1, 39):\n", + " url = base_url + f\"{round:02}/atletas/mercado.json\"\n", + " data = request.urlopen(url).read()\n", + " market = Market.model_validate_json(data)\n", + " if round == 1:\n", + " for player in market.players:\n", + " player.round = 0\n", + " markets.extend(market.players)\n", + "\n", + "markets[0]" + ] + }, + { + "cell_type": "markdown", + "id": "e23a272d", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "Let's get the list of markets for 2022 and flatten it into a single DataFrame:" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "047e15d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-------------------------------------------------------------------------------+\n", + "| round player team position … value score appreciation minimum |\n", + "+===============================================================================+\n", + "| 1 37424 1371 6 … 3.0 0.0 0.0 0.0 |\n", + "| 1 37646 314 3 … 5.0 0.0 0.0 2.3 |\n", + "| 1 37656 266 1 … 9.0 0.0 0.0 4.08 |\n", + "| … … … … … … … … … |\n", + "| 38 121398 354 4 … 1.0 0.0 0.0 0.0 |\n", + "| 38 121399 354 4 … 1.0 0.0 0.0 0.0 |\n", + "| 38 121400 354 5 … 1.0 0.0 0.0 0.0 |\n", + "+-------------------------------------------------------------------------------+\n", + "shape: (30_063, 10)\n" + ] + } + ], + "source": [ + "# | label: data-wrangling-dataframe\n", + "import polars as pl\n", + "\n", + "players = (\n", + " pl.DataFrame(markets)\n", + " .with_columns(round=pl.col(\"round\") + 1)\n", + " .sort(\"round\", \"player\")\n", + ")\n", + "pl.Config(\n", + " tbl_dataframe_shape_below=True,\n", + " tbl_formatting=\"ASCII_BORDERS_ONLY_CONDENSED\",\n", + " tbl_hide_column_data_types=True,\n", + " tbl_rows=6,\n", + ")\n", + "print(players)" + ] + }, + { + "cell_type": "markdown", + "id": "dd0ac934", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "Now, let's focus on a specific `player` to illustrate our data while we wrangle\n", + "it:" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "22ab906b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-------------------------------------------------------------------------------+\n", + "| round player team position … value score appreciation minimum |\n", + "+===============================================================================+\n", + "| 1 42234 264 1 … 10.0 0.0 0.0 4.53 |\n", + "| 2 42234 264 1 … 7.93 2.0 -2.07 5.52 |\n", + "| 3 42234 264 1 … 10.44 11.0 2.51 4.75 |\n", + "| … … … … … … … … … |\n", + "| 36 42234 264 1 … 11.51 0.0 0.03 3.63 |\n", + "| 37 42234 264 1 … 12.68 0.0 1.17 9.29 |\n", + "| 38 42234 264 1 … 11.06 0.0 -1.62 1.37 |\n", + "+-------------------------------------------------------------------------------+\n", + "shape: (38, 10)\n" + ] + } + ], + "source": [ + "# | label: data-wrangling-example\n", + "def filter_example(markets: pl.DataFrame, columns: List[str]):\n", + " example = players.filter(pl.col(\"player\") == 42234).select(\n", + " [\"round\", \"player\"] + columns\n", + " )\n", + " print(example)\n", + "\n", + "\n", + "filter_example(players, players.columns[2:])" + ] + }, + { + "cell_type": "markdown", + "id": "73ffae88", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "### Filtering participation\n", + "\n", + "Players will show up in the market for many rounds that they do not participate\n", + "in. However, for our analysis, we are only interested in players that actually\n", + "played a game in the round.\n", + "\n", + "Each player has a `status` field intended to indicate their participation in the\n", + "round. However, this field is often inaccurate, likely due to the API data being\n", + "updated before the round.\n", + "\n", + "One solution is to keep only rows where there is an increase in the number of\n", + "`games` the player has played:" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "cb9c234d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------------------+\n", + "| round player games |\n", + "+========================+\n", + "| 1 42234 0 |\n", + "| 2 42234 1 |\n", + "| 3 42234 2 |\n", + "| … … … |\n", + "| 36 42234 28 |\n", + "| 37 42234 29 |\n", + "| 38 42234 30 |\n", + "+------------------------+\n", + "shape: (31, 3)\n" + ] + } + ], + "source": [ + "# | label: data-wrangling-round-participation\n", + "players = players.filter(\n", + " pl.col(\"games\") != pl.col(\"games\").shift(1).over(\"player\").fill_null(-1)\n", + ")\n", + "filter_example(markets, [\"games\"])" + ] + }, + { + "cell_type": "markdown", + "id": "642b6687", + "metadata": { + "cell_marker": "r\"\"\"" + }, + "source": [ + "### Imputing scores\n", + "\n", + "Similarly, the player `score` field is often inaccurate, likely for the same\n", + "reasons as the `status` field. Fortunately, the `average` field is reliable,\n", + "allowing us to recover the `score`:\n", + "\n", + "$$\n", + "\\begin{align*}\n", + "\\mathrm{Average}(\\mathbf{s}_{1:t})\n", + "= \\frac{\\mathrm{Average}(\\mathbf{s}_{1:(t-1)}) + s_t}{2} \\\\\n", + "s_t\n", + "= 2\\mathrm{Average}(\\mathbf{s}_{1:t}) - \\mathrm{Average}(\\mathbf{s}_{1:(t-1)}),\n", + "\\end{align*}\n", + "$$\n", + "\n", + "where $\\mathbf{s}$ is the vector of scores for a given player across all rounds." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "389e6b06", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----------------------------------+\n", + "| round player score average |\n", + "+==================================+\n", + "| 1 42234 2.0 2.0 |\n", + "| 2 42234 11.0 6.5 |\n", + "| 3 42234 9.5 8.0 |\n", + "| … … … … |\n", + "| 36 42234 5.1 4.96 |\n", + "| 37 42234 4.62 4.79 |\n", + "| 38 42234 4.79 4.79 |\n", + "+----------------------------------+\n", + "shape: (31, 4)\n" + ] + } + ], + "source": [ + "# | label: data-wrangling-missing-scores\n", + "# TODO make this better\n", + "players = players.with_columns(\n", + " average=pl.col(\"average\").shift(-1).over(\"player\").fill_null(pl.col(\"average\"))\n", + ").with_columns(\n", + " score=2 * pl.col(\"average\")\n", + " - pl.col(\"average\").shift(1).over(\"player\").fill_null(pl.col(\"average\")),\n", + ")\n", + "filter_example(players, [\"score\", \"average\"])" + ] + }, + { + "cell_type": "markdown", + "id": "84a0559c", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "### Adding fixtures\n", + "\n", + "Let's fetch the list of fixtures to enrich our dataset. A fixture is an object\n", + "like:" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "f3f7a337", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Fixture(round=1, home=282, away=285)" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# | label: data-wrangling-fixtures\n", + "class Fixture(BaseModel):\n", + " round: int = Field(default=0)\n", + " home: int = Field(alias=\"clube_casa_id\")\n", + " away: int = Field(alias=\"clube_visitante_id\")\n", + "\n", + "\n", + "class Round(BaseModel):\n", + " round: int = Field(alias=\"rodada\")\n", + " fixtures: List[Fixture] = Field(alias=\"partidas\")\n", + "\n", + "\n", + "fixtures = []\n", + "for round in range(1, 39):\n", + " url = base_url + f\"{round:02}/partidas.json\"\n", + " data = request.urlopen(url).read()\n", + " round = Round.model_validate_json(data)\n", + " for fixture in round.fixtures:\n", + " fixture.round = round.round\n", + " fixtures.extend(round.fixtures)\n", + "fixtures[0]" + ] + }, + { + "cell_type": "markdown", + "id": "65e6bc44", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "Let's consolidate these fixtures into a single DataFrame and then pivot them\n", + "into a long format:" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "905f557b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------------------------+\n", + "| round team versus home |\n", + "+==============================+\n", + "| 1 282 285 1 |\n", + "| 1 266 277 1 |\n", + "| 1 276 293 1 |\n", + "| … … … … |\n", + "| 38 276 290 0 |\n", + "| 38 294 1371 0 |\n", + "| 38 263 293 0 |\n", + "+------------------------------+\n", + "shape: (760, 4)\n" + ] + } + ], + "source": [ + "# | label: data-wrangling-fixtures-long\n", + "fixtures = (\n", + " pl.DataFrame(fixtures)\n", + " .rename({\"home\": \"team\", \"away\": \"versus\"})\n", + " .with_columns(home=pl.lit(1))\n", + ")\n", + "mirrored = fixtures.rename({\"team\": \"versus\", \"versus\": \"team\"}).with_columns(\n", + " home=pl.lit(0)\n", + ")\n", + "fixtures = pl.concat([fixtures, mirrored], how=\"diagonal\")\n", + "print(fixtures)" + ] + }, + { + "cell_type": "markdown", + "id": "a30a39f2", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "Finally, let's join this data to our dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "fe762abe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------------------------------------+\n", + "| round player team versus home |\n", + "+=======================================+\n", + "| 1 42234 264 263 0 |\n", + "| 2 42234 264 314 1 |\n", + "| 3 42234 264 275 0 |\n", + "| … … … … … |\n", + "| 36 42234 264 354 1 |\n", + "| 37 42234 264 294 0 |\n", + "| 38 42234 264 282 1 |\n", + "+---------------------------------------+\n", + "shape: (31, 5)\n" + ] + } + ], + "source": [ + "# | label: data-wrangling-fixtures-join\n", + "players = players.join(fixtures, on=[\"round\", \"team\"], how=\"inner\")\n", + "filter_example(players, [\"team\", \"versus\", \"home\"])" + ] + }, + { + "cell_type": "markdown", + "id": "ee811e64", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "### Aligning variables\n", + "\n", + "In our subsequent analysis, the `average` field will exclude the `score` from\n", + "the given round. Additionally, the `appreciation` field will be calculated in\n", + "relation to the round's `score`." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "a9f96c40", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------------------------------------------------------+\n", + "| round player average value score appreciation |\n", + "+=========================================================+\n", + "| 1 42234 0.0 10.0 2.0 -2.07 |\n", + "| 2 42234 2.0 7.93 11.0 2.51 |\n", + "| 3 42234 6.5 10.44 9.5 1.25 |\n", + "| … … … … … … |\n", + "| 36 42234 4.82 11.51 5.1 1.17 |\n", + "| 37 42234 4.96 12.68 4.62 -1.62 |\n", + "| 38 42234 4.79 11.06 4.79 0.0 |\n", + "+---------------------------------------------------------+\n", + "shape: (31, 6)\n" + ] + } + ], + "source": [ + "# | label: data-wrangling-lookahead-variables\n", + "players = players.with_columns(\n", + " average=pl.col(\"average\").shift(1).over(\"player\").fill_null(0.0),\n", + " appreciation=pl.col(\"appreciation\").shift(-1).over(\"player\").fill_null(0.0),\n", + ")\n", + "filter_example(players, [\"average\", \"value\", \"score\", \"appreciation\"])" + ] + }, + { + "cell_type": "markdown", + "id": "10b9966f", + "metadata": { + "cell_marker": "r\"\"\"", + "lines_to_next_cell": 2 + }, + "source": [ + "## Team picking\n", + "\n", + "Now let's solve the problem of picking the best team a given market. Let $\n", + "\\mathcal{F}$ be the set of valid formations, then for each formation $f \\in\n", + "\\mathcal{F}$, solve:\n", + "\n", + "$$\n", + "\\begin{equation*} \\begin{array}{ll@{}ll}\n", + "\\text{maximize} & \\displaystyle \\hat{\\mathbf{s}}^T \\mathbf{x}, & \\mathbf{x} \\in \\{\\mathbf{0}, \\mathbf{1}\\} \\\\\n", + "\\text{subject to}\n", + "& \\displaystyle \\mathbf{v}^T \\mathbf{x} \\leq b \\\\\n", + "& \\displaystyle \\mathbf{P}^T \\mathbf{x} = f, \\\\\n", + "\\end{array} \\end{equation*}\n", + "$$\n", + "\n", + "where\n", + "\n", + "$\\mathbf{x}$ is a variable vector of player picks in the market;\n", + "$\\hat{\\mathbf{s}}$ is the vector of predicted player scores in the market;\n", + "$b$ is our available budget for that round;\n", + "$\\mathbf{P}$ is the matrix of dummy-encoded player formations in the market.\n", + "\n", + "Finally, take the solution with the highest objective." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1bb8505a", + "metadata": {}, + "outputs": [], + "source": [ + "# | echo: true\n", + "# | label: team-picking-problem\n", + "import numpy as np\n", + "import pulp\n", + "\n", + "\n", + "class Formation(BaseModel):\n", + " goalkeeper: int = Field(alias=\"gol\")\n", + " defender: int = Field(alias=\"zag\")\n", + " winger: int = Field(alias=\"lat\")\n", + " midfielder: int = Field(alias=\"mei\")\n", + " forward: int = Field(alias=\"ata\")\n", + " coach: int = Field(alias=\"tec\")\n", + "\n", + "\n", + "class Problem(BaseModel):\n", + " scores: List[float]\n", + " values: List[float]\n", + " budget: float\n", + " positions: List[List[int]]\n", + " formations: List[Formation]\n", + "\n", + " def solve(self) -> List[pulp.LpSolution]:\n", + " formations = [list(f.model_dump().values()) for f in self.formations]\n", + " problems = [self.construct(f) for f in formations]\n", + " [p.solve(pulp.COIN(msg=False)) for p in problems]\n", + " objectives = [p.objective.value() for p in problems]\n", + " best = np.argmax(np.array(objectives))\n", + " solution = problems[best]\n", + " variables = [v.value() for v in solution.variables()]\n", + " picks = np.array(variables)\n", + " return picks\n", + "\n", + " def construct(self, formation: List[int]) -> pulp.LpProblem:\n", + " n = len(self.scores)\n", + " m = len(formation)\n", + " problem = pulp.LpProblem(\"team_picking\", pulp.LpMaximize)\n", + " indexes = [\"pick_\" + str(i).zfill(len(str(n))) for i in range(n)]\n", + " picks = [pulp.LpVariable(i, cat=pulp.const.LpBinary) for i in indexes]\n", + " problem += pulp.lpDot(picks, self.scores)\n", + " problem += pulp.lpDot(picks, self.values) <= self.budget\n", + " for i in range(m):\n", + " problem += pulp.lpDot(picks, self.positions[i]) == formation[i]\n", + " return problem" + ] + }, + { + "cell_type": "markdown", + "id": "41b79536", + "metadata": { + "cell_marker": "r\"\"\"" + }, + "source": [ + "### Backtesting\n", + "\n", + "By solving the team picking problem for all rounds, we can backtest our\n", + "performance in the season. Before backtesting, let's get the set of valid\n", + "formations $\\mathcal{F}$:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e156ac2d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Formation(goalkeeper=1, defender=3, winger=0, midfielder=4, forward=3, coach=1),\n", + " Formation(goalkeeper=1, defender=3, winger=0, midfielder=5, forward=2, coach=1),\n", + " Formation(goalkeeper=1, defender=2, winger=2, midfielder=3, forward=3, coach=1),\n", + " Formation(goalkeeper=1, defender=2, winger=2, midfielder=4, forward=2, coach=1),\n", + " Formation(goalkeeper=1, defender=2, winger=2, midfielder=5, forward=1, coach=1),\n", + " Formation(goalkeeper=1, defender=3, winger=2, midfielder=3, forward=2, coach=1),\n", + " Formation(goalkeeper=1, defender=3, winger=2, midfielder=4, forward=1, coach=1)]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# | label: team-picking-formations\n", + "from pydantic import RootModel\n", + "\n", + "\n", + "class MetaFormation(BaseModel):\n", + " id: int = Field(alias=\"esquema_id\")\n", + " name: str = Field(alias=\"nome\")\n", + " formation: Formation = Field(alias=\"posicoes\")\n", + "\n", + "\n", + "class MetaFormations(RootModel):\n", + " root: List[MetaFormation]\n", + "\n", + "\n", + "url = base_url + \"38/esquemas.json\"\n", + "data = request.urlopen(url).read()\n", + "meta_formations = MetaFormations.model_validate_json(data).root\n", + "formations = [m.formation for m in meta_formations]\n", + "formations" + ] + }, + { + "cell_type": "markdown", + "id": "397da432", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "Knowing our formation constraints, we're ready to backtest. Starting with a\n", + "budget of C$ 100, for each round let's:\n", + "\n", + "1. Predict each player's score based on their performance on previous rounds;\n", + "2. Pick the team with the best total score;\n", + "3. Add the sum of the team player's appreciation to our budget." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "097e2d05", + "metadata": {}, + "outputs": [], + "source": [ + "# | echo: true\n", + "# | label: team-picking-backtest\n", + "from typing import Callable\n", + "import polars as pl\n", + "\n", + "\n", + "def backtest(\n", + " players: pl.DataFrame, predict: Callable, initial_budget: float = 100.0\n", + ") -> pl.DataFrame:\n", + " rounds = players.get_column(\"round\").max()\n", + " budget = [None] * rounds\n", + " teams = [None] * rounds\n", + " budget[0] = initial_budget\n", + " appreciation = 0\n", + " for round in range(rounds):\n", + " if round > 0:\n", + " budget[round] = budget[round - 1] + appreciation\n", + " data = players.filter(pl.col(\"round\") < round + 1)\n", + " candidates = players.filter(pl.col(\"round\") == round + 1)\n", + " candidates = predict(data, candidates)\n", + " problem = Problem(\n", + " scores=candidates.get_column(\"prediction\"),\n", + " values=candidates.get_column(\"value\"),\n", + " positions=candidates.get_column(\"position\").to_dummies(),\n", + " budget=budget[round],\n", + " formations=formations,\n", + " )\n", + " picks = problem.solve()\n", + " team = candidates.filter(picks == 1)\n", + " teams[round] = team\n", + " appreciation = team.get_column(\"appreciation\").sum()\n", + " teams = pl.concat(teams)\n", + " return teams" + ] + }, + { + "cell_type": "markdown", + "id": "e7f98049", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "\n", + "Before exploring predictions, we'll begin with a few hypothetical backtests\n", + "using actual observed scores for team selection. Backtesting this strategy, this\n", + "is our team in the first round:" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "acf78b03", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----------------------------------------------------------------------------+\n", + "| round player team position … minimum versus home prediction |\n", + "+=============================================================================+\n", + "| 1 71571 356 1 … 3.19 1371 1 11.0 |\n", + "| 1 42145 294 2 … 2.75 290 1 15.8 |\n", + "| 1 105584 264 2 … 2.75 263 0 10.5 |\n", + "| … … … … … … … … … |\n", + "| 1 89840 276 5 … 5.42 293 1 27.1 |\n", + "| 1 104530 294 5 … 2.3 290 1 11.0 |\n", + "| 1 97341 276 6 … 0.0 293 1 9.52 |\n", + "+-----------------------------------------------------------------------------+\n", + "shape: (12, 13)\n" + ] + } + ], + "source": [ + "# | label: team-picking-backtest-first-team\n", + "def predict_score(data: pl.DataFrame, candidates: pl.DataFrame) -> pl.DataFrame:\n", + " prediction = candidates.get_column(\"score\")\n", + " candidates = candidates.with_columns(prediction=prediction)\n", + " return candidates\n", + "\n", + "\n", + "teams = backtest(players, predict_score)\n", + "print(teams.filter(pl.col(\"round\") == 1).sort(\"position\"))" + ] + }, + { + "cell_type": "markdown", + "id": "5d5c18c8", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "And we can plot out cumulative performance during the season:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "fdc43591", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": { + "image/png": { + "height": 378.25, + "width": 512.975 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "# | label: team-picking-backtest-score\n", + "# | warning: false\n", + "from blog import theme\n", + "import seaborn.objects as so\n", + "\n", + "\n", + "def summarize(teams: pl.DataFrame, model: str) -> pl.DataFrame:\n", + " captains = (\n", + " teams.filter(pl.col(\"position\") != 6)\n", + " .filter(pl.col(\"prediction\") == pl.col(\"prediction\").over(\"round\").max())\n", + " .with_columns(captain=2.0)\n", + " .select(\"round\", \"player\", \"captain\")\n", + " ) # TODO review\n", + " campaign = (\n", + " teams.join(captains, on=[\"round\", \"player\"], how=\"left\")\n", + " .with_columns(score=pl.col(\"score\") * pl.col(\"captain\").fill_null(1.0))\n", + " .group_by(\"round\")\n", + " .agg(score=pl.col(\"score\").sum())\n", + " .with_columns(score=pl.col(\"score\").cum_sum())\n", + " )\n", + " score = campaign.get_column(\"score\").tail(1).round(2).item()\n", + " label = f\"{model} ({score})\"\n", + " campaign = campaign.with_columns(label=pl.lit(label))\n", + " return campaign\n", + "\n", + "\n", + "def add_line(\n", + " fig: so.Plot,\n", + " campaign: pl.DataFrame,\n", + " linestyle: str = \"solid\",\n", + " valign: str = \"center_baseline\",\n", + ") -> so.Plot:\n", + " text = campaign.tail(1)\n", + " fig = fig.add(\n", + " so.Line(linestyle=linestyle),\n", + " data=campaign,\n", + " legend=False,\n", + " ).add(\n", + " so.Text({\"clip_on\": False}, halign=\"left\", valign=valign),\n", + " data=text,\n", + " )\n", + " return fig\n", + "\n", + "\n", + "season = summarize(teams, \"Score\")\n", + "theme.set()\n", + "fig = so.Plot(season, x=\"round\", y=\"score\", color=\"label\", text=\"label\").label(\n", + " x=\"Round\", y=\"Cumulative score\"\n", + ")\n", + "fig = add_line(fig, season)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "da18256d", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "This might seem like a perfect campaign at first, but it's possible that, early\n", + "in the season, we didn't have enough budget to pick the best scoring teams. To\n", + "test this hypothesis, we backtest the same strategy with unlimited budget from\n", + "the start:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "cc11b6b8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": { + "image/png": { + "height": 377.825, + "width": 521.05 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "# | label: team-picking-backtest-score-unlimited-budget\n", + "# | warning: false\n", + "def add_backtest(\n", + " fig: so.Plot,\n", + " teams: pl.DataFrame,\n", + " model: str,\n", + " valign: str = \"center_baseline\",\n", + ") -> so.Plot:\n", + " campaign = summarize(teams, model)\n", + " fig = add_line(fig, campaign, valign=valign)\n", + " return fig\n", + "\n", + "\n", + "teams = backtest(players, predict_score, initial_budget=1000.0)\n", + "add_backtest(fig, teams, \"Score with unlimited budget\", valign=\"bottom\")" + ] + }, + { + "cell_type": "markdown", + "id": "439ab58e", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "Both runs are nearly identical, which is evidence that focusing on appreciation\n", + "is not so important if we have accurate predictions for the scores. If we\n", + "predict scores perfectly, we get a near perfect run.\n", + "\n", + "To put our backtests into perspective,\n", + "[the 2022 season champion had a total score of 3434.37](https://ge.globo.com/cartola/noticia/2022/12/03/cartola-2022-com-larga-vantagem-mosquito-bar-8-vence-liga-premiada-meliuz-e-fatura-r-20-mil.ghtml).\n", + "This is very impressive and not very far from the near perfect run." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "ee53cbc4", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": { + "image/png": { + "height": 378.25, + "width": 516.375 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "# | label: team-picking-backtest-champion\n", + "# | warning: false\n", + "champion = players.unique(\"round\").with_columns(\n", + " score=pl.lit(3434.37), label=pl.lit(\"Champion (3434.37)\")\n", + ")\n", + "fig = add_line(fig, champion, linestyle=\"dashed\")\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "c8e1fb5a", + "metadata": { + "cell_marker": "r\"\"\"" + }, + "source": [ + "## Score prediction\n", + "\n", + "For each round, we must predict $\\hat{s}$, the vector of score predictions,\n", + "using data from previous rounds.\n", + "\n", + "However, during the first round, we don't have any previous data to train our\n", + "model. In this case, we need to include prior information. One way to do that\n", + "would be to use data from previous seasons. However, we know a variable where\n", + "this information is already encoded: the player `value`. Each season starts with\n", + "players valued according to their past performance. Knowing this, all our models\n", + "start with $\\hat{s} = v$ in the first round.\n", + "\n", + "Let's use Bambi [@Capretto2022] and its default priors to fit our models. We\n", + "won't delve into convergence diagnostics, since we are more interested in the\n", + "average of the predictive posteriors and the backtest itself is measure of the\n", + "prediction quality.\n", + "\n", + "One question that arises here is: why not use non-parametric models such\n", + "as gradient boosted trees or neural nets? After some experimentation, I\n", + "concluded they are not a good fit for this problem: either because they\n", + "assume independence between observations, or because they are too data hungry.\n", + "Also, tuning these models for backtests might lead us into a rabbit hole\n", + "[@Bailey2013].\n", + "\n", + "### Player average\n", + "\n", + "$$\n", + "\\begin{align*}\n", + "\\mathbf{\\hat{s}} = \\mathbf{Z} \\mathbf{\\beta} \\\\\n", + "\\mathbf{s} \\sim N(\\mathbf{\\hat{s}}, \\sigma),\n", + "\\end{align*}\n", + "$$\n", + "\n", + "where\n", + "$\\mathbf{Z}$ is a dummy-encoded matrix of players;\n", + "$\\mathbf{\\beta}$ is a vector of parameters for each player.\n", + "\n", + "In this model, $\\mathbf{\\beta}$ is simply a vector of player averages. Let's\n", + "also consider that players that show up in the middle of the season have an\n", + "average of zero before their first round. This will be our baseline model." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b1238ec7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": { + "image/png": { + "height": 378.25, + "width": 516.375 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "# | label: score-prediction-player-average\n", + "# | warning: false\n", + "def predict_average(data: pl.DataFrame, candidates: pl.DataFrame):\n", + " candidates = candidates.with_columns(\n", + " prediction=pl.when(pl.col(\"round\") == 1)\n", + " .then(pl.col(\"value\"))\n", + " .otherwise(pl.col(\"average\"))\n", + " )\n", + " return candidates\n", + "\n", + "\n", + "teams = backtest(players, predict_average)\n", + "fig = add_backtest(fig, teams, \"Player average\")\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "4438a520", + "metadata": { + "cell_marker": "r\"\"\"" + }, + "source": [ + "### Player random effects\n", + "\n", + "$$\n", + "\\begin{align*}\n", + "\\mathbf{\\hat{s}} = \\alpha + \\mathbf{Z} \\mathbf{b} \\\\\n", + "\\mathbf{b} \\sim N(0, \\sigma_b),\n", + "\\end{align*}\n", + "$$\n", + "\n", + "where\n", + "$\\alpha$ is an intercept and\n", + "$\\mathbf{b}$ is a vector of player random effects.\n", + "\n", + "This model performs significantly better than the average model, possibly\n", + "because of the partial pooling between the random effects, that pulls large\n", + "effects towards the overall mean [@clark2019shrinkage]. In our dataset, it's\n", + "common for players that played one or two games to have large averages by\n", + "chance." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "20e48cec", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n", + "Compiling...\n", + "Compilation time = 0:00:15.348142\n", + "Sampling...\n", + "Sampling time = 0:00:03.736367\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.106630\n", + "Compiling...\n", + "Compilation time = 0:00:02.471332\n", + "Sampling...\n", + "Sampling time = 0:00:02.263076\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.099559\n", + "Compiling...\n", + "Compilation time = 0:00:00.570329\n", + "Sampling...\n", + "Sampling time = 0:00:02.436223\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.090894\n", + "Compiling...\n", + "Compilation time = 0:00:00.581795\n", + "Sampling...\n", + "Sampling time = 0:00:02.499836\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.087544\n", + "Compiling...\n", + "Compilation time = 0:00:00.474472\n", + "Sampling...\n", + "Sampling time = 0:00:02.679640\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.086805\n", + "Compiling...\n", + "Compilation time = 0:00:00.597276\n", + "Sampling...\n", + "Sampling time = 0:00:02.730077\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.086566\n", + "Compiling...\n", + "Compilation time = 0:00:00.476114\n", + "Sampling...\n", + "Sampling time = 0:00:02.974780\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.083834\n", + "Compiling...\n", + "Compilation time = 0:00:00.484789\n", + "Sampling...\n", + "Sampling time = 0:00:02.951227\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.090246\n", + "Compiling...\n", + "Compilation time = 0:00:00.626126\n", + "Sampling...\n", + "Sampling time = 0:00:03.073210\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.086071\n", + "Compiling...\n", + "Compilation time = 0:00:00.488828\n", + "Sampling...\n", + "Sampling time = 0:00:03.159322\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.087983\n", + "Compiling...\n", + "Compilation time = 0:00:00.639227\n", + "Sampling...\n", + "Sampling time = 0:00:03.413968\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.088858\n", + "Compiling...\n", + "Compilation time = 0:00:00.493007\n", + "Sampling...\n", + "Sampling time = 0:00:03.382153\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.094958\n", + "Compiling...\n", + "Compilation time = 0:00:00.657848\n", + "Sampling...\n", + "Sampling time = 0:00:03.589810\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.088574\n", + "Compiling...\n", + "Compilation time = 0:00:00.495813\n", + "Sampling...\n", + "Sampling time = 0:00:03.988393\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.086442\n", + "Compiling...\n", + "Compilation time = 0:00:00.674194\n", + "Sampling...\n", + "Sampling time = 0:00:03.989928\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.092058\n", + "Compiling...\n", + "Compilation time = 0:00:00.500662\n", + "Sampling...\n", + "Sampling time = 0:00:04.551274\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.090595\n", + "Compiling...\n", + "Compilation time = 0:00:00.693080\n", + "Sampling...\n", + "Sampling time = 0:00:04.646941\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.092757\n", + "Compiling...\n", + "Compilation time = 0:00:00.506404\n", + "Sampling...\n", + "Sampling time = 0:00:04.869855\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.092713\n", + "Compiling...\n", + "Compilation time = 0:00:00.706206\n", + "Sampling...\n", + "Sampling time = 0:00:04.670018\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.090660\n", + "Compiling...\n", + "Compilation time = 0:00:00.510140\n", + "Sampling...\n", + "Sampling time = 0:00:05.954047\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.091404\n", + "Compiling...\n", + "Compilation time = 0:00:00.726372\n", + "Sampling...\n", + "Sampling time = 0:00:05.583064\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.092434\n", + "Compiling...\n", + "Compilation time = 0:00:00.513472\n", + "Sampling...\n", + "Sampling time = 0:00:06.669829\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.092793\n", + "Compiling...\n", + "Compilation time = 0:00:00.515148\n", + "Sampling...\n", + "Sampling time = 0:00:06.961459\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.088096\n", + "Compiling...\n", + "Compilation time = 0:00:00.532672\n", + "Sampling...\n", + "Sampling time = 0:00:07.167323\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.093192\n", + "Compiling...\n", + "Compilation time = 0:00:00.535939\n", + "Sampling...\n", + "Sampling time = 0:00:07.055900\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.092713\n", + "Compiling...\n", + "Compilation time = 0:00:00.542286\n", + "Sampling...\n", + "Sampling time = 0:00:07.803232\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.092944\n", + "Compiling...\n", + "Compilation time = 0:00:00.544685\n", + "Sampling...\n", + "Sampling time = 0:00:08.286128\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.102901\n", + "Compiling...\n", + "Compilation time = 0:00:00.816501\n", + "Sampling...\n", + "Sampling time = 0:00:08.437252\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.088628\n", + "Compiling...\n", + "Compilation time = 0:00:00.559032\n", + "Sampling...\n", + "Sampling time = 0:00:08.789750\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.087069\n", + "Compiling...\n", + "Compilation time = 0:00:00.798131\n", + "Sampling...\n", + "Sampling time = 0:00:09.200497\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.092206\n", + "Compiling...\n", + "Compilation time = 0:00:00.542523\n", + "Sampling...\n", + "Sampling time = 0:00:09.187203\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.099589\n", + "Compiling...\n", + "Compilation time = 0:00:00.561396\n", + "Sampling...\n", + "Sampling time = 0:00:10.291747\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.093139\n", + "Compiling...\n", + "Compilation time = 0:00:00.581599\n", + "Sampling...\n", + "Sampling time = 0:00:10.123574\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.110857\n", + "Compiling...\n", + "Compilation time = 0:00:00.555584\n", + "Sampling...\n", + "Sampling time = 0:00:10.120113\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.113047\n", + "Compiling...\n", + "Compilation time = 0:00:00.857569\n", + "Sampling...\n", + "Sampling time = 0:00:10.850128\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.090888\n", + "Compiling...\n", + "Compilation time = 0:00:00.599466\n", + "Sampling...\n", + "Sampling time = 0:00:11.018571\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.090562\n", + "Compiling...\n", + "Compilation time = 0:00:00.894909\n", + "Sampling...\n", + "Sampling time = 0:00:11.113421\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.068803\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": { + "image/png": { + "height": 378.25, + "width": 516.375 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "# | label: score-prediction-player-random-effects\n", + "# | message: false\n", + "# | warning: false\n", + "from functools import partial\n", + "import arviz as az\n", + "import bambi as bmb\n", + "\n", + "\n", + "def predict_model(\n", + " data: pl.DataFrame, candidates: pl.DataFrame, **kwargs\n", + ") -> pl.DataFrame:\n", + " if data.height == 0:\n", + " predictions = candidates.get_column(\"value\")\n", + " else:\n", + " model = bmb.Model(data=data.to_pandas(), **kwargs)\n", + " inference = model.fit(\n", + " inference_method=\"nuts_numpyro\", random_seed=37, progressbar=False\n", + " )\n", + " predictions = model.predict(\n", + " inference,\n", + " data=candidates.to_pandas(),\n", + " sample_new_groups=True,\n", + " inplace=False,\n", + " )\n", + " summary = az.summary(predictions, var_names=[\"score_mean\"])\n", + " predictions = summary[\"mean\"].values\n", + " candidates = candidates.with_columns(prediction=pl.lit(predictions))\n", + " return candidates\n", + "\n", + "\n", + "predict_random_effects = partial(predict_model, formula=\"score ~ (1 | player)\")\n", + "teams = backtest(players, predict_random_effects)\n", + "fig = add_backtest(fig, teams, \"Random effects\")\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "821c47a1", + "metadata": { + "cell_marker": "r\"\"\"" + }, + "source": [ + "### Fixture mixed effects\n", + "\n", + "$$\n", + "\\mathbf{\\hat{s}} = \\alpha + \\mathbf{X} \\mathbf{\\beta} + \\mathbf{Z} \\mathbf{b},\n", + "$$\n", + "\n", + "where\n", + "$\\mathbf{X}$ is a matrix of the dummy-encoded fixture variables: the player\n", + " `team`, whether they are playing at `home`, and their `adversary` team\n", + " variables;\n", + "$\\mathbf{\\beta}$ is a vector of fixed effects.\n", + "\n", + "This model brings more context to our predictions. It also provides a reasonable\n", + "way to predict a new player, by setting their $b = 0$ (the mean of the random\n", + "effects). However, it does not improve significantly over our random effects\n", + "model." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "613ee872", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Compiling...\n", + "Compilation time = 0:00:07.905601\n", + "Sampling...\n", + "Sampling time = 0:00:40.474963\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.101650\n", + "Compiling...\n", + "Compilation time = 0:00:02.563196\n", + "Sampling...\n", + "Sampling time = 0:00:15.965689\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.081567\n", + "Compiling...\n", + "Compilation time = 0:00:01.121964\n", + "Sampling...\n", + "Sampling time = 0:00:06.425227\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.088375\n", + "Compiling...\n", + "Compilation time = 0:00:01.109532\n", + "Sampling...\n", + "Sampling time = 0:00:07.657055\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.085445\n", + "Compiling...\n", + "Compilation time = 0:00:01.138635\n", + "Sampling...\n", + "Sampling time = 0:00:08.078478\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.084024\n", + "Compiling...\n", + "Compilation time = 0:00:01.142378\n", + "Sampling...\n", + "Sampling time = 0:00:10.012693\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.080147\n", + "Compiling...\n", + "Compilation time = 0:00:01.494229\n", + "Sampling...\n", + "Sampling time = 0:00:10.154541\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.081909\n", + "Compiling...\n", + "Compilation time = 0:00:01.159511\n", + "Sampling...\n", + "Sampling time = 0:00:10.580360\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.086075\n", + "Compiling...\n", + "Compilation time = 0:00:01.512785\n", + "Sampling...\n", + "Sampling time = 0:00:11.477954\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.086296\n", + "Compiling...\n", + "Compilation time = 0:00:01.169140\n", + "Sampling...\n", + "Sampling time = 0:00:12.247918\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.084582\n", + "Compiling...\n", + "Compilation time = 0:00:01.150253\n", + "Sampling...\n", + "Sampling time = 0:00:12.878032\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.499189\n", + "Compiling...\n", + "Compilation time = 0:00:01.235270\n", + "Sampling...\n", + "Sampling time = 0:00:14.371604\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.082805\n", + "Compiling...\n", + "Compilation time = 0:00:01.162771\n", + "Sampling...\n", + "Sampling time = 0:00:15.366639\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.083889\n", + "Compiling...\n", + "Compilation time = 0:00:01.565987\n", + "Sampling...\n", + "Sampling time = 0:00:16.674839\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.086589\n", + "Compiling...\n", + "Compilation time = 0:00:01.177655\n", + "Sampling...\n", + "Sampling time = 0:00:17.277517\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.090385\n", + "Compiling...\n", + "Compilation time = 0:00:01.160624\n", + "Sampling...\n", + "Sampling time = 0:00:20.335498\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.098216\n", + "Compiling...\n", + "Compilation time = 0:00:01.276130\n", + "Sampling...\n", + "Sampling time = 0:00:20.817625\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.096836\n", + "Compiling...\n", + "Compilation time = 0:00:01.183428\n", + "Sampling...\n", + "Sampling time = 0:00:20.134373\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.092602\n", + "Compiling...\n", + "Compilation time = 0:00:01.585903\n", + "Sampling...\n", + "Sampling time = 0:00:20.545590\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.091502\n", + "Compiling...\n", + "Compilation time = 0:00:01.167017\n", + "Sampling...\n", + "Sampling time = 0:00:22.932675\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.088934\n", + "Compiling...\n", + "Compilation time = 0:00:01.759577\n", + "Sampling...\n", + "Sampling time = 0:00:23.422531\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.088727\n", + "Compiling...\n", + "Compilation time = 0:00:01.251229\n", + "Sampling...\n", + "Sampling time = 0:00:24.212809\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.093909\n", + "Compiling...\n", + "Compilation time = 0:00:01.162820\n", + "Sampling...\n", + "Sampling time = 0:00:25.859220\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.079607\n", + "Compiling...\n", + "Compilation time = 0:00:01.199369\n", + "Sampling...\n", + "Sampling time = 0:00:26.590709\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.087804\n", + "Compiling...\n", + "Compilation time = 0:00:01.197172\n", + "Sampling...\n", + "Sampling time = 0:00:27.408158\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.088161\n", + "Compiling...\n", + "Compilation time = 0:00:01.657817\n", + "Sampling...\n", + "Sampling time = 0:00:28.746087\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.090272\n", + "Compiling...\n", + "Compilation time = 0:00:01.192098\n", + "Sampling...\n", + "Sampling time = 0:00:29.634159\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.087828\n", + "Compiling...\n", + "Compilation time = 0:00:01.182441\n", + "Sampling...\n", + "Sampling time = 0:00:34.215767\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.091198\n", + "Compiling...\n", + "Compilation time = 0:00:01.210951\n", + "Sampling...\n", + "Sampling time = 0:00:36.042148\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.089578\n", + "Compiling...\n", + "Compilation time = 0:00:01.234012\n", + "Sampling...\n", + "Sampling time = 0:00:36.489777\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.092242\n", + "Compiling...\n", + "Compilation time = 0:00:01.723651\n", + "Sampling...\n", + "Sampling time = 0:00:38.450233\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.085619\n", + "Compiling...\n", + "Compilation time = 0:00:01.224424\n", + "Sampling...\n", + "Sampling time = 0:00:38.919846\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.088513\n", + "Compiling...\n", + "Compilation time = 0:00:01.729140\n", + "Sampling...\n", + "Sampling time = 0:00:42.741515\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.101450\n", + "Compiling...\n", + "Compilation time = 0:00:01.356056\n", + "Sampling...\n", + "Sampling time = 0:00:44.794694\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.105902\n", + "Compiling...\n", + "Compilation time = 0:00:01.444282\n", + "Sampling...\n", + "Sampling time = 0:00:49.125387\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.697565\n", + "Compiling...\n", + "Compilation time = 0:00:01.540290\n", + "Sampling...\n", + "Sampling time = 0:00:48.307869\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.086775\n", + "Compiling...\n", + "Compilation time = 0:00:01.261389\n", + "Sampling...\n", + "Sampling time = 0:00:49.365798\n", + "Transforming variables...\n", + "Transformation time = 0:00:00.095134\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n", + "/Users/assuncaolfi/Projects/site/blog/fantasy-football/.venv/lib/python3.11/site-packages/seaborn/_core/plot.py:1644: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n", + " df_subset = grouped_df.get_group(pd_key)\n" + ] + }, + { + "data": { + "image/png": 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YuPcCAOCRcf0avKtiRm4hjl6u2OFx7IDucLZv+M+Fu6zMTeFoa4mcghLkFJRArlAZbLMAotaI3x1ERERE1G7tPROBTysFXwDgYGMOa0v9gxWxWITPX5yCJV8GIzmrEAqVGpsOX8OfR8IQ0M0VI/p6Y2SgN1zr0efrVGgszlyLAwD09HbF+89NrbYXl1gsqhJk3e/4lWjt7VZjB/pWe7xMKsG7z0zB9Fe+Q2m5AluPXsUzM4bqrCaqjUYQsGbLcbg722LVstk1Nl2vzuaDIdrbJl9/YkKV4AuouN3sf89NxcNv/IS8olLsP3sTc8cZJjDJKSjRPraztmjSuX/bfxGrNx/TfiyTSvBATy8snDIIQ/p413ru3eAIAJztrfHNtpP4KfhclePUGgFnrsXh8q1E/O9fQRg70Ffv+kY8+6VOX6+OTjZ4YuogPBk0uFG3AAIVX3cAsLIwxdRhvRo1V0MM9e+CFx8ega+3nkRcSg7mvfkzHuzbFVOG9cLQPt4IPnUdX/11EgqlCoumDa4xDNbHX0euQq3WAGhco/v72VmZa6/P7ILiGlevERHDLyIiIiJqp/afj8THvx/XGZNJxXjridF6hz53uThY4Zc3H8YPey5h54mbUKjU0AgCrkan4Wp0GtZuO4ve3i6YM7o3xvb3QfV76VWq7U4TcABYPHNYnU3oa3PiSrT28axRATUeZ29jgZH9umH/2VsoLpXj3PX4Wlce3U+uUGH5o2PqFXwB93bdc7S1xJgBNQcz3u5O6NPVDWExqTh2Ocpg4dfdhv0AYFbHjon1dXfl1l1qtQYZuUUIi05B7y4da/1cFpbc6912PCQKIRFJcHOyxaJpgzGod2c421khPacQO45dwx8HLkGuVOG/P+yDj6czOrnWvTFDVl5RlYb2xaVyxKVkIyIho1G3Kd6ITcX12FQAwIzhfWrcXbK5PTVtCPy8XPD570eRkJ6Lk1djcPJqDMQiETSCACsLU3z92qPo271hK76Aig0gdh4LBVBxq2hj5rpf5eux8nVKRFUx/CIiIiKidufQxWh8uPE4BOHemFQixkfPTsTgXp0aNKeFmQxLHx6KxycFYs+ZcBy+FIPYlIpwQxCA63EZuB6XgV/3X8V7zwahZ5eO1c6j1mi0jcxNZJI6VwDV5eLNe83a+3Z3r/XYAB8P7D97CwBwMy6tXuFX544OGNXPp8r4ni/+BQDV3qaYkVuI6KQsAICfl0uduw36erkgLCYVtwzY40it0WgfSxsRQlZn9qgAjAjshjK5AimZBfjnchTCb6fj+11n8PfZm/jqlYdrDKoKSsq1j0MikjCwpxc+XzpLpydZJ1cHLJs/Gs72Vli16R+Ulivw2/6LeHPRpDprMzczwZfL50AQBGTmFSM6MRN7z9zAqdBYnAqNxdPTh+CFh0Y06N+96WBF/yuRCHjYQCFmdTQaAek5hSgqrfhcdu7ogOTMfKjurNIqLpXj442H8O+HR2J4364Neo19Z25qv1aPNPG/tfLGHCqVppYjiYjhFxERERG1K0cux+D9n/+BplLyJRGL8b/F4zHMv/FNt+2tzfH4pH54fFI/JGXk458rcTh0KRrxqXkAgLjUXCz+aDPWr3gE/j5Vw6jcwlLtihvvjo6N2hVOoxG0zdMdbCzrXGHj7myrfVy515U+pj7Yu9oeUG6V5rxfata93THLFUqcvBpT62vc3amyuFSO0nIFLMyaf8VQ5c9/cZm8liPr7/4A9KnpQ7Dn1HW8/+N+pGTm49V1u/D7e09Uew1UDjs8XeyxatnsGj8fc8f1ww+7zqCoVI7DFyLwnycm1Lma0MrcVNvb7a5/zXkQL6/egbCYVPwUfA5dPZwwcXBPff+5AIDM3CIcvRQJoOLWw9p2KG1OZXIFVny1G2euxcHCzAQfvzAdEwb3QH5RGQ5dCMfO49cQlZiJmKQsLFu1Dc/MGIrn5wyv9+vcbXRva2WOSUPq97mqS+Xrkf2+iGrH7xAiIiIiajeOXYnDexuO3hd8ifD+M+Mwsm/jVlhVx9PFDk9M7ocnJvfDuRuJ+HzzKaTlFKFcocRb3+3FzpWLq4QQ2fnF2se2Vo3bpa+guEz7b9Vnl8LKxxSV1i/o6dPVrX7FQbefVkhEEkIikvQ+t7hUbpDwq3Jvq6YOv6ozbXgfXLyZgH1nbyImKQsnrkRjXDW7W1pVWkm3cPIDtX4uZFIJenR2xcVbCSgqlSMnvwQd6tGD7i4HG0u8/1wQZr32PQQB+Gn3uXqHX1uPXtWurJrXhP2v6kOl1uClz7fhSmQSzE1l+P71+ejhXdFrzs7aHHPH9cPccf1w4ko0Pvz5IHIKSvDj7rMAUK8A7PyNeMSl5AAAZo70b/KAqrjS96ihNoAgaq2adt0uEREREVELdTI0Hu/8eARqzb3gSywS4d2nxmJ0v+p3SmxKQ3p3wnevzYS9dUWglZKZr21qX5mmcn113AZYl8q37OlDgO5quPpozM5/DVHff1tDuVTame/uyrPmNjzw3i12567HV3tM5aBSn8+Fvc29Zv2VA9b68nSxR+eOjgCA2JRsZFZqvF8XuUKFHcdDAQCdXOwbfUtvQwWfDMOVyIqg9fEpD2iDr/uN7OeDX95ZqA0Wf957HmnZBdUeW527t3dKxCI8PLZpb3kUBAGFd26nlErEcLS1bNL5idoarvwiIiIiojbvTFgC3vrhsE5IIBIBbz85GuMGdKvlzKblZGuJKUN88cehUABAfGpOlVvLHGzuvYlt7Eoja4t7q5bub15enZKye8c42DTtzobVqfyGfcZIf7zz9ORmf8368nCxh0wqgVKlRmxyNtQaTaM2INCHS6VVWXdvW63tmLzC0jrnFCqvdpQ0rv4ODtaIT61Y0ZSZV6T3KrJ9Z29qA8SHx/UzeGB615E7t10CwPDA2r//3ZxtMX1EH/x5KARqtQbHr0Rj/oQBdb5GQlouzobFaV+jo5NN44q+T2J6nrbJfWc3R6N9LolaC678IiIiIqI27fzNRLzx/UHtrVZARfD15uOjMXGQ/g3dm4pbpTfB1YUWTnaW2kbW9+8GWF+mJlI421sBAHIKiuvcES65Up8vV8emfbNencqvkZNfUsuRxiOTStC9UwcAFX3JYpOzm/015Uq19rGlefW3M7o522pvi01Ir/s6yal0rd29JhpKoVRVqk//2+3u9r+yMDPB9BF9GlVDY6RnF2ofO9vV/bm4u9INANJzCms58p4/D4doN9R4pBlu77wZl6Z93KuGzTOI6B6GX0RERETUZl28lYT/rD8I5X07ob2+YBSmDPE1Sk1ZlUKeDtWEEDKpBAN7VjTeLyqV43psaqNer3eXil5cggCERafUemxoVLL28aDenRv1uvro6GQLb7eKYOF6bCqUKnUdZxjHgwH3bou9fCuh2V8vMiFD+9ijQ80N4XvfCT2uRCbprOy6n1Kl1s7paGsJe+uGr+pTazSIubNDp0Qs0ntF08Wbt7XB4dRhvfTqQVeXzYcuY+ry9Rj74lp8svEQyhW1h7t32VrdWxFZVGnXzJoUlty73bXyysyaFJXKsff0DQBAV3cnPNCz8Rtp3O9yRKL28TD/5r9tm6i1Y/hFRERERG3S5YgUvLb+ABT3BSqvPTYCQcOqNhBvqKSMfBy+VPsuhXcpVWocvBCl/bhPNbs9AsCEQffq+3nP+UbVV3muXSeu1XhcXmEpToXGAqgISPy7VV9bUxs7sCKELCguw+6TYQZ5zfqaMqw3JHf6r+1qRI17Tl1HWnbtK4cUShV2/BOq/XjC4B41HjuiX8Ute1l5xdqvXXWOXorUNkefOLhHtbfIZecXY8ex0Crj9/v79A3tZgij+vvAzERW5znAvf5XAPDIuMb3vzp6KRKf/34U6TmFyC8qw9ajV7HurxN6nevr5aJ9fLKWzxtQ0YPv0IUI7cd+lc6tya4T11B65zbjueObttcXAJSUVezaCVTcnvxgQNc6ziAihl9ERERE1OZcjUrFq1/vh0KpG3y9PO9BzBxev93papNTUIrl6/7Guz8dwQe//IOM3JobiRcUl+M/3x5EWk5Fg/AAH/cad0icMqw3fO/canfiSjTWbjleY1Pz4yHRtQZkowd0h6dLxeqhwxcjsO/szSrHKJQqvPfjPu0b9udmPdjoZvv6WjD5ATjdufVs1R//4HhIdLXHKVVq/HXkinZFjSF5dLDD6AEVt8jGJmfjamRyHWdUtXrzMfz3h3149qNNuFLDrpblCiXe+nYvEjPyAAAjArvBx9O5xjmnDO2lXcX1ycZD1QZrKVn5+HLzMQCAmYkMc6sJnuJSsvHEe7/hw58P4rPfj2ivg/tdDk/EZ78fBVDRN+zxKYNq+Rffk5SRp93c4YGeXvB2d9LrvNocuhBedex81bHqzB3XD3fzv592n8W16Oq/nkqVGh9vPKhd6ebj6VznikiNRsBfh68AqNiRc+qwXnrVVB9/n7l5L1wb16/Jd5Ekaov4XUJEREREbcq1mDS88vU+yCv1JQKAZXOHYc6o3k36WgnpeSgqrXgTuu98FA5fjkFfHzf093WHs50FLExNkF9chpvxmTh2JU7bdN7Jzgof/GtajfNKJWL899mpePajTSgqlWPj3xdwKjQGk4f2gperA5QqNW6n5eBYSLT2jXkXd0eM7OdTZS6ZVIK3npqEF1dugUqtwdvf7sWxy1EY3rcrrMxNkZSZj90nw7T9xfr36IRZowJqrC2/qAxhMSl3/v33ek2FxaRo35DbWZnDv4ZVbfeztjDF209PwitrdkKuVOHlNTswqHdnjOrnA2d7K5SUKRBxOx1HLkUiK68YZiYy9O7aUacPkyE8MWUQjl6KhCAAq//8B7+8s7BeTcYd7Spul0vNLsDijzahf49OeNC/Czo620Kj0SA6KQv7ztxExp3dE9072OG/i6fUOqeFmQlee3wcXv86GBm5RZj31gbMGOGPnt6uEItFCI9Px47j17Srvv7v0THaILQyK3NTmN0JUP48FIJD58MxdqAvenq7wsrCDNn5xTh/PR4nQ2O0fayWzRuN3jWEt/fbfCgEmjsnNlX/KxGqfu71/Xp0cXfCvx8eiXV/nUBJuQKLP9qM0f19MLCnFxxtLaFUqhGXmo2D58K1QaSNpRk+fL7m79m7jodEIfXOjpDTh/eBuWn1PdsaqqikHD/uPgsAsDQzafJdJInaKpFQ283hRAagKCuCINS+PbPMzApisQQajRrK8oZvzUztC68bagheN9RQvHZahutx6Vi+5m+U3tfYfclDQzB/XM2BTmOk5RThm53ncfxKHNSaun+1HuDngbeenlJtCHG/22k5WL5qu/YNeE369+iEt5+aVOucx0Oi8e4Pf2uDkOoMC+iClUtm1nor2+XwRDz38eba6/HzxPdvPFrrMfe7dCsBr63bhcJaejBJxCLMHBmApfNG1avRelNZ+dsRbDkcAgB4/ckJeGhMYL3OP3QhHKs3H9MGXDUZ5t8F/312il79pQBgy+EQrNr0j86mDpXJpBL854nxmDmy5u+BvMJSfP7HURw8fwu1vUO0NDfBf56YgClD9VvRVFwmx5Sl36CkXAE3J1vs/vy5JllV+M+lSLy6bpfO2KMTB+Dlx8bqPceWwyFY99eJOjeC6OntineemVLrKry7Fn+4CVcikyASATtXPqvX93l9fPTzQWy/c3vq209PqvVrSkT3MPwio2P4Rc2F1w01BK8baiheO8YXkZCFJV/u0a6uuuv5WYOwcGL9QoqGSMkqxImrcbgSnYrbafkoLClHuUIFW0szONiaI6BbR4zs640Bfp4wMbfWe16FUoW9p2/gWEgUIhMykV9cBjOZFK5ONujv1wmThvRAgI+HXnPlFJTgryNXcPpaLFKzClBSroCdlTl6dHbFtOG9MXagb52rZ5or/AIqVpVtP3YVp0NjEZ+ag1K5EtYWpvBydcDAnl6YOdIfHZ1s6z1vUymTK/HY278gIT0X5qYy/PjmY/DrXHcPqMrKFUocvhCBU6GxiEjIQE5+CaRSMTrYW6F3FzdMGdZLu+FBfcSlZGPL4Su4FJ6A9JxCSMRidHSywZA+3pg7rh/cne30micqMROHLoTj4s0EpGYVoLhMDgcbC7g722F0fx9MHtoL9jb6N8y/EZuK93/cj9iUbCx9ZBQen6rfrZL62HzoMn7bdxHlChUmDPLDsvmj9e5BdldmbhEOnLuFU6GxSMnKR15RKUxlUjjYWqJ3l44YM8AXwwO7QiKuu2NQSZkcy7/cjtCoZAzu4421Lz/c0H9atQ5dCMcb3wRDECpuif1y+ZwmnZ+oLWP4RUbH8IuaC68bagheN9RQvHaMKzYlFy+u2o3CEt1VTc9OH4gnpzTNbVZNRSQS1yv8opYlOjETz3y0CcWlcrg4WGPjfx+Hs13VXTtJV1JGHuxtLJpkl8eWLr+oDGVypd47YerjZlwaFn+0CXKFCl6uDvjp7ccatWsnUXvDhvdERERE1KolZuRj6Zo9VYKvp6b2b3HBF7V+Pp06YPXyOTCVSZGRW4QXP92CnIISY5fV4nm62LeL4AsA7KzNmzT4ik7KwtIvtkGuUMHZ3gpfvzaXwRdRPTH8IiIiIqJWKzW7EC+t3oPcwjKd8ccm9MXTQQOMVBW1dYG+nlj50kyYyqSITcnGsx9tQlZe7X28iBoiMiEDz328GXlFpXC0tcRXr8416q2/RK0Vwy8iIiIiapUy84rx0uo9yMzTXXXz0KjeeGHWoHrtxEdUXw8GdMXXrz0CG0szJGfmIzYl29glURt0LToFBcVl8HSxx4a3F6CbR91N94moKqmxC2gprl+/jh07duDChQtIS0uDIAhwdHRE3759MW3aNIwaNarW83fs2IHXX39dr9d66qmnsGLFilqPKS4uxsaNG3H48GEkJSVBo9HA3d0dY8eOxRNPPAEHBwe9Xqup5iEiIiJqSXILS/HS6j1IzdZdbRM01A/L5g5j8EUGEejrgZ/eegy303IxuLe3scuhNmjuuH6QSsQY3b97vTYaICJd7b7hfWFhId5++20cOHCg1uNGjBiBNWvWwMKi+h849Qm/vvjiCwQFBdX4fGxsLBYvXoyUlJRqn3dycsL69evh7+9f6+s01TzNjQ3vqbnwuqGG4HVDDcVrx3AKisvx7y+DEZuSqzM+fmA3vLNojF67shkTG94TEREZVrtf+bVhwwYcOHAAdnZ2WLBgAcaMGQMPDw+Ul5cjJiYGv/76K44fP46TJ0/itddew1dffVXnnGFhYbU+L5PVvP1ucXExnn32WaSkpEAmk2HZsmUICgqCmZkZLl++jE8//RSJiYl4/vnnsWvXLjg7V7/stanmISIiImpJisvkWL727yrB18i+3nj7ydEtPvgiIiIiw2v34dfSpUvRoUMHTJo0SecWQFtbW7i4uGDYsGH473//i82bN+Pw4cOIiopC9+7da53T1LThu5j89NNPSE5OBgCsXLkSU6ZM0T43btw4FBcXY8WKFcjOzsY333yDd999t1nnISIiImopSsuVeHndPkQkZumMD+nlifeeHgepRGKkyoiIiKgla/d/GhOJRHj00Udr7X01b9487ePY2Nhmq0WtVmPz5s0AgMDAQJ3A6u7z3333nfbjHTt2oKxMd2ejppyHiIiIqKWQK1R4bf1+XI/L0Bnv5+uGj56bCBMZgy8iIiKqXrsPv/ShUCi0jzt06NBsr3PlyhXk5eUBACZPnlzl+e3btyMuLg5z5swBAJSXl+P06dPNNg8RERFRS6BQqvHGdwdxJTJVZ7xPFxesfH4yTE3a/c0MREREVAuGX3rYuHEjAMDHxwf9+vWr17kaTe2N3Cu7ceOG9nFgYKDOc2VlZVi3bh0CAgKwdOnSas9p6nmIiIiIjE2l1uDdn47g3M0knXG/Ts74YskUWJjV3EuViIiICGDPryo0Gg2USiVKSkpw69Yt/PLLLzh16hTc3NywZs0avbbNDg4Oxq5duxAVFYXs7GyYm5sjICAACxYswLhx42o8Ly4uTvvYw8ND57mNGzciMzMTn3/+OVxcXCCVSqFSqXTOaep5DEVqWveWvSKRWPv/MjOr5i6J2gheN9QQvG6ooXjtND21RoMPv9+PE6HxOuNdPZyw7rW5sLMyN1JlRO2XRqOBmBtLUDPh9UXNheHXfYKDg7FixQrtx46OjliyZAkef/xx2NjY6DXHq6++qvNxaWkpzp07h3PnzmHhwoV46623qj0vN/ferkX29vbax3l5efjxxx8xYsQIDBo0CABgY2OD3Nxc7e2NzTGPoYjF+vfoEIlEEInY04Pqh9cNNQSvG2ooXjtNQ6MR8PHGQzh4Plxn3MvVAetXzIODjaWRKiNqv6KjI3Dm1DHMfuhR2NjYGrscamMEQcD+fbthamqKMWMnMQSjJsXw6z7x8bp/WczJycFvv/2GkpISLFmyBBYW1a9Scnd3x8SJEyGTyTBw4EAMGjQIrq6uUCgUOH/+PD777DMkJSXht99+Q8+ePTF79uwqc9xtOm9iYqKzwuzbb79FcXExXn75Ze2YiYkJgIpgrbnmMRSNRl3nMSKRGCKRCIIgQBD0v5WU2jdeN9QQvG6ooXjtNB1BEPD57/9g94kwnXE3Z1t8/drDsLc20+v3h5asPn/8I2oJroWG4Ng/ByEIAi5eOINx46fUfRJRPSQlJSAq8hYAoLSkBFOCZkEqZWRBTYNX0n2WL1+O5cuXo6SkBMnJydi7dy9+//13bNiwAadPn8bmzZthZVX1VoZBgwZpV1NVZm5ujokTJ8Lf3x9BQUEoLi7G+vXrqw2/7qqccKekpGDTpk0ICgqCn5+fdlyfXmJNNU9zU8lL63yTIDOzgkgkgSBooCwvNlBl1NrxuqGG4HVDDcVrp2kIgoAvt5zBtuO6/Ug72Fti7dKpcLAQtfrPr0gkhom5tbHLaDcEQcChg3sRHRUB5w4uCJo2G5aWvDW5Pq6EXMSJ44cBAF27dcfoMRONXBG1RZ06dca48VNw9Mh+xMZGYffOvzBz9iOQSPjHAmo8riOsgaWlJXx9ffHyyy9jw4YNEIvFiIqKwurVqxs0X8eOHTFp0iQAQGJiIpKSkqocc3dVmVKp1I6tXr0agiDoNKcHALlcrnNOc8xDREREZEiCIGDVn6erBF8ONuZYu2wa3Jz0a0HRFvy15Tds+PFr7Ni+2diltHoJCXG4dTMMSqUCqSlJuHrlkrFLalXCb13XBl/e3l0xNWg2wwhqNn38AzF6bEW4mpgYjwP7dkMQBCNXRW0Bwy89BAYGon///gCAnTt3NngeHx8f7ePExMQqzzs4OAAA1Go1ioqKEBERgb1792LevHnw9PTUHqdUKlFUVKRzTnPMQ0RERGQoGo2AzzefwvYTN3XG7azNsGbpNHRysTNOYUaSkpyIgoJ8qFUqY5fS6omgu2GVHvtX0R1JSQk4dHAvAMC1oxuCps1h8EXNLiCgPwYNfhAAEBUVjlMn/zFyRdQW8LZHPXXu3BmXLl1CcXExcnNzGxQWmZvf25Go8qqsu7p06aJ9nJKSgs8//xzm5uZ44YUXdI5LS0vT3q7o7e3dbPMQERERGYJGI+CLP09h58lbOuN21mZYt2w6urq3vj/SKZUKREZF4fbteGRnZ6G8vBwSiQSmpmZwcnJCZ28f9OzlD1NTU2OX2uZ18vJGz17+iI4Kh7OzC/oGDjR2Sa2CXC7HoQN7oNFoYGZmjqCgOZDKZPWeJz8vFzdvXkNCQjwK8vOhVCpgZm4OezsHdOnqg169AmBm3rCdW5VKBf7ctBG+fj3Rp08gzOt5N4tGo0FU5C3ExkQhPT0VJaUlkEoksLCwgruHJ3z9eqFTp84Nqg0ALl08i9TUZAQGDkQnL+O/30pOTkRkxE0kJyWiuLgQGo0GFhaWcO7ggm4+fvDz66VXk/mtW35DcnLVxRy16dnLHxMnTdP7+CFDRyAjPRW3b8ch5PJ5eHt3hWcjvhZEDL/0VF5eDqBiBydLy4btLpSVlaV97OLiUuX53r17ax9v3LgRp06dwpIlS6oEbVeuXNE+7tOnT7PNQ0RERNTcNBoBn20+id2ndHd1tLc2x7rl09DFrfUFXxER4Th77oz298e7NBoNlEoliouLcPt2PM6eOYGhw0aib+AAnU2KqGmJRCJMnDStXm+8CThx/DAKCwsAAGPGToK1Tf1uO1apVDh5/AiuX79apc9wSXExSoqLkZyciIsXzmLs+Mno3r1HvWu8dfM6srMzkX06Ew4Ojujm41f3SXckJyXgyOF9yMvL1RlXq1SQy+XIy8vBjeuh6NLFB5OmzKh3UK3RaBB69TKKi4tQWFiAhY8vrtf5TamosBCHDu5FYmJ8lecKCwtQWFiA2JgoXL54DlOCZsLJqYMRqtQlEokwcfJ0/LJhPeRyOQ4e2IOFTzzLPxhQgzH80oNarUZISAgAwMvLq8HfcGfPngUAWFtbV7vSKjAwEPb29sjLy8OOHTvg6OiIRYsWVTnuwIEDAAAzMzMMGzas2eYhIiIiak4ajYCVm04i+LRu8OVgY451y6bBu5UFX4Ig4PSZU7h+/d4ulc5OzvD27gJrGxtIxGIUFRUhJSUZSclJUCjkuHTxLHy6+8HKig3wqeXIycnGzRvXAADuHp3g69ezXucLgoCd2zdrVwc5ODrBx8cX9g5OEIvFKCzIR1TkLWRmZqC8vAz79u6EbKYM3l261es1Qq9W9G+zsbFFl67d9T434XYcdu3cAo1GA4lEgm7dfOHu0QkWFpYoKy9FZkY6wm/dgEqlRFxcNHbt+BMPP7JQr1VRd0VFhqO4uKLFTN++A/Q+r6mVlBRj0x8bUFpaAgDw9PRCZ+9usLaxgVqlQl5eDm7eDENJcTFycrKwfesmzH9sEWxsbGucc9jw0SgvK6vztdPTU3Hh/GkAgKOjc71rt7CwxOAhw3Hi+BEUFRXiWuhlPDCI71upYdp9+PXDDz/g5MmTWLduHezs7Ko9Zv369UhNTQUAPPbYY1WeFwQBn376KQYMGIBx48ZVO8f+/fu1AdrMmTNhZmZW5RiJRIL58+fjm2++AQA8+eSTVVaZnT17FsePHwcAzJkzR+dWyqaeh4iIiKi5aDQCPv3jBPacidAZd7Axx1fLp6NzR3sjVdZwV66EaIMvKytrjB0zFu7uHlWO69dvAIpK5Tj2z0EMHzGGwRe1OFcun9c+fmDQ0HqfLxKJMGTYSBzYtxtDh41Ej559qqxuHPjAUFwPu4ojh/dBEAQcP3YInb276r0KMuF2HHJzcwAA/gH96xVMeXh6oWs3X6hVqhpXtQ0ZOgK7dvyJzMwMpKYmIyLiJnr21P9umatXLwIAzMzM0aNH7zqObj6WllYVn+vrVzF+wlS4uVX9mTR4yAgcPbwPN2+GobS0BOfPncKEiUE1zlndHNW5des6AEAqlaJ374AG1e/v3w8Xzp9BeXkZrl69hH79B0EqbfcxBjVAu75qIiIi8NVXX6G8vBwTJ07EwoULMXr0aHh6ekKtViMyMhKbNm3CwYMHAQCDBw/G/Pnzq8zz008/4eeff8bPP/+McePGYc6cOejduzcsLCyQmpqK4OBg/PzzzwAqVo7dv+NiZU8//TSCg4ORnJyMP/74Ax4eHhg8eDA0Gg0OHz6MlStXQhAEODk54fnnn2/2eYiIiIiamkYj4JPfT2DvWd3gy9HGAuv+bxo6u7a+4CsnJxsXL10AAJibmWPWzFmwtq75NjFHRyc89HDVP6oSGVtZaSnCwyt2XLW1tYOXV5c6zqieh0cnPL3437WGWX38AxETE4nb8bHIz89Dfn4e7O31W/F59UpFuCSVStGnT9961SaRSDA1aFattVlaWmHsuMnYvOkXAEBCfKze4VdaWgrS0yoWT/Tu07dBvdKaUr/+DyCw38Aa/70SiQRjxk1GTGwU5OXluH07ttGvWVRYiNiYSACAX4/eDe7rJpXJ0KuXP0JCLqC0pASRETfRq4FBGrVv7Tr88vPzw4YNG/DGG2/g9u3bWLduHdatW1ftsUFBQXjvvfcgq+YH15NPPomcnBz89ttvOHLkCI4cOVLtHP7+/li1ahWsrWv+656VlRW+//57LF68GCkpKVi+fHmVY5ycnLB+/Xo4O9e8dLSp5iEiIiJqSmqNBh//dgL7zkXqjDvZWmDd8unwcrUzTmGNdP7CeQiCAAB48MHhtQZfDSEIAiIjbiH8VhgyszIgLy+HhYUlvDp3weDBw+vsx1ReVobYuGgkJyUgKzMdhYWFUCjkkEplsLa2gbuHJwIC+sO5Q9W+tHcVFORjw49fAwCWLF2BstJSnDx5FIkJFX2EunT1wajRE2BqaorsrEycOvUP0lKTIRZL4OvXE8NHjK12xcbd5tk9evbBpMnToVarceN6KG7dCkNebg5UajVsbe3QpYsPBg4cUueb6JKSYnz/7Zoan7exscXTi/9d6xzVKS8vw7XQEMTFRiM3NwcqlRLmFhZwdnZB9+490KNnnzpXH507exLnz53SqSEvNwfXw64iPj4WhYX5EIslcHRyRh//QPTq5V/vOhsrLi4aarUaQMXXtDH96PQ518mpA27HV4QtCoVcr3lzc7Jx+3YcgIYHK3rV5nzv+0GuZ20AcDXkovY1Avr2r3dtzaGuf69UKoW9vQPS01KhkCsa/XqhoZe1vd76Bjbuts8u3bojJKTijwvR0REMv6hB2nX4BQD9+/fHvn37sG/fPvzzzz8ICwtDdnY2pFIpXFxc0L9/f8yYMQMDBtT8DSuVSrFixQrMmzcPW7duxenTp5GamoqysjI4Ozuje/fuCAoKwqRJk/Raotm1a1cEBwdj48aNOHToEJKSkiAIAtzc3DB27Fg8+eSTeu022VTztHbfrP+qQec5Oznj4Ycfqfa5rVu3ICs7q9rn6vLC89X/snX8+D+4FX6r2ufq8tBDc9HBuWpjypu3buDEieMNmnPkyFHo1bPqEu3MrExs2/ZXg+bs2aMnRo0aU+1zzfF12vzHz8jMzGjQvPw6Ge7r1NK+n+Y9+iQ6dnSvMs6vU8v6OvH7qXV+ndQaDT7+9TiioiKwIEBz39GF+Hvn73XOaeivkz6KiouQmJgAoCJY6dbNp8FzVTt/YSH2/b0TqanJuuNFhbhxPRRxcdF4bMHTtd4+uWvnFqSlpVQZVyoVyM3NRm5uNm5cD8XwEWPQf8DgOmvKyszA3j3btT2NAODWzTCUl5VhyNAR2PrX7zpBRujVyxCLxRg5anyNc5aVlqC4qBDBu7chIyNN57ncnGzk5mQjKvIWHpn/hMFvFU1PS8GunX+hrKxUZ/xu4/bb8bEIvXoJM2Y9oldt5eVlEAQB58+dwqWLZ7VhUwUV0lKTkZaajPy8HAx7cHQT/2tql5SUoH3cqVPz71BYdKep/t3wRR9X7/T6AhofrNTmbsN/AHB2rjkYrqy4qBDR0RWrWrt27V5r76yWRBAEFBUVAgCcq/nva30olUpcv34VAODu7qn3564mHTu6QyaTQalUIiU5EYIgcJMQqrd2H34BFcs8p02bhmnTGrcDjJeXF1555RW88sorja7JysoKL774Il588cUWMQ8RERFRY6g1Gny48TgOXIhCtzb2t7fEhATtqq+uXfXvWaSPcnk5dmzfhNzcHLi4dkTPnn1gYWGFgoI87U5ypSUlOHvmRK09err79UR+fh7cPTqhY0c3WFvbQiwWo6SkCImJtxEfFwONRoOTJ47C3t4RXbrWHuDt3bMdSqUCgwY/CAcHR1y+dA5ZWZmIi4tGZmY6AFR57uaNMAwfMbbG1VGFhQXYuXMLsrMy0dHNA76+PWFpaYXs7EyEhl6GvLwchYUF2L3zLzy64KkaP8+mpmaYMXNulfHDh/7WNv2uj4yMNGzbuglKZcVqmC5dfdCliw/MzMxRUJCHmzeuITc3B5mZGdi+dRMeXfBUtXeLVKZQKPDP0QMIu3YFMpkMffz7oWNHd2g0asTFxSA6qmITiEsXz6F3n0DY2trVu+6Guvv1AwBHp+a9QyQzIx0xd26N6+PfDyYmdW8sVl5ehvA7vaQ8PDo1OlipiSAIOHfmBADAxMQE/v799DovNDTk3oqnfgObpbbmcC00BCXFxQCgVwBem1u3wiC/s9tt38DGfw4kEgns7R2QmZkBhUKBvLxcODg4Nnpeal8YfhERERFRs1JrNPhg4zEcvBBt7FKaRUalVcYutdw22BDZWZkQi8V4cPhoDBg4RCfw8fPrjV9+/hYqlRIxMZEYP2FqjYFQ374D0K/fA9U/FzgQycmJ2PbX7xAEARcunK4z/FKpVHhk/pNwdHQCULE7+qGDewEASpUS8x59Qru7293n5PJyFOTnwb6GN625uTkQiUQYNXoCAiuFBt19e6Bnzz7YtOlnyMvLkZmZjri4aHStYXc/qVRabf0NaZItCAKOHtmvDb4mTAyqcstVYL8HsCd4G+LjYpCbm40L50/jweF1r9YKu3YFLi4dMXnqTJ0VTz17+SN491bExkRBEATExkShX//qv3bNoSA/H0BF4NAcq5bUajUK8vMQHR2By5fOQa1Ww8uri16fMwC4cT0USqUSQNMEK/crLytDaloyQi5fQHJSAqRSGSZPmVnnrcUAoFIqcT2sYsWTk1MHeHp6NXl9TUmpVCA7Ows3rofixvVQABWhdTcf3wbPKQgCQq9cBlCx8Udj5qrMzt5Re0dHYUE+wy+qN4ZfRERERNRs1BoN/vfLMRy62DaDLwAoKLh3a1RzhAXjxk+ptseNtY0NOrq5IynxNuTl5SgvK4O5hUW1c9TVi8rDoxPcPTohOSkB6WmpUCmVtTbpHjxkuDb4Aiqag981ZMgIbfB1/3Nl5WWobTuDEaPG6QRfd9nZO+CBB4bh1MmjAICbN67VGH41pdiYKGSkV9yC6dPdr9qvg0QiwYSJQdjw4zdQKhUIuxaCwYMfrLPJuZNTBzw097FqVzv5+vZCbEwUACA/P7cJ/iX6kcvlUKkqgiVTM7MmXcWYkZGGTb9v0BlzdnZBH/9A9PEP1Gu3Ro1Gg9CrFcGKtbUNunbTvQZ++uErFBYWoGcvf0ycVL+7enbt3IL4uBjtx6amZvDr0Vu7glEf4eE3UF5eBqDq7Zg3b1zTBsRPPfOiQVfz3S/k8nmcPHFU+7FEIkGnTt4YMHAwvDo3bIODuxIS4pCbmw0A8A/oV69dOGtjXqmvW0lJcZPMSe0Lwy9q82rqYdIYNfVaaYxRo8Y0qt9IdXr17F1tX5TG6ODcoVk+p80x5/zHFkEslkCjUUNZ3jT/keTXqe1/P8nMrKod59epZX2damLMr5PMzKpeP3Paw9dJpdbgfz//g0OXYnTGiwQbTH9oGjycmy4oaq7vJ32U37m9BwDMzMyadO6Obh61Nneu3F9KqVKiYfupVajcpL+srAzWtYQ3JiYmOh9LKq2qqu053d5WVdW0Og0AfP16asOvuzvpNbfY2Cjt4z59Ams8zsLCEl27dUdE+A3I5XLcvh1X54qXESPH1nibn62dnfbx3VVOhnB3hRsAyGQmtRxZf3m5OVXGyspKkZmZjoKCfL36fcXERGr7UvkH9G+yYAUA8vJ0Q0aVSomC/DykpiTB3t5BryDwbi8yU7OK4Kylyr3va6HRaFBcXIjU1GS4uLo16ufY3Wb/EokEffxr/p6pr8rXY+XrlEhfDL+IiIiIqMkplGq88+NhnLx2W2fcxcEKXy2fDnfnpt0N0ZjUapX2cVO+GQcASR3zVX5DfrfvmD4KCwtQXFQEhVIOjbqiP1Hl5vUa4f4NCYzP2toGllZWFU3mS4pRWloCCwvLZn3NuztZAoCbu0etx7q5eSAi/AYAID09tc7wSyyR1PicPr2vmoOguXcNiUVNey13dPPAjJlzodaoUVhQgJTkRMTGRuHG9VBERtzE5Ckzq6zkup82WJFK0ce/b5PWN3rMBGjUGpSVlSIvLxe3boUhLS0FaWkpiIy8hRkzHq51NV9CQjxy7mw40rt33zr7vhlTH/9AdO3aHUqlAgUF+YiOjkBmRjrOnzuF8FvXMWvOfL03H6gsNzdHuwtnd9+eTfr9Wfln692eakT1wfCLiIiIiJpUmVyJ/3x7EJfCdXcndHWwwlf/Nx1uTm0n+AIAieTer9Qt+U1ZRkYarl65hLi4aG0z6tbGxtpW25S7vKysWcMvQRC0t1dZWFrWuRKq8mqtgoL8Rr22pJZgrDlJpPdet/JunU3B1tZO51a//gMGISMjDbt2bEFpaQn279uFBQufgV0NoUtGRpp2x1M/v14wN6/+Ft+G6ty5q87Hg4cMx8njR3DtWggSE+Jx/PhhjBs/pcbzr16pCOZEIhEC+vZv0tqamqurm87HDwwahps3w3D44F4UFORjb/B2PLrgqXpfh3c/B0DT78KpkN+7HiUN6N9H1LRxPhERERG1a0Wlcixf+3eV4KujozW+boPBF6B7q2O5vGWGSmfPnMCm3zcg/Nb1Vht8AborouTypg1n7ldWVqpdTWeqx0os3dpa5+fY1PTetdzcn18AcHHpiGEPjgJQcXvnlUrhyf2uXrmkfRzYDI3u7yeVSjFqzATY3Ansbt64VmOvqfy8XNyOjwVQsRuoMft5NVSvXv7w9esFAMjOztS55Vcf5eXl2l04XTu6VQnYGkteKYw1M23a28upfWBkSkRERERNIq+oDMvX/o2opGydcS9XO6xdGgRn++p76rV2tra2SLvTg6qosBBOlRrBtwRh167gwvnTACpWpfTq5Q+/Hr3h6OQMMzNz7e1EBw/swa2bYcYstU4C7t2WJxI3XTP2al+rHreRVpxw72FT3/5qKBKJBBYWligtLYFKpaxz44OmUHlnzoQ7t8zdr6SkGFGRtwAA7u6ecG7iXVVrIhaL0blzF4RduwKNRoPExNvoUU0vr6tXL2mvl+bYgdJQunT10d66m3A7Dt2799D73BvXrzbzLpyl2sdWVm3vjyjU/Bh+EREREVGjZeUV46U1e5GQnq8z3t3TCV++NBX21o1pxd6yubi4ICIiHACQkZkBb+/G7ZbWlARBwPnzp7Qfjx03uUmbUBuaUnGv0bWZWfNeU5VXQSkUdTfYVijvrUyxaOJb8gzJwdEJpaUlAIDMrAy4udXe66yxLCwsIRaL7zRdL6r2mISEeO1mCSkpSfjyiw9rnfPWzTBtkNuQnR8rq7wRRHFR9fVV3iVy+9Y/6pxzw49fax8be+fHyqwrhUo1fS2qo9FocC00BEDF17M+oZm+MrMytI8dW9gfGKh1aJ1/kiAiIiKiFiMlqxDPf7G7SvDl39UV65ZPa9PBFwB08vTSNp6Pi6t+5Yqx5OXlantk2dratergCwAKCvMBVKxgs7Rs3mb3UqkUllYVqxVLSorr3HWxID9P+9japul2MjW0yrerpac3/66aGo1G2yuvpkb/ghF76alU9za0MDGtvu9bvVcJtlCqSpt31GfThZiYSBQWFgCoaKbf1D3riooK7/0cs7OHmXnb/m8KNQ+u/CIiIiKiBotPzcXSNXuRXVCqMz7Qzx2fPD8J5qYtd8ezpmJtbY1OnbyQkHAb+fl5iI+PazGrv8rLyrSPbW3tjVhJ4xUU5KO0pGJFknMHlzob0DeFjq7uiImJBACkpibDy8u7xmNTUu71uetUy3Etnbd3V1y+dA4AkJyYgH79HmjW18uqtKLH1q76a7STlzdmzJxb51y7d/0FAPDs1Flbd+WVWw2RmZmufWxXw/fQ+IlBUNURjkZE3ERkxM2K4ydM1W7WYKnnpg1Jibdx/PhhFBbkw83dE2PHTYZNE4esWZX/rZU2cKjL3X5sYrEY/gH9mrQmAEhOStA+9r5vYwIifTH8IiIiIqIGiUzMwvK1fyO/WLe59/CAznj/mXEwlbWfXzUHDRqMxMQECIKAk6dOwMXFFRYWdd/6plKpIG3GnctMKzXjLyoqrPE4tVqNnOysZqtDX6WlJTXu4Fi5H5mXl2HCxe6+PbXh143roTWGX6WlJYiLjQZQcdtXc98q2JzcPTrB1s4eBfl5iI+PQUlJMSwt69+vLzU1GUqlstbAENBtZO/n17PaY6ytbeoVYllb2+j0ErufWq3GtdAQ+Af0q/X7Ly83R9uHzNLSCh6eXtUe16lT5zpryshI0z727NS5Xrc6FhYWYNfOv6BSVQRst+NjsWf3Vjy64GntqtPa3LwZBk9Pr1rDMpVKhbCwq9qPfX176VVbRkYaUlOSAADdfPxgZWWt13n1cf16qPZxr94BTT4/tQ+87ZGIiIiI6u1aTBr+vWpPleBrwgM++PDZ8e0q+AIAJ0cnDBxYsdKkpKQEu3bvQGZWZo3HFxYWIHj3VuwJ3tast0w5ODjC/E7/qby8HNy+HVvlmPLyMuzds13nzXl9+v00pV07t1T72ulpKbh86TwAQCKVom/gAIPU083HF3Z3ViNFRd5C+J1m4JWpVCocOrgXSmVFX7AhQ0foFUi0VCKRCP37V1zLGo0GNyoFD/qKigrHtq1/YPeuv3A97GqN13jI5fPaHQKtrKzRs5d/g+vWl1wux84df+LE8cPYtXMLCgryqz2usLAAwcHbtLdk9h8wuMlv59NXfFy0Nvi6KzMzA7m5OXWee/LEURw6sAdbt/yG5OTEao9RKZU4sH838vNyAQBduvjAybmDXrVVDi/79m3678ucnCyk3Knbs1NndHBxbfLXoPahff1WQkRERESNduFWEv6z/iDkSpXO+KwRPfHyvOEQN/MufC1V/34DUFpaihs3riM/Px/btv2Fjq4d0cnLCzbWNoBIhNLSUqSmpiAxMUHbwDvsWggCmuFNI1ARZAT2G4izZ04AAIJ3bUUf/0C4uHSESCxGZkY6wm9dR1lZKTw9vZB05/ai06eOYdLk6QZvxJ2RnoZff/kePXv5w9W1IwARUlOScONGqPbzNXDgkFpXl5SVlSItNaXK+N3eTSqVSrtK6y4zc/NqV2tJJBKMmzAVO7ZtgkajwYF9uxETHYEuXXxgYmqK/Lw83LxxDXl5FSGEh6dXq++rBgC9egXg/LnTKC0tweVL59Grd0C9VvRYmFtALBJDqVTgyOF9uBJyET7dfeHg4ASxRIKC/HxER4VrA1eJRIKg6XPq1WeqoaRSqXYzg6TE29j4y3fo0sUHnp5esLS0hFwuR1paCiLCb2j7vHXr5ot+/Zv39s/aVf8zVZ+ftHd74xUWFmDrlt/g4ekFb++usLGxgyBokJWVifBb17Whs62tnd4bBFTehdPZ2QXuHp56nVcfJ44d0T4eOHBIk89P7QfDLyIiIiLS24mr8Xjnp8NQqnQbUD82PgAvzB7cqle8NJZIJMKI4SPh7OSMc+fPory8HGnpaUhLT6v2eKlUhn79B6J3n+YNSwY+MBSZGemIiYmEWq1G6NXLVY7p4x+IMWMnYfeuv3A7PhapKUnIyEgzePj14PDROH3qGK5euVjt8/4B/TBk6Iha58jOztL2fqpOaWlJlec9PDrh4UcWVnu8p6cXgqbNxsEDeyCXyxETHYmY6Mgqx3l7d0XQtDlt4ntAKpNhzLhJ2Bu8HQqFHCeOH8HUoFl6n+/h6YW5jyzEP0cPIC0tBbm52bhwPrvaY+3tHTB56ky4uHRsqvJrJZFIMDVoFi6cP42Qy+ehVCoRHRWO6KjwKseKRCL0HzAYQ4eNNOrXtUsXH5w6eVRn04UOHVxh7+BY57n9BwyGlbUNTp04iqKiQiQnJej00Kqss3dXTJw0Te+G8tdCQ7ShdHOsxoyMuImEhIrbTv169IZX55bRS5FaJ4ZfRERERKSXAxei8OHGY1BrdG9henb6QDwxuV+beNPfFHr06Ilu3bohKioK8bfjkZOTjbKyMojFYpiZmcPe3h5dunaHr1+vBvVSqi+xWIyg6XNw69Z13LpxDVlZmVCplLC0tIKbuyf6+AfCw6MTACBo2mycO3sK0dER6GyExtIDHxgKT08vXAm5iOSURJSVlkJmYgJXFzf0CQiEj4+fwWsCgK7dfPHEIndcCw1BfFwMCgrzoVQoYGZmDhfXjujZyx8+Pn5t6nvAx8cPfj16IyL8BqIib6FTp871WtXWwcUV8x59ErfjYxETE4mUlCSUFBdBrVbDysoaDg5O8PXrhW4+vs3a9646YrEYQ4aOQB//QERG3ER8fCzycnNQVlYKU1MzWFlZo5OXN3r18oeDo5NBa6uOtY0Nps+cixPHD6MgPx/u7h4YO36K3tebr29PdO3ig8iocMTHRSMzIx0lJSWQSMSwtLJGR1c39OjZB5569C6rrKysFDKZDBKJFH49ejfgX1azvNwcHD1yAEDFLbGjx0xs0vmp/REJbWVfVmq1FGVFEITaty+WmVlBLJZAo1FDWV5soMqoteN1Qw3B64Yaqq1fOztO3MTnm09VGV/68FA8Mrb5+/S0JSKRGCbmTd8UurWq3Ito+ctvGrkaqkwul2Prll+RlZUJsViMWXPm69XcndoPlUqFgoI8ODo6N9mc5eVl2LzpF+Tn5UIqlWHOw4+26k0kqGVgw3siIiIiqtX24zeqBF9ikQhvLBzF4IuoDTM1NcWsOfNha2cPjUaDPbu3ITUl2dhlUQsilUqbNPiSy8uxa8cW5OflVqxanTabwRc1CYZfRERERFSjv89G4Is/T+uMScRivPf0WAQNM84taERkOJaWVpg9Zz5sbGyhUMixY/vmGncNJGqM8vIybN+6CWlpKRCLxZg4aTq8u3QzdlnURjD8IiIiIqJq/RMSi49/O6EzZiKT4NPnJ2LsAL4hIWov7Ozs8cj8J+Ds3AFKpQJJibeNXRK1Qfn5ecjJyYZMJsOMmXPh16OXsUuiNoQN74mIiIioijPXE/DuT0ehqdQeVioR45N/TcTgXp2MWBkRGYOVlTUefuRx3LoZhsB+A41dDrVBrq5umD7jIZiamsK1o7uxy6E2huEXEREREekIiUzBm98dglpzb0MasUiE954ex+CLqB0zNTVl8EXNyqtzF2OXQG0Uwy8iIiIi0roRl4HXvtkPhUqtM/7mE6Mxuh/flFDT8+zUGS4uHWFja2vsUoiIqI1i+EVEREREAICopGy8/NXfKJOrdMZfmT8ckwd3N1JV1NYNHjLc2CUQEVEbx4b3RERERITb6XlYtnYvikoVOuMvzh6M2SPZdJiIiIhaL4ZfRERERO1canYhlq7ei/yicp3xRVP647EJfY1TFBEREVETYfhFRERE1I5l5RXjpdV7kJVfojP+yFh/PDNtgJGqIiIiImo6DL+IiIiI2qm8ojK8tGYvUrOLdManP9gDLz00BCKRyEiVERERETUdhl9ERERE7VBRqRzL1u5FQnq+zviEgd3w6qPDGXwRERFRm8Hwi4iIiKidKS1X4uWv9iE6KUdnfHhAZ7z15GhIxPwVkYiIiNoO/mZDRERE1I7IlSqsWH8AN+IydMYH9vDA+8+Mg1QiMVJlRERERM2D4RcRERFRO6FSq/HW94cREpmiMx7QzRWf/GsiTGVSI1VGRERE1HwYfhERERG1Ayq1Bu9t+AdnrifojPt1csZnL06GuanMSJURERERNS+GX0RERERtnEYj4KNfj+NoSKzOuHdHe6x6aQqszE2NVBkRERFR82P4RURERNSGCYKAlZtO4sCFKJ1xd2cbrFkWBDsrcyNVRkRERGQYDL+IiIiI2ihBEPDlX2cQfDpcZ9zVwQrrlk+Dk62lkSojIiIiMhyGX0RERERtkCAI+GbnBWw7dkNn3MnWAmuXT4Org7WRKiMiIiIyLIZfRERERG3QT3sv449DoTpj9tbmWLt8GjycbY1TFBEREZERMPwiIiIiamN+O3gVG/4O0RmzsTTF2mVB6Oxqb6SqiIiIiIyD4RcRERFRG7LlaBjW77ygM2ZlboI1S4PQ1d3RSFURERERGQ/DLyIiIqI2YtfJW1iz9azOmIWpDKuWTIVvJ2cjVUVERERkXAy/iIiIiNqAfecisXLTSZ0xU5kUn/17Mnp3cTFSVURERETGx/CLiIiIqJU7cjkGH/16XGfMRCrByhcmIdDHzThFEREREbUQDL+IiIiIWrETofF4b8NRaARBOyaViPHhcxMwsIeHESsjIiIiahkYfhERERG1UuduJOLtHw5DrbkXfEnEIrz/zDgM6+NlxMqIiIiIWg6GX0RERESt0OWIZLz+3UGo1BrtmEgEvL1oDEYFdjFiZUREREQtC8MvIiIiolbmWkwaXvvmABRKtc74GwtHYcJAHyNVRURERNQyMfwiIiIiakVuxmfg5a/2oVyh0hl/Zf5wTB3qZ6SqiIiIiFouhl9ERERErURkYhaWr/0bpeVKnfGXHhqC2SN7GakqIiIiopaN4RcRERFRKxCbkoNla/5GcZlCZ/xfMx/AvHEBRqqKiIiIqOVj+EVERETUwt1Oz8NLq/eioKRcZ/ypqf3x+KR+RqqKiIiIqHVg+EVERETUgiVnFuClL/cgr6hMZ/yxCX3xdNAAI1VFRERE1How/CIiIiJqodJyirBk9R5kF5TqjD88ujdemDUIIpHISJURERERtR4Mv4iIiIhaoMy8Yiz5cg8ycot1xmcO74llc4cx+CIiIiLSE8MvIiIiohYmp6AUL63eg9TsQp3xKUN88cr84Qy+iIiIiOqB4RcRERFRC5JXVIaXVu9BYkaBzvi4Ad3w+sKREIsZfBERERHVB8MvIiIiohaisESOZWv3Ij4tT2d8ZF9vvLNoNCRi/upGREREVF/8DYqIiIioBSgpU2D52r2ITsrRGR/apxPef2YcpBKJkSojIiIiat0YfhEREREZWWm5Ei9/tQ/hCVk64wP93PHhsxMgkzL4IiIiImoohl9ERERERlSuUOK1b/YjLDZdZ7yvT0d8+sIkmMqkRqqMiIiIqG1g+EVERERkJHKlCv/59iCuRKXqjPfu4oLPXpwMMxOZkSojIiIiajsYfhEREREZgVKlxts/HMbFW8k6435ezli1ZAoszUyMVBkRERFR28Lwi4iIiMjAVGoN3ttwFKfDEnTGu7k7YvVLU2FlbmqkyoiIiIjaHoZfRERERAak0Qj46Nfj+OdKnM64d0d7rFkWBBtLMyNVRkRERNQ2MfwiIiIiMhBBEPDZ5pM4cCFKZ9zD2QZrlgXB3trcSJURERERtV0Mv4iIiIgMQBAErNl6FrtPheuMuzpYYe3yaXCytTRSZURERERtG8MvIiIiIgP4bvdF/PXPdZ0xJ1sLrF0+Da4O1kaqioiIiKjtY/hFRERE1Mx+2ReCXw9c1RmzszbD2uXT4OFsa6SqiIiIiNoHhl9EREREzejPI9fwffAlnTFrC1OsXToNnV3tjVQVERERUfvB8IuIiIiomew8eRNrt53TGbM0M8HqpVPRzcPRSFURERERtS8Mv4iIiIiawb5zkfhs0ymdMTMTKb7492T08OpgpKqIiIiI2h+GX0RERERN7OjlGHz063GdMROpBCtfmAT/bh2NUxQRERFRO8Xwi4iIiKgJnbgag/9u+AcaQdCOSSVifPTcBAzw8zBiZURERETtE8MvIiIioiZy7no8Xv96D9QajXZMIhbhvafHYWgfLyNWRkRERNR+MfwiIiIiagJXIpPw8podUKrU2jGRCHjridEY3a+LESsjIiIiat+kxi6gpbh+/Tp27NiBCxcuIC0tDYIgwNHREX379sW0adMwatQoveYpLi7Gxo0bcfjwYSQlJUGj0cDd3R1jx47FE088AQcHh1Y5DxEREdXsZnwGlq/ZC7lCpTP+nwUjMXFQdyNVRUREREQAIBKESg0p2qHCwkK8/fbbOHDgQK3HjRgxAmvWrIGFhUWNx8TGxmLx4sVISUmp9nknJyesX78e/v7+tb5WS5unuSnKiiAImlqPkZlZQSyWQKNRQ1lebKDKqLXjdUMNweuG6isqKRtLvgxGUalCZ3z5I8Pw8Og+RqqKWjKRSAwTc2tjl0FERNRutPvwa/Xq1Vi/fj3s7OywYMECjBkzBh4eHigvL0dMTAx+/fVXHD9+HAAwfvx4fPXVV9XOU1xcjBkzZiA5ORkymQzLli1DUFAQzMzMcPnyZXz66adITEyEk5MTdu3aBWdn51YxjyEw/KLmwuuGGoLXDdXH7bQ8vLBqN/KLynXGX5g1CAsmBhqpKmrpGH4REREZVrvv+bV06VK8++672L9/P5YsWYJevXrB1tYWLi4uGDZsGL777jvMnz8fAHD48GFERUVVO89PP/2E5ORkAMDKlSvxzDPPwNXVFXZ2dhg3bhxefPFFAEB2dja++eabGutpafMQERFR9VKyCvHS6j1Vgq/FM4Yw+CIiIiJqQdp9+CUSifDoo4/W2vtq3rx52sexsbFVnler1di8eTMAIDAwEFOmTKny/Hfffaf9eMeOHSgrK2vx8xAREVH1MvOKsXTNHmQXlOqML5g8EItnDjVSVURERERUnXYffulDobjXw6NDhw5Vnr9y5Qry8vIAAJMnT67y/Pbt2xEXF4c5c+YAAMrLy3H69OkWPw8RERFVlVtYhpdW70VqdpHO+JzRAVg2bzREIpGRKiMiIiKi6jD80sPGjRsBAD4+PujXr1+V52/cuKF9HBioe5tDWVkZ1q1bh4CAACxdurTac1rqPERERKSrsESO5Wv3IjEjX2d84iAfvLZwHIMvIiIiohZIauwCWhqNRgOlUomSkhLcunULv/zyC06dOgU3NzesWbOm2l9q4+LitI89PDx0ntu4cSMyMzPx+eefw8XFBVKpFCqVSuecljoPERER3VNSrsDLX/2N6OQcnfGRfb3x5uOjIRYz+CIiIiJqiRh+3Sc4OBgrVqzQfuzo6IglS5bg8ccfh42NTbXn5Obmah/b29trH+fl5eHHH3/EiBEjMGjQIACAjY0NcnNztbcltuR5DEVqalHnMSKRWPv/MjOr5i6J2gheN9QQvG6oOuUKJf7z7d+4GZ+pMz6kT2d89OIMmMikvHaIiIiIWiiGX/eJj4/X+TgnJwe//fYbSkpKsGTJElhYVA1q7jaLNzEx0VkZ9u2336K4uBgvv/yydszExAQAUFqq2yC3Jc5jKGKxRO9jRSIRRCL9jycCeN1Qw/C6obuUKjX+89UehEQk6Yz38/XEZy/NhpmpTGec1w4RERFRy8Lw6z7Lly/H8uXLUVJSguTkZOzduxe///47NmzYgNOnT2Pz5s2wsqr+r7li8b0WaikpKdi0aROCgoLg5+enHddoNHXW0NLmaW4ajbrOY0QiMUQiEQRBgCAYv2ZqHXjdUEPwuqHKVGoN3lq/F2fCdNsD9PR2xRfLZsJUJtb+d4zXDtVHff74R0RERI3D8KsGlpaW8PX1ha+vL8aMGYNHH30UUVFRWL16Nd566y2dY++uBlMqldqx1atXQxAEnabyACCXy3XOacnzGIpKXlrnmwSZmRVEIgkEQQNlebGBKqPWjtcNNQSvG7pLoxHw4a/HcPRylM54V3cHrPr3JJiKlFCW3/tvLa8d0pdIJIaJubWxyyAiImo3uNujHgIDA9G/f38AwM6dO6s87+DgAABQq9UoKipCREQE9u7di3nz5sHT01N7nFKpRFFRkc45LXkeIiKi9koQBKzachr7z+sGX51cbLFmaRBsLM2MVBkRERER1RfDLz117twZAFBcXKzTUB4AunTpon2ckpKCzz//HObm5njhhRd0jktLS9PeZujt7V3lNVraPERERO2RIAj4ZucF7DhxU2fc1cEKa5ZOg4ON8VZLExEREVH9MfzSU3l5OYCKJraWlpY6z/Xu3Vv7eOPGjTh16hSeeuqpKquprly5on3cp0+fKq/R0uYhIiJqj37ZfwV/HArVGXO0scDaZdPg4sBdHImIiIhaG4ZfelCr1QgJCQEAeHl5wdTUVOf5wMBA2NvbAwB27NgBR0dHLFq0qMo8Bw4cAACYmZlh2LBhVZ5vafMQERG1N1uOhuGH4Es6Y7aWZlizLAgeHWyNVBURERERNUa7D79++OEHLFy4EPn5+TUes379eqSmpgIAHnvssSrPSyQSzJ8/X/vxk08+WWV12NmzZ3H8+HEAwJw5c2Bubt7i5yEiImpP9p6JwJqtZ3XGLM1MsHrpVHRxY29MIiIiotZKJAiCYOwijCUiIgKPPPIIysvLYWdnh4ULF2L06NHw9PSEWq1GZGQkNm3ahIMHDwIABg8ejB9//BEymazKXMXFxZgxYwaSk5Ph6uqKFStWYPDgwdBoNDh8+DBWrlyJ0tJSODk5YdeuXXB2dq62ppY2jyEoyor02u1RLJZAo1FzBy3SG68bagheN+3TidB4vPndIWgq/VpkZiLF6pemwr9bR73m4LVD+uJuj0RERIbVrsMvAAgJCcEbb7yB27dv13pcUFAQ3nvvPVhZ1dzrIzY2FosXL0ZKSkq1zzs5OWH9+vXw9/ev9bVa2jzNjeEXNRdeN9QQvG7anyuRKfi/dfugUKm1YzKpGJ+/OAUDe3joPQ+vHdIXwy8iIiLDavfhF1DR02vfvn34559/EBYWhuzsbEilUri4uKB///6YMWMGBgwYoNdcxcXF2LhxIw4dOoSkpCQIggA3NzeMHTsWTz75ZJWm861lnubE8IuaC68bagheN+1LZGI2Xly1G6XlSu2YWCTCh89NwMi+9dsJmdcO6YvhFxERkWEx/CKjY/hFzYXXDTUEr5v2IykjH//6fDfyisp0xl9fOBLThvWo93y8dkhfDL+IiIgMq903vCciIqL2Jyu/BMvW/l0l+Hp+1qAGBV9ERERE1HIx/CIiIqJ2pbBEjuVr/0ZaTpHO+PxxAVgwoa9xiiIiIiKiZsPwi4iIiNqNcoUSr32zH3GpuTrjUwZ3x4uzB0MkEhmpMiIiIiJqLgy/iIiIqF1QqdV464fDCItN1xkf1scL/1k4EmIxgy8iIiKitojhFxEREbV5Go2Aj349gbPXE3XGA7q54oPF4yGVSIxUGRERERE1N4ZfRERE1KYJgoB128/hwIUonfFu7o5Y+cJkmJpIjVQZERERERkCwy8iIiJq0347eBVbjobpjLk52WDVS1NgbWFqpKqIiIiIyFAYfhEREVGbtfvULXy766LOmIONOVYvnQonW0sjVUVEREREhsTwi4iIiNqk41fj8NmmUzpjlmYmWLVkKjycbY1UFREREREZGsMvIiIianNCIlPw7k9HoBEE7ZiJTIKVL05Cd08nI1ZGRERERIbG8IuIiIjalIiELKxYfwBKlUY7JhGL8L9nxiPQx82IlRERERGRMTD8IiIiojYjJjkHy9buRWm5Umf8PwtGYnhAZ+MURURERERGxfCLiIiI2oT41Fy8tGYPCkvkOuP/nj0YU4f6GakqIiIiIjI2hl9ERETU6iVm5OOl1XuRX1SuM75wYiAendDXOEURERERUYvA8IuIiIhatZSsQiz5cg9yCkt1xh8Z0wf/mvmAkaoiIiIiopaC4RcRERG1Wum5RVjyZTCy8kt0xmeN6ImXHh4KkUhkpMqIiIiIqKVg+EVEREStUlZeMZZ8uQfpucU649OG+eHlecMZfBERERERAIZfRERE1ArlFJRiyeq9SMkq1BmfNKg7XntsBMRiBl9EREREVIHhFxEREbUqeUVleGnNHiRm5OuMj+3fFW88PgoSMX+9ISIiIqJ7+NshERERtRqFJeVYtmYv4lPzdMZHBHTGu0+NgVTCX22IiIiISBd/QyQiIqJWobhMjmVr/0Z0co7O+JDenfD+M+MhlUiMVBkRERERtWQMv4iIiKjFKylX4P/W7UNEQpbO+MAeHvjouQkwkTH4IiIiIqLqMfwiIiKiFq1MrsSrX+/HjbgMnfFAn4749PmJMJVJjVQZEREREbUGDL+IiIioxZIrVFix/gBCo9N0xvt0ccFnL06BmYnMSJURERERUWvB8IuIiIhaJIVSjde/O4jLESk64z28nPHFkimwMGPwRURERER1Y/hFRERELY5KrcbbPx7G+ZtJOuPdPZ3w5UtBsDI3NVJlRERERNTaMPwiIiKiFkWt0eB/vxzDqWu3dca7uDlg9dKpsLFk8EVERERE+mP4RURERC2GRiNg5R8ncfhSjM64l6sd1i4Lgp2VuZEqIyIiIqLWiuEXERERtQiCIGDN1jPYcyZCZ9zNyQZrlwbBwcbCSJURERERUWvG8IuIiIhahO+DL2HrsRs6Yx3sLbFuWRCc7a2MVBURERERtXYMv4iIiMjofj1wBRv3X9EZc7Axx9pl09DRycZIVRERERFRW8Dwi4iIiIxq67Hr+HbXRZ0xawtTrFkahE4udsYpioiIiIjaDIZfREREZDR7z0Tgyy1ndMYszGT48qWp6OruaKSqiIiIiKgtYfhFRERERnH4Ugw+/v24zpipTIrPX5yCnp07GKcoIiIiImpzGH4RERGRwZ26dhvv//wPBOHemEwqxifPT0Rfn47GK4yIiIiI2hyGX0RERGRQl8KT8dYPh6DWaLRjErEI7z8zHoN6ehqxMiIiIiJqixh+ERERkcFci0nDivUHoFTdC75EIuDtJ8dgZF9vI1ZGRERERG0Vwy8iIiIyiPCETLzy1X6UK1Q64yseG4kJD/gYqSoiIiIiausYfhEREVGzi03JxfK1f6OkXKEzvvThoZj+YA8jVUVERERE7QHDLyIiImpWSRn5WLpmDwpL5Drjz04fiEfG+hupKiIiIiJqLxh+ERERUbNJyy7ES6v3IrewTGd84cRAPDG5n5GqIiIiIqL2hOEXERERNYuM3GL8+8s9yMgr1hl/aFRv/GvmAxCJREaqjIiIiIjaE4ZfRERE1OSyC0qw5MtgpOUU6YxPHeqLZXOHMfgiIiIiIoNh+EVERERNKrewDC99uRfJWYU64+MHdsN/FoyEWMzgi4iIiIgMh+EXERERNZmC4nIsXbMHt9PzdMZHBXbB20+OgUTMXz2IiIiIyLD4GygRERE1icISOZau2YvYlFyd8Qf9vfDe02MhlfDXDiIiIiIyPP4WSkRERI1WUqbA/637G1FJ2Trjg3t54oPFEyCTSoxUGRERERG1dwy/iIiIqFFKy5V4+at9uHU7U2e8v687Pn5uIkxkDL6IiIiIyHgYfhEREVGDlSuUeO2b/QiLTdcZD+jWEStfmARTE6mRKiMiIiIiqsDwi4iIiBpErlRhxfqDuBKVqjPey9sFn/97MsxNZUaqjIiIiIjoHoZfREREVG9KlRpvfn8Il8KTdcb9Ojlj1ZIpsDQzMVJlRERERES6GH4RERFRvajUarzz4xGcvZ6oM97N3RGrl06FtYWpkSojIiIiIqqK4RcRERHpTaXW4L2f/8GJ0Hidce+O9lizLAg2lmZGqoyIiIiIqHoMv4iIiEgvao0GH/16HEcvx+qMd3Kxxdpl02BvbW6kyoiIiIiIasbwi4iIiOqk0QhY+cdJHLgQpTPu5mSDdcumwdHWwkiVERERERHVzmj7j6vVakRHRyMjIwNFRUVQqVSYOXOmscohIiKiGgiCgK92nMOeMxE64y4OVli3fBqc7a2MVBkRERERUd0MHn5dvXoVv/76K44dOwa5XK7zXHXhV3BwMAIDA+Hp6WmgComIiKiy3w6G4s8jYTpjznaWWLd8Gjo6WhupKiIiIiIi/Rgs/BIEAR9++CE2bdoEQRAgCILO8yKRqMo5X375Jb7//nsMHjwYP//8s6FKJSIiojuCT4fj210XdMbsrc2xdtk0eDjbGqkqIiIiIiL9GSz8evvtt7F9+3YIggA3NzdMmzYNPXr0QHx8PNasWVPtOf3794cgCDh//jzCwsLg7+9vqHKJiIjaveNX47Dyj5M6Y5ZmJli1ZCq8XO2MUxQRERERUT0ZJPw6e/Ystm3bBpFIhLlz5+Ktt96CiYkJAOD48eM1njdixAj06NEDERERCA4OZvhFRERkICGRKXj3pyPQVFqpbSKVYOULk+DbycmIlRERERER1Y9Bdnv866+/AAA9evTA+++/rw2+9DF27FgIgoBLly41V3lERERUSWRiFlasPwClSqMdE4tEeP+ZcQjs7mbEyoiIiIiI6s8g4VdoaChEIhFmzZpV73O9vb0BAKmpqU1dFhEREd0nMSMfy9f9jdJypc74fxaOxIi+3kaqioiIiIio4QwSfuXk5AAAOnbsWO9z764SKysra9KaiIiISFdWXjGWrdmL/KJynfEXZw9G0FA/I1VFRERERNQ4Bgm/zM3NAdwLweojPj4eAGBnZ9eUJREREVElhSXlWLb2b6TnFuuMPzY+AI9N6GucooiIiIiImoBBwi9fX18AwOHDh+t1niAICA4OhkgkQs+ePZujNCIionavTK7Ey1/tR3xans540FA/vDB7sJGqIiIiIiJqGgYJv6ZMmQJBEHDmzBns2rVL7/PWrl2LmJgYAMDkyZObqToiIqL2S6lS483vD+FmfIbO+IiAznjtsREQiURGqoyIiIiIqGkYJPx66KGH0K1bNwiCgDfeeAMffPABYmNjazw+PDwcL7/8Mr799luIRCJ0794d06dPN0SpRERE7YZGI+CDjcdw/maSznigT0e898w4SCUG+TWBiIiIiKhZiQRBEAzxQunp6Zg3bx7S09O1f0U2MzODqakp8vPzIRKJ0KlTJ+Tk5KCkpARAxW2PLi4u+Ouvv+Di4mKIMskIFGVFEARNrcfIzKwgFkug0aihLC+u9Viiu3jdUEO0l+tGEAR8+dcZbDt2Q2fcx9MRX//fdFiZmxqpstarvVw71HgikRgm5tbGLoOIiKjdMNifdF1dXbFlyxaMHz8egiBAEASUlZWhoKBAG4YlJiaiuLhY+/zIkSPx559/MvgiIiJqYr/su1Il+PJwtsGXS6Yy+CIiIiKiNsVgK78qu3r1Kvbt24cTJ04gNTUVKpUKACCVSuHi4oIRI0Zg4sSJGDyYTXbbA678oubC64Yaoj1cNztO3MTnm0/pjDnZWuDbV2fCzcnGSFW1fu3h2qGmwZVfREREhmWU8Ot+eXkVu0vZ29sbuRIyBoZf1Fx43VBDtPXr5vjVOLz5/SFU/q+/tYUJvnl5Brq6OxqvsDagrV871HQYfhERERmW1NgFAAy9iIiIDCE8IRPvbfhHJ/gylUnx+YtTGHwRERERUZvFbZyIiIjagYzcYrz29QHIlSrtmEQsxofPjkefrq5GrIyIiIiIqHkx/CIiImrjSsuVeO2b/cgpLNUZf+2x4Rjax8tIVRERERERGQbDLyIiojZMrdHg3Z+OIDo5R2f8sfEBmDash5GqIiIiIiIyHIP0/Hr88cerjIlEIqxatQqOji2nx0hISAiCg4Nx8eJFZGdnQy6Xw9nZGX379sXs2bMxbNiwWs/fsWMHXn/9db1e66mnnsKKFStqPaa4uBgbN27E4cOHkZSUBI1GA3d3d4wdOxZPPPEEHBwc9HqtppqHiIhan6+2n8eZ6wk6YyMCOuP5WdxRmYiIiIjaB4OEXxcvXoRIJELljSVFIhHkcrkhXr5OZWVleOeddxAcHFzlueTkZCQnJ2Pv3r2YPXs2PvjgA0gkkka/Zq9evWp9PjY2FosXL0ZKSorOeHR0NKKjo7Ft2zasX78e/v7+BpmHiIhan10nb2HL0TCdMd9OTnj3qbEQi0VGqoqIiIiIyLAMEn6JRBW/YPv4+MDOzk47bmpqaoiXr5VCocAzzzyDy5cvQywW45FHHsGMGTPg5eUFpVKJ8PBwrFq1CpGRkdixYwccHR3xyiuv1DlvWFhYrc/LZLIanysuLsazzz6LlJQUyGQyLFu2DEFBQTAzM8Ply5fx6aefIjExEc8//zx27doFZ2fnZp2HiIhan0vhyfjiz1M6Y062Flj5/CSYm9b83yAiIiIiorbGIOGXjY0NCgsL8eyzz2LatGmGeEm9mZiY4LHHHkNiYiI++eSTKrc2uri4YNCgQZg2bRqSkpLw66+/4umnn4a9vX2t8zYm2Pvpp5+QnJwMAFi5ciWmTJmifW7cuHEoLi7GihUrkJ2djW+++Qbvvvtus85DRESty+20PLz5/SGoNfdWXJuZSPHZi5PhbG9lxMqIiIiIiAzPIA3v/fz8AFTc/tgSTZkyBceOHauxp5e5uTkWLFgAAJDL5c3671Cr1di8eTMAIDAwUCewuvv8d999p/14x44dKCsra7Z5iIiodckvLsMrX+9HcZlCOyYSAf99aix8O3GFLxERERG1PwYJvx5++GEIgoDdu3cjOjq6XuceP34cPXr0QM+ePZupugpSae2L4Ly9vbWPs7Kymq2OK1euIC8vDwAwefLkKs9v374dcXFxmDNnDgCgvLwcp0+fbrZ5iIio9VAo1Xj924NIzS7UGX9x1mCM6Otdw1lERERERG2bQcKvoKAgzJo1CwqFAosXL0Z4eHi9zhcEQadZvjGUlpZqH9va2tbrXI1Go/exN27c0D4ODAzUea6srAzr1q1DQEAAli5dWu05TT0PERG1DoIg4OPfj+NaTLrO+LRhfpg/PsBIVRERERERGZ9Ben4BwMcffwwnJyf8/PPPePjhh7FgwQIsWrQILi4uhiqhUSo3sO/bt2+dxwcHB2PXrl2IiopCdnY2zM3NERAQgAULFmDcuHE1nhcXF6d97OHhofPcxo0bkZmZic8//xwuLi6QSqVQqVQ65zT1PIYgNbWo8xiRSKz9f5kZ+9WQfnjdUEO01uvmp+BzOHhBd3X1gB6eeH3RZMikjd+lmOrWWq8dIiIiorbOYOGXSqXC3Llz0aFDB3zxxRfYuHEjfv/9dwwePLjWMOn27duGKrFGCoUCe/bsAVCxisrT07POc1599VWdj0tLS3Hu3DmcO3cOCxcuxFtvvVXtebm5udrHlZvq5+Xl4ccff8SIESMwaNAgABUbCeTm5mpvb2yOeQxBLNb/TZlIJIJIxDdxVD+8bqghWtN1c+hCOL7dcUZnzMvVAZ+9NBumJiZGqqr9ak3XDhEREVF7YJDwa/jw4cjJyaly66JKpcKZM2dw5syZGs5sGb7//nttn6+XXnqpxuPc3d0xceJEyGQyDBw4EIMGDYKrqysUCgXOnz+Pzz77DElJSfjtt9/Qs2dPzJ49u8ocd5vOm5iYQCQSace//fZbFBcX4+WXX9aOmdx5Q1P5lsymnscQNBp1nceIRGKIRKI7t8DqfxsptW+8bqghWtt1cyM2Df/9fp/OmK2lGb5cPgtW5jK9fsZS02ht1w4ZV33++EdERESNY5Dwq7YG8cbu5VWXK1eu4NtvvwVQ0bh/6NChNR47aNAg7WqqyszNzTFx4kT4+/sjKCgIxcXFWL9+fbXh111i8b12bCkpKdi0aROCgoK0O2cC+vUSa6p5mpNKXlrnmwSZmRVEIgkEQQNlebGBKqPWjtcNNURrum7Scorwf6t3QK5UacekEjE++tcEuNrKWnz9bU1runbIuEQiMUzMrY1dBhERUbthsNseRSIRli9frle/rMpCQ0OxatWq5imqDsnJyXjxxRehVCrRu3fvGm9V1FfHjh0xadIkbNu2DYmJiUhKSqpyC6WFRUX/K6VSqR1bvXo1BEHQaU4PAHK5XOec5piHiIhappIyBV79ej/yisp0xv+zYCQCfdyMVBURERERUctjsPALAHx9ffHAAw/U6xxj3YqXkZGBRYsWITc3F56enli/fj3MzMwaPa+Pj4/2cWJiYpXwy8HBAQCgVqtRVFSElJQU7N27F4899pjOsUqlEkVFRTrnNMc8RETU8giCgPd/+Qdxqbk6449PCsSUIb5GqoqIiIiIqGUS131I02jptzdWlp2djUWLFiExMRHu7u7YuHEjOnTo0CRzm5ubax9XXpV1V5cuXbSPU1JS8Pnnn8Pc3BwvvPCCznFpaWna2xW9vb2bbR4iImp5th67gVPXbuuMjQrsgmen1+8PTERERERE7YFBVn5FREQ0+NxRo0Y16vz6ysjIwBNPPIH4+Hh4eXlhw4YNcHd3b7L5K/c/c3FxqfJ87969tY83btyIU6dOYcmSJVVWZV25ckX7uE+fPs02DxERtSyRiVn4esc5nTG/Ts54Z9FoiMWiGs4iIiIiImq/DLbyqzVISkrC/PnzER8fDz8/P2zatAkeHh5N+hpnz54FAFhbW1e70iowMBD29vYAgB07dsDR0RGLFi2qctyBAwcAAGZmZhg2bFizzUNERC1HSbkCb/9wGErVvU1CrMxN8MGz42FmIjNiZURERERELRfDrzuioqIwf/58pKSkYOjQofjjjz/g5OSk9/mCIOCTTz7BkSNHajxm//79CAkJAQDMnDmz2h5iEokE8+fP13785JNPwtLSUueYs2fP4vjx4wCAOXPm6NxK2dTzEBFRyyAIAlb+cRLJWYU64/9ZMBJuTjZGqoqIiIiIqOUTCa2pGVczuXr1Kp577jkUFBRg8uTJ+OCDDyCT1fwXdJFIBBMTE52xH3/8EZ999hkAYNy4cZgzZw569+4NCwsLpKamIjg4GD///DNUKhW8vLywfft2WFtXv8V1cXExZsyYgeTkZLi6umLFihUYPHgwNBoNDh8+jJUrV6K0tBROTk7YtWsXnJ2dm3We5qYoK4IgaGo9RmZmBbFYAo1Gze3jSW+8bqghWup1s+dMOD7+7YTO2KwRPfHqoyOMVBHdr6VeO9TyiERimJhX/3sgERERNT2GXwAWLlyIixcv6n18586dcfDgQZ0xlUqFL774Ar/99lu1jezv8vf3x6pVq6rs8ni/2NhYLF68GCkpKdU+7+TkhPXr18Pf398g8zQnhl/UXHjdUEO0xOsmLjUXT3+8A3KlSjvWzd0RP6yYBVMTg27cTLVoidcOtUwMv4iIiAyLvzE3QHW9uqRSKVasWIF58+Zh69atOH36NFJTU1FWVgZnZ2d0794dQUFBmDRpEqTSuj/tXbt2RXBwMDZu3IhDhw4hKSkJgiDAzc0NY8eOxZNPPlmleX1zzkNERMZRrlDi7R8P6wRfZiZSvL94HIMvIiIiIiI9GGXlV2hoKPbv34+wsDAkJSWhqKgIEokEjo6O6NixI4YNG4axY8eiW7duhi6NjIArv6i58Lqhhmhp180nv59A8OlwnbE3Hx+FqUP9jFQR1aSlXTvUcnHlFxERkWEZNPyKjo7GO++8g9DQUJ3xuyWIRLpbtE+dOhWvvvoqXFxcDFUiGQHDL2ouvG6oIVrSdXPkcgze+VF3I5WJg3zwzpNjqvw3k4yvJV071LIx/CIiIjIsg+32eOnSJcybNw+hoaEQBEHnfzKZDFKptMr433//jUceeaTGflVERERtVXJWAT75XbfBvWcHW7wyfziDLyIiIiKiejBIs5CioiIsW7YMJSUlAIC+fftizpw56NevHzw9PbU7JyoUCiQlJSEkJATbt2/HtWvXkJ6ejqeeegrBwcEwNTU1RLlERERGpVSp8c6PR1Bafm8DFROpBP9bPB6WZia1nElERERERPczyMqvTZs2IScnByKRCP/3f/+HP//8Ew8//DC6du2qDb4AwMTEBF27dsXcuXOxZcsWLFu2DACQmJiILVu2GKJUIiIio/tm5wVEJGTpjC15aAi6ezoZqSIiIiIiotbLIOHXsWPHIBKJMHToUDz77LN6n/evf/0LQ4YMgSAIOHjwYDNWSERE1DKcDruNLUfDdMZG9vXG7JG9jFQREREREVHrZpDwKyEhAQAwceLEep87adIkABXN8omIiNqyjNxifLDxmM6Yq4MVXl84in2+iIiIiIgayCDhV1FREQDAwcGh3uc6OjoCAEpLS5u0JiIiopZEpdbgvxuOoLBErh2TiEV475lxsLFkz0siIiIiooYySPhlY2MDAA3atTEtLQ0AYGdn15QlERERtSgb/r6MazHpOmPPzngAfbq4GqkiIiIiIqK2wSDhl5+fHwRBwO7duyEIgt7nCYKA4OBgiEQi+Pj4NGOFRERExnM5Ihkb91/RGRvU0xOPje9rnIKIiIiIiNoQg4RfU6ZMAQCEh4fj3XffhUKhqPMclUqF999/H9evXwcATJgwoVlrJCIiMobcwlK8t+EfVP7bkKONBd5+cgzEYvb5IiIiIiJqLJFQn6VYDaRWqzF37lzcvHkTIpEIrq6umD59Ovr3749OnTrBysoKAFBSUoKkpCSEhIRg9+7dSEtLgyAI6Ny5M/bu3QupVNrcpZIRKMqKIAiaWo+RmVlBLJZAo1FDWV5soMqoteN1Qw1hyOtGrdHg5a/24eKtZO2YSASsWToNA/zcm/W1qenxZw7pSyQSw8Tc2thlEBERtRsGCb8AIDc3F48++ihu376t945VgiDAxcUFv/76K7y8vJq5QjIWhl/UXHjdUEMY8rr5Pvgiftmne7vjoin9sXj6wGZ9XWoe/JlD+mL4RUREZFgGue0RqNjpcdu2bVi0aBEkEgkEQaj1f2KxGDNnzsTOnTsZfBERUZtzOux2leAroFtHLJra30gVERERERG1TQZb+VVZbm4uTp48ibNnzyItLQ15eXlQq9WwsbGBp6cnAgICMHHiRHTo0MHQpZERcOUXNRdeN9QQhrhukjML8NTH21Fcdq8HpoONOX5+4yE421k2y2tS8+PPHNIXV34REREZllHCL6LKGH5Rc+F1Qw3R3NdNmVyJZ1fuRGxKrnZMIhZh7fJpCPRxa/LXI8PhzxzSF8MvIiIiwzLYbY9ERETtnSAI+PT3EzrBFwD8e84QBl9ERERERM2E4RcREZGBbDt+A4cuxeiMjR3QFXPH9DFSRUREREREbR/DLyIiIgO4FpOGtVvP6Yx5u9nj9QWj9N4FmYiIiOguQa0ydgnUhrW164vhFxERUTPLLijBW98fhlpzr7+hpZkJPn5uIizMZEasjIiIiFobQRCgOLge8t9XQFApjV0OtUFCST7K1j8N5ZV9xi6lyRgs/Dp37hxWr14Njab2xuaVqdVqfPrppzh37lzdBxMREbVAKrUab/9wGDmFpTrjbz85Gp1c7IxTFBEREbVKgloFxY6PoDy9CeqYi1DfOmHskqgNUp7fDiEjDoqdH0Nx4jdjl9MkpIZ4EZVKhTfffBNpaWnIycnB//73P73O+/DDD7Fp0yYcPXoUBw8e5G0hRETU6ny1/TyuxaTrjD0+KRAj+nobqSIiao0EjQaKXZ9AdfM4xB19YDbvfxBZORi7LIMS1CpALOF7Amq3BI0a8r/+qw28ZOP/Ban/OCNXRW2RbPQiCAUZUF3dD+WR7wGVAiZjnzZ2WY1ikPBr3759SE1NhUgkwsyZM/U+75FHHsHmzZuRlJSEw4cPY8KECc1XJBERURM7dCkaf/1zXWdsoJ87Fk8faKSKiKi1UsdegurqfgCAJiEMyrN/wWTCv4xclWGprh2CYs8XENm5wmTKS5D6DDJ2SU1KFXUeqtCD0CTdhFCcAwAQWTlA7OYLaY/hkPg9CJGZpV5zCWoVVFf3QX3zODQZcRBKCwCJDCLbDpB06gNJr1GQdOkPkcQgbwepiSj2fHEv+Bq3GCYjHjNyRdRWicRimMx6HYJKCfX1I1Ae/wUiSzvIBs8xdmkNZpCfdkeOHAEABAQEoH///nqf5+vri6FDh+Ls2bMMv4iIqFWJTcnBJ7/p3org4mCF954ZB4mYLTeJqJ7uX+0kaqc/R1QKCNmJEIpyjF1JkxGU8orVPBGnqz6Xnw51fjrUt07A7JmvIfHyr3M+TW4Kyn9bASE7QfcJtQpCVgJUWQlQ3TwOi1d3AAy/Wg3F8V+hurwHACAdPAcmIx83ckXU1olEIpjOeRPlJXnQxIVAsW8tRHaukPoNM3ZpDWKQn3bXr1+HSCTC6NGj633ugw8+iDNnzuDatWvNUBkREVHTKy6T4/VvD6JccW+XHJlUjI+emwA7K3MjVkZETU0TdgSav1frjImnvwpxr5HVHq/+6z0IsZdQuUW15f9O1fk6kq4DIQ2cXHHbo2s3yIY81IiqqSWR7/hIG3yJnDpBGjgZYvuOgFoFTVE2NAnXIZQV6hV8CUo5yn9ZDiEvDQAg7tIf0h4PQmTtBKG8BEJBBtSxlyD27A2RSev+75FQnAdNfjogkULS0cfY5TQrdUoklMc2AADEnXrDZNK/6z2HoNFAHXkGqhvHoEmJgFCcC2jUENk4QezeA7J+UyDpOkD/+eSlUF0/AvXNE9BkJ0EoyQWkJhBZO0PSbSBk/YMg7tC53nUCgCrmIlQhf0OTdANCcR4glUHs1AkSn0GQDpwBsY1zjeeq46+ifMNL9X5N8//7q+L7rpmV/bQEmtuh9TpHGjgJprPf1OvYxnzuqiOSSGE2912UffM0hMIsyHd9Asm/f4XIyr5e87QEBgm/cnIq/jLTuXPnep/r5uYGAMjKymrKkoiIiJqFRiPg/Z+PITmrUGf85XnD0cOrg5GqIiJDEpJvATWEX0JqZIPmFIlEMJ39Bkxnv9GY0qiF0WTEQX3jHwAVoYbZU+uq3oo4XP/5VCF7tcGX7MH5MJn4QtWDxjzV0HJbDE1eGspWzQVQEQxI9AwGWiNBpYB8+weARg1ITWD60Dv1vl1Vk5UI+a6PoUm8UXX+nGSoc5KhDjsMSZ+xMJ3xGkSmFrXOp4o6D8XuzyAUZuo+oZRDKCuCKjMOqvPbIBv7DExGLNC7TkFeCvn2D6EOP6n7hFoJTUoENCkRUJ7fDouXt0JkZqX3vO1Bc37uRJb2MJm5AvJfXwFK8iEP/gxmj37UhNUbhkHCL/Gd2zuUyvpvw6pWqwGgXrtEEhERGctvB6/idNhtnbFpw/ww/cEeximIiAxHJAYEDYTkm9U+LeSkAGV3gnGxpOLNLLVr6kphhHTAjEb34NKZb1Dr7c1D96iu7IOQdRsAIHvw0XqvTtKkx6Ds52VAaQEAQOzZG5KewyG2dQEAqJPDoQrZC8hLoL5+FHK1CmbzP6i5ntCDkO/4CBA0gFgCie9QSLoOhMjKHoKiDOqYSxWBrkYN5eHvIDIxg2xw3StVBZUS5b++Ak1iRa9UsUtXSPpOgNiuI4SyImiyE6C+fhTSwMm1hjdily4wXfCJXp8bxb6vIOQmAxa2BttAxGT8cxDKCuo8TpMcAeXxXwAA4g5daj22qT53tZH6DILK70GoI05DHX4K6sTrkHTq06C5jMUg4ZerqysSEhJw7do1BAUF1evcGzcqfoB36MC/lhMRUct24VYSvg++qDPm5+WM/5v3oJEqIiKDcusOpEQAWYkQ5KVVVk8IKRHax2L3HtAkVV2FQe1L5d5lYjuXxs9X3LTzkXEJGg2UZ/+q+EBmBtnQufWeQx1/FSgrAiQymEx/BbJ+U3Sel/YZC9mg2Sj/4XkIxblQ3zoBVcSZavs6CYIAVcRpQNBAZNsBpvP+B4lHT51jZIGToeozFvJNbwCCBoqD6yHtMw4iS7ta61Qe+V4b3sgefBSy8c9BdF+PVGHiC0Adi2JEFraQ+tbdk0qTEV8RfAGQ9Q+CSGZa5zlNQdKpt17HlV89WPFAZgppv6m1HttUn7u6yEYs0N6irTy9GZJHW1f4ZZBOmf3794cgCNi5c2e9bl8sKirCzp07IRKJEBAQ0IwVEhERNU5adiHe/ekIBOHemK2lGT58dgJMZWwoTNQeiLwDKx4IGgjJ4VWeF1LvjEmkkHi1rjcN1FyEug+p13RNPB8ZlTr6HIScJACAtM8YiMyt6z2HbMjDMF24Eqbz/lcl+LpL7OAG2fhntR+rruyr9jiRSASzef+DyeQlMFu0pkrwdZfUbxikAXc2q1MpoAo7UmuNmtxUKM9vAwBIegyHycTnq4Q3ACASSyCSymqdS1/K81srHoglkD4wq0nmbCqaggzt7YtS//EQWdjUfKwBP3cSz14Qu/kCANQRZ6DJTW3UfIZmkN/GH3roIWzfvh0lJSVYvHgxvvvuO7i41P6XiJKSEixbtgx5eXkQiUSYPn26IUolIiKqN7lChTe/P4zCErl2TCwS4f1nxqGjY/1/USWi1knsHQj16c0A7vT96qq7y/ndlV8i9x6AzKzWuTRFOShbObPG50V2rrB4eatedcn3rYXqXMWxkp4ja7ylSVApUfb1oopdAkWiije3dwO9GggqJVRX/obq+lFo0mMApRwiS3uIPXtBNmAaJN0G6lUjAAiKMijPbYX6xjFocpIAkRhiBw9Ieo+CbEj9V7w0B1Xk2Ypm0sk3IZQWAGZWEHfoAqn/WEj7TYVILDF2iU1Gkx4L5YXtUMddgVCYBUhNILbvCInfcMiGPFTvIEaQl0IVsgeq8NPQZMYD5cWAqQXE9m6QdOkHaf8giJ06VV9LpR5f91NdPQDV1QNVxmUjFsKkUqBTY10qBVShB6G+dRKajNiKr6tIDJGFDcSu3SDp9gCkARMaFDw1lvrmce1jiR6rmWoi9RlU5zESn8Hax5rMuFqP1WcFmsRnEFShB/SaT3lhB6BWAWJJ9X3qmphQWgjVtUMVdfoOa3GrJFXnd2hvi5cNrv32ZUN/7iR+w6BJjQQEDdThJyEeNq/ZX7OpGCT8CgwMxEMPPYRt27YhMjISU6dOxSOPPIIxY8bA19cXVlYV950q/p+98w6Polr/+GdmSzbZ9J5ACL33ohQBK4pir1e9197rT9Rr7/3avZZru6LXXhFRAUEU6UjvkEB679m+M/P7Y5NNlk3ZtE3A83keHjZzzpw9O3t2d+Y77/t9nU4yMzP5448/+OSTTygoKECSJGbOnMmMGTOCMVWBQCAQCNqEpmk8+eFydmf7RjZfe+YkJg3r3U2zEggEbUVzO9G2LUWrKkIedRJSXK+2D5I6BELCwGGF3J2+4zusUJINgNQ3uBkNxhOvRdm9Eq0iH2XnbygZG5qs6uZa+ZlH+AL0R53dqvClVhVj//BOtOIDPtu16mKUHcUoO35FP/40jGfe1aoopNaUYX//VrTSbN/thftQC/fh3vhjQyRJN+AxHX/Sa07vxVKJemAjzgMbcW9YgOnvzyGZm6+CphTsR6su8v6tluY0tGVvR3Na/fbRDTiq2YgNJfNPNJe9YZ7WhmIr7j0r/fpLxrBW31fwpDQ5l/zH15fO7UQt2IdasA/Xum8xXfI0urQRrY4FoOTswPHJfZ4Kg42x1aDa9qDm78G18jP0E07HeMpNrRquB4IU1/pvsFq4H/sn96NV+EewaFV2lKpilD2rcC75D4Zj/oZh+qWdFnkUCErmRs8DSULXf0LLnTuIFBblfazZajp3PGt1Cz1B2bsaADl9DHIA71tHca2fDy7PDUvD5HO6/Pnagua04/pzAVB3PJIHttg/2MdON2ASrmWeyqNK5kYMQvzy59FHH6WgoICVK1disVh4//33ef99z0HT6XTIsuxniK9pGkOGDOGFF14I1jQFAoFAIGgTH/y4kV827PfZNmNMX/5+cusXFwKBoGegWatQvnoC8jxpicr25eiu+0+bPWAkWYfUZzTavjVoBXvRFLfXwFwr2OsxiAakvmMhp2lTfO9YoRFNmjY7vn0WLBVtm5fRRMhZ/8T+we2gaTh+eJnQmz/wMVdXK4tw/fahp39MKsZZ17c4plpd4vEIqvJUe5MHTEI/fAaSOQatuhj3pp9QC/bh3rgQDCGEzPm/ZsfSnHbsH/yfV/iSolPQT5yDHJeGZqtB2b0CZe8ar/lzsNFUFfv/7kHNWO+ZX0I6+nGzkWN7odlrUXavRNn9B2rebuzz5mK67u1mjevdqz9vMkoJPL49fsg6wh5qPmXM8e0zaJWFTbf97x7/4fqOIfSqfzc7HoDz1w9wLXvP80dYFIYJc5BTBnsiPbK2eFLiLBXYP7yT0BveRY5tWShW9q/H/sm9XrFBThvpSeGLTESz16DmbMe9fTk4LLh3/obhuCv8xC8pPNbn86DVVuL8zvO33H8Chqnn+z1vcyl53jGs1djnzfUKcrqBk9ANmYYUmQCq4vG/ytnhST9z2nBvWYLhmItbHLMz0SyV3s+XFJmIZDJ37RPaGgSqxsJVe9Eaj9eC35daU+b97OsGHdWwv+JGK83x+NiFRSPH9UYythwxG9C8FDfudd955pXYr8tFxbbi3vyzx6ON1qO+gn3sAOTEfg3P387qxd1F0MQvnU7HO++8w3vvvcfrr7+O3d5wh8Ltdvv1lySJSy65hLlz5xIW1nHlXyAQCASCzubXjZm8s2C9z7Z+KTE8cPlxSJLUTbMSCARtQSvPQ/niEagoaNhYUwqWSmhHKozUbyzavjWeC/3CDOjl8Ueh3uzeGIqUOqR18UtvbNK02WkIaZdLlK7/ePQTz8C9fj5aaRauVZ9jnH5Jw7g/vgIuO0gSIWffg2QMbXE858JXPBfmkoTxrHv8TbSPPhfn9//C/ecPuNd9i37UCejSRzc5lmv1F97oMTl9DKZ//Mvn+Q2TzsC94zccXz4KSturx3cU97pvvMKXfuLpGOfc4SNuGSbMwbXpZ5zfPOmJivrjU4wz/94pzy1FJnS4AmRjWqsUqBTsa6gw13sYpkuf8xEu9KNPRD/xTOz/vRVsNTgXvIDpshebHU+z1+L45knP50GSMM6+BcOUQ4SqCXMwzr4V128fIvcdixwZ7zeOZAjx+TyojT6vclRCQAbnh+LessgrfOnHnULIOff79TFMPhe1pgzXio894m6QTNEB1EbRaFJ8Wpc/n9JIyKj3deoIqs94g5vvl7W1oV9SfzRVwbXiY9xrvvaNFDSGoh99EsaTru2QOKfsWI5W7REVDUf3sKgvTfP6d0mRCeiGTW+xf7CPHYBkMiOFx6LVlnv+uRxB/Vx0hKA68MqyzDXXXMO5557LkiVLWLZsGVlZWZSUlKCqKnFxcaSkpDB9+nRmz55NWlrXf8gFAoFAIGgPe7JLeOy/vukvUWYTz904m/DQw+MkQCD4q6Pl7kL56nGfiAcAqc8oiGpfpXGpb0PUp5a3E6lO/NLyPBeCUtrIbvOFMp58I8reNWhVRbiWz0M/ZhZyZALuvatRdq0AAkt3VLK3oez8zdu/KRNtSZYxzvk/lIwNaJWFuFZ/1aT4pbmduOp80tAbCTn/wSaFN/2ImailVzQdHdWFaE47zqWeKCg5eSDG0+c2+f4Zxp2Csm8NyraluNd+jeGYvzUpWoWcc7+PyOJc9j6uX/8LgOnKVwNKSWzMob5vtvduQT24GQDz4yvaNBaAc9GbnlRHvZGQi55oMmJHlzIQ40nX4fz+eZT961GLD/hEgzTGtXyet6KlfvJ5/sJXHVJIWKvRhp2NWpLlfawbcWyz/eSIOEJOvTUIM/JFq2kQLzojEqs1lO2/eh83VemxLWiqinunx7AdWYdu8JTm+9YJUeB5nfZ5c1Ez//Tv6LTh3vA9yt41mC5/CTmhaX+41qgXlzCFox97crvG6CqU/evQ6talftJZrQrfwT52XsKioE5c02rLkGJSOzZekOiW8lOxsbFceOGFXHjhhd3x9AKBQCAQdIjSKgv/fPNnHK6GyGWdLPPUdbPoldB8RR6BQNBzUHf9gbrgBb9IIil9NPI597U7elOK6+URzqqK0XJ2Ql0VMa0uCiLYfl8+cwsJw3jW3TjmzQWnDefPrxNy9r04f3jZ0x6TEpAA4d7WIPwbJp3V/PPpjehHn4jr9/+h7FnpkwZaj5q11WN8DuiGTEWOaj7aToqIa3VunY2yf513fvqJp7coXOrHn4qybSlaTRlq7s5mI916KlqdfxnUvxfNC8D6MbNw/vASqArunb9jbEL80jStocqfzoDx2Mu6ZN7tpbGgpGbvgA4YyncJjbzcOitlrTnUinzveyVFp6AbPrND4ynblqKV5wGgG3UCcmRCs301a4O/mHPRm6gHNyP3GYVhxiXoeg2DEDNq4X5cv32IsmcVWnUxji8exnTdf5D0xrbNK3cnal3UrX78qa1GuAab+sIk6I0YJp3eav9gHrvGSEZTQwSy095S1x6Ffw1MgUAgEAgEzeJwubn3rUUUV1h8tt918XTGDT487nwJBH9lNE1DXfsN6nfP+AtfI49DvvBRJFN4h55D6jvW81y5Hg8xrSzPG13WneIXgH7gUejHnwZ4LlAdnz3oMfuWJELOvjegi8F6g2UMJqTEvi32lVPq0p3cTk+Fv0PHOrDJ+1hXd9x6EsreVd7Hcq+hLfbVpQzyPlbr01wPI5R9a70G9629VskYihTnydJp7rWqebvRako946WPCkr0UlvQNYpucq34H475//KJButuNLWRNZDctTErjvnPe78PjbNv7lCqrWatxvlzna9cSFirFTcbe4OpBzdjmHYRpqtfRz9kGlJ4LJIhBF3aCEIueQbd0GM8/Qr3o9RHlrUB16o6cUmSelzKo1qS7RHbAf2I41osnFFPMI+dD43Wo6b4W1g1h1ttT9J+59EtkV8CgUAgEByOaJrG0x8uZ8eBYp/tFx4/ijOOGdZNsxIIBIGiqQrqkrfRNi70a5Om/Q15+sWd4tcn9RuHtmUxWCvRyvPQ6sWBsChI6Nvh8TuKcfbNKPvWotWUeoWsQNIdwfM96DVY1xt9xKGmUBtVb9Sqi6GRQAR4Db0hsOp8waaxt5RasB+tpWIDqtrwsLqk+X49lMavVasqbrJapC+eC1mtmddaH/kDICcN6PD8Ohtd7+EYjr/SU7lO03Bv+B73hu+REvujHzwZ3bBjkNNGdpuHp6RriMzR7JYWenYM54qPvZ52GekzGT18RrvH0jQNx9dPeP2mjKfc1GI0JwCNRD7dqBMwnnJTk90kScIw8+8ou/8AwL11CfrRJwY8N7W6BGXncs/zDDq61UINwca15kvQPJ8pfStG916CdOwOpfF6DNTva0uZg7vXlfLi5HhGxHSPPYgQvwQCgUAgCJAPf97E4vW+lR0nj0jjpnOb97IQCAQ9A81pQ/3uObQM3yIVyDrk2bcgd+BC4FCk9DEgyaCpaLk7G1Ie00f3iGIYkikc/ZTzcC1+y7vNMOnMwHa2Vnmjg7BVN1lRsFmauIDXLJUN8wrp4mp27UCrbRC7nN//K/Ad61IlDycaC3vutd/gXvtNYPs1I8zUR31BcDyr2oPxuCvQpY/Buex91KwtAGjFmbiKM3H98QlSVCL6SWdimHJBl6ceHooUGtHwh6Nr1pN7/zqcS95GAvabenN91D/40uKit9nQrvFcS99rENTHzcYw8YxW92n8uTced2WLfeWUwaA3eiJJS7Jb7Hso7rXfQl2Ukn7yeW3at6vRbDW4Ny8CQO49HF3vwG6oBuvY+dF4PQYYKf3CtgpyLG4u+62IV6ckMDUp+CmnIu1RIBAIBIIA+G3TAf4zf53Ptr7JMTx29YnodeLnVCDoyWi15Sgf3+MvfIWEIV/waKcKXwBSWCTURbpoOTu9kV/dnfJYj2arafCWqcPx46toWtempGj1opnv1oaHPUAY7DSafK1HKM28Vk1riIRD6rm/k7r+4wm9+t+E/t/nGE+9Fd2go6EumkWrKsb1yzvY/v0P1LLcoM5LauSTpVmrOn18JW8P9k8fQNJUSvVR3D9gLi/NTGu/8LV+Pq7f5gEg9x2D8Yw7A9uxsXjSyudG0um9oqBWWxbw3DSXA9eG7z1jxPVGN/CogPcNBq4/F4DTBngqjAZMEI7doWiahmar8xrT6ZHCYwPa75UpCQyLNmJ1a1z/RzF/FNraPYf2IiK/BAKBQCBohb05pTz636U+2yLNITx34ymisqNA0MPRSrJQvngEDk3NikxAd/7DrXpWtRep31i0wn1oBzdDXbU7qYd4Wjl+eMlTgU9nQDdgAsreNaiZf+JePx/DUWe1vHNYFMg6UBWk2F6E/d9nzXbNs7g55ec8kkJ1nNcvguuHNRH9Y2oc3WJt3wvqQqTwGLQ6r7LQO79u0QS+p/FlZg2L86zsrXJR4VAw6iSSQ/WMjw9hVq8wJiea0MsNgmNjj6GQcx/wqYRX4VD4LKOG5QU2cixual0qYXqZPuF6piSaOLnCwfBDUpkkc6OLYkfXpe11FnJsKvKU8zFMOR/N5UDZsRzn8g/QynLRKgqwf3wvoTfPQ5KDI+RJcb1BZwDFhVp0AE1VOq1SrFqWg/2ju5CcNqp1Ydwx8J88cPwIJie2LxrHveM3TwEEQE4ehOmSZwI2VPcV+Spb36FepG/DsXBvWeyJWgUMR5/TIyJw69FUxROVBkjhsehGHBfwvsE4dn5DlOV4hTopvk/AxzImRMd7MxK59NciMmtc3La6hPdnJDEmLnjn0UGT4MvKysjMzGyxT0FBAf/+97+ZO3cud955J5988gl2++FTPUAgEAgERx5lVVb++cbP2J2+lR2fvHYWvRN7ZhqHQCDwoB7cgvLR3f7CV1J/dP94vsuEL/D4fgGe59ZUiE5Cik7usucLFPfO31G2LgHAMP0SQs590CNoAc7Fb6JWFrW4vyRJSJEeAUizVLQaLaZokG9VyK51NdkuRTQIJGplQZN9upPGfkX1PkaHCw9vLGdlkZ0Su4JbA6tbI7PGxVcHapm7tgTnIebTUnTTr3V5vpXZP+fz2s4qtlU4qXSquDWodqlsr3Dyzp5qnt9W6ff8cqPxepKRfCBIhhD0Y08m9Pp3kaJTANBKDqIe3By8Oej0yCkDPX+47KhF/gUj2oNakY/9/dvAUkGtHMr/9buL62aOb7/wtWcVji8f9Qjiif0wXfZCm4qG6BoVV2jsEdgUmuL2mry3pfqra/VXngfGUPTjTg14v2Cg7Pzd66Oon3g6kj7wyLtgHLtDUesKuQABp2fWE23U8e70RBJNOmyKxg0ri8m3Bm6Y31GCFvn1zDPPsGjRIu6++24uvfRSv/alS5cyd+5cHA6Hd9vChQv5+OOP+fjjj4mOjg7WVAUCgUAgADyVHe95axFFFb5eG3MvOoYJQ3qWUapAIPBF3bYU9cfXfAyBAaQBE5HPvBspJKzTn3Pcj5Xex3o1lWVyCKGq59x2achwlm+ycEyigROSQwi+24kndcq54AXAE1VimPl3JL0R48k34Pz2GXBYcXz3LKGXv9jiOLrBR+Ne9x04rKgFe9GlDmn3nHS9h1P/Dqk5OyAAj6Bgohs0GfemnwBPNTVdK1UQu5tDi6lNTjRxfGooiSYdNS6NAqub1cV2xsSGEKb3jYPQDTzKk3qqaSgHt2A45m/sqnRy+5oSnCroJZjTx8y4uBCijDLlDpWsWhe/F9q4ZGAEhyKnjYQQMzgsKFlb0FyOgM2xW6VxtEknZOsqmkaRTSHf4ibVrCc1zHOZLJnM6IZPx73qC8ATMaXrP77jTxggukFTvGKDemAjunoxrJ2oFfnY37sVrbqEGjmMW/vfzVHjxjOrd9v99iwulfWrlzN26RMYVBcZpjTuSvknplU2piZpnNcvggGRrQs5UkI6GEPBaUM5sNnrP/hLnpWnNpdTaPOk8+08Lx21YJ/Xt0tO7N/keOuK7Vz+e4OIP6lmO28Ue4JwvoyYxnM/lAKlfvstmd2LXmZ/eeTfOyp5Y5dv2umFJYu4pORHTKqTX6KP5pXUi3HIDZFuqWE6fjk1sAIerjV1wpysQz/prID2qafxsduzZT1P1RxNvtVNhVMhyqijV5iOE3uFcXbfcKJKWj929Wwrd/BDtoW1JXZKbAoWt0qcSceACAN3HlxP/RmwbtDkNs0XIDlMz/NHx3Pl70VUOlXuX1/K+zOSghKNF5TIr5ycHBYuXIjL5Wry7lBBQQF33XUXdrvdk0Pa6F9mZiZz584NxjQFAoFAIPCiaRrP/O83dhzwjYI477iRnDVjeDfNSiAQtIamaah/fIr6w0v+wte4U5DPe7BLhK9Dcct6fo2aSL4hnnxDPD+HjubnAhcPbLFy+vJKMqqdXT6HQ3H88JI3oifk9LnetCT9uFOR+4wCQM1Yj2vDghbH0Y841vvYteKTDs1J7jvG6wfl3rUCrZnUR01VcG9Z1KHnag+6wZPB4DE6d639Fs3Zs7NSSu0Nvj9XDo7k/RlJXDowklm9zZzbL5ybR0Tz8XHJ3D0mxm9fOSIOuc9oAJR9a1CLDvCfXVU466y7XpuawFOT4jm/fwSzepu5aEAE/xwTy8KTe3FCqv9nStIb0A+b7vnDVoN7/Xed9jqlRumyalVxCz2bR1MVrw/d91kWTvwxj3/8VsTa4kPeY5ej0R/BTZfTjz3Zm6Lm+tO/Sm1bUMvrhK+qIip14dw84J84UoZx+8joNo/1e4GNRz//gdG/eISvXaF9uW7AfeQQzr5qF/P21XD2knze2d26V5kk69AN9hQOUnavoLCkhJtWFnPr6hKv8FWP+88fvI/1Y04KaK5/K2343vgiPrB9WuK4ynXcmf8RKa4yYpQazi/7hZsLmk//bgklfw9q1lYAdMNnIkfGt20ASSY7ZSIAKdmrySoqptiu4FI93wVbyp28sK2SMxbnk7PiO+9uzR27WpfK/RtKuXBZIR/tr/GkTDtVnCoUWBU25lcQvf83ANSwGO/71lYmJpi4YnAkAGtLHPxvf027xmkrQRG/5s+fj6qqJCQkcOGFF/q1v/3221itViRJYtasWbzxxhu8+OKLDBkyBE3TWLVqFRs3bgzGVAUCgUAgAGDewnUsWrvPZ9tRw3tz63lTu2lGAoGgNTTFjfrjK6grPvZrk4+7HPnkmzrNM6clbh5s4pUJZqLPvpMDf3+HHX97m6ETpzMiyvPcpQ6NRbnB9bdy7/gNZZvHu1A3+iR0AyZ62yRJIuT0ud6LbOfPr7coKOj6T/CIQoCyfRnOX95BU9Um+06p3sKNBZ83O5YcEY+uXkyz1eBc+LLfzXLN5cDx5WOomcG/HpBCwjAcf4VnHhX5OD57AK2ZSo5q8UEc3z2L5gy+kXM9ta6G9+HiJqKxWsN48g0eMVJVsH98D6W5noiZ3mY9M1MaBC7NWo3jx1dRCzNaHM9w/JWeyBTAueRt3DuWN9lPc7tw/fEp7j0rA5qnZDIjxacDoOZsbzblS1OaT6lyLngB+7s3oeTtabaPkrfH4xdVh65OJA4WcmwqumEzAE8VSqVOKGkralkO9vduQasqosoUy7UDH2Rn2ADuGxvr4/sWCN9n1fLZjz9x796XCNFcZMaO4MDZz/PoMf14amIcp6WFoZPArcFL2yv53/7qVsc0TL3A88DlYOcHj7Aqz180c+9ZhXujRwCUEvo2K7wMjjLwxtQE3piawHsjnBxT46niWdVrLHcfP9bb9sbUBPqEeyK9oo0y8aamfxvm9DH77HOPfpNfn3Mt63jh6Hh0dYdyUGRgfmfuVQ2FR9pkdI/nRs+jm8p5yHACACbNyX9K3+WJMWZenZLAA2NjmRjvibQcVvInMTt/Bpo/djUulat+L+Lbgx5/vt5mPdcNjeSZSXG8MiWB20dGc4llNWbVIw5/HnciNVr7iiMAXDssisS6Y/76ziqs7qZ/QzqToKQ9rlmzBkmSOOmkkzAafReC0+lk/vz5SJLE1KlTefXVV71tkydP5qSTTsJqtbJw4ULGjw9eiKlAIBAI/ros/3Mfb3y9wmdbn6RoHr/6JFHZUSDooWgOK+o3T6MdPOTCRKdHnnMH8vAZQZvLmBgdE+P8LwquG2TiiywHT++wtZqppVmrUHK2+2+vi0LRXA4/oUAKjULXZ6T/PpZKHHXpjoRGEDL7Fr8+cvIA9FPOx73yM3BYcM5/DtM/nm92fsbT78T+9vVoNaW4fvsQ987f0Y89GTkuzWPQXZpN+I4VvFq8H4D5Of1h0nlNj3XcFdj2rganDfemn1DLctCPPQUpNBK1NAf3hu/RqoqQ+45BPbil2Tl1FYYpF3iKAhzYhLJvLdaXLsIw/lTk1CEgyWhVRSgZG1D2r/MYSmsaIWffE/R5Aj4+XvWpe21BlzYCw4y/4/ptHlpFPq9V3sPi6CmUpYzGvTMVzVKBmrUN987fwGVHydhA6PXvNJvOKMekYDztdpzfPQtuJ47PHsQ1YBL6YdORIuLQHBbU/D0o23/1RCWGRiDf+D5yAP54hmkX4Jz/L3A7sb19A4ajz0ZO6IumutHK83HvWI4ufTQhTVQddG9birsuwtH+1tVMShrOldJw8o0JJB2IwVVgQcn8E2Xvam8VPd2YWchJ/dp8TDuKYfrFKDuXg6bh/Pl1TNe+1aYUMbUs1yN81ZSh6Qy8EzeHXs5ijjGWM6EiF3dF0/vJCX2RY30tHjRNo2Djcp4++Cp6FNzh8Qw76UKGyfuhrqbBnGi4WHHw1u5qNGDVCphjjyNqwFhvtcFD2WUeSGbqLE7IX8zR1Vv5dO+9lAyZxVolgcJqC0fV7sCxdZXHP1FvJOT8h5F0Ta/v6BAdx9ZFIjo2LcRdV3U0ceYFpDaKUNxX5SS71iOOntcvnBBd08e0b4SBvhEN3+f2UAOH1lU06mQqnSpK3ccvEOFZrSnDvX0Z4CkSoEsf3eo+jSlzqPxZ6iDDPJDfe81iRt5i+hVvov8Pt6AfPxs5Lo3zoxxs27+KgVnL0aHhlA1ENnPs9lQ6yao7HpcMjOCu0TEYGwmjmq0G63ffAVArm3gv8njKdlXyzzGBVXs8FLNe5tKBEby4vZJql8rXB2r5+6DIdo0VKEERv7KyPAaHw4f7p4n88ccf3qiva6+91qctNjaWOXPm8Pnnn7N58+ZgTFUgEAgEf3H2ZhfzwFsLaBx4EBEWwr9uPIWIMFHZUSDoiWjVpShfPgLFB30bTBHoznsAKW1Ed0yrSS5ID+HXIhcUttxPLcrE8b8WBBRLhV+73HcsoVe95tfVseBFsHiucI0nXY8U7p/yBh4RStm2DK26GGXfWlwbF2IYf1qTfeXoJEzXvYX9o7vRijLRSg7iWvIfnz71Fxrrw4eTF9O8L5ic2BfTxU9h/98/we1Ezd6OM9tX+NNPOhP9pLOwv3FFs+N0FZJOj+kfz+P4+kmU7cvAWoXrj0+b7huTin7UCUGeYediPPFqpNBwnIvfwqi6mVOxAipW4Nh5SMcQM4aJZ0ArVf0M409FCo3A8fUTHp+4jPU4M9b7d5R16EcchxQa2AWwYeIZqHl7cG/4HmzVuJbP8+vjLs3GMPMfflU6dSOPx1hdgnP5PLDXElO0kxuoe4HZ4JOULMnoJ5+L8eQbA5pXZ6PrNRT90efgXvM1au7OwKqyNkLJ2uqp7gpIios78z70tjm2Nb+f8dz7/cQvSZK4XNuCu07+0deW4vj8Ib99BwM+zoGZoN35VZPil82tcvWKYizxl2CzWZlT8QdpjiLStn7EoaEvUkQ8IX97IiDvM81u8fr1SdEp6IZM82mvT7XTSXDRgMCjJPUjj/d8DzRCN+oEb4RberieY5JMrY7jXvcdKJ5CIPo2Rn0BxJt0fHZ8Mh/srebYgf9EXiDj3vwzWnkurl/e8far/+Yt1sdwd9/beCK0D019G09MMPHFCcn8mm/jssH+n0Hnkv+AxZM2/3LqpVTpI5ifZeGu0THI7fTrOq9/OK/trMSlwkf7a7h0YESXen8FRfyqrKwEICrKvyrWr7/+CkBMTAxHH320X3u9YJafn991ExQIBAKBAKiosTH3lW+xORqqkulkiSevnUVaUnT3TUwgEDSLVpSJ8uWjUHdx5yU6Gd0FjyLF9bziFFPiDTSdONeA61Dn8nbi3rYMZYfnfHtr2CD6DJtNL6DSqfD1gVoW51rJqnXjVDV6mfVcO+oqjl/5NADOn/6NbuBRyJEJTY4tRSay9LRXyFvzE4Nz/2CgNYsopRZFF4I+Jhl32miurx7LdvNAzgpv2VBbN2AioTf9l9wl/0M+8Cfh9jJqdWHsDevH8rTZhPWZyt9khZYcce5bX8p3WRauHhLJHaNi+OZgLe/vqSbf6iY9XM9Nw6M5sVcYqqbx/p5qvjxQS7FNYWCUgbtGxXBUYvMXrJLeiOnCR1EmzMG16SfU7O1otWUgSUjhcchpI9APm45u+IyAU2udqsb2cof3QvSq3wvJ3pnLmLgQzu8XztSk7iiL4MEw7SJ0gybz308/YlLtTno7SwiRFKSwaOSUgegGTfZE55kCM0rXD5vO2r/9l5KV39Kr4E962/IIUx3Y9WE4Y3qTMOwojBNOQ45JCXiOeRY3n/W7GpttKEfl/sJQSwZRSi0u2YgtKpWoQeMJn3iqn/AF8F2WhfsLpmAeOIZTK1YypWYrA2w5RCm1GDQ31Toz2SEpbDIPYUHsTPKsibxb6mJqUuuXz6V2ha8O1PBHoZ3MGhe1LpVQvURsiI4xsSEcmxrKialhbUo3NJ50Pcq+dWhlOTgXvYHcezi61MEB798e5Li0ThvLqQshLNL/fQAI1cvcMCyKD/ZVE33BA4TYtuP+cyFqzg6cNeVYZBM5IUlMmDkb/fjTAq4kqZZmIUUloRUfQH/0WUhyQ+R+pVNhQbYnVO24lNA2RUnqR8xEO/U2XCs/RXM50I88nnWj/sGBtZ5UzYsHBCbgaNZKT0qw3oh+9IkBP39jzHqZm4ZHe/449350o0/0HjvNUgEhYcixvfglZjKPaZOx6MLYV+1iSHTTgnWfcAOXDfaPWnZvW+YRmQHdkGlsTTgRatxUOlVK7QqJoe2TlaKNOsbHhbC2xEGuxc3OSicjYrruRrOktVafuBMYN24cdrudl19+mZNPPtmnbcaMGZSUlDBnzhz+9a9/+e37yy+/cPPNN6PX69m+3T/0W3D447TVoGkt5/gaTOHIsg5VVXA147MgEByKWDeCtqBpGne98ROrtvn6htz5t+mcM7PnRI0Iei7iOyf4qAc2oX7zFBzqsZQ6BN15DyKZo9s0Xr5VodCuMTJKh7GZFJjmeGiLhQV5HuH8naPNTaY91vNNjpPHt3k8v87vF86jE/zLzr+4rYJ397TulXNy7zCeOyoeQwsX0o2rn311QjL5VoVHN5ZR5mj6/GtifAj/nZmEroULOLeqMXdtKUvymvYuC9VJ3DU6hsc2eSIFzko389Sk5qUrRdN4+M8yvqnzm2kKgwx3joppNjWmXvw6v1848SYdbx5SoU0GPj0+mY/31/B9tu/zhOklFp6cSlI7L+LaSqHVzbV/FLO/2tVsn3P6mnl0QlyL78OaYht2d8Pl3EvbK9lXN+YbU/1FS7NBZlJC0yKfXVFZU+Rr9n7jqhIABkUa+L8mjNF7mfUMimo58supaNyzvpSfW/C5Gxlj5K1jEokNCUw4/CKzhic3l+Nq4RIiVCdx+8joJqNJvj1Yy/0byprZs2maqwbYmIXZFh7ZWIbF3fIldnKojrtGxzA7LfAqi2rhfmzv3QL2WqTIREzX/wc5InCD9JxaFyf/7AkoGRFj5MsTAhca28OqIhtXr/B4B87qFcbLU5oW0cHzfeJQNMwGX2uJy5YXsr7Uk+q987z0ds1DLctFMsf4CLVv767i5e2VALw/I5HJiR0Tmq9ZUcTKIjtheonlp/Um3BCYRYbmcqBV5CMndm067cvbK3h7t+f35P6xMVwyMPD0QiV3F/b3bwGXAykujdBr3uDSdXY2lXnel/knpbT6HdASjd+LO0dFc+UQ/4CpziIo3+6JiYlkZ2d70x/rWbduHcXFxUiSxPTp05vct6bGE45oNre9/KpAIBAIBIHy5a/b/YSvc2eOEMKXQNBDUbcuQf3p314/nnqkwZORz7gTydB62kk9mqbxxj477+13oAFT4/W8OsncoujQEUodDRfGcc1c7J+aZubj/TWMiQthfFwIfcL1hOllyh0Kuyud/JBtodbtMc5PCa1ssnpfU3yeWcs3B2tRNM8F6YyUUEJ1EtvKnXySUY1ThQ2lDhZkWTirb/MRFg9vLPMKX2F6ifP7hTMqNgSXqvFn3f6P1wlfgXDPulIW5njGSzTpOLdfOIOiDDgVjbUldr7PsuBS4ektFZgNMue0MLcVhTYKbQpHJ5g4u6+ZrFo3b+6qQgVuq6sgd1RCCGf3DSe7rs3q1liYbenSC696imxuLv610FvJbmqiiRN7hRFn0lFodfNdloVdlU6+OWjBpJN5YFzznjoPbCgj33qoA5GHeuGqMRPjQ/jw2Kb9tMrsapP7AOyrdjXZdsOwqBYvfFVN48aVxayqq6DYP8LA2X3NpJn11LhUfi2wsSzfxvYKJ9esKOaz45NbFHIB3tpVyas7PMKmDJzUO4xjkkxEGnUU2dysKbbze4ENm6KxOM/KxQMjOPRTNiXR5CMOrim282FdGtw/BkYwuYkowJSwloW5DSV2/rmuFBXQS3B6upkJ8SaijDI2t0aRzc3qIjuri+0U2hR+K7C1SfySkwdiuuQZ7PPuQKsuxv7BHYRe8TJSeGCeS7sqG5I5B0a236g8UKqcDcpkdEjLYpBeltpsvB8oclxvn7/dqsZnGZ73emCkocPCV0a1i1V1ovGZ6eaAhS8AyRCC1MXCF/i+FzEBCswAamEG9v/d7RG+IuIxXf4ikjmaKmeet090G8ZrisZrcWdl11ZBDor4NWrUKLKysliwYAGXXXYZISEhaJrG66+/DoDBYODYY49tct+9e/cCHv8vgUAgEAi6gn25pbz+zWqfbaMHpnLbBaKyo0DQ09A0DfWPT9Ca8FySJpyOfOLVbaro6FI1Ht1qZWF+QwTOqlI3pXaNpNCuuRj7rajhBH9iQtMpHkOjjaw9M63ZC8Ibhkdz4dICCm0KH2dUc8PwKCICuOj68kAtcSEyT02KZ3pyw0Xf7DQzKWE6nt7i8Qb7Jd/arPi1qdTurQgWFyLz0bHJPobQZ6aH8/eBEdywsrhZYaYxy/OtXuFrWLSR92ckEmVseA/PSA/njD7hXPdHEU4Vnt1SzszkUOKaqc5WaFO4qH84D46L9Ub8fJdVS4FVabFtVxdfeNXz1OYKCm0KEvDExDjOPuQ4XzIwgkc2lvPVgVo+zajh1LQwxscHLua2RO9WIpc6e7xPMmq8wtf5/TzHvfGaPrdfBPOzarl3fRm7Kp38d2811w5tXoDcUubg33XCV4RB4vWpiUw8JJLt0oGR5FncvL6zkltHRDcpYieH6UlulOpW0UgcGBJt9Bqmt4V5+6qpH+XRCf7vK8BVQ6LYVenkg73V3BOgYN0YXd8xhPztCRyfPoBWfADbe7diuuJl5MjWI8ByLA2VL9PDu1782lHR8Hka0UyaXXewOM/qFZ4vboPXV3N8tL/aW8Dk4gFda9jeXnY2ei+GB/heKAX7sH/wf2CtQgqPxXTZC8jRyVhcqtcYP9GkI6GZ7+FA6dtoLeZamq/O2hkERfw644wz+OGHH9i/fz/nnXce06dPZ9u2baxfv95bBTIysumFsmzZMiRJYuzYscGYqkAgEAj+YtidLh569xdcjUosh4eF8Ph1p6HXdewHXSAQdC6a4kL96d9o25Ye0iIhn3AV0qQz22SWW+1SmfunlQ3lvifcySaJSGPnC1+KpvHWXjs7qzwXXiNijExpwWeqpUiIhLroqNd3VuFSPaLAMcmtRzCY9RLvzUhicBPROrPTzF7xq74KWlM0Tie8f1ysj/BVz6AoI88fncDFv7bi7A+8sqMSAAl4elKcj/BVz9GJJv4xKJJ391RT49L46kAt1w1rWiSJC5G5b2ysz1pIMOkosCottlU6W7bh6Aw2ldq9EXN/GxDRpEAiSxIPjI1lVZGNfKvCR/trmhW/fjnVN6qlI2livcx6v32Gf+XJ3JkUH8K8ZiLGmsPmVnmt7r0dEmXgofGxTQpRZ6aHs6LQxo85Vj7eX8OVgyObXftPbi73CkxPTYr3E74av5aW0my7ggM1DQL6rN7Ni2fDoo08e1T756YfPAXp8pewf3wPWkUeWvEBCED8KrU3CNExrURidRRV07zrXC/BjJTu8687lI/2edL/IgwSZ6R3LLusyqmwIMtzI2BKookBQYioays5tS6vENk/Qt/k93VTqNnbPcJXbG+P8BWbCsCSPKu3quVxqR1/XxuvxRJ76zdLOkJQxK8ZM2YwdepUVq1axf79+9m/f7+3zWQycfvttze53+LFi8nKykKSpGYjwwQCgUAg6AivfrmarMJKn233X34yqQlRwrdJIOhBaHYL6rdPox3c7NugMyCfMRd56DFtGi/fqnDzBgsHan0Fj2ijxHPjzYS20fOrMVsqFKx12pEGWN0aGbUKSwtdHLR4ni85VMeLR8d3qLJVciN/qnJHYBcNlwyMaFL4Ak/1sFCdhE3RsLmbFoJsbpV1JZ5InrgQmVm9mr/IDyQiIM/iZk+VRzQYExvS7NwAzu0X7vVB+znX0qz4FaLzT6EKqfu7pTan0uVWyPzUyPfqwv7Np24adRKnpZl5Z081y/NtuFSt1XTAnsbKIjs1Ls8xPb9fRItpxGenh/NjjpUSu8KWcgcTmhD7Dta42F53ET8ixsgJ7YjO6kqijTrA88HfUubo0oIFuvTRhF79OmppDrqBkwLax9rIh6wj32+B8GOO1RtpNjvNHDQvvdbYWu5gS7lnDZ3TN5wwfcdEwC8za7HVfW9cMrDjUWRdwbt7GiISr2iiimNzGI4+G3Q69MOmI5k9UYouVeO9vZ7vYBm4rBn/xbYQqm9Yi7ZWvPI6StBW4csvv8ztt9/OqlWrvNtiY2N57rnnSEvzryThdDp58sknkSSJPn36MGvWrGBNVSAQCAR/EZZvyuS7Fb6120+fPpJZk4ehql1790kgEASOVl2K8sUjUHLQtyE00mNs33tYm8bbWeXm1vUWypy+J9p9wmRem2Smj7ljUZ//3mtvsX1GooHHJiW2qUJWpUMh2+LG4lJxqhqaBvuqGlJZAtVt9K2IbWaDjE1RaG64TWUO6gOkJsSb2l3ivp41xQ3FCsbHt1zlKz3cQFyITJlDZX+1C7uiYtJ1bQRLZ/Nbgef1huqkVn2XhsV4hECHqrG/2sWwHpQ6Fgj1rxVgZGzLc69/reBJl2tK/Fqa3yAcHt+DIonqOS41lI11JuC3ri7huqFRnNsvPGAT/7YiJ/Zrk1G60qjOXVf5a4GnkuKzWzx+f2a9p+hAT6E+6kvCE3nZEdyqxid13mG9wnQc2wPX5J+ldr464LmROyLG2KKPY1MYJp7h8/d7e6rJqCuoccnAiICjyFqi8W+Su5OqHDf7XF06eiMiIyN5//332b17N5mZmcTExDB27FhCQ5teJEajkblz5/LAAw/w4IMPduiumEAgEAgEh1JUXsvTH/3msy0tMYo7Lzm+m2YkEAiaQivK9AhftYeYp0enoLvwEaTYXm0a7/ciF//cbOHQ7IoxMTpemmAmxth1YsrsVAOX9A1hZIwRYwDCV4ld4YO91SzOtZAXgHdWZ9Day6/3ygHoE97xS4mCRq8rLYDxepv1lDmcKBrkWxT6Rx4+4pemaRTUhQQadZKPONQUBxul0RVZ3Yed+NXYv2d3pZPyFlKaGrcUNrPWG6fiDu6Bx+LSgZGsK7GzotCO1a3x0vZKXttRybi4EGakhHJCaliniAXtxdhI8KptqUxmB9A0jXvXNVSSvXt0DClhPSPqq8jmZlFd5OX05FD6dND3bEkj77CLBkR0+EZAZ1PhULh7XSkanujWJ1qpHNsaG0vtvLGzEoD0cD23dZKo2XgthnRxRGLQV+LQoUMZOnRoQH3POOMMJk2aREpK15ZhFQgEAsFfC0VVeey/S6mxOrzb9DqZR686kTBTzzuhFgj+qqiZG1G/fRqch4gEqUPQnf8QUljbKvN9keXg2R02Dr3sOynZwGNjwjB10on3O0ebmRjnubD6JtvB49s9869xaYyIDuz0+9d8K3etK/VJVeoJVDRKr2xLVbNmx3M2Gi+AFKTGz1nTRRfwXUWlU6X+7axyNl9ZsSlqmklD7cmUNVorj2wMvPJnbTOvtbFnVXQXitTtJUQn8ea0RL46UMv7e6vJrnXj1mB9qYP1pQ5e2FbJyBgjfx8UwZw0c9CDOyKNXf/ZeW1HFb8Ver7vzko3c37/npMK+GlGjffz1xkpiv+rqw5q0kmc269tEVVdjUvVuGNNiffmwsPjYxnSAcG40Orm9jWluDVPdd9XpyR0OGW0nsZrMbKLP9c9Q4ZtASF8CQQCgaCz+fDnTWzaV+Cz7fozj2JoekIzewgEgmCjblmM+vPrcEgKsjR4CvIZc5EMgVe/UzWNV/fYmZfp8Gu7rH8Itw7pePpec5ydZmR+rpOtlQp/lLj5Kc/Jqb1bnvveKie3rymh/prg6IQQLh4YyYgYI3EhOu/d8W8P1nL/hrIumXdzNMqcojOOWFtttho/fxcHCfQolMNP+2o3zaU+KT7vfc9882VJ4oL+EVzQP4ItZQ5+L7TxR6GNHRVOVGB7hZN/rivj+ywLr01NCGrabmOPwEpH5y+ozzNreGu3pxjGxPgQHhkf1+nP0V4cisaXmZ70v/RwPcckdax66rZyB5vqUlzn9DHX+b31DDRN4771pawt8czvqsGRbU53bEyVU+GaP4optSvoJfjXUfEMasGbsa00LjTS1d5wPV78EggEAoGgM9mWWcj7P2zw2XbU8N5cdOKYbpqRQCBojKYqqMvnoa39xq9NmniGp6qjHPiFhkPReGirlcUFLp/tMvDPEaFckN6yz1RHkSSJ+0aGccnKGhQN/rXLxtREI4kt2MO8vbvKK3zN6WPm2UlxPcYCpPGdeUsnRCNFGto2XuM+sQEY6vckoo0yegncGqSZ9Sya3baU3cONuBAd+/F87pad2ovkDqa/xZkOr6i/MXEhjIkL4ZYR0ZTZFb45WMs7u6uodWusLLLz7JYKHg6iQNS4EuHuRn6BncGSPCtPbPJE9w2NNvD6tESMPUidXpBdS0WdyHLxgIgOf59+VBf1VT9eT+LZLRUszPGkd57T18zc0THtHsvmVrlxZQkZ1S4k4LEJcRzXyYUmdlU2rMWurpbZ8+JFBQKBQCDoImptDh55bylKo7vK0REmHrzseOTDrIqWQHAkojltqN881YTwJSGfcA26k65tk/BV6VS5fl2tn/Bl0sGLE8xdLnzVMyRSx0V1z1Xh1PjXTmuL/deVNESo3T4yuscIX+CpCFlPXiNPp/aSZm4QRHIDGK++gpxBhvguMhLvKiRJIinUM+dyh4Km9ayU1s4mJazh/Sltwe8r4PEaRYVkVrta6NnziDPpuGZoFPOOTfZegH97sBZ7EEP6hkcbqS+st62888Sv3wqs3Lm2BEWDgZEG3jkmiYhOSInuTD7a5xGrwvQSZ3cgCgqgxObm5xwLABPiQxjag/znXt5ewYd1wtypaWE8OqH94qpD0bhpVYk3wu2+sTEdiiBrjsZrcVRM1x7LnrUqBQKBQCDoIjRN47mPV1BQVuOz/YF/HEdcVM8qly4Q/BXRqopRProbbd9a3wZ9CPI59yIfdWabxsuxKFy+upbNFb4X3XFGiXePDmdmUnCNp28YbCLR5LnyXJjn5I/C5s3OK+u8kvQSpPYQs+h6Rsc2CIZbO+ECuvF4G0v901Ibc7DGRXldutbEeFOPiiwJlOnJnpA/i1vziXg4Eql/rQAbWnlv2zreyqKWiwW0lcYrqSslyWHRRq/3klP1LfjQ1ZgNsreiarVL7ZT193uBjdtWe1K0B0cZ+GBmEnE9LCJzdZGNfXVi6Znp5g57FX6aUdup3mE2t8ojG8uY8n0OJ/6Yyyf7a1rfqQle21HJ27s91SxPSwvj2aPi250e7FA0bl5VzJpiOxLw4LhYLhkY2a6xWmN9qac6sgwck9y1FTOF+CUQCASCvwQ/rdnLLxv2+2w7/7iRTB2V3k0zEggE9Wh5e1Dm3QHFB3wbwmPRXfoM8pCpbRpva4Wby1bXkmXxjaroFy7z4dTwgE3nOxOzXuKuYQ0n9o9sLMPaTJpfVF1qoVvzVHxsjp2NLl6DFUMUb9LRP8Jz/HIsbjbWXbg0xbcHa1sdb0i0kf51FfA2lznYX938BfnXBxrGO6GTU2+CxazeDfN+b091N86k65mRHEponUD5aUYNtg6myY6ONZJaF022qsjOjoqOC2r1NE7nLbS2L6LRGaCBnaNRtFew5duz0hsidxp/ntrDbwVWbl1djFP1RJX9d0YSsT0wGtM3RbFjAo5D0fjigGe8pFAdJ3bC99AL2yr5IrOWKqdKvlXhic3l/JLXcnTwoby6vZI3d3n81s5KN3dI+LIrKjetLGZlkR0ZeHRCLH/rotTOtcV2bxXXmSmhXb5+hPglEAgEgiOenKJKXvhshc+2Qb3juPGcyd00I4FAUI+68zeUT+4FS6VvQ9IAdJe9iJQyqE3jLSt0cu3aWiqcvheiE2J1fDAl3Hvx3B2cmGJkWoJHOMq3KryyvbLJfuPjG8yYP9rnL5AomsbrOyv5uNFFXbEteBEklzaKAHh8U7lPqfp6/ru32nsx1hpXDvGMpwL3rS9rcry1xXY+2u85Fr3N+h5XXS1QJieGMqMuuuGnXCuvbK9AbSb9cUWhjZe2VQRzep2K2SBz43BPRdYci5vbVpc069WVUe3ioT+bF4TBkzZ6V51/kQrctrqkWbG03KHwxKZy8gMUskbFGL1C1MIcS5NClqsZI37weNFd/Gsh960vpcjW/HN+kVlDZo2nPS5Epk94cIX40/qYvam3C7Jr2+3btzTfyq2rS3CqMCk+hA9mJhHTA4WvrFoXvxd4ogQnJ5o67Cn1Q7bFG316Yf9w9J1gmfFzrsVv2485/tua48VtFd5CA5cNiuDJiXHtLuBidavc8EcJq4rtGGR4fnI85/XrOk+zzzIbfsOuGNw1kWWN6Vlx1AKBQCAQdDIut8LD7y3F5mg4GQ0x6Hn0qhMJMYifQYGgu9A0DfWPT9D++NSvTRo8Bfn0uUjGtlXk+viAnRd22f2ioGanGnhkVFiPSJO7Z0Qo5/1eg0OFj/fXcGqamTFxvt5j/xgUwS95VjTg3T3VHKxxcUxyKJFGmYM1bn7IriWzxs2waCN5FjfVLpVPMmqYlBDChPiOVTELhHP6hfO//TVk1rjYU+Xi7CX5XNg/gj7heortCj9mW9lc7mBCfAh/BpDudna6mZ9yLKwssrO9wskZi/M5r184AyINOBSNNcV2FmRbUDRPKuhD42K9FS8PRx4aH8vFywoptiv8Z3c1v+TbOKOPmb4RBpyKxoEaF8sKrOyu9KRqDYg0cEb64Sn2XTYokhWFNtaVOPijyM4pP+Vxdt9wRsQYkSVP2t+qIhsrizyfW1XTeGJifLPjndzbzNl9bXx70EK+VeGcJQXM6WNmUoKJCINMmUPhz1IHv+RZsSsa2yscfHRsMoZWRIqEUD2npoWxMMfKgRo35/ySz3n9IkgJ01HtVMmodrEwx8J9Y2OZnWb22//5rRXsrHSys9LJwhwL05NDOSrBRGKoDrfqiSZbkmdlW0WDWHf7yOguqzLbHAZZ4h+DIvnX1gpqXBpv7qzizjYaov+SZ+WONSW4NU/008UDI9hQ0nwEaD3j400+EXaNKbS62d1EGmbjSoDL832joZLD9K36bf1vfw31I3RGiuL/6gR4gwznd5Io1NQKCHRV/GtrBf/d65nTUQkhHJ1g4reCllOCZUliRop/eqFD0bh2RTEb6zy+LugfgUmW/I77ocSadD7p64GyqdTO4lzP2OPiQpiY0PW/XeKsXyAQCARHNG/PX8fu7BKfbbddMJW+Ke2vfiMQCDqG5nKgLnwFbdfvfm3SlPORZ/4dSQo8QUHRNF7YZePTg/4XT1cPCOHGwaYeYxrfO0zH1QNDeX2vDRV46M8yvjoxxefifEK8iTtHx/D81go04Jd8G7/k+17QjI0N4d/TEvj2YC0vbKuk1K7wU441KOKXUZZ4d3oif19eSJ5VIc+q8OIhUWxjYo38e2oCJ/+UT3UrlfkkSeKVKQncsaaU3wttFNoU/r3TP2osVCfx4uSELveF6WpSw/R8enwyN6wsZm+Vi4xqFy81EwV4dEKInzh6OKGXJd4+Jol715fyU66VCqfK+3ubTvdMM+s5tQlh6VCemBBHcqiet3ZV4dbguywL32X5R8qY9RKnpZm9Ju+t8dD4OLJr3WyrcJJZ4+a5rf5Rd6/vrOKU3mF+3yd3jopB1Typvi4VluXbWJbftAgRppd4cFwsZ3aToHnpwAgWZlvYWenkw33VnJluZlBU4Ebjy/KtXs+rIpvC/60pDWi/paf2alb8Wl1s5/4NZS3uf+Mq33O5s9LNPDWpeaG01qXyXV3qda8wHcc1Ifi0hbXFdvZUeQTp2b3NneZtdkqa2SeKF+DUPq1/DgAWNYoaW1fiYF1JSQu9PaSZ9cxI8a80W2pXvMIXeG7OHDqvpjinr7nN4pdb1XhkYzkaYJThkfGxbdq/vQjxSyAQCARHLOt25vDxki0+244d148zjxnWTTMSCARabQXK109A/h7fBlmPPPsW5NEntGk8m6Jx32YLy4t8U410Etw3MpRz0nqecHD5ABM/Frg4UONmX7WLd3dXccPwaJ8+VwyOZFSMkY/217Cx1E6VUyXKKDMs2sicPmbm9DEjSxJXDYnCrJf53/4aTkgNniiUHKbn6xNTmbevmqX5VrJq3Ohl6B9h4Ix0Mxf2j0AvSySE6loVvwDC9DJvHZPIsnwrC7ItbClzUO5Q0EkSvc16pieHcunACJJ7WAGA9pISpufLE1JYkG3h5xwLuyqdVDlVTDqJVLOeifEmTu/jHxV4OGLUSbwwOYFzi2x8e7CWzWUOSuwKsiSRYNIxOtbIib3COLFXWEA+RZIkccuIaE7uHcaXB2pZV2wn1+LGpWrEhOgYFm1kerKJM9PD22RuHmGQ+ejYZL48UMNPOVb2V7uwulXCDTKDIg1MSw7l7L7hTQrpZoPMoxPiuGpIJD9kW1hV5JlTpdPzOmNCZIZGGZmcaOKsvuHdWg3RIEs8PSmO85cW4FTh/9aU8unxyV06p1CdRHJocNMiM2tcpITp2V/t4qIBER2Ossu1uEgw6SixK50SRVbPHSOjcakai3KtmPUSVw6O6lJPw/ROTrXtG972VNKnt5R7ixDcNjKmTeJrR5C0I73GrqDH47TVoGktnxQZTOHIsg5VVXDZO2bOKPjrINbNX5uKGhv/ePxLyqobwrUTY8x8+MD5RJqbj4wQ60bQXsTaaR2tKBPlq8eh+pC706GR6M69HyltRJvGK3Oo3LbBwo4qX7+rMB38a7yZqQnBregYKJIkYwztOh8VgUAgaI3vDtZy/4YyNGBakok3pyV2iodVTyOr1kVciK7DVR7Bk66/u8rFsFbSLQXN8/H+ap7c7ImqnNUrjBcnxwct/VcY3gsEAoHgiEPTNJ788Fcf4UuS4OErTmhR+BIIBF2Hum8tykd3+wtf8X08xvZtFL4O1CpctqrWT/hKNEm8PyWixwpfAoFA0BM4q284c0dFA7CyyM4960txt2Dqf7iSHm7oFOELPFGHQvhqPwuya3lmi0f4OjohhOeOCp7wBSLtUSAQCARHIF8s28aqbdk+2y6bPZ5xg1O7aUYCwV8XTdPQ1n6D+usHcIgVvdR/AvKZdyOZAvM3qefPcjd3/Gmh2uU73qAImdcmhpMUKu7vCgQCQWtcOSQKi1vjzV1V/Jhjxa2W8q+j41stECAQtJXvDtbywIYyVOo8K6cmBr0IjRC/BAKBQHBEsXV/Af/+eo3PtpH9k7jytIndNCOB4K+LprhQf34DbesSvzZp4unIJ1yNJLfNB+anfCcPb7VyqI3U5Hg9/xpnJtwgLtoEAoEgUG4ZEU1siMzTmyvYUu7x2ksKFTKBoHP5rdBT5OXYlFBenByPSRf8m1RiVQsEAoHgiKG82soD7yxBURuuis0mI49ceQL6bviRFQj+ymj2WtRvnkLL2urbIMnIs65HHn9q28bTNN7PcPDvvXa/tjN7G7l/ZKiIVhAIBIJ2cMnASBJD9fSPMAjhS9AlPHtUPGNja7h0UERAhS26ArGyBQKBQHBE4FZUHnr3F0qrrD7bH7j8OFLjI7tpVgLBXxOtsgjly0eh1Df9mBAz8tn3IPcb16bx3KrG0ztsfJPj9Gu7cbCJqweENFmBTSAQCASBcVKvrqswKBAYZYnLBnfv+bgQvwQCgUBwRPCf+evYuDffZ9ulJ49l5th+3TQjgeCviZa/F+Wrx8BS6dsQnYzugoeR4tLaNJ7FrXH3JgurStw+2/USPDI6jNN6CfNhgUAgEAgELSPEL4FAIBAc9vy26QAfL97ss238kFSuPeOo7pmQQPAXRd27GnX+8+B2+Db0GobuvAeQwqLaNF6xXeWW9bXsrfE1+IrQS7w4IYyJcaKio0AgEAgEgtYR4pdAIBAIDmuyiyp5fN4yn23xUWE8dtWJwudLIAgSmqahrf8edem7+FV0HDYdec7/IenbFqG1t1rh1g21FNl9x0sNlXltkpn+4W0zyhcIBAKBQPDXRYhfAoFAIDhssTlc3PefxVjtLu82nSzz5LWziI0U3hUCQTDQVAX1l3fQ/vzBr02afB7ysf9AktomRP9e5OLezRasiu/24VE6XploJj5ECNsCgUAgEAgCR4hfAoFAIDgs0TSNZz/+ncz8cp/tt54/hVEDkrtpVgLBXwvNaUOd/xza/vW+DZKMfMqNyGNPadt4msb/Djh4abf9kPgxmJGo55mxZkL1wtheIBAIBAJB2xDiVyP+/PNPvv/+e9atW0dpaSkOh4OEhATGjh3LOeecw7Rp0wIap7a2lnnz5rFkyRJycnJQVZVevXpxwgkncNlllxEbG3tYjiMQCAQ9iW9+28Hidft8tp00aSDnHTuym2YkEPy10GrKUL58DIoyfBuMoZ6Kjv0ntGk8l6rx1HYb3+X6V3S8MN3IXcNDu608ukAgEAgEgsMbSdO0Q2+s/eWw2Ww89NBDfP/99y32O+ecc3jiiSfQ6Zr3mMjIyOCaa64hLy+vyfb4+HjefPNNRo8e3eJz9bRxuhKnrQZNU1vsYzCFI8s6VFXBZa8N0swEhzti3Ry5bM8s4sYX5uNWGr47+qXE8M4/zyHM1DEDbLFuBO3lr7R2tOKDKF8+CtUlvg0R8Z6Kjoltq7Ja4VS5c6OFjeW+eY4ycNfwUC5MNyIdQcKXJMkYQyO6exoCgUAgEPxl+MuLX06nkyuuuIINGzYgyzIXXnghZ555Junp6bhcLnbt2sWLL77Inj17ALjmmmu48847mxyrtraWM888k9zcXAwGA7fffjtz5szBZDKxYcMGnn32WbKzs4mPj+e7774jISHhsBinqxHil6CrEOvmyKSixsYVT31FcYXFuy0sxMB7955LenJ0h8cX60bQXv4qa0fN3Ij67dPgtPk2JA1Ad/5DSBFxbRovo0bh9j8t5Fp9zwXC9fDsODNTE468io5C/BIIBAKBILj85d1CjUYjl1xyCYmJibz77rs88sgjjBs3jtjYWJKSkjj22GP5/PPPSUtLA+DDDz+koqKiybHee+89cnNzAXjuuee4+uqrSU5OJjo6mhNPPJGbbroJgNLSUt54441m59TTxhEIBIKegqKqPPzeLz7CF8B9/zi2U4QvgUDQMurmn1G/eMRP+JIGTEJ36TNtFr5Wlbi4fHWNn/DVO0xm3tSII1L4EggEAoFAEHy6JfIrIyODJUuWsH37dgoLC6mpqUFVVZYsWeLX1+l0YjS2rTR2e3C73ej1zVugffDBBzz99NMAvPrqq5x88sk+7YqiMG3aNCoqKhg3bhyfffaZX/ucOXPIzMwEwGQysWbNGkJDQ3v0OMFARH4Jugqxbo483vpuLR/+vMln299OHMMt503ptOcQ60bQXo7ktaNpKupvH6Kt/sqvTRp/GvJJ1yLJzdtC+I+n8VmWk+d32jj0DGB8rI4XxpuJNh6592hF5JdAIBAIBMElqGcVOTk5XH311cyZM4dXXnmFpUuXsn37drKysrwRSo3Zu3cv5557Lh9//HGXz60l4QugX78G74qSkhK/9o0bN3ojwmbPnu3X/vXXX5OZmcm5554LgN1u548//ujx4wgEAkFPYcWWg37C19hBKdxw9tHdNCOB4K+B5nahzn++CeFLQj7hGuRZ17dJ+HKpGk/vsPFcE8LXmb2NvHVU+BEtfAkEAoFAIAg+QTuz2Lp1K+eeey4rV65E0zR0Oh0DBgxgwIABze7z9ddfs2/fPl5++WUsFkuz/YKB1Wr1Po6KivJr3759u/fxuHHjfNpsNhuvvfYaY8aM4bbbbmtyn546jkAgEPQEcourePyDZT7b4iLDeOzqE9HrxEWyQNBVaLZalM8fRNv1u2+DPgT5nHuRjzqzTUb01S6Vm9db+DLbt6KjBNwx1MTDo0IxyEeOsb1AIBAIBIKeQcvhTp1ETU0NN9xwA9XV1URFRXHHHXdwxhlnEBoayvLly7n++uub3O+GG27giy++oLa2lvnz53PxxRcHY7pNsnXrVu/jsWPH+rXXpw8C9O7d26dt3rx5FBcX8/zzz5OUlIRer8ftdvvs01PHCQb6kLBW+0iS7P3fYArv6ikJjhDEujkysDtc3P/OL9TaGi6WdbLE0zedQUpSUqc/n1g3gvZypK0dtbIQ58f3QMlB3wZzDCEXP4Xce1ibxjtY4+Lm1WUcrHX7bA/TSzw3KZZjU4JvvSAQCAQCgeCvQVDEr3nz5lFWVobJZOLDDz9kyJAhAe0XHR3NrFmzmD9/Pr///nu3iV9Op5MFCxYAniiqevP7xpSXl3sfx8TEeB9XVFTw7rvvMmPGDI4+2pOaExkZSXl5eZPG+T1tnGAgtyFVQpIkJCnw/gIBiHVzOKNpGs9+tJR9Ob7p5rdddBwThqV36XOLdSNoL0fC2lEK9uH86C60mjKf7VJ8H0z/+BdyTGqbxltdZOP/1pRS7fJNdEwN0/HGtEQGR3W9v6tAIBAIBIK/LkERv5YtW4YkSZxzzjkBC1/1jB8/nvnz57Nr164uml3rvP32216fr1tvvbXJPjabp+qR0Wj0Cf9/6623qK2tZe7cud5t9Qb+jVMpe+o4wUBVlVb7SJKMJElomtaqOb5AUI9YN4c/3/y6hR/+8E3JPmHiYC46aVxA3x3tQawbQXs5UtaOsn8dzs8f9qvoKPcZifFvT0JYVJs+f19k1vLklkqUQ0osjY018srkOOJMui77PPdk2nLzTyAQCAQCQccIiviVk5MDwMSJE9u8b1ycp2R240imYLJx40beeustAM4//3ymTp3aYn9ZbvCeycvL45NPPmHOnDkMHTrUu11VWz8h7mnjdCVuhzWgao+SpEPT1COugpag6xDr5vBmV1Yxz3+81GdbenI091x6DG5H1/lAinUjaC9HwtpRty5B/fE1OOR3WRo6Den0ubhlHQT42lRN45Xddj484PBrm9PLwIMjQzFiw2XvlKkfVohqjwKBQCAQBJegiF8Oh+ekp7EQEyi1tZ4TrNaqMXYFubm53HTTTbhcLkaOHMkDDzzQbN+wMI9vlcvl8m57+eWX0TTNx1QeGo5H/T49eRyBQCDoDqotdu7/z2Jc7oYL8NAQPU9dOwuzSaRHCQSdjaZpqH98gvbHp35t0qQzkU+4yutpFgh2RePBLVZ+KXT5bJeAW4aYuLx/SJuM8gUCgUAgEAg6QlBKZCUnJwOwc+fONu+7du1awN+0vaspKiriiiuuoLy8nLS0NN58801MJlOz/WNjYwFQFIWamhp2797NDz/8wEUXXeTjEeZyuaipqfHZpyePIxAIBMFGVTUe+2AZheW+0SX3XHos/VLF95RA0Nloihv1x1eaEL4k5BOvQXfiNW0SvsodKtetrfUTvkw6eGF8GFcMMAnhSyAQCHoYqvbXSz8XBJfuXmNBEb8mT56Mpml8+eWXVFVVBbxfRkYGP/zwA5IkMWXKlC6coS+lpaVcccUVZGdn06tXL+bNm0diYmKL+/Tv39/7OC8vj+eff57Q0FBuvPFGn34FBQXeNMN+/fr1+HEEAoEg2Hy0aBOrtmX7bDvv2JGcNGlgN81IIDhy0RxW1C8fQ9v6i2+D3oh89j3Ik85s03hZtQqXr65la6XvCW58iMR7k8M5LllEbgoEAkFPw6lY+X7nQ6zL+aS7pyI4Qims2cP/Nl5Hce3+bptDUMSvSy+9FJ1OR2VlJddddx3FxcWt7pORkcENN9yAy+VCp9Nx0UUXBWGmnoivSy+9lIyMDNLT0/nwww/p1atXq/uNHDnS+3jevHmsWLGCK6+80i+aauPGjd7Ho0aN6vHjCAQCQTDZsDuPd75f77NtRL9EbjkveDdABIK/ClpNGcrH96Ad2OjbYIpA97cnkYdOa9N4m8rdXLa6lhyrr19Y/3CZeVMjGB4VfAsLgUAgELSM1VnBt9vvJbdqC3/mfUW1vbC7pyQ4AlmV9V+qHYV8u/1esis3dcscgnIWMnjwYO644w7+9a9/sWXLFk466SSOO+44Ro0aRUFBgbffggULKC0tZf369fz+++8oioIkSdx5550+kUxdRU5ODpdddhl5eXkMHTqU9957j/j4+ID2HTduHDExMVRUVPDNN98QFxfHFVdc4dfv559/BsBkMjFtmv9JZU8bRyAQCIJFSUUtD7/3C6rWUBIuymzi8WtOwqAXVdEEgs5EK8lC+eIRqC7xbYhOQnfBo0hxbbObWJTv5MGtVlyH1K85Kk7P8+PDiDAE5X7rEYHVWUFR7b5m202GCFIihgVxRgKB4EjF5qrim+33UGnPwyCbOG3Yg0Sakrt7WoIjkNlD7uX7nQ9RYsngh12PctrQB0mPmRDUOQTtFtxVV12F3W7njTfewOFwsGjRIhYtWgTg9X24++67vf01TUOWZa677jouv/zyLp/f3r17ufLKKykpKWHq1Km89tprhIeHB7y/Tqfjb3/7G2+88QYAl19+OWaz2afPqlWrWL58OQDnnnsuoaGhPX4cgUAgCAZuReHBd3+hosbm3SZJ8PCVJ5AcKyqiCQSdiZq1FfXrJ+HQqqnJg9Bd8BCSOSbgsTRN44NMB6/u8S/ZeHovAw+OCsMgC3+vtlBYs5sf9zzZbHtq5EjOGflMEGckEAiOROpTHSvteegkA6cNe5DeUWO6e1qCI5RQQxRnDn+Cr7ffTYUth5/3PM2ZI54kOWJI0OYQ1NtwN910E1999RVTp05FlmU0TWvyH8DEiRP59NNP/SoTdgWbNm3i0ksvpaSkhNmzZ/Paa69hMBhwOBxN/nM6nU2Oc9VVV3mN+T/++GN+/PFHysvLKS0t5dNPP+Wmm25C0zTi4+O54YYbmp1PTxtHIBAIupo3v13H1gzfMPsrTp3A5BFpzewhEAjag7prBernD/kJX9KASeguebpNwpdb1Xhyu61J4ev6QSYeHS2EL4FAIOiJaJrGT3uepsSSAUicNPhOIXwJuhyTIYIzhz+O2RiLS7Xzw65HqXG0bonVWUia1ii/JIhUVFSwYsUKcnNzKSsrAyAuLo7U1FSOOeaYgNMNO4O///3vrFu3LuD+ffv29UatHUpGRgbXXHMNeXl5TbbHx8fz5ptvMnr06Bafo6eN05U4bTVomtpiH4MpHFnWoaoKLntti30FgnrEujk8WL4pk/v+s9hn21HDe/PCzaeik4OfKiXWjaC99PS1o/65EHXxW4DvqZ807hTkWTcgyYGnF1vcGndvsrCqxO2zXS/BQ6PCOL33X8/YPr9mJwszmorYkjDIJkz6CGJNvUkJH87A2GlER7ZN3P/3qjmAiPwSCAQdZ0v+fFYcfAeAcannMK3vlR0es9RykF8zXqOodg8AZ414it5RbbvGrLYXsqt4KQcr1lHjKMGl2gnTR5EcMZShiSeQHjOxTeNpmsbe0uXsLllGmSULh7uGMEM0SRFDGJ50Mn2ix7U6xtrsj1mfe2g15JaJCEnksgnvt2mfjqKoLvaU/EpG2SpKLPuxu2sJ0YcTbUolPWYiI5JOIdQQGdBYRTV72Vu6nNyqrVic5bgUG2GGaGLC0hgcfyyD4qejkw3tnmt+9Xa+23E/qqbQK3I0Z414MihVoLvNeTQmJoYzzjiju56+Q7RUFXHAgAF8//33zJs3j8WLF5OTk4OmaaSmpnLCCSdw+eWX+5nOHw7jCAQCQVeQU1TJk/OW+2xLjDHzyJUndIvwJRAciWiahvrHJ2h/+J+8yzP/gTTl/DaddBbbVW5ZX8veGt8bV+F6eGG8maPi239CfGSi4VJtuJw2apzFZFVvZH3B54xJPZOj0y5FJ4tCAAKBIHhU2HJZnf0hALFh6UxJv6xD47kUO+tzPmVzwXeomtL6Ds2wteAHVma9j6L6ZlnVOEuoKSthX9kKBsYdw0mD5gYkvDjctfy85xlyqjY3Od7+sj8YkXQKM/vfgCwd3t6yRbX7WLTnWaodvlkUNlclNlclBTU72Zj3FTP738CQhOOaHcfptrLi4NvsKv7Fr63GWUKNs4Tsyo1sKZjPyYPvJjq09cKATZEaOZJxqefwZ96X5FVvZWvB94xJbVt16fbQbZFfAkE9IvJL0FWIddOzsTtdXPvsd+zPK/Nu0+tk3ph7JiP7J3XbvMS6EbSXnrh2NFVBXfI22saFvg2yDvnU25BHHd+m8fZWK9yyoZZiu+/pY0qoxGsTwxkQcXhfQHSExpFfqeEjGJlwirfNrTqodZWRV72NvNrt3u3pMZM4begDAV14icgvgUDQGSza8yz7ylYA7YvOakxWxQaWZ75JjaPIr60tY28p+J4VB94GwCCbGJp4IskRQ9BJBspt2ewoWoTF6TlfHBx/LLMG39nieJqmsmDXI2RXeqoZR4YkMTL5NCJNSdTYi9hRtIhKuyczamzq2RzT96pmx6qw5VFpazqLqjFu1c7ivc+joZIeM4nThz0c0GvvKOXWbL7cegcu1WNB0DtqNOnRE4kIScTiKievahuZ5avrekucNGguQxKO9RvH4bYwf+cDFNcVXIkMSWJwwrFEm3ph0JmosOWys2ixV2CLMqVwweiXCNEH7pPeGKdi4+NN12FxlhOiD+fyCR9g0JnaNVagiFtNAoFAIAg6mqbx/Kd/+AhfALecN6VbhS+B4EhCc7tQf3gRbdcK3wZ9CPLZ9yAPnNSm8VaVuLh7kwWLb6Yjw6N0vDLRTHyIiNasx2yIJT1qvN/2MYlzKKjdxaLMF3CpNrIq1rOt8EfGpJzeDbMUCARtpdSSiU4OIaadES/dTbW9mP1lKwFICh/SbuHL4ixnxYF32F/W8PsyOH4mRl0Y24t+atNYFdYcVh70pAiGGWI5Z+QzRIem+vQZnXIG326/hzLrQfaWLmdo4vH0ifb/jq1nV/EvXuErwTyQs0Y8SYi+ofjbqOTT+Hr7Pymx7Gdz/rcMjp9JYvjAJseKCe0V0Pu9rWAhGp6AjtHJc1rt31n8fuAtr/B13ICbGZF0ik/7mJQzyKrYwMLdj6NqCisOvM2AuKnoZV97gjLrAaps+QCMTj6daX2v9IuwG5NyBj/sepS86m1U2QtYn/MZx/S7ul3zNupCGZ18BquzP8DhrmVn0aIuj/4SZykCgUAgCDoLVu7mx9V7fLadMGEA5x07sptmJBAcWWgOK+qXj/oLX6ZwdH97os3C1zfZDm7d4C98zUzU8+7R4UL4agMp4cM4Ju0K79/bCn7oxtkIBIKmUFQXDreFSlseWRUbWJfzKZ9vuZ3PttzKtsLD9zO7tXCBV6AZlXxqu8dZuv9lr/AVE9qbs0Y8yazBdxFqiG7zWNuLfkLVPD8u0/pe4Sd8AZj04Rw34Bbv3xvzvm52PE3T+DPvSwAkZGYNmusjfAFoaNQ6S71/b8j9vM3zPvQ5txQuACDKlNqiMNeZWJ2V5FZtAyA5Ypif8FVPesxEBsXPAMDurqaoZo9fn9TIkVww5mWO6Xs1M/pf12RqqUFn4viBtwEeq4TdJctazeBqiRFJJyNLnnisLQXf09VJiUGJ/HrppZc4/fTTGTiwaTVVIBAIBH8d9mSX8uJnf/hsS0+O5p6/zwyK2aVAcKSjWatQPn8ECvf5NkTEobvwMaSE9IDHUjWNV3bb+fCAw6/tonQjdw4PRSc+t21mQMxUVubOw6lYqLTnUessI9wY16XPaXfVcKBiHXlVWym1ZFLjKMap2NDLIYSHxJMaOYJRyacSb+7f4ji/H/gPWws8F3knDLydYYknttjf4iznvxsuA7SAUjZtriq2FHzPwYr1VNny0dAwG2NJixrHmJTTiQlrvVDAN9vvIb96u8+2S8a9RUyopwp6Yc1uNud/R371TmyuSq8YcPPU4IgaVfZCDpSvIb96B+XWbCzOctyqA6M+jGhTL/pEj2Nk8mmYjS1XX/140/VU2HKRkLly0keEGqJa7H+gfC0Ldz8OwPhe5zE1/fIW+5daDrK1cAF5VVupdZSik41EmZLpF3s0Y1LOCCjdqT5dt54wQwxXTvrI+/fekt/YUbSIUmsmDreF1tZJZx27xlidlewu+YWMstXUOIqwuaq9a6IpDpSvY0a/61ocszOOXVeQURf1JSGTHtO2myCNmdznHxRU72RC7/MZl3pOh8zPM8o8KXmypGdg3DHN9kuOGEK0qReV9jzyqrZhdVYSZoz261dcu48qewEAfaLHNfmdsbXgB2yuSnSSAUVzkVWxAadiw6gLbddryK78k0pbLgCjU+YE7XzWk27qEYxSIoa12Dc5Yhh7Sn4FoNZZ1mSfKFMKY1PPanGcKFMy0aG9qLTlYndXY3FVtPv3y2SIICViOHnVW6l2FFFiyWg2Aq8zCIr49Z///Ie3336bgQMHcvrpp3PqqafSu3fvYDy1QCAQCHoQNVYH97+9GKe7wRDVZNTz5LWzMJv+etXhBILORqssQvn8ISg/xJ8kthe6ix5HikoMeCybovHAZivLilw+2yVg7jATl/TrWm+OYFNuy2Zj4TfUOMsYm3Q6/aKP6rLnkiUdkSGJlFoPAB6BqKvFrwW7HvFWYWuMS7VRYcuhwpbDzqLFTE2/nHG9zml2nEFx073iV2b5mlbFrwPla6m/OBsUN73FvrlVW/lp95M4FIvP9ip7AVX2AnYWL2ZGv+sYmTy7xXGaosZRQkxob3YWLebXjH+3KG50JU63lU823YCiufzaHO5aimr3UFS7h62FC5k95N4W09IGxc9gXc4naKhklq9hRNLJLT53Rtkq7+PB8ce22Hdj3jeszvrA5zgpiosSSwYllgy2Ff7IaUMfJDliSIvjHIrVVYmiupAlPUv3v8LuEn9j7ebozGNXz/6ylfya8W8c7ppm+8SE9sao80QOmfQRJEUMRlFdzQo+XXXsOkqVvZAaRzEA8eb+AVf+a4rE8IFcNvEDTB0U8VRN8Xp5RYf2alVES4kcRqU9Dw2V4tp99I31F/CyK//0Pm4qAsupWNmU/w2RIR4xckvBfBTNRW7lZvrHTWnX69hcMB8AgxzKsISWvxM7E4MuzPu4qc9FY5xuq/exSR/RoedtvL/DVdOh36+06LHkVW8FPL8Bh734BZ5QwP379/PSSy/x0ksvMWbMGObMmcPs2bOJi+vaH3uBQCAQdD+apvH4B7+SX1rts/2eS2fSP1VUnRUIOopWchDls4egtty3IWUQugseQQprOSqkMSV2ldv/tLCzyrdyl0mGJ8eGcXzykSNWa5rKtpKfWF/whTf15tesN0kJH9bhC4SWqE/18Myh/RXSAmVw/Ayq7YWkRo4gKWIIESEJSJIOq7Oc3KotHKxYj6oprMx6n+jQ3vSLbVr8S44YRrgxnlpnKTmVm3Ap9hZNig+UrwE8kSYD4qY22y+7chMLdz2GonmEkcHxx9I7ahR6OYRyWzbbC3/G6ipneeYbmPQRDIxvPkJkSvpl2F0eMWPh7scAqHWUkFWxwSt89YmeQP/YKeRVb2Vf6e+tHr/OwqgPIz1mImXWg/SKHEW8uR9hxlhUzU2to5SMspUU1e7F4a7hx91PcPG4N5u9sBwUP5N1OZ8AHmGrJfFL1RQOVKwDIDa0D/Hmvs32XZ/zKWtzPgbApI9keNIsEswD0DSV/Ood7Cr5BZurku93PsSFY14mypTS7FinDX0IgDLrQdZkfwh16WY7ihaxu+QXZEnPkITj6B01mj/zvqLcmhWUYweQVfEni/Y8i4aKhMzA+OmkRY3BqDdTYy8io3w1hTW7sLtrmZp+Bf1ij252rK44dp1NiSXD+zg2rE+Hx+uo8AWeSM96kTBEZ26lt8eEvZ5yWw598Re/Si0HvI8Twwf5tW/Jn4/dXc20vldSn74HUGo92C7xq9yaQ07lZgCGJh6PUR/W8g6dSJQpGYNswqXaya7chKopzRZQya2rehmiDyc1ckSHnrexWGxqJeK0NeLCGqLRSyz7OzRWawRF/HrzzTdZvHgxy5Yto6qqCoAtW7awZcsWnn76aY4++mhOO+00Tj75ZMLDuycEVCAQCARdy8eLN/PH1oM+286eMZxZR/mfmAgEgrah5e5E+fJRsPtGzEh9xyGfex+SMfBUjn3VCrduqKXwkIqO8SESL08wMyL6yKmXVOssZXn2WxTU7vLZrmhO1A74mAT63PWEtcMnp62MSpnTrJnw6JTTyavaznc77kNDZUPu582KX5IkMSBuGlsK5uNWHeRUbmr2gtGp2Mit8tzRT40cSVgzqWguxc6v+19F0VyY9JGcMfwxv7v/o1POYP6OByix7Oe3zDdJix7n5+NTT1PpPxW2XNbnfo4kyZw0aC6D42cCeKNOgskpQ+5p9gJ1fK9z2ZD7OWuyP8KpWNlasKDZ9MSY0F4kmAdQYskgt2oLDndts+l0uVVbvResgxNmNju3Eksm63I+BSApfDBzhj3sk045OGEmI5NP4dsd9+Fw17I88w3OHP54s+PVr6PGAunBivVszv8WszGW04c9Sry5HwA7ihY1O049nXXsVE1heebrXuFl1uC7GBTvG5k4NvVsfj/wFtsKF/LTnqc5Y/hjLUaTdfax62yq61IBgR5j2G9sHLmkthy5BPisb4e76arKFfaGyOeIEN9oZ4e7ls353xFlSmFIwnHkV+/wttWnLbaVLQXzqY9uHRVEo3sAnWxgVMocNuZ9RaUtl0V7nuXYATf5pUBvLfiBnKrNSMgc2/+mDlVVdCpWKuuM8c3G2DalGDdFtKnB463a7l81tDMJijvpcccdx9NPP82qVat4//33ueiii4iLi0PTNBRFYfXq1TzwwANMnTqVm2++mZ9++gmn0xmMqQkEAoEgCGzam89b363z2TYsPYHbzp/WTTMSCI4c1P3rUT590F/4GjYd+fyH2iR8rSxxccWaGj/ha1CEzIdTI44Y4UvTNPaV/8FXu+/xE74AxiadSVgH72a3RJFlH1ZXBQChhmgiQrq+ym1zgkE9vaJGeqMBimr34lb8fd7qaRx1lVkX2dUU2RV/elNxBrUQqbWl4HtqnCUAHNv/xibTXkz6cE4aNBeQsLmr2FuyvKWX48e2woXUOIqY1vdKr/DVXbT2Xng8lDzRlXl1ZtbNUW9irWpuDpSva7ZfZqOUx0EtvP5VB/+LhopOMjB7yH1N+ojFm/szpc9lAORUbqLcmt3iHA9lddY8wBMVVi98BUpnHbvcqq3eFMC06HF+whd4hN4p6Zehkwyomrsucq15gnHsOoLFWeF9bNK3P+WxMzHoTN6IrypHYQB7NERquVRbkz1srirv40MF98353+FQLEzq/TdkSecTvWZ1VQY+8Trs7lqvj1Za1FhiA/Ak7GyOTruEvjEekTmjfBUfbryaZRmvkVu1FburhuUZr/P7gbcw6EI5Zcg9Ta71tpBRttorGveLaT0asjVMjdJvrc7yFnp2nKCW5tHpdEydOpVHHnmEFStW8PHHH3P55ZeTkpKCpmk4nU6WLl3KHXfcwZQpU/jnP//Jb7/9hqJ0fSi4QCAQCLqG4opaHnx3CWqjCi4RYSE8ce0sjIaWT2IFAkHLqNuWon71OLh9hQpp/GnIZ9yJpA/chPiLLAe3NVHRcVqCnvcnR5ASemRUdLS7a1ia9RrLs9/0u3gK0Zk5Pv1mJqVc0GXP71LsrMqd5/17WOKJPabYR3hIfN0jDZu7utl+yeFDiTAmAHjTJZsi0yflsfmbHfVph2GG6BZTI2PD0rw+SRnlq5rt1xRu1UGCeQCjgxyZ0R50soFQvUc4aXwh3xQe8cuzfurNzA9F01Tve5EUPoQoU3KT/WyuKnKrtgDQN/aoRuvBnyEJx3mFqMZeYoHgVh2MSj6tS7x9Aj12FY2ifHpFjmq2n1EXRmTd8Sqq2dtsdFKwjl1HcKsNvxMdifzpbNKixwGeVLqCav+bEfXUOIpZn/uZ9+/GqeONcSme73UJ2Ucstbmq2Vwwn+jQ3t7oR50c0mg/e5vnvqPwZ+9xHZ1yepv37wx0soFTh97P9H7XYdSZcSk2dhYt4rsd9/He+kvYXvQT8eb+XDL2zRa/XwNBUd1sqqu0KSG3ao4fCAa5YS261OZvunQG3Xb7TpIkJkyYwIQJE7jnnnvYsWMHixcvZsmSJWRmZmKxWPj+++/5/vvviY6O5pRTTuHhhx/urukKBAKBoB04XG7ufWsR5dW+F5gPX3E8KXFd56UjEPwVUNd9i7r0Pb/t8vRLkKZdFLCgomgaL+2y8/FB/5POC9ON3DksFL3cM8SZjpJTvZXfs/+D1V3p19YrYiQz067DbOx8D0KXYqfWVUZB7U62Fi/0RjnFm/szsVfXCW0tUeMoptZRhlOxesUrS6O77i2Vr69Pfdxc8B12dzX51Tv80sFUTSGrcgMAvaNGN1uJsNZRSpn1IAAJ5gFIUssia4J5AIU1uymubbs3zLhe5/iNPzb1zFZN+7sSVVOosuVjc1fhUuzedNv6iLnWjPkjQhJIiRhGQc1Osqs2NVmxrqBmlzfScHBdpFhTZFdu9D5fUhNeSY0x6ExEm3pRbsumuI0+PbKkY1yqf1GFkwf/E0V1eiO3WqMjx05tJGLpGwkgTVFvwq6hYnfXNpnmFaxj1xEai9Stfc6CydCEE9hf5qkC/vuBtzhrxFN+Kc1l1ix+2PUotkbRWc1VZqwXKA81z9+U/w0uxcak3hd5RbHG4piiti3zTNUUthUuBCAiJIm+Haie2VHcigOLoxRFdSIhExPam3JbtndNlloyWZ75OlPSL/fx2Gorm/K/ptzmiVYcnTKH6E5In5XlBkmq3nezq+gxsesjRoxgxIgR/N///R8ZGRksWrSIJUuWsGvXLioqKvjss8+E+CUQCASHEZqm8ez/fmdXVonP9stPHc/UUe3/4RUI/upomor66wdoa785pEVCPvkG5PGnBjyWza1x72YLvxX7nnBKwJ3DQvlbX2OPiUrqCG7Vwdr8z9hZutivTScZOCr1b4yIP6nTLgj3VaxgX8WKFvskRwzjtKEPBNUcubh2P1sK5nOwYn2zfjmBMjB+OpsLvgMgs3y1n/iVX73d+xwtmdNXN0p1cqmOFlP3wBO9AeBULE0KPc1h1IUxINY/6iFEH96sT1ZXklG2ih1Fi8ir2tpqlbbWGBQ/g4KanSiqk6yK9d5UyMbPBdSZujcvflXbG96LGkdpq++FVudzVOsobbHfofSKGt1kZFSg3kGdceyiQht8hhobpB9KvcAGnkij5iokBuvYdQSd1CAGOd1Npwx2B31jJ9E/dgqZ5aspsWTw6eabGJF0MjFhfXC4aymo3sne0t+QkOgVOYq8ak86a2gzXok62YBbdfgI+FZnJdsKfiAmNM1HAG4cyReo6FpPRtkqautuZIxKPrXbBMVKWz4Ldj1Mlb2A6NDenDzoLhLCB1Bpy2d3yVJ2Fi3B6irnYMV68qq2MWvwXQEVbziUguqdXk+7KFMqk/v8o1Pm73A3WDbo2/getJUeI341Jj09nZEjR5KXl0dWVhZWq7X1nQQCgUDQo/h86VZ+XrvXZ9u0UelcPaf77owJBIc7mtOOuuB5tL2H+CzJeuQz7kQe1rzIcCjFdpXbN1jYVe2bshaqg6fHmpmZFHjKZE+mxJrJr1lvUOUo8GuLD+3Lsek3EmMKjvmzQTaRZB7M0PjjGZoS3Gijtdn/Y33u59QbM3eU5IghRIQkUuMo5kD5Wmb0u86nvT7NTpZ09G9CdKrH6qz0Ps6v3k5+9faA5+B0WwIWv+LN/f0iQboDl2Ln5z3PeKPiOoOBccew4sDbaKhklK3yE78yy1cD0CtqVIsCU2PPo22FP7Ct8IeAnt+pWFrv1Ijk8KFt6l9PZx679OgJmPSR2N3V7C1dzoTe5xET2tuv39aCBbhUTzpc76jRzXqOBevYdYTGIm8wnzcQThz4f/y810F25UZqnaXeipn1mPSRzBp8p0fAqRO/mquUadCF4lYdKJrbW/3wz7wvcal2jkr7m49I1ZFU0C0F3wOeyMHhibPatG9nUeMo4evtd2NzVRIb2oezRz7jFWijQ1OZ3OfvTOp9EVsKFrAm+0Ncqp0fdz/J6cMfpU9dumkg1DpK+WnP06iaG4McyqlD7++01Fmn0qD1GLv4RkSPEb+cTicrVqxg0aJFLF++nJoaTzUSrc4jJjKyZ5jyCQQCgaB11u/K5d9f+16c90mK5uErj0c+QtKnBIJgo9WUoXz1OBQekiZjDEU+537kfmMDHmtPtcJtG2opOsTYPiFE4tWJZoZG9ZhTxHajagqbi75nY+G3aPgKfBISY5POZFzS2ejkzn+tqeEjGJlwCgCyJGPUmQnRhRMZkogs6YIeIbC98CevV46EzLDEExmccCxxYemE6MO9F/S/7HuJ3SVLAx53YNwxbMr/hhpHMSW1GSSED/C21Ue+9Ioa3Wy0TEfp6oqcXcGyjFe94o1BF8q41LPpG3MUUaYUjLpQ79qY9+eVXkP21ggzRtM7agw5VZvIqtiAW3Gg13lS+Ypr93vH6Sqj/2C9D5157HSygeMH3spPu59C1dzM3/EAU9IvIy1qHCZDBFZnBXtKfm0kwkhM7N35KcrBXMONo+3sruY9/boDoz6MOcMeZl/pCnYVL6bcmo1TsRFujKdf7NGMTT0LszGWTXkNEc8J5v5NjhVmiK5Lj9RwuGtRNYUdRT8TG5bOwDhfs/f6dGDPfoFXLSyq2UthjcefbHD8TEyG7rHyWJX1gTcVdGb/G5r8rtXJBsb3OodIUxI/73kaDZXfM9/iknFvBRTZbXfX8v3Oh7C6KpAlHbMG39mh1Em/8RutxXBjXKeN2xTdemZjs9lYvnw5ixcv5rfffsNm84Rf1gteoaGhHH/88cyZM4djjgn8TqZAIBAIuo/ckioeeMfX4N5sMvLsDacQHtqyr4ZAIGgarSgT5cvHoOaQFBlzDLrzH0ZKCdw4ekWxi3s2WbAe4lE+JFLHKxPMJB0BxvZVjkKWZ71BsTXDry3SmMSx6TeQZG7Zl6cjmA2xpEeN77Lx24KmqazP/dT797EDbmRE0imdMna9+AWeSK968avUkkmNw1OyflBcy5XFwozR3sfDE2dx/MBbO2VuPZEKW67X3F8nGzl35HNtrnbYHIPiZ5BTtQmXaie7ciP946YADSmPsqRv1ew6rFEa2UmD5jIk4bhOmVtn0BXHrn/sZOYMe4il+1+h1lnKkn0vNNNTYlr6FaRGjmx2rJ587OqJDW2oRFhizezGmTSNLOkYknAsQxKObbLdqdjIr9kJQHxYv2Z9BKNDe3l9BGscJewq/gW36uCotIv9xJ5aR4M1R0wb/Kvqo77A433VHSiqmwN1EbZGnbnF9QkwMG4acWF9KbMepNKeR5n1YKufIZdi54ddj9b5fEkcN+DWdqVMtkSJpWEtxob26dSxDyXo4ldtbS3Lli1j0aJFrFy5EofDE2pYL3gZjUamT5/OaaedxvHHH4/J1HMqUQgEAoGgZax2F/e8uYgaa0MYuSTBo1edQHpydPdNTCA4jFH3r0ed/xw4D/FoSezrEb4iEwIe64ssB8/usPnZQM9I1PP0WDNh+sM7MlPVVHaWLmF9wec+6Sz1DI07jsmpl/aoSmddTaU932tkHxmS3GnCF0BSxGAiQ5KodhSRWb6ao/tcAjROedTTP3ZKi2OEGxO9jy2NojCORPKqtnkfD46f0WnCF8CAuKn8lvkGiuZif9lKr/iVWVcVMz1mYqveZuEhjd4LZ896L7ry2NWnNcqS3s9wOyl8MEelXUJ6zIQWx+jJx66ehPAByJIOVVMort3X3dNpM9sLf/Sa0g9Laj7NMD6sr7fyaXblRnYWLSI+rF+Tnn9FjY5DXIBryuIs9xr0p0aOIL6ZCLSuxu6u9v7OhRljAoriiglN8xEGW/ocuVUnC3c/7o1wm9HvWoYlntDxiR9CcW2DRUpSxOBOH78xQRG/Kisr+eWXX1i8eDGrV6/G7fZ8qdQLXjqdjsmTJ3Paaacxa9YswsODbzopEAgEgo6hqhpPzFtGZn65z/brzjxaGNwLBO1EXf896tJ34ZDUGKn/BOSz/okUEphZuqZpvL7XznsZ/oLQxX2N3DEsFN1hbmxfZj3Iitz3moxoCNVHMj3tmh4TjRVM7K4a7+MoU3Knjz8w7hg25n9NmfUgVfZCokzJHChfC0DvqDGtpgNFmhKJCU2jwpZDUc0eFNXdJamoPQG7u/F70bRfUXsJ0ZvpEzOBA+VrOFixHkV1UWUvoMKWC7Rc5bGePtHj8ZS70Miv3s74Xv4VGbuLrjh2tY5Sft7zDC7VzsTeFzKh1/mUWDJwKTYMulBiQns3G110KD352NVj1IWREjGcvOptONy1lFgym00d7GkU1+5nXc4ngCfKaWgLkXV9YiZ601XX5XyCqrmbjPpSNYWcys2ApxhA76gxAc1lW+FCr0g6Ovn0tr4UP1yKnT8Ovsv+spUYdaGMSz0noGgykz6C+jUXaAETR6PPUVgLa7te+Mqt2gJIzOx3PaNSTgvoOdpKvYebhFz3Oeo6gvLLMm3aNFTVc9JWL3hJksT48eM57bTTmD17NrGxnV9WWiAQCATBY95PG1m+ybdi0gkTB/D3k8d2z4QEgsMYTVVQl7yNtnGhX5s04XTkE69Gkps2Xj4Ul6rx+DYrC/J8q6LJwN3DQ7mw7+GdjuxS7Gws/IZtJT95y7o3Jj1yPNPTrg74IranopONKKoTt+IvYLZE42ifGmfzleUU1UW5NbvRlsCM8QfGT2dj/teAx1h9YNw0SiyedNNB8S2nPHrHiJvG+tzPsLur2VW8mJHJgVcsPZzweS8cJc32q3EUN4iWWuAFCgbHz+BA+RqcioWcqs2U1Hr8AQ1yKP1iWk9VMhtjSI0cTn71DrIqNlBmzepUb5+O0BXHLqN8tTfqa3D8TAw6E6mRI9o1v5587BozNPFEr9iws2gRM/vf0M0zap0KWx4/7n7SG+U0s//1LUYxJpoHEmVKocpegKq5STAPoF/sZL9+jas1psdMCqh4hlt1sqPwZwDMxjhvhGVHWJX1ATuKPGM63DX8fuAtzMbYVtOUdbKB2NA0ym3Z2FyVFNXsISliSLP9ax2l5FfvADwm/TGN0mAb41YcLNz9BDlVm5CQOXaAp/pmV5BbtZUqu6cYTd+YSV3+Ox0UUwdFUdA0DU3TGD58OHfddRe//vorn3zyCZdccokQvgQCgeAw5/fNB3hnwXqfbYN6x3Hf348NKAxbIBA0oDmsqF8+5i98STLySdehm3VdwMKX1a1x+waLn/Bl0sHLE82HvfCVU72Zr/bcw9aShX7Cl0EOZXraNZzU747DXvgCiDalAlBuy6bW0byIdSgxob0w6T0myJW2XLIq/vTrY3fX8vOeZyi2NKQA1adKtkZi+EBvJM6B8jXeqC9PyqP/BWdTjE09mzCD53pgxcF3vWmTh6KobrYW/MDu4sBN+XsSKRHDvY/3l/3RZHpcuTWb73c+hEv1pDnb3NUoqtuvX1P0jTkKg+xJ6c0oW0VGXZXHfrFHew3wW2Nq+hVIyGioLNz1OOXWnCb72V01rDjwDqWWgwGN21G65Ng1iqjdXDAfi7McrQMG9D312DVmcPxMzHWm4ntKluNUbK3s0b3sKfmVL7bc7hWpRiSd0qqfmiRJTOh1vvfv+u/OxlTZC1hx4D/evyf1vjCg+ewtWY7NXQXAyKTZzVb/bAv7S1f4bdvXxLamaBwhtnT/K81+b9tcVfy052kUzXMuMDxxFka9f+S4S7Hzw+5HyanahCzpOXnw3V0mfIEnlbWecb3O7rLnqScokV/9+/fn1FNPZc6cOfTt2zcYTykQCASCIHEgv5zH/rvMZ1t0uIlnrj+Z0JDuLysvEBxOaFXFHmP7koO+DcZQ5DPvRh44KeCxyhwqt6y3sKva19k+2ijx2kQzI6MP39Qyq6uS1XkfkVnZtEjSL2oSU3pfhrkN1bt6OkMSjmdV1vu4VQdfb/8no5JnE2lKwe6qocSyv+4O/Y1++0mSzNjUM1mT/REAP+5+ghFJp5AYPhBJkimpzWB3yTLs7mp6RY4mr3or4IlGOGnQXCJNSa3ObUDcNDbmfUVB9S5ciieSJi16XKseU/WE6M0cP/AWftz9JIrq5MfdT5AWNY7+sZMxG2NxKlZKLBl1okc5ejmEpIghxIT2bnK8gppdPume4Embq69ACZAcMbTLqlA2R7y5L2lRY8mp2ozDXcvnW25hVPJpRIf2xqXYyK3aSkbZShTNTa/IUeRVb8OtOlhx4D9M7Xtlq5EpBp2JvrFHs6/0N/aX/uEVgdpS5TE5YigTep/PhtzPqXYU8tmWWxgcP5NeUaMx6kKxuaooqNlJRtkq3KqDnMpNXDDmZfSyscnx6o95vc8QeHzoGr8X/WKPanVeXXHs+sdNZU32R7hUOzuLFrGzaJFPu4RMqCHSWyVwWOKJLabkdvax6wp0sp6xKWeyMut9nIqF9TmfMq3vlW0ex604yKna4re90p7vfVxYs9v7fQBg0IUEnFqYXbmRDblfkF+93bttQq8LmJL+j4D2H5p4AvvKVpBTuYl9ZSuo3V7GkITjMOkjKLMeZGvhQm8K4NjUs30q1bZEvdG9LOk7zz+xiZvEgd42Hp50Mgcr1nOwYj3ltmw+f9pwoQAAsRhJREFU2XQDgxOOJTF8MCF6M063haLafewtXe5NjUwwD2RyE8fRrTr5fudDFNQVFRiZNBudbPT5rDZFmCGqxYiz5iio3sX+Om+25IhhrRr2dwaSprUhllYg6AKctppW77IYTOHIsg5VVXDZA8tpFgjEuul6qi0Orn7ma3JLGsoU62SJV26bw/ghgVfN6UmIdSNoLx1dO1r+XpSvHgNLpW9DZAK68x9CSgzc4DnLonDzegu5Vt/f195hMq9PMtPH3PG71d2BpqnsLlvOuoJPcSpWv3azIY5pvS/vFm+v/JqdLMx4EoBBMdM5Nv36ZvtKkowxtGUvrENRVDc/7n6crEr/yC3wpEVeOfEjQvRmvzZVU/h5zzNk1kUCNcWIpFOY2f8GFu56zPscpwy+h4HxrVdcL6nN4POtt/lsO3Hg/zG0jebIuVVb+WnPUy3610jIDE+axbS+V2LUNe159832e3wumpvirBFP0TtqdJvm1xlYnOV8u+M+Kuu8uA5FL4cwvd81DIidxqdbbsbiLENC5oqJH/pUxmyOA+VrWbj7ce/fJn0kV0z8sM0+apvyvmFV1gdNphLXY9SFcXSfSxmdfHqzUd7/XtW6d9HNU38IaE5dcexyq7awdP+r3uqkLREb2oc5wx4h0pTYYr/OOnZdhaK6+WrbXEosGciSjgtHv0KcuW+bxqi2F/HhxqvatE9ESCKXTXi/2fYyaxaZZavYXfIrVY1EtNjQPszof32bP6+eiNan63yrmmZ40skc2//GgCK4cqu28t2O+wAYknAcJw2a26b5NMfvmf9ha+ECn22nDn0g4MjZes+wHUWLaC1dfVD8TGb0u65J4b897ynAsMSTOGHgba13bISqKXy25VbKrVnoJAMXjHk5KGnCh+8tP4FAIBB0K4qq8tB7S3yEL4Dbzp922ApfAkF3oe5eibrgRXAf4ueUPAjd+Q8ihQduEbG90s0tGyxUOn1PgodH6Xh1opm4kKC4XnQ6FfZcVuS8T5Flj1+bhMSIhJOZmHz+EVvJUSfrOW3YQ+wsWsSu4qWUW7NQNBcmfQTx5v70i52MTmr61F6WdMwech+7S5axq3gJZZYDuFUnYcYYUiKGMyLpFHpFee66nzLkXtblfEJG2Ur6tFLhrp6E8AFEmVK9F6w6ydCkx05r9I4azaXj3mZH0U8crFhPhTUHl2rHqDMTHdqLtKgxDEuc1ar40JMxG2O5YPSLbMmfT0bZKirt+UhIhIfE0yd6AqNT5njTSM8d+Syrs+bhUKwBCV/gMV4P0Yd7BcQBcVPbVUBgXK9zSI+ZxPaiH8mt3EK1owhVUwg1RBIf1p/0mAkMTTihydSprqIrjl3vqDH8Y/w7lNtysDjLfNIknYqFKnsh+0p/o8KWS7ktm/k7H+Disa+jk5uPbO+Jx64xOlnPiYPu4Istt6NoLn7e+yznjXq+SeE8WCiqm6+33eVzUyMpfAijU+YwKH5Gu9ILTfpwzhz+OHtLf2N38VLKrFnY3TWEGWJICh/EiOTZ9IkeF/B41fZCwgyxWF3lnWJ0X8+U9Mu8VVrrDe8DFb7AE/F53ICbGZl8KntLlpNbtQWLswy7u5YQfThmYyy9IkcxOH5GuyK0WiPa1PZz/hUH3qbcmgXA5D5/D5o/noj8EnQ7IvJL0FWIddO1vP71aj5e4ns37fRpQ7nn0pmHtc+XWDeC9tKetaNpGtqar1GXf+DXJg2Zinz6HUiGwMWcFcUu7t5kwe6b6cjUBD3/GmcmTH/4fTbdqpPNRfPZUrwAVVP82uNC+zI97WoSwgKPjOtu2hP51dNxq07+u+EfONy1DIw7hlOG3NPdUxIIOgVNU1m6/1V2l/wCwPEDbmV40qxunlXH2VW8lKX7XwY00qLHcfqwRzrFw6q9/LznGUqtB+kfO5lBcdMDTkUMJpqmUWo9cNhUyeyJbC1YwO91fmsDYqdyypB7kKTg3JQTkV8CgUAgaDOL1+3zE75G9k9i7kXTD2vhSyAIJprbhbrodbStv/i1SZPPRT72sjadEH6b4+DJ7TaUQ25rntHLyAOjQjHIh99nM79mByty36faUejXppdDmJB8HiMTTu7WCzaBhz0ly7zRRp3mhSMQdBG5VVtxKXbCQ+JbFTIkSWZc6lle8au+munhzrDEE7C5qliV9T45lZtYsu8FTho0t9u+T08YeHuPj9yVJEkIXx1gT8mvrDjwDgC9Ikcza/BdQRO+4DAQv6qqqti7dy8AkyYFbvIqEAgEgq5hd1YJT3203GdbfFQYT103C6NBXIAKBIGg1ZajfP0k5B+SwifrkE++EXls4NWVNE3j7f0O3tpn92u7ekAINw42HXaitN1dy9r8T9hb/luT7WmRY5nW+3IijAlBnpmgKazOStZmfwJ4qvKlRY/t3gkJBK2w6uB/KbbsIy16HGcOf7zV/nZ3QwEFQyuFBw4nxvc6B5diZX3uZ+wr/R1VdTNr8N3tSpXtKD1d+BJ0jF3FS1m2/xU0VJIjhnLasAdaTB/uCnq8+LVp0yauv/56ZFlm586d3T0dgUAg+EtTXm3j3rcW4XQ1pB4Z9Tqeuf5k4qO6zytCIDic0PL3eISv2kNKkoeYkc+5F7nv2IDHcqsaz+yw8XWO02e7DPxzRCgXpId0fMJBRNM0DlStY1XuPG85+caE6qOZ0uvv9I8++rAT9I5Uqu1F/LTnKayucmRJz/R+13b3lASCVokJS6PYso+8qm2UWDJbjOaxuapZlfWB9++0qLFdP8EgcnSfSwk1RLHiwDsU1u7B5qokPCS+u6clOMLIqliPhkrfmEmcMvge9Lrgn5/0ePGrHmFNJhAIBN2LW1G4/+3FFFX4ehndfckMhvdL6qZZCQSHF+rWpag//xsUl29DTAq68x5Cik8LeCybonHPJgu/F7t9tofI8PTYMI5LNnbGlIOGxVnOytz/klW9scn2oXHHc1TKRd1qyizw4FSsHKxYT1bFBvaX/oGiedbz9H7Xkhg+sJtnJxC0zvDEWewp+RVVc/PV1rkMjD+GpPDBhBqi0ctGFNWFxVVOce1+DpSv8Rqx94+dfERGNo5OOR2zMY6Y0DQhfAm6hJMGzSU5YiijU07vttTaw0b8EggEAkH38u+v17Blf4HPtguOH8WpUzq/coxAcKShqQrqsvfR1s/3a5P6jUM+859IoeEBj1fhVLltg4Vtlb4G8JEGiVcmmBkbe/ic4mmayq6yZazL/wyXavNrjzb1Ynrvq0gOF981PYVaRxmL9/7L+7deDuG4ATczJOG4bpyVQBA4vaJGckzfq1iV9QGK5mJPya/sKfm12f4SMiOTZ3NM36uDOMvgMiBuandPQXAEo5MNjE09q1vncPicGQkEAoGg21i8fh9fLNvms23CkF7cfO6UbpqRQHD4oFmrUec/i3Zwi1+bdPQ5HmN7OfC7oPtrFP7vTwu5Vt9KyckmidePCqd/+OHjvVdpz2dFzrsUWvb4tcmSjrFJZzI28Yyg+4IIWiYmtBchOjORpmTSYyYyOvl0wozR3T0tgaBNjE09i74xR7G96CcKqndS7SjC4a5F01T0shGTIZKY0N6kRo5kUPwMokzJ3T1lgUDQATpN/LLb7UiSREiIf+7md9991+5xd+/e3YFZCQQCgaCjZOSV8cxHvqbTybHhPH7Nieh1wavQIhAcjmjFB1G+fhwqi3wb9Ebk2bcgj2xbpMyyQicPbLFi8w34YnCEzGuTwkk0HR6fSVVzs6XoBzYVfedNmWtMYtggZvS5mhhT726YnaA1JEnmqqM+EVU2BYc90aGpHNP3qu6ehkAgCAKdIn59+umnPPnkk0iSxAMPPMCFF17o037PPfcIU1KBQCA4DKm1Obj3rUXYnQ2eQka9jievm0V0+JFT7Ugg6ArU3StRf3gJXIdUYYyIR3fuA0gpgXsjqZrGO81UdDwqTs/z481EGA6Pc60Sawa/Z79DuT3Hr00vhzAp5UKGx5+EHMTy54K2I4QvgUAgEBxOdIr49fLLL+N2ey6MXnrpJT/xqx5hWi8QCASHD6qq8fgHv5JbUu2zfe7fjmFYemI3zUog6Ploqory+//QVn7m39h7OLpz7kUyxwQ8nsWt8dAWK8uK/COkzutj5O7hoRjkni98uRQ7fxZ+xfaSn9HwPydMixjDMWlXEm4UZssCgUAgEAg6l04RvyIiIqiurkbTNCIjI5vtd9lllzF06NA2jb17927mzZvX0SkKBAKBoI38b/EmVmw56LPt9GlDOX3asO6ZkEBwGKDZLTi/ehxtz0q/NmncbOSTrkXSBe5flWtV+L8NFvbX+vp76SW4e3go56cHv1R4e8it2cYfOe9R4yzxazPpIpjS++8MiJ4qMgUEAoFAIBB0CZ0ifj333HM8++yzSJLE3Xff3Wy/qVOnMnPmzDaNvXz5ciF+CQQCQZBZtzOHt+ev99k2ND2BOy46pptmJBD0fNSyXJyf3o9WkuXbIOuQZ12PPG52m8ZbW+rin5usVLl8o6RijBLPjzcz/jCo6OhwW1iT/z/2lv/eZPvAmGlM6fV3TPqIIM9MIBAIBALBX4lOOWsaP348n3/+eWcMJRAIBIJuprC8hoffW4raKFU9ymziyWtnEWLo+RfbAkF3oGb+iXv+v8Be69sQFu1Jc0wbEfBYmqbx6UEnL+62oRySHTg0UseLE8ykhPZ8P6yDVRv4I+e/2NyVfm3hhjiOSbuKtMgxwZ+YQCAQCASCvxziKkYgEAgEXhwuN/f9ZzFVlgZTbUmCR686gZQ4EZkhEDSFumEB6i/vgOabmkjyAI+xfWRCwGM5FI2nttv4Ps/p13ZyioGHR4cRquvZqYF2dw2rcueRUbm6iVaJkfEnMzHlfAw6U9DnJhAIBAKB4K+JEL8EAoFA4OWlz1eyO8vXk+faM47iqOFp3TQjgaDnoqkK6pK30TYu9GuTRhyLPPsWJEPgnlzFdpU7N1rYVqn4jgXcMsTE5f1DerwnVmblWlbmfoDdXe3XFmPqzfS0q0kyD+qGmQkEAoFAIPgrExTx64ILLkCSJFJTU9u8b3R0NJMmTeqCWQkEAoGgMQtW7uL7P3b5bDtmdF/+fvK4bpqRQNBz0RxW1O+eRcv807dBktGfdC3a+NPaJFRtq3Rzx58WSh2+eY7henhqrJnpiYGb5HcHVlcVq3L/y4Gq9X5tEjrGJp3BuKQz0ck9+3UIBAKBQCA4MpE0TfOvNS0QBBGnrQbt0FSRQzCYwpFlHaqq4DrUT0UgaAaxbgJnd1YJ1//rO5zuhoiT3gmRvHfvuUSEHR7V5DoLsW4EraFVFaN8+Sj8P3v3HR5VtbUB/D1nZpKZ9Eo6oYOUQCjSi3QQBUXBhmDB3kEBxYYNFNv1XsGKiA2pokhTei+h955GSEJ6Jpl29vcHn8GTk0CAZGaSvL/nuY/J2ufsrHiPSWbN3muXbmzvYYLnyDchN7rxqp6dJclWvH3ADFupX4Wx3jI+bueN+j66Ssi6agghcCJ7E7akzIHFof2eg02x6BnzCIK96jk/OTcmSTI8TNxKTkRE5Czc9khEVMvlFhTj5S9WqApfngY93ntsQK0rfBFdiUg5Csf8twBzjnrALxSe906FPrIJFMVR5r2l2RSBT48U48czFs1Y11A93mvjBV+D+za2L7RmYWPyt0jM260ZkyUd2obdjtZhQyBL/HOTiIiIXIt/jRAR1WIORcEb3/6NtCz1io2J9/VAw6hgF2VF5J6Uwxug/PExYC/VjD6iMXR3vAo5pG6F58qyKHhpdyF2ZWkLZWMaeOKppkbo3LS/lxACx7LWYWvKj7AqZs14qFcD9Ih5FEGmaBdkR0RERKTF4hcRUS327R+7sO1Qkip2x00tMaBjExdlROR+hBAQW+ZBWfe9Zkxq1hXykOchGSp+cuHBHDvGJRTifLG684SnDLwe54VBkR7XnXNVKbBmYn3S10jJ368Z00kGtAsfjlZ1BkOW3HerJhEREdU+LH4REdVSm/adxaw/1c264xqG4+nhnV2UEZH7EXYblOX/hdj/t2ZM6nwn5J6jIEkV35q4OMmC9w4WwVqqv1e4UcKH7bzR3N89/zQTQsHhC6uxPfVn2JRizXiYd2P0iHkEAcarP9yIiIiIqKq5519YRERUpZLTc/HmLPWL+SA/E94a2w8GPVdsEAGAMOfBsfBdIOmAekDWQx70JOS4fhWey6YIvH+oCPMTrZqxDsF6TG3jhSBP9+zvlVN8DhuSvkZa4RHNmE7ywI2RI9E8pD/kqygCEhERETmT2xe/hBBYvnw5Bg0a5OpUiIhqhGKrDS9/sRIFRZdehOtkCW+N7YfQAG8XZkbkPsSFFDjmvQFkn1MPGH0g3/4y5Ni4Cs+VXnyxv9febG1/r1H1PfFMUyP0svv191KEA/vT/8SutAVwCJtmPMLnBvSIGQs/zzAXZEdERERUcW5d/LLZbBg+fDiOHz8OX19fdOvWzdUpERFVa4oi8NZ3a3Ai5YIq/uTtnRDfmNuViABAObsPysJ3gWL1QRAIjIDuzjcgBUdVeK49WXa8uLsQmRZ1fy+jDLzmxv29LpjPYH3SV8gsOqMZM8hG3Bh5N24I7n1VWz6JiIiIXMUpxa977rkH9erVw9ixY1G/fv0K32cwGBAcHIxjx45h6dKlLH4REV2nb5fuxJqEU6pYn3YNMbJPxVexENVkyr5VUJb9F1BKrdKKaQnd7S9D8vKr0DxCCMxLtOKDQ0Wwq+teiPaS8WFbbzTxc78txnbFit3nF2Pv+d8hoGjGo33j0C3mQfh6hLogOyIiIqJr45TiV0JCAnbv3o0BAwZcVfELALp164YtW7Zgz549VZMcEVEtsWrHCXy7VN3gvkFkECaN6gVJcr8tV0TOJISAsuFHiE2/aMakVn0gD3wKkt5QobksDoEp+4vwW7K2v1eXED3ebeMFfw/3WzGVVnAU65O+Qq7lnGbMU+eDzlGj0CiwK39eEBERUbXj1tseASAiIgIAkJaW5uJMiIiqr0Onz+Od79eoYgG+RnzwxEB4GSv2gp6ophIOO5Tl/4PYt0ozJve8H1LnOytc8Ek12/Hs5nQczNH2yHqooSceb2KEzs2KR1ZHEXacm4tDmdrvHwAaBnRG56hRMBn8nZwZERERUeVw++JXcfHF47QVRbv0noiIriw9uwATZqyA1XZpG5dBL+O9RwcgIqRiW7iIaiphLYKyaCrEKfWqSOg9IA95HvIN3Ss81/aMYozbloVsq/pvFi8dMKW1F/qEu19/r6S8PdiQ9C0KbRc0Y16GQHSLfgCx/u1ckBkRERFR5XH74tc/2x3r1Knj2kSIiKqhIosNL32+HBfyzKr4hHt7onWjCBdlReQeRGEOHL++CaQdVw8YfaG78zVI0TdUbB4h8OMZCz45UgxHqf5esd4X+3s19HWv/l7F9nxsSZmDE9mbyhxvFtwbHSPvhofOy8mZEREREVU+ty1+CSHw22+/YeHChZAkCTfeeKOrUyIiqlYunuy4GseSMlXx+wa0weDOTV2UFZF7EFmpcMx9Hcgp1d/KPwy6kW9CCo6u0DzFDoG395uxNFW7zbFnHT3eau0NX4P7bHMUQuBUzlZsTvkexfY8zbifRxi6xzyMSN/mLsiOiIiIqGpUevFr27ZtePnll8sce+WVV+Dp6XnFOYQQyMjIgN1uhxACBoMBo0ePruxUiYhqtK//2IG1u0+rYt3i6uGxoR1dlBGRexCpx+CY9yZgzlUPhDWAbsQbkHyCKjTPuSIF43YV4nCe+mRICcBjjY14uJEnZDfq72WxF2Jj8jc4lbNNMyZBQqs6g9EufDj08pX/ViMiIiKqTiq9+BUYGIiUlBRIkgQhLq39F0IgMzPzMneWTa/X46233kKTJk0qM00iohpt5fbj+O7PBFWsUVQwXn+wN2TZfV6MEzmbcmIHlMVTAZtFFZfqxUO+fRIkz4pt89uVZceLCYXItqr3OfoaJExtH4QugY5y7nSNcwVHsPbs5ygoo7dXkLEuetQdi1CvBi7IjIiIiKjqVXrxKzIyEpGRkapYamoqJElCUFBQhVZ+SZIEf39/xMXFYdSoUWjYsGFlp0lEVGMdPH0e736/VhUL9DVh2hMD4W10v4bbRM6i7F0JZdl/AaFuSC+1uAnyzc9A0l355FMhBOYlWvHBoSLYS/X3auCrx3+71EFdbxm24oLKTP2aKcKBhLRF2HN+MQTUCcuSHm3DbkPrsCGQJbfthEFERER03Sr9Lx0fHx+sXr1aFWvWrBkA4N1330XPnj0r+0sSEdH/O59VgAkzlsNqV5/sOPXxAYgI9nVhZkSuI4SA2PQLlA0/asakzndA7jkaUgW2J1odAlMPFWFRklUz1rOOHtM61oGfpwGK4h6rvvIs6Vhz9nOkm49rxoJNsbgp9gkEGivW24yIiIioOuPbfERENYS52IaXPl+GrLwiVXzSqF5o1SDcRVkRuZZQHFBWzIDYs7zUiAS53yOQ299SoXkyihWMTyjEvhxtYevRRp54pLERnga5EjKuHCeyNmFj8rewKcWasVahg9EhYgR08pVXuhERERHVBCx+ERHVAIoiMGXW3zierO7nc//AeAzsyJ6JVDsJWzGUxe9DnNiuHtAZIA99EXLTLhWaZ1+2HeMSCpFpUW8b9NIBb7f2wk3h7rOd2OowY1PydziRvUkzZtIHoFfdRxHtF+eCzIiIiIhcxynFr396gBmNRmd8OSKiWufLJduxfu8ZVaxH63p45NYbXZMQkYsJcy4c86YAqUfVA0Yf6O54FVJMiwrNszjJgncPFsGmbhOGGC8ZH7fzRkNfXSVlfP3OFx7HmrP/Q741QzNW1y8ePeo+ApPezwWZEREREbmWU4pfpXuAERFR5Vm+7Ri+X75bFWscHYzXHujDkx2pVhLZaXD8+jqQlaIe8AuFbuSbkELqXnEOmyLw4eEizD2r7e/VJVSP99p4wc9NtjkqQsGe878hIW0hBNRVOp1kQMfIe9A8pF+F+poRERER1UTc9khEVI3tP5WG9+asVcWC/C6e7OhlZD8fqn1E8iE45r8NFOWpB+rUg27EG5B8Q644R5ZFwUu7C7ErS9vfa0wDTzzV1AidmxSSCqyZWHP2c6QVHtWMBRljcFPsUwgysak9ERER1W4sfhERVVNpWfmYOGMFbPZLKz089DpMfWwgwoN4siPVPsqBNVD+/BRw2FVxKTYO8u2vQDJ6X3GOw7l2vLCrEGnF6v5eRhl4I84LAyLdp7/Xyeyt2Jj0DayKWTPWImQAboy8C3rZffIlIiIichUWv4iIqqEiiw0TZixHdr76ZMeX7++Flg3CXJQVkWsIoUDZ8BPEpl80Y1LznpBvfg6S/sorIZelWPHmfjMspfp7RZgkfNzOG0393OPPpmJ7AbakfF9OU3s/9Kj7KOr6tXF+YkRERERuyj3+iiMiogoTQuDd79fieJL6ZMfRg9qi/42NXZQVkWsImwXK0k8gDm/QjEld74bc/Z4r9rpyCIH/HCnG96ctmrEOwXpMi/dCoId79Pc6m5uADUnfoMieoxmL9o1Dz7qPwcvg7/zEiIiIiNyY2xe/EhIS8Oeff6Jv377o1KmTq9MhInK575fvxt+7TqpiPdvUx9hbOrgoIyLXEIXZF/t7lT7RUaeHPPhZyC1vuuIcuVYFk/aYsSXTrhm7p54Hnm9mgt4NDo643GovWdKjY+TdaBHSH5LkHkU6IiIiInfi9OJXQUEB9u7diwsXLkBRlHKvE0IgPT0dP/zwAzIzM7F161b88ccfTsyUiMj9bNx3Bl8u2a6KNYgMwqtjevNkR6pVRPoZOOa9CeRlqAe8/KEbPhlS9A1XnONkvgPP7ypEkln994iHDLzS0gu3RrtHv6zLrfYKMtZFr9jHEGyKdX5iRERERNWE04pfDocDH374IX744QfYbLYK3yfExYazo0aNqqrUiIiqhTPnsvHGt39D/KsPt5+3J6Y9zpMdqXZRTuyA8tv7gFXd8w4hdaG78zVIAeFXnGNNmhWT95phLnWgYx2jhA/beqNlgOsXx19utZcEHeLDbkWbsGHQya7PlYiIiMidOe2vpXHjxmHFihUlxayK8vf3x9ixYzFy5MgqyoyIyP3lFVrw0ufLYC6+9OaBTpbw9tj+iAr1c2FmRM6l7Pwdyl9fAUK9WkuqHw952MQrnuioCIEvjxfjixPa/l6tA3WY3tYbIZ6u3zp4NncXNiR9W+5qr551H0WIVz2n50VERERUHTml+LV27VosX74ckiQhIiICw4cPR0REBI4dO4bZs2dDkiS8++67qnsOHjyIH374AVFRURg+fLgz0iQickt2h4LXvlmF5Iw8VfyZO7ugfbMoF2VF5FxCcUBZ9SVEwlLNmNT2Zsj9HoEk6y47R4FN4NV9hVh7Xtvf6/YYD0xoboKHzrXbh7nai4iIiKjyOeUvp0WLFgEAfHx8MH/+fAQHBwO4WBSbPXs2AOC2225T3XPbbbchICAA//3vf/Hkk0/ip59+ckaqRERuZ+bibdh+KFkVG9KlGe7o1dJFGRE5lyguhLJ4GsTpBPWAJEPu8zCk9rdc8UTHs4UX+3udLlCvGNNLwIvNTbizrscV56hqXO1FREREVDWcUvzav38/JEnCLbfcUlL4AnDFPzKfeOIJLF26FLt378bvv/+OW265papTJSJyK8u2HsNPq/aqYi0bhGH83d1d/kKdyBlEThoc86YAmYnqAQ8T5KEvQW505VNON2XYMHF3IQpKLfgK9JDwQVtvtAty7SoqrvYiIiIiqlpOaWpx4cIFAMANN6hPXvLwuHSKktVq1dwnyzKGDh0KIQSWLFlStUkSEbmZQ2fSMe2HdapYaIA33n20PzwMl9/eRVQTiKQDcMwepy18+YVCN+qDKxa+hBD47mQxnt6hLXzd4KfDT119XVr4EkLgdM4OzD8yoczCV5CxLoY1mYJ2EXew8EVERER0HZzyl5TDcfEopaCgIFXc2/tSU9qsrCyEh2tPZ6pXrx4A4PDhw1WXIBGRm8nMLcTEGcthtV86is5Dr8N7jw1AiP/lG3oTVXfCYYey8WeILfM0je0R0QS6O16F5BN42TmKHAJT9pmx/Jz2hOnBkQa82soLRhf298qzpGNzymwk5e3RjHG1FxEREVHlcspfVP7+/sjKyoLZbFbF69atW/LxgQMHyix+5eVdbPCcm5tbtUkSEbkJq82Bl79Yicxc9c/MCff1RPN6dVyUFZFziKxUOJZMB84d04xJN3SHfPNzkAyel50jtUjBC7sKcTTPoYrLAJ5tZsSo+p4u2zbsUOzYn7EUCWmL4RDaVe/s7UVERERU+ZxS/IqJiUFWVhbOnj2rigcEBCAqKgqpqan48ccf0bdvX829GzZsAHCxgEZEVNMJIfDBz+tx4NR5Vfzuvq0xqFMTF2VFVPWEEBD7VkFZ9SVgK9aMS11GQu5xLyTp8h0btmTYMGmPGbk2oYr7GSRMbeOFzqGGSs37aqTmH8Km5FnIsaRqxiTIaBM2FPFc7UVERERU6ZzS86tVq1YQQmD37t2asf79+0MIga1bt2LChAlIS0sDcLFP2Mcff4xVq1ZBkiS0bdvWGakSEbnU/LUHsHTzUVXsxubReOL2ji7KiKjqCXMelEXvQfnzP9rCl3cg5JFToOs56rKFL0UIfH2iGE/uKNQUvhr6yPihi4/LCl9FtlysOTsDS0++U2bhK8y7CW5v+g7as7cXERERUZWQhBDiypddn7Vr1+Kxxx6DyWTCrl27IMuX/njNzMxE//79UVRUVBLT6/Ww2y92phVCQJZlzJkzB+3atavqVMkFrEX5EKV7upRiMPpAlnVQFAdsxQVOyoyqu+r23Ow8kozn/7MUDuXSj+XoUD98PXE4/Lwvv82LKk91e26qO+XMHii/fwQUZGnGpMadIA9+GpLX5Vd/59sEXttXiLXn7Zqx3mEGTGntBW991W9zLP3sCKHg8IXV2HFuLqwOs+Z6T50POkbejSZBPa64oo1qFkmS4WHydXUaREREtYZT3l7s2rUrHnnkEQwePFhV+AKAkJAQTJ8+Hc8++yxstotNaf/5JwBIkoQXX3zR6YWvc+fOYerUqVi+fDkA4Pvvv0fHjpdfebFw4UJMmjSpQvM/+OCDmDBhwmWvKSgowOzZs7Fq1SokJSVBURRERUWhT58+GD16tOYAgaqeh4iqTkpGHiZ/tUpV+PIyGjDt8YEsfFGNJOw2KOu+h9i+SDto8ITcdyyk1gOu2JvrRL4D43YVItGsfhNFBvBkUyPGNPCE7IL+XpnmM9iY/C0yzCfLHG8a1As3Rt4Fo54FECIiIqKq5pTil8FgwAsvvFDueO/evbFgwQLMnDkT27dvR05ODvz9/dGuXTuMHj3aqYUvq9WKWbNmYebMmZoG/ZWpRYsWlx0/efIkxo4di5SUFFX8+PHjOH78OObPn48ZM2YgLi7OKfMQUdUxF9swYcZy5BVaSmKSBLzxYB/Uj2RxmmoekXH2YlP79NPawfDG0N06HlJw1BXnWZFqxRv7zShW97VHgEHCe/Fe6BTi/G2OVrsZWxK/x4G0pRDQLq4PMsaga/QDCPdp6vTciIiIiGort2ks0aRJE3z00UcuzWHDhg14++23cebMGQBAeHh4SQ+yq7Vv377LjhsM5f9BXlBQgEceeQQpKSkwGAx47rnnMGTIEBiNRuzcuRPTpk1DYmIiHn/8cSxevBihoaFVOg8RVR1FEZgy62+cSlVv+Xrk1hvRLa6ea5IiqiJCCIhdf0BZMwuwlz7pUILU5U7I3e6BpLv8nyc2ReDTI8X48YxFM3aDnw7T23kj0uTcbYRCCJy4sBEbz3wDs027hVMve6Jd+HC0DB0AWXKbP7+IiIiIagX+9QXAbrfjueeew6pVqwAAoaGhGD9+PMLDwzF69OhrmtPT89q3KX3zzTdITk4GALz//vsYPHhwyVjfvn1RUFCACRMmIDMzE59//jlef/31Kp2HiKrOjEVbsX7vGVWsd9sGuH9gvGsSIqoiojAbytJPIU7u1A76hUJ3yzhIdVtecZ4LFgUv7S5EQpZDMzYs2gMTW5jgqXPuNscC6wVsSPoayfllv/FVz789OkfdDx+PYKfmRUREREQXsbsqLjbY9/Pzg8FgwNixY7FixQoMGzbsin1GqoLD4cDPP/8MAIiPj1cVrP4Z/+KLL0o+X7hwoeqwgMqeh4iqzm8bDuHHVXtVscbRwXhl9E0u+flDVFWUkzvh+PqpMgtfUvOe0D30WYUKX3uz7bh7Y76m8GWQgVdbmvB6nJdTC19CCBy9sBbzj0wos/Dl4xGKAfXHo1/951n4IiIiInIhFr/+34QJE/Dnn39i/Pjx8Pb2dlkeCQkJyM7OBgAMGjRIM75gwQKcOnUKw4cPBwAUFxdj48aNVTYPEVWNHYeTMf1n9X9zwX5emPbEQJg8nd+niKiqKLuXQZk3BTDnqgc8vSDfMg66oS9CMvpcdg4hBH49a8HDWwuQYVH30Qo3Svi2kw9ur+vcgyEKrVlYceoDrE/6CjZF/eaRLOnRJmwo7mw2DXX9uYqTiIiIyNVY/Pp//v7+qFu3bpXMrSjKlS/6fwcOHCj5OD5e/QdzUVERPvvsM7Ru3RrPPvtsmfdU9jxEVPnOnMvGK1+uhONfPxs8DXpMe2IgwoN48hvVDEIIKBt/hrL8f4Ao9Xswujl0D34GueVNV5ynyCHw2j4z3jtYBHup/vE3BuvxY1dftAxwXhcHIQSOZa3H/CMTkJS/VzMe6dcSI+I+QYeIEdDLPKmViIiIyB24fc8vIQQ2b96MJk2aVKuG7EuWLMHixYtx7NgxZGZmwmQyoXXr1rjvvvvQt2/fcu87depUycfR0dGqsdmzZyM9PR3Tp09HWFgY9Ho97Ha76p7KnscZ9J5eV7xGkuSSfxqusEKA6B/u+Nxk55kx/vPlKChSN/ue8uhgtG7WwEVZ0b+543NT3QjFAduf/4Gy4zf1gCxD32sM9BVoag8ASYV2PLf1Ao7m2jRjDzbxwTPN/aGXnbfNsdB6AWtPzcDZnB2aMb3sic51xyAu4mYAEkTpgh8RERERuYzbF7/uv/9+7Ny5E08//TSeeOIJV6dTYS+++KLqc7PZjC1btmDLli0YNWoUJk+eXOZ9WVmXTogKDAws+Tg7Oxtff/01evTogY4dOwIA/Pz8kJWVVbK9sSrmcQZZ1lX4WkmSIEkVv54IcJ/nxmK148XPfkNqhnr71zMje6HvjTe4KCsqj7s8N9WNsFlgnf8WHIfWqQf0HvAcOQX6Zl0rNM/y5EK8tvMCCkot9/LSS3i3fTD6RzuvRYEQAscy12L96S9gsRdoxiN8m6NPo+cQYIosifHZISIiInIfbl/8atOmDXbs2IENGza4ffErKioKAwYMgMFgQIcOHdCxY0eEh4fDarVi69at+OCDD5CUlIQ5c+agefPmuP322zVz/NN03sPDQ9XweubMmSgoKMC4ceNKYh4eHgAuFtaqah5nUBTtiV2lSZIMSZIghOC76VRh7vTcCCHw5tdLsfd4iio+tEcr3DewXYX+OyDncKfnproRxQWw/jwZypk96gGTLzzueQ9y3ZZXfNaLHQLT9uZg3plCzVh9Xz0+7RSMBr4Gp/03Y7ZmY93pGTidvU0zppc90DFmFOLCh0CSZAgh+OxQhV3Nm39ERER0fdy++NWwYUMAcNmWvKvRsWPHktVU/2YymTBgwADExcVhyJAhKCgowIwZM8osfv1Dli+1Y0tJScFPP/2EIUOGoFmzZiXxivQSq6x5qpLdYr7iiwSD0QeSpIMQCmzF2nfdicriTs/N17/vwIqtR1Sxdk2j8MKITrBbtC/yyXXc6bmpTkRBFhxzXwfST6sHfEOgG/kmlNBYKFf493ky34GJuwtxokD7O6FPuAFvxnnBW2+BrdhSmamXSQiBkzlbsDl5NiwObd5h3o3RI+ZRBBgjYLdcfPOIzw5VlCTJ8DCxxyMREZGzuH3x659VSYWF1f/FYUREBAYOHIj58+cjMTERSUlJiImJUV3j5XWx/5XNdqm/ySeffAIhhKo5PQBYLBbVPVUxDxFdvxXbjuHbpbtUsbphAXjnkf4w6PnOP1V/IisFjrmvATnn1QPBMdDdNQWS3+V7dgoh8FuyFdMOFqG4VN3LIAPPNzPhrlj1SuaqVGTLxcbkWTiTq+3tpZMMaB8xAi1DB0KWeG4QERERUXXg9sWvgwcPAgB8fGpG0+HGjRuXfJyYmKgpfgUFBQEAHA4H8vPzkZKSgj/++AP33nuv6lqbzYb8/HzVPVUxDxFdn30nzuHdOWtVMX9vI6Y/NQh+3jwJjqo/ce44HL++AZjVvewQ2RS6O1+H5OV32fsLbALvHDBj+TltU/sYLxnT4r1wg79z/lwRQuBUzjZsTv4OxY58zXgdr4boWfcxBBgjy7ibiIiIiNyV2xa/iouLsWHDBvzyyy+QJAlt2rRxdUqVwmQylXz871VZ/2jQ4NJpbykpKZg+fTpMJpOm39m5c+dKtivWr1+/yuYhomuXnJGLiTNXwGa/tJTFoJcx9fEBiA71d2FmRJVDOb0bysJ3AWuRKi417AD5tgmQDMbL3n8o146Ju81IMmu3OQ6ONODlll7w1jtntVeB9QI2JX+HxLwEzZhOMqBd+HC0qnMzV3sRERERVUOVUvxau3YtHn/88cqYSuOf5rH3339/lczvbBkZGSUfh4WFacZbtmxZ8vHs2bOxYcMGPP3005pVWQkJl/44b9WqVZXNQ0TXJq/Qghf/tww5BcWq+MujeqF1owgXZUVUeZRD66D8/jGg2FVxqVVfyIOegqQr/08MIQR+OmPBJ0eKUeowRxh1wMTmJtwa7ZxtjopQcChzJXac+xV2RdtLLNSrAXrWfRSBxugqz4WIiIiIqkalrfwSQlz5omv0zDPPoEuXLlU2vzNt3rwZAODr61vmSqv4+HgEBgYiOzsbCxcuRHBwMB544AHNdcuXLwcAGI1GdO2qPTa+suYhoqtndzjwypcrcTYtRxV/8OZ2GNCxiWuSIqpEyo7foPz1lSYudb4Dcs/Rly1a5VgVvL7PjPXpds1YY18ZU+O90cDHOb3wMs1nsCHpG2QWaQ/VkSUd2oUPR1ydIZAl9uYjIiIiqs4qddujJElo3759pcxlNBoRGxuLoUOHVosVSUIITJs2De3bt0ffvn3LvGbZsmXYteti0+thw4bBaNRuB9HpdLj77rvx+eefAwDGjBkDb29v1TWbN2/G2rVrAQDDhw9XbaWs7HmI6OoIIfDBTxuw62iKKt6vQyM8NKRyfj4SuYoQAsq62RBb5mvG5D5jId849LL3J2TZMWlPIdKLtW+Y3VnXAy/cYIJRV/WrvWyOYuxKW4ADGcshoN1yGebdGN2iH0aQiau9iIiIiGqCSu/5NWfOnMqe0ikcDgfsdvW70P/uyWWz2UpORQQAWZZhMBhKPv/mm28wa9YszJo1C3379sXw4cPRsmVLeHl5ITU1FUuWLMGsWbMAALGxsZoTF//toYcewpIlS5CcnIwff/wR0dHR6NSpExRFwapVq/D+++9DCIGQkJDLbjetrHmIqOJ+WrUXv286ooq1ahCGl+/v5bST6oiqgnDYoCz/H8S+v9QDsh7ykOcgt+hV7r0OIfDNCQu+OF6sKTX56IHXWnmhX4RHpedclqS8PdiYNAsFtkzNmEE24cbIu3BDcG9I7O1FREREVGNIohL2K65duxaPPfYYJEnC4cOHKyMvp1u4cCEmTZpU4etvu+02TJ06teRzu92ODz/8EHPmzCmzkf0/4uLi8NFHH2lOeSzt5MmTGDt2LFJSUsocDwkJwYwZMxAXF+eUeaqStSgfQmjfef83g9EHsqyDojhgKy5wUmZU3Tn7uVm7+xRe+XIl/v1TNTLEF19NuB2BvlxZWV3w542WKMyGY+F7QPIh9YDBCPn2lyE3aFvuvfk2BS8mmLHtgnabY6sAHd5r44Uor6rfVmi25WBLyhycytla5nh9/xvROfp+eBsCr/lr8NmhipIkGR4mX1enQUREVGu47WmP1Y1er8eECRNw1113Yd68edi4cSNSU1NRVFSE0NBQNGnSBEOGDMHAgQOh11/5X3vDhg2xZMkSzJ49GytXrkRSUhKEEIiMjESfPn0wZswYTfP6qpyHiC7v8Nl0vPntalXhy8fkgelPDmbhi6o1kXYCjgXvAHkZ6gGTH3Qj3oAUWX4fu2SzA8/sKMTpQu0bHKMbeOLJJkYY5KpdESmEgqNZa7Et9WdYHWbNuLchGF2jxyDWv/wCHhERERFVb1z5RS7HlV9UVZz13JzPKsDDUxfiQt6lF9Y6WcbHzwxG+2bsGVTd8OfNJcrBdVD+/BSwW9UDAWHQjZgCKTiq3Hv3Ztvx/K5CZFvVf2YEeEh4K84L3eoYyrmz8mQXp2BD0jc4X3hUMyZBQovQgWgffgcMOm0PzmvBZ4cqiiu/iIiInIsrv4iIrkNhsRUv/m+ZqvAFAOPv7sbCF1VbQnFAWTsbYttCzZhUrzXkoRMgefmVe/+KVCte22eGtdT7GnEBOnzQ1ht1jFXbT8uuWLH3/BLsSV8CRTg048Gmeuge8zBCvbSnLhMRERFRzVMpxa/u3btjx44dlTEVEVG1YXcoeO2rVTiRckEVv7dfawzt3txFWRFdH1FUAGXJBxCndmnGpA5DIfd+EJJcdo8uIQS+PWnBf48Va8b6RxgwJc4LnlV8mmNy3j5sSv4OedbzmjG97Il24XegZegAyFLV9xkjIiIiIvdQKcUvnU4HX18u3Sai2uU/8zdjy8EkVaxXfH08flsnF2VEdH1EZhIc898CslPVAzoD5IFPQY7rU+69NkXg7f1FWJJi1Yw93NATjzcxQq7CE08LrVnYmvoDTuVsK3M8xq8NukaNga9naJXlQERERETuidseiYiuwbw1+zF/zQFVrFlsKF57oDfkKm7gTVQVlOPboCyZDliL1AM+QdANfwVSZNNy782zKRifYMaOUic66iXg1VYm3BrtWRUpAwAU4cDBjJXYlTYfNkW74syk90fnqPvRIKAjpCosvhERERGR+2Lxi4joKm3afxaf/rpZFasT6I33nxgIo0fVN/EmqkxCCIjNc6Gs/xFAqTNwIpteLHz5lH8qcFKhA8/sLMSZUic6+hkkfNjWC+2Dq+6/ifOFx7EpeRYuFJ0tY1RC85A+aB8+Ap567yrLgYiIiIjcH4tfRERX4XhyJl7/+i8o/zoo18vTgOlPDkaIP19gU/UirEVQln4CcWSTZkyK6wd5wBOQ9OUXr/Zk2fF8QiFySp3oGOMl4z/tvVHPp2r6ahXbC7Dj3FwcubAGmoIdgBBTfXSLeQChXg2r5OsTERERUfXilOLXpEmTrnsOSZLw7rvvVkI2RETXJjO3EC/+bxnMFltJTJYkTHm4LxpFB7swM6KrJ3LS4FjwNpB+Rj0gyZD7joXUbshltwkuS7XijTJOdGwTqMNH7bwR6FH5JzoKIXA8az22pf6MYke+Ztwgm9AhYgRuCOkLWaraEyWJiIiIqPpwSvFr0aJF19VnQwjB4hcRuVSRxYaX/rcc6dmFqvhzI7qiS6tYF2VFdG2UM3uhLJ4GFOWpB0x+kG+bCDk2rtx7hRD4+qQFn5dxouOgSANeb1U1JzpmFSVhY/IsnC88WuZ4w8Au6BR5L7wMAZX+tYmIiIioenPatkchtNsSrkSSJPj5+SE4OBhBQeX3GyEiqkqKIjBl1mocScxQxe+4qSXuuKmli7IiujZKwp9QVs4ERKklW3XqQTd8MqSA8HLvtToE3jpgxh8pNs3YI4088VhjY6U3lbc5ipFwfiH2py+HgEMz7u8Zga7RDyDKt0Wlfl0iIiIiqjmcUvz6+++/r/qe7777DnPmzEGTJk3wxRdfwMvLqwoyIyK6shmLtmLdntOqWOeWdfHMHV1clBHR1ROKA8rqbyF2/KYZk5p1g3zzc5A8jOXen2VR8NLuQuzKUheg9BLwepwXhkR5VGq+inDgZPZm7Dg3D4W2C5pxnWRAfNgwxNW5GTqZB00QERERUfmcUvyKioq66nteeeUVAMAPP/yASZMm4dNPP63stIiIrui3DYfw46q9qlijqGBMebgv9Dr2FKLqQViLoCyZDnF8W6kRCXLPUZA633nZFVv7c+x4MaEQ54vVq7j9DRI+bOeNdkGV9+fExaLXFuw+vxi5lnNlXhPj1wZdokbDz7NOpX1dIiIiIqq53Pq0xwkTJuDvv//GypUrsWXLFnTu3NnVKRFRLbLjcDKm/7xRFQv288IHTw6Ct7FyV7kQVRWRnwnHvLeA8yfVAwZPyLe+CLlJp/LvFQILkqx4/1ARbKV2Sdb1kvGfDt6I9a6cEx0VoeBk9ubLFr28DUHoHHU/6vm3r/TtlURERERUc7l18Uuv12PkyJH4+OOPsWDBAha/iMhpTqdm4ZUvV8KhXHrF72nQ4/0nByIsyMeFmRFVnEg7Cce8N4GCLPWATxB0d74GKbxRufcWOwTeO1CEJSlWzVj7ID0+aOuFgEo40VERCk5lb0HC+UXlFr0k6NAqdCDaht8Og678rZlERERERGVx6+IXADRp0gQAsGvXLhdnQkS1RVZeEV78fBkKii696Jck4I2H+uCGWG6zoupBOb4Nym8fALZSpzKGNYTuzlch+YaUe2+q2YHxCWYcztM2mB9V3xPPNDVCL1/fyquKFb1kNAnqgTZhQ7nFkYiIiIiumdsXv8xmMwAgMzPTxZkQUW1gsdkxceZypGbmq+JP3tYJPdvUd1FWRBUnhIDYsQTK318DUPfokhrdCHnoi5A8TOXevznDhpf3mJFrU99r0gFvxnmhX8T1bfmtaNGrcVB3xIcNY9GLiIiIiK6b2xe/VqxYAQDw8eE2IyKqWkIIvPv9Whw4dV4Vv7XbDbi7X2sXZUVUcUJxQFn5BcTuPzVjUoehkHs/CEkuu0eXIgS+OWnBjGPFpUpmQD1vGdPbeqOh77X391KEglM5W5CQxqIXERERETmXU4pfO3bsuKrrHQ4Hzp8/j+XLl2PNmjWQJAlt2rSpmuSIiP7fN3/sxKodJ1SxDs2iMP7ubmyuTW5PWMxQFk2FOJ2gHpBkyP0fg9x2cLn35tsUTN5rxvp0u2asd5gBb8Z5wcdw7f8NnM3dhW2pP1eg6DUUfp5h1/x1iIiIiIjK4pTi16hRo67rhaMsy3jooYcqMSMiIrUV247h26Xq3oKx4QF4+5H+0Osq5zQ7oqoictMvNrbPOKse8DBBHjYRcsN25d57PM+BcQmFSDKrj3OUATzV1IgxDTyv+Xe41VGELSlzcCxrXZnjLHoRERERkTM4bdujEKU3UVSMv78/XnvtNbRv376SMyIiumjfiXN4d85aVSzAx4jpTw6Gr5enS3IiqiiRehSO+W8BhTnqAb9Q6Ea8Dim0Xrn3Lku1Ysp+M4pL9bUP8JAwtY0XOoYYrjmvtIKjWJs4A/nWDM3YxaJXN7QJGwp/z/Br/hpERERERBXhlOLXU089ddX3eHl5oX79+ujcuTOMRh5rTkRVIyUjDxNnroDNfmnVi0EvY+pjAxAV6ufCzIiuTDmyEcrvHwF2q3ogogl0d7wKySewzPtsisDHR4rw8xmrZqyFvw4ftPVGhEm+ppwcih0JaQuwN/13iNIN91n0IiIiIiIXcNviFxFRVcs3W/Di/5Yhp6BYFX95VC/ENYpwUVZEVyaEArH5Vyjrf9CMSc26Qh7yPCRD2W8cXbAoeDGhELuzHZqx22M88FJzEzx117bNMbs4BWvPfo7MojOasQDPSNwU+wRCvHhqKhERERE5l9uf9khEVBXsDgcmf7UKZ9KyVfEHb26HAR2buCgroisTFjOUPz6GOLZFMyZ1vhNyz1GQpLJXbZ3Id+CZnQU4V6RekeUhAxNbmHBbzLVt8xVCwcHMVdie+jMcwqYZbxEyADdG3gW97HFN8xMRERERXQ8Wv4io1hFC4KNfNmHH4WRVvG/7RnhoCPsLkvsSWSlwLHgHyExUD8g6yIOeghzXr9x7N6bbMHFPIQpLHegYbpQwva03WgRc258EhbZsrEv8Ain5+zVjXoZA9Kz7KKJ9W13T3ERERERElYHFLyKqdeb+vQ+LNxxSxVrUD8Mro3td18m0RFVJObEDypLpgKVQPWDyg3zbRMixceXe+/MZC6YfKoJSKn5jsB5T470Q6HFt/b1O5WzDxqRvYXEUaMbqB3REt+gHYdT7XNPcRERERESVxanFr4MHD+LQoUPIzs6G1aptsnsl7B1GRNdr474z+GyBertYeJAPpj0+AJ4Gvh9A7kcI8a/+XqVOTg5rCN3wVyD51ynzXrsiMP1wEeae1f7OvbPuxf5eevnqC75WhxmbkmfjRPZGzZhBNqFr9Gg0CuzGYjIRERERuQWnvNI7d+4cnnrqKRw6dOjKF18Gi19EdD2OJWXi9W/+gvhX/cDb6IHpTw1GkJ+X6xIjKoewmKEs/QTi6GbNmNSiF+RBT5Xb2D7fJjBxTyE2Z6j3OcoAxjU34e5Yj2sqTp0rOIy1Z2eiwJapGYvwvgE9Yx+Fr0foVc9LRERERFRVnFL8evPNN3Hw4EFnfCkiojJl5BTipc+XochyqRCgkyW8NbYfGkQGuTAzorKV299LkiH3fhBSh6HlFq9SzA48u7MQJwvUGx29dMDUeG90r2O46nwcig070+ZjX/pSlF6BJks6tI8YgVahgyGX02yfiIiIiMhVnFL82rZtGyRJwoABAzB+/HjUqVMHHh488YmInKPIYsWEz5cjPVvdK+m5EV3RqUWMi7IiKt9l+3sNmwC5Xuty792bbcfzuwqRbVUXqMKNEj5t74MmfrqrzifDfBrrEmciuzhZMxZojMZNsU8g2BR71fMSERERETmDU4pf3t7eKC4uRv/+/REdHe2ML0lEBABQFIHXv1yGI4kZqvidN7XE8F4tXZQVUdmEEBBb5kFZNwfa/l4NoLv9FUgBYeXevyzFijf2m2Et1dm+pb8OH7f3Rojn1a3KUoQdu9N+w+7ziyE07fKBVqGD0D5iBPQy39AiIiIiIvfllL0JN910E4QQ2LJly5UvJiKqRP+dtw5rdh1XxTq3rItn7uziooyIyiYsZiiL3oOy7nuULnxJzXtCN+r9cgtfQgjMPFaEl/dqC1/9Iwz4qpPPVRe+sooSsfjY60g4v1BT+PI2BGFww0noFHUfC19ERERE5PacsvJr/Pjx2LNnDxYsWIDOnTtj8ODBzviyRFTLLdmwH7OXblPFGkYFYcrDfaGT2ZeI3Mfl+3s9AKnDsHL7e1kcAm/sM2P5OZtm7OFGnni8sRHyVTS2V4QD+9L/wK60BVCEQzPeOLA7OkeNgqfeu8JzEhERERG5kiSEEFe+7PplZmbizjvvxPnz59G3b194e1/dH82SJOHdd9+touzIlaxF+RBCu53m3wxGH8iyDorigK24wEmZUXW278Q5PPnx73A4Lj1bQX4mfD3xdoQH+bowM3J3zv55o5zcBeW396+pv9cFi4LndxVif466SGWQgddbeeHmqKtblZVTnIp1iTORbj6pGTPp/dE95iHE+re7qjlrE/6uooqSJBkeJv4uIiIichanFL9OnDiBMWPG4MKFC9c1z+HDhyspI3InLH5RZcs3W3D/2/NwPuvSs+Jh0OHzF25F8/rl90siApz780ZJ+BPKyplA6Z+BFejvdSzPged2FeBckfrXeICHhI/beqNNUMUXdwuh4EDGCuw4NxcOoV1B1iCgE7pGj4FRzxfrl8PfVVRRLH4RERE5l1O2Pb7zzjvIzMx0xpciIsKHP29QFb4A4LUxvVn4IrchhAJlzXcQ2xZqxqTmPSEPfhqSwVju/UuSLXj3QBEspWpm9X1k/Ke9N6K9Kn6iY54lHesSv0Ba4RHNmKfOB12jH0DDwE4Vno+IiIiIyN04pfi1Z88eSJKEAQMGYPz48ahTpw48PNggl4gq38rtx7FyxwlV7J4B7dC7XUMXZUSkJmwWKH98BHFkk3pAkiHfNAbSjbeV29+r2CHw/qEiLEqyasY6hejxfrwXfA0V62cnhMDhC39jW+pPsCsWzXisXzt0i3kIXgb/Cs1HREREROSunFL88vf3R3FxMfr374/o6GhnfEkiqoXOXcjHBz9tUMUax4TiieHdAaXYRVkRXSLMuXDMmwKkHlUPGDwhD30JcuOO5d6bVOjAi7vNOJqnbUI/oq4HXmxugl6uWGP7Amsm1id+hZSCA5oxD9kLXaLvR6PAbuUW4YiIiIiIqhOnHHc2ZMgQCCGwbdu2K19MRHQNHIqCKbNWo7D40ooYD70O7zx+Czw9nFLnJ7oscSEZjtnjtIUv70Do7p122cLXmjQr7tmUryl8GWVgSpwXJrX0qlDhSxEKDmasxPwjE8ssfEX7xuGOZtPQOKg7C19EREREVGM45RXhM888gwMHDmD+/Pno1KkTBg0a5IwvS0S1yA8r9mDviXOq2NMje6JhdCgURbtShsiZROIBOBa8DZRugh5SF7oRb0Dyr1PmfTZF4L9Hi/H96TK2JXrL+CDeG439Ktbf64L5DDYkf4uMMk5yNMhGdIq6F02DbmLRi4iIiIhqHKcUv7Zv345hw4bhzJkzGDduHJYtWwZvb++rmkOSJLz77rtVlCERVWeHz6bj6993qmKdWsRgZN94F2VEdIlycC2UpZ8ADrsqLtVrA/m2SZCMZf8+TC9WMHF3IXZna4u3/cINeK2VF3wMVy5U2RzF2JW2AAcylkNAe7JuhE9z9Ix5BL6eoRX7hoiIiIiIqhlJCCGufNn1adasWck7yUKIq35X+Z97Dh8+XBXpkYtZi/IhhPYF2b/x+HgqT5HFhjHvzEdSem5JLMDHiDmvjkB4WB0+N3TVKuvnjRACYvNcKOt/0IxJcf0gD3wSkq7s96B2XLBh4m4zsqzqX9F6CXi+mQl31/Oo0O/Ss7m7sCl5NgptFzRjBtmIDhEj0TykLyTJKV0Qajz+rqKKkiQZHiZfV6dBRERUazhl5VdkZKQzvgwR1UKfztusKnwBwKRRvRDs7+WijIgA4bBDWf5fiH1/acbkHqMgdRlRZvFKEQKzTlrw+bFizRqtMKOEafHeaB145V/dBdYL2JzyPc7m7ixzvJ5/B3SOGgUfj+AKfT9ERERERNWZU4pfq1evdsaXIaJaZt2e01iyUb0idFj35ujeup5rEiICIIoLoCx6D+LMXvWATg/55ucgt+hV5n25VgWT95qxMcOuGesUosc7rb0Q5Hn5FVqKcOBgxkrsSpsPWxknnPoYgtElegxi/dtW+PshIiIiIqrueAQaEVVLmbmFmDpnnSpWN8wfT9/R2UUZEQEiNx2OX98AMhPVA0Yf6IZPhlS3ZZn3Hcyx48XdhThXpN7mKAF4pJEnxjY2QneFbY4Z5lPYmPQNMovOaMYkyGgVOghtw2+HQWe8iu+IiIiIiKj6Y/GLiKodRRF4e/Ya5BZeWtmik2W88WBfmDwNLsyMajNx7gQc894ECrPVAwHhF090DI4u874FiRZMO1QEW6l9jgEGCe+08UKX0Ms/01aHGTvPzcehzJUQ0LbxrOPVEN1iHkKwKfaqvh8iIiIiopqCxS8iqnbmrdmP7YeSVbGxt3ZAs1ieVkeuoRzfDuW3aYDNoh6IbArdna9B8vLX3GNXBKYfLsLcs1bNWKsAHd6P90a4qfxtjkIInMndgc0p38Nsy9aMe8he6BA5Es2Ce0NmQ3siIiIiqsWcUvxKTU2tlHnYOJ+ITqZcwIxF21SxNo0jcG//1i7KiGo7ZfcyKCtmAKVOrZWadYU85AVIBk/NPXk2BS8lmLHtgra/1z31PPBcMxMMcvnbHC32QmxM/hancraWOd4goBM6R90HL0PgVX43REREREQ1j1OKX717967QkeyXI0kSDh06VEkZEVF1ZLHZ8ca3f8Nqd5TEfEweeO2B3tDJXNlCziWEAmXd9xBb5mvGpE7DIfcaDamMFVdnCxx4dlchzhaqi2VGHfBmnBf6R3hc9uumFRzFmrP/Q4HtgmbM1yMUXaMfRIxf3FV+N0RERERENZfTtj0Koe1DQkR0NWYu2oaTKVmq2Ph7uiM8yNdFGVFtJew2KEs/gTikPnQBkgy5/2OQ2w4u876tGTa8tNuMfLv6d2K4UcIn7b3R1K/8X8uKcGB32iLsPr9Y09tLgg6t69yM+PBh0MvalWZERERERLWZU4pft91221Xfs3r1auTl5aFnz5649dZbERQUVAWZEVF1se1QEuau3q+KDejYGP07NHZRRlRbiaICOBa+DSQeUA8YPCEPnQC58Y3ae4TA3LNWTD9cBEep94LiAnT4qJ03gj3LX72YZ0nHmrOfI918XDMWbIpFr7qPI8gUc03fDxERERFRTeeU4td777131fckJSXh/vvvx8aNGzFy5Eh06tSpCjIjouogp6AI78xeo4qFB/lg3F3dXJQR1VYiNx2OX98AMhPVA14BFxvbRzbR3GNTBN4/VIT5idrG9kOiDHi1pRc8dOW3BjiRtQkbk2fBphRpxlqFDkKHiJHQyTzllIiIiIioPG7bJCcmJgafffYZhBCYPHkycnNzXZ0SEbmAEALTfliPzFxzSUyWJLz+YB/4mLi9i5xHpJ2AY/Y4beErKAq6+6eXWfjKsSp4ckehpvAlAXi2qRFT4sovfFkdZqw5+znWJH6uKXyZ9P4Y1GACOkXdx8IXEREREdEVuG3xCwBatmyJ/v37IysrC3PnznV1OkTkAr9vOoJ1e06rYvcPikfrRhEuyohqI+XkTjh+mAgUZqsHoptDN+oDSIHhmntOFThw/+YC7Ch1oqOXDvi4nTfGNDSWexjM+cLjWHj0ZZzI3qQZi/Frg+FN30M0m9oTEREREVWIWxe/AKBnz54QQmDlypWuToWInOxkShY+nqt+8d+8Xh08eHM7F2VEtZGyZwWUeVMAW7EqLjXrBt3db0Py8tPcsynDhtGb85FkVp/oGGmS8V1nX/QMK3u1liIUJKQtwu/HpyDfmqEa00kGdIkajQH1x8Nk8L/O74qIiIiIqPZw2mmP1yowMBAAcPbsWRdnQkTOZC62YfJXK2GxXVo1Y/LU4/UHekOv07kwM6othBBwrP8BYtMvmjHpxtsg934AkiRr7vnpjAUfHS6GUuqe+EAdprf1RlA5je0LrJlYc/ZzpBUe1YwFGqPRO/YpNrUnIiIiIroGbl/8OnnyJADAZrO5OBMicqYPf9mAs2k5qtj4u7sjJizAJflQ7SLsNtgWT4XYW3rVsQS53yOQ29+iucemCLx3sAiLkrSN7YdGe+CVliYY5LK3OZ7M3oqNSd/Aqpg1Yy1C+uPGyLuhlz2u6XshIiIiIqrt3Lr4dfDgQXzzzTeQJAkxMXy3m6i2WLr5CJZtPaaKDe7cFIM6NXVRRlSbiOICWOa+DuXULvWA3gPy0BchN+msuSfTouClhELsznao4jKA528w4t56nmX298qzpGP7uV9wOmebZsyo80WPuo8g1r/tdX0/RERERES1nVOKX/fff/9VXa8oCs6fP4/k5GQIISBJEvr3719F2RGROzmVmoXpP29UxepHBGLcXd1clBHVJiI3HZYFb0OcP6UeMPlBd+drkKKaae7Zm23HiwmFyLAIVdxHD7zXxhvd6mj7e1kdZuw5vwT7M5ZBEXbNeJRvK/Sq+xi8DAHX9f0QEREREZGTil/bt28v90SryxHi4guJ5s2b4+GHH67stIjIzRRZbHj1q1WqPl+eBj3eGtsPJs+yG4QTVRaRfBiOBe8A5hz1QGAEdCPehBQUqb5eCMxLtOKDQ0Wwq+teiPGS8XE7bzT0VfenU4QDRy+sxc60+Si252lykCU9boy4Cy1DB2j6iRERERER0bVx2rbHfwpZFWUymVC/fn0MHDgQo0ePhqenZxVlRkTu4qO5G3H6XLYqNv7ubmgQGeSijKi2UPb/DWXZZ4Cj1CqsqGbQ3fEqJC/16YrFDoH3DhRhSYq2v9eNwXpMi/dCgIe6eJWctw9bU39EdnFymTmEmOqhe8xYhHjVu67vhYiIiIiI1JxS/Dpy5IgzvgwRVWPLth7F0s3qU+4GdWqCm7tot5kRVRYhFChrv4fYOl8zJjfvAWnws5AM6jdfUosUjN9ViMN5Ds09Yxp44skmRuj/1dg+uzgF21J+RFL+3jJz8DIEokPESDQO7MrVXkREREREVcCtG94TUe1w5lw2PvhpgyoWGx6AcXd1d1FGVBsIixnK7x9CHNc2mzf0Gg1dz9GwW9WnL27NsGHSHjNybOrVzF464I04L/SLuHQiY7E9H7vSFuBw5t8QUDRfQyd5oHWdIYirczMMOmMlfVdERERERFQai19E5FLFVhte+Woliq3qPl9vj+0PLyP7fFHVELnpcMybAmScUQ/oDDDcNhEerftDUS6t7BJC4LtTFvz3aLGmjBXrLePDtpf6ezkUGw5mrsTutMWwKmaUpXFgd3SIGAFvD27pJSIiIiKqaix+EZFLfTx3E06nqvt8vXBXVzSMYlGAqka5je29A6G7YzL0DdqpwoV2gdf3mfF3mk0z101hBkyJ84KPQYIQAmdyd2J76s/Is54v82uHezdFp6hRCPWqX1nfDhERERERXUGVFb8yMzMREhJy3fP8/PPPGDFiBHQ63ZUvJqJqZfm2Y/h9k7onYP8bG2MI+3xRFSm3sX1Yw4uN7f3Uv7dOFzgwblchTheq13tJAJ5sYsQDDT0hSxLyrRnYkPg1UgoOlPl1fT3qoGPkPajn3/6aTj8mIiIiIqJrVyXFrzlz5uCrr77Cl19+iWbNru1FrNVqxVNPPYUNGzYgPT0dzz77bCVnSUSudCYtGx/8tF4VqxsWgJfu6cHiAFW6yzW2l5p2gTzkBUge6r5bf6UU4ZVd+SgsVSfzN0h4t40XuoQaIITA0QtrsSVlDmxKsWZuD9kL8eHD0CKkP3Qyt/ESEREREblCpRe/CgoK8Omnn6KwsBDvv/8+vv3222uax8PDAwbDxRcW3333HUaNGoWgIG6DIqoJLFY7Xv1qFYosl6oKHgYd3h7bj32+qNJdrrG91PUuyN3vUZ2y6BACn+7PxldH8zTXN/XT4cO2Xojy0qHQlo0NSV8jKW+Pdl7IuCGkD9qG3w6T3q9Svx8iIiIiIro6lV78Wrx4MQoKCqDT6TB58uTrmuvll1/Ghg0bUFxcjN9//x2jR4+upCyJyJU+/nUTTqZkqWLPj+iKRtHBLsqIaqrLNbaXb34OcoueqnCWRcHknZnYkm7RzHVzlAGTW3rBUwZOZm/BpuRZsDgKNddF+rRAl+j7EWiMrsxvhYiIiIiIrpF85UuuzqZNmwAAXbp0QYMGDa5rrqioKHTv3h1CCGzYsKEy0iMiF1u54ziWbDysivXr0Ai3drvBRRlRTSWSD8Px3Qvawpd3IHT3TdUUvnZcsGHkxnxN4UsvARObm/BWnBcgCvD32c+w+ux/NYUvveyJrtEPYHDDSSx8ERERERG5kUpf+XX06FFIkoTOnTtXynwdO3bE33//jVOnTlXKfETkOonnc/D+D+o+XzF1/PHSvezzRZVLObwByu8fVqixvUMIfHW8GF+esECUmifEU8IH8d5oE6THmdyd2Jj0DYrs2u2QYd5N0bPuI/D3DK+C74aIiIiIiK5HpRe/cnJyAACRkZGVMt8/J0ZmZ2dXynxE5BoW28U+X2aLrSTmodfhrbH94G30cGFmVNMoCX9CWTEDKFXKKquxfXqxgpf3FGJXlkMzT9tgD0xtbYSfvghrz36N49naFcg6yYD2EXeiZeggyFKlL6YmIiIiIqJKUOnFL6vVCgCw2WxXuLJihLj44sXh0L4wIaLq49NfN+N48gVV7NkRXdAkJqScO4iujhACYtMvUDb8qBkrq7H9xnQbXt1nRo61VJEMwKPN/PFYMx8kZW7BgpNfotCmfQMmxNQAvWIf5RZHIiIiIiI3V+nFr8DAQGRmZiI5OblS5ktJSQEAnvRIVI39sfkIFm84pIr1adcQw7o3d1FGVNMIoUD56yuInb+rB2Qd5MHPQm7VuyRkUwT+d6wYs09pm9oHe0iYemMwOoTK2HR6Jg6mL9dcI0GHtuG3oU3YLZClSv81SkRERERElazS92jccMMNEELgr7/+qpT51q1bB0mS0KxZs0qZj4ic68jZDEz/Sb1dLCrUDxPv68k+X1QphMMO5fePtIUvvSfk4ZNVha9UswMPbS0os/DVMViPX7r7ItZ4Er/searMwlegMRrDmkxB2/DbWPgiIiIiIqomKv0v9969e2P9+vU4dOgQdu7cifbt21/zXAcPHsSuXbsgSRJ69+595RuIyK3kFBRh0hcrYLVf2rbsadDj3Uf7w9vEPl90/YStGMrC9yBO7VIPGL2hu/N1SNGXVhf+nWbFm/uKkG9Xb3PUScDjjY24v74Ou8//ir3pf0DTLwwS4uoMQbvw4dDJhqr6doiIiIiIqApI4p+mWpXEbDajd+/eyM3NRUREBH766SeEh1/96VdZWVm49957cfr0aQQGBmL16tUwmUyVmeplnTt3DlOnTsXy5Rff+f/+++/RsWPHCt1bUFCA2bNnY9WqVUhKSoKiKIiKikKfPn0wevToCm/hdLd5qoq1KB9CKJe9xmD0gSzroCgO2IoLnJQZXQ+7Q8ELny3FziMpqvjrD/TGgI5NnJIDn5uaTRQVwDHvTSDlsHrAJwi6kW9CqlMfAGBxCHx8pAhzz1o1c4QZJbzXxhv1vTKw5uznyCzSnizs7xmBnnUfRZh34yr5Pqjm4M8cqihJkuFh8nV1GkRERLVGpRe/AGDRokWYNGkSJElCUFAQJk+ejEGDBlX4/tWrV2PKlClIS0uDJEl45513cPvtt1d2mmWyWq2YNWsWZs6cCbPZXBKvaPHr5MmTGDt2bEmvstJCQkIwY8YMxMXFVat5qhKLXzXT54u24ocVe1SxEb1b4bkRXZ2WA5+bmkvkX4Bj7mtAxln1QEAEdHe9BSnw4psuZwscmLDHjKN52kNTetTR441WJpwv2IAtKd/Drmi3QrYMGYgOkSOglz2r5PugmoU/c6iiWPwiIiJyriopfgHA22+/jR9++KGkp0/dunUxYMAAxMXFISYmBgEBATAYDLDZbMjNzUVycjL27duHVatW4dSpUyWnPN53332YPHlyVaSosWHDBrz99ts4c+YMACA8PBxpaWkAKlb8KigowNChQ5GcnAyDwYDnnnsOQ4YMgdFoxM6dOzFt2jQkJiYiJCQEixcvRmhoaLWYp6qx+FXzrEk4hVe+XKmKtW4Ugc+eHwK9Tue0PPjc1EwiKxWOX14Fcs+rB+rUh27kFEg+gQCAP1OsePuAGUWl6l56CXiumRG3R1uxMflbnMndofka3h7B6NPwOYR5Nqiqb4NqIP7MoYpi8YuIiMi5qqxb7+TJkxESEoLPPvsMDocDiYmJ+Oqrryp0rxACOp0OTz31FB5//PGqSrGE3W7Hc889h1WrVgEAQkNDMX78eISHh2P06NEVnuebb74pOeXy/fffx+DBg0vG+vbti4KCAkyYMAGZmZn4/PPP8frrr1eLeYiuxunULLwze40qFuLvhbfH9nNq4YtqJpF2Eo65rwPmHPVATAvo7ngVktEHxQ6BaQeLsDhZu80x2kvG1DZeCNAdxaJjM1Boy9Zc0yCoM25q+DQ8dd4sYBARERER1QCVftrjvz322GNYuHAhevXqBUmSIIS44v8kSUKvXr2wcOFCpxS+AECv18PPzw8GgwFjx47FihUrMGzYsKs6ic7hcODnn38GAMTHx6sKTf+Mf/HFFyWfL1y4EEVFRW4/D9HVKCiyYNIXK2C22Epiep2Mdx7tj2B/LxdmRjWBSDwAx0+TNIUvqdGNF1d8GX1wpsCB+zfnl1n46h9hwA+dTSgw/4o/T76nKXzpZU90jxmLAY0nwGTwq8pvhYiIiIiInKjKz2lv2rQpZs6cibS0NKxbtw779u3D2bNnkZOTA4vFAk9PTwQEBCA2NhatW7dG9+7dERERUdVpaUyYMAGPPfYY6tate033JyQkIDv74gupsvqbLViwAKdOncLw4cOxYMECFBcXY+PGjejXr59bz0NUUYoi8NZ3a5B4PlcVf35kV7RqcPWHXhD9m3J8G5TF0wC7uqgltewNefAzkHR6LEux4q0ytjl6ysCLzU3oHXoBq8/8D5lFZzTzh5ga4KbYJxBgjLiqNz6IiIiIiMj9VXnx6x/h4eEYOXIkRo4c6awveVX8/f3h7+9/zfcfOHCg5OP4+HjVWFFRET777DO0bt0azz77LBYsWFByT+lik7vNQ1RR3y9PwIa9Z1Sxm7s0xbDuzV2TENUYyr6/oPz5H6BUb0DpxmGQez8IiyJh+n4zFiRpV3vV8764zVGxrceiY3PgEKWvkdC6zhC0C78DOtlpvxKJiIiIiMiJ+Jd+JTl16lTJx9HR0aqx2bNnIz09HdOnT0dYWBj0ej3sdrvqHnedxxn0nlfeDidJcsk/DUafqk6JrtLmfafx1e/qpuE31AvDxDED4eFhcFFWfG5qAvvWhVCW/UcT1/cdC323e5BYaMcL27JwNNemuebmGBNebKnHjsT/4XT2Ns24t0cw+jZ8HlH+rVRxPjd0rfjsEBEREbknFr8qSVZWVsnHgYGBJR9nZ2fj66+/Ro8ePUpOi/Tz80NWVlbJtkR3nscZZLniTdAlSYIksWm6O0lOz8GrXyzFv8+N9fcx4YNnboOX0ei6xP6Fz031ZNv1B2ylC1+SDI9bx8HQ/lYsSyrEq7suwGxXH1rsIQMvtwlCx8AT+OPQxzDbslBao+Bu6NXwKRj15Rco+NzQteKzQ0REROReWPyqJP80i/fw8FD1i5k5cyYKCgowbty4kpiHhwcAwGw2u/08zqAojiteI0nyvw5NUK54PTlHscWG8Z8uRF5hcUlMliS88/gQhAX5VOj/26rE56b6su//G7bf3lcHdQZ43DEZ9qY98O6uTMw9Xai5L9ZHj7fb6nAh5wv8fnitZlwvG9Gj3iNoGtobkiSV+YzyuaFrxWeHrsbVvPlHRERE14fFr0omy5cO0ExJScFPP/2EIUOGoFmzZiVxRbnyH8TuNk9VslvMV3yRYDD6QJJ0EEKBrbjASZnR5Qgh8Nas1TielKGKPzbsRrRtGOwW/z/xuamelOPboCx8F6rlhDo95Dsm42xYG7y0Jg1H8rRFq37hOgwLX4sdpxbCrlg046FeDXFT7BPw9wyH3aItnP2Dzw1dKz47VFGSJMPD5OvqNIiIiGoNFr8qiZfXxb5VNtulvjOffPIJhBB49tlnVddaLBbVPe48D1F55q89gJXbj6tiveIb4N7+bVyTENUIyuk9UBa9B/x7RZYkQx42EX+bWmHKpnwU2NX3GCTgwfrp8HfMwMH09DJmldAm7Fa0C78dssRfe0REREREtQ1fBVSSoKAgAIDD4UB+fj5SUlLwxx9/4N5770VMTEzJdTabDfn5+ap73HkeorLsOX4O/5m3RRWrFx6IV0b3Um2zJboaIvkQlAVvAY5/V7ckKDe/gOm2OMzdrd2aHWlSMCx0PlC8DmWtswkyxqBr9AMI92laZXkTEREREZF7Y/GrkjRo0KDk45SUFEyfPh0mkwlPPPGE6rpz586VbDOsX7++289DVFpGTiEmf7USjn9tl/UyGvDeYwPgbfRwYWZUnYm0E3D8+gZgU29XzOvzBJ7Oa4dDuVbNPfH+KWhn+gSwa4tinjoftI+4E82Cb4LMxuNERERERLUai1+VpGXLliUfz549Gxs2bMDTTz+tWU2VkJBQ8nGrVq3cfh6if7M7HJj85Upk5RWp4q+O6Y3Y8ADXJEXVnshMhOOX1wCLuoiV0/1B3FfQBeeL1f299JKC7v5L0My0CqUXGkqQcUNIX7QLH37ZkxyJiIiIiKj2YPGrksTHxyMwMBDZ2dlYuHAhgoOD8cADD2iuW758OQDAaDSia9eubj8P0b/NXrYb+0+dV8VGD2qLnm24apCujcg+B8fPk4GiPFU8t+PduMfSBxkWoYoH6HPQN+AL1PFI1MwV6dMCnaNGIcgUoxkjIiIiIqLaS77yJVQROp0Od999d8nnY8aMgbe3t+qazZs3Y+3atQCA4cOHw2Qyuf08RP84dCYd3/25SxXr2DwGD9/S3kUZUXUn8jLh+PkVoCBLFc9rexvuUm7WFL7qG/dgeMjbmsKXr0co+tV7HoMbTmLhi4iIiIiINFj8+n8OhwMWi0X1v3+flGiz2cod+8dDDz2E6OhoAMCPP/6IP//8E1lZWcjMzMTPP/+MJ598EkIIhISE4PHHHy83F3ebh8hitWPKrNVwKJeKEUF+Jrz2QG/oZP4YoasnCnMurvjKVZ/OmNdyEO6UhyOzVIuvZqYtGBD4NTzlS1tu9bIn2keMwB3N3ke9gPY8bIGIiIiIiMokCSHElS+r+RYuXIhJkyZV+PrbbrsNU6dO1cRPnjyJsWPHIiUlpcz7QkJCMGPGDMTFxV12fnebpypZi/IhhHLZawxGH8iyDorigK24rDPdqCp9PHcj5q05oIp98MQgdI2LdVFGFcPnxj2Jonw4fpoEpJ9RxfOb9sJw34eQbVMXsW7w2oRe/j9Dki79umoU2A03RoyEt0fln1LL54auFZ8dqihJkuFh8nV1GkRERLUGe35VsoYNG2LJkiWYPXs2Vq5ciaSkJAghEBkZiT59+mDMmDGapvPVYR6qvXYcTtYUvm7p2sztC1/knoTFDMfc1zWFr4IGnTDc50FN4auF1wb08J9bUvgK9WqAzlH3I8y7sbNSJiIiIiKiao4rv8jluPLLfeWbLRj11q9Izy4siUWG+GL25DvhbfRwYWYVw+fGvQibBcqvb0Ak7lfFC+u2xfCQZ3DBoX4/ppX3WnTzmwdJAnSSAV2ix6BpUA9IUtVuteVzQ9eKzw5VFFd+ERERORdXfhFRuT78ZaOq8CVJwOTRvatF4Yvci3DYoCx8V1P4Mke0wPDgJzWFrzjv1ejqtwCSBHjqvNG//jiE+zR1ZspERERERFRDsPhFRGVaveskVm4/rord068N2jSOcFFGVF0Jhx3Kbx9AnFKfFmoObYw7w57FBcVTFW/j/Rc6+y2CJAHehiAMajgBgcZoZ6ZMREREREQ1CItfRKSRmVuI939ar4o1igrG2Fs6uCgjqq6Eww5lyXSIo5tV8aKgWIyIGId04aWKt/VZgY6+SyBJQIAxCoMaTICPR7AzUyYiIiIiohqGxS8iUhFC4L3v1yKv0FISM+hlvPZAb3gYdC7MjKoboTguFr6ObFTFi/0jcVfUSzgv+aji7X3+RAffpZAkIMy7CfrXHwejXn0NERERERHR1WLxi4hUfttwGFsOJqliY2/pgEbRXH1DFScUx8WtjqUKXxbfMNwbPQGpsr8q3sH3D3TwXQYAiPVri971noZeZm85IiIiIiK6fix+EVGJ5PRc/Ge+enta60bhuLtfaxdlRNVRuSu+fMNwb91JSNSrC6k3+i5Be98VAICmQb3QLeZByBJXGRIRERERUeVg8YuIAAB2h4Ip361GsdVeEvPyNGDy6N7QybILM6PqpKTwdXiDKl7sG4Z76k5Ckj5EFe/suwjxvn8BAOLDhqFd+B2QJMlp+RIRERERUc3H4hcRAQB+WrUHB06dV8WeubMLokL9XJQRVTflFb4KfergvphJSC5V+OritwBtfFYDkNA1ejSah/RzYrZERERERFRbsPhFRDiWlImvf9+pinWLi8UtXZu5KCOqbi4Wvj7UFL7yvEJxb8zLSDOoC1/d/OYhzmctZEmPm2KfQIOAjs5Ml4iIiIiIahEWv4hqOYvNjjdn/Q27QymJBfgYMfG+ntx+RhUiFAeU3z+COLxeFc82heL+2FeQ5vHvwpeCHv6/oqX3BhhkE/rXfwGRvs2dmzAREREREdUqLH4R1XJf/rYDp1OzVbEJ9/ZEkJ+XizKi6qSk8HVonSp+wRiCB+q9rCp86WBF38DZaGjaA5M+AIMavoRgU6yzUyYiIiIiolqGxS+iWmz3sVT88vdeVWxQpyboGV/fRRlRdVJe4SvdMwQP138F5zxCS2JGOR+Dg75AuMdp+HmGY1CDCfDzrOPslImIiIiIqBZi8YuoliossuKt71ZDiEuxsCAfPD+yq+uSomqjvMLXeY8QjG2gLnz569IxJPhz+OszEO7dDH3rPwuTngcpEBERERGRc7D4RVRLfTJvE9KyClSxyaNvgo/J00UZUXUhFAeUPz7WFL7SPILxSEN14SvccBKDgr6ASVeIFiH90SnqXsgSf/UQEREREZHz8BUIUS20cd8ZLN18VBUb2ScO7ZpGuSgjqi5KCl8H16ri5wzBeKzUiq+GxgT0CfwenjLQLfoRNAnu6eRsiYiIiIiIWPwiqnXyCi14/0f1qXz1IwLx2LAbXZQRVRfCYYey9FNN4SvNEIzHG76C1H/18Grj/Rc6+y2Gj0cg+tV/DqFeDZ2cLRERERER0UUsfhHVMv+ZvxmZueaSz3WyhNce6A1PA38cUPmEtRjK4qkQJ3eq4mmGYDzW8BWk/H/hS4KC7v6/oqX3BoR7N0Wfes/Cy+DvipSJiIiIiIgAsPhFVKtsOZCIP7eotzveNyAeTeuGlnMHESDMuXD8+iZw7pgqXrrwpZcs6B/4LeoZD6B5SH90Zn8vIiIiIiJyA3xVQlRLFBRZMO1HdYPy+pGBeGBwOxdlRNWByEmDY+7rQFaKKp5qCMETDSeVFL5Mch5uDpqBcM9UdIt+BE3Z34uIiIiIiNwEi19EtcR/F2xFenZhyeeyJOGV+2+Ch0HnwqzInYm0k3D8+gZQmK2KHzPWxTMNXsQFQyAAIFB/DjcHfY4Ik0Dfeq+hjjf7exERERERkftg8YuoFthxOBlLNh5Wxe7u1xrN69Up5w6q7ZQze6AseAewFqniO3ya48V6z6FQ5wUAiPQ4hoFBXyLWty761nsGXoYAF2RLRERERERUPha/iGq4wmIr3puzVhWrGxaAh29p75J8yP0ph9ZB+f1jQLGr4isCOuHNmEdhkw0AgMam7egd8CNahfZCp8hR0Mn8lUJERERERO6Hr1SIargZi7YhLaug5HNJAl65vxdPd6QyKdsXQ/n7a038x5CB+DTyHghJBgC09/kTHf1WoFvMA2gW3MvJWRIREREREVUcX/0S1WAJR1OwcN1BVWxE7zi0ahjuoozIXQmhQFnzHcS2hZqxTyLuwY91BgMAdLCid+APaO17Ev3qv4o63o2cnSoREREREdFVYfGLqIYqstjw7hz16Y7RoX54dGgHF2VE7ko4bFCWfgpxcK0qbocOb9Z9BMsDuwIAvOQcDAr6Ejf4CQxoMAU+HsEuyJaIiIiIiOjqsPhFVEN98dt2pGbmqWKT7u8Fo4fBRRmROxIWM5SF70Gc2a2KF8pGvFTvWWz3bQUACDEkYnDQF2geWB+9Y5+CQWd0RbpERERERERXjcUvohpo34lzmLdmvyp2R6+WiG8c6aKMyB2Jwmw4fn0DSDupil/Q++HZ+i/iqFd9AEAD4270Cfge8WG90THyXsj/3/eLiIiIiIioOmDxi6iGsVjteHfOWghxKRYZ4ovHhnV0WU7kfkRWChxzXwdy0lTxRI8wPNPgJaR4hgEA2vksQ0ffZegacz+ah/R1RapERERERETXhcUvohrmq993IPF8rio28b5e8DJyuyNdpJzdB2XxNMCsfk4Omhrg+frjkG3whw423BTwI1r4HECfeuMR4xfnomyJiIiIiIiuD4tfRDXIwdPn8ctf+1SxYd2bo32zKBdlRO5ECAViy3wo638AhKIa2+wbh4mxz6BIZ4RJzsOgoC/RyDsfAxq8gSBTtGsSJiIiIiIiqgQsfhHVEBabHe/MXgvlX/sdwwJ98OTtnVyYFbkLUZQP5fePIE7u0Iz9Edgdb8c8BIekR7A+GYODZqKhXxD61Z8CL4O/C7IlIiIiIiKqPCx+EdUQs5buwpm0bFVswn094W3ycFFG5C5E6jE4Fk8FctNVcQUSvgq7DV+H3QZIEuoZ96FfwHdoGtQGPes+Br3MZ4eIiIiIiKo/Fr+IaoAjZzPw48o9qtiQLs3QqUWMaxIityCEgEj4E8rfXwEOu2osR+eDV+s+ga3/38sr3mclOvkuQdvwoWgXPhwST3QkIiIiIqIagsUvomrOZnfgne/XwKFc2u4Y4u+Fp+/o7MKsyNWEtQjKsv9CHFqnGdvn1Qgvxz6N8x7BkGFDr4Cf0dx7J7rHPIomQd1dkC0REREREVHVYfGLqJqbvSwBJ1OyVLEJ9/WEr5enizIiVxOZSXAsfBe4kKQZ+zlkAP4TcTfssh4+uiwMCPwGdU2Z6Fd/EiJ8bnBBtkRERERERFWLxS+iauxYUiZmL9utig3s2ARdW8W6KCNyNeXgOijLPgNsxap4oWzE2zEP46+Aiwcg1PPch96BcxBm8seABm/C3zPcFekSERERERFVORa/iKopi9WON7/9Gw5FKYkF+3nhuRFdXJgVuYqw26D8/TVEwlLN2AljNCbGPoOzxkjIcKCT329o7f03mgb3QJeo0TDojC7ImIiIiIiIyDlY/CKqpv63cCtOn1Of7jj+nu7w82Yho7YROefhWDQVSDuuGfsjsBumRj8Ai+wJH10W+gd+ixjjOXSLeQKNAru6IFsiIiIiIiLnYvGLqBravP8s5q89oIoN6tQEPdvUd1FG5CrKiR1Qfv8QKC5QxS2SAdOj7sfioF6AJKGu5wH0DfwedX3CcVPse/DzrOOahImIiIiIiJyMxS+iaiYrz4x3vl+rikWG+OGFkd1ckxC5hBAKxMZfoGz8STOW4hGKCbHP4KhXfUhwoJPvErTxWY348FvQLvx2yBJ/9BMRERERUe3BV0BE1YgQAu/MXoPs/KKSmE6W8MaDfeBt8nBhZuRMwloM5Y+PII5u1oyt82uLN2MeRb7eG95yNvoHzkJDnyz0qjsRUb4tXJAtERERERGRa7H4RVSNLFh7EFsOJqliD97cHi0bhLkoI3I2kZsOx/y3gPTTqrgdMj6PGIE5oTcDkoQYz0PoGzAbzQKboEfdF2HU+7ooYyIiIiIiItdi8YuomjiZkoX/LtiiisU1DMeogfEuyoicTSQegGPhu0BRniqerfPFxHpPI8GnOSQouNH3d3TwXYNOUXejeUg/SJLkooyJiIiIiIhcj8UvomrAYrPjjW//gtXuKIl5Gz3w+gO9odfJLsyMnEXZvRzKyhmA4lDFjxnrYnz953HOIxRecg76Bc5CC/8i9Il9E0Gmui7KloiIiIiIyH2w+EVUDcxctA0nU7JUsfF3d0NEiJ+LMiJnEQ47lL++gkhYqhlb7d8Bb8Q8iiKdEdEeh9E38Du0q9MRnaLuhV72dEG2RERERERE7ofFLyI3t/VgEuau3q+K9b+xMQZ0bOKijMhZhDkXyqKpEIn7NWNfhN2Ob8KGQUgy2vv8ia4B69Cz7sOoH9DBBZkSERERERG5Lxa/iNxYdn4R3pm9RhULD/LB+Lu7uSgjchaRfuZiY/vc86p4keyJ12MexZqAG+EpFaJv4Gx0ChHoVfdd+HgEuyhbIiIiIiIi98XiF5GbEkLgvTlrcSHPXBKTJQlvPNgHPiZuaavJlGNboCz5ELAVq+KphhCMr/88jptiEWo4i0FB36FPTD+0Ch0ISWLvNyIiIiIiorKw+EXkphZvOISN+86qYmMGt0VcowgXZURVTQgBsekXKBt+1IwleDfDhHrPIEfvh+ZeG3FLnc3oV/95BLOpPRERERER0WWx+EXkhs6cy8Z/5m1RxVrUD8OYwe1clBFVNWEthrL0Y4gjmzRj84P74MPIURCygt4Bc3BHbCA6RLwBvezhgkyJiIiIiIiqFxa/iNyM1ebA69/+BYvNXhLz8jTg9Qd7Q6/j1raaSOSmX+zvlX5aFbdDh+lRo7AgpC/8dBkYVmcuRjQahijfli7KlIiIiIiIqPph8YvIzXy5ZDuOJ11Qxcbd3Q3Rof4uyoiqknJ6D5Tf3geK8lTxHJ0PJtR7Bgk+zVHPcx8erHcQ/WKfg1Hv46JMiYiIiIiIqicWv4jcyI7Dyfhp1V5VrE/7hhjYsYmLMqKqIoSA2LoAyrrvAaGoxo4bYzC+3vM45xmCLn5L8cwNddEk6DFIkuSibImIiIiIiKovFr+I3ERuQTHe+m61KhYW6IMX7+7BokcNIyxmKEs/gTi6WTO2xr89Xo95DDDYMDpiMR5sdgt8PUNdkCUREREREVHNwOIXkRsQQmDqD+uQmWsuiUkS8NoDveHn7enCzKiyiQtJcCx4B7iQrIorkDAz/A58V+cWhHmcxUvNzqNn9BjIEvu8ERERERERXQ8Wv4jcwO+bjmDdHnWz81ED4hHfJNJFGVFVUI5uhvLHx4C1SBXP1Xljct0nsdUvDu39tuPNNo0R6RPvoiyJiIiIiIhqFha/iFwsPbsA/5mv3v7WLDYUD9/S3kUZUWUTigPK+jkQW+Zrxo4aY/FSvWeRafTBQzHb8FjzXtDLXO1HRERERERUWVj8InIhIQQ++GkDzMW2kpjJU483H+wDvU7nwsyosghzLpTfPoA4s0cztjSwG96LfgABnun4T1wROoYNcH6CRERERERENRyLX0Qu9NfOk9i0/6wq9viwTogJC3BNQlSpxLkTcCx8B8jLUMXt0OGjqPswL7gvugTtxzttWyHAM9BFWRIREREREdVsLH4RuUhuQTE+/nWjKtaqQRhu79nCRRlRZVL2rYKy/HPAYVPFM/QBmFjvGRz1icazDY5idJNukNjUnoiIiIiIqMqw+EXkIp/O24yc/OKSzw16GZNG9YIsSy7Miq6XsNug/PUlxO5lmrHd3k0wKfYZePoU4ps2drQI7uSCDImIiIiIiGoXFr+IXGDrwUQs33ZMFRszuB3qRXDrW3Um8jPhWPgekHpUM/ZLSH98EnkPeoSexjttW8GkN7ogQyIiIiIiotqHxS8iJysstmLaj+tVsYZRQbivfxvXJESVQiQfgn3BO5DMuap4seSBd2IewprgeExokoE7G3RwUYZERERERES1E4tfRE72xW/bcT6roORzWZIwaVQvGPQ83bG6UvYsh2PFDEiKQxVP9qiDl+o9i+JAT/zU3oQGfpEuypCIiIiIiKj2YvGLyIn2n0rDgrUHVLERfVqheb06LsqIrodw2GBfORPSnhUo3alto29rvFb3CXSPysHrbRrDQ8fiJhERERERkSuw+EXkJFabA+99vw5CXIpFhvhh7C3cBlcdicIcFM9/DYbUU5qxb+oMxfeRg/ByS4GbY3h6JxERERERkSux+EXkJLOXJeBMWrYqNvG+HjB5GlyUEV0re+phWOa9Dk+zWRUvkj3xRsyjOBXZCHM7BCPa2+SiDImIiIiIiOgfLH4ROcHJlAv4fvluVWxIl2Zo3yzaRRnRtcpOmAvTqh/gqQhVPMUjFOPrPY9G9UMwt3UEPHSlN0ISERERERGRK7D4RVTFHIqC9+asg0NRSmLBfl54anhnF2ZFV8tuL8L5P19H2MFDmrEdPs0xud6TeLRVMEbU83FBdkRERERERFQeFr+Iqti81ftx6Ey6Kjbu7m7w8/Z0UUZ0tTKz9qN48dsIP1+oGfs5ZAC+rzcSH7UPQutA/kglIiIiIiJyN3ylRlSFUjLy8OWSHapYr/j66BXfwEUZ0dVwKDYcOjoLkSv/QLhZUY1ZJT3ei34QSQ164ud2vgjxlF2UJREREREREV0Oi19EVUQIgfd/XIdiq70k5uvlgRfu6ubCrKiiMs1ncHTr+4jfmQQPh7q/V4Y+AC/Wew4tmzfHVzeYYJDZ34uIiIiIiMhdsfhFVEWWbjmKHUdSVLGnhndGiL+3izKiilCEA3vSFsOx8Rd0PFGkGd/n1QivNngWj8ZH4tZobl0lIiIiIiJydyx+EVWBC7lmfDZ/syrWrmkUhnRp5qKMqCLyLOlYf/IzNNu6H/XO2zXjvwX1xHcNH8D77QPQIoA/PomIiIiIiKoDvnojqgIfzd2IfLO15HNPgx4T7usBSeL2OHckhMCJ7I3Ye+xrdN+Rg+ACh2rcDhkfRd2H040HYXZbbwSxvxcREREREVG1weJXJVq4cCEmTZpUoWsffPBBTJgwodzxgoICzJ49G6tWrUJSUhIURUFUVBT69OmD0aNHIygoqEJfp7LmoYpbt/s01iScUsXG3toB0aH+LsqILsfqMGNj0rcwn9yAAbuLYLKpG9vn6Hwwsd4zaNYyHjOaGaFnfy8iIiIiIqJqhcUvF2nRokW5YydPnsTYsWORkqLuF3X8+HEcP34c8+fPx4wZMxAXF3fZr1FZ81DF5Zst+PCXDapYs9hQjOjdykUZ0eWkFRzFmjP/Q/SJVPQ7bIEO6sb2x4x18UqD5zG2fSwGR3m4KEsiIiIiIiK6Hix+VZF9+/ZddtxgMJQZLygowCOPPIKUlBQYDAY899xzGDJkCIxGI3bu3Ilp06YhMTERjz/+OBYvXozQ0NAqnYeuzv8WbkVmrrnkc50s4+VRvaDXcZucO1GEAwlpi7AvdRE6HLTihpRizTWr/G/E100ew3sdgtDMnz8qiYiIiIiIqiu+Iq8inp6el/2fLJf9r/6bb75BcnIyAOD999/Hww8/jPDwcAQEBKBv37548sknAQCZmZn4/PPPy/36lTUPVdzx5Ez8vumwKjZqYBs0ig52UUZUljzLefx+fAqOnF2A3lvtuCFFfaKjAgn/C78T6zu9gO96hrLwRUREREREVM2x+OVGHA4Hfv75ZwBAfHw8Bg8erBn/4osvSj5fuHAhiorUL9wrcx66OjMXbYP416652PAAjBnUznUJkYoQAseyNmDh0ZdhTz2GAZvsiM4rVF1TIBvxcoPnUb/f3Xg33hu+Bvb3IiIiIiIiqu5Y/HIjCQkJyM7OBgAMGjRIM75gwQKcOnUKw4cPBwAUFxdj48aNVTYPVdzuY6nYcjBJFXtqeGd4GHQuyoj+zWIvwJqz/8O6xJkIPevAoK1mBFrVBd9EjzC83eYtPH1zT9wa7cGTOYmIiIiIiGoIFr+cQFGUK18E4MCBAyUfx8fHq8aKiorw2WefoXXr1nj22WfLvKey56GKEULg80VbVbHWjSLQpWVdF2VE/5aadwBz9z2HU9mb0eiALwYcyISHcKiu2eLTCsv6TsW7fZsh1ocFSyIiIiIiopqEzWyqyJIlS7B48WIcO3YMmZmZMJlMaN26Ne677z707du3zHtOnTpV8nF0dLRqbPbs2UhPT8f06dMRFhYGvV4Pu92uuqey53EWvafXFa+RJLnknwajT1WndFXW7DqOg6fTVbFn7roJHiZfF2VEwMWm9tuTfsKulHmARUK7XX5olZusuW5++M2oP/QJPBfp7YIsyR25888bcm98doiIiIjcE4tfVeTFF19UfW42m7FlyxZs2bIFo0aNwuTJkzX3ZGVllXwcGBhY8nF2dja+/vpr9OjRAx07dgQA+Pn5ISsrq2R7Y1XM4yyyXPGVNpIkQZLcZ2WO3aHg8/kbVLGebRujTZMYF2VEAGCxF2LFsfeRmLMLSk4wBibkIcqiLnxZJAPmt3gMtw8djhCj+zxT5D7c7ecNVR98doiIiIjcC4tflSgqKgoDBgyAwWBAhw4d0LFjR4SHh8NqtWLr1q344IMPkJSUhDlz5qB58+a4/fbbVff/03Tew0Pdb2jmzJkoKCjAuHHjSmIeHh4ALhbVSquseZxFURxXvEaSZEiSBCEEhKjYNlJn+H39fpw5d6nYKEsSnhjetULfE1WNnKJU/Hn0HWQXJUNJaoq7D++Dt1KsuibDEIg9fSbjwU5tIUsVewap9nDXnzfk/vjs0NW4mjf/iIiI6Pqw+FWJOnbsWLKi6t9MJhMGDBiAuLg4DBkyBAUFBZgxY4am+PUPWb7Uii0lJQU//fQThgwZgmbNmpXEK9JHrLLmqWp2i/mKLxIMRh9Ikg5CKLAVFzgps8uzWO34YpH6oIBBnZsgJtjoNjnWNin5B/D3mf+gwGqD15E43JO4ETKE6pojPo1hGD4JgyLrwGEpBMteVJo7/ryh6oHPDlWUJMlsj0BEROREbHjvRBERERg4cCAAIDExEUlJ6tMBvbwu9r6y2WwlsU8++QRCCFVzegCwWCyqe6piHrq8BWsPICOnsORzD70ODw/p4MKMardDmauw7OQ0ZBR5ITqhPu5L3KApfO2OvgkNxk5D08g6LsqSiIiIiIiInI0rv5yscePGJR8nJiYiJuZSb6igoCAAgMPhQH5+PlJSUvDHH3/g3nvvVV1ns9mQn5+vuuffKmseKl++2YLZy3erYsN7tURYEBscO5si7NicPAeHL/yF1Lzm6L83Bx0KtquucUDG8Q4PoH2fYaqtwERERERERFTzsfjlZCaTqeTjf6/MAoAGDRqUfJySkoLp06fDZDLhiSeeUF137ty5ku2K9evX13yNypqHyvfjyj3IN1tKPvc2euD+gfEuzKh2Krbn468z/0FK/mGcyrgJDx/YjvqWVNU1hTovWG9/DS0btXRRlkRERERERORKLH45WUZGRsnHYWFhqrGWLS+9OJ89ezY2bNiAp59+WrMqKyEhoeTjVq1aab5GZc1DZcvIKcTcv/erYvcNaAN/H6OLMqqdsotTsPLUhzhfVIDEpKGYcGwhAh356mu86iBwzAcICYtl/x0iIiIiIqJaisUvJ9u8eTMAwNfXV7PaKj4+HoGBgcjOzsbChQsRHByMBx54QDPH8uXLAQBGoxFdu3bVjFfWPFS2WUt3wWKzl3we7OeFEb1ZPHSmpLw9+PvMf5FSHArryc548+xP8BTqlZSZIU0RNeY96P1DeZojERERERFRLcaG95VECIGpU6fir7/+KveaZcuWYdeuXQCAYcOGwWhUrxTS6XS4++67Sz4fM2YMvL29Vdds3rwZa9euBQAMHz5ctY2ysuchrcTzOfh902FV7IGb28HkaXBRRrWLEAL70pdixanp2JvXGoEHGuLVM7M1ha+cht0R9sB7kH3Zy46IiIiIiKi248qvSvLNN99g1qxZmDVrFvr27Yvhw4ejZcuW8PLyQmpqKpYsWYJZs2YBAGJjYzWnLv7joYcewpIlS5CcnIwff/wR0dHR6NSpExRFwapVq/D+++9DCIGQkBA8/vjj5eZTWfOQ2pe/bYdDuXSCYHSoH27t1syFGdUeDsWGjcnf4tCFzdiafSeGHzuEwdkrNNcVdbwLwTfdy8b2REREREREBACQhBDiypfRldjtdnz44YeYM2eOppH9v8XFxeGjjz5SnbpY2smTJzF27FikpKSUOR4SEoIZM2YgLi7usjlV1jxVzVqUDyGUy15jMPpAlnVQFIfLejcdPpuOh95bqIpNebgv+rZv5JJ8apMiWy5WnfkYJ3IzsTn9Hkw4MR/xhcdU1zgkHeTBz8AQ16ck5g7PDVU/fG7oWvHZoYqSJBkeJl9Xp0FERFRrsPhVyc6ePYt58+Zh48aNSE1NRVFREUJDQ9GkSRMMGTIEAwcOhF5/5QV3BQUFmD17NlauXImkpCQIIRAZGYk+ffpgzJgxmub1VT1PVaouxa9nPvkdO49cKiQ2rRuCbyYOhyxzhVFVSi88gb/P/AdHCkJw+NwgTDs5AzHWdNU1Vk8fmO6YDKmu+kRHd3huqPrhc0PXis8OVRSLX0RERM7F4he5XHUofm0/lITn/rNUFfvkmZtxY/PyV/DR9XEodiScX4g9aUuQUNAX9nONMO3Mf+DnMKuuswZEwjTyDUhBkZo5XP3cUPXE54auFZ8dqigWv4iIiJyLPb+IrkBRBGYu3q6KtW8WxcJXFbpQdBZrz87EOXM6Vuc8jJapuXg56QPooT610RHTCqbhL0PiCwgiIiIiIiIqB4tfRFewJuEkjiRmqGKP39bRRdnUbIpwYG/6H0hIW4AMax2suDAe96asx5j037UXt+oDj0FPQdLxpE0iIiIiIiIqH4tfRJdhdzjwxW/qVV+92zbADbF1XJRRzZVTfA7rEmci3XwCx83tsDn7Drx6dhb65O7QXCv3vB9S5zt5oiMRERERERFdEYtfRJfx+6YjSM7IK/lcJ0t4ZOiNLsyo5hFCwcHMVdie+gusigOb8+5Aak48/nd6OloUnVJfq/OA7pYXIN/QzUXZEhERERERUXXD4hdROYosNnz7xy5V7JauN6BuWIBrEqqB8q0ZWJ/4FVILDqLQ4Y8VWQ/BO88Ds06/jgjbBdW1wisA+jtehRTV1EXZEhERERERUXXE4hdROX5dvR8X8i6dLOhp0OOBm9u5MKOaQwiBY1nrsCVlDmxKMVIsjbAy+yHE55zGO2c/g49SrL4hpC70I96A5M/tpkRERERERHR1WPwiKkNuQTF+WLFHFRvZpxVCA7xdk1ANYrblYEPS10jM2w0hgL2FfbAlbyjuyPwb41LmQAehul6q3xbysAmQjPx3T0RERERERFePxS+iMny/fDcKi60ln/t6eeLe/m1cl1ANcTJ7KzYlz4LFUQCr4ok1OffhTFFrvJD6I+7KXKm5Xmp7M+R+j0CSdS7IloiIiIiIiGoCFr+ISsnIKcSCtQdUsdED4+Hr5emijKq/AmsmtqTMwZncnQCALFsYlmc/AqvFH9PPfoRu+XvVN0gy5D4PQ2p/C090JCIiIiIiouvC4hdRKb9tOASr3VHyeZ1Abwy/qaULM6q+HIod+zOWIiFtMRzi4kq6E0VtsTrnPgRb8vH56SloXJykvslghDz0JciNeaomERERERERXT8Wv4j+xe5wYMnGw6rYqAHx8DTwP5WrlZJ/AJuSv0Ou5RwAwCFkbM0bhr2FfdDcfBIfnf4IwfZc9U2+IdDd+RqksAYuyJiIiIiIiIhqIr6iJ/qX9XvOIDP30gmPXp4GDOzUxIUZVT+F1ixsTf0Bp3K2lcSy7XXwd/ZopNvq4aac7ZiSOBNGYVXfGN7oYuHLJ8jJGRMREREREVFNxuIX0b8sXH9Q9fnATk3gbfRwUTbViyLsOJCxAglpC2FTigEAQkjYV9gTW/OGwiEMuD/jdzx9bq7mXqlJJ8i3jIfkYXR22kRERERERFTDsfhF9P/OnMtGwtFUVey2Hi1clE31cq7gMDYlf4fs4uSSWL49CKtz7kOKtSn87fmYkDwT/XK3a+6VOg2H3Gs0JEl2ZspERERERERUS7D4RfT/Sq/6atM4Ag2juAXvcsy2HGxL/QknsjeVxIQAjhR1wsbcO2ATJtyUsx0TU75DkD1PfbOsgzzgCchtBjg5ayIiIiIiIqpNWPwiAmAutmHZlmOqGFd9lU8RDhzK/As7z82DTSkqiZsdflibczfOWOLgb8/HSyn/Rf+crdoJPL0h3z4Jcr02zkuaiIiIiIiIaiUWv4gArNxxHIXFlxqwB/qa0Cu+vgszcl/nC49jU/IsXCg6q4qfLIrHuty7UKz44KacHZiQMgvBpVd7AUB4Q+huHQ8pOMZJGRMREREREVFtxuIX1XpCCCxcq97yeGu3G2DQ61yUkXsqtudje+ovOJq1Vh1XTNiQOxLHizrA356PySn/xYCyVnvJesjd7oLU6Q5IOv7oISIiIiIiIufgK1Cq9Q6cOo8TKRdKPpclCUO73+DCjNyLEAqOZq3H9tSfYXEUqMYSi2/AmpxRKFT80St3ByYml7PaK6whdEOeg1SHq+mIiIiIiIjIuVj8olpvwTr1qq+ucbEID/J1UTbu5ULRWWxMmoV083FV3KZ4YnPebTho7g5/ez7eSvkcA3M2ayeQdZC73gWp851c7UVEREREREQuwVejVKtl5RVhTcJJVez2nmx0b3WYsSttAQ5mrICAUI2lWBpjXe5o5NgD0TN3JyYlz0KwPVc7SVgD6G5+DlJYAydlTURERERERKTF4hfVan9sPgKbXSn5PDrUDx2aRbswI9cSQuBUzlZsTfkBZnuOaizPHoytebfjRHGbK6/26jISUpcRXO1FRERERERELsdXplRrORQFi9ertzwO69ECsiy5KCPXyilOxabk75BaoP53YlU8satgAPYV9oVD6NA9NwEvJ3+DEK72IiIiIiIiomqAxS+qtbYeSEJa1qUG7h4GHW7u3NSFGbmGXbFg9/nfsC/9DyjCURIXQsKRok7YnjcMhYoPjI5ivJT6HW7PWqOdRNZB6jICcpcRkHQGJ2ZPREREREREdHksflGttbDUqq++7RvB38foomxc42xuAjanfI8Ca4YqnmppiE15dyLDFgMAaG4+iSlnZyDWmqadpE496Ia8wNVeRERERERE5JZY/KJaKSUjD1sPJqpitanRfaE1C5tTZuNM7k5VPM8ehC15t+FkcVsAgE44MCZ9CR5OWwQ9FPUkkgypy0jIXbnai4iIiIiIiNwXi19UKy3ecAjiX4cYNosNRfN6dVyXkJMIoeDIhbXYlvoTbEpRSdymeGJXQX/sLegDBy4WsqIs5zElcSbizMe1EwVFQXfLOEiRTZyVOhEREREREdE1YfGLah2LzY4/Nh1RxWrDqq9cSxo2JH6Nc4WHS2JCSDhadCO25g2FWfH/J4hbstdjXMoceCvFmnmk+EGQez8EyaN2bRElIiIiIiKi6onFL6p1Vu86hdzCS0UdXy9P9G3f0IUZVS1FOLA//U/sSlsAh7CVxM9Z62Nj7p3IsMWWxPzt+ZiU/C365O7QTuTlD3nwM5Abd3RG2kRERERERESVgsUvqnUWrlM3ur+5S1MYPWpmz6oL5jNYn/QVMovOlMRsige25t+K/YU9Acgl8Y75+/F64hcItedo5pEadoB88zOQvAOrPmkiIiIiIiKiSsTiF9UqRxMzcPD0eVXstu7NXZRN1bErViSkLcS+9KUQ/2pUn2ppiNU5o5DnCC2JeSpWPHluLu7OXKGdSO8Juc9DkOIHQZIkZ6ROREREREREVKlY/KJapfSqrw43RCMmLMA1yVSRcwWHsSHpG+RazpXEylvt1bjoLN5KnIGGxcnaicIbQXfreEjB0U7ImoiIiIiIiKhqsPhFtUa+2YKV20+oYjWp0b3VYcb21F9w+MLfqniqpRFW59ynWu0lCwX3ZPyJJ9LmwyDs6okkGVLnOyF3uxuSjj8iiIiIiIiIqHrjK1uqNf7cchQW26VCT51Ab3RtFXuZO6qPs7kJ2JT8LQpt2SWxS6u9blJdG2NJw2uJX6KN+Zh2Iv8w6G55AVJMzSkKEhERERERUe3G4hfVCkIILFqv3vI4tHtz6HVyOXdUD1aHGRuTvsXJnC2qeFmrvSShYETmKjydNheeilUzl9SyN+R+j0Iyeld53kRERERERETOwuIX1Qq7jqYg8Xxuyec6WcatXW9wYUbXz2zLxfJT7+OC5iTHodhf2Et1bZQlHa8mfYl2hUe0Exl9IA98EvIN3as2YSIiIiIiIiIXYPGLaoXSje57ta2PYH8vF2Vz/fIs57Hs5DTkWS+dXJliaYQ1pVZ7QQjcfmE1njv3E0yKRTOP1LA95EFPQ/INdkbaRERERERERE7H4hfVeBnZBdiw94wqVp0b3WeaT2P5qfdRZM8DUP5qrzBrJl5N+hodCw5oJ/H0gtx3LKRWfSFJkhOyJiIiIiIiInINFr+oxvtt42E4FFHyef3IQLRpFOHCjK5dSv4BrDr9MWxKMQDggi0Cy7Ie1az2uiVrPcad+wHejiLNHFL9eMiDn4HkF6oZIyIiIiIiIqppWPyiGs3ucGDJxsOq2O09WlTL1U4ns7dgbeIMKMIBAEi2NMHyrEdgFaaSa0JtWXg56Rt0y9+rncDDBLn3Q5DaDKiW3z8RERERERHRtWDxi2q09XvOIDPXXPK5yVOPgR2buDCja3MgYwW2pMwBcHEF21FzB6zJuQ/KP/8JC4FB2ZvwUur38HGYNfdLsXGQBz8LKSDMiVkTERERERERuR6LX1SjLVyvbnQ/sGMTeJs8XJTN1RNCYOe5X7Enfcn/fw4kFPTHtvyhJdcE23IwMXkWeuXt0k5g8ITcawykdjdDkmRnpU1ERERERETkNlj8ohrrzLlsJBxNVcVu61F9Gt0rwoENSd/gWNa6//9cxvrcEThk7l5yTZ+cbZiYPAsBjgLtBNHNobv5OUhBkc5KmYiIiIiIiMjtsPhFNdae4+dUn7duFI5G0cEuyubq2BUL/j7zGRLzdgMAbIonVmY/gLOWVgAAX3shXkqZjYE5m7U36z0g97wfUvtbIMk6Z6ZNRERERERE5HZY/KIaKzzYR/X5Pf3auCaRq1RsL8CKU9ORbj4OADA7fLE063Fk2GIBAJ3y9uHVpK9Qx56tvTmyKXRDnocUHO3MlImIiIiIiIjcFotfVGN1alEXL93bA1sPJKJbXD10b13P1SldUYH1ApadnIocy8Xtmtn2Olh64UnkOUJgdBTj2XM/444Lf2tv1Okhd78PUsfbuNqLiIiIiIiI6F8kIYRwdRJUu1mL8iGEctlrDEYfyLIOiuKArbiM/lY1QFZRMpafmoZCWxYA4JylAf7MehQW4YO4wmN4I3EmYqzp2hvr1IfulnGQ6tRzbsLVQG14bqjy8bmha8VnhypKkmR4mHxdnQYREVGtwZVfRG4greAoVp7+EBZHIQDgZFE8/soeDVkBnjw/F6PS/4AOperUkgyp8x2Qu90NSWdwQdZERERERERE/9fenUdHVd//H3/dyUIWCBCysAqIJVSQJEBF4StFUSmboSKFsFRQsSgiKvyKWgWpyxFLBX6ICW5tsCztV6IGfsiiFcEiUlkELZuQmBDWkATINjPJ3N8flCnTmURAMhNuno9zOH7mfj735j3H9znoK/d+bt1H+AUEWHbxVn36/euqMp2SpK9LbtU/ztyt68oPa1ZuujpW5Hqf1LTlub29Wv/Uz9UCAAAAAHB1IfwCAmj3iY+05cgSSaZcpqHNZ+7WtyU/17gTq/Tg8RUKMau8zjG6DZLt1vEyQsP8XzAAAAAAAFcZwi8gAEzTpS1Hluqbkx9JkpyuEH1SfK+cp1vojdwX1PXfb3r00KiZbIMek619sp+rBQAAAADg6kX4BfhZpcuhDbnpyi7+UpJ0xH6dPi0apdtP7NaUo68p3GX3OsfofKtsd/xGRnhDf5cLAAAAAMBVjfAL8KOKyhKty35Vx0v3yelqoC/OpOjwmWTNzH1Dt5zd6X1CeJRsv5gkW6fefq8VAAAAAAArIPwC/OSs/aTWHHpFxfYjyrMnaEPxaHUqPqqluU8rtrLYa71x3Y2yDZgso2FT/xcLAAAAAIBFEH4BflBQlq01h+ao2GHX5jOjtL+0pyYeW6Ffn1glm0yPtWZouIJunyCj6x0yDCNAFQMAAAAAYA2EX0AtyzuzUx/n/F99V/YTfVacqiblDr2V+7y6lB30Xtyqk4Lv+j8ymsT7v1AAAAAAACyI8AuoRXtPbdDH3y/TxtMjdaD8Rt1ZtFlPHX5HDV0VHutMGbL1+pVst4ySYQsKULUAAAAAAFgP4RdQC0zT1PZjmfrfnGxtPP07yRmqGfmLNKRok/fihtEKumuabG27+r9QAAAAAAAsjvALuMJcZqVWZy/Rn3Ku0aGKfkooy9aL3y9UW8cxr7XGT3rKNvBRGRGNA1ApAAAAAADWR/gFXEH2yjL9cdcaZR3vJ7srUqkFH2ny0eUKMas8FwaFyNbvfhndBrGpPQAAAAAAtYjwC7gCvi9x6i8HD+mT40EqqrxNTZ2nNTtvjnqf/dp7cbM2Chr6Wxlx7f1fKAAAAAAA9QzhF3CZCuwurT3i0Id5Z3SgJFTSuTc03nh2t36fm65mlae9zjGSfiHb7Q/ICAnzc7UAAAAAANRPhF/AJShxmvr7cYdW5zv1z1NOuWRICpVMU4ml+zWiYJ3uOP2l94lhkbINmCxbp//xe80AAAAAANRnhF/AD3BUmfrHSac+OuLUxhNO2V3nZww1cDl0Z9EXGlGwTgkV3/u+QOvrFXTXNBmN4/xVMgAAAAAA+DfCL8AHl2lqe2GVPjri0MfHnDrjND3m4x0FGl7wsVIKN6hJVUk1VzFk9B4h2/+kyrAF1X7RAAAAAADAC+EXcAF7lan/d8ShvxyyK7vU5TlpmupeukcjCtapz+ltCpLp+yKS1KyNbP0fkq1t19otGAAAAAAA1IjwC5B0xunS/37v0LIcu045PEOtsKoKDSj+h35VsF7XVRyu4SqGjOt+JqPHEBntkmQYRu0WDQAAAAAAfhDhF+q1I+UuLcmu0Pt5DpVXec61sp/Q8FPrNaTwM0VVlVV/kbBIGYn9ZUseKKNp89otGAAAAAAAXBLCL9RLe09XKuOQXeuPOVX17xu9YpxFSirdp8TS/Uoq3aeO5bmy1fRoY2w72XoMltG5r4yQMP8UDgAAAAAALgnhF+oN0zS1peBc6LW1wKF29qO664Kwq5Xj5A9fw7DJ1vEm2XoMkdp04dFGAAAAAADqOMIvWJ7TZWr94TL9Y/ceRZ/cq+Gl+/VC6f4a3tLorSosQsHJgxTUbaCMqNharBYAAAAAAFxJhF+wLNM0tX3LRgVtW6k+JQd1p+m85GtUxMYr/MYRCr2+r4zg0FqoEgAAAAAA1CbCL1jW3m+/VuKGP1z0+ipDOtU4WPYWbdT42r5q0qGfGkY2qb0CAQAAAABArSP8gmWdPZJT47wjWDrRJFjHo4N0JraZ4jr0V8e42xUe0tg/BQIAAAAAgFpH+FUPlJSUKCMjQ+vXr1deXp5cLpdatWqlfv366d5771V0dHSgS6wV0T9tq2O7mqq5s0iSVBgaqZNNDRU2q9TxpsEqbhSk1lGJ+mnM7fpZVJJshi3AFQMAAAAAgCuN8MviDh48qAkTJig/P9/j+IEDB3TgwAG99957SktLU9euXQNUYe2pCDuuDbfYZJy5TkERp+WMsEuGoQZBjZXQrK/ubNZPUQ3iAl0mAAAAAACoRYRfFlZSUqIHH3xQ+fn5CgkJ0WOPPabBgwcrLCxMX331lWbPnq3c3Fw99NBD+uCDDxQba623GLaJStI/w/+mygYn5ZIUF9FR18f0U/smPRVsY/N6AAAAAADqA8IvC3v77bd1+PBhSdIrr7yigQMHuuduv/12lZSUaPr06SooKNDrr7+umTNnBqrUWtG4QXMN7/SKjpXuU9OwNmoWfk2gSwIAAAAAAH7GJkcWVVVVpWXLlkmSkpOTPYKv8/OLFi1yf87MzFR5eblfa/SHhqExuq5pb4IvAAAAAADqKcIvi9q+fbuKis5t9D5gwACv+RUrVujQoUMaNmyYJKmiokKff/65X2sEAAAAAACobYRfFvXNN9+4x8nJyR5z5eXlWrBggRITEzVlyhSf5wAAAAAAAFgBe35Z1KFDh9zj1q1be8xlZGToxIkTmjNnjuLj4xUcHKzKykqPc/wpuEHED64xDJv7nyFhDWu7JFgEfYPLQd/gctE7AAAAdRPhl0UVFha6x02bNnWPi4qK9NZbb6lPnz7q2bOnJCkqKkqFhYXuxyT9zWYLuui1hmHIMC5+PSDRN7g89A0uF70DAABQtxB+WdT5zet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+ "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": { + "image/png": { + "height": 378.25, + "width": 516.375 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "# | label: score-prediction-fixture-mixed-effects\n", + "# | message: false\n", + "# | warning: false\n", + "predict_mixed_effects = partial(\n", + " predict_model,\n", + " formula=\"score ~ home + C(team) + C(versus) + (1 | player)\",\n", + ")\n", + "teams = backtest(players, predict_mixed_effects)\n", + "fig = add_backtest(fig, teams, \"Mixed effects\")\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "e0ab35e1", + "metadata": { + "cell_marker": "\"\"\"" + }, + "source": [ + "## Conclusion\n", + "\n", + "We developed a comprehensive framework for the fantasy football team picking\n", + "problem. There are more ideas we could explore to improve our chances of\n", + "winning:\n", + "\n", + "* enriching our data and models with player scouts;\n", + "* including more information in our priors;\n", + "* testing strategies that balance predicted score and appreciation;\n", + "* further model diagnostics.\n", + "\n", + "However, I suppose expert human player predictions have a certain edge over\n", + "those of hobbyist statistical models in fantasy leagues, due to the fact that\n", + "there are all sorts of relevant data unavailable in public datasets.\n", + "\n", + "At least, this seems to be the case for brazilian soccer, also known as \"a\n", + "little box of surprises\"." + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/blog/fantasy-football/index.tmp b/blog/fantasy-football/index.tmp deleted file mode 100644 index ad9b46f..0000000 --- a/blog/fantasy-football/index.tmp +++ /dev/null @@ -1,720 +0,0 @@ -# %% [markdown] -# --- -# title: Picking a fantasy football team -# date: 2023-09-21 -# categories: [football, optimization, prediction] -# execute: -# freeze: true -# --- - -# %% [markdown] -""" -[Cartola](http://cartola.globo.com) is a fantasy football league following the -Brazilian Championship A Series. - -Cartola offers a public API to access data for the current round. A couple -of years ago, I created a script to automate data retrieval to a -[repository](https://github.com/assuncaolfi/tophat/tree/main), which now hosts -comprehensive historical data since 2022. - -In this post, we will delve into the data for the 2022 season, formulate a mixed -integer linear program to pick the optimal team, and present initial concepts -for forecasting player scores using mixed effects linear models. - -## The game - -We begin the season with a budget of C$ 100, the game’s paper currency. - -Each round is preceded by a market session, where players are assigned a value. -We are tasked with forming a team of 11 players plus a coach, all within our -budget and adhering to a valid formation. A captain must be chosen from among -the players, excluding the coach. - -The market is available until the round starts. Players then earn scores based -on their real-life match performances. Our team's score is the aggregate of -our players' scores, with our captain’s score doubled in the 2022 season. - -Following the conclusion of the round, player values are recalibrated based -on performance -— with increases for scores above their average and decreases for -below-average performances. Our budget for the next round is our previous -budget, plus the sum of our players' value variations. - -## Data wrangling - -Let's talk about data structures: each round has a market, and each market is a -list of players. A player is a structure like this: -""" - -# %% -# | label: data-wrangling-players -from pydantic import BaseModel, Field, field_validator - -from rich.console import Console -from rich.pretty import pprint -from typing import Callable, Dict, List -import urllib.request - - -class Player(BaseModel): - round: int = Field(alias="rodada_id") - player: int = Field(alias="atleta_id") - team: int = Field(alias="clube_id") - position: int = Field(alias="posicao_id") - games: int = Field(alias="jogos_num") - average: float = Field(alias="media_num") - value: float = Field(alias="preco_num") - score: float = Field(alias="pontos_num") - appreciation: float = Field(alias="variacao_num") - minimum: float | Dict | None = Field(alias="minimo_para_valorizar") - - @field_validator("minimum") - @classmethod - def dict_is_zero(cls, v: float | Dict | None): - if v == {} or v is None: - v = 0.0 - return v - - -class Market(BaseModel): - players: List[Player] = Field(alias="atletas") - - -base_url = "https://raw.githubusercontent.com/assuncaolfi/tophat/main/2022/" -markets = [] -for round in range(1, 39): - url = base_url + f"{round:02}/atletas/mercado.json" - data = urllib.request.urlopen(url).read() - market = Market.model_validate_json(data) - if round == 1: - for player in market.players: - player.round = 0 - markets.extend(market.players) - -console = Console(color_system=None) -pprint(markets[0], console=console, expand_all=True) - - -# %% [markdown] -""" -Let's get the list of markets for 2022 and flatten it into a single DataFrame: -""" - -# %% -# | label: data-wrangling-dataframe -import polars as pl - -pl.Config.set_tbl_hide_column_data_types(True) - -players = ( - pl.DataFrame(markets) - .with_columns(round=pl.col("round") + 1) - .sort("round", "player") -) -pprint(players) - -# %% [markdown] -""" -Now, let's focus on a specific `player` to illustrate our data while we wrangle -it: -""" - - -# %% -# | label: data-wrangling-example -def print_example(markets: pl.DataFrame, columns: List[str]): - example = players.filter(pl.col("player") == 42234).select( - ["round", "player"] + columns - ) - pprint(example) - - -print_example(players, players.columns[2:]) - -# %% [markdown] -""" -### Filtering participation - -Players will show up in the market for many rounds that they do not participate -in. However, for our analysis, we are only interested in players that actually -played a game in the round. - -Each player has a `status` field intended to indicate their participation in the -round. However, this field is often inaccurate, likely due to the API data being -updated before the round. - -One solution is to keep only rows where there is an increase in the number of -`games` the player has played: -""" - -# %% -# | label: data-wrangling-round-participation -players = players.filter( - pl.col("games") != pl.col("games").shift(1).over("player").fill_null(-1) -) -print_example(markets, ["games"]) - -# %% [markdown] -r""" -### Imputing scores - -Similarly, the player `score` field is often inaccurate, likely for the same -reasons as the `status` field. Fortunately, the `average` field is reliable, -allowing us to recover the `score`: - -$$ -\begin{align*} -\mathrm{Average}(\mathbf{s}_{1:t}) -= \frac{\mathrm{Average}(\mathbf{s}_{1:(t-1)}) + s_t}{2} \\ -s_t -= 2\mathrm{Average}(\mathbf{s}_{1:t}) - \mathrm{Average}(\mathbf{s}_{1:(t-1)}), -\end{align*} -$$ - -where $\mathbf{s}$ is the vector of scores for a given player across all rounds. -""" - -# %% -# | label: data-wrangling-missing-scores -# TODO make this better -players = players.with_columns( - average=pl.col("average").shift(-1).over("player").fill_null(pl.col("average")) -).with_columns( - score=2 * pl.col("average") - - pl.col("average").shift(1).over("player").fill_null(pl.col("average")), -) -print_example(players, ["score", "average"]) - -# %% [markdown] -""" -### Adding fixtures - -Let's fetch the list of fixtures to enrich our dataset. A fixture is an object -like: -""" - - -# %% -# | label: data-wrangling-fixtures -class Fixture(BaseModel): - round: int = Field(default=0) - home: int = Field(alias="clube_casa_id") - away: int = Field(alias="clube_visitante_id") - - -class Round(BaseModel): - round: int = Field(alias="rodada") - fixtures: List[Fixture] = Field(alias="partidas") - - -fixtures = [] -for round in range(1, 39): - url = base_url + f"{round:02}/partidas.json" - data = urllib.request.urlopen(url).read() - round = Round.model_validate_json(data) - for fixture in round.fixtures: - fixture.round = round.round - fixtures.extend(round.fixtures) -pprint(fixtures[0]) - -# %% [markdown] -""" -Let's consolidate these fixtures into a single DataFrame and then pivot them -into a long format: -""" - -# %% -# | label: data-wrangling-fixtures-long -fixtures = ( - pl.DataFrame(fixtures) - .rename({"home": "team", "away": "versus"}) - .with_columns(home=pl.lit(1)) -) -mirrored = fixtures.rename({"team": "versus", "versus": "team"}).with_columns( - home=pl.lit(0) -) -fixtures = pl.concat([fixtures, mirrored], how="diagonal") -pprint(fixtures) - -# %% [markdown] -""" -Finally, let's join this data to our dataset: -""" - -# %% -# | label: data-wrangling-fixtures-join -players = players.join(fixtures, on=["round", "team"], how="inner") -print_example(players, ["team", "versus", "home"]) - -# %% [markdown] -""" -### Aligning variables - -In our subsequent analysis, the `average` field will exclude the `score` from -the given round. Additionally, the `appreciation` field will be calculated in -relation to the round's `score`. -""" - -# %% -# | label: data-wrangling-lookahead-variables -players = players.with_columns( - average=pl.col("average").shift(1).over("player").fill_null(0.0), - appreciation=pl.col("appreciation").shift(-1).over("player").fill_null(0.0), -) -print_example(players, ["average", "value", "score", "appreciation"]) - -# %% [markdown] -r""" -## Team picking - -Now let's solve the problem of picking the best team a given market. Let $ -\mathcal{F}$ be the set of valid formations, then for each formation $f \in -\mathcal{F}$, solve: - -$$ -\begin{equation*} \begin{array}{ll@{}ll} -\text{maximize} & \displaystyle \hat{\mathbf{s}}^T \mathbf{x}, & \mathbf{x} \in \{\mathbf{0}, \mathbf{1}\} \\ -\text{subject to} -& \displaystyle \mathbf{v}^T \mathbf{x} \leq b \\ -& \displaystyle \mathbf{P}^T \mathbf{x} = f, \\ -\end{array} \end{equation*} -$$ - -where - -$\mathbf{x}$ is a variable vector of player picks in the market; -$\hat{\mathbf{s}}$ is the vector of predicted player scores in the market; -$b$ is our available budget for that round; -$\mathbf{P}$ is the matrix of dummy-encoded player formations in the market. - -Finally, take the solution with the highest objective. -""" - - -# %% -# | label: team-picking-formation -import numpy as np -import pulp - - -class Formation(BaseModel): - goalkeeper: int = Field(alias="gol") - defender: int = Field(alias="zag") - winger: int = Field(alias="lat") - midfielder: int = Field(alias="mei") - forward: int = Field(alias="ata") - coach: int = Field(alias="tec") - - -# %% -# | echo: true -# | label: team-picking-problem -class Problem(BaseModel): - scores: List[float] - values: List[float] - budget: float - positions: List[List[int]] - formations: List[Formation] - - def solve(self) -> List[pulp.LpSolution]: - formations = [list(f.model_dump().values()) for f in self.formations] - problems = [self.construct(f) for f in formations] - [p.solve(pulp.COIN(msg=False)) for p in problems] - objectives = [p.objective.value() for p in problems] - best = np.argmax(np.array(objectives)) - solution = problems[best] - variables = [v.value() for v in solution.variables()] - picks = np.array(variables) - return picks - - def construct(self, formation: List[int]) -> pulp.LpProblem: - n = len(self.scores) - m = len(formation) - problem = pulp.LpProblem("team_picking", pulp.LpMaximize) - indexes = ["pick_" + str(i).zfill(len(str(n))) for i in range(n)] - picks = [pulp.LpVariable(i, cat=pulp.const.LpBinary) for i in indexes] - problem += pulp.lpDot(picks, self.scores) - problem += pulp.lpDot(picks, self.values) <= self.budget - for i in range(m): - problem += pulp.lpDot(picks, self.positions[i]) == formation[i] - return problem - - -# %% [markdown] -r""" -### Backtesting - -By solving the team picking problem for all rounds, we can backtest our -performance in the season. Before backtesting, let's get the set of valid -formations $\mathcal{F}$: -""" - -# %% -# | label: team-picking-formations -from pydantic import RootModel - - -class MetaFormation(BaseModel): - id: int = Field(alias="esquema_id") - name: str = Field(alias="nome") - formation: Formation = Field(alias="posicoes") - - -class MetaFormations(RootModel): - root: List[MetaFormation] - - -url = base_url + "38/esquemas.json" -data = urllib.request.urlopen(url).read() -meta_formations = MetaFormations.model_validate_json(data).root -formations = [m.formation for m in meta_formations] -pprint(formations) - -# %% [markdown] -""" -Knowing our formation constraints, we're ready to backtest. Starting with a -budget of C$ 100, for each round let's: - -1. Predict each player's score based on their performance on previous rounds; -2. Pick the team with the best total score; -3. Add the sum of the team player's appreciation to our budget. -""" - -# %% -# | label: team-picking-backtest-import -from typing import Callable - - -# %% -# | echo: true -# | label: team-picking-backtest -def backtest( - players: pl.DataFrame, predict: Callable, initial_budget: float = 100.0 -) -> pl.DataFrame: - rounds = players.get_column("round").max() - budget = [None] * rounds - teams = [None] * rounds - budget[0] = initial_budget - for round in range(rounds): - if round > 0: - budget[round] = budget[round - 1] + appreciation - data = players.filter(pl.col("round") < round + 1) - candidates = players.filter(pl.col("round") == round + 1) - candidates = predict(data, candidates) - problem = Problem( - scores=candidates.get_column("prediction"), - values=candidates.get_column("value"), - positions=candidates.get_column("position").to_dummies(), - budget=budget[round], - formations=formations, - ) - picks = problem.solve() - team = candidates.filter(picks == 1) - teams[round] = team - appreciation = team.get_column("appreciation").sum() - teams = pl.concat(teams) - return teams - - -# %% [markdown] -""" - -Before exploring predictions, we'll begin with a few hypothetical backtests -using actual observed scores for team selection. Backtesting this strategy, this -is our team in the first round: -""" - - -# %% -# | label: team-picking-backtest-first-team -def predict_score(data: pl.DataFrame, candidates: pl.DataFrame) -> pl.DataFrame: - prediction = candidates.get_column("score") - candidates = candidates.with_columns(prediction=prediction) - return candidates - - -teams = backtest(players, predict_score) -pprint(teams.filter(pl.col("round") == 1).sort("position")) - -# %% [markdown] -""" -And we can plot out cumulative performance during the season: -""" - -# %% -# | label: team-picking-backtest-score -from matplotlib import style -import seaborn.objects as so - -from matplotlib import font_manager - - -so.Plot.config.theme.update(style.library["Solarize_Light2"]) -font_path = "../../assets/FiraCode-Regular.ttf" -font_manager.fontManager.addfont(font_path) -prop = font_manager.FontProperties(fname=font_path) -so.Plot.config.theme.update( - {"font.family": "sans-serif", "font.sans-serif": prop.get_name()} -) - - -def summarize(teams: pl.DataFrame, model: str) -> pl.DataFrame: - captains = ( - teams.filter(pl.col("position") != 6) - .filter(pl.col("prediction") == pl.col("prediction").over("round").max()) - .with_columns(captain=2.0) - .select("round", "player", "captain") - ) # TODO review - campaign = ( - teams.join(captains, on=["round", "player"], how="left") - .with_columns(score=pl.col("score") * pl.col("captain").fill_null(1.0)) - .group_by("round") - .agg(score=pl.col("score").sum()) - .with_columns(score=pl.col("score").cumsum()) - ) - score = campaign.get_column("score").tail(1).round(2).item() - label = f"{model} ({score})" - campaign = campaign.with_columns(label=pl.lit(label)) - return campaign - - -def add_line( - fig: so.Plot, - campaign: pl.DataFrame, - linestyle: str = "solid", - valign: str = "center_baseline", -) -> so.Plot: - text = campaign.tail(1) - fig = fig.add( - so.Line(linestyle=linestyle), - data=campaign, - legend=False, - ).add( - so.Text({"clip_on": False}, halign="left", valign=valign), - data=text, - ) - return fig - - -season = summarize(teams, "Score") -fig = so.Plot(season, x="round", y="score", color="label", text="label").label( - x="Round", y="Cumulative score" -) -fig = add_line(fig, season) -fig - -# %% [markdown] -""" -This might seem like a perfect campaign at first, but it's possible that, early -in the season, we didn't have enough budget to pick the best scoring teams. To -test this hypothesis, we backtest the same strategy with unlimited budget from -the start: -""" - - -# %% -# | label: team-picking-backtest-score-unlimited-budget -# TODO use kwargs -def add_backtest( - fig: so.Plot, - teams: pl.DataFrame, - model: str, - valign: str = "center_baseline", -) -> so.Plot: - campaign = summarize(teams, model) - fig = add_line(fig, campaign, valign=valign) - return fig - - -teams = backtest(players, predict_score, initial_budget=1000.0) -add_backtest(fig, teams, "Score with unlimited budget", valign="bottom") - -# %% [markdown] -""" -Both runs are nearly identical, which is evidence that focusing on appreciation -is not so important if we have accurate predictions for the scores. If we -predict scores perfectly, we get a near perfect run. - -To put our backtests into perspective, -[the 2022 season champion had a total score of 3434.37](https://ge.globo.com/cartola/noticia/2022/12/03/cartola-2022-com-larga-vantagem-mosquito-bar-8-vence-liga-premiada-meliuz-e-fatura-r-20-mil.ghtml). -This is very impressive and not very far from the near perfect run. -""" - -# %% -# | label: team-picking-backtest-champion -champion = players.unique("round").with_columns( - score=pl.lit(3434.37), label=pl.lit("Champion (3434.37)") -) -fig = add_line(fig, champion, linestyle="dashed") -fig - - -# %% [markdown] -r""" -## Score prediction - -For each round, we must predict $\hat{s}$, the vector of score predictions, -using data from previous rounds. - -However, during the first round, we don't have any previous data to train our -model. In this case, we need to include prior information. One way to do that -would be to use data from previous seasons. However, we know a variable where -this information is already encoded: the player `value`. Each season starts with -players valued according to their past performance. Knowing this, all our models -start with $\hat{s} = v$ in the first round. - -Let's use Bambi [@Capretto2022] and its default priors to fit our models. We -won't delve into convergence diagnostics, since we are more interested in the -average of the predictive posteriors and the backtest itself is measure of the -prediction quality. - -One question that arises here is: why not use non-parametric models such -as gradient boosted trees or neural nets? After some experimentation, I -concluded they are not a good fit for this problem: either because they -assume independence between observations, or because they are too data hungry. -Also, tuning these models for backtests might lead us into a rabbit hole -[@Bailey2013]. - -### Player average - -$$ -\begin{align*} -\mathbf{\hat{s}} = \mathbf{Z} \mathbf{\beta} \\ -\mathbf{s} \sim N(\mathbf{\hat{s}}, \sigma), -\end{align*} -$$ - -where -$\mathbf{Z}$ is a dummy-encoded matrix of players; -$\mathbf{\beta}$ is a vector of parameters for each player. - -In this model, $\mathbf{\beta}$ is simply a vector of player averages. Let's -also consider that players that show up in the middle of the season have an -average of zero before their first round. This will be our baseline model. -""" - - -# %% -# | label: score-prediction-player-average -def predict_average(data: pl.DataFrame, candidates: pl.DataFrame): - candidates = candidates.with_columns( - prediction=pl.when(pl.col("round") == 1) - .then(pl.col("value")) - .otherwise(pl.col("average")) - ) - return candidates - - -teams = backtest(players, predict_average) -fig = add_backtest(fig, teams, "Player average") -fig - -# %% [markdown] -r""" -### Player random effects - -$$ -\begin{align*} -\mathbf{\hat{s}} = \alpha + \mathbf{Z} \mathbf{b} \\ -\mathbf{b} \sim N(0, \sigma_b), -\end{align*} -$$ - -where -$\alpha$ is an intercept and -$\mathbf{b}$ is a vector of player random effects. - -This model performs significantly better than the average model, possibly -because of the partial pooling between the random effects, that pulls large -effects towards the overall mean [@clark2019shrinkage]. In our dataset, it's -common for players that played one or two games to have large averages by -chance. -""" - -# %% -# | label: score-prediction-player-random-effects -# | message: false -# | warning: false -from functools import partial -import arviz as az -import bambi as bmb - - -def predict_model( - data: pl.DataFrame, candidates: pl.DataFrame, **kwargs -) -> pl.DataFrame: - if data.height == 0: - predictions = candidates.get_column("value") - else: - model = bmb.Model(data=data.to_pandas(), **kwargs) - inference = model.fit( - inference_method="nuts_numpyro", random_seed=37, progressbar=False - ) - predictions = model.predict( - inference, - data=candidates.to_pandas(), - sample_new_groups=True, - inplace=False, - ) - summary = az.summary(predictions, var_names=["score_mean"]) - predictions = summary["mean"].values - candidates = candidates.with_columns(prediction=pl.lit(predictions)) - return candidates - - -predict_random_effects = partial(predict_model, formula="score ~ (1 | player)") -teams = backtest(players, predict_random_effects) -fig = add_backtest(fig, teams, "Random effects") -fig - -# %% [markdown] -r""" -### Fixture mixed effects - -$$ -\mathbf{\hat{s}} = \alpha + \mathbf{X} \mathbf{\beta} + \mathbf{Z} \mathbf{b}, -$$ - -where -$\mathbf{X}$ is a matrix of the dummy-encoded fixture variables: the player - `team`, whether they are playing at `home`, and their `adversary` team - variables; -$\mathbf{\beta}$ is a vector of fixed effects. - -This model brings more context to our predictions. It also provides a reasonable -way to predict a new player, by setting their $b = 0$ (the mean of the random -effects). However, it does not improve significantly over our random effects -model. -""" - -# %% -# | label: score-prediction-fixture-mixed-effects -# | message: false -# | warning: false -predict_mixed_effects = partial( - predict_model, - formula="score ~ home + C(team) + C(versus) + (1 | player)", -) -teams = backtest(players, predict_mixed_effects) -fig = add_backtest(fig, teams, "Mixed effects") -fig - -# %% [markdown] -""" -## Conclusion - -We developed a comprehensive framework for the fantasy football team picking -problem. There are more ideas we could explore to improve our chances of -winning: - -* enriching our data and models with player scouts; -* including more information in our priors; -* testing strategies that balance predicted score and appreciation; -* further model diagnostics. - -However, I suppose expert human player predictions have a certain edge over -those of hobbyist statistical models in fantasy leagues, due to the fact that -there are all sorts of relevant data unavailable in public datasets. - -At least, this seems to be the case for brazilian soccer, also known as "a -little box of surprises". -""" diff --git a/blog/fantasy-football/pyproject.toml b/blog/fantasy-football/pyproject.toml index 50fd899..891a272 100644 --- a/blog/fantasy-football/pyproject.toml +++ b/blog/fantasy-football/pyproject.toml @@ -5,17 +5,21 @@ authors = [ { name = "Luís Assunção", email = "assuncaolfi@gmail.com" } ] dependencies = [ - "pydantic~=2.3.0", - "polars~=0.19.3", + "bambi~=0.13.0", + "blog @ git+https://github.com/assuncaolfi/site/", + "jupyter~=1.0.0", + "numpyro~=0.13.2", "numpy~=1.24.4", + "polars~=0.19.3", "pulp~=2.7.0", - "rich~=13.6.0", - "seaborn~=0.13.0", - "bambi~=0.13.0", "pyarrow~=14.0.1", - "numpyro~=0.13.2", + "pydantic~=2.3.0", + "seaborn~=0.13.0", ] -requires-python = ">= 3.8" +requires-python = ">= 3.11" + +[project.scripts] +hello = "fantasy_football:hello" [build-system] requires = ["hatchling"] @@ -23,10 +27,10 @@ build-backend = "hatchling.build" [tool.rye] managed = true -dev-dependencies = [ - "jupyter~=1.0.0", - "jupyter-cache~=0.6.1", -] +dev-dependencies = [] [tool.hatch.metadata] allow-direct-references = true + +[tool.hatch.build.targets.wheel] +packages = ["src/fantasy_football"] diff --git a/blog/fantasy-football/requirements-dev.lock b/blog/fantasy-football/requirements-dev.lock index 3f8c748..1fd3d19 100644 --- a/blog/fantasy-football/requirements-dev.lock +++ b/blog/fantasy-football/requirements-dev.lock @@ -5,158 +5,149 @@ # pre: false # features: [] # all-features: false +# with-sources: false -e file:. -annotated-types==0.5.0 -anyio==4.0.0 +annotated-types==0.6.0 +anyio==4.2.0 appnope==0.1.3 argon2-cffi==23.1.0 argon2-cffi-bindings==21.2.0 -arrow==1.2.3 -arviz==0.16.1 -asttokens==2.4.0 +arrow==1.3.0 +arviz==0.17.0 +asttokens==2.4.1 async-lru==2.0.4 -attrs==23.1.0 -babel==2.12.1 -backcall==0.2.0 +attrs==23.2.0 +babel==2.14.0 bambi==0.13.0 -beautifulsoup4==4.12.2 -bleach==6.0.0 +beautifulsoup4==4.12.3 +bleach==6.1.0 +blog @ git+https://github.com/assuncaolfi/site/ cachetools==5.3.2 -certifi==2023.7.22 -cffi==1.15.1 -charset-normalizer==3.2.0 -click==8.1.7 +certifi==2023.11.17 +cffi==1.16.0 +charset-normalizer==3.3.2 cloudpickle==3.0.0 -comm==0.1.4 +comm==0.2.1 cons==0.4.6 -contourpy==1.1.1 +contourpy==1.2.0 cycler==0.12.1 debugpy==1.8.0 decorator==5.1.1 defusedxml==0.7.1 etuples==0.3.9 -executing==1.2.0 -fastjsonschema==2.18.0 +executing==2.0.1 +fastjsonschema==2.19.1 fastprogress==1.0.3 filelock==3.13.1 -fonttools==4.43.1 +fonttools==4.47.2 formulae==0.5.1 fqdn==1.5.1 graphviz==0.20.1 h5netcdf==1.3.0 h5py==3.10.0 -idna==3.4 -importlib-metadata==6.8.0 -ipykernel==6.25.2 -ipython==8.15.0 -ipython-genutils==0.2.0 +idna==3.6 +ipykernel==6.29.0 +ipython==8.20.0 ipywidgets==8.1.1 isoduration==20.11.0 -jax==0.4.20 -jaxlib==0.4.20 -jedi==0.19.0 -jinja2==3.1.2 +jax==0.4.23 +jaxlib==0.4.23 +jedi==0.19.1 +jinja2==3.1.3 json5==0.9.14 jsonpointer==2.4 -jsonschema==4.19.1 -jsonschema-specifications==2023.7.1 +jsonschema==4.21.1 +jsonschema-specifications==2023.12.1 jupyter==1.0.0 -jupyter-cache==0.6.1 -jupyter-client==8.3.1 +jupyter-client==8.6.0 jupyter-console==6.6.3 -jupyter-core==5.3.1 -jupyter-events==0.7.0 -jupyter-lsp==2.2.0 -jupyter-server==2.7.3 -jupyter-server-terminals==0.4.4 -jupyterlab==4.0.6 -jupyterlab-pygments==0.2.2 -jupyterlab-server==2.25.0 +jupyter-core==5.7.1 +jupyter-events==0.9.0 +jupyter-lsp==2.2.2 +jupyter-server==2.12.5 +jupyter-server-terminals==0.5.2 +jupyterlab==4.0.11 +jupyterlab-pygments==0.3.0 +jupyterlab-server==2.25.2 jupyterlab-widgets==3.0.9 kiwisolver==1.4.5 logical-unification==0.4.6 -markdown-it-py==3.0.0 -markupsafe==2.1.3 -matplotlib==3.8.0 +markupsafe==2.1.4 +matplotlib==3.8.2 matplotlib-inline==0.1.6 -mdurl==0.1.2 minikanren==1.0.3 -mistune==3.0.1 -ml-dtypes==0.3.1 +mistune==3.0.2 +ml-dtypes==0.3.2 multipledispatch==1.0.0 -nbclient==0.7.4 -nbconvert==7.8.0 +nbclient==0.9.0 +nbconvert==7.14.2 nbformat==5.9.2 -nest-asyncio==1.5.8 -notebook==7.0.4 +nest-asyncio==1.6.0 +notebook==7.0.7 notebook-shim==0.2.3 numpy==1.24.4 numpyro==0.13.2 opt-einsum==3.3.0 -overrides==7.4.0 -packaging==23.1 -pandas==2.1.1 -pandocfilters==1.5.0 +overrides==7.6.0 +packaging==23.2 +pandas==2.2.0 +pandocfilters==1.5.1 parso==0.8.3 -pexpect==4.8.0 -pickleshare==0.7.5 -pillow==10.1.0 -platformdirs==3.10.0 -polars==0.19.3 -prometheus-client==0.17.1 -prompt-toolkit==3.0.39 -psutil==5.9.5 +pexpect==4.9.0 +pillow==10.2.0 +platformdirs==4.1.0 +polars==0.19.19 +prometheus-client==0.19.0 +prompt-toolkit==3.0.43 +psutil==5.9.8 ptyprocess==0.7.0 pulp==2.7.0 pure-eval==0.2.2 -pyarrow==14.0.1 +pyarrow==14.0.2 pycparser==2.21 pydantic==2.3.0 pydantic-core==2.6.3 -pygments==2.16.1 -pymc==5.9.2 +pygments==2.17.2 +pymc==5.10.3 pyparsing==3.1.1 -pytensor==2.17.4 +pytensor==2.18.6 python-dateutil==2.8.2 python-json-logger==2.0.7 pytz==2023.3.post1 pyyaml==6.0.1 -pyzmq==25.1.1 -qtconsole==5.4.4 -qtpy==2.4.0 -referencing==0.30.2 +pyzmq==25.1.2 +qtconsole==5.5.1 +qtpy==2.4.1 +referencing==0.32.1 requests==2.31.0 rfc3339-validator==0.1.4 rfc3986-validator==0.1.1 -rich==13.6.0 -rpds-py==0.10.3 -scipy==1.11.3 -seaborn==0.13.0 +rpds-py==0.17.1 +scipy==1.12.0 +seaborn==0.13.1 send2trash==1.8.2 six==1.16.0 sniffio==1.3.0 soupsieve==2.5 -sqlalchemy==2.0.21 -stack-data==0.6.2 -tabulate==0.9.0 -terminado==0.17.1 +stack-data==0.6.3 +terminado==0.18.0 tinycss2==1.2.1 toolz==0.12.0 -tornado==6.3.3 +tornado==6.4 tqdm==4.66.1 -traitlets==5.10.0 -typing-extensions==4.8.0 -tzdata==2023.3 +traitlets==5.14.1 +types-python-dateutil==2.8.19.20240106 +typing-extensions==4.9.0 +tzdata==2023.4 uri-template==1.3.0 -urllib3==2.0.5 -wcwidth==0.2.6 +urllib3==2.1.0 +wcwidth==0.2.13 webcolors==1.13 webencodings==0.5.1 -websocket-client==1.6.3 +websocket-client==1.7.0 widgetsnbextension==4.0.9 -xarray==2023.10.1 -xarray-einstats==0.6.0 -zipp==3.17.0 +xarray==2024.1.0 +xarray-einstats==0.7.0 # The following packages are considered to be unsafe in a requirements file: -setuptools==68.2.2 +setuptools==69.0.3 diff --git a/blog/fantasy-football/requirements.lock b/blog/fantasy-football/requirements.lock index 8088b5f..1fd3d19 100644 --- a/blog/fantasy-football/requirements.lock +++ b/blog/fantasy-football/requirements.lock @@ -5,60 +5,149 @@ # pre: false # features: [] # all-features: false +# with-sources: false -e file:. -annotated-types==0.5.0 -arviz==0.16.1 +annotated-types==0.6.0 +anyio==4.2.0 +appnope==0.1.3 +argon2-cffi==23.1.0 +argon2-cffi-bindings==21.2.0 +arrow==1.3.0 +arviz==0.17.0 +asttokens==2.4.1 +async-lru==2.0.4 +attrs==23.2.0 +babel==2.14.0 bambi==0.13.0 +beautifulsoup4==4.12.3 +bleach==6.1.0 +blog @ git+https://github.com/assuncaolfi/site/ cachetools==5.3.2 +certifi==2023.11.17 +cffi==1.16.0 +charset-normalizer==3.3.2 cloudpickle==3.0.0 +comm==0.2.1 cons==0.4.6 -contourpy==1.1.1 +contourpy==1.2.0 cycler==0.12.1 +debugpy==1.8.0 +decorator==5.1.1 +defusedxml==0.7.1 etuples==0.3.9 +executing==2.0.1 +fastjsonschema==2.19.1 fastprogress==1.0.3 filelock==3.13.1 -fonttools==4.43.1 +fonttools==4.47.2 formulae==0.5.1 +fqdn==1.5.1 graphviz==0.20.1 h5netcdf==1.3.0 h5py==3.10.0 -jax==0.4.20 -jaxlib==0.4.20 +idna==3.6 +ipykernel==6.29.0 +ipython==8.20.0 +ipywidgets==8.1.1 +isoduration==20.11.0 +jax==0.4.23 +jaxlib==0.4.23 +jedi==0.19.1 +jinja2==3.1.3 +json5==0.9.14 +jsonpointer==2.4 +jsonschema==4.21.1 +jsonschema-specifications==2023.12.1 +jupyter==1.0.0 +jupyter-client==8.6.0 +jupyter-console==6.6.3 +jupyter-core==5.7.1 +jupyter-events==0.9.0 +jupyter-lsp==2.2.2 +jupyter-server==2.12.5 +jupyter-server-terminals==0.5.2 +jupyterlab==4.0.11 +jupyterlab-pygments==0.3.0 +jupyterlab-server==2.25.2 +jupyterlab-widgets==3.0.9 kiwisolver==1.4.5 logical-unification==0.4.6 -markdown-it-py==3.0.0 -matplotlib==3.8.0 -mdurl==0.1.2 +markupsafe==2.1.4 +matplotlib==3.8.2 +matplotlib-inline==0.1.6 minikanren==1.0.3 -ml-dtypes==0.3.1 +mistune==3.0.2 +ml-dtypes==0.3.2 multipledispatch==1.0.0 +nbclient==0.9.0 +nbconvert==7.14.2 +nbformat==5.9.2 +nest-asyncio==1.6.0 +notebook==7.0.7 +notebook-shim==0.2.3 numpy==1.24.4 numpyro==0.13.2 opt-einsum==3.3.0 +overrides==7.6.0 packaging==23.2 -pandas==2.1.1 -pillow==10.1.0 -polars==0.19.3 +pandas==2.2.0 +pandocfilters==1.5.1 +parso==0.8.3 +pexpect==4.9.0 +pillow==10.2.0 +platformdirs==4.1.0 +polars==0.19.19 +prometheus-client==0.19.0 +prompt-toolkit==3.0.43 +psutil==5.9.8 +ptyprocess==0.7.0 pulp==2.7.0 -pyarrow==14.0.1 +pure-eval==0.2.2 +pyarrow==14.0.2 +pycparser==2.21 pydantic==2.3.0 pydantic-core==2.6.3 -pygments==2.16.1 -pymc==5.9.2 +pygments==2.17.2 +pymc==5.10.3 pyparsing==3.1.1 -pytensor==2.17.4 +pytensor==2.18.6 python-dateutil==2.8.2 +python-json-logger==2.0.7 pytz==2023.3.post1 -rich==13.6.0 -scipy==1.11.3 -seaborn==0.13.0 +pyyaml==6.0.1 +pyzmq==25.1.2 +qtconsole==5.5.1 +qtpy==2.4.1 +referencing==0.32.1 +requests==2.31.0 +rfc3339-validator==0.1.4 +rfc3986-validator==0.1.1 +rpds-py==0.17.1 +scipy==1.12.0 +seaborn==0.13.1 +send2trash==1.8.2 six==1.16.0 +sniffio==1.3.0 +soupsieve==2.5 +stack-data==0.6.3 +terminado==0.18.0 +tinycss2==1.2.1 toolz==0.12.0 +tornado==6.4 tqdm==4.66.1 -typing-extensions==4.8.0 -tzdata==2023.3 -xarray==2023.10.1 -xarray-einstats==0.6.0 +traitlets==5.14.1 +types-python-dateutil==2.8.19.20240106 +typing-extensions==4.9.0 +tzdata==2023.4 +uri-template==1.3.0 +urllib3==2.1.0 +wcwidth==0.2.13 +webcolors==1.13 +webencodings==0.5.1 +websocket-client==1.7.0 +widgetsnbextension==4.0.9 +xarray==2024.1.0 +xarray-einstats==0.7.0 # The following packages are considered to be unsafe in a requirements file: -setuptools==68.2.2 +setuptools==69.0.3 diff --git a/blog/fantasy-football/src/fantasy_football/__init__.py b/blog/fantasy-football/src/fantasy_football/__init__.py new file mode 100644 index 0000000..8c7debe --- /dev/null +++ b/blog/fantasy-football/src/fantasy_football/__init__.py @@ -0,0 +1,2 @@ +def hello(): + return "Hello from fantasy-football!" diff --git a/blog/non-monotonic/.ipynb_checkpoints/index-checkpoint.ipynb b/blog/non-monotonic/.ipynb_checkpoints/index-checkpoint.ipynb deleted file mode 100644 index 10abd06..0000000 --- a/blog/non-monotonic/.ipynb_checkpoints/index-checkpoint.ipynb +++ /dev/null @@ -1,642 +0,0 @@ -{ - "cells": [ - { - "cell_type": "raw", - "id": "f1aac84b", - "metadata": {}, - "source": [ - "---\n", - "title: 'Additive aging curve'\n", - "date: today\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "c40e21d3", - "metadata": {}, - "source": [ - "::: {.callout-warning}\n", - "This post is a draft.\n", - ":::\n", - "\n", - "Recently, I helped design an experiment measuring a binary response against a\n", - "continuous variable. If the user abandoned their cart at time zero, then we\n", - "delayed for a variable number of minutes before reminding them to finish their\n", - "purchase. The delay has a non-monotonic relationship to the response:\n", - "as the delay increases, so does the purchase rate; then the rate peaks; and finally it decreases.\n", - "\n", - "Causally, we may decompose this process into two: as the delay increases,\n", - "the user 1) becomes more available for and 2) loses interest in purchasing\n", - "the product. This is a common phenomena in different time-based scenarios. In\n", - "sports, the \"aging curve\" refers to how a player's performance increases with\n", - "age, then decreases. As the player gets older, they get 1) better at the sport\n", - "and 2) physically weaker.\n", - "\n", - "Andrew Gelman wrote about this a couple of times in his blog: see his posts\n", - "from [2018](https://statmodeling.stat.columbia.edu/2018/09/07/bothered-non-monotonicity-heres-one-quick-trick-make-happy/)\n", - "and [2023](https://statmodeling.stat.columbia.edu/2023/01/01/how-to-model-a-non-monotonic-relation/), \n", - "where Gelman suggests modeling these processes using an additive function like:\n", - "\n", - "$$g(x) = g_1(x) + g_2(x),$$\n", - "\n", - "where \n", - "$g_1(x)$ is a monotonically increasing function with a right asymptote; and \n", - "$g_2(x)$ is a monotonically decreasing function with a left asymptote.\n", - "\n", - "In this post, we'll analyse an experimental dataset by fitting and comparing\n", - "three different models: a non-parametric bootstrap, a semi-parametric spline\n", - "and a fully parametric decomposable curve like $g(x)$.\n", - "\n", - "## The Digit Span test\n", - "\n", - "The motivation for Gelman's 2018 post was a study relating age to peak cognitive \n", - "functioning [@Hartshorne2015]. According to the study, one of their experiments\n", - "was a large scale online experimentation platform:\n", - "\n", - "> Participants in Experiment 2 (N = 10,394; age range = 10–69 years old) [...]\n", - "> were visitors to TestMyBrain.org, who took part in experiments in order to\n", - "> contribute to scientific research and in exchange for performance-related\n", - "> feedback. [...] We continued data collection for each experiment for approximately\n", - "> 1 year, sufficient to obtain around 10,000 participants, which allowed fine-grained\n", - "> age-of-peak-performance analysis.\n", - "\n", - "The dataset for Experiment 2 is available online [@Germine_Hartshorne_2016] and \n", - "includes results of the Digit Span verbal working memory test, part of the Wechsler \n", - "Adult Intelligence Scale (WAIS) and Wechsler Memory Scale (WMS) supertests. In the \n", - "Digit Span test, subjects must repeat lists of digits, either in the same or reversed \n", - "order.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "digit-span", - "metadata": {}, - "outputs": [], - "source": [ - "# | label: digit-span\n", - "import polars as pl\n", - "\n", - "experiment = (\n", - " pl.read_csv(\"data/experiment-2.csv\")\n", - " .with_columns(digit_span=pl.col(\"DigitSpan\"))\n", - " .select(\"age\", \"digit_span\")\n", - " .filter(pl.col(\"age\").is_between(10, 70))\n", - " .with_columns(\n", - " y=(pl.col(\"digit_span\") - pl.col(\"digit_span\").mean())\n", - " / pl.col(\"digit_span\").std()\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "2499183c", - "metadata": {}, - "source": [ - "Let's plot the relationship between age and Digit Span performance:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "digit-span-plot", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": { - "image/png": { - "height": 378.25, - "width": 509.15 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "# | label: digit-span-plot\n", - "from blog import theme\n", - "import seaborn.objects as so\n", - "\n", - "theme.set()\n", - "fig = so.Plot(experiment.to_pandas(), x=\"age\", y=\"y\").label(\n", - " x=\"Age (years)\", y=\"Performance (z-score)\", title=\"Digit Span\"\n", - ")\n", - "fig.add(so.Dots())" - ] - }, - { - "cell_type": "markdown", - "id": "85b28969", - "metadata": {}, - "source": [ - "Visually, it's still unclear if this relationship follows an aging curve, but we'll get back to this matter in the next section.\n", - "\n", - "## Bootstrap estimates\n", - "\n", - "In the original paper, the authors describe a bootstrap resampling procedure\n", - "to estimate the distribution of ages of peak performance:\n", - "\n", - "> Estimates and standard errors for age of peak performance were calculated using\n", - "> a bootstrap resampling procedure identical to the one used in Experiment 1\n", - "> but applied to raw performance data. To dampen noise, we smoothed means for each\n", - "> age using a moving 3-year window prior to identifying age of peak performance\n", - "> in each sample. Other methods of dampening noise provide similar results.\n", - "\n", - "Let's decompose this method (as I understand it) into steps:\n", - "\n", - "1. With replacement, sample $n$ observations from the dataset;\n", - "2. Calculate the mean performance for each sample and age;\n", - "3. Repeat steps 1 and 2 $m$ times to get multiple samples;\n", - "4. Sort each sample by age and smooth age means using a 3-year rolling average;\n", - "5. Find the age of peak performance for each sample.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "bootstrap", - "metadata": {}, - "outputs": [], - "source": [ - "# | label: bootstrap\n", - "# | echo: true\n", - "import polars as pl\n", - "\n", - "n = experiment.height\n", - "m = 10000\n", - "nm = n * m\n", - "seed = 37\n", - "samples = (\n", - " experiment.sample(nm, with_replacement=True, seed=seed)\n", - " .with_columns(sample=pl.arange(1, nm + 1) % m)\n", - " .group_by(\"sample\", \"age\")\n", - " .agg(mean=pl.col(\"y\").mean())\n", - " .sort(\"sample\", \"age\")\n", - " .with_columns(smoothed_mean=pl.col(\"mean\").rolling_mean(3).over(\"sample\"))\n", - ")\n", - "peak_performance = samples.group_by(\"sample\").agg(\n", - " age=pl.col(\"age\").get(pl.col(\"smoothed_mean\").arg_max())\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "06107126", - "metadata": {}, - "source": [ - "This yields the following bootstrap distribution of ages of peak performance:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "bootstrap-distribution", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": { - "image/png": { - "height": 378.25, - "width": 509.15 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "# | label: bootstrap-distribution\n", - "distribution = (\n", - " peak_performance.group_by(\"age\")\n", - " .agg(count=pl.count())\n", - " .with_columns(p=pl.col(\"count\") / pl.col(\"count\").sum())\n", - ")\n", - "so.Plot(distribution, x=\"age\", y=\"p\").add(so.Bars()).label(\n", - " title=\"Bootstrap distribution of smoothed means\",\n", - " x=\"Age of peak performance (years)\",\n", - " y=\"Mass\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "b1cdbbd1", - "metadata": {}, - "source": [ - "This distribution suggests two important things:\n", - "\n", - "1. The most probable age of peak performance is, by far, 33;\n", - "2. There is a non-negligible probability that the age of peak performance happens in the early 20s, but a negligible probability that it happens in the late 20s.\n", - "\n", - "Thing 2 certainly deserves attention. This is possibly caused by a confound variable or some measuring error, but I won't investigate this any further. Instead, let's get back to estimating curves. We will use the samples from step 4 to summarize the distribution of mean performances. For each age, we calculate the mean and 90% interquantile range, yielding a nonparametric curve:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "bootstrap-curve", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": { - "image/png": { - "height": 378.25, - "width": 509.15 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "# | label: bootstrap-curve\n", - "def add_bands(fig: so.Plot, summary: pl.DataFrame, title: str):\n", - " fig = (\n", - " fig.add(so.Line(), data=summary, x=\"age\", y=\"mean\")\n", - " .add(\n", - " so.Band(),\n", - " data=summary,\n", - " x=\"age\",\n", - " y=\"mean\",\n", - " ymin=\"ymin\",\n", - " ymax=\"ymax\",\n", - " )\n", - " .label(title=title)\n", - " )\n", - " return fig\n", - "\n", - "\n", - "curve = samples.group_by(\"age\").agg(\n", - " mean=pl.col(\"smoothed_mean\").mean(),\n", - " ymin=pl.col(\"smoothed_mean\").quantile(0.05),\n", - " ymax=pl.col(\"smoothed_mean\").quantile(0.9),\n", - ")\n", - "add_bands(fig, curve, \"Bootstrap curve\")" - ] - }, - { - "cell_type": "markdown", - "id": "e84d94ad", - "metadata": {}, - "source": [ - "This figure is analogue to figure ... in the paper. Since this is an entirely empirical curve, there isn't much to interpret here (maybe unitary changes?). However, the curve shape indicates an aging-curve-likeness.\n", - "\n", - "## Penalized splines\n", - "\n", - "Splines are wiggly curves...\n", - "\n", - "$$\n", - "\\begin{align}\n", - "g(x) &= \\alpha + Z \\bf{b} \\\\\n", - "y &\\sim \\mathrm{Normal}(g(x), \\sigma) \\\\\n", - "\\alpha &\\sim \\mathrm{Student}(3, 0, 0.1) \\\\\n", - "\\sigma &\\sim \\mathrm{HalfCauchy}(1)\n", - "\\end{align}\n", - "$$\n", - "\n", - "Polynomials have runge swings...\n", - "\n", - "We could make assumptions about the data generating process to help us pick the number of knots. Instead, let's pick an arbitrary large number of knots (say, 15) and let the model itself learn how wiggly the curve should be.\n", - "\n", - "$$\n", - "\\begin{align}\n", - "b &= \\tau \\bf{z} \\\\\n", - "\\tau &\\sim \\mathrm{HalfCauchy}(1) \\\\\n", - "\\bf{z} &\\sim \\mathrm{Normal}(0, 1)\n", - "\\end{align}\n", - "$$\n", - "\n", - "https://www.pymc.io/projects/examples/en/latest/howto/spline.html \n", - "https://www.tjmahr.com/random-effects-penalized-splines-same-thing/ \n", - "https://elevanth.org/blog/2017/09/07/metamorphosis-multilevel-model/ \n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "spline-basis", - "metadata": {}, - "outputs": [], - "source": [ - "# | label: spline-basis\n", - "from patsy import dmatrix\n", - "import numpy as np\n", - "\n", - "\n", - "def b_spline(x: pl.Series) -> np.typing.NDArray:\n", - " B = dmatrix(\n", - " \"bs(x, df=15, degree=3, include_intercept=True) - 1\",\n", - " {\"x\": x},\n", - " )\n", - " B = np.asarray(B, order=\"F\")\n", - " return B\n", - "\n", - "\n", - "x = experiment.get_column(\"age\")\n", - "Z = b_spline(x)\n", - "y = experiment.get_column(\"y\")\n", - "x_range = np.arange(x.min(), x.max() + 1)\n", - "Z_range = b_spline(x_range)\n", - "# B_range" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "spline-model", - "metadata": {}, - "outputs": [], - "source": [ - "# | label: spline-model\n", - "# | echo: true\n", - "# | warning: false\n", - "import pymc as pm\n", - "\n", - "with pm.Model() as spline:\n", - " Z = pm.ConstantData(\"Z\", Z)\n", - " α = pm.StudentT(\"α\", 3, 0, sigma=0.1)\n", - " τ = pm.HalfCauchy(\"τ\", 1)\n", - " z = pm.Normal(\"z\", 0, 1, size=Z.shape[1])\n", - " b = pm.Deterministic(\"b\", τ * z)\n", - " μ = pm.Deterministic(\"μ\", α + pm.math.dot(Z, b.T))\n", - " σ = pm.HalfCauchy(\"σ\", 1)\n", - " pm.Normal(\"y\", μ, σ, observed=y)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "spline-sample", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Sequential sampling (4 chains in 1 job)\n", - "NUTS: [α, τ, z, σ]\n", - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 47 seconds.\n" - ] - } - ], - "source": [ - "# | label: spline-sample\n", - "with spline:\n", - " prediction = pm.Deterministic(\"prediction\", α + pm.math.dot(Z_range, b.T))\n", - " draws = pm.sample(chains=4, cores=1, progressbar=False, random_seed=seed)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "spline-plot", - "metadata": { - "0": "s", - "1": "p", - "10": "t", - "2": "l", - "3": "i", - "4": "n", - "5": "e", - "6": "-", - "7": "p", - "8": "l", - "9": "o" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": { - "image/png": { - "height": 378.25, - "width": 509.15 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "# | label: spline-plot\n", - "import arviz as az\n", - "\n", - "\n", - "def add_model(fig: so.Plot, array: az.InferenceData, title: str, hdi_prob: float = 0.9):\n", - " summary = az.summary(array, hdi_prob=hdi_prob)\n", - " summary[\"age\"] = x_range\n", - " summary[\"ymin\"] = summary[\"hdi_5%\"]\n", - " summary[\"ymax\"] = summary[\"hdi_95%\"]\n", - " fig = add_bands(fig, summary, title)\n", - " return fig\n", - "\n", - "\n", - "# az.plot_trace(traces, var_names=[\"α\", \"τ\", \"b\", \"σ\"])\n", - "# az.plot_forest(traces, var_names=[\"α\", \"b\"])\n", - "add_model(fig, draws.posterior.prediction, \"Spline\")" - ] - }, - { - "cell_type": "markdown", - "id": "db558d0d", - "metadata": {}, - "source": [ - "Splines are good interpolation tools... \n", - "\"when, where and how things change\"... https://www.youtube.com/watch?v=Zxokd_Eqrcg&t=506s \n", - "However, it's not a good idea to extrapolate... \n", - "\n", - "## Two component function\n", - "\n", - "All intervals are 80% credibility...\n", - "\n", - "$$\n", - "\\begin{align}\n", - "g_1(x) &= \\alpha_1 + \\beta_1 \\exp(-\\lambda_1 x) \\\\\n", - "g_2(x) &= \\alpha_2 + \\beta_2 (1 - \\exp(-\\lambda_2 x)) \\\\\n", - "g(x) &= g_1(x) + g_2(x) \\\\\n", - " &= \\alpha + \\beta_1 \\exp(-\\lambda_1 x) + \\beta_2 (1 - \\exp(-\\lambda_2 x))\n", - "\\end{align}\n", - "$$\n", - "\n", - "$$\n", - "\\begin{align}\n", - "y &\\sim \\mathrm{Normal}(g(x), \\sigma) \\\\\n", - "\\alpha &\\sim \\mathrm{Normal}(0, 2) \\\\\n", - "\\lambda &\\sim \\mathrm{Exponential}(0.01) \\\\\n", - "\\sigma &\\sim \\mathrm{Exponential}(1) \\\\\n", - "\\end{align}\n", - "$$\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "decomposable-models-additive", - "metadata": {}, - "outputs": [], - "source": [ - "# | label: decomposable-models-additive\n", - "# | echo: true\n", - "import pymc as pm\n", - "\n", - "\n", - "def g(x):\n", - " y = α[0] * pm.math.exp(-λ[0] * x) + α[1] + α[2] * (1 - pm.math.exp(-λ[1] * x))\n", - " return y\n", - "\n", - "\n", - "with pm.Model() as model:\n", - " x = pm.ConstantData(\"x\", x)\n", - " α = pm.Normal(\"alpha\", 0, 2, size=3)\n", - " λ = pm.HalfNormal(\"lambda\", 0.01, size=2)\n", - " μ = pm.Deterministic(\"mu\", g(x))\n", - " σ = pm.HalfNormal(\"sigma\", 1)\n", - " pm.Normal(\"observed\", mu=μ, sigma=σ, observed=y)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "decomposable-models-additive-draws", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [alpha, lambda, sigma]\n", - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 132 seconds.\n", - "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n", - "The effective sample size per chain is smaller than 100 for some parameters. A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": { - "image/png": { - "height": 378.25, - "width": 509.15 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "# | label: decomposable-models-additive-draws\n", - "# | warning: false\n", - "with model:\n", - " prediction = pm.Deterministic(\"prediction\", g(x_range))\n", - " peak = pm.Deterministic(\n", - " \"peak\", pm.math.log(α[0] * λ[0] / (α[2] * λ[1])) / (λ[0] - λ[1])\n", - " )\n", - " slope = pm.Deterministic(\n", - " \"slope\",\n", - " pm.math.log(α[0] * λ[0] ** 2 / (α[2] * λ[1] ** 2)) / (λ[0] - λ[1]),\n", - " )\n", - " # traces = pm.sample_prior_predictive(samples=2000, random_seed=seed)\n", - " traces = pm.sample(progressbar=False, random_seed=seed)\n", - "\n", - "# add_model(fig, traces.prior.prediction, \"Prior Curves (80% Coverage)\")\n", - "add_model(fig, traces.posterior.prediction, \"Posterior Curves (80% Coverage)\")" - ] - }, - { - "cell_type": "markdown", - "id": "63b527e6", - "metadata": {}, - "source": [ - "$$d/dx g(x) = a_2 b_2 e^(b_2 (-x)) - a_1 b_1 e^(b_1 (-x))$$\n", - "$$x = \\frac{\\log(\\frac{a_1 b_1}{a_2 b_2})}{b_1 - b_2}$$\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "decomposable-models-peak", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": { - "image/png": { - "height": 378.25, - "width": 509.15 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "# | label: decomposable-models-peak\n", - "# az.plot_posterior(traces, var_names=[\"peak\", \"slope\"], hdi_prob=0.8)\n", - "peak = traces.posterior.peak.to_dataframe()\n", - "so.Plot(peak, x=\"peak\").add(so.Area(), so.KDE()).label(\n", - " title=\"Posterior distribution\", x=\"Age of peak performance (years)\", y=\"Density\"\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git 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b/docs/blog/aging-curve/index.html @@ -0,0 +1,847 @@ + + + + + + + + + + +Luís Assunção - Additive aging curve + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Additive aging curve

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Published
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January 23, 2024

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Modified
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January 23, 2024

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This post is a draft.

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Recently, I helped design an experiment measuring a binary response against a continuous variable. If the user abandoned their cart at time zero, then we delayed for a variable number of minutes before reminding them to finish their purchase. The delay has a non-monotonic relationship to the response: as the delay increases, so does the purchase rate; then the rate peaks; and finally it decreases.

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Causally, we may decompose this process into two: as the delay increases, the user 1) becomes more available for and 2) loses interest in purchasing the product. This is a common phenomena in different time-based scenarios. In sports, the “aging curve” refers to how a player’s performance increases with age, then decreases. As the player gets older, they get 1) better at the sport and 2) physically weaker.

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Andrew Gelman wrote about this a couple of times in his blog: see his posts from 2018 and 2023, where Gelman suggests modeling these processes using an additive function like:

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\[g(x) = g_1(x) + g_2(x),\]

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where
+\(g_1(x)\) is a monotonically increasing function with a right asymptote; and
+\(g_2(x)\) is a monotonically decreasing function with a left asymptote.

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In this post, we’ll analyse an experimental dataset by fitting and comparing three different models: a non-parametric bootstrap, a semi-parametric spline and a fully parametric decomposable curve like \(g(x)\).

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The Digit Span test

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The motivation for Gelman’s 2018 post was a study relating age to peak cognitive functioning (Hartshorne and Germine 2015). According to the study, one of their experiments was a large scale online experimentation platform:

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+Hartshorne, Joshua K., and Laura T. Germine. 2015. “When Does Cognitive Functioning Peak? The Asynchronous Rise and Fall of Different Cognitive Abilities Across the Life Span.” Psychological Science 26 (4): 433–43. https://doi.org/10.1177/0956797614567339. +
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Participants in Experiment 2 (N = 10,394; age range = 10–69 years old) […] were visitors to TestMyBrain.org, who took part in experiments in order to contribute to scientific research and in exchange for performance-related feedback. […] We continued data collection for each experiment for approximately 1 year, sufficient to obtain around 10,000 participants, which allowed fine-grained age-of-peak-performance analysis.

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The dataset for Experiment 2 is available online (Germine and Hartshorne 2016) and includes results of the Digit Span verbal working memory test, part of the Wechsler Adult Intelligence Scale (WAIS) and Wechsler Memory Scale (WMS) supertests. In the Digit Span test, subjects must repeat lists of digits, either in the same or reversed order.

+
+Germine, Laura, and Joshua K Hartshorne. 2016. “Hartshorne & Germine (2015) When Does Cognitive Functioning Peak?” OSF. osf.io/f2saj. +

Let’s plot the relationship between age and Digit Span performance:

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Visually, it’s still unclear if this relationship follows an aging curve, but we’ll get back to this matter in the next section.

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Bootstrap estimates

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In the original paper, the authors describe a bootstrap resampling procedure to estimate the distribution of ages of peak performance:

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Estimates and standard errors for age of peak performance were calculated using a bootstrap resampling procedure identical to the one used in Experiment 1 but applied to raw performance data. To dampen noise, we smoothed means for each age using a moving 3-year window prior to identifying age of peak performance in each sample. Other methods of dampening noise provide similar results.

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Let’s decompose this method (as I understand it) into steps:

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  1. With replacement, sample \(n\) observations from the dataset;
    2. +
  2. Calculate the mean performance for each sample and age;
    3. +
  3. Repeat steps 1 and 2 \(m\) times to get multiple samples;
    4. +
  4. Sort each sample by age and smooth age means using a 3-year rolling average;
    5. +
  5. Find the age of peak performance for each sample.
    6. +
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+
import polars as pl
+
+n = experiment.height
+m = 10000
+nm = n * m
+seed = 37
+samples = (
+    experiment.sample(nm, with_replacement=True, seed=seed)
+    .with_columns(sample=pl.arange(1, nm + 1) % m)
+    .group_by("sample", "age")
+    .agg(mean=pl.col("y").mean())
+    .sort("sample", "age")
+    .with_columns(smoothed_mean=pl.col("mean").rolling_mean(3).over("sample"))
+)
+peak_performance = samples.group_by("sample").agg(
+    age=pl.col("age").get(pl.col("smoothed_mean").arg_max())
+)
+
+

This yields the following bootstrap distribution of ages of peak performance:

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This distribution suggests two important things:

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  1. The most probable age of peak performance is, by far, 33;
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  2. There is a non-negligible probability that the age of peak performance happens in the early 20s, but a negligible probability that it happens in the late 20s.
    3. +
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Thing 2 certainly deserves attention. This is possibly caused by a confound variable or some measuring error, but I won’t investigate this any further. Instead, let’s get back to estimating curves. We will use the samples from step 4 to summarize the distribution of mean performances. For each age, we calculate the mean and 90% interquantile range, yielding a nonparametric curve:

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This figure is analogue to figure … in the paper. Since this is an entirely empirical curve, there isn’t much to interpret here (maybe unitary changes?). However, the curve shape indicates an aging-curve-likeness.

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Penalized splines

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Splines are wiggly curves…

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\[ +\begin{align} +g(x) &= \alpha + Z \bf{b} \\ +y &\sim \mathrm{Normal}(g(x), \sigma) \\ +\alpha &\sim \mathrm{Student}(3, 0, 0.1) \\ +\sigma &\sim \mathrm{HalfCauchy}(1) +\end{align} +\]

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Polynomials have runge swings…

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We could make assumptions about the data generating process to help us pick the number of knots. Instead, let’s pick an arbitrary large number of knots (say, 15) and let the model itself learn how wiggly the curve should be.

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\[ +\begin{align} +b &= \tau \bf{z} \\ +\tau &\sim \mathrm{HalfCauchy}(1) \\ +\bf{z} &\sim \mathrm{Normal}(0, 1) +\end{align} +\]

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https://www.pymc.io/projects/examples/en/latest/howto/spline.html
+https://www.tjmahr.com/random-effects-penalized-splines-same-thing/
+https://elevanth.org/blog/2017/09/07/metamorphosis-multilevel-model/

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+
import pymc as pm
+
+with pm.Model() as spline:
+    Z = pm.ConstantData("Z", Z)
+    α = pm.StudentT("α", 3, 0, sigma=0.1)
+    τ = pm.HalfCauchy("τ", 1)
+    z = pm.Normal("z", 0, 1, size=Z.shape[1])
+    b = pm.Deterministic("b", τ * z)
+    μ = pm.Deterministic("μ", α + pm.math.dot(Z, b.T))
+    σ = pm.HalfCauchy("σ", 1)
+    pm.Normal("y", μ, σ, observed=y)
+
+
+
+
Auto-assigning NUTS sampler...
+Initializing NUTS using jitter+adapt_diag...
+Sequential sampling (4 chains in 1 job)
+NUTS: [α, τ, z, σ]
+Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 47 seconds.
+
+
+

::: {#cell-spline-plot .cell 0=‘s’ 1=‘p’ 2=‘l’ 3=‘i’ 4=‘n’ 5=‘e’ 6=‘-’ 7=‘p’ 8=‘l’ 9=‘o’ 10=‘t’ execution_count=9}

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:::

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Splines are good interpolation tools…
+“when, where and how things change”… https://www.youtube.com/watch?v=Zxokd_Eqrcg&t=506s
+However, it’s not a good idea to extrapolate…

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Two component function

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All intervals are 80% credibility…

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\[ +\begin{align} +g_1(x) &= \alpha_1 + \beta_1 \exp(-\lambda_1 x) \\ +g_2(x) &= \alpha_2 + \beta_2 (1 - \exp(-\lambda_2 x)) \\ +g(x) &= g_1(x) + g_2(x) \\ + &= \alpha + \beta_1 \exp(-\lambda_1 x) + \beta_2 (1 - \exp(-\lambda_2 x)) +\end{align} +\]

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\[ +\begin{align} +y &\sim \mathrm{Normal}(g(x), \sigma) \\ +\alpha &\sim \mathrm{Normal}(0, 2) \\ +\lambda &\sim \mathrm{Exponential}(0.01) \\ +\sigma &\sim \mathrm{Exponential}(1) \\ +\end{align} +\]

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+
import pymc as pm
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+
+def g(x):
+    y = α[0] * pm.math.exp(-λ[0] * x) + α[1] + α[2] * (1 - pm.math.exp(-λ[1] * x))
+    return y
+
+
+with pm.Model() as model:
+    x = pm.ConstantData("x", x)
+    α = pm.Normal("alpha", 0, 2, size=3)
+    λ = pm.HalfNormal("lambda", 0.01, size=2)
+    μ = pm.Deterministic("mu", g(x))
+    σ = pm.HalfNormal("sigma", 1)
+    pm.Normal("observed", mu=μ, sigma=σ, observed=y)
+
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+

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\[d/dx g(x) = a_2 b_2 e^(b_2 (-x)) - a_1 b_1 e^(b_1 (-x))\] \[x = \frac{\log(\frac{a_1 b_1}{a_2 b_2})}{b_1 - b_2}\]

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Citation

BibTeX citation:
@online{assunção2024,
+  author = {Assunção, Luís},
+  title = {Additive Aging Curve},
+  date = {2024-01-23},
+  url = {https://assuncaolfi.github.io/site/blog/aging-curve},
+  langid = {en}
+}
+
For attribution, please cite this work as:
+Assunção, Luís. 2024. “Additive Aging Curve.” January 23, +2024. https://assuncaolfi.github.io/site/blog/aging-curve. +
+ +
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