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Hello World.html
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<!DOCTYPE html>
<html lang="en" prefix="og: http://ogp.me/ns# fb: https://www.facebook.com/2008/fbml">
<head>
<title>Hello World - DIANA HEP</title>
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/* Overrides of notebook CSS for static HTML export */
div.entry-content {
overflow: visible;
padding: 8px;
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<script src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML" type="text/javascript"></script>
<script type="text/javascript">
init_mathjax = function() {
if (window.MathJax) {
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<link rel="canonical" href="/Hello World.html">
<meta name="author" content="Kyle Cranmer" />
<meta name="keywords" content="news" />
<meta name="description" content="Initial post for DIANA-HEP. Status of build: To do: setup DNS for www.diana-hep.org subdomain using this guide setup DNS for diana-hep.org apex domain using this guide ok, working with .net currently, need to decide. Progress: Created DIANA-HEP GitHub Organization Using Pelican to generate website Using Travis to ..." />
<meta property="og:site_name" content="DIANA HEP" />
<meta property="og:type" content="article"/>
<meta property="og:title" content="Hello World"/>
<meta property="og:url" content="/Hello World.html"/>
<meta property="og:description" content="Initial post for DIANA-HEP. Status of build: To do: setup DNS for www.diana-hep.org subdomain using this guide setup DNS for diana-hep.org apex domain using this guide ok, working with .net currently, need to decide. Progress: Created DIANA-HEP GitHub Organization Using Pelican to generate website Using Travis to ..."/>
<meta property="article:published_time" content="2015-05-10" />
<meta property="article:section" content="Blog" />
<meta property="article:tag" content="news" />
<meta property="article:author" content="Kyle Cranmer" />
<!-- Bootstrap -->
<link rel="stylesheet" href="/theme/css/bootstrap.cosmo.min.css" type="text/css"/>
<link href="/theme/css/font-awesome.min.css" rel="stylesheet">
<link href="/theme/css/pygments/default.css" rel="stylesheet">
<link href="/theme/tipuesearch/tipuesearch.css" rel="stylesheet">
<link rel="stylesheet" href="/theme/css/style.css" type="text/css"/>
<link href="/static/custom.css" rel="stylesheet">
</head>
<body>
<div class="navbar navbar-default navbar-fixed-top" role="navigation">
<div class="container">
<div class="navbar-header">
<button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".navbar-ex1-collapse">
<span class="sr-only">Toggle navigation</span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a href="/" class="navbar-brand">
DIANA HEP </a>
</div>
<div class="collapse navbar-collapse navbar-ex1-collapse">
<ul class="nav navbar-nav">
<li><a href="pages/about.html">About</a></li>
<li><a href="pages/jobs.html">Job Opportunities</a></li>
<li class="active">
<a href="/category/blog.html">Blog</a>
</li>
</ul>
<ul class="nav navbar-nav navbar-right">
<li><span>
<form class="navbar-search" action="/search.html">
<input type="text" class="search-query" placeholder="Search" name="q" id="tipue_search_input" required>
</form></span>
</li>
<li><a href="/archives.html"><i class="fa fa-th-list"></i><span class="icon-label">Archives</span></a></li>
</ul>
</div>
<!-- /.navbar-collapse -->
</div>
</div> <!-- /.navbar -->
<!-- Banner -->
<!-- End Banner -->
<div class="container">
<div class="row">
<div class="col-sm-9">
<section id="content">
<article>
<header class="page-header">
<h1>
<a href="/Hello World.html"
rel="bookmark"
title="Permalink to Hello World">
Hello World
</a>
</h1>
</header>
<div class="entry-content">
<div class="panel">
<div class="panel-body">
<footer class="post-info">
<span class="label label-default">Date</span>
<span class="published">
<i class="fa fa-calendar"></i><time datetime="2015-05-10T14:53:00+02:00"> Sun 10 May 2015</time>
</span>
<span class="label label-default">Tags</span>
<a href="/tag/news.html">news</a>
</footer><!-- /.post-info --> </div>
</div>
<p>Initial post for DIANA-HEP.</p>
<p>Status of build:</p>
<p><img alt="" src="https://travis-ci.org/diana-hep/diana-hep.github.io-source.svg?branch=master" /></p>
<p>To do: </p>
<ul>
<li>setup DNS for <code>www.diana-hep.org</code> subdomain using <a href="https://help.github.com/articles/tips-for-configuring-a-cname-record-with-your-dns-provider">this guide</a></li>
<li>setup DNS for <code>diana-hep.org</code> apex domain using <a href="https://help.github.com/articles/tips-for-configuring-an-a-record-with-your-dns-provider/">this guide</a><ul>
<li>ok, working with .net currently, need to decide.</li>
</ul>
</li>
</ul>
<p>Progress:</p>
<ul>
<li>Created <a href="https://github.com/diana-hep">DIANA-HEP GitHub Organization</a></li>
<li>Using <a href="http://getpelican.com">Pelican</a> to generate website</li>
<li>Using Travis to build website -- <a href="http://zonca.github.io/2013/09/automatically-build-pelican-and-publish-to-github-pages.html">how-to</a></li>
<li>deploying to GitHub pages <a href="http://diana-hep.github.io">http://diana-hep.github.io</a></li>
<li>created <code>CNAME</code> file to use custom url <code>diana-hep.org</code></li>
<li>Check math: <span class="math">\(pp \to \tilde{\chi}_1^0 \tilde{\chi}_1^\pm\)</span></li>
<li>Check code </li>
</ul>
<div class="highlight"><pre> <span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="k">print</span> <span class="s">'welcome to Pelican'</span>
</pre></div>
<ul>
<li>Check embed ipython notebook
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
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<p>Particle physicisits primarily use <a href="http://root.cern.ch">ROOT</a> for the data analysis framework. Part of that framework is a package called <a href="http://root.cern.ch/drupal/content/users-guide#roofit">RooFit</a> statistical modeling and fitting package. I have contributed to this package and added a layer on top called <a href="https://twiki.cern.ch/twiki/bin/view/RooStats/WebHome">RooStats</a> that provides with statistical inference in both frequentist and Bayesian paradigms based on statistical models made with RooFit. These are the tools that were used to claim the discover the Higgs boson, and those <a href="http://blogs.discovermagazine.com/cosmicvariance/2011/12/08/making-the-higgs-sausage/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+CosmicVariance+%28Cosmic+Variance%29#.UxFW0NyrvlI">statistical models get pretty complicated</a>.</p>
<p>Here I demonstrate a simple example of RooFit's ability to create a statistical model, generate some simulated data, fit that data, create the profile likelihood, and provide a covariance matrix from the likelihood fit.</p>
<p>ROOT is a C++ library, but it has python bindings known as PyROOT.</p>
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<div class=" highlight hl-ipython3"><pre><span class="kn">import</span> <span class="nn">ROOT</span>
<span class="kn">import</span> <span class="nn">rootnotes</span> <span class="c">#for plotting with iPython</span>
<span class="n">c1</span> <span class="o">=</span> <span class="n">rootnotes</span><span class="o">.</span><span class="n">default_canvas</span><span class="p">()</span>
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<p>Here we create a "workspace" object that provides a factory with a convenient syntax for statistical models and variables. The workspace also provides an I/O mechanism to read/write statistical models and data to/from files.</p>
<p>In this example, we create a mixture model of a falling exponential distribution and a Gaussian for a variable x.<br>
This is really a marked Poisson process, because in addition to the pdf on x, we also encode that we expect s=50 events from the Gaussian and b=100 events from the falling exponential.</p>
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<div class=" highlight hl-ipython3"><pre><span class="n">w</span> <span class="o">=</span> <span class="n">ROOT</span><span class="o">.</span><span class="n">RooWorkspace</span><span class="p">()</span>
<span class="n">w</span><span class="o">.</span><span class="n">factory</span><span class="p">(</span><span class="s">'Gaussian::g(x[-5,5],mu[-3,3],sigma[1])'</span><span class="p">)</span>
<span class="n">w</span><span class="o">.</span><span class="n">factory</span><span class="p">(</span><span class="s">'Exponential::e(x,tau[-.5,-3,0])'</span><span class="p">)</span>
<span class="n">w</span><span class="o">.</span><span class="n">factory</span><span class="p">(</span><span class="s">'SUM::model(s[50,0,100]*g,b[100,0,1000]*e)'</span><span class="p">)</span>
<span class="n">w</span><span class="o">.</span><span class="n">Print</span><span class="p">()</span> <span class="c">#this isn't displaying in iPython</span>
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<pre>TClass::TClass:0: RuntimeWarning: no dictionary for class stack<RooAbsArg*,deque<RooAbsArg*> > is available
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<p>Now that we have made the mdoel, we can generate some fake data, make a plot of the data, fit the model to that data, and overlay the fitted model in the plot</p>
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<div class=" highlight hl-ipython3"><pre><span class="n">x</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s">'x'</span><span class="p">)</span>
<span class="n">pdf</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="s">'model'</span><span class="p">)</span>
<span class="n">frame</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">frame</span><span class="p">()</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pdf</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">ROOT</span><span class="o">.</span><span class="n">RooArgSet</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">data</span><span class="o">.</span><span class="n">plotOn</span><span class="p">(</span><span class="n">frame</span><span class="p">)</span>
<span class="n">fitResult</span> <span class="o">=</span> <span class="n">pdf</span><span class="o">.</span><span class="n">fitTo</span><span class="p">(</span><span class="n">data</span><span class="p">,</span><span class="n">ROOT</span><span class="o">.</span><span class="n">RooFit</span><span class="o">.</span><span class="n">Save</span><span class="p">())</span>
<span class="n">pdf</span><span class="o">.</span><span class="n">plotOn</span><span class="p">(</span><span class="n">frame</span><span class="p">)</span>
<span class="n">frame</span><span class="o">.</span><span class="n">Draw</span><span class="p">()</span>
<span class="n">c1</span>
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<p>We can check the value of the parameters after the fit</p>
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<div class="prompt input_prompt">In [4]:</div>
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<div class=" highlight hl-ipython3"><pre><span class="n">mu</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s">'mu'</span><span class="p">)</span>
<span class="nb">print</span> <span class="s">'best fit value of mean for Gaussian is'</span><span class="p">,</span> <span class="n">mu</span><span class="o">.</span><span class="n">getVal</span><span class="p">(),</span> <span class="s">'±'</span><span class="p">,</span> <span class="n">mu</span><span class="o">.</span><span class="n">getError</span><span class="p">()</span>
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<pre>best fit value of mean for Gaussian is 0.324840840875 ± 0.158716435359
</pre>
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